List[str]:\n await create_guild_model(inter.guild)\n guild = await Guild.get(inter.guild_id)\n return guild.prefixes[:24]"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["async def process_prefix_list(\n guild: disnake.Guild,\n ctx: commands.Context = None,\n inter: AppCmdInter = None,\n allowed_mentions=None,\n):\n await create_guild_model(guild)\n guild = await Guild.get(guild.id)\n msg = f\"The following are the custom prefixes for {guild.name}:\\n\" + \", \".join(\n guild.prefixes\n )\n await send_message(msg=msg, ctx=ctx, inter=inter, allowed_mentions=allowed_mentions)","def complete_prefix(self, prefix):\n return tuple(\n k for k in self.commands.keys()\n if k.startswith(prefix)\n )","def autocomplete(self, prefix):\n if len(prefix) == 0:\n \n __________________________________________\n \n if prefix[0] not in self.branches:\n \n __________________________________________\n \n ______________________________________________","async def prefix(self, ctx):\n prefixes = await self.bot.get_prefix(ctx.message)\n formatted = self._format_prefixes(prefixes)\n await ctx.send(f\"You can mention me or use any of the following \"\n f\"prefixes like so: {formatted}\")","def get_prefix(bot, message):\n\n # Add prefixes that you want the bot to respond to\n prefixes = ['?', '!']\n\n # Check to see if we are outside of a guild. e.g DM's etc.\n if not message.guild:\n # Only allow ? to be used in DMs\n return '?'\n\n # If we are in a guild, we allow for the user to mention us or use any of the prefixes in our list.\n return commands.when_mentioned_or(*prefixes)(bot, message)","async def get_command_prefix(self, guild: Guild) -> str:\n if (prefix := self._cache[guild.id].prefix) is None:\n db_guild = await self.find_by_id(guild.id)\n\n if db_guild is None:\n await self.create_from_gateway_response(guild)\n db_guild = await self.find_by_id(guild.id)\n\n self._cache[guild.id].prefix = prefix = db_guild[\"command_prefix\"]\n\n return prefix","async def get_prefixes(self, guild: Guild) -> List[str]:\n if not guild:\n return []\n\n return await self.redis.smembers(\n f'dog:prefixes:{guild.id}', encoding='utf-8')","async def _set_prefix(self, ctx: Context, *prefixes: str):\n\n if not prefixes:\n await ctx.bot._prefix_cache.set_prefixes(\n guild=ctx.guild, prefixes=[]\n )\n await ctx.send(\"Server prefixes have been reset.\")\n return\n prefixes = sorted(prefixes, reverse=True)\n await ctx.bot._prefix_cache.set_prefixes(\n guild=ctx.guild, prefixes=prefixes\n )\n inline_prefixes = [f\"`{prefix}`\" for prefix in prefixes]\n await ctx.send(\n f\"Set {', '.join(inline_prefixes)} as server\"\n f\" {'prefix' if len(prefixes) == 1 else 'prefixes'}.\"\n )","async def prefix(self, ctx, prefix):\n if prefix.strip() == \"\":\n raise exceptions.Warning(\"Prefix cannot be empty.\")\n\n if prefix.startswith(\" \"):\n raise exceptions.Warning(\"Prefix cannot start with a space.\")\n\n if len(prefix) > 32:\n raise exceptions.Warning(\"Prefix cannot be over 32 characters.\")\n\n prefix = prefix.lstrip()\n await self.bot.db.execute(\n \"\"\"\n INSERT INTO guild_prefix (guild_id, prefix)\n VALUES (%s, %s)\n ON DUPLICATE KEY UPDATE\n prefix = VALUES(prefix)\n \"\"\",\n ctx.guild.id,\n prefix,\n )\n self.bot.cache.prefixes[str(ctx.guild.id)] = prefix\n await util.send_success(\n ctx,\n f\"Command prefix for this server is now `{prefix}`. \"\n f\"Example command usage: {prefix}ping\",\n )","async def set_command_prefix(self, guild: Guild, prefix: str) -> bool:\n\n async with self.db_pool.acquire() as conn:\n result = await conn.execute(\n f\"UPDATE {self.table_name} \"\n \"SET command_prefix = $1 \"\n \"WHERE id = $2\",\n prefix,\n guild.id,\n )\n\n if result := (int(result.split()[1]) == 1) is True:\n self._cache[guild.id].prefix = prefix\n\n return result","async def setprefix(self, ctx, *, prefix=bot_prefix):\n prefix = prefix.lower()\n current_server_prefix = await self.ex.get_server_prefix(ctx.guild.id)\n if len(prefix) > 8:\n await ctx.send(\"> **Your prefix can not be more than 8 characters.**\")\n else:\n # Default prefix '%' should never be in DB.\n if current_server_prefix == \"%\":\n if prefix != \"%\":\n await self.ex.conn.execute(\"INSERT INTO general.serverprefix VALUES ($1,$2)\", ctx.guild.id, prefix)\n self.ex.cache.server_prefixes[ctx.guild.id] = prefix\n else:\n if prefix != \"%\":\n await self.ex.conn.execute(\"UPDATE general.serverprefix SET prefix = $1 WHERE serverid = $2\",\n prefix, ctx.guild.id)\n self.ex.cache.server_prefixes[ctx.guild.id] = prefix\n else:\n await self.ex.conn.execute(\"DELETE FROM general.serverprefix WHERE serverid = $1\", ctx.guild.id)\n self.ex.cache.server_prefixes.pop(ctx.guild.id, None)\n await ctx.send(f\"> **This server's prefix has been set to {prefix}.**\")","async def _setprefix(self, ctx, arg1):\n if len(arg1) > 6:\n await ctx.send(\"Keep the prefix under 6 chars, please.\")\n return\n\n guildconfig = database.getGuild(ctx.guild.id)\n\n if not guildconfig:\n guildconfig = database.newGuild(ctx.guild.id)\n\n database.setPrefix(guildconfig.ID, arg1)\n await ctx.send(\"Prefix set successfully!\")","async def on_guild_join(self, guild: discord.Guild):\n with open(\"./config/prefixes.json\", \"r\") as f:\n prefixes = json.load(f)\n\n prefixes[str(guild.id)] = \".\"\n\n with open(\"./config/prefixes.json\", \"w\") as f:\n json.dump(prefixes, f, indent=4)","def suggestions(prefix : str = typer.Argument(...), n : int = typer.Option(5, help=\"Number of suggestions to display\")): \n response_url = url + \"/suggestions/\" + prefix + \"?suggestion_nums=\" + str(n) \n response = requests.get(response_url) \n for i in range(len(response.json())):\n typer.echo(response.json()[i])","async def nuke_commands(self):\n print(\"nuking...\")\n await self.delete_global_commands()\n print(\"nuked global commands\")\n async for guild in self._discord.fetch_guilds():\n print(\"nuking commands in\", guild.id)\n await self.delete_guild_commands(guild.id)\n print(\"nuked commands in\", guild.id, guild.name)\n print(\"nuked\")","def type_command(ctx, name_from, name_to):","async def prefix(self, _bot, message: discord.Message):\n mention = [self.user.mention + ' ', f'<@!{self.user.id}> ']\n additional_prefixes = await self.get_prefixes(message.guild)\n return self.cfg['bot']['prefixes'] + mention + additional_prefixes","async def set_prefix(self, ctx, prefix: commands.clean_content, allow_default=False):\n\n if len(prefix) > 25:\n return await ctx.send(\":no_entry: | prefixes can't be 25 characters or greater.\")\n\n if re.findall(r\"<a?:\\w*:\\d*>\", prefix):\n return await ctx.send(\":no_entry: | emoji's are not allowed as a guild's prefix\")\n\n if re.findall(r'https?://(?:[-\\\\w.]|(?:%[\\\\da-fA-F]{2}))+', prefix):\n return await ctx.send(\":no_entry: | urls are not allowed as a guild's prefix\")\n\n async with ctx.acquire():\n await ctx.db.execute(\"\"\"\n INSERT INTO guilds (guild_id, prefix, allow_default) VALUES ($1, $2, $3)\n ON CONFLICT (guild_id) DO UPDATE SET (prefix, allow_default) = ($2, $3)\n \n \"\"\", ctx.guild.id, prefix, allow_default)\n\n await ctx.send(f\"The prefix for this guild is now {prefix}\")","async def change_prefix(ctx, prefix):\n if \" \" in prefix:\n await ctx.send(\"Not a valid prefix\")\n else:\n Config.set_guild_prefix(ctx.guild, prefix)\n await ctx.send(\"Update prefix to `\" + str(prefix) + \"`\")","async def prefix(self, ctx, prefix: str = None):\n if not prefix:\n try:\n return await ctx.send(f'My prefix here is `{self.bot.prefixes[str(ctx.guild.id)]}`. You can change that with `{ctx.prefix}prefix <prefix>`')\n except KeyError:\n return await ctx.send(f'My prefix here is `{config.prefix[0]}`. You can change that with `{ctx.prefix}prefix <prefix>`')\n db = pymysql.connect(config.db_ip, config.db_user, config.db_pass, config.db_name)\n cur = db.cursor()\n cur.execute(\n f\"\"\"INSERT INTO settings (guildid, prefix) VALUES ({ctx.guild.id}, \"{prefix}\") ON DUPLICATE KEY UPDATE prefix = \"{prefix}\";\"\"\")\n db.commit()\n db.close()\n self.bot.prefixes = get_all_prefixes()\n await ctx.send(f':ok_hand: Successfully set my prefix here to `{prefix}`')","async def get_prefix(_bot, message):\n if isinstance(message.channel, discord.DMChannel):\n return _bot.config.dm_prefix\n\n if message.author == _bot.owner:\n return _bot.config.owner_prefix\n\n prefix_for_this_guild = await _bot.db.fetchrow(\n \"\"\"\n SELECT prefix\n FROM guilds\n WHERE id=$1\n \"\"\",\n message.guild.id,\n )\n\n if prefix_for_this_guild is None:\n await _bot.db.execute(\n \"\"\"\n INSERT INTO guilds (id, prefix)\n VALUES ($1, $2)\n \"\"\",\n message.guild.id,\n \",\",\n )\n prefix_for_this_guild = {\"prefix\": _bot.config.default_prefix}\n\n prefix_return = str(prefix_for_this_guild[\"prefix\"])\n return commands.when_mentioned_or(prefix_return)(_bot, message)","async def fetch_prefix(self, bot: 'ContestBot', message: discord.Message):\n user_id = bot.user.id\n base = [f'<@!{user_id}> ', f'<@{user_id}> ']\n\n if message.guild:\n with self.get_session() as session:\n guild: Guild = session.query(Guild).get(message.guild.id)\n base.append(guild.prefix)\n return base","async def before_any_command(ctx):\n ctx.timer = time()\n try:\n await ctx.trigger_typing()\n except discord.errors.Forbidden:\n pass","async def prefix(self, ctx, prefix):\n # Get the server language\n lang = getLang(ctx.message.guild.id)\n\n if len(prefix) > 10:\n with open(f\"embeds/{lang}/prefix.json\", \"r\") as f:\n await ctx.reply(embed=discord.Embed.from_dict(json.load(f)['len-error']), delete_after=20)\n\n # Change prefix\n with open('serverconfig/prefixes.json', 'r') as f:\n prefixes = json.load(f)\n old_prefix = prefixes[str(ctx.guild.id)]\n prefixes[str(ctx.guild.id)] = prefix\n with open('serverconfig/prefixes.json', 'w') as f:\n json.dump(prefixes, f, indent=4)\n\n # Get the embed of the right language and send with replaced variable\n with open(f\"embeds/{lang}/prefix.json\", \"r\") as f:\n embed = json.load(f)['embed']\n\n embed['description'] = embed['description'].replace(\"%VAR\", prefix)\n await ctx.reply(embed=discord.Embed.from_dict(embed), mention_author=False, delete_after=20)","async def prefix_process(bot, msg: discord.Message):\n if msg.guild:\n g_prefix = await bot.sql.get_guild_prefix(bot.db, guildID=msg.guild.id)\n if g_prefix:\n return g_prefix, bot.default_prefix\n return bot.default_prefix","def autocorrect(trie, prefix, max_count=None):\n\n autocompleted = autocomplete(trie, prefix, max_count)\n\n edits = get_valid_edits(trie, prefix)\n\n edits_items = [(key, trie[key]) for key in edits]\n sorted_edits_items = sorted(edits_items, key=lambda x: x[1], reverse=True)\n result = [word for word, _ in sorted_edits_items]\n\n if max_count is None:\n\n return autocompleted + result\n\n if len(autocompleted) < max_count:\n\n return autocompleted + result[:max_count - len(autocompleted)]\n\n return autocompleted","async def setprefix(self, ctx: Context, prefix: str):\n if ctx.guild is None:\n raise NoPrivateMessage(\"You cannot use this command in a DM\")\n\n if await GuildRepository().set_command_prefix(ctx.guild, prefix):\n message = f\"Command prefix changed to `{prefix}`.\"\n else:\n message = f\"`{prefix}` is already your prefix.\"\n\n await ctx.send(content=message)","def autocomplete():\n query = '' if request.args.get('query') is None else request.args.get('query')\n\n prefixed_words = []\n close_words = []\n for f in app.preprocessed.words:\n lowered = f.lower()\n if lowered.startswith(query) and lowered != query:\n prefixed_words.append(f)\n elif levenshtein(query, lowered) <= 1:\n close_words.append(f)\n\n result = {\n 'success': True,\n 'data': {\n 'suggestions': prefixed_words + close_words\n }\n }\n return jsonify(result)","async def adding_command_list(self):\n command_aliases=['anime','fun','mod','nekogif'] #This includes the aliases and the cog names\n #NOTE: fun command added\n for i in self.bot.commands:\n self.commands.append(i.name)\n \n for i in command_aliases:\n self.commands.append(i)","def complete(self, prefix):\n words = list(set([word for word in self._vocabulary if word.startswith(prefix)]))\n if len(words) <= self._max_completions:\n return words\n else:\n return words[:self._max_completions]","async def get_prefix(client, message):\n conf1 = json.load(open(\"json/serverconfig.json\", 'r'))\n guild = message.guild\n if guild:\n return conf1[str(guild.id)][\"prefix\"]\n else:\n return get_default_prefix()","async def process_prefix_add_remove(\n guild: disnake.Guild,\n prefix: str,\n ctx: commands.Context = None,\n inter: AppCmdInter = None,\n allowed_mentions=None,\n add=False,\n):\n await create_guild_model(guild)\n guild = await Guild.get(guild.id)\n\n if add:\n await guild.add_prefix(prefix)\n msg = f\"{prefix.lower()} has been added as a command prefix for {guild.name}.\"\n else:\n await guild.delete_prefix(prefix)\n msg = (\n f\"{prefix.lower()} has been removed as a command prefix from {guild.name}.\"\n )\n\n await send_message(msg=msg, ctx=ctx, inter=inter, allowed_mentions=allowed_mentions)","def autoComplete(prefix, Trie, top):\n\ttemp_node = Trie.search(prefix)\n\tsuggest_list = []\n\tword_list = []\n\tPqueue = PriorityQueue()\n\tfor child in temp_node.children.keys():\n\t\tnode = temp_node.children[child]\n\t\tPqueue.insert((child,node), node.weight)\n\tfor i in range(top):\n\t\tword_list.append(prefix)\n\twhile len(suggest_list) < top and Pqueue.is_empty() == False:\n\t\tcharacter, candidate_node = Pqueue.extract_highest()\n\t\tif candidate_node.isEnd == False:\n\t\t\tfor child in candidate_node.children.keys():\n\t\t\t\tPqueue.insert((character+child,candidate_node.children[child]), candidate_node.children[child].weight)\n\t\telse:\n\t\t\tword_list[len(suggest_list)] = word_list[len(suggest_list)] + character\n\t\t\tsuggest_list.append((word_list[len(suggest_list)], candidate_node.weight))\n\treturn suggest_list","async def get_prefix(self, bot, message):\n async with aiosqlite.connect('data\\\\config.db') as conn:\n async with conn.execute(\"SELECT * from CONFIG\") as cursor:\n async for row in cursor:\n if row[0] == message.guild.id:\n await levels.insert(message.author.id, message.author.guild.id)\n prefixes = [row[1]]\n return commands.when_mentioned_or(*prefixes)(bot, message)\n await conn.execute(f\"INSERT INTO COUNTING (GUILD) \\\n VALUES ({message.guild.id})\")\n await conn.execute(f\"INSERT INTO CONFIG (GUILD, PREFIX) \\\n VALUES ({message.guild.id}, '>')\")\n await AutoModSettingsManager().create(message.guild.id)\n await levels.insert(message.author.id, message.author.guild.id)\n prefixes = ['>']\n return commands.when_mentioned_or(*prefixes)(bot, message)","def autocomplete(trie, prefix, max_count=None):\n\n if type(prefix) != trie.type:\n raise TypeError\n\n result = []\n\n if max_count == 0:\n return result\n\n if prefix in trie:\n result.append((prefix, trie[prefix]))\n\n for i, elt in enumerate(prefix):\n if isinstance(prefix, tuple):\n elt = (elt,)\n\n if elt in trie.children:\n trie = trie.children[elt]\n else:\n return result\n\n if i == len(prefix) - 1:\n break\n\n for key, _ in trie:\n result.append((prefix + key, trie[key]))\n\n sorted_result = sorted(result, key=lambda x: x[1], reverse=True)\n\n if max_count is None or len(result) <= max_count:\n return [key for key, _ in sorted_result]\n\n return [key for key, _ in sorted_result[:max_count]]","def commands_for_submode(prefix):\n candidates = _lookup_command_candidates(prefix, command_registry)\n # print type(candidates), [x['self'] for x in candidates]\n # print [x.get('all-help') for x in candidates]\n if debug.cli():\n # print 'commands_for_submode:', sorted(dict([[x['name']['title'] if type(x['name']) == dict else x['name'], None] for x in candidates]).keys())\n pass\n return candidates","def prefix_type(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"prefix_type\")","def quick_completer(cmd, completions):\n if isinstance(completions, basestring):\n \n completions = completions.split()\n def do_complete(self,event):\n return completions\n \n ip.set_hook('complete_command',do_complete, str_key = cmd)","def get_command_prefix(self):\n return self.bot_data_file[\"commands_prefix\"]","async def get_prefix(bot: Bot, message: Message):\n return appearance.get_prefix(guild_id=message.guild.id)","def raw_command_completer(self, text, line, start_index, end_index):\n return [command for command in self.suggested_commands() if command.startswith(text)]","def autocomplete(self):\n # Don't complete if user hasn't sourced bash_completion file.\n if 'TORNADO_AUTO_COMPLETE' not in os.environ:\n return\n \n cwords = os.environ['COMP_WORDS'].split()[1:]\n cword = int(os.environ['COMP_CWORD'])\n \n try:\n curr = cwords[cword-1]\n except IndexError:\n curr = ''\n \n envs = self.get_envs()\n \n subcommands = get_commands().keys() + envs + ['help']\n options = [('--help', None)]\n \n # subcommand\n if cword == 1:\n print ' '.join(sorted(filter(lambda x: x.startswith(curr), subcommands)))\n # subcommand options\n # special case: the 'help' subcommand has no options\n elif cwords[0] in subcommands and cwords[0] != 'help':\n subcommand_cls = self.fetch_command(cwords[0])\n # special case: 'runfcgi' stores additional options as\n # 'key=value' pairs\n if cwords[0] == 'runfcgi':\n from django.core.servers.fastcgi import FASTCGI_OPTIONS\n options += [(k, 1) for k in FASTCGI_OPTIONS]\n # special case: add the names of installed apps to options\n elif cwords[0] in ('dumpdata', 'reset', 'sql', 'sqlall',\n 'sqlclear', 'sqlcustom', 'sqlindexes',\n 'sqlreset', 'sqlsequencereset', 'test'):\n try:\n from django.conf import settings\n # Get the last part of the dotted path as the app name.\n options += [(a.split('.')[-1], 0) for a in settings.INSTALLED_APPS]\n except ImportError:\n # Fail silently if DJANGO_SETTINGS_MODULE isn't set. The\n # user will find out once they execute the command.\n pass\n options += [(s_opt.get_opt_string(), s_opt.nargs) for s_opt in\n subcommand_cls.option_list]\n # filter out previously specified options from available options\n prev_opts = [x.split('=')[0] for x in cwords[1:cword-1]]\n options = filter(lambda (x, v): x not in prev_opts, options)\n \n # filter options by current input\n options = sorted([(k, v) for k, v in options if k.startswith(curr)])\n for option in options:\n opt_label = option[0]\n # append '=' to options which require args\n if option[1]:\n opt_label += '='\n print opt_label\n sys.exit(1)","async def help_bot(prefixe, message):\n\n msg_embed = {\n \"color\": 6158690, # Couleur de la barre\n \"fields\": [\n # Zone 1\n {\n \"name\": \"Personnaliser le bot\",\n \"value\": f\"\"\"`{prefixe}prefixechange`\"\"\"\n },\n # Zone 2\n {\n \"name\": \"Misc\",\n \"value\": f\"\"\"`$$$what` `{prefixe}help` `{prefixe}poll` `{prefixe}8ball` `{prefixe}russian`\"\"\"\n },\n # Zone 3\n {\n \"name\": \"Administration\",\n \"value\": f\"\"\"`{prefixe}clear`\"\"\"\n },\n # Zone 4\n {\n \"name\": \"Niveaux et Profils\",\n \"value\": f\"\"\"`{prefixe}profile` `{prefixe}rank` `{prefixe}alert`\"\"\"\n },\n # Zone 5\n {\n \"name\": \"Anniversaires\",\n \"value\": f\"\"\"`{prefixe}addbirth` `{prefixe}removebirth` `{prefixe}nextbirth`\"\"\"\n }],\n \"footer\":\n {\n \"icon_url\": \"https://cdn.discordapp.com/attachments/487002983557627936/715329727757549568/portrait2.jpg\",\n \"text\": \"Bot by Takase\"\n },\n \"title\": \"You Can (Not) Advance\",\n \"url\": \"https://arthurtakase.github.io\",\n \"description\": \"Un magnifique bot discord qui peut tout faire !\"\n }\n await message.channel.send(embed=discord.Embed.from_dict(msg_embed))\n return","async def prefix(self, ctx, *, new_prefix: str = None):\n\n if not ctx.guild:\n if new_prefix:\n await ctx.error(\"Prefix cannot be set in DMs.\")\n return\n\n await ctx.info(f\"Prefix is {self.bot.default_prefix}\")\n return\n\n if not new_prefix:\n guild_prefix = self.bot.prefixes.get(ctx.guild.id)\n prefix = guild_prefix or self.bot.default_prefix\n await ctx.info(f\"Prefix is {prefix}\")\n return\n\n if await checks.check_is_admin(ctx):\n await db.execute_sql(\n \"INSERT OR REPLACE INTO prefixes(guild_id, prefix)\"\n \"VALUES(?, ?)\", (ctx.guild.id, new_prefix)\n )\n\n self.bot.prefixes[ctx.guild.id] = new_prefix\n\n await ctx.info(f\"Prefix set to {new_prefix}\")\n return\n\n await ctx.error(\"Prefix can only be set by admins.\")\n return","def setup_command_dict(self):\n\n out = {}\n for k, v in self.command_aliases.items():\n for i in v:\n out[i] = k # string typed by player:function of MyThing\n return out","def cmd(name: str) -> Callable:\n return g.new_cmd_decorator(name, ['c', 'spellCommands',])","def _lookup_command_candidates(command_prefix, command_list):\n candidates = []\n current_mode = bigsh.run.finder.mode_stack.current_mode()\n try:\n for command in command_list:\n modes = _get_applicable_modes(command)\n if _match_current_modes(command, current_mode, modes):\n name = command['name']\n if (type(name) == str and\n name.startswith(command_prefix.lower())):\n candidates.append(command)\n # should check the type of command_prefix,\n # and for str, ame.match(command_prefix):\n if type(name) == dict:\n if 're' not in name:\n command['name']['re'] = re.compile(name['pattern'])\n if name['re'].match(command_prefix):\n candidates.append(command)\n if type(name) == dict and \\\n name['re'].match(command_prefix):\n candidates.append(command)\n \n except Exception, _e:\n if debug.cli():\n print _line(), 'Backtrace'\n traceback.print_exc()\n raise error.CommandDescriptionError('Missing mode or name', command)\n \n return candidates","def suggest_name(self, workspace: Workspace, role: str, *args) -> str:\n return role + \"-api\"","async def on_guild_remove(self, guild: discord.Guild):\n with open(\"./config/prefixes.json\", \"r\") as f:\n prefixes = json.load(f)\n\n prefixes.pop(str(guild.id))\n\n with open(\"./config/prefixes.json\", \"w\") as f:\n json.dump(prefixes, f, indent=4)","def getAutoCompleteList(self, rawCmd='', *args, **kwds):\n try:\n actKey = rawCmd[-1] #Was it activated by a '/', '.' or ' ' ?\n cmd = re.sub('#.*', '', rawCmd) # remove comments\n cmd = string.strip(cmd)\n if not cmd: return None \n \n # get lhs description\n (lhsDesc, remaining) = self.cmd.GetNextTerm( cmd ) \n \n lst = []\n \n #Get contents from the root\n if actKey == '/':\n if hasattr(self.cmd.root, 'GetContents'):\n lst = []\n for i in self.cmd.root.GetContents():\n lst.append(i[0])\n \n #Try different options\n elif actKey == '.':\n myDesc = string.split(cmd, ' ')[-1][:-1] \n if myDesc[0] == '/': lhsObj = self.cmd.GetObject(self.cmd.root, myDesc[1:])\n else: lhsObj = self.cmd.GetObject(self.cmd.currentObj, myDesc)\n \n #Object with get contents attr\n if hasattr(lhsObj, 'GetContents'):\n lst = []\n for i in lhsObj.GetContents():\n lst.append(i[0])\n \n #If it is a thermo provider, return available prop pkgs\n elif myDesc in self.cmd.thermoAdmin.GetAvThermoProviderNames():\n thAd = self.cmd.thermoAdmin\n lst = thAd.GetAvPropPkgNames(myDesc)\n \n #If a folder with unit ops, then retun av u ops\n elif myDesc in unitop.__all__:\n uop = guicmd.CommandInterface.__dict__.get(myDesc, None)\n if hasattr(uop, 'VALID_UNIT_OPERATIONS'):\n lst = uop.VALID_UNIT_OPERATIONS\n \n #Is it a command?\n elif guicmd.CommandInterface.commands.has_key(lhsDesc):\n cmdActOnObj = ('cd', 'view', 'delete', 'dir', 'valueOf')\n lst = []\n if lhsDesc == 'units':\n if actKey == ' ' and remaining == '':\n lst = self.cmd.units.GetSetNames()\n elif lhsDesc in cmdActOnObj:\n if actKey == ' ' and remaining == '':\n lst = ['..', '/']\n if hasattr(self.cmd.currentObj, 'GetContents'):\n for i in self.cmd.currentObj.GetContents():\n lst.append(i[0])\n elif lhsDesc == 'language':\n if actKey == ' ' and remaining == '':\n dct = guicmd.CommandInterface.MessageHandler.GetSupportedLanguages()\n #dct['languages'] should have the main languages supported\n lst = list(dct['languages'])\n \n lst.sort()\n return lst\n \n except:\n return []","def hotkey_prefix(self, hotkey_prefix):\n allowed_values = [\"None\", \"Show\", \"Hide\"] # noqa: E501\n if not hotkey_prefix.isdigit():\t\n if hotkey_prefix not in allowed_values:\n raise ValueError(\n \"Invalid value for `hotkey_prefix` ({0}), must be one of {1}\" # noqa: E501\n .format(hotkey_prefix, allowed_values))\n self._hotkey_prefix = hotkey_prefix\n else:\n self._hotkey_prefix = allowed_values[int(hotkey_prefix) if six.PY3 else long(hotkey_prefix)]","def load_aliases():\n for name in COMMANDS:\n load_alias(name)","def handle_admincommands(bot, ievent):\n cmnds = getcmndtable()\n if not ievent.rest: ievent.reply(\"commands: \", cmnds)\n else:\n try: ievent.reply(\"%s command is found in %s \" % (ievent.rest, cmnds[ievent.rest]))\n except KeyError: ievent.reply(\"no such commands available\")","def __getFullCommandName(self, command, type):\n return 'cmd_%s_%s' % (type, command)","def Complete():\n\n # TODO(iancottrell): support auto complete of more than just the command\n # try to parse the command line using parser\n print(' '.join(command.name for command in cr.Command.Plugins()))","async def any_shortcut(argument, context):\n shortcut = None\n check_order = [\"user\", \"channel\", \"category\", \"guild\"]\n for name in check_order:\n shortcuts = None\n try:\n shortcuts = getattr(context, name + \"_data\").shortcuts\n except AttributeError:\n continue\n\n if argument in shortcuts:\n shortcut = shortcuts[argument]\n break\n\n else:\n guild_shortcut = definitions.COMMANDS.get_sub_command_from_path(\n \"settings\", \"guild\", \"shortcut\", \"add\")\n\n \"\"\"category_shortcut = definitions.COMMANDS.get_sub_command_from_path(\n \"settings\", \"category\", \"shortcut\", \"add\")\n\n channel_shortcut = definitions.COMMANDS.get_sub_command_from_path(\n \"settings\", \"channel\", \"shortcut\", \"add\")\n\n user_shortcut = definitions.COMMANDS.get_sub_command_from_path(\n \"settings\", \"user\", \"shortcut\", \"add\")\"\"\"\n\n guild_shortcut_cmd = await guild_shortcut.get_command_string(context)\n \"\"\"category_shortcut_cmd = await category_shortcut.get_command_string(context)\n channel_shortcut_cmd = await channel_shortcut.get_command_string(context)\n user_shortcut_cmd = await user_shortcut.get_command_string(context)\"\"\"\n\n custom_msg = (\n \"{invalid_shortcut_name}\\n\\n\"\n \"ℹ {invalid_shortcut_name_info}\").format(\n invalid_shortcut_name=await context.language.get_text(\n \"invalid_shortcut_name\"),\n invalid_shortcut_name_info=await context.language.get_text(\n \"invalid_shortcut_name_info\", {\"guild_shortcut\": guild_shortcut_cmd}))\n\n await embed_messages.invalid_argument(context, argument, custom_msg)\n\n return shortcut","def _query_commands(self):\n # TODO: make this work\n self.player.respond(\"Hi there! Ask me to play artists or songs. \"\n \"I can also find songs that are similar to other \"\n \"artists.\")","def cmdprefix(self) -> str:\n return self.config[\"Core\"].get(\"CmdPrefix\", \"!\")","async def suggest(self, ctx, *, suggestion: str):\n emojis = [\"✅\", \"❌\"]\n author = ctx.author\n guild = ctx.guild\n embed = Embed(color=Color.blurple(), timestamp=datetime.utcnow())\n embed.add_field(name=\"Suggestion\", value=suggestion)\n embed.set_author(name=f\"Suggestion by - {author}\", icon_url=author.avatar_url)\n msg = await ctx.send(embed=embed)\n await ctx.message.delete()\n for i in range(len(emojis)):\n await msg.add_reaction(emojis[i])","def autocomplete(self): # pylint: disable-msg=R0914\n # Don't complete if user hasn't sourced bash_completion file.\n # This is found in django-trunk/extras/django_bash_completion\n if 'DJANGO_AUTO_COMPLETE' not in os.environ:\n return\n\n cwords = os.environ['COMP_WORDS'].split()[1:]\n cword = int(os.environ['COMP_CWORD'])\n\n try:\n curr = cwords[cword - 1]\n except IndexError:\n curr = ''\n\n subcommands = get_commands().keys() + ['help']\n options = [('--help', None)]\n\n # subcommand\n if cword == 1:\n debug_text = ' '.join(sorted(filter(lambda x: x.startswith(curr),\n subcommands)))\n print(debug_text)\n # subcommand options\n # special case: the 'help' subcommand has no options\n elif cwords[0] in subcommands and cwords[0] != 'help':\n subcommand_cls = self.fetch_command(cwords[0])\n # special case: add the names of installed apps to options\n if cwords[0] in ('dumpdata', 'sql', 'sqlall', 'sqlclear',\n 'sqlcustom', 'sqlindexes', 'sqlsequencereset', 'test'):\n try:\n database = Database()\n # Get the last part of the dotted path as the app name.\n options += [(a.split('.')[-1], 0) for \\\n a in database.get_app_list()]\n except ImportError:\n # Fail silently if DJANGO_SETTINGS_MODULE isn't set. The\n # user will find out once they execute the command.\n pass\n options += [(s_opt.get_opt_string(), s_opt.nargs) for s_opt in\n subcommand_cls.option_list]\n # filter out previously specified options from available options\n prev_opts = [x.split('=')[0] for x in cwords[1:cword - 1]]\n\n # Original Python 2 version\n #options = filter(lambda (x, v): x not in prev_opts, options)\n # Python 3 version?\n #options = filter(lambda x_v: x_v[0] not in prev_opts, options)\n options = [opt for opt in options if opt[0] not in prev_opts]\n\n # filter options by current input\n options = sorted([(k, v) for k, v in \\\n options if k.startswith(curr)])\n for option in options:\n opt_label = option[0]\n # append '=' to options which require args\n if option[1]:\n opt_label += '='\n print(opt_label)\n sys.exit(1)","def prefix_type(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"prefix_type\")","def suggested_commands(self):\n return self.commands()","def completions (cls, cc_prefix) :\n return cls.cc_trie.completions (cc_prefix)","def command_short():\n pass","def autocorrect(self, prefix, N):\r\n def valid_edits():\r\n letters, length = \"abcdefghijklmnopqrstuvwxyz\".upper(), len(prefix)\r\n yield from (\r\n #add letter\r\n prefix[:i] + l + prefix[i:] for i in range(length+1)\r\n for l in letters\r\n )\r\n yield from (\r\n #delete letter\r\n prefix[:i] + prefix[i+1:] for i in range(length)\r\n )\r\n yield from (\r\n #replace letter\r\n prefix[:i] + l + prefix[i+1:] for i in range(length)\r\n for l in letters\r\n )\r\n yield from (\r\n #swap letters\r\n prefix[:i]+prefix[j]+prefix[i+1:j]+prefix[i]+prefix[j+1:]\r\n for i in range(length) for j in range(i+1, length)\r\n )\r\n\r\n edits = []\r\n for e in valid_edits():\r\n f = self.find(e)\r\n if f and f.frequency: edits.append((e, f.frequency))\r\n edits.sort(key=lambda x: x[1])\r\n\r\n #Ensure no duplicates by using set.\r\n l = set(self.autocomplete(prefix, N))\r\n while len(l) < N and edits: l.add(edits.pop()[0])\r\n return list(l)","async def add_deleter(*, new_prefix: str):\n prefixes: list = location.deleter_prefixes['prefixes']\n text_prefixes = location.deleter_prefixes['text_prefixes']\n if new_prefix.strip() in prefixes:\n return f\"{error_sticker} У вас уже есть данный префикс <<{new_prefix}>>\"\n\n prefixes.append(new_prefix.strip())\n location.add_object_the_database(value={\n \"prefixes\": prefixes,\n 'text_prefixes': text_prefixes\n }, method='deleter_prefixes')\n return f\"{complete_sticker} Вы добавили новый префикс <<{new_prefix}>> для удалялки.\" \\\n f\"Сработает после перезапуска.\"","async def help(self, ctx: commands.Context):\n embed = discord.Embed(title=\"RenameX Commands :tools:\", timestamp=datetime.datetime.now(),\n color=discord.Color.green())\n embed.add_field(name=\":arrow_right: **`/rename [member;role;'all'] {bool: bypass_ignore}`**\",\n value=\"Change the nicknames of a player or perhaps even an entire server!\")\n await ctx.send(\"\", embed=embed)","def start_command_handler(update, context):\n add_typing(update, context)\n buttons = MultiItems(\"What would you like to receive?\", [\"Text\", \"File\", \"GoogleDoc\", \"Gallery\"])\n add_suggested_actions(update, context, buttons)","def cmd_type(args):","def guess_word(self, prefix):\n prefix = prefix.lower()\n current = self.search_prefix(prefix)\n if current:\n print(\"You typed: \" + '\"' + prefix + '\"')\n if current.get_end():\n print('\"' + prefix + '\" is a word, but you could have also been typing out:')\n else:\n print('\"' + prefix + '\" is not a word, perhaps you were typing out:')\n library = [prefix + word for word in self.get_library(current, library = [])]\n for word in library:\n print(word)\n return library # list includes empty string if its already a word\n print(\"I'm not quite sure what you meant by \" + '\"' + prefix + '\"...')\n return []","def startswith( self, prefix ):\n return len(self.commands) >= 1 and self.commands[0].startswith( prefix )","async def suggest(self, ctx, *, suggestion):\n suggch = self.bot.get_channel(454695415611260928)\n embed_suggest = discord.Embed(title=ctx.author.name, description=ctx.author.id, color=0x00FF00).set_thumbnail(url=ctx.author.avatar_url)\n embed_suggest.add_field(name=ctx.guild.name, value=ctx.guild.id, inline=False)\n embed_suggest.add_field(name=\"Suggestion\", value=suggestion, inline=False)\n await suggch.send(embed=embed_suggest)","def command_with_prefix(self):\n return self.endpoint_prefix.rstrip('/') + self.command","async def is_guild_shortcut_name(argument, context, verbose=True):\n return await is_shortcut_name(argument, context, \"guild\", verbose)","async def pre(self, ctx):\n settings = config.load_settings()\n guild = ctx.guild.id\n await ctx.send('Current prefix: ' + settings['guilds'][str(guild)]['prefix'])","def do_shortcuts(self, args):\n result = \"\\n\".join('%s: %s' % (sc[0], sc[1]) for sc in sorted(self.shortcuts))\n self.poutput(\"Shortcuts for other commands:\\n{}\\n\".format(result))","def __editAutoCompleteFromAPIs(self):\n self.activeWindow().autoCompleteFromAPIs()","def test_sendCommandWithPrefix(self):\n self.p.sendCommand(\"CMD\", (\"param1\", \"param2\"), \"irc.example.com\")\n self.check(b\":irc.example.com CMD param1 param2\\r\\n\")","def autocomplete(self, token):\n sub_dict = self.container\n for letter in token:\n try:\n sub_dict = sub_dict[letter]\n except KeyError:\n return []\n auto_list = []\n for word_fragment in self.traversal(start=sub_dict):\n if len(auto_list) < 4:\n auto_list.append(token + word_fragment)\n else:\n break\n return auto_list","def main():\r\n # Sample implementation of the autocomplete API\r\n query = lambda prefix: [d for d in database if d.startswith(prefix)][:5]\r\n\r\n print(\"test case 1: assigned list\")\r\n database = sorted([\r\n \"abracadara\", \"al\", \"alice\", \"alicia\", \"allen\", \"alter\", \"altercation\", \"bob\", \"eve\", \"evening\", \"event\",\r\n \"eventually\", \"mallory\"\r\n ])\r\n assert extract(query) == database\r\n print(\"Passed! Evolution: Pichu\")\r\n print(\"\\r\")\r\n\r\n print(\"test case 2: lots of 'username + a-z'\")\r\n database = ['usernamea', 'usernameb', 'usernamec', 'usernamed', 'usernamee', 'usernamef', 'usernameg', 'usernameh', 'usernamei', 'usernamej', 'usernamek', 'usernamel', 'usernamem', 'usernamen', 'usernameo', 'usernamep', 'usernameq', 'usernamer', 'usernames', 'usernamet', 'usernameu', 'usernamev', 'usernamew', 'usernamex', 'usernamey', 'usernamez']\r\n query = lambda prefix: [d for d in database if d.startswith(prefix)][:5]\r\n assert extract(query) == database\r\n print(\"Passed! Evolution: Pikachu\")\r\n print(\"\\r\")\r\n\r\n print(\"test case 3: empty\")\r\n database = sorted([\r\n ])\r\n assert extract(query) == database\r\n print(\"Passed! Evolution: Raichu\")","def apply_prefix(string):\n for short, long in PREFIXES.items():\n if string.startswith(long):\n return short + ':' + string[len(long):]\n return string","def __setupCommandHandlerTypes(self):\n # dict saving all command handler types\n self.__commandHandlers = {'channel': {}, 'query': {}, 'not_authed_dcc': {}, 'authed_dcc': {}}","def alias_matches(self, text):\n #print 'Completer->alias_matches:',text # dbg\n text = os.path.expanduser(text)\n aliases = self.alias_table.keys()\n if text == \"\":\n return aliases\n else:\n return [alias for alias in aliases if alias.startswith(text)]","def autocomplete():\n value = str(request.args.get('q'))\n result = s.query(Genes).filter(Genes.name.like(\"%\" + value + \"%\")).all()\n data = [i.name for i in result]\n return jsonify(matching_results=data)","async def prefix(self, ctx, *, prefix=None):\n\n current = self.bot.prefix\n embed = Embed(\n title=\"Current prefix\", color=self.bot.main_color, description=f\"{current}\"\n )\n\n if prefix is None:\n await ctx.send(embed=embed)\n else:\n embed.title = \"Changed prefix!\"\n embed.description = f\"Set prefix to `{prefix}`\"\n self.bot.config[\"prefix\"] = prefix\n await self.bot.config.update()\n await ctx.send(embed=embed)","def complete(self, text: str, word: str) -> List[str]:\n prefix = word.lower()\n result = [prop.name for prop in self.context.debug_info.properties\n if prop.name.lower().startswith(prefix)]\n\n # If the users didn't ask for a special property, don't suggest special\n # properties, as they are usually just noise for them.\n if not prefix.startswith('['):\n result = [n for n in result if not n.startswith('[')]\n\n return result","def __editAutoCompleteFromAll(self):\n self.activeWindow().autoCompleteFromAll()","def func(self):\n from evennia.utils.utils import string_suggestions, list_to_string\n\n msg = \"Command '%s' is not available.\" % self.raw\n cmdset = self.cmdset\n cmdset.make_unique(self.caller)\n all_cmds = [cmd for cmd in cmdset if cmd.auto_help and cmd.access(self.caller)]\n names = []\n for cmd in all_cmds:\n # noinspection PyProtectedMember\n names.extend(cmd._keyaliases)\n suggestions = string_suggestions(self.raw, set(names), cutoff=0.7)\n if suggestions:\n msg += \" Maybe you meant %s?\" % list_to_string(\n suggestions, \"or\", addquote=True\n )\n else:\n msg += ' Type \"help\" for help.'\n self.msg(msg)","def do_shortcuts(self, args):\n result = \"\\n\".join('%s: %s' % (sc[0], sc[1]) for sc in sorted(self.shortcuts))\n self.stdout.write(\"Single-key shortcuts for other commands:\\n{}\\n\".format(result))","def handle_suggest():\n return 0","def __editAutoComplete(self):\n self.activeWindow().autoComplete()","async def on_message(self, msg: discord.Message) -> None:\n # If the prefix is set\n if self.prefix != '':\n # Check if the received message was not sent by the bot itself\n if msg.author != self.user:\n if msg.content.startswith(self.prefix):\n command = msg.content[1:].split(' ')[0]\n args = msg.content[1:].split(' ')[1:]\n # Send command with arguments to on_command function\n await self.on_command(command, args, msg)","def autoprefix(prefix):\n pl = len(prefix)\n msg = '%%(s)r: expected some name after %(prefix)r!' % locals()\n def checker(s):\n if s.startswith(prefix):\n tail = s[pl:]\n if tail:\n return prefix + dotted_name(tail)\n else:\n raise ValueError(msg % locals())\n elif s:\n return prefix + dotted_name(s)\n else:\n return ''\n return checker","def _commands(self) -> Dict[str, List[str]]:\r\n pass","def getAutoCompleteKeys(self):\n return [ord('.'), ord(' '), ord('/')]","def load_alias(name):\n mod = importlib.import_module(\"umdone.commands.\" + name)\n main = getattr(mod, \"main\")\n builtins.aliases[name] = main\n builtins.aliases[name.replace(\"_\", \"-\")] = main","def autosuggest(self, suggest, focus=None, clip=None, display='full',\n format='json', lang=None):\n\n params = {\n 'addr': suggest,\n 'display': display,\n 'format': format,\n 'lang': lang or self.lang,\n }\n if focus:\n params.update({\n 'focus': focus\n })\n if clip:\n params.update({\n 'clip': clip\n })\n\n return self._request('/autosuggest', params)","async def prefix(self, ctx, *, pre):\n server = ctx.message.guild\n if pre.endswith('\\w'):\n pre = pre[:-2]+' '\n if len(pre.strip) > 0:\n msg = f'The server prefix has been set to `{pre}` Use `{pre}prefix <prefix>` to change it again.'\n else:\n await ctx.send('Invalid prefix.')\n return\n else:\n msg = f'The server prefix has been set to `{pre}` Use `{pre}prefix <prefix>` to change it again. ' \\\n f'If you would like to add a trailing whitespace to the prefix, use `{pre}prefix {pre}\\w`.'\n\n await self.bot.db.config.update_one({'_id': str(server.id)}, {'$set': {'prefix': str(pre)}}, upsert=True)\n self.bot.pre[str(server.id)] = str(pre)\n await ctx.send(msg)","def test_prefix(self):\n self.chck_triple('prefix')","def set_prefix(prefix):\n PLUGINS.set_prefix(prefix)"],"string":"[\n \"async def process_prefix_list(\\n guild: disnake.Guild,\\n ctx: commands.Context = None,\\n inter: AppCmdInter = None,\\n allowed_mentions=None,\\n):\\n await create_guild_model(guild)\\n guild = await Guild.get(guild.id)\\n msg = f\\\"The following are the custom prefixes for {guild.name}:\\\\n\\\" + \\\", \\\".join(\\n guild.prefixes\\n )\\n await send_message(msg=msg, ctx=ctx, inter=inter, allowed_mentions=allowed_mentions)\",\n \"def complete_prefix(self, prefix):\\n return tuple(\\n k for k in self.commands.keys()\\n if k.startswith(prefix)\\n )\",\n \"def autocomplete(self, prefix):\\n if len(prefix) == 0:\\n \\n __________________________________________\\n \\n if prefix[0] not in self.branches:\\n \\n __________________________________________\\n \\n ______________________________________________\",\n \"async def prefix(self, ctx):\\n prefixes = await self.bot.get_prefix(ctx.message)\\n formatted = self._format_prefixes(prefixes)\\n await ctx.send(f\\\"You can mention me or use any of the following \\\"\\n f\\\"prefixes like so: {formatted}\\\")\",\n \"def get_prefix(bot, message):\\n\\n # Add prefixes that you want the bot to respond to\\n prefixes = ['?', '!']\\n\\n # Check to see if we are outside of a guild. e.g DM's etc.\\n if not message.guild:\\n # Only allow ? to be used in DMs\\n return '?'\\n\\n # If we are in a guild, we allow for the user to mention us or use any of the prefixes in our list.\\n return commands.when_mentioned_or(*prefixes)(bot, message)\",\n \"async def get_command_prefix(self, guild: Guild) -> str:\\n if (prefix := self._cache[guild.id].prefix) is None:\\n db_guild = await self.find_by_id(guild.id)\\n\\n if db_guild is None:\\n await self.create_from_gateway_response(guild)\\n db_guild = await self.find_by_id(guild.id)\\n\\n self._cache[guild.id].prefix = prefix = db_guild[\\\"command_prefix\\\"]\\n\\n return prefix\",\n \"async def get_prefixes(self, guild: Guild) -> List[str]:\\n if not guild:\\n return []\\n\\n return await self.redis.smembers(\\n f'dog:prefixes:{guild.id}', encoding='utf-8')\",\n \"async def _set_prefix(self, ctx: Context, *prefixes: str):\\n\\n if not prefixes:\\n await ctx.bot._prefix_cache.set_prefixes(\\n guild=ctx.guild, prefixes=[]\\n )\\n await ctx.send(\\\"Server prefixes have been reset.\\\")\\n return\\n prefixes = sorted(prefixes, reverse=True)\\n await ctx.bot._prefix_cache.set_prefixes(\\n guild=ctx.guild, prefixes=prefixes\\n )\\n inline_prefixes = [f\\\"`{prefix}`\\\" for prefix in prefixes]\\n await ctx.send(\\n f\\\"Set {', '.join(inline_prefixes)} as server\\\"\\n f\\\" {'prefix' if len(prefixes) == 1 else 'prefixes'}.\\\"\\n )\",\n \"async def prefix(self, ctx, prefix):\\n if prefix.strip() == \\\"\\\":\\n raise exceptions.Warning(\\\"Prefix cannot be empty.\\\")\\n\\n if prefix.startswith(\\\" \\\"):\\n raise exceptions.Warning(\\\"Prefix cannot start with a space.\\\")\\n\\n if len(prefix) > 32:\\n raise exceptions.Warning(\\\"Prefix cannot be over 32 characters.\\\")\\n\\n prefix = prefix.lstrip()\\n await self.bot.db.execute(\\n \\\"\\\"\\\"\\n INSERT INTO guild_prefix (guild_id, prefix)\\n VALUES (%s, %s)\\n ON DUPLICATE KEY UPDATE\\n prefix = VALUES(prefix)\\n \\\"\\\"\\\",\\n ctx.guild.id,\\n prefix,\\n )\\n self.bot.cache.prefixes[str(ctx.guild.id)] = prefix\\n await util.send_success(\\n ctx,\\n f\\\"Command prefix for this server is now `{prefix}`. \\\"\\n f\\\"Example command usage: {prefix}ping\\\",\\n )\",\n \"async def set_command_prefix(self, guild: Guild, prefix: str) -> bool:\\n\\n async with self.db_pool.acquire() as conn:\\n result = await conn.execute(\\n f\\\"UPDATE {self.table_name} \\\"\\n \\\"SET command_prefix = $1 \\\"\\n \\\"WHERE id = $2\\\",\\n prefix,\\n guild.id,\\n )\\n\\n if result := (int(result.split()[1]) == 1) is True:\\n self._cache[guild.id].prefix = prefix\\n\\n return result\",\n \"async def setprefix(self, ctx, *, prefix=bot_prefix):\\n prefix = prefix.lower()\\n current_server_prefix = await self.ex.get_server_prefix(ctx.guild.id)\\n if len(prefix) > 8:\\n await ctx.send(\\\"> **Your prefix can not be more than 8 characters.**\\\")\\n else:\\n # Default prefix '%' should never be in DB.\\n if current_server_prefix == \\\"%\\\":\\n if prefix != \\\"%\\\":\\n await self.ex.conn.execute(\\\"INSERT INTO general.serverprefix VALUES ($1,$2)\\\", ctx.guild.id, prefix)\\n self.ex.cache.server_prefixes[ctx.guild.id] = prefix\\n else:\\n if prefix != \\\"%\\\":\\n await self.ex.conn.execute(\\\"UPDATE general.serverprefix SET prefix = $1 WHERE serverid = $2\\\",\\n prefix, ctx.guild.id)\\n self.ex.cache.server_prefixes[ctx.guild.id] = prefix\\n else:\\n await self.ex.conn.execute(\\\"DELETE FROM general.serverprefix WHERE serverid = $1\\\", ctx.guild.id)\\n self.ex.cache.server_prefixes.pop(ctx.guild.id, None)\\n await ctx.send(f\\\"> **This server's prefix has been set to {prefix}.**\\\")\",\n \"async def _setprefix(self, ctx, arg1):\\n if len(arg1) > 6:\\n await ctx.send(\\\"Keep the prefix under 6 chars, please.\\\")\\n return\\n\\n guildconfig = database.getGuild(ctx.guild.id)\\n\\n if not guildconfig:\\n guildconfig = database.newGuild(ctx.guild.id)\\n\\n database.setPrefix(guildconfig.ID, arg1)\\n await ctx.send(\\\"Prefix set successfully!\\\")\",\n \"async def on_guild_join(self, guild: discord.Guild):\\n with open(\\\"./config/prefixes.json\\\", \\\"r\\\") as f:\\n prefixes = json.load(f)\\n\\n prefixes[str(guild.id)] = \\\".\\\"\\n\\n with open(\\\"./config/prefixes.json\\\", \\\"w\\\") as f:\\n json.dump(prefixes, f, indent=4)\",\n \"def suggestions(prefix : str = typer.Argument(...), n : int = typer.Option(5, help=\\\"Number of suggestions to display\\\")): \\n response_url = url + \\\"/suggestions/\\\" + prefix + \\\"?suggestion_nums=\\\" + str(n) \\n response = requests.get(response_url) \\n for i in range(len(response.json())):\\n typer.echo(response.json()[i])\",\n \"async def nuke_commands(self):\\n print(\\\"nuking...\\\")\\n await self.delete_global_commands()\\n print(\\\"nuked global commands\\\")\\n async for guild in self._discord.fetch_guilds():\\n print(\\\"nuking commands in\\\", guild.id)\\n await self.delete_guild_commands(guild.id)\\n print(\\\"nuked commands in\\\", guild.id, guild.name)\\n print(\\\"nuked\\\")\",\n \"def type_command(ctx, name_from, name_to):\",\n \"async def prefix(self, _bot, message: discord.Message):\\n mention = [self.user.mention + ' ', f'<@!{self.user.id}> ']\\n additional_prefixes = await self.get_prefixes(message.guild)\\n return self.cfg['bot']['prefixes'] + mention + additional_prefixes\",\n \"async def set_prefix(self, ctx, prefix: commands.clean_content, allow_default=False):\\n\\n if len(prefix) > 25:\\n return await ctx.send(\\\":no_entry: | prefixes can't be 25 characters or greater.\\\")\\n\\n if re.findall(r\\\"<a?:\\\\w*:\\\\d*>\\\", prefix):\\n return await ctx.send(\\\":no_entry: | emoji's are not allowed as a guild's prefix\\\")\\n\\n if re.findall(r'https?://(?:[-\\\\\\\\w.]|(?:%[\\\\\\\\da-fA-F]{2}))+', prefix):\\n return await ctx.send(\\\":no_entry: | urls are not allowed as a guild's prefix\\\")\\n\\n async with ctx.acquire():\\n await ctx.db.execute(\\\"\\\"\\\"\\n INSERT INTO guilds (guild_id, prefix, allow_default) VALUES ($1, $2, $3)\\n ON CONFLICT (guild_id) DO UPDATE SET (prefix, allow_default) = ($2, $3)\\n \\n \\\"\\\"\\\", ctx.guild.id, prefix, allow_default)\\n\\n await ctx.send(f\\\"The prefix for this guild is now {prefix}\\\")\",\n \"async def change_prefix(ctx, prefix):\\n if \\\" \\\" in prefix:\\n await ctx.send(\\\"Not a valid prefix\\\")\\n else:\\n Config.set_guild_prefix(ctx.guild, prefix)\\n await ctx.send(\\\"Update prefix to `\\\" + str(prefix) + \\\"`\\\")\",\n \"async def prefix(self, ctx, prefix: str = None):\\n if not prefix:\\n try:\\n return await ctx.send(f'My prefix here is `{self.bot.prefixes[str(ctx.guild.id)]}`. You can change that with `{ctx.prefix}prefix <prefix>`')\\n except KeyError:\\n return await ctx.send(f'My prefix here is `{config.prefix[0]}`. You can change that with `{ctx.prefix}prefix <prefix>`')\\n db = pymysql.connect(config.db_ip, config.db_user, config.db_pass, config.db_name)\\n cur = db.cursor()\\n cur.execute(\\n f\\\"\\\"\\\"INSERT INTO settings (guildid, prefix) VALUES ({ctx.guild.id}, \\\"{prefix}\\\") ON DUPLICATE KEY UPDATE prefix = \\\"{prefix}\\\";\\\"\\\"\\\")\\n db.commit()\\n db.close()\\n self.bot.prefixes = get_all_prefixes()\\n await ctx.send(f':ok_hand: Successfully set my prefix here to `{prefix}`')\",\n \"async def get_prefix(_bot, message):\\n if isinstance(message.channel, discord.DMChannel):\\n return _bot.config.dm_prefix\\n\\n if message.author == _bot.owner:\\n return _bot.config.owner_prefix\\n\\n prefix_for_this_guild = await _bot.db.fetchrow(\\n \\\"\\\"\\\"\\n SELECT prefix\\n FROM guilds\\n WHERE id=$1\\n \\\"\\\"\\\",\\n message.guild.id,\\n )\\n\\n if prefix_for_this_guild is None:\\n await _bot.db.execute(\\n \\\"\\\"\\\"\\n INSERT INTO guilds (id, prefix)\\n VALUES ($1, $2)\\n \\\"\\\"\\\",\\n message.guild.id,\\n \\\",\\\",\\n )\\n prefix_for_this_guild = {\\\"prefix\\\": _bot.config.default_prefix}\\n\\n prefix_return = str(prefix_for_this_guild[\\\"prefix\\\"])\\n return commands.when_mentioned_or(prefix_return)(_bot, message)\",\n \"async def fetch_prefix(self, bot: 'ContestBot', message: discord.Message):\\n user_id = bot.user.id\\n base = [f'<@!{user_id}> ', f'<@{user_id}> ']\\n\\n if message.guild:\\n with self.get_session() as session:\\n guild: Guild = session.query(Guild).get(message.guild.id)\\n base.append(guild.prefix)\\n return base\",\n \"async def before_any_command(ctx):\\n ctx.timer = time()\\n try:\\n await ctx.trigger_typing()\\n except discord.errors.Forbidden:\\n pass\",\n \"async def prefix(self, ctx, prefix):\\n # Get the server language\\n lang = getLang(ctx.message.guild.id)\\n\\n if len(prefix) > 10:\\n with open(f\\\"embeds/{lang}/prefix.json\\\", \\\"r\\\") as f:\\n await ctx.reply(embed=discord.Embed.from_dict(json.load(f)['len-error']), delete_after=20)\\n\\n # Change prefix\\n with open('serverconfig/prefixes.json', 'r') as f:\\n prefixes = json.load(f)\\n old_prefix = prefixes[str(ctx.guild.id)]\\n prefixes[str(ctx.guild.id)] = prefix\\n with open('serverconfig/prefixes.json', 'w') as f:\\n json.dump(prefixes, f, indent=4)\\n\\n # Get the embed of the right language and send with replaced variable\\n with open(f\\\"embeds/{lang}/prefix.json\\\", \\\"r\\\") as f:\\n embed = json.load(f)['embed']\\n\\n embed['description'] = embed['description'].replace(\\\"%VAR\\\", prefix)\\n await ctx.reply(embed=discord.Embed.from_dict(embed), mention_author=False, delete_after=20)\",\n \"async def prefix_process(bot, msg: discord.Message):\\n if msg.guild:\\n g_prefix = await bot.sql.get_guild_prefix(bot.db, guildID=msg.guild.id)\\n if g_prefix:\\n return g_prefix, bot.default_prefix\\n return bot.default_prefix\",\n \"def autocorrect(trie, prefix, max_count=None):\\n\\n autocompleted = autocomplete(trie, prefix, max_count)\\n\\n edits = get_valid_edits(trie, prefix)\\n\\n edits_items = [(key, trie[key]) for key in edits]\\n sorted_edits_items = sorted(edits_items, key=lambda x: x[1], reverse=True)\\n result = [word for word, _ in sorted_edits_items]\\n\\n if max_count is None:\\n\\n return autocompleted + result\\n\\n if len(autocompleted) < max_count:\\n\\n return autocompleted + result[:max_count - len(autocompleted)]\\n\\n return autocompleted\",\n \"async def setprefix(self, ctx: Context, prefix: str):\\n if ctx.guild is None:\\n raise NoPrivateMessage(\\\"You cannot use this command in a DM\\\")\\n\\n if await GuildRepository().set_command_prefix(ctx.guild, prefix):\\n message = f\\\"Command prefix changed to `{prefix}`.\\\"\\n else:\\n message = f\\\"`{prefix}` is already your prefix.\\\"\\n\\n await ctx.send(content=message)\",\n \"def autocomplete():\\n query = '' if request.args.get('query') is None else request.args.get('query')\\n\\n prefixed_words = []\\n close_words = []\\n for f in app.preprocessed.words:\\n lowered = f.lower()\\n if lowered.startswith(query) and lowered != query:\\n prefixed_words.append(f)\\n elif levenshtein(query, lowered) <= 1:\\n close_words.append(f)\\n\\n result = {\\n 'success': True,\\n 'data': {\\n 'suggestions': prefixed_words + close_words\\n }\\n }\\n return jsonify(result)\",\n \"async def adding_command_list(self):\\n command_aliases=['anime','fun','mod','nekogif'] #This includes the aliases and the cog names\\n #NOTE: fun command added\\n for i in self.bot.commands:\\n self.commands.append(i.name)\\n \\n for i in command_aliases:\\n self.commands.append(i)\",\n \"def complete(self, prefix):\\n words = list(set([word for word in self._vocabulary if word.startswith(prefix)]))\\n if len(words) <= self._max_completions:\\n return words\\n else:\\n return words[:self._max_completions]\",\n \"async def get_prefix(client, message):\\n conf1 = json.load(open(\\\"json/serverconfig.json\\\", 'r'))\\n guild = message.guild\\n if guild:\\n return conf1[str(guild.id)][\\\"prefix\\\"]\\n else:\\n return get_default_prefix()\",\n \"async def process_prefix_add_remove(\\n guild: disnake.Guild,\\n prefix: str,\\n ctx: commands.Context = None,\\n inter: AppCmdInter = None,\\n allowed_mentions=None,\\n add=False,\\n):\\n await create_guild_model(guild)\\n guild = await Guild.get(guild.id)\\n\\n if add:\\n await guild.add_prefix(prefix)\\n msg = f\\\"{prefix.lower()} has been added as a command prefix for {guild.name}.\\\"\\n else:\\n await guild.delete_prefix(prefix)\\n msg = (\\n f\\\"{prefix.lower()} has been removed as a command prefix from {guild.name}.\\\"\\n )\\n\\n await send_message(msg=msg, ctx=ctx, inter=inter, allowed_mentions=allowed_mentions)\",\n \"def autoComplete(prefix, Trie, top):\\n\\ttemp_node = Trie.search(prefix)\\n\\tsuggest_list = []\\n\\tword_list = []\\n\\tPqueue = PriorityQueue()\\n\\tfor child in temp_node.children.keys():\\n\\t\\tnode = temp_node.children[child]\\n\\t\\tPqueue.insert((child,node), node.weight)\\n\\tfor i in range(top):\\n\\t\\tword_list.append(prefix)\\n\\twhile len(suggest_list) < top and Pqueue.is_empty() == False:\\n\\t\\tcharacter, candidate_node = Pqueue.extract_highest()\\n\\t\\tif candidate_node.isEnd == False:\\n\\t\\t\\tfor child in candidate_node.children.keys():\\n\\t\\t\\t\\tPqueue.insert((character+child,candidate_node.children[child]), candidate_node.children[child].weight)\\n\\t\\telse:\\n\\t\\t\\tword_list[len(suggest_list)] = word_list[len(suggest_list)] + character\\n\\t\\t\\tsuggest_list.append((word_list[len(suggest_list)], candidate_node.weight))\\n\\treturn suggest_list\",\n \"async def get_prefix(self, bot, message):\\n async with aiosqlite.connect('data\\\\\\\\config.db') as conn:\\n async with conn.execute(\\\"SELECT * from CONFIG\\\") as cursor:\\n async for row in cursor:\\n if row[0] == message.guild.id:\\n await levels.insert(message.author.id, message.author.guild.id)\\n prefixes = [row[1]]\\n return commands.when_mentioned_or(*prefixes)(bot, message)\\n await conn.execute(f\\\"INSERT INTO COUNTING (GUILD) \\\\\\n VALUES ({message.guild.id})\\\")\\n await conn.execute(f\\\"INSERT INTO CONFIG (GUILD, PREFIX) \\\\\\n VALUES ({message.guild.id}, '>')\\\")\\n await AutoModSettingsManager().create(message.guild.id)\\n await levels.insert(message.author.id, message.author.guild.id)\\n prefixes = ['>']\\n return commands.when_mentioned_or(*prefixes)(bot, message)\",\n \"def autocomplete(trie, prefix, max_count=None):\\n\\n if type(prefix) != trie.type:\\n raise TypeError\\n\\n result = []\\n\\n if max_count == 0:\\n return result\\n\\n if prefix in trie:\\n result.append((prefix, trie[prefix]))\\n\\n for i, elt in enumerate(prefix):\\n if isinstance(prefix, tuple):\\n elt = (elt,)\\n\\n if elt in trie.children:\\n trie = trie.children[elt]\\n else:\\n return result\\n\\n if i == len(prefix) - 1:\\n break\\n\\n for key, _ in trie:\\n result.append((prefix + key, trie[key]))\\n\\n sorted_result = sorted(result, key=lambda x: x[1], reverse=True)\\n\\n if max_count is None or len(result) <= max_count:\\n return [key for key, _ in sorted_result]\\n\\n return [key for key, _ in sorted_result[:max_count]]\",\n \"def commands_for_submode(prefix):\\n candidates = _lookup_command_candidates(prefix, command_registry)\\n # print type(candidates), [x['self'] for x in candidates]\\n # print [x.get('all-help') for x in candidates]\\n if debug.cli():\\n # print 'commands_for_submode:', sorted(dict([[x['name']['title'] if type(x['name']) == dict else x['name'], None] for x in candidates]).keys())\\n pass\\n return candidates\",\n \"def prefix_type(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"prefix_type\\\")\",\n \"def quick_completer(cmd, completions):\\n if isinstance(completions, basestring):\\n \\n completions = completions.split()\\n def do_complete(self,event):\\n return completions\\n \\n ip.set_hook('complete_command',do_complete, str_key = cmd)\",\n \"def get_command_prefix(self):\\n return self.bot_data_file[\\\"commands_prefix\\\"]\",\n \"async def get_prefix(bot: Bot, message: Message):\\n return appearance.get_prefix(guild_id=message.guild.id)\",\n \"def raw_command_completer(self, text, line, start_index, end_index):\\n return [command for command in self.suggested_commands() if command.startswith(text)]\",\n \"def autocomplete(self):\\n # Don't complete if user hasn't sourced bash_completion file.\\n if 'TORNADO_AUTO_COMPLETE' not in os.environ:\\n return\\n \\n cwords = os.environ['COMP_WORDS'].split()[1:]\\n cword = int(os.environ['COMP_CWORD'])\\n \\n try:\\n curr = cwords[cword-1]\\n except IndexError:\\n curr = ''\\n \\n envs = self.get_envs()\\n \\n subcommands = get_commands().keys() + envs + ['help']\\n options = [('--help', None)]\\n \\n # subcommand\\n if cword == 1:\\n print ' '.join(sorted(filter(lambda x: x.startswith(curr), subcommands)))\\n # subcommand options\\n # special case: the 'help' subcommand has no options\\n elif cwords[0] in subcommands and cwords[0] != 'help':\\n subcommand_cls = self.fetch_command(cwords[0])\\n # special case: 'runfcgi' stores additional options as\\n # 'key=value' pairs\\n if cwords[0] == 'runfcgi':\\n from django.core.servers.fastcgi import FASTCGI_OPTIONS\\n options += [(k, 1) for k in FASTCGI_OPTIONS]\\n # special case: add the names of installed apps to options\\n elif cwords[0] in ('dumpdata', 'reset', 'sql', 'sqlall',\\n 'sqlclear', 'sqlcustom', 'sqlindexes',\\n 'sqlreset', 'sqlsequencereset', 'test'):\\n try:\\n from django.conf import settings\\n # Get the last part of the dotted path as the app name.\\n options += [(a.split('.')[-1], 0) for a in settings.INSTALLED_APPS]\\n except ImportError:\\n # Fail silently if DJANGO_SETTINGS_MODULE isn't set. The\\n # user will find out once they execute the command.\\n pass\\n options += [(s_opt.get_opt_string(), s_opt.nargs) for s_opt in\\n subcommand_cls.option_list]\\n # filter out previously specified options from available options\\n prev_opts = [x.split('=')[0] for x in cwords[1:cword-1]]\\n options = filter(lambda (x, v): x not in prev_opts, options)\\n \\n # filter options by current input\\n options = sorted([(k, v) for k, v in options if k.startswith(curr)])\\n for option in options:\\n opt_label = option[0]\\n # append '=' to options which require args\\n if option[1]:\\n opt_label += '='\\n print opt_label\\n sys.exit(1)\",\n \"async def help_bot(prefixe, message):\\n\\n msg_embed = {\\n \\\"color\\\": 6158690, # Couleur de la barre\\n \\\"fields\\\": [\\n # Zone 1\\n {\\n \\\"name\\\": \\\"Personnaliser le bot\\\",\\n \\\"value\\\": f\\\"\\\"\\\"`{prefixe}prefixechange`\\\"\\\"\\\"\\n },\\n # Zone 2\\n {\\n \\\"name\\\": \\\"Misc\\\",\\n \\\"value\\\": f\\\"\\\"\\\"`$$$what` `{prefixe}help` `{prefixe}poll` `{prefixe}8ball` `{prefixe}russian`\\\"\\\"\\\"\\n },\\n # Zone 3\\n {\\n \\\"name\\\": \\\"Administration\\\",\\n \\\"value\\\": f\\\"\\\"\\\"`{prefixe}clear`\\\"\\\"\\\"\\n },\\n # Zone 4\\n {\\n \\\"name\\\": \\\"Niveaux et Profils\\\",\\n \\\"value\\\": f\\\"\\\"\\\"`{prefixe}profile` `{prefixe}rank` `{prefixe}alert`\\\"\\\"\\\"\\n },\\n # Zone 5\\n {\\n \\\"name\\\": \\\"Anniversaires\\\",\\n \\\"value\\\": f\\\"\\\"\\\"`{prefixe}addbirth` `{prefixe}removebirth` `{prefixe}nextbirth`\\\"\\\"\\\"\\n }],\\n \\\"footer\\\":\\n {\\n \\\"icon_url\\\": \\\"https://cdn.discordapp.com/attachments/487002983557627936/715329727757549568/portrait2.jpg\\\",\\n \\\"text\\\": \\\"Bot by Takase\\\"\\n },\\n \\\"title\\\": \\\"You Can (Not) Advance\\\",\\n \\\"url\\\": \\\"https://arthurtakase.github.io\\\",\\n \\\"description\\\": \\\"Un magnifique bot discord qui peut tout faire !\\\"\\n }\\n await message.channel.send(embed=discord.Embed.from_dict(msg_embed))\\n return\",\n \"async def prefix(self, ctx, *, new_prefix: str = None):\\n\\n if not ctx.guild:\\n if new_prefix:\\n await ctx.error(\\\"Prefix cannot be set in DMs.\\\")\\n return\\n\\n await ctx.info(f\\\"Prefix is {self.bot.default_prefix}\\\")\\n return\\n\\n if not new_prefix:\\n guild_prefix = self.bot.prefixes.get(ctx.guild.id)\\n prefix = guild_prefix or self.bot.default_prefix\\n await ctx.info(f\\\"Prefix is {prefix}\\\")\\n return\\n\\n if await checks.check_is_admin(ctx):\\n await db.execute_sql(\\n \\\"INSERT OR REPLACE INTO prefixes(guild_id, prefix)\\\"\\n \\\"VALUES(?, ?)\\\", (ctx.guild.id, new_prefix)\\n )\\n\\n self.bot.prefixes[ctx.guild.id] = new_prefix\\n\\n await ctx.info(f\\\"Prefix set to {new_prefix}\\\")\\n return\\n\\n await ctx.error(\\\"Prefix can only be set by admins.\\\")\\n return\",\n \"def setup_command_dict(self):\\n\\n out = {}\\n for k, v in self.command_aliases.items():\\n for i in v:\\n out[i] = k # string typed by player:function of MyThing\\n return out\",\n \"def cmd(name: str) -> Callable:\\n return g.new_cmd_decorator(name, ['c', 'spellCommands',])\",\n \"def _lookup_command_candidates(command_prefix, command_list):\\n candidates = []\\n current_mode = bigsh.run.finder.mode_stack.current_mode()\\n try:\\n for command in command_list:\\n modes = _get_applicable_modes(command)\\n if _match_current_modes(command, current_mode, modes):\\n name = command['name']\\n if (type(name) == str and\\n name.startswith(command_prefix.lower())):\\n candidates.append(command)\\n # should check the type of command_prefix,\\n # and for str, ame.match(command_prefix):\\n if type(name) == dict:\\n if 're' not in name:\\n command['name']['re'] = re.compile(name['pattern'])\\n if name['re'].match(command_prefix):\\n candidates.append(command)\\n if type(name) == dict and \\\\\\n name['re'].match(command_prefix):\\n candidates.append(command)\\n \\n except Exception, _e:\\n if debug.cli():\\n print _line(), 'Backtrace'\\n traceback.print_exc()\\n raise error.CommandDescriptionError('Missing mode or name', command)\\n \\n return candidates\",\n \"def suggest_name(self, workspace: Workspace, role: str, *args) -> str:\\n return role + \\\"-api\\\"\",\n \"async def on_guild_remove(self, guild: discord.Guild):\\n with open(\\\"./config/prefixes.json\\\", \\\"r\\\") as f:\\n prefixes = json.load(f)\\n\\n prefixes.pop(str(guild.id))\\n\\n with open(\\\"./config/prefixes.json\\\", \\\"w\\\") as f:\\n json.dump(prefixes, f, indent=4)\",\n \"def getAutoCompleteList(self, rawCmd='', *args, **kwds):\\n try:\\n actKey = rawCmd[-1] #Was it activated by a '/', '.' or ' ' ?\\n cmd = re.sub('#.*', '', rawCmd) # remove comments\\n cmd = string.strip(cmd)\\n if not cmd: return None \\n \\n # get lhs description\\n (lhsDesc, remaining) = self.cmd.GetNextTerm( cmd ) \\n \\n lst = []\\n \\n #Get contents from the root\\n if actKey == '/':\\n if hasattr(self.cmd.root, 'GetContents'):\\n lst = []\\n for i in self.cmd.root.GetContents():\\n lst.append(i[0])\\n \\n #Try different options\\n elif actKey == '.':\\n myDesc = string.split(cmd, ' ')[-1][:-1] \\n if myDesc[0] == '/': lhsObj = self.cmd.GetObject(self.cmd.root, myDesc[1:])\\n else: lhsObj = self.cmd.GetObject(self.cmd.currentObj, myDesc)\\n \\n #Object with get contents attr\\n if hasattr(lhsObj, 'GetContents'):\\n lst = []\\n for i in lhsObj.GetContents():\\n lst.append(i[0])\\n \\n #If it is a thermo provider, return available prop pkgs\\n elif myDesc in self.cmd.thermoAdmin.GetAvThermoProviderNames():\\n thAd = self.cmd.thermoAdmin\\n lst = thAd.GetAvPropPkgNames(myDesc)\\n \\n #If a folder with unit ops, then retun av u ops\\n elif myDesc in unitop.__all__:\\n uop = guicmd.CommandInterface.__dict__.get(myDesc, None)\\n if hasattr(uop, 'VALID_UNIT_OPERATIONS'):\\n lst = uop.VALID_UNIT_OPERATIONS\\n \\n #Is it a command?\\n elif guicmd.CommandInterface.commands.has_key(lhsDesc):\\n cmdActOnObj = ('cd', 'view', 'delete', 'dir', 'valueOf')\\n lst = []\\n if lhsDesc == 'units':\\n if actKey == ' ' and remaining == '':\\n lst = self.cmd.units.GetSetNames()\\n elif lhsDesc in cmdActOnObj:\\n if actKey == ' ' and remaining == '':\\n lst = ['..', '/']\\n if hasattr(self.cmd.currentObj, 'GetContents'):\\n for i in self.cmd.currentObj.GetContents():\\n lst.append(i[0])\\n elif lhsDesc == 'language':\\n if actKey == ' ' and remaining == '':\\n dct = guicmd.CommandInterface.MessageHandler.GetSupportedLanguages()\\n #dct['languages'] should have the main languages supported\\n lst = list(dct['languages'])\\n \\n lst.sort()\\n return lst\\n \\n except:\\n return []\",\n \"def hotkey_prefix(self, hotkey_prefix):\\n allowed_values = [\\\"None\\\", \\\"Show\\\", \\\"Hide\\\"] # noqa: E501\\n if not hotkey_prefix.isdigit():\\t\\n if hotkey_prefix not in allowed_values:\\n raise ValueError(\\n \\\"Invalid value for `hotkey_prefix` ({0}), must be one of {1}\\\" # noqa: E501\\n .format(hotkey_prefix, allowed_values))\\n self._hotkey_prefix = hotkey_prefix\\n else:\\n self._hotkey_prefix = allowed_values[int(hotkey_prefix) if six.PY3 else long(hotkey_prefix)]\",\n \"def load_aliases():\\n for name in COMMANDS:\\n load_alias(name)\",\n \"def handle_admincommands(bot, ievent):\\n cmnds = getcmndtable()\\n if not ievent.rest: ievent.reply(\\\"commands: \\\", cmnds)\\n else:\\n try: ievent.reply(\\\"%s command is found in %s \\\" % (ievent.rest, cmnds[ievent.rest]))\\n except KeyError: ievent.reply(\\\"no such commands available\\\")\",\n \"def __getFullCommandName(self, command, type):\\n return 'cmd_%s_%s' % (type, command)\",\n \"def Complete():\\n\\n # TODO(iancottrell): support auto complete of more than just the command\\n # try to parse the command line using parser\\n print(' '.join(command.name for command in cr.Command.Plugins()))\",\n \"async def any_shortcut(argument, context):\\n shortcut = None\\n check_order = [\\\"user\\\", \\\"channel\\\", \\\"category\\\", \\\"guild\\\"]\\n for name in check_order:\\n shortcuts = None\\n try:\\n shortcuts = getattr(context, name + \\\"_data\\\").shortcuts\\n except AttributeError:\\n continue\\n\\n if argument in shortcuts:\\n shortcut = shortcuts[argument]\\n break\\n\\n else:\\n guild_shortcut = definitions.COMMANDS.get_sub_command_from_path(\\n \\\"settings\\\", \\\"guild\\\", \\\"shortcut\\\", \\\"add\\\")\\n\\n \\\"\\\"\\\"category_shortcut = definitions.COMMANDS.get_sub_command_from_path(\\n \\\"settings\\\", \\\"category\\\", \\\"shortcut\\\", \\\"add\\\")\\n\\n channel_shortcut = definitions.COMMANDS.get_sub_command_from_path(\\n \\\"settings\\\", \\\"channel\\\", \\\"shortcut\\\", \\\"add\\\")\\n\\n user_shortcut = definitions.COMMANDS.get_sub_command_from_path(\\n \\\"settings\\\", \\\"user\\\", \\\"shortcut\\\", \\\"add\\\")\\\"\\\"\\\"\\n\\n guild_shortcut_cmd = await guild_shortcut.get_command_string(context)\\n \\\"\\\"\\\"category_shortcut_cmd = await category_shortcut.get_command_string(context)\\n channel_shortcut_cmd = await channel_shortcut.get_command_string(context)\\n user_shortcut_cmd = await user_shortcut.get_command_string(context)\\\"\\\"\\\"\\n\\n custom_msg = (\\n \\\"{invalid_shortcut_name}\\\\n\\\\n\\\"\\n \\\"ℹ {invalid_shortcut_name_info}\\\").format(\\n invalid_shortcut_name=await context.language.get_text(\\n \\\"invalid_shortcut_name\\\"),\\n invalid_shortcut_name_info=await context.language.get_text(\\n \\\"invalid_shortcut_name_info\\\", {\\\"guild_shortcut\\\": guild_shortcut_cmd}))\\n\\n await embed_messages.invalid_argument(context, argument, custom_msg)\\n\\n return shortcut\",\n \"def _query_commands(self):\\n # TODO: make this work\\n self.player.respond(\\\"Hi there! Ask me to play artists or songs. \\\"\\n \\\"I can also find songs that are similar to other \\\"\\n \\\"artists.\\\")\",\n \"def cmdprefix(self) -> str:\\n return self.config[\\\"Core\\\"].get(\\\"CmdPrefix\\\", \\\"!\\\")\",\n \"async def suggest(self, ctx, *, suggestion: str):\\n emojis = [\\\"✅\\\", \\\"❌\\\"]\\n author = ctx.author\\n guild = ctx.guild\\n embed = Embed(color=Color.blurple(), timestamp=datetime.utcnow())\\n embed.add_field(name=\\\"Suggestion\\\", value=suggestion)\\n embed.set_author(name=f\\\"Suggestion by - {author}\\\", icon_url=author.avatar_url)\\n msg = await ctx.send(embed=embed)\\n await ctx.message.delete()\\n for i in range(len(emojis)):\\n await msg.add_reaction(emojis[i])\",\n \"def autocomplete(self): # pylint: disable-msg=R0914\\n # Don't complete if user hasn't sourced bash_completion file.\\n # This is found in django-trunk/extras/django_bash_completion\\n if 'DJANGO_AUTO_COMPLETE' not in os.environ:\\n return\\n\\n cwords = os.environ['COMP_WORDS'].split()[1:]\\n cword = int(os.environ['COMP_CWORD'])\\n\\n try:\\n curr = cwords[cword - 1]\\n except IndexError:\\n curr = ''\\n\\n subcommands = get_commands().keys() + ['help']\\n options = [('--help', None)]\\n\\n # subcommand\\n if cword == 1:\\n debug_text = ' '.join(sorted(filter(lambda x: x.startswith(curr),\\n subcommands)))\\n print(debug_text)\\n # subcommand options\\n # special case: the 'help' subcommand has no options\\n elif cwords[0] in subcommands and cwords[0] != 'help':\\n subcommand_cls = self.fetch_command(cwords[0])\\n # special case: add the names of installed apps to options\\n if cwords[0] in ('dumpdata', 'sql', 'sqlall', 'sqlclear',\\n 'sqlcustom', 'sqlindexes', 'sqlsequencereset', 'test'):\\n try:\\n database = Database()\\n # Get the last part of the dotted path as the app name.\\n options += [(a.split('.')[-1], 0) for \\\\\\n a in database.get_app_list()]\\n except ImportError:\\n # Fail silently if DJANGO_SETTINGS_MODULE isn't set. The\\n # user will find out once they execute the command.\\n pass\\n options += [(s_opt.get_opt_string(), s_opt.nargs) for s_opt in\\n subcommand_cls.option_list]\\n # filter out previously specified options from available options\\n prev_opts = [x.split('=')[0] for x in cwords[1:cword - 1]]\\n\\n # Original Python 2 version\\n #options = filter(lambda (x, v): x not in prev_opts, options)\\n # Python 3 version?\\n #options = filter(lambda x_v: x_v[0] not in prev_opts, options)\\n options = [opt for opt in options if opt[0] not in prev_opts]\\n\\n # filter options by current input\\n options = sorted([(k, v) for k, v in \\\\\\n options if k.startswith(curr)])\\n for option in options:\\n opt_label = option[0]\\n # append '=' to options which require args\\n if option[1]:\\n opt_label += '='\\n print(opt_label)\\n sys.exit(1)\",\n \"def prefix_type(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"prefix_type\\\")\",\n \"def suggested_commands(self):\\n return self.commands()\",\n \"def completions (cls, cc_prefix) :\\n return cls.cc_trie.completions (cc_prefix)\",\n \"def command_short():\\n pass\",\n \"def autocorrect(self, prefix, N):\\r\\n def valid_edits():\\r\\n letters, length = \\\"abcdefghijklmnopqrstuvwxyz\\\".upper(), len(prefix)\\r\\n yield from (\\r\\n #add letter\\r\\n prefix[:i] + l + prefix[i:] for i in range(length+1)\\r\\n for l in letters\\r\\n )\\r\\n yield from (\\r\\n #delete letter\\r\\n prefix[:i] + prefix[i+1:] for i in range(length)\\r\\n )\\r\\n yield from (\\r\\n #replace letter\\r\\n prefix[:i] + l + prefix[i+1:] for i in range(length)\\r\\n for l in letters\\r\\n )\\r\\n yield from (\\r\\n #swap letters\\r\\n prefix[:i]+prefix[j]+prefix[i+1:j]+prefix[i]+prefix[j+1:]\\r\\n for i in range(length) for j in range(i+1, length)\\r\\n )\\r\\n\\r\\n edits = []\\r\\n for e in valid_edits():\\r\\n f = self.find(e)\\r\\n if f and f.frequency: edits.append((e, f.frequency))\\r\\n edits.sort(key=lambda x: x[1])\\r\\n\\r\\n #Ensure no duplicates by using set.\\r\\n l = set(self.autocomplete(prefix, N))\\r\\n while len(l) < N and edits: l.add(edits.pop()[0])\\r\\n return list(l)\",\n \"async def add_deleter(*, new_prefix: str):\\n prefixes: list = location.deleter_prefixes['prefixes']\\n text_prefixes = location.deleter_prefixes['text_prefixes']\\n if new_prefix.strip() in prefixes:\\n return f\\\"{error_sticker} У вас уже есть данный префикс <<{new_prefix}>>\\\"\\n\\n prefixes.append(new_prefix.strip())\\n location.add_object_the_database(value={\\n \\\"prefixes\\\": prefixes,\\n 'text_prefixes': text_prefixes\\n }, method='deleter_prefixes')\\n return f\\\"{complete_sticker} Вы добавили новый префикс <<{new_prefix}>> для удалялки.\\\" \\\\\\n f\\\"Сработает после перезапуска.\\\"\",\n \"async def help(self, ctx: commands.Context):\\n embed = discord.Embed(title=\\\"RenameX Commands :tools:\\\", timestamp=datetime.datetime.now(),\\n color=discord.Color.green())\\n embed.add_field(name=\\\":arrow_right: **`/rename [member;role;'all'] {bool: bypass_ignore}`**\\\",\\n value=\\\"Change the nicknames of a player or perhaps even an entire server!\\\")\\n await ctx.send(\\\"\\\", embed=embed)\",\n \"def start_command_handler(update, context):\\n add_typing(update, context)\\n buttons = MultiItems(\\\"What would you like to receive?\\\", [\\\"Text\\\", \\\"File\\\", \\\"GoogleDoc\\\", \\\"Gallery\\\"])\\n add_suggested_actions(update, context, buttons)\",\n \"def cmd_type(args):\",\n \"def guess_word(self, prefix):\\n prefix = prefix.lower()\\n current = self.search_prefix(prefix)\\n if current:\\n print(\\\"You typed: \\\" + '\\\"' + prefix + '\\\"')\\n if current.get_end():\\n print('\\\"' + prefix + '\\\" is a word, but you could have also been typing out:')\\n else:\\n print('\\\"' + prefix + '\\\" is not a word, perhaps you were typing out:')\\n library = [prefix + word for word in self.get_library(current, library = [])]\\n for word in library:\\n print(word)\\n return library # list includes empty string if its already a word\\n print(\\\"I'm not quite sure what you meant by \\\" + '\\\"' + prefix + '\\\"...')\\n return []\",\n \"def startswith( self, prefix ):\\n return len(self.commands) >= 1 and self.commands[0].startswith( prefix )\",\n \"async def suggest(self, ctx, *, suggestion):\\n suggch = self.bot.get_channel(454695415611260928)\\n embed_suggest = discord.Embed(title=ctx.author.name, description=ctx.author.id, color=0x00FF00).set_thumbnail(url=ctx.author.avatar_url)\\n embed_suggest.add_field(name=ctx.guild.name, value=ctx.guild.id, inline=False)\\n embed_suggest.add_field(name=\\\"Suggestion\\\", value=suggestion, inline=False)\\n await suggch.send(embed=embed_suggest)\",\n \"def command_with_prefix(self):\\n return self.endpoint_prefix.rstrip('/') + self.command\",\n \"async def is_guild_shortcut_name(argument, context, verbose=True):\\n return await is_shortcut_name(argument, context, \\\"guild\\\", verbose)\",\n \"async def pre(self, ctx):\\n settings = config.load_settings()\\n guild = ctx.guild.id\\n await ctx.send('Current prefix: ' + settings['guilds'][str(guild)]['prefix'])\",\n \"def do_shortcuts(self, args):\\n result = \\\"\\\\n\\\".join('%s: %s' % (sc[0], sc[1]) for sc in sorted(self.shortcuts))\\n self.poutput(\\\"Shortcuts for other commands:\\\\n{}\\\\n\\\".format(result))\",\n \"def __editAutoCompleteFromAPIs(self):\\n self.activeWindow().autoCompleteFromAPIs()\",\n \"def test_sendCommandWithPrefix(self):\\n self.p.sendCommand(\\\"CMD\\\", (\\\"param1\\\", \\\"param2\\\"), \\\"irc.example.com\\\")\\n self.check(b\\\":irc.example.com CMD param1 param2\\\\r\\\\n\\\")\",\n \"def autocomplete(self, token):\\n sub_dict = self.container\\n for letter in token:\\n try:\\n sub_dict = sub_dict[letter]\\n except KeyError:\\n return []\\n auto_list = []\\n for word_fragment in self.traversal(start=sub_dict):\\n if len(auto_list) < 4:\\n auto_list.append(token + word_fragment)\\n else:\\n break\\n return auto_list\",\n \"def main():\\r\\n # Sample implementation of the autocomplete API\\r\\n query = lambda prefix: [d for d in database if d.startswith(prefix)][:5]\\r\\n\\r\\n print(\\\"test case 1: assigned list\\\")\\r\\n database = sorted([\\r\\n \\\"abracadara\\\", \\\"al\\\", \\\"alice\\\", \\\"alicia\\\", \\\"allen\\\", \\\"alter\\\", \\\"altercation\\\", \\\"bob\\\", \\\"eve\\\", \\\"evening\\\", \\\"event\\\",\\r\\n \\\"eventually\\\", \\\"mallory\\\"\\r\\n ])\\r\\n assert extract(query) == database\\r\\n print(\\\"Passed! Evolution: Pichu\\\")\\r\\n print(\\\"\\\\r\\\")\\r\\n\\r\\n print(\\\"test case 2: lots of 'username + a-z'\\\")\\r\\n database = ['usernamea', 'usernameb', 'usernamec', 'usernamed', 'usernamee', 'usernamef', 'usernameg', 'usernameh', 'usernamei', 'usernamej', 'usernamek', 'usernamel', 'usernamem', 'usernamen', 'usernameo', 'usernamep', 'usernameq', 'usernamer', 'usernames', 'usernamet', 'usernameu', 'usernamev', 'usernamew', 'usernamex', 'usernamey', 'usernamez']\\r\\n query = lambda prefix: [d for d in database if d.startswith(prefix)][:5]\\r\\n assert extract(query) == database\\r\\n print(\\\"Passed! Evolution: Pikachu\\\")\\r\\n print(\\\"\\\\r\\\")\\r\\n\\r\\n print(\\\"test case 3: empty\\\")\\r\\n database = sorted([\\r\\n ])\\r\\n assert extract(query) == database\\r\\n print(\\\"Passed! Evolution: Raichu\\\")\",\n \"def apply_prefix(string):\\n for short, long in PREFIXES.items():\\n if string.startswith(long):\\n return short + ':' + string[len(long):]\\n return string\",\n \"def __setupCommandHandlerTypes(self):\\n # dict saving all command handler types\\n self.__commandHandlers = {'channel': {}, 'query': {}, 'not_authed_dcc': {}, 'authed_dcc': {}}\",\n \"def alias_matches(self, text):\\n #print 'Completer->alias_matches:',text # dbg\\n text = os.path.expanduser(text)\\n aliases = self.alias_table.keys()\\n if text == \\\"\\\":\\n return aliases\\n else:\\n return [alias for alias in aliases if alias.startswith(text)]\",\n \"def autocomplete():\\n value = str(request.args.get('q'))\\n result = s.query(Genes).filter(Genes.name.like(\\\"%\\\" + value + \\\"%\\\")).all()\\n data = [i.name for i in result]\\n return jsonify(matching_results=data)\",\n \"async def prefix(self, ctx, *, prefix=None):\\n\\n current = self.bot.prefix\\n embed = Embed(\\n title=\\\"Current prefix\\\", color=self.bot.main_color, description=f\\\"{current}\\\"\\n )\\n\\n if prefix is None:\\n await ctx.send(embed=embed)\\n else:\\n embed.title = \\\"Changed prefix!\\\"\\n embed.description = f\\\"Set prefix to `{prefix}`\\\"\\n self.bot.config[\\\"prefix\\\"] = prefix\\n await self.bot.config.update()\\n await ctx.send(embed=embed)\",\n \"def complete(self, text: str, word: str) -> List[str]:\\n prefix = word.lower()\\n result = [prop.name for prop in self.context.debug_info.properties\\n if prop.name.lower().startswith(prefix)]\\n\\n # If the users didn't ask for a special property, don't suggest special\\n # properties, as they are usually just noise for them.\\n if not prefix.startswith('['):\\n result = [n for n in result if not n.startswith('[')]\\n\\n return result\",\n \"def __editAutoCompleteFromAll(self):\\n self.activeWindow().autoCompleteFromAll()\",\n \"def func(self):\\n from evennia.utils.utils import string_suggestions, list_to_string\\n\\n msg = \\\"Command '%s' is not available.\\\" % self.raw\\n cmdset = self.cmdset\\n cmdset.make_unique(self.caller)\\n all_cmds = [cmd for cmd in cmdset if cmd.auto_help and cmd.access(self.caller)]\\n names = []\\n for cmd in all_cmds:\\n # noinspection PyProtectedMember\\n names.extend(cmd._keyaliases)\\n suggestions = string_suggestions(self.raw, set(names), cutoff=0.7)\\n if suggestions:\\n msg += \\\" Maybe you meant %s?\\\" % list_to_string(\\n suggestions, \\\"or\\\", addquote=True\\n )\\n else:\\n msg += ' Type \\\"help\\\" for help.'\\n self.msg(msg)\",\n \"def do_shortcuts(self, args):\\n result = \\\"\\\\n\\\".join('%s: %s' % (sc[0], sc[1]) for sc in sorted(self.shortcuts))\\n self.stdout.write(\\\"Single-key shortcuts for other commands:\\\\n{}\\\\n\\\".format(result))\",\n \"def handle_suggest():\\n return 0\",\n \"def __editAutoComplete(self):\\n self.activeWindow().autoComplete()\",\n \"async def on_message(self, msg: discord.Message) -> None:\\n # If the prefix is set\\n if self.prefix != '':\\n # Check if the received message was not sent by the bot itself\\n if msg.author != self.user:\\n if msg.content.startswith(self.prefix):\\n command = msg.content[1:].split(' ')[0]\\n args = msg.content[1:].split(' ')[1:]\\n # Send command with arguments to on_command function\\n await self.on_command(command, args, msg)\",\n \"def autoprefix(prefix):\\n pl = len(prefix)\\n msg = '%%(s)r: expected some name after %(prefix)r!' % locals()\\n def checker(s):\\n if s.startswith(prefix):\\n tail = s[pl:]\\n if tail:\\n return prefix + dotted_name(tail)\\n else:\\n raise ValueError(msg % locals())\\n elif s:\\n return prefix + dotted_name(s)\\n else:\\n return ''\\n return checker\",\n \"def _commands(self) -> Dict[str, List[str]]:\\r\\n pass\",\n \"def getAutoCompleteKeys(self):\\n return [ord('.'), ord(' '), ord('/')]\",\n \"def load_alias(name):\\n mod = importlib.import_module(\\\"umdone.commands.\\\" + name)\\n main = getattr(mod, \\\"main\\\")\\n builtins.aliases[name] = main\\n builtins.aliases[name.replace(\\\"_\\\", \\\"-\\\")] = main\",\n \"def autosuggest(self, suggest, focus=None, clip=None, display='full',\\n format='json', lang=None):\\n\\n params = {\\n 'addr': suggest,\\n 'display': display,\\n 'format': format,\\n 'lang': lang or self.lang,\\n }\\n if focus:\\n params.update({\\n 'focus': focus\\n })\\n if clip:\\n params.update({\\n 'clip': clip\\n })\\n\\n return self._request('/autosuggest', params)\",\n \"async def prefix(self, ctx, *, pre):\\n server = ctx.message.guild\\n if pre.endswith('\\\\w'):\\n pre = pre[:-2]+' '\\n if len(pre.strip) > 0:\\n msg = f'The server prefix has been set to `{pre}` Use `{pre}prefix <prefix>` to change it again.'\\n else:\\n await ctx.send('Invalid prefix.')\\n return\\n else:\\n msg = f'The server prefix has been set to `{pre}` Use `{pre}prefix <prefix>` to change it again. ' \\\\\\n f'If you would like to add a trailing whitespace to the prefix, use `{pre}prefix {pre}\\\\w`.'\\n\\n await self.bot.db.config.update_one({'_id': str(server.id)}, {'$set': {'prefix': str(pre)}}, upsert=True)\\n self.bot.pre[str(server.id)] = str(pre)\\n await ctx.send(msg)\",\n \"def test_prefix(self):\\n self.chck_triple('prefix')\",\n \"def set_prefix(prefix):\\n PLUGINS.set_prefix(prefix)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6334425","0.61928004","0.59438735","0.5882108","0.58367133","0.5759399","0.56862473","0.566724","0.5637605","0.5576028","0.55751395","0.55580425","0.5514969","0.54804295","0.5447843","0.54456246","0.5445153","0.54353315","0.54343086","0.53711796","0.53630424","0.53543186","0.5332114","0.53305995","0.53236854","0.52714264","0.52256155","0.52193826","0.5160899","0.5156524","0.5107623","0.5105302","0.5075894","0.5049495","0.5025002","0.5023256","0.5020888","0.50138867","0.5013275","0.5005295","0.4970627","0.49701878","0.49486127","0.49245164","0.49198225","0.49104246","0.4906404","0.49059013","0.4904478","0.48994085","0.48840013","0.48617265","0.48535788","0.48457405","0.48097396","0.48024875","0.47980866","0.47892693","0.47620627","0.4759452","0.47546917","0.47377917","0.4737074","0.47355598","0.4733865","0.47165766","0.47076824","0.47061804","0.4701103","0.46942523","0.4694121","0.4689228","0.46886423","0.46866286","0.46850935","0.4679139","0.466767","0.46582898","0.4657088","0.46459207","0.46418154","0.46379638","0.4636999","0.46315983","0.4624574","0.46245736","0.46199277","0.4615001","0.46051812","0.46018782","0.45900282","0.4588044","0.4585636","0.45768905","0.45600927","0.4555839","0.4529976","0.45242736","0.45236382","0.45187747"],"string":"[\n \"0.6334425\",\n \"0.61928004\",\n \"0.59438735\",\n \"0.5882108\",\n \"0.58367133\",\n \"0.5759399\",\n \"0.56862473\",\n \"0.566724\",\n \"0.5637605\",\n \"0.5576028\",\n \"0.55751395\",\n \"0.55580425\",\n \"0.5514969\",\n \"0.54804295\",\n \"0.5447843\",\n \"0.54456246\",\n \"0.5445153\",\n \"0.54353315\",\n \"0.54343086\",\n \"0.53711796\",\n \"0.53630424\",\n \"0.53543186\",\n \"0.5332114\",\n \"0.53305995\",\n \"0.53236854\",\n \"0.52714264\",\n \"0.52256155\",\n \"0.52193826\",\n \"0.5160899\",\n \"0.5156524\",\n \"0.5107623\",\n \"0.5105302\",\n \"0.5075894\",\n \"0.5049495\",\n \"0.5025002\",\n \"0.5023256\",\n \"0.5020888\",\n \"0.50138867\",\n \"0.5013275\",\n \"0.5005295\",\n \"0.4970627\",\n \"0.49701878\",\n \"0.49486127\",\n \"0.49245164\",\n \"0.49198225\",\n \"0.49104246\",\n \"0.4906404\",\n \"0.49059013\",\n \"0.4904478\",\n \"0.48994085\",\n \"0.48840013\",\n \"0.48617265\",\n \"0.48535788\",\n \"0.48457405\",\n \"0.48097396\",\n \"0.48024875\",\n \"0.47980866\",\n \"0.47892693\",\n \"0.47620627\",\n \"0.4759452\",\n \"0.47546917\",\n \"0.47377917\",\n \"0.4737074\",\n \"0.47355598\",\n \"0.4733865\",\n \"0.47165766\",\n \"0.47076824\",\n \"0.47061804\",\n \"0.4701103\",\n \"0.46942523\",\n \"0.4694121\",\n \"0.4689228\",\n \"0.46886423\",\n \"0.46866286\",\n \"0.46850935\",\n \"0.4679139\",\n \"0.466767\",\n \"0.46582898\",\n \"0.4657088\",\n \"0.46459207\",\n \"0.46418154\",\n \"0.46379638\",\n \"0.4636999\",\n \"0.46315983\",\n \"0.4624574\",\n \"0.46245736\",\n \"0.46199277\",\n \"0.4615001\",\n \"0.46051812\",\n \"0.46018782\",\n \"0.45900282\",\n \"0.4588044\",\n \"0.4585636\",\n \"0.45768905\",\n \"0.45600927\",\n \"0.4555839\",\n \"0.4529976\",\n \"0.45242736\",\n \"0.45236382\",\n \"0.45187747\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.79266447"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":16871,"cells":{"query":{"kind":"string","value":"Add a reaction role."},"document":{"kind":"string","value":"async def process_add_reaction_role(\n user_id, description, ctx=None, inter=None, allowed_mentions=None\n):\n user = await User.get(user_id)\n response_deferred = await defer_inter(inter, ephemeral=True)\n view = disnake.ui.View(timeout=None)\n view.add_item(RoleDropdown(description))\n await send_message(\n key=\"choose_roles\",\n user=user,\n view=view,\n ephemeral=True,\n response_deferred=response_deferred,\n inter=inter,\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":["async def rolemenu_add_role(self,\n interaction: discord.Interaction,\n name: str,\n role: discord.Role,\n emoji: str = None,\n description: str = None):\n doc = await self.db.find_one({\n \"guild_id\": interaction.guild.id,\n \"name\": name\n })\n if not doc:\n return await interaction.response.send_message(\n \"No role menu with that name exists.\", ephemeral=True)\n for role_doc in doc[\"roles\"]:\n if role_doc[\"id\"] == role.id:\n return await interaction.followup.send(\n \"Role is already in the menu.\", ephemeral=True)\n if len(doc[\"roles\"]) >= 25:\n return await interaction.response.send_message(\n \"This role menu is full.\", ephemeral=True)\n await interaction.response.defer(ephemeral=True)\n if role.guild != interaction.guild:\n return await interaction.response.send_message(\n \"This role is not in this server.\")\n if emoji:\n if emoji.startswith(\"<\") and emoji.endswith(\">\"):\n try:\n emoji = int(emoji[1:-1].split(\":\")[2])\n except ValueError:\n return await interaction.followup.send(\"Invalid emoji.\")\n else:\n try:\n message = await interaction.original_message()\n await message.add_reaction(emoji)\n except discord.HTTPException:\n return await interaction.followup.send(\"Invalid emoji.\")\n await self.db.update_one({\"_id\": doc[\"_id\"]}, {\n \"$push\": {\n \"roles\": {\n \"description\": description,\n \"id\": role.id,\n \"emoji\": emoji,\n \"date_added\": datetime.datetime.now(datetime.datetime.u)\n }\n }\n })\n doc = await self.db.find_one({\"_id\": doc[\"_id\"]})\n await interaction.followup.send(f\"Added {role.mention} to the menu.\")\n menu = Menu(self, interaction.guild, doc)\n await menu.update()","def addRole(self, role):\n self._client.addRole(role)","def addRole(self, role=None, roleName=None, kvDict=None):\n return _modelActionBase(self, instance=role, instanceName=roleName, kvDict=kvDict,\n model=get_model('role'), db=db, action='add', modelType='role')","def add_role(self, role):\n if role.name not in [r.name for r in self.roles]:\n return db[self.colNam].find_and_modify(query=dict(_id=self.id), update={'$push': {'roles': role.to_python()}})","async def add_role(\n client,\n event,\n user: ('user', 'User to add role to'),\n role: ('role', 'The role to give'),\n):\n # Check for permissions\n if not event.user_permissions.can_manage_roles:\n abort('You need `manage roles` permission to invoke this command.')\n \n if not event.guild.cached_permissions_for(client).can_manage_roles:\n abort('I need `manage roles` permission to execute this command.')\n \n if not event.user.has_higher_role_than(role):\n abort('You must have higher role than the role you are trying to give.')\n \n if not client.has_higher_role_than(role):\n abort('I must have higher role than the role you are trying to give.')\n \n # Using `.copy_to` on forms works as well.\n return ADD_ROLE_FORM.copy_with(\n title = f'Add role {role.name} to {user.full_name}',\n custom_id = f'add_role.{user.id}.{role.id}',\n )","async def command_assign_role(self, context, role: str):\n try:\n await context.author.add_roles(discord.utils.get(\n context.guild.roles, name=role))\n await context.message.add_reaction('👍')\n except Exception as e:\n await context.message.add_reaction('👎')\n await context.send('Role could not be assigned')\n print(f'Errored in command_assign_role.', e)","def add_role(self, role):\n try:\n self.db_proxy.nameCheck(role.theName, 'role')\n except ARM.ARMException as ex:\n self.close()\n raise ARMHTTPError(ex)\n\n role_params = RoleParameters(\n name=role.theName,\n rType=role.theType,\n sCode=role.theShortCode,\n desc=role.theDescription,\n cProperties=[]\n )\n\n role_id = self.db_proxy.addRole(role_params)\n\n return role_id","async def addrole(self, ctx, user: discord.Member=None, *, role=None):\r\n if user is None or role is None:\r\n return await ctx.send(\"Incorrect usage! *;addrole @user role*\")\r\n r = discord.utils.get(ctx.guild.roles, name=str(role))\r\n if r is None:\r\n return await ctx.send(f'{role} was not found')\r\n try:\r\n await user.add_roles(r)\r\n return await ctx.send(f\"**{str(user)}** has been given the role of **{role}** {self.bot.get_emoji(470063310386233344)}\")\r\n except discord.Forbidden:\r\n return await ctx.send(\"Bot does not have enough permissions to give roles.\")","def manage_addRole(self, role_id, title, description, RESPONSE=None,\n REQUEST=None):\n if not role_id:\n message = 'Please+provide+a+Role+ID'\n else:\n self.addRole(role_id, title, description)\n message = 'Role+added'\n\n if RESPONSE is not None:\n RESPONSE.redirect('%s/manage_roles?manage_tabs_message=%s' %\n (self.absolute_url(), message))","async def handle_role_reaction_press(interaction: disnake.MessageInteraction):\n if interaction.message not in await ReactionRoleMessage.get_all():\n return\n\n role_id = int(interaction.component.custom_id)\n member: disnake.Member = interaction.author\n user = await User.get(member.id)\n role = member.get_role(role_id)\n if role:\n await member.remove_roles(role, reason=\"Reaction Role Message\")\n await send_message(user=user, key=\"role_removed\", inter=interaction, ephemeral=True)\n else:\n role = interaction.guild.get_role(role_id)\n if role:\n try:\n await member.add_roles(role, reason=\"Reaction Role Message\")\n await send_message(user=user, key=\"role_added\", inter=interaction, ephemeral=True)\n except disnake.errors.Forbidden as e:\n await send_message(user=user, key=\"no_permissions\", inter=interaction, ephemeral=True)\n else:\n await send_message(user=user, key=\"role_not_found\", inter=interaction, ephemeral=True)","async def on_raw_reaction_add(self, payload: discord.RawReactionActionEvent):\n\n # Skipping bot reactions\n if payload.member.bot:\n return\n\n # Search the event by message id\n message_id = payload.message_id\n channel_id = payload.channel_id\n guild_id = payload.guild_id\n event_type = connector.getEventTypeByMessage(guild_id, message_id, channel_id)\n if event_type is None:\n return\n\n role = discord.utils.get(payload.member.guild.roles, id=event_type.role_id)\n\n if (payload.event_type == \"REACTION_ADD\") and (event_type.emoji == str(payload.emoji)):\n # Adding role to user\n await payload.member.add_roles(role)","def addReaction(self, *args, **kwargs):\n if isinstance(args[0], Reaction):\n reaction = args[0]\n else:\n reaction = self._sim.reaction(*args, **kwargs)\n\n self._sim.assignReaction(reaction, self)\n return self","async def on_raw_reaction_add(self, payload):\n\n # exclude all reactions which are not the original message\n if str(payload.message_id) != self.message_id:\n return\n\n # exclude the bot\n if payload.user_id == self.bot.user.id:\n return\n\n else:\n # get the model data for the role assigner object\n data = await self.get_objects(\n model=RoleAssigner, filter={\"bot__name\": str(self.bot_name)}\n )\n\n # role assigner object\n data = data[0]\n\n guild = self.get_guild(guild_id=payload.guild_id)\n\n user = self.get_user(guild=guild, user_id=payload.user_id)\n\n for db_role in data.roles.all():\n\n if db_role.emoji.startswith(\":\") and db_role.emoji.endswith(\":\"):\n\n ce = db_role.emoji[1:-1]\n\n else:\n ce = db_role.emoji\n\n if str(payload.emoji.name) == str(ce):\n\n role = self.get_role(guild, int(db_role.uid))\n\n if user not in role.members:\n\n await user.add_roles(role)\n\n print(\"Added \" + str(user) + \" to role: \" + str(role) + \"!\")\n\n else:\n print(\n \"User \" + str(user) + \" already in role: \" + str(role) + \"!\"\n )\n\n pass","def add_role():\r\n check_admin()\r\n\r\n add_role = True\r\n\r\n form = RoleForm()\r\n if form.validate_on_submit():\r\n role = Role(name=form.name.data,\r\n description=form.description.data)\r\n\r\n try:\r\n # add role to the database\r\n db.session.add(role)\r\n db.session.commit()\r\n flash('You have successfully added a new role.')\r\n except:\r\n # in case role name already exists\r\n flash('Error: role name already exists.',category='error')\r\n\r\n # redirect to the roles page\r\n return redirect(url_for('admin.list_roles'))\r\n\r\n # load role template\r\n return render_template('admin/roles/role.html', add_role=add_role,\r\n form=form, title='Add Role')","def add_role(self, name):\n role = Role.by_name(name)\n if not role:\n role = Role(name)\n db.add(role)\n if not role in self.roles:\n self.roles.append(role)","def add_role(self, role, parents=[]):\r\n self._roles.setdefault(role, set())\r\n self._roles[role].update(parents)","def add_role():\n role = roles.find_or_create_role(request.values.get('role_name', ''))\n user = users.get_or_404(int(request.values.get('user_id', '')))\n if not users.add_role_to_user(user, role):\n return {}, 500\n return {}","async def addrole(self, ctx, member: discord.Member, role: discord.Role):\n role = discord.utils.get(ctx.guild.roles, id=role.id)\n\n muted_role = discord.utils.get(ctx.guild.roles, name=\"Muted\")\n punished_role = discord.utils.get(ctx.guild.roles, name=\"Punished\")\n\n if role > ctx.author.top_role:\n return await ctx.send(\n embed=discord.Embed(\n title=\"You don't have permission to add this role\",\n timestamp=datetime.datetime.utcnow(),\n colour=discord.Colour.darker_grey(),\n )\n )\n\n if role == muted_role or role == punished_role:\n return await ctx.send(\n embed=discord.Embed(\n title=f\"Can not assign *{role}* role using this command.\",\n description=\"For more information run ```.help addrole```\",\n timestamp=datetime.datetime.utcnow(),\n colour=discord.Colour.red(),\n )\n )\n\n if role in member.roles:\n return await ctx.channel.send(\n embed=discord.Embed(\n title=f\"*{member}* already has *{role}* Role!\",\n timestamp=datetime.datetime.utcnow(),\n colour=discord.Colour.greyple(),\n )\n )\n\n await member.add_roles(role)\n await ctx.send(\n embed=discord.Embed(\n title=f\"*{role}* has been added to *{member}*\",\n timestamp=datetime.datetime.utcnow(),\n colour=discord.Colour.green(),\n )\n )","def create_role(self, role_id, role):\n raise exception.NotImplemented() # pragma: no cover","def addRole(self, role_id, title='', description=''):\n if self._roles.get(role_id) is not None:\n raise KeyError('Duplicate role: %s' % role_id)\n\n self._roles[role_id] = {'id': role_id, 'title': title,\n 'description': description}","def addUserRole(self, name, role):\n self._client.addUserRole(name, role)","async def on_raw_reaction_add(self, payload: discord.RawReactionActionEvent):\n role: discord.Role = await self.check_payload(payload)\n if payload.user_id == self.bot.user.id:\n return\n if role:\n guild = self.bot.get_guild(payload.guild_id)\n member = guild.get_member(payload.user_id)\n await member.add_roles(role)","def add_role():\n check_admin()\n add_role = True\n\n form = RoleForm()\n if form.validate_on_submit():\n role = Role(title=form.title.data)\n\n try:\n db.session.add(role)\n db.session.commit()\n flash('New role successfully created')\n except:\n flash('Error: Role title already exist')\n\n return redirect(url_for('admin.get_roles'))\n\n return render_template('admin/roles/role.html', form=form, add_role=add_role, title='Add Role')","def addReactant(self, *args):\n return _libsbml.Reaction_addReactant(self, *args)","async def on_raw_reaction_add(self, payload: discord.RawReactionActionEvent):\n # Get the message id of the message that the user reacted to.\n message_id = payload.message_id\n\n # Get the message id of the message we want the user to react to.\n actual_message_id = constants.MessageIDs.RULES_MSGID\n\n # Compare that id's match, and if true continue to give the role.\n if message_id == actual_message_id:\n guild_id = payload.guild_id\n guild = self.bot.get_guild(guild_id)\n role = get(payload.member.guild.roles, name='Not Verified')\n\n if role is not None:\n member = get(guild.members, id=payload.user_id)\n if member is not None:\n await payload.member.add_roles(role)\n print(f\"Added role to {member}\")\n else:\n print(\"User not found . . .\")\n else:\n print(\"Role not found . . .\")","def add_role(role):\n roleOfUser=Role.objects.create(type=role)\n return roleOfUser","def add_role(email, role):\n from enferno.user.models import Role\n u = User.query.filter(User.email == email).first()\n\n if u is None:\n print('Sorry, this user does not exist!')\n else:\n r = Role.query.filter(Role.name == role).first()\n if r is None:\n print('Sorry, this role does not exist!')\n u = click.prompt('Would you like to create one? Y/N', default='N')\n if u.lower() == 'y':\n r = Role(name=role)\n try:\n db.session.add(r)\n db.session.commit()\n print('Role created successfully, you may add it now to the user')\n except Exception as e:\n db.session.rollback()\n # add role to user\n u.roles.append(r)","def addRolePermission(self, role, _type):\n self._client.addRolePermission(role, _type)","def add_role(self, principal, role):\n return permissions.utils.add_local_role(self, principal, role)","async def apply_role(self, *, reason: str = None):\n if self.role not in self.member.roles:\n try:\n await self.member.add_roles(self.role, reason=reason)\n except discord.HTTPException:\n pass","def addReaction(self, *args):\n return _libsbml.Model_addReaction(self, *args)","def add_role():\n\tcheck_admin()\n\tadd_role = True\n\n\tform = RoleForm()\n\tif form.validate_on_submit():\n\t\trole= Role(name= form.name.data,description=form.description.data)\n\n\t\ttry:\n\t\t\t#add role to the database \n\t\t\tdb.session.add(role)\n\t\t\tdb.session.commit()\n\t\t\tflash('You have successfully added a new role ')\n\t\texcept:\n\t\t\t#incase the role already exists\n\t\t flash(\"Error:the role already exists\")\n\n\t\t#redirect to the roles page\n\t\treturn redirect(url_for('admin.list_roles'))\n\n\t\t#load the role template\n\treturn render_template('admin/roles/role.html', add_role=add_role, form = form,title='Add Role')","def add_employeeRole(self, id, role):\n cursor = self.dbconnect.get_cursor()\n try:\n cursor.execute('INSERT INTO employeeRoles values(%s,%s)',\n (id, role))\n # get id and return updated object\n self.dbconnect.commit()\n except(Exception, self.dbconnect.get_error()) as error:\n self.dbconnect.rollback()\n raise Exception('\\nUnable to save EmployeeRole!\\n(%s)' % (error))","async def role(ctx, role: discord.Role = None):\n if role is None:\n await ctx.send(\"List of assignable roles: \" + str(allowed_roles))\n if role.name in allowed_roles:\n if not role in ctx.message.author.roles:\n await ctx.message.author.add_roles(role)\n await ctx.send(\"Role added.\")\n else:\n await ctx.message.author.remove_roles(role)\n await ctx.send(\"Role removed.\") \n else:\n await ctx.send(\"That role doesn't exist, or you don't have permission to modify it.\")","async def addrole(self, ctx: context.CustomContext):\n\n await ctx.send(\n f\"{config.USER_INTERACTION_REQUIRED} Reply with the name of the role you want to create.\"\n )\n\n role_name = await ctx.converted_input(converter=converter.CaseInsensitiveRole)\n\n if isinstance(role_name, str):\n await ctx.send(\n f\"{config.YES} I will **create a new role** on this server named `{role_name}` for this.\"\n )\n try:\n discord_role = await ctx.guild.create_role(name=role_name)\n except discord.Forbidden:\n raise exceptions.ForbiddenError(\n exceptions.ForbiddenTask.CREATE_ROLE, role_name\n )\n\n else:\n discord_role = role_name\n\n await ctx.send(\n f\"{config.YES} I'll use the **pre-existing role** named `{discord_role.name}` for this.\"\n )\n\n role_join_message = await ctx.input(\n f\"{config.USER_INTERACTION_REQUIRED} Reply with a short message the user should see when they get the role.\"\n )\n\n try:\n await self.bot.db.execute(\n \"INSERT INTO selfrole (guild_id, role_id, join_message) VALUES ($1, $2, $3) \"\n \"ON CONFLICT (guild_id, role_id) DO UPDATE SET join_message = $3\",\n ctx.guild.id,\n discord_role.id,\n role_join_message,\n )\n except asyncpg.UniqueViolationError:\n return await ctx.send(\n f\"{config.NO} `{discord_role.name}` is already a selfrole on this server.\"\n )\n\n await ctx.send(f\"{config.YES} `{discord_role.name}` was added as a selfrole.\")","def add_role_to_user(self, user, role):\n user, role = self._prepare_role_modify_args(user, role)\n if role not in user.roles:\n user.roles.append(role)\n # noinspection PyUnresolvedReferences\n self.save(user)\n return True\n\n return False","async def register_reaction_role(self, ctx, target_role_id: int, emoji_name: str):\n\n await self.bot.wait_until_ready()\n\n if emoji_name[0] == \"<\":\n emoji_name = emoji_name[1:-1]\n\n if target_role_id in self.bot.config.staff_role_ids:\n return await ctx.send(\"Error: Dangerous role found!\")\n\n target_role = ctx.guild.get_role(target_role_id)\n\n if target_role is None:\n return await ctx.send(\"Error: Role not found!\")\n\n target_role_name = target_role.name\n\n for key in self.reaction_config[\"reaction_roles_emoji_map\"]:\n value = self.reaction_config[\"reaction_roles_emoji_map\"][key]\n if type(value) is str and target_role_name == value:\n return await ctx.send(f\"Error: {target_role_name}: already registered.\")\n\n self.reaction_config[\"reaction_roles_emoji_map\"][emoji_name] = target_role_name\n self.save_reaction_config(self.reaction_config)\n await self.reload_reaction_message(False)\n\n await ctx.send(f\"{target_role_name}: registered.\")","def define_role(self, role):\n\n self._db_manager.create_role(role)","def addRoleAccess(self, role, read, write, catalog='*', repository='*'):\n return self._client.addRoleAccess(role, read, write, catalog, repository)","async def addrole(self, ctx, rolename, user: discord.Member=None):\n author = ctx.message.author\n channel = ctx.message.channel\n server = ctx.message.server\n\n if user is None:\n user = author\n\n role = self._role_from_string(server, rolename)\n\n if role is None:\n await self.bot.say('That role cannot be found.')\n return\n\n if not channel.permissions_for(server.me).manage_roles:\n await self.bot.say('I don\\'t have manage_roles.')\n return\n\n if author.id == settings.owner:\n pass\n elif not channel.permissions_for(author).manage_roles:\n raise commands.CheckFailure\n\n await self.bot.add_roles(user, role)\n await self.bot.say('Added role {} to {}'.format(role.name, user.name))","async def on_raw_reaction_add(self, payload: discord.RawReactionActionEvent):\n \n guild = self.bot.get_guild(payload.guild_id)\n user = discord.utils.get(guild.members, id=payload.user_id)\n # Skipping bot reactions\n if user.bot:\n return\n\n # Search the event by message id\n message_id = payload.message_id\n channel_id = payload.channel_id\n guild_id = payload.guild_id\n event = connector.getEventByMessage(guild_id, message_id, channel_id)\n if event is None:\n return\n\n role = discord.utils.get(payload.member.guild.roles, id=event.role_id)\n\n if (payload.event_type == \"REACTION_ADD\") and (event.emoji == str(payload.emoji)):\n # Adding role to user\n await payload.member.add_roles(role)\n elif (payload.event_type == \"REACTION_REMOVE\") and (event.emoji == str(payload.emoji)) and (role in payload.member.roles):\n # Remove role from user\n await payload.member.remove_roles(role)","def add_role(self, role_id: str, current_user_id=None):\n if RoleModel.is_valid_role(role_id) and not self.has_role(role_id):\n user_role = UserRoleModel(user_id=self.id, role_id=role_id, lastchange_by=current_user_id)\n self.roles.append(user_role)","async def add(ctx, *args: commands.clean_content):\r\n if len(args) < 2:\r\n await ctx.send('Add takes 2+ parameters')\r\n return\r\n\r\n tgt_role = args[-1]\r\n if tgt_role.startswith('@'):\r\n tgt_role = tgt_role[1:]\r\n if not discord.utils.get(ctx.guild.roles, name=tgt_role):\r\n await ctx.send(f'Role {args[-1]} does not exist')\r\n return\r\n\r\n roles = list(args[:-1])\r\n\r\n for index, role in enumerate(roles):\r\n if role.startswith('@'):\r\n role = role[1:]\r\n roles[index] = role\r\n print(role)\r\n if not discord.utils.get(ctx.guild.roles, name=role):\r\n await ctx.send(f'Role {role} does not exist')\r\n return\r\n\r\n docid = db.insert({'guild': ctx.guild.id, 'roles': roles, 'target': tgt_role})\r\n await ctx.send(f'Rule {docid} created')\r\n await update_roles(ctx.guild)\r\n await check_guild_rules(ctx.guild)","async def _on_raw_reaction(\n self,\n payload: RawReactionActionEvent,\n reaction_type: EnumReactionType,\n ) -> None:\n if self.__is_self(payload.user_id):\n print(\"reaction added by the bot itself\")\n return\n\n guild = self._client.get_guild(payload.guild_id)\n\n if reaction_type == EnumReactionType.ADD:\n await self.__roles.add_role(\n guild, payload.message_id,\n payload.emoji, payload.user_id,\n\n )\n elif reaction_type == EnumReactionType.REMOVE:\n await self.__roles.remove_role(\n guild, payload.message_id,\n payload.emoji, payload.user_id,\n )\n else:\n raise InvalidReactionType","async def add_role_member(request, role_id):\n required_fields = [\"id\"]\n utils.validate_fields(required_fields, request.json)\n txn_key, txn_user_id = await utils.get_transactor_key(request)\n proposal_id = str(uuid4())\n batch_list = Role().member.propose.batch_list(\n signer_keypair=txn_key,\n signer_user_id=txn_user_id,\n proposal_id=proposal_id,\n role_id=role_id,\n pack_id=request.json.get(\"pack_id\"),\n next_id=request.json.get(\"id\"),\n reason=request.json.get(\"reason\"),\n metadata=request.json.get(\"metadata\"),\n )\n batch_status = await utils.send(\n request.app.config.VAL_CONN,\n batch_list,\n request.app.config.TIMEOUT,\n request.json.get(\"tracker\") and True,\n )\n if request.json.get(\"tracker\"):\n return utils.create_tracker_response(\"batch_status\", batch_status)\n return json({\"proposal_id\": proposal_id})","async def addRole(self, ctx, *roles_to_add):\n already_present_roles = [] # roles that will be deleted from \"roles_to_add\"\n\n available_roles = open(\"assets/roles.txt\", \"r\").readlines()\n available_roles = [role.lower().strip() for role in available_roles]\n\n output_msg = \"\"\n\n for role_to_add in roles_to_add:\n for role in available_roles:\n if role_to_add.lower() == role:\n output_msg += f\"Failed to add {role_to_add}: role already exists.\\n\"\n already_present_roles.append(role_to_add)\n break\n\n for role in already_present_roles:\n roles_to_add.remove(role)\n\n if roles_to_add:\n with open(\"assets/roles.txt\", \"a\") as f:\n for role in roles_to_add:\n f.write(f\"{role}\\n\")\n output_msg += f\"{role} has been added successfully.\\n\"\n\n await ctx.send(output_msg)","async def _role_repl(self, ctx: Context, *, role: discord.Role):\n\n msg = await ctx.send(\n _(\n \"Are you sure you want to set `{}` as replacement role?\"\n ).format(role.name)\n )\n start_adding_reactions(msg, ReactionPredicate.YES_OR_NO_EMOJIS)\n\n pred = ReactionPredicate.yes_or_no(msg, ctx.author)\n await ctx.bot.wait_for(\"reaction_add\", check=pred)\n\n if pred.result:\n await self.config.guild(ctx.guild).repl_id.set(role.id)\n await ctx.send(\n _(\"Set `{}` as replacement role!\").format(role.name)\n )\n else:\n await ctx.send(_(\"Aborted replacement role setup.\"))","async def massadd(\n self,\n ctx,\n role: discord.Role,\n member: commands.Greedy[discord.Member],\n ):\n role = discord.utils.get(ctx.guild.roles, id=role.id)\n\n muted_role = discord.utils.get(ctx.guild.roles, name=\"Muted\")\n punished_role = discord.utils.get(ctx.guild.roles, name=\"Punished\")\n\n if role > ctx.author.top_role:\n return await ctx.send(\n embed=discord.Embed(\n title=\"You don't have permission to add this role\",\n timestamp=datetime.datetime.utcnow(),\n colour=discord.Colour.darker_grey(),\n )\n )\n\n if role == muted_role or role == punished_role:\n return await ctx.send(\n embed=discord.Embed(\n title=f\"Can not assign *{role}* role using this command.\",\n description=\"For more information run ```.help massadd```\",\n timestamp=datetime.datetime.utcnow(),\n colour=discord.Colour.red(),\n )\n )\n\n for i in member:\n if role in i.roles:\n await ctx.channel.send(\n embed=discord.Embed(\n title=f\"*{i}* already has *{role}* Role!\",\n timestamp=datetime.datetime.utcnow(),\n colour=discord.Colour.greyple(),\n )\n )\n\n await i.add_roles(role)\n\n await ctx.send(\n embed=discord.Embed(\n title=f\"*{role}* has been added to **{len(member)}** members!\",\n timestamp=datetime.datetime.utcnow(),\n colour=discord.Colour.green(),\n )\n )","def add(self, user, role=None, roles=None):\n # TODO(adriant): resolve the roles and users into id's\n # user_id = base.getid(user)\n user_id = user\n # role_id = role\n if role:\n params = {\n 'roles': [role]\n }\n elif roles:\n params = {\n 'roles': roles\n }\n\n route = '/openstack/users/%s/roles'\n url = route % (user_id)\n try:\n self._put(url, json=params, response_key=None)\n except exc.HTTPBadRequest as e:\n print(e.message)\n return False\n\n return True","async def addrole(self, ctx, role: discord.Role):\n guild = ctx.message.guild\n excluded_roles = await self.config.guild(guild).excluded_roles()\n\n for excluded_role in excluded_roles:\n if excluded_role == role.id:\n await ctx.send(\"%s already added to role exclusion list\" % role.name)\n return\n\n excluded_roles.append(role.id)\n await self.config.guild(guild).excluded_roles.set(excluded_roles)\n\n await ctx.send(\"%s added to role exclusion list\" % role.name)","async def createrole(self, ctx, role: str):\n if role.lower() == \"muted\" or role.lower() == \"punished\":\n return await ctx.send(\"Can not create this roles.\")\n \"\"\"Create a new role\"\"\"\n role = await ctx.guild.create_role(name=role)\n return await ctx.send(\n embed=discord.Embed(\n title=f\"Role *{role}* has been created!\",\n timestamp=datetime.datetime.utcnow(),\n colour=discord.Colour.green(),\n )\n )","async def reaction_roles_post(inter: MessageInteraction, description, roles):\n view = disnake.ui.View(timeout=None)\n for role in roles:\n view.add_item(disnake.ui.Button(label=role.name, custom_id=role.id))\n messages = await send_message(msg=description, channel=inter.channel, view=view)\n for message in messages:\n await ReactionRoleMessage.insert(message.id)","def inheritRole(self, role=None, roleName=None, kvDict=None):\n\n return _modelActionBase(self, instance=role, instanceName=roleName, kvDict=kvDict,\n model=get_model('role'), db=db, action='add', modelType='role', inherit=True)","def addModifier(self, *args):\n return _libsbml.Reaction_addModifier(self, *args)","async def add_role_admin(request, role_id):\n required_fields = [\"id\"]\n utils.validate_fields(required_fields, request.json)\n\n txn_key, txn_user_id = await utils.get_transactor_key(request)\n proposal_id = str(uuid4())\n batch_list = Role().admin.propose.batch_list(\n signer_keypair=txn_key,\n signer_user_id=txn_user_id,\n proposal_id=proposal_id,\n role_id=role_id,\n next_id=request.json.get(\"id\"),\n reason=request.json.get(\"reason\"),\n metadata=request.json.get(\"metadata\"),\n )\n await utils.send(\n request.app.config.VAL_CONN, batch_list, request.app.config.TIMEOUT\n )\n return json({\"proposal_id\": proposal_id})","def add_keystone_v3_role_to_user_or_group(self, user_id, role_id,\n pro_dom_id, id_flag):\n LOG_OBJ.debug(\"Adding the role.\")\n\n _url = \"http://\" + self.host_ip + \":35357/v3/\" + id_flag + \"s/\" + \\\n str(pro_dom_id) + \"/users/\" + str(user_id) + \"/roles/\" + \\\n str(role_id)\n _headers = {'x-auth-token': self.cloud_admin_info[\"token_domain\"],\n 'content-type': 'application/json'}\n _body = None\n response = self.request(\"PUT\", _url, _headers, _body)\n\n if response is None:\n LOG_OBJ.error(\"No response from Server while adding role\")\n print (\"No response from Server while adding role\")\n return response\n\n if response.status not in [200, 201, 202, 203, 204]:\n LOG_OBJ.error(\"Adding role Failed with status %s \"\n \"and error : %s\" % (response.status, response.data))\n print (\"Adding role Failed with status %s and error : %s\" %\n (response.status, response.data))\n return response.status\n return True","def role(self, role):\n\n self._role = int(role)","async def addtagrole(self, ctx, _role):\r\n\t\tif _role == 0:\r\n\t\t\tself.settings.ServerConfig(ctx.guild.id, 'TagRole', 0)\r\n\t\t\tawait ctx.send('Tag role set to: {}'.format(0))\r\n\t\telse:\t\r\n\t\t\trole = self.settings.Get(ctx, 'role', _role)\r\n\t\t\tif not role: return await ctx.send('Can\\'t find role: {}'.format(_role))\r\n\r\n\t\t\tself.settings.ServerConfig(ctx.guild.id, 'TagRole', role.id)\r\n\t\t\tawait ctx.send('Tag role set to: {}'.format(role))","def add_modifier( modifier_id, reaction, model, compartment = \"default\", arguments = DEFAULT_ARGUMENTS):\n if modifier_id is None and len( reaction.getListOfModifiers()) == 1:\n return reaction.getListOfModifiers()[0].getId()\n else:\n if not modifier_id:\n reaction_name = reaction.getName();\n reaction_id = reaction.getId();\n modifier_id = reaction_id + \"_modifier_nr\" + str(len( reaction.getListOfModifiers()));\n modifier_name = reaction_name[0:3].lower() + \"modifier\";\n add_species( None, model, id = modifier_id, name = modifier_name, compartment = compartment, arguments = arguments);\n \n modifier_ref = reaction.createModifier()\n check( modifier_ref, 'create modifier reference', arguments = arguments);\n check( modifier_ref.setSpecies( modifier_id), 'assign modifier species', arguments = arguments);\n check( modifier_ref.setMetaId( \"metaid_0000\" + modifier_id), 'set meta ID', arguments = arguments);\n check( modifier_ref.addCVTerm(add_cvterm(GENERIC_REACTION_SBO_MAPPING[\"modifier\"])), 'set controlled vocab SBO term for cause', arguments = arguments);\n return modifier_ref","async def addRolesOnEmbed(msg):\n for field in msg.embeds[0].fields:\n await msg.add_reaction(field.name[0])","async def _role_spec(self, ctx: Context, *, role: discord.Role):\n\n msg = await ctx.send(\n _(\n \"Are you sure you want to set `{}` as spectator role?\"\n ).format(role.name)\n )\n start_adding_reactions(msg, ReactionPredicate.YES_OR_NO_EMOJIS)\n\n pred = ReactionPredicate.yes_or_no(msg, ctx.author)\n await ctx.bot.wait_for(\"reaction_add\", check=pred)\n\n if pred.result:\n await self.config.guild(ctx.guild).spec_id.set(role.id)\n await ctx.send(_(\"Set `{}` as spectator role!\").format(role.name))\n else:\n await ctx.send(_(\"Aborted spectator role setup.\"))","async def add_roles(self, ctx: commands.Context, *roles: discord.Role):\n if not roles:\n return await ctx.send_help()\n errored = \"\"\n message = \"\"\n added = []\n already_added = []\n for role in roles:\n if role >= ctx.author.top_role:\n errored += (\n \"{role}: You can't set a role equal to or higher than your own.\\n\".format(\n role=role.name\n )\n )\n continue\n if role >= ctx.guild.me.top_role:\n errored += (\n \"{role}: You can't set a role that's equal to or higher than the \"\n \"bot.\\n\".format(role=role.name)\n )\n continue\n async with self.config.guild(ctx.guild).autoroles() as roles_list:\n if role.id not in roles_list:\n roles_list.append(role.id)\n added.append(role.name)\n else:\n already_added.append(role.name)\n message += errored\n if added:\n message += \"\\nAdded role(s): {roles}\".format(roles=humanize_list(added))\n if already_added:\n message += \"\\nRole(s) already added: {roles}\".format(\n roles=humanize_list(already_added)\n )\n if message:\n for line in pagify(message):\n await ctx.send(line)","def create_role(self, **kwargs):\n\n role = self.role_model(**kwargs)\n return self.put(role)","def role(self, role):\n\n self._role = role","def role(self, role):\n\n self._role = role","def addRole(self, name, description=\"\"):\n params = {\n \"f\" : \"json\",\n \"rolename\" : name,\n \"description\" : description\n }\n aURL = self._url + \"/roles/add\"\n return self._con.post(path=aURL, postdata=params)","def create(self, role):\n model = models.load('Role', role)\n model.account_id = self.account_id\n\n return self.client.create_role(model)","def test_edit_role_add_new_role(self):\n # Add node with controller role\n Nodes().nodes_discovered[0].checkbox.click()\n RolesPanel().controller.click()\n Nodes().apply_changes.click()\n time.sleep(1)\n # Add cinder role\n with Nodes() as n:\n n.nodes[0].checkbox.click()\n n.edit_roles.click()\n RolesPanel().cinder.click()\n Nodes().apply_changes.click()\n time.sleep(1)\n with Nodes() as n:\n self.assertIn(ROLE_CONTROLLER, n.nodes[0].roles.text,\n 'Controller role')\n self.assertIn(ROLE_CINDER, n.nodes[0].roles.text,\n 'Cinder role')","def add_user_role(self, tenant_id, user_id, role_id):\n _url = \"http://\" + self.host_ip + \":35357/v2.0/tenants/\" + \\\n tenant_id + \"/users/\" + user_id + \"/roles/OS-KSADM/\" + role_id\n _headers = {'x-auth-token': self.cloud_admin_info['token_project']}\n _body = None\n response = self.request(\"PUT\", _url, _headers, _body)\n if response is None:\n LOG_OBJ.error(\"No response from Server while adding role\")\n return response\n if response.status not in [200, 201, 202, 203, 204]:\n LOG_OBJ.error(\"Add user role Failed with status %s \" %\n response.status)\n return response.status\n\n LOG_OBJ.info(\"Role: %s is added to user:%s successfully.\"\n % (role_id, user_id))\n return True","def add_admin(self, uid, name, role=None):\n uid = self._check_uid(uid)\n self._router_request(\n self._make_request_data(\n 'addAdminRole',\n data=dict(\n params=dict(\n uid=uid,\n name=name,\n role=role,\n )\n )\n )\n )\n\n return self.get_admin_by_name(uid, name)","def add_reaction(self, user_id, ts, reaction):\n if (user_id, ts) not in self.user_id_ts_to_reactions:\n logging.info(\"add_reaction for (user_id, ts) (%s, %f) not in self.user_id_ts_to_reactions %s\" % (user_id, ts, self.user_id_ts_to_reactions))\n return\n if reaction < 0 or reaction > 1:\n logging.info(\"Got unknown reaction %s for (user_id, ts) (%s, %f)\" % (reaction, user_id, ts))\n return\n self.user_id_ts_to_reactions[(user_id, ts)][reaction] += 1","def add_role(self, name: str) -> Role:\n role = self.find_role(name)\n if role is None:\n try:\n role = self.role_model()\n role.name = name\n self.get_session.add(role)\n self.get_session.commit()\n log.info(const.LOGMSG_INF_SEC_ADD_ROLE.format(name))\n return role\n except Exception as e:\n log.error(const.LOGMSG_ERR_SEC_ADD_ROLE.format(e))\n self.get_session.rollback()\n return role","async def rolemenu_create(self,\n interaction: discord.Interaction,\n name: str,\n channel: discord.TextChannel,\n mode: int,\n color: str,\n placeholder: str,\n description: str = None):\n doc = await self.db.find_one({\n \"guild_id\": interaction.guild.id,\n \"name\": name\n })\n if doc:\n return await interaction.response.send_message(\n \"Role menu with that name exists.\", ephemeral=True)\n if channel.guild != interaction.guild:\n return await interaction.response.send_message(\n \"This channel is not in this server.\")\n if not channel.permissions_for(interaction.guild.me).send_messages:\n return await interaction.response.send_message(\n \"I don't have permission to send messages in this channel.\")\n try:\n color = discord.Color.from_str(color)\n except ValueError:\n return await interaction.response.send_message(\"Invalid color.\")\n await self.db.insert_one({\n \"message_id\": None,\n \"name\": name,\n \"mode\": mode,\n \"channel_id\": channel.id,\n \"roles\": [],\n \"guild_id\": interaction.guild.id,\n \"color\": color.value,\n \"placeholder\": placeholder,\n \"description\": description\n })\n await interaction.response.send_message(\n \"Role menu created. It is currently empty, however, and \"\n \"you'll need to add roles with `/rolemenu role add.`\",\n ephemeral=True)","async def addroleall(self, ctx, role: discord.Role):\n muted_role = discord.utils.get(ctx.guild.roles, name=\"Muted\")\n punished_role = discord.utils.get(ctx.guild.roles, name=\"Punished\")\n\n if role > ctx.author.top_role:\n return await ctx.send(\n embed=discord.Embed(\n title=\"You don't have permission to add this role\",\n timestamp=datetime.datetime.utcnow(),\n colour=discord.Colour.darker_grey(),\n )\n )\n\n if role == muted_role or role == punished_role:\n return await ctx.send(\n embed=discord.Embed(\n title=f\"Can not assign *{role}* role using this command.\",\n description=\"For more information run ```.help addroleall```\",\n timestamp=datetime.datetime.utcnow(),\n colour=discord.Colour.red(),\n )\n )\n\n for i in ctx.guild.members:\n if not i.bot:\n await i.add_roles(role)\n\n await ctx.send(\n embed=discord.Embed(\n title=f\"*{role}* has been added to **{len(ctx.guild.members)}** members!\",\n timestamp=datetime.datetime.utcnow(),\n colour=discord.Colour.green(),\n )\n )","def patch(self, username, role):\n try:\n UserService.add_role_to_user(token_auth.current_user(), username, role)\n return {\"Success\": \"Role Added\"}, 200\n except UserServiceError as e:\n return {\"Error\": str(e).split(\"-\")[1], \"SubCode\": str(e).split(\"-\")[0]}, 403","async def on_guild_role_create(self, role):\n channel = self.client.get_channel(serverlogs.getChannel(role.guild.id, \"roles\"))\n if channel is not None:\n await self.log_role(role=role, type='Create', channel=channel, guild=role.guild)","async def addRoles(self, ctx: Context, person: Member, roles: Greedy[Role]):\n roles = remove_dupe_roles(roles)\n\n await person.add_roles(*roles)\n await ctx.send(f\"Adding {roles_str(person, roles)}\")","async def _create_new_role(self, context, name: str, target=GROUP_CATEGORY_NAME, channel=False, color: discord.Color = None):\n # todo - sanitize input, preventing \"-\" specifically\n target_role = get(context.guild.roles, name=target)\n target_position = target_role.position\n\n new_role = await context.guild.create_role(\n name=name, mentionable=True, reason=f\"Role created by {context.author}\")\n\n await context.guild.edit_role_positions(positions={new_role: target_position})","def create_role(self, role_name, description, rights):\n org_admin_resource = self.client.get_resource(self.href_admin)\n role = E.Role(\n E.Description(description), E.RightReferences(), name=role_name)\n if rights is None:\n rights = ()\n for right in tuple(rights):\n right_record = self.get_right(right)\n role.RightReferences.append(\n E.RightReference(\n name=right_record.get('name'),\n href=right_record.get('href'),\n type=EntityType.RIGHT.value))\n return self.client.post_linked_resource(\n org_admin_resource, RelationType.ADD, EntityType.ROLE.value, role)","def create_role(self, **kwargs):\n role = self.role_model(**kwargs)\n # noinspection PyUnresolvedReferences\n return self.save(role)","def update_role(self, role_id, role):\n raise exception.NotImplemented() # pragma: no cover","async def register_role(self, ctx, role_name: str, role_title: str, guild_id: int = None):\n role_title = role_title.lower()\n\n # Raises expected AttributeError\n if guild_id is None:\n guild = ctx.guild\n guild_id = guild.id\n else:\n guild = self.bot.get_guild(guild_id)\n guild_id = guild.id\n if guild is None:\n await ctx.send(\"Couldn't find the guild provided.\")\n return\n\n try:\n role_id = discord.utils.get(guild.roles, name=role_name).id\n except AttributeError:\n # role returned is None\n await ctx.send(\"Role with name {} in guild {} could not be found.\".format(role_name, guild.name))\n return\n\n db_key = \"guild:{}:roles:roles:{}\".format(guild_id, role_title)\n\n self.config.set(\"{}:role_id\".format(db_key), role_id)\n\n self.config.hset(\"guild:{}:roles:all:names\".format(guild.id), role_title, role_id)\n\n await ctx.send(\"New role '{}' registered with keyword '{}'\".format(role_name, role_title))","def start_router_realm_role(self, id, role_id, config, details=None):\n self.log.debug(\"{}.add_router_realm_role\".format(self.__class__.__name__),\n id=id, role_id=role_id, config=config)\n\n if id not in self.realms:\n raise ApplicationError(u\"crossbar.error.no_such_object\", \"No realm with ID '{}'\".format(id))\n\n if role_id in self.realms[id].roles:\n raise ApplicationError(u\"crossbar.error.already_exists\", \"A role with ID '{}' already exists in realm with ID '{}'\".format(role_id, id))\n\n self.realms[id].roles[role_id] = RouterRealmRole(role_id, config)\n\n realm = self.realms[id].config['name']\n self._router_factory.add_role(realm, config)","def test_add_role(self):\n pass","def role_add(role, nodes, node, node_vars, host_vars, extra):\n role_manager = get_role_manager()\n node += nodes\n nodes, node_vars, host_vars, extra_args = _split_vars(\n node, node_vars, host_vars, extra)\n if not nodes:\n raise ArgumentError('No nodes informed')\n\n added_nodes = role_manager.add_role(\n role, hosts_node_map=nodes, host_vars=host_vars,\n node_vars=node_vars, extra_args=extra_args)\n\n print(f\"{len(added_nodes)} nodes were added to role {role}: {', '.join(sorted(added_nodes))}\")\n return 0","def RIPReaction(sc, event):\n sc.api_call('reactions.add', as_user='true', channel=event['channel'],\n timestamp=event['ts'], name='rip')","async def persistrole(self, ctx, member: discord.Member, role: discord.Role):\n role = discord.utils.get(ctx.guild.roles, id=role.id)\n\n muted_role = discord.utils.get(ctx.guild.roles, name=\"Muted\")\n punished_role = discord.utils.get(ctx.guild.roles, name=\"Punished\")\n\n if role > ctx.author.top_role:\n return await ctx.send(\n embed=discord.Embed(\n title=\"You don't have permission to add this role\",\n timestamp=datetime.datetime.utcnow(),\n colour=discord.Colour.darker_grey(),\n )\n )\n\n if role == muted_role or role == punished_role:\n return await ctx.send(\n embed=discord.Embed(\n title=f\"Can not assign *{role}* role using this command.\",\n description=\"For more information run ```.help persistrole```\",\n timestamp=datetime.datetime.utcnow(),\n colour=discord.Colour.red(),\n )\n )\n\n if role in member.roles:\n return await ctx.channel.send(\n embed=discord.Embed(\n title=f\"*{member}* already has *{role}* Role!\",\n timestamp=datetime.datetime.utcnow(),\n colour=discord.Colour.greyple(),\n )\n )\n\n await member.add_roles(role)\n persistent_role = Roles(\n bot=self.bot,\n guild_id=ctx.guild.id,\n user_id=member.id,\n roles=role.id,\n )\n # Post to db for persistent role\n await persistent_role.post()\n\n await ctx.send(\n embed=discord.Embed(\n title=f\"Persisting Role: *{role}* has been added to *{member}*\",\n timestamp=datetime.datetime.utcnow(),\n colour=discord.Colour.green(),\n )\n )","async def create(self, ctx: commands.Context, title: str, *roles: discord.Role):\n brake = {}\n\n def check(_r, _u):\n return _u == ctx.author and _r.message == me and str(_r.emoji) not in brake\n\n me = await ctx.send(f'React with the reaction that will correspond to the role `{roles[0]}`')\n\n # Yes I know this is an ugly solution to avoid an unnecessary api request but it is ultimately the only solution\n # I could think of.\n try:\n r, u = await self.bot.wait_for('reaction_add', timeout=len(roles) * 20, check=check)\n brake[str(r.emoji)] = roles[0].id\n except asyncio.TimeoutError:\n await ctx.send(\"Timed out, please run the command again and this time be a little quicker to react.\")\n return\n\n try:\n for role in roles[1:]:\n await me.edit(content=f'React with the reaction that will correspond to the role `{role}`')\n r, u = await self.bot.wait_for('reaction_add', timeout=len(roles) * 20, check=check)\n brake[str(r.emoji)] = role.id\n except asyncio.TimeoutError:\n await ctx.send('Timed out, please run the command again and this time be a little quicker to react.')\n return\n\n await ctx.send(\n 'What channel do you wish to send this role menu in? Enter its id, name or mention it: #<channel>')\n\n for count in range(4):\n try:\n m = await self.bot.wait_for('message', check=lambda _m: _m.author == ctx.author and _m.channel == ctx.channel, timeout=30)\n chan = await self.tcc.convert(ctx, m.content)\n break\n except commands.BadArgument:\n if count == 3:\n await ctx.send(\"Too many tries to enter channel, make sure I can actually see the channel you're \"\n \"referring to and use the entire command again\")\n return\n await ctx.send(\"Please enter the correct channel, if in doubt, try mentioning it, the `#channel` thing\")\n except asyncio.TimeoutError:\n await ctx.send(\"Timed out\")\n return\n\n e = discord.Embed(title=f\"Role menu: {title}\",\n description=\"\\n\\n\".join(f\"{k} - {ctx.guild.get_role(v)}\"for k, v in brake.items()),\n colour=discord.Colour.blue())\n\n zero = await chan.send(embed=e)\n\n for k in brake:\n await zero.add_reaction(k)\n\n self._cache[ctx.guild.id][zero.id] = brake\n\n query = \"\"\"\n INSERT INTO selfrole_lookup (guildid, channelid, messageid) \n VALUES ($1, $2, $3)\n \"\"\"\n await self.bot.pool.execute(query, ctx.guild.id, chan.id, zero.id)\n\n query = \"\"\"\n INSERT INTO selfrole (messageid, emoji, roleid)\n VALUES ($1, $2, $3)\n \"\"\"\n for k, v in brake.items():\n await self.bot.pool.execute(query, zero.id, k, v)","async def add_role_owner(request, role_id):\n required_fields = [\"id\"]\n utils.validate_fields(required_fields, request.json)\n\n txn_key, txn_user_id = await utils.get_transactor_key(request)\n proposal_id = str(uuid4())\n batch_list = Role().owner.propose.batch_list(\n signer_keypair=txn_key,\n signer_user_id=txn_user_id,\n proposal_id=proposal_id,\n role_id=role_id,\n next_id=request.json.get(\"id\"),\n reason=request.json.get(\"reason\"),\n metadata=request.json.get(\"metadata\"),\n )\n await utils.send(\n request.app.config.VAL_CONN, batch_list, request.app.config.TIMEOUT\n )\n return json({\"proposal_id\": proposal_id})","def add_role():\n\n role_form = RoleForm(request.form)\n\n if role_form.validate_on_submit():\n name = request.form['name']\n\n if not name or name == '' :\n flash(\"No null or empty values are allowed.\",\"warn\")\n return render_template('user/add_edit_role.html', title='Add Role',\n add=True,form=role_form)\n\n else:\n new_role = Role(name = name)\n try:\n correct = True\n db.session.add(new_role)\n db.session.commit()\n except Exception as e:\n # Catch anything unknown\n print(e)\n correct = False\n finally:\n if not correct:\n # Cleanup and show error\n db.session.rollback()\n flash('Error when creating a Role.','error')\n else:\n flash(\"Our Role was created!\",\"success\")\n return redirect(url_for('user_ksat.manage_role'))\n\n return render_template('user/add_edit_role.html', title='Add Role',add=True,form=role_form)","async def _role_player(self, ctx: Context, *, role: discord.Role):\n\n msg = await ctx.send(\n _(\n \"Are you sure you want to set `{}` as player role?\"\n ).format(role.name)\n )\n start_adding_reactions(msg, ReactionPredicate.YES_OR_NO_EMOJIS)\n\n pred = ReactionPredicate.yes_or_no(msg, ctx.author)\n await ctx.bot.wait_for(\"reaction_add\", check=pred)\n\n if pred.result:\n await self.config.guild(ctx.guild).player_id.set(role.id)\n await ctx.send(_(\"Set `{}` as player role!\").format(role.name))\n else:\n await ctx.send(_(\"Aborted player role setup.\"))","async def rolemenu(self, message, args):\n if \"-h\" in args or \"--help\" in args or \"--ahelp\" in args:\n await self._print_rolemenu_help(message.channel)\n return\n update_reactions = not is_key_in_args(args, \"--no-update\")\n allow_role_change = not is_key_in_args(args, \"--no-change\")\n remove_role_on_reaction_removal = not is_key_in_args(args, \"--no-removal\")\n max_number_of_reactions_per_user = get_number_in_args(args, \"--max-reactions\", None)\n max_users_with_role = get_number_in_args(args, \"--max-users\", None)\n no_required_role = is_key_in_args(args, \"--all\")\n role_ids = message.raw_role_mentions\n if len(role_ids) * 2 != len(args):\n await message.channel.send(\"Error with `rolemenu`: all roles must match an emoji\")\n return await self._print_rolemenu_help(message.channel)\n menu = {}\n for i, role_id in enumerate(message.raw_role_mentions):\n emoji = args.pop(0)\n if emoji.strip(\"<>@&\") == str(role_id): # emoji and roles are just exchanged: no problem\n emoji = args.pop(0)\n elif args.pop(0).strip(\"<>@&\") != str(role_id): # error: two adjacent args must be role_id and emoji\n await message.channel.send(\"Error with `rolemenu`: a role must match an emoji\")\n return await self._print_rolemenu_help(message.channel)\n menu.update({emoji: [message.guild.get_role(role_id)]})\n manager = RoleByReactionManager.get(self.guild)\n options = RoleMenuOptions(required_roles=None if no_required_role else [RoleCollection.VISITOR.value],\n ignored_roles=[RoleCollection.MASTER.value],\n update_reactions=update_reactions,\n allow_role_change=allow_role_change,\n max_number_of_reactions_per_user=max_number_of_reactions_per_user,\n max_users_with_role=max_users_with_role,\n remove_role_on_reaction_removal=remove_role_on_reaction_removal\n )\n await manager.add(message, menu, options)\n logger.debug(\"Rolemenu created. Now you can edit your post to make it prettier.\")","def create_role (self):\n return self.create_association().create_role(self.create_topic(),\n self.create_topic())","async def create(\n cls,\n member: discord.Member,\n role: discord.Role,\n duration: timedelta,\n added_by: discord.Member,\n *,\n reason: str = None\n ) -> \"TempRole\":\n self = cls(\n member,\n role=role,\n duration=duration.total_seconds(),\n reason=reason,\n added_by=added_by.id,\n added_at=datetime.utcnow().timestamp(),\n )\n await self.save()\n return self","async def subscribe_command(self, ctx: commands.Context, *_) -> None: # We don't actually care about the args\n view = RoleButtonView(ctx.author, self.assignable_roles)\n await ctx.send(\n \"Click the buttons below to add or remove your roles!\",\n view=view,\n delete_after=DELETE_MESSAGE_AFTER\n )","async def createRole(self, ctx):\n await self.deleteRole(ctx=ctx, reason=\"Début de partie.\")\n await ctx.guild.create_role(name=self.categoryName)\n await asyncio.sleep(1)\n self.roleForPlayer = discord.utils.get(ctx.guild.roles, name=self.categoryName)\n print(\"Role created.\")\n member = await ctx.guild.fetch_member(bot.user.id)\n await member.add_roles(self.roleForPlayer, reason=\"Début de partie.\")\n for member in ctx.author.voice.channel.members:\n await member.add_roles(self.roleForPlayer, reason=\"Début de partie.\")","def addRoleSecurityFilter(self, role, _type, s=None, p=None, o=None, g=None):\n self._client.addRoleSecurityFilter(role, _type, s, p, o, g)","def setup_test_role(self):\n self.test_role = rand_name('role')\n resp, self.role = self.client.create_role(self.test_role)\n self.roles.append(self.role)","def append_role(self, user):\n\n tx = self.iroha.transaction(\n [\n self.iroha.command(\n \"AppendRole\",\n account_id=f\"{user.gov_id}@afyamkononi\",\n role_name=user.type,\n )\n ],\n creator_account=f\"{self.creator_account_details.gov_id}@afyamkononi\",\n )\n\n IrohaCrypto.sign_transaction(tx, self.creator_account_details.private_key)\n return self.send_transaction_and_return_status(tx)","def setRole(self, *args):\n return _libsbml.ReferenceGlyph_setRole(self, *args)"],"string":"[\n \"async def rolemenu_add_role(self,\\n interaction: discord.Interaction,\\n name: str,\\n role: discord.Role,\\n emoji: str = None,\\n description: str = None):\\n doc = await self.db.find_one({\\n \\\"guild_id\\\": interaction.guild.id,\\n \\\"name\\\": name\\n })\\n if not doc:\\n return await interaction.response.send_message(\\n \\\"No role menu with that name exists.\\\", ephemeral=True)\\n for role_doc in doc[\\\"roles\\\"]:\\n if role_doc[\\\"id\\\"] == role.id:\\n return await interaction.followup.send(\\n \\\"Role is already in the menu.\\\", ephemeral=True)\\n if len(doc[\\\"roles\\\"]) >= 25:\\n return await interaction.response.send_message(\\n \\\"This role menu is full.\\\", ephemeral=True)\\n await interaction.response.defer(ephemeral=True)\\n if role.guild != interaction.guild:\\n return await interaction.response.send_message(\\n \\\"This role is not in this server.\\\")\\n if emoji:\\n if emoji.startswith(\\\"<\\\") and emoji.endswith(\\\">\\\"):\\n try:\\n emoji = int(emoji[1:-1].split(\\\":\\\")[2])\\n except ValueError:\\n return await interaction.followup.send(\\\"Invalid emoji.\\\")\\n else:\\n try:\\n message = await interaction.original_message()\\n await message.add_reaction(emoji)\\n except discord.HTTPException:\\n return await interaction.followup.send(\\\"Invalid emoji.\\\")\\n await self.db.update_one({\\\"_id\\\": doc[\\\"_id\\\"]}, {\\n \\\"$push\\\": {\\n \\\"roles\\\": {\\n \\\"description\\\": description,\\n \\\"id\\\": role.id,\\n \\\"emoji\\\": emoji,\\n \\\"date_added\\\": datetime.datetime.now(datetime.datetime.u)\\n }\\n }\\n })\\n doc = await self.db.find_one({\\\"_id\\\": doc[\\\"_id\\\"]})\\n await interaction.followup.send(f\\\"Added {role.mention} to the menu.\\\")\\n menu = Menu(self, interaction.guild, doc)\\n await menu.update()\",\n \"def addRole(self, role):\\n self._client.addRole(role)\",\n \"def addRole(self, role=None, roleName=None, kvDict=None):\\n return _modelActionBase(self, instance=role, instanceName=roleName, kvDict=kvDict,\\n model=get_model('role'), db=db, action='add', modelType='role')\",\n \"def add_role(self, role):\\n if role.name not in [r.name for r in self.roles]:\\n return db[self.colNam].find_and_modify(query=dict(_id=self.id), update={'$push': {'roles': role.to_python()}})\",\n \"async def add_role(\\n client,\\n event,\\n user: ('user', 'User to add role to'),\\n role: ('role', 'The role to give'),\\n):\\n # Check for permissions\\n if not event.user_permissions.can_manage_roles:\\n abort('You need `manage roles` permission to invoke this command.')\\n \\n if not event.guild.cached_permissions_for(client).can_manage_roles:\\n abort('I need `manage roles` permission to execute this command.')\\n \\n if not event.user.has_higher_role_than(role):\\n abort('You must have higher role than the role you are trying to give.')\\n \\n if not client.has_higher_role_than(role):\\n abort('I must have higher role than the role you are trying to give.')\\n \\n # Using `.copy_to` on forms works as well.\\n return ADD_ROLE_FORM.copy_with(\\n title = f'Add role {role.name} to {user.full_name}',\\n custom_id = f'add_role.{user.id}.{role.id}',\\n )\",\n \"async def command_assign_role(self, context, role: str):\\n try:\\n await context.author.add_roles(discord.utils.get(\\n context.guild.roles, name=role))\\n await context.message.add_reaction('👍')\\n except Exception as e:\\n await context.message.add_reaction('👎')\\n await context.send('Role could not be assigned')\\n print(f'Errored in command_assign_role.', e)\",\n \"def add_role(self, role):\\n try:\\n self.db_proxy.nameCheck(role.theName, 'role')\\n except ARM.ARMException as ex:\\n self.close()\\n raise ARMHTTPError(ex)\\n\\n role_params = RoleParameters(\\n name=role.theName,\\n rType=role.theType,\\n sCode=role.theShortCode,\\n desc=role.theDescription,\\n cProperties=[]\\n )\\n\\n role_id = self.db_proxy.addRole(role_params)\\n\\n return role_id\",\n \"async def addrole(self, ctx, user: discord.Member=None, *, role=None):\\r\\n if user is None or role is None:\\r\\n return await ctx.send(\\\"Incorrect usage! *;addrole @user role*\\\")\\r\\n r = discord.utils.get(ctx.guild.roles, name=str(role))\\r\\n if r is None:\\r\\n return await ctx.send(f'{role} was not found')\\r\\n try:\\r\\n await user.add_roles(r)\\r\\n return await ctx.send(f\\\"**{str(user)}** has been given the role of **{role}** {self.bot.get_emoji(470063310386233344)}\\\")\\r\\n except discord.Forbidden:\\r\\n return await ctx.send(\\\"Bot does not have enough permissions to give roles.\\\")\",\n \"def manage_addRole(self, role_id, title, description, RESPONSE=None,\\n REQUEST=None):\\n if not role_id:\\n message = 'Please+provide+a+Role+ID'\\n else:\\n self.addRole(role_id, title, description)\\n message = 'Role+added'\\n\\n if RESPONSE is not None:\\n RESPONSE.redirect('%s/manage_roles?manage_tabs_message=%s' %\\n (self.absolute_url(), message))\",\n \"async def handle_role_reaction_press(interaction: disnake.MessageInteraction):\\n if interaction.message not in await ReactionRoleMessage.get_all():\\n return\\n\\n role_id = int(interaction.component.custom_id)\\n member: disnake.Member = interaction.author\\n user = await User.get(member.id)\\n role = member.get_role(role_id)\\n if role:\\n await member.remove_roles(role, reason=\\\"Reaction Role Message\\\")\\n await send_message(user=user, key=\\\"role_removed\\\", inter=interaction, ephemeral=True)\\n else:\\n role = interaction.guild.get_role(role_id)\\n if role:\\n try:\\n await member.add_roles(role, reason=\\\"Reaction Role Message\\\")\\n await send_message(user=user, key=\\\"role_added\\\", inter=interaction, ephemeral=True)\\n except disnake.errors.Forbidden as e:\\n await send_message(user=user, key=\\\"no_permissions\\\", inter=interaction, ephemeral=True)\\n else:\\n await send_message(user=user, key=\\\"role_not_found\\\", inter=interaction, ephemeral=True)\",\n \"async def on_raw_reaction_add(self, payload: discord.RawReactionActionEvent):\\n\\n # Skipping bot reactions\\n if payload.member.bot:\\n return\\n\\n # Search the event by message id\\n message_id = payload.message_id\\n channel_id = payload.channel_id\\n guild_id = payload.guild_id\\n event_type = connector.getEventTypeByMessage(guild_id, message_id, channel_id)\\n if event_type is None:\\n return\\n\\n role = discord.utils.get(payload.member.guild.roles, id=event_type.role_id)\\n\\n if (payload.event_type == \\\"REACTION_ADD\\\") and (event_type.emoji == str(payload.emoji)):\\n # Adding role to user\\n await payload.member.add_roles(role)\",\n \"def addReaction(self, *args, **kwargs):\\n if isinstance(args[0], Reaction):\\n reaction = args[0]\\n else:\\n reaction = self._sim.reaction(*args, **kwargs)\\n\\n self._sim.assignReaction(reaction, self)\\n return self\",\n \"async def on_raw_reaction_add(self, payload):\\n\\n # exclude all reactions which are not the original message\\n if str(payload.message_id) != self.message_id:\\n return\\n\\n # exclude the bot\\n if payload.user_id == self.bot.user.id:\\n return\\n\\n else:\\n # get the model data for the role assigner object\\n data = await self.get_objects(\\n model=RoleAssigner, filter={\\\"bot__name\\\": str(self.bot_name)}\\n )\\n\\n # role assigner object\\n data = data[0]\\n\\n guild = self.get_guild(guild_id=payload.guild_id)\\n\\n user = self.get_user(guild=guild, user_id=payload.user_id)\\n\\n for db_role in data.roles.all():\\n\\n if db_role.emoji.startswith(\\\":\\\") and db_role.emoji.endswith(\\\":\\\"):\\n\\n ce = db_role.emoji[1:-1]\\n\\n else:\\n ce = db_role.emoji\\n\\n if str(payload.emoji.name) == str(ce):\\n\\n role = self.get_role(guild, int(db_role.uid))\\n\\n if user not in role.members:\\n\\n await user.add_roles(role)\\n\\n print(\\\"Added \\\" + str(user) + \\\" to role: \\\" + str(role) + \\\"!\\\")\\n\\n else:\\n print(\\n \\\"User \\\" + str(user) + \\\" already in role: \\\" + str(role) + \\\"!\\\"\\n )\\n\\n pass\",\n \"def add_role():\\r\\n check_admin()\\r\\n\\r\\n add_role = True\\r\\n\\r\\n form = RoleForm()\\r\\n if form.validate_on_submit():\\r\\n role = Role(name=form.name.data,\\r\\n description=form.description.data)\\r\\n\\r\\n try:\\r\\n # add role to the database\\r\\n db.session.add(role)\\r\\n db.session.commit()\\r\\n flash('You have successfully added a new role.')\\r\\n except:\\r\\n # in case role name already exists\\r\\n flash('Error: role name already exists.',category='error')\\r\\n\\r\\n # redirect to the roles page\\r\\n return redirect(url_for('admin.list_roles'))\\r\\n\\r\\n # load role template\\r\\n return render_template('admin/roles/role.html', add_role=add_role,\\r\\n form=form, title='Add Role')\",\n \"def add_role(self, name):\\n role = Role.by_name(name)\\n if not role:\\n role = Role(name)\\n db.add(role)\\n if not role in self.roles:\\n self.roles.append(role)\",\n \"def add_role(self, role, parents=[]):\\r\\n self._roles.setdefault(role, set())\\r\\n self._roles[role].update(parents)\",\n \"def add_role():\\n role = roles.find_or_create_role(request.values.get('role_name', ''))\\n user = users.get_or_404(int(request.values.get('user_id', '')))\\n if not users.add_role_to_user(user, role):\\n return {}, 500\\n return {}\",\n \"async def addrole(self, ctx, member: discord.Member, role: discord.Role):\\n role = discord.utils.get(ctx.guild.roles, id=role.id)\\n\\n muted_role = discord.utils.get(ctx.guild.roles, name=\\\"Muted\\\")\\n punished_role = discord.utils.get(ctx.guild.roles, name=\\\"Punished\\\")\\n\\n if role > ctx.author.top_role:\\n return await ctx.send(\\n embed=discord.Embed(\\n title=\\\"You don't have permission to add this role\\\",\\n timestamp=datetime.datetime.utcnow(),\\n colour=discord.Colour.darker_grey(),\\n )\\n )\\n\\n if role == muted_role or role == punished_role:\\n return await ctx.send(\\n embed=discord.Embed(\\n title=f\\\"Can not assign *{role}* role using this command.\\\",\\n description=\\\"For more information run ```.help addrole```\\\",\\n timestamp=datetime.datetime.utcnow(),\\n colour=discord.Colour.red(),\\n )\\n )\\n\\n if role in member.roles:\\n return await ctx.channel.send(\\n embed=discord.Embed(\\n title=f\\\"*{member}* already has *{role}* Role!\\\",\\n timestamp=datetime.datetime.utcnow(),\\n colour=discord.Colour.greyple(),\\n )\\n )\\n\\n await member.add_roles(role)\\n await ctx.send(\\n embed=discord.Embed(\\n title=f\\\"*{role}* has been added to *{member}*\\\",\\n timestamp=datetime.datetime.utcnow(),\\n colour=discord.Colour.green(),\\n )\\n )\",\n \"def create_role(self, role_id, role):\\n raise exception.NotImplemented() # pragma: no cover\",\n \"def addRole(self, role_id, title='', description=''):\\n if self._roles.get(role_id) is not None:\\n raise KeyError('Duplicate role: %s' % role_id)\\n\\n self._roles[role_id] = {'id': role_id, 'title': title,\\n 'description': description}\",\n \"def addUserRole(self, name, role):\\n self._client.addUserRole(name, role)\",\n \"async def on_raw_reaction_add(self, payload: discord.RawReactionActionEvent):\\n role: discord.Role = await self.check_payload(payload)\\n if payload.user_id == self.bot.user.id:\\n return\\n if role:\\n guild = self.bot.get_guild(payload.guild_id)\\n member = guild.get_member(payload.user_id)\\n await member.add_roles(role)\",\n \"def add_role():\\n check_admin()\\n add_role = True\\n\\n form = RoleForm()\\n if form.validate_on_submit():\\n role = Role(title=form.title.data)\\n\\n try:\\n db.session.add(role)\\n db.session.commit()\\n flash('New role successfully created')\\n except:\\n flash('Error: Role title already exist')\\n\\n return redirect(url_for('admin.get_roles'))\\n\\n return render_template('admin/roles/role.html', form=form, add_role=add_role, title='Add Role')\",\n \"def addReactant(self, *args):\\n return _libsbml.Reaction_addReactant(self, *args)\",\n \"async def on_raw_reaction_add(self, payload: discord.RawReactionActionEvent):\\n # Get the message id of the message that the user reacted to.\\n message_id = payload.message_id\\n\\n # Get the message id of the message we want the user to react to.\\n actual_message_id = constants.MessageIDs.RULES_MSGID\\n\\n # Compare that id's match, and if true continue to give the role.\\n if message_id == actual_message_id:\\n guild_id = payload.guild_id\\n guild = self.bot.get_guild(guild_id)\\n role = get(payload.member.guild.roles, name='Not Verified')\\n\\n if role is not None:\\n member = get(guild.members, id=payload.user_id)\\n if member is not None:\\n await payload.member.add_roles(role)\\n print(f\\\"Added role to {member}\\\")\\n else:\\n print(\\\"User not found . . .\\\")\\n else:\\n print(\\\"Role not found . . .\\\")\",\n \"def add_role(role):\\n roleOfUser=Role.objects.create(type=role)\\n return roleOfUser\",\n \"def add_role(email, role):\\n from enferno.user.models import Role\\n u = User.query.filter(User.email == email).first()\\n\\n if u is None:\\n print('Sorry, this user does not exist!')\\n else:\\n r = Role.query.filter(Role.name == role).first()\\n if r is None:\\n print('Sorry, this role does not exist!')\\n u = click.prompt('Would you like to create one? Y/N', default='N')\\n if u.lower() == 'y':\\n r = Role(name=role)\\n try:\\n db.session.add(r)\\n db.session.commit()\\n print('Role created successfully, you may add it now to the user')\\n except Exception as e:\\n db.session.rollback()\\n # add role to user\\n u.roles.append(r)\",\n \"def addRolePermission(self, role, _type):\\n self._client.addRolePermission(role, _type)\",\n \"def add_role(self, principal, role):\\n return permissions.utils.add_local_role(self, principal, role)\",\n \"async def apply_role(self, *, reason: str = None):\\n if self.role not in self.member.roles:\\n try:\\n await self.member.add_roles(self.role, reason=reason)\\n except discord.HTTPException:\\n pass\",\n \"def addReaction(self, *args):\\n return _libsbml.Model_addReaction(self, *args)\",\n \"def add_role():\\n\\tcheck_admin()\\n\\tadd_role = True\\n\\n\\tform = RoleForm()\\n\\tif form.validate_on_submit():\\n\\t\\trole= Role(name= form.name.data,description=form.description.data)\\n\\n\\t\\ttry:\\n\\t\\t\\t#add role to the database \\n\\t\\t\\tdb.session.add(role)\\n\\t\\t\\tdb.session.commit()\\n\\t\\t\\tflash('You have successfully added a new role ')\\n\\t\\texcept:\\n\\t\\t\\t#incase the role already exists\\n\\t\\t flash(\\\"Error:the role already exists\\\")\\n\\n\\t\\t#redirect to the roles page\\n\\t\\treturn redirect(url_for('admin.list_roles'))\\n\\n\\t\\t#load the role template\\n\\treturn render_template('admin/roles/role.html', add_role=add_role, form = form,title='Add Role')\",\n \"def add_employeeRole(self, id, role):\\n cursor = self.dbconnect.get_cursor()\\n try:\\n cursor.execute('INSERT INTO employeeRoles values(%s,%s)',\\n (id, role))\\n # get id and return updated object\\n self.dbconnect.commit()\\n except(Exception, self.dbconnect.get_error()) as error:\\n self.dbconnect.rollback()\\n raise Exception('\\\\nUnable to save EmployeeRole!\\\\n(%s)' % (error))\",\n \"async def role(ctx, role: discord.Role = None):\\n if role is None:\\n await ctx.send(\\\"List of assignable roles: \\\" + str(allowed_roles))\\n if role.name in allowed_roles:\\n if not role in ctx.message.author.roles:\\n await ctx.message.author.add_roles(role)\\n await ctx.send(\\\"Role added.\\\")\\n else:\\n await ctx.message.author.remove_roles(role)\\n await ctx.send(\\\"Role removed.\\\") \\n else:\\n await ctx.send(\\\"That role doesn't exist, or you don't have permission to modify it.\\\")\",\n \"async def addrole(self, ctx: context.CustomContext):\\n\\n await ctx.send(\\n f\\\"{config.USER_INTERACTION_REQUIRED} Reply with the name of the role you want to create.\\\"\\n )\\n\\n role_name = await ctx.converted_input(converter=converter.CaseInsensitiveRole)\\n\\n if isinstance(role_name, str):\\n await ctx.send(\\n f\\\"{config.YES} I will **create a new role** on this server named `{role_name}` for this.\\\"\\n )\\n try:\\n discord_role = await ctx.guild.create_role(name=role_name)\\n except discord.Forbidden:\\n raise exceptions.ForbiddenError(\\n exceptions.ForbiddenTask.CREATE_ROLE, role_name\\n )\\n\\n else:\\n discord_role = role_name\\n\\n await ctx.send(\\n f\\\"{config.YES} I'll use the **pre-existing role** named `{discord_role.name}` for this.\\\"\\n )\\n\\n role_join_message = await ctx.input(\\n f\\\"{config.USER_INTERACTION_REQUIRED} Reply with a short message the user should see when they get the role.\\\"\\n )\\n\\n try:\\n await self.bot.db.execute(\\n \\\"INSERT INTO selfrole (guild_id, role_id, join_message) VALUES ($1, $2, $3) \\\"\\n \\\"ON CONFLICT (guild_id, role_id) DO UPDATE SET join_message = $3\\\",\\n ctx.guild.id,\\n discord_role.id,\\n role_join_message,\\n )\\n except asyncpg.UniqueViolationError:\\n return await ctx.send(\\n f\\\"{config.NO} `{discord_role.name}` is already a selfrole on this server.\\\"\\n )\\n\\n await ctx.send(f\\\"{config.YES} `{discord_role.name}` was added as a selfrole.\\\")\",\n \"def add_role_to_user(self, user, role):\\n user, role = self._prepare_role_modify_args(user, role)\\n if role not in user.roles:\\n user.roles.append(role)\\n # noinspection PyUnresolvedReferences\\n self.save(user)\\n return True\\n\\n return False\",\n \"async def register_reaction_role(self, ctx, target_role_id: int, emoji_name: str):\\n\\n await self.bot.wait_until_ready()\\n\\n if emoji_name[0] == \\\"<\\\":\\n emoji_name = emoji_name[1:-1]\\n\\n if target_role_id in self.bot.config.staff_role_ids:\\n return await ctx.send(\\\"Error: Dangerous role found!\\\")\\n\\n target_role = ctx.guild.get_role(target_role_id)\\n\\n if target_role is None:\\n return await ctx.send(\\\"Error: Role not found!\\\")\\n\\n target_role_name = target_role.name\\n\\n for key in self.reaction_config[\\\"reaction_roles_emoji_map\\\"]:\\n value = self.reaction_config[\\\"reaction_roles_emoji_map\\\"][key]\\n if type(value) is str and target_role_name == value:\\n return await ctx.send(f\\\"Error: {target_role_name}: already registered.\\\")\\n\\n self.reaction_config[\\\"reaction_roles_emoji_map\\\"][emoji_name] = target_role_name\\n self.save_reaction_config(self.reaction_config)\\n await self.reload_reaction_message(False)\\n\\n await ctx.send(f\\\"{target_role_name}: registered.\\\")\",\n \"def define_role(self, role):\\n\\n self._db_manager.create_role(role)\",\n \"def addRoleAccess(self, role, read, write, catalog='*', repository='*'):\\n return self._client.addRoleAccess(role, read, write, catalog, repository)\",\n \"async def addrole(self, ctx, rolename, user: discord.Member=None):\\n author = ctx.message.author\\n channel = ctx.message.channel\\n server = ctx.message.server\\n\\n if user is None:\\n user = author\\n\\n role = self._role_from_string(server, rolename)\\n\\n if role is None:\\n await self.bot.say('That role cannot be found.')\\n return\\n\\n if not channel.permissions_for(server.me).manage_roles:\\n await self.bot.say('I don\\\\'t have manage_roles.')\\n return\\n\\n if author.id == settings.owner:\\n pass\\n elif not channel.permissions_for(author).manage_roles:\\n raise commands.CheckFailure\\n\\n await self.bot.add_roles(user, role)\\n await self.bot.say('Added role {} to {}'.format(role.name, user.name))\",\n \"async def on_raw_reaction_add(self, payload: discord.RawReactionActionEvent):\\n \\n guild = self.bot.get_guild(payload.guild_id)\\n user = discord.utils.get(guild.members, id=payload.user_id)\\n # Skipping bot reactions\\n if user.bot:\\n return\\n\\n # Search the event by message id\\n message_id = payload.message_id\\n channel_id = payload.channel_id\\n guild_id = payload.guild_id\\n event = connector.getEventByMessage(guild_id, message_id, channel_id)\\n if event is None:\\n return\\n\\n role = discord.utils.get(payload.member.guild.roles, id=event.role_id)\\n\\n if (payload.event_type == \\\"REACTION_ADD\\\") and (event.emoji == str(payload.emoji)):\\n # Adding role to user\\n await payload.member.add_roles(role)\\n elif (payload.event_type == \\\"REACTION_REMOVE\\\") and (event.emoji == str(payload.emoji)) and (role in payload.member.roles):\\n # Remove role from user\\n await payload.member.remove_roles(role)\",\n \"def add_role(self, role_id: str, current_user_id=None):\\n if RoleModel.is_valid_role(role_id) and not self.has_role(role_id):\\n user_role = UserRoleModel(user_id=self.id, role_id=role_id, lastchange_by=current_user_id)\\n self.roles.append(user_role)\",\n \"async def add(ctx, *args: commands.clean_content):\\r\\n if len(args) < 2:\\r\\n await ctx.send('Add takes 2+ parameters')\\r\\n return\\r\\n\\r\\n tgt_role = args[-1]\\r\\n if tgt_role.startswith('@'):\\r\\n tgt_role = tgt_role[1:]\\r\\n if not discord.utils.get(ctx.guild.roles, name=tgt_role):\\r\\n await ctx.send(f'Role {args[-1]} does not exist')\\r\\n return\\r\\n\\r\\n roles = list(args[:-1])\\r\\n\\r\\n for index, role in enumerate(roles):\\r\\n if role.startswith('@'):\\r\\n role = role[1:]\\r\\n roles[index] = role\\r\\n print(role)\\r\\n if not discord.utils.get(ctx.guild.roles, name=role):\\r\\n await ctx.send(f'Role {role} does not exist')\\r\\n return\\r\\n\\r\\n docid = db.insert({'guild': ctx.guild.id, 'roles': roles, 'target': tgt_role})\\r\\n await ctx.send(f'Rule {docid} created')\\r\\n await update_roles(ctx.guild)\\r\\n await check_guild_rules(ctx.guild)\",\n \"async def _on_raw_reaction(\\n self,\\n payload: RawReactionActionEvent,\\n reaction_type: EnumReactionType,\\n ) -> None:\\n if self.__is_self(payload.user_id):\\n print(\\\"reaction added by the bot itself\\\")\\n return\\n\\n guild = self._client.get_guild(payload.guild_id)\\n\\n if reaction_type == EnumReactionType.ADD:\\n await self.__roles.add_role(\\n guild, payload.message_id,\\n payload.emoji, payload.user_id,\\n\\n )\\n elif reaction_type == EnumReactionType.REMOVE:\\n await self.__roles.remove_role(\\n guild, payload.message_id,\\n payload.emoji, payload.user_id,\\n )\\n else:\\n raise InvalidReactionType\",\n \"async def add_role_member(request, role_id):\\n required_fields = [\\\"id\\\"]\\n utils.validate_fields(required_fields, request.json)\\n txn_key, txn_user_id = await utils.get_transactor_key(request)\\n proposal_id = str(uuid4())\\n batch_list = Role().member.propose.batch_list(\\n signer_keypair=txn_key,\\n signer_user_id=txn_user_id,\\n proposal_id=proposal_id,\\n role_id=role_id,\\n pack_id=request.json.get(\\\"pack_id\\\"),\\n next_id=request.json.get(\\\"id\\\"),\\n reason=request.json.get(\\\"reason\\\"),\\n metadata=request.json.get(\\\"metadata\\\"),\\n )\\n batch_status = await utils.send(\\n request.app.config.VAL_CONN,\\n batch_list,\\n request.app.config.TIMEOUT,\\n request.json.get(\\\"tracker\\\") and True,\\n )\\n if request.json.get(\\\"tracker\\\"):\\n return utils.create_tracker_response(\\\"batch_status\\\", batch_status)\\n return json({\\\"proposal_id\\\": proposal_id})\",\n \"async def addRole(self, ctx, *roles_to_add):\\n already_present_roles = [] # roles that will be deleted from \\\"roles_to_add\\\"\\n\\n available_roles = open(\\\"assets/roles.txt\\\", \\\"r\\\").readlines()\\n available_roles = [role.lower().strip() for role in available_roles]\\n\\n output_msg = \\\"\\\"\\n\\n for role_to_add in roles_to_add:\\n for role in available_roles:\\n if role_to_add.lower() == role:\\n output_msg += f\\\"Failed to add {role_to_add}: role already exists.\\\\n\\\"\\n already_present_roles.append(role_to_add)\\n break\\n\\n for role in already_present_roles:\\n roles_to_add.remove(role)\\n\\n if roles_to_add:\\n with open(\\\"assets/roles.txt\\\", \\\"a\\\") as f:\\n for role in roles_to_add:\\n f.write(f\\\"{role}\\\\n\\\")\\n output_msg += f\\\"{role} has been added successfully.\\\\n\\\"\\n\\n await ctx.send(output_msg)\",\n \"async def _role_repl(self, ctx: Context, *, role: discord.Role):\\n\\n msg = await ctx.send(\\n _(\\n \\\"Are you sure you want to set `{}` as replacement role?\\\"\\n ).format(role.name)\\n )\\n start_adding_reactions(msg, ReactionPredicate.YES_OR_NO_EMOJIS)\\n\\n pred = ReactionPredicate.yes_or_no(msg, ctx.author)\\n await ctx.bot.wait_for(\\\"reaction_add\\\", check=pred)\\n\\n if pred.result:\\n await self.config.guild(ctx.guild).repl_id.set(role.id)\\n await ctx.send(\\n _(\\\"Set `{}` as replacement role!\\\").format(role.name)\\n )\\n else:\\n await ctx.send(_(\\\"Aborted replacement role setup.\\\"))\",\n \"async def massadd(\\n self,\\n ctx,\\n role: discord.Role,\\n member: commands.Greedy[discord.Member],\\n ):\\n role = discord.utils.get(ctx.guild.roles, id=role.id)\\n\\n muted_role = discord.utils.get(ctx.guild.roles, name=\\\"Muted\\\")\\n punished_role = discord.utils.get(ctx.guild.roles, name=\\\"Punished\\\")\\n\\n if role > ctx.author.top_role:\\n return await ctx.send(\\n embed=discord.Embed(\\n title=\\\"You don't have permission to add this role\\\",\\n timestamp=datetime.datetime.utcnow(),\\n colour=discord.Colour.darker_grey(),\\n )\\n )\\n\\n if role == muted_role or role == punished_role:\\n return await ctx.send(\\n embed=discord.Embed(\\n title=f\\\"Can not assign *{role}* role using this command.\\\",\\n description=\\\"For more information run ```.help massadd```\\\",\\n timestamp=datetime.datetime.utcnow(),\\n colour=discord.Colour.red(),\\n )\\n )\\n\\n for i in member:\\n if role in i.roles:\\n await ctx.channel.send(\\n embed=discord.Embed(\\n title=f\\\"*{i}* already has *{role}* Role!\\\",\\n timestamp=datetime.datetime.utcnow(),\\n colour=discord.Colour.greyple(),\\n )\\n )\\n\\n await i.add_roles(role)\\n\\n await ctx.send(\\n embed=discord.Embed(\\n title=f\\\"*{role}* has been added to **{len(member)}** members!\\\",\\n timestamp=datetime.datetime.utcnow(),\\n colour=discord.Colour.green(),\\n )\\n )\",\n \"def add(self, user, role=None, roles=None):\\n # TODO(adriant): resolve the roles and users into id's\\n # user_id = base.getid(user)\\n user_id = user\\n # role_id = role\\n if role:\\n params = {\\n 'roles': [role]\\n }\\n elif roles:\\n params = {\\n 'roles': roles\\n }\\n\\n route = '/openstack/users/%s/roles'\\n url = route % (user_id)\\n try:\\n self._put(url, json=params, response_key=None)\\n except exc.HTTPBadRequest as e:\\n print(e.message)\\n return False\\n\\n return True\",\n \"async def addrole(self, ctx, role: discord.Role):\\n guild = ctx.message.guild\\n excluded_roles = await self.config.guild(guild).excluded_roles()\\n\\n for excluded_role in excluded_roles:\\n if excluded_role == role.id:\\n await ctx.send(\\\"%s already added to role exclusion list\\\" % role.name)\\n return\\n\\n excluded_roles.append(role.id)\\n await self.config.guild(guild).excluded_roles.set(excluded_roles)\\n\\n await ctx.send(\\\"%s added to role exclusion list\\\" % role.name)\",\n \"async def createrole(self, ctx, role: str):\\n if role.lower() == \\\"muted\\\" or role.lower() == \\\"punished\\\":\\n return await ctx.send(\\\"Can not create this roles.\\\")\\n \\\"\\\"\\\"Create a new role\\\"\\\"\\\"\\n role = await ctx.guild.create_role(name=role)\\n return await ctx.send(\\n embed=discord.Embed(\\n title=f\\\"Role *{role}* has been created!\\\",\\n timestamp=datetime.datetime.utcnow(),\\n colour=discord.Colour.green(),\\n )\\n )\",\n \"async def reaction_roles_post(inter: MessageInteraction, description, roles):\\n view = disnake.ui.View(timeout=None)\\n for role in roles:\\n view.add_item(disnake.ui.Button(label=role.name, custom_id=role.id))\\n messages = await send_message(msg=description, channel=inter.channel, view=view)\\n for message in messages:\\n await ReactionRoleMessage.insert(message.id)\",\n \"def inheritRole(self, role=None, roleName=None, kvDict=None):\\n\\n return _modelActionBase(self, instance=role, instanceName=roleName, kvDict=kvDict,\\n model=get_model('role'), db=db, action='add', modelType='role', inherit=True)\",\n \"def addModifier(self, *args):\\n return _libsbml.Reaction_addModifier(self, *args)\",\n \"async def add_role_admin(request, role_id):\\n required_fields = [\\\"id\\\"]\\n utils.validate_fields(required_fields, request.json)\\n\\n txn_key, txn_user_id = await utils.get_transactor_key(request)\\n proposal_id = str(uuid4())\\n batch_list = Role().admin.propose.batch_list(\\n signer_keypair=txn_key,\\n signer_user_id=txn_user_id,\\n proposal_id=proposal_id,\\n role_id=role_id,\\n next_id=request.json.get(\\\"id\\\"),\\n reason=request.json.get(\\\"reason\\\"),\\n metadata=request.json.get(\\\"metadata\\\"),\\n )\\n await utils.send(\\n request.app.config.VAL_CONN, batch_list, request.app.config.TIMEOUT\\n )\\n return json({\\\"proposal_id\\\": proposal_id})\",\n \"def add_keystone_v3_role_to_user_or_group(self, user_id, role_id,\\n pro_dom_id, id_flag):\\n LOG_OBJ.debug(\\\"Adding the role.\\\")\\n\\n _url = \\\"http://\\\" + self.host_ip + \\\":35357/v3/\\\" + id_flag + \\\"s/\\\" + \\\\\\n str(pro_dom_id) + \\\"/users/\\\" + str(user_id) + \\\"/roles/\\\" + \\\\\\n str(role_id)\\n _headers = {'x-auth-token': self.cloud_admin_info[\\\"token_domain\\\"],\\n 'content-type': 'application/json'}\\n _body = None\\n response = self.request(\\\"PUT\\\", _url, _headers, _body)\\n\\n if response is None:\\n LOG_OBJ.error(\\\"No response from Server while adding role\\\")\\n print (\\\"No response from Server while adding role\\\")\\n return response\\n\\n if response.status not in [200, 201, 202, 203, 204]:\\n LOG_OBJ.error(\\\"Adding role Failed with status %s \\\"\\n \\\"and error : %s\\\" % (response.status, response.data))\\n print (\\\"Adding role Failed with status %s and error : %s\\\" %\\n (response.status, response.data))\\n return response.status\\n return True\",\n \"def role(self, role):\\n\\n self._role = int(role)\",\n \"async def addtagrole(self, ctx, _role):\\r\\n\\t\\tif _role == 0:\\r\\n\\t\\t\\tself.settings.ServerConfig(ctx.guild.id, 'TagRole', 0)\\r\\n\\t\\t\\tawait ctx.send('Tag role set to: {}'.format(0))\\r\\n\\t\\telse:\\t\\r\\n\\t\\t\\trole = self.settings.Get(ctx, 'role', _role)\\r\\n\\t\\t\\tif not role: return await ctx.send('Can\\\\'t find role: {}'.format(_role))\\r\\n\\r\\n\\t\\t\\tself.settings.ServerConfig(ctx.guild.id, 'TagRole', role.id)\\r\\n\\t\\t\\tawait ctx.send('Tag role set to: {}'.format(role))\",\n \"def add_modifier( modifier_id, reaction, model, compartment = \\\"default\\\", arguments = DEFAULT_ARGUMENTS):\\n if modifier_id is None and len( reaction.getListOfModifiers()) == 1:\\n return reaction.getListOfModifiers()[0].getId()\\n else:\\n if not modifier_id:\\n reaction_name = reaction.getName();\\n reaction_id = reaction.getId();\\n modifier_id = reaction_id + \\\"_modifier_nr\\\" + str(len( reaction.getListOfModifiers()));\\n modifier_name = reaction_name[0:3].lower() + \\\"modifier\\\";\\n add_species( None, model, id = modifier_id, name = modifier_name, compartment = compartment, arguments = arguments);\\n \\n modifier_ref = reaction.createModifier()\\n check( modifier_ref, 'create modifier reference', arguments = arguments);\\n check( modifier_ref.setSpecies( modifier_id), 'assign modifier species', arguments = arguments);\\n check( modifier_ref.setMetaId( \\\"metaid_0000\\\" + modifier_id), 'set meta ID', arguments = arguments);\\n check( modifier_ref.addCVTerm(add_cvterm(GENERIC_REACTION_SBO_MAPPING[\\\"modifier\\\"])), 'set controlled vocab SBO term for cause', arguments = arguments);\\n return modifier_ref\",\n \"async def addRolesOnEmbed(msg):\\n for field in msg.embeds[0].fields:\\n await msg.add_reaction(field.name[0])\",\n \"async def _role_spec(self, ctx: Context, *, role: discord.Role):\\n\\n msg = await ctx.send(\\n _(\\n \\\"Are you sure you want to set `{}` as spectator role?\\\"\\n ).format(role.name)\\n )\\n start_adding_reactions(msg, ReactionPredicate.YES_OR_NO_EMOJIS)\\n\\n pred = ReactionPredicate.yes_or_no(msg, ctx.author)\\n await ctx.bot.wait_for(\\\"reaction_add\\\", check=pred)\\n\\n if pred.result:\\n await self.config.guild(ctx.guild).spec_id.set(role.id)\\n await ctx.send(_(\\\"Set `{}` as spectator role!\\\").format(role.name))\\n else:\\n await ctx.send(_(\\\"Aborted spectator role setup.\\\"))\",\n \"async def add_roles(self, ctx: commands.Context, *roles: discord.Role):\\n if not roles:\\n return await ctx.send_help()\\n errored = \\\"\\\"\\n message = \\\"\\\"\\n added = []\\n already_added = []\\n for role in roles:\\n if role >= ctx.author.top_role:\\n errored += (\\n \\\"{role}: You can't set a role equal to or higher than your own.\\\\n\\\".format(\\n role=role.name\\n )\\n )\\n continue\\n if role >= ctx.guild.me.top_role:\\n errored += (\\n \\\"{role}: You can't set a role that's equal to or higher than the \\\"\\n \\\"bot.\\\\n\\\".format(role=role.name)\\n )\\n continue\\n async with self.config.guild(ctx.guild).autoroles() as roles_list:\\n if role.id not in roles_list:\\n roles_list.append(role.id)\\n added.append(role.name)\\n else:\\n already_added.append(role.name)\\n message += errored\\n if added:\\n message += \\\"\\\\nAdded role(s): {roles}\\\".format(roles=humanize_list(added))\\n if already_added:\\n message += \\\"\\\\nRole(s) already added: {roles}\\\".format(\\n roles=humanize_list(already_added)\\n )\\n if message:\\n for line in pagify(message):\\n await ctx.send(line)\",\n \"def create_role(self, **kwargs):\\n\\n role = self.role_model(**kwargs)\\n return self.put(role)\",\n \"def role(self, role):\\n\\n self._role = role\",\n \"def role(self, role):\\n\\n self._role = role\",\n \"def addRole(self, name, description=\\\"\\\"):\\n params = {\\n \\\"f\\\" : \\\"json\\\",\\n \\\"rolename\\\" : name,\\n \\\"description\\\" : description\\n }\\n aURL = self._url + \\\"/roles/add\\\"\\n return self._con.post(path=aURL, postdata=params)\",\n \"def create(self, role):\\n model = models.load('Role', role)\\n model.account_id = self.account_id\\n\\n return self.client.create_role(model)\",\n \"def test_edit_role_add_new_role(self):\\n # Add node with controller role\\n Nodes().nodes_discovered[0].checkbox.click()\\n RolesPanel().controller.click()\\n Nodes().apply_changes.click()\\n time.sleep(1)\\n # Add cinder role\\n with Nodes() as n:\\n n.nodes[0].checkbox.click()\\n n.edit_roles.click()\\n RolesPanel().cinder.click()\\n Nodes().apply_changes.click()\\n time.sleep(1)\\n with Nodes() as n:\\n self.assertIn(ROLE_CONTROLLER, n.nodes[0].roles.text,\\n 'Controller role')\\n self.assertIn(ROLE_CINDER, n.nodes[0].roles.text,\\n 'Cinder role')\",\n \"def add_user_role(self, tenant_id, user_id, role_id):\\n _url = \\\"http://\\\" + self.host_ip + \\\":35357/v2.0/tenants/\\\" + \\\\\\n tenant_id + \\\"/users/\\\" + user_id + \\\"/roles/OS-KSADM/\\\" + role_id\\n _headers = {'x-auth-token': self.cloud_admin_info['token_project']}\\n _body = None\\n response = self.request(\\\"PUT\\\", _url, _headers, _body)\\n if response is None:\\n LOG_OBJ.error(\\\"No response from Server while adding role\\\")\\n return response\\n if response.status not in [200, 201, 202, 203, 204]:\\n LOG_OBJ.error(\\\"Add user role Failed with status %s \\\" %\\n response.status)\\n return response.status\\n\\n LOG_OBJ.info(\\\"Role: %s is added to user:%s successfully.\\\"\\n % (role_id, user_id))\\n return True\",\n \"def add_admin(self, uid, name, role=None):\\n uid = self._check_uid(uid)\\n self._router_request(\\n self._make_request_data(\\n 'addAdminRole',\\n data=dict(\\n params=dict(\\n uid=uid,\\n name=name,\\n role=role,\\n )\\n )\\n )\\n )\\n\\n return self.get_admin_by_name(uid, name)\",\n \"def add_reaction(self, user_id, ts, reaction):\\n if (user_id, ts) not in self.user_id_ts_to_reactions:\\n logging.info(\\\"add_reaction for (user_id, ts) (%s, %f) not in self.user_id_ts_to_reactions %s\\\" % (user_id, ts, self.user_id_ts_to_reactions))\\n return\\n if reaction < 0 or reaction > 1:\\n logging.info(\\\"Got unknown reaction %s for (user_id, ts) (%s, %f)\\\" % (reaction, user_id, ts))\\n return\\n self.user_id_ts_to_reactions[(user_id, ts)][reaction] += 1\",\n \"def add_role(self, name: str) -> Role:\\n role = self.find_role(name)\\n if role is None:\\n try:\\n role = self.role_model()\\n role.name = name\\n self.get_session.add(role)\\n self.get_session.commit()\\n log.info(const.LOGMSG_INF_SEC_ADD_ROLE.format(name))\\n return role\\n except Exception as e:\\n log.error(const.LOGMSG_ERR_SEC_ADD_ROLE.format(e))\\n self.get_session.rollback()\\n return role\",\n \"async def rolemenu_create(self,\\n interaction: discord.Interaction,\\n name: str,\\n channel: discord.TextChannel,\\n mode: int,\\n color: str,\\n placeholder: str,\\n description: str = None):\\n doc = await self.db.find_one({\\n \\\"guild_id\\\": interaction.guild.id,\\n \\\"name\\\": name\\n })\\n if doc:\\n return await interaction.response.send_message(\\n \\\"Role menu with that name exists.\\\", ephemeral=True)\\n if channel.guild != interaction.guild:\\n return await interaction.response.send_message(\\n \\\"This channel is not in this server.\\\")\\n if not channel.permissions_for(interaction.guild.me).send_messages:\\n return await interaction.response.send_message(\\n \\\"I don't have permission to send messages in this channel.\\\")\\n try:\\n color = discord.Color.from_str(color)\\n except ValueError:\\n return await interaction.response.send_message(\\\"Invalid color.\\\")\\n await self.db.insert_one({\\n \\\"message_id\\\": None,\\n \\\"name\\\": name,\\n \\\"mode\\\": mode,\\n \\\"channel_id\\\": channel.id,\\n \\\"roles\\\": [],\\n \\\"guild_id\\\": interaction.guild.id,\\n \\\"color\\\": color.value,\\n \\\"placeholder\\\": placeholder,\\n \\\"description\\\": description\\n })\\n await interaction.response.send_message(\\n \\\"Role menu created. It is currently empty, however, and \\\"\\n \\\"you'll need to add roles with `/rolemenu role add.`\\\",\\n ephemeral=True)\",\n \"async def addroleall(self, ctx, role: discord.Role):\\n muted_role = discord.utils.get(ctx.guild.roles, name=\\\"Muted\\\")\\n punished_role = discord.utils.get(ctx.guild.roles, name=\\\"Punished\\\")\\n\\n if role > ctx.author.top_role:\\n return await ctx.send(\\n embed=discord.Embed(\\n title=\\\"You don't have permission to add this role\\\",\\n timestamp=datetime.datetime.utcnow(),\\n colour=discord.Colour.darker_grey(),\\n )\\n )\\n\\n if role == muted_role or role == punished_role:\\n return await ctx.send(\\n embed=discord.Embed(\\n title=f\\\"Can not assign *{role}* role using this command.\\\",\\n description=\\\"For more information run ```.help addroleall```\\\",\\n timestamp=datetime.datetime.utcnow(),\\n colour=discord.Colour.red(),\\n )\\n )\\n\\n for i in ctx.guild.members:\\n if not i.bot:\\n await i.add_roles(role)\\n\\n await ctx.send(\\n embed=discord.Embed(\\n title=f\\\"*{role}* has been added to **{len(ctx.guild.members)}** members!\\\",\\n timestamp=datetime.datetime.utcnow(),\\n colour=discord.Colour.green(),\\n )\\n )\",\n \"def patch(self, username, role):\\n try:\\n UserService.add_role_to_user(token_auth.current_user(), username, role)\\n return {\\\"Success\\\": \\\"Role Added\\\"}, 200\\n except UserServiceError as e:\\n return {\\\"Error\\\": str(e).split(\\\"-\\\")[1], \\\"SubCode\\\": str(e).split(\\\"-\\\")[0]}, 403\",\n \"async def on_guild_role_create(self, role):\\n channel = self.client.get_channel(serverlogs.getChannel(role.guild.id, \\\"roles\\\"))\\n if channel is not None:\\n await self.log_role(role=role, type='Create', channel=channel, guild=role.guild)\",\n \"async def addRoles(self, ctx: Context, person: Member, roles: Greedy[Role]):\\n roles = remove_dupe_roles(roles)\\n\\n await person.add_roles(*roles)\\n await ctx.send(f\\\"Adding {roles_str(person, roles)}\\\")\",\n \"async def _create_new_role(self, context, name: str, target=GROUP_CATEGORY_NAME, channel=False, color: discord.Color = None):\\n # todo - sanitize input, preventing \\\"-\\\" specifically\\n target_role = get(context.guild.roles, name=target)\\n target_position = target_role.position\\n\\n new_role = await context.guild.create_role(\\n name=name, mentionable=True, reason=f\\\"Role created by {context.author}\\\")\\n\\n await context.guild.edit_role_positions(positions={new_role: target_position})\",\n \"def create_role(self, role_name, description, rights):\\n org_admin_resource = self.client.get_resource(self.href_admin)\\n role = E.Role(\\n E.Description(description), E.RightReferences(), name=role_name)\\n if rights is None:\\n rights = ()\\n for right in tuple(rights):\\n right_record = self.get_right(right)\\n role.RightReferences.append(\\n E.RightReference(\\n name=right_record.get('name'),\\n href=right_record.get('href'),\\n type=EntityType.RIGHT.value))\\n return self.client.post_linked_resource(\\n org_admin_resource, RelationType.ADD, EntityType.ROLE.value, role)\",\n \"def create_role(self, **kwargs):\\n role = self.role_model(**kwargs)\\n # noinspection PyUnresolvedReferences\\n return self.save(role)\",\n \"def update_role(self, role_id, role):\\n raise exception.NotImplemented() # pragma: no cover\",\n \"async def register_role(self, ctx, role_name: str, role_title: str, guild_id: int = None):\\n role_title = role_title.lower()\\n\\n # Raises expected AttributeError\\n if guild_id is None:\\n guild = ctx.guild\\n guild_id = guild.id\\n else:\\n guild = self.bot.get_guild(guild_id)\\n guild_id = guild.id\\n if guild is None:\\n await ctx.send(\\\"Couldn't find the guild provided.\\\")\\n return\\n\\n try:\\n role_id = discord.utils.get(guild.roles, name=role_name).id\\n except AttributeError:\\n # role returned is None\\n await ctx.send(\\\"Role with name {} in guild {} could not be found.\\\".format(role_name, guild.name))\\n return\\n\\n db_key = \\\"guild:{}:roles:roles:{}\\\".format(guild_id, role_title)\\n\\n self.config.set(\\\"{}:role_id\\\".format(db_key), role_id)\\n\\n self.config.hset(\\\"guild:{}:roles:all:names\\\".format(guild.id), role_title, role_id)\\n\\n await ctx.send(\\\"New role '{}' registered with keyword '{}'\\\".format(role_name, role_title))\",\n \"def start_router_realm_role(self, id, role_id, config, details=None):\\n self.log.debug(\\\"{}.add_router_realm_role\\\".format(self.__class__.__name__),\\n id=id, role_id=role_id, config=config)\\n\\n if id not in self.realms:\\n raise ApplicationError(u\\\"crossbar.error.no_such_object\\\", \\\"No realm with ID '{}'\\\".format(id))\\n\\n if role_id in self.realms[id].roles:\\n raise ApplicationError(u\\\"crossbar.error.already_exists\\\", \\\"A role with ID '{}' already exists in realm with ID '{}'\\\".format(role_id, id))\\n\\n self.realms[id].roles[role_id] = RouterRealmRole(role_id, config)\\n\\n realm = self.realms[id].config['name']\\n self._router_factory.add_role(realm, config)\",\n \"def test_add_role(self):\\n pass\",\n \"def role_add(role, nodes, node, node_vars, host_vars, extra):\\n role_manager = get_role_manager()\\n node += nodes\\n nodes, node_vars, host_vars, extra_args = _split_vars(\\n node, node_vars, host_vars, extra)\\n if not nodes:\\n raise ArgumentError('No nodes informed')\\n\\n added_nodes = role_manager.add_role(\\n role, hosts_node_map=nodes, host_vars=host_vars,\\n node_vars=node_vars, extra_args=extra_args)\\n\\n print(f\\\"{len(added_nodes)} nodes were added to role {role}: {', '.join(sorted(added_nodes))}\\\")\\n return 0\",\n \"def RIPReaction(sc, event):\\n sc.api_call('reactions.add', as_user='true', channel=event['channel'],\\n timestamp=event['ts'], name='rip')\",\n \"async def persistrole(self, ctx, member: discord.Member, role: discord.Role):\\n role = discord.utils.get(ctx.guild.roles, id=role.id)\\n\\n muted_role = discord.utils.get(ctx.guild.roles, name=\\\"Muted\\\")\\n punished_role = discord.utils.get(ctx.guild.roles, name=\\\"Punished\\\")\\n\\n if role > ctx.author.top_role:\\n return await ctx.send(\\n embed=discord.Embed(\\n title=\\\"You don't have permission to add this role\\\",\\n timestamp=datetime.datetime.utcnow(),\\n colour=discord.Colour.darker_grey(),\\n )\\n )\\n\\n if role == muted_role or role == punished_role:\\n return await ctx.send(\\n embed=discord.Embed(\\n title=f\\\"Can not assign *{role}* role using this command.\\\",\\n description=\\\"For more information run ```.help persistrole```\\\",\\n timestamp=datetime.datetime.utcnow(),\\n colour=discord.Colour.red(),\\n )\\n )\\n\\n if role in member.roles:\\n return await ctx.channel.send(\\n embed=discord.Embed(\\n title=f\\\"*{member}* already has *{role}* Role!\\\",\\n timestamp=datetime.datetime.utcnow(),\\n colour=discord.Colour.greyple(),\\n )\\n )\\n\\n await member.add_roles(role)\\n persistent_role = Roles(\\n bot=self.bot,\\n guild_id=ctx.guild.id,\\n user_id=member.id,\\n roles=role.id,\\n )\\n # Post to db for persistent role\\n await persistent_role.post()\\n\\n await ctx.send(\\n embed=discord.Embed(\\n title=f\\\"Persisting Role: *{role}* has been added to *{member}*\\\",\\n timestamp=datetime.datetime.utcnow(),\\n colour=discord.Colour.green(),\\n )\\n )\",\n \"async def create(self, ctx: commands.Context, title: str, *roles: discord.Role):\\n brake = {}\\n\\n def check(_r, _u):\\n return _u == ctx.author and _r.message == me and str(_r.emoji) not in brake\\n\\n me = await ctx.send(f'React with the reaction that will correspond to the role `{roles[0]}`')\\n\\n # Yes I know this is an ugly solution to avoid an unnecessary api request but it is ultimately the only solution\\n # I could think of.\\n try:\\n r, u = await self.bot.wait_for('reaction_add', timeout=len(roles) * 20, check=check)\\n brake[str(r.emoji)] = roles[0].id\\n except asyncio.TimeoutError:\\n await ctx.send(\\\"Timed out, please run the command again and this time be a little quicker to react.\\\")\\n return\\n\\n try:\\n for role in roles[1:]:\\n await me.edit(content=f'React with the reaction that will correspond to the role `{role}`')\\n r, u = await self.bot.wait_for('reaction_add', timeout=len(roles) * 20, check=check)\\n brake[str(r.emoji)] = role.id\\n except asyncio.TimeoutError:\\n await ctx.send('Timed out, please run the command again and this time be a little quicker to react.')\\n return\\n\\n await ctx.send(\\n 'What channel do you wish to send this role menu in? Enter its id, name or mention it: #<channel>')\\n\\n for count in range(4):\\n try:\\n m = await self.bot.wait_for('message', check=lambda _m: _m.author == ctx.author and _m.channel == ctx.channel, timeout=30)\\n chan = await self.tcc.convert(ctx, m.content)\\n break\\n except commands.BadArgument:\\n if count == 3:\\n await ctx.send(\\\"Too many tries to enter channel, make sure I can actually see the channel you're \\\"\\n \\\"referring to and use the entire command again\\\")\\n return\\n await ctx.send(\\\"Please enter the correct channel, if in doubt, try mentioning it, the `#channel` thing\\\")\\n except asyncio.TimeoutError:\\n await ctx.send(\\\"Timed out\\\")\\n return\\n\\n e = discord.Embed(title=f\\\"Role menu: {title}\\\",\\n description=\\\"\\\\n\\\\n\\\".join(f\\\"{k} - {ctx.guild.get_role(v)}\\\"for k, v in brake.items()),\\n colour=discord.Colour.blue())\\n\\n zero = await chan.send(embed=e)\\n\\n for k in brake:\\n await zero.add_reaction(k)\\n\\n self._cache[ctx.guild.id][zero.id] = brake\\n\\n query = \\\"\\\"\\\"\\n INSERT INTO selfrole_lookup (guildid, channelid, messageid) \\n VALUES ($1, $2, $3)\\n \\\"\\\"\\\"\\n await self.bot.pool.execute(query, ctx.guild.id, chan.id, zero.id)\\n\\n query = \\\"\\\"\\\"\\n INSERT INTO selfrole (messageid, emoji, roleid)\\n VALUES ($1, $2, $3)\\n \\\"\\\"\\\"\\n for k, v in brake.items():\\n await self.bot.pool.execute(query, zero.id, k, v)\",\n \"async def add_role_owner(request, role_id):\\n required_fields = [\\\"id\\\"]\\n utils.validate_fields(required_fields, request.json)\\n\\n txn_key, txn_user_id = await utils.get_transactor_key(request)\\n proposal_id = str(uuid4())\\n batch_list = Role().owner.propose.batch_list(\\n signer_keypair=txn_key,\\n signer_user_id=txn_user_id,\\n proposal_id=proposal_id,\\n role_id=role_id,\\n next_id=request.json.get(\\\"id\\\"),\\n reason=request.json.get(\\\"reason\\\"),\\n metadata=request.json.get(\\\"metadata\\\"),\\n )\\n await utils.send(\\n request.app.config.VAL_CONN, batch_list, request.app.config.TIMEOUT\\n )\\n return json({\\\"proposal_id\\\": proposal_id})\",\n \"def add_role():\\n\\n role_form = RoleForm(request.form)\\n\\n if role_form.validate_on_submit():\\n name = request.form['name']\\n\\n if not name or name == '' :\\n flash(\\\"No null or empty values are allowed.\\\",\\\"warn\\\")\\n return render_template('user/add_edit_role.html', title='Add Role',\\n add=True,form=role_form)\\n\\n else:\\n new_role = Role(name = name)\\n try:\\n correct = True\\n db.session.add(new_role)\\n db.session.commit()\\n except Exception as e:\\n # Catch anything unknown\\n print(e)\\n correct = False\\n finally:\\n if not correct:\\n # Cleanup and show error\\n db.session.rollback()\\n flash('Error when creating a Role.','error')\\n else:\\n flash(\\\"Our Role was created!\\\",\\\"success\\\")\\n return redirect(url_for('user_ksat.manage_role'))\\n\\n return render_template('user/add_edit_role.html', title='Add Role',add=True,form=role_form)\",\n \"async def _role_player(self, ctx: Context, *, role: discord.Role):\\n\\n msg = await ctx.send(\\n _(\\n \\\"Are you sure you want to set `{}` as player role?\\\"\\n ).format(role.name)\\n )\\n start_adding_reactions(msg, ReactionPredicate.YES_OR_NO_EMOJIS)\\n\\n pred = ReactionPredicate.yes_or_no(msg, ctx.author)\\n await ctx.bot.wait_for(\\\"reaction_add\\\", check=pred)\\n\\n if pred.result:\\n await self.config.guild(ctx.guild).player_id.set(role.id)\\n await ctx.send(_(\\\"Set `{}` as player role!\\\").format(role.name))\\n else:\\n await ctx.send(_(\\\"Aborted player role setup.\\\"))\",\n \"async def rolemenu(self, message, args):\\n if \\\"-h\\\" in args or \\\"--help\\\" in args or \\\"--ahelp\\\" in args:\\n await self._print_rolemenu_help(message.channel)\\n return\\n update_reactions = not is_key_in_args(args, \\\"--no-update\\\")\\n allow_role_change = not is_key_in_args(args, \\\"--no-change\\\")\\n remove_role_on_reaction_removal = not is_key_in_args(args, \\\"--no-removal\\\")\\n max_number_of_reactions_per_user = get_number_in_args(args, \\\"--max-reactions\\\", None)\\n max_users_with_role = get_number_in_args(args, \\\"--max-users\\\", None)\\n no_required_role = is_key_in_args(args, \\\"--all\\\")\\n role_ids = message.raw_role_mentions\\n if len(role_ids) * 2 != len(args):\\n await message.channel.send(\\\"Error with `rolemenu`: all roles must match an emoji\\\")\\n return await self._print_rolemenu_help(message.channel)\\n menu = {}\\n for i, role_id in enumerate(message.raw_role_mentions):\\n emoji = args.pop(0)\\n if emoji.strip(\\\"<>@&\\\") == str(role_id): # emoji and roles are just exchanged: no problem\\n emoji = args.pop(0)\\n elif args.pop(0).strip(\\\"<>@&\\\") != str(role_id): # error: two adjacent args must be role_id and emoji\\n await message.channel.send(\\\"Error with `rolemenu`: a role must match an emoji\\\")\\n return await self._print_rolemenu_help(message.channel)\\n menu.update({emoji: [message.guild.get_role(role_id)]})\\n manager = RoleByReactionManager.get(self.guild)\\n options = RoleMenuOptions(required_roles=None if no_required_role else [RoleCollection.VISITOR.value],\\n ignored_roles=[RoleCollection.MASTER.value],\\n update_reactions=update_reactions,\\n allow_role_change=allow_role_change,\\n max_number_of_reactions_per_user=max_number_of_reactions_per_user,\\n max_users_with_role=max_users_with_role,\\n remove_role_on_reaction_removal=remove_role_on_reaction_removal\\n )\\n await manager.add(message, menu, options)\\n logger.debug(\\\"Rolemenu created. Now you can edit your post to make it prettier.\\\")\",\n \"def create_role (self):\\n return self.create_association().create_role(self.create_topic(),\\n self.create_topic())\",\n \"async def create(\\n cls,\\n member: discord.Member,\\n role: discord.Role,\\n duration: timedelta,\\n added_by: discord.Member,\\n *,\\n reason: str = None\\n ) -> \\\"TempRole\\\":\\n self = cls(\\n member,\\n role=role,\\n duration=duration.total_seconds(),\\n reason=reason,\\n added_by=added_by.id,\\n added_at=datetime.utcnow().timestamp(),\\n )\\n await self.save()\\n return self\",\n \"async def subscribe_command(self, ctx: commands.Context, *_) -> None: # We don't actually care about the args\\n view = RoleButtonView(ctx.author, self.assignable_roles)\\n await ctx.send(\\n \\\"Click the buttons below to add or remove your roles!\\\",\\n view=view,\\n delete_after=DELETE_MESSAGE_AFTER\\n )\",\n \"async def createRole(self, ctx):\\n await self.deleteRole(ctx=ctx, reason=\\\"Début de partie.\\\")\\n await ctx.guild.create_role(name=self.categoryName)\\n await asyncio.sleep(1)\\n self.roleForPlayer = discord.utils.get(ctx.guild.roles, name=self.categoryName)\\n print(\\\"Role created.\\\")\\n member = await ctx.guild.fetch_member(bot.user.id)\\n await member.add_roles(self.roleForPlayer, reason=\\\"Début de partie.\\\")\\n for member in ctx.author.voice.channel.members:\\n await member.add_roles(self.roleForPlayer, reason=\\\"Début de partie.\\\")\",\n \"def addRoleSecurityFilter(self, role, _type, s=None, p=None, o=None, g=None):\\n self._client.addRoleSecurityFilter(role, _type, s, p, o, g)\",\n \"def setup_test_role(self):\\n self.test_role = rand_name('role')\\n resp, self.role = self.client.create_role(self.test_role)\\n self.roles.append(self.role)\",\n \"def append_role(self, user):\\n\\n tx = self.iroha.transaction(\\n [\\n self.iroha.command(\\n \\\"AppendRole\\\",\\n account_id=f\\\"{user.gov_id}@afyamkononi\\\",\\n role_name=user.type,\\n )\\n ],\\n creator_account=f\\\"{self.creator_account_details.gov_id}@afyamkononi\\\",\\n )\\n\\n IrohaCrypto.sign_transaction(tx, self.creator_account_details.private_key)\\n return self.send_transaction_and_return_status(tx)\",\n \"def setRole(self, *args):\\n return _libsbml.ReferenceGlyph_setRole(self, *args)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.74878365","0.7185206","0.7157245","0.71171796","0.70440525","0.7006333","0.6889938","0.68790966","0.6827275","0.6738522","0.67022955","0.6666086","0.6641454","0.662785","0.6625806","0.66136146","0.6613041","0.66006094","0.65936184","0.6589437","0.6564518","0.6559268","0.6525426","0.65142536","0.6509755","0.64827925","0.6481195","0.64700073","0.6435421","0.6392427","0.6384179","0.6380597","0.63701934","0.63290584","0.6328608","0.6314073","0.6303391","0.6300162","0.6299403","0.62869775","0.627737","0.6266513","0.6248002","0.6221816","0.6217302","0.620689","0.61795145","0.61262965","0.6103749","0.6102782","0.60956645","0.60930926","0.6050882","0.6042979","0.6042156","0.599123","0.59810734","0.59526247","0.5952198","0.5952194","0.58770365","0.58543295","0.5834187","0.5826164","0.5826164","0.5823056","0.58137655","0.58102244","0.579393","0.5789773","0.57561505","0.5755799","0.5750139","0.5746301","0.5737452","0.57271284","0.5718277","0.568688","0.56760216","0.5668318","0.565665","0.5601706","0.5601236","0.55924535","0.5590567","0.5557365","0.5552391","0.55335414","0.55164","0.5513319","0.55114","0.5490928","0.5474138","0.5472714","0.54564613","0.5454981","0.5447197","0.54383385","0.5417601","0.54132974"],"string":"[\n \"0.74878365\",\n \"0.7185206\",\n \"0.7157245\",\n \"0.71171796\",\n \"0.70440525\",\n \"0.7006333\",\n \"0.6889938\",\n \"0.68790966\",\n \"0.6827275\",\n \"0.6738522\",\n \"0.67022955\",\n \"0.6666086\",\n \"0.6641454\",\n \"0.662785\",\n \"0.6625806\",\n \"0.66136146\",\n \"0.6613041\",\n \"0.66006094\",\n \"0.65936184\",\n \"0.6589437\",\n \"0.6564518\",\n \"0.6559268\",\n \"0.6525426\",\n \"0.65142536\",\n \"0.6509755\",\n \"0.64827925\",\n \"0.6481195\",\n \"0.64700073\",\n \"0.6435421\",\n \"0.6392427\",\n \"0.6384179\",\n \"0.6380597\",\n \"0.63701934\",\n \"0.63290584\",\n \"0.6328608\",\n \"0.6314073\",\n \"0.6303391\",\n \"0.6300162\",\n \"0.6299403\",\n \"0.62869775\",\n \"0.627737\",\n \"0.6266513\",\n \"0.6248002\",\n \"0.6221816\",\n \"0.6217302\",\n \"0.620689\",\n \"0.61795145\",\n \"0.61262965\",\n \"0.6103749\",\n \"0.6102782\",\n \"0.60956645\",\n \"0.60930926\",\n \"0.6050882\",\n \"0.6042979\",\n \"0.6042156\",\n \"0.599123\",\n \"0.59810734\",\n \"0.59526247\",\n \"0.5952198\",\n \"0.5952194\",\n \"0.58770365\",\n \"0.58543295\",\n \"0.5834187\",\n \"0.5826164\",\n \"0.5826164\",\n \"0.5823056\",\n \"0.58137655\",\n \"0.58102244\",\n \"0.579393\",\n \"0.5789773\",\n \"0.57561505\",\n \"0.5755799\",\n \"0.5750139\",\n \"0.5746301\",\n \"0.5737452\",\n \"0.57271284\",\n \"0.5718277\",\n \"0.568688\",\n \"0.56760216\",\n \"0.5668318\",\n \"0.565665\",\n \"0.5601706\",\n \"0.5601236\",\n \"0.55924535\",\n \"0.5590567\",\n \"0.5557365\",\n \"0.5552391\",\n \"0.55335414\",\n \"0.55164\",\n \"0.5513319\",\n \"0.55114\",\n \"0.5490928\",\n \"0.5474138\",\n \"0.5472714\",\n \"0.54564613\",\n \"0.5454981\",\n \"0.5447197\",\n \"0.54383385\",\n \"0.5417601\",\n \"0.54132974\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6762945"},"document_rank":{"kind":"string","value":"9"}}},{"rowIdx":16872,"cells":{"query":{"kind":"string","value":"Post the reaction roles message"},"document":{"kind":"string","value":"async def reaction_roles_post(inter: MessageInteraction, description, roles):\n view = disnake.ui.View(timeout=None)\n for role in roles:\n view.add_item(disnake.ui.Button(label=role.name, custom_id=role.id))\n messages = await send_message(msg=description, channel=inter.channel, view=view)\n for message in messages:\n await ReactionRoleMessage.insert(message.id)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["async def setroles(self, ctx):\n if not has_permissions(ctx, MOD_ROLE):\n await ctx.send(\"You do not have sufficient permissions to perform this command\", hidden=True)\n return False\n\n def check(m):\n return m.author == ctx.author\n\n roles_dict = {}\n while True:\n info_embed = Embed(title=\"/setroles - Enter information\", colour=Colour.dark_purple())\n info_embed.description = \"Please enter a message tagging the role and all the members who you would like \" \\\n \"to assign it to.\"\n info_embed.set_footer(text='\"done/finished/yes/y\" to continue\\n\"no/cancel/n/stop\" to cancel')\n\n for role in roles_dict:\n users_string = f\"{role.mention}\\n\"\n for user in roles_dict[role]:\n users_string += f\"{user.mention}\\n\"\n info_embed.add_field(name=f\"{role.name} ({len(roles_dict[role])})\", value=users_string)\n\n info_message = await ctx.send(embed=info_embed)\n\n response = await self.bot.wait_for(\"message\", check=check)\n if response.content.lower() in [\"done\", \"finished\", \"yes\", \"y\"]:\n if len(roles_dict.keys()) > 0:\n await response.delete()\n await info_message.delete()\n total_roles_count = 0\n embed = Embed(title=\"Roles Summary\", description=\"Please review the roles you are about to set\\n\\n\"\n \"*this message has a timeout of 5 minutes*\",\n colour=Colour.dark_purple())\n embed.set_footer(text=f\"✅ to set roles\\n❌ to cancel\")\n for role in roles_dict:\n users_string = f\"{role.mention}\\n\"\n for user in roles_dict[role]:\n users_string += f\"{user.mention}\\n\"\n total_roles_count += len(roles_dict[role])\n embed.add_field(name=f\"{role.name} ({len(roles_dict[role])})\", value=users_string)\n embed.description += f\"\\n*{total_roles_count} members in total*\"\n message = await ctx.send(embed=embed)\n await message.add_reaction(\"✅\")\n await message.add_reaction(\"❌\")\n\n def check_reaction(r, u):\n return r.message.id == message.id and u == ctx.author and str(r.emoji) in [\"✅\", \"❌\"]\n\n set_roles = False\n while True:\n try:\n reaction, user = await self.bot.wait_for(\"reaction_add\", timeout=300, check=check_reaction)\n if str(reaction.emoji) == \"✅\":\n await message.clear_reactions()\n embed.set_footer(text=Embed.Empty)\n embed.description = Embed.Empty\n await message.edit(embed=embed)\n set_roles = True\n break\n elif str(reaction.emoji) == \"❌\":\n raise TimeoutError\n else:\n await message.remove_reaction(reaction, user)\n except TimeoutError:\n await message.edit(content=\"Message Expired\", embed=None)\n await message.clear_reactions()\n break\n if set_roles:\n roles_embed = Embed(title=\"Setting Roles\", colour=Colour.green())\n roles_assigned = 0\n roles_msg = await ctx.send(embed=roles_embed)\n for role in roles_dict:\n users_string = f\"{role.mention}\\n\"\n for member in roles_dict[role]:\n users_string += f\"{member.mention}\\n\"\n await member.add_roles(role, reason=f\"role added by {ctx.author.name} with setroles\"\n f\" command\")\n roles_assigned += 1\n if roles_assigned % 5 == 0:\n roles_embed.description = f\"Progress: {roles_assigned}/{total_roles_count}\"\n await roles_msg.edit(embed=roles_embed)\n roles_embed.add_field(name=f\"{role.name} ({len(roles_dict[role])})\", value=users_string)\n await roles_msg.edit(embed=roles_embed)\n roles_embed.title = \"Roles Set\"\n roles_embed.description = f\"Progress: Done\"\n await roles_msg.edit(embed=roles_embed)\n await message.delete()\n return\n else:\n await error_embed(ctx, \"You didn't input anything, cancelled setroles command\")\n return\n elif response.content.lower() in [\"no\", \"cancel\", \"n\", \"stop\"]:\n await info_message.delete()\n await response.delete()\n await ctx.send(embed=Embed(title=\"Cancelled\", description=\"You cancelled the setroles command\",\n colour=Colour.dark_purple()))\n return\n else:\n members = response.mentions\n if len(members) > 0:\n if len(response.role_mentions) == 1:\n role = response.role_mentions[0]\n server = ctx.guild\n bot_member = server.get_member(self.bot.user.id)\n\n if bot_member.top_role.position <= role.position:\n await error_embed(ctx, \"This role is too high to be set by the bot. Please enter a \"\n \"different role.\")\n elif ctx.author.top_role.position <= role.position:\n await error_embed(ctx, \"You cannot give others this role\")\n else:\n roles_dict[role] = members\n else:\n await error_embed(ctx, \"You can only mention one role at a time\")\n else:\n await error_embed(ctx, \"You did not mention any members\")\n await info_message.delete()","async def addRolesOnEmbed(msg):\n for field in msg.embeds[0].fields:\n await msg.add_reaction(field.name[0])","async def handle_role_reaction_press(interaction: disnake.MessageInteraction):\n if interaction.message not in await ReactionRoleMessage.get_all():\n return\n\n role_id = int(interaction.component.custom_id)\n member: disnake.Member = interaction.author\n user = await User.get(member.id)\n role = member.get_role(role_id)\n if role:\n await member.remove_roles(role, reason=\"Reaction Role Message\")\n await send_message(user=user, key=\"role_removed\", inter=interaction, ephemeral=True)\n else:\n role = interaction.guild.get_role(role_id)\n if role:\n try:\n await member.add_roles(role, reason=\"Reaction Role Message\")\n await send_message(user=user, key=\"role_added\", inter=interaction, ephemeral=True)\n except disnake.errors.Forbidden as e:\n await send_message(user=user, key=\"no_permissions\", inter=interaction, ephemeral=True)\n else:\n await send_message(user=user, key=\"role_not_found\", inter=interaction, ephemeral=True)","async def update_reactions(self, message, data):\n\n emojis = []\n\n for role in data.roles.all():\n if role.emoji.startswith(\":\") and role.emoji.endswith(\":\"):\n em = discord.utils.get(message.guild.emojis, name=role.emoji[1:-1])\n emojis.append(em)\n else:\n emojis.append(role.emoji)\n\n for emoji in emojis:\n await message.add_reaction(emoji)\n\n for reaction in message.reactions:\n if reaction.emoji not in emojis:\n await message.clear_reaction(reaction.emoji)","async def create(self, ctx: commands.Context, title: str, *roles: discord.Role):\n brake = {}\n\n def check(_r, _u):\n return _u == ctx.author and _r.message == me and str(_r.emoji) not in brake\n\n me = await ctx.send(f'React with the reaction that will correspond to the role `{roles[0]}`')\n\n # Yes I know this is an ugly solution to avoid an unnecessary api request but it is ultimately the only solution\n # I could think of.\n try:\n r, u = await self.bot.wait_for('reaction_add', timeout=len(roles) * 20, check=check)\n brake[str(r.emoji)] = roles[0].id\n except asyncio.TimeoutError:\n await ctx.send(\"Timed out, please run the command again and this time be a little quicker to react.\")\n return\n\n try:\n for role in roles[1:]:\n await me.edit(content=f'React with the reaction that will correspond to the role `{role}`')\n r, u = await self.bot.wait_for('reaction_add', timeout=len(roles) * 20, check=check)\n brake[str(r.emoji)] = role.id\n except asyncio.TimeoutError:\n await ctx.send('Timed out, please run the command again and this time be a little quicker to react.')\n return\n\n await ctx.send(\n 'What channel do you wish to send this role menu in? Enter its id, name or mention it: #<channel>')\n\n for count in range(4):\n try:\n m = await self.bot.wait_for('message', check=lambda _m: _m.author == ctx.author and _m.channel == ctx.channel, timeout=30)\n chan = await self.tcc.convert(ctx, m.content)\n break\n except commands.BadArgument:\n if count == 3:\n await ctx.send(\"Too many tries to enter channel, make sure I can actually see the channel you're \"\n \"referring to and use the entire command again\")\n return\n await ctx.send(\"Please enter the correct channel, if in doubt, try mentioning it, the `#channel` thing\")\n except asyncio.TimeoutError:\n await ctx.send(\"Timed out\")\n return\n\n e = discord.Embed(title=f\"Role menu: {title}\",\n description=\"\\n\\n\".join(f\"{k} - {ctx.guild.get_role(v)}\"for k, v in brake.items()),\n colour=discord.Colour.blue())\n\n zero = await chan.send(embed=e)\n\n for k in brake:\n await zero.add_reaction(k)\n\n self._cache[ctx.guild.id][zero.id] = brake\n\n query = \"\"\"\n INSERT INTO selfrole_lookup (guildid, channelid, messageid) \n VALUES ($1, $2, $3)\n \"\"\"\n await self.bot.pool.execute(query, ctx.guild.id, chan.id, zero.id)\n\n query = \"\"\"\n INSERT INTO selfrole (messageid, emoji, roleid)\n VALUES ($1, $2, $3)\n \"\"\"\n for k, v in brake.items():\n await self.bot.pool.execute(query, zero.id, k, v)","async def rolemenu(self, message, args):\n if \"-h\" in args or \"--help\" in args or \"--ahelp\" in args:\n await self._print_rolemenu_help(message.channel)\n return\n update_reactions = not is_key_in_args(args, \"--no-update\")\n allow_role_change = not is_key_in_args(args, \"--no-change\")\n remove_role_on_reaction_removal = not is_key_in_args(args, \"--no-removal\")\n max_number_of_reactions_per_user = get_number_in_args(args, \"--max-reactions\", None)\n max_users_with_role = get_number_in_args(args, \"--max-users\", None)\n no_required_role = is_key_in_args(args, \"--all\")\n role_ids = message.raw_role_mentions\n if len(role_ids) * 2 != len(args):\n await message.channel.send(\"Error with `rolemenu`: all roles must match an emoji\")\n return await self._print_rolemenu_help(message.channel)\n menu = {}\n for i, role_id in enumerate(message.raw_role_mentions):\n emoji = args.pop(0)\n if emoji.strip(\"<>@&\") == str(role_id): # emoji and roles are just exchanged: no problem\n emoji = args.pop(0)\n elif args.pop(0).strip(\"<>@&\") != str(role_id): # error: two adjacent args must be role_id and emoji\n await message.channel.send(\"Error with `rolemenu`: a role must match an emoji\")\n return await self._print_rolemenu_help(message.channel)\n menu.update({emoji: [message.guild.get_role(role_id)]})\n manager = RoleByReactionManager.get(self.guild)\n options = RoleMenuOptions(required_roles=None if no_required_role else [RoleCollection.VISITOR.value],\n ignored_roles=[RoleCollection.MASTER.value],\n update_reactions=update_reactions,\n allow_role_change=allow_role_change,\n max_number_of_reactions_per_user=max_number_of_reactions_per_user,\n max_users_with_role=max_users_with_role,\n remove_role_on_reaction_removal=remove_role_on_reaction_removal\n )\n await manager.add(message, menu, options)\n logger.debug(\"Rolemenu created. Now you can edit your post to make it prettier.\")","async def on_raw_reaction_add(self, payload: discord.RawReactionActionEvent):\n # Get the message id of the message that the user reacted to.\n message_id = payload.message_id\n\n # Get the message id of the message we want the user to react to.\n actual_message_id = constants.MessageIDs.RULES_MSGID\n\n # Compare that id's match, and if true continue to give the role.\n if message_id == actual_message_id:\n guild_id = payload.guild_id\n guild = self.bot.get_guild(guild_id)\n role = get(payload.member.guild.roles, name='Not Verified')\n\n if role is not None:\n member = get(guild.members, id=payload.user_id)\n if member is not None:\n await payload.member.add_roles(role)\n print(f\"Added role to {member}\")\n else:\n print(\"User not found . . .\")\n else:\n print(\"Role not found . . .\")","async def process_add_reaction_role(\n user_id, description, ctx=None, inter=None, allowed_mentions=None\n):\n user = await User.get(user_id)\n response_deferred = await defer_inter(inter, ephemeral=True)\n view = disnake.ui.View(timeout=None)\n view.add_item(RoleDropdown(description))\n await send_message(\n key=\"choose_roles\",\n user=user,\n view=view,\n ephemeral=True,\n response_deferred=response_deferred,\n inter=inter,\n )","async def on_raw_reaction_add(self, payload):\n\n # exclude all reactions which are not the original message\n if str(payload.message_id) != self.message_id:\n return\n\n # exclude the bot\n if payload.user_id == self.bot.user.id:\n return\n\n else:\n # get the model data for the role assigner object\n data = await self.get_objects(\n model=RoleAssigner, filter={\"bot__name\": str(self.bot_name)}\n )\n\n # role assigner object\n data = data[0]\n\n guild = self.get_guild(guild_id=payload.guild_id)\n\n user = self.get_user(guild=guild, user_id=payload.user_id)\n\n for db_role in data.roles.all():\n\n if db_role.emoji.startswith(\":\") and db_role.emoji.endswith(\":\"):\n\n ce = db_role.emoji[1:-1]\n\n else:\n ce = db_role.emoji\n\n if str(payload.emoji.name) == str(ce):\n\n role = self.get_role(guild, int(db_role.uid))\n\n if user not in role.members:\n\n await user.add_roles(role)\n\n print(\"Added \" + str(user) + \" to role: \" + str(role) + \"!\")\n\n else:\n print(\n \"User \" + str(user) + \" already in role: \" + str(role) + \"!\"\n )\n\n pass","async def edit(self, ctx: commands.Context, message_id: int):\n\n # Standard wait_for check function for message inputs, makes sure the command user's messages in command channel are considered\n def message_check(m: discord.Message):\n return m.author == ctx.author and m.channel == ctx.channel\n\n # Standard reaction check that ensures no duplicate reacrole entry, just name the relevant message 'm' before adding this one to check kwarg in wait_for\n def reaction_check_nd(_r: discord.Reaction, _u):\n return _u == ctx.author and _r.message == m and str(_r.emoji) not in self._cache[ctx.guild.id][PM.id]\n\n if message_id in self._cache[ctx.guild.id]:\n\n # Not actually channel id int but I decided to name it that way anyway\n chanid = await self.bot.pool.fetchrow(\"SELECT channelid FROM selfrole_lookup WHERE messageid = $1\", message_id)\n chan: discord.TextChannel = ctx.guild.get_channel(chanid['channelid'])\n\n # Currently need message content for title, might start saving title in db to avoid this api call idk\n try:\n PM: discord.Message = await chan.fetch_message(message_id)\n except discord.NotFound:\n await ctx.send(\"It would seem that the message for the role menu you're trying to edit has been deleted, please try creating a new one\")\n return\n\n buttons = [\"\\U0001f1e6\", \"\\U0001f1e7\", \"\\U0001f1e8\", \"\\U0001f1e9\"]\n\n e1 = discord.Embed(title=\"What aspect of the menu do you wish to change?\",\n description=\"\\U0001f1e6 - Add a role\\n\\n\"\n \"\\U0001f1e7 - Remove existing role\\n\\n\"\n \"\\U0001f1e8 - Edit the reaction of a role\\n\\n\"\n \"\\U0001f1e9 - Change the title\",\n colour=discord.Colour.blue())\n # Send the initial menu\n menu = await ctx.send(embed=e1)\n\n for button in buttons:\n await menu.add_reaction(button)\n\n # We need the first reaction where the emoji is one of the buttons\n def button_check(_r, _u):\n return _u == ctx.author and _r.message == menu and str(_r.emoji) in buttons\n # Get the option the user chose\n try:\n r, u = await self.bot.wait_for('reaction_add', check=button_check, timeout=20)\n except asyncio.TimeoutError:\n await ctx.send(\"Timed out\")\n return\n\n # If user wanted to add a new role to the menu\n if str(r.emoji) == buttons[0]:\n await menu.clear_reactions()\n await menu.edit(content=\"What role do you wish to be added? Enter its mention, id, or name\", embed=None)\n\n # Get the role object for the new role to be added\n try:\n m = await self.bot.wait_for('message', check=message_check, timeout=30)\n newrole = await self.rc.convert(ctx, m.content)\n\n if newrole.id in self._cache[ctx.guild.id][PM.id].values():\n await ctx.send(\"Error: role already exists in the menu, perhaps you meant to edit it?\")\n return\n except asyncio.TimeoutError:\n await ctx.send(\"Timed out\")\n return\n except commands.BadArgument:\n await ctx.send(\"Role not found, please try again\")\n return\n\n m = await ctx.send(f\"React on this message with the reaction that will correspond to the role `{newrole}`\")\n\n # Get the reaction/emoji that will correspond to the new role and yank everything into db\n try:\n r, u = await self.bot.wait_for('reaction_add', check=reaction_check_nd, timeout=30)\n self._cache[ctx.guild.id][PM.id][str(r.emoji)] = newrole.id\n\n query = \"\"\"\n INSERT INTO selfrole (messageid, emoji, roleid)\n VALUES ($1, $2, $3)\n \"\"\"\n\n await self.bot.pool.execute(query, PM.id, str(r.emoji), newrole.id)\n\n # Standard way of getting the embed description of the role menu\n newmenudesc = \"\\n\\n\".join([f\"{k} - {ctx.guild.get_role(v)}\" for k, v in self._cache[ctx.guild.id][PM.id].items()])\n\n newembed = discord.Embed(title=PM.embeds[0].title,\n description=newmenudesc,\n colour=discord.Colour.blue())\n\n await PM.edit(embed=newembed)\n await PM.add_reaction(r.emoji)\n await ctx.send(\"Menu edit completed successfully, you may now delete the messages other than the menu itself\")\n\n except asyncio.TimeoutError:\n await ctx.send(\"Timed out\")\n\n elif str(r.emoji) == buttons[1]:\n # Gotta yank the buttons to make everything squeaky clean\n await menu.clear_reactions()\n await menu.edit(content=\"Enter the role you wish to remove from the menu, can be mention, id or name\",\n embed=None)\n\n try:\n # Get role from user\n m = await self.bot.wait_for('message', check=message_check, timeout=20)\n role = await self.rc.convert(ctx, m.content)\n\n # If user trying to edit reaction to role that wasn't even in the menu to begin with\n if role.id not in self._cache[ctx.guild.id][PM.id].values():\n raise commands.BadArgument(\"Role not in cache\")\n\n # Get the key to delete using the old fashioned way, and subsequently delete it\n targetkey = \"\"\n for key, value in self._cache[ctx.guild.id][PM.id].items():\n if value == role.id:\n targetkey = key\n break\n self._cache[ctx.guild.id][PM.id].pop(targetkey)\n\n # After everything is done and dusted, make database entry and edit the menu\n query = \"\"\"\n DELETE FROM selfrole WHERE messageid = $1 AND roleid = $2\n \"\"\"\n await self.bot.pool.execute(query, PM.id, role.id)\n newmenudesc = \"\\n\\n\".join(\n [f\"{k} - {ctx.guild.get_role(v)}\" for k, v in self._cache[ctx.guild.id][PM.id].items()])\n\n newembed = discord.Embed(title=PM.embeds[0].title,\n description=newmenudesc,\n colour=discord.Colour.blue())\n await PM.edit(embed=newembed)\n await PM.clear_reaction(targetkey)\n await ctx.send(\n \"Menu edit completed successfully, you may now delete the messages other than the menu itself\")\n except asyncio.TimeoutError:\n await ctx.send(\"Timed out\")\n return\n except commands.BadArgument:\n await ctx.send(\"I don't think that role exists in that menu, run the command again\")\n return\n\n elif str(r.emoji) == buttons[2]:\n # Same drill, remove buttons to make it look clean\n await menu.clear_reactions()\n await menu.edit(embed=None, content=\"Enter the role for which you wish to change the reaction.\")\n\n try:\n m = await self.bot.wait_for('message', check=message_check, timeout=20)\n role = await self.rc.convert(ctx, m.content)\n\n if role.id not in self._cache[ctx.guild.id][PM.id].values():\n raise commands.BadArgument(\"Role not in cache\")\n\n except asyncio.TimeoutError:\n await ctx.send(\"Timed out\")\n return\n except commands.BadArgument:\n await ctx.send(\"Couldn't find the role you wished to edit in the menu\")\n return\n\n # Get the reaction/emoji that will correspond to the new role and yank everything into db\n m = await ctx.send(f\"React on this message with the new reaction that will correspond to the role {role}\")\n try:\n r, u = await self.bot.wait_for('reaction_add', check=reaction_check_nd, timeout=30)\n\n # Can only delete entry if have the key so....\n TargetKey = \"\" # Set default value so IDE stops screaming\n for k, v in self._cache[ctx.guild.id][PM.id].items():\n if v == role.id:\n TargetKey = k\n\n # Make new entry and delete the old one\n self._cache[ctx.guild.id][PM.id][str(r.emoji)] = role.id\n self._cache[ctx.guild.id][PM.id].pop(TargetKey)\n\n # After everything is done and dusted, at last update the database entry\n await self.bot.pool.execute(\"UPDATE selfrole SET emoji = $1 WHERE roleid = $2 AND messageid = $3\", str(r.emoji), role.id, PM.id)\n\n # Hehehehehehe\n newmenudesc = \"\\n\\n\".join(\n [f\"{k} - {ctx.guild.get_role(v)}\" for k, v in self._cache[ctx.guild.id][PM.id].items()])\n\n newembed = discord.Embed(title=PM.embeds[0].title,\n description=newmenudesc,\n colour=discord.Colour.blue())\n\n await PM.edit(embed=newembed)\n await PM.clear_reaction(TargetKey)\n await PM.add_reaction(str(r.emoji))\n await ctx.send(\n \"Menu edit completed successfully, you may now delete the messages other than the menu itself\")\n except asyncio.TimeoutError:\n await ctx.send(\"Timed out\")\n return\n\n elif str(r.emoji) == buttons[3]:\n # This one speaks for itself I think.\n await menu.clear_reactions()\n await menu.edit(embed=None, content=\"Enter the new title you want the menu to have\")\n try:\n m = await self.bot.wait_for('message', check=message_check, timeout=30)\n e = discord.Embed(title=f\"Role menu: {m.content}\",\n description=PM.embeds[0].description,\n colour=PM.embeds[0].colour)\n await PM.edit(embed=e)\n except asyncio.TimeoutError:\n await ctx.send(\"Timed out\")\n return\n\n else:\n await ctx.send(\"Menu not found in this server, double check if the id was entered correctly\")","async def togglerole(self, ctx, role=\"\"):\r\n\r\n user = ctx.message.author\r\n joinmsg = \"Joined {0} role\"\r\n leavemsg = \"Left {0} role\"\r\n\r\n role = await commands.clean_content().convert(ctx, role)\r\n\r\n if role == \"\":\r\n embed = discord.Embed(title=\"Toggleable Roles:\", color=discord.Color.dark_teal())\r\n embed.description = \"\"\"\r\n - :race_car: Mario Kart 8 Deluxe: MK8D\r\n - :squid: Splatoon 2: spla2n\r\n - :card_box: Cards Against Humanity: cah\r\n - :bomb: Counter-Strike: Global Offensive: csgo\r\n - :gun: PUBG: pubg\r\n - :red_circle: Red Eclipse: redeclipse\r\n - :robot: Titanfall (2): titanfall\r\n - :boxing_glove: Super Smash Bros.: smash\r\n - :shopping_cart: Fortnite: fortnite\r\n \"\"\"\r\n await ctx.send(\"\", embed=embed)\r\n\r\n elif role.lower() == \"mk8d\":\r\n if self.bot.mk8d_role in user.roles:\r\n await user.remove_roles(self.bot.mk8d_role)\r\n await ctx.send(leavemsg.format(role.upper()))\r\n\r\n else:\r\n print(\"before error\")\r\n await user.add_roles(self.bot.mk8d_role)\r\n print(\"after error\")\r\n await ctx.send(joinmsg.format(role.upper()))\r\n\r\n elif role.lower() == \"spla2n\":\r\n if self.bot.spla2n_role in user.roles:\r\n await user.remove_roles(self.bot.spla2n_role)\r\n await ctx.send(leavemsg.format(role.lower()))\r\n\r\n else:\r\n await user.add_roles(self.bot.spla2n_role)\r\n await ctx.send(joinmsg.format(role.lower()))\r\n\r\n elif role.lower() == \"cah\":\r\n if self.bot.cah_role in user.roles:\r\n await user.remove_roles(self.bot.cah_role)\r\n await ctx.send(leavemsg.format(role.lower()))\r\n\r\n else:\r\n await user.add_roles(self.bot.cah_role)\r\n await ctx.send(joinmsg.format(role.lower()))\r\n\r\n elif role.lower() == \"csgo\":\r\n if self.bot.csgo_role in user.roles:\r\n await user.remove_roles(self.bot.csgo_role)\r\n await ctx.send(leavemsg.format(role.lower()))\r\n\r\n else:\r\n await user.add_roles(self.bot.csgo_role)\r\n await ctx.send(joinmsg.format(role.lower()))\r\n\r\n elif role.lower() == \"pubg\":\r\n if self.bot.pubg_role in user.roles:\r\n await user.remove_roles(self.bot.pubg_role)\r\n await ctx.send(leavemsg.format(role.lower()))\r\n\r\n else:\r\n await user.add_roles(self.bot.pubg_role)\r\n await ctx.send(joinmsg.format(role.lower()))\r\n\r\n elif role.lower() == \"redeclipse\":\r\n if self.bot.redeclipse_role in user.roles:\r\n await user.remove_roles(self.bot.redeclipse_role)\r\n await ctx.send(leavemsg.format(role.lower()))\r\n\r\n else:\r\n await user.add_roles(self.bot.redeclipse_role)\r\n await ctx.send(joinmsg.format(role.lower()))\r\n\r\n elif role.lower() == \"titanfall\":\r\n if self.bot.titanfall_role in user.roles:\r\n await user.remove_roles(self.bot.titanfall_role)\r\n await ctx.send(leavemsg.format(role.lower()))\r\n\r\n else:\r\n await user.add_roles(self.bot.titanfall_role)\r\n await ctx.send(joinmsg.format(role.lower()))\r\n\r\n elif role.lower() == \"smash\":\r\n if self.bot.smashbros_role in user.roles:\r\n await user.remove_roles(self.bot.smashbros_role)\r\n await ctx.send(leavemsg.format(role.lower()))\r\n\r\n else:\r\n await user.add_roles(self.bot.smashbros_role)\r\n await ctx.send(joinmsg.format(role.lower()))\r\n\r\n elif role.lower() == \"fortnite\":\r\n if self.bot.fortnite_role in user.roles:\r\n await user.remove_roles(self.bot.fortnite_role)\r\n await ctx.send(leavemsg.format(role.lower()))\r\n\r\n else:\r\n await user.add_roles(self.bot.fortnite_role)\r\n await ctx.send(joinmsg.format(role.lower()))\r\n else:\r\n msg = \"{} is not a togglable role\".format(role)\r\n await ctx.send(msg)","async def on_raw_reaction_add(self, payload: discord.RawReactionActionEvent):\n role: discord.Role = await self.check_payload(payload)\n if payload.user_id == self.bot.user.id:\n return\n if role:\n guild = self.bot.get_guild(payload.guild_id)\n member = guild.get_member(payload.user_id)\n await member.add_roles(role)","async def on_raw_reaction_add(self, payload: discord.RawReactionActionEvent):\n \n guild = self.bot.get_guild(payload.guild_id)\n user = discord.utils.get(guild.members, id=payload.user_id)\n # Skipping bot reactions\n if user.bot:\n return\n\n # Search the event by message id\n message_id = payload.message_id\n channel_id = payload.channel_id\n guild_id = payload.guild_id\n event = connector.getEventByMessage(guild_id, message_id, channel_id)\n if event is None:\n return\n\n role = discord.utils.get(payload.member.guild.roles, id=event.role_id)\n\n if (payload.event_type == \"REACTION_ADD\") and (event.emoji == str(payload.emoji)):\n # Adding role to user\n await payload.member.add_roles(role)\n elif (payload.event_type == \"REACTION_REMOVE\") and (event.emoji == str(payload.emoji)) and (role in payload.member.roles):\n # Remove role from user\n await payload.member.remove_roles(role)","async def subscribe_command(self, ctx: commands.Context, *_) -> None: # We don't actually care about the args\n view = RoleButtonView(ctx.author, self.assignable_roles)\n await ctx.send(\n \"Click the buttons below to add or remove your roles!\",\n view=view,\n delete_after=DELETE_MESSAGE_AFTER\n )","async def on_raw_reaction_add(self, payload: discord.RawReactionActionEvent):\n\n # Skipping bot reactions\n if payload.member.bot:\n return\n\n # Search the event by message id\n message_id = payload.message_id\n channel_id = payload.channel_id\n guild_id = payload.guild_id\n event_type = connector.getEventTypeByMessage(guild_id, message_id, channel_id)\n if event_type is None:\n return\n\n role = discord.utils.get(payload.member.guild.roles, id=event_type.role_id)\n\n if (payload.event_type == \"REACTION_ADD\") and (event_type.emoji == str(payload.emoji)):\n # Adding role to user\n await payload.member.add_roles(role)","async def add_roles(self, ctx: commands.Context, *roles: discord.Role):\n if not roles:\n return await ctx.send_help()\n errored = \"\"\n message = \"\"\n added = []\n already_added = []\n for role in roles:\n if role >= ctx.author.top_role:\n errored += (\n \"{role}: You can't set a role equal to or higher than your own.\\n\".format(\n role=role.name\n )\n )\n continue\n if role >= ctx.guild.me.top_role:\n errored += (\n \"{role}: You can't set a role that's equal to or higher than the \"\n \"bot.\\n\".format(role=role.name)\n )\n continue\n async with self.config.guild(ctx.guild).autoroles() as roles_list:\n if role.id not in roles_list:\n roles_list.append(role.id)\n added.append(role.name)\n else:\n already_added.append(role.name)\n message += errored\n if added:\n message += \"\\nAdded role(s): {roles}\".format(roles=humanize_list(added))\n if already_added:\n message += \"\\nRole(s) already added: {roles}\".format(\n roles=humanize_list(already_added)\n )\n if message:\n for line in pagify(message):\n await ctx.send(line)","async def rolemenu_add_role(self,\n interaction: discord.Interaction,\n name: str,\n role: discord.Role,\n emoji: str = None,\n description: str = None):\n doc = await self.db.find_one({\n \"guild_id\": interaction.guild.id,\n \"name\": name\n })\n if not doc:\n return await interaction.response.send_message(\n \"No role menu with that name exists.\", ephemeral=True)\n for role_doc in doc[\"roles\"]:\n if role_doc[\"id\"] == role.id:\n return await interaction.followup.send(\n \"Role is already in the menu.\", ephemeral=True)\n if len(doc[\"roles\"]) >= 25:\n return await interaction.response.send_message(\n \"This role menu is full.\", ephemeral=True)\n await interaction.response.defer(ephemeral=True)\n if role.guild != interaction.guild:\n return await interaction.response.send_message(\n \"This role is not in this server.\")\n if emoji:\n if emoji.startswith(\"<\") and emoji.endswith(\">\"):\n try:\n emoji = int(emoji[1:-1].split(\":\")[2])\n except ValueError:\n return await interaction.followup.send(\"Invalid emoji.\")\n else:\n try:\n message = await interaction.original_message()\n await message.add_reaction(emoji)\n except discord.HTTPException:\n return await interaction.followup.send(\"Invalid emoji.\")\n await self.db.update_one({\"_id\": doc[\"_id\"]}, {\n \"$push\": {\n \"roles\": {\n \"description\": description,\n \"id\": role.id,\n \"emoji\": emoji,\n \"date_added\": datetime.datetime.now(datetime.datetime.u)\n }\n }\n })\n doc = await self.db.find_one({\"_id\": doc[\"_id\"]})\n await interaction.followup.send(f\"Added {role.mention} to the menu.\")\n menu = Menu(self, interaction.guild, doc)\n await menu.update()","async def roles(self, ctx):\n\n pass","async def test_roles_command_command(self):\n self.ctx.guild.roles.append(self.moderator_role)\n\n self.cog.roles_info.can_run = unittest.mock.AsyncMock()\n self.cog.roles_info.can_run.return_value = True\n\n self.assertIsNone(await self.cog.roles_info(self.cog, self.ctx))\n self.ctx.send.assert_called_once()\n\n _, kwargs = self.ctx.send.call_args\n embed = kwargs.pop(\"embed\")\n\n self.assertEqual(embed.title, \"Role information (Total 1 role)\")\n self.assertEqual(embed.colour, discord.Colour.og_blurple())\n self.assertEqual(embed.description, f\"\\n`{self.moderator_role.id}` - {self.moderator_role.mention}\\n\")","async def send(self, msg, roles):\n try:\n if (\n self.active_role and\n roles.get_role_by_id(msg.author.id).role == self.active_role and\n len(msg.content) > 0 or hasattr(self, msg.content.strip().split()[0]+'_cmd')\n ):\n await self.error(\n to=msg.author,\n msg=\"Attention ! Ce message ressemble fortement à une commande, et il ne faudrait pas que les \"\n \"autres te surprennent ! [CE MESSAGE N'A PAS ÉTÉ RELAYÉ ; UTILISE `$public monMessage` SI TU \"\n \"VEUX VRAIMENT L'ENVOYER À TOUS LES JOUEURS]\"\n )\n else:\n await self.redirect(\n from_=roles.get_name_by_id(msg.author.id),\n to=roles.everyone.exclude(msg.author.id),\n msg=msg.content\n )\n\n except Exception as e:\n logger.error(\n \"The redirection of the message '%s' raised a(n) %s : %s\" % (msg.content, e.__class__.__name__, e)\n )\n await self.error(to=msg.author, msg=msgs.MESSAGE_HAS_RAISED % msg.content)","async def reactions(self, ctx):\n\n\t\tawait self.message_leaderboard(ctx, \"reactions\")","async def addroleall(self, ctx, role: discord.Role):\n muted_role = discord.utils.get(ctx.guild.roles, name=\"Muted\")\n punished_role = discord.utils.get(ctx.guild.roles, name=\"Punished\")\n\n if role > ctx.author.top_role:\n return await ctx.send(\n embed=discord.Embed(\n title=\"You don't have permission to add this role\",\n timestamp=datetime.datetime.utcnow(),\n colour=discord.Colour.darker_grey(),\n )\n )\n\n if role == muted_role or role == punished_role:\n return await ctx.send(\n embed=discord.Embed(\n title=f\"Can not assign *{role}* role using this command.\",\n description=\"For more information run ```.help addroleall```\",\n timestamp=datetime.datetime.utcnow(),\n colour=discord.Colour.red(),\n )\n )\n\n for i in ctx.guild.members:\n if not i.bot:\n await i.add_roles(role)\n\n await ctx.send(\n embed=discord.Embed(\n title=f\"*{role}* has been added to **{len(ctx.guild.members)}** members!\",\n timestamp=datetime.datetime.utcnow(),\n colour=discord.Colour.green(),\n )\n )","async def _on_raw_reaction(\n self,\n payload: RawReactionActionEvent,\n reaction_type: EnumReactionType,\n ) -> None:\n if self.__is_self(payload.user_id):\n print(\"reaction added by the bot itself\")\n return\n\n guild = self._client.get_guild(payload.guild_id)\n\n if reaction_type == EnumReactionType.ADD:\n await self.__roles.add_role(\n guild, payload.message_id,\n payload.emoji, payload.user_id,\n\n )\n elif reaction_type == EnumReactionType.REMOVE:\n await self.__roles.remove_role(\n guild, payload.message_id,\n payload.emoji, payload.user_id,\n )\n else:\n raise InvalidReactionType","async def on_raw_reaction_remove(self, payload):\n\n # exclude all reactions which are not the original message\n if str(payload.message_id) != self.message_id:\n return\n\n # exclude the bot\n if payload.user_id == self.bot.user.id:\n return\n\n else:\n # get the model data for the role assigner object\n data = await self.get_objects(\n model=RoleAssigner, filter={\"bot__name\": str(self.bot_name)}\n )\n\n # role assigner object\n data = data[0]\n\n guild = self.get_guild(guild_id=payload.guild_id)\n\n user = self.get_user(guild=guild, user_id=payload.user_id)\n\n for db_role in data.roles.all():\n\n if db_role.emoji.startswith(\":\") and db_role.emoji.endswith(\":\"):\n\n ce = db_role.emoji[1:-1]\n\n else:\n ce = db_role.emoji\n\n if str(payload.emoji.name) == str(ce):\n\n role = self.get_role(guild, int(db_role.uid))\n\n if user in role.members:\n\n await user.remove_roles(role)\n\n print(\"Removed \" + str(user) + \" from role: \" + str(role) + \"!\")\n\n else:\n print(\"User \" + str(user) + \" not in role: \" + str(role) + \"!\")\n\n pass","def grant_role(self, role, principal_ids):","async def role(ctx, role: discord.Role = None):\n if role is None:\n await ctx.send(\"List of assignable roles: \" + str(allowed_roles))\n if role.name in allowed_roles:\n if not role in ctx.message.author.roles:\n await ctx.message.author.add_roles(role)\n await ctx.send(\"Role added.\")\n else:\n await ctx.message.author.remove_roles(role)\n await ctx.send(\"Role removed.\") \n else:\n await ctx.send(\"That role doesn't exist, or you don't have permission to modify it.\")","def roles(self, roles):\n\n self._roles = roles","def roles(self, roles):\n\n self._roles = roles","def roles(self, roles):\n\n self._roles = roles","async def command_assign_role(self, context, role: str):\n try:\n await context.author.add_roles(discord.utils.get(\n context.guild.roles, name=role))\n await context.message.add_reaction('👍')\n except Exception as e:\n await context.message.add_reaction('👎')\n await context.send('Role could not be assigned')\n print(f'Errored in command_assign_role.', e)","def create_message_embed(self, data):\n\n # get the language object\n lang = data.bot.lang\n\n embed = self.generate_embed()\n\n if lang == \"en\":\n help_text = \"Role Selection\"\n\n elif lang == \"de\":\n help_text = \"Rollenvergabe\"\n\n embed.add_field(name=help_text, value=data.message_text, inline=False)\n\n for role in data.roles.all():\n embed.add_field(name=role.name, value=role.emoji, inline=False)\n\n return embed","async def list_roles(self, ctx: commands.Context):\n all_roles = await self.config.guild(ctx.guild).autoroles()\n maybe_not_found = []\n message = \"\"\n for role in all_roles:\n fetched_role = ctx.guild.get_role(role)\n if not fetched_role:\n maybe_not_found.append(role)\n continue\n message += \"- {name} (`{id}`).\\n\".format(name=fetched_role.name, id=fetched_role.id)\n if maybe_not_found:\n clean_list = list(set(all_roles) - set(maybe_not_found))\n await self.config.guild(ctx.guild).autoroles.set(clean_list)\n message += \"\\nSome roles has been removed since I was unable to find them.\"\n if message:\n for line in pagify(message):\n await ctx.send(line)\n else:\n await ctx.send(\"No role has been added.\")","async def tod_revenge(self, ctx, *args):\n roles = [y.name.lower() for y in ctx.author.roles]\n self.revenge = not self.revenge\n if self.revenge:\n message = \"Revenges are now __on__.\"\n else:\n message = \"Revenges are now __off__.\"\n await ctx.channel.send(message)","async def _set_roles(self, ctx: Context):\n\n guild: discord.Guild = ctx.guild\n\n host = await guild.create_role(\n name=\"Host\", colour=discord.Color(0xFFBF37),\n hoist=True, mentionable=True\n )\n await self.config.guild(guild).host_id.set(host.id)\n await ctx.author.add_roles(host)\n\n player = await guild.create_role(\n name=\"Player\", colour=discord.Color(0x37BFFF),\n hoist=True, mentionable=True\n )\n await self.config.guild(guild).player_id.set(player.id)\n\n repl = await guild.create_role(\n name=\"Replacement\", colour=discord.Color(0x86FF40)\n )\n await self.config.guild(guild).repl_id.set(repl.id)\n\n spec = await guild.create_role(\n name=\"Spectator\", colour=discord.Color(0xD837FF)\n )\n await self.config.guild(guild).spec_id.set(spec.id)\n\n dead = await guild.create_role(\n name=\"Dead\", colour=discord.Color(0xDC5757)\n )\n await self.config.guild(guild).dead_id.set(dead.id)\n\n txt = _(\n \"Host: {}\"\n \"\\nPlayer: {}\"\n \"\\nSpectator: {}\"\n \"\\nDead: {}\"\n \"\\nReplacement: {}\"\n ).format(\n host.mention,\n player.mention,\n spec.mention,\n dead.mention,\n repl.mention\n )\n\n embed = discord.Embed(\n color=0x37BFFF, title=\"Created Roles!\", description=txt\n )\n\n try:\n await ctx.send(embed=embed)\n except discord.Forbidden:\n await ctx.send(\"Set up required roles!\")","async def _role_repl(self, ctx: Context, *, role: discord.Role):\n\n msg = await ctx.send(\n _(\n \"Are you sure you want to set `{}` as replacement role?\"\n ).format(role.name)\n )\n start_adding_reactions(msg, ReactionPredicate.YES_OR_NO_EMOJIS)\n\n pred = ReactionPredicate.yes_or_no(msg, ctx.author)\n await ctx.bot.wait_for(\"reaction_add\", check=pred)\n\n if pred.result:\n await self.config.guild(ctx.guild).repl_id.set(role.id)\n await ctx.send(\n _(\"Set `{}` as replacement role!\").format(role.name)\n )\n else:\n await ctx.send(_(\"Aborted replacement role setup.\"))","async def update(self, ctx):\n\n # get the model data for the role assigner object\n data = await self.get_objects(\n model=RoleAssigner, filter={\"bot__name\": str(self.bot_name)}\n )\n\n # role assigner object\n data = data[0]\n\n # fetch the discord message\n guild_id = await self.get_deep_data(data, \"bot__server__uid\")\n\n guild = self.get_guild(int(guild_id))\n channel = self.get_channel(guild, int(data.message.cuid))\n message = await channel.fetch_message(int(data.message.uid))\n\n # update the message\n await message.edit(content=\"_ _\", embed=self.create_message_embed(data))\n\n await self.update_reactions(message, data)\n\n await ctx.send(\"Updated.\")","def save_roles(post, container_id):\n user_id = int(post['user_id'])\n role_id = int(post['role_id'])\n do_save_user_role(user_id, role_id, container_id, item_container)","async def rolemenu_create(self,\n interaction: discord.Interaction,\n name: str,\n channel: discord.TextChannel,\n mode: int,\n color: str,\n placeholder: str,\n description: str = None):\n doc = await self.db.find_one({\n \"guild_id\": interaction.guild.id,\n \"name\": name\n })\n if doc:\n return await interaction.response.send_message(\n \"Role menu with that name exists.\", ephemeral=True)\n if channel.guild != interaction.guild:\n return await interaction.response.send_message(\n \"This channel is not in this server.\")\n if not channel.permissions_for(interaction.guild.me).send_messages:\n return await interaction.response.send_message(\n \"I don't have permission to send messages in this channel.\")\n try:\n color = discord.Color.from_str(color)\n except ValueError:\n return await interaction.response.send_message(\"Invalid color.\")\n await self.db.insert_one({\n \"message_id\": None,\n \"name\": name,\n \"mode\": mode,\n \"channel_id\": channel.id,\n \"roles\": [],\n \"guild_id\": interaction.guild.id,\n \"color\": color.value,\n \"placeholder\": placeholder,\n \"description\": description\n })\n await interaction.response.send_message(\n \"Role menu created. It is currently empty, however, and \"\n \"you'll need to add roles with `/rolemenu role add.`\",\n ephemeral=True)","async def addRole(self, ctx, *roles_to_add):\n already_present_roles = [] # roles that will be deleted from \"roles_to_add\"\n\n available_roles = open(\"assets/roles.txt\", \"r\").readlines()\n available_roles = [role.lower().strip() for role in available_roles]\n\n output_msg = \"\"\n\n for role_to_add in roles_to_add:\n for role in available_roles:\n if role_to_add.lower() == role:\n output_msg += f\"Failed to add {role_to_add}: role already exists.\\n\"\n already_present_roles.append(role_to_add)\n break\n\n for role in already_present_roles:\n roles_to_add.remove(role)\n\n if roles_to_add:\n with open(\"assets/roles.txt\", \"a\") as f:\n for role in roles_to_add:\n f.write(f\"{role}\\n\")\n output_msg += f\"{role} has been added successfully.\\n\"\n\n await ctx.send(output_msg)","async def command_rolecall(self, context):\n print(self._fetch_category_roles(context))\n print(self._fetch_category_roles(context, COSMETIC_CATEGORY_NAME))","async def on_raw_reaction_remove(self, payload: discord.RawReactionActionEvent):\n role: discord.Role = await self.check_payload(payload)\n\n if role:\n guild = self.bot.get_guild(payload.guild_id)\n member: discord.Member = guild.get_member(payload.user_id)\n await member.remove_roles(role)","def roles_str(person: Member, roles: commands.Greedy[Role]) -> str:\n message = \"role\" if len(roles) == 1 else \"roles\"\n roleIds = [role.name for role in roles]\n\n return f\"{message} for {person}: {roleIds}\"","def create_cm_roles(self, role_list):\n return self._post(endpoint='{}/cm/service/roles'.format(self.api_version),\n data=role_list).json()","def getRoles(self):\n\t\tpayload = ''\n\t\tif self.Roles:\n\t\t\tif type(self.Roles) != int:\n\t\t\t\tfor x in range(0,len(self.Roles)):\n\t\t\t\t\tpayload += \"%s\" % (self.Roles[x])\n\t\t\t\treturn self.Roles\n\t\t\telse:\n\t\t\t\treturn None","async def editrole(self, ctx: context.CustomContext, *, role: Fuzzy[Selfrole]):\n\n new_join_message = await ctx.input(\n f\"{config.USER_INTERACTION_REQUIRED} Reply with the new join message for `{role.role.name}`.\"\n f\"\\n{config.HINT} The current join message is: `{role.join_message}`\"\n )\n\n await self.bot.db.execute(\n \"UPDATE selfrole SET join_message = $1 WHERE role_id = $2\",\n new_join_message,\n role.role.id,\n )\n\n await ctx.send(\n f\"{config.YES} The join message for `{role.role.name}` was updated.\"\n )","async def update_cog(self):\n\n # get the model data for the role assigner object\n data = await self.get_objects(\n model=RoleAssigner, filter={\"bot__name\": str(self.bot_name)}\n )\n\n # role assigner object\n data = data[0]\n\n # fetch the discord message\n guild_id = await self.get_deep_data(data, \"bot__server__uid\")\n\n guild = self.get_guild(int(guild_id))\n channel = self.get_channel(guild, int(data.message.cuid))\n message = await channel.fetch_message(int(data.message.uid))\n self.message_id = int(data.message.uid)\n\n # update the message\n await message.edit(content=\"_ _\", embed=self.create_message_embed(data))\n\n await self.update_reactions(message, data)","async def apply_role(self, *, reason: str = None):\n if self.role not in self.member.roles:\n try:\n await self.member.add_roles(self.role, reason=reason)\n except discord.HTTPException:\n pass","async def add_role_admin(request, role_id):\n required_fields = [\"id\"]\n utils.validate_fields(required_fields, request.json)\n\n txn_key, txn_user_id = await utils.get_transactor_key(request)\n proposal_id = str(uuid4())\n batch_list = Role().admin.propose.batch_list(\n signer_keypair=txn_key,\n signer_user_id=txn_user_id,\n proposal_id=proposal_id,\n role_id=role_id,\n next_id=request.json.get(\"id\"),\n reason=request.json.get(\"reason\"),\n metadata=request.json.get(\"metadata\"),\n )\n await utils.send(\n request.app.config.VAL_CONN, batch_list, request.app.config.TIMEOUT\n )\n return json({\"proposal_id\": proposal_id})","async def roles(self, ctx, *, role: Fuzzy[Selfrole] = None):\n\n if role:\n await self._toggle_role(ctx, role)\n else:\n await self._list_all_roles(ctx)","async def addRoles(self, ctx: Context, person: Member, roles: Greedy[Role]):\n roles = remove_dupe_roles(roles)\n\n await person.add_roles(*roles)\n await ctx.send(f\"Adding {roles_str(person, roles)}\")","async def createrole(self, ctx, role: str):\n if role.lower() == \"muted\" or role.lower() == \"punished\":\n return await ctx.send(\"Can not create this roles.\")\n \"\"\"Create a new role\"\"\"\n role = await ctx.guild.create_role(name=role)\n return await ctx.send(\n embed=discord.Embed(\n title=f\"Role *{role}* has been created!\",\n timestamp=datetime.datetime.utcnow(),\n colour=discord.Colour.green(),\n )\n )","async def on_reaction_add(reaction, user):\n if reaction.message.content.startswith('http'):\n curator = re.sub(r'\\d|\\W|(TravelFeed)','',str(user),re.IGNORECASE|re.DOTALL)\n if not user.id in discordcuratorlist and not user.id == botid:\n \"\"\"Checks if user who added reaction is a curator\"\"\"\n await loop.create_task(send_discord(\"Curator unauthorised: \"+curator, logchannel))\n return\n else:\n author, permlink = resolve_authorperm(reaction.message.content)\n post = Comment(construct_authorperm(author, permlink))\n if reaction.emoji == '🌍':\n await bot.add_reaction(reaction.message, \"⏳\")\n actionqueue.put(Post_Action(post, \"tf100\", curator, reaction.message))\n elif reaction.emoji == '🌐': \n await bot.add_reaction(reaction.message, \"⏳\") \n actionqueue.put(Post_Action(post, \"tf50\", curator, reaction.message))\n elif reaction.emoji == '👥':\n await bot.add_reaction(reaction.message, \"⏳\")\n actionqueue.put(Post_Action(post, \"coop100\", None, reaction.message))\n elif reaction.emoji == '👋':\n await bot.add_reaction(reaction.message, \"⏳\")\n actionqueue.put(Post_Action(post, \"ad10\", curator, reaction.message))\n elif reaction.emoji == '📏':\n await bot.add_reaction(reaction.message, \"⏳\")\n actionqueue.put(Post_Action(post, \"short0\", None, reaction.message))\n elif reaction.emoji == '🇬🇧':\n await bot.add_reaction(reaction.message, \"⏳\")\n actionqueue.put(Post_Action(post, \"lang0\", None, reaction.message))\n elif reaction.emoji == '📝':\n await bot.add_reaction(reaction.message, \"⏳\")\n actionqueue.put(Post_Action(post, \"copyright0\", None, reaction.message))","def role_command():","def myrole(var, wrapper, message):\n\n ps = get_participants()\n if wrapper.source not in ps:\n return\n\n role = get_main_role(wrapper.source)\n if role in Hidden:\n role = var.HIDDEN_ROLE\n\n evt = Event(\"myrole\", {\"role\": role, \"messages\": []})\n if not evt.dispatch(var, wrapper.source):\n return\n role = evt.data[\"role\"]\n\n wrapper.pm(messages[\"show_role\"].format(role))\n\n for msg in evt.data[\"messages\"]:\n wrapper.pm(msg)","async def remove_roles(self, ctx: commands.Context, *roles: discord.Role):\n if not roles:\n return await ctx.send_help()\n message = \"\"\n removed = []\n not_found = []\n async with self.config.guild(ctx.guild).autoroles() as roles_list:\n for role in roles:\n if role.id in roles_list:\n roles_list.remove(role.id)\n removed.append(role.name)\n else:\n not_found.append(role.name)\n if not_found:\n message += \"\\nRole(s) not found in autorole list: {roles}\".format(\n roles=humanize_list(not_found)\n )\n if removed:\n message += \"\\nRole(s) remove from autorole list: {roles}\".format(\n roles=humanize_list(removed)\n )\n if message:\n for line in pagify(message):\n await ctx.send(line)","def _updateRoles(self, obj, updateRoles={}, deleteRoles=[], cancelRoles=[]):\n #user_role_delta = {}\n pending = obj.getPendingCollaborations()\n\n collabs = obj.getCollaborators()\n\n user_role_delta = obj.generateCollaborationRequests(\n newUser=False, newRoles=updateRoles, deleteRoles=deleteRoles)\n \n for p in user_role_delta.keys():\n if p in pending.keys():\n new_changes = pending[p].roles.copy()\n for role in user_role_delta[p]:\n delta = user_role_delta[p][role]\n if role in new_changes:\n if new_changes[role] != delta:\n new_changes.pop(role)\n elif new_changes[role] == delta:\n #Shouldn't happen\n pass\n else:\n new_changes[role] = delta\n if not new_changes:\n obj.manage_delObjects(pending[p].id)\n else:\n obj.editCollaborationRequest(pending[p].id, new_changes)\n else:\n obj.addCollaborator(p)\n obj.requestCollaboration(p, user_role_delta[p])\n\n for u in cancelRoles:\n if u in obj.getPendingCollaborations():\n # Revert the new roles back to the published version\n obj.reverseCollaborationRequest(pending[u].id)\n # Delete the collaboration request\n obj.manage_delObjects(pending[u].id)\n\n #Get the collaborators again if they have changed\n all_roles = {}\n for rolename in obj.default_roles + getattr(obj, 'optional_roles', {}).keys():\n for r in getattr(obj,rolename.lower()+'s',[]):\n all_roles[r]=None\n for r in getattr(obj, 'pub_'+rolename.lower()+'s', []):\n all_roles[r]=None\n \n collabs = obj.getCollaborators()\n for c in collabs:\n if c not in all_roles.keys():\n obj.removeCollaborator(c)","async def removeroleall(self, ctx, role: discord.Role):\n muted_role = discord.utils.get(ctx.guild.roles, name=\"Muted\")\n punished_role = discord.utils.get(ctx.guild.roles, name=\"Punished\")\n\n if role > ctx.author.top_role:\n return await ctx.send(\n embed=discord.Embed(\n title=\"You don't have permission to remove this role\",\n timestamp=datetime.datetime.utcnow(),\n colour=discord.Colour.darker_grey(),\n )\n )\n\n if role == muted_role or role == punished_role:\n return await ctx.send(\n embed=discord.Embed(\n title=f\"Can not remove *{role}* role using this command.\",\n description=\"For more information run ```.help removeroleall```\",\n timestamp=datetime.datetime.utcnow(),\n colour=discord.Colour.red(),\n )\n )\n\n for i in ctx.guild.members:\n if not i.bot:\n await i.remove_roles(role)\n\n await ctx.send(\n embed=discord.Embed(\n title=f\"*{role}* has been removed from **{len(ctx.guild.members)}** members!\",\n timestamp=datetime.datetime.utcnow(),\n colour=discord.Colour.green(),\n )\n )","async def change_role_positions(self, roles: 'typing.Union[typing.Dict[role.Role, int], '\n 'typing.List[typing.Tuple[role.Role, int]]]'):\n if not self.me.guild_permissions.manage_roles:\n raise PermissionsError(\"manage_roles\")\n\n if isinstance(roles, dict):\n roles = roles.items()\n\n to_send = []\n for r, new_position in roles:\n if new_position >= self.me.top_role.position:\n raise HierarchyError(\"Cannot move role above our top role\")\n\n to_send.append((str(r.id), new_position))\n\n to_send = [(str(r.id), new_position) for (r, new_position) in roles]\n await self._bot.http.edit_role_positions(to_send)","def can_manage_roles(context):\n channel = context.channel\n member = context.message.author\n return dict(channel.permissions_for(member))['manage_roles']","async def on_raw_reaction_remove(self, payload: discord.RawReactionActionEvent):\n\n guild = self.bot.get_guild(payload.guild_id)\n user = guild.get_member(payload.user_id)\n # Skipping bot reactions\n if user.bot:\n return\n\n # Search the event by message id\n message_id = payload.message_id\n channel_id = payload.channel_id\n guild_id = payload.guild_id\n event = connector.getEventByMessage(guild_id, message_id, channel_id)\n if event is None:\n return\n\n emoji_id = str(payload.emoji).split(':')[2][:-1]\n role = discord.utils.get(guild.roles, id=event.role_id)\n if (payload.event_type == \"REACTION_REMOVE\") and (event.emoji.split(':')[2][:-1] == emoji_id) and (role in user.roles):\n # Remove role from user\n await user.remove_roles(role)","async def rollme_allow(self, ctx, *roles: typing.Union[discord.Role]):\n if not roles:\n raise Tools.NoRolesGiven\n\n await ctx.bot.add_self_roles(ctx, *roles)\n\n await ctx.send(embed = await Macro.send(\n f\"Allowed the self roles {', '.join([str(role) for role in roles])}\"\n ))","async def perm_check(ctx,roles_list: List[int]):\n for n,role in enumerate(ctx.author.roles):\n # If authorized\n if role.id in roles_list:\n return \"pass\"\n # Not authorized\n if n == len(ctx.author.roles) - 1:\n return await ctx.send(embed=Embed(title=\"> **⚠ Attention !**\",description=\"Vous n'avez pas la permission d'éxecutez cette commande !\",color=Colour.from_rgb(255,255,0)).set_author(name=ctx.author.name,icon_url=ctx.author.avatar_url))","async def role_claimer(event):\n \n # Double check.\n if not event.user_permissions.can_administrator:\n abort('Admin only')\n \n return InteractionResponse('Claim role by clicking on it', components = ROLE_CLAIMER_COMPONENTS)","async def ironman(self, ctx):\n if has_post_permission(ctx.guild.id, ctx.channel.id):\n out = (':tools: __**IRONMAN**__ :tools:\\n' \\\n 'If you want to become an ironman, please react to this post with a :thumbsup:. '\n 'This will **RESET** your account and give you the ironman role. '\n 'You will be unable to trade with other players or gamble. '\n 'In return, you will be able to proudly display your status as an ironman, '\n 'by the way.')\n msg = await ctx.send(out)\n\n if await self.confirm(ctx, msg, out):\n ctx.user_object.reset_account()\n ctx.user_object.is_ironman = True\n ctx.user_object.save()\n # ironman_role = discord.utils.get(ctx.guild.roles, name=\"Ironman\")\n # await ctx.author.add_roles(ironman_role, reason='Wanted to become an ironmeme.')\n name = get_display_name(ctx.author)\n await msg.edit(content=f':tools: __**IRONMAN**__ :tools:\\n'\n f'Congratulations, {name}, you are now '\n 'an ironman!')","async def register_reaction_role(self, ctx, target_role_id: int, emoji_name: str):\n\n await self.bot.wait_until_ready()\n\n if emoji_name[0] == \"<\":\n emoji_name = emoji_name[1:-1]\n\n if target_role_id in self.bot.config.staff_role_ids:\n return await ctx.send(\"Error: Dangerous role found!\")\n\n target_role = ctx.guild.get_role(target_role_id)\n\n if target_role is None:\n return await ctx.send(\"Error: Role not found!\")\n\n target_role_name = target_role.name\n\n for key in self.reaction_config[\"reaction_roles_emoji_map\"]:\n value = self.reaction_config[\"reaction_roles_emoji_map\"][key]\n if type(value) is str and target_role_name == value:\n return await ctx.send(f\"Error: {target_role_name}: already registered.\")\n\n self.reaction_config[\"reaction_roles_emoji_map\"][emoji_name] = target_role_name\n self.save_reaction_config(self.reaction_config)\n await self.reload_reaction_message(False)\n\n await ctx.send(f\"{target_role_name}: registered.\")","async def on_raw_reaction_add(self, payload):\n\n\t\tguild = self.bot.get_guild(payload.guild_id)\n\t\tif guild is not None:\n\t\t\tchannel = guild.get_channel(payload.channel_id)\n\t\t\tmessage = await channel.fetch_message(payload.message_id)\n\t\t\tuser = guild.get_member(payload.user_id)\n\n\t\t\t# Update cached leaderboards\n\t\t\tif not payload.member.bot:\n\t\t\t\tif payload.message_id in self.cachedMessages:\n\t\t\t\t\tif payload.emoji.name == \"➡️\":\n\t\t\t\t\t\tawait self.update_leaderboard_message(message, 1)\n\t\t\t\t\t\tawait message.remove_reaction(\"➡️\", user)\n\t\t\t\t\telif payload.emoji.name == \"⬅️\":\n\t\t\t\t\t\tawait self.update_leaderboard_message(message, -1)\n\t\t\t\t\t\tawait message.remove_reaction(\"⬅️\", user)\n\n\t\t\t# Update reaction leaderboards\n\t\t\tif not payload.member.bot:\n\t\t\t\treactionLeaderboard = self.leaderboards[str(payload.guild_id)][\"reactionLeaderboard\"]\n\n\t\t\t\tif payload.emoji.id is not None:\n\t\t\t\t\tfor guildEmoji in guild.emojis:\n\t\t\t\t\t\tif payload.emoji.id == guildEmoji.id:\n\t\t\t\t\t\t\tif (\"<:\" + str(payload.emoji.name) + \":\" + str(payload.emoji.id) + \">\") not in reactionLeaderboard:\n\t\t\t\t\t\t\t\treactionLeaderboard[\"<:\" + str(payload.emoji.name) + \":\" + str(payload.emoji.id) + \">\"] = 1\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\treactionLeaderboard[\"<:\" + str(payload.emoji.name) + \":\" + str(payload.emoji.id) + \">\"] += 1\n\n\n\n\t\t\t\t\t\t\tbreak\n\t\t\t\telse:\n\t\t\t\t\tif payload.emoji.name not in reactionLeaderboard:\n\t\t\t\t\t\treactionLeaderboard[str(payload.emoji.name)] = 1\n\t\t\t\t\telse:\n\t\t\t\t\t\treactionLeaderboard[str(payload.emoji.name)] += 1\n\n\t\t\t\tif str(payload.emoji.id) in self.leaderboards[str(payload.guild_id)][\"emojiLeaderboard\"]:\n\t\t\t\t\tself.leaderboards[str(payload.guild_id)][\"emojiLeaderboard\"][str(payload.emoji.id)] += 1","async def massadd(\n self,\n ctx,\n role: discord.Role,\n member: commands.Greedy[discord.Member],\n ):\n role = discord.utils.get(ctx.guild.roles, id=role.id)\n\n muted_role = discord.utils.get(ctx.guild.roles, name=\"Muted\")\n punished_role = discord.utils.get(ctx.guild.roles, name=\"Punished\")\n\n if role > ctx.author.top_role:\n return await ctx.send(\n embed=discord.Embed(\n title=\"You don't have permission to add this role\",\n timestamp=datetime.datetime.utcnow(),\n colour=discord.Colour.darker_grey(),\n )\n )\n\n if role == muted_role or role == punished_role:\n return await ctx.send(\n embed=discord.Embed(\n title=f\"Can not assign *{role}* role using this command.\",\n description=\"For more information run ```.help massadd```\",\n timestamp=datetime.datetime.utcnow(),\n colour=discord.Colour.red(),\n )\n )\n\n for i in member:\n if role in i.roles:\n await ctx.channel.send(\n embed=discord.Embed(\n title=f\"*{i}* already has *{role}* Role!\",\n timestamp=datetime.datetime.utcnow(),\n colour=discord.Colour.greyple(),\n )\n )\n\n await i.add_roles(role)\n\n await ctx.send(\n embed=discord.Embed(\n title=f\"*{role}* has been added to **{len(member)}** members!\",\n timestamp=datetime.datetime.utcnow(),\n colour=discord.Colour.green(),\n )\n )","async def send_react(self, reactions, *args, **kwargs):\n message = await self.send(*args, **kwargs)\n if isinstance(reactions, str): # Handle two-character emojis\n reactions = (reactions,)\n for reaction in reactions:\n await self.add_reaction(message, reaction)\n return message","async def on_raw_reaction_remove(self, payload: discord.RawReactionActionEvent):\n\n guild = self.bot.get_guild(payload.guild_id)\n user = guild.get_member(payload.user_id)\n # Skipping bot reactions\n if user.bot:\n return\n\n # Search the event by message id\n message_id = payload.message_id\n channel_id = payload.channel_id\n guild_id = payload.guild_id\n event_type = connector.getEventTypeByMessage(guild_id, message_id, channel_id)\n if event_type is None:\n return\n\n emoji_id = str(payload.emoji).split(':')[2][:-1]\n role = discord.utils.get(guild.roles, id=event_type.role_id)\n if (payload.event_type == \"REACTION_REMOVE\") and (event_type.emoji.split(':')[2][:-1] == emoji_id) and (role in user.roles):\n # Remove role from user\n await user.remove_roles(role)","async def log_role(self, role, type, channel, guild):\n\n lang = getLang.getLang(guild.id)\n with open(f\"embeds/{lang}/roleLogs.json\", \"r\") as f:\n logData = json.load(f)\n\n if type == \"Create\":\n logEmbed = discord.Embed(color=logData['Create']['Colour'])\n logEmbed.title = logData['Create']['Title']\n description = role.mention\n elif type == \"Delete\":\n logEmbed = discord.Embed(color=logData['Delete']['Colour'])\n logEmbed.title = logData['Delete']['Title']\n description = role.mention\n elif type == \"Update\":\n logEmbed = discord.Embed(color=logData['Update']['Colour'])\n logEmbed.title = logData['Update']['Title']\n description = role.mention\n\n # Send message to log channel\n logEmbed.description = description\n logEmbed.timestamp = datetime.utcnow()\n await channel.send(embed=logEmbed)","def put_node_roles(session, node_roles_data):\n # type: (Session, NodeRolesData) -> None\n if not session.network:\n raise ValueError(\"Network must be set to get node roles\")\n url_tail = \"/{}/{}/{}\".format(\n CoordConstsV2.RSC_NETWORKS, session.network, CoordConstsV2.RSC_NODE_ROLES\n )\n return _put_json(session, url_tail, node_roles_data)","def RolePayload(self):\n\t\tpre_return = ''\n\t\tif self.Roles:\n\t\t\tif len(self.Roles):\n\t\t\t\tfor x in range(0,len(self.Roles)):\n\t\t\t\t\tpre_return += \"<role>\" + str(self.Roles[x]) + \"</role>\"\n\t\t\t\treturn \"<roles type='array'>%s</roles>\" % (pre_return)\n\t\t\telse:\n\t\t\t\treturn \"<roles type='array'>%s</roles>\" % (self.Roles)\n\t\telse:\n\t\t\treturn None","async def _handle_role(self, member: discord.Member) -> tuple:\n list_to_add = await self.config.guild(member.guild).autoroles()\n list_to_remove = await self.config.guild(member.guild).temprole()\n actions = []\n if list_to_add:\n for role in list_to_add:\n to_add = member.guild.get_role(role)\n await member.add_roles(to_add, reason=\"Adding auto role by Verify.\")\n actions.append(\n \"added automatically role{plural}\".format(\n plural=\"s\" if len(list_to_add) > 1 else \"\"\n )\n )\n if list_to_remove:\n to_remove = member.guild.get_role(list_to_remove)\n if to_remove in member.roles:\n await member.remove_roles(to_remove, reason=\"Removing temporary role by Verify.\")\n actions.append(\"removed temporary role\")\n return (\n True,\n humanize_list(actions).capitalize() if actions else \"No action taken.\",\n )","async def add_role(\n client,\n event,\n user: ('user', 'User to add role to'),\n role: ('role', 'The role to give'),\n):\n # Check for permissions\n if not event.user_permissions.can_manage_roles:\n abort('You need `manage roles` permission to invoke this command.')\n \n if not event.guild.cached_permissions_for(client).can_manage_roles:\n abort('I need `manage roles` permission to execute this command.')\n \n if not event.user.has_higher_role_than(role):\n abort('You must have higher role than the role you are trying to give.')\n \n if not client.has_higher_role_than(role):\n abort('I must have higher role than the role you are trying to give.')\n \n # Using `.copy_to` on forms works as well.\n return ADD_ROLE_FORM.copy_with(\n title = f'Add role {role.name} to {user.full_name}',\n custom_id = f'add_role.{user.id}.{role.id}',\n )","async def on_guild_role_create(self, role):\n channel = self.client.get_channel(serverlogs.getChannel(role.guild.id, \"roles\"))\n if channel is not None:\n await self.log_role(role=role, type='Create', channel=channel, guild=role.guild)","async def _toggle_role(self, ctx, selfrole: Selfrole):\n\n if selfrole.role not in ctx.message.author.roles:\n try:\n await ctx.message.author.add_roles(selfrole.role)\n except discord.Forbidden:\n raise exceptions.ForbiddenError(\n exceptions.ForbiddenTask.ADD_ROLE, selfrole.role.name\n )\n\n await ctx.send(f\"{config.YES} {selfrole.join_message}\")\n\n elif selfrole.role in ctx.message.author.roles:\n try:\n await ctx.message.author.remove_roles(selfrole.role)\n except discord.Forbidden:\n raise exceptions.ForbiddenError(\n exceptions.ForbiddenTask.REMOVE_ROLE, selfrole.role.name\n )\n\n await ctx.send(\n f\"{config.YES} The `{selfrole.role.name}` role was removed from you.\"\n )","def espionage(roles, numbers):\n texts = []\n n = len(roles)\n right = random.randint(0, 1)\n for c in range(len(roles)):\n if right:\n if roles[(c + 1) % n]:\n texts.append(\"CODE RED: Espionage Detected\")\n else:\n texts.append(\"ALL CLEAR: Espionage NOT Detected\")\n\n else:\n if roles[(c - 1) % n]:\n texts.append(\"CODE RED: Espionage Detected\")\n else:\n texts.append(\"ALL CLEAR: Espionage NOT Detected\")\n send_text(numbers, texts)","async def reacrole(self, ctx: commands.Context):\n pass","async def create(self, ctx):\n\n # get the model data for the role assigner object\n data = await self.get_objects(\n model=RoleAssigner, filter={\"bot__name\": str(self.bot_name)}\n )\n\n # role assigner object\n data = data[0]\n\n message = await ctx.send(\"_ _\", embed=self.create_message_embed(data))\n\n data.message.uid = message.id\n data.message.cuid = message.channel.id\n\n self.message_id = data.message.uid\n\n await self.update_reactions(message, data)\n\n await self.update_objects(model_instance=data)","def roles(self):\n params = {\n \"f\" : \"json\"\n }\n uURL = self._url + \"/roles\"\n return self._con.get(path=uURL, params=params)","def get_roles(role):","def present_roles(self):\n print(\"User\" + str(self.unique_id) + \": roles=\")\n for group in self._roles:\n print(\"\\tGroup\" + str(group) + \" -> [\"\n + self.get_role_from_type(group, roles_influence) + \", \"\n + self.get_role_from_type(group, roles_neighbors) + \", \"\n + self.get_role_from_type(group, roles_activities) + \", \"\n + self.get_role_from_type(group, roles_attitude) + \"]\")\n print('')","async def setjoinrole(self, ctx, role):\r\n guild = ctx.message.guild\r\n role = discord.utils.get(guild.roles, name=role)\r\n functions.updatesql(server=ctx.guild.id, joinrole=role.id)\r\n await ctx.send(embed=discord.Embed(title='Sucsess!', color=discord.Colour.from_rgb(255, 0, 255)))","def admin_roles(request):\n user = User.objects.get(username=request.user.username)\n permisos = get_permisos_sistema(user)\n return render_to_response('admin/roles/roles.html',{'user':user,\n 'ver_roles':'Ver roles' in permisos,\n 'crear_rol': 'Crear rol' in permisos,\n 'mod_rol': 'Modificar rol' in permisos,\n 'eliminar_rol': 'Eliminar rol' in permisos},context_instance=RequestContext(request))","def update_client_roles(self,request,role):\n return\n #pdb.set_trace() #commend out by JZ on 10/3/14\n if not self.client_roles.has_key(request['protocol'].peer):\n self.client_roles.update({request['protocol'].peer:{role:time.time()}})\n else:\n self.client_roles[request['protocol'].peer].update({role:time.time()})","def getRolesStr(self):\n rolesJson = self.getRoles()\n rolesStr = json.dumps(rolesJson)\n return (200, rolesStr)","def getRolesStr(self):\n rolesJson = self.getRoles()\n rolesStr = json.dumps(rolesJson)\n return (200,rolesStr)","def updateRoles(request):\n\n # update org admins\n #updateRole('gsoc_org_admin')\n\n # update mentors\n #updateRole('gsoc_mentor')\n\n # update students\n # we can assume that students cannot have any other roles, so we do not\n # need to set ETA\n updateRole('gsoc_student')","def frole(wrapper: MessageDispatcher, message: str):\n pl = get_players(wrapper.game_state)\n\n parts = message.lower().split(\",\")\n for part in parts:\n try:\n (name, role) = part.split(\":\", 1)\n except ValueError:\n wrapper.send(messages[\"frole_incorrect\"].format(part))\n return\n umatch = users.complete_match(name.strip(), pl)\n rmatch = match_role(role.strip(), allow_special=False)\n role = None\n if rmatch:\n role = rmatch.get().key\n if not umatch or not rmatch:\n wrapper.send(messages[\"frole_incorrect\"].format(part))\n return\n FORCE_ROLES[role].add(umatch.get())\n\n wrapper.send(messages[\"operation_successful\"])","async def togglerole(self, ctx, role):\n \n user = ctx.message.author\n await ctx.message.delete()\n\n\n if role == \"MK8D\":\n if self.bot.mk8d_role in user.roles:\n await user.remove_roles(self.bot.mk8d_role)\n await user.send(\"Left MK8D role\")\n\n else:\n await user.add_roles(self.bot.mk8d_role)\n await user.send(\"Joined MK8D role\")\n else:\n await user.send(\"{} is not a togglable role\".format(role))","def syncRole(user, roleToAdd, listToAdd):\n print(user, \":\", roleToAdd)\n if roleToAdd == \"Doppelgänger\":\n listToAdd.append(\n Doppelganger(user=user, firstRole=roleToAdd, botRef=bot))\n\n elif roleToAdd == \"Sbire\":\n listToAdd.append(Minion(user=user, firstRole=roleToAdd, botRef=bot))\n\n elif roleToAdd == \"Loup-Garou\":\n listToAdd.append(Werewolf(user=user, firstRole=roleToAdd, botRef=bot))\n\n elif roleToAdd == \"Loup Alpha\":\n listToAdd.append(\n AlphaWerewolf(user=user, firstRole=roleToAdd, botRef=bot))\n\n elif roleToAdd == \"Loup Shamane\":\n listToAdd.append(\n ShamanWerewolf(user=user, firstRole=roleToAdd, botRef=bot))\n\n elif roleToAdd == \"Franc-Maçon\":\n listToAdd.append(Freemason(user=user, firstRole=roleToAdd, botRef=bot))\n\n elif roleToAdd == \"Voyante\":\n listToAdd.append(Seer(user=user, firstRole=roleToAdd, botRef=bot))\n\n elif roleToAdd == \"Chasseur de Fantômes\":\n listToAdd.append(GhostHunter(user=user, firstRole=roleToAdd, botRef=bot))\n\n elif roleToAdd == \"Apprentie voyante\":\n listToAdd.append(\n BeginnerSeer(user=user, firstRole=roleToAdd, botRef=bot))\n\n elif roleToAdd == \"Voleur\":\n listToAdd.append(Thief(user=user, firstRole=roleToAdd, botRef=bot))\n\n elif roleToAdd == \"Noiseuse\":\n listToAdd.append(\n Troublemaker(user=user, firstRole=roleToAdd, botRef=bot))\n\n elif roleToAdd == \"Soûlard\":\n listToAdd.append(Drunkard(user=user, firstRole=roleToAdd, botRef=bot))\n\n elif roleToAdd == \"Insomniaque\":\n listToAdd.append(Insomniac(user=user, firstRole=roleToAdd, botRef=bot))\n\n elif roleToAdd == \"Divinateur\":\n listToAdd.append(Diviner(user=user, firstRole=roleToAdd, botRef=bot))\n\n elif roleToAdd == \"Tanneur\":\n listToAdd.append(Tanner(user=user, firstRole=roleToAdd, botRef=bot))\n\n elif roleToAdd == \"Chasseur\":\n listToAdd.append(Hunter(user=user, firstRole=roleToAdd, botRef=bot))\n\n elif roleToAdd == \"Garde du corps\":\n listToAdd.append(BodyGuard(user=user, firstRole=roleToAdd, botRef=bot))\n\n elif roleToAdd == \"Loup rêveur\":\n listToAdd.append(\n SleepingWerewolf(user=user, firstRole=roleToAdd, botRef=bot))\n else:\n print(\"GROS PROBLEME\", roleToAdd)\n exit()","async def games(ctx):\n games_channel = discord.utils.get(ctx.message.author.guild.text_channels, name=CHANNEL_GAMES)\n member = ctx.message.author\n role = discord.utils.get(member.guild.roles, name=ROLE_GAMES)\n if role in member.roles:\n await member.remove_roles(role)\n await ctx.send(\"Removed you from the games club... feel free to come back anytime!\")\n await games_channel.send(f\"{member.mention} left the party.\")\n else:\n await member.add_roles(role)\n await ctx.send(f\"You are now in the channel. Come and have fun in {games_channel.mention}! :tada:\")\n await games_channel.send(f\"Please welcome {member.mention} to the party!!\")","async def x5lol(ctx):\n #Shortcut to Author of the Message\n atr = ctx.author\n #Shortcut to Author Current Voice Channel\n currentvc = atr.voice.channel.name\n #Shortcut to Voice Channel Members list\n usrs = atr.voice.channel.members\n #Specify what role will use \n role = ctx.guild.get_role(\"\"\"Insert role id\"\"\")\n \n pot = []\n #await ctx.send(atr.name +' '+currentvc)\n for i in usrs:\n if role in i.roles:\n #await ctx.send('O ' + str(i) + ' É ' + str(role))\n nick = (str(i.nick))\n if nick != 'None':\n pot.append(str(i.nick))\n else:\n pot.append(str(i))\n \n \n #NOTE:The math part of team ballance isn't done yet\n # For now, Bot shuffle the names, then send to Text Channel on Discord \n random.shuffle(pot)\n await ctx.send(pot)","def manage_removeRoles(self, role_ids, RESPONSE=None, REQUEST=None):\n role_ids = [_f for _f in role_ids if _f]\n\n if not role_ids:\n message = 'no+roles+selected'\n\n else:\n\n for role_id in role_ids:\n self.removeRole(role_id)\n\n message = 'Role+assignments+removed'\n\n if RESPONSE is not None:\n RESPONSE.redirect('%s/manage_roles?manage_tabs_message=%s' %\n (self.absolute_url(), message))","async def toggle_group(role_title, ctx):\n if ctx.guild is None:\n return 'whisper'\n server_roles = ctx.guild.roles\n #print(\"Server roles\", server_roles)\n user_roles = ctx.author.roles\n #print(\"Author roles\", user_roles)\n\n role_id = \"\"\n\n #Finding the role on the server. If it doesn't exist, we'll let the user know.\n found_role = False\n role_id_index = ''\n for i in server_roles:\n #print(i.name.lower())\n if i.name.lower() == role_title.lower(): #.lower is for consistency\n role_id = i\n found_role = True\n try:\n role_id_index = user_roles.index(i)\n except:\n pass\n\n if not found_role:\n return \"role not found\"\n else:\n if role_id in user_roles:\n # User has this role, need to remove it.\n user_roles.pop(role_id_index)\n await ctx.author.edit(roles=user_roles, reason=\"Automated role removal requested by user\")\n return \"removed\"\n else:\n # User does not have this role\n user_roles.append(role_id)\n await ctx.author.edit(roles=user_roles, reason=\"Automated role add requested by user\")\n return \"added\"","async def show_all_self_assignable_roles(self, interaction: Interaction, button: discord.ui.Button) -> None:\n view = RoleButtonView(interaction.user, self.assignable_roles)\n await interaction.response.send_message(\n view=view,\n ephemeral=True\n )","def add_role():\n check_admin()\n add_role = True\n\n form = RoleForm()\n if form.validate_on_submit():\n role = Role(title=form.title.data)\n\n try:\n db.session.add(role)\n db.session.commit()\n flash('New role successfully created')\n except:\n flash('Error: Role title already exist')\n\n return redirect(url_for('admin.get_roles'))\n\n return render_template('admin/roles/role.html', form=form, add_role=add_role, title='Add Role')","async def roledelete(ctx):\r\n await ctx.message.delete()\r\n roles = ctx.guild.roles\r\n roles.pop(0)\r\n for role in roles:\r\n if ctx.guild.roles[-1] > role:\r\n try:\r\n await role.delete()\r\n except:\r\n print(f\"{Fore.RED}[-]ROLE => {Fore.RESET}Failed to delete: {role}\")","def list_roles(var, wrapper, message):\n\n lpl = len(var.ALL_PLAYERS)\n specific = 0\n\n pieces = re.split(\" +\", message.strip())\n gamemode = var.CURRENT_GAMEMODE\n if gamemode.name == \"villagergame\":\n gamemode = var.GAME_MODES[\"default\"][0]()\n\n if (not pieces[0] or pieces[0].isdigit()) and not hasattr(gamemode, \"ROLE_GUIDE\"):\n wrapper.reply(\"There {0} \\u0002{1}\\u0002 playing. {2}roles is disabled for the {3} game mode.\".format(\"is\" if lpl == 1 else \"are\", lpl, botconfig.CMD_CHAR, gamemode.name), prefix_nick=True)\n return\n\n msg = []\n\n if not pieces[0] and lpl:\n msg.append(\"There {0} \\u0002{1}\\u0002 playing.\".format(\"is\" if lpl == 1 else \"are\", lpl))\n if var.PHASE in var.GAME_PHASES:\n msg.append(\"Using the {0} game mode.\".format(gamemode.name))\n pieces[0] = str(lpl)\n\n if pieces[0] and not pieces[0].isdigit():\n valid = var.GAME_MODES.keys() - var.DISABLED_GAMEMODES - {\"roles\", \"villagergame\"}\n mode = pieces.pop(0)\n if mode not in valid:\n matches = complete_match(mode, valid)\n if not matches:\n wrapper.reply(messages[\"invalid_mode\"].format(mode), prefix_nick=True)\n return\n if len(matches) > 1:\n wrapper.reply(messages[\"ambiguous_mode\"].format(mode, matches), prefix_nick=True)\n return\n\n mode = matches[0]\n\n gamemode = var.GAME_MODES[mode][0]()\n\n try:\n gamemode.ROLE_GUIDE\n except AttributeError:\n wrapper.reply(\"{0}roles is disabled for the {1} game mode.\".format(botconfig.CMD_CHAR, gamemode.name), prefix_nick=True)\n return\n\n strip = lambda x: re.sub(r\"\\(.*\\)\", \"\", x)\n rolecnt = Counter()\n roles = list((x, map(strip, y)) for x, y in gamemode.ROLE_GUIDE.items())\n roles.sort(key=lambda x: x[0])\n\n if pieces and pieces[0].isdigit():\n specific = int(pieces[0])\n new = []\n for role in itertools.chain.from_iterable([y for x, y in roles if x <= specific]):\n if role.startswith(\"-\"):\n rolecnt[role[1:]] -= 1\n new.remove(role[1:])\n else:\n rolecnt[role] += 1\n append = \"({0})\".format(rolecnt[role]) if rolecnt[role] > 1 else \"\"\n new.append(role + append)\n\n msg.append(\"[{0}]\".format(specific))\n msg.append(\", \".join(new))\n\n else:\n final = []\n\n for num, role_num in roles:\n snum = \"[{0}]\".format(num)\n if num <= lpl:\n snum = \"\\u0002{0}\\u0002\".format(snum)\n final.append(snum)\n new = []\n for role in role_num:\n if role.startswith(\"-\"):\n rolecnt[role[1:]] -= 1\n new.append(role)\n else:\n rolecnt[role] += 1\n append = \"({0})\".format(rolecnt[role]) if rolecnt[role] > 1 else \"\"\n new.append(role + append)\n\n final.append(\", \".join(new))\n\n msg.append(\" \".join(final))\n\n if not msg:\n msg.append(\"No roles are defined for {0}p games.\".format(specific or lpl))\n\n wrapper.send(*msg)","def updateMentors(request):\n\n return updateRole('gsoc_mentor')"],"string":"[\n \"async def setroles(self, ctx):\\n if not has_permissions(ctx, MOD_ROLE):\\n await ctx.send(\\\"You do not have sufficient permissions to perform this command\\\", hidden=True)\\n return False\\n\\n def check(m):\\n return m.author == ctx.author\\n\\n roles_dict = {}\\n while True:\\n info_embed = Embed(title=\\\"/setroles - Enter information\\\", colour=Colour.dark_purple())\\n info_embed.description = \\\"Please enter a message tagging the role and all the members who you would like \\\" \\\\\\n \\\"to assign it to.\\\"\\n info_embed.set_footer(text='\\\"done/finished/yes/y\\\" to continue\\\\n\\\"no/cancel/n/stop\\\" to cancel')\\n\\n for role in roles_dict:\\n users_string = f\\\"{role.mention}\\\\n\\\"\\n for user in roles_dict[role]:\\n users_string += f\\\"{user.mention}\\\\n\\\"\\n info_embed.add_field(name=f\\\"{role.name} ({len(roles_dict[role])})\\\", value=users_string)\\n\\n info_message = await ctx.send(embed=info_embed)\\n\\n response = await self.bot.wait_for(\\\"message\\\", check=check)\\n if response.content.lower() in [\\\"done\\\", \\\"finished\\\", \\\"yes\\\", \\\"y\\\"]:\\n if len(roles_dict.keys()) > 0:\\n await response.delete()\\n await info_message.delete()\\n total_roles_count = 0\\n embed = Embed(title=\\\"Roles Summary\\\", description=\\\"Please review the roles you are about to set\\\\n\\\\n\\\"\\n \\\"*this message has a timeout of 5 minutes*\\\",\\n colour=Colour.dark_purple())\\n embed.set_footer(text=f\\\"✅ to set roles\\\\n❌ to cancel\\\")\\n for role in roles_dict:\\n users_string = f\\\"{role.mention}\\\\n\\\"\\n for user in roles_dict[role]:\\n users_string += f\\\"{user.mention}\\\\n\\\"\\n total_roles_count += len(roles_dict[role])\\n embed.add_field(name=f\\\"{role.name} ({len(roles_dict[role])})\\\", value=users_string)\\n embed.description += f\\\"\\\\n*{total_roles_count} members in total*\\\"\\n message = await ctx.send(embed=embed)\\n await message.add_reaction(\\\"✅\\\")\\n await message.add_reaction(\\\"❌\\\")\\n\\n def check_reaction(r, u):\\n return r.message.id == message.id and u == ctx.author and str(r.emoji) in [\\\"✅\\\", \\\"❌\\\"]\\n\\n set_roles = False\\n while True:\\n try:\\n reaction, user = await self.bot.wait_for(\\\"reaction_add\\\", timeout=300, check=check_reaction)\\n if str(reaction.emoji) == \\\"✅\\\":\\n await message.clear_reactions()\\n embed.set_footer(text=Embed.Empty)\\n embed.description = Embed.Empty\\n await message.edit(embed=embed)\\n set_roles = True\\n break\\n elif str(reaction.emoji) == \\\"❌\\\":\\n raise TimeoutError\\n else:\\n await message.remove_reaction(reaction, user)\\n except TimeoutError:\\n await message.edit(content=\\\"Message Expired\\\", embed=None)\\n await message.clear_reactions()\\n break\\n if set_roles:\\n roles_embed = Embed(title=\\\"Setting Roles\\\", colour=Colour.green())\\n roles_assigned = 0\\n roles_msg = await ctx.send(embed=roles_embed)\\n for role in roles_dict:\\n users_string = f\\\"{role.mention}\\\\n\\\"\\n for member in roles_dict[role]:\\n users_string += f\\\"{member.mention}\\\\n\\\"\\n await member.add_roles(role, reason=f\\\"role added by {ctx.author.name} with setroles\\\"\\n f\\\" command\\\")\\n roles_assigned += 1\\n if roles_assigned % 5 == 0:\\n roles_embed.description = f\\\"Progress: {roles_assigned}/{total_roles_count}\\\"\\n await roles_msg.edit(embed=roles_embed)\\n roles_embed.add_field(name=f\\\"{role.name} ({len(roles_dict[role])})\\\", value=users_string)\\n await roles_msg.edit(embed=roles_embed)\\n roles_embed.title = \\\"Roles Set\\\"\\n roles_embed.description = f\\\"Progress: Done\\\"\\n await roles_msg.edit(embed=roles_embed)\\n await message.delete()\\n return\\n else:\\n await error_embed(ctx, \\\"You didn't input anything, cancelled setroles command\\\")\\n return\\n elif response.content.lower() in [\\\"no\\\", \\\"cancel\\\", \\\"n\\\", \\\"stop\\\"]:\\n await info_message.delete()\\n await response.delete()\\n await ctx.send(embed=Embed(title=\\\"Cancelled\\\", description=\\\"You cancelled the setroles command\\\",\\n colour=Colour.dark_purple()))\\n return\\n else:\\n members = response.mentions\\n if len(members) > 0:\\n if len(response.role_mentions) == 1:\\n role = response.role_mentions[0]\\n server = ctx.guild\\n bot_member = server.get_member(self.bot.user.id)\\n\\n if bot_member.top_role.position <= role.position:\\n await error_embed(ctx, \\\"This role is too high to be set by the bot. Please enter a \\\"\\n \\\"different role.\\\")\\n elif ctx.author.top_role.position <= role.position:\\n await error_embed(ctx, \\\"You cannot give others this role\\\")\\n else:\\n roles_dict[role] = members\\n else:\\n await error_embed(ctx, \\\"You can only mention one role at a time\\\")\\n else:\\n await error_embed(ctx, \\\"You did not mention any members\\\")\\n await info_message.delete()\",\n \"async def addRolesOnEmbed(msg):\\n for field in msg.embeds[0].fields:\\n await msg.add_reaction(field.name[0])\",\n \"async def handle_role_reaction_press(interaction: disnake.MessageInteraction):\\n if interaction.message not in await ReactionRoleMessage.get_all():\\n return\\n\\n role_id = int(interaction.component.custom_id)\\n member: disnake.Member = interaction.author\\n user = await User.get(member.id)\\n role = member.get_role(role_id)\\n if role:\\n await member.remove_roles(role, reason=\\\"Reaction Role Message\\\")\\n await send_message(user=user, key=\\\"role_removed\\\", inter=interaction, ephemeral=True)\\n else:\\n role = interaction.guild.get_role(role_id)\\n if role:\\n try:\\n await member.add_roles(role, reason=\\\"Reaction Role Message\\\")\\n await send_message(user=user, key=\\\"role_added\\\", inter=interaction, ephemeral=True)\\n except disnake.errors.Forbidden as e:\\n await send_message(user=user, key=\\\"no_permissions\\\", inter=interaction, ephemeral=True)\\n else:\\n await send_message(user=user, key=\\\"role_not_found\\\", inter=interaction, ephemeral=True)\",\n \"async def update_reactions(self, message, data):\\n\\n emojis = []\\n\\n for role in data.roles.all():\\n if role.emoji.startswith(\\\":\\\") and role.emoji.endswith(\\\":\\\"):\\n em = discord.utils.get(message.guild.emojis, name=role.emoji[1:-1])\\n emojis.append(em)\\n else:\\n emojis.append(role.emoji)\\n\\n for emoji in emojis:\\n await message.add_reaction(emoji)\\n\\n for reaction in message.reactions:\\n if reaction.emoji not in emojis:\\n await message.clear_reaction(reaction.emoji)\",\n \"async def create(self, ctx: commands.Context, title: str, *roles: discord.Role):\\n brake = {}\\n\\n def check(_r, _u):\\n return _u == ctx.author and _r.message == me and str(_r.emoji) not in brake\\n\\n me = await ctx.send(f'React with the reaction that will correspond to the role `{roles[0]}`')\\n\\n # Yes I know this is an ugly solution to avoid an unnecessary api request but it is ultimately the only solution\\n # I could think of.\\n try:\\n r, u = await self.bot.wait_for('reaction_add', timeout=len(roles) * 20, check=check)\\n brake[str(r.emoji)] = roles[0].id\\n except asyncio.TimeoutError:\\n await ctx.send(\\\"Timed out, please run the command again and this time be a little quicker to react.\\\")\\n return\\n\\n try:\\n for role in roles[1:]:\\n await me.edit(content=f'React with the reaction that will correspond to the role `{role}`')\\n r, u = await self.bot.wait_for('reaction_add', timeout=len(roles) * 20, check=check)\\n brake[str(r.emoji)] = role.id\\n except asyncio.TimeoutError:\\n await ctx.send('Timed out, please run the command again and this time be a little quicker to react.')\\n return\\n\\n await ctx.send(\\n 'What channel do you wish to send this role menu in? Enter its id, name or mention it: #<channel>')\\n\\n for count in range(4):\\n try:\\n m = await self.bot.wait_for('message', check=lambda _m: _m.author == ctx.author and _m.channel == ctx.channel, timeout=30)\\n chan = await self.tcc.convert(ctx, m.content)\\n break\\n except commands.BadArgument:\\n if count == 3:\\n await ctx.send(\\\"Too many tries to enter channel, make sure I can actually see the channel you're \\\"\\n \\\"referring to and use the entire command again\\\")\\n return\\n await ctx.send(\\\"Please enter the correct channel, if in doubt, try mentioning it, the `#channel` thing\\\")\\n except asyncio.TimeoutError:\\n await ctx.send(\\\"Timed out\\\")\\n return\\n\\n e = discord.Embed(title=f\\\"Role menu: {title}\\\",\\n description=\\\"\\\\n\\\\n\\\".join(f\\\"{k} - {ctx.guild.get_role(v)}\\\"for k, v in brake.items()),\\n colour=discord.Colour.blue())\\n\\n zero = await chan.send(embed=e)\\n\\n for k in brake:\\n await zero.add_reaction(k)\\n\\n self._cache[ctx.guild.id][zero.id] = brake\\n\\n query = \\\"\\\"\\\"\\n INSERT INTO selfrole_lookup (guildid, channelid, messageid) \\n VALUES ($1, $2, $3)\\n \\\"\\\"\\\"\\n await self.bot.pool.execute(query, ctx.guild.id, chan.id, zero.id)\\n\\n query = \\\"\\\"\\\"\\n INSERT INTO selfrole (messageid, emoji, roleid)\\n VALUES ($1, $2, $3)\\n \\\"\\\"\\\"\\n for k, v in brake.items():\\n await self.bot.pool.execute(query, zero.id, k, v)\",\n \"async def rolemenu(self, message, args):\\n if \\\"-h\\\" in args or \\\"--help\\\" in args or \\\"--ahelp\\\" in args:\\n await self._print_rolemenu_help(message.channel)\\n return\\n update_reactions = not is_key_in_args(args, \\\"--no-update\\\")\\n allow_role_change = not is_key_in_args(args, \\\"--no-change\\\")\\n remove_role_on_reaction_removal = not is_key_in_args(args, \\\"--no-removal\\\")\\n max_number_of_reactions_per_user = get_number_in_args(args, \\\"--max-reactions\\\", None)\\n max_users_with_role = get_number_in_args(args, \\\"--max-users\\\", None)\\n no_required_role = is_key_in_args(args, \\\"--all\\\")\\n role_ids = message.raw_role_mentions\\n if len(role_ids) * 2 != len(args):\\n await message.channel.send(\\\"Error with `rolemenu`: all roles must match an emoji\\\")\\n return await self._print_rolemenu_help(message.channel)\\n menu = {}\\n for i, role_id in enumerate(message.raw_role_mentions):\\n emoji = args.pop(0)\\n if emoji.strip(\\\"<>@&\\\") == str(role_id): # emoji and roles are just exchanged: no problem\\n emoji = args.pop(0)\\n elif args.pop(0).strip(\\\"<>@&\\\") != str(role_id): # error: two adjacent args must be role_id and emoji\\n await message.channel.send(\\\"Error with `rolemenu`: a role must match an emoji\\\")\\n return await self._print_rolemenu_help(message.channel)\\n menu.update({emoji: [message.guild.get_role(role_id)]})\\n manager = RoleByReactionManager.get(self.guild)\\n options = RoleMenuOptions(required_roles=None if no_required_role else [RoleCollection.VISITOR.value],\\n ignored_roles=[RoleCollection.MASTER.value],\\n update_reactions=update_reactions,\\n allow_role_change=allow_role_change,\\n max_number_of_reactions_per_user=max_number_of_reactions_per_user,\\n max_users_with_role=max_users_with_role,\\n remove_role_on_reaction_removal=remove_role_on_reaction_removal\\n )\\n await manager.add(message, menu, options)\\n logger.debug(\\\"Rolemenu created. Now you can edit your post to make it prettier.\\\")\",\n \"async def on_raw_reaction_add(self, payload: discord.RawReactionActionEvent):\\n # Get the message id of the message that the user reacted to.\\n message_id = payload.message_id\\n\\n # Get the message id of the message we want the user to react to.\\n actual_message_id = constants.MessageIDs.RULES_MSGID\\n\\n # Compare that id's match, and if true continue to give the role.\\n if message_id == actual_message_id:\\n guild_id = payload.guild_id\\n guild = self.bot.get_guild(guild_id)\\n role = get(payload.member.guild.roles, name='Not Verified')\\n\\n if role is not None:\\n member = get(guild.members, id=payload.user_id)\\n if member is not None:\\n await payload.member.add_roles(role)\\n print(f\\\"Added role to {member}\\\")\\n else:\\n print(\\\"User not found . . .\\\")\\n else:\\n print(\\\"Role not found . . .\\\")\",\n \"async def process_add_reaction_role(\\n user_id, description, ctx=None, inter=None, allowed_mentions=None\\n):\\n user = await User.get(user_id)\\n response_deferred = await defer_inter(inter, ephemeral=True)\\n view = disnake.ui.View(timeout=None)\\n view.add_item(RoleDropdown(description))\\n await send_message(\\n key=\\\"choose_roles\\\",\\n user=user,\\n view=view,\\n ephemeral=True,\\n response_deferred=response_deferred,\\n inter=inter,\\n )\",\n \"async def on_raw_reaction_add(self, payload):\\n\\n # exclude all reactions which are not the original message\\n if str(payload.message_id) != self.message_id:\\n return\\n\\n # exclude the bot\\n if payload.user_id == self.bot.user.id:\\n return\\n\\n else:\\n # get the model data for the role assigner object\\n data = await self.get_objects(\\n model=RoleAssigner, filter={\\\"bot__name\\\": str(self.bot_name)}\\n )\\n\\n # role assigner object\\n data = data[0]\\n\\n guild = self.get_guild(guild_id=payload.guild_id)\\n\\n user = self.get_user(guild=guild, user_id=payload.user_id)\\n\\n for db_role in data.roles.all():\\n\\n if db_role.emoji.startswith(\\\":\\\") and db_role.emoji.endswith(\\\":\\\"):\\n\\n ce = db_role.emoji[1:-1]\\n\\n else:\\n ce = db_role.emoji\\n\\n if str(payload.emoji.name) == str(ce):\\n\\n role = self.get_role(guild, int(db_role.uid))\\n\\n if user not in role.members:\\n\\n await user.add_roles(role)\\n\\n print(\\\"Added \\\" + str(user) + \\\" to role: \\\" + str(role) + \\\"!\\\")\\n\\n else:\\n print(\\n \\\"User \\\" + str(user) + \\\" already in role: \\\" + str(role) + \\\"!\\\"\\n )\\n\\n pass\",\n \"async def edit(self, ctx: commands.Context, message_id: int):\\n\\n # Standard wait_for check function for message inputs, makes sure the command user's messages in command channel are considered\\n def message_check(m: discord.Message):\\n return m.author == ctx.author and m.channel == ctx.channel\\n\\n # Standard reaction check that ensures no duplicate reacrole entry, just name the relevant message 'm' before adding this one to check kwarg in wait_for\\n def reaction_check_nd(_r: discord.Reaction, _u):\\n return _u == ctx.author and _r.message == m and str(_r.emoji) not in self._cache[ctx.guild.id][PM.id]\\n\\n if message_id in self._cache[ctx.guild.id]:\\n\\n # Not actually channel id int but I decided to name it that way anyway\\n chanid = await self.bot.pool.fetchrow(\\\"SELECT channelid FROM selfrole_lookup WHERE messageid = $1\\\", message_id)\\n chan: discord.TextChannel = ctx.guild.get_channel(chanid['channelid'])\\n\\n # Currently need message content for title, might start saving title in db to avoid this api call idk\\n try:\\n PM: discord.Message = await chan.fetch_message(message_id)\\n except discord.NotFound:\\n await ctx.send(\\\"It would seem that the message for the role menu you're trying to edit has been deleted, please try creating a new one\\\")\\n return\\n\\n buttons = [\\\"\\\\U0001f1e6\\\", \\\"\\\\U0001f1e7\\\", \\\"\\\\U0001f1e8\\\", \\\"\\\\U0001f1e9\\\"]\\n\\n e1 = discord.Embed(title=\\\"What aspect of the menu do you wish to change?\\\",\\n description=\\\"\\\\U0001f1e6 - Add a role\\\\n\\\\n\\\"\\n \\\"\\\\U0001f1e7 - Remove existing role\\\\n\\\\n\\\"\\n \\\"\\\\U0001f1e8 - Edit the reaction of a role\\\\n\\\\n\\\"\\n \\\"\\\\U0001f1e9 - Change the title\\\",\\n colour=discord.Colour.blue())\\n # Send the initial menu\\n menu = await ctx.send(embed=e1)\\n\\n for button in buttons:\\n await menu.add_reaction(button)\\n\\n # We need the first reaction where the emoji is one of the buttons\\n def button_check(_r, _u):\\n return _u == ctx.author and _r.message == menu and str(_r.emoji) in buttons\\n # Get the option the user chose\\n try:\\n r, u = await self.bot.wait_for('reaction_add', check=button_check, timeout=20)\\n except asyncio.TimeoutError:\\n await ctx.send(\\\"Timed out\\\")\\n return\\n\\n # If user wanted to add a new role to the menu\\n if str(r.emoji) == buttons[0]:\\n await menu.clear_reactions()\\n await menu.edit(content=\\\"What role do you wish to be added? Enter its mention, id, or name\\\", embed=None)\\n\\n # Get the role object for the new role to be added\\n try:\\n m = await self.bot.wait_for('message', check=message_check, timeout=30)\\n newrole = await self.rc.convert(ctx, m.content)\\n\\n if newrole.id in self._cache[ctx.guild.id][PM.id].values():\\n await ctx.send(\\\"Error: role already exists in the menu, perhaps you meant to edit it?\\\")\\n return\\n except asyncio.TimeoutError:\\n await ctx.send(\\\"Timed out\\\")\\n return\\n except commands.BadArgument:\\n await ctx.send(\\\"Role not found, please try again\\\")\\n return\\n\\n m = await ctx.send(f\\\"React on this message with the reaction that will correspond to the role `{newrole}`\\\")\\n\\n # Get the reaction/emoji that will correspond to the new role and yank everything into db\\n try:\\n r, u = await self.bot.wait_for('reaction_add', check=reaction_check_nd, timeout=30)\\n self._cache[ctx.guild.id][PM.id][str(r.emoji)] = newrole.id\\n\\n query = \\\"\\\"\\\"\\n INSERT INTO selfrole (messageid, emoji, roleid)\\n VALUES ($1, $2, $3)\\n \\\"\\\"\\\"\\n\\n await self.bot.pool.execute(query, PM.id, str(r.emoji), newrole.id)\\n\\n # Standard way of getting the embed description of the role menu\\n newmenudesc = \\\"\\\\n\\\\n\\\".join([f\\\"{k} - {ctx.guild.get_role(v)}\\\" for k, v in self._cache[ctx.guild.id][PM.id].items()])\\n\\n newembed = discord.Embed(title=PM.embeds[0].title,\\n description=newmenudesc,\\n colour=discord.Colour.blue())\\n\\n await PM.edit(embed=newembed)\\n await PM.add_reaction(r.emoji)\\n await ctx.send(\\\"Menu edit completed successfully, you may now delete the messages other than the menu itself\\\")\\n\\n except asyncio.TimeoutError:\\n await ctx.send(\\\"Timed out\\\")\\n\\n elif str(r.emoji) == buttons[1]:\\n # Gotta yank the buttons to make everything squeaky clean\\n await menu.clear_reactions()\\n await menu.edit(content=\\\"Enter the role you wish to remove from the menu, can be mention, id or name\\\",\\n embed=None)\\n\\n try:\\n # Get role from user\\n m = await self.bot.wait_for('message', check=message_check, timeout=20)\\n role = await self.rc.convert(ctx, m.content)\\n\\n # If user trying to edit reaction to role that wasn't even in the menu to begin with\\n if role.id not in self._cache[ctx.guild.id][PM.id].values():\\n raise commands.BadArgument(\\\"Role not in cache\\\")\\n\\n # Get the key to delete using the old fashioned way, and subsequently delete it\\n targetkey = \\\"\\\"\\n for key, value in self._cache[ctx.guild.id][PM.id].items():\\n if value == role.id:\\n targetkey = key\\n break\\n self._cache[ctx.guild.id][PM.id].pop(targetkey)\\n\\n # After everything is done and dusted, make database entry and edit the menu\\n query = \\\"\\\"\\\"\\n DELETE FROM selfrole WHERE messageid = $1 AND roleid = $2\\n \\\"\\\"\\\"\\n await self.bot.pool.execute(query, PM.id, role.id)\\n newmenudesc = \\\"\\\\n\\\\n\\\".join(\\n [f\\\"{k} - {ctx.guild.get_role(v)}\\\" for k, v in self._cache[ctx.guild.id][PM.id].items()])\\n\\n newembed = discord.Embed(title=PM.embeds[0].title,\\n description=newmenudesc,\\n colour=discord.Colour.blue())\\n await PM.edit(embed=newembed)\\n await PM.clear_reaction(targetkey)\\n await ctx.send(\\n \\\"Menu edit completed successfully, you may now delete the messages other than the menu itself\\\")\\n except asyncio.TimeoutError:\\n await ctx.send(\\\"Timed out\\\")\\n return\\n except commands.BadArgument:\\n await ctx.send(\\\"I don't think that role exists in that menu, run the command again\\\")\\n return\\n\\n elif str(r.emoji) == buttons[2]:\\n # Same drill, remove buttons to make it look clean\\n await menu.clear_reactions()\\n await menu.edit(embed=None, content=\\\"Enter the role for which you wish to change the reaction.\\\")\\n\\n try:\\n m = await self.bot.wait_for('message', check=message_check, timeout=20)\\n role = await self.rc.convert(ctx, m.content)\\n\\n if role.id not in self._cache[ctx.guild.id][PM.id].values():\\n raise commands.BadArgument(\\\"Role not in cache\\\")\\n\\n except asyncio.TimeoutError:\\n await ctx.send(\\\"Timed out\\\")\\n return\\n except commands.BadArgument:\\n await ctx.send(\\\"Couldn't find the role you wished to edit in the menu\\\")\\n return\\n\\n # Get the reaction/emoji that will correspond to the new role and yank everything into db\\n m = await ctx.send(f\\\"React on this message with the new reaction that will correspond to the role {role}\\\")\\n try:\\n r, u = await self.bot.wait_for('reaction_add', check=reaction_check_nd, timeout=30)\\n\\n # Can only delete entry if have the key so....\\n TargetKey = \\\"\\\" # Set default value so IDE stops screaming\\n for k, v in self._cache[ctx.guild.id][PM.id].items():\\n if v == role.id:\\n TargetKey = k\\n\\n # Make new entry and delete the old one\\n self._cache[ctx.guild.id][PM.id][str(r.emoji)] = role.id\\n self._cache[ctx.guild.id][PM.id].pop(TargetKey)\\n\\n # After everything is done and dusted, at last update the database entry\\n await self.bot.pool.execute(\\\"UPDATE selfrole SET emoji = $1 WHERE roleid = $2 AND messageid = $3\\\", str(r.emoji), role.id, PM.id)\\n\\n # Hehehehehehe\\n newmenudesc = \\\"\\\\n\\\\n\\\".join(\\n [f\\\"{k} - {ctx.guild.get_role(v)}\\\" for k, v in self._cache[ctx.guild.id][PM.id].items()])\\n\\n newembed = discord.Embed(title=PM.embeds[0].title,\\n description=newmenudesc,\\n colour=discord.Colour.blue())\\n\\n await PM.edit(embed=newembed)\\n await PM.clear_reaction(TargetKey)\\n await PM.add_reaction(str(r.emoji))\\n await ctx.send(\\n \\\"Menu edit completed successfully, you may now delete the messages other than the menu itself\\\")\\n except asyncio.TimeoutError:\\n await ctx.send(\\\"Timed out\\\")\\n return\\n\\n elif str(r.emoji) == buttons[3]:\\n # This one speaks for itself I think.\\n await menu.clear_reactions()\\n await menu.edit(embed=None, content=\\\"Enter the new title you want the menu to have\\\")\\n try:\\n m = await self.bot.wait_for('message', check=message_check, timeout=30)\\n e = discord.Embed(title=f\\\"Role menu: {m.content}\\\",\\n description=PM.embeds[0].description,\\n colour=PM.embeds[0].colour)\\n await PM.edit(embed=e)\\n except asyncio.TimeoutError:\\n await ctx.send(\\\"Timed out\\\")\\n return\\n\\n else:\\n await ctx.send(\\\"Menu not found in this server, double check if the id was entered correctly\\\")\",\n \"async def togglerole(self, ctx, role=\\\"\\\"):\\r\\n\\r\\n user = ctx.message.author\\r\\n joinmsg = \\\"Joined {0} role\\\"\\r\\n leavemsg = \\\"Left {0} role\\\"\\r\\n\\r\\n role = await commands.clean_content().convert(ctx, role)\\r\\n\\r\\n if role == \\\"\\\":\\r\\n embed = discord.Embed(title=\\\"Toggleable Roles:\\\", color=discord.Color.dark_teal())\\r\\n embed.description = \\\"\\\"\\\"\\r\\n - :race_car: Mario Kart 8 Deluxe: MK8D\\r\\n - :squid: Splatoon 2: spla2n\\r\\n - :card_box: Cards Against Humanity: cah\\r\\n - :bomb: Counter-Strike: Global Offensive: csgo\\r\\n - :gun: PUBG: pubg\\r\\n - :red_circle: Red Eclipse: redeclipse\\r\\n - :robot: Titanfall (2): titanfall\\r\\n - :boxing_glove: Super Smash Bros.: smash\\r\\n - :shopping_cart: Fortnite: fortnite\\r\\n \\\"\\\"\\\"\\r\\n await ctx.send(\\\"\\\", embed=embed)\\r\\n\\r\\n elif role.lower() == \\\"mk8d\\\":\\r\\n if self.bot.mk8d_role in user.roles:\\r\\n await user.remove_roles(self.bot.mk8d_role)\\r\\n await ctx.send(leavemsg.format(role.upper()))\\r\\n\\r\\n else:\\r\\n print(\\\"before error\\\")\\r\\n await user.add_roles(self.bot.mk8d_role)\\r\\n print(\\\"after error\\\")\\r\\n await ctx.send(joinmsg.format(role.upper()))\\r\\n\\r\\n elif role.lower() == \\\"spla2n\\\":\\r\\n if self.bot.spla2n_role in user.roles:\\r\\n await user.remove_roles(self.bot.spla2n_role)\\r\\n await ctx.send(leavemsg.format(role.lower()))\\r\\n\\r\\n else:\\r\\n await user.add_roles(self.bot.spla2n_role)\\r\\n await ctx.send(joinmsg.format(role.lower()))\\r\\n\\r\\n elif role.lower() == \\\"cah\\\":\\r\\n if self.bot.cah_role in user.roles:\\r\\n await user.remove_roles(self.bot.cah_role)\\r\\n await ctx.send(leavemsg.format(role.lower()))\\r\\n\\r\\n else:\\r\\n await user.add_roles(self.bot.cah_role)\\r\\n await ctx.send(joinmsg.format(role.lower()))\\r\\n\\r\\n elif role.lower() == \\\"csgo\\\":\\r\\n if self.bot.csgo_role in user.roles:\\r\\n await user.remove_roles(self.bot.csgo_role)\\r\\n await ctx.send(leavemsg.format(role.lower()))\\r\\n\\r\\n else:\\r\\n await user.add_roles(self.bot.csgo_role)\\r\\n await ctx.send(joinmsg.format(role.lower()))\\r\\n\\r\\n elif role.lower() == \\\"pubg\\\":\\r\\n if self.bot.pubg_role in user.roles:\\r\\n await user.remove_roles(self.bot.pubg_role)\\r\\n await ctx.send(leavemsg.format(role.lower()))\\r\\n\\r\\n else:\\r\\n await user.add_roles(self.bot.pubg_role)\\r\\n await ctx.send(joinmsg.format(role.lower()))\\r\\n\\r\\n elif role.lower() == \\\"redeclipse\\\":\\r\\n if self.bot.redeclipse_role in user.roles:\\r\\n await user.remove_roles(self.bot.redeclipse_role)\\r\\n await ctx.send(leavemsg.format(role.lower()))\\r\\n\\r\\n else:\\r\\n await user.add_roles(self.bot.redeclipse_role)\\r\\n await ctx.send(joinmsg.format(role.lower()))\\r\\n\\r\\n elif role.lower() == \\\"titanfall\\\":\\r\\n if self.bot.titanfall_role in user.roles:\\r\\n await user.remove_roles(self.bot.titanfall_role)\\r\\n await ctx.send(leavemsg.format(role.lower()))\\r\\n\\r\\n else:\\r\\n await user.add_roles(self.bot.titanfall_role)\\r\\n await ctx.send(joinmsg.format(role.lower()))\\r\\n\\r\\n elif role.lower() == \\\"smash\\\":\\r\\n if self.bot.smashbros_role in user.roles:\\r\\n await user.remove_roles(self.bot.smashbros_role)\\r\\n await ctx.send(leavemsg.format(role.lower()))\\r\\n\\r\\n else:\\r\\n await user.add_roles(self.bot.smashbros_role)\\r\\n await ctx.send(joinmsg.format(role.lower()))\\r\\n\\r\\n elif role.lower() == \\\"fortnite\\\":\\r\\n if self.bot.fortnite_role in user.roles:\\r\\n await user.remove_roles(self.bot.fortnite_role)\\r\\n await ctx.send(leavemsg.format(role.lower()))\\r\\n\\r\\n else:\\r\\n await user.add_roles(self.bot.fortnite_role)\\r\\n await ctx.send(joinmsg.format(role.lower()))\\r\\n else:\\r\\n msg = \\\"{} is not a togglable role\\\".format(role)\\r\\n await ctx.send(msg)\",\n \"async def on_raw_reaction_add(self, payload: discord.RawReactionActionEvent):\\n role: discord.Role = await self.check_payload(payload)\\n if payload.user_id == self.bot.user.id:\\n return\\n if role:\\n guild = self.bot.get_guild(payload.guild_id)\\n member = guild.get_member(payload.user_id)\\n await member.add_roles(role)\",\n \"async def on_raw_reaction_add(self, payload: discord.RawReactionActionEvent):\\n \\n guild = self.bot.get_guild(payload.guild_id)\\n user = discord.utils.get(guild.members, id=payload.user_id)\\n # Skipping bot reactions\\n if user.bot:\\n return\\n\\n # Search the event by message id\\n message_id = payload.message_id\\n channel_id = payload.channel_id\\n guild_id = payload.guild_id\\n event = connector.getEventByMessage(guild_id, message_id, channel_id)\\n if event is None:\\n return\\n\\n role = discord.utils.get(payload.member.guild.roles, id=event.role_id)\\n\\n if (payload.event_type == \\\"REACTION_ADD\\\") and (event.emoji == str(payload.emoji)):\\n # Adding role to user\\n await payload.member.add_roles(role)\\n elif (payload.event_type == \\\"REACTION_REMOVE\\\") and (event.emoji == str(payload.emoji)) and (role in payload.member.roles):\\n # Remove role from user\\n await payload.member.remove_roles(role)\",\n \"async def subscribe_command(self, ctx: commands.Context, *_) -> None: # We don't actually care about the args\\n view = RoleButtonView(ctx.author, self.assignable_roles)\\n await ctx.send(\\n \\\"Click the buttons below to add or remove your roles!\\\",\\n view=view,\\n delete_after=DELETE_MESSAGE_AFTER\\n )\",\n \"async def on_raw_reaction_add(self, payload: discord.RawReactionActionEvent):\\n\\n # Skipping bot reactions\\n if payload.member.bot:\\n return\\n\\n # Search the event by message id\\n message_id = payload.message_id\\n channel_id = payload.channel_id\\n guild_id = payload.guild_id\\n event_type = connector.getEventTypeByMessage(guild_id, message_id, channel_id)\\n if event_type is None:\\n return\\n\\n role = discord.utils.get(payload.member.guild.roles, id=event_type.role_id)\\n\\n if (payload.event_type == \\\"REACTION_ADD\\\") and (event_type.emoji == str(payload.emoji)):\\n # Adding role to user\\n await payload.member.add_roles(role)\",\n \"async def add_roles(self, ctx: commands.Context, *roles: discord.Role):\\n if not roles:\\n return await ctx.send_help()\\n errored = \\\"\\\"\\n message = \\\"\\\"\\n added = []\\n already_added = []\\n for role in roles:\\n if role >= ctx.author.top_role:\\n errored += (\\n \\\"{role}: You can't set a role equal to or higher than your own.\\\\n\\\".format(\\n role=role.name\\n )\\n )\\n continue\\n if role >= ctx.guild.me.top_role:\\n errored += (\\n \\\"{role}: You can't set a role that's equal to or higher than the \\\"\\n \\\"bot.\\\\n\\\".format(role=role.name)\\n )\\n continue\\n async with self.config.guild(ctx.guild).autoroles() as roles_list:\\n if role.id not in roles_list:\\n roles_list.append(role.id)\\n added.append(role.name)\\n else:\\n already_added.append(role.name)\\n message += errored\\n if added:\\n message += \\\"\\\\nAdded role(s): {roles}\\\".format(roles=humanize_list(added))\\n if already_added:\\n message += \\\"\\\\nRole(s) already added: {roles}\\\".format(\\n roles=humanize_list(already_added)\\n )\\n if message:\\n for line in pagify(message):\\n await ctx.send(line)\",\n \"async def rolemenu_add_role(self,\\n interaction: discord.Interaction,\\n name: str,\\n role: discord.Role,\\n emoji: str = None,\\n description: str = None):\\n doc = await self.db.find_one({\\n \\\"guild_id\\\": interaction.guild.id,\\n \\\"name\\\": name\\n })\\n if not doc:\\n return await interaction.response.send_message(\\n \\\"No role menu with that name exists.\\\", ephemeral=True)\\n for role_doc in doc[\\\"roles\\\"]:\\n if role_doc[\\\"id\\\"] == role.id:\\n return await interaction.followup.send(\\n \\\"Role is already in the menu.\\\", ephemeral=True)\\n if len(doc[\\\"roles\\\"]) >= 25:\\n return await interaction.response.send_message(\\n \\\"This role menu is full.\\\", ephemeral=True)\\n await interaction.response.defer(ephemeral=True)\\n if role.guild != interaction.guild:\\n return await interaction.response.send_message(\\n \\\"This role is not in this server.\\\")\\n if emoji:\\n if emoji.startswith(\\\"<\\\") and emoji.endswith(\\\">\\\"):\\n try:\\n emoji = int(emoji[1:-1].split(\\\":\\\")[2])\\n except ValueError:\\n return await interaction.followup.send(\\\"Invalid emoji.\\\")\\n else:\\n try:\\n message = await interaction.original_message()\\n await message.add_reaction(emoji)\\n except discord.HTTPException:\\n return await interaction.followup.send(\\\"Invalid emoji.\\\")\\n await self.db.update_one({\\\"_id\\\": doc[\\\"_id\\\"]}, {\\n \\\"$push\\\": {\\n \\\"roles\\\": {\\n \\\"description\\\": description,\\n \\\"id\\\": role.id,\\n \\\"emoji\\\": emoji,\\n \\\"date_added\\\": datetime.datetime.now(datetime.datetime.u)\\n }\\n }\\n })\\n doc = await self.db.find_one({\\\"_id\\\": doc[\\\"_id\\\"]})\\n await interaction.followup.send(f\\\"Added {role.mention} to the menu.\\\")\\n menu = Menu(self, interaction.guild, doc)\\n await menu.update()\",\n \"async def roles(self, ctx):\\n\\n pass\",\n \"async def test_roles_command_command(self):\\n self.ctx.guild.roles.append(self.moderator_role)\\n\\n self.cog.roles_info.can_run = unittest.mock.AsyncMock()\\n self.cog.roles_info.can_run.return_value = True\\n\\n self.assertIsNone(await self.cog.roles_info(self.cog, self.ctx))\\n self.ctx.send.assert_called_once()\\n\\n _, kwargs = self.ctx.send.call_args\\n embed = kwargs.pop(\\\"embed\\\")\\n\\n self.assertEqual(embed.title, \\\"Role information (Total 1 role)\\\")\\n self.assertEqual(embed.colour, discord.Colour.og_blurple())\\n self.assertEqual(embed.description, f\\\"\\\\n`{self.moderator_role.id}` - {self.moderator_role.mention}\\\\n\\\")\",\n \"async def send(self, msg, roles):\\n try:\\n if (\\n self.active_role and\\n roles.get_role_by_id(msg.author.id).role == self.active_role and\\n len(msg.content) > 0 or hasattr(self, msg.content.strip().split()[0]+'_cmd')\\n ):\\n await self.error(\\n to=msg.author,\\n msg=\\\"Attention ! Ce message ressemble fortement à une commande, et il ne faudrait pas que les \\\"\\n \\\"autres te surprennent ! [CE MESSAGE N'A PAS ÉTÉ RELAYÉ ; UTILISE `$public monMessage` SI TU \\\"\\n \\\"VEUX VRAIMENT L'ENVOYER À TOUS LES JOUEURS]\\\"\\n )\\n else:\\n await self.redirect(\\n from_=roles.get_name_by_id(msg.author.id),\\n to=roles.everyone.exclude(msg.author.id),\\n msg=msg.content\\n )\\n\\n except Exception as e:\\n logger.error(\\n \\\"The redirection of the message '%s' raised a(n) %s : %s\\\" % (msg.content, e.__class__.__name__, e)\\n )\\n await self.error(to=msg.author, msg=msgs.MESSAGE_HAS_RAISED % msg.content)\",\n \"async def reactions(self, ctx):\\n\\n\\t\\tawait self.message_leaderboard(ctx, \\\"reactions\\\")\",\n \"async def addroleall(self, ctx, role: discord.Role):\\n muted_role = discord.utils.get(ctx.guild.roles, name=\\\"Muted\\\")\\n punished_role = discord.utils.get(ctx.guild.roles, name=\\\"Punished\\\")\\n\\n if role > ctx.author.top_role:\\n return await ctx.send(\\n embed=discord.Embed(\\n title=\\\"You don't have permission to add this role\\\",\\n timestamp=datetime.datetime.utcnow(),\\n colour=discord.Colour.darker_grey(),\\n )\\n )\\n\\n if role == muted_role or role == punished_role:\\n return await ctx.send(\\n embed=discord.Embed(\\n title=f\\\"Can not assign *{role}* role using this command.\\\",\\n description=\\\"For more information run ```.help addroleall```\\\",\\n timestamp=datetime.datetime.utcnow(),\\n colour=discord.Colour.red(),\\n )\\n )\\n\\n for i in ctx.guild.members:\\n if not i.bot:\\n await i.add_roles(role)\\n\\n await ctx.send(\\n embed=discord.Embed(\\n title=f\\\"*{role}* has been added to **{len(ctx.guild.members)}** members!\\\",\\n timestamp=datetime.datetime.utcnow(),\\n colour=discord.Colour.green(),\\n )\\n )\",\n \"async def _on_raw_reaction(\\n self,\\n payload: RawReactionActionEvent,\\n reaction_type: EnumReactionType,\\n ) -> None:\\n if self.__is_self(payload.user_id):\\n print(\\\"reaction added by the bot itself\\\")\\n return\\n\\n guild = self._client.get_guild(payload.guild_id)\\n\\n if reaction_type == EnumReactionType.ADD:\\n await self.__roles.add_role(\\n guild, payload.message_id,\\n payload.emoji, payload.user_id,\\n\\n )\\n elif reaction_type == EnumReactionType.REMOVE:\\n await self.__roles.remove_role(\\n guild, payload.message_id,\\n payload.emoji, payload.user_id,\\n )\\n else:\\n raise InvalidReactionType\",\n \"async def on_raw_reaction_remove(self, payload):\\n\\n # exclude all reactions which are not the original message\\n if str(payload.message_id) != self.message_id:\\n return\\n\\n # exclude the bot\\n if payload.user_id == self.bot.user.id:\\n return\\n\\n else:\\n # get the model data for the role assigner object\\n data = await self.get_objects(\\n model=RoleAssigner, filter={\\\"bot__name\\\": str(self.bot_name)}\\n )\\n\\n # role assigner object\\n data = data[0]\\n\\n guild = self.get_guild(guild_id=payload.guild_id)\\n\\n user = self.get_user(guild=guild, user_id=payload.user_id)\\n\\n for db_role in data.roles.all():\\n\\n if db_role.emoji.startswith(\\\":\\\") and db_role.emoji.endswith(\\\":\\\"):\\n\\n ce = db_role.emoji[1:-1]\\n\\n else:\\n ce = db_role.emoji\\n\\n if str(payload.emoji.name) == str(ce):\\n\\n role = self.get_role(guild, int(db_role.uid))\\n\\n if user in role.members:\\n\\n await user.remove_roles(role)\\n\\n print(\\\"Removed \\\" + str(user) + \\\" from role: \\\" + str(role) + \\\"!\\\")\\n\\n else:\\n print(\\\"User \\\" + str(user) + \\\" not in role: \\\" + str(role) + \\\"!\\\")\\n\\n pass\",\n \"def grant_role(self, role, principal_ids):\",\n \"async def role(ctx, role: discord.Role = None):\\n if role is None:\\n await ctx.send(\\\"List of assignable roles: \\\" + str(allowed_roles))\\n if role.name in allowed_roles:\\n if not role in ctx.message.author.roles:\\n await ctx.message.author.add_roles(role)\\n await ctx.send(\\\"Role added.\\\")\\n else:\\n await ctx.message.author.remove_roles(role)\\n await ctx.send(\\\"Role removed.\\\") \\n else:\\n await ctx.send(\\\"That role doesn't exist, or you don't have permission to modify it.\\\")\",\n \"def roles(self, roles):\\n\\n self._roles = roles\",\n \"def roles(self, roles):\\n\\n self._roles = roles\",\n \"def roles(self, roles):\\n\\n self._roles = roles\",\n \"async def command_assign_role(self, context, role: str):\\n try:\\n await context.author.add_roles(discord.utils.get(\\n context.guild.roles, name=role))\\n await context.message.add_reaction('👍')\\n except Exception as e:\\n await context.message.add_reaction('👎')\\n await context.send('Role could not be assigned')\\n print(f'Errored in command_assign_role.', e)\",\n \"def create_message_embed(self, data):\\n\\n # get the language object\\n lang = data.bot.lang\\n\\n embed = self.generate_embed()\\n\\n if lang == \\\"en\\\":\\n help_text = \\\"Role Selection\\\"\\n\\n elif lang == \\\"de\\\":\\n help_text = \\\"Rollenvergabe\\\"\\n\\n embed.add_field(name=help_text, value=data.message_text, inline=False)\\n\\n for role in data.roles.all():\\n embed.add_field(name=role.name, value=role.emoji, inline=False)\\n\\n return embed\",\n \"async def list_roles(self, ctx: commands.Context):\\n all_roles = await self.config.guild(ctx.guild).autoroles()\\n maybe_not_found = []\\n message = \\\"\\\"\\n for role in all_roles:\\n fetched_role = ctx.guild.get_role(role)\\n if not fetched_role:\\n maybe_not_found.append(role)\\n continue\\n message += \\\"- {name} (`{id}`).\\\\n\\\".format(name=fetched_role.name, id=fetched_role.id)\\n if maybe_not_found:\\n clean_list = list(set(all_roles) - set(maybe_not_found))\\n await self.config.guild(ctx.guild).autoroles.set(clean_list)\\n message += \\\"\\\\nSome roles has been removed since I was unable to find them.\\\"\\n if message:\\n for line in pagify(message):\\n await ctx.send(line)\\n else:\\n await ctx.send(\\\"No role has been added.\\\")\",\n \"async def tod_revenge(self, ctx, *args):\\n roles = [y.name.lower() for y in ctx.author.roles]\\n self.revenge = not self.revenge\\n if self.revenge:\\n message = \\\"Revenges are now __on__.\\\"\\n else:\\n message = \\\"Revenges are now __off__.\\\"\\n await ctx.channel.send(message)\",\n \"async def _set_roles(self, ctx: Context):\\n\\n guild: discord.Guild = ctx.guild\\n\\n host = await guild.create_role(\\n name=\\\"Host\\\", colour=discord.Color(0xFFBF37),\\n hoist=True, mentionable=True\\n )\\n await self.config.guild(guild).host_id.set(host.id)\\n await ctx.author.add_roles(host)\\n\\n player = await guild.create_role(\\n name=\\\"Player\\\", colour=discord.Color(0x37BFFF),\\n hoist=True, mentionable=True\\n )\\n await self.config.guild(guild).player_id.set(player.id)\\n\\n repl = await guild.create_role(\\n name=\\\"Replacement\\\", colour=discord.Color(0x86FF40)\\n )\\n await self.config.guild(guild).repl_id.set(repl.id)\\n\\n spec = await guild.create_role(\\n name=\\\"Spectator\\\", colour=discord.Color(0xD837FF)\\n )\\n await self.config.guild(guild).spec_id.set(spec.id)\\n\\n dead = await guild.create_role(\\n name=\\\"Dead\\\", colour=discord.Color(0xDC5757)\\n )\\n await self.config.guild(guild).dead_id.set(dead.id)\\n\\n txt = _(\\n \\\"Host: {}\\\"\\n \\\"\\\\nPlayer: {}\\\"\\n \\\"\\\\nSpectator: {}\\\"\\n \\\"\\\\nDead: {}\\\"\\n \\\"\\\\nReplacement: {}\\\"\\n ).format(\\n host.mention,\\n player.mention,\\n spec.mention,\\n dead.mention,\\n repl.mention\\n )\\n\\n embed = discord.Embed(\\n color=0x37BFFF, title=\\\"Created Roles!\\\", description=txt\\n )\\n\\n try:\\n await ctx.send(embed=embed)\\n except discord.Forbidden:\\n await ctx.send(\\\"Set up required roles!\\\")\",\n \"async def _role_repl(self, ctx: Context, *, role: discord.Role):\\n\\n msg = await ctx.send(\\n _(\\n \\\"Are you sure you want to set `{}` as replacement role?\\\"\\n ).format(role.name)\\n )\\n start_adding_reactions(msg, ReactionPredicate.YES_OR_NO_EMOJIS)\\n\\n pred = ReactionPredicate.yes_or_no(msg, ctx.author)\\n await ctx.bot.wait_for(\\\"reaction_add\\\", check=pred)\\n\\n if pred.result:\\n await self.config.guild(ctx.guild).repl_id.set(role.id)\\n await ctx.send(\\n _(\\\"Set `{}` as replacement role!\\\").format(role.name)\\n )\\n else:\\n await ctx.send(_(\\\"Aborted replacement role setup.\\\"))\",\n \"async def update(self, ctx):\\n\\n # get the model data for the role assigner object\\n data = await self.get_objects(\\n model=RoleAssigner, filter={\\\"bot__name\\\": str(self.bot_name)}\\n )\\n\\n # role assigner object\\n data = data[0]\\n\\n # fetch the discord message\\n guild_id = await self.get_deep_data(data, \\\"bot__server__uid\\\")\\n\\n guild = self.get_guild(int(guild_id))\\n channel = self.get_channel(guild, int(data.message.cuid))\\n message = await channel.fetch_message(int(data.message.uid))\\n\\n # update the message\\n await message.edit(content=\\\"_ _\\\", embed=self.create_message_embed(data))\\n\\n await self.update_reactions(message, data)\\n\\n await ctx.send(\\\"Updated.\\\")\",\n \"def save_roles(post, container_id):\\n user_id = int(post['user_id'])\\n role_id = int(post['role_id'])\\n do_save_user_role(user_id, role_id, container_id, item_container)\",\n \"async def rolemenu_create(self,\\n interaction: discord.Interaction,\\n name: str,\\n channel: discord.TextChannel,\\n mode: int,\\n color: str,\\n placeholder: str,\\n description: str = None):\\n doc = await self.db.find_one({\\n \\\"guild_id\\\": interaction.guild.id,\\n \\\"name\\\": name\\n })\\n if doc:\\n return await interaction.response.send_message(\\n \\\"Role menu with that name exists.\\\", ephemeral=True)\\n if channel.guild != interaction.guild:\\n return await interaction.response.send_message(\\n \\\"This channel is not in this server.\\\")\\n if not channel.permissions_for(interaction.guild.me).send_messages:\\n return await interaction.response.send_message(\\n \\\"I don't have permission to send messages in this channel.\\\")\\n try:\\n color = discord.Color.from_str(color)\\n except ValueError:\\n return await interaction.response.send_message(\\\"Invalid color.\\\")\\n await self.db.insert_one({\\n \\\"message_id\\\": None,\\n \\\"name\\\": name,\\n \\\"mode\\\": mode,\\n \\\"channel_id\\\": channel.id,\\n \\\"roles\\\": [],\\n \\\"guild_id\\\": interaction.guild.id,\\n \\\"color\\\": color.value,\\n \\\"placeholder\\\": placeholder,\\n \\\"description\\\": description\\n })\\n await interaction.response.send_message(\\n \\\"Role menu created. It is currently empty, however, and \\\"\\n \\\"you'll need to add roles with `/rolemenu role add.`\\\",\\n ephemeral=True)\",\n \"async def addRole(self, ctx, *roles_to_add):\\n already_present_roles = [] # roles that will be deleted from \\\"roles_to_add\\\"\\n\\n available_roles = open(\\\"assets/roles.txt\\\", \\\"r\\\").readlines()\\n available_roles = [role.lower().strip() for role in available_roles]\\n\\n output_msg = \\\"\\\"\\n\\n for role_to_add in roles_to_add:\\n for role in available_roles:\\n if role_to_add.lower() == role:\\n output_msg += f\\\"Failed to add {role_to_add}: role already exists.\\\\n\\\"\\n already_present_roles.append(role_to_add)\\n break\\n\\n for role in already_present_roles:\\n roles_to_add.remove(role)\\n\\n if roles_to_add:\\n with open(\\\"assets/roles.txt\\\", \\\"a\\\") as f:\\n for role in roles_to_add:\\n f.write(f\\\"{role}\\\\n\\\")\\n output_msg += f\\\"{role} has been added successfully.\\\\n\\\"\\n\\n await ctx.send(output_msg)\",\n \"async def command_rolecall(self, context):\\n print(self._fetch_category_roles(context))\\n print(self._fetch_category_roles(context, COSMETIC_CATEGORY_NAME))\",\n \"async def on_raw_reaction_remove(self, payload: discord.RawReactionActionEvent):\\n role: discord.Role = await self.check_payload(payload)\\n\\n if role:\\n guild = self.bot.get_guild(payload.guild_id)\\n member: discord.Member = guild.get_member(payload.user_id)\\n await member.remove_roles(role)\",\n \"def roles_str(person: Member, roles: commands.Greedy[Role]) -> str:\\n message = \\\"role\\\" if len(roles) == 1 else \\\"roles\\\"\\n roleIds = [role.name for role in roles]\\n\\n return f\\\"{message} for {person}: {roleIds}\\\"\",\n \"def create_cm_roles(self, role_list):\\n return self._post(endpoint='{}/cm/service/roles'.format(self.api_version),\\n data=role_list).json()\",\n \"def getRoles(self):\\n\\t\\tpayload = ''\\n\\t\\tif self.Roles:\\n\\t\\t\\tif type(self.Roles) != int:\\n\\t\\t\\t\\tfor x in range(0,len(self.Roles)):\\n\\t\\t\\t\\t\\tpayload += \\\"%s\\\" % (self.Roles[x])\\n\\t\\t\\t\\treturn self.Roles\\n\\t\\t\\telse:\\n\\t\\t\\t\\treturn None\",\n \"async def editrole(self, ctx: context.CustomContext, *, role: Fuzzy[Selfrole]):\\n\\n new_join_message = await ctx.input(\\n f\\\"{config.USER_INTERACTION_REQUIRED} Reply with the new join message for `{role.role.name}`.\\\"\\n f\\\"\\\\n{config.HINT} The current join message is: `{role.join_message}`\\\"\\n )\\n\\n await self.bot.db.execute(\\n \\\"UPDATE selfrole SET join_message = $1 WHERE role_id = $2\\\",\\n new_join_message,\\n role.role.id,\\n )\\n\\n await ctx.send(\\n f\\\"{config.YES} The join message for `{role.role.name}` was updated.\\\"\\n )\",\n \"async def update_cog(self):\\n\\n # get the model data for the role assigner object\\n data = await self.get_objects(\\n model=RoleAssigner, filter={\\\"bot__name\\\": str(self.bot_name)}\\n )\\n\\n # role assigner object\\n data = data[0]\\n\\n # fetch the discord message\\n guild_id = await self.get_deep_data(data, \\\"bot__server__uid\\\")\\n\\n guild = self.get_guild(int(guild_id))\\n channel = self.get_channel(guild, int(data.message.cuid))\\n message = await channel.fetch_message(int(data.message.uid))\\n self.message_id = int(data.message.uid)\\n\\n # update the message\\n await message.edit(content=\\\"_ _\\\", embed=self.create_message_embed(data))\\n\\n await self.update_reactions(message, data)\",\n \"async def apply_role(self, *, reason: str = None):\\n if self.role not in self.member.roles:\\n try:\\n await self.member.add_roles(self.role, reason=reason)\\n except discord.HTTPException:\\n pass\",\n \"async def add_role_admin(request, role_id):\\n required_fields = [\\\"id\\\"]\\n utils.validate_fields(required_fields, request.json)\\n\\n txn_key, txn_user_id = await utils.get_transactor_key(request)\\n proposal_id = str(uuid4())\\n batch_list = Role().admin.propose.batch_list(\\n signer_keypair=txn_key,\\n signer_user_id=txn_user_id,\\n proposal_id=proposal_id,\\n role_id=role_id,\\n next_id=request.json.get(\\\"id\\\"),\\n reason=request.json.get(\\\"reason\\\"),\\n metadata=request.json.get(\\\"metadata\\\"),\\n )\\n await utils.send(\\n request.app.config.VAL_CONN, batch_list, request.app.config.TIMEOUT\\n )\\n return json({\\\"proposal_id\\\": proposal_id})\",\n \"async def roles(self, ctx, *, role: Fuzzy[Selfrole] = None):\\n\\n if role:\\n await self._toggle_role(ctx, role)\\n else:\\n await self._list_all_roles(ctx)\",\n \"async def addRoles(self, ctx: Context, person: Member, roles: Greedy[Role]):\\n roles = remove_dupe_roles(roles)\\n\\n await person.add_roles(*roles)\\n await ctx.send(f\\\"Adding {roles_str(person, roles)}\\\")\",\n \"async def createrole(self, ctx, role: str):\\n if role.lower() == \\\"muted\\\" or role.lower() == \\\"punished\\\":\\n return await ctx.send(\\\"Can not create this roles.\\\")\\n \\\"\\\"\\\"Create a new role\\\"\\\"\\\"\\n role = await ctx.guild.create_role(name=role)\\n return await ctx.send(\\n embed=discord.Embed(\\n title=f\\\"Role *{role}* has been created!\\\",\\n timestamp=datetime.datetime.utcnow(),\\n colour=discord.Colour.green(),\\n )\\n )\",\n \"async def on_reaction_add(reaction, user):\\n if reaction.message.content.startswith('http'):\\n curator = re.sub(r'\\\\d|\\\\W|(TravelFeed)','',str(user),re.IGNORECASE|re.DOTALL)\\n if not user.id in discordcuratorlist and not user.id == botid:\\n \\\"\\\"\\\"Checks if user who added reaction is a curator\\\"\\\"\\\"\\n await loop.create_task(send_discord(\\\"Curator unauthorised: \\\"+curator, logchannel))\\n return\\n else:\\n author, permlink = resolve_authorperm(reaction.message.content)\\n post = Comment(construct_authorperm(author, permlink))\\n if reaction.emoji == '🌍':\\n await bot.add_reaction(reaction.message, \\\"⏳\\\")\\n actionqueue.put(Post_Action(post, \\\"tf100\\\", curator, reaction.message))\\n elif reaction.emoji == '🌐': \\n await bot.add_reaction(reaction.message, \\\"⏳\\\") \\n actionqueue.put(Post_Action(post, \\\"tf50\\\", curator, reaction.message))\\n elif reaction.emoji == '👥':\\n await bot.add_reaction(reaction.message, \\\"⏳\\\")\\n actionqueue.put(Post_Action(post, \\\"coop100\\\", None, reaction.message))\\n elif reaction.emoji == '👋':\\n await bot.add_reaction(reaction.message, \\\"⏳\\\")\\n actionqueue.put(Post_Action(post, \\\"ad10\\\", curator, reaction.message))\\n elif reaction.emoji == '📏':\\n await bot.add_reaction(reaction.message, \\\"⏳\\\")\\n actionqueue.put(Post_Action(post, \\\"short0\\\", None, reaction.message))\\n elif reaction.emoji == '🇬🇧':\\n await bot.add_reaction(reaction.message, \\\"⏳\\\")\\n actionqueue.put(Post_Action(post, \\\"lang0\\\", None, reaction.message))\\n elif reaction.emoji == '📝':\\n await bot.add_reaction(reaction.message, \\\"⏳\\\")\\n actionqueue.put(Post_Action(post, \\\"copyright0\\\", None, reaction.message))\",\n \"def role_command():\",\n \"def myrole(var, wrapper, message):\\n\\n ps = get_participants()\\n if wrapper.source not in ps:\\n return\\n\\n role = get_main_role(wrapper.source)\\n if role in Hidden:\\n role = var.HIDDEN_ROLE\\n\\n evt = Event(\\\"myrole\\\", {\\\"role\\\": role, \\\"messages\\\": []})\\n if not evt.dispatch(var, wrapper.source):\\n return\\n role = evt.data[\\\"role\\\"]\\n\\n wrapper.pm(messages[\\\"show_role\\\"].format(role))\\n\\n for msg in evt.data[\\\"messages\\\"]:\\n wrapper.pm(msg)\",\n \"async def remove_roles(self, ctx: commands.Context, *roles: discord.Role):\\n if not roles:\\n return await ctx.send_help()\\n message = \\\"\\\"\\n removed = []\\n not_found = []\\n async with self.config.guild(ctx.guild).autoroles() as roles_list:\\n for role in roles:\\n if role.id in roles_list:\\n roles_list.remove(role.id)\\n removed.append(role.name)\\n else:\\n not_found.append(role.name)\\n if not_found:\\n message += \\\"\\\\nRole(s) not found in autorole list: {roles}\\\".format(\\n roles=humanize_list(not_found)\\n )\\n if removed:\\n message += \\\"\\\\nRole(s) remove from autorole list: {roles}\\\".format(\\n roles=humanize_list(removed)\\n )\\n if message:\\n for line in pagify(message):\\n await ctx.send(line)\",\n \"def _updateRoles(self, obj, updateRoles={}, deleteRoles=[], cancelRoles=[]):\\n #user_role_delta = {}\\n pending = obj.getPendingCollaborations()\\n\\n collabs = obj.getCollaborators()\\n\\n user_role_delta = obj.generateCollaborationRequests(\\n newUser=False, newRoles=updateRoles, deleteRoles=deleteRoles)\\n \\n for p in user_role_delta.keys():\\n if p in pending.keys():\\n new_changes = pending[p].roles.copy()\\n for role in user_role_delta[p]:\\n delta = user_role_delta[p][role]\\n if role in new_changes:\\n if new_changes[role] != delta:\\n new_changes.pop(role)\\n elif new_changes[role] == delta:\\n #Shouldn't happen\\n pass\\n else:\\n new_changes[role] = delta\\n if not new_changes:\\n obj.manage_delObjects(pending[p].id)\\n else:\\n obj.editCollaborationRequest(pending[p].id, new_changes)\\n else:\\n obj.addCollaborator(p)\\n obj.requestCollaboration(p, user_role_delta[p])\\n\\n for u in cancelRoles:\\n if u in obj.getPendingCollaborations():\\n # Revert the new roles back to the published version\\n obj.reverseCollaborationRequest(pending[u].id)\\n # Delete the collaboration request\\n obj.manage_delObjects(pending[u].id)\\n\\n #Get the collaborators again if they have changed\\n all_roles = {}\\n for rolename in obj.default_roles + getattr(obj, 'optional_roles', {}).keys():\\n for r in getattr(obj,rolename.lower()+'s',[]):\\n all_roles[r]=None\\n for r in getattr(obj, 'pub_'+rolename.lower()+'s', []):\\n all_roles[r]=None\\n \\n collabs = obj.getCollaborators()\\n for c in collabs:\\n if c not in all_roles.keys():\\n obj.removeCollaborator(c)\",\n \"async def removeroleall(self, ctx, role: discord.Role):\\n muted_role = discord.utils.get(ctx.guild.roles, name=\\\"Muted\\\")\\n punished_role = discord.utils.get(ctx.guild.roles, name=\\\"Punished\\\")\\n\\n if role > ctx.author.top_role:\\n return await ctx.send(\\n embed=discord.Embed(\\n title=\\\"You don't have permission to remove this role\\\",\\n timestamp=datetime.datetime.utcnow(),\\n colour=discord.Colour.darker_grey(),\\n )\\n )\\n\\n if role == muted_role or role == punished_role:\\n return await ctx.send(\\n embed=discord.Embed(\\n title=f\\\"Can not remove *{role}* role using this command.\\\",\\n description=\\\"For more information run ```.help removeroleall```\\\",\\n timestamp=datetime.datetime.utcnow(),\\n colour=discord.Colour.red(),\\n )\\n )\\n\\n for i in ctx.guild.members:\\n if not i.bot:\\n await i.remove_roles(role)\\n\\n await ctx.send(\\n embed=discord.Embed(\\n title=f\\\"*{role}* has been removed from **{len(ctx.guild.members)}** members!\\\",\\n timestamp=datetime.datetime.utcnow(),\\n colour=discord.Colour.green(),\\n )\\n )\",\n \"async def change_role_positions(self, roles: 'typing.Union[typing.Dict[role.Role, int], '\\n 'typing.List[typing.Tuple[role.Role, int]]]'):\\n if not self.me.guild_permissions.manage_roles:\\n raise PermissionsError(\\\"manage_roles\\\")\\n\\n if isinstance(roles, dict):\\n roles = roles.items()\\n\\n to_send = []\\n for r, new_position in roles:\\n if new_position >= self.me.top_role.position:\\n raise HierarchyError(\\\"Cannot move role above our top role\\\")\\n\\n to_send.append((str(r.id), new_position))\\n\\n to_send = [(str(r.id), new_position) for (r, new_position) in roles]\\n await self._bot.http.edit_role_positions(to_send)\",\n \"def can_manage_roles(context):\\n channel = context.channel\\n member = context.message.author\\n return dict(channel.permissions_for(member))['manage_roles']\",\n \"async def on_raw_reaction_remove(self, payload: discord.RawReactionActionEvent):\\n\\n guild = self.bot.get_guild(payload.guild_id)\\n user = guild.get_member(payload.user_id)\\n # Skipping bot reactions\\n if user.bot:\\n return\\n\\n # Search the event by message id\\n message_id = payload.message_id\\n channel_id = payload.channel_id\\n guild_id = payload.guild_id\\n event = connector.getEventByMessage(guild_id, message_id, channel_id)\\n if event is None:\\n return\\n\\n emoji_id = str(payload.emoji).split(':')[2][:-1]\\n role = discord.utils.get(guild.roles, id=event.role_id)\\n if (payload.event_type == \\\"REACTION_REMOVE\\\") and (event.emoji.split(':')[2][:-1] == emoji_id) and (role in user.roles):\\n # Remove role from user\\n await user.remove_roles(role)\",\n \"async def rollme_allow(self, ctx, *roles: typing.Union[discord.Role]):\\n if not roles:\\n raise Tools.NoRolesGiven\\n\\n await ctx.bot.add_self_roles(ctx, *roles)\\n\\n await ctx.send(embed = await Macro.send(\\n f\\\"Allowed the self roles {', '.join([str(role) for role in roles])}\\\"\\n ))\",\n \"async def perm_check(ctx,roles_list: List[int]):\\n for n,role in enumerate(ctx.author.roles):\\n # If authorized\\n if role.id in roles_list:\\n return \\\"pass\\\"\\n # Not authorized\\n if n == len(ctx.author.roles) - 1:\\n return await ctx.send(embed=Embed(title=\\\"> **⚠ Attention !**\\\",description=\\\"Vous n'avez pas la permission d'éxecutez cette commande !\\\",color=Colour.from_rgb(255,255,0)).set_author(name=ctx.author.name,icon_url=ctx.author.avatar_url))\",\n \"async def role_claimer(event):\\n \\n # Double check.\\n if not event.user_permissions.can_administrator:\\n abort('Admin only')\\n \\n return InteractionResponse('Claim role by clicking on it', components = ROLE_CLAIMER_COMPONENTS)\",\n \"async def ironman(self, ctx):\\n if has_post_permission(ctx.guild.id, ctx.channel.id):\\n out = (':tools: __**IRONMAN**__ :tools:\\\\n' \\\\\\n 'If you want to become an ironman, please react to this post with a :thumbsup:. '\\n 'This will **RESET** your account and give you the ironman role. '\\n 'You will be unable to trade with other players or gamble. '\\n 'In return, you will be able to proudly display your status as an ironman, '\\n 'by the way.')\\n msg = await ctx.send(out)\\n\\n if await self.confirm(ctx, msg, out):\\n ctx.user_object.reset_account()\\n ctx.user_object.is_ironman = True\\n ctx.user_object.save()\\n # ironman_role = discord.utils.get(ctx.guild.roles, name=\\\"Ironman\\\")\\n # await ctx.author.add_roles(ironman_role, reason='Wanted to become an ironmeme.')\\n name = get_display_name(ctx.author)\\n await msg.edit(content=f':tools: __**IRONMAN**__ :tools:\\\\n'\\n f'Congratulations, {name}, you are now '\\n 'an ironman!')\",\n \"async def register_reaction_role(self, ctx, target_role_id: int, emoji_name: str):\\n\\n await self.bot.wait_until_ready()\\n\\n if emoji_name[0] == \\\"<\\\":\\n emoji_name = emoji_name[1:-1]\\n\\n if target_role_id in self.bot.config.staff_role_ids:\\n return await ctx.send(\\\"Error: Dangerous role found!\\\")\\n\\n target_role = ctx.guild.get_role(target_role_id)\\n\\n if target_role is None:\\n return await ctx.send(\\\"Error: Role not found!\\\")\\n\\n target_role_name = target_role.name\\n\\n for key in self.reaction_config[\\\"reaction_roles_emoji_map\\\"]:\\n value = self.reaction_config[\\\"reaction_roles_emoji_map\\\"][key]\\n if type(value) is str and target_role_name == value:\\n return await ctx.send(f\\\"Error: {target_role_name}: already registered.\\\")\\n\\n self.reaction_config[\\\"reaction_roles_emoji_map\\\"][emoji_name] = target_role_name\\n self.save_reaction_config(self.reaction_config)\\n await self.reload_reaction_message(False)\\n\\n await ctx.send(f\\\"{target_role_name}: registered.\\\")\",\n \"async def on_raw_reaction_add(self, payload):\\n\\n\\t\\tguild = self.bot.get_guild(payload.guild_id)\\n\\t\\tif guild is not None:\\n\\t\\t\\tchannel = guild.get_channel(payload.channel_id)\\n\\t\\t\\tmessage = await channel.fetch_message(payload.message_id)\\n\\t\\t\\tuser = guild.get_member(payload.user_id)\\n\\n\\t\\t\\t# Update cached leaderboards\\n\\t\\t\\tif not payload.member.bot:\\n\\t\\t\\t\\tif payload.message_id in self.cachedMessages:\\n\\t\\t\\t\\t\\tif payload.emoji.name == \\\"➡️\\\":\\n\\t\\t\\t\\t\\t\\tawait self.update_leaderboard_message(message, 1)\\n\\t\\t\\t\\t\\t\\tawait message.remove_reaction(\\\"➡️\\\", user)\\n\\t\\t\\t\\t\\telif payload.emoji.name == \\\"⬅️\\\":\\n\\t\\t\\t\\t\\t\\tawait self.update_leaderboard_message(message, -1)\\n\\t\\t\\t\\t\\t\\tawait message.remove_reaction(\\\"⬅️\\\", user)\\n\\n\\t\\t\\t# Update reaction leaderboards\\n\\t\\t\\tif not payload.member.bot:\\n\\t\\t\\t\\treactionLeaderboard = self.leaderboards[str(payload.guild_id)][\\\"reactionLeaderboard\\\"]\\n\\n\\t\\t\\t\\tif payload.emoji.id is not None:\\n\\t\\t\\t\\t\\tfor guildEmoji in guild.emojis:\\n\\t\\t\\t\\t\\t\\tif payload.emoji.id == guildEmoji.id:\\n\\t\\t\\t\\t\\t\\t\\tif (\\\"<:\\\" + str(payload.emoji.name) + \\\":\\\" + str(payload.emoji.id) + \\\">\\\") not in reactionLeaderboard:\\n\\t\\t\\t\\t\\t\\t\\t\\treactionLeaderboard[\\\"<:\\\" + str(payload.emoji.name) + \\\":\\\" + str(payload.emoji.id) + \\\">\\\"] = 1\\n\\t\\t\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\t\\treactionLeaderboard[\\\"<:\\\" + str(payload.emoji.name) + \\\":\\\" + str(payload.emoji.id) + \\\">\\\"] += 1\\n\\n\\n\\n\\t\\t\\t\\t\\t\\t\\tbreak\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tif payload.emoji.name not in reactionLeaderboard:\\n\\t\\t\\t\\t\\t\\treactionLeaderboard[str(payload.emoji.name)] = 1\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\treactionLeaderboard[str(payload.emoji.name)] += 1\\n\\n\\t\\t\\t\\tif str(payload.emoji.id) in self.leaderboards[str(payload.guild_id)][\\\"emojiLeaderboard\\\"]:\\n\\t\\t\\t\\t\\tself.leaderboards[str(payload.guild_id)][\\\"emojiLeaderboard\\\"][str(payload.emoji.id)] += 1\",\n \"async def massadd(\\n self,\\n ctx,\\n role: discord.Role,\\n member: commands.Greedy[discord.Member],\\n ):\\n role = discord.utils.get(ctx.guild.roles, id=role.id)\\n\\n muted_role = discord.utils.get(ctx.guild.roles, name=\\\"Muted\\\")\\n punished_role = discord.utils.get(ctx.guild.roles, name=\\\"Punished\\\")\\n\\n if role > ctx.author.top_role:\\n return await ctx.send(\\n embed=discord.Embed(\\n title=\\\"You don't have permission to add this role\\\",\\n timestamp=datetime.datetime.utcnow(),\\n colour=discord.Colour.darker_grey(),\\n )\\n )\\n\\n if role == muted_role or role == punished_role:\\n return await ctx.send(\\n embed=discord.Embed(\\n title=f\\\"Can not assign *{role}* role using this command.\\\",\\n description=\\\"For more information run ```.help massadd```\\\",\\n timestamp=datetime.datetime.utcnow(),\\n colour=discord.Colour.red(),\\n )\\n )\\n\\n for i in member:\\n if role in i.roles:\\n await ctx.channel.send(\\n embed=discord.Embed(\\n title=f\\\"*{i}* already has *{role}* Role!\\\",\\n timestamp=datetime.datetime.utcnow(),\\n colour=discord.Colour.greyple(),\\n )\\n )\\n\\n await i.add_roles(role)\\n\\n await ctx.send(\\n embed=discord.Embed(\\n title=f\\\"*{role}* has been added to **{len(member)}** members!\\\",\\n timestamp=datetime.datetime.utcnow(),\\n colour=discord.Colour.green(),\\n )\\n )\",\n \"async def send_react(self, reactions, *args, **kwargs):\\n message = await self.send(*args, **kwargs)\\n if isinstance(reactions, str): # Handle two-character emojis\\n reactions = (reactions,)\\n for reaction in reactions:\\n await self.add_reaction(message, reaction)\\n return message\",\n \"async def on_raw_reaction_remove(self, payload: discord.RawReactionActionEvent):\\n\\n guild = self.bot.get_guild(payload.guild_id)\\n user = guild.get_member(payload.user_id)\\n # Skipping bot reactions\\n if user.bot:\\n return\\n\\n # Search the event by message id\\n message_id = payload.message_id\\n channel_id = payload.channel_id\\n guild_id = payload.guild_id\\n event_type = connector.getEventTypeByMessage(guild_id, message_id, channel_id)\\n if event_type is None:\\n return\\n\\n emoji_id = str(payload.emoji).split(':')[2][:-1]\\n role = discord.utils.get(guild.roles, id=event_type.role_id)\\n if (payload.event_type == \\\"REACTION_REMOVE\\\") and (event_type.emoji.split(':')[2][:-1] == emoji_id) and (role in user.roles):\\n # Remove role from user\\n await user.remove_roles(role)\",\n \"async def log_role(self, role, type, channel, guild):\\n\\n lang = getLang.getLang(guild.id)\\n with open(f\\\"embeds/{lang}/roleLogs.json\\\", \\\"r\\\") as f:\\n logData = json.load(f)\\n\\n if type == \\\"Create\\\":\\n logEmbed = discord.Embed(color=logData['Create']['Colour'])\\n logEmbed.title = logData['Create']['Title']\\n description = role.mention\\n elif type == \\\"Delete\\\":\\n logEmbed = discord.Embed(color=logData['Delete']['Colour'])\\n logEmbed.title = logData['Delete']['Title']\\n description = role.mention\\n elif type == \\\"Update\\\":\\n logEmbed = discord.Embed(color=logData['Update']['Colour'])\\n logEmbed.title = logData['Update']['Title']\\n description = role.mention\\n\\n # Send message to log channel\\n logEmbed.description = description\\n logEmbed.timestamp = datetime.utcnow()\\n await channel.send(embed=logEmbed)\",\n \"def put_node_roles(session, node_roles_data):\\n # type: (Session, NodeRolesData) -> None\\n if not session.network:\\n raise ValueError(\\\"Network must be set to get node roles\\\")\\n url_tail = \\\"/{}/{}/{}\\\".format(\\n CoordConstsV2.RSC_NETWORKS, session.network, CoordConstsV2.RSC_NODE_ROLES\\n )\\n return _put_json(session, url_tail, node_roles_data)\",\n \"def RolePayload(self):\\n\\t\\tpre_return = ''\\n\\t\\tif self.Roles:\\n\\t\\t\\tif len(self.Roles):\\n\\t\\t\\t\\tfor x in range(0,len(self.Roles)):\\n\\t\\t\\t\\t\\tpre_return += \\\"<role>\\\" + str(self.Roles[x]) + \\\"</role>\\\"\\n\\t\\t\\t\\treturn \\\"<roles type='array'>%s</roles>\\\" % (pre_return)\\n\\t\\t\\telse:\\n\\t\\t\\t\\treturn \\\"<roles type='array'>%s</roles>\\\" % (self.Roles)\\n\\t\\telse:\\n\\t\\t\\treturn None\",\n \"async def _handle_role(self, member: discord.Member) -> tuple:\\n list_to_add = await self.config.guild(member.guild).autoroles()\\n list_to_remove = await self.config.guild(member.guild).temprole()\\n actions = []\\n if list_to_add:\\n for role in list_to_add:\\n to_add = member.guild.get_role(role)\\n await member.add_roles(to_add, reason=\\\"Adding auto role by Verify.\\\")\\n actions.append(\\n \\\"added automatically role{plural}\\\".format(\\n plural=\\\"s\\\" if len(list_to_add) > 1 else \\\"\\\"\\n )\\n )\\n if list_to_remove:\\n to_remove = member.guild.get_role(list_to_remove)\\n if to_remove in member.roles:\\n await member.remove_roles(to_remove, reason=\\\"Removing temporary role by Verify.\\\")\\n actions.append(\\\"removed temporary role\\\")\\n return (\\n True,\\n humanize_list(actions).capitalize() if actions else \\\"No action taken.\\\",\\n )\",\n \"async def add_role(\\n client,\\n event,\\n user: ('user', 'User to add role to'),\\n role: ('role', 'The role to give'),\\n):\\n # Check for permissions\\n if not event.user_permissions.can_manage_roles:\\n abort('You need `manage roles` permission to invoke this command.')\\n \\n if not event.guild.cached_permissions_for(client).can_manage_roles:\\n abort('I need `manage roles` permission to execute this command.')\\n \\n if not event.user.has_higher_role_than(role):\\n abort('You must have higher role than the role you are trying to give.')\\n \\n if not client.has_higher_role_than(role):\\n abort('I must have higher role than the role you are trying to give.')\\n \\n # Using `.copy_to` on forms works as well.\\n return ADD_ROLE_FORM.copy_with(\\n title = f'Add role {role.name} to {user.full_name}',\\n custom_id = f'add_role.{user.id}.{role.id}',\\n )\",\n \"async def on_guild_role_create(self, role):\\n channel = self.client.get_channel(serverlogs.getChannel(role.guild.id, \\\"roles\\\"))\\n if channel is not None:\\n await self.log_role(role=role, type='Create', channel=channel, guild=role.guild)\",\n \"async def _toggle_role(self, ctx, selfrole: Selfrole):\\n\\n if selfrole.role not in ctx.message.author.roles:\\n try:\\n await ctx.message.author.add_roles(selfrole.role)\\n except discord.Forbidden:\\n raise exceptions.ForbiddenError(\\n exceptions.ForbiddenTask.ADD_ROLE, selfrole.role.name\\n )\\n\\n await ctx.send(f\\\"{config.YES} {selfrole.join_message}\\\")\\n\\n elif selfrole.role in ctx.message.author.roles:\\n try:\\n await ctx.message.author.remove_roles(selfrole.role)\\n except discord.Forbidden:\\n raise exceptions.ForbiddenError(\\n exceptions.ForbiddenTask.REMOVE_ROLE, selfrole.role.name\\n )\\n\\n await ctx.send(\\n f\\\"{config.YES} The `{selfrole.role.name}` role was removed from you.\\\"\\n )\",\n \"def espionage(roles, numbers):\\n texts = []\\n n = len(roles)\\n right = random.randint(0, 1)\\n for c in range(len(roles)):\\n if right:\\n if roles[(c + 1) % n]:\\n texts.append(\\\"CODE RED: Espionage Detected\\\")\\n else:\\n texts.append(\\\"ALL CLEAR: Espionage NOT Detected\\\")\\n\\n else:\\n if roles[(c - 1) % n]:\\n texts.append(\\\"CODE RED: Espionage Detected\\\")\\n else:\\n texts.append(\\\"ALL CLEAR: Espionage NOT Detected\\\")\\n send_text(numbers, texts)\",\n \"async def reacrole(self, ctx: commands.Context):\\n pass\",\n \"async def create(self, ctx):\\n\\n # get the model data for the role assigner object\\n data = await self.get_objects(\\n model=RoleAssigner, filter={\\\"bot__name\\\": str(self.bot_name)}\\n )\\n\\n # role assigner object\\n data = data[0]\\n\\n message = await ctx.send(\\\"_ _\\\", embed=self.create_message_embed(data))\\n\\n data.message.uid = message.id\\n data.message.cuid = message.channel.id\\n\\n self.message_id = data.message.uid\\n\\n await self.update_reactions(message, data)\\n\\n await self.update_objects(model_instance=data)\",\n \"def roles(self):\\n params = {\\n \\\"f\\\" : \\\"json\\\"\\n }\\n uURL = self._url + \\\"/roles\\\"\\n return self._con.get(path=uURL, params=params)\",\n \"def get_roles(role):\",\n \"def present_roles(self):\\n print(\\\"User\\\" + str(self.unique_id) + \\\": roles=\\\")\\n for group in self._roles:\\n print(\\\"\\\\tGroup\\\" + str(group) + \\\" -> [\\\"\\n + self.get_role_from_type(group, roles_influence) + \\\", \\\"\\n + self.get_role_from_type(group, roles_neighbors) + \\\", \\\"\\n + self.get_role_from_type(group, roles_activities) + \\\", \\\"\\n + self.get_role_from_type(group, roles_attitude) + \\\"]\\\")\\n print('')\",\n \"async def setjoinrole(self, ctx, role):\\r\\n guild = ctx.message.guild\\r\\n role = discord.utils.get(guild.roles, name=role)\\r\\n functions.updatesql(server=ctx.guild.id, joinrole=role.id)\\r\\n await ctx.send(embed=discord.Embed(title='Sucsess!', color=discord.Colour.from_rgb(255, 0, 255)))\",\n \"def admin_roles(request):\\n user = User.objects.get(username=request.user.username)\\n permisos = get_permisos_sistema(user)\\n return render_to_response('admin/roles/roles.html',{'user':user,\\n 'ver_roles':'Ver roles' in permisos,\\n 'crear_rol': 'Crear rol' in permisos,\\n 'mod_rol': 'Modificar rol' in permisos,\\n 'eliminar_rol': 'Eliminar rol' in permisos},context_instance=RequestContext(request))\",\n \"def update_client_roles(self,request,role):\\n return\\n #pdb.set_trace() #commend out by JZ on 10/3/14\\n if not self.client_roles.has_key(request['protocol'].peer):\\n self.client_roles.update({request['protocol'].peer:{role:time.time()}})\\n else:\\n self.client_roles[request['protocol'].peer].update({role:time.time()})\",\n \"def getRolesStr(self):\\n rolesJson = self.getRoles()\\n rolesStr = json.dumps(rolesJson)\\n return (200, rolesStr)\",\n \"def getRolesStr(self):\\n rolesJson = self.getRoles()\\n rolesStr = json.dumps(rolesJson)\\n return (200,rolesStr)\",\n \"def updateRoles(request):\\n\\n # update org admins\\n #updateRole('gsoc_org_admin')\\n\\n # update mentors\\n #updateRole('gsoc_mentor')\\n\\n # update students\\n # we can assume that students cannot have any other roles, so we do not\\n # need to set ETA\\n updateRole('gsoc_student')\",\n \"def frole(wrapper: MessageDispatcher, message: str):\\n pl = get_players(wrapper.game_state)\\n\\n parts = message.lower().split(\\\",\\\")\\n for part in parts:\\n try:\\n (name, role) = part.split(\\\":\\\", 1)\\n except ValueError:\\n wrapper.send(messages[\\\"frole_incorrect\\\"].format(part))\\n return\\n umatch = users.complete_match(name.strip(), pl)\\n rmatch = match_role(role.strip(), allow_special=False)\\n role = None\\n if rmatch:\\n role = rmatch.get().key\\n if not umatch or not rmatch:\\n wrapper.send(messages[\\\"frole_incorrect\\\"].format(part))\\n return\\n FORCE_ROLES[role].add(umatch.get())\\n\\n wrapper.send(messages[\\\"operation_successful\\\"])\",\n \"async def togglerole(self, ctx, role):\\n \\n user = ctx.message.author\\n await ctx.message.delete()\\n\\n\\n if role == \\\"MK8D\\\":\\n if self.bot.mk8d_role in user.roles:\\n await user.remove_roles(self.bot.mk8d_role)\\n await user.send(\\\"Left MK8D role\\\")\\n\\n else:\\n await user.add_roles(self.bot.mk8d_role)\\n await user.send(\\\"Joined MK8D role\\\")\\n else:\\n await user.send(\\\"{} is not a togglable role\\\".format(role))\",\n \"def syncRole(user, roleToAdd, listToAdd):\\n print(user, \\\":\\\", roleToAdd)\\n if roleToAdd == \\\"Doppelgänger\\\":\\n listToAdd.append(\\n Doppelganger(user=user, firstRole=roleToAdd, botRef=bot))\\n\\n elif roleToAdd == \\\"Sbire\\\":\\n listToAdd.append(Minion(user=user, firstRole=roleToAdd, botRef=bot))\\n\\n elif roleToAdd == \\\"Loup-Garou\\\":\\n listToAdd.append(Werewolf(user=user, firstRole=roleToAdd, botRef=bot))\\n\\n elif roleToAdd == \\\"Loup Alpha\\\":\\n listToAdd.append(\\n AlphaWerewolf(user=user, firstRole=roleToAdd, botRef=bot))\\n\\n elif roleToAdd == \\\"Loup Shamane\\\":\\n listToAdd.append(\\n ShamanWerewolf(user=user, firstRole=roleToAdd, botRef=bot))\\n\\n elif roleToAdd == \\\"Franc-Maçon\\\":\\n listToAdd.append(Freemason(user=user, firstRole=roleToAdd, botRef=bot))\\n\\n elif roleToAdd == \\\"Voyante\\\":\\n listToAdd.append(Seer(user=user, firstRole=roleToAdd, botRef=bot))\\n\\n elif roleToAdd == \\\"Chasseur de Fantômes\\\":\\n listToAdd.append(GhostHunter(user=user, firstRole=roleToAdd, botRef=bot))\\n\\n elif roleToAdd == \\\"Apprentie voyante\\\":\\n listToAdd.append(\\n BeginnerSeer(user=user, firstRole=roleToAdd, botRef=bot))\\n\\n elif roleToAdd == \\\"Voleur\\\":\\n listToAdd.append(Thief(user=user, firstRole=roleToAdd, botRef=bot))\\n\\n elif roleToAdd == \\\"Noiseuse\\\":\\n listToAdd.append(\\n Troublemaker(user=user, firstRole=roleToAdd, botRef=bot))\\n\\n elif roleToAdd == \\\"Soûlard\\\":\\n listToAdd.append(Drunkard(user=user, firstRole=roleToAdd, botRef=bot))\\n\\n elif roleToAdd == \\\"Insomniaque\\\":\\n listToAdd.append(Insomniac(user=user, firstRole=roleToAdd, botRef=bot))\\n\\n elif roleToAdd == \\\"Divinateur\\\":\\n listToAdd.append(Diviner(user=user, firstRole=roleToAdd, botRef=bot))\\n\\n elif roleToAdd == \\\"Tanneur\\\":\\n listToAdd.append(Tanner(user=user, firstRole=roleToAdd, botRef=bot))\\n\\n elif roleToAdd == \\\"Chasseur\\\":\\n listToAdd.append(Hunter(user=user, firstRole=roleToAdd, botRef=bot))\\n\\n elif roleToAdd == \\\"Garde du corps\\\":\\n listToAdd.append(BodyGuard(user=user, firstRole=roleToAdd, botRef=bot))\\n\\n elif roleToAdd == \\\"Loup rêveur\\\":\\n listToAdd.append(\\n SleepingWerewolf(user=user, firstRole=roleToAdd, botRef=bot))\\n else:\\n print(\\\"GROS PROBLEME\\\", roleToAdd)\\n exit()\",\n \"async def games(ctx):\\n games_channel = discord.utils.get(ctx.message.author.guild.text_channels, name=CHANNEL_GAMES)\\n member = ctx.message.author\\n role = discord.utils.get(member.guild.roles, name=ROLE_GAMES)\\n if role in member.roles:\\n await member.remove_roles(role)\\n await ctx.send(\\\"Removed you from the games club... feel free to come back anytime!\\\")\\n await games_channel.send(f\\\"{member.mention} left the party.\\\")\\n else:\\n await member.add_roles(role)\\n await ctx.send(f\\\"You are now in the channel. Come and have fun in {games_channel.mention}! :tada:\\\")\\n await games_channel.send(f\\\"Please welcome {member.mention} to the party!!\\\")\",\n \"async def x5lol(ctx):\\n #Shortcut to Author of the Message\\n atr = ctx.author\\n #Shortcut to Author Current Voice Channel\\n currentvc = atr.voice.channel.name\\n #Shortcut to Voice Channel Members list\\n usrs = atr.voice.channel.members\\n #Specify what role will use \\n role = ctx.guild.get_role(\\\"\\\"\\\"Insert role id\\\"\\\"\\\")\\n \\n pot = []\\n #await ctx.send(atr.name +' '+currentvc)\\n for i in usrs:\\n if role in i.roles:\\n #await ctx.send('O ' + str(i) + ' É ' + str(role))\\n nick = (str(i.nick))\\n if nick != 'None':\\n pot.append(str(i.nick))\\n else:\\n pot.append(str(i))\\n \\n \\n #NOTE:The math part of team ballance isn't done yet\\n # For now, Bot shuffle the names, then send to Text Channel on Discord \\n random.shuffle(pot)\\n await ctx.send(pot)\",\n \"def manage_removeRoles(self, role_ids, RESPONSE=None, REQUEST=None):\\n role_ids = [_f for _f in role_ids if _f]\\n\\n if not role_ids:\\n message = 'no+roles+selected'\\n\\n else:\\n\\n for role_id in role_ids:\\n self.removeRole(role_id)\\n\\n message = 'Role+assignments+removed'\\n\\n if RESPONSE is not None:\\n RESPONSE.redirect('%s/manage_roles?manage_tabs_message=%s' %\\n (self.absolute_url(), message))\",\n \"async def toggle_group(role_title, ctx):\\n if ctx.guild is None:\\n return 'whisper'\\n server_roles = ctx.guild.roles\\n #print(\\\"Server roles\\\", server_roles)\\n user_roles = ctx.author.roles\\n #print(\\\"Author roles\\\", user_roles)\\n\\n role_id = \\\"\\\"\\n\\n #Finding the role on the server. If it doesn't exist, we'll let the user know.\\n found_role = False\\n role_id_index = ''\\n for i in server_roles:\\n #print(i.name.lower())\\n if i.name.lower() == role_title.lower(): #.lower is for consistency\\n role_id = i\\n found_role = True\\n try:\\n role_id_index = user_roles.index(i)\\n except:\\n pass\\n\\n if not found_role:\\n return \\\"role not found\\\"\\n else:\\n if role_id in user_roles:\\n # User has this role, need to remove it.\\n user_roles.pop(role_id_index)\\n await ctx.author.edit(roles=user_roles, reason=\\\"Automated role removal requested by user\\\")\\n return \\\"removed\\\"\\n else:\\n # User does not have this role\\n user_roles.append(role_id)\\n await ctx.author.edit(roles=user_roles, reason=\\\"Automated role add requested by user\\\")\\n return \\\"added\\\"\",\n \"async def show_all_self_assignable_roles(self, interaction: Interaction, button: discord.ui.Button) -> None:\\n view = RoleButtonView(interaction.user, self.assignable_roles)\\n await interaction.response.send_message(\\n view=view,\\n ephemeral=True\\n )\",\n \"def add_role():\\n check_admin()\\n add_role = True\\n\\n form = RoleForm()\\n if form.validate_on_submit():\\n role = Role(title=form.title.data)\\n\\n try:\\n db.session.add(role)\\n db.session.commit()\\n flash('New role successfully created')\\n except:\\n flash('Error: Role title already exist')\\n\\n return redirect(url_for('admin.get_roles'))\\n\\n return render_template('admin/roles/role.html', form=form, add_role=add_role, title='Add Role')\",\n \"async def roledelete(ctx):\\r\\n await ctx.message.delete()\\r\\n roles = ctx.guild.roles\\r\\n roles.pop(0)\\r\\n for role in roles:\\r\\n if ctx.guild.roles[-1] > role:\\r\\n try:\\r\\n await role.delete()\\r\\n except:\\r\\n print(f\\\"{Fore.RED}[-]ROLE => {Fore.RESET}Failed to delete: {role}\\\")\",\n \"def list_roles(var, wrapper, message):\\n\\n lpl = len(var.ALL_PLAYERS)\\n specific = 0\\n\\n pieces = re.split(\\\" +\\\", message.strip())\\n gamemode = var.CURRENT_GAMEMODE\\n if gamemode.name == \\\"villagergame\\\":\\n gamemode = var.GAME_MODES[\\\"default\\\"][0]()\\n\\n if (not pieces[0] or pieces[0].isdigit()) and not hasattr(gamemode, \\\"ROLE_GUIDE\\\"):\\n wrapper.reply(\\\"There {0} \\\\u0002{1}\\\\u0002 playing. {2}roles is disabled for the {3} game mode.\\\".format(\\\"is\\\" if lpl == 1 else \\\"are\\\", lpl, botconfig.CMD_CHAR, gamemode.name), prefix_nick=True)\\n return\\n\\n msg = []\\n\\n if not pieces[0] and lpl:\\n msg.append(\\\"There {0} \\\\u0002{1}\\\\u0002 playing.\\\".format(\\\"is\\\" if lpl == 1 else \\\"are\\\", lpl))\\n if var.PHASE in var.GAME_PHASES:\\n msg.append(\\\"Using the {0} game mode.\\\".format(gamemode.name))\\n pieces[0] = str(lpl)\\n\\n if pieces[0] and not pieces[0].isdigit():\\n valid = var.GAME_MODES.keys() - var.DISABLED_GAMEMODES - {\\\"roles\\\", \\\"villagergame\\\"}\\n mode = pieces.pop(0)\\n if mode not in valid:\\n matches = complete_match(mode, valid)\\n if not matches:\\n wrapper.reply(messages[\\\"invalid_mode\\\"].format(mode), prefix_nick=True)\\n return\\n if len(matches) > 1:\\n wrapper.reply(messages[\\\"ambiguous_mode\\\"].format(mode, matches), prefix_nick=True)\\n return\\n\\n mode = matches[0]\\n\\n gamemode = var.GAME_MODES[mode][0]()\\n\\n try:\\n gamemode.ROLE_GUIDE\\n except AttributeError:\\n wrapper.reply(\\\"{0}roles is disabled for the {1} game mode.\\\".format(botconfig.CMD_CHAR, gamemode.name), prefix_nick=True)\\n return\\n\\n strip = lambda x: re.sub(r\\\"\\\\(.*\\\\)\\\", \\\"\\\", x)\\n rolecnt = Counter()\\n roles = list((x, map(strip, y)) for x, y in gamemode.ROLE_GUIDE.items())\\n roles.sort(key=lambda x: x[0])\\n\\n if pieces and pieces[0].isdigit():\\n specific = int(pieces[0])\\n new = []\\n for role in itertools.chain.from_iterable([y for x, y in roles if x <= specific]):\\n if role.startswith(\\\"-\\\"):\\n rolecnt[role[1:]] -= 1\\n new.remove(role[1:])\\n else:\\n rolecnt[role] += 1\\n append = \\\"({0})\\\".format(rolecnt[role]) if rolecnt[role] > 1 else \\\"\\\"\\n new.append(role + append)\\n\\n msg.append(\\\"[{0}]\\\".format(specific))\\n msg.append(\\\", \\\".join(new))\\n\\n else:\\n final = []\\n\\n for num, role_num in roles:\\n snum = \\\"[{0}]\\\".format(num)\\n if num <= lpl:\\n snum = \\\"\\\\u0002{0}\\\\u0002\\\".format(snum)\\n final.append(snum)\\n new = []\\n for role in role_num:\\n if role.startswith(\\\"-\\\"):\\n rolecnt[role[1:]] -= 1\\n new.append(role)\\n else:\\n rolecnt[role] += 1\\n append = \\\"({0})\\\".format(rolecnt[role]) if rolecnt[role] > 1 else \\\"\\\"\\n new.append(role + append)\\n\\n final.append(\\\", \\\".join(new))\\n\\n msg.append(\\\" \\\".join(final))\\n\\n if not msg:\\n msg.append(\\\"No roles are defined for {0}p games.\\\".format(specific or lpl))\\n\\n wrapper.send(*msg)\",\n \"def updateMentors(request):\\n\\n return updateRole('gsoc_mentor')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.70767516","0.7048763","0.70305854","0.67609715","0.66043484","0.6424783","0.63169235","0.6300088","0.6295008","0.6281987","0.62049335","0.6064257","0.605706","0.60347486","0.6028766","0.6005439","0.60031676","0.5964373","0.59568113","0.5954478","0.5919339","0.58383036","0.5838286","0.5750343","0.5709077","0.56854534","0.5673877","0.5673877","0.5673877","0.5662464","0.5651171","0.5646733","0.561437","0.56040806","0.55798054","0.5579122","0.5578049","0.5569587","0.5529546","0.5500897","0.5500284","0.5490795","0.5482033","0.5479841","0.5461189","0.54513353","0.54461575","0.5433383","0.54208565","0.5406829","0.54010576","0.53846836","0.5372685","0.5344638","0.5344606","0.53369504","0.53131926","0.53123057","0.5306749","0.53003424","0.529753","0.5292406","0.5292113","0.5291001","0.5286759","0.5278526","0.5276988","0.52688617","0.52645665","0.52504706","0.5247098","0.52447224","0.5238252","0.5237494","0.52359945","0.52348155","0.52309984","0.52120155","0.52109563","0.52097774","0.5205505","0.52027345","0.51962376","0.5193363","0.51851803","0.51846325","0.51718223","0.5170791","0.51690936","0.5160727","0.5158523","0.5156181","0.5144863","0.5143245","0.51270026","0.5119842","0.5118778","0.51173866","0.5116844","0.5113628"],"string":"[\n \"0.70767516\",\n \"0.7048763\",\n \"0.70305854\",\n \"0.67609715\",\n \"0.66043484\",\n \"0.6424783\",\n \"0.63169235\",\n \"0.6300088\",\n \"0.6295008\",\n \"0.6281987\",\n \"0.62049335\",\n \"0.6064257\",\n \"0.605706\",\n \"0.60347486\",\n \"0.6028766\",\n \"0.6005439\",\n \"0.60031676\",\n \"0.5964373\",\n \"0.59568113\",\n \"0.5954478\",\n \"0.5919339\",\n \"0.58383036\",\n \"0.5838286\",\n \"0.5750343\",\n \"0.5709077\",\n \"0.56854534\",\n \"0.5673877\",\n \"0.5673877\",\n \"0.5673877\",\n \"0.5662464\",\n \"0.5651171\",\n \"0.5646733\",\n \"0.561437\",\n \"0.56040806\",\n \"0.55798054\",\n \"0.5579122\",\n \"0.5578049\",\n \"0.5569587\",\n \"0.5529546\",\n \"0.5500897\",\n \"0.5500284\",\n \"0.5490795\",\n \"0.5482033\",\n \"0.5479841\",\n \"0.5461189\",\n \"0.54513353\",\n \"0.54461575\",\n \"0.5433383\",\n \"0.54208565\",\n \"0.5406829\",\n \"0.54010576\",\n \"0.53846836\",\n \"0.5372685\",\n \"0.5344638\",\n \"0.5344606\",\n \"0.53369504\",\n \"0.53131926\",\n \"0.53123057\",\n \"0.5306749\",\n \"0.53003424\",\n \"0.529753\",\n \"0.5292406\",\n \"0.5292113\",\n \"0.5291001\",\n \"0.5286759\",\n \"0.5278526\",\n \"0.5276988\",\n \"0.52688617\",\n \"0.52645665\",\n \"0.52504706\",\n \"0.5247098\",\n \"0.52447224\",\n \"0.5238252\",\n \"0.5237494\",\n \"0.52359945\",\n \"0.52348155\",\n \"0.52309984\",\n \"0.52120155\",\n \"0.52109563\",\n \"0.52097774\",\n \"0.5205505\",\n \"0.52027345\",\n \"0.51962376\",\n \"0.5193363\",\n \"0.51851803\",\n \"0.51846325\",\n \"0.51718223\",\n \"0.5170791\",\n \"0.51690936\",\n \"0.5160727\",\n \"0.5158523\",\n \"0.5156181\",\n \"0.5144863\",\n \"0.5143245\",\n \"0.51270026\",\n \"0.5119842\",\n \"0.5118778\",\n \"0.51173866\",\n \"0.5116844\",\n \"0.5113628\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.858319"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":16873,"cells":{"query":{"kind":"string","value":"Handles role reaction button presses. A 'on_button_click' listener."},"document":{"kind":"string","value":"async def handle_role_reaction_press(interaction: disnake.MessageInteraction):\n if interaction.message not in await ReactionRoleMessage.get_all():\n return\n\n role_id = int(interaction.component.custom_id)\n member: disnake.Member = interaction.author\n user = await User.get(member.id)\n role = member.get_role(role_id)\n if role:\n await member.remove_roles(role, reason=\"Reaction Role Message\")\n await send_message(user=user, key=\"role_removed\", inter=interaction, ephemeral=True)\n else:\n role = interaction.guild.get_role(role_id)\n if role:\n try:\n await member.add_roles(role, reason=\"Reaction Role Message\")\n await send_message(user=user, key=\"role_added\", inter=interaction, ephemeral=True)\n except disnake.errors.Forbidden as e:\n await send_message(user=user, key=\"no_permissions\", inter=interaction, ephemeral=True)\n else:\n await send_message(user=user, key=\"role_not_found\", inter=interaction, ephemeral=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":["async def subscribe_command(self, ctx: commands.Context, *_) -> None: # We don't actually care about the args\n view = RoleButtonView(ctx.author, self.assignable_roles)\n await ctx.send(\n \"Click the buttons below to add or remove your roles!\",\n view=view,\n delete_after=DELETE_MESSAGE_AFTER\n )","async def callback(self, interaction: Interaction) -> None:\n if isinstance(interaction.user, discord.User):\n log.trace(\"User %s is not a member\", interaction.user)\n await interaction.message.delete()\n self.view.stop()\n return\n\n await members.handle_role_change(\n interaction.user,\n interaction.user.remove_roles if self.assigned else interaction.user.add_roles,\n discord.Object(self.role.role_id),\n )\n\n self.assigned = not self.assigned\n try:\n await self.update_view(interaction)\n send_function = interaction.followup.send\n except discord.NotFound:\n log.debug(\"Subscribe message for %s removed before buttons could be updated\", interaction.user)\n self.view.stop()\n send_function = interaction.response.send_message\n\n await send_function(\n self.LABEL_FORMAT.format(action=\"Added\" if self.assigned else \"Removed\", role_name=self.role.name),\n ephemeral=True,\n )","def handle_button(self, event, event_type):\n # 0 for left\n # 1 for right\n # 2 for middle/center\n # 3 for side\n mouse_button_number = self._get_mouse_button_number(event)\n\n # Identify buttons 3,4,5\n if event_type in (25, 26):\n event_type = event_type + (mouse_button_number * 0.1)\n\n # Add buttons to events\n event_type_string, event_code, value, scan = self.codes[event_type]\n if event_type_string == \"Key\":\n scan_event, key_event = self.emulate_press(\n event_code, scan, value, self.timeval)\n self.events.append(scan_event)\n self.events.append(key_event)\n\n # doubleclick/n-click of button\n click_state = self._get_click_state(event)\n\n repeat = self.emulate_repeat(click_state, self.timeval)\n self.events.append(repeat)","async def _on_raw_reaction(\n self,\n payload: RawReactionActionEvent,\n reaction_type: EnumReactionType,\n ) -> None:\n if self.__is_self(payload.user_id):\n print(\"reaction added by the bot itself\")\n return\n\n guild = self._client.get_guild(payload.guild_id)\n\n if reaction_type == EnumReactionType.ADD:\n await self.__roles.add_role(\n guild, payload.message_id,\n payload.emoji, payload.user_id,\n\n )\n elif reaction_type == EnumReactionType.REMOVE:\n await self.__roles.remove_role(\n guild, payload.message_id,\n payload.emoji, payload.user_id,\n )\n else:\n raise InvalidReactionType","async def reaction_roles_post(inter: MessageInteraction, description, roles):\n view = disnake.ui.View(timeout=None)\n for role in roles:\n view.add_item(disnake.ui.Button(label=role.name, custom_id=role.id))\n messages = await send_message(msg=description, channel=inter.channel, view=view)\n for message in messages:\n await ReactionRoleMessage.insert(message.id)","def button_pressed(\n self, event_name: str, data: dict, kwargs: dict) -> None:\n action_name = self.get_state(self.entity_ids[CONF_ACTION_LIST])\n\n if action_name not in self.OBJECT_MAP:\n self.error('Unknown action: {0}'.format(action_name))\n return\n\n self.log('Running Dash action: {0}'.format(action_name))\n\n klass, args = self.OBJECT_MAP[action_name]\n button = klass(self, args) # type: ignore\n button.run()","def OnButton(self, event):\n button = event.GetEventObject().GetName()\n if button == \"Button1\":\n self.OnButton1()\n elif button == \"Button2\":\n self.OnButton2()\n elif button == \"Button3\":\n self.OnExit(event)","def clickedAction(self, events):\n print(\"The {} button was clicked!\".format(self.imgname))","def __on_click(self, evt):\n if evt.button() == Qt.LeftButton:\n return self._on_left_click(evt)\n if evt.button() == Qt.RightButton:\n return self._on_right_click(evt)","def handle_right_click(self):\n if not self.game_in_progress:\n return\n if self.first_click:\n self.first_click = False\n self.timer.start(1000)\n sender = self.sender()\n row = 0\n col = 0\n for row in range(self.rows):\n for col in range(self.cols):\n if self.button_array[row][col] == sender:\n break\n else:\n continue\n break\n # print 'Received right click:', row, ',', col\n status = self.board.getcellstatus(row, col)\n if status == CellStatus.Opened:\n return\n elif status == CellStatus.Closed:\n self.remainingminecount = self.remainingminecount - 1\n self.mines_lcd.display(str(self.remainingminecount))\n self.board.setcellstatus(row, col, CellStatus.MarkedAsMine)\n self.button_array[row][col].setIcon(QtGui.QIcon(\"icons/Flag.png\"))\n elif status == CellStatus.MarkedAsMine:\n self.remainingminecount = self.remainingminecount + 1\n self.mines_lcd.display(str(self.remainingminecount))\n self.board.setcellstatus(row, col, CellStatus.MarkedAsSuspectedMine)\n self.button_array[row][col].setIcon(QtGui.QIcon(\"icons/questionmark.png\"))\n elif status == CellStatus.MarkedAsSuspectedMine:\n self.board.setcellstatus(row, col, CellStatus.Closed)\n self.button_array[row][col].setIcon(QtGui.QIcon(\"icons/unopenedsquare.png\"))","def handle_button_event(evt_type: EventType, hue_resource: Button) -> None:\n LOGGER.debug(\"Received button event: %s\", hue_resource)\n\n # guard for missing button object on the resource\n if hue_resource.button is None:\n return\n\n hue_device = btn_controller.get_device(hue_resource.id)\n device = dev_reg.async_get_device({(DOMAIN, hue_device.id)})\n\n # Fire event\n data = {\n # send slugified entity name as id = backwards compatibility with previous version\n CONF_ID: slugify(f\"{hue_device.metadata.name} Button\"),\n CONF_DEVICE_ID: device.id, # type: ignore\n CONF_UNIQUE_ID: hue_resource.id,\n CONF_TYPE: hue_resource.button.last_event.value,\n CONF_SUBTYPE: hue_resource.metadata.control_id,\n }\n hass.bus.async_fire(ATTR_HUE_EVENT, data)","def button_clicked(self, event_name, data, kwargs):\n if data[\"entity_id\"] == self.button:\n if data[\"click_type\"] == \"single\":\n if float(self.cached_fade_in_time) > 0:\n self.log(\n \"Turning on {}\".format(self.friendly_name(self.wakeup_light))\n )\n self.call_service(\n \"light/turn_on\", entity_id=self.wakeup_light, brightness_pct=1\n )\n transition = int(\n float(self.cached_fade_in_time)\n * int(self.fade_in_time_multiplicator)\n )\n self.log(\n \"Transitioning light in over {} seconds\".format(transition)\n )\n self.timer_handle_list.append(\n self.run_in(\n self.run_fade_in, 1, transition=transition, brightness_pct=1\n )\n )","async def on_raw_reaction_add(self, payload: discord.RawReactionActionEvent):\n \n guild = self.bot.get_guild(payload.guild_id)\n user = discord.utils.get(guild.members, id=payload.user_id)\n # Skipping bot reactions\n if user.bot:\n return\n\n # Search the event by message id\n message_id = payload.message_id\n channel_id = payload.channel_id\n guild_id = payload.guild_id\n event = connector.getEventByMessage(guild_id, message_id, channel_id)\n if event is None:\n return\n\n role = discord.utils.get(payload.member.guild.roles, id=event.role_id)\n\n if (payload.event_type == \"REACTION_ADD\") and (event.emoji == str(payload.emoji)):\n # Adding role to user\n await payload.member.add_roles(role)\n elif (payload.event_type == \"REACTION_REMOVE\") and (event.emoji == str(payload.emoji)) and (role in payload.member.roles):\n # Remove role from user\n await payload.member.remove_roles(role)","def on_click(self):\n arcade.play_sound(button, volume=constants.MUSIC_VOLUME / 40)\n\n global entered_code\n\n if button_text == \"Enter Code\":\n self.answer = self.input_box.text\n entered_code = self.answer\n self.convert_string_to_int(self.answer)\n self.ui_manager.purge_ui_elements()\n self.minigame.window.show_view(FakeCodeGame.MyView(self.minigame.main_view))\n print(f\"EnterCode button. {self.answer}\")\n elif button_text == \"Exit Terminal\":\n reset_global_variables()\n self.minigame.window.show_view(self.minigame.main_view)\n\n \"\"\"\n The following functions check the submitted answer\n \"\"\"","def eventHandler(self, event: pygame.event):\n # change selected color if this button's rectangle was clicked\n if event.type == pygame.MOUSEBUTTONDOWN:\n if event.button == 1:\n if self.rect.collidepoint(event.pos): # is mouse over button\n self.image = self._images[ButtonImages.CLICKING_IMAGE.value]\n self.beingClicked = True\n for func, *args in self.functionsToInvokeWhenClicked:\n func(*args)\n elif event.type == pygame.MOUSEBUTTONUP and self.beingClicked:\n if event.button == 1:\n self.beingClicked = False\n self.image = self._images[ButtonImages.DEFAULT_IMAGE.value]","async def on_raw_reaction_add(self, payload: discord.RawReactionActionEvent):\n role: discord.Role = await self.check_payload(payload)\n if payload.user_id == self.bot.user.id:\n return\n if role:\n guild = self.bot.get_guild(payload.guild_id)\n member = guild.get_member(payload.user_id)\n await member.add_roles(role)","async def on_raw_reaction_add(self, payload: discord.RawReactionActionEvent):\n\n # Skipping bot reactions\n if payload.member.bot:\n return\n\n # Search the event by message id\n message_id = payload.message_id\n channel_id = payload.channel_id\n guild_id = payload.guild_id\n event_type = connector.getEventTypeByMessage(guild_id, message_id, channel_id)\n if event_type is None:\n return\n\n role = discord.utils.get(payload.member.guild.roles, id=event_type.role_id)\n\n if (payload.event_type == \"REACTION_ADD\") and (event_type.emoji == str(payload.emoji)):\n # Adding role to user\n await payload.member.add_roles(role)","def on_buttonBox_clicked(self, button):\n if button == self.buttonBox.button(QDialogButtonBox.Close):\n self.close()\n elif button == self.buttonBox.button(QDialogButtonBox.Cancel):\n self.__client.abort()\n self.__canceled = True\n elif button == self.__installButton:\n self.__install()\n elif button == self.__showDetailsButton:\n self.__showDetails()","def OnButton(self, event):\n\n\n event_id = event.GetId()\n event_obj = event.GetEventObject()\n print(\"Button 1 Clicked:\")\n print(\"ID=%d\" % event_id)\n print(\"object=%s\" % event_obj.GetLabel())","def OnButton(self, event):\r\n \r\n button = event.GetInt()\r\n\r\n if button == AUI_BUTTON_LEFT or button == AUI_BUTTON_RIGHT:\r\n if button == AUI_BUTTON_LEFT:\r\n if self.GetTabOffset() > 0:\r\n \r\n self.SetTabOffset(self.GetTabOffset()-1)\r\n self.Refresh()\r\n self.Update()\r\n else:\r\n self.SetTabOffset(self.GetTabOffset()+1)\r\n self.Refresh()\r\n self.Update()\r\n \r\n elif button == AUI_BUTTON_WINDOWLIST:\r\n idx = self.GetArtProvider().ShowDropDown(self, self._pages, self.GetActivePage())\r\n \r\n if idx != -1:\r\n \r\n e = AuiNotebookEvent(wxEVT_COMMAND_AUINOTEBOOK_PAGE_CHANGING, self.GetId())\r\n e.SetSelection(idx)\r\n e.SetOldSelection(self.GetActivePage())\r\n e.SetEventObject(self)\r\n self.GetEventHandler().ProcessEvent(e)\r\n \r\n else:\r\n event.Skip()","def callback(self, event):\n button = event[\"button\"]\n\n cmd = self._callbacks.get(self._uuidstr(self.global_id, button), None)\n cmd = self._callbacks.get(self._uuidstr(event[\"name\"], button), cmd)\n cmd = self._callbacks.get(self._uuidstr(event[\"instance\"], button), cmd)\n\n if cmd is None:\n return\n if callable(cmd):\n cmd(event)\n else:\n bumblebee.util.execute(cmd, False)","def button_clicked(self):\n sender = self.sender()\n if self._buttons_active:\n message_box = QtGui.QMessageBox()\n quit_button = message_box.addButton('Quitter',\n QtGui.QMessageBox.RejectRole)\n if (sender.row, sender.col, sender.text()) in self._solution:\n message_box.setText('Félicitations, vous avez deviné juste !')\n else:\n message_box.setText('Dommage, vous avez perdu !')\n message_box.setInformativeText('Vous pouvez proposer une '\n 'autre sortie, ou afficher de '\n 'nouveau les objets.')\n show_items_button = message_box.addButton('Voir objets',\n QtGui.QMessageBox.AcceptRole)\n play_again_button = message_box.addButton( 'Réessayer',\n QtGui.QMessageBox.AcceptRole)\n message_box.setDefaultButton(play_again_button)\n message_box.exec()\n if message_box.clickedButton() == quit_button:\n self.close()\n elif message_box.clickedButton() == show_items_button:\n self.reset_game()","def m_press(self, button: MButton):\n pass","def handle_button_press(button_state, mqtt_client, message):\n if button_state:\n ev3.Sound.speak(message).wait()\n mqtt_client.send_message(\"button_pressed\", [message])","async def on_raw_reaction_remove(self, payload: discord.RawReactionActionEvent):\n\n guild = self.bot.get_guild(payload.guild_id)\n user = guild.get_member(payload.user_id)\n # Skipping bot reactions\n if user.bot:\n return\n\n # Search the event by message id\n message_id = payload.message_id\n channel_id = payload.channel_id\n guild_id = payload.guild_id\n event = connector.getEventByMessage(guild_id, message_id, channel_id)\n if event is None:\n return\n\n emoji_id = str(payload.emoji).split(':')[2][:-1]\n role = discord.utils.get(guild.roles, id=event.role_id)\n if (payload.event_type == \"REACTION_REMOVE\") and (event.emoji.split(':')[2][:-1] == emoji_id) and (role in user.roles):\n # Remove role from user\n await user.remove_roles(role)","async def user_joined_button(self, payload: discord.RawReactionActionEvent) -> None:\n\n self.bits = flip_action_bits(LoggingActions.USER_JOINED, self.bits)\n await self.update_embed()","def on_click_return(self, event):\n self.on_click_callable(event[\"target\"])","def on_mouse_press(self, x, y, button, modifiers):\n \n menu: Menu = self.get_menu_for_display()\n\n menu_click_x, menu_click_y = self.get_menu_click(menu, x, y)\n\n if button == arcade.MOUSE_BUTTON_LEFT:\n if menu:\n menu.button_list.check_mouse_press_for_buttons(\n menu_click_x,\n menu_click_y,\n )","def button_click(self, btn, mbtn):\n self.last_action_ts = pygame.time.get_ticks() # update last action timestamp (idle shutdown countdown restarts)\n self.show_time = pygame.time.get_ticks() # refresh show time timestamp, so countdown restarts\n\n status = self.player.get_status()\n\n # which button was pressed?\n if btn is self.btn_play:\n logger.debug(\"button_click: btn_play \")\n player.pause() # toggle play/pause\n elif btn is self.btn_prev:\n logger.debug(\"button_click: btn_prev \")\n try:\n if int(status['song']) > 0: # only accept 'prev' button push if this is not the first song\n player.prev()\n except Exception as e:\n logger.error(e, exc_info=True) # log any exceptions\n elif btn is self.btn_next:\n logger.debug(\"button_click: btn_next \")\n try:\n if int(status['song']) < (int(status['playlistlength']) - 1):\n player.next()\n except Exception as e:\n logger.error(e, exc_info=True) # log any exceptions\n elif btn is self.background:\n logger.debug(\"button_click: background \")\n if status['state'] == 'play' or status['state']== 'pause':\n self.show_buttons()\n else:\n logger.debug(\"button_click: <unknown>\")","async def on_raw_reaction_remove(self, payload: discord.RawReactionActionEvent):\n\n guild = self.bot.get_guild(payload.guild_id)\n user = guild.get_member(payload.user_id)\n # Skipping bot reactions\n if user.bot:\n return\n\n # Search the event by message id\n message_id = payload.message_id\n channel_id = payload.channel_id\n guild_id = payload.guild_id\n event_type = connector.getEventTypeByMessage(guild_id, message_id, channel_id)\n if event_type is None:\n return\n\n emoji_id = str(payload.emoji).split(':')[2][:-1]\n role = discord.utils.get(guild.roles, id=event_type.role_id)\n if (payload.event_type == \"REACTION_REMOVE\") and (event_type.emoji.split(':')[2][:-1] == emoji_id) and (role in user.roles):\n # Remove role from user\n await user.remove_roles(role)","def OnButtonClick(self):\n self.choice()","def myrole(var, wrapper, message):\n\n ps = get_participants()\n if wrapper.source not in ps:\n return\n\n role = get_main_role(wrapper.source)\n if role in Hidden:\n role = var.HIDDEN_ROLE\n\n evt = Event(\"myrole\", {\"role\": role, \"messages\": []})\n if not evt.dispatch(var, wrapper.source):\n return\n role = evt.data[\"role\"]\n\n wrapper.pm(messages[\"show_role\"].format(role))\n\n for msg in evt.data[\"messages\"]:\n wrapper.pm(msg)","def press_R_button(self) -> None: # pylint: disable=invalid-name","def button_press_event(self, widget, event):\n x, y = event.x, event.y\n\n # x, y = coordinates where the button was pressed\n self.last_win_x, self.last_win_y = x, y\n\n button = 0\n # Prepare a button mask with bits set as follows:\n # left button: 0x1\n # middle button: 0x2\n # right button: 0x4\n # Others can be added as appropriate\n self.logger.debug(\"button down event at %dx%d, button=%x\" % (x, y, button))\n\n data_x, data_y = self.check_cursor_location()\n\n return self.make_ui_callback('button-press', button, data_x, data_y)","def _handle_r_click(self, event):\n Debug.printe(event, Debug.Level.INFO)\n\n # Create the menu to display\n p_menu = Menu(self._parent)\n p_menu.add_command(label=\"Add Picture\", command=lambda: self._add_new_wall_pic())\n p_menu.add_command(label=\"Delete Picture\", command=lambda: self._remove_wall_pic())\n p_menu.add_command(label=\"Delete All\", command=lambda: self.remove_all())\n p_menu.post(event.x_root, event.y_root)","def click_button(self):\n self.widgets.get('button').click()","def input(self, event: pygame.event) -> None:\n if event.type == pygame.MOUSEBUTTONDOWN and event.button == 1:\n self.user_clicked = True","async def on_raw_reaction_add(self, payload):\n\n # exclude all reactions which are not the original message\n if str(payload.message_id) != self.message_id:\n return\n\n # exclude the bot\n if payload.user_id == self.bot.user.id:\n return\n\n else:\n # get the model data for the role assigner object\n data = await self.get_objects(\n model=RoleAssigner, filter={\"bot__name\": str(self.bot_name)}\n )\n\n # role assigner object\n data = data[0]\n\n guild = self.get_guild(guild_id=payload.guild_id)\n\n user = self.get_user(guild=guild, user_id=payload.user_id)\n\n for db_role in data.roles.all():\n\n if db_role.emoji.startswith(\":\") and db_role.emoji.endswith(\":\"):\n\n ce = db_role.emoji[1:-1]\n\n else:\n ce = db_role.emoji\n\n if str(payload.emoji.name) == str(ce):\n\n role = self.get_role(guild, int(db_role.uid))\n\n if user not in role.members:\n\n await user.add_roles(role)\n\n print(\"Added \" + str(user) + \" to role: \" + str(role) + \"!\")\n\n else:\n print(\n \"User \" + str(user) + \" already in role: \" + str(role) + \"!\"\n )\n\n pass","async def on_raw_reaction_remove(self, payload: discord.RawReactionActionEvent):\n role: discord.Role = await self.check_payload(payload)\n\n if role:\n guild = self.bot.get_guild(payload.guild_id)\n member: discord.Member = guild.get_member(payload.user_id)\n await member.remove_roles(role)","def on_click(self, event):\n if event['button'] == 1 and 'button1' in self.options:\n subprocess.call(self.options['button1'].split())\n elif event['button'] == 2 and 'button2' in self.options:\n subprocess.call(self.options['button2'].split())\n elif event['button'] == 3 and 'button3' in self.options:\n subprocess.call(self.options['button3'].split())","async def process_add_reaction_role(\n user_id, description, ctx=None, inter=None, allowed_mentions=None\n):\n user = await User.get(user_id)\n response_deferred = await defer_inter(inter, ephemeral=True)\n view = disnake.ui.View(timeout=None)\n view.add_item(RoleDropdown(description))\n await send_message(\n key=\"choose_roles\",\n user=user,\n view=view,\n ephemeral=True,\n response_deferred=response_deferred,\n inter=inter,\n )","def _event_callback(self, event, *args, **kwargs):\n if isinstance(event, UserRoleModifiedEvent):\n # Event when User Roles are modified\n user_role_event = event\n org_id = user_role_event.origin\n actor_id = user_role_event.actor_id\n role_name = user_role_event.role_name\n log.debug(\"User Role modified: %s %s %s\" % (org_id, actor_id, role_name))\n\n # Evict the user and their roles from the cache so that it gets updated with the next call.\n if self.user_role_cache and self.user_role_cache.has_key(actor_id):\n log.debug(\"Evicting user from the user_role_cache: %s\" % actor_id)\n self.user_role_cache.evict(actor_id)\n elif isinstance(event, UserRoleCacheResetEvent):\n # An event is received to clear the user data cache\n self.user_role_cache.clear()","def on_mouse_release(self, x, y, button, modifiers):\n \n menu: Menu = self.get_menu_for_display()\n\n menu_click_x, menu_click_y = self.get_menu_click(menu, x, y)\n\n if button == arcade.MOUSE_BUTTON_LEFT:\n if menu:\n menu.button_list.check_mouse_release_for_buttons(\n menu_click_x,\n menu_click_y,\n )","def on_mouse_press(self, x: float, y: float, button, modifiers):\n #dialogue buttons\n check_mouse_press_for_buttons(x, y, self.levels[self.current_level].dialogue_list)\n\n #room info prompt buttons\n check_mouse_press_for_buttons(x, y, self.levels[self.current_level].room_info_list)","def button_clicked(self, button, button_idx):\n if self.turn % 2 == 0:\n if self.board[button_idx-1] == 0:\n self.place_move_x(button, button_idx-1)\n gameOver = self.check_x_won()\n if self.player_mode == 'pvc' and gameOver is None:\n self.play_cpu()\n else:\n if self.player_mode == 'pvp':\n if self.board[button_idx-1] == 0:\n self.place_move_o(button, button_idx-1)\n\n self.check_game()\n self.player_highlight()","async def on_raw_reaction_remove(self, payload):\n\n # exclude all reactions which are not the original message\n if str(payload.message_id) != self.message_id:\n return\n\n # exclude the bot\n if payload.user_id == self.bot.user.id:\n return\n\n else:\n # get the model data for the role assigner object\n data = await self.get_objects(\n model=RoleAssigner, filter={\"bot__name\": str(self.bot_name)}\n )\n\n # role assigner object\n data = data[0]\n\n guild = self.get_guild(guild_id=payload.guild_id)\n\n user = self.get_user(guild=guild, user_id=payload.user_id)\n\n for db_role in data.roles.all():\n\n if db_role.emoji.startswith(\":\") and db_role.emoji.endswith(\":\"):\n\n ce = db_role.emoji[1:-1]\n\n else:\n ce = db_role.emoji\n\n if str(payload.emoji.name) == str(ce):\n\n role = self.get_role(guild, int(db_role.uid))\n\n if user in role.members:\n\n await user.remove_roles(role)\n\n print(\"Removed \" + str(user) + \" from role: \" + str(role) + \"!\")\n\n else:\n print(\"User \" + str(user) + \" not in role: \" + str(role) + \"!\")\n\n pass","def ev_mousebuttondown(self, event: tcod.event.MouseButtonDown) -> T | None:","async def on_raw_reaction_add(self, payload: discord.RawReactionActionEvent):\n # Get the message id of the message that the user reacted to.\n message_id = payload.message_id\n\n # Get the message id of the message we want the user to react to.\n actual_message_id = constants.MessageIDs.RULES_MSGID\n\n # Compare that id's match, and if true continue to give the role.\n if message_id == actual_message_id:\n guild_id = payload.guild_id\n guild = self.bot.get_guild(guild_id)\n role = get(payload.member.guild.roles, name='Not Verified')\n\n if role is not None:\n member = get(guild.members, id=payload.user_id)\n if member is not None:\n await payload.member.add_roles(role)\n print(f\"Added role to {member}\")\n else:\n print(\"User not found . . .\")\n else:\n print(\"Role not found . . .\")","def on_mouse_up(self, pos, mouse_button):\n for item in button.Button.all_buttons:\n if item.collidepoint(pos):\n self.buttons_clicked.append((item, mouse_button))\n item.on_click(mouse_button)","async def rolemenu(self, message, args):\n if \"-h\" in args or \"--help\" in args or \"--ahelp\" in args:\n await self._print_rolemenu_help(message.channel)\n return\n update_reactions = not is_key_in_args(args, \"--no-update\")\n allow_role_change = not is_key_in_args(args, \"--no-change\")\n remove_role_on_reaction_removal = not is_key_in_args(args, \"--no-removal\")\n max_number_of_reactions_per_user = get_number_in_args(args, \"--max-reactions\", None)\n max_users_with_role = get_number_in_args(args, \"--max-users\", None)\n no_required_role = is_key_in_args(args, \"--all\")\n role_ids = message.raw_role_mentions\n if len(role_ids) * 2 != len(args):\n await message.channel.send(\"Error with `rolemenu`: all roles must match an emoji\")\n return await self._print_rolemenu_help(message.channel)\n menu = {}\n for i, role_id in enumerate(message.raw_role_mentions):\n emoji = args.pop(0)\n if emoji.strip(\"<>@&\") == str(role_id): # emoji and roles are just exchanged: no problem\n emoji = args.pop(0)\n elif args.pop(0).strip(\"<>@&\") != str(role_id): # error: two adjacent args must be role_id and emoji\n await message.channel.send(\"Error with `rolemenu`: a role must match an emoji\")\n return await self._print_rolemenu_help(message.channel)\n menu.update({emoji: [message.guild.get_role(role_id)]})\n manager = RoleByReactionManager.get(self.guild)\n options = RoleMenuOptions(required_roles=None if no_required_role else [RoleCollection.VISITOR.value],\n ignored_roles=[RoleCollection.MASTER.value],\n update_reactions=update_reactions,\n allow_role_change=allow_role_change,\n max_number_of_reactions_per_user=max_number_of_reactions_per_user,\n max_users_with_role=max_users_with_role,\n remove_role_on_reaction_removal=remove_role_on_reaction_removal\n )\n await manager.add(message, menu, options)\n logger.debug(\"Rolemenu created. Now you can edit your post to make it prettier.\")","def on_click(self) -> None:\n pass","async def togglerole(self, ctx, role=\"\"):\r\n\r\n user = ctx.message.author\r\n joinmsg = \"Joined {0} role\"\r\n leavemsg = \"Left {0} role\"\r\n\r\n role = await commands.clean_content().convert(ctx, role)\r\n\r\n if role == \"\":\r\n embed = discord.Embed(title=\"Toggleable Roles:\", color=discord.Color.dark_teal())\r\n embed.description = \"\"\"\r\n - :race_car: Mario Kart 8 Deluxe: MK8D\r\n - :squid: Splatoon 2: spla2n\r\n - :card_box: Cards Against Humanity: cah\r\n - :bomb: Counter-Strike: Global Offensive: csgo\r\n - :gun: PUBG: pubg\r\n - :red_circle: Red Eclipse: redeclipse\r\n - :robot: Titanfall (2): titanfall\r\n - :boxing_glove: Super Smash Bros.: smash\r\n - :shopping_cart: Fortnite: fortnite\r\n \"\"\"\r\n await ctx.send(\"\", embed=embed)\r\n\r\n elif role.lower() == \"mk8d\":\r\n if self.bot.mk8d_role in user.roles:\r\n await user.remove_roles(self.bot.mk8d_role)\r\n await ctx.send(leavemsg.format(role.upper()))\r\n\r\n else:\r\n print(\"before error\")\r\n await user.add_roles(self.bot.mk8d_role)\r\n print(\"after error\")\r\n await ctx.send(joinmsg.format(role.upper()))\r\n\r\n elif role.lower() == \"spla2n\":\r\n if self.bot.spla2n_role in user.roles:\r\n await user.remove_roles(self.bot.spla2n_role)\r\n await ctx.send(leavemsg.format(role.lower()))\r\n\r\n else:\r\n await user.add_roles(self.bot.spla2n_role)\r\n await ctx.send(joinmsg.format(role.lower()))\r\n\r\n elif role.lower() == \"cah\":\r\n if self.bot.cah_role in user.roles:\r\n await user.remove_roles(self.bot.cah_role)\r\n await ctx.send(leavemsg.format(role.lower()))\r\n\r\n else:\r\n await user.add_roles(self.bot.cah_role)\r\n await ctx.send(joinmsg.format(role.lower()))\r\n\r\n elif role.lower() == \"csgo\":\r\n if self.bot.csgo_role in user.roles:\r\n await user.remove_roles(self.bot.csgo_role)\r\n await ctx.send(leavemsg.format(role.lower()))\r\n\r\n else:\r\n await user.add_roles(self.bot.csgo_role)\r\n await ctx.send(joinmsg.format(role.lower()))\r\n\r\n elif role.lower() == \"pubg\":\r\n if self.bot.pubg_role in user.roles:\r\n await user.remove_roles(self.bot.pubg_role)\r\n await ctx.send(leavemsg.format(role.lower()))\r\n\r\n else:\r\n await user.add_roles(self.bot.pubg_role)\r\n await ctx.send(joinmsg.format(role.lower()))\r\n\r\n elif role.lower() == \"redeclipse\":\r\n if self.bot.redeclipse_role in user.roles:\r\n await user.remove_roles(self.bot.redeclipse_role)\r\n await ctx.send(leavemsg.format(role.lower()))\r\n\r\n else:\r\n await user.add_roles(self.bot.redeclipse_role)\r\n await ctx.send(joinmsg.format(role.lower()))\r\n\r\n elif role.lower() == \"titanfall\":\r\n if self.bot.titanfall_role in user.roles:\r\n await user.remove_roles(self.bot.titanfall_role)\r\n await ctx.send(leavemsg.format(role.lower()))\r\n\r\n else:\r\n await user.add_roles(self.bot.titanfall_role)\r\n await ctx.send(joinmsg.format(role.lower()))\r\n\r\n elif role.lower() == \"smash\":\r\n if self.bot.smashbros_role in user.roles:\r\n await user.remove_roles(self.bot.smashbros_role)\r\n await ctx.send(leavemsg.format(role.lower()))\r\n\r\n else:\r\n await user.add_roles(self.bot.smashbros_role)\r\n await ctx.send(joinmsg.format(role.lower()))\r\n\r\n elif role.lower() == \"fortnite\":\r\n if self.bot.fortnite_role in user.roles:\r\n await user.remove_roles(self.bot.fortnite_role)\r\n await ctx.send(leavemsg.format(role.lower()))\r\n\r\n else:\r\n await user.add_roles(self.bot.fortnite_role)\r\n await ctx.send(joinmsg.format(role.lower()))\r\n else:\r\n msg = \"{} is not a togglable role\".format(role)\r\n await ctx.send(msg)","async def user_left_button(self, payload: discord.RawReactionActionEvent) -> None:\n\n self.bits = flip_action_bits(LoggingActions.USER_LEFT, self.bits)\n await self.update_embed()","def button_multiclick_fired(self, payload: str):\n json_object = util.deserialize_json(payload)\n logger.debug(f\"Process Multiclick action\")\n clicks = int(json_object['clicks'])\n arduino_name = json_object['name']\n arduino = self.arduinos.get(arduino_name, None)\n if arduino is None:\n # Unknown arduino\n logger.info(f\"Could not find arduino with name '{arduino_name}'.\")\n return\n button = arduino.button_pins[json_object['button_pin']]\n logger.debug(f\"Compare button click {button.clicks} with payload {clicks}\")\n if button.clicks == clicks:\n self.__action_executor.execute_actions(button.get_button_after_release_actions(button.clicks), button,\n arduino.name)\n button.clicks = 0\n button.stop_multi_click_timer()\n else:\n logger.debug(f\"New click has been received\")\n return","async def show_all_self_assignable_roles(self, interaction: Interaction, button: discord.ui.Button) -> None:\n view = RoleButtonView(interaction.user, self.assignable_roles)\n await interaction.response.send_message(\n view=view,\n ephemeral=True\n )","def handle_left_click(self):\n if not self.game_in_progress:\n return\n if self.first_click:\n self.first_click = False\n self.timer.start(1000)\n sender = self.sender()\n row = 0\n col = 0\n for row in range(self.rows):\n for col in range(self.cols):\n if self.button_array[row][col] == sender:\n break\n else:\n continue\n break\n # print 'Received left click:', row, ',', col\n celllist = self.board.opencell(row, col)\n if celllist == []:\n return\n for cell in celllist:\n row = cell[0]\n col = cell[1]\n cell_property = self.board.getcellproperty(row, col)\n if cell_property == CellProperty.Empty:\n self.button_array[row][col].setIcon(QtGui.QIcon(\"icons/OpenedSquare.png\"))\n elif cell_property == CellProperty.Mine:\n # Game over\n for row in range(self.rows):\n for col in range(self.cols):\n cell_property = self.board.getcellproperty(row, col)\n if cell_property == CellProperty.Mine:\n self.button_array[row][col].setIcon(QtGui.QIcon(\"icons/mine.ico\"))\n self.status_button.setIcon(QtGui.QIcon(\"icons/smiley3.ico\"))\n self.game_in_progress = False\n self.timer.stop()\n return\n elif cell_property == CellProperty.MineCountOne:\n self.button_array[row][col].setIcon(QtGui.QIcon(\"icons/1.png\"))\n elif cell_property == CellProperty.MineCountTwo:\n self.button_array[row][col].setIcon(QtGui.QIcon(\"icons/2.png\"))\n elif cell_property == CellProperty.MineCountThree:\n self.button_array[row][col].setIcon(QtGui.QIcon(\"icons/3.png\"))\n elif cell_property == CellProperty.MineCountFour:\n self.button_array[row][col].setIcon(QtGui.QIcon(\"icons/4.png\"))\n elif cell_property == CellProperty.MineCountFive:\n self.button_array[row][col].setIcon(QtGui.QIcon(\"icons/5.png\"))\n elif cell_property == CellProperty.MineCountSix:\n self.button_array[row][col].setIcon(QtGui.QIcon(\"icons/6.png\"))\n elif cell_property == CellProperty.MineCountSeven:\n self.button_array[row][col].setIcon(QtGui.QIcon(\"icons/7.png\"))\n elif cell_property == CellProperty.MineCountEight:\n self.button_array[row][col].setIcon(QtGui.QIcon(\"icons/8.png\"))\n\n game_status = self.board.continuegame()\n print 'Game Status:', game_status\n if game_status == GameStatus.GameWon:\n self.timer.stop()\n self.game_in_progress = False\n player_name = QtGui.QInputDialog.getText(self, \"Name Please !!\",\\\n \"Enter your name for leader board:\")\n # TODO: Replace 1 with the time taken by the end user.\n LeaderBoard.insertnewscore(CURRENT_GAME_LEVEL, player_name[0], self.time)\n self.status_button.setIcon(QtGui.QIcon(\"icons/smiley.ico\"))\n print \"You have won the game\"","def _on_button_press_event(self, widget, event):\n if event.button == 3:\n self.menu.popup(None, None, None, None, event.button, event.time)\n self.menu.show_all()","async def _role_player(self, ctx: Context, *, role: discord.Role):\n\n msg = await ctx.send(\n _(\n \"Are you sure you want to set `{}` as player role?\"\n ).format(role.name)\n )\n start_adding_reactions(msg, ReactionPredicate.YES_OR_NO_EMOJIS)\n\n pred = ReactionPredicate.yes_or_no(msg, ctx.author)\n await ctx.bot.wait_for(\"reaction_add\", check=pred)\n\n if pred.result:\n await self.config.guild(ctx.guild).player_id.set(role.id)\n await ctx.send(_(\"Set `{}` as player role!\").format(role.name))\n else:\n await ctx.send(_(\"Aborted player role setup.\"))","def OnButton(self, event):\r\n\r\n if event.GetEventObject() != self._pButton:\r\n event.Skip()\r\n return\r\n\r\n self.Collapse(not self.IsCollapsed())\r\n\r\n # this change was generated by the user - send the event\r\n ev = wx.CollapsiblePaneEvent(self, self.GetId(), self.IsCollapsed())\r\n self.GetEventHandler().ProcessEvent(ev)","def on_buttonBox_clicked(self, button):\n if button == self.findButton:\n self.__doSearch()\n elif button == self.stopButton:\n self.__stopSearch()","def button(*args, actOnPress: bool=True, actionIsSubstitute: bool=True, align: Union[AnyStr,\n bool]=\"\", annotation: Union[AnyStr, bool]=\"\", backgroundColor: Union[List[float,\n float, float], bool]=None, command: Union[Script, bool]=None, defineTemplate:\n AnyStr=\"\", docTag: Union[AnyStr, bool]=\"\", dragCallback: Script=None, dropCallback:\n Script=None, enable: bool=True, enableBackground: bool=True, enableKeyboardFocus:\n bool=True, exists: bool=True, fullPathName: bool=True, height: Union[int, bool]=0,\n highlightColor: Union[List[float, float, float], bool]=None, isObscured: bool=True,\n label: Union[AnyStr, bool]=\"\", manage: bool=True, noBackground: bool=True,\n numberOfPopupMenus: bool=True, parent: Union[AnyStr, bool]=\"\", popupMenuArray:\n bool=True, preventOverride: bool=True, recomputeSize: bool=True, statusBarMessage:\n AnyStr=\"\", useTemplate: AnyStr=\"\", visible: bool=True, visibleChangeCommand:\n Union[Script, bool]=None, width: Union[int, bool]=0, q=True, query=True, e=True,\n edit=True, **kwargs)->Union[AnyStr, Any]:\n pass","def click_login_button(self):","def handle_event(self, event):\n if self.sub_event is not None:\n self.sub_event.handle_event(event)\n else:\n self.confirm_response.handle_event(event)","def on_click(button):\n global ttt, choices, count, sym, result, x_pos, o_pos\n\n if count % 2 == 0:\n sym = \"X\"\n else:\n sym = \"O\"\n count += 1\n\n button.config(\n text=sym,\n state='disabled',\n disabledforeground=\"red\") # For cross\n\n x, y = get_coordinates(button)\n x += 1\n y += 1\n x_pos.append((x, y))\n state = gen_state(to_move='O', x_positions=x_pos,\n o_positions=o_pos)\n try:\n choice = choices.get()\n if \"Random\" in choice:\n a, b = random_player(ttt, state)\n elif \"Pro\" in choice:\n a, b = minimax_decision(state, ttt)\n else:\n a, b = alphabeta_player(ttt, state)\n except (ValueError, IndexError, TypeError) as e:\n disable_game()\n result.set(\"It's a draw :|\")\n return\n if 1 <= a <= 3 and 1 <= b <= 3:\n o_pos.append((a, b))\n button_to_change = get_button(a - 1, b - 1)\n if count % 2 == 0: # Used again, will become handy when user is given the choice of turn.\n sym = \"X\"\n else:\n sym = \"O\"\n count += 1\n\n if check_victory(button):\n result.set(\"You win :)\")\n disable_game()\n else:\n button_to_change.config(text=sym, state='disabled',\n disabledforeground=\"black\")\n if check_victory(button_to_change):\n result.set(\"You lose :(\")\n disable_game()","def reaction_check(self, payload):\n if payload.message_id != self.message.id:\n return False\n if payload.user_id not in (*self.bot.owner_ids, self._author_id):\n return False\n return payload.emoji in self.buttons","def button_press_event(self, widget, event, menu):\n\t\tif event.type == gtk.gdk.BUTTON_PRESS and event.button == 3:\n\t\t\tmenu.popup(None, None, None, event.button, event.time)\n\t\treturn False","def button_release_event(self, widget, event):\n x, y = event.x, event.y\n\n # x, y = coordinates where the button was released\n self.last_win_x, self.last_win_y = x, y\n\n button = 0\n # prepare button mask as in button_press_event()\n\n data_x, data_y = self.check_cursor_location()\n\n return self.make_ui_callback('button-release', button, data_x, data_y)","def ev_controllerbuttondown(self, event: tcod.event.ControllerButton) -> T | None:","def handle_event(self, event):\n # Get rid of all non keydown events\n if event.type != pygame.KEYDOWN:\n return\n\n if not self._initial_prompt.is_over():\n self._initial_prompt.handle_event(event)\n\n # If response is 2 then the user selected SEE YA!. Pass control to\n # seeya dialogue.\n elif self._response == 2 and not self._seeya_dialogue.is_over():\n self._seeya_dialogue.handle_event(event)\n\n # If response is 0 then the user selected BUY. Pass control to buy\n # menu.\n elif self._response == 0 and not self._buy_menu.is_over():\n self._buy_menu.handle_event(event)\n\n elif self._response == 1 and not self._sell_menu.is_over():\n self._sell_menu.handle_event(event)","def _press(self, event):","def on_click(self):\n arcade.play_sound(button, volume=constants.MUSIC_VOLUME / 40)\n\n global success\n global fails\n if success or fails == 20:\n reset_global_variables()\n self.minigame.window.show_view(self.minigame.main_view)\n else:\n self.minigame.window.show_view(self.minigame.main_view)\n print(f\"Exit Button.\")","def click(self) -> None:\n if self.is_enabled():\n try:\n self.element.click()\n logging.info(\"Class booked!\")\n except:\n logging.info(\"The button could not be clicked, trying to execute the element.\")\n self.driver.execute_script(\"arguments[0].click();\", self.element)\n finally:\n logging.info(\"Could not book the class\")\n\n else:\n warnings.warn('The Button cannot be clicked.')","def handle_remote_button(self, request):\n self._verify_auth_parameters(request)\n content = yield from request.content.read()\n parsed = dmap.parse(content, tag_definitions.lookup_tag)\n self.last_button_pressed = dmap.first(parsed, 'cmbe')\n return web.Response(status=200)","def RightClick(self):\n self._PressRightButton()\n self._ReleaseAllButtons()","def on_mouse_release(self, x: float, y: float, button, modifiers):\n #dialogue buttons\n check_mouse_release_for_buttons(x, y, self.levels[self.current_level].dialogue_list)\n\n #room info prompt buttons\n check_mouse_release_for_buttons(x, y, self.levels[self.current_level].room_info_list)","def slotAccept(self, id):\n self.category = self.buttonGroup.find(id).text().ascii()\n self.accept()","def HandlePress(self, event: tkEvent):\n pass","def ev_mousebuttondown(self, event: MouseButtonDown) -> None:","async def edit(self, ctx: commands.Context, message_id: int):\n\n # Standard wait_for check function for message inputs, makes sure the command user's messages in command channel are considered\n def message_check(m: discord.Message):\n return m.author == ctx.author and m.channel == ctx.channel\n\n # Standard reaction check that ensures no duplicate reacrole entry, just name the relevant message 'm' before adding this one to check kwarg in wait_for\n def reaction_check_nd(_r: discord.Reaction, _u):\n return _u == ctx.author and _r.message == m and str(_r.emoji) not in self._cache[ctx.guild.id][PM.id]\n\n if message_id in self._cache[ctx.guild.id]:\n\n # Not actually channel id int but I decided to name it that way anyway\n chanid = await self.bot.pool.fetchrow(\"SELECT channelid FROM selfrole_lookup WHERE messageid = $1\", message_id)\n chan: discord.TextChannel = ctx.guild.get_channel(chanid['channelid'])\n\n # Currently need message content for title, might start saving title in db to avoid this api call idk\n try:\n PM: discord.Message = await chan.fetch_message(message_id)\n except discord.NotFound:\n await ctx.send(\"It would seem that the message for the role menu you're trying to edit has been deleted, please try creating a new one\")\n return\n\n buttons = [\"\\U0001f1e6\", \"\\U0001f1e7\", \"\\U0001f1e8\", \"\\U0001f1e9\"]\n\n e1 = discord.Embed(title=\"What aspect of the menu do you wish to change?\",\n description=\"\\U0001f1e6 - Add a role\\n\\n\"\n \"\\U0001f1e7 - Remove existing role\\n\\n\"\n \"\\U0001f1e8 - Edit the reaction of a role\\n\\n\"\n \"\\U0001f1e9 - Change the title\",\n colour=discord.Colour.blue())\n # Send the initial menu\n menu = await ctx.send(embed=e1)\n\n for button in buttons:\n await menu.add_reaction(button)\n\n # We need the first reaction where the emoji is one of the buttons\n def button_check(_r, _u):\n return _u == ctx.author and _r.message == menu and str(_r.emoji) in buttons\n # Get the option the user chose\n try:\n r, u = await self.bot.wait_for('reaction_add', check=button_check, timeout=20)\n except asyncio.TimeoutError:\n await ctx.send(\"Timed out\")\n return\n\n # If user wanted to add a new role to the menu\n if str(r.emoji) == buttons[0]:\n await menu.clear_reactions()\n await menu.edit(content=\"What role do you wish to be added? Enter its mention, id, or name\", embed=None)\n\n # Get the role object for the new role to be added\n try:\n m = await self.bot.wait_for('message', check=message_check, timeout=30)\n newrole = await self.rc.convert(ctx, m.content)\n\n if newrole.id in self._cache[ctx.guild.id][PM.id].values():\n await ctx.send(\"Error: role already exists in the menu, perhaps you meant to edit it?\")\n return\n except asyncio.TimeoutError:\n await ctx.send(\"Timed out\")\n return\n except commands.BadArgument:\n await ctx.send(\"Role not found, please try again\")\n return\n\n m = await ctx.send(f\"React on this message with the reaction that will correspond to the role `{newrole}`\")\n\n # Get the reaction/emoji that will correspond to the new role and yank everything into db\n try:\n r, u = await self.bot.wait_for('reaction_add', check=reaction_check_nd, timeout=30)\n self._cache[ctx.guild.id][PM.id][str(r.emoji)] = newrole.id\n\n query = \"\"\"\n INSERT INTO selfrole (messageid, emoji, roleid)\n VALUES ($1, $2, $3)\n \"\"\"\n\n await self.bot.pool.execute(query, PM.id, str(r.emoji), newrole.id)\n\n # Standard way of getting the embed description of the role menu\n newmenudesc = \"\\n\\n\".join([f\"{k} - {ctx.guild.get_role(v)}\" for k, v in self._cache[ctx.guild.id][PM.id].items()])\n\n newembed = discord.Embed(title=PM.embeds[0].title,\n description=newmenudesc,\n colour=discord.Colour.blue())\n\n await PM.edit(embed=newembed)\n await PM.add_reaction(r.emoji)\n await ctx.send(\"Menu edit completed successfully, you may now delete the messages other than the menu itself\")\n\n except asyncio.TimeoutError:\n await ctx.send(\"Timed out\")\n\n elif str(r.emoji) == buttons[1]:\n # Gotta yank the buttons to make everything squeaky clean\n await menu.clear_reactions()\n await menu.edit(content=\"Enter the role you wish to remove from the menu, can be mention, id or name\",\n embed=None)\n\n try:\n # Get role from user\n m = await self.bot.wait_for('message', check=message_check, timeout=20)\n role = await self.rc.convert(ctx, m.content)\n\n # If user trying to edit reaction to role that wasn't even in the menu to begin with\n if role.id not in self._cache[ctx.guild.id][PM.id].values():\n raise commands.BadArgument(\"Role not in cache\")\n\n # Get the key to delete using the old fashioned way, and subsequently delete it\n targetkey = \"\"\n for key, value in self._cache[ctx.guild.id][PM.id].items():\n if value == role.id:\n targetkey = key\n break\n self._cache[ctx.guild.id][PM.id].pop(targetkey)\n\n # After everything is done and dusted, make database entry and edit the menu\n query = \"\"\"\n DELETE FROM selfrole WHERE messageid = $1 AND roleid = $2\n \"\"\"\n await self.bot.pool.execute(query, PM.id, role.id)\n newmenudesc = \"\\n\\n\".join(\n [f\"{k} - {ctx.guild.get_role(v)}\" for k, v in self._cache[ctx.guild.id][PM.id].items()])\n\n newembed = discord.Embed(title=PM.embeds[0].title,\n description=newmenudesc,\n colour=discord.Colour.blue())\n await PM.edit(embed=newembed)\n await PM.clear_reaction(targetkey)\n await ctx.send(\n \"Menu edit completed successfully, you may now delete the messages other than the menu itself\")\n except asyncio.TimeoutError:\n await ctx.send(\"Timed out\")\n return\n except commands.BadArgument:\n await ctx.send(\"I don't think that role exists in that menu, run the command again\")\n return\n\n elif str(r.emoji) == buttons[2]:\n # Same drill, remove buttons to make it look clean\n await menu.clear_reactions()\n await menu.edit(embed=None, content=\"Enter the role for which you wish to change the reaction.\")\n\n try:\n m = await self.bot.wait_for('message', check=message_check, timeout=20)\n role = await self.rc.convert(ctx, m.content)\n\n if role.id not in self._cache[ctx.guild.id][PM.id].values():\n raise commands.BadArgument(\"Role not in cache\")\n\n except asyncio.TimeoutError:\n await ctx.send(\"Timed out\")\n return\n except commands.BadArgument:\n await ctx.send(\"Couldn't find the role you wished to edit in the menu\")\n return\n\n # Get the reaction/emoji that will correspond to the new role and yank everything into db\n m = await ctx.send(f\"React on this message with the new reaction that will correspond to the role {role}\")\n try:\n r, u = await self.bot.wait_for('reaction_add', check=reaction_check_nd, timeout=30)\n\n # Can only delete entry if have the key so....\n TargetKey = \"\" # Set default value so IDE stops screaming\n for k, v in self._cache[ctx.guild.id][PM.id].items():\n if v == role.id:\n TargetKey = k\n\n # Make new entry and delete the old one\n self._cache[ctx.guild.id][PM.id][str(r.emoji)] = role.id\n self._cache[ctx.guild.id][PM.id].pop(TargetKey)\n\n # After everything is done and dusted, at last update the database entry\n await self.bot.pool.execute(\"UPDATE selfrole SET emoji = $1 WHERE roleid = $2 AND messageid = $3\", str(r.emoji), role.id, PM.id)\n\n # Hehehehehehe\n newmenudesc = \"\\n\\n\".join(\n [f\"{k} - {ctx.guild.get_role(v)}\" for k, v in self._cache[ctx.guild.id][PM.id].items()])\n\n newembed = discord.Embed(title=PM.embeds[0].title,\n description=newmenudesc,\n colour=discord.Colour.blue())\n\n await PM.edit(embed=newembed)\n await PM.clear_reaction(TargetKey)\n await PM.add_reaction(str(r.emoji))\n await ctx.send(\n \"Menu edit completed successfully, you may now delete the messages other than the menu itself\")\n except asyncio.TimeoutError:\n await ctx.send(\"Timed out\")\n return\n\n elif str(r.emoji) == buttons[3]:\n # This one speaks for itself I think.\n await menu.clear_reactions()\n await menu.edit(embed=None, content=\"Enter the new title you want the menu to have\")\n try:\n m = await self.bot.wait_for('message', check=message_check, timeout=30)\n e = discord.Embed(title=f\"Role menu: {m.content}\",\n description=PM.embeds[0].description,\n colour=PM.embeds[0].colour)\n await PM.edit(embed=e)\n except asyncio.TimeoutError:\n await ctx.send(\"Timed out\")\n return\n\n else:\n await ctx.send(\"Menu not found in this server, double check if the id was entered correctly\")","def proceed_menu_button_click(cb: telebot.types.CallbackQuery):\n logger.info(f'Got callback {cb.data} from id: {cb.from_user.id}.')\n\n message = bot.send_message(\n cb.from_user.id,\n 'Starting the game.'\n )\n user = utils.get_user_or_none(cb.from_user)\n if user:\n utils.update_dissolving_messages(user, 'starting_the_game', message)\n\n if cb.data == 'ai':\n # TODO inline keyboard for selecting AI difficulty level\n logger.info(f'Starting AI game for id: {cb.from_user.id}.')\n utils.start_new_game(bot, cb.from_user, 'ai')\n\n elif cb.data == 'person':\n logger.info(f'Starting PVP game for id: {cb.from_user.id}.')\n utils.start_new_game(bot, cb.from_user, 'person')\n\n logger.info(f'Deleting message {cb.message.text} from chat {cb.from_user.id}.')\n bot.delete_message(\n cb.from_user.id,\n cb.message.message_id\n )","async def message_edit_button(self, payload: discord.RawReactionActionEvent) -> None:\n\n self.bits = flip_action_bits(LoggingActions.MESSAGE_EDIT, self.bits)\n await self.update_embed()","async def _toggle_role(self, ctx, selfrole: Selfrole):\n\n if selfrole.role not in ctx.message.author.roles:\n try:\n await ctx.message.author.add_roles(selfrole.role)\n except discord.Forbidden:\n raise exceptions.ForbiddenError(\n exceptions.ForbiddenTask.ADD_ROLE, selfrole.role.name\n )\n\n await ctx.send(f\"{config.YES} {selfrole.join_message}\")\n\n elif selfrole.role in ctx.message.author.roles:\n try:\n await ctx.message.author.remove_roles(selfrole.role)\n except discord.Forbidden:\n raise exceptions.ForbiddenError(\n exceptions.ForbiddenTask.REMOVE_ROLE, selfrole.role.name\n )\n\n await ctx.send(\n f\"{config.YES} The `{selfrole.role.name}` role was removed from you.\"\n )","def on_next_turn_click(self, button):\n if self.referee.is_game_over():\n Gtk.main_quit()\n else:\n self.do_next_turn(button)\n # if the game is over after this turn, we will shutdown on the next click,\n # so visually alert the player with the button label\n if self.referee.is_game_over():\n button.set_label(GAME_OVER_MSG)","def button_pressed(self, channel):\n print(\"button_pressed() channel = \"+str(channel))\n GPIO.remove_event_detect(channel) # turn button off to avoid double hits\n match channel:\n case self.prev_button: \n print(\"Previous button - sending message: mycroft.audio.service.prev\")\n self.send_message(\"mycroft.audio.service.prev\")\n case self.stop_button: \n print(\"Stop button - sending message: mycroft.stop\")\n self.send_message(\"mycroft.stop\")\n case self.next_button: \n print(\"Next button - sending message: mycroft.audio.service.next\")\n self.send_message(\"mycroft.audio.service.next\")\n case _: # not expected\n print(\"Did not expect channel = \"+str(channel))\n GPIO.add_event_detect(channel, GPIO.FALLING, callback=self.button_pressed, bouncetime=5) # turn button back on","def click(self, mouse_pos):\n for button in self.enabled_buttons(): # type: Button\n if button.is_position_on_button(mouse_pos):\n self.sound.play_sound(self.click_sound)\n button.click()","def handle_event(self, event):\n if not self.can_afford:\n self.cant_afford_dialogue.handle_event(event)\n else:\n self.how_many_selector.handle_event(event)","def event_handler(self):\r\n for event in pygame.event.get():\r\n if event.type == QUIT:\r\n pygame.quit()\r\n sys.exit()\r\n elif event.type == MOUSEBUTTONDOWN and event.button == LEFT_CLICK:\r\n self.left_mouse_down_handler(event)\r\n elif event.type == MOUSEBUTTONUP and event.button == LEFT_CLICK:\r\n self.left_mouse_up_handler(event)\r\n elif event.type == MOUSEBUTTONDOWN and event.button == RIGHT_CLICK:\r\n self.right_mouse_down_handler(event)\r\n elif event.type == MOUSEBUTTONUP and event.button == RIGHT_CLICK:\r\n self.right_mouse_up_handler(event)\r\n elif event.type == MOUSEMOTION:\r\n self.mouse_motion_handler(event)\r\n elif event.type == MOUSEBUTTONUP and event.button in [2, 4, 5]:\r\n self.shortcut_click(event)","async def _handle_role(self, member: discord.Member) -> tuple:\n list_to_add = await self.config.guild(member.guild).autoroles()\n list_to_remove = await self.config.guild(member.guild).temprole()\n actions = []\n if list_to_add:\n for role in list_to_add:\n to_add = member.guild.get_role(role)\n await member.add_roles(to_add, reason=\"Adding auto role by Verify.\")\n actions.append(\n \"added automatically role{plural}\".format(\n plural=\"s\" if len(list_to_add) > 1 else \"\"\n )\n )\n if list_to_remove:\n to_remove = member.guild.get_role(list_to_remove)\n if to_remove in member.roles:\n await member.remove_roles(to_remove, reason=\"Removing temporary role by Verify.\")\n actions.append(\"removed temporary role\")\n return (\n True,\n humanize_list(actions).capitalize() if actions else \"No action taken.\",\n )","def on_mouse_click(self, event):\n if not self.is_game_over:\n try:\n # i, j coordinates of the click event\n i = int(round(event.ydata))\n j = int(round(event.xdata))\n\n # Left button\n if event.button == 1 or event.button == 2:\n self.reveal(i, j)\n\n # Right button\n elif event.button == 3:\n self.flag(i, j)\n\n except (TypeError, IndexError):\n pass","def check_replay_button(self, mouse_x, mouse_y):\r\n for button in self._replay_button_list:\r\n if button.get_button_rect().collidepoint(mouse_x, mouse_y):\r\n button_clicked = button\r\n break\r\n else:\r\n button_clicked = None\r\n\r\n if button_clicked is not None and button_clicked.get_num_atom() == 1:\r\n self.setup_new_game()\r\n elif button_clicked is not None and button_clicked.get_num_atom() == 2:\r\n sys.exit()","def keypress_callback():\n data = request.get_json()\n buttonid = data['buttonid']\n assert buttonid in pyautogui.KEYBOARD_KEYS, \"Unknown buttonid received\"\n pyautogui.press(buttonid)\n return json.dumps({'success':True}), 200, {'ContentType':'application/json'}","def onclick(self, fun, btn=1, add=None):\n self._onscreenclick(fun, btn, add)","def on_confirm_button(self, negotiation_outcome):\n # Send message.\n self.ros_node.send_message(UserInput.NEGOTIATION,\n negotiation_outcome)\n # Reset label and button.\n self.confirm_label.setText(\"Selection sent.\")\n self.confirm_button.setStyleSheet('QPushButton {color: gray;}')\n self.confirm_button.setEnabled(False)","def on_right_click(self, client, game) -> None:\n pass","def click_modal_button(self, title):\n locator = lex_locators[\"modal\"][\"button\"].format(title)\n self.selenium.wait_until_page_contains_element(locator)\n self.selenium.wait_until_element_is_enabled(locator)\n self._jsclick(locator)","def post(self, role, user_id, action):\n self._login_as_staff()\n return self.client.post(self.path(role=role), {'user_id': user_id, 'action': action})","def press(button_id: str) -> None:\n try:\n self.query_one(f\"#{button_id}\", Button).press()\n except NoMatches:\n pass","def manage_updateRole(self, role_id, title, description, RESPONSE=None,\n REQUEST=None):\n self.updateRole(role_id, title, description)\n\n message = 'Role+updated'\n\n if RESPONSE is not None:\n RESPONSE.redirect('%s/manage_roles?role_id=%s&'\n 'manage_tabs_message=%s' %\n (self.absolute_url(), role_id, message))","def _ClickGaiaButton(self, button_text, alt_text):\n get_button_js = '''\n (function() {\n var buttons = document.querySelectorAll('[role=\"button\"]');\n if (buttons == null)\n return false;\n for (var i=0; i < buttons.length; ++i) {\n if ((buttons[i].textContent.indexOf('%s') != -1) ||\n (buttons[i].textContent.indexOf('%s') != -1)) {\n buttons[i].click();\n return true;\n }\n }\n return false;\n })();\n ''' % (button_text, alt_text)\n self._GaiaWebviewContext().WaitForJavaScriptCondition(\n get_button_js, timeout=20)","def _onMouseButton(e):\n\n mouse_event = MouseButtonEvent(e)\n orca_state.lastInputEvent = mouse_event\n\n # A mouse button event looks like: mouse:button:1p, where the\n # number is the button number and the 'p' is either 'p' or 'r',\n # meaning pressed or released. We only want to stop speech on\n # button presses.\n #\n if mouse_event.pressed:\n speech.stop()"],"string":"[\n \"async def subscribe_command(self, ctx: commands.Context, *_) -> None: # We don't actually care about the args\\n view = RoleButtonView(ctx.author, self.assignable_roles)\\n await ctx.send(\\n \\\"Click the buttons below to add or remove your roles!\\\",\\n view=view,\\n delete_after=DELETE_MESSAGE_AFTER\\n )\",\n \"async def callback(self, interaction: Interaction) -> None:\\n if isinstance(interaction.user, discord.User):\\n log.trace(\\\"User %s is not a member\\\", interaction.user)\\n await interaction.message.delete()\\n self.view.stop()\\n return\\n\\n await members.handle_role_change(\\n interaction.user,\\n interaction.user.remove_roles if self.assigned else interaction.user.add_roles,\\n discord.Object(self.role.role_id),\\n )\\n\\n self.assigned = not self.assigned\\n try:\\n await self.update_view(interaction)\\n send_function = interaction.followup.send\\n except discord.NotFound:\\n log.debug(\\\"Subscribe message for %s removed before buttons could be updated\\\", interaction.user)\\n self.view.stop()\\n send_function = interaction.response.send_message\\n\\n await send_function(\\n self.LABEL_FORMAT.format(action=\\\"Added\\\" if self.assigned else \\\"Removed\\\", role_name=self.role.name),\\n ephemeral=True,\\n )\",\n \"def handle_button(self, event, event_type):\\n # 0 for left\\n # 1 for right\\n # 2 for middle/center\\n # 3 for side\\n mouse_button_number = self._get_mouse_button_number(event)\\n\\n # Identify buttons 3,4,5\\n if event_type in (25, 26):\\n event_type = event_type + (mouse_button_number * 0.1)\\n\\n # Add buttons to events\\n event_type_string, event_code, value, scan = self.codes[event_type]\\n if event_type_string == \\\"Key\\\":\\n scan_event, key_event = self.emulate_press(\\n event_code, scan, value, self.timeval)\\n self.events.append(scan_event)\\n self.events.append(key_event)\\n\\n # doubleclick/n-click of button\\n click_state = self._get_click_state(event)\\n\\n repeat = self.emulate_repeat(click_state, self.timeval)\\n self.events.append(repeat)\",\n \"async def _on_raw_reaction(\\n self,\\n payload: RawReactionActionEvent,\\n reaction_type: EnumReactionType,\\n ) -> None:\\n if self.__is_self(payload.user_id):\\n print(\\\"reaction added by the bot itself\\\")\\n return\\n\\n guild = self._client.get_guild(payload.guild_id)\\n\\n if reaction_type == EnumReactionType.ADD:\\n await self.__roles.add_role(\\n guild, payload.message_id,\\n payload.emoji, payload.user_id,\\n\\n )\\n elif reaction_type == EnumReactionType.REMOVE:\\n await self.__roles.remove_role(\\n guild, payload.message_id,\\n payload.emoji, payload.user_id,\\n )\\n else:\\n raise InvalidReactionType\",\n \"async def reaction_roles_post(inter: MessageInteraction, description, roles):\\n view = disnake.ui.View(timeout=None)\\n for role in roles:\\n view.add_item(disnake.ui.Button(label=role.name, custom_id=role.id))\\n messages = await send_message(msg=description, channel=inter.channel, view=view)\\n for message in messages:\\n await ReactionRoleMessage.insert(message.id)\",\n \"def button_pressed(\\n self, event_name: str, data: dict, kwargs: dict) -> None:\\n action_name = self.get_state(self.entity_ids[CONF_ACTION_LIST])\\n\\n if action_name not in self.OBJECT_MAP:\\n self.error('Unknown action: {0}'.format(action_name))\\n return\\n\\n self.log('Running Dash action: {0}'.format(action_name))\\n\\n klass, args = self.OBJECT_MAP[action_name]\\n button = klass(self, args) # type: ignore\\n button.run()\",\n \"def OnButton(self, event):\\n button = event.GetEventObject().GetName()\\n if button == \\\"Button1\\\":\\n self.OnButton1()\\n elif button == \\\"Button2\\\":\\n self.OnButton2()\\n elif button == \\\"Button3\\\":\\n self.OnExit(event)\",\n \"def clickedAction(self, events):\\n print(\\\"The {} button was clicked!\\\".format(self.imgname))\",\n \"def __on_click(self, evt):\\n if evt.button() == Qt.LeftButton:\\n return self._on_left_click(evt)\\n if evt.button() == Qt.RightButton:\\n return self._on_right_click(evt)\",\n \"def handle_right_click(self):\\n if not self.game_in_progress:\\n return\\n if self.first_click:\\n self.first_click = False\\n self.timer.start(1000)\\n sender = self.sender()\\n row = 0\\n col = 0\\n for row in range(self.rows):\\n for col in range(self.cols):\\n if self.button_array[row][col] == sender:\\n break\\n else:\\n continue\\n break\\n # print 'Received right click:', row, ',', col\\n status = self.board.getcellstatus(row, col)\\n if status == CellStatus.Opened:\\n return\\n elif status == CellStatus.Closed:\\n self.remainingminecount = self.remainingminecount - 1\\n self.mines_lcd.display(str(self.remainingminecount))\\n self.board.setcellstatus(row, col, CellStatus.MarkedAsMine)\\n self.button_array[row][col].setIcon(QtGui.QIcon(\\\"icons/Flag.png\\\"))\\n elif status == CellStatus.MarkedAsMine:\\n self.remainingminecount = self.remainingminecount + 1\\n self.mines_lcd.display(str(self.remainingminecount))\\n self.board.setcellstatus(row, col, CellStatus.MarkedAsSuspectedMine)\\n self.button_array[row][col].setIcon(QtGui.QIcon(\\\"icons/questionmark.png\\\"))\\n elif status == CellStatus.MarkedAsSuspectedMine:\\n self.board.setcellstatus(row, col, CellStatus.Closed)\\n self.button_array[row][col].setIcon(QtGui.QIcon(\\\"icons/unopenedsquare.png\\\"))\",\n \"def handle_button_event(evt_type: EventType, hue_resource: Button) -> None:\\n LOGGER.debug(\\\"Received button event: %s\\\", hue_resource)\\n\\n # guard for missing button object on the resource\\n if hue_resource.button is None:\\n return\\n\\n hue_device = btn_controller.get_device(hue_resource.id)\\n device = dev_reg.async_get_device({(DOMAIN, hue_device.id)})\\n\\n # Fire event\\n data = {\\n # send slugified entity name as id = backwards compatibility with previous version\\n CONF_ID: slugify(f\\\"{hue_device.metadata.name} Button\\\"),\\n CONF_DEVICE_ID: device.id, # type: ignore\\n CONF_UNIQUE_ID: hue_resource.id,\\n CONF_TYPE: hue_resource.button.last_event.value,\\n CONF_SUBTYPE: hue_resource.metadata.control_id,\\n }\\n hass.bus.async_fire(ATTR_HUE_EVENT, data)\",\n \"def button_clicked(self, event_name, data, kwargs):\\n if data[\\\"entity_id\\\"] == self.button:\\n if data[\\\"click_type\\\"] == \\\"single\\\":\\n if float(self.cached_fade_in_time) > 0:\\n self.log(\\n \\\"Turning on {}\\\".format(self.friendly_name(self.wakeup_light))\\n )\\n self.call_service(\\n \\\"light/turn_on\\\", entity_id=self.wakeup_light, brightness_pct=1\\n )\\n transition = int(\\n float(self.cached_fade_in_time)\\n * int(self.fade_in_time_multiplicator)\\n )\\n self.log(\\n \\\"Transitioning light in over {} seconds\\\".format(transition)\\n )\\n self.timer_handle_list.append(\\n self.run_in(\\n self.run_fade_in, 1, transition=transition, brightness_pct=1\\n )\\n )\",\n \"async def on_raw_reaction_add(self, payload: discord.RawReactionActionEvent):\\n \\n guild = self.bot.get_guild(payload.guild_id)\\n user = discord.utils.get(guild.members, id=payload.user_id)\\n # Skipping bot reactions\\n if user.bot:\\n return\\n\\n # Search the event by message id\\n message_id = payload.message_id\\n channel_id = payload.channel_id\\n guild_id = payload.guild_id\\n event = connector.getEventByMessage(guild_id, message_id, channel_id)\\n if event is None:\\n return\\n\\n role = discord.utils.get(payload.member.guild.roles, id=event.role_id)\\n\\n if (payload.event_type == \\\"REACTION_ADD\\\") and (event.emoji == str(payload.emoji)):\\n # Adding role to user\\n await payload.member.add_roles(role)\\n elif (payload.event_type == \\\"REACTION_REMOVE\\\") and (event.emoji == str(payload.emoji)) and (role in payload.member.roles):\\n # Remove role from user\\n await payload.member.remove_roles(role)\",\n \"def on_click(self):\\n arcade.play_sound(button, volume=constants.MUSIC_VOLUME / 40)\\n\\n global entered_code\\n\\n if button_text == \\\"Enter Code\\\":\\n self.answer = self.input_box.text\\n entered_code = self.answer\\n self.convert_string_to_int(self.answer)\\n self.ui_manager.purge_ui_elements()\\n self.minigame.window.show_view(FakeCodeGame.MyView(self.minigame.main_view))\\n print(f\\\"EnterCode button. {self.answer}\\\")\\n elif button_text == \\\"Exit Terminal\\\":\\n reset_global_variables()\\n self.minigame.window.show_view(self.minigame.main_view)\\n\\n \\\"\\\"\\\"\\n The following functions check the submitted answer\\n \\\"\\\"\\\"\",\n \"def eventHandler(self, event: pygame.event):\\n # change selected color if this button's rectangle was clicked\\n if event.type == pygame.MOUSEBUTTONDOWN:\\n if event.button == 1:\\n if self.rect.collidepoint(event.pos): # is mouse over button\\n self.image = self._images[ButtonImages.CLICKING_IMAGE.value]\\n self.beingClicked = True\\n for func, *args in self.functionsToInvokeWhenClicked:\\n func(*args)\\n elif event.type == pygame.MOUSEBUTTONUP and self.beingClicked:\\n if event.button == 1:\\n self.beingClicked = False\\n self.image = self._images[ButtonImages.DEFAULT_IMAGE.value]\",\n \"async def on_raw_reaction_add(self, payload: discord.RawReactionActionEvent):\\n role: discord.Role = await self.check_payload(payload)\\n if payload.user_id == self.bot.user.id:\\n return\\n if role:\\n guild = self.bot.get_guild(payload.guild_id)\\n member = guild.get_member(payload.user_id)\\n await member.add_roles(role)\",\n \"async def on_raw_reaction_add(self, payload: discord.RawReactionActionEvent):\\n\\n # Skipping bot reactions\\n if payload.member.bot:\\n return\\n\\n # Search the event by message id\\n message_id = payload.message_id\\n channel_id = payload.channel_id\\n guild_id = payload.guild_id\\n event_type = connector.getEventTypeByMessage(guild_id, message_id, channel_id)\\n if event_type is None:\\n return\\n\\n role = discord.utils.get(payload.member.guild.roles, id=event_type.role_id)\\n\\n if (payload.event_type == \\\"REACTION_ADD\\\") and (event_type.emoji == str(payload.emoji)):\\n # Adding role to user\\n await payload.member.add_roles(role)\",\n \"def on_buttonBox_clicked(self, button):\\n if button == self.buttonBox.button(QDialogButtonBox.Close):\\n self.close()\\n elif button == self.buttonBox.button(QDialogButtonBox.Cancel):\\n self.__client.abort()\\n self.__canceled = True\\n elif button == self.__installButton:\\n self.__install()\\n elif button == self.__showDetailsButton:\\n self.__showDetails()\",\n \"def OnButton(self, event):\\n\\n\\n event_id = event.GetId()\\n event_obj = event.GetEventObject()\\n print(\\\"Button 1 Clicked:\\\")\\n print(\\\"ID=%d\\\" % event_id)\\n print(\\\"object=%s\\\" % event_obj.GetLabel())\",\n \"def OnButton(self, event):\\r\\n \\r\\n button = event.GetInt()\\r\\n\\r\\n if button == AUI_BUTTON_LEFT or button == AUI_BUTTON_RIGHT:\\r\\n if button == AUI_BUTTON_LEFT:\\r\\n if self.GetTabOffset() > 0:\\r\\n \\r\\n self.SetTabOffset(self.GetTabOffset()-1)\\r\\n self.Refresh()\\r\\n self.Update()\\r\\n else:\\r\\n self.SetTabOffset(self.GetTabOffset()+1)\\r\\n self.Refresh()\\r\\n self.Update()\\r\\n \\r\\n elif button == AUI_BUTTON_WINDOWLIST:\\r\\n idx = self.GetArtProvider().ShowDropDown(self, self._pages, self.GetActivePage())\\r\\n \\r\\n if idx != -1:\\r\\n \\r\\n e = AuiNotebookEvent(wxEVT_COMMAND_AUINOTEBOOK_PAGE_CHANGING, self.GetId())\\r\\n e.SetSelection(idx)\\r\\n e.SetOldSelection(self.GetActivePage())\\r\\n e.SetEventObject(self)\\r\\n self.GetEventHandler().ProcessEvent(e)\\r\\n \\r\\n else:\\r\\n event.Skip()\",\n \"def callback(self, event):\\n button = event[\\\"button\\\"]\\n\\n cmd = self._callbacks.get(self._uuidstr(self.global_id, button), None)\\n cmd = self._callbacks.get(self._uuidstr(event[\\\"name\\\"], button), cmd)\\n cmd = self._callbacks.get(self._uuidstr(event[\\\"instance\\\"], button), cmd)\\n\\n if cmd is None:\\n return\\n if callable(cmd):\\n cmd(event)\\n else:\\n bumblebee.util.execute(cmd, False)\",\n \"def button_clicked(self):\\n sender = self.sender()\\n if self._buttons_active:\\n message_box = QtGui.QMessageBox()\\n quit_button = message_box.addButton('Quitter',\\n QtGui.QMessageBox.RejectRole)\\n if (sender.row, sender.col, sender.text()) in self._solution:\\n message_box.setText('Félicitations, vous avez deviné juste !')\\n else:\\n message_box.setText('Dommage, vous avez perdu !')\\n message_box.setInformativeText('Vous pouvez proposer une '\\n 'autre sortie, ou afficher de '\\n 'nouveau les objets.')\\n show_items_button = message_box.addButton('Voir objets',\\n QtGui.QMessageBox.AcceptRole)\\n play_again_button = message_box.addButton( 'Réessayer',\\n QtGui.QMessageBox.AcceptRole)\\n message_box.setDefaultButton(play_again_button)\\n message_box.exec()\\n if message_box.clickedButton() == quit_button:\\n self.close()\\n elif message_box.clickedButton() == show_items_button:\\n self.reset_game()\",\n \"def m_press(self, button: MButton):\\n pass\",\n \"def handle_button_press(button_state, mqtt_client, message):\\n if button_state:\\n ev3.Sound.speak(message).wait()\\n mqtt_client.send_message(\\\"button_pressed\\\", [message])\",\n \"async def on_raw_reaction_remove(self, payload: discord.RawReactionActionEvent):\\n\\n guild = self.bot.get_guild(payload.guild_id)\\n user = guild.get_member(payload.user_id)\\n # Skipping bot reactions\\n if user.bot:\\n return\\n\\n # Search the event by message id\\n message_id = payload.message_id\\n channel_id = payload.channel_id\\n guild_id = payload.guild_id\\n event = connector.getEventByMessage(guild_id, message_id, channel_id)\\n if event is None:\\n return\\n\\n emoji_id = str(payload.emoji).split(':')[2][:-1]\\n role = discord.utils.get(guild.roles, id=event.role_id)\\n if (payload.event_type == \\\"REACTION_REMOVE\\\") and (event.emoji.split(':')[2][:-1] == emoji_id) and (role in user.roles):\\n # Remove role from user\\n await user.remove_roles(role)\",\n \"async def user_joined_button(self, payload: discord.RawReactionActionEvent) -> None:\\n\\n self.bits = flip_action_bits(LoggingActions.USER_JOINED, self.bits)\\n await self.update_embed()\",\n \"def on_click_return(self, event):\\n self.on_click_callable(event[\\\"target\\\"])\",\n \"def on_mouse_press(self, x, y, button, modifiers):\\n \\n menu: Menu = self.get_menu_for_display()\\n\\n menu_click_x, menu_click_y = self.get_menu_click(menu, x, y)\\n\\n if button == arcade.MOUSE_BUTTON_LEFT:\\n if menu:\\n menu.button_list.check_mouse_press_for_buttons(\\n menu_click_x,\\n menu_click_y,\\n )\",\n \"def button_click(self, btn, mbtn):\\n self.last_action_ts = pygame.time.get_ticks() # update last action timestamp (idle shutdown countdown restarts)\\n self.show_time = pygame.time.get_ticks() # refresh show time timestamp, so countdown restarts\\n\\n status = self.player.get_status()\\n\\n # which button was pressed?\\n if btn is self.btn_play:\\n logger.debug(\\\"button_click: btn_play \\\")\\n player.pause() # toggle play/pause\\n elif btn is self.btn_prev:\\n logger.debug(\\\"button_click: btn_prev \\\")\\n try:\\n if int(status['song']) > 0: # only accept 'prev' button push if this is not the first song\\n player.prev()\\n except Exception as e:\\n logger.error(e, exc_info=True) # log any exceptions\\n elif btn is self.btn_next:\\n logger.debug(\\\"button_click: btn_next \\\")\\n try:\\n if int(status['song']) < (int(status['playlistlength']) - 1):\\n player.next()\\n except Exception as e:\\n logger.error(e, exc_info=True) # log any exceptions\\n elif btn is self.background:\\n logger.debug(\\\"button_click: background \\\")\\n if status['state'] == 'play' or status['state']== 'pause':\\n self.show_buttons()\\n else:\\n logger.debug(\\\"button_click: <unknown>\\\")\",\n \"async def on_raw_reaction_remove(self, payload: discord.RawReactionActionEvent):\\n\\n guild = self.bot.get_guild(payload.guild_id)\\n user = guild.get_member(payload.user_id)\\n # Skipping bot reactions\\n if user.bot:\\n return\\n\\n # Search the event by message id\\n message_id = payload.message_id\\n channel_id = payload.channel_id\\n guild_id = payload.guild_id\\n event_type = connector.getEventTypeByMessage(guild_id, message_id, channel_id)\\n if event_type is None:\\n return\\n\\n emoji_id = str(payload.emoji).split(':')[2][:-1]\\n role = discord.utils.get(guild.roles, id=event_type.role_id)\\n if (payload.event_type == \\\"REACTION_REMOVE\\\") and (event_type.emoji.split(':')[2][:-1] == emoji_id) and (role in user.roles):\\n # Remove role from user\\n await user.remove_roles(role)\",\n \"def OnButtonClick(self):\\n self.choice()\",\n \"def myrole(var, wrapper, message):\\n\\n ps = get_participants()\\n if wrapper.source not in ps:\\n return\\n\\n role = get_main_role(wrapper.source)\\n if role in Hidden:\\n role = var.HIDDEN_ROLE\\n\\n evt = Event(\\\"myrole\\\", {\\\"role\\\": role, \\\"messages\\\": []})\\n if not evt.dispatch(var, wrapper.source):\\n return\\n role = evt.data[\\\"role\\\"]\\n\\n wrapper.pm(messages[\\\"show_role\\\"].format(role))\\n\\n for msg in evt.data[\\\"messages\\\"]:\\n wrapper.pm(msg)\",\n \"def press_R_button(self) -> None: # pylint: disable=invalid-name\",\n \"def button_press_event(self, widget, event):\\n x, y = event.x, event.y\\n\\n # x, y = coordinates where the button was pressed\\n self.last_win_x, self.last_win_y = x, y\\n\\n button = 0\\n # Prepare a button mask with bits set as follows:\\n # left button: 0x1\\n # middle button: 0x2\\n # right button: 0x4\\n # Others can be added as appropriate\\n self.logger.debug(\\\"button down event at %dx%d, button=%x\\\" % (x, y, button))\\n\\n data_x, data_y = self.check_cursor_location()\\n\\n return self.make_ui_callback('button-press', button, data_x, data_y)\",\n \"def _handle_r_click(self, event):\\n Debug.printe(event, Debug.Level.INFO)\\n\\n # Create the menu to display\\n p_menu = Menu(self._parent)\\n p_menu.add_command(label=\\\"Add Picture\\\", command=lambda: self._add_new_wall_pic())\\n p_menu.add_command(label=\\\"Delete Picture\\\", command=lambda: self._remove_wall_pic())\\n p_menu.add_command(label=\\\"Delete All\\\", command=lambda: self.remove_all())\\n p_menu.post(event.x_root, event.y_root)\",\n \"def click_button(self):\\n self.widgets.get('button').click()\",\n \"def input(self, event: pygame.event) -> None:\\n if event.type == pygame.MOUSEBUTTONDOWN and event.button == 1:\\n self.user_clicked = True\",\n \"async def on_raw_reaction_add(self, payload):\\n\\n # exclude all reactions which are not the original message\\n if str(payload.message_id) != self.message_id:\\n return\\n\\n # exclude the bot\\n if payload.user_id == self.bot.user.id:\\n return\\n\\n else:\\n # get the model data for the role assigner object\\n data = await self.get_objects(\\n model=RoleAssigner, filter={\\\"bot__name\\\": str(self.bot_name)}\\n )\\n\\n # role assigner object\\n data = data[0]\\n\\n guild = self.get_guild(guild_id=payload.guild_id)\\n\\n user = self.get_user(guild=guild, user_id=payload.user_id)\\n\\n for db_role in data.roles.all():\\n\\n if db_role.emoji.startswith(\\\":\\\") and db_role.emoji.endswith(\\\":\\\"):\\n\\n ce = db_role.emoji[1:-1]\\n\\n else:\\n ce = db_role.emoji\\n\\n if str(payload.emoji.name) == str(ce):\\n\\n role = self.get_role(guild, int(db_role.uid))\\n\\n if user not in role.members:\\n\\n await user.add_roles(role)\\n\\n print(\\\"Added \\\" + str(user) + \\\" to role: \\\" + str(role) + \\\"!\\\")\\n\\n else:\\n print(\\n \\\"User \\\" + str(user) + \\\" already in role: \\\" + str(role) + \\\"!\\\"\\n )\\n\\n pass\",\n \"async def on_raw_reaction_remove(self, payload: discord.RawReactionActionEvent):\\n role: discord.Role = await self.check_payload(payload)\\n\\n if role:\\n guild = self.bot.get_guild(payload.guild_id)\\n member: discord.Member = guild.get_member(payload.user_id)\\n await member.remove_roles(role)\",\n \"def on_click(self, event):\\n if event['button'] == 1 and 'button1' in self.options:\\n subprocess.call(self.options['button1'].split())\\n elif event['button'] == 2 and 'button2' in self.options:\\n subprocess.call(self.options['button2'].split())\\n elif event['button'] == 3 and 'button3' in self.options:\\n subprocess.call(self.options['button3'].split())\",\n \"async def process_add_reaction_role(\\n user_id, description, ctx=None, inter=None, allowed_mentions=None\\n):\\n user = await User.get(user_id)\\n response_deferred = await defer_inter(inter, ephemeral=True)\\n view = disnake.ui.View(timeout=None)\\n view.add_item(RoleDropdown(description))\\n await send_message(\\n key=\\\"choose_roles\\\",\\n user=user,\\n view=view,\\n ephemeral=True,\\n response_deferred=response_deferred,\\n inter=inter,\\n )\",\n \"def _event_callback(self, event, *args, **kwargs):\\n if isinstance(event, UserRoleModifiedEvent):\\n # Event when User Roles are modified\\n user_role_event = event\\n org_id = user_role_event.origin\\n actor_id = user_role_event.actor_id\\n role_name = user_role_event.role_name\\n log.debug(\\\"User Role modified: %s %s %s\\\" % (org_id, actor_id, role_name))\\n\\n # Evict the user and their roles from the cache so that it gets updated with the next call.\\n if self.user_role_cache and self.user_role_cache.has_key(actor_id):\\n log.debug(\\\"Evicting user from the user_role_cache: %s\\\" % actor_id)\\n self.user_role_cache.evict(actor_id)\\n elif isinstance(event, UserRoleCacheResetEvent):\\n # An event is received to clear the user data cache\\n self.user_role_cache.clear()\",\n \"def on_mouse_release(self, x, y, button, modifiers):\\n \\n menu: Menu = self.get_menu_for_display()\\n\\n menu_click_x, menu_click_y = self.get_menu_click(menu, x, y)\\n\\n if button == arcade.MOUSE_BUTTON_LEFT:\\n if menu:\\n menu.button_list.check_mouse_release_for_buttons(\\n menu_click_x,\\n menu_click_y,\\n )\",\n \"def on_mouse_press(self, x: float, y: float, button, modifiers):\\n #dialogue buttons\\n check_mouse_press_for_buttons(x, y, self.levels[self.current_level].dialogue_list)\\n\\n #room info prompt buttons\\n check_mouse_press_for_buttons(x, y, self.levels[self.current_level].room_info_list)\",\n \"def button_clicked(self, button, button_idx):\\n if self.turn % 2 == 0:\\n if self.board[button_idx-1] == 0:\\n self.place_move_x(button, button_idx-1)\\n gameOver = self.check_x_won()\\n if self.player_mode == 'pvc' and gameOver is None:\\n self.play_cpu()\\n else:\\n if self.player_mode == 'pvp':\\n if self.board[button_idx-1] == 0:\\n self.place_move_o(button, button_idx-1)\\n\\n self.check_game()\\n self.player_highlight()\",\n \"async def on_raw_reaction_remove(self, payload):\\n\\n # exclude all reactions which are not the original message\\n if str(payload.message_id) != self.message_id:\\n return\\n\\n # exclude the bot\\n if payload.user_id == self.bot.user.id:\\n return\\n\\n else:\\n # get the model data for the role assigner object\\n data = await self.get_objects(\\n model=RoleAssigner, filter={\\\"bot__name\\\": str(self.bot_name)}\\n )\\n\\n # role assigner object\\n data = data[0]\\n\\n guild = self.get_guild(guild_id=payload.guild_id)\\n\\n user = self.get_user(guild=guild, user_id=payload.user_id)\\n\\n for db_role in data.roles.all():\\n\\n if db_role.emoji.startswith(\\\":\\\") and db_role.emoji.endswith(\\\":\\\"):\\n\\n ce = db_role.emoji[1:-1]\\n\\n else:\\n ce = db_role.emoji\\n\\n if str(payload.emoji.name) == str(ce):\\n\\n role = self.get_role(guild, int(db_role.uid))\\n\\n if user in role.members:\\n\\n await user.remove_roles(role)\\n\\n print(\\\"Removed \\\" + str(user) + \\\" from role: \\\" + str(role) + \\\"!\\\")\\n\\n else:\\n print(\\\"User \\\" + str(user) + \\\" not in role: \\\" + str(role) + \\\"!\\\")\\n\\n pass\",\n \"def ev_mousebuttondown(self, event: tcod.event.MouseButtonDown) -> T | None:\",\n \"async def on_raw_reaction_add(self, payload: discord.RawReactionActionEvent):\\n # Get the message id of the message that the user reacted to.\\n message_id = payload.message_id\\n\\n # Get the message id of the message we want the user to react to.\\n actual_message_id = constants.MessageIDs.RULES_MSGID\\n\\n # Compare that id's match, and if true continue to give the role.\\n if message_id == actual_message_id:\\n guild_id = payload.guild_id\\n guild = self.bot.get_guild(guild_id)\\n role = get(payload.member.guild.roles, name='Not Verified')\\n\\n if role is not None:\\n member = get(guild.members, id=payload.user_id)\\n if member is not None:\\n await payload.member.add_roles(role)\\n print(f\\\"Added role to {member}\\\")\\n else:\\n print(\\\"User not found . . .\\\")\\n else:\\n print(\\\"Role not found . . .\\\")\",\n \"def on_mouse_up(self, pos, mouse_button):\\n for item in button.Button.all_buttons:\\n if item.collidepoint(pos):\\n self.buttons_clicked.append((item, mouse_button))\\n item.on_click(mouse_button)\",\n \"async def rolemenu(self, message, args):\\n if \\\"-h\\\" in args or \\\"--help\\\" in args or \\\"--ahelp\\\" in args:\\n await self._print_rolemenu_help(message.channel)\\n return\\n update_reactions = not is_key_in_args(args, \\\"--no-update\\\")\\n allow_role_change = not is_key_in_args(args, \\\"--no-change\\\")\\n remove_role_on_reaction_removal = not is_key_in_args(args, \\\"--no-removal\\\")\\n max_number_of_reactions_per_user = get_number_in_args(args, \\\"--max-reactions\\\", None)\\n max_users_with_role = get_number_in_args(args, \\\"--max-users\\\", None)\\n no_required_role = is_key_in_args(args, \\\"--all\\\")\\n role_ids = message.raw_role_mentions\\n if len(role_ids) * 2 != len(args):\\n await message.channel.send(\\\"Error with `rolemenu`: all roles must match an emoji\\\")\\n return await self._print_rolemenu_help(message.channel)\\n menu = {}\\n for i, role_id in enumerate(message.raw_role_mentions):\\n emoji = args.pop(0)\\n if emoji.strip(\\\"<>@&\\\") == str(role_id): # emoji and roles are just exchanged: no problem\\n emoji = args.pop(0)\\n elif args.pop(0).strip(\\\"<>@&\\\") != str(role_id): # error: two adjacent args must be role_id and emoji\\n await message.channel.send(\\\"Error with `rolemenu`: a role must match an emoji\\\")\\n return await self._print_rolemenu_help(message.channel)\\n menu.update({emoji: [message.guild.get_role(role_id)]})\\n manager = RoleByReactionManager.get(self.guild)\\n options = RoleMenuOptions(required_roles=None if no_required_role else [RoleCollection.VISITOR.value],\\n ignored_roles=[RoleCollection.MASTER.value],\\n update_reactions=update_reactions,\\n allow_role_change=allow_role_change,\\n max_number_of_reactions_per_user=max_number_of_reactions_per_user,\\n max_users_with_role=max_users_with_role,\\n remove_role_on_reaction_removal=remove_role_on_reaction_removal\\n )\\n await manager.add(message, menu, options)\\n logger.debug(\\\"Rolemenu created. Now you can edit your post to make it prettier.\\\")\",\n \"def on_click(self) -> None:\\n pass\",\n \"async def togglerole(self, ctx, role=\\\"\\\"):\\r\\n\\r\\n user = ctx.message.author\\r\\n joinmsg = \\\"Joined {0} role\\\"\\r\\n leavemsg = \\\"Left {0} role\\\"\\r\\n\\r\\n role = await commands.clean_content().convert(ctx, role)\\r\\n\\r\\n if role == \\\"\\\":\\r\\n embed = discord.Embed(title=\\\"Toggleable Roles:\\\", color=discord.Color.dark_teal())\\r\\n embed.description = \\\"\\\"\\\"\\r\\n - :race_car: Mario Kart 8 Deluxe: MK8D\\r\\n - :squid: Splatoon 2: spla2n\\r\\n - :card_box: Cards Against Humanity: cah\\r\\n - :bomb: Counter-Strike: Global Offensive: csgo\\r\\n - :gun: PUBG: pubg\\r\\n - :red_circle: Red Eclipse: redeclipse\\r\\n - :robot: Titanfall (2): titanfall\\r\\n - :boxing_glove: Super Smash Bros.: smash\\r\\n - :shopping_cart: Fortnite: fortnite\\r\\n \\\"\\\"\\\"\\r\\n await ctx.send(\\\"\\\", embed=embed)\\r\\n\\r\\n elif role.lower() == \\\"mk8d\\\":\\r\\n if self.bot.mk8d_role in user.roles:\\r\\n await user.remove_roles(self.bot.mk8d_role)\\r\\n await ctx.send(leavemsg.format(role.upper()))\\r\\n\\r\\n else:\\r\\n print(\\\"before error\\\")\\r\\n await user.add_roles(self.bot.mk8d_role)\\r\\n print(\\\"after error\\\")\\r\\n await ctx.send(joinmsg.format(role.upper()))\\r\\n\\r\\n elif role.lower() == \\\"spla2n\\\":\\r\\n if self.bot.spla2n_role in user.roles:\\r\\n await user.remove_roles(self.bot.spla2n_role)\\r\\n await ctx.send(leavemsg.format(role.lower()))\\r\\n\\r\\n else:\\r\\n await user.add_roles(self.bot.spla2n_role)\\r\\n await ctx.send(joinmsg.format(role.lower()))\\r\\n\\r\\n elif role.lower() == \\\"cah\\\":\\r\\n if self.bot.cah_role in user.roles:\\r\\n await user.remove_roles(self.bot.cah_role)\\r\\n await ctx.send(leavemsg.format(role.lower()))\\r\\n\\r\\n else:\\r\\n await user.add_roles(self.bot.cah_role)\\r\\n await ctx.send(joinmsg.format(role.lower()))\\r\\n\\r\\n elif role.lower() == \\\"csgo\\\":\\r\\n if self.bot.csgo_role in user.roles:\\r\\n await user.remove_roles(self.bot.csgo_role)\\r\\n await ctx.send(leavemsg.format(role.lower()))\\r\\n\\r\\n else:\\r\\n await user.add_roles(self.bot.csgo_role)\\r\\n await ctx.send(joinmsg.format(role.lower()))\\r\\n\\r\\n elif role.lower() == \\\"pubg\\\":\\r\\n if self.bot.pubg_role in user.roles:\\r\\n await user.remove_roles(self.bot.pubg_role)\\r\\n await ctx.send(leavemsg.format(role.lower()))\\r\\n\\r\\n else:\\r\\n await user.add_roles(self.bot.pubg_role)\\r\\n await ctx.send(joinmsg.format(role.lower()))\\r\\n\\r\\n elif role.lower() == \\\"redeclipse\\\":\\r\\n if self.bot.redeclipse_role in user.roles:\\r\\n await user.remove_roles(self.bot.redeclipse_role)\\r\\n await ctx.send(leavemsg.format(role.lower()))\\r\\n\\r\\n else:\\r\\n await user.add_roles(self.bot.redeclipse_role)\\r\\n await ctx.send(joinmsg.format(role.lower()))\\r\\n\\r\\n elif role.lower() == \\\"titanfall\\\":\\r\\n if self.bot.titanfall_role in user.roles:\\r\\n await user.remove_roles(self.bot.titanfall_role)\\r\\n await ctx.send(leavemsg.format(role.lower()))\\r\\n\\r\\n else:\\r\\n await user.add_roles(self.bot.titanfall_role)\\r\\n await ctx.send(joinmsg.format(role.lower()))\\r\\n\\r\\n elif role.lower() == \\\"smash\\\":\\r\\n if self.bot.smashbros_role in user.roles:\\r\\n await user.remove_roles(self.bot.smashbros_role)\\r\\n await ctx.send(leavemsg.format(role.lower()))\\r\\n\\r\\n else:\\r\\n await user.add_roles(self.bot.smashbros_role)\\r\\n await ctx.send(joinmsg.format(role.lower()))\\r\\n\\r\\n elif role.lower() == \\\"fortnite\\\":\\r\\n if self.bot.fortnite_role in user.roles:\\r\\n await user.remove_roles(self.bot.fortnite_role)\\r\\n await ctx.send(leavemsg.format(role.lower()))\\r\\n\\r\\n else:\\r\\n await user.add_roles(self.bot.fortnite_role)\\r\\n await ctx.send(joinmsg.format(role.lower()))\\r\\n else:\\r\\n msg = \\\"{} is not a togglable role\\\".format(role)\\r\\n await ctx.send(msg)\",\n \"async def user_left_button(self, payload: discord.RawReactionActionEvent) -> None:\\n\\n self.bits = flip_action_bits(LoggingActions.USER_LEFT, self.bits)\\n await self.update_embed()\",\n \"def button_multiclick_fired(self, payload: str):\\n json_object = util.deserialize_json(payload)\\n logger.debug(f\\\"Process Multiclick action\\\")\\n clicks = int(json_object['clicks'])\\n arduino_name = json_object['name']\\n arduino = self.arduinos.get(arduino_name, None)\\n if arduino is None:\\n # Unknown arduino\\n logger.info(f\\\"Could not find arduino with name '{arduino_name}'.\\\")\\n return\\n button = arduino.button_pins[json_object['button_pin']]\\n logger.debug(f\\\"Compare button click {button.clicks} with payload {clicks}\\\")\\n if button.clicks == clicks:\\n self.__action_executor.execute_actions(button.get_button_after_release_actions(button.clicks), button,\\n arduino.name)\\n button.clicks = 0\\n button.stop_multi_click_timer()\\n else:\\n logger.debug(f\\\"New click has been received\\\")\\n return\",\n \"async def show_all_self_assignable_roles(self, interaction: Interaction, button: discord.ui.Button) -> None:\\n view = RoleButtonView(interaction.user, self.assignable_roles)\\n await interaction.response.send_message(\\n view=view,\\n ephemeral=True\\n )\",\n \"def handle_left_click(self):\\n if not self.game_in_progress:\\n return\\n if self.first_click:\\n self.first_click = False\\n self.timer.start(1000)\\n sender = self.sender()\\n row = 0\\n col = 0\\n for row in range(self.rows):\\n for col in range(self.cols):\\n if self.button_array[row][col] == sender:\\n break\\n else:\\n continue\\n break\\n # print 'Received left click:', row, ',', col\\n celllist = self.board.opencell(row, col)\\n if celllist == []:\\n return\\n for cell in celllist:\\n row = cell[0]\\n col = cell[1]\\n cell_property = self.board.getcellproperty(row, col)\\n if cell_property == CellProperty.Empty:\\n self.button_array[row][col].setIcon(QtGui.QIcon(\\\"icons/OpenedSquare.png\\\"))\\n elif cell_property == CellProperty.Mine:\\n # Game over\\n for row in range(self.rows):\\n for col in range(self.cols):\\n cell_property = self.board.getcellproperty(row, col)\\n if cell_property == CellProperty.Mine:\\n self.button_array[row][col].setIcon(QtGui.QIcon(\\\"icons/mine.ico\\\"))\\n self.status_button.setIcon(QtGui.QIcon(\\\"icons/smiley3.ico\\\"))\\n self.game_in_progress = False\\n self.timer.stop()\\n return\\n elif cell_property == CellProperty.MineCountOne:\\n self.button_array[row][col].setIcon(QtGui.QIcon(\\\"icons/1.png\\\"))\\n elif cell_property == CellProperty.MineCountTwo:\\n self.button_array[row][col].setIcon(QtGui.QIcon(\\\"icons/2.png\\\"))\\n elif cell_property == CellProperty.MineCountThree:\\n self.button_array[row][col].setIcon(QtGui.QIcon(\\\"icons/3.png\\\"))\\n elif cell_property == CellProperty.MineCountFour:\\n self.button_array[row][col].setIcon(QtGui.QIcon(\\\"icons/4.png\\\"))\\n elif cell_property == CellProperty.MineCountFive:\\n self.button_array[row][col].setIcon(QtGui.QIcon(\\\"icons/5.png\\\"))\\n elif cell_property == CellProperty.MineCountSix:\\n self.button_array[row][col].setIcon(QtGui.QIcon(\\\"icons/6.png\\\"))\\n elif cell_property == CellProperty.MineCountSeven:\\n self.button_array[row][col].setIcon(QtGui.QIcon(\\\"icons/7.png\\\"))\\n elif cell_property == CellProperty.MineCountEight:\\n self.button_array[row][col].setIcon(QtGui.QIcon(\\\"icons/8.png\\\"))\\n\\n game_status = self.board.continuegame()\\n print 'Game Status:', game_status\\n if game_status == GameStatus.GameWon:\\n self.timer.stop()\\n self.game_in_progress = False\\n player_name = QtGui.QInputDialog.getText(self, \\\"Name Please !!\\\",\\\\\\n \\\"Enter your name for leader board:\\\")\\n # TODO: Replace 1 with the time taken by the end user.\\n LeaderBoard.insertnewscore(CURRENT_GAME_LEVEL, player_name[0], self.time)\\n self.status_button.setIcon(QtGui.QIcon(\\\"icons/smiley.ico\\\"))\\n print \\\"You have won the game\\\"\",\n \"def _on_button_press_event(self, widget, event):\\n if event.button == 3:\\n self.menu.popup(None, None, None, None, event.button, event.time)\\n self.menu.show_all()\",\n \"async def _role_player(self, ctx: Context, *, role: discord.Role):\\n\\n msg = await ctx.send(\\n _(\\n \\\"Are you sure you want to set `{}` as player role?\\\"\\n ).format(role.name)\\n )\\n start_adding_reactions(msg, ReactionPredicate.YES_OR_NO_EMOJIS)\\n\\n pred = ReactionPredicate.yes_or_no(msg, ctx.author)\\n await ctx.bot.wait_for(\\\"reaction_add\\\", check=pred)\\n\\n if pred.result:\\n await self.config.guild(ctx.guild).player_id.set(role.id)\\n await ctx.send(_(\\\"Set `{}` as player role!\\\").format(role.name))\\n else:\\n await ctx.send(_(\\\"Aborted player role setup.\\\"))\",\n \"def OnButton(self, event):\\r\\n\\r\\n if event.GetEventObject() != self._pButton:\\r\\n event.Skip()\\r\\n return\\r\\n\\r\\n self.Collapse(not self.IsCollapsed())\\r\\n\\r\\n # this change was generated by the user - send the event\\r\\n ev = wx.CollapsiblePaneEvent(self, self.GetId(), self.IsCollapsed())\\r\\n self.GetEventHandler().ProcessEvent(ev)\",\n \"def on_buttonBox_clicked(self, button):\\n if button == self.findButton:\\n self.__doSearch()\\n elif button == self.stopButton:\\n self.__stopSearch()\",\n \"def button(*args, actOnPress: bool=True, actionIsSubstitute: bool=True, align: Union[AnyStr,\\n bool]=\\\"\\\", annotation: Union[AnyStr, bool]=\\\"\\\", backgroundColor: Union[List[float,\\n float, float], bool]=None, command: Union[Script, bool]=None, defineTemplate:\\n AnyStr=\\\"\\\", docTag: Union[AnyStr, bool]=\\\"\\\", dragCallback: Script=None, dropCallback:\\n Script=None, enable: bool=True, enableBackground: bool=True, enableKeyboardFocus:\\n bool=True, exists: bool=True, fullPathName: bool=True, height: Union[int, bool]=0,\\n highlightColor: Union[List[float, float, float], bool]=None, isObscured: bool=True,\\n label: Union[AnyStr, bool]=\\\"\\\", manage: bool=True, noBackground: bool=True,\\n numberOfPopupMenus: bool=True, parent: Union[AnyStr, bool]=\\\"\\\", popupMenuArray:\\n bool=True, preventOverride: bool=True, recomputeSize: bool=True, statusBarMessage:\\n AnyStr=\\\"\\\", useTemplate: AnyStr=\\\"\\\", visible: bool=True, visibleChangeCommand:\\n Union[Script, bool]=None, width: Union[int, bool]=0, q=True, query=True, e=True,\\n edit=True, **kwargs)->Union[AnyStr, Any]:\\n pass\",\n \"def click_login_button(self):\",\n \"def handle_event(self, event):\\n if self.sub_event is not None:\\n self.sub_event.handle_event(event)\\n else:\\n self.confirm_response.handle_event(event)\",\n \"def on_click(button):\\n global ttt, choices, count, sym, result, x_pos, o_pos\\n\\n if count % 2 == 0:\\n sym = \\\"X\\\"\\n else:\\n sym = \\\"O\\\"\\n count += 1\\n\\n button.config(\\n text=sym,\\n state='disabled',\\n disabledforeground=\\\"red\\\") # For cross\\n\\n x, y = get_coordinates(button)\\n x += 1\\n y += 1\\n x_pos.append((x, y))\\n state = gen_state(to_move='O', x_positions=x_pos,\\n o_positions=o_pos)\\n try:\\n choice = choices.get()\\n if \\\"Random\\\" in choice:\\n a, b = random_player(ttt, state)\\n elif \\\"Pro\\\" in choice:\\n a, b = minimax_decision(state, ttt)\\n else:\\n a, b = alphabeta_player(ttt, state)\\n except (ValueError, IndexError, TypeError) as e:\\n disable_game()\\n result.set(\\\"It's a draw :|\\\")\\n return\\n if 1 <= a <= 3 and 1 <= b <= 3:\\n o_pos.append((a, b))\\n button_to_change = get_button(a - 1, b - 1)\\n if count % 2 == 0: # Used again, will become handy when user is given the choice of turn.\\n sym = \\\"X\\\"\\n else:\\n sym = \\\"O\\\"\\n count += 1\\n\\n if check_victory(button):\\n result.set(\\\"You win :)\\\")\\n disable_game()\\n else:\\n button_to_change.config(text=sym, state='disabled',\\n disabledforeground=\\\"black\\\")\\n if check_victory(button_to_change):\\n result.set(\\\"You lose :(\\\")\\n disable_game()\",\n \"def reaction_check(self, payload):\\n if payload.message_id != self.message.id:\\n return False\\n if payload.user_id not in (*self.bot.owner_ids, self._author_id):\\n return False\\n return payload.emoji in self.buttons\",\n \"def button_press_event(self, widget, event, menu):\\n\\t\\tif event.type == gtk.gdk.BUTTON_PRESS and event.button == 3:\\n\\t\\t\\tmenu.popup(None, None, None, event.button, event.time)\\n\\t\\treturn False\",\n \"def button_release_event(self, widget, event):\\n x, y = event.x, event.y\\n\\n # x, y = coordinates where the button was released\\n self.last_win_x, self.last_win_y = x, y\\n\\n button = 0\\n # prepare button mask as in button_press_event()\\n\\n data_x, data_y = self.check_cursor_location()\\n\\n return self.make_ui_callback('button-release', button, data_x, data_y)\",\n \"def ev_controllerbuttondown(self, event: tcod.event.ControllerButton) -> T | None:\",\n \"def handle_event(self, event):\\n # Get rid of all non keydown events\\n if event.type != pygame.KEYDOWN:\\n return\\n\\n if not self._initial_prompt.is_over():\\n self._initial_prompt.handle_event(event)\\n\\n # If response is 2 then the user selected SEE YA!. Pass control to\\n # seeya dialogue.\\n elif self._response == 2 and not self._seeya_dialogue.is_over():\\n self._seeya_dialogue.handle_event(event)\\n\\n # If response is 0 then the user selected BUY. Pass control to buy\\n # menu.\\n elif self._response == 0 and not self._buy_menu.is_over():\\n self._buy_menu.handle_event(event)\\n\\n elif self._response == 1 and not self._sell_menu.is_over():\\n self._sell_menu.handle_event(event)\",\n \"def _press(self, event):\",\n \"def on_click(self):\\n arcade.play_sound(button, volume=constants.MUSIC_VOLUME / 40)\\n\\n global success\\n global fails\\n if success or fails == 20:\\n reset_global_variables()\\n self.minigame.window.show_view(self.minigame.main_view)\\n else:\\n self.minigame.window.show_view(self.minigame.main_view)\\n print(f\\\"Exit Button.\\\")\",\n \"def click(self) -> None:\\n if self.is_enabled():\\n try:\\n self.element.click()\\n logging.info(\\\"Class booked!\\\")\\n except:\\n logging.info(\\\"The button could not be clicked, trying to execute the element.\\\")\\n self.driver.execute_script(\\\"arguments[0].click();\\\", self.element)\\n finally:\\n logging.info(\\\"Could not book the class\\\")\\n\\n else:\\n warnings.warn('The Button cannot be clicked.')\",\n \"def handle_remote_button(self, request):\\n self._verify_auth_parameters(request)\\n content = yield from request.content.read()\\n parsed = dmap.parse(content, tag_definitions.lookup_tag)\\n self.last_button_pressed = dmap.first(parsed, 'cmbe')\\n return web.Response(status=200)\",\n \"def RightClick(self):\\n self._PressRightButton()\\n self._ReleaseAllButtons()\",\n \"def on_mouse_release(self, x: float, y: float, button, modifiers):\\n #dialogue buttons\\n check_mouse_release_for_buttons(x, y, self.levels[self.current_level].dialogue_list)\\n\\n #room info prompt buttons\\n check_mouse_release_for_buttons(x, y, self.levels[self.current_level].room_info_list)\",\n \"def slotAccept(self, id):\\n self.category = self.buttonGroup.find(id).text().ascii()\\n self.accept()\",\n \"def HandlePress(self, event: tkEvent):\\n pass\",\n \"def ev_mousebuttondown(self, event: MouseButtonDown) -> None:\",\n \"async def edit(self, ctx: commands.Context, message_id: int):\\n\\n # Standard wait_for check function for message inputs, makes sure the command user's messages in command channel are considered\\n def message_check(m: discord.Message):\\n return m.author == ctx.author and m.channel == ctx.channel\\n\\n # Standard reaction check that ensures no duplicate reacrole entry, just name the relevant message 'm' before adding this one to check kwarg in wait_for\\n def reaction_check_nd(_r: discord.Reaction, _u):\\n return _u == ctx.author and _r.message == m and str(_r.emoji) not in self._cache[ctx.guild.id][PM.id]\\n\\n if message_id in self._cache[ctx.guild.id]:\\n\\n # Not actually channel id int but I decided to name it that way anyway\\n chanid = await self.bot.pool.fetchrow(\\\"SELECT channelid FROM selfrole_lookup WHERE messageid = $1\\\", message_id)\\n chan: discord.TextChannel = ctx.guild.get_channel(chanid['channelid'])\\n\\n # Currently need message content for title, might start saving title in db to avoid this api call idk\\n try:\\n PM: discord.Message = await chan.fetch_message(message_id)\\n except discord.NotFound:\\n await ctx.send(\\\"It would seem that the message for the role menu you're trying to edit has been deleted, please try creating a new one\\\")\\n return\\n\\n buttons = [\\\"\\\\U0001f1e6\\\", \\\"\\\\U0001f1e7\\\", \\\"\\\\U0001f1e8\\\", \\\"\\\\U0001f1e9\\\"]\\n\\n e1 = discord.Embed(title=\\\"What aspect of the menu do you wish to change?\\\",\\n description=\\\"\\\\U0001f1e6 - Add a role\\\\n\\\\n\\\"\\n \\\"\\\\U0001f1e7 - Remove existing role\\\\n\\\\n\\\"\\n \\\"\\\\U0001f1e8 - Edit the reaction of a role\\\\n\\\\n\\\"\\n \\\"\\\\U0001f1e9 - Change the title\\\",\\n colour=discord.Colour.blue())\\n # Send the initial menu\\n menu = await ctx.send(embed=e1)\\n\\n for button in buttons:\\n await menu.add_reaction(button)\\n\\n # We need the first reaction where the emoji is one of the buttons\\n def button_check(_r, _u):\\n return _u == ctx.author and _r.message == menu and str(_r.emoji) in buttons\\n # Get the option the user chose\\n try:\\n r, u = await self.bot.wait_for('reaction_add', check=button_check, timeout=20)\\n except asyncio.TimeoutError:\\n await ctx.send(\\\"Timed out\\\")\\n return\\n\\n # If user wanted to add a new role to the menu\\n if str(r.emoji) == buttons[0]:\\n await menu.clear_reactions()\\n await menu.edit(content=\\\"What role do you wish to be added? Enter its mention, id, or name\\\", embed=None)\\n\\n # Get the role object for the new role to be added\\n try:\\n m = await self.bot.wait_for('message', check=message_check, timeout=30)\\n newrole = await self.rc.convert(ctx, m.content)\\n\\n if newrole.id in self._cache[ctx.guild.id][PM.id].values():\\n await ctx.send(\\\"Error: role already exists in the menu, perhaps you meant to edit it?\\\")\\n return\\n except asyncio.TimeoutError:\\n await ctx.send(\\\"Timed out\\\")\\n return\\n except commands.BadArgument:\\n await ctx.send(\\\"Role not found, please try again\\\")\\n return\\n\\n m = await ctx.send(f\\\"React on this message with the reaction that will correspond to the role `{newrole}`\\\")\\n\\n # Get the reaction/emoji that will correspond to the new role and yank everything into db\\n try:\\n r, u = await self.bot.wait_for('reaction_add', check=reaction_check_nd, timeout=30)\\n self._cache[ctx.guild.id][PM.id][str(r.emoji)] = newrole.id\\n\\n query = \\\"\\\"\\\"\\n INSERT INTO selfrole (messageid, emoji, roleid)\\n VALUES ($1, $2, $3)\\n \\\"\\\"\\\"\\n\\n await self.bot.pool.execute(query, PM.id, str(r.emoji), newrole.id)\\n\\n # Standard way of getting the embed description of the role menu\\n newmenudesc = \\\"\\\\n\\\\n\\\".join([f\\\"{k} - {ctx.guild.get_role(v)}\\\" for k, v in self._cache[ctx.guild.id][PM.id].items()])\\n\\n newembed = discord.Embed(title=PM.embeds[0].title,\\n description=newmenudesc,\\n colour=discord.Colour.blue())\\n\\n await PM.edit(embed=newembed)\\n await PM.add_reaction(r.emoji)\\n await ctx.send(\\\"Menu edit completed successfully, you may now delete the messages other than the menu itself\\\")\\n\\n except asyncio.TimeoutError:\\n await ctx.send(\\\"Timed out\\\")\\n\\n elif str(r.emoji) == buttons[1]:\\n # Gotta yank the buttons to make everything squeaky clean\\n await menu.clear_reactions()\\n await menu.edit(content=\\\"Enter the role you wish to remove from the menu, can be mention, id or name\\\",\\n embed=None)\\n\\n try:\\n # Get role from user\\n m = await self.bot.wait_for('message', check=message_check, timeout=20)\\n role = await self.rc.convert(ctx, m.content)\\n\\n # If user trying to edit reaction to role that wasn't even in the menu to begin with\\n if role.id not in self._cache[ctx.guild.id][PM.id].values():\\n raise commands.BadArgument(\\\"Role not in cache\\\")\\n\\n # Get the key to delete using the old fashioned way, and subsequently delete it\\n targetkey = \\\"\\\"\\n for key, value in self._cache[ctx.guild.id][PM.id].items():\\n if value == role.id:\\n targetkey = key\\n break\\n self._cache[ctx.guild.id][PM.id].pop(targetkey)\\n\\n # After everything is done and dusted, make database entry and edit the menu\\n query = \\\"\\\"\\\"\\n DELETE FROM selfrole WHERE messageid = $1 AND roleid = $2\\n \\\"\\\"\\\"\\n await self.bot.pool.execute(query, PM.id, role.id)\\n newmenudesc = \\\"\\\\n\\\\n\\\".join(\\n [f\\\"{k} - {ctx.guild.get_role(v)}\\\" for k, v in self._cache[ctx.guild.id][PM.id].items()])\\n\\n newembed = discord.Embed(title=PM.embeds[0].title,\\n description=newmenudesc,\\n colour=discord.Colour.blue())\\n await PM.edit(embed=newembed)\\n await PM.clear_reaction(targetkey)\\n await ctx.send(\\n \\\"Menu edit completed successfully, you may now delete the messages other than the menu itself\\\")\\n except asyncio.TimeoutError:\\n await ctx.send(\\\"Timed out\\\")\\n return\\n except commands.BadArgument:\\n await ctx.send(\\\"I don't think that role exists in that menu, run the command again\\\")\\n return\\n\\n elif str(r.emoji) == buttons[2]:\\n # Same drill, remove buttons to make it look clean\\n await menu.clear_reactions()\\n await menu.edit(embed=None, content=\\\"Enter the role for which you wish to change the reaction.\\\")\\n\\n try:\\n m = await self.bot.wait_for('message', check=message_check, timeout=20)\\n role = await self.rc.convert(ctx, m.content)\\n\\n if role.id not in self._cache[ctx.guild.id][PM.id].values():\\n raise commands.BadArgument(\\\"Role not in cache\\\")\\n\\n except asyncio.TimeoutError:\\n await ctx.send(\\\"Timed out\\\")\\n return\\n except commands.BadArgument:\\n await ctx.send(\\\"Couldn't find the role you wished to edit in the menu\\\")\\n return\\n\\n # Get the reaction/emoji that will correspond to the new role and yank everything into db\\n m = await ctx.send(f\\\"React on this message with the new reaction that will correspond to the role {role}\\\")\\n try:\\n r, u = await self.bot.wait_for('reaction_add', check=reaction_check_nd, timeout=30)\\n\\n # Can only delete entry if have the key so....\\n TargetKey = \\\"\\\" # Set default value so IDE stops screaming\\n for k, v in self._cache[ctx.guild.id][PM.id].items():\\n if v == role.id:\\n TargetKey = k\\n\\n # Make new entry and delete the old one\\n self._cache[ctx.guild.id][PM.id][str(r.emoji)] = role.id\\n self._cache[ctx.guild.id][PM.id].pop(TargetKey)\\n\\n # After everything is done and dusted, at last update the database entry\\n await self.bot.pool.execute(\\\"UPDATE selfrole SET emoji = $1 WHERE roleid = $2 AND messageid = $3\\\", str(r.emoji), role.id, PM.id)\\n\\n # Hehehehehehe\\n newmenudesc = \\\"\\\\n\\\\n\\\".join(\\n [f\\\"{k} - {ctx.guild.get_role(v)}\\\" for k, v in self._cache[ctx.guild.id][PM.id].items()])\\n\\n newembed = discord.Embed(title=PM.embeds[0].title,\\n description=newmenudesc,\\n colour=discord.Colour.blue())\\n\\n await PM.edit(embed=newembed)\\n await PM.clear_reaction(TargetKey)\\n await PM.add_reaction(str(r.emoji))\\n await ctx.send(\\n \\\"Menu edit completed successfully, you may now delete the messages other than the menu itself\\\")\\n except asyncio.TimeoutError:\\n await ctx.send(\\\"Timed out\\\")\\n return\\n\\n elif str(r.emoji) == buttons[3]:\\n # This one speaks for itself I think.\\n await menu.clear_reactions()\\n await menu.edit(embed=None, content=\\\"Enter the new title you want the menu to have\\\")\\n try:\\n m = await self.bot.wait_for('message', check=message_check, timeout=30)\\n e = discord.Embed(title=f\\\"Role menu: {m.content}\\\",\\n description=PM.embeds[0].description,\\n colour=PM.embeds[0].colour)\\n await PM.edit(embed=e)\\n except asyncio.TimeoutError:\\n await ctx.send(\\\"Timed out\\\")\\n return\\n\\n else:\\n await ctx.send(\\\"Menu not found in this server, double check if the id was entered correctly\\\")\",\n \"def proceed_menu_button_click(cb: telebot.types.CallbackQuery):\\n logger.info(f'Got callback {cb.data} from id: {cb.from_user.id}.')\\n\\n message = bot.send_message(\\n cb.from_user.id,\\n 'Starting the game.'\\n )\\n user = utils.get_user_or_none(cb.from_user)\\n if user:\\n utils.update_dissolving_messages(user, 'starting_the_game', message)\\n\\n if cb.data == 'ai':\\n # TODO inline keyboard for selecting AI difficulty level\\n logger.info(f'Starting AI game for id: {cb.from_user.id}.')\\n utils.start_new_game(bot, cb.from_user, 'ai')\\n\\n elif cb.data == 'person':\\n logger.info(f'Starting PVP game for id: {cb.from_user.id}.')\\n utils.start_new_game(bot, cb.from_user, 'person')\\n\\n logger.info(f'Deleting message {cb.message.text} from chat {cb.from_user.id}.')\\n bot.delete_message(\\n cb.from_user.id,\\n cb.message.message_id\\n )\",\n \"async def message_edit_button(self, payload: discord.RawReactionActionEvent) -> None:\\n\\n self.bits = flip_action_bits(LoggingActions.MESSAGE_EDIT, self.bits)\\n await self.update_embed()\",\n \"async def _toggle_role(self, ctx, selfrole: Selfrole):\\n\\n if selfrole.role not in ctx.message.author.roles:\\n try:\\n await ctx.message.author.add_roles(selfrole.role)\\n except discord.Forbidden:\\n raise exceptions.ForbiddenError(\\n exceptions.ForbiddenTask.ADD_ROLE, selfrole.role.name\\n )\\n\\n await ctx.send(f\\\"{config.YES} {selfrole.join_message}\\\")\\n\\n elif selfrole.role in ctx.message.author.roles:\\n try:\\n await ctx.message.author.remove_roles(selfrole.role)\\n except discord.Forbidden:\\n raise exceptions.ForbiddenError(\\n exceptions.ForbiddenTask.REMOVE_ROLE, selfrole.role.name\\n )\\n\\n await ctx.send(\\n f\\\"{config.YES} The `{selfrole.role.name}` role was removed from you.\\\"\\n )\",\n \"def on_next_turn_click(self, button):\\n if self.referee.is_game_over():\\n Gtk.main_quit()\\n else:\\n self.do_next_turn(button)\\n # if the game is over after this turn, we will shutdown on the next click,\\n # so visually alert the player with the button label\\n if self.referee.is_game_over():\\n button.set_label(GAME_OVER_MSG)\",\n \"def button_pressed(self, channel):\\n print(\\\"button_pressed() channel = \\\"+str(channel))\\n GPIO.remove_event_detect(channel) # turn button off to avoid double hits\\n match channel:\\n case self.prev_button: \\n print(\\\"Previous button - sending message: mycroft.audio.service.prev\\\")\\n self.send_message(\\\"mycroft.audio.service.prev\\\")\\n case self.stop_button: \\n print(\\\"Stop button - sending message: mycroft.stop\\\")\\n self.send_message(\\\"mycroft.stop\\\")\\n case self.next_button: \\n print(\\\"Next button - sending message: mycroft.audio.service.next\\\")\\n self.send_message(\\\"mycroft.audio.service.next\\\")\\n case _: # not expected\\n print(\\\"Did not expect channel = \\\"+str(channel))\\n GPIO.add_event_detect(channel, GPIO.FALLING, callback=self.button_pressed, bouncetime=5) # turn button back on\",\n \"def click(self, mouse_pos):\\n for button in self.enabled_buttons(): # type: Button\\n if button.is_position_on_button(mouse_pos):\\n self.sound.play_sound(self.click_sound)\\n button.click()\",\n \"def handle_event(self, event):\\n if not self.can_afford:\\n self.cant_afford_dialogue.handle_event(event)\\n else:\\n self.how_many_selector.handle_event(event)\",\n \"def event_handler(self):\\r\\n for event in pygame.event.get():\\r\\n if event.type == QUIT:\\r\\n pygame.quit()\\r\\n sys.exit()\\r\\n elif event.type == MOUSEBUTTONDOWN and event.button == LEFT_CLICK:\\r\\n self.left_mouse_down_handler(event)\\r\\n elif event.type == MOUSEBUTTONUP and event.button == LEFT_CLICK:\\r\\n self.left_mouse_up_handler(event)\\r\\n elif event.type == MOUSEBUTTONDOWN and event.button == RIGHT_CLICK:\\r\\n self.right_mouse_down_handler(event)\\r\\n elif event.type == MOUSEBUTTONUP and event.button == RIGHT_CLICK:\\r\\n self.right_mouse_up_handler(event)\\r\\n elif event.type == MOUSEMOTION:\\r\\n self.mouse_motion_handler(event)\\r\\n elif event.type == MOUSEBUTTONUP and event.button in [2, 4, 5]:\\r\\n self.shortcut_click(event)\",\n \"async def _handle_role(self, member: discord.Member) -> tuple:\\n list_to_add = await self.config.guild(member.guild).autoroles()\\n list_to_remove = await self.config.guild(member.guild).temprole()\\n actions = []\\n if list_to_add:\\n for role in list_to_add:\\n to_add = member.guild.get_role(role)\\n await member.add_roles(to_add, reason=\\\"Adding auto role by Verify.\\\")\\n actions.append(\\n \\\"added automatically role{plural}\\\".format(\\n plural=\\\"s\\\" if len(list_to_add) > 1 else \\\"\\\"\\n )\\n )\\n if list_to_remove:\\n to_remove = member.guild.get_role(list_to_remove)\\n if to_remove in member.roles:\\n await member.remove_roles(to_remove, reason=\\\"Removing temporary role by Verify.\\\")\\n actions.append(\\\"removed temporary role\\\")\\n return (\\n True,\\n humanize_list(actions).capitalize() if actions else \\\"No action taken.\\\",\\n )\",\n \"def on_mouse_click(self, event):\\n if not self.is_game_over:\\n try:\\n # i, j coordinates of the click event\\n i = int(round(event.ydata))\\n j = int(round(event.xdata))\\n\\n # Left button\\n if event.button == 1 or event.button == 2:\\n self.reveal(i, j)\\n\\n # Right button\\n elif event.button == 3:\\n self.flag(i, j)\\n\\n except (TypeError, IndexError):\\n pass\",\n \"def check_replay_button(self, mouse_x, mouse_y):\\r\\n for button in self._replay_button_list:\\r\\n if button.get_button_rect().collidepoint(mouse_x, mouse_y):\\r\\n button_clicked = button\\r\\n break\\r\\n else:\\r\\n button_clicked = None\\r\\n\\r\\n if button_clicked is not None and button_clicked.get_num_atom() == 1:\\r\\n self.setup_new_game()\\r\\n elif button_clicked is not None and button_clicked.get_num_atom() == 2:\\r\\n sys.exit()\",\n \"def keypress_callback():\\n data = request.get_json()\\n buttonid = data['buttonid']\\n assert buttonid in pyautogui.KEYBOARD_KEYS, \\\"Unknown buttonid received\\\"\\n pyautogui.press(buttonid)\\n return json.dumps({'success':True}), 200, {'ContentType':'application/json'}\",\n \"def onclick(self, fun, btn=1, add=None):\\n self._onscreenclick(fun, btn, add)\",\n \"def on_confirm_button(self, negotiation_outcome):\\n # Send message.\\n self.ros_node.send_message(UserInput.NEGOTIATION,\\n negotiation_outcome)\\n # Reset label and button.\\n self.confirm_label.setText(\\\"Selection sent.\\\")\\n self.confirm_button.setStyleSheet('QPushButton {color: gray;}')\\n self.confirm_button.setEnabled(False)\",\n \"def on_right_click(self, client, game) -> None:\\n pass\",\n \"def click_modal_button(self, title):\\n locator = lex_locators[\\\"modal\\\"][\\\"button\\\"].format(title)\\n self.selenium.wait_until_page_contains_element(locator)\\n self.selenium.wait_until_element_is_enabled(locator)\\n self._jsclick(locator)\",\n \"def post(self, role, user_id, action):\\n self._login_as_staff()\\n return self.client.post(self.path(role=role), {'user_id': user_id, 'action': action})\",\n \"def press(button_id: str) -> None:\\n try:\\n self.query_one(f\\\"#{button_id}\\\", Button).press()\\n except NoMatches:\\n pass\",\n \"def manage_updateRole(self, role_id, title, description, RESPONSE=None,\\n REQUEST=None):\\n self.updateRole(role_id, title, description)\\n\\n message = 'Role+updated'\\n\\n if RESPONSE is not None:\\n RESPONSE.redirect('%s/manage_roles?role_id=%s&'\\n 'manage_tabs_message=%s' %\\n (self.absolute_url(), role_id, message))\",\n \"def _ClickGaiaButton(self, button_text, alt_text):\\n get_button_js = '''\\n (function() {\\n var buttons = document.querySelectorAll('[role=\\\"button\\\"]');\\n if (buttons == null)\\n return false;\\n for (var i=0; i < buttons.length; ++i) {\\n if ((buttons[i].textContent.indexOf('%s') != -1) ||\\n (buttons[i].textContent.indexOf('%s') != -1)) {\\n buttons[i].click();\\n return true;\\n }\\n }\\n return false;\\n })();\\n ''' % (button_text, alt_text)\\n self._GaiaWebviewContext().WaitForJavaScriptCondition(\\n get_button_js, timeout=20)\",\n \"def _onMouseButton(e):\\n\\n mouse_event = MouseButtonEvent(e)\\n orca_state.lastInputEvent = mouse_event\\n\\n # A mouse button event looks like: mouse:button:1p, where the\\n # number is the button number and the 'p' is either 'p' or 'r',\\n # meaning pressed or released. We only want to stop speech on\\n # button presses.\\n #\\n if mouse_event.pressed:\\n speech.stop()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5762249","0.5668957","0.5659755","0.5601932","0.5597881","0.55494606","0.5509019","0.550365","0.54933995","0.54303074","0.5379965","0.53338444","0.5327616","0.5321051","0.5281076","0.52789605","0.52783185","0.5262219","0.52602375","0.5247453","0.5199621","0.5156527","0.5153453","0.5151972","0.51352996","0.51328313","0.5132546","0.51267046","0.5106546","0.5105769","0.5104905","0.5088549","0.50815374","0.5071633","0.5050712","0.50485414","0.5038496","0.5036337","0.5033176","0.5006564","0.5006027","0.49716136","0.49675182","0.49328336","0.4923141","0.4914458","0.49040633","0.4901954","0.48875967","0.4858086","0.48527363","0.48522556","0.4850257","0.4816004","0.48100084","0.48045588","0.47844878","0.47826782","0.47822934","0.47737372","0.476707","0.4757249","0.47520489","0.47457427","0.4742094","0.4721721","0.4717471","0.47152945","0.47126135","0.4706438","0.47054768","0.4704515","0.47033885","0.47006994","0.46994513","0.46809232","0.46774706","0.46643496","0.46637234","0.46633738","0.46611902","0.4657816","0.4656989","0.46494249","0.464505","0.46403375","0.46379507","0.46337938","0.46226028","0.46200106","0.46184063","0.46180218","0.4617832","0.46149847","0.46112204","0.46100956","0.4606513","0.46060577","0.45779836","0.45744967"],"string":"[\n \"0.5762249\",\n \"0.5668957\",\n \"0.5659755\",\n \"0.5601932\",\n \"0.5597881\",\n \"0.55494606\",\n \"0.5509019\",\n \"0.550365\",\n \"0.54933995\",\n \"0.54303074\",\n \"0.5379965\",\n \"0.53338444\",\n \"0.5327616\",\n \"0.5321051\",\n \"0.5281076\",\n \"0.52789605\",\n \"0.52783185\",\n \"0.5262219\",\n \"0.52602375\",\n \"0.5247453\",\n \"0.5199621\",\n \"0.5156527\",\n \"0.5153453\",\n \"0.5151972\",\n \"0.51352996\",\n \"0.51328313\",\n \"0.5132546\",\n \"0.51267046\",\n \"0.5106546\",\n \"0.5105769\",\n \"0.5104905\",\n \"0.5088549\",\n \"0.50815374\",\n \"0.5071633\",\n \"0.5050712\",\n \"0.50485414\",\n \"0.5038496\",\n \"0.5036337\",\n \"0.5033176\",\n \"0.5006564\",\n \"0.5006027\",\n \"0.49716136\",\n \"0.49675182\",\n \"0.49328336\",\n \"0.4923141\",\n \"0.4914458\",\n \"0.49040633\",\n \"0.4901954\",\n \"0.48875967\",\n \"0.4858086\",\n \"0.48527363\",\n \"0.48522556\",\n \"0.4850257\",\n \"0.4816004\",\n \"0.48100084\",\n \"0.48045588\",\n \"0.47844878\",\n \"0.47826782\",\n \"0.47822934\",\n \"0.47737372\",\n \"0.476707\",\n \"0.4757249\",\n \"0.47520489\",\n \"0.47457427\",\n \"0.4742094\",\n \"0.4721721\",\n \"0.4717471\",\n \"0.47152945\",\n \"0.47126135\",\n \"0.4706438\",\n \"0.47054768\",\n \"0.4704515\",\n \"0.47033885\",\n \"0.47006994\",\n \"0.46994513\",\n \"0.46809232\",\n \"0.46774706\",\n \"0.46643496\",\n \"0.46637234\",\n \"0.46633738\",\n \"0.46611902\",\n \"0.4657816\",\n \"0.4656989\",\n \"0.46494249\",\n \"0.464505\",\n \"0.46403375\",\n \"0.46379507\",\n \"0.46337938\",\n \"0.46226028\",\n \"0.46200106\",\n \"0.46184063\",\n \"0.46180218\",\n \"0.4617832\",\n \"0.46149847\",\n \"0.46112204\",\n \"0.46100956\",\n \"0.4606513\",\n \"0.46060577\",\n \"0.45779836\",\n \"0.45744967\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6394534"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":16874,"cells":{"query":{"kind":"string","value":"Plateau in refractive index below 330nm for Glass, edge of data artifact"},"document":{"kind":"string","value":"def refractive_index(self):\n wd = np.arange(80,820,10)\n nd = self.boundary.imat.refractive_index(wd) \n\n plt.plot(wd, nd)\n\n return wd, nd"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_index_of_surface_gate(data, setup={}):\n alts = data['alt']\n return np.argmin(np.abs(alts), 1)","def test_no_backg_subt():\n \n test_object = fa.read_in_envision(data_csv=HsHis6_PEX5C_vs_HsPEX5C, platemap_csv=Hs_His6_PEX5C_vs_HsPEX5C_platemap, data_type='plate', size=384)\n test_object.calculate_r_i(correct=True, plot_i=False, thr=80)","def intensity(self) -> int:","def get_sn2005ek(colorplt=False):\n z = 0.016551\n ebv = 0.210\n D = cosmo.luminosity_distance([z])[0].value * 1e+6 # in pc\n dis_mod = 5*np.log10(D / 10)\n t_max = 53639.9\n print (\"adopt r band t_max from Drout+13\")\n \n # tb = pd.read_csv('/Users/yuhanyao/Desktop/ZTF18abfcmjw/data/Drout2013/table1', sep='\\t')\n # tb = tb.drop(columns=[\"Unnamed: 6\"])\n \n mjds = np.array([53639.3, 53640.3, 53641.3, 53642.2, 53643.2, 53645.3,\n 53646.5, 53648.0, 53649.2, 53650.4, 53651.3, 53652.5,\n 53654.2, 53655.2, 53656.2, 53657.2])\n \n Bmags = np.array([18.25, 18.38, 18.65, np.nan, 19.10, 19.71,\n 20.07, np.nan, 20.67, 20.90, 21.05, np.nan,\n 21.74, np.nan, np.nan, np.nan])\n \n Bmag_uncs = np.array([0.02, 0.03, 0.02, np.nan, 0.05, 0.07, \n 0.07, np.nan, 0.04, 0.04, 0.04, np.nan,\n 0.12, np.nan, np.nan, np.nan])\n \n Vmags = np.array([17.83, 18.03, 17.92, np.nan, 18.24, 18.66,\n 18.93, 19.48, 19.63, 19.86, 19.98, 20.35,\n 20.60, 20.74, 20.88, 21.22])\n \n Vmag_uncs = np.array([0.02, 0.03, 0.01, np.nan, 0.02, 0.02,\n 0.02, 0.06, 0.03, 0.03, 0.04, 0.05, \n 0.08, 0.10, 0.08, 0.13])\n \n Rmags = np.array([17.46, 17.41, 17.60, 17.69, 17.86, 18.18, \n np.nan, 18.83, 19.03, 19.26, 19.48, 19.75,\n 20.08, np.nan, 20.47, np.nan])\n \n Rmag_uncs = np.array([0.01, 0.02, 0.01, 0.02, 0.01, 0.01,\n np.nan, 0.03, 0.02, 0.02, 0.02, 0.04,\n 0.05, np.nan, 0.08, np.nan])\n\n Imags = np.array([17.20, 17.13, 17.18, np.nan, 17.47, 17.71, \n np.nan, 18.13, 18.26, 18.51, 18.61, 18.74, \n 19.01, np.nan, 19.47, np.nan])\n \n Imag_uncs = np.array([0.02, 0.04, 0.02, np.nan, 0.03, 0.02,\n np.nan, 0.06, 0.02, 0.02, 0.02, 0.03,\n 0.05, np.nan, 0.06, np.nan])\n \n mymjds = np.hstack([mjds, mjds, mjds, mjds])\n mymags = np.hstack([Bmags, Vmags, Rmags, Imags])\n myemags = np.hstack([Bmag_uncs, Vmag_uncs, Rmag_uncs, Imag_uncs])\n myfilts = np.hstack([ np.repeat(\"B\", len(Bmags)),\n np.repeat(\"V\", len(Bmags)),\n np.repeat(\"R\", len(Rmags)),\n np.repeat(\"I\", len(Imags)) ])\n ix = ~np.isnan(mymags)\n tb = pd.DataFrame({'mjd': mymjds[ix],\n 'mag': mymags[ix],\n 'emag': myemags[ix],\n \"filter\": myfilts[ix]})\n \n ixB = tb['filter'].values==\"B\"\n ixV = tb['filter'].values==\"V\"\n ixR = tb['filter'].values==\"R\"\n ixI = tb['filter'].values==\"I\"\n \n tb['wave'] = np.zeros(len(tb))\n tb['wave'].values[ixB] = 4359\n tb['wave'].values[ixV] = 5430\n tb['wave'].values[ixR] = 6349\n tb['wave'].values[ixI] = 8797\n \n tb['mag0'] = tb['mag'] - extinction.ccm89(tb['wave'].values, 3.1*ebv, 3.1)\n tb['mag0_abs'] = tb['mag0'] - dis_mod\n tb['tmax_rf'] = (tb['mjd'] - t_max) / (1+z)\n if colorplt==False:\n return tb\n else:\n tb = add_datecol(tb)\n ix = np.in1d(tb[\"filter\"].values, np.array(['B', 'R', 'I']))\n tb = tb[ix]\n\n dates = get_date_span(tb)\n datesave = []\n for i in range(len(dates)):\n x = dates[i]\n ix = tb[\"date\"].values == x\n tbsub = tb[ix]\n if len(tbsub)!=0:\n flts = tbsub['filter'].values\n if \"R\" in flts and np.sum(np.unique(flts))!=1:\n datesave.append(x)\n datesave = np.array(datesave)\n \n mcolor = []\n mcolor_unc = []\n mjds = []\n colorname = []\n for i in range(len(datesave)):\n x = datesave[i]\n ix = tb[\"date\"].values == x\n tbsub = tb[ix]\n gtb = tbsub[tbsub[\"filter\"].values==\"B\"]\n rtb = tbsub[tbsub[\"filter\"].values==\"R\"]\n itb = tbsub[tbsub[\"filter\"].values==\"I\"]\n if len(gtb)!=0:\n gmjds = gtb[\"mjd\"].values\n gmags = gtb[\"mag0\"].values\n gemags = gtb[\"emag\"].values\n gwtgs = 1/gemags**2\n gmag = np.sum(gmags * gwtgs) / np.sum(gwtgs)\n gmjd = np.sum(gmjds * gwtgs) / np.sum(gwtgs)\n gemag = 1/ np.sqrt(np.sum(gwtgs))\n if len(rtb)!=0:\n rmjds = rtb[\"mjd\"].values\n rmags = rtb[\"mag0\"].values\n remags = rtb[\"emag\"].values\n rwtgs = 1/remags**2\n rmag = np.sum(rmags * rwtgs) / np.sum(rwtgs)\n rmjd = np.sum(rmjds * rwtgs) / np.sum(rwtgs)\n remag = 1/ np.sqrt(np.sum(rwtgs))\n if len(itb)!=0:\n imjds = itb[\"mjd\"].values\n imags = itb[\"mag0\"].values\n iemags = itb[\"emag\"].values\n iwtgs = 1/iemags**2\n imag = np.sum(imags * iwtgs) / np.sum(iwtgs)\n imjd = np.sum(imjds * iwtgs) / np.sum(iwtgs)\n iemag = 1/ np.sqrt(np.sum(iwtgs))\n if len(gtb)!=0 and len(rtb)!=0:\n mcolor.append(gmag - rmag)\n mjds.append( 0.5 * (gmjd + rmjd) )\n mcolor_unc.append( np.sqrt(gemag**2 + remag**2) )\n colorname.append(\"BmR\")\n if len(rtb)!=0 and len(itb)!=0:\n mcolor.append(rmag - imag)\n mjds.append( 0.5 * (rmjd + imjd) )\n mcolor_unc.append( np.sqrt(remag**2 + iemag**2) )\n colorname.append(\"RmI\")\n \n ctb = Table(data = [mjds, mcolor, mcolor_unc, colorname],\n names = [\"mjd\", \"c\", \"ec\", \"cname\"])\n \n ctb['tmax_rf'] = (ctb['mjd'] - t_max) / (1+z)\n ctb = ctb.to_pandas()\n return ctb","def bndy_plasma(self):\n self.ne[0], self.ne[-1] = 1e11, 1e11\n self.ni[0], self.ni[-1] = 1e11, 1e11\n self.nn[0], self.nn[-1] = 1e11, 1e11\n self.Te[0], self.Te[-1] = 0.1, 0.1\n self.Ti[0], self.Ti[-1] = 0.01, 0.01\n # self.coll_em[0], self.coll_em[-1] = 1e5, 1e5\n # self.coll_im[0], self.coll_im[-1] = 1e5, 1e5","def nonlinear_refractive_index(name):\n if name == 'Ne':\n return 8e-9 # cm^2/TW\n elif name == 'He':\n return 3e-9\n elif name == 'Ar':\n return 1e-7\n else:\n raise ValueError('Unknown substance %s' % name)","def front_column_model_p_gain():","def get_specific_heat() -> float:\n return 1006.0","def giniIndex(p_m1):\n G = p_m1*(1-p_m1)*2 \n return G","def referenceIllum(temp, wavelength):\n ct=temp\n if ct <= 0:\n return 0\n if ct < 4000:\n return planckian(ct, wavelength)\n if ct < 5000:\n p=planckian(ct, wavelength)\n d=dseries(ct, wavelength)\n return p+(d-p)*(ct-4000)/1500.0\n return dseries(ct, wavelength)","def get_ptf10iuv(colorplt = False):\n z = 0.0251485\n ebv = 0.0371 # SFD\n D = cosmo.luminosity_distance([z])[0].value * 1e+6 # in pc\n dis_mod = 5*np.log10(D / 10)\n print (\"adopt g band t_max estimated by myself\")\n t_max = 55357.387 \n tb = pd.read_csv('../data/otherSN/Kasliwal2012/PTF10iuv', sep='\\t')\n tb = tb.drop(columns=[\"Unnamed: 4\"])\n tb = tb.rename(columns={'Filter' : 'filter',\n 'MJD': 'mjd'})\n tb = tb[~np.array([x[0]=='>' for x in tb['Mag'].values])]\n tb['mag'] = np.array([float(x.split(\" +or-\")[0]) for x in tb['Mag'].values])\n tb['emag'] = np.array([float(x.split(\" +or-\")[1]) for x in tb['Mag'].values])\n tb = tb.drop(columns=[\"Mag\"])\n \n ixg = tb['filter'].values == \"g\"\n ixr = tb['filter'].values == \"r\"\n ixi = tb['filter'].values == \"i\"\n ixz = tb['filter'].values == \"z\"\n ixB = tb['filter'].values == \"B\"\n tb['wave'] = np.zeros(len(tb))\n tb['wave'].values[ixB] = 4359\n tb['wave'].values[ixg] = 4814\n tb['wave'].values[ixr] = 6422\n tb['wave'].values[ixi] = 7883\n tb['wave'].values[ixz] = 9670\n \n tb['mag0'] = tb['mag'] - extinction.ccm89(tb['wave'].values, 3.1*ebv, 3.1)\n tb['mag0_abs'] = tb['mag0'] - dis_mod\n tb['tmax_rf'] = (tb['mjd'] - t_max) / (1+z)\n tb = tb.sort_values(by = \"mjd\")\n if colorplt==False:\n return tb\n \n else:\n tb = add_datecol(tb)\n ix = np.in1d(tb[\"filter\"].values, np.array(['g', 'r', 'i']))\n tb = tb[ix]\n tb = tb[tb.mjd > 55352.5]\n tb = tb[tb.mjd < 55593.5]\n \n dates = get_date_span(tb)\n datesave = []\n for i in range(len(dates)):\n x = dates[i]\n ix = tb[\"date\"].values == x\n tbsub = tb[ix]\n if len(tbsub)!=0:\n flts = tbsub['filter'].values\n if \"r\" in flts and np.sum(np.unique(flts))!=1:\n datesave.append(x)\n datesave = np.array(datesave)\n \n mcolor = []\n mcolor_unc = []\n mjds = []\n colorname = []\n for i in range(len(datesave)):\n x = datesave[i]\n ix = tb[\"date\"].values == x\n tbsub = tb[ix]\n gtb = tbsub[tbsub[\"filter\"].values==\"g\"]\n rtb = tbsub[tbsub[\"filter\"].values==\"r\"]\n itb = tbsub[tbsub[\"filter\"].values==\"i\"]\n if len(gtb)!=0:\n gmjds = gtb[\"mjd\"].values\n gmags = gtb[\"mag0\"].values\n gemags = gtb[\"emag\"].values\n gwtgs = 1/gemags**2\n gmag = np.sum(gmags * gwtgs) / np.sum(gwtgs)\n gmjd = np.sum(gmjds * gwtgs) / np.sum(gwtgs)\n gemag = 1/ np.sqrt(np.sum(gwtgs))\n if len(rtb)!=0:\n rmjds = rtb[\"mjd\"].values\n rmags = rtb[\"mag0\"].values\n remags = rtb[\"emag\"].values\n rwtgs = 1/remags**2\n rmag = np.sum(rmags * rwtgs) / np.sum(rwtgs)\n rmjd = np.sum(rmjds * rwtgs) / np.sum(rwtgs)\n remag = 1/ np.sqrt(np.sum(rwtgs))\n if len(itb)!=0:\n imjds = itb[\"mjd\"].values\n imags = itb[\"mag0\"].values\n iemags = itb[\"emag\"].values\n iwtgs = 1/iemags**2\n imag = np.sum(imags * iwtgs) / np.sum(iwtgs)\n imjd = np.sum(imjds * iwtgs) / np.sum(iwtgs)\n iemag = 1/ np.sqrt(np.sum(iwtgs))\n if len(gtb)!=0 and len(rtb)!=0:\n mcolor.append(gmag - rmag)\n mjds.append( 0.5 * (gmjd + rmjd) )\n mcolor_unc.append( np.sqrt(gemag**2 + remag**2) )\n colorname.append(\"gmr\")\n if len(rtb)!=0 and len(itb)!=0:\n mcolor.append(rmag - imag)\n mjds.append( 0.5 * (rmjd + imjd) )\n mcolor_unc.append( np.sqrt(remag**2 + iemag**2) )\n colorname.append(\"rmi\")\n \n ctb = Table(data = [mjds, mcolor, mcolor_unc, colorname],\n names = [\"mjd\", \"c\", \"ec\", \"cname\"])\n \n ctb['tmax_rf'] = (ctb['mjd'] - t_max) / (1+z)\n ctb = ctb.to_pandas()\n return ctb","def calibration(self) -> int:","def comp_amplification_index(self):\n \n self.grid_tuning_in=self.inputs.grid_tuning_in\n self.grid_tuning_out=gl.comp_grid_tuning_index(self.L,self.nx,(self.r[0:self.n_e**2,:]).T) \n self.grid_tuning_out_inhib=gl.comp_grid_tuning_index(self.L,self.nx,(self.r[self.n_e**2:,:]).T)\n\n self.grid_amp_index=self.grid_tuning_out/self.grid_tuning_in","def test_WIMP_cut_region_on_true_data(bolo_name, mass, analysis):\n\t\n\n\t#Load 2D PDF\n\tfWIMP2D, f = PyRPl.open_ROOT_object(\"./ROOT_files/WIMP_PDF2D_\" + analysis + \".root\", \"WIMP_\" + mass + \"_GeV\")\n\n\t#Load cut value on PDF for 95% WIMP box\n\tcut_val_90, cut_val_99 = 0,0\n\twith open (\"./Text_files/WIMP_PDF_90_and_99_cut_value_\" + analysis + \".txt\", \"r\") as fcut:\n\t\tstuff = [elem.rstrip().split(\",\") for elem in fcut.readlines()]\n\t\tfor elem in stuff:\n\t\t\tmass_val = elem[0]\n\t\t\tif int(mass)==int(mass_val):\n\t\t\t\tcut_val_90 = float(elem[1])\n\t\t\t\tcut_val_99 = float(elem[2])\n\t\n\n\tdata_path = \"/home/irfulx204/mnt/tmain/Desktop/Run308_Analyse_ERA/Fond_ERA_merged/\"\n\tfilou = TFile(data_path + bolo_name + \"_\" + analysis + \"_fond.root\", \"read\")\n\ttree = filou.Get(\"data\")\n\tnum_pass_cut =0\n\n\thpass = TH2F(\"hpass\", \"hpass\", 100, 0, 15, 100, 0, 15)\n\n\t# #T Check that the events are found where expected\n\t# arr1 = np.random.uniform(0,15,size=(200000,2))\n\t# for i in range(arr1.shape[0]):\n\t# \tPDF_val = fWIMP2D.Eval(arr1[i][0], arr1[i][1])\n\t# \tif (cut_val_99<PDF_val<cut_val_90):\n\t# \t# if (cut_val_99<PDF_val<cut_val_90):\n\t# \t\tnum_pass_cut+=1\n\t# \t\thpass.Fill(arr1[i][0], arr1[i][1])\t\t\n\n\t# hpass.Draw()\n\t# raw_input()\n\n\tfor k in range(tree.GetEntries()):\n\t\ttree.GetEntry(k)\n\t\tER=(1+8./3)*0.5*(tree.EC1+tree.EC2)-0.33*(1.5*tree.EIA+4*tree.EIB+1.5*tree.EIC+4*tree.EID)\n\t\tPDF_val = fWIMP2D.Eval(ER, 0.5*(tree.EIB+tree.EID))\n\t\tif (cut_val_99<PDF_val<cut_val_90 and 0.5*(tree.EIB+tree.EID)>0.7):\n\t\t# if (cut_val_99<PDF_val<cut_val_90):\n\t\t\tnum_pass_cut+=1\n\t\t\thpass.Fill(0.5*(tree.EC1+tree.EC2), 0.5*(tree.EIB+tree.EID))\n\n\tprint num_pass_cut\n\thpass.Draw()\n\traw_input()","def mamajek08_logRpHK_edge():\n return -4.355226174791392","def search_diversification(self):\n self.stage = 2\n # Find index of least explored region,\n # express in binary\n b = bin(np.argmin(self.LTM))[2:]\n # Pad binary number\n if len(b) < self.x0.shape[0]:\n pad = self.x0.shape[0] - len(b)\n b = ''.join(['0' for i in range(pad)]) + b\n # Store digits in column vector\n d = len(b)\n b = np.array(list(b), dtype='int').reshape((d,1))\n # Generate random positive vector\n base = self.r_uni(low=0, high=2, size=(d,1))\n # Transform vector into appropriate region\n return base * (b * 2 - 1)","def refractive_index_glass(wavelength, type='soda-lime-low-iron'):\n\n if type.lower() == 'soda-lime-low-iron':\n wavelength = wavelength / 1000\n n = 1.5130 - 0.003169 * wavelength ** 2 + 0.003962 * wavelength ** -2 + 0 * 1j\n\n # n[wavelength < 0.3] = n[wavelength < 0.3] + 1j*0\n elif type.upper() == 'BK7':\n wavelength = wavelength / 1000\n n = np.sqrt(1 + \\\n (1.03961212 * wavelength ** 2) / (\n wavelength ** 2 - 0.00600069867) + \\\n (0.231792344 * wavelength ** 2) / (\n wavelength ** 2 - 0.0200179144) + \\\n (1.01046945 * wavelength ** 2) / (\n wavelength ** 2 - 103.560653)\n )\n\n return n","def molar_mass_dry_air():\n return 28.9647","def sky_orbits(test=True):\n \n t = Table.read('/home/ana/data/baumgardt_positions.fits')\n \n ind_disterr = ~np.isfinite(t['e_Rsun'])\n t['e_Rsun'][ind_disterr] = 0.1 * t['Rsun'][ind_disterr]\n e_max = np.nanmax(t['e_Rsun'][~ind_disterr])\n ind_cap = t['e_Rsun']>e_max\n t['e_Rsun'][ind_cap] = e_max\n \n clusters = ['NGC 3201', 'NGC 4590', 'NGC 5824', 'NGC 5272', 'NGC 5139', 'NGC 5024']\n #clusters = ['NGC 5824', 'NGC 5024']\n N = len(clusters)\n \n match = dict()\n match['NGC 3201'] = dict(streams=['gjoll'], direction=[-1], nstep=[35], gc_label='NGC\\n3201', gcra_off=0*u.deg, gcdec_off=-13*u.deg, gcl_off=0*u.deg, gcb_off=-13*u.deg, stream_label=['$Gj\\\\\\\" oll$'], stream_ra=[-156*u.deg], stream_dec=[-4.5*u.deg], eq_angle=[-45*u.deg], stream_l=[-148*u.deg], stream_b=[-33*u.deg], gal_angle=[22*u.deg])\n \n match['NGC 4590'] = dict(streams=['fjorm'], direction=[1], nstep=[100], gc_label='NGC\\n4590', gcra_off=-15*u.deg, gcdec_off=0*u.deg, gcl_off=-13*u.deg, gcb_off=-10*u.deg, stream_label=['$Fj\\\\\\\" orm$'], stream_ra=[-22*u.deg], stream_dec=[66*u.deg], eq_angle=[35*u.deg], stream_l=[110*u.deg], stream_b=[50*u.deg], gal_angle=[-50*u.deg])\n \n match['NGC 5024'] = dict(streams=['sylgr', 'ravi'], direction=[-1, 1], nstep=[300,500], gc_label='NGC\\n5024', gcra_off=-15*u.deg, gcdec_off=0*u.deg, gcl_off=10*u.deg, gcb_off=-20*u.deg, stream_label=['Sylgr', 'Ravi'], stream_ra=[-70*u.deg, 83*u.deg], stream_dec=[2*u.deg, -47*u.deg], eq_angle=[25*u.deg, 65*u.deg], stream_l=[-110*u.deg, -18.5*u.deg], stream_b=[62*u.deg, -47*u.deg], gal_angle=[30*u.deg, -10*u.deg])\n \n match['NGC 5139'] = dict(streams=['fimbulthul'], direction=[-1], nstep=[70], gc_label='NGC\\n5139', gcra_off=-5*u.deg, gcdec_off=-15*u.deg, gcl_off=0*u.deg, gcb_off=-12*u.deg, stream_label=['Fimbulthul'], stream_ra=[-20*u.deg], stream_dec=[-15*u.deg], eq_angle=[0*u.deg], stream_l=[-20*u.deg], stream_b=[45*u.deg], gal_angle=[0*u.deg])\n \n match['NGC 5272'] = dict(streams=['svol'], direction=[1], nstep=[70], gc_label='NGC\\n5272', gcra_off=-15*u.deg, gcdec_off=10*u.deg, gcl_off=-23*u.deg, gcb_off=-17*u.deg, stream_label=['$Sv\\\\\\\" ol$'], stream_ra=[-2*u.deg], stream_dec=[34*u.deg], eq_angle=[-10*u.deg], stream_l=[55*u.deg], stream_b=[55*u.deg], gal_angle=[-65*u.deg])\n \n match['NGC 5824'] = dict(streams=['triangulum', 'turbio'], direction=[1,1], nstep=[700,1], gc_label='NGC\\n5824', gcra_off=15*u.deg, gcdec_off=-5*u.deg, gcl_off=15*u.deg, gcb_off=-5*u.deg, stream_label=['Triangulum', 'Turbio'], stream_ra=[152*u.deg, 130*u.deg], stream_dec=[32*u.deg, -51*u.deg], eq_angle=[-48*u.deg, 30*u.deg], stream_l=[120*u.deg, -82*u.deg], stream_b=[-31*u.deg, -57*u.deg], gal_angle=[70*u.deg, 105*u.deg])\n \n dt = 0.5*u.Myr\n wangle = 180*u.deg\n ra_off = 120*u.deg\n l_off = 0*u.deg\n \n colors = [mpl.cm.plasma(0.95*x/N) for x in range(N)]\n \n np.random.seed(27529)\n if test:\n Nsample = 1\n else:\n Nsample = 100\n \n plt.close()\n fig = plt.figure(figsize=(12,12))\n \n ax0 = fig.add_subplot(211, projection='mollweide')\n ax1 = fig.add_subplot(212, projection='mollweide')\n ax = [ax0, ax1]\n \n for i in range(N):\n #ind = t['Name']== clusters[i]\n ind = t['Name']==clusters[i]\n t_ = t[ind]\n \n c = coord.SkyCoord(ra=t_['RAJ2000'], dec=t_['DEJ2000'], distance=t_['Rsun'], pm_ra_cosdec=t_['pmRA_'], pm_dec=t_['pmDE'], radial_velocity=t_['RV'], frame='icrs')\n cgal = c.transform_to(coord.Galactic)\n #w0 = gd.PhaseSpacePosition(c.transform_to(gc_frame).cartesian)[0]\n \n color = colors[i]\n alpha_text = 0.8\n \n plt.sca(ax[0])\n plt.plot((c.ra + ra_off).wrap_at(wangle).rad, c.dec.rad, '+', color=color, mew=3, ms=15, label=t_['Name'][0])\n plt.text((c.ra + ra_off + match[clusters[i]]['gcra_off']).wrap_at(wangle).rad, (c.dec + match[clusters[i]]['gcdec_off']).rad, match[clusters[i]]['gc_label'], fontsize='small', ha='center', va='center', alpha=alpha_text)\n \n plt.sca(ax[1])\n plt.plot((cgal.l + l_off).wrap_at(wangle).rad, cgal.b.rad, '+', color=color, mew=3, ms=15, label=t_['Name'][0])\n plt.text((cgal.l + l_off + match[clusters[i]]['gcl_off']).wrap_at(wangle).rad, (cgal.b + match[clusters[i]]['gcb_off']).rad, match[clusters[i]]['gc_label'], fontsize='small', ha='center', va='center', alpha=alpha_text)\n \n\n for j in range(len(match[clusters[i]]['direction'])):\n # sample gc positional uncertainties\n for k in range(-1, Nsample):\n if k==-1:\n c = coord.SkyCoord(ra=t_['RAJ2000'], dec=t_['DEJ2000'], distance=t_['Rsun'], pm_ra_cosdec=t_['pmRA_'], pm_dec=t_['pmDE'], radial_velocity=t_['RV'], frame='icrs')\n w0 = gd.PhaseSpacePosition(c.transform_to(gc_frame).cartesian)[0]\n \n lw = 1.5\n alpha = 1\n else:\n c = coord.SkyCoord(ra=t_['RAJ2000'], dec=t_['DEJ2000'], distance=t_['Rsun'] + np.random.randn()*t_['e_Rsun'], pm_ra_cosdec=t_['pmRA_'] + np.random.randn()*t_['e_pmRA_'], pm_dec=t_['pmDE'] + np.random.randn()*t_['e_pmDE'], radial_velocity=t_['RV'] + np.random.randn()*t_['e_RV'], frame='icrs')\n w0 = gd.PhaseSpacePosition(c.transform_to(gc_frame).cartesian)[0]\n \n lw = 1\n alpha = 0.1\n \n orbit = ham.integrate_orbit(w0, dt=dt*match[clusters[i]]['direction'][j], n_steps=match[clusters[i]]['nstep'][j])\n orbit_eq = orbit.to_coord_frame(coord.ICRS, galactocentric_frame=gc_frame)\n orbit_gal = orbit.to_coord_frame(coord.Galactic, galactocentric_frame=gc_frame)\n \n \n plt.sca(ax[0])\n dra = (orbit_eq.ra+ra_off).wrap_at(wangle)[1:] - (orbit_eq.ra+ra_off).wrap_at(wangle)[:-1]\n if np.any(np.abs(dra)>180*u.deg):\n pos_break = dra>180*u.deg\n ind_break = np.argmax(pos_break)\n ipad = 1\n plt.plot((orbit_eq.ra+ra_off).wrap_at(wangle).rad[:ind_break-ipad], orbit_eq.dec.rad[:ind_break-ipad], '-', color=color, lw=lw, label='', alpha=alpha)\n plt.plot((orbit_eq.ra+ra_off).wrap_at(wangle).rad[ind_break+ipad:], orbit_eq.dec.rad[ind_break+ipad:], '-', color=color, lw=lw, label='', alpha=alpha)\n else:\n plt.plot((orbit_eq.ra+ra_off).wrap_at(wangle).rad, orbit_eq.dec.rad, '-', color=color, lw=lw, label='', alpha=alpha)\n \n plt.sca(ax[1])\n dl = orbit_gal.l.wrap_at(wangle)[1:] - orbit_gal.l.wrap_at(wangle)[:-1]\n if np.any(np.abs(dl)>180*u.deg):\n pos_break = dl>180*u.deg\n ind_break = np.argmax(pos_break)\n ipad = 1\n plt.plot((orbit_gal.l+l_off).wrap_at(wangle).rad[:ind_break-ipad], orbit_gal.b.rad[:ind_break-ipad], '-', color=color, lw=lw, label='', alpha=alpha)\n plt.plot((orbit_gal.l+l_off).wrap_at(wangle).rad[ind_break+ipad:], orbit_gal.b.rad[ind_break+ipad:], '-', color=color, lw=lw, label='', alpha=alpha)\n else:\n plt.plot((orbit_gal.l+l_off).wrap_at(wangle).rad, orbit_gal.b.rad, '-', color=color, lw=lw, label='', alpha=alpha)\n \n # add streams\n pkl = pickle.load(open('../data/streams/data_{:s}.pkl'.format(match[clusters[i]]['streams'][j]), 'rb'))\n cs = coord.SkyCoord(ra=pkl['dec'][0], dec=pkl['dec'][1], frame='icrs')\n cs_gal = cs.transform_to(coord.Galactic)\n \n plt.sca(ax[0])\n plt.plot((cs.ra+ra_off).wrap_at(wangle).rad, cs.dec.rad, 'o', color=color, ms=8, label=match[clusters[i]]['streams'][j])\n plt.text(coord.Longitude(match[clusters[i]]['stream_ra'][j]).wrap_at(wangle).rad, coord.Latitude(match[clusters[i]]['stream_dec'][j]).rad, match[clusters[i]]['stream_label'][j], fontsize='small', alpha=alpha_text, rotation=match[clusters[i]]['eq_angle'][j].value, ha='center', va='center')\n \n plt.sca(ax[1])\n plt.plot((cs_gal.l+l_off).wrap_at(wangle).rad, cs_gal.b.rad, 'o', color=color, ms=8, label=match[clusters[i]]['streams'][j])\n plt.text(coord.Longitude(match[clusters[i]]['stream_l'][j]).wrap_at(wangle).rad, coord.Latitude(match[clusters[i]]['stream_b'][j]).rad, match[clusters[i]]['stream_label'][j], fontsize='small', alpha=alpha_text, rotation=match[clusters[i]]['gal_angle'][j].value, ha='center', va='center')\n \n \n plt.sca(ax[0])\n plt.grid(ls=':')\n plt.xlabel('R.A. [deg]')\n plt.ylabel('Dec [deg]')\n\n plt.gca().xaxis.set_ticklabels([])\n \n xloc = coord.Longitude(np.arange(-150,180,30)*u.deg)\n xloc = np.delete(xloc, [3])\n yloc = coord.Latitude(5*u.deg)\n Nx = len(xloc)\n \n for i in range(Nx):\n plt.text(xloc[i].wrap_at(wangle).rad, yloc.rad, '{:.0f}$\\degree$'.format((xloc[i]-ra_off).wrap_at(wangle).degree), alpha=0.6, ha='center', va='center')\n \n \n plt.sca(ax[1])\n plt.grid(ls=':')\n plt.xlabel('Galactic longitude [deg]')\n plt.ylabel('Galactic latitude [deg]')\n \n plt.gca().xaxis.set_ticklabels([])\n \n xloc = coord.Longitude(np.arange(-150,180,30)*u.deg)\n xloc = np.delete(xloc, [2,3])\n yloc = coord.Latitude(5*u.deg)\n Nx = len(xloc)\n \n for i in range(Nx):\n plt.text(xloc[i].wrap_at(wangle).rad, yloc.rad, '{:.0f}$\\degree$'.format((xloc[i]+l_off).wrap_at(wangle).degree), alpha=0.6, ha='center', va='center')\n \n \n \n plt.tight_layout(h_pad=2)\n plt.savefig('../paper/sky_orbits.pdf')","def ggpl_house():\n\n\t# .lines ogni riga ha due coppie di x/y che costituiscono un segmento\n\tverts = []\n\tcells = []\n\ti = 0\n\treader = csv.reader(open(\"lines/muri_esterni.lines\", 'rb'), delimiter=',') \n\tfor row in reader:\n\t\tverts.append([float(row[0]), float(row[1])])\n\t\tverts.append([float(row[2]), float(row[3])])\n\t\ti+=2\n\t\tcells.append([i-1,i])\n\n\texternalWalls = MKPOL([verts,cells,None])\n\tfloor = SOLIDIFY(externalWalls)\n\tfloor = S([1,2,3])([.04,.04,.04])(floor)\n\texternalWalls = S([1,2,3])([.04,.04,.04])(externalWalls)\n\texternalWalls = OFFSET([.2,.2,4])(externalWalls)\n\theightWalls = SIZE([3])(externalWalls)[0]\n\tthicknessWalls = SIZE([2])(externalWalls)[0]\n\n\tverts = []\n\tcells = []\n\ti = 0\n\treader = csv.reader(open(\"lines/muri_interni.lines\", 'rb'), delimiter=',') \n\tfor row in reader:\n\t\tverts.append([float(row[0]), float(row[1])])\n\t\tverts.append([float(row[2]), float(row[3])])\n\t\ti+=2\n\t\tcells.append([i-1,i])\n\n\tinternalWalls = MKPOL([verts,cells,None])\n\tinternalWalls = S([1,2,3])([.04,.04,.04])(internalWalls)\n\tinternalWalls = OFFSET([.2,.2,4])(internalWalls)\n\twalls = STRUCT([externalWalls, internalWalls])\n\n\tverts = []\n\tcells = []\n\ti = 0\n\treader = csv.reader(open(\"lines/porte.lines\", 'rb'), delimiter=',') \n\tfor row in reader:\n\t\tverts.append([float(row[0]), float(row[1])])\n\t\tverts.append([float(row[2]), float(row[3])])\n\t\ti+=2\n\t\tcells.append([i-1,i])\n\n\tdoors = MKPOL([verts,cells,None])\n\tdoors = SOLIDIFY(doors)\n\tdoors = S([1,2,3])([.04,.04,.04])(doors)\n\tdoors = OFFSET([.2,.2,3])(doors)\n\twalls = DIFFERENCE([walls, doors])\n\n\n\tverts = []\n\tcells = []\n\ti = 0\n\treader = csv.reader(open(\"lines/finestre.lines\", 'rb'), delimiter=',') \n\tfor row in reader:\n\t\tverts.append([float(row[0]), float(row[1])])\n\t\tverts.append([float(row[2]), float(row[3])])\n\t\ti+=2\n\t\tcells.append([i-1,i])\n\n\twindows = MKPOL([verts,cells,None])\n\twindows = SOLIDIFY(windows)\n\twindows = S([1,2,3])([.04,.04,.04])(windows)\n\twindows = OFFSET([.2,.2,2])(windows)\n\theightWindows = SIZE([3])(windows)[0]\n\twindows = T(3)((heightWalls-heightWindows)/2.)(windows)\n\twalls = DIFFERENCE([walls, windows])\n\n\tfloor = TEXTURE(\"texture/floor.jpg\")(floor)\n\twalls = TEXTURE(\"texture/wall.jpg\")(walls)\n\thome = STRUCT([floor, walls])\n\treturn home","def top_of_climb_index(self):\n return self.altitudes.argmax()","def split_necessity(self):\n return max(self._color_var_rel) * self.n_pix\n # return reduce(int.__mul__, (l-u for u,l in self.bounds)) * self.n_pix","def get_iPTF14gqr(colorplt=False):\n z = 0.063\n # ebv = 0.082\n D = cosmo.luminosity_distance([z])[0].value * 1e+6 # in pc\n dis_mod = 5*np.log10(D / 10)\n t_exp = 56943.74 # \n t_max = 56950.26 # g band max light + 3\n \n tb = Table(fits.open('../data/otherSN/De2018/tables1.fit')[1].data)\n tb.rename_column('MJD' , 'mjd')\n tb['texp_rf'] = (tb['mjd'] - t_exp) / (1+z)\n tb['tmax_rf'] = (tb['mjd'] - t_max) / (1+z)\n # tb = tb[tb[\"Filt\"]==\"g \"]\n tb = tb[~np.isnan(tb['e_mag'])]\n tb.rename_column('Filt' , 'filter')\n tb.rename_column('e_mag' , 'emag')\n tb.rename_column('mag' , 'mag0')\n \n ixg = tb['filter']==\"g \"\n ixB = tb['filter']==\"B \"\n ixV = tb['filter']==\"V \"\n ixr = tb['filter']==\"r \"\n ixi = tb['filter']==\"i \"\n ixUVW1 = tb['filter']==\"UVW1\"\n ixUVW2 = tb['filter']==\"UVW2\"\n \n tb['wave'] = np.zeros(len(tb))\n tb['wave'][ixUVW2] = 2079\n tb['wave'][ixUVW1] = 2614\n tb['wave'][ixB] = 4359\n tb['wave'][ixg] = 4814\n tb['wave'][ixV] = 5430\n tb['wave'][ixr] = 6422\n tb['wave'][ixi] = 7883\n \n tb['mag0_abs'] = tb['mag0'] - dis_mod\n \n tb = tb.to_pandas()\n tb[\"texp_rf\"] = tb[\"Phase\"]\n tb = tb.drop(columns=[\"recno\", \"Phase\", \"l_mag\"])\n \"\"\"\n ix = np.any([tb['Tel'].values==\"P60 \",\n tb[\"filter\"].values=='g '], axis=0)\n tb = tb[ix]\n \"\"\"\n tb = add_datecol(tb)\n tb = add_physcol(tb)\n tt = tb[\"tmax_rf\"].values\n epochs = [\" \" for x in range(len(tt))]\n epochs = np.array(epochs)\n \"\"\"\n ix = (tt>-5.6)&(tt<-5.55)\n epochs[ix] = \"epoch 01\"\n \"\"\"\n ix = (tt>-5.55)&(tt<-5.50)\n epochs[ix] = \"epoch 02\"\n \n ix = (tt>-5.50)&(tt<-5.45)\n epochs[ix] = \"epoch 03\"\n \n ix = (tt>-5.2)&(tt<-5.0)\n epochs[ix] = \"epoch 04\"\n ix = (tt>-5.0)&(tt<-4.7)\n epochs[ix] = \"epoch 05\"\n \n ix = (tt>-4.7)&(tt<-4.5)\n epochs[ix] = \"epoch 06\"\n ix = (tt>-4.5)&(tt<-3.5)\n epochs[ix] = \"epoch 07\"\n ix = (tt>-3.5)&(tt<-2.5)\n epochs[ix] = \"epoch 08\"\n ix = (tt>-1.5)&(tt<-1)\n epochs[ix] = \"epoch 09\"\n ix = (tt>-1)&(tt<-0.82)\n epochs[ix] = \"epoch 10\"\n ix = (tt>-0.82)&(tt<-0.6)\n epochs[ix] = \"epoch 11\"\n ix = (tt>-0.5)&(tt<0.5)\n epochs[ix] = \"epoch 12\"\n ix = (tt>0.5)&(tt<1.5)\n epochs[ix] = \"epoch 13\"\n ix = (tt>1.5)&(tt<2.5)\n epochs[ix] = \"epoch 14\"\n ix = (tt>3.5)&(tt<4.5)\n epochs[ix] = \"epoch 15\"\n ix = (tt>4.5)&(tt<5)\n epochs[ix] = \"epoch 16\"\n ix = (tt>5)&(tt<5.6)\n epochs[ix] = \"epoch 17\"\n ix = (tt>5.6)&(tt<5.8)\n epochs[ix] = \"epoch 18\"\n ix = (tt>6)&(tt<7)\n epochs[ix] = \"epoch 19\"\n ix = (tt>7)&(tt<8)\n epochs[ix] = \"epoch 20\"\n ix = (tt>8)&(tt<9)\n epochs[ix] = \"epoch 21\"\n tb[\"epoch\"] = epochs\n\n if colorplt==False:\n return tb\n else:\n tb = add_datecol(tb)\n ix = np.in1d(tb[\"filter\"].values, np.array(['g ', 'r ', 'i ']))\n tb = tb[ix]\n\n dates = get_date_span(tb)\n datesave = []\n for i in range(len(dates)):\n x = dates[i]\n ix = tb[\"date\"].values == x\n tbsub = tb[ix]\n if len(tbsub)!=0:\n flts = tbsub['filter'].values\n if \"r \" in flts and np.sum(np.unique(flts))!=1:\n datesave.append(x)\n datesave = np.array(datesave)\n \n mcolor = []\n mcolor_unc = []\n mjds = []\n colorname = []\n for i in range(len(datesave)):\n x = datesave[i]\n ix = tb[\"date\"].values == x\n tbsub = tb[ix]\n gtb = tbsub[tbsub[\"filter\"].values==\"g \"]\n rtb = tbsub[tbsub[\"filter\"].values==\"r \"]\n itb = tbsub[tbsub[\"filter\"].values==\"i \"]\n if len(gtb)!=0:\n gmjds = gtb[\"mjd\"].values\n gmags = gtb[\"mag0\"].values\n gemags = gtb[\"emag\"].values\n gwtgs = 1/gemags**2\n gmag = np.sum(gmags * gwtgs) / np.sum(gwtgs)\n gmjd = np.sum(gmjds * gwtgs) / np.sum(gwtgs)\n gemag = 1/ np.sqrt(np.sum(gwtgs))\n if len(rtb)!=0:\n rmjds = rtb[\"mjd\"].values\n rmags = rtb[\"mag0\"].values\n remags = rtb[\"emag\"].values\n rwtgs = 1/remags**2\n rmag = np.sum(rmags * rwtgs) / np.sum(rwtgs)\n rmjd = np.sum(rmjds * rwtgs) / np.sum(rwtgs)\n remag = 1/ np.sqrt(np.sum(rwtgs))\n if len(itb)!=0:\n imjds = itb[\"mjd\"].values\n imags = itb[\"mag0\"].values\n iemags = itb[\"emag\"].values\n iwtgs = 1/iemags**2\n imag = np.sum(imags * iwtgs) / np.sum(iwtgs)\n imjd = np.sum(imjds * iwtgs) / np.sum(iwtgs)\n iemag = 1/ np.sqrt(np.sum(iwtgs))\n if len(gtb)!=0 and len(rtb)!=0:\n mcolor.append(gmag - rmag)\n mjds.append( 0.5 * (gmjd + rmjd) )\n mcolor_unc.append( np.sqrt(gemag**2 + remag**2) )\n colorname.append(\"gmr\")\n if len(rtb)!=0 and len(itb)!=0:\n mcolor.append(rmag - imag)\n mjds.append( 0.5 * (rmjd + imjd) )\n mcolor_unc.append( np.sqrt(remag**2 + iemag**2) )\n colorname.append(\"rmi\")\n \n ctb = Table(data = [mjds, mcolor, mcolor_unc, colorname],\n names = [\"mjd\", \"c\", \"ec\", \"cname\"])\n \n ctb['tmax_rf'] = (ctb['mjd'] - t_max) / (1+z)\n ctb = ctb.to_pandas()\n return ctb","def narrowIncandPeakInfoLG(self):\r\n\t\tself.narrowIncandBaseline_LG = (np.mean(self.lowGainNarrowBandIncandData[0:10]))\r\n\t\t\t\t\r\n\t\traw_narrowIncand_max_LG = np.amax(self.lowGainNarrowBandIncandData)\r\n\t\tnarrowIncand_max_LG = raw_narrowIncand_max_LG - self.narrowIncandBaseline_LG\t\t\r\n\t\tnarrowIncand_max_index_LG = np.argmax(self.lowGainNarrowBandIncandData)\r\n\t\t\r\n\t\tself.narrowIncandMax_LG =narrowIncand_max_LG\r\n\t\tself.narrowIncandMaxPos_LG = narrowIncand_max_index_LG","def get_index(self):\n return (np.sqrt(self.dielectric))","def get_absolute_color_index(A_g, bmag, rmag):\n assert (A_g is not None) & (str(A_g) != \"nan\")\n assert (bmag is not None) & (str(bmag) != \"nan\")\n assert (rmag is not None) & (str(rmag) != \"nan\")\n # E(Bp-Rp) = A_Bp-A_Rp = (Bp-Rp)_obs - E(Bp-Rp)\n Ab_minus_Ar = get_excess_from_extiction(A_g)\n bp_rp = bmag - rmag # color index\n Bp_Rp = bp_rp - Ab_minus_Ar\n return Bp_Rp","def find_components_old(image,deltaPix,lens_rad_arcsec = 5.0,lens_rad_ratio = None, gal_rad_ratio = 0.1,min_size_arcsec=0.3,thresh=0.4, show_locations=False):\n\n # convert minimum component size in pixel units\n min_size = int(min_size_arcsec / deltaPix)\n \n #Convert lens radius and central galaxy radius to pixels\n if lens_rad_ratio == None:\n lens_rad = int(lens_rad_arcsec / deltaPix)\n else: lens_rad = int(len(image) * lens_rad_ratio)\n gal_rad = int(len(image) * gal_rad_ratio)\n \n # downscale source image to data resolution (for speed + easier for converting to data units)\n #down = image_util.re_size(image, factor=supersampling_factor_source)\n \n # apply laplacian of gaussian (LoG) filter to enhance maxima\n filtered = - gaussian_laplace(deepcopy(image), sigma = min_size, mode='constant', cval=0.)\n \n# print(filtered.min(),filtered.max(),filtered.min() + thresh * np.abs(filtered.min()))\n \n \n # assume all value below max*threshold can not be maxima, so put all to zero\n# filtered[filtered < thresh*filtered.max()] = 0.\n \n# assume all value below min*threshold can not be maxima, so put all to zero\n filtered[filtered < filtered.min() + thresh * np.abs(filtered.min())] = 0.\n \n if show_locations:\n plt.figure(figsize = (8,8))\n plt.subplot(1,2,1)\n plt.imshow(image, origin='lower', norm=SymLogNorm(5))\n plt.title('Image')\n\n plt.subplot(1,2,2)\n plt.imshow(filtered, origin='lower', norm=SymLogNorm(5))\n plt.title('Filtered Image')\n plt.show()\n \n # find coordinates of local maxima\n #print(int(0.5 * min_size))\n max_idx_2d_small = peak_local_max(filtered, min_distance=0)\n max_idx_2d_large = peak_local_max(filtered, min_distance=1)\n \n x_list_small, y_list_small = max_idx_2d_small[:, 1], max_idx_2d_small[:, 0]\n x_list_large, y_list_large = max_idx_2d_large[:, 1], max_idx_2d_large[:, 0]\n \n im_center_x, im_center_y = len(image) / 2., len(image) / 2.\n \n R = np.sqrt((x_list_large - im_center_x)**2 + (y_list_large - im_center_y)**2)\n new_center_x, new_center_y = x_list_large[R < gal_rad], y_list_large[R < gal_rad]\n \n if (len(new_center_x) > 1) and (len(x_list_large[R == R.min()]) ==1 ): \n new_center_x, new_center_y = x_list_large[R == R.min()], y_list_large[R == R.min()]\n elif (len(new_center_x) > 1) and (len(x_list_large[R == R.min()]) > 1 ): \n new_center_x, new_center_y = im_center_x, im_center_y\n elif len(new_center_x) == 0: \n new_center_x, new_center_y = im_center_x, im_center_y\n \n \n R_small = np.sqrt((x_list_small - new_center_x)**2 + (y_list_small - new_center_y)**2)\n R_large = np.sqrt((x_list_large - new_center_x)**2 + (y_list_large - new_center_y)**2)\n \n x_sats, y_sats = x_list_small[R_small > lens_rad], y_list_small[R_small > lens_rad]\n \n # show maxima on image for debug\n if show_locations:\n fig = plt.figure(figsize=(4, 4))\n #plt.imshow(image, origin='lower', cmap=cmap_flux, norm=LogNorm(1e-2))\n plt.imshow(image, origin='lower', norm=SymLogNorm(5))\n \n for i in range(len(x_sats)):\n plt.scatter([x_sats[i]], [y_sats[i]], c='red', s=60, marker='+')\n# plt.annotate(i+1, (x_list[i], y_list[i]), color='black')\n \n# for i in range(len(x_mask)):\n# plt.scatter([x_mask[i]], [y_mask[i]], c='red', s=100, marker='*')\n# plt.annotate(i+1, (x_mask[i], y_mask[i]), color='red')\n plt.scatter(new_center_x, new_center_y,c='red', s=100, marker='*')\n \n draw_lens_circle = Circle((new_center_x, new_center_y),lens_rad ,fill=False)\n draw_gal_circle = Circle((new_center_x, new_center_y),gal_rad, fill = False)\n plt.gcf().gca().add_artist(draw_lens_circle)\n plt.gcf().gca().add_artist(draw_gal_circle)\n plt.title('Detected Components')\n plt.text(1, 1, \"detected components\", color='red')\n fig.axes[0].get_xaxis().set_visible(True); fig.axes[0].get_yaxis().set_visible(True)\n plt.show()\n return (x_sats, y_sats), (new_center_x, new_center_y)","def localize_red_clump(star_catalog,close_cat_idx,log):\n\n def select_within_range(mags, colours, mag_min, mag_max, col_min, col_max):\n \"\"\"Function to identify the set of array indices with values\n between the range indicated\"\"\"\n\n idx1 = np.where(colours >= col_min)[0]\n idx2 = np.where(colours <= col_max)[0]\n idx3 = np.where(mags >= mag_min)[0]\n idx4 = np.where(mags <= mag_max)[0]\n idx = set(idx1).intersection(set(idx2))\n idx = idx.intersection(set(idx3))\n idx = list(idx.intersection(set(idx4)))\n\n return idx\n\n RC = photometry_classes.Star()\n\n inst_i = star_catalog['cal_ref_mag_ip'][close_cat_idx]\n inst_r = star_catalog['cal_ref_mag_rp'][close_cat_idx]\n inst_g = star_catalog['cal_ref_mag_gp'][close_cat_idx]\n cal_i = star_catalog['imag'][close_cat_idx]\n cal_r = star_catalog['rmag'][close_cat_idx]\n cal_g = star_catalog['gmag'][close_cat_idx]\n inst_ri = inst_r - inst_i # Catalogue column order is red -> blue\n inst_gi = inst_g - inst_i\n inst_gr = inst_g - inst_r\n cal_ri = cal_r - cal_i\n cal_gi = cal_g - cal_i\n cal_gr = cal_g - cal_r\n\n log.info('\\n')\n log.info('Localizing the Red Clump')\n log.info('Median (r-i), i: '+str(np.median(inst_ri))+', '+str(np.median(inst_i)))\n log.info('Median (g-i), i: '+str(np.median(inst_gi))+', '+str(np.median(inst_i)))\n log.info('Median (g-r), g: '+str(np.median(inst_gr))+', '+str(np.median(inst_g)))\n\n ri_min = 0.8\n ri_max = 1.2\n i_min = 15.5\n i_max = 16.5\n\n r_min = 16.2\n r_max = 17.5\n\n gi_min = 2.5\n gi_max = 3.5\n\n gr_min = 1.5\n gr_max = 2.2\n g_min = 17.8\n g_max = 19.5\n\n log.info('Selected Red Clump giants between:')\n log.info('i = '+str(i_min)+' to '+str(i_max))\n log.info('r = '+str(r_min)+' to '+str(r_max))\n log.info('(r-i) = '+str(ri_min)+' to '+str(ri_max))\n log.info('g = '+str(g_min)+' to '+str(g_max))\n log.info('(g-r) = '+str(gr_min)+' to '+str(gr_max))\n log.info('(g-i) = '+str(gi_min)+' to '+str(gi_max))\n\n idx = select_within_range(inst_i, inst_ri, i_min, i_max, ri_min, ri_max)\n\n (RC.ri, RC.sig_ri, RC.i, RC.sig_i) = calc_distribution_centroid_and_spread_2d(inst_ri[idx], inst_i[idx], use_iqr=True)\n\n idx = select_within_range(inst_r, inst_ri, r_min, r_max, ri_min, ri_max)\n\n (RC.r, RC.sig_r) = calc_distribution_centre_and_spread(inst_r[idx], use_iqr=True)\n\n idx = select_within_range(inst_g, inst_gr, g_min, g_max, gr_min, gr_max)\n\n (RC.gr, RC.sig_gr, RC.g, RC.sig_g) = calc_distribution_centroid_and_spread_2d(inst_gr[idx], inst_g[idx], use_iqr=True)\n\n idx = select_within_range(inst_g, inst_gi, g_min, g_max, gi_min, gi_max)\n\n (RC.gi, RC.sig_gi, RC.g, RC.sig_g) = calc_distribution_centroid_and_spread_2d(inst_gi[idx], inst_g[idx], use_iqr=True)\n\n log.info('\\n')\n log.info('Centroid of Red Clump Stars at:')\n log.info(RC.summary(show_mags=True))\n log.info(RC.summary(show_mags=False,show_colours=True))\n\n RC.transform_to_JohnsonCousins()\n\n log.info(RC.summary(show_mags=False,johnsons=True))\n\n return RC","def kohonen():\n# plb.close('all')\n \n dim = 28*28\n data_range = 255.0\n \n # load in data and labels \n data = np.array(np.loadtxt('data.txt'))\n labels = np.loadtxt('labels.txt')\n\n # select 4 digits \n name = \"Stettler\"\n targetdigits = name2digits(name) # assign the four digits that should be used\n print(targetdigits) # output the digits that were selected\n\n # this selects all data vectors that corresponds to one of the four digits\n data = data[np.logical_or.reduce([labels==x for x in targetdigits]),:]\n \n dy, dx = data.shape\n \n #set the size of the Kohonen map. In this case it will be 6 X 6\n size_k = 6\n \n #set the width of the neighborhood via the width of the gaussian that\n #describes it\n sigma = 2.0\n \n #initialise the centers randomly\n centers = np.random.rand(size_k**2, dim) * data_range\n \n #build a neighborhood matrix\n neighbor = np.arange(size_k**2).reshape((size_k, size_k))\n\n #set the learning rate\n eta = 0.9 # HERE YOU HAVE TO SET YOUR OWN LEARNING RATE\n \n #set the maximal iteration count\n tmax = 5000 # this might or might not work; use your own convergence criterion\n \n #set the random order in which the datapoints should be presented\n i_random = np.arange(tmax) % dy\n np.random.shuffle(i_random)\n \n for t, i in enumerate(i_random):\n som_step(centers, data[i,:],neighbor,eta,sigma)\n\n # for visualization, you can use this:\n for i in range(size_k**2):\n plb.subplot(size_k,size_k,i)\n \n plb.imshow(np.reshape(centers[i,:], [28, 28]),interpolation='bilinear')\n plb.axis('off')\n \n # leave the window open at the end of the loop\n plb.show()\n plb.draw()","def get_nc_BGrid_GFDL(grdfile, name='GFDL_CM2.1_North_Pacific', area='regional', \\\n xrange=(60,175), yrange=(120, 190), ystart=235):\n\n nc = pyroms.io.Dataset(grdfile)\n\n lon_t = nc.variables['geolon_t'][:]\n lat_t = nc.variables['geolat_t'][:]\n lon_uv = nc.variables['geolon_c'][:]\n lat_uv = nc.variables['geolat_c'][:]\n\n h = nc.variables['ht'][:]\n\n f = nc.variables['coriolis_param'][:]\n\n kmt = nc.variables['kmt'][:]\n z_t = nc.variables['st_ocean'][:]\n z_t_edges = nc.variables['st_edges_ocean'][:]\n\n kmu = nc.variables['kmu'][:]\n z_uv = nc.variables['sw_ocean'][:]\n z_uv_edges = nc.variables['sw_edges_ocean'][:]\n\n # compute mask at t-point\n M_t, L_t = kmt.shape\n N_t = z_t.shape[0]\n mask_t = np.zeros((N_t, M_t, L_t))\n for j in range(M_t):\n for i in range(L_t):\n try:\n mask_t[0:int(kmt[j,i]), j,i] = 1\n except:\n mask_t[:, j,i] = 0\n\n # compute mask at uv-point\n M_uv, L_uv = kmu.shape\n N_uv = z_uv.shape[0]\n mask_uv = np.zeros((N_uv, M_uv, L_uv))\n for j in range(M_uv):\n for i in range(L_uv):\n try:\n mask_uv[0:int(kmu[j,i]), j,i] = 1\n except:\n mask_uv[:, j,i] = 0\n\n if area == 'npolar':\n #add two rows in the north and the south\n lon_t = lon_t[np.r_[0,0,:np.size(lon_t,0),-1,-1]]\n lon_t = lon_t[:,np.r_[0,:np.size(lon_t,1),-1]]\n lon_t[:,0] = lon_t[:,1] - (lon_t[:,2]-lon_t[:,1])\n lon_t[:,-1] = lon_t[:,-2] + (lon_t[:,-2]-lon_t[:,-3])\n lat_t = lat_t[np.r_[0,0,:np.size(lat_t,0),-1,-1]]\n lat_t = lat_t[:,np.r_[0,:np.size(lat_t,1),-1]]\n lat_t[0,:] = -85\n lat_t[1,:] = -80\n lat_t[-2,:] = 90\n lat_t[-1,:] = 91\n lon_uv = lon_uv[np.r_[0,0,:np.size(lon_uv,0),-1,-1]]\n lon_uv = lon_uv[:,np.r_[0,:np.size(lon_uv,1),-1]]\n lon_uv[:,0] = lon_uv[:,1] - (lon_uv[:,2]-lon_t[:,1])\n lon_uv[:,-1] = lon_uv[:,-2] + (lon_uv[:,-2]-lon_uv[:,-3])\n lat_uv = lat_uv[np.r_[0,0,:np.size(lat_uv,0),-1,-1]]\n lat_uv = lat_uv[:,np.r_[0,:np.size(lat_uv,1),-1]]\n lat_uv[0,:] = -85\n lat_uv[1,:] = -80\n lat_uv[-2,:] = 90\n lat_uv[-1,:] = 91\n mask_t = mask_t[:,np.r_[0,0,:np.size(mask_t,1),-1,-1],:]\n mask_t = mask_t[:,:,np.r_[0,:np.size(mask_t,2),-1]]\n mask_t[:,:,0] = mask_t[:,:,-2]\n mask_t[:,:,-1] = mask_t[:,:,1]\n mask_uv = mask_uv[:,np.r_[0,0,:np.size(mask_uv,1),-1,-1],:]\n mask_uv = mask_uv[:,:,np.r_[0,:np.size(mask_uv,2),-1]]\n mask_uv[:,:,0] = mask_uv[:,:,-2]\n mask_uv[:,:,-1] = mask_uv[:,:,1]\n h = h[np.r_[0,0,:np.size(h,0),-1,-1]]\n h = h[:,np.r_[0,:np.size(h,1),-1]]\n h[:,0] = h[:,-2]\n h[:,-1] = h[:,1]\n f = f[np.r_[0,0,:np.size(f,0),-1,-1]]\n f = f[:,np.r_[0,:np.size(f,1),-1]]\n f[:,0] = f[:,-2]\n f[:,-1] = f[:,1]\n m,l = h.shape\n xrange=(1,l-2)\n yrange=(ystart+2,m-2)\n\n if area == 'tripole':\n #add two rows in the north and the south\n fold1 = L_t//2\n lon_t = lon_t[np.r_[0,0,:np.size(lon_t,0),-1,-1]]\n lon_t[-2,:fold1] = lon_t[-3,L_t:fold1-1:-1]\n lon_t[-2,L_t:fold1-1:-1] = lon_t[-3,:fold1]\n lon_t[-1,:fold1] = lon_t[-4,L_t:fold1-1:-1]\n lon_t[-1,L_t:fold1-1:-1] = lon_t[-4,:fold1]\n\n lon_t = lon_t[:,np.r_[0,:np.size(lon_t,1),-1]]\n lon_t[:,0] = lon_t[:,1] - (lon_t[:,2]-lon_t[:,1])\n lon_t[:,-1] = lon_t[:,-2] + (lon_t[:,-2]-lon_t[:,-3])\n lat_t = lat_t[np.r_[0,0,:np.size(lat_t,0),-1,-1]]\n lat_t = lat_t[:,np.r_[0,:np.size(lat_t,1),-1]]\n lat_t[0,:] = -85\n lat_t[1,:] = -80\n lat_t[-2,:] = lat_t[-3,:]\n lat_t[-1,:] = lat_t[-4,:]\n lon_uv = lon_uv[np.r_[0,0,:np.size(lon_uv,0),-1,-1]]\n\n lon_uv[-2,:fold1] = lon_uv[-4,L_t:fold1-1:-1]\n lon_uv[-2,L_t:fold1-1:-1] = lon_uv[-4,:fold1]\n lon_uv[-1,:fold1] = lon_uv[-5,L_t:fold1-1:-1]\n lon_uv[-1,L_t:fold1-1:-1] = lon_uv[-5,:fold1]\n\n lon_uv = lon_uv[:,np.r_[0,:np.size(lon_uv,1),-1]]\n lon_uv[:,0] = lon_uv[:,1] - (lon_uv[:,2]-lon_t[:,1])\n lon_uv[:,-1] = lon_uv[:,-2] + (lon_uv[:,-2]-lon_uv[:,-3])\n lat_uv = lat_uv[np.r_[0,0,:np.size(lat_uv,0),-1,-1]]\n lat_uv = lat_uv[:,np.r_[0,:np.size(lat_uv,1),-1]]\n lat_uv[0,:] = -85\n lat_uv[1,:] = -80\n lat_uv[-2,:] = lat_uv[-3,:]\n lat_uv[-1,:] = lat_uv[-4,:]\n mask_t = mask_t[:,np.r_[0,0,:np.size(mask_t,1),-1,-1],:]\n mask_t = mask_t[:,:,np.r_[0,:np.size(mask_t,2),-1]]\n mask_t[:,:,0] = mask_t[:,:,-2]\n mask_t[:,:,-1] = mask_t[:,:,1]\n mask_uv = mask_uv[:,np.r_[0,0,:np.size(mask_uv,1),-1,-1],:]\n mask_uv = mask_uv[:,:,np.r_[0,:np.size(mask_uv,2),-1]]\n mask_uv[:,:,0] = mask_uv[:,:,-2]\n mask_uv[:,:,-1] = mask_uv[:,:,1]\n h = h[np.r_[0,0,:np.size(h,0),-1,-1]]\n h = h[:,np.r_[0,:np.size(h,1),-1]]\n h[:,0] = h[:,-2]\n h[:,-1] = h[:,1]\n f = f[np.r_[0,0,:np.size(f,0),-1,-1]]\n f = f[:,np.r_[0,:np.size(f,1),-1]]\n f[:,0] = f[:,-2]\n f[:,-1] = f[:,1]\n m,l = h.shape\n xrange=(1,l-2)\n yrange=(ystart+2,m-2)\n\n return BGrid_GFDL(lon_t, lat_t, lon_uv, lat_uv, \\\n mask_t, mask_uv, h, z_t, z_t_edges, \\\n z_uv, z_uv_edges, f, \\\n name, xrange=xrange, yrange=yrange)","def incandPeakInfoLG(self):\r\n\t\tself.incandBaseline_LG = (np.mean(self.lowGainWideBandIncandData[0:10]))\r\n\t\t\t\t\r\n\t\traw_incand_max_LG = np.amax(self.lowGainWideBandIncandData)\r\n\t\tincand_max_LG = raw_incand_max_LG - self.incandBaseline_LG\t\t\r\n\t\tincand_max_index_LG = np.argmax(self.lowGainWideBandIncandData)\r\n\t\t\r\n\t\tself.incandMax_LG =incand_max_LG\r\n\t\tself.incandMaxPos_LG = incand_max_index_LG","def latent_heat_vapourisation(self, tair):\n return (2.501 - 0.00237 * tair) * 1E06","def galaxy(img):\n return img[420:490, 710:770]","def refl_analysis(self,dials_model):\n Z = self.refl_table\n indices = Z['miller_index']\n expts = ExperimentListFactory.from_json_file(dials_model,\n check_format=False)\n self.dials_model=expts[0]\n CRYS = self.dials_model.crystal\n UC = CRYS.get_unit_cell()\n strong_resolutions = UC.d(indices)\n order = flex.sort_permutation(strong_resolutions, reverse=True)\n Z[\"spots_order\"] = order\n self.spots_pixels = flex.size_t()\n spots_offset = flex.int(len(order),-1)\n spots_size = flex.int(len(order),-1)\n\n P = panels = Z['panel']\n S = shoeboxes = Z['shoebox']\n N_visited = 0; N_bad = 0\n for oidx in range(len(order)): #loop through the shoeboxes in correct order\n sidx = order[oidx] # index into the Miller indices\n ipanel = P[sidx]\n slow_size = 254\n fast_size = 254\n panel_size=slow_size*fast_size\n bbox = S[sidx].bbox\n first_position = spots_offset[sidx] = self.spots_pixels.size()\n for islow in range(max(0,bbox[2]-3), min(slow_size,bbox[3]+3)):\n for ifast in range(max(0,bbox[0]-3), min(fast_size,bbox[1]+3)):\n value = self.trusted_mask[ipanel][islow*slow_size + ifast]\n N_visited += 1\n if value: self.spots_pixels.append(ipanel*panel_size+islow*slow_size+ifast)\n else: N_bad+=1\n spot_size = spots_size[sidx] = self.spots_pixels.size() - first_position\n Z[\"spots_offset\"] = spots_offset\n Z[\"spots_size\"] = spots_size\n print (N_visited,\"pixels were visited in the %d shoeboxes (with borders)\"%len(order))\n print (N_bad,\"of these were bad pixels, leaving %d in target\"%(len(self.spots_pixels)))","def width_h_invis(self):\n if m_higgs > 2.0 * self.mx:\n coupling = self.gsxx * self.stheta / np.sqrt(1 - self.stheta**2)\n\n val = (\n (coupling**2 * (m_higgs**2 - 4 * self.mx**2) ** 1.5)\n / (8.0 * m_higgs**2 * np.pi)\n ).real\n\n assert val >= 0\n\n return val\n else:\n return 0.0","def gpt2_1w (station, dmjd,dlat,dlon,hell,it):\n\n# need to find diffpod and difflon\n if (dlon < 0):\n plon = (dlon + 2*np.pi)*180/np.pi;\n else:\n plon = dlon*180/np.pi;\n# transform to polar distance in degrees\n ppod = (-dlat + np.pi/2)*180/np.pi; \n\n# % find the index (line in the grid file) of the nearest point\n# \t % changed for the 1 degree grid (GP)\n ipod = np.floor(ppod+1); \n ilon = np.floor(plon+1);\n \n# normalized (to one) differences, can be positive or negative\n#\t% changed for the 1 degree grid (GP)\n diffpod = (ppod - (ipod - 0.5));\n difflon = (plon - (ilon - 0.5));\n\n\n# change the reference epoch to January 1 2000\n print('Modified Julian Day', dmjd)\n dmjd1 = dmjd-51544.5 \n\n pi2 = 2*np.pi\n pi4 = 4*np.pi\n\n# mean gravity in m/s**2\n gm = 9.80665;\n# molar mass of dry air in kg/mol\n dMtr = 28.965E-3 \n# dMtr = 28.965*10^-3 \n# universal gas constant in J/K/mol\n Rg = 8.3143 \n\n# factors for amplitudes, i.e. whether you want time varying\n if (it==1):\n print('>>>> no refraction time variation ')\n cosfy = 0; coshy = 0; sinfy = 0; sinhy = 0;\n else: \n cosfy = np.cos(pi2*dmjd1/365.25)\n coshy = np.cos(pi4*dmjd1/365.25) \n sinfy = np.sin(pi2*dmjd1/365.25) \n sinhy = np.sin(pi4*dmjd1/365.25) \n cossin = np.matrix([1, cosfy, sinfy, coshy, sinhy])\n# initialization of new vectors\n p = 0; T = 0; dT = 0; Tm = 0; e = 0; ah = 0; aw = 0; la = 0; undu = 0;\n undul = np.zeros(4)\n Ql = np.zeros(4)\n dTl = np.zeros(4)\n Tl = np.zeros(4)\n pl = np.zeros(4)\n ahl = np.zeros(4)\n awl = np.zeros(4)\n lal = np.zeros(4)\n Tml = np.zeros(4)\n el = np.zeros(4)\n#\n pgrid, Tgrid, Qgrid, dTgrid, u, Hs, ahgrid, awgrid, lagrid, Tmgrid = read_4by5(station,dlat,dlon,hell)\n#\n for l in [0,1,2,3]:\n KL = l #silly to have this as a variable like this \n# transforming ellipsoidal height to orthometric height:\n# Hortho = -N + Hell\n undul[l] = u[KL] \n hgt = hell-undul[l] \n# pressure, temperature at the height of the grid\n T0 = Tgrid[KL,0] + Tgrid[KL,1]*cosfy + Tgrid[KL,2]*sinfy + Tgrid[KL,3]*coshy + Tgrid[KL,4]*sinhy;\n tg = float(Tgrid[KL,:] *cossin.T)\n# print(T0,tg)\n\n p0 = pgrid[KL,0] + pgrid[KL,1]*cosfy + pgrid[KL,2]*sinfy + pgrid[KL,3]*coshy + pgrid[KL,4]*sinhy;\n \n# humidity \n Ql[l] = Qgrid[KL,0] + Qgrid[KL,1]*cosfy + Qgrid[KL,2]*sinfy + Qgrid[KL,3]*coshy + Qgrid[KL,4]*sinhy;\n \n# reduction = stationheight - gridheight\n Hs1 = Hs[KL]\n redh = hgt - Hs1;\n\n# lapse rate of the temperature in degree / m\n dTl[l] = dTgrid[KL,0] + dTgrid[KL,1]*cosfy + dTgrid[KL,2]*sinfy + dTgrid[KL,3]*coshy + dTgrid[KL,4]*sinhy;\n \n# temperature reduction to station height\n Tl[l] = T0 + dTl[l]*redh - 273.15;\n\n# virtual temperature\n Tv = T0*(1+0.6077*Ql[l]) \n c = gm*dMtr/(Rg*Tv) \n \n# pressure in hPa\n pl[l] = (p0*np.exp(-c*redh))/100 \n \n# hydrostatic coefficient ah\n ahl[l] = ahgrid[KL,0] + ahgrid[KL,1]*cosfy + ahgrid[KL,2]*sinfy + ahgrid[KL,3]*coshy + ahgrid[KL,4]*sinhy;\n \n# wet coefficient aw\n awl[l] = awgrid[KL,0] + awgrid[KL,1]*cosfy + awgrid[KL,2]*sinfy + awgrid[KL,3]*coshy + awgrid[KL,4]*sinhy;\n\t\t\t\t\t \n# water vapor decrease factor la - added by GP\n lal[l] = lagrid[KL,0] + lagrid[KL,1]*cosfy + lagrid[KL,2]*sinfy + lagrid[KL,3]*coshy + lagrid[KL,4]*sinhy;\n\t\t\t\t\t \n# mean temperature of the water vapor Tm - added by GP\n Tml[l] = Tmgrid[KL,0] + Tmgrid[KL,1]*cosfy + Tmgrid[KL,2]*sinfy + Tmgrid[KL,3]*coshy + Tmgrid[KL,4]*sinhy;\n\t\t\t\t\t \t\t \n# water vapor pressure in hPa - changed by GP\n e0 = Ql[l]*p0/(0.622+0.378*Ql[l])/100; # % on the grid\n aa = (100*pl[l]/p0)\n bb = lal[l]+1\n el[l] = e0*np.power(aa,bb) # % on the station height - (14) Askne and Nordius, 1987\n \n dnpod1 = np.abs(diffpod); # % distance nearer point\n dnpod2 = 1 - dnpod1; # % distance to distant point\n dnlon1 = np.abs(difflon);\n dnlon2 = 1 - dnlon1;\n \n# pressure\n R1 = dnpod2*pl[0]+dnpod1*pl[1];\n R2 = dnpod2*pl[2]+dnpod1*pl[3];\n p = dnlon2*R1+dnlon1*R2;\n \n# temperature\n R1 = dnpod2*Tl[0]+dnpod1*Tl[1];\n R2 = dnpod2*Tl[2]+dnpod1*Tl[3];\n T = dnlon2*R1+dnlon1*R2;\n \n# temperature in degree per km\n R1 = dnpod2*dTl[0]+dnpod1*dTl[1];\n R2 = dnpod2*dTl[2]+dnpod1*dTl[3];\n dT = (dnlon2*R1+dnlon1*R2)*1000;\n \n# water vapor pressure in hPa - changed by GP\n R1 = dnpod2*el[0]+dnpod1*el[1];\n R2 = dnpod2*el[2]+dnpod1*el[3];\n e = dnlon2*R1+dnlon1*R2;\n \n# hydrostatic\n R1 = dnpod2*ahl[0]+dnpod1*ahl[1];\n R2 = dnpod2*ahl[2]+dnpod1*ahl[3];\n ah = dnlon2*R1+dnlon1*R2;\n \n# wet\n R1 = dnpod2*awl[0]+dnpod1*awl[1];\n R2 = dnpod2*awl[2]+dnpod1*awl[3];\n aw = dnlon2*R1+dnlon1*R2;\n \n# undulation\n R1 = dnpod2*undul[0]+dnpod1*undul[1];\n R2 = dnpod2*undul[2]+dnpod1*undul[3];\n undu = dnlon2*R1+dnlon1*R2;\n\n# water vapor decrease factor la - added by GP\n R1 = dnpod2*lal[0]+dnpod1*lal[1];\n R2 = dnpod2*lal[2]+dnpod1*lal[3];\n la = dnlon2*R1+dnlon1*R2;\n\t\t\n# mean temperature of the water vapor Tm - added by GP\n R1 = dnpod2*Tml[0]+dnpod1*Tml[1];\n R2 = dnpod2*Tml[2]+dnpod1*Tml[3];\n Tm = dnlon2*R1+dnlon1*R2; \n\n return p, T, dT,Tm,e,ah,aw,la,undu","def plateau_finder(data, tol=0.0003):\n from scipy.ndimage.filters import generic_filter\n tol = 0.0003\n filt_data = generic_filter(data, np.std, size=3)\n plat_index = np.where(filt_data < (np.min(filt_data) + tol))[0]\n\n plateaus = group_consecutive(plat_index)\n\n return plateaus","def get_iPTF16hgs(colorplt = False):\n z = 0.017\n ebv = 0\n D = cosmo.luminosity_distance([z])[0].value * 1e+6 # in pc\n dis_mod = 5*np.log10(D / 10)\n \n tb = pd.read_csv('../data/otherSN/iPTF16hgs/table1.txt', sep=\"\\t\")\n tb = tb.drop(columns=[\"Unnamed: 5\"])\n tb = tb.rename(columns={'Filter' : 'filter',\n 'MJD': 'mjd'})\n tb = tb[~np.array([x[0]=='>' for x in tb['Magnitude'].values])]\n tb['mag'] = np.array([float(x.split(\" +or-\")[0]) for x in tb['Magnitude'].values])\n tb['emag'] = np.array([float(x.split(\" +or-\")[1]) for x in tb['Magnitude'].values])\n tb = tb.drop(columns=[\"Magnitude\"])\n \n ixg = tb['filter'].values == \"g\"\n ixr = tb['filter'].values == \"r\"\n ixi = tb['filter'].values == \"i\"\n tb['wave'] = np.zeros(len(tb))\n tb['wave'].values[ixg] = 4814\n tb['wave'].values[ixr] = 6422\n tb['wave'].values[ixi] = 7883\n tb['mag0'] = tb['mag'] - extinction.ccm89(tb['wave'].values, 3.1*ebv, 3.1)\n tb['mag0_abs'] = tb['mag0'] - dis_mod\n t_max = 57691.59 # from the paper\n tb['tmax_of'] = tb['mjd'] - t_max\n tb['tmax_rf'] = (tb['mjd'] - t_max) / (1+z)\n \"\"\"\n plt.errorbar(tb[\"tmax_rf\"].values[ixg], tb[\"mag\"].values[ixg], tb[\"emag\"].values[ixg], fmt=\".g\")\n plt.errorbar(tb[\"tmax_rf\"].values[ixr], tb[\"mag\"].values[ixr], tb[\"emag\"].values[ixr], fmt=\".r\")\n plt.errorbar(tb[\"tmax_rf\"].values[ixi], tb[\"mag\"].values[ixi], tb[\"emag\"].values[ixi], fmt=\".y\")\n \"\"\"\n tb = add_datecol(tb)\n tb = add_physcol(tb)\n #tb = tb.drop(columns=[\"datetime64\"])\n if colorplt==False:\n return tb\n else:\n #tb = tb[tb.mjd > 55352.5]\n #tb = tb[tb.mjd < 55593.5]\n \n dates = get_date_span(tb)\n datesave = []\n for i in range(len(dates)):\n x = dates[i]\n ix = tb[\"date\"].values == x\n tbsub = tb[ix]\n if len(tbsub)!=0:\n flts = tbsub['filter'].values\n if \"r\" in flts and np.sum(np.unique(flts))!=1:\n datesave.append(x)\n datesave = np.array(datesave)\n \n mcolor = []\n mcolor_unc = []\n mjds = []\n colorname = []\n for i in range(len(datesave)):\n x = datesave[i]\n ix = tb[\"date\"].values == x\n tbsub = tb[ix]\n gtb = tbsub[tbsub[\"filter\"].values==\"g\"]\n rtb = tbsub[tbsub[\"filter\"].values==\"r\"]\n itb = tbsub[tbsub[\"filter\"].values==\"i\"]\n if len(gtb)!=0:\n gmjds = gtb[\"mjd\"].values\n gmags = gtb[\"mag0\"].values\n gemags = gtb[\"emag\"].values\n gwtgs = 1/gemags**2\n gmag = np.sum(gmags * gwtgs) / np.sum(gwtgs)\n gmjd = np.sum(gmjds * gwtgs) / np.sum(gwtgs)\n gemag = 1/ np.sqrt(np.sum(gwtgs))\n if len(rtb)!=0:\n rmjds = rtb[\"mjd\"].values\n rmags = rtb[\"mag0\"].values\n remags = rtb[\"emag\"].values\n rwtgs = 1/remags**2\n rmag = np.sum(rmags * rwtgs) / np.sum(rwtgs)\n rmjd = np.sum(rmjds * rwtgs) / np.sum(rwtgs)\n remag = 1/ np.sqrt(np.sum(rwtgs))\n if len(itb)!=0:\n imjds = itb[\"mjd\"].values\n imags = itb[\"mag0\"].values\n iemags = itb[\"emag\"].values\n iwtgs = 1/iemags**2\n imag = np.sum(imags * iwtgs) / np.sum(iwtgs)\n imjd = np.sum(imjds * iwtgs) / np.sum(iwtgs)\n iemag = 1/ np.sqrt(np.sum(iwtgs))\n if len(gtb)!=0 and len(rtb)!=0:\n mcolor.append(gmag - rmag)\n mjds.append( 0.5 * (gmjd + rmjd) )\n mcolor_unc.append( np.sqrt(gemag**2 + remag**2) )\n colorname.append(\"gmr\")\n if len(rtb)!=0 and len(itb)!=0:\n mcolor.append(rmag - imag)\n mjds.append( 0.5 * (rmjd + imjd) )\n mcolor_unc.append( np.sqrt(remag**2 + iemag**2) )\n colorname.append(\"rmi\")\n \n ctb = Table(data = [mjds, mcolor, mcolor_unc, colorname],\n names = [\"mjd\", \"c\", \"ec\", \"cname\"])\n \n ctb['tmax_rf'] = (ctb['mjd'] - t_max) / (1+z)\n ctb = ctb.to_pandas()\n return ctb","def bands(self) -> int:\n ...","def n_band(self):\n pass","def get_bandgap(cluster, spin, dirr=\"/home/pv278/Platinum/\"):\n outfile = open(get_path(dirr, cluster, spin)).readlines()\n line = [l for l in outfile if \"occ\" in l]\n if line:\n Eocc = line[-1].split()[-1]\n else:\n Eocc = None\n line = [l for l in outfile if \"virt\" in l]\n if line:\n Evirt = line[0].split()[4]\n else:\n Evirt = None\n \n if Eocc:\n return (float(Evirt) - float(Eocc)) * 27.211\n else:\n return","def _update_farness_map(self,ind):","def stain_image(image, num_stains, color):","def adjust_for_refractive_index(df):\n refractive_index_salt_water = 1.33\n df['Height'] = (df['Height']) / refractive_index_salt_water\n return df","def minimum_spanning_arborescence(sol):","def pan_corr(file):\n\n # # infile = 'd:\\\\Projekti\\\\Satelit\\\\CO\\\\Razpis\\\\Flat field images_new2020\\\\flatfield\\\\NHDBflat_1D'\n # # infile = 'd:\\Projekti\\Satelit\\CO\\Razpis\\_POSNETKI\\Jure_naloga_banje_raw_pyt\\\\NHDRGoreMorje_3D'\n #\n # # in_path = 'd:\\Projekti\\Satelit\\CO\\Razpis\\Flat field images_new2020\\\\20201028 Vignetting\\\\flatfield\\\\'\n # # in_pan_ref_file = 'NHDPflat_3D_py.tif'\n # in_path = 'd:\\Projekti\\Satelit\\CO\\Razpis\\_POSNETKI\\Peking_PAN\\\\'\n # in_pan_ref_file = 'NHDPfoc_swp6_1D_py.tif'\n # in_ref = in_path + in_pan_ref_file\n #\n # inreffil = gdal.Open(in_ref)\n # image_ref = inreffil.ReadAsArray()\n # # size_ref = image_ref.shape\n # # pix_count = size_ref[0]*size_ref[1]\n #\n # image_ref = image_ref[800:930, 1420:1640]\n # size_ref = image_ref.shape\n # pix_count = size_ref[0] * size_ref[1]\n #\n # g1 = 0.\n # g2 = 0.\n # r1 = 0.\n # b1 = 0.\n #\n # for i in range(size_ref[0]):\n # for j in range(size_ref[1]):\n # if (i % 2) == 0 and (j % 2) == 0: g1 = g1 + image_ref[i, j]\n # if (i % 2) == 1 and (j % 2) == 1: g2 = g2 + image_ref[i, j]\n # if (i % 2) == 0 and (j % 2) == 1: r1 = r1 + image_ref[i, j]\n # if (i % 2) == 1 and (j % 2) == 0: b1 = b1 + image_ref[i, j]\n #\n # g1_avg = g1 / pix_count * 4\n # g2_avg = g2 / pix_count * 4\n # r1_avg = r1 / pix_count * 4\n # b1_avg = b1 / pix_count * 4\n #\n # raz_g1 = 1\n # raz_g2 = g1_avg/g2_avg\n # raz_r1 = g1_avg/r1_avg\n # raz_b1 = g1_avg/b1_avg\n #\n # avg = (g1+g2+r1+b1)/pix_count\n #\n # print(g1_avg, g2_avg, r1_avg, b1_avg, avg)\n\n raz_g1 = 1\n raz_g2 = 1.0245196396115988\n raz_r1 = 1.0131841989689434\n raz_b1 = 1.0517113199247086\n\n print('razmerje:', raz_g1, raz_g2, raz_r1, raz_b1)\n\n # in_path = 'd:\\Projekti\\Satelit\\CO\\Razpis\\_POSNETKI\\Peking_PAN\\\\'\n # in_pan_ref_file = 'NHDPfoc_swp6_4D_py.tif'\n # in_path = 'd:\\Projekti\\Satelit\\CO\\Razpis\\Flat field images_new2020\\\\20201028 Vignetting\\\\flatfield\\\\'\n # in_pan_ref_file = 'NHDPflat_3D_py.tif'\n\n # in_path = 'd:\\Projekti\\Satelit\\CO\\Razpis\\_POSNETKI\\Slo_PAN\\_26_30\\\\'\n # in_pan_ref_file = [filename for filename in os.listdir(in_path) if filename.lower().startswith(\"nhd\") and filename.lower().endswith(\"tif\")]\n\n \n\n \n\n # print('image', i)\n in_ref=file\n inreffil = gdal.Open(in_ref)\n image_ref = inreffil.ReadAsArray()\n size_ref = image_ref.shape\n # pix_count = size_ref[0] * size_ref[1]\n # pix_count = np.count_nonzero(image_ref)\n # pix_count = 3664*650\n\n # g1 = 0.\n # g2 = 0.\n # r1 = 0.\n # b1 = 0.\n #\n # for i in range(size_ref[0]):\n # for j in range(size_ref[1]):\n # if (i % 2) == 0 and (j % 2) == 0: g1 = g1 + image_ref[i, j]\n # if (i % 2) == 1 and (j % 2) == 1: g2 = g2 + image_ref[i, j]\n # if (i % 2) == 0 and (j % 2) == 1: r1 = r1 + image_ref[i, j]\n # if (i % 2) == 1 and (j % 2) == 0: b1 = b1 + image_ref[i, j]\n #\n # g1_avg = g1 / pix_count * 4\n # g2_avg = g2 / pix_count * 4\n # r1_avg = r1 / pix_count * 4\n # b1_avg = b1 / pix_count * 4\n #\n # avg = (g1 + g2 + r1 + b1) / pix_count\n #\n # print(g1_avg, g2_avg, r1_avg, b1_avg, avg)\n\n # popravek\n im_p_pop = np.zeros((size_ref[0], size_ref[1]), np.uint16)\n\n\n for i in range(size_ref[0]):\n for j in range(size_ref[1]):\n if (i % 2) == 0 and (j % 2) == 0 and image_ref[i, j] != 0: im_p_pop[i, j] = image_ref[i, j] * raz_g1\n if (i % 2) == 1 and (j % 2) == 1 and image_ref[i, j] != 0: im_p_pop[i, j] = image_ref[i, j] * raz_g2\n if (i % 2) == 0 and (j % 2) == 1 and image_ref[i, j] != 0: im_p_pop[i, j] = image_ref[i, j] * raz_r1\n if (i % 2) == 1 and (j % 2) == 0 and image_ref[i, j] != 0: im_p_pop[i, j] = image_ref[i, j] * raz_b1\n \n _,_,_,_,P=return_flatfield_set_path(2)\n P_flat=gdal_array.LoadFile(P)\n \n # im_p_pop=simple_flatfield_corr(P_flat, im_p_pop, 2, 1) \n \n # outout\n \n im_p_pop=BLUE_simple_flatfield_corr(P_flat, im_p_pop)\n \n out=os.path.abspath(file)+\"/corr/\"+os.path.basename(file)[:-4] + \"_pop_flat_corr.tif\"\n\n \n # out = in_ref[:-4] + \"_pop_flat_corr.tif\"\n\n driver = gdal.GetDriverByName('GTiff')\n\n # outRaster = driver.Create(out, size[1], size[0], 3, gdal.GDT_UInt16)\n outRaster = driver.Create(out, size_ref[1], size_ref[0], 1, gdal.GDT_UInt16)\n\n outband = outRaster.GetRasterBand(1)\n outband.WriteArray(im_p_pop)\n outband.FlushCache()","def test_G_ind(self):\r\n a = array([[29, 11], [273, 191], [8, 31], [64, 64]])\r\n self.assertFloatEqual(G_ind(a)[0], 28.59642)\r\n self.assertFloatEqual(G_ind(a, True)[0], 28.31244)","def bg_ex(self, f):\n dat = sif.readSIF(os.path.join(self.path, f))[0][0]\n return dat - np.min(dat[:511, :511])","def global_analysis(tomo, b_th, c=18):\n\n ## Thesholding and Volume analysis\n if c == 6:\n con_mat = [ [[0, 0, 0], [0, 1, 0], [0, 0, 0]],\n [[0, 1, 0], [1, 1, 1], [0, 1, 0]],\n [[0, 0, 0], [0, 1, 0], [0, 0, 0]] ]\n elif c == 18:\n con_mat = [[[0, 1, 0], [1, 1, 1], [0, 1, 0]],\n [[1, 1, 1], [1, 1, 1], [1, 1, 1]],\n [[0, 1, 0], [1, 1, 1], [0, 1, 0]]]\n elif c == 26:\n con_mat = [[[1, 1, 1], [1, 1, 1], [1, 1, 1]],\n [[1, 1, 1], [1, 1, 1], [1, 1, 1]],\n [[1, 1, 1], [1, 1, 1], [1, 1, 1]]]\n else:\n raise ValueError\n tomo_lbl, num_lbls = sp.ndimage.label(tomo >= b_th, structure=np.ones(shape=[3, 3, 3]))\n tomo_out = np.zeros(shape=tomo.shape, dtype=int)\n lut = np.zeros(shape=num_lbls+1, dtype=int)\n\n ## COUNTING REGIONS METHODS\n # import time\n # hold_t = time.time()\n # for lbl in range(1, num_lbls + 1):\n # ids = tomo == lbl\n # feat_sz = len(ids)\n # tomo_out[ids] = feat_sz\n # # print('[1]:', lbl, 'of', num_lbls)\n # print time.time() - hold_t\n\n ## COUNTING PIXELS METHOD\n ## Count loop\n # cont, total = 0, np.prod(tomo.shape)\n # import time\n # hold_t = time.time()\n for x in range(tomo.shape[0]):\n for y in range(tomo.shape[1]):\n for z in range(tomo.shape[2]):\n id = tomo_lbl[x, y, z]\n lut[id] += 1\n # cont += 1\n # print('[1]:', cont, 'of', total)\n #\n ## Write loop\n # cont, total = 0, np.prod(tomo.shape)\n\n for x in range(tomo.shape[0]):\n for y in range(tomo.shape[1]):\n for z in range(tomo.shape[2]):\n id = tomo_lbl[x, y, z]\n if id > 0:\n tomo_out[x, y, z] = lut[id]\n # cont += 1\n # print('[1]:', cont, 'of', total)\n # print time.time() - hold_t\n\n return tomo_out","def upper_covers(self, x):","def prada(self):\n scale_factor = 1.0 / (1.0 + self.snapshot.header.redshift)\n r200c_physical = self.r200c * scale_factor / 1000.0 # units Mpc\n\n v200 = (\n (self.snapshot.const.G * self.m200c)\n / r200c_physical\n * self.snapshot.const.Mpc ** 2\n / 1000.0 ** 2\n ) ** 0.5 # units km/s\n\n def y(x, vmax, v200):\n func = np.log(1 + x) - (x / (1 + x))\n return ((0.216 * x) / func) ** 0.5 - (vmax / v200)\n\n concentration = np.zeros((len(self.vmax)))\n for halo in range(self.N_halos):\n if v200[halo] > self.vmax[halo]:\n concentration[halo] = -9999.0\n else:\n try:\n concentration[halo] = newton(\n y, x0=5.0, args=(self.vmax[halo], v200[halo])\n )\n except:\n concentration[halo] = -9999.0\n\n return concentration","def footprint_corner_indices():","def __AOD04(self):\n\n assert self.__lmax <= 100\n\n k = np.zeros(101)\n\n k[0] = 0\n k[1] = 0\n k[2] = -0.308\n k[3] = -0.195\n k[4] = -0.132\n k[5] = -0.103\n k[6] = -0.089\n k[7] = -0.082\n k[8] = -0.078\n k[9] = -0.073\n\n for i in range(10, 18):\n k[i] = -(0.682 + 0.27 * (i - 10) / 8) / i\n for i in range(18, 32):\n k[i] = -(0.952 + 0.288 * (i - 18) / 14) / i\n for i in range(32, 56):\n k[i] = -(1.24 + 0.162 * (i - 32) / 24) / i\n for i in range(56, 101):\n k[i] = -(1.402 + 0.059 * (i - 56) / 44) / i\n\n # print(k[0:(self.__Nmax+1)])\n\n return k[0:(self.__lmax + 1)]","def test_lifted_index():\n pressure = np.array([1014., 1000., 997., 981.2, 947.4, 925., 914.9, 911.,\n 902., 883., 850., 822.3, 816., 807., 793.2, 770.,\n 765.1, 753., 737.5, 737., 713., 700., 688., 685.,\n 680., 666., 659.8, 653., 643., 634., 615., 611.8,\n 566.2, 516., 500., 487., 484.2, 481., 475., 460.,\n 400.]) * units.hPa\n temperature = np.array([24.2, 24.2, 24., 23.1, 21., 19.6, 18.7, 18.4,\n 19.2, 19.4, 17.2, 15.3, 14.8, 14.4, 13.4, 11.6,\n 11.1, 10., 8.8, 8.8, 8.2, 7., 5.6, 5.6,\n 5.6, 4.4, 3.8, 3.2, 3., 3.2, 1.8, 1.5,\n -3.4, -9.3, -11.3, -13.1, -13.1, -13.1, -13.7, -15.1,\n -23.5]) * units.degC\n dewpoint = np.array([23.2, 23.1, 22.8, 22., 20.2, 19., 17.6, 17.,\n 16.8, 15.5, 14., 11.7, 11.2, 8.4, 7., 4.6,\n 5., 6., 4.2, 4.1, -1.8, -2., -1.4, -0.4,\n -3.4, -5.6, -4.3, -2.8, -7., -25.8, -31.2, -31.4,\n -34.1, -37.3, -32.3, -34.1, -37.3, -41.1, -37.7, -58.1,\n -57.5]) * units.degC\n parcel_prof = parcel_profile(pressure, temperature[0], dewpoint[0])\n li = lifted_index(pressure, temperature, parcel_prof)\n assert_almost_equal(li, -7.9115691 * units.delta_degree_Celsius, 2)","def mamajek08_logRpHK_Ro_edge():\n Ro_edge = 0.31935816876122064\n return Ro_edge","def get_evaporation_latent_heat() -> float:\n theta = 28.0\n return 2500.8 - 2.3668 * theta","def findzpd(self):\n dc=0.5*self.rms*self.ndstep\n #fixed at 0.1 of the dispersion\n dd=0.1*self.ws.coef[1]\n\n #set upt he docef values\n dcoef=self.ws.coef*0.0\n dcoef[0]=dc\n dcoef[1]=dd\n self.ws=st.findxcor(self.xarr, self.farr, self.swarr, self.sfarr, self.ws, \n dcoef=dcoef, ndstep=self.ndstep, best=False, inttype='interp')\n self.plotArt()\n self.redraw_canvas()","def index_of_refraction(self):\n return self.microsphere.index_of_refraction(self.wavelength)","def get_gini(rows):\n count_zero = 0 # number of rows labelled healthy\n count_one = 0 # number of rows labelled depressed\n\n for row in rows:\n if row[len(row) - 1] == 0:\n count_zero = count_zero + 1\n else:\n count_one = count_one + 1\n return 1 - (count_zero / float(len(rows))) ** 2 - (count_one / float(len(rows))) ** 2","def land(self, n, e):\n pass","def P_AI_Rocky(in_dict):\n # START\n fs = 16\n plt.rc('font', size=fs)\n fig = plt.figure(figsize=(14,12))\n ds = nc.Dataset(in_dict['fn'])\n\n # PLOT CODE\n aa = [-122.8, -122.54, 47.92, 48.22]\n import cmocean\n cmap = cmocean.cm.balance\n # cmap = 'RdYlBu_r'\n\n from warnings import filterwarnings\n filterwarnings('ignore') # skip some warning messages\n \n # plot Code\n \n # calculate divergence and vorticity\n uu = ds['u'][0, -1, :, :]\n vv = ds['v'][0, -1, :, :]\n u = zfun.fillit(uu)\n v = zfun.fillit(vv)\n u[np.isnan(u)] = 0\n v[np.isnan(v)] = 0\n \n G = zrfun.get_basic_info(in_dict['fn'], only_G=True)\n \n dive = ((np.diff(u, axis=1)/G['DX'][:, 1:-1])[1:-1, :]\n + (np.diff(v, axis = 0)/G['DY'][1:-1, :])[:, 1:-1])\n #dive[G['mask_rho'][1:-1,1:-1]==False] = np.nan\n \n vort = np.diff(v, axis=1)/G['DX'][1:,1:] - np.diff(u, axis=0)/G['DY'][1:,1:]\n #vort[G['mask_rho'][1:,1:]==False] = np.nan\n \n scl = 2e-3\n \n # panel 1\n ax = fig.add_subplot(121)\n # cs = plt.pcolormesh(G['lon_psi'], G['lat_psi'], dive/scl, cmap=cmap,\n # vmin=-1, vmax=1)\n cs = plt.pcolormesh(G['lon_rho'][1:-1,1:-1], G['lat_rho'][1:-1,1:-1], dive/scl, cmap=cmap,\n vmin=-1, vmax=1, shading='gouraud')\n tstr = (r'Surface Divergence (%0.1e $s^{-1}$)' % (scl))\n #pfun.add_bathy_contours(ax, ds, txt=True)\n pfun.add_coast(ax)\n ax.axis(aa)\n pfun.dar(ax)\n ax.set_xlabel('Longitude')\n ax.set_ylabel('Latitude')\n ax.set_title(tstr)\n pfun.add_info(ax, in_dict['fn'])\n ax.set_xticks([-122.8, -122.7, -122.6])\n ax.set_yticks([48, 48.1, 48.2])\n #\n # panel 2\n ax = fig.add_subplot(122)\n # cs = plt.pcolormesh(G['lon_rho'], G['lat_rho'], vort/scl, cmap=cmap,\n # vmin=-1, vmax=1)\n cs = plt.pcolormesh(G['lon_psi'], G['lat_psi'], vort/scl, cmap=cmap,\n vmin=-1, vmax=1, shading='gouraud')\n tstr = (r'Surface Vorticity (%0.1e $s^{-1}$)' % (scl))\n ax.set_xticks([-122.8, -122.7, -122.6])\n ax.set_yticks([])\n #fig.colorbar(cs)\n \n # Inset colorbar\n from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n cbaxes = inset_axes(ax, width=\"4%\", height=\"40%\", loc='lower left')\n fig.colorbar(cs, cax=cbaxes, orientation='vertical')\n \n #pfun.add_bathy_contours(ax, ds)\n pfun.add_coast(ax)\n ax.axis(aa)\n pfun.dar(ax)\n ax.set_xlabel('Longitude')\n ax.set_title(tstr) \n \n #fig.tight_layout()\n # FINISH\n ds.close()\n if len(in_dict['fn_out']) > 0:\n plt.savefig(in_dict['fn_out'])\n plt.close()\n else:\n plt.show()\n plt.rcdefaults()","def __len__(self):\n return 9 # logsfr_ratios has 6 bins","def test_max_entangled_reconstruction(self, wires):\n circuit = max_entangled_circuit(wires)\n bits, recipes = circuit()\n shadow = ClassicalShadow(bits, recipes)\n\n state = shadow.global_snapshots()\n assert state.shape == (10000, 2**wires, 2**wires)\n\n state = np.mean(state, axis=0)\n expected = np.zeros((2**wires, 2**wires))\n expected[np.array([0, 0, -1, -1]), np.array([0, -1, 0, -1])] = 0.5\n\n assert qml.math.allclose(state, expected, atol=1e-1)","def luminance(self):\n \n return (self.r + self.g + self.b) // 3","def fuel_from_mass(m):\n return (m/3).astype(int) - 2","def plc_temp(coil_df):","def cygx3IndFlux(self):\n # --------------------------------------------------------------------------------------------- #\n # Read data\n fitsNnam = os.path.join(self.workpath, 'LCresults.fits')\n lcTab = Table.read(fitsNnam)\n detect = lcTab['ts'] >= self.tsmin\n lcTab = lcTab[detect] \n\n ind08 = (lcTab['mjd'] > 54700) & (lcTab['mjd'] < 54900) \n flux08 = lcTab['flux'][ind08]\n fluxerr08 = lcTab['fluxerr'][ind08]\n index08 = lcTab['index'][ind08]\n indexerr08 = lcTab['indexerr'][ind08]\n\n ind09 = (lcTab['mjd'] > 54900) & (lcTab['mjd'] < 55100) \n flux09 = lcTab['flux'][ind09]\n fluxerr09 = lcTab['fluxerr'][ind09]\n index09 = lcTab['index'][ind09]\n indexerr09 = lcTab['indexerr'][ind09]\n\n scale = 10**int(np.floor(np.log10( np.mean( np.concatenate( (flux08, flux09), axis=0) ) ))) \n\n # --------------------------------------------------------------------------------------------- #\n # Plot\n indplt = FermiPlot(savepath='', xsize=8.5, ysize=6)\n indplt.figname = os.path.join(self.workpath, 'IndvsFlux.pdf')\n indplt.xlabel = r'Flux ($10^{%d}$ ph\\,cm$^{-2}$\\,s$^{-1}$)'%(int(np.log10(scale)))\n indplt.ylabel = r'Index'\n indplt.mksize = 2\n indplt.color = self.lblue\n indplt.label = r'2008'\n indplt.plot(x=flux08/scale, xerr=fluxerr08/scale, y=index08, yerr=indexerr08)\n indplt.color = self.loran\n indplt.label = r'2009'\n indplt.plot(x=flux09/scale, xerr=fluxerr09/scale, y=index09, yerr=indexerr09)\n indplt.save()\n\n print(\"\\t=== Figure '{}' created ===\".format(indplt.figname)) \n return","def GEEviLandsat(ptsFile,metric,timeStep,sensor,buf,poly,username,folderOut, scalePix = 30):\n \n # load required libraries\n import ee\n import math\n \n # Initialize the Earth Engine object, using the authentication credentials.\n ee.Initialize()\n\n ID_field = \"geeID\"\n\n #load pts or poly file\n pts1 = ee.FeatureCollection('users/' + username + '/' + str(ptsFile))\n\n #define dictionary for raster random names\n sensor_d = {}\n sensor_d['L4'] = 'LANDSAT/LT04/C01/T1_SR'\n sensor_d['L5'] = 'LANDSAT/LT05/C01/T1_SR'\n sensor_d['L7'] = 'LANDSAT/LE07/C01/T1_SR'\n sensor_d['L8'] = 'LANDSAT/LC08/C01/T1_SR'\n\n time_d = {}\n time_d['lowest'] = 'rl'\n time_d['month'] = 'rm'\n time_d['year'] = 'ry'\n \n \n #Computes the bits we need to extract.\n def getQABits(image, start, end, newName):\n pattern = 0\n listB = list(range(start, end+1))\n for one in listB:\n pattern += math.pow(2, one)\n pattern = int(pattern)\n \n return (image.select([0], [newName])\n .bitwiseAnd(pattern)\n .rightShift(start))\n \n for sen in sensor:\n LS = ee.ImageCollection(sensor_d[sen])\n #senL = [sen]\n \n def maskbyBits(img):\n QA = img.select('pixel_qa')\n QA1 = getQABits(QA, 3, 3, 'QA')\n QA2 = getQABits(QA, 5, 5, 'QA')\n\n mask = QA1.eq(0).And(QA2.eq(0))\n return img.updateMask(mask)\n \n LSm = LS.map(maskbyBits)\n \n lastImage = ee.Image(ee.ImageCollection(sensor_d[sen])\n .sort('system:time_start',False)\n .first())\n lastImageDate = lastImage.get('system:index').getInfo()\n\n firstImage = ee.Image(ee.ImageCollection(sensor_d[sen])\n .sort('system:time_start',True)\n .first())\n firstImageDate = firstImage.get('system:index').getInfo()\n \n startYear = int(firstImageDate[(len(firstImageDate)-8):(len(firstImageDate)-4)])\n endYear = int(lastImageDate[(len(lastImageDate)-8):(len(lastImageDate)-4)])\n startMonth = int(firstImageDate[(len(firstImageDate)-4):(len(firstImageDate)-2)])\n endMonth = int(lastImageDate[(len(lastImageDate)-4):(len(lastImageDate)-2)])-1\n startYearAll = startYear + 1\n endYearAll = endYear - 1\n \n years = list(range(startYear, endYearAll + 1))\n monthsEE = ee.List(list(range(startMonth,(12*len(years)+endMonth))))\n yearsEE = ee.List(list(range(startYearAll, endYearAll + 1)))\n \n for met in metric:\n # metL = [met]\n\n if (sen == 'L8' and met == \"NDVI\"):\n bands = ['B5', 'B4']\n elif (sen != 'L8' and met == \"NDVI\"):\n bands = ['B4', 'B3']\n elif (sen == 'L8' and met == \"NDWI\"):\n bands = ['B5', 'B6']\n elif (sen != 'L8' and met == \"NDWI\"):\n bands = ['B4', 'B5']\n elif (sen == 'L8' and met == \"NBR\"):\n bands = ['B5', 'B7']\n elif (sen != 'L8' and met == \"NBR\"):\n bands = ['B4', 'B7']\n #else:\n #print(\"wrong metric specified\")\n \n def addVI(image):\n vi = (image.normalizedDifference(bands)\n .rename('VI'))\n return image.addBands(vi)\n\n withVI = LSm.map(addVI)\n\n VI_col = withVI.select('VI')\n\n if timeStep == 'year':\n\n def map_m(i):\n i = ee.Number(i).int()\n image2 = (VI_col\n .filter(ee.Filter.calendarRange(i, i, 'year'))\n .first())\n filtered = (VI_col\n .filter(ee.Filter.calendarRange(i, i, 'year'))\n .mean()\n .copyProperties(image2,['system:time_start','system:time_end']))\n return filtered\n\n img_col = ee.ImageCollection(yearsEE.map(map_m).flatten())\n\n elif timeStep == 'month':\n \n def map_m(i):\n i = ee.Number(i)\n y = i.divide(12).add(years[0]).int()\n m = i.mod(12).add(1)\n image2 = (VI_col\n .filter(ee.Filter.calendarRange(m, m, 'month'))\n .filter(ee.Filter.calendarRange(y, y, 'year'))\n .first())\n filtered = (VI_col\n .filter(ee.Filter.calendarRange(m, m, 'month'))\n .filter(ee.Filter.calendarRange(y, y, 'year'))\n .mean()\n .copyProperties(image2,['system:time_start','system:time_end']))\n return filtered\n\n img_col = ee.ImageCollection(monthsEE.map(map_m).flatten())\n\n elif timeStep == 'lowest':\n\n img_col = VI_col\n\n #else:\n #print(\"incorrect time step specified\")\n \n if buf > 0:\n bufL = [buf]\n def bufferPoly(feature):\n return feature.buffer(bufL[0])\n\n ptsB = pts1.map(bufferPoly)\n def table_m(image):\n table = (image\n .select('VI')\n .reduceRegions(collection = ptsB.select([ID_field]),\n reducer = ee.Reducer.mean(),\n scale = scalePix))\n \n def table_add_date(f):\n return f.set('startDate', ee.Date(image.get('system:time_start')))\n\n return table.map(table_add_date)\n\n triplets = img_col.map(table_m).flatten()\n\n task_tc = ee.batch.Export.table.toDrive(collection = triplets\n .filter(ee.Filter.neq('mean', None))\n .select(['.*'],None,False),\n description = str(time_d[timeStep])+'_'+str(sen)+'_'+str(met)+'_'+str(years[0])+'_'+str(years[len(years)-1])+'_ptsB',\n folder = folderOut,\n fileFormat = 'CSV')\n task_tc.start()\n \n \n #print ('buffered pts by:' + str(buf) + ' for Landsat: ' + sen + '_' + met)\n\n elif poly > 0:\n \n def table_m(image):\n table = (image\n .select('VI')\n .reduceRegions(collection = pts1.select([ID_field]),\n reducer = ee.Reducer.mean(),\n scale = scalePix))\n \n def table_add_date(f):\n return f.set('startDate', ee.Date(image.get('system:time_start')))\n\n return table.map(table_add_date)\n\n triplets = img_col.map(table_m).flatten()\n\n task_tc = ee.batch.Export.table.toDrive(collection = triplets\n .filter(ee.Filter.neq('mean', None))\n .select(['.*'],None,False),\n description = str(time_d[timeStep])+'_'+str(sen)+'_'+str(met)+'_'+str(years[0])+'_'+str(years[len(years)-1])+'_poly1',\n folder = folderOut,\n fileFormat = 'CSV')\n task_tc.start()\n \n \n #print ('spatial mean in poly: no buffer for Landsat: ' + sen + '_' + met)\n\n else:\n def table_m(image):\n table = (image\n .select('VI')\n .reduceRegions(collection = pts1.select([ID_field]),\n reducer = ee.Reducer.mean(),\n scale = scalePix))\n \n def table_add_date(f):\n return f.set('startDate', ee.Date(image.get('system:time_start')))\n\n return table.map(table_add_date)\n\n triplets = img_col.map(table_m).flatten()\n\n task_tc = ee.batch.Export.table.toDrive(collection = triplets\n .filter(ee.Filter.neq('mean', None))\n .select(['.*'],None,False),\n description = str(time_d[timeStep])+'_'+str(sen)+'_'+str(met)+'_'+str(years[0])+'_'+str(years[len(years)-1])+'_pts1',\n folder = folderOut,\n fileFormat = 'CSV')\n task_tc.start()\n \n #print('value at point: no buffer for Landsat: ' + sen + '_' + met)","def viscosity(altitude):\n t_ref = temperature(0) # R\n t = temperature(altitude) # R\n s = 198.72 # R\n mu_ref = 3.737 * 10 ** (-7) # [slug/(ft*s)]\n mu = mu_ref*((t/t_ref)**(3/2))*(t_ref + s)/(t + s) # [slug/(ft*s)]\n return mu","def load_gred_dat(self, wannier_txt_file, index_word = \"WANNIER FUNCTIONS - LIST OF ACTIVE BANDS\", permutation = None):\n f = open(wannier_txt_file, \"r\")\n F = f.read()\n f.close()\n F = os.linesep.join([s for s in F.splitlines() if s]) #remove empty lines\n F = F.split(index_word)[1].split(\"WANNIER\")[0].split(\"G = \")\n \n bands = np.array([literal_eval(i) for i in F[0].split()])-1 # indexing begins at 0\n\n for i in np.arange(1,len(F[1:])+1):\n # Reading block index vector\n \n \n G = -1*np.array([literal_eval(j) for j in F[i].split(\")\")[0].split(\"(\")[1].split()])\n\n gmap = self.mapping[self._c2i(G)]\n \n # parse block\n \n B = F[i].split(\")\")[1]\n \n # read elements in block\n\n for line in B.split(\"\\n\")[1:]:\n # note : Crystal is column-major (fortran)\n row_list = [literal_eval(j) for j in line.split()]\n if len(row_list)!=0:\n if len(row_list)==1:\n # row_list contains index\n columns = np.array(row_list) -1\n else:\n if type(row_list[1]) is int:\n # line contains indices\n columns = np.array(row_list) -1\n \n else:\n # line contains elements\n row = row_list[0] - 1\n elements = np.array(row_list[1:]) \n \n self.blocks[ gmap ][row, columns + bands[0]] = elements #row and column \n return bands","def test_gleckler_index(self):\n\n # generate sample data\n # sample data\n tmp = np.zeros((5, 3, 1))\n tmp[:,0,0] = np.ones(5)*1.\n tmp[:,1,0] = np.ones(5)*2.\n tmp[:,2,0] = np.ones(5)*5.\n\n # The data is like ...\n #| 1 | 2 | 5 |\n #| 1 | 2 | 5 |\n #| 1 | 2 | 5 |\n #| 1 | 2 | 5 |\n #| 1 | 2 | 5 |\n\n x = self.D.copy()\n x._temporal_subsetting(0, 4)\n\n x.data = np.ma.array(tmp, mask=tmp!=tmp)\n x.std = np.ones(x.data.shape)\n x.time[0] = pl.datestr2num('2000-02-15')\n x.time[1] = pl.datestr2num('2000-03-15')\n x.time[2] = pl.datestr2num('2000-04-15')\n x.time[3] = pl.datestr2num('2000-05-15')\n x.time[4] = pl.datestr2num('2000-06-15')\n\n y = self.D.copy()\n y._temporal_subsetting(0, 4)\n tmp = np.ones(x.data.shape) # sample data 2\n y.data = np.ma.array(tmp, mask=tmp!=tmp)\n y.time[0] = pl.datestr2num('2000-02-15')\n y.time[1] = pl.datestr2num('2000-03-15')\n y.time[2] = pl.datestr2num('2000-04-15')\n y.time[3] = pl.datestr2num('2000-05-15')\n y.time[4] = pl.datestr2num('2000-06-15')\n\n # Case 1: same area weights\n # cell area\n tmp = np.ones((3, 1))\n x.cell_area = tmp*1.\n\n #| 1-1 | 2-1 | 5-1 |\n #| 1-1 | 2-1 | 5-1 |\n #| 1-1 | 2-1 | 5-1 |\n #| 1-1 | 2-1 | 5-1 |\n #| 1-1 | 2-1 | 5-1 |\n #===================\n #| 0 | 5 | 5*4**2=5*16. = 80 |\n #==> E2 = sqrt(85./(15.))\n D = GlecklerPlot()\n r = D.calc_index(x, y, 'a', 'b', time_weighting=False)\n\n wt = np.ones(5) / 5.\n ref = np.sqrt(((85./15.) * wt).sum())\n t = np.abs(1. - r / ref)\n self.assertLess(t, 0.000001) # relative error\n\n D = GlecklerPlot()\n r = D.calc_index(x, y, 'a', 'b')\n\n wt = np.asarray([29., 31., 30., 31., 30.])\n wt = wt / wt.sum()\n ref = np.sqrt(((85./15.) * wt).sum())\n t = np.abs(1. - r / ref)\n self.assertLess(t, 0.000001) # relative error\n\n\n\n # Case 2: Different area weights\n # cell area\n tmp = np.ones((3, 1))\n tmp[1, 0] = 2.\n x.cell_area = tmp*1.\n\n #| 1-1=0 | 2-1=1 | 5-1=16 |\n #| 1-1=0 | 2-1=1 | 5-1=16 |\n #| 1-1=0 | 2-1=1 | 5-1=16 |\n #| 1-1=0 | 2-1=1 | 5-1=16 |\n #| 1-1=0 | 2-1=1 | 5-1=16 |\n #--------------------------\n # w = 0.25 w = 0.5 w=0.25|\n #--------------------------\n\n # 0.25*0 + 0.5 * 1 + 0.25 * 16 = 0 + 0.5 + 4 = 4.5\n # the mean of that is 4.5 for each timestep\n # mean because the overall weights are calculated as such that\n # they give a total weight if 1\n\n # diagnostic\n D = GlecklerPlot()\n r = D.calc_index(x, y, 'a', 'b', time_weighting=False)\n\n wt = np.ones(5) / 5.\n ref = np.sqrt((4.5 * wt).sum())\n t = np.abs(1. - r / ref)\n self.assertLess(t, 0.000001) # relative error\n\n wt = np.asarray([29., 31., 30., 31., 30.])\n wt = wt / wt.sum()\n ref = np.sqrt((4.5 * wt).sum())\n t = np.abs(1. - r / ref)\n self.assertLess(t, 0.000001) # relative error\n\n # Case 3: use different std\n x.std = np.ones(x.data.shape)\n x.std[:, 2, 0] = 0.5\n\n #| 1-1=0 | 2-1=1 | 5-1=16 / 0.5 |\n #| 1-1=0 | 2-1=1 | 5-1=16 / 0.5 |\n #| 1-1=0 | 2-1=1 | 5-1=16 / 0.5 |\n #| 1-1=0 | 2-1=1 | 5-1=16 / 0.5 |\n #| 1-1=0 | 2-1=1 | 5-1=16 / 0.5 |\n #--------------------------------\n # w = 0.25 w = 0.5 w=0.25|\n # 0 + 0.5 + 0.25*32 = 0.5 + 8 = 8.5\n\n D = GlecklerPlot()\n r = D.calc_index(x, y, 'a', 'b', time_weighting=False)\n\n wt = np.ones(5) / 5.\n ref = np.sqrt((8.5 * wt).sum())\n t = np.abs(1. - r / ref)\n self.assertLess(t, 0.000001) # relative error\n\n wt = np.asarray([29., 31., 30., 31., 30.])\n wt = wt / wt.sum()\n ref = np.sqrt((8.5 * wt).sum())\n t = np.abs(1. - r / ref)\n self.assertLess(t, 0.000001) # relative error","def vis_loss(x, wn, nm1_air, n_glass, wl_los=[1.48,1.63,1.95,2.16], wl_his=[1.63,1.81,2.16,2.40], n_sub=4):\n if len(x)>1:\n phase = 2*np.pi*1e6*(x[0]*nm1_air + x[1]*n_glass + (x[0] - 1.0))*wn\n else:\n phase = 2*np.pi*1e6*(x[0]*nm1_air + (x[0] - 1.0))*wn\n N_wn = len(wn)\n if wl_los is None:\n ix_los = N_wn//n_sub*np.arange(n_sub)\n ix_his = N_wn//n_sub*(np.arange(n_sub)+1)\n else:\n #Wavelength and wavenumber are back to front, so this is \n #slightly confusing.\n ix_his = [np.where(1./wn <= wl_lo)[0][0] for wl_lo in wl_los][::-1]\n ix_los = [np.where(1./wn <= wl_hi)[0][0] for wl_hi in wl_his][::-1]\n mnsq = 0.\n for ix_lo,ix_hi in zip(ix_los, ix_his):\n phase_sub = phase[ix_lo:ix_hi]\n mnsq += np.var(phase_sub)\n return 100**2*mnsq/n_sub","def internal_external_canopy_heat_capacity(lumped_cover_heat_capacity: float) -> float:\n return 0.1 * lumped_cover_heat_capacity","def sample_pin_position_range():\n #Create a sample goniometer\n g = TopazInHouseGoniometer()\n\n #Initialize the leg limits\n g.relative_sample_position = column([0.0, 0.0, 0.0])\n g.getplatepos(0.0, 0.0, 0.0)\n g.calculate_leg_xy_limits(visualize=True)\n\n# if True:\n# pylab.show()\n# return\n\n n = 17\n positions = np.linspace(-8, 8, n) #Range calculated in mm\n allowed = np.zeros( (n,n,n) )\n for (ix, x) in enumerate(positions):\n print \"Calculating x\", x\n for (iy, y) in enumerate(positions):\n for (iz, z) in enumerate(positions):\n #Set up\n g.relative_sample_position = column([x, y, z])\n allowed[ix,iy,iz] = g.are_angles_allowed([0., 0., 0.], return_reason=False)\n\n #Do a plot\n\n pylab.figure(1, figsize=[15,15])\n pylab.title(\"Allowable XZ sample positions\")\n for (iy, y) in enumerate(positions):\n print \"At y of\", y, \", # of points = \", np.sum( allowed[:, iy,:])\n if iy < 16:\n pylab.subplot(4,4,iy+1)\n pylab.pcolor(positions, positions, allowed[:, iy, :].transpose(), norm=pylab.Normalize(0, 1))\n pylab.xlabel(\"x\")\n pylab.ylabel(\"z\")\n pylab.title(\"y = %.3f mm\" % y)\n pylab.draw()\n pylab.axis('equal')\n pylab.show()\n #pylab.","def hapax(self):\n return self.nlegomena(1)","def degree_of_map(self):\n if 2*self.component1.genus == self.space.genus and self.space.n == 0:\n return 2\n else:\n return 1","def phosphorene_4band():\n a = 0.222\n ax = 0.438\n ay = 0.332\n theta = 96.79 * (pi / 180)\n phi = 103.69 * (pi / 180)\n\n lat = pb.Lattice(a1=[ax, 0], a2=[0, ay])\n\n h = a * sin(phi - pi / 2)\n s = 0.5 * ax - a * cos(theta / 2)\n lat.add_sublattices(\n ('A', [-s/2, -ay/2, h], 0),\n ('B', [ s/2, -ay/2, 0], 0),\n ('C', [-s/2 + ax/2, 0, 0], 0),\n ('D', [ s/2 + ax/2, 0, h], 0)\n )\n\n lat.register_hopping_energies({\n 't1': -1.22,\n 't2': 3.665,\n 't3': -0.205,\n 't4': -0.105,\n 't5': -0.055\n })\n\n lat.add_hoppings(\n # t1\n ([-1, 0], 'A', 'D', 't1'),\n ([-1, -1], 'A', 'D', 't1'),\n ([ 0, 0], 'B', 'C', 't1'),\n ([ 0, -1], 'B', 'C', 't1'),\n # t2\n ([ 0, 0], 'A', 'B', 't2'),\n ([ 0, 0], 'C', 'D', 't2'),\n # t3\n ([ 0, 0], 'A', 'D', 't3'),\n ([ 0, -1], 'A', 'D', 't3'),\n ([ 1, 1], 'C', 'B', 't3'),\n ([ 1, 0], 'C', 'B', 't3'),\n # t4\n ([ 0, 0], 'A', 'C', 't4'),\n ([ 0, -1], 'A', 'C', 't4'),\n ([-1, 0], 'A', 'C', 't4'),\n ([-1, -1], 'A', 'C', 't4'),\n ([ 0, 0], 'B', 'D', 't4'),\n ([ 0, -1], 'B', 'D', 't4'),\n ([-1, 0], 'B', 'D', 't4'),\n ([-1, -1], 'B', 'D', 't4'),\n # t5\n ([-1, 0], 'A', 'B', 't5'),\n ([-1, 0], 'C', 'D', 't5')\n )\n\n return lat","def lap_mat(self):","def snow_depth(lon, lat, month):\n\n im = month - 1\n\n h0 = np.array( [28.01, 30.28, 33.89, 36.80, 36.93, 36.59,\n 11.02, 4.64, 15.81, 22.66, 25.57, 26.67] )\n a = np.array( [ 0.1270, 0.1056, 0.5486, 0.4046, 0.0214, 0.7021,\n 0.3008, 0.3100, 0.2119, 0.3594, 0.1496, -0.1876] )\n b = np.array( [-1.1833, -0.5908, -0.1996, -0.4005, -1.1795, -1.4819,\n -1.2591, -0.6350, -1.0292, -1.3483, -1.4643, -1.4229] )\n c = np.array( [-0.1164, -0.0263, 0.0280, 0.0256, -0.1076, -0.1195,\n -0.0811, -0.0655, -0.0868, -0.1063, -0.1409, -0.1413] )\n d = np.array( [-0.0051, -0.0049, 0.0216, 0.0024, -0.0244, -0.0009,\n -0.0043, 0.0059, -0.0177, 0.0051, -0.0079, -0.0316] )\n e = np.array( [ 0.0243, 0.0044, -0.0176, -0.0641, -0.0142, -0.0603,\n -0.0959, -0.0005, -0.0723, -0.0577, -0.0258, -0.0029] )\n\n x = (90. - lat) * np.cos( np.radians(lon) )\n y = (90. - lat) * np.sin( np.radians(lon) )\n\n h = ( h0[im] + ( a[im] * x ) + ( b[im] * y ) + ( c[im] * x * y ) +\n ( d[im] * x * x ) + ( e[im] * y * y ) )\n\n return h","def test_showalter_index():\n pressure = units.Quantity(np.array([931.0, 925.0, 911.0, 891.0, 886.9, 855.0, 850.0, 825.6,\n 796.3, 783.0, 768.0, 759.0, 745.0, 740.4, 733.0, 715.0,\n 700.0, 695.0, 687.2, 684.0, 681.0, 677.0, 674.0, 661.9,\n 657.0, 639.0, 637.6, 614.0, 592.0, 568.9, 547.4, 526.8,\n 500.0, 487.5, 485.0]), 'hPa')\n temps = units.Quantity(np.array([18.4, 19.8, 20.0, 19.6, 19.3, 16.8, 16.4, 15.1, 13.4,\n 12.6, 11.2, 10.4, 8.6, 8.3, 7.8, 5.8, 4.6, 4.2, 3.4, 3.0,\n 3.0, 4.4, 5.0, 5.1, 5.2, 3.4, 3.3, 2.4, 1.4, -0.4, -2.2,\n -3.9, -6.3, -7.6, -7.9]), 'degC')\n dewp = units.Quantity(np.array([9.4, 8.8, 6.0, 8.6, 8.4, 6.8, 6.4, 4.0, 1.0, -0.4, -1.1,\n -1.6, 1.6, -0.2, -3.2, -3.2, -4.4, -2.8, -3.6, -4.0, -6.0,\n -17.6, -25.0, -31.2, -33.8, -29.6, -30.1, -39.0, -47.6,\n -48.9, -50.2, -51.5, -53.3, -55.5, -55.9]), 'degC')\n\n result = showalter_index(pressure, temps, dewp)\n assert_almost_equal(result, units.Quantity(7.6024, 'delta_degC'), 4)","def getHoracekFromEQ(self, ep):\n #check if power is in MW or W (assumes Pinj > 500W)\n if self.Pinj < 500:\n Pinj = self.Pinj * 1e6\n else:\n Pinj = self.Pinj\n\n #find the plasma volume from the equilibrium\n Rlim = ep.g['lcfs'][:,0]\n Zlim = ep.g['lcfs'][:,1]\n\n #calculate cross sectional area using shoelace formula\n i=np.arange(len(Rlim))\n crossSecArea=np.abs(np.sum(Rlim[i-1]*Zlim[i]-Rlim[i]*Zlim[i-1])*0.5)\n\n #calculate (approximate) volume inside separatrix [m^3]\n #assumes RmAxis is close to R of center of mass of crossSecArea\n vol = crossSecArea * 2 * np.pi * ep.g['RmAxis']\n\n #assuming plasma is centered in machine here\n zMin = ep.g['ZmAxis'] - 0.25\n zMax = ep.g['ZmAxis'] + 0.25\n zLCFS = ep.g['lcfs'][:,1]\n rLCFS = ep.g['lcfs'][:,0]\n #this prevents us from getting locations not at midplane\n idx = np.where(np.logical_and(zLCFS>zMin,zLCFS<zMax))\n Rmax = ep.g['lcfs'][:,0][idx].max()\n Rmin = ep.g['lcfs'][:,0][idx].min()\n # geometric quantities\n Rgeo = (Rmax + Rmin) / 2.0\n a = (Rmax - Rmin) / 2.0\n aspect = a/Rgeo\n\n #maximum z point and elongation\n idx2 = np.where(np.logical_and(rLCFS>Rmin,rLCFS<Rmax))\n b = ep.g['lcfs'][:,1][idx].max() #assumes equatorial plane is z=0\n k = b / a\n\n #lambda q from Horacek engineering scaling figure 6a\n self.lqCF = 10 * (Pinj / vol)**(-0.38) * aspect**(1.3) * k**(-1.3) * 1e3 #in mm\n return","def find_aretes(params, df):\n# colonnes = [\"num_graphe\",\"sommets_matE\",\"aretes_matE\"];\n# df = pd.read_csv(params[\"path_save\"]+params[\"file_save\"], names = colonnes, skiprows=[0]);\n nb_rd = 1+1.6*round(random.random(),1);\n nb_min_max = random.randrange(df[\"aretes_matE\"].min(), df[\"aretes_matE\"].max());\n aretes = nb_min_max * nb_rd;\n return aretes;\n pass","def visitcomp(plate,mjd,indiv=False,apred='test') :\n #plt.close('all')\n load=apload.ApLoad(apred=apred)\n a=load.apVisitSum(plate,mjd)[1].data\n\n #dr14=apload.ApLoad(dr='dr14')\n p=yanny.yanny(os.environ['PLATELIST_DIR']+'/platePlans.par',np=True)\n j=np.where(p['PLATEPLANS']['plateid'] == plate)[0][0]\n locid=p['PLATEPLANS']['locationid'][j]\n b=fits.open(os.environ['APOGEE_REDUX']+'/r8/fields/apo25m/{:04d}/apVisitSum-{:04d}-{:05d}.fits'.format(\n locid,plate,mjd))[1].data\n\n fig,ax=plots.multi(2,2)\n i1,i2=match.match(a['FIBERID'],b['FIBERID'])\n plots.plotc(ax[0,0],a['FIBERID'][i1],a['VHELIO'][i1]-b['VHELIO'][i2],a['RV_TEFF'][i1],zr=[3500,5500],xt='Fiber',yt=r'$\\Delta$ VHELIO')\n plots.plotc(ax[0,1],a['FIBERID'][i1],a['VHELIO'][i1]-b['VHELIO'][i2],a['RV_TEFF'][i1],zr=[3500,5500],yr=[-2.,2.],xt='Fiber',yt=r'$\\Delta$ VHELIO')\n plots.plotc(ax[1,0],a['FIBERID'][i1],a['RV_TEFF'][i1]-b['RV_TEFF'][i2],a['RV_TEFF'][i1],zr=[3500,5500],xt='Fiber',yt=r'$\\Delta$ RV_TEFF')\n plots.plotc(ax[1,1],a['RV_TEFF'][i1]-b['RV_TEFF'][i2],a['VHELIO'][i1]-b['VHELIO'][i2],a['RV_TEFF'][i1],zr=[3500,5500],xt=r'$\\Delta$ RV_TEFF',yt=r'$\\Delta$ VHELIO')\n out=load.filename('Plate',chips=True,plate=plate,mjd=mjd)\n outdir=os.path.dirname(out)\n outname=os.path.basename(out).replace('-a','').replace('.fits','_dr14comp.png')\n fig.tight_layout()\n pdb.set_trace()\n print(outdir+'/plots/'+outname)\n fig.savefig(outdir+'/plots/'+outname)\n\n if indiv :\n va=load.apPlate(plate,mjd)\n vb={}\n for chip in chips :\n tmp=fits.open(os.environ['APOGEE_REDUX']+'/r8/apo25m/{:04d}/{:05d}/apPlate-{:s}-{:04d}-{:05d}.fits'.format(\n plate,mjd,chip,plate,mjd))\n vb[chip] = tmp\n\n fig,ax=plots.multi(1,3,hspace=0.3) \n pfig,pax=plots.multi(1,3,hspace=0.3) \n wfig,wax=plots.multi(1,3,hspace=0.3) \n for i in range(len(i1)) :\n fiber = a['FIBERID'][i1[i]]\n if (a['VHELIO'][i1[i]]-b['VHELIO'][i2[i]]) > 0.5 :\n print(fiber,a['VHELIO'][i1[i]],b['VHELIO'][i2[i]],a['RV_TEFF'][i1[i]],b['RV_TEFF'][i2[i]])\n applot.chip(va,ax=ax,row=300-fiber,color='r')\n #applot.chip(va,ax=pax,row=300-fiber,color='r',pixel=True)\n applot.chip(vb,ax=ax,row=300-fiber,color='b')\n #applot.chip(vb,ax=pax,row=300-fiber,color='b',pixel=True)\n for ichip,chip in enumerate(chips) :\n pax[ichip].plot(va[chip][1].data[300-fiber,:]/vb[chip][1].data[300-fiber,:])\n wax[ichip].plot(va[chip][4].data[300-fiber,:]-vb[chip][4].data[300-fiber,:])\n plt.show()\n\n pdb.set_trace()\n for ichip in range(3) :\n ax[ichip].cla()\n pax[ichip].cla()\n wax[ichip].cla()\n plt.close()","def NPL(self):\n self.edge = np.zeros((np.sum(self.Adjmatrix), 3))\n Temp = 0\n for i in range(self.nodenum):\n for j in range(self.nodenum):\n if(self.Adjmatrix[i, j] == 1):\n self.edge[Temp, 0], self.edge[Temp, 1], self.edge[Temp, 2] = i, j, self.Dismatrix[i, j]\n Temp += 1\n \n self.Totallength = ((np.max(self.Geox) - np.min(self.Geox))**2 + (np.max(self.Geoy) - np.min(self.Geoy))**2)**0.5\n self.norm_edge = self.edge[:, 2]/self.Totallength","def test_isentropic_pressure_masked_column():\n lev = [100000., 95000.] * units.Pa\n tmp = np.ma.ones((len(lev), 5, 5))\n tmp[0, :] = 296.\n tmp[1, :] = 292.\n tmp[:, :, -1] = np.ma.masked\n tmp = units.Quantity(tmp, units.kelvin)\n isentprs = isentropic_interpolation([296.] * units.kelvin, lev, tmp)\n trueprs = np.ones((1, 5, 5)) * (1000. * units.hPa)\n trueprs[:, :, -1] = np.nan\n assert isentprs[0].shape == (1, 5, 5)\n assert_almost_equal(isentprs[0], trueprs, 3)","def make_montage_axial(overlay, underlay, png_name, cbar_name):\n import matplotlib\n import commands\n# matplotlib.use('Agg')\n import os\n matplotlib.rcParams.update({'font.size': 5})\n import matplotlib.cm as cm\n ###\n try:\n from mpl_toolkits.axes_grid1 import ImageGrid\n except:\n from mpl_toolkits.axes_grid import ImageGrid\n import matplotlib.pyplot as plt\n import matplotlib.colors as col\n import nibabel as nb\n import numpy as np\n\n def determine_start_and_end(data, direction, percent):\n x, y, z = data.shape\n xx1 = 0\n xx2 = x - 1\n zz1 = 0\n zz2 = z - 1\n total_non_zero_voxels = len(np.nonzero(data.flatten())[0])\n thresh = percent * float(total_non_zero_voxels)\n start = None\n end = None\n if 'axial' in direction:\n while(zz2 > 0):\n d = len(np.nonzero(data[:, :, zz2].flatten())[0])\n if float(d) > thresh:\n break\n zz2 -= 1\n while(zz1 < zz2):\n d = len(np.nonzero(data[:, :, zz1].flatten())[0])\n if float(d) > thresh:\n break\n zz1 += 1\n start = zz1\n end = zz2\n else:\n while(xx2 > 0):\n d = len(np.nonzero(data[xx2, :, :].flatten())[0])\n if float(d) > thresh:\n break\n xx2 -= 1\n while(xx1 < xx2):\n d = len(np.nonzero(data[xx1, :, :].flatten())[0])\n if float(d) > thresh:\n break\n xx1 += 1\n start = xx1\n end = xx2\n return start, end\n def get_spacing(across, down, dimension):\n space = 10\n prod = (across*down*space)\n if prod > dimension:\n while(across*down*space) > dimension:\n space -= 1\n else:\n while(across*down*space) < dimension:\n space += 1\n return space\n\n Y = nb.load(underlay).get_data()\n X = nb.load(overlay).get_data()\n X = X.astype(np.float16)\n Y = Y.astype(np.float16)\n\n if 'skull_vis' in png_name:\n X[X < 20.0] = 0.0\n if 'skull_vis' in png_name or 't1_edge_on_mean_func_in_t1' in png_name or 'MNI_edge_on_mean_func_mni' in png_name:\n max_ = np.nanmax(np.abs(X.flatten()))\n X[X != 0.0] = max_\n print '^^', np.unique(X)\n z1, z2 = determine_start_and_end(Y, 'axial', 0.0001)\n spacing = get_spacing(6, 3, z2 - z1)\n x, y, z = Y.shape\n fig = plt.figure(1)\n max_ = np.max(np.abs(Y))\n\n if ('snr' in png_name) or ('reho' in png_name) or ('vmhc' in png_name) or ('sca_' in png_name) or ('alff' in png_name) or ('centrality' in png_name) or ('temporal_regression_sca' in png_name) or ('temporal_dual_regression' in png_name):\n grid = ImageGrid(fig, 111, nrows_ncols=(3, 6), share_all=True, aspect=True, cbar_mode=\"single\", cbar_pad=0.2, direction=\"row\")\n else:\n grid = ImageGrid(fig, 111, nrows_ncols=(3, 6), share_all=True, aspect=True, direction=\"row\")\n\n zz = z1\n for i in range(6*3):\n\n if zz >= z2:\n break\n\n im = grid[i].imshow(np.rot90(Y[:, :, zz]), cmap=cm.Greys_r)\n zz += spacing\n\n x, y, z = X.shape\n X[X == 0.0] = np.nan\n max_ = np.nanmax(np.abs(X.flatten()))\n print '~~', max_\n\n\n zz = z1\n im = None\n print '~~~', z1, ' ', z2\n for i in range(6*3):\n\n\n if zz >= z2:\n break\n if cbar_name is 'red_to_blue':\n\n im = grid[i].imshow(np.rot90(X[:, :, zz]), cmap=cm.get_cmap(cbar_name), alpha=0.82, vmin=0, vmax=max_) ###\n elif cbar_name is 'green':\n im = grid[i].imshow(np.rot90(X[:, :, zz]), cmap=cm.get_cmap(cbar_name), alpha=0.82, vmin=0, vmax=max_)\n else:\n im = grid[i].imshow(np.rot90(X[:, :, zz]), cmap=cm.get_cmap(cbar_name), alpha=0.82, vmin=- max_, vmax=max_)\n\n grid[i].axes.get_xaxis().set_visible(False)\n grid[i].axes.get_yaxis().set_visible(False)\n zz += spacing\n\n cbar = grid.cbar_axes[0].colorbar(im)\n\n if 'snr' in png_name:\n cbar.ax.set_yticks(drange(0, max_))\n\n elif ('reho' in png_name) or ('vmhc' in png_name) or ('sca_' in png_name) or ('alff' in png_name) or ('centrality' in png_name) or ('temporal_regression_sca' in png_name) or ('temporal_dual_regression' in png_name):\n cbar.ax.set_yticks(drange(-max_, max_))\n\n\n plt.show()\n plt.axis(\"off\")\n png_name = str(os.path.join(os.getcwd(), png_name))\n plt.savefig(png_name, dpi=300, bbox_inches='tight')\n plt.close()\n\n matplotlib.rcdefaults()\n\n return png_name","def zeige_auf_sensehat(self):\n if self.sense is not None:\n sense.clear(farbe_tot)\n for zelle in self.lebendig:\n x, y = zelle\n if 0 <= x < 8 and 0 <= y < 8:\n sense.set_pixel(zelle[0], zelle[1]. farbe_lebendig)","def foetus_pics(self):\n pic = 0\n if 0.0 not in self.allele:\n self.contamination = 2\n pic = 3\n elif 0.0 == self.allele[1]:\n pic = 1\n else:\n pic = 2\n return pic","def cloud_cover_to_ghi_linear(cloud_cover, ghi_clear, offset=35):\n\n offset = offset / 100.\n cloud_cover = cloud_cover / 100.\n ghi = (offset + (1 - offset) * (1 - cloud_cover)) * ghi_clear\n return ghi","def branching_factor(data, loc):\n\n return 20","def delta(self):\r\n return 1 - xl.Refractive_Index_Re(self.compound, self.energy, self.density)","def test_epix100a():\n basedir = '/reg/g/psdm/detector/alignment/cspad/calib-cxi-ds1-2014-05-15/'\n fname_geometry = basedir + 'calib/CsPad::CalibV1/CxiDs1.0:Cspad.0/geometry/2-end.data'\n fname_data = basedir + 'cspad-arr-cxid2714-r0023-lysozyme-rings.txt'\n\n #basedir = '/reg/neh/home1/dubrovin/LCLS/GeometryCalib/calib-xpp-Epix100a-2014-11-05/'\n #fname_geometry = basedir + 'calib/Epix100a::CalibV1/NoDetector.0:Epix100a.0/geometry/0-end.data'\n #fname_data = basedir + 'epix100a-ndarr-ave-clb-xppi0614-r0073.dat'\n\n geometry = GeometryAccess(fname_geometry, pbits=0o377)\n amp_range = (-4,10)\n\n rows, cols = geometry.get_pixel_coord_indexes()\n\n root, ext = os.path.splitext(fname_data)\n arr = np.load(fname_data) if ext == '.npy' else np.loadtxt(fname_data, dtype=np.float)\n\n logger.info('shapes rows: %s cols: %s weight: %s' % (str(rows.shape), str(cols.shape), str(arr.shape)))\n img = img_from_pixel_arrays(rows,cols,W=arr)\n\n axim = gg.plotImageLarge(img,amp_range=amp_range)\n gg.move(500,10)\n gg.show()","def getIndexRef(row, col, frame, midRange, maxIndex): \n indexRef = numpy.zeros(12).reshape(4,3)\n indexRef[:,0] = numpy.arange(-1, 3) * midRange + row\n indexRef[:,1] = numpy.arange(-1, 3) * midRange + col\n indexRef[:,2] = numpy.arange(-1, 3) * midRange + frame\n \n for i in range(4):\n for j in range(3):\n if indexRef[i,j] < 0 or indexRef[i,j] > maxIndex:\n indexRef[i,j] = -1\n return indexRef","def test_lifted_index_500hpa_missing():\n pressure = np.array([1014., 1000., 997., 981.2, 947.4, 925., 914.9, 911.,\n 902., 883., 850., 822.3, 816., 807., 793.2, 770.,\n 765.1, 753., 737.5, 737., 713., 700., 688., 685.,\n 680., 666., 659.8, 653., 643., 634., 615., 611.8,\n 566.2, 516., 487., 484.2, 481., 475., 460.,\n 400.]) * units.hPa\n temperature = np.array([24.2, 24.2, 24., 23.1, 21., 19.6, 18.7, 18.4,\n 19.2, 19.4, 17.2, 15.3, 14.8, 14.4, 13.4, 11.6,\n 11.1, 10., 8.8, 8.8, 8.2, 7., 5.6, 5.6,\n 5.6, 4.4, 3.8, 3.2, 3., 3.2, 1.8, 1.5,\n -3.4, -9.3, -13.1, -13.1, -13.1, -13.7, -15.1,\n -23.5]) * units.degC\n dewpoint = np.array([23.2, 23.1, 22.8, 22., 20.2, 19., 17.6, 17.,\n 16.8, 15.5, 14., 11.7, 11.2, 8.4, 7., 4.6,\n 5., 6., 4.2, 4.1, -1.8, -2., -1.4, -0.4,\n -3.4, -5.6, -4.3, -2.8, -7., -25.8, -31.2, -31.4,\n -34.1, -37.3, -34.1, -37.3, -41.1, -37.7, -58.1,\n -57.5]) * units.degC\n parcel_prof = parcel_profile(pressure, temperature[0], dewpoint[0])\n li = lifted_index(pressure, temperature, parcel_prof)\n assert_almost_equal(li, -7.9806301 * units.delta_degree_Celsius, 1)","def knolg(self):\n if self._knolg is None:\n if self.nptir > 0:\n self._knolg = self.get_mesh_l2g_numbering()\n else:\n self._knolg = np.zeros((self.npoin3), dtype=np.int32)\n\n return self._knolg","def reScaleLandsat(self,img):\n \n\t\tthermalBand = ee.List(['thermal'])\n\t\tthermal = ee.Image(img).select(thermalBand).multiply(10)\n \n\t\totherBands = ee.Image(img).bandNames().removeAll(thermalBand)\n\t\tscaled = ee.Image(img).select(otherBands).divide(0.0001)\n \n\t\timage = ee.Image(scaled.addBands(thermal)).int16()\n \n\t\treturn image.copyProperties(img)","def manage_grad(self,test=0):\n\t\tsx1 = self.abs_sobel_thresh(orient='x', thresh_min=25, thresh_max=55)\n\t\tsd_less = self.dir_threshold(sobel_kernel=9, thresh=(1.0721,np.pi/2))\n\t\tsd_less2 = self.dir_threshold(sobel_kernel=9, thresh=(0.0982,0.3927))\n\t\tsx3 = binary_substr(binary_substr(sx1,sd_less),sd_less2)\n\n\t\thls = cv2.cvtColor(self.img, cv2.COLOR_RGB2HLS)\n\t\th_less = ch_threshold(hls, 0, (85,256))\n\t\tsx3 = binary_substr(sx3, h_less)\n\t\th_less2 = ch_threshold(hls, 0, (-1,6))\n\t\tsx3 = binary_substr(sx3, h_less2)\n\t\tif test==1:\n\t\t print(\"nothing to test\")\n\t\treturn sx3","def decreaseSearch(self):\n\n self.detections = self.detections()\n self.detections.location = self.location\n for index, row in enumerate(self.magdata):\n\n if row[3] < self.upper*self.background and row[3] < self.background - 2*self.std:\n # less than 50% of background and 2 std\n #print \"Large decrease (less than \" + str(self.upper*self.background) + \")\", row[3]\n self.detections.largeDecrease.append([index,self.timestamps[index], row[3]])\n \n elif row[3] < self.lower*self.background and row[3] < self.background - self.std:\n # less than 25% of background and 1 std\n #print \"Decrease (less than \" + str(self.lower*self.background) + \")\", row[3]\n self.detections.smallDecrease.append([index,self.timestamps[index], row[3]])\n \n elif row[3] > (1 + self.upper)*self.background and row[3] > self.background + 2*self.std:\n # greater than 50% of background\n #print \"Large increase (greater than \" + str((1 + self.upper)*self.background) + \")\", row[3]\n self.detections.largeIncrease.append([index,self.timestamps[index], row[3]])\n \n elif row[3] > (1 + self.lower)*self.background and row[3] > self.background + self.std:\n # greater than 25% of background\n #print \"Increase (greater than \" + str((1 + self.lower)*self.background) + \")\", row[3]\n self.detections.smallIncrease.append([index,self.timestamps[index], row[3]])\n\n \n if resultCounter(self.detections) == 0:\n self.detections.results.append(None)\n print \"No observable jumps/dips in magnetic field strength\"\n else:\n for attribute, bins in classIterator(self.detections):\n if 'crease' in attribute:\n self.detections.results.append('Number of ' + attribute + ': ' + str(len(bins)))","def grass_drass():","def test_ridge(self):\n fun = get_problem('ridge', dimension=self.dimension, lower=-64, upper=64)\n self.assertAlmostEqual(fun(self.array10), 371.0, delta=1e-4)"],"string":"[\n \"def get_index_of_surface_gate(data, setup={}):\\n alts = data['alt']\\n return np.argmin(np.abs(alts), 1)\",\n \"def test_no_backg_subt():\\n \\n test_object = fa.read_in_envision(data_csv=HsHis6_PEX5C_vs_HsPEX5C, platemap_csv=Hs_His6_PEX5C_vs_HsPEX5C_platemap, data_type='plate', size=384)\\n test_object.calculate_r_i(correct=True, plot_i=False, thr=80)\",\n \"def intensity(self) -> int:\",\n \"def get_sn2005ek(colorplt=False):\\n z = 0.016551\\n ebv = 0.210\\n D = cosmo.luminosity_distance([z])[0].value * 1e+6 # in pc\\n dis_mod = 5*np.log10(D / 10)\\n t_max = 53639.9\\n print (\\\"adopt r band t_max from Drout+13\\\")\\n \\n # tb = pd.read_csv('/Users/yuhanyao/Desktop/ZTF18abfcmjw/data/Drout2013/table1', sep='\\\\t')\\n # tb = tb.drop(columns=[\\\"Unnamed: 6\\\"])\\n \\n mjds = np.array([53639.3, 53640.3, 53641.3, 53642.2, 53643.2, 53645.3,\\n 53646.5, 53648.0, 53649.2, 53650.4, 53651.3, 53652.5,\\n 53654.2, 53655.2, 53656.2, 53657.2])\\n \\n Bmags = np.array([18.25, 18.38, 18.65, np.nan, 19.10, 19.71,\\n 20.07, np.nan, 20.67, 20.90, 21.05, np.nan,\\n 21.74, np.nan, np.nan, np.nan])\\n \\n Bmag_uncs = np.array([0.02, 0.03, 0.02, np.nan, 0.05, 0.07, \\n 0.07, np.nan, 0.04, 0.04, 0.04, np.nan,\\n 0.12, np.nan, np.nan, np.nan])\\n \\n Vmags = np.array([17.83, 18.03, 17.92, np.nan, 18.24, 18.66,\\n 18.93, 19.48, 19.63, 19.86, 19.98, 20.35,\\n 20.60, 20.74, 20.88, 21.22])\\n \\n Vmag_uncs = np.array([0.02, 0.03, 0.01, np.nan, 0.02, 0.02,\\n 0.02, 0.06, 0.03, 0.03, 0.04, 0.05, \\n 0.08, 0.10, 0.08, 0.13])\\n \\n Rmags = np.array([17.46, 17.41, 17.60, 17.69, 17.86, 18.18, \\n np.nan, 18.83, 19.03, 19.26, 19.48, 19.75,\\n 20.08, np.nan, 20.47, np.nan])\\n \\n Rmag_uncs = np.array([0.01, 0.02, 0.01, 0.02, 0.01, 0.01,\\n np.nan, 0.03, 0.02, 0.02, 0.02, 0.04,\\n 0.05, np.nan, 0.08, np.nan])\\n\\n Imags = np.array([17.20, 17.13, 17.18, np.nan, 17.47, 17.71, \\n np.nan, 18.13, 18.26, 18.51, 18.61, 18.74, \\n 19.01, np.nan, 19.47, np.nan])\\n \\n Imag_uncs = np.array([0.02, 0.04, 0.02, np.nan, 0.03, 0.02,\\n np.nan, 0.06, 0.02, 0.02, 0.02, 0.03,\\n 0.05, np.nan, 0.06, np.nan])\\n \\n mymjds = np.hstack([mjds, mjds, mjds, mjds])\\n mymags = np.hstack([Bmags, Vmags, Rmags, Imags])\\n myemags = np.hstack([Bmag_uncs, Vmag_uncs, Rmag_uncs, Imag_uncs])\\n myfilts = np.hstack([ np.repeat(\\\"B\\\", len(Bmags)),\\n np.repeat(\\\"V\\\", len(Bmags)),\\n np.repeat(\\\"R\\\", len(Rmags)),\\n np.repeat(\\\"I\\\", len(Imags)) ])\\n ix = ~np.isnan(mymags)\\n tb = pd.DataFrame({'mjd': mymjds[ix],\\n 'mag': mymags[ix],\\n 'emag': myemags[ix],\\n \\\"filter\\\": myfilts[ix]})\\n \\n ixB = tb['filter'].values==\\\"B\\\"\\n ixV = tb['filter'].values==\\\"V\\\"\\n ixR = tb['filter'].values==\\\"R\\\"\\n ixI = tb['filter'].values==\\\"I\\\"\\n \\n tb['wave'] = np.zeros(len(tb))\\n tb['wave'].values[ixB] = 4359\\n tb['wave'].values[ixV] = 5430\\n tb['wave'].values[ixR] = 6349\\n tb['wave'].values[ixI] = 8797\\n \\n tb['mag0'] = tb['mag'] - extinction.ccm89(tb['wave'].values, 3.1*ebv, 3.1)\\n tb['mag0_abs'] = tb['mag0'] - dis_mod\\n tb['tmax_rf'] = (tb['mjd'] - t_max) / (1+z)\\n if colorplt==False:\\n return tb\\n else:\\n tb = add_datecol(tb)\\n ix = np.in1d(tb[\\\"filter\\\"].values, np.array(['B', 'R', 'I']))\\n tb = tb[ix]\\n\\n dates = get_date_span(tb)\\n datesave = []\\n for i in range(len(dates)):\\n x = dates[i]\\n ix = tb[\\\"date\\\"].values == x\\n tbsub = tb[ix]\\n if len(tbsub)!=0:\\n flts = tbsub['filter'].values\\n if \\\"R\\\" in flts and np.sum(np.unique(flts))!=1:\\n datesave.append(x)\\n datesave = np.array(datesave)\\n \\n mcolor = []\\n mcolor_unc = []\\n mjds = []\\n colorname = []\\n for i in range(len(datesave)):\\n x = datesave[i]\\n ix = tb[\\\"date\\\"].values == x\\n tbsub = tb[ix]\\n gtb = tbsub[tbsub[\\\"filter\\\"].values==\\\"B\\\"]\\n rtb = tbsub[tbsub[\\\"filter\\\"].values==\\\"R\\\"]\\n itb = tbsub[tbsub[\\\"filter\\\"].values==\\\"I\\\"]\\n if len(gtb)!=0:\\n gmjds = gtb[\\\"mjd\\\"].values\\n gmags = gtb[\\\"mag0\\\"].values\\n gemags = gtb[\\\"emag\\\"].values\\n gwtgs = 1/gemags**2\\n gmag = np.sum(gmags * gwtgs) / np.sum(gwtgs)\\n gmjd = np.sum(gmjds * gwtgs) / np.sum(gwtgs)\\n gemag = 1/ np.sqrt(np.sum(gwtgs))\\n if len(rtb)!=0:\\n rmjds = rtb[\\\"mjd\\\"].values\\n rmags = rtb[\\\"mag0\\\"].values\\n remags = rtb[\\\"emag\\\"].values\\n rwtgs = 1/remags**2\\n rmag = np.sum(rmags * rwtgs) / np.sum(rwtgs)\\n rmjd = np.sum(rmjds * rwtgs) / np.sum(rwtgs)\\n remag = 1/ np.sqrt(np.sum(rwtgs))\\n if len(itb)!=0:\\n imjds = itb[\\\"mjd\\\"].values\\n imags = itb[\\\"mag0\\\"].values\\n iemags = itb[\\\"emag\\\"].values\\n iwtgs = 1/iemags**2\\n imag = np.sum(imags * iwtgs) / np.sum(iwtgs)\\n imjd = np.sum(imjds * iwtgs) / np.sum(iwtgs)\\n iemag = 1/ np.sqrt(np.sum(iwtgs))\\n if len(gtb)!=0 and len(rtb)!=0:\\n mcolor.append(gmag - rmag)\\n mjds.append( 0.5 * (gmjd + rmjd) )\\n mcolor_unc.append( np.sqrt(gemag**2 + remag**2) )\\n colorname.append(\\\"BmR\\\")\\n if len(rtb)!=0 and len(itb)!=0:\\n mcolor.append(rmag - imag)\\n mjds.append( 0.5 * (rmjd + imjd) )\\n mcolor_unc.append( np.sqrt(remag**2 + iemag**2) )\\n colorname.append(\\\"RmI\\\")\\n \\n ctb = Table(data = [mjds, mcolor, mcolor_unc, colorname],\\n names = [\\\"mjd\\\", \\\"c\\\", \\\"ec\\\", \\\"cname\\\"])\\n \\n ctb['tmax_rf'] = (ctb['mjd'] - t_max) / (1+z)\\n ctb = ctb.to_pandas()\\n return ctb\",\n \"def bndy_plasma(self):\\n self.ne[0], self.ne[-1] = 1e11, 1e11\\n self.ni[0], self.ni[-1] = 1e11, 1e11\\n self.nn[0], self.nn[-1] = 1e11, 1e11\\n self.Te[0], self.Te[-1] = 0.1, 0.1\\n self.Ti[0], self.Ti[-1] = 0.01, 0.01\\n # self.coll_em[0], self.coll_em[-1] = 1e5, 1e5\\n # self.coll_im[0], self.coll_im[-1] = 1e5, 1e5\",\n \"def nonlinear_refractive_index(name):\\n if name == 'Ne':\\n return 8e-9 # cm^2/TW\\n elif name == 'He':\\n return 3e-9\\n elif name == 'Ar':\\n return 1e-7\\n else:\\n raise ValueError('Unknown substance %s' % name)\",\n \"def front_column_model_p_gain():\",\n \"def get_specific_heat() -> float:\\n return 1006.0\",\n \"def giniIndex(p_m1):\\n G = p_m1*(1-p_m1)*2 \\n return G\",\n \"def referenceIllum(temp, wavelength):\\n ct=temp\\n if ct <= 0:\\n return 0\\n if ct < 4000:\\n return planckian(ct, wavelength)\\n if ct < 5000:\\n p=planckian(ct, wavelength)\\n d=dseries(ct, wavelength)\\n return p+(d-p)*(ct-4000)/1500.0\\n return dseries(ct, wavelength)\",\n \"def get_ptf10iuv(colorplt = False):\\n z = 0.0251485\\n ebv = 0.0371 # SFD\\n D = cosmo.luminosity_distance([z])[0].value * 1e+6 # in pc\\n dis_mod = 5*np.log10(D / 10)\\n print (\\\"adopt g band t_max estimated by myself\\\")\\n t_max = 55357.387 \\n tb = pd.read_csv('../data/otherSN/Kasliwal2012/PTF10iuv', sep='\\\\t')\\n tb = tb.drop(columns=[\\\"Unnamed: 4\\\"])\\n tb = tb.rename(columns={'Filter' : 'filter',\\n 'MJD': 'mjd'})\\n tb = tb[~np.array([x[0]=='>' for x in tb['Mag'].values])]\\n tb['mag'] = np.array([float(x.split(\\\" +or-\\\")[0]) for x in tb['Mag'].values])\\n tb['emag'] = np.array([float(x.split(\\\" +or-\\\")[1]) for x in tb['Mag'].values])\\n tb = tb.drop(columns=[\\\"Mag\\\"])\\n \\n ixg = tb['filter'].values == \\\"g\\\"\\n ixr = tb['filter'].values == \\\"r\\\"\\n ixi = tb['filter'].values == \\\"i\\\"\\n ixz = tb['filter'].values == \\\"z\\\"\\n ixB = tb['filter'].values == \\\"B\\\"\\n tb['wave'] = np.zeros(len(tb))\\n tb['wave'].values[ixB] = 4359\\n tb['wave'].values[ixg] = 4814\\n tb['wave'].values[ixr] = 6422\\n tb['wave'].values[ixi] = 7883\\n tb['wave'].values[ixz] = 9670\\n \\n tb['mag0'] = tb['mag'] - extinction.ccm89(tb['wave'].values, 3.1*ebv, 3.1)\\n tb['mag0_abs'] = tb['mag0'] - dis_mod\\n tb['tmax_rf'] = (tb['mjd'] - t_max) / (1+z)\\n tb = tb.sort_values(by = \\\"mjd\\\")\\n if colorplt==False:\\n return tb\\n \\n else:\\n tb = add_datecol(tb)\\n ix = np.in1d(tb[\\\"filter\\\"].values, np.array(['g', 'r', 'i']))\\n tb = tb[ix]\\n tb = tb[tb.mjd > 55352.5]\\n tb = tb[tb.mjd < 55593.5]\\n \\n dates = get_date_span(tb)\\n datesave = []\\n for i in range(len(dates)):\\n x = dates[i]\\n ix = tb[\\\"date\\\"].values == x\\n tbsub = tb[ix]\\n if len(tbsub)!=0:\\n flts = tbsub['filter'].values\\n if \\\"r\\\" in flts and np.sum(np.unique(flts))!=1:\\n datesave.append(x)\\n datesave = np.array(datesave)\\n \\n mcolor = []\\n mcolor_unc = []\\n mjds = []\\n colorname = []\\n for i in range(len(datesave)):\\n x = datesave[i]\\n ix = tb[\\\"date\\\"].values == x\\n tbsub = tb[ix]\\n gtb = tbsub[tbsub[\\\"filter\\\"].values==\\\"g\\\"]\\n rtb = tbsub[tbsub[\\\"filter\\\"].values==\\\"r\\\"]\\n itb = tbsub[tbsub[\\\"filter\\\"].values==\\\"i\\\"]\\n if len(gtb)!=0:\\n gmjds = gtb[\\\"mjd\\\"].values\\n gmags = gtb[\\\"mag0\\\"].values\\n gemags = gtb[\\\"emag\\\"].values\\n gwtgs = 1/gemags**2\\n gmag = np.sum(gmags * gwtgs) / np.sum(gwtgs)\\n gmjd = np.sum(gmjds * gwtgs) / np.sum(gwtgs)\\n gemag = 1/ np.sqrt(np.sum(gwtgs))\\n if len(rtb)!=0:\\n rmjds = rtb[\\\"mjd\\\"].values\\n rmags = rtb[\\\"mag0\\\"].values\\n remags = rtb[\\\"emag\\\"].values\\n rwtgs = 1/remags**2\\n rmag = np.sum(rmags * rwtgs) / np.sum(rwtgs)\\n rmjd = np.sum(rmjds * rwtgs) / np.sum(rwtgs)\\n remag = 1/ np.sqrt(np.sum(rwtgs))\\n if len(itb)!=0:\\n imjds = itb[\\\"mjd\\\"].values\\n imags = itb[\\\"mag0\\\"].values\\n iemags = itb[\\\"emag\\\"].values\\n iwtgs = 1/iemags**2\\n imag = np.sum(imags * iwtgs) / np.sum(iwtgs)\\n imjd = np.sum(imjds * iwtgs) / np.sum(iwtgs)\\n iemag = 1/ np.sqrt(np.sum(iwtgs))\\n if len(gtb)!=0 and len(rtb)!=0:\\n mcolor.append(gmag - rmag)\\n mjds.append( 0.5 * (gmjd + rmjd) )\\n mcolor_unc.append( np.sqrt(gemag**2 + remag**2) )\\n colorname.append(\\\"gmr\\\")\\n if len(rtb)!=0 and len(itb)!=0:\\n mcolor.append(rmag - imag)\\n mjds.append( 0.5 * (rmjd + imjd) )\\n mcolor_unc.append( np.sqrt(remag**2 + iemag**2) )\\n colorname.append(\\\"rmi\\\")\\n \\n ctb = Table(data = [mjds, mcolor, mcolor_unc, colorname],\\n names = [\\\"mjd\\\", \\\"c\\\", \\\"ec\\\", \\\"cname\\\"])\\n \\n ctb['tmax_rf'] = (ctb['mjd'] - t_max) / (1+z)\\n ctb = ctb.to_pandas()\\n return ctb\",\n \"def calibration(self) -> int:\",\n \"def comp_amplification_index(self):\\n \\n self.grid_tuning_in=self.inputs.grid_tuning_in\\n self.grid_tuning_out=gl.comp_grid_tuning_index(self.L,self.nx,(self.r[0:self.n_e**2,:]).T) \\n self.grid_tuning_out_inhib=gl.comp_grid_tuning_index(self.L,self.nx,(self.r[self.n_e**2:,:]).T)\\n\\n self.grid_amp_index=self.grid_tuning_out/self.grid_tuning_in\",\n \"def test_WIMP_cut_region_on_true_data(bolo_name, mass, analysis):\\n\\t\\n\\n\\t#Load 2D PDF\\n\\tfWIMP2D, f = PyRPl.open_ROOT_object(\\\"./ROOT_files/WIMP_PDF2D_\\\" + analysis + \\\".root\\\", \\\"WIMP_\\\" + mass + \\\"_GeV\\\")\\n\\n\\t#Load cut value on PDF for 95% WIMP box\\n\\tcut_val_90, cut_val_99 = 0,0\\n\\twith open (\\\"./Text_files/WIMP_PDF_90_and_99_cut_value_\\\" + analysis + \\\".txt\\\", \\\"r\\\") as fcut:\\n\\t\\tstuff = [elem.rstrip().split(\\\",\\\") for elem in fcut.readlines()]\\n\\t\\tfor elem in stuff:\\n\\t\\t\\tmass_val = elem[0]\\n\\t\\t\\tif int(mass)==int(mass_val):\\n\\t\\t\\t\\tcut_val_90 = float(elem[1])\\n\\t\\t\\t\\tcut_val_99 = float(elem[2])\\n\\t\\n\\n\\tdata_path = \\\"/home/irfulx204/mnt/tmain/Desktop/Run308_Analyse_ERA/Fond_ERA_merged/\\\"\\n\\tfilou = TFile(data_path + bolo_name + \\\"_\\\" + analysis + \\\"_fond.root\\\", \\\"read\\\")\\n\\ttree = filou.Get(\\\"data\\\")\\n\\tnum_pass_cut =0\\n\\n\\thpass = TH2F(\\\"hpass\\\", \\\"hpass\\\", 100, 0, 15, 100, 0, 15)\\n\\n\\t# #T Check that the events are found where expected\\n\\t# arr1 = np.random.uniform(0,15,size=(200000,2))\\n\\t# for i in range(arr1.shape[0]):\\n\\t# \\tPDF_val = fWIMP2D.Eval(arr1[i][0], arr1[i][1])\\n\\t# \\tif (cut_val_99<PDF_val<cut_val_90):\\n\\t# \\t# if (cut_val_99<PDF_val<cut_val_90):\\n\\t# \\t\\tnum_pass_cut+=1\\n\\t# \\t\\thpass.Fill(arr1[i][0], arr1[i][1])\\t\\t\\n\\n\\t# hpass.Draw()\\n\\t# raw_input()\\n\\n\\tfor k in range(tree.GetEntries()):\\n\\t\\ttree.GetEntry(k)\\n\\t\\tER=(1+8./3)*0.5*(tree.EC1+tree.EC2)-0.33*(1.5*tree.EIA+4*tree.EIB+1.5*tree.EIC+4*tree.EID)\\n\\t\\tPDF_val = fWIMP2D.Eval(ER, 0.5*(tree.EIB+tree.EID))\\n\\t\\tif (cut_val_99<PDF_val<cut_val_90 and 0.5*(tree.EIB+tree.EID)>0.7):\\n\\t\\t# if (cut_val_99<PDF_val<cut_val_90):\\n\\t\\t\\tnum_pass_cut+=1\\n\\t\\t\\thpass.Fill(0.5*(tree.EC1+tree.EC2), 0.5*(tree.EIB+tree.EID))\\n\\n\\tprint num_pass_cut\\n\\thpass.Draw()\\n\\traw_input()\",\n \"def mamajek08_logRpHK_edge():\\n return -4.355226174791392\",\n \"def search_diversification(self):\\n self.stage = 2\\n # Find index of least explored region,\\n # express in binary\\n b = bin(np.argmin(self.LTM))[2:]\\n # Pad binary number\\n if len(b) < self.x0.shape[0]:\\n pad = self.x0.shape[0] - len(b)\\n b = ''.join(['0' for i in range(pad)]) + b\\n # Store digits in column vector\\n d = len(b)\\n b = np.array(list(b), dtype='int').reshape((d,1))\\n # Generate random positive vector\\n base = self.r_uni(low=0, high=2, size=(d,1))\\n # Transform vector into appropriate region\\n return base * (b * 2 - 1)\",\n \"def refractive_index_glass(wavelength, type='soda-lime-low-iron'):\\n\\n if type.lower() == 'soda-lime-low-iron':\\n wavelength = wavelength / 1000\\n n = 1.5130 - 0.003169 * wavelength ** 2 + 0.003962 * wavelength ** -2 + 0 * 1j\\n\\n # n[wavelength < 0.3] = n[wavelength < 0.3] + 1j*0\\n elif type.upper() == 'BK7':\\n wavelength = wavelength / 1000\\n n = np.sqrt(1 + \\\\\\n (1.03961212 * wavelength ** 2) / (\\n wavelength ** 2 - 0.00600069867) + \\\\\\n (0.231792344 * wavelength ** 2) / (\\n wavelength ** 2 - 0.0200179144) + \\\\\\n (1.01046945 * wavelength ** 2) / (\\n wavelength ** 2 - 103.560653)\\n )\\n\\n return n\",\n \"def molar_mass_dry_air():\\n return 28.9647\",\n \"def sky_orbits(test=True):\\n \\n t = Table.read('/home/ana/data/baumgardt_positions.fits')\\n \\n ind_disterr = ~np.isfinite(t['e_Rsun'])\\n t['e_Rsun'][ind_disterr] = 0.1 * t['Rsun'][ind_disterr]\\n e_max = np.nanmax(t['e_Rsun'][~ind_disterr])\\n ind_cap = t['e_Rsun']>e_max\\n t['e_Rsun'][ind_cap] = e_max\\n \\n clusters = ['NGC 3201', 'NGC 4590', 'NGC 5824', 'NGC 5272', 'NGC 5139', 'NGC 5024']\\n #clusters = ['NGC 5824', 'NGC 5024']\\n N = len(clusters)\\n \\n match = dict()\\n match['NGC 3201'] = dict(streams=['gjoll'], direction=[-1], nstep=[35], gc_label='NGC\\\\n3201', gcra_off=0*u.deg, gcdec_off=-13*u.deg, gcl_off=0*u.deg, gcb_off=-13*u.deg, stream_label=['$Gj\\\\\\\\\\\\\\\" oll$'], stream_ra=[-156*u.deg], stream_dec=[-4.5*u.deg], eq_angle=[-45*u.deg], stream_l=[-148*u.deg], stream_b=[-33*u.deg], gal_angle=[22*u.deg])\\n \\n match['NGC 4590'] = dict(streams=['fjorm'], direction=[1], nstep=[100], gc_label='NGC\\\\n4590', gcra_off=-15*u.deg, gcdec_off=0*u.deg, gcl_off=-13*u.deg, gcb_off=-10*u.deg, stream_label=['$Fj\\\\\\\\\\\\\\\" orm$'], stream_ra=[-22*u.deg], stream_dec=[66*u.deg], eq_angle=[35*u.deg], stream_l=[110*u.deg], stream_b=[50*u.deg], gal_angle=[-50*u.deg])\\n \\n match['NGC 5024'] = dict(streams=['sylgr', 'ravi'], direction=[-1, 1], nstep=[300,500], gc_label='NGC\\\\n5024', gcra_off=-15*u.deg, gcdec_off=0*u.deg, gcl_off=10*u.deg, gcb_off=-20*u.deg, stream_label=['Sylgr', 'Ravi'], stream_ra=[-70*u.deg, 83*u.deg], stream_dec=[2*u.deg, -47*u.deg], eq_angle=[25*u.deg, 65*u.deg], stream_l=[-110*u.deg, -18.5*u.deg], stream_b=[62*u.deg, -47*u.deg], gal_angle=[30*u.deg, -10*u.deg])\\n \\n match['NGC 5139'] = dict(streams=['fimbulthul'], direction=[-1], nstep=[70], gc_label='NGC\\\\n5139', gcra_off=-5*u.deg, gcdec_off=-15*u.deg, gcl_off=0*u.deg, gcb_off=-12*u.deg, stream_label=['Fimbulthul'], stream_ra=[-20*u.deg], stream_dec=[-15*u.deg], eq_angle=[0*u.deg], stream_l=[-20*u.deg], stream_b=[45*u.deg], gal_angle=[0*u.deg])\\n \\n match['NGC 5272'] = dict(streams=['svol'], direction=[1], nstep=[70], gc_label='NGC\\\\n5272', gcra_off=-15*u.deg, gcdec_off=10*u.deg, gcl_off=-23*u.deg, gcb_off=-17*u.deg, stream_label=['$Sv\\\\\\\\\\\\\\\" ol$'], stream_ra=[-2*u.deg], stream_dec=[34*u.deg], eq_angle=[-10*u.deg], stream_l=[55*u.deg], stream_b=[55*u.deg], gal_angle=[-65*u.deg])\\n \\n match['NGC 5824'] = dict(streams=['triangulum', 'turbio'], direction=[1,1], nstep=[700,1], gc_label='NGC\\\\n5824', gcra_off=15*u.deg, gcdec_off=-5*u.deg, gcl_off=15*u.deg, gcb_off=-5*u.deg, stream_label=['Triangulum', 'Turbio'], stream_ra=[152*u.deg, 130*u.deg], stream_dec=[32*u.deg, -51*u.deg], eq_angle=[-48*u.deg, 30*u.deg], stream_l=[120*u.deg, -82*u.deg], stream_b=[-31*u.deg, -57*u.deg], gal_angle=[70*u.deg, 105*u.deg])\\n \\n dt = 0.5*u.Myr\\n wangle = 180*u.deg\\n ra_off = 120*u.deg\\n l_off = 0*u.deg\\n \\n colors = [mpl.cm.plasma(0.95*x/N) for x in range(N)]\\n \\n np.random.seed(27529)\\n if test:\\n Nsample = 1\\n else:\\n Nsample = 100\\n \\n plt.close()\\n fig = plt.figure(figsize=(12,12))\\n \\n ax0 = fig.add_subplot(211, projection='mollweide')\\n ax1 = fig.add_subplot(212, projection='mollweide')\\n ax = [ax0, ax1]\\n \\n for i in range(N):\\n #ind = t['Name']== clusters[i]\\n ind = t['Name']==clusters[i]\\n t_ = t[ind]\\n \\n c = coord.SkyCoord(ra=t_['RAJ2000'], dec=t_['DEJ2000'], distance=t_['Rsun'], pm_ra_cosdec=t_['pmRA_'], pm_dec=t_['pmDE'], radial_velocity=t_['RV'], frame='icrs')\\n cgal = c.transform_to(coord.Galactic)\\n #w0 = gd.PhaseSpacePosition(c.transform_to(gc_frame).cartesian)[0]\\n \\n color = colors[i]\\n alpha_text = 0.8\\n \\n plt.sca(ax[0])\\n plt.plot((c.ra + ra_off).wrap_at(wangle).rad, c.dec.rad, '+', color=color, mew=3, ms=15, label=t_['Name'][0])\\n plt.text((c.ra + ra_off + match[clusters[i]]['gcra_off']).wrap_at(wangle).rad, (c.dec + match[clusters[i]]['gcdec_off']).rad, match[clusters[i]]['gc_label'], fontsize='small', ha='center', va='center', alpha=alpha_text)\\n \\n plt.sca(ax[1])\\n plt.plot((cgal.l + l_off).wrap_at(wangle).rad, cgal.b.rad, '+', color=color, mew=3, ms=15, label=t_['Name'][0])\\n plt.text((cgal.l + l_off + match[clusters[i]]['gcl_off']).wrap_at(wangle).rad, (cgal.b + match[clusters[i]]['gcb_off']).rad, match[clusters[i]]['gc_label'], fontsize='small', ha='center', va='center', alpha=alpha_text)\\n \\n\\n for j in range(len(match[clusters[i]]['direction'])):\\n # sample gc positional uncertainties\\n for k in range(-1, Nsample):\\n if k==-1:\\n c = coord.SkyCoord(ra=t_['RAJ2000'], dec=t_['DEJ2000'], distance=t_['Rsun'], pm_ra_cosdec=t_['pmRA_'], pm_dec=t_['pmDE'], radial_velocity=t_['RV'], frame='icrs')\\n w0 = gd.PhaseSpacePosition(c.transform_to(gc_frame).cartesian)[0]\\n \\n lw = 1.5\\n alpha = 1\\n else:\\n c = coord.SkyCoord(ra=t_['RAJ2000'], dec=t_['DEJ2000'], distance=t_['Rsun'] + np.random.randn()*t_['e_Rsun'], pm_ra_cosdec=t_['pmRA_'] + np.random.randn()*t_['e_pmRA_'], pm_dec=t_['pmDE'] + np.random.randn()*t_['e_pmDE'], radial_velocity=t_['RV'] + np.random.randn()*t_['e_RV'], frame='icrs')\\n w0 = gd.PhaseSpacePosition(c.transform_to(gc_frame).cartesian)[0]\\n \\n lw = 1\\n alpha = 0.1\\n \\n orbit = ham.integrate_orbit(w0, dt=dt*match[clusters[i]]['direction'][j], n_steps=match[clusters[i]]['nstep'][j])\\n orbit_eq = orbit.to_coord_frame(coord.ICRS, galactocentric_frame=gc_frame)\\n orbit_gal = orbit.to_coord_frame(coord.Galactic, galactocentric_frame=gc_frame)\\n \\n \\n plt.sca(ax[0])\\n dra = (orbit_eq.ra+ra_off).wrap_at(wangle)[1:] - (orbit_eq.ra+ra_off).wrap_at(wangle)[:-1]\\n if np.any(np.abs(dra)>180*u.deg):\\n pos_break = dra>180*u.deg\\n ind_break = np.argmax(pos_break)\\n ipad = 1\\n plt.plot((orbit_eq.ra+ra_off).wrap_at(wangle).rad[:ind_break-ipad], orbit_eq.dec.rad[:ind_break-ipad], '-', color=color, lw=lw, label='', alpha=alpha)\\n plt.plot((orbit_eq.ra+ra_off).wrap_at(wangle).rad[ind_break+ipad:], orbit_eq.dec.rad[ind_break+ipad:], '-', color=color, lw=lw, label='', alpha=alpha)\\n else:\\n plt.plot((orbit_eq.ra+ra_off).wrap_at(wangle).rad, orbit_eq.dec.rad, '-', color=color, lw=lw, label='', alpha=alpha)\\n \\n plt.sca(ax[1])\\n dl = orbit_gal.l.wrap_at(wangle)[1:] - orbit_gal.l.wrap_at(wangle)[:-1]\\n if np.any(np.abs(dl)>180*u.deg):\\n pos_break = dl>180*u.deg\\n ind_break = np.argmax(pos_break)\\n ipad = 1\\n plt.plot((orbit_gal.l+l_off).wrap_at(wangle).rad[:ind_break-ipad], orbit_gal.b.rad[:ind_break-ipad], '-', color=color, lw=lw, label='', alpha=alpha)\\n plt.plot((orbit_gal.l+l_off).wrap_at(wangle).rad[ind_break+ipad:], orbit_gal.b.rad[ind_break+ipad:], '-', color=color, lw=lw, label='', alpha=alpha)\\n else:\\n plt.plot((orbit_gal.l+l_off).wrap_at(wangle).rad, orbit_gal.b.rad, '-', color=color, lw=lw, label='', alpha=alpha)\\n \\n # add streams\\n pkl = pickle.load(open('../data/streams/data_{:s}.pkl'.format(match[clusters[i]]['streams'][j]), 'rb'))\\n cs = coord.SkyCoord(ra=pkl['dec'][0], dec=pkl['dec'][1], frame='icrs')\\n cs_gal = cs.transform_to(coord.Galactic)\\n \\n plt.sca(ax[0])\\n plt.plot((cs.ra+ra_off).wrap_at(wangle).rad, cs.dec.rad, 'o', color=color, ms=8, label=match[clusters[i]]['streams'][j])\\n plt.text(coord.Longitude(match[clusters[i]]['stream_ra'][j]).wrap_at(wangle).rad, coord.Latitude(match[clusters[i]]['stream_dec'][j]).rad, match[clusters[i]]['stream_label'][j], fontsize='small', alpha=alpha_text, rotation=match[clusters[i]]['eq_angle'][j].value, ha='center', va='center')\\n \\n plt.sca(ax[1])\\n plt.plot((cs_gal.l+l_off).wrap_at(wangle).rad, cs_gal.b.rad, 'o', color=color, ms=8, label=match[clusters[i]]['streams'][j])\\n plt.text(coord.Longitude(match[clusters[i]]['stream_l'][j]).wrap_at(wangle).rad, coord.Latitude(match[clusters[i]]['stream_b'][j]).rad, match[clusters[i]]['stream_label'][j], fontsize='small', alpha=alpha_text, rotation=match[clusters[i]]['gal_angle'][j].value, ha='center', va='center')\\n \\n \\n plt.sca(ax[0])\\n plt.grid(ls=':')\\n plt.xlabel('R.A. [deg]')\\n plt.ylabel('Dec [deg]')\\n\\n plt.gca().xaxis.set_ticklabels([])\\n \\n xloc = coord.Longitude(np.arange(-150,180,30)*u.deg)\\n xloc = np.delete(xloc, [3])\\n yloc = coord.Latitude(5*u.deg)\\n Nx = len(xloc)\\n \\n for i in range(Nx):\\n plt.text(xloc[i].wrap_at(wangle).rad, yloc.rad, '{:.0f}$\\\\degree$'.format((xloc[i]-ra_off).wrap_at(wangle).degree), alpha=0.6, ha='center', va='center')\\n \\n \\n plt.sca(ax[1])\\n plt.grid(ls=':')\\n plt.xlabel('Galactic longitude [deg]')\\n plt.ylabel('Galactic latitude [deg]')\\n \\n plt.gca().xaxis.set_ticklabels([])\\n \\n xloc = coord.Longitude(np.arange(-150,180,30)*u.deg)\\n xloc = np.delete(xloc, [2,3])\\n yloc = coord.Latitude(5*u.deg)\\n Nx = len(xloc)\\n \\n for i in range(Nx):\\n plt.text(xloc[i].wrap_at(wangle).rad, yloc.rad, '{:.0f}$\\\\degree$'.format((xloc[i]+l_off).wrap_at(wangle).degree), alpha=0.6, ha='center', va='center')\\n \\n \\n \\n plt.tight_layout(h_pad=2)\\n plt.savefig('../paper/sky_orbits.pdf')\",\n \"def ggpl_house():\\n\\n\\t# .lines ogni riga ha due coppie di x/y che costituiscono un segmento\\n\\tverts = []\\n\\tcells = []\\n\\ti = 0\\n\\treader = csv.reader(open(\\\"lines/muri_esterni.lines\\\", 'rb'), delimiter=',') \\n\\tfor row in reader:\\n\\t\\tverts.append([float(row[0]), float(row[1])])\\n\\t\\tverts.append([float(row[2]), float(row[3])])\\n\\t\\ti+=2\\n\\t\\tcells.append([i-1,i])\\n\\n\\texternalWalls = MKPOL([verts,cells,None])\\n\\tfloor = SOLIDIFY(externalWalls)\\n\\tfloor = S([1,2,3])([.04,.04,.04])(floor)\\n\\texternalWalls = S([1,2,3])([.04,.04,.04])(externalWalls)\\n\\texternalWalls = OFFSET([.2,.2,4])(externalWalls)\\n\\theightWalls = SIZE([3])(externalWalls)[0]\\n\\tthicknessWalls = SIZE([2])(externalWalls)[0]\\n\\n\\tverts = []\\n\\tcells = []\\n\\ti = 0\\n\\treader = csv.reader(open(\\\"lines/muri_interni.lines\\\", 'rb'), delimiter=',') \\n\\tfor row in reader:\\n\\t\\tverts.append([float(row[0]), float(row[1])])\\n\\t\\tverts.append([float(row[2]), float(row[3])])\\n\\t\\ti+=2\\n\\t\\tcells.append([i-1,i])\\n\\n\\tinternalWalls = MKPOL([verts,cells,None])\\n\\tinternalWalls = S([1,2,3])([.04,.04,.04])(internalWalls)\\n\\tinternalWalls = OFFSET([.2,.2,4])(internalWalls)\\n\\twalls = STRUCT([externalWalls, internalWalls])\\n\\n\\tverts = []\\n\\tcells = []\\n\\ti = 0\\n\\treader = csv.reader(open(\\\"lines/porte.lines\\\", 'rb'), delimiter=',') \\n\\tfor row in reader:\\n\\t\\tverts.append([float(row[0]), float(row[1])])\\n\\t\\tverts.append([float(row[2]), float(row[3])])\\n\\t\\ti+=2\\n\\t\\tcells.append([i-1,i])\\n\\n\\tdoors = MKPOL([verts,cells,None])\\n\\tdoors = SOLIDIFY(doors)\\n\\tdoors = S([1,2,3])([.04,.04,.04])(doors)\\n\\tdoors = OFFSET([.2,.2,3])(doors)\\n\\twalls = DIFFERENCE([walls, doors])\\n\\n\\n\\tverts = []\\n\\tcells = []\\n\\ti = 0\\n\\treader = csv.reader(open(\\\"lines/finestre.lines\\\", 'rb'), delimiter=',') \\n\\tfor row in reader:\\n\\t\\tverts.append([float(row[0]), float(row[1])])\\n\\t\\tverts.append([float(row[2]), float(row[3])])\\n\\t\\ti+=2\\n\\t\\tcells.append([i-1,i])\\n\\n\\twindows = MKPOL([verts,cells,None])\\n\\twindows = SOLIDIFY(windows)\\n\\twindows = S([1,2,3])([.04,.04,.04])(windows)\\n\\twindows = OFFSET([.2,.2,2])(windows)\\n\\theightWindows = SIZE([3])(windows)[0]\\n\\twindows = T(3)((heightWalls-heightWindows)/2.)(windows)\\n\\twalls = DIFFERENCE([walls, windows])\\n\\n\\tfloor = TEXTURE(\\\"texture/floor.jpg\\\")(floor)\\n\\twalls = TEXTURE(\\\"texture/wall.jpg\\\")(walls)\\n\\thome = STRUCT([floor, walls])\\n\\treturn home\",\n \"def top_of_climb_index(self):\\n return self.altitudes.argmax()\",\n \"def split_necessity(self):\\n return max(self._color_var_rel) * self.n_pix\\n # return reduce(int.__mul__, (l-u for u,l in self.bounds)) * self.n_pix\",\n \"def get_iPTF14gqr(colorplt=False):\\n z = 0.063\\n # ebv = 0.082\\n D = cosmo.luminosity_distance([z])[0].value * 1e+6 # in pc\\n dis_mod = 5*np.log10(D / 10)\\n t_exp = 56943.74 # \\n t_max = 56950.26 # g band max light + 3\\n \\n tb = Table(fits.open('../data/otherSN/De2018/tables1.fit')[1].data)\\n tb.rename_column('MJD' , 'mjd')\\n tb['texp_rf'] = (tb['mjd'] - t_exp) / (1+z)\\n tb['tmax_rf'] = (tb['mjd'] - t_max) / (1+z)\\n # tb = tb[tb[\\\"Filt\\\"]==\\\"g \\\"]\\n tb = tb[~np.isnan(tb['e_mag'])]\\n tb.rename_column('Filt' , 'filter')\\n tb.rename_column('e_mag' , 'emag')\\n tb.rename_column('mag' , 'mag0')\\n \\n ixg = tb['filter']==\\\"g \\\"\\n ixB = tb['filter']==\\\"B \\\"\\n ixV = tb['filter']==\\\"V \\\"\\n ixr = tb['filter']==\\\"r \\\"\\n ixi = tb['filter']==\\\"i \\\"\\n ixUVW1 = tb['filter']==\\\"UVW1\\\"\\n ixUVW2 = tb['filter']==\\\"UVW2\\\"\\n \\n tb['wave'] = np.zeros(len(tb))\\n tb['wave'][ixUVW2] = 2079\\n tb['wave'][ixUVW1] = 2614\\n tb['wave'][ixB] = 4359\\n tb['wave'][ixg] = 4814\\n tb['wave'][ixV] = 5430\\n tb['wave'][ixr] = 6422\\n tb['wave'][ixi] = 7883\\n \\n tb['mag0_abs'] = tb['mag0'] - dis_mod\\n \\n tb = tb.to_pandas()\\n tb[\\\"texp_rf\\\"] = tb[\\\"Phase\\\"]\\n tb = tb.drop(columns=[\\\"recno\\\", \\\"Phase\\\", \\\"l_mag\\\"])\\n \\\"\\\"\\\"\\n ix = np.any([tb['Tel'].values==\\\"P60 \\\",\\n tb[\\\"filter\\\"].values=='g '], axis=0)\\n tb = tb[ix]\\n \\\"\\\"\\\"\\n tb = add_datecol(tb)\\n tb = add_physcol(tb)\\n tt = tb[\\\"tmax_rf\\\"].values\\n epochs = [\\\" \\\" for x in range(len(tt))]\\n epochs = np.array(epochs)\\n \\\"\\\"\\\"\\n ix = (tt>-5.6)&(tt<-5.55)\\n epochs[ix] = \\\"epoch 01\\\"\\n \\\"\\\"\\\"\\n ix = (tt>-5.55)&(tt<-5.50)\\n epochs[ix] = \\\"epoch 02\\\"\\n \\n ix = (tt>-5.50)&(tt<-5.45)\\n epochs[ix] = \\\"epoch 03\\\"\\n \\n ix = (tt>-5.2)&(tt<-5.0)\\n epochs[ix] = \\\"epoch 04\\\"\\n ix = (tt>-5.0)&(tt<-4.7)\\n epochs[ix] = \\\"epoch 05\\\"\\n \\n ix = (tt>-4.7)&(tt<-4.5)\\n epochs[ix] = \\\"epoch 06\\\"\\n ix = (tt>-4.5)&(tt<-3.5)\\n epochs[ix] = \\\"epoch 07\\\"\\n ix = (tt>-3.5)&(tt<-2.5)\\n epochs[ix] = \\\"epoch 08\\\"\\n ix = (tt>-1.5)&(tt<-1)\\n epochs[ix] = \\\"epoch 09\\\"\\n ix = (tt>-1)&(tt<-0.82)\\n epochs[ix] = \\\"epoch 10\\\"\\n ix = (tt>-0.82)&(tt<-0.6)\\n epochs[ix] = \\\"epoch 11\\\"\\n ix = (tt>-0.5)&(tt<0.5)\\n epochs[ix] = \\\"epoch 12\\\"\\n ix = (tt>0.5)&(tt<1.5)\\n epochs[ix] = \\\"epoch 13\\\"\\n ix = (tt>1.5)&(tt<2.5)\\n epochs[ix] = \\\"epoch 14\\\"\\n ix = (tt>3.5)&(tt<4.5)\\n epochs[ix] = \\\"epoch 15\\\"\\n ix = (tt>4.5)&(tt<5)\\n epochs[ix] = \\\"epoch 16\\\"\\n ix = (tt>5)&(tt<5.6)\\n epochs[ix] = \\\"epoch 17\\\"\\n ix = (tt>5.6)&(tt<5.8)\\n epochs[ix] = \\\"epoch 18\\\"\\n ix = (tt>6)&(tt<7)\\n epochs[ix] = \\\"epoch 19\\\"\\n ix = (tt>7)&(tt<8)\\n epochs[ix] = \\\"epoch 20\\\"\\n ix = (tt>8)&(tt<9)\\n epochs[ix] = \\\"epoch 21\\\"\\n tb[\\\"epoch\\\"] = epochs\\n\\n if colorplt==False:\\n return tb\\n else:\\n tb = add_datecol(tb)\\n ix = np.in1d(tb[\\\"filter\\\"].values, np.array(['g ', 'r ', 'i ']))\\n tb = tb[ix]\\n\\n dates = get_date_span(tb)\\n datesave = []\\n for i in range(len(dates)):\\n x = dates[i]\\n ix = tb[\\\"date\\\"].values == x\\n tbsub = tb[ix]\\n if len(tbsub)!=0:\\n flts = tbsub['filter'].values\\n if \\\"r \\\" in flts and np.sum(np.unique(flts))!=1:\\n datesave.append(x)\\n datesave = np.array(datesave)\\n \\n mcolor = []\\n mcolor_unc = []\\n mjds = []\\n colorname = []\\n for i in range(len(datesave)):\\n x = datesave[i]\\n ix = tb[\\\"date\\\"].values == x\\n tbsub = tb[ix]\\n gtb = tbsub[tbsub[\\\"filter\\\"].values==\\\"g \\\"]\\n rtb = tbsub[tbsub[\\\"filter\\\"].values==\\\"r \\\"]\\n itb = tbsub[tbsub[\\\"filter\\\"].values==\\\"i \\\"]\\n if len(gtb)!=0:\\n gmjds = gtb[\\\"mjd\\\"].values\\n gmags = gtb[\\\"mag0\\\"].values\\n gemags = gtb[\\\"emag\\\"].values\\n gwtgs = 1/gemags**2\\n gmag = np.sum(gmags * gwtgs) / np.sum(gwtgs)\\n gmjd = np.sum(gmjds * gwtgs) / np.sum(gwtgs)\\n gemag = 1/ np.sqrt(np.sum(gwtgs))\\n if len(rtb)!=0:\\n rmjds = rtb[\\\"mjd\\\"].values\\n rmags = rtb[\\\"mag0\\\"].values\\n remags = rtb[\\\"emag\\\"].values\\n rwtgs = 1/remags**2\\n rmag = np.sum(rmags * rwtgs) / np.sum(rwtgs)\\n rmjd = np.sum(rmjds * rwtgs) / np.sum(rwtgs)\\n remag = 1/ np.sqrt(np.sum(rwtgs))\\n if len(itb)!=0:\\n imjds = itb[\\\"mjd\\\"].values\\n imags = itb[\\\"mag0\\\"].values\\n iemags = itb[\\\"emag\\\"].values\\n iwtgs = 1/iemags**2\\n imag = np.sum(imags * iwtgs) / np.sum(iwtgs)\\n imjd = np.sum(imjds * iwtgs) / np.sum(iwtgs)\\n iemag = 1/ np.sqrt(np.sum(iwtgs))\\n if len(gtb)!=0 and len(rtb)!=0:\\n mcolor.append(gmag - rmag)\\n mjds.append( 0.5 * (gmjd + rmjd) )\\n mcolor_unc.append( np.sqrt(gemag**2 + remag**2) )\\n colorname.append(\\\"gmr\\\")\\n if len(rtb)!=0 and len(itb)!=0:\\n mcolor.append(rmag - imag)\\n mjds.append( 0.5 * (rmjd + imjd) )\\n mcolor_unc.append( np.sqrt(remag**2 + iemag**2) )\\n colorname.append(\\\"rmi\\\")\\n \\n ctb = Table(data = [mjds, mcolor, mcolor_unc, colorname],\\n names = [\\\"mjd\\\", \\\"c\\\", \\\"ec\\\", \\\"cname\\\"])\\n \\n ctb['tmax_rf'] = (ctb['mjd'] - t_max) / (1+z)\\n ctb = ctb.to_pandas()\\n return ctb\",\n \"def narrowIncandPeakInfoLG(self):\\r\\n\\t\\tself.narrowIncandBaseline_LG = (np.mean(self.lowGainNarrowBandIncandData[0:10]))\\r\\n\\t\\t\\t\\t\\r\\n\\t\\traw_narrowIncand_max_LG = np.amax(self.lowGainNarrowBandIncandData)\\r\\n\\t\\tnarrowIncand_max_LG = raw_narrowIncand_max_LG - self.narrowIncandBaseline_LG\\t\\t\\r\\n\\t\\tnarrowIncand_max_index_LG = np.argmax(self.lowGainNarrowBandIncandData)\\r\\n\\t\\t\\r\\n\\t\\tself.narrowIncandMax_LG =narrowIncand_max_LG\\r\\n\\t\\tself.narrowIncandMaxPos_LG = narrowIncand_max_index_LG\",\n \"def get_index(self):\\n return (np.sqrt(self.dielectric))\",\n \"def get_absolute_color_index(A_g, bmag, rmag):\\n assert (A_g is not None) & (str(A_g) != \\\"nan\\\")\\n assert (bmag is not None) & (str(bmag) != \\\"nan\\\")\\n assert (rmag is not None) & (str(rmag) != \\\"nan\\\")\\n # E(Bp-Rp) = A_Bp-A_Rp = (Bp-Rp)_obs - E(Bp-Rp)\\n Ab_minus_Ar = get_excess_from_extiction(A_g)\\n bp_rp = bmag - rmag # color index\\n Bp_Rp = bp_rp - Ab_minus_Ar\\n return Bp_Rp\",\n \"def find_components_old(image,deltaPix,lens_rad_arcsec = 5.0,lens_rad_ratio = None, gal_rad_ratio = 0.1,min_size_arcsec=0.3,thresh=0.4, show_locations=False):\\n\\n # convert minimum component size in pixel units\\n min_size = int(min_size_arcsec / deltaPix)\\n \\n #Convert lens radius and central galaxy radius to pixels\\n if lens_rad_ratio == None:\\n lens_rad = int(lens_rad_arcsec / deltaPix)\\n else: lens_rad = int(len(image) * lens_rad_ratio)\\n gal_rad = int(len(image) * gal_rad_ratio)\\n \\n # downscale source image to data resolution (for speed + easier for converting to data units)\\n #down = image_util.re_size(image, factor=supersampling_factor_source)\\n \\n # apply laplacian of gaussian (LoG) filter to enhance maxima\\n filtered = - gaussian_laplace(deepcopy(image), sigma = min_size, mode='constant', cval=0.)\\n \\n# print(filtered.min(),filtered.max(),filtered.min() + thresh * np.abs(filtered.min()))\\n \\n \\n # assume all value below max*threshold can not be maxima, so put all to zero\\n# filtered[filtered < thresh*filtered.max()] = 0.\\n \\n# assume all value below min*threshold can not be maxima, so put all to zero\\n filtered[filtered < filtered.min() + thresh * np.abs(filtered.min())] = 0.\\n \\n if show_locations:\\n plt.figure(figsize = (8,8))\\n plt.subplot(1,2,1)\\n plt.imshow(image, origin='lower', norm=SymLogNorm(5))\\n plt.title('Image')\\n\\n plt.subplot(1,2,2)\\n plt.imshow(filtered, origin='lower', norm=SymLogNorm(5))\\n plt.title('Filtered Image')\\n plt.show()\\n \\n # find coordinates of local maxima\\n #print(int(0.5 * min_size))\\n max_idx_2d_small = peak_local_max(filtered, min_distance=0)\\n max_idx_2d_large = peak_local_max(filtered, min_distance=1)\\n \\n x_list_small, y_list_small = max_idx_2d_small[:, 1], max_idx_2d_small[:, 0]\\n x_list_large, y_list_large = max_idx_2d_large[:, 1], max_idx_2d_large[:, 0]\\n \\n im_center_x, im_center_y = len(image) / 2., len(image) / 2.\\n \\n R = np.sqrt((x_list_large - im_center_x)**2 + (y_list_large - im_center_y)**2)\\n new_center_x, new_center_y = x_list_large[R < gal_rad], y_list_large[R < gal_rad]\\n \\n if (len(new_center_x) > 1) and (len(x_list_large[R == R.min()]) ==1 ): \\n new_center_x, new_center_y = x_list_large[R == R.min()], y_list_large[R == R.min()]\\n elif (len(new_center_x) > 1) and (len(x_list_large[R == R.min()]) > 1 ): \\n new_center_x, new_center_y = im_center_x, im_center_y\\n elif len(new_center_x) == 0: \\n new_center_x, new_center_y = im_center_x, im_center_y\\n \\n \\n R_small = np.sqrt((x_list_small - new_center_x)**2 + (y_list_small - new_center_y)**2)\\n R_large = np.sqrt((x_list_large - new_center_x)**2 + (y_list_large - new_center_y)**2)\\n \\n x_sats, y_sats = x_list_small[R_small > lens_rad], y_list_small[R_small > lens_rad]\\n \\n # show maxima on image for debug\\n if show_locations:\\n fig = plt.figure(figsize=(4, 4))\\n #plt.imshow(image, origin='lower', cmap=cmap_flux, norm=LogNorm(1e-2))\\n plt.imshow(image, origin='lower', norm=SymLogNorm(5))\\n \\n for i in range(len(x_sats)):\\n plt.scatter([x_sats[i]], [y_sats[i]], c='red', s=60, marker='+')\\n# plt.annotate(i+1, (x_list[i], y_list[i]), color='black')\\n \\n# for i in range(len(x_mask)):\\n# plt.scatter([x_mask[i]], [y_mask[i]], c='red', s=100, marker='*')\\n# plt.annotate(i+1, (x_mask[i], y_mask[i]), color='red')\\n plt.scatter(new_center_x, new_center_y,c='red', s=100, marker='*')\\n \\n draw_lens_circle = Circle((new_center_x, new_center_y),lens_rad ,fill=False)\\n draw_gal_circle = Circle((new_center_x, new_center_y),gal_rad, fill = False)\\n plt.gcf().gca().add_artist(draw_lens_circle)\\n plt.gcf().gca().add_artist(draw_gal_circle)\\n plt.title('Detected Components')\\n plt.text(1, 1, \\\"detected components\\\", color='red')\\n fig.axes[0].get_xaxis().set_visible(True); fig.axes[0].get_yaxis().set_visible(True)\\n plt.show()\\n return (x_sats, y_sats), (new_center_x, new_center_y)\",\n \"def localize_red_clump(star_catalog,close_cat_idx,log):\\n\\n def select_within_range(mags, colours, mag_min, mag_max, col_min, col_max):\\n \\\"\\\"\\\"Function to identify the set of array indices with values\\n between the range indicated\\\"\\\"\\\"\\n\\n idx1 = np.where(colours >= col_min)[0]\\n idx2 = np.where(colours <= col_max)[0]\\n idx3 = np.where(mags >= mag_min)[0]\\n idx4 = np.where(mags <= mag_max)[0]\\n idx = set(idx1).intersection(set(idx2))\\n idx = idx.intersection(set(idx3))\\n idx = list(idx.intersection(set(idx4)))\\n\\n return idx\\n\\n RC = photometry_classes.Star()\\n\\n inst_i = star_catalog['cal_ref_mag_ip'][close_cat_idx]\\n inst_r = star_catalog['cal_ref_mag_rp'][close_cat_idx]\\n inst_g = star_catalog['cal_ref_mag_gp'][close_cat_idx]\\n cal_i = star_catalog['imag'][close_cat_idx]\\n cal_r = star_catalog['rmag'][close_cat_idx]\\n cal_g = star_catalog['gmag'][close_cat_idx]\\n inst_ri = inst_r - inst_i # Catalogue column order is red -> blue\\n inst_gi = inst_g - inst_i\\n inst_gr = inst_g - inst_r\\n cal_ri = cal_r - cal_i\\n cal_gi = cal_g - cal_i\\n cal_gr = cal_g - cal_r\\n\\n log.info('\\\\n')\\n log.info('Localizing the Red Clump')\\n log.info('Median (r-i), i: '+str(np.median(inst_ri))+', '+str(np.median(inst_i)))\\n log.info('Median (g-i), i: '+str(np.median(inst_gi))+', '+str(np.median(inst_i)))\\n log.info('Median (g-r), g: '+str(np.median(inst_gr))+', '+str(np.median(inst_g)))\\n\\n ri_min = 0.8\\n ri_max = 1.2\\n i_min = 15.5\\n i_max = 16.5\\n\\n r_min = 16.2\\n r_max = 17.5\\n\\n gi_min = 2.5\\n gi_max = 3.5\\n\\n gr_min = 1.5\\n gr_max = 2.2\\n g_min = 17.8\\n g_max = 19.5\\n\\n log.info('Selected Red Clump giants between:')\\n log.info('i = '+str(i_min)+' to '+str(i_max))\\n log.info('r = '+str(r_min)+' to '+str(r_max))\\n log.info('(r-i) = '+str(ri_min)+' to '+str(ri_max))\\n log.info('g = '+str(g_min)+' to '+str(g_max))\\n log.info('(g-r) = '+str(gr_min)+' to '+str(gr_max))\\n log.info('(g-i) = '+str(gi_min)+' to '+str(gi_max))\\n\\n idx = select_within_range(inst_i, inst_ri, i_min, i_max, ri_min, ri_max)\\n\\n (RC.ri, RC.sig_ri, RC.i, RC.sig_i) = calc_distribution_centroid_and_spread_2d(inst_ri[idx], inst_i[idx], use_iqr=True)\\n\\n idx = select_within_range(inst_r, inst_ri, r_min, r_max, ri_min, ri_max)\\n\\n (RC.r, RC.sig_r) = calc_distribution_centre_and_spread(inst_r[idx], use_iqr=True)\\n\\n idx = select_within_range(inst_g, inst_gr, g_min, g_max, gr_min, gr_max)\\n\\n (RC.gr, RC.sig_gr, RC.g, RC.sig_g) = calc_distribution_centroid_and_spread_2d(inst_gr[idx], inst_g[idx], use_iqr=True)\\n\\n idx = select_within_range(inst_g, inst_gi, g_min, g_max, gi_min, gi_max)\\n\\n (RC.gi, RC.sig_gi, RC.g, RC.sig_g) = calc_distribution_centroid_and_spread_2d(inst_gi[idx], inst_g[idx], use_iqr=True)\\n\\n log.info('\\\\n')\\n log.info('Centroid of Red Clump Stars at:')\\n log.info(RC.summary(show_mags=True))\\n log.info(RC.summary(show_mags=False,show_colours=True))\\n\\n RC.transform_to_JohnsonCousins()\\n\\n log.info(RC.summary(show_mags=False,johnsons=True))\\n\\n return RC\",\n \"def kohonen():\\n# plb.close('all')\\n \\n dim = 28*28\\n data_range = 255.0\\n \\n # load in data and labels \\n data = np.array(np.loadtxt('data.txt'))\\n labels = np.loadtxt('labels.txt')\\n\\n # select 4 digits \\n name = \\\"Stettler\\\"\\n targetdigits = name2digits(name) # assign the four digits that should be used\\n print(targetdigits) # output the digits that were selected\\n\\n # this selects all data vectors that corresponds to one of the four digits\\n data = data[np.logical_or.reduce([labels==x for x in targetdigits]),:]\\n \\n dy, dx = data.shape\\n \\n #set the size of the Kohonen map. In this case it will be 6 X 6\\n size_k = 6\\n \\n #set the width of the neighborhood via the width of the gaussian that\\n #describes it\\n sigma = 2.0\\n \\n #initialise the centers randomly\\n centers = np.random.rand(size_k**2, dim) * data_range\\n \\n #build a neighborhood matrix\\n neighbor = np.arange(size_k**2).reshape((size_k, size_k))\\n\\n #set the learning rate\\n eta = 0.9 # HERE YOU HAVE TO SET YOUR OWN LEARNING RATE\\n \\n #set the maximal iteration count\\n tmax = 5000 # this might or might not work; use your own convergence criterion\\n \\n #set the random order in which the datapoints should be presented\\n i_random = np.arange(tmax) % dy\\n np.random.shuffle(i_random)\\n \\n for t, i in enumerate(i_random):\\n som_step(centers, data[i,:],neighbor,eta,sigma)\\n\\n # for visualization, you can use this:\\n for i in range(size_k**2):\\n plb.subplot(size_k,size_k,i)\\n \\n plb.imshow(np.reshape(centers[i,:], [28, 28]),interpolation='bilinear')\\n plb.axis('off')\\n \\n # leave the window open at the end of the loop\\n plb.show()\\n plb.draw()\",\n \"def get_nc_BGrid_GFDL(grdfile, name='GFDL_CM2.1_North_Pacific', area='regional', \\\\\\n xrange=(60,175), yrange=(120, 190), ystart=235):\\n\\n nc = pyroms.io.Dataset(grdfile)\\n\\n lon_t = nc.variables['geolon_t'][:]\\n lat_t = nc.variables['geolat_t'][:]\\n lon_uv = nc.variables['geolon_c'][:]\\n lat_uv = nc.variables['geolat_c'][:]\\n\\n h = nc.variables['ht'][:]\\n\\n f = nc.variables['coriolis_param'][:]\\n\\n kmt = nc.variables['kmt'][:]\\n z_t = nc.variables['st_ocean'][:]\\n z_t_edges = nc.variables['st_edges_ocean'][:]\\n\\n kmu = nc.variables['kmu'][:]\\n z_uv = nc.variables['sw_ocean'][:]\\n z_uv_edges = nc.variables['sw_edges_ocean'][:]\\n\\n # compute mask at t-point\\n M_t, L_t = kmt.shape\\n N_t = z_t.shape[0]\\n mask_t = np.zeros((N_t, M_t, L_t))\\n for j in range(M_t):\\n for i in range(L_t):\\n try:\\n mask_t[0:int(kmt[j,i]), j,i] = 1\\n except:\\n mask_t[:, j,i] = 0\\n\\n # compute mask at uv-point\\n M_uv, L_uv = kmu.shape\\n N_uv = z_uv.shape[0]\\n mask_uv = np.zeros((N_uv, M_uv, L_uv))\\n for j in range(M_uv):\\n for i in range(L_uv):\\n try:\\n mask_uv[0:int(kmu[j,i]), j,i] = 1\\n except:\\n mask_uv[:, j,i] = 0\\n\\n if area == 'npolar':\\n #add two rows in the north and the south\\n lon_t = lon_t[np.r_[0,0,:np.size(lon_t,0),-1,-1]]\\n lon_t = lon_t[:,np.r_[0,:np.size(lon_t,1),-1]]\\n lon_t[:,0] = lon_t[:,1] - (lon_t[:,2]-lon_t[:,1])\\n lon_t[:,-1] = lon_t[:,-2] + (lon_t[:,-2]-lon_t[:,-3])\\n lat_t = lat_t[np.r_[0,0,:np.size(lat_t,0),-1,-1]]\\n lat_t = lat_t[:,np.r_[0,:np.size(lat_t,1),-1]]\\n lat_t[0,:] = -85\\n lat_t[1,:] = -80\\n lat_t[-2,:] = 90\\n lat_t[-1,:] = 91\\n lon_uv = lon_uv[np.r_[0,0,:np.size(lon_uv,0),-1,-1]]\\n lon_uv = lon_uv[:,np.r_[0,:np.size(lon_uv,1),-1]]\\n lon_uv[:,0] = lon_uv[:,1] - (lon_uv[:,2]-lon_t[:,1])\\n lon_uv[:,-1] = lon_uv[:,-2] + (lon_uv[:,-2]-lon_uv[:,-3])\\n lat_uv = lat_uv[np.r_[0,0,:np.size(lat_uv,0),-1,-1]]\\n lat_uv = lat_uv[:,np.r_[0,:np.size(lat_uv,1),-1]]\\n lat_uv[0,:] = -85\\n lat_uv[1,:] = -80\\n lat_uv[-2,:] = 90\\n lat_uv[-1,:] = 91\\n mask_t = mask_t[:,np.r_[0,0,:np.size(mask_t,1),-1,-1],:]\\n mask_t = mask_t[:,:,np.r_[0,:np.size(mask_t,2),-1]]\\n mask_t[:,:,0] = mask_t[:,:,-2]\\n mask_t[:,:,-1] = mask_t[:,:,1]\\n mask_uv = mask_uv[:,np.r_[0,0,:np.size(mask_uv,1),-1,-1],:]\\n mask_uv = mask_uv[:,:,np.r_[0,:np.size(mask_uv,2),-1]]\\n mask_uv[:,:,0] = mask_uv[:,:,-2]\\n mask_uv[:,:,-1] = mask_uv[:,:,1]\\n h = h[np.r_[0,0,:np.size(h,0),-1,-1]]\\n h = h[:,np.r_[0,:np.size(h,1),-1]]\\n h[:,0] = h[:,-2]\\n h[:,-1] = h[:,1]\\n f = f[np.r_[0,0,:np.size(f,0),-1,-1]]\\n f = f[:,np.r_[0,:np.size(f,1),-1]]\\n f[:,0] = f[:,-2]\\n f[:,-1] = f[:,1]\\n m,l = h.shape\\n xrange=(1,l-2)\\n yrange=(ystart+2,m-2)\\n\\n if area == 'tripole':\\n #add two rows in the north and the south\\n fold1 = L_t//2\\n lon_t = lon_t[np.r_[0,0,:np.size(lon_t,0),-1,-1]]\\n lon_t[-2,:fold1] = lon_t[-3,L_t:fold1-1:-1]\\n lon_t[-2,L_t:fold1-1:-1] = lon_t[-3,:fold1]\\n lon_t[-1,:fold1] = lon_t[-4,L_t:fold1-1:-1]\\n lon_t[-1,L_t:fold1-1:-1] = lon_t[-4,:fold1]\\n\\n lon_t = lon_t[:,np.r_[0,:np.size(lon_t,1),-1]]\\n lon_t[:,0] = lon_t[:,1] - (lon_t[:,2]-lon_t[:,1])\\n lon_t[:,-1] = lon_t[:,-2] + (lon_t[:,-2]-lon_t[:,-3])\\n lat_t = lat_t[np.r_[0,0,:np.size(lat_t,0),-1,-1]]\\n lat_t = lat_t[:,np.r_[0,:np.size(lat_t,1),-1]]\\n lat_t[0,:] = -85\\n lat_t[1,:] = -80\\n lat_t[-2,:] = lat_t[-3,:]\\n lat_t[-1,:] = lat_t[-4,:]\\n lon_uv = lon_uv[np.r_[0,0,:np.size(lon_uv,0),-1,-1]]\\n\\n lon_uv[-2,:fold1] = lon_uv[-4,L_t:fold1-1:-1]\\n lon_uv[-2,L_t:fold1-1:-1] = lon_uv[-4,:fold1]\\n lon_uv[-1,:fold1] = lon_uv[-5,L_t:fold1-1:-1]\\n lon_uv[-1,L_t:fold1-1:-1] = lon_uv[-5,:fold1]\\n\\n lon_uv = lon_uv[:,np.r_[0,:np.size(lon_uv,1),-1]]\\n lon_uv[:,0] = lon_uv[:,1] - (lon_uv[:,2]-lon_t[:,1])\\n lon_uv[:,-1] = lon_uv[:,-2] + (lon_uv[:,-2]-lon_uv[:,-3])\\n lat_uv = lat_uv[np.r_[0,0,:np.size(lat_uv,0),-1,-1]]\\n lat_uv = lat_uv[:,np.r_[0,:np.size(lat_uv,1),-1]]\\n lat_uv[0,:] = -85\\n lat_uv[1,:] = -80\\n lat_uv[-2,:] = lat_uv[-3,:]\\n lat_uv[-1,:] = lat_uv[-4,:]\\n mask_t = mask_t[:,np.r_[0,0,:np.size(mask_t,1),-1,-1],:]\\n mask_t = mask_t[:,:,np.r_[0,:np.size(mask_t,2),-1]]\\n mask_t[:,:,0] = mask_t[:,:,-2]\\n mask_t[:,:,-1] = mask_t[:,:,1]\\n mask_uv = mask_uv[:,np.r_[0,0,:np.size(mask_uv,1),-1,-1],:]\\n mask_uv = mask_uv[:,:,np.r_[0,:np.size(mask_uv,2),-1]]\\n mask_uv[:,:,0] = mask_uv[:,:,-2]\\n mask_uv[:,:,-1] = mask_uv[:,:,1]\\n h = h[np.r_[0,0,:np.size(h,0),-1,-1]]\\n h = h[:,np.r_[0,:np.size(h,1),-1]]\\n h[:,0] = h[:,-2]\\n h[:,-1] = h[:,1]\\n f = f[np.r_[0,0,:np.size(f,0),-1,-1]]\\n f = f[:,np.r_[0,:np.size(f,1),-1]]\\n f[:,0] = f[:,-2]\\n f[:,-1] = f[:,1]\\n m,l = h.shape\\n xrange=(1,l-2)\\n yrange=(ystart+2,m-2)\\n\\n return BGrid_GFDL(lon_t, lat_t, lon_uv, lat_uv, \\\\\\n mask_t, mask_uv, h, z_t, z_t_edges, \\\\\\n z_uv, z_uv_edges, f, \\\\\\n name, xrange=xrange, yrange=yrange)\",\n \"def incandPeakInfoLG(self):\\r\\n\\t\\tself.incandBaseline_LG = (np.mean(self.lowGainWideBandIncandData[0:10]))\\r\\n\\t\\t\\t\\t\\r\\n\\t\\traw_incand_max_LG = np.amax(self.lowGainWideBandIncandData)\\r\\n\\t\\tincand_max_LG = raw_incand_max_LG - self.incandBaseline_LG\\t\\t\\r\\n\\t\\tincand_max_index_LG = np.argmax(self.lowGainWideBandIncandData)\\r\\n\\t\\t\\r\\n\\t\\tself.incandMax_LG =incand_max_LG\\r\\n\\t\\tself.incandMaxPos_LG = incand_max_index_LG\",\n \"def latent_heat_vapourisation(self, tair):\\n return (2.501 - 0.00237 * tair) * 1E06\",\n \"def galaxy(img):\\n return img[420:490, 710:770]\",\n \"def refl_analysis(self,dials_model):\\n Z = self.refl_table\\n indices = Z['miller_index']\\n expts = ExperimentListFactory.from_json_file(dials_model,\\n check_format=False)\\n self.dials_model=expts[0]\\n CRYS = self.dials_model.crystal\\n UC = CRYS.get_unit_cell()\\n strong_resolutions = UC.d(indices)\\n order = flex.sort_permutation(strong_resolutions, reverse=True)\\n Z[\\\"spots_order\\\"] = order\\n self.spots_pixels = flex.size_t()\\n spots_offset = flex.int(len(order),-1)\\n spots_size = flex.int(len(order),-1)\\n\\n P = panels = Z['panel']\\n S = shoeboxes = Z['shoebox']\\n N_visited = 0; N_bad = 0\\n for oidx in range(len(order)): #loop through the shoeboxes in correct order\\n sidx = order[oidx] # index into the Miller indices\\n ipanel = P[sidx]\\n slow_size = 254\\n fast_size = 254\\n panel_size=slow_size*fast_size\\n bbox = S[sidx].bbox\\n first_position = spots_offset[sidx] = self.spots_pixels.size()\\n for islow in range(max(0,bbox[2]-3), min(slow_size,bbox[3]+3)):\\n for ifast in range(max(0,bbox[0]-3), min(fast_size,bbox[1]+3)):\\n value = self.trusted_mask[ipanel][islow*slow_size + ifast]\\n N_visited += 1\\n if value: self.spots_pixels.append(ipanel*panel_size+islow*slow_size+ifast)\\n else: N_bad+=1\\n spot_size = spots_size[sidx] = self.spots_pixels.size() - first_position\\n Z[\\\"spots_offset\\\"] = spots_offset\\n Z[\\\"spots_size\\\"] = spots_size\\n print (N_visited,\\\"pixels were visited in the %d shoeboxes (with borders)\\\"%len(order))\\n print (N_bad,\\\"of these were bad pixels, leaving %d in target\\\"%(len(self.spots_pixels)))\",\n \"def width_h_invis(self):\\n if m_higgs > 2.0 * self.mx:\\n coupling = self.gsxx * self.stheta / np.sqrt(1 - self.stheta**2)\\n\\n val = (\\n (coupling**2 * (m_higgs**2 - 4 * self.mx**2) ** 1.5)\\n / (8.0 * m_higgs**2 * np.pi)\\n ).real\\n\\n assert val >= 0\\n\\n return val\\n else:\\n return 0.0\",\n \"def gpt2_1w (station, dmjd,dlat,dlon,hell,it):\\n\\n# need to find diffpod and difflon\\n if (dlon < 0):\\n plon = (dlon + 2*np.pi)*180/np.pi;\\n else:\\n plon = dlon*180/np.pi;\\n# transform to polar distance in degrees\\n ppod = (-dlat + np.pi/2)*180/np.pi; \\n\\n# % find the index (line in the grid file) of the nearest point\\n# \\t % changed for the 1 degree grid (GP)\\n ipod = np.floor(ppod+1); \\n ilon = np.floor(plon+1);\\n \\n# normalized (to one) differences, can be positive or negative\\n#\\t% changed for the 1 degree grid (GP)\\n diffpod = (ppod - (ipod - 0.5));\\n difflon = (plon - (ilon - 0.5));\\n\\n\\n# change the reference epoch to January 1 2000\\n print('Modified Julian Day', dmjd)\\n dmjd1 = dmjd-51544.5 \\n\\n pi2 = 2*np.pi\\n pi4 = 4*np.pi\\n\\n# mean gravity in m/s**2\\n gm = 9.80665;\\n# molar mass of dry air in kg/mol\\n dMtr = 28.965E-3 \\n# dMtr = 28.965*10^-3 \\n# universal gas constant in J/K/mol\\n Rg = 8.3143 \\n\\n# factors for amplitudes, i.e. whether you want time varying\\n if (it==1):\\n print('>>>> no refraction time variation ')\\n cosfy = 0; coshy = 0; sinfy = 0; sinhy = 0;\\n else: \\n cosfy = np.cos(pi2*dmjd1/365.25)\\n coshy = np.cos(pi4*dmjd1/365.25) \\n sinfy = np.sin(pi2*dmjd1/365.25) \\n sinhy = np.sin(pi4*dmjd1/365.25) \\n cossin = np.matrix([1, cosfy, sinfy, coshy, sinhy])\\n# initialization of new vectors\\n p = 0; T = 0; dT = 0; Tm = 0; e = 0; ah = 0; aw = 0; la = 0; undu = 0;\\n undul = np.zeros(4)\\n Ql = np.zeros(4)\\n dTl = np.zeros(4)\\n Tl = np.zeros(4)\\n pl = np.zeros(4)\\n ahl = np.zeros(4)\\n awl = np.zeros(4)\\n lal = np.zeros(4)\\n Tml = np.zeros(4)\\n el = np.zeros(4)\\n#\\n pgrid, Tgrid, Qgrid, dTgrid, u, Hs, ahgrid, awgrid, lagrid, Tmgrid = read_4by5(station,dlat,dlon,hell)\\n#\\n for l in [0,1,2,3]:\\n KL = l #silly to have this as a variable like this \\n# transforming ellipsoidal height to orthometric height:\\n# Hortho = -N + Hell\\n undul[l] = u[KL] \\n hgt = hell-undul[l] \\n# pressure, temperature at the height of the grid\\n T0 = Tgrid[KL,0] + Tgrid[KL,1]*cosfy + Tgrid[KL,2]*sinfy + Tgrid[KL,3]*coshy + Tgrid[KL,4]*sinhy;\\n tg = float(Tgrid[KL,:] *cossin.T)\\n# print(T0,tg)\\n\\n p0 = pgrid[KL,0] + pgrid[KL,1]*cosfy + pgrid[KL,2]*sinfy + pgrid[KL,3]*coshy + pgrid[KL,4]*sinhy;\\n \\n# humidity \\n Ql[l] = Qgrid[KL,0] + Qgrid[KL,1]*cosfy + Qgrid[KL,2]*sinfy + Qgrid[KL,3]*coshy + Qgrid[KL,4]*sinhy;\\n \\n# reduction = stationheight - gridheight\\n Hs1 = Hs[KL]\\n redh = hgt - Hs1;\\n\\n# lapse rate of the temperature in degree / m\\n dTl[l] = dTgrid[KL,0] + dTgrid[KL,1]*cosfy + dTgrid[KL,2]*sinfy + dTgrid[KL,3]*coshy + dTgrid[KL,4]*sinhy;\\n \\n# temperature reduction to station height\\n Tl[l] = T0 + dTl[l]*redh - 273.15;\\n\\n# virtual temperature\\n Tv = T0*(1+0.6077*Ql[l]) \\n c = gm*dMtr/(Rg*Tv) \\n \\n# pressure in hPa\\n pl[l] = (p0*np.exp(-c*redh))/100 \\n \\n# hydrostatic coefficient ah\\n ahl[l] = ahgrid[KL,0] + ahgrid[KL,1]*cosfy + ahgrid[KL,2]*sinfy + ahgrid[KL,3]*coshy + ahgrid[KL,4]*sinhy;\\n \\n# wet coefficient aw\\n awl[l] = awgrid[KL,0] + awgrid[KL,1]*cosfy + awgrid[KL,2]*sinfy + awgrid[KL,3]*coshy + awgrid[KL,4]*sinhy;\\n\\t\\t\\t\\t\\t \\n# water vapor decrease factor la - added by GP\\n lal[l] = lagrid[KL,0] + lagrid[KL,1]*cosfy + lagrid[KL,2]*sinfy + lagrid[KL,3]*coshy + lagrid[KL,4]*sinhy;\\n\\t\\t\\t\\t\\t \\n# mean temperature of the water vapor Tm - added by GP\\n Tml[l] = Tmgrid[KL,0] + Tmgrid[KL,1]*cosfy + Tmgrid[KL,2]*sinfy + Tmgrid[KL,3]*coshy + Tmgrid[KL,4]*sinhy;\\n\\t\\t\\t\\t\\t \\t\\t \\n# water vapor pressure in hPa - changed by GP\\n e0 = Ql[l]*p0/(0.622+0.378*Ql[l])/100; # % on the grid\\n aa = (100*pl[l]/p0)\\n bb = lal[l]+1\\n el[l] = e0*np.power(aa,bb) # % on the station height - (14) Askne and Nordius, 1987\\n \\n dnpod1 = np.abs(diffpod); # % distance nearer point\\n dnpod2 = 1 - dnpod1; # % distance to distant point\\n dnlon1 = np.abs(difflon);\\n dnlon2 = 1 - dnlon1;\\n \\n# pressure\\n R1 = dnpod2*pl[0]+dnpod1*pl[1];\\n R2 = dnpod2*pl[2]+dnpod1*pl[3];\\n p = dnlon2*R1+dnlon1*R2;\\n \\n# temperature\\n R1 = dnpod2*Tl[0]+dnpod1*Tl[1];\\n R2 = dnpod2*Tl[2]+dnpod1*Tl[3];\\n T = dnlon2*R1+dnlon1*R2;\\n \\n# temperature in degree per km\\n R1 = dnpod2*dTl[0]+dnpod1*dTl[1];\\n R2 = dnpod2*dTl[2]+dnpod1*dTl[3];\\n dT = (dnlon2*R1+dnlon1*R2)*1000;\\n \\n# water vapor pressure in hPa - changed by GP\\n R1 = dnpod2*el[0]+dnpod1*el[1];\\n R2 = dnpod2*el[2]+dnpod1*el[3];\\n e = dnlon2*R1+dnlon1*R2;\\n \\n# hydrostatic\\n R1 = dnpod2*ahl[0]+dnpod1*ahl[1];\\n R2 = dnpod2*ahl[2]+dnpod1*ahl[3];\\n ah = dnlon2*R1+dnlon1*R2;\\n \\n# wet\\n R1 = dnpod2*awl[0]+dnpod1*awl[1];\\n R2 = dnpod2*awl[2]+dnpod1*awl[3];\\n aw = dnlon2*R1+dnlon1*R2;\\n \\n# undulation\\n R1 = dnpod2*undul[0]+dnpod1*undul[1];\\n R2 = dnpod2*undul[2]+dnpod1*undul[3];\\n undu = dnlon2*R1+dnlon1*R2;\\n\\n# water vapor decrease factor la - added by GP\\n R1 = dnpod2*lal[0]+dnpod1*lal[1];\\n R2 = dnpod2*lal[2]+dnpod1*lal[3];\\n la = dnlon2*R1+dnlon1*R2;\\n\\t\\t\\n# mean temperature of the water vapor Tm - added by GP\\n R1 = dnpod2*Tml[0]+dnpod1*Tml[1];\\n R2 = dnpod2*Tml[2]+dnpod1*Tml[3];\\n Tm = dnlon2*R1+dnlon1*R2; \\n\\n return p, T, dT,Tm,e,ah,aw,la,undu\",\n \"def plateau_finder(data, tol=0.0003):\\n from scipy.ndimage.filters import generic_filter\\n tol = 0.0003\\n filt_data = generic_filter(data, np.std, size=3)\\n plat_index = np.where(filt_data < (np.min(filt_data) + tol))[0]\\n\\n plateaus = group_consecutive(plat_index)\\n\\n return plateaus\",\n \"def get_iPTF16hgs(colorplt = False):\\n z = 0.017\\n ebv = 0\\n D = cosmo.luminosity_distance([z])[0].value * 1e+6 # in pc\\n dis_mod = 5*np.log10(D / 10)\\n \\n tb = pd.read_csv('../data/otherSN/iPTF16hgs/table1.txt', sep=\\\"\\\\t\\\")\\n tb = tb.drop(columns=[\\\"Unnamed: 5\\\"])\\n tb = tb.rename(columns={'Filter' : 'filter',\\n 'MJD': 'mjd'})\\n tb = tb[~np.array([x[0]=='>' for x in tb['Magnitude'].values])]\\n tb['mag'] = np.array([float(x.split(\\\" +or-\\\")[0]) for x in tb['Magnitude'].values])\\n tb['emag'] = np.array([float(x.split(\\\" +or-\\\")[1]) for x in tb['Magnitude'].values])\\n tb = tb.drop(columns=[\\\"Magnitude\\\"])\\n \\n ixg = tb['filter'].values == \\\"g\\\"\\n ixr = tb['filter'].values == \\\"r\\\"\\n ixi = tb['filter'].values == \\\"i\\\"\\n tb['wave'] = np.zeros(len(tb))\\n tb['wave'].values[ixg] = 4814\\n tb['wave'].values[ixr] = 6422\\n tb['wave'].values[ixi] = 7883\\n tb['mag0'] = tb['mag'] - extinction.ccm89(tb['wave'].values, 3.1*ebv, 3.1)\\n tb['mag0_abs'] = tb['mag0'] - dis_mod\\n t_max = 57691.59 # from the paper\\n tb['tmax_of'] = tb['mjd'] - t_max\\n tb['tmax_rf'] = (tb['mjd'] - t_max) / (1+z)\\n \\\"\\\"\\\"\\n plt.errorbar(tb[\\\"tmax_rf\\\"].values[ixg], tb[\\\"mag\\\"].values[ixg], tb[\\\"emag\\\"].values[ixg], fmt=\\\".g\\\")\\n plt.errorbar(tb[\\\"tmax_rf\\\"].values[ixr], tb[\\\"mag\\\"].values[ixr], tb[\\\"emag\\\"].values[ixr], fmt=\\\".r\\\")\\n plt.errorbar(tb[\\\"tmax_rf\\\"].values[ixi], tb[\\\"mag\\\"].values[ixi], tb[\\\"emag\\\"].values[ixi], fmt=\\\".y\\\")\\n \\\"\\\"\\\"\\n tb = add_datecol(tb)\\n tb = add_physcol(tb)\\n #tb = tb.drop(columns=[\\\"datetime64\\\"])\\n if colorplt==False:\\n return tb\\n else:\\n #tb = tb[tb.mjd > 55352.5]\\n #tb = tb[tb.mjd < 55593.5]\\n \\n dates = get_date_span(tb)\\n datesave = []\\n for i in range(len(dates)):\\n x = dates[i]\\n ix = tb[\\\"date\\\"].values == x\\n tbsub = tb[ix]\\n if len(tbsub)!=0:\\n flts = tbsub['filter'].values\\n if \\\"r\\\" in flts and np.sum(np.unique(flts))!=1:\\n datesave.append(x)\\n datesave = np.array(datesave)\\n \\n mcolor = []\\n mcolor_unc = []\\n mjds = []\\n colorname = []\\n for i in range(len(datesave)):\\n x = datesave[i]\\n ix = tb[\\\"date\\\"].values == x\\n tbsub = tb[ix]\\n gtb = tbsub[tbsub[\\\"filter\\\"].values==\\\"g\\\"]\\n rtb = tbsub[tbsub[\\\"filter\\\"].values==\\\"r\\\"]\\n itb = tbsub[tbsub[\\\"filter\\\"].values==\\\"i\\\"]\\n if len(gtb)!=0:\\n gmjds = gtb[\\\"mjd\\\"].values\\n gmags = gtb[\\\"mag0\\\"].values\\n gemags = gtb[\\\"emag\\\"].values\\n gwtgs = 1/gemags**2\\n gmag = np.sum(gmags * gwtgs) / np.sum(gwtgs)\\n gmjd = np.sum(gmjds * gwtgs) / np.sum(gwtgs)\\n gemag = 1/ np.sqrt(np.sum(gwtgs))\\n if len(rtb)!=0:\\n rmjds = rtb[\\\"mjd\\\"].values\\n rmags = rtb[\\\"mag0\\\"].values\\n remags = rtb[\\\"emag\\\"].values\\n rwtgs = 1/remags**2\\n rmag = np.sum(rmags * rwtgs) / np.sum(rwtgs)\\n rmjd = np.sum(rmjds * rwtgs) / np.sum(rwtgs)\\n remag = 1/ np.sqrt(np.sum(rwtgs))\\n if len(itb)!=0:\\n imjds = itb[\\\"mjd\\\"].values\\n imags = itb[\\\"mag0\\\"].values\\n iemags = itb[\\\"emag\\\"].values\\n iwtgs = 1/iemags**2\\n imag = np.sum(imags * iwtgs) / np.sum(iwtgs)\\n imjd = np.sum(imjds * iwtgs) / np.sum(iwtgs)\\n iemag = 1/ np.sqrt(np.sum(iwtgs))\\n if len(gtb)!=0 and len(rtb)!=0:\\n mcolor.append(gmag - rmag)\\n mjds.append( 0.5 * (gmjd + rmjd) )\\n mcolor_unc.append( np.sqrt(gemag**2 + remag**2) )\\n colorname.append(\\\"gmr\\\")\\n if len(rtb)!=0 and len(itb)!=0:\\n mcolor.append(rmag - imag)\\n mjds.append( 0.5 * (rmjd + imjd) )\\n mcolor_unc.append( np.sqrt(remag**2 + iemag**2) )\\n colorname.append(\\\"rmi\\\")\\n \\n ctb = Table(data = [mjds, mcolor, mcolor_unc, colorname],\\n names = [\\\"mjd\\\", \\\"c\\\", \\\"ec\\\", \\\"cname\\\"])\\n \\n ctb['tmax_rf'] = (ctb['mjd'] - t_max) / (1+z)\\n ctb = ctb.to_pandas()\\n return ctb\",\n \"def bands(self) -> int:\\n ...\",\n \"def n_band(self):\\n pass\",\n \"def get_bandgap(cluster, spin, dirr=\\\"/home/pv278/Platinum/\\\"):\\n outfile = open(get_path(dirr, cluster, spin)).readlines()\\n line = [l for l in outfile if \\\"occ\\\" in l]\\n if line:\\n Eocc = line[-1].split()[-1]\\n else:\\n Eocc = None\\n line = [l for l in outfile if \\\"virt\\\" in l]\\n if line:\\n Evirt = line[0].split()[4]\\n else:\\n Evirt = None\\n \\n if Eocc:\\n return (float(Evirt) - float(Eocc)) * 27.211\\n else:\\n return\",\n \"def _update_farness_map(self,ind):\",\n \"def stain_image(image, num_stains, color):\",\n \"def adjust_for_refractive_index(df):\\n refractive_index_salt_water = 1.33\\n df['Height'] = (df['Height']) / refractive_index_salt_water\\n return df\",\n \"def minimum_spanning_arborescence(sol):\",\n \"def pan_corr(file):\\n\\n # # infile = 'd:\\\\\\\\Projekti\\\\\\\\Satelit\\\\\\\\CO\\\\\\\\Razpis\\\\\\\\Flat field images_new2020\\\\\\\\flatfield\\\\\\\\NHDBflat_1D'\\n # # infile = 'd:\\\\Projekti\\\\Satelit\\\\CO\\\\Razpis\\\\_POSNETKI\\\\Jure_naloga_banje_raw_pyt\\\\\\\\NHDRGoreMorje_3D'\\n #\\n # # in_path = 'd:\\\\Projekti\\\\Satelit\\\\CO\\\\Razpis\\\\Flat field images_new2020\\\\\\\\20201028 Vignetting\\\\\\\\flatfield\\\\\\\\'\\n # # in_pan_ref_file = 'NHDPflat_3D_py.tif'\\n # in_path = 'd:\\\\Projekti\\\\Satelit\\\\CO\\\\Razpis\\\\_POSNETKI\\\\Peking_PAN\\\\\\\\'\\n # in_pan_ref_file = 'NHDPfoc_swp6_1D_py.tif'\\n # in_ref = in_path + in_pan_ref_file\\n #\\n # inreffil = gdal.Open(in_ref)\\n # image_ref = inreffil.ReadAsArray()\\n # # size_ref = image_ref.shape\\n # # pix_count = size_ref[0]*size_ref[1]\\n #\\n # image_ref = image_ref[800:930, 1420:1640]\\n # size_ref = image_ref.shape\\n # pix_count = size_ref[0] * size_ref[1]\\n #\\n # g1 = 0.\\n # g2 = 0.\\n # r1 = 0.\\n # b1 = 0.\\n #\\n # for i in range(size_ref[0]):\\n # for j in range(size_ref[1]):\\n # if (i % 2) == 0 and (j % 2) == 0: g1 = g1 + image_ref[i, j]\\n # if (i % 2) == 1 and (j % 2) == 1: g2 = g2 + image_ref[i, j]\\n # if (i % 2) == 0 and (j % 2) == 1: r1 = r1 + image_ref[i, j]\\n # if (i % 2) == 1 and (j % 2) == 0: b1 = b1 + image_ref[i, j]\\n #\\n # g1_avg = g1 / pix_count * 4\\n # g2_avg = g2 / pix_count * 4\\n # r1_avg = r1 / pix_count * 4\\n # b1_avg = b1 / pix_count * 4\\n #\\n # raz_g1 = 1\\n # raz_g2 = g1_avg/g2_avg\\n # raz_r1 = g1_avg/r1_avg\\n # raz_b1 = g1_avg/b1_avg\\n #\\n # avg = (g1+g2+r1+b1)/pix_count\\n #\\n # print(g1_avg, g2_avg, r1_avg, b1_avg, avg)\\n\\n raz_g1 = 1\\n raz_g2 = 1.0245196396115988\\n raz_r1 = 1.0131841989689434\\n raz_b1 = 1.0517113199247086\\n\\n print('razmerje:', raz_g1, raz_g2, raz_r1, raz_b1)\\n\\n # in_path = 'd:\\\\Projekti\\\\Satelit\\\\CO\\\\Razpis\\\\_POSNETKI\\\\Peking_PAN\\\\\\\\'\\n # in_pan_ref_file = 'NHDPfoc_swp6_4D_py.tif'\\n # in_path = 'd:\\\\Projekti\\\\Satelit\\\\CO\\\\Razpis\\\\Flat field images_new2020\\\\\\\\20201028 Vignetting\\\\\\\\flatfield\\\\\\\\'\\n # in_pan_ref_file = 'NHDPflat_3D_py.tif'\\n\\n # in_path = 'd:\\\\Projekti\\\\Satelit\\\\CO\\\\Razpis\\\\_POSNETKI\\\\Slo_PAN\\\\_26_30\\\\\\\\'\\n # in_pan_ref_file = [filename for filename in os.listdir(in_path) if filename.lower().startswith(\\\"nhd\\\") and filename.lower().endswith(\\\"tif\\\")]\\n\\n \\n\\n \\n\\n # print('image', i)\\n in_ref=file\\n inreffil = gdal.Open(in_ref)\\n image_ref = inreffil.ReadAsArray()\\n size_ref = image_ref.shape\\n # pix_count = size_ref[0] * size_ref[1]\\n # pix_count = np.count_nonzero(image_ref)\\n # pix_count = 3664*650\\n\\n # g1 = 0.\\n # g2 = 0.\\n # r1 = 0.\\n # b1 = 0.\\n #\\n # for i in range(size_ref[0]):\\n # for j in range(size_ref[1]):\\n # if (i % 2) == 0 and (j % 2) == 0: g1 = g1 + image_ref[i, j]\\n # if (i % 2) == 1 and (j % 2) == 1: g2 = g2 + image_ref[i, j]\\n # if (i % 2) == 0 and (j % 2) == 1: r1 = r1 + image_ref[i, j]\\n # if (i % 2) == 1 and (j % 2) == 0: b1 = b1 + image_ref[i, j]\\n #\\n # g1_avg = g1 / pix_count * 4\\n # g2_avg = g2 / pix_count * 4\\n # r1_avg = r1 / pix_count * 4\\n # b1_avg = b1 / pix_count * 4\\n #\\n # avg = (g1 + g2 + r1 + b1) / pix_count\\n #\\n # print(g1_avg, g2_avg, r1_avg, b1_avg, avg)\\n\\n # popravek\\n im_p_pop = np.zeros((size_ref[0], size_ref[1]), np.uint16)\\n\\n\\n for i in range(size_ref[0]):\\n for j in range(size_ref[1]):\\n if (i % 2) == 0 and (j % 2) == 0 and image_ref[i, j] != 0: im_p_pop[i, j] = image_ref[i, j] * raz_g1\\n if (i % 2) == 1 and (j % 2) == 1 and image_ref[i, j] != 0: im_p_pop[i, j] = image_ref[i, j] * raz_g2\\n if (i % 2) == 0 and (j % 2) == 1 and image_ref[i, j] != 0: im_p_pop[i, j] = image_ref[i, j] * raz_r1\\n if (i % 2) == 1 and (j % 2) == 0 and image_ref[i, j] != 0: im_p_pop[i, j] = image_ref[i, j] * raz_b1\\n \\n _,_,_,_,P=return_flatfield_set_path(2)\\n P_flat=gdal_array.LoadFile(P)\\n \\n # im_p_pop=simple_flatfield_corr(P_flat, im_p_pop, 2, 1) \\n \\n # outout\\n \\n im_p_pop=BLUE_simple_flatfield_corr(P_flat, im_p_pop)\\n \\n out=os.path.abspath(file)+\\\"/corr/\\\"+os.path.basename(file)[:-4] + \\\"_pop_flat_corr.tif\\\"\\n\\n \\n # out = in_ref[:-4] + \\\"_pop_flat_corr.tif\\\"\\n\\n driver = gdal.GetDriverByName('GTiff')\\n\\n # outRaster = driver.Create(out, size[1], size[0], 3, gdal.GDT_UInt16)\\n outRaster = driver.Create(out, size_ref[1], size_ref[0], 1, gdal.GDT_UInt16)\\n\\n outband = outRaster.GetRasterBand(1)\\n outband.WriteArray(im_p_pop)\\n outband.FlushCache()\",\n \"def test_G_ind(self):\\r\\n a = array([[29, 11], [273, 191], [8, 31], [64, 64]])\\r\\n self.assertFloatEqual(G_ind(a)[0], 28.59642)\\r\\n self.assertFloatEqual(G_ind(a, True)[0], 28.31244)\",\n \"def bg_ex(self, f):\\n dat = sif.readSIF(os.path.join(self.path, f))[0][0]\\n return dat - np.min(dat[:511, :511])\",\n \"def global_analysis(tomo, b_th, c=18):\\n\\n ## Thesholding and Volume analysis\\n if c == 6:\\n con_mat = [ [[0, 0, 0], [0, 1, 0], [0, 0, 0]],\\n [[0, 1, 0], [1, 1, 1], [0, 1, 0]],\\n [[0, 0, 0], [0, 1, 0], [0, 0, 0]] ]\\n elif c == 18:\\n con_mat = [[[0, 1, 0], [1, 1, 1], [0, 1, 0]],\\n [[1, 1, 1], [1, 1, 1], [1, 1, 1]],\\n [[0, 1, 0], [1, 1, 1], [0, 1, 0]]]\\n elif c == 26:\\n con_mat = [[[1, 1, 1], [1, 1, 1], [1, 1, 1]],\\n [[1, 1, 1], [1, 1, 1], [1, 1, 1]],\\n [[1, 1, 1], [1, 1, 1], [1, 1, 1]]]\\n else:\\n raise ValueError\\n tomo_lbl, num_lbls = sp.ndimage.label(tomo >= b_th, structure=np.ones(shape=[3, 3, 3]))\\n tomo_out = np.zeros(shape=tomo.shape, dtype=int)\\n lut = np.zeros(shape=num_lbls+1, dtype=int)\\n\\n ## COUNTING REGIONS METHODS\\n # import time\\n # hold_t = time.time()\\n # for lbl in range(1, num_lbls + 1):\\n # ids = tomo == lbl\\n # feat_sz = len(ids)\\n # tomo_out[ids] = feat_sz\\n # # print('[1]:', lbl, 'of', num_lbls)\\n # print time.time() - hold_t\\n\\n ## COUNTING PIXELS METHOD\\n ## Count loop\\n # cont, total = 0, np.prod(tomo.shape)\\n # import time\\n # hold_t = time.time()\\n for x in range(tomo.shape[0]):\\n for y in range(tomo.shape[1]):\\n for z in range(tomo.shape[2]):\\n id = tomo_lbl[x, y, z]\\n lut[id] += 1\\n # cont += 1\\n # print('[1]:', cont, 'of', total)\\n #\\n ## Write loop\\n # cont, total = 0, np.prod(tomo.shape)\\n\\n for x in range(tomo.shape[0]):\\n for y in range(tomo.shape[1]):\\n for z in range(tomo.shape[2]):\\n id = tomo_lbl[x, y, z]\\n if id > 0:\\n tomo_out[x, y, z] = lut[id]\\n # cont += 1\\n # print('[1]:', cont, 'of', total)\\n # print time.time() - hold_t\\n\\n return tomo_out\",\n \"def upper_covers(self, x):\",\n \"def prada(self):\\n scale_factor = 1.0 / (1.0 + self.snapshot.header.redshift)\\n r200c_physical = self.r200c * scale_factor / 1000.0 # units Mpc\\n\\n v200 = (\\n (self.snapshot.const.G * self.m200c)\\n / r200c_physical\\n * self.snapshot.const.Mpc ** 2\\n / 1000.0 ** 2\\n ) ** 0.5 # units km/s\\n\\n def y(x, vmax, v200):\\n func = np.log(1 + x) - (x / (1 + x))\\n return ((0.216 * x) / func) ** 0.5 - (vmax / v200)\\n\\n concentration = np.zeros((len(self.vmax)))\\n for halo in range(self.N_halos):\\n if v200[halo] > self.vmax[halo]:\\n concentration[halo] = -9999.0\\n else:\\n try:\\n concentration[halo] = newton(\\n y, x0=5.0, args=(self.vmax[halo], v200[halo])\\n )\\n except:\\n concentration[halo] = -9999.0\\n\\n return concentration\",\n \"def footprint_corner_indices():\",\n \"def __AOD04(self):\\n\\n assert self.__lmax <= 100\\n\\n k = np.zeros(101)\\n\\n k[0] = 0\\n k[1] = 0\\n k[2] = -0.308\\n k[3] = -0.195\\n k[4] = -0.132\\n k[5] = -0.103\\n k[6] = -0.089\\n k[7] = -0.082\\n k[8] = -0.078\\n k[9] = -0.073\\n\\n for i in range(10, 18):\\n k[i] = -(0.682 + 0.27 * (i - 10) / 8) / i\\n for i in range(18, 32):\\n k[i] = -(0.952 + 0.288 * (i - 18) / 14) / i\\n for i in range(32, 56):\\n k[i] = -(1.24 + 0.162 * (i - 32) / 24) / i\\n for i in range(56, 101):\\n k[i] = -(1.402 + 0.059 * (i - 56) / 44) / i\\n\\n # print(k[0:(self.__Nmax+1)])\\n\\n return k[0:(self.__lmax + 1)]\",\n \"def test_lifted_index():\\n pressure = np.array([1014., 1000., 997., 981.2, 947.4, 925., 914.9, 911.,\\n 902., 883., 850., 822.3, 816., 807., 793.2, 770.,\\n 765.1, 753., 737.5, 737., 713., 700., 688., 685.,\\n 680., 666., 659.8, 653., 643., 634., 615., 611.8,\\n 566.2, 516., 500., 487., 484.2, 481., 475., 460.,\\n 400.]) * units.hPa\\n temperature = np.array([24.2, 24.2, 24., 23.1, 21., 19.6, 18.7, 18.4,\\n 19.2, 19.4, 17.2, 15.3, 14.8, 14.4, 13.4, 11.6,\\n 11.1, 10., 8.8, 8.8, 8.2, 7., 5.6, 5.6,\\n 5.6, 4.4, 3.8, 3.2, 3., 3.2, 1.8, 1.5,\\n -3.4, -9.3, -11.3, -13.1, -13.1, -13.1, -13.7, -15.1,\\n -23.5]) * units.degC\\n dewpoint = np.array([23.2, 23.1, 22.8, 22., 20.2, 19., 17.6, 17.,\\n 16.8, 15.5, 14., 11.7, 11.2, 8.4, 7., 4.6,\\n 5., 6., 4.2, 4.1, -1.8, -2., -1.4, -0.4,\\n -3.4, -5.6, -4.3, -2.8, -7., -25.8, -31.2, -31.4,\\n -34.1, -37.3, -32.3, -34.1, -37.3, -41.1, -37.7, -58.1,\\n -57.5]) * units.degC\\n parcel_prof = parcel_profile(pressure, temperature[0], dewpoint[0])\\n li = lifted_index(pressure, temperature, parcel_prof)\\n assert_almost_equal(li, -7.9115691 * units.delta_degree_Celsius, 2)\",\n \"def mamajek08_logRpHK_Ro_edge():\\n Ro_edge = 0.31935816876122064\\n return Ro_edge\",\n \"def get_evaporation_latent_heat() -> float:\\n theta = 28.0\\n return 2500.8 - 2.3668 * theta\",\n \"def findzpd(self):\\n dc=0.5*self.rms*self.ndstep\\n #fixed at 0.1 of the dispersion\\n dd=0.1*self.ws.coef[1]\\n\\n #set upt he docef values\\n dcoef=self.ws.coef*0.0\\n dcoef[0]=dc\\n dcoef[1]=dd\\n self.ws=st.findxcor(self.xarr, self.farr, self.swarr, self.sfarr, self.ws, \\n dcoef=dcoef, ndstep=self.ndstep, best=False, inttype='interp')\\n self.plotArt()\\n self.redraw_canvas()\",\n \"def index_of_refraction(self):\\n return self.microsphere.index_of_refraction(self.wavelength)\",\n \"def get_gini(rows):\\n count_zero = 0 # number of rows labelled healthy\\n count_one = 0 # number of rows labelled depressed\\n\\n for row in rows:\\n if row[len(row) - 1] == 0:\\n count_zero = count_zero + 1\\n else:\\n count_one = count_one + 1\\n return 1 - (count_zero / float(len(rows))) ** 2 - (count_one / float(len(rows))) ** 2\",\n \"def land(self, n, e):\\n pass\",\n \"def P_AI_Rocky(in_dict):\\n # START\\n fs = 16\\n plt.rc('font', size=fs)\\n fig = plt.figure(figsize=(14,12))\\n ds = nc.Dataset(in_dict['fn'])\\n\\n # PLOT CODE\\n aa = [-122.8, -122.54, 47.92, 48.22]\\n import cmocean\\n cmap = cmocean.cm.balance\\n # cmap = 'RdYlBu_r'\\n\\n from warnings import filterwarnings\\n filterwarnings('ignore') # skip some warning messages\\n \\n # plot Code\\n \\n # calculate divergence and vorticity\\n uu = ds['u'][0, -1, :, :]\\n vv = ds['v'][0, -1, :, :]\\n u = zfun.fillit(uu)\\n v = zfun.fillit(vv)\\n u[np.isnan(u)] = 0\\n v[np.isnan(v)] = 0\\n \\n G = zrfun.get_basic_info(in_dict['fn'], only_G=True)\\n \\n dive = ((np.diff(u, axis=1)/G['DX'][:, 1:-1])[1:-1, :]\\n + (np.diff(v, axis = 0)/G['DY'][1:-1, :])[:, 1:-1])\\n #dive[G['mask_rho'][1:-1,1:-1]==False] = np.nan\\n \\n vort = np.diff(v, axis=1)/G['DX'][1:,1:] - np.diff(u, axis=0)/G['DY'][1:,1:]\\n #vort[G['mask_rho'][1:,1:]==False] = np.nan\\n \\n scl = 2e-3\\n \\n # panel 1\\n ax = fig.add_subplot(121)\\n # cs = plt.pcolormesh(G['lon_psi'], G['lat_psi'], dive/scl, cmap=cmap,\\n # vmin=-1, vmax=1)\\n cs = plt.pcolormesh(G['lon_rho'][1:-1,1:-1], G['lat_rho'][1:-1,1:-1], dive/scl, cmap=cmap,\\n vmin=-1, vmax=1, shading='gouraud')\\n tstr = (r'Surface Divergence (%0.1e $s^{-1}$)' % (scl))\\n #pfun.add_bathy_contours(ax, ds, txt=True)\\n pfun.add_coast(ax)\\n ax.axis(aa)\\n pfun.dar(ax)\\n ax.set_xlabel('Longitude')\\n ax.set_ylabel('Latitude')\\n ax.set_title(tstr)\\n pfun.add_info(ax, in_dict['fn'])\\n ax.set_xticks([-122.8, -122.7, -122.6])\\n ax.set_yticks([48, 48.1, 48.2])\\n #\\n # panel 2\\n ax = fig.add_subplot(122)\\n # cs = plt.pcolormesh(G['lon_rho'], G['lat_rho'], vort/scl, cmap=cmap,\\n # vmin=-1, vmax=1)\\n cs = plt.pcolormesh(G['lon_psi'], G['lat_psi'], vort/scl, cmap=cmap,\\n vmin=-1, vmax=1, shading='gouraud')\\n tstr = (r'Surface Vorticity (%0.1e $s^{-1}$)' % (scl))\\n ax.set_xticks([-122.8, -122.7, -122.6])\\n ax.set_yticks([])\\n #fig.colorbar(cs)\\n \\n # Inset colorbar\\n from mpl_toolkits.axes_grid1.inset_locator import inset_axes\\n cbaxes = inset_axes(ax, width=\\\"4%\\\", height=\\\"40%\\\", loc='lower left')\\n fig.colorbar(cs, cax=cbaxes, orientation='vertical')\\n \\n #pfun.add_bathy_contours(ax, ds)\\n pfun.add_coast(ax)\\n ax.axis(aa)\\n pfun.dar(ax)\\n ax.set_xlabel('Longitude')\\n ax.set_title(tstr) \\n \\n #fig.tight_layout()\\n # FINISH\\n ds.close()\\n if len(in_dict['fn_out']) > 0:\\n plt.savefig(in_dict['fn_out'])\\n plt.close()\\n else:\\n plt.show()\\n plt.rcdefaults()\",\n \"def __len__(self):\\n return 9 # logsfr_ratios has 6 bins\",\n \"def test_max_entangled_reconstruction(self, wires):\\n circuit = max_entangled_circuit(wires)\\n bits, recipes = circuit()\\n shadow = ClassicalShadow(bits, recipes)\\n\\n state = shadow.global_snapshots()\\n assert state.shape == (10000, 2**wires, 2**wires)\\n\\n state = np.mean(state, axis=0)\\n expected = np.zeros((2**wires, 2**wires))\\n expected[np.array([0, 0, -1, -1]), np.array([0, -1, 0, -1])] = 0.5\\n\\n assert qml.math.allclose(state, expected, atol=1e-1)\",\n \"def luminance(self):\\n \\n return (self.r + self.g + self.b) // 3\",\n \"def fuel_from_mass(m):\\n return (m/3).astype(int) - 2\",\n \"def plc_temp(coil_df):\",\n \"def cygx3IndFlux(self):\\n # --------------------------------------------------------------------------------------------- #\\n # Read data\\n fitsNnam = os.path.join(self.workpath, 'LCresults.fits')\\n lcTab = Table.read(fitsNnam)\\n detect = lcTab['ts'] >= self.tsmin\\n lcTab = lcTab[detect] \\n\\n ind08 = (lcTab['mjd'] > 54700) & (lcTab['mjd'] < 54900) \\n flux08 = lcTab['flux'][ind08]\\n fluxerr08 = lcTab['fluxerr'][ind08]\\n index08 = lcTab['index'][ind08]\\n indexerr08 = lcTab['indexerr'][ind08]\\n\\n ind09 = (lcTab['mjd'] > 54900) & (lcTab['mjd'] < 55100) \\n flux09 = lcTab['flux'][ind09]\\n fluxerr09 = lcTab['fluxerr'][ind09]\\n index09 = lcTab['index'][ind09]\\n indexerr09 = lcTab['indexerr'][ind09]\\n\\n scale = 10**int(np.floor(np.log10( np.mean( np.concatenate( (flux08, flux09), axis=0) ) ))) \\n\\n # --------------------------------------------------------------------------------------------- #\\n # Plot\\n indplt = FermiPlot(savepath='', xsize=8.5, ysize=6)\\n indplt.figname = os.path.join(self.workpath, 'IndvsFlux.pdf')\\n indplt.xlabel = r'Flux ($10^{%d}$ ph\\\\,cm$^{-2}$\\\\,s$^{-1}$)'%(int(np.log10(scale)))\\n indplt.ylabel = r'Index'\\n indplt.mksize = 2\\n indplt.color = self.lblue\\n indplt.label = r'2008'\\n indplt.plot(x=flux08/scale, xerr=fluxerr08/scale, y=index08, yerr=indexerr08)\\n indplt.color = self.loran\\n indplt.label = r'2009'\\n indplt.plot(x=flux09/scale, xerr=fluxerr09/scale, y=index09, yerr=indexerr09)\\n indplt.save()\\n\\n print(\\\"\\\\t=== Figure '{}' created ===\\\".format(indplt.figname)) \\n return\",\n \"def GEEviLandsat(ptsFile,metric,timeStep,sensor,buf,poly,username,folderOut, scalePix = 30):\\n \\n # load required libraries\\n import ee\\n import math\\n \\n # Initialize the Earth Engine object, using the authentication credentials.\\n ee.Initialize()\\n\\n ID_field = \\\"geeID\\\"\\n\\n #load pts or poly file\\n pts1 = ee.FeatureCollection('users/' + username + '/' + str(ptsFile))\\n\\n #define dictionary for raster random names\\n sensor_d = {}\\n sensor_d['L4'] = 'LANDSAT/LT04/C01/T1_SR'\\n sensor_d['L5'] = 'LANDSAT/LT05/C01/T1_SR'\\n sensor_d['L7'] = 'LANDSAT/LE07/C01/T1_SR'\\n sensor_d['L8'] = 'LANDSAT/LC08/C01/T1_SR'\\n\\n time_d = {}\\n time_d['lowest'] = 'rl'\\n time_d['month'] = 'rm'\\n time_d['year'] = 'ry'\\n \\n \\n #Computes the bits we need to extract.\\n def getQABits(image, start, end, newName):\\n pattern = 0\\n listB = list(range(start, end+1))\\n for one in listB:\\n pattern += math.pow(2, one)\\n pattern = int(pattern)\\n \\n return (image.select([0], [newName])\\n .bitwiseAnd(pattern)\\n .rightShift(start))\\n \\n for sen in sensor:\\n LS = ee.ImageCollection(sensor_d[sen])\\n #senL = [sen]\\n \\n def maskbyBits(img):\\n QA = img.select('pixel_qa')\\n QA1 = getQABits(QA, 3, 3, 'QA')\\n QA2 = getQABits(QA, 5, 5, 'QA')\\n\\n mask = QA1.eq(0).And(QA2.eq(0))\\n return img.updateMask(mask)\\n \\n LSm = LS.map(maskbyBits)\\n \\n lastImage = ee.Image(ee.ImageCollection(sensor_d[sen])\\n .sort('system:time_start',False)\\n .first())\\n lastImageDate = lastImage.get('system:index').getInfo()\\n\\n firstImage = ee.Image(ee.ImageCollection(sensor_d[sen])\\n .sort('system:time_start',True)\\n .first())\\n firstImageDate = firstImage.get('system:index').getInfo()\\n \\n startYear = int(firstImageDate[(len(firstImageDate)-8):(len(firstImageDate)-4)])\\n endYear = int(lastImageDate[(len(lastImageDate)-8):(len(lastImageDate)-4)])\\n startMonth = int(firstImageDate[(len(firstImageDate)-4):(len(firstImageDate)-2)])\\n endMonth = int(lastImageDate[(len(lastImageDate)-4):(len(lastImageDate)-2)])-1\\n startYearAll = startYear + 1\\n endYearAll = endYear - 1\\n \\n years = list(range(startYear, endYearAll + 1))\\n monthsEE = ee.List(list(range(startMonth,(12*len(years)+endMonth))))\\n yearsEE = ee.List(list(range(startYearAll, endYearAll + 1)))\\n \\n for met in metric:\\n # metL = [met]\\n\\n if (sen == 'L8' and met == \\\"NDVI\\\"):\\n bands = ['B5', 'B4']\\n elif (sen != 'L8' and met == \\\"NDVI\\\"):\\n bands = ['B4', 'B3']\\n elif (sen == 'L8' and met == \\\"NDWI\\\"):\\n bands = ['B5', 'B6']\\n elif (sen != 'L8' and met == \\\"NDWI\\\"):\\n bands = ['B4', 'B5']\\n elif (sen == 'L8' and met == \\\"NBR\\\"):\\n bands = ['B5', 'B7']\\n elif (sen != 'L8' and met == \\\"NBR\\\"):\\n bands = ['B4', 'B7']\\n #else:\\n #print(\\\"wrong metric specified\\\")\\n \\n def addVI(image):\\n vi = (image.normalizedDifference(bands)\\n .rename('VI'))\\n return image.addBands(vi)\\n\\n withVI = LSm.map(addVI)\\n\\n VI_col = withVI.select('VI')\\n\\n if timeStep == 'year':\\n\\n def map_m(i):\\n i = ee.Number(i).int()\\n image2 = (VI_col\\n .filter(ee.Filter.calendarRange(i, i, 'year'))\\n .first())\\n filtered = (VI_col\\n .filter(ee.Filter.calendarRange(i, i, 'year'))\\n .mean()\\n .copyProperties(image2,['system:time_start','system:time_end']))\\n return filtered\\n\\n img_col = ee.ImageCollection(yearsEE.map(map_m).flatten())\\n\\n elif timeStep == 'month':\\n \\n def map_m(i):\\n i = ee.Number(i)\\n y = i.divide(12).add(years[0]).int()\\n m = i.mod(12).add(1)\\n image2 = (VI_col\\n .filter(ee.Filter.calendarRange(m, m, 'month'))\\n .filter(ee.Filter.calendarRange(y, y, 'year'))\\n .first())\\n filtered = (VI_col\\n .filter(ee.Filter.calendarRange(m, m, 'month'))\\n .filter(ee.Filter.calendarRange(y, y, 'year'))\\n .mean()\\n .copyProperties(image2,['system:time_start','system:time_end']))\\n return filtered\\n\\n img_col = ee.ImageCollection(monthsEE.map(map_m).flatten())\\n\\n elif timeStep == 'lowest':\\n\\n img_col = VI_col\\n\\n #else:\\n #print(\\\"incorrect time step specified\\\")\\n \\n if buf > 0:\\n bufL = [buf]\\n def bufferPoly(feature):\\n return feature.buffer(bufL[0])\\n\\n ptsB = pts1.map(bufferPoly)\\n def table_m(image):\\n table = (image\\n .select('VI')\\n .reduceRegions(collection = ptsB.select([ID_field]),\\n reducer = ee.Reducer.mean(),\\n scale = scalePix))\\n \\n def table_add_date(f):\\n return f.set('startDate', ee.Date(image.get('system:time_start')))\\n\\n return table.map(table_add_date)\\n\\n triplets = img_col.map(table_m).flatten()\\n\\n task_tc = ee.batch.Export.table.toDrive(collection = triplets\\n .filter(ee.Filter.neq('mean', None))\\n .select(['.*'],None,False),\\n description = str(time_d[timeStep])+'_'+str(sen)+'_'+str(met)+'_'+str(years[0])+'_'+str(years[len(years)-1])+'_ptsB',\\n folder = folderOut,\\n fileFormat = 'CSV')\\n task_tc.start()\\n \\n \\n #print ('buffered pts by:' + str(buf) + ' for Landsat: ' + sen + '_' + met)\\n\\n elif poly > 0:\\n \\n def table_m(image):\\n table = (image\\n .select('VI')\\n .reduceRegions(collection = pts1.select([ID_field]),\\n reducer = ee.Reducer.mean(),\\n scale = scalePix))\\n \\n def table_add_date(f):\\n return f.set('startDate', ee.Date(image.get('system:time_start')))\\n\\n return table.map(table_add_date)\\n\\n triplets = img_col.map(table_m).flatten()\\n\\n task_tc = ee.batch.Export.table.toDrive(collection = triplets\\n .filter(ee.Filter.neq('mean', None))\\n .select(['.*'],None,False),\\n description = str(time_d[timeStep])+'_'+str(sen)+'_'+str(met)+'_'+str(years[0])+'_'+str(years[len(years)-1])+'_poly1',\\n folder = folderOut,\\n fileFormat = 'CSV')\\n task_tc.start()\\n \\n \\n #print ('spatial mean in poly: no buffer for Landsat: ' + sen + '_' + met)\\n\\n else:\\n def table_m(image):\\n table = (image\\n .select('VI')\\n .reduceRegions(collection = pts1.select([ID_field]),\\n reducer = ee.Reducer.mean(),\\n scale = scalePix))\\n \\n def table_add_date(f):\\n return f.set('startDate', ee.Date(image.get('system:time_start')))\\n\\n return table.map(table_add_date)\\n\\n triplets = img_col.map(table_m).flatten()\\n\\n task_tc = ee.batch.Export.table.toDrive(collection = triplets\\n .filter(ee.Filter.neq('mean', None))\\n .select(['.*'],None,False),\\n description = str(time_d[timeStep])+'_'+str(sen)+'_'+str(met)+'_'+str(years[0])+'_'+str(years[len(years)-1])+'_pts1',\\n folder = folderOut,\\n fileFormat = 'CSV')\\n task_tc.start()\\n \\n #print('value at point: no buffer for Landsat: ' + sen + '_' + met)\",\n \"def viscosity(altitude):\\n t_ref = temperature(0) # R\\n t = temperature(altitude) # R\\n s = 198.72 # R\\n mu_ref = 3.737 * 10 ** (-7) # [slug/(ft*s)]\\n mu = mu_ref*((t/t_ref)**(3/2))*(t_ref + s)/(t + s) # [slug/(ft*s)]\\n return mu\",\n \"def load_gred_dat(self, wannier_txt_file, index_word = \\\"WANNIER FUNCTIONS - LIST OF ACTIVE BANDS\\\", permutation = None):\\n f = open(wannier_txt_file, \\\"r\\\")\\n F = f.read()\\n f.close()\\n F = os.linesep.join([s for s in F.splitlines() if s]) #remove empty lines\\n F = F.split(index_word)[1].split(\\\"WANNIER\\\")[0].split(\\\"G = \\\")\\n \\n bands = np.array([literal_eval(i) for i in F[0].split()])-1 # indexing begins at 0\\n\\n for i in np.arange(1,len(F[1:])+1):\\n # Reading block index vector\\n \\n \\n G = -1*np.array([literal_eval(j) for j in F[i].split(\\\")\\\")[0].split(\\\"(\\\")[1].split()])\\n\\n gmap = self.mapping[self._c2i(G)]\\n \\n # parse block\\n \\n B = F[i].split(\\\")\\\")[1]\\n \\n # read elements in block\\n\\n for line in B.split(\\\"\\\\n\\\")[1:]:\\n # note : Crystal is column-major (fortran)\\n row_list = [literal_eval(j) for j in line.split()]\\n if len(row_list)!=0:\\n if len(row_list)==1:\\n # row_list contains index\\n columns = np.array(row_list) -1\\n else:\\n if type(row_list[1]) is int:\\n # line contains indices\\n columns = np.array(row_list) -1\\n \\n else:\\n # line contains elements\\n row = row_list[0] - 1\\n elements = np.array(row_list[1:]) \\n \\n self.blocks[ gmap ][row, columns + bands[0]] = elements #row and column \\n return bands\",\n \"def test_gleckler_index(self):\\n\\n # generate sample data\\n # sample data\\n tmp = np.zeros((5, 3, 1))\\n tmp[:,0,0] = np.ones(5)*1.\\n tmp[:,1,0] = np.ones(5)*2.\\n tmp[:,2,0] = np.ones(5)*5.\\n\\n # The data is like ...\\n #| 1 | 2 | 5 |\\n #| 1 | 2 | 5 |\\n #| 1 | 2 | 5 |\\n #| 1 | 2 | 5 |\\n #| 1 | 2 | 5 |\\n\\n x = self.D.copy()\\n x._temporal_subsetting(0, 4)\\n\\n x.data = np.ma.array(tmp, mask=tmp!=tmp)\\n x.std = np.ones(x.data.shape)\\n x.time[0] = pl.datestr2num('2000-02-15')\\n x.time[1] = pl.datestr2num('2000-03-15')\\n x.time[2] = pl.datestr2num('2000-04-15')\\n x.time[3] = pl.datestr2num('2000-05-15')\\n x.time[4] = pl.datestr2num('2000-06-15')\\n\\n y = self.D.copy()\\n y._temporal_subsetting(0, 4)\\n tmp = np.ones(x.data.shape) # sample data 2\\n y.data = np.ma.array(tmp, mask=tmp!=tmp)\\n y.time[0] = pl.datestr2num('2000-02-15')\\n y.time[1] = pl.datestr2num('2000-03-15')\\n y.time[2] = pl.datestr2num('2000-04-15')\\n y.time[3] = pl.datestr2num('2000-05-15')\\n y.time[4] = pl.datestr2num('2000-06-15')\\n\\n # Case 1: same area weights\\n # cell area\\n tmp = np.ones((3, 1))\\n x.cell_area = tmp*1.\\n\\n #| 1-1 | 2-1 | 5-1 |\\n #| 1-1 | 2-1 | 5-1 |\\n #| 1-1 | 2-1 | 5-1 |\\n #| 1-1 | 2-1 | 5-1 |\\n #| 1-1 | 2-1 | 5-1 |\\n #===================\\n #| 0 | 5 | 5*4**2=5*16. = 80 |\\n #==> E2 = sqrt(85./(15.))\\n D = GlecklerPlot()\\n r = D.calc_index(x, y, 'a', 'b', time_weighting=False)\\n\\n wt = np.ones(5) / 5.\\n ref = np.sqrt(((85./15.) * wt).sum())\\n t = np.abs(1. - r / ref)\\n self.assertLess(t, 0.000001) # relative error\\n\\n D = GlecklerPlot()\\n r = D.calc_index(x, y, 'a', 'b')\\n\\n wt = np.asarray([29., 31., 30., 31., 30.])\\n wt = wt / wt.sum()\\n ref = np.sqrt(((85./15.) * wt).sum())\\n t = np.abs(1. - r / ref)\\n self.assertLess(t, 0.000001) # relative error\\n\\n\\n\\n # Case 2: Different area weights\\n # cell area\\n tmp = np.ones((3, 1))\\n tmp[1, 0] = 2.\\n x.cell_area = tmp*1.\\n\\n #| 1-1=0 | 2-1=1 | 5-1=16 |\\n #| 1-1=0 | 2-1=1 | 5-1=16 |\\n #| 1-1=0 | 2-1=1 | 5-1=16 |\\n #| 1-1=0 | 2-1=1 | 5-1=16 |\\n #| 1-1=0 | 2-1=1 | 5-1=16 |\\n #--------------------------\\n # w = 0.25 w = 0.5 w=0.25|\\n #--------------------------\\n\\n # 0.25*0 + 0.5 * 1 + 0.25 * 16 = 0 + 0.5 + 4 = 4.5\\n # the mean of that is 4.5 for each timestep\\n # mean because the overall weights are calculated as such that\\n # they give a total weight if 1\\n\\n # diagnostic\\n D = GlecklerPlot()\\n r = D.calc_index(x, y, 'a', 'b', time_weighting=False)\\n\\n wt = np.ones(5) / 5.\\n ref = np.sqrt((4.5 * wt).sum())\\n t = np.abs(1. - r / ref)\\n self.assertLess(t, 0.000001) # relative error\\n\\n wt = np.asarray([29., 31., 30., 31., 30.])\\n wt = wt / wt.sum()\\n ref = np.sqrt((4.5 * wt).sum())\\n t = np.abs(1. - r / ref)\\n self.assertLess(t, 0.000001) # relative error\\n\\n # Case 3: use different std\\n x.std = np.ones(x.data.shape)\\n x.std[:, 2, 0] = 0.5\\n\\n #| 1-1=0 | 2-1=1 | 5-1=16 / 0.5 |\\n #| 1-1=0 | 2-1=1 | 5-1=16 / 0.5 |\\n #| 1-1=0 | 2-1=1 | 5-1=16 / 0.5 |\\n #| 1-1=0 | 2-1=1 | 5-1=16 / 0.5 |\\n #| 1-1=0 | 2-1=1 | 5-1=16 / 0.5 |\\n #--------------------------------\\n # w = 0.25 w = 0.5 w=0.25|\\n # 0 + 0.5 + 0.25*32 = 0.5 + 8 = 8.5\\n\\n D = GlecklerPlot()\\n r = D.calc_index(x, y, 'a', 'b', time_weighting=False)\\n\\n wt = np.ones(5) / 5.\\n ref = np.sqrt((8.5 * wt).sum())\\n t = np.abs(1. - r / ref)\\n self.assertLess(t, 0.000001) # relative error\\n\\n wt = np.asarray([29., 31., 30., 31., 30.])\\n wt = wt / wt.sum()\\n ref = np.sqrt((8.5 * wt).sum())\\n t = np.abs(1. - r / ref)\\n self.assertLess(t, 0.000001) # relative error\",\n \"def vis_loss(x, wn, nm1_air, n_glass, wl_los=[1.48,1.63,1.95,2.16], wl_his=[1.63,1.81,2.16,2.40], n_sub=4):\\n if len(x)>1:\\n phase = 2*np.pi*1e6*(x[0]*nm1_air + x[1]*n_glass + (x[0] - 1.0))*wn\\n else:\\n phase = 2*np.pi*1e6*(x[0]*nm1_air + (x[0] - 1.0))*wn\\n N_wn = len(wn)\\n if wl_los is None:\\n ix_los = N_wn//n_sub*np.arange(n_sub)\\n ix_his = N_wn//n_sub*(np.arange(n_sub)+1)\\n else:\\n #Wavelength and wavenumber are back to front, so this is \\n #slightly confusing.\\n ix_his = [np.where(1./wn <= wl_lo)[0][0] for wl_lo in wl_los][::-1]\\n ix_los = [np.where(1./wn <= wl_hi)[0][0] for wl_hi in wl_his][::-1]\\n mnsq = 0.\\n for ix_lo,ix_hi in zip(ix_los, ix_his):\\n phase_sub = phase[ix_lo:ix_hi]\\n mnsq += np.var(phase_sub)\\n return 100**2*mnsq/n_sub\",\n \"def internal_external_canopy_heat_capacity(lumped_cover_heat_capacity: float) -> float:\\n return 0.1 * lumped_cover_heat_capacity\",\n \"def sample_pin_position_range():\\n #Create a sample goniometer\\n g = TopazInHouseGoniometer()\\n\\n #Initialize the leg limits\\n g.relative_sample_position = column([0.0, 0.0, 0.0])\\n g.getplatepos(0.0, 0.0, 0.0)\\n g.calculate_leg_xy_limits(visualize=True)\\n\\n# if True:\\n# pylab.show()\\n# return\\n\\n n = 17\\n positions = np.linspace(-8, 8, n) #Range calculated in mm\\n allowed = np.zeros( (n,n,n) )\\n for (ix, x) in enumerate(positions):\\n print \\\"Calculating x\\\", x\\n for (iy, y) in enumerate(positions):\\n for (iz, z) in enumerate(positions):\\n #Set up\\n g.relative_sample_position = column([x, y, z])\\n allowed[ix,iy,iz] = g.are_angles_allowed([0., 0., 0.], return_reason=False)\\n\\n #Do a plot\\n\\n pylab.figure(1, figsize=[15,15])\\n pylab.title(\\\"Allowable XZ sample positions\\\")\\n for (iy, y) in enumerate(positions):\\n print \\\"At y of\\\", y, \\\", # of points = \\\", np.sum( allowed[:, iy,:])\\n if iy < 16:\\n pylab.subplot(4,4,iy+1)\\n pylab.pcolor(positions, positions, allowed[:, iy, :].transpose(), norm=pylab.Normalize(0, 1))\\n pylab.xlabel(\\\"x\\\")\\n pylab.ylabel(\\\"z\\\")\\n pylab.title(\\\"y = %.3f mm\\\" % y)\\n pylab.draw()\\n pylab.axis('equal')\\n pylab.show()\\n #pylab.\",\n \"def hapax(self):\\n return self.nlegomena(1)\",\n \"def degree_of_map(self):\\n if 2*self.component1.genus == self.space.genus and self.space.n == 0:\\n return 2\\n else:\\n return 1\",\n \"def phosphorene_4band():\\n a = 0.222\\n ax = 0.438\\n ay = 0.332\\n theta = 96.79 * (pi / 180)\\n phi = 103.69 * (pi / 180)\\n\\n lat = pb.Lattice(a1=[ax, 0], a2=[0, ay])\\n\\n h = a * sin(phi - pi / 2)\\n s = 0.5 * ax - a * cos(theta / 2)\\n lat.add_sublattices(\\n ('A', [-s/2, -ay/2, h], 0),\\n ('B', [ s/2, -ay/2, 0], 0),\\n ('C', [-s/2 + ax/2, 0, 0], 0),\\n ('D', [ s/2 + ax/2, 0, h], 0)\\n )\\n\\n lat.register_hopping_energies({\\n 't1': -1.22,\\n 't2': 3.665,\\n 't3': -0.205,\\n 't4': -0.105,\\n 't5': -0.055\\n })\\n\\n lat.add_hoppings(\\n # t1\\n ([-1, 0], 'A', 'D', 't1'),\\n ([-1, -1], 'A', 'D', 't1'),\\n ([ 0, 0], 'B', 'C', 't1'),\\n ([ 0, -1], 'B', 'C', 't1'),\\n # t2\\n ([ 0, 0], 'A', 'B', 't2'),\\n ([ 0, 0], 'C', 'D', 't2'),\\n # t3\\n ([ 0, 0], 'A', 'D', 't3'),\\n ([ 0, -1], 'A', 'D', 't3'),\\n ([ 1, 1], 'C', 'B', 't3'),\\n ([ 1, 0], 'C', 'B', 't3'),\\n # t4\\n ([ 0, 0], 'A', 'C', 't4'),\\n ([ 0, -1], 'A', 'C', 't4'),\\n ([-1, 0], 'A', 'C', 't4'),\\n ([-1, -1], 'A', 'C', 't4'),\\n ([ 0, 0], 'B', 'D', 't4'),\\n ([ 0, -1], 'B', 'D', 't4'),\\n ([-1, 0], 'B', 'D', 't4'),\\n ([-1, -1], 'B', 'D', 't4'),\\n # t5\\n ([-1, 0], 'A', 'B', 't5'),\\n ([-1, 0], 'C', 'D', 't5')\\n )\\n\\n return lat\",\n \"def lap_mat(self):\",\n \"def snow_depth(lon, lat, month):\\n\\n im = month - 1\\n\\n h0 = np.array( [28.01, 30.28, 33.89, 36.80, 36.93, 36.59,\\n 11.02, 4.64, 15.81, 22.66, 25.57, 26.67] )\\n a = np.array( [ 0.1270, 0.1056, 0.5486, 0.4046, 0.0214, 0.7021,\\n 0.3008, 0.3100, 0.2119, 0.3594, 0.1496, -0.1876] )\\n b = np.array( [-1.1833, -0.5908, -0.1996, -0.4005, -1.1795, -1.4819,\\n -1.2591, -0.6350, -1.0292, -1.3483, -1.4643, -1.4229] )\\n c = np.array( [-0.1164, -0.0263, 0.0280, 0.0256, -0.1076, -0.1195,\\n -0.0811, -0.0655, -0.0868, -0.1063, -0.1409, -0.1413] )\\n d = np.array( [-0.0051, -0.0049, 0.0216, 0.0024, -0.0244, -0.0009,\\n -0.0043, 0.0059, -0.0177, 0.0051, -0.0079, -0.0316] )\\n e = np.array( [ 0.0243, 0.0044, -0.0176, -0.0641, -0.0142, -0.0603,\\n -0.0959, -0.0005, -0.0723, -0.0577, -0.0258, -0.0029] )\\n\\n x = (90. - lat) * np.cos( np.radians(lon) )\\n y = (90. - lat) * np.sin( np.radians(lon) )\\n\\n h = ( h0[im] + ( a[im] * x ) + ( b[im] * y ) + ( c[im] * x * y ) +\\n ( d[im] * x * x ) + ( e[im] * y * y ) )\\n\\n return h\",\n \"def test_showalter_index():\\n pressure = units.Quantity(np.array([931.0, 925.0, 911.0, 891.0, 886.9, 855.0, 850.0, 825.6,\\n 796.3, 783.0, 768.0, 759.0, 745.0, 740.4, 733.0, 715.0,\\n 700.0, 695.0, 687.2, 684.0, 681.0, 677.0, 674.0, 661.9,\\n 657.0, 639.0, 637.6, 614.0, 592.0, 568.9, 547.4, 526.8,\\n 500.0, 487.5, 485.0]), 'hPa')\\n temps = units.Quantity(np.array([18.4, 19.8, 20.0, 19.6, 19.3, 16.8, 16.4, 15.1, 13.4,\\n 12.6, 11.2, 10.4, 8.6, 8.3, 7.8, 5.8, 4.6, 4.2, 3.4, 3.0,\\n 3.0, 4.4, 5.0, 5.1, 5.2, 3.4, 3.3, 2.4, 1.4, -0.4, -2.2,\\n -3.9, -6.3, -7.6, -7.9]), 'degC')\\n dewp = units.Quantity(np.array([9.4, 8.8, 6.0, 8.6, 8.4, 6.8, 6.4, 4.0, 1.0, -0.4, -1.1,\\n -1.6, 1.6, -0.2, -3.2, -3.2, -4.4, -2.8, -3.6, -4.0, -6.0,\\n -17.6, -25.0, -31.2, -33.8, -29.6, -30.1, -39.0, -47.6,\\n -48.9, -50.2, -51.5, -53.3, -55.5, -55.9]), 'degC')\\n\\n result = showalter_index(pressure, temps, dewp)\\n assert_almost_equal(result, units.Quantity(7.6024, 'delta_degC'), 4)\",\n \"def getHoracekFromEQ(self, ep):\\n #check if power is in MW or W (assumes Pinj > 500W)\\n if self.Pinj < 500:\\n Pinj = self.Pinj * 1e6\\n else:\\n Pinj = self.Pinj\\n\\n #find the plasma volume from the equilibrium\\n Rlim = ep.g['lcfs'][:,0]\\n Zlim = ep.g['lcfs'][:,1]\\n\\n #calculate cross sectional area using shoelace formula\\n i=np.arange(len(Rlim))\\n crossSecArea=np.abs(np.sum(Rlim[i-1]*Zlim[i]-Rlim[i]*Zlim[i-1])*0.5)\\n\\n #calculate (approximate) volume inside separatrix [m^3]\\n #assumes RmAxis is close to R of center of mass of crossSecArea\\n vol = crossSecArea * 2 * np.pi * ep.g['RmAxis']\\n\\n #assuming plasma is centered in machine here\\n zMin = ep.g['ZmAxis'] - 0.25\\n zMax = ep.g['ZmAxis'] + 0.25\\n zLCFS = ep.g['lcfs'][:,1]\\n rLCFS = ep.g['lcfs'][:,0]\\n #this prevents us from getting locations not at midplane\\n idx = np.where(np.logical_and(zLCFS>zMin,zLCFS<zMax))\\n Rmax = ep.g['lcfs'][:,0][idx].max()\\n Rmin = ep.g['lcfs'][:,0][idx].min()\\n # geometric quantities\\n Rgeo = (Rmax + Rmin) / 2.0\\n a = (Rmax - Rmin) / 2.0\\n aspect = a/Rgeo\\n\\n #maximum z point and elongation\\n idx2 = np.where(np.logical_and(rLCFS>Rmin,rLCFS<Rmax))\\n b = ep.g['lcfs'][:,1][idx].max() #assumes equatorial plane is z=0\\n k = b / a\\n\\n #lambda q from Horacek engineering scaling figure 6a\\n self.lqCF = 10 * (Pinj / vol)**(-0.38) * aspect**(1.3) * k**(-1.3) * 1e3 #in mm\\n return\",\n \"def find_aretes(params, df):\\n# colonnes = [\\\"num_graphe\\\",\\\"sommets_matE\\\",\\\"aretes_matE\\\"];\\n# df = pd.read_csv(params[\\\"path_save\\\"]+params[\\\"file_save\\\"], names = colonnes, skiprows=[0]);\\n nb_rd = 1+1.6*round(random.random(),1);\\n nb_min_max = random.randrange(df[\\\"aretes_matE\\\"].min(), df[\\\"aretes_matE\\\"].max());\\n aretes = nb_min_max * nb_rd;\\n return aretes;\\n pass\",\n \"def visitcomp(plate,mjd,indiv=False,apred='test') :\\n #plt.close('all')\\n load=apload.ApLoad(apred=apred)\\n a=load.apVisitSum(plate,mjd)[1].data\\n\\n #dr14=apload.ApLoad(dr='dr14')\\n p=yanny.yanny(os.environ['PLATELIST_DIR']+'/platePlans.par',np=True)\\n j=np.where(p['PLATEPLANS']['plateid'] == plate)[0][0]\\n locid=p['PLATEPLANS']['locationid'][j]\\n b=fits.open(os.environ['APOGEE_REDUX']+'/r8/fields/apo25m/{:04d}/apVisitSum-{:04d}-{:05d}.fits'.format(\\n locid,plate,mjd))[1].data\\n\\n fig,ax=plots.multi(2,2)\\n i1,i2=match.match(a['FIBERID'],b['FIBERID'])\\n plots.plotc(ax[0,0],a['FIBERID'][i1],a['VHELIO'][i1]-b['VHELIO'][i2],a['RV_TEFF'][i1],zr=[3500,5500],xt='Fiber',yt=r'$\\\\Delta$ VHELIO')\\n plots.plotc(ax[0,1],a['FIBERID'][i1],a['VHELIO'][i1]-b['VHELIO'][i2],a['RV_TEFF'][i1],zr=[3500,5500],yr=[-2.,2.],xt='Fiber',yt=r'$\\\\Delta$ VHELIO')\\n plots.plotc(ax[1,0],a['FIBERID'][i1],a['RV_TEFF'][i1]-b['RV_TEFF'][i2],a['RV_TEFF'][i1],zr=[3500,5500],xt='Fiber',yt=r'$\\\\Delta$ RV_TEFF')\\n plots.plotc(ax[1,1],a['RV_TEFF'][i1]-b['RV_TEFF'][i2],a['VHELIO'][i1]-b['VHELIO'][i2],a['RV_TEFF'][i1],zr=[3500,5500],xt=r'$\\\\Delta$ RV_TEFF',yt=r'$\\\\Delta$ VHELIO')\\n out=load.filename('Plate',chips=True,plate=plate,mjd=mjd)\\n outdir=os.path.dirname(out)\\n outname=os.path.basename(out).replace('-a','').replace('.fits','_dr14comp.png')\\n fig.tight_layout()\\n pdb.set_trace()\\n print(outdir+'/plots/'+outname)\\n fig.savefig(outdir+'/plots/'+outname)\\n\\n if indiv :\\n va=load.apPlate(plate,mjd)\\n vb={}\\n for chip in chips :\\n tmp=fits.open(os.environ['APOGEE_REDUX']+'/r8/apo25m/{:04d}/{:05d}/apPlate-{:s}-{:04d}-{:05d}.fits'.format(\\n plate,mjd,chip,plate,mjd))\\n vb[chip] = tmp\\n\\n fig,ax=plots.multi(1,3,hspace=0.3) \\n pfig,pax=plots.multi(1,3,hspace=0.3) \\n wfig,wax=plots.multi(1,3,hspace=0.3) \\n for i in range(len(i1)) :\\n fiber = a['FIBERID'][i1[i]]\\n if (a['VHELIO'][i1[i]]-b['VHELIO'][i2[i]]) > 0.5 :\\n print(fiber,a['VHELIO'][i1[i]],b['VHELIO'][i2[i]],a['RV_TEFF'][i1[i]],b['RV_TEFF'][i2[i]])\\n applot.chip(va,ax=ax,row=300-fiber,color='r')\\n #applot.chip(va,ax=pax,row=300-fiber,color='r',pixel=True)\\n applot.chip(vb,ax=ax,row=300-fiber,color='b')\\n #applot.chip(vb,ax=pax,row=300-fiber,color='b',pixel=True)\\n for ichip,chip in enumerate(chips) :\\n pax[ichip].plot(va[chip][1].data[300-fiber,:]/vb[chip][1].data[300-fiber,:])\\n wax[ichip].plot(va[chip][4].data[300-fiber,:]-vb[chip][4].data[300-fiber,:])\\n plt.show()\\n\\n pdb.set_trace()\\n for ichip in range(3) :\\n ax[ichip].cla()\\n pax[ichip].cla()\\n wax[ichip].cla()\\n plt.close()\",\n \"def NPL(self):\\n self.edge = np.zeros((np.sum(self.Adjmatrix), 3))\\n Temp = 0\\n for i in range(self.nodenum):\\n for j in range(self.nodenum):\\n if(self.Adjmatrix[i, j] == 1):\\n self.edge[Temp, 0], self.edge[Temp, 1], self.edge[Temp, 2] = i, j, self.Dismatrix[i, j]\\n Temp += 1\\n \\n self.Totallength = ((np.max(self.Geox) - np.min(self.Geox))**2 + (np.max(self.Geoy) - np.min(self.Geoy))**2)**0.5\\n self.norm_edge = self.edge[:, 2]/self.Totallength\",\n \"def test_isentropic_pressure_masked_column():\\n lev = [100000., 95000.] * units.Pa\\n tmp = np.ma.ones((len(lev), 5, 5))\\n tmp[0, :] = 296.\\n tmp[1, :] = 292.\\n tmp[:, :, -1] = np.ma.masked\\n tmp = units.Quantity(tmp, units.kelvin)\\n isentprs = isentropic_interpolation([296.] * units.kelvin, lev, tmp)\\n trueprs = np.ones((1, 5, 5)) * (1000. * units.hPa)\\n trueprs[:, :, -1] = np.nan\\n assert isentprs[0].shape == (1, 5, 5)\\n assert_almost_equal(isentprs[0], trueprs, 3)\",\n \"def make_montage_axial(overlay, underlay, png_name, cbar_name):\\n import matplotlib\\n import commands\\n# matplotlib.use('Agg')\\n import os\\n matplotlib.rcParams.update({'font.size': 5})\\n import matplotlib.cm as cm\\n ###\\n try:\\n from mpl_toolkits.axes_grid1 import ImageGrid\\n except:\\n from mpl_toolkits.axes_grid import ImageGrid\\n import matplotlib.pyplot as plt\\n import matplotlib.colors as col\\n import nibabel as nb\\n import numpy as np\\n\\n def determine_start_and_end(data, direction, percent):\\n x, y, z = data.shape\\n xx1 = 0\\n xx2 = x - 1\\n zz1 = 0\\n zz2 = z - 1\\n total_non_zero_voxels = len(np.nonzero(data.flatten())[0])\\n thresh = percent * float(total_non_zero_voxels)\\n start = None\\n end = None\\n if 'axial' in direction:\\n while(zz2 > 0):\\n d = len(np.nonzero(data[:, :, zz2].flatten())[0])\\n if float(d) > thresh:\\n break\\n zz2 -= 1\\n while(zz1 < zz2):\\n d = len(np.nonzero(data[:, :, zz1].flatten())[0])\\n if float(d) > thresh:\\n break\\n zz1 += 1\\n start = zz1\\n end = zz2\\n else:\\n while(xx2 > 0):\\n d = len(np.nonzero(data[xx2, :, :].flatten())[0])\\n if float(d) > thresh:\\n break\\n xx2 -= 1\\n while(xx1 < xx2):\\n d = len(np.nonzero(data[xx1, :, :].flatten())[0])\\n if float(d) > thresh:\\n break\\n xx1 += 1\\n start = xx1\\n end = xx2\\n return start, end\\n def get_spacing(across, down, dimension):\\n space = 10\\n prod = (across*down*space)\\n if prod > dimension:\\n while(across*down*space) > dimension:\\n space -= 1\\n else:\\n while(across*down*space) < dimension:\\n space += 1\\n return space\\n\\n Y = nb.load(underlay).get_data()\\n X = nb.load(overlay).get_data()\\n X = X.astype(np.float16)\\n Y = Y.astype(np.float16)\\n\\n if 'skull_vis' in png_name:\\n X[X < 20.0] = 0.0\\n if 'skull_vis' in png_name or 't1_edge_on_mean_func_in_t1' in png_name or 'MNI_edge_on_mean_func_mni' in png_name:\\n max_ = np.nanmax(np.abs(X.flatten()))\\n X[X != 0.0] = max_\\n print '^^', np.unique(X)\\n z1, z2 = determine_start_and_end(Y, 'axial', 0.0001)\\n spacing = get_spacing(6, 3, z2 - z1)\\n x, y, z = Y.shape\\n fig = plt.figure(1)\\n max_ = np.max(np.abs(Y))\\n\\n if ('snr' in png_name) or ('reho' in png_name) or ('vmhc' in png_name) or ('sca_' in png_name) or ('alff' in png_name) or ('centrality' in png_name) or ('temporal_regression_sca' in png_name) or ('temporal_dual_regression' in png_name):\\n grid = ImageGrid(fig, 111, nrows_ncols=(3, 6), share_all=True, aspect=True, cbar_mode=\\\"single\\\", cbar_pad=0.2, direction=\\\"row\\\")\\n else:\\n grid = ImageGrid(fig, 111, nrows_ncols=(3, 6), share_all=True, aspect=True, direction=\\\"row\\\")\\n\\n zz = z1\\n for i in range(6*3):\\n\\n if zz >= z2:\\n break\\n\\n im = grid[i].imshow(np.rot90(Y[:, :, zz]), cmap=cm.Greys_r)\\n zz += spacing\\n\\n x, y, z = X.shape\\n X[X == 0.0] = np.nan\\n max_ = np.nanmax(np.abs(X.flatten()))\\n print '~~', max_\\n\\n\\n zz = z1\\n im = None\\n print '~~~', z1, ' ', z2\\n for i in range(6*3):\\n\\n\\n if zz >= z2:\\n break\\n if cbar_name is 'red_to_blue':\\n\\n im = grid[i].imshow(np.rot90(X[:, :, zz]), cmap=cm.get_cmap(cbar_name), alpha=0.82, vmin=0, vmax=max_) ###\\n elif cbar_name is 'green':\\n im = grid[i].imshow(np.rot90(X[:, :, zz]), cmap=cm.get_cmap(cbar_name), alpha=0.82, vmin=0, vmax=max_)\\n else:\\n im = grid[i].imshow(np.rot90(X[:, :, zz]), cmap=cm.get_cmap(cbar_name), alpha=0.82, vmin=- max_, vmax=max_)\\n\\n grid[i].axes.get_xaxis().set_visible(False)\\n grid[i].axes.get_yaxis().set_visible(False)\\n zz += spacing\\n\\n cbar = grid.cbar_axes[0].colorbar(im)\\n\\n if 'snr' in png_name:\\n cbar.ax.set_yticks(drange(0, max_))\\n\\n elif ('reho' in png_name) or ('vmhc' in png_name) or ('sca_' in png_name) or ('alff' in png_name) or ('centrality' in png_name) or ('temporal_regression_sca' in png_name) or ('temporal_dual_regression' in png_name):\\n cbar.ax.set_yticks(drange(-max_, max_))\\n\\n\\n plt.show()\\n plt.axis(\\\"off\\\")\\n png_name = str(os.path.join(os.getcwd(), png_name))\\n plt.savefig(png_name, dpi=300, bbox_inches='tight')\\n plt.close()\\n\\n matplotlib.rcdefaults()\\n\\n return png_name\",\n \"def zeige_auf_sensehat(self):\\n if self.sense is not None:\\n sense.clear(farbe_tot)\\n for zelle in self.lebendig:\\n x, y = zelle\\n if 0 <= x < 8 and 0 <= y < 8:\\n sense.set_pixel(zelle[0], zelle[1]. farbe_lebendig)\",\n \"def foetus_pics(self):\\n pic = 0\\n if 0.0 not in self.allele:\\n self.contamination = 2\\n pic = 3\\n elif 0.0 == self.allele[1]:\\n pic = 1\\n else:\\n pic = 2\\n return pic\",\n \"def cloud_cover_to_ghi_linear(cloud_cover, ghi_clear, offset=35):\\n\\n offset = offset / 100.\\n cloud_cover = cloud_cover / 100.\\n ghi = (offset + (1 - offset) * (1 - cloud_cover)) * ghi_clear\\n return ghi\",\n \"def branching_factor(data, loc):\\n\\n return 20\",\n \"def delta(self):\\r\\n return 1 - xl.Refractive_Index_Re(self.compound, self.energy, self.density)\",\n \"def test_epix100a():\\n basedir = '/reg/g/psdm/detector/alignment/cspad/calib-cxi-ds1-2014-05-15/'\\n fname_geometry = basedir + 'calib/CsPad::CalibV1/CxiDs1.0:Cspad.0/geometry/2-end.data'\\n fname_data = basedir + 'cspad-arr-cxid2714-r0023-lysozyme-rings.txt'\\n\\n #basedir = '/reg/neh/home1/dubrovin/LCLS/GeometryCalib/calib-xpp-Epix100a-2014-11-05/'\\n #fname_geometry = basedir + 'calib/Epix100a::CalibV1/NoDetector.0:Epix100a.0/geometry/0-end.data'\\n #fname_data = basedir + 'epix100a-ndarr-ave-clb-xppi0614-r0073.dat'\\n\\n geometry = GeometryAccess(fname_geometry, pbits=0o377)\\n amp_range = (-4,10)\\n\\n rows, cols = geometry.get_pixel_coord_indexes()\\n\\n root, ext = os.path.splitext(fname_data)\\n arr = np.load(fname_data) if ext == '.npy' else np.loadtxt(fname_data, dtype=np.float)\\n\\n logger.info('shapes rows: %s cols: %s weight: %s' % (str(rows.shape), str(cols.shape), str(arr.shape)))\\n img = img_from_pixel_arrays(rows,cols,W=arr)\\n\\n axim = gg.plotImageLarge(img,amp_range=amp_range)\\n gg.move(500,10)\\n gg.show()\",\n \"def getIndexRef(row, col, frame, midRange, maxIndex): \\n indexRef = numpy.zeros(12).reshape(4,3)\\n indexRef[:,0] = numpy.arange(-1, 3) * midRange + row\\n indexRef[:,1] = numpy.arange(-1, 3) * midRange + col\\n indexRef[:,2] = numpy.arange(-1, 3) * midRange + frame\\n \\n for i in range(4):\\n for j in range(3):\\n if indexRef[i,j] < 0 or indexRef[i,j] > maxIndex:\\n indexRef[i,j] = -1\\n return indexRef\",\n \"def test_lifted_index_500hpa_missing():\\n pressure = np.array([1014., 1000., 997., 981.2, 947.4, 925., 914.9, 911.,\\n 902., 883., 850., 822.3, 816., 807., 793.2, 770.,\\n 765.1, 753., 737.5, 737., 713., 700., 688., 685.,\\n 680., 666., 659.8, 653., 643., 634., 615., 611.8,\\n 566.2, 516., 487., 484.2, 481., 475., 460.,\\n 400.]) * units.hPa\\n temperature = np.array([24.2, 24.2, 24., 23.1, 21., 19.6, 18.7, 18.4,\\n 19.2, 19.4, 17.2, 15.3, 14.8, 14.4, 13.4, 11.6,\\n 11.1, 10., 8.8, 8.8, 8.2, 7., 5.6, 5.6,\\n 5.6, 4.4, 3.8, 3.2, 3., 3.2, 1.8, 1.5,\\n -3.4, -9.3, -13.1, -13.1, -13.1, -13.7, -15.1,\\n -23.5]) * units.degC\\n dewpoint = np.array([23.2, 23.1, 22.8, 22., 20.2, 19., 17.6, 17.,\\n 16.8, 15.5, 14., 11.7, 11.2, 8.4, 7., 4.6,\\n 5., 6., 4.2, 4.1, -1.8, -2., -1.4, -0.4,\\n -3.4, -5.6, -4.3, -2.8, -7., -25.8, -31.2, -31.4,\\n -34.1, -37.3, -34.1, -37.3, -41.1, -37.7, -58.1,\\n -57.5]) * units.degC\\n parcel_prof = parcel_profile(pressure, temperature[0], dewpoint[0])\\n li = lifted_index(pressure, temperature, parcel_prof)\\n assert_almost_equal(li, -7.9806301 * units.delta_degree_Celsius, 1)\",\n \"def knolg(self):\\n if self._knolg is None:\\n if self.nptir > 0:\\n self._knolg = self.get_mesh_l2g_numbering()\\n else:\\n self._knolg = np.zeros((self.npoin3), dtype=np.int32)\\n\\n return self._knolg\",\n \"def reScaleLandsat(self,img):\\n \\n\\t\\tthermalBand = ee.List(['thermal'])\\n\\t\\tthermal = ee.Image(img).select(thermalBand).multiply(10)\\n \\n\\t\\totherBands = ee.Image(img).bandNames().removeAll(thermalBand)\\n\\t\\tscaled = ee.Image(img).select(otherBands).divide(0.0001)\\n \\n\\t\\timage = ee.Image(scaled.addBands(thermal)).int16()\\n \\n\\t\\treturn image.copyProperties(img)\",\n \"def manage_grad(self,test=0):\\n\\t\\tsx1 = self.abs_sobel_thresh(orient='x', thresh_min=25, thresh_max=55)\\n\\t\\tsd_less = self.dir_threshold(sobel_kernel=9, thresh=(1.0721,np.pi/2))\\n\\t\\tsd_less2 = self.dir_threshold(sobel_kernel=9, thresh=(0.0982,0.3927))\\n\\t\\tsx3 = binary_substr(binary_substr(sx1,sd_less),sd_less2)\\n\\n\\t\\thls = cv2.cvtColor(self.img, cv2.COLOR_RGB2HLS)\\n\\t\\th_less = ch_threshold(hls, 0, (85,256))\\n\\t\\tsx3 = binary_substr(sx3, h_less)\\n\\t\\th_less2 = ch_threshold(hls, 0, (-1,6))\\n\\t\\tsx3 = binary_substr(sx3, h_less2)\\n\\t\\tif test==1:\\n\\t\\t print(\\\"nothing to test\\\")\\n\\t\\treturn sx3\",\n \"def decreaseSearch(self):\\n\\n self.detections = self.detections()\\n self.detections.location = self.location\\n for index, row in enumerate(self.magdata):\\n\\n if row[3] < self.upper*self.background and row[3] < self.background - 2*self.std:\\n # less than 50% of background and 2 std\\n #print \\\"Large decrease (less than \\\" + str(self.upper*self.background) + \\\")\\\", row[3]\\n self.detections.largeDecrease.append([index,self.timestamps[index], row[3]])\\n \\n elif row[3] < self.lower*self.background and row[3] < self.background - self.std:\\n # less than 25% of background and 1 std\\n #print \\\"Decrease (less than \\\" + str(self.lower*self.background) + \\\")\\\", row[3]\\n self.detections.smallDecrease.append([index,self.timestamps[index], row[3]])\\n \\n elif row[3] > (1 + self.upper)*self.background and row[3] > self.background + 2*self.std:\\n # greater than 50% of background\\n #print \\\"Large increase (greater than \\\" + str((1 + self.upper)*self.background) + \\\")\\\", row[3]\\n self.detections.largeIncrease.append([index,self.timestamps[index], row[3]])\\n \\n elif row[3] > (1 + self.lower)*self.background and row[3] > self.background + self.std:\\n # greater than 25% of background\\n #print \\\"Increase (greater than \\\" + str((1 + self.lower)*self.background) + \\\")\\\", row[3]\\n self.detections.smallIncrease.append([index,self.timestamps[index], row[3]])\\n\\n \\n if resultCounter(self.detections) == 0:\\n self.detections.results.append(None)\\n print \\\"No observable jumps/dips in magnetic field strength\\\"\\n else:\\n for attribute, bins in classIterator(self.detections):\\n if 'crease' in attribute:\\n self.detections.results.append('Number of ' + attribute + ': ' + str(len(bins)))\",\n \"def grass_drass():\",\n \"def test_ridge(self):\\n fun = get_problem('ridge', dimension=self.dimension, lower=-64, upper=64)\\n self.assertAlmostEqual(fun(self.array10), 371.0, delta=1e-4)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5831233","0.5830266","0.5719483","0.563902","0.55948025","0.55843574","0.5583961","0.5577517","0.55708694","0.5549658","0.55016595","0.5486436","0.54773384","0.54688746","0.5463584","0.54448295","0.53914726","0.5348724","0.5341465","0.5329872","0.5314997","0.53144276","0.53135145","0.5303402","0.5301894","0.52767056","0.5256424","0.5252162","0.524737","0.52465284","0.5237794","0.522824","0.52238476","0.52234894","0.52154356","0.521172","0.52068496","0.5202252","0.5194222","0.51876545","0.5183452","0.51811016","0.51790136","0.51710284","0.51676494","0.51634187","0.5163412","0.51541424","0.51501024","0.5145557","0.51452005","0.51433456","0.5141259","0.5139859","0.5135346","0.51341057","0.5124994","0.5124117","0.5119168","0.51144975","0.5110063","0.5106856","0.5106252","0.51061785","0.51060385","0.5103962","0.5099556","0.5097507","0.50920355","0.5086726","0.5085617","0.5083085","0.5073521","0.5065906","0.5065582","0.5062455","0.50605047","0.5053246","0.50502306","0.5045421","0.50447875","0.5040772","0.50407517","0.5035871","0.50252","0.5023634","0.5023113","0.5020982","0.5011357","0.50107545","0.501021","0.5007149","0.5005769","0.5001943","0.49990335","0.49930602","0.49911022","0.4988728","0.4984947","0.49844474"],"string":"[\n \"0.5831233\",\n \"0.5830266\",\n \"0.5719483\",\n \"0.563902\",\n \"0.55948025\",\n \"0.55843574\",\n \"0.5583961\",\n \"0.5577517\",\n \"0.55708694\",\n \"0.5549658\",\n \"0.55016595\",\n \"0.5486436\",\n \"0.54773384\",\n \"0.54688746\",\n \"0.5463584\",\n \"0.54448295\",\n \"0.53914726\",\n \"0.5348724\",\n \"0.5341465\",\n \"0.5329872\",\n \"0.5314997\",\n \"0.53144276\",\n \"0.53135145\",\n \"0.5303402\",\n \"0.5301894\",\n \"0.52767056\",\n \"0.5256424\",\n \"0.5252162\",\n \"0.524737\",\n \"0.52465284\",\n \"0.5237794\",\n \"0.522824\",\n \"0.52238476\",\n \"0.52234894\",\n \"0.52154356\",\n \"0.521172\",\n \"0.52068496\",\n \"0.5202252\",\n \"0.5194222\",\n \"0.51876545\",\n \"0.5183452\",\n \"0.51811016\",\n \"0.51790136\",\n \"0.51710284\",\n \"0.51676494\",\n \"0.51634187\",\n \"0.5163412\",\n \"0.51541424\",\n \"0.51501024\",\n \"0.5145557\",\n \"0.51452005\",\n \"0.51433456\",\n \"0.5141259\",\n \"0.5139859\",\n \"0.5135346\",\n \"0.51341057\",\n \"0.5124994\",\n \"0.5124117\",\n \"0.5119168\",\n \"0.51144975\",\n \"0.5110063\",\n \"0.5106856\",\n \"0.5106252\",\n \"0.51061785\",\n \"0.51060385\",\n \"0.5103962\",\n \"0.5099556\",\n \"0.5097507\",\n \"0.50920355\",\n \"0.5086726\",\n \"0.5085617\",\n \"0.5083085\",\n \"0.5073521\",\n \"0.5065906\",\n \"0.5065582\",\n \"0.5062455\",\n \"0.50605047\",\n \"0.5053246\",\n \"0.50502306\",\n \"0.5045421\",\n \"0.50447875\",\n \"0.5040772\",\n \"0.50407517\",\n \"0.5035871\",\n \"0.50252\",\n \"0.5023634\",\n \"0.5023113\",\n \"0.5020982\",\n \"0.5011357\",\n \"0.50107545\",\n \"0.501021\",\n \"0.5007149\",\n \"0.5005769\",\n \"0.5001943\",\n \"0.49990335\",\n \"0.49930602\",\n \"0.49911022\",\n \"0.4988728\",\n \"0.4984947\",\n \"0.49844474\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.618945"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":16875,"cells":{"query":{"kind":"string","value":"Read h5 format data file"},"document":{"kind":"string","value":"def read_data(path):\n with h5py.File(path, 'r') as hf:\n data = np.array(hf.get('data'))\n return data"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def read_hdf5(path_to_file):\n\n print(\"\\nReading HDF5 file: \", path_to_file)\n file = h5py.File(path_to_file, 'r')\n\n # List the groups\n groups = list(file.keys())\n print(\"Groups available: \", groups)\n\n # Read Zemax Metadata\n zemax_metadata = {}\n print(\"\\nZemax Metadata:\")\n for key in file['Zemax Metadata'].attrs.keys():\n print('{} : {}'.format(key, file['Zemax Metadata'].attrs[key]))\n zemax_metadata[key] = file['Zemax Metadata'].attrs[key]\n\n # Read the analysis groups\n for group_name in groups:\n if group_name != 'Zemax Metadata':\n analysis_group = file[group_name]\n print('\\nAnalysis: ', group_name)\n # For each Analysis Group we loop over subgroups\n for subgroup_key in analysis_group.keys():\n subgroup = analysis_group[subgroup_key]\n print('Subgroup #', subgroup_key)\n # List the metadata of the subgroup\n for att_key in subgroup.attrs.keys():\n print(' {} : {}'.format(att_key, subgroup.attrs[att_key]))\n\n file.close()\n\n return zemax_metadata","def read_h5meta(filepath):\n with open(filepath) as file:\n content = parse_h5meta(file)\n\n return content","def read_hdf5(file_path):\n if not os.path.exists(file_path):\n logging.fatal(\"Cannot read feature file {}.\".format(file_path))\n exit()\n hdf5_file = h5py.File(file_path, 'r')\n data = np.array(hdf5_file['data'])\n hdf5_file.close()\n\n return data","def load_h5_file(file_path):\n # load\n fr = h5py.File(file_path, 'r')\n a_group_key = list(fr.keys())[0]\n data = list(fr[a_group_key])\n # transform to appropriate numpy array \n data=data[0:]\n data = np.stack(data, axis=0)\n return data","def load_h5_file(file_path):\n # load\n fr = h5py.File(file_path, 'r')\n a_group_key = list(fr.keys())[0]\n data = list(fr[a_group_key])\n # transform to appropriate numpy array \n data=data[0:]\n data = np.stack(data, axis=0)\n return data","def _read_h5_dataset(self):\n dev = self.getParentObj()\n top = dev.getFileDescriptor()\n for attr in self._attr_list:\n data = top.get(attr)\n if data is None:\n msg = \"Unable to open object (Object %s doesn't exist)\" % attr\n raise TaurusException(msg)\n top = data\n return data","def loadh5(fname, path='/data'):\n fp = open_read(fname)\n slab = fp.get_node(path)\n mat = slab.read()\n fp.close()\n return mat","def read_h5(fpath):\n _check_h5_installed()\n import h5py as h5\n\n f = h5.File(fpath, 'r')\n res = dict()\n\n if 'est_n_dips' in f.keys():\n res['est_n_dips'] = list(f['est_n_dips'][:])\n else:\n res['est_n_dips'] = 'Not available.'\n\n if 'exponents' in f.keys():\n res['exponents'] = f['exponents'][:]\n else:\n res['exponents'] = 'Not available.'\n\n if 'ch_names' in f.keys():\n _temp = list(f['ch_names'][:].flatten())\n res['ch_names'] = list(x.decode('utf-8', 'ignore') for x in _temp)\n del _temp\n else:\n res['ch_names'] = 'Not available.'\n\n for _k in ['prob_map', 'est_locs', 'model_sel', 'est_dip_mom_std']:\n if _k in f.keys():\n res[_k] = list(f[_k][_key][:] for _key in sorted(f[_k].keys(),\n key=lambda x: int(x)))\n else:\n res[_k] = 'Not available.'\n\n for _k in ['final_dip_mom_std', 'tmin', 'tmax', 'fmin', 'fmax', 'subsample']:\n if _k in f.keys():\n res[_k] = f[_k][()]\n else:\n res[_k] = None\n\n for _k in ['lambda', 'noise_std', 'dip_mom_std', 'max_n_dips',\n 'subject', 'subject_viz', 'data_path', 'fwd_path',\n 'cov_path', 'src_path', 'lf_path', 'fwd_fixed_ori']:\n if _k in f.keys():\n res[_k] = f[_k][()]\n else:\n res[_k] = 'Not available.'\n\n if 'est_dip_moms' in f.keys():\n est_dip_moms_temp = np.asarray(list(f['est_dip_moms'][_key][:] for _key in sorted(f['est_dip_moms'].keys(),\n key=lambda x: int(x))))\n if f['fwd_fixed_ori'][()]:\n est_dip_moms_aux = np.zeros((res['est_locs'][-1].shape[0], est_dip_moms_temp.shape[0]))\n for i in range(est_dip_moms_temp.shape[0]):\n _temp = est_dip_moms_temp[i, :].reshape(-1, 1)\n for j in range(res['est_locs'][-1].shape[0]):\n est_dip_moms_aux[j, i] += _temp[j]\n elif f['fwd_fixed_ori'][()] == 'Not available.':\n print('Uknown forward source orientation. Skipping dipole moments.')\n else:\n est_dip_moms_aux = np.zeros((res['est_locs'][-1].shape[0], est_dip_moms_temp.shape[0], 3))\n for i in range(est_dip_moms_temp.shape[0]):\n _temp = est_dip_moms_temp[i, :].reshape(-1, 3)\n for j in range(res['est_locs'][-1].shape[0]):\n est_dip_moms_aux[j, i, :] += _temp[j]\n res['est_dip_moms'] = est_dip_moms_aux\n f.close()\n return res","def read_data(path):\n with h5py.File(path, 'r') as hf:\t\n input_ = np.array(hf.get('input'))\n label_ = np.array(hf.get('label'))\n return input_, label_","def load_h5(filename: str, **kwargs):\n return open_h5(filename, 'r', **kwargs)","def read_hdf5(filename, namelist=None, **kwargs):\n\n print('Reading %s...'%filename)\n\n fid = h5py.File(filename, mode='r')\n \n data = read_hdf5_tree(fid, namelist, **kwargs)\n\n fid.close()\n \n print('Finished reading %s.'%filename)\n return data","def read_data_test(path):\n with h5py.File(path, 'r') as hf:\n input_ = np.array(hf.get('data'))\n label_ = np.array(hf.get('label'))\n\t\n return input_, label_","def read_data(path):\n with h5py.File(path, 'r') as hf:\n data = np.array(hf.get('data'))\n label = np.array(hf.get('label'))\n return data, label","def read_h5file(self, fname, datasetname):\n with h5py.File(fname, 'r') as f:\n atom_pos = f.get(datasetname + '/r').value # atom position -> N x 3 array\n ion_list = f.get(\n datasetname + '/xyz').value # length = N, contain atom type id for each atom\n self.atom_pos = atom_pos[np.argsort(ion_list)]\n _, idx = np.unique(np.sort(ion_list), return_index=True)\n self.split_idx = np.append(idx, [len(ion_list)])\n\n # get atom factor table, sorted by atom type id\n atom_type = f.get(\n datasetname + '/T').value # atom type array, each type is represented by an integer\n self.num_atom_types = len(atom_type)\n ff_table = f.get(datasetname + '/ff').value\n self.ff_table = ff_table[np.argsort(atom_type)]\n\n self.q_sample = f.get(datasetname + '/halfQ').value\n self.num_q_samples = len(self.q_sample)\n self.compton_q_sample = f.get(datasetname + '/Sq_halfQ').value\n self.num_compton_q_samples = len(self.compton_q_sample)\n self.sBound = f.get(datasetname + '/Sq_bound').value\n self.nFree = f.get(datasetname + '/Sq_free').value","def _readHDF5(self):\n\n h5 = h5py.File(self.pointInputFile, 'r')\n self.coords = h5['geometry/vertices'][:]\n self.stations = h5['stations'][:]\n self.dispRaw = h5['vertex_fields/displacement'][self.timeStep,:,:]\n h5.close()\n\n self.numStations = self.coords.shape[0]\n\n return","def read_data(path):\n with h5py.File(path, 'r') as hf:\n data = np.array(hf.get('data'))\n label = np.array(hf.get('label'))\n return data, label","def loadhdf5file(file_h5, key='data'):\n\n with h5py.File(file_h5, 'r') as data:\n # Add datasets to dictionary\n info_value = {}\n info_attrs = {}\n\n for i in np.arange(len(data.items())):\n info_value.update({str(list(data.items())[i][0]): data[str(list(data.items())[i][0])].value})\n\n for i in np.arange(len(data[key].attrs)):\n info_attrs.update({list(data[key].attrs.keys())[i]: list(data[key].attrs.values())[i]})\n\n return info_value, info_attrs","def readH5 (dataset):\n if dataset.attrs['type']==PhotoZTemplateSED.typestr:\n return PhotoZTemplateSED()\n else:\n return None","def read_lh5(in_file, key=None, cols=None, ilo=0, ihi=None):\n if \".lh5\" not in in_file:\n print(\"Error, unknown file:\", in_file)\n exit()\n \n # open the file in context manager to avoid weird crashes \n t_start = time.time()\n with h5py.File(os.path.expanduser(in_file)) as hf:\n \n header = get_lh5_header(f_lh5, verbose=False)\n\n # pick off first table by default, or let the user specify the name\n table = list(header.keys())[0] if key is None else key\n df_hdr = header[table] \n \n # this function reads the Table into memory\n df = read_table(table, hf, df_hdr, ilo, ihi)\n\n # t_elapsed = time.time() - t_start\n # print(\"elapsed: {t_elapsed:.4f} sec\")\n \n return df","def FromH5(self,h5File=None):\r\n\r\n logStr = \"{0:s}.{1:s}: \".format(self.__class__.__name__, sys._getframe().f_code.co_name)\r\n logger.debug(\"{0:s}{1:s}\".format(logStr,'Start.')) \r\n\r\n if h5File == None:\r\n h5File=self.h5File\r\n\r\n #Check if h5File exists\r\n if not os.path.exists(h5File): \r\n logStrFinal=\"{0:s}{1:s}: Not Existing!\".format(logStr,h5File) \r\n raise XmError(logStrFinal) \r\n \r\n try:\r\n self.dataFrames={} \r\n with pd.HDFStore(h5File) as h5Store:\r\n h5Keys=sorted(h5Store.keys())\r\n for h5Key in h5Keys:\r\n match=re.search('(/)(\\w+$)',h5Key)\r\n key=match.group(2)\r\n logger.debug(\"{0:s}{1:s}: Reading h5Key {2:s} to tableName {3:s}.\".format(logStr,h5File,h5Key,key)) \r\n self.dataFrames[key]=h5Store[h5Key]\r\n \r\n\r\n except Exception as e:\r\n logStrFinal=\"{:s}Exception: Line: {:d}: {!s:s}: {:s}\".format(logStr,sys.exc_info()[-1].tb_lineno,type(e),str(e))\r\n logger.error(logStrFinal) \r\n raise XmError(logStrFinal) \r\n \r\n finally:\r\n h5Store.close()\r\n logger.debug(\"{0:s}{1:s}\".format(logStr,'_Done.'))","def data_reading(data_file):\n\n # The number of samples is needed to read the HDF5 file, which is stored in the name\n # Split by '/' to remove the directory, and by '.' to remove the file format\n file_name = data_file.split(\"/\")[-1].split(\".\")[-2]\n # The file name ends with the number of samples and before that the number of included neighbours\n n_samples = int(file_name.split(\"_\")[-1])\n n_neighbours = int(file_name.split(\"_\")[-2])\n\n # Read the data\n h5f = h5py.File(data_file, 'r')\n\n # The data set name is the name of the path where the data file can be found\n data = h5f[\"dataset_{}\".format(n_samples)][:]\n\n # Close the H5py file\n h5f.close()\n\n return data, n_samples, n_neighbours","def data_reading(data_file):\n\n # The number of samples is needed to read the HDF5 file, which is stored in the name\n # Split by '/' to remove the directory, and by '.' to remove the file format\n file_name = data_file.split(\"/\")[-1].split(\".\")[-2]\n # The file name ends with the number of samples and before that the number of included neighbours\n n_samples = int(file_name.split(\"_\")[-1])\n n_neighbours = int(file_name.split(\"_\")[-2])\n\n # Read the data\n h5f = h5py.File(data_file, 'r')\n\n # The data set name is the name of the path where the data file can be found\n data = h5f[\"dataset_{}\".format(n_samples)][:]\n\n # Close the H5py file\n h5f.close()\n\n return data, n_samples, n_neighbours","def _read(self, item):\n return read_hdf5(self.file_name, title=self._get_h5_path(item))","def open_h5meta(filepath):\n data = dict()\n h5meta_content = read_h5meta(filepath)\n for file in h5meta_content[\"filelist\"]:\n data[file] = read_detector_data(file)\n\n return data","def read_data(path):\n with h5py.File(path, 'r') as hf:\n data = np.array(hf.get('data'))\n label = np.array(hf.get('label'))\n\n data, label=data[:,:,:,0:2], label[:,:,:,0]\n #data=np.expand_dims(data,axis=-1)\n label=np.expand_dims(label,axis=-1)\n\n return data, label","def load_h5(fname: str, path: str='/') -> dict:\n try:\n with h5py.File(fname, 'r') as f:\n dataMap = recursively_load_dict_contents_from_group(f, path)\n except IOError as e:\n print(f\"Cannot open HDF5 file {fname}\")\n print(f\"IOError: {e}\")\n\n return dataMap","def from_hdf(filename):\n # Function for iteratively parsing the file to create the dictionary\n def visit_group(obj, sdict):\n name = obj.name.split('/')[-1]\n #indent = len(obj.name.split('/'))-1\n #print \" \"*indent,name, obj.value if (type(obj) == h5py.Dataset) else \":\"\n if type(obj) in [ h5py.Dataset ]:\n sdict[name] = obj.value\n if type(obj) in [ h5py.Group, h5py.File ]:\n sdict[name] = {}\n for sobj in obj.values():\n visit_group(sobj, sdict[name])\n\n data = {}\n try:\n h5file = h5py.File(os.path.expandvars(filename), 'r')\n # Run over the whole dataset\n for obj in h5file.values():\n visit_group(obj, data)\n except IOError, e:\n logging.error(\"Unable to read HDF5 file \\'%s\\'\" % filename)\n logging.error(e)\n raise e\n finally:\n h5file.close()\n\n return data","def read_generic_hdf5(fname):\n f = h5py.File(fname, \"r\")\n fcontent = {}\n\n def filldict(x, y):\n # create a new container\n tmp = {}\n # add attributes if present\n if len(y.attrs) > 0:\n tmp['attrs'] = dict(y.attrs)\n # add data if it is a dataset\n if isinstance(y, h5py.Dataset):\n tmp['data'] = np.array(y)\n # only add to the dictionary, if we have something meaningful to add\n if tmp != {}:\n fcontent[x] = tmp\n\n f.visititems(filldict)\n\n f.close()\n return fcontent","def read_uvh5(\n self,\n filename,\n antenna_nums=None,\n antenna_names=None,\n ant_str=None,\n bls=None,\n frequencies=None,\n freq_chans=None,\n times=None,\n time_range=None,\n polarizations=None,\n blt_inds=None,\n keep_all_metadata=True,\n read_data=True,\n data_array_dtype=np.complex128,\n multidim_index=False,\n background_lsts=True,\n run_check=True,\n check_extra=True,\n run_check_acceptability=True,\n strict_uvw_antpos_check=False,\n ):\n if not os.path.exists(filename):\n raise IOError(filename + \" not found\")\n\n # open hdf5 file for reading\n with h5py.File(filename, \"r\") as f:\n # extract header information\n header = f[\"/Header\"]\n self._read_header(\n header,\n filename,\n run_check_acceptability=run_check_acceptability,\n background_lsts=background_lsts,\n )\n\n if not read_data:\n # don't read in the data. This means the object is incomplete,\n # but that may not matter for many purposes.\n return\n\n # Now read in the data\n dgrp = f[\"/Data\"]\n self._get_data(\n dgrp,\n antenna_nums,\n antenna_names,\n ant_str,\n bls,\n frequencies,\n freq_chans,\n times,\n time_range,\n polarizations,\n blt_inds,\n data_array_dtype,\n keep_all_metadata,\n multidim_index,\n run_check,\n check_extra,\n run_check_acceptability,\n strict_uvw_antpos_check,\n )\n\n return","def read_data(path):\n with h5py.File(path, \"r\") as f:\n transformed_poses = np.array(f['transformed_poses'])\n extracted_poses = np.array(f['poses'])\n target = np.array(f['target'])\n \n return extracted_poses, transformed_poses, target","def read_h5_file(folder, filen):\n \n ### file path\n \n fpath = folder + filen + '.h5'\n assert os.path.exists(fpath), \"The out.h5 file does NOT exist for \" + fpath\n fl = h5py.File(fpath, 'r')\n \n ### cell information\n \n xu = np.array(fl['/cells/comu'], dtype=np.float32)\n \n ### simulation information\n \n lx = fl['/info/box/x'][...]\n ly = fl['/info/box/y'][...]\n dt = fl['/info/dt'][...]\n nsteps = fl['/info/nsteps'][...]\n nfils = fl['/info/nfils'][...]\n nbeads = fl['/info/nbeads'][...]\n nsamp = fl['/info/nsamp'][...]\n nbpf = fl['/info/nbpf'][...]\n \n ### simulation parameters\n \n density = fl['/param/density'][...]\n kappa = fl['/param/kappa'][...]\n km = fl['/param/km'][...]\n pa = fl['/param/pa'][...]\n pp = fl['/param/pp'][...]\n bl = fl['/param/bl'][...]\n sigma = fl['/param/sigma'][...]\n \n fl.close()\n \n ### generate classes to submerge data\n \n sim = misc_tools.Simulation(lx, ly, dt, nsteps, nfils, nbeads, nsamp, nbpf, \\\n density, kappa, km, pa, pp, bl, sigma)\n fils = misc_tools.Cells(xu, nbpf, sim)\n \n return sim, fils","def load_data(file_path):\n with h5py.File(file_path) as f:\n # load meta info\n fs, channels, p_names, signals = _get_info(f)\n\n # load raw data\n data = [f['protocol{}/raw_data'.format(k + 1)][:] for k in range(len(p_names))]\n df = pd.DataFrame(np.concatenate(data), columns=channels)\n\n # load signals data\n signals_data = [f['protocol{}/signals_data'.format(k + 1)][:] for k in range(len(p_names))]\n df_signals = pd.DataFrame(np.concatenate(signals_data), columns=['signal_'+s for s in signals])\n df = pd.concat([df, df_signals], axis=1)\n\n # load timestamps\n if 'timestamp' in df:\n timestamp_data = [f['protocol{}/timestamp_data'.format(k + 1)][:] for k in range(len(p_names))]\n df['timestamps'] = np.concatenate(timestamp_data)\n\n # events data\n events_data = [f['protocol{}/mark_data'.format(k + 1)][:] for k in range(len(p_names))]\n df['events'] = np.concatenate(events_data)\n\n # set block names and numbers\n df['block_name'] = np.concatenate([[p]*len(d) for p, d in zip(p_names, data)])\n df['block_number'] = np.concatenate([[j + 1]*len(d) for j, d in enumerate(data)])\n return df, fs, channels, p_names","def load_data(infile, nstep): \n \n f = h5py.File(infile, 'r')\n \n edges_grp = f['edges']\n xedges = np.asarray(edges_grp['x'][nstep], dtype=float)\n yedges = np.asarray(edges_grp['y'][nstep], dtype=float)\n\n time = np.asarray(f['time'][nstep])\n\n tables_grp = f['tables']\n rho_hist = np.asarray(tables_grp['rho'][nstep], dtype=float)\n vx_hist = np.asarray(tables_grp['vx'][nstep], dtype=float)\n vy_hist = np.asarray(tables_grp['vy'][nstep], dtype=float)\n vorticity = np.asarray(tables_grp['vorticity'][nstep], dtype=float) \n \n box_grp = f['box']\n lx = box_grp['x'][...]\n ly = box_grp['y'][...]\n \n #nsteps = f['nsteps'][...]\n f.close()\n\n return lx, ly, time, xedges, yedges, rho_hist, vx_hist, vy_hist, vorticity","def read_h5(self):\n infile = h5py.File(self.inf_name,'r')\n\n vardict = self.labdict\n #store data with the correct labels\n for k in infile['plasma/1d'].keys():\n try:\n vardict[k] = infile[self.labdict[k]].value\n except:\n vardict[k] = []\n\n vardict['a_ions']=infile['/plasma/anum'].value\n vardict['znum']=infile['/plasma/znum'].value\n \n\n self.rho_in = vardict['rho']\n self._rho_vol = infile['distributions/rhoDist/abscissae/dim1'].value[1:]\n self._volumes = infile['distributions/rhoDist/shellVolume'].value\n self.nrho_in = np.size(self.rho_in)\n\n if vardict['a_ions'][0]!='/':\n self.nspec = len(vardict['a_ions'])\n else:\n self.nspec = vardict['ni'].shape[1]\n print(\"Number of ions: \", self.nspec)\n if len(vardict['a_ions'])!=len(vardict['znum']):\n print(\"ERROR! array of A and Z don't have the same length\")\n\n self.A = vardict['a_ions']\n self.Z = vardict['znum']\n self.nion = self.nspec\n \n self.te_in = vardict['te'][:]\n self.ne_in = vardict['ne'][:]\n self.ti_in = vardict['ti'][:]\n ni1_in = vardict['ni'][:,0]\n self.ni_in = np.zeros((self.nion, self.nrho_in),dtype=float)\n self.ni_in[0,:] = ni1_in\n if self.nion==2:\n ni2_in = vardict['ni'][:,1]\n self.ni_in[1,:] = ni2_in\n elif self.nion==3:\n ni2_in = vardict['ni'][:,1]\n ni3_in = vardict['ni'][:,2]\n self.ni_in[1,:] = ni2_in\n self.ni_in[2,:] = ni3_in\n\n try:\n self.vt_in = vardict['vtor']\n except:\n self.vt_in = np.zeros(self.nrho_in,dtype=float)\n\n try:\n self.zeff_in = vardict['zeff'][:]\n except:\n self.zeff_in = np.zeros(self.nrho_in,dtype=float)\n\n self.ni = np.zeros((self.nion, self.nrho),dtype = float)\n self.spline()","def read_hdf5(self, file_name,\r\n projections_start=None,\r\n projections_end=None,\r\n projections_step=None,\r\n slices_start=None,\r\n slices_end=None,\r\n slices_step=None,\r\n pixels_start=None,\r\n pixels_end=None,\r\n pixels_step=None,\r\n white_start=None,\r\n white_end=None,\r\n dark_start=None,\r\n dark_end=None,\r\n dtype='float32'):\r\n print \"Reading data...\"\r\n self.file_name = file_name\r\n\r\n # Initialize f to null.\r\n f = None\r\n\r\n # Get the file_name in lower case.\r\n lFn = file_name.lower()\r\n\r\n # Split the string with the delimeter '.'\r\n end = lFn.split('.')\r\n\r\n # If the string has an extension.\r\n if len(end) > 1:\r\n # Check.\r\n if end[len(end) - 1] == 'h5' or end[len(end) - 1] == 'hdf':\r\n f = Hdf5()\r\n\r\n # If f != None the call read on it.\r\n if not f == None:\r\n # Read data from exchange group.\r\n self.data = f.read(file_name,\r\n array_name='exchange/data',\r\n x_start=projections_start,\r\n x_end=projections_end,\r\n x_step=projections_step,\r\n y_start=slices_start,\r\n y_end=slices_end,\r\n y_step=slices_step,\r\n z_start=pixels_start,\r\n z_end=pixels_end,\r\n z_step=pixels_step).astype(dtype)\r\n\r\n # Read white field data from exchange group.\r\n print white_start, white_end, slices_start, slices_end\r\n self.white = f.read(file_name,\r\n array_name='exchange/data_white',\r\n x_start=white_start,\r\n x_end=white_end,\r\n y_start=slices_start,\r\n y_end=slices_end,\r\n y_step=slices_step,\r\n z_start=pixels_start,\r\n z_end=pixels_end,\r\n z_step=pixels_step).astype(dtype)\r\n\r\n # Read dark field data from exchange group.\r\n self.dark = f.read(file_name,\r\n array_name='exchange/data_dark',\r\n x_start=dark_start,\r\n x_end=dark_end,\r\n y_start=slices_start,\r\n y_end=slices_end,\r\n y_step=slices_step,\r\n z_start=pixels_start,\r\n z_end=pixels_end,\r\n z_step=pixels_step).astype(dtype)\r\n\r\n # Assign the rotation center.\r\n self.center = self.data.shape[2] / 2\r\n else:\r\n print 'Unsupported file.'","def parse_hdf5(inp, close=True, **kwargs):\n import json\n import h5py\n # Path\n path = kwargs.pop('path', '/')\n # Open\n if isinstance(inp, basestring):\n hdf5 = h5py.File(inp, 'r')\n else:\n hdf5 = inp\n # Data\n data = hdf5[path+'data'][()]\n # Meta\n if 'meta' in hdf5[path].keys():\n meta = json.loads(hdf5[path+'meta'][()])\n # Headers\n for jj,heads in enumerate(meta['headers']):\n try:\n meta['headers'][jj] = fits.Header.fromstring(meta['headers'][jj])\n except TypeError: # dict\n if not isinstance(meta['headers'][jj], dict):\n raise IOError(\"Bad meta type\")\n else:\n meta = None\n # Units\n units = json.loads(hdf5[path+'units'][()])\n for key,item in units.items():\n if item == 'dimensionless_unit':\n units[key] = u.dimensionless_unscaled\n else:\n units[key] = getattr(u, item)\n # Other arrays\n try:\n sig = data['sig']\n except (NameError, IndexError):\n sig = None\n try:\n co = data['co']\n except (NameError, IndexError):\n co = None\n # Finish\n if close:\n hdf5.close()\n return XSpectrum1D(data['wave'], data['flux'], sig=sig, co=co,\n meta=meta, units=units, **kwargs)","def load_h5(fname, surfmap=True):\n filenames = glob.glob(fname)\n print(\"Files found: {}\".format(filenames))\n fin = h5py.File(filenames[0])\n meas = fin['measurement0'] # Wavefront data located in 'measurement0'\n opdsets = meas['genraw']\n wvl = opdsets.attrs['wavelength'][:]\n wvl = float(wvl[:-3])\n # Get the x pixel spacing\n try:\n iscale = float(opdsets.attrs['xpix'][:-3])\n except TypeError:\n iscale = 0.0\n print(\"No Calibration Dimensioning Found in H5 file\")\n # Return either surface map or fringe map\n if surfmap is True:\n data = np.asarray(opdsets['data'])\n data[data > 1e10] = np.nan # Eliminates \"bad\" data sets to NAN\n data *= wvl * mask_data(filenames[0])\n else:\n data = np.asarray(meas['reserve_interferogram']['frame4']['data'])\n return data, wvl, iscale","def loadHDF5(ofile):\n \n ## Groups \n size_grp = ofile['size']\n beads_grp = ofile['beads']\n props_grp = ofile['props']\n \n ## Datasets\n size = np.asarray(size_grp['size'])\n x = np.asarray(beads_grp['x'])\n comx = np.asarray(props_grp['comx'])\n \n ## Filament list\n fil_grp = props_grp['filament_list']\n fil_list_1 = np.asarray(fil_grp['1'])\n \n print size\n print x\n print comx\n print fil_list_1\n \n return","def read_hdf5(ID_images:str, path:str = \"data/dataset/\"):\n images, labels = [], []\n\n #open the HDF5 file\n file = h5py.File(path +ID_images+\"_lens.h5\", \"r\")\n\n images = np.array(file[\"/images\"]).astype(\"float64\")\n labels = pd.read_hdf(path +ID_images+'_meta.h5', \"table\")\n\n return images, labels","def _loadHDF5File(self, filename):\n matfile = h5py.File(filename)\n\n self.StokesI = np.transpose(matfile['StokesI'][:,:])\n self.StokesQ = np.transpose(matfile['StokesQ'][:,:])\n self.StokesU = np.transpose(matfile['StokesU'][:,:])\n self.StokesV = np.transpose(matfile['StokesV'][:,:])\n self.detectorPosition = matfile['detectorPosition'][:,0]\n self.detectorDirection = matfile['detectorDirection'][:,0]\n self.detectorVisang = matfile['detectorVisang'][0,0]\n\n try: self.wall = matfile['wall'][:,:]\n except KeyError: pass\n\n try: self.separatrix = matfile['separatrix'][:,:]\n except KeyError: pass","def load_document_representations(path):\n return h5py.File(path, \"r\")","def h5ToDf(filename):\n log.info(f\"Import data from: {filename}\")\n with h5py.File(filename, \"r\") as hf :\n d = {}\n for name in list(hf.keys()):\n d[name] = np.array(hf[name][:])\n df = pd.DataFrame(data=d)\n return(df)","def h5ToDf(filename):\n log.info(f\"Import data from: {filename}\")\n with h5py.File(filename, \"r\") as hf :\n d = {}\n for name in list(hf.keys()):\n d[name] = np.array(hf[name][:])\n df = pd.DataFrame(data=d)\n return(df)","def read_hdf5(filename, **extras):\n groups = {'sampling': {}, 'obs': {}}\n res = {}\n with h5py.File(filename, \"r\") as hf:\n # loop over the groups\n for group, d in groups.items():\n # read the arrays in that group into the dictionary for that group\n for k, v in hf[group].items():\n d[k] = np.array(v)\n # unserialize the attributes and put them in the dictionary\n for k, v in hf[group].attrs.items():\n try:\n d[k] = json.loads(v)\n except:\n d[k] = v\n # do top-level attributes.\n for k, v in hf.attrs.items():\n try:\n res[k] = json.loads(v)\n except:\n res[k] = v\n res.update(groups['sampling'])\n res['obs'] = groups['obs']\n try:\n res['obs']['filters'] = load_filters([str(f) for f in res['obs']['filters']])\n except:\n pass\n try:\n res['rstate'] = pickle.loads(res['rstate'])\n except:\n pass\n try:\n mp = [names_to_functions(p.copy()) for p in res['model_params']]\n res['model_params'] = mp\n except:\n pass\n\n return res","def read(filename: str) -> orm.Data:\n return from_bands_inspect(load(hdf5_file=filename))","def from_h5(cls, filename, pulse_number=None):\n df = pd.read_hdf(filename)\n return cls._sort_and_filter_dataframe(df, pulse_number)","def read_wabbit_hdf5(file, verbose=True, return_iteration=False):\n import h5py\n import numpy as np\n\n if verbose:\n print(\"~~~~~~~~~~~~~~~~~~~~~~~~~\")\n print(\"Reading file %s\" % (file) )\n\n fid = h5py.File(file,'r')\n b = fid['coords_origin'][:]\n x0 = np.array(b, dtype=float)\n\n b = fid['coords_spacing'][:]\n dx = np.array(b, dtype=float)\n\n b = fid['blocks'][:]\n data = np.array(b, dtype=float)\n\n b = fid['block_treecode'][:]\n treecode = np.array(b, dtype=float)\n\n # get the dataset handle\n dset_id = fid.get('blocks')\n \n # from the dset handle, read the attributes\n time = dset_id.attrs.get('time')\n iteration = dset_id.attrs.get('iteration')\n box = dset_id.attrs.get('domain-size')\n version=dset_id.attrs.get('version')\n\n\n fid.close()\n\n jmin, jmax = get_max_min_level( treecode )\n N = data.shape[0]\n Bs = data.shape[1:]\n Bs = np.asarray(Bs[::-1]) # we have to flip the array since hdf5 stores in [Nz, Ny, Nx] order\n \n if version == 20200408 or version == 20231602:\n Bs = Bs-1\n #print(\"!!!Warning old (old branch: newGhostNodes) version of wabbit format detected!!!\")\n else:\n print(\"This file includes redundant points\")\n \n if verbose:\n print(\"Time=%e it=%i N=%i Bs[0]=%i Bs[1]=%i Jmin=%i Jmax=%i\" % (time, iteration, N, Bs[0], Bs[1], jmin, jmax) )\n print(\"~~~~~~~~~~~~~~~~~~~~~~~~~\")\n\n\n if return_iteration:\n return time, x0, dx, box, data, treecode, iteration[0]\n else:\n return time, x0, dx, box, data, treecode","def hdf5_file(self):\n if self._hdf5_file is None:\n self._hdf5_file = h5py.File(self.hdf5_path, 'r', swmr=self.hdf5_use_swmr, libver='latest')\n return self._hdf5_file","def readH5 (dataset):\n ## also use old name \n if dataset.attrs['type']==PhotoZDoubleGauss.typestr:\n sigma=float(dataset.attrs['sigma'])\n Acat=float(dataset.attrs['Acat'])\n zcat=float(dataset.attrs['zcat'])\n sigmacat=float(dataset.attrs['sigmacat'])\n return PhotoZDoubleGauss(sigma,Acat,zcat,sigmacat)\n else:\n return None","def read_conn(path, simulation):\n with h5py.File(path+simulation+'-conn','r') as f: \n print(path+simulation+'-conn' )\n out_deg=np.array(f['conn']) \n return out_deg","def test_h5file(dl2_shower_geometry_file):\n from ctapipe.io.tableloader import TableLoader\n\n # no input raises error\n with pytest.raises(ValueError):\n with TableLoader():\n pass\n\n # test we can use an already open file\n with tables.open_file(dl2_shower_geometry_file, mode=\"r+\") as h5file:\n with TableLoader(h5file=h5file) as loader:\n assert 25 in loader.subarray.tel\n loader.read_subarray_events()\n loader.read_telescope_events()","def load_h5py_file(fname, offsets = [0, 0, 0]):\n # Load the data\n f = h5py.File(fname, 'r') # r for read only\n print(\"Available fields: \", list(f.keys())) # f is a dictionary. Let's look at the keys\n\n # Create variables from loaded dictionary\n neural_data = f['ripple_data'][:,0:32]\n emg_data = f['ripple_data'][:,32:]\n force_data = f['data'][0:6,:].transpose()\n fs = f['mySampleRate'][:]\n\n # Transform matrix for force data\n TF = [[1.117\t, -0.096747,\t 1.7516, 0.03441, -0.88072, 0.042127, -0.89026],\n [0.3134, 0.0041349, 0.0045219, -0.055942, 1.5273, 0.037719,-1.5227],\n [0.135\t, 1.4494, -0.061075, 1.6259, 0.083867, 1.5999, 0.0058155]]\n TF = np.array(TF)\n\n # Read force data\n force_data = np.concatenate((np.ones((len(force_data),1)), force_data), axis=1)\n force_data = force_data @ TF.transpose()\n\n # Make baseband zero\n force_data[:,0] = force_data[:,0] - offsets[0]\n force_data[:,1] = force_data[:,1] - offsets[1]\n force_data[:,2] = force_data[:,2] - offsets[2]\n\n # Use sent and received pulse signals to allign DAQ and RIPPLE data\n pulse_sent = f['data'][6,:].transpose()\n ps_ind, = np.nonzero(pulse_sent>1)\n ps_ind = ps_ind[0]\n\n pulse_received = f['ttl_data'][:,0]\n pr_ind, = np.nonzero(pulse_received>2000)\n pr_ind = pr_ind[0]\n\n p_diff = ps_ind - pr_ind\n\n # Align data\n if p_diff > 0:\n pulse_sent = np.concatenate((pulse_sent[p_diff:], np.zeros((p_diff,))), axis=0)\n trailing = np.mean(force_data[-int(fs*0.1):], axis=0) * np.ones((p_diff,1))\n force_data = np.concatenate((force_data[p_diff:,:], trailing))\n else:\n pulse_sent = np.concatenate((np.zeros((-p_diff,)), pulse_sent[:p_diff]), axis=0)\n leading = np.mean(force_data[:int(fs * 0.1)], axis=0) * np.ones((-p_diff, 1))\n force_data = np.concatenate((leading, force_data[:p_diff,:]))\n\n # Choose force channel for analysis\n force_data = force_data[:,1]\n force_data = -force_data # Invert the sign (increased as applied force increased)\n\n # Choose EMG data\n emg_data = emg_data[:,(5,15)]-emg_data[:,(23,25)]\n\n # Re-order EMG data so that 1. Dorsal 2. Biceps 3. Ventral 4. Triceps\n positions3 = (0,1)\n emg_data = emg_data[:,positions3]\n\n # Corresponding time vectors\n time = f['ripple_time'][:]\n return neural_data, emg_data, force_data, time, fs","def load_hdf(fn, key='data'):\n with open_file(fn, mode=\"r\") as f:\n data = f.get_node('/'+key).read()\n return data","def read_wabbit_hdf5_dir(dir):\n import numpy as np\n import re\n import ntpath\n import os\n\n it=0\n data={'time': [],'x0':[],'dx':[],'treecode':[]}\n # we loop over all files in the given directory\n for file in os.listdir(dir):\n # filter out the good ones (ending with .h5)\n if file.endswith(\".h5\"):\n # from the file we can get the fieldname\n fieldname=re.split('_',file)[0]\n print(fieldname)\n time, x0, dx, box, field, treecode = read_wabbit_hdf5(os.path.join(dir, file))\n #increase the counter\n data['time'].append(time[0])\n data['x0'].append(x0)\n data['dx'].append(dx)\n data['treecode'].append(treecode)\n if fieldname not in data:\n # add the new field to the dictionary\n data[fieldname]=[]\n data[fieldname].append(field)\n else: # append the field to the existing data field\n data[fieldname].append(field)\n it=it+1\n # the size of the domain\n data['box']=box\n #return time, x0, dx, box, data, treecode\n return data","def readmd5_sum_file(path):\n with open(path, \"r\") as f:\n hashes = {}\n while True:\n line = f.readline()\n if not line:\n break\n h, name = line.rstrip().split(' ', 1)\n hashes[name] = h\n return hashes","def open_h5(filename: str, mode: str, **kwargs):\n import h5py\n return h5py.File(filename, mode, **kwargs)","def read_detector_data(filepath):\n with h5py.File(filepath, \"r\") as h5f:\n data = h5f[\"/entry1/area_detector2/data\"][:]\n\n # reshape data to a correct shape (2006 issue)\n n, cols, rows = data.shape\n data = data.reshape(n, rows, cols)\n\n det_data = {\"data\": data}\n\n det_data[\"rot_angle\"] = h5f[\"/entry1/area_detector2/rotation_angle\"][:] # om, sometimes ph\n det_data[\"pol_angle\"] = h5f[\"/entry1/ZEBRA/area_detector2/polar_angle\"][:] # gammad\n det_data[\"tlt_angle\"] = h5f[\"/entry1/ZEBRA/area_detector2/tilt_angle\"][:] # nud\n det_data[\"ddist\"] = h5f[\"/entry1/ZEBRA/area_detector2/distance\"][:]\n det_data[\"wave\"] = h5f[\"/entry1/ZEBRA/monochromator/wavelength\"][:]\n det_data[\"chi_angle\"] = h5f[\"/entry1/sample/chi\"][:] # ch\n det_data[\"phi_angle\"] = h5f[\"/entry1/sample/phi\"][:] # ph\n det_data[\"UB\"] = h5f[\"/entry1/sample/UB\"][:].reshape(3, 3)\n\n return det_data","def read_hdf5_group(filename, gname, vars_name=None):\n fid = h5py.File(filename, 'r')\n gid = fid.get(gname)\n if vars_name is None: vars_name = list(gid.keys())\n\n data = {}\n for var_name in vars_name:\n try:\n dset = gid.get(var_name)\n shape = dset.shape\n data[var_name] = np.zeros(shape)\n dset.read_direct(data[var_name])\n except:\n pass\n fid.close()\n print('Read from ', ''.join((filename,'/',gname)))\n print('Variables names = ')\n print('\\n'.join(vars_name))\n\n return data, vars_name","def load(file):\n if isinstance(file, bob.io.base.HDF5File):\n return file.read(\"array\")\n else:\n return bob.io.base.load(file)","def get_data_hdf(file):\n data = {}\n\n def func(name, dset):\n if not hasattr(dset, 'shape'):\n return # not array, can't be image\n if isinstance(dset, h5py.Dataset):\n data[dset.name] = dset.name\n\n file_h5 = h5py.File(file, 'r')\n file_h5.visititems(func)\n return file_h5, data","def view_hdf5(filename):\n # handling input errors\n if not isinstance(filename, str):\n raise TypeError('Passed value of `filename` is not a string! Instead, it is: '\n + str(type(filename)))\n if not filename.split('/')[-1].split('.')[-1] == 'hdf5':\n raise TypeError('`filename` is not type = .hdf5! Instead, it is: '\n + filename.split('/')[-1].split('.')[-1])\n # pring groups and datasets in first three layers\n print('**** {} ****'.format(filename))\n hdf5 = h5py.File(filename, 'r')\n for _, layer_1 in enumerate(list(hdf5.keys())):\n if isinstance(hdf5[layer_1], h5py.Group):\n print('\\033[1m{}\\033[0m'.format(layer_1))\n for _, layer_2 in enumerate(list(hdf5[layer_1].keys())):\n if isinstance(hdf5['{}/{}'.format(layer_1, layer_2)], h5py.Group):\n print('| \\033[1m{}\\033[0m'.format(layer_2))\n for _, layer_3 in enumerate(list(hdf5['{}/{}'.format(layer_1, layer_2)])):\n if isinstance(hdf5['{}/{}/{}'.format(layer_1, layer_2, layer_3)],\n h5py.Group):\n print('| | \\033[1m{}\\033[0m/...'.format(layer_3))\n else:\n print('| | {}'.format(layer_3))\n else:\n print('| {}'.format(layer_2))\n else:\n print('{}'.format(layer_1))\n hdf5.close()","def get_lh5_header(in_file, verbose=False):\n hf = h5py.File(in_file)\n\n # pretty print the raw structure, with all attributes\n if verbose:\n def print_groups(name, obj):\n if isinstance(obj, h5py.Group):\n print(f\"GROUP /{name}\")\n indent = \" \"\n if isinstance(obj, h5py.Dataset):\n print(\" DATASET\", obj.shape, obj.name)\n indent = \" \"\n for att, val in obj.attrs.items():\n print(f\"{indent}ATTRIBUTE {att}:\", val)\n print(\" \")\n hf.visititems(print_groups) # accesses __call__\n \n # find each LH5 \"Table\" contained in the file, and create a DataFrame header\n tables = {}\n for g_top in hf.keys():\n \n h5group = hf[f\"/{g_top}\"]\n attrs = {att:val for att, val in h5group.attrs.items()}\n \n # LH5 table condition\n if \"datatype\" in attrs.keys() and \"table{\" in attrs[\"datatype\"]:\n \n # call our nice iterator at this group level\n table = {g_top:[]}\n for (path, name, size, dtype, units, spec) in get_datasets(h5group):\n table[g_top].append((name, size, dtype, units, spec))\n \n hdr = pd.DataFrame(table[g_top], columns=['name','size','dtype',\n 'units','spec'])\n \n # fix waveform datatype to match flattened_data\n if 'waveform' in hdr['name'].values:\n wf_dt = h5group['waveform/values/flattened_data'].dtype\n hdr.loc[hdr['name'] == 'waveform', ['dtype']] = wf_dt\n \n tables[g_top] = hdr\n\n return tables","def read_h5_file_arvind_format(folder, filen):\n \n ### file path\n \n fpath = folder + filen + '.h5'\n assert os.path.exists(fpath), \"The out.h5 file does NOT exist for \" + fpath\n fl = h5py.File(fpath, 'r')\n \n ### cell information\n \n xu = np.array(fl['/positions/xu'], dtype=np.float32)\n pol = np.array(fl['/positions/ori'], dtype=np.float32)\n pol = np.array([xt.T for xt in pol[:]])\n \n ### simulation information\n \n lx = fl['/info/box/x'][...]\n ly = fl['/info/box/y'][...]\n dt = fl['/info/dt'][...]\n nsteps = fl['/info/nsteps'][...]\n nfils = fl['/info/nfils'][...]\n nbeads = fl['/info/nbeads'][...]\n nsamp = fl['/info/nsamp'][...]\n nbpf = fl['/info/nbpf'][...]\n \n ### simulation parameters\n \n density = fl['/param/density'][...]\n kappa = fl['/param/kappa'][...]\n km = fl['/param/km'][...]\n pa = fl['/param/pa'][...]\n pp = fl['/param/pp'][...]\n bl = fl['/param/bl'][...]\n sigma = fl['/param/sigma'][...]\n \n fl.close()\n \n ### generate classes to submerge data\n \n sim = misc_tools.Simulation(lx, ly, dt, nsteps, nfils, nbeads, nsamp, nbpf, \\\n density, kappa, km, pa, pp, bl, sigma)\n fils = misc_tools.Cells(xu, pol, nbpf, sim)\n \n return sim, fils","def _getH5File(db):\n if isinstance(db, Database3):\n return db.h5db\n else:\n raise TypeError(\"Unsupported Database type ({})!\".format(type(db)))","def _read_file(self) -> Tuple[np.ndarray, h5py.File]:\n assert os.path.exists(self.datafile)\n LOGGER.info(f\"Found datafile: {self.datafile}\")\n\n # get ELM indices from datafile\n hf = h5py.File(self.datafile, \"r\")\n LOGGER.info(f\"Number of ELM events in the datafile: {len(hf)}\")\n elm_index = np.array([int(key) for key in hf], dtype=np.int32)\n return elm_index, hf","def readHtk(filename):\n with open(filename, \"rb\") as f:\n # Read header\n nSamples, sampPeriod, sampSize, parmKind = struct.unpack(\">iihh\", f.read(12))\n # Read data\n data = struct.unpack(\">%df\" % (nSamples * sampSize / 4), f.read(nSamples * sampSize))\n # return numpy.array(data).reshape(nSamples, int(sampSize / 4))\n return nSamples, sampPeriod, sampSize, parmKind, data","def hdf5_data(self):\n if self._hdf5_data is None:\n self._hdf5_data = self.parent.request_data(Hdf5DataSpec)\n return self._hdf5_data","def read_lastfm_data(filepath):\n start = time.time()\n with h5py.File(filepath, 'r') as f:\n m = f.get('artist_user_plays')\n plays = csr_matrix((m.get('data'), m.get('indices'), m.get('indptr')))\n logging.debug('lastfm_360k.hdf5 loaded in ' + str(time.time() - start))\n return np.array(f['artist']), np.array(f['user']), plays","def ReadHDF5(self,filename):\n\n if self.elements is not None and self.points is not None:\n self.__reset__()\n\n DictOutput = loadmat(filename)\n\n # GENERIC READER - READS EVERYTHING FROM HDF5 AND ASSIGNS IT TO MESH OBJECT\n for key, value in DictOutput.items():\n if isinstance(DictOutput[key],np.ndarray):\n if \"elements\" in key or \"edge\" in key or \"face\" in key:\n setattr(self, key, np.ascontiguousarray(value).astype(np.uint64))\n else:\n setattr(self, key, np.ascontiguousarray(value))\n else:\n setattr(self, key, value)\n\n if isinstance(self.element_type,np.ndarray):\n self.element_type = str(self.element_type[0])\n if isinstance(self.nelem,np.ndarray):\n self.nelem = int(self.nelem[0])\n\n for key in self.__dict__.keys():\n if isinstance(self.__dict__[str(key)],np.ndarray):\n if self.__dict__[str(key)].size == 1:\n self.__dict__[str(key)] = np.asscalar(self.__dict__[str(key)])","def read_data_set(filepath):\n st = time.time()\n with h5py.File(filepath, 'r') as f:\n m = f.get('artist_user_plays')\n plays = csr_matrix((m.get('data'), m.get('indices'), m.get('indptr')))\n logger.debug('data loaded in:\\t' + str(time.time() - st))\n return np.array(f['artist']), np.array(f['user']), plays","def read_dhalo_trees(basename):\n \n # Read in the tree file(s)\n ifile = 0\n nfiles = 1\n data = collections.OrderedDict()\n while ifile < nfiles:\n treefile = h5py.File(\"%s.%d.hdf5\" % (basename, ifile), \"r\")\n if ifile == 0:\n nfiles = treefile[\"fileInfo\"].attrs[\"numberOfFiles\"]\n for uname in treefile[\"haloTrees\"].keys():\n name = str(uname)\n if ifile == 0:\n data[name] = []\n data[name].append(treefile[\"haloTrees\"][name][...])\n treefile.close()\n ifile += 1\n \n # Combine arrays from separate files and return\n for name in data.keys():\n data[name] = np.concatenate(data[name], axis=0)\n return data","def read_treecode_hdf5(file):\n import h5py\n import numpy as np\n\n fid = h5py.File(file,'r')\n\n b = fid['block_treecode'][:]\n treecode = np.array(b, dtype=float)\n\n return treecode","def print_h5(fname: str) -> None:\n try:\n with h5py.File(fname, 'r') as h:\n print(fname)\n recursively_print_structure(h, ' ')\n except IOError as e:\n print(f\"Cannot open HDF5 file {fname}\")\n print(f\"IOError: {e}\")","def h5(self):\n return self._h5","def read_dataset(fname, dname, start=0, stop=-1, skip=1):\n data = None\n\n try:\n with h5py.File(fname, 'r') as f:\n if stop == -1:\n return f[dname][start::skip,]\n else:\n return f[dname][start:stop:skip,]\n except IOError:\n print(\"Error: cannot find file %s.\" % fname)\n\n return data","def extract_h5(args, var, data, lfile):\n import h5py\n import numpy as np\n try:\n raw = h5py.File(lfile, 'r')\n except:\n print(\"Error: File could not be read: \" + lfile)\n return\n # Get time\n time = np.array(raw['sys.exec.out.time'])\n # Get data\n for v in var:\n if v not in data: # havent extracted yet\n if v in raw:\n data[v] = np.array(raw[v])\n else:\n # if args.verbose :\n # print \"Warning: \" + v + \" not found in \" +\n # os.path.basename(lfile)\n continue\n else:\n continue # already extracted\n raw.close()\n return data, time","def ToH5(self,h5File=None):\r\n\r\n logStr = \"{0:s}.{1:s}: \".format(self.__class__.__name__, sys._getframe().f_code.co_name)\r\n logger.debug(\"{0:s}{1:s}\".format(logStr,'Start.')) \r\n \r\n try: \r\n if h5File == None:\r\n h5File=self.h5File\r\n\r\n #Delete .h5 File if exists\r\n if os.path.exists(h5File): \r\n logger.debug(\"{0:s}{1:s}: Delete ...\".format(logStr,h5File)) \r\n os.remove(h5File)\r\n\r\n #Determine .h5 BaseKey\r\n\r\n relPath2XmlromCurDir=os.path.normpath(os.path.relpath(os.path.normpath(self.xmlFile),start=os.path.normpath(os.path.curdir))) # ..\\..\\..\\..\\..\\3S\\Modelle\\....XML\r\n #print(repr(relPath2XmlromCurDir)) # '..\\\\..\\\\..\\\\..\\\\..\\\\3S\\\\Modelle\\\\....XML'\r\n h5KeySep='/'\r\n h5KeyCharForDot='_'\r\n h5KeyCharForMinus='_'\r\n relPath2XmlromCurDirH5BaseKey=re.sub('\\.',h5KeyCharForDot,re.sub(r'\\\\',h5KeySep,re.sub('-',h5KeyCharForMinus,re.sub('.xml','',relPath2XmlromCurDir,flags=re.IGNORECASE))))\r\n #__/__/__/__/__/3S/Modelle/...\r\n\r\n warnings.filterwarnings('ignore',category=pd.io.pytables.PerformanceWarning) #your performance may suffer as PyTables will pickle object types that it cannot map directly to c-types \r\n warnings.filterwarnings('ignore',category=tables.exceptions.NaturalNameWarning) #\\lib\\site-packages\\tables\\path.py:100: NaturalNameWarning: object name is not a valid Python identifier: '3S'; it does not match the pattern ``^[a-zA-Z_][a-zA-Z0-9_]*$``; you will not be able to use natural naming to access this object; using ``getattr()`` will still work, though)\r\n \r\n #Write .h5 File\r\n logger.debug(\"{0:s}pd.HDFStore({1:s}) ...\".format(logStr,h5File)) \r\n with pd.HDFStore(h5File) as h5Store: \r\n #for tableName,table in self.dataFrames.items():\r\n for tableName in sorted(self.dataFrames.keys()):\r\n table=self.dataFrames[tableName]\r\n h5Key=relPath2XmlromCurDirH5BaseKey+h5KeySep+tableName \r\n logger.debug(\"{0:s}{1:s}: Writing DataFrame {2:s} with h5Key={3:s}\".format(logStr,h5File,tableName,h5Key)) \r\n try:\r\n h5Store.put(h5Key,table)#,format='table') \r\n except Exception as e:\r\n logger.error(\"{0:s}{1:s}: Writing DataFrame {2:s} with h5Key={3:s} FAILED!\".format(logStr,h5File,tableName,h5Key)) \r\n raise e\r\n \r\n\r\n except Exception as e:\r\n logStrFinal=\"{:s}Exception: Line: {:d}: {!s:s}: {:s}\".format(logStr,sys.exc_info()[-1].tb_lineno,type(e),str(e))\r\n logger.error(logStrFinal) \r\n raise XmError(logStrFinal) \r\n \r\n finally:\r\n h5Store.close()\r\n logger.debug(\"{0:s}{1:s}\".format(logStr,'_Done.'))","def load_hdf5_with_structure(file):\n n_classes = 80\n n_boxes = 5\n anchors = [[0.738768, 0.874946], [2.42204, 2.65704], [4.30971, 7.04493], [10.246, 4.59428], [12.6868, 11.8741]]\n\n yolov2 = YOLOv2(n_classes=n_classes, n_boxes=n_boxes)\n chainer.serializers.load_hdf5(file, yolov2)\n model = YOLOv2Predictor(yolov2)\n model.init_anchor(anchors)\n #model.predictor.train = False\n model.predictor.finetune = False\n return model","def read(self):\n\n if self.filename.endswith(\".pkl\"):\n logging.debug(\"Loading pickle file %s\", self.filename)\n data = pd.read_pickle(self.filename)\n\n elif self.filename.endswith(\".hdf5\"):\n logging.debug(\"Loading HDF5 file %s\", self.filename)\n with h5py.File(self.filename, \"r\") as data_file:\n\n data = pd.DataFrame(\n {\n column: data_file[\"/haloTrees/%s\" % column].value\n for column in self.columns\n }\n ).set_index(\"nodeIndex\")\n\n # with open(\"./data/cache.pkl\", \"w\") as pickle_file:\n # data.to_pickle(pickle_file)\n\n else:\n raise TypeError(\"Unknown filetype %s\" % self.filename)\n\n return data","def h5_to_df(h5_file, group_name):\n col_dict = {}\n h5_set = hdf_file[group_name]\n for col in h5_set.keys():\n my_array = np.asarray(h5_set[col])\n col_dict[col] = my_array\n df = pd.DataFrame(col_dict)\n return df","def summarize_hdf5(hdf5_file):\n # characterize the h5file in a mostly content-agnostic way\n summary = {\n 'sums': {},\n 'shapes': {}\n }\n\n def characterize_object(obj_name, obj_data):\n \"\"\"retain some properties of each dataset in an hdf5 file\"\"\"\n if isinstance(obj_data, h5py.Dataset):\n summary['shapes'][obj_name] = obj_data.shape\n # note that this will break if the hdf5 file contains non-numeric datasets\n summary['sums'][obj_name] = obj_data[...].sum()\n print(\"dataset %s version = %s\" % (obj_name, hdf5_file.get_version(obj_name)))\n\n hdf5_file.visititems(characterize_object)\n\n return summary","def collect_storage_info(fname):\n # Open the HDF5 file for simple byte I/O...\n fb = fname.open('rb')\n if not fb.seekable():\n raise OSError('Byte stream for %s not seekable' % fname)\n fb.seek(0, SEEK_SET)\n\n storage = dict()\n\n def _stinfo(objname, obj):\n if isinstance(obj, h5py.Dataset):\n stinfo = obj.storage\n if isinstance(stinfo, h5py.h5d.ContiguousStorageInfo):\n fb.seek(stinfo.file_addr, SEEK_SET)\n byte_stream = fb.read(stinfo.size)\n if len(byte_stream) != stinfo.size:\n raise IOError(\n 'Read %d bytes instead of %d bytes for %s from %s' %\n (len(byte_stream), stinfo.size, obj.name, fname))\n\n storage[obj.name] = {'file_addr': stinfo.file_addr,\n 'size': stinfo.size,\n 'uuid': str(uuid4()),\n 'md5': md5(byte_stream).hexdigest()}\n else:\n chunk_info = list()\n storage[obj.name] = {'chunk_size': obj.chunks,\n 'filters': obj._filters}\n for si in stinfo:\n fb.seek(si.file_addr, SEEK_SET)\n byte_stream = fb.read(si.size)\n if len(byte_stream) != si.size:\n raise IOError(\n 'Read %d bytes instead of %d bytes for chunk #%d '\n 'of %s from %s' % (len(byte_stream), si.size,\n si.order, obj.name, fname))\n\n chunk_info.append({'order': si.order,\n 'file_addr': si.file_addr,\n 'size': si.size,\n 'logical_addr': si.logical_addr,\n 'uuid': str(uuid4()),\n 'md5': md5(byte_stream).hexdigest()})\n\n storage[obj.name]['chunk_info'] = chunk_info\n\n with h5py.File(str(fname), 'r') as f:\n f.visititems(_stinfo)\n fb.close()\n\n return storage","def get_data(data_dir, hdf5):\r\n\r\n # Get the filenames of the lists containing image paths and labels.\r\n train_file, val_file = build_dataset_index(data_dir)\r\n\r\n # Check if (creating and) loading from hdf5 database is desired.\r\n if hdf5:\r\n # Create folder to store dataset.\r\n if not os.path.exists('hdf5'):\r\n os.makedirs('hdf5')\r\n # Check if hdf5 databases already exist and create them if not.\r\n if not os.path.exists('hdf5/tiny-imagenet_train.h5'):\r\n from tflearn.data_utils import build_hdf5_image_dataset\r\n print ' Creating hdf5 train dataset.'\r\n build_hdf5_image_dataset(train_file, image_shape=(64, 64), mode='file', output_path='hdf5/tiny-imagenet_train.h5', categorical_labels=True, normalize=True)\r\n\r\n if not os.path.exists('hdf5/tiny-imagenet_val.h5'):\r\n from tflearn.data_utils import build_hdf5_image_dataset\r\n print ' Creating hdf5 val dataset.'\r\n build_hdf5_image_dataset(val_file, image_shape=(64, 64), mode='file', output_path='hdf5/tiny-imagenet_val.h5', categorical_labels=True, normalize=True)\r\n\r\n # Load training data from hdf5 dataset.\r\n h5f = h5py.File('hdf5/tiny-imagenet_train.h5', 'r')\r\n X = h5f['X']\r\n Y = h5f['Y']\r\n\r\n # Load validation data.\r\n h5f = h5py.File('hdf5/tiny-imagenet_val.h5', 'r')\r\n X_test = h5f['X']\r\n Y_test = h5f['Y'] \r\n\r\n # Load images directly from disk when they are required.\r\n else:\r\n from tflearn.data_utils import image_preloader\r\n X, Y = image_preloader(train_file, image_shape=(64, 64), mode='file', categorical_labels=True, normalize=True, filter_channel=True)\r\n X_test, Y_test = image_preloader(val_file, image_shape=(64, 64), mode='file', categorical_labels=True, normalize=True, filter_channel=True)\r\n\r\n # Randomly shuffle the dataset.\r\n X, Y = shuffle(X, Y)\r\n\r\n return X, Y, X_test, Y_test","def read_data(path, batch_size, qp, frac, kernel, model):\n # load h5 file and get dictionaries\n inputs_dict, labels_dict, _ = get_dataset_dict(path, qp)\n\n # create training / validation dictionaries\n block_keys = [k for k in inputs_dict]\n train_inputs_dict, train_labels_dict, val_inputs_dict, val_labels_dict = (dict() for _ in range(4))\n\n # get inputs / labels for block & frac position\n for block in block_keys:\n inputs = inputs_dict[block][frac]\n\n # only use inputs that can be split 80 / 20 train / validation and fill out a batch\n split_percentage = 4/5\n if len(inputs) < batch_size / split_percentage:\n continue\n\n # if model contains non-linear activations, use same input & label size\n inputs = inputs[:, kernel:-kernel, kernel:-kernel, :] if \"scratch\" not in model else inputs\n\n labels = labels_dict[block][frac]\n\n # shuffle the pairs\n inputs, labels = array_shuffle(len(inputs), inputs, labels)\n\n # split 80 / 20\n (train_inputs, train_labels), (val_inputs, val_labels) = split_data(split_percentage, inputs, labels)\n\n # put into correct dictionary entry\n train_inputs_dict[block] = train_inputs\n train_labels_dict[block] = train_labels\n val_inputs_dict[block] = val_inputs\n val_labels_dict[block] = val_labels\n\n return train_inputs_dict, train_labels_dict, val_inputs_dict, val_labels_dict","def setup(self):\n print(\"Looking for \", self.filename)\n if os.path.exists(self.filename):\n n, ext = os.path.splitext(self.filename)[:2]\n if ext == \".h5\" or ext == \".hdf5\":\n with h5py.File(self.filename, \"r\") as file:\n keys = list(file.keys())\n self.data = file[keys[0]].value\n print(\"Behavior Data length is \", self.data.shape[2])\n\n else:\n raise FileNotFoundError","def parse_hdob_file(path):\n col_names = [\"obs_time\", \"lat\", \"lon\", \"static_air_press\", \"geo_pot_height\",\n \"sfc_press_dval\", \"t_air\", \"t_dew\", \"wind_dir_spd\", \"wind_peak\",\n \"sfc_wind_peak\", \"rain_rate\", \"qc_flags\"]\n file_header = ''\n obs_data = []\n\n # Determine if 'path' is a path or url\n if isfile(path):\n # open & read local file\n with open(path, 'r') as fh:\n for idx, line in enumerate(fh):\n line = line.rstrip('\\n')\n\n if (idx == 3):\n file_header = line\n elif ((idx > 3) and (idx < 24)):\n curr_line = line.split(' ')\n curr_line = [x for x in curr_line if x != ' ']\n obs_data.append(curr_line)\n hdob_df = pd.DataFrame(data=obs_data, index=range(0, len(obs_data)), columns=col_names)\n hdob_obj = HDOBFile(file_header, hdob_df)\n print(hdob_obj)\n # elif (isURL):","def hdf5_dataset(path, fields):\n\n # Reads dataset row by row\n def _generator():\n with h5py.File(path, 'r') as file:\n datasets = [file[field] for field in fields]\n for row in zip(*datasets):\n yield row\n\n # Reads hdf5 metadata (types and shapes)\n with h5py.File(path, 'r') as file:\n datasets = [file[field] for field in fields]\n\n types = tuple(ds.dtype for ds in datasets)\n shapes = tuple(ds.shape[1:] for ds in datasets)\n\n # Create dataset\n ds = tf.data.Dataset.from_generator(_generator, types, shapes)\n return ds","def read_dataset_v1():\n path = load_config()\n T = feather.read_dataframe(path['data_dir'] / 'T_dat.feather')\n E = feather.read_dataframe(path['data_dir'] / 'E_dat.feather')\n M = feather.read_dataframe(path['data_dir'] / 'Meta.feather')\n data = sio.loadmat(path['data_dir'] / 'highvar_genes.mat', squeeze_me=True)\n return T, E, M, data","def test_hdf5_load_all():\n skip_if_no_h5py()\n import h5py\n\n # save random data to HDF5\n handle, filename = tempfile.mkstemp()\n dataset = random_one_hot_dense_design_matrix(np.random.RandomState(1),\n num_examples=10, dim=5,\n num_classes=3)\n with h5py.File(filename, 'w') as f:\n f.create_dataset('X', data=dataset.get_design_matrix())\n f.create_dataset('y', data=dataset.get_targets())\n\n # instantiate Train object\n trainer = yaml_parse.load(load_all_yaml % {'filename': filename})\n trainer.main_loop()\n\n # cleanup\n os.remove(filename)","def h5_path(self):\n return self._h5_path","def read(self, simtype):\n\n if simtype == 'original':\n if self.filename.endswith(\".pkl\"):\n logging.debug(\"Loading pickle file %s\", self.filename)\n data = pd.read_pickle(self.filename)\n\n elif self.filename.endswith(\".hdf5\"):\n logging.debug(\"Loading HDF5 file %s\", self.filename)\n with h5py.File(self.filename, \"r\") as data_file:\n #print('treeIndex', data_file[\"treeIndex\"].keys())\n #print('haloTrees', data_file[\"haloTrees\"].keys())\n \n # Find dimensionality of keys\n columns_1dim = [] \n columns_2dim = [] \n for column in self.columns:\n if len(data_file[\"/haloTrees/%s\" % column].shape) == 1:\n columns_1dim.append(column)\n else:\n columns_2dim.append(column)\n \n # 1D keys\n data = pd.DataFrame(\n {\n column: data_file[\"/haloTrees/%s\" % column].value\n for column in columns_1dim\n },\n columns=columns_1dim\n ).set_index(\"nodeIndex\")\n del columns_1dim\n\n # 2D keys\n for column in columns_2dim:\n if column == 'position':\n pos = data_file[\"/haloTrees/%s\" % column].value\n data['X'] = pd.Series(pos[:, 0], index=data.index)\n data['Y'] = pd.Series(pos[:, 1], index=data.index)\n data['Z'] = pd.Series(pos[:, 2], index=data.index)\n del columns_2dim\n\n data.rename(index=str,\n columns={\"snapshotNumber\": \"snapnum\"})\n ## eliminate fake elements with isIntegrated=1\n #data = data[data.isInterpolated != 1]\n\n else:\n raise TypeError(\"Unknown filetype %s\" % self.filename)\n if simtype == 'EAGLE':\n if self.filename.endswith(\".pkl\"):\n logging.debug(\"Loading pickle file %s\", self.filename)\n data = pd.read_pickle(self.filename)\n\n elif self.filename.endswith(\".hdf5\"):\n logging.debug(\"Loading HDF5 file %s\", self.filename)\n data_file = h5py.File(self.filename, 'r')\n column_mt = []\n column_sh = []\n for column in self.columns:\n if column in data_file['MergerTree']:\n column_mt.append(column)\n else:\n column_sh.append(column)\n\n data = pd.DataFrame(\n {\n column: data_file[\"/MergerTree/%s\" % column].value\n for column in column_mt\n },\n columns=column_mt\n ).set_index(\"HaloID\")\n #.set_index(data_file[\"/Merger/HaloID\"].value)\n\n for column in column_sh:\n data[column] = pd.Series(data_file[\"/Subhalo/%s\" % column].value,\n index=data.index)\n data = data.rename(index=str,\n columns={\"SnapNum\": \"snapnum\", #\"HaloID\": \"nodeIndex\",\n \"DescendantID\" : \"descendantIndex\"})\n else:\n raise TypeError(\"Unknown filetype %s\" % self.filename)\n\n return data","def load_field_data_h5(h5, verbose=True):\n data = {'components':{}}\n\n # Load attributes\n attrs, other = load_field_attrs(h5.attrs, verbose=verbose)\n attrs.update(other)\n data['attrs'] = attrs\n \n # Loop over records and components\n for g, comps in field_record_components.items():\n if g not in h5:\n continue\n \n # Get the full openPMD unitDimension \n required_dim = expected_record_unit_dimension[g]\n \n for comp in comps:\n if comp not in h5[g]:\n continue\n name = g+'/'+comp\n cdat = component_data(h5[name])\n \n # Check dimensions\n dim = h5[name].attrs['unitDimension']\n assert np.all(dim == required_dim), f'{name} with dimension {required_dim} expected for {name}, found: {dim}'\n \n # Check shape\n s1 = tuple(attrs['gridSize'])\n s2 = cdat.shape\n assert s1 == s2, f'Expected shape: {s1} != found shape: {s2}'\n \n # Static fields should be real\n if attrs['harmonic'] == 0:\n cdat = np.real(cdat)\n \n # Finally set\n \n data['components'][name] = cdat \n \n \n return data","def get_dataset(filepath: PathLike, internal_path: str) -> np.ndarray:\n with h5py.File(filepath, mode='r') as f:\n array = f[internal_path][...]\n return array","def read(self, fname, frame=None):\n\n self.resetvals()\n url = urlparse(fname)\n if not self.url:\n self.url = url\n# if frame:\n# self.hdf5_location.set_index(frame)\n self.filename = self.url.path\n if os.path.isfile(self.filename):\n self.hdf5 = h5py.File(self.filename, \"r\")\n else:\n error = \"No such file or directory: %s\" % self.filename\n logger.error(error)\n raise RuntimeError(error)\n self.ds = self.hdf5[self.url.query]\n if isinstance(self.ds, h5py.Group) and (\"data\" in self.ds):\n self.ds = self.ds[\"data\"]\n\n if self.url.fragment:\n slices = self.get_slice()\n self.data = self.ds[self.get_slice()]\n self.nframes = self.ds.shape[self.main_dim]\n else:\n self.data = self.ds[:]\n self.nframes = 1\n self.dim2, self.dim1 = self.data.shape\n self.bytecode = str(self.data.dtype)\n return self","def read_metadata(self, data_set):\n # checks if there is an open file in the dataset as load_data does\n # reading of metadata before reading the complete dataset\n if not hasattr(self, '_h5_base_group'):\n self._open_file(data_set)\n if 'metadata' in data_set._h5_base_group.keys():\n metadata_group = data_set._h5_base_group['metadata']\n self.read_dict_from_hdf5(data_set.metadata, metadata_group)\n return data_set","def save_as_hdf5(self, filename):","def read_skyh5(\n self, filename, run_check=True, check_extra=True, run_check_acceptability=True\n ):\n with h5py.File(filename, \"r\") as fileobj:\n if \"/Header\" not in fileobj:\n raise ValueError(\"This is not a proper skyh5 file.\")\n\n init_params = {\"filename\": os.path.basename(filename)}\n\n with h5py.File(filename, \"r\") as fileobj:\n # extract header information\n header = fileobj[\"/Header\"]\n header_params = [\n \"_Ncomponents\",\n \"_Nfreqs\",\n \"_component_type\",\n \"_spectral_type\",\n \"_history\",\n \"_name\",\n \"_nside\",\n \"_hpx_order\",\n \"_hpx_inds\",\n \"_freq_array\",\n \"_freq_edge_array\",\n \"_reference_frequency\",\n \"_spectral_index\",\n \"_stokes_error\",\n \"_beam_amp\",\n \"_extended_model_group\",\n ]\n\n optional_params = [\n \"_hpx_order\",\n \"_freq_array\",\n \"_freq_edge_array\",\n \"_reference_frequency\",\n \"_spectral_index\",\n \"_stokes_error\",\n \"_beam_amp\",\n \"_extended_model_group\",\n ]\n\n self.component_type = header[\"component_type\"][()].tobytes().decode(\"utf-8\")\n\n if self.component_type != \"healpix\":\n optional_params.extend([\"_nside\", \"_hpx_inds\"])\n if \"skycoord\" in header:\n skycoord_dict = {}\n for key in header[\"skycoord\"]:\n if key in [\"frame\", \"representation_type\"]:\n expected_type = str\n else:\n expected_type = None\n skycoord_dict[key] = _get_value_hdf5_group(\n header[\"skycoord\"], key, expected_type\n )\n init_params[\"skycoord\"] = SkyCoord(**skycoord_dict)\n else:\n if \"lat\" in header and \"lon\" in header and \"frame\" in header:\n header_params += [\"lat\", \"lon\", \"frame\"]\n optional_params += [\"lat\", \"lon\", \"frame\"]\n elif \"ra\" in header and \"dec\" in header:\n header_params += [\"ra\", \"dec\"]\n optional_params += [\"ra\", \"dec\"]\n else:\n raise ValueError(\n \"No component location information found in file.\"\n )\n warnings.warn(\n \"Parameter skycoord not found in skyh5 file. \"\n \"This skyh5 file was written by an older version of pyradiosky. \"\n \"Consider re-writing this file to ensure future compatibility\"\n )\n else:\n optional_params.append(\"_name\")\n\n if \"hpx_frame\" in header:\n if isinstance(header[\"hpx_frame\"], h5py.Dataset):\n # hpx_frame was stored as a string\n frame_str = _get_value_hdf5_group(header, \"hpx_frame\", str)\n dummy_coord = SkyCoord(0, 0, unit=\"rad\", frame=frame_str)\n init_params[\n \"hpx_frame\"\n ] = dummy_coord.frame.replicate_without_data(copy=True)\n else:\n # hpx_frame was stored as a nested dset\n skycoord_dict = {}\n for key in header[\"hpx_frame\"]:\n if key in [\"frame\", \"representation_type\"]:\n expected_type = str\n else:\n expected_type = None\n skycoord_dict[key] = _get_value_hdf5_group(\n header[\"hpx_frame\"], key, expected_type\n )\n dummy_coord = SkyCoord(0, 0, unit=\"rad\", **skycoord_dict)\n init_params[\n \"hpx_frame\"\n ] = dummy_coord.frame.replicate_without_data(copy=True)\n elif \"frame\" in header:\n # frame was stored as a string\n frame_str = _get_value_hdf5_group(header, \"frame\", str)\n dummy_coord = SkyCoord(0, 0, unit=\"rad\", frame=frame_str)\n init_params[\"hpx_frame\"] = dummy_coord.frame.replicate_without_data(\n copy=True\n )\n\n for par in header_params:\n if par in [\"lat\", \"lon\", \"frame\", \"ra\", \"dec\"]:\n parname = par\n if par == \"frame\":\n expected_type = \"str\"\n else:\n expected_type = Quantity\n else:\n param = getattr(self, par)\n parname = param.name\n expected_type = param.expected_type\n\n # skip optional params if not present\n if par in optional_params:\n if parname not in header:\n continue\n\n if parname not in header:\n raise ValueError(\n f\"Expected parameter {parname} is missing in file.\"\n )\n\n value = _get_value_hdf5_group(header, parname, expected_type)\n\n if parname == \"nside\":\n value = int(value)\n\n init_params[parname] = value\n\n # check that the parameters not passed to the init make sense\n if init_params[\"component_type\"] == \"healpix\":\n if init_params[\"Ncomponents\"] != init_params[\"hpx_inds\"].size:\n raise ValueError(\n \"Ncomponents is not equal to the size of 'hpx_inds'.\"\n )\n else:\n if init_params[\"Ncomponents\"] != init_params[\"name\"].size:\n raise ValueError(\"Ncomponents is not equal to the size of 'name'.\")\n\n if \"freq_array\" in init_params.keys():\n if init_params[\"Nfreqs\"] != init_params[\"freq_array\"].size:\n raise ValueError(\"Nfreqs is not equal to the size of 'freq_array'.\")\n\n if init_params[\"spectral_type\"] == \"subband\":\n if \"freq_edge_array\" not in init_params.keys():\n try:\n init_params[\n \"freq_edge_array\"\n ] = _get_freq_edges_from_centers(\n init_params[\"freq_array\"], self._freq_array.tols\n )\n except ValueError:\n warnings.warn(\n \"No freq_edge_array in this file and frequencies are \"\n \"not evenly spaced, so spectral_type will be set to \"\n \"'full' rather than 'subband'.\"\n )\n init_params[\"spectral_type\"] = \"full\"\n\n # remove parameters not needed in __init__\n init_params.pop(\"Ncomponents\")\n init_params.pop(\"Nfreqs\")\n\n # get stokes array\n dgrp = fileobj[\"/Data\"]\n init_params[\"stokes\"] = dgrp[\"stokes\"] * units.Unit(\n dgrp[\"stokes\"].attrs[\"unit\"]\n )\n # frame is a new parameter, check if it exists and try to read\n # otherwise default to ICRS (the old assumed frame.)\n if \"skycoord\" not in init_params and self.component_type != \"healpix\":\n if \"frame\" in header:\n init_params[\"frame\"] = header[\"frame\"][()].tobytes().decode(\"utf8\")\n else:\n warnings.warn(\n \"No frame available in this file, assuming 'icrs'. \"\n \"Consider re-writing this file to ensure future compatility.\"\n )\n init_params[\"frame\"] = \"icrs\"\n\n if self.component_type == \"healpix\" and \"hpx_frame\" in init_params.keys():\n init_params[\"frame\"] = init_params[\"hpx_frame\"]\n del init_params[\"hpx_frame\"]\n\n if self.component_type == \"healpix\" and \"frame\" not in init_params:\n warnings.warn(\n \"No frame available in this file, assuming 'icrs'. \"\n \"Consider re-writing this file to ensure future compatility.\"\n )\n init_params[\"frame\"] = \"icrs\"\n\n self.__init__(**init_params)\n\n if run_check:\n self.check(\n check_extra=check_extra, run_check_acceptability=run_check_acceptability\n )","def recursively_load_dict_contents_from_group(h5file: \"h5py.File\", \n path: str,\n ) -> dict:\n ans = {}\n for key, item in h5file[path].items():\n if isinstance(item, h5py._hl.dataset.Dataset):\n ans[key] = item.value\n elif isinstance(item, h5py._hl.group.Group):\n ans[key] = recursively_load_dict_contents_from_group(h5file, f\"{path}{key}/\")\n return ans","def read_image_from_h5(h5_path, *args, **kwargs):\n # TODO: Implement the method\n\n h5_pathRd = h5.File(h5_path, \"a\")\n image = h5_pathRd[\"data\"][\"image\"]\n\n return image","def printAllColumnsInH5(pathToData):\n\n #Check if a correct path is given\n\n if not os.path.isfile(pathToData):\n raise ValueError(\"h5 file not found. Wrong path given?\")\n elif os.path.isfile(pathToData):\n Data = h5.File(pathToData, 'r')\n\n\n Files = Data.keys()\n\n for File in Files:\n print()\n print('Filename = %s' %(File))\n print('----------------------')\n\n #Every time you see Xr*' '\n #It means I add X spaces to line it\n print('\\t column name%sunit%slength'%(29*' ',16*' '))\n print('\\t '+'-----------------'*4)\n \n #In this file give me all the column names\n columns = Data[File].keys()\n \n #for every column in the columns\n for nrc,column in enumerate(columns):\n #always want the column name printed in 40 char\n spaces = ' '*(40 - len(column))\n length = Data[File][column].shape[0]\n #always want the unit name printed over 20 chars\n unit = Data[File][column].attrs['units']\n spaces2 = ' '*(20 - len(unit))\n #--\n length = Data[File][column].shape[0]\n\n print('\\t %s%s%s%s%s'%(column,spaces, unit,spaces2, length))\n #Every 4 lines print a dashed line to read output easier\n if (nrc%5==4):\n print('\\t '+'-----------------'*4)\n Data.close()"],"string":"[\n \"def read_hdf5(path_to_file):\\n\\n print(\\\"\\\\nReading HDF5 file: \\\", path_to_file)\\n file = h5py.File(path_to_file, 'r')\\n\\n # List the groups\\n groups = list(file.keys())\\n print(\\\"Groups available: \\\", groups)\\n\\n # Read Zemax Metadata\\n zemax_metadata = {}\\n print(\\\"\\\\nZemax Metadata:\\\")\\n for key in file['Zemax Metadata'].attrs.keys():\\n print('{} : {}'.format(key, file['Zemax Metadata'].attrs[key]))\\n zemax_metadata[key] = file['Zemax Metadata'].attrs[key]\\n\\n # Read the analysis groups\\n for group_name in groups:\\n if group_name != 'Zemax Metadata':\\n analysis_group = file[group_name]\\n print('\\\\nAnalysis: ', group_name)\\n # For each Analysis Group we loop over subgroups\\n for subgroup_key in analysis_group.keys():\\n subgroup = analysis_group[subgroup_key]\\n print('Subgroup #', subgroup_key)\\n # List the metadata of the subgroup\\n for att_key in subgroup.attrs.keys():\\n print(' {} : {}'.format(att_key, subgroup.attrs[att_key]))\\n\\n file.close()\\n\\n return zemax_metadata\",\n \"def read_h5meta(filepath):\\n with open(filepath) as file:\\n content = parse_h5meta(file)\\n\\n return content\",\n \"def read_hdf5(file_path):\\n if not os.path.exists(file_path):\\n logging.fatal(\\\"Cannot read feature file {}.\\\".format(file_path))\\n exit()\\n hdf5_file = h5py.File(file_path, 'r')\\n data = np.array(hdf5_file['data'])\\n hdf5_file.close()\\n\\n return data\",\n \"def load_h5_file(file_path):\\n # load\\n fr = h5py.File(file_path, 'r')\\n a_group_key = list(fr.keys())[0]\\n data = list(fr[a_group_key])\\n # transform to appropriate numpy array \\n data=data[0:]\\n data = np.stack(data, axis=0)\\n return data\",\n \"def load_h5_file(file_path):\\n # load\\n fr = h5py.File(file_path, 'r')\\n a_group_key = list(fr.keys())[0]\\n data = list(fr[a_group_key])\\n # transform to appropriate numpy array \\n data=data[0:]\\n data = np.stack(data, axis=0)\\n return data\",\n \"def _read_h5_dataset(self):\\n dev = self.getParentObj()\\n top = dev.getFileDescriptor()\\n for attr in self._attr_list:\\n data = top.get(attr)\\n if data is None:\\n msg = \\\"Unable to open object (Object %s doesn't exist)\\\" % attr\\n raise TaurusException(msg)\\n top = data\\n return data\",\n \"def loadh5(fname, path='/data'):\\n fp = open_read(fname)\\n slab = fp.get_node(path)\\n mat = slab.read()\\n fp.close()\\n return mat\",\n \"def read_h5(fpath):\\n _check_h5_installed()\\n import h5py as h5\\n\\n f = h5.File(fpath, 'r')\\n res = dict()\\n\\n if 'est_n_dips' in f.keys():\\n res['est_n_dips'] = list(f['est_n_dips'][:])\\n else:\\n res['est_n_dips'] = 'Not available.'\\n\\n if 'exponents' in f.keys():\\n res['exponents'] = f['exponents'][:]\\n else:\\n res['exponents'] = 'Not available.'\\n\\n if 'ch_names' in f.keys():\\n _temp = list(f['ch_names'][:].flatten())\\n res['ch_names'] = list(x.decode('utf-8', 'ignore') for x in _temp)\\n del _temp\\n else:\\n res['ch_names'] = 'Not available.'\\n\\n for _k in ['prob_map', 'est_locs', 'model_sel', 'est_dip_mom_std']:\\n if _k in f.keys():\\n res[_k] = list(f[_k][_key][:] for _key in sorted(f[_k].keys(),\\n key=lambda x: int(x)))\\n else:\\n res[_k] = 'Not available.'\\n\\n for _k in ['final_dip_mom_std', 'tmin', 'tmax', 'fmin', 'fmax', 'subsample']:\\n if _k in f.keys():\\n res[_k] = f[_k][()]\\n else:\\n res[_k] = None\\n\\n for _k in ['lambda', 'noise_std', 'dip_mom_std', 'max_n_dips',\\n 'subject', 'subject_viz', 'data_path', 'fwd_path',\\n 'cov_path', 'src_path', 'lf_path', 'fwd_fixed_ori']:\\n if _k in f.keys():\\n res[_k] = f[_k][()]\\n else:\\n res[_k] = 'Not available.'\\n\\n if 'est_dip_moms' in f.keys():\\n est_dip_moms_temp = np.asarray(list(f['est_dip_moms'][_key][:] for _key in sorted(f['est_dip_moms'].keys(),\\n key=lambda x: int(x))))\\n if f['fwd_fixed_ori'][()]:\\n est_dip_moms_aux = np.zeros((res['est_locs'][-1].shape[0], est_dip_moms_temp.shape[0]))\\n for i in range(est_dip_moms_temp.shape[0]):\\n _temp = est_dip_moms_temp[i, :].reshape(-1, 1)\\n for j in range(res['est_locs'][-1].shape[0]):\\n est_dip_moms_aux[j, i] += _temp[j]\\n elif f['fwd_fixed_ori'][()] == 'Not available.':\\n print('Uknown forward source orientation. Skipping dipole moments.')\\n else:\\n est_dip_moms_aux = np.zeros((res['est_locs'][-1].shape[0], est_dip_moms_temp.shape[0], 3))\\n for i in range(est_dip_moms_temp.shape[0]):\\n _temp = est_dip_moms_temp[i, :].reshape(-1, 3)\\n for j in range(res['est_locs'][-1].shape[0]):\\n est_dip_moms_aux[j, i, :] += _temp[j]\\n res['est_dip_moms'] = est_dip_moms_aux\\n f.close()\\n return res\",\n \"def read_data(path):\\n with h5py.File(path, 'r') as hf:\\t\\n input_ = np.array(hf.get('input'))\\n label_ = np.array(hf.get('label'))\\n return input_, label_\",\n \"def load_h5(filename: str, **kwargs):\\n return open_h5(filename, 'r', **kwargs)\",\n \"def read_hdf5(filename, namelist=None, **kwargs):\\n\\n print('Reading %s...'%filename)\\n\\n fid = h5py.File(filename, mode='r')\\n \\n data = read_hdf5_tree(fid, namelist, **kwargs)\\n\\n fid.close()\\n \\n print('Finished reading %s.'%filename)\\n return data\",\n \"def read_data_test(path):\\n with h5py.File(path, 'r') as hf:\\n input_ = np.array(hf.get('data'))\\n label_ = np.array(hf.get('label'))\\n\\t\\n return input_, label_\",\n \"def read_data(path):\\n with h5py.File(path, 'r') as hf:\\n data = np.array(hf.get('data'))\\n label = np.array(hf.get('label'))\\n return data, label\",\n \"def read_h5file(self, fname, datasetname):\\n with h5py.File(fname, 'r') as f:\\n atom_pos = f.get(datasetname + '/r').value # atom position -> N x 3 array\\n ion_list = f.get(\\n datasetname + '/xyz').value # length = N, contain atom type id for each atom\\n self.atom_pos = atom_pos[np.argsort(ion_list)]\\n _, idx = np.unique(np.sort(ion_list), return_index=True)\\n self.split_idx = np.append(idx, [len(ion_list)])\\n\\n # get atom factor table, sorted by atom type id\\n atom_type = f.get(\\n datasetname + '/T').value # atom type array, each type is represented by an integer\\n self.num_atom_types = len(atom_type)\\n ff_table = f.get(datasetname + '/ff').value\\n self.ff_table = ff_table[np.argsort(atom_type)]\\n\\n self.q_sample = f.get(datasetname + '/halfQ').value\\n self.num_q_samples = len(self.q_sample)\\n self.compton_q_sample = f.get(datasetname + '/Sq_halfQ').value\\n self.num_compton_q_samples = len(self.compton_q_sample)\\n self.sBound = f.get(datasetname + '/Sq_bound').value\\n self.nFree = f.get(datasetname + '/Sq_free').value\",\n \"def _readHDF5(self):\\n\\n h5 = h5py.File(self.pointInputFile, 'r')\\n self.coords = h5['geometry/vertices'][:]\\n self.stations = h5['stations'][:]\\n self.dispRaw = h5['vertex_fields/displacement'][self.timeStep,:,:]\\n h5.close()\\n\\n self.numStations = self.coords.shape[0]\\n\\n return\",\n \"def read_data(path):\\n with h5py.File(path, 'r') as hf:\\n data = np.array(hf.get('data'))\\n label = np.array(hf.get('label'))\\n return data, label\",\n \"def loadhdf5file(file_h5, key='data'):\\n\\n with h5py.File(file_h5, 'r') as data:\\n # Add datasets to dictionary\\n info_value = {}\\n info_attrs = {}\\n\\n for i in np.arange(len(data.items())):\\n info_value.update({str(list(data.items())[i][0]): data[str(list(data.items())[i][0])].value})\\n\\n for i in np.arange(len(data[key].attrs)):\\n info_attrs.update({list(data[key].attrs.keys())[i]: list(data[key].attrs.values())[i]})\\n\\n return info_value, info_attrs\",\n \"def readH5 (dataset):\\n if dataset.attrs['type']==PhotoZTemplateSED.typestr:\\n return PhotoZTemplateSED()\\n else:\\n return None\",\n \"def read_lh5(in_file, key=None, cols=None, ilo=0, ihi=None):\\n if \\\".lh5\\\" not in in_file:\\n print(\\\"Error, unknown file:\\\", in_file)\\n exit()\\n \\n # open the file in context manager to avoid weird crashes \\n t_start = time.time()\\n with h5py.File(os.path.expanduser(in_file)) as hf:\\n \\n header = get_lh5_header(f_lh5, verbose=False)\\n\\n # pick off first table by default, or let the user specify the name\\n table = list(header.keys())[0] if key is None else key\\n df_hdr = header[table] \\n \\n # this function reads the Table into memory\\n df = read_table(table, hf, df_hdr, ilo, ihi)\\n\\n # t_elapsed = time.time() - t_start\\n # print(\\\"elapsed: {t_elapsed:.4f} sec\\\")\\n \\n return df\",\n \"def FromH5(self,h5File=None):\\r\\n\\r\\n logStr = \\\"{0:s}.{1:s}: \\\".format(self.__class__.__name__, sys._getframe().f_code.co_name)\\r\\n logger.debug(\\\"{0:s}{1:s}\\\".format(logStr,'Start.')) \\r\\n\\r\\n if h5File == None:\\r\\n h5File=self.h5File\\r\\n\\r\\n #Check if h5File exists\\r\\n if not os.path.exists(h5File): \\r\\n logStrFinal=\\\"{0:s}{1:s}: Not Existing!\\\".format(logStr,h5File) \\r\\n raise XmError(logStrFinal) \\r\\n \\r\\n try:\\r\\n self.dataFrames={} \\r\\n with pd.HDFStore(h5File) as h5Store:\\r\\n h5Keys=sorted(h5Store.keys())\\r\\n for h5Key in h5Keys:\\r\\n match=re.search('(/)(\\\\w+$)',h5Key)\\r\\n key=match.group(2)\\r\\n logger.debug(\\\"{0:s}{1:s}: Reading h5Key {2:s} to tableName {3:s}.\\\".format(logStr,h5File,h5Key,key)) \\r\\n self.dataFrames[key]=h5Store[h5Key]\\r\\n \\r\\n\\r\\n except Exception as e:\\r\\n logStrFinal=\\\"{:s}Exception: Line: {:d}: {!s:s}: {:s}\\\".format(logStr,sys.exc_info()[-1].tb_lineno,type(e),str(e))\\r\\n logger.error(logStrFinal) \\r\\n raise XmError(logStrFinal) \\r\\n \\r\\n finally:\\r\\n h5Store.close()\\r\\n logger.debug(\\\"{0:s}{1:s}\\\".format(logStr,'_Done.'))\",\n \"def data_reading(data_file):\\n\\n # The number of samples is needed to read the HDF5 file, which is stored in the name\\n # Split by '/' to remove the directory, and by '.' to remove the file format\\n file_name = data_file.split(\\\"/\\\")[-1].split(\\\".\\\")[-2]\\n # The file name ends with the number of samples and before that the number of included neighbours\\n n_samples = int(file_name.split(\\\"_\\\")[-1])\\n n_neighbours = int(file_name.split(\\\"_\\\")[-2])\\n\\n # Read the data\\n h5f = h5py.File(data_file, 'r')\\n\\n # The data set name is the name of the path where the data file can be found\\n data = h5f[\\\"dataset_{}\\\".format(n_samples)][:]\\n\\n # Close the H5py file\\n h5f.close()\\n\\n return data, n_samples, n_neighbours\",\n \"def data_reading(data_file):\\n\\n # The number of samples is needed to read the HDF5 file, which is stored in the name\\n # Split by '/' to remove the directory, and by '.' to remove the file format\\n file_name = data_file.split(\\\"/\\\")[-1].split(\\\".\\\")[-2]\\n # The file name ends with the number of samples and before that the number of included neighbours\\n n_samples = int(file_name.split(\\\"_\\\")[-1])\\n n_neighbours = int(file_name.split(\\\"_\\\")[-2])\\n\\n # Read the data\\n h5f = h5py.File(data_file, 'r')\\n\\n # The data set name is the name of the path where the data file can be found\\n data = h5f[\\\"dataset_{}\\\".format(n_samples)][:]\\n\\n # Close the H5py file\\n h5f.close()\\n\\n return data, n_samples, n_neighbours\",\n \"def _read(self, item):\\n return read_hdf5(self.file_name, title=self._get_h5_path(item))\",\n \"def open_h5meta(filepath):\\n data = dict()\\n h5meta_content = read_h5meta(filepath)\\n for file in h5meta_content[\\\"filelist\\\"]:\\n data[file] = read_detector_data(file)\\n\\n return data\",\n \"def read_data(path):\\n with h5py.File(path, 'r') as hf:\\n data = np.array(hf.get('data'))\\n label = np.array(hf.get('label'))\\n\\n data, label=data[:,:,:,0:2], label[:,:,:,0]\\n #data=np.expand_dims(data,axis=-1)\\n label=np.expand_dims(label,axis=-1)\\n\\n return data, label\",\n \"def load_h5(fname: str, path: str='/') -> dict:\\n try:\\n with h5py.File(fname, 'r') as f:\\n dataMap = recursively_load_dict_contents_from_group(f, path)\\n except IOError as e:\\n print(f\\\"Cannot open HDF5 file {fname}\\\")\\n print(f\\\"IOError: {e}\\\")\\n\\n return dataMap\",\n \"def from_hdf(filename):\\n # Function for iteratively parsing the file to create the dictionary\\n def visit_group(obj, sdict):\\n name = obj.name.split('/')[-1]\\n #indent = len(obj.name.split('/'))-1\\n #print \\\" \\\"*indent,name, obj.value if (type(obj) == h5py.Dataset) else \\\":\\\"\\n if type(obj) in [ h5py.Dataset ]:\\n sdict[name] = obj.value\\n if type(obj) in [ h5py.Group, h5py.File ]:\\n sdict[name] = {}\\n for sobj in obj.values():\\n visit_group(sobj, sdict[name])\\n\\n data = {}\\n try:\\n h5file = h5py.File(os.path.expandvars(filename), 'r')\\n # Run over the whole dataset\\n for obj in h5file.values():\\n visit_group(obj, data)\\n except IOError, e:\\n logging.error(\\\"Unable to read HDF5 file \\\\'%s\\\\'\\\" % filename)\\n logging.error(e)\\n raise e\\n finally:\\n h5file.close()\\n\\n return data\",\n \"def read_generic_hdf5(fname):\\n f = h5py.File(fname, \\\"r\\\")\\n fcontent = {}\\n\\n def filldict(x, y):\\n # create a new container\\n tmp = {}\\n # add attributes if present\\n if len(y.attrs) > 0:\\n tmp['attrs'] = dict(y.attrs)\\n # add data if it is a dataset\\n if isinstance(y, h5py.Dataset):\\n tmp['data'] = np.array(y)\\n # only add to the dictionary, if we have something meaningful to add\\n if tmp != {}:\\n fcontent[x] = tmp\\n\\n f.visititems(filldict)\\n\\n f.close()\\n return fcontent\",\n \"def read_uvh5(\\n self,\\n filename,\\n antenna_nums=None,\\n antenna_names=None,\\n ant_str=None,\\n bls=None,\\n frequencies=None,\\n freq_chans=None,\\n times=None,\\n time_range=None,\\n polarizations=None,\\n blt_inds=None,\\n keep_all_metadata=True,\\n read_data=True,\\n data_array_dtype=np.complex128,\\n multidim_index=False,\\n background_lsts=True,\\n run_check=True,\\n check_extra=True,\\n run_check_acceptability=True,\\n strict_uvw_antpos_check=False,\\n ):\\n if not os.path.exists(filename):\\n raise IOError(filename + \\\" not found\\\")\\n\\n # open hdf5 file for reading\\n with h5py.File(filename, \\\"r\\\") as f:\\n # extract header information\\n header = f[\\\"/Header\\\"]\\n self._read_header(\\n header,\\n filename,\\n run_check_acceptability=run_check_acceptability,\\n background_lsts=background_lsts,\\n )\\n\\n if not read_data:\\n # don't read in the data. This means the object is incomplete,\\n # but that may not matter for many purposes.\\n return\\n\\n # Now read in the data\\n dgrp = f[\\\"/Data\\\"]\\n self._get_data(\\n dgrp,\\n antenna_nums,\\n antenna_names,\\n ant_str,\\n bls,\\n frequencies,\\n freq_chans,\\n times,\\n time_range,\\n polarizations,\\n blt_inds,\\n data_array_dtype,\\n keep_all_metadata,\\n multidim_index,\\n run_check,\\n check_extra,\\n run_check_acceptability,\\n strict_uvw_antpos_check,\\n )\\n\\n return\",\n \"def read_data(path):\\n with h5py.File(path, \\\"r\\\") as f:\\n transformed_poses = np.array(f['transformed_poses'])\\n extracted_poses = np.array(f['poses'])\\n target = np.array(f['target'])\\n \\n return extracted_poses, transformed_poses, target\",\n \"def read_h5_file(folder, filen):\\n \\n ### file path\\n \\n fpath = folder + filen + '.h5'\\n assert os.path.exists(fpath), \\\"The out.h5 file does NOT exist for \\\" + fpath\\n fl = h5py.File(fpath, 'r')\\n \\n ### cell information\\n \\n xu = np.array(fl['/cells/comu'], dtype=np.float32)\\n \\n ### simulation information\\n \\n lx = fl['/info/box/x'][...]\\n ly = fl['/info/box/y'][...]\\n dt = fl['/info/dt'][...]\\n nsteps = fl['/info/nsteps'][...]\\n nfils = fl['/info/nfils'][...]\\n nbeads = fl['/info/nbeads'][...]\\n nsamp = fl['/info/nsamp'][...]\\n nbpf = fl['/info/nbpf'][...]\\n \\n ### simulation parameters\\n \\n density = fl['/param/density'][...]\\n kappa = fl['/param/kappa'][...]\\n km = fl['/param/km'][...]\\n pa = fl['/param/pa'][...]\\n pp = fl['/param/pp'][...]\\n bl = fl['/param/bl'][...]\\n sigma = fl['/param/sigma'][...]\\n \\n fl.close()\\n \\n ### generate classes to submerge data\\n \\n sim = misc_tools.Simulation(lx, ly, dt, nsteps, nfils, nbeads, nsamp, nbpf, \\\\\\n density, kappa, km, pa, pp, bl, sigma)\\n fils = misc_tools.Cells(xu, nbpf, sim)\\n \\n return sim, fils\",\n \"def load_data(file_path):\\n with h5py.File(file_path) as f:\\n # load meta info\\n fs, channels, p_names, signals = _get_info(f)\\n\\n # load raw data\\n data = [f['protocol{}/raw_data'.format(k + 1)][:] for k in range(len(p_names))]\\n df = pd.DataFrame(np.concatenate(data), columns=channels)\\n\\n # load signals data\\n signals_data = [f['protocol{}/signals_data'.format(k + 1)][:] for k in range(len(p_names))]\\n df_signals = pd.DataFrame(np.concatenate(signals_data), columns=['signal_'+s for s in signals])\\n df = pd.concat([df, df_signals], axis=1)\\n\\n # load timestamps\\n if 'timestamp' in df:\\n timestamp_data = [f['protocol{}/timestamp_data'.format(k + 1)][:] for k in range(len(p_names))]\\n df['timestamps'] = np.concatenate(timestamp_data)\\n\\n # events data\\n events_data = [f['protocol{}/mark_data'.format(k + 1)][:] for k in range(len(p_names))]\\n df['events'] = np.concatenate(events_data)\\n\\n # set block names and numbers\\n df['block_name'] = np.concatenate([[p]*len(d) for p, d in zip(p_names, data)])\\n df['block_number'] = np.concatenate([[j + 1]*len(d) for j, d in enumerate(data)])\\n return df, fs, channels, p_names\",\n \"def load_data(infile, nstep): \\n \\n f = h5py.File(infile, 'r')\\n \\n edges_grp = f['edges']\\n xedges = np.asarray(edges_grp['x'][nstep], dtype=float)\\n yedges = np.asarray(edges_grp['y'][nstep], dtype=float)\\n\\n time = np.asarray(f['time'][nstep])\\n\\n tables_grp = f['tables']\\n rho_hist = np.asarray(tables_grp['rho'][nstep], dtype=float)\\n vx_hist = np.asarray(tables_grp['vx'][nstep], dtype=float)\\n vy_hist = np.asarray(tables_grp['vy'][nstep], dtype=float)\\n vorticity = np.asarray(tables_grp['vorticity'][nstep], dtype=float) \\n \\n box_grp = f['box']\\n lx = box_grp['x'][...]\\n ly = box_grp['y'][...]\\n \\n #nsteps = f['nsteps'][...]\\n f.close()\\n\\n return lx, ly, time, xedges, yedges, rho_hist, vx_hist, vy_hist, vorticity\",\n \"def read_h5(self):\\n infile = h5py.File(self.inf_name,'r')\\n\\n vardict = self.labdict\\n #store data with the correct labels\\n for k in infile['plasma/1d'].keys():\\n try:\\n vardict[k] = infile[self.labdict[k]].value\\n except:\\n vardict[k] = []\\n\\n vardict['a_ions']=infile['/plasma/anum'].value\\n vardict['znum']=infile['/plasma/znum'].value\\n \\n\\n self.rho_in = vardict['rho']\\n self._rho_vol = infile['distributions/rhoDist/abscissae/dim1'].value[1:]\\n self._volumes = infile['distributions/rhoDist/shellVolume'].value\\n self.nrho_in = np.size(self.rho_in)\\n\\n if vardict['a_ions'][0]!='/':\\n self.nspec = len(vardict['a_ions'])\\n else:\\n self.nspec = vardict['ni'].shape[1]\\n print(\\\"Number of ions: \\\", self.nspec)\\n if len(vardict['a_ions'])!=len(vardict['znum']):\\n print(\\\"ERROR! array of A and Z don't have the same length\\\")\\n\\n self.A = vardict['a_ions']\\n self.Z = vardict['znum']\\n self.nion = self.nspec\\n \\n self.te_in = vardict['te'][:]\\n self.ne_in = vardict['ne'][:]\\n self.ti_in = vardict['ti'][:]\\n ni1_in = vardict['ni'][:,0]\\n self.ni_in = np.zeros((self.nion, self.nrho_in),dtype=float)\\n self.ni_in[0,:] = ni1_in\\n if self.nion==2:\\n ni2_in = vardict['ni'][:,1]\\n self.ni_in[1,:] = ni2_in\\n elif self.nion==3:\\n ni2_in = vardict['ni'][:,1]\\n ni3_in = vardict['ni'][:,2]\\n self.ni_in[1,:] = ni2_in\\n self.ni_in[2,:] = ni3_in\\n\\n try:\\n self.vt_in = vardict['vtor']\\n except:\\n self.vt_in = np.zeros(self.nrho_in,dtype=float)\\n\\n try:\\n self.zeff_in = vardict['zeff'][:]\\n except:\\n self.zeff_in = np.zeros(self.nrho_in,dtype=float)\\n\\n self.ni = np.zeros((self.nion, self.nrho),dtype = float)\\n self.spline()\",\n \"def read_hdf5(self, file_name,\\r\\n projections_start=None,\\r\\n projections_end=None,\\r\\n projections_step=None,\\r\\n slices_start=None,\\r\\n slices_end=None,\\r\\n slices_step=None,\\r\\n pixels_start=None,\\r\\n pixels_end=None,\\r\\n pixels_step=None,\\r\\n white_start=None,\\r\\n white_end=None,\\r\\n dark_start=None,\\r\\n dark_end=None,\\r\\n dtype='float32'):\\r\\n print \\\"Reading data...\\\"\\r\\n self.file_name = file_name\\r\\n\\r\\n # Initialize f to null.\\r\\n f = None\\r\\n\\r\\n # Get the file_name in lower case.\\r\\n lFn = file_name.lower()\\r\\n\\r\\n # Split the string with the delimeter '.'\\r\\n end = lFn.split('.')\\r\\n\\r\\n # If the string has an extension.\\r\\n if len(end) > 1:\\r\\n # Check.\\r\\n if end[len(end) - 1] == 'h5' or end[len(end) - 1] == 'hdf':\\r\\n f = Hdf5()\\r\\n\\r\\n # If f != None the call read on it.\\r\\n if not f == None:\\r\\n # Read data from exchange group.\\r\\n self.data = f.read(file_name,\\r\\n array_name='exchange/data',\\r\\n x_start=projections_start,\\r\\n x_end=projections_end,\\r\\n x_step=projections_step,\\r\\n y_start=slices_start,\\r\\n y_end=slices_end,\\r\\n y_step=slices_step,\\r\\n z_start=pixels_start,\\r\\n z_end=pixels_end,\\r\\n z_step=pixels_step).astype(dtype)\\r\\n\\r\\n # Read white field data from exchange group.\\r\\n print white_start, white_end, slices_start, slices_end\\r\\n self.white = f.read(file_name,\\r\\n array_name='exchange/data_white',\\r\\n x_start=white_start,\\r\\n x_end=white_end,\\r\\n y_start=slices_start,\\r\\n y_end=slices_end,\\r\\n y_step=slices_step,\\r\\n z_start=pixels_start,\\r\\n z_end=pixels_end,\\r\\n z_step=pixels_step).astype(dtype)\\r\\n\\r\\n # Read dark field data from exchange group.\\r\\n self.dark = f.read(file_name,\\r\\n array_name='exchange/data_dark',\\r\\n x_start=dark_start,\\r\\n x_end=dark_end,\\r\\n y_start=slices_start,\\r\\n y_end=slices_end,\\r\\n y_step=slices_step,\\r\\n z_start=pixels_start,\\r\\n z_end=pixels_end,\\r\\n z_step=pixels_step).astype(dtype)\\r\\n\\r\\n # Assign the rotation center.\\r\\n self.center = self.data.shape[2] / 2\\r\\n else:\\r\\n print 'Unsupported file.'\",\n \"def parse_hdf5(inp, close=True, **kwargs):\\n import json\\n import h5py\\n # Path\\n path = kwargs.pop('path', '/')\\n # Open\\n if isinstance(inp, basestring):\\n hdf5 = h5py.File(inp, 'r')\\n else:\\n hdf5 = inp\\n # Data\\n data = hdf5[path+'data'][()]\\n # Meta\\n if 'meta' in hdf5[path].keys():\\n meta = json.loads(hdf5[path+'meta'][()])\\n # Headers\\n for jj,heads in enumerate(meta['headers']):\\n try:\\n meta['headers'][jj] = fits.Header.fromstring(meta['headers'][jj])\\n except TypeError: # dict\\n if not isinstance(meta['headers'][jj], dict):\\n raise IOError(\\\"Bad meta type\\\")\\n else:\\n meta = None\\n # Units\\n units = json.loads(hdf5[path+'units'][()])\\n for key,item in units.items():\\n if item == 'dimensionless_unit':\\n units[key] = u.dimensionless_unscaled\\n else:\\n units[key] = getattr(u, item)\\n # Other arrays\\n try:\\n sig = data['sig']\\n except (NameError, IndexError):\\n sig = None\\n try:\\n co = data['co']\\n except (NameError, IndexError):\\n co = None\\n # Finish\\n if close:\\n hdf5.close()\\n return XSpectrum1D(data['wave'], data['flux'], sig=sig, co=co,\\n meta=meta, units=units, **kwargs)\",\n \"def load_h5(fname, surfmap=True):\\n filenames = glob.glob(fname)\\n print(\\\"Files found: {}\\\".format(filenames))\\n fin = h5py.File(filenames[0])\\n meas = fin['measurement0'] # Wavefront data located in 'measurement0'\\n opdsets = meas['genraw']\\n wvl = opdsets.attrs['wavelength'][:]\\n wvl = float(wvl[:-3])\\n # Get the x pixel spacing\\n try:\\n iscale = float(opdsets.attrs['xpix'][:-3])\\n except TypeError:\\n iscale = 0.0\\n print(\\\"No Calibration Dimensioning Found in H5 file\\\")\\n # Return either surface map or fringe map\\n if surfmap is True:\\n data = np.asarray(opdsets['data'])\\n data[data > 1e10] = np.nan # Eliminates \\\"bad\\\" data sets to NAN\\n data *= wvl * mask_data(filenames[0])\\n else:\\n data = np.asarray(meas['reserve_interferogram']['frame4']['data'])\\n return data, wvl, iscale\",\n \"def loadHDF5(ofile):\\n \\n ## Groups \\n size_grp = ofile['size']\\n beads_grp = ofile['beads']\\n props_grp = ofile['props']\\n \\n ## Datasets\\n size = np.asarray(size_grp['size'])\\n x = np.asarray(beads_grp['x'])\\n comx = np.asarray(props_grp['comx'])\\n \\n ## Filament list\\n fil_grp = props_grp['filament_list']\\n fil_list_1 = np.asarray(fil_grp['1'])\\n \\n print size\\n print x\\n print comx\\n print fil_list_1\\n \\n return\",\n \"def read_hdf5(ID_images:str, path:str = \\\"data/dataset/\\\"):\\n images, labels = [], []\\n\\n #open the HDF5 file\\n file = h5py.File(path +ID_images+\\\"_lens.h5\\\", \\\"r\\\")\\n\\n images = np.array(file[\\\"/images\\\"]).astype(\\\"float64\\\")\\n labels = pd.read_hdf(path +ID_images+'_meta.h5', \\\"table\\\")\\n\\n return images, labels\",\n \"def _loadHDF5File(self, filename):\\n matfile = h5py.File(filename)\\n\\n self.StokesI = np.transpose(matfile['StokesI'][:,:])\\n self.StokesQ = np.transpose(matfile['StokesQ'][:,:])\\n self.StokesU = np.transpose(matfile['StokesU'][:,:])\\n self.StokesV = np.transpose(matfile['StokesV'][:,:])\\n self.detectorPosition = matfile['detectorPosition'][:,0]\\n self.detectorDirection = matfile['detectorDirection'][:,0]\\n self.detectorVisang = matfile['detectorVisang'][0,0]\\n\\n try: self.wall = matfile['wall'][:,:]\\n except KeyError: pass\\n\\n try: self.separatrix = matfile['separatrix'][:,:]\\n except KeyError: pass\",\n \"def load_document_representations(path):\\n return h5py.File(path, \\\"r\\\")\",\n \"def h5ToDf(filename):\\n log.info(f\\\"Import data from: {filename}\\\")\\n with h5py.File(filename, \\\"r\\\") as hf :\\n d = {}\\n for name in list(hf.keys()):\\n d[name] = np.array(hf[name][:])\\n df = pd.DataFrame(data=d)\\n return(df)\",\n \"def h5ToDf(filename):\\n log.info(f\\\"Import data from: {filename}\\\")\\n with h5py.File(filename, \\\"r\\\") as hf :\\n d = {}\\n for name in list(hf.keys()):\\n d[name] = np.array(hf[name][:])\\n df = pd.DataFrame(data=d)\\n return(df)\",\n \"def read_hdf5(filename, **extras):\\n groups = {'sampling': {}, 'obs': {}}\\n res = {}\\n with h5py.File(filename, \\\"r\\\") as hf:\\n # loop over the groups\\n for group, d in groups.items():\\n # read the arrays in that group into the dictionary for that group\\n for k, v in hf[group].items():\\n d[k] = np.array(v)\\n # unserialize the attributes and put them in the dictionary\\n for k, v in hf[group].attrs.items():\\n try:\\n d[k] = json.loads(v)\\n except:\\n d[k] = v\\n # do top-level attributes.\\n for k, v in hf.attrs.items():\\n try:\\n res[k] = json.loads(v)\\n except:\\n res[k] = v\\n res.update(groups['sampling'])\\n res['obs'] = groups['obs']\\n try:\\n res['obs']['filters'] = load_filters([str(f) for f in res['obs']['filters']])\\n except:\\n pass\\n try:\\n res['rstate'] = pickle.loads(res['rstate'])\\n except:\\n pass\\n try:\\n mp = [names_to_functions(p.copy()) for p in res['model_params']]\\n res['model_params'] = mp\\n except:\\n pass\\n\\n return res\",\n \"def read(filename: str) -> orm.Data:\\n return from_bands_inspect(load(hdf5_file=filename))\",\n \"def from_h5(cls, filename, pulse_number=None):\\n df = pd.read_hdf(filename)\\n return cls._sort_and_filter_dataframe(df, pulse_number)\",\n \"def read_wabbit_hdf5(file, verbose=True, return_iteration=False):\\n import h5py\\n import numpy as np\\n\\n if verbose:\\n print(\\\"~~~~~~~~~~~~~~~~~~~~~~~~~\\\")\\n print(\\\"Reading file %s\\\" % (file) )\\n\\n fid = h5py.File(file,'r')\\n b = fid['coords_origin'][:]\\n x0 = np.array(b, dtype=float)\\n\\n b = fid['coords_spacing'][:]\\n dx = np.array(b, dtype=float)\\n\\n b = fid['blocks'][:]\\n data = np.array(b, dtype=float)\\n\\n b = fid['block_treecode'][:]\\n treecode = np.array(b, dtype=float)\\n\\n # get the dataset handle\\n dset_id = fid.get('blocks')\\n \\n # from the dset handle, read the attributes\\n time = dset_id.attrs.get('time')\\n iteration = dset_id.attrs.get('iteration')\\n box = dset_id.attrs.get('domain-size')\\n version=dset_id.attrs.get('version')\\n\\n\\n fid.close()\\n\\n jmin, jmax = get_max_min_level( treecode )\\n N = data.shape[0]\\n Bs = data.shape[1:]\\n Bs = np.asarray(Bs[::-1]) # we have to flip the array since hdf5 stores in [Nz, Ny, Nx] order\\n \\n if version == 20200408 or version == 20231602:\\n Bs = Bs-1\\n #print(\\\"!!!Warning old (old branch: newGhostNodes) version of wabbit format detected!!!\\\")\\n else:\\n print(\\\"This file includes redundant points\\\")\\n \\n if verbose:\\n print(\\\"Time=%e it=%i N=%i Bs[0]=%i Bs[1]=%i Jmin=%i Jmax=%i\\\" % (time, iteration, N, Bs[0], Bs[1], jmin, jmax) )\\n print(\\\"~~~~~~~~~~~~~~~~~~~~~~~~~\\\")\\n\\n\\n if return_iteration:\\n return time, x0, dx, box, data, treecode, iteration[0]\\n else:\\n return time, x0, dx, box, data, treecode\",\n \"def hdf5_file(self):\\n if self._hdf5_file is None:\\n self._hdf5_file = h5py.File(self.hdf5_path, 'r', swmr=self.hdf5_use_swmr, libver='latest')\\n return self._hdf5_file\",\n \"def readH5 (dataset):\\n ## also use old name \\n if dataset.attrs['type']==PhotoZDoubleGauss.typestr:\\n sigma=float(dataset.attrs['sigma'])\\n Acat=float(dataset.attrs['Acat'])\\n zcat=float(dataset.attrs['zcat'])\\n sigmacat=float(dataset.attrs['sigmacat'])\\n return PhotoZDoubleGauss(sigma,Acat,zcat,sigmacat)\\n else:\\n return None\",\n \"def read_conn(path, simulation):\\n with h5py.File(path+simulation+'-conn','r') as f: \\n print(path+simulation+'-conn' )\\n out_deg=np.array(f['conn']) \\n return out_deg\",\n \"def test_h5file(dl2_shower_geometry_file):\\n from ctapipe.io.tableloader import TableLoader\\n\\n # no input raises error\\n with pytest.raises(ValueError):\\n with TableLoader():\\n pass\\n\\n # test we can use an already open file\\n with tables.open_file(dl2_shower_geometry_file, mode=\\\"r+\\\") as h5file:\\n with TableLoader(h5file=h5file) as loader:\\n assert 25 in loader.subarray.tel\\n loader.read_subarray_events()\\n loader.read_telescope_events()\",\n \"def load_h5py_file(fname, offsets = [0, 0, 0]):\\n # Load the data\\n f = h5py.File(fname, 'r') # r for read only\\n print(\\\"Available fields: \\\", list(f.keys())) # f is a dictionary. Let's look at the keys\\n\\n # Create variables from loaded dictionary\\n neural_data = f['ripple_data'][:,0:32]\\n emg_data = f['ripple_data'][:,32:]\\n force_data = f['data'][0:6,:].transpose()\\n fs = f['mySampleRate'][:]\\n\\n # Transform matrix for force data\\n TF = [[1.117\\t, -0.096747,\\t 1.7516, 0.03441, -0.88072, 0.042127, -0.89026],\\n [0.3134, 0.0041349, 0.0045219, -0.055942, 1.5273, 0.037719,-1.5227],\\n [0.135\\t, 1.4494, -0.061075, 1.6259, 0.083867, 1.5999, 0.0058155]]\\n TF = np.array(TF)\\n\\n # Read force data\\n force_data = np.concatenate((np.ones((len(force_data),1)), force_data), axis=1)\\n force_data = force_data @ TF.transpose()\\n\\n # Make baseband zero\\n force_data[:,0] = force_data[:,0] - offsets[0]\\n force_data[:,1] = force_data[:,1] - offsets[1]\\n force_data[:,2] = force_data[:,2] - offsets[2]\\n\\n # Use sent and received pulse signals to allign DAQ and RIPPLE data\\n pulse_sent = f['data'][6,:].transpose()\\n ps_ind, = np.nonzero(pulse_sent>1)\\n ps_ind = ps_ind[0]\\n\\n pulse_received = f['ttl_data'][:,0]\\n pr_ind, = np.nonzero(pulse_received>2000)\\n pr_ind = pr_ind[0]\\n\\n p_diff = ps_ind - pr_ind\\n\\n # Align data\\n if p_diff > 0:\\n pulse_sent = np.concatenate((pulse_sent[p_diff:], np.zeros((p_diff,))), axis=0)\\n trailing = np.mean(force_data[-int(fs*0.1):], axis=0) * np.ones((p_diff,1))\\n force_data = np.concatenate((force_data[p_diff:,:], trailing))\\n else:\\n pulse_sent = np.concatenate((np.zeros((-p_diff,)), pulse_sent[:p_diff]), axis=0)\\n leading = np.mean(force_data[:int(fs * 0.1)], axis=0) * np.ones((-p_diff, 1))\\n force_data = np.concatenate((leading, force_data[:p_diff,:]))\\n\\n # Choose force channel for analysis\\n force_data = force_data[:,1]\\n force_data = -force_data # Invert the sign (increased as applied force increased)\\n\\n # Choose EMG data\\n emg_data = emg_data[:,(5,15)]-emg_data[:,(23,25)]\\n\\n # Re-order EMG data so that 1. Dorsal 2. Biceps 3. Ventral 4. Triceps\\n positions3 = (0,1)\\n emg_data = emg_data[:,positions3]\\n\\n # Corresponding time vectors\\n time = f['ripple_time'][:]\\n return neural_data, emg_data, force_data, time, fs\",\n \"def load_hdf(fn, key='data'):\\n with open_file(fn, mode=\\\"r\\\") as f:\\n data = f.get_node('/'+key).read()\\n return data\",\n \"def read_wabbit_hdf5_dir(dir):\\n import numpy as np\\n import re\\n import ntpath\\n import os\\n\\n it=0\\n data={'time': [],'x0':[],'dx':[],'treecode':[]}\\n # we loop over all files in the given directory\\n for file in os.listdir(dir):\\n # filter out the good ones (ending with .h5)\\n if file.endswith(\\\".h5\\\"):\\n # from the file we can get the fieldname\\n fieldname=re.split('_',file)[0]\\n print(fieldname)\\n time, x0, dx, box, field, treecode = read_wabbit_hdf5(os.path.join(dir, file))\\n #increase the counter\\n data['time'].append(time[0])\\n data['x0'].append(x0)\\n data['dx'].append(dx)\\n data['treecode'].append(treecode)\\n if fieldname not in data:\\n # add the new field to the dictionary\\n data[fieldname]=[]\\n data[fieldname].append(field)\\n else: # append the field to the existing data field\\n data[fieldname].append(field)\\n it=it+1\\n # the size of the domain\\n data['box']=box\\n #return time, x0, dx, box, data, treecode\\n return data\",\n \"def readmd5_sum_file(path):\\n with open(path, \\\"r\\\") as f:\\n hashes = {}\\n while True:\\n line = f.readline()\\n if not line:\\n break\\n h, name = line.rstrip().split(' ', 1)\\n hashes[name] = h\\n return hashes\",\n \"def open_h5(filename: str, mode: str, **kwargs):\\n import h5py\\n return h5py.File(filename, mode, **kwargs)\",\n \"def read_detector_data(filepath):\\n with h5py.File(filepath, \\\"r\\\") as h5f:\\n data = h5f[\\\"/entry1/area_detector2/data\\\"][:]\\n\\n # reshape data to a correct shape (2006 issue)\\n n, cols, rows = data.shape\\n data = data.reshape(n, rows, cols)\\n\\n det_data = {\\\"data\\\": data}\\n\\n det_data[\\\"rot_angle\\\"] = h5f[\\\"/entry1/area_detector2/rotation_angle\\\"][:] # om, sometimes ph\\n det_data[\\\"pol_angle\\\"] = h5f[\\\"/entry1/ZEBRA/area_detector2/polar_angle\\\"][:] # gammad\\n det_data[\\\"tlt_angle\\\"] = h5f[\\\"/entry1/ZEBRA/area_detector2/tilt_angle\\\"][:] # nud\\n det_data[\\\"ddist\\\"] = h5f[\\\"/entry1/ZEBRA/area_detector2/distance\\\"][:]\\n det_data[\\\"wave\\\"] = h5f[\\\"/entry1/ZEBRA/monochromator/wavelength\\\"][:]\\n det_data[\\\"chi_angle\\\"] = h5f[\\\"/entry1/sample/chi\\\"][:] # ch\\n det_data[\\\"phi_angle\\\"] = h5f[\\\"/entry1/sample/phi\\\"][:] # ph\\n det_data[\\\"UB\\\"] = h5f[\\\"/entry1/sample/UB\\\"][:].reshape(3, 3)\\n\\n return det_data\",\n \"def read_hdf5_group(filename, gname, vars_name=None):\\n fid = h5py.File(filename, 'r')\\n gid = fid.get(gname)\\n if vars_name is None: vars_name = list(gid.keys())\\n\\n data = {}\\n for var_name in vars_name:\\n try:\\n dset = gid.get(var_name)\\n shape = dset.shape\\n data[var_name] = np.zeros(shape)\\n dset.read_direct(data[var_name])\\n except:\\n pass\\n fid.close()\\n print('Read from ', ''.join((filename,'/',gname)))\\n print('Variables names = ')\\n print('\\\\n'.join(vars_name))\\n\\n return data, vars_name\",\n \"def load(file):\\n if isinstance(file, bob.io.base.HDF5File):\\n return file.read(\\\"array\\\")\\n else:\\n return bob.io.base.load(file)\",\n \"def get_data_hdf(file):\\n data = {}\\n\\n def func(name, dset):\\n if not hasattr(dset, 'shape'):\\n return # not array, can't be image\\n if isinstance(dset, h5py.Dataset):\\n data[dset.name] = dset.name\\n\\n file_h5 = h5py.File(file, 'r')\\n file_h5.visititems(func)\\n return file_h5, data\",\n \"def view_hdf5(filename):\\n # handling input errors\\n if not isinstance(filename, str):\\n raise TypeError('Passed value of `filename` is not a string! Instead, it is: '\\n + str(type(filename)))\\n if not filename.split('/')[-1].split('.')[-1] == 'hdf5':\\n raise TypeError('`filename` is not type = .hdf5! Instead, it is: '\\n + filename.split('/')[-1].split('.')[-1])\\n # pring groups and datasets in first three layers\\n print('**** {} ****'.format(filename))\\n hdf5 = h5py.File(filename, 'r')\\n for _, layer_1 in enumerate(list(hdf5.keys())):\\n if isinstance(hdf5[layer_1], h5py.Group):\\n print('\\\\033[1m{}\\\\033[0m'.format(layer_1))\\n for _, layer_2 in enumerate(list(hdf5[layer_1].keys())):\\n if isinstance(hdf5['{}/{}'.format(layer_1, layer_2)], h5py.Group):\\n print('| \\\\033[1m{}\\\\033[0m'.format(layer_2))\\n for _, layer_3 in enumerate(list(hdf5['{}/{}'.format(layer_1, layer_2)])):\\n if isinstance(hdf5['{}/{}/{}'.format(layer_1, layer_2, layer_3)],\\n h5py.Group):\\n print('| | \\\\033[1m{}\\\\033[0m/...'.format(layer_3))\\n else:\\n print('| | {}'.format(layer_3))\\n else:\\n print('| {}'.format(layer_2))\\n else:\\n print('{}'.format(layer_1))\\n hdf5.close()\",\n \"def get_lh5_header(in_file, verbose=False):\\n hf = h5py.File(in_file)\\n\\n # pretty print the raw structure, with all attributes\\n if verbose:\\n def print_groups(name, obj):\\n if isinstance(obj, h5py.Group):\\n print(f\\\"GROUP /{name}\\\")\\n indent = \\\" \\\"\\n if isinstance(obj, h5py.Dataset):\\n print(\\\" DATASET\\\", obj.shape, obj.name)\\n indent = \\\" \\\"\\n for att, val in obj.attrs.items():\\n print(f\\\"{indent}ATTRIBUTE {att}:\\\", val)\\n print(\\\" \\\")\\n hf.visititems(print_groups) # accesses __call__\\n \\n # find each LH5 \\\"Table\\\" contained in the file, and create a DataFrame header\\n tables = {}\\n for g_top in hf.keys():\\n \\n h5group = hf[f\\\"/{g_top}\\\"]\\n attrs = {att:val for att, val in h5group.attrs.items()}\\n \\n # LH5 table condition\\n if \\\"datatype\\\" in attrs.keys() and \\\"table{\\\" in attrs[\\\"datatype\\\"]:\\n \\n # call our nice iterator at this group level\\n table = {g_top:[]}\\n for (path, name, size, dtype, units, spec) in get_datasets(h5group):\\n table[g_top].append((name, size, dtype, units, spec))\\n \\n hdr = pd.DataFrame(table[g_top], columns=['name','size','dtype',\\n 'units','spec'])\\n \\n # fix waveform datatype to match flattened_data\\n if 'waveform' in hdr['name'].values:\\n wf_dt = h5group['waveform/values/flattened_data'].dtype\\n hdr.loc[hdr['name'] == 'waveform', ['dtype']] = wf_dt\\n \\n tables[g_top] = hdr\\n\\n return tables\",\n \"def read_h5_file_arvind_format(folder, filen):\\n \\n ### file path\\n \\n fpath = folder + filen + '.h5'\\n assert os.path.exists(fpath), \\\"The out.h5 file does NOT exist for \\\" + fpath\\n fl = h5py.File(fpath, 'r')\\n \\n ### cell information\\n \\n xu = np.array(fl['/positions/xu'], dtype=np.float32)\\n pol = np.array(fl['/positions/ori'], dtype=np.float32)\\n pol = np.array([xt.T for xt in pol[:]])\\n \\n ### simulation information\\n \\n lx = fl['/info/box/x'][...]\\n ly = fl['/info/box/y'][...]\\n dt = fl['/info/dt'][...]\\n nsteps = fl['/info/nsteps'][...]\\n nfils = fl['/info/nfils'][...]\\n nbeads = fl['/info/nbeads'][...]\\n nsamp = fl['/info/nsamp'][...]\\n nbpf = fl['/info/nbpf'][...]\\n \\n ### simulation parameters\\n \\n density = fl['/param/density'][...]\\n kappa = fl['/param/kappa'][...]\\n km = fl['/param/km'][...]\\n pa = fl['/param/pa'][...]\\n pp = fl['/param/pp'][...]\\n bl = fl['/param/bl'][...]\\n sigma = fl['/param/sigma'][...]\\n \\n fl.close()\\n \\n ### generate classes to submerge data\\n \\n sim = misc_tools.Simulation(lx, ly, dt, nsteps, nfils, nbeads, nsamp, nbpf, \\\\\\n density, kappa, km, pa, pp, bl, sigma)\\n fils = misc_tools.Cells(xu, pol, nbpf, sim)\\n \\n return sim, fils\",\n \"def _getH5File(db):\\n if isinstance(db, Database3):\\n return db.h5db\\n else:\\n raise TypeError(\\\"Unsupported Database type ({})!\\\".format(type(db)))\",\n \"def _read_file(self) -> Tuple[np.ndarray, h5py.File]:\\n assert os.path.exists(self.datafile)\\n LOGGER.info(f\\\"Found datafile: {self.datafile}\\\")\\n\\n # get ELM indices from datafile\\n hf = h5py.File(self.datafile, \\\"r\\\")\\n LOGGER.info(f\\\"Number of ELM events in the datafile: {len(hf)}\\\")\\n elm_index = np.array([int(key) for key in hf], dtype=np.int32)\\n return elm_index, hf\",\n \"def readHtk(filename):\\n with open(filename, \\\"rb\\\") as f:\\n # Read header\\n nSamples, sampPeriod, sampSize, parmKind = struct.unpack(\\\">iihh\\\", f.read(12))\\n # Read data\\n data = struct.unpack(\\\">%df\\\" % (nSamples * sampSize / 4), f.read(nSamples * sampSize))\\n # return numpy.array(data).reshape(nSamples, int(sampSize / 4))\\n return nSamples, sampPeriod, sampSize, parmKind, data\",\n \"def hdf5_data(self):\\n if self._hdf5_data is None:\\n self._hdf5_data = self.parent.request_data(Hdf5DataSpec)\\n return self._hdf5_data\",\n \"def read_lastfm_data(filepath):\\n start = time.time()\\n with h5py.File(filepath, 'r') as f:\\n m = f.get('artist_user_plays')\\n plays = csr_matrix((m.get('data'), m.get('indices'), m.get('indptr')))\\n logging.debug('lastfm_360k.hdf5 loaded in ' + str(time.time() - start))\\n return np.array(f['artist']), np.array(f['user']), plays\",\n \"def ReadHDF5(self,filename):\\n\\n if self.elements is not None and self.points is not None:\\n self.__reset__()\\n\\n DictOutput = loadmat(filename)\\n\\n # GENERIC READER - READS EVERYTHING FROM HDF5 AND ASSIGNS IT TO MESH OBJECT\\n for key, value in DictOutput.items():\\n if isinstance(DictOutput[key],np.ndarray):\\n if \\\"elements\\\" in key or \\\"edge\\\" in key or \\\"face\\\" in key:\\n setattr(self, key, np.ascontiguousarray(value).astype(np.uint64))\\n else:\\n setattr(self, key, np.ascontiguousarray(value))\\n else:\\n setattr(self, key, value)\\n\\n if isinstance(self.element_type,np.ndarray):\\n self.element_type = str(self.element_type[0])\\n if isinstance(self.nelem,np.ndarray):\\n self.nelem = int(self.nelem[0])\\n\\n for key in self.__dict__.keys():\\n if isinstance(self.__dict__[str(key)],np.ndarray):\\n if self.__dict__[str(key)].size == 1:\\n self.__dict__[str(key)] = np.asscalar(self.__dict__[str(key)])\",\n \"def read_data_set(filepath):\\n st = time.time()\\n with h5py.File(filepath, 'r') as f:\\n m = f.get('artist_user_plays')\\n plays = csr_matrix((m.get('data'), m.get('indices'), m.get('indptr')))\\n logger.debug('data loaded in:\\\\t' + str(time.time() - st))\\n return np.array(f['artist']), np.array(f['user']), plays\",\n \"def read_dhalo_trees(basename):\\n \\n # Read in the tree file(s)\\n ifile = 0\\n nfiles = 1\\n data = collections.OrderedDict()\\n while ifile < nfiles:\\n treefile = h5py.File(\\\"%s.%d.hdf5\\\" % (basename, ifile), \\\"r\\\")\\n if ifile == 0:\\n nfiles = treefile[\\\"fileInfo\\\"].attrs[\\\"numberOfFiles\\\"]\\n for uname in treefile[\\\"haloTrees\\\"].keys():\\n name = str(uname)\\n if ifile == 0:\\n data[name] = []\\n data[name].append(treefile[\\\"haloTrees\\\"][name][...])\\n treefile.close()\\n ifile += 1\\n \\n # Combine arrays from separate files and return\\n for name in data.keys():\\n data[name] = np.concatenate(data[name], axis=0)\\n return data\",\n \"def read_treecode_hdf5(file):\\n import h5py\\n import numpy as np\\n\\n fid = h5py.File(file,'r')\\n\\n b = fid['block_treecode'][:]\\n treecode = np.array(b, dtype=float)\\n\\n return treecode\",\n \"def print_h5(fname: str) -> None:\\n try:\\n with h5py.File(fname, 'r') as h:\\n print(fname)\\n recursively_print_structure(h, ' ')\\n except IOError as e:\\n print(f\\\"Cannot open HDF5 file {fname}\\\")\\n print(f\\\"IOError: {e}\\\")\",\n \"def h5(self):\\n return self._h5\",\n \"def read_dataset(fname, dname, start=0, stop=-1, skip=1):\\n data = None\\n\\n try:\\n with h5py.File(fname, 'r') as f:\\n if stop == -1:\\n return f[dname][start::skip,]\\n else:\\n return f[dname][start:stop:skip,]\\n except IOError:\\n print(\\\"Error: cannot find file %s.\\\" % fname)\\n\\n return data\",\n \"def extract_h5(args, var, data, lfile):\\n import h5py\\n import numpy as np\\n try:\\n raw = h5py.File(lfile, 'r')\\n except:\\n print(\\\"Error: File could not be read: \\\" + lfile)\\n return\\n # Get time\\n time = np.array(raw['sys.exec.out.time'])\\n # Get data\\n for v in var:\\n if v not in data: # havent extracted yet\\n if v in raw:\\n data[v] = np.array(raw[v])\\n else:\\n # if args.verbose :\\n # print \\\"Warning: \\\" + v + \\\" not found in \\\" +\\n # os.path.basename(lfile)\\n continue\\n else:\\n continue # already extracted\\n raw.close()\\n return data, time\",\n \"def ToH5(self,h5File=None):\\r\\n\\r\\n logStr = \\\"{0:s}.{1:s}: \\\".format(self.__class__.__name__, sys._getframe().f_code.co_name)\\r\\n logger.debug(\\\"{0:s}{1:s}\\\".format(logStr,'Start.')) \\r\\n \\r\\n try: \\r\\n if h5File == None:\\r\\n h5File=self.h5File\\r\\n\\r\\n #Delete .h5 File if exists\\r\\n if os.path.exists(h5File): \\r\\n logger.debug(\\\"{0:s}{1:s}: Delete ...\\\".format(logStr,h5File)) \\r\\n os.remove(h5File)\\r\\n\\r\\n #Determine .h5 BaseKey\\r\\n\\r\\n relPath2XmlromCurDir=os.path.normpath(os.path.relpath(os.path.normpath(self.xmlFile),start=os.path.normpath(os.path.curdir))) # ..\\\\..\\\\..\\\\..\\\\..\\\\3S\\\\Modelle\\\\....XML\\r\\n #print(repr(relPath2XmlromCurDir)) # '..\\\\\\\\..\\\\\\\\..\\\\\\\\..\\\\\\\\..\\\\\\\\3S\\\\\\\\Modelle\\\\\\\\....XML'\\r\\n h5KeySep='/'\\r\\n h5KeyCharForDot='_'\\r\\n h5KeyCharForMinus='_'\\r\\n relPath2XmlromCurDirH5BaseKey=re.sub('\\\\.',h5KeyCharForDot,re.sub(r'\\\\\\\\',h5KeySep,re.sub('-',h5KeyCharForMinus,re.sub('.xml','',relPath2XmlromCurDir,flags=re.IGNORECASE))))\\r\\n #__/__/__/__/__/3S/Modelle/...\\r\\n\\r\\n warnings.filterwarnings('ignore',category=pd.io.pytables.PerformanceWarning) #your performance may suffer as PyTables will pickle object types that it cannot map directly to c-types \\r\\n warnings.filterwarnings('ignore',category=tables.exceptions.NaturalNameWarning) #\\\\lib\\\\site-packages\\\\tables\\\\path.py:100: NaturalNameWarning: object name is not a valid Python identifier: '3S'; it does not match the pattern ``^[a-zA-Z_][a-zA-Z0-9_]*$``; you will not be able to use natural naming to access this object; using ``getattr()`` will still work, though)\\r\\n \\r\\n #Write .h5 File\\r\\n logger.debug(\\\"{0:s}pd.HDFStore({1:s}) ...\\\".format(logStr,h5File)) \\r\\n with pd.HDFStore(h5File) as h5Store: \\r\\n #for tableName,table in self.dataFrames.items():\\r\\n for tableName in sorted(self.dataFrames.keys()):\\r\\n table=self.dataFrames[tableName]\\r\\n h5Key=relPath2XmlromCurDirH5BaseKey+h5KeySep+tableName \\r\\n logger.debug(\\\"{0:s}{1:s}: Writing DataFrame {2:s} with h5Key={3:s}\\\".format(logStr,h5File,tableName,h5Key)) \\r\\n try:\\r\\n h5Store.put(h5Key,table)#,format='table') \\r\\n except Exception as e:\\r\\n logger.error(\\\"{0:s}{1:s}: Writing DataFrame {2:s} with h5Key={3:s} FAILED!\\\".format(logStr,h5File,tableName,h5Key)) \\r\\n raise e\\r\\n \\r\\n\\r\\n except Exception as e:\\r\\n logStrFinal=\\\"{:s}Exception: Line: {:d}: {!s:s}: {:s}\\\".format(logStr,sys.exc_info()[-1].tb_lineno,type(e),str(e))\\r\\n logger.error(logStrFinal) \\r\\n raise XmError(logStrFinal) \\r\\n \\r\\n finally:\\r\\n h5Store.close()\\r\\n logger.debug(\\\"{0:s}{1:s}\\\".format(logStr,'_Done.'))\",\n \"def load_hdf5_with_structure(file):\\n n_classes = 80\\n n_boxes = 5\\n anchors = [[0.738768, 0.874946], [2.42204, 2.65704], [4.30971, 7.04493], [10.246, 4.59428], [12.6868, 11.8741]]\\n\\n yolov2 = YOLOv2(n_classes=n_classes, n_boxes=n_boxes)\\n chainer.serializers.load_hdf5(file, yolov2)\\n model = YOLOv2Predictor(yolov2)\\n model.init_anchor(anchors)\\n #model.predictor.train = False\\n model.predictor.finetune = False\\n return model\",\n \"def read(self):\\n\\n if self.filename.endswith(\\\".pkl\\\"):\\n logging.debug(\\\"Loading pickle file %s\\\", self.filename)\\n data = pd.read_pickle(self.filename)\\n\\n elif self.filename.endswith(\\\".hdf5\\\"):\\n logging.debug(\\\"Loading HDF5 file %s\\\", self.filename)\\n with h5py.File(self.filename, \\\"r\\\") as data_file:\\n\\n data = pd.DataFrame(\\n {\\n column: data_file[\\\"/haloTrees/%s\\\" % column].value\\n for column in self.columns\\n }\\n ).set_index(\\\"nodeIndex\\\")\\n\\n # with open(\\\"./data/cache.pkl\\\", \\\"w\\\") as pickle_file:\\n # data.to_pickle(pickle_file)\\n\\n else:\\n raise TypeError(\\\"Unknown filetype %s\\\" % self.filename)\\n\\n return data\",\n \"def h5_to_df(h5_file, group_name):\\n col_dict = {}\\n h5_set = hdf_file[group_name]\\n for col in h5_set.keys():\\n my_array = np.asarray(h5_set[col])\\n col_dict[col] = my_array\\n df = pd.DataFrame(col_dict)\\n return df\",\n \"def summarize_hdf5(hdf5_file):\\n # characterize the h5file in a mostly content-agnostic way\\n summary = {\\n 'sums': {},\\n 'shapes': {}\\n }\\n\\n def characterize_object(obj_name, obj_data):\\n \\\"\\\"\\\"retain some properties of each dataset in an hdf5 file\\\"\\\"\\\"\\n if isinstance(obj_data, h5py.Dataset):\\n summary['shapes'][obj_name] = obj_data.shape\\n # note that this will break if the hdf5 file contains non-numeric datasets\\n summary['sums'][obj_name] = obj_data[...].sum()\\n print(\\\"dataset %s version = %s\\\" % (obj_name, hdf5_file.get_version(obj_name)))\\n\\n hdf5_file.visititems(characterize_object)\\n\\n return summary\",\n \"def collect_storage_info(fname):\\n # Open the HDF5 file for simple byte I/O...\\n fb = fname.open('rb')\\n if not fb.seekable():\\n raise OSError('Byte stream for %s not seekable' % fname)\\n fb.seek(0, SEEK_SET)\\n\\n storage = dict()\\n\\n def _stinfo(objname, obj):\\n if isinstance(obj, h5py.Dataset):\\n stinfo = obj.storage\\n if isinstance(stinfo, h5py.h5d.ContiguousStorageInfo):\\n fb.seek(stinfo.file_addr, SEEK_SET)\\n byte_stream = fb.read(stinfo.size)\\n if len(byte_stream) != stinfo.size:\\n raise IOError(\\n 'Read %d bytes instead of %d bytes for %s from %s' %\\n (len(byte_stream), stinfo.size, obj.name, fname))\\n\\n storage[obj.name] = {'file_addr': stinfo.file_addr,\\n 'size': stinfo.size,\\n 'uuid': str(uuid4()),\\n 'md5': md5(byte_stream).hexdigest()}\\n else:\\n chunk_info = list()\\n storage[obj.name] = {'chunk_size': obj.chunks,\\n 'filters': obj._filters}\\n for si in stinfo:\\n fb.seek(si.file_addr, SEEK_SET)\\n byte_stream = fb.read(si.size)\\n if len(byte_stream) != si.size:\\n raise IOError(\\n 'Read %d bytes instead of %d bytes for chunk #%d '\\n 'of %s from %s' % (len(byte_stream), si.size,\\n si.order, obj.name, fname))\\n\\n chunk_info.append({'order': si.order,\\n 'file_addr': si.file_addr,\\n 'size': si.size,\\n 'logical_addr': si.logical_addr,\\n 'uuid': str(uuid4()),\\n 'md5': md5(byte_stream).hexdigest()})\\n\\n storage[obj.name]['chunk_info'] = chunk_info\\n\\n with h5py.File(str(fname), 'r') as f:\\n f.visititems(_stinfo)\\n fb.close()\\n\\n return storage\",\n \"def get_data(data_dir, hdf5):\\r\\n\\r\\n # Get the filenames of the lists containing image paths and labels.\\r\\n train_file, val_file = build_dataset_index(data_dir)\\r\\n\\r\\n # Check if (creating and) loading from hdf5 database is desired.\\r\\n if hdf5:\\r\\n # Create folder to store dataset.\\r\\n if not os.path.exists('hdf5'):\\r\\n os.makedirs('hdf5')\\r\\n # Check if hdf5 databases already exist and create them if not.\\r\\n if not os.path.exists('hdf5/tiny-imagenet_train.h5'):\\r\\n from tflearn.data_utils import build_hdf5_image_dataset\\r\\n print ' Creating hdf5 train dataset.'\\r\\n build_hdf5_image_dataset(train_file, image_shape=(64, 64), mode='file', output_path='hdf5/tiny-imagenet_train.h5', categorical_labels=True, normalize=True)\\r\\n\\r\\n if not os.path.exists('hdf5/tiny-imagenet_val.h5'):\\r\\n from tflearn.data_utils import build_hdf5_image_dataset\\r\\n print ' Creating hdf5 val dataset.'\\r\\n build_hdf5_image_dataset(val_file, image_shape=(64, 64), mode='file', output_path='hdf5/tiny-imagenet_val.h5', categorical_labels=True, normalize=True)\\r\\n\\r\\n # Load training data from hdf5 dataset.\\r\\n h5f = h5py.File('hdf5/tiny-imagenet_train.h5', 'r')\\r\\n X = h5f['X']\\r\\n Y = h5f['Y']\\r\\n\\r\\n # Load validation data.\\r\\n h5f = h5py.File('hdf5/tiny-imagenet_val.h5', 'r')\\r\\n X_test = h5f['X']\\r\\n Y_test = h5f['Y'] \\r\\n\\r\\n # Load images directly from disk when they are required.\\r\\n else:\\r\\n from tflearn.data_utils import image_preloader\\r\\n X, Y = image_preloader(train_file, image_shape=(64, 64), mode='file', categorical_labels=True, normalize=True, filter_channel=True)\\r\\n X_test, Y_test = image_preloader(val_file, image_shape=(64, 64), mode='file', categorical_labels=True, normalize=True, filter_channel=True)\\r\\n\\r\\n # Randomly shuffle the dataset.\\r\\n X, Y = shuffle(X, Y)\\r\\n\\r\\n return X, Y, X_test, Y_test\",\n \"def read_data(path, batch_size, qp, frac, kernel, model):\\n # load h5 file and get dictionaries\\n inputs_dict, labels_dict, _ = get_dataset_dict(path, qp)\\n\\n # create training / validation dictionaries\\n block_keys = [k for k in inputs_dict]\\n train_inputs_dict, train_labels_dict, val_inputs_dict, val_labels_dict = (dict() for _ in range(4))\\n\\n # get inputs / labels for block & frac position\\n for block in block_keys:\\n inputs = inputs_dict[block][frac]\\n\\n # only use inputs that can be split 80 / 20 train / validation and fill out a batch\\n split_percentage = 4/5\\n if len(inputs) < batch_size / split_percentage:\\n continue\\n\\n # if model contains non-linear activations, use same input & label size\\n inputs = inputs[:, kernel:-kernel, kernel:-kernel, :] if \\\"scratch\\\" not in model else inputs\\n\\n labels = labels_dict[block][frac]\\n\\n # shuffle the pairs\\n inputs, labels = array_shuffle(len(inputs), inputs, labels)\\n\\n # split 80 / 20\\n (train_inputs, train_labels), (val_inputs, val_labels) = split_data(split_percentage, inputs, labels)\\n\\n # put into correct dictionary entry\\n train_inputs_dict[block] = train_inputs\\n train_labels_dict[block] = train_labels\\n val_inputs_dict[block] = val_inputs\\n val_labels_dict[block] = val_labels\\n\\n return train_inputs_dict, train_labels_dict, val_inputs_dict, val_labels_dict\",\n \"def setup(self):\\n print(\\\"Looking for \\\", self.filename)\\n if os.path.exists(self.filename):\\n n, ext = os.path.splitext(self.filename)[:2]\\n if ext == \\\".h5\\\" or ext == \\\".hdf5\\\":\\n with h5py.File(self.filename, \\\"r\\\") as file:\\n keys = list(file.keys())\\n self.data = file[keys[0]].value\\n print(\\\"Behavior Data length is \\\", self.data.shape[2])\\n\\n else:\\n raise FileNotFoundError\",\n \"def parse_hdob_file(path):\\n col_names = [\\\"obs_time\\\", \\\"lat\\\", \\\"lon\\\", \\\"static_air_press\\\", \\\"geo_pot_height\\\",\\n \\\"sfc_press_dval\\\", \\\"t_air\\\", \\\"t_dew\\\", \\\"wind_dir_spd\\\", \\\"wind_peak\\\",\\n \\\"sfc_wind_peak\\\", \\\"rain_rate\\\", \\\"qc_flags\\\"]\\n file_header = ''\\n obs_data = []\\n\\n # Determine if 'path' is a path or url\\n if isfile(path):\\n # open & read local file\\n with open(path, 'r') as fh:\\n for idx, line in enumerate(fh):\\n line = line.rstrip('\\\\n')\\n\\n if (idx == 3):\\n file_header = line\\n elif ((idx > 3) and (idx < 24)):\\n curr_line = line.split(' ')\\n curr_line = [x for x in curr_line if x != ' ']\\n obs_data.append(curr_line)\\n hdob_df = pd.DataFrame(data=obs_data, index=range(0, len(obs_data)), columns=col_names)\\n hdob_obj = HDOBFile(file_header, hdob_df)\\n print(hdob_obj)\\n # elif (isURL):\",\n \"def hdf5_dataset(path, fields):\\n\\n # Reads dataset row by row\\n def _generator():\\n with h5py.File(path, 'r') as file:\\n datasets = [file[field] for field in fields]\\n for row in zip(*datasets):\\n yield row\\n\\n # Reads hdf5 metadata (types and shapes)\\n with h5py.File(path, 'r') as file:\\n datasets = [file[field] for field in fields]\\n\\n types = tuple(ds.dtype for ds in datasets)\\n shapes = tuple(ds.shape[1:] for ds in datasets)\\n\\n # Create dataset\\n ds = tf.data.Dataset.from_generator(_generator, types, shapes)\\n return ds\",\n \"def read_dataset_v1():\\n path = load_config()\\n T = feather.read_dataframe(path['data_dir'] / 'T_dat.feather')\\n E = feather.read_dataframe(path['data_dir'] / 'E_dat.feather')\\n M = feather.read_dataframe(path['data_dir'] / 'Meta.feather')\\n data = sio.loadmat(path['data_dir'] / 'highvar_genes.mat', squeeze_me=True)\\n return T, E, M, data\",\n \"def test_hdf5_load_all():\\n skip_if_no_h5py()\\n import h5py\\n\\n # save random data to HDF5\\n handle, filename = tempfile.mkstemp()\\n dataset = random_one_hot_dense_design_matrix(np.random.RandomState(1),\\n num_examples=10, dim=5,\\n num_classes=3)\\n with h5py.File(filename, 'w') as f:\\n f.create_dataset('X', data=dataset.get_design_matrix())\\n f.create_dataset('y', data=dataset.get_targets())\\n\\n # instantiate Train object\\n trainer = yaml_parse.load(load_all_yaml % {'filename': filename})\\n trainer.main_loop()\\n\\n # cleanup\\n os.remove(filename)\",\n \"def h5_path(self):\\n return self._h5_path\",\n \"def read(self, simtype):\\n\\n if simtype == 'original':\\n if self.filename.endswith(\\\".pkl\\\"):\\n logging.debug(\\\"Loading pickle file %s\\\", self.filename)\\n data = pd.read_pickle(self.filename)\\n\\n elif self.filename.endswith(\\\".hdf5\\\"):\\n logging.debug(\\\"Loading HDF5 file %s\\\", self.filename)\\n with h5py.File(self.filename, \\\"r\\\") as data_file:\\n #print('treeIndex', data_file[\\\"treeIndex\\\"].keys())\\n #print('haloTrees', data_file[\\\"haloTrees\\\"].keys())\\n \\n # Find dimensionality of keys\\n columns_1dim = [] \\n columns_2dim = [] \\n for column in self.columns:\\n if len(data_file[\\\"/haloTrees/%s\\\" % column].shape) == 1:\\n columns_1dim.append(column)\\n else:\\n columns_2dim.append(column)\\n \\n # 1D keys\\n data = pd.DataFrame(\\n {\\n column: data_file[\\\"/haloTrees/%s\\\" % column].value\\n for column in columns_1dim\\n },\\n columns=columns_1dim\\n ).set_index(\\\"nodeIndex\\\")\\n del columns_1dim\\n\\n # 2D keys\\n for column in columns_2dim:\\n if column == 'position':\\n pos = data_file[\\\"/haloTrees/%s\\\" % column].value\\n data['X'] = pd.Series(pos[:, 0], index=data.index)\\n data['Y'] = pd.Series(pos[:, 1], index=data.index)\\n data['Z'] = pd.Series(pos[:, 2], index=data.index)\\n del columns_2dim\\n\\n data.rename(index=str,\\n columns={\\\"snapshotNumber\\\": \\\"snapnum\\\"})\\n ## eliminate fake elements with isIntegrated=1\\n #data = data[data.isInterpolated != 1]\\n\\n else:\\n raise TypeError(\\\"Unknown filetype %s\\\" % self.filename)\\n if simtype == 'EAGLE':\\n if self.filename.endswith(\\\".pkl\\\"):\\n logging.debug(\\\"Loading pickle file %s\\\", self.filename)\\n data = pd.read_pickle(self.filename)\\n\\n elif self.filename.endswith(\\\".hdf5\\\"):\\n logging.debug(\\\"Loading HDF5 file %s\\\", self.filename)\\n data_file = h5py.File(self.filename, 'r')\\n column_mt = []\\n column_sh = []\\n for column in self.columns:\\n if column in data_file['MergerTree']:\\n column_mt.append(column)\\n else:\\n column_sh.append(column)\\n\\n data = pd.DataFrame(\\n {\\n column: data_file[\\\"/MergerTree/%s\\\" % column].value\\n for column in column_mt\\n },\\n columns=column_mt\\n ).set_index(\\\"HaloID\\\")\\n #.set_index(data_file[\\\"/Merger/HaloID\\\"].value)\\n\\n for column in column_sh:\\n data[column] = pd.Series(data_file[\\\"/Subhalo/%s\\\" % column].value,\\n index=data.index)\\n data = data.rename(index=str,\\n columns={\\\"SnapNum\\\": \\\"snapnum\\\", #\\\"HaloID\\\": \\\"nodeIndex\\\",\\n \\\"DescendantID\\\" : \\\"descendantIndex\\\"})\\n else:\\n raise TypeError(\\\"Unknown filetype %s\\\" % self.filename)\\n\\n return data\",\n \"def load_field_data_h5(h5, verbose=True):\\n data = {'components':{}}\\n\\n # Load attributes\\n attrs, other = load_field_attrs(h5.attrs, verbose=verbose)\\n attrs.update(other)\\n data['attrs'] = attrs\\n \\n # Loop over records and components\\n for g, comps in field_record_components.items():\\n if g not in h5:\\n continue\\n \\n # Get the full openPMD unitDimension \\n required_dim = expected_record_unit_dimension[g]\\n \\n for comp in comps:\\n if comp not in h5[g]:\\n continue\\n name = g+'/'+comp\\n cdat = component_data(h5[name])\\n \\n # Check dimensions\\n dim = h5[name].attrs['unitDimension']\\n assert np.all(dim == required_dim), f'{name} with dimension {required_dim} expected for {name}, found: {dim}'\\n \\n # Check shape\\n s1 = tuple(attrs['gridSize'])\\n s2 = cdat.shape\\n assert s1 == s2, f'Expected shape: {s1} != found shape: {s2}'\\n \\n # Static fields should be real\\n if attrs['harmonic'] == 0:\\n cdat = np.real(cdat)\\n \\n # Finally set\\n \\n data['components'][name] = cdat \\n \\n \\n return data\",\n \"def get_dataset(filepath: PathLike, internal_path: str) -> np.ndarray:\\n with h5py.File(filepath, mode='r') as f:\\n array = f[internal_path][...]\\n return array\",\n \"def read(self, fname, frame=None):\\n\\n self.resetvals()\\n url = urlparse(fname)\\n if not self.url:\\n self.url = url\\n# if frame:\\n# self.hdf5_location.set_index(frame)\\n self.filename = self.url.path\\n if os.path.isfile(self.filename):\\n self.hdf5 = h5py.File(self.filename, \\\"r\\\")\\n else:\\n error = \\\"No such file or directory: %s\\\" % self.filename\\n logger.error(error)\\n raise RuntimeError(error)\\n self.ds = self.hdf5[self.url.query]\\n if isinstance(self.ds, h5py.Group) and (\\\"data\\\" in self.ds):\\n self.ds = self.ds[\\\"data\\\"]\\n\\n if self.url.fragment:\\n slices = self.get_slice()\\n self.data = self.ds[self.get_slice()]\\n self.nframes = self.ds.shape[self.main_dim]\\n else:\\n self.data = self.ds[:]\\n self.nframes = 1\\n self.dim2, self.dim1 = self.data.shape\\n self.bytecode = str(self.data.dtype)\\n return self\",\n \"def read_metadata(self, data_set):\\n # checks if there is an open file in the dataset as load_data does\\n # reading of metadata before reading the complete dataset\\n if not hasattr(self, '_h5_base_group'):\\n self._open_file(data_set)\\n if 'metadata' in data_set._h5_base_group.keys():\\n metadata_group = data_set._h5_base_group['metadata']\\n self.read_dict_from_hdf5(data_set.metadata, metadata_group)\\n return data_set\",\n \"def save_as_hdf5(self, filename):\",\n \"def read_skyh5(\\n self, filename, run_check=True, check_extra=True, run_check_acceptability=True\\n ):\\n with h5py.File(filename, \\\"r\\\") as fileobj:\\n if \\\"/Header\\\" not in fileobj:\\n raise ValueError(\\\"This is not a proper skyh5 file.\\\")\\n\\n init_params = {\\\"filename\\\": os.path.basename(filename)}\\n\\n with h5py.File(filename, \\\"r\\\") as fileobj:\\n # extract header information\\n header = fileobj[\\\"/Header\\\"]\\n header_params = [\\n \\\"_Ncomponents\\\",\\n \\\"_Nfreqs\\\",\\n \\\"_component_type\\\",\\n \\\"_spectral_type\\\",\\n \\\"_history\\\",\\n \\\"_name\\\",\\n \\\"_nside\\\",\\n \\\"_hpx_order\\\",\\n \\\"_hpx_inds\\\",\\n \\\"_freq_array\\\",\\n \\\"_freq_edge_array\\\",\\n \\\"_reference_frequency\\\",\\n \\\"_spectral_index\\\",\\n \\\"_stokes_error\\\",\\n \\\"_beam_amp\\\",\\n \\\"_extended_model_group\\\",\\n ]\\n\\n optional_params = [\\n \\\"_hpx_order\\\",\\n \\\"_freq_array\\\",\\n \\\"_freq_edge_array\\\",\\n \\\"_reference_frequency\\\",\\n \\\"_spectral_index\\\",\\n \\\"_stokes_error\\\",\\n \\\"_beam_amp\\\",\\n \\\"_extended_model_group\\\",\\n ]\\n\\n self.component_type = header[\\\"component_type\\\"][()].tobytes().decode(\\\"utf-8\\\")\\n\\n if self.component_type != \\\"healpix\\\":\\n optional_params.extend([\\\"_nside\\\", \\\"_hpx_inds\\\"])\\n if \\\"skycoord\\\" in header:\\n skycoord_dict = {}\\n for key in header[\\\"skycoord\\\"]:\\n if key in [\\\"frame\\\", \\\"representation_type\\\"]:\\n expected_type = str\\n else:\\n expected_type = None\\n skycoord_dict[key] = _get_value_hdf5_group(\\n header[\\\"skycoord\\\"], key, expected_type\\n )\\n init_params[\\\"skycoord\\\"] = SkyCoord(**skycoord_dict)\\n else:\\n if \\\"lat\\\" in header and \\\"lon\\\" in header and \\\"frame\\\" in header:\\n header_params += [\\\"lat\\\", \\\"lon\\\", \\\"frame\\\"]\\n optional_params += [\\\"lat\\\", \\\"lon\\\", \\\"frame\\\"]\\n elif \\\"ra\\\" in header and \\\"dec\\\" in header:\\n header_params += [\\\"ra\\\", \\\"dec\\\"]\\n optional_params += [\\\"ra\\\", \\\"dec\\\"]\\n else:\\n raise ValueError(\\n \\\"No component location information found in file.\\\"\\n )\\n warnings.warn(\\n \\\"Parameter skycoord not found in skyh5 file. \\\"\\n \\\"This skyh5 file was written by an older version of pyradiosky. \\\"\\n \\\"Consider re-writing this file to ensure future compatibility\\\"\\n )\\n else:\\n optional_params.append(\\\"_name\\\")\\n\\n if \\\"hpx_frame\\\" in header:\\n if isinstance(header[\\\"hpx_frame\\\"], h5py.Dataset):\\n # hpx_frame was stored as a string\\n frame_str = _get_value_hdf5_group(header, \\\"hpx_frame\\\", str)\\n dummy_coord = SkyCoord(0, 0, unit=\\\"rad\\\", frame=frame_str)\\n init_params[\\n \\\"hpx_frame\\\"\\n ] = dummy_coord.frame.replicate_without_data(copy=True)\\n else:\\n # hpx_frame was stored as a nested dset\\n skycoord_dict = {}\\n for key in header[\\\"hpx_frame\\\"]:\\n if key in [\\\"frame\\\", \\\"representation_type\\\"]:\\n expected_type = str\\n else:\\n expected_type = None\\n skycoord_dict[key] = _get_value_hdf5_group(\\n header[\\\"hpx_frame\\\"], key, expected_type\\n )\\n dummy_coord = SkyCoord(0, 0, unit=\\\"rad\\\", **skycoord_dict)\\n init_params[\\n \\\"hpx_frame\\\"\\n ] = dummy_coord.frame.replicate_without_data(copy=True)\\n elif \\\"frame\\\" in header:\\n # frame was stored as a string\\n frame_str = _get_value_hdf5_group(header, \\\"frame\\\", str)\\n dummy_coord = SkyCoord(0, 0, unit=\\\"rad\\\", frame=frame_str)\\n init_params[\\\"hpx_frame\\\"] = dummy_coord.frame.replicate_without_data(\\n copy=True\\n )\\n\\n for par in header_params:\\n if par in [\\\"lat\\\", \\\"lon\\\", \\\"frame\\\", \\\"ra\\\", \\\"dec\\\"]:\\n parname = par\\n if par == \\\"frame\\\":\\n expected_type = \\\"str\\\"\\n else:\\n expected_type = Quantity\\n else:\\n param = getattr(self, par)\\n parname = param.name\\n expected_type = param.expected_type\\n\\n # skip optional params if not present\\n if par in optional_params:\\n if parname not in header:\\n continue\\n\\n if parname not in header:\\n raise ValueError(\\n f\\\"Expected parameter {parname} is missing in file.\\\"\\n )\\n\\n value = _get_value_hdf5_group(header, parname, expected_type)\\n\\n if parname == \\\"nside\\\":\\n value = int(value)\\n\\n init_params[parname] = value\\n\\n # check that the parameters not passed to the init make sense\\n if init_params[\\\"component_type\\\"] == \\\"healpix\\\":\\n if init_params[\\\"Ncomponents\\\"] != init_params[\\\"hpx_inds\\\"].size:\\n raise ValueError(\\n \\\"Ncomponents is not equal to the size of 'hpx_inds'.\\\"\\n )\\n else:\\n if init_params[\\\"Ncomponents\\\"] != init_params[\\\"name\\\"].size:\\n raise ValueError(\\\"Ncomponents is not equal to the size of 'name'.\\\")\\n\\n if \\\"freq_array\\\" in init_params.keys():\\n if init_params[\\\"Nfreqs\\\"] != init_params[\\\"freq_array\\\"].size:\\n raise ValueError(\\\"Nfreqs is not equal to the size of 'freq_array'.\\\")\\n\\n if init_params[\\\"spectral_type\\\"] == \\\"subband\\\":\\n if \\\"freq_edge_array\\\" not in init_params.keys():\\n try:\\n init_params[\\n \\\"freq_edge_array\\\"\\n ] = _get_freq_edges_from_centers(\\n init_params[\\\"freq_array\\\"], self._freq_array.tols\\n )\\n except ValueError:\\n warnings.warn(\\n \\\"No freq_edge_array in this file and frequencies are \\\"\\n \\\"not evenly spaced, so spectral_type will be set to \\\"\\n \\\"'full' rather than 'subband'.\\\"\\n )\\n init_params[\\\"spectral_type\\\"] = \\\"full\\\"\\n\\n # remove parameters not needed in __init__\\n init_params.pop(\\\"Ncomponents\\\")\\n init_params.pop(\\\"Nfreqs\\\")\\n\\n # get stokes array\\n dgrp = fileobj[\\\"/Data\\\"]\\n init_params[\\\"stokes\\\"] = dgrp[\\\"stokes\\\"] * units.Unit(\\n dgrp[\\\"stokes\\\"].attrs[\\\"unit\\\"]\\n )\\n # frame is a new parameter, check if it exists and try to read\\n # otherwise default to ICRS (the old assumed frame.)\\n if \\\"skycoord\\\" not in init_params and self.component_type != \\\"healpix\\\":\\n if \\\"frame\\\" in header:\\n init_params[\\\"frame\\\"] = header[\\\"frame\\\"][()].tobytes().decode(\\\"utf8\\\")\\n else:\\n warnings.warn(\\n \\\"No frame available in this file, assuming 'icrs'. \\\"\\n \\\"Consider re-writing this file to ensure future compatility.\\\"\\n )\\n init_params[\\\"frame\\\"] = \\\"icrs\\\"\\n\\n if self.component_type == \\\"healpix\\\" and \\\"hpx_frame\\\" in init_params.keys():\\n init_params[\\\"frame\\\"] = init_params[\\\"hpx_frame\\\"]\\n del init_params[\\\"hpx_frame\\\"]\\n\\n if self.component_type == \\\"healpix\\\" and \\\"frame\\\" not in init_params:\\n warnings.warn(\\n \\\"No frame available in this file, assuming 'icrs'. \\\"\\n \\\"Consider re-writing this file to ensure future compatility.\\\"\\n )\\n init_params[\\\"frame\\\"] = \\\"icrs\\\"\\n\\n self.__init__(**init_params)\\n\\n if run_check:\\n self.check(\\n check_extra=check_extra, run_check_acceptability=run_check_acceptability\\n )\",\n \"def recursively_load_dict_contents_from_group(h5file: \\\"h5py.File\\\", \\n path: str,\\n ) -> dict:\\n ans = {}\\n for key, item in h5file[path].items():\\n if isinstance(item, h5py._hl.dataset.Dataset):\\n ans[key] = item.value\\n elif isinstance(item, h5py._hl.group.Group):\\n ans[key] = recursively_load_dict_contents_from_group(h5file, f\\\"{path}{key}/\\\")\\n return ans\",\n \"def read_image_from_h5(h5_path, *args, **kwargs):\\n # TODO: Implement the method\\n\\n h5_pathRd = h5.File(h5_path, \\\"a\\\")\\n image = h5_pathRd[\\\"data\\\"][\\\"image\\\"]\\n\\n return image\",\n \"def printAllColumnsInH5(pathToData):\\n\\n #Check if a correct path is given\\n\\n if not os.path.isfile(pathToData):\\n raise ValueError(\\\"h5 file not found. Wrong path given?\\\")\\n elif os.path.isfile(pathToData):\\n Data = h5.File(pathToData, 'r')\\n\\n\\n Files = Data.keys()\\n\\n for File in Files:\\n print()\\n print('Filename = %s' %(File))\\n print('----------------------')\\n\\n #Every time you see Xr*' '\\n #It means I add X spaces to line it\\n print('\\\\t column name%sunit%slength'%(29*' ',16*' '))\\n print('\\\\t '+'-----------------'*4)\\n \\n #In this file give me all the column names\\n columns = Data[File].keys()\\n \\n #for every column in the columns\\n for nrc,column in enumerate(columns):\\n #always want the column name printed in 40 char\\n spaces = ' '*(40 - len(column))\\n length = Data[File][column].shape[0]\\n #always want the unit name printed over 20 chars\\n unit = Data[File][column].attrs['units']\\n spaces2 = ' '*(20 - len(unit))\\n #--\\n length = Data[File][column].shape[0]\\n\\n print('\\\\t %s%s%s%s%s'%(column,spaces, unit,spaces2, length))\\n #Every 4 lines print a dashed line to read output easier\\n if (nrc%5==4):\\n print('\\\\t '+'-----------------'*4)\\n Data.close()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7600226","0.756424","0.7475437","0.74319535","0.74319535","0.7393015","0.72876596","0.7209565","0.71935135","0.7171935","0.7154797","0.7107951","0.70604193","0.70279646","0.70197207","0.70096785","0.6915676","0.68875426","0.6883653","0.6829923","0.6789821","0.6789821","0.67532575","0.6752257","0.67111564","0.67087764","0.66596407","0.6645489","0.663117","0.6611474","0.6608719","0.6588371","0.6480647","0.64715487","0.6458041","0.6438385","0.64314","0.64301884","0.6424387","0.63818383","0.636999","0.63600415","0.63600415","0.6316384","0.6308739","0.62755364","0.6269743","0.6263793","0.6257808","0.6227594","0.6208816","0.62078476","0.61968774","0.6185224","0.61686885","0.61606175","0.61510617","0.6150992","0.6144234","0.61322385","0.6104563","0.6103382","0.60869795","0.60798466","0.6076474","0.6066799","0.6065294","0.60476065","0.60128564","0.6001852","0.5998544","0.59917384","0.59665143","0.59626395","0.5923686","0.5923164","0.59194654","0.59180355","0.59080535","0.58902556","0.58806264","0.5843823","0.5839159","0.5830269","0.5806004","0.5805572","0.5798957","0.57909244","0.5790205","0.57764643","0.5756443","0.57554644","0.5748696","0.5737882","0.5735171","0.57292116","0.56985956","0.5696817","0.5690602","0.5689763"],"string":"[\n \"0.7600226\",\n \"0.756424\",\n \"0.7475437\",\n \"0.74319535\",\n \"0.74319535\",\n \"0.7393015\",\n \"0.72876596\",\n \"0.7209565\",\n \"0.71935135\",\n \"0.7171935\",\n \"0.7154797\",\n \"0.7107951\",\n \"0.70604193\",\n \"0.70279646\",\n \"0.70197207\",\n \"0.70096785\",\n \"0.6915676\",\n \"0.68875426\",\n \"0.6883653\",\n \"0.6829923\",\n \"0.6789821\",\n \"0.6789821\",\n \"0.67532575\",\n \"0.6752257\",\n \"0.67111564\",\n \"0.67087764\",\n \"0.66596407\",\n \"0.6645489\",\n \"0.663117\",\n \"0.6611474\",\n \"0.6608719\",\n \"0.6588371\",\n \"0.6480647\",\n \"0.64715487\",\n \"0.6458041\",\n \"0.6438385\",\n \"0.64314\",\n \"0.64301884\",\n \"0.6424387\",\n \"0.63818383\",\n \"0.636999\",\n \"0.63600415\",\n \"0.63600415\",\n \"0.6316384\",\n \"0.6308739\",\n \"0.62755364\",\n \"0.6269743\",\n \"0.6263793\",\n \"0.6257808\",\n \"0.6227594\",\n \"0.6208816\",\n \"0.62078476\",\n \"0.61968774\",\n \"0.6185224\",\n \"0.61686885\",\n \"0.61606175\",\n \"0.61510617\",\n \"0.6150992\",\n \"0.6144234\",\n \"0.61322385\",\n \"0.6104563\",\n \"0.6103382\",\n \"0.60869795\",\n \"0.60798466\",\n \"0.6076474\",\n \"0.6066799\",\n \"0.6065294\",\n \"0.60476065\",\n \"0.60128564\",\n \"0.6001852\",\n \"0.5998544\",\n \"0.59917384\",\n \"0.59665143\",\n \"0.59626395\",\n \"0.5923686\",\n \"0.5923164\",\n \"0.59194654\",\n \"0.59180355\",\n \"0.59080535\",\n \"0.58902556\",\n \"0.58806264\",\n \"0.5843823\",\n \"0.5839159\",\n \"0.5830269\",\n \"0.5806004\",\n \"0.5805572\",\n \"0.5798957\",\n \"0.57909244\",\n \"0.5790205\",\n \"0.57764643\",\n \"0.5756443\",\n \"0.57554644\",\n \"0.5748696\",\n \"0.5737882\",\n \"0.5735171\",\n \"0.57292116\",\n \"0.56985956\",\n \"0.5696817\",\n \"0.5690602\",\n \"0.5689763\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7660385"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":16876,"cells":{"query":{"kind":"string","value":"Preprocess single image file (1) Read original image as YCbCr format (and grayscale as default) (2) Normalize (3) Apply image file with bicubic interpolation"},"document":{"kind":"string","value":"def preprocess(path, scale=3):\n image = imread(path, is_grayscale=True)\n # Must be normalized\n image = (image-127.5 )/ 127.5 \n input_ = scipy.ndimage.interpolation.zoom(input_, (scale/1.), prefilter=False)\n return input_"},"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 normalise(image):","def preprocess(img):\n \n scaler=StandardScaler() ## scaler object to perform preprocessing\n img=scaler.fit_transform(img) ## zero-center and normalize\n \n return img","def preprocess(path, img_w, img_h):\n #print(path)\n img = cv2.imread(path)\n #print(img.shape)\n #resizing the image to particular size (64, 128, 3)\n img = fix_size(img, img_w, img_h)\n #print(img.shape)\n \n #assigining values less than zero to zer0 and greater than zero to 1\n img = np.clip(img, 0, 255)\n\n #changing the interger to more useful and complex integer\n img = np.uint8(img)\n\n #convert an image to one color space to another\n img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n \n #chaging the values datatype to float\n img = img.astype(np.float32)\n\n #normalization\n img /= 255\n return img","def preprocess_image(self, img):\r\n\r\n # if channel 1 then as grayscale\r\n try:\r\n if img.shape[1] / img.shape[0] < 6.4:\r\n img = pad_image(img, (self.crnn_cfg().width, self.crnn_cfg().height), self.crnn_cfg().nb_channels)\r\n else:\r\n img = resize_image(img, (self.crnn_cfg().width, self.crnn_cfg().height))\r\n if self.crnn_cfg().nb_channels == 1:\r\n img = img.transpose([1, 0])\r\n else:\r\n img = img.transpose([1, 0, 2])\r\n\r\n img = np.flip(img, 1)\r\n img = img / 255.0\r\n if self.crnn_cfg().nb_channels == 1:\r\n img = img[:, :, np.newaxis]\r\n return img\r\n except:\r\n print('Error in method {0} in module {1}'.format('preprocess_image', 'crnn_bridge.py'))\r\n return None","def preprocessImage(imgPath, imgSize, binary=True):\n img = cv2.imread(imgPath)\n # Binary\n if binary:\n brightness = 0\n contrast = 50\n img = np.int16(img)\n img = img * (contrast/127+1) - contrast + brightness\n img = np.clip(img, 0, 255)\n img = np.uint8(img)\n\n img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n T = threshold_local(img, 11, offset=10, method=\"gaussian\")\n img = (img > T).astype(\"uint8\") * 255\n\n # Increase line width\n kernel = np.ones((3, 3), np.uint8)\n img = cv2.erode(img, kernel, iterations=1)\n else:\n img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\n # Create target image and copy sample image into it\n (wt, ht) = imgSize\n (h, w) = img.shape\n fx = w / wt\n fy = h / ht\n f = max(fx, fy)\n\n # Scaling\n newSize = (max(min(wt, int(w / f)), 1), max(min(ht, int(h / f)), 1))\n img = cv2.resize(img, newSize)\n target = np.ones([ht, wt]) * 255\n target[0:newSize[1], 0:newSize[0]] = img\n\n # Transpose\n img = cv2.transpose(target)\n\n # Normalize using mean stander division\n (m, s) = cv2.meanStdDev(img)\n m = m[0][0]\n s = s[0][0]\n img = img - m\n img = img / s if s > 0 else img\n\n return img","def _preprocess_image(self, image_raw):\n\n image = tf.io.decode_raw(image_raw, tf.float64)\n \n if self.rescale is not None:\n image_out = image * self.rescale\n else:\n image_out = image\n\n return image_out","def pre_process(img, img_size):\n resized = cv2.resize(\n img, (img_size, img_size), interpolation=cv2.INTER_LINEAR\n )\n img_in = cv2.cvtColor(resized, cv2.COLOR_BGR2RGB)\n img_in = np.transpose(img_in, (2, 0, 1)).astype(np.float32)\n img_in = np.expand_dims(img_in, axis=0)\n img_in /= 255.0\n img_in = np.ascontiguousarray(img_in)\n return img_in","def process_image(img_file):\n img = cv2.imread(img_file)\n img = cv2.resize(img, IMAGE_DIM).transpose(\n (2, 0, 1)).astype('float32') / 255.0\n return img","def preprocess(path, scale=3):\n image = imread(path, is_grayscale=True)\n label_ = modcrop(image, scale)\n\n # Must be normalized\n \n label_ = label_ / 255.\n \n\n\n input_ = scipy.ndimage.interpolation.zoom(label_, (1. / scale), prefilter=False)\n input_ = scipy.ndimage.interpolation.zoom(input_, (scale / 1.), prefilter=False)\n\n return input_, label_","def preprocess(self, img):\n img_ = image.load_img(img, target_size=(299, 299))\n img_ = image.img_to_array(img_)\n img_ = np.expand_dims(img_, axis=0)\n img_ = preprocess_input(img_)\n return img_","def _preprocess_image(self, image_raw):\n\n image = tf.io.decode_raw(image_raw, tf.float64)\n\n return image * self.rescale","def preprocess(image):\n image = rgb2yuv(image)\n return image","def preprocess(path, path2 , scale):\n image = imread(path)\n label_ = imread(path2)\n\n #label_ = modcrop(label, scale)\n\n # Must be normalized\n input_ = image / 255.\n label_ = label_ / 255.\n\n #input_ = scipy.ndimage.interpolation.zoom(label_, (1./scale), prefilter=False)\n #input_ = scipy.ndimage.interpolation.zoom(input_, (scale/1.), prefilter=False)\n\n return input_, label_","def _preprocessing(self, input_image):\n if self.resize:\n input_image = self._np_resize_image(input_image,\n self.input_size,\n dtype='int')\n image = self._np_transpose(input_image)\n image = self._np_normalize(image)\n image = self._np_flip_n_cat(image)\n return image","def preprocess_input(img):\n img /= 255.\n img -= 0.5\n img *= 2.\n return img","def __preprocesses_image(self, image, size):\n\n # Resize image\n image = cv2.resize(image, (size, size))\n\n # Normalize image (min-max)\n image = np.float32((image - np.min(image)) / (np.max(image) - np.min(image) + 0.00001))\n\n return image","def preprocess(img_name, quality=None):\n img = plt.imread(img_name)\n Y, X, C = img.shape\n img = img[:Y-Y%2, :X-X%2, :3]\n if quality is not None:\n img = jpeg_compress(img, quality)\n img = img_to_tensor(img).cuda().type(torch.float)\n return img","def preprocess_image(image):\n\n gray_image = grayscale_image(image)\n # any other preprocessing steps such as bluring would go here\n\n binary_image = binarize_image(gray_image)\n\n return binary_image","def load_and_preprocess_image(path):\n\n img = cv2.imread(path, 0) # Load image into greyscale\n img = cv2.equalizeHist(img) # Histogram equilization\n return img","def image_preprocess(image, image_size, mean_rgb, stddev_rgb):\n input_processor = dataloader.DetectionInputProcessor(image, image_size)\n input_processor.normalize_image(mean_rgb, stddev_rgb)\n input_processor.set_scale_factors_to_output_size()\n image = input_processor.resize_and_crop_image()\n image_scale = input_processor.image_scale_to_original\n return image, image_scale","def preprocess_image(self, inputs):\n raise NotImplementedError('preprocess_image method not implemented.')","def _preprocess(self, image):\n\n # Scale from [0, 255] to [0, 1] and BGR to RGB \n return (image / 255.0)[:, :, ::-1]","def process_image(img):\n img[0] = img[0] * 0.229\n img[1] = img[1] * 0.224\n img[2] = img[2] * 0.225\n img[0] += 0.485\n img[1] += 0.456\n img[2] += 0.406\n\n return img.cpu().numpy().transpose((1, 2, 0))","def preprocess(image):\n return image - MEAN_PIXEL","def preprocess(self, img):\n return img - np.mean(img)","def preprocess_image(image_path, desired_size=SIZE):\n im = Image.open(image_path)\n im = im.resize((desired_size,) * 2, resample=Image.LANCZOS)\n\n return im","def process_image(image_path):\n with Image.open(image_path) as image:\n transform = test_transforms()\n image = transform(image).numpy()\n\n return image","def preprocess_image(self, img_path):\n im = Image.open(img_path)\n im = im.resize((IM_WIDTH, IM_HEIGHT))\n im = np.array(im) / 255.0\n \n return im","def preprocess(image):\n return (image / 255) * 2 - 1","def chainercv_preprocess(image):\n image = skio.imread(image)\n image = image.transpose(2, 0, 1)\n return [image]","def process_image(self, image):\r\n img = cv2.imread(image)\r\n img = img.astype(float)/127 - 1\r\n return np.expand_dims(img, axis=0)","def _postprocess(img):\n img = _scale_to_zero_one(img)\n img = img.reshape(1, -1) # to avoid a scikit-learn deprecation warning later\n return img","def preprocess(img):\n if img.ndim != 3:\n raise TypeError('bad ndim of img')\n if img.dtype != np.uint8:\n raise TypeError('bad dtype of img')\n img = cv2.resize(img, (224, 224))\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n img = img.astype(np.float32)\n img *= (2.0/255) # normalize to: 0.0~2.0\n img -= 1.0 # subtract mean to make it: -1.0~1.0\n img = np.expand_dims(img, axis=0)\n return img","def load_and_downscale(input_filename):\n\n image = Image.from_file(input_filename)\n blue_channel = image[:,:,2]\n downscaled = downscale_local_mean(blue_channel, (2, 2))\n\n return downscaled","def preprocess(img):\r\n\r\n sorted = np.sort(img, axis=None)\r\n min = sorted[int(removeLower*sorted.size)]\r\n max = sorted[int((1.0-removeUpper)*sorted.size)]\r\n\r\n size = int(cropsize + np.random.rand()*(height-cropsize))\r\n topLeft_x = int(np.random.rand()*(height-size))\r\n topLeft_y = int(np.random.rand()*(height-size))\r\n\r\n crop = np.clip(img[topLeft_y:(topLeft_y+cropsize), topLeft_x:(topLeft_x+cropsize)], min, max)\r\n\r\n resized = cv2.resize(crop, (cropsize, cropsize), interpolation=cv2.INTER_AREA)\r\n\r\n resized[np.isnan(resized)] = 0.5\r\n resized[np.isinf(resized)] = 0.5\r\n\r\n return scale0to1(flip_rotate(resized))","def preprocess_input(x, data_format=None):\n if data_format is None:\n data_format = K.image_data_format()\n assert data_format in {'channels_last', 'channels_first'}\n\n if data_format == 'channels_first':\n # 'RGB'->'BGR'\n x = x[:, ::-1, :, :]\n # Zero-center by mean pixel\n x[:, 0, :, :] -= 103.939\n x[:, 1, :, :] -= 116.779\n x[:, 2, :, :] -= 123.68\n else:\n # 'RGB'->'BGR'\n x = x[:, :, :, ::-1]\n # Zero-center by mean pixel\n x[:, :, :, 0] -= 103.939\n x[:, :, :, 1] -= 116.779\n x[:, :, :, 2] -= 123.68\n return x","def postprocess_img(img):\n img = img.transpose((1, 2, 0))\n img += 1.0\n img = (img * 128.0).astype(np.uint8)\n return img","def preprocess(self, data):\n data_unnorm = data / 2.0 + 0.5\n \n if self.permute == 1:\n permute = [2, 1, 0]\n data_rgb_unnorm = data_unnorm[:, permute]\n elif self.permute == 0:\n data_rgb_unnorm = data_unnorm\n \n data_rgb_unnorm = F.upsample(data_rgb_unnorm, size=self.size, mode='bilinear')\n data_rgb = (data_rgb_unnorm - self.normalize_mean) / self.normalize_std\n return data_rgb","def preprocess_img_inv(img):\n img = img.data.numpy().copy()\n\n img[0] = img[0] * TORCH_IMG_STD[0] + TORCH_IMG_MEAN[0]\n img[1] = img[1] * TORCH_IMG_STD[1] + TORCH_IMG_MEAN[1]\n img[2] = img[2] * TORCH_IMG_STD[2] + TORCH_IMG_MEAN[2]\n img = img.transpose(1, 2, 0) * 255.0\n\n return img.round().astype('uint8')","def preprocess_img(img):\n # Make a copy of img as array\n img = np.array(img)\n\n # Convert into tensor\n img = torch.Tensor(img).permute(2, 0, 1) / 255.0\n\n # Normalize\n for t, m, s in zip(img, TORCH_IMG_MEAN, TORCH_IMG_STD):\n t.sub_(m).div_(s)\n\n return img","def preprocessing(I):\n I = I[35:195] # crop\n I = I[::2,::2,0] # downsample by factor of 2\n I = scipy.misc.imresize(I,size=(img_dim,img_dim))\n I[I == 144] = 0 # erase background (background type 1)\n I[I == 109] = 0 # erase background (background type 2)\n #I[I != 0] = 1 # everything else (paddles, ball) just set to 1\n I = I/255\n return I.astype(np.float).ravel() #flattens","def img_preprocessing(save_path,img_path, filename):\n\n\tsave_path_filename = save_path + filename\n\n\t#Check if file exits\n\tif not os.path.exists(img_path + filename):\n\t\tlogger.error(\" image path {} does not exit\".\n\t\t\t\t\t\t\t\tformat(img_path + filename))\n\n\timage = plt.imread(img_path + filename)\n\n\timg_gray_orig_0 = rgb2gray(image)\n\n\timg_gray_orig = img_resize(img_gray_orig_0, 2*IMG_SIZE)\n\n\timg_just_bone = img_preprocess_core(img_gray_orig)\n\n\ttry:\n\t img_bone = img_pad_resize(img_just_bone, 2*IMG_SIZE) \n\t #Second iteration of image segmentation\n\t img_just_bone = img_preprocess_core(img_bone)\n\t img_bone = img_pad_resize(img_just_bone, IMG_SIZE)\n\n\t plt.imsave(save_path_filename, img_bone)\n\t \n\texcept ValueError:\n\t\tlogger.error(\"Unable to run 2nd interaton for {}\".format(filename))","def preprocess(self):\n meta_file_path = os.path.join(database_directory, 'data.txt')\n meta = pd.read_csv(meta_file_path, delimiter=' ', header=None)\n meta = meta[meta[0] != '45567.jpg'] # Corrupt image.\n meta.to_pickle(os.path.join(database_directory, 'meta.pkl'))\n for file_name in meta.iloc[:, 0].values:\n if file_name.endswith('.jpg'):\n file_path = os.path.join(database_directory, file_name)\n image = imageio.imread(file_path).astype(np.uint8)\n image = transform.resize(image, (self.preprocessed_image_size, self.preprocessed_image_size),\n preserve_range=True)\n image = image.transpose((2, 0, 1))\n np.save(file_path.replace('.jpg', '.npy'), image)","def pre_processing_image(img):\n\n #print(img.shape)\n # apply gamma correction and show the images\n #adjusted = adjust_gamma(img, gamma=0.65)\n\n adjusted = exposure.adjust_gamma(img, gamma=1.65)\n #print(adjusted.shape)\n\n # log transform of image\n\n logarithmic_corrected = exposure.adjust_log(adjusted, 1)\n #print(logarithmic_corrected.shape)\n\n # denoising\n #dst2 = cv2.fastNlMeansDenoisingColored(logarithmic_corrected, None, 10, 10, 7, 21)\n #print(dst2.shape)\n dst2 = logarithmic_corrected\n return dst2","def compute_img(self):\r\n self.load_img()\r\n self.check_shape()\r\n self.convert_img()\r\n self.img_computed = True","def preprocess_image(x, mode='caffe'):\n # mostly identical to \"https://github.com/keras-team/keras-applications/blob/master/keras_applications/imagenet_utils.py\"\n # except for converting RGB -> BGR since we assume BGR already\n\n # covert always to float32 to keep compatibility with opencv\n x = x.astype(np.float32)\n \n if mode == 'tf':\n x /= 127.5\n x -= 1.\n elif mode == 'caffe':\n x[..., 0] -= 103.939\n x[..., 1] -= 116.779\n x[..., 2] -= 123.68\n\n return x","def preprocess_image(img):\n return (img.astype(np.float32)/255.0 - FACENET_MEAN) / FACENET_STD","def preprocess_image(image, height, width):\n image = np.uint8(\n resize(rgb2gray(image), [height, width], mode='constant')*255)\n image = np.reshape(image, [height, width, 1])/255.\n return image","def process_image(image_path):\n\n # open the image\n image = Image.open(image_path)\n \n # print(\"Original Image size: \", image.size)\n \n # first resize the images where the shortest side is 256 px\n width, height = image.size\n size = 256, 256\n \n newwidth, newheight = None, None\n \n # if the height is the shorter side\n if height < width:\n # find ratio between larger and smaller side\n ratio = float(width) / float(height)\n # resize smaller side to 256\n newheight = 256\n # resize larger side to 256 * ratio\n newwidth = int(floor(ratio * size[0])) \n # else, the width is the shorter side\n else:\n # find ratio between larger and smaller side\n ratio = float(height)/float(width)\n # resize smaller side to 256\n newwidth = 256\n # resize larger side to 256 * ratio\n newheight = int(floor(ratio * size[1]))\n \n \n # print(\"W: {}, H: {}\".format(newwidth, newheight))\n \n # resize the image\n image = image.resize((newwidth, newheight), Image.ANTIALIAS)\n\n # print(\"Resized Image (keep aspect ratio): \", image.size)\n \n # perform center crop\n # https://stackoverflow.com/questions/16646183/crop-an-image-in-the-centre-using-pil\n width, height = image.size # Get dimensions\n new_height, new_width = 224, 224\n \n left = (width - new_width)/2\n top = (height - new_height)/2\n right = (width + new_width)/2\n bottom = (height + new_height)/2\n\n image = image.crop((left, top, right, bottom))\n # print(\"cropped image size: \", image.size)\n \n # convert encoded color channels and convert to floats (divide by 255)\n np_image = np.array(image) / 255\n # print(np_image)\n \n # normalize\n mean = [0.485, 0.456, 0.406]\n std = [0.229, 0.224, 0.225]\n np_image = (np_image - mean) / std\n \n # finally, transpose\n # print(\"shape 1: \", np_image.shape)\n np_image = np_image.transpose((2, 0, 1))\n # print(\"transposed shape: \", np_image.shape)\n \n # Originally, I was returning a numpy array, as I thought these were the instructions, but\n # when trying to test, it would not work. \n # Found solution at: https://knowledge.udacity.com/questions/29173\n # We have to convert to a tensor before we return it\n return torch.Tensor(np_image)","def load_and_process_image(self, im_path):\n image = Image.open(im_path).convert('RGB')\n image = transforms.ToTensor()(image)\n image = 2 * image - 1\n return image","def _read_and_preprocess(paths, scale_len, crop_len):\n\n imgs = []\n\n for path in paths:\n img = imread(path)\n\n # force 3-channel images\n if img.ndim == 2:\n img = gray2rgb(img)\n elif img.shape[2] == 4:\n img = img[:, :, :3]\n\n # compute the resize dimension\n resize_f = float(scale_len) / min(img.shape[:2])\n new_dims = (int(np.round(img.shape[0] * resize_f)),\n int(np.round(img.shape[1] * resize_f)))\n\n # prevent the input image from blowing up\n # factor of 2 is more or less an arbitrary number\n max_dim = 2 * scale_len\n new_dims = (min(new_dims[0], max_dim),\n min(new_dims[1], max_dim))\n\n # resize and center crop\n img = resize(img, new_dims)\n top = int(np.ceil((img.shape[0] - crop_len) / 2.0))\n left = int(np.ceil((img.shape[1] - crop_len) / 2.0))\n img = img[top:(top+crop_len), left:(left+crop_len)]\n\n # preprocessing (tanh)\n img = 2 * img - 1\n\n imgs.append(img)\n\n return np.array(imgs)","def preprocess_image(image, model_image_size):\n #resized_image = cv2.resize(image, tuple(reversed(model_image_size)), cv2.INTER_AREA)\n resized_image = letterbox_resize(image, tuple(reversed(model_image_size)))\n image_data = np.asarray(resized_image).astype('float32')\n image_data = normalize_image(image_data)\n image_data = np.expand_dims(image_data, 0) # Add batch dimension.\n return image_data","def preprocess(self, resized_inputs):\n return (2.0 / 255.0) * resized_inputs - 1.0","def _preprocessing(image) -> np.ndarray:\n # TODO: Turn mapping into generic function.\n processed_image = cv.cvtColor(image, cv.COLOR_BGR2GRAY)\n processed_image[~mask] = 255\n return processed_image","def post_process_image(image):\n\n image = (image - np.min(image)) * (255 / (np.max(image) - np.min(image)))\n\n return image","def preprocess(Data, minn, maxx, dim=128, low=0.1, hi=1.0):\n for key in Data:\n print (key)\n\n Data[key][0] = Data[key][0].reshape(len(Data[key][0]), dim, dim, 1)\n for i, img in enumerate(Data[key][0]):\n img = img / 255.\n # img[img > 0.] = 1. - img[img > 0.] #inv color\n minn, maxx = np.min(img[img > 0]), np.max(img[img > 0])\n img[img > 0] = low + (img[img > 0] - minn) * (hi - low) / (maxx - minn)\n Data[key][0][i] = img","def process_path(file_path: str):\r\n img = tf.io.read_file(file_path)\r\n img = tf.image.decode_jpeg(img, channels=3)\r\n img = tf.image.resize(img, [IMG_SIZE, IMG_SIZE])\r\n return tf.keras.applications.efficientnet.preprocess_input(img) # Shape: IMG_SIZE x IMG_SIZE x 3\r","def caffe_preprocess(img):\n out = numpy.copy(img)\n out -= IMAGE_256_MEAN[None, None, :]\n out = out[:, :, [2, 1, 0]] # swap channel from RGB to BGR\n return out","def process_image(image):\n # Open the image using PIL\n pil_image = Image.open(image)\n \n # Resize the image to 256x256 while maintining aspect ratio\n if pil_image.width > pil_image.height:\n resize_dim = (int(pil_image.width*256 / pil_image.height), 256)\n else:\n resize_dim = (256, int(pil_image.height*256 / pil_image.width))\n \n pil_image = pil_image.resize(resize_dim)\n \n # Crop image to center 224 pixles\n crop_box_dim = 224\n left = (pil_image.width - crop_box_dim)/2\n top = (pil_image.height - crop_box_dim)/2\n right = pil_image.width - (pil_image.width - crop_box_dim)/2\n bottom = pil_image.height - (pil_image.height - crop_box_dim)/2\n pil_image = pil_image.crop((left, top, right, bottom))\n \n # Update color channels\n np_image = np.array(pil_image)\n np_image_means = np.array([0.485, 0.456, 0.406])\n np_image_stddev = np.array([0.229, 0.224, 0.225])\n np_image = (np_image/255 - np_image_means) / np_image_stddev\n \n # PIL images and numpy arrays have color channels in the 3rd dimension\n # Transpose them to first dimension to match what PyTorch expects\n np_image = np_image.transpose((2,0,1))\n\n return np_image","def preprocess_image(image):\n image = tf.image.decode_jpeg(image, channels=3, dct_method=\"INTEGER_ACCURATE\")\n return scale(image)","def colorImgPreProcess(self, image):\n #do processing on the image while it's still in color\n image = cv2.medianBlur(image, 7) #kernal size must be odd\n #image = cv2.bilateralFilter(image, 9, 75, 75) #TODO: uncomment when it won't cause C++ errors with ROS\n #self.closeImages() #uncomment if showing output image\n return image","def preprocess(data):\n # expect image to be passed in as BGR\n rgb_data = cv2.cvtColor(data, cv2.COLOR_BGR2RGB)\n batched_rgb_data = np.expand_dims(rgb_data, axis = 0).astype('float64')\n new_data = resnet50.preprocess_input(batched_rgb_data)[0]\n\n return new_data","def process_new_image(name):\n #preprocess the image\n img = cv2.imread(name)\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n img = img/255.-.5\n return img","def _load_preprocess_image(self, image_file):\n\n image_raw = tf.io.read_file(image_file)\n\n image = self._preprocess_image(image_raw)\n\n return image","def preprocess(self, img):\n undistorted = undistort(img, self.mtx, self.dist)\n warped = warp(self.transform_matrix, undistorted)\n binary_img = binarize(warped, s_thres=(100, 255), l_thres=(50, 255))\n return binary_img","def normalize(image):\r\n return image / 127.5 - 1.","def get_image_and_prep(self,file_path):\r\n img = np.array(Image.open(file_path).convert('1'))\r\n img = img.reshape(28,28,1)\r\n return img","def pre_process_data(input_path: list, cuts: int, shape: int = 32, normalize: bool = True) -> list:\n images = []\n images_uncut = []\n for files_path in input_path:\n\n files = os.listdir(files_path) # TODO paths\n for f in files:\n file_path = f'{files_path}/{f}'\n im_uncut = cv2.imread(file_path)\n im_uncut = cv2.cvtColor(im_uncut, cv2.COLOR_RGB2GRAY)\n images_uncut.append(cv2.resize(im_uncut, (shape * cuts, shape * cuts)))\n x = np.array(images_uncut)\n\n if normalize:\n x_mean = np.mean(x, axis=(0, 1, 2))\n x_std = np.std(x, axis=(0, 1, 2))\n x = (x - x_mean) / (x_std + 1e-9)\n\n for im in x:\n height = im.shape[0]\n width = im.shape[1]\n frac_h = height // cuts\n frac_w = width // cuts\n i = 0\n image = []\n for h in range(cuts):\n for w in range(cuts):\n crop = im[h * frac_h:(h + 1) * frac_h, w * frac_w:(w + 1) * frac_w]\n crop_rehaped = cv2.resize(crop, (shape, shape))\n image.append([crop_rehaped, i, number_to_angle(i, cuts), neighbours(i, cuts)])\n i = i + 1\n images.append(image)\n # return np.array(images) # todo back to array\n return images","def preprocess_(image, input_shape):\n if isinstance(image, str):\n filename = image\n image = Image.open(image)\n if isinstance(image, np.ndarray):\n image = Image.fromarray(image)\n assert (isinstance(image, Image.Image)) \n\n w_ori, h_ori = image.size\n h, w = input_shape\n image = image.convert(\"RGB\")\n\n if w_ori > h or h_ori > w:\n scale = max(1.0*w_ori / w, 1.0*h_ori / h)\n image.thumbnail((w,h), Image.BILINEAR)\n else:\n scale = 1\n\n image = image.crop((0, 0, w, h))\n img_data = np.array(image).reshape((1, h, w, 3))\n img_data = img_data / 256. - 0.5\n\n return img_data, scale, w_ori, h_ori","def _load_preprocess_image(self, image_file):\n image_raw = tf.io.read_file(image_file)\n\n image = self._preprocess_image(image_raw)\n\n return image","def _preprocessing(self, path: str) -> np.array:\n if Checker.check_input_type_bool(path, 'nii'):\n image = sitk.ReadImage(path)\n self.space = image.GetSpacing()\n image = sitk.GetArrayFromImage(image).astype('float32')\n\n elif Checker.check_input_type_bool(path, 'npy'):\n image = np.load(path)\n self.space = [1., 1., 1.]\n warnings.warn(\n '.npy is not recommended as an image format.'\n 'Since spacing cannot be identified from .npy, spacing is set as [1., 1., 1.].', UserWarning)\n\n elif Checker.check_input_type_bool(path, 'dcm'):\n raise ValueError(\n '.dcm is not supported.'\n 'Please convert dcm dummies to nii format.')\n\n else:\n input_ext = path.split('.')[-1]\n raise ValueError(\n f'.{input_ext} format is not supported.')\n\n self.img_shape = image.shape\n\n # normalize\n windowing_range = [-40., 120.]\n windowing_min = windowing_range[0] - windowing_range[1] // 2\n windowing_max = windowing_range[0] + windowing_range[1] // 2\n image = ndimage.zoom(image, [.5, .5, .5], order=1, mode='constant')\n image = np.clip(image, windowing_min, windowing_max)\n image = (image - windowing_min) / (windowing_max - windowing_min)\n image = image[np.newaxis, ..., np.newaxis]\n return image","def retinanet_preprocess_image(x, mode='caffe'):\n # mostly identical to \"https://github.com/keras-team/keras-applications/blob/master/keras_applications/imagenet_utils.py\"\n # except for converting RGB -> BGR since we assume BGR already\n\n # covert always to float32 to keep compatibility with opencv\n x = x.astype(np.float32)\n\n if mode == 'tf':\n x /= 127.5\n x -= 1.\n elif mode == 'caffe':\n x[..., 0] -= 103.939\n x[..., 1] -= 116.779\n x[..., 2] -= 123.68\n\n return x","def get_preprocessed_image(file_name):\n\n im = np.array(Image.open(file_name)).astype(np.float32)\n assert im.ndim == 3, 'Only RGB images are supported.'\n im = im - _IMAGENET_MEANS\n im = im[:, :, ::-1] # Convert to BGR\n img_h, img_w, img_c = im.shape\n assert img_c == 3, 'Only RGB images are supported.'\n if img_h > 500 or img_w > 500:\n raise ValueError('Please resize your images to be not bigger than 500 x 500.')\n\n pad_h = 500 - img_h\n pad_w = 500 - img_w\n im = np.pad(im, pad_width=((0, pad_h), (0, pad_w), (0, 0)), mode='constant', constant_values=0)\n return np.expand_dims(im.astype(np.float32), 0), img_h, img_w","def _preprocess_fn(data):\n\n # Validate input\n if not isinstance(data, dict) or 'image' not in data:\n raise ValueError('Argument `data` must be a dictionary, '\n 'not %s' % str(type(data)))\n\n # Apply all the individual steps in sequence.\n image = data['image']\n image = decode_image(image)\n image = normalize_value_range(image)\n image = get_multiscale_patches(image, **preprocessing_kwargs)\n\n data['image'] = image\n return data","def normalize(\n self,\n image: np.ndarray,\n data_format: Optional[Union[str, ChannelDimension]] = None,\n input_data_format: Optional[Union[str, ChannelDimension]] = None,\n ) -> np.ndarray:\n image = rescale(image=image, scale=1 / 127.5, data_format=data_format, input_data_format=input_data_format)\n image = image - 1\n return image","def normalize_image(img):\n\n # Load image and convert to grayscale\n img = rgb2gray(img)\n\n # Normalize values, range 0 to 255\n img = (img - img.min()) / (img.max() - img.min())\n img *= 255\n\n # Make int values\n img = img.astype(int)\n\n # Return new image\n return img","def process_image(img_file, bbox_file, openpose_file, input_res=224):\n normalize_img = Normalize(mean=cfg.IMG_NORM_MEAN, std=cfg.IMG_NORM_STD)\n # tmp = cv2.imread(img_file)\n img = cv2.imread(img_file)[:,:,::-1].copy() # PyTorch does not support negative stride at the moment\n if bbox_file is None and openpose_file is None:\n # Assume that the person is centerered in the image\n height = img.shape[0]\n width = img.shape[1]\n center = np.array([width // 2, height // 2])\n scale = max(height, width) / 200\n else:\n if bbox_file is not None:\n center, scale = bbox_from_json(bbox_file)\n elif openpose_file is not None:\n center, scale = bbox_from_openpose(openpose_file)\n img = crop(img, center, scale, (input_res, input_res))\n img = img.astype(np.float32) / 255.\n img = torch.from_numpy(img).permute(2,0,1)\n norm_img = normalize_img(img.clone())[None]\n return img, norm_img","def normalize_image(self, image, colorspace=None):\n float_image = image.astype(numpy.float32)\n if float_image.__array_interface__[\n \"data\"][0] == image.__array_interface__[\"data\"][0]:\n float_image = float_image.copy()\n float_image -= float_image.min()\n max_value = float_image.max()\n if max_value:\n max_value /= 255.0\n float_image /= max_value\n else:\n float_image[:] = 127.5\n normalized_image = float_image.astype(numpy.uint8)\n if (colorspace != \"RGB\" and len(normalized_image.shape) == 3\n and normalized_image.shape[2] == 3):\n import cv2\n normalized_image = cv2.cvtColor(\n normalized_image, getattr(cv2, \"COLOR_\" + colorspace + \"2RGB\"))\n return normalized_image","def read_img(self, path):\n img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)\n resized = cv2.resize(img, (28, 28))\n inverted = np.invert(np.array(resized).reshape((28, 28, 1)))\n denoised_and_reshaped = de_noise(inverted).reshape((28, 28, 1))\n normalized = denoised_and_reshaped / 255.\n return normalized","def _process_img(self, img):\n\n # ==\n # Construct transforms\n trans_list = [transforms.Resize(self.img_size)]\n if self.grayscale:\n trans_list += [transforms.Grayscale(num_output_channels=1)]\n\n img_transforms = transforms.Compose(trans_list)\n\n # ==\n # Transform and output\n img = img_transforms(img)\n obs = np.array(img, dtype=np.float32)\n\n # Ensure channel is in first dimension (torch conv standard)\n if len(np.shape(obs)) == 2:\n obs = np.expand_dims(obs, axis=0)\n elif len(np.shape(obs)) == 3:\n # PIL have channel on dim 2, swap with dim 0\n obs = np.swapaxes(obs, 2, 0)\n pass\n else:\n raise RuntimeError\n\n # Scale values to [0, 1]\n if self.scale_observation:\n obs = obs / 255.0\n\n # (Optinal) Flatten to vector\n if self.flatten_obs:\n obs = obs.flatten()\n\n return obs","def preprocessing(image_path, side=416):\n\n image = Image.open(image_path)\n width, height = image.size\n scale = side / max([width, height])\n width_scaled = int(width * scale)\n height_scaled = int(height * scale)\n image_scaled = image.resize((width_scaled, height_scaled),\n resample=Image.LANCZOS)\n image_array = np.array(image_scaled, dtype=np.float32)\n image_padded = np.full([side, side, 3], 128, dtype=np.float32)\n width_offset = (side - width_scaled) // 2\n height_offset = (side - height_scaled) // 2\n image_padded[\n height_offset:height_offset + height_scaled, width_offset:width_offset + width_scaled, :] = image_array\n image_norm = image_padded / 255\n return image_norm","def normalize_image(img):\n arr = np.array(img)\n new_img = Image.fromarray(normalize(arr).astype('uint8'),'L')\n return new_img","def image_normalize(image, image_dims):\r\n if image.dtype != 'float32':\r\n image = image.astype(dtype=np.float32)\r\n if np.size(image_dims) == 2:\r\n maxpx = np.max(image[:, :])\r\n if maxpx == float(0):\r\n maxpx = 1e-12 # fuzz factor\r\n minpx = np.min(image[:, :])\r\n image[:, :] = (image[:, :] - minpx) / (maxpx - minpx)\r\n else:\r\n for i in range(image_dims[2]): # find max/min for each channel\r\n maxpx = np.max(image[:, :, i])\r\n if maxpx == float(0):\r\n maxpx = 1e-12 # fuzz factor\r\n minpx = np.min(image[:, :, i])\r\n image[:, :, i] = (image[:, :, i] - minpx) / (maxpx - minpx)\r\n return image","def post_process_image(self, image):\r\n A = np.min(image)\r\n B = np.max(image)\r\n k = 255\r\n R, C = image.shape\r\n for i in range(R):\r\n for j in range(C):\r\n image[i][j] = (k / (B-A)) * (image[i][j] - A)\r\n average_image = np.average(image)\r\n if average_image > 50:\r\n return image.astype(dtype='uint8')\r\n else:\r\n return (255-image).astype(dtype='uint8')","def process_image(img_file, bbox_file, openpose_file, input_res=224):\n normalize_img = Normalize(mean=constants.IMG_NORM_MEAN, std=constants.IMG_NORM_STD)\n img = cv2.imread(img_file)[:,:,::-1].copy() # PyTorch does not support negative stride at the moment\n print(img.shape)\n if bbox_file is None and openpose_file is None:\n # Assume that the person is centerered in the image\n height = img.shape[0]\n width = img.shape[1]\n center = np.array([width // 2, height // 2])\n scale = max(height, width) / 200\n else:\n if bbox_file is not None:\n center, scale = bbox_from_json(bbox_file)\n elif openpose_file is not None:\n center, scale = bbox_from_openpose(openpose_file)\n img = crop(img, center, scale, (input_res, input_res))\n img = img.astype(np.float32) / 255.\n img = torch.from_numpy(img).permute(2,0,1)\n norm_img = normalize_img(img.clone())[None]\n return img, norm_img","def pre_processing_function(label, filename: str, augmentor: Augmentor = None):\n image = imread(filename)\n if augmentor is not None:\n image = np.round(augmentor.run(image)).astype(np.uint8)\n\n return image, label","def preprocess_img(imgBGR, erode_dilate=True):\n rows, cols, _ = imgBGR.shape\n imgHSV = cv2.cvtColor(imgBGR, cv2.COLOR_BGR2HSV)\n\n Bmin = np.array([100, 43, 46])\n Bmax = np.array([124, 255, 255])\n img_Bbin = cv2.inRange(imgHSV,Bmin, Bmax)\n \n Rmin1 = np.array([0, 43, 46])\n Rmax1 = np.array([10, 255, 255])\n img_Rbin1 = cv2.inRange(imgHSV,Rmin1, Rmax1)\n \n Rmin2 = np.array([156, 43, 46])\n Rmax2 = np.array([180, 255, 255])\n img_Rbin2 = cv2.inRange(imgHSV,Rmin2, Rmax2)\n img_Rbin = np.maximum(img_Rbin1, img_Rbin2)\n img_bin = np.maximum(img_Bbin, img_Rbin)\n\n if erode_dilate is True:\n kernelErosion = np.ones((9,9), np.uint8)\n kernelDilation = np.ones((9,9), np.uint8) \n img_bin = cv2.erode(img_bin, kernelErosion, iterations=2)\n img_bin = cv2.dilate(img_bin, kernelDilation, iterations=2)\n\n return img_bin","def _apply_transform(self, img: np.ndarray): \n img = self.transform(image=img)[\"image\"]\n return img","def _apply_transform(self, img: np.ndarray): \n img = self.transform(image=img)[\"image\"]\n return img","def preprocess(exam, data_folder, save_path, image_format):\n for v in ['L-CC', 'L-MLO', 'R-CC', 'R-MLO']:\n if len(exam[v]) == 0:\n continue\n else:\n for image in exam[v]:\n image_path = data_folder + '/' + image + '.' + image_format\n # Extract subdirectories\n subdirs = \"/\".join(image.split('/')[:-1])\n save_dirs = os.path.join(save_path, subdirs)\n # Extract image id\n image_id = image.split('/')[-1]\n # Create save directories\n os.makedirs(save_dirs, exist_ok=True)\n png_save_path = os.path.join(save_dirs, image_id + '.png')\n with Image(filename=image_path, format=image_format) as img:\n with img.clone() as i:\n i.auto_level()\n with i.convert('png') as png_image:\n png_image.transform(resize='896x1152!')\n png_image.save(filename=png_save_path)","def process_image(image):\r\n image = random_brightness(image)\r\n image = crop_image(image)\r\n image = resize(image)\r\n return image","def preprocess(img, out_shape=None):\n if out_shape is not None:\n img = resize(img, out_shape, mode='constant')\n\n # Normalize the image\n mean = img.mean()\n std = img.std()\n return (img - mean) / std","def normalize(image):\n return image / 127.5 - 1.","def preprocess(self, image, input_shape):\n # image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n h, w, _ = image.shape\n\n input_h, input_w = input_shape\n scale = min(float(input_w) / float(w), float(input_h) / float(h))\n nw = int(w * scale)\n nh = int(h * scale)\n\n image = cv2.resize(image, (nw, nh))\n\n new_image = np.zeros((input_h, input_w, 3), np.float32)\n new_image.fill(128)\n bh, bw, _ = new_image.shape\n new_image[int((bh - nh) / 2):(nh + int((bh - nh) / 2)), int((bw - nw) / 2):(nw + int((bw - nw) / 2)), :] = image\n\n new_image /= 255.\n new_image = np.expand_dims(new_image, 0) # Add batch dimension.\n return new_image","def main():\n test_image = load_image()\n\n pixelate_image(\n normalize_image(test_image)\n )\n pass","def preprocess_image(image: Image) -> np.ndarray:\n return np.array(image.convert('L'))","def preprocess_image(normalize_lighting=False, min_value=0., max_value=1.):\n if min_value > max_value:\n min_value, max_value = max_value, min_value\n rescaling_range = max_value - min_value\n\n def rescale(image):\n if image.max() > 1:\n # Project values interval on [0.0; 1.0]\n image = image / 255.\n # Project values on [min_value; max_value]\n image = image * rescaling_range + min_value\n return image\n\n def normalize_lighting_and_rescale(image):\n # Normalize image lighting (this results in an image with values from [0.0; 1.0])\n image = image / np.mean(image, axis=(0, 1))\n image = image / image.max()\n # Project values on [min_value; max_value]\n image = image * rescaling_range + min_value\n return image\n\n if normalize_lighting is True:\n return normalize_lighting_and_rescale\n else:\n return rescale","def preprocess_fn(self, X_data, Y_data):\n # Normalize image data to [0,1]\n X_data = tf.math.divide(tf.math.subtract(X_data, tf.math.reduce_min(\n X_data)), tf.math.subtract(tf.math.reduce_max(X_data), tf.math.reduce_min(X_data)))\n Y_data = tf.math.divide(tf.math.subtract(Y_data, tf.math.reduce_min(\n Y_data)), tf.math.subtract(tf.math.reduce_max(Y_data), tf.math.reduce_min(Y_data)))\n\n return X_data, Y_data","def image_transform(im, format='pytorch'):\n if format == 'pytorch':\n im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)\n im = im.astype(np.float32)\n im = im / 255.\n im = (im - np.array([0.485, 0.456, 0.406], dtype=np.float32) ) / \\\n np.array([0.229, 0.224, 0.225], dtype=np.float32)\n elif format == 'caffe':\n # return BGR image\n im = im.astype(np.float32)\n im = im - np.array([103.939, 116.779, 123.68], dtype=np.float32)\n return im","def __processImage(self, f):\n try:\n imgobj = Image.open(f).convert('RGB')\n except:\n return None\n w, h = imgobj.size\n if w < h:\n # reduce width to required dimension and adjust height accordingly\n new_h = int(h * self.PROCESSING_DIM / w)\n resizedImg = imgobj.resize((self.PROCESSING_DIM, new_h))\n\n y_start = int(new_h / 2 - self.PROCESSING_DIM / 2)\n processedImage = resizedImg.crop((0, y_start, self.PROCESSING_DIM, y_start + self.PROCESSING_DIM))\n\n else:\n # reduce height to required dimension and adjust width accordingly\n new_w = int(w * self.PROCESSING_DIM / h)\n resizedImg = imgobj.resize((new_w, self.PROCESSING_DIM))\n\n x_start = int(new_w / 2 - self.PROCESSING_DIM / 2)\n processedImage = resizedImg.crop((x_start, 0, x_start + self.PROCESSING_DIM, self.PROCESSING_DIM))\n\n return processedImage"],"string":"[\n \"def normalise(image):\",\n \"def preprocess(img):\\n \\n scaler=StandardScaler() ## scaler object to perform preprocessing\\n img=scaler.fit_transform(img) ## zero-center and normalize\\n \\n return img\",\n \"def preprocess(path, img_w, img_h):\\n #print(path)\\n img = cv2.imread(path)\\n #print(img.shape)\\n #resizing the image to particular size (64, 128, 3)\\n img = fix_size(img, img_w, img_h)\\n #print(img.shape)\\n \\n #assigining values less than zero to zer0 and greater than zero to 1\\n img = np.clip(img, 0, 255)\\n\\n #changing the interger to more useful and complex integer\\n img = np.uint8(img)\\n\\n #convert an image to one color space to another\\n img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\\n \\n #chaging the values datatype to float\\n img = img.astype(np.float32)\\n\\n #normalization\\n img /= 255\\n return img\",\n \"def preprocess_image(self, img):\\r\\n\\r\\n # if channel 1 then as grayscale\\r\\n try:\\r\\n if img.shape[1] / img.shape[0] < 6.4:\\r\\n img = pad_image(img, (self.crnn_cfg().width, self.crnn_cfg().height), self.crnn_cfg().nb_channels)\\r\\n else:\\r\\n img = resize_image(img, (self.crnn_cfg().width, self.crnn_cfg().height))\\r\\n if self.crnn_cfg().nb_channels == 1:\\r\\n img = img.transpose([1, 0])\\r\\n else:\\r\\n img = img.transpose([1, 0, 2])\\r\\n\\r\\n img = np.flip(img, 1)\\r\\n img = img / 255.0\\r\\n if self.crnn_cfg().nb_channels == 1:\\r\\n img = img[:, :, np.newaxis]\\r\\n return img\\r\\n except:\\r\\n print('Error in method {0} in module {1}'.format('preprocess_image', 'crnn_bridge.py'))\\r\\n return None\",\n \"def preprocessImage(imgPath, imgSize, binary=True):\\n img = cv2.imread(imgPath)\\n # Binary\\n if binary:\\n brightness = 0\\n contrast = 50\\n img = np.int16(img)\\n img = img * (contrast/127+1) - contrast + brightness\\n img = np.clip(img, 0, 255)\\n img = np.uint8(img)\\n\\n img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\\n T = threshold_local(img, 11, offset=10, method=\\\"gaussian\\\")\\n img = (img > T).astype(\\\"uint8\\\") * 255\\n\\n # Increase line width\\n kernel = np.ones((3, 3), np.uint8)\\n img = cv2.erode(img, kernel, iterations=1)\\n else:\\n img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\\n\\n # Create target image and copy sample image into it\\n (wt, ht) = imgSize\\n (h, w) = img.shape\\n fx = w / wt\\n fy = h / ht\\n f = max(fx, fy)\\n\\n # Scaling\\n newSize = (max(min(wt, int(w / f)), 1), max(min(ht, int(h / f)), 1))\\n img = cv2.resize(img, newSize)\\n target = np.ones([ht, wt]) * 255\\n target[0:newSize[1], 0:newSize[0]] = img\\n\\n # Transpose\\n img = cv2.transpose(target)\\n\\n # Normalize using mean stander division\\n (m, s) = cv2.meanStdDev(img)\\n m = m[0][0]\\n s = s[0][0]\\n img = img - m\\n img = img / s if s > 0 else img\\n\\n return img\",\n \"def _preprocess_image(self, image_raw):\\n\\n image = tf.io.decode_raw(image_raw, tf.float64)\\n \\n if self.rescale is not None:\\n image_out = image * self.rescale\\n else:\\n image_out = image\\n\\n return image_out\",\n \"def pre_process(img, img_size):\\n resized = cv2.resize(\\n img, (img_size, img_size), interpolation=cv2.INTER_LINEAR\\n )\\n img_in = cv2.cvtColor(resized, cv2.COLOR_BGR2RGB)\\n img_in = np.transpose(img_in, (2, 0, 1)).astype(np.float32)\\n img_in = np.expand_dims(img_in, axis=0)\\n img_in /= 255.0\\n img_in = np.ascontiguousarray(img_in)\\n return img_in\",\n \"def process_image(img_file):\\n img = cv2.imread(img_file)\\n img = cv2.resize(img, IMAGE_DIM).transpose(\\n (2, 0, 1)).astype('float32') / 255.0\\n return img\",\n \"def preprocess(path, scale=3):\\n image = imread(path, is_grayscale=True)\\n label_ = modcrop(image, scale)\\n\\n # Must be normalized\\n \\n label_ = label_ / 255.\\n \\n\\n\\n input_ = scipy.ndimage.interpolation.zoom(label_, (1. / scale), prefilter=False)\\n input_ = scipy.ndimage.interpolation.zoom(input_, (scale / 1.), prefilter=False)\\n\\n return input_, label_\",\n \"def preprocess(self, img):\\n img_ = image.load_img(img, target_size=(299, 299))\\n img_ = image.img_to_array(img_)\\n img_ = np.expand_dims(img_, axis=0)\\n img_ = preprocess_input(img_)\\n return img_\",\n \"def _preprocess_image(self, image_raw):\\n\\n image = tf.io.decode_raw(image_raw, tf.float64)\\n\\n return image * self.rescale\",\n \"def preprocess(image):\\n image = rgb2yuv(image)\\n return image\",\n \"def preprocess(path, path2 , scale):\\n image = imread(path)\\n label_ = imread(path2)\\n\\n #label_ = modcrop(label, scale)\\n\\n # Must be normalized\\n input_ = image / 255.\\n label_ = label_ / 255.\\n\\n #input_ = scipy.ndimage.interpolation.zoom(label_, (1./scale), prefilter=False)\\n #input_ = scipy.ndimage.interpolation.zoom(input_, (scale/1.), prefilter=False)\\n\\n return input_, label_\",\n \"def _preprocessing(self, input_image):\\n if self.resize:\\n input_image = self._np_resize_image(input_image,\\n self.input_size,\\n dtype='int')\\n image = self._np_transpose(input_image)\\n image = self._np_normalize(image)\\n image = self._np_flip_n_cat(image)\\n return image\",\n \"def preprocess_input(img):\\n img /= 255.\\n img -= 0.5\\n img *= 2.\\n return img\",\n \"def __preprocesses_image(self, image, size):\\n\\n # Resize image\\n image = cv2.resize(image, (size, size))\\n\\n # Normalize image (min-max)\\n image = np.float32((image - np.min(image)) / (np.max(image) - np.min(image) + 0.00001))\\n\\n return image\",\n \"def preprocess(img_name, quality=None):\\n img = plt.imread(img_name)\\n Y, X, C = img.shape\\n img = img[:Y-Y%2, :X-X%2, :3]\\n if quality is not None:\\n img = jpeg_compress(img, quality)\\n img = img_to_tensor(img).cuda().type(torch.float)\\n return img\",\n \"def preprocess_image(image):\\n\\n gray_image = grayscale_image(image)\\n # any other preprocessing steps such as bluring would go here\\n\\n binary_image = binarize_image(gray_image)\\n\\n return binary_image\",\n \"def load_and_preprocess_image(path):\\n\\n img = cv2.imread(path, 0) # Load image into greyscale\\n img = cv2.equalizeHist(img) # Histogram equilization\\n return img\",\n \"def image_preprocess(image, image_size, mean_rgb, stddev_rgb):\\n input_processor = dataloader.DetectionInputProcessor(image, image_size)\\n input_processor.normalize_image(mean_rgb, stddev_rgb)\\n input_processor.set_scale_factors_to_output_size()\\n image = input_processor.resize_and_crop_image()\\n image_scale = input_processor.image_scale_to_original\\n return image, image_scale\",\n \"def preprocess_image(self, inputs):\\n raise NotImplementedError('preprocess_image method not implemented.')\",\n \"def _preprocess(self, image):\\n\\n # Scale from [0, 255] to [0, 1] and BGR to RGB \\n return (image / 255.0)[:, :, ::-1]\",\n \"def process_image(img):\\n img[0] = img[0] * 0.229\\n img[1] = img[1] * 0.224\\n img[2] = img[2] * 0.225\\n img[0] += 0.485\\n img[1] += 0.456\\n img[2] += 0.406\\n\\n return img.cpu().numpy().transpose((1, 2, 0))\",\n \"def preprocess(image):\\n return image - MEAN_PIXEL\",\n \"def preprocess(self, img):\\n return img - np.mean(img)\",\n \"def preprocess_image(image_path, desired_size=SIZE):\\n im = Image.open(image_path)\\n im = im.resize((desired_size,) * 2, resample=Image.LANCZOS)\\n\\n return im\",\n \"def process_image(image_path):\\n with Image.open(image_path) as image:\\n transform = test_transforms()\\n image = transform(image).numpy()\\n\\n return image\",\n \"def preprocess_image(self, img_path):\\n im = Image.open(img_path)\\n im = im.resize((IM_WIDTH, IM_HEIGHT))\\n im = np.array(im) / 255.0\\n \\n return im\",\n \"def preprocess(image):\\n return (image / 255) * 2 - 1\",\n \"def chainercv_preprocess(image):\\n image = skio.imread(image)\\n image = image.transpose(2, 0, 1)\\n return [image]\",\n \"def process_image(self, image):\\r\\n img = cv2.imread(image)\\r\\n img = img.astype(float)/127 - 1\\r\\n return np.expand_dims(img, axis=0)\",\n \"def _postprocess(img):\\n img = _scale_to_zero_one(img)\\n img = img.reshape(1, -1) # to avoid a scikit-learn deprecation warning later\\n return img\",\n \"def preprocess(img):\\n if img.ndim != 3:\\n raise TypeError('bad ndim of img')\\n if img.dtype != np.uint8:\\n raise TypeError('bad dtype of img')\\n img = cv2.resize(img, (224, 224))\\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\\n img = img.astype(np.float32)\\n img *= (2.0/255) # normalize to: 0.0~2.0\\n img -= 1.0 # subtract mean to make it: -1.0~1.0\\n img = np.expand_dims(img, axis=0)\\n return img\",\n \"def load_and_downscale(input_filename):\\n\\n image = Image.from_file(input_filename)\\n blue_channel = image[:,:,2]\\n downscaled = downscale_local_mean(blue_channel, (2, 2))\\n\\n return downscaled\",\n \"def preprocess(img):\\r\\n\\r\\n sorted = np.sort(img, axis=None)\\r\\n min = sorted[int(removeLower*sorted.size)]\\r\\n max = sorted[int((1.0-removeUpper)*sorted.size)]\\r\\n\\r\\n size = int(cropsize + np.random.rand()*(height-cropsize))\\r\\n topLeft_x = int(np.random.rand()*(height-size))\\r\\n topLeft_y = int(np.random.rand()*(height-size))\\r\\n\\r\\n crop = np.clip(img[topLeft_y:(topLeft_y+cropsize), topLeft_x:(topLeft_x+cropsize)], min, max)\\r\\n\\r\\n resized = cv2.resize(crop, (cropsize, cropsize), interpolation=cv2.INTER_AREA)\\r\\n\\r\\n resized[np.isnan(resized)] = 0.5\\r\\n resized[np.isinf(resized)] = 0.5\\r\\n\\r\\n return scale0to1(flip_rotate(resized))\",\n \"def preprocess_input(x, data_format=None):\\n if data_format is None:\\n data_format = K.image_data_format()\\n assert data_format in {'channels_last', 'channels_first'}\\n\\n if data_format == 'channels_first':\\n # 'RGB'->'BGR'\\n x = x[:, ::-1, :, :]\\n # Zero-center by mean pixel\\n x[:, 0, :, :] -= 103.939\\n x[:, 1, :, :] -= 116.779\\n x[:, 2, :, :] -= 123.68\\n else:\\n # 'RGB'->'BGR'\\n x = x[:, :, :, ::-1]\\n # Zero-center by mean pixel\\n x[:, :, :, 0] -= 103.939\\n x[:, :, :, 1] -= 116.779\\n x[:, :, :, 2] -= 123.68\\n return x\",\n \"def postprocess_img(img):\\n img = img.transpose((1, 2, 0))\\n img += 1.0\\n img = (img * 128.0).astype(np.uint8)\\n return img\",\n \"def preprocess(self, data):\\n data_unnorm = data / 2.0 + 0.5\\n \\n if self.permute == 1:\\n permute = [2, 1, 0]\\n data_rgb_unnorm = data_unnorm[:, permute]\\n elif self.permute == 0:\\n data_rgb_unnorm = data_unnorm\\n \\n data_rgb_unnorm = F.upsample(data_rgb_unnorm, size=self.size, mode='bilinear')\\n data_rgb = (data_rgb_unnorm - self.normalize_mean) / self.normalize_std\\n return data_rgb\",\n \"def preprocess_img_inv(img):\\n img = img.data.numpy().copy()\\n\\n img[0] = img[0] * TORCH_IMG_STD[0] + TORCH_IMG_MEAN[0]\\n img[1] = img[1] * TORCH_IMG_STD[1] + TORCH_IMG_MEAN[1]\\n img[2] = img[2] * TORCH_IMG_STD[2] + TORCH_IMG_MEAN[2]\\n img = img.transpose(1, 2, 0) * 255.0\\n\\n return img.round().astype('uint8')\",\n \"def preprocess_img(img):\\n # Make a copy of img as array\\n img = np.array(img)\\n\\n # Convert into tensor\\n img = torch.Tensor(img).permute(2, 0, 1) / 255.0\\n\\n # Normalize\\n for t, m, s in zip(img, TORCH_IMG_MEAN, TORCH_IMG_STD):\\n t.sub_(m).div_(s)\\n\\n return img\",\n \"def preprocessing(I):\\n I = I[35:195] # crop\\n I = I[::2,::2,0] # downsample by factor of 2\\n I = scipy.misc.imresize(I,size=(img_dim,img_dim))\\n I[I == 144] = 0 # erase background (background type 1)\\n I[I == 109] = 0 # erase background (background type 2)\\n #I[I != 0] = 1 # everything else (paddles, ball) just set to 1\\n I = I/255\\n return I.astype(np.float).ravel() #flattens\",\n \"def img_preprocessing(save_path,img_path, filename):\\n\\n\\tsave_path_filename = save_path + filename\\n\\n\\t#Check if file exits\\n\\tif not os.path.exists(img_path + filename):\\n\\t\\tlogger.error(\\\" image path {} does not exit\\\".\\n\\t\\t\\t\\t\\t\\t\\t\\tformat(img_path + filename))\\n\\n\\timage = plt.imread(img_path + filename)\\n\\n\\timg_gray_orig_0 = rgb2gray(image)\\n\\n\\timg_gray_orig = img_resize(img_gray_orig_0, 2*IMG_SIZE)\\n\\n\\timg_just_bone = img_preprocess_core(img_gray_orig)\\n\\n\\ttry:\\n\\t img_bone = img_pad_resize(img_just_bone, 2*IMG_SIZE) \\n\\t #Second iteration of image segmentation\\n\\t img_just_bone = img_preprocess_core(img_bone)\\n\\t img_bone = img_pad_resize(img_just_bone, IMG_SIZE)\\n\\n\\t plt.imsave(save_path_filename, img_bone)\\n\\t \\n\\texcept ValueError:\\n\\t\\tlogger.error(\\\"Unable to run 2nd interaton for {}\\\".format(filename))\",\n \"def preprocess(self):\\n meta_file_path = os.path.join(database_directory, 'data.txt')\\n meta = pd.read_csv(meta_file_path, delimiter=' ', header=None)\\n meta = meta[meta[0] != '45567.jpg'] # Corrupt image.\\n meta.to_pickle(os.path.join(database_directory, 'meta.pkl'))\\n for file_name in meta.iloc[:, 0].values:\\n if file_name.endswith('.jpg'):\\n file_path = os.path.join(database_directory, file_name)\\n image = imageio.imread(file_path).astype(np.uint8)\\n image = transform.resize(image, (self.preprocessed_image_size, self.preprocessed_image_size),\\n preserve_range=True)\\n image = image.transpose((2, 0, 1))\\n np.save(file_path.replace('.jpg', '.npy'), image)\",\n \"def pre_processing_image(img):\\n\\n #print(img.shape)\\n # apply gamma correction and show the images\\n #adjusted = adjust_gamma(img, gamma=0.65)\\n\\n adjusted = exposure.adjust_gamma(img, gamma=1.65)\\n #print(adjusted.shape)\\n\\n # log transform of image\\n\\n logarithmic_corrected = exposure.adjust_log(adjusted, 1)\\n #print(logarithmic_corrected.shape)\\n\\n # denoising\\n #dst2 = cv2.fastNlMeansDenoisingColored(logarithmic_corrected, None, 10, 10, 7, 21)\\n #print(dst2.shape)\\n dst2 = logarithmic_corrected\\n return dst2\",\n \"def compute_img(self):\\r\\n self.load_img()\\r\\n self.check_shape()\\r\\n self.convert_img()\\r\\n self.img_computed = True\",\n \"def preprocess_image(x, mode='caffe'):\\n # mostly identical to \\\"https://github.com/keras-team/keras-applications/blob/master/keras_applications/imagenet_utils.py\\\"\\n # except for converting RGB -> BGR since we assume BGR already\\n\\n # covert always to float32 to keep compatibility with opencv\\n x = x.astype(np.float32)\\n \\n if mode == 'tf':\\n x /= 127.5\\n x -= 1.\\n elif mode == 'caffe':\\n x[..., 0] -= 103.939\\n x[..., 1] -= 116.779\\n x[..., 2] -= 123.68\\n\\n return x\",\n \"def preprocess_image(img):\\n return (img.astype(np.float32)/255.0 - FACENET_MEAN) / FACENET_STD\",\n \"def preprocess_image(image, height, width):\\n image = np.uint8(\\n resize(rgb2gray(image), [height, width], mode='constant')*255)\\n image = np.reshape(image, [height, width, 1])/255.\\n return image\",\n \"def process_image(image_path):\\n\\n # open the image\\n image = Image.open(image_path)\\n \\n # print(\\\"Original Image size: \\\", image.size)\\n \\n # first resize the images where the shortest side is 256 px\\n width, height = image.size\\n size = 256, 256\\n \\n newwidth, newheight = None, None\\n \\n # if the height is the shorter side\\n if height < width:\\n # find ratio between larger and smaller side\\n ratio = float(width) / float(height)\\n # resize smaller side to 256\\n newheight = 256\\n # resize larger side to 256 * ratio\\n newwidth = int(floor(ratio * size[0])) \\n # else, the width is the shorter side\\n else:\\n # find ratio between larger and smaller side\\n ratio = float(height)/float(width)\\n # resize smaller side to 256\\n newwidth = 256\\n # resize larger side to 256 * ratio\\n newheight = int(floor(ratio * size[1]))\\n \\n \\n # print(\\\"W: {}, H: {}\\\".format(newwidth, newheight))\\n \\n # resize the image\\n image = image.resize((newwidth, newheight), Image.ANTIALIAS)\\n\\n # print(\\\"Resized Image (keep aspect ratio): \\\", image.size)\\n \\n # perform center crop\\n # https://stackoverflow.com/questions/16646183/crop-an-image-in-the-centre-using-pil\\n width, height = image.size # Get dimensions\\n new_height, new_width = 224, 224\\n \\n left = (width - new_width)/2\\n top = (height - new_height)/2\\n right = (width + new_width)/2\\n bottom = (height + new_height)/2\\n\\n image = image.crop((left, top, right, bottom))\\n # print(\\\"cropped image size: \\\", image.size)\\n \\n # convert encoded color channels and convert to floats (divide by 255)\\n np_image = np.array(image) / 255\\n # print(np_image)\\n \\n # normalize\\n mean = [0.485, 0.456, 0.406]\\n std = [0.229, 0.224, 0.225]\\n np_image = (np_image - mean) / std\\n \\n # finally, transpose\\n # print(\\\"shape 1: \\\", np_image.shape)\\n np_image = np_image.transpose((2, 0, 1))\\n # print(\\\"transposed shape: \\\", np_image.shape)\\n \\n # Originally, I was returning a numpy array, as I thought these were the instructions, but\\n # when trying to test, it would not work. \\n # Found solution at: https://knowledge.udacity.com/questions/29173\\n # We have to convert to a tensor before we return it\\n return torch.Tensor(np_image)\",\n \"def load_and_process_image(self, im_path):\\n image = Image.open(im_path).convert('RGB')\\n image = transforms.ToTensor()(image)\\n image = 2 * image - 1\\n return image\",\n \"def _read_and_preprocess(paths, scale_len, crop_len):\\n\\n imgs = []\\n\\n for path in paths:\\n img = imread(path)\\n\\n # force 3-channel images\\n if img.ndim == 2:\\n img = gray2rgb(img)\\n elif img.shape[2] == 4:\\n img = img[:, :, :3]\\n\\n # compute the resize dimension\\n resize_f = float(scale_len) / min(img.shape[:2])\\n new_dims = (int(np.round(img.shape[0] * resize_f)),\\n int(np.round(img.shape[1] * resize_f)))\\n\\n # prevent the input image from blowing up\\n # factor of 2 is more or less an arbitrary number\\n max_dim = 2 * scale_len\\n new_dims = (min(new_dims[0], max_dim),\\n min(new_dims[1], max_dim))\\n\\n # resize and center crop\\n img = resize(img, new_dims)\\n top = int(np.ceil((img.shape[0] - crop_len) / 2.0))\\n left = int(np.ceil((img.shape[1] - crop_len) / 2.0))\\n img = img[top:(top+crop_len), left:(left+crop_len)]\\n\\n # preprocessing (tanh)\\n img = 2 * img - 1\\n\\n imgs.append(img)\\n\\n return np.array(imgs)\",\n \"def preprocess_image(image, model_image_size):\\n #resized_image = cv2.resize(image, tuple(reversed(model_image_size)), cv2.INTER_AREA)\\n resized_image = letterbox_resize(image, tuple(reversed(model_image_size)))\\n image_data = np.asarray(resized_image).astype('float32')\\n image_data = normalize_image(image_data)\\n image_data = np.expand_dims(image_data, 0) # Add batch dimension.\\n return image_data\",\n \"def preprocess(self, resized_inputs):\\n return (2.0 / 255.0) * resized_inputs - 1.0\",\n \"def _preprocessing(image) -> np.ndarray:\\n # TODO: Turn mapping into generic function.\\n processed_image = cv.cvtColor(image, cv.COLOR_BGR2GRAY)\\n processed_image[~mask] = 255\\n return processed_image\",\n \"def post_process_image(image):\\n\\n image = (image - np.min(image)) * (255 / (np.max(image) - np.min(image)))\\n\\n return image\",\n \"def preprocess(Data, minn, maxx, dim=128, low=0.1, hi=1.0):\\n for key in Data:\\n print (key)\\n\\n Data[key][0] = Data[key][0].reshape(len(Data[key][0]), dim, dim, 1)\\n for i, img in enumerate(Data[key][0]):\\n img = img / 255.\\n # img[img > 0.] = 1. - img[img > 0.] #inv color\\n minn, maxx = np.min(img[img > 0]), np.max(img[img > 0])\\n img[img > 0] = low + (img[img > 0] - minn) * (hi - low) / (maxx - minn)\\n Data[key][0][i] = img\",\n \"def process_path(file_path: str):\\r\\n img = tf.io.read_file(file_path)\\r\\n img = tf.image.decode_jpeg(img, channels=3)\\r\\n img = tf.image.resize(img, [IMG_SIZE, IMG_SIZE])\\r\\n return tf.keras.applications.efficientnet.preprocess_input(img) # Shape: IMG_SIZE x IMG_SIZE x 3\\r\",\n \"def caffe_preprocess(img):\\n out = numpy.copy(img)\\n out -= IMAGE_256_MEAN[None, None, :]\\n out = out[:, :, [2, 1, 0]] # swap channel from RGB to BGR\\n return out\",\n \"def process_image(image):\\n # Open the image using PIL\\n pil_image = Image.open(image)\\n \\n # Resize the image to 256x256 while maintining aspect ratio\\n if pil_image.width > pil_image.height:\\n resize_dim = (int(pil_image.width*256 / pil_image.height), 256)\\n else:\\n resize_dim = (256, int(pil_image.height*256 / pil_image.width))\\n \\n pil_image = pil_image.resize(resize_dim)\\n \\n # Crop image to center 224 pixles\\n crop_box_dim = 224\\n left = (pil_image.width - crop_box_dim)/2\\n top = (pil_image.height - crop_box_dim)/2\\n right = pil_image.width - (pil_image.width - crop_box_dim)/2\\n bottom = pil_image.height - (pil_image.height - crop_box_dim)/2\\n pil_image = pil_image.crop((left, top, right, bottom))\\n \\n # Update color channels\\n np_image = np.array(pil_image)\\n np_image_means = np.array([0.485, 0.456, 0.406])\\n np_image_stddev = np.array([0.229, 0.224, 0.225])\\n np_image = (np_image/255 - np_image_means) / np_image_stddev\\n \\n # PIL images and numpy arrays have color channels in the 3rd dimension\\n # Transpose them to first dimension to match what PyTorch expects\\n np_image = np_image.transpose((2,0,1))\\n\\n return np_image\",\n \"def preprocess_image(image):\\n image = tf.image.decode_jpeg(image, channels=3, dct_method=\\\"INTEGER_ACCURATE\\\")\\n return scale(image)\",\n \"def colorImgPreProcess(self, image):\\n #do processing on the image while it's still in color\\n image = cv2.medianBlur(image, 7) #kernal size must be odd\\n #image = cv2.bilateralFilter(image, 9, 75, 75) #TODO: uncomment when it won't cause C++ errors with ROS\\n #self.closeImages() #uncomment if showing output image\\n return image\",\n \"def preprocess(data):\\n # expect image to be passed in as BGR\\n rgb_data = cv2.cvtColor(data, cv2.COLOR_BGR2RGB)\\n batched_rgb_data = np.expand_dims(rgb_data, axis = 0).astype('float64')\\n new_data = resnet50.preprocess_input(batched_rgb_data)[0]\\n\\n return new_data\",\n \"def process_new_image(name):\\n #preprocess the image\\n img = cv2.imread(name)\\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\\n img = img/255.-.5\\n return img\",\n \"def _load_preprocess_image(self, image_file):\\n\\n image_raw = tf.io.read_file(image_file)\\n\\n image = self._preprocess_image(image_raw)\\n\\n return image\",\n \"def preprocess(self, img):\\n undistorted = undistort(img, self.mtx, self.dist)\\n warped = warp(self.transform_matrix, undistorted)\\n binary_img = binarize(warped, s_thres=(100, 255), l_thres=(50, 255))\\n return binary_img\",\n \"def normalize(image):\\r\\n return image / 127.5 - 1.\",\n \"def get_image_and_prep(self,file_path):\\r\\n img = np.array(Image.open(file_path).convert('1'))\\r\\n img = img.reshape(28,28,1)\\r\\n return img\",\n \"def pre_process_data(input_path: list, cuts: int, shape: int = 32, normalize: bool = True) -> list:\\n images = []\\n images_uncut = []\\n for files_path in input_path:\\n\\n files = os.listdir(files_path) # TODO paths\\n for f in files:\\n file_path = f'{files_path}/{f}'\\n im_uncut = cv2.imread(file_path)\\n im_uncut = cv2.cvtColor(im_uncut, cv2.COLOR_RGB2GRAY)\\n images_uncut.append(cv2.resize(im_uncut, (shape * cuts, shape * cuts)))\\n x = np.array(images_uncut)\\n\\n if normalize:\\n x_mean = np.mean(x, axis=(0, 1, 2))\\n x_std = np.std(x, axis=(0, 1, 2))\\n x = (x - x_mean) / (x_std + 1e-9)\\n\\n for im in x:\\n height = im.shape[0]\\n width = im.shape[1]\\n frac_h = height // cuts\\n frac_w = width // cuts\\n i = 0\\n image = []\\n for h in range(cuts):\\n for w in range(cuts):\\n crop = im[h * frac_h:(h + 1) * frac_h, w * frac_w:(w + 1) * frac_w]\\n crop_rehaped = cv2.resize(crop, (shape, shape))\\n image.append([crop_rehaped, i, number_to_angle(i, cuts), neighbours(i, cuts)])\\n i = i + 1\\n images.append(image)\\n # return np.array(images) # todo back to array\\n return images\",\n \"def preprocess_(image, input_shape):\\n if isinstance(image, str):\\n filename = image\\n image = Image.open(image)\\n if isinstance(image, np.ndarray):\\n image = Image.fromarray(image)\\n assert (isinstance(image, Image.Image)) \\n\\n w_ori, h_ori = image.size\\n h, w = input_shape\\n image = image.convert(\\\"RGB\\\")\\n\\n if w_ori > h or h_ori > w:\\n scale = max(1.0*w_ori / w, 1.0*h_ori / h)\\n image.thumbnail((w,h), Image.BILINEAR)\\n else:\\n scale = 1\\n\\n image = image.crop((0, 0, w, h))\\n img_data = np.array(image).reshape((1, h, w, 3))\\n img_data = img_data / 256. - 0.5\\n\\n return img_data, scale, w_ori, h_ori\",\n \"def _load_preprocess_image(self, image_file):\\n image_raw = tf.io.read_file(image_file)\\n\\n image = self._preprocess_image(image_raw)\\n\\n return image\",\n \"def _preprocessing(self, path: str) -> np.array:\\n if Checker.check_input_type_bool(path, 'nii'):\\n image = sitk.ReadImage(path)\\n self.space = image.GetSpacing()\\n image = sitk.GetArrayFromImage(image).astype('float32')\\n\\n elif Checker.check_input_type_bool(path, 'npy'):\\n image = np.load(path)\\n self.space = [1., 1., 1.]\\n warnings.warn(\\n '.npy is not recommended as an image format.'\\n 'Since spacing cannot be identified from .npy, spacing is set as [1., 1., 1.].', UserWarning)\\n\\n elif Checker.check_input_type_bool(path, 'dcm'):\\n raise ValueError(\\n '.dcm is not supported.'\\n 'Please convert dcm dummies to nii format.')\\n\\n else:\\n input_ext = path.split('.')[-1]\\n raise ValueError(\\n f'.{input_ext} format is not supported.')\\n\\n self.img_shape = image.shape\\n\\n # normalize\\n windowing_range = [-40., 120.]\\n windowing_min = windowing_range[0] - windowing_range[1] // 2\\n windowing_max = windowing_range[0] + windowing_range[1] // 2\\n image = ndimage.zoom(image, [.5, .5, .5], order=1, mode='constant')\\n image = np.clip(image, windowing_min, windowing_max)\\n image = (image - windowing_min) / (windowing_max - windowing_min)\\n image = image[np.newaxis, ..., np.newaxis]\\n return image\",\n \"def retinanet_preprocess_image(x, mode='caffe'):\\n # mostly identical to \\\"https://github.com/keras-team/keras-applications/blob/master/keras_applications/imagenet_utils.py\\\"\\n # except for converting RGB -> BGR since we assume BGR already\\n\\n # covert always to float32 to keep compatibility with opencv\\n x = x.astype(np.float32)\\n\\n if mode == 'tf':\\n x /= 127.5\\n x -= 1.\\n elif mode == 'caffe':\\n x[..., 0] -= 103.939\\n x[..., 1] -= 116.779\\n x[..., 2] -= 123.68\\n\\n return x\",\n \"def get_preprocessed_image(file_name):\\n\\n im = np.array(Image.open(file_name)).astype(np.float32)\\n assert im.ndim == 3, 'Only RGB images are supported.'\\n im = im - _IMAGENET_MEANS\\n im = im[:, :, ::-1] # Convert to BGR\\n img_h, img_w, img_c = im.shape\\n assert img_c == 3, 'Only RGB images are supported.'\\n if img_h > 500 or img_w > 500:\\n raise ValueError('Please resize your images to be not bigger than 500 x 500.')\\n\\n pad_h = 500 - img_h\\n pad_w = 500 - img_w\\n im = np.pad(im, pad_width=((0, pad_h), (0, pad_w), (0, 0)), mode='constant', constant_values=0)\\n return np.expand_dims(im.astype(np.float32), 0), img_h, img_w\",\n \"def _preprocess_fn(data):\\n\\n # Validate input\\n if not isinstance(data, dict) or 'image' not in data:\\n raise ValueError('Argument `data` must be a dictionary, '\\n 'not %s' % str(type(data)))\\n\\n # Apply all the individual steps in sequence.\\n image = data['image']\\n image = decode_image(image)\\n image = normalize_value_range(image)\\n image = get_multiscale_patches(image, **preprocessing_kwargs)\\n\\n data['image'] = image\\n return data\",\n \"def normalize(\\n self,\\n image: np.ndarray,\\n data_format: Optional[Union[str, ChannelDimension]] = None,\\n input_data_format: Optional[Union[str, ChannelDimension]] = None,\\n ) -> np.ndarray:\\n image = rescale(image=image, scale=1 / 127.5, data_format=data_format, input_data_format=input_data_format)\\n image = image - 1\\n return image\",\n \"def normalize_image(img):\\n\\n # Load image and convert to grayscale\\n img = rgb2gray(img)\\n\\n # Normalize values, range 0 to 255\\n img = (img - img.min()) / (img.max() - img.min())\\n img *= 255\\n\\n # Make int values\\n img = img.astype(int)\\n\\n # Return new image\\n return img\",\n \"def process_image(img_file, bbox_file, openpose_file, input_res=224):\\n normalize_img = Normalize(mean=cfg.IMG_NORM_MEAN, std=cfg.IMG_NORM_STD)\\n # tmp = cv2.imread(img_file)\\n img = cv2.imread(img_file)[:,:,::-1].copy() # PyTorch does not support negative stride at the moment\\n if bbox_file is None and openpose_file is None:\\n # Assume that the person is centerered in the image\\n height = img.shape[0]\\n width = img.shape[1]\\n center = np.array([width // 2, height // 2])\\n scale = max(height, width) / 200\\n else:\\n if bbox_file is not None:\\n center, scale = bbox_from_json(bbox_file)\\n elif openpose_file is not None:\\n center, scale = bbox_from_openpose(openpose_file)\\n img = crop(img, center, scale, (input_res, input_res))\\n img = img.astype(np.float32) / 255.\\n img = torch.from_numpy(img).permute(2,0,1)\\n norm_img = normalize_img(img.clone())[None]\\n return img, norm_img\",\n \"def normalize_image(self, image, colorspace=None):\\n float_image = image.astype(numpy.float32)\\n if float_image.__array_interface__[\\n \\\"data\\\"][0] == image.__array_interface__[\\\"data\\\"][0]:\\n float_image = float_image.copy()\\n float_image -= float_image.min()\\n max_value = float_image.max()\\n if max_value:\\n max_value /= 255.0\\n float_image /= max_value\\n else:\\n float_image[:] = 127.5\\n normalized_image = float_image.astype(numpy.uint8)\\n if (colorspace != \\\"RGB\\\" and len(normalized_image.shape) == 3\\n and normalized_image.shape[2] == 3):\\n import cv2\\n normalized_image = cv2.cvtColor(\\n normalized_image, getattr(cv2, \\\"COLOR_\\\" + colorspace + \\\"2RGB\\\"))\\n return normalized_image\",\n \"def read_img(self, path):\\n img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)\\n resized = cv2.resize(img, (28, 28))\\n inverted = np.invert(np.array(resized).reshape((28, 28, 1)))\\n denoised_and_reshaped = de_noise(inverted).reshape((28, 28, 1))\\n normalized = denoised_and_reshaped / 255.\\n return normalized\",\n \"def _process_img(self, img):\\n\\n # ==\\n # Construct transforms\\n trans_list = [transforms.Resize(self.img_size)]\\n if self.grayscale:\\n trans_list += [transforms.Grayscale(num_output_channels=1)]\\n\\n img_transforms = transforms.Compose(trans_list)\\n\\n # ==\\n # Transform and output\\n img = img_transforms(img)\\n obs = np.array(img, dtype=np.float32)\\n\\n # Ensure channel is in first dimension (torch conv standard)\\n if len(np.shape(obs)) == 2:\\n obs = np.expand_dims(obs, axis=0)\\n elif len(np.shape(obs)) == 3:\\n # PIL have channel on dim 2, swap with dim 0\\n obs = np.swapaxes(obs, 2, 0)\\n pass\\n else:\\n raise RuntimeError\\n\\n # Scale values to [0, 1]\\n if self.scale_observation:\\n obs = obs / 255.0\\n\\n # (Optinal) Flatten to vector\\n if self.flatten_obs:\\n obs = obs.flatten()\\n\\n return obs\",\n \"def preprocessing(image_path, side=416):\\n\\n image = Image.open(image_path)\\n width, height = image.size\\n scale = side / max([width, height])\\n width_scaled = int(width * scale)\\n height_scaled = int(height * scale)\\n image_scaled = image.resize((width_scaled, height_scaled),\\n resample=Image.LANCZOS)\\n image_array = np.array(image_scaled, dtype=np.float32)\\n image_padded = np.full([side, side, 3], 128, dtype=np.float32)\\n width_offset = (side - width_scaled) // 2\\n height_offset = (side - height_scaled) // 2\\n image_padded[\\n height_offset:height_offset + height_scaled, width_offset:width_offset + width_scaled, :] = image_array\\n image_norm = image_padded / 255\\n return image_norm\",\n \"def normalize_image(img):\\n arr = np.array(img)\\n new_img = Image.fromarray(normalize(arr).astype('uint8'),'L')\\n return new_img\",\n \"def image_normalize(image, image_dims):\\r\\n if image.dtype != 'float32':\\r\\n image = image.astype(dtype=np.float32)\\r\\n if np.size(image_dims) == 2:\\r\\n maxpx = np.max(image[:, :])\\r\\n if maxpx == float(0):\\r\\n maxpx = 1e-12 # fuzz factor\\r\\n minpx = np.min(image[:, :])\\r\\n image[:, :] = (image[:, :] - minpx) / (maxpx - minpx)\\r\\n else:\\r\\n for i in range(image_dims[2]): # find max/min for each channel\\r\\n maxpx = np.max(image[:, :, i])\\r\\n if maxpx == float(0):\\r\\n maxpx = 1e-12 # fuzz factor\\r\\n minpx = np.min(image[:, :, i])\\r\\n image[:, :, i] = (image[:, :, i] - minpx) / (maxpx - minpx)\\r\\n return image\",\n \"def post_process_image(self, image):\\r\\n A = np.min(image)\\r\\n B = np.max(image)\\r\\n k = 255\\r\\n R, C = image.shape\\r\\n for i in range(R):\\r\\n for j in range(C):\\r\\n image[i][j] = (k / (B-A)) * (image[i][j] - A)\\r\\n average_image = np.average(image)\\r\\n if average_image > 50:\\r\\n return image.astype(dtype='uint8')\\r\\n else:\\r\\n return (255-image).astype(dtype='uint8')\",\n \"def process_image(img_file, bbox_file, openpose_file, input_res=224):\\n normalize_img = Normalize(mean=constants.IMG_NORM_MEAN, std=constants.IMG_NORM_STD)\\n img = cv2.imread(img_file)[:,:,::-1].copy() # PyTorch does not support negative stride at the moment\\n print(img.shape)\\n if bbox_file is None and openpose_file is None:\\n # Assume that the person is centerered in the image\\n height = img.shape[0]\\n width = img.shape[1]\\n center = np.array([width // 2, height // 2])\\n scale = max(height, width) / 200\\n else:\\n if bbox_file is not None:\\n center, scale = bbox_from_json(bbox_file)\\n elif openpose_file is not None:\\n center, scale = bbox_from_openpose(openpose_file)\\n img = crop(img, center, scale, (input_res, input_res))\\n img = img.astype(np.float32) / 255.\\n img = torch.from_numpy(img).permute(2,0,1)\\n norm_img = normalize_img(img.clone())[None]\\n return img, norm_img\",\n \"def pre_processing_function(label, filename: str, augmentor: Augmentor = None):\\n image = imread(filename)\\n if augmentor is not None:\\n image = np.round(augmentor.run(image)).astype(np.uint8)\\n\\n return image, label\",\n \"def preprocess_img(imgBGR, erode_dilate=True):\\n rows, cols, _ = imgBGR.shape\\n imgHSV = cv2.cvtColor(imgBGR, cv2.COLOR_BGR2HSV)\\n\\n Bmin = np.array([100, 43, 46])\\n Bmax = np.array([124, 255, 255])\\n img_Bbin = cv2.inRange(imgHSV,Bmin, Bmax)\\n \\n Rmin1 = np.array([0, 43, 46])\\n Rmax1 = np.array([10, 255, 255])\\n img_Rbin1 = cv2.inRange(imgHSV,Rmin1, Rmax1)\\n \\n Rmin2 = np.array([156, 43, 46])\\n Rmax2 = np.array([180, 255, 255])\\n img_Rbin2 = cv2.inRange(imgHSV,Rmin2, Rmax2)\\n img_Rbin = np.maximum(img_Rbin1, img_Rbin2)\\n img_bin = np.maximum(img_Bbin, img_Rbin)\\n\\n if erode_dilate is True:\\n kernelErosion = np.ones((9,9), np.uint8)\\n kernelDilation = np.ones((9,9), np.uint8) \\n img_bin = cv2.erode(img_bin, kernelErosion, iterations=2)\\n img_bin = cv2.dilate(img_bin, kernelDilation, iterations=2)\\n\\n return img_bin\",\n \"def _apply_transform(self, img: np.ndarray): \\n img = self.transform(image=img)[\\\"image\\\"]\\n return img\",\n \"def _apply_transform(self, img: np.ndarray): \\n img = self.transform(image=img)[\\\"image\\\"]\\n return img\",\n \"def preprocess(exam, data_folder, save_path, image_format):\\n for v in ['L-CC', 'L-MLO', 'R-CC', 'R-MLO']:\\n if len(exam[v]) == 0:\\n continue\\n else:\\n for image in exam[v]:\\n image_path = data_folder + '/' + image + '.' + image_format\\n # Extract subdirectories\\n subdirs = \\\"/\\\".join(image.split('/')[:-1])\\n save_dirs = os.path.join(save_path, subdirs)\\n # Extract image id\\n image_id = image.split('/')[-1]\\n # Create save directories\\n os.makedirs(save_dirs, exist_ok=True)\\n png_save_path = os.path.join(save_dirs, image_id + '.png')\\n with Image(filename=image_path, format=image_format) as img:\\n with img.clone() as i:\\n i.auto_level()\\n with i.convert('png') as png_image:\\n png_image.transform(resize='896x1152!')\\n png_image.save(filename=png_save_path)\",\n \"def process_image(image):\\r\\n image = random_brightness(image)\\r\\n image = crop_image(image)\\r\\n image = resize(image)\\r\\n return image\",\n \"def preprocess(img, out_shape=None):\\n if out_shape is not None:\\n img = resize(img, out_shape, mode='constant')\\n\\n # Normalize the image\\n mean = img.mean()\\n std = img.std()\\n return (img - mean) / std\",\n \"def normalize(image):\\n return image / 127.5 - 1.\",\n \"def preprocess(self, image, input_shape):\\n # image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\\n h, w, _ = image.shape\\n\\n input_h, input_w = input_shape\\n scale = min(float(input_w) / float(w), float(input_h) / float(h))\\n nw = int(w * scale)\\n nh = int(h * scale)\\n\\n image = cv2.resize(image, (nw, nh))\\n\\n new_image = np.zeros((input_h, input_w, 3), np.float32)\\n new_image.fill(128)\\n bh, bw, _ = new_image.shape\\n new_image[int((bh - nh) / 2):(nh + int((bh - nh) / 2)), int((bw - nw) / 2):(nw + int((bw - nw) / 2)), :] = image\\n\\n new_image /= 255.\\n new_image = np.expand_dims(new_image, 0) # Add batch dimension.\\n return new_image\",\n \"def main():\\n test_image = load_image()\\n\\n pixelate_image(\\n normalize_image(test_image)\\n )\\n pass\",\n \"def preprocess_image(image: Image) -> np.ndarray:\\n return np.array(image.convert('L'))\",\n \"def preprocess_image(normalize_lighting=False, min_value=0., max_value=1.):\\n if min_value > max_value:\\n min_value, max_value = max_value, min_value\\n rescaling_range = max_value - min_value\\n\\n def rescale(image):\\n if image.max() > 1:\\n # Project values interval on [0.0; 1.0]\\n image = image / 255.\\n # Project values on [min_value; max_value]\\n image = image * rescaling_range + min_value\\n return image\\n\\n def normalize_lighting_and_rescale(image):\\n # Normalize image lighting (this results in an image with values from [0.0; 1.0])\\n image = image / np.mean(image, axis=(0, 1))\\n image = image / image.max()\\n # Project values on [min_value; max_value]\\n image = image * rescaling_range + min_value\\n return image\\n\\n if normalize_lighting is True:\\n return normalize_lighting_and_rescale\\n else:\\n return rescale\",\n \"def preprocess_fn(self, X_data, Y_data):\\n # Normalize image data to [0,1]\\n X_data = tf.math.divide(tf.math.subtract(X_data, tf.math.reduce_min(\\n X_data)), tf.math.subtract(tf.math.reduce_max(X_data), tf.math.reduce_min(X_data)))\\n Y_data = tf.math.divide(tf.math.subtract(Y_data, tf.math.reduce_min(\\n Y_data)), tf.math.subtract(tf.math.reduce_max(Y_data), tf.math.reduce_min(Y_data)))\\n\\n return X_data, Y_data\",\n \"def image_transform(im, format='pytorch'):\\n if format == 'pytorch':\\n im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)\\n im = im.astype(np.float32)\\n im = im / 255.\\n im = (im - np.array([0.485, 0.456, 0.406], dtype=np.float32) ) / \\\\\\n np.array([0.229, 0.224, 0.225], dtype=np.float32)\\n elif format == 'caffe':\\n # return BGR image\\n im = im.astype(np.float32)\\n im = im - np.array([103.939, 116.779, 123.68], dtype=np.float32)\\n return im\",\n \"def __processImage(self, f):\\n try:\\n imgobj = Image.open(f).convert('RGB')\\n except:\\n return None\\n w, h = imgobj.size\\n if w < h:\\n # reduce width to required dimension and adjust height accordingly\\n new_h = int(h * self.PROCESSING_DIM / w)\\n resizedImg = imgobj.resize((self.PROCESSING_DIM, new_h))\\n\\n y_start = int(new_h / 2 - self.PROCESSING_DIM / 2)\\n processedImage = resizedImg.crop((0, y_start, self.PROCESSING_DIM, y_start + self.PROCESSING_DIM))\\n\\n else:\\n # reduce height to required dimension and adjust width accordingly\\n new_w = int(w * self.PROCESSING_DIM / h)\\n resizedImg = imgobj.resize((new_w, self.PROCESSING_DIM))\\n\\n x_start = int(new_w / 2 - self.PROCESSING_DIM / 2)\\n processedImage = resizedImg.crop((x_start, 0, x_start + self.PROCESSING_DIM, self.PROCESSING_DIM))\\n\\n return processedImage\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67356676","0.6674581","0.6586979","0.6573389","0.6537538","0.65024185","0.64687973","0.64650357","0.64465904","0.6442704","0.6418419","0.640712","0.6392954","0.6363671","0.6362483","0.6359591","0.6263613","0.6258477","0.6247825","0.624503","0.6207113","0.6171945","0.616526","0.614976","0.61284715","0.6109691","0.6104963","0.6102594","0.6096845","0.60599726","0.6050089","0.60462236","0.6042504","0.6024278","0.60126513","0.60108954","0.60063094","0.6003471","0.5990815","0.5978517","0.5976508","0.59751403","0.59654385","0.596438","0.5951336","0.5940581","0.59337276","0.5921208","0.5916405","0.59132856","0.5907197","0.5902749","0.5901027","0.5900503","0.5897701","0.58898485","0.58788043","0.5868862","0.5868065","0.5862698","0.5859621","0.58556926","0.58497804","0.5841603","0.5839103","0.5816334","0.58138126","0.5808921","0.58067113","0.5801539","0.57997334","0.5794179","0.578975","0.5786447","0.57824683","0.5777242","0.577078","0.5744092","0.57370335","0.5727059","0.572165","0.5715814","0.57016295","0.56989294","0.5698158","0.5696485","0.56798804","0.5678309","0.5678309","0.56771046","0.5675958","0.56758285","0.5674319","0.56721914","0.56613594","0.56577003","0.5655528","0.56542474","0.5645321","0.56439346"],"string":"[\n \"0.67356676\",\n \"0.6674581\",\n \"0.6586979\",\n \"0.6573389\",\n \"0.6537538\",\n \"0.65024185\",\n \"0.64687973\",\n \"0.64650357\",\n \"0.64465904\",\n \"0.6442704\",\n \"0.6418419\",\n \"0.640712\",\n \"0.6392954\",\n \"0.6363671\",\n \"0.6362483\",\n \"0.6359591\",\n \"0.6263613\",\n \"0.6258477\",\n \"0.6247825\",\n \"0.624503\",\n \"0.6207113\",\n \"0.6171945\",\n \"0.616526\",\n \"0.614976\",\n \"0.61284715\",\n \"0.6109691\",\n \"0.6104963\",\n \"0.6102594\",\n \"0.6096845\",\n \"0.60599726\",\n \"0.6050089\",\n \"0.60462236\",\n \"0.6042504\",\n \"0.6024278\",\n \"0.60126513\",\n \"0.60108954\",\n \"0.60063094\",\n \"0.6003471\",\n \"0.5990815\",\n \"0.5978517\",\n \"0.5976508\",\n \"0.59751403\",\n \"0.59654385\",\n \"0.596438\",\n \"0.5951336\",\n \"0.5940581\",\n \"0.59337276\",\n \"0.5921208\",\n \"0.5916405\",\n \"0.59132856\",\n \"0.5907197\",\n \"0.5902749\",\n \"0.5901027\",\n \"0.5900503\",\n \"0.5897701\",\n \"0.58898485\",\n \"0.58788043\",\n \"0.5868862\",\n \"0.5868065\",\n \"0.5862698\",\n \"0.5859621\",\n \"0.58556926\",\n \"0.58497804\",\n \"0.5841603\",\n \"0.5839103\",\n \"0.5816334\",\n \"0.58138126\",\n \"0.5808921\",\n \"0.58067113\",\n \"0.5801539\",\n \"0.57997334\",\n \"0.5794179\",\n \"0.578975\",\n \"0.5786447\",\n \"0.57824683\",\n \"0.5777242\",\n \"0.577078\",\n \"0.5744092\",\n \"0.57370335\",\n \"0.5727059\",\n \"0.572165\",\n \"0.5715814\",\n \"0.57016295\",\n \"0.56989294\",\n \"0.5698158\",\n \"0.5696485\",\n \"0.56798804\",\n \"0.5678309\",\n \"0.5678309\",\n \"0.56771046\",\n \"0.5675958\",\n \"0.56758285\",\n \"0.5674319\",\n \"0.56721914\",\n \"0.56613594\",\n \"0.56577003\",\n \"0.5655528\",\n \"0.56542474\",\n \"0.5645321\",\n \"0.56439346\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6635638"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":16877,"cells":{"query":{"kind":"string","value":"Make input data as h5 file format Depending on 'is_train' (flag value), savepath would be changed."},"document":{"kind":"string","value":"def make_data(sess, data, data_dir):\n if FLAGS.is_train:\n #savepath = os.path.join(os.getcwd(), os.path.join('checkpoint',data_dir,'train.h5'))\n savepath = os.path.join('.', os.path.join('checkpoint',data_dir,'train.h5'))\n if not os.path.exists(os.path.join('.',os.path.join('checkpoint',data_dir))):\n os.makedirs(os.path.join('.',os.path.join('checkpoint',data_dir)))\n with h5py.File(savepath, 'w') as hf:\n hf.create_dataset('data', data=data)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def save_to_hd5(out_file, x_train, y_train, x_val, y_val, x_test, y_test):\n data = h5py.File(out_file, \"w\")\n train_data = data.create_group(\"train_data\")\n train_data.create_dataset(\"x_train\", data=x_train)\n train_data.create_dataset(\"y_train\", data=y_train)\n if x_val is not None:\n val_data = data.create_group(\"val_data\")\n val_data.create_dataset(\"x_val\", data=x_val)\n val_data.create_dataset(\"y_val\", data=y_val)\n if x_test is not None:\n test_data = data.create_group(\"test_data\")\n test_data.create_dataset(\"x_test\", data=x_test)\n test_data.create_dataset(\"y_test\", data=y_test)\n data.close()","def make_data_hf(input_, label_, config):\n # Check the check dir, if not, create one\n if not os.path.isdir(os.path.join(os.getcwd(),config.checkpoint_dir)):\n os.makedirs(os.path.join(os.getcwd(),config.checkpoint_dir))\n\n if config.is_train:\n savepath = os.path.join(os.getcwd(), config.checkpoint_dir + '/train.h5')\n else:\n savepath = os.path.join(os.getcwd(), config.checkpoint_dir + '/test.h5')\n\n with h5py.File(savepath, 'w') as hf:\n #checkimage(input_[1])\n hf.create_dataset('input', data=input_)\n hf.create_dataset('label', data=label_)","def save_as_hdf5(self, filename):","def make_data(config, data, label):\n if not os.path.isdir(os.path.join(os.getcwd(), config.checkpoint_dir)):\n os.makedirs(os.path.join(os.getcwd(), config.checkpoint_dir))\n\n if config.is_train:\n savepath = os.path.join(os.getcwd(), config.checkpoint_dir +'/train.h5')\n else:\n savepath = os.path.join(os.getcwd(), config.checkpoint_dir +'/test.h5')\n\n with h5py.File(savepath, 'w') as hf:\n hf.create_dataset('data', data=data)\n hf.create_dataset('label', data=label)","def make_data(_is_train, data, label):\n save_path = os.path.join(os.getcwd(), \"SRCNN\", 'checkpoint')\n if not os.path.exists(save_path):\n os.makedirs(save_path)\n\n if _is_train:\n save_path = os.path.join(save_path, 'train.h5')\n else:\n save_path = os.path.join(save_path, 'test.h5')\n\n # data 和 label 預設類型是 numpy array ，但若建立時內部陣列維度不相等，內部數據將被轉為 dtype=object\n # 導致 h5py 無法儲存: TypeError: Object dtype dtype('O') has no native HDF5 equivalent\n with h5py.File(save_path, 'w') as hf:\n hf.create_dataset('data', data=data)\n hf.create_dataset('label', data=label)","def make_data(sess, data, label):\n if FLAGS.is_train:\n savepath = os.path.join(os.getcwd(), 'checkpoint/train.h5')\n else:\n savepath = os.path.join(os.getcwd(), 'checkpoint/test.h5')\n\n with h5py.File(savepath, 'w') as hf:\n hf.create_dataset('data', data=data)\n hf.create_dataset('label', data=label)","def transform_to_h5():\n # this took about 10 minutes for set1\n for setname in ['set1', 'set2']:\n filename = os.path.join(ltrc_dirname, '{}.h5'.format(setname))\n f = h5py.File(filename, 'w')\n\n for name in ['train', 'valid', 'test']:\n g = f.create_group(name)\n filename = os.path.join(ltrc_dirname, '{}.{}.txt'.format(setname, name))\n X, y, q = load_svmlight_file(filename, query_id=True)\n g.create_dataset('X', data=X.todense(), compression='gzip')\n g.create_dataset('y', data=y, compression='gzip')\n g.create_dataset('q', data=q, compression='gzip')\n f.close()\n # Now you can do this\n # f['/valid/X'].shape\n # Out[24]: (71083, 699)","def saveTrainingData(model, hdf5, view_names=None, sample_names=None, feature_names=None, likelihoods=None):\n data = model.getTrainingData()\n data_grp = hdf5.create_group(\"data\")\n featuredata_grp = hdf5.create_group(\"features\")\n hdf5.create_dataset(\"samples\", data=np.array(sample_names, dtype='S50'))\n\n if likelihoods is not None:\n data_grp.attrs['likelihood'] = np.array(likelihoods, dtype='S50')\n\n for m in range(len(data)):\n view = view_names[m] if view_names is not None else str(m)\n data_grp.create_dataset(view, data=data[m].data.T)\n if feature_names is not None:\n # data_grp.attrs['features'] = np.array(feature_names[m], dtype='S')\n featuredata_grp.create_dataset(view, data=np.array(feature_names[m], dtype='S50'))","def convert(self, out_path: str)->None:\n tape_data_hdf5 = self.createTapeHDF5Dict()\n \n self.deleteFile(out_path)\n self.to_hdf5(tape_data_hdf5, out_path)\n print(\"HDF5 file has been successfully saved at {}\".format(out_path))","def prepare_hdf5_file(hdf5_file, n_train, n_valid, n_test):\n n_total = n_train + n_valid + n_test\n splits = create_splits(n_train, n_valid, n_test)\n hdf5_file.attrs['split'] = H5PYDataset.create_split_array(splits)\n vlen_dtype = h5py.special_dtype(vlen=numpy.dtype('uint8'))\n hdf5_file.create_dataset('encoded_images', shape=(n_total,),\n dtype=vlen_dtype)\n hdf5_file.create_dataset('targets', shape=(n_total, 1), dtype=numpy.int16)\n hdf5_file.create_dataset('filenames', shape=(n_total, 1), dtype='S32')","def create_devh5(self):\n if os.path.exists(self.dev_h5_path):\n print(\"[LOGGING]: \" + self.dev_h5_path + \" exists!\")\n return\n\n with h5py.File(self.dev_h5_path, 'w') as f:\n\n # create a group: f['train']\n train = f.create_group('train')\n self.extract_fea_for_datagroup(train, mode='train')\n\n # f['test']\n test = f.create_group('test')\n self.extract_fea_for_datagroup(test, mode='test')\n\n f.close()","def save_model(self, fpath):\n self._make_model_folder(fpath)\n self.model.save(os.path.join(fpath, U.MODEL_NAME), save_format=\"h5\")\n return","def numpy_to_h5py(in_dir=config.dir_npy, split = config.split):\n\n in_files=[x[:-13] for x in os.listdir(in_dir) if x.endswith('_voc_stft.npy') and not x.startswith('._')]\n\n random.shuffle(in_files)\n\n\n num_files = len(in_files)\n\n split_idx = int(num_files*split)\n\n trn_files = in_files[:split_idx]\n\n val_files = in_files[split_idx:]\n\n num_val_files = len(val_files)\n\n print('Processing %d training files' % split_idx)\n logger.info('Processing %d training files' % split_idx)\n\n logger.info('Training file: %s' % config.h5py_file_train)\n\n voc_shape_trn = [split_idx, 5170,config.input_features]\n\n mix_shape_trn = [split_idx, 5170,config.input_features]\n\n feats_shape_trn = [split_idx, 5170,config.output_features]\n\n hdf5_file = h5py.File(config.h5py_file_train, mode='w')\n\n hdf5_file.create_dataset(\"voc_stft\", voc_shape_trn, np.float32)\n\n hdf5_file.create_dataset(\"back_stft\", voc_shape_trn, np.float32)\n\n hdf5_file.create_dataset(\"mix_stft\", mix_shape_trn, np.float32)\n\n hdf5_file.create_dataset(\"feats\", feats_shape_trn, np.float32)\n\n\n i = 0\n\n for f in trn_files:\n\n voc_stft = np.load(in_dir+f+'_voc_stft.npy')\n\n voc_stft = voc_stft.astype('float32')\n\n mix_stft = np.load(in_dir+f+'_mix_stft.npy')\n\n mix_stft = mix_stft.astype('float32')\n\n back_stft = np.load(in_dir+f+'_back_stft.npy')\n\n back_stft = back_stft.astype('float32')\n\n synth_feats = np.load(in_dir+f+'_synth_feats.npy')\n\n synth_feats = synth_feats.astype('float32')\n\n hdf5_file[\"voc_stft\"][i,...] = voc_stft\n\n hdf5_file[\"mix_stft\"][i,...] = mix_stft\n\n hdf5_file[\"back_stft\"][i,...] = back_stft\n\n hdf5_file[\"feats\"][i,...] = synth_feats\n\n i+=1\n utils.progress(i, split_idx)\n\n logger.info('Processed training file: %s' % f)\n\n hdf5_file.close()\n\n print('Processing %d validation files' % num_val_files)\n logger.info('Processing %d validation files' % num_val_files)\n\n logger.info('Validation file: %s' % config.h5py_file_val)\n\n voc_shape_trn = [num_val_files, 5170,config.input_features]\n\n mix_shape_trn = [num_val_files, 5170,config.input_features]\n\n feats_shape_trn = [num_val_files, 5170,config.output_features]\n\n hdf5_file = h5py.File(config.h5py_file_val, mode='w')\n\n hdf5_file.create_dataset(\"voc_stft\", voc_shape_trn, np.float32)\n\n hdf5_file.create_dataset(\"mix_stft\", mix_shape_trn, np.float32)\n\n hdf5_file.create_dataset(\"back_stft\", voc_shape_trn, np.float32)\n\n hdf5_file.create_dataset(\"feats\", feats_shape_trn, np.float32)\n\n\n i = 0\n\n for f in val_files:\n\n voc_stft = np.load(in_dir+f+'_voc_stft.npy')\n\n voc_stft = voc_stft.astype('float32')\n\n mix_stft = np.load(in_dir+f+'_mix_stft.npy')\n\n mix_stft = mix_stft.astype('float32')\n\n synth_feats = np.load(in_dir+f+'_synth_feats.npy')\n\n synth_feats = synth_feats.astype('float32')\n\n back_stft = np.load(in_dir+f+'_back_stft.npy')\n\n back_stft = back_stft.astype('float32')\n\n hdf5_file[\"voc_stft\"][i,...] = voc_stft\n\n hdf5_file[\"mix_stft\"][i,...] = mix_stft\n\n hdf5_file[\"back_stft\"][i,...] = back_stft\n\n hdf5_file[\"feats\"][i,...] = synth_feats\n\n i+=1\n utils.progress(i, num_val_files)\n\n logger.info('Processed validation file: %s' % f)\n\n hdf5_file.close()\n # return original_ffts","def gene_h5_train_file(data_path, h5_name):\n img = []\n y_cls_mask = []\n y_reg_cls_mask = []\n os.chdir(data_path)\n jpgfiles = glob.glob('*.jpg')\n idx = 1\n # the position of generator objector is very important\n gene_obj = image_output_pair(data_path, 1/255.0)\n while True:\n if idx == len(jpgfiles):\n break\n print '\\t{0}/{1}'.format(idx, len(jpgfiles))\n # the position of generator objector is very important\n # gene_obj = image_output_pair(data_path, 1/255.0)\n img_it, y_cls_mask_it, y_reg_cls_mask_it = gene_obj.next()\n img.append(img_it)\n y_cls_mask.append(y_cls_mask_it)\n y_reg_cls_mask.append(y_reg_cls_mask_it)\n idx += 1\n\n # img => (320, 320, 3)\n # after np.stack => (19041, 320, 320, 3)\n img_input = np.stack(img, axis=0)\n y_cls = np.stack(y_cls_mask, axis=0)\n y_reg = np.stack(y_reg_cls_mask, axis=0)\n print 'input data shape is {0}'.format(img_input.shape)\n print 'y_cls data shape is {0}'.format(y_cls.shape)\n print 'y_reg data shape is {0}'.format(y_reg.shape)\n \n # wirte data\n h5 = '/home/yuquanjie/Documents/train_' + h5_name\n file_write = h5py.File(h5, 'w')\n file_write.create_dataset('X_train', data=img_input)\n file_write.create_dataset('Y_train_cls', data=y_cls)\n file_write.create_dataset('Y_train_merge', data=y_reg)\n file_write.close()","def to_hdf5(self, path: Union[str, Path]):\n import h5py # : import-outside-toplevel\n\n with h5py.File(path, \"w\") as hdf:\n for k, v in self._to_list_dict().items():\n if k in self._cal_paras:\n hdf.create_dataset(k, data=v.tolist())\n elif v:\n hdf[k] = v","def save_model(model):\n\n # model.save(os.path.join(output))\n # model.save(output)\n\n # key = \"{}/{}/examples\".format(prefix,data_partition_name)\n # url = 's3://{}/{}'.format(bucket, key)\n # boto3.Session().resource('s3').Bucket(bucket).Object(key).upload_file('data.csv')\n # print('Done writing to {}'.format(url))\n \n model.save('output/sentiment_model.h5')\n\n s3 = boto3.resource('s3')\n s3.meta.client.upload_file('output/sentiment_model.h5', 'ieor4577-hw4', 'sentiment_model.h5')\n\n # tf.saved_model.save(model, os.path.join(output, \"1\"))\n print(\"Model successfully saved\")","def save(self,\n path,\n save_model=False):\n if save_model:\n self.model.save(path)\n\n h5dict = H5Dict(path)\n self._update_hdf5(h5dict, self.generator_train.command_dict, 'train')\n \n try:\n self._update_hdf5(h5dict, self.generator_val.command_dict, 'val')\n except AttributeError:\n pass","def export_to_hdf5(cls, h5_file, model, loads):\n #encoding = model._encoding\n #comments = []\n sid = []\n node = []\n cid = []\n mag = []\n xyz = []\n for load in loads:\n #comments.append(loads.comment)\n sid.append(load.sid)\n node.append(load.node)\n cid.append(load.cid)\n mag.append(load.mag)\n xyz.append(load.xyz)\n\n #h5_file.create_dataset('_comment', data=comments)\n h5_file.create_dataset('sid', data=sid)\n h5_file.create_dataset('node', data=node)\n h5_file.create_dataset('cid', data=cid)\n h5_file.create_dataset('mag', data=mag)\n h5_file.create_dataset('xyz', data=xyz)","def save(self, path: utils.URLPath):\n save_somclassifier_config(self.config, path / \"config.json\")\n self.model.save(str(path / \"model.h5\"))\n io_functions.save_joblib(self.binarizer, path / \"binarizer.joblib\")\n\n io_functions.save_json(self.data_ids[\"validation\"], path / \"ids_validate.json\")\n io_functions.save_json(self.data_ids[\"train\"], path / \"ids_train.json\")","def writeH5Dataset( self, foldername, time, nameConvention = \"grid\" ):\n filename = \"{0}/{1}_{2:06}.h5\".format(foldername,nameConvention,time)\n file = h5py.File(filename,'w',driver='mpio',comm=self.global_comm)\n dset = file.create_dataset(\"dset\",self._layout.fullShape, dtype = self._f.dtype)\n slices = tuple([slice(s,e) for s,e in zip(self._layout.starts,self._layout.ends)])\n dset[slices]=self._f[:]\n attr_data = np.array(self._layout.dims_order)\n dset.attrs.create(\"Layout\", attr_data, (self._nDims,), h5py.h5t.STD_I32BE)\n file.close()","def save(model, save_name):\n dirs = configparser.ConfigParser()\n dirs.read(\"config/dir_config.ini\")\n\n save_name = os.path.splitext(save_name)[0]\n path = os.path.join(dirs[\"save_dirs\"][\"models\"], save_name + \".h5\")\n info = os.path.join(dirs[\"save_dirs\"][\"models\"], save_name + \"_info.txt\")\n\n with open(info, \"w\") as file:\n model.summary(print_fn=lambda x: file.write(f\"{x}\\n\"))\n model.save(path, overwrite=False)","def save_all(self, file):\n self.model.save(file + \"_model.h5\")\n return","def save_model(model, output_dir, epoch, step, metric, current_score,\n best_score, name=\"model\"):\n assert hasattr(model, \"loss\") and model.loss is not None\n assert hasattr(model, \"optimizer\") and model.optimizer is not None\n\n model.save(os.path.join(output_dir, f\"{name}.h5\"))\n\n file_io.write_csv(\n os.path.join(output_dir, f\"{name}.step\"),\n [epoch, step, metric, current_score, best_score])","def save(self, filename: Union[str, Path]):\n self.model.save(filename, save_format=\"h5\")\n with h5py.File(filename, mode='a') as f:\n f.attrs['spectrum_binner'] = self.spectrum_binner.to_json()\n f.attrs['additional_input'] = self.nr_of_additional_inputs","def save_image_to_h5(image, h5_path, *args, **kwargs):\n # TODO: Implement the method\n\n f = h5.File(h5_path, \"w\")\n grp = f.create_group(\"data\")\n grp.create_dataset(\"image\", data=image)","def write(self,data): \n if not os.path.exists(self.output_dir):\n os.makedirs(self.output_dir)\n\n # We will store these in a separate file and link them to the level2s\n fname = data.filename.split('/')[-1]\n \n if os.path.exists(self.outfile):\n output = h5py.File(self.outfile,'a')\n else:\n output = h5py.File(self.outfile,'w')\n\n # Set permissions and group\n if self.set_permissions:\n try:\n os.chmod(self.outfile,0o664)\n shutil.chown(self.outfile, group=self.permissions_group)\n except PermissionError:\n self.logger(f'{fname}:{self.name}: Warning, couldnt set the file permissions.')\n\n # Store datasets in root\n data_out = {'tod':self.all_tod,\n 'weights':self.all_weights,\n 'mask':self.all_mask,\n 'cal_factors':self.all_cal_factors,\n 'frequency':self.all_frequency,\n 'auto_rms':self.all_auto}\n\n for dname, dset in data_out.items():\n if dname in output:\n del output[dname]\n output.create_dataset(dname, data=dset)\n\n output.attrs['version'] = __level3_version__\n output['cal_factors'].attrs['source'] = self.cal_source\n output['cal_factors'].attrs['calibrator_obsid'] = self.nearest_calibrator\n\n output.close()\n \n if self.level3 in data.keys():\n del data[self.level3]\n data[self.level3] = h5py.ExternalLink(self.outfile,'/')","def save_h5(self, filename):\n try:\n shutil.copyfile(filename, '{0}_bak'.format(filename))\n except IOError:\n print 'could not make backup of trainer param file (which is \\\n normal if we haven\\'t saved one until now)'\n paramfile = tables.openFile(filename, 'w')\n paramfile.createArray(paramfile.root, 'learningrate',\n self.learningrate)\n paramfile.createArray(paramfile.root, 'verbose', self.verbose)\n paramfile.createArray(paramfile.root, 'loadsize', self.loadsize)\n paramfile.createArray(paramfile.root, 'batchsize', self.batchsize)\n paramfile.createArray(paramfile.root, 'momentum',\n self.momentum)\n paramfile.createArray(paramfile.root, 'epochcount',\n self.epochcount)\n paramfile.createArray(paramfile.root, 'momentum_batchcounter',\n self.momentum_batchcounter)\n incsgrp = paramfile.createGroup(paramfile.root, 'incs', 'increments')\n for p in self._params:\n paramfile.createArray(incsgrp, p.name, self._incs[p].get_value())\n if self.rmsprop is not None:\n avg_grad_sqrs_grp = paramfile.createGroup(paramfile.root, 'avg_grad_sqrs')\n for p in self._params:\n paramfile.createArray(avg_grad_sqrs_grp, p.name, self._avg_grad_sqrs[p].get_value())\n paramfile.close()","def save_model_data():\n print(\"Getting the training, validation, and testing sets...\")\n df = get_data()\n\n # First read in & group by type\n image_styles = {key: [] for key in df['Period'].unique()}\n for pic in df.to_dict(\"records\"):\n db = pic['file'][:pic['file'].find(\"_\")]\n img = Image.open(os.path.join(MODEL_DIR, f\"{db}/sculpture_images/{pic['file']}\"))\n img.load()\n image_styles[pic['Period']].append(img)\n\n\n # Split each type up...not just the whole thing\n for style in image_styles.keys():\n # Split into Train/Test - 75/25\n feats, labels = image_styles[style], [style] * len(image_styles[style])\n feat_train, feat_test, label_train, label_test = train_test_split(feats, labels, test_size=.25, random_state=42)\n\n # Create dirs if needed\n for pic_type in ['train', 'test']:\n if not os.path.exists(os.path.join(MODEL_DIR, f\"model_data/gan/{pic_type}/{style}\")):\n os.makedirs(os.path.join(MODEL_DIR, f\"model_data/gan/{pic_type}/{style}\"))\n\n # Save in train/validation/test folders\n for style_type_pics in [[\"train\", feat_train], [\"test\", feat_test]]:\n for pic in range(len(style_type_pics[1])):\n file_name = style + format(pic, '03d') + \".jpg\"\n if not os.path.isfile(os.path.join(MODEL_DIR, f\"model_data/gan/{style_type_pics[0]}/{style}/{file_name}\")):\n style_type_pics[1][pic].save(os.path.join(MODEL_DIR, f\"model_data/gan/{style_type_pics[0]}/{style}/{file_name}\"))\n\n print(\"Split data for\", style)","def save(self, path, name):\n if not self._frozen:\n raise Exception(\"Dataset must be frozen\")\n # create directory\n pathlib.Path(os.path.join(path,name)).mkdir(parents=True, exist_ok=True)\n self._raw_data.to_hdf(os.path.join(path,name,\"dataset.h5\"), key=\"raw_data\")\n self._proc_data.to_hdf(os.path.join(path,name,\"dataset.h5\"), key=\"proc_data\")\n np.save(os.path.join(path,name,\"_X_train.npy\"), self._X_train)\n np.save(os.path.join(path,name,\"_X_test.npy\"), self._X_test)\n np.save(os.path.join(path,name,\"_y_train.npy\"), self._y_train)\n np.save(os.path.join(path,name,\"_y_test.npy\"), self._y_test)\n \n np.save(os.path.join(path,name,\"_X_mean.npy\"), self._X_mean)\n np.save(os.path.join(path,name,\"_X_std.npy\"), self._X_std)\n np.save(os.path.join(path,name,\"_y_mean.npy\"), self._y_mean)\n np.save(os.path.join(path,name,\"_y_std.npy\"), self._y_std)\n \n with open(os.path.join(path,name,\"_seed.pkl\"), \"wb\") as fp: #Pickling\n pickle.dump(self._seed, fp)\n with open(os.path.join(path,name,\"_train_part.pkl\"), \"wb\") as fp: #Pickling\n pickle.dump(self._train_part, fp)\n with open(os.path.join(path,name,\"_test_part.pkl\"), \"wb\") as fp: #Pickling\n pickle.dump(self._test_part, fp)\n with open(os.path.join(path,name,\"_columns.pkl\"), \"wb\") as fp: #Pickling\n pickle.dump(self._columns, fp)","def __save_datasets(self):\n self.train.to_csv('{}/{}/{}'.format(path_to_train_set, img_format, 'train.csv'))\n self.valid.to_csv('{}/{}/{}'.format(path_to_valid_set, img_format, 'valid.csv'))\n self.test.to_csv('{}/{}/{}'.format(path_to_test_set, img_format, 'test.csv'))","def save_batch(dataset, steps, outdir, batch_nbr):\n\twith h5py.File(outdir+\"/batch%d.h5\" % (batch_nbr), 'w') as f:\n\t\tf.create_dataset('left_camera_image', data=np.array(dataset['left_camera']))\n\t\tf.create_dataset('left_camera_steering_angle', data=np.array(dataset['left_camera/steering_angle']))\n\t\tf.create_dataset('center_camera_image', data=np.array(dataset['center_camera']))\n\t\tf.create_dataset('center_camera_steering_angle', data=np.array(dataset['center_camera/steering_angle']))\n\t\tf.create_dataset('right_camera_image', data=np.array(dataset['right_camera']))\n\t\tf.create_dataset('right_camera_steering_angle', data=np.array(dataset['right_camera/steering_angle']))\n\treturn batch_nbr + 1","def save_to_hdf(df, fname, output_subdir=None):\n path = Path(fname)\n newfname = path.with_suffix('.h5').name\n folderpath = HOME / 'output'\n if output_subdir:\n folderpath = folderpath / output_subdir\n path = folderpath / newfname\n df.to_hdf(str(path), 'df', format='t')\n return str(path)","def create_output_file(self):\n if self.options['storage_method'] == 'hdf5':\n try:\n fp = h5py.File(self.file_name, \"w\")\n except IOError:\n print \"Unable to open output file '%s'\" % self.file_name\n sys.exit(1)\n # remember file pointer\n self.file_pointer = fp\n print \"Creating file '%s'\" % self.file_name\n elif self.options['storage_method'] == 'none':\n # save command for later processing\n self.h5commands.append((\"create_file\", self.file_name))","def save_model(self):\n\n print('Save model')\n self.feature_extractor.save_weights(\n self.path_save_model + self.name_model + '.h5')\n\n print('Mean and std')\n np.save(self.path_save_model + 'mean.npy', self.mean)\n np.save(self.path_save_model + 'std.npy', self.std)","def save(self, folder):\n self.generator.save_weights('%s/generator.h5'%folder)\n self.critic.save_weights('%s/critic.h5'%folder)","def save_training(self):\n\n filename = str(hashlib.sha1(str(self.training_data).encode(\"utf-8\"))\n .hexdigest())\n path = \"./training/\" + filename + \".json\"\n\n data = {\n \"states\": self.states,\n \"transitions\": self.transitions,\n \"matrix\": self.matrix.tolist()\n }\n\n with open(path, \"w\") as outfile:\n json.dump(data, outfile)","def saveModel(model, outfile, train_opts, model_opts, view_names=None, sample_names=None, feature_names=None):\n\n # QC checks\n assert model.trained == True, \"Model is not trained yet\"\n assert len(np.unique(view_names)) == len(view_names), 'View names must be unique'\n assert len(np.unique(sample_names)) == len(sample_names), 'Sample names must be unique'\n\n # Create output directory\n if not os.path.isdir(os.path.dirname(outfile)):\n print(\"Output directory does not exist, creating it...\")\n os.makedirs(os.path.dirname(outfile))\n\n # For some reason h5py orders the datasets alphabetically, so we have to sort the likelihoods accordingly\n idx = sorted(range(len(view_names)), key=lambda k: view_names[k])\n tmp = [model_opts[\"likelihood\"][idx[m]] for m in range(len(model_opts[\"likelihood\"]))]\n model_opts[\"likelihood\"] = tmp\n\n # Open HDF5 handler\n hdf5 = h5py.File(outfile,'w')\n\n # Save expectations\n saveExpectations(model,hdf5,view_names)\n\n # Save parameters\n # saveParameters(model,hdf5,view_names)\n\n # Save training statistics\n saveTrainingStats(model,hdf5)\n\n # Save training options\n saveTrainingOpts(train_opts,hdf5)\n\n # Save model options\n saveModelOpts(model_opts,hdf5)\n\n # Save training data\n saveTrainingData(model, hdf5, view_names, sample_names, feature_names, model_opts[\"likelihood\"])\n\n # Close HDF5 file\n hdf5.close()","def SaveResultsToH5(self):\n\n try: \n wildcard = \"HDF5 files (*.hdf5)|*.hdf5\"\n dialog = wx.FileDialog(None, \"Save as .hdf5\", wildcard=wildcard,\n style=wx.SAVE|wx.OVERWRITE_PROMPT)\n\n if dialog.ShowModal() == wx.ID_OK:\n filepath = dialog.GetPath()\n self.page1.filename = dialog.GetFilename()\n dir = dialog.GetDirectory()\n \n self.common.path = dir\n self.common.filename = self.page1.filename\n\n wx.BeginBusyCursor() \n self.stk.write_results_h5(filepath, self.data_struct, self.anlz) \n wx.EndBusyCursor() \n\n except:\n\n wx.EndBusyCursor()\n wx.MessageBox(\"Could not save HDF5 file.\")\n \n dialog.Destroy()\n self.refresh_widgets()\n \n return","def save_hdf5(self, filename):\n filename += '.h5'\n try:\n hf = h5py.File(filename, 'w')\n hf.create_dataset('Array', data=self.flat_array)\n hf.close()\n except TypeError as err:\n if isinstance(self.mess_inst, MessagesGUI):\n self.mess_inst.message('TypeError [{}] when attempting to save HDF5'.format(err))\n else:\n print('TypeError [{}] when attempting to save HDF5'.format(err))","def dataset_to_hdf5(raw_path: Path, target_path: Path):\n _prepare_target_dir(target_path)\n\n logging.info(\"Creating hdf5 blob file...\")\n with pd.HDFStore(target_path) as store:\n logging.info(\"Converting session by session...\")\n session_paths = [p for p in raw_path.glob(\"*/*/\")]\n\n # loop all session folders\n sessions_index = []\n for p in tqdm(session_paths):\n # Skip files (.DS_Store) and excluded session\n if (not os.path.isdir(p)) or (p.name in EXCLUDE_SESSIONS):\n logging.debug(f\"Skipping {p.resolve()}\")\n continue\n\n # Derive subject and session from path\n subject = p.parent.name\n session = p.name\n session_no = session.split(\"_\")[-1] #\n\n # Read\n df_act = _read_activity(p)\n df_sens = _read_sensors(p)\n\n # Join task/scenario information to sensor data\n df_sens = _join_activity(df_act, df_sens)\n\n # Save to hdf5. Renaming, because keys can't start with digits\n store.put(\n f\"subject_{subject}/session_{subject}_{session_no}/activity\",\n df_act,\n format=\"f\",\n )\n store.put(\n f\"subject_{subject}/session_{subject}_{session_no}/sensors_100hz\",\n df_sens,\n format=\"f\",\n )\n\n # Compose index table\n sessions_index.append(\n {\n \"subject\": subject,\n \"session\": f\"{subject}_session_{session_no}\",\n \"key\": f\"subject_{subject}/session_{subject}_{session_no}\",\n \"task_type\": df_sens[\"task_type\"].max(),\n }\n )\n\n # Save index table to hdf5\n df_index = pd.DataFrame(sessions_index)\n store.put(f\"index\", df_index, format=\"f\")","def export_experiment(session, saver, last_step, global_step, output_dir,\n eval_set, features, labels, images, route):\n output_filename = 'output_%s_%s_%d.h5' % (\n FLAGS.dataset, eval_set, global_step)\n output_directory = os.path.join(output_dir, 'classify', 'output')\n\n if not os.path.exists(output_directory):\n os.makedirs(output_directory)\n\n with h5py.File(os.path.join(output_directory, output_filename), 'w') as hf:\n hf.create_dataset('features', data=features, compression='lzf')\n hf.create_dataset('labels', data=labels, compression='lzf')\n hf.create_dataset('images', data=images, compression='lzf')\n hf.create_dataset('route', data=route, compression='lzf')\n\n session_directory = os.path.join(\n output_dir, 'eval', FLAGS.dataset, eval_set)\n saver.save(session, os.path.join(session_directory, 'model.ckpt'),\n global_step=last_step)","def _generate_testdata_h5(cls, test_filepath):\n # Generate some test data\n data = numpy.indices( (10, 100, 200, 3) )\n assert data.shape == (4, 10, 100, 200, 3)\n data = data.astype( numpy.uint32 )\n cls.original_data = data\n\n # Choose names\n cls.dvid_dataset = \"datasetA\"\n cls.data_uuid = \"abcde\"\n cls.data_name = \"indices_data\"\n cls.volume_location = \"/datasets/{dvid_dataset}/volumes/{data_name}\".format( **cls.__dict__ )\n cls.node_location = \"/datasets/{dvid_dataset}/nodes/{data_uuid}\".format( **cls.__dict__ )\n cls.voxels_metadata = voxels.VoxelsMetadata.create_default_metadata(data.shape, data.dtype, \"cxyzt\", 1.0, \"\")\n\n # Write to h5 file\n with H5MockServerDataFile( test_filepath ) as test_h5file:\n test_h5file.add_node( cls.dvid_dataset, cls.data_uuid )\n test_h5file.add_volume( cls.dvid_dataset, cls.data_name, data, cls.voxels_metadata )\n\n test_h5file.add_node( \"datasetB\", \"12345\" )\n test_h5file.add_volume( \"datasetB\", cls.data_name, data, cls.voxels_metadata )","def save_dataset(fname, dname, data):\n with h5py.File(fname, 'w') as w:\n try:\n dset = w.create_dataset(dname, (data.shape[0], data.shape[1]))\n dset[:] = data\n except:\n pass\n\n w.flush()","def saveh5(fname, mat, name='data'):\n fp = open_write(fname)\n save_vec(mat, fp, fp.root, name)\n fp.close()","def save_frame_to_hdf5_file(fsrc, key = 'images', compression = 0):\n preparation = \"\"\n preparation += \"from h5py import File;\"\n preparation += \"from tempfile import gettempdir;\"\n preparation += \"import os;\"\n preparation += \"root = gettempdir()\"\n preparation += \"filename_dst = os.path.join(root,'test_destination.hdf5')\"\n preparation += \"filename_dst = os.path.join(root,'test_destination.hdf5')\"\n testcode = ''","def saveTrainingStats(model, hdf5):\n stats = model.getTrainingStats()\n stats_grp = hdf5.create_group(\"training_stats\")\n stats_grp.create_dataset(\"activeK\", data=stats[\"activeK\"])\n stats_grp.create_dataset(\"elbo\", data=stats[\"elbo\"])\n stats_grp.create_dataset(\"elbo_terms\", data=stats[\"elbo_terms\"].T)\n stats_grp['elbo_terms'].attrs['colnames'] = [a.encode('utf8') for a in stats[\"elbo_terms\"].columns.values]","def save_model(self):\n self.pred_net.save((self.save_path / \"iqn_pred_net\").absolute().as_posix())\n self.target_net.save((self.save_path / \"iqn_target_net\").absolute().as_posix())","def save(self):\n\n or_none = lambda x: x if x is not None else \"none\"\n with h5py.File(self.filename, \"a\") as hf:\n for attr in self._SAVE_ATTRS + self._save_attrs:\n hf.attrs[attr] = or_none(getattr(self, attr, None))","def main(config):\n save_main_dir = config[\"save_main_dir\"]\n\n if not os.path.isdir(save_main_dir):\n print(\"{} does not exist, creating it now...\", save_main_dir)\n pathlib.Path(save_main_dir).mkdir(parents=True, exist_ok=True)\n\n app = inviwopy.app\n network = app.network\n \n # Resize the canvas to improve rendering speed, only affects visual output\n if config[\"should_resize\"]:\n ivw_helpers.set_canvas_sizes(128, 128)\n\n hdf5_path = os.path.join(save_main_dir, config[\"hdf5_name\"])\n with h5py.File(hdf5_path, mode='w', libver='latest') as hdf5_file:\n hdf5_file.swmr_mode = True\n create_hdf_storage(hdf5_file, config)\n count = {\"train\": 0, \"val\": 0}\n for set_type in 'train', 'val':\n capture_lf_samples(hdf5_file, set_type, config, network, count) \n print(\"Finished writing to HDF5 in {}\".format(hdf5_path))","def write_hdf5(data, labels, output_filename):\n\n x = data.astype(numpy.float32)\n y = labels.astype(numpy.float32)\n\n with h5py.File(output_filename, 'w') as h:\n h.create_dataset('data', data=x, shape=x.shape)\n h.create_dataset('label', data=y, shape=y.shape)\n # h.create_dataset()","def write_data_to_h5(data, filename):\n f = h5py.File(filename, 'w', libver='latest')\n dset = f.create_dataset('array', shape=(data.shape), data=data, compression='gzip', compression_opts=9)\n f.close()","def save_model(self, is_best, state, epoch):\n path = os.path.join(self.logpath_models, 'model-%d.pth.tar' % epoch)\n torch.save(state, path)\n if is_best:\n shutil.copyfile(path, path + 'model_best.pth.tar')","def save_weight(model):\n file = h5py.File(WEIGHT_SAVE, 'w')\n weight = model.get_weights()\n for i in range(len(weight)):\n file.create_dataset('weight' + str(i), data=weight[i])\n file.close()","def write_hdf5(features, in_dir, cmd_args):\n data_set = in_dir.split('/')[-2]\n file_name = os.path.join(cmd_args.output, data_set,\n '_'.join([data_set, '-'.join([cmd_args.window,\n str(cmd_args.windowsize)+'s',\n str(cmd_args.overlap)+'%']),\n cmd_args.bands.replace(',', '-')])) + '.hdf5'\n\n logging.info(\"\\t\\tWriting features to {}.\\n\".format(file_name))\n\n hdf5_file = h5py.File(file_name, 'w')\n hdf5_file.create_dataset('data', features.shape, data=features)\n hdf5_file.close()\n\n return file_name","def export(self, output_dir, config, train_ratio=0.7, delimiter=\",\"):\n model_dir = os.path.join(output_dir, self.model_id)\n if not os.path.exists(model_dir):\n os.makedirs(model_dir)\n X_tr, X_te = self.get_train_test_embeddings(config, train_ratio)\n #save text feature matrix\n idx = config[\"dimension\"]\n tr_text = csr_matrix(X_tr[:,:idx])\n te_text = csr_matrix(X_te[:,:idx])\n print(\"text\", tr_text.shape, te_text.shape)\n save_npz(os.path.join(model_dir, \"train_text\"), tr_text)\n save_npz(os.path.join(model_dir, \"test_text\"), te_text)\n #save history feature matrix\n if config.get(\"user_history\", False):\n tr_history = X_tr[:,idx:idx+4]\n te_history = X_te[:,idx:idx+4]\n np.savetxt(os.path.join(model_dir, \"train_history.csv\"), tr_history, delimiter=delimiter)\n np.savetxt(os.path.join(model_dir, \"test_history.csv\"), te_history, delimiter=delimiter)\n idx += 4\n print(\"history\", tr_history.shape, te_history.shape)\n # save node embeddings\n if \"user_ne\" in config and X_tr.shape[1] > idx:\n tr_network = X_tr[:,idx:]\n te_network = X_te[:,idx:]\n np.savetxt(os.path.join(model_dir, \"train_network.csv\"), tr_network, delimiter=delimiter)\n np.savetxt(os.path.join(model_dir, \"test_network.csv\"), te_network, delimiter=delimiter)\n print(\"network\", tr_network.shape, te_network.shape)\n #save labels\n np.savetxt(os.path.join(model_dir, \"train_label.csv\"), self.tr_label, delimiter=delimiter, fmt='%i')\n np.savetxt(os.path.join(model_dir, \"test_label.csv\"), self.te_label, delimiter=delimiter, fmt='%i')\n #save meta\n self.tr_meta[self._exported_meta_columns].to_csv(os.path.join(model_dir, \"train_meta.csv\"), index=False, sep=delimiter)\n self.te_meta[self._exported_meta_columns].to_csv(os.path.join(model_dir, \"test_meta.csv\"), index=False, sep=delimiter)\n print(\"Model was exported\")\n return model_dir","def split_and_save(data_path: str,\n train_portion: int, validation_portion: int, test_portion: int,\n train_test_shuffle: bool = True, train_test_stratify_col: str = None,\n train_val_shuffle: bool = True, train_val_stratify_col: str = None,\n train_test_random_num: int = 42, train_val_random_num: int = 42\n ):\n if 'raw' not in data_path:\n raise ValueError(\"data path should be {data_dir}/{version}/raw/{data_name}.{file_format}.\"\n \"\\nYou have written:\", data_path)\n\n data_name = ''.join(data_path.split('/')[-1].split('.')[:-1])\n data_dir = '/'.join(data_path.split('/')[:-2])\n file_format = data_path.split('/')[-1].split('.')[-1]\n separator = '\\t' if file_format == 'tsv' else ',' if file_format == 'csv' else None\n\n df = pd.read_csv(data_path, sep=separator)\n\n total_portion = train_portion + validation_portion + test_portion\n train_val_size = (train_portion + validation_portion) / total_portion\n test_size = test_portion / total_portion\n\n train_val_portion = train_portion + validation_portion\n\n train_size = train_portion / train_val_portion\n val_size = validation_portion / train_val_portion\n\n print(\"====== SPECIFICATION ======\")\n print(f\"Train + Val: Test = {round(train_val_size * 100, 2)}%: {round(test_size * 100, 2)}%\")\n print(f\"Train: Validation = {round(train_size * 100, 2)}%: {round(val_size * 100, 2)}%\")\n print()\n\n train_val, test = train_test_split(df,\n train_size=train_val_size,\n test_size=test_size,\n random_state=train_test_random_num,\n shuffle=train_test_shuffle,\n stratify=df[train_test_stratify_col]\n if train_test_stratify_col else train_test_stratify_col)\n\n train, val = train_test_split(train_val,\n train_size=train_size,\n test_size=val_size,\n random_state=train_val_random_num,\n shuffle=train_val_shuffle,\n stratify=df[train_val_stratify_col]\n if train_val_stratify_col else train_val_stratify_col)\n\n train.to_csv(f'{data_dir}/train_{data_name}.tsv', sep='\\t', index=False)\n val.to_csv(f'{data_dir}/val_{data_name}.tsv', sep='\\t', index=False)\n test.to_csv(f'{data_dir}/test_{data_name}.tsv', sep='\\t', index=False)\n\n print(\"====== RESULT ======\")\n print(f\"Total Data Count: {len(df)}\")\n print(f\"Train Data Count: {len(train)} ({len(train) / len(df) * 100} %)\")\n print(f\"Val Data Count: {len(val)} ({len(val) / len(df) * 100} %)\")\n print(f\"Test Data Count: {len(test)} ({len(test) / len(df) * 100} %)\")","def make_data(self, which_set, p, center=False, variance_normalize=False,\n shuffle=False, save_dummy=False):\n\n print \"Making h5 file for %s\" % which_set #TODO(dhjelm): switch to logging.\n\n if which_set == 'train':\n source_path = serial.preprocess(p + 'train.npy')\n data_path = serial.preprocess(p + 'train.h5')\n label_path = serial.preprocess(p + 'train_labels.npy')\n else:\n assert which_set == 'test'\n source_path = serial.preprocess(p + 'test.npy')\n data_path = serial.preprocess(p + 'test.h5')\n label_path = serial.preprocess(p + 'test_labels.npy')\n\n data_path = \"\".join(data_path.split(\".\")[0] + '_dummy.h5')\n\n # Get the topological view and labels.\n topo_view = np.load(source_path)\n y = np.load(label_path)\n num_labels = np.amax(y) + 1\n\n # Shape information and mask.\n samples, rows, columns, depth = topo_view.shape\n if self.mask is not None:\n assert self.mask.shape == (rows, columns, depth)\n size = len(np.where(self.mask.flatten() == 1)[0].tolist())\n else:\n size = rows * columns * depth\n\n self.view_converter = MRIViewConverter((rows, columns, depth))\n X = self.view_converter.topo_view_to_design_mat(topo_view, self.mask)\n\n # TODO(dhjelm): one_hot is going away.\n one_hot = np.zeros((size, num_labels), dtype=config.floatX)\n for i in xrange(y.shape[0]):\n one_hot[i, y[i] - 1] = 1.\n\n if center:\n X -= X.mean(axis=0)\n\n if variance_normalize:\n X /= X.std(axis=0)\n\n rng = make_np_rng(None, 322, which_method=\"shuffle\")\n if shuffle:\n index = range(samples)\n rng.shuffle(index)\n X = X[index, :]\n one_hot = one_hot[index, :]\n\n assert not np.any(np.isnan(X))\n\n h5file, node = self.init_hdf5(data_path, ([samples, size], [samples, num_labels]))\n MRI_Big.fill_hdf5(h5file, X, one_hot, node)\n h5file.close()","def save_reconstructions(reconstructions, out_dir):\n if (not (os.path.exists(out_dir))):\n os.mkdir(out_dir)\n out_dir.mkdir(exist_ok=True)\n print('Saved directory is',out_dir)\n for fname, recons in reconstructions.items():\n with h5py.File(out_dir / fname, 'w') as f:\n f.create_dataset('reconstruction', data=recons)","def save_dataset(self):\n if os.path.exists(self.output_path):\n print('Directory already exists. EXITING.')\n sys.exit()\n if not os.path.exists(self.output_path): os.mkdir(self.output_path)\n for window_num, cur_window in enumerate(self.time_windows):\n window_dir = (os.path.join(self.output_path, ('window %s' % str(window_num + 1))))\n if not os.path.exists(window_dir): os.mkdir(window_dir)\n for filepath in cur_window:\n topic = os.path.basename(os.path.dirname(filepath))\n topic_dir = os.path.join(os.path.join(window_dir, topic))\n if not os.path.exists(topic_dir): os.mkdir(topic_dir)\n copy(filepath, topic_dir)\n self.ground_truth.append((len(os.listdir(window_dir))))","def save_model(model):\n # ***\n # Please remove the comment to enable model save.\n # However, it will overwrite the baseline model we provided.\n # ***\n model.save(\"model/model.h5\")\n print(\"Model Saved Successfully.\")","def to_hdf5(self, filename):\n\n f = h5py.File(filename, 'w')\n f['xyz'] = self.xyz\n f.close()\n\n return","def save_w_matrix(wih, who, epochs=5, hnodes=400, path='saved_data/', iscupy=False):\n \n print(\"Saving Data...\")\n\n t = time.asctime(time.localtime(time.time()))\n t = t.replace(':', '_')\n\n filepath = path + t + \"/\"\n try:\n os.mkdir(filepath)\n except:\n pass\n filename_wih = f\"hn{hnodes}_wih_epoch_{epochs}.csv\"\n filename_who = f\"hn{hnodes}_who_epoch_{epochs}.csv\"\n\n if iscupy:\n wih = cupy.asnumpy(wih)\n who = cupy.asnumpy(who)\n\n numpy.savetxt(filepath + filename_wih, wih)\n numpy.savetxt(filepath + filename_who, who)\n print(\"Saving Succeeded!\")","def write(self,data): \n \n if not os.path.exists(self.output_dir):\n os.makedirs(self.output_dir)\n\n # We will store these in a separate file and link them to the level2s\n fname = data.filename.split('/')[-1]\n units = {'A':'K','x0':'degrees','y0':'degrees','sigx':'degrees','sigy':'degrees','sigy_scale':'none','B':'K','phi':'radians'}\n\n outfile = '{}/{}_{}'.format(self.output_dir,self.prefix,fname)\n\n print ('WRITING: ',outfile)\n output = h5py.File(outfile,'a')\n\n # Set permissions and group\n os.chmod(outfile,0o664)\n shutil.chown(outfile, group='comap')\n\n ##\n ## Narrow channel fits\n ##\n\n for valerr in ['Values','Errors','Chi2']:\n if f'Gauss_Narrow_{valerr}' in output:\n del output[f'Gauss_Narrow_{valerr}']\n gauss_fits = output.create_group(f'Gauss_Narrow_{valerr}')\n gauss_fits.attrs['FitFunc'] = self.model.__name__\n gauss_fits.attrs['source_el'] = self.source_positions['mean_el']\n gauss_fits.attrs['source_az'] = self.source_positions['mean_az']\n\n dnames = self.map_parameters\n dsets = [self.map_fits[valerr][...,iparam] for iparam in range(self.map_fits[valerr].shape[-1])]\n\n for (dname, dset) in zip(dnames, dsets):\n if dname in output:\n del output[dname]\n print(dname,dset.shape,units[dname])\n gauss_dset = gauss_fits.create_dataset(dname, data=dset)\n gauss_dset.attrs['Unit'] = units[dname]\n \n\n output.attrs['SourceFittingVersion'] = __version__\n output.attrs['source'] = self.getSource(data)\n output.close()\n self.linkfile(data)","def save_data_01(outputfilepath,summary):\n ext=os.path.splitext(outputfilepath)[-1].lower()\n if ext=='.mat':\n savemat(outputfilepath,summary,long_field_names=True)\n return outputfilepath\n elif ext=='.npz':\n np.savez(outputfilepath,summary=summary)\n elif ext=='.pdb':\n make_pdb_file_01(outputfilepath,np.array(summary['X_evol'][-1]))\n else:\n if ext!='.json':\n outputfilepath+='.json'\n with open(outputfilepath, 'w') as result:\n # serialize\n summary['json_task']='not stored'\n serialized_summary=jsonify_01(summary)\n json.dump(serialized_summary, result,indent=4,sort_keys=True)","def make_hdf5file(self, dataset_paths: list = None, dataset_values: list = None) -> None:\n\n assert dataset_paths is not None and len(dataset_paths) != 0, (\"`dataset_path` not valid. Expected at least \" \\\n f\"one list element, got {len(dataset_paths)}.\")\n\n assert dataset_values is not None and len(dataset_values) != 0, (\"`dataset_values` not valid. Expected at least \" \\\n f\"one list element, got {len(dataset_values)}.\")\n\n\n # Remove file if already exists and create a new one\n if os.path.isfile(os.path.join(self.FOFDirectory, self.filename)):\n os.remove(os.path.join(self.FOFDirectory, self.filename))\n print(f'[ FOFOutput ]\\t==> Removed old {self.filename} file.')\n\n # Create file and optional groups within it\n FOFfile = h5py.File(os.path.join(self.FOFDirectory, self.filename), 'w')\n print(f'[ FOFOutput ]\\t==> Created new {self.filename} file.')\n\n # Push the attributes to file, if any\n if self.attrs is not None and len(self.attrs.keys()) > 0:\n for key, text in zip(self.attrs.keys(), self.attrs.values()):\n FOFfile.attrs[key] = text\n\n for internal_path, dataset_content in zip(dataset_paths, dataset_values):\n\n assert not internal_path.endswith('/'), \"Invalid hdf5 internal path\"\n assert type(dataset_content) is np.ndarray, \"Can only push numpy.ndarrays into hdf5 files.\"\n\n nested_groups = self.groups_from_path(internal_path)\n if len(nested_groups) == 1:\n FOFfile.create_dataset(nested_groups[0], data=dataset_content)\n else:\n for nested_group in nested_groups[:-1]:\n g = FOFfile.create_group(nested_group)\n g.create_dataset(nested_groups[-1], data = dataset_content)\n\n print(f'[ FOFOutput ]\\t==> Created {internal_path} dataset in {self.filename} file.')\n\n FOFfile.close()","def export_to_hdf5(cls, h5_file, model, eids):\n #comments = []\n pids = []\n nodes = []\n x = []\n g0 = []\n offt = []\n unused_bit = []\n pa = []\n pb = []\n wa = []\n wb = []\n nan = np.full(3, np.nan)\n encoding = model._encoding\n for eid in eids:\n element = model.elements[eid]\n #comments.append(element.comment)\n pids.append(element.pid)\n nodes.append(element.nodes)\n if element.g0 is None:\n x.append(element.x)\n g0.append(-1)\n else:\n x.append(nan)\n g0.append(element.g0)\n\n offti = element.offt\n if isinstance(offti, integer_types):\n offti = str(offti)\n offt.append(offti.encode(encoding))\n pa.append(element.pa)\n pb.append(element.pb)\n wa.append(element.wa)\n wb.append(element.wb)\n #h5_file.create_dataset('_comment', data=comments)\n h5_file.create_dataset('eid', data=eids)\n h5_file.create_dataset('nodes', data=nodes)\n h5_file.create_dataset('pid', data=pids)\n #print('x =', x)\n #print('g0 =', g0)\n h5_file.create_dataset('x', data=x)\n h5_file.create_dataset('g0', data=g0)\n h5_file.create_dataset('offt', data=offt)\n\n h5_file.create_dataset('pa', data=pa)\n h5_file.create_dataset('pb', data=pb)\n\n h5_file.create_dataset('wa', data=wa)\n h5_file.create_dataset('wb', data=wb)","def _write_h5_out(self, fout, save_hybrid_meta=True):\n\n with Outputs(fout, mode='a') as out:\n if 'meta' in out.datasets and save_hybrid_meta:\n hybrid_meta = to_records_array(self.hybrid_meta)\n out['meta'] = hybrid_meta\n\n for dset, data in self.profiles.items():\n out[dset] = data","def make_data(input_filepath, output_filepath):\n\n df_train = pd.read_csv(input_filepath+'train_u6lujuX_CVtuZ9i.csv', index_col=0)\n df_test = pd.read_csv(input_filepath+'test_Y3wMUE5_7gLdaTN.csv', index_col=0)\n print('Sizes', df_train.shape, df_test.shape)\n print(\"Outcome dispersion:\\n\", df_train['Loan_Status'].value_counts())\n\n\n # recode and save outcome vector\n y = df_train['Loan_Status'].map({'N': 0, 'Y': 1})\n\n del df_train['Loan_Status']\n\n # all in one dataframe\n df = pd.concat([df_train, df_test])\n print(df.shape)\n\n from src.features.build_features import make_features\n df = make_features(df)\n\n # Divide data on train and test again and save\n data_train = df[df.index.isin(df_train.index)]\n data_test = df[df.index.isin(df_test.index)]\n print(data_train.shape, data_test.shape)\n\n data_tmp = data_train.copy()\n data_tmp['y'] = y\n\n\n data_tmp.to_csv(output_filepath + 'train_ready.csv', index=False)\n data_test.to_csv(output_filepath + 'test_ready.csv', index=False)\n id_test = pd.DataFrame(data=df_test.index, columns=['Loan_ID'])\n id_test.to_csv(output_filepath + 'id_test.csv', index=False)","def generate_data(self):\n self.remove_hdf5_file()\n hdf5_handler = self.create_hdf5_file()\n self.populate_hdf5_file(hdf5_handler, self.dataset)","def set_model_for_train(self):\n if self.train_time is None:\n self.train_time = datetime.now().strftime('%Y-%m-%d_%H-%M-%S')\n\n self.curr_folder = data_functions.create_path(\n self.save_path, self.train_time)\n logger.info(f\"training results will be stored in: {self.curr_folder}\")\n\n self.save_model_params()\n self.train_generator, self.val_generator = \\\n self.clarifruit_train_val_generators()\n keras_logs_path = self.set_model_checkpint()\n\n return keras_logs_path","def get_data(data_dir, hdf5):\r\n\r\n # Get the filenames of the lists containing image paths and labels.\r\n train_file, val_file = build_dataset_index(data_dir)\r\n\r\n # Check if (creating and) loading from hdf5 database is desired.\r\n if hdf5:\r\n # Create folder to store dataset.\r\n if not os.path.exists('hdf5'):\r\n os.makedirs('hdf5')\r\n # Check if hdf5 databases already exist and create them if not.\r\n if not os.path.exists('hdf5/tiny-imagenet_train.h5'):\r\n from tflearn.data_utils import build_hdf5_image_dataset\r\n print ' Creating hdf5 train dataset.'\r\n build_hdf5_image_dataset(train_file, image_shape=(64, 64), mode='file', output_path='hdf5/tiny-imagenet_train.h5', categorical_labels=True, normalize=True)\r\n\r\n if not os.path.exists('hdf5/tiny-imagenet_val.h5'):\r\n from tflearn.data_utils import build_hdf5_image_dataset\r\n print ' Creating hdf5 val dataset.'\r\n build_hdf5_image_dataset(val_file, image_shape=(64, 64), mode='file', output_path='hdf5/tiny-imagenet_val.h5', categorical_labels=True, normalize=True)\r\n\r\n # Load training data from hdf5 dataset.\r\n h5f = h5py.File('hdf5/tiny-imagenet_train.h5', 'r')\r\n X = h5f['X']\r\n Y = h5f['Y']\r\n\r\n # Load validation data.\r\n h5f = h5py.File('hdf5/tiny-imagenet_val.h5', 'r')\r\n X_test = h5f['X']\r\n Y_test = h5f['Y'] \r\n\r\n # Load images directly from disk when they are required.\r\n else:\r\n from tflearn.data_utils import image_preloader\r\n X, Y = image_preloader(train_file, image_shape=(64, 64), mode='file', categorical_labels=True, normalize=True, filter_channel=True)\r\n X_test, Y_test = image_preloader(val_file, image_shape=(64, 64), mode='file', categorical_labels=True, normalize=True, filter_channel=True)\r\n\r\n # Randomly shuffle the dataset.\r\n X, Y = shuffle(X, Y)\r\n\r\n return X, Y, X_test, Y_test","def make_train_input_tensors(dataset, dataset_name,\n trial_split='train',\n update_params=None,\n save_file=True,\n return_dict=True,\n save_path=\"train_input.h5\",\n include_behavior=False,\n include_forward_pred=False):\n assert isinstance(dataset, NWBDataset), \"`dataset` must be an instance of NWBDataset\"\n assert dataset_name in PARAMS.keys(), f\"`dataset_name` must be one of {list(PARAMS.keys())}\"\n assert isinstance(trial_split, (pd.Series, np.ndarray, list)) or trial_split in ['train', 'val'], \\\n \"Invalid `trial_split` argument. Please refer to the documentation for valid choices\"\n\n # Fetch and update params\n params = PARAMS[dataset_name].copy()\n if update_params is not None:\n params.update(update_params)\n # Add filename extension if necessary\n if not save_path.endswith('.h5'):\n save_path = save_path + '.h5'\n\n # unpack params\n spk_field = params['spk_field']\n hospk_field = params['hospk_field']\n make_params = params['make_params'].copy()\n \n # Prep mask\n trial_mask = _prep_mask(dataset, trial_split)\n\n # Make output spiking arrays and put into data_dict\n train_dict = make_stacked_array(dataset, [spk_field, hospk_field], make_params, trial_mask)\n data_dict = {\n 'train_spikes_heldin': train_dict[spk_field],\n 'train_spikes_heldout': train_dict[hospk_field],\n }\n\n # Add behavior data if necessary\n if include_behavior:\n behavior_source = params['behavior_source']\n behavior_field = params['behavior_field']\n behavior_make_params = _prep_behavior(dataset, params.get('lag', None), make_params)\n # Retrieve behavior data from indicated source\n if behavior_source == 'data':\n train_behavior = make_stacked_array(dataset, behavior_field, behavior_make_params, trial_mask)[behavior_field]\n else:\n train_behavior = dataset.trial_info[trial_mask][behavior_field].to_numpy()\n # Filter out behavior on certain trials if necessary\n if 'behavior_mask' in params:\n if callable(params['behavior_mask']):\n behavior_mask = params['behavior_mask'](dataset.trial_info[trial_mask])\n else:\n behavior_mask, _ = params['behavior_mask']\n train_behavior[~behavior_mask] = np.nan\n data_dict['train_behavior'] = train_behavior\n \n # Add forward prediction data if necessary\n if include_forward_pred:\n fp_len = params['fp_len']\n fp_steps = fp_len / dataset.bin_width\n fp_make_params = _prep_fp(make_params, fp_steps, dataset.bin_width)\n fp_dict = make_stacked_array(dataset, [spk_field, hospk_field], fp_make_params, trial_mask)\n data_dict['train_spikes_heldin_forward'] = fp_dict[spk_field]\n data_dict['train_spikes_heldout_forward'] = fp_dict[hospk_field]\n\n # Save and return data\n if save_file:\n save_to_h5(data_dict, save_path, overwrite=True)\n if return_dict:\n return data_dict","def test_hdf5_convert_to_one_hot():\n skip_if_no_h5py()\n import h5py\n\n # save random data to HDF5\n handle, filename = tempfile.mkstemp()\n dataset = random_dense_design_matrix(np.random.RandomState(1),\n num_examples=10, dim=5, num_classes=3)\n with h5py.File(filename, 'w') as f:\n f.create_dataset('X', data=dataset.get_design_matrix())\n f.create_dataset('y', data=dataset.get_targets())\n\n # instantiate Train object\n trainer = yaml_parse.load(convert_to_one_hot_yaml % {'filename': filename})\n trainer.main_loop()\n\n # cleanup\n os.remove(filename)","def save_train_test(train_df, test_df, save_path: str, save_as_dict: bool=True):\n if save_as_dict:\n data = {\n 'train_df': train_df,\n 'test_df': test_df\n }\n save_pkl_file(data, save_path)\n else:\n # Remove the extension so we can add 'train' or 'test' to the save_path.\n save_path = save_path[:-4]\n save_pkl_file(train_df, f\"{save_path}_train.csv\")\n save_pkl_file(test_df, f\"{save_path}_test.csv\")","def create_and_write_output(predictions_path,output_path,inpDir):\n \n filenames= sorted(os.listdir(predictions_path)) \n for filename in filenames:\n \n # read the 3 channel output image from the neural network\n image=cv2.imread(os.path.join(predictions_path,filename))\n \n # create binary image output using the create_binary function\n out_image=create_binary(image) \n \n # read and store the metadata from the input image\n with BioReader(os.path.join(inpDir,filename)) as br:\n metadata = br.metadata\n\n # Write the binary output consisting of the metadata using bfio.\n output_image_5channel=np.zeros((out_image.shape[0],out_image.shape[1],1,1,1),dtype=np.uint8)\n output_image_5channel[:,:,0,0,0]=out_image \n\n with BioWriter(os.path.join(output_path,filename), metadata=metadata) as bw:\n bw.dtype = output_image_5channel.dtype\n bw.write(output_image_5channel)","def convert_and_save(filename, output_dir, is_train_data):\n mat_contents = sio.loadmat(filename)\n if 'X' in mat_contents:\n data = mat_contents['X']\n # data = data[:-1, :]\n if is_train_data:\n # data = data[:, :5000]\n # Finding the normalization statistics from the train data\n global mu, sig2 \n mu = np.sum(data, axis=1) / data.shape[1]\n sig2 = np.sum(data**2, axis=1) / data.shape[1] \n # Normalizing feature data\n data -= mu.reshape((data.shape[0], 1))\n data /= sig2.reshape((data.shape[0], 1))\n\n if 'Y' in mat_contents:\n data = mat_contents['Y']\n if is_train_data:\n # data = data[:, :5000]\n None\n\n print(\"Input filename is \" + filename)\n save_name = filename.split(str(os.sep))[-1]\n save_name = save_name.split('.')[0]\n np.save(os.path.normpath(os.path.join(output_dir, save_name)), data.T)","def save(self, uri):\r\n pf = PyFolder(os.path.dirname(os.path.realpath(uri)), allow_override=True)\r\n pf[os.path.basename(uri)+\"_options.json\"] = {\r\n 'input_cells': self._input_cells,\r\n 'latent_space': self._latent_space,\r\n }\r\n\r\n save_model(self._autoencoder, uri+\"_lstm_autoencoder.hdf5\")\r\n save_model(self._encoder, uri+\"_lstm_encoder.hdf5\")","def save_model(name, model):\n # Load path\n project_dir = Path(__file__).resolve().parents[2]\n model_path = os.path.join(project_dir, 'models', name + '.h5')\n\n # Save model\n model.save(model_path)","def saveTrainingOpts(opts, hdf5):\n # Remove dictionaries from the options\n for k,v in opts.copy().items():\n if type(v)==dict:\n for k1,v1 in v.items():\n opts[str(k)+\"_\"+str(k1)] = v1\n opts.pop(k)\n\n # Create HDF5 data set\n hdf5.create_dataset(\"training_opts\", data=np.array(list(opts.values()), dtype=np.float))\n hdf5['training_opts'].attrs['names'] = np.asarray(list(opts.keys())).astype('S')","def save_h5_file(self, result_dict, loc_id):\n if self.storage_type == 's3':\n file_path = '{0}/{1}_combined.h5'.format(\n self.output_path,\n loc_id\n )\n hf_result = h5py.File(file_path, 'w')\n for k, v in result_dict.items():\n hf_result.create_dataset(k, data=v)\n hf_result.close()\n else:\n file_path = os.path.join(self.output_path, '{0}_combined.h5'.format(loc_id))\n hf_result = h5py.File(file_path, 'w')\n for k, v in result_dict.items():\n hf_result.create_dataset(k, data=v)\n hf_result.close()\n return file_path","def save_model(self, epoch):\n # Set the name for the model\n gen_lungs_filename = 'gen_lungs_model_epoch_{}.h5'.format(epoch + 1)\n disc_lungs_filename = 'disc_lungs_model_epoch_{}.h5'.format(epoch + 1)\n train_summary_lungs_filename = 'train_summary_lungs_epoch_{}.csv'.format(epoch + 1)\n\n gen_organs_filename = 'gen_organs_model_epoch_{}.h5'.format(epoch + 1)\n disc_organs_filename = 'disc_organs_model_epoch_{}.h5'.format(epoch + 1)\n train_summary_organs_filename = 'train_summary_organs_epoch_{}.csv'.format(epoch + 1)\n\n # Save the model and train summary\n self.generator_lungs.save(op.join(self.model_dir, gen_lungs_filename), include_optimizer=True)\n self.disc_lungs.save(op.join(self.model_dir, disc_lungs_filename), include_optimizer=True)\n self.summary_writer_lungs.to_csv(op.join(self.train_summary_dir, train_summary_lungs_filename))\n\n self.generator_organs.save(op.join(self.model_dir, gen_organs_filename), include_optimizer=True)\n self.disc_organs.save(op.join(self.model_dir, disc_organs_filename), include_optimizer=True)\n self.summary_writer_organs.to_csv(op.join(self.train_summary_dir, train_summary_organs_filename))\n return self","def set_model_output(self, path):\n\n file = f'model_R{str(self.time_span).replace(\".\", \"_\")} ({str(self.date_time).replace(\":\",\"_\")}).csv'\n self.model_output_file = path_inc(path, file)","def test_hdf5_load_all():\n skip_if_no_h5py()\n import h5py\n\n # save random data to HDF5\n handle, filename = tempfile.mkstemp()\n dataset = random_one_hot_dense_design_matrix(np.random.RandomState(1),\n num_examples=10, dim=5,\n num_classes=3)\n with h5py.File(filename, 'w') as f:\n f.create_dataset('X', data=dataset.get_design_matrix())\n f.create_dataset('y', data=dataset.get_targets())\n\n # instantiate Train object\n trainer = yaml_parse.load(load_all_yaml % {'filename': filename})\n trainer.main_loop()\n\n # cleanup\n os.remove(filename)","def create_train_file(img_folder_path: str, train_file_path: str) -> None:\n files = []\n for ext in (\"*.gif\", \"*.png\", \"*.jpg\", \"*.bmp\"):\n img_path = glob(join(img_folder_path, ext))\n if img_path:\n files.extend(img_path)\n\n write_to_train_file(files, train_file_path)\n\n print(\"Training files are created in \" + img_folder_path)","def write_hdf5(filename, data):\n \n if '.h5' in filename:\n fid = h5py.File(filename, 'w')\n else:\n filename = filename+'.h5'\n fid = h5py.File(filename, 'w')\n\n print('Writing %s...'%filename)\n\n write_hdf5_group(fid, data)\n\n fid.close()\n print('Finished writting %s.'%filename)\n return","def create_train(train_img_path):\n\n f = open(\"train.txt\", \"w+\")\n for subdirs, dirs, files in os.walk(train_img_path):\n for filename in files:\n if filename.endswith(\".jpg\"):\n train_image_path = os.path.join(train_img_path, filename)\n print(train_image_path)\n f.write(train_image_path + \"\\n\")\n f.close()","def save(self, save_path: os.PathLike):\n os.makedirs(save_path, exist_ok=True)\n save_path = Path(save_path)\n joblib.dump(self.dataset, save_path / \"dataset.pkl\")\n hparams = dict(\n latent_dim=self.latent_dim,\n batch_size=self.batch_size,\n learning_rate=self.learning_rate,\n momentum=self.momentum,\n )\n if self.trained_epochs > 0:\n joblib.dump(self.encoders, save_path / \"encoders.pkl\")\n joblib.dump(self.gps_normalizer, save_path / \"gps_normalizer.pkl\")\n self.gen.save(save_path / \"generator_model\")\n self.dis.save(save_path / \"discriminator_model\")\n self.gan.save(save_path / \"gan_model\")\n train_state = dict(\n patience=self.patience,\n trained_epochs=self.trained_epochs,\n timesteps=self.timesteps,\n vocab_sizes=self.vocab_sizes,\n )\n joblib.dump(train_state, save_path / \"train_state.pkl\")\n joblib.dump(hparams, save_path / \"hparams.pkl\")\n\n return self","def cli(dataset_path, out_file):\n data = pd.read_csv(dataset_path)\n train_X, test_X, train_y, test_y = train_test_split(\n X, y, test_size=0.2, random_state=1\n )\n\n model = Predictor()\n model.fit(train_X, train_y)\n model.dump(out_file)","def main(input_filepath, output_filepath):\n\n logging.info(\"reading %s\", input_filepath)\n train_test = pd.read_hdf(input_filepath, 'train_test')\n meta = pd.read_hdf(input_filepath, 'meta')\n meta_org = pd.read_hdf(input_filepath, 'meta_org')\n\n sel_series = train_test[train_test.entry_type.isin(['train', 'cold_start'])]\\\n ['series_id'].unique()\n train_series, validate_series = train_test_split(sel_series, random_state=1)\n\n logging.info(\"calc train_test\")\n train_test = calc_final_features(train_test, meta, meta_org=meta_org, verbose=True)\n\n sel = train_test[train_test.entry_type.isin(['train', 'cold_start'])]\n train = sel[sel.series_id.isin(train_series)]\n validate = sel[sel.series_id.isin(validate_series)]\n test = train_test[train_test.entry_type.isin(['test'])]\n\n logging.info(\"writing %s\", output_filepath)\n train.to_hdf(output_filepath, \"train\", mode=\"w\")\n validate.to_hdf(output_filepath, \"validate\", mode=\"a\")\n test.to_hdf(output_filepath, \"test\", mode=\"a\")\n for k in ['meta', 'submission']:\n df = pd.read_hdf(input_filepath, k)\n df.to_hdf(output_filepath, k, mode=\"a\")","def write_output_to_pickle(image_lst, img_dir, model,save_path=\"output_inception\", k=5, only_first_name=False):\n imgs = image_lst\n output_layer = []\n \n # Looping over images\n \n for img in tqdm(imgs):\n tv = model.transfer_values(image_path=join(img_dir, img))\n output_layer.append(tv)\n # scores = model.get_scores(pred=pred, k=k, only_first_name=only_first_name)\n # top_k.append(scores)\n \n # Building a dataframe with columns for image names, prediction array, scores\n # print(\"THIS:\",np.array(output_layer).ndim)\n # print(output_layer[0])\n \n df = pd.DataFrame({\n \"img\": imgs,\n \"transfer layer\": output_layer,\n })\n \n # saving to pickle\n \n df.to_pickle(save_path)","def tree_model(feature_train, help_rank_train, model_name):\n decision_tree = DecisionTreeClassifier()\n decision_tree = decision_tree.fit(feature_train, help_rank_train)\n tree_model = open(model_name,'wb')\n dump(decision_tree, tree_model, -1)\n return","def save_model(self, epoch=None, out_tag='my_lstm'):\n\n Utils.check_dir('./models/')\n if epoch is not None:\n self.model.save_weights('{}/models/{}_model_epoch_{}.hdf5'.format(os.getcwd(), out_tag, epoch))\n with open(\"{}/models/{}_model_architecture_epoch_{}.json\".format(os.getcwd(), out_tag, epoch), \"w\") as f_out:\n f_out.write(self.model.to_json())\n else: \n self.model.save_weights('{}/models/{}_model.hdf5'.format(os.getcwd(), out_tag))\n with open(\"{}/models/{}_model_architecture.json\".format(os.getcwd(), out_tag), \"w\") as f_out:\n f_out.write(self.model.to_json())","def generate_train_txt(name, path):\n with open(path + '/test.txt', 'a') as file:\n file.write('/content/YOLO_metric/data/obj/' + name + '\\n')","def save_model(self, path):\n self._model.save(os.path.join(path, 'trained_model.h5'))\n plot_model(self._model, to_file=os.path.join(path, 'model_structure.png'), show_shapes=True, show_layer_names=True)","def save_hdf_file(file_path, idata, key_path='entry', overwrite=True):\r\n if (\"\\\\\" in file_path):\r\n raise ValueError(\r\n \"Please use a file path following the Unix convention\")\r\n file_base, file_ext = os.path.splitext(file_path)\r\n if not ((file_ext == '.hdf') or (file_ext == '.h5')):\r\n file_ext = '.hdf'\r\n file_path = file_base + file_ext\r\n _create_folder(file_path)\r\n if not overwrite:\r\n file_path = _create_file_name(file_path)\r\n ofile = None\r\n try:\r\n ofile = h5py.File(file_path, 'w')\r\n except IOError:\r\n print((\"Couldn't write file: {}\").format(file_path))\r\n raise\r\n grp = ofile.create_group(key_path)\r\n grp.create_dataset(\"data\", data=idata)\r\n ofile.close()\r\n return file_path","def save(self,file_path):\n hf = h5py.File(file_path, 'w')\n hf.attrs[\"annotations\"] = str(self.annotations)\n f=hf.create_group(\"funds\")\n for i,fund in enumerate(self.funds_set):\n grp = f.create_group(\"fund_{0}\".format(i))\n grp.create_dataset(\"data\",data=fund.__getstate__()[\"data\"])\n grp[\"params\"]=str(fund.__getstate__()[\"params\"])\n g=hf.create_group(\"portfolios\")\n for i,portfolio in enumerate(self.portfolios_set):\n grp = g.create_group(\"portfolio_{0}\".format(i))\n grp.attrs[\"capital\"]=portfolio.__getstate__()[\"capital\"]\n grp.create_dataset(\"commitments\",data=portfolio.__getstate__()[\"commitments\"])\n grp.create_dataset(\"vintages\", data=portfolio.__getstate__()[\"vintages\"])\n funds=[hash(portfolio.funds[i]) for i in range(len(portfolio))]\n grp.create_dataset(\"funds\", data=np.array(funds,dtype=h5py.string_dtype(encoding='utf-8')))\n hf.close()","def write_h5(fname: str, data: dict) -> None:\n try:\n with h5py.File(fname, 'w') as f:\n recursively_save_dict_contents_to_group(f,'/',data)\n except IOError as e:\n print(f\"Cannot write HDF5 file {fname}\")\n print(f\"IOError: {e}\")","def train_teacher (nb_teachers, teacher_id):\n # Load the dataset\n X_train, X_test, y_train, y_test = models.get_dataset()\n\n print(X_train.shape)\n print(y_train.shape)\n print(X_test.shape)\n print(y_test.shape)\n \n # Retrieve subset of data for this teacher\n data, labels = partition.partition_dataset(X_train,\n y_train,\n nb_teachers,\n teacher_id)\n\n print(\"Length of training data: \" + str(len(labels)))\n\n # Define teacher checkpoint filename and full path\n\n filename = str(nb_teachers) + '_teachers_' + str(teacher_id) + '.hdf5'\n filename2 = str(nb_teachers) + '_teachers_' + str(teacher_id) + '.h5'\n \n # Perform teacher training need to modify \n \n\n # Create teacher model\n model, opt = models.create_two_layer_mlp(46) # num of cols\n model.compile(loss='binary_crossentropy',\n optimizer=\"Adam\",\n metrics=['accuracy'])\n model, hist = models.training(model, data, X_test, labels, y_test,filename)\n\n #modify\n model_json = model.to_json()\n with open(\"model.json\", \"w\") as json_file:\n json_file.write(model_json)\n# serialize weights to HDF5\n model.save_weights(filename2)\n print(\"Saved model to disk\")\n return True","def load_data():\r\n train_dataset = h5py.File('train_catvnoncat.h5', \"r\") # Change the directory as per your system\r\n train_set_x_orig = np.array(train_dataset[\"train_set_x\"][:]) # your train set features\r\n train_set_y_orig = np.array(train_dataset[\"train_set_y\"][:]) # your train set labels\r\n\r\n test_dataset = h5py.File('test_catvnoncat.h5', \"r\") # Change the directory as per your system\r\n test_set_x_orig = np.array(test_dataset[\"test_set_x\"][:]) # your test set features\r\n test_set_y_orig = np.array(test_dataset[\"test_set_y\"][:]) # your test set labels\r\n\r\n classes = np.array(test_dataset[\"list_classes\"][:]) # the list of classes\r\n \r\n train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))\r\n test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))\r\n \r\n return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes","def train(\n model_path=\"./trained_model/\",\n model_file_name=\"model.h5\",\n training_data_path=\"./train.csv\",\n):\n config = SConfig(training_data_path=training_data_path)\n s2s = Seq2Seq(config)\n s2s.fit()\n s2s.save_model(path_to_model=model_path, model_file_name=model_file_name)"],"string":"[\n \"def save_to_hd5(out_file, x_train, y_train, x_val, y_val, x_test, y_test):\\n data = h5py.File(out_file, \\\"w\\\")\\n train_data = data.create_group(\\\"train_data\\\")\\n train_data.create_dataset(\\\"x_train\\\", data=x_train)\\n train_data.create_dataset(\\\"y_train\\\", data=y_train)\\n if x_val is not None:\\n val_data = data.create_group(\\\"val_data\\\")\\n val_data.create_dataset(\\\"x_val\\\", data=x_val)\\n val_data.create_dataset(\\\"y_val\\\", data=y_val)\\n if x_test is not None:\\n test_data = data.create_group(\\\"test_data\\\")\\n test_data.create_dataset(\\\"x_test\\\", data=x_test)\\n test_data.create_dataset(\\\"y_test\\\", data=y_test)\\n data.close()\",\n \"def make_data_hf(input_, label_, config):\\n # Check the check dir, if not, create one\\n if not os.path.isdir(os.path.join(os.getcwd(),config.checkpoint_dir)):\\n os.makedirs(os.path.join(os.getcwd(),config.checkpoint_dir))\\n\\n if config.is_train:\\n savepath = os.path.join(os.getcwd(), config.checkpoint_dir + '/train.h5')\\n else:\\n savepath = os.path.join(os.getcwd(), config.checkpoint_dir + '/test.h5')\\n\\n with h5py.File(savepath, 'w') as hf:\\n #checkimage(input_[1])\\n hf.create_dataset('input', data=input_)\\n hf.create_dataset('label', data=label_)\",\n \"def save_as_hdf5(self, filename):\",\n \"def make_data(config, data, label):\\n if not os.path.isdir(os.path.join(os.getcwd(), config.checkpoint_dir)):\\n os.makedirs(os.path.join(os.getcwd(), config.checkpoint_dir))\\n\\n if config.is_train:\\n savepath = os.path.join(os.getcwd(), config.checkpoint_dir +'/train.h5')\\n else:\\n savepath = os.path.join(os.getcwd(), config.checkpoint_dir +'/test.h5')\\n\\n with h5py.File(savepath, 'w') as hf:\\n hf.create_dataset('data', data=data)\\n hf.create_dataset('label', data=label)\",\n \"def make_data(_is_train, data, label):\\n save_path = os.path.join(os.getcwd(), \\\"SRCNN\\\", 'checkpoint')\\n if not os.path.exists(save_path):\\n os.makedirs(save_path)\\n\\n if _is_train:\\n save_path = os.path.join(save_path, 'train.h5')\\n else:\\n save_path = os.path.join(save_path, 'test.h5')\\n\\n # data 和 label 預設類型是 numpy array ，但若建立時內部陣列維度不相等，內部數據將被轉為 dtype=object\\n # 導致 h5py 無法儲存: TypeError: Object dtype dtype('O') has no native HDF5 equivalent\\n with h5py.File(save_path, 'w') as hf:\\n hf.create_dataset('data', data=data)\\n hf.create_dataset('label', data=label)\",\n \"def make_data(sess, data, label):\\n if FLAGS.is_train:\\n savepath = os.path.join(os.getcwd(), 'checkpoint/train.h5')\\n else:\\n savepath = os.path.join(os.getcwd(), 'checkpoint/test.h5')\\n\\n with h5py.File(savepath, 'w') as hf:\\n hf.create_dataset('data', data=data)\\n hf.create_dataset('label', data=label)\",\n \"def transform_to_h5():\\n # this took about 10 minutes for set1\\n for setname in ['set1', 'set2']:\\n filename = os.path.join(ltrc_dirname, '{}.h5'.format(setname))\\n f = h5py.File(filename, 'w')\\n\\n for name in ['train', 'valid', 'test']:\\n g = f.create_group(name)\\n filename = os.path.join(ltrc_dirname, '{}.{}.txt'.format(setname, name))\\n X, y, q = load_svmlight_file(filename, query_id=True)\\n g.create_dataset('X', data=X.todense(), compression='gzip')\\n g.create_dataset('y', data=y, compression='gzip')\\n g.create_dataset('q', data=q, compression='gzip')\\n f.close()\\n # Now you can do this\\n # f['/valid/X'].shape\\n # Out[24]: (71083, 699)\",\n \"def saveTrainingData(model, hdf5, view_names=None, sample_names=None, feature_names=None, likelihoods=None):\\n data = model.getTrainingData()\\n data_grp = hdf5.create_group(\\\"data\\\")\\n featuredata_grp = hdf5.create_group(\\\"features\\\")\\n hdf5.create_dataset(\\\"samples\\\", data=np.array(sample_names, dtype='S50'))\\n\\n if likelihoods is not None:\\n data_grp.attrs['likelihood'] = np.array(likelihoods, dtype='S50')\\n\\n for m in range(len(data)):\\n view = view_names[m] if view_names is not None else str(m)\\n data_grp.create_dataset(view, data=data[m].data.T)\\n if feature_names is not None:\\n # data_grp.attrs['features'] = np.array(feature_names[m], dtype='S')\\n featuredata_grp.create_dataset(view, data=np.array(feature_names[m], dtype='S50'))\",\n \"def convert(self, out_path: str)->None:\\n tape_data_hdf5 = self.createTapeHDF5Dict()\\n \\n self.deleteFile(out_path)\\n self.to_hdf5(tape_data_hdf5, out_path)\\n print(\\\"HDF5 file has been successfully saved at {}\\\".format(out_path))\",\n \"def prepare_hdf5_file(hdf5_file, n_train, n_valid, n_test):\\n n_total = n_train + n_valid + n_test\\n splits = create_splits(n_train, n_valid, n_test)\\n hdf5_file.attrs['split'] = H5PYDataset.create_split_array(splits)\\n vlen_dtype = h5py.special_dtype(vlen=numpy.dtype('uint8'))\\n hdf5_file.create_dataset('encoded_images', shape=(n_total,),\\n dtype=vlen_dtype)\\n hdf5_file.create_dataset('targets', shape=(n_total, 1), dtype=numpy.int16)\\n hdf5_file.create_dataset('filenames', shape=(n_total, 1), dtype='S32')\",\n \"def create_devh5(self):\\n if os.path.exists(self.dev_h5_path):\\n print(\\\"[LOGGING]: \\\" + self.dev_h5_path + \\\" exists!\\\")\\n return\\n\\n with h5py.File(self.dev_h5_path, 'w') as f:\\n\\n # create a group: f['train']\\n train = f.create_group('train')\\n self.extract_fea_for_datagroup(train, mode='train')\\n\\n # f['test']\\n test = f.create_group('test')\\n self.extract_fea_for_datagroup(test, mode='test')\\n\\n f.close()\",\n \"def save_model(self, fpath):\\n self._make_model_folder(fpath)\\n self.model.save(os.path.join(fpath, U.MODEL_NAME), save_format=\\\"h5\\\")\\n return\",\n \"def numpy_to_h5py(in_dir=config.dir_npy, split = config.split):\\n\\n in_files=[x[:-13] for x in os.listdir(in_dir) if x.endswith('_voc_stft.npy') and not x.startswith('._')]\\n\\n random.shuffle(in_files)\\n\\n\\n num_files = len(in_files)\\n\\n split_idx = int(num_files*split)\\n\\n trn_files = in_files[:split_idx]\\n\\n val_files = in_files[split_idx:]\\n\\n num_val_files = len(val_files)\\n\\n print('Processing %d training files' % split_idx)\\n logger.info('Processing %d training files' % split_idx)\\n\\n logger.info('Training file: %s' % config.h5py_file_train)\\n\\n voc_shape_trn = [split_idx, 5170,config.input_features]\\n\\n mix_shape_trn = [split_idx, 5170,config.input_features]\\n\\n feats_shape_trn = [split_idx, 5170,config.output_features]\\n\\n hdf5_file = h5py.File(config.h5py_file_train, mode='w')\\n\\n hdf5_file.create_dataset(\\\"voc_stft\\\", voc_shape_trn, np.float32)\\n\\n hdf5_file.create_dataset(\\\"back_stft\\\", voc_shape_trn, np.float32)\\n\\n hdf5_file.create_dataset(\\\"mix_stft\\\", mix_shape_trn, np.float32)\\n\\n hdf5_file.create_dataset(\\\"feats\\\", feats_shape_trn, np.float32)\\n\\n\\n i = 0\\n\\n for f in trn_files:\\n\\n voc_stft = np.load(in_dir+f+'_voc_stft.npy')\\n\\n voc_stft = voc_stft.astype('float32')\\n\\n mix_stft = np.load(in_dir+f+'_mix_stft.npy')\\n\\n mix_stft = mix_stft.astype('float32')\\n\\n back_stft = np.load(in_dir+f+'_back_stft.npy')\\n\\n back_stft = back_stft.astype('float32')\\n\\n synth_feats = np.load(in_dir+f+'_synth_feats.npy')\\n\\n synth_feats = synth_feats.astype('float32')\\n\\n hdf5_file[\\\"voc_stft\\\"][i,...] = voc_stft\\n\\n hdf5_file[\\\"mix_stft\\\"][i,...] = mix_stft\\n\\n hdf5_file[\\\"back_stft\\\"][i,...] = back_stft\\n\\n hdf5_file[\\\"feats\\\"][i,...] = synth_feats\\n\\n i+=1\\n utils.progress(i, split_idx)\\n\\n logger.info('Processed training file: %s' % f)\\n\\n hdf5_file.close()\\n\\n print('Processing %d validation files' % num_val_files)\\n logger.info('Processing %d validation files' % num_val_files)\\n\\n logger.info('Validation file: %s' % config.h5py_file_val)\\n\\n voc_shape_trn = [num_val_files, 5170,config.input_features]\\n\\n mix_shape_trn = [num_val_files, 5170,config.input_features]\\n\\n feats_shape_trn = [num_val_files, 5170,config.output_features]\\n\\n hdf5_file = h5py.File(config.h5py_file_val, mode='w')\\n\\n hdf5_file.create_dataset(\\\"voc_stft\\\", voc_shape_trn, np.float32)\\n\\n hdf5_file.create_dataset(\\\"mix_stft\\\", mix_shape_trn, np.float32)\\n\\n hdf5_file.create_dataset(\\\"back_stft\\\", voc_shape_trn, np.float32)\\n\\n hdf5_file.create_dataset(\\\"feats\\\", feats_shape_trn, np.float32)\\n\\n\\n i = 0\\n\\n for f in val_files:\\n\\n voc_stft = np.load(in_dir+f+'_voc_stft.npy')\\n\\n voc_stft = voc_stft.astype('float32')\\n\\n mix_stft = np.load(in_dir+f+'_mix_stft.npy')\\n\\n mix_stft = mix_stft.astype('float32')\\n\\n synth_feats = np.load(in_dir+f+'_synth_feats.npy')\\n\\n synth_feats = synth_feats.astype('float32')\\n\\n back_stft = np.load(in_dir+f+'_back_stft.npy')\\n\\n back_stft = back_stft.astype('float32')\\n\\n hdf5_file[\\\"voc_stft\\\"][i,...] = voc_stft\\n\\n hdf5_file[\\\"mix_stft\\\"][i,...] = mix_stft\\n\\n hdf5_file[\\\"back_stft\\\"][i,...] = back_stft\\n\\n hdf5_file[\\\"feats\\\"][i,...] = synth_feats\\n\\n i+=1\\n utils.progress(i, num_val_files)\\n\\n logger.info('Processed validation file: %s' % f)\\n\\n hdf5_file.close()\\n # return original_ffts\",\n \"def gene_h5_train_file(data_path, h5_name):\\n img = []\\n y_cls_mask = []\\n y_reg_cls_mask = []\\n os.chdir(data_path)\\n jpgfiles = glob.glob('*.jpg')\\n idx = 1\\n # the position of generator objector is very important\\n gene_obj = image_output_pair(data_path, 1/255.0)\\n while True:\\n if idx == len(jpgfiles):\\n break\\n print '\\\\t{0}/{1}'.format(idx, len(jpgfiles))\\n # the position of generator objector is very important\\n # gene_obj = image_output_pair(data_path, 1/255.0)\\n img_it, y_cls_mask_it, y_reg_cls_mask_it = gene_obj.next()\\n img.append(img_it)\\n y_cls_mask.append(y_cls_mask_it)\\n y_reg_cls_mask.append(y_reg_cls_mask_it)\\n idx += 1\\n\\n # img => (320, 320, 3)\\n # after np.stack => (19041, 320, 320, 3)\\n img_input = np.stack(img, axis=0)\\n y_cls = np.stack(y_cls_mask, axis=0)\\n y_reg = np.stack(y_reg_cls_mask, axis=0)\\n print 'input data shape is {0}'.format(img_input.shape)\\n print 'y_cls data shape is {0}'.format(y_cls.shape)\\n print 'y_reg data shape is {0}'.format(y_reg.shape)\\n \\n # wirte data\\n h5 = '/home/yuquanjie/Documents/train_' + h5_name\\n file_write = h5py.File(h5, 'w')\\n file_write.create_dataset('X_train', data=img_input)\\n file_write.create_dataset('Y_train_cls', data=y_cls)\\n file_write.create_dataset('Y_train_merge', data=y_reg)\\n file_write.close()\",\n \"def to_hdf5(self, path: Union[str, Path]):\\n import h5py # : import-outside-toplevel\\n\\n with h5py.File(path, \\\"w\\\") as hdf:\\n for k, v in self._to_list_dict().items():\\n if k in self._cal_paras:\\n hdf.create_dataset(k, data=v.tolist())\\n elif v:\\n hdf[k] = v\",\n \"def save_model(model):\\n\\n # model.save(os.path.join(output))\\n # model.save(output)\\n\\n # key = \\\"{}/{}/examples\\\".format(prefix,data_partition_name)\\n # url = 's3://{}/{}'.format(bucket, key)\\n # boto3.Session().resource('s3').Bucket(bucket).Object(key).upload_file('data.csv')\\n # print('Done writing to {}'.format(url))\\n \\n model.save('output/sentiment_model.h5')\\n\\n s3 = boto3.resource('s3')\\n s3.meta.client.upload_file('output/sentiment_model.h5', 'ieor4577-hw4', 'sentiment_model.h5')\\n\\n # tf.saved_model.save(model, os.path.join(output, \\\"1\\\"))\\n print(\\\"Model successfully saved\\\")\",\n \"def save(self,\\n path,\\n save_model=False):\\n if save_model:\\n self.model.save(path)\\n\\n h5dict = H5Dict(path)\\n self._update_hdf5(h5dict, self.generator_train.command_dict, 'train')\\n \\n try:\\n self._update_hdf5(h5dict, self.generator_val.command_dict, 'val')\\n except AttributeError:\\n pass\",\n \"def export_to_hdf5(cls, h5_file, model, loads):\\n #encoding = model._encoding\\n #comments = []\\n sid = []\\n node = []\\n cid = []\\n mag = []\\n xyz = []\\n for load in loads:\\n #comments.append(loads.comment)\\n sid.append(load.sid)\\n node.append(load.node)\\n cid.append(load.cid)\\n mag.append(load.mag)\\n xyz.append(load.xyz)\\n\\n #h5_file.create_dataset('_comment', data=comments)\\n h5_file.create_dataset('sid', data=sid)\\n h5_file.create_dataset('node', data=node)\\n h5_file.create_dataset('cid', data=cid)\\n h5_file.create_dataset('mag', data=mag)\\n h5_file.create_dataset('xyz', data=xyz)\",\n \"def save(self, path: utils.URLPath):\\n save_somclassifier_config(self.config, path / \\\"config.json\\\")\\n self.model.save(str(path / \\\"model.h5\\\"))\\n io_functions.save_joblib(self.binarizer, path / \\\"binarizer.joblib\\\")\\n\\n io_functions.save_json(self.data_ids[\\\"validation\\\"], path / \\\"ids_validate.json\\\")\\n io_functions.save_json(self.data_ids[\\\"train\\\"], path / \\\"ids_train.json\\\")\",\n \"def writeH5Dataset( self, foldername, time, nameConvention = \\\"grid\\\" ):\\n filename = \\\"{0}/{1}_{2:06}.h5\\\".format(foldername,nameConvention,time)\\n file = h5py.File(filename,'w',driver='mpio',comm=self.global_comm)\\n dset = file.create_dataset(\\\"dset\\\",self._layout.fullShape, dtype = self._f.dtype)\\n slices = tuple([slice(s,e) for s,e in zip(self._layout.starts,self._layout.ends)])\\n dset[slices]=self._f[:]\\n attr_data = np.array(self._layout.dims_order)\\n dset.attrs.create(\\\"Layout\\\", attr_data, (self._nDims,), h5py.h5t.STD_I32BE)\\n file.close()\",\n \"def save(model, save_name):\\n dirs = configparser.ConfigParser()\\n dirs.read(\\\"config/dir_config.ini\\\")\\n\\n save_name = os.path.splitext(save_name)[0]\\n path = os.path.join(dirs[\\\"save_dirs\\\"][\\\"models\\\"], save_name + \\\".h5\\\")\\n info = os.path.join(dirs[\\\"save_dirs\\\"][\\\"models\\\"], save_name + \\\"_info.txt\\\")\\n\\n with open(info, \\\"w\\\") as file:\\n model.summary(print_fn=lambda x: file.write(f\\\"{x}\\\\n\\\"))\\n model.save(path, overwrite=False)\",\n \"def save_all(self, file):\\n self.model.save(file + \\\"_model.h5\\\")\\n return\",\n \"def save_model(model, output_dir, epoch, step, metric, current_score,\\n best_score, name=\\\"model\\\"):\\n assert hasattr(model, \\\"loss\\\") and model.loss is not None\\n assert hasattr(model, \\\"optimizer\\\") and model.optimizer is not None\\n\\n model.save(os.path.join(output_dir, f\\\"{name}.h5\\\"))\\n\\n file_io.write_csv(\\n os.path.join(output_dir, f\\\"{name}.step\\\"),\\n [epoch, step, metric, current_score, best_score])\",\n \"def save(self, filename: Union[str, Path]):\\n self.model.save(filename, save_format=\\\"h5\\\")\\n with h5py.File(filename, mode='a') as f:\\n f.attrs['spectrum_binner'] = self.spectrum_binner.to_json()\\n f.attrs['additional_input'] = self.nr_of_additional_inputs\",\n \"def save_image_to_h5(image, h5_path, *args, **kwargs):\\n # TODO: Implement the method\\n\\n f = h5.File(h5_path, \\\"w\\\")\\n grp = f.create_group(\\\"data\\\")\\n grp.create_dataset(\\\"image\\\", data=image)\",\n \"def write(self,data): \\n if not os.path.exists(self.output_dir):\\n os.makedirs(self.output_dir)\\n\\n # We will store these in a separate file and link them to the level2s\\n fname = data.filename.split('/')[-1]\\n \\n if os.path.exists(self.outfile):\\n output = h5py.File(self.outfile,'a')\\n else:\\n output = h5py.File(self.outfile,'w')\\n\\n # Set permissions and group\\n if self.set_permissions:\\n try:\\n os.chmod(self.outfile,0o664)\\n shutil.chown(self.outfile, group=self.permissions_group)\\n except PermissionError:\\n self.logger(f'{fname}:{self.name}: Warning, couldnt set the file permissions.')\\n\\n # Store datasets in root\\n data_out = {'tod':self.all_tod,\\n 'weights':self.all_weights,\\n 'mask':self.all_mask,\\n 'cal_factors':self.all_cal_factors,\\n 'frequency':self.all_frequency,\\n 'auto_rms':self.all_auto}\\n\\n for dname, dset in data_out.items():\\n if dname in output:\\n del output[dname]\\n output.create_dataset(dname, data=dset)\\n\\n output.attrs['version'] = __level3_version__\\n output['cal_factors'].attrs['source'] = self.cal_source\\n output['cal_factors'].attrs['calibrator_obsid'] = self.nearest_calibrator\\n\\n output.close()\\n \\n if self.level3 in data.keys():\\n del data[self.level3]\\n data[self.level3] = h5py.ExternalLink(self.outfile,'/')\",\n \"def save_h5(self, filename):\\n try:\\n shutil.copyfile(filename, '{0}_bak'.format(filename))\\n except IOError:\\n print 'could not make backup of trainer param file (which is \\\\\\n normal if we haven\\\\'t saved one until now)'\\n paramfile = tables.openFile(filename, 'w')\\n paramfile.createArray(paramfile.root, 'learningrate',\\n self.learningrate)\\n paramfile.createArray(paramfile.root, 'verbose', self.verbose)\\n paramfile.createArray(paramfile.root, 'loadsize', self.loadsize)\\n paramfile.createArray(paramfile.root, 'batchsize', self.batchsize)\\n paramfile.createArray(paramfile.root, 'momentum',\\n self.momentum)\\n paramfile.createArray(paramfile.root, 'epochcount',\\n self.epochcount)\\n paramfile.createArray(paramfile.root, 'momentum_batchcounter',\\n self.momentum_batchcounter)\\n incsgrp = paramfile.createGroup(paramfile.root, 'incs', 'increments')\\n for p in self._params:\\n paramfile.createArray(incsgrp, p.name, self._incs[p].get_value())\\n if self.rmsprop is not None:\\n avg_grad_sqrs_grp = paramfile.createGroup(paramfile.root, 'avg_grad_sqrs')\\n for p in self._params:\\n paramfile.createArray(avg_grad_sqrs_grp, p.name, self._avg_grad_sqrs[p].get_value())\\n paramfile.close()\",\n \"def save_model_data():\\n print(\\\"Getting the training, validation, and testing sets...\\\")\\n df = get_data()\\n\\n # First read in & group by type\\n image_styles = {key: [] for key in df['Period'].unique()}\\n for pic in df.to_dict(\\\"records\\\"):\\n db = pic['file'][:pic['file'].find(\\\"_\\\")]\\n img = Image.open(os.path.join(MODEL_DIR, f\\\"{db}/sculpture_images/{pic['file']}\\\"))\\n img.load()\\n image_styles[pic['Period']].append(img)\\n\\n\\n # Split each type up...not just the whole thing\\n for style in image_styles.keys():\\n # Split into Train/Test - 75/25\\n feats, labels = image_styles[style], [style] * len(image_styles[style])\\n feat_train, feat_test, label_train, label_test = train_test_split(feats, labels, test_size=.25, random_state=42)\\n\\n # Create dirs if needed\\n for pic_type in ['train', 'test']:\\n if not os.path.exists(os.path.join(MODEL_DIR, f\\\"model_data/gan/{pic_type}/{style}\\\")):\\n os.makedirs(os.path.join(MODEL_DIR, f\\\"model_data/gan/{pic_type}/{style}\\\"))\\n\\n # Save in train/validation/test folders\\n for style_type_pics in [[\\\"train\\\", feat_train], [\\\"test\\\", feat_test]]:\\n for pic in range(len(style_type_pics[1])):\\n file_name = style + format(pic, '03d') + \\\".jpg\\\"\\n if not os.path.isfile(os.path.join(MODEL_DIR, f\\\"model_data/gan/{style_type_pics[0]}/{style}/{file_name}\\\")):\\n style_type_pics[1][pic].save(os.path.join(MODEL_DIR, f\\\"model_data/gan/{style_type_pics[0]}/{style}/{file_name}\\\"))\\n\\n print(\\\"Split data for\\\", style)\",\n \"def save(self, path, name):\\n if not self._frozen:\\n raise Exception(\\\"Dataset must be frozen\\\")\\n # create directory\\n pathlib.Path(os.path.join(path,name)).mkdir(parents=True, exist_ok=True)\\n self._raw_data.to_hdf(os.path.join(path,name,\\\"dataset.h5\\\"), key=\\\"raw_data\\\")\\n self._proc_data.to_hdf(os.path.join(path,name,\\\"dataset.h5\\\"), key=\\\"proc_data\\\")\\n np.save(os.path.join(path,name,\\\"_X_train.npy\\\"), self._X_train)\\n np.save(os.path.join(path,name,\\\"_X_test.npy\\\"), self._X_test)\\n np.save(os.path.join(path,name,\\\"_y_train.npy\\\"), self._y_train)\\n np.save(os.path.join(path,name,\\\"_y_test.npy\\\"), self._y_test)\\n \\n np.save(os.path.join(path,name,\\\"_X_mean.npy\\\"), self._X_mean)\\n np.save(os.path.join(path,name,\\\"_X_std.npy\\\"), self._X_std)\\n np.save(os.path.join(path,name,\\\"_y_mean.npy\\\"), self._y_mean)\\n np.save(os.path.join(path,name,\\\"_y_std.npy\\\"), self._y_std)\\n \\n with open(os.path.join(path,name,\\\"_seed.pkl\\\"), \\\"wb\\\") as fp: #Pickling\\n pickle.dump(self._seed, fp)\\n with open(os.path.join(path,name,\\\"_train_part.pkl\\\"), \\\"wb\\\") as fp: #Pickling\\n pickle.dump(self._train_part, fp)\\n with open(os.path.join(path,name,\\\"_test_part.pkl\\\"), \\\"wb\\\") as fp: #Pickling\\n pickle.dump(self._test_part, fp)\\n with open(os.path.join(path,name,\\\"_columns.pkl\\\"), \\\"wb\\\") as fp: #Pickling\\n pickle.dump(self._columns, fp)\",\n \"def __save_datasets(self):\\n self.train.to_csv('{}/{}/{}'.format(path_to_train_set, img_format, 'train.csv'))\\n self.valid.to_csv('{}/{}/{}'.format(path_to_valid_set, img_format, 'valid.csv'))\\n self.test.to_csv('{}/{}/{}'.format(path_to_test_set, img_format, 'test.csv'))\",\n \"def save_batch(dataset, steps, outdir, batch_nbr):\\n\\twith h5py.File(outdir+\\\"/batch%d.h5\\\" % (batch_nbr), 'w') as f:\\n\\t\\tf.create_dataset('left_camera_image', data=np.array(dataset['left_camera']))\\n\\t\\tf.create_dataset('left_camera_steering_angle', data=np.array(dataset['left_camera/steering_angle']))\\n\\t\\tf.create_dataset('center_camera_image', data=np.array(dataset['center_camera']))\\n\\t\\tf.create_dataset('center_camera_steering_angle', data=np.array(dataset['center_camera/steering_angle']))\\n\\t\\tf.create_dataset('right_camera_image', data=np.array(dataset['right_camera']))\\n\\t\\tf.create_dataset('right_camera_steering_angle', data=np.array(dataset['right_camera/steering_angle']))\\n\\treturn batch_nbr + 1\",\n \"def save_to_hdf(df, fname, output_subdir=None):\\n path = Path(fname)\\n newfname = path.with_suffix('.h5').name\\n folderpath = HOME / 'output'\\n if output_subdir:\\n folderpath = folderpath / output_subdir\\n path = folderpath / newfname\\n df.to_hdf(str(path), 'df', format='t')\\n return str(path)\",\n \"def create_output_file(self):\\n if self.options['storage_method'] == 'hdf5':\\n try:\\n fp = h5py.File(self.file_name, \\\"w\\\")\\n except IOError:\\n print \\\"Unable to open output file '%s'\\\" % self.file_name\\n sys.exit(1)\\n # remember file pointer\\n self.file_pointer = fp\\n print \\\"Creating file '%s'\\\" % self.file_name\\n elif self.options['storage_method'] == 'none':\\n # save command for later processing\\n self.h5commands.append((\\\"create_file\\\", self.file_name))\",\n \"def save_model(self):\\n\\n print('Save model')\\n self.feature_extractor.save_weights(\\n self.path_save_model + self.name_model + '.h5')\\n\\n print('Mean and std')\\n np.save(self.path_save_model + 'mean.npy', self.mean)\\n np.save(self.path_save_model + 'std.npy', self.std)\",\n \"def save(self, folder):\\n self.generator.save_weights('%s/generator.h5'%folder)\\n self.critic.save_weights('%s/critic.h5'%folder)\",\n \"def save_training(self):\\n\\n filename = str(hashlib.sha1(str(self.training_data).encode(\\\"utf-8\\\"))\\n .hexdigest())\\n path = \\\"./training/\\\" + filename + \\\".json\\\"\\n\\n data = {\\n \\\"states\\\": self.states,\\n \\\"transitions\\\": self.transitions,\\n \\\"matrix\\\": self.matrix.tolist()\\n }\\n\\n with open(path, \\\"w\\\") as outfile:\\n json.dump(data, outfile)\",\n \"def saveModel(model, outfile, train_opts, model_opts, view_names=None, sample_names=None, feature_names=None):\\n\\n # QC checks\\n assert model.trained == True, \\\"Model is not trained yet\\\"\\n assert len(np.unique(view_names)) == len(view_names), 'View names must be unique'\\n assert len(np.unique(sample_names)) == len(sample_names), 'Sample names must be unique'\\n\\n # Create output directory\\n if not os.path.isdir(os.path.dirname(outfile)):\\n print(\\\"Output directory does not exist, creating it...\\\")\\n os.makedirs(os.path.dirname(outfile))\\n\\n # For some reason h5py orders the datasets alphabetically, so we have to sort the likelihoods accordingly\\n idx = sorted(range(len(view_names)), key=lambda k: view_names[k])\\n tmp = [model_opts[\\\"likelihood\\\"][idx[m]] for m in range(len(model_opts[\\\"likelihood\\\"]))]\\n model_opts[\\\"likelihood\\\"] = tmp\\n\\n # Open HDF5 handler\\n hdf5 = h5py.File(outfile,'w')\\n\\n # Save expectations\\n saveExpectations(model,hdf5,view_names)\\n\\n # Save parameters\\n # saveParameters(model,hdf5,view_names)\\n\\n # Save training statistics\\n saveTrainingStats(model,hdf5)\\n\\n # Save training options\\n saveTrainingOpts(train_opts,hdf5)\\n\\n # Save model options\\n saveModelOpts(model_opts,hdf5)\\n\\n # Save training data\\n saveTrainingData(model, hdf5, view_names, sample_names, feature_names, model_opts[\\\"likelihood\\\"])\\n\\n # Close HDF5 file\\n hdf5.close()\",\n \"def SaveResultsToH5(self):\\n\\n try: \\n wildcard = \\\"HDF5 files (*.hdf5)|*.hdf5\\\"\\n dialog = wx.FileDialog(None, \\\"Save as .hdf5\\\", wildcard=wildcard,\\n style=wx.SAVE|wx.OVERWRITE_PROMPT)\\n\\n if dialog.ShowModal() == wx.ID_OK:\\n filepath = dialog.GetPath()\\n self.page1.filename = dialog.GetFilename()\\n dir = dialog.GetDirectory()\\n \\n self.common.path = dir\\n self.common.filename = self.page1.filename\\n\\n wx.BeginBusyCursor() \\n self.stk.write_results_h5(filepath, self.data_struct, self.anlz) \\n wx.EndBusyCursor() \\n\\n except:\\n\\n wx.EndBusyCursor()\\n wx.MessageBox(\\\"Could not save HDF5 file.\\\")\\n \\n dialog.Destroy()\\n self.refresh_widgets()\\n \\n return\",\n \"def save_hdf5(self, filename):\\n filename += '.h5'\\n try:\\n hf = h5py.File(filename, 'w')\\n hf.create_dataset('Array', data=self.flat_array)\\n hf.close()\\n except TypeError as err:\\n if isinstance(self.mess_inst, MessagesGUI):\\n self.mess_inst.message('TypeError [{}] when attempting to save HDF5'.format(err))\\n else:\\n print('TypeError [{}] when attempting to save HDF5'.format(err))\",\n \"def dataset_to_hdf5(raw_path: Path, target_path: Path):\\n _prepare_target_dir(target_path)\\n\\n logging.info(\\\"Creating hdf5 blob file...\\\")\\n with pd.HDFStore(target_path) as store:\\n logging.info(\\\"Converting session by session...\\\")\\n session_paths = [p for p in raw_path.glob(\\\"*/*/\\\")]\\n\\n # loop all session folders\\n sessions_index = []\\n for p in tqdm(session_paths):\\n # Skip files (.DS_Store) and excluded session\\n if (not os.path.isdir(p)) or (p.name in EXCLUDE_SESSIONS):\\n logging.debug(f\\\"Skipping {p.resolve()}\\\")\\n continue\\n\\n # Derive subject and session from path\\n subject = p.parent.name\\n session = p.name\\n session_no = session.split(\\\"_\\\")[-1] #\\n\\n # Read\\n df_act = _read_activity(p)\\n df_sens = _read_sensors(p)\\n\\n # Join task/scenario information to sensor data\\n df_sens = _join_activity(df_act, df_sens)\\n\\n # Save to hdf5. Renaming, because keys can't start with digits\\n store.put(\\n f\\\"subject_{subject}/session_{subject}_{session_no}/activity\\\",\\n df_act,\\n format=\\\"f\\\",\\n )\\n store.put(\\n f\\\"subject_{subject}/session_{subject}_{session_no}/sensors_100hz\\\",\\n df_sens,\\n format=\\\"f\\\",\\n )\\n\\n # Compose index table\\n sessions_index.append(\\n {\\n \\\"subject\\\": subject,\\n \\\"session\\\": f\\\"{subject}_session_{session_no}\\\",\\n \\\"key\\\": f\\\"subject_{subject}/session_{subject}_{session_no}\\\",\\n \\\"task_type\\\": df_sens[\\\"task_type\\\"].max(),\\n }\\n )\\n\\n # Save index table to hdf5\\n df_index = pd.DataFrame(sessions_index)\\n store.put(f\\\"index\\\", df_index, format=\\\"f\\\")\",\n \"def export_experiment(session, saver, last_step, global_step, output_dir,\\n eval_set, features, labels, images, route):\\n output_filename = 'output_%s_%s_%d.h5' % (\\n FLAGS.dataset, eval_set, global_step)\\n output_directory = os.path.join(output_dir, 'classify', 'output')\\n\\n if not os.path.exists(output_directory):\\n os.makedirs(output_directory)\\n\\n with h5py.File(os.path.join(output_directory, output_filename), 'w') as hf:\\n hf.create_dataset('features', data=features, compression='lzf')\\n hf.create_dataset('labels', data=labels, compression='lzf')\\n hf.create_dataset('images', data=images, compression='lzf')\\n hf.create_dataset('route', data=route, compression='lzf')\\n\\n session_directory = os.path.join(\\n output_dir, 'eval', FLAGS.dataset, eval_set)\\n saver.save(session, os.path.join(session_directory, 'model.ckpt'),\\n global_step=last_step)\",\n \"def _generate_testdata_h5(cls, test_filepath):\\n # Generate some test data\\n data = numpy.indices( (10, 100, 200, 3) )\\n assert data.shape == (4, 10, 100, 200, 3)\\n data = data.astype( numpy.uint32 )\\n cls.original_data = data\\n\\n # Choose names\\n cls.dvid_dataset = \\\"datasetA\\\"\\n cls.data_uuid = \\\"abcde\\\"\\n cls.data_name = \\\"indices_data\\\"\\n cls.volume_location = \\\"/datasets/{dvid_dataset}/volumes/{data_name}\\\".format( **cls.__dict__ )\\n cls.node_location = \\\"/datasets/{dvid_dataset}/nodes/{data_uuid}\\\".format( **cls.__dict__ )\\n cls.voxels_metadata = voxels.VoxelsMetadata.create_default_metadata(data.shape, data.dtype, \\\"cxyzt\\\", 1.0, \\\"\\\")\\n\\n # Write to h5 file\\n with H5MockServerDataFile( test_filepath ) as test_h5file:\\n test_h5file.add_node( cls.dvid_dataset, cls.data_uuid )\\n test_h5file.add_volume( cls.dvid_dataset, cls.data_name, data, cls.voxels_metadata )\\n\\n test_h5file.add_node( \\\"datasetB\\\", \\\"12345\\\" )\\n test_h5file.add_volume( \\\"datasetB\\\", cls.data_name, data, cls.voxels_metadata )\",\n \"def save_dataset(fname, dname, data):\\n with h5py.File(fname, 'w') as w:\\n try:\\n dset = w.create_dataset(dname, (data.shape[0], data.shape[1]))\\n dset[:] = data\\n except:\\n pass\\n\\n w.flush()\",\n \"def saveh5(fname, mat, name='data'):\\n fp = open_write(fname)\\n save_vec(mat, fp, fp.root, name)\\n fp.close()\",\n \"def save_frame_to_hdf5_file(fsrc, key = 'images', compression = 0):\\n preparation = \\\"\\\"\\n preparation += \\\"from h5py import File;\\\"\\n preparation += \\\"from tempfile import gettempdir;\\\"\\n preparation += \\\"import os;\\\"\\n preparation += \\\"root = gettempdir()\\\"\\n preparation += \\\"filename_dst = os.path.join(root,'test_destination.hdf5')\\\"\\n preparation += \\\"filename_dst = os.path.join(root,'test_destination.hdf5')\\\"\\n testcode = ''\",\n \"def saveTrainingStats(model, hdf5):\\n stats = model.getTrainingStats()\\n stats_grp = hdf5.create_group(\\\"training_stats\\\")\\n stats_grp.create_dataset(\\\"activeK\\\", data=stats[\\\"activeK\\\"])\\n stats_grp.create_dataset(\\\"elbo\\\", data=stats[\\\"elbo\\\"])\\n stats_grp.create_dataset(\\\"elbo_terms\\\", data=stats[\\\"elbo_terms\\\"].T)\\n stats_grp['elbo_terms'].attrs['colnames'] = [a.encode('utf8') for a in stats[\\\"elbo_terms\\\"].columns.values]\",\n \"def save_model(self):\\n self.pred_net.save((self.save_path / \\\"iqn_pred_net\\\").absolute().as_posix())\\n self.target_net.save((self.save_path / \\\"iqn_target_net\\\").absolute().as_posix())\",\n \"def save(self):\\n\\n or_none = lambda x: x if x is not None else \\\"none\\\"\\n with h5py.File(self.filename, \\\"a\\\") as hf:\\n for attr in self._SAVE_ATTRS + self._save_attrs:\\n hf.attrs[attr] = or_none(getattr(self, attr, None))\",\n \"def main(config):\\n save_main_dir = config[\\\"save_main_dir\\\"]\\n\\n if not os.path.isdir(save_main_dir):\\n print(\\\"{} does not exist, creating it now...\\\", save_main_dir)\\n pathlib.Path(save_main_dir).mkdir(parents=True, exist_ok=True)\\n\\n app = inviwopy.app\\n network = app.network\\n \\n # Resize the canvas to improve rendering speed, only affects visual output\\n if config[\\\"should_resize\\\"]:\\n ivw_helpers.set_canvas_sizes(128, 128)\\n\\n hdf5_path = os.path.join(save_main_dir, config[\\\"hdf5_name\\\"])\\n with h5py.File(hdf5_path, mode='w', libver='latest') as hdf5_file:\\n hdf5_file.swmr_mode = True\\n create_hdf_storage(hdf5_file, config)\\n count = {\\\"train\\\": 0, \\\"val\\\": 0}\\n for set_type in 'train', 'val':\\n capture_lf_samples(hdf5_file, set_type, config, network, count) \\n print(\\\"Finished writing to HDF5 in {}\\\".format(hdf5_path))\",\n \"def write_hdf5(data, labels, output_filename):\\n\\n x = data.astype(numpy.float32)\\n y = labels.astype(numpy.float32)\\n\\n with h5py.File(output_filename, 'w') as h:\\n h.create_dataset('data', data=x, shape=x.shape)\\n h.create_dataset('label', data=y, shape=y.shape)\\n # h.create_dataset()\",\n \"def write_data_to_h5(data, filename):\\n f = h5py.File(filename, 'w', libver='latest')\\n dset = f.create_dataset('array', shape=(data.shape), data=data, compression='gzip', compression_opts=9)\\n f.close()\",\n \"def save_model(self, is_best, state, epoch):\\n path = os.path.join(self.logpath_models, 'model-%d.pth.tar' % epoch)\\n torch.save(state, path)\\n if is_best:\\n shutil.copyfile(path, path + 'model_best.pth.tar')\",\n \"def save_weight(model):\\n file = h5py.File(WEIGHT_SAVE, 'w')\\n weight = model.get_weights()\\n for i in range(len(weight)):\\n file.create_dataset('weight' + str(i), data=weight[i])\\n file.close()\",\n \"def write_hdf5(features, in_dir, cmd_args):\\n data_set = in_dir.split('/')[-2]\\n file_name = os.path.join(cmd_args.output, data_set,\\n '_'.join([data_set, '-'.join([cmd_args.window,\\n str(cmd_args.windowsize)+'s',\\n str(cmd_args.overlap)+'%']),\\n cmd_args.bands.replace(',', '-')])) + '.hdf5'\\n\\n logging.info(\\\"\\\\t\\\\tWriting features to {}.\\\\n\\\".format(file_name))\\n\\n hdf5_file = h5py.File(file_name, 'w')\\n hdf5_file.create_dataset('data', features.shape, data=features)\\n hdf5_file.close()\\n\\n return file_name\",\n \"def export(self, output_dir, config, train_ratio=0.7, delimiter=\\\",\\\"):\\n model_dir = os.path.join(output_dir, self.model_id)\\n if not os.path.exists(model_dir):\\n os.makedirs(model_dir)\\n X_tr, X_te = self.get_train_test_embeddings(config, train_ratio)\\n #save text feature matrix\\n idx = config[\\\"dimension\\\"]\\n tr_text = csr_matrix(X_tr[:,:idx])\\n te_text = csr_matrix(X_te[:,:idx])\\n print(\\\"text\\\", tr_text.shape, te_text.shape)\\n save_npz(os.path.join(model_dir, \\\"train_text\\\"), tr_text)\\n save_npz(os.path.join(model_dir, \\\"test_text\\\"), te_text)\\n #save history feature matrix\\n if config.get(\\\"user_history\\\", False):\\n tr_history = X_tr[:,idx:idx+4]\\n te_history = X_te[:,idx:idx+4]\\n np.savetxt(os.path.join(model_dir, \\\"train_history.csv\\\"), tr_history, delimiter=delimiter)\\n np.savetxt(os.path.join(model_dir, \\\"test_history.csv\\\"), te_history, delimiter=delimiter)\\n idx += 4\\n print(\\\"history\\\", tr_history.shape, te_history.shape)\\n # save node embeddings\\n if \\\"user_ne\\\" in config and X_tr.shape[1] > idx:\\n tr_network = X_tr[:,idx:]\\n te_network = X_te[:,idx:]\\n np.savetxt(os.path.join(model_dir, \\\"train_network.csv\\\"), tr_network, delimiter=delimiter)\\n np.savetxt(os.path.join(model_dir, \\\"test_network.csv\\\"), te_network, delimiter=delimiter)\\n print(\\\"network\\\", tr_network.shape, te_network.shape)\\n #save labels\\n np.savetxt(os.path.join(model_dir, \\\"train_label.csv\\\"), self.tr_label, delimiter=delimiter, fmt='%i')\\n np.savetxt(os.path.join(model_dir, \\\"test_label.csv\\\"), self.te_label, delimiter=delimiter, fmt='%i')\\n #save meta\\n self.tr_meta[self._exported_meta_columns].to_csv(os.path.join(model_dir, \\\"train_meta.csv\\\"), index=False, sep=delimiter)\\n self.te_meta[self._exported_meta_columns].to_csv(os.path.join(model_dir, \\\"test_meta.csv\\\"), index=False, sep=delimiter)\\n print(\\\"Model was exported\\\")\\n return model_dir\",\n \"def split_and_save(data_path: str,\\n train_portion: int, validation_portion: int, test_portion: int,\\n train_test_shuffle: bool = True, train_test_stratify_col: str = None,\\n train_val_shuffle: bool = True, train_val_stratify_col: str = None,\\n train_test_random_num: int = 42, train_val_random_num: int = 42\\n ):\\n if 'raw' not in data_path:\\n raise ValueError(\\\"data path should be {data_dir}/{version}/raw/{data_name}.{file_format}.\\\"\\n \\\"\\\\nYou have written:\\\", data_path)\\n\\n data_name = ''.join(data_path.split('/')[-1].split('.')[:-1])\\n data_dir = '/'.join(data_path.split('/')[:-2])\\n file_format = data_path.split('/')[-1].split('.')[-1]\\n separator = '\\\\t' if file_format == 'tsv' else ',' if file_format == 'csv' else None\\n\\n df = pd.read_csv(data_path, sep=separator)\\n\\n total_portion = train_portion + validation_portion + test_portion\\n train_val_size = (train_portion + validation_portion) / total_portion\\n test_size = test_portion / total_portion\\n\\n train_val_portion = train_portion + validation_portion\\n\\n train_size = train_portion / train_val_portion\\n val_size = validation_portion / train_val_portion\\n\\n print(\\\"====== SPECIFICATION ======\\\")\\n print(f\\\"Train + Val: Test = {round(train_val_size * 100, 2)}%: {round(test_size * 100, 2)}%\\\")\\n print(f\\\"Train: Validation = {round(train_size * 100, 2)}%: {round(val_size * 100, 2)}%\\\")\\n print()\\n\\n train_val, test = train_test_split(df,\\n train_size=train_val_size,\\n test_size=test_size,\\n random_state=train_test_random_num,\\n shuffle=train_test_shuffle,\\n stratify=df[train_test_stratify_col]\\n if train_test_stratify_col else train_test_stratify_col)\\n\\n train, val = train_test_split(train_val,\\n train_size=train_size,\\n test_size=val_size,\\n random_state=train_val_random_num,\\n shuffle=train_val_shuffle,\\n stratify=df[train_val_stratify_col]\\n if train_val_stratify_col else train_val_stratify_col)\\n\\n train.to_csv(f'{data_dir}/train_{data_name}.tsv', sep='\\\\t', index=False)\\n val.to_csv(f'{data_dir}/val_{data_name}.tsv', sep='\\\\t', index=False)\\n test.to_csv(f'{data_dir}/test_{data_name}.tsv', sep='\\\\t', index=False)\\n\\n print(\\\"====== RESULT ======\\\")\\n print(f\\\"Total Data Count: {len(df)}\\\")\\n print(f\\\"Train Data Count: {len(train)} ({len(train) / len(df) * 100} %)\\\")\\n print(f\\\"Val Data Count: {len(val)} ({len(val) / len(df) * 100} %)\\\")\\n print(f\\\"Test Data Count: {len(test)} ({len(test) / len(df) * 100} %)\\\")\",\n \"def make_data(self, which_set, p, center=False, variance_normalize=False,\\n shuffle=False, save_dummy=False):\\n\\n print \\\"Making h5 file for %s\\\" % which_set #TODO(dhjelm): switch to logging.\\n\\n if which_set == 'train':\\n source_path = serial.preprocess(p + 'train.npy')\\n data_path = serial.preprocess(p + 'train.h5')\\n label_path = serial.preprocess(p + 'train_labels.npy')\\n else:\\n assert which_set == 'test'\\n source_path = serial.preprocess(p + 'test.npy')\\n data_path = serial.preprocess(p + 'test.h5')\\n label_path = serial.preprocess(p + 'test_labels.npy')\\n\\n data_path = \\\"\\\".join(data_path.split(\\\".\\\")[0] + '_dummy.h5')\\n\\n # Get the topological view and labels.\\n topo_view = np.load(source_path)\\n y = np.load(label_path)\\n num_labels = np.amax(y) + 1\\n\\n # Shape information and mask.\\n samples, rows, columns, depth = topo_view.shape\\n if self.mask is not None:\\n assert self.mask.shape == (rows, columns, depth)\\n size = len(np.where(self.mask.flatten() == 1)[0].tolist())\\n else:\\n size = rows * columns * depth\\n\\n self.view_converter = MRIViewConverter((rows, columns, depth))\\n X = self.view_converter.topo_view_to_design_mat(topo_view, self.mask)\\n\\n # TODO(dhjelm): one_hot is going away.\\n one_hot = np.zeros((size, num_labels), dtype=config.floatX)\\n for i in xrange(y.shape[0]):\\n one_hot[i, y[i] - 1] = 1.\\n\\n if center:\\n X -= X.mean(axis=0)\\n\\n if variance_normalize:\\n X /= X.std(axis=0)\\n\\n rng = make_np_rng(None, 322, which_method=\\\"shuffle\\\")\\n if shuffle:\\n index = range(samples)\\n rng.shuffle(index)\\n X = X[index, :]\\n one_hot = one_hot[index, :]\\n\\n assert not np.any(np.isnan(X))\\n\\n h5file, node = self.init_hdf5(data_path, ([samples, size], [samples, num_labels]))\\n MRI_Big.fill_hdf5(h5file, X, one_hot, node)\\n h5file.close()\",\n \"def save_reconstructions(reconstructions, out_dir):\\n if (not (os.path.exists(out_dir))):\\n os.mkdir(out_dir)\\n out_dir.mkdir(exist_ok=True)\\n print('Saved directory is',out_dir)\\n for fname, recons in reconstructions.items():\\n with h5py.File(out_dir / fname, 'w') as f:\\n f.create_dataset('reconstruction', data=recons)\",\n \"def save_dataset(self):\\n if os.path.exists(self.output_path):\\n print('Directory already exists. EXITING.')\\n sys.exit()\\n if not os.path.exists(self.output_path): os.mkdir(self.output_path)\\n for window_num, cur_window in enumerate(self.time_windows):\\n window_dir = (os.path.join(self.output_path, ('window %s' % str(window_num + 1))))\\n if not os.path.exists(window_dir): os.mkdir(window_dir)\\n for filepath in cur_window:\\n topic = os.path.basename(os.path.dirname(filepath))\\n topic_dir = os.path.join(os.path.join(window_dir, topic))\\n if not os.path.exists(topic_dir): os.mkdir(topic_dir)\\n copy(filepath, topic_dir)\\n self.ground_truth.append((len(os.listdir(window_dir))))\",\n \"def save_model(model):\\n # ***\\n # Please remove the comment to enable model save.\\n # However, it will overwrite the baseline model we provided.\\n # ***\\n model.save(\\\"model/model.h5\\\")\\n print(\\\"Model Saved Successfully.\\\")\",\n \"def to_hdf5(self, filename):\\n\\n f = h5py.File(filename, 'w')\\n f['xyz'] = self.xyz\\n f.close()\\n\\n return\",\n \"def save_w_matrix(wih, who, epochs=5, hnodes=400, path='saved_data/', iscupy=False):\\n \\n print(\\\"Saving Data...\\\")\\n\\n t = time.asctime(time.localtime(time.time()))\\n t = t.replace(':', '_')\\n\\n filepath = path + t + \\\"/\\\"\\n try:\\n os.mkdir(filepath)\\n except:\\n pass\\n filename_wih = f\\\"hn{hnodes}_wih_epoch_{epochs}.csv\\\"\\n filename_who = f\\\"hn{hnodes}_who_epoch_{epochs}.csv\\\"\\n\\n if iscupy:\\n wih = cupy.asnumpy(wih)\\n who = cupy.asnumpy(who)\\n\\n numpy.savetxt(filepath + filename_wih, wih)\\n numpy.savetxt(filepath + filename_who, who)\\n print(\\\"Saving Succeeded!\\\")\",\n \"def write(self,data): \\n \\n if not os.path.exists(self.output_dir):\\n os.makedirs(self.output_dir)\\n\\n # We will store these in a separate file and link them to the level2s\\n fname = data.filename.split('/')[-1]\\n units = {'A':'K','x0':'degrees','y0':'degrees','sigx':'degrees','sigy':'degrees','sigy_scale':'none','B':'K','phi':'radians'}\\n\\n outfile = '{}/{}_{}'.format(self.output_dir,self.prefix,fname)\\n\\n print ('WRITING: ',outfile)\\n output = h5py.File(outfile,'a')\\n\\n # Set permissions and group\\n os.chmod(outfile,0o664)\\n shutil.chown(outfile, group='comap')\\n\\n ##\\n ## Narrow channel fits\\n ##\\n\\n for valerr in ['Values','Errors','Chi2']:\\n if f'Gauss_Narrow_{valerr}' in output:\\n del output[f'Gauss_Narrow_{valerr}']\\n gauss_fits = output.create_group(f'Gauss_Narrow_{valerr}')\\n gauss_fits.attrs['FitFunc'] = self.model.__name__\\n gauss_fits.attrs['source_el'] = self.source_positions['mean_el']\\n gauss_fits.attrs['source_az'] = self.source_positions['mean_az']\\n\\n dnames = self.map_parameters\\n dsets = [self.map_fits[valerr][...,iparam] for iparam in range(self.map_fits[valerr].shape[-1])]\\n\\n for (dname, dset) in zip(dnames, dsets):\\n if dname in output:\\n del output[dname]\\n print(dname,dset.shape,units[dname])\\n gauss_dset = gauss_fits.create_dataset(dname, data=dset)\\n gauss_dset.attrs['Unit'] = units[dname]\\n \\n\\n output.attrs['SourceFittingVersion'] = __version__\\n output.attrs['source'] = self.getSource(data)\\n output.close()\\n self.linkfile(data)\",\n \"def save_data_01(outputfilepath,summary):\\n ext=os.path.splitext(outputfilepath)[-1].lower()\\n if ext=='.mat':\\n savemat(outputfilepath,summary,long_field_names=True)\\n return outputfilepath\\n elif ext=='.npz':\\n np.savez(outputfilepath,summary=summary)\\n elif ext=='.pdb':\\n make_pdb_file_01(outputfilepath,np.array(summary['X_evol'][-1]))\\n else:\\n if ext!='.json':\\n outputfilepath+='.json'\\n with open(outputfilepath, 'w') as result:\\n # serialize\\n summary['json_task']='not stored'\\n serialized_summary=jsonify_01(summary)\\n json.dump(serialized_summary, result,indent=4,sort_keys=True)\",\n \"def make_hdf5file(self, dataset_paths: list = None, dataset_values: list = None) -> None:\\n\\n assert dataset_paths is not None and len(dataset_paths) != 0, (\\\"`dataset_path` not valid. Expected at least \\\" \\\\\\n f\\\"one list element, got {len(dataset_paths)}.\\\")\\n\\n assert dataset_values is not None and len(dataset_values) != 0, (\\\"`dataset_values` not valid. Expected at least \\\" \\\\\\n f\\\"one list element, got {len(dataset_values)}.\\\")\\n\\n\\n # Remove file if already exists and create a new one\\n if os.path.isfile(os.path.join(self.FOFDirectory, self.filename)):\\n os.remove(os.path.join(self.FOFDirectory, self.filename))\\n print(f'[ FOFOutput ]\\\\t==> Removed old {self.filename} file.')\\n\\n # Create file and optional groups within it\\n FOFfile = h5py.File(os.path.join(self.FOFDirectory, self.filename), 'w')\\n print(f'[ FOFOutput ]\\\\t==> Created new {self.filename} file.')\\n\\n # Push the attributes to file, if any\\n if self.attrs is not None and len(self.attrs.keys()) > 0:\\n for key, text in zip(self.attrs.keys(), self.attrs.values()):\\n FOFfile.attrs[key] = text\\n\\n for internal_path, dataset_content in zip(dataset_paths, dataset_values):\\n\\n assert not internal_path.endswith('/'), \\\"Invalid hdf5 internal path\\\"\\n assert type(dataset_content) is np.ndarray, \\\"Can only push numpy.ndarrays into hdf5 files.\\\"\\n\\n nested_groups = self.groups_from_path(internal_path)\\n if len(nested_groups) == 1:\\n FOFfile.create_dataset(nested_groups[0], data=dataset_content)\\n else:\\n for nested_group in nested_groups[:-1]:\\n g = FOFfile.create_group(nested_group)\\n g.create_dataset(nested_groups[-1], data = dataset_content)\\n\\n print(f'[ FOFOutput ]\\\\t==> Created {internal_path} dataset in {self.filename} file.')\\n\\n FOFfile.close()\",\n \"def export_to_hdf5(cls, h5_file, model, eids):\\n #comments = []\\n pids = []\\n nodes = []\\n x = []\\n g0 = []\\n offt = []\\n unused_bit = []\\n pa = []\\n pb = []\\n wa = []\\n wb = []\\n nan = np.full(3, np.nan)\\n encoding = model._encoding\\n for eid in eids:\\n element = model.elements[eid]\\n #comments.append(element.comment)\\n pids.append(element.pid)\\n nodes.append(element.nodes)\\n if element.g0 is None:\\n x.append(element.x)\\n g0.append(-1)\\n else:\\n x.append(nan)\\n g0.append(element.g0)\\n\\n offti = element.offt\\n if isinstance(offti, integer_types):\\n offti = str(offti)\\n offt.append(offti.encode(encoding))\\n pa.append(element.pa)\\n pb.append(element.pb)\\n wa.append(element.wa)\\n wb.append(element.wb)\\n #h5_file.create_dataset('_comment', data=comments)\\n h5_file.create_dataset('eid', data=eids)\\n h5_file.create_dataset('nodes', data=nodes)\\n h5_file.create_dataset('pid', data=pids)\\n #print('x =', x)\\n #print('g0 =', g0)\\n h5_file.create_dataset('x', data=x)\\n h5_file.create_dataset('g0', data=g0)\\n h5_file.create_dataset('offt', data=offt)\\n\\n h5_file.create_dataset('pa', data=pa)\\n h5_file.create_dataset('pb', data=pb)\\n\\n h5_file.create_dataset('wa', data=wa)\\n h5_file.create_dataset('wb', data=wb)\",\n \"def _write_h5_out(self, fout, save_hybrid_meta=True):\\n\\n with Outputs(fout, mode='a') as out:\\n if 'meta' in out.datasets and save_hybrid_meta:\\n hybrid_meta = to_records_array(self.hybrid_meta)\\n out['meta'] = hybrid_meta\\n\\n for dset, data in self.profiles.items():\\n out[dset] = data\",\n \"def make_data(input_filepath, output_filepath):\\n\\n df_train = pd.read_csv(input_filepath+'train_u6lujuX_CVtuZ9i.csv', index_col=0)\\n df_test = pd.read_csv(input_filepath+'test_Y3wMUE5_7gLdaTN.csv', index_col=0)\\n print('Sizes', df_train.shape, df_test.shape)\\n print(\\\"Outcome dispersion:\\\\n\\\", df_train['Loan_Status'].value_counts())\\n\\n\\n # recode and save outcome vector\\n y = df_train['Loan_Status'].map({'N': 0, 'Y': 1})\\n\\n del df_train['Loan_Status']\\n\\n # all in one dataframe\\n df = pd.concat([df_train, df_test])\\n print(df.shape)\\n\\n from src.features.build_features import make_features\\n df = make_features(df)\\n\\n # Divide data on train and test again and save\\n data_train = df[df.index.isin(df_train.index)]\\n data_test = df[df.index.isin(df_test.index)]\\n print(data_train.shape, data_test.shape)\\n\\n data_tmp = data_train.copy()\\n data_tmp['y'] = y\\n\\n\\n data_tmp.to_csv(output_filepath + 'train_ready.csv', index=False)\\n data_test.to_csv(output_filepath + 'test_ready.csv', index=False)\\n id_test = pd.DataFrame(data=df_test.index, columns=['Loan_ID'])\\n id_test.to_csv(output_filepath + 'id_test.csv', index=False)\",\n \"def generate_data(self):\\n self.remove_hdf5_file()\\n hdf5_handler = self.create_hdf5_file()\\n self.populate_hdf5_file(hdf5_handler, self.dataset)\",\n \"def set_model_for_train(self):\\n if self.train_time is None:\\n self.train_time = datetime.now().strftime('%Y-%m-%d_%H-%M-%S')\\n\\n self.curr_folder = data_functions.create_path(\\n self.save_path, self.train_time)\\n logger.info(f\\\"training results will be stored in: {self.curr_folder}\\\")\\n\\n self.save_model_params()\\n self.train_generator, self.val_generator = \\\\\\n self.clarifruit_train_val_generators()\\n keras_logs_path = self.set_model_checkpint()\\n\\n return keras_logs_path\",\n \"def get_data(data_dir, hdf5):\\r\\n\\r\\n # Get the filenames of the lists containing image paths and labels.\\r\\n train_file, val_file = build_dataset_index(data_dir)\\r\\n\\r\\n # Check if (creating and) loading from hdf5 database is desired.\\r\\n if hdf5:\\r\\n # Create folder to store dataset.\\r\\n if not os.path.exists('hdf5'):\\r\\n os.makedirs('hdf5')\\r\\n # Check if hdf5 databases already exist and create them if not.\\r\\n if not os.path.exists('hdf5/tiny-imagenet_train.h5'):\\r\\n from tflearn.data_utils import build_hdf5_image_dataset\\r\\n print ' Creating hdf5 train dataset.'\\r\\n build_hdf5_image_dataset(train_file, image_shape=(64, 64), mode='file', output_path='hdf5/tiny-imagenet_train.h5', categorical_labels=True, normalize=True)\\r\\n\\r\\n if not os.path.exists('hdf5/tiny-imagenet_val.h5'):\\r\\n from tflearn.data_utils import build_hdf5_image_dataset\\r\\n print ' Creating hdf5 val dataset.'\\r\\n build_hdf5_image_dataset(val_file, image_shape=(64, 64), mode='file', output_path='hdf5/tiny-imagenet_val.h5', categorical_labels=True, normalize=True)\\r\\n\\r\\n # Load training data from hdf5 dataset.\\r\\n h5f = h5py.File('hdf5/tiny-imagenet_train.h5', 'r')\\r\\n X = h5f['X']\\r\\n Y = h5f['Y']\\r\\n\\r\\n # Load validation data.\\r\\n h5f = h5py.File('hdf5/tiny-imagenet_val.h5', 'r')\\r\\n X_test = h5f['X']\\r\\n Y_test = h5f['Y'] \\r\\n\\r\\n # Load images directly from disk when they are required.\\r\\n else:\\r\\n from tflearn.data_utils import image_preloader\\r\\n X, Y = image_preloader(train_file, image_shape=(64, 64), mode='file', categorical_labels=True, normalize=True, filter_channel=True)\\r\\n X_test, Y_test = image_preloader(val_file, image_shape=(64, 64), mode='file', categorical_labels=True, normalize=True, filter_channel=True)\\r\\n\\r\\n # Randomly shuffle the dataset.\\r\\n X, Y = shuffle(X, Y)\\r\\n\\r\\n return X, Y, X_test, Y_test\",\n \"def make_train_input_tensors(dataset, dataset_name,\\n trial_split='train',\\n update_params=None,\\n save_file=True,\\n return_dict=True,\\n save_path=\\\"train_input.h5\\\",\\n include_behavior=False,\\n include_forward_pred=False):\\n assert isinstance(dataset, NWBDataset), \\\"`dataset` must be an instance of NWBDataset\\\"\\n assert dataset_name in PARAMS.keys(), f\\\"`dataset_name` must be one of {list(PARAMS.keys())}\\\"\\n assert isinstance(trial_split, (pd.Series, np.ndarray, list)) or trial_split in ['train', 'val'], \\\\\\n \\\"Invalid `trial_split` argument. Please refer to the documentation for valid choices\\\"\\n\\n # Fetch and update params\\n params = PARAMS[dataset_name].copy()\\n if update_params is not None:\\n params.update(update_params)\\n # Add filename extension if necessary\\n if not save_path.endswith('.h5'):\\n save_path = save_path + '.h5'\\n\\n # unpack params\\n spk_field = params['spk_field']\\n hospk_field = params['hospk_field']\\n make_params = params['make_params'].copy()\\n \\n # Prep mask\\n trial_mask = _prep_mask(dataset, trial_split)\\n\\n # Make output spiking arrays and put into data_dict\\n train_dict = make_stacked_array(dataset, [spk_field, hospk_field], make_params, trial_mask)\\n data_dict = {\\n 'train_spikes_heldin': train_dict[spk_field],\\n 'train_spikes_heldout': train_dict[hospk_field],\\n }\\n\\n # Add behavior data if necessary\\n if include_behavior:\\n behavior_source = params['behavior_source']\\n behavior_field = params['behavior_field']\\n behavior_make_params = _prep_behavior(dataset, params.get('lag', None), make_params)\\n # Retrieve behavior data from indicated source\\n if behavior_source == 'data':\\n train_behavior = make_stacked_array(dataset, behavior_field, behavior_make_params, trial_mask)[behavior_field]\\n else:\\n train_behavior = dataset.trial_info[trial_mask][behavior_field].to_numpy()\\n # Filter out behavior on certain trials if necessary\\n if 'behavior_mask' in params:\\n if callable(params['behavior_mask']):\\n behavior_mask = params['behavior_mask'](dataset.trial_info[trial_mask])\\n else:\\n behavior_mask, _ = params['behavior_mask']\\n train_behavior[~behavior_mask] = np.nan\\n data_dict['train_behavior'] = train_behavior\\n \\n # Add forward prediction data if necessary\\n if include_forward_pred:\\n fp_len = params['fp_len']\\n fp_steps = fp_len / dataset.bin_width\\n fp_make_params = _prep_fp(make_params, fp_steps, dataset.bin_width)\\n fp_dict = make_stacked_array(dataset, [spk_field, hospk_field], fp_make_params, trial_mask)\\n data_dict['train_spikes_heldin_forward'] = fp_dict[spk_field]\\n data_dict['train_spikes_heldout_forward'] = fp_dict[hospk_field]\\n\\n # Save and return data\\n if save_file:\\n save_to_h5(data_dict, save_path, overwrite=True)\\n if return_dict:\\n return data_dict\",\n \"def test_hdf5_convert_to_one_hot():\\n skip_if_no_h5py()\\n import h5py\\n\\n # save random data to HDF5\\n handle, filename = tempfile.mkstemp()\\n dataset = random_dense_design_matrix(np.random.RandomState(1),\\n num_examples=10, dim=5, num_classes=3)\\n with h5py.File(filename, 'w') as f:\\n f.create_dataset('X', data=dataset.get_design_matrix())\\n f.create_dataset('y', data=dataset.get_targets())\\n\\n # instantiate Train object\\n trainer = yaml_parse.load(convert_to_one_hot_yaml % {'filename': filename})\\n trainer.main_loop()\\n\\n # cleanup\\n os.remove(filename)\",\n \"def save_train_test(train_df, test_df, save_path: str, save_as_dict: bool=True):\\n if save_as_dict:\\n data = {\\n 'train_df': train_df,\\n 'test_df': test_df\\n }\\n save_pkl_file(data, save_path)\\n else:\\n # Remove the extension so we can add 'train' or 'test' to the save_path.\\n save_path = save_path[:-4]\\n save_pkl_file(train_df, f\\\"{save_path}_train.csv\\\")\\n save_pkl_file(test_df, f\\\"{save_path}_test.csv\\\")\",\n \"def create_and_write_output(predictions_path,output_path,inpDir):\\n \\n filenames= sorted(os.listdir(predictions_path)) \\n for filename in filenames:\\n \\n # read the 3 channel output image from the neural network\\n image=cv2.imread(os.path.join(predictions_path,filename))\\n \\n # create binary image output using the create_binary function\\n out_image=create_binary(image) \\n \\n # read and store the metadata from the input image\\n with BioReader(os.path.join(inpDir,filename)) as br:\\n metadata = br.metadata\\n\\n # Write the binary output consisting of the metadata using bfio.\\n output_image_5channel=np.zeros((out_image.shape[0],out_image.shape[1],1,1,1),dtype=np.uint8)\\n output_image_5channel[:,:,0,0,0]=out_image \\n\\n with BioWriter(os.path.join(output_path,filename), metadata=metadata) as bw:\\n bw.dtype = output_image_5channel.dtype\\n bw.write(output_image_5channel)\",\n \"def convert_and_save(filename, output_dir, is_train_data):\\n mat_contents = sio.loadmat(filename)\\n if 'X' in mat_contents:\\n data = mat_contents['X']\\n # data = data[:-1, :]\\n if is_train_data:\\n # data = data[:, :5000]\\n # Finding the normalization statistics from the train data\\n global mu, sig2 \\n mu = np.sum(data, axis=1) / data.shape[1]\\n sig2 = np.sum(data**2, axis=1) / data.shape[1] \\n # Normalizing feature data\\n data -= mu.reshape((data.shape[0], 1))\\n data /= sig2.reshape((data.shape[0], 1))\\n\\n if 'Y' in mat_contents:\\n data = mat_contents['Y']\\n if is_train_data:\\n # data = data[:, :5000]\\n None\\n\\n print(\\\"Input filename is \\\" + filename)\\n save_name = filename.split(str(os.sep))[-1]\\n save_name = save_name.split('.')[0]\\n np.save(os.path.normpath(os.path.join(output_dir, save_name)), data.T)\",\n \"def save(self, uri):\\r\\n pf = PyFolder(os.path.dirname(os.path.realpath(uri)), allow_override=True)\\r\\n pf[os.path.basename(uri)+\\\"_options.json\\\"] = {\\r\\n 'input_cells': self._input_cells,\\r\\n 'latent_space': self._latent_space,\\r\\n }\\r\\n\\r\\n save_model(self._autoencoder, uri+\\\"_lstm_autoencoder.hdf5\\\")\\r\\n save_model(self._encoder, uri+\\\"_lstm_encoder.hdf5\\\")\",\n \"def save_model(name, model):\\n # Load path\\n project_dir = Path(__file__).resolve().parents[2]\\n model_path = os.path.join(project_dir, 'models', name + '.h5')\\n\\n # Save model\\n model.save(model_path)\",\n \"def saveTrainingOpts(opts, hdf5):\\n # Remove dictionaries from the options\\n for k,v in opts.copy().items():\\n if type(v)==dict:\\n for k1,v1 in v.items():\\n opts[str(k)+\\\"_\\\"+str(k1)] = v1\\n opts.pop(k)\\n\\n # Create HDF5 data set\\n hdf5.create_dataset(\\\"training_opts\\\", data=np.array(list(opts.values()), dtype=np.float))\\n hdf5['training_opts'].attrs['names'] = np.asarray(list(opts.keys())).astype('S')\",\n \"def save_h5_file(self, result_dict, loc_id):\\n if self.storage_type == 's3':\\n file_path = '{0}/{1}_combined.h5'.format(\\n self.output_path,\\n loc_id\\n )\\n hf_result = h5py.File(file_path, 'w')\\n for k, v in result_dict.items():\\n hf_result.create_dataset(k, data=v)\\n hf_result.close()\\n else:\\n file_path = os.path.join(self.output_path, '{0}_combined.h5'.format(loc_id))\\n hf_result = h5py.File(file_path, 'w')\\n for k, v in result_dict.items():\\n hf_result.create_dataset(k, data=v)\\n hf_result.close()\\n return file_path\",\n \"def save_model(self, epoch):\\n # Set the name for the model\\n gen_lungs_filename = 'gen_lungs_model_epoch_{}.h5'.format(epoch + 1)\\n disc_lungs_filename = 'disc_lungs_model_epoch_{}.h5'.format(epoch + 1)\\n train_summary_lungs_filename = 'train_summary_lungs_epoch_{}.csv'.format(epoch + 1)\\n\\n gen_organs_filename = 'gen_organs_model_epoch_{}.h5'.format(epoch + 1)\\n disc_organs_filename = 'disc_organs_model_epoch_{}.h5'.format(epoch + 1)\\n train_summary_organs_filename = 'train_summary_organs_epoch_{}.csv'.format(epoch + 1)\\n\\n # Save the model and train summary\\n self.generator_lungs.save(op.join(self.model_dir, gen_lungs_filename), include_optimizer=True)\\n self.disc_lungs.save(op.join(self.model_dir, disc_lungs_filename), include_optimizer=True)\\n self.summary_writer_lungs.to_csv(op.join(self.train_summary_dir, train_summary_lungs_filename))\\n\\n self.generator_organs.save(op.join(self.model_dir, gen_organs_filename), include_optimizer=True)\\n self.disc_organs.save(op.join(self.model_dir, disc_organs_filename), include_optimizer=True)\\n self.summary_writer_organs.to_csv(op.join(self.train_summary_dir, train_summary_organs_filename))\\n return self\",\n \"def set_model_output(self, path):\\n\\n file = f'model_R{str(self.time_span).replace(\\\".\\\", \\\"_\\\")} ({str(self.date_time).replace(\\\":\\\",\\\"_\\\")}).csv'\\n self.model_output_file = path_inc(path, file)\",\n \"def test_hdf5_load_all():\\n skip_if_no_h5py()\\n import h5py\\n\\n # save random data to HDF5\\n handle, filename = tempfile.mkstemp()\\n dataset = random_one_hot_dense_design_matrix(np.random.RandomState(1),\\n num_examples=10, dim=5,\\n num_classes=3)\\n with h5py.File(filename, 'w') as f:\\n f.create_dataset('X', data=dataset.get_design_matrix())\\n f.create_dataset('y', data=dataset.get_targets())\\n\\n # instantiate Train object\\n trainer = yaml_parse.load(load_all_yaml % {'filename': filename})\\n trainer.main_loop()\\n\\n # cleanup\\n os.remove(filename)\",\n \"def create_train_file(img_folder_path: str, train_file_path: str) -> None:\\n files = []\\n for ext in (\\\"*.gif\\\", \\\"*.png\\\", \\\"*.jpg\\\", \\\"*.bmp\\\"):\\n img_path = glob(join(img_folder_path, ext))\\n if img_path:\\n files.extend(img_path)\\n\\n write_to_train_file(files, train_file_path)\\n\\n print(\\\"Training files are created in \\\" + img_folder_path)\",\n \"def write_hdf5(filename, data):\\n \\n if '.h5' in filename:\\n fid = h5py.File(filename, 'w')\\n else:\\n filename = filename+'.h5'\\n fid = h5py.File(filename, 'w')\\n\\n print('Writing %s...'%filename)\\n\\n write_hdf5_group(fid, data)\\n\\n fid.close()\\n print('Finished writting %s.'%filename)\\n return\",\n \"def create_train(train_img_path):\\n\\n f = open(\\\"train.txt\\\", \\\"w+\\\")\\n for subdirs, dirs, files in os.walk(train_img_path):\\n for filename in files:\\n if filename.endswith(\\\".jpg\\\"):\\n train_image_path = os.path.join(train_img_path, filename)\\n print(train_image_path)\\n f.write(train_image_path + \\\"\\\\n\\\")\\n f.close()\",\n \"def save(self, save_path: os.PathLike):\\n os.makedirs(save_path, exist_ok=True)\\n save_path = Path(save_path)\\n joblib.dump(self.dataset, save_path / \\\"dataset.pkl\\\")\\n hparams = dict(\\n latent_dim=self.latent_dim,\\n batch_size=self.batch_size,\\n learning_rate=self.learning_rate,\\n momentum=self.momentum,\\n )\\n if self.trained_epochs > 0:\\n joblib.dump(self.encoders, save_path / \\\"encoders.pkl\\\")\\n joblib.dump(self.gps_normalizer, save_path / \\\"gps_normalizer.pkl\\\")\\n self.gen.save(save_path / \\\"generator_model\\\")\\n self.dis.save(save_path / \\\"discriminator_model\\\")\\n self.gan.save(save_path / \\\"gan_model\\\")\\n train_state = dict(\\n patience=self.patience,\\n trained_epochs=self.trained_epochs,\\n timesteps=self.timesteps,\\n vocab_sizes=self.vocab_sizes,\\n )\\n joblib.dump(train_state, save_path / \\\"train_state.pkl\\\")\\n joblib.dump(hparams, save_path / \\\"hparams.pkl\\\")\\n\\n return self\",\n \"def cli(dataset_path, out_file):\\n data = pd.read_csv(dataset_path)\\n train_X, test_X, train_y, test_y = train_test_split(\\n X, y, test_size=0.2, random_state=1\\n )\\n\\n model = Predictor()\\n model.fit(train_X, train_y)\\n model.dump(out_file)\",\n \"def main(input_filepath, output_filepath):\\n\\n logging.info(\\\"reading %s\\\", input_filepath)\\n train_test = pd.read_hdf(input_filepath, 'train_test')\\n meta = pd.read_hdf(input_filepath, 'meta')\\n meta_org = pd.read_hdf(input_filepath, 'meta_org')\\n\\n sel_series = train_test[train_test.entry_type.isin(['train', 'cold_start'])]\\\\\\n ['series_id'].unique()\\n train_series, validate_series = train_test_split(sel_series, random_state=1)\\n\\n logging.info(\\\"calc train_test\\\")\\n train_test = calc_final_features(train_test, meta, meta_org=meta_org, verbose=True)\\n\\n sel = train_test[train_test.entry_type.isin(['train', 'cold_start'])]\\n train = sel[sel.series_id.isin(train_series)]\\n validate = sel[sel.series_id.isin(validate_series)]\\n test = train_test[train_test.entry_type.isin(['test'])]\\n\\n logging.info(\\\"writing %s\\\", output_filepath)\\n train.to_hdf(output_filepath, \\\"train\\\", mode=\\\"w\\\")\\n validate.to_hdf(output_filepath, \\\"validate\\\", mode=\\\"a\\\")\\n test.to_hdf(output_filepath, \\\"test\\\", mode=\\\"a\\\")\\n for k in ['meta', 'submission']:\\n df = pd.read_hdf(input_filepath, k)\\n df.to_hdf(output_filepath, k, mode=\\\"a\\\")\",\n \"def write_output_to_pickle(image_lst, img_dir, model,save_path=\\\"output_inception\\\", k=5, only_first_name=False):\\n imgs = image_lst\\n output_layer = []\\n \\n # Looping over images\\n \\n for img in tqdm(imgs):\\n tv = model.transfer_values(image_path=join(img_dir, img))\\n output_layer.append(tv)\\n # scores = model.get_scores(pred=pred, k=k, only_first_name=only_first_name)\\n # top_k.append(scores)\\n \\n # Building a dataframe with columns for image names, prediction array, scores\\n # print(\\\"THIS:\\\",np.array(output_layer).ndim)\\n # print(output_layer[0])\\n \\n df = pd.DataFrame({\\n \\\"img\\\": imgs,\\n \\\"transfer layer\\\": output_layer,\\n })\\n \\n # saving to pickle\\n \\n df.to_pickle(save_path)\",\n \"def tree_model(feature_train, help_rank_train, model_name):\\n decision_tree = DecisionTreeClassifier()\\n decision_tree = decision_tree.fit(feature_train, help_rank_train)\\n tree_model = open(model_name,'wb')\\n dump(decision_tree, tree_model, -1)\\n return\",\n \"def save_model(self, epoch=None, out_tag='my_lstm'):\\n\\n Utils.check_dir('./models/')\\n if epoch is not None:\\n self.model.save_weights('{}/models/{}_model_epoch_{}.hdf5'.format(os.getcwd(), out_tag, epoch))\\n with open(\\\"{}/models/{}_model_architecture_epoch_{}.json\\\".format(os.getcwd(), out_tag, epoch), \\\"w\\\") as f_out:\\n f_out.write(self.model.to_json())\\n else: \\n self.model.save_weights('{}/models/{}_model.hdf5'.format(os.getcwd(), out_tag))\\n with open(\\\"{}/models/{}_model_architecture.json\\\".format(os.getcwd(), out_tag), \\\"w\\\") as f_out:\\n f_out.write(self.model.to_json())\",\n \"def generate_train_txt(name, path):\\n with open(path + '/test.txt', 'a') as file:\\n file.write('/content/YOLO_metric/data/obj/' + name + '\\\\n')\",\n \"def save_model(self, path):\\n self._model.save(os.path.join(path, 'trained_model.h5'))\\n plot_model(self._model, to_file=os.path.join(path, 'model_structure.png'), show_shapes=True, show_layer_names=True)\",\n \"def save_hdf_file(file_path, idata, key_path='entry', overwrite=True):\\r\\n if (\\\"\\\\\\\\\\\" in file_path):\\r\\n raise ValueError(\\r\\n \\\"Please use a file path following the Unix convention\\\")\\r\\n file_base, file_ext = os.path.splitext(file_path)\\r\\n if not ((file_ext == '.hdf') or (file_ext == '.h5')):\\r\\n file_ext = '.hdf'\\r\\n file_path = file_base + file_ext\\r\\n _create_folder(file_path)\\r\\n if not overwrite:\\r\\n file_path = _create_file_name(file_path)\\r\\n ofile = None\\r\\n try:\\r\\n ofile = h5py.File(file_path, 'w')\\r\\n except IOError:\\r\\n print((\\\"Couldn't write file: {}\\\").format(file_path))\\r\\n raise\\r\\n grp = ofile.create_group(key_path)\\r\\n grp.create_dataset(\\\"data\\\", data=idata)\\r\\n ofile.close()\\r\\n return file_path\",\n \"def save(self,file_path):\\n hf = h5py.File(file_path, 'w')\\n hf.attrs[\\\"annotations\\\"] = str(self.annotations)\\n f=hf.create_group(\\\"funds\\\")\\n for i,fund in enumerate(self.funds_set):\\n grp = f.create_group(\\\"fund_{0}\\\".format(i))\\n grp.create_dataset(\\\"data\\\",data=fund.__getstate__()[\\\"data\\\"])\\n grp[\\\"params\\\"]=str(fund.__getstate__()[\\\"params\\\"])\\n g=hf.create_group(\\\"portfolios\\\")\\n for i,portfolio in enumerate(self.portfolios_set):\\n grp = g.create_group(\\\"portfolio_{0}\\\".format(i))\\n grp.attrs[\\\"capital\\\"]=portfolio.__getstate__()[\\\"capital\\\"]\\n grp.create_dataset(\\\"commitments\\\",data=portfolio.__getstate__()[\\\"commitments\\\"])\\n grp.create_dataset(\\\"vintages\\\", data=portfolio.__getstate__()[\\\"vintages\\\"])\\n funds=[hash(portfolio.funds[i]) for i in range(len(portfolio))]\\n grp.create_dataset(\\\"funds\\\", data=np.array(funds,dtype=h5py.string_dtype(encoding='utf-8')))\\n hf.close()\",\n \"def write_h5(fname: str, data: dict) -> None:\\n try:\\n with h5py.File(fname, 'w') as f:\\n recursively_save_dict_contents_to_group(f,'/',data)\\n except IOError as e:\\n print(f\\\"Cannot write HDF5 file {fname}\\\")\\n print(f\\\"IOError: {e}\\\")\",\n \"def train_teacher (nb_teachers, teacher_id):\\n # Load the dataset\\n X_train, X_test, y_train, y_test = models.get_dataset()\\n\\n print(X_train.shape)\\n print(y_train.shape)\\n print(X_test.shape)\\n print(y_test.shape)\\n \\n # Retrieve subset of data for this teacher\\n data, labels = partition.partition_dataset(X_train,\\n y_train,\\n nb_teachers,\\n teacher_id)\\n\\n print(\\\"Length of training data: \\\" + str(len(labels)))\\n\\n # Define teacher checkpoint filename and full path\\n\\n filename = str(nb_teachers) + '_teachers_' + str(teacher_id) + '.hdf5'\\n filename2 = str(nb_teachers) + '_teachers_' + str(teacher_id) + '.h5'\\n \\n # Perform teacher training need to modify \\n \\n\\n # Create teacher model\\n model, opt = models.create_two_layer_mlp(46) # num of cols\\n model.compile(loss='binary_crossentropy',\\n optimizer=\\\"Adam\\\",\\n metrics=['accuracy'])\\n model, hist = models.training(model, data, X_test, labels, y_test,filename)\\n\\n #modify\\n model_json = model.to_json()\\n with open(\\\"model.json\\\", \\\"w\\\") as json_file:\\n json_file.write(model_json)\\n# serialize weights to HDF5\\n model.save_weights(filename2)\\n print(\\\"Saved model to disk\\\")\\n return True\",\n \"def load_data():\\r\\n train_dataset = h5py.File('train_catvnoncat.h5', \\\"r\\\") # Change the directory as per your system\\r\\n train_set_x_orig = np.array(train_dataset[\\\"train_set_x\\\"][:]) # your train set features\\r\\n train_set_y_orig = np.array(train_dataset[\\\"train_set_y\\\"][:]) # your train set labels\\r\\n\\r\\n test_dataset = h5py.File('test_catvnoncat.h5', \\\"r\\\") # Change the directory as per your system\\r\\n test_set_x_orig = np.array(test_dataset[\\\"test_set_x\\\"][:]) # your test set features\\r\\n test_set_y_orig = np.array(test_dataset[\\\"test_set_y\\\"][:]) # your test set labels\\r\\n\\r\\n classes = np.array(test_dataset[\\\"list_classes\\\"][:]) # the list of classes\\r\\n \\r\\n train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))\\r\\n test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))\\r\\n \\r\\n return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes\",\n \"def train(\\n model_path=\\\"./trained_model/\\\",\\n model_file_name=\\\"model.h5\\\",\\n training_data_path=\\\"./train.csv\\\",\\n):\\n config = SConfig(training_data_path=training_data_path)\\n s2s = Seq2Seq(config)\\n s2s.fit()\\n s2s.save_model(path_to_model=model_path, model_file_name=model_file_name)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7217005","0.70193493","0.684831","0.6649944","0.6624915","0.656537","0.6552267","0.64465964","0.63645554","0.6273017","0.62496","0.61882937","0.6150306","0.610883","0.6098114","0.6088304","0.60781205","0.6075722","0.6039973","0.6017013","0.59959936","0.5990799","0.5982824","0.5934962","0.59158707","0.58937216","0.5867237","0.58626014","0.5850171","0.58397275","0.58374685","0.5835646","0.58273876","0.5822247","0.58196396","0.58125573","0.58093965","0.57877135","0.57790375","0.5767544","0.57674617","0.5765889","0.57646894","0.57385767","0.57355505","0.5733944","0.5720436","0.57050306","0.56992596","0.56982785","0.5696457","0.56952214","0.56902754","0.5688742","0.56867856","0.5685535","0.5684669","0.5684033","0.56837475","0.56805986","0.56787413","0.5658619","0.5642396","0.5640764","0.56376743","0.56326073","0.56318986","0.56237453","0.5615669","0.5612311","0.5604015","0.5595032","0.5593377","0.5582238","0.55743426","0.5569","0.5567635","0.5565695","0.55526793","0.5546549","0.5543563","0.5539595","0.5537432","0.5533444","0.5531805","0.5529146","0.5525446","0.5523127","0.5521531","0.55073285","0.55070996","0.5501883","0.55001396","0.5497032","0.5489433","0.54857135","0.54824734","0.5479708","0.5473499","0.54691637"],"string":"[\n \"0.7217005\",\n \"0.70193493\",\n \"0.684831\",\n \"0.6649944\",\n \"0.6624915\",\n \"0.656537\",\n \"0.6552267\",\n \"0.64465964\",\n \"0.63645554\",\n \"0.6273017\",\n \"0.62496\",\n \"0.61882937\",\n \"0.6150306\",\n \"0.610883\",\n \"0.6098114\",\n \"0.6088304\",\n \"0.60781205\",\n \"0.6075722\",\n \"0.6039973\",\n \"0.6017013\",\n \"0.59959936\",\n \"0.5990799\",\n \"0.5982824\",\n \"0.5934962\",\n \"0.59158707\",\n \"0.58937216\",\n \"0.5867237\",\n \"0.58626014\",\n \"0.5850171\",\n \"0.58397275\",\n \"0.58374685\",\n \"0.5835646\",\n \"0.58273876\",\n \"0.5822247\",\n \"0.58196396\",\n \"0.58125573\",\n \"0.58093965\",\n \"0.57877135\",\n \"0.57790375\",\n \"0.5767544\",\n \"0.57674617\",\n \"0.5765889\",\n \"0.57646894\",\n \"0.57385767\",\n \"0.57355505\",\n \"0.5733944\",\n \"0.5720436\",\n \"0.57050306\",\n \"0.56992596\",\n \"0.56982785\",\n \"0.5696457\",\n \"0.56952214\",\n \"0.56902754\",\n \"0.5688742\",\n \"0.56867856\",\n \"0.5685535\",\n \"0.5684669\",\n \"0.5684033\",\n \"0.56837475\",\n \"0.56805986\",\n \"0.56787413\",\n \"0.5658619\",\n \"0.5642396\",\n \"0.5640764\",\n \"0.56376743\",\n \"0.56326073\",\n \"0.56318986\",\n \"0.56237453\",\n \"0.5615669\",\n \"0.5612311\",\n \"0.5604015\",\n \"0.5595032\",\n \"0.5593377\",\n \"0.5582238\",\n \"0.55743426\",\n \"0.5569\",\n \"0.5567635\",\n \"0.5565695\",\n \"0.55526793\",\n \"0.5546549\",\n \"0.5543563\",\n \"0.5539595\",\n \"0.5537432\",\n \"0.5533444\",\n \"0.5531805\",\n \"0.5529146\",\n \"0.5525446\",\n \"0.5523127\",\n \"0.5521531\",\n \"0.55073285\",\n \"0.55070996\",\n \"0.5501883\",\n \"0.55001396\",\n \"0.5497032\",\n \"0.5489433\",\n \"0.54857135\",\n \"0.54824734\",\n \"0.5479708\",\n \"0.5473499\",\n \"0.54691637\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.71147066"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":16878,"cells":{"query":{"kind":"string","value":"Read image using its path. Default value is grayscale, and image is read by YCbCr format as the paper said."},"document":{"kind":"string","value":"def imread(path, is_grayscale=True):\n if is_grayscale:\n return scipy.misc.imread(path, flatten=True, mode='YCbCr').astype(np.float)\n else:\n return scipy.misc.imread(path, mode='YCbCr').astype(np.float)"},"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 imread(path, is_grayscale=True):\n if is_grayscale:\n #flatten=True 以灰度图的形式读取 \n return scipy.misc.imread(path, flatten=True, mode='YCbCr').astype(np.float)\n else:\n return scipy.misc.imread(path, mode='YCbCr').astype(np.float)","def imread(path, is_grayscale=True):\n if is_grayscale:\n # flatten=True: 形成單層的灰階通道\n return scipy.misc.imread(path, flatten=True, mode='YCbCr').astype(np.float)\n else:\n return scipy.misc.imread(path, mode='YCbCr').astype(np.float)","def imread(path, is_grayscale=True):\n if is_grayscale:\n return imageio.imread(path, as_gray=True, pilmode='YCbCr').astype(np.float32)\n else:\n return imageio.imread(path, pilmode='YCbCr').astype(np.float32)","def readImage(self, path, tt=1):\n return cv2.imread( path, tt)","def _read_input_file(self, path: Path):\n img = cv2.imread(str(path))\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # shape: (H, W, C)\n return img","def __read_image(self, path):\n path = 'data/' + path\n image = cv2.imread(path)\n\n # Convert greyscale image to BGR\n if image.shape[-1] == 1:\n image = np.dstack([image, image, image])\n\n # Convert BGR image to RGB image\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n\n return image","def read_image(path):\n img = misc.imread(path)\n return img","def read_img(path):\r\n if os.path.isfile(path):\r\n return cv2.imread(path)\r\n else:\r\n raise ValueError('hiiiiiiiiii')","def imread(path):\n with open(path, 'rb') as f:\n with PIL.Image.open(f) as img:\n return img.convert('RGB')","def imread(path, is_grayscale=False):\r\n\r\n if is_grayscale:\r\n return scipy.misc.imread(path, flatten=True).astype(np.float32)\r\n # img1=cv.imread(path).astype(np.float32)\r\n # return cv.cvtColor(img1,cv.COLOR_BGR2YCrCb)\r\n else:\r\n # img1=cv.imread(path).astype(np.float32)\r\n # return cv.cvtColor(img1,cv.COLOR_BGR2YCrCb)\r\n\r\n return scipy.misc.imread(path).astype(np.float32)","def read_image(path):\n img = ndimage.imread(path, mode=\"RGB\") \n return img","def read_image(path):\n\n image = cv2.imread(path)\n return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)","def read_image(path: str):\n return Image.open(path, mode=\"r\")","def read_image(image_path: str):\n\treturn cv.imread(image_path, cv.IMREAD_UNCHANGED)","def read_image(image_path, gray=False):\n if gray:\n return cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)\n \n image = cv2.imread(image_path) \n return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)","def read_image(image_path, *args, **kwargs):\n # TODO: Implement the method\n image2 = Image.open(image_path)\n image = num.asarray(image2)\n\n return image","def read_rgb_image(img_path, format='ndarray'):\n got_img = False\n if not osp.exists(img_path):\n raise IOError(\"{} does not exist\".format(img_path))\n while not got_img:\n try:\n if format == 'PIL':\n img = Image.open(img_path).convert(\"RGB\")\n elif format == 'ndarray':\n img = cv2.imread(img_path)\n if len(img.shape) == 3:\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n got_img = True\n except IOError:\n print(\"IOError incurred when reading '{}'. Will redo. Don't worry. Just chill.\".format(img_path))\n return img","def imread(path, as_gray=False, **kwargs):\n image = iio.imread(path, as_gray, **kwargs)\n if image.dtype == np.uint8:\n image = image / 127.5 - 1\n elif image.dtype == np.uint16:\n image = image / 32767.5 - 1\n elif image.dtype in [np.float32, np.float64]:\n image = image * 2 - 1.0\n else:\n raise Exception(\"Inavailable image dtype: %s!\" % image.dtype)\n return image","def _open_image(self, path):\n return cv.imread(path, 1)\n # .astype(float)","def imread(path):\n img = cv2.imread(path)\n return img","def read_image(image_path):\n return np.array(load_img(image_path, color_mode='grayscale')) / 255","def Read(image_path):\n # use cv2.imread() to read an images.\n # syntax : cv2.imread(filename, flag=None)\n return cv2.imread(image_path, 0)","def read_image(img_path):\n img = imageio.imread(uri=img_path)\n return img","def read(\n img_path: Union[str, pathlib.Path], mode: int = cv2.IMREAD_UNCHANGED\n) -> np.ndarray:\n return cv2.imread(str(img_path), mode)","def read(path: Union[Path, str]) -> np.ndarray:\n return _reader.imread(str(path))","def read_image(image_path: str, gray: bool=False) -> np.ndarray:\n if gray:\n return cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)\n\n image = cv2.imread(image_path)\n return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)","def read_image(fname):\n\timg = cv2.imread(fname,cv2.IMREAD_GRAYSCALE)\n\treturn img","def load_image(self, image_path):\n # Load image\n image = cv2.imread(image_path)\n #TODO 如果是灰度图先转为RGB的\n # If grayscale. Convert to RGB for consistency.\n # if image.ndim != 3:\n # image = skimage.color.gray2rgb(image)\n # If has an alpha channel, remove it for consistency\n if image.shape[-1] == 4:\n image = image[..., :3]\n return image\n pass","def get_input(path):\n img = imread(path)\n return img","def read_image_greyscale(path: str) -> np.ndarray:\n img = imread(path)\n if len(img.shape) > 2:\n img = np.dot(img[..., :3], [0.299, 0.587, 0.114])\n return img","def read_image(filename, grayscale=False):\n # Convert to grayscale\n if grayscale:\n return cv2.imread(filename, cv2.IMREAD_GRAYSCALE)\n return cv2.imread(filename, cv2.IMREAD_COLOR)","def read_image(img_path, channels_first, color=True, color_mode='BGR', dtype=np.float32, resize_dim=None):\n\n if not path.exists(img_path):\n raise ValueError('Provided path \"{}\" does NOT exist.'.format(img_path))\n\n image = cv2.imread(img_path, cv2.IMREAD_COLOR if color else cv2.IMREAD_GRAYSCALE)\n\n if color and color_mode == 'RGB':\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n\n if resize_dim is not None:\n image = cv2.resize(image, dsize=resize_dim[::-1], interpolation=cv2.INTER_LINEAR)\n\n if color and channels_first:\n image = np.transpose(image, (2, 0, 1))\n\n return image.astype(dtype)","def read_image(path, file_format='nii.gz'):\n path = path + '.' + file_format\n if file_format == 'npy':\n image = np.load(path)\n elif file_format == 'npz':\n image = np.load(path)['arr_0']\n elif file_format in ('png', 'jpg'):\n image = np.array(imageio.imread(path))\n elif file_format == 'dcm':\n image = np.array(imageio.volread(path, 'DICOM'))\n elif file_format in ('nii', 'nii.gz'):\n image = nib.load(path).get_data()\n else:\n raise ValueError('invalid --input_type : {}'.format(file_format))\n\n return image","def load_image(image_path):\n # Case insenstive check of the image type.\n img_lower = image_path.lower()\n if (\n img_lower.endswith(\n \".jpg\",\n -4,\n )\n or img_lower.endswith(\n \".png\",\n -4,\n )\n or img_lower.endswith(\n \".jpeg\",\n -5,\n )\n ):\n try:\n image_data = cv2.imread(image_path)\n image_data = cv2.cvtColor(image_data, cv2.COLOR_BGR2RGB)\n config_utils.logger.info(\"img shape: '{}'.\".format(image_data.shape))\n except Exception as e:\n config_utils.logger.error(\n \"Unable to read the image at: {}. Error: {}\".format(image_path, e)\n )\n exit(1)\n elif img_lower.endswith(\n \".npy\",\n -4,\n ):\n image_data = load(image_path)\n else:\n config_utils.logger.error(\"Images of format jpg,jpeg,png and npy are only supported.\")\n exit(1)\n return image_data","def read_image(filepath, format=None):\n image = Image.open(filepath)\n\n # capture and ignore this bug:\n # https://github.com/python-pillow/Pillow/issues/3973\n try:\n image = ImageOps.exif_transpose(image)\n except Exception:\n pass\n\n if format is not None:\n # PIL only supports RGB, so convert to RGB and flip channels over below\n conversion_format = format\n if format == \"BGR\":\n conversion_format = \"RGB\"\n image = image.convert(conversion_format)\n image = np.asarray(image)\n if format == \"BGR\":\n # flip channels if needed\n image = image[:, :, ::-1]\n # PIL squeezes out the channel dimension for \"L\", so make it HWC\n if format == \"L\":\n image = np.expand_dims(image, -1)\n return image","def imread(filename):\n filename = process(filename)\n ext = os.path.splitext(filename)[1]\n if ext.lower() == '.pfm':\n return load_pfm(filename)\n elif ext.lower() == '.dng':\n return load_dng(filename)\n else:\n loaded = cv2.imread(filename, flags=cv2.IMREAD_ANYDEPTH + cv2.IMREAD_COLOR)\n if loaded is None:\n raise IOError('Could not read {0}'.format(filename))\n else:\n return loaded","def read_color_image(path):\n with open(path, 'rb') as f:\n img = Image.fromarray(read_ppm(f), mode='RGB')\n img = tf.keras.preprocessing.image.img_to_array(img, dtype=int)\n img = tf.convert_to_tensor(img)\n return img","def readImage(imageName, flags=cv2.IMREAD_GRAYSCALE):\r\n if imageName.__class__ != \"\".__class__:\r\n print(\"ERROR - readImage: Input type must be a string (image name). Was: \", imageName.__class__)\r\n return None\r\n\r\n # cv2 loads an image in BGR format. Here we have a default value used to load it in grayscale mode\r\n image = cv2.imread(imageName, flags)\r\n return image","def read_image(img_path):\r\n got_img = False\r\n while not got_img:\r\n try:\r\n img = Image.open(img_path).convert('RGB')\r\n got_img = True\r\n except IOError:\r\n print(\"IOError incurred when reading '{}'. Will redo. Don't worry. Just chill.\".format(img_path))\r\n pass\r\n return img","def read_im(im_path):\n im = cv2.imread(im_path)\n return im","def read_image(self, filePath):\n if filePath.endswith(\".dcm\"):\n image = sitk.ReadImage(filePath)\n image = sitk.GetArrayFromImage(image).astype(\"int16\")\n image = np.expand_dims(image[0,:,:], -1)\n elif filePath.endswith(\".png\"):\n image = cv2.imread(filePath)\n image = np.array(image, dtype = \"int16\")\n elif filePath.endswith(\".mha\"):\n image = sitk.ReadImage(filePath)\n image = sitk.GetArrayFromImage(image).astype(\"int16\")\n image = np.transpose(image,(1,2,0))\n return image","def read_image(img_path, show=False):\n img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)\n\n if show:\n show_image(img)\n\n return img","def read_image(image_path, rgb=False):\n if rgb:\n image = Image.open(image_path)\n else:\n image = Image.open(image_path).convert('L')\n image = np.array(image, dtype=np.float32)\n return image","def read_image(image_path, rgb=False):\n if rgb:\n image = Image.open(image_path)\n else:\n image = Image.open(image_path).convert('L')\n image = np.array(image, dtype=np.float32)\n return image","def _read_image(self, image_path:str, label:str):\n # Get the full path to the image\n image = \"\"\n if label == \"real\":\n image = os.path.join(self.root, \"real\", image_path)\n else:\n image = os.path.join(self.root, \"fake\", image_path)\n \n # Read the image\n image = cv2.imread(image)\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n\n # Normalize the image\n image = image / 255.0\n\n # Convert the image to floating point to use it as\n # an input to the PyTorch model\n image = image.astype(np.float32)\n\n return image","def read_image(img_path):\n\tgot_img = False\n\twhile not got_img:\n\t\ttry:\n\t\t\timg = Image.open(img_path).convert('RGB')\n\t\t\timg = img.resize((100,100),Image.ANTIALIAS)\n\t\t\tgot_img = True\n\t\texcept IOError:\n\t\t\tprint(\"IOError incurred when reading '{}'. Will redo. Don't worry. Just chill.\".format(img_path))\n\t\t\tpass\n\treturn img","def read_gray_scale_image(data_path):\n return cv2.imread(data_path, cv2.IMREAD_GRAYSCALE)","def read_colored_image(data_path):\n return cv2.imread(data_path, cv2.IMREAD_COLOR)","def load_image(self, path):\n\n image = cv2.imread(path) / 255\n h, w, _ = image.shape\n image = cv2.resize(image, (self.input_size, self.input_size))\n nh, nw, _ = image.shape\n return image, (nh/h, nw/w)","def read_image(img_name, grey=False, use_opencv=False, uint8=False):\n data_dir = Path('..') / 'img'\n if use_opencv:\n if grey:\n img = cv_imread(data_dir / img_name, 0)\n else:\n img = cv_imread(data_dir / img_name)\n else:\n img = imread(data_dir / img_name, as_gray=grey)\n if uint8 and img.dtype != 'uint8':\n img = np.uint8(img)\n return img","def load_image(self, path):\n if path:\n self.original_image = cv2.imread(path, 1)\n self.prepare_images()","def read_img(img_path:str) -> object:\n img = cv2.imread(img_path)\n return img","def image(fname):\n return cv2.imread(fname)","def read_image(img_path):\n got_img = False\n if not osp.exists(img_path):\n raise IOError(\"{} does not exist\".format(img_path))\n while not got_img:\n try:\n img = Image.open(img_path).convert('RGB')\n got_img = True\n except IOError:\n print(\"IOError incurred when reading '{}'. Will redo. Don't worry. Just chill.\".format(img_path))\n pass\n return img","def read_image(img_path):\n got_img = False\n if not os.path.exists(img_path):\n raise IOError(\"{} does not exist\".format(img_path))\n while not got_img:\n try:\n img = Image.open(img_path).convert('RGB')\n got_img = True\n except IOError:\n print(\"IOError incurred when reading '{}'. Will redo. Don't worry. Just chill.\".format(img_path))\n pass\n return img","def load(image_path):\n\tpil_image = Image.open(image_path).convert(\"RGB\")\n\t# convert to BGR format\n\timage = np.array(pil_image)[:, :, [2, 1, 0]]\n\treturn image","def read_image(image_path):\n im = Image.open(image_path, 'r')\n return np.array(im)","def load_img(path):\n img = cv2.imread(path)\n return img","def readColorImage(filename):\n\t#imread flags>0(cv2.IMREAD_COLOR):3ChannelColors\n\t#白黒画像でも強制的にRGBで扱ってしまう．\n\timg = cv2.imread(filename, cv2.IMREAD_COLOR)\n\tprint('Read \"' + filename + '\".')\n\treturn img","def read_image(image_png_path, image_png_file):\n # Image\n f = os.path.join(image_png_path, image_png_file)\n #image = cv2.imread(f, cv2.IMREAD_GRAYSCALE)*2\n image = cv2.imread(f, -1)\n return image","def get_img(path):\n # imread function converts an image to a 2d grayscale array\n img = imread(path, as_gray=True).astype(int)\n\n # resize function resize image to a specific size;\n img = resize(img, (height, width), anti_aliasing=True, preserve_range=True)\n\n return img","def load(path):\n print(\"path\", path)\n print(Path(path).is_file())\n if Path(path).is_file():\n img = image.imread(path)\n print(f\"Loading image of dimensions {img.shape[0]} x \"\n f\"{img.shape[1]}\")\n return np.array(img)\n raise FileNotFoundError","def read_image(image_path: str) -> np.ndarray:\n assert image_path.exists()\n try:\n with Image.open(image_path) as img:\n image = np.array(img)\n except OSError as e:\n raise OSError(e)\n return image","def img_read(name):\n\n img = cv2.imread(name)\n\n return img","def imgLoad(path, gray=False):\n\tif gray:\n\t\treturn to_tensor(Image.open(path).convert('L'))[None,...]\n\treturn to_tensor(Image.open(path))[None,...]","def imgLoad(path, gray=False):\n\tif gray:\n\t\treturn to_tensor(Image.open(path).convert('L'))[None,...]\n\treturn to_tensor(Image.open(path))[None,...]","def read_image(image_path):\n if not os.path.exists(image_path):\n raise IOError('File does not exist: %s' % image_path)\n else:\n return Image.open(image_path)","def imgRead(filename: str, representation: int) -> np.ndarray:\r\n if representation==LOAD_GRAY_SCALE:\r\n img = cv2.imread(filename,0)\r\n else:\r\n img = cv2.imread(filename)\r\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\r\n return img.astype('uint8')","def read_image(filename, representation):\n image = imread(filename)\n image = image.astype(np.float64)\n image /= (COLOR_SIZE-1)\n\n if representation == RGB_NUMBER:\n return image\n image_gray = rgb2gray(image)\n return image_gray","def load_image(path: str):\n if path.endswith('.npy'):\n return np.load(path)\n if path.endswith(('.nii', '.nii.gz', '.hdr', '.img')):\n import nibabel as nib\n return nib.load(path).get_data()\n if path.endswith('.tif'):\n from PIL import Image\n with Image.open(path) as image:\n return np.asarray(image)\n\n raise ValueError(f\"Couldn't read image from path: {path}.\\n\"\n \"Unknown file extension.\")","def load_img(path, grayscale=False, target_size=None):\n img = io.imread(path, grayscale)\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n if target_size:\n img = cv2.resize(img, (target_size[1], target_size[0]))\n return img","def load(path) -> Image:\n return Image.open(path)","def open_image(image_path, mode=\"RGB\"):\n print(\"Opening image file in '%s'.\" % image_path)\n return Image.open(image_path).convert(mode)","def read_image(filename, representation=1):\n\n # if representation == 1:\n # # converting to gray\n # im = cv2.imread(filename, cv2.IMREAD_ANYDEPTH)\n # im = im / 65535\n # else:\n # if representation == 2:\n # im = cv2.imread(filename, cv2.IMREAD_COLOR)\n # # setting the image's matrix to be between 0 and 1\n # im = im / 65535\n # return im\n\n im = imread(filename)\n if representation == 1:\n # converting to gray\n im = rgb2gray(im) / 255\n else:\n if representation == 2:\n im = im.astype(np.float64)\n # setting the image's matrix to be between 0 and 1\n im = im / 255\n return im","def read_image(filename, representation):\n img = imread(filename)\n img = int2float(img)\n if representation == GS_REP:\n img = rgb2gray(img)\n return img","def load(path):\n img = plt.imread(path)\n dimensions = f\"{img.shape[0]} x {img.shape[1]}\"\n print(f\"Loaded image at {path} of dimensions {dimensions}\")\n return img","def _load(f, as_gray=False):\n # importing io is quite slow since it scans all the backends\n # we lazy import it here\n from skimage.io import imread\n return imread(os.path.join(data_dir, f), plugin='pil', as_gray=as_gray)","def load_image(file_path):\r\n return Image.open(file_path)","def read_image(path):\n reader = sitk.ImageSeriesReader()\n dicom_filenames = reader.GetGDCMSeriesFileNames(path)\n reader.SetFileNames(dicom_filenames)\n reader.LoadPrivateTagsOn()\n img = reader.Execute()\n img.SetOrigin((0, 0, 0))\n return img","def _load_image(path, filename, bits, mode):\n if filename.rsplit('.')[1].lower() == 'dcm':\n ds = pydicom.dcmread(os.path.join(path, filename))\n m = ('I;16' if bits == 16 else 'L') if mode == 'L' else 'RGB'\n image = Image.frombuffer(\n m, (ds.Columns, ds.Rows), ds.PixelData, 'raw', m, 0, 1)\n else:\n image = Image.open(os.path.join(path, filename)).convert(mode)\n return image","def read_image(img_path):\n got_img = False\n if not osp.exists(img_path):\n raise IOError(\"{} does not exist\".format(img_path))\n while not got_img:\n try:\n img = Image.open(img_path).convert('RGB')\n got_img = True\n except IOError:\n from ipdb import set_trace; set_trace()\n print(\"IOError incurred when reading '{}'. Will redo. Don't worry. Just chill.\".format(img_path))\n pass\n return img","def load_single_image(path: str) -> np.uint8:\n if not os.path.exists(path):\n print(f\"Warning, try to load non-exist image {path}\")\n return None\n if path.endswith(\".npy\"):\n img = np.load(path)\n elif path.endswith(\".png\") or path.endswith(\".jpeg\") or path.endswith(\".jpg\"):\n img = plt.imread(path)\n if img.dtype != \"uint8\":\n img = (255 * img).astype(np.uint8)\n return img","def load_img(fname):\n img = cv2.imread(fname)\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n return img","def read_image_mode(path, mode):\n image = PIL.Image.open(path)\n if image.mode != mode:\n raise ValueError('The image mode is {0} whereas the given mode is {1}.'.format(image.mode, mode))\n return numpy.asarray(image)","def loadImage(self, path: str) -> ndarray:\n try:\n self.img = np.asarray(Image.open(path))\n\n except FileNotFoundError:\n\n print(\"NO such File {}\".format(path))\n return None\n return self.img","def readImage(path,rgb = [0,1,2], *rescale):\n image = plt.imread(path)[:,:,rgb]\n height = image.shape[0]\n width = image.shape[1]\n try:\n # rescale the image by using bilinear interpolation\n for x in rescale:\n height = x[0]\n width = x[1]\n image = imresize(image, x, interp='bilinear', mode=None)\n except IndexError:\n pass\n #print(\"no given rescale command \")\n return np.array(image)","def imread(fname):\r\n return skimage.io.imread(fname)","def read_img(filename: str, folder: str, gray=False) -> np.ndarray:\n return (\n cv2.imread(os.path.join(folder, filename), cv2.CV_8UC1)\n if gray\n else cv2.imread(os.path.join(folder, filename))\n )","def read_img(self, path):\n img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)\n resized = cv2.resize(img, (28, 28))\n inverted = np.invert(np.array(resized).reshape((28, 28, 1)))\n denoised_and_reshaped = de_noise(inverted).reshape((28, 28, 1))\n normalized = denoised_and_reshaped / 255.\n return normalized","def imread(img_path):\n if not os.path.exists(img_path):\n raise ImageNotFoundError(f\"Image {img_path} could'nt be located\")\n\n img = cv2.imread(img_path)\n\n if img is None:\n raise InvalidImageError(f\"Image {img_path} could'nt be loaded\")\n\n return img","def read_image(filename, representation):\n im = imread(filename)\n if representation == GS_REP:\n im = rgb2gray(im)\n im = np.divide(im, MAX_VALUE - 1)\n return im","def LoadImage(self, filename, mode):\n print(\"TODO: CHECK FOR >PNG?\")\n path = \"static/CVImages/\" + filename\n print(\" path \" + path)\n img = cv2.imread(path, mode) # 0 for black, 1 for rgb\n return img","def load_image_file(filename, mode='RGB'):\n return imread(filename, mode=mode)","def read_image(filename, representation):\n image = imread(filename)\n\n # Convert image to grayscale, if required.\n if representation == GRAY_OUT and image.ndim == RGB_DIM:\n image = rgb2gray(image)\n else:\n # Normalize image to [0, 1].\n image = image.astype(np.float64)\n image /= TO_FRACTION\n return image","def load_single_image(image_path, dim=100):\n if not isinstance(image_path, str):\n img = Image.open(image_path)\n img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)\n img = preprocess_data(img, dim)\n else:\n img = cv2.imread(image_path, cv2.IMREAD_COLOR)\n img = preprocess_data(img, dim)\n\n img = np.array([img])\n\n return img","def load_image(self, image_id):\n # Load image\n# print(self.image_info[image_id]['path'])\n image = cv2.imread(self.image_info[image_id]['path'],cv2.IMREAD_GRAYSCALE) \n image = image[:,:, np.newaxis] #Add 1 dimension for grayscale images\n return image","def read_image_color(path: str, color: str) -> np.ndarray:\n img = imread(path)\n if color == 'red':\n return img[:, :, 0]\n elif color == 'green':\n return img[:, :, 1]\n elif color == 'blue':\n return img[:, :, 2]\n return read_image_greyscale(path)","def read_image(image_file_path: str):\n\n pixels = numpy.array(Image.open(image_file_path))\n\n return pixels","def read_img(filename, use_flat_32_type, one_channel, flip):\r\n\r\n if one_channel:\r\n img = cv2.imread(filename, -1)\r\n else:\r\n img = cv2.imread(filename)\r\n if img is None:\r\n print('in conv_data_generator.py - read_img function - image is None ; filename=', filename)\r\n return img\r\n if use_flat_32_type & (img is not None):\r\n img = img.astype(np.float32)\r\n if img.shape[:2] == (288, 512):\r\n if flip:\r\n img = cv2.flip(img, 1)\r\n return img\r\n else:\r\n print(\"something is strange here - input does not follow the normal habbit - please check or cvhange the code according to input size\")\r\n return False","def read_image_from_path(path: str, num_channels: Optional[int]=None, return_num_bytes=False) ->Union[Optional[torch.Tensor], Tuple[Optional[torch.Tensor], int]]:\n bytes_obj = get_bytes_obj_from_path(path)\n image = read_image_from_bytes_obj(bytes_obj, num_channels)\n if return_num_bytes:\n if bytes_obj is not None:\n num_bytes = len(bytes_obj)\n else:\n num_bytes = None\n return image, num_bytes\n else:\n return image"],"string":"[\n \"def imread(path, is_grayscale=True):\\n if is_grayscale:\\n #flatten=True 以灰度图的形式读取 \\n return scipy.misc.imread(path, flatten=True, mode='YCbCr').astype(np.float)\\n else:\\n return scipy.misc.imread(path, mode='YCbCr').astype(np.float)\",\n \"def imread(path, is_grayscale=True):\\n if is_grayscale:\\n # flatten=True: 形成單層的灰階通道\\n return scipy.misc.imread(path, flatten=True, mode='YCbCr').astype(np.float)\\n else:\\n return scipy.misc.imread(path, mode='YCbCr').astype(np.float)\",\n \"def imread(path, is_grayscale=True):\\n if is_grayscale:\\n return imageio.imread(path, as_gray=True, pilmode='YCbCr').astype(np.float32)\\n else:\\n return imageio.imread(path, pilmode='YCbCr').astype(np.float32)\",\n \"def readImage(self, path, tt=1):\\n return cv2.imread( path, tt)\",\n \"def _read_input_file(self, path: Path):\\n img = cv2.imread(str(path))\\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # shape: (H, W, C)\\n return img\",\n \"def __read_image(self, path):\\n path = 'data/' + path\\n image = cv2.imread(path)\\n\\n # Convert greyscale image to BGR\\n if image.shape[-1] == 1:\\n image = np.dstack([image, image, image])\\n\\n # Convert BGR image to RGB image\\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\\n\\n return image\",\n \"def read_image(path):\\n img = misc.imread(path)\\n return img\",\n \"def read_img(path):\\r\\n if os.path.isfile(path):\\r\\n return cv2.imread(path)\\r\\n else:\\r\\n raise ValueError('hiiiiiiiiii')\",\n \"def imread(path):\\n with open(path, 'rb') as f:\\n with PIL.Image.open(f) as img:\\n return img.convert('RGB')\",\n \"def imread(path, is_grayscale=False):\\r\\n\\r\\n if is_grayscale:\\r\\n return scipy.misc.imread(path, flatten=True).astype(np.float32)\\r\\n # img1=cv.imread(path).astype(np.float32)\\r\\n # return cv.cvtColor(img1,cv.COLOR_BGR2YCrCb)\\r\\n else:\\r\\n # img1=cv.imread(path).astype(np.float32)\\r\\n # return cv.cvtColor(img1,cv.COLOR_BGR2YCrCb)\\r\\n\\r\\n return scipy.misc.imread(path).astype(np.float32)\",\n \"def read_image(path):\\n img = ndimage.imread(path, mode=\\\"RGB\\\") \\n return img\",\n \"def read_image(path):\\n\\n image = cv2.imread(path)\\n return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\",\n \"def read_image(path: str):\\n return Image.open(path, mode=\\\"r\\\")\",\n \"def read_image(image_path: str):\\n\\treturn cv.imread(image_path, cv.IMREAD_UNCHANGED)\",\n \"def read_image(image_path, gray=False):\\n if gray:\\n return cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)\\n \\n image = cv2.imread(image_path) \\n return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\",\n \"def read_image(image_path, *args, **kwargs):\\n # TODO: Implement the method\\n image2 = Image.open(image_path)\\n image = num.asarray(image2)\\n\\n return image\",\n \"def read_rgb_image(img_path, format='ndarray'):\\n got_img = False\\n if not osp.exists(img_path):\\n raise IOError(\\\"{} does not exist\\\".format(img_path))\\n while not got_img:\\n try:\\n if format == 'PIL':\\n img = Image.open(img_path).convert(\\\"RGB\\\")\\n elif format == 'ndarray':\\n img = cv2.imread(img_path)\\n if len(img.shape) == 3:\\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\\n got_img = True\\n except IOError:\\n print(\\\"IOError incurred when reading '{}'. Will redo. Don't worry. Just chill.\\\".format(img_path))\\n return img\",\n \"def imread(path, as_gray=False, **kwargs):\\n image = iio.imread(path, as_gray, **kwargs)\\n if image.dtype == np.uint8:\\n image = image / 127.5 - 1\\n elif image.dtype == np.uint16:\\n image = image / 32767.5 - 1\\n elif image.dtype in [np.float32, np.float64]:\\n image = image * 2 - 1.0\\n else:\\n raise Exception(\\\"Inavailable image dtype: %s!\\\" % image.dtype)\\n return image\",\n \"def _open_image(self, path):\\n return cv.imread(path, 1)\\n # .astype(float)\",\n \"def imread(path):\\n img = cv2.imread(path)\\n return img\",\n \"def read_image(image_path):\\n return np.array(load_img(image_path, color_mode='grayscale')) / 255\",\n \"def Read(image_path):\\n # use cv2.imread() to read an images.\\n # syntax : cv2.imread(filename, flag=None)\\n return cv2.imread(image_path, 0)\",\n \"def read_image(img_path):\\n img = imageio.imread(uri=img_path)\\n return img\",\n \"def read(\\n img_path: Union[str, pathlib.Path], mode: int = cv2.IMREAD_UNCHANGED\\n) -> np.ndarray:\\n return cv2.imread(str(img_path), mode)\",\n \"def read(path: Union[Path, str]) -> np.ndarray:\\n return _reader.imread(str(path))\",\n \"def read_image(image_path: str, gray: bool=False) -> np.ndarray:\\n if gray:\\n return cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)\\n\\n image = cv2.imread(image_path)\\n return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\",\n \"def read_image(fname):\\n\\timg = cv2.imread(fname,cv2.IMREAD_GRAYSCALE)\\n\\treturn img\",\n \"def load_image(self, image_path):\\n # Load image\\n image = cv2.imread(image_path)\\n #TODO 如果是灰度图先转为RGB的\\n # If grayscale. Convert to RGB for consistency.\\n # if image.ndim != 3:\\n # image = skimage.color.gray2rgb(image)\\n # If has an alpha channel, remove it for consistency\\n if image.shape[-1] == 4:\\n image = image[..., :3]\\n return image\\n pass\",\n \"def get_input(path):\\n img = imread(path)\\n return img\",\n \"def read_image_greyscale(path: str) -> np.ndarray:\\n img = imread(path)\\n if len(img.shape) > 2:\\n img = np.dot(img[..., :3], [0.299, 0.587, 0.114])\\n return img\",\n \"def read_image(filename, grayscale=False):\\n # Convert to grayscale\\n if grayscale:\\n return cv2.imread(filename, cv2.IMREAD_GRAYSCALE)\\n return cv2.imread(filename, cv2.IMREAD_COLOR)\",\n \"def read_image(img_path, channels_first, color=True, color_mode='BGR', dtype=np.float32, resize_dim=None):\\n\\n if not path.exists(img_path):\\n raise ValueError('Provided path \\\"{}\\\" does NOT exist.'.format(img_path))\\n\\n image = cv2.imread(img_path, cv2.IMREAD_COLOR if color else cv2.IMREAD_GRAYSCALE)\\n\\n if color and color_mode == 'RGB':\\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\\n\\n if resize_dim is not None:\\n image = cv2.resize(image, dsize=resize_dim[::-1], interpolation=cv2.INTER_LINEAR)\\n\\n if color and channels_first:\\n image = np.transpose(image, (2, 0, 1))\\n\\n return image.astype(dtype)\",\n \"def read_image(path, file_format='nii.gz'):\\n path = path + '.' + file_format\\n if file_format == 'npy':\\n image = np.load(path)\\n elif file_format == 'npz':\\n image = np.load(path)['arr_0']\\n elif file_format in ('png', 'jpg'):\\n image = np.array(imageio.imread(path))\\n elif file_format == 'dcm':\\n image = np.array(imageio.volread(path, 'DICOM'))\\n elif file_format in ('nii', 'nii.gz'):\\n image = nib.load(path).get_data()\\n else:\\n raise ValueError('invalid --input_type : {}'.format(file_format))\\n\\n return image\",\n \"def load_image(image_path):\\n # Case insenstive check of the image type.\\n img_lower = image_path.lower()\\n if (\\n img_lower.endswith(\\n \\\".jpg\\\",\\n -4,\\n )\\n or img_lower.endswith(\\n \\\".png\\\",\\n -4,\\n )\\n or img_lower.endswith(\\n \\\".jpeg\\\",\\n -5,\\n )\\n ):\\n try:\\n image_data = cv2.imread(image_path)\\n image_data = cv2.cvtColor(image_data, cv2.COLOR_BGR2RGB)\\n config_utils.logger.info(\\\"img shape: '{}'.\\\".format(image_data.shape))\\n except Exception as e:\\n config_utils.logger.error(\\n \\\"Unable to read the image at: {}. Error: {}\\\".format(image_path, e)\\n )\\n exit(1)\\n elif img_lower.endswith(\\n \\\".npy\\\",\\n -4,\\n ):\\n image_data = load(image_path)\\n else:\\n config_utils.logger.error(\\\"Images of format jpg,jpeg,png and npy are only supported.\\\")\\n exit(1)\\n return image_data\",\n \"def read_image(filepath, format=None):\\n image = Image.open(filepath)\\n\\n # capture and ignore this bug:\\n # https://github.com/python-pillow/Pillow/issues/3973\\n try:\\n image = ImageOps.exif_transpose(image)\\n except Exception:\\n pass\\n\\n if format is not None:\\n # PIL only supports RGB, so convert to RGB and flip channels over below\\n conversion_format = format\\n if format == \\\"BGR\\\":\\n conversion_format = \\\"RGB\\\"\\n image = image.convert(conversion_format)\\n image = np.asarray(image)\\n if format == \\\"BGR\\\":\\n # flip channels if needed\\n image = image[:, :, ::-1]\\n # PIL squeezes out the channel dimension for \\\"L\\\", so make it HWC\\n if format == \\\"L\\\":\\n image = np.expand_dims(image, -1)\\n return image\",\n \"def imread(filename):\\n filename = process(filename)\\n ext = os.path.splitext(filename)[1]\\n if ext.lower() == '.pfm':\\n return load_pfm(filename)\\n elif ext.lower() == '.dng':\\n return load_dng(filename)\\n else:\\n loaded = cv2.imread(filename, flags=cv2.IMREAD_ANYDEPTH + cv2.IMREAD_COLOR)\\n if loaded is None:\\n raise IOError('Could not read {0}'.format(filename))\\n else:\\n return loaded\",\n \"def read_color_image(path):\\n with open(path, 'rb') as f:\\n img = Image.fromarray(read_ppm(f), mode='RGB')\\n img = tf.keras.preprocessing.image.img_to_array(img, dtype=int)\\n img = tf.convert_to_tensor(img)\\n return img\",\n \"def readImage(imageName, flags=cv2.IMREAD_GRAYSCALE):\\r\\n if imageName.__class__ != \\\"\\\".__class__:\\r\\n print(\\\"ERROR - readImage: Input type must be a string (image name). Was: \\\", imageName.__class__)\\r\\n return None\\r\\n\\r\\n # cv2 loads an image in BGR format. Here we have a default value used to load it in grayscale mode\\r\\n image = cv2.imread(imageName, flags)\\r\\n return image\",\n \"def read_image(img_path):\\r\\n got_img = False\\r\\n while not got_img:\\r\\n try:\\r\\n img = Image.open(img_path).convert('RGB')\\r\\n got_img = True\\r\\n except IOError:\\r\\n print(\\\"IOError incurred when reading '{}'. Will redo. Don't worry. Just chill.\\\".format(img_path))\\r\\n pass\\r\\n return img\",\n \"def read_im(im_path):\\n im = cv2.imread(im_path)\\n return im\",\n \"def read_image(self, filePath):\\n if filePath.endswith(\\\".dcm\\\"):\\n image = sitk.ReadImage(filePath)\\n image = sitk.GetArrayFromImage(image).astype(\\\"int16\\\")\\n image = np.expand_dims(image[0,:,:], -1)\\n elif filePath.endswith(\\\".png\\\"):\\n image = cv2.imread(filePath)\\n image = np.array(image, dtype = \\\"int16\\\")\\n elif filePath.endswith(\\\".mha\\\"):\\n image = sitk.ReadImage(filePath)\\n image = sitk.GetArrayFromImage(image).astype(\\\"int16\\\")\\n image = np.transpose(image,(1,2,0))\\n return image\",\n \"def read_image(img_path, show=False):\\n img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)\\n\\n if show:\\n show_image(img)\\n\\n return img\",\n \"def read_image(image_path, rgb=False):\\n if rgb:\\n image = Image.open(image_path)\\n else:\\n image = Image.open(image_path).convert('L')\\n image = np.array(image, dtype=np.float32)\\n return image\",\n \"def read_image(image_path, rgb=False):\\n if rgb:\\n image = Image.open(image_path)\\n else:\\n image = Image.open(image_path).convert('L')\\n image = np.array(image, dtype=np.float32)\\n return image\",\n \"def _read_image(self, image_path:str, label:str):\\n # Get the full path to the image\\n image = \\\"\\\"\\n if label == \\\"real\\\":\\n image = os.path.join(self.root, \\\"real\\\", image_path)\\n else:\\n image = os.path.join(self.root, \\\"fake\\\", image_path)\\n \\n # Read the image\\n image = cv2.imread(image)\\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\\n\\n # Normalize the image\\n image = image / 255.0\\n\\n # Convert the image to floating point to use it as\\n # an input to the PyTorch model\\n image = image.astype(np.float32)\\n\\n return image\",\n \"def read_image(img_path):\\n\\tgot_img = False\\n\\twhile not got_img:\\n\\t\\ttry:\\n\\t\\t\\timg = Image.open(img_path).convert('RGB')\\n\\t\\t\\timg = img.resize((100,100),Image.ANTIALIAS)\\n\\t\\t\\tgot_img = True\\n\\t\\texcept IOError:\\n\\t\\t\\tprint(\\\"IOError incurred when reading '{}'. Will redo. Don't worry. Just chill.\\\".format(img_path))\\n\\t\\t\\tpass\\n\\treturn img\",\n \"def read_gray_scale_image(data_path):\\n return cv2.imread(data_path, cv2.IMREAD_GRAYSCALE)\",\n \"def read_colored_image(data_path):\\n return cv2.imread(data_path, cv2.IMREAD_COLOR)\",\n \"def load_image(self, path):\\n\\n image = cv2.imread(path) / 255\\n h, w, _ = image.shape\\n image = cv2.resize(image, (self.input_size, self.input_size))\\n nh, nw, _ = image.shape\\n return image, (nh/h, nw/w)\",\n \"def read_image(img_name, grey=False, use_opencv=False, uint8=False):\\n data_dir = Path('..') / 'img'\\n if use_opencv:\\n if grey:\\n img = cv_imread(data_dir / img_name, 0)\\n else:\\n img = cv_imread(data_dir / img_name)\\n else:\\n img = imread(data_dir / img_name, as_gray=grey)\\n if uint8 and img.dtype != 'uint8':\\n img = np.uint8(img)\\n return img\",\n \"def load_image(self, path):\\n if path:\\n self.original_image = cv2.imread(path, 1)\\n self.prepare_images()\",\n \"def read_img(img_path:str) -> object:\\n img = cv2.imread(img_path)\\n return img\",\n \"def image(fname):\\n return cv2.imread(fname)\",\n \"def read_image(img_path):\\n got_img = False\\n if not osp.exists(img_path):\\n raise IOError(\\\"{} does not exist\\\".format(img_path))\\n while not got_img:\\n try:\\n img = Image.open(img_path).convert('RGB')\\n got_img = True\\n except IOError:\\n print(\\\"IOError incurred when reading '{}'. Will redo. Don't worry. Just chill.\\\".format(img_path))\\n pass\\n return img\",\n \"def read_image(img_path):\\n got_img = False\\n if not os.path.exists(img_path):\\n raise IOError(\\\"{} does not exist\\\".format(img_path))\\n while not got_img:\\n try:\\n img = Image.open(img_path).convert('RGB')\\n got_img = True\\n except IOError:\\n print(\\\"IOError incurred when reading '{}'. Will redo. Don't worry. Just chill.\\\".format(img_path))\\n pass\\n return img\",\n \"def load(image_path):\\n\\tpil_image = Image.open(image_path).convert(\\\"RGB\\\")\\n\\t# convert to BGR format\\n\\timage = np.array(pil_image)[:, :, [2, 1, 0]]\\n\\treturn image\",\n \"def read_image(image_path):\\n im = Image.open(image_path, 'r')\\n return np.array(im)\",\n \"def load_img(path):\\n img = cv2.imread(path)\\n return img\",\n \"def readColorImage(filename):\\n\\t#imread flags>0(cv2.IMREAD_COLOR):3ChannelColors\\n\\t#白黒画像でも強制的にRGBで扱ってしまう．\\n\\timg = cv2.imread(filename, cv2.IMREAD_COLOR)\\n\\tprint('Read \\\"' + filename + '\\\".')\\n\\treturn img\",\n \"def read_image(image_png_path, image_png_file):\\n # Image\\n f = os.path.join(image_png_path, image_png_file)\\n #image = cv2.imread(f, cv2.IMREAD_GRAYSCALE)*2\\n image = cv2.imread(f, -1)\\n return image\",\n \"def get_img(path):\\n # imread function converts an image to a 2d grayscale array\\n img = imread(path, as_gray=True).astype(int)\\n\\n # resize function resize image to a specific size;\\n img = resize(img, (height, width), anti_aliasing=True, preserve_range=True)\\n\\n return img\",\n \"def load(path):\\n print(\\\"path\\\", path)\\n print(Path(path).is_file())\\n if Path(path).is_file():\\n img = image.imread(path)\\n print(f\\\"Loading image of dimensions {img.shape[0]} x \\\"\\n f\\\"{img.shape[1]}\\\")\\n return np.array(img)\\n raise FileNotFoundError\",\n \"def read_image(image_path: str) -> np.ndarray:\\n assert image_path.exists()\\n try:\\n with Image.open(image_path) as img:\\n image = np.array(img)\\n except OSError as e:\\n raise OSError(e)\\n return image\",\n \"def img_read(name):\\n\\n img = cv2.imread(name)\\n\\n return img\",\n \"def imgLoad(path, gray=False):\\n\\tif gray:\\n\\t\\treturn to_tensor(Image.open(path).convert('L'))[None,...]\\n\\treturn to_tensor(Image.open(path))[None,...]\",\n \"def imgLoad(path, gray=False):\\n\\tif gray:\\n\\t\\treturn to_tensor(Image.open(path).convert('L'))[None,...]\\n\\treturn to_tensor(Image.open(path))[None,...]\",\n \"def read_image(image_path):\\n if not os.path.exists(image_path):\\n raise IOError('File does not exist: %s' % image_path)\\n else:\\n return Image.open(image_path)\",\n \"def imgRead(filename: str, representation: int) -> np.ndarray:\\r\\n if representation==LOAD_GRAY_SCALE:\\r\\n img = cv2.imread(filename,0)\\r\\n else:\\r\\n img = cv2.imread(filename)\\r\\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\\r\\n return img.astype('uint8')\",\n \"def read_image(filename, representation):\\n image = imread(filename)\\n image = image.astype(np.float64)\\n image /= (COLOR_SIZE-1)\\n\\n if representation == RGB_NUMBER:\\n return image\\n image_gray = rgb2gray(image)\\n return image_gray\",\n \"def load_image(path: str):\\n if path.endswith('.npy'):\\n return np.load(path)\\n if path.endswith(('.nii', '.nii.gz', '.hdr', '.img')):\\n import nibabel as nib\\n return nib.load(path).get_data()\\n if path.endswith('.tif'):\\n from PIL import Image\\n with Image.open(path) as image:\\n return np.asarray(image)\\n\\n raise ValueError(f\\\"Couldn't read image from path: {path}.\\\\n\\\"\\n \\\"Unknown file extension.\\\")\",\n \"def load_img(path, grayscale=False, target_size=None):\\n img = io.imread(path, grayscale)\\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\\n if target_size:\\n img = cv2.resize(img, (target_size[1], target_size[0]))\\n return img\",\n \"def load(path) -> Image:\\n return Image.open(path)\",\n \"def open_image(image_path, mode=\\\"RGB\\\"):\\n print(\\\"Opening image file in '%s'.\\\" % image_path)\\n return Image.open(image_path).convert(mode)\",\n \"def read_image(filename, representation=1):\\n\\n # if representation == 1:\\n # # converting to gray\\n # im = cv2.imread(filename, cv2.IMREAD_ANYDEPTH)\\n # im = im / 65535\\n # else:\\n # if representation == 2:\\n # im = cv2.imread(filename, cv2.IMREAD_COLOR)\\n # # setting the image's matrix to be between 0 and 1\\n # im = im / 65535\\n # return im\\n\\n im = imread(filename)\\n if representation == 1:\\n # converting to gray\\n im = rgb2gray(im) / 255\\n else:\\n if representation == 2:\\n im = im.astype(np.float64)\\n # setting the image's matrix to be between 0 and 1\\n im = im / 255\\n return im\",\n \"def read_image(filename, representation):\\n img = imread(filename)\\n img = int2float(img)\\n if representation == GS_REP:\\n img = rgb2gray(img)\\n return img\",\n \"def load(path):\\n img = plt.imread(path)\\n dimensions = f\\\"{img.shape[0]} x {img.shape[1]}\\\"\\n print(f\\\"Loaded image at {path} of dimensions {dimensions}\\\")\\n return img\",\n \"def _load(f, as_gray=False):\\n # importing io is quite slow since it scans all the backends\\n # we lazy import it here\\n from skimage.io import imread\\n return imread(os.path.join(data_dir, f), plugin='pil', as_gray=as_gray)\",\n \"def load_image(file_path):\\r\\n return Image.open(file_path)\",\n \"def read_image(path):\\n reader = sitk.ImageSeriesReader()\\n dicom_filenames = reader.GetGDCMSeriesFileNames(path)\\n reader.SetFileNames(dicom_filenames)\\n reader.LoadPrivateTagsOn()\\n img = reader.Execute()\\n img.SetOrigin((0, 0, 0))\\n return img\",\n \"def _load_image(path, filename, bits, mode):\\n if filename.rsplit('.')[1].lower() == 'dcm':\\n ds = pydicom.dcmread(os.path.join(path, filename))\\n m = ('I;16' if bits == 16 else 'L') if mode == 'L' else 'RGB'\\n image = Image.frombuffer(\\n m, (ds.Columns, ds.Rows), ds.PixelData, 'raw', m, 0, 1)\\n else:\\n image = Image.open(os.path.join(path, filename)).convert(mode)\\n return image\",\n \"def read_image(img_path):\\n got_img = False\\n if not osp.exists(img_path):\\n raise IOError(\\\"{} does not exist\\\".format(img_path))\\n while not got_img:\\n try:\\n img = Image.open(img_path).convert('RGB')\\n got_img = True\\n except IOError:\\n from ipdb import set_trace; set_trace()\\n print(\\\"IOError incurred when reading '{}'. Will redo. Don't worry. Just chill.\\\".format(img_path))\\n pass\\n return img\",\n \"def load_single_image(path: str) -> np.uint8:\\n if not os.path.exists(path):\\n print(f\\\"Warning, try to load non-exist image {path}\\\")\\n return None\\n if path.endswith(\\\".npy\\\"):\\n img = np.load(path)\\n elif path.endswith(\\\".png\\\") or path.endswith(\\\".jpeg\\\") or path.endswith(\\\".jpg\\\"):\\n img = plt.imread(path)\\n if img.dtype != \\\"uint8\\\":\\n img = (255 * img).astype(np.uint8)\\n return img\",\n \"def load_img(fname):\\n img = cv2.imread(fname)\\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\\n return img\",\n \"def read_image_mode(path, mode):\\n image = PIL.Image.open(path)\\n if image.mode != mode:\\n raise ValueError('The image mode is {0} whereas the given mode is {1}.'.format(image.mode, mode))\\n return numpy.asarray(image)\",\n \"def loadImage(self, path: str) -> ndarray:\\n try:\\n self.img = np.asarray(Image.open(path))\\n\\n except FileNotFoundError:\\n\\n print(\\\"NO such File {}\\\".format(path))\\n return None\\n return self.img\",\n \"def readImage(path,rgb = [0,1,2], *rescale):\\n image = plt.imread(path)[:,:,rgb]\\n height = image.shape[0]\\n width = image.shape[1]\\n try:\\n # rescale the image by using bilinear interpolation\\n for x in rescale:\\n height = x[0]\\n width = x[1]\\n image = imresize(image, x, interp='bilinear', mode=None)\\n except IndexError:\\n pass\\n #print(\\\"no given rescale command \\\")\\n return np.array(image)\",\n \"def imread(fname):\\r\\n return skimage.io.imread(fname)\",\n \"def read_img(filename: str, folder: str, gray=False) -> np.ndarray:\\n return (\\n cv2.imread(os.path.join(folder, filename), cv2.CV_8UC1)\\n if gray\\n else cv2.imread(os.path.join(folder, filename))\\n )\",\n \"def read_img(self, path):\\n img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)\\n resized = cv2.resize(img, (28, 28))\\n inverted = np.invert(np.array(resized).reshape((28, 28, 1)))\\n denoised_and_reshaped = de_noise(inverted).reshape((28, 28, 1))\\n normalized = denoised_and_reshaped / 255.\\n return normalized\",\n \"def imread(img_path):\\n if not os.path.exists(img_path):\\n raise ImageNotFoundError(f\\\"Image {img_path} could'nt be located\\\")\\n\\n img = cv2.imread(img_path)\\n\\n if img is None:\\n raise InvalidImageError(f\\\"Image {img_path} could'nt be loaded\\\")\\n\\n return img\",\n \"def read_image(filename, representation):\\n im = imread(filename)\\n if representation == GS_REP:\\n im = rgb2gray(im)\\n im = np.divide(im, MAX_VALUE - 1)\\n return im\",\n \"def LoadImage(self, filename, mode):\\n print(\\\"TODO: CHECK FOR >PNG?\\\")\\n path = \\\"static/CVImages/\\\" + filename\\n print(\\\" path \\\" + path)\\n img = cv2.imread(path, mode) # 0 for black, 1 for rgb\\n return img\",\n \"def load_image_file(filename, mode='RGB'):\\n return imread(filename, mode=mode)\",\n \"def read_image(filename, representation):\\n image = imread(filename)\\n\\n # Convert image to grayscale, if required.\\n if representation == GRAY_OUT and image.ndim == RGB_DIM:\\n image = rgb2gray(image)\\n else:\\n # Normalize image to [0, 1].\\n image = image.astype(np.float64)\\n image /= TO_FRACTION\\n return image\",\n \"def load_single_image(image_path, dim=100):\\n if not isinstance(image_path, str):\\n img = Image.open(image_path)\\n img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)\\n img = preprocess_data(img, dim)\\n else:\\n img = cv2.imread(image_path, cv2.IMREAD_COLOR)\\n img = preprocess_data(img, dim)\\n\\n img = np.array([img])\\n\\n return img\",\n \"def load_image(self, image_id):\\n # Load image\\n# print(self.image_info[image_id]['path'])\\n image = cv2.imread(self.image_info[image_id]['path'],cv2.IMREAD_GRAYSCALE) \\n image = image[:,:, np.newaxis] #Add 1 dimension for grayscale images\\n return image\",\n \"def read_image_color(path: str, color: str) -> np.ndarray:\\n img = imread(path)\\n if color == 'red':\\n return img[:, :, 0]\\n elif color == 'green':\\n return img[:, :, 1]\\n elif color == 'blue':\\n return img[:, :, 2]\\n return read_image_greyscale(path)\",\n \"def read_image(image_file_path: str):\\n\\n pixels = numpy.array(Image.open(image_file_path))\\n\\n return pixels\",\n \"def read_img(filename, use_flat_32_type, one_channel, flip):\\r\\n\\r\\n if one_channel:\\r\\n img = cv2.imread(filename, -1)\\r\\n else:\\r\\n img = cv2.imread(filename)\\r\\n if img is None:\\r\\n print('in conv_data_generator.py - read_img function - image is None ; filename=', filename)\\r\\n return img\\r\\n if use_flat_32_type & (img is not None):\\r\\n img = img.astype(np.float32)\\r\\n if img.shape[:2] == (288, 512):\\r\\n if flip:\\r\\n img = cv2.flip(img, 1)\\r\\n return img\\r\\n else:\\r\\n print(\\\"something is strange here - input does not follow the normal habbit - please check or cvhange the code according to input size\\\")\\r\\n return False\",\n \"def read_image_from_path(path: str, num_channels: Optional[int]=None, return_num_bytes=False) ->Union[Optional[torch.Tensor], Tuple[Optional[torch.Tensor], int]]:\\n bytes_obj = get_bytes_obj_from_path(path)\\n image = read_image_from_bytes_obj(bytes_obj, num_channels)\\n if return_num_bytes:\\n if bytes_obj is not None:\\n num_bytes = len(bytes_obj)\\n else:\\n num_bytes = None\\n return image, num_bytes\\n else:\\n return image\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.778447","0.77261835","0.7638995","0.75208247","0.74676746","0.74573797","0.7367039","0.722754","0.721408","0.72122663","0.71996546","0.7183188","0.70761466","0.7057287","0.7026387","0.70155853","0.6943071","0.6942862","0.69331473","0.689974","0.6891293","0.6876135","0.6834026","0.6803568","0.676568","0.6737664","0.67365336","0.6699616","0.66899395","0.6669361","0.6669006","0.66289794","0.6590289","0.65866315","0.6573189","0.6571991","0.6568552","0.6555358","0.65517306","0.65451986","0.6542543","0.65253013","0.65149254","0.65149254","0.65104145","0.6509684","0.6509467","0.6495821","0.6484357","0.64508873","0.6424278","0.641785","0.6407544","0.64056927","0.6405302","0.64028883","0.6400016","0.6399601","0.6381181","0.6361318","0.63404685","0.63296664","0.631333","0.63111454","0.6302808","0.6302808","0.6299929","0.6297955","0.62888205","0.62874424","0.62847847","0.6271138","0.62693775","0.6268033","0.6267054","0.6259557","0.62594885","0.6252937","0.62339115","0.623373","0.6231839","0.622336","0.622218","0.62210935","0.6215373","0.6209609","0.6206921","0.6204267","0.62018895","0.6193951","0.6179212","0.61703837","0.61673254","0.61607265","0.61430335","0.61421657","0.6124951","0.61236924","0.61169535","0.6112236"],"string":"[\n \"0.778447\",\n \"0.77261835\",\n \"0.7638995\",\n \"0.75208247\",\n \"0.74676746\",\n \"0.74573797\",\n \"0.7367039\",\n \"0.722754\",\n \"0.721408\",\n \"0.72122663\",\n \"0.71996546\",\n \"0.7183188\",\n \"0.70761466\",\n \"0.7057287\",\n \"0.7026387\",\n \"0.70155853\",\n \"0.6943071\",\n \"0.6942862\",\n \"0.69331473\",\n \"0.689974\",\n \"0.6891293\",\n \"0.6876135\",\n \"0.6834026\",\n \"0.6803568\",\n \"0.676568\",\n \"0.6737664\",\n \"0.67365336\",\n \"0.6699616\",\n \"0.66899395\",\n \"0.6669361\",\n \"0.6669006\",\n \"0.66289794\",\n \"0.6590289\",\n \"0.65866315\",\n \"0.6573189\",\n \"0.6571991\",\n \"0.6568552\",\n \"0.6555358\",\n \"0.65517306\",\n \"0.65451986\",\n \"0.6542543\",\n \"0.65253013\",\n \"0.65149254\",\n \"0.65149254\",\n \"0.65104145\",\n \"0.6509684\",\n \"0.6509467\",\n \"0.6495821\",\n \"0.6484357\",\n \"0.64508873\",\n \"0.6424278\",\n \"0.641785\",\n \"0.6407544\",\n \"0.64056927\",\n \"0.6405302\",\n \"0.64028883\",\n \"0.6400016\",\n \"0.6399601\",\n \"0.6381181\",\n \"0.6361318\",\n \"0.63404685\",\n \"0.63296664\",\n \"0.631333\",\n \"0.63111454\",\n \"0.6302808\",\n \"0.6302808\",\n \"0.6299929\",\n \"0.6297955\",\n \"0.62888205\",\n \"0.62874424\",\n \"0.62847847\",\n \"0.6271138\",\n \"0.62693775\",\n \"0.6268033\",\n \"0.6267054\",\n \"0.6259557\",\n \"0.62594885\",\n \"0.6252937\",\n \"0.62339115\",\n \"0.623373\",\n \"0.6231839\",\n \"0.622336\",\n \"0.622218\",\n \"0.62210935\",\n \"0.6215373\",\n \"0.6209609\",\n \"0.6206921\",\n \"0.6204267\",\n \"0.62018895\",\n \"0.6193951\",\n \"0.6179212\",\n \"0.61703837\",\n \"0.61673254\",\n \"0.61607265\",\n \"0.61430335\",\n \"0.61421657\",\n \"0.6124951\",\n \"0.61236924\",\n \"0.61169535\",\n \"0.6112236\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7768587"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":16879,"cells":{"query":{"kind":"string","value":"To scale down and up the original image, first thing to do is to have no remainder while scaling operation. We need to find modulo of height (and width) and scale factor. Then, subtract the modulo from height (and width) of original image size. There would be no remainder even after scaling operation."},"document":{"kind":"string","value":"def modcrop(image, scale=3):\n if len(image.shape) == 3:\n h, w, _ = image.shape\n h = h - np.mod(h, scale)\n w = w - np.mod(w, scale)\n image = image[0:h, 0:w, :]\n else:\n h, w = image.shape\n h = h - np.mod(h, scale)\n w = w - np.mod(w, scale)\n image = image[0:h, 0:w]\n return image"},"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 scale_down(image:np.array)->np.array:\n src = image\n scale_percent = 25\n width = int(src.shape[1] * scale_percent / 100)\n height = int(src.shape[0] * scale_percent / 100)\n dsize = (width, height)\n output = cv2.resize(src, dsize)\n return output","def Rescale(self):\r\n picWidth,picHeight = self.oldSize = self.GetSizeTuple()\r\n bitmap = self.scaled = self.bitmap\r\n if not bitmap: return\r\n imgWidth,imgHeight = bitmap.GetWidth(),bitmap.GetHeight()\r\n if self.scaling == 2 or (self.scaling == 1 and (imgWidth > picWidth or imgHeight > picHeight)):\r\n image = bitmap.ConvertToImage()\r\n factor = min(1.0*picWidth/imgWidth,1.0*picHeight/imgHeight)\r\n newWidth,newHeight = int(factor*imgWidth),int(factor*imgHeight)\r\n self.scaled = image.Scale(newWidth,newHeight).ConvertToBitmap()\r\n #self.scaled = image.Scale(newWidth,newHeight,wx.IMAGE_QUALITY_HIGH ).ConvertToBitmap()\r","def _scale(self, image):\n\n if image.GetWidth() != self._width or image.GetHeight()!= self._height:\n image.Rescale(self._width, self._height)\n \n return image","def scale(input_img, size):\n width, height = size\n old_height, old_width = input_img.shape\n x_scale = float(height) / old_height\n y_scale = float(width) / old_width\n\n output_img = np.zeros((height, width), dtype=np.uint8)\n for xidx in xrange(height):\n old_x = float(xidx) / x_scale\n for yidx in xrange(width):\n old_y = float(yidx) / y_scale\n if old_x.is_integer() or old_y.is_integer():\n output_img[xidx, yidx] = input_img[int(old_x), int(old_y)]\n else: # use bilinear interpolation\n x1 = int(np.floor(old_x))\n x2 = int(np.ceil(old_x)) if int(np.ceil(old_x)) < old_height else old_height - 1\n y1 = int(np.floor(old_y))\n y2 = int(np.ceil(old_y)) if int(np.ceil(old_y)) < old_width else old_width - 1\n\n q11 = input_img[x1, y1]\n q12 = input_img[x1, y2]\n q21 = input_img[x2, y1]\n q22 = input_img[x2, y2]\n\n output_img[xidx, yidx] = (q11 * (x2 - old_x) * (y2 - old_y)\n + q21 * (old_x - x1) * (y2 - old_y)\n + q12 * (x2 - old_x) * (old_y - y1)\n + q22 * (old_x - x1) * (old_y - y1)) \\\n / ((x2 - x1) * (y2 - y1) + 1e-10)\n\n return output_img","def rescale(self, img):\n\n if self.scale != 1:\n return imutils.resize(img, width=int(img.shape[1] * self.scale))\n else:\n return img","def scale(img, scale):\n return resize(img, x_scale=scale, y_scale=scale)","def scaleZoomed(self, bx, by):\n ix, iy = self._image.get_size()\n if ix > iy:\n # fit to width\n scale = bx/float(iy)\n sy = scale * ix\n if sy > by:\n scale = by/float(iy)\n sx = scale * ix\n sy = by\n else:\n sx = bx\n else:\n # fit to height\n scale = by/float(ix)\n sx = scale * iy\n if sx > bx:\n scale = bx/float(ix)\n sx = bx\n sy = scale * iy\n else:\n sy = by\n\n self._image = pygame.transform.scale(self._image, (int(sx), int(sy)))","def scale(image, maxval=1024):\n image += maxval # minimum value is now 0\n image /= maxval*2\n\n return(image)","def scale_image(self, pixels, size):\n x_min, x_max = np.amin(pixels[:,0]), np.amax(pixels[:,0])\n y_min, y_max = np.amin(pixels[:,1]), np.amax(pixels[:,1])\n z_min, z_max = np.amin(pixels[:,2]), np.amax(pixels[:,2])\n \n pixels[:,0] -= x_min \n pixels[:,1] -= y_min\n pixels[:,2] -= z_min\n \n x_max -= x_min\n y_max -= y_min\n z_max -= z_min\n \n scale_factor = size / max(x_max, y_max, z_max) \n # All points are now between [0..max]\n\n pixels *= scale_factor\n return pixels","def rescale_image(image, scale=0.50):\r\n \r\n wi = int(image.shape[1]*scale)\r\n hei = int(image.shape[0]*scale)\r\n dimension = (wi, hei)\r\n return cv.resize(image, dimension, interpolation = cv.INTER_AREA)","def downScaleResolution(kv, factor=10):\n sub_img_name = kv[0]\n sub_image = kv[1]\n img_dimension = len(sub_image)\n big_image = sub_image\n Nbig = img_dimension\n Nsmall = Nbig//factor\n small_image = big_image.reshape([Nsmall, Nbig // Nsmall, Nsmall, Nbig // Nsmall]).mean(3).mean(1)\n return (sub_img_name,small_image)","def rescale(self, factor):\n scaled_size = (int(self.width * factor), int(self.height * factor))\n return self.resize(scaled_size)","def upscale_main_side(output_img):\n #Fixing pixel which y % 2 != 1\n outputx, outputy = output_img.size\n for oy in range(0, outputy-1, 2):\n for ox in range(1, outputx, 2):\n pixel1 = output_img.getpixel((ox-1, oy))\n p1 = pixel1[0]\n p2 = pixel1[1]\n p3 = pixel1[2]\n if ox == outputx-1 :\n output_img.putpixel((ox, oy), (p1, p2, p3))\n else:\n pixel2 = output_img.getpixel((ox+1, oy))\n P1 = pixel2[0]\n P2 = pixel2[1]\n P3 = pixel2[2]\n output_img.putpixel((ox, oy), (int((p1+P1)/2), int((p2+P2)/2), int((p3+P3)/2)))\n #print(f'pixel:{ox, oy} output:{output_img.getpixel((ox,oy))}')\n #Fixing pixel which y % 2 == 1\n for oy in range(1, outputy-1, 2):\n for ox in range(0, outputx):\n pixel1 = output_img.getpixel((ox, oy-1))\n p1 = pixel1[0]\n p2 = pixel1[1]\n p3 = pixel1[2]\n if oy == outputx:\n output_img.putpixel((ox, oy), (p1, p2, p3))\n break\n else:\n pixel2 = output_img.getpixel((ox, oy+1))\n P1 = pixel2[0]\n P2 = pixel2[1]\n P3 = pixel2[2]\n output_img.putpixel((ox, oy), (int((p1+P1)/2), int((p2+P2)/2), int((p3+P3)/2)))\n #print(f'pixel:{ox, oy} output:{output_img.getpixel((ox,oy))}')\n #Save image \n result_img = output_path+'/output.'+working_img.format.lower()\n output_img.save(result_img)\n print('Upscale finished..!')\n output_img.show()","def scale(val, src, dst):\r\n return ((val - src[0]) / (src[1]-src[0])) * (dst[1]-dst[0]) + dst[0]","def scale(val, src, dst):\n return ((val - src[0]) / (src[1] - src[0])) * (dst[1] - dst[0]) + dst[0]","def scale(self):","def __scale_image(image, scale: float):\r\n height, width, _ = image.shape\r\n\r\n width_scaled = int(np.ceil(width * scale))\r\n height_scaled = int(np.ceil(height * scale))\r\n\r\n im_data = cv2.resize(image, (width_scaled, height_scaled), interpolation=cv2.INTER_AREA)\r\n\r\n # Normalize the image's pixels\r\n im_data_normalized = (im_data - 127.5) * 0.0078125\r\n\r\n return im_data_normalized","def scale(val, src, dst):\n return ((val - src[0]) / (src[1]-src[0])) * (dst[1]-dst[0]) + dst[0]","def scale(val, src, dst):\n return ((val - src[0]) / (src[1]-src[0])) * (dst[1]-dst[0]) + dst[0]","def scale_image(image=np.zeros((100,100)), \n new_width=100,\n ascii_block_size=(2,3)):\n \n original_width, original_height = image.shape\n aspect_ratio = original_height / float(original_width)\n w,h = ascii_block_size\n new_height = int(h/w * aspect_ratio * new_width)\n\n return skimage.transform.resize(image, (new_width, new_height))","def scale(self, up):\n s = 1.1 if up else 0.9\n self.scaling_matrix = np.dot(\n self.scaling_matrix,\n F.scaling([s, s, s])\n )\n\n self.aabb.scale(s)","def scale_image(img, factor=1):\n return cv2.resize(img, (int(img.shape[1] * factor), int(img.shape[0] * factor)))","def rescale(input_image, shift=None, scale=None):\n if scale is None and shift is None:\n return input_image\n\n output_image = sitk.ShiftScale(input_image, float(shift), float(scale))\n return output_image","def set_scale_factors_to_output_size(self):\n # Compute the scale_factor using rounded scaled image size.\n height = tf.shape(self._image)[0]\n width = tf.shape(self._image)[1]\n max_image_size = tf.to_float(tf.maximum(height, width))\n image_scale = tf.to_float(self._output_size) / max_image_size\n scaled_height = tf.to_int32(tf.to_float(height) * image_scale)\n scaled_width = tf.to_int32(tf.to_float(width) * image_scale)\n self._image_scale = image_scale\n self._scaled_height = scaled_height\n self._scaled_width = scaled_width","def scale_image(img, factor=1):\n\treturn cv2.resize(img,(int(img.shape[1]*factor), int(img.shape[0]*factor)))","def scale_image_button(src):\n return pygame.transform.scale(pygame.image.load(src), (30, 30))","def rescale_image(image, rescale_width):\r\n\r\n # image size\r\n image_height = pdb.gimp_image_height(image)\r\n image_width = pdb.gimp_image_width(image)\r\n\r\n # new image height\r\n rescale_height = round(image_height * (rescale_width * 1.0 / image_width))\r\n\r\n pdb.gimp_image_scale(image, rescale_width, rescale_height)\r\n gimp.message('Rescaled image')","def calculate_image_scale(source_width, source_height, target_width, target_height):\n if source_width == target_width and source_height == target_height:\n return 1.0\n\n source_ratio = source_width / source_height\n target_ratio = target_width / target_height\n\n if target_ratio < source_ratio:\n scale = target_width / source_width\n else:\n scale = target_height / source_height\n\n return scale","def downscale_image(im, max_dim=2048):\n a, b = im.size\n if max(a, b) <= max_dim:\n return 1.0, im\n\n scale = 1.0 * max_dim / max(a, b)\n new_im = im.resize((int(a * scale), int(b * scale)), Image.ANTIALIAS)\n return scale, new_im","def rescale_by_height(image, target_height, method=cv2.INTER_LANCZOS4):\r\n w = int(round(target_height * image.shape[1] / image.shape[0]))\r\n return cv2.resize(image, (w, target_height), interpolation=method)","def rescale_box(box, img_size_orig, img_size_new):\n orig_w, orig_h = img_size_orig\n new_w, new_h = img_size_new\n scale_x = new_w / orig_w\n scale_y = new_h / orig_h\n sx, sy, ex, ey = box\n return [sx * scale_x, sy * scale_y, ex * scale_x, ey * scale_y]","def scale(val, src, dst):\n try:\n return ((val - src[0]) / (src[1]-src[0])) * (dst[1]-dst[0]) + dst[0]\n except ZeroDivisionError:\n return 0.0","def rescale_intrinsic(self):\n # scale focal length and principal points wrt image resizeing\n if self.downscale > 1:\n self.K = self.K_orig.copy()\n self.K[0, 0] /= float(self.downscale)\n self.K[1, 1] /= float(self.downscale)\n self.K[0, 2] /= float(self.downscale)\n self.K[1, 2] /= float(self.downscale)\n self.intrinsic = self.K\n else:\n self.K = self.intrinsic = self.K_orig.copy()","def scale_img(img):\r\n # Scale values of img between 0 and 255.\r\n img -= np.amin(img)\r\n img /= np.amax(img)\r\n img *= 255\r\n return img","def imrescale(img,scale,return_scale=False,interpolation='bilinear'):\n h,w = img.shape[:2]\n if isinstance(scale,(float,int)):\n if scale <= 0:\n raise ValueError('Invalid scale:{},must be positive.'.format(scale))\n scale_factor = scale\n \n elif isinstance(scale,tuple):\n max_long_edge = max(scale)\n max_short_edge = min(scale)\n scale_factor = min(max_long_edge/max(h,w),\n max_short_edge/min(h,w))\n else:\n raise ValueError('Scale must be a number or tuple of int,but got {}'.format(type(scale)))\n \n new_size = _scale_size((w,h),scale_factor)\n rescaled_img = imresize(img,new_size,interpolation = interpolation)\n if return_scale:\n return rescaled_img,scale_factor\n else:\n return rescaled_img","def rescale_image(image: np.ndarray, scale: float) -> np.ndarray:\n (height, width) = image.shape[:2]\n new_dims = (int(width * scale), int(height * scale))\n return cv2.resize(image, new_dims, interpolation=cv2.INTER_CUBIC)","def PScaleImage (inImage, scale, err):\n ################################################################\n # Checks\n if not Image.PIsA(inImage):\n raise TypeError,\"inImage MUST be a Python Obit Image\"\n if not OErr.OErrIsA(err):\n raise TypeError,\"err MUST be an OErr\"\n #\n # Open image\n Image.POpen (inImage, Image.READWRITE, err)\n # Get input descriptor to see how many planes\n inDesc = Image.PGetDesc(inImage)\n inDescDict = ImageDesc.PGetDict(inDesc)\n ndim = inDescDict[\"naxis\"]\n inNaxis = inDescDict[\"inaxes\"]\n # Work buffer\n inImageArray = Image.PGetFArray(inImage)\n ImageBuffer = FArray.PCopy(inImageArray, err)\n # Reset max/min\n inDescDict[\"minval\"] = 1.0e20\n inDescDict[\"maxval\"] = -1.0e20\n inImage.Desc.Dict = inDescDict # Update descriptor on image\n Image.PDirty(inImage) # Force update\n Image.PClose (inImage, err)\n\n # list of planes to loop over (0-rel)\n if (ndim>0) and (inNaxis[2]>0): \n planes = range(inNaxis[2])\n else:\n planes = [0]\n \n # Loop over planes\n for iPlane in planes:\n doPlane = [iPlane+1,1,1,1,1]\n # Get image plane\n Image.PGetPlane (inImage, ImageBuffer, doPlane, err)\n\n # Scaling factor\n if type(scale)==list:\n scl = scale[iPlane]\n else:\n scl = scale\n\n # Scale\n FArray.PSMul(ImageBuffer, scl)\n\n # Write output\n Image.PPutPlane (inImage, ImageBuffer, doPlane, err)\n\n # Scale any CC table\n highVer = Image.PGetHighVer (inImage, \"AIPS CC\")\n if (iPlane+1<=highVer):\n CCTab = Image.PImageGetTable (inImage, Image.READWRITE, \"AIPS CC\", iPlane+1, err)\n PCCScale (CCTab, 1, 0, scl, err)\n # end loop over planes\n # Write history\n inHistory = History.History(\"history\", inImage.List, err)\n # Add this programs history\n inHistory.Open(History.READWRITE, err)\n inHistory.TimeStamp(\" Start Obit ScaleImage\",err)\n if type(scale)==list:\n i = -1\n for iPlane in planes:\n i = i + 1\n scl = scale[i]\n inHistory.WriteRec(-1,\"ScaleImage / scale[\"+str(i+1)+\"] = \"+str(scl),err)\n else:\n inHistory.WriteRec(-1,\"ScaleImage / scale = \"+str(scale),err)\n inHistory.Close(err)","def imageScale(scale):\n\t\treturn max(1, int(scale * (InterfaceTools.getCanvasSize()[0] / height)))","def retinanet_compute_resize_scale(image_shape, min_side=800, max_side=1333):\n (rows, cols, _) = image_shape\n\n smallest_side = min(rows, cols)\n\n # rescale the image so the smallest side is min_side\n scale = float(min_side) / float(smallest_side)\n\n # check if the largest side is now greater than max_side, which can happen\n # when images have a large aspect ratio\n largest_side = max(rows, cols)\n if largest_side * scale > max_side:\n scale = float(max_side) / float(largest_side)\n\n return scale","def scaled_image(width, height):\r\n total = width*height\r\n scale_dict = {0.2: 6500000, 0.3: 4000000, 0.4: 2250000, 0.5: 1000000, 0.8: 500000, 1: 0}\r\n for k, v in scale_dict.items():\r\n if total > v:\r\n return k","def __scale_width_height(img, target_width=None, target_height=None, method=Image.BICUBIC):\n if target_height > 0 and target_width:\n raise ValueError(\n f\"Expected target_width>0 and target_height>0, but got target_width={target_width}, target_height={target_height}\")\n\n ow, oh = img.size\n if target_width is not None and target_height is None:\n target_height = int(target_width * oh) / ow\n elif target_width is None and target_height is not None:\n target_width = int(target_height * ow) / oh\n elif target_width is None and target_height is None:\n return img\n\n return img.resize((target_width, target_height), method)","def _shrink_main(self, amt):\n self.ratio -= amt\n self.ratio = max(self.min_ratio, self.ratio)","def adjust(self, image):\n ...","def transform_input(crop_size, upscale_factor):\n return Compose([\n Scale(crop_size // upscale_factor),\n ])","def scale_invert(self):","def scale(self, scale=1):\n self.x *= scale\n self.y *= scale\n self.width *= scale\n self.height *= scale\n\n # Always update the corners after operation\n self.update_corners()\n return","def scale(self, value):\n\t\toldscale = self.oldmax - self.oldmin\n\t\tnewscale = self.newmax - self.newmin\n\t\treturn (newscale * (value - self.oldmin) / oldscale) + self.newmin","def scaleImageToEqualSize(sourceImg, targetImg):\n return cv2.resize(sourceImg,\n None, # no specific out dim\n fx=targetImg.shape[1] / sourceImg.shape[1], # scale factor Y\n fy=targetImg.shape[0] / sourceImg.shape[0], # scale factor X\n interpolation=cv2.INTER_AREA)","def scale_image(image: Image, scale: float) -> Image:\n width = round(image.width * scale)\n height = round(image.height * scale)\n image.thumbnail((width, height))\n return image","def _scale_to_mbs_frame(self : \"animation\",\n img : \"np.ndarray\"\n ) -> \"np.ndarray\":\n xnew = img.shape[0] + self._mbs - img.shape[0]%self._mbs\n ynew = img.shape[1] + self._mbs - img.shape[1]%self._mbs\n return (255*resize(img, (xnew, ynew))).astype(np.uint8)","def zoom_to_size(self, *p):\n\t\tif self.image is None or self.allocation is None:\n\t\t\treturn\n\t\tif __debug__: print self.allocation.width, self.image.get_width()\n\t\tif __debug__: print self.allocation.width, self.image.get_width(), self.allocation.width/self.image.get_width()\n\t\tz = min(\n\t\t\tself.allocation.width/self.image.get_width(),\n\t\t\tself.allocation.height/self.image.get_height()\n\t\t\t)\n\t\tif __debug__: print \"zoom_to_size\", \"z=\", z\n\t\tself.zoom = z","def resize_img(self,scale=1):\n reduced = self.image.reduce((scale,scale))\n reduced.save(\"../edited/{}\".format(self.image.filename))\n\n reduced = Image.open(\"../edited/{}\".format(self.image.filename))\n return reduced","def scale_image(self, image):\n err_m1 = \"image must be a numpy.ndarray with shape (h, w, 3)\"\n if not isinstance(image, np.ndarray):\n raise TypeError(err_m1)\n if len(image.shape) != 3 or image.shape[2] != 3:\n raise TypeError(err_m1)\n shape0 = image.shape[0]\n shape1 = image.shape[1]\n\n if shape0 > shape1:\n new_w = int(image.shape[1] * 512 / image.shape[0])\n new_h = 512\n elif shape0 < shape1:\n new_h = int(image.shape[0] * 512 / image.shape[1])\n new_w = 512\n else:\n new_h = 512\n new_w = 512\n tf.image.ResizeMethod.BICUBIC\n image = tf.expand_dims(image, 0)\n image = tf.image.resize_bicubic(image,\n (new_h, new_w),\n align_corners=False)\n image = image / 255\n image = tf.clip_by_value(image, clip_value_min=0, clip_value_max=1)\n\n return image","def scale(input):\n return (input - np.min(input)) / ((np.max(input) - np.min(input)))","def scale_img(img, scale=1.0):\n dim = (int(img.shape[1]*scale), int(img.shape[0]*scale))\n return cv2.resize(img.copy(), dim, interpolation=cv2.INTER_AREA)","def fit_image(self, img, width, height):\n if img.get_height()/height > img.get_width()/width:\n # scale is determined by width\n w = width\n h = int(math.ceil(img.get_height() * (w/img.get_width())))\n else:\n # scale is determined by height\n h = height\n w = int(math.ceil(img.get_width() * (h/img.get_height())))\n img = pygame.transform.smoothscale(img, (w,h))\n rect = img.get_rect()\n rect = rect.move((width-w)//2, (height-h)//2)\n img2 = pygame.Surface((width, height))\n img2.blit(img, rect)\n return img2","def scale(image):\n image = tf.cast(image, tf.float32)\n image /= 255\n return image","def shrink_kernel(self, kernel, up_scale):\n up_scale = torch.tensor(up_scale).float()\n # boundary padding based on the scaling law\n pad_in = (torch.ceil(up_scale**2).int())*((kernel.shape[2]-1)//2)\n pad_h = (torch.ceil(up_scale).int())*((kernel.shape[3]-1)//2)\n pad_w = (torch.ceil(up_scale).int())*((kernel.shape[4]-1)//2)\n padded_kernel = F.pad(kernel, (pad_w, pad_w, pad_h, pad_h, pad_in, pad_in))\n delta = up_scale%1\n \n if delta == 0:\n shrink_factor = 1\n else:\n # shrink_factor for coordinates.\n shrink_factor = (((kernel.shape[4]-1))/(padded_kernel.shape[-1]-1)*(up_scale+1))\n \n # Adjustment to deal with weird filtering on the grid sample function.\n shrink_factor = 1.5*(shrink_factor-0.5)**3 + 0.57 \n\n grid = torch.meshgrid(torch.linspace(-1, 1, kernel.shape[2])*(shrink_factor**2),\n torch.linspace(-1, 1, kernel.shape[3])*shrink_factor, \n torch.linspace(-1, 1, kernel.shape[4])*shrink_factor)\n\n grid = torch.cat([grid[2].unsqueeze(0).unsqueeze(-1), \n grid[1].unsqueeze(0).unsqueeze(-1), \n grid[0].unsqueeze(0).unsqueeze(-1)], dim = -1).repeat(kernel.shape[0],1,1,1,1)\n\n new_kernel = F.grid_sample(padded_kernel, grid.to(device))\n if kernel.shape[-1] - 2*up_scale > 0:\n new_kernel = new_kernel * (kernel.shape[-1]**2/((kernel.shape[-1] - 2*up_scale)**2 + 0.01))\n return new_kernel","def resize_image(image: Image) -> Image:\n expected_x: int = 1024\n expected_y: int = 768\n x, y = image.size\n if x > expected_x or y > expected_y:\n scale = min(expected_x / x, expected_y / y)\n return image.resize((int(x * scale), int(y * scale)))\n else:\n return image.resize((int(0.9*x), int(0.9*y)))","async def _create_upscale(input_file, *, output_file, ratio):\n # If the output file already exists, use it if the timestamp matches. If the\n # timestamps are different, regenerate it.\n if output_file.exists():\n input_stat = input_file.stat()\n output_stat = output_file.stat()\n if input_stat.st_mtime == output_stat.st_mtime:\n return\n\n # Reencode the image we're upscaling to an RGB BMP. The upscaler isn't very robust\n # at handling various files and file paths, so this lets us give it a simple, controlled\n # input that won't confuse it. Bake any transparency (it doesn't handle transparency)\n # and convert to RGB.\n input_temp_file = misc.get_temporary_path('.bmp')\n output_temp_file = misc.get_temporary_path('.jpg')\n\n with input_file.open('rb') as f:\n f = remove_photoshop_tiff_data(f)\n image = Image.open(f)\n image.load()\n \n # If we tell PIL to convert P to RGB and the palette has transparency, it seems\n # to use an undefined RGB color for transparency. We just want it to comp onto\n # black. To avoid this, convert to RGBA, so it'll use the black underlay path\n # below.\n if image.mode == 'P':\n image = image.convert('RGBA')\n\n # Bake RGBA to RGB with a black background.\n if image.mode == 'RGBA':\n bg = Image.new('RGBA', image.size, (0,0,0,255))\n image = Image.alpha_composite(bg, image)\n\n # Convert anything else to RGB.\n if image.mode != 'RGB':\n image = image.convert('RGB')\n\n with input_temp_file.open('w+b') as output:\n image.save(output, format='bmp')\n\n try:\n # This is a GPU upscaler. Only process one image at a time, so we don't spam GPU jobs\n # if the client tries to load too aggressively. Doing it here allows the above check\n # to complete without blocking if the image is already cached.\n async with _lock:\n assert ratio in (2,3,4)\n result = await _run_upscale([\n _upscaler,\n '-s', str(ratio),\n '-i', str(input_temp_file),\n '-o', str(output_temp_file),\n ])\n\n if result != 0:\n raise Exception('Error upscaling image')\n\n # The upscaler doesn't return a result code.\n if not output_temp_file.exists():\n raise Exception('Error upscaling image (no file generated)')\n\n shutil.copyfile(output_temp_file, output_file)\n finally:\n # Clean up.\n input_temp_file.unlink(missing_ok=True)\n output_temp_file.unlink(missing_ok=True)\n\n # Set the cache file's timestamp to match the input file, so we can tell if the input\n # file changes.\n st = input_file.stat()\n os.utime(output_file, (st.st_atime, st.st_mtime))","def scale(self, scale):\n \n scale_matrix = wf.scaleMatrix(scale, self.width/2, self.height/2, 0)\n self.transform(scale_matrix)","def rescale_by_width(image, target_width, method=cv2.INTER_LANCZOS4):\r\n h = int(round(target_width * image.shape[0] / image.shape[1]))\r\n return cv2.resize(image, (target_width, h), interpolation=method)","def scale0to1(img):\r\n\r\n min = np.min(img)\r\n max = np.max(img)\r\n\r\n if min == max:\r\n img.fill(0.5)\r\n else:\r\n img = (img-min) / (max-min)\r\n\r\n return img.astype(np.float32)","def rescale(tx):\n mins = np.amin(tx, axis=0)\n maxs = np.amax(tx, axis=0)\n txscale = (tx - mins) / (maxs - mins)\n return txscale","def _scale(x):\n scaleFactor = 1\n _ret = int(x/scaleFactor)\n return _ret","def coral_image_resize(im, scaling_method, scaling_factor, height_cm):\n\n if scaling_method == 'scale':\n scale = float(scaling_factor) # here scaling_factor is the desired image scaling.\n elif scaling_method == 'ratio':\n scale = float(scaling_factor) * height_cm / im.shape[0] # here scaling_factor is the desited px_cm_ratio.\n im = scipy.misc.imresize(im, scale)\n return (im, scale)","def scale0to1(img):\r\n\r\n img = img.astype(np.float32)\r\n\r\n if img.shape[-1] != 1:\r\n img = np.sqrt(np.sum(img**2, axis=-1, keepdims=True))\r\n min = np.min(img)\r\n max = np.max(img)\r\n\r\n if np.absolute(min-max) < 1.e-6:\r\n img.fill(0.5)\r\n else:\r\n img = (img - min)/(max - min)\r\n\r\n return img.astype(np.float32)","def change_resolution(img):\n scale_factor = np.random.choice(list(range(0, 6, 2)))\n if scale_factor == 0:\n return img\n downsample = nn.AvgPool2d(scale_factor)\n upsample = nn.UpsamplingNearest2d(scale_factor=scale_factor)\n new_res_img = upsample(downsample(img.unsqueeze(dim=1))).squeeze()\n return new_res_img","def scaleImage(img, min):\n scales = []\n img_ = img.copy()\n h, w, c = img.shape\n factor = 0.8\n while h >= min and w >= min:\n img_ = cv2.resize(img_, (w, h))\n scales.append(img_)\n h = int(h * factor)\n w = int(w * factor)\n\n return scales","def scale_image(image, scale=1, interpolation='linear'):\n if interpolation == 'linear':\n return cv2.resize(image, None, fx=scale, fy=scale, interpolation=cv2.INTER_LINEAR)\n if interpolation == 'nearest':\n return cv2.resize(image, None, fx=scale, fy=scale, interpolation=cv2.INTER_NEAREST)","def _rescale(x, xlim, ylim):\n m = (ylim[1] - ylim[0]) / (xlim[1] - xlim[0])\n c = ylim[1] - m * xlim[1]\n y = m * x + c\n return y","def rescale(num, old_min, old_max, new_min, new_max):\n old_range = old_max - old_min\n new_range = new_max - new_min\n new_val = new_min + (((num - old_min) * new_range)/old_range)\n\n return new_val","def image_resize(img, min_size=600, max_size=1000):\n C, H, W = img.shape\n scale1 = min_size / min(H, W)\n scale2 = max_size / max(H, W)\n scale = min(scale1, scale2)\n img = img / 255\n img = transform.resize(img, (C, H * scale, W * scale),\n mode='reflect', anti_aliasing=False)\n # img = pytorch_normalize(img)\n # img = caffe_normalize(img)\n return img","def scale(self, value):\n return (float(value) - float(self.minimum)) / \\\n float(self.maximum - self.minimum) * 2.0 - 1.0","def __call__(self, input_image):\n output_image = input_image\n if self.shift is not None or self.scale is not None:\n output_image = rescale(output_image, shift=self.shift, scale=self.scale)\n if self.random_shift is not None or self.random_scale is not None:\n current_random_shift = random.float_uniform(-self.random_shift, self.random_shift)\n current_random_scale = 1 + random.float_uniform(-self.random_scale, self.random_scale)\n output_image = rescale(output_image, shift=current_random_shift, scale=current_random_scale)\n if self.clamp_min is not None or self.clamp_max is not None:\n output_image = clamp(output_image, clamp_min=self.clamp_min, clamp_max=self.clamp_max)\n\n return output_image","def rescale(x):\n if x.min() != 0:\n raise ValueError('input should have min zero.')\n\n x /= x.max() # max 1\n x *= 2 # max 2\n x -= 1 # range -1, 1\n\n if x.min() != -1 and x.max() != 1:\n raise Exception\n\n return x","def scaling(mat, factor):\n\treturn mat / (mat + factor)","def rescale_image_01(image):\n # scale image to from [0, 255] to [0.0, 1.0]\n image = image.astype(np.float32)\n return image / 255","def scale_image(image, new_range):\n min_val = np.min(image).astype(np.float32)\n max_val = np.max(image).astype(np.float32)\n min_val_new = np.array(min(new_range), dtype=np.float32)\n max_val_new = np.array(max(new_range), dtype=np.float32)\n scaled_image = (image - min_val) / (max_val - min_val) * (max_val_new - min_val_new) + min_val_new\n return scaled_image.astype(np.uint8)","def edit_scale(scale, direction):\n if direction in (up, shift_up, plus):\n scale = scale*2\n elif direction in (down, shift_down, minus):\n scale = scale/2\n return scale","def resize_img(input_img, scale=1.1):\n if scale == 1.0:\n return input_img\n rows, cols, ch = input_img.shape\n d = rows * (scale - 1) # overall image size change from rows, cols, to rows - 2d, cols - 2d\n pts1 = np.float32([[d, d], [rows - 1 - d, d], [d, cols - 1 - d], [rows - 1 - d, cols - 1 - d]])\n pts2 = np.float32([[0, 0], [rows - 1, 0], [0, cols - 1], [rows - 1, cols - 1]])\n M = cv2.getPerspectiveTransform(pts1, pts2)\n dst = cv2.warpPerspective(input_img, M, (cols, rows), borderMode=1)\n return dst","def process_image(image):\n image = resize(image)\n return image","def img_scale(self):\n return min(400, abs(self.size))","def rescale_and_resize_image(img_name, img_size, save_img):\n \n try:\n \n # Open image\n img = Image.open(img_name)\n \n # Resize image\n img = img.resize((int(img_size), int(img_size)), Image.ANTIALIAS) \n \n # Get data from image\n img_list = list(img.getdata())\n \n # Find minimum and maximum value pixels in the image\n img_max = max(img_list)\n img_min = min(img_list)\n \n # Determine factor to scale to a 8-bit image\n scale_factor = 255.0/(img_max - img_min)\n \n img_list_new = [0] * img_size * img_size\n \n # Rescale all pixels to the range 0 to 255 (in line with unit8 values)\n for i in range(0,img_size):\n for j in range(0,img_size):\n img_list_new[i*img_size + j] = int((img_list[i*img_size + j]-img_min)*scale_factor)\n if (img_list_new[i*img_size + j] > 255) or (img_list_new[i*img_size + j] < 0) or (img_list_new[i*img_size + j]-int(img_list_new[i*img_size + j]) != 0):\n print(\"img_list_new[%d][%d] = %r\" % (i,j,img_list_new[i*img_size + j]))\n \n img.putdata(img_list_new)\n \n # Convert to uint8 greyscale image\n img = img.convert('L')\n \n # Save image\n if save_img:\n img.save(img_name)\n else:\n ph = ImageTk.PhotoImage(img)\n return ph\n \n finally:\n \n # Close image\n img.close()","def scale(self, value):\r\n return (float(value)-float(self.minimum))/float(self.maximum-self.minimum)*2.0 - 1.0","def scale(self, sx, sy):\n frameWidth *= sx\n frameHeight *= sy\n repaint()","def scale_it(val):\n return scale(val, 0, 1, bpm_range[0], bpm_range[1])","def downscale_image(img, bottom, x, y):\n width, height = tuple(img.shape[1::-1])\n img = img[int(round((1 - bottom) * (height - 1))):(height - 1), 1:(width - 1)]\n img = cv2.resize(img, (x, y))\n #img = cv2.Canny(img, 100, 200)\n ret, img = cv2.threshold(img, img.mean(), 255, cv2.THRESH_BINARY)\n return img","def normalise_image(image, use_torch=True):\n if use_torch:\n image = torch.abs(image)\n else:\n image = np.abs(image)\n if (image.max() - image.min()) < 1e-5:\n return image - image.min() + 1e-5\n else:\n return (image - image.min()) / (image.max() - image.min())","def preprocess(img, min_size, max_size):\n if(min_size > max_size):\n raise Exception('min_size should not exceed max_size')\n \n width, height = img.size\n minDim = min(width,height)\n maxDim = max(width,height)\n scale_shorter_side = min_size/minDim\n scale_longer_side = maxDim * scale_shorter_side\n if(scale_longer_side > max_size):\n scale = max_size/maxDim\n else:\n scale = scale_shorter_side\n transform = transforms.Compose([\n transforms.Resize((round(img.height*scale),round(img.width * scale))),\n transforms.ToTensor(),\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n ])\n img = transform(img)\n return scale,img","def normalise(image):","def scale0to1(img):\r\n\r\n img = img.astype(np.float32)\r\n\r\n min = np.min(img)\r\n max = np.max(img)\r\n\r\n if np.absolute(min-max) < 1.e-6:\r\n img.fill(0.5)\r\n else:\r\n img = (img-min) / (max-min)\r\n\r\n return img.astype(np.float32)","def zoom_augmentation():\n # Get the width and the height of the zoomed version\n x_len, y_len = np.random.randint(250, 350, size=2)\n # Get left upper ,right and lower bound of the pixels in the original image\n left = np.random.randint(x_size-x_len)\n upper = np.random.randint(y_size-y_len)\n right, lower = left + x_len, upper+y_len\n # Crops the box and resizes it to the original image size\n box = (left, upper, right, lower)\n return lambda image: image.transform(image.size, Image.EXTENT, box)","def rescaled(M,newmin,newmax):\n mmin,mmax = M.min(),M.max()\n M2 = M.copy()\n M2 -= mmin\n M2 *= (newmax-newmin) / (mmax-mmin)\n M2 += newmin\n return M2","def retinanet_resize_image(img, min_side=800, max_side=1333):\n # compute scale to resize the image\n scale = retinanet_compute_resize_scale(img.shape, min_side=min_side, max_side=max_side)\n # scale = round(scale, 4)\n # print(\"scale: \", scale)\n\n # resize the image with the computed scale\n img = cv2.resize(img, None, fx=scale, fy=scale)\n\n return img, scale","def stretch(number, scale):\n\tif number >= 0:\n\t\treturn scale * math.ceil(number / scale)\n\telse:\n\t\treturn scale * math.floor(number / scale)","def rescaleFrame(frame, scale=.75):\n\n\twidth = int(frame.shape[1] * scale)\n\theight = int(frame.shape[0] * scale)\n\n\tdimensions = (width, height)\n\t\n\treturn cv.resize(frame, dimensions, interpolation=cv.INTER_AREA)","def resizeImage(image, maxW, maxH):\n\timageW, imageH = image.size\n\tif imageW == maxW and imageH == maxH:\n\t\treturn image\n\t# find which axis requires the biggest zoom (smallest relative max dimension)\n\tzoomW = float(imageW) / float(maxW)\n\tzoomH = float(imageH) / float(maxH)\n\tzoom = max(zoomW, zoomH)\n\tif zoomW >= zoomH:\t# size is defined by width\n\t\tmaxH = int(imageH//zoom)\t# calculate the new height\n\telse:\n\t\tmaxW = int(imageW//zoom)\n\treturn image.resize((maxW, maxH))","def reScaleLandsat(self,img):\n \n\t\tthermalBand = ee.List(['thermal'])\n\t\tthermal = ee.Image(img).select(thermalBand).multiply(10)\n \n\t\totherBands = ee.Image(img).bandNames().removeAll(thermalBand)\n\t\tscaled = ee.Image(img).select(otherBands).divide(0.0001)\n \n\t\timage = ee.Image(scaled.addBands(thermal)).int16()\n \n\t\treturn image.copyProperties(img)"],"string":"[\n \"def scale_down(image:np.array)->np.array:\\n src = image\\n scale_percent = 25\\n width = int(src.shape[1] * scale_percent / 100)\\n height = int(src.shape[0] * scale_percent / 100)\\n dsize = (width, height)\\n output = cv2.resize(src, dsize)\\n return output\",\n \"def Rescale(self):\\r\\n picWidth,picHeight = self.oldSize = self.GetSizeTuple()\\r\\n bitmap = self.scaled = self.bitmap\\r\\n if not bitmap: return\\r\\n imgWidth,imgHeight = bitmap.GetWidth(),bitmap.GetHeight()\\r\\n if self.scaling == 2 or (self.scaling == 1 and (imgWidth > picWidth or imgHeight > picHeight)):\\r\\n image = bitmap.ConvertToImage()\\r\\n factor = min(1.0*picWidth/imgWidth,1.0*picHeight/imgHeight)\\r\\n newWidth,newHeight = int(factor*imgWidth),int(factor*imgHeight)\\r\\n self.scaled = image.Scale(newWidth,newHeight).ConvertToBitmap()\\r\\n #self.scaled = image.Scale(newWidth,newHeight,wx.IMAGE_QUALITY_HIGH ).ConvertToBitmap()\\r\",\n \"def _scale(self, image):\\n\\n if image.GetWidth() != self._width or image.GetHeight()!= self._height:\\n image.Rescale(self._width, self._height)\\n \\n return image\",\n \"def scale(input_img, size):\\n width, height = size\\n old_height, old_width = input_img.shape\\n x_scale = float(height) / old_height\\n y_scale = float(width) / old_width\\n\\n output_img = np.zeros((height, width), dtype=np.uint8)\\n for xidx in xrange(height):\\n old_x = float(xidx) / x_scale\\n for yidx in xrange(width):\\n old_y = float(yidx) / y_scale\\n if old_x.is_integer() or old_y.is_integer():\\n output_img[xidx, yidx] = input_img[int(old_x), int(old_y)]\\n else: # use bilinear interpolation\\n x1 = int(np.floor(old_x))\\n x2 = int(np.ceil(old_x)) if int(np.ceil(old_x)) < old_height else old_height - 1\\n y1 = int(np.floor(old_y))\\n y2 = int(np.ceil(old_y)) if int(np.ceil(old_y)) < old_width else old_width - 1\\n\\n q11 = input_img[x1, y1]\\n q12 = input_img[x1, y2]\\n q21 = input_img[x2, y1]\\n q22 = input_img[x2, y2]\\n\\n output_img[xidx, yidx] = (q11 * (x2 - old_x) * (y2 - old_y)\\n + q21 * (old_x - x1) * (y2 - old_y)\\n + q12 * (x2 - old_x) * (old_y - y1)\\n + q22 * (old_x - x1) * (old_y - y1)) \\\\\\n / ((x2 - x1) * (y2 - y1) + 1e-10)\\n\\n return output_img\",\n \"def rescale(self, img):\\n\\n if self.scale != 1:\\n return imutils.resize(img, width=int(img.shape[1] * self.scale))\\n else:\\n return img\",\n \"def scale(img, scale):\\n return resize(img, x_scale=scale, y_scale=scale)\",\n \"def scaleZoomed(self, bx, by):\\n ix, iy = self._image.get_size()\\n if ix > iy:\\n # fit to width\\n scale = bx/float(iy)\\n sy = scale * ix\\n if sy > by:\\n scale = by/float(iy)\\n sx = scale * ix\\n sy = by\\n else:\\n sx = bx\\n else:\\n # fit to height\\n scale = by/float(ix)\\n sx = scale * iy\\n if sx > bx:\\n scale = bx/float(ix)\\n sx = bx\\n sy = scale * iy\\n else:\\n sy = by\\n\\n self._image = pygame.transform.scale(self._image, (int(sx), int(sy)))\",\n \"def scale(image, maxval=1024):\\n image += maxval # minimum value is now 0\\n image /= maxval*2\\n\\n return(image)\",\n \"def scale_image(self, pixels, size):\\n x_min, x_max = np.amin(pixels[:,0]), np.amax(pixels[:,0])\\n y_min, y_max = np.amin(pixels[:,1]), np.amax(pixels[:,1])\\n z_min, z_max = np.amin(pixels[:,2]), np.amax(pixels[:,2])\\n \\n pixels[:,0] -= x_min \\n pixels[:,1] -= y_min\\n pixels[:,2] -= z_min\\n \\n x_max -= x_min\\n y_max -= y_min\\n z_max -= z_min\\n \\n scale_factor = size / max(x_max, y_max, z_max) \\n # All points are now between [0..max]\\n\\n pixels *= scale_factor\\n return pixels\",\n \"def rescale_image(image, scale=0.50):\\r\\n \\r\\n wi = int(image.shape[1]*scale)\\r\\n hei = int(image.shape[0]*scale)\\r\\n dimension = (wi, hei)\\r\\n return cv.resize(image, dimension, interpolation = cv.INTER_AREA)\",\n \"def downScaleResolution(kv, factor=10):\\n sub_img_name = kv[0]\\n sub_image = kv[1]\\n img_dimension = len(sub_image)\\n big_image = sub_image\\n Nbig = img_dimension\\n Nsmall = Nbig//factor\\n small_image = big_image.reshape([Nsmall, Nbig // Nsmall, Nsmall, Nbig // Nsmall]).mean(3).mean(1)\\n return (sub_img_name,small_image)\",\n \"def rescale(self, factor):\\n scaled_size = (int(self.width * factor), int(self.height * factor))\\n return self.resize(scaled_size)\",\n \"def upscale_main_side(output_img):\\n #Fixing pixel which y % 2 != 1\\n outputx, outputy = output_img.size\\n for oy in range(0, outputy-1, 2):\\n for ox in range(1, outputx, 2):\\n pixel1 = output_img.getpixel((ox-1, oy))\\n p1 = pixel1[0]\\n p2 = pixel1[1]\\n p3 = pixel1[2]\\n if ox == outputx-1 :\\n output_img.putpixel((ox, oy), (p1, p2, p3))\\n else:\\n pixel2 = output_img.getpixel((ox+1, oy))\\n P1 = pixel2[0]\\n P2 = pixel2[1]\\n P3 = pixel2[2]\\n output_img.putpixel((ox, oy), (int((p1+P1)/2), int((p2+P2)/2), int((p3+P3)/2)))\\n #print(f'pixel:{ox, oy} output:{output_img.getpixel((ox,oy))}')\\n #Fixing pixel which y % 2 == 1\\n for oy in range(1, outputy-1, 2):\\n for ox in range(0, outputx):\\n pixel1 = output_img.getpixel((ox, oy-1))\\n p1 = pixel1[0]\\n p2 = pixel1[1]\\n p3 = pixel1[2]\\n if oy == outputx:\\n output_img.putpixel((ox, oy), (p1, p2, p3))\\n break\\n else:\\n pixel2 = output_img.getpixel((ox, oy+1))\\n P1 = pixel2[0]\\n P2 = pixel2[1]\\n P3 = pixel2[2]\\n output_img.putpixel((ox, oy), (int((p1+P1)/2), int((p2+P2)/2), int((p3+P3)/2)))\\n #print(f'pixel:{ox, oy} output:{output_img.getpixel((ox,oy))}')\\n #Save image \\n result_img = output_path+'/output.'+working_img.format.lower()\\n output_img.save(result_img)\\n print('Upscale finished..!')\\n output_img.show()\",\n \"def scale(val, src, dst):\\r\\n return ((val - src[0]) / (src[1]-src[0])) * (dst[1]-dst[0]) + dst[0]\",\n \"def scale(val, src, dst):\\n return ((val - src[0]) / (src[1] - src[0])) * (dst[1] - dst[0]) + dst[0]\",\n \"def scale(self):\",\n \"def __scale_image(image, scale: float):\\r\\n height, width, _ = image.shape\\r\\n\\r\\n width_scaled = int(np.ceil(width * scale))\\r\\n height_scaled = int(np.ceil(height * scale))\\r\\n\\r\\n im_data = cv2.resize(image, (width_scaled, height_scaled), interpolation=cv2.INTER_AREA)\\r\\n\\r\\n # Normalize the image's pixels\\r\\n im_data_normalized = (im_data - 127.5) * 0.0078125\\r\\n\\r\\n return im_data_normalized\",\n \"def scale(val, src, dst):\\n return ((val - src[0]) / (src[1]-src[0])) * (dst[1]-dst[0]) + dst[0]\",\n \"def scale(val, src, dst):\\n return ((val - src[0]) / (src[1]-src[0])) * (dst[1]-dst[0]) + dst[0]\",\n \"def scale_image(image=np.zeros((100,100)), \\n new_width=100,\\n ascii_block_size=(2,3)):\\n \\n original_width, original_height = image.shape\\n aspect_ratio = original_height / float(original_width)\\n w,h = ascii_block_size\\n new_height = int(h/w * aspect_ratio * new_width)\\n\\n return skimage.transform.resize(image, (new_width, new_height))\",\n \"def scale(self, up):\\n s = 1.1 if up else 0.9\\n self.scaling_matrix = np.dot(\\n self.scaling_matrix,\\n F.scaling([s, s, s])\\n )\\n\\n self.aabb.scale(s)\",\n \"def scale_image(img, factor=1):\\n return cv2.resize(img, (int(img.shape[1] * factor), int(img.shape[0] * factor)))\",\n \"def rescale(input_image, shift=None, scale=None):\\n if scale is None and shift is None:\\n return input_image\\n\\n output_image = sitk.ShiftScale(input_image, float(shift), float(scale))\\n return output_image\",\n \"def set_scale_factors_to_output_size(self):\\n # Compute the scale_factor using rounded scaled image size.\\n height = tf.shape(self._image)[0]\\n width = tf.shape(self._image)[1]\\n max_image_size = tf.to_float(tf.maximum(height, width))\\n image_scale = tf.to_float(self._output_size) / max_image_size\\n scaled_height = tf.to_int32(tf.to_float(height) * image_scale)\\n scaled_width = tf.to_int32(tf.to_float(width) * image_scale)\\n self._image_scale = image_scale\\n self._scaled_height = scaled_height\\n self._scaled_width = scaled_width\",\n \"def scale_image(img, factor=1):\\n\\treturn cv2.resize(img,(int(img.shape[1]*factor), int(img.shape[0]*factor)))\",\n \"def scale_image_button(src):\\n return pygame.transform.scale(pygame.image.load(src), (30, 30))\",\n \"def rescale_image(image, rescale_width):\\r\\n\\r\\n # image size\\r\\n image_height = pdb.gimp_image_height(image)\\r\\n image_width = pdb.gimp_image_width(image)\\r\\n\\r\\n # new image height\\r\\n rescale_height = round(image_height * (rescale_width * 1.0 / image_width))\\r\\n\\r\\n pdb.gimp_image_scale(image, rescale_width, rescale_height)\\r\\n gimp.message('Rescaled image')\",\n \"def calculate_image_scale(source_width, source_height, target_width, target_height):\\n if source_width == target_width and source_height == target_height:\\n return 1.0\\n\\n source_ratio = source_width / source_height\\n target_ratio = target_width / target_height\\n\\n if target_ratio < source_ratio:\\n scale = target_width / source_width\\n else:\\n scale = target_height / source_height\\n\\n return scale\",\n \"def downscale_image(im, max_dim=2048):\\n a, b = im.size\\n if max(a, b) <= max_dim:\\n return 1.0, im\\n\\n scale = 1.0 * max_dim / max(a, b)\\n new_im = im.resize((int(a * scale), int(b * scale)), Image.ANTIALIAS)\\n return scale, new_im\",\n \"def rescale_by_height(image, target_height, method=cv2.INTER_LANCZOS4):\\r\\n w = int(round(target_height * image.shape[1] / image.shape[0]))\\r\\n return cv2.resize(image, (w, target_height), interpolation=method)\",\n \"def rescale_box(box, img_size_orig, img_size_new):\\n orig_w, orig_h = img_size_orig\\n new_w, new_h = img_size_new\\n scale_x = new_w / orig_w\\n scale_y = new_h / orig_h\\n sx, sy, ex, ey = box\\n return [sx * scale_x, sy * scale_y, ex * scale_x, ey * scale_y]\",\n \"def scale(val, src, dst):\\n try:\\n return ((val - src[0]) / (src[1]-src[0])) * (dst[1]-dst[0]) + dst[0]\\n except ZeroDivisionError:\\n return 0.0\",\n \"def rescale_intrinsic(self):\\n # scale focal length and principal points wrt image resizeing\\n if self.downscale > 1:\\n self.K = self.K_orig.copy()\\n self.K[0, 0] /= float(self.downscale)\\n self.K[1, 1] /= float(self.downscale)\\n self.K[0, 2] /= float(self.downscale)\\n self.K[1, 2] /= float(self.downscale)\\n self.intrinsic = self.K\\n else:\\n self.K = self.intrinsic = self.K_orig.copy()\",\n \"def scale_img(img):\\r\\n # Scale values of img between 0 and 255.\\r\\n img -= np.amin(img)\\r\\n img /= np.amax(img)\\r\\n img *= 255\\r\\n return img\",\n \"def imrescale(img,scale,return_scale=False,interpolation='bilinear'):\\n h,w = img.shape[:2]\\n if isinstance(scale,(float,int)):\\n if scale <= 0:\\n raise ValueError('Invalid scale:{},must be positive.'.format(scale))\\n scale_factor = scale\\n \\n elif isinstance(scale,tuple):\\n max_long_edge = max(scale)\\n max_short_edge = min(scale)\\n scale_factor = min(max_long_edge/max(h,w),\\n max_short_edge/min(h,w))\\n else:\\n raise ValueError('Scale must be a number or tuple of int,but got {}'.format(type(scale)))\\n \\n new_size = _scale_size((w,h),scale_factor)\\n rescaled_img = imresize(img,new_size,interpolation = interpolation)\\n if return_scale:\\n return rescaled_img,scale_factor\\n else:\\n return rescaled_img\",\n \"def rescale_image(image: np.ndarray, scale: float) -> np.ndarray:\\n (height, width) = image.shape[:2]\\n new_dims = (int(width * scale), int(height * scale))\\n return cv2.resize(image, new_dims, interpolation=cv2.INTER_CUBIC)\",\n \"def PScaleImage (inImage, scale, err):\\n ################################################################\\n # Checks\\n if not Image.PIsA(inImage):\\n raise TypeError,\\\"inImage MUST be a Python Obit Image\\\"\\n if not OErr.OErrIsA(err):\\n raise TypeError,\\\"err MUST be an OErr\\\"\\n #\\n # Open image\\n Image.POpen (inImage, Image.READWRITE, err)\\n # Get input descriptor to see how many planes\\n inDesc = Image.PGetDesc(inImage)\\n inDescDict = ImageDesc.PGetDict(inDesc)\\n ndim = inDescDict[\\\"naxis\\\"]\\n inNaxis = inDescDict[\\\"inaxes\\\"]\\n # Work buffer\\n inImageArray = Image.PGetFArray(inImage)\\n ImageBuffer = FArray.PCopy(inImageArray, err)\\n # Reset max/min\\n inDescDict[\\\"minval\\\"] = 1.0e20\\n inDescDict[\\\"maxval\\\"] = -1.0e20\\n inImage.Desc.Dict = inDescDict # Update descriptor on image\\n Image.PDirty(inImage) # Force update\\n Image.PClose (inImage, err)\\n\\n # list of planes to loop over (0-rel)\\n if (ndim>0) and (inNaxis[2]>0): \\n planes = range(inNaxis[2])\\n else:\\n planes = [0]\\n \\n # Loop over planes\\n for iPlane in planes:\\n doPlane = [iPlane+1,1,1,1,1]\\n # Get image plane\\n Image.PGetPlane (inImage, ImageBuffer, doPlane, err)\\n\\n # Scaling factor\\n if type(scale)==list:\\n scl = scale[iPlane]\\n else:\\n scl = scale\\n\\n # Scale\\n FArray.PSMul(ImageBuffer, scl)\\n\\n # Write output\\n Image.PPutPlane (inImage, ImageBuffer, doPlane, err)\\n\\n # Scale any CC table\\n highVer = Image.PGetHighVer (inImage, \\\"AIPS CC\\\")\\n if (iPlane+1<=highVer):\\n CCTab = Image.PImageGetTable (inImage, Image.READWRITE, \\\"AIPS CC\\\", iPlane+1, err)\\n PCCScale (CCTab, 1, 0, scl, err)\\n # end loop over planes\\n # Write history\\n inHistory = History.History(\\\"history\\\", inImage.List, err)\\n # Add this programs history\\n inHistory.Open(History.READWRITE, err)\\n inHistory.TimeStamp(\\\" Start Obit ScaleImage\\\",err)\\n if type(scale)==list:\\n i = -1\\n for iPlane in planes:\\n i = i + 1\\n scl = scale[i]\\n inHistory.WriteRec(-1,\\\"ScaleImage / scale[\\\"+str(i+1)+\\\"] = \\\"+str(scl),err)\\n else:\\n inHistory.WriteRec(-1,\\\"ScaleImage / scale = \\\"+str(scale),err)\\n inHistory.Close(err)\",\n \"def imageScale(scale):\\n\\t\\treturn max(1, int(scale * (InterfaceTools.getCanvasSize()[0] / height)))\",\n \"def retinanet_compute_resize_scale(image_shape, min_side=800, max_side=1333):\\n (rows, cols, _) = image_shape\\n\\n smallest_side = min(rows, cols)\\n\\n # rescale the image so the smallest side is min_side\\n scale = float(min_side) / float(smallest_side)\\n\\n # check if the largest side is now greater than max_side, which can happen\\n # when images have a large aspect ratio\\n largest_side = max(rows, cols)\\n if largest_side * scale > max_side:\\n scale = float(max_side) / float(largest_side)\\n\\n return scale\",\n \"def scaled_image(width, height):\\r\\n total = width*height\\r\\n scale_dict = {0.2: 6500000, 0.3: 4000000, 0.4: 2250000, 0.5: 1000000, 0.8: 500000, 1: 0}\\r\\n for k, v in scale_dict.items():\\r\\n if total > v:\\r\\n return k\",\n \"def __scale_width_height(img, target_width=None, target_height=None, method=Image.BICUBIC):\\n if target_height > 0 and target_width:\\n raise ValueError(\\n f\\\"Expected target_width>0 and target_height>0, but got target_width={target_width}, target_height={target_height}\\\")\\n\\n ow, oh = img.size\\n if target_width is not None and target_height is None:\\n target_height = int(target_width * oh) / ow\\n elif target_width is None and target_height is not None:\\n target_width = int(target_height * ow) / oh\\n elif target_width is None and target_height is None:\\n return img\\n\\n return img.resize((target_width, target_height), method)\",\n \"def _shrink_main(self, amt):\\n self.ratio -= amt\\n self.ratio = max(self.min_ratio, self.ratio)\",\n \"def adjust(self, image):\\n ...\",\n \"def transform_input(crop_size, upscale_factor):\\n return Compose([\\n Scale(crop_size // upscale_factor),\\n ])\",\n \"def scale_invert(self):\",\n \"def scale(self, scale=1):\\n self.x *= scale\\n self.y *= scale\\n self.width *= scale\\n self.height *= scale\\n\\n # Always update the corners after operation\\n self.update_corners()\\n return\",\n \"def scale(self, value):\\n\\t\\toldscale = self.oldmax - self.oldmin\\n\\t\\tnewscale = self.newmax - self.newmin\\n\\t\\treturn (newscale * (value - self.oldmin) / oldscale) + self.newmin\",\n \"def scaleImageToEqualSize(sourceImg, targetImg):\\n return cv2.resize(sourceImg,\\n None, # no specific out dim\\n fx=targetImg.shape[1] / sourceImg.shape[1], # scale factor Y\\n fy=targetImg.shape[0] / sourceImg.shape[0], # scale factor X\\n interpolation=cv2.INTER_AREA)\",\n \"def scale_image(image: Image, scale: float) -> Image:\\n width = round(image.width * scale)\\n height = round(image.height * scale)\\n image.thumbnail((width, height))\\n return image\",\n \"def _scale_to_mbs_frame(self : \\\"animation\\\",\\n img : \\\"np.ndarray\\\"\\n ) -> \\\"np.ndarray\\\":\\n xnew = img.shape[0] + self._mbs - img.shape[0]%self._mbs\\n ynew = img.shape[1] + self._mbs - img.shape[1]%self._mbs\\n return (255*resize(img, (xnew, ynew))).astype(np.uint8)\",\n \"def zoom_to_size(self, *p):\\n\\t\\tif self.image is None or self.allocation is None:\\n\\t\\t\\treturn\\n\\t\\tif __debug__: print self.allocation.width, self.image.get_width()\\n\\t\\tif __debug__: print self.allocation.width, self.image.get_width(), self.allocation.width/self.image.get_width()\\n\\t\\tz = min(\\n\\t\\t\\tself.allocation.width/self.image.get_width(),\\n\\t\\t\\tself.allocation.height/self.image.get_height()\\n\\t\\t\\t)\\n\\t\\tif __debug__: print \\\"zoom_to_size\\\", \\\"z=\\\", z\\n\\t\\tself.zoom = z\",\n \"def resize_img(self,scale=1):\\n reduced = self.image.reduce((scale,scale))\\n reduced.save(\\\"../edited/{}\\\".format(self.image.filename))\\n\\n reduced = Image.open(\\\"../edited/{}\\\".format(self.image.filename))\\n return reduced\",\n \"def scale_image(self, image):\\n err_m1 = \\\"image must be a numpy.ndarray with shape (h, w, 3)\\\"\\n if not isinstance(image, np.ndarray):\\n raise TypeError(err_m1)\\n if len(image.shape) != 3 or image.shape[2] != 3:\\n raise TypeError(err_m1)\\n shape0 = image.shape[0]\\n shape1 = image.shape[1]\\n\\n if shape0 > shape1:\\n new_w = int(image.shape[1] * 512 / image.shape[0])\\n new_h = 512\\n elif shape0 < shape1:\\n new_h = int(image.shape[0] * 512 / image.shape[1])\\n new_w = 512\\n else:\\n new_h = 512\\n new_w = 512\\n tf.image.ResizeMethod.BICUBIC\\n image = tf.expand_dims(image, 0)\\n image = tf.image.resize_bicubic(image,\\n (new_h, new_w),\\n align_corners=False)\\n image = image / 255\\n image = tf.clip_by_value(image, clip_value_min=0, clip_value_max=1)\\n\\n return image\",\n \"def scale(input):\\n return (input - np.min(input)) / ((np.max(input) - np.min(input)))\",\n \"def scale_img(img, scale=1.0):\\n dim = (int(img.shape[1]*scale), int(img.shape[0]*scale))\\n return cv2.resize(img.copy(), dim, interpolation=cv2.INTER_AREA)\",\n \"def fit_image(self, img, width, height):\\n if img.get_height()/height > img.get_width()/width:\\n # scale is determined by width\\n w = width\\n h = int(math.ceil(img.get_height() * (w/img.get_width())))\\n else:\\n # scale is determined by height\\n h = height\\n w = int(math.ceil(img.get_width() * (h/img.get_height())))\\n img = pygame.transform.smoothscale(img, (w,h))\\n rect = img.get_rect()\\n rect = rect.move((width-w)//2, (height-h)//2)\\n img2 = pygame.Surface((width, height))\\n img2.blit(img, rect)\\n return img2\",\n \"def scale(image):\\n image = tf.cast(image, tf.float32)\\n image /= 255\\n return image\",\n \"def shrink_kernel(self, kernel, up_scale):\\n up_scale = torch.tensor(up_scale).float()\\n # boundary padding based on the scaling law\\n pad_in = (torch.ceil(up_scale**2).int())*((kernel.shape[2]-1)//2)\\n pad_h = (torch.ceil(up_scale).int())*((kernel.shape[3]-1)//2)\\n pad_w = (torch.ceil(up_scale).int())*((kernel.shape[4]-1)//2)\\n padded_kernel = F.pad(kernel, (pad_w, pad_w, pad_h, pad_h, pad_in, pad_in))\\n delta = up_scale%1\\n \\n if delta == 0:\\n shrink_factor = 1\\n else:\\n # shrink_factor for coordinates.\\n shrink_factor = (((kernel.shape[4]-1))/(padded_kernel.shape[-1]-1)*(up_scale+1))\\n \\n # Adjustment to deal with weird filtering on the grid sample function.\\n shrink_factor = 1.5*(shrink_factor-0.5)**3 + 0.57 \\n\\n grid = torch.meshgrid(torch.linspace(-1, 1, kernel.shape[2])*(shrink_factor**2),\\n torch.linspace(-1, 1, kernel.shape[3])*shrink_factor, \\n torch.linspace(-1, 1, kernel.shape[4])*shrink_factor)\\n\\n grid = torch.cat([grid[2].unsqueeze(0).unsqueeze(-1), \\n grid[1].unsqueeze(0).unsqueeze(-1), \\n grid[0].unsqueeze(0).unsqueeze(-1)], dim = -1).repeat(kernel.shape[0],1,1,1,1)\\n\\n new_kernel = F.grid_sample(padded_kernel, grid.to(device))\\n if kernel.shape[-1] - 2*up_scale > 0:\\n new_kernel = new_kernel * (kernel.shape[-1]**2/((kernel.shape[-1] - 2*up_scale)**2 + 0.01))\\n return new_kernel\",\n \"def resize_image(image: Image) -> Image:\\n expected_x: int = 1024\\n expected_y: int = 768\\n x, y = image.size\\n if x > expected_x or y > expected_y:\\n scale = min(expected_x / x, expected_y / y)\\n return image.resize((int(x * scale), int(y * scale)))\\n else:\\n return image.resize((int(0.9*x), int(0.9*y)))\",\n \"async def _create_upscale(input_file, *, output_file, ratio):\\n # If the output file already exists, use it if the timestamp matches. If the\\n # timestamps are different, regenerate it.\\n if output_file.exists():\\n input_stat = input_file.stat()\\n output_stat = output_file.stat()\\n if input_stat.st_mtime == output_stat.st_mtime:\\n return\\n\\n # Reencode the image we're upscaling to an RGB BMP. The upscaler isn't very robust\\n # at handling various files and file paths, so this lets us give it a simple, controlled\\n # input that won't confuse it. Bake any transparency (it doesn't handle transparency)\\n # and convert to RGB.\\n input_temp_file = misc.get_temporary_path('.bmp')\\n output_temp_file = misc.get_temporary_path('.jpg')\\n\\n with input_file.open('rb') as f:\\n f = remove_photoshop_tiff_data(f)\\n image = Image.open(f)\\n image.load()\\n \\n # If we tell PIL to convert P to RGB and the palette has transparency, it seems\\n # to use an undefined RGB color for transparency. We just want it to comp onto\\n # black. To avoid this, convert to RGBA, so it'll use the black underlay path\\n # below.\\n if image.mode == 'P':\\n image = image.convert('RGBA')\\n\\n # Bake RGBA to RGB with a black background.\\n if image.mode == 'RGBA':\\n bg = Image.new('RGBA', image.size, (0,0,0,255))\\n image = Image.alpha_composite(bg, image)\\n\\n # Convert anything else to RGB.\\n if image.mode != 'RGB':\\n image = image.convert('RGB')\\n\\n with input_temp_file.open('w+b') as output:\\n image.save(output, format='bmp')\\n\\n try:\\n # This is a GPU upscaler. Only process one image at a time, so we don't spam GPU jobs\\n # if the client tries to load too aggressively. Doing it here allows the above check\\n # to complete without blocking if the image is already cached.\\n async with _lock:\\n assert ratio in (2,3,4)\\n result = await _run_upscale([\\n _upscaler,\\n '-s', str(ratio),\\n '-i', str(input_temp_file),\\n '-o', str(output_temp_file),\\n ])\\n\\n if result != 0:\\n raise Exception('Error upscaling image')\\n\\n # The upscaler doesn't return a result code.\\n if not output_temp_file.exists():\\n raise Exception('Error upscaling image (no file generated)')\\n\\n shutil.copyfile(output_temp_file, output_file)\\n finally:\\n # Clean up.\\n input_temp_file.unlink(missing_ok=True)\\n output_temp_file.unlink(missing_ok=True)\\n\\n # Set the cache file's timestamp to match the input file, so we can tell if the input\\n # file changes.\\n st = input_file.stat()\\n os.utime(output_file, (st.st_atime, st.st_mtime))\",\n \"def scale(self, scale):\\n \\n scale_matrix = wf.scaleMatrix(scale, self.width/2, self.height/2, 0)\\n self.transform(scale_matrix)\",\n \"def rescale_by_width(image, target_width, method=cv2.INTER_LANCZOS4):\\r\\n h = int(round(target_width * image.shape[0] / image.shape[1]))\\r\\n return cv2.resize(image, (target_width, h), interpolation=method)\",\n \"def scale0to1(img):\\r\\n\\r\\n min = np.min(img)\\r\\n max = np.max(img)\\r\\n\\r\\n if min == max:\\r\\n img.fill(0.5)\\r\\n else:\\r\\n img = (img-min) / (max-min)\\r\\n\\r\\n return img.astype(np.float32)\",\n \"def rescale(tx):\\n mins = np.amin(tx, axis=0)\\n maxs = np.amax(tx, axis=0)\\n txscale = (tx - mins) / (maxs - mins)\\n return txscale\",\n \"def _scale(x):\\n scaleFactor = 1\\n _ret = int(x/scaleFactor)\\n return _ret\",\n \"def coral_image_resize(im, scaling_method, scaling_factor, height_cm):\\n\\n if scaling_method == 'scale':\\n scale = float(scaling_factor) # here scaling_factor is the desired image scaling.\\n elif scaling_method == 'ratio':\\n scale = float(scaling_factor) * height_cm / im.shape[0] # here scaling_factor is the desited px_cm_ratio.\\n im = scipy.misc.imresize(im, scale)\\n return (im, scale)\",\n \"def scale0to1(img):\\r\\n\\r\\n img = img.astype(np.float32)\\r\\n\\r\\n if img.shape[-1] != 1:\\r\\n img = np.sqrt(np.sum(img**2, axis=-1, keepdims=True))\\r\\n min = np.min(img)\\r\\n max = np.max(img)\\r\\n\\r\\n if np.absolute(min-max) < 1.e-6:\\r\\n img.fill(0.5)\\r\\n else:\\r\\n img = (img - min)/(max - min)\\r\\n\\r\\n return img.astype(np.float32)\",\n \"def change_resolution(img):\\n scale_factor = np.random.choice(list(range(0, 6, 2)))\\n if scale_factor == 0:\\n return img\\n downsample = nn.AvgPool2d(scale_factor)\\n upsample = nn.UpsamplingNearest2d(scale_factor=scale_factor)\\n new_res_img = upsample(downsample(img.unsqueeze(dim=1))).squeeze()\\n return new_res_img\",\n \"def scaleImage(img, min):\\n scales = []\\n img_ = img.copy()\\n h, w, c = img.shape\\n factor = 0.8\\n while h >= min and w >= min:\\n img_ = cv2.resize(img_, (w, h))\\n scales.append(img_)\\n h = int(h * factor)\\n w = int(w * factor)\\n\\n return scales\",\n \"def scale_image(image, scale=1, interpolation='linear'):\\n if interpolation == 'linear':\\n return cv2.resize(image, None, fx=scale, fy=scale, interpolation=cv2.INTER_LINEAR)\\n if interpolation == 'nearest':\\n return cv2.resize(image, None, fx=scale, fy=scale, interpolation=cv2.INTER_NEAREST)\",\n \"def _rescale(x, xlim, ylim):\\n m = (ylim[1] - ylim[0]) / (xlim[1] - xlim[0])\\n c = ylim[1] - m * xlim[1]\\n y = m * x + c\\n return y\",\n \"def rescale(num, old_min, old_max, new_min, new_max):\\n old_range = old_max - old_min\\n new_range = new_max - new_min\\n new_val = new_min + (((num - old_min) * new_range)/old_range)\\n\\n return new_val\",\n \"def image_resize(img, min_size=600, max_size=1000):\\n C, H, W = img.shape\\n scale1 = min_size / min(H, W)\\n scale2 = max_size / max(H, W)\\n scale = min(scale1, scale2)\\n img = img / 255\\n img = transform.resize(img, (C, H * scale, W * scale),\\n mode='reflect', anti_aliasing=False)\\n # img = pytorch_normalize(img)\\n # img = caffe_normalize(img)\\n return img\",\n \"def scale(self, value):\\n return (float(value) - float(self.minimum)) / \\\\\\n float(self.maximum - self.minimum) * 2.0 - 1.0\",\n \"def __call__(self, input_image):\\n output_image = input_image\\n if self.shift is not None or self.scale is not None:\\n output_image = rescale(output_image, shift=self.shift, scale=self.scale)\\n if self.random_shift is not None or self.random_scale is not None:\\n current_random_shift = random.float_uniform(-self.random_shift, self.random_shift)\\n current_random_scale = 1 + random.float_uniform(-self.random_scale, self.random_scale)\\n output_image = rescale(output_image, shift=current_random_shift, scale=current_random_scale)\\n if self.clamp_min is not None or self.clamp_max is not None:\\n output_image = clamp(output_image, clamp_min=self.clamp_min, clamp_max=self.clamp_max)\\n\\n return output_image\",\n \"def rescale(x):\\n if x.min() != 0:\\n raise ValueError('input should have min zero.')\\n\\n x /= x.max() # max 1\\n x *= 2 # max 2\\n x -= 1 # range -1, 1\\n\\n if x.min() != -1 and x.max() != 1:\\n raise Exception\\n\\n return x\",\n \"def scaling(mat, factor):\\n\\treturn mat / (mat + factor)\",\n \"def rescale_image_01(image):\\n # scale image to from [0, 255] to [0.0, 1.0]\\n image = image.astype(np.float32)\\n return image / 255\",\n \"def scale_image(image, new_range):\\n min_val = np.min(image).astype(np.float32)\\n max_val = np.max(image).astype(np.float32)\\n min_val_new = np.array(min(new_range), dtype=np.float32)\\n max_val_new = np.array(max(new_range), dtype=np.float32)\\n scaled_image = (image - min_val) / (max_val - min_val) * (max_val_new - min_val_new) + min_val_new\\n return scaled_image.astype(np.uint8)\",\n \"def edit_scale(scale, direction):\\n if direction in (up, shift_up, plus):\\n scale = scale*2\\n elif direction in (down, shift_down, minus):\\n scale = scale/2\\n return scale\",\n \"def resize_img(input_img, scale=1.1):\\n if scale == 1.0:\\n return input_img\\n rows, cols, ch = input_img.shape\\n d = rows * (scale - 1) # overall image size change from rows, cols, to rows - 2d, cols - 2d\\n pts1 = np.float32([[d, d], [rows - 1 - d, d], [d, cols - 1 - d], [rows - 1 - d, cols - 1 - d]])\\n pts2 = np.float32([[0, 0], [rows - 1, 0], [0, cols - 1], [rows - 1, cols - 1]])\\n M = cv2.getPerspectiveTransform(pts1, pts2)\\n dst = cv2.warpPerspective(input_img, M, (cols, rows), borderMode=1)\\n return dst\",\n \"def process_image(image):\\n image = resize(image)\\n return image\",\n \"def img_scale(self):\\n return min(400, abs(self.size))\",\n \"def rescale_and_resize_image(img_name, img_size, save_img):\\n \\n try:\\n \\n # Open image\\n img = Image.open(img_name)\\n \\n # Resize image\\n img = img.resize((int(img_size), int(img_size)), Image.ANTIALIAS) \\n \\n # Get data from image\\n img_list = list(img.getdata())\\n \\n # Find minimum and maximum value pixels in the image\\n img_max = max(img_list)\\n img_min = min(img_list)\\n \\n # Determine factor to scale to a 8-bit image\\n scale_factor = 255.0/(img_max - img_min)\\n \\n img_list_new = [0] * img_size * img_size\\n \\n # Rescale all pixels to the range 0 to 255 (in line with unit8 values)\\n for i in range(0,img_size):\\n for j in range(0,img_size):\\n img_list_new[i*img_size + j] = int((img_list[i*img_size + j]-img_min)*scale_factor)\\n if (img_list_new[i*img_size + j] > 255) or (img_list_new[i*img_size + j] < 0) or (img_list_new[i*img_size + j]-int(img_list_new[i*img_size + j]) != 0):\\n print(\\\"img_list_new[%d][%d] = %r\\\" % (i,j,img_list_new[i*img_size + j]))\\n \\n img.putdata(img_list_new)\\n \\n # Convert to uint8 greyscale image\\n img = img.convert('L')\\n \\n # Save image\\n if save_img:\\n img.save(img_name)\\n else:\\n ph = ImageTk.PhotoImage(img)\\n return ph\\n \\n finally:\\n \\n # Close image\\n img.close()\",\n \"def scale(self, value):\\r\\n return (float(value)-float(self.minimum))/float(self.maximum-self.minimum)*2.0 - 1.0\",\n \"def scale(self, sx, sy):\\n frameWidth *= sx\\n frameHeight *= sy\\n repaint()\",\n \"def scale_it(val):\\n return scale(val, 0, 1, bpm_range[0], bpm_range[1])\",\n \"def downscale_image(img, bottom, x, y):\\n width, height = tuple(img.shape[1::-1])\\n img = img[int(round((1 - bottom) * (height - 1))):(height - 1), 1:(width - 1)]\\n img = cv2.resize(img, (x, y))\\n #img = cv2.Canny(img, 100, 200)\\n ret, img = cv2.threshold(img, img.mean(), 255, cv2.THRESH_BINARY)\\n return img\",\n \"def normalise_image(image, use_torch=True):\\n if use_torch:\\n image = torch.abs(image)\\n else:\\n image = np.abs(image)\\n if (image.max() - image.min()) < 1e-5:\\n return image - image.min() + 1e-5\\n else:\\n return (image - image.min()) / (image.max() - image.min())\",\n \"def preprocess(img, min_size, max_size):\\n if(min_size > max_size):\\n raise Exception('min_size should not exceed max_size')\\n \\n width, height = img.size\\n minDim = min(width,height)\\n maxDim = max(width,height)\\n scale_shorter_side = min_size/minDim\\n scale_longer_side = maxDim * scale_shorter_side\\n if(scale_longer_side > max_size):\\n scale = max_size/maxDim\\n else:\\n scale = scale_shorter_side\\n transform = transforms.Compose([\\n transforms.Resize((round(img.height*scale),round(img.width * scale))),\\n transforms.ToTensor(),\\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\\n ])\\n img = transform(img)\\n return scale,img\",\n \"def normalise(image):\",\n \"def scale0to1(img):\\r\\n\\r\\n img = img.astype(np.float32)\\r\\n\\r\\n min = np.min(img)\\r\\n max = np.max(img)\\r\\n\\r\\n if np.absolute(min-max) < 1.e-6:\\r\\n img.fill(0.5)\\r\\n else:\\r\\n img = (img-min) / (max-min)\\r\\n\\r\\n return img.astype(np.float32)\",\n \"def zoom_augmentation():\\n # Get the width and the height of the zoomed version\\n x_len, y_len = np.random.randint(250, 350, size=2)\\n # Get left upper ,right and lower bound of the pixels in the original image\\n left = np.random.randint(x_size-x_len)\\n upper = np.random.randint(y_size-y_len)\\n right, lower = left + x_len, upper+y_len\\n # Crops the box and resizes it to the original image size\\n box = (left, upper, right, lower)\\n return lambda image: image.transform(image.size, Image.EXTENT, box)\",\n \"def rescaled(M,newmin,newmax):\\n mmin,mmax = M.min(),M.max()\\n M2 = M.copy()\\n M2 -= mmin\\n M2 *= (newmax-newmin) / (mmax-mmin)\\n M2 += newmin\\n return M2\",\n \"def retinanet_resize_image(img, min_side=800, max_side=1333):\\n # compute scale to resize the image\\n scale = retinanet_compute_resize_scale(img.shape, min_side=min_side, max_side=max_side)\\n # scale = round(scale, 4)\\n # print(\\\"scale: \\\", scale)\\n\\n # resize the image with the computed scale\\n img = cv2.resize(img, None, fx=scale, fy=scale)\\n\\n return img, scale\",\n \"def stretch(number, scale):\\n\\tif number >= 0:\\n\\t\\treturn scale * math.ceil(number / scale)\\n\\telse:\\n\\t\\treturn scale * math.floor(number / scale)\",\n \"def rescaleFrame(frame, scale=.75):\\n\\n\\twidth = int(frame.shape[1] * scale)\\n\\theight = int(frame.shape[0] * scale)\\n\\n\\tdimensions = (width, height)\\n\\t\\n\\treturn cv.resize(frame, dimensions, interpolation=cv.INTER_AREA)\",\n \"def resizeImage(image, maxW, maxH):\\n\\timageW, imageH = image.size\\n\\tif imageW == maxW and imageH == maxH:\\n\\t\\treturn image\\n\\t# find which axis requires the biggest zoom (smallest relative max dimension)\\n\\tzoomW = float(imageW) / float(maxW)\\n\\tzoomH = float(imageH) / float(maxH)\\n\\tzoom = max(zoomW, zoomH)\\n\\tif zoomW >= zoomH:\\t# size is defined by width\\n\\t\\tmaxH = int(imageH//zoom)\\t# calculate the new height\\n\\telse:\\n\\t\\tmaxW = int(imageW//zoom)\\n\\treturn image.resize((maxW, maxH))\",\n \"def reScaleLandsat(self,img):\\n \\n\\t\\tthermalBand = ee.List(['thermal'])\\n\\t\\tthermal = ee.Image(img).select(thermalBand).multiply(10)\\n \\n\\t\\totherBands = ee.Image(img).bandNames().removeAll(thermalBand)\\n\\t\\tscaled = ee.Image(img).select(otherBands).divide(0.0001)\\n \\n\\t\\timage = ee.Image(scaled.addBands(thermal)).int16()\\n \\n\\t\\treturn image.copyProperties(img)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72712576","0.7073858","0.7063879","0.6822762","0.6660474","0.66532725","0.6620047","0.6618878","0.660835","0.65937185","0.653122","0.65161115","0.64969885","0.648761","0.64868206","0.64707977","0.64599156","0.6456482","0.6456482","0.64336264","0.64038795","0.63927114","0.63902193","0.6381375","0.6361869","0.6342903","0.6317131","0.62809163","0.6273473","0.6260965","0.6232873","0.6194964","0.61906636","0.617151","0.6150135","0.61471784","0.6087807","0.60872245","0.6055202","0.6035415","0.60173947","0.60082746","0.5999263","0.5985565","0.5974501","0.59723544","0.59723115","0.5963878","0.5947828","0.5945838","0.5930303","0.59299225","0.59268844","0.59237474","0.59143513","0.591199","0.5904124","0.58986944","0.58971316","0.5886442","0.5882318","0.5878745","0.58661383","0.5857454","0.5845814","0.584215","0.583654","0.58309233","0.58279365","0.58148944","0.58044934","0.57965046","0.5792034","0.57888275","0.5788714","0.5785491","0.57847154","0.57779014","0.57685876","0.57639825","0.5762937","0.5762538","0.5757272","0.57551533","0.5739954","0.57395476","0.57361966","0.5732592","0.5729771","0.5727582","0.57232714","0.57226634","0.5722379","0.5715134","0.57121253","0.57121235","0.5708632","0.570302","0.57010126"],"string":"[\n \"0.72712576\",\n \"0.7073858\",\n \"0.7063879\",\n \"0.6822762\",\n \"0.6660474\",\n \"0.66532725\",\n \"0.6620047\",\n \"0.6618878\",\n \"0.660835\",\n \"0.65937185\",\n \"0.653122\",\n \"0.65161115\",\n \"0.64969885\",\n \"0.648761\",\n \"0.64868206\",\n \"0.64707977\",\n \"0.64599156\",\n \"0.6456482\",\n \"0.6456482\",\n \"0.64336264\",\n \"0.64038795\",\n \"0.63927114\",\n \"0.63902193\",\n \"0.6381375\",\n \"0.6361869\",\n \"0.6342903\",\n \"0.6317131\",\n \"0.62809163\",\n \"0.6273473\",\n \"0.6260965\",\n \"0.6232873\",\n \"0.6194964\",\n \"0.61906636\",\n \"0.617151\",\n \"0.6150135\",\n \"0.61471784\",\n \"0.6087807\",\n \"0.60872245\",\n \"0.6055202\",\n \"0.6035415\",\n \"0.60173947\",\n \"0.60082746\",\n \"0.5999263\",\n \"0.5985565\",\n \"0.5974501\",\n \"0.59723544\",\n \"0.59723115\",\n \"0.5963878\",\n \"0.5947828\",\n \"0.5945838\",\n \"0.5930303\",\n \"0.59299225\",\n \"0.59268844\",\n \"0.59237474\",\n \"0.59143513\",\n \"0.591199\",\n \"0.5904124\",\n \"0.58986944\",\n \"0.58971316\",\n \"0.5886442\",\n \"0.5882318\",\n \"0.5878745\",\n \"0.58661383\",\n \"0.5857454\",\n \"0.5845814\",\n \"0.584215\",\n \"0.583654\",\n \"0.58309233\",\n \"0.58279365\",\n \"0.58148944\",\n \"0.58044934\",\n \"0.57965046\",\n \"0.5792034\",\n \"0.57888275\",\n \"0.5788714\",\n \"0.5785491\",\n \"0.57847154\",\n \"0.57779014\",\n \"0.57685876\",\n \"0.57639825\",\n \"0.5762937\",\n \"0.5762538\",\n \"0.5757272\",\n \"0.57551533\",\n \"0.5739954\",\n \"0.57395476\",\n \"0.57361966\",\n \"0.5732592\",\n \"0.5729771\",\n \"0.5727582\",\n \"0.57232714\",\n \"0.57226634\",\n \"0.5722379\",\n \"0.5715134\",\n \"0.57121253\",\n \"0.57121235\",\n \"0.5708632\",\n \"0.570302\",\n \"0.57010126\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5729484"},"document_rank":{"kind":"string","value":"90"}}},{"rowIdx":16880,"cells":{"query":{"kind":"string","value":"Read image files and make their subimages and saved them as a h5 file format."},"document":{"kind":"string","value":"def input_setup_MS(sess,config,data_dir,index=0):\n # Load data path\n if config.is_train:\n data = prepare_data(sess, dataset=data_dir)\n\n sub_input_sequence = []\n padding = 0 \n if config.is_train:\n for i in xrange(len(data)):\n input_=(imread(data[i])-127.5)/127.5\n\n if len(input_.shape) == 3:\n h, w, _ = input_.shape\n else:\n h, w = input_.shape\n for x in range(0, h-config.image_size_MS+1, config.stride_MS):\n for y in range(0, w-config.image_size_MS+1, config.stride_MS):\n sub_input = input_[x:x+config.image_size_MS, y:y+config.image_size_MS] \n if data_dir == \"Train\":\n sub_input=cv2.resize(sub_input, (config.image_size_MS/4,config.image_size_MS/4),interpolation=cv2.INTER_CUBIC)\n sub_input = sub_input.reshape([config.image_size_MS/4, config.image_size_MS/4, 1])\n print('error')\n else:\n sub_input = sub_input.reshape([config.image_size_MS, config.image_size_MS, 1]) \n sub_input_sequence.append(sub_input)\n arrdata = np.asarray(sub_input_sequence) \n print(arrdata.shape)\n make_data(sess, arrdata, data_dir)"},"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 create_and_write_output(predictions_path,output_path,inpDir):\n \n filenames= sorted(os.listdir(predictions_path)) \n for filename in filenames:\n \n # read the 3 channel output image from the neural network\n image=cv2.imread(os.path.join(predictions_path,filename))\n \n # create binary image output using the create_binary function\n out_image=create_binary(image) \n \n # read and store the metadata from the input image\n with BioReader(os.path.join(inpDir,filename)) as br:\n metadata = br.metadata\n\n # Write the binary output consisting of the metadata using bfio.\n output_image_5channel=np.zeros((out_image.shape[0],out_image.shape[1],1,1,1),dtype=np.uint8)\n output_image_5channel[:,:,0,0,0]=out_image \n\n with BioWriter(os.path.join(output_path,filename), metadata=metadata) as bw:\n bw.dtype = output_image_5channel.dtype\n bw.write(output_image_5channel)","def _preprocess(self):\n print(\"Note: if root path is changed, the previously generated json files need to be re-generated (delete them first)\")\n if osp.exists(self.imgs_labeled_dir) and \\\n osp.exists(self.imgs_detected_dir) and \\\n osp.exists(self.split_classic_det_json_path) and \\\n osp.exists(self.split_classic_lab_json_path) and \\\n osp.exists(self.split_new_det_json_path) and \\\n osp.exists(self.split_new_lab_json_path):\n return\n\n mkdir_if_missing(self.imgs_detected_dir)\n mkdir_if_missing(self.imgs_labeled_dir)\n\n print(\"Extract image data from {} and save as png\".format(self.raw_mat_path))\n mat = h5py.File(self.raw_mat_path, 'r')\n\n def _deref(ref):\n return mat[ref][:].T\n\n def _process_images(img_refs, campid, pid, save_dir):\n img_paths = [] # Note: some persons only have images for one view\n for imgid, img_ref in enumerate(img_refs):\n img = _deref(img_ref)\n # skip empty cell\n if img.size == 0 or img.ndim < 3: continue\n # images are saved with the following format, index-1 (ensure uniqueness)\n # campid: index of camera pair (1-5)\n # pid: index of person in 'campid'-th camera pair\n # viewid: index of view, {1, 2}\n # imgid: index of image, (1-10)\n viewid = 1 if imgid < 5 else 2\n img_name = '{:01d}_{:03d}_{:01d}_{:02d}.png'.format(campid+1, pid+1, viewid, imgid+1)\n img_path = osp.join(save_dir, img_name)\n imageio.imwrite(img_path, img)\n img_paths.append(img_path)\n return img_paths\n\n def _extract_img(name):\n print(\"Processing {} images (extract and save) ...\".format(name))\n meta_data = []\n imgs_dir = self.imgs_detected_dir if name == 'detected' else self.imgs_labeled_dir\n for campid, camp_ref in enumerate(mat[name][0]):\n camp = _deref(camp_ref)\n num_pids = camp.shape[0]\n for pid in range(num_pids):\n img_paths = _process_images(camp[pid,:], campid, pid, imgs_dir)\n assert len(img_paths) > 0, \"campid{}-pid{} has no images\".format(campid, pid)\n meta_data.append((campid+1, pid+1, img_paths))\n print(\"done camera pair {} with {} identities\".format(campid+1, num_pids))\n return meta_data\n\n meta_detected = _extract_img('detected')\n meta_labeled = _extract_img('labeled')\n\n def _extract_classic_split(meta_data, test_split):\n train, test = [], []\n num_train_pids, num_test_pids = 0, 0\n num_train_imgs, num_test_imgs = 0, 0\n for i, (campid, pid, img_paths) in enumerate(meta_data):\n \n if [campid, pid] in test_split:\n for img_path in img_paths:\n camid = int(osp.basename(img_path).split('_')[2])\n test.append((img_path, num_test_pids, camid))\n num_test_pids += 1\n num_test_imgs += len(img_paths)\n else:\n for img_path in img_paths:\n camid = int(osp.basename(img_path).split('_')[2])\n train.append((img_path, num_train_pids, camid))\n num_train_pids += 1\n num_train_imgs += len(img_paths)\n return train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs\n\n print(\"Creating classic splits (# = 20) ...\")\n splits_classic_det, splits_classic_lab = [], []\n for split_ref in mat['testsets'][0]:\n test_split = _deref(split_ref).tolist()\n\n # create split for detected images\n train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs = \\\n _extract_classic_split(meta_detected, test_split)\n splits_classic_det.append({\n 'train': train, 'query': test, 'gallery': test,\n 'num_train_pids': num_train_pids, 'num_train_imgs': num_train_imgs,\n 'num_query_pids': num_test_pids, 'num_query_imgs': num_test_imgs,\n 'num_gallery_pids': num_test_pids, 'num_gallery_imgs': num_test_imgs,\n })\n\n # create split for labeled images\n train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs = \\\n _extract_classic_split(meta_labeled, test_split)\n splits_classic_lab.append({\n 'train': train, 'query': test, 'gallery': test,\n 'num_train_pids': num_train_pids, 'num_train_imgs': num_train_imgs,\n 'num_query_pids': num_test_pids, 'num_query_imgs': num_test_imgs,\n 'num_gallery_pids': num_test_pids, 'num_gallery_imgs': num_test_imgs,\n })\n \n write_json(splits_classic_det, self.split_classic_det_json_path)\n write_json(splits_classic_lab, self.split_classic_lab_json_path)\n\n def _extract_set(filelist, pids, pid2label, idxs, img_dir, relabel):\n tmp_set = []\n unique_pids = set()\n for idx in idxs:\n img_name = filelist[idx][0]\n camid = int(img_name.split('_')[2])\n pid = pids[idx]\n if relabel: pid = pid2label[pid]\n img_path = osp.join(img_dir, img_name)\n tmp_set.append((img_path, int(pid), camid))\n unique_pids.add(pid)\n return tmp_set, len(unique_pids), len(idxs)\n\n def _extract_new_split(split_dict, img_dir):\n train_idxs = split_dict['train_idx'].flatten() - 1 # index-0\n pids = split_dict['labels'].flatten()\n train_pids = set(pids[train_idxs])\n pid2label = {pid: label for label, pid in enumerate(train_pids)}\n query_idxs = split_dict['query_idx'].flatten() - 1\n gallery_idxs = split_dict['gallery_idx'].flatten() - 1\n filelist = split_dict['filelist'].flatten()\n train_info = _extract_set(filelist, pids, pid2label, train_idxs, img_dir, relabel=True)\n query_info = _extract_set(filelist, pids, pid2label, query_idxs, img_dir, relabel=False)\n gallery_info = _extract_set(filelist, pids, pid2label, gallery_idxs, img_dir, relabel=False)\n return train_info, query_info, gallery_info\n\n print(\"Creating new splits for detected images (767/700) ...\")\n train_info, query_info, gallery_info = _extract_new_split(\n loadmat(self.split_new_det_mat_path),\n self.imgs_detected_dir,\n )\n splits = [{\n 'train': train_info[0], 'query': query_info[0], 'gallery': gallery_info[0],\n 'num_train_pids': train_info[1], 'num_train_imgs': train_info[2],\n 'num_query_pids': query_info[1], 'num_query_imgs': query_info[2],\n 'num_gallery_pids': gallery_info[1], 'num_gallery_imgs': gallery_info[2],\n }]\n write_json(splits, self.split_new_det_json_path)\n\n print(\"Creating new splits for labeled images (767/700) ...\")\n train_info, query_info, gallery_info = _extract_new_split(\n loadmat(self.split_new_lab_mat_path),\n self.imgs_labeled_dir,\n )\n splits = [{\n 'train': train_info[0], 'query': query_info[0], 'gallery': gallery_info[0],\n 'num_train_pids': train_info[1], 'num_train_imgs': train_info[2],\n 'num_query_pids': query_info[1], 'num_query_imgs': query_info[2],\n 'num_gallery_pids': gallery_info[1], 'num_gallery_imgs': gallery_info[2],\n }]\n write_json(splits, self.split_new_lab_json_path)","def gene_h5_train_file(data_path, h5_name):\n img = []\n y_cls_mask = []\n y_reg_cls_mask = []\n os.chdir(data_path)\n jpgfiles = glob.glob('*.jpg')\n idx = 1\n # the position of generator objector is very important\n gene_obj = image_output_pair(data_path, 1/255.0)\n while True:\n if idx == len(jpgfiles):\n break\n print '\\t{0}/{1}'.format(idx, len(jpgfiles))\n # the position of generator objector is very important\n # gene_obj = image_output_pair(data_path, 1/255.0)\n img_it, y_cls_mask_it, y_reg_cls_mask_it = gene_obj.next()\n img.append(img_it)\n y_cls_mask.append(y_cls_mask_it)\n y_reg_cls_mask.append(y_reg_cls_mask_it)\n idx += 1\n\n # img => (320, 320, 3)\n # after np.stack => (19041, 320, 320, 3)\n img_input = np.stack(img, axis=0)\n y_cls = np.stack(y_cls_mask, axis=0)\n y_reg = np.stack(y_reg_cls_mask, axis=0)\n print 'input data shape is {0}'.format(img_input.shape)\n print 'y_cls data shape is {0}'.format(y_cls.shape)\n print 'y_reg data shape is {0}'.format(y_reg.shape)\n \n # wirte data\n h5 = '/home/yuquanjie/Documents/train_' + h5_name\n file_write = h5py.File(h5, 'w')\n file_write.create_dataset('X_train', data=img_input)\n file_write.create_dataset('Y_train_cls', data=y_cls)\n file_write.create_dataset('Y_train_merge', data=y_reg)\n file_write.close()","def preprocess_images(file_path, new_file_path):\n if not os.path.isdir(new_file_path):\n os.mkdir(new_file_path)\n i = 0\n for dir in listdir(file_path):\n j = 0\n for image_path in listdir(file_path + '/' + dir):\n image = open_image(image_path)\n cv2.imwrite(file_path + '/' + image_path + '/' str(i) + '/' +str(i) + '.jpg', image)\n j += 1\n i += 1","def bulk_process_images(inputpath, outputpath, extension):\n\n for dirpath, dirnames, filenames in os.walk(inputpath):\n structure = os.path.join(outputpath, dirpath[len(inputpath) + 1:])\n for file in filenames:\n if file.endswith(extension):\n src = os.path.join(dirpath, file)\n dest = os.path.join(structure, file)\n img = load_and_preprocess_image(src)\n cv2.imwrite(dest, img)","def create_train(train_img_path):\n\n f = open(\"train.txt\", \"w+\")\n for subdirs, dirs, files in os.walk(train_img_path):\n for filename in files:\n if filename.endswith(\".jpg\"):\n train_image_path = os.path.join(train_img_path, filename)\n print(train_image_path)\n f.write(train_image_path + \"\\n\")\n f.close()","def save_step_1(imgs, output_path='./output/step1'):\n # ... your code here ...\n i=0\n for each in imgs:\n i+=1\n cv2.imwrite(output_path+\"/output\"+str(i)+\".jpg\", each)","def main():\n \n # for inserting other images, add tem to /input folder and list them here\n images = (\n 'image-0',\n 'image-1',\n 'image-2'\n )\n\n for image_name in images:\n print(image_name, \"image:\")\n\n image = open_image(image_name)\n display_image(image, \"Original input \" + image_name)\n\n grayscale_v = transform_colors(image)\n display_image(grayscale_v[:,:,0], \"Grayscale \" + image_name)\n save_image(image_name + \"-grayscale\", grayscale_v[:,:,0])\n\n contours_v, contours = get_contours(grayscale_v)\n display_image(contours_v, \"Contours \" + image_name)\n save_image(image_name + \"-contours\", contours_v)\n\n labeled_img, areas = get_measures(image, contours[1:])\n display_image(labeled_img, \"Labeled \" + image_name)\n save_image(image_name + \"-labeled\", labeled_img)\n\n areas_histogram(areas, image_name)","def save_image_to_h5(image, h5_path, *args, **kwargs):\n # TODO: Implement the method\n\n f = h5.File(h5_path, \"w\")\n grp = f.create_group(\"data\")\n grp.create_dataset(\"image\", data=image)","def prepare_hdf5_file(hdf5_file, n_train, n_valid, n_test):\n n_total = n_train + n_valid + n_test\n splits = create_splits(n_train, n_valid, n_test)\n hdf5_file.attrs['split'] = H5PYDataset.create_split_array(splits)\n vlen_dtype = h5py.special_dtype(vlen=numpy.dtype('uint8'))\n hdf5_file.create_dataset('encoded_images', shape=(n_total,),\n dtype=vlen_dtype)\n hdf5_file.create_dataset('targets', shape=(n_total, 1), dtype=numpy.int16)\n hdf5_file.create_dataset('filenames', shape=(n_total, 1), dtype='S32')","def appendpics(pathofimg, w_sub, h_sub, step):\n num = 0\n dirlist = []\n images = [] # images in each folder\n for root, dirs, fileswer in os.walk(pathofimg):\n if len(dirs)!= 0:\n for dir in dirs:\n dirlist.append(dir)\n for rooert, dirwerwes, files in os.walk(pathofimg+'/'+dir):\n for file in files:\n if(file.endswith('.png')):\n images.append(Image.open(pathofimg+'/'+dir+'/'+file))\n if(len(images)==81):\n break\n target = montage(images, w_sub, h_sub, step)\n target.save(pathofimg +'/'+ dir + '.png', quality=100)\n else:\n dir = 'Generated'\n for file in fileswer:\n if (file.endswith('.png')):\n images.append(Image.open(pathofimg +'/'+ file))\n target1 = montage(images, w_sub, h_sub, step)\n savepath = pathofimg +'/'+ 'generated'\n os.makedirs(savepath)\n target1.save(savepath +'/'+ dir + '.png', quality=100)","def get_files(self):\n train_images = glob(os.path.join(self.images_dir, '*%s' % self.im_extension)) \n train_labels = [x.replace(self.im_extension, '.npy').replace('images', 'groundTruth') for x in train_images]\n val_images = glob(os.path.join(self.val_images_dir, '*%s' % self.im_extension))\n val_labels = [x.replace(self.im_extension, '.npy').replace('images', 'groundTruth') for x in val_images]\n train_images = np.array(train_images)\n train_labels = np.array(train_labels)\n val_images = np.array(val_images)\n val_labels = np.array(val_labels)\n test_images = np.array(\n glob('/media/data_cifs/pytorch_projects/datasets/BSDS500_crops/data/images/test_nocrop/*.jpg'))\n test_labels = np.array(\n [x.replace('images', 'groundTruth').replace('.jpg', '.npy') for x in test_images])\n test_labels = np.array(\n [np.load(x) for x in test_labels])\n keep_idx = np.array([True if x.shape[0] > x.shape[1] else False for x in test_labels])\n test_images = test_images[keep_idx]\n test_labels = test_labels[keep_idx]\n test_images = np.stack([misc.imread(x) for x in test_images], 0)\n test_labels = np.stack(test_labels, 0)\n test_labels = test_labels[..., None]\n\n # Add constant padding to bottom/right\n if self.pad:\n test_images = util.pad(test_images, ((0, 0), (self.pad // 2, self.pad - self.pad // 2), (self.pad // 2, self.pad - self.pad // 2), (0, 0)), mode='linear_ramp')\n test_labels = util.pad(test_labels, ((0, 0), (self.pad // 2, self.pad - self.pad // 2), (self.pad // 2, self.pad - self.pad // 2), (0, 0)), mode='constant', constant_values=0)\n\n # Select images for training\n sort_idx = np.argsort(train_images)\n train_images = train_images[sort_idx[:self.train_size]]\n train_labels = train_labels[sort_idx[:self.train_size]]\n\n # Build CV dict\n cv_files, cv_labels = {}, {}\n cv_files[self.folds['train']] = train_images\n cv_files[self.folds['val']] = val_images\n cv_files[self.folds['test']] = test_images\n cv_labels[self.folds['train']] = train_labels\n cv_labels[self.folds['val']] = val_labels\n cv_labels[self.folds['test']] = test_labels\n return cv_files, cv_labels","def write_data_files(self):\n \n logging.info('\\n Start writing data files \\n')\n \n for i, (data_file, label_file) in enumerate(self.files):\n data_file, label_file = Path(data_file), Path(label_file)\n logging.info('Writing .hdf5 file for : [{}]'.format(str(data_file)))\n \n file_name = self.save_data_folder / '{}.hdf5'.format(label_file.name[:-4])\n if file_name.exists():\n continue\n \n with h5py.File(str(file_name), 'w') as writer:\n self.serialize_samples(\n writer, data_file, label_file)","def _extract_images(source_path, target_path, merge_labels):\n\n images_path = os.path.join(source_path, 'imagesTr')\n labels_path = os.path.join(source_path, 'labelsTr')\n\n # Filenames have the form 'hippocampus_XX.nii.gz'\n filenames = [x for x in os.listdir(images_path) if x[:5] == 'hippo']\n\n # Create directories\n if not os.path.isdir(target_path):\n os.makedirs(target_path)\n\n for filename in filenames:\n\n # Extract only T2-weighted\n x = sitk.ReadImage(os.path.join(images_path, filename))\n x = sitk.GetArrayFromImage(x)\n y = sitk.ReadImage(os.path.join(labels_path, filename))\n y = sitk.GetArrayFromImage(y)\n\n # Shape expected: (35, 51, 35)\n # Average label shape: (24.5, 37.8, 21.0)\n assert x.shape == y.shape\n\n # No longer distinguish between hippocampus proper and subiculum\n if merge_labels:\n y[y == 2] = 1\n\n # Save new images so they can be loaded directly\n study_name = filename.replace('_', '').split('.nii')[0]\n sitk.WriteImage(sitk.GetImageFromArray(x), join_path([target_path, study_name + \".nii.gz\"]))\n sitk.WriteImage(sitk.GetImageFromArray(y), join_path([target_path, study_name + \"_gt.nii.gz\"]))","def save_step_2(imgs, match_list, output_path=\"./output/step2\"):\n # ... your code here ...\n for i in range(len(imgs)):\n name1,tail1 = str.split(filenames[match_list[i][0]],\".\")\n name2,tail2 = str.split(filenames[match_list[i][2]],\".\")\n cv2.imwrite(output_path+\"/\"+name1+\"_\"+str(match_list[i][1])+\"_\"+name2+\"_\"+str(match_list[i][3])+\"_\"+str(match_list[i][4])+\".jpg\", imgs[i])","def analyzeImages(path, name_type, box1_size, box2_size, box3_size, box4_size, box5_size):\n \n folders = [f for f in sorted(glob.glob(path + \"/**\"))]\n \n for folder in folders: \n \n # to save this data frame in a csv file\n \n files = [f for f in sorted(glob.glob(folder + \"/**\" + \".jpg\"))]\n \n centroidsDf = pd.DataFrame(np.zeros([len(files), 11]),columns=[\"frame\", \"fly1_x\", \"fly1_y\", \"fly2_x\", \"fly2_y\", \"fly3_x\", \"fly3_y\", \"fly4_x\", \"fly4_y\", \"fly5_x\", \"fly5_y\"]);\n headsDf = pd.DataFrame(np.zeros([len(files), 11]),columns=[\"frame\", \"fly1_x\", \"fly1_y\", \"fly2_x\", \"fly2_y\", \"fly3_x\", \"fly3_y\", \"fly4_x\", \"fly4_y\", \"fly5_x\", \"fly5_y\"]);\n \n img_array1 = []\n img_array2 = []\n img_array3 = []\n img_array4 = []\n img_array5 = []\n\n for file in files:\n \n print(file)\n \n centroidsDf[\"frame\"][files.index(file)] = files.index(file)+1\n headsDf[\"frame\"][files.index(file)] = files.index(file)+1\n \n img = cv2.imread(file)\n \n ## FLY 1 ##\n\n box1 = img[box1_size[0]:box1_size[1], box1_size[2]:box1_size[3]]\n \n # image processing to get the centroid and head positions\n x_centroid, y_centroid, x_head, y_head = findCentroid(box1, file) \n \n # add the centroid and head locations on the image \n box1 = cv2.circle(box1, (x_centroid, y_centroid), 7, (255, 0, 0), -1)\n box1 = cv2.circle(box1, (x_head, y_head), 7, (0, 0, 255), -1)\n img_array1.append(box1)\n \n # add the positions in the final data frame\n centroidsDf[\"fly1_x\"][files.index(file)] = x_centroid\n centroidsDf[\"fly1_y\"][files.index(file)] = y_centroid\n \n headsDf[\"fly1_x\"][files.index(file)] = x_head\n headsDf[\"fly1_y\"][files.index(file)] = y_head\n \n ## FLY 2 ##\n \n box2 = img[box2_size[0]:box2_size[1], box2_size[2]:box2_size[3]]\n \n # image processing to get the centroid and head positions\n x_centroid, y_centroid, x_head, y_head = findCentroid(box2, file)\n \n # add the centroid and head locations on the image \n box2 = cv2.circle(box2, (x_centroid, y_centroid), 7, (255, 0, 0), -1)\n box2 = cv2.circle(box2, (x_head, y_head), 7, (0, 0, 255), -1)\n img_array2.append(box2)\n \n # add the positions in the final data frame \n centroidsDf[\"fly2_x\"][files.index(file)] = x_centroid\n centroidsDf[\"fly2_y\"][files.index(file)] = y_centroid\n \n headsDf[\"fly2_x\"][files.index(file)] = x_head\n headsDf[\"fly2_y\"][files.index(file)] = y_head\n \n ## FLY 3 ##\n\n box3 = img[box3_size[0]:box3_size[1], box3_size[2]:box3_size[3]]\n \n # image processing to get the centroid and head positions\n x_centroid, y_centroid, x_head, y_head = findCentroid(box3, file)\n \n # add the centroid and head locations on the image \n box3 = cv2.circle(box3, (x_centroid, y_centroid), 7, (255, 0, 0), -1)\n box3 = cv2.circle(box3, (x_head, y_head), 7, (0, 0, 255), -1)\n img_array3.append(box3)\n\n # add the positions in the final data frame\n centroidsDf[\"fly3_x\"][files.index(file)] = x_centroid\n centroidsDf[\"fly3_y\"][files.index(file)] = y_centroid\n \n headsDf[\"fly3_x\"][files.index(file)] = x_head\n headsDf[\"fly3_y\"][files.index(file)] = y_head\n \n ## FLY 4 ##\n \n box4 = img[box4_size[0]:box4_size[1], box4_size[2]:box4_size[3]]\n \n # image processing to get the centroid and head positions\n x_centroid, y_centroid, x_head, y_head = findCentroid(box4, file)\n \n # add the centroid and head locations on the image \n box4 = cv2.circle(box4, (x_centroid, y_centroid), 7, (255, 0, 0), -1)\n box4 = cv2.circle(box4, (x_head, y_head), 7, (0, 0, 255), -1)\n img_array4.append(box4)\n \n # add the positions in the final data frame\n centroidsDf[\"fly4_x\"][files.index(file)] = x_centroid\n centroidsDf[\"fly4_y\"][files.index(file)] = y_centroid\n \n headsDf[\"fly4_x\"][files.index(file)] = x_head\n headsDf[\"fly4_y\"][files.index(file)] = y_head\n \n ## FLY 5 ##\n \n # the fifth fly is not present in all the genetic strains \n if (box5_size != []):\n box5 = img[box5_size[0]:box5_size[1], box5_size[2]:box5_size[3]]\n \n # image processing to get the centroid and head positions\n x_centroid, y_centroid, x_head, y_head = findCentroid(box5, file)\n \n # add the centroid and head locations on the image \n box5 = cv2.circle(box5, (x_centroid, y_centroid), 7, (255, 0, 0), -1)\n box5 = cv2.circle(box5, (x_head, y_head), 7, (0, 0, 255), -1)\n img_array5.append(box5)\n \n # add the positions in the final data frame\n centroidsDf[\"fly5_x\"][files.index(file)] = x_centroid\n centroidsDf[\"fly5_y\"][files.index(file)] = y_centroid\n \n headsDf[\"fly5_x\"][files.index(file)] = x_head\n headsDf[\"fly5_y\"][files.index(file)] = y_head\n \n # save the data frame in a .csv file, \n # one for the centroids and one for the heads\n #centroidsDf.to_csv(folder+\"/centroids.csv\", index = None, header=True)\n #headsDf.to_csv(folder+\"/heads.csv\", index = None, header=True)\n \n \n ## CREATE THE VIDEOS ##\n \n height, width, _ = box1.shape\n size = (width,height)\n out = cv2.VideoWriter(folder+name_type+'_1_' + str(folders.index(folder)+1)+ '.mp4',cv2.VideoWriter_fourcc(*'DIVX'), 1.5, size)\n for i in range(len(img_array1)):\n out.write(img_array1[i])\n out.release()\n \n height, width, _ = box2.shape\n size = (width,height)\n out = cv2.VideoWriter(folder+name_type+'_2_' + str(folders.index(folder)+1)+ '.mp4' ,cv2.VideoWriter_fourcc(*'DIVX'), 1.5, size)\n for i in range(len(img_array2)):\n out.write(img_array2[i])\n out.release()\n \n height, width, _ = box3.shape\n size = (width,height)\n out = cv2.VideoWriter(folder+name_type+'_3_' + str(folders.index(folder)+1)+ '.mp4' ,cv2.VideoWriter_fourcc(*'DIVX'), 1.5, size)\n for i in range(len(img_array3)):\n out.write(img_array3[i])\n out.release()\n \n height, width, _ = box4.shape\n size = (width,height)\n out = cv2.VideoWriter(folder+name_type+'_4_' + str(folders.index(folder)+1)+ '.mp4' ,cv2.VideoWriter_fourcc(*'DIVX'), 1.5, size)\n for i in range(len(img_array4)):\n out.write(img_array4[i])\n out.release()\n \n if (box5_size != []):\n height, width, _ = box5.shape\n size = (width,height)\n out = cv2.VideoWriter(folder+name_type+'_5_' + str(folders.index(folder)+1)+ '.mp4' ,cv2.VideoWriter_fourcc(*'DIVX'), 1.5, size)\n for i in range(len(img_array5)):\n out.write(img_array5[i])\n out.release()","def main(vis_dirs, outdir):\n assert len(vis_dirs) == 4\n\n if not os.path.exists(outdir):\n os.mkdir(outdir)\n\n for i, filename in enumerate(tqdm(os.listdir(vis_dirs[-1]))):\n # if i % 100 == 0:\n # print(i)\n\n files = [os.path.join(vis_dir, filename) for vis_dir in vis_dirs]\n outimg = os.path.join(outdir, filename)\n merge_four_images(files, outimg)\n\n print (\"Finished! Result dir is %s\" % outdir)","def process_files(self):\n for filename in self.temp_directory.iterdir():\n im = Image.open(str(filename))\n scaled = im.resize((640, 480))\n scaled.save(str(filename))","def pickle_images(path, df, image_size=\"\"):\n all_imgs = []\n labels = []\n folder = path\n for f in os.listdir(folder): \n if f.endswith(\".png\"): \n img = read_transposed_image(folder+f)\n i = f.split(\"_\")[0]\n name = \".\".join(\"_\".join(f.split(\"_\")[1:]).split(\".\")[:-1])\n label = df[(df.id == i) & (df.filename == name)].label\n if len(label) > 0:\n label = label.iloc[0]\n if image_size != \"\":\n new_image = resize_and_pad(img, image_size)\n new_dir = folder+str(image_size)\n if not os.path.exists(new_dir):\n os.makedirs(new_dir)\n misc.toimage(new_image).save(new_dir+\"/\"+f)\n all_imgs.append(new_image)\n else:\n all_imgs.append(img) \n labels.append(label)\n d = {\"data\":all_imgs, \"labels\":labels}\n with open('data_'+str(image_size)+'.pkl', 'wb') as handle:\n cPickle.dump(d, handle, protocol=cPickle.HIGHEST_PROTOCOL)","def mosaic(self):\n \n if len(self.tiles) > 1:\n hdflist = sorted(glob.glob(self.fullPath + '/*.hdf'))\n for i in range(0,len(hdflist),2):\n ms = pymodis.convertmodis_gdal.createMosaicGDAL(hdfnames = [hdflist[i], hdflist[i+1]], subset = self.subset, outformat = 'GTiff')\n ms.run(str(hdflist[i].split('.h')[0]) + 'mos.tif')\n ms.write_vrt(output = str(hdflist[i].split('.h')[0]), separate = True)\n mosaicCount = len(glob.glob(self.fullPath + '/*mos.tif'))\n logger.log('SUCCESS', 'Mosaic complete! MODIS tiles %s were successfully mosaicked into %d mosaic images.' % (str(self.tiles), mosaicCount))","def save(images, output):\n for image, frame in images:\n image.save(output(frame))","def process_all_images(input_path: str, output_path: str, resized_image_shape: Tuple,transformations:List[TransformationsEnum]):\n\n output_images_path = os.path.join(output_path, \"images\")\n csv_file_path = os.path.join(output_path, \"metadata.csv\")\n\n prepare_folders(output_path, output_images_path)\n prepare_csv(csv_file_path)\n\n df = pd.read_csv(csv_file_path)\n current_id = 1 #has to check the current id in the folder or be set to 1 if none\n categories_names = list(os.listdir(input_path))\n\n encoder = LabelBinarizer()\n encoder.fit(categories_names)\n\n\n for folder_name in os.listdir(input_path):\n current_category_name = folder_name\n category_path = os.path.join(input_path, folder_name)\n images_in_category = list(Path(category_path).glob(\"*.jpg\"))\n df, current_id = process_image(\n df, current_id, encoder, current_category_name, images_in_category,output_images_path, resized_image_shape,transformations\n )\n\n df.to_csv(csv_file_path, index=False, quotechar='\"', encoding='ascii')\n\n print(\"done, processed\", len(df), \"images\")","def jarvis(input_path, output_path): \n \n if not os.path.exists(f'{output_path}'):\n os.makedirs(f'{output_path}')\n\n folder_list = [sample for sample in os.listdir(input_path) if os.path.isdir(f'{input_path}{sample}')]\n\n for folder in folder_list:\n\n file_list = [filename for filename in os.listdir(f'{input_path}{folder}/') if '.tif' in filename]\n mutant = '_'.join(folder.split(' '))\n\n for x, filename in enumerate(file_list):\n pathname = os.path.join(input_path, folder, filename)\n new_name = f'{output_path}{mutant}_{x}.tif'\n copyfile(pathname, new_name)\n # array_stack = skimage.io.imread(f'{pathname}').transpose(1, 2, 0)\n logger.info(f'{new_name}')","def combineImages(path=None, imgfiles=None, cols=3, size=300):\n\n font = ImageFont.truetype(\"Arial.ttf\", 15)\n x=size\n w=20\n i=0; j=0\n if imgfiles == None:\n imgfiles = findFiles(path, 'png')\n width = cols*(x+w)\n height = int(math.ceil(float(len(imgfiles))/cols)*x)\n new_im = Image.new('RGBA', (width, height), 'white')\n for f in imgfiles:\n name = os.path.basename(f).split('.')[0]\n if not os.path.exists(f):\n continue\n im = Image.open(f)\n im.thumbnail((x,x))\n new_im.paste(im, (i*x+w,j*x+w))\n draw = ImageDraw.Draw(new_im)\n draw.text((i*x+w,j*x+w), name, (0,0,0), font=font)\n i+=1\n if i>=cols:\n i=0; j+=1\n #new_im.show()\n path = os.path.split(imgfiles[0])[0]\n new_im.save(os.path.join(path,\"summary.png\"))\n return","def save_step_4(imgs, output_path=\"./output/step4\"):\n # ... your code here ...\n cv2.imwrite(output_path+\"/output.jpg\", imgs)","def write_data(data_to_write_train, data_to_write_test, train_path, eval_path ,test_path):\n aligned_lists_train = data_to_write_train[0]\n raw_lists_train = data_to_write_train[2]\n\n aligned_lists_eval = data_to_write_test[0]\n raw_lists_eval = data_to_write_test[2]\n\n aligned_lists_test = data_to_write_test[1]\n raw_lists_test = data_to_write_test[3]\n\n filelist = list([train_path, eval_path, test_path])\n\n for file in filelist:\n aligned_path = os.path.join(file, 'aligned_image')\n raw_path = os.path.join(file, 'raw_image')\n os.mkdir(aligned_path)\n os.mkdir(raw_path)\n\n # raw image data\n for Idx, train_raw in enumerate(raw_lists_train):\n img = Image.open(train_raw)\n img.save(train_path+'/raw_image/img_'+f'{Idx:04d}.jpg')\n if Idx%100 == 0:\n print('\\t%d images are saved'% Idx); \n print('\\tTrain raw images saved! ')\n\n for Idx, eval_raw in enumerate(raw_lists_eval):\n img = Image.open(eval_raw)\n img.save(eval_path+'/raw_image/img_'+f'{Idx:04d}.jpg')\n if Idx%100 == 0:\n print('\\t%d images are saved'% Idx); \n print('\\tEval raw images saved! ')\n\n for Idx, test_raw in enumerate(raw_lists_test):\n img = Image.open(test_raw)\n img.save(test_path+'/raw_image/img_'+f'{Idx:04d}.jpg')\n if Idx%100 == 0:\n print('\\t%d images are saved'% Idx); \n print('\\tTest raw images saved! ')\n\n # aligned image data\n for Idx, train_aligned in enumerate(aligned_lists_train):\n img = Image.open(train_aligned)\n img.save(train_path+'/aligned_image/img_'+f'{Idx:04d}.jpg')\n if Idx%100 == 0:\n print('\\t%d images are saved'% Idx); \n print('\\tTrain aligned images saved! ')\n\n for Idx, eval_aligned in enumerate(aligned_lists_eval):\n img = Image.open(eval_aligned)\n img.save(eval_path+'/aligned_image/img_'+f'{Idx:04d}.jpg')\n if Idx%100 == 0:\n print('\\t%d images are saved'% Idx); \n print('\\tEval aligned images saved! ')\n\n for Idx, test_aligned in enumerate(aligned_lists_test):\n img = Image.open(test_aligned)\n img.save(test_path+'/aligned_image/img_'+f'{Idx:04d}.jpg')\n if Idx%100 == 0:\n print('\\t%d images are saved'% Idx); \n print('\\tTest aligned images saved! ')","def form_sample_folder(self, input_folder, target_folder, sample_name):\n print(f'processing {sample_name} folder.')\n # first make a subfolder to contain the images - e.g. 'target_folder/sample_name'\n sample_dir = join(target_folder, sample_name)\n if not os.path.exists(sample_dir):\n mkdir(sample_dir)\n # resize and move the mask images - e.g. 'target_folder/sample_name/imgs_necrosis.png'\n img_file_nec = join(input_folder, 'Necrosis',\n 'Tissue Slides.'+sample_name+'.png')\n img_res = self.process_img(img_file_nec, self.rescale_ratio)\n img_nec = img_res.copy()\n cv2.imwrite(join(sample_dir, 'necrosis.png'), img_res)\n\n img_file_perf = join(input_folder, 'Perfusion',\n 'Tissue Slides.'+sample_name+'.png')\n img_res = self.process_img(img_file_perf, self.rescale_ratio)\n cv2.imwrite(join(sample_dir, 'perfusion.png'), img_res)\n\n # resize and move the maker HE and EF5 images\n files = listdir(input_folder)\n img_files = [x for x in files if x.split(\n '.')[-1] in ('tif', 'jpg', 'png')]\n for img_file in img_files:\n if (sample_name+'_' in img_file) or (sample_name+'-' in img_file):\n if ('HE-G' in img_file) or ('HE-green' in img_file) or ('HEgreen' in img_file):\n img_res = self.process_img(\n join(input_folder, img_file), self.rescale_ratio)\n if not os.path.exists(join(sample_dir, 'HE-green.png')):\n cv2.imwrite(join(sample_dir, 'HE-green.png'), img_res)\n else:\n warnings.warn(\n f\"file already exists, while processing {img_file}\")\n elif ('HE-R' in img_file) or ('HE-red' in img_file) or ('HEred' in img_file):\n img_res = self.process_img(\n join(input_folder, img_file), self.rescale_ratio)\n if not os.path.exists(join(sample_dir, 'HE-red.png')):\n cv2.imwrite(join(sample_dir, 'HE-red.png'), img_res)\n else:\n warnings.warn(\n f\"file already exists, while processing {img_file}\")\n elif ('HE-B' in img_file) or ('HE-blue' in img_file) or ('HE-blue' in img_file):\n img_res = self.process_img(\n join(input_folder, img_file), self.rescale_ratio)\n if not os.path.exists(join(sample_dir, 'HE-blue.png')):\n cv2.imwrite(join(sample_dir, 'HE-blue.png'), img_res)\n else:\n warnings.warn(\n f\"file already exists, while processing {img_file}\")\n elif 'EF5' in img_file:\n img_res = self.process_img(\n join(input_folder, img_file), self.rescale_ratio)\n img_ef5 = img_res.copy()\n if not os.path.exists(join(sample_dir, 'EF5.png')):\n cv2.imwrite(join(sample_dir, 'EF5.png'), img_res)\n else:\n warnings.warn(\n f\"file already exists, while processing {img_file}\")\n\n masked_ef5 = (img_ef5 * (img_nec <= 0)).astype(img_ef5.dtype)\n cv2.imwrite(join(sample_dir, 'EF5_masked.png'), masked_ef5)\n assert len(listdir(sample_dir)) == 7\n return","def createAllImageFiles(poly, name) :\n \n for i in range(len(poly.getPaths())):\n fileName = name + \"_\" + str(i) + \".dot\"\n imgName = name + \"_\" + str(i) + \".jpg\"\n \n Command = \"neato -Tjpeg \" + fileName + \" -o \" + imgName\n run(Command, shell=True)","def splitTransform(self):\n\t\t#path_merge = \"transform\"\n\t\t#path_train = \"transform/data/\"\n\t\t#path_label = \"transform/label/\"\n\t\tpath_merge = \"train/merge\"\n\t\tpath_train = \"train/image\"\n\t\tpath_label = \"train/label\"\n\t\ttrain_imgs = glob.glob(path_merge+\"/*.\"+self.img_type)\n\t\tfor imgname in train_imgs:\n\t\t\tmidname = imgname[imgname.rindex(\"/\")+1:imgname.rindex(\".\"+self.img_type)]\n\t\t\timg = cv2.imread(imgname)\n\t\t\timg_train = img[:,:,2]#cv2 read image rgb->bgr\n\t\t\timg_label = img[:,:,0]\n\t\t\tcv2.imwrite(path_train+midname+\".\"+self.img_type,img_train)\n\t\t\tcv2.imwrite(path_label+midname+\".\"+self.img_type,img_label)","def save_images(images, filenames, output_dir):\n for i, filename in enumerate(filenames):\n dirname = os.path.dirname(filename)\n\n if dirname!='':\n dirpath = os.path.join(output_dir, dirname)\n if not os.path.exists(dirpath):\n os.makedirs(dirpath)\n\n # Images for inception classifier are normalized to be in [-1, 1] interval,\n # so rescale them back to [0, 1].\n with tf.gfile.Open(os.path.join(output_dir, filename), 'w') as f:\n img = (((images[i, :, :, :] + 1.0) * 0.5) * 255.0).astype(np.uint8)\n Image.fromarray(img).save(f)","def process_data(output_folder):\n # select imgs\n img_folder = join(output_folder, 'img')\n select_img(output_folder, img_folder, 'HE-green')\n\n mask_folder = join(output_folder, 'mask')\n select_img(output_folder, mask_folder, '_EF5')","def save_images(images, filenames, output_dir):\n for i, filename in enumerate(filenames):\n\n with tf.gfile.Open(os.path.join(output_dir, filename), 'w') as f:\n image = images[i, :, :, :]\n image[:, :, 0] += _R_MEAN\n image[:, :, 1] += _G_MEAN\n image[:, :, 2] += _B_MEAN\n image = imresize(image, [299, 299])\n imsave(f, image, format='png')","def process_images(path, dataset):\n \n print(f\"Processing images {os.path.join(path, dataset)}\", flush=True)\n label_file = os.path.join(path, dataset + '-labels-idx1-ubyte')\n with open(label_file, 'rb') as file:\n _, num = struct.unpack(\">II\", file.read(8))\n labels = numpy.fromfile(file, dtype=numpy.int8) #int8\n new_labels = numpy.zeros((num, 10))\n new_labels[numpy.arange(num), labels] = 1\n\n img_file = os.path.join(path, dataset + '-images-idx3-ubyte')\n with open(img_file, 'rb') as file:\n _, num, rows, cols = struct.unpack(\">IIII\", file.read(16))\n imgs = numpy.fromfile(file, dtype=numpy.uint8).reshape(num, rows, cols) #uint8\n imgs = imgs.astype(numpy.float32) / 255.0\n\n os.remove(label_file); os.remove(img_file)\n print(f\"Saving files under {os.path.join(path, dataset)} path\", flush=True)\n numpy.savez_compressed(os.path.join(path, dataset), imgs=imgs, labels=labels)","def hdfpath_to_nifti1image(file_path, h5path):\n with h5py.File(file_path, 'r') as f:\n return hdfgroup_to_nifti1image(f[h5path])","def save_imgs(self, epoch):\n row, column = 5, 5\n\n # Generates r*c images from the model, saves them individually and as a gallery\n images_generated = self.generate_images(row * column)\n\n # ???\n images_generated = 0.5 * images_generated + 0.5\n\n for index, np_array_image in enumerate(images_generated):\n path = f\"{self.output_directory}/generated_{self.img_size[0]}x{self.img_size[1]}\"\n if not os.path.exists(path):\n os.makedirs(path)\n imsave(path + f\"/{unique_name()}_{epoch}_{index}.png\", np_array_image)\n\n # 4D array:\n nindex, height, width, intensity = images_generated.shape\n\n nrows = nindex // column\n\n assert nindex == nrows * column\n\n # Form the gallery by combining the data at pixel levels (may not be the best approach)\n # want result.shape = (height*n-rows, width*n-cols, intensity)\n gallery = (\n images_generated.reshape(nrows, column, height, width, intensity)\n .swapaxes(1, 2)\n .reshape(height * nrows, width * column, intensity)\n )\n\n path = f\"{self.output_directory}/gallery_generated_{self.img_size[0]}x{self.img_size[1]}\"\n if not os.path.exists(path):\n os.makedirs(path)\n imsave(path + f\"/{unique_name()}_{epoch}.png\", gallery)","def save_imgs(self):\n print(\"Saving the images with required categories ...\")\n os.makedirs(self.imgs_dir, exist_ok=True)\n # Save the images into a local folder\n for im in tqdm(self.images):\n img_data = requests.get(im['coco_url']).content\n with open(os.path.join(self.imgs_dir, im['file_name']), 'wb') as handler:\n handler.write(img_data)","def numpy_to_h5py(in_dir=config.dir_npy, split = config.split):\n\n in_files=[x[:-13] for x in os.listdir(in_dir) if x.endswith('_voc_stft.npy') and not x.startswith('._')]\n\n random.shuffle(in_files)\n\n\n num_files = len(in_files)\n\n split_idx = int(num_files*split)\n\n trn_files = in_files[:split_idx]\n\n val_files = in_files[split_idx:]\n\n num_val_files = len(val_files)\n\n print('Processing %d training files' % split_idx)\n logger.info('Processing %d training files' % split_idx)\n\n logger.info('Training file: %s' % config.h5py_file_train)\n\n voc_shape_trn = [split_idx, 5170,config.input_features]\n\n mix_shape_trn = [split_idx, 5170,config.input_features]\n\n feats_shape_trn = [split_idx, 5170,config.output_features]\n\n hdf5_file = h5py.File(config.h5py_file_train, mode='w')\n\n hdf5_file.create_dataset(\"voc_stft\", voc_shape_trn, np.float32)\n\n hdf5_file.create_dataset(\"back_stft\", voc_shape_trn, np.float32)\n\n hdf5_file.create_dataset(\"mix_stft\", mix_shape_trn, np.float32)\n\n hdf5_file.create_dataset(\"feats\", feats_shape_trn, np.float32)\n\n\n i = 0\n\n for f in trn_files:\n\n voc_stft = np.load(in_dir+f+'_voc_stft.npy')\n\n voc_stft = voc_stft.astype('float32')\n\n mix_stft = np.load(in_dir+f+'_mix_stft.npy')\n\n mix_stft = mix_stft.astype('float32')\n\n back_stft = np.load(in_dir+f+'_back_stft.npy')\n\n back_stft = back_stft.astype('float32')\n\n synth_feats = np.load(in_dir+f+'_synth_feats.npy')\n\n synth_feats = synth_feats.astype('float32')\n\n hdf5_file[\"voc_stft\"][i,...] = voc_stft\n\n hdf5_file[\"mix_stft\"][i,...] = mix_stft\n\n hdf5_file[\"back_stft\"][i,...] = back_stft\n\n hdf5_file[\"feats\"][i,...] = synth_feats\n\n i+=1\n utils.progress(i, split_idx)\n\n logger.info('Processed training file: %s' % f)\n\n hdf5_file.close()\n\n print('Processing %d validation files' % num_val_files)\n logger.info('Processing %d validation files' % num_val_files)\n\n logger.info('Validation file: %s' % config.h5py_file_val)\n\n voc_shape_trn = [num_val_files, 5170,config.input_features]\n\n mix_shape_trn = [num_val_files, 5170,config.input_features]\n\n feats_shape_trn = [num_val_files, 5170,config.output_features]\n\n hdf5_file = h5py.File(config.h5py_file_val, mode='w')\n\n hdf5_file.create_dataset(\"voc_stft\", voc_shape_trn, np.float32)\n\n hdf5_file.create_dataset(\"mix_stft\", mix_shape_trn, np.float32)\n\n hdf5_file.create_dataset(\"back_stft\", voc_shape_trn, np.float32)\n\n hdf5_file.create_dataset(\"feats\", feats_shape_trn, np.float32)\n\n\n i = 0\n\n for f in val_files:\n\n voc_stft = np.load(in_dir+f+'_voc_stft.npy')\n\n voc_stft = voc_stft.astype('float32')\n\n mix_stft = np.load(in_dir+f+'_mix_stft.npy')\n\n mix_stft = mix_stft.astype('float32')\n\n synth_feats = np.load(in_dir+f+'_synth_feats.npy')\n\n synth_feats = synth_feats.astype('float32')\n\n back_stft = np.load(in_dir+f+'_back_stft.npy')\n\n back_stft = back_stft.astype('float32')\n\n hdf5_file[\"voc_stft\"][i,...] = voc_stft\n\n hdf5_file[\"mix_stft\"][i,...] = mix_stft\n\n hdf5_file[\"back_stft\"][i,...] = back_stft\n\n hdf5_file[\"feats\"][i,...] = synth_feats\n\n i+=1\n utils.progress(i, num_val_files)\n\n logger.info('Processed validation file: %s' % f)\n\n hdf5_file.close()\n # return original_ffts","def process_images():\n create_dirs()\n for root, dirs, files in os.walk(IN):\n for name in files:\n if name[0] == '.':\n continue\n process_image(name)","def _crop_write_image(self, inroot, images, outroot):\n for image in images:\n inimage_path = osp.join(inroot, image)\n cvimg = cv2.imread(inimage_path)\n cvimg = cvimg[60:-30, 25:-25]\n h, w, _ = cvimg.shape\n assert h == w == 128\n outimage_path = osp.join(outroot, image)\n cv2.imwrite(outimage_path, cvimg)\n print(outimage_path)","def preprocessfolder(self):\n imgs, _ = getFilesAndHdf(str(self.in_directory.text()))\n self.img_list = sorted(imgs)\n self.updateImageGroups()","def save_step_3(img_pairs, match_list, output_path=\"./output/step3\"):\n # ... your code here ...\n for i in range(len(img_pairs)):\n name1,tail1 = str.split(filenames[match_list[i][0]],\".\")\n name2,tail2 = str.split(filenames[match_list[i][1]],\".\")\n cv2.imwrite(output_path+\"/\"+name1+\"_\"+name2+\".jpg\", img_pairs[i][0])\n cv2.imwrite(output_path+\"/\"+name2+\"_\"+name1+\".jpg\", img_pairs[i][1])","def save_images(images, filenames, output_dir):\n for i, filename in enumerate(filenames):\n # Images for inception classifier are normalized to be in [-1, 1] interval,\n # so rescale them back to [0, 1].\n with tf.gfile.Open(os.path.join(output_dir, filename), 'w') as f:\n img = np.round(((images[i] + 1.0) * 0.5) * 255.0).astype(np.uint8)\n Image.fromarray(img).save(f, format='PNG')","def load_images(input_dir=\"/tmp/mapswipe/project-1\", n_images=2000, seed=1):\n class_map = {1: \"1\", 0: \"5\"}\n output_dir = \"/Users/thead/git/dreamview/data/\"\n\n X_ = []\n y_ = []\n for new_klass in class_map:\n images = []\n for klass in class_map[new_klass]:\n for img in glob.glob(input_dir + \"/%s/*/*/*/aerial.jpeg\" % klass):\n if os.stat(img).st_size > 0:\n images.append(img)\n\n images = shuffle(images, random_state=seed+42+new_klass)\n images = images[:n_images]\n X_ += images\n y_ += [new_klass] * len(images)\n\n # XXX deduce array size from an actual image\n X = np.zeros((2*n_images, 256*256), dtype=np.ubyte)\n y = np.zeros(2*n_images, dtype=np.int)\n\n for n, (img_path, klass) in enumerate(zip(X_, y_)):\n # the order of these OPs has been chosen on purpose, don't mess\n # without checking what happens\n img = imread(img_path)\n img = equalize_adapthist(img)\n img = rgb2grey(img)\n img = img_as_ubyte(img)\n\n if not n % 10:\n fname = os.path.split(img_path)[:-1]\n fname = os.path.join(*fname, \"aerial-processed.jpeg\")\n imsave(fname, img)\n\n X[n,:] = img.ravel()\n y[n] = klass\n\n return X, y","def save_test_images(images):\n for description, img in images.items():\n save_to_image(img, description)\n save_to_netcdf(img, description)","def extract_write_to_file(self, num_extracted, write_dir, sub_h, sub_w, margin=10):\n\n file_seed = len(os.listdir(write_dir))\n\n for i in range(num_extracted):\n file_num = str(file_seed + i)\n write_path = os.path.join(write_dir, file_num + \".\" + 'jpg')\n\n print('extracting {}/{} images of dimension {}x{}'.format(i, num_extracted, sub_h, sub_w))\n print('writting to location: {}'.format(write_path))\n\n self.extract_single(sub_h, sub_w, write_path)","def save_images(images, filenames, output_dir):\n for i, filename in enumerate(filenames):\n # Images for inception classifier are normalized to be in [-1, 1] interval,\n # so rescale them back to [0, 1].\n with tf.gfile.Open(os.path.join(output_dir, filename), 'w') as f:\n img = (((images[i, :, :, :] + 1.0) * 0.5) * 255.0).astype(np.uint8)\n Image.fromarray(img).save(f, format='PNG')","def load(self):\n\n # get files in folder\n files = [f for f in listdir(self.data_path)]\n print(\"loading images from folder: %s\" % self.data_path)\n\n images = []\n image_targets = []\n for f in files:\n filepath = path.join(self.data_path, f)\n images.append(io.imread(filepath, as_grey=True))\n image_targets.append(self.target)\n\n # define new size and resize images\n new_size = (2 ** self.size_exponent, 2 ** self.size_exponent)\n for i in range(0, len(images)):\n # images[i] = transform.resize(images[i], new_size)\n images[i] = misc.imresize(images[i], new_size) / 16\n\n self.images = images\n self.targets = image_targets","def load_data_withIdx(subjIdx_list, filenames, test_ratio = 0.2):\n dataset_path = filenames['data']\n train_path = filenames['train']\n test_path = filenames['test']\n eval_path = filenames['eval']\n raw_image_lists = []\n aligned_image_lists = []\n print('[INFO] Data Processing started ... ')\n\n for subIdx in subjIdx_list:\n subj_path = dataset_path+'/subj_'+f'{subIdx:02d}'\n print('\\tProccessing images for subject_' +f'{subIdx:02d}'+ ' ...', end =\" \")\n\n if(not os.path.isdir(subj_path)):\n print(' no file exists for subject_' +f'{subIdx:02d}'+ ' !!')\n continue\n\n raw_list = sorted(glob.glob(subj_path + '/raw_images/*.jpg'))\n aligned_list = sorted(glob.glob(subj_path + '/aligned_images/*.jpg')) \n\n assert len(raw_list) == len(aligned_list) , \"data size mismatch! raw_img:{0}, alig_img:{1}\".format(len(raw_list) ,len(aligned_list)) \n \n raw_image_lists += raw_list\n aligned_image_lists += aligned_list\n print(' Done!')\n\n data_to_write_train = train_test_split_data(aligned_image_lists, raw_image_lists, test_ratio)\n data_to_write_test = train_test_split_data(data_to_write_train[1], data_to_write_train[3], 0.5)\n print('[INFO] Processing Done! ')\n print(\"[INFO] Number of train data :{0:4d}, Number of eval data :{1:4d}, Number of test data :{2:4d} \"\n . format(len(data_to_write_train[0]), len(data_to_write_test[0]), len(data_to_write_test[1]))) \n # data write part \n write_data(data_to_write_train, data_to_write_test, train_path, eval_path ,test_path)","def buildImages(files, targets, type):\n images = []\n for file in files:\n targets.append(file)\n with open(file, \"rb\") as f:\n if type == \"Byte\":\n images.append(bytePlot(list(f.read())))\n elif type == \"Markov\":\n images.append(markovPlot(list(f.read())))\n elif type == \"Hilbert\":\n images.append(hilbertPlot(list(f.read())))\n smp.imsave(\"{}.png\".format(file), images[-1])\n return images, targets","def jarvis(input_path, output_path): \n\n if not os.path.exists(f'{output_path}'):\n os.makedirs(f'{output_path}')\n\n file_list = [filename for filename in os.listdir(f'{input_path}') if '.tif' in filename]\n\n for filename in file_list:\n pathname = os.path.join(input_path, filename)\n new_name = f\"{output_path}{filename.replace('.lif - ', '_').replace('_5x-', '_')}\"\n copyfile(pathname, new_name)\n logger.info(f'{new_name}')","def collect_and_rename() -> None:\n image_source_folder = 'image_dir'\n label_source_folder = 'annotation_dir'\n image_target_folder = 'images'\n label_target_folder = 'labels'\n for i, (subdir, _, files) in enumerate(os.walk(image_source_folder), -1):\n # it walks the parent folder first, not a file\n if i == -1: \n continue\n subdir_name = subdir.split('\\\\')[1]\n for file_name in files:\n with open(f'{image_source_folder}/{subdir_name}/{file_name}') as image_file, \\\n open(f'{label_source_folder}/{subdir_name}/{file_name}'.split('.')[0] + '.txt') as label_file:\n shutil.copy2(image_file.name, f'{image_target_folder}/{\"%06d\" % i}.jpg')\n shutil.copy2(label_file.name, f'{label_target_folder}/{\"%06d\" % i}.txt')\n print(f'Processed {i} images')","def read_image_data(self):\n\n for sequence_name in self.sequence_name_list:\n sequence = self.sequences[sequence_name]\n for image_id in sequence.image_id_list:\n sequence.image_dict[image_id].image_path = '{}{}/{}'.format(self.root_dir, self.name, sequence.image_dict[image_id].filename)","def run():\n\n today = datetime.now().strftime(\"%Y-%m-%d\")\n log_file = os.path.abspath(\"logs/{}.log\".format(today))\n logger = RsmasLogger(\"pipeline\", log_file)\n\n images = get_list_of_images()\n # LOG: list of images to process\n logger.log(loglevel.INFO, [img.key for img in images])\n\n for im in images:\n\n logger.log(loglevel.INFO, \"Processing image: {}\".format(im.key))\n\n file_path = \"{}/{}\".format(im.bucket_name, im.key)\n full_path = \"{}_full.jpg\"\n mod_path = \"{}_mod.jpg\"\n aws_path = \"{}/{}/{}/{}\"\n try:\n haz_id, haz_name, sat_name, sat_dir, img_type, img_date, center = summary.pull_summary_data(\n \"/vsis3/{}\".format(file_path))\n sat_id = Satellite.from_params(sat_name, bool(sat_dir))\n except:\n # LOG: error in image metadata format\n logger.log(loglevel.ERROR, '\\tThere was an error in the metadata format of the image. Skipping.')\n continue\n\n aws_path = aws_path.format(haz_id, sat_id, img_type, img_date)\n full_path = full_path.format(img_date)\n mod_path = mod_path.format(img_date)\n\n # 1. Read in image file\n with rasterio.open(\"s3://{}\".format(file_path)) as data:\n band = data.read(1)\n img = plot.show(band)\n img.get_figure().savefig(full_path, dpi=300)\n\n # 3. Compress image\n compressed = immanip.compress_image(full_path, compression_amount=0.3)\n\n # 4 - 5. Pad image and add date on image\n text_image = immanip.add_text_to_image(compressed, img_date)\n\n # 6. Save image locally\n text_image.save(mod_path.format(img_date))\n mod_path_aws = save.get_s3_url(\"{}/{}\".format(aws_path, mod_path))\n full_path_aws = save.get_s3_url(\"{}/{}\".format(aws_path, full_path))\n\n tif_path_aws = save.get_s3_url(\"{}/{}\".format(aws_path, im.key))\n\n # LOG: images successfully moved to S3 bucket\n # LOG: mod_path_aws, full_path_aws, tif_path_aws\n\n hazard = Hazard(haz_id, haz_name, HazardType.VOLCANO, Location(center[0], center[1]), Date(img_date), 0)\n satellite = Satellite.from_params(sat_name, bool(sat_dir))\n image = Image(str(randint(1, 10000000)),\n haz_id,\n satellite,\n ImageType.from_string(img_type),\n Date(img_date),\n ImageURL(full_path_aws),\n ImageURL(tif_path_aws),\n ImageURL(mod_path_aws))\n\n try:\n db = Database()\n except ConnectionError:\n logger.log(loglevel.ERROR, \"\\tThere was an error while connecting to the database. Skipping this image.\")\n continue\n\n db.create_new_hazard(hazard)\n db.create_new_satellite(satellite)\n db.create_new_image(image)\n\n db.close()\n\n # LOG: database successfully updated\n logger.log(loglevel.INFO, \"\\tDatabase succesfully updated.\")\n\n save.save_image_s3(mod_path, \"{}/{}\".format(aws_path, mod_path))\n save.save_image_s3(full_path, \"{}/{}\".format(aws_path, full_path))\n save.move_tif(im.key, \"{}/{}\".format(aws_path, im.key))\n\n logger.log(loglevel.INFO, \"\\tImages were successfully uploaded to the S3 bucket\")\n logger.log(loglevel.INFO, \"\\t\\tmod_path_aws: {}\".format(mod_path_aws))\n logger.log(loglevel.INFO, \"\\t\\tfull_path_aws: {}\".format(full_path_aws))\n logger.log(loglevel.INFO, \"\\t\\ttif_path_aws: {}\".format(tif_path_aws))\n\n # LOG: image completed\n logger.log(loglevel.INFO, \"\\tProcessing of {} completed.\".format(im.key))\n\n # LOG: finished processing images\n logger.log(loglevel.INFO, \"Processing complete.\")","def extract_data(filename, images_dir, output_dir, trials_idx, block_nums, goal_dict):\n num_images = len(trials_idx) * len(block_nums)\n f = h5py.File(os.path.join(output_dir, filename), 'w')\n X = f.create_dataset('X', (num_images, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS*NUM_FRAMES), dtype=TYPE)\n Y = f.create_dataset('Y', (num_images, 2), dtype=TYPE)\n\n image_count = 0\n for trial_num in trials_idx:\n for block_num in block_nums:\n print('Blocks ' + str(block_num) + ' Trial ' + str(trial_num))\n for frame_num in xrange(0, NUM_FRAMES):\n temp = imread(images_dir+'RTr_Bl'+str(block_num)+'_'+str(trial_num)+'_'+str(frame_num)+IMAGE_FORMAT)\n temp = imresize(temp, [temp.shape[0]//DOWN_SAMPLE, temp.shape[1]//DOWN_SAMPLE, temp.shape[2]])\n X[image_count, 0:temp.shape[0], 0:temp.shape[1], frame_num*NUM_CHANNELS:(frame_num+1)*NUM_CHANNELS] = temp\n label = goal_dict['RTr_Bl'+str(block_num)+'_'+str(trial_num)]\n Y[image_count, :] = [label, 1-label]\n image_count += 1\n\n f.close()\n\n # TODO Use pixel depth normalization???\n #data = (data - (PIXEL_DEPTH / 2.0)) / PIXEL_DEPTH","def process_imgdir(self,imgdir):\n #Write images into resultdir\n resultdir = os.path.join(imgdir, 'results')\n #Read images from input dir\n inputdir = os.path.join(imgdir, 'inputs')\n shutil.rmtree(resultdir)\n os.mkdir(resultdir)\n #Read files from input images\n for fullname in os.listdir(inputdir):\n filepath = os.path.join(inputdir, fullname)\n if os.path.isfile(filepath):\n basename = os.path.basename(filepath)\n image = cv2.imread(filepath, cv2.IMREAD_COLOR)\n if len(image.shape) == 3 and image.shape[2] == 3:\n print('Processing %s ...' % basename)\n else:\n sys.stderr.write('Skipping %s, not RGB' % basename)\n continue\n #Extract haze from the scene and then save the image\n dehazed = self.get_scene_radiance(image)\n cv2.imwrite(os.path.join(resultdir, basename), dehazed)\n return os.path.join(resultdir, basename)","def write(self):\n f, ds = self.opendset()\n #\n # Now add the images\n #\n start_time = time.clock() # time this\n nframes = 0 # number completed\n print_every = 1; marker = \" .\";\n print('Frames written (of %s):' % self.ntowrite, end=\"\")\n for i in range(self.nfiles):\n if nframes >= self.ntowrite: break\n\n logging.debug('processing file %d of %d' % (i+1, self.nfiles))\n img_i = fabio.open(self.files[i])\n nfi = img_i.nframes\n for j in range(nfi):\n msg = '... file %d/image %d' % (i, j)\n logging.debug(msg)\n if j < self.nempty:\n logging.debug('... empty frame ... skipping')\n else:\n ds[nframes, :, :] = img_i.data\n nframes += 1\n if numpy.mod(nframes, print_every) == 0:\n print(marker, nframes, end=\"\")\n print_every *= 2\n sys.stdout.flush()\n logging.debug('... wrote image %s of %s' %\\\n (nframes, self.ntowrite))\n if nframes >= self.ntowrite:\n logging.debug('wrote last frame: stopping')\n break\n if j < nfi - 1:\n # on last frame in file, fabio will look for next file\n img_i = img_i.next()\n\n f.close()\n print(\"\\nTime to write: %f seconds \" %(time.clock()-start_time))","def write_frames(self, images):\n for img in images:\n self.write_frame(img)","def store_hdf5(images, labels, ID:str, path:str = \"data/dataset/\"):\n\n #create a new HDF5 file\n file = h5py.File(path+ID+\"_lens.h5\", \"w\")\n\n #create a dataset in the file\n dataset = file.create_dataset( \"images\", np.shape(images), h5py.h5t.IEEE_F64BE, data=images)\n file.close()\n\n labels.to_hdf(path +ID+'_meta.h5', \"table\")","def save_images(images, filenames, output_dir):\n for i, filename in enumerate(filenames):\n # Images for inception classifier are normalized to be in [-1, 1] interval,\n # so rescale them back to [0, 1].\n with tf.gfile.Open(os.path.join(output_dir, filename), 'w') as f:\n imsave(f, (images[i] + 1.0) * 0.5, format='png')","def save_images(images, filenames, output_dir):\n for i, filename in enumerate(filenames):\n # Images for inception classifier are normalized to be in [-1, 1] interval,\n # so rescale them back to [0, 1].\n with tf.gfile.Open(os.path.join(output_dir, filename), 'w') as f:\n imsave(f, (images[i, :, :, :] + 1.0) * 0.5, format='png')","def div_imgs(dir_path: str, holdout: int) -> None:\n VH_REGEX = re.compile(r'(.*)_([0-9]+).vh.tif')\n\n for file in os.listdir(dir_path):\n m = re.match(VH_REGEX, file)\n if not m:\n continue\n\n pre, num = m.groups()\n vv = f'{pre}_{num}.vv.tif'\n mask = f'{pre}_{num}.mask.tif'\n\n if not os.path.isfile(os.path.join(dir_path, mask)):\n print(f\"Tile: {file} is missing a mask {mask}!\")\n\n if not os.path.isfile(os.path.join(dir_path, vv)):\n print(f\"Tile: {file} is missing a mask {vv}!\")\n\n test_or_train = 'train' if random.random() > holdout else 'test'\n\n folder = os.path.join(dir_path, test_or_train)\n if not os.path.isdir(folder):\n os.makedirs(folder)\n\n os.rename(\n os.path.join(dir_path, file), os.path.join(folder, file)\n )\n os.rename(\n os.path.join(dir_path, vv),\n os.path.join(folder, vv)\n )\n os.rename(\n os.path.join(dir_path, mask),\n os.path.join(folder, mask)\n )","def save_as_hdf5(self, filename):","def process_subdirectory(subdir_path, regex_images, output_folder_if_pickle,\n min_n_leftside_metadata=10, only_ionogram_content_extraction_on_leftside_metadata=True, to_pickle=True):\n # Run segment_images on the subdirectory \n df_img,df_loss,df_outlier = segment_images(subdir_path, regex_images)\n\n # Determine ionogram grid mappings used to map (x,y) pixel coordinates of ionogram trace to (Hz, km) values\n stack = all_stack(df_img)\n col_peaks,row_peaks,mapping_Hz, mapping_km = get_grid_mappings(stack)\n\n # Translate metadata located on the left\n df_img_left = df_img[df_img['metadata_type']== 'left']\n \n if len(df_img_left.index) > min_n_leftside_metadata:\n # Determine leftside metadata grid (pixel coordinates to number, category mappings)\n df_img_left, df_loss_meta,dict_mapping,dict_hist= get_leftside_metadata(df_img_left,subdir_path)\n df_all_loss = df_loss.append(df_loss_meta)\n else:\n df_all_loss = df_loss\n \n # Extract the coordinates of the ionogram trace (black), Map the (x,y) pixel coordinates to (Hz, km) values and Extract select parameters i.e. fmin\n if only_ionogram_content_extraction_on_leftside_metadata:\n df_processed, df_loss_coord = extract_coord_subdir_and_param(df_img_left,subdir_path,col_peaks,row_peaks,mapping_Hz,mapping_km)\n else:\n df_processed, df_loss_coord = extract_coord_subdir_and_param(df_img,subdir_path,col_peaks,row_peaks,mapping_Hz,mapping_km)\n\n df_processed['mapping_Hz'] = [mapping_Hz] * len(df_processed.index)\n df_processed['mapping_km'] = [mapping_km] * len(df_processed.index)\n\n if to_pickle:\n start,subdir_name = ntpath.split(subdir_path[:-1])\n start,dir_name = ntpath.split(start)\n df_processed.to_pickle(os.pardir + '/pickle/' + str(dir_name)+'_'+str(subdir_name)+'.pkl')\n \n df_all_loss = df_all_loss.append(df_loss_coord)\n return df_processed, df_all_loss,df_outlier","def compress_img():\n in_path = 'output/templates/rgb/'\n out_path = 'output/templates/imgs/'\n names = os.listdir(in_path)\n for i, name in enumerate(names):\n img = cv2.imread(in_path + name, 0)\n if any(np.array(img.shape) > 1000):\n img = cv2.resize(img, None, fx=0.5, fy=0.5, interpolation=cv2.INTER_CUBIC)\n cv2.imwrite(out_path + name, img)\n\n return","def generate_all(files, alignement_h5f, input_h5f,\n nframes=7, vad=None):\n def try_remove(fname):\n try:\n os.remove(fname)\n except:\n pass\n try:\n directory = os.path.dirname(os.path.abspath(input_h5f))\n\n # create temporary files:\n _, fb_h5f = tempfile.mkstemp(dir=directory)\n _, fb_mvn_h5f = tempfile.mkstemp(dir=directory)\n os.remove(fb_h5f)\n os.remove(fb_mvn_h5f)\n\n # generate mfccs:\n h5features_compute(files, alignement_h5f, featfunc=do_mfccs)\n\n # generate stacked mvn fbanks:\n h5features_compute(files, fb_h5f, featfunc=do_fbank)\n mean_variance_normalisation(fb_h5f, fb_mvn_h5f, vad=vad)\n h5features_feats2stackedfeats(fb_mvn_h5f, input_h5f, nframes=nframes)\n finally:\n try_remove(fb_h5f)\n try_remove(fb_mvn_h5f)","def processImages(self):\n for file in os.listdir(self.config[\"tempPath\"]):\n self.logger.debug(\"Calling generateImages for the file: {0}\".format(file))\n self.generateText(file)","def save_images(images, filenames, output_dir):\n for i, filename in enumerate(filenames):\n # Images for inception classifier are normalized to be in [-1, 1] interval,\n # so rescale them back to [0, 1].\n with tf.gfile.Open(os.path.join(output_dir, filename), 'w') as f:\n imsave(f, (images[i, :, :, :] + 1.0) * 0.5, format='png')","def create_random_data(output_path: str, num_images: int = 5) -> None:\n train_path = os.path.join(output_path, \"train\")\n class1_train_path = os.path.join(train_path, \"class1\")\n class2_train_path = os.path.join(train_path, \"class2\")\n\n val_path = os.path.join(output_path, \"val\")\n class1_val_path = os.path.join(val_path, \"class1\")\n class2_val_path = os.path.join(val_path, \"class2\")\n\n test_path = os.path.join(output_path, \"test\")\n class1_test_path = os.path.join(test_path, \"class1\")\n class2_test_path = os.path.join(test_path, \"class2\")\n\n paths = [\n class1_train_path,\n class1_val_path,\n class1_test_path,\n class2_train_path,\n class2_val_path,\n class2_test_path,\n ]\n\n for path in paths:\n try:\n os.makedirs(path)\n except FileExistsError:\n pass\n\n for i in range(num_images):\n pixels = numpy.random.rand(64, 64, 3) * 255\n im = Image.fromarray(pixels.astype(\"uint8\")).convert(\"RGB\")\n im.save(os.path.join(path, f\"rand_image_{i}.jpeg\"))\n\n process_images(output_path)","def save_images_in_folder(folder, images, size = (224, 224), start_index = 0):\n\n # Loop over the images\n for i, image in enumerate(images):\n # Resize image to target size\n image = cv2.resize(image, dsize = size)\n \n # Convert image back to BGR color space\n image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n \n # Create the path where the image will be save, images will be indexed\n path = os.path.join(folder, str(start_index + i) + '.png')\n \n # Write / Save image \n cv2.imwrite(path, image)","def transform_to_h5():\n # this took about 10 minutes for set1\n for setname in ['set1', 'set2']:\n filename = os.path.join(ltrc_dirname, '{}.h5'.format(setname))\n f = h5py.File(filename, 'w')\n\n for name in ['train', 'valid', 'test']:\n g = f.create_group(name)\n filename = os.path.join(ltrc_dirname, '{}.{}.txt'.format(setname, name))\n X, y, q = load_svmlight_file(filename, query_id=True)\n g.create_dataset('X', data=X.todense(), compression='gzip')\n g.create_dataset('y', data=y, compression='gzip')\n g.create_dataset('q', data=q, compression='gzip')\n f.close()\n # Now you can do this\n # f['/valid/X'].shape\n # Out[24]: (71083, 699)","def preprocess(exam, data_folder, save_path, image_format):\n for v in ['L-CC', 'L-MLO', 'R-CC', 'R-MLO']:\n if len(exam[v]) == 0:\n continue\n else:\n for image in exam[v]:\n image_path = data_folder + '/' + image + '.' + image_format\n # Extract subdirectories\n subdirs = \"/\".join(image.split('/')[:-1])\n save_dirs = os.path.join(save_path, subdirs)\n # Extract image id\n image_id = image.split('/')[-1]\n # Create save directories\n os.makedirs(save_dirs, exist_ok=True)\n png_save_path = os.path.join(save_dirs, image_id + '.png')\n with Image(filename=image_path, format=image_format) as img:\n with img.clone() as i:\n i.auto_level()\n with i.convert('png') as png_image:\n png_image.transform(resize='896x1152!')\n png_image.save(filename=png_save_path)","def sum_images(filelist):\n\n nfiles = np.size(filelist)\n\n print(\"Summing together {} files\".format(nfiles))\n\n ims = []\n\n for fname in filelist:\n hdu = f.open(fname)\n ims.append(hdu[0].data)\n\n ims = np.array(ims)\n\n sum_im = np.nansum(ims, axis=0)\n hdu[0].data = sum_im\n\n #Add pipeline version and history keywords\n vers = version.get_version()\n hdu[0].header.set('PL_VERS',vers,'Version of pipeline used for processing')\n hdu[0].header['HISTORY'] = \"Summed up the following images:\"\n\n for fname in filelist:\n hdu[0].header['HISTORY'] = fname\n\n hdu[0].header['HISTORY'] = \"######################\"\n\n outname = filelist[-1].split('.')[0]+'_summed.fits'\n\n print(\"Writing out final file to {}\".format(outname))\n\n hdu.writeto(outname, overwrite=True)","def saveHDF5(ifile, cs, xi, yi, phi, cidx, idx, size, comx, comy, rgysq, pl, st, sw, ens, fils):\n \n ## Groups\n size_grp = ifile.create_group('size')\n beads_grp = ifile.create_group('beads')\n props_grp = ifile.create_group('props')\n \n ## Datasets\n size_grp.create_dataset('size', data=cs, compression='gzip')\n \n beads_grp.create_dataset('x', data=xi, compression='gzip')\n beads_grp.create_dataset('y', data=yi, compression='gzip')\n beads_grp.create_dataset('phi', data=phi, compression='gzip')\n beads_grp.create_dataset('idx', data=cidx, compression='gzip')\n \n idx_h5 = np.asarray(idx, dtype=int)\n size_h5 = np.asarray(size, dtype=int)\n comx_h5 = np.asarray(comx, dtype=float)\n comy_h5 = np.asarray(comy, dtype=float)\n rgysq_h5 = np.asarray(rgysq, dtype=float)\n planarity_h5 = np.asarray(pl, dtype=float)\n straightness_h5 = np.asarray(st, dtype=float)\n swirliness_h5 = np.asarray(sw, dtype=float)\n enstrophy_h5 = np.asarray(ens, dtype=float)\n \n props_grp.create_dataset('idx', data=idx_h5, compression='gzip')\n props_grp.create_dataset('size', data=size_h5, compression='gzip')\n props_grp.create_dataset('comx', data=comx_h5, compression='gzip')\n props_grp.create_dataset('comy', data=comy_h5, compression='gzip')\n props_grp.create_dataset('rgysq', data=rgysq_h5, compression='gzip')\n props_grp.create_dataset('planarity', data=planarity_h5, compression='gzip')\n props_grp.create_dataset('straightness', data=straightness_h5, compression='gzip')\n props_grp.create_dataset('swirliness', data=swirliness_h5, compression='gzip')\n props_grp.create_dataset('enstrophy', data=enstrophy_h5, compression='gzip')\n \n ## Filament list\n fil_grp = props_grp.create_group('filament_list')\n for sz_idx in np.arange(len(size_h5)):\n fil_list = np.asarray(fils[sz_idx], dtype=int)\n fil_grp.create_dataset(str(sz_idx), data=fil_list, compression='gzip')\n \n return","def ReadData(self, path):\n os.chdir(path)\n folders=os.listdir()\n if 'data.hdf5' in folders:\n print('Loading data from hdf5 file! Might take some time, be patient!')\n file=h5py.File('data.hdf5','r+')\n data=(np.array(list(file['imgs'])),np.array(list(file['lables'])))\n self.real_labels=list(file['real_labels'])\n file.close()\n\n else:\n print('1. Collecting data.')\n err_logs = []\n img=[]\n lable=[]\n for folder in tqdm(folders):\n\n os.chdir(os.path.join(path,folder))\n for file in os.listdir():\n try:\n dat=(plt.imread(open(file,'rb')))\n img.append(resize_image(dat, (resize_x, resize_y),\n mode='constant',\n ))\n lable.append(folder)\n if folder not in self.real_labels:\n self.real_labels.append(folder)\n \n except OSError:\n err_logs.append([folder, file])\n print('\\nError logs:')\n for e in range(len(err_logs)):\n print('\\tFolder: {} | Some OSError for file: {}'.format(err_logs[e][0],\n err_logs[e][0]))\n \n \n print('2. Encoding data to categorical.')\n # Encode Letters into numerical categories.\n le = LabelEncoder()\n le.fit(lable)\n lable = le.transform(lable)\n lable = np.array(lable).reshape(-1, 1)\n \n print('3. Onehot encoding.')\n # Onehot encoding.\n ohe = OneHotEncoder(sparse=False)\n ohe.fit(lable)\n lable = ohe.transform(lable)\n \n # Shaffle data.\n print('4. Shuffle data.')\n img, lable = shuffle(img, lable)\n\t\t \n print('5. Saving data.')\n data=(np.asarray(img), np.asarray(lable))\n os.chdir(path)\n \n file=h5py.File('data.hdf5','w')\n x=file.create_dataset('imgs',data=np.array(img))\n y=file.create_dataset('lables',data=np.array(lable))\n print(self.real_labels)\n rl=file.create_dataset('real_labels',data=np.string_(self.real_labels))\n file.close()\n print('Data set is stored in Data.hdf5 file. ')\n\n return data","def create_ela_files(originals='./data/CASIA1_originals', originals_ela='./data/CASIA1_originals_ela',\n fakes='./data/CASIA1_fakes', fakes_ela='./data/CASIA1_fakes_ela'):\n for i, item in enumerate(os.listdir(originals)):\n image = ela(os.path.join(originals, item))\n cv2.imwrite(os.path.join(originals_ela, item), image)\n \n for i, item in enumerate(os.listdir(fakes)):\n image = ela(os.path.join(fakes, item))\n cv2.imwrite(os.path.join(fakes_ela, item), image)","def exporting_cropped_images (fpath_tiff):\n src = rasterio.open(fpath_tiff, 'r')\n outfolder_irregular = '/train/irregular'\n outfolder_healthy = '/train/healthy'\n outfolder_concrete = '/train/concrete'\n outfolder_incomplete = '/train/incomplete'\n outfolder_other = '/train/other'\n outfolder = '/train/batch'\n #os.makedirs (outfolder, exist_ok = True)","def main():\n nb_processed = 0\n for dataset_name in DATASETS:\n print(\"-----------------\")\n print(\"Dataset: '%s'\" % (dataset_name,))\n print(\"-----------------\")\n\n dataset_dir = os.path.join(WRITE_MAIN_DIR, dataset_name)\n if not os.path.isdir(dataset_dir):\n os.makedirs(dataset_dir)\n\n dataset = Dataset([os.path.join(READ_MAIN_DIR, dataset_name)])\n print(\"Found %d images total.\" % (len(dataset.fps),))\n\n errors = []\n\n scale_height, scale_width = SCALES[dataset_name]\n target_aspect_ratio = scale_width / scale_height\n\n # iterate over every image in the current dataset,\n # augment that image N times, add cols/rows until target aspect ratio\n # is reached, resize it (e.g. 64x64), save it\n for img_idx, (image_filepath, image) in enumerate(zip(dataset.fps, dataset.get_images())):\n print(\"[%s] Image %d of %d (%.2f%%)...\" \\\n % (dataset_name, img_idx+1, len(dataset.fps),\n 100*(img_idx+1)/len(dataset.fps)))\n\n # IOErrors during loading of images result here in a None value\n if image is None:\n print(\"Error / None\")\n errors.append((\n image_filepath,\n \"Failed to load image '%s' (idx %d for dataset %s)\" \\\n % (image_filepath, img_idx, dataset_name)\n ))\n else:\n # resize too big images to smaller ones before any augmentation\n # (for performance reasons)\n height = image.shape[0]\n width = image.shape[1]\n aspect_ratio = width / height\n if width > 1000 or height > 1000:\n image = misc.imresize(image, (1000, int(1000 * aspect_ratio)))\n\n # augment image\n # converts augmented versions automatically to float32, 0-1\n augmentations = augment(image, **AUGMENTATIONS[dataset_name])\n\n # create list of original image + augmented versions\n images_aug = [image / 255.0]\n images_aug.extend(augmentations)\n\n # for each augmented version of the images:\n # resize it to target aspect ratio (e.g. same width and height),\n # save it\n for aug_idx, image_aug in enumerate(images_aug):\n image_aug = to_aspect_ratio_add(image_aug, target_aspect_ratio)\n filename = \"{:0>6}_{:0>3}.jpg\".format(img_idx, aug_idx)\n img_scaled = misc.imresize(image_aug, (scale_height, scale_width))\n misc.imsave(os.path.join(dataset_dir, filename), img_scaled)\n\n nb_processed += 1\n\n print(\"Processed %d images with %d errors.\" % (nb_processed, len(errors)))\n for (fp, err) in errors:\n print(\"File %s error:\" % (fp,))\n print(err)\n print(\"Finished.\")","def HDF5_to_HDF5(self, **kwds):\n # split extension from HDF5 file\n if isinstance(self.filename, str):\n fileBasename,fileExtension=os.path.splitext(self.filename)\n else:\n fileBasename,fileExtension=os.path.splitext(self.filename.filename)\n # output HDF5 file\n hdf5_file = os.path.expanduser(f'{fileBasename}.h5')\n # copy everything from the HDF5 file\n with h5py.File(self.filename,mode='r') as source:\n dest = h5py.File(hdf5_file,mode='w')\n # value checks on output HDF5\n if not hasattr(dest, 'create_dataset'):\n raise ValueError('dest must be a group, got {!r}'.format(dest))\n # for each key in the root of the hdf5 file structure\n for k in source.keys():\n self.copy_from_HDF5(source[k], dest, name=k, **kwds)","def read_hdf5(ID_images:str, path:str = \"data/dataset/\"):\n images, labels = [], []\n\n #open the HDF5 file\n file = h5py.File(path +ID_images+\"_lens.h5\", \"r\")\n\n images = np.array(file[\"/images\"]).astype(\"float64\")\n labels = pd.read_hdf(path +ID_images+'_meta.h5', \"table\")\n\n return images, labels","def gen_test_output(sess, logits, image_folder, image_pl, data_folder,\n learning_phase, image_shape, nw_shape):\n image_paths = glob(os.path.join(data_folder, image_folder, '*.png'))\n for image_file in image_paths[:5]:\n\n in_image = scipy.misc.imread(image_file, mode='RGB')\n image = scipy.misc.imresize(in_image, image_shape)\n\n street_im = get_seg_img(sess, logits, image_pl, image, image_shape,\n nw_shape, learning_phase)\n\n street_im = scipy.misc.imresize(street_im, in_image.shape)\n yield os.path.basename(image_file), np.array(street_im)","def main(output_dir, img_size, max_boxes, train_size, val_size):\n im_id = 0\n ann_id = 0\n\n def make_split(split, split_size):\n nonlocal im_id\n nonlocal ann_id\n\n image_dir = join(output_dir, 'train')\n make_dir(image_dir)\n\n images = []\n annotations = []\n for _ in range(split_size):\n img, boxes = make_scene(img_size, max_boxes)\n img = np.transpose(img, (1, 2, 0))\n file_name = '{}.png'.format(im_id)\n Image.fromarray(img).save(\n join(image_dir, file_name))\n images.append({\n 'id': im_id,\n 'height': img_size,\n 'width': img_size,\n 'file_name': file_name\n })\n for box in boxes:\n annotations.append({\n 'id': ann_id,\n 'image_id': im_id,\n 'category_id': 1,\n 'area': (box[2] - box[0]) * (box[3] - box[1]),\n 'bbox': [box[1], box[0], box[3]-box[1], box[2]-box[0]]\n })\n ann_id += 1\n im_id += 1\n\n categories = [{'id': 1, 'name': 'rectangle'}]\n labels = {\n 'images': images,\n 'annotations': annotations,\n 'categories': categories}\n json_to_file(labels, join(output_dir, '{}.json'.format(split)))\n\n make_split('train', train_size)\n make_split('valid', val_size)","def main():\n\n\n\n skulls_folder = os.listdir(RAW_IMAGE_DIRECTORY)\n\n # fetch and sort the .mnc and .tag files\n mnc_files = [f for f in skulls_folder if 'mnc' in f]\n tag_files = [f for f in skulls_folder if 'tag' in f]\n mnc_names = [i.split('.mnc')[0] for i in mnc_files]\n \n mnc_files.sort()\n tag_files.sort()\n mnc_names.sort()\n\n # Process and package ndarrays as tuples inside npy file\n package_to_npy(RAW_IMAGE_DIRECTORY, mnc_files, tag_files, mnc_names)\n \n print('\\n' * 5)\n\n # Push the npy files to GCP Cloud Storage\n upload_to_gcp(PROCESSED_IMAGE_DIRECTORY, GCP_PROJECT_NAME, GCP_BUCKET_NAME)","def create_png_images(self):\n if self.subject is None:\n print Console.WARNING + 'You need to specify a subject first' + Console.ENDC\n return\n\n check_dir_of = self.locations.check_dir_of\n check_dir_of(self.locations.HISTO_PNG_U)\n check_dir_of(self.locations.HISTO_PNG)\n check_dir_of(self.locations.SOURCE_PNG)\n\n\n\n fmap_img = ImageUtils.load_nifti_image(self.locations.HIST_FMAP) #loading subject nifti files\n volumes = []\n try:\n for s in self.locations.SOURCES:\n volumes.append(ImageUtils.load_nifti_image(s))\n except IOError as e:\n print Console.FAIL + 'There are errors loading nifi files for subject %s'%self.subject + Console.ENDC\n return False\n \n\n num_slices = volumes[0].shape[2] #use first volume to check expected number of slices\n\n self.locations.create_empty_dir(self.locations.IMAGES_DIR)\n\n print 'Creating input PNGs for %s'%self.subject\n for k, vol in enumerate(volumes):\n for i in range(num_slices):\n imslice = ImageUtils.data_to_bytescale_rgb(vol[:, :, i])\n im = Image.fromarray(imslice)\n im.save(self.locations.SOURCE_PNG % (self.locations.LABELS[k],i))\n\n \n print 'Creating histology PNGs for %s'%self.subject\n for i in range(num_slices):\n\n im_unscaled = ImageUtils.data_to_unscaled_rgb(fmap_img[:, :, i]); #keeps the original values\n im_unscaled = Image.fromarray(im_unscaled)\n im_unscaled = im_unscaled.filter(ImageFilter.GaussianBlur(radius=2)) #Filter requested by Ali Khan\n im_unscaled.save(self.locations.HISTO_PNG_U % i)\n\n im_scaled = ImageUtils.data_to_bytescale_rgb(fmap_img[:,:,i]); # bytescaled histology\n im_scaled = Image.fromarray(im_scaled)\n im_scaled = im_scaled.filter(ImageFilter.GaussianBlur(radius=2)) #Filter requested by Ali Khan\n im_scaled.save(self.locations.HISTO_PNG % i)\n\n print\n return True","def print_images_in_statistics(self):\n self._print_images_statistics(self._images_in_folder, self._pose_class_names)","def print_images_out_statistics(self):\n self._print_images_statistics(self._images_out_folder, self._pose_class_names)","def create_training_images(fn, i, p_hr, p_lr, size, qualityFactor, downsize=True):\n dest = p_lr/fn.relative_to(p_hr)\n dest.parent.mkdir(parents=True, exist_ok=True)\n img = PIL.Image.open(fn)\n if downsize:\n targ_sz = resize_to(img, size, use_min=True) # W x H\n img = img.resize(targ_sz, resample=PIL.Image.BILINEAR).convert('RGB')\n img.save(dest.with_suffix(\".jpg\"), \"JPEG\", quality=qualityFactor)","def read_images(folder):\n distinct_frames = DistinctFrames()\n\n for file in sorted(sorted(os.listdir(folder)),\n key=len): # sorting files on basis of 1) length and 2) numerical order\n '''\n Sorting is done 2 times because\n if files in the folder are\n 1. image100.pkl\n 2. image22.pkl\n 3. image21.pkl\n firstly sort them to image100.pkl,image21.pkl,image22.pkl then according to length to image21.pkl,image22.pkl,image100.pkl\n '''\n try:\n img_obj = load_from_memory(file, folder)\n time_stamp = img_obj.get_time()\n distinct_frames.add_img_obj(img_obj)\n print(\"Reading image ..\" + str(time_stamp) + \" from \" + folder) # for debug purpose\n except:\n # exception will occur for files like .DS_Store and jpg directory\n continue\n\n if distinct_frames.no_of_frames() != 0:\n distinct_frames.calculate_time()\n\n return distinct_frames","def append_component_images(pldm_fw_up_pkg, image_files):\n for image in image_files:\n with open(image, \"rb\") as file:\n for line in file:\n pldm_fw_up_pkg.write(line)","def global_metadata(paths):\n\n # Weakly group images to partition image set size- crucial optimization step\n if os.path.exists(paths.image_preprocess):\n clumped_paths = json.loads(open(paths.image_preprocess).read())\n else:\n clumped_paths = network.alpha_categorize(paths)\n print(\"Hashed source images\")\n\n with open(paths.image_preprocess, 'w') as json_file:\n json.dump(clumped_paths, json_file)\n\n # Combinatorial image grouping to graph\n image_graph = network.load_graph(paths.image_network_path)\n\n total = len(list(chain(*clumped_paths.values())))\n counter = 0.\n\n for image_paths in clumped_paths.values():\n counter += len(image_paths)\n print(str(int(counter / float(total) * 100)) + \"% complete\")\n\n if len(image_paths) > 1:\n image_grouping = images.load_paths(paths.default_patches, image_paths)\n image_graph = metadata.network.network_images(\n image_grouping, threshold=0, network=image_graph)\n else:\n image_graph.add_node(image_paths[0])\n\n metadata.network.save_graph(paths.image_network_path, image_graph)\n print(\"Updated image graph.\")\n\n # Create informational json files for templates and files\n templates.build(paths, image_graph)\n mappings.build(paths, image_graph)\n print(\"Created JSON metadata files.\")","def read_images(path, image_size=None):\n c = 0\n X = []\n y = []\n folder_names = []\n for dirname, dirnames, filenames in os.walk(path):\n for subdirname in dirnames:\n folder_names.append(subdirname)\n subject_path = os.path.join(dirname, subdirname)\n for filename in os.listdir(subject_path):\n #try:\n im = cv2.imread(os.path.join(subject_path, filename), cv2.IMREAD_GRAYSCALE)\n # resize to given size (if given)\n if (image_size is not None):\n im = cv2.resize(im, image_size)\n X.append(np.asarray(im, dtype=np.uint8))\n y.append(c)\n #except IOError, (errno, strerror):\n # print \"I/O error({0}): {1}\".format(errno, strerror)\n # except:\n # print \"Unexpected error:\", sys.exc_info()[0]\n # raise\n c = c+1\n return [X,y,folder_names]","def extractOneImageStack(filename, h5path=None, destPath=None):\n if destPath == None:\n destPath = \".\"\n if filename.find(\"%\") > 0:\n h5file = h5py.File(filename, driver=\"family\")\n else:\n h5file = h5py.File(filename)\n if (h5path is None) or (h5path not in h5file):\n list_of_names = []\n h5file.visit(list_of_names.append)\n print(os.linesep + \"Where is your stack of images dataset (in which group) ?\")\n for onePath in list_of_names:\n if isinstance(h5file[onePath], h5py.highlevel.Group):\n print(\"\\t\" + onePath)\n\n h5path = raw_input(\"Where is your image stack ? \")\n if not h5path in h5file:\n print(\"This HDF5path %s is not in the file %s\" % (filename, h5path))\n sys.exit(1)\n group = h5file[h5path]\n if \"data\" in group:\n data = group[\"data\"]\n else:\n maxData = None\n for obj in group:\n if isinstance(group[obj], h5py.highlevel.Dataset):\n if len(group[obj].shape) > len(maxData.shape):\n maxData = group[obj]\n elif len(group[obj].shape) == len(maxData.shape):\n if group[obj].shape[0] > maxData.shape[0]:\n maxData = group[obj]\n if maxData is None:\n raise Exception(\"No dataset found in provided group %s from file %s\" % (h5path, filename))\n else:\n data = maxData\n dsSize = data.shape[0]\n\n headerGrp = []\n for obj in group:\n if isinstance(group[obj], h5py.highlevel.Group):\n testHeaderGrp = group[obj]\n for headerdataset in testHeaderGrp:\n if testHeaderGrp[headerdataset].len() != dsSize:\n break\n else:\n headerGrp = testHeaderGrp\n if \"Filenames\" in group:\n EdfFilenames = group[\"Filenames\"]\n elif \"filenames\" in group:\n EdfFilenames = group[\"filenames\"]\n else:\n base = os.path.splitext(filename)[0]\n grpname = h5path.replace(\"/\", \"_\")\n EdfFilenames = [\"%s-%s-%04i.edf\" % (base, grpname, i) for i in range(dsSize) ]\n for idx, oneFile in enumerate(EdfFilenames):\n print(\"Extracting \" + oneFile)\n headers = {}\n for key in headerGrp:\n if key == \"filename\":\n continue\n headers[key] = headerGrp[key][idx]\n edf = fabio.edfimage.edfimage(data=data[idx], header=headers)\n edf.write(os.path.join(destPath, oneFile))","def make_hdf5file(self, dataset_paths: list = None, dataset_values: list = None) -> None:\n\n assert dataset_paths is not None and len(dataset_paths) != 0, (\"`dataset_path` not valid. Expected at least \" \\\n f\"one list element, got {len(dataset_paths)}.\")\n\n assert dataset_values is not None and len(dataset_values) != 0, (\"`dataset_values` not valid. Expected at least \" \\\n f\"one list element, got {len(dataset_values)}.\")\n\n\n # Remove file if already exists and create a new one\n if os.path.isfile(os.path.join(self.FOFDirectory, self.filename)):\n os.remove(os.path.join(self.FOFDirectory, self.filename))\n print(f'[ FOFOutput ]\\t==> Removed old {self.filename} file.')\n\n # Create file and optional groups within it\n FOFfile = h5py.File(os.path.join(self.FOFDirectory, self.filename), 'w')\n print(f'[ FOFOutput ]\\t==> Created new {self.filename} file.')\n\n # Push the attributes to file, if any\n if self.attrs is not None and len(self.attrs.keys()) > 0:\n for key, text in zip(self.attrs.keys(), self.attrs.values()):\n FOFfile.attrs[key] = text\n\n for internal_path, dataset_content in zip(dataset_paths, dataset_values):\n\n assert not internal_path.endswith('/'), \"Invalid hdf5 internal path\"\n assert type(dataset_content) is np.ndarray, \"Can only push numpy.ndarrays into hdf5 files.\"\n\n nested_groups = self.groups_from_path(internal_path)\n if len(nested_groups) == 1:\n FOFfile.create_dataset(nested_groups[0], data=dataset_content)\n else:\n for nested_group in nested_groups[:-1]:\n g = FOFfile.create_group(nested_group)\n g.create_dataset(nested_groups[-1], data = dataset_content)\n\n print(f'[ FOFOutput ]\\t==> Created {internal_path} dataset in {self.filename} file.')\n\n FOFfile.close()","def create_image_lists(image_dir):\n if not gfile.Exists(image_dir):\n print(\"Image directory '\" + image_dir + \"' not found.\")\n return None\n result = {}\n sub_dirs = [x[0] for x in os.walk(image_dir)]\n # The root directory comes first, so skip it.\n is_root_dir = True\n for sub_dir in sub_dirs:\n print('in sub loop')\n extensions = ['jpg', 'jpeg', 'JPG', 'JPEG']\n file_list = []\n dir_name = os.path.basename(image_dir)\n print(\"Looking for images in '\" + image_dir + \"'\")\n for extension in extensions:\n file_glob = os.path.join(image_dir, dir_name, '*.' + extension)\n file_list.extend(glob.glob(file_glob))\n if not file_list:\n print('No files found')\n continue\n if len(file_list) < 20:\n print('WARNING: Folder has less than 20 images, which may cause issues.')\n label_name = re.sub(r'[^a-z0-9]+', ' ', dir_name.lower())\n testing_images = []\n for file_name in file_list:\n base_name = os.path.basename(file_name)\n # We want to ignore anything after '_nohash_' in the file name when\n # deciding which set to put an image in, the data set creator has a way of\n # grouping photos that are close variations of each other. For example\n # this is used in the plant disease data set to group multiple pictures of\n # the same leaf.\n hash_name = re.sub(r'_nohash_.*$', '', file_name)\n # This looks a bit magical, but we need to decide whether this file should\n # go into the training, testing, or validation sets, and we want to keep\n # existing files in the same set even if more files are subsequently\n # added.\n # To do that, we need a stable way of deciding based on just the file name\n # itself, so we do a hash of that and then use that to generate a\n # probability value that we use to assign it.\n testing_images.append(base_name)\n return testing_images","def splitMerge(self):\n\t\tpath_merge = self.aug_merge_path\n\t\tpath_train = self.aug_train_path\n\t\tpath_label = self.aug_label_path\n\t\tfor i in range(self.slices):\n\t\t\tpath = path_merge + \"/\" + str(i)\n\t\t\t# print(path)\n\t\t\ttrain_imgs = glob.glob(path+\"/*.\"+self.img_type)\n\t\t\t# print(len(train_imgs))\n\t\t\t# break\n\t\t\tfor imgname in train_imgs:\n\t\t\t\tmidname = imgname[imgname.rindex(\"/\")+1:imgname.rindex(\".\"+self.img_type)]\n\t\t\t\timg = cv2.imread(imgname)\n\t\t\t\timg_train = img[:,:,2]#cv2 read image rgb->bgr\n\t\t\t\timg_label = img[:,:,0]\n\t\t\t\tcv2.imwrite(path_train+\"/\"+midname+\".\"+self.img_type,img_train)\n\t\t\t\tcv2.imwrite(path_label+\"/\"+midname+\".\"+self.img_type,img_label)","def create_noobj_folder(\n folder: PathLike, \n img_ext: str = \".jpg\",\n):\n folder = Path(folder).expanduser().resolve()\n images = glob(folder, img_ext)\n \n for image in images:\n filename = image.name\n _folder = image.parent.name\n path = folder / (image.stem + \".xml\")\n img_w, img_h = get_image_size(image)\n\n tree = ET.Element(\"annotation\")\n\n et_folder = ET.SubElement(tree, \"folder\")\n et_folder.text = _folder\n\n et_filename = ET.SubElement(tree, \"filename\")\n et_filename.text = filename\n\n et_path = ET.SubElement(tree, \"path\")\n et_path.text = str(path)\n\n et_img_size = ET.SubElement(tree, \"size\")\n ET.SubElement(et_img_size, \"width\").text = str(img_w)\n ET.SubElement(et_img_size, \"height\").text = str(img_h)\n ET.SubElement(et_img_size, \"depth\").text = \"3\"\n\n content = ET.tostring(tree, encoding=\"unicode\", pretty_print=True)\n try: \n path.write_text(content)\n except KeyboardInterrupt:\n path.write_text(content)\n exit()","def add_random(fr_data_path, random_path, output_path):\n with h5py.File(random_path, 'r') as data:\n random = np.asarray(data['images'].value)\n \n means = np.mean(np.mean(random, axis=-1), axis=-1)\n empty = means == 0.0\n error = np.isnan(means)\n discard = empty | error\n\n random_i = np.where(~discard)\n random = random[random_i]\n\n random = center_on_brightest(random)\n \n with h5py.File(fr_data_path, 'r') as data:\n images = np.asarray(data[\"images\"].value) \n images = center_on_brightest(images)\n \n labels = np.where(np.asarray(data['labels']), 2, 1)\n\n images = np.concatenate((images, random), axis=0)\n labels = np.concatenate((labels, np.full((random.shape[0],), \n fill_value=0)), axis=0)\n\n with h5py.File(output_path, 'w') as f:\n f.create_dataset('images', data=images)\n f.create_dataset('labels', data=labels)\n\n with h5py.File(fr_data_path, 'r') as data: \n f.copy(data, 'fri_data')\n f.copy(data, 'frii_data')","def __call__(self):\n train_number = 1\n test_number = 1\n for cur_file in os.listdir(self.txt_dir):\n if(cur_file.split(\".\")[0].split(\"-\")[-1] == \"ellipseList\"):\n with open(os.path.join(self.txt_dir, cur_file), 'r') as txt_file:\n while(True):\n line = txt_file.readline().rstrip(\"\\n\")\n if not line : break\n\n # Image load\n img_folder_ = line.split(\"/\") \n img_dir_ = \\\n self.img_dir + \"\\\\\" + img_folder_[0] + \"\\\\\" + img_folder_[1] + \"\\\\\" + img_folder_[2] + \"\\\\\" + img_folder_[3] + \"\\\\\" + img_folder_[4] + \".jpg\"\n img = cv2.imread(img_dir_, cv2.IMREAD_COLOR)\n \n if(train_number > 2500) :\n image_name = \"IMG_{}.jpg\".format(test_number)\n cv2.imwrite(os.path.join(self.new_img_dir, \"test\", image_name), img)\n test_number = test_number + 1\n\n else :\n image_name = \"IMG_{}.jpg\".format(train_number)\n cv2.imwrite(os.path.join(self.new_img_dir, \"train\", image_name), img)\n train_number = train_number + 1\n \n object_num = int(txt_file.readline().rstrip(\"\\n\"))\n file_data = OrderedDict()\n object_data = OrderedDict()\n file_data[\"Img_id\"] = image_name.split('.')[0]\n file_data[\"Objects\"] = []\n\n #Do \"for\" operation as much as the number of objects\n for i in range(object_num):\n coord_ = txt_file.readline().split(\" \")\n rad = abs(float(coord_[2]))\n h = float(coord_[0])*np.sin(rad)\n w = float(coord_[1])*np.sin(rad)\n object_data[\"Object_class\"] = \"Face\"\n object_data[\"Coordinate\"] = { \"Xmin\" : float(coord_[3]) - w, \"Ymin\" : float(coord_[4]) - h, \"Xmax\" : float(coord_[3]) + w, \"Ymax\" : float(coord_[4]) + h }\n file_data[\"Objects\"].append(object_data)\n self.json_list.append(file_data)\n\n # Save train annotation\n if(train_number == 2501):\n self._save_annotation(\"train\")\n train_number = train_number + 1\n self._save_annotation(\"test\")","def read_input_files(self):\r\n\r\n for input_file in self.list_of_input_files:\r\n input_file.read_header_of_file()\r\n self.list_of_header_objects.extend(input_file.list_of_header_objects)\r\n self.list_of_header_objects_without_ID.extend(input_file.list_of_header_objects_without_ID)\r\n self.list_of_contigs.extend(input_file.list_of_contigs)\r\n\r\n self.list_of_header_objects = list(toolz.unique(self.list_of_header_objects, key=lambda x: x.tag_and_ID))\r\n self.list_of_header_objects_without_ID = list(\r\n toolz.unique(self.list_of_header_objects_without_ID, key=lambda x: x.line))\r\n self.list_of_contigs = list(toolz.unique(self.list_of_contigs, key=lambda x: x.line))\r\n self.list_of_header_objects.extend(self.list_of_header_objects_without_ID)\r\n self.list_of_header_objects.sort(key=lambda x: x.line)\r\n self.list_of_header_objects.extend(self.list_of_contigs)\r\n self.list_of_header_objects.sort(key=lambda x: x.tag, reverse=False)\r\n self.create_body_header_line_for_output()\r\n self.write_header_in_output_file()\r\n\r\n list_of_chrom = list(self.indices.keys())\r\n list_of_chrom.sort(key=lambda x: self.alphanum_key(x))\r\n for chrom in list_of_chrom:\r\n self.list_of_body_objects.clear()\r\n for input_file in self.list_of_input_files:\r\n input_file.read_specific_chrom_body_of_file(chrom)\r\n self.list_of_body_objects.extend(input_file.list_of_body_objects)\r\n\r\n self.adjust_body_records_to_samples()\r\n self.list_of_body_objects = list(toolz.unique(self.list_of_body_objects, key=lambda x: x.line))\r\n self.list_of_body_objects.sort(key=lambda x: self.alphanum_key(x.line))\r\n self.verify_and_merge_body_records()\r\n self.write_specific_chrom_in_output_file()","def decompose_images(data_dir, output_dir, save_individually, **kwargs):\n\n # parse parameters\n opt = TestOptions()\n opt.parse(kwargs)\n # print(kwargs)\n\n # torch setting\n pytorch_settings.set_(with_random=False, determine=True)\n\n # visualize\n V.create_a_visualizer(opt)\n\n # NIID-Net Manager\n model = create_model(opt)\n model.switch_to_eval()\n\n # List all image files in the directory (exclude subdirectory)\n image_file_list = list_files(data_dir, ['jpg', 'jpeg', 'png', 'tif', 'JPG'])\n print('Total image in the directory %s: %d' % (data_dir, len(image_file_list)))\n\n # Decompose images\n for file_name in image_file_list:\n # Read image\n img_path = os.path.join(data_dir, file_name)\n o_img = Image.open(img_path)\n o_img = o_img.convert(\"RGB\")\n\n # Resize input image\n # input_img = resize_image(o_img)\n input_img = o_img\n\n # Predict\n input_img = TF.to_tensor(input_img).unsqueeze(0)\n pred_N, pred_R, pred_L, pred_S, rendered_img = model.predict({'input_srgb': input_img}, normal=True, IID=True)\n\n # Save results\n idx = 0\n pred_imgs = {\n 'pred_N': pred_N[idx].cpu(),\n 'pred_R': pred_R[idx].cpu(),\n 'pred_L': pred_L[idx].cpu(),\n 'pred_S': pred_S[idx].cpu(),\n 'rendered_img': rendered_img[idx].cpu(),\n 'input_srgb': input_img[idx],\n }\n f = '%s_decomposed' % (file_name[:file_name.rfind('.')])\n image_util.save_intrinsic_images(output_dir, pred_imgs, label=f, individual=save_individually)\n torch.save(pred_imgs, os.path.join(output_dir, f+'.pth.tar'))\n print('Decompose %s successfully!' % file_name)","def resize_multiple_images(src_path, dst_path):\n for filename in os.listdir(src_path):\n img=Image.open(src_path+'/'+filename)\n new_img = img.resize((96,96,))\n #new_img.resize(96,96,1)\n if not os.path.exists(dst_path):\n os.makedirs(dst_path)\n new_img.save(dst_path+'/'+filename)\n print('Resized and saved {} successfully.'.format(filename))","def test_save_image(self):\n\n from m3_save_images.m3_save_images import save_images\n folder_destination_name = \"unittest-sorted-images\"\n path_source = \"../img\"\n image_name = [\"00ff00.png\", \"aqua.png\", \"black.jpg\", \"yellow.png\", \"red2.jpg\", \"green.jpg\"]\n image_color = [\"Lime\", \"Aqua\", \"Black\", \"Yellow\", \"Red\", \"Green\"]\n # new empty folder is needed for testing save_image() function\n if os.path.isdir(folder_destination_name):\n shutil.rmtree(folder_destination_name)\n os.mkdir(folder_destination_name)\n # creating folders\n for i in range(0, 4):\n save_images(folder_destination_name, path_source, image_name[i], image_color[i])\n self.assertEqual(''.join(os.listdir(os.path.join(folder_destination_name, image_color[i]))), image_name[i])\n save_images(folder_destination_name, path_source, image_name[i], image_color[5])\n self.assertNotEqual(''.join(os.listdir(os.path.join(folder_destination_name, image_color[i]))), image_name[5])"],"string":"[\n \"def create_and_write_output(predictions_path,output_path,inpDir):\\n \\n filenames= sorted(os.listdir(predictions_path)) \\n for filename in filenames:\\n \\n # read the 3 channel output image from the neural network\\n image=cv2.imread(os.path.join(predictions_path,filename))\\n \\n # create binary image output using the create_binary function\\n out_image=create_binary(image) \\n \\n # read and store the metadata from the input image\\n with BioReader(os.path.join(inpDir,filename)) as br:\\n metadata = br.metadata\\n\\n # Write the binary output consisting of the metadata using bfio.\\n output_image_5channel=np.zeros((out_image.shape[0],out_image.shape[1],1,1,1),dtype=np.uint8)\\n output_image_5channel[:,:,0,0,0]=out_image \\n\\n with BioWriter(os.path.join(output_path,filename), metadata=metadata) as bw:\\n bw.dtype = output_image_5channel.dtype\\n bw.write(output_image_5channel)\",\n \"def _preprocess(self):\\n print(\\\"Note: if root path is changed, the previously generated json files need to be re-generated (delete them first)\\\")\\n if osp.exists(self.imgs_labeled_dir) and \\\\\\n osp.exists(self.imgs_detected_dir) and \\\\\\n osp.exists(self.split_classic_det_json_path) and \\\\\\n osp.exists(self.split_classic_lab_json_path) and \\\\\\n osp.exists(self.split_new_det_json_path) and \\\\\\n osp.exists(self.split_new_lab_json_path):\\n return\\n\\n mkdir_if_missing(self.imgs_detected_dir)\\n mkdir_if_missing(self.imgs_labeled_dir)\\n\\n print(\\\"Extract image data from {} and save as png\\\".format(self.raw_mat_path))\\n mat = h5py.File(self.raw_mat_path, 'r')\\n\\n def _deref(ref):\\n return mat[ref][:].T\\n\\n def _process_images(img_refs, campid, pid, save_dir):\\n img_paths = [] # Note: some persons only have images for one view\\n for imgid, img_ref in enumerate(img_refs):\\n img = _deref(img_ref)\\n # skip empty cell\\n if img.size == 0 or img.ndim < 3: continue\\n # images are saved with the following format, index-1 (ensure uniqueness)\\n # campid: index of camera pair (1-5)\\n # pid: index of person in 'campid'-th camera pair\\n # viewid: index of view, {1, 2}\\n # imgid: index of image, (1-10)\\n viewid = 1 if imgid < 5 else 2\\n img_name = '{:01d}_{:03d}_{:01d}_{:02d}.png'.format(campid+1, pid+1, viewid, imgid+1)\\n img_path = osp.join(save_dir, img_name)\\n imageio.imwrite(img_path, img)\\n img_paths.append(img_path)\\n return img_paths\\n\\n def _extract_img(name):\\n print(\\\"Processing {} images (extract and save) ...\\\".format(name))\\n meta_data = []\\n imgs_dir = self.imgs_detected_dir if name == 'detected' else self.imgs_labeled_dir\\n for campid, camp_ref in enumerate(mat[name][0]):\\n camp = _deref(camp_ref)\\n num_pids = camp.shape[0]\\n for pid in range(num_pids):\\n img_paths = _process_images(camp[pid,:], campid, pid, imgs_dir)\\n assert len(img_paths) > 0, \\\"campid{}-pid{} has no images\\\".format(campid, pid)\\n meta_data.append((campid+1, pid+1, img_paths))\\n print(\\\"done camera pair {} with {} identities\\\".format(campid+1, num_pids))\\n return meta_data\\n\\n meta_detected = _extract_img('detected')\\n meta_labeled = _extract_img('labeled')\\n\\n def _extract_classic_split(meta_data, test_split):\\n train, test = [], []\\n num_train_pids, num_test_pids = 0, 0\\n num_train_imgs, num_test_imgs = 0, 0\\n for i, (campid, pid, img_paths) in enumerate(meta_data):\\n \\n if [campid, pid] in test_split:\\n for img_path in img_paths:\\n camid = int(osp.basename(img_path).split('_')[2])\\n test.append((img_path, num_test_pids, camid))\\n num_test_pids += 1\\n num_test_imgs += len(img_paths)\\n else:\\n for img_path in img_paths:\\n camid = int(osp.basename(img_path).split('_')[2])\\n train.append((img_path, num_train_pids, camid))\\n num_train_pids += 1\\n num_train_imgs += len(img_paths)\\n return train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs\\n\\n print(\\\"Creating classic splits (# = 20) ...\\\")\\n splits_classic_det, splits_classic_lab = [], []\\n for split_ref in mat['testsets'][0]:\\n test_split = _deref(split_ref).tolist()\\n\\n # create split for detected images\\n train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs = \\\\\\n _extract_classic_split(meta_detected, test_split)\\n splits_classic_det.append({\\n 'train': train, 'query': test, 'gallery': test,\\n 'num_train_pids': num_train_pids, 'num_train_imgs': num_train_imgs,\\n 'num_query_pids': num_test_pids, 'num_query_imgs': num_test_imgs,\\n 'num_gallery_pids': num_test_pids, 'num_gallery_imgs': num_test_imgs,\\n })\\n\\n # create split for labeled images\\n train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs = \\\\\\n _extract_classic_split(meta_labeled, test_split)\\n splits_classic_lab.append({\\n 'train': train, 'query': test, 'gallery': test,\\n 'num_train_pids': num_train_pids, 'num_train_imgs': num_train_imgs,\\n 'num_query_pids': num_test_pids, 'num_query_imgs': num_test_imgs,\\n 'num_gallery_pids': num_test_pids, 'num_gallery_imgs': num_test_imgs,\\n })\\n \\n write_json(splits_classic_det, self.split_classic_det_json_path)\\n write_json(splits_classic_lab, self.split_classic_lab_json_path)\\n\\n def _extract_set(filelist, pids, pid2label, idxs, img_dir, relabel):\\n tmp_set = []\\n unique_pids = set()\\n for idx in idxs:\\n img_name = filelist[idx][0]\\n camid = int(img_name.split('_')[2])\\n pid = pids[idx]\\n if relabel: pid = pid2label[pid]\\n img_path = osp.join(img_dir, img_name)\\n tmp_set.append((img_path, int(pid), camid))\\n unique_pids.add(pid)\\n return tmp_set, len(unique_pids), len(idxs)\\n\\n def _extract_new_split(split_dict, img_dir):\\n train_idxs = split_dict['train_idx'].flatten() - 1 # index-0\\n pids = split_dict['labels'].flatten()\\n train_pids = set(pids[train_idxs])\\n pid2label = {pid: label for label, pid in enumerate(train_pids)}\\n query_idxs = split_dict['query_idx'].flatten() - 1\\n gallery_idxs = split_dict['gallery_idx'].flatten() - 1\\n filelist = split_dict['filelist'].flatten()\\n train_info = _extract_set(filelist, pids, pid2label, train_idxs, img_dir, relabel=True)\\n query_info = _extract_set(filelist, pids, pid2label, query_idxs, img_dir, relabel=False)\\n gallery_info = _extract_set(filelist, pids, pid2label, gallery_idxs, img_dir, relabel=False)\\n return train_info, query_info, gallery_info\\n\\n print(\\\"Creating new splits for detected images (767/700) ...\\\")\\n train_info, query_info, gallery_info = _extract_new_split(\\n loadmat(self.split_new_det_mat_path),\\n self.imgs_detected_dir,\\n )\\n splits = [{\\n 'train': train_info[0], 'query': query_info[0], 'gallery': gallery_info[0],\\n 'num_train_pids': train_info[1], 'num_train_imgs': train_info[2],\\n 'num_query_pids': query_info[1], 'num_query_imgs': query_info[2],\\n 'num_gallery_pids': gallery_info[1], 'num_gallery_imgs': gallery_info[2],\\n }]\\n write_json(splits, self.split_new_det_json_path)\\n\\n print(\\\"Creating new splits for labeled images (767/700) ...\\\")\\n train_info, query_info, gallery_info = _extract_new_split(\\n loadmat(self.split_new_lab_mat_path),\\n self.imgs_labeled_dir,\\n )\\n splits = [{\\n 'train': train_info[0], 'query': query_info[0], 'gallery': gallery_info[0],\\n 'num_train_pids': train_info[1], 'num_train_imgs': train_info[2],\\n 'num_query_pids': query_info[1], 'num_query_imgs': query_info[2],\\n 'num_gallery_pids': gallery_info[1], 'num_gallery_imgs': gallery_info[2],\\n }]\\n write_json(splits, self.split_new_lab_json_path)\",\n \"def gene_h5_train_file(data_path, h5_name):\\n img = []\\n y_cls_mask = []\\n y_reg_cls_mask = []\\n os.chdir(data_path)\\n jpgfiles = glob.glob('*.jpg')\\n idx = 1\\n # the position of generator objector is very important\\n gene_obj = image_output_pair(data_path, 1/255.0)\\n while True:\\n if idx == len(jpgfiles):\\n break\\n print '\\\\t{0}/{1}'.format(idx, len(jpgfiles))\\n # the position of generator objector is very important\\n # gene_obj = image_output_pair(data_path, 1/255.0)\\n img_it, y_cls_mask_it, y_reg_cls_mask_it = gene_obj.next()\\n img.append(img_it)\\n y_cls_mask.append(y_cls_mask_it)\\n y_reg_cls_mask.append(y_reg_cls_mask_it)\\n idx += 1\\n\\n # img => (320, 320, 3)\\n # after np.stack => (19041, 320, 320, 3)\\n img_input = np.stack(img, axis=0)\\n y_cls = np.stack(y_cls_mask, axis=0)\\n y_reg = np.stack(y_reg_cls_mask, axis=0)\\n print 'input data shape is {0}'.format(img_input.shape)\\n print 'y_cls data shape is {0}'.format(y_cls.shape)\\n print 'y_reg data shape is {0}'.format(y_reg.shape)\\n \\n # wirte data\\n h5 = '/home/yuquanjie/Documents/train_' + h5_name\\n file_write = h5py.File(h5, 'w')\\n file_write.create_dataset('X_train', data=img_input)\\n file_write.create_dataset('Y_train_cls', data=y_cls)\\n file_write.create_dataset('Y_train_merge', data=y_reg)\\n file_write.close()\",\n \"def preprocess_images(file_path, new_file_path):\\n if not os.path.isdir(new_file_path):\\n os.mkdir(new_file_path)\\n i = 0\\n for dir in listdir(file_path):\\n j = 0\\n for image_path in listdir(file_path + '/' + dir):\\n image = open_image(image_path)\\n cv2.imwrite(file_path + '/' + image_path + '/' str(i) + '/' +str(i) + '.jpg', image)\\n j += 1\\n i += 1\",\n \"def bulk_process_images(inputpath, outputpath, extension):\\n\\n for dirpath, dirnames, filenames in os.walk(inputpath):\\n structure = os.path.join(outputpath, dirpath[len(inputpath) + 1:])\\n for file in filenames:\\n if file.endswith(extension):\\n src = os.path.join(dirpath, file)\\n dest = os.path.join(structure, file)\\n img = load_and_preprocess_image(src)\\n cv2.imwrite(dest, img)\",\n \"def create_train(train_img_path):\\n\\n f = open(\\\"train.txt\\\", \\\"w+\\\")\\n for subdirs, dirs, files in os.walk(train_img_path):\\n for filename in files:\\n if filename.endswith(\\\".jpg\\\"):\\n train_image_path = os.path.join(train_img_path, filename)\\n print(train_image_path)\\n f.write(train_image_path + \\\"\\\\n\\\")\\n f.close()\",\n \"def save_step_1(imgs, output_path='./output/step1'):\\n # ... your code here ...\\n i=0\\n for each in imgs:\\n i+=1\\n cv2.imwrite(output_path+\\\"/output\\\"+str(i)+\\\".jpg\\\", each)\",\n \"def main():\\n \\n # for inserting other images, add tem to /input folder and list them here\\n images = (\\n 'image-0',\\n 'image-1',\\n 'image-2'\\n )\\n\\n for image_name in images:\\n print(image_name, \\\"image:\\\")\\n\\n image = open_image(image_name)\\n display_image(image, \\\"Original input \\\" + image_name)\\n\\n grayscale_v = transform_colors(image)\\n display_image(grayscale_v[:,:,0], \\\"Grayscale \\\" + image_name)\\n save_image(image_name + \\\"-grayscale\\\", grayscale_v[:,:,0])\\n\\n contours_v, contours = get_contours(grayscale_v)\\n display_image(contours_v, \\\"Contours \\\" + image_name)\\n save_image(image_name + \\\"-contours\\\", contours_v)\\n\\n labeled_img, areas = get_measures(image, contours[1:])\\n display_image(labeled_img, \\\"Labeled \\\" + image_name)\\n save_image(image_name + \\\"-labeled\\\", labeled_img)\\n\\n areas_histogram(areas, image_name)\",\n \"def save_image_to_h5(image, h5_path, *args, **kwargs):\\n # TODO: Implement the method\\n\\n f = h5.File(h5_path, \\\"w\\\")\\n grp = f.create_group(\\\"data\\\")\\n grp.create_dataset(\\\"image\\\", data=image)\",\n \"def prepare_hdf5_file(hdf5_file, n_train, n_valid, n_test):\\n n_total = n_train + n_valid + n_test\\n splits = create_splits(n_train, n_valid, n_test)\\n hdf5_file.attrs['split'] = H5PYDataset.create_split_array(splits)\\n vlen_dtype = h5py.special_dtype(vlen=numpy.dtype('uint8'))\\n hdf5_file.create_dataset('encoded_images', shape=(n_total,),\\n dtype=vlen_dtype)\\n hdf5_file.create_dataset('targets', shape=(n_total, 1), dtype=numpy.int16)\\n hdf5_file.create_dataset('filenames', shape=(n_total, 1), dtype='S32')\",\n \"def appendpics(pathofimg, w_sub, h_sub, step):\\n num = 0\\n dirlist = []\\n images = [] # images in each folder\\n for root, dirs, fileswer in os.walk(pathofimg):\\n if len(dirs)!= 0:\\n for dir in dirs:\\n dirlist.append(dir)\\n for rooert, dirwerwes, files in os.walk(pathofimg+'/'+dir):\\n for file in files:\\n if(file.endswith('.png')):\\n images.append(Image.open(pathofimg+'/'+dir+'/'+file))\\n if(len(images)==81):\\n break\\n target = montage(images, w_sub, h_sub, step)\\n target.save(pathofimg +'/'+ dir + '.png', quality=100)\\n else:\\n dir = 'Generated'\\n for file in fileswer:\\n if (file.endswith('.png')):\\n images.append(Image.open(pathofimg +'/'+ file))\\n target1 = montage(images, w_sub, h_sub, step)\\n savepath = pathofimg +'/'+ 'generated'\\n os.makedirs(savepath)\\n target1.save(savepath +'/'+ dir + '.png', quality=100)\",\n \"def get_files(self):\\n train_images = glob(os.path.join(self.images_dir, '*%s' % self.im_extension)) \\n train_labels = [x.replace(self.im_extension, '.npy').replace('images', 'groundTruth') for x in train_images]\\n val_images = glob(os.path.join(self.val_images_dir, '*%s' % self.im_extension))\\n val_labels = [x.replace(self.im_extension, '.npy').replace('images', 'groundTruth') for x in val_images]\\n train_images = np.array(train_images)\\n train_labels = np.array(train_labels)\\n val_images = np.array(val_images)\\n val_labels = np.array(val_labels)\\n test_images = np.array(\\n glob('/media/data_cifs/pytorch_projects/datasets/BSDS500_crops/data/images/test_nocrop/*.jpg'))\\n test_labels = np.array(\\n [x.replace('images', 'groundTruth').replace('.jpg', '.npy') for x in test_images])\\n test_labels = np.array(\\n [np.load(x) for x in test_labels])\\n keep_idx = np.array([True if x.shape[0] > x.shape[1] else False for x in test_labels])\\n test_images = test_images[keep_idx]\\n test_labels = test_labels[keep_idx]\\n test_images = np.stack([misc.imread(x) for x in test_images], 0)\\n test_labels = np.stack(test_labels, 0)\\n test_labels = test_labels[..., None]\\n\\n # Add constant padding to bottom/right\\n if self.pad:\\n test_images = util.pad(test_images, ((0, 0), (self.pad // 2, self.pad - self.pad // 2), (self.pad // 2, self.pad - self.pad // 2), (0, 0)), mode='linear_ramp')\\n test_labels = util.pad(test_labels, ((0, 0), (self.pad // 2, self.pad - self.pad // 2), (self.pad // 2, self.pad - self.pad // 2), (0, 0)), mode='constant', constant_values=0)\\n\\n # Select images for training\\n sort_idx = np.argsort(train_images)\\n train_images = train_images[sort_idx[:self.train_size]]\\n train_labels = train_labels[sort_idx[:self.train_size]]\\n\\n # Build CV dict\\n cv_files, cv_labels = {}, {}\\n cv_files[self.folds['train']] = train_images\\n cv_files[self.folds['val']] = val_images\\n cv_files[self.folds['test']] = test_images\\n cv_labels[self.folds['train']] = train_labels\\n cv_labels[self.folds['val']] = val_labels\\n cv_labels[self.folds['test']] = test_labels\\n return cv_files, cv_labels\",\n \"def write_data_files(self):\\n \\n logging.info('\\\\n Start writing data files \\\\n')\\n \\n for i, (data_file, label_file) in enumerate(self.files):\\n data_file, label_file = Path(data_file), Path(label_file)\\n logging.info('Writing .hdf5 file for : [{}]'.format(str(data_file)))\\n \\n file_name = self.save_data_folder / '{}.hdf5'.format(label_file.name[:-4])\\n if file_name.exists():\\n continue\\n \\n with h5py.File(str(file_name), 'w') as writer:\\n self.serialize_samples(\\n writer, data_file, label_file)\",\n \"def _extract_images(source_path, target_path, merge_labels):\\n\\n images_path = os.path.join(source_path, 'imagesTr')\\n labels_path = os.path.join(source_path, 'labelsTr')\\n\\n # Filenames have the form 'hippocampus_XX.nii.gz'\\n filenames = [x for x in os.listdir(images_path) if x[:5] == 'hippo']\\n\\n # Create directories\\n if not os.path.isdir(target_path):\\n os.makedirs(target_path)\\n\\n for filename in filenames:\\n\\n # Extract only T2-weighted\\n x = sitk.ReadImage(os.path.join(images_path, filename))\\n x = sitk.GetArrayFromImage(x)\\n y = sitk.ReadImage(os.path.join(labels_path, filename))\\n y = sitk.GetArrayFromImage(y)\\n\\n # Shape expected: (35, 51, 35)\\n # Average label shape: (24.5, 37.8, 21.0)\\n assert x.shape == y.shape\\n\\n # No longer distinguish between hippocampus proper and subiculum\\n if merge_labels:\\n y[y == 2] = 1\\n\\n # Save new images so they can be loaded directly\\n study_name = filename.replace('_', '').split('.nii')[0]\\n sitk.WriteImage(sitk.GetImageFromArray(x), join_path([target_path, study_name + \\\".nii.gz\\\"]))\\n sitk.WriteImage(sitk.GetImageFromArray(y), join_path([target_path, study_name + \\\"_gt.nii.gz\\\"]))\",\n \"def save_step_2(imgs, match_list, output_path=\\\"./output/step2\\\"):\\n # ... your code here ...\\n for i in range(len(imgs)):\\n name1,tail1 = str.split(filenames[match_list[i][0]],\\\".\\\")\\n name2,tail2 = str.split(filenames[match_list[i][2]],\\\".\\\")\\n cv2.imwrite(output_path+\\\"/\\\"+name1+\\\"_\\\"+str(match_list[i][1])+\\\"_\\\"+name2+\\\"_\\\"+str(match_list[i][3])+\\\"_\\\"+str(match_list[i][4])+\\\".jpg\\\", imgs[i])\",\n \"def analyzeImages(path, name_type, box1_size, box2_size, box3_size, box4_size, box5_size):\\n \\n folders = [f for f in sorted(glob.glob(path + \\\"/**\\\"))]\\n \\n for folder in folders: \\n \\n # to save this data frame in a csv file\\n \\n files = [f for f in sorted(glob.glob(folder + \\\"/**\\\" + \\\".jpg\\\"))]\\n \\n centroidsDf = pd.DataFrame(np.zeros([len(files), 11]),columns=[\\\"frame\\\", \\\"fly1_x\\\", \\\"fly1_y\\\", \\\"fly2_x\\\", \\\"fly2_y\\\", \\\"fly3_x\\\", \\\"fly3_y\\\", \\\"fly4_x\\\", \\\"fly4_y\\\", \\\"fly5_x\\\", \\\"fly5_y\\\"]);\\n headsDf = pd.DataFrame(np.zeros([len(files), 11]),columns=[\\\"frame\\\", \\\"fly1_x\\\", \\\"fly1_y\\\", \\\"fly2_x\\\", \\\"fly2_y\\\", \\\"fly3_x\\\", \\\"fly3_y\\\", \\\"fly4_x\\\", \\\"fly4_y\\\", \\\"fly5_x\\\", \\\"fly5_y\\\"]);\\n \\n img_array1 = []\\n img_array2 = []\\n img_array3 = []\\n img_array4 = []\\n img_array5 = []\\n\\n for file in files:\\n \\n print(file)\\n \\n centroidsDf[\\\"frame\\\"][files.index(file)] = files.index(file)+1\\n headsDf[\\\"frame\\\"][files.index(file)] = files.index(file)+1\\n \\n img = cv2.imread(file)\\n \\n ## FLY 1 ##\\n\\n box1 = img[box1_size[0]:box1_size[1], box1_size[2]:box1_size[3]]\\n \\n # image processing to get the centroid and head positions\\n x_centroid, y_centroid, x_head, y_head = findCentroid(box1, file) \\n \\n # add the centroid and head locations on the image \\n box1 = cv2.circle(box1, (x_centroid, y_centroid), 7, (255, 0, 0), -1)\\n box1 = cv2.circle(box1, (x_head, y_head), 7, (0, 0, 255), -1)\\n img_array1.append(box1)\\n \\n # add the positions in the final data frame\\n centroidsDf[\\\"fly1_x\\\"][files.index(file)] = x_centroid\\n centroidsDf[\\\"fly1_y\\\"][files.index(file)] = y_centroid\\n \\n headsDf[\\\"fly1_x\\\"][files.index(file)] = x_head\\n headsDf[\\\"fly1_y\\\"][files.index(file)] = y_head\\n \\n ## FLY 2 ##\\n \\n box2 = img[box2_size[0]:box2_size[1], box2_size[2]:box2_size[3]]\\n \\n # image processing to get the centroid and head positions\\n x_centroid, y_centroid, x_head, y_head = findCentroid(box2, file)\\n \\n # add the centroid and head locations on the image \\n box2 = cv2.circle(box2, (x_centroid, y_centroid), 7, (255, 0, 0), -1)\\n box2 = cv2.circle(box2, (x_head, y_head), 7, (0, 0, 255), -1)\\n img_array2.append(box2)\\n \\n # add the positions in the final data frame \\n centroidsDf[\\\"fly2_x\\\"][files.index(file)] = x_centroid\\n centroidsDf[\\\"fly2_y\\\"][files.index(file)] = y_centroid\\n \\n headsDf[\\\"fly2_x\\\"][files.index(file)] = x_head\\n headsDf[\\\"fly2_y\\\"][files.index(file)] = y_head\\n \\n ## FLY 3 ##\\n\\n box3 = img[box3_size[0]:box3_size[1], box3_size[2]:box3_size[3]]\\n \\n # image processing to get the centroid and head positions\\n x_centroid, y_centroid, x_head, y_head = findCentroid(box3, file)\\n \\n # add the centroid and head locations on the image \\n box3 = cv2.circle(box3, (x_centroid, y_centroid), 7, (255, 0, 0), -1)\\n box3 = cv2.circle(box3, (x_head, y_head), 7, (0, 0, 255), -1)\\n img_array3.append(box3)\\n\\n # add the positions in the final data frame\\n centroidsDf[\\\"fly3_x\\\"][files.index(file)] = x_centroid\\n centroidsDf[\\\"fly3_y\\\"][files.index(file)] = y_centroid\\n \\n headsDf[\\\"fly3_x\\\"][files.index(file)] = x_head\\n headsDf[\\\"fly3_y\\\"][files.index(file)] = y_head\\n \\n ## FLY 4 ##\\n \\n box4 = img[box4_size[0]:box4_size[1], box4_size[2]:box4_size[3]]\\n \\n # image processing to get the centroid and head positions\\n x_centroid, y_centroid, x_head, y_head = findCentroid(box4, file)\\n \\n # add the centroid and head locations on the image \\n box4 = cv2.circle(box4, (x_centroid, y_centroid), 7, (255, 0, 0), -1)\\n box4 = cv2.circle(box4, (x_head, y_head), 7, (0, 0, 255), -1)\\n img_array4.append(box4)\\n \\n # add the positions in the final data frame\\n centroidsDf[\\\"fly4_x\\\"][files.index(file)] = x_centroid\\n centroidsDf[\\\"fly4_y\\\"][files.index(file)] = y_centroid\\n \\n headsDf[\\\"fly4_x\\\"][files.index(file)] = x_head\\n headsDf[\\\"fly4_y\\\"][files.index(file)] = y_head\\n \\n ## FLY 5 ##\\n \\n # the fifth fly is not present in all the genetic strains \\n if (box5_size != []):\\n box5 = img[box5_size[0]:box5_size[1], box5_size[2]:box5_size[3]]\\n \\n # image processing to get the centroid and head positions\\n x_centroid, y_centroid, x_head, y_head = findCentroid(box5, file)\\n \\n # add the centroid and head locations on the image \\n box5 = cv2.circle(box5, (x_centroid, y_centroid), 7, (255, 0, 0), -1)\\n box5 = cv2.circle(box5, (x_head, y_head), 7, (0, 0, 255), -1)\\n img_array5.append(box5)\\n \\n # add the positions in the final data frame\\n centroidsDf[\\\"fly5_x\\\"][files.index(file)] = x_centroid\\n centroidsDf[\\\"fly5_y\\\"][files.index(file)] = y_centroid\\n \\n headsDf[\\\"fly5_x\\\"][files.index(file)] = x_head\\n headsDf[\\\"fly5_y\\\"][files.index(file)] = y_head\\n \\n # save the data frame in a .csv file, \\n # one for the centroids and one for the heads\\n #centroidsDf.to_csv(folder+\\\"/centroids.csv\\\", index = None, header=True)\\n #headsDf.to_csv(folder+\\\"/heads.csv\\\", index = None, header=True)\\n \\n \\n ## CREATE THE VIDEOS ##\\n \\n height, width, _ = box1.shape\\n size = (width,height)\\n out = cv2.VideoWriter(folder+name_type+'_1_' + str(folders.index(folder)+1)+ '.mp4',cv2.VideoWriter_fourcc(*'DIVX'), 1.5, size)\\n for i in range(len(img_array1)):\\n out.write(img_array1[i])\\n out.release()\\n \\n height, width, _ = box2.shape\\n size = (width,height)\\n out = cv2.VideoWriter(folder+name_type+'_2_' + str(folders.index(folder)+1)+ '.mp4' ,cv2.VideoWriter_fourcc(*'DIVX'), 1.5, size)\\n for i in range(len(img_array2)):\\n out.write(img_array2[i])\\n out.release()\\n \\n height, width, _ = box3.shape\\n size = (width,height)\\n out = cv2.VideoWriter(folder+name_type+'_3_' + str(folders.index(folder)+1)+ '.mp4' ,cv2.VideoWriter_fourcc(*'DIVX'), 1.5, size)\\n for i in range(len(img_array3)):\\n out.write(img_array3[i])\\n out.release()\\n \\n height, width, _ = box4.shape\\n size = (width,height)\\n out = cv2.VideoWriter(folder+name_type+'_4_' + str(folders.index(folder)+1)+ '.mp4' ,cv2.VideoWriter_fourcc(*'DIVX'), 1.5, size)\\n for i in range(len(img_array4)):\\n out.write(img_array4[i])\\n out.release()\\n \\n if (box5_size != []):\\n height, width, _ = box5.shape\\n size = (width,height)\\n out = cv2.VideoWriter(folder+name_type+'_5_' + str(folders.index(folder)+1)+ '.mp4' ,cv2.VideoWriter_fourcc(*'DIVX'), 1.5, size)\\n for i in range(len(img_array5)):\\n out.write(img_array5[i])\\n out.release()\",\n \"def main(vis_dirs, outdir):\\n assert len(vis_dirs) == 4\\n\\n if not os.path.exists(outdir):\\n os.mkdir(outdir)\\n\\n for i, filename in enumerate(tqdm(os.listdir(vis_dirs[-1]))):\\n # if i % 100 == 0:\\n # print(i)\\n\\n files = [os.path.join(vis_dir, filename) for vis_dir in vis_dirs]\\n outimg = os.path.join(outdir, filename)\\n merge_four_images(files, outimg)\\n\\n print (\\\"Finished! Result dir is %s\\\" % outdir)\",\n \"def process_files(self):\\n for filename in self.temp_directory.iterdir():\\n im = Image.open(str(filename))\\n scaled = im.resize((640, 480))\\n scaled.save(str(filename))\",\n \"def pickle_images(path, df, image_size=\\\"\\\"):\\n all_imgs = []\\n labels = []\\n folder = path\\n for f in os.listdir(folder): \\n if f.endswith(\\\".png\\\"): \\n img = read_transposed_image(folder+f)\\n i = f.split(\\\"_\\\")[0]\\n name = \\\".\\\".join(\\\"_\\\".join(f.split(\\\"_\\\")[1:]).split(\\\".\\\")[:-1])\\n label = df[(df.id == i) & (df.filename == name)].label\\n if len(label) > 0:\\n label = label.iloc[0]\\n if image_size != \\\"\\\":\\n new_image = resize_and_pad(img, image_size)\\n new_dir = folder+str(image_size)\\n if not os.path.exists(new_dir):\\n os.makedirs(new_dir)\\n misc.toimage(new_image).save(new_dir+\\\"/\\\"+f)\\n all_imgs.append(new_image)\\n else:\\n all_imgs.append(img) \\n labels.append(label)\\n d = {\\\"data\\\":all_imgs, \\\"labels\\\":labels}\\n with open('data_'+str(image_size)+'.pkl', 'wb') as handle:\\n cPickle.dump(d, handle, protocol=cPickle.HIGHEST_PROTOCOL)\",\n \"def mosaic(self):\\n \\n if len(self.tiles) > 1:\\n hdflist = sorted(glob.glob(self.fullPath + '/*.hdf'))\\n for i in range(0,len(hdflist),2):\\n ms = pymodis.convertmodis_gdal.createMosaicGDAL(hdfnames = [hdflist[i], hdflist[i+1]], subset = self.subset, outformat = 'GTiff')\\n ms.run(str(hdflist[i].split('.h')[0]) + 'mos.tif')\\n ms.write_vrt(output = str(hdflist[i].split('.h')[0]), separate = True)\\n mosaicCount = len(glob.glob(self.fullPath + '/*mos.tif'))\\n logger.log('SUCCESS', 'Mosaic complete! MODIS tiles %s were successfully mosaicked into %d mosaic images.' % (str(self.tiles), mosaicCount))\",\n \"def save(images, output):\\n for image, frame in images:\\n image.save(output(frame))\",\n \"def process_all_images(input_path: str, output_path: str, resized_image_shape: Tuple,transformations:List[TransformationsEnum]):\\n\\n output_images_path = os.path.join(output_path, \\\"images\\\")\\n csv_file_path = os.path.join(output_path, \\\"metadata.csv\\\")\\n\\n prepare_folders(output_path, output_images_path)\\n prepare_csv(csv_file_path)\\n\\n df = pd.read_csv(csv_file_path)\\n current_id = 1 #has to check the current id in the folder or be set to 1 if none\\n categories_names = list(os.listdir(input_path))\\n\\n encoder = LabelBinarizer()\\n encoder.fit(categories_names)\\n\\n\\n for folder_name in os.listdir(input_path):\\n current_category_name = folder_name\\n category_path = os.path.join(input_path, folder_name)\\n images_in_category = list(Path(category_path).glob(\\\"*.jpg\\\"))\\n df, current_id = process_image(\\n df, current_id, encoder, current_category_name, images_in_category,output_images_path, resized_image_shape,transformations\\n )\\n\\n df.to_csv(csv_file_path, index=False, quotechar='\\\"', encoding='ascii')\\n\\n print(\\\"done, processed\\\", len(df), \\\"images\\\")\",\n \"def jarvis(input_path, output_path): \\n \\n if not os.path.exists(f'{output_path}'):\\n os.makedirs(f'{output_path}')\\n\\n folder_list = [sample for sample in os.listdir(input_path) if os.path.isdir(f'{input_path}{sample}')]\\n\\n for folder in folder_list:\\n\\n file_list = [filename for filename in os.listdir(f'{input_path}{folder}/') if '.tif' in filename]\\n mutant = '_'.join(folder.split(' '))\\n\\n for x, filename in enumerate(file_list):\\n pathname = os.path.join(input_path, folder, filename)\\n new_name = f'{output_path}{mutant}_{x}.tif'\\n copyfile(pathname, new_name)\\n # array_stack = skimage.io.imread(f'{pathname}').transpose(1, 2, 0)\\n logger.info(f'{new_name}')\",\n \"def combineImages(path=None, imgfiles=None, cols=3, size=300):\\n\\n font = ImageFont.truetype(\\\"Arial.ttf\\\", 15)\\n x=size\\n w=20\\n i=0; j=0\\n if imgfiles == None:\\n imgfiles = findFiles(path, 'png')\\n width = cols*(x+w)\\n height = int(math.ceil(float(len(imgfiles))/cols)*x)\\n new_im = Image.new('RGBA', (width, height), 'white')\\n for f in imgfiles:\\n name = os.path.basename(f).split('.')[0]\\n if not os.path.exists(f):\\n continue\\n im = Image.open(f)\\n im.thumbnail((x,x))\\n new_im.paste(im, (i*x+w,j*x+w))\\n draw = ImageDraw.Draw(new_im)\\n draw.text((i*x+w,j*x+w), name, (0,0,0), font=font)\\n i+=1\\n if i>=cols:\\n i=0; j+=1\\n #new_im.show()\\n path = os.path.split(imgfiles[0])[0]\\n new_im.save(os.path.join(path,\\\"summary.png\\\"))\\n return\",\n \"def save_step_4(imgs, output_path=\\\"./output/step4\\\"):\\n # ... your code here ...\\n cv2.imwrite(output_path+\\\"/output.jpg\\\", imgs)\",\n \"def write_data(data_to_write_train, data_to_write_test, train_path, eval_path ,test_path):\\n aligned_lists_train = data_to_write_train[0]\\n raw_lists_train = data_to_write_train[2]\\n\\n aligned_lists_eval = data_to_write_test[0]\\n raw_lists_eval = data_to_write_test[2]\\n\\n aligned_lists_test = data_to_write_test[1]\\n raw_lists_test = data_to_write_test[3]\\n\\n filelist = list([train_path, eval_path, test_path])\\n\\n for file in filelist:\\n aligned_path = os.path.join(file, 'aligned_image')\\n raw_path = os.path.join(file, 'raw_image')\\n os.mkdir(aligned_path)\\n os.mkdir(raw_path)\\n\\n # raw image data\\n for Idx, train_raw in enumerate(raw_lists_train):\\n img = Image.open(train_raw)\\n img.save(train_path+'/raw_image/img_'+f'{Idx:04d}.jpg')\\n if Idx%100 == 0:\\n print('\\\\t%d images are saved'% Idx); \\n print('\\\\tTrain raw images saved! ')\\n\\n for Idx, eval_raw in enumerate(raw_lists_eval):\\n img = Image.open(eval_raw)\\n img.save(eval_path+'/raw_image/img_'+f'{Idx:04d}.jpg')\\n if Idx%100 == 0:\\n print('\\\\t%d images are saved'% Idx); \\n print('\\\\tEval raw images saved! ')\\n\\n for Idx, test_raw in enumerate(raw_lists_test):\\n img = Image.open(test_raw)\\n img.save(test_path+'/raw_image/img_'+f'{Idx:04d}.jpg')\\n if Idx%100 == 0:\\n print('\\\\t%d images are saved'% Idx); \\n print('\\\\tTest raw images saved! ')\\n\\n # aligned image data\\n for Idx, train_aligned in enumerate(aligned_lists_train):\\n img = Image.open(train_aligned)\\n img.save(train_path+'/aligned_image/img_'+f'{Idx:04d}.jpg')\\n if Idx%100 == 0:\\n print('\\\\t%d images are saved'% Idx); \\n print('\\\\tTrain aligned images saved! ')\\n\\n for Idx, eval_aligned in enumerate(aligned_lists_eval):\\n img = Image.open(eval_aligned)\\n img.save(eval_path+'/aligned_image/img_'+f'{Idx:04d}.jpg')\\n if Idx%100 == 0:\\n print('\\\\t%d images are saved'% Idx); \\n print('\\\\tEval aligned images saved! ')\\n\\n for Idx, test_aligned in enumerate(aligned_lists_test):\\n img = Image.open(test_aligned)\\n img.save(test_path+'/aligned_image/img_'+f'{Idx:04d}.jpg')\\n if Idx%100 == 0:\\n print('\\\\t%d images are saved'% Idx); \\n print('\\\\tTest aligned images saved! ')\",\n \"def form_sample_folder(self, input_folder, target_folder, sample_name):\\n print(f'processing {sample_name} folder.')\\n # first make a subfolder to contain the images - e.g. 'target_folder/sample_name'\\n sample_dir = join(target_folder, sample_name)\\n if not os.path.exists(sample_dir):\\n mkdir(sample_dir)\\n # resize and move the mask images - e.g. 'target_folder/sample_name/imgs_necrosis.png'\\n img_file_nec = join(input_folder, 'Necrosis',\\n 'Tissue Slides.'+sample_name+'.png')\\n img_res = self.process_img(img_file_nec, self.rescale_ratio)\\n img_nec = img_res.copy()\\n cv2.imwrite(join(sample_dir, 'necrosis.png'), img_res)\\n\\n img_file_perf = join(input_folder, 'Perfusion',\\n 'Tissue Slides.'+sample_name+'.png')\\n img_res = self.process_img(img_file_perf, self.rescale_ratio)\\n cv2.imwrite(join(sample_dir, 'perfusion.png'), img_res)\\n\\n # resize and move the maker HE and EF5 images\\n files = listdir(input_folder)\\n img_files = [x for x in files if x.split(\\n '.')[-1] in ('tif', 'jpg', 'png')]\\n for img_file in img_files:\\n if (sample_name+'_' in img_file) or (sample_name+'-' in img_file):\\n if ('HE-G' in img_file) or ('HE-green' in img_file) or ('HEgreen' in img_file):\\n img_res = self.process_img(\\n join(input_folder, img_file), self.rescale_ratio)\\n if not os.path.exists(join(sample_dir, 'HE-green.png')):\\n cv2.imwrite(join(sample_dir, 'HE-green.png'), img_res)\\n else:\\n warnings.warn(\\n f\\\"file already exists, while processing {img_file}\\\")\\n elif ('HE-R' in img_file) or ('HE-red' in img_file) or ('HEred' in img_file):\\n img_res = self.process_img(\\n join(input_folder, img_file), self.rescale_ratio)\\n if not os.path.exists(join(sample_dir, 'HE-red.png')):\\n cv2.imwrite(join(sample_dir, 'HE-red.png'), img_res)\\n else:\\n warnings.warn(\\n f\\\"file already exists, while processing {img_file}\\\")\\n elif ('HE-B' in img_file) or ('HE-blue' in img_file) or ('HE-blue' in img_file):\\n img_res = self.process_img(\\n join(input_folder, img_file), self.rescale_ratio)\\n if not os.path.exists(join(sample_dir, 'HE-blue.png')):\\n cv2.imwrite(join(sample_dir, 'HE-blue.png'), img_res)\\n else:\\n warnings.warn(\\n f\\\"file already exists, while processing {img_file}\\\")\\n elif 'EF5' in img_file:\\n img_res = self.process_img(\\n join(input_folder, img_file), self.rescale_ratio)\\n img_ef5 = img_res.copy()\\n if not os.path.exists(join(sample_dir, 'EF5.png')):\\n cv2.imwrite(join(sample_dir, 'EF5.png'), img_res)\\n else:\\n warnings.warn(\\n f\\\"file already exists, while processing {img_file}\\\")\\n\\n masked_ef5 = (img_ef5 * (img_nec <= 0)).astype(img_ef5.dtype)\\n cv2.imwrite(join(sample_dir, 'EF5_masked.png'), masked_ef5)\\n assert len(listdir(sample_dir)) == 7\\n return\",\n \"def createAllImageFiles(poly, name) :\\n \\n for i in range(len(poly.getPaths())):\\n fileName = name + \\\"_\\\" + str(i) + \\\".dot\\\"\\n imgName = name + \\\"_\\\" + str(i) + \\\".jpg\\\"\\n \\n Command = \\\"neato -Tjpeg \\\" + fileName + \\\" -o \\\" + imgName\\n run(Command, shell=True)\",\n \"def splitTransform(self):\\n\\t\\t#path_merge = \\\"transform\\\"\\n\\t\\t#path_train = \\\"transform/data/\\\"\\n\\t\\t#path_label = \\\"transform/label/\\\"\\n\\t\\tpath_merge = \\\"train/merge\\\"\\n\\t\\tpath_train = \\\"train/image\\\"\\n\\t\\tpath_label = \\\"train/label\\\"\\n\\t\\ttrain_imgs = glob.glob(path_merge+\\\"/*.\\\"+self.img_type)\\n\\t\\tfor imgname in train_imgs:\\n\\t\\t\\tmidname = imgname[imgname.rindex(\\\"/\\\")+1:imgname.rindex(\\\".\\\"+self.img_type)]\\n\\t\\t\\timg = cv2.imread(imgname)\\n\\t\\t\\timg_train = img[:,:,2]#cv2 read image rgb->bgr\\n\\t\\t\\timg_label = img[:,:,0]\\n\\t\\t\\tcv2.imwrite(path_train+midname+\\\".\\\"+self.img_type,img_train)\\n\\t\\t\\tcv2.imwrite(path_label+midname+\\\".\\\"+self.img_type,img_label)\",\n \"def save_images(images, filenames, output_dir):\\n for i, filename in enumerate(filenames):\\n dirname = os.path.dirname(filename)\\n\\n if dirname!='':\\n dirpath = os.path.join(output_dir, dirname)\\n if not os.path.exists(dirpath):\\n os.makedirs(dirpath)\\n\\n # Images for inception classifier are normalized to be in [-1, 1] interval,\\n # so rescale them back to [0, 1].\\n with tf.gfile.Open(os.path.join(output_dir, filename), 'w') as f:\\n img = (((images[i, :, :, :] + 1.0) * 0.5) * 255.0).astype(np.uint8)\\n Image.fromarray(img).save(f)\",\n \"def process_data(output_folder):\\n # select imgs\\n img_folder = join(output_folder, 'img')\\n select_img(output_folder, img_folder, 'HE-green')\\n\\n mask_folder = join(output_folder, 'mask')\\n select_img(output_folder, mask_folder, '_EF5')\",\n \"def save_images(images, filenames, output_dir):\\n for i, filename in enumerate(filenames):\\n\\n with tf.gfile.Open(os.path.join(output_dir, filename), 'w') as f:\\n image = images[i, :, :, :]\\n image[:, :, 0] += _R_MEAN\\n image[:, :, 1] += _G_MEAN\\n image[:, :, 2] += _B_MEAN\\n image = imresize(image, [299, 299])\\n imsave(f, image, format='png')\",\n \"def process_images(path, dataset):\\n \\n print(f\\\"Processing images {os.path.join(path, dataset)}\\\", flush=True)\\n label_file = os.path.join(path, dataset + '-labels-idx1-ubyte')\\n with open(label_file, 'rb') as file:\\n _, num = struct.unpack(\\\">II\\\", file.read(8))\\n labels = numpy.fromfile(file, dtype=numpy.int8) #int8\\n new_labels = numpy.zeros((num, 10))\\n new_labels[numpy.arange(num), labels] = 1\\n\\n img_file = os.path.join(path, dataset + '-images-idx3-ubyte')\\n with open(img_file, 'rb') as file:\\n _, num, rows, cols = struct.unpack(\\\">IIII\\\", file.read(16))\\n imgs = numpy.fromfile(file, dtype=numpy.uint8).reshape(num, rows, cols) #uint8\\n imgs = imgs.astype(numpy.float32) / 255.0\\n\\n os.remove(label_file); os.remove(img_file)\\n print(f\\\"Saving files under {os.path.join(path, dataset)} path\\\", flush=True)\\n numpy.savez_compressed(os.path.join(path, dataset), imgs=imgs, labels=labels)\",\n \"def hdfpath_to_nifti1image(file_path, h5path):\\n with h5py.File(file_path, 'r') as f:\\n return hdfgroup_to_nifti1image(f[h5path])\",\n \"def save_imgs(self, epoch):\\n row, column = 5, 5\\n\\n # Generates r*c images from the model, saves them individually and as a gallery\\n images_generated = self.generate_images(row * column)\\n\\n # ???\\n images_generated = 0.5 * images_generated + 0.5\\n\\n for index, np_array_image in enumerate(images_generated):\\n path = f\\\"{self.output_directory}/generated_{self.img_size[0]}x{self.img_size[1]}\\\"\\n if not os.path.exists(path):\\n os.makedirs(path)\\n imsave(path + f\\\"/{unique_name()}_{epoch}_{index}.png\\\", np_array_image)\\n\\n # 4D array:\\n nindex, height, width, intensity = images_generated.shape\\n\\n nrows = nindex // column\\n\\n assert nindex == nrows * column\\n\\n # Form the gallery by combining the data at pixel levels (may not be the best approach)\\n # want result.shape = (height*n-rows, width*n-cols, intensity)\\n gallery = (\\n images_generated.reshape(nrows, column, height, width, intensity)\\n .swapaxes(1, 2)\\n .reshape(height * nrows, width * column, intensity)\\n )\\n\\n path = f\\\"{self.output_directory}/gallery_generated_{self.img_size[0]}x{self.img_size[1]}\\\"\\n if not os.path.exists(path):\\n os.makedirs(path)\\n imsave(path + f\\\"/{unique_name()}_{epoch}.png\\\", gallery)\",\n \"def save_imgs(self):\\n print(\\\"Saving the images with required categories ...\\\")\\n os.makedirs(self.imgs_dir, exist_ok=True)\\n # Save the images into a local folder\\n for im in tqdm(self.images):\\n img_data = requests.get(im['coco_url']).content\\n with open(os.path.join(self.imgs_dir, im['file_name']), 'wb') as handler:\\n handler.write(img_data)\",\n \"def numpy_to_h5py(in_dir=config.dir_npy, split = config.split):\\n\\n in_files=[x[:-13] for x in os.listdir(in_dir) if x.endswith('_voc_stft.npy') and not x.startswith('._')]\\n\\n random.shuffle(in_files)\\n\\n\\n num_files = len(in_files)\\n\\n split_idx = int(num_files*split)\\n\\n trn_files = in_files[:split_idx]\\n\\n val_files = in_files[split_idx:]\\n\\n num_val_files = len(val_files)\\n\\n print('Processing %d training files' % split_idx)\\n logger.info('Processing %d training files' % split_idx)\\n\\n logger.info('Training file: %s' % config.h5py_file_train)\\n\\n voc_shape_trn = [split_idx, 5170,config.input_features]\\n\\n mix_shape_trn = [split_idx, 5170,config.input_features]\\n\\n feats_shape_trn = [split_idx, 5170,config.output_features]\\n\\n hdf5_file = h5py.File(config.h5py_file_train, mode='w')\\n\\n hdf5_file.create_dataset(\\\"voc_stft\\\", voc_shape_trn, np.float32)\\n\\n hdf5_file.create_dataset(\\\"back_stft\\\", voc_shape_trn, np.float32)\\n\\n hdf5_file.create_dataset(\\\"mix_stft\\\", mix_shape_trn, np.float32)\\n\\n hdf5_file.create_dataset(\\\"feats\\\", feats_shape_trn, np.float32)\\n\\n\\n i = 0\\n\\n for f in trn_files:\\n\\n voc_stft = np.load(in_dir+f+'_voc_stft.npy')\\n\\n voc_stft = voc_stft.astype('float32')\\n\\n mix_stft = np.load(in_dir+f+'_mix_stft.npy')\\n\\n mix_stft = mix_stft.astype('float32')\\n\\n back_stft = np.load(in_dir+f+'_back_stft.npy')\\n\\n back_stft = back_stft.astype('float32')\\n\\n synth_feats = np.load(in_dir+f+'_synth_feats.npy')\\n\\n synth_feats = synth_feats.astype('float32')\\n\\n hdf5_file[\\\"voc_stft\\\"][i,...] = voc_stft\\n\\n hdf5_file[\\\"mix_stft\\\"][i,...] = mix_stft\\n\\n hdf5_file[\\\"back_stft\\\"][i,...] = back_stft\\n\\n hdf5_file[\\\"feats\\\"][i,...] = synth_feats\\n\\n i+=1\\n utils.progress(i, split_idx)\\n\\n logger.info('Processed training file: %s' % f)\\n\\n hdf5_file.close()\\n\\n print('Processing %d validation files' % num_val_files)\\n logger.info('Processing %d validation files' % num_val_files)\\n\\n logger.info('Validation file: %s' % config.h5py_file_val)\\n\\n voc_shape_trn = [num_val_files, 5170,config.input_features]\\n\\n mix_shape_trn = [num_val_files, 5170,config.input_features]\\n\\n feats_shape_trn = [num_val_files, 5170,config.output_features]\\n\\n hdf5_file = h5py.File(config.h5py_file_val, mode='w')\\n\\n hdf5_file.create_dataset(\\\"voc_stft\\\", voc_shape_trn, np.float32)\\n\\n hdf5_file.create_dataset(\\\"mix_stft\\\", mix_shape_trn, np.float32)\\n\\n hdf5_file.create_dataset(\\\"back_stft\\\", voc_shape_trn, np.float32)\\n\\n hdf5_file.create_dataset(\\\"feats\\\", feats_shape_trn, np.float32)\\n\\n\\n i = 0\\n\\n for f in val_files:\\n\\n voc_stft = np.load(in_dir+f+'_voc_stft.npy')\\n\\n voc_stft = voc_stft.astype('float32')\\n\\n mix_stft = np.load(in_dir+f+'_mix_stft.npy')\\n\\n mix_stft = mix_stft.astype('float32')\\n\\n synth_feats = np.load(in_dir+f+'_synth_feats.npy')\\n\\n synth_feats = synth_feats.astype('float32')\\n\\n back_stft = np.load(in_dir+f+'_back_stft.npy')\\n\\n back_stft = back_stft.astype('float32')\\n\\n hdf5_file[\\\"voc_stft\\\"][i,...] = voc_stft\\n\\n hdf5_file[\\\"mix_stft\\\"][i,...] = mix_stft\\n\\n hdf5_file[\\\"back_stft\\\"][i,...] = back_stft\\n\\n hdf5_file[\\\"feats\\\"][i,...] = synth_feats\\n\\n i+=1\\n utils.progress(i, num_val_files)\\n\\n logger.info('Processed validation file: %s' % f)\\n\\n hdf5_file.close()\\n # return original_ffts\",\n \"def process_images():\\n create_dirs()\\n for root, dirs, files in os.walk(IN):\\n for name in files:\\n if name[0] == '.':\\n continue\\n process_image(name)\",\n \"def _crop_write_image(self, inroot, images, outroot):\\n for image in images:\\n inimage_path = osp.join(inroot, image)\\n cvimg = cv2.imread(inimage_path)\\n cvimg = cvimg[60:-30, 25:-25]\\n h, w, _ = cvimg.shape\\n assert h == w == 128\\n outimage_path = osp.join(outroot, image)\\n cv2.imwrite(outimage_path, cvimg)\\n print(outimage_path)\",\n \"def preprocessfolder(self):\\n imgs, _ = getFilesAndHdf(str(self.in_directory.text()))\\n self.img_list = sorted(imgs)\\n self.updateImageGroups()\",\n \"def save_step_3(img_pairs, match_list, output_path=\\\"./output/step3\\\"):\\n # ... your code here ...\\n for i in range(len(img_pairs)):\\n name1,tail1 = str.split(filenames[match_list[i][0]],\\\".\\\")\\n name2,tail2 = str.split(filenames[match_list[i][1]],\\\".\\\")\\n cv2.imwrite(output_path+\\\"/\\\"+name1+\\\"_\\\"+name2+\\\".jpg\\\", img_pairs[i][0])\\n cv2.imwrite(output_path+\\\"/\\\"+name2+\\\"_\\\"+name1+\\\".jpg\\\", img_pairs[i][1])\",\n \"def save_images(images, filenames, output_dir):\\n for i, filename in enumerate(filenames):\\n # Images for inception classifier are normalized to be in [-1, 1] interval,\\n # so rescale them back to [0, 1].\\n with tf.gfile.Open(os.path.join(output_dir, filename), 'w') as f:\\n img = np.round(((images[i] + 1.0) * 0.5) * 255.0).astype(np.uint8)\\n Image.fromarray(img).save(f, format='PNG')\",\n \"def load_images(input_dir=\\\"/tmp/mapswipe/project-1\\\", n_images=2000, seed=1):\\n class_map = {1: \\\"1\\\", 0: \\\"5\\\"}\\n output_dir = \\\"/Users/thead/git/dreamview/data/\\\"\\n\\n X_ = []\\n y_ = []\\n for new_klass in class_map:\\n images = []\\n for klass in class_map[new_klass]:\\n for img in glob.glob(input_dir + \\\"/%s/*/*/*/aerial.jpeg\\\" % klass):\\n if os.stat(img).st_size > 0:\\n images.append(img)\\n\\n images = shuffle(images, random_state=seed+42+new_klass)\\n images = images[:n_images]\\n X_ += images\\n y_ += [new_klass] * len(images)\\n\\n # XXX deduce array size from an actual image\\n X = np.zeros((2*n_images, 256*256), dtype=np.ubyte)\\n y = np.zeros(2*n_images, dtype=np.int)\\n\\n for n, (img_path, klass) in enumerate(zip(X_, y_)):\\n # the order of these OPs has been chosen on purpose, don't mess\\n # without checking what happens\\n img = imread(img_path)\\n img = equalize_adapthist(img)\\n img = rgb2grey(img)\\n img = img_as_ubyte(img)\\n\\n if not n % 10:\\n fname = os.path.split(img_path)[:-1]\\n fname = os.path.join(*fname, \\\"aerial-processed.jpeg\\\")\\n imsave(fname, img)\\n\\n X[n,:] = img.ravel()\\n y[n] = klass\\n\\n return X, y\",\n \"def save_test_images(images):\\n for description, img in images.items():\\n save_to_image(img, description)\\n save_to_netcdf(img, description)\",\n \"def extract_write_to_file(self, num_extracted, write_dir, sub_h, sub_w, margin=10):\\n\\n file_seed = len(os.listdir(write_dir))\\n\\n for i in range(num_extracted):\\n file_num = str(file_seed + i)\\n write_path = os.path.join(write_dir, file_num + \\\".\\\" + 'jpg')\\n\\n print('extracting {}/{} images of dimension {}x{}'.format(i, num_extracted, sub_h, sub_w))\\n print('writting to location: {}'.format(write_path))\\n\\n self.extract_single(sub_h, sub_w, write_path)\",\n \"def save_images(images, filenames, output_dir):\\n for i, filename in enumerate(filenames):\\n # Images for inception classifier are normalized to be in [-1, 1] interval,\\n # so rescale them back to [0, 1].\\n with tf.gfile.Open(os.path.join(output_dir, filename), 'w') as f:\\n img = (((images[i, :, :, :] + 1.0) * 0.5) * 255.0).astype(np.uint8)\\n Image.fromarray(img).save(f, format='PNG')\",\n \"def load(self):\\n\\n # get files in folder\\n files = [f for f in listdir(self.data_path)]\\n print(\\\"loading images from folder: %s\\\" % self.data_path)\\n\\n images = []\\n image_targets = []\\n for f in files:\\n filepath = path.join(self.data_path, f)\\n images.append(io.imread(filepath, as_grey=True))\\n image_targets.append(self.target)\\n\\n # define new size and resize images\\n new_size = (2 ** self.size_exponent, 2 ** self.size_exponent)\\n for i in range(0, len(images)):\\n # images[i] = transform.resize(images[i], new_size)\\n images[i] = misc.imresize(images[i], new_size) / 16\\n\\n self.images = images\\n self.targets = image_targets\",\n \"def load_data_withIdx(subjIdx_list, filenames, test_ratio = 0.2):\\n dataset_path = filenames['data']\\n train_path = filenames['train']\\n test_path = filenames['test']\\n eval_path = filenames['eval']\\n raw_image_lists = []\\n aligned_image_lists = []\\n print('[INFO] Data Processing started ... ')\\n\\n for subIdx in subjIdx_list:\\n subj_path = dataset_path+'/subj_'+f'{subIdx:02d}'\\n print('\\\\tProccessing images for subject_' +f'{subIdx:02d}'+ ' ...', end =\\\" \\\")\\n\\n if(not os.path.isdir(subj_path)):\\n print(' no file exists for subject_' +f'{subIdx:02d}'+ ' !!')\\n continue\\n\\n raw_list = sorted(glob.glob(subj_path + '/raw_images/*.jpg'))\\n aligned_list = sorted(glob.glob(subj_path + '/aligned_images/*.jpg')) \\n\\n assert len(raw_list) == len(aligned_list) , \\\"data size mismatch! raw_img:{0}, alig_img:{1}\\\".format(len(raw_list) ,len(aligned_list)) \\n \\n raw_image_lists += raw_list\\n aligned_image_lists += aligned_list\\n print(' Done!')\\n\\n data_to_write_train = train_test_split_data(aligned_image_lists, raw_image_lists, test_ratio)\\n data_to_write_test = train_test_split_data(data_to_write_train[1], data_to_write_train[3], 0.5)\\n print('[INFO] Processing Done! ')\\n print(\\\"[INFO] Number of train data :{0:4d}, Number of eval data :{1:4d}, Number of test data :{2:4d} \\\"\\n . format(len(data_to_write_train[0]), len(data_to_write_test[0]), len(data_to_write_test[1]))) \\n # data write part \\n write_data(data_to_write_train, data_to_write_test, train_path, eval_path ,test_path)\",\n \"def buildImages(files, targets, type):\\n images = []\\n for file in files:\\n targets.append(file)\\n with open(file, \\\"rb\\\") as f:\\n if type == \\\"Byte\\\":\\n images.append(bytePlot(list(f.read())))\\n elif type == \\\"Markov\\\":\\n images.append(markovPlot(list(f.read())))\\n elif type == \\\"Hilbert\\\":\\n images.append(hilbertPlot(list(f.read())))\\n smp.imsave(\\\"{}.png\\\".format(file), images[-1])\\n return images, targets\",\n \"def jarvis(input_path, output_path): \\n\\n if not os.path.exists(f'{output_path}'):\\n os.makedirs(f'{output_path}')\\n\\n file_list = [filename for filename in os.listdir(f'{input_path}') if '.tif' in filename]\\n\\n for filename in file_list:\\n pathname = os.path.join(input_path, filename)\\n new_name = f\\\"{output_path}{filename.replace('.lif - ', '_').replace('_5x-', '_')}\\\"\\n copyfile(pathname, new_name)\\n logger.info(f'{new_name}')\",\n \"def collect_and_rename() -> None:\\n image_source_folder = 'image_dir'\\n label_source_folder = 'annotation_dir'\\n image_target_folder = 'images'\\n label_target_folder = 'labels'\\n for i, (subdir, _, files) in enumerate(os.walk(image_source_folder), -1):\\n # it walks the parent folder first, not a file\\n if i == -1: \\n continue\\n subdir_name = subdir.split('\\\\\\\\')[1]\\n for file_name in files:\\n with open(f'{image_source_folder}/{subdir_name}/{file_name}') as image_file, \\\\\\n open(f'{label_source_folder}/{subdir_name}/{file_name}'.split('.')[0] + '.txt') as label_file:\\n shutil.copy2(image_file.name, f'{image_target_folder}/{\\\"%06d\\\" % i}.jpg')\\n shutil.copy2(label_file.name, f'{label_target_folder}/{\\\"%06d\\\" % i}.txt')\\n print(f'Processed {i} images')\",\n \"def read_image_data(self):\\n\\n for sequence_name in self.sequence_name_list:\\n sequence = self.sequences[sequence_name]\\n for image_id in sequence.image_id_list:\\n sequence.image_dict[image_id].image_path = '{}{}/{}'.format(self.root_dir, self.name, sequence.image_dict[image_id].filename)\",\n \"def run():\\n\\n today = datetime.now().strftime(\\\"%Y-%m-%d\\\")\\n log_file = os.path.abspath(\\\"logs/{}.log\\\".format(today))\\n logger = RsmasLogger(\\\"pipeline\\\", log_file)\\n\\n images = get_list_of_images()\\n # LOG: list of images to process\\n logger.log(loglevel.INFO, [img.key for img in images])\\n\\n for im in images:\\n\\n logger.log(loglevel.INFO, \\\"Processing image: {}\\\".format(im.key))\\n\\n file_path = \\\"{}/{}\\\".format(im.bucket_name, im.key)\\n full_path = \\\"{}_full.jpg\\\"\\n mod_path = \\\"{}_mod.jpg\\\"\\n aws_path = \\\"{}/{}/{}/{}\\\"\\n try:\\n haz_id, haz_name, sat_name, sat_dir, img_type, img_date, center = summary.pull_summary_data(\\n \\\"/vsis3/{}\\\".format(file_path))\\n sat_id = Satellite.from_params(sat_name, bool(sat_dir))\\n except:\\n # LOG: error in image metadata format\\n logger.log(loglevel.ERROR, '\\\\tThere was an error in the metadata format of the image. Skipping.')\\n continue\\n\\n aws_path = aws_path.format(haz_id, sat_id, img_type, img_date)\\n full_path = full_path.format(img_date)\\n mod_path = mod_path.format(img_date)\\n\\n # 1. Read in image file\\n with rasterio.open(\\\"s3://{}\\\".format(file_path)) as data:\\n band = data.read(1)\\n img = plot.show(band)\\n img.get_figure().savefig(full_path, dpi=300)\\n\\n # 3. Compress image\\n compressed = immanip.compress_image(full_path, compression_amount=0.3)\\n\\n # 4 - 5. Pad image and add date on image\\n text_image = immanip.add_text_to_image(compressed, img_date)\\n\\n # 6. Save image locally\\n text_image.save(mod_path.format(img_date))\\n mod_path_aws = save.get_s3_url(\\\"{}/{}\\\".format(aws_path, mod_path))\\n full_path_aws = save.get_s3_url(\\\"{}/{}\\\".format(aws_path, full_path))\\n\\n tif_path_aws = save.get_s3_url(\\\"{}/{}\\\".format(aws_path, im.key))\\n\\n # LOG: images successfully moved to S3 bucket\\n # LOG: mod_path_aws, full_path_aws, tif_path_aws\\n\\n hazard = Hazard(haz_id, haz_name, HazardType.VOLCANO, Location(center[0], center[1]), Date(img_date), 0)\\n satellite = Satellite.from_params(sat_name, bool(sat_dir))\\n image = Image(str(randint(1, 10000000)),\\n haz_id,\\n satellite,\\n ImageType.from_string(img_type),\\n Date(img_date),\\n ImageURL(full_path_aws),\\n ImageURL(tif_path_aws),\\n ImageURL(mod_path_aws))\\n\\n try:\\n db = Database()\\n except ConnectionError:\\n logger.log(loglevel.ERROR, \\\"\\\\tThere was an error while connecting to the database. Skipping this image.\\\")\\n continue\\n\\n db.create_new_hazard(hazard)\\n db.create_new_satellite(satellite)\\n db.create_new_image(image)\\n\\n db.close()\\n\\n # LOG: database successfully updated\\n logger.log(loglevel.INFO, \\\"\\\\tDatabase succesfully updated.\\\")\\n\\n save.save_image_s3(mod_path, \\\"{}/{}\\\".format(aws_path, mod_path))\\n save.save_image_s3(full_path, \\\"{}/{}\\\".format(aws_path, full_path))\\n save.move_tif(im.key, \\\"{}/{}\\\".format(aws_path, im.key))\\n\\n logger.log(loglevel.INFO, \\\"\\\\tImages were successfully uploaded to the S3 bucket\\\")\\n logger.log(loglevel.INFO, \\\"\\\\t\\\\tmod_path_aws: {}\\\".format(mod_path_aws))\\n logger.log(loglevel.INFO, \\\"\\\\t\\\\tfull_path_aws: {}\\\".format(full_path_aws))\\n logger.log(loglevel.INFO, \\\"\\\\t\\\\ttif_path_aws: {}\\\".format(tif_path_aws))\\n\\n # LOG: image completed\\n logger.log(loglevel.INFO, \\\"\\\\tProcessing of {} completed.\\\".format(im.key))\\n\\n # LOG: finished processing images\\n logger.log(loglevel.INFO, \\\"Processing complete.\\\")\",\n \"def extract_data(filename, images_dir, output_dir, trials_idx, block_nums, goal_dict):\\n num_images = len(trials_idx) * len(block_nums)\\n f = h5py.File(os.path.join(output_dir, filename), 'w')\\n X = f.create_dataset('X', (num_images, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS*NUM_FRAMES), dtype=TYPE)\\n Y = f.create_dataset('Y', (num_images, 2), dtype=TYPE)\\n\\n image_count = 0\\n for trial_num in trials_idx:\\n for block_num in block_nums:\\n print('Blocks ' + str(block_num) + ' Trial ' + str(trial_num))\\n for frame_num in xrange(0, NUM_FRAMES):\\n temp = imread(images_dir+'RTr_Bl'+str(block_num)+'_'+str(trial_num)+'_'+str(frame_num)+IMAGE_FORMAT)\\n temp = imresize(temp, [temp.shape[0]//DOWN_SAMPLE, temp.shape[1]//DOWN_SAMPLE, temp.shape[2]])\\n X[image_count, 0:temp.shape[0], 0:temp.shape[1], frame_num*NUM_CHANNELS:(frame_num+1)*NUM_CHANNELS] = temp\\n label = goal_dict['RTr_Bl'+str(block_num)+'_'+str(trial_num)]\\n Y[image_count, :] = [label, 1-label]\\n image_count += 1\\n\\n f.close()\\n\\n # TODO Use pixel depth normalization???\\n #data = (data - (PIXEL_DEPTH / 2.0)) / PIXEL_DEPTH\",\n \"def process_imgdir(self,imgdir):\\n #Write images into resultdir\\n resultdir = os.path.join(imgdir, 'results')\\n #Read images from input dir\\n inputdir = os.path.join(imgdir, 'inputs')\\n shutil.rmtree(resultdir)\\n os.mkdir(resultdir)\\n #Read files from input images\\n for fullname in os.listdir(inputdir):\\n filepath = os.path.join(inputdir, fullname)\\n if os.path.isfile(filepath):\\n basename = os.path.basename(filepath)\\n image = cv2.imread(filepath, cv2.IMREAD_COLOR)\\n if len(image.shape) == 3 and image.shape[2] == 3:\\n print('Processing %s ...' % basename)\\n else:\\n sys.stderr.write('Skipping %s, not RGB' % basename)\\n continue\\n #Extract haze from the scene and then save the image\\n dehazed = self.get_scene_radiance(image)\\n cv2.imwrite(os.path.join(resultdir, basename), dehazed)\\n return os.path.join(resultdir, basename)\",\n \"def write(self):\\n f, ds = self.opendset()\\n #\\n # Now add the images\\n #\\n start_time = time.clock() # time this\\n nframes = 0 # number completed\\n print_every = 1; marker = \\\" .\\\";\\n print('Frames written (of %s):' % self.ntowrite, end=\\\"\\\")\\n for i in range(self.nfiles):\\n if nframes >= self.ntowrite: break\\n\\n logging.debug('processing file %d of %d' % (i+1, self.nfiles))\\n img_i = fabio.open(self.files[i])\\n nfi = img_i.nframes\\n for j in range(nfi):\\n msg = '... file %d/image %d' % (i, j)\\n logging.debug(msg)\\n if j < self.nempty:\\n logging.debug('... empty frame ... skipping')\\n else:\\n ds[nframes, :, :] = img_i.data\\n nframes += 1\\n if numpy.mod(nframes, print_every) == 0:\\n print(marker, nframes, end=\\\"\\\")\\n print_every *= 2\\n sys.stdout.flush()\\n logging.debug('... wrote image %s of %s' %\\\\\\n (nframes, self.ntowrite))\\n if nframes >= self.ntowrite:\\n logging.debug('wrote last frame: stopping')\\n break\\n if j < nfi - 1:\\n # on last frame in file, fabio will look for next file\\n img_i = img_i.next()\\n\\n f.close()\\n print(\\\"\\\\nTime to write: %f seconds \\\" %(time.clock()-start_time))\",\n \"def write_frames(self, images):\\n for img in images:\\n self.write_frame(img)\",\n \"def store_hdf5(images, labels, ID:str, path:str = \\\"data/dataset/\\\"):\\n\\n #create a new HDF5 file\\n file = h5py.File(path+ID+\\\"_lens.h5\\\", \\\"w\\\")\\n\\n #create a dataset in the file\\n dataset = file.create_dataset( \\\"images\\\", np.shape(images), h5py.h5t.IEEE_F64BE, data=images)\\n file.close()\\n\\n labels.to_hdf(path +ID+'_meta.h5', \\\"table\\\")\",\n \"def save_images(images, filenames, output_dir):\\n for i, filename in enumerate(filenames):\\n # Images for inception classifier are normalized to be in [-1, 1] interval,\\n # so rescale them back to [0, 1].\\n with tf.gfile.Open(os.path.join(output_dir, filename), 'w') as f:\\n imsave(f, (images[i] + 1.0) * 0.5, format='png')\",\n \"def save_images(images, filenames, output_dir):\\n for i, filename in enumerate(filenames):\\n # Images for inception classifier are normalized to be in [-1, 1] interval,\\n # so rescale them back to [0, 1].\\n with tf.gfile.Open(os.path.join(output_dir, filename), 'w') as f:\\n imsave(f, (images[i, :, :, :] + 1.0) * 0.5, format='png')\",\n \"def div_imgs(dir_path: str, holdout: int) -> None:\\n VH_REGEX = re.compile(r'(.*)_([0-9]+).vh.tif')\\n\\n for file in os.listdir(dir_path):\\n m = re.match(VH_REGEX, file)\\n if not m:\\n continue\\n\\n pre, num = m.groups()\\n vv = f'{pre}_{num}.vv.tif'\\n mask = f'{pre}_{num}.mask.tif'\\n\\n if not os.path.isfile(os.path.join(dir_path, mask)):\\n print(f\\\"Tile: {file} is missing a mask {mask}!\\\")\\n\\n if not os.path.isfile(os.path.join(dir_path, vv)):\\n print(f\\\"Tile: {file} is missing a mask {vv}!\\\")\\n\\n test_or_train = 'train' if random.random() > holdout else 'test'\\n\\n folder = os.path.join(dir_path, test_or_train)\\n if not os.path.isdir(folder):\\n os.makedirs(folder)\\n\\n os.rename(\\n os.path.join(dir_path, file), os.path.join(folder, file)\\n )\\n os.rename(\\n os.path.join(dir_path, vv),\\n os.path.join(folder, vv)\\n )\\n os.rename(\\n os.path.join(dir_path, mask),\\n os.path.join(folder, mask)\\n )\",\n \"def save_as_hdf5(self, filename):\",\n \"def process_subdirectory(subdir_path, regex_images, output_folder_if_pickle,\\n min_n_leftside_metadata=10, only_ionogram_content_extraction_on_leftside_metadata=True, to_pickle=True):\\n # Run segment_images on the subdirectory \\n df_img,df_loss,df_outlier = segment_images(subdir_path, regex_images)\\n\\n # Determine ionogram grid mappings used to map (x,y) pixel coordinates of ionogram trace to (Hz, km) values\\n stack = all_stack(df_img)\\n col_peaks,row_peaks,mapping_Hz, mapping_km = get_grid_mappings(stack)\\n\\n # Translate metadata located on the left\\n df_img_left = df_img[df_img['metadata_type']== 'left']\\n \\n if len(df_img_left.index) > min_n_leftside_metadata:\\n # Determine leftside metadata grid (pixel coordinates to number, category mappings)\\n df_img_left, df_loss_meta,dict_mapping,dict_hist= get_leftside_metadata(df_img_left,subdir_path)\\n df_all_loss = df_loss.append(df_loss_meta)\\n else:\\n df_all_loss = df_loss\\n \\n # Extract the coordinates of the ionogram trace (black), Map the (x,y) pixel coordinates to (Hz, km) values and Extract select parameters i.e. fmin\\n if only_ionogram_content_extraction_on_leftside_metadata:\\n df_processed, df_loss_coord = extract_coord_subdir_and_param(df_img_left,subdir_path,col_peaks,row_peaks,mapping_Hz,mapping_km)\\n else:\\n df_processed, df_loss_coord = extract_coord_subdir_and_param(df_img,subdir_path,col_peaks,row_peaks,mapping_Hz,mapping_km)\\n\\n df_processed['mapping_Hz'] = [mapping_Hz] * len(df_processed.index)\\n df_processed['mapping_km'] = [mapping_km] * len(df_processed.index)\\n\\n if to_pickle:\\n start,subdir_name = ntpath.split(subdir_path[:-1])\\n start,dir_name = ntpath.split(start)\\n df_processed.to_pickle(os.pardir + '/pickle/' + str(dir_name)+'_'+str(subdir_name)+'.pkl')\\n \\n df_all_loss = df_all_loss.append(df_loss_coord)\\n return df_processed, df_all_loss,df_outlier\",\n \"def compress_img():\\n in_path = 'output/templates/rgb/'\\n out_path = 'output/templates/imgs/'\\n names = os.listdir(in_path)\\n for i, name in enumerate(names):\\n img = cv2.imread(in_path + name, 0)\\n if any(np.array(img.shape) > 1000):\\n img = cv2.resize(img, None, fx=0.5, fy=0.5, interpolation=cv2.INTER_CUBIC)\\n cv2.imwrite(out_path + name, img)\\n\\n return\",\n \"def generate_all(files, alignement_h5f, input_h5f,\\n nframes=7, vad=None):\\n def try_remove(fname):\\n try:\\n os.remove(fname)\\n except:\\n pass\\n try:\\n directory = os.path.dirname(os.path.abspath(input_h5f))\\n\\n # create temporary files:\\n _, fb_h5f = tempfile.mkstemp(dir=directory)\\n _, fb_mvn_h5f = tempfile.mkstemp(dir=directory)\\n os.remove(fb_h5f)\\n os.remove(fb_mvn_h5f)\\n\\n # generate mfccs:\\n h5features_compute(files, alignement_h5f, featfunc=do_mfccs)\\n\\n # generate stacked mvn fbanks:\\n h5features_compute(files, fb_h5f, featfunc=do_fbank)\\n mean_variance_normalisation(fb_h5f, fb_mvn_h5f, vad=vad)\\n h5features_feats2stackedfeats(fb_mvn_h5f, input_h5f, nframes=nframes)\\n finally:\\n try_remove(fb_h5f)\\n try_remove(fb_mvn_h5f)\",\n \"def processImages(self):\\n for file in os.listdir(self.config[\\\"tempPath\\\"]):\\n self.logger.debug(\\\"Calling generateImages for the file: {0}\\\".format(file))\\n self.generateText(file)\",\n \"def save_images(images, filenames, output_dir):\\n for i, filename in enumerate(filenames):\\n # Images for inception classifier are normalized to be in [-1, 1] interval,\\n # so rescale them back to [0, 1].\\n with tf.gfile.Open(os.path.join(output_dir, filename), 'w') as f:\\n imsave(f, (images[i, :, :, :] + 1.0) * 0.5, format='png')\",\n \"def create_random_data(output_path: str, num_images: int = 5) -> None:\\n train_path = os.path.join(output_path, \\\"train\\\")\\n class1_train_path = os.path.join(train_path, \\\"class1\\\")\\n class2_train_path = os.path.join(train_path, \\\"class2\\\")\\n\\n val_path = os.path.join(output_path, \\\"val\\\")\\n class1_val_path = os.path.join(val_path, \\\"class1\\\")\\n class2_val_path = os.path.join(val_path, \\\"class2\\\")\\n\\n test_path = os.path.join(output_path, \\\"test\\\")\\n class1_test_path = os.path.join(test_path, \\\"class1\\\")\\n class2_test_path = os.path.join(test_path, \\\"class2\\\")\\n\\n paths = [\\n class1_train_path,\\n class1_val_path,\\n class1_test_path,\\n class2_train_path,\\n class2_val_path,\\n class2_test_path,\\n ]\\n\\n for path in paths:\\n try:\\n os.makedirs(path)\\n except FileExistsError:\\n pass\\n\\n for i in range(num_images):\\n pixels = numpy.random.rand(64, 64, 3) * 255\\n im = Image.fromarray(pixels.astype(\\\"uint8\\\")).convert(\\\"RGB\\\")\\n im.save(os.path.join(path, f\\\"rand_image_{i}.jpeg\\\"))\\n\\n process_images(output_path)\",\n \"def save_images_in_folder(folder, images, size = (224, 224), start_index = 0):\\n\\n # Loop over the images\\n for i, image in enumerate(images):\\n # Resize image to target size\\n image = cv2.resize(image, dsize = size)\\n \\n # Convert image back to BGR color space\\n image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\\n \\n # Create the path where the image will be save, images will be indexed\\n path = os.path.join(folder, str(start_index + i) + '.png')\\n \\n # Write / Save image \\n cv2.imwrite(path, image)\",\n \"def transform_to_h5():\\n # this took about 10 minutes for set1\\n for setname in ['set1', 'set2']:\\n filename = os.path.join(ltrc_dirname, '{}.h5'.format(setname))\\n f = h5py.File(filename, 'w')\\n\\n for name in ['train', 'valid', 'test']:\\n g = f.create_group(name)\\n filename = os.path.join(ltrc_dirname, '{}.{}.txt'.format(setname, name))\\n X, y, q = load_svmlight_file(filename, query_id=True)\\n g.create_dataset('X', data=X.todense(), compression='gzip')\\n g.create_dataset('y', data=y, compression='gzip')\\n g.create_dataset('q', data=q, compression='gzip')\\n f.close()\\n # Now you can do this\\n # f['/valid/X'].shape\\n # Out[24]: (71083, 699)\",\n \"def preprocess(exam, data_folder, save_path, image_format):\\n for v in ['L-CC', 'L-MLO', 'R-CC', 'R-MLO']:\\n if len(exam[v]) == 0:\\n continue\\n else:\\n for image in exam[v]:\\n image_path = data_folder + '/' + image + '.' + image_format\\n # Extract subdirectories\\n subdirs = \\\"/\\\".join(image.split('/')[:-1])\\n save_dirs = os.path.join(save_path, subdirs)\\n # Extract image id\\n image_id = image.split('/')[-1]\\n # Create save directories\\n os.makedirs(save_dirs, exist_ok=True)\\n png_save_path = os.path.join(save_dirs, image_id + '.png')\\n with Image(filename=image_path, format=image_format) as img:\\n with img.clone() as i:\\n i.auto_level()\\n with i.convert('png') as png_image:\\n png_image.transform(resize='896x1152!')\\n png_image.save(filename=png_save_path)\",\n \"def sum_images(filelist):\\n\\n nfiles = np.size(filelist)\\n\\n print(\\\"Summing together {} files\\\".format(nfiles))\\n\\n ims = []\\n\\n for fname in filelist:\\n hdu = f.open(fname)\\n ims.append(hdu[0].data)\\n\\n ims = np.array(ims)\\n\\n sum_im = np.nansum(ims, axis=0)\\n hdu[0].data = sum_im\\n\\n #Add pipeline version and history keywords\\n vers = version.get_version()\\n hdu[0].header.set('PL_VERS',vers,'Version of pipeline used for processing')\\n hdu[0].header['HISTORY'] = \\\"Summed up the following images:\\\"\\n\\n for fname in filelist:\\n hdu[0].header['HISTORY'] = fname\\n\\n hdu[0].header['HISTORY'] = \\\"######################\\\"\\n\\n outname = filelist[-1].split('.')[0]+'_summed.fits'\\n\\n print(\\\"Writing out final file to {}\\\".format(outname))\\n\\n hdu.writeto(outname, overwrite=True)\",\n \"def saveHDF5(ifile, cs, xi, yi, phi, cidx, idx, size, comx, comy, rgysq, pl, st, sw, ens, fils):\\n \\n ## Groups\\n size_grp = ifile.create_group('size')\\n beads_grp = ifile.create_group('beads')\\n props_grp = ifile.create_group('props')\\n \\n ## Datasets\\n size_grp.create_dataset('size', data=cs, compression='gzip')\\n \\n beads_grp.create_dataset('x', data=xi, compression='gzip')\\n beads_grp.create_dataset('y', data=yi, compression='gzip')\\n beads_grp.create_dataset('phi', data=phi, compression='gzip')\\n beads_grp.create_dataset('idx', data=cidx, compression='gzip')\\n \\n idx_h5 = np.asarray(idx, dtype=int)\\n size_h5 = np.asarray(size, dtype=int)\\n comx_h5 = np.asarray(comx, dtype=float)\\n comy_h5 = np.asarray(comy, dtype=float)\\n rgysq_h5 = np.asarray(rgysq, dtype=float)\\n planarity_h5 = np.asarray(pl, dtype=float)\\n straightness_h5 = np.asarray(st, dtype=float)\\n swirliness_h5 = np.asarray(sw, dtype=float)\\n enstrophy_h5 = np.asarray(ens, dtype=float)\\n \\n props_grp.create_dataset('idx', data=idx_h5, compression='gzip')\\n props_grp.create_dataset('size', data=size_h5, compression='gzip')\\n props_grp.create_dataset('comx', data=comx_h5, compression='gzip')\\n props_grp.create_dataset('comy', data=comy_h5, compression='gzip')\\n props_grp.create_dataset('rgysq', data=rgysq_h5, compression='gzip')\\n props_grp.create_dataset('planarity', data=planarity_h5, compression='gzip')\\n props_grp.create_dataset('straightness', data=straightness_h5, compression='gzip')\\n props_grp.create_dataset('swirliness', data=swirliness_h5, compression='gzip')\\n props_grp.create_dataset('enstrophy', data=enstrophy_h5, compression='gzip')\\n \\n ## Filament list\\n fil_grp = props_grp.create_group('filament_list')\\n for sz_idx in np.arange(len(size_h5)):\\n fil_list = np.asarray(fils[sz_idx], dtype=int)\\n fil_grp.create_dataset(str(sz_idx), data=fil_list, compression='gzip')\\n \\n return\",\n \"def ReadData(self, path):\\n os.chdir(path)\\n folders=os.listdir()\\n if 'data.hdf5' in folders:\\n print('Loading data from hdf5 file! Might take some time, be patient!')\\n file=h5py.File('data.hdf5','r+')\\n data=(np.array(list(file['imgs'])),np.array(list(file['lables'])))\\n self.real_labels=list(file['real_labels'])\\n file.close()\\n\\n else:\\n print('1. Collecting data.')\\n err_logs = []\\n img=[]\\n lable=[]\\n for folder in tqdm(folders):\\n\\n os.chdir(os.path.join(path,folder))\\n for file in os.listdir():\\n try:\\n dat=(plt.imread(open(file,'rb')))\\n img.append(resize_image(dat, (resize_x, resize_y),\\n mode='constant',\\n ))\\n lable.append(folder)\\n if folder not in self.real_labels:\\n self.real_labels.append(folder)\\n \\n except OSError:\\n err_logs.append([folder, file])\\n print('\\\\nError logs:')\\n for e in range(len(err_logs)):\\n print('\\\\tFolder: {} | Some OSError for file: {}'.format(err_logs[e][0],\\n err_logs[e][0]))\\n \\n \\n print('2. Encoding data to categorical.')\\n # Encode Letters into numerical categories.\\n le = LabelEncoder()\\n le.fit(lable)\\n lable = le.transform(lable)\\n lable = np.array(lable).reshape(-1, 1)\\n \\n print('3. Onehot encoding.')\\n # Onehot encoding.\\n ohe = OneHotEncoder(sparse=False)\\n ohe.fit(lable)\\n lable = ohe.transform(lable)\\n \\n # Shaffle data.\\n print('4. Shuffle data.')\\n img, lable = shuffle(img, lable)\\n\\t\\t \\n print('5. Saving data.')\\n data=(np.asarray(img), np.asarray(lable))\\n os.chdir(path)\\n \\n file=h5py.File('data.hdf5','w')\\n x=file.create_dataset('imgs',data=np.array(img))\\n y=file.create_dataset('lables',data=np.array(lable))\\n print(self.real_labels)\\n rl=file.create_dataset('real_labels',data=np.string_(self.real_labels))\\n file.close()\\n print('Data set is stored in Data.hdf5 file. ')\\n\\n return data\",\n \"def create_ela_files(originals='./data/CASIA1_originals', originals_ela='./data/CASIA1_originals_ela',\\n fakes='./data/CASIA1_fakes', fakes_ela='./data/CASIA1_fakes_ela'):\\n for i, item in enumerate(os.listdir(originals)):\\n image = ela(os.path.join(originals, item))\\n cv2.imwrite(os.path.join(originals_ela, item), image)\\n \\n for i, item in enumerate(os.listdir(fakes)):\\n image = ela(os.path.join(fakes, item))\\n cv2.imwrite(os.path.join(fakes_ela, item), image)\",\n \"def exporting_cropped_images (fpath_tiff):\\n src = rasterio.open(fpath_tiff, 'r')\\n outfolder_irregular = '/train/irregular'\\n outfolder_healthy = '/train/healthy'\\n outfolder_concrete = '/train/concrete'\\n outfolder_incomplete = '/train/incomplete'\\n outfolder_other = '/train/other'\\n outfolder = '/train/batch'\\n #os.makedirs (outfolder, exist_ok = True)\",\n \"def main():\\n nb_processed = 0\\n for dataset_name in DATASETS:\\n print(\\\"-----------------\\\")\\n print(\\\"Dataset: '%s'\\\" % (dataset_name,))\\n print(\\\"-----------------\\\")\\n\\n dataset_dir = os.path.join(WRITE_MAIN_DIR, dataset_name)\\n if not os.path.isdir(dataset_dir):\\n os.makedirs(dataset_dir)\\n\\n dataset = Dataset([os.path.join(READ_MAIN_DIR, dataset_name)])\\n print(\\\"Found %d images total.\\\" % (len(dataset.fps),))\\n\\n errors = []\\n\\n scale_height, scale_width = SCALES[dataset_name]\\n target_aspect_ratio = scale_width / scale_height\\n\\n # iterate over every image in the current dataset,\\n # augment that image N times, add cols/rows until target aspect ratio\\n # is reached, resize it (e.g. 64x64), save it\\n for img_idx, (image_filepath, image) in enumerate(zip(dataset.fps, dataset.get_images())):\\n print(\\\"[%s] Image %d of %d (%.2f%%)...\\\" \\\\\\n % (dataset_name, img_idx+1, len(dataset.fps),\\n 100*(img_idx+1)/len(dataset.fps)))\\n\\n # IOErrors during loading of images result here in a None value\\n if image is None:\\n print(\\\"Error / None\\\")\\n errors.append((\\n image_filepath,\\n \\\"Failed to load image '%s' (idx %d for dataset %s)\\\" \\\\\\n % (image_filepath, img_idx, dataset_name)\\n ))\\n else:\\n # resize too big images to smaller ones before any augmentation\\n # (for performance reasons)\\n height = image.shape[0]\\n width = image.shape[1]\\n aspect_ratio = width / height\\n if width > 1000 or height > 1000:\\n image = misc.imresize(image, (1000, int(1000 * aspect_ratio)))\\n\\n # augment image\\n # converts augmented versions automatically to float32, 0-1\\n augmentations = augment(image, **AUGMENTATIONS[dataset_name])\\n\\n # create list of original image + augmented versions\\n images_aug = [image / 255.0]\\n images_aug.extend(augmentations)\\n\\n # for each augmented version of the images:\\n # resize it to target aspect ratio (e.g. same width and height),\\n # save it\\n for aug_idx, image_aug in enumerate(images_aug):\\n image_aug = to_aspect_ratio_add(image_aug, target_aspect_ratio)\\n filename = \\\"{:0>6}_{:0>3}.jpg\\\".format(img_idx, aug_idx)\\n img_scaled = misc.imresize(image_aug, (scale_height, scale_width))\\n misc.imsave(os.path.join(dataset_dir, filename), img_scaled)\\n\\n nb_processed += 1\\n\\n print(\\\"Processed %d images with %d errors.\\\" % (nb_processed, len(errors)))\\n for (fp, err) in errors:\\n print(\\\"File %s error:\\\" % (fp,))\\n print(err)\\n print(\\\"Finished.\\\")\",\n \"def HDF5_to_HDF5(self, **kwds):\\n # split extension from HDF5 file\\n if isinstance(self.filename, str):\\n fileBasename,fileExtension=os.path.splitext(self.filename)\\n else:\\n fileBasename,fileExtension=os.path.splitext(self.filename.filename)\\n # output HDF5 file\\n hdf5_file = os.path.expanduser(f'{fileBasename}.h5')\\n # copy everything from the HDF5 file\\n with h5py.File(self.filename,mode='r') as source:\\n dest = h5py.File(hdf5_file,mode='w')\\n # value checks on output HDF5\\n if not hasattr(dest, 'create_dataset'):\\n raise ValueError('dest must be a group, got {!r}'.format(dest))\\n # for each key in the root of the hdf5 file structure\\n for k in source.keys():\\n self.copy_from_HDF5(source[k], dest, name=k, **kwds)\",\n \"def read_hdf5(ID_images:str, path:str = \\\"data/dataset/\\\"):\\n images, labels = [], []\\n\\n #open the HDF5 file\\n file = h5py.File(path +ID_images+\\\"_lens.h5\\\", \\\"r\\\")\\n\\n images = np.array(file[\\\"/images\\\"]).astype(\\\"float64\\\")\\n labels = pd.read_hdf(path +ID_images+'_meta.h5', \\\"table\\\")\\n\\n return images, labels\",\n \"def gen_test_output(sess, logits, image_folder, image_pl, data_folder,\\n learning_phase, image_shape, nw_shape):\\n image_paths = glob(os.path.join(data_folder, image_folder, '*.png'))\\n for image_file in image_paths[:5]:\\n\\n in_image = scipy.misc.imread(image_file, mode='RGB')\\n image = scipy.misc.imresize(in_image, image_shape)\\n\\n street_im = get_seg_img(sess, logits, image_pl, image, image_shape,\\n nw_shape, learning_phase)\\n\\n street_im = scipy.misc.imresize(street_im, in_image.shape)\\n yield os.path.basename(image_file), np.array(street_im)\",\n \"def main(output_dir, img_size, max_boxes, train_size, val_size):\\n im_id = 0\\n ann_id = 0\\n\\n def make_split(split, split_size):\\n nonlocal im_id\\n nonlocal ann_id\\n\\n image_dir = join(output_dir, 'train')\\n make_dir(image_dir)\\n\\n images = []\\n annotations = []\\n for _ in range(split_size):\\n img, boxes = make_scene(img_size, max_boxes)\\n img = np.transpose(img, (1, 2, 0))\\n file_name = '{}.png'.format(im_id)\\n Image.fromarray(img).save(\\n join(image_dir, file_name))\\n images.append({\\n 'id': im_id,\\n 'height': img_size,\\n 'width': img_size,\\n 'file_name': file_name\\n })\\n for box in boxes:\\n annotations.append({\\n 'id': ann_id,\\n 'image_id': im_id,\\n 'category_id': 1,\\n 'area': (box[2] - box[0]) * (box[3] - box[1]),\\n 'bbox': [box[1], box[0], box[3]-box[1], box[2]-box[0]]\\n })\\n ann_id += 1\\n im_id += 1\\n\\n categories = [{'id': 1, 'name': 'rectangle'}]\\n labels = {\\n 'images': images,\\n 'annotations': annotations,\\n 'categories': categories}\\n json_to_file(labels, join(output_dir, '{}.json'.format(split)))\\n\\n make_split('train', train_size)\\n make_split('valid', val_size)\",\n \"def main():\\n\\n\\n\\n skulls_folder = os.listdir(RAW_IMAGE_DIRECTORY)\\n\\n # fetch and sort the .mnc and .tag files\\n mnc_files = [f for f in skulls_folder if 'mnc' in f]\\n tag_files = [f for f in skulls_folder if 'tag' in f]\\n mnc_names = [i.split('.mnc')[0] for i in mnc_files]\\n \\n mnc_files.sort()\\n tag_files.sort()\\n mnc_names.sort()\\n\\n # Process and package ndarrays as tuples inside npy file\\n package_to_npy(RAW_IMAGE_DIRECTORY, mnc_files, tag_files, mnc_names)\\n \\n print('\\\\n' * 5)\\n\\n # Push the npy files to GCP Cloud Storage\\n upload_to_gcp(PROCESSED_IMAGE_DIRECTORY, GCP_PROJECT_NAME, GCP_BUCKET_NAME)\",\n \"def create_png_images(self):\\n if self.subject is None:\\n print Console.WARNING + 'You need to specify a subject first' + Console.ENDC\\n return\\n\\n check_dir_of = self.locations.check_dir_of\\n check_dir_of(self.locations.HISTO_PNG_U)\\n check_dir_of(self.locations.HISTO_PNG)\\n check_dir_of(self.locations.SOURCE_PNG)\\n\\n\\n\\n fmap_img = ImageUtils.load_nifti_image(self.locations.HIST_FMAP) #loading subject nifti files\\n volumes = []\\n try:\\n for s in self.locations.SOURCES:\\n volumes.append(ImageUtils.load_nifti_image(s))\\n except IOError as e:\\n print Console.FAIL + 'There are errors loading nifi files for subject %s'%self.subject + Console.ENDC\\n return False\\n \\n\\n num_slices = volumes[0].shape[2] #use first volume to check expected number of slices\\n\\n self.locations.create_empty_dir(self.locations.IMAGES_DIR)\\n\\n print 'Creating input PNGs for %s'%self.subject\\n for k, vol in enumerate(volumes):\\n for i in range(num_slices):\\n imslice = ImageUtils.data_to_bytescale_rgb(vol[:, :, i])\\n im = Image.fromarray(imslice)\\n im.save(self.locations.SOURCE_PNG % (self.locations.LABELS[k],i))\\n\\n \\n print 'Creating histology PNGs for %s'%self.subject\\n for i in range(num_slices):\\n\\n im_unscaled = ImageUtils.data_to_unscaled_rgb(fmap_img[:, :, i]); #keeps the original values\\n im_unscaled = Image.fromarray(im_unscaled)\\n im_unscaled = im_unscaled.filter(ImageFilter.GaussianBlur(radius=2)) #Filter requested by Ali Khan\\n im_unscaled.save(self.locations.HISTO_PNG_U % i)\\n\\n im_scaled = ImageUtils.data_to_bytescale_rgb(fmap_img[:,:,i]); # bytescaled histology\\n im_scaled = Image.fromarray(im_scaled)\\n im_scaled = im_scaled.filter(ImageFilter.GaussianBlur(radius=2)) #Filter requested by Ali Khan\\n im_scaled.save(self.locations.HISTO_PNG % i)\\n\\n print\\n return True\",\n \"def print_images_in_statistics(self):\\n self._print_images_statistics(self._images_in_folder, self._pose_class_names)\",\n \"def print_images_out_statistics(self):\\n self._print_images_statistics(self._images_out_folder, self._pose_class_names)\",\n \"def create_training_images(fn, i, p_hr, p_lr, size, qualityFactor, downsize=True):\\n dest = p_lr/fn.relative_to(p_hr)\\n dest.parent.mkdir(parents=True, exist_ok=True)\\n img = PIL.Image.open(fn)\\n if downsize:\\n targ_sz = resize_to(img, size, use_min=True) # W x H\\n img = img.resize(targ_sz, resample=PIL.Image.BILINEAR).convert('RGB')\\n img.save(dest.with_suffix(\\\".jpg\\\"), \\\"JPEG\\\", quality=qualityFactor)\",\n \"def read_images(folder):\\n distinct_frames = DistinctFrames()\\n\\n for file in sorted(sorted(os.listdir(folder)),\\n key=len): # sorting files on basis of 1) length and 2) numerical order\\n '''\\n Sorting is done 2 times because\\n if files in the folder are\\n 1. image100.pkl\\n 2. image22.pkl\\n 3. image21.pkl\\n firstly sort them to image100.pkl,image21.pkl,image22.pkl then according to length to image21.pkl,image22.pkl,image100.pkl\\n '''\\n try:\\n img_obj = load_from_memory(file, folder)\\n time_stamp = img_obj.get_time()\\n distinct_frames.add_img_obj(img_obj)\\n print(\\\"Reading image ..\\\" + str(time_stamp) + \\\" from \\\" + folder) # for debug purpose\\n except:\\n # exception will occur for files like .DS_Store and jpg directory\\n continue\\n\\n if distinct_frames.no_of_frames() != 0:\\n distinct_frames.calculate_time()\\n\\n return distinct_frames\",\n \"def append_component_images(pldm_fw_up_pkg, image_files):\\n for image in image_files:\\n with open(image, \\\"rb\\\") as file:\\n for line in file:\\n pldm_fw_up_pkg.write(line)\",\n \"def global_metadata(paths):\\n\\n # Weakly group images to partition image set size- crucial optimization step\\n if os.path.exists(paths.image_preprocess):\\n clumped_paths = json.loads(open(paths.image_preprocess).read())\\n else:\\n clumped_paths = network.alpha_categorize(paths)\\n print(\\\"Hashed source images\\\")\\n\\n with open(paths.image_preprocess, 'w') as json_file:\\n json.dump(clumped_paths, json_file)\\n\\n # Combinatorial image grouping to graph\\n image_graph = network.load_graph(paths.image_network_path)\\n\\n total = len(list(chain(*clumped_paths.values())))\\n counter = 0.\\n\\n for image_paths in clumped_paths.values():\\n counter += len(image_paths)\\n print(str(int(counter / float(total) * 100)) + \\\"% complete\\\")\\n\\n if len(image_paths) > 1:\\n image_grouping = images.load_paths(paths.default_patches, image_paths)\\n image_graph = metadata.network.network_images(\\n image_grouping, threshold=0, network=image_graph)\\n else:\\n image_graph.add_node(image_paths[0])\\n\\n metadata.network.save_graph(paths.image_network_path, image_graph)\\n print(\\\"Updated image graph.\\\")\\n\\n # Create informational json files for templates and files\\n templates.build(paths, image_graph)\\n mappings.build(paths, image_graph)\\n print(\\\"Created JSON metadata files.\\\")\",\n \"def read_images(path, image_size=None):\\n c = 0\\n X = []\\n y = []\\n folder_names = []\\n for dirname, dirnames, filenames in os.walk(path):\\n for subdirname in dirnames:\\n folder_names.append(subdirname)\\n subject_path = os.path.join(dirname, subdirname)\\n for filename in os.listdir(subject_path):\\n #try:\\n im = cv2.imread(os.path.join(subject_path, filename), cv2.IMREAD_GRAYSCALE)\\n # resize to given size (if given)\\n if (image_size is not None):\\n im = cv2.resize(im, image_size)\\n X.append(np.asarray(im, dtype=np.uint8))\\n y.append(c)\\n #except IOError, (errno, strerror):\\n # print \\\"I/O error({0}): {1}\\\".format(errno, strerror)\\n # except:\\n # print \\\"Unexpected error:\\\", sys.exc_info()[0]\\n # raise\\n c = c+1\\n return [X,y,folder_names]\",\n \"def extractOneImageStack(filename, h5path=None, destPath=None):\\n if destPath == None:\\n destPath = \\\".\\\"\\n if filename.find(\\\"%\\\") > 0:\\n h5file = h5py.File(filename, driver=\\\"family\\\")\\n else:\\n h5file = h5py.File(filename)\\n if (h5path is None) or (h5path not in h5file):\\n list_of_names = []\\n h5file.visit(list_of_names.append)\\n print(os.linesep + \\\"Where is your stack of images dataset (in which group) ?\\\")\\n for onePath in list_of_names:\\n if isinstance(h5file[onePath], h5py.highlevel.Group):\\n print(\\\"\\\\t\\\" + onePath)\\n\\n h5path = raw_input(\\\"Where is your image stack ? \\\")\\n if not h5path in h5file:\\n print(\\\"This HDF5path %s is not in the file %s\\\" % (filename, h5path))\\n sys.exit(1)\\n group = h5file[h5path]\\n if \\\"data\\\" in group:\\n data = group[\\\"data\\\"]\\n else:\\n maxData = None\\n for obj in group:\\n if isinstance(group[obj], h5py.highlevel.Dataset):\\n if len(group[obj].shape) > len(maxData.shape):\\n maxData = group[obj]\\n elif len(group[obj].shape) == len(maxData.shape):\\n if group[obj].shape[0] > maxData.shape[0]:\\n maxData = group[obj]\\n if maxData is None:\\n raise Exception(\\\"No dataset found in provided group %s from file %s\\\" % (h5path, filename))\\n else:\\n data = maxData\\n dsSize = data.shape[0]\\n\\n headerGrp = []\\n for obj in group:\\n if isinstance(group[obj], h5py.highlevel.Group):\\n testHeaderGrp = group[obj]\\n for headerdataset in testHeaderGrp:\\n if testHeaderGrp[headerdataset].len() != dsSize:\\n break\\n else:\\n headerGrp = testHeaderGrp\\n if \\\"Filenames\\\" in group:\\n EdfFilenames = group[\\\"Filenames\\\"]\\n elif \\\"filenames\\\" in group:\\n EdfFilenames = group[\\\"filenames\\\"]\\n else:\\n base = os.path.splitext(filename)[0]\\n grpname = h5path.replace(\\\"/\\\", \\\"_\\\")\\n EdfFilenames = [\\\"%s-%s-%04i.edf\\\" % (base, grpname, i) for i in range(dsSize) ]\\n for idx, oneFile in enumerate(EdfFilenames):\\n print(\\\"Extracting \\\" + oneFile)\\n headers = {}\\n for key in headerGrp:\\n if key == \\\"filename\\\":\\n continue\\n headers[key] = headerGrp[key][idx]\\n edf = fabio.edfimage.edfimage(data=data[idx], header=headers)\\n edf.write(os.path.join(destPath, oneFile))\",\n \"def make_hdf5file(self, dataset_paths: list = None, dataset_values: list = None) -> None:\\n\\n assert dataset_paths is not None and len(dataset_paths) != 0, (\\\"`dataset_path` not valid. Expected at least \\\" \\\\\\n f\\\"one list element, got {len(dataset_paths)}.\\\")\\n\\n assert dataset_values is not None and len(dataset_values) != 0, (\\\"`dataset_values` not valid. Expected at least \\\" \\\\\\n f\\\"one list element, got {len(dataset_values)}.\\\")\\n\\n\\n # Remove file if already exists and create a new one\\n if os.path.isfile(os.path.join(self.FOFDirectory, self.filename)):\\n os.remove(os.path.join(self.FOFDirectory, self.filename))\\n print(f'[ FOFOutput ]\\\\t==> Removed old {self.filename} file.')\\n\\n # Create file and optional groups within it\\n FOFfile = h5py.File(os.path.join(self.FOFDirectory, self.filename), 'w')\\n print(f'[ FOFOutput ]\\\\t==> Created new {self.filename} file.')\\n\\n # Push the attributes to file, if any\\n if self.attrs is not None and len(self.attrs.keys()) > 0:\\n for key, text in zip(self.attrs.keys(), self.attrs.values()):\\n FOFfile.attrs[key] = text\\n\\n for internal_path, dataset_content in zip(dataset_paths, dataset_values):\\n\\n assert not internal_path.endswith('/'), \\\"Invalid hdf5 internal path\\\"\\n assert type(dataset_content) is np.ndarray, \\\"Can only push numpy.ndarrays into hdf5 files.\\\"\\n\\n nested_groups = self.groups_from_path(internal_path)\\n if len(nested_groups) == 1:\\n FOFfile.create_dataset(nested_groups[0], data=dataset_content)\\n else:\\n for nested_group in nested_groups[:-1]:\\n g = FOFfile.create_group(nested_group)\\n g.create_dataset(nested_groups[-1], data = dataset_content)\\n\\n print(f'[ FOFOutput ]\\\\t==> Created {internal_path} dataset in {self.filename} file.')\\n\\n FOFfile.close()\",\n \"def create_image_lists(image_dir):\\n if not gfile.Exists(image_dir):\\n print(\\\"Image directory '\\\" + image_dir + \\\"' not found.\\\")\\n return None\\n result = {}\\n sub_dirs = [x[0] for x in os.walk(image_dir)]\\n # The root directory comes first, so skip it.\\n is_root_dir = True\\n for sub_dir in sub_dirs:\\n print('in sub loop')\\n extensions = ['jpg', 'jpeg', 'JPG', 'JPEG']\\n file_list = []\\n dir_name = os.path.basename(image_dir)\\n print(\\\"Looking for images in '\\\" + image_dir + \\\"'\\\")\\n for extension in extensions:\\n file_glob = os.path.join(image_dir, dir_name, '*.' + extension)\\n file_list.extend(glob.glob(file_glob))\\n if not file_list:\\n print('No files found')\\n continue\\n if len(file_list) < 20:\\n print('WARNING: Folder has less than 20 images, which may cause issues.')\\n label_name = re.sub(r'[^a-z0-9]+', ' ', dir_name.lower())\\n testing_images = []\\n for file_name in file_list:\\n base_name = os.path.basename(file_name)\\n # We want to ignore anything after '_nohash_' in the file name when\\n # deciding which set to put an image in, the data set creator has a way of\\n # grouping photos that are close variations of each other. For example\\n # this is used in the plant disease data set to group multiple pictures of\\n # the same leaf.\\n hash_name = re.sub(r'_nohash_.*$', '', file_name)\\n # This looks a bit magical, but we need to decide whether this file should\\n # go into the training, testing, or validation sets, and we want to keep\\n # existing files in the same set even if more files are subsequently\\n # added.\\n # To do that, we need a stable way of deciding based on just the file name\\n # itself, so we do a hash of that and then use that to generate a\\n # probability value that we use to assign it.\\n testing_images.append(base_name)\\n return testing_images\",\n \"def splitMerge(self):\\n\\t\\tpath_merge = self.aug_merge_path\\n\\t\\tpath_train = self.aug_train_path\\n\\t\\tpath_label = self.aug_label_path\\n\\t\\tfor i in range(self.slices):\\n\\t\\t\\tpath = path_merge + \\\"/\\\" + str(i)\\n\\t\\t\\t# print(path)\\n\\t\\t\\ttrain_imgs = glob.glob(path+\\\"/*.\\\"+self.img_type)\\n\\t\\t\\t# print(len(train_imgs))\\n\\t\\t\\t# break\\n\\t\\t\\tfor imgname in train_imgs:\\n\\t\\t\\t\\tmidname = imgname[imgname.rindex(\\\"/\\\")+1:imgname.rindex(\\\".\\\"+self.img_type)]\\n\\t\\t\\t\\timg = cv2.imread(imgname)\\n\\t\\t\\t\\timg_train = img[:,:,2]#cv2 read image rgb->bgr\\n\\t\\t\\t\\timg_label = img[:,:,0]\\n\\t\\t\\t\\tcv2.imwrite(path_train+\\\"/\\\"+midname+\\\".\\\"+self.img_type,img_train)\\n\\t\\t\\t\\tcv2.imwrite(path_label+\\\"/\\\"+midname+\\\".\\\"+self.img_type,img_label)\",\n \"def create_noobj_folder(\\n folder: PathLike, \\n img_ext: str = \\\".jpg\\\",\\n):\\n folder = Path(folder).expanduser().resolve()\\n images = glob(folder, img_ext)\\n \\n for image in images:\\n filename = image.name\\n _folder = image.parent.name\\n path = folder / (image.stem + \\\".xml\\\")\\n img_w, img_h = get_image_size(image)\\n\\n tree = ET.Element(\\\"annotation\\\")\\n\\n et_folder = ET.SubElement(tree, \\\"folder\\\")\\n et_folder.text = _folder\\n\\n et_filename = ET.SubElement(tree, \\\"filename\\\")\\n et_filename.text = filename\\n\\n et_path = ET.SubElement(tree, \\\"path\\\")\\n et_path.text = str(path)\\n\\n et_img_size = ET.SubElement(tree, \\\"size\\\")\\n ET.SubElement(et_img_size, \\\"width\\\").text = str(img_w)\\n ET.SubElement(et_img_size, \\\"height\\\").text = str(img_h)\\n ET.SubElement(et_img_size, \\\"depth\\\").text = \\\"3\\\"\\n\\n content = ET.tostring(tree, encoding=\\\"unicode\\\", pretty_print=True)\\n try: \\n path.write_text(content)\\n except KeyboardInterrupt:\\n path.write_text(content)\\n exit()\",\n \"def add_random(fr_data_path, random_path, output_path):\\n with h5py.File(random_path, 'r') as data:\\n random = np.asarray(data['images'].value)\\n \\n means = np.mean(np.mean(random, axis=-1), axis=-1)\\n empty = means == 0.0\\n error = np.isnan(means)\\n discard = empty | error\\n\\n random_i = np.where(~discard)\\n random = random[random_i]\\n\\n random = center_on_brightest(random)\\n \\n with h5py.File(fr_data_path, 'r') as data:\\n images = np.asarray(data[\\\"images\\\"].value) \\n images = center_on_brightest(images)\\n \\n labels = np.where(np.asarray(data['labels']), 2, 1)\\n\\n images = np.concatenate((images, random), axis=0)\\n labels = np.concatenate((labels, np.full((random.shape[0],), \\n fill_value=0)), axis=0)\\n\\n with h5py.File(output_path, 'w') as f:\\n f.create_dataset('images', data=images)\\n f.create_dataset('labels', data=labels)\\n\\n with h5py.File(fr_data_path, 'r') as data: \\n f.copy(data, 'fri_data')\\n f.copy(data, 'frii_data')\",\n \"def __call__(self):\\n train_number = 1\\n test_number = 1\\n for cur_file in os.listdir(self.txt_dir):\\n if(cur_file.split(\\\".\\\")[0].split(\\\"-\\\")[-1] == \\\"ellipseList\\\"):\\n with open(os.path.join(self.txt_dir, cur_file), 'r') as txt_file:\\n while(True):\\n line = txt_file.readline().rstrip(\\\"\\\\n\\\")\\n if not line : break\\n\\n # Image load\\n img_folder_ = line.split(\\\"/\\\") \\n img_dir_ = \\\\\\n self.img_dir + \\\"\\\\\\\\\\\" + img_folder_[0] + \\\"\\\\\\\\\\\" + img_folder_[1] + \\\"\\\\\\\\\\\" + img_folder_[2] + \\\"\\\\\\\\\\\" + img_folder_[3] + \\\"\\\\\\\\\\\" + img_folder_[4] + \\\".jpg\\\"\\n img = cv2.imread(img_dir_, cv2.IMREAD_COLOR)\\n \\n if(train_number > 2500) :\\n image_name = \\\"IMG_{}.jpg\\\".format(test_number)\\n cv2.imwrite(os.path.join(self.new_img_dir, \\\"test\\\", image_name), img)\\n test_number = test_number + 1\\n\\n else :\\n image_name = \\\"IMG_{}.jpg\\\".format(train_number)\\n cv2.imwrite(os.path.join(self.new_img_dir, \\\"train\\\", image_name), img)\\n train_number = train_number + 1\\n \\n object_num = int(txt_file.readline().rstrip(\\\"\\\\n\\\"))\\n file_data = OrderedDict()\\n object_data = OrderedDict()\\n file_data[\\\"Img_id\\\"] = image_name.split('.')[0]\\n file_data[\\\"Objects\\\"] = []\\n\\n #Do \\\"for\\\" operation as much as the number of objects\\n for i in range(object_num):\\n coord_ = txt_file.readline().split(\\\" \\\")\\n rad = abs(float(coord_[2]))\\n h = float(coord_[0])*np.sin(rad)\\n w = float(coord_[1])*np.sin(rad)\\n object_data[\\\"Object_class\\\"] = \\\"Face\\\"\\n object_data[\\\"Coordinate\\\"] = { \\\"Xmin\\\" : float(coord_[3]) - w, \\\"Ymin\\\" : float(coord_[4]) - h, \\\"Xmax\\\" : float(coord_[3]) + w, \\\"Ymax\\\" : float(coord_[4]) + h }\\n file_data[\\\"Objects\\\"].append(object_data)\\n self.json_list.append(file_data)\\n\\n # Save train annotation\\n if(train_number == 2501):\\n self._save_annotation(\\\"train\\\")\\n train_number = train_number + 1\\n self._save_annotation(\\\"test\\\")\",\n \"def read_input_files(self):\\r\\n\\r\\n for input_file in self.list_of_input_files:\\r\\n input_file.read_header_of_file()\\r\\n self.list_of_header_objects.extend(input_file.list_of_header_objects)\\r\\n self.list_of_header_objects_without_ID.extend(input_file.list_of_header_objects_without_ID)\\r\\n self.list_of_contigs.extend(input_file.list_of_contigs)\\r\\n\\r\\n self.list_of_header_objects = list(toolz.unique(self.list_of_header_objects, key=lambda x: x.tag_and_ID))\\r\\n self.list_of_header_objects_without_ID = list(\\r\\n toolz.unique(self.list_of_header_objects_without_ID, key=lambda x: x.line))\\r\\n self.list_of_contigs = list(toolz.unique(self.list_of_contigs, key=lambda x: x.line))\\r\\n self.list_of_header_objects.extend(self.list_of_header_objects_without_ID)\\r\\n self.list_of_header_objects.sort(key=lambda x: x.line)\\r\\n self.list_of_header_objects.extend(self.list_of_contigs)\\r\\n self.list_of_header_objects.sort(key=lambda x: x.tag, reverse=False)\\r\\n self.create_body_header_line_for_output()\\r\\n self.write_header_in_output_file()\\r\\n\\r\\n list_of_chrom = list(self.indices.keys())\\r\\n list_of_chrom.sort(key=lambda x: self.alphanum_key(x))\\r\\n for chrom in list_of_chrom:\\r\\n self.list_of_body_objects.clear()\\r\\n for input_file in self.list_of_input_files:\\r\\n input_file.read_specific_chrom_body_of_file(chrom)\\r\\n self.list_of_body_objects.extend(input_file.list_of_body_objects)\\r\\n\\r\\n self.adjust_body_records_to_samples()\\r\\n self.list_of_body_objects = list(toolz.unique(self.list_of_body_objects, key=lambda x: x.line))\\r\\n self.list_of_body_objects.sort(key=lambda x: self.alphanum_key(x.line))\\r\\n self.verify_and_merge_body_records()\\r\\n self.write_specific_chrom_in_output_file()\",\n \"def decompose_images(data_dir, output_dir, save_individually, **kwargs):\\n\\n # parse parameters\\n opt = TestOptions()\\n opt.parse(kwargs)\\n # print(kwargs)\\n\\n # torch setting\\n pytorch_settings.set_(with_random=False, determine=True)\\n\\n # visualize\\n V.create_a_visualizer(opt)\\n\\n # NIID-Net Manager\\n model = create_model(opt)\\n model.switch_to_eval()\\n\\n # List all image files in the directory (exclude subdirectory)\\n image_file_list = list_files(data_dir, ['jpg', 'jpeg', 'png', 'tif', 'JPG'])\\n print('Total image in the directory %s: %d' % (data_dir, len(image_file_list)))\\n\\n # Decompose images\\n for file_name in image_file_list:\\n # Read image\\n img_path = os.path.join(data_dir, file_name)\\n o_img = Image.open(img_path)\\n o_img = o_img.convert(\\\"RGB\\\")\\n\\n # Resize input image\\n # input_img = resize_image(o_img)\\n input_img = o_img\\n\\n # Predict\\n input_img = TF.to_tensor(input_img).unsqueeze(0)\\n pred_N, pred_R, pred_L, pred_S, rendered_img = model.predict({'input_srgb': input_img}, normal=True, IID=True)\\n\\n # Save results\\n idx = 0\\n pred_imgs = {\\n 'pred_N': pred_N[idx].cpu(),\\n 'pred_R': pred_R[idx].cpu(),\\n 'pred_L': pred_L[idx].cpu(),\\n 'pred_S': pred_S[idx].cpu(),\\n 'rendered_img': rendered_img[idx].cpu(),\\n 'input_srgb': input_img[idx],\\n }\\n f = '%s_decomposed' % (file_name[:file_name.rfind('.')])\\n image_util.save_intrinsic_images(output_dir, pred_imgs, label=f, individual=save_individually)\\n torch.save(pred_imgs, os.path.join(output_dir, f+'.pth.tar'))\\n print('Decompose %s successfully!' % file_name)\",\n \"def resize_multiple_images(src_path, dst_path):\\n for filename in os.listdir(src_path):\\n img=Image.open(src_path+'/'+filename)\\n new_img = img.resize((96,96,))\\n #new_img.resize(96,96,1)\\n if not os.path.exists(dst_path):\\n os.makedirs(dst_path)\\n new_img.save(dst_path+'/'+filename)\\n print('Resized and saved {} successfully.'.format(filename))\",\n \"def test_save_image(self):\\n\\n from m3_save_images.m3_save_images import save_images\\n folder_destination_name = \\\"unittest-sorted-images\\\"\\n path_source = \\\"../img\\\"\\n image_name = [\\\"00ff00.png\\\", \\\"aqua.png\\\", \\\"black.jpg\\\", \\\"yellow.png\\\", \\\"red2.jpg\\\", \\\"green.jpg\\\"]\\n image_color = [\\\"Lime\\\", \\\"Aqua\\\", \\\"Black\\\", \\\"Yellow\\\", \\\"Red\\\", \\\"Green\\\"]\\n # new empty folder is needed for testing save_image() function\\n if os.path.isdir(folder_destination_name):\\n shutil.rmtree(folder_destination_name)\\n os.mkdir(folder_destination_name)\\n # creating folders\\n for i in range(0, 4):\\n save_images(folder_destination_name, path_source, image_name[i], image_color[i])\\n self.assertEqual(''.join(os.listdir(os.path.join(folder_destination_name, image_color[i]))), image_name[i])\\n save_images(folder_destination_name, path_source, image_name[i], image_color[5])\\n self.assertNotEqual(''.join(os.listdir(os.path.join(folder_destination_name, image_color[i]))), image_name[5])\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67509365","0.6384123","0.635404","0.62878686","0.6287435","0.62700456","0.6264196","0.62362057","0.6180607","0.6161816","0.6144989","0.6094709","0.6090776","0.6078111","0.60713965","0.60598326","0.6051427","0.60020304","0.5998716","0.5987901","0.59787405","0.5956464","0.59554833","0.59206235","0.5919363","0.5918594","0.58958215","0.5887884","0.58809143","0.587855","0.5856731","0.58481556","0.584211","0.58359003","0.5834821","0.58246946","0.5811717","0.580348","0.5795319","0.5793594","0.5786635","0.5785196","0.57844615","0.5783898","0.5763457","0.57570297","0.57549566","0.57522905","0.57365113","0.5730137","0.57190466","0.5708668","0.5700997","0.5699917","0.56986004","0.5692065","0.56852186","0.56821156","0.56753874","0.56707275","0.56688756","0.566151","0.5660219","0.56573534","0.56518096","0.5651804","0.56517893","0.56488556","0.56379783","0.563319","0.5630987","0.56271666","0.5626634","0.5624007","0.56234","0.5622638","0.5619962","0.5617768","0.5613786","0.55948514","0.5588134","0.55774236","0.55640006","0.55629337","0.55586845","0.55571157","0.55427057","0.55374455","0.55369854","0.55303174","0.552553","0.5524029","0.55201966","0.55175346","0.55157006","0.5510418","0.5510261","0.5508093","0.55044794","0.5501496","0.54953754"],"string":"[\n \"0.67509365\",\n \"0.6384123\",\n \"0.635404\",\n \"0.62878686\",\n \"0.6287435\",\n \"0.62700456\",\n \"0.6264196\",\n \"0.62362057\",\n \"0.6180607\",\n \"0.6161816\",\n \"0.6144989\",\n \"0.6094709\",\n \"0.6090776\",\n \"0.6078111\",\n \"0.60713965\",\n \"0.60598326\",\n \"0.6051427\",\n \"0.60020304\",\n \"0.5998716\",\n \"0.5987901\",\n \"0.59787405\",\n \"0.5956464\",\n \"0.59554833\",\n \"0.59206235\",\n \"0.5919363\",\n \"0.5918594\",\n \"0.58958215\",\n \"0.5887884\",\n \"0.58809143\",\n \"0.587855\",\n \"0.5856731\",\n \"0.58481556\",\n \"0.584211\",\n \"0.58359003\",\n \"0.5834821\",\n \"0.58246946\",\n \"0.5811717\",\n \"0.580348\",\n \"0.5795319\",\n \"0.5793594\",\n \"0.5786635\",\n \"0.5785196\",\n \"0.57844615\",\n \"0.5783898\",\n \"0.5763457\",\n \"0.57570297\",\n \"0.57549566\",\n \"0.57522905\",\n \"0.57365113\",\n \"0.5730137\",\n \"0.57190466\",\n \"0.5708668\",\n \"0.5700997\",\n \"0.5699917\",\n \"0.56986004\",\n \"0.5692065\",\n \"0.56852186\",\n \"0.56821156\",\n \"0.56753874\",\n \"0.56707275\",\n \"0.56688756\",\n \"0.566151\",\n \"0.5660219\",\n \"0.56573534\",\n \"0.56518096\",\n \"0.5651804\",\n \"0.56517893\",\n \"0.56488556\",\n \"0.56379783\",\n \"0.563319\",\n \"0.5630987\",\n \"0.56271666\",\n \"0.5626634\",\n \"0.5624007\",\n \"0.56234\",\n \"0.5622638\",\n \"0.5619962\",\n \"0.5617768\",\n \"0.5613786\",\n \"0.55948514\",\n \"0.5588134\",\n \"0.55774236\",\n \"0.55640006\",\n \"0.55629337\",\n \"0.55586845\",\n \"0.55571157\",\n \"0.55427057\",\n \"0.55374455\",\n \"0.55369854\",\n \"0.55303174\",\n \"0.552553\",\n \"0.5524029\",\n \"0.55201966\",\n \"0.55175346\",\n \"0.55157006\",\n \"0.5510418\",\n \"0.5510261\",\n \"0.5508093\",\n \"0.55044794\",\n \"0.5501496\",\n \"0.54953754\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":16881,"cells":{"query":{"kind":"string","value":"Read image files and make their subimages and saved them as a h5 file format."},"document":{"kind":"string","value":"def input_setup_PAN(sess,config,data_dir,index=0):\n if config.is_train:\n data = prepare_data(sess, dataset=data_dir)\n\n sub_input_sequence = []\n padding = 0 \n if config.is_train:\n for i in xrange(len(data)):\n input_=(imread(data[i])-127.5)/127.5\n\n if len(input_.shape) == 3:\n h, w, _ = input_.shape\n else:\n h, w = input_.shape\n for x in range(0, h-config.image_size_PAN+1, config.stride_PAN):\n for y in range(0, w-config.image_size_PAN+1, config.stride_PAN):\n sub_input = input_[x:x+config.image_size_PAN, y:y+config.image_size_PAN] \n # Make channel value\n if data_dir == \"Train\":\n sub_input=cv2.resize(sub_input, (config.image_size_PAN/4,config.image_size_PAN/4),interpolation=cv2.INTER_CUBIC)\n sub_input = sub_input.reshape([config.image_size_PAN/4, config.image_size_PAN/4, 1])\n print('error')\n else:\n sub_input = sub_input.reshape([config.image_size_PAN, config.image_size_PAN, 1]) \n sub_input_sequence.append(sub_input)\n arrdata = np.asarray(sub_input_sequence) \n print(arrdata.shape)\n make_data(sess, arrdata, data_dir)\n\n\n if not config.is_train:\n print(nx,ny)\n print(h_real,w_real)\n return nx, ny,h_real,w_real"},"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 create_and_write_output(predictions_path,output_path,inpDir):\n \n filenames= sorted(os.listdir(predictions_path)) \n for filename in filenames:\n \n # read the 3 channel output image from the neural network\n image=cv2.imread(os.path.join(predictions_path,filename))\n \n # create binary image output using the create_binary function\n out_image=create_binary(image) \n \n # read and store the metadata from the input image\n with BioReader(os.path.join(inpDir,filename)) as br:\n metadata = br.metadata\n\n # Write the binary output consisting of the metadata using bfio.\n output_image_5channel=np.zeros((out_image.shape[0],out_image.shape[1],1,1,1),dtype=np.uint8)\n output_image_5channel[:,:,0,0,0]=out_image \n\n with BioWriter(os.path.join(output_path,filename), metadata=metadata) as bw:\n bw.dtype = output_image_5channel.dtype\n bw.write(output_image_5channel)","def _preprocess(self):\n print(\"Note: if root path is changed, the previously generated json files need to be re-generated (delete them first)\")\n if osp.exists(self.imgs_labeled_dir) and \\\n osp.exists(self.imgs_detected_dir) and \\\n osp.exists(self.split_classic_det_json_path) and \\\n osp.exists(self.split_classic_lab_json_path) and \\\n osp.exists(self.split_new_det_json_path) and \\\n osp.exists(self.split_new_lab_json_path):\n return\n\n mkdir_if_missing(self.imgs_detected_dir)\n mkdir_if_missing(self.imgs_labeled_dir)\n\n print(\"Extract image data from {} and save as png\".format(self.raw_mat_path))\n mat = h5py.File(self.raw_mat_path, 'r')\n\n def _deref(ref):\n return mat[ref][:].T\n\n def _process_images(img_refs, campid, pid, save_dir):\n img_paths = [] # Note: some persons only have images for one view\n for imgid, img_ref in enumerate(img_refs):\n img = _deref(img_ref)\n # skip empty cell\n if img.size == 0 or img.ndim < 3: continue\n # images are saved with the following format, index-1 (ensure uniqueness)\n # campid: index of camera pair (1-5)\n # pid: index of person in 'campid'-th camera pair\n # viewid: index of view, {1, 2}\n # imgid: index of image, (1-10)\n viewid = 1 if imgid < 5 else 2\n img_name = '{:01d}_{:03d}_{:01d}_{:02d}.png'.format(campid+1, pid+1, viewid, imgid+1)\n img_path = osp.join(save_dir, img_name)\n imageio.imwrite(img_path, img)\n img_paths.append(img_path)\n return img_paths\n\n def _extract_img(name):\n print(\"Processing {} images (extract and save) ...\".format(name))\n meta_data = []\n imgs_dir = self.imgs_detected_dir if name == 'detected' else self.imgs_labeled_dir\n for campid, camp_ref in enumerate(mat[name][0]):\n camp = _deref(camp_ref)\n num_pids = camp.shape[0]\n for pid in range(num_pids):\n img_paths = _process_images(camp[pid,:], campid, pid, imgs_dir)\n assert len(img_paths) > 0, \"campid{}-pid{} has no images\".format(campid, pid)\n meta_data.append((campid+1, pid+1, img_paths))\n print(\"done camera pair {} with {} identities\".format(campid+1, num_pids))\n return meta_data\n\n meta_detected = _extract_img('detected')\n meta_labeled = _extract_img('labeled')\n\n def _extract_classic_split(meta_data, test_split):\n train, test = [], []\n num_train_pids, num_test_pids = 0, 0\n num_train_imgs, num_test_imgs = 0, 0\n for i, (campid, pid, img_paths) in enumerate(meta_data):\n \n if [campid, pid] in test_split:\n for img_path in img_paths:\n camid = int(osp.basename(img_path).split('_')[2])\n test.append((img_path, num_test_pids, camid))\n num_test_pids += 1\n num_test_imgs += len(img_paths)\n else:\n for img_path in img_paths:\n camid = int(osp.basename(img_path).split('_')[2])\n train.append((img_path, num_train_pids, camid))\n num_train_pids += 1\n num_train_imgs += len(img_paths)\n return train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs\n\n print(\"Creating classic splits (# = 20) ...\")\n splits_classic_det, splits_classic_lab = [], []\n for split_ref in mat['testsets'][0]:\n test_split = _deref(split_ref).tolist()\n\n # create split for detected images\n train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs = \\\n _extract_classic_split(meta_detected, test_split)\n splits_classic_det.append({\n 'train': train, 'query': test, 'gallery': test,\n 'num_train_pids': num_train_pids, 'num_train_imgs': num_train_imgs,\n 'num_query_pids': num_test_pids, 'num_query_imgs': num_test_imgs,\n 'num_gallery_pids': num_test_pids, 'num_gallery_imgs': num_test_imgs,\n })\n\n # create split for labeled images\n train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs = \\\n _extract_classic_split(meta_labeled, test_split)\n splits_classic_lab.append({\n 'train': train, 'query': test, 'gallery': test,\n 'num_train_pids': num_train_pids, 'num_train_imgs': num_train_imgs,\n 'num_query_pids': num_test_pids, 'num_query_imgs': num_test_imgs,\n 'num_gallery_pids': num_test_pids, 'num_gallery_imgs': num_test_imgs,\n })\n \n write_json(splits_classic_det, self.split_classic_det_json_path)\n write_json(splits_classic_lab, self.split_classic_lab_json_path)\n\n def _extract_set(filelist, pids, pid2label, idxs, img_dir, relabel):\n tmp_set = []\n unique_pids = set()\n for idx in idxs:\n img_name = filelist[idx][0]\n camid = int(img_name.split('_')[2])\n pid = pids[idx]\n if relabel: pid = pid2label[pid]\n img_path = osp.join(img_dir, img_name)\n tmp_set.append((img_path, int(pid), camid))\n unique_pids.add(pid)\n return tmp_set, len(unique_pids), len(idxs)\n\n def _extract_new_split(split_dict, img_dir):\n train_idxs = split_dict['train_idx'].flatten() - 1 # index-0\n pids = split_dict['labels'].flatten()\n train_pids = set(pids[train_idxs])\n pid2label = {pid: label for label, pid in enumerate(train_pids)}\n query_idxs = split_dict['query_idx'].flatten() - 1\n gallery_idxs = split_dict['gallery_idx'].flatten() - 1\n filelist = split_dict['filelist'].flatten()\n train_info = _extract_set(filelist, pids, pid2label, train_idxs, img_dir, relabel=True)\n query_info = _extract_set(filelist, pids, pid2label, query_idxs, img_dir, relabel=False)\n gallery_info = _extract_set(filelist, pids, pid2label, gallery_idxs, img_dir, relabel=False)\n return train_info, query_info, gallery_info\n\n print(\"Creating new splits for detected images (767/700) ...\")\n train_info, query_info, gallery_info = _extract_new_split(\n loadmat(self.split_new_det_mat_path),\n self.imgs_detected_dir,\n )\n splits = [{\n 'train': train_info[0], 'query': query_info[0], 'gallery': gallery_info[0],\n 'num_train_pids': train_info[1], 'num_train_imgs': train_info[2],\n 'num_query_pids': query_info[1], 'num_query_imgs': query_info[2],\n 'num_gallery_pids': gallery_info[1], 'num_gallery_imgs': gallery_info[2],\n }]\n write_json(splits, self.split_new_det_json_path)\n\n print(\"Creating new splits for labeled images (767/700) ...\")\n train_info, query_info, gallery_info = _extract_new_split(\n loadmat(self.split_new_lab_mat_path),\n self.imgs_labeled_dir,\n )\n splits = [{\n 'train': train_info[0], 'query': query_info[0], 'gallery': gallery_info[0],\n 'num_train_pids': train_info[1], 'num_train_imgs': train_info[2],\n 'num_query_pids': query_info[1], 'num_query_imgs': query_info[2],\n 'num_gallery_pids': gallery_info[1], 'num_gallery_imgs': gallery_info[2],\n }]\n write_json(splits, self.split_new_lab_json_path)","def gene_h5_train_file(data_path, h5_name):\n img = []\n y_cls_mask = []\n y_reg_cls_mask = []\n os.chdir(data_path)\n jpgfiles = glob.glob('*.jpg')\n idx = 1\n # the position of generator objector is very important\n gene_obj = image_output_pair(data_path, 1/255.0)\n while True:\n if idx == len(jpgfiles):\n break\n print '\\t{0}/{1}'.format(idx, len(jpgfiles))\n # the position of generator objector is very important\n # gene_obj = image_output_pair(data_path, 1/255.0)\n img_it, y_cls_mask_it, y_reg_cls_mask_it = gene_obj.next()\n img.append(img_it)\n y_cls_mask.append(y_cls_mask_it)\n y_reg_cls_mask.append(y_reg_cls_mask_it)\n idx += 1\n\n # img => (320, 320, 3)\n # after np.stack => (19041, 320, 320, 3)\n img_input = np.stack(img, axis=0)\n y_cls = np.stack(y_cls_mask, axis=0)\n y_reg = np.stack(y_reg_cls_mask, axis=0)\n print 'input data shape is {0}'.format(img_input.shape)\n print 'y_cls data shape is {0}'.format(y_cls.shape)\n print 'y_reg data shape is {0}'.format(y_reg.shape)\n \n # wirte data\n h5 = '/home/yuquanjie/Documents/train_' + h5_name\n file_write = h5py.File(h5, 'w')\n file_write.create_dataset('X_train', data=img_input)\n file_write.create_dataset('Y_train_cls', data=y_cls)\n file_write.create_dataset('Y_train_merge', data=y_reg)\n file_write.close()","def preprocess_images(file_path, new_file_path):\n if not os.path.isdir(new_file_path):\n os.mkdir(new_file_path)\n i = 0\n for dir in listdir(file_path):\n j = 0\n for image_path in listdir(file_path + '/' + dir):\n image = open_image(image_path)\n cv2.imwrite(file_path + '/' + image_path + '/' str(i) + '/' +str(i) + '.jpg', image)\n j += 1\n i += 1","def bulk_process_images(inputpath, outputpath, extension):\n\n for dirpath, dirnames, filenames in os.walk(inputpath):\n structure = os.path.join(outputpath, dirpath[len(inputpath) + 1:])\n for file in filenames:\n if file.endswith(extension):\n src = os.path.join(dirpath, file)\n dest = os.path.join(structure, file)\n img = load_and_preprocess_image(src)\n cv2.imwrite(dest, img)","def create_train(train_img_path):\n\n f = open(\"train.txt\", \"w+\")\n for subdirs, dirs, files in os.walk(train_img_path):\n for filename in files:\n if filename.endswith(\".jpg\"):\n train_image_path = os.path.join(train_img_path, filename)\n print(train_image_path)\n f.write(train_image_path + \"\\n\")\n f.close()","def save_step_1(imgs, output_path='./output/step1'):\n # ... your code here ...\n i=0\n for each in imgs:\n i+=1\n cv2.imwrite(output_path+\"/output\"+str(i)+\".jpg\", each)","def main():\n \n # for inserting other images, add tem to /input folder and list them here\n images = (\n 'image-0',\n 'image-1',\n 'image-2'\n )\n\n for image_name in images:\n print(image_name, \"image:\")\n\n image = open_image(image_name)\n display_image(image, \"Original input \" + image_name)\n\n grayscale_v = transform_colors(image)\n display_image(grayscale_v[:,:,0], \"Grayscale \" + image_name)\n save_image(image_name + \"-grayscale\", grayscale_v[:,:,0])\n\n contours_v, contours = get_contours(grayscale_v)\n display_image(contours_v, \"Contours \" + image_name)\n save_image(image_name + \"-contours\", contours_v)\n\n labeled_img, areas = get_measures(image, contours[1:])\n display_image(labeled_img, \"Labeled \" + image_name)\n save_image(image_name + \"-labeled\", labeled_img)\n\n areas_histogram(areas, image_name)","def save_image_to_h5(image, h5_path, *args, **kwargs):\n # TODO: Implement the method\n\n f = h5.File(h5_path, \"w\")\n grp = f.create_group(\"data\")\n grp.create_dataset(\"image\", data=image)","def prepare_hdf5_file(hdf5_file, n_train, n_valid, n_test):\n n_total = n_train + n_valid + n_test\n splits = create_splits(n_train, n_valid, n_test)\n hdf5_file.attrs['split'] = H5PYDataset.create_split_array(splits)\n vlen_dtype = h5py.special_dtype(vlen=numpy.dtype('uint8'))\n hdf5_file.create_dataset('encoded_images', shape=(n_total,),\n dtype=vlen_dtype)\n hdf5_file.create_dataset('targets', shape=(n_total, 1), dtype=numpy.int16)\n hdf5_file.create_dataset('filenames', shape=(n_total, 1), dtype='S32')","def appendpics(pathofimg, w_sub, h_sub, step):\n num = 0\n dirlist = []\n images = [] # images in each folder\n for root, dirs, fileswer in os.walk(pathofimg):\n if len(dirs)!= 0:\n for dir in dirs:\n dirlist.append(dir)\n for rooert, dirwerwes, files in os.walk(pathofimg+'/'+dir):\n for file in files:\n if(file.endswith('.png')):\n images.append(Image.open(pathofimg+'/'+dir+'/'+file))\n if(len(images)==81):\n break\n target = montage(images, w_sub, h_sub, step)\n target.save(pathofimg +'/'+ dir + '.png', quality=100)\n else:\n dir = 'Generated'\n for file in fileswer:\n if (file.endswith('.png')):\n images.append(Image.open(pathofimg +'/'+ file))\n target1 = montage(images, w_sub, h_sub, step)\n savepath = pathofimg +'/'+ 'generated'\n os.makedirs(savepath)\n target1.save(savepath +'/'+ dir + '.png', quality=100)","def get_files(self):\n train_images = glob(os.path.join(self.images_dir, '*%s' % self.im_extension)) \n train_labels = [x.replace(self.im_extension, '.npy').replace('images', 'groundTruth') for x in train_images]\n val_images = glob(os.path.join(self.val_images_dir, '*%s' % self.im_extension))\n val_labels = [x.replace(self.im_extension, '.npy').replace('images', 'groundTruth') for x in val_images]\n train_images = np.array(train_images)\n train_labels = np.array(train_labels)\n val_images = np.array(val_images)\n val_labels = np.array(val_labels)\n test_images = np.array(\n glob('/media/data_cifs/pytorch_projects/datasets/BSDS500_crops/data/images/test_nocrop/*.jpg'))\n test_labels = np.array(\n [x.replace('images', 'groundTruth').replace('.jpg', '.npy') for x in test_images])\n test_labels = np.array(\n [np.load(x) for x in test_labels])\n keep_idx = np.array([True if x.shape[0] > x.shape[1] else False for x in test_labels])\n test_images = test_images[keep_idx]\n test_labels = test_labels[keep_idx]\n test_images = np.stack([misc.imread(x) for x in test_images], 0)\n test_labels = np.stack(test_labels, 0)\n test_labels = test_labels[..., None]\n\n # Add constant padding to bottom/right\n if self.pad:\n test_images = util.pad(test_images, ((0, 0), (self.pad // 2, self.pad - self.pad // 2), (self.pad // 2, self.pad - self.pad // 2), (0, 0)), mode='linear_ramp')\n test_labels = util.pad(test_labels, ((0, 0), (self.pad // 2, self.pad - self.pad // 2), (self.pad // 2, self.pad - self.pad // 2), (0, 0)), mode='constant', constant_values=0)\n\n # Select images for training\n sort_idx = np.argsort(train_images)\n train_images = train_images[sort_idx[:self.train_size]]\n train_labels = train_labels[sort_idx[:self.train_size]]\n\n # Build CV dict\n cv_files, cv_labels = {}, {}\n cv_files[self.folds['train']] = train_images\n cv_files[self.folds['val']] = val_images\n cv_files[self.folds['test']] = test_images\n cv_labels[self.folds['train']] = train_labels\n cv_labels[self.folds['val']] = val_labels\n cv_labels[self.folds['test']] = test_labels\n return cv_files, cv_labels","def write_data_files(self):\n \n logging.info('\\n Start writing data files \\n')\n \n for i, (data_file, label_file) in enumerate(self.files):\n data_file, label_file = Path(data_file), Path(label_file)\n logging.info('Writing .hdf5 file for : [{}]'.format(str(data_file)))\n \n file_name = self.save_data_folder / '{}.hdf5'.format(label_file.name[:-4])\n if file_name.exists():\n continue\n \n with h5py.File(str(file_name), 'w') as writer:\n self.serialize_samples(\n writer, data_file, label_file)","def _extract_images(source_path, target_path, merge_labels):\n\n images_path = os.path.join(source_path, 'imagesTr')\n labels_path = os.path.join(source_path, 'labelsTr')\n\n # Filenames have the form 'hippocampus_XX.nii.gz'\n filenames = [x for x in os.listdir(images_path) if x[:5] == 'hippo']\n\n # Create directories\n if not os.path.isdir(target_path):\n os.makedirs(target_path)\n\n for filename in filenames:\n\n # Extract only T2-weighted\n x = sitk.ReadImage(os.path.join(images_path, filename))\n x = sitk.GetArrayFromImage(x)\n y = sitk.ReadImage(os.path.join(labels_path, filename))\n y = sitk.GetArrayFromImage(y)\n\n # Shape expected: (35, 51, 35)\n # Average label shape: (24.5, 37.8, 21.0)\n assert x.shape == y.shape\n\n # No longer distinguish between hippocampus proper and subiculum\n if merge_labels:\n y[y == 2] = 1\n\n # Save new images so they can be loaded directly\n study_name = filename.replace('_', '').split('.nii')[0]\n sitk.WriteImage(sitk.GetImageFromArray(x), join_path([target_path, study_name + \".nii.gz\"]))\n sitk.WriteImage(sitk.GetImageFromArray(y), join_path([target_path, study_name + \"_gt.nii.gz\"]))","def save_step_2(imgs, match_list, output_path=\"./output/step2\"):\n # ... your code here ...\n for i in range(len(imgs)):\n name1,tail1 = str.split(filenames[match_list[i][0]],\".\")\n name2,tail2 = str.split(filenames[match_list[i][2]],\".\")\n cv2.imwrite(output_path+\"/\"+name1+\"_\"+str(match_list[i][1])+\"_\"+name2+\"_\"+str(match_list[i][3])+\"_\"+str(match_list[i][4])+\".jpg\", imgs[i])","def analyzeImages(path, name_type, box1_size, box2_size, box3_size, box4_size, box5_size):\n \n folders = [f for f in sorted(glob.glob(path + \"/**\"))]\n \n for folder in folders: \n \n # to save this data frame in a csv file\n \n files = [f for f in sorted(glob.glob(folder + \"/**\" + \".jpg\"))]\n \n centroidsDf = pd.DataFrame(np.zeros([len(files), 11]),columns=[\"frame\", \"fly1_x\", \"fly1_y\", \"fly2_x\", \"fly2_y\", \"fly3_x\", \"fly3_y\", \"fly4_x\", \"fly4_y\", \"fly5_x\", \"fly5_y\"]);\n headsDf = pd.DataFrame(np.zeros([len(files), 11]),columns=[\"frame\", \"fly1_x\", \"fly1_y\", \"fly2_x\", \"fly2_y\", \"fly3_x\", \"fly3_y\", \"fly4_x\", \"fly4_y\", \"fly5_x\", \"fly5_y\"]);\n \n img_array1 = []\n img_array2 = []\n img_array3 = []\n img_array4 = []\n img_array5 = []\n\n for file in files:\n \n print(file)\n \n centroidsDf[\"frame\"][files.index(file)] = files.index(file)+1\n headsDf[\"frame\"][files.index(file)] = files.index(file)+1\n \n img = cv2.imread(file)\n \n ## FLY 1 ##\n\n box1 = img[box1_size[0]:box1_size[1], box1_size[2]:box1_size[3]]\n \n # image processing to get the centroid and head positions\n x_centroid, y_centroid, x_head, y_head = findCentroid(box1, file) \n \n # add the centroid and head locations on the image \n box1 = cv2.circle(box1, (x_centroid, y_centroid), 7, (255, 0, 0), -1)\n box1 = cv2.circle(box1, (x_head, y_head), 7, (0, 0, 255), -1)\n img_array1.append(box1)\n \n # add the positions in the final data frame\n centroidsDf[\"fly1_x\"][files.index(file)] = x_centroid\n centroidsDf[\"fly1_y\"][files.index(file)] = y_centroid\n \n headsDf[\"fly1_x\"][files.index(file)] = x_head\n headsDf[\"fly1_y\"][files.index(file)] = y_head\n \n ## FLY 2 ##\n \n box2 = img[box2_size[0]:box2_size[1], box2_size[2]:box2_size[3]]\n \n # image processing to get the centroid and head positions\n x_centroid, y_centroid, x_head, y_head = findCentroid(box2, file)\n \n # add the centroid and head locations on the image \n box2 = cv2.circle(box2, (x_centroid, y_centroid), 7, (255, 0, 0), -1)\n box2 = cv2.circle(box2, (x_head, y_head), 7, (0, 0, 255), -1)\n img_array2.append(box2)\n \n # add the positions in the final data frame \n centroidsDf[\"fly2_x\"][files.index(file)] = x_centroid\n centroidsDf[\"fly2_y\"][files.index(file)] = y_centroid\n \n headsDf[\"fly2_x\"][files.index(file)] = x_head\n headsDf[\"fly2_y\"][files.index(file)] = y_head\n \n ## FLY 3 ##\n\n box3 = img[box3_size[0]:box3_size[1], box3_size[2]:box3_size[3]]\n \n # image processing to get the centroid and head positions\n x_centroid, y_centroid, x_head, y_head = findCentroid(box3, file)\n \n # add the centroid and head locations on the image \n box3 = cv2.circle(box3, (x_centroid, y_centroid), 7, (255, 0, 0), -1)\n box3 = cv2.circle(box3, (x_head, y_head), 7, (0, 0, 255), -1)\n img_array3.append(box3)\n\n # add the positions in the final data frame\n centroidsDf[\"fly3_x\"][files.index(file)] = x_centroid\n centroidsDf[\"fly3_y\"][files.index(file)] = y_centroid\n \n headsDf[\"fly3_x\"][files.index(file)] = x_head\n headsDf[\"fly3_y\"][files.index(file)] = y_head\n \n ## FLY 4 ##\n \n box4 = img[box4_size[0]:box4_size[1], box4_size[2]:box4_size[3]]\n \n # image processing to get the centroid and head positions\n x_centroid, y_centroid, x_head, y_head = findCentroid(box4, file)\n \n # add the centroid and head locations on the image \n box4 = cv2.circle(box4, (x_centroid, y_centroid), 7, (255, 0, 0), -1)\n box4 = cv2.circle(box4, (x_head, y_head), 7, (0, 0, 255), -1)\n img_array4.append(box4)\n \n # add the positions in the final data frame\n centroidsDf[\"fly4_x\"][files.index(file)] = x_centroid\n centroidsDf[\"fly4_y\"][files.index(file)] = y_centroid\n \n headsDf[\"fly4_x\"][files.index(file)] = x_head\n headsDf[\"fly4_y\"][files.index(file)] = y_head\n \n ## FLY 5 ##\n \n # the fifth fly is not present in all the genetic strains \n if (box5_size != []):\n box5 = img[box5_size[0]:box5_size[1], box5_size[2]:box5_size[3]]\n \n # image processing to get the centroid and head positions\n x_centroid, y_centroid, x_head, y_head = findCentroid(box5, file)\n \n # add the centroid and head locations on the image \n box5 = cv2.circle(box5, (x_centroid, y_centroid), 7, (255, 0, 0), -1)\n box5 = cv2.circle(box5, (x_head, y_head), 7, (0, 0, 255), -1)\n img_array5.append(box5)\n \n # add the positions in the final data frame\n centroidsDf[\"fly5_x\"][files.index(file)] = x_centroid\n centroidsDf[\"fly5_y\"][files.index(file)] = y_centroid\n \n headsDf[\"fly5_x\"][files.index(file)] = x_head\n headsDf[\"fly5_y\"][files.index(file)] = y_head\n \n # save the data frame in a .csv file, \n # one for the centroids and one for the heads\n #centroidsDf.to_csv(folder+\"/centroids.csv\", index = None, header=True)\n #headsDf.to_csv(folder+\"/heads.csv\", index = None, header=True)\n \n \n ## CREATE THE VIDEOS ##\n \n height, width, _ = box1.shape\n size = (width,height)\n out = cv2.VideoWriter(folder+name_type+'_1_' + str(folders.index(folder)+1)+ '.mp4',cv2.VideoWriter_fourcc(*'DIVX'), 1.5, size)\n for i in range(len(img_array1)):\n out.write(img_array1[i])\n out.release()\n \n height, width, _ = box2.shape\n size = (width,height)\n out = cv2.VideoWriter(folder+name_type+'_2_' + str(folders.index(folder)+1)+ '.mp4' ,cv2.VideoWriter_fourcc(*'DIVX'), 1.5, size)\n for i in range(len(img_array2)):\n out.write(img_array2[i])\n out.release()\n \n height, width, _ = box3.shape\n size = (width,height)\n out = cv2.VideoWriter(folder+name_type+'_3_' + str(folders.index(folder)+1)+ '.mp4' ,cv2.VideoWriter_fourcc(*'DIVX'), 1.5, size)\n for i in range(len(img_array3)):\n out.write(img_array3[i])\n out.release()\n \n height, width, _ = box4.shape\n size = (width,height)\n out = cv2.VideoWriter(folder+name_type+'_4_' + str(folders.index(folder)+1)+ '.mp4' ,cv2.VideoWriter_fourcc(*'DIVX'), 1.5, size)\n for i in range(len(img_array4)):\n out.write(img_array4[i])\n out.release()\n \n if (box5_size != []):\n height, width, _ = box5.shape\n size = (width,height)\n out = cv2.VideoWriter(folder+name_type+'_5_' + str(folders.index(folder)+1)+ '.mp4' ,cv2.VideoWriter_fourcc(*'DIVX'), 1.5, size)\n for i in range(len(img_array5)):\n out.write(img_array5[i])\n out.release()","def main(vis_dirs, outdir):\n assert len(vis_dirs) == 4\n\n if not os.path.exists(outdir):\n os.mkdir(outdir)\n\n for i, filename in enumerate(tqdm(os.listdir(vis_dirs[-1]))):\n # if i % 100 == 0:\n # print(i)\n\n files = [os.path.join(vis_dir, filename) for vis_dir in vis_dirs]\n outimg = os.path.join(outdir, filename)\n merge_four_images(files, outimg)\n\n print (\"Finished! Result dir is %s\" % outdir)","def process_files(self):\n for filename in self.temp_directory.iterdir():\n im = Image.open(str(filename))\n scaled = im.resize((640, 480))\n scaled.save(str(filename))","def pickle_images(path, df, image_size=\"\"):\n all_imgs = []\n labels = []\n folder = path\n for f in os.listdir(folder): \n if f.endswith(\".png\"): \n img = read_transposed_image(folder+f)\n i = f.split(\"_\")[0]\n name = \".\".join(\"_\".join(f.split(\"_\")[1:]).split(\".\")[:-1])\n label = df[(df.id == i) & (df.filename == name)].label\n if len(label) > 0:\n label = label.iloc[0]\n if image_size != \"\":\n new_image = resize_and_pad(img, image_size)\n new_dir = folder+str(image_size)\n if not os.path.exists(new_dir):\n os.makedirs(new_dir)\n misc.toimage(new_image).save(new_dir+\"/\"+f)\n all_imgs.append(new_image)\n else:\n all_imgs.append(img) \n labels.append(label)\n d = {\"data\":all_imgs, \"labels\":labels}\n with open('data_'+str(image_size)+'.pkl', 'wb') as handle:\n cPickle.dump(d, handle, protocol=cPickle.HIGHEST_PROTOCOL)","def mosaic(self):\n \n if len(self.tiles) > 1:\n hdflist = sorted(glob.glob(self.fullPath + '/*.hdf'))\n for i in range(0,len(hdflist),2):\n ms = pymodis.convertmodis_gdal.createMosaicGDAL(hdfnames = [hdflist[i], hdflist[i+1]], subset = self.subset, outformat = 'GTiff')\n ms.run(str(hdflist[i].split('.h')[0]) + 'mos.tif')\n ms.write_vrt(output = str(hdflist[i].split('.h')[0]), separate = True)\n mosaicCount = len(glob.glob(self.fullPath + '/*mos.tif'))\n logger.log('SUCCESS', 'Mosaic complete! MODIS tiles %s were successfully mosaicked into %d mosaic images.' % (str(self.tiles), mosaicCount))","def save(images, output):\n for image, frame in images:\n image.save(output(frame))","def process_all_images(input_path: str, output_path: str, resized_image_shape: Tuple,transformations:List[TransformationsEnum]):\n\n output_images_path = os.path.join(output_path, \"images\")\n csv_file_path = os.path.join(output_path, \"metadata.csv\")\n\n prepare_folders(output_path, output_images_path)\n prepare_csv(csv_file_path)\n\n df = pd.read_csv(csv_file_path)\n current_id = 1 #has to check the current id in the folder or be set to 1 if none\n categories_names = list(os.listdir(input_path))\n\n encoder = LabelBinarizer()\n encoder.fit(categories_names)\n\n\n for folder_name in os.listdir(input_path):\n current_category_name = folder_name\n category_path = os.path.join(input_path, folder_name)\n images_in_category = list(Path(category_path).glob(\"*.jpg\"))\n df, current_id = process_image(\n df, current_id, encoder, current_category_name, images_in_category,output_images_path, resized_image_shape,transformations\n )\n\n df.to_csv(csv_file_path, index=False, quotechar='\"', encoding='ascii')\n\n print(\"done, processed\", len(df), \"images\")","def jarvis(input_path, output_path): \n \n if not os.path.exists(f'{output_path}'):\n os.makedirs(f'{output_path}')\n\n folder_list = [sample for sample in os.listdir(input_path) if os.path.isdir(f'{input_path}{sample}')]\n\n for folder in folder_list:\n\n file_list = [filename for filename in os.listdir(f'{input_path}{folder}/') if '.tif' in filename]\n mutant = '_'.join(folder.split(' '))\n\n for x, filename in enumerate(file_list):\n pathname = os.path.join(input_path, folder, filename)\n new_name = f'{output_path}{mutant}_{x}.tif'\n copyfile(pathname, new_name)\n # array_stack = skimage.io.imread(f'{pathname}').transpose(1, 2, 0)\n logger.info(f'{new_name}')","def combineImages(path=None, imgfiles=None, cols=3, size=300):\n\n font = ImageFont.truetype(\"Arial.ttf\", 15)\n x=size\n w=20\n i=0; j=0\n if imgfiles == None:\n imgfiles = findFiles(path, 'png')\n width = cols*(x+w)\n height = int(math.ceil(float(len(imgfiles))/cols)*x)\n new_im = Image.new('RGBA', (width, height), 'white')\n for f in imgfiles:\n name = os.path.basename(f).split('.')[0]\n if not os.path.exists(f):\n continue\n im = Image.open(f)\n im.thumbnail((x,x))\n new_im.paste(im, (i*x+w,j*x+w))\n draw = ImageDraw.Draw(new_im)\n draw.text((i*x+w,j*x+w), name, (0,0,0), font=font)\n i+=1\n if i>=cols:\n i=0; j+=1\n #new_im.show()\n path = os.path.split(imgfiles[0])[0]\n new_im.save(os.path.join(path,\"summary.png\"))\n return","def write_data(data_to_write_train, data_to_write_test, train_path, eval_path ,test_path):\n aligned_lists_train = data_to_write_train[0]\n raw_lists_train = data_to_write_train[2]\n\n aligned_lists_eval = data_to_write_test[0]\n raw_lists_eval = data_to_write_test[2]\n\n aligned_lists_test = data_to_write_test[1]\n raw_lists_test = data_to_write_test[3]\n\n filelist = list([train_path, eval_path, test_path])\n\n for file in filelist:\n aligned_path = os.path.join(file, 'aligned_image')\n raw_path = os.path.join(file, 'raw_image')\n os.mkdir(aligned_path)\n os.mkdir(raw_path)\n\n # raw image data\n for Idx, train_raw in enumerate(raw_lists_train):\n img = Image.open(train_raw)\n img.save(train_path+'/raw_image/img_'+f'{Idx:04d}.jpg')\n if Idx%100 == 0:\n print('\\t%d images are saved'% Idx); \n print('\\tTrain raw images saved! ')\n\n for Idx, eval_raw in enumerate(raw_lists_eval):\n img = Image.open(eval_raw)\n img.save(eval_path+'/raw_image/img_'+f'{Idx:04d}.jpg')\n if Idx%100 == 0:\n print('\\t%d images are saved'% Idx); \n print('\\tEval raw images saved! ')\n\n for Idx, test_raw in enumerate(raw_lists_test):\n img = Image.open(test_raw)\n img.save(test_path+'/raw_image/img_'+f'{Idx:04d}.jpg')\n if Idx%100 == 0:\n print('\\t%d images are saved'% Idx); \n print('\\tTest raw images saved! ')\n\n # aligned image data\n for Idx, train_aligned in enumerate(aligned_lists_train):\n img = Image.open(train_aligned)\n img.save(train_path+'/aligned_image/img_'+f'{Idx:04d}.jpg')\n if Idx%100 == 0:\n print('\\t%d images are saved'% Idx); \n print('\\tTrain aligned images saved! ')\n\n for Idx, eval_aligned in enumerate(aligned_lists_eval):\n img = Image.open(eval_aligned)\n img.save(eval_path+'/aligned_image/img_'+f'{Idx:04d}.jpg')\n if Idx%100 == 0:\n print('\\t%d images are saved'% Idx); \n print('\\tEval aligned images saved! ')\n\n for Idx, test_aligned in enumerate(aligned_lists_test):\n img = Image.open(test_aligned)\n img.save(test_path+'/aligned_image/img_'+f'{Idx:04d}.jpg')\n if Idx%100 == 0:\n print('\\t%d images are saved'% Idx); \n print('\\tTest aligned images saved! ')","def save_step_4(imgs, output_path=\"./output/step4\"):\n # ... your code here ...\n cv2.imwrite(output_path+\"/output.jpg\", imgs)","def form_sample_folder(self, input_folder, target_folder, sample_name):\n print(f'processing {sample_name} folder.')\n # first make a subfolder to contain the images - e.g. 'target_folder/sample_name'\n sample_dir = join(target_folder, sample_name)\n if not os.path.exists(sample_dir):\n mkdir(sample_dir)\n # resize and move the mask images - e.g. 'target_folder/sample_name/imgs_necrosis.png'\n img_file_nec = join(input_folder, 'Necrosis',\n 'Tissue Slides.'+sample_name+'.png')\n img_res = self.process_img(img_file_nec, self.rescale_ratio)\n img_nec = img_res.copy()\n cv2.imwrite(join(sample_dir, 'necrosis.png'), img_res)\n\n img_file_perf = join(input_folder, 'Perfusion',\n 'Tissue Slides.'+sample_name+'.png')\n img_res = self.process_img(img_file_perf, self.rescale_ratio)\n cv2.imwrite(join(sample_dir, 'perfusion.png'), img_res)\n\n # resize and move the maker HE and EF5 images\n files = listdir(input_folder)\n img_files = [x for x in files if x.split(\n '.')[-1] in ('tif', 'jpg', 'png')]\n for img_file in img_files:\n if (sample_name+'_' in img_file) or (sample_name+'-' in img_file):\n if ('HE-G' in img_file) or ('HE-green' in img_file) or ('HEgreen' in img_file):\n img_res = self.process_img(\n join(input_folder, img_file), self.rescale_ratio)\n if not os.path.exists(join(sample_dir, 'HE-green.png')):\n cv2.imwrite(join(sample_dir, 'HE-green.png'), img_res)\n else:\n warnings.warn(\n f\"file already exists, while processing {img_file}\")\n elif ('HE-R' in img_file) or ('HE-red' in img_file) or ('HEred' in img_file):\n img_res = self.process_img(\n join(input_folder, img_file), self.rescale_ratio)\n if not os.path.exists(join(sample_dir, 'HE-red.png')):\n cv2.imwrite(join(sample_dir, 'HE-red.png'), img_res)\n else:\n warnings.warn(\n f\"file already exists, while processing {img_file}\")\n elif ('HE-B' in img_file) or ('HE-blue' in img_file) or ('HE-blue' in img_file):\n img_res = self.process_img(\n join(input_folder, img_file), self.rescale_ratio)\n if not os.path.exists(join(sample_dir, 'HE-blue.png')):\n cv2.imwrite(join(sample_dir, 'HE-blue.png'), img_res)\n else:\n warnings.warn(\n f\"file already exists, while processing {img_file}\")\n elif 'EF5' in img_file:\n img_res = self.process_img(\n join(input_folder, img_file), self.rescale_ratio)\n img_ef5 = img_res.copy()\n if not os.path.exists(join(sample_dir, 'EF5.png')):\n cv2.imwrite(join(sample_dir, 'EF5.png'), img_res)\n else:\n warnings.warn(\n f\"file already exists, while processing {img_file}\")\n\n masked_ef5 = (img_ef5 * (img_nec <= 0)).astype(img_ef5.dtype)\n cv2.imwrite(join(sample_dir, 'EF5_masked.png'), masked_ef5)\n assert len(listdir(sample_dir)) == 7\n return","def createAllImageFiles(poly, name) :\n \n for i in range(len(poly.getPaths())):\n fileName = name + \"_\" + str(i) + \".dot\"\n imgName = name + \"_\" + str(i) + \".jpg\"\n \n Command = \"neato -Tjpeg \" + fileName + \" -o \" + imgName\n run(Command, shell=True)","def splitTransform(self):\n\t\t#path_merge = \"transform\"\n\t\t#path_train = \"transform/data/\"\n\t\t#path_label = \"transform/label/\"\n\t\tpath_merge = \"train/merge\"\n\t\tpath_train = \"train/image\"\n\t\tpath_label = \"train/label\"\n\t\ttrain_imgs = glob.glob(path_merge+\"/*.\"+self.img_type)\n\t\tfor imgname in train_imgs:\n\t\t\tmidname = imgname[imgname.rindex(\"/\")+1:imgname.rindex(\".\"+self.img_type)]\n\t\t\timg = cv2.imread(imgname)\n\t\t\timg_train = img[:,:,2]#cv2 read image rgb->bgr\n\t\t\timg_label = img[:,:,0]\n\t\t\tcv2.imwrite(path_train+midname+\".\"+self.img_type,img_train)\n\t\t\tcv2.imwrite(path_label+midname+\".\"+self.img_type,img_label)","def save_images(images, filenames, output_dir):\n for i, filename in enumerate(filenames):\n dirname = os.path.dirname(filename)\n\n if dirname!='':\n dirpath = os.path.join(output_dir, dirname)\n if not os.path.exists(dirpath):\n os.makedirs(dirpath)\n\n # Images for inception classifier are normalized to be in [-1, 1] interval,\n # so rescale them back to [0, 1].\n with tf.gfile.Open(os.path.join(output_dir, filename), 'w') as f:\n img = (((images[i, :, :, :] + 1.0) * 0.5) * 255.0).astype(np.uint8)\n Image.fromarray(img).save(f)","def process_data(output_folder):\n # select imgs\n img_folder = join(output_folder, 'img')\n select_img(output_folder, img_folder, 'HE-green')\n\n mask_folder = join(output_folder, 'mask')\n select_img(output_folder, mask_folder, '_EF5')","def save_images(images, filenames, output_dir):\n for i, filename in enumerate(filenames):\n\n with tf.gfile.Open(os.path.join(output_dir, filename), 'w') as f:\n image = images[i, :, :, :]\n image[:, :, 0] += _R_MEAN\n image[:, :, 1] += _G_MEAN\n image[:, :, 2] += _B_MEAN\n image = imresize(image, [299, 299])\n imsave(f, image, format='png')","def process_images(path, dataset):\n \n print(f\"Processing images {os.path.join(path, dataset)}\", flush=True)\n label_file = os.path.join(path, dataset + '-labels-idx1-ubyte')\n with open(label_file, 'rb') as file:\n _, num = struct.unpack(\">II\", file.read(8))\n labels = numpy.fromfile(file, dtype=numpy.int8) #int8\n new_labels = numpy.zeros((num, 10))\n new_labels[numpy.arange(num), labels] = 1\n\n img_file = os.path.join(path, dataset + '-images-idx3-ubyte')\n with open(img_file, 'rb') as file:\n _, num, rows, cols = struct.unpack(\">IIII\", file.read(16))\n imgs = numpy.fromfile(file, dtype=numpy.uint8).reshape(num, rows, cols) #uint8\n imgs = imgs.astype(numpy.float32) / 255.0\n\n os.remove(label_file); os.remove(img_file)\n print(f\"Saving files under {os.path.join(path, dataset)} path\", flush=True)\n numpy.savez_compressed(os.path.join(path, dataset), imgs=imgs, labels=labels)","def hdfpath_to_nifti1image(file_path, h5path):\n with h5py.File(file_path, 'r') as f:\n return hdfgroup_to_nifti1image(f[h5path])","def save_imgs(self, epoch):\n row, column = 5, 5\n\n # Generates r*c images from the model, saves them individually and as a gallery\n images_generated = self.generate_images(row * column)\n\n # ???\n images_generated = 0.5 * images_generated + 0.5\n\n for index, np_array_image in enumerate(images_generated):\n path = f\"{self.output_directory}/generated_{self.img_size[0]}x{self.img_size[1]}\"\n if not os.path.exists(path):\n os.makedirs(path)\n imsave(path + f\"/{unique_name()}_{epoch}_{index}.png\", np_array_image)\n\n # 4D array:\n nindex, height, width, intensity = images_generated.shape\n\n nrows = nindex // column\n\n assert nindex == nrows * column\n\n # Form the gallery by combining the data at pixel levels (may not be the best approach)\n # want result.shape = (height*n-rows, width*n-cols, intensity)\n gallery = (\n images_generated.reshape(nrows, column, height, width, intensity)\n .swapaxes(1, 2)\n .reshape(height * nrows, width * column, intensity)\n )\n\n path = f\"{self.output_directory}/gallery_generated_{self.img_size[0]}x{self.img_size[1]}\"\n if not os.path.exists(path):\n os.makedirs(path)\n imsave(path + f\"/{unique_name()}_{epoch}.png\", gallery)","def save_imgs(self):\n print(\"Saving the images with required categories ...\")\n os.makedirs(self.imgs_dir, exist_ok=True)\n # Save the images into a local folder\n for im in tqdm(self.images):\n img_data = requests.get(im['coco_url']).content\n with open(os.path.join(self.imgs_dir, im['file_name']), 'wb') as handler:\n handler.write(img_data)","def numpy_to_h5py(in_dir=config.dir_npy, split = config.split):\n\n in_files=[x[:-13] for x in os.listdir(in_dir) if x.endswith('_voc_stft.npy') and not x.startswith('._')]\n\n random.shuffle(in_files)\n\n\n num_files = len(in_files)\n\n split_idx = int(num_files*split)\n\n trn_files = in_files[:split_idx]\n\n val_files = in_files[split_idx:]\n\n num_val_files = len(val_files)\n\n print('Processing %d training files' % split_idx)\n logger.info('Processing %d training files' % split_idx)\n\n logger.info('Training file: %s' % config.h5py_file_train)\n\n voc_shape_trn = [split_idx, 5170,config.input_features]\n\n mix_shape_trn = [split_idx, 5170,config.input_features]\n\n feats_shape_trn = [split_idx, 5170,config.output_features]\n\n hdf5_file = h5py.File(config.h5py_file_train, mode='w')\n\n hdf5_file.create_dataset(\"voc_stft\", voc_shape_trn, np.float32)\n\n hdf5_file.create_dataset(\"back_stft\", voc_shape_trn, np.float32)\n\n hdf5_file.create_dataset(\"mix_stft\", mix_shape_trn, np.float32)\n\n hdf5_file.create_dataset(\"feats\", feats_shape_trn, np.float32)\n\n\n i = 0\n\n for f in trn_files:\n\n voc_stft = np.load(in_dir+f+'_voc_stft.npy')\n\n voc_stft = voc_stft.astype('float32')\n\n mix_stft = np.load(in_dir+f+'_mix_stft.npy')\n\n mix_stft = mix_stft.astype('float32')\n\n back_stft = np.load(in_dir+f+'_back_stft.npy')\n\n back_stft = back_stft.astype('float32')\n\n synth_feats = np.load(in_dir+f+'_synth_feats.npy')\n\n synth_feats = synth_feats.astype('float32')\n\n hdf5_file[\"voc_stft\"][i,...] = voc_stft\n\n hdf5_file[\"mix_stft\"][i,...] = mix_stft\n\n hdf5_file[\"back_stft\"][i,...] = back_stft\n\n hdf5_file[\"feats\"][i,...] = synth_feats\n\n i+=1\n utils.progress(i, split_idx)\n\n logger.info('Processed training file: %s' % f)\n\n hdf5_file.close()\n\n print('Processing %d validation files' % num_val_files)\n logger.info('Processing %d validation files' % num_val_files)\n\n logger.info('Validation file: %s' % config.h5py_file_val)\n\n voc_shape_trn = [num_val_files, 5170,config.input_features]\n\n mix_shape_trn = [num_val_files, 5170,config.input_features]\n\n feats_shape_trn = [num_val_files, 5170,config.output_features]\n\n hdf5_file = h5py.File(config.h5py_file_val, mode='w')\n\n hdf5_file.create_dataset(\"voc_stft\", voc_shape_trn, np.float32)\n\n hdf5_file.create_dataset(\"mix_stft\", mix_shape_trn, np.float32)\n\n hdf5_file.create_dataset(\"back_stft\", voc_shape_trn, np.float32)\n\n hdf5_file.create_dataset(\"feats\", feats_shape_trn, np.float32)\n\n\n i = 0\n\n for f in val_files:\n\n voc_stft = np.load(in_dir+f+'_voc_stft.npy')\n\n voc_stft = voc_stft.astype('float32')\n\n mix_stft = np.load(in_dir+f+'_mix_stft.npy')\n\n mix_stft = mix_stft.astype('float32')\n\n synth_feats = np.load(in_dir+f+'_synth_feats.npy')\n\n synth_feats = synth_feats.astype('float32')\n\n back_stft = np.load(in_dir+f+'_back_stft.npy')\n\n back_stft = back_stft.astype('float32')\n\n hdf5_file[\"voc_stft\"][i,...] = voc_stft\n\n hdf5_file[\"mix_stft\"][i,...] = mix_stft\n\n hdf5_file[\"back_stft\"][i,...] = back_stft\n\n hdf5_file[\"feats\"][i,...] = synth_feats\n\n i+=1\n utils.progress(i, num_val_files)\n\n logger.info('Processed validation file: %s' % f)\n\n hdf5_file.close()\n # return original_ffts","def process_images():\n create_dirs()\n for root, dirs, files in os.walk(IN):\n for name in files:\n if name[0] == '.':\n continue\n process_image(name)","def preprocessfolder(self):\n imgs, _ = getFilesAndHdf(str(self.in_directory.text()))\n self.img_list = sorted(imgs)\n self.updateImageGroups()","def _crop_write_image(self, inroot, images, outroot):\n for image in images:\n inimage_path = osp.join(inroot, image)\n cvimg = cv2.imread(inimage_path)\n cvimg = cvimg[60:-30, 25:-25]\n h, w, _ = cvimg.shape\n assert h == w == 128\n outimage_path = osp.join(outroot, image)\n cv2.imwrite(outimage_path, cvimg)\n print(outimage_path)","def save_step_3(img_pairs, match_list, output_path=\"./output/step3\"):\n # ... your code here ...\n for i in range(len(img_pairs)):\n name1,tail1 = str.split(filenames[match_list[i][0]],\".\")\n name2,tail2 = str.split(filenames[match_list[i][1]],\".\")\n cv2.imwrite(output_path+\"/\"+name1+\"_\"+name2+\".jpg\", img_pairs[i][0])\n cv2.imwrite(output_path+\"/\"+name2+\"_\"+name1+\".jpg\", img_pairs[i][1])","def load_images(input_dir=\"/tmp/mapswipe/project-1\", n_images=2000, seed=1):\n class_map = {1: \"1\", 0: \"5\"}\n output_dir = \"/Users/thead/git/dreamview/data/\"\n\n X_ = []\n y_ = []\n for new_klass in class_map:\n images = []\n for klass in class_map[new_klass]:\n for img in glob.glob(input_dir + \"/%s/*/*/*/aerial.jpeg\" % klass):\n if os.stat(img).st_size > 0:\n images.append(img)\n\n images = shuffle(images, random_state=seed+42+new_klass)\n images = images[:n_images]\n X_ += images\n y_ += [new_klass] * len(images)\n\n # XXX deduce array size from an actual image\n X = np.zeros((2*n_images, 256*256), dtype=np.ubyte)\n y = np.zeros(2*n_images, dtype=np.int)\n\n for n, (img_path, klass) in enumerate(zip(X_, y_)):\n # the order of these OPs has been chosen on purpose, don't mess\n # without checking what happens\n img = imread(img_path)\n img = equalize_adapthist(img)\n img = rgb2grey(img)\n img = img_as_ubyte(img)\n\n if not n % 10:\n fname = os.path.split(img_path)[:-1]\n fname = os.path.join(*fname, \"aerial-processed.jpeg\")\n imsave(fname, img)\n\n X[n,:] = img.ravel()\n y[n] = klass\n\n return X, y","def save_test_images(images):\n for description, img in images.items():\n save_to_image(img, description)\n save_to_netcdf(img, description)","def save_images(images, filenames, output_dir):\n for i, filename in enumerate(filenames):\n # Images for inception classifier are normalized to be in [-1, 1] interval,\n # so rescale them back to [0, 1].\n with tf.gfile.Open(os.path.join(output_dir, filename), 'w') as f:\n img = np.round(((images[i] + 1.0) * 0.5) * 255.0).astype(np.uint8)\n Image.fromarray(img).save(f, format='PNG')","def extract_write_to_file(self, num_extracted, write_dir, sub_h, sub_w, margin=10):\n\n file_seed = len(os.listdir(write_dir))\n\n for i in range(num_extracted):\n file_num = str(file_seed + i)\n write_path = os.path.join(write_dir, file_num + \".\" + 'jpg')\n\n print('extracting {}/{} images of dimension {}x{}'.format(i, num_extracted, sub_h, sub_w))\n print('writting to location: {}'.format(write_path))\n\n self.extract_single(sub_h, sub_w, write_path)","def load(self):\n\n # get files in folder\n files = [f for f in listdir(self.data_path)]\n print(\"loading images from folder: %s\" % self.data_path)\n\n images = []\n image_targets = []\n for f in files:\n filepath = path.join(self.data_path, f)\n images.append(io.imread(filepath, as_grey=True))\n image_targets.append(self.target)\n\n # define new size and resize images\n new_size = (2 ** self.size_exponent, 2 ** self.size_exponent)\n for i in range(0, len(images)):\n # images[i] = transform.resize(images[i], new_size)\n images[i] = misc.imresize(images[i], new_size) / 16\n\n self.images = images\n self.targets = image_targets","def save_images(images, filenames, output_dir):\n for i, filename in enumerate(filenames):\n # Images for inception classifier are normalized to be in [-1, 1] interval,\n # so rescale them back to [0, 1].\n with tf.gfile.Open(os.path.join(output_dir, filename), 'w') as f:\n img = (((images[i, :, :, :] + 1.0) * 0.5) * 255.0).astype(np.uint8)\n Image.fromarray(img).save(f, format='PNG')","def load_data_withIdx(subjIdx_list, filenames, test_ratio = 0.2):\n dataset_path = filenames['data']\n train_path = filenames['train']\n test_path = filenames['test']\n eval_path = filenames['eval']\n raw_image_lists = []\n aligned_image_lists = []\n print('[INFO] Data Processing started ... ')\n\n for subIdx in subjIdx_list:\n subj_path = dataset_path+'/subj_'+f'{subIdx:02d}'\n print('\\tProccessing images for subject_' +f'{subIdx:02d}'+ ' ...', end =\" \")\n\n if(not os.path.isdir(subj_path)):\n print(' no file exists for subject_' +f'{subIdx:02d}'+ ' !!')\n continue\n\n raw_list = sorted(glob.glob(subj_path + '/raw_images/*.jpg'))\n aligned_list = sorted(glob.glob(subj_path + '/aligned_images/*.jpg')) \n\n assert len(raw_list) == len(aligned_list) , \"data size mismatch! raw_img:{0}, alig_img:{1}\".format(len(raw_list) ,len(aligned_list)) \n \n raw_image_lists += raw_list\n aligned_image_lists += aligned_list\n print(' Done!')\n\n data_to_write_train = train_test_split_data(aligned_image_lists, raw_image_lists, test_ratio)\n data_to_write_test = train_test_split_data(data_to_write_train[1], data_to_write_train[3], 0.5)\n print('[INFO] Processing Done! ')\n print(\"[INFO] Number of train data :{0:4d}, Number of eval data :{1:4d}, Number of test data :{2:4d} \"\n . format(len(data_to_write_train[0]), len(data_to_write_test[0]), len(data_to_write_test[1]))) \n # data write part \n write_data(data_to_write_train, data_to_write_test, train_path, eval_path ,test_path)","def buildImages(files, targets, type):\n images = []\n for file in files:\n targets.append(file)\n with open(file, \"rb\") as f:\n if type == \"Byte\":\n images.append(bytePlot(list(f.read())))\n elif type == \"Markov\":\n images.append(markovPlot(list(f.read())))\n elif type == \"Hilbert\":\n images.append(hilbertPlot(list(f.read())))\n smp.imsave(\"{}.png\".format(file), images[-1])\n return images, targets","def jarvis(input_path, output_path): \n\n if not os.path.exists(f'{output_path}'):\n os.makedirs(f'{output_path}')\n\n file_list = [filename for filename in os.listdir(f'{input_path}') if '.tif' in filename]\n\n for filename in file_list:\n pathname = os.path.join(input_path, filename)\n new_name = f\"{output_path}{filename.replace('.lif - ', '_').replace('_5x-', '_')}\"\n copyfile(pathname, new_name)\n logger.info(f'{new_name}')","def collect_and_rename() -> None:\n image_source_folder = 'image_dir'\n label_source_folder = 'annotation_dir'\n image_target_folder = 'images'\n label_target_folder = 'labels'\n for i, (subdir, _, files) in enumerate(os.walk(image_source_folder), -1):\n # it walks the parent folder first, not a file\n if i == -1: \n continue\n subdir_name = subdir.split('\\\\')[1]\n for file_name in files:\n with open(f'{image_source_folder}/{subdir_name}/{file_name}') as image_file, \\\n open(f'{label_source_folder}/{subdir_name}/{file_name}'.split('.')[0] + '.txt') as label_file:\n shutil.copy2(image_file.name, f'{image_target_folder}/{\"%06d\" % i}.jpg')\n shutil.copy2(label_file.name, f'{label_target_folder}/{\"%06d\" % i}.txt')\n print(f'Processed {i} images')","def read_image_data(self):\n\n for sequence_name in self.sequence_name_list:\n sequence = self.sequences[sequence_name]\n for image_id in sequence.image_id_list:\n sequence.image_dict[image_id].image_path = '{}{}/{}'.format(self.root_dir, self.name, sequence.image_dict[image_id].filename)","def run():\n\n today = datetime.now().strftime(\"%Y-%m-%d\")\n log_file = os.path.abspath(\"logs/{}.log\".format(today))\n logger = RsmasLogger(\"pipeline\", log_file)\n\n images = get_list_of_images()\n # LOG: list of images to process\n logger.log(loglevel.INFO, [img.key for img in images])\n\n for im in images:\n\n logger.log(loglevel.INFO, \"Processing image: {}\".format(im.key))\n\n file_path = \"{}/{}\".format(im.bucket_name, im.key)\n full_path = \"{}_full.jpg\"\n mod_path = \"{}_mod.jpg\"\n aws_path = \"{}/{}/{}/{}\"\n try:\n haz_id, haz_name, sat_name, sat_dir, img_type, img_date, center = summary.pull_summary_data(\n \"/vsis3/{}\".format(file_path))\n sat_id = Satellite.from_params(sat_name, bool(sat_dir))\n except:\n # LOG: error in image metadata format\n logger.log(loglevel.ERROR, '\\tThere was an error in the metadata format of the image. Skipping.')\n continue\n\n aws_path = aws_path.format(haz_id, sat_id, img_type, img_date)\n full_path = full_path.format(img_date)\n mod_path = mod_path.format(img_date)\n\n # 1. Read in image file\n with rasterio.open(\"s3://{}\".format(file_path)) as data:\n band = data.read(1)\n img = plot.show(band)\n img.get_figure().savefig(full_path, dpi=300)\n\n # 3. Compress image\n compressed = immanip.compress_image(full_path, compression_amount=0.3)\n\n # 4 - 5. Pad image and add date on image\n text_image = immanip.add_text_to_image(compressed, img_date)\n\n # 6. Save image locally\n text_image.save(mod_path.format(img_date))\n mod_path_aws = save.get_s3_url(\"{}/{}\".format(aws_path, mod_path))\n full_path_aws = save.get_s3_url(\"{}/{}\".format(aws_path, full_path))\n\n tif_path_aws = save.get_s3_url(\"{}/{}\".format(aws_path, im.key))\n\n # LOG: images successfully moved to S3 bucket\n # LOG: mod_path_aws, full_path_aws, tif_path_aws\n\n hazard = Hazard(haz_id, haz_name, HazardType.VOLCANO, Location(center[0], center[1]), Date(img_date), 0)\n satellite = Satellite.from_params(sat_name, bool(sat_dir))\n image = Image(str(randint(1, 10000000)),\n haz_id,\n satellite,\n ImageType.from_string(img_type),\n Date(img_date),\n ImageURL(full_path_aws),\n ImageURL(tif_path_aws),\n ImageURL(mod_path_aws))\n\n try:\n db = Database()\n except ConnectionError:\n logger.log(loglevel.ERROR, \"\\tThere was an error while connecting to the database. Skipping this image.\")\n continue\n\n db.create_new_hazard(hazard)\n db.create_new_satellite(satellite)\n db.create_new_image(image)\n\n db.close()\n\n # LOG: database successfully updated\n logger.log(loglevel.INFO, \"\\tDatabase succesfully updated.\")\n\n save.save_image_s3(mod_path, \"{}/{}\".format(aws_path, mod_path))\n save.save_image_s3(full_path, \"{}/{}\".format(aws_path, full_path))\n save.move_tif(im.key, \"{}/{}\".format(aws_path, im.key))\n\n logger.log(loglevel.INFO, \"\\tImages were successfully uploaded to the S3 bucket\")\n logger.log(loglevel.INFO, \"\\t\\tmod_path_aws: {}\".format(mod_path_aws))\n logger.log(loglevel.INFO, \"\\t\\tfull_path_aws: {}\".format(full_path_aws))\n logger.log(loglevel.INFO, \"\\t\\ttif_path_aws: {}\".format(tif_path_aws))\n\n # LOG: image completed\n logger.log(loglevel.INFO, \"\\tProcessing of {} completed.\".format(im.key))\n\n # LOG: finished processing images\n logger.log(loglevel.INFO, \"Processing complete.\")","def extract_data(filename, images_dir, output_dir, trials_idx, block_nums, goal_dict):\n num_images = len(trials_idx) * len(block_nums)\n f = h5py.File(os.path.join(output_dir, filename), 'w')\n X = f.create_dataset('X', (num_images, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS*NUM_FRAMES), dtype=TYPE)\n Y = f.create_dataset('Y', (num_images, 2), dtype=TYPE)\n\n image_count = 0\n for trial_num in trials_idx:\n for block_num in block_nums:\n print('Blocks ' + str(block_num) + ' Trial ' + str(trial_num))\n for frame_num in xrange(0, NUM_FRAMES):\n temp = imread(images_dir+'RTr_Bl'+str(block_num)+'_'+str(trial_num)+'_'+str(frame_num)+IMAGE_FORMAT)\n temp = imresize(temp, [temp.shape[0]//DOWN_SAMPLE, temp.shape[1]//DOWN_SAMPLE, temp.shape[2]])\n X[image_count, 0:temp.shape[0], 0:temp.shape[1], frame_num*NUM_CHANNELS:(frame_num+1)*NUM_CHANNELS] = temp\n label = goal_dict['RTr_Bl'+str(block_num)+'_'+str(trial_num)]\n Y[image_count, :] = [label, 1-label]\n image_count += 1\n\n f.close()\n\n # TODO Use pixel depth normalization???\n #data = (data - (PIXEL_DEPTH / 2.0)) / PIXEL_DEPTH","def process_imgdir(self,imgdir):\n #Write images into resultdir\n resultdir = os.path.join(imgdir, 'results')\n #Read images from input dir\n inputdir = os.path.join(imgdir, 'inputs')\n shutil.rmtree(resultdir)\n os.mkdir(resultdir)\n #Read files from input images\n for fullname in os.listdir(inputdir):\n filepath = os.path.join(inputdir, fullname)\n if os.path.isfile(filepath):\n basename = os.path.basename(filepath)\n image = cv2.imread(filepath, cv2.IMREAD_COLOR)\n if len(image.shape) == 3 and image.shape[2] == 3:\n print('Processing %s ...' % basename)\n else:\n sys.stderr.write('Skipping %s, not RGB' % basename)\n continue\n #Extract haze from the scene and then save the image\n dehazed = self.get_scene_radiance(image)\n cv2.imwrite(os.path.join(resultdir, basename), dehazed)\n return os.path.join(resultdir, basename)","def write(self):\n f, ds = self.opendset()\n #\n # Now add the images\n #\n start_time = time.clock() # time this\n nframes = 0 # number completed\n print_every = 1; marker = \" .\";\n print('Frames written (of %s):' % self.ntowrite, end=\"\")\n for i in range(self.nfiles):\n if nframes >= self.ntowrite: break\n\n logging.debug('processing file %d of %d' % (i+1, self.nfiles))\n img_i = fabio.open(self.files[i])\n nfi = img_i.nframes\n for j in range(nfi):\n msg = '... file %d/image %d' % (i, j)\n logging.debug(msg)\n if j < self.nempty:\n logging.debug('... empty frame ... skipping')\n else:\n ds[nframes, :, :] = img_i.data\n nframes += 1\n if numpy.mod(nframes, print_every) == 0:\n print(marker, nframes, end=\"\")\n print_every *= 2\n sys.stdout.flush()\n logging.debug('... wrote image %s of %s' %\\\n (nframes, self.ntowrite))\n if nframes >= self.ntowrite:\n logging.debug('wrote last frame: stopping')\n break\n if j < nfi - 1:\n # on last frame in file, fabio will look for next file\n img_i = img_i.next()\n\n f.close()\n print(\"\\nTime to write: %f seconds \" %(time.clock()-start_time))","def write_frames(self, images):\n for img in images:\n self.write_frame(img)","def store_hdf5(images, labels, ID:str, path:str = \"data/dataset/\"):\n\n #create a new HDF5 file\n file = h5py.File(path+ID+\"_lens.h5\", \"w\")\n\n #create a dataset in the file\n dataset = file.create_dataset( \"images\", np.shape(images), h5py.h5t.IEEE_F64BE, data=images)\n file.close()\n\n labels.to_hdf(path +ID+'_meta.h5', \"table\")","def save_images(images, filenames, output_dir):\n for i, filename in enumerate(filenames):\n # Images for inception classifier are normalized to be in [-1, 1] interval,\n # so rescale them back to [0, 1].\n with tf.gfile.Open(os.path.join(output_dir, filename), 'w') as f:\n imsave(f, (images[i] + 1.0) * 0.5, format='png')","def save_images(images, filenames, output_dir):\n for i, filename in enumerate(filenames):\n # Images for inception classifier are normalized to be in [-1, 1] interval,\n # so rescale them back to [0, 1].\n with tf.gfile.Open(os.path.join(output_dir, filename), 'w') as f:\n imsave(f, (images[i, :, :, :] + 1.0) * 0.5, format='png')","def div_imgs(dir_path: str, holdout: int) -> None:\n VH_REGEX = re.compile(r'(.*)_([0-9]+).vh.tif')\n\n for file in os.listdir(dir_path):\n m = re.match(VH_REGEX, file)\n if not m:\n continue\n\n pre, num = m.groups()\n vv = f'{pre}_{num}.vv.tif'\n mask = f'{pre}_{num}.mask.tif'\n\n if not os.path.isfile(os.path.join(dir_path, mask)):\n print(f\"Tile: {file} is missing a mask {mask}!\")\n\n if not os.path.isfile(os.path.join(dir_path, vv)):\n print(f\"Tile: {file} is missing a mask {vv}!\")\n\n test_or_train = 'train' if random.random() > holdout else 'test'\n\n folder = os.path.join(dir_path, test_or_train)\n if not os.path.isdir(folder):\n os.makedirs(folder)\n\n os.rename(\n os.path.join(dir_path, file), os.path.join(folder, file)\n )\n os.rename(\n os.path.join(dir_path, vv),\n os.path.join(folder, vv)\n )\n os.rename(\n os.path.join(dir_path, mask),\n os.path.join(folder, mask)\n )","def process_subdirectory(subdir_path, regex_images, output_folder_if_pickle,\n min_n_leftside_metadata=10, only_ionogram_content_extraction_on_leftside_metadata=True, to_pickle=True):\n # Run segment_images on the subdirectory \n df_img,df_loss,df_outlier = segment_images(subdir_path, regex_images)\n\n # Determine ionogram grid mappings used to map (x,y) pixel coordinates of ionogram trace to (Hz, km) values\n stack = all_stack(df_img)\n col_peaks,row_peaks,mapping_Hz, mapping_km = get_grid_mappings(stack)\n\n # Translate metadata located on the left\n df_img_left = df_img[df_img['metadata_type']== 'left']\n \n if len(df_img_left.index) > min_n_leftside_metadata:\n # Determine leftside metadata grid (pixel coordinates to number, category mappings)\n df_img_left, df_loss_meta,dict_mapping,dict_hist= get_leftside_metadata(df_img_left,subdir_path)\n df_all_loss = df_loss.append(df_loss_meta)\n else:\n df_all_loss = df_loss\n \n # Extract the coordinates of the ionogram trace (black), Map the (x,y) pixel coordinates to (Hz, km) values and Extract select parameters i.e. fmin\n if only_ionogram_content_extraction_on_leftside_metadata:\n df_processed, df_loss_coord = extract_coord_subdir_and_param(df_img_left,subdir_path,col_peaks,row_peaks,mapping_Hz,mapping_km)\n else:\n df_processed, df_loss_coord = extract_coord_subdir_and_param(df_img,subdir_path,col_peaks,row_peaks,mapping_Hz,mapping_km)\n\n df_processed['mapping_Hz'] = [mapping_Hz] * len(df_processed.index)\n df_processed['mapping_km'] = [mapping_km] * len(df_processed.index)\n\n if to_pickle:\n start,subdir_name = ntpath.split(subdir_path[:-1])\n start,dir_name = ntpath.split(start)\n df_processed.to_pickle(os.pardir + '/pickle/' + str(dir_name)+'_'+str(subdir_name)+'.pkl')\n \n df_all_loss = df_all_loss.append(df_loss_coord)\n return df_processed, df_all_loss,df_outlier","def save_as_hdf5(self, filename):","def compress_img():\n in_path = 'output/templates/rgb/'\n out_path = 'output/templates/imgs/'\n names = os.listdir(in_path)\n for i, name in enumerate(names):\n img = cv2.imread(in_path + name, 0)\n if any(np.array(img.shape) > 1000):\n img = cv2.resize(img, None, fx=0.5, fy=0.5, interpolation=cv2.INTER_CUBIC)\n cv2.imwrite(out_path + name, img)\n\n return","def processImages(self):\n for file in os.listdir(self.config[\"tempPath\"]):\n self.logger.debug(\"Calling generateImages for the file: {0}\".format(file))\n self.generateText(file)","def generate_all(files, alignement_h5f, input_h5f,\n nframes=7, vad=None):\n def try_remove(fname):\n try:\n os.remove(fname)\n except:\n pass\n try:\n directory = os.path.dirname(os.path.abspath(input_h5f))\n\n # create temporary files:\n _, fb_h5f = tempfile.mkstemp(dir=directory)\n _, fb_mvn_h5f = tempfile.mkstemp(dir=directory)\n os.remove(fb_h5f)\n os.remove(fb_mvn_h5f)\n\n # generate mfccs:\n h5features_compute(files, alignement_h5f, featfunc=do_mfccs)\n\n # generate stacked mvn fbanks:\n h5features_compute(files, fb_h5f, featfunc=do_fbank)\n mean_variance_normalisation(fb_h5f, fb_mvn_h5f, vad=vad)\n h5features_feats2stackedfeats(fb_mvn_h5f, input_h5f, nframes=nframes)\n finally:\n try_remove(fb_h5f)\n try_remove(fb_mvn_h5f)","def save_images(images, filenames, output_dir):\n for i, filename in enumerate(filenames):\n # Images for inception classifier are normalized to be in [-1, 1] interval,\n # so rescale them back to [0, 1].\n with tf.gfile.Open(os.path.join(output_dir, filename), 'w') as f:\n imsave(f, (images[i, :, :, :] + 1.0) * 0.5, format='png')","def create_random_data(output_path: str, num_images: int = 5) -> None:\n train_path = os.path.join(output_path, \"train\")\n class1_train_path = os.path.join(train_path, \"class1\")\n class2_train_path = os.path.join(train_path, \"class2\")\n\n val_path = os.path.join(output_path, \"val\")\n class1_val_path = os.path.join(val_path, \"class1\")\n class2_val_path = os.path.join(val_path, \"class2\")\n\n test_path = os.path.join(output_path, \"test\")\n class1_test_path = os.path.join(test_path, \"class1\")\n class2_test_path = os.path.join(test_path, \"class2\")\n\n paths = [\n class1_train_path,\n class1_val_path,\n class1_test_path,\n class2_train_path,\n class2_val_path,\n class2_test_path,\n ]\n\n for path in paths:\n try:\n os.makedirs(path)\n except FileExistsError:\n pass\n\n for i in range(num_images):\n pixels = numpy.random.rand(64, 64, 3) * 255\n im = Image.fromarray(pixels.astype(\"uint8\")).convert(\"RGB\")\n im.save(os.path.join(path, f\"rand_image_{i}.jpeg\"))\n\n process_images(output_path)","def save_images_in_folder(folder, images, size = (224, 224), start_index = 0):\n\n # Loop over the images\n for i, image in enumerate(images):\n # Resize image to target size\n image = cv2.resize(image, dsize = size)\n \n # Convert image back to BGR color space\n image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n \n # Create the path where the image will be save, images will be indexed\n path = os.path.join(folder, str(start_index + i) + '.png')\n \n # Write / Save image \n cv2.imwrite(path, image)","def transform_to_h5():\n # this took about 10 minutes for set1\n for setname in ['set1', 'set2']:\n filename = os.path.join(ltrc_dirname, '{}.h5'.format(setname))\n f = h5py.File(filename, 'w')\n\n for name in ['train', 'valid', 'test']:\n g = f.create_group(name)\n filename = os.path.join(ltrc_dirname, '{}.{}.txt'.format(setname, name))\n X, y, q = load_svmlight_file(filename, query_id=True)\n g.create_dataset('X', data=X.todense(), compression='gzip')\n g.create_dataset('y', data=y, compression='gzip')\n g.create_dataset('q', data=q, compression='gzip')\n f.close()\n # Now you can do this\n # f['/valid/X'].shape\n # Out[24]: (71083, 699)","def preprocess(exam, data_folder, save_path, image_format):\n for v in ['L-CC', 'L-MLO', 'R-CC', 'R-MLO']:\n if len(exam[v]) == 0:\n continue\n else:\n for image in exam[v]:\n image_path = data_folder + '/' + image + '.' + image_format\n # Extract subdirectories\n subdirs = \"/\".join(image.split('/')[:-1])\n save_dirs = os.path.join(save_path, subdirs)\n # Extract image id\n image_id = image.split('/')[-1]\n # Create save directories\n os.makedirs(save_dirs, exist_ok=True)\n png_save_path = os.path.join(save_dirs, image_id + '.png')\n with Image(filename=image_path, format=image_format) as img:\n with img.clone() as i:\n i.auto_level()\n with i.convert('png') as png_image:\n png_image.transform(resize='896x1152!')\n png_image.save(filename=png_save_path)","def sum_images(filelist):\n\n nfiles = np.size(filelist)\n\n print(\"Summing together {} files\".format(nfiles))\n\n ims = []\n\n for fname in filelist:\n hdu = f.open(fname)\n ims.append(hdu[0].data)\n\n ims = np.array(ims)\n\n sum_im = np.nansum(ims, axis=0)\n hdu[0].data = sum_im\n\n #Add pipeline version and history keywords\n vers = version.get_version()\n hdu[0].header.set('PL_VERS',vers,'Version of pipeline used for processing')\n hdu[0].header['HISTORY'] = \"Summed up the following images:\"\n\n for fname in filelist:\n hdu[0].header['HISTORY'] = fname\n\n hdu[0].header['HISTORY'] = \"######################\"\n\n outname = filelist[-1].split('.')[0]+'_summed.fits'\n\n print(\"Writing out final file to {}\".format(outname))\n\n hdu.writeto(outname, overwrite=True)","def saveHDF5(ifile, cs, xi, yi, phi, cidx, idx, size, comx, comy, rgysq, pl, st, sw, ens, fils):\n \n ## Groups\n size_grp = ifile.create_group('size')\n beads_grp = ifile.create_group('beads')\n props_grp = ifile.create_group('props')\n \n ## Datasets\n size_grp.create_dataset('size', data=cs, compression='gzip')\n \n beads_grp.create_dataset('x', data=xi, compression='gzip')\n beads_grp.create_dataset('y', data=yi, compression='gzip')\n beads_grp.create_dataset('phi', data=phi, compression='gzip')\n beads_grp.create_dataset('idx', data=cidx, compression='gzip')\n \n idx_h5 = np.asarray(idx, dtype=int)\n size_h5 = np.asarray(size, dtype=int)\n comx_h5 = np.asarray(comx, dtype=float)\n comy_h5 = np.asarray(comy, dtype=float)\n rgysq_h5 = np.asarray(rgysq, dtype=float)\n planarity_h5 = np.asarray(pl, dtype=float)\n straightness_h5 = np.asarray(st, dtype=float)\n swirliness_h5 = np.asarray(sw, dtype=float)\n enstrophy_h5 = np.asarray(ens, dtype=float)\n \n props_grp.create_dataset('idx', data=idx_h5, compression='gzip')\n props_grp.create_dataset('size', data=size_h5, compression='gzip')\n props_grp.create_dataset('comx', data=comx_h5, compression='gzip')\n props_grp.create_dataset('comy', data=comy_h5, compression='gzip')\n props_grp.create_dataset('rgysq', data=rgysq_h5, compression='gzip')\n props_grp.create_dataset('planarity', data=planarity_h5, compression='gzip')\n props_grp.create_dataset('straightness', data=straightness_h5, compression='gzip')\n props_grp.create_dataset('swirliness', data=swirliness_h5, compression='gzip')\n props_grp.create_dataset('enstrophy', data=enstrophy_h5, compression='gzip')\n \n ## Filament list\n fil_grp = props_grp.create_group('filament_list')\n for sz_idx in np.arange(len(size_h5)):\n fil_list = np.asarray(fils[sz_idx], dtype=int)\n fil_grp.create_dataset(str(sz_idx), data=fil_list, compression='gzip')\n \n return","def ReadData(self, path):\n os.chdir(path)\n folders=os.listdir()\n if 'data.hdf5' in folders:\n print('Loading data from hdf5 file! Might take some time, be patient!')\n file=h5py.File('data.hdf5','r+')\n data=(np.array(list(file['imgs'])),np.array(list(file['lables'])))\n self.real_labels=list(file['real_labels'])\n file.close()\n\n else:\n print('1. Collecting data.')\n err_logs = []\n img=[]\n lable=[]\n for folder in tqdm(folders):\n\n os.chdir(os.path.join(path,folder))\n for file in os.listdir():\n try:\n dat=(plt.imread(open(file,'rb')))\n img.append(resize_image(dat, (resize_x, resize_y),\n mode='constant',\n ))\n lable.append(folder)\n if folder not in self.real_labels:\n self.real_labels.append(folder)\n \n except OSError:\n err_logs.append([folder, file])\n print('\\nError logs:')\n for e in range(len(err_logs)):\n print('\\tFolder: {} | Some OSError for file: {}'.format(err_logs[e][0],\n err_logs[e][0]))\n \n \n print('2. Encoding data to categorical.')\n # Encode Letters into numerical categories.\n le = LabelEncoder()\n le.fit(lable)\n lable = le.transform(lable)\n lable = np.array(lable).reshape(-1, 1)\n \n print('3. Onehot encoding.')\n # Onehot encoding.\n ohe = OneHotEncoder(sparse=False)\n ohe.fit(lable)\n lable = ohe.transform(lable)\n \n # Shaffle data.\n print('4. Shuffle data.')\n img, lable = shuffle(img, lable)\n\t\t \n print('5. Saving data.')\n data=(np.asarray(img), np.asarray(lable))\n os.chdir(path)\n \n file=h5py.File('data.hdf5','w')\n x=file.create_dataset('imgs',data=np.array(img))\n y=file.create_dataset('lables',data=np.array(lable))\n print(self.real_labels)\n rl=file.create_dataset('real_labels',data=np.string_(self.real_labels))\n file.close()\n print('Data set is stored in Data.hdf5 file. ')\n\n return data","def create_ela_files(originals='./data/CASIA1_originals', originals_ela='./data/CASIA1_originals_ela',\n fakes='./data/CASIA1_fakes', fakes_ela='./data/CASIA1_fakes_ela'):\n for i, item in enumerate(os.listdir(originals)):\n image = ela(os.path.join(originals, item))\n cv2.imwrite(os.path.join(originals_ela, item), image)\n \n for i, item in enumerate(os.listdir(fakes)):\n image = ela(os.path.join(fakes, item))\n cv2.imwrite(os.path.join(fakes_ela, item), image)","def main():\n nb_processed = 0\n for dataset_name in DATASETS:\n print(\"-----------------\")\n print(\"Dataset: '%s'\" % (dataset_name,))\n print(\"-----------------\")\n\n dataset_dir = os.path.join(WRITE_MAIN_DIR, dataset_name)\n if not os.path.isdir(dataset_dir):\n os.makedirs(dataset_dir)\n\n dataset = Dataset([os.path.join(READ_MAIN_DIR, dataset_name)])\n print(\"Found %d images total.\" % (len(dataset.fps),))\n\n errors = []\n\n scale_height, scale_width = SCALES[dataset_name]\n target_aspect_ratio = scale_width / scale_height\n\n # iterate over every image in the current dataset,\n # augment that image N times, add cols/rows until target aspect ratio\n # is reached, resize it (e.g. 64x64), save it\n for img_idx, (image_filepath, image) in enumerate(zip(dataset.fps, dataset.get_images())):\n print(\"[%s] Image %d of %d (%.2f%%)...\" \\\n % (dataset_name, img_idx+1, len(dataset.fps),\n 100*(img_idx+1)/len(dataset.fps)))\n\n # IOErrors during loading of images result here in a None value\n if image is None:\n print(\"Error / None\")\n errors.append((\n image_filepath,\n \"Failed to load image '%s' (idx %d for dataset %s)\" \\\n % (image_filepath, img_idx, dataset_name)\n ))\n else:\n # resize too big images to smaller ones before any augmentation\n # (for performance reasons)\n height = image.shape[0]\n width = image.shape[1]\n aspect_ratio = width / height\n if width > 1000 or height > 1000:\n image = misc.imresize(image, (1000, int(1000 * aspect_ratio)))\n\n # augment image\n # converts augmented versions automatically to float32, 0-1\n augmentations = augment(image, **AUGMENTATIONS[dataset_name])\n\n # create list of original image + augmented versions\n images_aug = [image / 255.0]\n images_aug.extend(augmentations)\n\n # for each augmented version of the images:\n # resize it to target aspect ratio (e.g. same width and height),\n # save it\n for aug_idx, image_aug in enumerate(images_aug):\n image_aug = to_aspect_ratio_add(image_aug, target_aspect_ratio)\n filename = \"{:0>6}_{:0>3}.jpg\".format(img_idx, aug_idx)\n img_scaled = misc.imresize(image_aug, (scale_height, scale_width))\n misc.imsave(os.path.join(dataset_dir, filename), img_scaled)\n\n nb_processed += 1\n\n print(\"Processed %d images with %d errors.\" % (nb_processed, len(errors)))\n for (fp, err) in errors:\n print(\"File %s error:\" % (fp,))\n print(err)\n print(\"Finished.\")","def exporting_cropped_images (fpath_tiff):\n src = rasterio.open(fpath_tiff, 'r')\n outfolder_irregular = '/train/irregular'\n outfolder_healthy = '/train/healthy'\n outfolder_concrete = '/train/concrete'\n outfolder_incomplete = '/train/incomplete'\n outfolder_other = '/train/other'\n outfolder = '/train/batch'\n #os.makedirs (outfolder, exist_ok = True)","def HDF5_to_HDF5(self, **kwds):\n # split extension from HDF5 file\n if isinstance(self.filename, str):\n fileBasename,fileExtension=os.path.splitext(self.filename)\n else:\n fileBasename,fileExtension=os.path.splitext(self.filename.filename)\n # output HDF5 file\n hdf5_file = os.path.expanduser(f'{fileBasename}.h5')\n # copy everything from the HDF5 file\n with h5py.File(self.filename,mode='r') as source:\n dest = h5py.File(hdf5_file,mode='w')\n # value checks on output HDF5\n if not hasattr(dest, 'create_dataset'):\n raise ValueError('dest must be a group, got {!r}'.format(dest))\n # for each key in the root of the hdf5 file structure\n for k in source.keys():\n self.copy_from_HDF5(source[k], dest, name=k, **kwds)","def read_hdf5(ID_images:str, path:str = \"data/dataset/\"):\n images, labels = [], []\n\n #open the HDF5 file\n file = h5py.File(path +ID_images+\"_lens.h5\", \"r\")\n\n images = np.array(file[\"/images\"]).astype(\"float64\")\n labels = pd.read_hdf(path +ID_images+'_meta.h5', \"table\")\n\n return images, labels","def gen_test_output(sess, logits, image_folder, image_pl, data_folder,\n learning_phase, image_shape, nw_shape):\n image_paths = glob(os.path.join(data_folder, image_folder, '*.png'))\n for image_file in image_paths[:5]:\n\n in_image = scipy.misc.imread(image_file, mode='RGB')\n image = scipy.misc.imresize(in_image, image_shape)\n\n street_im = get_seg_img(sess, logits, image_pl, image, image_shape,\n nw_shape, learning_phase)\n\n street_im = scipy.misc.imresize(street_im, in_image.shape)\n yield os.path.basename(image_file), np.array(street_im)","def main(output_dir, img_size, max_boxes, train_size, val_size):\n im_id = 0\n ann_id = 0\n\n def make_split(split, split_size):\n nonlocal im_id\n nonlocal ann_id\n\n image_dir = join(output_dir, 'train')\n make_dir(image_dir)\n\n images = []\n annotations = []\n for _ in range(split_size):\n img, boxes = make_scene(img_size, max_boxes)\n img = np.transpose(img, (1, 2, 0))\n file_name = '{}.png'.format(im_id)\n Image.fromarray(img).save(\n join(image_dir, file_name))\n images.append({\n 'id': im_id,\n 'height': img_size,\n 'width': img_size,\n 'file_name': file_name\n })\n for box in boxes:\n annotations.append({\n 'id': ann_id,\n 'image_id': im_id,\n 'category_id': 1,\n 'area': (box[2] - box[0]) * (box[3] - box[1]),\n 'bbox': [box[1], box[0], box[3]-box[1], box[2]-box[0]]\n })\n ann_id += 1\n im_id += 1\n\n categories = [{'id': 1, 'name': 'rectangle'}]\n labels = {\n 'images': images,\n 'annotations': annotations,\n 'categories': categories}\n json_to_file(labels, join(output_dir, '{}.json'.format(split)))\n\n make_split('train', train_size)\n make_split('valid', val_size)","def main():\n\n\n\n skulls_folder = os.listdir(RAW_IMAGE_DIRECTORY)\n\n # fetch and sort the .mnc and .tag files\n mnc_files = [f for f in skulls_folder if 'mnc' in f]\n tag_files = [f for f in skulls_folder if 'tag' in f]\n mnc_names = [i.split('.mnc')[0] for i in mnc_files]\n \n mnc_files.sort()\n tag_files.sort()\n mnc_names.sort()\n\n # Process and package ndarrays as tuples inside npy file\n package_to_npy(RAW_IMAGE_DIRECTORY, mnc_files, tag_files, mnc_names)\n \n print('\\n' * 5)\n\n # Push the npy files to GCP Cloud Storage\n upload_to_gcp(PROCESSED_IMAGE_DIRECTORY, GCP_PROJECT_NAME, GCP_BUCKET_NAME)","def create_png_images(self):\n if self.subject is None:\n print Console.WARNING + 'You need to specify a subject first' + Console.ENDC\n return\n\n check_dir_of = self.locations.check_dir_of\n check_dir_of(self.locations.HISTO_PNG_U)\n check_dir_of(self.locations.HISTO_PNG)\n check_dir_of(self.locations.SOURCE_PNG)\n\n\n\n fmap_img = ImageUtils.load_nifti_image(self.locations.HIST_FMAP) #loading subject nifti files\n volumes = []\n try:\n for s in self.locations.SOURCES:\n volumes.append(ImageUtils.load_nifti_image(s))\n except IOError as e:\n print Console.FAIL + 'There are errors loading nifi files for subject %s'%self.subject + Console.ENDC\n return False\n \n\n num_slices = volumes[0].shape[2] #use first volume to check expected number of slices\n\n self.locations.create_empty_dir(self.locations.IMAGES_DIR)\n\n print 'Creating input PNGs for %s'%self.subject\n for k, vol in enumerate(volumes):\n for i in range(num_slices):\n imslice = ImageUtils.data_to_bytescale_rgb(vol[:, :, i])\n im = Image.fromarray(imslice)\n im.save(self.locations.SOURCE_PNG % (self.locations.LABELS[k],i))\n\n \n print 'Creating histology PNGs for %s'%self.subject\n for i in range(num_slices):\n\n im_unscaled = ImageUtils.data_to_unscaled_rgb(fmap_img[:, :, i]); #keeps the original values\n im_unscaled = Image.fromarray(im_unscaled)\n im_unscaled = im_unscaled.filter(ImageFilter.GaussianBlur(radius=2)) #Filter requested by Ali Khan\n im_unscaled.save(self.locations.HISTO_PNG_U % i)\n\n im_scaled = ImageUtils.data_to_bytescale_rgb(fmap_img[:,:,i]); # bytescaled histology\n im_scaled = Image.fromarray(im_scaled)\n im_scaled = im_scaled.filter(ImageFilter.GaussianBlur(radius=2)) #Filter requested by Ali Khan\n im_scaled.save(self.locations.HISTO_PNG % i)\n\n print\n return True","def print_images_in_statistics(self):\n self._print_images_statistics(self._images_in_folder, self._pose_class_names)","def print_images_out_statistics(self):\n self._print_images_statistics(self._images_out_folder, self._pose_class_names)","def create_training_images(fn, i, p_hr, p_lr, size, qualityFactor, downsize=True):\n dest = p_lr/fn.relative_to(p_hr)\n dest.parent.mkdir(parents=True, exist_ok=True)\n img = PIL.Image.open(fn)\n if downsize:\n targ_sz = resize_to(img, size, use_min=True) # W x H\n img = img.resize(targ_sz, resample=PIL.Image.BILINEAR).convert('RGB')\n img.save(dest.with_suffix(\".jpg\"), \"JPEG\", quality=qualityFactor)","def read_images(folder):\n distinct_frames = DistinctFrames()\n\n for file in sorted(sorted(os.listdir(folder)),\n key=len): # sorting files on basis of 1) length and 2) numerical order\n '''\n Sorting is done 2 times because\n if files in the folder are\n 1. image100.pkl\n 2. image22.pkl\n 3. image21.pkl\n firstly sort them to image100.pkl,image21.pkl,image22.pkl then according to length to image21.pkl,image22.pkl,image100.pkl\n '''\n try:\n img_obj = load_from_memory(file, folder)\n time_stamp = img_obj.get_time()\n distinct_frames.add_img_obj(img_obj)\n print(\"Reading image ..\" + str(time_stamp) + \" from \" + folder) # for debug purpose\n except:\n # exception will occur for files like .DS_Store and jpg directory\n continue\n\n if distinct_frames.no_of_frames() != 0:\n distinct_frames.calculate_time()\n\n return distinct_frames","def global_metadata(paths):\n\n # Weakly group images to partition image set size- crucial optimization step\n if os.path.exists(paths.image_preprocess):\n clumped_paths = json.loads(open(paths.image_preprocess).read())\n else:\n clumped_paths = network.alpha_categorize(paths)\n print(\"Hashed source images\")\n\n with open(paths.image_preprocess, 'w') as json_file:\n json.dump(clumped_paths, json_file)\n\n # Combinatorial image grouping to graph\n image_graph = network.load_graph(paths.image_network_path)\n\n total = len(list(chain(*clumped_paths.values())))\n counter = 0.\n\n for image_paths in clumped_paths.values():\n counter += len(image_paths)\n print(str(int(counter / float(total) * 100)) + \"% complete\")\n\n if len(image_paths) > 1:\n image_grouping = images.load_paths(paths.default_patches, image_paths)\n image_graph = metadata.network.network_images(\n image_grouping, threshold=0, network=image_graph)\n else:\n image_graph.add_node(image_paths[0])\n\n metadata.network.save_graph(paths.image_network_path, image_graph)\n print(\"Updated image graph.\")\n\n # Create informational json files for templates and files\n templates.build(paths, image_graph)\n mappings.build(paths, image_graph)\n print(\"Created JSON metadata files.\")","def append_component_images(pldm_fw_up_pkg, image_files):\n for image in image_files:\n with open(image, \"rb\") as file:\n for line in file:\n pldm_fw_up_pkg.write(line)","def read_images(path, image_size=None):\n c = 0\n X = []\n y = []\n folder_names = []\n for dirname, dirnames, filenames in os.walk(path):\n for subdirname in dirnames:\n folder_names.append(subdirname)\n subject_path = os.path.join(dirname, subdirname)\n for filename in os.listdir(subject_path):\n #try:\n im = cv2.imread(os.path.join(subject_path, filename), cv2.IMREAD_GRAYSCALE)\n # resize to given size (if given)\n if (image_size is not None):\n im = cv2.resize(im, image_size)\n X.append(np.asarray(im, dtype=np.uint8))\n y.append(c)\n #except IOError, (errno, strerror):\n # print \"I/O error({0}): {1}\".format(errno, strerror)\n # except:\n # print \"Unexpected error:\", sys.exc_info()[0]\n # raise\n c = c+1\n return [X,y,folder_names]","def extractOneImageStack(filename, h5path=None, destPath=None):\n if destPath == None:\n destPath = \".\"\n if filename.find(\"%\") > 0:\n h5file = h5py.File(filename, driver=\"family\")\n else:\n h5file = h5py.File(filename)\n if (h5path is None) or (h5path not in h5file):\n list_of_names = []\n h5file.visit(list_of_names.append)\n print(os.linesep + \"Where is your stack of images dataset (in which group) ?\")\n for onePath in list_of_names:\n if isinstance(h5file[onePath], h5py.highlevel.Group):\n print(\"\\t\" + onePath)\n\n h5path = raw_input(\"Where is your image stack ? \")\n if not h5path in h5file:\n print(\"This HDF5path %s is not in the file %s\" % (filename, h5path))\n sys.exit(1)\n group = h5file[h5path]\n if \"data\" in group:\n data = group[\"data\"]\n else:\n maxData = None\n for obj in group:\n if isinstance(group[obj], h5py.highlevel.Dataset):\n if len(group[obj].shape) > len(maxData.shape):\n maxData = group[obj]\n elif len(group[obj].shape) == len(maxData.shape):\n if group[obj].shape[0] > maxData.shape[0]:\n maxData = group[obj]\n if maxData is None:\n raise Exception(\"No dataset found in provided group %s from file %s\" % (h5path, filename))\n else:\n data = maxData\n dsSize = data.shape[0]\n\n headerGrp = []\n for obj in group:\n if isinstance(group[obj], h5py.highlevel.Group):\n testHeaderGrp = group[obj]\n for headerdataset in testHeaderGrp:\n if testHeaderGrp[headerdataset].len() != dsSize:\n break\n else:\n headerGrp = testHeaderGrp\n if \"Filenames\" in group:\n EdfFilenames = group[\"Filenames\"]\n elif \"filenames\" in group:\n EdfFilenames = group[\"filenames\"]\n else:\n base = os.path.splitext(filename)[0]\n grpname = h5path.replace(\"/\", \"_\")\n EdfFilenames = [\"%s-%s-%04i.edf\" % (base, grpname, i) for i in range(dsSize) ]\n for idx, oneFile in enumerate(EdfFilenames):\n print(\"Extracting \" + oneFile)\n headers = {}\n for key in headerGrp:\n if key == \"filename\":\n continue\n headers[key] = headerGrp[key][idx]\n edf = fabio.edfimage.edfimage(data=data[idx], header=headers)\n edf.write(os.path.join(destPath, oneFile))","def make_hdf5file(self, dataset_paths: list = None, dataset_values: list = None) -> None:\n\n assert dataset_paths is not None and len(dataset_paths) != 0, (\"`dataset_path` not valid. Expected at least \" \\\n f\"one list element, got {len(dataset_paths)}.\")\n\n assert dataset_values is not None and len(dataset_values) != 0, (\"`dataset_values` not valid. Expected at least \" \\\n f\"one list element, got {len(dataset_values)}.\")\n\n\n # Remove file if already exists and create a new one\n if os.path.isfile(os.path.join(self.FOFDirectory, self.filename)):\n os.remove(os.path.join(self.FOFDirectory, self.filename))\n print(f'[ FOFOutput ]\\t==> Removed old {self.filename} file.')\n\n # Create file and optional groups within it\n FOFfile = h5py.File(os.path.join(self.FOFDirectory, self.filename), 'w')\n print(f'[ FOFOutput ]\\t==> Created new {self.filename} file.')\n\n # Push the attributes to file, if any\n if self.attrs is not None and len(self.attrs.keys()) > 0:\n for key, text in zip(self.attrs.keys(), self.attrs.values()):\n FOFfile.attrs[key] = text\n\n for internal_path, dataset_content in zip(dataset_paths, dataset_values):\n\n assert not internal_path.endswith('/'), \"Invalid hdf5 internal path\"\n assert type(dataset_content) is np.ndarray, \"Can only push numpy.ndarrays into hdf5 files.\"\n\n nested_groups = self.groups_from_path(internal_path)\n if len(nested_groups) == 1:\n FOFfile.create_dataset(nested_groups[0], data=dataset_content)\n else:\n for nested_group in nested_groups[:-1]:\n g = FOFfile.create_group(nested_group)\n g.create_dataset(nested_groups[-1], data = dataset_content)\n\n print(f'[ FOFOutput ]\\t==> Created {internal_path} dataset in {self.filename} file.')\n\n FOFfile.close()","def create_image_lists(image_dir):\n if not gfile.Exists(image_dir):\n print(\"Image directory '\" + image_dir + \"' not found.\")\n return None\n result = {}\n sub_dirs = [x[0] for x in os.walk(image_dir)]\n # The root directory comes first, so skip it.\n is_root_dir = True\n for sub_dir in sub_dirs:\n print('in sub loop')\n extensions = ['jpg', 'jpeg', 'JPG', 'JPEG']\n file_list = []\n dir_name = os.path.basename(image_dir)\n print(\"Looking for images in '\" + image_dir + \"'\")\n for extension in extensions:\n file_glob = os.path.join(image_dir, dir_name, '*.' + extension)\n file_list.extend(glob.glob(file_glob))\n if not file_list:\n print('No files found')\n continue\n if len(file_list) < 20:\n print('WARNING: Folder has less than 20 images, which may cause issues.')\n label_name = re.sub(r'[^a-z0-9]+', ' ', dir_name.lower())\n testing_images = []\n for file_name in file_list:\n base_name = os.path.basename(file_name)\n # We want to ignore anything after '_nohash_' in the file name when\n # deciding which set to put an image in, the data set creator has a way of\n # grouping photos that are close variations of each other. For example\n # this is used in the plant disease data set to group multiple pictures of\n # the same leaf.\n hash_name = re.sub(r'_nohash_.*$', '', file_name)\n # This looks a bit magical, but we need to decide whether this file should\n # go into the training, testing, or validation sets, and we want to keep\n # existing files in the same set even if more files are subsequently\n # added.\n # To do that, we need a stable way of deciding based on just the file name\n # itself, so we do a hash of that and then use that to generate a\n # probability value that we use to assign it.\n testing_images.append(base_name)\n return testing_images","def splitMerge(self):\n\t\tpath_merge = self.aug_merge_path\n\t\tpath_train = self.aug_train_path\n\t\tpath_label = self.aug_label_path\n\t\tfor i in range(self.slices):\n\t\t\tpath = path_merge + \"/\" + str(i)\n\t\t\t# print(path)\n\t\t\ttrain_imgs = glob.glob(path+\"/*.\"+self.img_type)\n\t\t\t# print(len(train_imgs))\n\t\t\t# break\n\t\t\tfor imgname in train_imgs:\n\t\t\t\tmidname = imgname[imgname.rindex(\"/\")+1:imgname.rindex(\".\"+self.img_type)]\n\t\t\t\timg = cv2.imread(imgname)\n\t\t\t\timg_train = img[:,:,2]#cv2 read image rgb->bgr\n\t\t\t\timg_label = img[:,:,0]\n\t\t\t\tcv2.imwrite(path_train+\"/\"+midname+\".\"+self.img_type,img_train)\n\t\t\t\tcv2.imwrite(path_label+\"/\"+midname+\".\"+self.img_type,img_label)","def create_noobj_folder(\n folder: PathLike, \n img_ext: str = \".jpg\",\n):\n folder = Path(folder).expanduser().resolve()\n images = glob(folder, img_ext)\n \n for image in images:\n filename = image.name\n _folder = image.parent.name\n path = folder / (image.stem + \".xml\")\n img_w, img_h = get_image_size(image)\n\n tree = ET.Element(\"annotation\")\n\n et_folder = ET.SubElement(tree, \"folder\")\n et_folder.text = _folder\n\n et_filename = ET.SubElement(tree, \"filename\")\n et_filename.text = filename\n\n et_path = ET.SubElement(tree, \"path\")\n et_path.text = str(path)\n\n et_img_size = ET.SubElement(tree, \"size\")\n ET.SubElement(et_img_size, \"width\").text = str(img_w)\n ET.SubElement(et_img_size, \"height\").text = str(img_h)\n ET.SubElement(et_img_size, \"depth\").text = \"3\"\n\n content = ET.tostring(tree, encoding=\"unicode\", pretty_print=True)\n try: \n path.write_text(content)\n except KeyboardInterrupt:\n path.write_text(content)\n exit()","def __call__(self):\n train_number = 1\n test_number = 1\n for cur_file in os.listdir(self.txt_dir):\n if(cur_file.split(\".\")[0].split(\"-\")[-1] == \"ellipseList\"):\n with open(os.path.join(self.txt_dir, cur_file), 'r') as txt_file:\n while(True):\n line = txt_file.readline().rstrip(\"\\n\")\n if not line : break\n\n # Image load\n img_folder_ = line.split(\"/\") \n img_dir_ = \\\n self.img_dir + \"\\\\\" + img_folder_[0] + \"\\\\\" + img_folder_[1] + \"\\\\\" + img_folder_[2] + \"\\\\\" + img_folder_[3] + \"\\\\\" + img_folder_[4] + \".jpg\"\n img = cv2.imread(img_dir_, cv2.IMREAD_COLOR)\n \n if(train_number > 2500) :\n image_name = \"IMG_{}.jpg\".format(test_number)\n cv2.imwrite(os.path.join(self.new_img_dir, \"test\", image_name), img)\n test_number = test_number + 1\n\n else :\n image_name = \"IMG_{}.jpg\".format(train_number)\n cv2.imwrite(os.path.join(self.new_img_dir, \"train\", image_name), img)\n train_number = train_number + 1\n \n object_num = int(txt_file.readline().rstrip(\"\\n\"))\n file_data = OrderedDict()\n object_data = OrderedDict()\n file_data[\"Img_id\"] = image_name.split('.')[0]\n file_data[\"Objects\"] = []\n\n #Do \"for\" operation as much as the number of objects\n for i in range(object_num):\n coord_ = txt_file.readline().split(\" \")\n rad = abs(float(coord_[2]))\n h = float(coord_[0])*np.sin(rad)\n w = float(coord_[1])*np.sin(rad)\n object_data[\"Object_class\"] = \"Face\"\n object_data[\"Coordinate\"] = { \"Xmin\" : float(coord_[3]) - w, \"Ymin\" : float(coord_[4]) - h, \"Xmax\" : float(coord_[3]) + w, \"Ymax\" : float(coord_[4]) + h }\n file_data[\"Objects\"].append(object_data)\n self.json_list.append(file_data)\n\n # Save train annotation\n if(train_number == 2501):\n self._save_annotation(\"train\")\n train_number = train_number + 1\n self._save_annotation(\"test\")","def read_input_files(self):\r\n\r\n for input_file in self.list_of_input_files:\r\n input_file.read_header_of_file()\r\n self.list_of_header_objects.extend(input_file.list_of_header_objects)\r\n self.list_of_header_objects_without_ID.extend(input_file.list_of_header_objects_without_ID)\r\n self.list_of_contigs.extend(input_file.list_of_contigs)\r\n\r\n self.list_of_header_objects = list(toolz.unique(self.list_of_header_objects, key=lambda x: x.tag_and_ID))\r\n self.list_of_header_objects_without_ID = list(\r\n toolz.unique(self.list_of_header_objects_without_ID, key=lambda x: x.line))\r\n self.list_of_contigs = list(toolz.unique(self.list_of_contigs, key=lambda x: x.line))\r\n self.list_of_header_objects.extend(self.list_of_header_objects_without_ID)\r\n self.list_of_header_objects.sort(key=lambda x: x.line)\r\n self.list_of_header_objects.extend(self.list_of_contigs)\r\n self.list_of_header_objects.sort(key=lambda x: x.tag, reverse=False)\r\n self.create_body_header_line_for_output()\r\n self.write_header_in_output_file()\r\n\r\n list_of_chrom = list(self.indices.keys())\r\n list_of_chrom.sort(key=lambda x: self.alphanum_key(x))\r\n for chrom in list_of_chrom:\r\n self.list_of_body_objects.clear()\r\n for input_file in self.list_of_input_files:\r\n input_file.read_specific_chrom_body_of_file(chrom)\r\n self.list_of_body_objects.extend(input_file.list_of_body_objects)\r\n\r\n self.adjust_body_records_to_samples()\r\n self.list_of_body_objects = list(toolz.unique(self.list_of_body_objects, key=lambda x: x.line))\r\n self.list_of_body_objects.sort(key=lambda x: self.alphanum_key(x.line))\r\n self.verify_and_merge_body_records()\r\n self.write_specific_chrom_in_output_file()","def add_random(fr_data_path, random_path, output_path):\n with h5py.File(random_path, 'r') as data:\n random = np.asarray(data['images'].value)\n \n means = np.mean(np.mean(random, axis=-1), axis=-1)\n empty = means == 0.0\n error = np.isnan(means)\n discard = empty | error\n\n random_i = np.where(~discard)\n random = random[random_i]\n\n random = center_on_brightest(random)\n \n with h5py.File(fr_data_path, 'r') as data:\n images = np.asarray(data[\"images\"].value) \n images = center_on_brightest(images)\n \n labels = np.where(np.asarray(data['labels']), 2, 1)\n\n images = np.concatenate((images, random), axis=0)\n labels = np.concatenate((labels, np.full((random.shape[0],), \n fill_value=0)), axis=0)\n\n with h5py.File(output_path, 'w') as f:\n f.create_dataset('images', data=images)\n f.create_dataset('labels', data=labels)\n\n with h5py.File(fr_data_path, 'r') as data: \n f.copy(data, 'fri_data')\n f.copy(data, 'frii_data')","def decompose_images(data_dir, output_dir, save_individually, **kwargs):\n\n # parse parameters\n opt = TestOptions()\n opt.parse(kwargs)\n # print(kwargs)\n\n # torch setting\n pytorch_settings.set_(with_random=False, determine=True)\n\n # visualize\n V.create_a_visualizer(opt)\n\n # NIID-Net Manager\n model = create_model(opt)\n model.switch_to_eval()\n\n # List all image files in the directory (exclude subdirectory)\n image_file_list = list_files(data_dir, ['jpg', 'jpeg', 'png', 'tif', 'JPG'])\n print('Total image in the directory %s: %d' % (data_dir, len(image_file_list)))\n\n # Decompose images\n for file_name in image_file_list:\n # Read image\n img_path = os.path.join(data_dir, file_name)\n o_img = Image.open(img_path)\n o_img = o_img.convert(\"RGB\")\n\n # Resize input image\n # input_img = resize_image(o_img)\n input_img = o_img\n\n # Predict\n input_img = TF.to_tensor(input_img).unsqueeze(0)\n pred_N, pred_R, pred_L, pred_S, rendered_img = model.predict({'input_srgb': input_img}, normal=True, IID=True)\n\n # Save results\n idx = 0\n pred_imgs = {\n 'pred_N': pred_N[idx].cpu(),\n 'pred_R': pred_R[idx].cpu(),\n 'pred_L': pred_L[idx].cpu(),\n 'pred_S': pred_S[idx].cpu(),\n 'rendered_img': rendered_img[idx].cpu(),\n 'input_srgb': input_img[idx],\n }\n f = '%s_decomposed' % (file_name[:file_name.rfind('.')])\n image_util.save_intrinsic_images(output_dir, pred_imgs, label=f, individual=save_individually)\n torch.save(pred_imgs, os.path.join(output_dir, f+'.pth.tar'))\n print('Decompose %s successfully!' % file_name)","def resize_multiple_images(src_path, dst_path):\n for filename in os.listdir(src_path):\n img=Image.open(src_path+'/'+filename)\n new_img = img.resize((96,96,))\n #new_img.resize(96,96,1)\n if not os.path.exists(dst_path):\n os.makedirs(dst_path)\n new_img.save(dst_path+'/'+filename)\n print('Resized and saved {} successfully.'.format(filename))","def test_save_image(self):\n\n from m3_save_images.m3_save_images import save_images\n folder_destination_name = \"unittest-sorted-images\"\n path_source = \"../img\"\n image_name = [\"00ff00.png\", \"aqua.png\", \"black.jpg\", \"yellow.png\", \"red2.jpg\", \"green.jpg\"]\n image_color = [\"Lime\", \"Aqua\", \"Black\", \"Yellow\", \"Red\", \"Green\"]\n # new empty folder is needed for testing save_image() function\n if os.path.isdir(folder_destination_name):\n shutil.rmtree(folder_destination_name)\n os.mkdir(folder_destination_name)\n # creating folders\n for i in range(0, 4):\n save_images(folder_destination_name, path_source, image_name[i], image_color[i])\n self.assertEqual(''.join(os.listdir(os.path.join(folder_destination_name, image_color[i]))), image_name[i])\n save_images(folder_destination_name, path_source, image_name[i], image_color[5])\n self.assertNotEqual(''.join(os.listdir(os.path.join(folder_destination_name, image_color[i]))), image_name[5])"],"string":"[\n \"def create_and_write_output(predictions_path,output_path,inpDir):\\n \\n filenames= sorted(os.listdir(predictions_path)) \\n for filename in filenames:\\n \\n # read the 3 channel output image from the neural network\\n image=cv2.imread(os.path.join(predictions_path,filename))\\n \\n # create binary image output using the create_binary function\\n out_image=create_binary(image) \\n \\n # read and store the metadata from the input image\\n with BioReader(os.path.join(inpDir,filename)) as br:\\n metadata = br.metadata\\n\\n # Write the binary output consisting of the metadata using bfio.\\n output_image_5channel=np.zeros((out_image.shape[0],out_image.shape[1],1,1,1),dtype=np.uint8)\\n output_image_5channel[:,:,0,0,0]=out_image \\n\\n with BioWriter(os.path.join(output_path,filename), metadata=metadata) as bw:\\n bw.dtype = output_image_5channel.dtype\\n bw.write(output_image_5channel)\",\n \"def _preprocess(self):\\n print(\\\"Note: if root path is changed, the previously generated json files need to be re-generated (delete them first)\\\")\\n if osp.exists(self.imgs_labeled_dir) and \\\\\\n osp.exists(self.imgs_detected_dir) and \\\\\\n osp.exists(self.split_classic_det_json_path) and \\\\\\n osp.exists(self.split_classic_lab_json_path) and \\\\\\n osp.exists(self.split_new_det_json_path) and \\\\\\n osp.exists(self.split_new_lab_json_path):\\n return\\n\\n mkdir_if_missing(self.imgs_detected_dir)\\n mkdir_if_missing(self.imgs_labeled_dir)\\n\\n print(\\\"Extract image data from {} and save as png\\\".format(self.raw_mat_path))\\n mat = h5py.File(self.raw_mat_path, 'r')\\n\\n def _deref(ref):\\n return mat[ref][:].T\\n\\n def _process_images(img_refs, campid, pid, save_dir):\\n img_paths = [] # Note: some persons only have images for one view\\n for imgid, img_ref in enumerate(img_refs):\\n img = _deref(img_ref)\\n # skip empty cell\\n if img.size == 0 or img.ndim < 3: continue\\n # images are saved with the following format, index-1 (ensure uniqueness)\\n # campid: index of camera pair (1-5)\\n # pid: index of person in 'campid'-th camera pair\\n # viewid: index of view, {1, 2}\\n # imgid: index of image, (1-10)\\n viewid = 1 if imgid < 5 else 2\\n img_name = '{:01d}_{:03d}_{:01d}_{:02d}.png'.format(campid+1, pid+1, viewid, imgid+1)\\n img_path = osp.join(save_dir, img_name)\\n imageio.imwrite(img_path, img)\\n img_paths.append(img_path)\\n return img_paths\\n\\n def _extract_img(name):\\n print(\\\"Processing {} images (extract and save) ...\\\".format(name))\\n meta_data = []\\n imgs_dir = self.imgs_detected_dir if name == 'detected' else self.imgs_labeled_dir\\n for campid, camp_ref in enumerate(mat[name][0]):\\n camp = _deref(camp_ref)\\n num_pids = camp.shape[0]\\n for pid in range(num_pids):\\n img_paths = _process_images(camp[pid,:], campid, pid, imgs_dir)\\n assert len(img_paths) > 0, \\\"campid{}-pid{} has no images\\\".format(campid, pid)\\n meta_data.append((campid+1, pid+1, img_paths))\\n print(\\\"done camera pair {} with {} identities\\\".format(campid+1, num_pids))\\n return meta_data\\n\\n meta_detected = _extract_img('detected')\\n meta_labeled = _extract_img('labeled')\\n\\n def _extract_classic_split(meta_data, test_split):\\n train, test = [], []\\n num_train_pids, num_test_pids = 0, 0\\n num_train_imgs, num_test_imgs = 0, 0\\n for i, (campid, pid, img_paths) in enumerate(meta_data):\\n \\n if [campid, pid] in test_split:\\n for img_path in img_paths:\\n camid = int(osp.basename(img_path).split('_')[2])\\n test.append((img_path, num_test_pids, camid))\\n num_test_pids += 1\\n num_test_imgs += len(img_paths)\\n else:\\n for img_path in img_paths:\\n camid = int(osp.basename(img_path).split('_')[2])\\n train.append((img_path, num_train_pids, camid))\\n num_train_pids += 1\\n num_train_imgs += len(img_paths)\\n return train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs\\n\\n print(\\\"Creating classic splits (# = 20) ...\\\")\\n splits_classic_det, splits_classic_lab = [], []\\n for split_ref in mat['testsets'][0]:\\n test_split = _deref(split_ref).tolist()\\n\\n # create split for detected images\\n train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs = \\\\\\n _extract_classic_split(meta_detected, test_split)\\n splits_classic_det.append({\\n 'train': train, 'query': test, 'gallery': test,\\n 'num_train_pids': num_train_pids, 'num_train_imgs': num_train_imgs,\\n 'num_query_pids': num_test_pids, 'num_query_imgs': num_test_imgs,\\n 'num_gallery_pids': num_test_pids, 'num_gallery_imgs': num_test_imgs,\\n })\\n\\n # create split for labeled images\\n train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs = \\\\\\n _extract_classic_split(meta_labeled, test_split)\\n splits_classic_lab.append({\\n 'train': train, 'query': test, 'gallery': test,\\n 'num_train_pids': num_train_pids, 'num_train_imgs': num_train_imgs,\\n 'num_query_pids': num_test_pids, 'num_query_imgs': num_test_imgs,\\n 'num_gallery_pids': num_test_pids, 'num_gallery_imgs': num_test_imgs,\\n })\\n \\n write_json(splits_classic_det, self.split_classic_det_json_path)\\n write_json(splits_classic_lab, self.split_classic_lab_json_path)\\n\\n def _extract_set(filelist, pids, pid2label, idxs, img_dir, relabel):\\n tmp_set = []\\n unique_pids = set()\\n for idx in idxs:\\n img_name = filelist[idx][0]\\n camid = int(img_name.split('_')[2])\\n pid = pids[idx]\\n if relabel: pid = pid2label[pid]\\n img_path = osp.join(img_dir, img_name)\\n tmp_set.append((img_path, int(pid), camid))\\n unique_pids.add(pid)\\n return tmp_set, len(unique_pids), len(idxs)\\n\\n def _extract_new_split(split_dict, img_dir):\\n train_idxs = split_dict['train_idx'].flatten() - 1 # index-0\\n pids = split_dict['labels'].flatten()\\n train_pids = set(pids[train_idxs])\\n pid2label = {pid: label for label, pid in enumerate(train_pids)}\\n query_idxs = split_dict['query_idx'].flatten() - 1\\n gallery_idxs = split_dict['gallery_idx'].flatten() - 1\\n filelist = split_dict['filelist'].flatten()\\n train_info = _extract_set(filelist, pids, pid2label, train_idxs, img_dir, relabel=True)\\n query_info = _extract_set(filelist, pids, pid2label, query_idxs, img_dir, relabel=False)\\n gallery_info = _extract_set(filelist, pids, pid2label, gallery_idxs, img_dir, relabel=False)\\n return train_info, query_info, gallery_info\\n\\n print(\\\"Creating new splits for detected images (767/700) ...\\\")\\n train_info, query_info, gallery_info = _extract_new_split(\\n loadmat(self.split_new_det_mat_path),\\n self.imgs_detected_dir,\\n )\\n splits = [{\\n 'train': train_info[0], 'query': query_info[0], 'gallery': gallery_info[0],\\n 'num_train_pids': train_info[1], 'num_train_imgs': train_info[2],\\n 'num_query_pids': query_info[1], 'num_query_imgs': query_info[2],\\n 'num_gallery_pids': gallery_info[1], 'num_gallery_imgs': gallery_info[2],\\n }]\\n write_json(splits, self.split_new_det_json_path)\\n\\n print(\\\"Creating new splits for labeled images (767/700) ...\\\")\\n train_info, query_info, gallery_info = _extract_new_split(\\n loadmat(self.split_new_lab_mat_path),\\n self.imgs_labeled_dir,\\n )\\n splits = [{\\n 'train': train_info[0], 'query': query_info[0], 'gallery': gallery_info[0],\\n 'num_train_pids': train_info[1], 'num_train_imgs': train_info[2],\\n 'num_query_pids': query_info[1], 'num_query_imgs': query_info[2],\\n 'num_gallery_pids': gallery_info[1], 'num_gallery_imgs': gallery_info[2],\\n }]\\n write_json(splits, self.split_new_lab_json_path)\",\n \"def gene_h5_train_file(data_path, h5_name):\\n img = []\\n y_cls_mask = []\\n y_reg_cls_mask = []\\n os.chdir(data_path)\\n jpgfiles = glob.glob('*.jpg')\\n idx = 1\\n # the position of generator objector is very important\\n gene_obj = image_output_pair(data_path, 1/255.0)\\n while True:\\n if idx == len(jpgfiles):\\n break\\n print '\\\\t{0}/{1}'.format(idx, len(jpgfiles))\\n # the position of generator objector is very important\\n # gene_obj = image_output_pair(data_path, 1/255.0)\\n img_it, y_cls_mask_it, y_reg_cls_mask_it = gene_obj.next()\\n img.append(img_it)\\n y_cls_mask.append(y_cls_mask_it)\\n y_reg_cls_mask.append(y_reg_cls_mask_it)\\n idx += 1\\n\\n # img => (320, 320, 3)\\n # after np.stack => (19041, 320, 320, 3)\\n img_input = np.stack(img, axis=0)\\n y_cls = np.stack(y_cls_mask, axis=0)\\n y_reg = np.stack(y_reg_cls_mask, axis=0)\\n print 'input data shape is {0}'.format(img_input.shape)\\n print 'y_cls data shape is {0}'.format(y_cls.shape)\\n print 'y_reg data shape is {0}'.format(y_reg.shape)\\n \\n # wirte data\\n h5 = '/home/yuquanjie/Documents/train_' + h5_name\\n file_write = h5py.File(h5, 'w')\\n file_write.create_dataset('X_train', data=img_input)\\n file_write.create_dataset('Y_train_cls', data=y_cls)\\n file_write.create_dataset('Y_train_merge', data=y_reg)\\n file_write.close()\",\n \"def preprocess_images(file_path, new_file_path):\\n if not os.path.isdir(new_file_path):\\n os.mkdir(new_file_path)\\n i = 0\\n for dir in listdir(file_path):\\n j = 0\\n for image_path in listdir(file_path + '/' + dir):\\n image = open_image(image_path)\\n cv2.imwrite(file_path + '/' + image_path + '/' str(i) + '/' +str(i) + '.jpg', image)\\n j += 1\\n i += 1\",\n \"def bulk_process_images(inputpath, outputpath, extension):\\n\\n for dirpath, dirnames, filenames in os.walk(inputpath):\\n structure = os.path.join(outputpath, dirpath[len(inputpath) + 1:])\\n for file in filenames:\\n if file.endswith(extension):\\n src = os.path.join(dirpath, file)\\n dest = os.path.join(structure, file)\\n img = load_and_preprocess_image(src)\\n cv2.imwrite(dest, img)\",\n \"def create_train(train_img_path):\\n\\n f = open(\\\"train.txt\\\", \\\"w+\\\")\\n for subdirs, dirs, files in os.walk(train_img_path):\\n for filename in files:\\n if filename.endswith(\\\".jpg\\\"):\\n train_image_path = os.path.join(train_img_path, filename)\\n print(train_image_path)\\n f.write(train_image_path + \\\"\\\\n\\\")\\n f.close()\",\n \"def save_step_1(imgs, output_path='./output/step1'):\\n # ... your code here ...\\n i=0\\n for each in imgs:\\n i+=1\\n cv2.imwrite(output_path+\\\"/output\\\"+str(i)+\\\".jpg\\\", each)\",\n \"def main():\\n \\n # for inserting other images, add tem to /input folder and list them here\\n images = (\\n 'image-0',\\n 'image-1',\\n 'image-2'\\n )\\n\\n for image_name in images:\\n print(image_name, \\\"image:\\\")\\n\\n image = open_image(image_name)\\n display_image(image, \\\"Original input \\\" + image_name)\\n\\n grayscale_v = transform_colors(image)\\n display_image(grayscale_v[:,:,0], \\\"Grayscale \\\" + image_name)\\n save_image(image_name + \\\"-grayscale\\\", grayscale_v[:,:,0])\\n\\n contours_v, contours = get_contours(grayscale_v)\\n display_image(contours_v, \\\"Contours \\\" + image_name)\\n save_image(image_name + \\\"-contours\\\", contours_v)\\n\\n labeled_img, areas = get_measures(image, contours[1:])\\n display_image(labeled_img, \\\"Labeled \\\" + image_name)\\n save_image(image_name + \\\"-labeled\\\", labeled_img)\\n\\n areas_histogram(areas, image_name)\",\n \"def save_image_to_h5(image, h5_path, *args, **kwargs):\\n # TODO: Implement the method\\n\\n f = h5.File(h5_path, \\\"w\\\")\\n grp = f.create_group(\\\"data\\\")\\n grp.create_dataset(\\\"image\\\", data=image)\",\n \"def prepare_hdf5_file(hdf5_file, n_train, n_valid, n_test):\\n n_total = n_train + n_valid + n_test\\n splits = create_splits(n_train, n_valid, n_test)\\n hdf5_file.attrs['split'] = H5PYDataset.create_split_array(splits)\\n vlen_dtype = h5py.special_dtype(vlen=numpy.dtype('uint8'))\\n hdf5_file.create_dataset('encoded_images', shape=(n_total,),\\n dtype=vlen_dtype)\\n hdf5_file.create_dataset('targets', shape=(n_total, 1), dtype=numpy.int16)\\n hdf5_file.create_dataset('filenames', shape=(n_total, 1), dtype='S32')\",\n \"def appendpics(pathofimg, w_sub, h_sub, step):\\n num = 0\\n dirlist = []\\n images = [] # images in each folder\\n for root, dirs, fileswer in os.walk(pathofimg):\\n if len(dirs)!= 0:\\n for dir in dirs:\\n dirlist.append(dir)\\n for rooert, dirwerwes, files in os.walk(pathofimg+'/'+dir):\\n for file in files:\\n if(file.endswith('.png')):\\n images.append(Image.open(pathofimg+'/'+dir+'/'+file))\\n if(len(images)==81):\\n break\\n target = montage(images, w_sub, h_sub, step)\\n target.save(pathofimg +'/'+ dir + '.png', quality=100)\\n else:\\n dir = 'Generated'\\n for file in fileswer:\\n if (file.endswith('.png')):\\n images.append(Image.open(pathofimg +'/'+ file))\\n target1 = montage(images, w_sub, h_sub, step)\\n savepath = pathofimg +'/'+ 'generated'\\n os.makedirs(savepath)\\n target1.save(savepath +'/'+ dir + '.png', quality=100)\",\n \"def get_files(self):\\n train_images = glob(os.path.join(self.images_dir, '*%s' % self.im_extension)) \\n train_labels = [x.replace(self.im_extension, '.npy').replace('images', 'groundTruth') for x in train_images]\\n val_images = glob(os.path.join(self.val_images_dir, '*%s' % self.im_extension))\\n val_labels = [x.replace(self.im_extension, '.npy').replace('images', 'groundTruth') for x in val_images]\\n train_images = np.array(train_images)\\n train_labels = np.array(train_labels)\\n val_images = np.array(val_images)\\n val_labels = np.array(val_labels)\\n test_images = np.array(\\n glob('/media/data_cifs/pytorch_projects/datasets/BSDS500_crops/data/images/test_nocrop/*.jpg'))\\n test_labels = np.array(\\n [x.replace('images', 'groundTruth').replace('.jpg', '.npy') for x in test_images])\\n test_labels = np.array(\\n [np.load(x) for x in test_labels])\\n keep_idx = np.array([True if x.shape[0] > x.shape[1] else False for x in test_labels])\\n test_images = test_images[keep_idx]\\n test_labels = test_labels[keep_idx]\\n test_images = np.stack([misc.imread(x) for x in test_images], 0)\\n test_labels = np.stack(test_labels, 0)\\n test_labels = test_labels[..., None]\\n\\n # Add constant padding to bottom/right\\n if self.pad:\\n test_images = util.pad(test_images, ((0, 0), (self.pad // 2, self.pad - self.pad // 2), (self.pad // 2, self.pad - self.pad // 2), (0, 0)), mode='linear_ramp')\\n test_labels = util.pad(test_labels, ((0, 0), (self.pad // 2, self.pad - self.pad // 2), (self.pad // 2, self.pad - self.pad // 2), (0, 0)), mode='constant', constant_values=0)\\n\\n # Select images for training\\n sort_idx = np.argsort(train_images)\\n train_images = train_images[sort_idx[:self.train_size]]\\n train_labels = train_labels[sort_idx[:self.train_size]]\\n\\n # Build CV dict\\n cv_files, cv_labels = {}, {}\\n cv_files[self.folds['train']] = train_images\\n cv_files[self.folds['val']] = val_images\\n cv_files[self.folds['test']] = test_images\\n cv_labels[self.folds['train']] = train_labels\\n cv_labels[self.folds['val']] = val_labels\\n cv_labels[self.folds['test']] = test_labels\\n return cv_files, cv_labels\",\n \"def write_data_files(self):\\n \\n logging.info('\\\\n Start writing data files \\\\n')\\n \\n for i, (data_file, label_file) in enumerate(self.files):\\n data_file, label_file = Path(data_file), Path(label_file)\\n logging.info('Writing .hdf5 file for : [{}]'.format(str(data_file)))\\n \\n file_name = self.save_data_folder / '{}.hdf5'.format(label_file.name[:-4])\\n if file_name.exists():\\n continue\\n \\n with h5py.File(str(file_name), 'w') as writer:\\n self.serialize_samples(\\n writer, data_file, label_file)\",\n \"def _extract_images(source_path, target_path, merge_labels):\\n\\n images_path = os.path.join(source_path, 'imagesTr')\\n labels_path = os.path.join(source_path, 'labelsTr')\\n\\n # Filenames have the form 'hippocampus_XX.nii.gz'\\n filenames = [x for x in os.listdir(images_path) if x[:5] == 'hippo']\\n\\n # Create directories\\n if not os.path.isdir(target_path):\\n os.makedirs(target_path)\\n\\n for filename in filenames:\\n\\n # Extract only T2-weighted\\n x = sitk.ReadImage(os.path.join(images_path, filename))\\n x = sitk.GetArrayFromImage(x)\\n y = sitk.ReadImage(os.path.join(labels_path, filename))\\n y = sitk.GetArrayFromImage(y)\\n\\n # Shape expected: (35, 51, 35)\\n # Average label shape: (24.5, 37.8, 21.0)\\n assert x.shape == y.shape\\n\\n # No longer distinguish between hippocampus proper and subiculum\\n if merge_labels:\\n y[y == 2] = 1\\n\\n # Save new images so they can be loaded directly\\n study_name = filename.replace('_', '').split('.nii')[0]\\n sitk.WriteImage(sitk.GetImageFromArray(x), join_path([target_path, study_name + \\\".nii.gz\\\"]))\\n sitk.WriteImage(sitk.GetImageFromArray(y), join_path([target_path, study_name + \\\"_gt.nii.gz\\\"]))\",\n \"def save_step_2(imgs, match_list, output_path=\\\"./output/step2\\\"):\\n # ... your code here ...\\n for i in range(len(imgs)):\\n name1,tail1 = str.split(filenames[match_list[i][0]],\\\".\\\")\\n name2,tail2 = str.split(filenames[match_list[i][2]],\\\".\\\")\\n cv2.imwrite(output_path+\\\"/\\\"+name1+\\\"_\\\"+str(match_list[i][1])+\\\"_\\\"+name2+\\\"_\\\"+str(match_list[i][3])+\\\"_\\\"+str(match_list[i][4])+\\\".jpg\\\", imgs[i])\",\n \"def analyzeImages(path, name_type, box1_size, box2_size, box3_size, box4_size, box5_size):\\n \\n folders = [f for f in sorted(glob.glob(path + \\\"/**\\\"))]\\n \\n for folder in folders: \\n \\n # to save this data frame in a csv file\\n \\n files = [f for f in sorted(glob.glob(folder + \\\"/**\\\" + \\\".jpg\\\"))]\\n \\n centroidsDf = pd.DataFrame(np.zeros([len(files), 11]),columns=[\\\"frame\\\", \\\"fly1_x\\\", \\\"fly1_y\\\", \\\"fly2_x\\\", \\\"fly2_y\\\", \\\"fly3_x\\\", \\\"fly3_y\\\", \\\"fly4_x\\\", \\\"fly4_y\\\", \\\"fly5_x\\\", \\\"fly5_y\\\"]);\\n headsDf = pd.DataFrame(np.zeros([len(files), 11]),columns=[\\\"frame\\\", \\\"fly1_x\\\", \\\"fly1_y\\\", \\\"fly2_x\\\", \\\"fly2_y\\\", \\\"fly3_x\\\", \\\"fly3_y\\\", \\\"fly4_x\\\", \\\"fly4_y\\\", \\\"fly5_x\\\", \\\"fly5_y\\\"]);\\n \\n img_array1 = []\\n img_array2 = []\\n img_array3 = []\\n img_array4 = []\\n img_array5 = []\\n\\n for file in files:\\n \\n print(file)\\n \\n centroidsDf[\\\"frame\\\"][files.index(file)] = files.index(file)+1\\n headsDf[\\\"frame\\\"][files.index(file)] = files.index(file)+1\\n \\n img = cv2.imread(file)\\n \\n ## FLY 1 ##\\n\\n box1 = img[box1_size[0]:box1_size[1], box1_size[2]:box1_size[3]]\\n \\n # image processing to get the centroid and head positions\\n x_centroid, y_centroid, x_head, y_head = findCentroid(box1, file) \\n \\n # add the centroid and head locations on the image \\n box1 = cv2.circle(box1, (x_centroid, y_centroid), 7, (255, 0, 0), -1)\\n box1 = cv2.circle(box1, (x_head, y_head), 7, (0, 0, 255), -1)\\n img_array1.append(box1)\\n \\n # add the positions in the final data frame\\n centroidsDf[\\\"fly1_x\\\"][files.index(file)] = x_centroid\\n centroidsDf[\\\"fly1_y\\\"][files.index(file)] = y_centroid\\n \\n headsDf[\\\"fly1_x\\\"][files.index(file)] = x_head\\n headsDf[\\\"fly1_y\\\"][files.index(file)] = y_head\\n \\n ## FLY 2 ##\\n \\n box2 = img[box2_size[0]:box2_size[1], box2_size[2]:box2_size[3]]\\n \\n # image processing to get the centroid and head positions\\n x_centroid, y_centroid, x_head, y_head = findCentroid(box2, file)\\n \\n # add the centroid and head locations on the image \\n box2 = cv2.circle(box2, (x_centroid, y_centroid), 7, (255, 0, 0), -1)\\n box2 = cv2.circle(box2, (x_head, y_head), 7, (0, 0, 255), -1)\\n img_array2.append(box2)\\n \\n # add the positions in the final data frame \\n centroidsDf[\\\"fly2_x\\\"][files.index(file)] = x_centroid\\n centroidsDf[\\\"fly2_y\\\"][files.index(file)] = y_centroid\\n \\n headsDf[\\\"fly2_x\\\"][files.index(file)] = x_head\\n headsDf[\\\"fly2_y\\\"][files.index(file)] = y_head\\n \\n ## FLY 3 ##\\n\\n box3 = img[box3_size[0]:box3_size[1], box3_size[2]:box3_size[3]]\\n \\n # image processing to get the centroid and head positions\\n x_centroid, y_centroid, x_head, y_head = findCentroid(box3, file)\\n \\n # add the centroid and head locations on the image \\n box3 = cv2.circle(box3, (x_centroid, y_centroid), 7, (255, 0, 0), -1)\\n box3 = cv2.circle(box3, (x_head, y_head), 7, (0, 0, 255), -1)\\n img_array3.append(box3)\\n\\n # add the positions in the final data frame\\n centroidsDf[\\\"fly3_x\\\"][files.index(file)] = x_centroid\\n centroidsDf[\\\"fly3_y\\\"][files.index(file)] = y_centroid\\n \\n headsDf[\\\"fly3_x\\\"][files.index(file)] = x_head\\n headsDf[\\\"fly3_y\\\"][files.index(file)] = y_head\\n \\n ## FLY 4 ##\\n \\n box4 = img[box4_size[0]:box4_size[1], box4_size[2]:box4_size[3]]\\n \\n # image processing to get the centroid and head positions\\n x_centroid, y_centroid, x_head, y_head = findCentroid(box4, file)\\n \\n # add the centroid and head locations on the image \\n box4 = cv2.circle(box4, (x_centroid, y_centroid), 7, (255, 0, 0), -1)\\n box4 = cv2.circle(box4, (x_head, y_head), 7, (0, 0, 255), -1)\\n img_array4.append(box4)\\n \\n # add the positions in the final data frame\\n centroidsDf[\\\"fly4_x\\\"][files.index(file)] = x_centroid\\n centroidsDf[\\\"fly4_y\\\"][files.index(file)] = y_centroid\\n \\n headsDf[\\\"fly4_x\\\"][files.index(file)] = x_head\\n headsDf[\\\"fly4_y\\\"][files.index(file)] = y_head\\n \\n ## FLY 5 ##\\n \\n # the fifth fly is not present in all the genetic strains \\n if (box5_size != []):\\n box5 = img[box5_size[0]:box5_size[1], box5_size[2]:box5_size[3]]\\n \\n # image processing to get the centroid and head positions\\n x_centroid, y_centroid, x_head, y_head = findCentroid(box5, file)\\n \\n # add the centroid and head locations on the image \\n box5 = cv2.circle(box5, (x_centroid, y_centroid), 7, (255, 0, 0), -1)\\n box5 = cv2.circle(box5, (x_head, y_head), 7, (0, 0, 255), -1)\\n img_array5.append(box5)\\n \\n # add the positions in the final data frame\\n centroidsDf[\\\"fly5_x\\\"][files.index(file)] = x_centroid\\n centroidsDf[\\\"fly5_y\\\"][files.index(file)] = y_centroid\\n \\n headsDf[\\\"fly5_x\\\"][files.index(file)] = x_head\\n headsDf[\\\"fly5_y\\\"][files.index(file)] = y_head\\n \\n # save the data frame in a .csv file, \\n # one for the centroids and one for the heads\\n #centroidsDf.to_csv(folder+\\\"/centroids.csv\\\", index = None, header=True)\\n #headsDf.to_csv(folder+\\\"/heads.csv\\\", index = None, header=True)\\n \\n \\n ## CREATE THE VIDEOS ##\\n \\n height, width, _ = box1.shape\\n size = (width,height)\\n out = cv2.VideoWriter(folder+name_type+'_1_' + str(folders.index(folder)+1)+ '.mp4',cv2.VideoWriter_fourcc(*'DIVX'), 1.5, size)\\n for i in range(len(img_array1)):\\n out.write(img_array1[i])\\n out.release()\\n \\n height, width, _ = box2.shape\\n size = (width,height)\\n out = cv2.VideoWriter(folder+name_type+'_2_' + str(folders.index(folder)+1)+ '.mp4' ,cv2.VideoWriter_fourcc(*'DIVX'), 1.5, size)\\n for i in range(len(img_array2)):\\n out.write(img_array2[i])\\n out.release()\\n \\n height, width, _ = box3.shape\\n size = (width,height)\\n out = cv2.VideoWriter(folder+name_type+'_3_' + str(folders.index(folder)+1)+ '.mp4' ,cv2.VideoWriter_fourcc(*'DIVX'), 1.5, size)\\n for i in range(len(img_array3)):\\n out.write(img_array3[i])\\n out.release()\\n \\n height, width, _ = box4.shape\\n size = (width,height)\\n out = cv2.VideoWriter(folder+name_type+'_4_' + str(folders.index(folder)+1)+ '.mp4' ,cv2.VideoWriter_fourcc(*'DIVX'), 1.5, size)\\n for i in range(len(img_array4)):\\n out.write(img_array4[i])\\n out.release()\\n \\n if (box5_size != []):\\n height, width, _ = box5.shape\\n size = (width,height)\\n out = cv2.VideoWriter(folder+name_type+'_5_' + str(folders.index(folder)+1)+ '.mp4' ,cv2.VideoWriter_fourcc(*'DIVX'), 1.5, size)\\n for i in range(len(img_array5)):\\n out.write(img_array5[i])\\n out.release()\",\n \"def main(vis_dirs, outdir):\\n assert len(vis_dirs) == 4\\n\\n if not os.path.exists(outdir):\\n os.mkdir(outdir)\\n\\n for i, filename in enumerate(tqdm(os.listdir(vis_dirs[-1]))):\\n # if i % 100 == 0:\\n # print(i)\\n\\n files = [os.path.join(vis_dir, filename) for vis_dir in vis_dirs]\\n outimg = os.path.join(outdir, filename)\\n merge_four_images(files, outimg)\\n\\n print (\\\"Finished! Result dir is %s\\\" % outdir)\",\n \"def process_files(self):\\n for filename in self.temp_directory.iterdir():\\n im = Image.open(str(filename))\\n scaled = im.resize((640, 480))\\n scaled.save(str(filename))\",\n \"def pickle_images(path, df, image_size=\\\"\\\"):\\n all_imgs = []\\n labels = []\\n folder = path\\n for f in os.listdir(folder): \\n if f.endswith(\\\".png\\\"): \\n img = read_transposed_image(folder+f)\\n i = f.split(\\\"_\\\")[0]\\n name = \\\".\\\".join(\\\"_\\\".join(f.split(\\\"_\\\")[1:]).split(\\\".\\\")[:-1])\\n label = df[(df.id == i) & (df.filename == name)].label\\n if len(label) > 0:\\n label = label.iloc[0]\\n if image_size != \\\"\\\":\\n new_image = resize_and_pad(img, image_size)\\n new_dir = folder+str(image_size)\\n if not os.path.exists(new_dir):\\n os.makedirs(new_dir)\\n misc.toimage(new_image).save(new_dir+\\\"/\\\"+f)\\n all_imgs.append(new_image)\\n else:\\n all_imgs.append(img) \\n labels.append(label)\\n d = {\\\"data\\\":all_imgs, \\\"labels\\\":labels}\\n with open('data_'+str(image_size)+'.pkl', 'wb') as handle:\\n cPickle.dump(d, handle, protocol=cPickle.HIGHEST_PROTOCOL)\",\n \"def mosaic(self):\\n \\n if len(self.tiles) > 1:\\n hdflist = sorted(glob.glob(self.fullPath + '/*.hdf'))\\n for i in range(0,len(hdflist),2):\\n ms = pymodis.convertmodis_gdal.createMosaicGDAL(hdfnames = [hdflist[i], hdflist[i+1]], subset = self.subset, outformat = 'GTiff')\\n ms.run(str(hdflist[i].split('.h')[0]) + 'mos.tif')\\n ms.write_vrt(output = str(hdflist[i].split('.h')[0]), separate = True)\\n mosaicCount = len(glob.glob(self.fullPath + '/*mos.tif'))\\n logger.log('SUCCESS', 'Mosaic complete! MODIS tiles %s were successfully mosaicked into %d mosaic images.' % (str(self.tiles), mosaicCount))\",\n \"def save(images, output):\\n for image, frame in images:\\n image.save(output(frame))\",\n \"def process_all_images(input_path: str, output_path: str, resized_image_shape: Tuple,transformations:List[TransformationsEnum]):\\n\\n output_images_path = os.path.join(output_path, \\\"images\\\")\\n csv_file_path = os.path.join(output_path, \\\"metadata.csv\\\")\\n\\n prepare_folders(output_path, output_images_path)\\n prepare_csv(csv_file_path)\\n\\n df = pd.read_csv(csv_file_path)\\n current_id = 1 #has to check the current id in the folder or be set to 1 if none\\n categories_names = list(os.listdir(input_path))\\n\\n encoder = LabelBinarizer()\\n encoder.fit(categories_names)\\n\\n\\n for folder_name in os.listdir(input_path):\\n current_category_name = folder_name\\n category_path = os.path.join(input_path, folder_name)\\n images_in_category = list(Path(category_path).glob(\\\"*.jpg\\\"))\\n df, current_id = process_image(\\n df, current_id, encoder, current_category_name, images_in_category,output_images_path, resized_image_shape,transformations\\n )\\n\\n df.to_csv(csv_file_path, index=False, quotechar='\\\"', encoding='ascii')\\n\\n print(\\\"done, processed\\\", len(df), \\\"images\\\")\",\n \"def jarvis(input_path, output_path): \\n \\n if not os.path.exists(f'{output_path}'):\\n os.makedirs(f'{output_path}')\\n\\n folder_list = [sample for sample in os.listdir(input_path) if os.path.isdir(f'{input_path}{sample}')]\\n\\n for folder in folder_list:\\n\\n file_list = [filename for filename in os.listdir(f'{input_path}{folder}/') if '.tif' in filename]\\n mutant = '_'.join(folder.split(' '))\\n\\n for x, filename in enumerate(file_list):\\n pathname = os.path.join(input_path, folder, filename)\\n new_name = f'{output_path}{mutant}_{x}.tif'\\n copyfile(pathname, new_name)\\n # array_stack = skimage.io.imread(f'{pathname}').transpose(1, 2, 0)\\n logger.info(f'{new_name}')\",\n \"def combineImages(path=None, imgfiles=None, cols=3, size=300):\\n\\n font = ImageFont.truetype(\\\"Arial.ttf\\\", 15)\\n x=size\\n w=20\\n i=0; j=0\\n if imgfiles == None:\\n imgfiles = findFiles(path, 'png')\\n width = cols*(x+w)\\n height = int(math.ceil(float(len(imgfiles))/cols)*x)\\n new_im = Image.new('RGBA', (width, height), 'white')\\n for f in imgfiles:\\n name = os.path.basename(f).split('.')[0]\\n if not os.path.exists(f):\\n continue\\n im = Image.open(f)\\n im.thumbnail((x,x))\\n new_im.paste(im, (i*x+w,j*x+w))\\n draw = ImageDraw.Draw(new_im)\\n draw.text((i*x+w,j*x+w), name, (0,0,0), font=font)\\n i+=1\\n if i>=cols:\\n i=0; j+=1\\n #new_im.show()\\n path = os.path.split(imgfiles[0])[0]\\n new_im.save(os.path.join(path,\\\"summary.png\\\"))\\n return\",\n \"def write_data(data_to_write_train, data_to_write_test, train_path, eval_path ,test_path):\\n aligned_lists_train = data_to_write_train[0]\\n raw_lists_train = data_to_write_train[2]\\n\\n aligned_lists_eval = data_to_write_test[0]\\n raw_lists_eval = data_to_write_test[2]\\n\\n aligned_lists_test = data_to_write_test[1]\\n raw_lists_test = data_to_write_test[3]\\n\\n filelist = list([train_path, eval_path, test_path])\\n\\n for file in filelist:\\n aligned_path = os.path.join(file, 'aligned_image')\\n raw_path = os.path.join(file, 'raw_image')\\n os.mkdir(aligned_path)\\n os.mkdir(raw_path)\\n\\n # raw image data\\n for Idx, train_raw in enumerate(raw_lists_train):\\n img = Image.open(train_raw)\\n img.save(train_path+'/raw_image/img_'+f'{Idx:04d}.jpg')\\n if Idx%100 == 0:\\n print('\\\\t%d images are saved'% Idx); \\n print('\\\\tTrain raw images saved! ')\\n\\n for Idx, eval_raw in enumerate(raw_lists_eval):\\n img = Image.open(eval_raw)\\n img.save(eval_path+'/raw_image/img_'+f'{Idx:04d}.jpg')\\n if Idx%100 == 0:\\n print('\\\\t%d images are saved'% Idx); \\n print('\\\\tEval raw images saved! ')\\n\\n for Idx, test_raw in enumerate(raw_lists_test):\\n img = Image.open(test_raw)\\n img.save(test_path+'/raw_image/img_'+f'{Idx:04d}.jpg')\\n if Idx%100 == 0:\\n print('\\\\t%d images are saved'% Idx); \\n print('\\\\tTest raw images saved! ')\\n\\n # aligned image data\\n for Idx, train_aligned in enumerate(aligned_lists_train):\\n img = Image.open(train_aligned)\\n img.save(train_path+'/aligned_image/img_'+f'{Idx:04d}.jpg')\\n if Idx%100 == 0:\\n print('\\\\t%d images are saved'% Idx); \\n print('\\\\tTrain aligned images saved! ')\\n\\n for Idx, eval_aligned in enumerate(aligned_lists_eval):\\n img = Image.open(eval_aligned)\\n img.save(eval_path+'/aligned_image/img_'+f'{Idx:04d}.jpg')\\n if Idx%100 == 0:\\n print('\\\\t%d images are saved'% Idx); \\n print('\\\\tEval aligned images saved! ')\\n\\n for Idx, test_aligned in enumerate(aligned_lists_test):\\n img = Image.open(test_aligned)\\n img.save(test_path+'/aligned_image/img_'+f'{Idx:04d}.jpg')\\n if Idx%100 == 0:\\n print('\\\\t%d images are saved'% Idx); \\n print('\\\\tTest aligned images saved! ')\",\n \"def save_step_4(imgs, output_path=\\\"./output/step4\\\"):\\n # ... your code here ...\\n cv2.imwrite(output_path+\\\"/output.jpg\\\", imgs)\",\n \"def form_sample_folder(self, input_folder, target_folder, sample_name):\\n print(f'processing {sample_name} folder.')\\n # first make a subfolder to contain the images - e.g. 'target_folder/sample_name'\\n sample_dir = join(target_folder, sample_name)\\n if not os.path.exists(sample_dir):\\n mkdir(sample_dir)\\n # resize and move the mask images - e.g. 'target_folder/sample_name/imgs_necrosis.png'\\n img_file_nec = join(input_folder, 'Necrosis',\\n 'Tissue Slides.'+sample_name+'.png')\\n img_res = self.process_img(img_file_nec, self.rescale_ratio)\\n img_nec = img_res.copy()\\n cv2.imwrite(join(sample_dir, 'necrosis.png'), img_res)\\n\\n img_file_perf = join(input_folder, 'Perfusion',\\n 'Tissue Slides.'+sample_name+'.png')\\n img_res = self.process_img(img_file_perf, self.rescale_ratio)\\n cv2.imwrite(join(sample_dir, 'perfusion.png'), img_res)\\n\\n # resize and move the maker HE and EF5 images\\n files = listdir(input_folder)\\n img_files = [x for x in files if x.split(\\n '.')[-1] in ('tif', 'jpg', 'png')]\\n for img_file in img_files:\\n if (sample_name+'_' in img_file) or (sample_name+'-' in img_file):\\n if ('HE-G' in img_file) or ('HE-green' in img_file) or ('HEgreen' in img_file):\\n img_res = self.process_img(\\n join(input_folder, img_file), self.rescale_ratio)\\n if not os.path.exists(join(sample_dir, 'HE-green.png')):\\n cv2.imwrite(join(sample_dir, 'HE-green.png'), img_res)\\n else:\\n warnings.warn(\\n f\\\"file already exists, while processing {img_file}\\\")\\n elif ('HE-R' in img_file) or ('HE-red' in img_file) or ('HEred' in img_file):\\n img_res = self.process_img(\\n join(input_folder, img_file), self.rescale_ratio)\\n if not os.path.exists(join(sample_dir, 'HE-red.png')):\\n cv2.imwrite(join(sample_dir, 'HE-red.png'), img_res)\\n else:\\n warnings.warn(\\n f\\\"file already exists, while processing {img_file}\\\")\\n elif ('HE-B' in img_file) or ('HE-blue' in img_file) or ('HE-blue' in img_file):\\n img_res = self.process_img(\\n join(input_folder, img_file), self.rescale_ratio)\\n if not os.path.exists(join(sample_dir, 'HE-blue.png')):\\n cv2.imwrite(join(sample_dir, 'HE-blue.png'), img_res)\\n else:\\n warnings.warn(\\n f\\\"file already exists, while processing {img_file}\\\")\\n elif 'EF5' in img_file:\\n img_res = self.process_img(\\n join(input_folder, img_file), self.rescale_ratio)\\n img_ef5 = img_res.copy()\\n if not os.path.exists(join(sample_dir, 'EF5.png')):\\n cv2.imwrite(join(sample_dir, 'EF5.png'), img_res)\\n else:\\n warnings.warn(\\n f\\\"file already exists, while processing {img_file}\\\")\\n\\n masked_ef5 = (img_ef5 * (img_nec <= 0)).astype(img_ef5.dtype)\\n cv2.imwrite(join(sample_dir, 'EF5_masked.png'), masked_ef5)\\n assert len(listdir(sample_dir)) == 7\\n return\",\n \"def createAllImageFiles(poly, name) :\\n \\n for i in range(len(poly.getPaths())):\\n fileName = name + \\\"_\\\" + str(i) + \\\".dot\\\"\\n imgName = name + \\\"_\\\" + str(i) + \\\".jpg\\\"\\n \\n Command = \\\"neato -Tjpeg \\\" + fileName + \\\" -o \\\" + imgName\\n run(Command, shell=True)\",\n \"def splitTransform(self):\\n\\t\\t#path_merge = \\\"transform\\\"\\n\\t\\t#path_train = \\\"transform/data/\\\"\\n\\t\\t#path_label = \\\"transform/label/\\\"\\n\\t\\tpath_merge = \\\"train/merge\\\"\\n\\t\\tpath_train = \\\"train/image\\\"\\n\\t\\tpath_label = \\\"train/label\\\"\\n\\t\\ttrain_imgs = glob.glob(path_merge+\\\"/*.\\\"+self.img_type)\\n\\t\\tfor imgname in train_imgs:\\n\\t\\t\\tmidname = imgname[imgname.rindex(\\\"/\\\")+1:imgname.rindex(\\\".\\\"+self.img_type)]\\n\\t\\t\\timg = cv2.imread(imgname)\\n\\t\\t\\timg_train = img[:,:,2]#cv2 read image rgb->bgr\\n\\t\\t\\timg_label = img[:,:,0]\\n\\t\\t\\tcv2.imwrite(path_train+midname+\\\".\\\"+self.img_type,img_train)\\n\\t\\t\\tcv2.imwrite(path_label+midname+\\\".\\\"+self.img_type,img_label)\",\n \"def save_images(images, filenames, output_dir):\\n for i, filename in enumerate(filenames):\\n dirname = os.path.dirname(filename)\\n\\n if dirname!='':\\n dirpath = os.path.join(output_dir, dirname)\\n if not os.path.exists(dirpath):\\n os.makedirs(dirpath)\\n\\n # Images for inception classifier are normalized to be in [-1, 1] interval,\\n # so rescale them back to [0, 1].\\n with tf.gfile.Open(os.path.join(output_dir, filename), 'w') as f:\\n img = (((images[i, :, :, :] + 1.0) * 0.5) * 255.0).astype(np.uint8)\\n Image.fromarray(img).save(f)\",\n \"def process_data(output_folder):\\n # select imgs\\n img_folder = join(output_folder, 'img')\\n select_img(output_folder, img_folder, 'HE-green')\\n\\n mask_folder = join(output_folder, 'mask')\\n select_img(output_folder, mask_folder, '_EF5')\",\n \"def save_images(images, filenames, output_dir):\\n for i, filename in enumerate(filenames):\\n\\n with tf.gfile.Open(os.path.join(output_dir, filename), 'w') as f:\\n image = images[i, :, :, :]\\n image[:, :, 0] += _R_MEAN\\n image[:, :, 1] += _G_MEAN\\n image[:, :, 2] += _B_MEAN\\n image = imresize(image, [299, 299])\\n imsave(f, image, format='png')\",\n \"def process_images(path, dataset):\\n \\n print(f\\\"Processing images {os.path.join(path, dataset)}\\\", flush=True)\\n label_file = os.path.join(path, dataset + '-labels-idx1-ubyte')\\n with open(label_file, 'rb') as file:\\n _, num = struct.unpack(\\\">II\\\", file.read(8))\\n labels = numpy.fromfile(file, dtype=numpy.int8) #int8\\n new_labels = numpy.zeros((num, 10))\\n new_labels[numpy.arange(num), labels] = 1\\n\\n img_file = os.path.join(path, dataset + '-images-idx3-ubyte')\\n with open(img_file, 'rb') as file:\\n _, num, rows, cols = struct.unpack(\\\">IIII\\\", file.read(16))\\n imgs = numpy.fromfile(file, dtype=numpy.uint8).reshape(num, rows, cols) #uint8\\n imgs = imgs.astype(numpy.float32) / 255.0\\n\\n os.remove(label_file); os.remove(img_file)\\n print(f\\\"Saving files under {os.path.join(path, dataset)} path\\\", flush=True)\\n numpy.savez_compressed(os.path.join(path, dataset), imgs=imgs, labels=labels)\",\n \"def hdfpath_to_nifti1image(file_path, h5path):\\n with h5py.File(file_path, 'r') as f:\\n return hdfgroup_to_nifti1image(f[h5path])\",\n \"def save_imgs(self, epoch):\\n row, column = 5, 5\\n\\n # Generates r*c images from the model, saves them individually and as a gallery\\n images_generated = self.generate_images(row * column)\\n\\n # ???\\n images_generated = 0.5 * images_generated + 0.5\\n\\n for index, np_array_image in enumerate(images_generated):\\n path = f\\\"{self.output_directory}/generated_{self.img_size[0]}x{self.img_size[1]}\\\"\\n if not os.path.exists(path):\\n os.makedirs(path)\\n imsave(path + f\\\"/{unique_name()}_{epoch}_{index}.png\\\", np_array_image)\\n\\n # 4D array:\\n nindex, height, width, intensity = images_generated.shape\\n\\n nrows = nindex // column\\n\\n assert nindex == nrows * column\\n\\n # Form the gallery by combining the data at pixel levels (may not be the best approach)\\n # want result.shape = (height*n-rows, width*n-cols, intensity)\\n gallery = (\\n images_generated.reshape(nrows, column, height, width, intensity)\\n .swapaxes(1, 2)\\n .reshape(height * nrows, width * column, intensity)\\n )\\n\\n path = f\\\"{self.output_directory}/gallery_generated_{self.img_size[0]}x{self.img_size[1]}\\\"\\n if not os.path.exists(path):\\n os.makedirs(path)\\n imsave(path + f\\\"/{unique_name()}_{epoch}.png\\\", gallery)\",\n \"def save_imgs(self):\\n print(\\\"Saving the images with required categories ...\\\")\\n os.makedirs(self.imgs_dir, exist_ok=True)\\n # Save the images into a local folder\\n for im in tqdm(self.images):\\n img_data = requests.get(im['coco_url']).content\\n with open(os.path.join(self.imgs_dir, im['file_name']), 'wb') as handler:\\n handler.write(img_data)\",\n \"def numpy_to_h5py(in_dir=config.dir_npy, split = config.split):\\n\\n in_files=[x[:-13] for x in os.listdir(in_dir) if x.endswith('_voc_stft.npy') and not x.startswith('._')]\\n\\n random.shuffle(in_files)\\n\\n\\n num_files = len(in_files)\\n\\n split_idx = int(num_files*split)\\n\\n trn_files = in_files[:split_idx]\\n\\n val_files = in_files[split_idx:]\\n\\n num_val_files = len(val_files)\\n\\n print('Processing %d training files' % split_idx)\\n logger.info('Processing %d training files' % split_idx)\\n\\n logger.info('Training file: %s' % config.h5py_file_train)\\n\\n voc_shape_trn = [split_idx, 5170,config.input_features]\\n\\n mix_shape_trn = [split_idx, 5170,config.input_features]\\n\\n feats_shape_trn = [split_idx, 5170,config.output_features]\\n\\n hdf5_file = h5py.File(config.h5py_file_train, mode='w')\\n\\n hdf5_file.create_dataset(\\\"voc_stft\\\", voc_shape_trn, np.float32)\\n\\n hdf5_file.create_dataset(\\\"back_stft\\\", voc_shape_trn, np.float32)\\n\\n hdf5_file.create_dataset(\\\"mix_stft\\\", mix_shape_trn, np.float32)\\n\\n hdf5_file.create_dataset(\\\"feats\\\", feats_shape_trn, np.float32)\\n\\n\\n i = 0\\n\\n for f in trn_files:\\n\\n voc_stft = np.load(in_dir+f+'_voc_stft.npy')\\n\\n voc_stft = voc_stft.astype('float32')\\n\\n mix_stft = np.load(in_dir+f+'_mix_stft.npy')\\n\\n mix_stft = mix_stft.astype('float32')\\n\\n back_stft = np.load(in_dir+f+'_back_stft.npy')\\n\\n back_stft = back_stft.astype('float32')\\n\\n synth_feats = np.load(in_dir+f+'_synth_feats.npy')\\n\\n synth_feats = synth_feats.astype('float32')\\n\\n hdf5_file[\\\"voc_stft\\\"][i,...] = voc_stft\\n\\n hdf5_file[\\\"mix_stft\\\"][i,...] = mix_stft\\n\\n hdf5_file[\\\"back_stft\\\"][i,...] = back_stft\\n\\n hdf5_file[\\\"feats\\\"][i,...] = synth_feats\\n\\n i+=1\\n utils.progress(i, split_idx)\\n\\n logger.info('Processed training file: %s' % f)\\n\\n hdf5_file.close()\\n\\n print('Processing %d validation files' % num_val_files)\\n logger.info('Processing %d validation files' % num_val_files)\\n\\n logger.info('Validation file: %s' % config.h5py_file_val)\\n\\n voc_shape_trn = [num_val_files, 5170,config.input_features]\\n\\n mix_shape_trn = [num_val_files, 5170,config.input_features]\\n\\n feats_shape_trn = [num_val_files, 5170,config.output_features]\\n\\n hdf5_file = h5py.File(config.h5py_file_val, mode='w')\\n\\n hdf5_file.create_dataset(\\\"voc_stft\\\", voc_shape_trn, np.float32)\\n\\n hdf5_file.create_dataset(\\\"mix_stft\\\", mix_shape_trn, np.float32)\\n\\n hdf5_file.create_dataset(\\\"back_stft\\\", voc_shape_trn, np.float32)\\n\\n hdf5_file.create_dataset(\\\"feats\\\", feats_shape_trn, np.float32)\\n\\n\\n i = 0\\n\\n for f in val_files:\\n\\n voc_stft = np.load(in_dir+f+'_voc_stft.npy')\\n\\n voc_stft = voc_stft.astype('float32')\\n\\n mix_stft = np.load(in_dir+f+'_mix_stft.npy')\\n\\n mix_stft = mix_stft.astype('float32')\\n\\n synth_feats = np.load(in_dir+f+'_synth_feats.npy')\\n\\n synth_feats = synth_feats.astype('float32')\\n\\n back_stft = np.load(in_dir+f+'_back_stft.npy')\\n\\n back_stft = back_stft.astype('float32')\\n\\n hdf5_file[\\\"voc_stft\\\"][i,...] = voc_stft\\n\\n hdf5_file[\\\"mix_stft\\\"][i,...] = mix_stft\\n\\n hdf5_file[\\\"back_stft\\\"][i,...] = back_stft\\n\\n hdf5_file[\\\"feats\\\"][i,...] = synth_feats\\n\\n i+=1\\n utils.progress(i, num_val_files)\\n\\n logger.info('Processed validation file: %s' % f)\\n\\n hdf5_file.close()\\n # return original_ffts\",\n \"def process_images():\\n create_dirs()\\n for root, dirs, files in os.walk(IN):\\n for name in files:\\n if name[0] == '.':\\n continue\\n process_image(name)\",\n \"def preprocessfolder(self):\\n imgs, _ = getFilesAndHdf(str(self.in_directory.text()))\\n self.img_list = sorted(imgs)\\n self.updateImageGroups()\",\n \"def _crop_write_image(self, inroot, images, outroot):\\n for image in images:\\n inimage_path = osp.join(inroot, image)\\n cvimg = cv2.imread(inimage_path)\\n cvimg = cvimg[60:-30, 25:-25]\\n h, w, _ = cvimg.shape\\n assert h == w == 128\\n outimage_path = osp.join(outroot, image)\\n cv2.imwrite(outimage_path, cvimg)\\n print(outimage_path)\",\n \"def save_step_3(img_pairs, match_list, output_path=\\\"./output/step3\\\"):\\n # ... your code here ...\\n for i in range(len(img_pairs)):\\n name1,tail1 = str.split(filenames[match_list[i][0]],\\\".\\\")\\n name2,tail2 = str.split(filenames[match_list[i][1]],\\\".\\\")\\n cv2.imwrite(output_path+\\\"/\\\"+name1+\\\"_\\\"+name2+\\\".jpg\\\", img_pairs[i][0])\\n cv2.imwrite(output_path+\\\"/\\\"+name2+\\\"_\\\"+name1+\\\".jpg\\\", img_pairs[i][1])\",\n \"def load_images(input_dir=\\\"/tmp/mapswipe/project-1\\\", n_images=2000, seed=1):\\n class_map = {1: \\\"1\\\", 0: \\\"5\\\"}\\n output_dir = \\\"/Users/thead/git/dreamview/data/\\\"\\n\\n X_ = []\\n y_ = []\\n for new_klass in class_map:\\n images = []\\n for klass in class_map[new_klass]:\\n for img in glob.glob(input_dir + \\\"/%s/*/*/*/aerial.jpeg\\\" % klass):\\n if os.stat(img).st_size > 0:\\n images.append(img)\\n\\n images = shuffle(images, random_state=seed+42+new_klass)\\n images = images[:n_images]\\n X_ += images\\n y_ += [new_klass] * len(images)\\n\\n # XXX deduce array size from an actual image\\n X = np.zeros((2*n_images, 256*256), dtype=np.ubyte)\\n y = np.zeros(2*n_images, dtype=np.int)\\n\\n for n, (img_path, klass) in enumerate(zip(X_, y_)):\\n # the order of these OPs has been chosen on purpose, don't mess\\n # without checking what happens\\n img = imread(img_path)\\n img = equalize_adapthist(img)\\n img = rgb2grey(img)\\n img = img_as_ubyte(img)\\n\\n if not n % 10:\\n fname = os.path.split(img_path)[:-1]\\n fname = os.path.join(*fname, \\\"aerial-processed.jpeg\\\")\\n imsave(fname, img)\\n\\n X[n,:] = img.ravel()\\n y[n] = klass\\n\\n return X, y\",\n \"def save_test_images(images):\\n for description, img in images.items():\\n save_to_image(img, description)\\n save_to_netcdf(img, description)\",\n \"def save_images(images, filenames, output_dir):\\n for i, filename in enumerate(filenames):\\n # Images for inception classifier are normalized to be in [-1, 1] interval,\\n # so rescale them back to [0, 1].\\n with tf.gfile.Open(os.path.join(output_dir, filename), 'w') as f:\\n img = np.round(((images[i] + 1.0) * 0.5) * 255.0).astype(np.uint8)\\n Image.fromarray(img).save(f, format='PNG')\",\n \"def extract_write_to_file(self, num_extracted, write_dir, sub_h, sub_w, margin=10):\\n\\n file_seed = len(os.listdir(write_dir))\\n\\n for i in range(num_extracted):\\n file_num = str(file_seed + i)\\n write_path = os.path.join(write_dir, file_num + \\\".\\\" + 'jpg')\\n\\n print('extracting {}/{} images of dimension {}x{}'.format(i, num_extracted, sub_h, sub_w))\\n print('writting to location: {}'.format(write_path))\\n\\n self.extract_single(sub_h, sub_w, write_path)\",\n \"def load(self):\\n\\n # get files in folder\\n files = [f for f in listdir(self.data_path)]\\n print(\\\"loading images from folder: %s\\\" % self.data_path)\\n\\n images = []\\n image_targets = []\\n for f in files:\\n filepath = path.join(self.data_path, f)\\n images.append(io.imread(filepath, as_grey=True))\\n image_targets.append(self.target)\\n\\n # define new size and resize images\\n new_size = (2 ** self.size_exponent, 2 ** self.size_exponent)\\n for i in range(0, len(images)):\\n # images[i] = transform.resize(images[i], new_size)\\n images[i] = misc.imresize(images[i], new_size) / 16\\n\\n self.images = images\\n self.targets = image_targets\",\n \"def save_images(images, filenames, output_dir):\\n for i, filename in enumerate(filenames):\\n # Images for inception classifier are normalized to be in [-1, 1] interval,\\n # so rescale them back to [0, 1].\\n with tf.gfile.Open(os.path.join(output_dir, filename), 'w') as f:\\n img = (((images[i, :, :, :] + 1.0) * 0.5) * 255.0).astype(np.uint8)\\n Image.fromarray(img).save(f, format='PNG')\",\n \"def load_data_withIdx(subjIdx_list, filenames, test_ratio = 0.2):\\n dataset_path = filenames['data']\\n train_path = filenames['train']\\n test_path = filenames['test']\\n eval_path = filenames['eval']\\n raw_image_lists = []\\n aligned_image_lists = []\\n print('[INFO] Data Processing started ... ')\\n\\n for subIdx in subjIdx_list:\\n subj_path = dataset_path+'/subj_'+f'{subIdx:02d}'\\n print('\\\\tProccessing images for subject_' +f'{subIdx:02d}'+ ' ...', end =\\\" \\\")\\n\\n if(not os.path.isdir(subj_path)):\\n print(' no file exists for subject_' +f'{subIdx:02d}'+ ' !!')\\n continue\\n\\n raw_list = sorted(glob.glob(subj_path + '/raw_images/*.jpg'))\\n aligned_list = sorted(glob.glob(subj_path + '/aligned_images/*.jpg')) \\n\\n assert len(raw_list) == len(aligned_list) , \\\"data size mismatch! raw_img:{0}, alig_img:{1}\\\".format(len(raw_list) ,len(aligned_list)) \\n \\n raw_image_lists += raw_list\\n aligned_image_lists += aligned_list\\n print(' Done!')\\n\\n data_to_write_train = train_test_split_data(aligned_image_lists, raw_image_lists, test_ratio)\\n data_to_write_test = train_test_split_data(data_to_write_train[1], data_to_write_train[3], 0.5)\\n print('[INFO] Processing Done! ')\\n print(\\\"[INFO] Number of train data :{0:4d}, Number of eval data :{1:4d}, Number of test data :{2:4d} \\\"\\n . format(len(data_to_write_train[0]), len(data_to_write_test[0]), len(data_to_write_test[1]))) \\n # data write part \\n write_data(data_to_write_train, data_to_write_test, train_path, eval_path ,test_path)\",\n \"def buildImages(files, targets, type):\\n images = []\\n for file in files:\\n targets.append(file)\\n with open(file, \\\"rb\\\") as f:\\n if type == \\\"Byte\\\":\\n images.append(bytePlot(list(f.read())))\\n elif type == \\\"Markov\\\":\\n images.append(markovPlot(list(f.read())))\\n elif type == \\\"Hilbert\\\":\\n images.append(hilbertPlot(list(f.read())))\\n smp.imsave(\\\"{}.png\\\".format(file), images[-1])\\n return images, targets\",\n \"def jarvis(input_path, output_path): \\n\\n if not os.path.exists(f'{output_path}'):\\n os.makedirs(f'{output_path}')\\n\\n file_list = [filename for filename in os.listdir(f'{input_path}') if '.tif' in filename]\\n\\n for filename in file_list:\\n pathname = os.path.join(input_path, filename)\\n new_name = f\\\"{output_path}{filename.replace('.lif - ', '_').replace('_5x-', '_')}\\\"\\n copyfile(pathname, new_name)\\n logger.info(f'{new_name}')\",\n \"def collect_and_rename() -> None:\\n image_source_folder = 'image_dir'\\n label_source_folder = 'annotation_dir'\\n image_target_folder = 'images'\\n label_target_folder = 'labels'\\n for i, (subdir, _, files) in enumerate(os.walk(image_source_folder), -1):\\n # it walks the parent folder first, not a file\\n if i == -1: \\n continue\\n subdir_name = subdir.split('\\\\\\\\')[1]\\n for file_name in files:\\n with open(f'{image_source_folder}/{subdir_name}/{file_name}') as image_file, \\\\\\n open(f'{label_source_folder}/{subdir_name}/{file_name}'.split('.')[0] + '.txt') as label_file:\\n shutil.copy2(image_file.name, f'{image_target_folder}/{\\\"%06d\\\" % i}.jpg')\\n shutil.copy2(label_file.name, f'{label_target_folder}/{\\\"%06d\\\" % i}.txt')\\n print(f'Processed {i} images')\",\n \"def read_image_data(self):\\n\\n for sequence_name in self.sequence_name_list:\\n sequence = self.sequences[sequence_name]\\n for image_id in sequence.image_id_list:\\n sequence.image_dict[image_id].image_path = '{}{}/{}'.format(self.root_dir, self.name, sequence.image_dict[image_id].filename)\",\n \"def run():\\n\\n today = datetime.now().strftime(\\\"%Y-%m-%d\\\")\\n log_file = os.path.abspath(\\\"logs/{}.log\\\".format(today))\\n logger = RsmasLogger(\\\"pipeline\\\", log_file)\\n\\n images = get_list_of_images()\\n # LOG: list of images to process\\n logger.log(loglevel.INFO, [img.key for img in images])\\n\\n for im in images:\\n\\n logger.log(loglevel.INFO, \\\"Processing image: {}\\\".format(im.key))\\n\\n file_path = \\\"{}/{}\\\".format(im.bucket_name, im.key)\\n full_path = \\\"{}_full.jpg\\\"\\n mod_path = \\\"{}_mod.jpg\\\"\\n aws_path = \\\"{}/{}/{}/{}\\\"\\n try:\\n haz_id, haz_name, sat_name, sat_dir, img_type, img_date, center = summary.pull_summary_data(\\n \\\"/vsis3/{}\\\".format(file_path))\\n sat_id = Satellite.from_params(sat_name, bool(sat_dir))\\n except:\\n # LOG: error in image metadata format\\n logger.log(loglevel.ERROR, '\\\\tThere was an error in the metadata format of the image. Skipping.')\\n continue\\n\\n aws_path = aws_path.format(haz_id, sat_id, img_type, img_date)\\n full_path = full_path.format(img_date)\\n mod_path = mod_path.format(img_date)\\n\\n # 1. Read in image file\\n with rasterio.open(\\\"s3://{}\\\".format(file_path)) as data:\\n band = data.read(1)\\n img = plot.show(band)\\n img.get_figure().savefig(full_path, dpi=300)\\n\\n # 3. Compress image\\n compressed = immanip.compress_image(full_path, compression_amount=0.3)\\n\\n # 4 - 5. Pad image and add date on image\\n text_image = immanip.add_text_to_image(compressed, img_date)\\n\\n # 6. Save image locally\\n text_image.save(mod_path.format(img_date))\\n mod_path_aws = save.get_s3_url(\\\"{}/{}\\\".format(aws_path, mod_path))\\n full_path_aws = save.get_s3_url(\\\"{}/{}\\\".format(aws_path, full_path))\\n\\n tif_path_aws = save.get_s3_url(\\\"{}/{}\\\".format(aws_path, im.key))\\n\\n # LOG: images successfully moved to S3 bucket\\n # LOG: mod_path_aws, full_path_aws, tif_path_aws\\n\\n hazard = Hazard(haz_id, haz_name, HazardType.VOLCANO, Location(center[0], center[1]), Date(img_date), 0)\\n satellite = Satellite.from_params(sat_name, bool(sat_dir))\\n image = Image(str(randint(1, 10000000)),\\n haz_id,\\n satellite,\\n ImageType.from_string(img_type),\\n Date(img_date),\\n ImageURL(full_path_aws),\\n ImageURL(tif_path_aws),\\n ImageURL(mod_path_aws))\\n\\n try:\\n db = Database()\\n except ConnectionError:\\n logger.log(loglevel.ERROR, \\\"\\\\tThere was an error while connecting to the database. Skipping this image.\\\")\\n continue\\n\\n db.create_new_hazard(hazard)\\n db.create_new_satellite(satellite)\\n db.create_new_image(image)\\n\\n db.close()\\n\\n # LOG: database successfully updated\\n logger.log(loglevel.INFO, \\\"\\\\tDatabase succesfully updated.\\\")\\n\\n save.save_image_s3(mod_path, \\\"{}/{}\\\".format(aws_path, mod_path))\\n save.save_image_s3(full_path, \\\"{}/{}\\\".format(aws_path, full_path))\\n save.move_tif(im.key, \\\"{}/{}\\\".format(aws_path, im.key))\\n\\n logger.log(loglevel.INFO, \\\"\\\\tImages were successfully uploaded to the S3 bucket\\\")\\n logger.log(loglevel.INFO, \\\"\\\\t\\\\tmod_path_aws: {}\\\".format(mod_path_aws))\\n logger.log(loglevel.INFO, \\\"\\\\t\\\\tfull_path_aws: {}\\\".format(full_path_aws))\\n logger.log(loglevel.INFO, \\\"\\\\t\\\\ttif_path_aws: {}\\\".format(tif_path_aws))\\n\\n # LOG: image completed\\n logger.log(loglevel.INFO, \\\"\\\\tProcessing of {} completed.\\\".format(im.key))\\n\\n # LOG: finished processing images\\n logger.log(loglevel.INFO, \\\"Processing complete.\\\")\",\n \"def extract_data(filename, images_dir, output_dir, trials_idx, block_nums, goal_dict):\\n num_images = len(trials_idx) * len(block_nums)\\n f = h5py.File(os.path.join(output_dir, filename), 'w')\\n X = f.create_dataset('X', (num_images, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS*NUM_FRAMES), dtype=TYPE)\\n Y = f.create_dataset('Y', (num_images, 2), dtype=TYPE)\\n\\n image_count = 0\\n for trial_num in trials_idx:\\n for block_num in block_nums:\\n print('Blocks ' + str(block_num) + ' Trial ' + str(trial_num))\\n for frame_num in xrange(0, NUM_FRAMES):\\n temp = imread(images_dir+'RTr_Bl'+str(block_num)+'_'+str(trial_num)+'_'+str(frame_num)+IMAGE_FORMAT)\\n temp = imresize(temp, [temp.shape[0]//DOWN_SAMPLE, temp.shape[1]//DOWN_SAMPLE, temp.shape[2]])\\n X[image_count, 0:temp.shape[0], 0:temp.shape[1], frame_num*NUM_CHANNELS:(frame_num+1)*NUM_CHANNELS] = temp\\n label = goal_dict['RTr_Bl'+str(block_num)+'_'+str(trial_num)]\\n Y[image_count, :] = [label, 1-label]\\n image_count += 1\\n\\n f.close()\\n\\n # TODO Use pixel depth normalization???\\n #data = (data - (PIXEL_DEPTH / 2.0)) / PIXEL_DEPTH\",\n \"def process_imgdir(self,imgdir):\\n #Write images into resultdir\\n resultdir = os.path.join(imgdir, 'results')\\n #Read images from input dir\\n inputdir = os.path.join(imgdir, 'inputs')\\n shutil.rmtree(resultdir)\\n os.mkdir(resultdir)\\n #Read files from input images\\n for fullname in os.listdir(inputdir):\\n filepath = os.path.join(inputdir, fullname)\\n if os.path.isfile(filepath):\\n basename = os.path.basename(filepath)\\n image = cv2.imread(filepath, cv2.IMREAD_COLOR)\\n if len(image.shape) == 3 and image.shape[2] == 3:\\n print('Processing %s ...' % basename)\\n else:\\n sys.stderr.write('Skipping %s, not RGB' % basename)\\n continue\\n #Extract haze from the scene and then save the image\\n dehazed = self.get_scene_radiance(image)\\n cv2.imwrite(os.path.join(resultdir, basename), dehazed)\\n return os.path.join(resultdir, basename)\",\n \"def write(self):\\n f, ds = self.opendset()\\n #\\n # Now add the images\\n #\\n start_time = time.clock() # time this\\n nframes = 0 # number completed\\n print_every = 1; marker = \\\" .\\\";\\n print('Frames written (of %s):' % self.ntowrite, end=\\\"\\\")\\n for i in range(self.nfiles):\\n if nframes >= self.ntowrite: break\\n\\n logging.debug('processing file %d of %d' % (i+1, self.nfiles))\\n img_i = fabio.open(self.files[i])\\n nfi = img_i.nframes\\n for j in range(nfi):\\n msg = '... file %d/image %d' % (i, j)\\n logging.debug(msg)\\n if j < self.nempty:\\n logging.debug('... empty frame ... skipping')\\n else:\\n ds[nframes, :, :] = img_i.data\\n nframes += 1\\n if numpy.mod(nframes, print_every) == 0:\\n print(marker, nframes, end=\\\"\\\")\\n print_every *= 2\\n sys.stdout.flush()\\n logging.debug('... wrote image %s of %s' %\\\\\\n (nframes, self.ntowrite))\\n if nframes >= self.ntowrite:\\n logging.debug('wrote last frame: stopping')\\n break\\n if j < nfi - 1:\\n # on last frame in file, fabio will look for next file\\n img_i = img_i.next()\\n\\n f.close()\\n print(\\\"\\\\nTime to write: %f seconds \\\" %(time.clock()-start_time))\",\n \"def write_frames(self, images):\\n for img in images:\\n self.write_frame(img)\",\n \"def store_hdf5(images, labels, ID:str, path:str = \\\"data/dataset/\\\"):\\n\\n #create a new HDF5 file\\n file = h5py.File(path+ID+\\\"_lens.h5\\\", \\\"w\\\")\\n\\n #create a dataset in the file\\n dataset = file.create_dataset( \\\"images\\\", np.shape(images), h5py.h5t.IEEE_F64BE, data=images)\\n file.close()\\n\\n labels.to_hdf(path +ID+'_meta.h5', \\\"table\\\")\",\n \"def save_images(images, filenames, output_dir):\\n for i, filename in enumerate(filenames):\\n # Images for inception classifier are normalized to be in [-1, 1] interval,\\n # so rescale them back to [0, 1].\\n with tf.gfile.Open(os.path.join(output_dir, filename), 'w') as f:\\n imsave(f, (images[i] + 1.0) * 0.5, format='png')\",\n \"def save_images(images, filenames, output_dir):\\n for i, filename in enumerate(filenames):\\n # Images for inception classifier are normalized to be in [-1, 1] interval,\\n # so rescale them back to [0, 1].\\n with tf.gfile.Open(os.path.join(output_dir, filename), 'w') as f:\\n imsave(f, (images[i, :, :, :] + 1.0) * 0.5, format='png')\",\n \"def div_imgs(dir_path: str, holdout: int) -> None:\\n VH_REGEX = re.compile(r'(.*)_([0-9]+).vh.tif')\\n\\n for file in os.listdir(dir_path):\\n m = re.match(VH_REGEX, file)\\n if not m:\\n continue\\n\\n pre, num = m.groups()\\n vv = f'{pre}_{num}.vv.tif'\\n mask = f'{pre}_{num}.mask.tif'\\n\\n if not os.path.isfile(os.path.join(dir_path, mask)):\\n print(f\\\"Tile: {file} is missing a mask {mask}!\\\")\\n\\n if not os.path.isfile(os.path.join(dir_path, vv)):\\n print(f\\\"Tile: {file} is missing a mask {vv}!\\\")\\n\\n test_or_train = 'train' if random.random() > holdout else 'test'\\n\\n folder = os.path.join(dir_path, test_or_train)\\n if not os.path.isdir(folder):\\n os.makedirs(folder)\\n\\n os.rename(\\n os.path.join(dir_path, file), os.path.join(folder, file)\\n )\\n os.rename(\\n os.path.join(dir_path, vv),\\n os.path.join(folder, vv)\\n )\\n os.rename(\\n os.path.join(dir_path, mask),\\n os.path.join(folder, mask)\\n )\",\n \"def process_subdirectory(subdir_path, regex_images, output_folder_if_pickle,\\n min_n_leftside_metadata=10, only_ionogram_content_extraction_on_leftside_metadata=True, to_pickle=True):\\n # Run segment_images on the subdirectory \\n df_img,df_loss,df_outlier = segment_images(subdir_path, regex_images)\\n\\n # Determine ionogram grid mappings used to map (x,y) pixel coordinates of ionogram trace to (Hz, km) values\\n stack = all_stack(df_img)\\n col_peaks,row_peaks,mapping_Hz, mapping_km = get_grid_mappings(stack)\\n\\n # Translate metadata located on the left\\n df_img_left = df_img[df_img['metadata_type']== 'left']\\n \\n if len(df_img_left.index) > min_n_leftside_metadata:\\n # Determine leftside metadata grid (pixel coordinates to number, category mappings)\\n df_img_left, df_loss_meta,dict_mapping,dict_hist= get_leftside_metadata(df_img_left,subdir_path)\\n df_all_loss = df_loss.append(df_loss_meta)\\n else:\\n df_all_loss = df_loss\\n \\n # Extract the coordinates of the ionogram trace (black), Map the (x,y) pixel coordinates to (Hz, km) values and Extract select parameters i.e. fmin\\n if only_ionogram_content_extraction_on_leftside_metadata:\\n df_processed, df_loss_coord = extract_coord_subdir_and_param(df_img_left,subdir_path,col_peaks,row_peaks,mapping_Hz,mapping_km)\\n else:\\n df_processed, df_loss_coord = extract_coord_subdir_and_param(df_img,subdir_path,col_peaks,row_peaks,mapping_Hz,mapping_km)\\n\\n df_processed['mapping_Hz'] = [mapping_Hz] * len(df_processed.index)\\n df_processed['mapping_km'] = [mapping_km] * len(df_processed.index)\\n\\n if to_pickle:\\n start,subdir_name = ntpath.split(subdir_path[:-1])\\n start,dir_name = ntpath.split(start)\\n df_processed.to_pickle(os.pardir + '/pickle/' + str(dir_name)+'_'+str(subdir_name)+'.pkl')\\n \\n df_all_loss = df_all_loss.append(df_loss_coord)\\n return df_processed, df_all_loss,df_outlier\",\n \"def save_as_hdf5(self, filename):\",\n \"def compress_img():\\n in_path = 'output/templates/rgb/'\\n out_path = 'output/templates/imgs/'\\n names = os.listdir(in_path)\\n for i, name in enumerate(names):\\n img = cv2.imread(in_path + name, 0)\\n if any(np.array(img.shape) > 1000):\\n img = cv2.resize(img, None, fx=0.5, fy=0.5, interpolation=cv2.INTER_CUBIC)\\n cv2.imwrite(out_path + name, img)\\n\\n return\",\n \"def processImages(self):\\n for file in os.listdir(self.config[\\\"tempPath\\\"]):\\n self.logger.debug(\\\"Calling generateImages for the file: {0}\\\".format(file))\\n self.generateText(file)\",\n \"def generate_all(files, alignement_h5f, input_h5f,\\n nframes=7, vad=None):\\n def try_remove(fname):\\n try:\\n os.remove(fname)\\n except:\\n pass\\n try:\\n directory = os.path.dirname(os.path.abspath(input_h5f))\\n\\n # create temporary files:\\n _, fb_h5f = tempfile.mkstemp(dir=directory)\\n _, fb_mvn_h5f = tempfile.mkstemp(dir=directory)\\n os.remove(fb_h5f)\\n os.remove(fb_mvn_h5f)\\n\\n # generate mfccs:\\n h5features_compute(files, alignement_h5f, featfunc=do_mfccs)\\n\\n # generate stacked mvn fbanks:\\n h5features_compute(files, fb_h5f, featfunc=do_fbank)\\n mean_variance_normalisation(fb_h5f, fb_mvn_h5f, vad=vad)\\n h5features_feats2stackedfeats(fb_mvn_h5f, input_h5f, nframes=nframes)\\n finally:\\n try_remove(fb_h5f)\\n try_remove(fb_mvn_h5f)\",\n \"def save_images(images, filenames, output_dir):\\n for i, filename in enumerate(filenames):\\n # Images for inception classifier are normalized to be in [-1, 1] interval,\\n # so rescale them back to [0, 1].\\n with tf.gfile.Open(os.path.join(output_dir, filename), 'w') as f:\\n imsave(f, (images[i, :, :, :] + 1.0) * 0.5, format='png')\",\n \"def create_random_data(output_path: str, num_images: int = 5) -> None:\\n train_path = os.path.join(output_path, \\\"train\\\")\\n class1_train_path = os.path.join(train_path, \\\"class1\\\")\\n class2_train_path = os.path.join(train_path, \\\"class2\\\")\\n\\n val_path = os.path.join(output_path, \\\"val\\\")\\n class1_val_path = os.path.join(val_path, \\\"class1\\\")\\n class2_val_path = os.path.join(val_path, \\\"class2\\\")\\n\\n test_path = os.path.join(output_path, \\\"test\\\")\\n class1_test_path = os.path.join(test_path, \\\"class1\\\")\\n class2_test_path = os.path.join(test_path, \\\"class2\\\")\\n\\n paths = [\\n class1_train_path,\\n class1_val_path,\\n class1_test_path,\\n class2_train_path,\\n class2_val_path,\\n class2_test_path,\\n ]\\n\\n for path in paths:\\n try:\\n os.makedirs(path)\\n except FileExistsError:\\n pass\\n\\n for i in range(num_images):\\n pixels = numpy.random.rand(64, 64, 3) * 255\\n im = Image.fromarray(pixels.astype(\\\"uint8\\\")).convert(\\\"RGB\\\")\\n im.save(os.path.join(path, f\\\"rand_image_{i}.jpeg\\\"))\\n\\n process_images(output_path)\",\n \"def save_images_in_folder(folder, images, size = (224, 224), start_index = 0):\\n\\n # Loop over the images\\n for i, image in enumerate(images):\\n # Resize image to target size\\n image = cv2.resize(image, dsize = size)\\n \\n # Convert image back to BGR color space\\n image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\\n \\n # Create the path where the image will be save, images will be indexed\\n path = os.path.join(folder, str(start_index + i) + '.png')\\n \\n # Write / Save image \\n cv2.imwrite(path, image)\",\n \"def transform_to_h5():\\n # this took about 10 minutes for set1\\n for setname in ['set1', 'set2']:\\n filename = os.path.join(ltrc_dirname, '{}.h5'.format(setname))\\n f = h5py.File(filename, 'w')\\n\\n for name in ['train', 'valid', 'test']:\\n g = f.create_group(name)\\n filename = os.path.join(ltrc_dirname, '{}.{}.txt'.format(setname, name))\\n X, y, q = load_svmlight_file(filename, query_id=True)\\n g.create_dataset('X', data=X.todense(), compression='gzip')\\n g.create_dataset('y', data=y, compression='gzip')\\n g.create_dataset('q', data=q, compression='gzip')\\n f.close()\\n # Now you can do this\\n # f['/valid/X'].shape\\n # Out[24]: (71083, 699)\",\n \"def preprocess(exam, data_folder, save_path, image_format):\\n for v in ['L-CC', 'L-MLO', 'R-CC', 'R-MLO']:\\n if len(exam[v]) == 0:\\n continue\\n else:\\n for image in exam[v]:\\n image_path = data_folder + '/' + image + '.' + image_format\\n # Extract subdirectories\\n subdirs = \\\"/\\\".join(image.split('/')[:-1])\\n save_dirs = os.path.join(save_path, subdirs)\\n # Extract image id\\n image_id = image.split('/')[-1]\\n # Create save directories\\n os.makedirs(save_dirs, exist_ok=True)\\n png_save_path = os.path.join(save_dirs, image_id + '.png')\\n with Image(filename=image_path, format=image_format) as img:\\n with img.clone() as i:\\n i.auto_level()\\n with i.convert('png') as png_image:\\n png_image.transform(resize='896x1152!')\\n png_image.save(filename=png_save_path)\",\n \"def sum_images(filelist):\\n\\n nfiles = np.size(filelist)\\n\\n print(\\\"Summing together {} files\\\".format(nfiles))\\n\\n ims = []\\n\\n for fname in filelist:\\n hdu = f.open(fname)\\n ims.append(hdu[0].data)\\n\\n ims = np.array(ims)\\n\\n sum_im = np.nansum(ims, axis=0)\\n hdu[0].data = sum_im\\n\\n #Add pipeline version and history keywords\\n vers = version.get_version()\\n hdu[0].header.set('PL_VERS',vers,'Version of pipeline used for processing')\\n hdu[0].header['HISTORY'] = \\\"Summed up the following images:\\\"\\n\\n for fname in filelist:\\n hdu[0].header['HISTORY'] = fname\\n\\n hdu[0].header['HISTORY'] = \\\"######################\\\"\\n\\n outname = filelist[-1].split('.')[0]+'_summed.fits'\\n\\n print(\\\"Writing out final file to {}\\\".format(outname))\\n\\n hdu.writeto(outname, overwrite=True)\",\n \"def saveHDF5(ifile, cs, xi, yi, phi, cidx, idx, size, comx, comy, rgysq, pl, st, sw, ens, fils):\\n \\n ## Groups\\n size_grp = ifile.create_group('size')\\n beads_grp = ifile.create_group('beads')\\n props_grp = ifile.create_group('props')\\n \\n ## Datasets\\n size_grp.create_dataset('size', data=cs, compression='gzip')\\n \\n beads_grp.create_dataset('x', data=xi, compression='gzip')\\n beads_grp.create_dataset('y', data=yi, compression='gzip')\\n beads_grp.create_dataset('phi', data=phi, compression='gzip')\\n beads_grp.create_dataset('idx', data=cidx, compression='gzip')\\n \\n idx_h5 = np.asarray(idx, dtype=int)\\n size_h5 = np.asarray(size, dtype=int)\\n comx_h5 = np.asarray(comx, dtype=float)\\n comy_h5 = np.asarray(comy, dtype=float)\\n rgysq_h5 = np.asarray(rgysq, dtype=float)\\n planarity_h5 = np.asarray(pl, dtype=float)\\n straightness_h5 = np.asarray(st, dtype=float)\\n swirliness_h5 = np.asarray(sw, dtype=float)\\n enstrophy_h5 = np.asarray(ens, dtype=float)\\n \\n props_grp.create_dataset('idx', data=idx_h5, compression='gzip')\\n props_grp.create_dataset('size', data=size_h5, compression='gzip')\\n props_grp.create_dataset('comx', data=comx_h5, compression='gzip')\\n props_grp.create_dataset('comy', data=comy_h5, compression='gzip')\\n props_grp.create_dataset('rgysq', data=rgysq_h5, compression='gzip')\\n props_grp.create_dataset('planarity', data=planarity_h5, compression='gzip')\\n props_grp.create_dataset('straightness', data=straightness_h5, compression='gzip')\\n props_grp.create_dataset('swirliness', data=swirliness_h5, compression='gzip')\\n props_grp.create_dataset('enstrophy', data=enstrophy_h5, compression='gzip')\\n \\n ## Filament list\\n fil_grp = props_grp.create_group('filament_list')\\n for sz_idx in np.arange(len(size_h5)):\\n fil_list = np.asarray(fils[sz_idx], dtype=int)\\n fil_grp.create_dataset(str(sz_idx), data=fil_list, compression='gzip')\\n \\n return\",\n \"def ReadData(self, path):\\n os.chdir(path)\\n folders=os.listdir()\\n if 'data.hdf5' in folders:\\n print('Loading data from hdf5 file! Might take some time, be patient!')\\n file=h5py.File('data.hdf5','r+')\\n data=(np.array(list(file['imgs'])),np.array(list(file['lables'])))\\n self.real_labels=list(file['real_labels'])\\n file.close()\\n\\n else:\\n print('1. Collecting data.')\\n err_logs = []\\n img=[]\\n lable=[]\\n for folder in tqdm(folders):\\n\\n os.chdir(os.path.join(path,folder))\\n for file in os.listdir():\\n try:\\n dat=(plt.imread(open(file,'rb')))\\n img.append(resize_image(dat, (resize_x, resize_y),\\n mode='constant',\\n ))\\n lable.append(folder)\\n if folder not in self.real_labels:\\n self.real_labels.append(folder)\\n \\n except OSError:\\n err_logs.append([folder, file])\\n print('\\\\nError logs:')\\n for e in range(len(err_logs)):\\n print('\\\\tFolder: {} | Some OSError for file: {}'.format(err_logs[e][0],\\n err_logs[e][0]))\\n \\n \\n print('2. Encoding data to categorical.')\\n # Encode Letters into numerical categories.\\n le = LabelEncoder()\\n le.fit(lable)\\n lable = le.transform(lable)\\n lable = np.array(lable).reshape(-1, 1)\\n \\n print('3. Onehot encoding.')\\n # Onehot encoding.\\n ohe = OneHotEncoder(sparse=False)\\n ohe.fit(lable)\\n lable = ohe.transform(lable)\\n \\n # Shaffle data.\\n print('4. Shuffle data.')\\n img, lable = shuffle(img, lable)\\n\\t\\t \\n print('5. Saving data.')\\n data=(np.asarray(img), np.asarray(lable))\\n os.chdir(path)\\n \\n file=h5py.File('data.hdf5','w')\\n x=file.create_dataset('imgs',data=np.array(img))\\n y=file.create_dataset('lables',data=np.array(lable))\\n print(self.real_labels)\\n rl=file.create_dataset('real_labels',data=np.string_(self.real_labels))\\n file.close()\\n print('Data set is stored in Data.hdf5 file. ')\\n\\n return data\",\n \"def create_ela_files(originals='./data/CASIA1_originals', originals_ela='./data/CASIA1_originals_ela',\\n fakes='./data/CASIA1_fakes', fakes_ela='./data/CASIA1_fakes_ela'):\\n for i, item in enumerate(os.listdir(originals)):\\n image = ela(os.path.join(originals, item))\\n cv2.imwrite(os.path.join(originals_ela, item), image)\\n \\n for i, item in enumerate(os.listdir(fakes)):\\n image = ela(os.path.join(fakes, item))\\n cv2.imwrite(os.path.join(fakes_ela, item), image)\",\n \"def main():\\n nb_processed = 0\\n for dataset_name in DATASETS:\\n print(\\\"-----------------\\\")\\n print(\\\"Dataset: '%s'\\\" % (dataset_name,))\\n print(\\\"-----------------\\\")\\n\\n dataset_dir = os.path.join(WRITE_MAIN_DIR, dataset_name)\\n if not os.path.isdir(dataset_dir):\\n os.makedirs(dataset_dir)\\n\\n dataset = Dataset([os.path.join(READ_MAIN_DIR, dataset_name)])\\n print(\\\"Found %d images total.\\\" % (len(dataset.fps),))\\n\\n errors = []\\n\\n scale_height, scale_width = SCALES[dataset_name]\\n target_aspect_ratio = scale_width / scale_height\\n\\n # iterate over every image in the current dataset,\\n # augment that image N times, add cols/rows until target aspect ratio\\n # is reached, resize it (e.g. 64x64), save it\\n for img_idx, (image_filepath, image) in enumerate(zip(dataset.fps, dataset.get_images())):\\n print(\\\"[%s] Image %d of %d (%.2f%%)...\\\" \\\\\\n % (dataset_name, img_idx+1, len(dataset.fps),\\n 100*(img_idx+1)/len(dataset.fps)))\\n\\n # IOErrors during loading of images result here in a None value\\n if image is None:\\n print(\\\"Error / None\\\")\\n errors.append((\\n image_filepath,\\n \\\"Failed to load image '%s' (idx %d for dataset %s)\\\" \\\\\\n % (image_filepath, img_idx, dataset_name)\\n ))\\n else:\\n # resize too big images to smaller ones before any augmentation\\n # (for performance reasons)\\n height = image.shape[0]\\n width = image.shape[1]\\n aspect_ratio = width / height\\n if width > 1000 or height > 1000:\\n image = misc.imresize(image, (1000, int(1000 * aspect_ratio)))\\n\\n # augment image\\n # converts augmented versions automatically to float32, 0-1\\n augmentations = augment(image, **AUGMENTATIONS[dataset_name])\\n\\n # create list of original image + augmented versions\\n images_aug = [image / 255.0]\\n images_aug.extend(augmentations)\\n\\n # for each augmented version of the images:\\n # resize it to target aspect ratio (e.g. same width and height),\\n # save it\\n for aug_idx, image_aug in enumerate(images_aug):\\n image_aug = to_aspect_ratio_add(image_aug, target_aspect_ratio)\\n filename = \\\"{:0>6}_{:0>3}.jpg\\\".format(img_idx, aug_idx)\\n img_scaled = misc.imresize(image_aug, (scale_height, scale_width))\\n misc.imsave(os.path.join(dataset_dir, filename), img_scaled)\\n\\n nb_processed += 1\\n\\n print(\\\"Processed %d images with %d errors.\\\" % (nb_processed, len(errors)))\\n for (fp, err) in errors:\\n print(\\\"File %s error:\\\" % (fp,))\\n print(err)\\n print(\\\"Finished.\\\")\",\n \"def exporting_cropped_images (fpath_tiff):\\n src = rasterio.open(fpath_tiff, 'r')\\n outfolder_irregular = '/train/irregular'\\n outfolder_healthy = '/train/healthy'\\n outfolder_concrete = '/train/concrete'\\n outfolder_incomplete = '/train/incomplete'\\n outfolder_other = '/train/other'\\n outfolder = '/train/batch'\\n #os.makedirs (outfolder, exist_ok = True)\",\n \"def HDF5_to_HDF5(self, **kwds):\\n # split extension from HDF5 file\\n if isinstance(self.filename, str):\\n fileBasename,fileExtension=os.path.splitext(self.filename)\\n else:\\n fileBasename,fileExtension=os.path.splitext(self.filename.filename)\\n # output HDF5 file\\n hdf5_file = os.path.expanduser(f'{fileBasename}.h5')\\n # copy everything from the HDF5 file\\n with h5py.File(self.filename,mode='r') as source:\\n dest = h5py.File(hdf5_file,mode='w')\\n # value checks on output HDF5\\n if not hasattr(dest, 'create_dataset'):\\n raise ValueError('dest must be a group, got {!r}'.format(dest))\\n # for each key in the root of the hdf5 file structure\\n for k in source.keys():\\n self.copy_from_HDF5(source[k], dest, name=k, **kwds)\",\n \"def read_hdf5(ID_images:str, path:str = \\\"data/dataset/\\\"):\\n images, labels = [], []\\n\\n #open the HDF5 file\\n file = h5py.File(path +ID_images+\\\"_lens.h5\\\", \\\"r\\\")\\n\\n images = np.array(file[\\\"/images\\\"]).astype(\\\"float64\\\")\\n labels = pd.read_hdf(path +ID_images+'_meta.h5', \\\"table\\\")\\n\\n return images, labels\",\n \"def gen_test_output(sess, logits, image_folder, image_pl, data_folder,\\n learning_phase, image_shape, nw_shape):\\n image_paths = glob(os.path.join(data_folder, image_folder, '*.png'))\\n for image_file in image_paths[:5]:\\n\\n in_image = scipy.misc.imread(image_file, mode='RGB')\\n image = scipy.misc.imresize(in_image, image_shape)\\n\\n street_im = get_seg_img(sess, logits, image_pl, image, image_shape,\\n nw_shape, learning_phase)\\n\\n street_im = scipy.misc.imresize(street_im, in_image.shape)\\n yield os.path.basename(image_file), np.array(street_im)\",\n \"def main(output_dir, img_size, max_boxes, train_size, val_size):\\n im_id = 0\\n ann_id = 0\\n\\n def make_split(split, split_size):\\n nonlocal im_id\\n nonlocal ann_id\\n\\n image_dir = join(output_dir, 'train')\\n make_dir(image_dir)\\n\\n images = []\\n annotations = []\\n for _ in range(split_size):\\n img, boxes = make_scene(img_size, max_boxes)\\n img = np.transpose(img, (1, 2, 0))\\n file_name = '{}.png'.format(im_id)\\n Image.fromarray(img).save(\\n join(image_dir, file_name))\\n images.append({\\n 'id': im_id,\\n 'height': img_size,\\n 'width': img_size,\\n 'file_name': file_name\\n })\\n for box in boxes:\\n annotations.append({\\n 'id': ann_id,\\n 'image_id': im_id,\\n 'category_id': 1,\\n 'area': (box[2] - box[0]) * (box[3] - box[1]),\\n 'bbox': [box[1], box[0], box[3]-box[1], box[2]-box[0]]\\n })\\n ann_id += 1\\n im_id += 1\\n\\n categories = [{'id': 1, 'name': 'rectangle'}]\\n labels = {\\n 'images': images,\\n 'annotations': annotations,\\n 'categories': categories}\\n json_to_file(labels, join(output_dir, '{}.json'.format(split)))\\n\\n make_split('train', train_size)\\n make_split('valid', val_size)\",\n \"def main():\\n\\n\\n\\n skulls_folder = os.listdir(RAW_IMAGE_DIRECTORY)\\n\\n # fetch and sort the .mnc and .tag files\\n mnc_files = [f for f in skulls_folder if 'mnc' in f]\\n tag_files = [f for f in skulls_folder if 'tag' in f]\\n mnc_names = [i.split('.mnc')[0] for i in mnc_files]\\n \\n mnc_files.sort()\\n tag_files.sort()\\n mnc_names.sort()\\n\\n # Process and package ndarrays as tuples inside npy file\\n package_to_npy(RAW_IMAGE_DIRECTORY, mnc_files, tag_files, mnc_names)\\n \\n print('\\\\n' * 5)\\n\\n # Push the npy files to GCP Cloud Storage\\n upload_to_gcp(PROCESSED_IMAGE_DIRECTORY, GCP_PROJECT_NAME, GCP_BUCKET_NAME)\",\n \"def create_png_images(self):\\n if self.subject is None:\\n print Console.WARNING + 'You need to specify a subject first' + Console.ENDC\\n return\\n\\n check_dir_of = self.locations.check_dir_of\\n check_dir_of(self.locations.HISTO_PNG_U)\\n check_dir_of(self.locations.HISTO_PNG)\\n check_dir_of(self.locations.SOURCE_PNG)\\n\\n\\n\\n fmap_img = ImageUtils.load_nifti_image(self.locations.HIST_FMAP) #loading subject nifti files\\n volumes = []\\n try:\\n for s in self.locations.SOURCES:\\n volumes.append(ImageUtils.load_nifti_image(s))\\n except IOError as e:\\n print Console.FAIL + 'There are errors loading nifi files for subject %s'%self.subject + Console.ENDC\\n return False\\n \\n\\n num_slices = volumes[0].shape[2] #use first volume to check expected number of slices\\n\\n self.locations.create_empty_dir(self.locations.IMAGES_DIR)\\n\\n print 'Creating input PNGs for %s'%self.subject\\n for k, vol in enumerate(volumes):\\n for i in range(num_slices):\\n imslice = ImageUtils.data_to_bytescale_rgb(vol[:, :, i])\\n im = Image.fromarray(imslice)\\n im.save(self.locations.SOURCE_PNG % (self.locations.LABELS[k],i))\\n\\n \\n print 'Creating histology PNGs for %s'%self.subject\\n for i in range(num_slices):\\n\\n im_unscaled = ImageUtils.data_to_unscaled_rgb(fmap_img[:, :, i]); #keeps the original values\\n im_unscaled = Image.fromarray(im_unscaled)\\n im_unscaled = im_unscaled.filter(ImageFilter.GaussianBlur(radius=2)) #Filter requested by Ali Khan\\n im_unscaled.save(self.locations.HISTO_PNG_U % i)\\n\\n im_scaled = ImageUtils.data_to_bytescale_rgb(fmap_img[:,:,i]); # bytescaled histology\\n im_scaled = Image.fromarray(im_scaled)\\n im_scaled = im_scaled.filter(ImageFilter.GaussianBlur(radius=2)) #Filter requested by Ali Khan\\n im_scaled.save(self.locations.HISTO_PNG % i)\\n\\n print\\n return True\",\n \"def print_images_in_statistics(self):\\n self._print_images_statistics(self._images_in_folder, self._pose_class_names)\",\n \"def print_images_out_statistics(self):\\n self._print_images_statistics(self._images_out_folder, self._pose_class_names)\",\n \"def create_training_images(fn, i, p_hr, p_lr, size, qualityFactor, downsize=True):\\n dest = p_lr/fn.relative_to(p_hr)\\n dest.parent.mkdir(parents=True, exist_ok=True)\\n img = PIL.Image.open(fn)\\n if downsize:\\n targ_sz = resize_to(img, size, use_min=True) # W x H\\n img = img.resize(targ_sz, resample=PIL.Image.BILINEAR).convert('RGB')\\n img.save(dest.with_suffix(\\\".jpg\\\"), \\\"JPEG\\\", quality=qualityFactor)\",\n \"def read_images(folder):\\n distinct_frames = DistinctFrames()\\n\\n for file in sorted(sorted(os.listdir(folder)),\\n key=len): # sorting files on basis of 1) length and 2) numerical order\\n '''\\n Sorting is done 2 times because\\n if files in the folder are\\n 1. image100.pkl\\n 2. image22.pkl\\n 3. image21.pkl\\n firstly sort them to image100.pkl,image21.pkl,image22.pkl then according to length to image21.pkl,image22.pkl,image100.pkl\\n '''\\n try:\\n img_obj = load_from_memory(file, folder)\\n time_stamp = img_obj.get_time()\\n distinct_frames.add_img_obj(img_obj)\\n print(\\\"Reading image ..\\\" + str(time_stamp) + \\\" from \\\" + folder) # for debug purpose\\n except:\\n # exception will occur for files like .DS_Store and jpg directory\\n continue\\n\\n if distinct_frames.no_of_frames() != 0:\\n distinct_frames.calculate_time()\\n\\n return distinct_frames\",\n \"def global_metadata(paths):\\n\\n # Weakly group images to partition image set size- crucial optimization step\\n if os.path.exists(paths.image_preprocess):\\n clumped_paths = json.loads(open(paths.image_preprocess).read())\\n else:\\n clumped_paths = network.alpha_categorize(paths)\\n print(\\\"Hashed source images\\\")\\n\\n with open(paths.image_preprocess, 'w') as json_file:\\n json.dump(clumped_paths, json_file)\\n\\n # Combinatorial image grouping to graph\\n image_graph = network.load_graph(paths.image_network_path)\\n\\n total = len(list(chain(*clumped_paths.values())))\\n counter = 0.\\n\\n for image_paths in clumped_paths.values():\\n counter += len(image_paths)\\n print(str(int(counter / float(total) * 100)) + \\\"% complete\\\")\\n\\n if len(image_paths) > 1:\\n image_grouping = images.load_paths(paths.default_patches, image_paths)\\n image_graph = metadata.network.network_images(\\n image_grouping, threshold=0, network=image_graph)\\n else:\\n image_graph.add_node(image_paths[0])\\n\\n metadata.network.save_graph(paths.image_network_path, image_graph)\\n print(\\\"Updated image graph.\\\")\\n\\n # Create informational json files for templates and files\\n templates.build(paths, image_graph)\\n mappings.build(paths, image_graph)\\n print(\\\"Created JSON metadata files.\\\")\",\n \"def append_component_images(pldm_fw_up_pkg, image_files):\\n for image in image_files:\\n with open(image, \\\"rb\\\") as file:\\n for line in file:\\n pldm_fw_up_pkg.write(line)\",\n \"def read_images(path, image_size=None):\\n c = 0\\n X = []\\n y = []\\n folder_names = []\\n for dirname, dirnames, filenames in os.walk(path):\\n for subdirname in dirnames:\\n folder_names.append(subdirname)\\n subject_path = os.path.join(dirname, subdirname)\\n for filename in os.listdir(subject_path):\\n #try:\\n im = cv2.imread(os.path.join(subject_path, filename), cv2.IMREAD_GRAYSCALE)\\n # resize to given size (if given)\\n if (image_size is not None):\\n im = cv2.resize(im, image_size)\\n X.append(np.asarray(im, dtype=np.uint8))\\n y.append(c)\\n #except IOError, (errno, strerror):\\n # print \\\"I/O error({0}): {1}\\\".format(errno, strerror)\\n # except:\\n # print \\\"Unexpected error:\\\", sys.exc_info()[0]\\n # raise\\n c = c+1\\n return [X,y,folder_names]\",\n \"def extractOneImageStack(filename, h5path=None, destPath=None):\\n if destPath == None:\\n destPath = \\\".\\\"\\n if filename.find(\\\"%\\\") > 0:\\n h5file = h5py.File(filename, driver=\\\"family\\\")\\n else:\\n h5file = h5py.File(filename)\\n if (h5path is None) or (h5path not in h5file):\\n list_of_names = []\\n h5file.visit(list_of_names.append)\\n print(os.linesep + \\\"Where is your stack of images dataset (in which group) ?\\\")\\n for onePath in list_of_names:\\n if isinstance(h5file[onePath], h5py.highlevel.Group):\\n print(\\\"\\\\t\\\" + onePath)\\n\\n h5path = raw_input(\\\"Where is your image stack ? \\\")\\n if not h5path in h5file:\\n print(\\\"This HDF5path %s is not in the file %s\\\" % (filename, h5path))\\n sys.exit(1)\\n group = h5file[h5path]\\n if \\\"data\\\" in group:\\n data = group[\\\"data\\\"]\\n else:\\n maxData = None\\n for obj in group:\\n if isinstance(group[obj], h5py.highlevel.Dataset):\\n if len(group[obj].shape) > len(maxData.shape):\\n maxData = group[obj]\\n elif len(group[obj].shape) == len(maxData.shape):\\n if group[obj].shape[0] > maxData.shape[0]:\\n maxData = group[obj]\\n if maxData is None:\\n raise Exception(\\\"No dataset found in provided group %s from file %s\\\" % (h5path, filename))\\n else:\\n data = maxData\\n dsSize = data.shape[0]\\n\\n headerGrp = []\\n for obj in group:\\n if isinstance(group[obj], h5py.highlevel.Group):\\n testHeaderGrp = group[obj]\\n for headerdataset in testHeaderGrp:\\n if testHeaderGrp[headerdataset].len() != dsSize:\\n break\\n else:\\n headerGrp = testHeaderGrp\\n if \\\"Filenames\\\" in group:\\n EdfFilenames = group[\\\"Filenames\\\"]\\n elif \\\"filenames\\\" in group:\\n EdfFilenames = group[\\\"filenames\\\"]\\n else:\\n base = os.path.splitext(filename)[0]\\n grpname = h5path.replace(\\\"/\\\", \\\"_\\\")\\n EdfFilenames = [\\\"%s-%s-%04i.edf\\\" % (base, grpname, i) for i in range(dsSize) ]\\n for idx, oneFile in enumerate(EdfFilenames):\\n print(\\\"Extracting \\\" + oneFile)\\n headers = {}\\n for key in headerGrp:\\n if key == \\\"filename\\\":\\n continue\\n headers[key] = headerGrp[key][idx]\\n edf = fabio.edfimage.edfimage(data=data[idx], header=headers)\\n edf.write(os.path.join(destPath, oneFile))\",\n \"def make_hdf5file(self, dataset_paths: list = None, dataset_values: list = None) -> None:\\n\\n assert dataset_paths is not None and len(dataset_paths) != 0, (\\\"`dataset_path` not valid. Expected at least \\\" \\\\\\n f\\\"one list element, got {len(dataset_paths)}.\\\")\\n\\n assert dataset_values is not None and len(dataset_values) != 0, (\\\"`dataset_values` not valid. Expected at least \\\" \\\\\\n f\\\"one list element, got {len(dataset_values)}.\\\")\\n\\n\\n # Remove file if already exists and create a new one\\n if os.path.isfile(os.path.join(self.FOFDirectory, self.filename)):\\n os.remove(os.path.join(self.FOFDirectory, self.filename))\\n print(f'[ FOFOutput ]\\\\t==> Removed old {self.filename} file.')\\n\\n # Create file and optional groups within it\\n FOFfile = h5py.File(os.path.join(self.FOFDirectory, self.filename), 'w')\\n print(f'[ FOFOutput ]\\\\t==> Created new {self.filename} file.')\\n\\n # Push the attributes to file, if any\\n if self.attrs is not None and len(self.attrs.keys()) > 0:\\n for key, text in zip(self.attrs.keys(), self.attrs.values()):\\n FOFfile.attrs[key] = text\\n\\n for internal_path, dataset_content in zip(dataset_paths, dataset_values):\\n\\n assert not internal_path.endswith('/'), \\\"Invalid hdf5 internal path\\\"\\n assert type(dataset_content) is np.ndarray, \\\"Can only push numpy.ndarrays into hdf5 files.\\\"\\n\\n nested_groups = self.groups_from_path(internal_path)\\n if len(nested_groups) == 1:\\n FOFfile.create_dataset(nested_groups[0], data=dataset_content)\\n else:\\n for nested_group in nested_groups[:-1]:\\n g = FOFfile.create_group(nested_group)\\n g.create_dataset(nested_groups[-1], data = dataset_content)\\n\\n print(f'[ FOFOutput ]\\\\t==> Created {internal_path} dataset in {self.filename} file.')\\n\\n FOFfile.close()\",\n \"def create_image_lists(image_dir):\\n if not gfile.Exists(image_dir):\\n print(\\\"Image directory '\\\" + image_dir + \\\"' not found.\\\")\\n return None\\n result = {}\\n sub_dirs = [x[0] for x in os.walk(image_dir)]\\n # The root directory comes first, so skip it.\\n is_root_dir = True\\n for sub_dir in sub_dirs:\\n print('in sub loop')\\n extensions = ['jpg', 'jpeg', 'JPG', 'JPEG']\\n file_list = []\\n dir_name = os.path.basename(image_dir)\\n print(\\\"Looking for images in '\\\" + image_dir + \\\"'\\\")\\n for extension in extensions:\\n file_glob = os.path.join(image_dir, dir_name, '*.' + extension)\\n file_list.extend(glob.glob(file_glob))\\n if not file_list:\\n print('No files found')\\n continue\\n if len(file_list) < 20:\\n print('WARNING: Folder has less than 20 images, which may cause issues.')\\n label_name = re.sub(r'[^a-z0-9]+', ' ', dir_name.lower())\\n testing_images = []\\n for file_name in file_list:\\n base_name = os.path.basename(file_name)\\n # We want to ignore anything after '_nohash_' in the file name when\\n # deciding which set to put an image in, the data set creator has a way of\\n # grouping photos that are close variations of each other. For example\\n # this is used in the plant disease data set to group multiple pictures of\\n # the same leaf.\\n hash_name = re.sub(r'_nohash_.*$', '', file_name)\\n # This looks a bit magical, but we need to decide whether this file should\\n # go into the training, testing, or validation sets, and we want to keep\\n # existing files in the same set even if more files are subsequently\\n # added.\\n # To do that, we need a stable way of deciding based on just the file name\\n # itself, so we do a hash of that and then use that to generate a\\n # probability value that we use to assign it.\\n testing_images.append(base_name)\\n return testing_images\",\n \"def splitMerge(self):\\n\\t\\tpath_merge = self.aug_merge_path\\n\\t\\tpath_train = self.aug_train_path\\n\\t\\tpath_label = self.aug_label_path\\n\\t\\tfor i in range(self.slices):\\n\\t\\t\\tpath = path_merge + \\\"/\\\" + str(i)\\n\\t\\t\\t# print(path)\\n\\t\\t\\ttrain_imgs = glob.glob(path+\\\"/*.\\\"+self.img_type)\\n\\t\\t\\t# print(len(train_imgs))\\n\\t\\t\\t# break\\n\\t\\t\\tfor imgname in train_imgs:\\n\\t\\t\\t\\tmidname = imgname[imgname.rindex(\\\"/\\\")+1:imgname.rindex(\\\".\\\"+self.img_type)]\\n\\t\\t\\t\\timg = cv2.imread(imgname)\\n\\t\\t\\t\\timg_train = img[:,:,2]#cv2 read image rgb->bgr\\n\\t\\t\\t\\timg_label = img[:,:,0]\\n\\t\\t\\t\\tcv2.imwrite(path_train+\\\"/\\\"+midname+\\\".\\\"+self.img_type,img_train)\\n\\t\\t\\t\\tcv2.imwrite(path_label+\\\"/\\\"+midname+\\\".\\\"+self.img_type,img_label)\",\n \"def create_noobj_folder(\\n folder: PathLike, \\n img_ext: str = \\\".jpg\\\",\\n):\\n folder = Path(folder).expanduser().resolve()\\n images = glob(folder, img_ext)\\n \\n for image in images:\\n filename = image.name\\n _folder = image.parent.name\\n path = folder / (image.stem + \\\".xml\\\")\\n img_w, img_h = get_image_size(image)\\n\\n tree = ET.Element(\\\"annotation\\\")\\n\\n et_folder = ET.SubElement(tree, \\\"folder\\\")\\n et_folder.text = _folder\\n\\n et_filename = ET.SubElement(tree, \\\"filename\\\")\\n et_filename.text = filename\\n\\n et_path = ET.SubElement(tree, \\\"path\\\")\\n et_path.text = str(path)\\n\\n et_img_size = ET.SubElement(tree, \\\"size\\\")\\n ET.SubElement(et_img_size, \\\"width\\\").text = str(img_w)\\n ET.SubElement(et_img_size, \\\"height\\\").text = str(img_h)\\n ET.SubElement(et_img_size, \\\"depth\\\").text = \\\"3\\\"\\n\\n content = ET.tostring(tree, encoding=\\\"unicode\\\", pretty_print=True)\\n try: \\n path.write_text(content)\\n except KeyboardInterrupt:\\n path.write_text(content)\\n exit()\",\n \"def __call__(self):\\n train_number = 1\\n test_number = 1\\n for cur_file in os.listdir(self.txt_dir):\\n if(cur_file.split(\\\".\\\")[0].split(\\\"-\\\")[-1] == \\\"ellipseList\\\"):\\n with open(os.path.join(self.txt_dir, cur_file), 'r') as txt_file:\\n while(True):\\n line = txt_file.readline().rstrip(\\\"\\\\n\\\")\\n if not line : break\\n\\n # Image load\\n img_folder_ = line.split(\\\"/\\\") \\n img_dir_ = \\\\\\n self.img_dir + \\\"\\\\\\\\\\\" + img_folder_[0] + \\\"\\\\\\\\\\\" + img_folder_[1] + \\\"\\\\\\\\\\\" + img_folder_[2] + \\\"\\\\\\\\\\\" + img_folder_[3] + \\\"\\\\\\\\\\\" + img_folder_[4] + \\\".jpg\\\"\\n img = cv2.imread(img_dir_, cv2.IMREAD_COLOR)\\n \\n if(train_number > 2500) :\\n image_name = \\\"IMG_{}.jpg\\\".format(test_number)\\n cv2.imwrite(os.path.join(self.new_img_dir, \\\"test\\\", image_name), img)\\n test_number = test_number + 1\\n\\n else :\\n image_name = \\\"IMG_{}.jpg\\\".format(train_number)\\n cv2.imwrite(os.path.join(self.new_img_dir, \\\"train\\\", image_name), img)\\n train_number = train_number + 1\\n \\n object_num = int(txt_file.readline().rstrip(\\\"\\\\n\\\"))\\n file_data = OrderedDict()\\n object_data = OrderedDict()\\n file_data[\\\"Img_id\\\"] = image_name.split('.')[0]\\n file_data[\\\"Objects\\\"] = []\\n\\n #Do \\\"for\\\" operation as much as the number of objects\\n for i in range(object_num):\\n coord_ = txt_file.readline().split(\\\" \\\")\\n rad = abs(float(coord_[2]))\\n h = float(coord_[0])*np.sin(rad)\\n w = float(coord_[1])*np.sin(rad)\\n object_data[\\\"Object_class\\\"] = \\\"Face\\\"\\n object_data[\\\"Coordinate\\\"] = { \\\"Xmin\\\" : float(coord_[3]) - w, \\\"Ymin\\\" : float(coord_[4]) - h, \\\"Xmax\\\" : float(coord_[3]) + w, \\\"Ymax\\\" : float(coord_[4]) + h }\\n file_data[\\\"Objects\\\"].append(object_data)\\n self.json_list.append(file_data)\\n\\n # Save train annotation\\n if(train_number == 2501):\\n self._save_annotation(\\\"train\\\")\\n train_number = train_number + 1\\n self._save_annotation(\\\"test\\\")\",\n \"def read_input_files(self):\\r\\n\\r\\n for input_file in self.list_of_input_files:\\r\\n input_file.read_header_of_file()\\r\\n self.list_of_header_objects.extend(input_file.list_of_header_objects)\\r\\n self.list_of_header_objects_without_ID.extend(input_file.list_of_header_objects_without_ID)\\r\\n self.list_of_contigs.extend(input_file.list_of_contigs)\\r\\n\\r\\n self.list_of_header_objects = list(toolz.unique(self.list_of_header_objects, key=lambda x: x.tag_and_ID))\\r\\n self.list_of_header_objects_without_ID = list(\\r\\n toolz.unique(self.list_of_header_objects_without_ID, key=lambda x: x.line))\\r\\n self.list_of_contigs = list(toolz.unique(self.list_of_contigs, key=lambda x: x.line))\\r\\n self.list_of_header_objects.extend(self.list_of_header_objects_without_ID)\\r\\n self.list_of_header_objects.sort(key=lambda x: x.line)\\r\\n self.list_of_header_objects.extend(self.list_of_contigs)\\r\\n self.list_of_header_objects.sort(key=lambda x: x.tag, reverse=False)\\r\\n self.create_body_header_line_for_output()\\r\\n self.write_header_in_output_file()\\r\\n\\r\\n list_of_chrom = list(self.indices.keys())\\r\\n list_of_chrom.sort(key=lambda x: self.alphanum_key(x))\\r\\n for chrom in list_of_chrom:\\r\\n self.list_of_body_objects.clear()\\r\\n for input_file in self.list_of_input_files:\\r\\n input_file.read_specific_chrom_body_of_file(chrom)\\r\\n self.list_of_body_objects.extend(input_file.list_of_body_objects)\\r\\n\\r\\n self.adjust_body_records_to_samples()\\r\\n self.list_of_body_objects = list(toolz.unique(self.list_of_body_objects, key=lambda x: x.line))\\r\\n self.list_of_body_objects.sort(key=lambda x: self.alphanum_key(x.line))\\r\\n self.verify_and_merge_body_records()\\r\\n self.write_specific_chrom_in_output_file()\",\n \"def add_random(fr_data_path, random_path, output_path):\\n with h5py.File(random_path, 'r') as data:\\n random = np.asarray(data['images'].value)\\n \\n means = np.mean(np.mean(random, axis=-1), axis=-1)\\n empty = means == 0.0\\n error = np.isnan(means)\\n discard = empty | error\\n\\n random_i = np.where(~discard)\\n random = random[random_i]\\n\\n random = center_on_brightest(random)\\n \\n with h5py.File(fr_data_path, 'r') as data:\\n images = np.asarray(data[\\\"images\\\"].value) \\n images = center_on_brightest(images)\\n \\n labels = np.where(np.asarray(data['labels']), 2, 1)\\n\\n images = np.concatenate((images, random), axis=0)\\n labels = np.concatenate((labels, np.full((random.shape[0],), \\n fill_value=0)), axis=0)\\n\\n with h5py.File(output_path, 'w') as f:\\n f.create_dataset('images', data=images)\\n f.create_dataset('labels', data=labels)\\n\\n with h5py.File(fr_data_path, 'r') as data: \\n f.copy(data, 'fri_data')\\n f.copy(data, 'frii_data')\",\n \"def decompose_images(data_dir, output_dir, save_individually, **kwargs):\\n\\n # parse parameters\\n opt = TestOptions()\\n opt.parse(kwargs)\\n # print(kwargs)\\n\\n # torch setting\\n pytorch_settings.set_(with_random=False, determine=True)\\n\\n # visualize\\n V.create_a_visualizer(opt)\\n\\n # NIID-Net Manager\\n model = create_model(opt)\\n model.switch_to_eval()\\n\\n # List all image files in the directory (exclude subdirectory)\\n image_file_list = list_files(data_dir, ['jpg', 'jpeg', 'png', 'tif', 'JPG'])\\n print('Total image in the directory %s: %d' % (data_dir, len(image_file_list)))\\n\\n # Decompose images\\n for file_name in image_file_list:\\n # Read image\\n img_path = os.path.join(data_dir, file_name)\\n o_img = Image.open(img_path)\\n o_img = o_img.convert(\\\"RGB\\\")\\n\\n # Resize input image\\n # input_img = resize_image(o_img)\\n input_img = o_img\\n\\n # Predict\\n input_img = TF.to_tensor(input_img).unsqueeze(0)\\n pred_N, pred_R, pred_L, pred_S, rendered_img = model.predict({'input_srgb': input_img}, normal=True, IID=True)\\n\\n # Save results\\n idx = 0\\n pred_imgs = {\\n 'pred_N': pred_N[idx].cpu(),\\n 'pred_R': pred_R[idx].cpu(),\\n 'pred_L': pred_L[idx].cpu(),\\n 'pred_S': pred_S[idx].cpu(),\\n 'rendered_img': rendered_img[idx].cpu(),\\n 'input_srgb': input_img[idx],\\n }\\n f = '%s_decomposed' % (file_name[:file_name.rfind('.')])\\n image_util.save_intrinsic_images(output_dir, pred_imgs, label=f, individual=save_individually)\\n torch.save(pred_imgs, os.path.join(output_dir, f+'.pth.tar'))\\n print('Decompose %s successfully!' % file_name)\",\n \"def resize_multiple_images(src_path, dst_path):\\n for filename in os.listdir(src_path):\\n img=Image.open(src_path+'/'+filename)\\n new_img = img.resize((96,96,))\\n #new_img.resize(96,96,1)\\n if not os.path.exists(dst_path):\\n os.makedirs(dst_path)\\n new_img.save(dst_path+'/'+filename)\\n print('Resized and saved {} successfully.'.format(filename))\",\n \"def test_save_image(self):\\n\\n from m3_save_images.m3_save_images import save_images\\n folder_destination_name = \\\"unittest-sorted-images\\\"\\n path_source = \\\"../img\\\"\\n image_name = [\\\"00ff00.png\\\", \\\"aqua.png\\\", \\\"black.jpg\\\", \\\"yellow.png\\\", \\\"red2.jpg\\\", \\\"green.jpg\\\"]\\n image_color = [\\\"Lime\\\", \\\"Aqua\\\", \\\"Black\\\", \\\"Yellow\\\", \\\"Red\\\", \\\"Green\\\"]\\n # new empty folder is needed for testing save_image() function\\n if os.path.isdir(folder_destination_name):\\n shutil.rmtree(folder_destination_name)\\n os.mkdir(folder_destination_name)\\n # creating folders\\n for i in range(0, 4):\\n save_images(folder_destination_name, path_source, image_name[i], image_color[i])\\n self.assertEqual(''.join(os.listdir(os.path.join(folder_destination_name, image_color[i]))), image_name[i])\\n save_images(folder_destination_name, path_source, image_name[i], image_color[5])\\n self.assertNotEqual(''.join(os.listdir(os.path.join(folder_destination_name, image_color[i]))), image_name[5])\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67501724","0.6385571","0.635447","0.6286945","0.62869215","0.62683004","0.62626487","0.6237303","0.617954","0.61628073","0.6144604","0.6095783","0.60903996","0.60788053","0.60707295","0.60599494","0.60516787","0.60040814","0.59981525","0.5988912","0.597845","0.5957897","0.59545064","0.5920445","0.591906","0.59170395","0.5895527","0.58866835","0.5882584","0.5877743","0.5858677","0.5847465","0.5842437","0.583656","0.5834043","0.58248204","0.5812721","0.58044267","0.5795689","0.5793437","0.5786518","0.5785831","0.578453","0.5784472","0.576345","0.5757228","0.57562894","0.5753973","0.5737438","0.57291555","0.5719523","0.57123846","0.5702252","0.570127","0.5700121","0.56927806","0.5685981","0.56826264","0.5674865","0.56701386","0.5669397","0.56616974","0.5661676","0.5655385","0.5653309","0.56520545","0.5651201","0.5648776","0.5636202","0.56341356","0.5632167","0.5629045","0.5627169","0.5626596","0.56232053","0.56210834","0.5621029","0.5618597","0.5616784","0.5595624","0.5587647","0.55766827","0.5566492","0.5565505","0.5560576","0.5556037","0.55456334","0.55387354","0.55382985","0.55315185","0.5527238","0.55248356","0.5521541","0.5519669","0.55134666","0.5511353","0.55109775","0.55103284","0.5505433","0.55016387","0.549538"],"string":"[\n \"0.67501724\",\n \"0.6385571\",\n \"0.635447\",\n \"0.6286945\",\n \"0.62869215\",\n \"0.62683004\",\n \"0.62626487\",\n \"0.6237303\",\n \"0.617954\",\n \"0.61628073\",\n \"0.6144604\",\n \"0.6095783\",\n \"0.60903996\",\n \"0.60788053\",\n \"0.60707295\",\n \"0.60599494\",\n \"0.60516787\",\n \"0.60040814\",\n \"0.59981525\",\n \"0.5988912\",\n \"0.597845\",\n \"0.5957897\",\n \"0.59545064\",\n \"0.5920445\",\n \"0.591906\",\n \"0.59170395\",\n \"0.5895527\",\n \"0.58866835\",\n \"0.5882584\",\n \"0.5877743\",\n \"0.5858677\",\n \"0.5847465\",\n \"0.5842437\",\n \"0.583656\",\n \"0.5834043\",\n \"0.58248204\",\n \"0.5812721\",\n \"0.58044267\",\n \"0.5795689\",\n \"0.5793437\",\n \"0.5786518\",\n \"0.5785831\",\n \"0.578453\",\n \"0.5784472\",\n \"0.576345\",\n \"0.5757228\",\n \"0.57562894\",\n \"0.5753973\",\n \"0.5737438\",\n \"0.57291555\",\n \"0.5719523\",\n \"0.57123846\",\n \"0.5702252\",\n \"0.570127\",\n \"0.5700121\",\n \"0.56927806\",\n \"0.5685981\",\n \"0.56826264\",\n \"0.5674865\",\n \"0.56701386\",\n \"0.5669397\",\n \"0.56616974\",\n \"0.5661676\",\n \"0.5655385\",\n \"0.5653309\",\n \"0.56520545\",\n \"0.5651201\",\n \"0.5648776\",\n \"0.5636202\",\n \"0.56341356\",\n \"0.5632167\",\n \"0.5629045\",\n \"0.5627169\",\n \"0.5626596\",\n \"0.56232053\",\n \"0.56210834\",\n \"0.5621029\",\n \"0.5618597\",\n \"0.5616784\",\n \"0.5595624\",\n \"0.5587647\",\n \"0.55766827\",\n \"0.5566492\",\n \"0.5565505\",\n \"0.5560576\",\n \"0.5556037\",\n \"0.55456334\",\n \"0.55387354\",\n \"0.55382985\",\n \"0.55315185\",\n \"0.5527238\",\n \"0.55248356\",\n \"0.5521541\",\n \"0.5519669\",\n \"0.55134666\",\n \"0.5511353\",\n \"0.55109775\",\n \"0.55103284\",\n \"0.5505433\",\n \"0.55016387\",\n \"0.549538\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":16882,"cells":{"query":{"kind":"string","value":"Dynamically generate options for resource group form field based on the user's selection for Environment. This method requires the user to set the resource_group parameter as dependent on environment."},"document":{"kind":"string","value":"def generate_options_for_resource_group(control_value=None, **kwargs):\n if control_value is None:\n return []\n\n env = Environment.objects.get(id=control_value)\n\n if CB_VERSION_93_PLUS:\n # Get the Resource Groups as defined on the Environment. The Resource Group is a\n # CustomField that is only updated on the Env when the user syncs this field on the\n # Environment specific parameters.\n resource_groups = env.custom_field_options.filter(\n field__name=\"resource_group_arm\"\n )\n return [rg.str_value for rg in resource_groups]\n else:\n rh = env.resource_handler.cast()\n groups = rh.armresourcegroup_set.all()\n return [g.name for g in groups]"},"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 generate_options_for_cloudbolt_environment(group=None, **kwargs):\n envs = Environment.objects.filter(\n resource_handler__resource_technology__name='Google Cloud Platform') \\\n .select_related('resource_handler')\n if group:\n group_env_ids = [env.id for env in group.get_available_environments()]\n envs = envs.filter(id__in=group_env_ids)\n return [\n (env.id, u'{env} ({project})'.format(\n env=env, project=env.gcp_project))\n for env in envs\n ]","def resource_group(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"resource_group\")","def resource_group(self) -> str:\n return pulumi.get(self, \"resource_group\")","def resource_group_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"resource_group_name\")","def get_resource_group_name(self) -> str:\n # read the original value passed by the command\n resource_group_name = self.raw_param.get(\"resource_group_name\")\n\n # this parameter does not need dynamic completion\n # this parameter does not need validation\n return resource_group_name","def generate_options_for_gcp_resource_handler(group=None, **kwargs):\n if not GCPHandler.objects.exists():\n raise CloudBoltException(\n \"Ordering this Blueprint requires having a \"\n \"configured Google Cloud Platform resource handler.\"\n \"and at least one Project added to the resource handler\"\n )\n\n resource_handlers = GCPHandler.objects.all()\n envs = Environment.objects.filter(\n resource_handler__resource_technology__name=\"Google Cloud Platform\"\n ).select_related(\"resource_handler\")\n\n if group:\n group_env_ids = [env.id for env in group.get_available_environments()]\n envs = envs.filter(id__in=group_env_ids)\n env_resource_handler_ids = {env.resource_handler_id for env in envs}\n resource_handlers = resource_handlers.filter(id__in=env_resource_handler_ids)\n\n return [(resource_handler.id, \"{resource_handler}\".format(resource_handler=resource_handler))\n for resource_handler in resource_handlers]","def modify_resource_group_with_options(\n self,\n request: dds_20151201_models.ModifyResourceGroupRequest,\n runtime: util_models.RuntimeOptions,\n ) -> dds_20151201_models.ModifyResourceGroupResponse:\n UtilClient.validate_model(request)\n query = {}\n if not UtilClient.is_unset(request.dbinstance_id):\n query['DBInstanceId'] = request.dbinstance_id\n if not UtilClient.is_unset(request.owner_account):\n query['OwnerAccount'] = request.owner_account\n if not UtilClient.is_unset(request.owner_id):\n query['OwnerId'] = request.owner_id\n if not UtilClient.is_unset(request.region_id):\n query['RegionId'] = request.region_id\n if not UtilClient.is_unset(request.resource_group_id):\n query['ResourceGroupId'] = request.resource_group_id\n if not UtilClient.is_unset(request.resource_owner_account):\n query['ResourceOwnerAccount'] = request.resource_owner_account\n if not UtilClient.is_unset(request.resource_owner_id):\n query['ResourceOwnerId'] = request.resource_owner_id\n if not UtilClient.is_unset(request.security_token):\n query['SecurityToken'] = request.security_token\n req = open_api_models.OpenApiRequest(\n query=OpenApiUtilClient.query(query)\n )\n params = open_api_models.Params(\n action='ModifyResourceGroup',\n version='2015-12-01',\n protocol='HTTPS',\n pathname='/',\n method='POST',\n auth_type='AK',\n style='RPC',\n req_body_type='formData',\n body_type='json'\n )\n return TeaCore.from_map(\n dds_20151201_models.ModifyResourceGroupResponse(),\n self.call_api(params, req, runtime)\n )","def gen_inventory(self):\n if isinstance(self.resource, list):\n self.my_add_group(self.resource, 'default_group')\n elif isinstance(self.resource, dict):\n for groupname, hosts_and_vars in self.resource.iteritems():\n self.my_add_group(hosts_and_vars.get(\"hosts\"), groupname, hosts_and_vars.get(\"vars\"))","def resource_group(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"resource_group\")","def resource_group(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"resource_group\")","def resource_group_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"resource_group_name\")","def formfield(self, **kwargs):\n if self.plugin_class:\n self._choices = self.plugin_class.get_all_choices(field=self)\n return super(TemplateNameField, self).formfield(**kwargs)","def optgroups(self, name, value, attrs=None):\n options = []\n\n for index, (name, product_data) in enumerate(self.product_fields.items()):\n quantity = product_data['quantity']\n name = product_data['name']\n price = product_data['price']\n if index:\n label = 'product_{}'.format(str(index))\n else:\n label = 'product'\n\n options.append({\n 'value': quantity,\n 'price': price,\n 'name': 'products',\n 'label': name,\n 'type': self.input_type,\n 'template_name': self.option_template_name,\n 'wrap_label': True,\n 'index': index\n })\n\n return options","def resource_group_name(self) -> str:\n return pulumi.get(self, \"resource_group_name\")","def pre_security_group_create(self, resource_dict):\n pass","def __init__(__self__, *,\n resource_group: pulumi.Input[str],\n server: Optional[pulumi.Input[str]] = None):\n pulumi.set(__self__, \"resource_group\", resource_group)\n if server is not None:\n pulumi.set(__self__, \"server\", server)","def list_static_site_build_app_settings(\n self,\n resource_group_name, # type: str\n name, # type: str\n environment_name, # type: str\n **kwargs # type: Any\n ):\n # type: (...) -> \"_models.StringDictionary\"\n cls = kwargs.pop('cls', None) # type: ClsType[\"_models.StringDictionary\"]\n error_map = {\n 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError\n }\n error_map.update(kwargs.pop('error_map', {}))\n api_version = \"2021-01-01\"\n accept = \"application/json\"\n\n # Construct URL\n url = self.list_static_site_build_app_settings.metadata['url'] # type: ignore\n path_format_arguments = {\n 'resourceGroupName': self._serialize.url(\"resource_group_name\", resource_group_name, 'str', max_length=90, min_length=1, pattern=r'^[-\\w\\._\\(\\)]+[^\\.]$'),\n 'name': self._serialize.url(\"name\", name, 'str'),\n 'environmentName': self._serialize.url(\"environment_name\", environment_name, 'str'),\n 'subscriptionId': self._serialize.url(\"self._config.subscription_id\", self._config.subscription_id, 'str'),\n }\n url = self._client.format_url(url, **path_format_arguments)\n\n # Construct parameters\n query_parameters = {} # type: Dict[str, Any]\n query_parameters['api-version'] = self._serialize.query(\"api_version\", api_version, 'str')\n\n # Construct headers\n header_parameters = {} # type: Dict[str, Any]\n header_parameters['Accept'] = self._serialize.header(\"accept\", accept, 'str')\n\n request = self._client.post(url, query_parameters, header_parameters)\n pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)\n response = pipeline_response.http_response\n\n if response.status_code not in [200]:\n map_error(status_code=response.status_code, response=response, error_map=error_map)\n error = self._deserialize.failsafe_deserialize(_models.DefaultErrorResponse, response)\n raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)\n\n deserialized = self._deserialize('StringDictionary', pipeline_response)\n\n if cls:\n return cls(pipeline_response, deserialized, {})\n\n return deserialized","def test_create_resource_group(self):\n pass","def setup(self):\r\n self.text_input_values = {}\r\n if self.tag == 'radiotextgroup':\r\n self.html_input_type = \"radio\"\r\n elif self.tag == 'checkboxtextgroup':\r\n self.html_input_type = \"checkbox\"\r\n else:\r\n raise Exception(\"ChoiceGroup: unexpected tag {0}\".format(self.tag))\r\n\r\n if self.value == '':\r\n # Make `value` an empty dictionary, if it currently has an empty\r\n # value. This is necessary because the template expects a\r\n # dictionary.\r\n self.value = {}\r\n self.choices = self.extract_choices(self.xml)","def node_resource_group(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"node_resource_group\")","def infra_resource_group(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"infra_resource_group\")","def post_security_group_create(self, resource_dict):\n pass","def create_tags(self, resource_name: str, **kwargs) -> dict:\n init_tags = self._global_tags\n init_tags.update(kwargs)\n tags = {}\n for key, value in init_tags.items():\n if key == \"business_unit\":\n self._check_business_unit(value, self._allowed_business_units)\n if key in [\"is_production\"]:\n raise KeyError(f\"{key} is not an allowed argument\")\n tags[key.replace(\"_\", \"-\").lower()] = value\n tags[\"is-production\"] = tags[\"environment-name\"] in [\"alpha\", \"prod\"]\n tags[\"Name\"] = resource_name\n return tags","def resource_group_id(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"resource_group_id\")","def resource_group_id(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"resource_group_id\")","def resource_group_id(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"resource_group_id\")","def resource_group_id(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"resource_group_id\")","def resource_group_id(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"resource_group_id\")","def resource_group_id(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"resource_group_id\")","def resource_group_id(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"resource_group_id\")","def resource_group_id(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"resource_group_id\")","def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n group_choices = Group.objects.all().values_list(\"pk\", \"name\").order_by(\"name\")\n selected_group_ids = []\n if \"instance\" in kwargs:\n preset = kwargs[\"instance\"]\n selected_group_ids = [\n group.pk for group in preset.groups_with_permission(Permission.VIEW)\n ]\n self.fields[\"groups\"].choices = group_choices\n self.fields[\"groups\"].initial = selected_group_ids","def register(self):\n group = cfg.OptGroup(self.group_name, title='Azure Options')\n self._config.register_group(group)\n self._config.register_opts(self._options, group=group)","def __init__(__self__,\n resource_name: str,\n args: GroupArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def setOptManagerResources(o): # pylint: disable=global-statement\n # pylint: disable=global-statement\n global OPT_MANAGER_RESOURCES_PGAAS\n OPT_MANAGER_RESOURCES_PGAAS = \"{}/pgaas\".format(o)","async def modify_resource_group_with_options_async(\n self,\n request: dds_20151201_models.ModifyResourceGroupRequest,\n runtime: util_models.RuntimeOptions,\n ) -> dds_20151201_models.ModifyResourceGroupResponse:\n UtilClient.validate_model(request)\n query = {}\n if not UtilClient.is_unset(request.dbinstance_id):\n query['DBInstanceId'] = request.dbinstance_id\n if not UtilClient.is_unset(request.owner_account):\n query['OwnerAccount'] = request.owner_account\n if not UtilClient.is_unset(request.owner_id):\n query['OwnerId'] = request.owner_id\n if not UtilClient.is_unset(request.region_id):\n query['RegionId'] = request.region_id\n if not UtilClient.is_unset(request.resource_group_id):\n query['ResourceGroupId'] = request.resource_group_id\n if not UtilClient.is_unset(request.resource_owner_account):\n query['ResourceOwnerAccount'] = request.resource_owner_account\n if not UtilClient.is_unset(request.resource_owner_id):\n query['ResourceOwnerId'] = request.resource_owner_id\n if not UtilClient.is_unset(request.security_token):\n query['SecurityToken'] = request.security_token\n req = open_api_models.OpenApiRequest(\n query=OpenApiUtilClient.query(query)\n )\n params = open_api_models.Params(\n action='ModifyResourceGroup',\n version='2015-12-01',\n protocol='HTTPS',\n pathname='/',\n method='POST',\n auth_type='AK',\n style='RPC',\n req_body_type='formData',\n body_type='json'\n )\n return TeaCore.from_map(\n dds_20151201_models.ModifyResourceGroupResponse(),\n await self.call_api_async(params, req, runtime)\n )","def register_opts(self, conf):\n config.register_opt_group(conf, project_config.service_available_group,\n project_config.ServiceAvailableGroup)\n\n config.register_opt_group(conf, project_config.placement_group,\n project_config.PlacementGroup)\n\n config.register_opt_group(conf, project_config.valet_group,\n project_config.opt_valet)","def read_input_group(self, **kwargs: str) -> _resource.ResourceGroup:\n\n root = secret_alnum_string(5)\n new_resources = {name: self._new_input_resource_file(file, root) for name, file in kwargs.items()}\n rg = _resource.ResourceGroup(None, root, **new_resources)\n self._resource_map.update({rg._uid: rg})\n return rg","def GenerateConfig(context):\n\n resources = [{\n 'name': 'my-build',\n 'action': 'gcp-types/cloudbuild-v1:cloudbuild.projects.builds.create',\n 'metadata': {\n 'runtimePolicy': ['UPDATE_ALWAYS']\n },\n 'properties': {\n 'steps': [\n {\n 'name': 'gcr.io/cloud-builders/gcloud',\n 'args': ['deployment-manager',\n context.properties['resourceToList'],\n 'list']\n }\n ],\n 'timeout': '120s'\n }\n }]\n return { 'resources': resources }","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n auto_healing_policies: Optional[pulumi.Input[pulumi.InputType['RegionInstanceGroupManagerAutoHealingPoliciesArgs']]] = None,\n base_instance_name: Optional[pulumi.Input[str]] = None,\n description: Optional[pulumi.Input[str]] = None,\n distribution_policy_zones: Optional[pulumi.Input[List[pulumi.Input[str]]]] = None,\n name: Optional[pulumi.Input[str]] = None,\n named_ports: Optional[pulumi.Input[List[pulumi.Input[pulumi.InputType['RegionInstanceGroupManagerNamedPortArgs']]]]] = None,\n project: Optional[pulumi.Input[str]] = None,\n region: Optional[pulumi.Input[str]] = None,\n stateful_disks: Optional[pulumi.Input[List[pulumi.Input[pulumi.InputType['RegionInstanceGroupManagerStatefulDiskArgs']]]]] = None,\n target_pools: Optional[pulumi.Input[List[pulumi.Input[str]]]] = None,\n target_size: Optional[pulumi.Input[float]] = None,\n update_policy: Optional[pulumi.Input[pulumi.InputType['RegionInstanceGroupManagerUpdatePolicyArgs']]] = None,\n versions: Optional[pulumi.Input[List[pulumi.Input[pulumi.InputType['RegionInstanceGroupManagerVersionArgs']]]]] = None,\n wait_for_instances: Optional[pulumi.Input[bool]] = None,\n __props__=None,\n __name__=None,\n __opts__=None):\n if __name__ is not None:\n warnings.warn(\"explicit use of __name__ is deprecated\", DeprecationWarning)\n resource_name = __name__\n if __opts__ is not None:\n warnings.warn(\"explicit use of __opts__ is deprecated, use 'opts' instead\", DeprecationWarning)\n opts = __opts__\n if opts is None:\n opts = pulumi.ResourceOptions()\n if not isinstance(opts, pulumi.ResourceOptions):\n raise TypeError('Expected resource options to be a ResourceOptions instance')\n if opts.version is None:\n opts.version = _utilities.get_version()\n if opts.id is None:\n if __props__ is not None:\n raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')\n __props__ = dict()\n\n __props__['auto_healing_policies'] = auto_healing_policies\n if base_instance_name is None:\n raise TypeError(\"Missing required property 'base_instance_name'\")\n __props__['base_instance_name'] = base_instance_name\n __props__['description'] = description\n __props__['distribution_policy_zones'] = distribution_policy_zones\n __props__['name'] = name\n __props__['named_ports'] = named_ports\n __props__['project'] = project\n if region is None:\n raise TypeError(\"Missing required property 'region'\")\n __props__['region'] = region\n __props__['stateful_disks'] = stateful_disks\n __props__['target_pools'] = target_pools\n __props__['target_size'] = target_size\n __props__['update_policy'] = update_policy\n if versions is None:\n raise TypeError(\"Missing required property 'versions'\")\n __props__['versions'] = versions\n __props__['wait_for_instances'] = wait_for_instances\n __props__['fingerprint'] = None\n __props__['instance_group'] = None\n __props__['self_link'] = None\n super(RegionInstanceGroupManager, __self__).__init__(\n 'gcp:compute/regionInstanceGroupManager:RegionInstanceGroupManager',\n resource_name,\n __props__,\n opts)","def list_static_site_build_function_app_settings(\n self,\n resource_group_name, # type: str\n name, # type: str\n environment_name, # type: str\n **kwargs # type: Any\n ):\n # type: (...) -> \"_models.StringDictionary\"\n cls = kwargs.pop('cls', None) # type: ClsType[\"_models.StringDictionary\"]\n error_map = {\n 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError\n }\n error_map.update(kwargs.pop('error_map', {}))\n api_version = \"2021-01-01\"\n accept = \"application/json\"\n\n # Construct URL\n url = self.list_static_site_build_function_app_settings.metadata['url'] # type: ignore\n path_format_arguments = {\n 'resourceGroupName': self._serialize.url(\"resource_group_name\", resource_group_name, 'str', max_length=90, min_length=1, pattern=r'^[-\\w\\._\\(\\)]+[^\\.]$'),\n 'name': self._serialize.url(\"name\", name, 'str'),\n 'environmentName': self._serialize.url(\"environment_name\", environment_name, 'str'),\n 'subscriptionId': self._serialize.url(\"self._config.subscription_id\", self._config.subscription_id, 'str'),\n }\n url = self._client.format_url(url, **path_format_arguments)\n\n # Construct parameters\n query_parameters = {} # type: Dict[str, Any]\n query_parameters['api-version'] = self._serialize.query(\"api_version\", api_version, 'str')\n\n # Construct headers\n header_parameters = {} # type: Dict[str, Any]\n header_parameters['Accept'] = self._serialize.header(\"accept\", accept, 'str')\n\n request = self._client.post(url, query_parameters, header_parameters)\n pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)\n response = pipeline_response.http_response\n\n if response.status_code not in [200]:\n map_error(status_code=response.status_code, response=response, error_map=error_map)\n error = self._deserialize.failsafe_deserialize(_models.DefaultErrorResponse, response)\n raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)\n\n deserialized = self._deserialize('StringDictionary', pipeline_response)\n\n if cls:\n return cls(pipeline_response, deserialized, {})\n\n return deserialized","def get_additional_options(cls):\n return {\n \"worksheet_type\": EnumResourceParameter(\n name=\"worksheet_type\",\n description=_(\"Worksheet Type\"),\n values=WORKSHEET_OPTIONS,\n default=\"student-basic\"\n )\n }","def get_node_resource_group(self) -> Union[str, None]:\n # read the original value passed by the command\n node_resource_group = self.raw_param.get(\"node_resource_group\")\n # try to read the property value corresponding to the parameter from the `mc` object\n if self.mc and self.mc.node_resource_group is not None:\n node_resource_group = self.mc.node_resource_group\n\n # this parameter does not need dynamic completion\n # this parameter does not need validation\n return node_resource_group","def settings_group_options():\n return [('', _('No group')), *[(str(a.id), str(a)) for a in Group.objects.all()]]","def __init__(self, *args, **kwargs):\n super(UserForm, self).__init__(*args, **kwargs)\n\n self.fields[\"groups\"].label = \"roles\"","def resource_group_id(self) -> Optional[str]:\n return pulumi.get(self, \"resource_group_id\")","def test_get_resource_group_list(self):\n pass","def resource_group_name(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"resource_group_name\")","def resource_group_name(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"resource_group_name\")"],"string":"[\n \"def generate_options_for_cloudbolt_environment(group=None, **kwargs):\\n envs = Environment.objects.filter(\\n resource_handler__resource_technology__name='Google Cloud Platform') \\\\\\n .select_related('resource_handler')\\n if group:\\n group_env_ids = [env.id for env in group.get_available_environments()]\\n envs = envs.filter(id__in=group_env_ids)\\n return [\\n (env.id, u'{env} ({project})'.format(\\n env=env, project=env.gcp_project))\\n for env in envs\\n ]\",\n \"def resource_group(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"resource_group\\\")\",\n \"def resource_group(self) -> str:\\n return pulumi.get(self, \\\"resource_group\\\")\",\n \"def resource_group_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def get_resource_group_name(self) -> str:\\n # read the original value passed by the command\\n resource_group_name = self.raw_param.get(\\\"resource_group_name\\\")\\n\\n # this parameter does not need dynamic completion\\n # this parameter does not need validation\\n return resource_group_name\",\n \"def generate_options_for_gcp_resource_handler(group=None, **kwargs):\\n if not GCPHandler.objects.exists():\\n raise CloudBoltException(\\n \\\"Ordering this Blueprint requires having a \\\"\\n \\\"configured Google Cloud Platform resource handler.\\\"\\n \\\"and at least one Project added to the resource handler\\\"\\n )\\n\\n resource_handlers = GCPHandler.objects.all()\\n envs = Environment.objects.filter(\\n resource_handler__resource_technology__name=\\\"Google Cloud Platform\\\"\\n ).select_related(\\\"resource_handler\\\")\\n\\n if group:\\n group_env_ids = [env.id for env in group.get_available_environments()]\\n envs = envs.filter(id__in=group_env_ids)\\n env_resource_handler_ids = {env.resource_handler_id for env in envs}\\n resource_handlers = resource_handlers.filter(id__in=env_resource_handler_ids)\\n\\n return [(resource_handler.id, \\\"{resource_handler}\\\".format(resource_handler=resource_handler))\\n for resource_handler in resource_handlers]\",\n \"def modify_resource_group_with_options(\\n self,\\n request: dds_20151201_models.ModifyResourceGroupRequest,\\n runtime: util_models.RuntimeOptions,\\n ) -> dds_20151201_models.ModifyResourceGroupResponse:\\n UtilClient.validate_model(request)\\n query = {}\\n if not UtilClient.is_unset(request.dbinstance_id):\\n query['DBInstanceId'] = request.dbinstance_id\\n if not UtilClient.is_unset(request.owner_account):\\n query['OwnerAccount'] = request.owner_account\\n if not UtilClient.is_unset(request.owner_id):\\n query['OwnerId'] = request.owner_id\\n if not UtilClient.is_unset(request.region_id):\\n query['RegionId'] = request.region_id\\n if not UtilClient.is_unset(request.resource_group_id):\\n query['ResourceGroupId'] = request.resource_group_id\\n if not UtilClient.is_unset(request.resource_owner_account):\\n query['ResourceOwnerAccount'] = request.resource_owner_account\\n if not UtilClient.is_unset(request.resource_owner_id):\\n query['ResourceOwnerId'] = request.resource_owner_id\\n if not UtilClient.is_unset(request.security_token):\\n query['SecurityToken'] = request.security_token\\n req = open_api_models.OpenApiRequest(\\n query=OpenApiUtilClient.query(query)\\n )\\n params = open_api_models.Params(\\n action='ModifyResourceGroup',\\n version='2015-12-01',\\n protocol='HTTPS',\\n pathname='/',\\n method='POST',\\n auth_type='AK',\\n style='RPC',\\n req_body_type='formData',\\n body_type='json'\\n )\\n return TeaCore.from_map(\\n dds_20151201_models.ModifyResourceGroupResponse(),\\n self.call_api(params, req, runtime)\\n )\",\n \"def gen_inventory(self):\\n if isinstance(self.resource, list):\\n self.my_add_group(self.resource, 'default_group')\\n elif isinstance(self.resource, dict):\\n for groupname, hosts_and_vars in self.resource.iteritems():\\n self.my_add_group(hosts_and_vars.get(\\\"hosts\\\"), groupname, hosts_and_vars.get(\\\"vars\\\"))\",\n \"def resource_group(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"resource_group\\\")\",\n \"def resource_group(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"resource_group\\\")\",\n \"def resource_group_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def formfield(self, **kwargs):\\n if self.plugin_class:\\n self._choices = self.plugin_class.get_all_choices(field=self)\\n return super(TemplateNameField, self).formfield(**kwargs)\",\n \"def optgroups(self, name, value, attrs=None):\\n options = []\\n\\n for index, (name, product_data) in enumerate(self.product_fields.items()):\\n quantity = product_data['quantity']\\n name = product_data['name']\\n price = product_data['price']\\n if index:\\n label = 'product_{}'.format(str(index))\\n else:\\n label = 'product'\\n\\n options.append({\\n 'value': quantity,\\n 'price': price,\\n 'name': 'products',\\n 'label': name,\\n 'type': self.input_type,\\n 'template_name': self.option_template_name,\\n 'wrap_label': True,\\n 'index': index\\n })\\n\\n return options\",\n \"def resource_group_name(self) -> str:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def pre_security_group_create(self, resource_dict):\\n pass\",\n \"def __init__(__self__, *,\\n resource_group: pulumi.Input[str],\\n server: Optional[pulumi.Input[str]] = None):\\n pulumi.set(__self__, \\\"resource_group\\\", resource_group)\\n if server is not None:\\n pulumi.set(__self__, \\\"server\\\", server)\",\n \"def list_static_site_build_app_settings(\\n self,\\n resource_group_name, # type: str\\n name, # type: str\\n environment_name, # type: str\\n **kwargs # type: Any\\n ):\\n # type: (...) -> \\\"_models.StringDictionary\\\"\\n cls = kwargs.pop('cls', None) # type: ClsType[\\\"_models.StringDictionary\\\"]\\n error_map = {\\n 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError\\n }\\n error_map.update(kwargs.pop('error_map', {}))\\n api_version = \\\"2021-01-01\\\"\\n accept = \\\"application/json\\\"\\n\\n # Construct URL\\n url = self.list_static_site_build_app_settings.metadata['url'] # type: ignore\\n path_format_arguments = {\\n 'resourceGroupName': self._serialize.url(\\\"resource_group_name\\\", resource_group_name, 'str', max_length=90, min_length=1, pattern=r'^[-\\\\w\\\\._\\\\(\\\\)]+[^\\\\.]$'),\\n 'name': self._serialize.url(\\\"name\\\", name, 'str'),\\n 'environmentName': self._serialize.url(\\\"environment_name\\\", environment_name, 'str'),\\n 'subscriptionId': self._serialize.url(\\\"self._config.subscription_id\\\", self._config.subscription_id, 'str'),\\n }\\n url = self._client.format_url(url, **path_format_arguments)\\n\\n # Construct parameters\\n query_parameters = {} # type: Dict[str, Any]\\n query_parameters['api-version'] = self._serialize.query(\\\"api_version\\\", api_version, 'str')\\n\\n # Construct headers\\n header_parameters = {} # type: Dict[str, Any]\\n header_parameters['Accept'] = self._serialize.header(\\\"accept\\\", accept, 'str')\\n\\n request = self._client.post(url, query_parameters, header_parameters)\\n pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)\\n response = pipeline_response.http_response\\n\\n if response.status_code not in [200]:\\n map_error(status_code=response.status_code, response=response, error_map=error_map)\\n error = self._deserialize.failsafe_deserialize(_models.DefaultErrorResponse, response)\\n raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)\\n\\n deserialized = self._deserialize('StringDictionary', pipeline_response)\\n\\n if cls:\\n return cls(pipeline_response, deserialized, {})\\n\\n return deserialized\",\n \"def test_create_resource_group(self):\\n pass\",\n \"def setup(self):\\r\\n self.text_input_values = {}\\r\\n if self.tag == 'radiotextgroup':\\r\\n self.html_input_type = \\\"radio\\\"\\r\\n elif self.tag == 'checkboxtextgroup':\\r\\n self.html_input_type = \\\"checkbox\\\"\\r\\n else:\\r\\n raise Exception(\\\"ChoiceGroup: unexpected tag {0}\\\".format(self.tag))\\r\\n\\r\\n if self.value == '':\\r\\n # Make `value` an empty dictionary, if it currently has an empty\\r\\n # value. This is necessary because the template expects a\\r\\n # dictionary.\\r\\n self.value = {}\\r\\n self.choices = self.extract_choices(self.xml)\",\n \"def node_resource_group(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"node_resource_group\\\")\",\n \"def infra_resource_group(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"infra_resource_group\\\")\",\n \"def post_security_group_create(self, resource_dict):\\n pass\",\n \"def create_tags(self, resource_name: str, **kwargs) -> dict:\\n init_tags = self._global_tags\\n init_tags.update(kwargs)\\n tags = {}\\n for key, value in init_tags.items():\\n if key == \\\"business_unit\\\":\\n self._check_business_unit(value, self._allowed_business_units)\\n if key in [\\\"is_production\\\"]:\\n raise KeyError(f\\\"{key} is not an allowed argument\\\")\\n tags[key.replace(\\\"_\\\", \\\"-\\\").lower()] = value\\n tags[\\\"is-production\\\"] = tags[\\\"environment-name\\\"] in [\\\"alpha\\\", \\\"prod\\\"]\\n tags[\\\"Name\\\"] = resource_name\\n return tags\",\n \"def resource_group_id(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"resource_group_id\\\")\",\n \"def resource_group_id(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"resource_group_id\\\")\",\n \"def resource_group_id(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"resource_group_id\\\")\",\n \"def resource_group_id(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"resource_group_id\\\")\",\n \"def resource_group_id(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"resource_group_id\\\")\",\n \"def resource_group_id(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"resource_group_id\\\")\",\n \"def resource_group_id(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"resource_group_id\\\")\",\n \"def resource_group_id(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"resource_group_id\\\")\",\n \"def __init__(self, *args, **kwargs):\\n super().__init__(*args, **kwargs)\\n group_choices = Group.objects.all().values_list(\\\"pk\\\", \\\"name\\\").order_by(\\\"name\\\")\\n selected_group_ids = []\\n if \\\"instance\\\" in kwargs:\\n preset = kwargs[\\\"instance\\\"]\\n selected_group_ids = [\\n group.pk for group in preset.groups_with_permission(Permission.VIEW)\\n ]\\n self.fields[\\\"groups\\\"].choices = group_choices\\n self.fields[\\\"groups\\\"].initial = selected_group_ids\",\n \"def register(self):\\n group = cfg.OptGroup(self.group_name, title='Azure Options')\\n self._config.register_group(group)\\n self._config.register_opts(self._options, group=group)\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: GroupArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def setOptManagerResources(o): # pylint: disable=global-statement\\n # pylint: disable=global-statement\\n global OPT_MANAGER_RESOURCES_PGAAS\\n OPT_MANAGER_RESOURCES_PGAAS = \\\"{}/pgaas\\\".format(o)\",\n \"async def modify_resource_group_with_options_async(\\n self,\\n request: dds_20151201_models.ModifyResourceGroupRequest,\\n runtime: util_models.RuntimeOptions,\\n ) -> dds_20151201_models.ModifyResourceGroupResponse:\\n UtilClient.validate_model(request)\\n query = {}\\n if not UtilClient.is_unset(request.dbinstance_id):\\n query['DBInstanceId'] = request.dbinstance_id\\n if not UtilClient.is_unset(request.owner_account):\\n query['OwnerAccount'] = request.owner_account\\n if not UtilClient.is_unset(request.owner_id):\\n query['OwnerId'] = request.owner_id\\n if not UtilClient.is_unset(request.region_id):\\n query['RegionId'] = request.region_id\\n if not UtilClient.is_unset(request.resource_group_id):\\n query['ResourceGroupId'] = request.resource_group_id\\n if not UtilClient.is_unset(request.resource_owner_account):\\n query['ResourceOwnerAccount'] = request.resource_owner_account\\n if not UtilClient.is_unset(request.resource_owner_id):\\n query['ResourceOwnerId'] = request.resource_owner_id\\n if not UtilClient.is_unset(request.security_token):\\n query['SecurityToken'] = request.security_token\\n req = open_api_models.OpenApiRequest(\\n query=OpenApiUtilClient.query(query)\\n )\\n params = open_api_models.Params(\\n action='ModifyResourceGroup',\\n version='2015-12-01',\\n protocol='HTTPS',\\n pathname='/',\\n method='POST',\\n auth_type='AK',\\n style='RPC',\\n req_body_type='formData',\\n body_type='json'\\n )\\n return TeaCore.from_map(\\n dds_20151201_models.ModifyResourceGroupResponse(),\\n await self.call_api_async(params, req, runtime)\\n )\",\n \"def register_opts(self, conf):\\n config.register_opt_group(conf, project_config.service_available_group,\\n project_config.ServiceAvailableGroup)\\n\\n config.register_opt_group(conf, project_config.placement_group,\\n project_config.PlacementGroup)\\n\\n config.register_opt_group(conf, project_config.valet_group,\\n project_config.opt_valet)\",\n \"def read_input_group(self, **kwargs: str) -> _resource.ResourceGroup:\\n\\n root = secret_alnum_string(5)\\n new_resources = {name: self._new_input_resource_file(file, root) for name, file in kwargs.items()}\\n rg = _resource.ResourceGroup(None, root, **new_resources)\\n self._resource_map.update({rg._uid: rg})\\n return rg\",\n \"def GenerateConfig(context):\\n\\n resources = [{\\n 'name': 'my-build',\\n 'action': 'gcp-types/cloudbuild-v1:cloudbuild.projects.builds.create',\\n 'metadata': {\\n 'runtimePolicy': ['UPDATE_ALWAYS']\\n },\\n 'properties': {\\n 'steps': [\\n {\\n 'name': 'gcr.io/cloud-builders/gcloud',\\n 'args': ['deployment-manager',\\n context.properties['resourceToList'],\\n 'list']\\n }\\n ],\\n 'timeout': '120s'\\n }\\n }]\\n return { 'resources': resources }\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n auto_healing_policies: Optional[pulumi.Input[pulumi.InputType['RegionInstanceGroupManagerAutoHealingPoliciesArgs']]] = None,\\n base_instance_name: Optional[pulumi.Input[str]] = None,\\n description: Optional[pulumi.Input[str]] = None,\\n distribution_policy_zones: Optional[pulumi.Input[List[pulumi.Input[str]]]] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n named_ports: Optional[pulumi.Input[List[pulumi.Input[pulumi.InputType['RegionInstanceGroupManagerNamedPortArgs']]]]] = None,\\n project: Optional[pulumi.Input[str]] = None,\\n region: Optional[pulumi.Input[str]] = None,\\n stateful_disks: Optional[pulumi.Input[List[pulumi.Input[pulumi.InputType['RegionInstanceGroupManagerStatefulDiskArgs']]]]] = None,\\n target_pools: Optional[pulumi.Input[List[pulumi.Input[str]]]] = None,\\n target_size: Optional[pulumi.Input[float]] = None,\\n update_policy: Optional[pulumi.Input[pulumi.InputType['RegionInstanceGroupManagerUpdatePolicyArgs']]] = None,\\n versions: Optional[pulumi.Input[List[pulumi.Input[pulumi.InputType['RegionInstanceGroupManagerVersionArgs']]]]] = None,\\n wait_for_instances: Optional[pulumi.Input[bool]] = None,\\n __props__=None,\\n __name__=None,\\n __opts__=None):\\n if __name__ is not None:\\n warnings.warn(\\\"explicit use of __name__ is deprecated\\\", DeprecationWarning)\\n resource_name = __name__\\n if __opts__ is not None:\\n warnings.warn(\\\"explicit use of __opts__ is deprecated, use 'opts' instead\\\", DeprecationWarning)\\n opts = __opts__\\n if opts is None:\\n opts = pulumi.ResourceOptions()\\n if not isinstance(opts, pulumi.ResourceOptions):\\n raise TypeError('Expected resource options to be a ResourceOptions instance')\\n if opts.version is None:\\n opts.version = _utilities.get_version()\\n if opts.id is None:\\n if __props__ is not None:\\n raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')\\n __props__ = dict()\\n\\n __props__['auto_healing_policies'] = auto_healing_policies\\n if base_instance_name is None:\\n raise TypeError(\\\"Missing required property 'base_instance_name'\\\")\\n __props__['base_instance_name'] = base_instance_name\\n __props__['description'] = description\\n __props__['distribution_policy_zones'] = distribution_policy_zones\\n __props__['name'] = name\\n __props__['named_ports'] = named_ports\\n __props__['project'] = project\\n if region is None:\\n raise TypeError(\\\"Missing required property 'region'\\\")\\n __props__['region'] = region\\n __props__['stateful_disks'] = stateful_disks\\n __props__['target_pools'] = target_pools\\n __props__['target_size'] = target_size\\n __props__['update_policy'] = update_policy\\n if versions is None:\\n raise TypeError(\\\"Missing required property 'versions'\\\")\\n __props__['versions'] = versions\\n __props__['wait_for_instances'] = wait_for_instances\\n __props__['fingerprint'] = None\\n __props__['instance_group'] = None\\n __props__['self_link'] = None\\n super(RegionInstanceGroupManager, __self__).__init__(\\n 'gcp:compute/regionInstanceGroupManager:RegionInstanceGroupManager',\\n resource_name,\\n __props__,\\n opts)\",\n \"def list_static_site_build_function_app_settings(\\n self,\\n resource_group_name, # type: str\\n name, # type: str\\n environment_name, # type: str\\n **kwargs # type: Any\\n ):\\n # type: (...) -> \\\"_models.StringDictionary\\\"\\n cls = kwargs.pop('cls', None) # type: ClsType[\\\"_models.StringDictionary\\\"]\\n error_map = {\\n 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError\\n }\\n error_map.update(kwargs.pop('error_map', {}))\\n api_version = \\\"2021-01-01\\\"\\n accept = \\\"application/json\\\"\\n\\n # Construct URL\\n url = self.list_static_site_build_function_app_settings.metadata['url'] # type: ignore\\n path_format_arguments = {\\n 'resourceGroupName': self._serialize.url(\\\"resource_group_name\\\", resource_group_name, 'str', max_length=90, min_length=1, pattern=r'^[-\\\\w\\\\._\\\\(\\\\)]+[^\\\\.]$'),\\n 'name': self._serialize.url(\\\"name\\\", name, 'str'),\\n 'environmentName': self._serialize.url(\\\"environment_name\\\", environment_name, 'str'),\\n 'subscriptionId': self._serialize.url(\\\"self._config.subscription_id\\\", self._config.subscription_id, 'str'),\\n }\\n url = self._client.format_url(url, **path_format_arguments)\\n\\n # Construct parameters\\n query_parameters = {} # type: Dict[str, Any]\\n query_parameters['api-version'] = self._serialize.query(\\\"api_version\\\", api_version, 'str')\\n\\n # Construct headers\\n header_parameters = {} # type: Dict[str, Any]\\n header_parameters['Accept'] = self._serialize.header(\\\"accept\\\", accept, 'str')\\n\\n request = self._client.post(url, query_parameters, header_parameters)\\n pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs)\\n response = pipeline_response.http_response\\n\\n if response.status_code not in [200]:\\n map_error(status_code=response.status_code, response=response, error_map=error_map)\\n error = self._deserialize.failsafe_deserialize(_models.DefaultErrorResponse, response)\\n raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)\\n\\n deserialized = self._deserialize('StringDictionary', pipeline_response)\\n\\n if cls:\\n return cls(pipeline_response, deserialized, {})\\n\\n return deserialized\",\n \"def get_additional_options(cls):\\n return {\\n \\\"worksheet_type\\\": EnumResourceParameter(\\n name=\\\"worksheet_type\\\",\\n description=_(\\\"Worksheet Type\\\"),\\n values=WORKSHEET_OPTIONS,\\n default=\\\"student-basic\\\"\\n )\\n }\",\n \"def get_node_resource_group(self) -> Union[str, None]:\\n # read the original value passed by the command\\n node_resource_group = self.raw_param.get(\\\"node_resource_group\\\")\\n # try to read the property value corresponding to the parameter from the `mc` object\\n if self.mc and self.mc.node_resource_group is not None:\\n node_resource_group = self.mc.node_resource_group\\n\\n # this parameter does not need dynamic completion\\n # this parameter does not need validation\\n return node_resource_group\",\n \"def settings_group_options():\\n return [('', _('No group')), *[(str(a.id), str(a)) for a in Group.objects.all()]]\",\n \"def __init__(self, *args, **kwargs):\\n super(UserForm, self).__init__(*args, **kwargs)\\n\\n self.fields[\\\"groups\\\"].label = \\\"roles\\\"\",\n \"def resource_group_id(self) -> Optional[str]:\\n return pulumi.get(self, \\\"resource_group_id\\\")\",\n \"def test_get_resource_group_list(self):\\n pass\",\n \"def resource_group_name(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\",\n \"def resource_group_name(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"resource_group_name\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6358487","0.5937777","0.55043066","0.54691523","0.54691523","0.54691523","0.54691523","0.54691523","0.54691523","0.54691523","0.54691523","0.54691523","0.54691523","0.54691523","0.54691523","0.54691523","0.54691523","0.54691523","0.54691523","0.54691523","0.54691523","0.54691523","0.54691523","0.54691523","0.54691523","0.54691523","0.54691523","0.54691523","0.54691523","0.54691523","0.54691523","0.54691523","0.54691523","0.54691523","0.54691523","0.54691523","0.54691523","0.54691523","0.54691523","0.54691523","0.54691523","0.5410062","0.5369061","0.5332479","0.5317821","0.53166884","0.53166884","0.52996236","0.52996236","0.52996236","0.52996236","0.52996236","0.52996236","0.52996236","0.52996236","0.52996236","0.52996236","0.52996236","0.52996236","0.52996236","0.52996236","0.52996236","0.5236991","0.5130091","0.5122761","0.5113318","0.5048693","0.5042145","0.50341487","0.50149375","0.5011061","0.49961466","0.4951374","0.4951162","0.49387255","0.49387255","0.49387255","0.49387255","0.49387255","0.49387255","0.49387255","0.49387255","0.48791036","0.48728904","0.48627618","0.48607996","0.4855658","0.48495233","0.48336372","0.48202616","0.48091415","0.48082525","0.48063555","0.48057005","0.4796697","0.479299","0.4777172","0.47746876","0.47711435","0.47711435"],"string":"[\n \"0.6358487\",\n \"0.5937777\",\n \"0.55043066\",\n \"0.54691523\",\n \"0.54691523\",\n \"0.54691523\",\n \"0.54691523\",\n \"0.54691523\",\n \"0.54691523\",\n \"0.54691523\",\n \"0.54691523\",\n \"0.54691523\",\n \"0.54691523\",\n \"0.54691523\",\n \"0.54691523\",\n \"0.54691523\",\n \"0.54691523\",\n \"0.54691523\",\n \"0.54691523\",\n \"0.54691523\",\n \"0.54691523\",\n \"0.54691523\",\n \"0.54691523\",\n \"0.54691523\",\n \"0.54691523\",\n \"0.54691523\",\n \"0.54691523\",\n \"0.54691523\",\n \"0.54691523\",\n \"0.54691523\",\n \"0.54691523\",\n \"0.54691523\",\n \"0.54691523\",\n \"0.54691523\",\n \"0.54691523\",\n \"0.54691523\",\n \"0.54691523\",\n \"0.54691523\",\n \"0.54691523\",\n \"0.54691523\",\n \"0.54691523\",\n \"0.5410062\",\n \"0.5369061\",\n \"0.5332479\",\n \"0.5317821\",\n \"0.53166884\",\n \"0.53166884\",\n \"0.52996236\",\n \"0.52996236\",\n \"0.52996236\",\n \"0.52996236\",\n \"0.52996236\",\n \"0.52996236\",\n \"0.52996236\",\n \"0.52996236\",\n \"0.52996236\",\n \"0.52996236\",\n \"0.52996236\",\n \"0.52996236\",\n \"0.52996236\",\n \"0.52996236\",\n \"0.52996236\",\n \"0.5236991\",\n \"0.5130091\",\n \"0.5122761\",\n \"0.5113318\",\n \"0.5048693\",\n \"0.5042145\",\n \"0.50341487\",\n \"0.50149375\",\n \"0.5011061\",\n \"0.49961466\",\n \"0.4951374\",\n \"0.4951162\",\n \"0.49387255\",\n \"0.49387255\",\n \"0.49387255\",\n \"0.49387255\",\n \"0.49387255\",\n \"0.49387255\",\n \"0.49387255\",\n \"0.49387255\",\n \"0.48791036\",\n \"0.48728904\",\n \"0.48627618\",\n \"0.48607996\",\n \"0.4855658\",\n \"0.48495233\",\n \"0.48336372\",\n \"0.48202616\",\n \"0.48091415\",\n \"0.48082525\",\n \"0.48063555\",\n \"0.48057005\",\n \"0.4796697\",\n \"0.479299\",\n \"0.4777172\",\n \"0.47746876\",\n \"0.47711435\",\n \"0.47711435\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6859024"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":16883,"cells":{"query":{"kind":"string","value":"Get the client using newer methods from the CloudBolt main repo if this CB is running a version greater than 9.2.1. These internal methods implicitly take care of much of the other features in CloudBolt such as proxy and ssl verification. Otherwise, manually instantiate clients without support for those other CloudBolt settings."},"document":{"kind":"string","value":"def _get_client(handler):\n if CB_VERSION_93_PLUS:\n from resourcehandlers.azure_arm.azure_wrapper import configure_arm_client\n\n wrapper = handler.get_api_wrapper()\n sql_client = configure_arm_client(wrapper, sql.SqlManagementClient)\n else:\n # TODO: Remove once versions <= 9.2.1 are no longer supported.\n credentials = ServicePrincipalCredentials(\n client_id=handler.client_id, secret=handler.secret, tenant=handler.tenant_id\n )\n sql_client = sql.SqlManagementClient(credentials, handler.serviceaccount)\n\n set_progress(\"Connection to Azure established\")\n\n return sql_client"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _get_client_impl(self):\n api_version = self._get_api_version(None)\n if api_version not in self._client_impls:\n self._create_client_impl(api_version)\n return self._client_impls[api_version]","def raw_client(self):\r\n warnings.warn(\"raw_client is deprecated. use self.client.get_client instead\",\r\n DeprecationWarning, stacklevel=2)\r\n return self.client.get_client(write=True)","def get_client():\n return Client(__address, authkey='strumamor')","def _get_client(self):\n if self._client is None:\n self._client = self.boto.client(service_name='elb', region_name=self.boto.cli_region)\n\n return self._client","def _base(self):\n from hubspot3.base import BaseClient\n\n return BaseClient(**self.auth, **self.options)","def get_client(version, **kwargs):\n endpoint = kwargs.get('os_endpoint') or kwargs.get('ceilometer_url')\n\n return Client(version, endpoint, **kwargs)","def get(cls, configuration: HttpClientConfiguration) -> HttpClient:\n client_type = configuration.client_type\n\n if client_type == HttpClientType.UAA:\n return cls._get_instance(configuration, ClientAuthType.TOKEN_UAA)\n\n elif client_type == HttpClientType.CONSOLE:\n return cls._get_instance(configuration, ClientAuthType.LOGIN_PAGE)\n\n elif client_type == HttpClientType.CONSOLE_NO_AUTH:\n return cls._get_instance(configuration, ClientAuthType.NO_AUTH)\n\n elif client_type == HttpClientType.APPLICATION:\n return cls._get_instance(configuration, ClientAuthType.TOKEN_CF)\n\n elif client_type == HttpClientType.CLOUD_FOUNDRY:\n return cls._get_instance(configuration, ClientAuthType.TOKEN_CF)\n\n elif client_type == HttpClientType.BROKER:\n return cls._get_instance(configuration, ClientAuthType.HTTP_BASIC)\n\n elif client_type == HttpClientType.WEBHDFS:\n return cls._get_instance(configuration, ClientAuthType.WEBHDFS)\n \n elif client_type == HttpClientType.SERVICE_TOOL:\n return cls._get_instance(configuration, ClientAuthType.NO_AUTH)\n\n elif client_type == HttpClientType.CLOUDERA:\n return cls._get_instance(configuration, ClientAuthType.HTTP_BASIC)\n\n else:\n raise HttpClientFactoryInvalidClientTypeException(client_type)","def _get_client(self):\n _client = KOPS(provider=self.provider, config=self.config)\n return _client","def get_client(self):\n return self.client","def client(self) -> 'BaseClient':\n return self","def getClient(self):\n authDict = Configuration().read_auth_data_from_config()\n client_key = authDict['client_key']\n client_secret = authDict['client_secret']\n token = authDict['token']\n token_secret = authDict['token_secret']\n\n authorize_OAuth_ob = authorizeOAuth.AuthorizeOAuth(client_key,\n client_secret,\n token,\n token_secret,\n Configuration().read_board_id_config())\n\n trello_client_wrapper = authorize_OAuth_ob.getClient()\n self.set_list(trello_client_wrapper)\n return trello_client_wrapper","def make_client(instance):\r\n neutron_client = utils.get_client_class(\r\n API_NAME,\r\n instance._api_version[API_NAME],\r\n API_VERSIONS,\r\n )\r\n instance.initialize()\r\n url = instance._url\r\n url = url.rstrip(\"/\")\r\n if '2.0' == instance._api_version[API_NAME]:\r\n client = neutron_client(username=instance._username,\r\n tenant_name=instance._tenant_name,\r\n password=instance._password,\r\n region_name=instance._region_name,\r\n auth_url=instance._auth_url,\r\n endpoint_url=url,\r\n token=instance._token,\r\n auth_strategy=instance._auth_strategy,\r\n insecure=instance._insecure,\r\n ca_cert=instance._ca_cert)\r\n return client\r\n else:\r\n raise exceptions.UnsupportedVersion(_(\"API version %s is not \"\r\n \"supported\") %\r\n instance._api_version[API_NAME])","def client():\n return Client(**common_data.AUTH_ARGS)","def _get_glance_client(self):\n session = self._get_keystone_session()\n return glanceclient.client.Client(2, session=session)","def _generate_client(self):\n mongoConf = self._config.get('Connectivity', 'MongoDB') # type: dict\n if mongoConf.get('username') and mongoConf.get('password'):\n return pymongo.MongoClient(\n \"mongodb://{0}:{1}@{2}:{3}/{4}\".format(\n mongoConf.get('username', ''),\n mongoConf.get('password', ''),\n mongoConf.get('host', 'localhost'),\n mongoConf.get('port', 27017),\n mongoConf.get('db', 'grease')\n ),\n w=1\n )\n else:\n return pymongo.MongoClient(\n host=mongoConf.get('host', 'localhost'),\n port=mongoConf.get('port', 27017),\n w=1\n )","def get_contribs_client():\n from mpcontribs.client import Client\n\n headers = get_consumer()\n\n if is_localhost():\n return Client(apikey=get_user_api_key())\n else:\n return Client(headers=headers)","def get_client():\n\n return MongoClientManager().client","def base_client(self):\n return self._client","def get_client():\n client_class = _import_by_path(settings.REDISIO_CLIENT_CLASS)\n return client_class(host=settings.REDISIO_HOST,\n port=settings.REDISIO_PORT,\n db=settings.REDISIO_DB)","def get_client(httpx_settings: Optional[dict] = None) -> \"PrefectClient\":\n ctx = prefect.context.get_settings_context()\n api = PREFECT_API_URL.value()\n\n if not api:\n # create an ephemeral API if none was provided\n from prefect.server.api.server import create_app\n\n api = create_app(ctx.settings, ephemeral=True)\n\n return PrefectClient(\n api,\n api_key=PREFECT_API_KEY.value(),\n httpx_settings=httpx_settings,\n )","def _get_client():\n\n return datastore.Client()","def _get_client():\n\n return datastore.Client()","def client(self):\n\n if self._client is None:\n self._client = self._get_client()\n return self._client","def _getMemcacheClient(self, refresh=False):\n if refresh or not hasattr(self, \"memcacheClient\"):\n\n if config.Memcached.Pools.Default.MemcacheSocket:\n client_addr = \"unix:{}\".format(config.Memcached.Pools.Default.MemcacheSocket)\n else:\n client_addr = \"{}:{}\".format(\n config.Memcached.Pools.Default.BindAddress,\n config.Memcached.Pools.Default.Port,\n )\n self.memcacheClient = ClientFactory.getClient([client_addr], debug=0, pickleProtocol=2)\n return self.memcacheClient","def get_api_ironic_client(get_session):\n def _get_api_ironic_client(version, is_api):\n if version == '1':\n if is_api:\n return api_clients.IronicApiClientV1(session=get_session())\n else:\n return client_v1.get_client(api_version=version,\n session=get_session())\n\n raise ValueError(\"Unexpected ironic version: {!r}\".format(version))\n\n return _get_api_ironic_client","def client():\n\n client = Client()\n return client","def get_client() -> RabbitmqClient:\n # Replace the parameters with proper values for host, port, login and password\n # Change the value of exchange if needed.\n #\n # For any parameter that is not given here, the client tries to use a value from an environment variable\n # and most of the parameters also have a default value that is used if neither the constructor parameter\n # nor the environmental variable exist.\n # See tools/clients.py for details about the environmental variables and the default values.\n return RabbitmqClient(\n host=\"\",\n port=0,\n login=\"\",\n password=\"\",\n exchange=\"procem.examples_testing\",\n ssl=True,\n ssl_version=\"PROTOCOL_TLS\",\n exchange_autodelete=True,\n exchange_durable=False\n )","def client(self):\r\n if self._client is None:\r\n self._client = self._client_cls(self._server, self._params, self)\r\n return self._client","def get_client() -> 'MongoCLient':\n client = pymongo.MongoClient()\n db = client['c3']\n c = db['json']\n return c","def _http_client(self):\n\n self.__enforce_connected()\n return self.collection._http_client","def configure_client(self):\n self.client = self.get_redis_client()\n return self.client","def client():\n return IceCubedSyncClient(\"api_key\", \"secret\")","def create_rbclient(self):\n return RBClient(url=self.TEST_SERVER_URL,\n transport_cls=URLMapTransport)","def affirm_client(self):\n if self.user_name and self.password and self.host:\n uri = f'mongodb://{self.user_name}:{self.password}@\\\n {self.host}/{self.db_name}'\n client = MongoClient(uri)\n elif self.host:\n client = MongoClient(self.host)\n else:\n client = MongoClient()\n return client[self.db_name]","def get_conn(self) -> WebClient:\n return self.client","def get_api_client():\n\n global _API_CLIENT_HANDLE\n\n if not _API_CLIENT_HANDLE:\n context = get_context()\n server_config = context.get_server_config()\n\n pc_ip = server_config.get(\"pc_ip\")\n pc_port = server_config.get(\"pc_port\")\n username = server_config.get(\"pc_username\")\n password = server_config.get(\"pc_password\")\n\n update_api_client(host=pc_ip, port=pc_port, auth=(username, password))\n\n return _API_CLIENT_HANDLE","def _client(self) -> httpx.Client:\n return httpx.Client(\n base_url=self._base_url,\n headers=self._authorization_headers,\n proxies=self._proxies,\n )","def api_client() -> APIClient:\n return APIClient()","def client(self):\n\t\t# pylint: disable=invalid-name\n\t\treturn self._client","def get_client(args):\n if args.auth_strategy == 'userpass':\n creds = {'username': args.username, 'password': args.password}\n else:\n creds = None\n\n try:\n client = Client(rucio_host=args.host, auth_host=args.auth_host,\n account=args.account,\n auth_type=args.auth_strategy, creds=creds,\n ca_cert=args.ca_certificate, timeout=args.timeout)\n except CannotAuthenticate, error:\n logger.error(error)\n if not args.auth_strategy:\n if 'RUCIO_AUTH_TYPE' in os.environ:\n auth_type = os.environ['RUCIO_AUTH_TYPE']\n else:\n try:\n auth_type = config_get('client', 'auth_type')\n except (NoOptionError, NoSectionError):\n logger.error('Cannot get AUTH_TYPE')\n sys.exit(FAILURE)\n if auth_type == 'x509_proxy':\n logger.error('Please verify that your proxy is still valid and renew it if needed.')\n sys.exit(FAILURE)\n return client","def get_unibox_client(self):\n if self._gls_unibox_client is None:\n client = Client(\n self.gls_server,\n self.gls_port\n )\n client.test = self.gls_is_test\n self._gls_unibox_client = client\n\n return self._gls_unibox_client","def _get_keystone_client(self, auth_creds):\n discover = keystone_discover.Discover(**auth_creds)\n\n for version_data in discover.version_data():\n version = version_data[\"version\"][0]\n if version <= 2:\n return keystone_client_v2.Client(insecure=True, **auth_creds)\n elif version == 3:\n return keystone_client_v3.Client(insecure=True, **auth_creds)\n\n raise Exception(\"Failed to discover keystone version \"\n \"for auth_url {0}\".format(\n auth_creds.get(\"auth_url\"))\n )","def GetClientInstance(release_track=calliope_base.ReleaseTrack.ALPHA):\n api_version = _RELEASE_TRACK_TO_API_VERSION.get(release_track)\n return core_apis.GetClientInstance(_API_NAME, api_version)","def get_conn(self) -> ServiceBusAdministrationClient:\n conn = self.get_connection(self.conn_id)\n connection_string: str = str(conn.schema)\n if connection_string:\n client = ServiceBusAdministrationClient.from_connection_string(connection_string)\n else:\n extras = conn.extra_dejson\n credential: str | DefaultAzureCredential = self._get_field(extras=extras, field_name=\"credential\")\n fully_qualified_namespace = self._get_field(extras=extras, field_name=\"fully_qualified_namespace\")\n if not credential:\n credential = DefaultAzureCredential()\n client = ServiceBusAdministrationClient(\n fully_qualified_namespace=fully_qualified_namespace,\n credential=credential, # type: ignore[arg-type]\n )\n self.log.info(\"Create and returns ServiceBusAdministrationClient\")\n return client","def _build_client(self):\n if self.url:\n return koji.ClientSession(self.url)\n else:\n _configuration = koji.read_config(self.profile,\n user_config=self.user_config)\n \"\"\"\n NOTE: This check is here because if the user does not have and koji\n config files, read_config will 'helpfully' return you a useless\n default config. The required baseurl ('server' in _configuration)\n has a default, so we cannot check that. However, topurl defaults\n to None, so we currently use this to devine if the returned config\n is the useless default.\n \"\"\"\n if not _configuration.get('topurl'):\n raise koji.ConfigurationError(\"no configuration for profile \\\n name: {0}\".format(self.profile))\n return koji.ClientSession(_configuration.get('server'),\n opts=_configuration)","def get_client(host, port=None, username=None,\n password=None, tenant=None,\n auth_url=None, auth_strategy=None,\n auth_token=None, region=None,\n is_silent_upload=False, insecure=False):\n\n if auth_url:\n force_strategy = 'keystone'\n else:\n force_strategy = None\n\n creds = dict(username=username,\n password=password,\n tenant=tenant,\n auth_url=auth_url,\n strategy=force_strategy or auth_strategy,\n region=region,\n )\n\n if creds['strategy'] == 'keystone' and not creds['auth_url']:\n msg = (\"--auth_url option or OS_AUTH_URL environment variable \"\n \"required when keystone authentication strategy is enabled\\n\")\n raise exception.ClientConfigurationError(msg)\n\n use_ssl = (creds['auth_url'] is not None and\n creds['auth_url'].find('https') != -1)\n\n client = HeatClient\n\n return client(host=host,\n port=port,\n use_ssl=use_ssl,\n auth_tok=auth_token,\n creds=creds,\n insecure=insecure)","def _get_auth_client(self, request):\n if self._auth_client is None:\n # Use PyFxa defaults if not specified\n server_url = fxa_conf(request, 'oauth_uri')\n auth_cache = self._get_cache(request)\n self._auth_client = OAuthClient(server_url=server_url, cache=auth_cache)\n\n return self._auth_client","async def get_docker_client(self) -> \"DockerClient\":","def Client(api_version, *args, **kwargs):\r\n neutron_client = utils.get_client_class(\r\n API_NAME,\r\n api_version,\r\n API_VERSIONS,\r\n )\r\n return neutron_client(*args, **kwargs)","def make_client(instance):\n network_client = utils.get_client_class(\n API_NAME,\n instance._api_version[API_NAME],\n API_VERSIONS)\n LOG.debug('Instantiating network client: %s', network_client)\n\n endpoint = instance.get_endpoint_for_service_type(\n API_NAME,\n region_name=instance._region_name,\n )\n\n return network_client(\n username=instance._username,\n tenant_name=instance._project_name,\n password=instance._password,\n region_name=instance._region_name,\n auth_url=instance._auth_url,\n endpoint_url=endpoint,\n token=instance.auth.get_token(instance.session),\n insecure=instance._insecure,\n ca_cert=instance._cacert,\n )","def _init_raw_client(self) -> None:\n if self.credentials:\n auth = HTTPBasicAuth(self.credentials['username'], self.credentials['password'])\n else:\n auth = None\n base_url = \"http://\" if self.untrusted else \"https://\"\n base_url += self.url\n self.raw_client = client.DockerRegistryClient(base_url=base_url, auth=auth)","def get_conn(self) -> ServiceBusClient:\n conn = self.get_connection(self.conn_id)\n connection_string: str = str(conn.schema)\n if connection_string:\n client = ServiceBusClient.from_connection_string(connection_string, logging_enable=True)\n else:\n extras = conn.extra_dejson\n credential: str | DefaultAzureCredential = self._get_field(extras=extras, field_name=\"credential\")\n fully_qualified_namespace = self._get_field(extras=extras, field_name=\"fully_qualified_namespace\")\n if not credential:\n credential = DefaultAzureCredential()\n client = ServiceBusClient(\n fully_qualified_namespace=fully_qualified_namespace,\n credential=credential, # type: ignore[arg-type]\n )\n\n self.log.info(\"Create and returns ServiceBusClient\")\n return client","def _keystone_client(context, version=(3, 0)):\n auth_plugin = token.Token(\n auth_url=CONF.keystone_authtoken.auth_uri,\n token=context.auth_token,\n project_id=context.project_id)\n client_session = session.Session(auth=auth_plugin,\n verify=False if\n CONF.keystone_authtoken.insecure else\n (CONF.keystone_authtoken.cafile or True))\n return client.Client(auth_url=CONF.keystone_authtoken.auth_uri,\n session=client_session, version=version)","def api_client() -> APIClient:\n\n return APIClient()","def make_client(self, context):\n return Client(self.settings['client_routing'], context=context)","def get_client(self):\n token = self.get_access_token()\n if self.client is None:\n credentials = AccessTokenCredentials(token, 'vetware/1.0')\n # credentials = SignedJwtAssertionCredentials(self.email, self.private_key,\n # \"https://www.googleapis.com/auth/calendar\")\n http = credentials.authorize(Http())\n self.client = build('calendar', 'v3', http=http)\n return self.client","def ironic_client_v1(get_api_ironic_client):\n return get_api_ironic_client(version='1', is_api=False)","def get_client_class(api_name, version, version_map):\r\n try:\r\n client_path = version_map[str(version)]\r\n except (KeyError, ValueError):\r\n msg = _(\"Invalid %(api_name)s client version '%(version)s'. must be \"\r\n \"one of: %(map_keys)s\")\r\n msg = msg % {'api_name': api_name, 'version': version,\r\n 'map_keys': ', '.join(version_map.keys())}\r\n raise exceptions.UnsupportedVersion(msg)\r\n\r\n return import_class(client_path)","def get_redis_client(self):\n\n client = Client(\n #connection_pool=connection_pool,\n host=self.backend_settings.get('HOST', 'localhost'),\n port=int(self.backend_settings.get('PORT', 6379)),\n io_loop=self.io_loop,\n password=self.backend_settings.get('PASSWORD', None),\n selected_db=int(self.backend_settings.get('DB', 0)),\n reconnect_callback=self.listen)\n\n return client","def test_get_client(self):\n pass","def get_client_adapter(self, is_controller=True, is_cli=False):\n from datrium.rpc.client.impl.http_remote_method_invoker import HTTPRemoteMethodInvoker\n return HTTPRemoteMethodInvoker(is_controller=is_controller, is_cli=is_cli)","def make_client(service_key, constructor=None, options=None, **kwargs):\n cloud = get_config(service_key=service_key, options=options, **kwargs)\n if not constructor:\n constructor = cloud_config._get_client(service_key)\n return cloud.get_legacy_client(service_key, constructor)","def operations_client(self) -> operations_v1.OperationsClient:\n # Sanity check: Only create a new client if we do not already have one.\n if \"operations_client\" not in self.__dict__:\n self.__dict__[\"operations_client\"] = operations_v1.OperationsClient(\n self.grpc_channel\n )\n\n # Return the client from cache.\n return self.__dict__[\"operations_client\"]","def decapod_client(get_decapod_client):\n return get_decapod_client()","def get_client(self):\n if self._client_state is not None:\n # Copy client state because `from_json_snapshot` modifies it...\n client = AxClient.from_json_snapshot(copy.deepcopy(self._client_state))\n else:\n client = AxClient(\n random_seed=self.seed,\n enforce_sequential_optimization=False,\n verbose_logging=False,\n )\n\n client.create_experiment(\n parameters=orion_space_to_axoptimizer_space(self.space),\n choose_generation_strategy_kwargs={\n \"num_initialization_trials\": self.n_initial_trials,\n \"max_parallelism_override\": self.max_trials,\n },\n objectives={\n \"objective\": ObjectiveProperties(minimize=True),\n **{\n o: ObjectiveProperties(minimize=True)\n for o in self.extra_objectives\n },\n },\n outcome_constraints=self.constraints,\n )\n\n yield client\n\n self._client_state = client.to_json_snapshot()","def operations_client(self) -> operations_v1.OperationsAsyncClient:\n # Quick check: Only create a new client if we do not already have one.\n if self._operations_client is None:\n self._operations_client = operations_v1.OperationsAsyncClient(\n self.grpc_channel\n )\n\n # Return the client from cache.\n return self._operations_client","def operations_client(self) -> operations_v1.OperationsAsyncClient:\n # Quick check: Only create a new client if we do not already have one.\n if self._operations_client is None:\n self._operations_client = operations_v1.OperationsAsyncClient(\n self.grpc_channel\n )\n\n # Return the client from cache.\n return self._operations_client","def pure_client():\n return VoximplantClient(\n host='https://api.host.com',\n account_id='100500',\n api_key='secret',\n )","def _get_client(self):\n return Github(\n base_url=github_trigger.api_endpoint(),\n login_or_token=self.auth_token if self.auth_token else github_trigger.client_id(),\n password=None if self.auth_token else github_trigger.client_secret(),\n timeout=5,\n )","def create_client(self) -> None:\n pass","def GetClient(release_track):\n return apis.GetClientInstance(_API_NAME, GetApiVersion(release_track))","def get_httpx_client() -> httpx.Client:\n return httpx.Client(**CLIENT_PARAMETERS) # type: ignore","def internal_keystoneclient(request):\n token = cache.get(CACHE_CLIENT, None)\n old_client = cache.get(CACHE_TOKEN, None)\n if not token:\n #LOG.debug('There is no token cached -> New Password Session')\n idm_password_session = _password_session(request)\n keystoneclient = client.Client(session=idm_password_session)\n cache.set(CACHE_CLIENT, keystoneclient.session.get_token(), INTERNAL_CLIENT_CACHE_TIME)\n cache.set(CACHE_TOKEN, keystoneclient, INTERNAL_CLIENT_CACHE_TIME)\n #LOG.debug('Saved token: %s',keystoneclient.session.get_token())\n else:\n #LOG.debug('There is a cached token! (%s)',token)\n old_client._auth_token = token\n keystoneclient = old_client\n\n #LOG.debug('Using token: %s',keystoneclient.session.get_token())\n return keystoneclient","def _get_client(self):\n credentials = service_account.Credentials.from_service_account_info(self.service_account_info)\n client = googleapiclient.discovery.build('container', 'v1', credentials=credentials)\n\n return client","def _client(self) -> httpx.AsyncClient:\n return httpx.AsyncClient(\n base_url=self._base_url,\n headers=self._authorization_headers,\n proxies=self._proxies,\n )","def get_keystoneclient(self):\n try:\n if self.token is None:\n client = keystoneclient.Client(user_name=self.cluster_account.cluster_user_name,\n password=self.cluster_account.cluster_password,\n auth_url=self.cluster_account.cluster.auth_url,\n tenant_name=self.name,\n )\n self.token = json.dumps(client.auth_ref)\n else:\n client = keystoneclient.Client(auth_ref=json.loads(self.token))\n # keystoneclient authenticates lazily, i.e. It doensn't actually\n # authenticates until the first time it needs the token for\n # someting. We'd like to find out about failures now (in\n # particular, it's easier to clear a bad token here than somewhere\n # else in the code. authenticate() forces it to auth right now:\n client.authenticate()\n return client\n except AuthorizationFailure:\n # Clear the token if auth failed:\n self.token = None\n raise","def get_client(service_account_json):\n api_scopes = ['https://www.googleapis.com/auth/cloud-platform']\n api_version = 'v1'\n discovery_api = 'https://cloudiot.googleapis.com/$discovery/rest'\n service_name = 'cloudiotcore'\n\n credentials = service_account.Credentials.from_service_account_file(\n service_account_json)\n scoped_credentials = credentials.with_scopes(api_scopes)\n\n discovery_url = '{}?version={}'.format(\n discovery_api, api_version)\n\n return discovery.build(\n service_name,\n api_version,\n discoveryServiceUrl=discovery_url,\n credentials=scoped_credentials)","def client(self):\n raise NotImplementedError()","def Client(self):\n return self._client","def client(self) -> WebClient:\n return WebClient(**self._get_conn_params())","def client(metadata_response) -> airbase.AirbaseClient:\n return airbase.AirbaseClient()","def _create_client_impl(self, api_version):\n if api_version == v7_2_VERSION:\n from .v7_2 import KeyVaultClient as ImplClient\n else:\n raise NotImplementedError(\"APIVersion {} is not available\".format(api_version))\n\n impl = ImplClient(credentials=self._credentials)\n impl.config = self.config\n\n # if __enter__ has previously been called and the impl client has __enter__ defined we need to call it\n if self._entered and hasattr(impl, '__enter__'):\n impl.__enter__()\n\n self._client_impls[api_version] = impl\n return impl","def get_client():\n return storage.Client(project=project_id)","def get_cinder(self, version='2'):\n if self.cinder is None:\n iface = os.getenv('OS_ENDPOINT_TYPE', \"public\")\n self.cinder = cinderclient.Client(version,\n session=self.get_session(),\n interface=iface)\n return self.cinder","def get_client(self, name):\n return self.get_clients(as_dict=True).get(name)","def get_conn():\n return ProfitBricksService(\n username=config.get_cloud_config_value(\n \"username\", get_configured_provider(), __opts__, search_global=False\n ),\n password=config.get_cloud_config_value(\n \"password\", get_configured_provider(), __opts__, search_global=False\n ),\n )","def api_ironic_client_v1(get_api_ironic_client):\n return get_api_ironic_client(version='1', is_api=True)","def get_client_version(self):\n return self.__aceQLHttpApi.get_client_version()","def _require_client(self, client):\n if client is None:\n client = self._client\n return client","def _get_monasca_client(self):\n\n monasca_url = self.token_helper.get_service_endpoint('monitoring')\n keystone_url = self.token_helper.get_service_endpoint('identity') + 'v3'\n # All monasca data is stored in the admin project, so get a token\n # to that project\n token = self.token_helper.get_token_for_project('admin')\n\n return client.Client(api_version=api_version,\n endpoint=monasca_url,\n token=token,\n auth_url=keystone_url,\n project_name='admin',\n project_domain_name='Default',\n insecure=get_conf(\"insecure\"),\n user_agent=api.USER_AGENT)","def _get_client(self):\n try:\n client = boto3_cached_conn(\n 'iam', **self.conn_details)\n\n if not client:\n raise ValueError(f\"boto3_cached_conn returned null IAM client for {self.account_number}\")\n\n return client\n\n except Exception as e:\n self.on_failure.send(self, error=e)\n self.current_app.logger.exception(f\"Failed to obtain boto3 IAM client for account {self.account_number}.\", exc_info=False)\n raise e","def operations_client(self) -> operations_v1.OperationsClient:\n # Quick check: Only create a new client if we do not already have one.\n if self._operations_client is None:\n self._operations_client = operations_v1.OperationsClient(self.grpc_channel)\n\n # Return the client from cache.\n return self._operations_client","def get_auth_client(username=None, api_key=None,\n baseurl=None, cdb_client=None):\n from carto.sql import SQLClient\n from carto.auth import APIKeyAuthClient\n\n if cdb_client:\n sql = SQLClient(cdb_client)\n elif username is not None and api_key is not None:\n baseurl = get_baseurl(username=username, baseurl=baseurl)\n auth_client = APIKeyAuthClient(baseurl, api_key)\n sql = SQLClient(auth_client)\n else:\n raise Exception(\"`username` and `api_key` or `cdb_client` has to be \"\n \"specified.\")\n return sql","def _get_neutron_client(self):\n session = self._get_keystone_session()\n return neutronclient.v2_0.client.Client(session=session)","def get_client(client_mgr):\n manager = getattr(client_mgr, 'manager', client_mgr)\n net_client = getattr(manager, 'networks_client')\n try:\n _params = manager.default_params_with_timeout_values.copy()\n except Exception:\n _params = {}\n client = LoadBalancersClient(net_client.auth_provider,\n net_client.service,\n net_client.region,\n net_client.endpoint_type,\n **_params)\n return client","def client(self):\n return self._client","def get_vc3_client():\n c = SafeConfigParser()\n c.readfp(open(app.config['VC3_CLIENT_CONFIG']))\n\n try:\n client_api = client.VC3ClientAPI(c)\n return client_api\n except Exception as e:\n app.logger.error(\"Couldn't get vc3 client: {0}\".format(e))\n raise","def buildProtocol(self, addr):\n if hasattr(settings, \"DISCORD_SESSION_CLASS\"):\n protocol_class = class_from_module(\n settings.DISCORD_SESSION_CLASS, fallback=DiscordClient\n )\n protocol = protocol_class()\n else:\n protocol = DiscordClient()\n\n protocol.factory = self\n protocol.sessionhandler = self.sessionhandler\n return protocol","def futures(self) -> _AsyncClient:\n return self._async_client","def _get_client(self):\n self.logger.info('Connecting to MySQL running at \"%s\"...',\n self._connection_params['host'])\n\n # https://dev.mysql.com/doc/connector-python\n return connector.connect(**self._connection_params)"],"string":"[\n \"def _get_client_impl(self):\\n api_version = self._get_api_version(None)\\n if api_version not in self._client_impls:\\n self._create_client_impl(api_version)\\n return self._client_impls[api_version]\",\n \"def raw_client(self):\\r\\n warnings.warn(\\\"raw_client is deprecated. use self.client.get_client instead\\\",\\r\\n DeprecationWarning, stacklevel=2)\\r\\n return self.client.get_client(write=True)\",\n \"def get_client():\\n return Client(__address, authkey='strumamor')\",\n \"def _get_client(self):\\n if self._client is None:\\n self._client = self.boto.client(service_name='elb', region_name=self.boto.cli_region)\\n\\n return self._client\",\n \"def _base(self):\\n from hubspot3.base import BaseClient\\n\\n return BaseClient(**self.auth, **self.options)\",\n \"def get_client(version, **kwargs):\\n endpoint = kwargs.get('os_endpoint') or kwargs.get('ceilometer_url')\\n\\n return Client(version, endpoint, **kwargs)\",\n \"def get(cls, configuration: HttpClientConfiguration) -> HttpClient:\\n client_type = configuration.client_type\\n\\n if client_type == HttpClientType.UAA:\\n return cls._get_instance(configuration, ClientAuthType.TOKEN_UAA)\\n\\n elif client_type == HttpClientType.CONSOLE:\\n return cls._get_instance(configuration, ClientAuthType.LOGIN_PAGE)\\n\\n elif client_type == HttpClientType.CONSOLE_NO_AUTH:\\n return cls._get_instance(configuration, ClientAuthType.NO_AUTH)\\n\\n elif client_type == HttpClientType.APPLICATION:\\n return cls._get_instance(configuration, ClientAuthType.TOKEN_CF)\\n\\n elif client_type == HttpClientType.CLOUD_FOUNDRY:\\n return cls._get_instance(configuration, ClientAuthType.TOKEN_CF)\\n\\n elif client_type == HttpClientType.BROKER:\\n return cls._get_instance(configuration, ClientAuthType.HTTP_BASIC)\\n\\n elif client_type == HttpClientType.WEBHDFS:\\n return cls._get_instance(configuration, ClientAuthType.WEBHDFS)\\n \\n elif client_type == HttpClientType.SERVICE_TOOL:\\n return cls._get_instance(configuration, ClientAuthType.NO_AUTH)\\n\\n elif client_type == HttpClientType.CLOUDERA:\\n return cls._get_instance(configuration, ClientAuthType.HTTP_BASIC)\\n\\n else:\\n raise HttpClientFactoryInvalidClientTypeException(client_type)\",\n \"def _get_client(self):\\n _client = KOPS(provider=self.provider, config=self.config)\\n return _client\",\n \"def get_client(self):\\n return self.client\",\n \"def client(self) -> 'BaseClient':\\n return self\",\n \"def getClient(self):\\n authDict = Configuration().read_auth_data_from_config()\\n client_key = authDict['client_key']\\n client_secret = authDict['client_secret']\\n token = authDict['token']\\n token_secret = authDict['token_secret']\\n\\n authorize_OAuth_ob = authorizeOAuth.AuthorizeOAuth(client_key,\\n client_secret,\\n token,\\n token_secret,\\n Configuration().read_board_id_config())\\n\\n trello_client_wrapper = authorize_OAuth_ob.getClient()\\n self.set_list(trello_client_wrapper)\\n return trello_client_wrapper\",\n \"def make_client(instance):\\r\\n neutron_client = utils.get_client_class(\\r\\n API_NAME,\\r\\n instance._api_version[API_NAME],\\r\\n API_VERSIONS,\\r\\n )\\r\\n instance.initialize()\\r\\n url = instance._url\\r\\n url = url.rstrip(\\\"/\\\")\\r\\n if '2.0' == instance._api_version[API_NAME]:\\r\\n client = neutron_client(username=instance._username,\\r\\n tenant_name=instance._tenant_name,\\r\\n password=instance._password,\\r\\n region_name=instance._region_name,\\r\\n auth_url=instance._auth_url,\\r\\n endpoint_url=url,\\r\\n token=instance._token,\\r\\n auth_strategy=instance._auth_strategy,\\r\\n insecure=instance._insecure,\\r\\n ca_cert=instance._ca_cert)\\r\\n return client\\r\\n else:\\r\\n raise exceptions.UnsupportedVersion(_(\\\"API version %s is not \\\"\\r\\n \\\"supported\\\") %\\r\\n instance._api_version[API_NAME])\",\n \"def client():\\n return Client(**common_data.AUTH_ARGS)\",\n \"def _get_glance_client(self):\\n session = self._get_keystone_session()\\n return glanceclient.client.Client(2, session=session)\",\n \"def _generate_client(self):\\n mongoConf = self._config.get('Connectivity', 'MongoDB') # type: dict\\n if mongoConf.get('username') and mongoConf.get('password'):\\n return pymongo.MongoClient(\\n \\\"mongodb://{0}:{1}@{2}:{3}/{4}\\\".format(\\n mongoConf.get('username', ''),\\n mongoConf.get('password', ''),\\n mongoConf.get('host', 'localhost'),\\n mongoConf.get('port', 27017),\\n mongoConf.get('db', 'grease')\\n ),\\n w=1\\n )\\n else:\\n return pymongo.MongoClient(\\n host=mongoConf.get('host', 'localhost'),\\n port=mongoConf.get('port', 27017),\\n w=1\\n )\",\n \"def get_contribs_client():\\n from mpcontribs.client import Client\\n\\n headers = get_consumer()\\n\\n if is_localhost():\\n return Client(apikey=get_user_api_key())\\n else:\\n return Client(headers=headers)\",\n \"def get_client():\\n\\n return MongoClientManager().client\",\n \"def base_client(self):\\n return self._client\",\n \"def get_client():\\n client_class = _import_by_path(settings.REDISIO_CLIENT_CLASS)\\n return client_class(host=settings.REDISIO_HOST,\\n port=settings.REDISIO_PORT,\\n db=settings.REDISIO_DB)\",\n \"def get_client(httpx_settings: Optional[dict] = None) -> \\\"PrefectClient\\\":\\n ctx = prefect.context.get_settings_context()\\n api = PREFECT_API_URL.value()\\n\\n if not api:\\n # create an ephemeral API if none was provided\\n from prefect.server.api.server import create_app\\n\\n api = create_app(ctx.settings, ephemeral=True)\\n\\n return PrefectClient(\\n api,\\n api_key=PREFECT_API_KEY.value(),\\n httpx_settings=httpx_settings,\\n )\",\n \"def _get_client():\\n\\n return datastore.Client()\",\n \"def _get_client():\\n\\n return datastore.Client()\",\n \"def client(self):\\n\\n if self._client is None:\\n self._client = self._get_client()\\n return self._client\",\n \"def _getMemcacheClient(self, refresh=False):\\n if refresh or not hasattr(self, \\\"memcacheClient\\\"):\\n\\n if config.Memcached.Pools.Default.MemcacheSocket:\\n client_addr = \\\"unix:{}\\\".format(config.Memcached.Pools.Default.MemcacheSocket)\\n else:\\n client_addr = \\\"{}:{}\\\".format(\\n config.Memcached.Pools.Default.BindAddress,\\n config.Memcached.Pools.Default.Port,\\n )\\n self.memcacheClient = ClientFactory.getClient([client_addr], debug=0, pickleProtocol=2)\\n return self.memcacheClient\",\n \"def get_api_ironic_client(get_session):\\n def _get_api_ironic_client(version, is_api):\\n if version == '1':\\n if is_api:\\n return api_clients.IronicApiClientV1(session=get_session())\\n else:\\n return client_v1.get_client(api_version=version,\\n session=get_session())\\n\\n raise ValueError(\\\"Unexpected ironic version: {!r}\\\".format(version))\\n\\n return _get_api_ironic_client\",\n \"def client():\\n\\n client = Client()\\n return client\",\n \"def get_client() -> RabbitmqClient:\\n # Replace the parameters with proper values for host, port, login and password\\n # Change the value of exchange if needed.\\n #\\n # For any parameter that is not given here, the client tries to use a value from an environment variable\\n # and most of the parameters also have a default value that is used if neither the constructor parameter\\n # nor the environmental variable exist.\\n # See tools/clients.py for details about the environmental variables and the default values.\\n return RabbitmqClient(\\n host=\\\"\\\",\\n port=0,\\n login=\\\"\\\",\\n password=\\\"\\\",\\n exchange=\\\"procem.examples_testing\\\",\\n ssl=True,\\n ssl_version=\\\"PROTOCOL_TLS\\\",\\n exchange_autodelete=True,\\n exchange_durable=False\\n )\",\n \"def client(self):\\r\\n if self._client is None:\\r\\n self._client = self._client_cls(self._server, self._params, self)\\r\\n return self._client\",\n \"def get_client() -> 'MongoCLient':\\n client = pymongo.MongoClient()\\n db = client['c3']\\n c = db['json']\\n return c\",\n \"def _http_client(self):\\n\\n self.__enforce_connected()\\n return self.collection._http_client\",\n \"def configure_client(self):\\n self.client = self.get_redis_client()\\n return self.client\",\n \"def client():\\n return IceCubedSyncClient(\\\"api_key\\\", \\\"secret\\\")\",\n \"def create_rbclient(self):\\n return RBClient(url=self.TEST_SERVER_URL,\\n transport_cls=URLMapTransport)\",\n \"def affirm_client(self):\\n if self.user_name and self.password and self.host:\\n uri = f'mongodb://{self.user_name}:{self.password}@\\\\\\n {self.host}/{self.db_name}'\\n client = MongoClient(uri)\\n elif self.host:\\n client = MongoClient(self.host)\\n else:\\n client = MongoClient()\\n return client[self.db_name]\",\n \"def get_conn(self) -> WebClient:\\n return self.client\",\n \"def get_api_client():\\n\\n global _API_CLIENT_HANDLE\\n\\n if not _API_CLIENT_HANDLE:\\n context = get_context()\\n server_config = context.get_server_config()\\n\\n pc_ip = server_config.get(\\\"pc_ip\\\")\\n pc_port = server_config.get(\\\"pc_port\\\")\\n username = server_config.get(\\\"pc_username\\\")\\n password = server_config.get(\\\"pc_password\\\")\\n\\n update_api_client(host=pc_ip, port=pc_port, auth=(username, password))\\n\\n return _API_CLIENT_HANDLE\",\n \"def _client(self) -> httpx.Client:\\n return httpx.Client(\\n base_url=self._base_url,\\n headers=self._authorization_headers,\\n proxies=self._proxies,\\n )\",\n \"def api_client() -> APIClient:\\n return APIClient()\",\n \"def client(self):\\n\\t\\t# pylint: disable=invalid-name\\n\\t\\treturn self._client\",\n \"def get_client(args):\\n if args.auth_strategy == 'userpass':\\n creds = {'username': args.username, 'password': args.password}\\n else:\\n creds = None\\n\\n try:\\n client = Client(rucio_host=args.host, auth_host=args.auth_host,\\n account=args.account,\\n auth_type=args.auth_strategy, creds=creds,\\n ca_cert=args.ca_certificate, timeout=args.timeout)\\n except CannotAuthenticate, error:\\n logger.error(error)\\n if not args.auth_strategy:\\n if 'RUCIO_AUTH_TYPE' in os.environ:\\n auth_type = os.environ['RUCIO_AUTH_TYPE']\\n else:\\n try:\\n auth_type = config_get('client', 'auth_type')\\n except (NoOptionError, NoSectionError):\\n logger.error('Cannot get AUTH_TYPE')\\n sys.exit(FAILURE)\\n if auth_type == 'x509_proxy':\\n logger.error('Please verify that your proxy is still valid and renew it if needed.')\\n sys.exit(FAILURE)\\n return client\",\n \"def get_unibox_client(self):\\n if self._gls_unibox_client is None:\\n client = Client(\\n self.gls_server,\\n self.gls_port\\n )\\n client.test = self.gls_is_test\\n self._gls_unibox_client = client\\n\\n return self._gls_unibox_client\",\n \"def _get_keystone_client(self, auth_creds):\\n discover = keystone_discover.Discover(**auth_creds)\\n\\n for version_data in discover.version_data():\\n version = version_data[\\\"version\\\"][0]\\n if version <= 2:\\n return keystone_client_v2.Client(insecure=True, **auth_creds)\\n elif version == 3:\\n return keystone_client_v3.Client(insecure=True, **auth_creds)\\n\\n raise Exception(\\\"Failed to discover keystone version \\\"\\n \\\"for auth_url {0}\\\".format(\\n auth_creds.get(\\\"auth_url\\\"))\\n )\",\n \"def GetClientInstance(release_track=calliope_base.ReleaseTrack.ALPHA):\\n api_version = _RELEASE_TRACK_TO_API_VERSION.get(release_track)\\n return core_apis.GetClientInstance(_API_NAME, api_version)\",\n \"def get_conn(self) -> ServiceBusAdministrationClient:\\n conn = self.get_connection(self.conn_id)\\n connection_string: str = str(conn.schema)\\n if connection_string:\\n client = ServiceBusAdministrationClient.from_connection_string(connection_string)\\n else:\\n extras = conn.extra_dejson\\n credential: str | DefaultAzureCredential = self._get_field(extras=extras, field_name=\\\"credential\\\")\\n fully_qualified_namespace = self._get_field(extras=extras, field_name=\\\"fully_qualified_namespace\\\")\\n if not credential:\\n credential = DefaultAzureCredential()\\n client = ServiceBusAdministrationClient(\\n fully_qualified_namespace=fully_qualified_namespace,\\n credential=credential, # type: ignore[arg-type]\\n )\\n self.log.info(\\\"Create and returns ServiceBusAdministrationClient\\\")\\n return client\",\n \"def _build_client(self):\\n if self.url:\\n return koji.ClientSession(self.url)\\n else:\\n _configuration = koji.read_config(self.profile,\\n user_config=self.user_config)\\n \\\"\\\"\\\"\\n NOTE: This check is here because if the user does not have and koji\\n config files, read_config will 'helpfully' return you a useless\\n default config. The required baseurl ('server' in _configuration)\\n has a default, so we cannot check that. However, topurl defaults\\n to None, so we currently use this to devine if the returned config\\n is the useless default.\\n \\\"\\\"\\\"\\n if not _configuration.get('topurl'):\\n raise koji.ConfigurationError(\\\"no configuration for profile \\\\\\n name: {0}\\\".format(self.profile))\\n return koji.ClientSession(_configuration.get('server'),\\n opts=_configuration)\",\n \"def get_client(host, port=None, username=None,\\n password=None, tenant=None,\\n auth_url=None, auth_strategy=None,\\n auth_token=None, region=None,\\n is_silent_upload=False, insecure=False):\\n\\n if auth_url:\\n force_strategy = 'keystone'\\n else:\\n force_strategy = None\\n\\n creds = dict(username=username,\\n password=password,\\n tenant=tenant,\\n auth_url=auth_url,\\n strategy=force_strategy or auth_strategy,\\n region=region,\\n )\\n\\n if creds['strategy'] == 'keystone' and not creds['auth_url']:\\n msg = (\\\"--auth_url option or OS_AUTH_URL environment variable \\\"\\n \\\"required when keystone authentication strategy is enabled\\\\n\\\")\\n raise exception.ClientConfigurationError(msg)\\n\\n use_ssl = (creds['auth_url'] is not None and\\n creds['auth_url'].find('https') != -1)\\n\\n client = HeatClient\\n\\n return client(host=host,\\n port=port,\\n use_ssl=use_ssl,\\n auth_tok=auth_token,\\n creds=creds,\\n insecure=insecure)\",\n \"def _get_auth_client(self, request):\\n if self._auth_client is None:\\n # Use PyFxa defaults if not specified\\n server_url = fxa_conf(request, 'oauth_uri')\\n auth_cache = self._get_cache(request)\\n self._auth_client = OAuthClient(server_url=server_url, cache=auth_cache)\\n\\n return self._auth_client\",\n \"async def get_docker_client(self) -> \\\"DockerClient\\\":\",\n \"def Client(api_version, *args, **kwargs):\\r\\n neutron_client = utils.get_client_class(\\r\\n API_NAME,\\r\\n api_version,\\r\\n API_VERSIONS,\\r\\n )\\r\\n return neutron_client(*args, **kwargs)\",\n \"def make_client(instance):\\n network_client = utils.get_client_class(\\n API_NAME,\\n instance._api_version[API_NAME],\\n API_VERSIONS)\\n LOG.debug('Instantiating network client: %s', network_client)\\n\\n endpoint = instance.get_endpoint_for_service_type(\\n API_NAME,\\n region_name=instance._region_name,\\n )\\n\\n return network_client(\\n username=instance._username,\\n tenant_name=instance._project_name,\\n password=instance._password,\\n region_name=instance._region_name,\\n auth_url=instance._auth_url,\\n endpoint_url=endpoint,\\n token=instance.auth.get_token(instance.session),\\n insecure=instance._insecure,\\n ca_cert=instance._cacert,\\n )\",\n \"def _init_raw_client(self) -> None:\\n if self.credentials:\\n auth = HTTPBasicAuth(self.credentials['username'], self.credentials['password'])\\n else:\\n auth = None\\n base_url = \\\"http://\\\" if self.untrusted else \\\"https://\\\"\\n base_url += self.url\\n self.raw_client = client.DockerRegistryClient(base_url=base_url, auth=auth)\",\n \"def get_conn(self) -> ServiceBusClient:\\n conn = self.get_connection(self.conn_id)\\n connection_string: str = str(conn.schema)\\n if connection_string:\\n client = ServiceBusClient.from_connection_string(connection_string, logging_enable=True)\\n else:\\n extras = conn.extra_dejson\\n credential: str | DefaultAzureCredential = self._get_field(extras=extras, field_name=\\\"credential\\\")\\n fully_qualified_namespace = self._get_field(extras=extras, field_name=\\\"fully_qualified_namespace\\\")\\n if not credential:\\n credential = DefaultAzureCredential()\\n client = ServiceBusClient(\\n fully_qualified_namespace=fully_qualified_namespace,\\n credential=credential, # type: ignore[arg-type]\\n )\\n\\n self.log.info(\\\"Create and returns ServiceBusClient\\\")\\n return client\",\n \"def _keystone_client(context, version=(3, 0)):\\n auth_plugin = token.Token(\\n auth_url=CONF.keystone_authtoken.auth_uri,\\n token=context.auth_token,\\n project_id=context.project_id)\\n client_session = session.Session(auth=auth_plugin,\\n verify=False if\\n CONF.keystone_authtoken.insecure else\\n (CONF.keystone_authtoken.cafile or True))\\n return client.Client(auth_url=CONF.keystone_authtoken.auth_uri,\\n session=client_session, version=version)\",\n \"def api_client() -> APIClient:\\n\\n return APIClient()\",\n \"def make_client(self, context):\\n return Client(self.settings['client_routing'], context=context)\",\n \"def get_client(self):\\n token = self.get_access_token()\\n if self.client is None:\\n credentials = AccessTokenCredentials(token, 'vetware/1.0')\\n # credentials = SignedJwtAssertionCredentials(self.email, self.private_key,\\n # \\\"https://www.googleapis.com/auth/calendar\\\")\\n http = credentials.authorize(Http())\\n self.client = build('calendar', 'v3', http=http)\\n return self.client\",\n \"def ironic_client_v1(get_api_ironic_client):\\n return get_api_ironic_client(version='1', is_api=False)\",\n \"def get_client_class(api_name, version, version_map):\\r\\n try:\\r\\n client_path = version_map[str(version)]\\r\\n except (KeyError, ValueError):\\r\\n msg = _(\\\"Invalid %(api_name)s client version '%(version)s'. must be \\\"\\r\\n \\\"one of: %(map_keys)s\\\")\\r\\n msg = msg % {'api_name': api_name, 'version': version,\\r\\n 'map_keys': ', '.join(version_map.keys())}\\r\\n raise exceptions.UnsupportedVersion(msg)\\r\\n\\r\\n return import_class(client_path)\",\n \"def get_redis_client(self):\\n\\n client = Client(\\n #connection_pool=connection_pool,\\n host=self.backend_settings.get('HOST', 'localhost'),\\n port=int(self.backend_settings.get('PORT', 6379)),\\n io_loop=self.io_loop,\\n password=self.backend_settings.get('PASSWORD', None),\\n selected_db=int(self.backend_settings.get('DB', 0)),\\n reconnect_callback=self.listen)\\n\\n return client\",\n \"def test_get_client(self):\\n pass\",\n \"def get_client_adapter(self, is_controller=True, is_cli=False):\\n from datrium.rpc.client.impl.http_remote_method_invoker import HTTPRemoteMethodInvoker\\n return HTTPRemoteMethodInvoker(is_controller=is_controller, is_cli=is_cli)\",\n \"def make_client(service_key, constructor=None, options=None, **kwargs):\\n cloud = get_config(service_key=service_key, options=options, **kwargs)\\n if not constructor:\\n constructor = cloud_config._get_client(service_key)\\n return cloud.get_legacy_client(service_key, constructor)\",\n \"def operations_client(self) -> operations_v1.OperationsClient:\\n # Sanity check: Only create a new client if we do not already have one.\\n if \\\"operations_client\\\" not in self.__dict__:\\n self.__dict__[\\\"operations_client\\\"] = operations_v1.OperationsClient(\\n self.grpc_channel\\n )\\n\\n # Return the client from cache.\\n return self.__dict__[\\\"operations_client\\\"]\",\n \"def decapod_client(get_decapod_client):\\n return get_decapod_client()\",\n \"def get_client(self):\\n if self._client_state is not None:\\n # Copy client state because `from_json_snapshot` modifies it...\\n client = AxClient.from_json_snapshot(copy.deepcopy(self._client_state))\\n else:\\n client = AxClient(\\n random_seed=self.seed,\\n enforce_sequential_optimization=False,\\n verbose_logging=False,\\n )\\n\\n client.create_experiment(\\n parameters=orion_space_to_axoptimizer_space(self.space),\\n choose_generation_strategy_kwargs={\\n \\\"num_initialization_trials\\\": self.n_initial_trials,\\n \\\"max_parallelism_override\\\": self.max_trials,\\n },\\n objectives={\\n \\\"objective\\\": ObjectiveProperties(minimize=True),\\n **{\\n o: ObjectiveProperties(minimize=True)\\n for o in self.extra_objectives\\n },\\n },\\n outcome_constraints=self.constraints,\\n )\\n\\n yield client\\n\\n self._client_state = client.to_json_snapshot()\",\n \"def operations_client(self) -> operations_v1.OperationsAsyncClient:\\n # Quick check: Only create a new client if we do not already have one.\\n if self._operations_client is None:\\n self._operations_client = operations_v1.OperationsAsyncClient(\\n self.grpc_channel\\n )\\n\\n # Return the client from cache.\\n return self._operations_client\",\n \"def operations_client(self) -> operations_v1.OperationsAsyncClient:\\n # Quick check: Only create a new client if we do not already have one.\\n if self._operations_client is None:\\n self._operations_client = operations_v1.OperationsAsyncClient(\\n self.grpc_channel\\n )\\n\\n # Return the client from cache.\\n return self._operations_client\",\n \"def pure_client():\\n return VoximplantClient(\\n host='https://api.host.com',\\n account_id='100500',\\n api_key='secret',\\n )\",\n \"def _get_client(self):\\n return Github(\\n base_url=github_trigger.api_endpoint(),\\n login_or_token=self.auth_token if self.auth_token else github_trigger.client_id(),\\n password=None if self.auth_token else github_trigger.client_secret(),\\n timeout=5,\\n )\",\n \"def create_client(self) -> None:\\n pass\",\n \"def GetClient(release_track):\\n return apis.GetClientInstance(_API_NAME, GetApiVersion(release_track))\",\n \"def get_httpx_client() -> httpx.Client:\\n return httpx.Client(**CLIENT_PARAMETERS) # type: ignore\",\n \"def internal_keystoneclient(request):\\n token = cache.get(CACHE_CLIENT, None)\\n old_client = cache.get(CACHE_TOKEN, None)\\n if not token:\\n #LOG.debug('There is no token cached -> New Password Session')\\n idm_password_session = _password_session(request)\\n keystoneclient = client.Client(session=idm_password_session)\\n cache.set(CACHE_CLIENT, keystoneclient.session.get_token(), INTERNAL_CLIENT_CACHE_TIME)\\n cache.set(CACHE_TOKEN, keystoneclient, INTERNAL_CLIENT_CACHE_TIME)\\n #LOG.debug('Saved token: %s',keystoneclient.session.get_token())\\n else:\\n #LOG.debug('There is a cached token! (%s)',token)\\n old_client._auth_token = token\\n keystoneclient = old_client\\n\\n #LOG.debug('Using token: %s',keystoneclient.session.get_token())\\n return keystoneclient\",\n \"def _get_client(self):\\n credentials = service_account.Credentials.from_service_account_info(self.service_account_info)\\n client = googleapiclient.discovery.build('container', 'v1', credentials=credentials)\\n\\n return client\",\n \"def _client(self) -> httpx.AsyncClient:\\n return httpx.AsyncClient(\\n base_url=self._base_url,\\n headers=self._authorization_headers,\\n proxies=self._proxies,\\n )\",\n \"def get_keystoneclient(self):\\n try:\\n if self.token is None:\\n client = keystoneclient.Client(user_name=self.cluster_account.cluster_user_name,\\n password=self.cluster_account.cluster_password,\\n auth_url=self.cluster_account.cluster.auth_url,\\n tenant_name=self.name,\\n )\\n self.token = json.dumps(client.auth_ref)\\n else:\\n client = keystoneclient.Client(auth_ref=json.loads(self.token))\\n # keystoneclient authenticates lazily, i.e. It doensn't actually\\n # authenticates until the first time it needs the token for\\n # someting. We'd like to find out about failures now (in\\n # particular, it's easier to clear a bad token here than somewhere\\n # else in the code. authenticate() forces it to auth right now:\\n client.authenticate()\\n return client\\n except AuthorizationFailure:\\n # Clear the token if auth failed:\\n self.token = None\\n raise\",\n \"def get_client(service_account_json):\\n api_scopes = ['https://www.googleapis.com/auth/cloud-platform']\\n api_version = 'v1'\\n discovery_api = 'https://cloudiot.googleapis.com/$discovery/rest'\\n service_name = 'cloudiotcore'\\n\\n credentials = service_account.Credentials.from_service_account_file(\\n service_account_json)\\n scoped_credentials = credentials.with_scopes(api_scopes)\\n\\n discovery_url = '{}?version={}'.format(\\n discovery_api, api_version)\\n\\n return discovery.build(\\n service_name,\\n api_version,\\n discoveryServiceUrl=discovery_url,\\n credentials=scoped_credentials)\",\n \"def client(self):\\n raise NotImplementedError()\",\n \"def Client(self):\\n return self._client\",\n \"def client(self) -> WebClient:\\n return WebClient(**self._get_conn_params())\",\n \"def client(metadata_response) -> airbase.AirbaseClient:\\n return airbase.AirbaseClient()\",\n \"def _create_client_impl(self, api_version):\\n if api_version == v7_2_VERSION:\\n from .v7_2 import KeyVaultClient as ImplClient\\n else:\\n raise NotImplementedError(\\\"APIVersion {} is not available\\\".format(api_version))\\n\\n impl = ImplClient(credentials=self._credentials)\\n impl.config = self.config\\n\\n # if __enter__ has previously been called and the impl client has __enter__ defined we need to call it\\n if self._entered and hasattr(impl, '__enter__'):\\n impl.__enter__()\\n\\n self._client_impls[api_version] = impl\\n return impl\",\n \"def get_client():\\n return storage.Client(project=project_id)\",\n \"def get_cinder(self, version='2'):\\n if self.cinder is None:\\n iface = os.getenv('OS_ENDPOINT_TYPE', \\\"public\\\")\\n self.cinder = cinderclient.Client(version,\\n session=self.get_session(),\\n interface=iface)\\n return self.cinder\",\n \"def get_client(self, name):\\n return self.get_clients(as_dict=True).get(name)\",\n \"def get_conn():\\n return ProfitBricksService(\\n username=config.get_cloud_config_value(\\n \\\"username\\\", get_configured_provider(), __opts__, search_global=False\\n ),\\n password=config.get_cloud_config_value(\\n \\\"password\\\", get_configured_provider(), __opts__, search_global=False\\n ),\\n )\",\n \"def api_ironic_client_v1(get_api_ironic_client):\\n return get_api_ironic_client(version='1', is_api=True)\",\n \"def get_client_version(self):\\n return self.__aceQLHttpApi.get_client_version()\",\n \"def _require_client(self, client):\\n if client is None:\\n client = self._client\\n return client\",\n \"def _get_monasca_client(self):\\n\\n monasca_url = self.token_helper.get_service_endpoint('monitoring')\\n keystone_url = self.token_helper.get_service_endpoint('identity') + 'v3'\\n # All monasca data is stored in the admin project, so get a token\\n # to that project\\n token = self.token_helper.get_token_for_project('admin')\\n\\n return client.Client(api_version=api_version,\\n endpoint=monasca_url,\\n token=token,\\n auth_url=keystone_url,\\n project_name='admin',\\n project_domain_name='Default',\\n insecure=get_conf(\\\"insecure\\\"),\\n user_agent=api.USER_AGENT)\",\n \"def _get_client(self):\\n try:\\n client = boto3_cached_conn(\\n 'iam', **self.conn_details)\\n\\n if not client:\\n raise ValueError(f\\\"boto3_cached_conn returned null IAM client for {self.account_number}\\\")\\n\\n return client\\n\\n except Exception as e:\\n self.on_failure.send(self, error=e)\\n self.current_app.logger.exception(f\\\"Failed to obtain boto3 IAM client for account {self.account_number}.\\\", exc_info=False)\\n raise e\",\n \"def operations_client(self) -> operations_v1.OperationsClient:\\n # Quick check: Only create a new client if we do not already have one.\\n if self._operations_client is None:\\n self._operations_client = operations_v1.OperationsClient(self.grpc_channel)\\n\\n # Return the client from cache.\\n return self._operations_client\",\n \"def get_auth_client(username=None, api_key=None,\\n baseurl=None, cdb_client=None):\\n from carto.sql import SQLClient\\n from carto.auth import APIKeyAuthClient\\n\\n if cdb_client:\\n sql = SQLClient(cdb_client)\\n elif username is not None and api_key is not None:\\n baseurl = get_baseurl(username=username, baseurl=baseurl)\\n auth_client = APIKeyAuthClient(baseurl, api_key)\\n sql = SQLClient(auth_client)\\n else:\\n raise Exception(\\\"`username` and `api_key` or `cdb_client` has to be \\\"\\n \\\"specified.\\\")\\n return sql\",\n \"def _get_neutron_client(self):\\n session = self._get_keystone_session()\\n return neutronclient.v2_0.client.Client(session=session)\",\n \"def get_client(client_mgr):\\n manager = getattr(client_mgr, 'manager', client_mgr)\\n net_client = getattr(manager, 'networks_client')\\n try:\\n _params = manager.default_params_with_timeout_values.copy()\\n except Exception:\\n _params = {}\\n client = LoadBalancersClient(net_client.auth_provider,\\n net_client.service,\\n net_client.region,\\n net_client.endpoint_type,\\n **_params)\\n return client\",\n \"def client(self):\\n return self._client\",\n \"def get_vc3_client():\\n c = SafeConfigParser()\\n c.readfp(open(app.config['VC3_CLIENT_CONFIG']))\\n\\n try:\\n client_api = client.VC3ClientAPI(c)\\n return client_api\\n except Exception as e:\\n app.logger.error(\\\"Couldn't get vc3 client: {0}\\\".format(e))\\n raise\",\n \"def buildProtocol(self, addr):\\n if hasattr(settings, \\\"DISCORD_SESSION_CLASS\\\"):\\n protocol_class = class_from_module(\\n settings.DISCORD_SESSION_CLASS, fallback=DiscordClient\\n )\\n protocol = protocol_class()\\n else:\\n protocol = DiscordClient()\\n\\n protocol.factory = self\\n protocol.sessionhandler = self.sessionhandler\\n return protocol\",\n \"def futures(self) -> _AsyncClient:\\n return self._async_client\",\n \"def _get_client(self):\\n self.logger.info('Connecting to MySQL running at \\\"%s\\\"...',\\n self._connection_params['host'])\\n\\n # https://dev.mysql.com/doc/connector-python\\n return connector.connect(**self._connection_params)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7103304","0.6904759","0.65741783","0.6360245","0.6288746","0.62776977","0.6226399","0.6196101","0.6190632","0.6187057","0.6171933","0.61647344","0.61564815","0.6148251","0.61479414","0.60850495","0.6072552","0.60554427","0.604616","0.6028892","0.5996603","0.5996603","0.59921736","0.5963985","0.59475595","0.5946585","0.59328294","0.5903525","0.589581","0.588521","0.5877647","0.5877122","0.58730555","0.58628345","0.5850559","0.5845853","0.5840732","0.5835176","0.5833741","0.5833498","0.5822472","0.58220863","0.58168983","0.58110213","0.58006483","0.5798592","0.57804173","0.5779848","0.5756844","0.5737197","0.57318896","0.5726947","0.5726772","0.5724653","0.5719921","0.5719638","0.57122105","0.570958","0.57091814","0.5706371","0.57005453","0.5693453","0.56932324","0.56866664","0.5662466","0.5661655","0.5661655","0.56553024","0.56517726","0.5647958","0.5647125","0.56415963","0.5639085","0.5632547","0.5629498","0.5627369","0.5623316","0.56173956","0.5617316","0.5608021","0.5602873","0.55866855","0.5576384","0.5566331","0.556476","0.5542993","0.5541543","0.5538831","0.5538655","0.5530595","0.5529568","0.55263025","0.55168426","0.55127794","0.55091125","0.5508122","0.55080765","0.5506847","0.5505413","0.550201"],"string":"[\n \"0.7103304\",\n \"0.6904759\",\n \"0.65741783\",\n \"0.6360245\",\n \"0.6288746\",\n \"0.62776977\",\n \"0.6226399\",\n \"0.6196101\",\n \"0.6190632\",\n \"0.6187057\",\n \"0.6171933\",\n \"0.61647344\",\n \"0.61564815\",\n \"0.6148251\",\n \"0.61479414\",\n \"0.60850495\",\n \"0.6072552\",\n \"0.60554427\",\n \"0.604616\",\n \"0.6028892\",\n \"0.5996603\",\n \"0.5996603\",\n \"0.59921736\",\n \"0.5963985\",\n \"0.59475595\",\n \"0.5946585\",\n \"0.59328294\",\n \"0.5903525\",\n \"0.589581\",\n \"0.588521\",\n \"0.5877647\",\n \"0.5877122\",\n \"0.58730555\",\n \"0.58628345\",\n \"0.5850559\",\n \"0.5845853\",\n \"0.5840732\",\n \"0.5835176\",\n \"0.5833741\",\n \"0.5833498\",\n \"0.5822472\",\n \"0.58220863\",\n \"0.58168983\",\n \"0.58110213\",\n \"0.58006483\",\n \"0.5798592\",\n \"0.57804173\",\n \"0.5779848\",\n \"0.5756844\",\n \"0.5737197\",\n \"0.57318896\",\n \"0.5726947\",\n \"0.5726772\",\n \"0.5724653\",\n \"0.5719921\",\n \"0.5719638\",\n \"0.57122105\",\n \"0.570958\",\n \"0.57091814\",\n \"0.5706371\",\n \"0.57005453\",\n \"0.5693453\",\n \"0.56932324\",\n \"0.56866664\",\n \"0.5662466\",\n \"0.5661655\",\n \"0.5661655\",\n \"0.56553024\",\n \"0.56517726\",\n \"0.5647958\",\n \"0.5647125\",\n \"0.56415963\",\n \"0.5639085\",\n \"0.5632547\",\n \"0.5629498\",\n \"0.5627369\",\n \"0.5623316\",\n \"0.56173956\",\n \"0.5617316\",\n \"0.5608021\",\n \"0.5602873\",\n \"0.55866855\",\n \"0.5576384\",\n \"0.5566331\",\n \"0.556476\",\n \"0.5542993\",\n \"0.5541543\",\n \"0.5538831\",\n \"0.5538655\",\n \"0.5530595\",\n \"0.5529568\",\n \"0.55263025\",\n \"0.55168426\",\n \"0.55127794\",\n \"0.55091125\",\n \"0.5508122\",\n \"0.55080765\",\n \"0.5506847\",\n \"0.5505413\",\n \"0.550201\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5521968"},"document_rank":{"kind":"string","value":"92"}}},{"rowIdx":16884,"cells":{"query":{"kind":"string","value":"Deserialize datetime object into string form for JSON processing."},"document":{"kind":"string","value":"def dump_datetime(value):\n if value is None:\n return None\n return [value.strftime(\"%Y-%m-%d\"), value.strftime(\"%H:%M:%S\")]"},"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 serialize_datetime(self, obj):\r\n if isinstance(obj, datetime.datetime):\r\n return obj.isoformat()\r\n raise TypeError(\"Type not serializable\")","def json_datetime_serializer(obj):\n\n if isinstance(obj, datetime):\n serial = obj.isoformat()\n return serial\n raise TypeError(\"{} is not JSON serializable.\".format(obj))","def json_serialize_datetime(self, obj):\n if isinstance(obj, datetime.datetime):\n return int(time.mktime(obj.timetuple()))\n\n raise TypeError(\"Type %s not serializable\" % type(obj))","def _serialize_datetime(val):\n return datetime_to_iso8601(val)","def convert_for_json(obj):\n if isinstance(obj, datetime.datetime):\n return obj.__str__()\n return obj","def json_serial(obj):\n if isinstance(obj, datetime):\n return obj.isoformat()","def deserialise(obj):\n if isinstance(obj, str) and 12 < len(obj) < 40:\n try:\n # some tests try tricking us with timezones - but we assume naive datetime objects in utc\n # 1970-01-21T21:14:37+12:45 -> 1970-01-21 08:29:37 (1970-01-21T08:29:37)\n x = obj\n obj = du_parser.parse(obj).astimezone(tz=du_tz.tzutc()).replace(tzinfo=None)\n LOG.info('datetime rehydrated: %s -> %s (%s)' % (x, obj, obj.isoformat()))\n except Exception as e:\n LOG.debug('not a date: %s (%s)' % (obj, e))\n return obj","def object_deserializer(obj):\n for key, val in obj.items():\n if isinstance(val, six.string_types) and DATETIME_REGEX.search(val):\n try:\n obj[key] = dates.localize_datetime(parser.parse(val))\n except ValueError:\n obj[key] = val\n return obj","def test_datetime_encoding(self):\n data = {\n 'date': datetime.datetime(1970, 2, 3, 11, 20, 42),\n 'int': 4,\n 'str': 'string'\n }\n s = json.dumps(data, cls=stellr.stellr.StellrJSONEncoder)\n self.assertEqual(\n s, '{\"date\": \"1970-02-03T11:20:42Z\", \"int\": 4, \"str\": \"string\"}')","def json_serial(obj):\n if isinstance(obj, datetime):\n serial = obj.isoformat()\n return serial","def convert_datetime_to_iso(datetime_obj):\r\n return Date().to_json(datetime_obj)","def _serialize_datetime(cls, dt): # pylint: disable=invalid-name\n if dt:\n return JSONEncoder().default(dt)\n return None","def serialize_dt(value):\n return value.isoformat() if hasattr(value, 'isoformat') else value","def json_serial2(self, obj):\n if isinstance(obj, (datetime.datetime, datetime.date)):\n return obj.isoformat()\n raise TypeError(\"Type %s not serializable\" % type(obj))","def json_hook(obj):\n if \"$dt\" in obj:\n return datetime.datetime.strptime(obj[\"$dt\"], \"%Y-%m-%dT%H:%M:%S.%f\")\n return obj","def json_serial(obj):\n if isinstance(obj, datetime):\n serial = obj.isoformat()\n return serial\n raise TypeError (\"Type not serializable\")","def json_serial(obj):\n if isinstance(obj, datetime):\n serial = obj.isoformat()\n return serial\n raise TypeError(\"Type not serializable\")","def json_serial(obj):\n\n if isinstance(obj, (datetime, date)):\n return obj.isoformat()","def json_serial(obj):\n\n if isinstance(obj, (datetime)):\n return obj.isoformat()\n raise TypeError(\"Type %s not serializable\" % type(obj))","def json_serializer(obj):\n if isinstance(obj, (datetime.datetime, datetime.date)):\n serial = obj.isoformat()\n return serial","def _json_decoder_hook(obj):\n if \"starttime\" in obj:\n obj[\"starttime\"] = datetime.strptime(obj[\"starttime\"], \"%Y-%m-%dT%H:%M:%SZ\")\n if \"endtime\" in obj:\n obj[\"endtime\"] = datetime.strptime(obj[\"endtime\"], \"%Y-%m-%dT%H:%M:%SZ\")\n return obj","def json_serial(obj):\n\n if isinstance(obj, datetime):\n serial = obj.isoformat()\n return serial\n raise TypeError (\"Type not serializable\")","def json_serial(obj):\n\n if isinstance(obj, datetime):\n serial = obj.isoformat()\n return serial\n raise TypeError(\"Type not serializable\")","def json_serial(obj):\n\n if isinstance(obj, datetime):\n serial = obj.isoformat()\n return serial\n raise TypeError(\"Type not serializable\")","def json_serial(obj):\r\n\r\n\t\tif isinstance(obj,(datetime, date)):\r\n\t\t\treturn obj.isoformat()\r\n\t\traise TypeError (\"Type %s not serializable\" % type(obj))","def json_serial(obj):\n if isinstance(obj, (dt.datetime, dt.date)):\n return obj.isoformat()\n raise TypeError(\"Type %s not serializable\" % type(obj))","def test_serialization_deserialization(self):\n\n original_time = now()\n serialized_time = DatetimeMapper.forward(original_time)\n assert serialized_time == original_time.strftime(\"%Y-%m-%dT%H:%M:%S.%f\")\n\n deserialized_time = DatetimeMapper.backward(serialized_time)\n assert original_time == deserialized_time\n\n deserialized_time = DatetimeMapper.backward(None)\n assert isinstance(deserialized_time, datetime)","def deserialize_datetime(string):\n try:\n from dateutil.parser import parse\n return parse(string)\n except ImportError:\n return string","def json_serial(obj):\n\n\tif isinstance(obj, (dt.datetime, dt.date)):\n\t\treturn obj.isoformat()\n\traise TypeError (\"Type %s not serializable\" % type(obj))","def _serialize_date(val):\n return date_to_iso8601(val)","def dump_datetime(value):\n return value.strftime('%Y-%m-%dT%H:%M:%SZ')","def json_serial(obj):\n\n if isinstance(obj, (datetime, date)):\n return str(obj) #.isoformat()\n raise TypeError (\"Type %s not serializable\" % type(obj))","def json_serial(obj):\n if isinstance(obj, (datetime, date)):\n return obj.isoformat()\n raise TypeError(\"Type %s not serializable\" % type(obj))","def json_serial(obj):\n if isinstance(obj, (datetime, date)):\n return obj.isoformat()\n raise TypeError(\"Type %s not serializable\" % type(obj))","def json_serial(obj):\n if isinstance(obj, (datetime.datetime, datetime.date)):\n return obj.isoformat()\n raise TypeError(\"Type %s not serializable\" % type(obj))","def json_serial(obj):\r\n\r\n if isinstance(obj, (datetime, date)):\r\n return obj.isoformat()\r\n raise TypeError (\"Type %s not serializable\" % type(obj))","def serializer(obj):\n if isinstance(obj, datetime):\n return obj.strftime(ISO_TIME_FORMAT)\n else:\n raise TypeError('%s is not serializable' % obj)","def serialize(dt):\n # Two different date formats (unix timestamp=int, timestamp=string)\n # So, deal with both until sorted out on the elasticsearch side.\n try:\n return datetime.datetime.fromtimestamp(dt / 1000).isoformat()\n except:\n from dateutil import parser\n return parser.parse(dt).isoformat()","def to_json(self):\n new_dict = self.__dict__.copy()\n new_dict.update({'__class__': str(self.__class__.__name__)})\n for key, value in new_dict.items():\n if isinstance(value, datetime):\n new_dict[key] = value.strftime(self.timeformat)\n return new_dict","def json_serial(obj):\n\n if isinstance(obj, (datetime, date,date)):\n return obj.isoformat()\n raise TypeError (\"Type %s not serializable\" % type(obj))","def json_serial(obj):\n\n if isinstance(obj, datetime):\n serial = obj.isoformat()\n return serial\n # return unix_time_millis(obj)\n raise TypeError(\"Type not serializable\")","def json_date_parser(dct):\n date = \"%Y-%m-%d\"\n date_time = \"%Y-%m-%dT%H:%M:%S\"\n date_time2 = \"%Y-%m-%dT%H:%M\"\n date_time3 = \"%Y-%m-%d %H:%M\"\n date_time_offset = \"%Y-%m-%dT%H:%M:%S%z\"\n time = \"%H:%M:%S\"\n formats = [date, date_time, date_time2, date_time3, date_time_offset, time]\n for k, v in dct.items():\n if isinstance(v, str):\n for fmt in formats:\n try:\n dct[k] = datetime.strptime(v, fmt)\n except ValueError:\n pass\n return dct","def test_date_to_json(self):\n now = datetime.now()\n the_date = datetime(2018, 10, 13, 12, 12, 12)\n assert ct.date_to_json(the_date) == \"2018-10-13 12:12:12\"\n assert isinstance(ct.date_to_json(), str)\n assert ct.date_to_json()[:4] == str(now.year)","def json_serial(obj):\n\n if isinstance(obj, (datetime, date)):\n return obj.isoformat()\n raise TypeError (\"Type %s not serializable\" % type(obj))","def json_serial(obj):\n\n if isinstance(obj, (datetime, date)):\n return obj.isoformat()\n raise TypeError (\"Type %s not serializable\" % type(obj))","def json_serial(obj):\n\n if isinstance(obj, (datetime, date)):\n return obj.isoformat()\n raise TypeError (\"Type %s not serializable\" % type(obj))","def json_serial(obj):\n\n if isinstance(obj, (datetime, date)):\n return obj.isoformat()\n raise TypeError (\"Type %s not serializable\" % type(obj))","def json_serial(obj):\n\n if isinstance(obj, (datetime, date)):\n return obj.isoformat()\n raise TypeError(\"Type %s not serializable\" % type(obj))","def object_serializer(obj):\n return dates.format_iso_datetime(obj) if hasattr(obj, 'isoformat') else obj","def time_to_js(obj):\n if isinstance(obj, (datetime.date, datetime.datetime)):\n return obj.isoformat()","def object_to_json(obj):\n if isinstance(obj, (datetime.datetime, datetime.date, datetime.time)):\n return obj.isoformat()\n return str(obj)","def date2str(datetime_object):\n if datetime_object is None:\n return 'None'\n return datetime_object.strftime('%Y-%m-%dT%H:%M:%S.%f')[0:-3]","def test_json_to_date(self):\n json_date_string = \"2018-10-13 12:12:12\"\n the_date = ct.json_to_date(json_date_string)\n assert isinstance(the_date, datetime)\n assert the_date.year == 2018\n assert the_date.month == 10","def serialize_to_json(data):\n\n # Define the actual datetime serializer\n # See: https://stackoverflow.com/questions/8556398/generate-rfc-3339-timestamp-in-python#8556555\n def datetime_serializer(element):\n if isinstance(element, datetime):\n return element.isoformat(\"T\") + \"Z\"\n\n return json.dumps(data, default=datetime_serializer)","def to_json(self, value):\r\n if value is None:\r\n return None\r\n if isinstance(value, time.struct_time):\r\n # struct_times are always utc\r\n return time.strftime('%Y-%m-%dT%H:%M:%SZ', value)\r\n elif isinstance(value, datetime.datetime):\r\n if value.tzinfo is None or value.utcoffset().total_seconds() == 0:\r\n # isoformat adds +00:00 rather than Z\r\n return value.strftime('%Y-%m-%dT%H:%M:%SZ')\r\n else:\r\n return value.isoformat()\r\n else:\r\n raise TypeError(\"Cannot convert {!r} to json\".format(value))","def datetime_simplify(self, datetime_ref):\n\n return {\n \"simplify_strategy\": \"datetime\",\n \"value\": datetime_ref.strftime('%b %d %Y %H:%M:%S'),\n \"timezone\" : str(datetime_ref.tzinfo)\n }","def date_to_python(self, value):\r\n # this throws away fractions of a second\r\n return datetime(*strptime(value[:-5], \"%Y-%m-%dT%H:%M:%S\")[0:6])","def json_conversion_handler(x):\n if isinstance(x, datetime.datetime):\n return x.isoformat()\n raise TypeError(\"Unknown type\")","def serialize(obj):\n\n # if isinstance(obj, date):\n # serial = obj.isoformat()\n # return serial\n #\n # if isinstance(obj, time):\n # serial = obj.isoformat()\n # return serial\n\n return obj.to_json()","def _DumpEntityHandler(obj):\n if isinstance(obj, datetime):\n return obj.strftime('%Y-%m-%d %H:%M:%S')\n else:\n raise TypeError('%r is not JSON serializable' % obj)","def _serialize_time(val):\n return val.isoformat()","def serialize_date(dt):\n if dt.tzinfo:\n dt = dt.astimezone(UTC).replace(tzinfo=None)\n return dt.isoformat()","def decode_datetime(self, string):\n if isinstance(string, str):\n if 'T' in string:\n return datetime.strptime(string, \"%Y%m%dT%H%M%S\")\n else:\n return datetime.strptime(string, \"%Y%m%d\")\n else:\n return string","def loads(s, obj_cls=None, **kwargs):\n if s is None or s == \"\":\n return None\n d = json.loads(s, object_hook=_decode_datetime, **kwargs)\n if obj_cls is None:\n return d\n else:\n return loadd(d, obj_cls)","def datetime_from_json(data):\n\n a = data['meta']['dateobs']\n year = int(a[:4])\n month = int(a[4:6])\n day = int(a[6:8])\n hour = int(a[9:11])\n time1 = datetime.datetime(year, month, day, hour)\n return time1","def encode_datetime(self, datetime_obj):\n if isinstance(datetime_obj, np.datetime64):\n datetime_obj = pd.Timestamp(datetime_obj).to_pydatetime()\n\n if isinstance(datetime_obj, dt.datetime):\n return datetime_obj.strftime(\"%Y%m%dT%H%M%S\")\n elif isinstance(datetime_obj, dt.date):\n return datetime_obj.strftime(\"%Y%m%d\")\n else:\n return datetime_obj","def datetime_to_str(obj):\n return obj.strftime(\"%Y-%m-%d %H:%M:%S\")","def dumps(obj, **kwargs):\n if obj is None:\n return \"\"\n d = dumpd(obj)\n return json.dumps(d, default=_encode_datetime, **kwargs)","def serialize_date(value):\n return datetime.strptime(value, '%d %b %Y')","def dump_datetime(value):\n if value is None:\n return\n return [value.strftime(\"%Y-%m-%d\"), value.strftime(\"%H:%M:%S\")]","def datetime_format(value):\n try:\n datetime_obj = Deserializer.deserialize_iso(value)\n except DeserializationError:\n message = \"Argument {} is not a valid ISO-8601 datetime format\"\n raise ValueError(message.format(value))\n else:\n return datetime_obj","def myconverter(o: object):\n if isinstance(o, datetime.datetime):\n return o.__str__()","def json_encoder(obj):\n if isinstance(obj, datetime.datetime):\n return time.mktime(obj.timetuple())\n if isinstance(obj, datetime.timedelta):\n return obj.total_seconds()","def serialize_datetime(date_time) -> timezone:\n if isinstance(date_time, struct_time):\n return timezone.datetime.fromtimestamp(mktime(date_time)).replace(tzinfo=pytz.UTC)\n else:\n raise TypeError(\"Can't convert this type to datetime\")","def json_serial(obj):\n if isinstance(obj, LegipyModel):\n return obj.to_json()\n elif isinstance(obj, (datetime.date, datetime.datetime)):\n return obj.isoformat()\n raise TypeError(\"Type {0} not serializable\".format(repr(type(obj))))","def set_datetimes_to_str_in_place(json_dict: Dict[str, Any]) -> None:\n for key, value in json_dict.items():\n if isinstance(value, datetime.datetime):\n json_dict[key] = str(value)","def _json_date_encoder(obj):\r\n if isinstance(obj, datetime.date):\r\n return obj.isoformat()\r\n elif isinstance(obj, decimal.Decimal):\r\n return float(obj)","def default(self, obj): # pylint: disable=method-hidden\r\n\r\n if isinstance(obj, datetime):\r\n if obj.tzinfo is None:\r\n # Localize to UTC naive datetime objects\r\n obj = UTC.localize(obj)\r\n else:\r\n # Convert to UTC datetime objects from other timezones\r\n obj = obj.astimezone(UTC)\r\n return obj.isoformat()\r\n elif isinstance(obj, date):\r\n return obj.isoformat()\r\n\r\n return super(DateTimeJSONEncoder, self).default(obj)","def datetime_to_str(obj: \"datetime\") -> str:\n if obj.tzinfo is not None and obj.tzinfo.utcoffset(obj) is not None:\n # aware time; translate to UTC\n obj = obj.astimezone(timezone.utc)\n obj = obj.replace(tzinfo=None)\n return obj.isoformat() + \"Z\"","def serialize_datetime_utc_to_json(date: datetime, name: str):\n srlz_date = serialize_datetime_utc(date)\n if srlz_date:\n return json.dumps({\"date\": srlz_date, \"name\": name})\n else:\n return None","def serialize(obj):\n\n if isinstance(obj, datetime.datetime):\n serial = obj.replace(microsecond=0).replace(tzinfo=None).isoformat() + \"Z\"\n return serial\n\n if isinstance(obj, bytes):\n return obj.decode('utf-8')\n\n return obj.__dict__","def serialize(obj):\n\n if isinstance(obj, datetime.datetime):\n serial = obj.replace(microsecond=0).replace(tzinfo=None).isoformat() + \"Z\"\n return serial\n\n if isinstance(obj, bytes):\n return obj.decode('utf-8')\n\n return obj.__dict__","def to_python(self, value):\n return string_to_partialdate(value)","def deserialize_date(string):\n try:\n from dateutil.parser import parse\n return parse(string).date()\n except ImportError:\n return string","def dehydrate_datetime(value):\n\n def seconds_and_nanoseconds(dt):\n if isinstance(dt, datetime):\n dt = DateTime.from_native(dt)\n zone_epoch = DateTime(1970, 1, 1, tzinfo=dt.tzinfo)\n t = dt.to_clock_time() - zone_epoch.to_clock_time()\n return t.seconds, t.nanoseconds\n\n tz = value.tzinfo\n if tz is None:\n # without time zone\n value = utc.localize(value)\n seconds, nanoseconds = seconds_and_nanoseconds(value)\n return Structure(ord(b\"d\"), seconds, nanoseconds)\n elif hasattr(tz, \"zone\") and tz.zone:\n # with named time zone\n seconds, nanoseconds = seconds_and_nanoseconds(value)\n return Structure(ord(b\"f\"), seconds, nanoseconds, tz.zone)\n else:\n # with time offset\n seconds, nanoseconds = seconds_and_nanoseconds(value)\n return Structure(ord(b\"F\"), seconds, nanoseconds, tz.utcoffset(value).seconds)","def _sanitizer(self, obj):\n if isinstance(obj, datetime.datetime):\n return obj.isoformat()\n if hasattr(obj, \"to_dict\"):\n return obj.to_dict()\n return obj","def date_to_str(obj: \"date\") -> str:\n return obj.isoformat()","def to_json(self, value):\r\n try:\r\n result = super(StringOrDate, self).to_json(value)\r\n except:\r\n return value\r\n if result is None:\r\n return value\r\n else:\r\n return result","def parse(data, datetime_field=None):\n\n parsed_data = json.loads(data)\n if datetime_field:\n if isinstance(parsed_data, list):\n for item in parsed_data:\n date = item.get(datetime_field)\n if date:\n item[datetime_field] = dateparse.parse_datetime(date)\n elif isinstance(parsed_data, dict):\n date = parsed_data.get(datetime_field)\n if date:\n parsed_data[datetime_field] = dateparse.parse_datetime(date)\n\n return parsed_data","def test_deserialize(self):\r\n\r\n # test that from_json produces no exceptions\r\n self.assertDeserializeEqual('10:20:30', '\"10:20:30\"')","def test_dumps_datetime(self):\n try:\n _build_test_dirs()\n dicti = {\n 'datetime': datetime.datetime.now(),\n 'array': [1, 2, 3],\n 'string': 'trololo',\n 'int': 1,\n 'float': 4.32,\n 'true': True,\n 'false': False,\n 'null': None\n }\n with open(_TEST_FILE, 'w+') as fileobj:\n morejson.dump(dicti, fileobj)\n with open(_TEST_FILE, 'r') as fileobj:\n self.assertEqual(dicti, morejson.load(fileobj))\n finally:\n _dismantle_test_dirs()","def serialize_obj(obj: Any) -> Any:\n if isinstance(obj, (datetime.date, datetime.datetime)):\n return obj.isoformat()\n else:\n return obj","def serialize_obj(obj: Any) -> Any:\n if isinstance(obj, (datetime.date, datetime.datetime)):\n return obj.isoformat()\n else:\n return obj","def json_serial(obj):\n\n if isinstance(obj, (datetime, date)):\n return obj.isoformat()\n if isinstance(obj, complex):\n return str(obj)\n raise TypeError (\"Type %s not serializable\" % type(obj))","def unmarshall_time(tyme):\r\n return datetime.datetime(day=tyme['day'],\r\n month=tyme['month'],\r\n year=tyme['year'],\r\n hour=tyme['hour'],\r\n minute=tyme['minute'],\r\n second=tyme['second'],\r\n microsecond=tyme['microsecond'])","def jsonSerial(obj):\n\n if isinstance(obj, (datetime, date)):\n return obj.isoformat()\n\n if isinstance(obj, enum.Enum):\n return obj.value\n\n raise TypeError (\"Type %s not serializable\" % type(obj))","def json_converter(obj):\n if isinstance(obj, Decimal):\n return float(obj)\n elif isinstance(obj, date):\n return obj.isoformat()\n else:\n raise TypeError(\"Cannot convert provided type {}\".format(type(obj)))"],"string":"[\n \"def serialize_datetime(self, obj):\\r\\n if isinstance(obj, datetime.datetime):\\r\\n return obj.isoformat()\\r\\n raise TypeError(\\\"Type not serializable\\\")\",\n \"def json_datetime_serializer(obj):\\n\\n if isinstance(obj, datetime):\\n serial = obj.isoformat()\\n return serial\\n raise TypeError(\\\"{} is not JSON serializable.\\\".format(obj))\",\n \"def json_serialize_datetime(self, obj):\\n if isinstance(obj, datetime.datetime):\\n return int(time.mktime(obj.timetuple()))\\n\\n raise TypeError(\\\"Type %s not serializable\\\" % type(obj))\",\n \"def _serialize_datetime(val):\\n return datetime_to_iso8601(val)\",\n \"def convert_for_json(obj):\\n if isinstance(obj, datetime.datetime):\\n return obj.__str__()\\n return obj\",\n \"def json_serial(obj):\\n if isinstance(obj, datetime):\\n return obj.isoformat()\",\n \"def deserialise(obj):\\n if isinstance(obj, str) and 12 < len(obj) < 40:\\n try:\\n # some tests try tricking us with timezones - but we assume naive datetime objects in utc\\n # 1970-01-21T21:14:37+12:45 -> 1970-01-21 08:29:37 (1970-01-21T08:29:37)\\n x = obj\\n obj = du_parser.parse(obj).astimezone(tz=du_tz.tzutc()).replace(tzinfo=None)\\n LOG.info('datetime rehydrated: %s -> %s (%s)' % (x, obj, obj.isoformat()))\\n except Exception as e:\\n LOG.debug('not a date: %s (%s)' % (obj, e))\\n return obj\",\n \"def object_deserializer(obj):\\n for key, val in obj.items():\\n if isinstance(val, six.string_types) and DATETIME_REGEX.search(val):\\n try:\\n obj[key] = dates.localize_datetime(parser.parse(val))\\n except ValueError:\\n obj[key] = val\\n return obj\",\n \"def test_datetime_encoding(self):\\n data = {\\n 'date': datetime.datetime(1970, 2, 3, 11, 20, 42),\\n 'int': 4,\\n 'str': 'string'\\n }\\n s = json.dumps(data, cls=stellr.stellr.StellrJSONEncoder)\\n self.assertEqual(\\n s, '{\\\"date\\\": \\\"1970-02-03T11:20:42Z\\\", \\\"int\\\": 4, \\\"str\\\": \\\"string\\\"}')\",\n \"def json_serial(obj):\\n if isinstance(obj, datetime):\\n serial = obj.isoformat()\\n return serial\",\n \"def convert_datetime_to_iso(datetime_obj):\\r\\n return Date().to_json(datetime_obj)\",\n \"def _serialize_datetime(cls, dt): # pylint: disable=invalid-name\\n if dt:\\n return JSONEncoder().default(dt)\\n return None\",\n \"def serialize_dt(value):\\n return value.isoformat() if hasattr(value, 'isoformat') else value\",\n \"def json_serial2(self, obj):\\n if isinstance(obj, (datetime.datetime, datetime.date)):\\n return obj.isoformat()\\n raise TypeError(\\\"Type %s not serializable\\\" % type(obj))\",\n \"def json_hook(obj):\\n if \\\"$dt\\\" in obj:\\n return datetime.datetime.strptime(obj[\\\"$dt\\\"], \\\"%Y-%m-%dT%H:%M:%S.%f\\\")\\n return obj\",\n \"def json_serial(obj):\\n if isinstance(obj, datetime):\\n serial = obj.isoformat()\\n return serial\\n raise TypeError (\\\"Type not serializable\\\")\",\n \"def json_serial(obj):\\n if isinstance(obj, datetime):\\n serial = obj.isoformat()\\n return serial\\n raise TypeError(\\\"Type not serializable\\\")\",\n \"def json_serial(obj):\\n\\n if isinstance(obj, (datetime, date)):\\n return obj.isoformat()\",\n \"def json_serial(obj):\\n\\n if isinstance(obj, (datetime)):\\n return obj.isoformat()\\n raise TypeError(\\\"Type %s not serializable\\\" % type(obj))\",\n \"def json_serializer(obj):\\n if isinstance(obj, (datetime.datetime, datetime.date)):\\n serial = obj.isoformat()\\n return serial\",\n \"def _json_decoder_hook(obj):\\n if \\\"starttime\\\" in obj:\\n obj[\\\"starttime\\\"] = datetime.strptime(obj[\\\"starttime\\\"], \\\"%Y-%m-%dT%H:%M:%SZ\\\")\\n if \\\"endtime\\\" in obj:\\n obj[\\\"endtime\\\"] = datetime.strptime(obj[\\\"endtime\\\"], \\\"%Y-%m-%dT%H:%M:%SZ\\\")\\n return obj\",\n \"def json_serial(obj):\\n\\n if isinstance(obj, datetime):\\n serial = obj.isoformat()\\n return serial\\n raise TypeError (\\\"Type not serializable\\\")\",\n \"def json_serial(obj):\\n\\n if isinstance(obj, datetime):\\n serial = obj.isoformat()\\n return serial\\n raise TypeError(\\\"Type not serializable\\\")\",\n \"def json_serial(obj):\\n\\n if isinstance(obj, datetime):\\n serial = obj.isoformat()\\n return serial\\n raise TypeError(\\\"Type not serializable\\\")\",\n \"def json_serial(obj):\\r\\n\\r\\n\\t\\tif isinstance(obj,(datetime, date)):\\r\\n\\t\\t\\treturn obj.isoformat()\\r\\n\\t\\traise TypeError (\\\"Type %s not serializable\\\" % type(obj))\",\n \"def json_serial(obj):\\n if isinstance(obj, (dt.datetime, dt.date)):\\n return obj.isoformat()\\n raise TypeError(\\\"Type %s not serializable\\\" % type(obj))\",\n \"def test_serialization_deserialization(self):\\n\\n original_time = now()\\n serialized_time = DatetimeMapper.forward(original_time)\\n assert serialized_time == original_time.strftime(\\\"%Y-%m-%dT%H:%M:%S.%f\\\")\\n\\n deserialized_time = DatetimeMapper.backward(serialized_time)\\n assert original_time == deserialized_time\\n\\n deserialized_time = DatetimeMapper.backward(None)\\n assert isinstance(deserialized_time, datetime)\",\n \"def deserialize_datetime(string):\\n try:\\n from dateutil.parser import parse\\n return parse(string)\\n except ImportError:\\n return string\",\n \"def json_serial(obj):\\n\\n\\tif isinstance(obj, (dt.datetime, dt.date)):\\n\\t\\treturn obj.isoformat()\\n\\traise TypeError (\\\"Type %s not serializable\\\" % type(obj))\",\n \"def _serialize_date(val):\\n return date_to_iso8601(val)\",\n \"def dump_datetime(value):\\n return value.strftime('%Y-%m-%dT%H:%M:%SZ')\",\n \"def json_serial(obj):\\n\\n if isinstance(obj, (datetime, date)):\\n return str(obj) #.isoformat()\\n raise TypeError (\\\"Type %s not serializable\\\" % type(obj))\",\n \"def json_serial(obj):\\n if isinstance(obj, (datetime, date)):\\n return obj.isoformat()\\n raise TypeError(\\\"Type %s not serializable\\\" % type(obj))\",\n \"def json_serial(obj):\\n if isinstance(obj, (datetime, date)):\\n return obj.isoformat()\\n raise TypeError(\\\"Type %s not serializable\\\" % type(obj))\",\n \"def json_serial(obj):\\n if isinstance(obj, (datetime.datetime, datetime.date)):\\n return obj.isoformat()\\n raise TypeError(\\\"Type %s not serializable\\\" % type(obj))\",\n \"def json_serial(obj):\\r\\n\\r\\n if isinstance(obj, (datetime, date)):\\r\\n return obj.isoformat()\\r\\n raise TypeError (\\\"Type %s not serializable\\\" % type(obj))\",\n \"def serializer(obj):\\n if isinstance(obj, datetime):\\n return obj.strftime(ISO_TIME_FORMAT)\\n else:\\n raise TypeError('%s is not serializable' % obj)\",\n \"def serialize(dt):\\n # Two different date formats (unix timestamp=int, timestamp=string)\\n # So, deal with both until sorted out on the elasticsearch side.\\n try:\\n return datetime.datetime.fromtimestamp(dt / 1000).isoformat()\\n except:\\n from dateutil import parser\\n return parser.parse(dt).isoformat()\",\n \"def to_json(self):\\n new_dict = self.__dict__.copy()\\n new_dict.update({'__class__': str(self.__class__.__name__)})\\n for key, value in new_dict.items():\\n if isinstance(value, datetime):\\n new_dict[key] = value.strftime(self.timeformat)\\n return new_dict\",\n \"def json_serial(obj):\\n\\n if isinstance(obj, (datetime, date,date)):\\n return obj.isoformat()\\n raise TypeError (\\\"Type %s not serializable\\\" % type(obj))\",\n \"def json_serial(obj):\\n\\n if isinstance(obj, datetime):\\n serial = obj.isoformat()\\n return serial\\n # return unix_time_millis(obj)\\n raise TypeError(\\\"Type not serializable\\\")\",\n \"def json_date_parser(dct):\\n date = \\\"%Y-%m-%d\\\"\\n date_time = \\\"%Y-%m-%dT%H:%M:%S\\\"\\n date_time2 = \\\"%Y-%m-%dT%H:%M\\\"\\n date_time3 = \\\"%Y-%m-%d %H:%M\\\"\\n date_time_offset = \\\"%Y-%m-%dT%H:%M:%S%z\\\"\\n time = \\\"%H:%M:%S\\\"\\n formats = [date, date_time, date_time2, date_time3, date_time_offset, time]\\n for k, v in dct.items():\\n if isinstance(v, str):\\n for fmt in formats:\\n try:\\n dct[k] = datetime.strptime(v, fmt)\\n except ValueError:\\n pass\\n return dct\",\n \"def test_date_to_json(self):\\n now = datetime.now()\\n the_date = datetime(2018, 10, 13, 12, 12, 12)\\n assert ct.date_to_json(the_date) == \\\"2018-10-13 12:12:12\\\"\\n assert isinstance(ct.date_to_json(), str)\\n assert ct.date_to_json()[:4] == str(now.year)\",\n \"def json_serial(obj):\\n\\n if isinstance(obj, (datetime, date)):\\n return obj.isoformat()\\n raise TypeError (\\\"Type %s not serializable\\\" % type(obj))\",\n \"def json_serial(obj):\\n\\n if isinstance(obj, (datetime, date)):\\n return obj.isoformat()\\n raise TypeError (\\\"Type %s not serializable\\\" % type(obj))\",\n \"def json_serial(obj):\\n\\n if isinstance(obj, (datetime, date)):\\n return obj.isoformat()\\n raise TypeError (\\\"Type %s not serializable\\\" % type(obj))\",\n \"def json_serial(obj):\\n\\n if isinstance(obj, (datetime, date)):\\n return obj.isoformat()\\n raise TypeError (\\\"Type %s not serializable\\\" % type(obj))\",\n \"def json_serial(obj):\\n\\n if isinstance(obj, (datetime, date)):\\n return obj.isoformat()\\n raise TypeError(\\\"Type %s not serializable\\\" % type(obj))\",\n \"def object_serializer(obj):\\n return dates.format_iso_datetime(obj) if hasattr(obj, 'isoformat') else obj\",\n \"def time_to_js(obj):\\n if isinstance(obj, (datetime.date, datetime.datetime)):\\n return obj.isoformat()\",\n \"def object_to_json(obj):\\n if isinstance(obj, (datetime.datetime, datetime.date, datetime.time)):\\n return obj.isoformat()\\n return str(obj)\",\n \"def date2str(datetime_object):\\n if datetime_object is None:\\n return 'None'\\n return datetime_object.strftime('%Y-%m-%dT%H:%M:%S.%f')[0:-3]\",\n \"def test_json_to_date(self):\\n json_date_string = \\\"2018-10-13 12:12:12\\\"\\n the_date = ct.json_to_date(json_date_string)\\n assert isinstance(the_date, datetime)\\n assert the_date.year == 2018\\n assert the_date.month == 10\",\n \"def serialize_to_json(data):\\n\\n # Define the actual datetime serializer\\n # See: https://stackoverflow.com/questions/8556398/generate-rfc-3339-timestamp-in-python#8556555\\n def datetime_serializer(element):\\n if isinstance(element, datetime):\\n return element.isoformat(\\\"T\\\") + \\\"Z\\\"\\n\\n return json.dumps(data, default=datetime_serializer)\",\n \"def to_json(self, value):\\r\\n if value is None:\\r\\n return None\\r\\n if isinstance(value, time.struct_time):\\r\\n # struct_times are always utc\\r\\n return time.strftime('%Y-%m-%dT%H:%M:%SZ', value)\\r\\n elif isinstance(value, datetime.datetime):\\r\\n if value.tzinfo is None or value.utcoffset().total_seconds() == 0:\\r\\n # isoformat adds +00:00 rather than Z\\r\\n return value.strftime('%Y-%m-%dT%H:%M:%SZ')\\r\\n else:\\r\\n return value.isoformat()\\r\\n else:\\r\\n raise TypeError(\\\"Cannot convert {!r} to json\\\".format(value))\",\n \"def datetime_simplify(self, datetime_ref):\\n\\n return {\\n \\\"simplify_strategy\\\": \\\"datetime\\\",\\n \\\"value\\\": datetime_ref.strftime('%b %d %Y %H:%M:%S'),\\n \\\"timezone\\\" : str(datetime_ref.tzinfo)\\n }\",\n \"def date_to_python(self, value):\\r\\n # this throws away fractions of a second\\r\\n return datetime(*strptime(value[:-5], \\\"%Y-%m-%dT%H:%M:%S\\\")[0:6])\",\n \"def json_conversion_handler(x):\\n if isinstance(x, datetime.datetime):\\n return x.isoformat()\\n raise TypeError(\\\"Unknown type\\\")\",\n \"def serialize(obj):\\n\\n # if isinstance(obj, date):\\n # serial = obj.isoformat()\\n # return serial\\n #\\n # if isinstance(obj, time):\\n # serial = obj.isoformat()\\n # return serial\\n\\n return obj.to_json()\",\n \"def _DumpEntityHandler(obj):\\n if isinstance(obj, datetime):\\n return obj.strftime('%Y-%m-%d %H:%M:%S')\\n else:\\n raise TypeError('%r is not JSON serializable' % obj)\",\n \"def _serialize_time(val):\\n return val.isoformat()\",\n \"def serialize_date(dt):\\n if dt.tzinfo:\\n dt = dt.astimezone(UTC).replace(tzinfo=None)\\n return dt.isoformat()\",\n \"def decode_datetime(self, string):\\n if isinstance(string, str):\\n if 'T' in string:\\n return datetime.strptime(string, \\\"%Y%m%dT%H%M%S\\\")\\n else:\\n return datetime.strptime(string, \\\"%Y%m%d\\\")\\n else:\\n return string\",\n \"def loads(s, obj_cls=None, **kwargs):\\n if s is None or s == \\\"\\\":\\n return None\\n d = json.loads(s, object_hook=_decode_datetime, **kwargs)\\n if obj_cls is None:\\n return d\\n else:\\n return loadd(d, obj_cls)\",\n \"def datetime_from_json(data):\\n\\n a = data['meta']['dateobs']\\n year = int(a[:4])\\n month = int(a[4:6])\\n day = int(a[6:8])\\n hour = int(a[9:11])\\n time1 = datetime.datetime(year, month, day, hour)\\n return time1\",\n \"def encode_datetime(self, datetime_obj):\\n if isinstance(datetime_obj, np.datetime64):\\n datetime_obj = pd.Timestamp(datetime_obj).to_pydatetime()\\n\\n if isinstance(datetime_obj, dt.datetime):\\n return datetime_obj.strftime(\\\"%Y%m%dT%H%M%S\\\")\\n elif isinstance(datetime_obj, dt.date):\\n return datetime_obj.strftime(\\\"%Y%m%d\\\")\\n else:\\n return datetime_obj\",\n \"def datetime_to_str(obj):\\n return obj.strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\",\n \"def dumps(obj, **kwargs):\\n if obj is None:\\n return \\\"\\\"\\n d = dumpd(obj)\\n return json.dumps(d, default=_encode_datetime, **kwargs)\",\n \"def serialize_date(value):\\n return datetime.strptime(value, '%d %b %Y')\",\n \"def dump_datetime(value):\\n if value is None:\\n return\\n return [value.strftime(\\\"%Y-%m-%d\\\"), value.strftime(\\\"%H:%M:%S\\\")]\",\n \"def datetime_format(value):\\n try:\\n datetime_obj = Deserializer.deserialize_iso(value)\\n except DeserializationError:\\n message = \\\"Argument {} is not a valid ISO-8601 datetime format\\\"\\n raise ValueError(message.format(value))\\n else:\\n return datetime_obj\",\n \"def myconverter(o: object):\\n if isinstance(o, datetime.datetime):\\n return o.__str__()\",\n \"def json_encoder(obj):\\n if isinstance(obj, datetime.datetime):\\n return time.mktime(obj.timetuple())\\n if isinstance(obj, datetime.timedelta):\\n return obj.total_seconds()\",\n \"def serialize_datetime(date_time) -> timezone:\\n if isinstance(date_time, struct_time):\\n return timezone.datetime.fromtimestamp(mktime(date_time)).replace(tzinfo=pytz.UTC)\\n else:\\n raise TypeError(\\\"Can't convert this type to datetime\\\")\",\n \"def json_serial(obj):\\n if isinstance(obj, LegipyModel):\\n return obj.to_json()\\n elif isinstance(obj, (datetime.date, datetime.datetime)):\\n return obj.isoformat()\\n raise TypeError(\\\"Type {0} not serializable\\\".format(repr(type(obj))))\",\n \"def set_datetimes_to_str_in_place(json_dict: Dict[str, Any]) -> None:\\n for key, value in json_dict.items():\\n if isinstance(value, datetime.datetime):\\n json_dict[key] = str(value)\",\n \"def _json_date_encoder(obj):\\r\\n if isinstance(obj, datetime.date):\\r\\n return obj.isoformat()\\r\\n elif isinstance(obj, decimal.Decimal):\\r\\n return float(obj)\",\n \"def default(self, obj): # pylint: disable=method-hidden\\r\\n\\r\\n if isinstance(obj, datetime):\\r\\n if obj.tzinfo is None:\\r\\n # Localize to UTC naive datetime objects\\r\\n obj = UTC.localize(obj)\\r\\n else:\\r\\n # Convert to UTC datetime objects from other timezones\\r\\n obj = obj.astimezone(UTC)\\r\\n return obj.isoformat()\\r\\n elif isinstance(obj, date):\\r\\n return obj.isoformat()\\r\\n\\r\\n return super(DateTimeJSONEncoder, self).default(obj)\",\n \"def datetime_to_str(obj: \\\"datetime\\\") -> str:\\n if obj.tzinfo is not None and obj.tzinfo.utcoffset(obj) is not None:\\n # aware time; translate to UTC\\n obj = obj.astimezone(timezone.utc)\\n obj = obj.replace(tzinfo=None)\\n return obj.isoformat() + \\\"Z\\\"\",\n \"def serialize_datetime_utc_to_json(date: datetime, name: str):\\n srlz_date = serialize_datetime_utc(date)\\n if srlz_date:\\n return json.dumps({\\\"date\\\": srlz_date, \\\"name\\\": name})\\n else:\\n return None\",\n \"def serialize(obj):\\n\\n if isinstance(obj, datetime.datetime):\\n serial = obj.replace(microsecond=0).replace(tzinfo=None).isoformat() + \\\"Z\\\"\\n return serial\\n\\n if isinstance(obj, bytes):\\n return obj.decode('utf-8')\\n\\n return obj.__dict__\",\n \"def serialize(obj):\\n\\n if isinstance(obj, datetime.datetime):\\n serial = obj.replace(microsecond=0).replace(tzinfo=None).isoformat() + \\\"Z\\\"\\n return serial\\n\\n if isinstance(obj, bytes):\\n return obj.decode('utf-8')\\n\\n return obj.__dict__\",\n \"def to_python(self, value):\\n return string_to_partialdate(value)\",\n \"def deserialize_date(string):\\n try:\\n from dateutil.parser import parse\\n return parse(string).date()\\n except ImportError:\\n return string\",\n \"def dehydrate_datetime(value):\\n\\n def seconds_and_nanoseconds(dt):\\n if isinstance(dt, datetime):\\n dt = DateTime.from_native(dt)\\n zone_epoch = DateTime(1970, 1, 1, tzinfo=dt.tzinfo)\\n t = dt.to_clock_time() - zone_epoch.to_clock_time()\\n return t.seconds, t.nanoseconds\\n\\n tz = value.tzinfo\\n if tz is None:\\n # without time zone\\n value = utc.localize(value)\\n seconds, nanoseconds = seconds_and_nanoseconds(value)\\n return Structure(ord(b\\\"d\\\"), seconds, nanoseconds)\\n elif hasattr(tz, \\\"zone\\\") and tz.zone:\\n # with named time zone\\n seconds, nanoseconds = seconds_and_nanoseconds(value)\\n return Structure(ord(b\\\"f\\\"), seconds, nanoseconds, tz.zone)\\n else:\\n # with time offset\\n seconds, nanoseconds = seconds_and_nanoseconds(value)\\n return Structure(ord(b\\\"F\\\"), seconds, nanoseconds, tz.utcoffset(value).seconds)\",\n \"def _sanitizer(self, obj):\\n if isinstance(obj, datetime.datetime):\\n return obj.isoformat()\\n if hasattr(obj, \\\"to_dict\\\"):\\n return obj.to_dict()\\n return obj\",\n \"def date_to_str(obj: \\\"date\\\") -> str:\\n return obj.isoformat()\",\n \"def to_json(self, value):\\r\\n try:\\r\\n result = super(StringOrDate, self).to_json(value)\\r\\n except:\\r\\n return value\\r\\n if result is None:\\r\\n return value\\r\\n else:\\r\\n return result\",\n \"def parse(data, datetime_field=None):\\n\\n parsed_data = json.loads(data)\\n if datetime_field:\\n if isinstance(parsed_data, list):\\n for item in parsed_data:\\n date = item.get(datetime_field)\\n if date:\\n item[datetime_field] = dateparse.parse_datetime(date)\\n elif isinstance(parsed_data, dict):\\n date = parsed_data.get(datetime_field)\\n if date:\\n parsed_data[datetime_field] = dateparse.parse_datetime(date)\\n\\n return parsed_data\",\n \"def test_deserialize(self):\\r\\n\\r\\n # test that from_json produces no exceptions\\r\\n self.assertDeserializeEqual('10:20:30', '\\\"10:20:30\\\"')\",\n \"def test_dumps_datetime(self):\\n try:\\n _build_test_dirs()\\n dicti = {\\n 'datetime': datetime.datetime.now(),\\n 'array': [1, 2, 3],\\n 'string': 'trololo',\\n 'int': 1,\\n 'float': 4.32,\\n 'true': True,\\n 'false': False,\\n 'null': None\\n }\\n with open(_TEST_FILE, 'w+') as fileobj:\\n morejson.dump(dicti, fileobj)\\n with open(_TEST_FILE, 'r') as fileobj:\\n self.assertEqual(dicti, morejson.load(fileobj))\\n finally:\\n _dismantle_test_dirs()\",\n \"def serialize_obj(obj: Any) -> Any:\\n if isinstance(obj, (datetime.date, datetime.datetime)):\\n return obj.isoformat()\\n else:\\n return obj\",\n \"def serialize_obj(obj: Any) -> Any:\\n if isinstance(obj, (datetime.date, datetime.datetime)):\\n return obj.isoformat()\\n else:\\n return obj\",\n \"def json_serial(obj):\\n\\n if isinstance(obj, (datetime, date)):\\n return obj.isoformat()\\n if isinstance(obj, complex):\\n return str(obj)\\n raise TypeError (\\\"Type %s not serializable\\\" % type(obj))\",\n \"def unmarshall_time(tyme):\\r\\n return datetime.datetime(day=tyme['day'],\\r\\n month=tyme['month'],\\r\\n year=tyme['year'],\\r\\n hour=tyme['hour'],\\r\\n minute=tyme['minute'],\\r\\n second=tyme['second'],\\r\\n microsecond=tyme['microsecond'])\",\n \"def jsonSerial(obj):\\n\\n if isinstance(obj, (datetime, date)):\\n return obj.isoformat()\\n\\n if isinstance(obj, enum.Enum):\\n return obj.value\\n\\n raise TypeError (\\\"Type %s not serializable\\\" % type(obj))\",\n \"def json_converter(obj):\\n if isinstance(obj, Decimal):\\n return float(obj)\\n elif isinstance(obj, date):\\n return obj.isoformat()\\n else:\\n raise TypeError(\\\"Cannot convert provided type {}\\\".format(type(obj)))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.69946605","0.69832784","0.6899136","0.6865027","0.6813981","0.6750276","0.6649766","0.6633779","0.65959406","0.6547565","0.65440494","0.6539357","0.6486496","0.6480947","0.64407444","0.63945824","0.6365423","0.63568634","0.6337701","0.6311433","0.62932414","0.6281401","0.62533945","0.62533945","0.62398726","0.6202354","0.6192055","0.6177737","0.6174645","0.6159559","0.61190903","0.6108999","0.61023974","0.61023974","0.6091736","0.607565","0.6056529","0.6027362","0.60240066","0.60159695","0.60109514","0.60096043","0.6003769","0.60036963","0.60036963","0.60036963","0.60036963","0.5995087","0.5972152","0.59657574","0.59537226","0.5931409","0.59252524","0.5919171","0.58976763","0.5876612","0.58755356","0.58645153","0.5854626","0.5828439","0.58248883","0.5813387","0.58089316","0.58015645","0.5789683","0.5788708","0.57787365","0.5777174","0.57650024","0.5763224","0.57593584","0.5752235","0.57512456","0.5695357","0.5694719","0.56826174","0.56446457","0.5624713","0.56069595","0.5601363","0.5598727","0.5598727","0.55964035","0.558799","0.55832875","0.55788124","0.5542831","0.553619","0.5522106","0.5520491","0.5502282","0.5497314","0.5497314","0.54966307","0.5494005","0.5478229","0.5472546"],"string":"[\n \"0.69946605\",\n \"0.69832784\",\n \"0.6899136\",\n \"0.6865027\",\n \"0.6813981\",\n \"0.6750276\",\n \"0.6649766\",\n \"0.6633779\",\n \"0.65959406\",\n \"0.6547565\",\n \"0.65440494\",\n \"0.6539357\",\n \"0.6486496\",\n \"0.6480947\",\n \"0.64407444\",\n \"0.63945824\",\n \"0.6365423\",\n \"0.63568634\",\n \"0.6337701\",\n \"0.6311433\",\n \"0.62932414\",\n \"0.6281401\",\n \"0.62533945\",\n \"0.62533945\",\n \"0.62398726\",\n \"0.6202354\",\n \"0.6192055\",\n \"0.6177737\",\n \"0.6174645\",\n \"0.6159559\",\n \"0.61190903\",\n \"0.6108999\",\n \"0.61023974\",\n \"0.61023974\",\n \"0.6091736\",\n \"0.607565\",\n \"0.6056529\",\n \"0.6027362\",\n \"0.60240066\",\n \"0.60159695\",\n \"0.60109514\",\n \"0.60096043\",\n \"0.6003769\",\n \"0.60036963\",\n \"0.60036963\",\n \"0.60036963\",\n \"0.60036963\",\n \"0.5995087\",\n \"0.5972152\",\n \"0.59657574\",\n \"0.59537226\",\n \"0.5931409\",\n \"0.59252524\",\n \"0.5919171\",\n \"0.58976763\",\n \"0.5876612\",\n \"0.58755356\",\n \"0.58645153\",\n \"0.5854626\",\n \"0.5828439\",\n \"0.58248883\",\n \"0.5813387\",\n \"0.58089316\",\n \"0.58015645\",\n \"0.5789683\",\n \"0.5788708\",\n \"0.57787365\",\n \"0.5777174\",\n \"0.57650024\",\n \"0.5763224\",\n \"0.57593584\",\n \"0.5752235\",\n \"0.57512456\",\n \"0.5695357\",\n \"0.5694719\",\n \"0.56826174\",\n \"0.56446457\",\n \"0.5624713\",\n \"0.56069595\",\n \"0.5601363\",\n \"0.5598727\",\n \"0.5598727\",\n \"0.55964035\",\n \"0.558799\",\n \"0.55832875\",\n \"0.55788124\",\n \"0.5542831\",\n \"0.553619\",\n \"0.5522106\",\n \"0.5520491\",\n \"0.5502282\",\n \"0.5497314\",\n \"0.5497314\",\n \"0.54966307\",\n \"0.5494005\",\n \"0.5478229\",\n \"0.5472546\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5694063"},"document_rank":{"kind":"string","value":"78"}}},{"rowIdx":16885,"cells":{"query":{"kind":"string","value":"Run the linter and return the exit code."},"document":{"kind":"string","value":"def run(args=None):\n parser = init_argument_parser()\n options = parser.parse_args(args if args is not None else sys.argv[1:])\n\n stdin_filename = None\n file_names = set(options.files)\n checked_files = set()\n\n # Read input from STDIN\n if not sys.stdin.isatty():\n with tempfile.NamedTemporaryFile('w', suffix='.sls', delete=False) as stdin_tmpfile:\n stdin_tmpfile.write(sys.stdin.read())\n stdin_filename = stdin_tmpfile.name\n file_names.add(stdin_filename)\n\n # Read, parse and validate the configuration\n options_dict = vars(options)\n try:\n config = Configuration(options_dict)\n except SaltLintConfigError as exc:\n print(exc)\n return 2\n\n # Show a help message on the screen\n if not file_names and not (options.listrules or options.listtags):\n parser.print_help(file=sys.stderr)\n return 1\n\n # Collect the rules from the configuration\n collection = RulesCollection(config)\n for rulesdir in config.rulesdirs:\n collection.extend(RulesCollection.create_from_directory(rulesdir, config))\n\n # Show the rules listing\n if options.listrules:\n print(collection)\n return 0\n\n # Show the tags listing\n if options.listtags:\n print(collection.listtags())\n return 0\n\n formatter = initialize_formatter(config)\n\n problems = []\n for file_name in file_names:\n runner = Runner(collection, file_name, config, checked_files)\n problems.extend(runner.run())\n\n # Delete stdin temporary file\n if stdin_filename:\n os.unlink(stdin_filename)\n\n if problems:\n sorted_problems = sort_problems(problems)\n formatter.process(sorted_problems)\n return 2\n return 0"},"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 lint_command(argv) -> CommandResult:\n app = FlakeHellApplication(program=NAME, version=VERSION)\n try:\n app.run(argv)\n app.exit()\n except SystemExit as exc:\n return int(exc.code), ''\n raise RuntimeError('unreachable')","def run(self):\n success = False\n try:\n # Download file if a source_zip_url was given\n if self.source_zip_url:\n App.logger.debug(\"Linting url: \" + self.source_zip_url)\n self.download_archive()\n # unzip the input archive if a source_zip_file exists\n if self.source_zip_file:\n App.logger.debug(\"Linting zip: \" + self.source_zip_file)\n self.unzip_archive()\n # lint files\n if self.source_dir:\n self.rc = RC(directory=self.source_dir)\n App.logger.debug(\"Linting '{0}' files...\".format(self.source_dir))\n success = self.lint()\n App.logger.debug(\"...finished.\")\n except Exception as e:\n message = 'Linting process ended abnormally: {0}'.format(e.message)\n App.logger.error(message)\n self.log.warnings.append(message)\n App.logger.error('{0}: {1}'.format(str(e), traceback.format_exc()))\n result = {\n 'success': success,\n 'warnings': self.log.warnings,\n }\n App.logger.debug(\"Linter results: \" + str(result))\n return result","def run(self):\n # first-party\n from run import run # pylint: disable=no-name-in-module\n\n # run the app\n exit_code = 0\n try:\n run()\n except SystemExit as e:\n exit_code = e.code\n\n self.log.data('run', 'Exit Code', exit_code)\n return exit_code","def main():\n import argparse\n\n # parse sys.argv\n parser = argparse.ArgumentParser(description='stylecheck')\n parser.add_argument('-v', '--version', action='version',\n version=('%(prog)s ' + __version__))\n parser.add_argument('-r', '--root_dir', type=str, default='../../ken3/',\n help='root dir path (default: \\'../../ken3/\\')')\n args = parser.parse_args()\n\n # run each test\n result = list(run(pick_names(args.root_dir), args.root_dir))\n if result:\n print(result)\n return len(result)","def main(args=None):\n cli = Cli(args)\n try:\n cli.run()\n except FatalError as err:\n sys.stderr.write(err.args[0] + os.linesep)\n return 2\n\n cli.print_output()\n cli.print_messages()\n return cli.code","def lint(to_lint):\n exit_code = 0\n for linter, options in (('pyflakes', []), ('pep8', pep8_options)):\n try:\n output = local[linter](*(options + to_lint))\n except commands.ProcessExecutionError as e:\n output = e.stdout\n\n if output:\n exit_code = 1\n print \"{0} Errors:\".format(linter)\n print output\n\n output = hacked_pep257(to_lint)\n if output:\n exit_code = 1\n print \"Docstring Errors:\".format(linter.upper())\n print output\n\n sys.exit(exit_code)","def run(self):\n check_paths = PACKAGES + [\n 'setup.py',\n 'tests',\n 'util',\n ]\n ignore = [\n 'doc/',\n ]\n\n # try to install missing dependencies and import prospector\n try:\n from prospector.run import main\n except ImportError:\n # try to install and then import\n self.distribution.fetch_build_eggs(['prospector[with_pyroma]'])\n from prospector.run import main\n\n self.install_deps_temp()\n\n # run linter\n\n # change working directory to package root\n package_root = os.path.abspath(os.path.dirname(__file__))\n os.chdir(package_root)\n\n # write command line\n files = discover_python_files(check_paths, ignore)\n sys.argv = ['prospector']\n sys.argv.extend(files)\n\n # run prospector\n errno = main()\n\n sys.exit(errno)","def _run(self, config):\n \n files = self.transaction.get_files(\n config.check_files, config.ignore_files\n )\n # Exit when no files has to be checked.\n if not files:\n self.logger.debug(\"PyLint check skipped. No files for check.\")\n return self.success()\n \n # Defining pylint home directory.\n os.environ['PYLINTHOME'] = config.pylint_home\n self.logger.debug(\"PyLint Home is used at '%s'.\", config.pylint_home)\n \n # Determine which pylintrc file is used for the validation.\n if config.pylintrc:\n self.logger.debug(\"Pylintrc is used at '%s'.\", config.pylintrc)\n os.environ['PYLINTRC'] = config.pylintrc\n else:\n self.logger.debug(\"Default PyLintRC is used.\")\n \n # Only added or updated files will be checked.\n files = [\n self.transaction.get_file(name) \n for name, attr in files.iteritems() \n if attr in [\"A\", \"U\", \"UU\"]\n ]\n \n if not files:\n self.logger.debug(\"No files to validate. PyLint check skipped.\")\n return self.success()\n \n output = StringIO.StringIO()\n reporter = TextReporter(output)\n \n # Mock to prevent the sys.exit called by pylint.lint.Run.__init__\n lint.sys.exit = lambda _: 0\n \n self.logger.debug(\"PyLint is running...\")\n lint.Run([\"--reports=n\"] + files, reporter=reporter)\n \n output = output.getvalue()\n self.logger.debug(\"PyLint output:\\n %s\", output)\n if output:\n return self.error(output)\n else:\n return self.success()","def run_pylint(self, fname):\n if '.py' not in fname:\n return False\n if not op.isfile(fname):\n self.logging.info('------------------------------------------------------------------')\n self.logging.info(\"FILE {} DOES NOT EXIST.\".format(fname))\n self.logging.info('------------------------------------------------------------------')\n self.logging.info('\\n')\n return False\n self.logging.info(\"{}\\n\".format(fname))\n self.fname = fname\n if self.rcfile and op.isfile(self.rcfile):\n command_arg = [fname, '--rcfile', self.rcfile, '--score', 'no']\n else:\n command_arg = [fname, '--score', 'no']\n try:\n with redirect_stdout(PrintLogger(name=\"pylint\", log_level=\"INFO\")):\n if int(pylint.__version__[0]) < 2:\n self.results = Run(command_arg, exit=False) # pylint: disable=unexpected-keyword-arg\n else:\n # Use the default one\n self.results = Run(command_arg, do_exit=False) # pylint: disable=unexpected-keyword-arg\n return True\n except Exception as error: # pylint: disable=broad-except\n # We want to crash if ANYTHING goes wrong\n self.logging.warning('------------------------------------------------------------------')\n self.logging.warning(\"PYLINT CRASHED WHILE HANDLING {}\".format(fname))\n self.logging.warning(\"{}: {}\".format(type(error), error.args))\n self.logging.warning('------------------------------------------------------------------')\n self.logging.info('\\n')\n self.failed_files.append(fname)\n return False","def main() -> None:\n try:\n run()\n except errors.BaseError as e:\n sys.stderr.write(f'{str(e)}\\n')\n sys.exit(e.code)","def run():\n args = parse_args(sys.argv[1:])\n fnames = args.fnames\n runner = PylintRunner(args)\n runner.run(fnames)","def lint():\n load_env_vars('dev')\n from tools.static_code_analysis import Lint\n pylint = Lint()\n score = pylint.run_test()\n pylint.create_badge(score)","def run(self, raw_args=None):\n\t\targs = self.parser.parse_args(raw_args)\n\n\t\tcore = Core()\n\n\t\ttry:\n\t\t\treport = core.lint(**vars(args))\n\t\texcept Exception as err:\n\t\t\tself.parser.error(str(err))\n\n\t\tprint(report)\n\t\tself.parser.exit()","def run(args):\n parser = make_parser()\n opts = parser.parse_args(args)\n\n try:\n return opts.__command__.run(opts)\n\n except NextstrainCliError as error:\n exit_status = 1\n\n if DEBUGGING:\n traceback.print_exc()\n else:\n if isinstance(error, UsageError):\n warn(opts.__parser__.format_usage())\n exit_status = 2\n\n warn(error)\n\n return exit_status\n\n except AssertionError:\n traceback.print_exc()\n warn(\"\\n\")\n warn(dedent(\"\"\"\\\n An error occurred (see above) that likely indicates a bug in the\n Nextstrain CLI.\n\n To report this, please open a new issue and include the error above:\n <https://github.com/nextstrain/cli/issues/new/choose>\n \"\"\"))\n return 1","def lint(ctx):\r\n print('Running linting...')\r\n ctx.run('pylint metrics')","def determine_exit_code(self) -> int:","def lint(context):\n context.run(\" \".join([\n \"autopep8\",\n \"--recursive\",\n \"--jobs 0\",\n \"--in-place\",\n \"--aggressive\",\n \"-v\",\n PACKAGE_NAME,\n \"tests\"\n ]))\n context.run(\"pylint %s\" % PACKAGE_NAME)","def _run_xsoar_linter(self, py_num: str, lint_files: List[Path]) -> Tuple[int, str]:\n status = SUCCESS\n FAIL_PYLINT = 0b10\n with pylint_plugin(self._pack_abs_dir):\n log_prompt = f\"{self._pack_name} - XSOAR Linter\"\n logger.info(f\"{log_prompt} - Start\")\n myenv = os.environ.copy()\n if myenv.get(\"PYTHONPATH\"):\n myenv[\"PYTHONPATH\"] += \":\" + str(self._pack_abs_dir)\n else:\n myenv[\"PYTHONPATH\"] = str(self._pack_abs_dir)\n if self._facts[\"is_long_running\"]:\n myenv[\"LONGRUNNING\"] = \"True\"\n\n py_ver = parse(py_num).major # type: ignore\n if py_ver < 3:\n myenv[\"PY2\"] = \"True\"\n myenv[\"is_script\"] = str(self._facts[\"is_script\"])\n # as Xsoar checker is a pylint plugin and runs as part of pylint code, we can not pass args to it.\n # as a result we can use the env vars as a getway.\n myenv[\"commands\"] = (\n \",\".join([str(elem) for elem in self._facts[\"commands\"]])\n if self._facts[\"commands\"]\n else \"\"\n )\n stdout, stderr, exit_code = run_command_os(\n command=build_xsoar_linter_command(\n files=lint_files, # type: ignore\n support_level=self._facts.get(\"support_level\", \"base\"),\n formatting_script=self._facts.get(\"formatting_script\", False),\n ),\n cwd=self._pack_abs_dir,\n env=myenv,\n )\n if exit_code & FAIL_PYLINT:\n logger.error(f\"{log_prompt} - Finished, errors found\")\n status = FAIL\n if exit_code & WARNING:\n logger.warning(f\"{log_prompt} - Finished, warnings found\")\n if not status:\n status = WARNING\n # if pylint did not run and failure exit code has been returned from run commnad\n elif exit_code & FAIL:\n status = FAIL\n logger.debug(f\"{log_prompt} - Actual XSOAR linter error -\")\n logger.debug(\n f\"{log_prompt} - Full format stdout: {RL if stdout else ''}{stdout}\"\n )\n # for contrib prs which are not merged from master and do not have pylint in dev-requirements-py2.\n if os.environ.get(\"CI\"):\n stdout = \"Xsoar linter could not run, Please merge from master\"\n else:\n stdout = (\n \"Xsoar linter could not run, please make sure you have\"\n \" the necessary Pylint version for both py2 and py3\"\n )\n logger.error(f\"{log_prompt} - Finished, errors found\")\n\n logger.debug(f\"{log_prompt} - Finished, exit-code: {exit_code}\")\n logger.debug(f\"{log_prompt} - Finished, stdout: {RL if stdout else ''}{stdout}\")\n logger.debug(f\"{log_prompt} - Finished, stderr: {RL if stderr else ''}{stderr}\")\n\n if not exit_code:\n logger.info(f\"{log_prompt} - Successfully finished\")\n\n return status, stdout","def lint(\n command,\n):\n print(\n \"\"\"\nRunning flakeheaven, a Python code linter\n===================================\n\"\"\"\n )\n command.run(\"flakeheaven lint\", echo=True, pty=POSIX)","def exit_code(self) -> int:\n _args: list[Arg] = []\n _ctx = self._select(\"exitCode\", _args)\n return _ctx.execute_sync(int)","def main(self):\n try:\n self.parse_args()\n self.run()\n return 0\n except AnalysisBackendError as e:\n L.error(e)\n return 1","def main() -> int:\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--overwrite\",\n help=\n \"Overwrites the unformatted source files with the well-formatted code in place. \" # pylint: disable=line-too-long\n \"If not set, an exception is raised if any of the files do not conform to the style guide.\", # pylint: disable=line-too-long\n action='store_true')\n\n args = parser.parse_args()\n\n tox_environments = [\"check\", \"_auto_version\"]\n\n if args.overwrite:\n tox_environments.insert(0, \"format\")\n\n tox_from_path = shutil.which(\"tox\")\n\n maybe_scripts = sysconfig.get_path(\"scripts\")\n tox_from_scripts = pathlib.Path(maybe_scripts).joinpath(\n \"tox\") if maybe_scripts is not None else None\n\n if tox_from_path is not None:\n tox = pathlib.Path(tox_from_path)\n elif tox_from_scripts is not None and tox_from_scripts.is_file():\n tox = tox_from_scripts\n else:\n message = textwrap.dedent(f'''\\\n 'tox' executable not found in PATH or scripts directory\n PATH: {os.environ['PATH']}\n scripts: {maybe_scripts}\n ''')\n raise ToxNotFoundError(message)\n\n repo_root = pathlib.Path(__file__).parent\n\n tox_ini = repo_root.joinpath(\"tox.ini\")\n\n command = [\n fspath(tox), \"-c\",\n fspath(tox_ini), \"-e\", \",\".join(tox_environments)\n ]\n completed_process = subprocess.run(command) # pylint: disable=W1510\n\n return completed_process.returncode","def commands_lint():\n lint()","def run_ansible_lint(\n *argv: str,\n cwd: str = None,\n bin: str = None,\n env: Dict[str, str] = None) -> subprocess.CompletedProcess:\n if not bin:\n bin = sys.executable\n args = [sys.executable, \"-m\", \"ansiblelint\", *argv]\n else:\n args = [bin, *argv]\n\n return subprocess.run(\n args,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n shell=False, # needed when command is a list\n check=False,\n cwd=cwd,\n env=env,\n universal_newlines=True\n )","def main(self, argv=None):\n\n p = self.build_parser()\n\n args = p.parse_args(argv)\n\n try:\n return self.run(args) or 0\n except CommandFailed as e:\n print(str(e))\n return 1","def run_and_check_exit_code(args=None, code=0):\n try:\n TT.main([\"dummy\"] + ([] if args is None else args))\n except SystemExit as exc:\n return exc.code == code\n\n return True","def lint(cline):\n print(\"Linting with pylint.\")\n cline.run(r\"git ls-files '*.py' | xargs python3 -m pylint -j 0\")\n print(\"Type checking with mypy.\")\n cline.run(r\"git ls-files '*.py' | xargs python3 -m mypy\")","def run_check(self, path):\n\n result = pycodestyle.StyleGuide().check_files(paths=[path])\n\n if result.total_errors != 0:\n self.assertEqual(\n result.total_errors, 0,\n \"Found code style errors (and warnings).\")","def lint(fix_imports, check):\n skip = [\"node_modules\", \"requirements\", \"migrations\"]\n root_files = glob(\"*.py\")\n root_directories = [\n name for name in next(os.walk(\".\"))[1] if not name.startswith(\".\")\n ]\n files_and_directories = [\n arg for arg in root_files + root_directories if arg not in skip\n ]\n\n def execute_tool(description, *args):\n \"\"\"Execute a checking tool with its arguments.\"\"\"\n command_line = list(args) + files_and_directories\n click.echo(f\"{description}: {' '.join(command_line)}\")\n rv = call(command_line)\n if rv != 0:\n exit(rv)\n\n isort_args = []\n black_args = []\n if check:\n isort_args.append(\"--check\")\n black_args.append(\"--check\")\n if fix_imports:\n execute_tool(\"Fixing import order\", \"isort\", *isort_args)\n execute_tool(\"Formatting style\", \"black\", *black_args)\n execute_tool(\"Checking code style\", \"flake8\")","def _run_lint_in_host(self, no_bandit: bool, no_mypy: bool, no_xsoar_linter: bool):\n log_prompt = f\"{self._pack_name} - Run Lint In Host\"\n logger.info(f\"{log_prompt} - Started\")\n exit_code: int = SUCCESS\n for lint_check in [\"XSOAR_linter\", \"bandit\", \"mypy\"]:\n exit_code = SUCCESS\n output = \"\"\n if self._facts[\"lint_files\"]:\n if lint_check == \"XSOAR_linter\" and not no_xsoar_linter:\n exit_code, output = self._run_xsoar_linter(\n py_num=self._facts[\"python_version\"],\n lint_files=self._facts[\"lint_files\"],\n )\n elif lint_check == \"bandit\" and not no_bandit:\n exit_code, output = self._run_bandit(\n lint_files=self._facts[\"lint_files\"]\n )\n\n elif (\n lint_check == \"mypy\"\n and not no_mypy\n and parse(self._facts[\"python_version\"]).major >= 3 # type: ignore[union-attr]\n ):\n # mypy does not support python2 now\n exit_code, output = self._run_mypy(\n py_num=self._facts[\"python_version\"],\n lint_files=self._facts[\"lint_files\"],\n )\n\n self._handle_lint_results(exit_code, lint_check, output)\n logger.info(f\"{log_prompt} - Finished successfully\")","def do_lint(cls, py_lint_args):\n results = {'lints' : [], 'created_at' : str(datetime.datetime.utcnow()),\n 'task_name' : py_lint_args.name, 'url' : py_lint_args.url}\n url = urllib.parse.urlparse(py_lint_args.url)\n passed = 0\n issues = 0\n failures = 0\n if url.scheme == 'file':\n for root, dummy_dirs, files in os.walk(url.path):\n for my_file in files:\n if my_file[-3:] == '.py':\n my_file = os.path.join(root, my_file)\n logging.info('Checking %s.', my_file)\n my_result = {'file' : re.sub(\n '^%s' % url.path, '', my_file).lstrip('/')}\n try:\n lint_results = cls.run_pylint(my_file)\n my_result.update(cls.lint_results_to_dict(\n lint_results))\n if my_result['outcome'] == 'success':\n passed += 1\n else:\n assert my_result['outcome'] == 'issues'\n issues += 1\n except Exception as bad: #pylint: disable=broad-except\n failures += 1\n my_result['outcome'] = 'failed'\n my_result['longrepr'] = str(bad)\n results['lints'].append(my_result)\n results['summary'] = {\n 'issues' : issues, 'passed' : passed, 'failures' : failures}\n return results\n else:\n raise ValueError('URL scheme of %s not handled yet.' % url.scheme)","def entry_point() -> int:\n return run(argv=sys.argv[1:], stdout=sys.stdout, stderr=sys.stderr)","def main():\n try:\n userParams, repoParams = splitArgv(sys.argv)\n parser = ArgumentParser(description = \"StyleCop parameters\")\n\n parser.add_argument(\"--repo\", dest=\"repo\", action=\"store\",\n help=\"Repository that use this script in hook\")\n parser.add_argument(\"--stage\", dest=\"stage\", action=\"store\",\n help=\"Stage of work with VCS\")\n parser.add_argument(\"--config\", dest=\"config\", action=\"store\",\n help=\"StyleCop config file\")\n\n\n args = parser.parse_args(userParams)\n\n configParser = ConfigParser()\n configString = FileReader.readFile(args.config)\n config = configParser.parse(configString)\n\n factory = repos.ReposFactory()\n repository = factory.getRepository(args.repo, args.stage)\n changedFiles = repository.getChangedFiles(repoParams)\n\n extensionsDict = config.getDictionary(\"extensions\")\n\n checkersFactory = CheckersFactory(extensionsDict)\n\n # List of strings of style violations\n errors = []\n\n for file in changedFiles:\n ext = getFileExtension(file)\n checker = checkersFactory.getChecker(ext)\n sourceString = FileReader.readFile(file)\n\n errors += checker.check(sourceString)\n\n \n\n except ValueError as er:\n pass\n except Exception as ex:\n pass\n \n if len(errors) > 0:\n repository.sendError(\"Total number of style errors: \" + errors)\n repository.sendError(\"Update failed\")\n \n # If there were no errors we permit this update\n return len(errors)","def checkPythonCommand(args=[None], stderr=None):\n\ttheOutput = None\n\ttry:\n\t\tif args is None or args is [None]:\n\t\t\ttheOutput = subprocess.check_output([\"exit 1 ; #\"])\n\t\telse:\n\t\t\tif str(\"coverage \") in args[0]:\n\t\t\t\targs[0] = str(\"coverage\")\n\t\t\t\targs.insert(1, str(\"run\"))\n\t\t\t\targs.insert(2, str(\"-p\"))\n\t\t\t\targs.insert(2, str(\"--source=code\"))\n\t\t\ttheOutput = subprocess.check_output(args, stderr=stderr)\n\texcept Exception:\n\t\ttheOutput = None\n\tif isinstance(theOutput, bytes):\n\t\ttheOutput = theOutput.decode('utf8')\n\treturn theOutput","def call(self, cmd):\n exitcode, _stdout, _stderr = self.run(cmd, nonzero_e = None)\n return exitcode","def lint(arg):\n local('pylint --rcfile=.pylintrc ' + arg)","def run_tool(\n self, args: List[str], source: bool = False, cwd: Union[str, Path] = None\n ) -> int:\n predef = {}\n predef[\"env\"] = os.environ\n\n if source:\n predef[\"env\"][\"VPROJECT\"] = str(self.config.path.vproject)\n predef[\"env\"][\"NOASSERT\"] = \"1\"\n if cwd:\n predef[\"cwd\"] = cwd\n\n try:\n result = self.run_subprocess(args, **predef)\n except Exception as e:\n raise Exception(f\"failed to execute tool with parameters: {args}\") from e\n return result.returncode","def test_pylint_score_main_script(self):\n my_dir = pathlib.Path(__file__).resolve().parent\n root_dir = my_dir.parent.parent.parent\n pylintrc = root_dir / \".pylintrc\"\n script = root_dir / \"backend\" / \"quality_report.py\"\n self.assert_pylint_score(\"{0} --rcfile {1}\".format(script, pylintrc), 10.0)","def test_pylint(self):\n files_list = []\n\n for root, dirnames, filenames in os.walk(PROJECT_DIR):\n if ignore(root):\n continue\n\n for filename in fnmatch.filter(filenames, '*.py'):\n files_list.append(os.path.join(root, filename))\n\n for file in files_list:\n # (pylint_stdout, pylint_stderr) = epylint.py_run(\n # command_options=\"{} --errors-only\".format(file),\n # return_std=True)\n\n # print(pylint_stdout.getvalue())\n # print(pylint_stderr.getvalue())\n\n call([\n 'pylint',\n '--errors-only',\n file])","def lint(self):\n return {\n \"actions\": [\n (create_dir, [\"build/lint\"]),\n TaskCreator.get_flake8() + \" \" + self.project_name_sc + \" | tee build/lint/flake8.log\",\n TaskCreator.get_pylint() + \" --output-format=parseable --reports=no \" + self.project_name_sc + \" | tee build/lint/pylint.log\"\n ],\n \"verbosity\": 2\n }","def get_exit_code(self):","def main():\n unittest.main(exit=False, verbosity=2)\n return 0","def test_main_succeed_en(runner: CliRunner) -> None:\n result = runner.invoke(__main__.main, \"-c tests/clippings-en.txt\")\n assert result.exit_code == 0","def run_check(self, ctx: RunContext): # noqa\n params = ctx.get_params(\"pycodestyle\")\n options = ctx.options\n if options:\n params.setdefault(\"max_line_length\", options.max_line_length)\n\n if params:\n parser = get_parser()\n for option in parser.option_list:\n if option.dest and option.dest in params:\n value = params[option.dest]\n if isinstance(value, str):\n params[option.dest] = option.convert_value(option, value)\n\n style = StyleGuide(reporter=_PycodestyleReport, **params)\n options = style.options\n options.report.ctx = ctx # type: ignore\n checker = Checker(ctx.filename, lines=ctx.lines, options=options)\n checker.check_all()","def check_exit_code(results):\n assert results[\"metrics\"][\"Exit code\"] == 0","def run(self, fnames):\n logging.info(\"Starting\")\n for fname in fnames:\n linted = self.run_pylint(fname=fname)\n if linted:\n custom_ok, override_standard = self.check_custom_rules()\n override = custom_ok and override_standard\n success = self.check_no_silent_crash(override=override)\n if success:\n self.eval_results(custom_ok, override)\n exit_code = self.report_results()\n if not self.keep_results:\n self.clean_up()\n sys.exit(exit_code)","def main():\n logging.basicConfig()\n refstackclient = RefstackClient()\n parser = RefstackClientParser()\n args = parser.parse_args(sys.argv[1:])\n try:\n result = refstackclient.main(**args)\n if result != testcase.TestCase.EX_OK:\n return result\n except Exception: # pylint: disable=broad-except\n return testcase.TestCase.EX_RUN_ERROR","def run():\n parser = create_arguments_parser()\n arguments = parser.parse_args()\n\n num_warnings = 0\n for filename in arguments.filenames:\n num_warnings += analyse_file(filename, arguments)\n \n if num_warnings == 0:\n print (\"No warnings\")\n elif num_warnings == 1:\n print (\"There was a single warning\")\n else:\n print (\"There were \" + str(num_warnings))","def lint(self) -> pulumi.Output[Optional[bool]]:\n return pulumi.get(self, \"lint\")","def main(tests, sources, fail_fast, config=None):\n try:\n smokr.run_tests(tests.split(','), sources.split(','),\n fail_fast, config)\n return 0\n except AssertionError:\n sys.exit(1)","def test_lint_pass(self, style):\n result = run_linter_throw(\"path/to/file\",\n \"{s}\\n{m}{e}\\n\",\n style,\n whitelist=[\"file/newline_last_char\"])\n self.assertTrue(result)","def main(self):\n try:\n config_provider = ConfigurationProvider(MaskReplacer(), DirectorySelector())\n parser = CommandParser(\n config_provider,\n RubyRipperCdRipper(config_provider),\n DiscogsMetadataService(),\n GenreSelector())\n commands = parser.from_args(self._get_arguments())\n for command in commands:\n command.validate()\n command.execute()\n return 0\n except Exception as ex:\n # This will be replaced with proper logging output.\n sys.stderr.write('{0}\\n'.format(ex.message))\n sys.stderr.write('{0}\\n'.format(traceback.format_exc()))\n return 255","def lint_cmd(scriptname, command, argv):\n if not '--quiet' in argv and not '-q' in argv:\n out('dennis version {version}'.format(version=__version__))\n\n parser = build_parser(\n 'usage: %prog lint [ DIR | FILENAME <FILENAME> ... ]',\n description='Lints a .po file for mismatched Python string '\n 'formatting tokens.',\n epilog='Note: You can ignore rules on a string-by-string basis by '\n 'adding an extracted comment \"dennis-ignore: <comma-separated-rules>\". '\n 'See documentation for details.',\n sections=[\n (format_vars(), True),\n (format_lint_rules(), True),\n ])\n parser.add_option(\n '--vars',\n dest='vars',\n help=('Comma-separated list of variable types. See Available Variable '\n 'Formats.'),\n metavar='VARS',\n default='pysprintf,pyformat')\n parser.add_option(\n '--rules',\n dest='rules',\n help=('Comma-separated list of lint rules to use. Defaults to all '\n 'rules. See Available Lint Rules.'),\n metavar='RULES',\n default='')\n parser.add_option(\n '-q', '--quiet',\n action='store_true',\n dest='quiet',\n help='quiet all output')\n parser.add_option(\n '--errorsonly',\n action='store_true',\n dest='errorsonly',\n help='only print errors')\n\n (options, args) = parser.parse_args(argv)\n\n if not args:\n parser.print_help()\n return 1\n\n linter = Linter(options.vars.split(','), options.rules.split(','))\n\n if os.path.isdir(args[0]):\n po_files = []\n for root, dirs, files in os.walk(args[0]):\n po_files.extend(\n [os.path.join(root, fn) for fn in files\n if fn.endswith('.po')])\n\n else:\n po_files = args\n\n po_files = [os.path.abspath(fn) for fn in po_files if fn.endswith('.po')]\n\n files_to_errors = {}\n total_error_count = 0\n total_warning_count = 0\n total_files_with_errors = 0\n\n for fn in po_files:\n try:\n if not os.path.exists(fn):\n raise IOError('File \"{fn}\" does not exist.'.format(fn=fn))\n\n results = linter.verify_file(fn)\n\n except IOError as ioe:\n # This is not a valid .po file. So mark it as an error.\n err('>>> Problem opening file: {fn}'.format(fn=fn))\n err(repr(ioe))\n out('')\n\n # FIXME - should we track this separately as an invalid\n # file?\n files_to_errors[fn] = (1, 0)\n total_error_count += 1\n continue\n\n # Extract all the problematic LintItems--they have non-empty\n # missing or invalid lists.\n problem_results = [\n r for r in results if r.has_problems(options.errorsonly)]\n\n # We don't want to print output for files that are fine, so we\n # update the bookkeeping and move on.\n if not problem_results:\n files_to_errors[fn] = (0, 0)\n continue\n\n if not options.quiet:\n out(TERM.bold_green,\n '>>> Working on: {fn}'.format(fn=fn),\n TERM.normal)\n\n error_count = 0\n warning_count = 0\n\n for entry in problem_results:\n total_error_count += len(entry.errors)\n error_count += len(entry.errors)\n\n if not options.quiet:\n # TODO: This is totally shite code.\n for code, trstr, msg in entry.errors:\n out(TERM.bold_red,\n code,\n ': ',\n msg,\n TERM.normal)\n for field, s in zip(trstr.msgid_fields, trstr.msgid_strings):\n out(field, ' \"', s, '\"')\n out(trstr.msgstr_field, ' \"', trstr.msgstr_string, '\"')\n out('')\n\n total_warning_count += len(entry.warnings)\n warning_count += len(entry.warnings)\n\n if not options.quiet and not options.errorsonly:\n for code, trstr, msg in entry.warnings:\n out(TERM.bold_yellow,\n code,\n ': ',\n msg,\n TERM.normal)\n for field, s in zip(trstr.msgid_fields, trstr.msgid_strings):\n out(field, ' \"', s, '\"')\n out(trstr.msgstr_field, ' \"', trstr.msgstr_string)\n out('')\n\n files_to_errors[fn] = (error_count, warning_count)\n\n if error_count > 0:\n total_files_with_errors += 1\n\n if not options.quiet:\n out('Totals')\n if not options.errorsonly:\n out(' Warnings: {warnings:5}'.format(warnings=warning_count))\n out(' Errors: {errors:5}\\n'.format(errors=error_count))\n\n if len(po_files) > 1 and not options.quiet:\n out('Final totals')\n out(' Number of files examined: {count:5}'.format(\n count=len(po_files)))\n out(' Total number of files with errors: {count:5}'.format(\n count=total_files_with_errors))\n if not options.errorsonly:\n out(' Total number of warnings: {count:5}'.format(\n count=total_warning_count))\n out(' Total number of errors: {count:5}'.format(\n count=total_error_count))\n out('')\n\n file_counts = [\n (counts[0], counts[1], fn.split(os.sep)[-3], fn.split(os.sep)[-1])\n for (fn, counts) in files_to_errors.items()\n ]\n\n # If we're showing errors only, then don't talk about warnings.\n if options.errorsonly:\n header = 'Errors Filename'\n line = ' {errors:5} {locale} ({fn})'\n else:\n header = 'Warnings Errors Filename'\n line = ' {warnings:5} {errors:5} {locale} ({fn})'\n\n file_counts = list(reversed(sorted(file_counts)))\n printed_header = False\n for error_count, warning_count, locale, fn in file_counts:\n if not error_count and not warning_count:\n continue\n if not printed_header:\n out(header)\n printed_header = True\n\n out(line.format(\n warnings=warning_count, errors=error_count, fn=fn,\n locale=locale))\n\n # Return 0 if everything was fine or 1 if there were errors.\n return 1 if total_error_count else 0","def lint():\n run('python3 -m pylint --load-plugins pylint_django api',\n warn=True, echo=True)","def lint(session):\n session.install(\"-r\", \"requirements-test.txt\")\n session.install(\"-r\", \"requirements.txt\")\n session.install(\"flake8-import-order\")\n session.run(\"black\", \"--check\", *BLACK_PATHS)\n session.run(\n \"flake8\",\n \"--import-order-style=google\",\n \"--application-import-names=google,tests\",\n \"google\",\n \"tests\",\n )\n session.run(\"mypy\", \"google\", \"tests\")\n session.run(\"python\", \"setup.py\", \"sdist\")\n session.run(\"twine\", \"check\", \"dist/*\")","def _run(args: List[str], check: bool = False) -> Tuple[int, str]:\n result = subprocess.run(args=args, stdout=subprocess.PIPE)\n if check and result.returncode != 0:\n raise subprocess.CalledProcessError(result.returncode, args)\n return result.returncode, result.stdout.decode('utf-8', 'strict')","def task_lint():\n return {\"actions\": [[\"prospector\"]], \"verbosity\": 1}","def error_handler(self):\n if self.ctx.exit_code is not None:\n return self.ctx.exit_code","def _main(args):\n start = datetime.now()\n try:\n main.main(args, standalone_mode=False)\n except Abort:\n click.secho(\"Operation aborted!\", fg=\"yellow\", bold=True)\n except Exception as err: # pylint: disable=broad-except\n click.secho(\"Error: \" + str(err), fg=\"red\")\n return 1\n finally:\n if \"--help\" not in sys.argv:\n # display overall runtime for reference when performing update\n end = datetime.now()\n runtime = end - start\n click.secho(\"Operation complete. Total runtime: \" + humanize.naturaldelta(runtime), fg=\"green\")\n return 0","def main(argv=None):\n # Parse arguments\n parser = argparse.ArgumentParser()\n parser.add_argument(\"filenames\", nargs=\"*\", help=\"File names to check\")\n args = parser.parse_args(argv)\n filenames = args.filenames\n\n # Find files with conflicts\n python_files = [filename for filename in filenames if is_textfile(filename)]\n files_with_conflicts = []\n for text_file in python_files:\n files_with_conflicts += find_git_conflicts(text_file)\n\n # Return response\n exit_code = 0\n if files_with_conflicts:\n exit_code = 1\n print(\"Git Conflicts Detected in file(s): \\n - {}\".format(\" - \".join(files_with_conflicts)))\n\n return exit_code","def main():\n coverage = calculate_code_coverage()\n platform = os.uname()[0]\n if coverage < CODE_COVERAGE_GOAL[platform]:\n data = {\n 'expected': CODE_COVERAGE_GOAL[platform],\n 'observed': coverage,\n }\n print '\\033[91mFAIL: %(observed).2f%% does not meet goal of %(expected).2f%%\\033[0m' % data\n sys.exit(1)","def test_2():\n try:\n import flake8 # noqa: F401\n except ImportError:\n return None\n\n cwd = os.getcwd()\n os.chdir(PACKAGE_DIR)\n try:\n subprocess.check_call(['flake8'])\n os.chdir(cwd)\n except CalledProcessError:\n os.chdir(cwd)\n raise CalledProcessError","def sys_main() -> int:\n try:\n main()\n return 0\n except Exception:\n print(traceback.format_exc())\n return 1","def run_pylint(options):\r\n errors = getattr(options, 'errors', False)\r\n systems = getattr(options, 'system', 'lms,cms,common').split(',')\r\n\r\n for system in systems:\r\n # Directory to put the pylint report in.\r\n # This makes the folder if it doesn't already exist.\r\n report_dir = get_or_make_dir(os.path.join(Env.REPORT_DIR, system))\r\n\r\n flags = '-E' if errors else ''\r\n\r\n apps = [system]\r\n\r\n for directory in ['djangoapps', 'lib']:\r\n dirs = os.listdir(os.path.join(system, directory))\r\n apps.extend([d for d in dirs if os.path.isdir(os.path.join(system, directory, d))])\r\n\r\n apps_list = ' '.join(apps)\r\n\r\n pythonpath_prefix = (\r\n \"PYTHONPATH={system}:{system}/djangoapps:{system}/\"\r\n \"lib:common/djangoapps:common/lib\".format(\r\n system=system\r\n )\r\n )\r\n\r\n sh(\r\n \"{pythonpath_prefix} pylint {flags} -f parseable {apps} | \"\r\n \"tee {report_dir}/pylint.report\".format(\r\n pythonpath_prefix=pythonpath_prefix,\r\n flags=flags,\r\n apps=apps_list,\r\n report_dir=report_dir\r\n )\r\n )","def main(): # pragma: nocover\n p = argparse.ArgumentParser()\n p.add_argument(\n 'directory',\n help=\"Directory to check\"\n )\n p.add_argument(\n '--verbose', '-v', action='store_true',\n help=\"increase verbosity\"\n )\n args = p.parse_args()\n\n import sys\n _changed = changed(sys.argv[1], args=args)\n sys.exit(_changed)","def test_lint_pass(self):\n contents = \"contents\\ncontents\\n\"\n result = run_linter_throw(\"path/to/file\",\n contents,\n FormatStyle(\"#\", \"#\", \"\"),\n whitelist=[\"file/trailing_whitespace\"])\n self.assertTrue(result)","def test():\n tests = unittest.TestLoader().discover('project/tests', pattern='test*.py')\n result = unittest.TextTestRunner(verbosity=2).run(tests)\n if result.wasSuccessful():\n return 0\n sys.exit(result)","def test_error_control(testdir):\n testdir.makepyfile(\"\"\"import sys\"\"\")\n result = testdir.runpytest('--pylint', '--pylint-error-types=EF')\n assert '1 passed' in result.stdout.str()","def run(self):\n checker = Checker(self.name)\n try:\n self.f(checker)\n except Exception as e:\n # Find line number and filename where the exception was thrown.\n _, _, traceback = sys.exc_info()\n traceback = traceback.tb_next\n lineno = traceback.tb_lineno\n filepath = traceback.tb_frame.f_code.co_filename\n filename = os.path.basename(filepath)\n\n # We can't continue, so return a single result with this error.\n self.checks = [Result(ERROR, e, self.name, \"\", filename, lineno)]\n dots(ERROR)\n else:\n self.checks = checker.results","def run(self, inp=\"\"):\n result = self.run_process(inp)\n if result.returncode == 0:\n outcome = self.PASS\n elif result.returncode < 0:\n outcome = self.FAIL\n else:\n outcome = self.UNRESOLVED\n return (result, outcome)","def main():\n argument_parser = argparse.ArgumentParser(add_help=True)\n argument_parser.add_argument(\"directory\", type=str,\n help=\"Directory to detect test smells.\")\n args = argument_parser.parse_args()\n \n if len(sys.argv) < 1:\n \n argument_parser.print_help()\n \n else:\n \n if os.path.exists(args.directory) or os.path.isdir(args.directory):\n\n #Stage 1: project level rule checking\n files = python_parser.get_python_files(os.path.abspath(args.directory))\n results_list = project_rule_runner(files)\n \n #Stage 2: test case level rule checking\n #test_case_pairs_list is a list of test cases paired with their file of origin\n filtered_files = python_parser.filter_python_files(files)\n test_case_pairs_list = python_parser.get_test_case_asts(filtered_files)\n \n for test_case_pair in test_case_pairs_list:\n results_list = results_list + test_case_rule_runner(test_case_pair)\n \n #Stage 3: test method level rule checking\n test_method_list = list()\n \n for test_case_pair in test_case_pairs_list:\n test_method_list = test_method_list + python_parser.get_test_asts(test_case_pair)\n \n for test_method in test_method_list: \n results_list = results_list + test_method_rule_runner(test_method)\n \n #Output formatting\n format_output(results_list)\n \n else:\n print(\"Invalid path given.\")","def test():\n # subprocess.check_output() has been introduced in Python 2.7\n if sys.version_info < (2, 7):\n print(\"Python version too old, skipping test.\")\n return 2\n\n try:\n subprocess.check_output(['shellcheck', '--version'])\n except OSError as exc:\n if exc.errno == errno.ENOENT:\n print(\"shellcheck not found, aborting test.\")\n return 2\n raise\n except subprocess.CalledProcessError:\n print(\"shellcheck on an simple stream exited with error\")\n return 1\n\n retval = 0\n basedir = os.path.join(os.path.dirname(__file__), os.path.pardir)\n for dirpath, _, files in os.walk(basedir):\n # Ignore files in .git directory\n if '/.git' in dirpath:\n continue\n # Ignore collected hardware data files\n if '/specific/collected_hwdata' in dirpath:\n continue\n\n for filename in sorted(files):\n # Skip .toprc, which is not UTF-8\n if filename == 'toprc' and dirpath.endswith('dotfiles'):\n continue\n\n filepath = os.path.join(dirpath, filename)\n\n # If there is no extension and the file is known not to be a\n # shell script, the first line is used to find shell scripts\n if '.' not in filename:\n is_shellconfig = (\n dirpath.endswith('/dotfiles/shell') or\n '/dotfiles/bash' in filepath or\n '/dotfiles/profile' in filepath or\n '/dotfiles/zsh' in filepath)\n if not is_shellconfig:\n with open(filepath, 'r') as cur_file:\n firstline = cur_file.readline().rstrip()\n if 'sh' not in firstline:\n continue\n elif not filename.lower().endswith('.sh'):\n continue\n\n exclwarns = get_excluded_warnings(filepath[len(basedir):])\n exitcode = subprocess.call(\n ['shellcheck', '-e', ','.join(exclwarns), filepath])\n if exitcode != 0:\n retval = 1\n\n if not custom_lint_rules(filepath):\n retval = 1\n return retval","def run(self):\n\n pwd = self.chdir()\n if pwd is None: return -1\n res = mkstuff.run_cmd(self.bindir + '/' + self.func + ' ' + self.args)\n os.chdir(pwd)\n return res","def test_main_fail_no_arguments(runner: CliRunner) -> None:\n result = runner.invoke(__main__.main)\n assert result.exit_code != 0","def main(cliargs=None):\n\n try:\n args = parsecli(cliargs)\n # checkargs(args)\n result = 0 # process(args)\n log.info(\"Done.\")\n return result\n\n except KeyboardInterrupt:\n return 10","def _run_ci_test():\n _run_install(False)\n _run_coverage_html(False)\n _run_typecheck_xml(False)\n _run_lint(True)","def pylint(context):\n exec_cmd = 'find . -name \"*.py\" | xargs pylint'\n run_cmd(context, exec_cmd)","def main(cls, args=None):\n parser = cls.CreateParser()\n cls.AddCommandLineArgs(parser, None)\n options, extra_args = parser.parse_known_args(args=args)\n cls.ProcessCommandLineArgs(parser, options, extra_args, None)\n return min(cls().Run(options, extra_args), 255)","def main(args):\n\n # Get additional args for the script\n if len(args) > 1:\n scriptargs = args[1:]\n else:\n scriptargs = None\n\n scriptpath = find_script(args[0])\n\n if not scriptpath:\n print('\\nCannot find that script!: {}'.format(args[0]))\n return 1\n\n # Check for extra input needed.\n forgotargs = check_input(scriptpath, scriptargs)\n if forgotargs:\n if not scriptargs:\n scriptargs = forgotargs\n else:\n scriptargs.extend(forgotargs)\n # Shell script..\n shellret = shell_script(scriptpath, scriptargs)\n if shellret:\n print('wrun: script returned non-zero!: {}'.format(shellret))\n\n return shellret","def run(self, args): # pylint: disable=too-many-return-statements\n # safely log all args to tests.log\n self._log_args(args)\n\n # get a configured instance of the App\n self.app = self.app_init(args)\n\n # Setup\n exit_code = self.run_app_method(self.app, 'setup')\n if exit_code != 0:\n return exit_code\n\n # Run\n exit_code = self.run_app_method(self.app, 'run')\n if exit_code != 0:\n return exit_code\n\n # Teardown\n exit_code = self.run_app_method(self.app, 'teardown')\n if exit_code != 0:\n return exit_code\n\n try:\n # call exit for message_tc output, but don't exit\n self.app.tcex.playbook.exit(msg=self.app.exit_message)\n except SystemExit:\n pass\n\n return self._exit(self.app.tcex.exit_code)","def main():\n args = get_args()\n prg = args.program\n\n if not os.path.isfile(prg):\n die('Missing expected program \"{}\"'.format(prg))\n\n for name in args.name:\n cmd = '{} \"{}\"'.format(prg, name)\n rv, out = getstatusoutput(cmd)\n if rv != 0:\n warn('Failed to run: {}\\nError: {}'.format(cmd, out))\n else:\n print('Success: \"{}\"'.format(out))\n\n print('Done.')","def pylint(config, python_file):\n try:\n cmd = [config.pylint_exe]\n\n if config.pylint_params:\n cmd += config.pylint_params.split()\n if '--rcfile' not in config.pylint_params:\n cmd.append('--rcfile={}'.format(config.pylintrc))\n else:\n cmd.append('--rcfile={}'.format(config.pylintrc))\n\n cmd += ['--reports=y', '--persistent=n', python_file]\n res = command.execute(cmd)\n LOGGER.debug(cmd)\n return res.stdout\n except OSError:\n print \"\\nAn error occurred. Is pylint installed?\"\n sys.exit(1)","def execute(self, args=None):\n parser = argparse.ArgumentParser(description='run tests on jupyter notebook')\n\n parser.add_argument('notebook', type=str, help='the jupyter notebook to test')\n parser.add_argument('-t', '--target', type=str, metavar='', help='where to store results')\n parser.add_argument('-c', '--context', type=str, metavar='', help='context directory')\n parser.add_argument('-v', '--verbose', action='count', default=0, help='verbosity level')\n\n args = parser.parse_args(args)\n\n logger.setLevel(loglevel(args.verbose))\n logger.debug(f'args: {args}')\n\n results = self._grade_notebook(\n Path(args.notebook).absolute(),\n target_dir=Path(args.target).absolute() if args.target else None,\n context=Path(args.context).absolute() if args.context else None\n )\n\n return results.summary().failed","def do_exit(self, args):\n return sys.exit(1)","def test_main_succeeds(app_tester: ApplicationTester) -> None:\n assert app_tester.execute(\"\") == 0","def main_parse_args(args):\n # Must return so that check command return value is passed back to calling routine\n # otherwise py.test will fail\n return main(parse_args(args))","def main_parse_args(args):\n # Must return so that check command return value is passed back to calling routine\n # otherwise py.test will fail\n return main(parse_args(args))","def run_main(): # pragma: no cover\n RunTestsCLI.run()","def main() -> int:\n parser = argparse.ArgumentParser()\n parser.add_argument('--dir-metadata',\n type=pathlib.Path, required=True)\n\n args = parser.parse_args()\n\n with LockedMetadata(args.dir_metadata, __file__) as md:\n summary_dict = {}\n passing_tests = []\n failing_tests = []\n for f in md.tests_pickle_files:\n try:\n trr = TestRunResult.construct_from_pickle(f)\n summary_dict[f\"{trr.testname}.{trr.seed}\"] = \\\n ('PASS' if trr.passed else\n 'FAILED' + (\" {T}\" if (trr.failure_mode == Failure_Modes.TIMEOUT) else \"\"))\n if trr.passed:\n passing_tests.append(trr)\n else:\n failing_tests.append(trr)\n except RuntimeError as e:\n failing_tests.append(\n TestRunResult(\n name='broken_test',\n failure_message=str(e)\n ))\n\n md.regr_log = md.dir_run/'regr.log'\n md.regr_log_junit = md.dir_run/'regr_junit.xml'\n md.regr_log_junit_merged = md.dir_run/'regr_junit_merged.xml'\n\n # Write results as junit_xml\n with open(md.regr_log_junit,\n 'w',\n encoding='UTF-8') as junit_xml,\\\n open(md.regr_log_junit_merged,\n 'w',\n encoding='UTF-8') as junit_merged_xml:\n output_run_results_junit_xml(passing_tests, failing_tests,\n junit_xml,\n junit_merged_xml)\n\n with open(md.regr_log, 'w', encoding='UTF-8') as outfile:\n # Write results as regr.log (custom logfile format)\n output_results_text(passing_tests, failing_tests, summary_dict,\n outfile)\n\n test_summary_dict = create_test_summary_dict(passing_tests +\n failing_tests)\n\n cov_summary_dict = {}\n if md.simulator == \"xlm\":\n cov_summary_dict = create_cov_summary_dict(md)\n else:\n print(\"Warning: Not generating coverage summary, unsupported \" \\\n f\"simulator {md.simulator}\")\n\n html_report_filename = md.dir_run/'report.html'\n with open(html_report_filename, 'w') as outfile:\n output_results_html(md, passing_tests + failing_tests,\n test_summary_dict, cov_summary_dict, outfile)\n\n json_report_filename = md.dir_run/'report.json'\n with open(json_report_filename, 'w') as json_report_file:\n output_results_dvsim_json(md, test_summary_dict, cov_summary_dict,\n json_report_file)\n\n svg_summary_filename = md.dir_run/'summary.svg'\n with open(svg_summary_filename, 'w') as svg_summary_file:\n output_results_svg(test_summary_dict, cov_summary_dict,\n svg_summary_file)\n\n # Print a summary line to the terminal\n print(gen_summary_line(passing_tests, failing_tests))\n\n # Succeed if no tests failed\n return 1 if failing_tests else 0","def do_command(): # pragma: no cover\n args = parse_args(sys.argv[1:])\n status = run(args)\n sys.exit(status)","def lint(session):\n session.install(\"pre-commit\")\n session.run(\"pre-commit\", \"run\", \"--all-files\")","def run(cmd):\n print ' '.join(cmd)\n try:\n check_call(cmd)\n except CalledProcessError as cpe:\n print \"Error: return code: \" + str(cpe.returncode)\n sys.exit(cpe.returncode)","def run_tests(self):\n import nose\n from pocketlint.formatcheck import main as pocket_main\n\n nose_args = ['nosetests']\n if self.verbose:\n nose_args.append('-v')\n else:\n nose_args.append('-q')\n\n module = self.test_suite\n if self.test_module:\n module = self.test_module\n\n nose_args.extend([\n '--with-coverage',\n '--cover-package=' + module,\n '--cover-erase',\n '--cover-test',\n module.replace('.', '/'),\n ])\n\n pocket_args = [\n 'README.rst',\n 'release-notes.rst',\n 'setup.py',\n ]\n for root, dirs, files in os.walk('chevah/keycert', topdown=False):\n for name in files:\n pocket_args.append(os.path.join(root, name))\n\n nose_code = nose.run(argv=nose_args)\n if nose_code:\n nose_code = 0\n else:\n nose_code = 1\n\n pocket_code = pocket_main(pocket_args)\n if not pocket_code:\n print('Linter OK')\n\n coverage_args = [\n 'report',\n '--include=chevah/keycert/tests/*',\n '--fail-under=100',\n ]\n covergate_code = load_entry_point(\n 'coverage', 'console_scripts', 'coverage')(argv=coverage_args)\n if not covergate_code:\n print('Tests coverage OK')\n\n sys.exit(pocket_code or nose_code or covergate_code)","def test():\n tests = unittest.TestLoader().discover('tests')\n result = unittest.TextTestRunner(verbosity=2).run(tests)\n if result.wasSuccessful():\n return 0\n else:\n return 1","def run(args):\n\n try:\n ec = subprocess.call(' '.join(args), shell=True)\n except OSError as e:\n print(\"Execution failed:\", e, file=sys.stderr)\n ec = 1\n\n if ec:\n sys.exit(ec)","def test_failed():\n build()\n sh(\"%s %s --last-failed\" % (PYTHON, RUNNER_PY))","def RunChecks(self):\n results = []\n\n affected_files = self.input_api.AffectedFiles(\n file_filter=self.file_filter, include_deletes=False)\n affected_js_files = filter(\n lambda f: f.LocalPath().endswith('.js'), affected_files)\n\n if affected_js_files:\n self.input_api.logging.info(\n 'Running appengine eslint on %d JS file(s)', len(affected_js_files))\n results += self.RunESLintChecks(affected_js_files)\n\n\n if results:\n results.append(self.output_api.PresubmitNotifyResult(\n 'See the JavaScript style guide at https://goo.gl/Ld1CqR.'))\n\n return results","def invoke(self):\n self.exitCode = self.script()","def main():\n fix_sys_path()\n result = unittest.TextTestRunner(verbosity=2).run(createTestSuite())\n\n if result.testsRun != EXPECTED_TEST_COUNT:\n raise Exception(\n 'Expected %s tests to be run, not %s.' % (EXPECTED_TEST_COUNT, result.testsRun))\n\n if len(result.errors) != 0 or len(result.failures) != 0:\n raise Exception(\n \"Functional test suite failed: %s errors, %s failures of %s tests run.\" % (\n len(result.errors), len(result.failures), result.testsRun))","def getReturnCode(self):\n retcode = self.sendCmd(\"echo $?\")\n try:\n return int(retcode)\n except:\n return retcode"],"string":"[\n \"def lint_command(argv) -> CommandResult:\\n app = FlakeHellApplication(program=NAME, version=VERSION)\\n try:\\n app.run(argv)\\n app.exit()\\n except SystemExit as exc:\\n return int(exc.code), ''\\n raise RuntimeError('unreachable')\",\n \"def run(self):\\n success = False\\n try:\\n # Download file if a source_zip_url was given\\n if self.source_zip_url:\\n App.logger.debug(\\\"Linting url: \\\" + self.source_zip_url)\\n self.download_archive()\\n # unzip the input archive if a source_zip_file exists\\n if self.source_zip_file:\\n App.logger.debug(\\\"Linting zip: \\\" + self.source_zip_file)\\n self.unzip_archive()\\n # lint files\\n if self.source_dir:\\n self.rc = RC(directory=self.source_dir)\\n App.logger.debug(\\\"Linting '{0}' files...\\\".format(self.source_dir))\\n success = self.lint()\\n App.logger.debug(\\\"...finished.\\\")\\n except Exception as e:\\n message = 'Linting process ended abnormally: {0}'.format(e.message)\\n App.logger.error(message)\\n self.log.warnings.append(message)\\n App.logger.error('{0}: {1}'.format(str(e), traceback.format_exc()))\\n result = {\\n 'success': success,\\n 'warnings': self.log.warnings,\\n }\\n App.logger.debug(\\\"Linter results: \\\" + str(result))\\n return result\",\n \"def run(self):\\n # first-party\\n from run import run # pylint: disable=no-name-in-module\\n\\n # run the app\\n exit_code = 0\\n try:\\n run()\\n except SystemExit as e:\\n exit_code = e.code\\n\\n self.log.data('run', 'Exit Code', exit_code)\\n return exit_code\",\n \"def main():\\n import argparse\\n\\n # parse sys.argv\\n parser = argparse.ArgumentParser(description='stylecheck')\\n parser.add_argument('-v', '--version', action='version',\\n version=('%(prog)s ' + __version__))\\n parser.add_argument('-r', '--root_dir', type=str, default='../../ken3/',\\n help='root dir path (default: \\\\'../../ken3/\\\\')')\\n args = parser.parse_args()\\n\\n # run each test\\n result = list(run(pick_names(args.root_dir), args.root_dir))\\n if result:\\n print(result)\\n return len(result)\",\n \"def main(args=None):\\n cli = Cli(args)\\n try:\\n cli.run()\\n except FatalError as err:\\n sys.stderr.write(err.args[0] + os.linesep)\\n return 2\\n\\n cli.print_output()\\n cli.print_messages()\\n return cli.code\",\n \"def lint(to_lint):\\n exit_code = 0\\n for linter, options in (('pyflakes', []), ('pep8', pep8_options)):\\n try:\\n output = local[linter](*(options + to_lint))\\n except commands.ProcessExecutionError as e:\\n output = e.stdout\\n\\n if output:\\n exit_code = 1\\n print \\\"{0} Errors:\\\".format(linter)\\n print output\\n\\n output = hacked_pep257(to_lint)\\n if output:\\n exit_code = 1\\n print \\\"Docstring Errors:\\\".format(linter.upper())\\n print output\\n\\n sys.exit(exit_code)\",\n \"def run(self):\\n check_paths = PACKAGES + [\\n 'setup.py',\\n 'tests',\\n 'util',\\n ]\\n ignore = [\\n 'doc/',\\n ]\\n\\n # try to install missing dependencies and import prospector\\n try:\\n from prospector.run import main\\n except ImportError:\\n # try to install and then import\\n self.distribution.fetch_build_eggs(['prospector[with_pyroma]'])\\n from prospector.run import main\\n\\n self.install_deps_temp()\\n\\n # run linter\\n\\n # change working directory to package root\\n package_root = os.path.abspath(os.path.dirname(__file__))\\n os.chdir(package_root)\\n\\n # write command line\\n files = discover_python_files(check_paths, ignore)\\n sys.argv = ['prospector']\\n sys.argv.extend(files)\\n\\n # run prospector\\n errno = main()\\n\\n sys.exit(errno)\",\n \"def _run(self, config):\\n \\n files = self.transaction.get_files(\\n config.check_files, config.ignore_files\\n )\\n # Exit when no files has to be checked.\\n if not files:\\n self.logger.debug(\\\"PyLint check skipped. No files for check.\\\")\\n return self.success()\\n \\n # Defining pylint home directory.\\n os.environ['PYLINTHOME'] = config.pylint_home\\n self.logger.debug(\\\"PyLint Home is used at '%s'.\\\", config.pylint_home)\\n \\n # Determine which pylintrc file is used for the validation.\\n if config.pylintrc:\\n self.logger.debug(\\\"Pylintrc is used at '%s'.\\\", config.pylintrc)\\n os.environ['PYLINTRC'] = config.pylintrc\\n else:\\n self.logger.debug(\\\"Default PyLintRC is used.\\\")\\n \\n # Only added or updated files will be checked.\\n files = [\\n self.transaction.get_file(name) \\n for name, attr in files.iteritems() \\n if attr in [\\\"A\\\", \\\"U\\\", \\\"UU\\\"]\\n ]\\n \\n if not files:\\n self.logger.debug(\\\"No files to validate. PyLint check skipped.\\\")\\n return self.success()\\n \\n output = StringIO.StringIO()\\n reporter = TextReporter(output)\\n \\n # Mock to prevent the sys.exit called by pylint.lint.Run.__init__\\n lint.sys.exit = lambda _: 0\\n \\n self.logger.debug(\\\"PyLint is running...\\\")\\n lint.Run([\\\"--reports=n\\\"] + files, reporter=reporter)\\n \\n output = output.getvalue()\\n self.logger.debug(\\\"PyLint output:\\\\n %s\\\", output)\\n if output:\\n return self.error(output)\\n else:\\n return self.success()\",\n \"def run_pylint(self, fname):\\n if '.py' not in fname:\\n return False\\n if not op.isfile(fname):\\n self.logging.info('------------------------------------------------------------------')\\n self.logging.info(\\\"FILE {} DOES NOT EXIST.\\\".format(fname))\\n self.logging.info('------------------------------------------------------------------')\\n self.logging.info('\\\\n')\\n return False\\n self.logging.info(\\\"{}\\\\n\\\".format(fname))\\n self.fname = fname\\n if self.rcfile and op.isfile(self.rcfile):\\n command_arg = [fname, '--rcfile', self.rcfile, '--score', 'no']\\n else:\\n command_arg = [fname, '--score', 'no']\\n try:\\n with redirect_stdout(PrintLogger(name=\\\"pylint\\\", log_level=\\\"INFO\\\")):\\n if int(pylint.__version__[0]) < 2:\\n self.results = Run(command_arg, exit=False) # pylint: disable=unexpected-keyword-arg\\n else:\\n # Use the default one\\n self.results = Run(command_arg, do_exit=False) # pylint: disable=unexpected-keyword-arg\\n return True\\n except Exception as error: # pylint: disable=broad-except\\n # We want to crash if ANYTHING goes wrong\\n self.logging.warning('------------------------------------------------------------------')\\n self.logging.warning(\\\"PYLINT CRASHED WHILE HANDLING {}\\\".format(fname))\\n self.logging.warning(\\\"{}: {}\\\".format(type(error), error.args))\\n self.logging.warning('------------------------------------------------------------------')\\n self.logging.info('\\\\n')\\n self.failed_files.append(fname)\\n return False\",\n \"def main() -> None:\\n try:\\n run()\\n except errors.BaseError as e:\\n sys.stderr.write(f'{str(e)}\\\\n')\\n sys.exit(e.code)\",\n \"def run():\\n args = parse_args(sys.argv[1:])\\n fnames = args.fnames\\n runner = PylintRunner(args)\\n runner.run(fnames)\",\n \"def lint():\\n load_env_vars('dev')\\n from tools.static_code_analysis import Lint\\n pylint = Lint()\\n score = pylint.run_test()\\n pylint.create_badge(score)\",\n \"def run(self, raw_args=None):\\n\\t\\targs = self.parser.parse_args(raw_args)\\n\\n\\t\\tcore = Core()\\n\\n\\t\\ttry:\\n\\t\\t\\treport = core.lint(**vars(args))\\n\\t\\texcept Exception as err:\\n\\t\\t\\tself.parser.error(str(err))\\n\\n\\t\\tprint(report)\\n\\t\\tself.parser.exit()\",\n \"def run(args):\\n parser = make_parser()\\n opts = parser.parse_args(args)\\n\\n try:\\n return opts.__command__.run(opts)\\n\\n except NextstrainCliError as error:\\n exit_status = 1\\n\\n if DEBUGGING:\\n traceback.print_exc()\\n else:\\n if isinstance(error, UsageError):\\n warn(opts.__parser__.format_usage())\\n exit_status = 2\\n\\n warn(error)\\n\\n return exit_status\\n\\n except AssertionError:\\n traceback.print_exc()\\n warn(\\\"\\\\n\\\")\\n warn(dedent(\\\"\\\"\\\"\\\\\\n An error occurred (see above) that likely indicates a bug in the\\n Nextstrain CLI.\\n\\n To report this, please open a new issue and include the error above:\\n <https://github.com/nextstrain/cli/issues/new/choose>\\n \\\"\\\"\\\"))\\n return 1\",\n \"def lint(ctx):\\r\\n print('Running linting...')\\r\\n ctx.run('pylint metrics')\",\n \"def determine_exit_code(self) -> int:\",\n \"def lint(context):\\n context.run(\\\" \\\".join([\\n \\\"autopep8\\\",\\n \\\"--recursive\\\",\\n \\\"--jobs 0\\\",\\n \\\"--in-place\\\",\\n \\\"--aggressive\\\",\\n \\\"-v\\\",\\n PACKAGE_NAME,\\n \\\"tests\\\"\\n ]))\\n context.run(\\\"pylint %s\\\" % PACKAGE_NAME)\",\n \"def _run_xsoar_linter(self, py_num: str, lint_files: List[Path]) -> Tuple[int, str]:\\n status = SUCCESS\\n FAIL_PYLINT = 0b10\\n with pylint_plugin(self._pack_abs_dir):\\n log_prompt = f\\\"{self._pack_name} - XSOAR Linter\\\"\\n logger.info(f\\\"{log_prompt} - Start\\\")\\n myenv = os.environ.copy()\\n if myenv.get(\\\"PYTHONPATH\\\"):\\n myenv[\\\"PYTHONPATH\\\"] += \\\":\\\" + str(self._pack_abs_dir)\\n else:\\n myenv[\\\"PYTHONPATH\\\"] = str(self._pack_abs_dir)\\n if self._facts[\\\"is_long_running\\\"]:\\n myenv[\\\"LONGRUNNING\\\"] = \\\"True\\\"\\n\\n py_ver = parse(py_num).major # type: ignore\\n if py_ver < 3:\\n myenv[\\\"PY2\\\"] = \\\"True\\\"\\n myenv[\\\"is_script\\\"] = str(self._facts[\\\"is_script\\\"])\\n # as Xsoar checker is a pylint plugin and runs as part of pylint code, we can not pass args to it.\\n # as a result we can use the env vars as a getway.\\n myenv[\\\"commands\\\"] = (\\n \\\",\\\".join([str(elem) for elem in self._facts[\\\"commands\\\"]])\\n if self._facts[\\\"commands\\\"]\\n else \\\"\\\"\\n )\\n stdout, stderr, exit_code = run_command_os(\\n command=build_xsoar_linter_command(\\n files=lint_files, # type: ignore\\n support_level=self._facts.get(\\\"support_level\\\", \\\"base\\\"),\\n formatting_script=self._facts.get(\\\"formatting_script\\\", False),\\n ),\\n cwd=self._pack_abs_dir,\\n env=myenv,\\n )\\n if exit_code & FAIL_PYLINT:\\n logger.error(f\\\"{log_prompt} - Finished, errors found\\\")\\n status = FAIL\\n if exit_code & WARNING:\\n logger.warning(f\\\"{log_prompt} - Finished, warnings found\\\")\\n if not status:\\n status = WARNING\\n # if pylint did not run and failure exit code has been returned from run commnad\\n elif exit_code & FAIL:\\n status = FAIL\\n logger.debug(f\\\"{log_prompt} - Actual XSOAR linter error -\\\")\\n logger.debug(\\n f\\\"{log_prompt} - Full format stdout: {RL if stdout else ''}{stdout}\\\"\\n )\\n # for contrib prs which are not merged from master and do not have pylint in dev-requirements-py2.\\n if os.environ.get(\\\"CI\\\"):\\n stdout = \\\"Xsoar linter could not run, Please merge from master\\\"\\n else:\\n stdout = (\\n \\\"Xsoar linter could not run, please make sure you have\\\"\\n \\\" the necessary Pylint version for both py2 and py3\\\"\\n )\\n logger.error(f\\\"{log_prompt} - Finished, errors found\\\")\\n\\n logger.debug(f\\\"{log_prompt} - Finished, exit-code: {exit_code}\\\")\\n logger.debug(f\\\"{log_prompt} - Finished, stdout: {RL if stdout else ''}{stdout}\\\")\\n logger.debug(f\\\"{log_prompt} - Finished, stderr: {RL if stderr else ''}{stderr}\\\")\\n\\n if not exit_code:\\n logger.info(f\\\"{log_prompt} - Successfully finished\\\")\\n\\n return status, stdout\",\n \"def lint(\\n command,\\n):\\n print(\\n \\\"\\\"\\\"\\nRunning flakeheaven, a Python code linter\\n===================================\\n\\\"\\\"\\\"\\n )\\n command.run(\\\"flakeheaven lint\\\", echo=True, pty=POSIX)\",\n \"def exit_code(self) -> int:\\n _args: list[Arg] = []\\n _ctx = self._select(\\\"exitCode\\\", _args)\\n return _ctx.execute_sync(int)\",\n \"def main(self):\\n try:\\n self.parse_args()\\n self.run()\\n return 0\\n except AnalysisBackendError as e:\\n L.error(e)\\n return 1\",\n \"def main() -> int:\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\n \\\"--overwrite\\\",\\n help=\\n \\\"Overwrites the unformatted source files with the well-formatted code in place. \\\" # pylint: disable=line-too-long\\n \\\"If not set, an exception is raised if any of the files do not conform to the style guide.\\\", # pylint: disable=line-too-long\\n action='store_true')\\n\\n args = parser.parse_args()\\n\\n tox_environments = [\\\"check\\\", \\\"_auto_version\\\"]\\n\\n if args.overwrite:\\n tox_environments.insert(0, \\\"format\\\")\\n\\n tox_from_path = shutil.which(\\\"tox\\\")\\n\\n maybe_scripts = sysconfig.get_path(\\\"scripts\\\")\\n tox_from_scripts = pathlib.Path(maybe_scripts).joinpath(\\n \\\"tox\\\") if maybe_scripts is not None else None\\n\\n if tox_from_path is not None:\\n tox = pathlib.Path(tox_from_path)\\n elif tox_from_scripts is not None and tox_from_scripts.is_file():\\n tox = tox_from_scripts\\n else:\\n message = textwrap.dedent(f'''\\\\\\n 'tox' executable not found in PATH or scripts directory\\n PATH: {os.environ['PATH']}\\n scripts: {maybe_scripts}\\n ''')\\n raise ToxNotFoundError(message)\\n\\n repo_root = pathlib.Path(__file__).parent\\n\\n tox_ini = repo_root.joinpath(\\\"tox.ini\\\")\\n\\n command = [\\n fspath(tox), \\\"-c\\\",\\n fspath(tox_ini), \\\"-e\\\", \\\",\\\".join(tox_environments)\\n ]\\n completed_process = subprocess.run(command) # pylint: disable=W1510\\n\\n return completed_process.returncode\",\n \"def commands_lint():\\n lint()\",\n \"def run_ansible_lint(\\n *argv: str,\\n cwd: str = None,\\n bin: str = None,\\n env: Dict[str, str] = None) -> subprocess.CompletedProcess:\\n if not bin:\\n bin = sys.executable\\n args = [sys.executable, \\\"-m\\\", \\\"ansiblelint\\\", *argv]\\n else:\\n args = [bin, *argv]\\n\\n return subprocess.run(\\n args,\\n stdout=subprocess.PIPE,\\n stderr=subprocess.PIPE,\\n shell=False, # needed when command is a list\\n check=False,\\n cwd=cwd,\\n env=env,\\n universal_newlines=True\\n )\",\n \"def main(self, argv=None):\\n\\n p = self.build_parser()\\n\\n args = p.parse_args(argv)\\n\\n try:\\n return self.run(args) or 0\\n except CommandFailed as e:\\n print(str(e))\\n return 1\",\n \"def run_and_check_exit_code(args=None, code=0):\\n try:\\n TT.main([\\\"dummy\\\"] + ([] if args is None else args))\\n except SystemExit as exc:\\n return exc.code == code\\n\\n return True\",\n \"def lint(cline):\\n print(\\\"Linting with pylint.\\\")\\n cline.run(r\\\"git ls-files '*.py' | xargs python3 -m pylint -j 0\\\")\\n print(\\\"Type checking with mypy.\\\")\\n cline.run(r\\\"git ls-files '*.py' | xargs python3 -m mypy\\\")\",\n \"def run_check(self, path):\\n\\n result = pycodestyle.StyleGuide().check_files(paths=[path])\\n\\n if result.total_errors != 0:\\n self.assertEqual(\\n result.total_errors, 0,\\n \\\"Found code style errors (and warnings).\\\")\",\n \"def lint(fix_imports, check):\\n skip = [\\\"node_modules\\\", \\\"requirements\\\", \\\"migrations\\\"]\\n root_files = glob(\\\"*.py\\\")\\n root_directories = [\\n name for name in next(os.walk(\\\".\\\"))[1] if not name.startswith(\\\".\\\")\\n ]\\n files_and_directories = [\\n arg for arg in root_files + root_directories if arg not in skip\\n ]\\n\\n def execute_tool(description, *args):\\n \\\"\\\"\\\"Execute a checking tool with its arguments.\\\"\\\"\\\"\\n command_line = list(args) + files_and_directories\\n click.echo(f\\\"{description}: {' '.join(command_line)}\\\")\\n rv = call(command_line)\\n if rv != 0:\\n exit(rv)\\n\\n isort_args = []\\n black_args = []\\n if check:\\n isort_args.append(\\\"--check\\\")\\n black_args.append(\\\"--check\\\")\\n if fix_imports:\\n execute_tool(\\\"Fixing import order\\\", \\\"isort\\\", *isort_args)\\n execute_tool(\\\"Formatting style\\\", \\\"black\\\", *black_args)\\n execute_tool(\\\"Checking code style\\\", \\\"flake8\\\")\",\n \"def _run_lint_in_host(self, no_bandit: bool, no_mypy: bool, no_xsoar_linter: bool):\\n log_prompt = f\\\"{self._pack_name} - Run Lint In Host\\\"\\n logger.info(f\\\"{log_prompt} - Started\\\")\\n exit_code: int = SUCCESS\\n for lint_check in [\\\"XSOAR_linter\\\", \\\"bandit\\\", \\\"mypy\\\"]:\\n exit_code = SUCCESS\\n output = \\\"\\\"\\n if self._facts[\\\"lint_files\\\"]:\\n if lint_check == \\\"XSOAR_linter\\\" and not no_xsoar_linter:\\n exit_code, output = self._run_xsoar_linter(\\n py_num=self._facts[\\\"python_version\\\"],\\n lint_files=self._facts[\\\"lint_files\\\"],\\n )\\n elif lint_check == \\\"bandit\\\" and not no_bandit:\\n exit_code, output = self._run_bandit(\\n lint_files=self._facts[\\\"lint_files\\\"]\\n )\\n\\n elif (\\n lint_check == \\\"mypy\\\"\\n and not no_mypy\\n and parse(self._facts[\\\"python_version\\\"]).major >= 3 # type: ignore[union-attr]\\n ):\\n # mypy does not support python2 now\\n exit_code, output = self._run_mypy(\\n py_num=self._facts[\\\"python_version\\\"],\\n lint_files=self._facts[\\\"lint_files\\\"],\\n )\\n\\n self._handle_lint_results(exit_code, lint_check, output)\\n logger.info(f\\\"{log_prompt} - Finished successfully\\\")\",\n \"def do_lint(cls, py_lint_args):\\n results = {'lints' : [], 'created_at' : str(datetime.datetime.utcnow()),\\n 'task_name' : py_lint_args.name, 'url' : py_lint_args.url}\\n url = urllib.parse.urlparse(py_lint_args.url)\\n passed = 0\\n issues = 0\\n failures = 0\\n if url.scheme == 'file':\\n for root, dummy_dirs, files in os.walk(url.path):\\n for my_file in files:\\n if my_file[-3:] == '.py':\\n my_file = os.path.join(root, my_file)\\n logging.info('Checking %s.', my_file)\\n my_result = {'file' : re.sub(\\n '^%s' % url.path, '', my_file).lstrip('/')}\\n try:\\n lint_results = cls.run_pylint(my_file)\\n my_result.update(cls.lint_results_to_dict(\\n lint_results))\\n if my_result['outcome'] == 'success':\\n passed += 1\\n else:\\n assert my_result['outcome'] == 'issues'\\n issues += 1\\n except Exception as bad: #pylint: disable=broad-except\\n failures += 1\\n my_result['outcome'] = 'failed'\\n my_result['longrepr'] = str(bad)\\n results['lints'].append(my_result)\\n results['summary'] = {\\n 'issues' : issues, 'passed' : passed, 'failures' : failures}\\n return results\\n else:\\n raise ValueError('URL scheme of %s not handled yet.' % url.scheme)\",\n \"def entry_point() -> int:\\n return run(argv=sys.argv[1:], stdout=sys.stdout, stderr=sys.stderr)\",\n \"def main():\\n try:\\n userParams, repoParams = splitArgv(sys.argv)\\n parser = ArgumentParser(description = \\\"StyleCop parameters\\\")\\n\\n parser.add_argument(\\\"--repo\\\", dest=\\\"repo\\\", action=\\\"store\\\",\\n help=\\\"Repository that use this script in hook\\\")\\n parser.add_argument(\\\"--stage\\\", dest=\\\"stage\\\", action=\\\"store\\\",\\n help=\\\"Stage of work with VCS\\\")\\n parser.add_argument(\\\"--config\\\", dest=\\\"config\\\", action=\\\"store\\\",\\n help=\\\"StyleCop config file\\\")\\n\\n\\n args = parser.parse_args(userParams)\\n\\n configParser = ConfigParser()\\n configString = FileReader.readFile(args.config)\\n config = configParser.parse(configString)\\n\\n factory = repos.ReposFactory()\\n repository = factory.getRepository(args.repo, args.stage)\\n changedFiles = repository.getChangedFiles(repoParams)\\n\\n extensionsDict = config.getDictionary(\\\"extensions\\\")\\n\\n checkersFactory = CheckersFactory(extensionsDict)\\n\\n # List of strings of style violations\\n errors = []\\n\\n for file in changedFiles:\\n ext = getFileExtension(file)\\n checker = checkersFactory.getChecker(ext)\\n sourceString = FileReader.readFile(file)\\n\\n errors += checker.check(sourceString)\\n\\n \\n\\n except ValueError as er:\\n pass\\n except Exception as ex:\\n pass\\n \\n if len(errors) > 0:\\n repository.sendError(\\\"Total number of style errors: \\\" + errors)\\n repository.sendError(\\\"Update failed\\\")\\n \\n # If there were no errors we permit this update\\n return len(errors)\",\n \"def checkPythonCommand(args=[None], stderr=None):\\n\\ttheOutput = None\\n\\ttry:\\n\\t\\tif args is None or args is [None]:\\n\\t\\t\\ttheOutput = subprocess.check_output([\\\"exit 1 ; #\\\"])\\n\\t\\telse:\\n\\t\\t\\tif str(\\\"coverage \\\") in args[0]:\\n\\t\\t\\t\\targs[0] = str(\\\"coverage\\\")\\n\\t\\t\\t\\targs.insert(1, str(\\\"run\\\"))\\n\\t\\t\\t\\targs.insert(2, str(\\\"-p\\\"))\\n\\t\\t\\t\\targs.insert(2, str(\\\"--source=code\\\"))\\n\\t\\t\\ttheOutput = subprocess.check_output(args, stderr=stderr)\\n\\texcept Exception:\\n\\t\\ttheOutput = None\\n\\tif isinstance(theOutput, bytes):\\n\\t\\ttheOutput = theOutput.decode('utf8')\\n\\treturn theOutput\",\n \"def call(self, cmd):\\n exitcode, _stdout, _stderr = self.run(cmd, nonzero_e = None)\\n return exitcode\",\n \"def lint(arg):\\n local('pylint --rcfile=.pylintrc ' + arg)\",\n \"def run_tool(\\n self, args: List[str], source: bool = False, cwd: Union[str, Path] = None\\n ) -> int:\\n predef = {}\\n predef[\\\"env\\\"] = os.environ\\n\\n if source:\\n predef[\\\"env\\\"][\\\"VPROJECT\\\"] = str(self.config.path.vproject)\\n predef[\\\"env\\\"][\\\"NOASSERT\\\"] = \\\"1\\\"\\n if cwd:\\n predef[\\\"cwd\\\"] = cwd\\n\\n try:\\n result = self.run_subprocess(args, **predef)\\n except Exception as e:\\n raise Exception(f\\\"failed to execute tool with parameters: {args}\\\") from e\\n return result.returncode\",\n \"def test_pylint_score_main_script(self):\\n my_dir = pathlib.Path(__file__).resolve().parent\\n root_dir = my_dir.parent.parent.parent\\n pylintrc = root_dir / \\\".pylintrc\\\"\\n script = root_dir / \\\"backend\\\" / \\\"quality_report.py\\\"\\n self.assert_pylint_score(\\\"{0} --rcfile {1}\\\".format(script, pylintrc), 10.0)\",\n \"def test_pylint(self):\\n files_list = []\\n\\n for root, dirnames, filenames in os.walk(PROJECT_DIR):\\n if ignore(root):\\n continue\\n\\n for filename in fnmatch.filter(filenames, '*.py'):\\n files_list.append(os.path.join(root, filename))\\n\\n for file in files_list:\\n # (pylint_stdout, pylint_stderr) = epylint.py_run(\\n # command_options=\\\"{} --errors-only\\\".format(file),\\n # return_std=True)\\n\\n # print(pylint_stdout.getvalue())\\n # print(pylint_stderr.getvalue())\\n\\n call([\\n 'pylint',\\n '--errors-only',\\n file])\",\n \"def lint(self):\\n return {\\n \\\"actions\\\": [\\n (create_dir, [\\\"build/lint\\\"]),\\n TaskCreator.get_flake8() + \\\" \\\" + self.project_name_sc + \\\" | tee build/lint/flake8.log\\\",\\n TaskCreator.get_pylint() + \\\" --output-format=parseable --reports=no \\\" + self.project_name_sc + \\\" | tee build/lint/pylint.log\\\"\\n ],\\n \\\"verbosity\\\": 2\\n }\",\n \"def get_exit_code(self):\",\n \"def main():\\n unittest.main(exit=False, verbosity=2)\\n return 0\",\n \"def test_main_succeed_en(runner: CliRunner) -> None:\\n result = runner.invoke(__main__.main, \\\"-c tests/clippings-en.txt\\\")\\n assert result.exit_code == 0\",\n \"def run_check(self, ctx: RunContext): # noqa\\n params = ctx.get_params(\\\"pycodestyle\\\")\\n options = ctx.options\\n if options:\\n params.setdefault(\\\"max_line_length\\\", options.max_line_length)\\n\\n if params:\\n parser = get_parser()\\n for option in parser.option_list:\\n if option.dest and option.dest in params:\\n value = params[option.dest]\\n if isinstance(value, str):\\n params[option.dest] = option.convert_value(option, value)\\n\\n style = StyleGuide(reporter=_PycodestyleReport, **params)\\n options = style.options\\n options.report.ctx = ctx # type: ignore\\n checker = Checker(ctx.filename, lines=ctx.lines, options=options)\\n checker.check_all()\",\n \"def check_exit_code(results):\\n assert results[\\\"metrics\\\"][\\\"Exit code\\\"] == 0\",\n \"def run(self, fnames):\\n logging.info(\\\"Starting\\\")\\n for fname in fnames:\\n linted = self.run_pylint(fname=fname)\\n if linted:\\n custom_ok, override_standard = self.check_custom_rules()\\n override = custom_ok and override_standard\\n success = self.check_no_silent_crash(override=override)\\n if success:\\n self.eval_results(custom_ok, override)\\n exit_code = self.report_results()\\n if not self.keep_results:\\n self.clean_up()\\n sys.exit(exit_code)\",\n \"def main():\\n logging.basicConfig()\\n refstackclient = RefstackClient()\\n parser = RefstackClientParser()\\n args = parser.parse_args(sys.argv[1:])\\n try:\\n result = refstackclient.main(**args)\\n if result != testcase.TestCase.EX_OK:\\n return result\\n except Exception: # pylint: disable=broad-except\\n return testcase.TestCase.EX_RUN_ERROR\",\n \"def run():\\n parser = create_arguments_parser()\\n arguments = parser.parse_args()\\n\\n num_warnings = 0\\n for filename in arguments.filenames:\\n num_warnings += analyse_file(filename, arguments)\\n \\n if num_warnings == 0:\\n print (\\\"No warnings\\\")\\n elif num_warnings == 1:\\n print (\\\"There was a single warning\\\")\\n else:\\n print (\\\"There were \\\" + str(num_warnings))\",\n \"def lint(self) -> pulumi.Output[Optional[bool]]:\\n return pulumi.get(self, \\\"lint\\\")\",\n \"def main(tests, sources, fail_fast, config=None):\\n try:\\n smokr.run_tests(tests.split(','), sources.split(','),\\n fail_fast, config)\\n return 0\\n except AssertionError:\\n sys.exit(1)\",\n \"def test_lint_pass(self, style):\\n result = run_linter_throw(\\\"path/to/file\\\",\\n \\\"{s}\\\\n{m}{e}\\\\n\\\",\\n style,\\n whitelist=[\\\"file/newline_last_char\\\"])\\n self.assertTrue(result)\",\n \"def main(self):\\n try:\\n config_provider = ConfigurationProvider(MaskReplacer(), DirectorySelector())\\n parser = CommandParser(\\n config_provider,\\n RubyRipperCdRipper(config_provider),\\n DiscogsMetadataService(),\\n GenreSelector())\\n commands = parser.from_args(self._get_arguments())\\n for command in commands:\\n command.validate()\\n command.execute()\\n return 0\\n except Exception as ex:\\n # This will be replaced with proper logging output.\\n sys.stderr.write('{0}\\\\n'.format(ex.message))\\n sys.stderr.write('{0}\\\\n'.format(traceback.format_exc()))\\n return 255\",\n \"def lint_cmd(scriptname, command, argv):\\n if not '--quiet' in argv and not '-q' in argv:\\n out('dennis version {version}'.format(version=__version__))\\n\\n parser = build_parser(\\n 'usage: %prog lint [ DIR | FILENAME <FILENAME> ... ]',\\n description='Lints a .po file for mismatched Python string '\\n 'formatting tokens.',\\n epilog='Note: You can ignore rules on a string-by-string basis by '\\n 'adding an extracted comment \\\"dennis-ignore: <comma-separated-rules>\\\". '\\n 'See documentation for details.',\\n sections=[\\n (format_vars(), True),\\n (format_lint_rules(), True),\\n ])\\n parser.add_option(\\n '--vars',\\n dest='vars',\\n help=('Comma-separated list of variable types. See Available Variable '\\n 'Formats.'),\\n metavar='VARS',\\n default='pysprintf,pyformat')\\n parser.add_option(\\n '--rules',\\n dest='rules',\\n help=('Comma-separated list of lint rules to use. Defaults to all '\\n 'rules. See Available Lint Rules.'),\\n metavar='RULES',\\n default='')\\n parser.add_option(\\n '-q', '--quiet',\\n action='store_true',\\n dest='quiet',\\n help='quiet all output')\\n parser.add_option(\\n '--errorsonly',\\n action='store_true',\\n dest='errorsonly',\\n help='only print errors')\\n\\n (options, args) = parser.parse_args(argv)\\n\\n if not args:\\n parser.print_help()\\n return 1\\n\\n linter = Linter(options.vars.split(','), options.rules.split(','))\\n\\n if os.path.isdir(args[0]):\\n po_files = []\\n for root, dirs, files in os.walk(args[0]):\\n po_files.extend(\\n [os.path.join(root, fn) for fn in files\\n if fn.endswith('.po')])\\n\\n else:\\n po_files = args\\n\\n po_files = [os.path.abspath(fn) for fn in po_files if fn.endswith('.po')]\\n\\n files_to_errors = {}\\n total_error_count = 0\\n total_warning_count = 0\\n total_files_with_errors = 0\\n\\n for fn in po_files:\\n try:\\n if not os.path.exists(fn):\\n raise IOError('File \\\"{fn}\\\" does not exist.'.format(fn=fn))\\n\\n results = linter.verify_file(fn)\\n\\n except IOError as ioe:\\n # This is not a valid .po file. So mark it as an error.\\n err('>>> Problem opening file: {fn}'.format(fn=fn))\\n err(repr(ioe))\\n out('')\\n\\n # FIXME - should we track this separately as an invalid\\n # file?\\n files_to_errors[fn] = (1, 0)\\n total_error_count += 1\\n continue\\n\\n # Extract all the problematic LintItems--they have non-empty\\n # missing or invalid lists.\\n problem_results = [\\n r for r in results if r.has_problems(options.errorsonly)]\\n\\n # We don't want to print output for files that are fine, so we\\n # update the bookkeeping and move on.\\n if not problem_results:\\n files_to_errors[fn] = (0, 0)\\n continue\\n\\n if not options.quiet:\\n out(TERM.bold_green,\\n '>>> Working on: {fn}'.format(fn=fn),\\n TERM.normal)\\n\\n error_count = 0\\n warning_count = 0\\n\\n for entry in problem_results:\\n total_error_count += len(entry.errors)\\n error_count += len(entry.errors)\\n\\n if not options.quiet:\\n # TODO: This is totally shite code.\\n for code, trstr, msg in entry.errors:\\n out(TERM.bold_red,\\n code,\\n ': ',\\n msg,\\n TERM.normal)\\n for field, s in zip(trstr.msgid_fields, trstr.msgid_strings):\\n out(field, ' \\\"', s, '\\\"')\\n out(trstr.msgstr_field, ' \\\"', trstr.msgstr_string, '\\\"')\\n out('')\\n\\n total_warning_count += len(entry.warnings)\\n warning_count += len(entry.warnings)\\n\\n if not options.quiet and not options.errorsonly:\\n for code, trstr, msg in entry.warnings:\\n out(TERM.bold_yellow,\\n code,\\n ': ',\\n msg,\\n TERM.normal)\\n for field, s in zip(trstr.msgid_fields, trstr.msgid_strings):\\n out(field, ' \\\"', s, '\\\"')\\n out(trstr.msgstr_field, ' \\\"', trstr.msgstr_string)\\n out('')\\n\\n files_to_errors[fn] = (error_count, warning_count)\\n\\n if error_count > 0:\\n total_files_with_errors += 1\\n\\n if not options.quiet:\\n out('Totals')\\n if not options.errorsonly:\\n out(' Warnings: {warnings:5}'.format(warnings=warning_count))\\n out(' Errors: {errors:5}\\\\n'.format(errors=error_count))\\n\\n if len(po_files) > 1 and not options.quiet:\\n out('Final totals')\\n out(' Number of files examined: {count:5}'.format(\\n count=len(po_files)))\\n out(' Total number of files with errors: {count:5}'.format(\\n count=total_files_with_errors))\\n if not options.errorsonly:\\n out(' Total number of warnings: {count:5}'.format(\\n count=total_warning_count))\\n out(' Total number of errors: {count:5}'.format(\\n count=total_error_count))\\n out('')\\n\\n file_counts = [\\n (counts[0], counts[1], fn.split(os.sep)[-3], fn.split(os.sep)[-1])\\n for (fn, counts) in files_to_errors.items()\\n ]\\n\\n # If we're showing errors only, then don't talk about warnings.\\n if options.errorsonly:\\n header = 'Errors Filename'\\n line = ' {errors:5} {locale} ({fn})'\\n else:\\n header = 'Warnings Errors Filename'\\n line = ' {warnings:5} {errors:5} {locale} ({fn})'\\n\\n file_counts = list(reversed(sorted(file_counts)))\\n printed_header = False\\n for error_count, warning_count, locale, fn in file_counts:\\n if not error_count and not warning_count:\\n continue\\n if not printed_header:\\n out(header)\\n printed_header = True\\n\\n out(line.format(\\n warnings=warning_count, errors=error_count, fn=fn,\\n locale=locale))\\n\\n # Return 0 if everything was fine or 1 if there were errors.\\n return 1 if total_error_count else 0\",\n \"def lint():\\n run('python3 -m pylint --load-plugins pylint_django api',\\n warn=True, echo=True)\",\n \"def lint(session):\\n session.install(\\\"-r\\\", \\\"requirements-test.txt\\\")\\n session.install(\\\"-r\\\", \\\"requirements.txt\\\")\\n session.install(\\\"flake8-import-order\\\")\\n session.run(\\\"black\\\", \\\"--check\\\", *BLACK_PATHS)\\n session.run(\\n \\\"flake8\\\",\\n \\\"--import-order-style=google\\\",\\n \\\"--application-import-names=google,tests\\\",\\n \\\"google\\\",\\n \\\"tests\\\",\\n )\\n session.run(\\\"mypy\\\", \\\"google\\\", \\\"tests\\\")\\n session.run(\\\"python\\\", \\\"setup.py\\\", \\\"sdist\\\")\\n session.run(\\\"twine\\\", \\\"check\\\", \\\"dist/*\\\")\",\n \"def _run(args: List[str], check: bool = False) -> Tuple[int, str]:\\n result = subprocess.run(args=args, stdout=subprocess.PIPE)\\n if check and result.returncode != 0:\\n raise subprocess.CalledProcessError(result.returncode, args)\\n return result.returncode, result.stdout.decode('utf-8', 'strict')\",\n \"def task_lint():\\n return {\\\"actions\\\": [[\\\"prospector\\\"]], \\\"verbosity\\\": 1}\",\n \"def error_handler(self):\\n if self.ctx.exit_code is not None:\\n return self.ctx.exit_code\",\n \"def _main(args):\\n start = datetime.now()\\n try:\\n main.main(args, standalone_mode=False)\\n except Abort:\\n click.secho(\\\"Operation aborted!\\\", fg=\\\"yellow\\\", bold=True)\\n except Exception as err: # pylint: disable=broad-except\\n click.secho(\\\"Error: \\\" + str(err), fg=\\\"red\\\")\\n return 1\\n finally:\\n if \\\"--help\\\" not in sys.argv:\\n # display overall runtime for reference when performing update\\n end = datetime.now()\\n runtime = end - start\\n click.secho(\\\"Operation complete. Total runtime: \\\" + humanize.naturaldelta(runtime), fg=\\\"green\\\")\\n return 0\",\n \"def main(argv=None):\\n # Parse arguments\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\\"filenames\\\", nargs=\\\"*\\\", help=\\\"File names to check\\\")\\n args = parser.parse_args(argv)\\n filenames = args.filenames\\n\\n # Find files with conflicts\\n python_files = [filename for filename in filenames if is_textfile(filename)]\\n files_with_conflicts = []\\n for text_file in python_files:\\n files_with_conflicts += find_git_conflicts(text_file)\\n\\n # Return response\\n exit_code = 0\\n if files_with_conflicts:\\n exit_code = 1\\n print(\\\"Git Conflicts Detected in file(s): \\\\n - {}\\\".format(\\\" - \\\".join(files_with_conflicts)))\\n\\n return exit_code\",\n \"def main():\\n coverage = calculate_code_coverage()\\n platform = os.uname()[0]\\n if coverage < CODE_COVERAGE_GOAL[platform]:\\n data = {\\n 'expected': CODE_COVERAGE_GOAL[platform],\\n 'observed': coverage,\\n }\\n print '\\\\033[91mFAIL: %(observed).2f%% does not meet goal of %(expected).2f%%\\\\033[0m' % data\\n sys.exit(1)\",\n \"def test_2():\\n try:\\n import flake8 # noqa: F401\\n except ImportError:\\n return None\\n\\n cwd = os.getcwd()\\n os.chdir(PACKAGE_DIR)\\n try:\\n subprocess.check_call(['flake8'])\\n os.chdir(cwd)\\n except CalledProcessError:\\n os.chdir(cwd)\\n raise CalledProcessError\",\n \"def sys_main() -> int:\\n try:\\n main()\\n return 0\\n except Exception:\\n print(traceback.format_exc())\\n return 1\",\n \"def run_pylint(options):\\r\\n errors = getattr(options, 'errors', False)\\r\\n systems = getattr(options, 'system', 'lms,cms,common').split(',')\\r\\n\\r\\n for system in systems:\\r\\n # Directory to put the pylint report in.\\r\\n # This makes the folder if it doesn't already exist.\\r\\n report_dir = get_or_make_dir(os.path.join(Env.REPORT_DIR, system))\\r\\n\\r\\n flags = '-E' if errors else ''\\r\\n\\r\\n apps = [system]\\r\\n\\r\\n for directory in ['djangoapps', 'lib']:\\r\\n dirs = os.listdir(os.path.join(system, directory))\\r\\n apps.extend([d for d in dirs if os.path.isdir(os.path.join(system, directory, d))])\\r\\n\\r\\n apps_list = ' '.join(apps)\\r\\n\\r\\n pythonpath_prefix = (\\r\\n \\\"PYTHONPATH={system}:{system}/djangoapps:{system}/\\\"\\r\\n \\\"lib:common/djangoapps:common/lib\\\".format(\\r\\n system=system\\r\\n )\\r\\n )\\r\\n\\r\\n sh(\\r\\n \\\"{pythonpath_prefix} pylint {flags} -f parseable {apps} | \\\"\\r\\n \\\"tee {report_dir}/pylint.report\\\".format(\\r\\n pythonpath_prefix=pythonpath_prefix,\\r\\n flags=flags,\\r\\n apps=apps_list,\\r\\n report_dir=report_dir\\r\\n )\\r\\n )\",\n \"def main(): # pragma: nocover\\n p = argparse.ArgumentParser()\\n p.add_argument(\\n 'directory',\\n help=\\\"Directory to check\\\"\\n )\\n p.add_argument(\\n '--verbose', '-v', action='store_true',\\n help=\\\"increase verbosity\\\"\\n )\\n args = p.parse_args()\\n\\n import sys\\n _changed = changed(sys.argv[1], args=args)\\n sys.exit(_changed)\",\n \"def test_lint_pass(self):\\n contents = \\\"contents\\\\ncontents\\\\n\\\"\\n result = run_linter_throw(\\\"path/to/file\\\",\\n contents,\\n FormatStyle(\\\"#\\\", \\\"#\\\", \\\"\\\"),\\n whitelist=[\\\"file/trailing_whitespace\\\"])\\n self.assertTrue(result)\",\n \"def test():\\n tests = unittest.TestLoader().discover('project/tests', pattern='test*.py')\\n result = unittest.TextTestRunner(verbosity=2).run(tests)\\n if result.wasSuccessful():\\n return 0\\n sys.exit(result)\",\n \"def test_error_control(testdir):\\n testdir.makepyfile(\\\"\\\"\\\"import sys\\\"\\\"\\\")\\n result = testdir.runpytest('--pylint', '--pylint-error-types=EF')\\n assert '1 passed' in result.stdout.str()\",\n \"def run(self):\\n checker = Checker(self.name)\\n try:\\n self.f(checker)\\n except Exception as e:\\n # Find line number and filename where the exception was thrown.\\n _, _, traceback = sys.exc_info()\\n traceback = traceback.tb_next\\n lineno = traceback.tb_lineno\\n filepath = traceback.tb_frame.f_code.co_filename\\n filename = os.path.basename(filepath)\\n\\n # We can't continue, so return a single result with this error.\\n self.checks = [Result(ERROR, e, self.name, \\\"\\\", filename, lineno)]\\n dots(ERROR)\\n else:\\n self.checks = checker.results\",\n \"def run(self, inp=\\\"\\\"):\\n result = self.run_process(inp)\\n if result.returncode == 0:\\n outcome = self.PASS\\n elif result.returncode < 0:\\n outcome = self.FAIL\\n else:\\n outcome = self.UNRESOLVED\\n return (result, outcome)\",\n \"def main():\\n argument_parser = argparse.ArgumentParser(add_help=True)\\n argument_parser.add_argument(\\\"directory\\\", type=str,\\n help=\\\"Directory to detect test smells.\\\")\\n args = argument_parser.parse_args()\\n \\n if len(sys.argv) < 1:\\n \\n argument_parser.print_help()\\n \\n else:\\n \\n if os.path.exists(args.directory) or os.path.isdir(args.directory):\\n\\n #Stage 1: project level rule checking\\n files = python_parser.get_python_files(os.path.abspath(args.directory))\\n results_list = project_rule_runner(files)\\n \\n #Stage 2: test case level rule checking\\n #test_case_pairs_list is a list of test cases paired with their file of origin\\n filtered_files = python_parser.filter_python_files(files)\\n test_case_pairs_list = python_parser.get_test_case_asts(filtered_files)\\n \\n for test_case_pair in test_case_pairs_list:\\n results_list = results_list + test_case_rule_runner(test_case_pair)\\n \\n #Stage 3: test method level rule checking\\n test_method_list = list()\\n \\n for test_case_pair in test_case_pairs_list:\\n test_method_list = test_method_list + python_parser.get_test_asts(test_case_pair)\\n \\n for test_method in test_method_list: \\n results_list = results_list + test_method_rule_runner(test_method)\\n \\n #Output formatting\\n format_output(results_list)\\n \\n else:\\n print(\\\"Invalid path given.\\\")\",\n \"def test():\\n # subprocess.check_output() has been introduced in Python 2.7\\n if sys.version_info < (2, 7):\\n print(\\\"Python version too old, skipping test.\\\")\\n return 2\\n\\n try:\\n subprocess.check_output(['shellcheck', '--version'])\\n except OSError as exc:\\n if exc.errno == errno.ENOENT:\\n print(\\\"shellcheck not found, aborting test.\\\")\\n return 2\\n raise\\n except subprocess.CalledProcessError:\\n print(\\\"shellcheck on an simple stream exited with error\\\")\\n return 1\\n\\n retval = 0\\n basedir = os.path.join(os.path.dirname(__file__), os.path.pardir)\\n for dirpath, _, files in os.walk(basedir):\\n # Ignore files in .git directory\\n if '/.git' in dirpath:\\n continue\\n # Ignore collected hardware data files\\n if '/specific/collected_hwdata' in dirpath:\\n continue\\n\\n for filename in sorted(files):\\n # Skip .toprc, which is not UTF-8\\n if filename == 'toprc' and dirpath.endswith('dotfiles'):\\n continue\\n\\n filepath = os.path.join(dirpath, filename)\\n\\n # If there is no extension and the file is known not to be a\\n # shell script, the first line is used to find shell scripts\\n if '.' not in filename:\\n is_shellconfig = (\\n dirpath.endswith('/dotfiles/shell') or\\n '/dotfiles/bash' in filepath or\\n '/dotfiles/profile' in filepath or\\n '/dotfiles/zsh' in filepath)\\n if not is_shellconfig:\\n with open(filepath, 'r') as cur_file:\\n firstline = cur_file.readline().rstrip()\\n if 'sh' not in firstline:\\n continue\\n elif not filename.lower().endswith('.sh'):\\n continue\\n\\n exclwarns = get_excluded_warnings(filepath[len(basedir):])\\n exitcode = subprocess.call(\\n ['shellcheck', '-e', ','.join(exclwarns), filepath])\\n if exitcode != 0:\\n retval = 1\\n\\n if not custom_lint_rules(filepath):\\n retval = 1\\n return retval\",\n \"def run(self):\\n\\n pwd = self.chdir()\\n if pwd is None: return -1\\n res = mkstuff.run_cmd(self.bindir + '/' + self.func + ' ' + self.args)\\n os.chdir(pwd)\\n return res\",\n \"def test_main_fail_no_arguments(runner: CliRunner) -> None:\\n result = runner.invoke(__main__.main)\\n assert result.exit_code != 0\",\n \"def main(cliargs=None):\\n\\n try:\\n args = parsecli(cliargs)\\n # checkargs(args)\\n result = 0 # process(args)\\n log.info(\\\"Done.\\\")\\n return result\\n\\n except KeyboardInterrupt:\\n return 10\",\n \"def _run_ci_test():\\n _run_install(False)\\n _run_coverage_html(False)\\n _run_typecheck_xml(False)\\n _run_lint(True)\",\n \"def pylint(context):\\n exec_cmd = 'find . -name \\\"*.py\\\" | xargs pylint'\\n run_cmd(context, exec_cmd)\",\n \"def main(cls, args=None):\\n parser = cls.CreateParser()\\n cls.AddCommandLineArgs(parser, None)\\n options, extra_args = parser.parse_known_args(args=args)\\n cls.ProcessCommandLineArgs(parser, options, extra_args, None)\\n return min(cls().Run(options, extra_args), 255)\",\n \"def main(args):\\n\\n # Get additional args for the script\\n if len(args) > 1:\\n scriptargs = args[1:]\\n else:\\n scriptargs = None\\n\\n scriptpath = find_script(args[0])\\n\\n if not scriptpath:\\n print('\\\\nCannot find that script!: {}'.format(args[0]))\\n return 1\\n\\n # Check for extra input needed.\\n forgotargs = check_input(scriptpath, scriptargs)\\n if forgotargs:\\n if not scriptargs:\\n scriptargs = forgotargs\\n else:\\n scriptargs.extend(forgotargs)\\n # Shell script..\\n shellret = shell_script(scriptpath, scriptargs)\\n if shellret:\\n print('wrun: script returned non-zero!: {}'.format(shellret))\\n\\n return shellret\",\n \"def run(self, args): # pylint: disable=too-many-return-statements\\n # safely log all args to tests.log\\n self._log_args(args)\\n\\n # get a configured instance of the App\\n self.app = self.app_init(args)\\n\\n # Setup\\n exit_code = self.run_app_method(self.app, 'setup')\\n if exit_code != 0:\\n return exit_code\\n\\n # Run\\n exit_code = self.run_app_method(self.app, 'run')\\n if exit_code != 0:\\n return exit_code\\n\\n # Teardown\\n exit_code = self.run_app_method(self.app, 'teardown')\\n if exit_code != 0:\\n return exit_code\\n\\n try:\\n # call exit for message_tc output, but don't exit\\n self.app.tcex.playbook.exit(msg=self.app.exit_message)\\n except SystemExit:\\n pass\\n\\n return self._exit(self.app.tcex.exit_code)\",\n \"def main():\\n args = get_args()\\n prg = args.program\\n\\n if not os.path.isfile(prg):\\n die('Missing expected program \\\"{}\\\"'.format(prg))\\n\\n for name in args.name:\\n cmd = '{} \\\"{}\\\"'.format(prg, name)\\n rv, out = getstatusoutput(cmd)\\n if rv != 0:\\n warn('Failed to run: {}\\\\nError: {}'.format(cmd, out))\\n else:\\n print('Success: \\\"{}\\\"'.format(out))\\n\\n print('Done.')\",\n \"def pylint(config, python_file):\\n try:\\n cmd = [config.pylint_exe]\\n\\n if config.pylint_params:\\n cmd += config.pylint_params.split()\\n if '--rcfile' not in config.pylint_params:\\n cmd.append('--rcfile={}'.format(config.pylintrc))\\n else:\\n cmd.append('--rcfile={}'.format(config.pylintrc))\\n\\n cmd += ['--reports=y', '--persistent=n', python_file]\\n res = command.execute(cmd)\\n LOGGER.debug(cmd)\\n return res.stdout\\n except OSError:\\n print \\\"\\\\nAn error occurred. Is pylint installed?\\\"\\n sys.exit(1)\",\n \"def execute(self, args=None):\\n parser = argparse.ArgumentParser(description='run tests on jupyter notebook')\\n\\n parser.add_argument('notebook', type=str, help='the jupyter notebook to test')\\n parser.add_argument('-t', '--target', type=str, metavar='', help='where to store results')\\n parser.add_argument('-c', '--context', type=str, metavar='', help='context directory')\\n parser.add_argument('-v', '--verbose', action='count', default=0, help='verbosity level')\\n\\n args = parser.parse_args(args)\\n\\n logger.setLevel(loglevel(args.verbose))\\n logger.debug(f'args: {args}')\\n\\n results = self._grade_notebook(\\n Path(args.notebook).absolute(),\\n target_dir=Path(args.target).absolute() if args.target else None,\\n context=Path(args.context).absolute() if args.context else None\\n )\\n\\n return results.summary().failed\",\n \"def do_exit(self, args):\\n return sys.exit(1)\",\n \"def test_main_succeeds(app_tester: ApplicationTester) -> None:\\n assert app_tester.execute(\\\"\\\") == 0\",\n \"def main_parse_args(args):\\n # Must return so that check command return value is passed back to calling routine\\n # otherwise py.test will fail\\n return main(parse_args(args))\",\n \"def main_parse_args(args):\\n # Must return so that check command return value is passed back to calling routine\\n # otherwise py.test will fail\\n return main(parse_args(args))\",\n \"def run_main(): # pragma: no cover\\n RunTestsCLI.run()\",\n \"def main() -> int:\\n parser = argparse.ArgumentParser()\\n parser.add_argument('--dir-metadata',\\n type=pathlib.Path, required=True)\\n\\n args = parser.parse_args()\\n\\n with LockedMetadata(args.dir_metadata, __file__) as md:\\n summary_dict = {}\\n passing_tests = []\\n failing_tests = []\\n for f in md.tests_pickle_files:\\n try:\\n trr = TestRunResult.construct_from_pickle(f)\\n summary_dict[f\\\"{trr.testname}.{trr.seed}\\\"] = \\\\\\n ('PASS' if trr.passed else\\n 'FAILED' + (\\\" {T}\\\" if (trr.failure_mode == Failure_Modes.TIMEOUT) else \\\"\\\"))\\n if trr.passed:\\n passing_tests.append(trr)\\n else:\\n failing_tests.append(trr)\\n except RuntimeError as e:\\n failing_tests.append(\\n TestRunResult(\\n name='broken_test',\\n failure_message=str(e)\\n ))\\n\\n md.regr_log = md.dir_run/'regr.log'\\n md.regr_log_junit = md.dir_run/'regr_junit.xml'\\n md.regr_log_junit_merged = md.dir_run/'regr_junit_merged.xml'\\n\\n # Write results as junit_xml\\n with open(md.regr_log_junit,\\n 'w',\\n encoding='UTF-8') as junit_xml,\\\\\\n open(md.regr_log_junit_merged,\\n 'w',\\n encoding='UTF-8') as junit_merged_xml:\\n output_run_results_junit_xml(passing_tests, failing_tests,\\n junit_xml,\\n junit_merged_xml)\\n\\n with open(md.regr_log, 'w', encoding='UTF-8') as outfile:\\n # Write results as regr.log (custom logfile format)\\n output_results_text(passing_tests, failing_tests, summary_dict,\\n outfile)\\n\\n test_summary_dict = create_test_summary_dict(passing_tests +\\n failing_tests)\\n\\n cov_summary_dict = {}\\n if md.simulator == \\\"xlm\\\":\\n cov_summary_dict = create_cov_summary_dict(md)\\n else:\\n print(\\\"Warning: Not generating coverage summary, unsupported \\\" \\\\\\n f\\\"simulator {md.simulator}\\\")\\n\\n html_report_filename = md.dir_run/'report.html'\\n with open(html_report_filename, 'w') as outfile:\\n output_results_html(md, passing_tests + failing_tests,\\n test_summary_dict, cov_summary_dict, outfile)\\n\\n json_report_filename = md.dir_run/'report.json'\\n with open(json_report_filename, 'w') as json_report_file:\\n output_results_dvsim_json(md, test_summary_dict, cov_summary_dict,\\n json_report_file)\\n\\n svg_summary_filename = md.dir_run/'summary.svg'\\n with open(svg_summary_filename, 'w') as svg_summary_file:\\n output_results_svg(test_summary_dict, cov_summary_dict,\\n svg_summary_file)\\n\\n # Print a summary line to the terminal\\n print(gen_summary_line(passing_tests, failing_tests))\\n\\n # Succeed if no tests failed\\n return 1 if failing_tests else 0\",\n \"def do_command(): # pragma: no cover\\n args = parse_args(sys.argv[1:])\\n status = run(args)\\n sys.exit(status)\",\n \"def lint(session):\\n session.install(\\\"pre-commit\\\")\\n session.run(\\\"pre-commit\\\", \\\"run\\\", \\\"--all-files\\\")\",\n \"def run(cmd):\\n print ' '.join(cmd)\\n try:\\n check_call(cmd)\\n except CalledProcessError as cpe:\\n print \\\"Error: return code: \\\" + str(cpe.returncode)\\n sys.exit(cpe.returncode)\",\n \"def run_tests(self):\\n import nose\\n from pocketlint.formatcheck import main as pocket_main\\n\\n nose_args = ['nosetests']\\n if self.verbose:\\n nose_args.append('-v')\\n else:\\n nose_args.append('-q')\\n\\n module = self.test_suite\\n if self.test_module:\\n module = self.test_module\\n\\n nose_args.extend([\\n '--with-coverage',\\n '--cover-package=' + module,\\n '--cover-erase',\\n '--cover-test',\\n module.replace('.', '/'),\\n ])\\n\\n pocket_args = [\\n 'README.rst',\\n 'release-notes.rst',\\n 'setup.py',\\n ]\\n for root, dirs, files in os.walk('chevah/keycert', topdown=False):\\n for name in files:\\n pocket_args.append(os.path.join(root, name))\\n\\n nose_code = nose.run(argv=nose_args)\\n if nose_code:\\n nose_code = 0\\n else:\\n nose_code = 1\\n\\n pocket_code = pocket_main(pocket_args)\\n if not pocket_code:\\n print('Linter OK')\\n\\n coverage_args = [\\n 'report',\\n '--include=chevah/keycert/tests/*',\\n '--fail-under=100',\\n ]\\n covergate_code = load_entry_point(\\n 'coverage', 'console_scripts', 'coverage')(argv=coverage_args)\\n if not covergate_code:\\n print('Tests coverage OK')\\n\\n sys.exit(pocket_code or nose_code or covergate_code)\",\n \"def test():\\n tests = unittest.TestLoader().discover('tests')\\n result = unittest.TextTestRunner(verbosity=2).run(tests)\\n if result.wasSuccessful():\\n return 0\\n else:\\n return 1\",\n \"def run(args):\\n\\n try:\\n ec = subprocess.call(' '.join(args), shell=True)\\n except OSError as e:\\n print(\\\"Execution failed:\\\", e, file=sys.stderr)\\n ec = 1\\n\\n if ec:\\n sys.exit(ec)\",\n \"def test_failed():\\n build()\\n sh(\\\"%s %s --last-failed\\\" % (PYTHON, RUNNER_PY))\",\n \"def RunChecks(self):\\n results = []\\n\\n affected_files = self.input_api.AffectedFiles(\\n file_filter=self.file_filter, include_deletes=False)\\n affected_js_files = filter(\\n lambda f: f.LocalPath().endswith('.js'), affected_files)\\n\\n if affected_js_files:\\n self.input_api.logging.info(\\n 'Running appengine eslint on %d JS file(s)', len(affected_js_files))\\n results += self.RunESLintChecks(affected_js_files)\\n\\n\\n if results:\\n results.append(self.output_api.PresubmitNotifyResult(\\n 'See the JavaScript style guide at https://goo.gl/Ld1CqR.'))\\n\\n return results\",\n \"def invoke(self):\\n self.exitCode = self.script()\",\n \"def main():\\n fix_sys_path()\\n result = unittest.TextTestRunner(verbosity=2).run(createTestSuite())\\n\\n if result.testsRun != EXPECTED_TEST_COUNT:\\n raise Exception(\\n 'Expected %s tests to be run, not %s.' % (EXPECTED_TEST_COUNT, result.testsRun))\\n\\n if len(result.errors) != 0 or len(result.failures) != 0:\\n raise Exception(\\n \\\"Functional test suite failed: %s errors, %s failures of %s tests run.\\\" % (\\n len(result.errors), len(result.failures), result.testsRun))\",\n \"def getReturnCode(self):\\n retcode = self.sendCmd(\\\"echo $?\\\")\\n try:\\n return int(retcode)\\n except:\\n return retcode\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7377753","0.70567983","0.6740494","0.67318183","0.6663239","0.6513022","0.6471138","0.6430389","0.63975984","0.63959485","0.63455653","0.6329475","0.63124686","0.6284871","0.6266829","0.6210904","0.6164137","0.6136608","0.61339283","0.6081217","0.6057421","0.59995157","0.5978652","0.5938565","0.59358144","0.5835184","0.5828818","0.58077073","0.57918495","0.5777865","0.577294","0.57654893","0.5754143","0.57403326","0.5724559","0.57113135","0.57068086","0.57044935","0.5690074","0.5689152","0.56699383","0.56671834","0.5662552","0.5659295","0.5631633","0.5630905","0.56187606","0.5602341","0.55888623","0.55847514","0.5552851","0.5550983","0.5539403","0.5536737","0.5529506","0.5515951","0.55140644","0.5509745","0.55060315","0.5499701","0.5486855","0.5461078","0.5454926","0.545371","0.54348946","0.5416862","0.5416831","0.54167455","0.541194","0.5410402","0.54085565","0.5408173","0.5399651","0.5378474","0.5372431","0.5368749","0.5367178","0.5361721","0.5350958","0.534499","0.53424364","0.5336445","0.5327029","0.5317324","0.5316877","0.5315795","0.5315795","0.5311217","0.52971846","0.5294127","0.5293068","0.5286449","0.5282121","0.52807","0.5279241","0.52680075","0.5261712","0.52616096","0.52573436","0.5256965"],"string":"[\n \"0.7377753\",\n \"0.70567983\",\n \"0.6740494\",\n \"0.67318183\",\n \"0.6663239\",\n \"0.6513022\",\n \"0.6471138\",\n \"0.6430389\",\n \"0.63975984\",\n \"0.63959485\",\n \"0.63455653\",\n \"0.6329475\",\n \"0.63124686\",\n \"0.6284871\",\n \"0.6266829\",\n \"0.6210904\",\n \"0.6164137\",\n \"0.6136608\",\n \"0.61339283\",\n \"0.6081217\",\n \"0.6057421\",\n \"0.59995157\",\n \"0.5978652\",\n \"0.5938565\",\n \"0.59358144\",\n \"0.5835184\",\n \"0.5828818\",\n \"0.58077073\",\n \"0.57918495\",\n \"0.5777865\",\n \"0.577294\",\n \"0.57654893\",\n \"0.5754143\",\n \"0.57403326\",\n \"0.5724559\",\n \"0.57113135\",\n \"0.57068086\",\n \"0.57044935\",\n \"0.5690074\",\n \"0.5689152\",\n \"0.56699383\",\n \"0.56671834\",\n \"0.5662552\",\n \"0.5659295\",\n \"0.5631633\",\n \"0.5630905\",\n \"0.56187606\",\n \"0.5602341\",\n \"0.55888623\",\n \"0.55847514\",\n \"0.5552851\",\n \"0.5550983\",\n \"0.5539403\",\n \"0.5536737\",\n \"0.5529506\",\n \"0.5515951\",\n \"0.55140644\",\n \"0.5509745\",\n \"0.55060315\",\n \"0.5499701\",\n \"0.5486855\",\n \"0.5461078\",\n \"0.5454926\",\n \"0.545371\",\n \"0.54348946\",\n \"0.5416862\",\n \"0.5416831\",\n \"0.54167455\",\n \"0.541194\",\n \"0.5410402\",\n \"0.54085565\",\n \"0.5408173\",\n \"0.5399651\",\n \"0.5378474\",\n \"0.5372431\",\n \"0.5368749\",\n \"0.5367178\",\n \"0.5361721\",\n \"0.5350958\",\n \"0.534499\",\n \"0.53424364\",\n \"0.5336445\",\n \"0.5327029\",\n \"0.5317324\",\n \"0.5316877\",\n \"0.5315795\",\n \"0.5315795\",\n \"0.5311217\",\n \"0.52971846\",\n \"0.5294127\",\n \"0.5293068\",\n \"0.5286449\",\n \"0.5282121\",\n \"0.52807\",\n \"0.5279241\",\n \"0.52680075\",\n \"0.5261712\",\n \"0.52616096\",\n \"0.52573436\",\n \"0.5256965\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.53075475"},"document_rank":{"kind":"string","value":"88"}}},{"rowIdx":16886,"cells":{"query":{"kind":"string","value":"Returns a new initialized argument parser."},"document":{"kind":"string","value":"def init_argument_parser():\n parser = argparse.ArgumentParser(prog=NAME, description=DESCRIPTION)\n\n # The files argument is optional as STDIN is always read\n parser.add_argument(dest='files', metavar='FILE', nargs='*', default=[],\n help='one or more files or paths')\n\n parser.add_argument('--version', action='version', version='%(prog)s {}'.format(VERSION))\n parser.add_argument('-L', dest='listrules', default=False,\n action='store_true', help=\"list all the rules\")\n parser.add_argument('-r', action='append', dest='rulesdir',\n default=[],\n help=\"specify one or more rules directories using \"\n \"one or more -r arguments. Any -r flags override \"\n \"the default rules in %s, unless -R is also used.\"\n % default_rulesdir)\n parser.add_argument('-R', action='store_true',\n default=False,\n dest='use_default_rules',\n help=\"Use default rules in %s in addition to any extra \"\n \"rules directories specified with -r. There is \"\n \"no need to specify this if no -r flags are used.\"\n % default_rulesdir)\n parser.add_argument('-t', dest='tags',\n action='append',\n default=[],\n help=\"only check rules whose id/tags match these values\")\n parser.add_argument('-T', dest='listtags', action='store_true',\n help=\"list all the tags\")\n parser.add_argument('-v', dest='verbosity', action='count',\n help=\"Increase verbosity level\",\n default=0)\n parser.add_argument('-x', dest='skip_list', default=[], action='append',\n help=\"only check rules whose id/tags do not \" +\n \"match these values\")\n parser.add_argument('--nocolor', '--nocolour', dest='colored',\n default=hasattr(sys.stdout, 'isatty') and sys.stdout.isatty(),\n action='store_false',\n help=\"disable colored output\")\n parser.add_argument('--force-color', '--force-colour', dest='colored',\n action='store_true',\n help=\"Try force colored output (relying on salt's code)\")\n parser.add_argument('--exclude', dest='exclude_paths', action='append',\n help='path to directories or files to skip. This option'\n ' is repeatable.',\n default=[])\n parser.add_argument('--json', dest='json', action='store_true', default=False,\n help='parse the output as JSON')\n parser.add_argument('--severity', dest='severity', action='store_true', default=False,\n help='add the severity to the standard output')\n parser.add_argument('-c', help='Specify configuration file to use. Defaults to \".salt-lint\"')\n\n return parser"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_parser():\n\n parser = parser.ArgumentParser()\n return parser","def get_parser():\n parser = ArgumentParser(\n description=__doc__, formatter_class=ArgumentDefaultsHelpFormatter\n )\n parser.add_argument(\n \"-s\", \"--sentence\", dest=\"sentence\", help=\"sentence, splitted by ';'\"\n )\n return parser","def _create_parser(self):\n default_options = self._create_defaults()\n\n all_categories = ['build', 'whitespace']\n\n mock_stderr = self._MockStdErr()\n\n return ArgumentParser(\n all_categories=all_categories,\n base_filter_rules=[],\n default_options=default_options,\n mock_stderr=mock_stderr,\n usage='test usage')","def get_parser(self):\n parser = ArgumentParser()\n parser.add_argument(\n \"-c\", default='', dest='cmd',\n help=(\"just like python -c or sh -c (pass in a command)\"))\n parser.add_argument(\n \"-e\", \"--exec\", default='', dest='execfile',\n help='a filename to execute')\n parser.add_argument(\n \"-v\", '--version', default=False, dest='version',\n action='store_true',\n help=(\"show version information\"))\n parser.add_argument(\"--shell\", dest=\"shell\",\n default=False, help=\"application shell\",\n action='store_true')\n parser.add_argument(\"--config\", dest='config',\n default=\"\",\n help=\"use config file\")\n return parser","def command_line_argument_parser() -> argparse.ArgumentParser:\n parser = argparse.ArgumentParser(\n description=description(),\n epilog=epilog(),\n formatter_class=argparse.RawDescriptionHelpFormatter,\n )\n return parser","def command_line_argument_parser() -> argparse.ArgumentParser:\n parser = argparse.ArgumentParser(\n description=description(),\n epilog=epilog(),\n formatter_class=argparse.RawDescriptionHelpFormatter,\n )\n return parser","def get_parser():\n p = argparse.ArgumentParser(description='such a good program')\n p.add_argument('infile')\n p.add_argument('outfile')\n return p","def _init_parser():\n\t\n\t_parser = argparse.ArgumentParser()\n\t_parser.add_argument(\"--pull\", help=\"pull scripts from UR3\", action=\"store_true\")\n\t_parser.add_argument(\"--create\", help=\"create data base from script files\", action=\"store_true\")\n\t_parser.add_argument(\"--clear\", help=\"clear all data base\", action=\"store_true\")\n\treturn _parser","def get_parser():\n from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter\n parser = ArgumentParser(description=__doc__,\n formatter_class=ArgumentDefaultsHelpFormatter)\n parser.add_argument(\"-m\", \"--model\",\n dest=\"modelfile\",\n help=\"where is the model file (.tar)?\",\n metavar=\"FILE\",\n type=lambda x: utils.is_valid_file(parser, x),\n required=True)\n parser.add_argument(\"-i\", \"--input\",\n dest=\"inputvec\",\n help=\"\"\"a file which contains an input vector\n [[0.12, 0.312, 1.21 ...]]\"\"\",\n metavar=\"FILE\",\n type=lambda x: utils.is_valid_file(parser, x),\n required=True)\n return parser","def get_parser(self):\n parser = argparse.ArgumentParser(description='Short sample app')\n\n parser.add_argument('-a', action=\"store_true\", default=False)\n parser.add_argument('-b', action=\"store\", dest=\"b\")\n parser.add_argument('-c', action=\"store\", dest=\"c\", type=int)\n return parser","def init_argparse(self, parser=None):\n if parser:\n p = parser\n else:\n p = argparse.ArgumentParser()\n\n # generic options\n p.add_argument(\"-q\", \"--quiet\", action=\"store_true\",\n help=\"log only errors and warnings\")\n p.add_argument(\"-v\", \"--verbose\", action=\"count\",\n help=\"log verbosely\")\n p.add_argument(\"-V\", \"--version\", action=\"store_true\",\n help=\"print version info and exit\")\n p.add_argument(\"--set\", action=\"append\",\n help=\"override config setting (--set 'PARAM=VAL')\")\n p.add_argument(\"command\", help=\"command name\")\n p.add_argument(\"args\", nargs=argparse.REMAINDER, help=\"arguments for command\")\n return p","def _create_parser(self):\n parser = argparse.ArgumentParser(\n description=description,\n formatter_class=argparse.RawTextHelpFormatter\n )\n\n parser.add_argument(\n '-v',\n '--verbose',\n action='store_true',\n default=False,\n help='Verbose mode (turn on logging.info)')\n\n parser.add_argument(\n '-d',\n '--debug',\n action='store_true',\n default=False,\n help='Debug (turn on logging.debug)')\n\n return parser","def get_parser():\n parser = argparse.ArgumentParser()\n parser.add_argument('--dataset', type=str)\n parser.add_argument('--method', type=str)\n parser.add_argument('--size_part', type=float, default=None)\n parser.add_argument('--start', type=int, default=0)\n parser.add_argument('--count', type=int, default=None)\n return parser","def create_parser():\n parser = argparse.ArgumentParser(\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n add_help=False)\n parser.add_argument(\n '--help', '-h',\n action='store_true',\n dest='help',\n help=\"\"\"show this help message and exit\"\"\")\n parser.add_argument(\n '--verbose', '-v',\n action='count',\n default=0,\n help=\"\"\"Enable verbose output from '%(prog)s'. A second and third\n '-v' increases verbosity.\"\"\")\n parser.add_argument(\n '--sequential',\n action='store_true',\n help=\"\"\"Execute analyzer sequentialy.\"\"\")\n parser.add_argument(\n '--cdb',\n metavar='<file>',\n default=\"compile_commands.json\",\n help=\"\"\"The JSON compilation database.\"\"\")\n return parser","def parser(self):\n return self.arg_parser","def build_parser(self, parser: ArgumentParser) -> None:","def parser(cls, *args, **kwargs):\n\n parser = ArgumentParser(*args, **kwargs)\n parser.add_argument('-a', \"--address\",\n help=\"Force entry point address\", default=None)\n parser.add_argument('-b', \"--dumpblocs\", action=\"store_true\",\n help=\"Log disasm blocks\")\n parser.add_argument('-z', \"--singlestep\", action=\"store_true\",\n help=\"Log single step\")\n parser.add_argument('-d', \"--debugging\", action=\"store_true\",\n help=\"Debug shell\")\n parser.add_argument('-g', \"--gdbserver\", type=int,\n help=\"Listen on port @port\")\n parser.add_argument(\"-j\", \"--jitter\",\n help=\"Jitter engine. Possible values are: gcc (default), tcc, llvm, python\",\n default=\"gcc\")\n parser.add_argument(\n '-q', \"--quiet-function-calls\", action=\"store_true\",\n help=\"Don't log function calls\")\n parser.add_argument('-i', \"--dependencies\", action=\"store_true\",\n help=\"Load PE and its dependencies\")\n\n for base_cls in cls._classes_():\n base_cls.update_parser(parser)\n return parser","def get_parser():\n # Parent and only parser.\n parser = argparse.ArgumentParser(\n add_help=True,\n formatter_class=argparse.RawTextHelpFormatter)\n parser.add_argument('mode', action='store',\n choices=range(len(MODES)),\n type=int,\n help='Select mode of file download.\\n'\n ' e.g: 0(rated) or 1(list).')\n parser.add_argument('torr_page', action='store',\n choices=range(len(TORRENTS)),\n type=int,\n help='Select tracking page to download from.\\n'\n ' e.g: 0 to .. ' + str(len(TORRENTS)-1) + '.')\n parser.add_argument('str_search', action='store',\n type=str,\n help='Input torrent string to search.\\n'\n ' e.g: \"String search\"')\n return(parser)","def get_parser():\n parser = ArgumentParser(description=\"Script used to generate Freeplane \"\n + \"mindmap files\")\n\n # This is use when people in Linaro aren't using their email address.\n parser.add_argument('--disable-altname', required=False,\n action=\"store_true\", default=False,\n help=\"Use alternative names (from cfg.yaml) to the tree\")\n\n parser.add_argument('--assignee', required=False,\n action=\"store_true\", default=False,\n help=\"Add assignees (from cfg.yaml) to the tree\")\n\n parser.add_argument('-a', '--author', required=False,\n action=\"store_true\", default=False,\n help=\"If set, git statistic only count the commit \"\n + \"from the author\")\n\n parser.add_argument('-p', '--path', required=False, action=\"store\",\n default=\"/home/jyx/devel/optee_projects/reference/linux\",\n help='Full path to the kernel tree')\n\n parser.add_argument('-s', '--since', required=False, action=\"store\",\n default=None,\n help='Used with the git log --since command')\n\n parser.add_argument('-o', '--output', required=False, action=\"store\",\n default=\"linux-kernel.mm\",\n help='Output filename')\n\n parser.add_argument('-v', required=False, action=\"store_true\",\n default=False,\n help='Output some verbose debugging info')\n\n return parser","def get_parser():\n parser = argparse.ArgumentParser(description='Parser des liens sur les sites Jahia et Wordpress.')\n parser.add_argument('ficher_des_sites', help='le fichier contenant les sites a parser.')\n parser.add_argument('-v', '--version', help='affiche la version du parser',\n action='version', version='%(prog)s ' + __version__)\n return parser","def get_parser():\n parser = argparse.ArgumentParser(description='Parser des liens sur les sites Jahia et Wordpress.')\n parser.add_argument('ficher_des_sites', help='le fichier contenant les sites a parser.')\n parser.add_argument('-v', '--version', help='affiche la version du parser',\n action='version', version='%(prog)s ' + __version__)\n return parser","def get_parser(name):\n parser = argparse.ArgumentParser(name, formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n # print default value always\n parser.add_argument = partial(parser.add_argument, help=' ')\n return parser","def get_parser(name):\n parser = argparse.ArgumentParser(name, formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n # print default value always\n parser.add_argument = partial(parser.add_argument, help=' ')\n return parser","def get_parser():\n parser = argparse.ArgumentParser()\n # parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)\n parser.add_argument('files', nargs='+')\n return parser","def create_arguments_parser():\n description = \"Statically analyse SBML files for modelling errors\"\n parent_arg_parser = rate_checker_sbml.create_arguments_parser()\n parser = argparse.ArgumentParser(description=description,\n parents=[parent_arg_parser])\n return parser","def create_parser(self, prog_name, subcommand):\n return OptionParser(prog=prog_name,\n usage=self.usage(subcommand),\n version=self.get_version(),\n option_list=self.option_list)","def get_parser():\n parser = ArgumentParser(description=__doc__,\n formatter_class=ArgumentDefaultsHelpFormatter,\n prog='pv2')\n subparsers = parser.add_subparsers(dest='cmd')\n # subparsers.add_parser('selfcheck',\n # add_help=False,\n # help=\"Self-check of the sst toolkit.\")\n # parser.add_argument('--version',\n # action='version',\n # version=('sst %s' % str(sst.__version__)))\n subparsers.add_parser('eval',\n add_help=False,\n parents=[evaluate.get_parser()],\n formatter_class=ArgumentDefaultsHelpFormatter,\n help=(\"Evaluate a single image\"))\n subparsers.add_parser('train',\n add_help=False,\n parents=[train.get_parser()],\n formatter_class=ArgumentDefaultsHelpFormatter,\n help=(\"Train a new model.\"))\n subparsers.add_parser('plot',\n add_help=False,\n parents=[plot.get_parser()],\n formatter_class=ArgumentDefaultsHelpFormatter,\n help=(\"Plot summary information.\"))\n return parser","def get_parser():\r\n parser = argparse.ArgumentParser(description=( # pylint: disable=redefined-outer-name\r\n \"Automatically finds translation errors in all edx-platform *.po files, \"\r\n \"for all languages, unless one or more language(s) is specified to check.\"\r\n ))\r\n\r\n parser.add_argument(\r\n '-l', '--language',\r\n type=str,\r\n nargs='*',\r\n help=\"Specify one or more specific language code(s) to check (eg 'ko_KR').\"\r\n )\r\n\r\n parser.add_argument(\r\n '-e', '--empty',\r\n action='store_true',\r\n help=\"Includes empty translation strings in .prob files.\"\r\n )\r\n\r\n parser.add_argument(\r\n '-v', '--verbose',\r\n action='count', default=0,\r\n help=\"Turns on info-level logging.\"\r\n )\r\n\r\n return parser","def _to_parser(self) -> ArgumentParser:\n parser = ArgumentParser()\n for arg in self.arg_list:\n parser.add_argument(arg._flag, **arg._parser_kwargs)\n return parser","def build_argument_parser():\n description=\"A simple tool to batch rename given files.\"\n parser = ArgumentParser(description=description)\n parser.add_argument(\"-i\", \"--input-list\", required=False,\n help=\"the path to the input list file.\")\n parser.add_argument(\"-p\", \"--glob-pattern\", default=DEFAULT_GLOB_PATTERN,\n help=\"a glob pattern to filter input files.\")\n return parser","def get_parser():\n module_parser = ArgumentParser(\n formatter_class=ArgumentDefaultsHelpFormatter)\n module_parser.add_argument(\"-i\", dest=\"data_path\", type=str,\n help=\"the location dataset\")\n module_parser.add_argument(\"-o\", dest=\"output_path\", type=str,\n help='base dir for outputs')\n module_parser.add_argument(\"-subdir\", dest=\"subdir\", type=str,\n choices=['test', 'train', 'val', 'all'],\n help='subdir: trn, test, val, or all ...')\n module_parser.add_argument(\"-n\", dest=\"n_train\", type=int,\n help='n: number of images for training')\n module_parser.add_argument(\"-Rx\", dest=\"x_res\", type=int,\n help='x resulution for final img')\n module_parser.add_argument(\"-Ry\", dest=\"y_res\", type=int,\n help='y resolution of final image')\n module_parser.add_argument(\"-d\", dest=\"d\",\n type=int,\n default=0,\n help='debug')\n return module_parser","def get_argument_parser():\n parser = argparse.ArgumentParser()\n parser.add_argument(\n 'names',\n help=(\n 'list of name-location pairs '\n '(location can be nat/hhs/cen/state or specific location labels)'))\n parser.add_argument(\n '--first',\n '-f',\n type=int,\n help='first epiweek override')\n parser.add_argument(\n '--last',\n '-l',\n type=int,\n help='last epiweek override')\n parser.add_argument(\n '--epiweek',\n '-w',\n type=int,\n help='epiweek override')\n parser.add_argument(\n '--test',\n '-t',\n default=False,\n action='store_true',\n help='dry run only')\n parser.add_argument(\n '--valid',\n '-v',\n default=False,\n action='store_true',\n help='do not fall back to stable wILI; require unstable wILI')\n return parser","def arg_parser():\n import argparse\n return argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)","def get_parser():\n if sys.version_info[0] < 3:\n # Using a version of Python < 3.\n parser = ArgumentParser(version=VERSION) # pylint: disable=E1123\n else:\n parser = ArgumentParser()\n parser.add_argument('--version', action='version', version=VERSION)\n\n subparsers = parser.add_subparsers(\n title='actions', help='Types of zappa commands',\n dest='command')\n\n parser_update_stack = subparsers.add_parser(\n 'update', help='Update a zappa deploy')\n parser_update_stack.add_argument(\n '--name', required=True,\n help='Name of the deployment (dev, prod, etc.)')\n\n parser_create_stack = subparsers.add_parser(\n 'deploy', help='Create a zappa deploy')\n parser_create_stack.add_argument(\n '--name', required=True,\n help='Name of the deployment (dev, prod, etc.)')\n\n return parser","def create_parser():\n now = datetime.datetime.today()\n default_date = \"{}-{}-{}\".format(now.day, now.month, now.year)\n parser = argparse.ArgumentParser(description=\"Git plugin for automatic insertion of @since and @author annotations \"\n \"into *.java source files in a project.\",\n epilog=\"© Avner & Oded\")\n parser.add_argument(\"-v\", \"--version\", help=\"Display the version of this plugin\", action='store_true')\n parser.add_argument(\"--since\", nargs='?', help=\"Add the @since annotations to project\", const=default_date)\n parser.add_argument(\"--author\", nargs='?', help=\"Add the @author annotations to project\", const=getpass.getuser())\n\n return parser","def create_parser(self, prog_name, subcommand):\r\n return OptionParser(prog=prog_name,\r\n usage=self.usage(subcommand),\r\n version='',\r\n add_help_option = False,\r\n option_list=self.option_list)","def init(argParser=\"default\", defaultLog=None, verbosity=0, **kwargs):\n\n if argParser is not None:\n if isinstance(argParser, str):\n # construct new parser based on command name\n args = _makeParser(argParser, **kwargs).parse_args()\n else:\n # use provided parser\n args = argParser.parse_args()\n\n defaultLog = None if not hasattr(args, \"log\") or args.log.lower() == \"none\" else args.log\n verbosity = args.verbose if hasattr(args, \"verbose\") else 0\n\n else:\n # don't parse anything, use default values\n args = None\n\n setupLogging(defaultLog, verbosity=verbosity)\n\n return args","def build_parser(usage, **kwargs):\n return BetterArgumentParser(usage=usage, version=VERSION, **kwargs)","def create_parser():\n p = NewParser()\n\n p.add_argument('reference', type=str,\n help = \"Fasta reference file that reads were mapped to.\")\n\n p.add_argument('gff', type=str,\n help = \"GFF file containing reference genome annotations.\")\n\n p.add_argument('vcf', type=str,\n help = \"VCF file to parse.\")\n\n args = p.parse_args(sys.argv[1:])\n return args","def init_parser():\n parser = argparse.ArgumentParser(\n description='Backup application code and data.')\n parser.add_argument('-a', '--app-id', required=True,\n help='the application ID to run the backup for')\n parser.add_argument('--source-code', action='store_true',\n default=False, help='backup the source code too. Disabled by default.')\n parser.add_argument('-d', '--debug', required=False, action=\"store_true\",\n default=False, help='display debug messages')\n parser.add_argument('--skip', required=False, nargs=\"+\",\n help='skip the following kinds, separated by spaces')\n\n return parser","def create_parser():\n parser = argparse.ArgumentParser()\n parser.add_argument('url', help='url to scrape')\n return parser","def get_base_argument_parser(\n **kwargs\n) -> ArgumentParser:\n\n parser = ArgumentParser(\n allow_abbrev=False,\n add_help=False,\n **kwargs\n )\n\n parser.add_argument(\n '--help',\n action='store_true',\n help='Pass this flag to print usage and argument descriptions.'\n )\n\n parser.add_argument(\n '--log',\n choices=['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'],\n help='Logging level.'\n )\n\n return parser","def get_parser():\n parser = argparse.ArgumentParser(description=\"Update golang.org/x/<name> in vendor folder\")\n parser.add_argument('-q', '--quiet', dest='verbose', action='store_false', help='work quietly')\n parser.add_argument('--revision', help='update deps to this revision', default='')\n parser.add_argument('name', help='name of the golang.org/x/ package. Can be empty', default='', nargs='?')\n return parser","def get_parser():\n _program_name = Path(__file__).stem\n example = f''' Example: >> {_program_name} sample.odb\\n '''\n parser = ArgumentParser(description=__doc__.split('..')[0], # Don't include module author part of doc string\n formatter_class=ArgumentDefaultsHelpFormatter, epilog=example, prog=_program_name)\n parser.add_argument(nargs=1,\n dest='input_file',\n type=str,\n help='odb or odbreport file for extracting data',\n metavar='sample.odb')\n parser.add_argument('-o', '--output-file',\n dest='output_file',\n type=str,\n help='file for printing output',\n metavar='sample.h5')\n parser.add_argument('-f', '--output-file-type',\n dest='output_type',\n choices=['yaml', 'json', 'h5'],\n type=str,\n default='h5',\n help='Type of file in which to store output data',\n metavar='h5')\n parser.add_argument('-r', '--odb-report-args',\n dest='odb_report_args',\n type=str,\n help='Arguments to give to the odbreport command. Require the ``option=value`` interface style.',\n metavar='\"step=step1 results\"')\n parser.add_argument('-a', '--abaqus-command',\n dest='abaqus_command',\n type=str,\n default=_settings._default_abaqus_command,\n help='Abaqus command to use',\n metavar='/path/to/abaqus')\n parser.add_argument('-d', '--delete-report-file',\n action=\"store_true\",\n dest='delete_report_file',\n default=False,\n help='Delete after parsing the file created by the odbreport command')\n parser.add_argument('-v', '--verbose',\n action=\"store_true\",\n dest='verbose',\n default=False,\n help='Print all messages')\n return parser","def parser_create():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-c\", \"--config-file\", type=str, help=\"yaml configuration file name\")\n return parser.parse_args()","def make_parser():\n parser = argparse.ArgumentParser(description='')\n parser.add_argument('-p', '--platform', dest='platform', type=str, required=False, default='')\n return parser","def make_parser():\n parser = argparse.ArgumentParser(description=config.DESCRIPTION)\n parser.add_argument('url_file', metavar='URL_FILE', type=str,\n help=config.HELP_URL_FILE)\n parser.add_argument('-d', metavar='DEST_DIR', dest='destination_dir', default=config.DEFAULT_DESTINATION_DIR, type=str,\n help=config.HELP_DESTINATION_DIR)\n parser.add_argument('-l', metavar='LOG_FILE', dest='log_file', default=config.DEFAULT_LOG_FILE, type=str,\n help=config.HELP_LOG_FILE % config.DEFAULT_LOG_FILE)\n\n return parser","def create_parser():\n parser = argparse.ArgumentParser(\n description=\"First example\",\n epilog=\"Batch 2017\")\n\n # script\n parser.add_argument('--script',\n required=True,\n action='store',\n dest='script',\n help=\"A script to execute\")\n\n parser.add_argument('--dataset',\n required=True,\n action='store',\n dest='dataset',\n help=\"A dataset to use\")\n#\n# parser.add_argument('--features',\n# required=True,\n# action='store',\n# dest='features',\n# help=\"Number of features\")\n return parser","def initFromCLI(cls, name=None):\n ap = cls._makeArgumentParser()\n ns = ap.parse_args()\n instance = cls.initFromOptions(ns, name=name)\n return instance","def create_arg_parser():\n arg_parser = argparse.ArgumentParser()\n arg_parser.add_argument(\n '-f',\n '--file',\n required=True,\n help='Name of clean data file'\n )\n return arg_parser","def _create_parser():\n parser = ArgumentParser(description=\"A CLI that sends messages to an Azure event hub.\")\n\n parser.add_argument(\"--connection-string\", type=str, required=True,\n help=\"The Azure event hub connection string\")\n\n parser.add_argument(\"--name\", type=str, required=True,\n help=\"The Azure event hub name\")\n\n parser.add_argument(\"--interval\", type=int, required=False,\n help=\"The number of seconds to wait between sends. Defaults to 10 seconds.\")\n\n parser.add_argument(\"--what-if\", type=bool, required=False,\n help=\"Run the program without sending messages to the Event Hub. \"\n \"The app will log what would have been sent to the Event Hub.\")\n\n return parser","def create_parser():\n parser = argparse.ArgumentParser()\n parser.add_argument('webpage', help='webpage to search')\n\n return parser","def create_parser(self, prog_name):\n return OptionParser(\n\t\t\tprog=prog_name,\n\t\t\t#usage=self.usage(subcommand),\n\t\t\toption_list=self.option_list\n\t\t)","def create_importer_parser():\n parser = argparse.ArgumentParser()\n _create_shared_args(parser)\n _create_importer_args(parser)\n\n return parser","def build_parser ():\n\n parser = argparse.ArgumentParser (description = __doc__)\n\n parser.add_argument (\n '-v', '--verbose', dest='verbose', action='count',\n help='increase output verbosity', default=0\n )\n parser.add_argument (\n '-l', '--live', dest='get_live_data', action='store_true',\n help='get live data from OSM database',\n )\n parser.add_argument (\n '-e', '--edit', action='store_true',\n help='edit the OSM database',\n )\n parser.add_argument (\n '-u', '--user', dest='my_edits', action='store_true',\n help='only report about my edits',\n )\n parser.add_argument (\n '--min-length', dest=\"min_length\", type=float, default=1000.0,\n help='way must be longer than this to get a ref (in m) (default=1000)',\n )\n parser.add_argument (\n '--batch-size', dest=\"batch_size\", type=int, default=10,\n help='apply OSM edits in changesets of this size (default=10)',\n )\n return parser","def initialise_parser(**kwargs):\n kwargs['formatter_class'] = ap.ArgumentDefaultsHelpFormatter\n p = MpArgumentParser(**kwargs)\n\n # -- version\n path_file = os.path.sep.join(\n os.path.abspath(__file__).split(os.path.sep)[:-2])\n with open(os.path.join(path_file, 'VERSION'), 'r') as version_file:\n version = version_file.readline()\n p.add_argument('--version', action='version', version=version)\n\n p.add_argument(\"-v\", \"--verbose\", action=\"store_true\", help=\"Verbose mode\")\n\n return p","def create_cli_parser() -> argparse.ArgumentParser:\n parser = argparse.ArgumentParser()\n parser.add_argument(\"config\", nargs=\"?\", help=\"path to yaml configuration file\")\n return parser","def get_parser():\n parser = argparse.ArgumentParser(\n description=\"\"\"Start a Classy Vision training job.\n\n This can be used for training on your local machine, using CPU or GPU, and\n for distributed training. This script also supports Tensorboard, Visdom and\n checkpointing.\"\"\"\n )\n\n parser = add_generic_args(parser)\n return parser","def _setup_parser():\n parser = argparse.ArgumentParser(add_help=True)\n parser.add_argument('--eval_model', type=str, default=None)\n parser.add_argument('--stack', type=int, default=1)\n parser.add_argument('--flare', action='store_true')\n parser.add_argument('--mixreg', action='store_true')\n\n env_group = parser.add_argument_group(\"Env Args\")\n env_group.add_argument('--env_name', type=str, default=ENV_NAME)\n env_group.add_argument('--num_envs', type=int, default=NUM_ENVS)\n env_group.add_argument('--num_levels', type=int, default=NUM_LEVELS)\n env_group.add_argument('--start_level', type=int, default=START_LEVEL)\n\n agent_group = parser.add_argument_group(\"Agent Args\")\n PPOAgent.add_to_argparse(agent_group)\n\n model_group = parser.add_argument_group(\"Model Args\")\n ImpalaPPO.add_to_argparse(model_group)\n\n return parser","def make_argument_parser():\n parser = argparse.ArgumentParser()\n parser.add_argument('--device',\n type=str,\n choices=['CPU', 'GPU'],\n help='Execution device.',\n required=True)\n parser.add_argument('-N',\n type=int,\n default=DEFAULT_N,\n help='Number of particles.')\n parser.add_argument('--rho',\n type=float,\n default=DEFAULT_RHO,\n help='Number density.')\n parser.add_argument('--dimensions',\n type=int,\n choices=[2, 3],\n help='Number of dimensions.',\n default=DEFAULT_DIMENSIONS)\n parser.add_argument('--warmup_steps',\n type=int,\n default=DEFAULT_WARMUP_STEPS,\n help='Number of timesteps to run before timing.')\n parser.add_argument('--benchmark_steps',\n type=int,\n default=DEFAULT_BENCHMARK_STEPS,\n help='Number of timesteps to run in the benchmark.')\n parser.add_argument('--repeat',\n type=int,\n default=DEFAULT_REPEAT,\n help='Number of times to repeat the run.')\n parser.add_argument('-v',\n '--verbose',\n action='store_true',\n help='Verbose output.')\n return parser","def _build_arg_parser():\n parser = argparse.ArgumentParser(\n description=_description,\n add_help=True,\n )\n add_generic_args(parser)\n add_diff_args(parser)\n add_filename_args(parser, [\"base\", \"remote\"])\n\n parser.add_argument(\n '-o', '--output',\n default=None,\n help=\"if supplied, the diff is written to this file. \"\n \"Otherwise it is printed to the terminal.\")\n\n return parser","def init_parser():\n parser = OptionParser()\n parser.add_option(\"-n\", \"--interactive\", action=\"store_true\", help=\"run in interactive (non-daemon) mode\")\n parser.add_option(\"-r\", \"--run\", action=\"store_true\", help=\"starts process identified by -app parameter\")\n parser.add_option(\"-k\", \"--kill\", action=\"store_true\", help=\"kill process identified by -app parameter\")\n parser.add_option(\"-a\", \"--app\", action=\"store\", help=\"application to start (process name)\")\n parser.add_option(\"-q\", \"--query\", action=\"store_true\", help=\"query application's state\")\n parser.add_option(\"-i\", \"--install_ve\", action=\"store_true\", help=\"install a virtualenv for the runtime to use\")\n parser.add_option(\"-s\", \"--shell\", action=\"store_true\", help=\"run an ipython shell within the virtualenv\")\n parser.add_option(\"-t\", \"--tests\", action=\"store_true\", help=\"run tests\")\n parser.add_option(\"-x\", \"--xunit\", action=\"store_true\", help=\"run tests with coverage and xunit output for Jenkins\")\n parser.add_option(\"-z\", \"--analyze\", action=\"store_true\", help=\"run pylint on project\")\n parser.add_option(\"-l\", \"--list\", action=\"store_true\", help=\"list available applications\")\n parser.add_option(\"-o\", \"--outfile\", action=\"store\", help=\"save results from a report to a file\")\n return parser","def _parser(s, remainder):\n parser = _ArgumentParser()\n def run():\n return s.parse(parser, remainder)\n parser.run = run\n return parser","def get_parser():\n\tparser = argparse.ArgumentParser('preprocessing.py',\n\t\tformatter_class=argparse.RawDescriptionHelpFormatter,\n\t\tdescription=\"\"\"\nRun a piepline for one NICER ObsID data. \n\t\t\"\"\"\n\t\t)\n\tversion = '%(prog)s ' + __version__\n\tparser.add_argument('obsid', type=str, \n\t\thelp='ObsID (e.g., 4012010109)')\t\n\treturn parser","def build_arg_parser():\n\n main = ArgumentParser(description='AMFinder command-line arguments.',\n allow_abbrev=False,\n formatter_class=RawTextHelpFormatter)\n\n subparsers = main.add_subparsers(dest='run_mode', required=True,\n help='action to be performed.')\n\n _ = training_subparser(subparsers)\n _ = prediction_subparser(subparsers)\n _ = diagnostic_subparser(subparsers)\n\n return main","def parser(self, prog, **kwargs):\n prog += ' ' + self.name\n parser = argparse.ArgumentParser(prog=prog,\n description=self.summary,\n **kwargs)\n for args, kwargs in self.args or ():\n parser.add_argument(*args, **kwargs)\n return parser","def get_argparser(self):\n parser = argparse.ArgumentParser(description='Command Configuration')\n parser.add_argument('--coin', choices=['bitcoin', 'ethereum', 'litecoin'], default='bitcoin')\n parser.add_argument('--start_date', default='2019-10-21')\n parser.add_argument('--end_date', default='2019-10-31')\n parser.add_argument('--language', choices=['en', 'it', 'es', 'fr', 'de', 'ru', 'zh'], default='en')\n\n argparser = parser.parse_args()\n return argparser.__dict__","def init_parser():\n parser = OptionParser()\n\n parser.add_option(\n \"-d\",\n \"--debug\",\n dest=\"debug\",\n help=\"Toggle debugging\",\n action=\"store_true\",\n default=False,\n )\n\n parser.add_option(\n \"-f\",\n \"--questions-file\",\n dest=\"file\",\n help=(\"Use this file instead of the default \"\n \"questions.yaml\"),\n metavar=\"FILE\",\n )\n\n parser.add_option(\n \"-p\",\n \"--generate-pdf\",\n dest=\"pdf\",\n help=(\"Generate the speaker PDF\"),\n action=\"store_true\",\n default=False,\n )\n\n parser.add_option(\n \"-v\",\n \"--version\",\n dest=\"version\",\n help=\"Show program version\",\n action=\"store_true\",\n default=False,\n )\n\n options = parser.parse_args()[0]\n return options","def build_parser():\n parser = argparse.ArgumentParser(description='The classic FizzBuzz game in programmatic form.', add_help=False)\n parser.add_argument('-h', '--help', default=argparse.SUPPRESS, action='help',\n help='Show this help message and exit.')\n parser.add_argument('-s', '--start', default=1, type=int, action='store', metavar='START',\n help='The number to start FizzBuzzing at (inclusive).')\n parser.add_argument('stop', type=int, action='store', metavar='STOP',\n help='The number to end FizzBuzzing at (exclusive).')\n return parser","def get_arg_parser(parser=None):\n # add arguments that are specific to the component\n if parser is None:\n parser = argparse.ArgumentParser(description=__doc__)\n\n parser.add_argument(\"--raw_training_data\", type=str, required=True, help=\"\")\n parser.add_argument(\"--raw_testing_data\", type=str, required=True, help=\"\")\n parser.add_argument(\"--train_output\", type=str, required=True, help=\"\")\n parser.add_argument(\"--test_output\", type=str, required=True, help=\"\")\n parser.add_argument(\"--metrics_prefix\", type=str, required=False, help=\"Metrics prefix\")\n return parser","def create_parser():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--getErrors\",\n type=str,\n default=None,\n help=\"get error messages - send \\'yes\\' \")\n parser.add_argument(\"--host\",\n type=str,\n default=\"localhost\",\n help=\"Host of redis. Default : localhost\")\n parser.add_argument(\"--port\",\n type=int,\n default=6379,\n help=\"Port of redis. Default : 6379\")\n parser.add_argument(\"--db\",\n type=int,\n default=0,\n help=\"Db of redis. Default : 0\")\n parser.add_argument(\"--cleanTemp\",\n type=str,\n default=None,\n help=\"clean trash files from db - send \\'yes\\' \")\n return parser","def get_parser():\n # parse parameters\n parser = argparse.ArgumentParser(description=\"Evaluate sentences with RTTL\")\n\n # main parameters\n parser.add_argument(\"--dump_path\", type=str, default=\"./dumped/\", help=\"Experiment dump path\")\n parser.add_argument(\"--exp_name\", type=str, default=\"\", help=\"Experiment name\")\n parser.add_argument(\"--exp_id\", type=str, default=\"\", help=\"Experiment ID\")\n parser.add_argument(\"--batch_size\", type=int, default=32, help=\"Number of sentences per batch\")\n\n # model / output paths\n parser.add_argument(\"--model_path\", type=str, default=\"\", help=\"Model path\")\n parser.add_argument(\"--output_path\", type=str, default=\"\", help=\"Output path for scores\")\n parser.add_argument(\"--input_path\", type=str, default=\"\", help=\"Input path for source sentences\")\n\n # parser.add_argument(\"--max_vocab\", type=int, default=-1, help=\"Maximum vocabulary size (-1 to disable)\")\n # parser.add_argument(\"--min_count\", type=int, default=0, help=\"Minimum vocabulary count\")\n\n # source language / target language\n parser.add_argument(\"--src_lang\", type=str, default=\"\", help=\"Source language\")\n parser.add_argument(\"--tgt_lang\", type=str, default=\"\", help=\"Target language\")\n\n return parser","def getArgumentParser():\n parser = argparse.ArgumentParser(description=\"Script for running optimization for the ZH dark photon SR\")\n parser.add_argument('-i',\n '--infile',\n dest='infile',\n help='Input CSV file',\n default = '/afs/cern.ch/user/e/ehofgard/public/data/all_data')\n parser.add_argument('-o',\n '--output',\n dest='outdir',\n help='Output directory for plots, selection lists, etc',\n default='outdir')\n \n return parser","def get_parser():\n parser = argparse.ArgumentParser(\n description='Converts HTML from file or url to a clean text version')\n parser.add_argument('input', nargs='?', default=None,\n help='Html input either from a file or an url '\n '(default:stdin)')\n parser.add_argument('-o', '--output', type=str,\n help='Output file (default:stdout).')\n parser.add_argument('-e', '--encoding', type=str,\n help='Content encoding for reading and writing files '\n '(default:utf-8)',\n default='utf-8')\n parser.add_argument('-i', '--display-image-captions',\n action='store_true', default=False,\n help='Display image captions (default:false).')\n parser.add_argument('-d', '--deduplicate-image-captions',\n action='store_true', default=False,\n help='Deduplicate image captions (default:false).')\n parser.add_argument('-l', '--display-link-targets',\n action='store_true', default=False,\n help='Display link targets (default:false).')\n parser.add_argument('-a', '--display-anchor-urls',\n action='store_true', default=False,\n help='Deduplicate image captions (default:false).')\n parser.add_argument('--indentation', default='extended',\n help='How to handle indentation (extended or strict;'\n ' default: extended).')\n parser.add_argument('-v', '--version',\n action='store_true', default=False,\n help='display version information')\n return parser","def create_parser():\n pass","def init_parser(self):\n usage = \"%s [options] [subcommand]\" % (self.command)\n description = \"An SBTools test plug-in.\"\n\n parser = SBToolOptionParser(self, self.sbtools, usage, description=description)\n return parser","def create_arg_parser():\n server_modes = ['builtin', 'waitress']\n\n parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n parser.add_argument('h', metavar='HOST', help='Server HOST (e.g. \"localhost\")', type=str)\n parser.add_argument('p', metavar='PORT', help='Server PORT (e.g. \"5001\")', type=int)\n parser.add_argument('m', metavar='SERVER_MODE', help=\", \".join(server_modes), choices=server_modes, type=str)\n parser.add_argument('--debug', help=\"Run builtin server in debug mode\", action='store_true', default=False)\n\n return parser","def get_parser():\n\tparser = argparse.ArgumentParser(description=\"Twitter Searcher\")\n\tparser.add_argument(\"-q\",\n\t\t\t\t\t\t\"--query\",\n\t\t\t\t\t\tdest=\"query\",\n\t\t\t\t\t\thelp=\"Query/Filter\",\n\t\t\t\t\t\tdefault='*')\n\tparser.add_argument(\"-d\",\n\t\t\t\t\t\"--data-dir\",\n\t\t\t\t\tdest=\"city\",\n\t\t\t\t\thelp=\"Output/Data Directory\")\n\treturn parser","def makeParser():\n parser = argparse.ArgumentParser(\n description=(\n \"Print a JSON object containing reference to read \"\n \"distances extracted from a SAM file.\"\n )\n )\n\n parser.add_argument(\n \"--samFile\",\n action=\"append\",\n required=True,\n help=\"The SAM file(s) to load. May be repeated.\",\n )\n\n parser.add_argument(\n \"--minMatchingReads\",\n type=int,\n help=(\n \"The minimum number of reads that must match a reference for it \"\n \"to be included.\"\n ),\n )\n\n parser.add_argument(\n \"--scoreTag\",\n help=(\n \"The score tag to use for the alignment score. If not given, \"\n \"1 will be used to indicate that a read matched a reference \"\n \"(non-matches are not included). The default is no score tag, \"\n 'which is not that useful. A good choice is \"AS\", for the '\n \"alignment score, but that has to be present in the SAM file, \"\n \"which means that the aligner (bowtie2, bwa, etc. has to have \"\n \"produced such a tag.\"\n ),\n )\n\n parser.add_argument(\n \"--verbose\", action=\"store_true\", help=\"Print extra information.\"\n )\n\n return parser","def create_argument_parser() -> argparse.ArgumentParser:\n\n parser = argparse.ArgumentParser(\n prog=\"mafiabot\",\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=\"Mafia Telegram Bot command line interface.\",\n )\n\n # parser.add_argument(\n # \"--argument\",\n # action=\"store_true\",\n # default=,\n # help=\"\",\n # )\n\n add_logging_options(parser)\n\n return parser","def initialize_parser():\n ftypes = [\n \"png\",\n \"jpg\",\n \"jpeg\",\n \"pdf\",\n \"ps\",\n \"eps\",\n \"rgba\",\n \"svg\",\n \"tiff\",\n \"tif\",\n \"pgf\",\n \"svgz\",\n \"raw\",\n ]\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"-s\",\n \"--savefig\",\n action=\"store\",\n default=False,\n choices=ftypes,\n help=\"Save figure to a file\",\n )\n return parser","def argument_parser() -> argparse.ArgumentParser:\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"source\",\n type=argparse.FileType(\"r\"),\n )\n parser.add_argument(\n \"-v\",\n \"--verbose\",\n action=\"store_true\",\n help=\"be verbose in output\",\n )\n parser.add_argument(\n \"-p\",\n \"--permissive\",\n action=\"store_true\",\n help=\"allow permissive validation (just checking presence)\",\n )\n return parser","def _CreateParser():\n parser = commandline.ArgumentParser(description=__doc__, caching=True)\n\n # TODO(rcui): Have this use the UI-V2 format of having source and target\n # device be specified as positional arguments.\n parser.add_argument('--force', action='store_true', default=False,\n help='Skip all prompts (i.e., for disabling of rootfs '\n 'verification). This may result in the target '\n 'machine being rebooted.')\n sdk_board_env = os.environ.get(cros_chrome_sdk.SDKFetcher.SDK_BOARD_ENV)\n parser.add_argument('--board', default=sdk_board_env,\n help=\"The board the Chrome build is targeted for. When \"\n \"in a 'cros chrome-sdk' shell, defaults to the SDK \"\n \"board.\")\n parser.add_argument('--build-dir', type='path',\n help='The directory with Chrome build artifacts to '\n 'deploy from. Typically of format '\n '<chrome_root>/out/Debug. When this option is used, '\n 'the GYP_DEFINES environment variable must be set.')\n parser.add_argument('--target-dir', type='path',\n default=None,\n help='Target directory on device to deploy Chrome into.')\n parser.add_argument('-g', '--gs-path', type='gs_path',\n help='GS path that contains the chrome to deploy.')\n parser.add_argument('--nostartui', action='store_false', dest='startui',\n default=True,\n help=\"Don't restart the ui daemon after deployment.\")\n parser.add_argument('--nostrip', action='store_false', dest='dostrip',\n default=True,\n help=\"Don't strip binaries during deployment. Warning: \"\n 'the resulting binaries will be very large!')\n parser.add_argument('-p', '--port', type=int, default=remote.DEFAULT_SSH_PORT,\n help='Port of the target device to connect to.')\n parser.add_argument('-t', '--to',\n help='The IP address of the CrOS device to deploy to.')\n parser.add_argument('-v', '--verbose', action='store_true', default=False,\n help='Show more debug output.')\n parser.add_argument('--mount-dir', type='path', default=None,\n help='Deploy Chrome in target directory and bind it '\n 'to the directory specified by this flag.'\n 'Any existing mount on this directory will be '\n 'umounted first.')\n parser.add_argument('--mount', action='store_true', default=False,\n help='Deploy Chrome to default target directory and bind '\n 'it to the default mount directory.'\n 'Any existing mount on this directory will be '\n 'umounted first.')\n\n group = parser.add_argument_group('Advanced Options')\n group.add_argument('-l', '--local-pkg-path', type='path',\n help='Path to local chrome prebuilt package to deploy.')\n group.add_argument('--sloppy', action='store_true', default=False,\n help='Ignore when mandatory artifacts are missing.')\n group.add_argument('--staging-flags', default=None, type=ValidateGypDefines,\n help=('Extra flags to control staging. Valid flags are - '\n '%s' % ', '.join(chrome_util.STAGING_FLAGS)))\n # TODO(stevenjb): Remove --strict entirely once removed from the ebuild.\n group.add_argument('--strict', action='store_true', default=False,\n help='Deprecated. Default behavior is \"strict\". Use '\n '--sloppy to omit warnings for missing optional '\n 'files.')\n group.add_argument('--strip-flags', default=None,\n help=\"Flags to call the 'strip' binutil tool with. \"\n \"Overrides the default arguments.\")\n group.add_argument('--ping', action='store_true', default=False,\n help='Ping the device before connection attempt.')\n group.add_argument('--mash', action='store_true', default=False,\n help='Copy additional files for mus+ash. Will not fit in '\n 'the default target-dir.')\n\n group = parser.add_argument_group(\n 'Metadata Overrides (Advanced)',\n description='Provide all of these overrides in order to remove '\n 'dependencies on metadata.json existence.')\n group.add_argument('--target-tc', action='store', default=None,\n help='Override target toolchain name, e.g. '\n 'x86_64-cros-linux-gnu')\n group.add_argument('--toolchain-url', action='store', default=None,\n help='Override toolchain url format pattern, e.g. '\n '2014/04/%%(target)s-2014.04.23.220740.tar.xz')\n\n # GYP_DEFINES that Chrome was built with. Influences which files are staged\n # when --build-dir is set. Defaults to reading from the GYP_DEFINES\n # enviroment variable. WILL BE DEPRECATED.\n parser.add_argument('--gyp-defines', default=None, type=ValidateGypDefines,\n help=argparse.SUPPRESS)\n\n # GN_ARGS (args.gn) used to build Chrome. Influences which files are staged\n # when --build-dir is set. Defaults to reading from the GN_ARGS env variable.\n # CURRENLY IGNORED, ADDED FOR FORWARD COMPATABILITY.\n parser.add_argument('--gn-args', default=None, type=ValidateGnArgs,\n help=argparse.SUPPRESS)\n\n # Path of an empty directory to stage chrome artifacts to. Defaults to a\n # temporary directory that is removed when the script finishes. If the path\n # is specified, then it will not be removed.\n parser.add_argument('--staging-dir', type='path', default=None,\n help=argparse.SUPPRESS)\n # Only prepare the staging directory, and skip deploying to the device.\n parser.add_argument('--staging-only', action='store_true', default=False,\n help=argparse.SUPPRESS)\n # Path to a binutil 'strip' tool to strip binaries with. The passed-in path\n # is used as-is, and not normalized. Used by the Chrome ebuild to skip\n # fetching the SDK toolchain.\n parser.add_argument('--strip-bin', default=None, help=argparse.SUPPRESS)\n return parser","def build_parser():\n parser = argparse.ArgumentParser()\n parser.add_argument('-r', '--reference', required=True, help=\"Reference Genome URL\")\n parser.add_argument('-n', '--normal', required=True, help='Normal BAM URL. Format: UUID.normal.bam')\n parser.add_argument('-t', '--tumor', required=True, help='Tumor BAM URL. Format: UUID.tumor.bam')\n parser.add_argument('-d', '--dbsnp', required=True, help='dbsnp_132_b37.leftAligned.vcf URL')\n parser.add_argument('-c', '--cosmic', required=True, help='b37_cosmic_v54_120711.vcf URL')\n parser.add_argument('-u', '--mutect', required=True, help='Mutect.jar')\n parser.add_argument('-w', '--work_dir', required=True, help='Where you wanna work from? (full path please)')\n\n return parser","def create_parser(self, prog_name, subcommand):\n parser = CommandParser(\n self, prog=\"%s %s\" % (os.path.basename(prog_name), subcommand),\n description=self.help or None,\n )\n parser.add_argument(\n '--version', action='version', version=self.get_version())\n\n self.add_arguments(parser)\n return parser","def get_parser():\n\tparser = argparse.ArgumentParser('tallyup.py',\n\t\tformatter_class=argparse.RawDescriptionHelpFormatter,\n\t\tdescription=\"\"\"\nTally up a student score file.\n\t\t\"\"\"\n\t\t)\n\tversion = '%(prog)s ' + __version__\n\tparser.add_argument('--version', '-v', action='version', version=version,\n\t\thelp='show version of this command.')\n\tparser.add_argument('--csvfile', '-i', type=str, required=True, \n\t\thelp='input csv file.')\n\treturn parser","def setup_parser(self):\n parser = argparse.ArgumentParser(description=DESCRIPTION)\n parser.add_argument('words', metavar='W', nargs='+', help=POSITIONAL_HELP)\n parser.add_argument('-a','--any', dest=\"search_funct\", action=\"store_const\", \n const='any', default='all', help=SEARCH_HELP)\n parser.add_argument('-o','--only-id', action='store_true', help=ID_HELP)\n parser.add_argument('-u', '--update', action='store_true', help=UPDATE_HELP)\n return parser","def get_parser():\n parser = argparse.ArgumentParser(description=\"Tweet Downloader\")\n parser.add_argument(\"-d\",\n \"--data\",\n dest=\"data\",\n help=\"Read data from file or display initial setting\",\n default=False)\n\n return parser","def build_arg_parser():\n parser = argparse.ArgumentParser(\n description='Standard Arguments for talking to vCenter')\n\n # because -h is reserved for 'help' we use -s for service\n parser.add_argument('-s', '--host',\n required=True,\n action='store',\n help='vSphere service to connect to')\n\n # because we want -p for password, we use -o for port\n parser.add_argument('-o', '--port',\n type=int,\n default=443,\n action='store',\n help='Port to connect on')\n\n parser.add_argument('-u', '--user',\n required=True,\n action='store',\n help='User name to use when connecting to host')\n\n parser.add_argument('-p', '--password',\n required=False,\n action='store',\n help='Password to use when connecting to host')\n return parser","def make_argument_parser():\n parser = Benchmark.make_argument_parser()\n parser.add_argument('--skip-reference',\n action='store_true',\n help='Skip the reference simulation run.')\n return parser","def build_cli(self):\n parser = argparse.ArgumentParser(\"xsgen\",\n conflict_handler='resolve', argument_default=NotSpecified)\n for plugin in self.plugins:\n plugin.update_argparser(parser)\n self.parser = parser\n return parser","def get_parser():\n parser = ArgumentParser(\n description='phpMyAdmin work reporting tool\\n\\nGenerates list of commits and issues handled in given period.',\n epilog='Credentials can be also stored in ~/.config/phpmyadmin:\\n\\n[github]\\nuser=USER\\ntoken=TOKEN',\n formatter_class=RawDescriptionHelpFormatter,\n )\n parser.add_argument(\n '-u', '--user',\n help='GitHub username, used for both reporting and authentication'\n )\n parser.add_argument(\n '-t', '--token',\n help='GitHub authentication token'\n )\n parser.add_argument(\n '-s', '--start-date',\n type=dateutil.parser.parse,\n default=datetime.now() - timedelta(days=7),\n help='Starting datetime, defaults to 7 days ago'\n )\n parser.add_argument(\n '-e', '--end-date',\n type=dateutil.parser.parse,\n default=datetime.now(),\n help='Ending datetime, defaults to current timestamp'\n )\n parser.add_argument(\n '-f', '--format',\n choices=('markdown', ),\n default='markdown',\n help='Output format',\n )\n parser.add_argument(\n '-w', '--weekly',\n action='store_true',\n help='Weekly report not including private repositories'\n )\n parser.add_argument(\n '-W', '--last-week',\n action='store_true',\n help='Create report for last week'\n )\n parser.add_argument(\n '-M', '--last-month',\n action='store_true',\n help='Create report for last month'\n )\n parser.add_argument(\n '--this-week',\n action='store_true',\n help='Create report for this week'\n )\n return parser","def _get_parserobj(self, option_list):\n if '--version' in self.parselines[0]:\n if 'optparse' == self.parser_type:\n parser = OptionParser(version=\"dummy\")\n else:\n parser = ArgumentParser(\n version='dummy',\n formatter_class=RawDescriptionHelpFormatter)\n else:\n if 'optparse' == self.parser_type:\n parser = OptionParser()\n else:\n parser = ArgumentParser(\n formatter_class=RawDescriptionHelpFormatter)\n for opt in option_list:\n if opt['short'] and self.parser_type is 'optparse':\n parser.add_option(opt['short'], opt['long'],\n metavar=opt['metavar'],\n help=opt['help'].strip())\n elif not opt['short'] and self.parser_type is 'optparse':\n parser.add_option(opt['long'],\n metavar=opt['metavar'],\n help=opt['help'].strip())\n elif opt['short'] and self.parser_type is 'argparse':\n parser.add_argument(opt['short'], opt['long'],\n metavar=opt['metavar'],\n help=opt['help'].strip())\n elif not opt['short'] and self.parser_type is 'argparse':\n parser.add_argument(opt['long'],\n metavar=opt['metavar'],\n help=opt['help'].strip())\n else:\n raise InvalidParserTypeError(\"Invalid paresr type.\")\n return parser","def get_parser():\n parser = libdot.ArgumentParser(description=__doc__)\n parser.add_argument('paths', nargs='+',\n help='Image files or directories to crush.')\n return parser","def get_cmd_line_parser(version=None, *args, **kwargs):\n parser = argparse.ArgumentParser(*args, **kwargs)\n if version:\n parser.add_argument(\n '--version', help='Print the version and exit.', action='version',\n version='%(prog)s {}'.format(version))\n DebugAction.add_parser_argument(parser)\n VerboseAction.add_parser_argument(parser)\n\n return parser","def make_parser():\n p = argparse.ArgumentParser(\n description=\"Visualize and analyze error from oblique/straight tag observations\"\n )\n\n p.add_argument(\"-n\", help=\"name of the test in the config file\")\n\n p.add_argument(\"-t\", help=\"throw out bad tags\", action=\"store_true\")\n\n p.add_argument(\"-v\", help=\"visualize data\", action=\"store_true\")\n\n p.add_argument(\"-i\", help=\"print result data\", action=\"store_true\")\n\n return p","def make_parser(description, agg=False, allowed_experiments=sum([exps for exps in EXPERIMENTS.values()], [])):\n\n return RamArgumentParser(description=description,\n agg=agg,\n allowed_experiments=allowed_experiments)","def create_parser():\n parser = argparse.ArgumentParser()\n\n # required args\n parser.add_argument('--project_id',\n help='Project id for project containing BQ data',\n default=KEY_FILE,\n type=str,\n required=True)\n\n # data and model args\n parser.add_argument('--training_budget',\n help='Training budget in hours',\n default=1,\n type=int)\n parser.add_argument('--key_file',\n help='JSON key file for API access',\n default=KEY_FILE,\n type=str)\n parser.add_argument('--location',\n help='GCP region to run',\n default=LOCATION,\n type=str)\n parser.add_argument('--automl_dataset',\n help='Name of AutoML dataset',\n default=AUTOML_DATASET,\n type=str)\n parser.add_argument('--automl_model',\n help='Name of AutoML model',\n default=AUTOML_MODEL,\n type=str)\n parser.add_argument('--bq_dataset',\n help='BigQuery dataset to import from',\n default=BQ_DATASET,\n type=str)\n parser.add_argument('--bq_table',\n help='BigQuery table to import from',\n default=BQ_TABLE,\n type=str)\n parser.add_argument('--batch_gcs_input',\n help='GCS URI for batch predict CSV',\n default=BATCH_GCS_INPUT,\n type=str)\n parser.add_argument('--batch_gcs_output',\n help='GCS URI for batch predict output',\n default=BATCH_GCS_OUTPUT,\n type=str)\n return parser","def make_argument_parser():\r\n parser = argparse.ArgumentParser()\r\n parser.add_argument(\"data_directory\",\r\n help=\"Directory where the data files live.\")\r\n parser.add_argument(\"out\", help=\"Output directory of files.\")\r\n parser.add_argument(\"-t\", \"--test\", action=\"store_true\",\r\n help=(\"Test mode, avoids slow classifiers and uses\"\r\n \" 3 folds\"))\r\n parser.add_argument(\"--folds\", default=10,\r\n help=\"Number of folds for n-fold cross validation\")\r\n parser.add_argument(\"--data_pattern\", default=\"*.mat\",\r\n help=\"Pattern for data files\")\r\n parser.add_argument(\"--label_pattern\", default=\"*.mat\",\r\n help=\"Pattern for label files\")\r\n return parser","def from_argparse_args(cls, args: Union[Namespace, ArgumentParser], **kwargs):\n return from_argparse_args(cls, args, **kwargs)","def get_parser():\n parser = argparse.ArgumentParser(usage = \"{prog} [options]\")\n parser.add_argument(\"-n\", \"--nights\", type=str, help=\"nights as comma separated string\")\n parser.add_argument(\"--obstypes\", type=str, default=None, help=\"comma separated list of exposure types to include in \"+\\\n \"the exposure table, e.g. science,arc,flat,dark,zero, ...\")\n parser.add_argument(\"-i\", \"--path-to-data\", type=str, default=None, help=\"path to the raw input data\")\n parser.add_argument(\"-o\",\"--exp-table-path\", type=str, default=None, help=\"path to save exposure tables, without monthly subdirectory\")\n parser.add_argument(\"--overwrite-files\", action=\"store_true\", help=\"overwrite existing exposure tables\")\n parser.add_argument(\"--verbose\", action=\"store_true\", help=\"print verbose output\")\n\n return parser"],"string":"[\n \"def get_parser():\\n\\n parser = parser.ArgumentParser()\\n return parser\",\n \"def get_parser():\\n parser = ArgumentParser(\\n description=__doc__, formatter_class=ArgumentDefaultsHelpFormatter\\n )\\n parser.add_argument(\\n \\\"-s\\\", \\\"--sentence\\\", dest=\\\"sentence\\\", help=\\\"sentence, splitted by ';'\\\"\\n )\\n return parser\",\n \"def _create_parser(self):\\n default_options = self._create_defaults()\\n\\n all_categories = ['build', 'whitespace']\\n\\n mock_stderr = self._MockStdErr()\\n\\n return ArgumentParser(\\n all_categories=all_categories,\\n base_filter_rules=[],\\n default_options=default_options,\\n mock_stderr=mock_stderr,\\n usage='test usage')\",\n \"def get_parser(self):\\n parser = ArgumentParser()\\n parser.add_argument(\\n \\\"-c\\\", default='', dest='cmd',\\n help=(\\\"just like python -c or sh -c (pass in a command)\\\"))\\n parser.add_argument(\\n \\\"-e\\\", \\\"--exec\\\", default='', dest='execfile',\\n help='a filename to execute')\\n parser.add_argument(\\n \\\"-v\\\", '--version', default=False, dest='version',\\n action='store_true',\\n help=(\\\"show version information\\\"))\\n parser.add_argument(\\\"--shell\\\", dest=\\\"shell\\\",\\n default=False, help=\\\"application shell\\\",\\n action='store_true')\\n parser.add_argument(\\\"--config\\\", dest='config',\\n default=\\\"\\\",\\n help=\\\"use config file\\\")\\n return parser\",\n \"def command_line_argument_parser() -> argparse.ArgumentParser:\\n parser = argparse.ArgumentParser(\\n description=description(),\\n epilog=epilog(),\\n formatter_class=argparse.RawDescriptionHelpFormatter,\\n )\\n return parser\",\n \"def command_line_argument_parser() -> argparse.ArgumentParser:\\n parser = argparse.ArgumentParser(\\n description=description(),\\n epilog=epilog(),\\n formatter_class=argparse.RawDescriptionHelpFormatter,\\n )\\n return parser\",\n \"def get_parser():\\n p = argparse.ArgumentParser(description='such a good program')\\n p.add_argument('infile')\\n p.add_argument('outfile')\\n return p\",\n \"def _init_parser():\\n\\t\\n\\t_parser = argparse.ArgumentParser()\\n\\t_parser.add_argument(\\\"--pull\\\", help=\\\"pull scripts from UR3\\\", action=\\\"store_true\\\")\\n\\t_parser.add_argument(\\\"--create\\\", help=\\\"create data base from script files\\\", action=\\\"store_true\\\")\\n\\t_parser.add_argument(\\\"--clear\\\", help=\\\"clear all data base\\\", action=\\\"store_true\\\")\\n\\treturn _parser\",\n \"def get_parser():\\n from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter\\n parser = ArgumentParser(description=__doc__,\\n formatter_class=ArgumentDefaultsHelpFormatter)\\n parser.add_argument(\\\"-m\\\", \\\"--model\\\",\\n dest=\\\"modelfile\\\",\\n help=\\\"where is the model file (.tar)?\\\",\\n metavar=\\\"FILE\\\",\\n type=lambda x: utils.is_valid_file(parser, x),\\n required=True)\\n parser.add_argument(\\\"-i\\\", \\\"--input\\\",\\n dest=\\\"inputvec\\\",\\n help=\\\"\\\"\\\"a file which contains an input vector\\n [[0.12, 0.312, 1.21 ...]]\\\"\\\"\\\",\\n metavar=\\\"FILE\\\",\\n type=lambda x: utils.is_valid_file(parser, x),\\n required=True)\\n return parser\",\n \"def get_parser(self):\\n parser = argparse.ArgumentParser(description='Short sample app')\\n\\n parser.add_argument('-a', action=\\\"store_true\\\", default=False)\\n parser.add_argument('-b', action=\\\"store\\\", dest=\\\"b\\\")\\n parser.add_argument('-c', action=\\\"store\\\", dest=\\\"c\\\", type=int)\\n return parser\",\n \"def init_argparse(self, parser=None):\\n if parser:\\n p = parser\\n else:\\n p = argparse.ArgumentParser()\\n\\n # generic options\\n p.add_argument(\\\"-q\\\", \\\"--quiet\\\", action=\\\"store_true\\\",\\n help=\\\"log only errors and warnings\\\")\\n p.add_argument(\\\"-v\\\", \\\"--verbose\\\", action=\\\"count\\\",\\n help=\\\"log verbosely\\\")\\n p.add_argument(\\\"-V\\\", \\\"--version\\\", action=\\\"store_true\\\",\\n help=\\\"print version info and exit\\\")\\n p.add_argument(\\\"--set\\\", action=\\\"append\\\",\\n help=\\\"override config setting (--set 'PARAM=VAL')\\\")\\n p.add_argument(\\\"command\\\", help=\\\"command name\\\")\\n p.add_argument(\\\"args\\\", nargs=argparse.REMAINDER, help=\\\"arguments for command\\\")\\n return p\",\n \"def _create_parser(self):\\n parser = argparse.ArgumentParser(\\n description=description,\\n formatter_class=argparse.RawTextHelpFormatter\\n )\\n\\n parser.add_argument(\\n '-v',\\n '--verbose',\\n action='store_true',\\n default=False,\\n help='Verbose mode (turn on logging.info)')\\n\\n parser.add_argument(\\n '-d',\\n '--debug',\\n action='store_true',\\n default=False,\\n help='Debug (turn on logging.debug)')\\n\\n return parser\",\n \"def get_parser():\\n parser = argparse.ArgumentParser()\\n parser.add_argument('--dataset', type=str)\\n parser.add_argument('--method', type=str)\\n parser.add_argument('--size_part', type=float, default=None)\\n parser.add_argument('--start', type=int, default=0)\\n parser.add_argument('--count', type=int, default=None)\\n return parser\",\n \"def create_parser():\\n parser = argparse.ArgumentParser(\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n add_help=False)\\n parser.add_argument(\\n '--help', '-h',\\n action='store_true',\\n dest='help',\\n help=\\\"\\\"\\\"show this help message and exit\\\"\\\"\\\")\\n parser.add_argument(\\n '--verbose', '-v',\\n action='count',\\n default=0,\\n help=\\\"\\\"\\\"Enable verbose output from '%(prog)s'. A second and third\\n '-v' increases verbosity.\\\"\\\"\\\")\\n parser.add_argument(\\n '--sequential',\\n action='store_true',\\n help=\\\"\\\"\\\"Execute analyzer sequentialy.\\\"\\\"\\\")\\n parser.add_argument(\\n '--cdb',\\n metavar='<file>',\\n default=\\\"compile_commands.json\\\",\\n help=\\\"\\\"\\\"The JSON compilation database.\\\"\\\"\\\")\\n return parser\",\n \"def parser(self):\\n return self.arg_parser\",\n \"def build_parser(self, parser: ArgumentParser) -> None:\",\n \"def parser(cls, *args, **kwargs):\\n\\n parser = ArgumentParser(*args, **kwargs)\\n parser.add_argument('-a', \\\"--address\\\",\\n help=\\\"Force entry point address\\\", default=None)\\n parser.add_argument('-b', \\\"--dumpblocs\\\", action=\\\"store_true\\\",\\n help=\\\"Log disasm blocks\\\")\\n parser.add_argument('-z', \\\"--singlestep\\\", action=\\\"store_true\\\",\\n help=\\\"Log single step\\\")\\n parser.add_argument('-d', \\\"--debugging\\\", action=\\\"store_true\\\",\\n help=\\\"Debug shell\\\")\\n parser.add_argument('-g', \\\"--gdbserver\\\", type=int,\\n help=\\\"Listen on port @port\\\")\\n parser.add_argument(\\\"-j\\\", \\\"--jitter\\\",\\n help=\\\"Jitter engine. Possible values are: gcc (default), tcc, llvm, python\\\",\\n default=\\\"gcc\\\")\\n parser.add_argument(\\n '-q', \\\"--quiet-function-calls\\\", action=\\\"store_true\\\",\\n help=\\\"Don't log function calls\\\")\\n parser.add_argument('-i', \\\"--dependencies\\\", action=\\\"store_true\\\",\\n help=\\\"Load PE and its dependencies\\\")\\n\\n for base_cls in cls._classes_():\\n base_cls.update_parser(parser)\\n return parser\",\n \"def get_parser():\\n # Parent and only parser.\\n parser = argparse.ArgumentParser(\\n add_help=True,\\n formatter_class=argparse.RawTextHelpFormatter)\\n parser.add_argument('mode', action='store',\\n choices=range(len(MODES)),\\n type=int,\\n help='Select mode of file download.\\\\n'\\n ' e.g: 0(rated) or 1(list).')\\n parser.add_argument('torr_page', action='store',\\n choices=range(len(TORRENTS)),\\n type=int,\\n help='Select tracking page to download from.\\\\n'\\n ' e.g: 0 to .. ' + str(len(TORRENTS)-1) + '.')\\n parser.add_argument('str_search', action='store',\\n type=str,\\n help='Input torrent string to search.\\\\n'\\n ' e.g: \\\"String search\\\"')\\n return(parser)\",\n \"def get_parser():\\n parser = ArgumentParser(description=\\\"Script used to generate Freeplane \\\"\\n + \\\"mindmap files\\\")\\n\\n # This is use when people in Linaro aren't using their email address.\\n parser.add_argument('--disable-altname', required=False,\\n action=\\\"store_true\\\", default=False,\\n help=\\\"Use alternative names (from cfg.yaml) to the tree\\\")\\n\\n parser.add_argument('--assignee', required=False,\\n action=\\\"store_true\\\", default=False,\\n help=\\\"Add assignees (from cfg.yaml) to the tree\\\")\\n\\n parser.add_argument('-a', '--author', required=False,\\n action=\\\"store_true\\\", default=False,\\n help=\\\"If set, git statistic only count the commit \\\"\\n + \\\"from the author\\\")\\n\\n parser.add_argument('-p', '--path', required=False, action=\\\"store\\\",\\n default=\\\"/home/jyx/devel/optee_projects/reference/linux\\\",\\n help='Full path to the kernel tree')\\n\\n parser.add_argument('-s', '--since', required=False, action=\\\"store\\\",\\n default=None,\\n help='Used with the git log --since command')\\n\\n parser.add_argument('-o', '--output', required=False, action=\\\"store\\\",\\n default=\\\"linux-kernel.mm\\\",\\n help='Output filename')\\n\\n parser.add_argument('-v', required=False, action=\\\"store_true\\\",\\n default=False,\\n help='Output some verbose debugging info')\\n\\n return parser\",\n \"def get_parser():\\n parser = argparse.ArgumentParser(description='Parser des liens sur les sites Jahia et Wordpress.')\\n parser.add_argument('ficher_des_sites', help='le fichier contenant les sites a parser.')\\n parser.add_argument('-v', '--version', help='affiche la version du parser',\\n action='version', version='%(prog)s ' + __version__)\\n return parser\",\n \"def get_parser():\\n parser = argparse.ArgumentParser(description='Parser des liens sur les sites Jahia et Wordpress.')\\n parser.add_argument('ficher_des_sites', help='le fichier contenant les sites a parser.')\\n parser.add_argument('-v', '--version', help='affiche la version du parser',\\n action='version', version='%(prog)s ' + __version__)\\n return parser\",\n \"def get_parser(name):\\n parser = argparse.ArgumentParser(name, formatter_class=argparse.ArgumentDefaultsHelpFormatter)\\n # print default value always\\n parser.add_argument = partial(parser.add_argument, help=' ')\\n return parser\",\n \"def get_parser(name):\\n parser = argparse.ArgumentParser(name, formatter_class=argparse.ArgumentDefaultsHelpFormatter)\\n # print default value always\\n parser.add_argument = partial(parser.add_argument, help=' ')\\n return parser\",\n \"def get_parser():\\n parser = argparse.ArgumentParser()\\n # parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)\\n parser.add_argument('files', nargs='+')\\n return parser\",\n \"def create_arguments_parser():\\n description = \\\"Statically analyse SBML files for modelling errors\\\"\\n parent_arg_parser = rate_checker_sbml.create_arguments_parser()\\n parser = argparse.ArgumentParser(description=description,\\n parents=[parent_arg_parser])\\n return parser\",\n \"def create_parser(self, prog_name, subcommand):\\n return OptionParser(prog=prog_name,\\n usage=self.usage(subcommand),\\n version=self.get_version(),\\n option_list=self.option_list)\",\n \"def get_parser():\\n parser = ArgumentParser(description=__doc__,\\n formatter_class=ArgumentDefaultsHelpFormatter,\\n prog='pv2')\\n subparsers = parser.add_subparsers(dest='cmd')\\n # subparsers.add_parser('selfcheck',\\n # add_help=False,\\n # help=\\\"Self-check of the sst toolkit.\\\")\\n # parser.add_argument('--version',\\n # action='version',\\n # version=('sst %s' % str(sst.__version__)))\\n subparsers.add_parser('eval',\\n add_help=False,\\n parents=[evaluate.get_parser()],\\n formatter_class=ArgumentDefaultsHelpFormatter,\\n help=(\\\"Evaluate a single image\\\"))\\n subparsers.add_parser('train',\\n add_help=False,\\n parents=[train.get_parser()],\\n formatter_class=ArgumentDefaultsHelpFormatter,\\n help=(\\\"Train a new model.\\\"))\\n subparsers.add_parser('plot',\\n add_help=False,\\n parents=[plot.get_parser()],\\n formatter_class=ArgumentDefaultsHelpFormatter,\\n help=(\\\"Plot summary information.\\\"))\\n return parser\",\n \"def get_parser():\\r\\n parser = argparse.ArgumentParser(description=( # pylint: disable=redefined-outer-name\\r\\n \\\"Automatically finds translation errors in all edx-platform *.po files, \\\"\\r\\n \\\"for all languages, unless one or more language(s) is specified to check.\\\"\\r\\n ))\\r\\n\\r\\n parser.add_argument(\\r\\n '-l', '--language',\\r\\n type=str,\\r\\n nargs='*',\\r\\n help=\\\"Specify one or more specific language code(s) to check (eg 'ko_KR').\\\"\\r\\n )\\r\\n\\r\\n parser.add_argument(\\r\\n '-e', '--empty',\\r\\n action='store_true',\\r\\n help=\\\"Includes empty translation strings in .prob files.\\\"\\r\\n )\\r\\n\\r\\n parser.add_argument(\\r\\n '-v', '--verbose',\\r\\n action='count', default=0,\\r\\n help=\\\"Turns on info-level logging.\\\"\\r\\n )\\r\\n\\r\\n return parser\",\n \"def _to_parser(self) -> ArgumentParser:\\n parser = ArgumentParser()\\n for arg in self.arg_list:\\n parser.add_argument(arg._flag, **arg._parser_kwargs)\\n return parser\",\n \"def build_argument_parser():\\n description=\\\"A simple tool to batch rename given files.\\\"\\n parser = ArgumentParser(description=description)\\n parser.add_argument(\\\"-i\\\", \\\"--input-list\\\", required=False,\\n help=\\\"the path to the input list file.\\\")\\n parser.add_argument(\\\"-p\\\", \\\"--glob-pattern\\\", default=DEFAULT_GLOB_PATTERN,\\n help=\\\"a glob pattern to filter input files.\\\")\\n return parser\",\n \"def get_parser():\\n module_parser = ArgumentParser(\\n formatter_class=ArgumentDefaultsHelpFormatter)\\n module_parser.add_argument(\\\"-i\\\", dest=\\\"data_path\\\", type=str,\\n help=\\\"the location dataset\\\")\\n module_parser.add_argument(\\\"-o\\\", dest=\\\"output_path\\\", type=str,\\n help='base dir for outputs')\\n module_parser.add_argument(\\\"-subdir\\\", dest=\\\"subdir\\\", type=str,\\n choices=['test', 'train', 'val', 'all'],\\n help='subdir: trn, test, val, or all ...')\\n module_parser.add_argument(\\\"-n\\\", dest=\\\"n_train\\\", type=int,\\n help='n: number of images for training')\\n module_parser.add_argument(\\\"-Rx\\\", dest=\\\"x_res\\\", type=int,\\n help='x resulution for final img')\\n module_parser.add_argument(\\\"-Ry\\\", dest=\\\"y_res\\\", type=int,\\n help='y resolution of final image')\\n module_parser.add_argument(\\\"-d\\\", dest=\\\"d\\\",\\n type=int,\\n default=0,\\n help='debug')\\n return module_parser\",\n \"def get_argument_parser():\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\n 'names',\\n help=(\\n 'list of name-location pairs '\\n '(location can be nat/hhs/cen/state or specific location labels)'))\\n parser.add_argument(\\n '--first',\\n '-f',\\n type=int,\\n help='first epiweek override')\\n parser.add_argument(\\n '--last',\\n '-l',\\n type=int,\\n help='last epiweek override')\\n parser.add_argument(\\n '--epiweek',\\n '-w',\\n type=int,\\n help='epiweek override')\\n parser.add_argument(\\n '--test',\\n '-t',\\n default=False,\\n action='store_true',\\n help='dry run only')\\n parser.add_argument(\\n '--valid',\\n '-v',\\n default=False,\\n action='store_true',\\n help='do not fall back to stable wILI; require unstable wILI')\\n return parser\",\n \"def arg_parser():\\n import argparse\\n return argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)\",\n \"def get_parser():\\n if sys.version_info[0] < 3:\\n # Using a version of Python < 3.\\n parser = ArgumentParser(version=VERSION) # pylint: disable=E1123\\n else:\\n parser = ArgumentParser()\\n parser.add_argument('--version', action='version', version=VERSION)\\n\\n subparsers = parser.add_subparsers(\\n title='actions', help='Types of zappa commands',\\n dest='command')\\n\\n parser_update_stack = subparsers.add_parser(\\n 'update', help='Update a zappa deploy')\\n parser_update_stack.add_argument(\\n '--name', required=True,\\n help='Name of the deployment (dev, prod, etc.)')\\n\\n parser_create_stack = subparsers.add_parser(\\n 'deploy', help='Create a zappa deploy')\\n parser_create_stack.add_argument(\\n '--name', required=True,\\n help='Name of the deployment (dev, prod, etc.)')\\n\\n return parser\",\n \"def create_parser():\\n now = datetime.datetime.today()\\n default_date = \\\"{}-{}-{}\\\".format(now.day, now.month, now.year)\\n parser = argparse.ArgumentParser(description=\\\"Git plugin for automatic insertion of @since and @author annotations \\\"\\n \\\"into *.java source files in a project.\\\",\\n epilog=\\\"© Avner & Oded\\\")\\n parser.add_argument(\\\"-v\\\", \\\"--version\\\", help=\\\"Display the version of this plugin\\\", action='store_true')\\n parser.add_argument(\\\"--since\\\", nargs='?', help=\\\"Add the @since annotations to project\\\", const=default_date)\\n parser.add_argument(\\\"--author\\\", nargs='?', help=\\\"Add the @author annotations to project\\\", const=getpass.getuser())\\n\\n return parser\",\n \"def create_parser(self, prog_name, subcommand):\\r\\n return OptionParser(prog=prog_name,\\r\\n usage=self.usage(subcommand),\\r\\n version='',\\r\\n add_help_option = False,\\r\\n option_list=self.option_list)\",\n \"def init(argParser=\\\"default\\\", defaultLog=None, verbosity=0, **kwargs):\\n\\n if argParser is not None:\\n if isinstance(argParser, str):\\n # construct new parser based on command name\\n args = _makeParser(argParser, **kwargs).parse_args()\\n else:\\n # use provided parser\\n args = argParser.parse_args()\\n\\n defaultLog = None if not hasattr(args, \\\"log\\\") or args.log.lower() == \\\"none\\\" else args.log\\n verbosity = args.verbose if hasattr(args, \\\"verbose\\\") else 0\\n\\n else:\\n # don't parse anything, use default values\\n args = None\\n\\n setupLogging(defaultLog, verbosity=verbosity)\\n\\n return args\",\n \"def build_parser(usage, **kwargs):\\n return BetterArgumentParser(usage=usage, version=VERSION, **kwargs)\",\n \"def create_parser():\\n p = NewParser()\\n\\n p.add_argument('reference', type=str,\\n help = \\\"Fasta reference file that reads were mapped to.\\\")\\n\\n p.add_argument('gff', type=str,\\n help = \\\"GFF file containing reference genome annotations.\\\")\\n\\n p.add_argument('vcf', type=str,\\n help = \\\"VCF file to parse.\\\")\\n\\n args = p.parse_args(sys.argv[1:])\\n return args\",\n \"def init_parser():\\n parser = argparse.ArgumentParser(\\n description='Backup application code and data.')\\n parser.add_argument('-a', '--app-id', required=True,\\n help='the application ID to run the backup for')\\n parser.add_argument('--source-code', action='store_true',\\n default=False, help='backup the source code too. Disabled by default.')\\n parser.add_argument('-d', '--debug', required=False, action=\\\"store_true\\\",\\n default=False, help='display debug messages')\\n parser.add_argument('--skip', required=False, nargs=\\\"+\\\",\\n help='skip the following kinds, separated by spaces')\\n\\n return parser\",\n \"def create_parser():\\n parser = argparse.ArgumentParser()\\n parser.add_argument('url', help='url to scrape')\\n return parser\",\n \"def get_base_argument_parser(\\n **kwargs\\n) -> ArgumentParser:\\n\\n parser = ArgumentParser(\\n allow_abbrev=False,\\n add_help=False,\\n **kwargs\\n )\\n\\n parser.add_argument(\\n '--help',\\n action='store_true',\\n help='Pass this flag to print usage and argument descriptions.'\\n )\\n\\n parser.add_argument(\\n '--log',\\n choices=['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'],\\n help='Logging level.'\\n )\\n\\n return parser\",\n \"def get_parser():\\n parser = argparse.ArgumentParser(description=\\\"Update golang.org/x/<name> in vendor folder\\\")\\n parser.add_argument('-q', '--quiet', dest='verbose', action='store_false', help='work quietly')\\n parser.add_argument('--revision', help='update deps to this revision', default='')\\n parser.add_argument('name', help='name of the golang.org/x/ package. Can be empty', default='', nargs='?')\\n return parser\",\n \"def get_parser():\\n _program_name = Path(__file__).stem\\n example = f''' Example: >> {_program_name} sample.odb\\\\n '''\\n parser = ArgumentParser(description=__doc__.split('..')[0], # Don't include module author part of doc string\\n formatter_class=ArgumentDefaultsHelpFormatter, epilog=example, prog=_program_name)\\n parser.add_argument(nargs=1,\\n dest='input_file',\\n type=str,\\n help='odb or odbreport file for extracting data',\\n metavar='sample.odb')\\n parser.add_argument('-o', '--output-file',\\n dest='output_file',\\n type=str,\\n help='file for printing output',\\n metavar='sample.h5')\\n parser.add_argument('-f', '--output-file-type',\\n dest='output_type',\\n choices=['yaml', 'json', 'h5'],\\n type=str,\\n default='h5',\\n help='Type of file in which to store output data',\\n metavar='h5')\\n parser.add_argument('-r', '--odb-report-args',\\n dest='odb_report_args',\\n type=str,\\n help='Arguments to give to the odbreport command. Require the ``option=value`` interface style.',\\n metavar='\\\"step=step1 results\\\"')\\n parser.add_argument('-a', '--abaqus-command',\\n dest='abaqus_command',\\n type=str,\\n default=_settings._default_abaqus_command,\\n help='Abaqus command to use',\\n metavar='/path/to/abaqus')\\n parser.add_argument('-d', '--delete-report-file',\\n action=\\\"store_true\\\",\\n dest='delete_report_file',\\n default=False,\\n help='Delete after parsing the file created by the odbreport command')\\n parser.add_argument('-v', '--verbose',\\n action=\\\"store_true\\\",\\n dest='verbose',\\n default=False,\\n help='Print all messages')\\n return parser\",\n \"def parser_create():\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\\"-c\\\", \\\"--config-file\\\", type=str, help=\\\"yaml configuration file name\\\")\\n return parser.parse_args()\",\n \"def make_parser():\\n parser = argparse.ArgumentParser(description='')\\n parser.add_argument('-p', '--platform', dest='platform', type=str, required=False, default='')\\n return parser\",\n \"def make_parser():\\n parser = argparse.ArgumentParser(description=config.DESCRIPTION)\\n parser.add_argument('url_file', metavar='URL_FILE', type=str,\\n help=config.HELP_URL_FILE)\\n parser.add_argument('-d', metavar='DEST_DIR', dest='destination_dir', default=config.DEFAULT_DESTINATION_DIR, type=str,\\n help=config.HELP_DESTINATION_DIR)\\n parser.add_argument('-l', metavar='LOG_FILE', dest='log_file', default=config.DEFAULT_LOG_FILE, type=str,\\n help=config.HELP_LOG_FILE % config.DEFAULT_LOG_FILE)\\n\\n return parser\",\n \"def create_parser():\\n parser = argparse.ArgumentParser(\\n description=\\\"First example\\\",\\n epilog=\\\"Batch 2017\\\")\\n\\n # script\\n parser.add_argument('--script',\\n required=True,\\n action='store',\\n dest='script',\\n help=\\\"A script to execute\\\")\\n\\n parser.add_argument('--dataset',\\n required=True,\\n action='store',\\n dest='dataset',\\n help=\\\"A dataset to use\\\")\\n#\\n# parser.add_argument('--features',\\n# required=True,\\n# action='store',\\n# dest='features',\\n# help=\\\"Number of features\\\")\\n return parser\",\n \"def initFromCLI(cls, name=None):\\n ap = cls._makeArgumentParser()\\n ns = ap.parse_args()\\n instance = cls.initFromOptions(ns, name=name)\\n return instance\",\n \"def create_arg_parser():\\n arg_parser = argparse.ArgumentParser()\\n arg_parser.add_argument(\\n '-f',\\n '--file',\\n required=True,\\n help='Name of clean data file'\\n )\\n return arg_parser\",\n \"def _create_parser():\\n parser = ArgumentParser(description=\\\"A CLI that sends messages to an Azure event hub.\\\")\\n\\n parser.add_argument(\\\"--connection-string\\\", type=str, required=True,\\n help=\\\"The Azure event hub connection string\\\")\\n\\n parser.add_argument(\\\"--name\\\", type=str, required=True,\\n help=\\\"The Azure event hub name\\\")\\n\\n parser.add_argument(\\\"--interval\\\", type=int, required=False,\\n help=\\\"The number of seconds to wait between sends. Defaults to 10 seconds.\\\")\\n\\n parser.add_argument(\\\"--what-if\\\", type=bool, required=False,\\n help=\\\"Run the program without sending messages to the Event Hub. \\\"\\n \\\"The app will log what would have been sent to the Event Hub.\\\")\\n\\n return parser\",\n \"def create_parser():\\n parser = argparse.ArgumentParser()\\n parser.add_argument('webpage', help='webpage to search')\\n\\n return parser\",\n \"def create_parser(self, prog_name):\\n return OptionParser(\\n\\t\\t\\tprog=prog_name,\\n\\t\\t\\t#usage=self.usage(subcommand),\\n\\t\\t\\toption_list=self.option_list\\n\\t\\t)\",\n \"def create_importer_parser():\\n parser = argparse.ArgumentParser()\\n _create_shared_args(parser)\\n _create_importer_args(parser)\\n\\n return parser\",\n \"def build_parser ():\\n\\n parser = argparse.ArgumentParser (description = __doc__)\\n\\n parser.add_argument (\\n '-v', '--verbose', dest='verbose', action='count',\\n help='increase output verbosity', default=0\\n )\\n parser.add_argument (\\n '-l', '--live', dest='get_live_data', action='store_true',\\n help='get live data from OSM database',\\n )\\n parser.add_argument (\\n '-e', '--edit', action='store_true',\\n help='edit the OSM database',\\n )\\n parser.add_argument (\\n '-u', '--user', dest='my_edits', action='store_true',\\n help='only report about my edits',\\n )\\n parser.add_argument (\\n '--min-length', dest=\\\"min_length\\\", type=float, default=1000.0,\\n help='way must be longer than this to get a ref (in m) (default=1000)',\\n )\\n parser.add_argument (\\n '--batch-size', dest=\\\"batch_size\\\", type=int, default=10,\\n help='apply OSM edits in changesets of this size (default=10)',\\n )\\n return parser\",\n \"def initialise_parser(**kwargs):\\n kwargs['formatter_class'] = ap.ArgumentDefaultsHelpFormatter\\n p = MpArgumentParser(**kwargs)\\n\\n # -- version\\n path_file = os.path.sep.join(\\n os.path.abspath(__file__).split(os.path.sep)[:-2])\\n with open(os.path.join(path_file, 'VERSION'), 'r') as version_file:\\n version = version_file.readline()\\n p.add_argument('--version', action='version', version=version)\\n\\n p.add_argument(\\\"-v\\\", \\\"--verbose\\\", action=\\\"store_true\\\", help=\\\"Verbose mode\\\")\\n\\n return p\",\n \"def create_cli_parser() -> argparse.ArgumentParser:\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\\"config\\\", nargs=\\\"?\\\", help=\\\"path to yaml configuration file\\\")\\n return parser\",\n \"def get_parser():\\n parser = argparse.ArgumentParser(\\n description=\\\"\\\"\\\"Start a Classy Vision training job.\\n\\n This can be used for training on your local machine, using CPU or GPU, and\\n for distributed training. This script also supports Tensorboard, Visdom and\\n checkpointing.\\\"\\\"\\\"\\n )\\n\\n parser = add_generic_args(parser)\\n return parser\",\n \"def _setup_parser():\\n parser = argparse.ArgumentParser(add_help=True)\\n parser.add_argument('--eval_model', type=str, default=None)\\n parser.add_argument('--stack', type=int, default=1)\\n parser.add_argument('--flare', action='store_true')\\n parser.add_argument('--mixreg', action='store_true')\\n\\n env_group = parser.add_argument_group(\\\"Env Args\\\")\\n env_group.add_argument('--env_name', type=str, default=ENV_NAME)\\n env_group.add_argument('--num_envs', type=int, default=NUM_ENVS)\\n env_group.add_argument('--num_levels', type=int, default=NUM_LEVELS)\\n env_group.add_argument('--start_level', type=int, default=START_LEVEL)\\n\\n agent_group = parser.add_argument_group(\\\"Agent Args\\\")\\n PPOAgent.add_to_argparse(agent_group)\\n\\n model_group = parser.add_argument_group(\\\"Model Args\\\")\\n ImpalaPPO.add_to_argparse(model_group)\\n\\n return parser\",\n \"def make_argument_parser():\\n parser = argparse.ArgumentParser()\\n parser.add_argument('--device',\\n type=str,\\n choices=['CPU', 'GPU'],\\n help='Execution device.',\\n required=True)\\n parser.add_argument('-N',\\n type=int,\\n default=DEFAULT_N,\\n help='Number of particles.')\\n parser.add_argument('--rho',\\n type=float,\\n default=DEFAULT_RHO,\\n help='Number density.')\\n parser.add_argument('--dimensions',\\n type=int,\\n choices=[2, 3],\\n help='Number of dimensions.',\\n default=DEFAULT_DIMENSIONS)\\n parser.add_argument('--warmup_steps',\\n type=int,\\n default=DEFAULT_WARMUP_STEPS,\\n help='Number of timesteps to run before timing.')\\n parser.add_argument('--benchmark_steps',\\n type=int,\\n default=DEFAULT_BENCHMARK_STEPS,\\n help='Number of timesteps to run in the benchmark.')\\n parser.add_argument('--repeat',\\n type=int,\\n default=DEFAULT_REPEAT,\\n help='Number of times to repeat the run.')\\n parser.add_argument('-v',\\n '--verbose',\\n action='store_true',\\n help='Verbose output.')\\n return parser\",\n \"def _build_arg_parser():\\n parser = argparse.ArgumentParser(\\n description=_description,\\n add_help=True,\\n )\\n add_generic_args(parser)\\n add_diff_args(parser)\\n add_filename_args(parser, [\\\"base\\\", \\\"remote\\\"])\\n\\n parser.add_argument(\\n '-o', '--output',\\n default=None,\\n help=\\\"if supplied, the diff is written to this file. \\\"\\n \\\"Otherwise it is printed to the terminal.\\\")\\n\\n return parser\",\n \"def init_parser():\\n parser = OptionParser()\\n parser.add_option(\\\"-n\\\", \\\"--interactive\\\", action=\\\"store_true\\\", help=\\\"run in interactive (non-daemon) mode\\\")\\n parser.add_option(\\\"-r\\\", \\\"--run\\\", action=\\\"store_true\\\", help=\\\"starts process identified by -app parameter\\\")\\n parser.add_option(\\\"-k\\\", \\\"--kill\\\", action=\\\"store_true\\\", help=\\\"kill process identified by -app parameter\\\")\\n parser.add_option(\\\"-a\\\", \\\"--app\\\", action=\\\"store\\\", help=\\\"application to start (process name)\\\")\\n parser.add_option(\\\"-q\\\", \\\"--query\\\", action=\\\"store_true\\\", help=\\\"query application's state\\\")\\n parser.add_option(\\\"-i\\\", \\\"--install_ve\\\", action=\\\"store_true\\\", help=\\\"install a virtualenv for the runtime to use\\\")\\n parser.add_option(\\\"-s\\\", \\\"--shell\\\", action=\\\"store_true\\\", help=\\\"run an ipython shell within the virtualenv\\\")\\n parser.add_option(\\\"-t\\\", \\\"--tests\\\", action=\\\"store_true\\\", help=\\\"run tests\\\")\\n parser.add_option(\\\"-x\\\", \\\"--xunit\\\", action=\\\"store_true\\\", help=\\\"run tests with coverage and xunit output for Jenkins\\\")\\n parser.add_option(\\\"-z\\\", \\\"--analyze\\\", action=\\\"store_true\\\", help=\\\"run pylint on project\\\")\\n parser.add_option(\\\"-l\\\", \\\"--list\\\", action=\\\"store_true\\\", help=\\\"list available applications\\\")\\n parser.add_option(\\\"-o\\\", \\\"--outfile\\\", action=\\\"store\\\", help=\\\"save results from a report to a file\\\")\\n return parser\",\n \"def _parser(s, remainder):\\n parser = _ArgumentParser()\\n def run():\\n return s.parse(parser, remainder)\\n parser.run = run\\n return parser\",\n \"def get_parser():\\n\\tparser = argparse.ArgumentParser('preprocessing.py',\\n\\t\\tformatter_class=argparse.RawDescriptionHelpFormatter,\\n\\t\\tdescription=\\\"\\\"\\\"\\nRun a piepline for one NICER ObsID data. \\n\\t\\t\\\"\\\"\\\"\\n\\t\\t)\\n\\tversion = '%(prog)s ' + __version__\\n\\tparser.add_argument('obsid', type=str, \\n\\t\\thelp='ObsID (e.g., 4012010109)')\\t\\n\\treturn parser\",\n \"def build_arg_parser():\\n\\n main = ArgumentParser(description='AMFinder command-line arguments.',\\n allow_abbrev=False,\\n formatter_class=RawTextHelpFormatter)\\n\\n subparsers = main.add_subparsers(dest='run_mode', required=True,\\n help='action to be performed.')\\n\\n _ = training_subparser(subparsers)\\n _ = prediction_subparser(subparsers)\\n _ = diagnostic_subparser(subparsers)\\n\\n return main\",\n \"def parser(self, prog, **kwargs):\\n prog += ' ' + self.name\\n parser = argparse.ArgumentParser(prog=prog,\\n description=self.summary,\\n **kwargs)\\n for args, kwargs in self.args or ():\\n parser.add_argument(*args, **kwargs)\\n return parser\",\n \"def get_argparser(self):\\n parser = argparse.ArgumentParser(description='Command Configuration')\\n parser.add_argument('--coin', choices=['bitcoin', 'ethereum', 'litecoin'], default='bitcoin')\\n parser.add_argument('--start_date', default='2019-10-21')\\n parser.add_argument('--end_date', default='2019-10-31')\\n parser.add_argument('--language', choices=['en', 'it', 'es', 'fr', 'de', 'ru', 'zh'], default='en')\\n\\n argparser = parser.parse_args()\\n return argparser.__dict__\",\n \"def init_parser():\\n parser = OptionParser()\\n\\n parser.add_option(\\n \\\"-d\\\",\\n \\\"--debug\\\",\\n dest=\\\"debug\\\",\\n help=\\\"Toggle debugging\\\",\\n action=\\\"store_true\\\",\\n default=False,\\n )\\n\\n parser.add_option(\\n \\\"-f\\\",\\n \\\"--questions-file\\\",\\n dest=\\\"file\\\",\\n help=(\\\"Use this file instead of the default \\\"\\n \\\"questions.yaml\\\"),\\n metavar=\\\"FILE\\\",\\n )\\n\\n parser.add_option(\\n \\\"-p\\\",\\n \\\"--generate-pdf\\\",\\n dest=\\\"pdf\\\",\\n help=(\\\"Generate the speaker PDF\\\"),\\n action=\\\"store_true\\\",\\n default=False,\\n )\\n\\n parser.add_option(\\n \\\"-v\\\",\\n \\\"--version\\\",\\n dest=\\\"version\\\",\\n help=\\\"Show program version\\\",\\n action=\\\"store_true\\\",\\n default=False,\\n )\\n\\n options = parser.parse_args()[0]\\n return options\",\n \"def build_parser():\\n parser = argparse.ArgumentParser(description='The classic FizzBuzz game in programmatic form.', add_help=False)\\n parser.add_argument('-h', '--help', default=argparse.SUPPRESS, action='help',\\n help='Show this help message and exit.')\\n parser.add_argument('-s', '--start', default=1, type=int, action='store', metavar='START',\\n help='The number to start FizzBuzzing at (inclusive).')\\n parser.add_argument('stop', type=int, action='store', metavar='STOP',\\n help='The number to end FizzBuzzing at (exclusive).')\\n return parser\",\n \"def get_arg_parser(parser=None):\\n # add arguments that are specific to the component\\n if parser is None:\\n parser = argparse.ArgumentParser(description=__doc__)\\n\\n parser.add_argument(\\\"--raw_training_data\\\", type=str, required=True, help=\\\"\\\")\\n parser.add_argument(\\\"--raw_testing_data\\\", type=str, required=True, help=\\\"\\\")\\n parser.add_argument(\\\"--train_output\\\", type=str, required=True, help=\\\"\\\")\\n parser.add_argument(\\\"--test_output\\\", type=str, required=True, help=\\\"\\\")\\n parser.add_argument(\\\"--metrics_prefix\\\", type=str, required=False, help=\\\"Metrics prefix\\\")\\n return parser\",\n \"def create_parser():\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\\"--getErrors\\\",\\n type=str,\\n default=None,\\n help=\\\"get error messages - send \\\\'yes\\\\' \\\")\\n parser.add_argument(\\\"--host\\\",\\n type=str,\\n default=\\\"localhost\\\",\\n help=\\\"Host of redis. Default : localhost\\\")\\n parser.add_argument(\\\"--port\\\",\\n type=int,\\n default=6379,\\n help=\\\"Port of redis. Default : 6379\\\")\\n parser.add_argument(\\\"--db\\\",\\n type=int,\\n default=0,\\n help=\\\"Db of redis. Default : 0\\\")\\n parser.add_argument(\\\"--cleanTemp\\\",\\n type=str,\\n default=None,\\n help=\\\"clean trash files from db - send \\\\'yes\\\\' \\\")\\n return parser\",\n \"def get_parser():\\n # parse parameters\\n parser = argparse.ArgumentParser(description=\\\"Evaluate sentences with RTTL\\\")\\n\\n # main parameters\\n parser.add_argument(\\\"--dump_path\\\", type=str, default=\\\"./dumped/\\\", help=\\\"Experiment dump path\\\")\\n parser.add_argument(\\\"--exp_name\\\", type=str, default=\\\"\\\", help=\\\"Experiment name\\\")\\n parser.add_argument(\\\"--exp_id\\\", type=str, default=\\\"\\\", help=\\\"Experiment ID\\\")\\n parser.add_argument(\\\"--batch_size\\\", type=int, default=32, help=\\\"Number of sentences per batch\\\")\\n\\n # model / output paths\\n parser.add_argument(\\\"--model_path\\\", type=str, default=\\\"\\\", help=\\\"Model path\\\")\\n parser.add_argument(\\\"--output_path\\\", type=str, default=\\\"\\\", help=\\\"Output path for scores\\\")\\n parser.add_argument(\\\"--input_path\\\", type=str, default=\\\"\\\", help=\\\"Input path for source sentences\\\")\\n\\n # parser.add_argument(\\\"--max_vocab\\\", type=int, default=-1, help=\\\"Maximum vocabulary size (-1 to disable)\\\")\\n # parser.add_argument(\\\"--min_count\\\", type=int, default=0, help=\\\"Minimum vocabulary count\\\")\\n\\n # source language / target language\\n parser.add_argument(\\\"--src_lang\\\", type=str, default=\\\"\\\", help=\\\"Source language\\\")\\n parser.add_argument(\\\"--tgt_lang\\\", type=str, default=\\\"\\\", help=\\\"Target language\\\")\\n\\n return parser\",\n \"def getArgumentParser():\\n parser = argparse.ArgumentParser(description=\\\"Script for running optimization for the ZH dark photon SR\\\")\\n parser.add_argument('-i',\\n '--infile',\\n dest='infile',\\n help='Input CSV file',\\n default = '/afs/cern.ch/user/e/ehofgard/public/data/all_data')\\n parser.add_argument('-o',\\n '--output',\\n dest='outdir',\\n help='Output directory for plots, selection lists, etc',\\n default='outdir')\\n \\n return parser\",\n \"def get_parser():\\n parser = argparse.ArgumentParser(\\n description='Converts HTML from file or url to a clean text version')\\n parser.add_argument('input', nargs='?', default=None,\\n help='Html input either from a file or an url '\\n '(default:stdin)')\\n parser.add_argument('-o', '--output', type=str,\\n help='Output file (default:stdout).')\\n parser.add_argument('-e', '--encoding', type=str,\\n help='Content encoding for reading and writing files '\\n '(default:utf-8)',\\n default='utf-8')\\n parser.add_argument('-i', '--display-image-captions',\\n action='store_true', default=False,\\n help='Display image captions (default:false).')\\n parser.add_argument('-d', '--deduplicate-image-captions',\\n action='store_true', default=False,\\n help='Deduplicate image captions (default:false).')\\n parser.add_argument('-l', '--display-link-targets',\\n action='store_true', default=False,\\n help='Display link targets (default:false).')\\n parser.add_argument('-a', '--display-anchor-urls',\\n action='store_true', default=False,\\n help='Deduplicate image captions (default:false).')\\n parser.add_argument('--indentation', default='extended',\\n help='How to handle indentation (extended or strict;'\\n ' default: extended).')\\n parser.add_argument('-v', '--version',\\n action='store_true', default=False,\\n help='display version information')\\n return parser\",\n \"def create_parser():\\n pass\",\n \"def init_parser(self):\\n usage = \\\"%s [options] [subcommand]\\\" % (self.command)\\n description = \\\"An SBTools test plug-in.\\\"\\n\\n parser = SBToolOptionParser(self, self.sbtools, usage, description=description)\\n return parser\",\n \"def create_arg_parser():\\n server_modes = ['builtin', 'waitress']\\n\\n parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)\\n parser.add_argument('h', metavar='HOST', help='Server HOST (e.g. \\\"localhost\\\")', type=str)\\n parser.add_argument('p', metavar='PORT', help='Server PORT (e.g. \\\"5001\\\")', type=int)\\n parser.add_argument('m', metavar='SERVER_MODE', help=\\\", \\\".join(server_modes), choices=server_modes, type=str)\\n parser.add_argument('--debug', help=\\\"Run builtin server in debug mode\\\", action='store_true', default=False)\\n\\n return parser\",\n \"def get_parser():\\n\\tparser = argparse.ArgumentParser(description=\\\"Twitter Searcher\\\")\\n\\tparser.add_argument(\\\"-q\\\",\\n\\t\\t\\t\\t\\t\\t\\\"--query\\\",\\n\\t\\t\\t\\t\\t\\tdest=\\\"query\\\",\\n\\t\\t\\t\\t\\t\\thelp=\\\"Query/Filter\\\",\\n\\t\\t\\t\\t\\t\\tdefault='*')\\n\\tparser.add_argument(\\\"-d\\\",\\n\\t\\t\\t\\t\\t\\\"--data-dir\\\",\\n\\t\\t\\t\\t\\tdest=\\\"city\\\",\\n\\t\\t\\t\\t\\thelp=\\\"Output/Data Directory\\\")\\n\\treturn parser\",\n \"def makeParser():\\n parser = argparse.ArgumentParser(\\n description=(\\n \\\"Print a JSON object containing reference to read \\\"\\n \\\"distances extracted from a SAM file.\\\"\\n )\\n )\\n\\n parser.add_argument(\\n \\\"--samFile\\\",\\n action=\\\"append\\\",\\n required=True,\\n help=\\\"The SAM file(s) to load. May be repeated.\\\",\\n )\\n\\n parser.add_argument(\\n \\\"--minMatchingReads\\\",\\n type=int,\\n help=(\\n \\\"The minimum number of reads that must match a reference for it \\\"\\n \\\"to be included.\\\"\\n ),\\n )\\n\\n parser.add_argument(\\n \\\"--scoreTag\\\",\\n help=(\\n \\\"The score tag to use for the alignment score. If not given, \\\"\\n \\\"1 will be used to indicate that a read matched a reference \\\"\\n \\\"(non-matches are not included). The default is no score tag, \\\"\\n 'which is not that useful. A good choice is \\\"AS\\\", for the '\\n \\\"alignment score, but that has to be present in the SAM file, \\\"\\n \\\"which means that the aligner (bowtie2, bwa, etc. has to have \\\"\\n \\\"produced such a tag.\\\"\\n ),\\n )\\n\\n parser.add_argument(\\n \\\"--verbose\\\", action=\\\"store_true\\\", help=\\\"Print extra information.\\\"\\n )\\n\\n return parser\",\n \"def create_argument_parser() -> argparse.ArgumentParser:\\n\\n parser = argparse.ArgumentParser(\\n prog=\\\"mafiabot\\\",\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n description=\\\"Mafia Telegram Bot command line interface.\\\",\\n )\\n\\n # parser.add_argument(\\n # \\\"--argument\\\",\\n # action=\\\"store_true\\\",\\n # default=,\\n # help=\\\"\\\",\\n # )\\n\\n add_logging_options(parser)\\n\\n return parser\",\n \"def initialize_parser():\\n ftypes = [\\n \\\"png\\\",\\n \\\"jpg\\\",\\n \\\"jpeg\\\",\\n \\\"pdf\\\",\\n \\\"ps\\\",\\n \\\"eps\\\",\\n \\\"rgba\\\",\\n \\\"svg\\\",\\n \\\"tiff\\\",\\n \\\"tif\\\",\\n \\\"pgf\\\",\\n \\\"svgz\\\",\\n \\\"raw\\\",\\n ]\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\n \\\"-s\\\",\\n \\\"--savefig\\\",\\n action=\\\"store\\\",\\n default=False,\\n choices=ftypes,\\n help=\\\"Save figure to a file\\\",\\n )\\n return parser\",\n \"def argument_parser() -> argparse.ArgumentParser:\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\n \\\"source\\\",\\n type=argparse.FileType(\\\"r\\\"),\\n )\\n parser.add_argument(\\n \\\"-v\\\",\\n \\\"--verbose\\\",\\n action=\\\"store_true\\\",\\n help=\\\"be verbose in output\\\",\\n )\\n parser.add_argument(\\n \\\"-p\\\",\\n \\\"--permissive\\\",\\n action=\\\"store_true\\\",\\n help=\\\"allow permissive validation (just checking presence)\\\",\\n )\\n return parser\",\n \"def _CreateParser():\\n parser = commandline.ArgumentParser(description=__doc__, caching=True)\\n\\n # TODO(rcui): Have this use the UI-V2 format of having source and target\\n # device be specified as positional arguments.\\n parser.add_argument('--force', action='store_true', default=False,\\n help='Skip all prompts (i.e., for disabling of rootfs '\\n 'verification). This may result in the target '\\n 'machine being rebooted.')\\n sdk_board_env = os.environ.get(cros_chrome_sdk.SDKFetcher.SDK_BOARD_ENV)\\n parser.add_argument('--board', default=sdk_board_env,\\n help=\\\"The board the Chrome build is targeted for. When \\\"\\n \\\"in a 'cros chrome-sdk' shell, defaults to the SDK \\\"\\n \\\"board.\\\")\\n parser.add_argument('--build-dir', type='path',\\n help='The directory with Chrome build artifacts to '\\n 'deploy from. Typically of format '\\n '<chrome_root>/out/Debug. When this option is used, '\\n 'the GYP_DEFINES environment variable must be set.')\\n parser.add_argument('--target-dir', type='path',\\n default=None,\\n help='Target directory on device to deploy Chrome into.')\\n parser.add_argument('-g', '--gs-path', type='gs_path',\\n help='GS path that contains the chrome to deploy.')\\n parser.add_argument('--nostartui', action='store_false', dest='startui',\\n default=True,\\n help=\\\"Don't restart the ui daemon after deployment.\\\")\\n parser.add_argument('--nostrip', action='store_false', dest='dostrip',\\n default=True,\\n help=\\\"Don't strip binaries during deployment. Warning: \\\"\\n 'the resulting binaries will be very large!')\\n parser.add_argument('-p', '--port', type=int, default=remote.DEFAULT_SSH_PORT,\\n help='Port of the target device to connect to.')\\n parser.add_argument('-t', '--to',\\n help='The IP address of the CrOS device to deploy to.')\\n parser.add_argument('-v', '--verbose', action='store_true', default=False,\\n help='Show more debug output.')\\n parser.add_argument('--mount-dir', type='path', default=None,\\n help='Deploy Chrome in target directory and bind it '\\n 'to the directory specified by this flag.'\\n 'Any existing mount on this directory will be '\\n 'umounted first.')\\n parser.add_argument('--mount', action='store_true', default=False,\\n help='Deploy Chrome to default target directory and bind '\\n 'it to the default mount directory.'\\n 'Any existing mount on this directory will be '\\n 'umounted first.')\\n\\n group = parser.add_argument_group('Advanced Options')\\n group.add_argument('-l', '--local-pkg-path', type='path',\\n help='Path to local chrome prebuilt package to deploy.')\\n group.add_argument('--sloppy', action='store_true', default=False,\\n help='Ignore when mandatory artifacts are missing.')\\n group.add_argument('--staging-flags', default=None, type=ValidateGypDefines,\\n help=('Extra flags to control staging. Valid flags are - '\\n '%s' % ', '.join(chrome_util.STAGING_FLAGS)))\\n # TODO(stevenjb): Remove --strict entirely once removed from the ebuild.\\n group.add_argument('--strict', action='store_true', default=False,\\n help='Deprecated. Default behavior is \\\"strict\\\". Use '\\n '--sloppy to omit warnings for missing optional '\\n 'files.')\\n group.add_argument('--strip-flags', default=None,\\n help=\\\"Flags to call the 'strip' binutil tool with. \\\"\\n \\\"Overrides the default arguments.\\\")\\n group.add_argument('--ping', action='store_true', default=False,\\n help='Ping the device before connection attempt.')\\n group.add_argument('--mash', action='store_true', default=False,\\n help='Copy additional files for mus+ash. Will not fit in '\\n 'the default target-dir.')\\n\\n group = parser.add_argument_group(\\n 'Metadata Overrides (Advanced)',\\n description='Provide all of these overrides in order to remove '\\n 'dependencies on metadata.json existence.')\\n group.add_argument('--target-tc', action='store', default=None,\\n help='Override target toolchain name, e.g. '\\n 'x86_64-cros-linux-gnu')\\n group.add_argument('--toolchain-url', action='store', default=None,\\n help='Override toolchain url format pattern, e.g. '\\n '2014/04/%%(target)s-2014.04.23.220740.tar.xz')\\n\\n # GYP_DEFINES that Chrome was built with. Influences which files are staged\\n # when --build-dir is set. Defaults to reading from the GYP_DEFINES\\n # enviroment variable. WILL BE DEPRECATED.\\n parser.add_argument('--gyp-defines', default=None, type=ValidateGypDefines,\\n help=argparse.SUPPRESS)\\n\\n # GN_ARGS (args.gn) used to build Chrome. Influences which files are staged\\n # when --build-dir is set. Defaults to reading from the GN_ARGS env variable.\\n # CURRENLY IGNORED, ADDED FOR FORWARD COMPATABILITY.\\n parser.add_argument('--gn-args', default=None, type=ValidateGnArgs,\\n help=argparse.SUPPRESS)\\n\\n # Path of an empty directory to stage chrome artifacts to. Defaults to a\\n # temporary directory that is removed when the script finishes. If the path\\n # is specified, then it will not be removed.\\n parser.add_argument('--staging-dir', type='path', default=None,\\n help=argparse.SUPPRESS)\\n # Only prepare the staging directory, and skip deploying to the device.\\n parser.add_argument('--staging-only', action='store_true', default=False,\\n help=argparse.SUPPRESS)\\n # Path to a binutil 'strip' tool to strip binaries with. The passed-in path\\n # is used as-is, and not normalized. Used by the Chrome ebuild to skip\\n # fetching the SDK toolchain.\\n parser.add_argument('--strip-bin', default=None, help=argparse.SUPPRESS)\\n return parser\",\n \"def build_parser():\\n parser = argparse.ArgumentParser()\\n parser.add_argument('-r', '--reference', required=True, help=\\\"Reference Genome URL\\\")\\n parser.add_argument('-n', '--normal', required=True, help='Normal BAM URL. Format: UUID.normal.bam')\\n parser.add_argument('-t', '--tumor', required=True, help='Tumor BAM URL. Format: UUID.tumor.bam')\\n parser.add_argument('-d', '--dbsnp', required=True, help='dbsnp_132_b37.leftAligned.vcf URL')\\n parser.add_argument('-c', '--cosmic', required=True, help='b37_cosmic_v54_120711.vcf URL')\\n parser.add_argument('-u', '--mutect', required=True, help='Mutect.jar')\\n parser.add_argument('-w', '--work_dir', required=True, help='Where you wanna work from? (full path please)')\\n\\n return parser\",\n \"def create_parser(self, prog_name, subcommand):\\n parser = CommandParser(\\n self, prog=\\\"%s %s\\\" % (os.path.basename(prog_name), subcommand),\\n description=self.help or None,\\n )\\n parser.add_argument(\\n '--version', action='version', version=self.get_version())\\n\\n self.add_arguments(parser)\\n return parser\",\n \"def get_parser():\\n\\tparser = argparse.ArgumentParser('tallyup.py',\\n\\t\\tformatter_class=argparse.RawDescriptionHelpFormatter,\\n\\t\\tdescription=\\\"\\\"\\\"\\nTally up a student score file.\\n\\t\\t\\\"\\\"\\\"\\n\\t\\t)\\n\\tversion = '%(prog)s ' + __version__\\n\\tparser.add_argument('--version', '-v', action='version', version=version,\\n\\t\\thelp='show version of this command.')\\n\\tparser.add_argument('--csvfile', '-i', type=str, required=True, \\n\\t\\thelp='input csv file.')\\n\\treturn parser\",\n \"def setup_parser(self):\\n parser = argparse.ArgumentParser(description=DESCRIPTION)\\n parser.add_argument('words', metavar='W', nargs='+', help=POSITIONAL_HELP)\\n parser.add_argument('-a','--any', dest=\\\"search_funct\\\", action=\\\"store_const\\\", \\n const='any', default='all', help=SEARCH_HELP)\\n parser.add_argument('-o','--only-id', action='store_true', help=ID_HELP)\\n parser.add_argument('-u', '--update', action='store_true', help=UPDATE_HELP)\\n return parser\",\n \"def get_parser():\\n parser = argparse.ArgumentParser(description=\\\"Tweet Downloader\\\")\\n parser.add_argument(\\\"-d\\\",\\n \\\"--data\\\",\\n dest=\\\"data\\\",\\n help=\\\"Read data from file or display initial setting\\\",\\n default=False)\\n\\n return parser\",\n \"def build_arg_parser():\\n parser = argparse.ArgumentParser(\\n description='Standard Arguments for talking to vCenter')\\n\\n # because -h is reserved for 'help' we use -s for service\\n parser.add_argument('-s', '--host',\\n required=True,\\n action='store',\\n help='vSphere service to connect to')\\n\\n # because we want -p for password, we use -o for port\\n parser.add_argument('-o', '--port',\\n type=int,\\n default=443,\\n action='store',\\n help='Port to connect on')\\n\\n parser.add_argument('-u', '--user',\\n required=True,\\n action='store',\\n help='User name to use when connecting to host')\\n\\n parser.add_argument('-p', '--password',\\n required=False,\\n action='store',\\n help='Password to use when connecting to host')\\n return parser\",\n \"def make_argument_parser():\\n parser = Benchmark.make_argument_parser()\\n parser.add_argument('--skip-reference',\\n action='store_true',\\n help='Skip the reference simulation run.')\\n return parser\",\n \"def build_cli(self):\\n parser = argparse.ArgumentParser(\\\"xsgen\\\",\\n conflict_handler='resolve', argument_default=NotSpecified)\\n for plugin in self.plugins:\\n plugin.update_argparser(parser)\\n self.parser = parser\\n return parser\",\n \"def get_parser():\\n parser = ArgumentParser(\\n description='phpMyAdmin work reporting tool\\\\n\\\\nGenerates list of commits and issues handled in given period.',\\n epilog='Credentials can be also stored in ~/.config/phpmyadmin:\\\\n\\\\n[github]\\\\nuser=USER\\\\ntoken=TOKEN',\\n formatter_class=RawDescriptionHelpFormatter,\\n )\\n parser.add_argument(\\n '-u', '--user',\\n help='GitHub username, used for both reporting and authentication'\\n )\\n parser.add_argument(\\n '-t', '--token',\\n help='GitHub authentication token'\\n )\\n parser.add_argument(\\n '-s', '--start-date',\\n type=dateutil.parser.parse,\\n default=datetime.now() - timedelta(days=7),\\n help='Starting datetime, defaults to 7 days ago'\\n )\\n parser.add_argument(\\n '-e', '--end-date',\\n type=dateutil.parser.parse,\\n default=datetime.now(),\\n help='Ending datetime, defaults to current timestamp'\\n )\\n parser.add_argument(\\n '-f', '--format',\\n choices=('markdown', ),\\n default='markdown',\\n help='Output format',\\n )\\n parser.add_argument(\\n '-w', '--weekly',\\n action='store_true',\\n help='Weekly report not including private repositories'\\n )\\n parser.add_argument(\\n '-W', '--last-week',\\n action='store_true',\\n help='Create report for last week'\\n )\\n parser.add_argument(\\n '-M', '--last-month',\\n action='store_true',\\n help='Create report for last month'\\n )\\n parser.add_argument(\\n '--this-week',\\n action='store_true',\\n help='Create report for this week'\\n )\\n return parser\",\n \"def _get_parserobj(self, option_list):\\n if '--version' in self.parselines[0]:\\n if 'optparse' == self.parser_type:\\n parser = OptionParser(version=\\\"dummy\\\")\\n else:\\n parser = ArgumentParser(\\n version='dummy',\\n formatter_class=RawDescriptionHelpFormatter)\\n else:\\n if 'optparse' == self.parser_type:\\n parser = OptionParser()\\n else:\\n parser = ArgumentParser(\\n formatter_class=RawDescriptionHelpFormatter)\\n for opt in option_list:\\n if opt['short'] and self.parser_type is 'optparse':\\n parser.add_option(opt['short'], opt['long'],\\n metavar=opt['metavar'],\\n help=opt['help'].strip())\\n elif not opt['short'] and self.parser_type is 'optparse':\\n parser.add_option(opt['long'],\\n metavar=opt['metavar'],\\n help=opt['help'].strip())\\n elif opt['short'] and self.parser_type is 'argparse':\\n parser.add_argument(opt['short'], opt['long'],\\n metavar=opt['metavar'],\\n help=opt['help'].strip())\\n elif not opt['short'] and self.parser_type is 'argparse':\\n parser.add_argument(opt['long'],\\n metavar=opt['metavar'],\\n help=opt['help'].strip())\\n else:\\n raise InvalidParserTypeError(\\\"Invalid paresr type.\\\")\\n return parser\",\n \"def get_parser():\\n parser = libdot.ArgumentParser(description=__doc__)\\n parser.add_argument('paths', nargs='+',\\n help='Image files or directories to crush.')\\n return parser\",\n \"def get_cmd_line_parser(version=None, *args, **kwargs):\\n parser = argparse.ArgumentParser(*args, **kwargs)\\n if version:\\n parser.add_argument(\\n '--version', help='Print the version and exit.', action='version',\\n version='%(prog)s {}'.format(version))\\n DebugAction.add_parser_argument(parser)\\n VerboseAction.add_parser_argument(parser)\\n\\n return parser\",\n \"def make_parser():\\n p = argparse.ArgumentParser(\\n description=\\\"Visualize and analyze error from oblique/straight tag observations\\\"\\n )\\n\\n p.add_argument(\\\"-n\\\", help=\\\"name of the test in the config file\\\")\\n\\n p.add_argument(\\\"-t\\\", help=\\\"throw out bad tags\\\", action=\\\"store_true\\\")\\n\\n p.add_argument(\\\"-v\\\", help=\\\"visualize data\\\", action=\\\"store_true\\\")\\n\\n p.add_argument(\\\"-i\\\", help=\\\"print result data\\\", action=\\\"store_true\\\")\\n\\n return p\",\n \"def make_parser(description, agg=False, allowed_experiments=sum([exps for exps in EXPERIMENTS.values()], [])):\\n\\n return RamArgumentParser(description=description,\\n agg=agg,\\n allowed_experiments=allowed_experiments)\",\n \"def create_parser():\\n parser = argparse.ArgumentParser()\\n\\n # required args\\n parser.add_argument('--project_id',\\n help='Project id for project containing BQ data',\\n default=KEY_FILE,\\n type=str,\\n required=True)\\n\\n # data and model args\\n parser.add_argument('--training_budget',\\n help='Training budget in hours',\\n default=1,\\n type=int)\\n parser.add_argument('--key_file',\\n help='JSON key file for API access',\\n default=KEY_FILE,\\n type=str)\\n parser.add_argument('--location',\\n help='GCP region to run',\\n default=LOCATION,\\n type=str)\\n parser.add_argument('--automl_dataset',\\n help='Name of AutoML dataset',\\n default=AUTOML_DATASET,\\n type=str)\\n parser.add_argument('--automl_model',\\n help='Name of AutoML model',\\n default=AUTOML_MODEL,\\n type=str)\\n parser.add_argument('--bq_dataset',\\n help='BigQuery dataset to import from',\\n default=BQ_DATASET,\\n type=str)\\n parser.add_argument('--bq_table',\\n help='BigQuery table to import from',\\n default=BQ_TABLE,\\n type=str)\\n parser.add_argument('--batch_gcs_input',\\n help='GCS URI for batch predict CSV',\\n default=BATCH_GCS_INPUT,\\n type=str)\\n parser.add_argument('--batch_gcs_output',\\n help='GCS URI for batch predict output',\\n default=BATCH_GCS_OUTPUT,\\n type=str)\\n return parser\",\n \"def make_argument_parser():\\r\\n parser = argparse.ArgumentParser()\\r\\n parser.add_argument(\\\"data_directory\\\",\\r\\n help=\\\"Directory where the data files live.\\\")\\r\\n parser.add_argument(\\\"out\\\", help=\\\"Output directory of files.\\\")\\r\\n parser.add_argument(\\\"-t\\\", \\\"--test\\\", action=\\\"store_true\\\",\\r\\n help=(\\\"Test mode, avoids slow classifiers and uses\\\"\\r\\n \\\" 3 folds\\\"))\\r\\n parser.add_argument(\\\"--folds\\\", default=10,\\r\\n help=\\\"Number of folds for n-fold cross validation\\\")\\r\\n parser.add_argument(\\\"--data_pattern\\\", default=\\\"*.mat\\\",\\r\\n help=\\\"Pattern for data files\\\")\\r\\n parser.add_argument(\\\"--label_pattern\\\", default=\\\"*.mat\\\",\\r\\n help=\\\"Pattern for label files\\\")\\r\\n return parser\",\n \"def from_argparse_args(cls, args: Union[Namespace, ArgumentParser], **kwargs):\\n return from_argparse_args(cls, args, **kwargs)\",\n \"def get_parser():\\n parser = argparse.ArgumentParser(usage = \\\"{prog} [options]\\\")\\n parser.add_argument(\\\"-n\\\", \\\"--nights\\\", type=str, help=\\\"nights as comma separated string\\\")\\n parser.add_argument(\\\"--obstypes\\\", type=str, default=None, help=\\\"comma separated list of exposure types to include in \\\"+\\\\\\n \\\"the exposure table, e.g. science,arc,flat,dark,zero, ...\\\")\\n parser.add_argument(\\\"-i\\\", \\\"--path-to-data\\\", type=str, default=None, help=\\\"path to the raw input data\\\")\\n parser.add_argument(\\\"-o\\\",\\\"--exp-table-path\\\", type=str, default=None, help=\\\"path to save exposure tables, without monthly subdirectory\\\")\\n parser.add_argument(\\\"--overwrite-files\\\", action=\\\"store_true\\\", help=\\\"overwrite existing exposure tables\\\")\\n parser.add_argument(\\\"--verbose\\\", action=\\\"store_true\\\", help=\\\"print verbose output\\\")\\n\\n return parser\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7757528","0.7674221","0.766556","0.75634253","0.7486213","0.7486213","0.74758685","0.74569213","0.74259055","0.7384082","0.737286","0.7365048","0.7327146","0.73229235","0.7290896","0.7204032","0.7203092","0.71918964","0.71847415","0.7169976","0.7169976","0.7162135","0.7162135","0.7156622","0.7155229","0.71293","0.71070313","0.71067065","0.7106706","0.71024156","0.7084872","0.7084712","0.70725656","0.70683694","0.7066554","0.70533764","0.7051313","0.7047623","0.7046198","0.70294785","0.70243704","0.7014136","0.70105505","0.7003668","0.6997002","0.6993853","0.69878334","0.6967649","0.6967301","0.69672066","0.69512695","0.6939229","0.69343454","0.6934242","0.6928272","0.6927337","0.692655","0.6926392","0.69225127","0.6919122","0.6895132","0.6882321","0.68750817","0.68605924","0.6845323","0.6836636","0.6835077","0.6827151","0.68215907","0.6801604","0.679724","0.67914915","0.6790726","0.6790546","0.67829436","0.67711","0.6761225","0.6757608","0.6741913","0.67387563","0.6736886","0.6731584","0.67153233","0.6712486","0.67117715","0.67105407","0.670984","0.6702067","0.67000717","0.6699785","0.6688574","0.6684566","0.6683434","0.668225","0.6673251","0.66677135","0.6667645","0.6657135","0.66540736","0.66505575","0.6649819"],"string":"[\n \"0.7757528\",\n \"0.7674221\",\n \"0.766556\",\n \"0.75634253\",\n \"0.7486213\",\n \"0.7486213\",\n \"0.74758685\",\n \"0.74569213\",\n \"0.74259055\",\n \"0.7384082\",\n \"0.737286\",\n \"0.7365048\",\n \"0.7327146\",\n \"0.73229235\",\n \"0.7290896\",\n \"0.7204032\",\n \"0.7203092\",\n \"0.71918964\",\n \"0.71847415\",\n \"0.7169976\",\n \"0.7169976\",\n \"0.7162135\",\n \"0.7162135\",\n \"0.7156622\",\n \"0.7155229\",\n \"0.71293\",\n \"0.71070313\",\n \"0.71067065\",\n \"0.7106706\",\n \"0.71024156\",\n \"0.7084872\",\n \"0.7084712\",\n \"0.70725656\",\n \"0.70683694\",\n \"0.7066554\",\n \"0.70533764\",\n \"0.7051313\",\n \"0.7047623\",\n \"0.7046198\",\n \"0.70294785\",\n \"0.70243704\",\n \"0.7014136\",\n \"0.70105505\",\n \"0.7003668\",\n \"0.6997002\",\n \"0.6993853\",\n \"0.69878334\",\n \"0.6967649\",\n \"0.6967301\",\n \"0.69672066\",\n \"0.69512695\",\n \"0.6939229\",\n \"0.69343454\",\n \"0.6934242\",\n \"0.6928272\",\n \"0.6927337\",\n \"0.692655\",\n \"0.6926392\",\n \"0.69225127\",\n \"0.6919122\",\n \"0.6895132\",\n \"0.6882321\",\n \"0.68750817\",\n \"0.68605924\",\n \"0.6845323\",\n \"0.6836636\",\n \"0.6835077\",\n \"0.6827151\",\n \"0.68215907\",\n \"0.6801604\",\n \"0.679724\",\n \"0.67914915\",\n \"0.6790726\",\n \"0.6790546\",\n \"0.67829436\",\n \"0.67711\",\n \"0.6761225\",\n \"0.6757608\",\n \"0.6741913\",\n \"0.67387563\",\n \"0.6736886\",\n \"0.6731584\",\n \"0.67153233\",\n \"0.6712486\",\n \"0.67117715\",\n \"0.67105407\",\n \"0.670984\",\n \"0.6702067\",\n \"0.67000717\",\n \"0.6699785\",\n \"0.6688574\",\n \"0.6684566\",\n \"0.6683434\",\n \"0.668225\",\n \"0.6673251\",\n \"0.66677135\",\n \"0.6667645\",\n \"0.6657135\",\n \"0.66540736\",\n \"0.66505575\",\n \"0.6649819\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":16887,"cells":{"query":{"kind":"string","value":"Return the initialized output formatter based upon the configuration."},"document":{"kind":"string","value":"def initialize_formatter(config):\n if config.json: # pylint: disable=R1705\n return formatters.JsonFormatter()\n elif config.severity: # pylint: disable=R1705\n return formatters.SeverityFormatter(config.colored)\n return formatters.Formatter(config.colored)"},"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 make_formatter(self):\n return SectionedFormatter(\n sections=self.sections,\n width=self.terminal_width,\n max_width=self.max_content_width,\n )","def set_formatter_string(config: dict):\n formatter_str = \"%(levelname)s %(name)s\"\n\n if config.get(\"formatter\"):\n return config[\"formatter\"]\n\n if config.get(\"extended\"):\n formatter_str += \".%(funcName)s():\"\n\n if config.get(\"timestamp\"):\n formatter_str = \"%(asctime)s \" + formatter_str\n\n formatter_str += \" %(message)s\"\n\n return formatter_str","def get_formatter(style):\n if style == 'authoryear':\n return AuthorYearFormatter\n return AuthorYearFormatter","def get_format(self):\n format = QtGui.QTextCharFormat()\n\n # Set foreground color\n if self.foreground_color is not None:\n color = self.color_map[self.foreground_color][self.intensity]\n format.setForeground(QtGui.QColor(color))\n\n # Set background color\n if self.background_color is not None:\n color = self.color_map[self.background_color][self.intensity]\n format.setBackground(QtGui.QColor(color))\n\n # Set font weight/style options\n if self.bold:\n format.setFontWeight(QtGui.QFont.Bold)\n else:\n format.setFontWeight(QtGui.QFont.Normal)\n format.setFontItalic(self.italic)\n format.setFontUnderline(self.underline)\n\n return format","def set_formatter(self, enable_time=False, enable_msg_count=True):\n self.formatter = logging.Formatter(\n '{}%(name)s - %(levelname)s - %(message)s'.format(\n \"%(asctime)s - \" if enable_time else \"\",\n ))\n for handler in self.handlers:\n handler.setFormatter(self.formatter)\n return self.formatter","def format( self ) :\n\n return( self.__format )","def assign_format(self):\n if self.is_output or self.is_req_output:\n if self.pname in self.tool_data[self.tool_name]['output_fmt']:\n return self.tool_data[self.tool_name]['output_fmt'][self.pname]\n elif self.pname in self.gen_out_fmt:\n return self.gen_out_fmt[self.pname]\n elif self.is_input:\n if self.pname in self.tool_data[self.tool_name]['input_fmt']:\n print(self.tool_data[self.tool_name])\n return self.tool_data[self.tool_name]['input_fmt'][self.pname]\n elif self.pname in self.gen_in_fmt:\n return self.gen_in_fmt[self.pname]\n else:\n # Not sure yet what this will be used for, but I think we need it.\n return ''","def _config_formatter(self):\n filter = int( self.markup_filter )\n if filter == self.MARKUP_MARKDOWN:\n return { 'filter_name':'markdown' }\n elif filter == self.MARKUP_MARKDOWN_CODEHILITE:\n return { 'filter_name' : 'markdown',\n 'extensions' : [ 'codehilite' ] }\n elif filter == self.MARKUP_REST:\n return { 'filter_name':'restructuredtext' }\n elif filter == self.MARKUP_TEXTILE:\n return { 'filter_name' : 'textile' }\n else:\n raise ValueError( 'Invalid option for Entry.markup_filter' )","def set_formatter(self, formatter):\n self.format = formatter","def get_format(cls):\n return cls._format","def set_parser(self, output_format):\n self.output_parser = output_parsers.get(output_format, lambda x:x)","def init_logging(log_format: str='default', level: str='INFO') -> Union[DefaultFormatter, DebugFormatter]:\n stream_handler = logging.StreamHandler()\n if log_format == 'default':\n formatter = DefaultFormatter\n elif log_format == 'human':\n formatter = DebugFormatter\n else:\n raise ValueError('Unrecognized Format: {}'.format(log_format))\n stream_handler.setFormatter(formatter())\n ROOT_LOGGER.addHandler(stream_handler)\n ROOT_LOGGER.setLevel(level)\n return formatter","def getLogFormat(self):\n\t\treturn AbsentSafeRawConfigParser.absentSafeGet(self, \n\t\t\tLogConfigParser.__LOG_CONFIG_SECTION, \n\t\t\tLogConfigParser.__LOG_FORMAT_KEY)","def format(self) -> pulumi.Output[Optional['outputs.FlowLogFormatParametersResponse']]:\n return pulumi.get(self, \"format\")","def get_formatter(name):\n\n # Is it already in the path?\n try:\n return import_module('pycclone.formatters.' + name)\n except ImportError:\n pass\n\n # Import from user folder\n fpath = os.path.join(USERDIR, 'formatters', name, name + '.py')\n return load_source('pycclone.formatters.' + name, fpath)","def get_format(self):\n pass","def output(self, formatter, **overrides):\n return self.where(output=formatter, **overrides)","def format_(self):\n return self.set_format or self.default_format or self.FALLBACK_FORMAT","def format(self):\n return self._format","def get_formatter(self, group):\n return getattr(self, \"format_\" + group + \"_standings\")","def default_formatter(self, data):\n return data","def parser_formatter(format_class, **kwargs):\n try:\n return lambda prog: format_class(prog, **kwargs)\n except TypeError:\n return format_class","def parser_formatter(format_class, **kwargs):\n try:\n return lambda prog: format_class(prog, **kwargs)\n except TypeError:\n return format_class","def get_formatter(self, **kwargs):\n config = dict([\n (attr, getattr(self, attr))\n for attr in [\n \"include_sign\",\n \"group_with_commas\",\n \"num_decimal_places\",\n ]\n ])\n config.update(kwargs)\n return \"\".join([\n \"{\",\n config.get(\"field_name\", \"\"),\n \":\",\n \"+\" if config[\"include_sign\"] else \"\",\n \",\" if config[\"group_with_commas\"] else \"\",\n \".\", str(config[\"num_decimal_places\"]), \"f\",\n \"}\",\n ])","def get_format_table(self):\n try:\n with open(self._config.values['format'], 'r') as f:\n return f.read()\n except:\n return None","def define_log_renderer(self):\n # it must accept a logger, method_name and event_dict (just like processors)\n # but must return the rendered string, not a dictionary.\n # TODO tty logic\n if self.args.log_format == \"json\":\n return structlog.processors.JSONRenderer()\n\n if self.args.log_format == \"pretty\":\n return structlog.dev.ConsoleRenderer()\n\n if self.args.log_file is not None:\n return structlog.processors.JSONRenderer()\n\n if sys.stderr.isatty() and not self.args.quiet:\n return structlog.dev.ConsoleRenderer()\n\n return structlog.processors.JSONRenderer()","def initFormat(self):\n ChoiceFormat.initFormat(self)\n fullFormat = ''.join(self.formatList)\n try:\n self.sep = [sep for sep in CombinationFormat.outputSepList\n if sep not in fullFormat][0] + ' '\n except IndexError:\n self.sep = CombinationFormat.outputSepList[0] + ' '","def active_figure_format_config(self):\n if self.ui.active_figure_format_config_widget is None:\n return None # No active override config\n else:\n # Otherwise we have a config widget:\n figure_format_config = self.ui.active_figure_format_config_widget.figure_format_config\n return figure_format_config","def getFormatManager(self) -> ghidra.app.util.viewer.format.FormatManager:\n ...","def uctt_plugin_factory_output_config(\n environment: Environment, instance_id: str = ''):\n return OutputCliPlugin(environment, instance_id)","def _CreateOutputMediator(self, config=None, storage_object=None):\n formatter_mediator = formatters_mediator.FormatterMediator()\n return mediator.OutputMediator(\n formatter_mediator, storage_object, config=config)","def formatter(formatter_name):\n\n def _formatter_decorator(f):\n def _formatter_wrapper(*wrapper_args, **wrapper_kwargs):\n ctx = wrapper_args[1]\n if not ctx.json and formatter_name in _formatter_functions:\n ctx.format_function = _formatter_functions[formatter_name]\n return f(*wrapper_args, **wrapper_kwargs)\n\n return _formatter_wrapper\n\n return _formatter_decorator","def add_formatter(config: Configurator, name: str, func: t.Callable) -> None:\n config.registry.settings.setdefault(\"pyramid_openapi3_formatters\", {})\n reg = config.registry.settings[\"pyramid_openapi3_formatters\"]\n reg[name] = func","def __format__(self, format_specification=''):\n return super().__format__(format_specification=format_specification)","def get(self):\n _config_file = None\n _parsed_config = configparser.ConfigParser()\n try:\n _config_file = open(self._config_path, \"r\")\n except OSError as e:\n logger.error(str(e))\n Utils.exiter(1)\n try:\n _parsed_config.read_file(_config_file)\n except configparser.ParsingError as e:\n logger.error(str(e))\n Utils.exiter(1)\n\n _defaults = _parsed_config.defaults()\n _t = {}\n for (_k, _v) in _defaults:\n _t[self._format_keys(_k)] = self._format_values(_v)\n self.config[self._format_keys(\"defaults\")] = _t\n\n for _s in _parsed_config.sections():\n _t = {}\n for (_k, _v) in _parsed_config.items(_s):\n _t[self._format_keys(_k)] = self._format_values(_v)\n self.config[self._format_keys(_s)] = _t\n logger.debug(f\"Got config: {json.dumps(self.config, indent=2)}\")\n return self.config","def __init__(self, fmt, datefmt=None):\n logging.Formatter.__init__(self, fmt, datefmt)","def __init__(self, proposition_formatter=None):\n self.proposition_formatter = proposition_formatter","def get_format(self):\n return self._format[0]","def setOutputFormat(self, value):\n return self._set(outputFormat=value)","def format(self):\n return self[\"format\"]","def format(self):\n return self[\"format\"]","def prettify(self):\n return self._config_dict","def show_configuration(\n ctx, user: bool, token: bool, organization: bool, output_format: str,\n use_default: bool, name: Optional[str]):\n config_set = __ensure_configuration_exists(ctx)\n\n if name or use_default:\n config = __ensure_name_in_config_set(ctx, name, config_set)\n else:\n config = config_set.active_config\n\n formatter = ConfigFormatter.build(config, format=output_format)\n out = formatter.format(user=user, token=token, organization=organization)\n\n click.echo(out)","def initFormat(self):\n pass","def conditional_formattings(self):\n return self.container['conditional_formattings']","def __init__(self, output_mediator):\n event_formatting_helper = shared_json.JSONEventFormattingHelper(\n output_mediator)\n super(JSONLineOutputModule, self).__init__(\n output_mediator, event_formatting_helper)","def get_conf_parser(self):\n return ConfigParser.ConfigParser()","def after_init(self):\n if self.options.format.appended:\n self.error_format = self.options.format.appended[0]","def format(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"format\")","def _createConfigParser(self):\n return ConfigParser.ConfigParser()","def format(self, formatter):\n WikiParser.format(self, formatter)\n # reset those states every time format is done\n self.break_next = False\n self.prev_list = False","def hadoop_output_format(self):\n if self.options.snappy:\n return 'org.apache.hadoop.mapred.SequenceFileOutputFormat'","def format(self) -> Optional[pulumi.Input['FlowLogFormatParametersArgs']]:\n return pulumi.get(self, \"format\")","def init_logger():\n LOG_LEVEL = logging.INFO\n LOGFORMAT = \"%(log_color)s%(levelname)-1s: %(log_color)s%(message)s\"\n logging.root.setLevel(LOG_LEVEL)\n formatter = ColoredFormatter(LOGFORMAT)\n stream = logging.StreamHandler()\n stream.setLevel(LOG_LEVEL)\n stream.setFormatter(formatter)\n log = logging.getLogger('pythonConfig')\n log.setLevel(LOG_LEVEL)\n log.addHandler(stream)\n return log","def format(self) -> str:\n return pulumi.get(self, \"format\")","def __init__(self,\n config_file=None,\n input_path=None,\n output_format=None,\n input_format=None,\n context='',\n ):\n\n absolute_path = path.join(context, config_file)\n import_dir_path = path.dirname(absolute_path)\n import_filename = path.basename(absolute_path)\n\n root_configs = {\n 'imports': [\n import_filename\n ],\n 'parameters': {\n 'input': {},\n 'output': {}\n },\n }\n\n if input_path is not None:\n root_configs['parameters']['input']['path'] = input_path\n\n if input_format is not None:\n root_configs['parameters']['input']['format'] = input_format\n\n if output_format is not None:\n root_configs['parameters']['output']['format'] = output_format\n\n self.configs = Config(import_dir_path, configs=root_configs)\n\n try:\n self.input_dir_path = path.join(context, self.params('input', 'path'))\n except Exception as e:\n Logger.debug(e)\n Logger.error('inp_path_missing')\n\n try:\n self.configs.get('parameters', 'output', 'path')\n except Exception as e:\n # setting the default output folder\n # this could be generalized and encapsulated as a setter on the config class\n self.configs.configs['parameters']['output']['path'] = 'output'\n self.configs.config_paths['parameters']['output']['path'] = context\n\n reader = self.params('input', 'format')\n if not is_str(reader) and issubclass(reader, Reader):\n self.module_loader.set_reader(reader)\n else:\n try:\n self.module_loader.load_reader(reader)\n Logger.info('input_format_detected', self.params('input', 'format'))\n except KeyError as e:\n Logger.debug(e)\n Logger.error('inp_format_missing')\n except Exception as e:\n Logger.debug(e)\n Logger.error('bad_inp_format', self.params('input', 'format'), str(e))\n\n writer = self.params('output', 'format')\n if not is_str(writer) and issubclass(writer, Writer):\n self.module_loader.set_writer(writer)\n else:\n try:\n self.module_loader.load_writer(writer)\n Logger.info('output_format_detected', self.params('output', 'format'))\n except KeyError as e:\n Logger.debug(e)\n Logger.error('out_format_missing')\n except Exception as e:\n Logger.debug(e)\n Logger.error('bad_out_format', self.params('output', 'format'), str(e))","def config_logger(name: str = '', level: int = logging.INFO,\n format_str: str = '%(levelname)s - [%(processName)s(%(process)d)] [%(asctime)s] - %(filename)s:%(lineno)d: %(message)s',\n handler: Type[Handler] = logging.StreamHandler, propagate: bool = True):\n _handler = handler()\n _handler.setFormatter(logging.Formatter(format_str))\n _logger = logging.getLogger(name)\n _logger.addHandler(_handler)\n _logger.setLevel(level)\n _logger.propagate = propagate\n return _logger","def output_config(self) -> 'outputs.PreventionJobTriggerInspectJobActionSaveFindingsOutputConfig':\n return pulumi.get(self, \"output_config\")","def get_configuration_template(self):\n return CONFIG_TEMPLATE","def get_logger(name):\n return StyleAdapter(logging.getLogger(name))","def with_default_format(self, other):\n return evolve(\n self,\n set_format=self.set_format or other,\n default_format=other\n )","def output_formats(self) -> List[DataFormat]:\n return [DataFormat.NGEN_OUTPUT]","def __init__(self, beaver_config, file_config, logger=None):\n self._current_host = beaver_config.get('hostname')\n self._file_config = file_config\n\n if beaver_config.get('format') == 'msgpack':\n import msgpack\n packer = msgpack.Packer()\n self._formatter = packer.pack\n elif beaver_config.get('format') == 'json':\n # priority: ujson > simplejson > jsonlib2 > json\n priority = ['ujson', 'simplejson', 'jsonlib2', 'json']\n for mod in priority:\n try:\n json = __import__(mod)\n self._formatter = json.dumps\n except ImportError:\n pass\n else:\n break\n elif beaver_config.get('format') == 'string':\n def string_formatter(self, data):\n return \"[{0}] [{1}] {2}\".format(data['@source_host'], data['@timestamp'], data['@message'])\n self._formatter = string_formatter\n else:\n def null_formatter(self, data):\n return data['@message']\n self._formatter = null_formatter","def format(self):\n return self.getparam(\"FORMAT\")","def format(self):\n return self.getparam(\"FORMAT\")","def s3_output_format_config(self) -> Optional[pulumi.Input['FlowDestinationFlowConfigDestinationConnectorPropertiesS3S3OutputFormatConfigArgs']]:\n return pulumi.get(self, \"s3_output_format_config\")","def get_parser(self):\n config_parser = ConfigParser.ConfigParser()\n config_parser.read(self.get_config)\n return config_parser","def formats():\n return _FORMATS","def config(self):\n return \"\\n\".join([ c.config(True) for p, c in self.configs_ ])","def format(self) -> str:\n return self._format","def format(self) -> str:\n return self._format","def _get_format(self, token):\n if token in self._formats:\n return self._formats[token]\n\n if self._style is None:\n result = self._get_format_from_document(token, self._document)\n else:\n result = self._get_format_from_style(token, self._style)\n\n self._formats[token] = result\n return result","def __format__(self, format_spec):\n # This calls the compiled regex stored on ANSIString's class to analyze the format spec.\n # It returns a dictionary.\n format_data = self.re_format.match(format_spec).groupdict()\n clean = self.clean()\n base_output = ANSIString(self.raw())\n align = format_data.get(\"align\", \"<\")\n fill = format_data.get(\"fill\", \" \")\n\n # Need to coerce width into an integer. We can be certain that it's numeric thanks to regex.\n width = format_data.get(\"width\", None)\n if width is None:\n width = len(clean)\n else:\n width = int(width)\n\n if align == \"<\":\n base_output = self.ljust(width, fill)\n elif align == \">\":\n base_output = self.rjust(width, fill)\n elif align == \"^\":\n base_output = self.center(width, fill)\n elif align == \"=\":\n pass\n\n # Return the raw string with ANSI markup, ready to be displayed.\n return base_output.raw()","def outputLogFormatter(log):\n if log.get(\"blockNumber\"):\n log[\"blockNumber\"] = to_decimal(log[\"blockNumber\"])\n if log.get(\"transactionIndex\"):\n log[\"transactionIndex\"] = to_decimal(log[\"transactionIndex\"])\n if log.get(\"logIndex\"):\n log[\"logIndex\"] = to_decimal(log[\"logIndex\"])\n\n return log","def getFormats(self):\n return self.formats","def get_config(self):\n config = self._kwargs.copy()\n config.update({\n 'metric': self.__class__.__name__,\n 'name': self.name,\n 'output_names': self.output_names,\n 'label_names': self.label_names})\n return config","def _formatter(self, result):\n seclabels = []\n if 'seclabels' in result and result['seclabels'] is not None:\n for sec in result['seclabels']:\n sec = re.search(r'([^=]+)=(.*$)', sec)\n seclabels.append({\n 'provider': sec.group(1),\n 'label': sec.group(2)\n })\n\n result['seclabels'] = seclabels\n return result","def setOutputFormat(self,format,outputWidth,outputHeight):\n self.PDFreactorConfiguration.in1[\"outputFormat\"] = format\n self.PDFreactorConfiguration.in1[\"outputWidth\"] = outputWidth\n self.PDFreactorConfiguration.in1[\"outputHeight\"] = outputHeight","def logging_template():\n template = (\n '[loggers]\\n'\n 'keys=root\\n'\n '\\n'\n '[handlers]\\n'\n 'keys=consoleHandler\\n'\n '\\n'\n '[formatters]\\n'\n 'keys=simpleFormatter\\n'\n '\\n'\n '[logger_root]\\n'\n 'level=DEBUG\\n'\n 'handlers=consoleHandler\\n'\n '\\n'\n '[handler_consoleHandler]\\n'\n 'class=StreamHandler\\n'\n 'level=DEBUG\\n'\n 'formatter=simpleFormatter\\n'\n 'args=(sys.stdout,)\\n'\n '\\n'\n '[formatter_simpleFormatter]\\n'\n 'format=%(asctime)s - %(name)s - %(levelname)s - %(message)s\\n'\n 'datefmt=\\n')\n return template","def __format__(self, format_spec):\n if format_spec == \"polite\":\n return self.polite\n elif format_spec == \"casual\":\n return self.casual\n else:\n # Using string addition here to avoid triggering flake8-sfs\n # while still giving a meaningful self-contained example:\n raise ValueError(format_spec + \" not a format defined by Client object\")","def config(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"config\")","def test_default_outfmt(self):\n args = argparse.Namespace(cfg=os.path.join(TEST_DATA_DIR, 'minimal-cfg-file.ini'))\n self.cfg = configure(args)\n cfg = ElasticBlastConfig(self.cfg, task = ElbCommand.SUBMIT)\n self.assertEqual(cfg.blast.options.strip(), f'-outfmt {ELB_DFLT_OUTFMT}')","def s3_output_format_config(self) -> pulumi.Input['FlowDestinationFlowConfigDestinationConnectorPropertiesUpsolverS3OutputFormatConfigArgs']:\n return pulumi.get(self, \"s3_output_format_config\")","def logging_config(self) -> Optional['outputs.FeatureSpecFleetobservabilityLoggingConfig']:\n return pulumi.get(self, \"logging_config\")","def default_logger_creator(config):\n cfg = config[\"logger_config\"].copy()\n cls = cfg.pop(\"type\")\n # Provide default for logdir, in case the user does\n # not specify this in the \"logger_config\" dict.\n logdir_ = cfg.pop(\"logdir\", logdir)\n return from_config(cls=cls, _args=[cfg], logdir=logdir_)","def _logger(self):\n logger = logging.getLogger(self.NAME)\n logger.setLevel(self.LOG_LEVEL)\n shandler = logging.StreamHandler(sys.stdout)\n fmt = '\\033[1;32m%(levelname)-5s %(module)s:%(funcName)s():'\n fmt += '%(lineno)d %(asctime)s\\033[0m| %(message)s'\n shandler.setFormatter(logging.Formatter(fmt))\n logger.addHandler(shandler)\n return logger","def page_formatter(self, page):\n return self._page_formatter(page)","def initFormat(self):\n self.formatList = []","def __str__(self):\n config_str = 'Configurations\\n'\n config_str += pprint.pformat(self.__dict__)\n return config_str","def get_config_template(self) -> cconfig.Config:","def format(self):\n ...","def asformat(self, format):","def basicConfig(**kwargs):\n # Add thread safety in case someone mistakenly calls\n # basicConfig() from multiple threads\n logging._acquireLock()\n try:\n if len(logging.root.handlers) == 0:\n handlers = kwargs.pop(\"handlers\", None)\n if handlers is None:\n if \"stream\" in kwargs and \"filename\" in kwargs:\n raise ValueError(\"'stream' and 'filename' should not be \"\n \"specified together\")\n else:\n if \"stream\" in kwargs or \"filename\" in kwargs:\n raise ValueError(\"'stream' or 'filename' should not be \"\n \"specified together with 'handlers'\")\n if handlers is None:\n filename = kwargs.pop(\"filename\", None)\n mode = kwargs.pop(\"filemode\", 'a')\n if filename:\n h = logging.FileHandler(filename, mode)\n else:\n stream = kwargs.pop(\"stream\", None)\n h = logging.StreamHandler(stream)\n handlers = [h]\n dfs = kwargs.pop(\"datefmt\", None)\n style = kwargs.pop(\"style\", '%')\n if style not in _STYLES:\n raise ValueError('Style must be one of: %s' % ','.join(\n _STYLES.keys()))\n fs = kwargs.pop(\"format\", BASIC_FORMAT)\n fmt = JsonFormatter(fs, dfs, style)\n for h in handlers:\n if h.formatter is None:\n h.setFormatter(fmt)\n logging.root.addHandler(h)\n level = kwargs.pop(\"level\", None)\n if level is not None:\n logging.root.setLevel(level)\n if kwargs:\n keys = ', '.join(kwargs.keys())\n raise ValueError('Unrecognised argument(s): %s' % keys)\n finally:\n logging._releaseLock()","def getFormat(self, settings: ghidra.docking.settings.Settings) -> int:\n ...","def output(self) -> DataFileFormatOutput:\n return self._output","def __call__(self, input_type, output_format):\n self.configure()\n return self._object_graph.provide(\\\n self._plugins[input_type][output_format])","def get(cls):\n\t\tif cls.__ConfigParser is None:\n\t\t\traise RuntimeError('Enter the Config context first.')\n\t\treturn cls.__ConfigParser","def initFormat(self):\n self.html = True","def initFormat(self):\n self.html = True","def logging_config(self) -> 'outputs.LoggingConfigResponse':\n return pulumi.get(self, \"logging_config\")"],"string":"[\n \"def make_formatter(self):\\n return SectionedFormatter(\\n sections=self.sections,\\n width=self.terminal_width,\\n max_width=self.max_content_width,\\n )\",\n \"def set_formatter_string(config: dict):\\n formatter_str = \\\"%(levelname)s %(name)s\\\"\\n\\n if config.get(\\\"formatter\\\"):\\n return config[\\\"formatter\\\"]\\n\\n if config.get(\\\"extended\\\"):\\n formatter_str += \\\".%(funcName)s():\\\"\\n\\n if config.get(\\\"timestamp\\\"):\\n formatter_str = \\\"%(asctime)s \\\" + formatter_str\\n\\n formatter_str += \\\" %(message)s\\\"\\n\\n return formatter_str\",\n \"def get_formatter(style):\\n if style == 'authoryear':\\n return AuthorYearFormatter\\n return AuthorYearFormatter\",\n \"def get_format(self):\\n format = QtGui.QTextCharFormat()\\n\\n # Set foreground color\\n if self.foreground_color is not None:\\n color = self.color_map[self.foreground_color][self.intensity]\\n format.setForeground(QtGui.QColor(color))\\n\\n # Set background color\\n if self.background_color is not None:\\n color = self.color_map[self.background_color][self.intensity]\\n format.setBackground(QtGui.QColor(color))\\n\\n # Set font weight/style options\\n if self.bold:\\n format.setFontWeight(QtGui.QFont.Bold)\\n else:\\n format.setFontWeight(QtGui.QFont.Normal)\\n format.setFontItalic(self.italic)\\n format.setFontUnderline(self.underline)\\n\\n return format\",\n \"def set_formatter(self, enable_time=False, enable_msg_count=True):\\n self.formatter = logging.Formatter(\\n '{}%(name)s - %(levelname)s - %(message)s'.format(\\n \\\"%(asctime)s - \\\" if enable_time else \\\"\\\",\\n ))\\n for handler in self.handlers:\\n handler.setFormatter(self.formatter)\\n return self.formatter\",\n \"def format( self ) :\\n\\n return( self.__format )\",\n \"def assign_format(self):\\n if self.is_output or self.is_req_output:\\n if self.pname in self.tool_data[self.tool_name]['output_fmt']:\\n return self.tool_data[self.tool_name]['output_fmt'][self.pname]\\n elif self.pname in self.gen_out_fmt:\\n return self.gen_out_fmt[self.pname]\\n elif self.is_input:\\n if self.pname in self.tool_data[self.tool_name]['input_fmt']:\\n print(self.tool_data[self.tool_name])\\n return self.tool_data[self.tool_name]['input_fmt'][self.pname]\\n elif self.pname in self.gen_in_fmt:\\n return self.gen_in_fmt[self.pname]\\n else:\\n # Not sure yet what this will be used for, but I think we need it.\\n return ''\",\n \"def _config_formatter(self):\\n filter = int( self.markup_filter )\\n if filter == self.MARKUP_MARKDOWN:\\n return { 'filter_name':'markdown' }\\n elif filter == self.MARKUP_MARKDOWN_CODEHILITE:\\n return { 'filter_name' : 'markdown',\\n 'extensions' : [ 'codehilite' ] }\\n elif filter == self.MARKUP_REST:\\n return { 'filter_name':'restructuredtext' }\\n elif filter == self.MARKUP_TEXTILE:\\n return { 'filter_name' : 'textile' }\\n else:\\n raise ValueError( 'Invalid option for Entry.markup_filter' )\",\n \"def set_formatter(self, formatter):\\n self.format = formatter\",\n \"def get_format(cls):\\n return cls._format\",\n \"def set_parser(self, output_format):\\n self.output_parser = output_parsers.get(output_format, lambda x:x)\",\n \"def init_logging(log_format: str='default', level: str='INFO') -> Union[DefaultFormatter, DebugFormatter]:\\n stream_handler = logging.StreamHandler()\\n if log_format == 'default':\\n formatter = DefaultFormatter\\n elif log_format == 'human':\\n formatter = DebugFormatter\\n else:\\n raise ValueError('Unrecognized Format: {}'.format(log_format))\\n stream_handler.setFormatter(formatter())\\n ROOT_LOGGER.addHandler(stream_handler)\\n ROOT_LOGGER.setLevel(level)\\n return formatter\",\n \"def getLogFormat(self):\\n\\t\\treturn AbsentSafeRawConfigParser.absentSafeGet(self, \\n\\t\\t\\tLogConfigParser.__LOG_CONFIG_SECTION, \\n\\t\\t\\tLogConfigParser.__LOG_FORMAT_KEY)\",\n \"def format(self) -> pulumi.Output[Optional['outputs.FlowLogFormatParametersResponse']]:\\n return pulumi.get(self, \\\"format\\\")\",\n \"def get_formatter(name):\\n\\n # Is it already in the path?\\n try:\\n return import_module('pycclone.formatters.' + name)\\n except ImportError:\\n pass\\n\\n # Import from user folder\\n fpath = os.path.join(USERDIR, 'formatters', name, name + '.py')\\n return load_source('pycclone.formatters.' + name, fpath)\",\n \"def get_format(self):\\n pass\",\n \"def output(self, formatter, **overrides):\\n return self.where(output=formatter, **overrides)\",\n \"def format_(self):\\n return self.set_format or self.default_format or self.FALLBACK_FORMAT\",\n \"def format(self):\\n return self._format\",\n \"def get_formatter(self, group):\\n return getattr(self, \\\"format_\\\" + group + \\\"_standings\\\")\",\n \"def default_formatter(self, data):\\n return data\",\n \"def parser_formatter(format_class, **kwargs):\\n try:\\n return lambda prog: format_class(prog, **kwargs)\\n except TypeError:\\n return format_class\",\n \"def parser_formatter(format_class, **kwargs):\\n try:\\n return lambda prog: format_class(prog, **kwargs)\\n except TypeError:\\n return format_class\",\n \"def get_formatter(self, **kwargs):\\n config = dict([\\n (attr, getattr(self, attr))\\n for attr in [\\n \\\"include_sign\\\",\\n \\\"group_with_commas\\\",\\n \\\"num_decimal_places\\\",\\n ]\\n ])\\n config.update(kwargs)\\n return \\\"\\\".join([\\n \\\"{\\\",\\n config.get(\\\"field_name\\\", \\\"\\\"),\\n \\\":\\\",\\n \\\"+\\\" if config[\\\"include_sign\\\"] else \\\"\\\",\\n \\\",\\\" if config[\\\"group_with_commas\\\"] else \\\"\\\",\\n \\\".\\\", str(config[\\\"num_decimal_places\\\"]), \\\"f\\\",\\n \\\"}\\\",\\n ])\",\n \"def get_format_table(self):\\n try:\\n with open(self._config.values['format'], 'r') as f:\\n return f.read()\\n except:\\n return None\",\n \"def define_log_renderer(self):\\n # it must accept a logger, method_name and event_dict (just like processors)\\n # but must return the rendered string, not a dictionary.\\n # TODO tty logic\\n if self.args.log_format == \\\"json\\\":\\n return structlog.processors.JSONRenderer()\\n\\n if self.args.log_format == \\\"pretty\\\":\\n return structlog.dev.ConsoleRenderer()\\n\\n if self.args.log_file is not None:\\n return structlog.processors.JSONRenderer()\\n\\n if sys.stderr.isatty() and not self.args.quiet:\\n return structlog.dev.ConsoleRenderer()\\n\\n return structlog.processors.JSONRenderer()\",\n \"def initFormat(self):\\n ChoiceFormat.initFormat(self)\\n fullFormat = ''.join(self.formatList)\\n try:\\n self.sep = [sep for sep in CombinationFormat.outputSepList\\n if sep not in fullFormat][0] + ' '\\n except IndexError:\\n self.sep = CombinationFormat.outputSepList[0] + ' '\",\n \"def active_figure_format_config(self):\\n if self.ui.active_figure_format_config_widget is None:\\n return None # No active override config\\n else:\\n # Otherwise we have a config widget:\\n figure_format_config = self.ui.active_figure_format_config_widget.figure_format_config\\n return figure_format_config\",\n \"def getFormatManager(self) -> ghidra.app.util.viewer.format.FormatManager:\\n ...\",\n \"def uctt_plugin_factory_output_config(\\n environment: Environment, instance_id: str = ''):\\n return OutputCliPlugin(environment, instance_id)\",\n \"def _CreateOutputMediator(self, config=None, storage_object=None):\\n formatter_mediator = formatters_mediator.FormatterMediator()\\n return mediator.OutputMediator(\\n formatter_mediator, storage_object, config=config)\",\n \"def formatter(formatter_name):\\n\\n def _formatter_decorator(f):\\n def _formatter_wrapper(*wrapper_args, **wrapper_kwargs):\\n ctx = wrapper_args[1]\\n if not ctx.json and formatter_name in _formatter_functions:\\n ctx.format_function = _formatter_functions[formatter_name]\\n return f(*wrapper_args, **wrapper_kwargs)\\n\\n return _formatter_wrapper\\n\\n return _formatter_decorator\",\n \"def add_formatter(config: Configurator, name: str, func: t.Callable) -> None:\\n config.registry.settings.setdefault(\\\"pyramid_openapi3_formatters\\\", {})\\n reg = config.registry.settings[\\\"pyramid_openapi3_formatters\\\"]\\n reg[name] = func\",\n \"def __format__(self, format_specification=''):\\n return super().__format__(format_specification=format_specification)\",\n \"def get(self):\\n _config_file = None\\n _parsed_config = configparser.ConfigParser()\\n try:\\n _config_file = open(self._config_path, \\\"r\\\")\\n except OSError as e:\\n logger.error(str(e))\\n Utils.exiter(1)\\n try:\\n _parsed_config.read_file(_config_file)\\n except configparser.ParsingError as e:\\n logger.error(str(e))\\n Utils.exiter(1)\\n\\n _defaults = _parsed_config.defaults()\\n _t = {}\\n for (_k, _v) in _defaults:\\n _t[self._format_keys(_k)] = self._format_values(_v)\\n self.config[self._format_keys(\\\"defaults\\\")] = _t\\n\\n for _s in _parsed_config.sections():\\n _t = {}\\n for (_k, _v) in _parsed_config.items(_s):\\n _t[self._format_keys(_k)] = self._format_values(_v)\\n self.config[self._format_keys(_s)] = _t\\n logger.debug(f\\\"Got config: {json.dumps(self.config, indent=2)}\\\")\\n return self.config\",\n \"def __init__(self, fmt, datefmt=None):\\n logging.Formatter.__init__(self, fmt, datefmt)\",\n \"def __init__(self, proposition_formatter=None):\\n self.proposition_formatter = proposition_formatter\",\n \"def get_format(self):\\n return self._format[0]\",\n \"def setOutputFormat(self, value):\\n return self._set(outputFormat=value)\",\n \"def format(self):\\n return self[\\\"format\\\"]\",\n \"def format(self):\\n return self[\\\"format\\\"]\",\n \"def prettify(self):\\n return self._config_dict\",\n \"def show_configuration(\\n ctx, user: bool, token: bool, organization: bool, output_format: str,\\n use_default: bool, name: Optional[str]):\\n config_set = __ensure_configuration_exists(ctx)\\n\\n if name or use_default:\\n config = __ensure_name_in_config_set(ctx, name, config_set)\\n else:\\n config = config_set.active_config\\n\\n formatter = ConfigFormatter.build(config, format=output_format)\\n out = formatter.format(user=user, token=token, organization=organization)\\n\\n click.echo(out)\",\n \"def initFormat(self):\\n pass\",\n \"def conditional_formattings(self):\\n return self.container['conditional_formattings']\",\n \"def __init__(self, output_mediator):\\n event_formatting_helper = shared_json.JSONEventFormattingHelper(\\n output_mediator)\\n super(JSONLineOutputModule, self).__init__(\\n output_mediator, event_formatting_helper)\",\n \"def get_conf_parser(self):\\n return ConfigParser.ConfigParser()\",\n \"def after_init(self):\\n if self.options.format.appended:\\n self.error_format = self.options.format.appended[0]\",\n \"def format(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"format\\\")\",\n \"def _createConfigParser(self):\\n return ConfigParser.ConfigParser()\",\n \"def format(self, formatter):\\n WikiParser.format(self, formatter)\\n # reset those states every time format is done\\n self.break_next = False\\n self.prev_list = False\",\n \"def hadoop_output_format(self):\\n if self.options.snappy:\\n return 'org.apache.hadoop.mapred.SequenceFileOutputFormat'\",\n \"def format(self) -> Optional[pulumi.Input['FlowLogFormatParametersArgs']]:\\n return pulumi.get(self, \\\"format\\\")\",\n \"def init_logger():\\n LOG_LEVEL = logging.INFO\\n LOGFORMAT = \\\"%(log_color)s%(levelname)-1s: %(log_color)s%(message)s\\\"\\n logging.root.setLevel(LOG_LEVEL)\\n formatter = ColoredFormatter(LOGFORMAT)\\n stream = logging.StreamHandler()\\n stream.setLevel(LOG_LEVEL)\\n stream.setFormatter(formatter)\\n log = logging.getLogger('pythonConfig')\\n log.setLevel(LOG_LEVEL)\\n log.addHandler(stream)\\n return log\",\n \"def format(self) -> str:\\n return pulumi.get(self, \\\"format\\\")\",\n \"def __init__(self,\\n config_file=None,\\n input_path=None,\\n output_format=None,\\n input_format=None,\\n context='',\\n ):\\n\\n absolute_path = path.join(context, config_file)\\n import_dir_path = path.dirname(absolute_path)\\n import_filename = path.basename(absolute_path)\\n\\n root_configs = {\\n 'imports': [\\n import_filename\\n ],\\n 'parameters': {\\n 'input': {},\\n 'output': {}\\n },\\n }\\n\\n if input_path is not None:\\n root_configs['parameters']['input']['path'] = input_path\\n\\n if input_format is not None:\\n root_configs['parameters']['input']['format'] = input_format\\n\\n if output_format is not None:\\n root_configs['parameters']['output']['format'] = output_format\\n\\n self.configs = Config(import_dir_path, configs=root_configs)\\n\\n try:\\n self.input_dir_path = path.join(context, self.params('input', 'path'))\\n except Exception as e:\\n Logger.debug(e)\\n Logger.error('inp_path_missing')\\n\\n try:\\n self.configs.get('parameters', 'output', 'path')\\n except Exception as e:\\n # setting the default output folder\\n # this could be generalized and encapsulated as a setter on the config class\\n self.configs.configs['parameters']['output']['path'] = 'output'\\n self.configs.config_paths['parameters']['output']['path'] = context\\n\\n reader = self.params('input', 'format')\\n if not is_str(reader) and issubclass(reader, Reader):\\n self.module_loader.set_reader(reader)\\n else:\\n try:\\n self.module_loader.load_reader(reader)\\n Logger.info('input_format_detected', self.params('input', 'format'))\\n except KeyError as e:\\n Logger.debug(e)\\n Logger.error('inp_format_missing')\\n except Exception as e:\\n Logger.debug(e)\\n Logger.error('bad_inp_format', self.params('input', 'format'), str(e))\\n\\n writer = self.params('output', 'format')\\n if not is_str(writer) and issubclass(writer, Writer):\\n self.module_loader.set_writer(writer)\\n else:\\n try:\\n self.module_loader.load_writer(writer)\\n Logger.info('output_format_detected', self.params('output', 'format'))\\n except KeyError as e:\\n Logger.debug(e)\\n Logger.error('out_format_missing')\\n except Exception as e:\\n Logger.debug(e)\\n Logger.error('bad_out_format', self.params('output', 'format'), str(e))\",\n \"def config_logger(name: str = '', level: int = logging.INFO,\\n format_str: str = '%(levelname)s - [%(processName)s(%(process)d)] [%(asctime)s] - %(filename)s:%(lineno)d: %(message)s',\\n handler: Type[Handler] = logging.StreamHandler, propagate: bool = True):\\n _handler = handler()\\n _handler.setFormatter(logging.Formatter(format_str))\\n _logger = logging.getLogger(name)\\n _logger.addHandler(_handler)\\n _logger.setLevel(level)\\n _logger.propagate = propagate\\n return _logger\",\n \"def output_config(self) -> 'outputs.PreventionJobTriggerInspectJobActionSaveFindingsOutputConfig':\\n return pulumi.get(self, \\\"output_config\\\")\",\n \"def get_configuration_template(self):\\n return CONFIG_TEMPLATE\",\n \"def get_logger(name):\\n return StyleAdapter(logging.getLogger(name))\",\n \"def with_default_format(self, other):\\n return evolve(\\n self,\\n set_format=self.set_format or other,\\n default_format=other\\n )\",\n \"def output_formats(self) -> List[DataFormat]:\\n return [DataFormat.NGEN_OUTPUT]\",\n \"def __init__(self, beaver_config, file_config, logger=None):\\n self._current_host = beaver_config.get('hostname')\\n self._file_config = file_config\\n\\n if beaver_config.get('format') == 'msgpack':\\n import msgpack\\n packer = msgpack.Packer()\\n self._formatter = packer.pack\\n elif beaver_config.get('format') == 'json':\\n # priority: ujson > simplejson > jsonlib2 > json\\n priority = ['ujson', 'simplejson', 'jsonlib2', 'json']\\n for mod in priority:\\n try:\\n json = __import__(mod)\\n self._formatter = json.dumps\\n except ImportError:\\n pass\\n else:\\n break\\n elif beaver_config.get('format') == 'string':\\n def string_formatter(self, data):\\n return \\\"[{0}] [{1}] {2}\\\".format(data['@source_host'], data['@timestamp'], data['@message'])\\n self._formatter = string_formatter\\n else:\\n def null_formatter(self, data):\\n return data['@message']\\n self._formatter = null_formatter\",\n \"def format(self):\\n return self.getparam(\\\"FORMAT\\\")\",\n \"def format(self):\\n return self.getparam(\\\"FORMAT\\\")\",\n \"def s3_output_format_config(self) -> Optional[pulumi.Input['FlowDestinationFlowConfigDestinationConnectorPropertiesS3S3OutputFormatConfigArgs']]:\\n return pulumi.get(self, \\\"s3_output_format_config\\\")\",\n \"def get_parser(self):\\n config_parser = ConfigParser.ConfigParser()\\n config_parser.read(self.get_config)\\n return config_parser\",\n \"def formats():\\n return _FORMATS\",\n \"def config(self):\\n return \\\"\\\\n\\\".join([ c.config(True) for p, c in self.configs_ ])\",\n \"def format(self) -> str:\\n return self._format\",\n \"def format(self) -> str:\\n return self._format\",\n \"def _get_format(self, token):\\n if token in self._formats:\\n return self._formats[token]\\n\\n if self._style is None:\\n result = self._get_format_from_document(token, self._document)\\n else:\\n result = self._get_format_from_style(token, self._style)\\n\\n self._formats[token] = result\\n return result\",\n \"def __format__(self, format_spec):\\n # This calls the compiled regex stored on ANSIString's class to analyze the format spec.\\n # It returns a dictionary.\\n format_data = self.re_format.match(format_spec).groupdict()\\n clean = self.clean()\\n base_output = ANSIString(self.raw())\\n align = format_data.get(\\\"align\\\", \\\"<\\\")\\n fill = format_data.get(\\\"fill\\\", \\\" \\\")\\n\\n # Need to coerce width into an integer. We can be certain that it's numeric thanks to regex.\\n width = format_data.get(\\\"width\\\", None)\\n if width is None:\\n width = len(clean)\\n else:\\n width = int(width)\\n\\n if align == \\\"<\\\":\\n base_output = self.ljust(width, fill)\\n elif align == \\\">\\\":\\n base_output = self.rjust(width, fill)\\n elif align == \\\"^\\\":\\n base_output = self.center(width, fill)\\n elif align == \\\"=\\\":\\n pass\\n\\n # Return the raw string with ANSI markup, ready to be displayed.\\n return base_output.raw()\",\n \"def outputLogFormatter(log):\\n if log.get(\\\"blockNumber\\\"):\\n log[\\\"blockNumber\\\"] = to_decimal(log[\\\"blockNumber\\\"])\\n if log.get(\\\"transactionIndex\\\"):\\n log[\\\"transactionIndex\\\"] = to_decimal(log[\\\"transactionIndex\\\"])\\n if log.get(\\\"logIndex\\\"):\\n log[\\\"logIndex\\\"] = to_decimal(log[\\\"logIndex\\\"])\\n\\n return log\",\n \"def getFormats(self):\\n return self.formats\",\n \"def get_config(self):\\n config = self._kwargs.copy()\\n config.update({\\n 'metric': self.__class__.__name__,\\n 'name': self.name,\\n 'output_names': self.output_names,\\n 'label_names': self.label_names})\\n return config\",\n \"def _formatter(self, result):\\n seclabels = []\\n if 'seclabels' in result and result['seclabels'] is not None:\\n for sec in result['seclabels']:\\n sec = re.search(r'([^=]+)=(.*$)', sec)\\n seclabels.append({\\n 'provider': sec.group(1),\\n 'label': sec.group(2)\\n })\\n\\n result['seclabels'] = seclabels\\n return result\",\n \"def setOutputFormat(self,format,outputWidth,outputHeight):\\n self.PDFreactorConfiguration.in1[\\\"outputFormat\\\"] = format\\n self.PDFreactorConfiguration.in1[\\\"outputWidth\\\"] = outputWidth\\n self.PDFreactorConfiguration.in1[\\\"outputHeight\\\"] = outputHeight\",\n \"def logging_template():\\n template = (\\n '[loggers]\\\\n'\\n 'keys=root\\\\n'\\n '\\\\n'\\n '[handlers]\\\\n'\\n 'keys=consoleHandler\\\\n'\\n '\\\\n'\\n '[formatters]\\\\n'\\n 'keys=simpleFormatter\\\\n'\\n '\\\\n'\\n '[logger_root]\\\\n'\\n 'level=DEBUG\\\\n'\\n 'handlers=consoleHandler\\\\n'\\n '\\\\n'\\n '[handler_consoleHandler]\\\\n'\\n 'class=StreamHandler\\\\n'\\n 'level=DEBUG\\\\n'\\n 'formatter=simpleFormatter\\\\n'\\n 'args=(sys.stdout,)\\\\n'\\n '\\\\n'\\n '[formatter_simpleFormatter]\\\\n'\\n 'format=%(asctime)s - %(name)s - %(levelname)s - %(message)s\\\\n'\\n 'datefmt=\\\\n')\\n return template\",\n \"def __format__(self, format_spec):\\n if format_spec == \\\"polite\\\":\\n return self.polite\\n elif format_spec == \\\"casual\\\":\\n return self.casual\\n else:\\n # Using string addition here to avoid triggering flake8-sfs\\n # while still giving a meaningful self-contained example:\\n raise ValueError(format_spec + \\\" not a format defined by Client object\\\")\",\n \"def config(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"config\\\")\",\n \"def test_default_outfmt(self):\\n args = argparse.Namespace(cfg=os.path.join(TEST_DATA_DIR, 'minimal-cfg-file.ini'))\\n self.cfg = configure(args)\\n cfg = ElasticBlastConfig(self.cfg, task = ElbCommand.SUBMIT)\\n self.assertEqual(cfg.blast.options.strip(), f'-outfmt {ELB_DFLT_OUTFMT}')\",\n \"def s3_output_format_config(self) -> pulumi.Input['FlowDestinationFlowConfigDestinationConnectorPropertiesUpsolverS3OutputFormatConfigArgs']:\\n return pulumi.get(self, \\\"s3_output_format_config\\\")\",\n \"def logging_config(self) -> Optional['outputs.FeatureSpecFleetobservabilityLoggingConfig']:\\n return pulumi.get(self, \\\"logging_config\\\")\",\n \"def default_logger_creator(config):\\n cfg = config[\\\"logger_config\\\"].copy()\\n cls = cfg.pop(\\\"type\\\")\\n # Provide default for logdir, in case the user does\\n # not specify this in the \\\"logger_config\\\" dict.\\n logdir_ = cfg.pop(\\\"logdir\\\", logdir)\\n return from_config(cls=cls, _args=[cfg], logdir=logdir_)\",\n \"def _logger(self):\\n logger = logging.getLogger(self.NAME)\\n logger.setLevel(self.LOG_LEVEL)\\n shandler = logging.StreamHandler(sys.stdout)\\n fmt = '\\\\033[1;32m%(levelname)-5s %(module)s:%(funcName)s():'\\n fmt += '%(lineno)d %(asctime)s\\\\033[0m| %(message)s'\\n shandler.setFormatter(logging.Formatter(fmt))\\n logger.addHandler(shandler)\\n return logger\",\n \"def page_formatter(self, page):\\n return self._page_formatter(page)\",\n \"def initFormat(self):\\n self.formatList = []\",\n \"def __str__(self):\\n config_str = 'Configurations\\\\n'\\n config_str += pprint.pformat(self.__dict__)\\n return config_str\",\n \"def get_config_template(self) -> cconfig.Config:\",\n \"def format(self):\\n ...\",\n \"def asformat(self, format):\",\n \"def basicConfig(**kwargs):\\n # Add thread safety in case someone mistakenly calls\\n # basicConfig() from multiple threads\\n logging._acquireLock()\\n try:\\n if len(logging.root.handlers) == 0:\\n handlers = kwargs.pop(\\\"handlers\\\", None)\\n if handlers is None:\\n if \\\"stream\\\" in kwargs and \\\"filename\\\" in kwargs:\\n raise ValueError(\\\"'stream' and 'filename' should not be \\\"\\n \\\"specified together\\\")\\n else:\\n if \\\"stream\\\" in kwargs or \\\"filename\\\" in kwargs:\\n raise ValueError(\\\"'stream' or 'filename' should not be \\\"\\n \\\"specified together with 'handlers'\\\")\\n if handlers is None:\\n filename = kwargs.pop(\\\"filename\\\", None)\\n mode = kwargs.pop(\\\"filemode\\\", 'a')\\n if filename:\\n h = logging.FileHandler(filename, mode)\\n else:\\n stream = kwargs.pop(\\\"stream\\\", None)\\n h = logging.StreamHandler(stream)\\n handlers = [h]\\n dfs = kwargs.pop(\\\"datefmt\\\", None)\\n style = kwargs.pop(\\\"style\\\", '%')\\n if style not in _STYLES:\\n raise ValueError('Style must be one of: %s' % ','.join(\\n _STYLES.keys()))\\n fs = kwargs.pop(\\\"format\\\", BASIC_FORMAT)\\n fmt = JsonFormatter(fs, dfs, style)\\n for h in handlers:\\n if h.formatter is None:\\n h.setFormatter(fmt)\\n logging.root.addHandler(h)\\n level = kwargs.pop(\\\"level\\\", None)\\n if level is not None:\\n logging.root.setLevel(level)\\n if kwargs:\\n keys = ', '.join(kwargs.keys())\\n raise ValueError('Unrecognised argument(s): %s' % keys)\\n finally:\\n logging._releaseLock()\",\n \"def getFormat(self, settings: ghidra.docking.settings.Settings) -> int:\\n ...\",\n \"def output(self) -> DataFileFormatOutput:\\n return self._output\",\n \"def __call__(self, input_type, output_format):\\n self.configure()\\n return self._object_graph.provide(\\\\\\n self._plugins[input_type][output_format])\",\n \"def get(cls):\\n\\t\\tif cls.__ConfigParser is None:\\n\\t\\t\\traise RuntimeError('Enter the Config context first.')\\n\\t\\treturn cls.__ConfigParser\",\n \"def initFormat(self):\\n self.html = True\",\n \"def initFormat(self):\\n self.html = True\",\n \"def logging_config(self) -> 'outputs.LoggingConfigResponse':\\n return pulumi.get(self, \\\"logging_config\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6507591","0.59741753","0.5937114","0.5926568","0.5859191","0.58575606","0.5845092","0.5838448","0.5783071","0.57651764","0.57422423","0.5739221","0.5724411","0.56877214","0.56793237","0.56695","0.5667886","0.566292","0.56339353","0.56309354","0.56209934","0.55973196","0.55973196","0.55743825","0.55599713","0.5544095","0.5518256","0.5431601","0.54015267","0.53977734","0.53657454","0.53453386","0.53085667","0.5266493","0.52212644","0.52092683","0.52003163","0.51996905","0.5191781","0.518004","0.518004","0.5175296","0.5171896","0.51399","0.5137848","0.5136781","0.5105564","0.50771636","0.50637215","0.5060657","0.5038085","0.50338334","0.50312686","0.50128895","0.49978852","0.49842048","0.4977144","0.49717867","0.49535152","0.49491355","0.49355006","0.49352017","0.49325678","0.49292234","0.49292234","0.49275655","0.49166912","0.49156383","0.49124235","0.48863924","0.48863924","0.48834985","0.48806","0.4868781","0.48519367","0.48381013","0.48372135","0.48361558","0.48355603","0.4831666","0.4812357","0.4809463","0.48084736","0.48080933","0.4806583","0.4799399","0.47982916","0.47969946","0.47925204","0.47860527","0.4785408","0.47798842","0.47774336","0.47763792","0.476883","0.47681886","0.47567046","0.47495624","0.47495624","0.47483197"],"string":"[\n \"0.6507591\",\n \"0.59741753\",\n \"0.5937114\",\n \"0.5926568\",\n \"0.5859191\",\n \"0.58575606\",\n \"0.5845092\",\n \"0.5838448\",\n \"0.5783071\",\n \"0.57651764\",\n \"0.57422423\",\n \"0.5739221\",\n \"0.5724411\",\n \"0.56877214\",\n \"0.56793237\",\n \"0.56695\",\n \"0.5667886\",\n \"0.566292\",\n \"0.56339353\",\n \"0.56309354\",\n \"0.56209934\",\n \"0.55973196\",\n \"0.55973196\",\n \"0.55743825\",\n \"0.55599713\",\n \"0.5544095\",\n \"0.5518256\",\n \"0.5431601\",\n \"0.54015267\",\n \"0.53977734\",\n \"0.53657454\",\n \"0.53453386\",\n \"0.53085667\",\n \"0.5266493\",\n \"0.52212644\",\n \"0.52092683\",\n \"0.52003163\",\n \"0.51996905\",\n \"0.5191781\",\n \"0.518004\",\n \"0.518004\",\n \"0.5175296\",\n \"0.5171896\",\n \"0.51399\",\n \"0.5137848\",\n \"0.5136781\",\n \"0.5105564\",\n \"0.50771636\",\n \"0.50637215\",\n \"0.5060657\",\n \"0.5038085\",\n \"0.50338334\",\n \"0.50312686\",\n \"0.50128895\",\n \"0.49978852\",\n \"0.49842048\",\n \"0.4977144\",\n \"0.49717867\",\n \"0.49535152\",\n \"0.49491355\",\n \"0.49355006\",\n \"0.49352017\",\n \"0.49325678\",\n \"0.49292234\",\n \"0.49292234\",\n \"0.49275655\",\n \"0.49166912\",\n \"0.49156383\",\n \"0.49124235\",\n \"0.48863924\",\n \"0.48863924\",\n \"0.48834985\",\n \"0.48806\",\n \"0.4868781\",\n \"0.48519367\",\n \"0.48381013\",\n \"0.48372135\",\n \"0.48361558\",\n \"0.48355603\",\n \"0.4831666\",\n \"0.4812357\",\n \"0.4809463\",\n \"0.48084736\",\n \"0.48080933\",\n \"0.4806583\",\n \"0.4799399\",\n \"0.47982916\",\n \"0.47969946\",\n \"0.47925204\",\n \"0.47860527\",\n \"0.4785408\",\n \"0.47798842\",\n \"0.47774336\",\n \"0.47763792\",\n \"0.476883\",\n \"0.47681886\",\n \"0.47567046\",\n \"0.47495624\",\n \"0.47495624\",\n \"0.47483197\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.74388397"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":16888,"cells":{"query":{"kind":"string","value":"Returns the sorted list of problems."},"document":{"kind":"string","value":"def sort_problems(problems):\n # Note: sort() doesn't return the sorted list; rather, it sorts the list\n # in place\n problems.sort(\n key=lambda problem: (\n problem.filename,\n problem.linenumber,\n problem.rule.id\n )\n )\n return problems"},"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 problem_list(self):\r\n return [{\r\n 'location': location, 'problem_name': name,\r\n 'num_graded': self.DUMMY_DATA['problem_list_num_graded'],\r\n 'num_pending': self.DUMMY_DATA['problem_list_num_pending'],\r\n 'num_required': self.DUMMY_DATA['problem_list_num_required']\r\n } for location, name in self.problems.items()\r\n ]","def get_problems(self):\n\n with self.__orm.session_scope() as session:\n try:\n problems = session.query(Problem.name).all()\n return [problem[0] for problem in problems]\n except NoResultFound:\n return []","def problems(self):\n return self.configuration.problems","def getPossibilities(self):\n \n return sorted(self._possibilities)","def get_all(self):\n return gnome_sort(self.__assignments, sort_function=lambda assignment_a, assignment_b: assignment_a.get_assignment_id() <= assignment_b.get_assignment_id())","def get_problems():\n problems = list()\n solved = database.session.query(Submission).\\\n filter(Submission.username == current_user.username).\\\n filter(Submission.result == \"good\").\\\n all()\n solved_set = set()\n for solve in solved:\n solved_set.add(solve.pid)\n\n for problem in database.session.query(Problem).all():\n problems.append({\n 'pid': problem.pid,\n 'name': problem.name,\n 'shortname': problem.shortname,\n 'appeared': problem.appeared,\n 'difficulty': problem.difficulty,\n 'comp_release': problem.comp_release,\n 'added': problem.added,\n 'solved': problem.pid in solved_set,\n 'url': url_for_problem(problem)\n })\n return serve_response(problems)","def sort_solutions(self, solutions):\r\n if self.breeding_rules.sorting_order is ScoresSortingOrder.ASCENDING:\r\n reverse = False\r\n else:\r\n reverse = True\r\n return sorted(solutions, reverse=reverse, key=lambda solution: solution.score)","def get_sorted_disciplines(self):\n results = self.__create_discipline_and_grade_dto()\n results.sort(self.__compare_dtos_on_grade)\n return results","def getPriorityList(self):","def _toposort_with_ordered_mech_tuples(self, data):\n result = []\n for dependency_set in toposort(data):\n d_iter = iter(dependency_set)\n result.extend(sorted(dependency_set, key=lambda item : next(d_iter).mechanism.name))\n return result","def _get_problem_list(self):\r\n self._success_response({'problem_list': self.server.problem_list})","def sorted(x) -> List:\n pass","def list_unique_problems(arn=None, nextToken=None):\n pass","def sort(self):\n self.notes.sort()","def displaySorted(self):\r\n os.system('cls')\r\n for i in self.sortedList:\r\n print(str(i[2]) + \": \" + i[0].showRule())","def tiles_by_tissue_percentage(self):\n sorted_list = sorted(self.tiles, key=lambda t: t.tissue_percentage, reverse=True)\n return sorted_list","def organizeM():\n scores = []\n today_listM = strainer('name', 'sort', 'event')\n today_listM.extend(strainer('name', 'sort', 'todo'))\n data = list(today_listM)\n while len(data) != 0:\n number = lowest_number(data)\n scores.append(number)\n data.remove(number)\n return scores","def sorted_carnivores(self):\n fitness_dict = {carn: carn.fitness for carn in self.carnivores}\n sorted_tuples = dict(sorted(fitness_dict.items(), key=lambda x: x[1], reverse=True))\n\n return list(sorted_tuples.keys())","def print_sorted(self):\n new_list = []\n for x in sorted(self):\n new_list.append(x)\n print(new_list)","def display_problems():\n\n res = choose_problems()\n\n cc_name1 = res[0][0]\n url_link1 = res[0][1]\n cc_name2 = res[1][0]\n url_link2 = res[1][1]\n cc_name3 = res[2][0]\n url_link3 = res[2][1]\n\n #TODO: implement datetime (i.e. \"11.07.21\")\n print('Weekly Wednesday Problems')\n print(f'Problem 1: {cc_name1} - {url_link1}')\n print(f'Problem 2: {cc_name2} - {url_link2}')\n print(f'Problem 3: {cc_name3} - {url_link3}')\n\n return cc_name1, url_link1, cc_name2, url_link2, cc_name3, url_link3","def sorted(self): \n pass","def printSolutions(self):\n\t\tprint \"Computing solutions...\"\n\t\t\n\t\tsolutions = self.problem.getSolutions()\n\t\tnumberOfSolutions = len(solutions)\n\t\t\n\t\tfor i, solution in enumerate(solutions):\n\t\t\titems = solution.items()\n\t\t\t# sort by time\n\t\t\titems.sort(lambda a, b: cmp(a[1], b[1]))\n\t\t\t# sort by order\n\t\t\titems.sort(lambda a, b: cmp(a[0][0], b[0][0]))\n\t\t\t\n\t\t\tprint \"Solution number\", i + 1\n\t\t\t\n\t\t\ti = 1\n\t\t\tfor j in items:\n\t\t\t\tif j[0][0:1] != str(i):\n\t\t\t\t\tif \"enter\" in j[0] or \"finish\" in j[0]:\n\t\t\t\t\t\tprint j,\n\t\t\t\telse:\n\t\t\t\t\tprint \"\\n\",\n\t\t\t\t\tprint \"Order no:\", i\n\t\t\t\t\tif \"enter\" in j[0] or \"finish\" in j[0]:\n\t\t\t\t\t\tprint j,\n\t\t\t\t\ti += 1\n\t\t\tprint \"\\n==============================================\\n\",\n\t\tprint \"Number of solutions:\", numberOfSolutions\n\t\treturn solutions, numberOfSolutions","def get_conflicts(self):\n return []","def cheat(self) -> List[str]:\n all_possible_words = self.trie.get_all_possible_words(\n self.get_current_reels_letters()\n )\n better_words = OrderedDict()\n for word in all_possible_words:\n score = self.scorer.calculate_word_score(word)\n if len(better_words) > 2:\n first_word = next(iter(better_words.items()))\n if first_word[0] < score:\n better_words.popitem(last=False)\n better_words[score] = word\n else:\n better_words[score] = word\n better_words = OrderedDict(sorted(better_words.items()))\n return [f\"{word} ({score})\" for score, word in better_words.items()]","def get_all_sorted(self):\n self.sort_and_reduce()\n return self.data","def get_listu_postaja(self):\n popis = sorted(list(self.postaje))\n return popis","def get_assignments_ordered() -> list:\n user_assignments = Assignment.query.all() if current_user.is_admin else current_user.classgroup.assignments\n open_assignments = [x for x in user_assignments if not x.expired]\n closed_assignments = [x for x in user_assignments if x.expired]\n return open_assignments + closed_assignments","def sort_results(self):\n pass","def issues(self) -> List[IssueType]:\n return [IssueType.FREE_SPACE]","def solution(self):\n return [(\"simple 1\", 1.),\n (\"simple 2\", 1.),\n (\"simple 3\", 1.),\n (\"simple 4\", 1.),\n (\"simple 5\", 1.),\n (\"simple 10\", 1.),\n (\"simple 15\", 1.),\n (\"thai 1\", 1.),\n (\"thai 2\", 1.),\n (\"thai 3\", 1.),\n (\"thai 4\", 1.),\n (\"thai 5\", 1.),\n (\"thai 10\", 1.),\n (\"thai 15\", 1.),\n ]","def solve_sort(self):\n if self.k < 0 or self.k > len(self.numbers):\n return None\n\n self.numbers.sort() # in place\n return self.numbers[-self.k]","def sorted_herbivores(self):\n fitness_dict = {herb: herb.fitness for herb in self.herbivores}\n sorted_tuples = sorted(fitness_dict.items(), key=lambda x: x[1], reverse=False)\n\n return sorted_tuples","def _get_suggestion_issues() -> Tuple[Optional[Issue], List[Issue]]:\n article_stages_for_opened_issues = (\n \"waiting\",\n \"proofreading_editor\",\n \"precheck\",\n \"spellcheck\",\n \"markup\",\n \"proofreading_spell\",\n \"proofreading_chief_dpt\",\n \"proofreading_chief\",\n \"publishing\",\n )\n opened_issues = (\n Issue.on_current_site.filter(posts__stage__slug__in=article_stages_for_opened_issues)\n .order_by(\"number\")\n .distinct()\n .all()[:5]\n )\n\n if not opened_issues:\n return None, []\n\n # Convert QuerySet to ordinary list to split for initial and least\n opened_issues: List[Issue] = list(opened_issues)\n initial_issue = opened_issues.pop()\n opened_issues.reverse()\n\n return initial_issue, opened_issues","def entries(self) -> \"list[tuple[Codepoint, EffectiveWidth]]\":\n result = list(self.entry_set)\n result.sort()\n return result","def ordering(self):\n value = []\n for i in self:\n if isinstance(i, PQ):\n value.extend(i.ordering())\n else:\n value.append(i)\n\n return value","def problem_list(self):\r\n return self.q(css='a.problem-button').text","def force_no_ordering(self):\n return []","def sort(self):\r\n self.candidates.sort(key=self.sortFitness)\r\n return","def get_listu_komponenti(self):\n popis = sorted(list(self.komponente.keys()))\n return popis","def get_sorted_results(self):\n results = self.results.values()\n return sorted(results, key=lambda r: r.rank(), reverse=True)","def get_sorted_topics_courses(self):\n return sorted(self.topics_courses, key=lambda course_topic: course_topic.stats.score, reverse=True)","def get_listu_uredjaja(self):\n lista = sorted(list(self.uredjaji.keys()))\n return lista","def sort(self):\n self.tasks = sorted(self.tasks, key=lambda k: k.priority, reverse=True)","def get_warning_msgs():\n return sorted(TypeWarning.warnings, key=lambda warning: warning.localization.line)","def generate_problems(self):\n\n prepped_equation = self.prep_equation()\n var_ranges = self.generate_var_ranges()\n input_array = self.generate_input_array(var_ranges)\n valid_combos = self.generate_valid_combos(prepped_equation, var_ranges, input_array)\n self.write_problems(valid_combos)\n self.dict['problems'] = valid_combos\n logging.info(f\"Generated {len(self.dict['problems'])} valid problems.\")\n logging.info(self.dict['problems'])","def get_problem_list(self, course_id, grader_id):\r\n params = {'course_id': course_id.to_deprecated_string(), 'grader_id': grader_id}\r\n result = self.get(self.get_problem_list_url, params)\r\n tags = [u'course_id:{}'.format(course_id)]\r\n self._record_result('get_problem_list', result, tags)\r\n dog_stats_api.histogram(\r\n self._metric_name('get_problem_list.result.length'),\r\n len(result.get('problem_list', []))\r\n )\r\n return result","def load_problems(filename):\n problems = []\n f = open(filename, 'r')\n while 1:\n try:\n coins = f.readline()\n if coins:\n coins = [int(num) for num in coins.replace('[', '')\n .replace(']', '')\n .replace(' ', '')\n .split(',') if num not in '\\n']\n else:\n break\n change = f.readline()\n if change:\n change = change.replace('\\n', '')\n change = int(change)\n else:\n break\n except Exception:\n break\n\n problems.append((coins, change))\n\n return problems","def _sort(self):\n self.population.sort()\n self.population.reverse()","def sort_list():\n fun_list = basic_list_exception.make_list()\n fun_list.sort()\n return fun_list","def issues(self):\r\n return issues.Issues(self)","def _load_problems(statement_id) -> List[Problem]:\n problems_statement_problems = db.session.query(Problem, StatementProblem) \\\n .join(StatementProblem, StatementProblem.problem_id == Problem.id) \\\n .filter(StatementProblem.statement_id == statement_id) \\\n .filter(StatementProblem.hidden == 0) \\\n .options(Load(Problem).load_only('id', 'name')) \\\n .options(Load(StatementProblem).load_only('rank'))\n\n problems = []\n # Yes it is ugly but I think its better than rewrite query\n for problem, sp in problems_statement_problems.all():\n problem.rank = sp.rank\n problems.append(problem)\n\n return problems","def sort(self):\n self.sorted = list()\n self.pushed = set()\n for item in self.unsorted:\n popped = []\n self.push(item)\n while len(self.stack):\n try:\n top = self.top()\n ref = next(top[1])\n refd = self.index.get(ref)\n if refd is None:\n log.debug('\"%s\" not found, skipped', Repr(ref))\n continue\n self.push(refd)\n except StopIteration:\n popped.append(self.pop())\n continue\n for p in popped:\n self.sorted.append(p)\n self.unsorted = self.sorted\n return self.sorted","def sort_priors(self):\n return","def get_list(self):\n return sorted(self.__entries.keys())","def get_conflicts(self):\n packages = []\n for obj in self.objects_used:\n packages.extend(obj.get_conflicts())\n # Remove duplicates (not necessary but it's cleaner)\n packages = list(set(packages))\n return packages","def tiles_by_score(self):\n sorted_list = sorted(self.tiles, key=lambda t: t.score, reverse=True)\n return sorted_list","def localResultSet(options=None):\n options = {} if options is None else options\n results = {}\n for problem in generateProblem(options):\n _, solutions = solver.numberOfSolutions(\n problem, returnWays=True)\n results[tuple(problem)] = difficulty(solutions)\n\n # sort from easiest to hardest\n results = sorted(list(results.iteritems()), key=lambda x: x[1])\n return results","def completed_puzzles(self):\r\n from foldit.models import PuzzleComplete\r\n\r\n return sorted(\r\n PuzzleComplete.completed_puzzles(self.system.anonymous_student_id),\r\n key=lambda d: (d['set'], d['subset']))","def get_ordered_dependency_list(self):\n # Validate the graph\n self.validate()\n # Generate the dependency list\n dep_list = []\n for rosdep_key in self:\n if self[rosdep_key]['is_root']:\n dep_list.extend(self.__get_ordered_uninstalled(rosdep_key))\n # Make the list unique and remove empty entries\n result = []\n for item in dep_list:\n if item not in result and item[1] != []:\n result.append(item)\n # Squash the results by installer_key\n squashed_result = []\n previous_installer_key = None\n for installer_key, resolved in result:\n if previous_installer_key != installer_key:\n squashed_result.append((installer_key, []))\n previous_installer_key = installer_key\n squashed_result[-1][1].extend(resolved)\n return squashed_result","def sort(self):\n out = []\n while len(self.x) > 0:\n self.x[0], self.x[-1] = self.x[-1], self.x[0]\n out.append(self.x.pop())\n BinaryMaxHeap.max_heapify(self.x, 0)\n\n return out","def toposort(prereqs_d):\r\n\r\n# all1 = set(prereqs_d.keys())\r\n# all2 = set()\r\n# for x, y in prereqs_d.items():\r\n# all2.update(y)\r\n# print all1.difference(all2)\r\n\r\n seq = []\r\n done = set()\r\n postreqs_d = {}\r\n for x, prereqs in prereqs_d.items():\r\n for prereq in prereqs:\r\n postreqs_d.setdefault(prereq, set()).add(x)\r\n next = set([k for k in prereqs_d if not prereqs_d[k]])\r\n while next:\r\n bases = next\r\n next = set()\r\n for x in bases:\r\n done.add(x)\r\n seq.append(x)\r\n for x in bases:\r\n for postreq in postreqs_d.get(x, []):\r\n if not prereqs_d[postreq].difference(done):\r\n next.add(postreq)\r\n if len(prereqs_d) != len(seq):\r\n raise Exception(\"Cannot sort topologically: there might be cycles, \"\r\n \"prereqs_d does not have a key for each element or \"\r\n \"some orderings contain invalid elements.\")\r\n return seq","def get_cases(self) -> list:\n case_list = [(key, selection_text(case))\n for key, case in self.cases.items()\n if hasattr(case, 'case_id')]\n case_list.sort(key=lambda x: x[1])\n return case_list","def find_over_counting_problems(self) -> list[OverCountingProblem]:\n for categorization in self.categorization_a, self.categorization_b:\n if not categorization.hierarchical:\n raise ValueError(\n f\"{categorization} is not hierarchical, without \"\n f\"a hierarchy, over counting can not be evaluated.\"\n )\n if not categorization.total_sum: # type: ignore\n raise ValueError(\n f\"For {categorization} it is not specified that the \"\n f\"sum of a set of children equals the parent, so \"\n f\"over counting can not be evaluated.\"\n )\n\n problems = []\n for categorization in self.categorization_a, self.categorization_b:\n # used to cache costly descendant evaluation\n descendants: dict[str, set[str]] = {}\n for category in categorization.values():\n prob = self._check_over_counting_category(\n category, categorization, descendants # type: ignore\n )\n if prob:\n problems.append(prob)\n\n return problems","def __qualitaetsListeProteins(self):\n rv = []\n pam30_sortierbar = {}\n for key in pam30.keys():\n pam30_sortierbar[str(pam30[key]) + \";\" + ''.join(key)] = pam30[key]\n if key[0] != key[1]:\n pam30_sortierbar[\n str(pam30[key]) + \";\" + ''.join((key[1], key[0]))\n ] = pam30[key]\n sorted_keys = list(pam30_sortierbar.keys())\n sorted_keys.sort(key=lambda k: int(k.split(\";\")[0]), reverse=True)\n # debugging kept for historical reasons\n # for key in iter(sorted_keys):\n # print(key.split(\";\")[1] + \" has score \" + str(pam30_sortierbar[key]))\n for key in iter(sorted_keys):\n rv.append(key.split(\";\")[1])\n return(rv)","def get_all_from_file(cls, fileID, problemDir):\n\n # Load the module\n if problemDir not in sys.path:\n sys.path.insert(0, problemDir)\n try:\n problemModule = __import__(fileID)\n except Exception as e: # pragma: no cover\n warn(\"Could not import file \" + fileID)\n print(e)\n return []\n\n foundProblems = [] # Holds the TestProblems we find in this file\n\n # Look for a dictionary\n PROBLEM_LIST = [\"problems\"]\n for problemList in PROBLEM_LIST:\n if problemList in [name for name in dir(problemModule)]:\n problems = getattr(problemModule, \"problems\")\n for problemDict in problems:\n foundProblems.append(cls.process_problem_dict(**problemDict))\n if len(foundProblems) == 0: # pragma: no cover\n warn(fileID + \" contains no problem objects.\")\n return foundProblems","def reorder( self ):\n self.sorted.sort(self.compareFunction)","def all_games(self):\r\n return sorted(self.games + list(g for sp in self.sub_pools for g in sp.games) + self.finals,\r\n key=lambda g: (g.datetime, g.pitch.rank))","def sort_suggestions(\n suggestions: List[Tuple[Set[str], float]]\n) -> List[Tuple[Set[str], float]]:\n confidence_list = [suggestion[1] for suggestion in suggestions]\n sort_index = sorted(range(len(confidence_list)), key=lambda k: confidence_list[k])\n # Inverse the sort\n sort_index = sort_index[::-1]\n return [suggestions[i] for i in sort_index]","def as_sorted_list(options):\n if len(options) > 0:\n options.sort(None, key=lambda o: o.number)\n return options","def reordered_rules(self):\n return tuple(r for r in self.rules if r.reordered)","def test_api_can_sort_issues_correctly(self):\n issues_list = Issue.objects.values_list('experience_needed').order_by('experience_needed')\n response = self.client.get('/issues/?ordering=experience_needed', format=\"json\")\n response_content = json.loads(response.content)\n for i in xrange(len(issues_list)):\n self.assertEqual(issues_list[i][0], response_content[i]['experience_needed'])","def challenges(self):\n return [gc.challenge for gc in GrandChallenge.objects.filter(round=self.round_number).order_by('challenge__status')]","def get_puzzle(self):\n return [[str(self.puzzle[i][j]) for j in range(len(self.puzzle[0]))] for i in range(len(self.puzzle))]","def sorted(self):\n sorted_list = [x for x in self.events.iteritems()]\n sorted_list.sort(key=lambda x: x[1])\n return sorted_list","def get_problem_list(self, course_id, user_id):\r\n return {'success': True, 'error': 'No problems found.'}","def get_bugs(self):\n return self.execute(TABELLE['bugs']['select'])","def GetRulePriorities(self, G, results):\n rules = results.Defrules\n proceededRules = []\n curRulesSet = []\n curSalience = 10000\n\n # Fill the list of input templates, would be unshanged through all processing\n inputTemplates = set()\n for rule in rules:\n for tmp in rule.InputTemplates:\n inputTemplates.add(tmp.TemplateName)\n\n # We have to proceed all rules\n while len(proceededRules) < len(rules):\n outputTemplates = set()\n\n # Tke outputing template names from not proceeded rules\n for rule in rules:\n if not rule in proceededRules:\n for tmp in rule.OutputTemplates:\n outputTemplates.add(tmp.TemplateName)\n\n # Valid input templates at current iteration are the ones which are not produced with existing rules\n validInputs = inputTemplates.difference(outputTemplates)\n\n # Get applicable rules from not proceeded\n curRuleSet = [rule for rule in rules if not rule in proceededRules and len(set([templ.TemplateName for templ in rule.InputTemplates]).difference(validInputs)) == 0]\n\n # Set the proper priority for selected rules\n for rule in curRuleSet:\n rule.Priority = curSalience\n\n # Update proceeded list\n proceededRules += curRuleSet\n\n # Decrease salience\n curSalience -= 1","def part_build_order(self):\n priority = []\n for geo_item in self.build_order:\n if geo_item in self.parts:\n priority += [geo_item]\n return priority","def getSorted(self):\n return sorted(self.contacts)","def test_returns_sorted_projects_by_difficulty_if_sort_by_set_to_difficulty(self):\n # Arrange\n # Set difficulty of test_project_1 to easy.\n self.test_project_1.difficulty = ProjectDifficulty.EASY.value\n self.test_project_1.save()\n # Set project_2 to be allowed for all users removing as private.\n self.test_project_2.private = False\n self.test_project_2.difficulty = ProjectDifficulty.MODERATE.value\n self.test_project_2.save()\n # Set difficulty of test_project_1 to hard and status to published.\n self.test_project_3.status = ProjectStatus.PUBLISHED.value\n self.test_project_3.difficulty = ProjectDifficulty.CHALLENGING.value\n self.test_project_3.save()\n\n # Test for descending order\n # Act\n response_desc = self.client.get(\n self.url,\n headers={\"Authorization\": self.user_session_token},\n query_string={\"orderBy\": \"difficulty\", \"orderByType\": \"DESC\"},\n )\n # Assert\n self.assertEqual(response_desc.status_code, 200)\n self.assertEqual(len(response_desc.json[\"results\"]), 3)\n expected_desc_order = [\n self.test_project_3.id,\n self.test_project_2.id,\n self.test_project_1.id,\n ]\n self.assertListEqual(\n [i[\"projectId\"] for i in response_desc.json[\"results\"]], expected_desc_order\n )\n\n # Test for ascending order\n # Act\n response_asc = self.client.get(\n self.url,\n headers={\"Authorization\": self.user_session_token},\n query_string={\"orderBy\": \"difficulty\", \"orderByType\": \"ASC\"},\n )\n # Assert\n self.assertEqual(response_asc.status_code, 200)\n self.assertEqual(len(response_asc.json[\"results\"]), 3)\n self.assertListEqual(\n [i[\"projectId\"] for i in response_asc.json[\"results\"]],\n expected_desc_order[::-1],\n )","def sortChoices(self):\n self.formatList.sort()","def sort_collection(self):\n sorted_tallies = []\n sorted = []\n collection_counter = self.create_collection_counter() \n for k, v in collection_counter.iteritems():\n sorted_tallies.append((str(v) + ': ' + k))\n print sorted_tallies\n print 8*'\\n', 100*'L'\n print 'PRINT SORTED TALLIES RAN (LINE 74)'\n for element in sorted_tallies:\n print element\n print 8*'\\n', 100*'M'\n print 'PRINT ELEMENT IN SORTED TALLIES LOOP HAS RAN'\n return sorted_tallies","def sort(self):\n self.cards.sort()","def sort(self):\n self.cards.sort()","def items(self):\n result = self._dict.keys( )\n try: result.sort( )\n except: pass\n\n return result","def topological_sort(g) -> list:\n dfs(g)\n res = [v for v in g]\n quick_sort(res, key=lambda v: v.finish_time)\n res.reverse()\n return res","def getList(self):\n\treturn self.sorted_list.getList()","def sort(self, descending):\n\t\tself.membersWithErrors.sort(key = lambda l: l[1], reverse = descending)","def sort_population(self):\n self.population.sort(key=lambda x: x.fitness, reverse=True)","def get_tents(self):\n answ = []\n for tent in self.sort_tents(self.tents.copy()):\n answ.append(str(tent))\n return answ","def sorted_tags(self):\n return sorted(self.tags, key=lambda x: x.name)","def give_puzzle(self) -> List[int]:\n return self._puzzle","def get_all_solutions(use_local_cache) -> List[SolutionInfo]:\n if use_local_cache:\n scores = get_from_file()\n else:\n scores = get_from_web()\n\n return [SolutionInfo(user=item['login'], hole=item['hole'], lang=item['lang'],\n strokes=int(item['strokes']), submitted=item['submitted'])\n for item in scores]","def get_listu_dilucijskih_jedinica(self):\n popis = sorted(list(self.dilucijskeJedinice.keys()))\n return popis","def get_top(self, difficulty):\n return list(self.database[difficulty].find().sort('time').limit(MAX_LEADERS))","def sort(self):\n self.list.sort(key=lambda x: ''.join)","def allBroken(request, page=1):\n objects = im.Issue.objects.filter(resolved_state__isnull=True)\n args = utils.generatePageList(request, objects, page)\n args['issues'] = args['objects']\n issues_list = {'Issues on Unusable Machines':[]}\n for issue in args['issues']:\n iss_id = issue.item.item_id\n machine = mac.Item.objects.get(item_id=iss_id)\n\n if machine.unusable:\n issues_list['Issues on Unusable Machines'].append(issue)\n\n args['object_list'] = issues_list.items() \n args['no_results'] = args['page'].object_list.count() < 1\n return render_to_response(\"grouped_issue_list.html\", args,\n context_instance=RequestContext(request))","def get_all_bugs(self) -> List:\n #starting point\n offset = 0\n #list for all bugs\n resultBugList = []\n #list for bug IDs\n bugIDList = []\n #checks if there are still results returned\n notEmpty = True\n\n #queries in 500 bug steps until the result list is empty\n while notEmpty:\n print(\"entered\")\n #interpretation of result as list plus formatting for eval errors\n result = ast.literal_eval(self.session.get(self.bugURL + \"&offset=\" + str(offset)).text.\n replace('true', 'True').replace('false', 'False').replace('null', 'None'))[\"bugs\"]\n #checks if the query needs to be set again with a new offset\n if result:\n resultBugList += result\n else:\n notEmpty = False\n\n #gets the ID out of all comments\n partList = [bug[\"id\"] for bug in result]\n bugIDList += partList\n #sets new starting point\n offset += 500\n\n #inserts bug ids and bugs into db if given one\n if self.mongoDB:\n for id in bugIDList:\n self.mongoDB[\"BugIDs\"].insert_one({\"ID\": id})\n self.mongoDB[\"BugsData\"].insert_many(resultBugList)\n\n #creates files for bug ids and bugs if given a folder\n if self.folder:\n #saves bug list as python object\n with open(self.folderpath + \"bugIDListP.pickle\", \"wb\") as a:\n pickle.dump(bugIDList, a)\n #saves bug list as csv\n with open(self.folderpath + \"bugIDList.csv\", \"w\") as b:\n for id in bugIDList:\n b.write(str(id) + \"\\n\")\n with open(self.folderpath + \"bugsData.txt\", \"w\") as c:\n for bug in resultBugList:\n c.write(str(bug) + \"\\n\")\n\n #returns List Object for further processing\n return(bugIDList)","def sort():\n return -1","def get_best_n_description(self, n: int) -> list:\n self._population.sort(key=lambda chromosome: chromosome.get_fitness())\n descriptions = []\n\n number = min(n, len(self._population))\n for i in range(number):\n descriptions.append(str(self._population[i]))\n\n return descriptions"],"string":"[\n \"def problem_list(self):\\r\\n return [{\\r\\n 'location': location, 'problem_name': name,\\r\\n 'num_graded': self.DUMMY_DATA['problem_list_num_graded'],\\r\\n 'num_pending': self.DUMMY_DATA['problem_list_num_pending'],\\r\\n 'num_required': self.DUMMY_DATA['problem_list_num_required']\\r\\n } for location, name in self.problems.items()\\r\\n ]\",\n \"def get_problems(self):\\n\\n with self.__orm.session_scope() as session:\\n try:\\n problems = session.query(Problem.name).all()\\n return [problem[0] for problem in problems]\\n except NoResultFound:\\n return []\",\n \"def problems(self):\\n return self.configuration.problems\",\n \"def getPossibilities(self):\\n \\n return sorted(self._possibilities)\",\n \"def get_all(self):\\n return gnome_sort(self.__assignments, sort_function=lambda assignment_a, assignment_b: assignment_a.get_assignment_id() <= assignment_b.get_assignment_id())\",\n \"def get_problems():\\n problems = list()\\n solved = database.session.query(Submission).\\\\\\n filter(Submission.username == current_user.username).\\\\\\n filter(Submission.result == \\\"good\\\").\\\\\\n all()\\n solved_set = set()\\n for solve in solved:\\n solved_set.add(solve.pid)\\n\\n for problem in database.session.query(Problem).all():\\n problems.append({\\n 'pid': problem.pid,\\n 'name': problem.name,\\n 'shortname': problem.shortname,\\n 'appeared': problem.appeared,\\n 'difficulty': problem.difficulty,\\n 'comp_release': problem.comp_release,\\n 'added': problem.added,\\n 'solved': problem.pid in solved_set,\\n 'url': url_for_problem(problem)\\n })\\n return serve_response(problems)\",\n \"def sort_solutions(self, solutions):\\r\\n if self.breeding_rules.sorting_order is ScoresSortingOrder.ASCENDING:\\r\\n reverse = False\\r\\n else:\\r\\n reverse = True\\r\\n return sorted(solutions, reverse=reverse, key=lambda solution: solution.score)\",\n \"def get_sorted_disciplines(self):\\n results = self.__create_discipline_and_grade_dto()\\n results.sort(self.__compare_dtos_on_grade)\\n return results\",\n \"def getPriorityList(self):\",\n \"def _toposort_with_ordered_mech_tuples(self, data):\\n result = []\\n for dependency_set in toposort(data):\\n d_iter = iter(dependency_set)\\n result.extend(sorted(dependency_set, key=lambda item : next(d_iter).mechanism.name))\\n return result\",\n \"def _get_problem_list(self):\\r\\n self._success_response({'problem_list': self.server.problem_list})\",\n \"def sorted(x) -> List:\\n pass\",\n \"def list_unique_problems(arn=None, nextToken=None):\\n pass\",\n \"def sort(self):\\n self.notes.sort()\",\n \"def displaySorted(self):\\r\\n os.system('cls')\\r\\n for i in self.sortedList:\\r\\n print(str(i[2]) + \\\": \\\" + i[0].showRule())\",\n \"def tiles_by_tissue_percentage(self):\\n sorted_list = sorted(self.tiles, key=lambda t: t.tissue_percentage, reverse=True)\\n return sorted_list\",\n \"def organizeM():\\n scores = []\\n today_listM = strainer('name', 'sort', 'event')\\n today_listM.extend(strainer('name', 'sort', 'todo'))\\n data = list(today_listM)\\n while len(data) != 0:\\n number = lowest_number(data)\\n scores.append(number)\\n data.remove(number)\\n return scores\",\n \"def sorted_carnivores(self):\\n fitness_dict = {carn: carn.fitness for carn in self.carnivores}\\n sorted_tuples = dict(sorted(fitness_dict.items(), key=lambda x: x[1], reverse=True))\\n\\n return list(sorted_tuples.keys())\",\n \"def print_sorted(self):\\n new_list = []\\n for x in sorted(self):\\n new_list.append(x)\\n print(new_list)\",\n \"def display_problems():\\n\\n res = choose_problems()\\n\\n cc_name1 = res[0][0]\\n url_link1 = res[0][1]\\n cc_name2 = res[1][0]\\n url_link2 = res[1][1]\\n cc_name3 = res[2][0]\\n url_link3 = res[2][1]\\n\\n #TODO: implement datetime (i.e. \\\"11.07.21\\\")\\n print('Weekly Wednesday Problems')\\n print(f'Problem 1: {cc_name1} - {url_link1}')\\n print(f'Problem 2: {cc_name2} - {url_link2}')\\n print(f'Problem 3: {cc_name3} - {url_link3}')\\n\\n return cc_name1, url_link1, cc_name2, url_link2, cc_name3, url_link3\",\n \"def sorted(self): \\n pass\",\n \"def printSolutions(self):\\n\\t\\tprint \\\"Computing solutions...\\\"\\n\\t\\t\\n\\t\\tsolutions = self.problem.getSolutions()\\n\\t\\tnumberOfSolutions = len(solutions)\\n\\t\\t\\n\\t\\tfor i, solution in enumerate(solutions):\\n\\t\\t\\titems = solution.items()\\n\\t\\t\\t# sort by time\\n\\t\\t\\titems.sort(lambda a, b: cmp(a[1], b[1]))\\n\\t\\t\\t# sort by order\\n\\t\\t\\titems.sort(lambda a, b: cmp(a[0][0], b[0][0]))\\n\\t\\t\\t\\n\\t\\t\\tprint \\\"Solution number\\\", i + 1\\n\\t\\t\\t\\n\\t\\t\\ti = 1\\n\\t\\t\\tfor j in items:\\n\\t\\t\\t\\tif j[0][0:1] != str(i):\\n\\t\\t\\t\\t\\tif \\\"enter\\\" in j[0] or \\\"finish\\\" in j[0]:\\n\\t\\t\\t\\t\\t\\tprint j,\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tprint \\\"\\\\n\\\",\\n\\t\\t\\t\\t\\tprint \\\"Order no:\\\", i\\n\\t\\t\\t\\t\\tif \\\"enter\\\" in j[0] or \\\"finish\\\" in j[0]:\\n\\t\\t\\t\\t\\t\\tprint j,\\n\\t\\t\\t\\t\\ti += 1\\n\\t\\t\\tprint \\\"\\\\n==============================================\\\\n\\\",\\n\\t\\tprint \\\"Number of solutions:\\\", numberOfSolutions\\n\\t\\treturn solutions, numberOfSolutions\",\n \"def get_conflicts(self):\\n return []\",\n \"def cheat(self) -> List[str]:\\n all_possible_words = self.trie.get_all_possible_words(\\n self.get_current_reels_letters()\\n )\\n better_words = OrderedDict()\\n for word in all_possible_words:\\n score = self.scorer.calculate_word_score(word)\\n if len(better_words) > 2:\\n first_word = next(iter(better_words.items()))\\n if first_word[0] < score:\\n better_words.popitem(last=False)\\n better_words[score] = word\\n else:\\n better_words[score] = word\\n better_words = OrderedDict(sorted(better_words.items()))\\n return [f\\\"{word} ({score})\\\" for score, word in better_words.items()]\",\n \"def get_all_sorted(self):\\n self.sort_and_reduce()\\n return self.data\",\n \"def get_listu_postaja(self):\\n popis = sorted(list(self.postaje))\\n return popis\",\n \"def get_assignments_ordered() -> list:\\n user_assignments = Assignment.query.all() if current_user.is_admin else current_user.classgroup.assignments\\n open_assignments = [x for x in user_assignments if not x.expired]\\n closed_assignments = [x for x in user_assignments if x.expired]\\n return open_assignments + closed_assignments\",\n \"def sort_results(self):\\n pass\",\n \"def issues(self) -> List[IssueType]:\\n return [IssueType.FREE_SPACE]\",\n \"def solution(self):\\n return [(\\\"simple 1\\\", 1.),\\n (\\\"simple 2\\\", 1.),\\n (\\\"simple 3\\\", 1.),\\n (\\\"simple 4\\\", 1.),\\n (\\\"simple 5\\\", 1.),\\n (\\\"simple 10\\\", 1.),\\n (\\\"simple 15\\\", 1.),\\n (\\\"thai 1\\\", 1.),\\n (\\\"thai 2\\\", 1.),\\n (\\\"thai 3\\\", 1.),\\n (\\\"thai 4\\\", 1.),\\n (\\\"thai 5\\\", 1.),\\n (\\\"thai 10\\\", 1.),\\n (\\\"thai 15\\\", 1.),\\n ]\",\n \"def solve_sort(self):\\n if self.k < 0 or self.k > len(self.numbers):\\n return None\\n\\n self.numbers.sort() # in place\\n return self.numbers[-self.k]\",\n \"def sorted_herbivores(self):\\n fitness_dict = {herb: herb.fitness for herb in self.herbivores}\\n sorted_tuples = sorted(fitness_dict.items(), key=lambda x: x[1], reverse=False)\\n\\n return sorted_tuples\",\n \"def _get_suggestion_issues() -> Tuple[Optional[Issue], List[Issue]]:\\n article_stages_for_opened_issues = (\\n \\\"waiting\\\",\\n \\\"proofreading_editor\\\",\\n \\\"precheck\\\",\\n \\\"spellcheck\\\",\\n \\\"markup\\\",\\n \\\"proofreading_spell\\\",\\n \\\"proofreading_chief_dpt\\\",\\n \\\"proofreading_chief\\\",\\n \\\"publishing\\\",\\n )\\n opened_issues = (\\n Issue.on_current_site.filter(posts__stage__slug__in=article_stages_for_opened_issues)\\n .order_by(\\\"number\\\")\\n .distinct()\\n .all()[:5]\\n )\\n\\n if not opened_issues:\\n return None, []\\n\\n # Convert QuerySet to ordinary list to split for initial and least\\n opened_issues: List[Issue] = list(opened_issues)\\n initial_issue = opened_issues.pop()\\n opened_issues.reverse()\\n\\n return initial_issue, opened_issues\",\n \"def entries(self) -> \\\"list[tuple[Codepoint, EffectiveWidth]]\\\":\\n result = list(self.entry_set)\\n result.sort()\\n return result\",\n \"def ordering(self):\\n value = []\\n for i in self:\\n if isinstance(i, PQ):\\n value.extend(i.ordering())\\n else:\\n value.append(i)\\n\\n return value\",\n \"def problem_list(self):\\r\\n return self.q(css='a.problem-button').text\",\n \"def force_no_ordering(self):\\n return []\",\n \"def sort(self):\\r\\n self.candidates.sort(key=self.sortFitness)\\r\\n return\",\n \"def get_listu_komponenti(self):\\n popis = sorted(list(self.komponente.keys()))\\n return popis\",\n \"def get_sorted_results(self):\\n results = self.results.values()\\n return sorted(results, key=lambda r: r.rank(), reverse=True)\",\n \"def get_sorted_topics_courses(self):\\n return sorted(self.topics_courses, key=lambda course_topic: course_topic.stats.score, reverse=True)\",\n \"def get_listu_uredjaja(self):\\n lista = sorted(list(self.uredjaji.keys()))\\n return lista\",\n \"def sort(self):\\n self.tasks = sorted(self.tasks, key=lambda k: k.priority, reverse=True)\",\n \"def get_warning_msgs():\\n return sorted(TypeWarning.warnings, key=lambda warning: warning.localization.line)\",\n \"def generate_problems(self):\\n\\n prepped_equation = self.prep_equation()\\n var_ranges = self.generate_var_ranges()\\n input_array = self.generate_input_array(var_ranges)\\n valid_combos = self.generate_valid_combos(prepped_equation, var_ranges, input_array)\\n self.write_problems(valid_combos)\\n self.dict['problems'] = valid_combos\\n logging.info(f\\\"Generated {len(self.dict['problems'])} valid problems.\\\")\\n logging.info(self.dict['problems'])\",\n \"def get_problem_list(self, course_id, grader_id):\\r\\n params = {'course_id': course_id.to_deprecated_string(), 'grader_id': grader_id}\\r\\n result = self.get(self.get_problem_list_url, params)\\r\\n tags = [u'course_id:{}'.format(course_id)]\\r\\n self._record_result('get_problem_list', result, tags)\\r\\n dog_stats_api.histogram(\\r\\n self._metric_name('get_problem_list.result.length'),\\r\\n len(result.get('problem_list', []))\\r\\n )\\r\\n return result\",\n \"def load_problems(filename):\\n problems = []\\n f = open(filename, 'r')\\n while 1:\\n try:\\n coins = f.readline()\\n if coins:\\n coins = [int(num) for num in coins.replace('[', '')\\n .replace(']', '')\\n .replace(' ', '')\\n .split(',') if num not in '\\\\n']\\n else:\\n break\\n change = f.readline()\\n if change:\\n change = change.replace('\\\\n', '')\\n change = int(change)\\n else:\\n break\\n except Exception:\\n break\\n\\n problems.append((coins, change))\\n\\n return problems\",\n \"def _sort(self):\\n self.population.sort()\\n self.population.reverse()\",\n \"def sort_list():\\n fun_list = basic_list_exception.make_list()\\n fun_list.sort()\\n return fun_list\",\n \"def issues(self):\\r\\n return issues.Issues(self)\",\n \"def _load_problems(statement_id) -> List[Problem]:\\n problems_statement_problems = db.session.query(Problem, StatementProblem) \\\\\\n .join(StatementProblem, StatementProblem.problem_id == Problem.id) \\\\\\n .filter(StatementProblem.statement_id == statement_id) \\\\\\n .filter(StatementProblem.hidden == 0) \\\\\\n .options(Load(Problem).load_only('id', 'name')) \\\\\\n .options(Load(StatementProblem).load_only('rank'))\\n\\n problems = []\\n # Yes it is ugly but I think its better than rewrite query\\n for problem, sp in problems_statement_problems.all():\\n problem.rank = sp.rank\\n problems.append(problem)\\n\\n return problems\",\n \"def sort(self):\\n self.sorted = list()\\n self.pushed = set()\\n for item in self.unsorted:\\n popped = []\\n self.push(item)\\n while len(self.stack):\\n try:\\n top = self.top()\\n ref = next(top[1])\\n refd = self.index.get(ref)\\n if refd is None:\\n log.debug('\\\"%s\\\" not found, skipped', Repr(ref))\\n continue\\n self.push(refd)\\n except StopIteration:\\n popped.append(self.pop())\\n continue\\n for p in popped:\\n self.sorted.append(p)\\n self.unsorted = self.sorted\\n return self.sorted\",\n \"def sort_priors(self):\\n return\",\n \"def get_list(self):\\n return sorted(self.__entries.keys())\",\n \"def get_conflicts(self):\\n packages = []\\n for obj in self.objects_used:\\n packages.extend(obj.get_conflicts())\\n # Remove duplicates (not necessary but it's cleaner)\\n packages = list(set(packages))\\n return packages\",\n \"def tiles_by_score(self):\\n sorted_list = sorted(self.tiles, key=lambda t: t.score, reverse=True)\\n return sorted_list\",\n \"def localResultSet(options=None):\\n options = {} if options is None else options\\n results = {}\\n for problem in generateProblem(options):\\n _, solutions = solver.numberOfSolutions(\\n problem, returnWays=True)\\n results[tuple(problem)] = difficulty(solutions)\\n\\n # sort from easiest to hardest\\n results = sorted(list(results.iteritems()), key=lambda x: x[1])\\n return results\",\n \"def completed_puzzles(self):\\r\\n from foldit.models import PuzzleComplete\\r\\n\\r\\n return sorted(\\r\\n PuzzleComplete.completed_puzzles(self.system.anonymous_student_id),\\r\\n key=lambda d: (d['set'], d['subset']))\",\n \"def get_ordered_dependency_list(self):\\n # Validate the graph\\n self.validate()\\n # Generate the dependency list\\n dep_list = []\\n for rosdep_key in self:\\n if self[rosdep_key]['is_root']:\\n dep_list.extend(self.__get_ordered_uninstalled(rosdep_key))\\n # Make the list unique and remove empty entries\\n result = []\\n for item in dep_list:\\n if item not in result and item[1] != []:\\n result.append(item)\\n # Squash the results by installer_key\\n squashed_result = []\\n previous_installer_key = None\\n for installer_key, resolved in result:\\n if previous_installer_key != installer_key:\\n squashed_result.append((installer_key, []))\\n previous_installer_key = installer_key\\n squashed_result[-1][1].extend(resolved)\\n return squashed_result\",\n \"def sort(self):\\n out = []\\n while len(self.x) > 0:\\n self.x[0], self.x[-1] = self.x[-1], self.x[0]\\n out.append(self.x.pop())\\n BinaryMaxHeap.max_heapify(self.x, 0)\\n\\n return out\",\n \"def toposort(prereqs_d):\\r\\n\\r\\n# all1 = set(prereqs_d.keys())\\r\\n# all2 = set()\\r\\n# for x, y in prereqs_d.items():\\r\\n# all2.update(y)\\r\\n# print all1.difference(all2)\\r\\n\\r\\n seq = []\\r\\n done = set()\\r\\n postreqs_d = {}\\r\\n for x, prereqs in prereqs_d.items():\\r\\n for prereq in prereqs:\\r\\n postreqs_d.setdefault(prereq, set()).add(x)\\r\\n next = set([k for k in prereqs_d if not prereqs_d[k]])\\r\\n while next:\\r\\n bases = next\\r\\n next = set()\\r\\n for x in bases:\\r\\n done.add(x)\\r\\n seq.append(x)\\r\\n for x in bases:\\r\\n for postreq in postreqs_d.get(x, []):\\r\\n if not prereqs_d[postreq].difference(done):\\r\\n next.add(postreq)\\r\\n if len(prereqs_d) != len(seq):\\r\\n raise Exception(\\\"Cannot sort topologically: there might be cycles, \\\"\\r\\n \\\"prereqs_d does not have a key for each element or \\\"\\r\\n \\\"some orderings contain invalid elements.\\\")\\r\\n return seq\",\n \"def get_cases(self) -> list:\\n case_list = [(key, selection_text(case))\\n for key, case in self.cases.items()\\n if hasattr(case, 'case_id')]\\n case_list.sort(key=lambda x: x[1])\\n return case_list\",\n \"def find_over_counting_problems(self) -> list[OverCountingProblem]:\\n for categorization in self.categorization_a, self.categorization_b:\\n if not categorization.hierarchical:\\n raise ValueError(\\n f\\\"{categorization} is not hierarchical, without \\\"\\n f\\\"a hierarchy, over counting can not be evaluated.\\\"\\n )\\n if not categorization.total_sum: # type: ignore\\n raise ValueError(\\n f\\\"For {categorization} it is not specified that the \\\"\\n f\\\"sum of a set of children equals the parent, so \\\"\\n f\\\"over counting can not be evaluated.\\\"\\n )\\n\\n problems = []\\n for categorization in self.categorization_a, self.categorization_b:\\n # used to cache costly descendant evaluation\\n descendants: dict[str, set[str]] = {}\\n for category in categorization.values():\\n prob = self._check_over_counting_category(\\n category, categorization, descendants # type: ignore\\n )\\n if prob:\\n problems.append(prob)\\n\\n return problems\",\n \"def __qualitaetsListeProteins(self):\\n rv = []\\n pam30_sortierbar = {}\\n for key in pam30.keys():\\n pam30_sortierbar[str(pam30[key]) + \\\";\\\" + ''.join(key)] = pam30[key]\\n if key[0] != key[1]:\\n pam30_sortierbar[\\n str(pam30[key]) + \\\";\\\" + ''.join((key[1], key[0]))\\n ] = pam30[key]\\n sorted_keys = list(pam30_sortierbar.keys())\\n sorted_keys.sort(key=lambda k: int(k.split(\\\";\\\")[0]), reverse=True)\\n # debugging kept for historical reasons\\n # for key in iter(sorted_keys):\\n # print(key.split(\\\";\\\")[1] + \\\" has score \\\" + str(pam30_sortierbar[key]))\\n for key in iter(sorted_keys):\\n rv.append(key.split(\\\";\\\")[1])\\n return(rv)\",\n \"def get_all_from_file(cls, fileID, problemDir):\\n\\n # Load the module\\n if problemDir not in sys.path:\\n sys.path.insert(0, problemDir)\\n try:\\n problemModule = __import__(fileID)\\n except Exception as e: # pragma: no cover\\n warn(\\\"Could not import file \\\" + fileID)\\n print(e)\\n return []\\n\\n foundProblems = [] # Holds the TestProblems we find in this file\\n\\n # Look for a dictionary\\n PROBLEM_LIST = [\\\"problems\\\"]\\n for problemList in PROBLEM_LIST:\\n if problemList in [name for name in dir(problemModule)]:\\n problems = getattr(problemModule, \\\"problems\\\")\\n for problemDict in problems:\\n foundProblems.append(cls.process_problem_dict(**problemDict))\\n if len(foundProblems) == 0: # pragma: no cover\\n warn(fileID + \\\" contains no problem objects.\\\")\\n return foundProblems\",\n \"def reorder( self ):\\n self.sorted.sort(self.compareFunction)\",\n \"def all_games(self):\\r\\n return sorted(self.games + list(g for sp in self.sub_pools for g in sp.games) + self.finals,\\r\\n key=lambda g: (g.datetime, g.pitch.rank))\",\n \"def sort_suggestions(\\n suggestions: List[Tuple[Set[str], float]]\\n) -> List[Tuple[Set[str], float]]:\\n confidence_list = [suggestion[1] for suggestion in suggestions]\\n sort_index = sorted(range(len(confidence_list)), key=lambda k: confidence_list[k])\\n # Inverse the sort\\n sort_index = sort_index[::-1]\\n return [suggestions[i] for i in sort_index]\",\n \"def as_sorted_list(options):\\n if len(options) > 0:\\n options.sort(None, key=lambda o: o.number)\\n return options\",\n \"def reordered_rules(self):\\n return tuple(r for r in self.rules if r.reordered)\",\n \"def test_api_can_sort_issues_correctly(self):\\n issues_list = Issue.objects.values_list('experience_needed').order_by('experience_needed')\\n response = self.client.get('/issues/?ordering=experience_needed', format=\\\"json\\\")\\n response_content = json.loads(response.content)\\n for i in xrange(len(issues_list)):\\n self.assertEqual(issues_list[i][0], response_content[i]['experience_needed'])\",\n \"def challenges(self):\\n return [gc.challenge for gc in GrandChallenge.objects.filter(round=self.round_number).order_by('challenge__status')]\",\n \"def get_puzzle(self):\\n return [[str(self.puzzle[i][j]) for j in range(len(self.puzzle[0]))] for i in range(len(self.puzzle))]\",\n \"def sorted(self):\\n sorted_list = [x for x in self.events.iteritems()]\\n sorted_list.sort(key=lambda x: x[1])\\n return sorted_list\",\n \"def get_problem_list(self, course_id, user_id):\\r\\n return {'success': True, 'error': 'No problems found.'}\",\n \"def get_bugs(self):\\n return self.execute(TABELLE['bugs']['select'])\",\n \"def GetRulePriorities(self, G, results):\\n rules = results.Defrules\\n proceededRules = []\\n curRulesSet = []\\n curSalience = 10000\\n\\n # Fill the list of input templates, would be unshanged through all processing\\n inputTemplates = set()\\n for rule in rules:\\n for tmp in rule.InputTemplates:\\n inputTemplates.add(tmp.TemplateName)\\n\\n # We have to proceed all rules\\n while len(proceededRules) < len(rules):\\n outputTemplates = set()\\n\\n # Tke outputing template names from not proceeded rules\\n for rule in rules:\\n if not rule in proceededRules:\\n for tmp in rule.OutputTemplates:\\n outputTemplates.add(tmp.TemplateName)\\n\\n # Valid input templates at current iteration are the ones which are not produced with existing rules\\n validInputs = inputTemplates.difference(outputTemplates)\\n\\n # Get applicable rules from not proceeded\\n curRuleSet = [rule for rule in rules if not rule in proceededRules and len(set([templ.TemplateName for templ in rule.InputTemplates]).difference(validInputs)) == 0]\\n\\n # Set the proper priority for selected rules\\n for rule in curRuleSet:\\n rule.Priority = curSalience\\n\\n # Update proceeded list\\n proceededRules += curRuleSet\\n\\n # Decrease salience\\n curSalience -= 1\",\n \"def part_build_order(self):\\n priority = []\\n for geo_item in self.build_order:\\n if geo_item in self.parts:\\n priority += [geo_item]\\n return priority\",\n \"def getSorted(self):\\n return sorted(self.contacts)\",\n \"def test_returns_sorted_projects_by_difficulty_if_sort_by_set_to_difficulty(self):\\n # Arrange\\n # Set difficulty of test_project_1 to easy.\\n self.test_project_1.difficulty = ProjectDifficulty.EASY.value\\n self.test_project_1.save()\\n # Set project_2 to be allowed for all users removing as private.\\n self.test_project_2.private = False\\n self.test_project_2.difficulty = ProjectDifficulty.MODERATE.value\\n self.test_project_2.save()\\n # Set difficulty of test_project_1 to hard and status to published.\\n self.test_project_3.status = ProjectStatus.PUBLISHED.value\\n self.test_project_3.difficulty = ProjectDifficulty.CHALLENGING.value\\n self.test_project_3.save()\\n\\n # Test for descending order\\n # Act\\n response_desc = self.client.get(\\n self.url,\\n headers={\\\"Authorization\\\": self.user_session_token},\\n query_string={\\\"orderBy\\\": \\\"difficulty\\\", \\\"orderByType\\\": \\\"DESC\\\"},\\n )\\n # Assert\\n self.assertEqual(response_desc.status_code, 200)\\n self.assertEqual(len(response_desc.json[\\\"results\\\"]), 3)\\n expected_desc_order = [\\n self.test_project_3.id,\\n self.test_project_2.id,\\n self.test_project_1.id,\\n ]\\n self.assertListEqual(\\n [i[\\\"projectId\\\"] for i in response_desc.json[\\\"results\\\"]], expected_desc_order\\n )\\n\\n # Test for ascending order\\n # Act\\n response_asc = self.client.get(\\n self.url,\\n headers={\\\"Authorization\\\": self.user_session_token},\\n query_string={\\\"orderBy\\\": \\\"difficulty\\\", \\\"orderByType\\\": \\\"ASC\\\"},\\n )\\n # Assert\\n self.assertEqual(response_asc.status_code, 200)\\n self.assertEqual(len(response_asc.json[\\\"results\\\"]), 3)\\n self.assertListEqual(\\n [i[\\\"projectId\\\"] for i in response_asc.json[\\\"results\\\"]],\\n expected_desc_order[::-1],\\n )\",\n \"def sortChoices(self):\\n self.formatList.sort()\",\n \"def sort_collection(self):\\n sorted_tallies = []\\n sorted = []\\n collection_counter = self.create_collection_counter() \\n for k, v in collection_counter.iteritems():\\n sorted_tallies.append((str(v) + ': ' + k))\\n print sorted_tallies\\n print 8*'\\\\n', 100*'L'\\n print 'PRINT SORTED TALLIES RAN (LINE 74)'\\n for element in sorted_tallies:\\n print element\\n print 8*'\\\\n', 100*'M'\\n print 'PRINT ELEMENT IN SORTED TALLIES LOOP HAS RAN'\\n return sorted_tallies\",\n \"def sort(self):\\n self.cards.sort()\",\n \"def sort(self):\\n self.cards.sort()\",\n \"def items(self):\\n result = self._dict.keys( )\\n try: result.sort( )\\n except: pass\\n\\n return result\",\n \"def topological_sort(g) -> list:\\n dfs(g)\\n res = [v for v in g]\\n quick_sort(res, key=lambda v: v.finish_time)\\n res.reverse()\\n return res\",\n \"def getList(self):\\n\\treturn self.sorted_list.getList()\",\n \"def sort(self, descending):\\n\\t\\tself.membersWithErrors.sort(key = lambda l: l[1], reverse = descending)\",\n \"def sort_population(self):\\n self.population.sort(key=lambda x: x.fitness, reverse=True)\",\n \"def get_tents(self):\\n answ = []\\n for tent in self.sort_tents(self.tents.copy()):\\n answ.append(str(tent))\\n return answ\",\n \"def sorted_tags(self):\\n return sorted(self.tags, key=lambda x: x.name)\",\n \"def give_puzzle(self) -> List[int]:\\n return self._puzzle\",\n \"def get_all_solutions(use_local_cache) -> List[SolutionInfo]:\\n if use_local_cache:\\n scores = get_from_file()\\n else:\\n scores = get_from_web()\\n\\n return [SolutionInfo(user=item['login'], hole=item['hole'], lang=item['lang'],\\n strokes=int(item['strokes']), submitted=item['submitted'])\\n for item in scores]\",\n \"def get_listu_dilucijskih_jedinica(self):\\n popis = sorted(list(self.dilucijskeJedinice.keys()))\\n return popis\",\n \"def get_top(self, difficulty):\\n return list(self.database[difficulty].find().sort('time').limit(MAX_LEADERS))\",\n \"def sort(self):\\n self.list.sort(key=lambda x: ''.join)\",\n \"def allBroken(request, page=1):\\n objects = im.Issue.objects.filter(resolved_state__isnull=True)\\n args = utils.generatePageList(request, objects, page)\\n args['issues'] = args['objects']\\n issues_list = {'Issues on Unusable Machines':[]}\\n for issue in args['issues']:\\n iss_id = issue.item.item_id\\n machine = mac.Item.objects.get(item_id=iss_id)\\n\\n if machine.unusable:\\n issues_list['Issues on Unusable Machines'].append(issue)\\n\\n args['object_list'] = issues_list.items() \\n args['no_results'] = args['page'].object_list.count() < 1\\n return render_to_response(\\\"grouped_issue_list.html\\\", args,\\n context_instance=RequestContext(request))\",\n \"def get_all_bugs(self) -> List:\\n #starting point\\n offset = 0\\n #list for all bugs\\n resultBugList = []\\n #list for bug IDs\\n bugIDList = []\\n #checks if there are still results returned\\n notEmpty = True\\n\\n #queries in 500 bug steps until the result list is empty\\n while notEmpty:\\n print(\\\"entered\\\")\\n #interpretation of result as list plus formatting for eval errors\\n result = ast.literal_eval(self.session.get(self.bugURL + \\\"&offset=\\\" + str(offset)).text.\\n replace('true', 'True').replace('false', 'False').replace('null', 'None'))[\\\"bugs\\\"]\\n #checks if the query needs to be set again with a new offset\\n if result:\\n resultBugList += result\\n else:\\n notEmpty = False\\n\\n #gets the ID out of all comments\\n partList = [bug[\\\"id\\\"] for bug in result]\\n bugIDList += partList\\n #sets new starting point\\n offset += 500\\n\\n #inserts bug ids and bugs into db if given one\\n if self.mongoDB:\\n for id in bugIDList:\\n self.mongoDB[\\\"BugIDs\\\"].insert_one({\\\"ID\\\": id})\\n self.mongoDB[\\\"BugsData\\\"].insert_many(resultBugList)\\n\\n #creates files for bug ids and bugs if given a folder\\n if self.folder:\\n #saves bug list as python object\\n with open(self.folderpath + \\\"bugIDListP.pickle\\\", \\\"wb\\\") as a:\\n pickle.dump(bugIDList, a)\\n #saves bug list as csv\\n with open(self.folderpath + \\\"bugIDList.csv\\\", \\\"w\\\") as b:\\n for id in bugIDList:\\n b.write(str(id) + \\\"\\\\n\\\")\\n with open(self.folderpath + \\\"bugsData.txt\\\", \\\"w\\\") as c:\\n for bug in resultBugList:\\n c.write(str(bug) + \\\"\\\\n\\\")\\n\\n #returns List Object for further processing\\n return(bugIDList)\",\n \"def sort():\\n return -1\",\n \"def get_best_n_description(self, n: int) -> list:\\n self._population.sort(key=lambda chromosome: chromosome.get_fitness())\\n descriptions = []\\n\\n number = min(n, len(self._population))\\n for i in range(number):\\n descriptions.append(str(self._population[i]))\\n\\n return descriptions\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6703581","0.6595729","0.62168723","0.6204288","0.6180413","0.61678904","0.6021558","0.5934097","0.5893262","0.58796406","0.58487594","0.5641037","0.5635462","0.5628445","0.5586737","0.5583212","0.54723084","0.5472102","0.547204","0.5451602","0.54358476","0.541411","0.54058313","0.5405021","0.5364568","0.5360199","0.5359589","0.5339819","0.53395486","0.53188103","0.53144026","0.53069776","0.5302474","0.5296865","0.52899605","0.5289288","0.5285551","0.5281136","0.52793646","0.52751744","0.5270713","0.526183","0.52596843","0.5253269","0.52450466","0.52343833","0.5233722","0.52289957","0.52176505","0.52074224","0.5185482","0.51824474","0.51703006","0.51702577","0.5157157","0.5156434","0.5147699","0.5146875","0.5139695","0.5136904","0.51333535","0.5108489","0.5107471","0.5105318","0.5105298","0.51041764","0.509951","0.5097441","0.50829196","0.5077681","0.50704694","0.50646985","0.50621957","0.5054395","0.5047853","0.5046832","0.5033864","0.50283784","0.500607","0.5004702","0.4992297","0.49922523","0.4988069","0.4988069","0.49876946","0.49811125","0.49793348","0.4978345","0.49765566","0.49746576","0.4971899","0.49671626","0.49424842","0.49407166","0.4937525","0.49374062","0.49344566","0.49304086","0.49282423","0.49279392"],"string":"[\n \"0.6703581\",\n \"0.6595729\",\n \"0.62168723\",\n \"0.6204288\",\n \"0.6180413\",\n \"0.61678904\",\n \"0.6021558\",\n \"0.5934097\",\n \"0.5893262\",\n \"0.58796406\",\n \"0.58487594\",\n \"0.5641037\",\n \"0.5635462\",\n \"0.5628445\",\n \"0.5586737\",\n \"0.5583212\",\n \"0.54723084\",\n \"0.5472102\",\n \"0.547204\",\n \"0.5451602\",\n \"0.54358476\",\n \"0.541411\",\n \"0.54058313\",\n \"0.5405021\",\n \"0.5364568\",\n \"0.5360199\",\n \"0.5359589\",\n \"0.5339819\",\n \"0.53395486\",\n \"0.53188103\",\n \"0.53144026\",\n \"0.53069776\",\n \"0.5302474\",\n \"0.5296865\",\n \"0.52899605\",\n \"0.5289288\",\n \"0.5285551\",\n \"0.5281136\",\n \"0.52793646\",\n \"0.52751744\",\n \"0.5270713\",\n \"0.526183\",\n \"0.52596843\",\n \"0.5253269\",\n \"0.52450466\",\n \"0.52343833\",\n \"0.5233722\",\n \"0.52289957\",\n \"0.52176505\",\n \"0.52074224\",\n \"0.5185482\",\n \"0.51824474\",\n \"0.51703006\",\n \"0.51702577\",\n \"0.5157157\",\n \"0.5156434\",\n \"0.5147699\",\n \"0.5146875\",\n \"0.5139695\",\n \"0.5136904\",\n \"0.51333535\",\n \"0.5108489\",\n \"0.5107471\",\n \"0.5105318\",\n \"0.5105298\",\n \"0.51041764\",\n \"0.509951\",\n \"0.5097441\",\n \"0.50829196\",\n \"0.5077681\",\n \"0.50704694\",\n \"0.50646985\",\n \"0.50621957\",\n \"0.5054395\",\n \"0.5047853\",\n \"0.5046832\",\n \"0.5033864\",\n \"0.50283784\",\n \"0.500607\",\n \"0.5004702\",\n \"0.4992297\",\n \"0.49922523\",\n \"0.4988069\",\n \"0.4988069\",\n \"0.49876946\",\n \"0.49811125\",\n \"0.49793348\",\n \"0.4978345\",\n \"0.49765566\",\n \"0.49746576\",\n \"0.4971899\",\n \"0.49671626\",\n \"0.49424842\",\n \"0.49407166\",\n \"0.4937525\",\n \"0.49374062\",\n \"0.49344566\",\n \"0.49304086\",\n \"0.49282423\",\n \"0.49279392\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.75329185"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":16889,"cells":{"query":{"kind":"string","value":"This is the stub name that will be used to generate the processed filenames and is the assumed stub for the raw data filename."},"document":{"kind":"string","value":"def get_dataset_name(self):\n return self.dataset_name"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _getfilename(self):\n pass","def data_filename(self) -> str: # type: ignore[return-value]\n return os.path.abspath(self.name) # type: ignore","def GetFileName(self) -> \"char const *\":\n return _itkVTKPolyDataReaderPython.itkVTKPolyDataReaderMF2_GetFileName(self)","def filename(self):\r\n\t\treturn None","def GetFileName():\r\n d = GetData()\r\n return d.filename","def handle_file_name(self):\r\n self.tmp_name = (os.path.basename(self.source_file_name)).split('.')[0]\r\n result_name = self.tmp_name + '_result_'\r\n log_name = self.tmp_name + '_log.csv'\r\n \r\n self.result_file_name = os.path.join(self.save_path , result_name) \r\n self.log_file_name = os.path.join(self.log_path , log_name)","def filename(self):\n return '%s%s' % (self.identifier, self.extension)","def GetFileName(self) -> \"char const *\":\n return _itkVTKPolyDataReaderPython.itkVTKPolyDataReaderMF3_GetFileName(self)","def raw_file_names(self):\n return self.filename","def GetFileName(self) -> \"char const *\":\n return _itkVTKPolyDataReaderPython.itkVTKPolyDataReaderMD2_GetFileName(self)","def init_data_file_name():\n now = datetime.datetime.now().isoformat().split('.')[0].replace(':', '-')\n filename = 'show-commands-' + now + \".txt\"\n return filename","def _initNames(self):\n self.outselect = os.path.join(self.workpath, 'FT1_selected'+self.suffix+'.fits')\n self.outmktime = os.path.join(self.workpath, 'FT1_filtered'+self.suffix+'.fits')\n self.outltcube = os.path.join(self.workpath, 'LtCube'+self.suffix+'.fits')\n self.outbincub = os.path.join(self.workpath, 'BinCube'+self.suffix+'.fits')\n self.outbinmap = os.path.join(self.workpath, 'CMAP'+self.suffix+'.fits')\n self.outbinexp = os.path.join(self.workpath, 'BinExpMap'+self.suffix+'.fits')\n self.outexpmap = os.path.join(self.workpath, 'ExpMap'+self.suffix+'.fits')\n self.outsrcmap = os.path.join(self.workpath, 'SrcMaps'+self.suffix+'.fits')\n self.outgtlike = os.path.join(self.workpath, 'Results'+self.suffix+'.dat')\n self.outmodel = os.path.join(self.workpath, 'OutModel'+self.suffix+'.xml')\n self.outapert = os.path.join(self.workpath, 'LC_ApPhoto'+self.suffix+'.fits')\n self.outgtmod = os.path.join(self.workpath, 'GtModel'+self.suffix+'.fits')\n self.outresid = os.path.join(self.workpath, 'Resid'+self.suffix+'.fits')\n self.outresig = os.path.join(self.workpath, 'ResSigma'+self.suffix+'.fits')\n self.outtsmap = os.path.join(self.workpath, 'TSMmap'+self.suffix+'.fits')\n return\n # self.outfind = self.dir + self.src + '_FindSrc'+self.suffix+'.txt'","def data_fname(self):\n # NOTE: second argument of rsplit() is 'maxsplit'\n return self.fname.rsplit('.', 1)[0] + '.dat'","async def filename_generator(self):\n chars = list(string.ascii_letters+string.digits)\n name = ''\n for i in range(random.randint(9, 25)):\n name += random.choice(chars)\n\n if name not in self.player['audio_files']:\n return name\n\n return await self.filename_generator()","def filename(self, *args) -> \"PyObject *\":\n return _ida_fpro.qfile_t_filename(self, *args)","def generateFilename(self, name):\n return self.context.generateUniqueId(type_name='Module')","def __newFileName(self):\n now = datetime.now()\n dateTimeAppend = now.strftime('%y%m%d_%H%M%S')\n self.__fileName = '{}/{}_{}.wav'.format(RECORDING,\n FILE_NAME_PREFIX, \n dateTimeAppend)","def _gen_basename(param_dict, clargs):\n if param_dict['output_basename'] in ['', 'auto']:\n return clargs.input_fname.lower().split('.json')[0]\n\n else:\n return param_dict['output_basename']","def _generate_raw_file_name(self, well, channel, desc):\n \n return \"bPLATE_w\" + well + \"_\" + desc + \"_c\" + channel + \".png\"","def base_filename(self):\n return self.filename.split('.')[0]","def get_data_output_name(prefix: str):\n return settings.data_dir + 'standardized_data_' + prefix","def testFilenameReturn(self):\n self.assertEqual(\n self.filename,\n self.mr.filename\n )\n\n self.mr._filename = 'burp'\n\n self.assertEqual(\n 'burp',\n self.mr.filename\n )","def _get_output_filename(dataset_dir, split_name):\n return '%s/fer_%s.tfrecord' % (dataset_dir, split_name)","def name(self):\n return self._filename","def test_create_final_name(self):\n \n date = \"111111\"\n fcid = \"A11A22BCXX\"\n sample_name = \"P101_150B_index5\"\n \n test_names = [(\"1_{}_{}_1_nophix_1_fastq.txt.gz\".format(date,fcid),\n \"1_{}_{}_{}_1.fastq.gz\".format(date,fcid,sample_name)),\n (\"1_{}_{}_1_nophix_1_fastq.txt\".format(date,fcid),\n \"1_{}_{}_{}_1.fastq\".format(date,fcid,sample_name)),\n (\"1_{}_{}_1_1_fastq.txt.gz\".format(date,fcid),\n \"1_{}_{}_{}_1.fastq.gz\".format(date,fcid,sample_name)),\n (\"{}_CGATGT_L001_R1_001.fastq.gz\".format(sample_name),\n \"1_{}_{}_{}_1.fastq.gz\".format(date,fcid,sample_name)),\n (\"{}_NoIndex_L001_R2_001.fastq.gz\".format(sample_name),\n \"1_{}_{}_{}_2.fastq.gz\".format(date,fcid,sample_name)),\n (\"{}_CGATGT_L001_R1_001.fastq..gz\".format(sample_name),\n \"1_{}_{}_{}_1.fastq.gz\".format(date,fcid,sample_name)),\n (\"{}_CGATGT_L001_R1_001.fastq\".format(sample_name),\n \"1_{}_{}_{}_1.fastq\".format(date,fcid,sample_name))]\n \n for test_fname, exp_result in test_names:\n obs_result = create_final_name(test_fname,date,fcid,sample_name)\n self.assertEqual(obs_result,\n exp_result,\n \"Did not get expected final name ({:s}) for file name {:s}\".format(exp_result,test_fname))\n \n # Try without the _index part of file name\n sample_name_noindex = \"P101_150\"\n test_names = [(\"1_{}_{}_1_nophix_1_fastq.txt.gz\".format(date,fcid),\n \"1_{}_{}_{}_1.fastq.gz\".format(date,fcid,sample_name_noindex)),\n (\"{}_CGATGT_L001_R1_001.fastq.gz\".format(sample_name_noindex),\n \"1_{}_{}_{}_1.fastq.gz\".format(date,fcid,sample_name_noindex)),\n (\"{}_NoIndex_L001_R2_001.fastq.gz\".format(sample_name_noindex),\n \"1_{}_{}_{}_2.fastq.gz\".format(date,fcid,sample_name_noindex))]\n \n for test_fname, exp_result in test_names:\n obs_result = create_final_name(test_fname,date,fcid,sample_name_noindex)\n self.assertEqual(obs_result,\n exp_result,\n \"Did not get expected final name ({:s}) for file name {:s}\".format(exp_result,test_fname))\n \n # Try some illegal file names and assert that they raise exceptions\n test_names = [\"1_{}_{}_1_nophix_1_fastq.gz\".format(date,fcid),\n \"a_{}_{}_1_nophix_1_fastq.txt\".format(date,fcid),\n \"{}_CGATRGT_L1_R1_001.fastq.gz\".format(sample_name)]\n for test_name in test_names:\n with self.assertRaises(ValueError):\n create_final_name(test_name,date,fcid,sample_name)\n \n # Try a file with undetermined reads\n sample_name = \"lane1\"\n test_names = [(\"{}_Undetermined_L001_R1_001.fastq.gz\".format(sample_name),\n \"1_{}_{}_{}_1.fastq.gz\".format(date,fcid,sample_name)),] \n for test_fname, exp_result in test_names:\n obs_result = create_final_name(test_fname,date,fcid,sample_name)\n self.assertEqual(obs_result,\n exp_result,\n \"Did not get expected final name ({:s}) for file name {:s}\".format(exp_result,test_fname))","def filename(self):\n return self.hfile.GetName()","def genBaseName(fileName):\n return fileName.split(\"_\")[0].split(\".\")[0]","def setFileName(self, filename):\r\n #We need to extract the last position of the path. This element is the filename\r\n Splifilename = filename.split(\"\\\\\")\r\n Isofilname = Splifilename[-1]\r\n self.actualFile = Isofilname","def unique_filename(data):\n file = data\n get_ext = file.filename.split(\".\")[-1]\n new_name = \"%s.%s\" % (uuid.uuid4().hex, get_ext)\n return new_name","def generate_raw_filename(self, source_name, table_name, environment, seq_number, upload_time, load_type,\n file_format):\n file_date = upload_time.strftime(\n \"%Y-%m-%d-%H-%M-%S-%f\")[:-3] # [:-3] => Removing the 3 last characters as %f is for millis.\n res = f'{source_name}/{source_name}_{table_name}/' \\\n f'{source_name}_{environment}_{table_name}_{str(seq_number).zfill(3)}_' \\\n f'{file_date}_utc_{load_type}.{file_format}'\n res = res.lower()\n\n # Check if no illegal chars were passed\n #test = FileNameStandardConvention(res)\n #test.check_naming_convention()\n return res","def GetFileName(self) -> \"char const *\":\n return _itkVTKPolyDataReaderPython.itkVTKPolyDataReaderMD3_GetFileName(self)","def file(self):\n\n dlos_filename = super(DlosPhotoz, self).file()\n\n photoz_str = 'DLOS_photoz_'\n \n file_name = photoz_str.join( \n dlos_filename.split('DLOS_')\n ) \n\n return file_name","def get_file_inter_name(self):\n\t\tf = tempfile.NamedTemporaryFile(encoding='utf-8',mode='r',delete=False)\n\t\tf.close()\n\t\treturn f.name","def generate_file_name(self):\n self._session_iterator = None # New file invalidate old interator\n self._img_count += 1\n self._current_file = '{0}/frame_{1}.jpg'.format(self._relative_path,self._img_count)\n return self.current_file","def path_name(self):","def inferTargetName(self):\n target_name = None\n\n for filename in self.files:\n if DUMMY_CPP_FILE_NAME in filename:\n continue\n\n if \"/\" in filename or \"$\" in filename:\n return\n\n name, _ = os.path.splitext(filename)\n\n if target_name is None:\n target_name = name\n elif target_name != name:\n return\n\n if target_name is None:\n return\n\n self.name = target_name","def _get_output_filename(dataset_dir, split_name):\n return '%s/%s*.tfrecord' % (dataset_dir, split_name)","def file_name(self):\n return self.lib.file_name","def base_name(self):\n return \".\".join(posixpath.basename(self.file_name).split(\".\")[:-1])","def name(self):\n #type: ()->Text\n return (\n os.path.splitext(os.path.basename(self.fileName))[0])","def temp_name(self, filename):\n if self.params.get('nopart', False) or filename == '-' or \\\n (os.path.exists(encodeFilename(filename)) and not os.path.isfile(encodeFilename(filename))):\n return filename\n return filename + '.part'","def _get_datafile_name(self, field_name, saveformat, timestep):\n # These formats produce a new file each time\n counted_formats = ('xml', 'xml.gz')\n\n metadata = {}\n\n # Make filename, with or without save count in name\n if saveformat in counted_formats:\n filename = \"%s%d.%s\" % (field_name, timestep, saveformat)\n # If we have a new filename each time, store the name in metadata\n #metadata = [('filename', filename)]\n metadata['filename'] = filename\n elif saveformat == \"shelve\":\n filename = \"%s.%s\" % (field_name, \"db\")\n else:\n filename = \"%s.%s\" % (field_name, saveformat)\n if saveformat == 'hdf5':\n metadata['dataset'] = field_name+str(timestep)\n\n savedir = self.get_savedir(field_name)\n fullname = os.path.join(savedir, filename)\n return fullname, metadata","def filename(self):\n # type: () -> str\n return self._filename","def exe_filename(self):","def _proto_filename_to_generated_header(proto_file: str) -> str:\n filename = os.path.splitext(proto_file)[0]\n return f'{filename}.rpc{PROTO_H_EXTENSION}'","def set_filename(self, file_name):","def dataset_part_filename(dataset_part, num_data):\n if num_data >= 0:\n return '{}_data_{}.npz'.format(dataset_part, str(num_data))\n return '{}_data.npz'.format(dataset_part)","def filename(self):\r\n return self._filename","def __GetLibFileName(cls, src, name):\n bin_path = FileUtils.GetBinPathForFile(src)\n return os.path.join(os.path.dirname(bin_path), '_%s.so' % name)","def source_file_name_feature(self):\n return \"_\".join((C_FILE_NAME, self.file_image_name.value))","def filename(self) -> Optional[str]:\n ...","def get_file_name(replay_dir, template_name):\n suffix = '.json' if not template_name.endswith('.json') else ''\n file_name = f'{template_name}{suffix}'\n return os.path.join(replay_dir, file_name)","def _get_output_filename(dataset_dir, split_name):\n return '%s/cifar100_%s.tfrecord' % (dataset_dir, split_name)","def generate_file_filename(instance, filename):\n return _generate_filename(instance, filename, 'photos')","def _filename(self, corotid):\n from datasource import DataSource\n self.corotid = corotid\n self.corot = DataSource(database='corot', user='sro', host='pina.aip.de')\n \n query = \"\"\"SELECT run_code, hlfccdid, win_id \n FROM corot \n WHERE corotid = %d;\"\"\" % self.corotid\n result = self.corot.query(query)\n \n par = {'run': result[0][0],\n 'half': result[0][1].rstrip('RL'), \n 'win': result[0][2]}\n filename = '/work2/jwe/CoRoT/%(run)s/data/%(run)s_%(half)s_%(win)04d.fits' % par\n logger.info('%d = %s' % (corotid,filename))\n return filename","def file_name(self):\n return self.__file_name","def name(self):\n return self._fname","def name(self):\n return self._fname","def file_name(id, title, kind=\"src\"):\n fn_template = conf.template_source_file_name\n if kind == \"tst\":\n fn_template = conf.template_test_file_name\n\n return fn_template.format(id=id, title=title.replace(\"-\", \"_\"))","def get_name(name, file: str) -> str:\n return os.path.basename(file) if name == \"__main__\" else name","def _opt_file_name(self):\r\n return os.path.join(DATA_DIR, \"otp.pickle\")","def test_file_name(self):\n fitting_report.create_prob_group(result=self.result,\n support_pages_dir=self.dir.name,\n options=self.options)\n file_name = self.result.fitting_report_link\n expected = os.path.abspath(\n os.path.join(self.dir.name, 'prob_0_cf1_m10_[s1]_jj0.html'))\n\n self.assertEqual(file_name, expected)","def test_get_pathless_raw_file_name_json(self):\n\n this_pathless_file_name = probsevere_io._get_pathless_raw_file_name(\n unix_time_sec=VALID_TIME_UNIX_SEC,\n file_extension=probsevere_io.JSON_FILE_EXTENSION)\n\n self.assertTrue(this_pathless_file_name == PATHLESS_JSON_FILE_NAME)","def test_default_format_file_name():\n\toutput_name = format_file_name(test_file)\n\tassert (output_name == test_file[:-4] + \"_no_grave.h5m\") == True","def filename(self):\n if self.__filename__ is None:\n return _filename\n else:\n return self.__filename__","def get_file_name(self):\n return str(self.get_file())","def filename(self):\n translator = {ord(\" \"): \"_\", ord(\",\"): None}\n return f'{self._full_name.translate(translator)}.txt'","def get_name(self):\n return self.file_name","def __GetWrapperFileName(cls, src):\n return FileUtils.GetBinPathForFile(src).replace('.i', '.swig.cc')","def get_file_name(self):\n return self.path.name[6:]","def rf_libFileName(self):\n self.leFileName.clear()\n selItems = self.twLibTree.selectedItems()\n if selItems:\n if selItems[0].itemType == 'file':\n fileName = selItems[0].path.split(os.sep)[-1].replace('.py', '')\n self.leFileName.setText(fileName)","def whisper_filename(self):\r\n source_name = self.source_id and self.source.name or ''\r\n return get_valid_filename(\"{0}__{1}.wsp\".format(source_name,\r\n self.name))","def get_filename(\n self,\n name,\n ext=\".npz\",\n map_tag=None,\n iter_index=None,\n extra_tag=None,\n bp_opts=False,\n ):\n if self.output_root is None:\n return None\n\n if bp_opts:\n if self.ensemble_mean:\n name = \"{}_mean\".format(name)\n elif self.ensemble_median:\n name = \"{}_median\".format(name)\n elif self.sim_index is not None:\n name = \"{}_sim{:04d}\".format(name, self.sim_index)\n if self.signal_type_sim:\n name = \"{}_{}\".format(name, self.signal_type_sim)\n if self.noise_type_sim:\n name = \"{}_{}\".format(name, self.noise_type_sim)\n else:\n if self.data_type != \"raw\":\n name = \"{}_{}\".format(name, self.data_type)\n if getattr(self, \"template_cleaned\", False):\n name = \"{}_clean_{}\".format(name, self.template_type)\n if getattr(self, \"planck_sub\", False):\n name = \"{}_planck_sub\".format(name)\n if self.weighted_bins:\n name = \"{}_wbins\".format(name)\n if getattr(self, \"return_cls\", False):\n name = \"{}_cl\".format(name)\n\n if map_tag is not None:\n name = \"{}_map_{}\".format(name, map_tag)\n if iter_index is not None:\n name = \"{}_iter{:03d}\".format(name, iter_index)\n if extra_tag is not None:\n name = \"{}_{}\".format(name, extra_tag)\n\n tag = \"_{}\".format(self.output_tag) if self.output_tag else \"\"\n if not ext.startswith(\".\"):\n ext = \".{}\".format(ext)\n return os.path.join(self.output_root, \"{}{}{}\".format(name, tag, ext))","def results_filename(self): # FIXME: was called output_name in util\n assert self.filename.endswith(\".krun\")\n return self.filename[:-5] + \"_results.json.bz2\"","def mk_filename(pattern, queryresponserow, resp, url):\n name = None\n if resp:\n cdheader = resp.headers.get(\"Content-Disposition\", None)\n if cdheader:\n _, params = parse_header(cdheader)\n name = params.get('filename', \"\")\n # Work around https://github.com/sunpy/sunpy/issues/3372\n if name.count('\"') >= 2:\n name = name.split('\"')[1]\n\n # This is a hack to to prevent IRIS data from being labelled as XML files\n if name is None and \"VOEvent_IRIS\" not in queryresponserow['fileid']:\n # Advice from the VSO is to fallback to providerid + fileid for a filename\n # As it's possible multiple providers give the same fileid.\n # However, I haven't implemented this yet as it would be a breaking\n # change to the filenames we expect.\n fileid = queryresponserow['fileid']\n\n # Some providers make fileid a path\n # Some also don't specify a file extension, but not a lot we can do\n # about that.\n name = fileid.split(\"/\")[-1]\n\n # If somehow we have got this far with an empty string, fallback to url segment\n if not name:\n name = url.split('/')[-1]\n\n # Remove any not-filename appropriate characters\n name = slugify(name)\n\n # If absolutely everything else fails make a filename based on download time\n if not name:\n name = f\"vso_file_{datetime.datetime.now().strftime('%Y%m%d%H%M%S%f')}\"\n\n fname = pattern.format(file=name,\n **queryresponserow.response_block_map)\n\n return fname","def get_filename(self) -> str:\n\t\treturn self.xml_name","def recovery_file_name():\n return \".failed_download_batches.json\"","def Fragment_a_spin_file_name(self):\n return self.Fragment_a_spin_file","def built_file_basename(self, name, type=None, **kw):\n if not kw.get('bare'):\n if type == self.EXECUTABLE:\n name = name + self._exe\n elif type == self.STATIC_LIB:\n name = self.lib_ + name + self._lib\n elif type == self.SHARED_LIB:\n name = self.dll_ + name + self._dll\n return name","def file_on_disk_name(instance, filename):\n return generate_file_on_disk_name(instance.checksum, filename)","def _get_data_filename(report, yyyymmdd):\n return os.path.join(_DATA_DIRECTORY, report + '_' + yyyymmdd + '.pickle')","def setMethodFilename(self,filename):\n if filename[-4:] != \".imf\":\n filename = filename+\".imf\"\n for i in [1]:\n caputS(self.pvStem+\"PORT\"+str(i)+\":MethodFileName\",filename)","def __init__(self, filename):\r\n self.__output__ = open(format(filename, '08X') + '.gen', 'wb')","def generate_namefile(pathfolder, methodvalues):\n datestr = datetime.datetime.now().date().strftime('%F')\n paramsstr = str(hash(str(methodvalues)))\n namefile = datestr + '-' + methodvalues['codename'] + '_' + paramsstr\n namefile = os.path.join(pathfolder, namefile)\n return namefile","def _blob_file(self, blob_name):\r\n return f\"{self._blob_folder()}/{blob_name}\"","def Fragment_b_spin_file_name(self):\n return self.Fragment_b_spin_file","def filenames(self):\n pass","def get_file_name(self):\n return self._fileName","def output_filename(self, prefix, suffix):\n filename = \"%s%s%s\" % (prefix, _ExecutionWrapper._file_index, suffix)\n _ExecutionWrapper._file_index += 1\n return filename","def test_get_original_file_name_without_duplication_marker(self):\n test_file_name = \"uploaded_file_name\"\n expected_file_name = \"uploaded_file_name\"\n cfs = CustomFileStorage()\n self.assertEqual(cfs.get_original_file_name(test_file_name), expected_file_name)","def generate_filename(player_name):\n name = player_name.split()\n filename = '_'.join(name).lower()\n return filename","def generate_file_name(old_file_name: str) -> str:\r\n return old_file_name.split(\".\")[0] + '_features' + '.npy'","def createname(cls):\n name = config.get(\"pyzombie_filesystem\", \"execbase\")\n name = \"{0}_{1}\".format(name, datetime.utcnow().strftime(\"%Y%jT%H%M%SZ\"))\n if os.path.isdir(Executable.execdirpath(name)):\n #Need to handle the rare case of duplicate resource names---this\n #will happen all the time in testing, but rarely in production.\n index = 0\n altname = \"{0}_{1:03}\".format(name, index)\n while os.path.isdir(Executable.execdirpath(altname)):\n index = index + 1\n altname = \"{0}_{1:03}\".format(name, index)\n name = altname\n return name","def _get_file_name(name: types.TSeedName) -> str:\n return f\"{name}.yml\"","def rsvp_responses_filename(data_filename):\n m = re.match('(.*).csv', data_filename)\n assert m is not None, 'Invalid filename: {}'.format(data_filename)\n rsvp_filename = m.group(1)+'-responses.csv'\n return rsvp_filename","def get_file_name(self):\n return self.__file_name","def file_name(self):\n return self._file_name","def file_name(self):\n return self._file_name","def file_name(self):\n return self._file_name","def file_name(self):\n return self._file_name","def file_name(self):\n return self._file_name"],"string":"[\n \"def _getfilename(self):\\n pass\",\n \"def data_filename(self) -> str: # type: ignore[return-value]\\n return os.path.abspath(self.name) # type: ignore\",\n \"def GetFileName(self) -> \\\"char const *\\\":\\n return _itkVTKPolyDataReaderPython.itkVTKPolyDataReaderMF2_GetFileName(self)\",\n \"def filename(self):\\r\\n\\t\\treturn None\",\n \"def GetFileName():\\r\\n d = GetData()\\r\\n return d.filename\",\n \"def handle_file_name(self):\\r\\n self.tmp_name = (os.path.basename(self.source_file_name)).split('.')[0]\\r\\n result_name = self.tmp_name + '_result_'\\r\\n log_name = self.tmp_name + '_log.csv'\\r\\n \\r\\n self.result_file_name = os.path.join(self.save_path , result_name) \\r\\n self.log_file_name = os.path.join(self.log_path , log_name)\",\n \"def filename(self):\\n return '%s%s' % (self.identifier, self.extension)\",\n \"def GetFileName(self) -> \\\"char const *\\\":\\n return _itkVTKPolyDataReaderPython.itkVTKPolyDataReaderMF3_GetFileName(self)\",\n \"def raw_file_names(self):\\n return self.filename\",\n \"def GetFileName(self) -> \\\"char const *\\\":\\n return _itkVTKPolyDataReaderPython.itkVTKPolyDataReaderMD2_GetFileName(self)\",\n \"def init_data_file_name():\\n now = datetime.datetime.now().isoformat().split('.')[0].replace(':', '-')\\n filename = 'show-commands-' + now + \\\".txt\\\"\\n return filename\",\n \"def _initNames(self):\\n self.outselect = os.path.join(self.workpath, 'FT1_selected'+self.suffix+'.fits')\\n self.outmktime = os.path.join(self.workpath, 'FT1_filtered'+self.suffix+'.fits')\\n self.outltcube = os.path.join(self.workpath, 'LtCube'+self.suffix+'.fits')\\n self.outbincub = os.path.join(self.workpath, 'BinCube'+self.suffix+'.fits')\\n self.outbinmap = os.path.join(self.workpath, 'CMAP'+self.suffix+'.fits')\\n self.outbinexp = os.path.join(self.workpath, 'BinExpMap'+self.suffix+'.fits')\\n self.outexpmap = os.path.join(self.workpath, 'ExpMap'+self.suffix+'.fits')\\n self.outsrcmap = os.path.join(self.workpath, 'SrcMaps'+self.suffix+'.fits')\\n self.outgtlike = os.path.join(self.workpath, 'Results'+self.suffix+'.dat')\\n self.outmodel = os.path.join(self.workpath, 'OutModel'+self.suffix+'.xml')\\n self.outapert = os.path.join(self.workpath, 'LC_ApPhoto'+self.suffix+'.fits')\\n self.outgtmod = os.path.join(self.workpath, 'GtModel'+self.suffix+'.fits')\\n self.outresid = os.path.join(self.workpath, 'Resid'+self.suffix+'.fits')\\n self.outresig = os.path.join(self.workpath, 'ResSigma'+self.suffix+'.fits')\\n self.outtsmap = os.path.join(self.workpath, 'TSMmap'+self.suffix+'.fits')\\n return\\n # self.outfind = self.dir + self.src + '_FindSrc'+self.suffix+'.txt'\",\n \"def data_fname(self):\\n # NOTE: second argument of rsplit() is 'maxsplit'\\n return self.fname.rsplit('.', 1)[0] + '.dat'\",\n \"async def filename_generator(self):\\n chars = list(string.ascii_letters+string.digits)\\n name = ''\\n for i in range(random.randint(9, 25)):\\n name += random.choice(chars)\\n\\n if name not in self.player['audio_files']:\\n return name\\n\\n return await self.filename_generator()\",\n \"def filename(self, *args) -> \\\"PyObject *\\\":\\n return _ida_fpro.qfile_t_filename(self, *args)\",\n \"def generateFilename(self, name):\\n return self.context.generateUniqueId(type_name='Module')\",\n \"def __newFileName(self):\\n now = datetime.now()\\n dateTimeAppend = now.strftime('%y%m%d_%H%M%S')\\n self.__fileName = '{}/{}_{}.wav'.format(RECORDING,\\n FILE_NAME_PREFIX, \\n dateTimeAppend)\",\n \"def _gen_basename(param_dict, clargs):\\n if param_dict['output_basename'] in ['', 'auto']:\\n return clargs.input_fname.lower().split('.json')[0]\\n\\n else:\\n return param_dict['output_basename']\",\n \"def _generate_raw_file_name(self, well, channel, desc):\\n \\n return \\\"bPLATE_w\\\" + well + \\\"_\\\" + desc + \\\"_c\\\" + channel + \\\".png\\\"\",\n \"def base_filename(self):\\n return self.filename.split('.')[0]\",\n \"def get_data_output_name(prefix: str):\\n return settings.data_dir + 'standardized_data_' + prefix\",\n \"def testFilenameReturn(self):\\n self.assertEqual(\\n self.filename,\\n self.mr.filename\\n )\\n\\n self.mr._filename = 'burp'\\n\\n self.assertEqual(\\n 'burp',\\n self.mr.filename\\n )\",\n \"def _get_output_filename(dataset_dir, split_name):\\n return '%s/fer_%s.tfrecord' % (dataset_dir, split_name)\",\n \"def name(self):\\n return self._filename\",\n \"def test_create_final_name(self):\\n \\n date = \\\"111111\\\"\\n fcid = \\\"A11A22BCXX\\\"\\n sample_name = \\\"P101_150B_index5\\\"\\n \\n test_names = [(\\\"1_{}_{}_1_nophix_1_fastq.txt.gz\\\".format(date,fcid),\\n \\\"1_{}_{}_{}_1.fastq.gz\\\".format(date,fcid,sample_name)),\\n (\\\"1_{}_{}_1_nophix_1_fastq.txt\\\".format(date,fcid),\\n \\\"1_{}_{}_{}_1.fastq\\\".format(date,fcid,sample_name)),\\n (\\\"1_{}_{}_1_1_fastq.txt.gz\\\".format(date,fcid),\\n \\\"1_{}_{}_{}_1.fastq.gz\\\".format(date,fcid,sample_name)),\\n (\\\"{}_CGATGT_L001_R1_001.fastq.gz\\\".format(sample_name),\\n \\\"1_{}_{}_{}_1.fastq.gz\\\".format(date,fcid,sample_name)),\\n (\\\"{}_NoIndex_L001_R2_001.fastq.gz\\\".format(sample_name),\\n \\\"1_{}_{}_{}_2.fastq.gz\\\".format(date,fcid,sample_name)),\\n (\\\"{}_CGATGT_L001_R1_001.fastq..gz\\\".format(sample_name),\\n \\\"1_{}_{}_{}_1.fastq.gz\\\".format(date,fcid,sample_name)),\\n (\\\"{}_CGATGT_L001_R1_001.fastq\\\".format(sample_name),\\n \\\"1_{}_{}_{}_1.fastq\\\".format(date,fcid,sample_name))]\\n \\n for test_fname, exp_result in test_names:\\n obs_result = create_final_name(test_fname,date,fcid,sample_name)\\n self.assertEqual(obs_result,\\n exp_result,\\n \\\"Did not get expected final name ({:s}) for file name {:s}\\\".format(exp_result,test_fname))\\n \\n # Try without the _index part of file name\\n sample_name_noindex = \\\"P101_150\\\"\\n test_names = [(\\\"1_{}_{}_1_nophix_1_fastq.txt.gz\\\".format(date,fcid),\\n \\\"1_{}_{}_{}_1.fastq.gz\\\".format(date,fcid,sample_name_noindex)),\\n (\\\"{}_CGATGT_L001_R1_001.fastq.gz\\\".format(sample_name_noindex),\\n \\\"1_{}_{}_{}_1.fastq.gz\\\".format(date,fcid,sample_name_noindex)),\\n (\\\"{}_NoIndex_L001_R2_001.fastq.gz\\\".format(sample_name_noindex),\\n \\\"1_{}_{}_{}_2.fastq.gz\\\".format(date,fcid,sample_name_noindex))]\\n \\n for test_fname, exp_result in test_names:\\n obs_result = create_final_name(test_fname,date,fcid,sample_name_noindex)\\n self.assertEqual(obs_result,\\n exp_result,\\n \\\"Did not get expected final name ({:s}) for file name {:s}\\\".format(exp_result,test_fname))\\n \\n # Try some illegal file names and assert that they raise exceptions\\n test_names = [\\\"1_{}_{}_1_nophix_1_fastq.gz\\\".format(date,fcid),\\n \\\"a_{}_{}_1_nophix_1_fastq.txt\\\".format(date,fcid),\\n \\\"{}_CGATRGT_L1_R1_001.fastq.gz\\\".format(sample_name)]\\n for test_name in test_names:\\n with self.assertRaises(ValueError):\\n create_final_name(test_name,date,fcid,sample_name)\\n \\n # Try a file with undetermined reads\\n sample_name = \\\"lane1\\\"\\n test_names = [(\\\"{}_Undetermined_L001_R1_001.fastq.gz\\\".format(sample_name),\\n \\\"1_{}_{}_{}_1.fastq.gz\\\".format(date,fcid,sample_name)),] \\n for test_fname, exp_result in test_names:\\n obs_result = create_final_name(test_fname,date,fcid,sample_name)\\n self.assertEqual(obs_result,\\n exp_result,\\n \\\"Did not get expected final name ({:s}) for file name {:s}\\\".format(exp_result,test_fname))\",\n \"def filename(self):\\n return self.hfile.GetName()\",\n \"def genBaseName(fileName):\\n return fileName.split(\\\"_\\\")[0].split(\\\".\\\")[0]\",\n \"def setFileName(self, filename):\\r\\n #We need to extract the last position of the path. This element is the filename\\r\\n Splifilename = filename.split(\\\"\\\\\\\\\\\")\\r\\n Isofilname = Splifilename[-1]\\r\\n self.actualFile = Isofilname\",\n \"def unique_filename(data):\\n file = data\\n get_ext = file.filename.split(\\\".\\\")[-1]\\n new_name = \\\"%s.%s\\\" % (uuid.uuid4().hex, get_ext)\\n return new_name\",\n \"def generate_raw_filename(self, source_name, table_name, environment, seq_number, upload_time, load_type,\\n file_format):\\n file_date = upload_time.strftime(\\n \\\"%Y-%m-%d-%H-%M-%S-%f\\\")[:-3] # [:-3] => Removing the 3 last characters as %f is for millis.\\n res = f'{source_name}/{source_name}_{table_name}/' \\\\\\n f'{source_name}_{environment}_{table_name}_{str(seq_number).zfill(3)}_' \\\\\\n f'{file_date}_utc_{load_type}.{file_format}'\\n res = res.lower()\\n\\n # Check if no illegal chars were passed\\n #test = FileNameStandardConvention(res)\\n #test.check_naming_convention()\\n return res\",\n \"def GetFileName(self) -> \\\"char const *\\\":\\n return _itkVTKPolyDataReaderPython.itkVTKPolyDataReaderMD3_GetFileName(self)\",\n \"def file(self):\\n\\n dlos_filename = super(DlosPhotoz, self).file()\\n\\n photoz_str = 'DLOS_photoz_'\\n \\n file_name = photoz_str.join( \\n dlos_filename.split('DLOS_')\\n ) \\n\\n return file_name\",\n \"def get_file_inter_name(self):\\n\\t\\tf = tempfile.NamedTemporaryFile(encoding='utf-8',mode='r',delete=False)\\n\\t\\tf.close()\\n\\t\\treturn f.name\",\n \"def generate_file_name(self):\\n self._session_iterator = None # New file invalidate old interator\\n self._img_count += 1\\n self._current_file = '{0}/frame_{1}.jpg'.format(self._relative_path,self._img_count)\\n return self.current_file\",\n \"def path_name(self):\",\n \"def inferTargetName(self):\\n target_name = None\\n\\n for filename in self.files:\\n if DUMMY_CPP_FILE_NAME in filename:\\n continue\\n\\n if \\\"/\\\" in filename or \\\"$\\\" in filename:\\n return\\n\\n name, _ = os.path.splitext(filename)\\n\\n if target_name is None:\\n target_name = name\\n elif target_name != name:\\n return\\n\\n if target_name is None:\\n return\\n\\n self.name = target_name\",\n \"def _get_output_filename(dataset_dir, split_name):\\n return '%s/%s*.tfrecord' % (dataset_dir, split_name)\",\n \"def file_name(self):\\n return self.lib.file_name\",\n \"def base_name(self):\\n return \\\".\\\".join(posixpath.basename(self.file_name).split(\\\".\\\")[:-1])\",\n \"def name(self):\\n #type: ()->Text\\n return (\\n os.path.splitext(os.path.basename(self.fileName))[0])\",\n \"def temp_name(self, filename):\\n if self.params.get('nopart', False) or filename == '-' or \\\\\\n (os.path.exists(encodeFilename(filename)) and not os.path.isfile(encodeFilename(filename))):\\n return filename\\n return filename + '.part'\",\n \"def _get_datafile_name(self, field_name, saveformat, timestep):\\n # These formats produce a new file each time\\n counted_formats = ('xml', 'xml.gz')\\n\\n metadata = {}\\n\\n # Make filename, with or without save count in name\\n if saveformat in counted_formats:\\n filename = \\\"%s%d.%s\\\" % (field_name, timestep, saveformat)\\n # If we have a new filename each time, store the name in metadata\\n #metadata = [('filename', filename)]\\n metadata['filename'] = filename\\n elif saveformat == \\\"shelve\\\":\\n filename = \\\"%s.%s\\\" % (field_name, \\\"db\\\")\\n else:\\n filename = \\\"%s.%s\\\" % (field_name, saveformat)\\n if saveformat == 'hdf5':\\n metadata['dataset'] = field_name+str(timestep)\\n\\n savedir = self.get_savedir(field_name)\\n fullname = os.path.join(savedir, filename)\\n return fullname, metadata\",\n \"def filename(self):\\n # type: () -> str\\n return self._filename\",\n \"def exe_filename(self):\",\n \"def _proto_filename_to_generated_header(proto_file: str) -> str:\\n filename = os.path.splitext(proto_file)[0]\\n return f'{filename}.rpc{PROTO_H_EXTENSION}'\",\n \"def set_filename(self, file_name):\",\n \"def dataset_part_filename(dataset_part, num_data):\\n if num_data >= 0:\\n return '{}_data_{}.npz'.format(dataset_part, str(num_data))\\n return '{}_data.npz'.format(dataset_part)\",\n \"def filename(self):\\r\\n return self._filename\",\n \"def __GetLibFileName(cls, src, name):\\n bin_path = FileUtils.GetBinPathForFile(src)\\n return os.path.join(os.path.dirname(bin_path), '_%s.so' % name)\",\n \"def source_file_name_feature(self):\\n return \\\"_\\\".join((C_FILE_NAME, self.file_image_name.value))\",\n \"def filename(self) -> Optional[str]:\\n ...\",\n \"def get_file_name(replay_dir, template_name):\\n suffix = '.json' if not template_name.endswith('.json') else ''\\n file_name = f'{template_name}{suffix}'\\n return os.path.join(replay_dir, file_name)\",\n \"def _get_output_filename(dataset_dir, split_name):\\n return '%s/cifar100_%s.tfrecord' % (dataset_dir, split_name)\",\n \"def generate_file_filename(instance, filename):\\n return _generate_filename(instance, filename, 'photos')\",\n \"def _filename(self, corotid):\\n from datasource import DataSource\\n self.corotid = corotid\\n self.corot = DataSource(database='corot', user='sro', host='pina.aip.de')\\n \\n query = \\\"\\\"\\\"SELECT run_code, hlfccdid, win_id \\n FROM corot \\n WHERE corotid = %d;\\\"\\\"\\\" % self.corotid\\n result = self.corot.query(query)\\n \\n par = {'run': result[0][0],\\n 'half': result[0][1].rstrip('RL'), \\n 'win': result[0][2]}\\n filename = '/work2/jwe/CoRoT/%(run)s/data/%(run)s_%(half)s_%(win)04d.fits' % par\\n logger.info('%d = %s' % (corotid,filename))\\n return filename\",\n \"def file_name(self):\\n return self.__file_name\",\n \"def name(self):\\n return self._fname\",\n \"def name(self):\\n return self._fname\",\n \"def file_name(id, title, kind=\\\"src\\\"):\\n fn_template = conf.template_source_file_name\\n if kind == \\\"tst\\\":\\n fn_template = conf.template_test_file_name\\n\\n return fn_template.format(id=id, title=title.replace(\\\"-\\\", \\\"_\\\"))\",\n \"def get_name(name, file: str) -> str:\\n return os.path.basename(file) if name == \\\"__main__\\\" else name\",\n \"def _opt_file_name(self):\\r\\n return os.path.join(DATA_DIR, \\\"otp.pickle\\\")\",\n \"def test_file_name(self):\\n fitting_report.create_prob_group(result=self.result,\\n support_pages_dir=self.dir.name,\\n options=self.options)\\n file_name = self.result.fitting_report_link\\n expected = os.path.abspath(\\n os.path.join(self.dir.name, 'prob_0_cf1_m10_[s1]_jj0.html'))\\n\\n self.assertEqual(file_name, expected)\",\n \"def test_get_pathless_raw_file_name_json(self):\\n\\n this_pathless_file_name = probsevere_io._get_pathless_raw_file_name(\\n unix_time_sec=VALID_TIME_UNIX_SEC,\\n file_extension=probsevere_io.JSON_FILE_EXTENSION)\\n\\n self.assertTrue(this_pathless_file_name == PATHLESS_JSON_FILE_NAME)\",\n \"def test_default_format_file_name():\\n\\toutput_name = format_file_name(test_file)\\n\\tassert (output_name == test_file[:-4] + \\\"_no_grave.h5m\\\") == True\",\n \"def filename(self):\\n if self.__filename__ is None:\\n return _filename\\n else:\\n return self.__filename__\",\n \"def get_file_name(self):\\n return str(self.get_file())\",\n \"def filename(self):\\n translator = {ord(\\\" \\\"): \\\"_\\\", ord(\\\",\\\"): None}\\n return f'{self._full_name.translate(translator)}.txt'\",\n \"def get_name(self):\\n return self.file_name\",\n \"def __GetWrapperFileName(cls, src):\\n return FileUtils.GetBinPathForFile(src).replace('.i', '.swig.cc')\",\n \"def get_file_name(self):\\n return self.path.name[6:]\",\n \"def rf_libFileName(self):\\n self.leFileName.clear()\\n selItems = self.twLibTree.selectedItems()\\n if selItems:\\n if selItems[0].itemType == 'file':\\n fileName = selItems[0].path.split(os.sep)[-1].replace('.py', '')\\n self.leFileName.setText(fileName)\",\n \"def whisper_filename(self):\\r\\n source_name = self.source_id and self.source.name or ''\\r\\n return get_valid_filename(\\\"{0}__{1}.wsp\\\".format(source_name,\\r\\n self.name))\",\n \"def get_filename(\\n self,\\n name,\\n ext=\\\".npz\\\",\\n map_tag=None,\\n iter_index=None,\\n extra_tag=None,\\n bp_opts=False,\\n ):\\n if self.output_root is None:\\n return None\\n\\n if bp_opts:\\n if self.ensemble_mean:\\n name = \\\"{}_mean\\\".format(name)\\n elif self.ensemble_median:\\n name = \\\"{}_median\\\".format(name)\\n elif self.sim_index is not None:\\n name = \\\"{}_sim{:04d}\\\".format(name, self.sim_index)\\n if self.signal_type_sim:\\n name = \\\"{}_{}\\\".format(name, self.signal_type_sim)\\n if self.noise_type_sim:\\n name = \\\"{}_{}\\\".format(name, self.noise_type_sim)\\n else:\\n if self.data_type != \\\"raw\\\":\\n name = \\\"{}_{}\\\".format(name, self.data_type)\\n if getattr(self, \\\"template_cleaned\\\", False):\\n name = \\\"{}_clean_{}\\\".format(name, self.template_type)\\n if getattr(self, \\\"planck_sub\\\", False):\\n name = \\\"{}_planck_sub\\\".format(name)\\n if self.weighted_bins:\\n name = \\\"{}_wbins\\\".format(name)\\n if getattr(self, \\\"return_cls\\\", False):\\n name = \\\"{}_cl\\\".format(name)\\n\\n if map_tag is not None:\\n name = \\\"{}_map_{}\\\".format(name, map_tag)\\n if iter_index is not None:\\n name = \\\"{}_iter{:03d}\\\".format(name, iter_index)\\n if extra_tag is not None:\\n name = \\\"{}_{}\\\".format(name, extra_tag)\\n\\n tag = \\\"_{}\\\".format(self.output_tag) if self.output_tag else \\\"\\\"\\n if not ext.startswith(\\\".\\\"):\\n ext = \\\".{}\\\".format(ext)\\n return os.path.join(self.output_root, \\\"{}{}{}\\\".format(name, tag, ext))\",\n \"def results_filename(self): # FIXME: was called output_name in util\\n assert self.filename.endswith(\\\".krun\\\")\\n return self.filename[:-5] + \\\"_results.json.bz2\\\"\",\n \"def mk_filename(pattern, queryresponserow, resp, url):\\n name = None\\n if resp:\\n cdheader = resp.headers.get(\\\"Content-Disposition\\\", None)\\n if cdheader:\\n _, params = parse_header(cdheader)\\n name = params.get('filename', \\\"\\\")\\n # Work around https://github.com/sunpy/sunpy/issues/3372\\n if name.count('\\\"') >= 2:\\n name = name.split('\\\"')[1]\\n\\n # This is a hack to to prevent IRIS data from being labelled as XML files\\n if name is None and \\\"VOEvent_IRIS\\\" not in queryresponserow['fileid']:\\n # Advice from the VSO is to fallback to providerid + fileid for a filename\\n # As it's possible multiple providers give the same fileid.\\n # However, I haven't implemented this yet as it would be a breaking\\n # change to the filenames we expect.\\n fileid = queryresponserow['fileid']\\n\\n # Some providers make fileid a path\\n # Some also don't specify a file extension, but not a lot we can do\\n # about that.\\n name = fileid.split(\\\"/\\\")[-1]\\n\\n # If somehow we have got this far with an empty string, fallback to url segment\\n if not name:\\n name = url.split('/')[-1]\\n\\n # Remove any not-filename appropriate characters\\n name = slugify(name)\\n\\n # If absolutely everything else fails make a filename based on download time\\n if not name:\\n name = f\\\"vso_file_{datetime.datetime.now().strftime('%Y%m%d%H%M%S%f')}\\\"\\n\\n fname = pattern.format(file=name,\\n **queryresponserow.response_block_map)\\n\\n return fname\",\n \"def get_filename(self) -> str:\\n\\t\\treturn self.xml_name\",\n \"def recovery_file_name():\\n return \\\".failed_download_batches.json\\\"\",\n \"def Fragment_a_spin_file_name(self):\\n return self.Fragment_a_spin_file\",\n \"def built_file_basename(self, name, type=None, **kw):\\n if not kw.get('bare'):\\n if type == self.EXECUTABLE:\\n name = name + self._exe\\n elif type == self.STATIC_LIB:\\n name = self.lib_ + name + self._lib\\n elif type == self.SHARED_LIB:\\n name = self.dll_ + name + self._dll\\n return name\",\n \"def file_on_disk_name(instance, filename):\\n return generate_file_on_disk_name(instance.checksum, filename)\",\n \"def _get_data_filename(report, yyyymmdd):\\n return os.path.join(_DATA_DIRECTORY, report + '_' + yyyymmdd + '.pickle')\",\n \"def setMethodFilename(self,filename):\\n if filename[-4:] != \\\".imf\\\":\\n filename = filename+\\\".imf\\\"\\n for i in [1]:\\n caputS(self.pvStem+\\\"PORT\\\"+str(i)+\\\":MethodFileName\\\",filename)\",\n \"def __init__(self, filename):\\r\\n self.__output__ = open(format(filename, '08X') + '.gen', 'wb')\",\n \"def generate_namefile(pathfolder, methodvalues):\\n datestr = datetime.datetime.now().date().strftime('%F')\\n paramsstr = str(hash(str(methodvalues)))\\n namefile = datestr + '-' + methodvalues['codename'] + '_' + paramsstr\\n namefile = os.path.join(pathfolder, namefile)\\n return namefile\",\n \"def _blob_file(self, blob_name):\\r\\n return f\\\"{self._blob_folder()}/{blob_name}\\\"\",\n \"def Fragment_b_spin_file_name(self):\\n return self.Fragment_b_spin_file\",\n \"def filenames(self):\\n pass\",\n \"def get_file_name(self):\\n return self._fileName\",\n \"def output_filename(self, prefix, suffix):\\n filename = \\\"%s%s%s\\\" % (prefix, _ExecutionWrapper._file_index, suffix)\\n _ExecutionWrapper._file_index += 1\\n return filename\",\n \"def test_get_original_file_name_without_duplication_marker(self):\\n test_file_name = \\\"uploaded_file_name\\\"\\n expected_file_name = \\\"uploaded_file_name\\\"\\n cfs = CustomFileStorage()\\n self.assertEqual(cfs.get_original_file_name(test_file_name), expected_file_name)\",\n \"def generate_filename(player_name):\\n name = player_name.split()\\n filename = '_'.join(name).lower()\\n return filename\",\n \"def generate_file_name(old_file_name: str) -> str:\\r\\n return old_file_name.split(\\\".\\\")[0] + '_features' + '.npy'\",\n \"def createname(cls):\\n name = config.get(\\\"pyzombie_filesystem\\\", \\\"execbase\\\")\\n name = \\\"{0}_{1}\\\".format(name, datetime.utcnow().strftime(\\\"%Y%jT%H%M%SZ\\\"))\\n if os.path.isdir(Executable.execdirpath(name)):\\n #Need to handle the rare case of duplicate resource names---this\\n #will happen all the time in testing, but rarely in production.\\n index = 0\\n altname = \\\"{0}_{1:03}\\\".format(name, index)\\n while os.path.isdir(Executable.execdirpath(altname)):\\n index = index + 1\\n altname = \\\"{0}_{1:03}\\\".format(name, index)\\n name = altname\\n return name\",\n \"def _get_file_name(name: types.TSeedName) -> str:\\n return f\\\"{name}.yml\\\"\",\n \"def rsvp_responses_filename(data_filename):\\n m = re.match('(.*).csv', data_filename)\\n assert m is not None, 'Invalid filename: {}'.format(data_filename)\\n rsvp_filename = m.group(1)+'-responses.csv'\\n return rsvp_filename\",\n \"def get_file_name(self):\\n return self.__file_name\",\n \"def file_name(self):\\n return self._file_name\",\n \"def file_name(self):\\n return self._file_name\",\n \"def file_name(self):\\n return self._file_name\",\n \"def file_name(self):\\n return self._file_name\",\n \"def file_name(self):\\n return self._file_name\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6384839","0.63739693","0.6017186","0.6008499","0.5945327","0.59224993","0.58893055","0.5883021","0.5876685","0.5855248","0.58311427","0.5830096","0.5818306","0.5797959","0.57899445","0.5784805","0.5756411","0.5752741","0.57516646","0.57437134","0.5695716","0.5688893","0.56796145","0.5673257","0.56699","0.56645286","0.5661614","0.56551886","0.564528","0.564341","0.5641678","0.5638578","0.56361514","0.56340903","0.56254196","0.56249017","0.5623625","0.5619447","0.5618773","0.56163436","0.56138515","0.5607489","0.56062126","0.5604661","0.5602729","0.5593918","0.55880845","0.5581938","0.55753154","0.5574037","0.55686843","0.5565574","0.5556711","0.5552707","0.5544132","0.5540051","0.55385274","0.55385274","0.553822","0.5538141","0.55265343","0.5518867","0.55165476","0.5513746","0.5510825","0.551019","0.55034345","0.55010694","0.5487697","0.54851246","0.5469827","0.5467364","0.54667157","0.54524744","0.545002","0.5449532","0.5448165","0.5443437","0.54400235","0.5435071","0.54243284","0.5424208","0.54214","0.54176986","0.5413843","0.5412591","0.5408093","0.5403002","0.5400612","0.5398239","0.5395766","0.5391922","0.5391722","0.539143","0.5383787","0.53815585","0.5379133","0.5379133","0.5379133","0.5379133","0.5379133"],"string":"[\n \"0.6384839\",\n \"0.63739693\",\n \"0.6017186\",\n \"0.6008499\",\n \"0.5945327\",\n \"0.59224993\",\n \"0.58893055\",\n \"0.5883021\",\n \"0.5876685\",\n \"0.5855248\",\n \"0.58311427\",\n \"0.5830096\",\n \"0.5818306\",\n \"0.5797959\",\n \"0.57899445\",\n \"0.5784805\",\n \"0.5756411\",\n \"0.5752741\",\n \"0.57516646\",\n \"0.57437134\",\n \"0.5695716\",\n \"0.5688893\",\n \"0.56796145\",\n \"0.5673257\",\n \"0.56699\",\n \"0.56645286\",\n \"0.5661614\",\n \"0.56551886\",\n \"0.564528\",\n \"0.564341\",\n \"0.5641678\",\n \"0.5638578\",\n \"0.56361514\",\n \"0.56340903\",\n \"0.56254196\",\n \"0.56249017\",\n \"0.5623625\",\n \"0.5619447\",\n \"0.5618773\",\n \"0.56163436\",\n \"0.56138515\",\n \"0.5607489\",\n \"0.56062126\",\n \"0.5604661\",\n \"0.5602729\",\n \"0.5593918\",\n \"0.55880845\",\n \"0.5581938\",\n \"0.55753154\",\n \"0.5574037\",\n \"0.55686843\",\n \"0.5565574\",\n \"0.5556711\",\n \"0.5552707\",\n \"0.5544132\",\n \"0.5540051\",\n \"0.55385274\",\n \"0.55385274\",\n \"0.553822\",\n \"0.5538141\",\n \"0.55265343\",\n \"0.5518867\",\n \"0.55165476\",\n \"0.5513746\",\n \"0.5510825\",\n \"0.551019\",\n \"0.55034345\",\n \"0.55010694\",\n \"0.5487697\",\n \"0.54851246\",\n \"0.5469827\",\n \"0.5467364\",\n \"0.54667157\",\n \"0.54524744\",\n \"0.545002\",\n \"0.5449532\",\n \"0.5448165\",\n \"0.5443437\",\n \"0.54400235\",\n \"0.5435071\",\n \"0.54243284\",\n \"0.5424208\",\n \"0.54214\",\n \"0.54176986\",\n \"0.5413843\",\n \"0.5412591\",\n \"0.5408093\",\n \"0.5403002\",\n \"0.5400612\",\n \"0.5398239\",\n \"0.5395766\",\n \"0.5391922\",\n \"0.5391722\",\n \"0.539143\",\n \"0.5383787\",\n \"0.53815585\",\n \"0.5379133\",\n \"0.5379133\",\n \"0.5379133\",\n \"0.5379133\",\n \"0.5379133\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":16890,"cells":{"query":{"kind":"string","value":"Returns the name of the class attribute to be used for classification."},"document":{"kind":"string","value":"def get_class_attribute(self):\n return self.class_attr"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def name(self) -> str:\n return self.class_names[self.class_num]","def get_attribute_class(self, attr_name):\n return self.attrs.get_attribute_class(attr_name)","def class_name(self) -> str:\n return pulumi.get(self, \"class_name\")","def get_attribute_class(self):\n return self._attr_cls","def class_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"class_name\")","def className(self):\n namevalue = self.__class__.__name__\n return str(namevalue)","def get_attribute_class(self, name):\n self.validate_attribute_name(name)\n return self.schema[name].get_attribute_class()","def get_class_name(self):\n\n if \"class\" in self._root.attrib:\n return self._root.attrib['class']\n else:\n return self._root.tag","def f_get_class_name(self):\n return self.__class__.__name__","def class_attribute(self):\n\n return getattr(self.parent.class_, self.key)","def name(self):\n return self._get_device_class_name()","def __id_attr_name(self):\n return self._attr_name()","def class_name(self) -> str:\n return self.__class__.__name__","def AttributeString(self) -> str:","def AttributeString(self) -> str:","def class_name(cls):\n return cls.__name__","def class_name(cls):\n return cls.__name__","def getClassName(self):\n n = type(self).__name__\n return n","def name_to_label(self, name):\n\t\treturn self.classes[name]","def name_to_label(self, name):\n\t\t\treturn self.classes[name]","def get_attr_name(self, instance):\n if not hasattr(self, '_attr_name'):\n cls = type(instance)\n self._attr_name = get_descriptor_attr_name(self, cls)\n\n assert self._attr_name is not None, (\n 'Could not find the attribute for %r on %r' % (self, cls))\n\n return self._attr_name","def classname(cls):\n return cls.__name__.lower()","def name_to_label(self, name):\n return self.classes[name]","def attribute_key(self) -> str:\n return pulumi.get(self, \"attribute_key\")","def _get_classname(cls):\n return cls.__name__","def attribute_name(name: str) -> str:\n return text.snake_case(utils.safe_snake(name))","def type_name(attr_type: AttrType) -> str:\n return attr_type.native_name or class_name(attr_type.name)","def classifier(self):\n return self.config.get('classifier', \"general\")","def getCustomAttribute(self):\n\t\treturn self.Attribute","def name(cls) -> str:\n return cls.__name__ # type: ignore[attr-defined]","def classname(self):\n if not self.has_classname():\n return \"\"\n\n classname_offset = self.unpack_dword(0x30)\n classname_length = self.unpack_word(0x4A)\n\n offset = self.abs_offset_from_hbin_offset(classname_offset)\n d = HBINCell(self._buf, offset, self)\n return struct.unpack_from(str(\"<%ds\") % (classname_length), self._buf, d.data_offset())[0].decode(\"utf-16le\").rstrip(\"\\x00\")","def get_name(cls):\n return cls.__name__","def get_attribute(self, attribute: str) -> str:\n pass","def get_attr(self):\n attr = self._bld.FindOrCreateAttribute(self._sobj, self.sname)\n return attr._narrow(self.stype)","def getAttrName(self, context):\r\n return self.attr if self.attr is not None else context.attr","def attribute(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"attribute\")","def attribute(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"attribute\")","def generate_class_name(self):\n self.class_name = self.profile_id_dict[self.profile_id]\n return self.class_name","def name(self) -> \"str\":\n return self._attrs.get(\"name\")","def name(self) -> \"str\":\n return self._attrs.get(\"name\")","def name(self) -> \"str\":\n return self._attrs.get(\"name\")","def name(cls):\n return cls._NAME","def attributeType(self) -> unicode:\n ...","def stringifier_attribute(self):\n return self._stringifier_attribute","def class_name(name: str) -> str:\n return text.pascal_case(utils.safe_snake(name, \"type\"))","def get_name(cls):\n\t\treturn '' if cls is SAM3X else cls.__name__","def name(cls):\n return cls.NAME","def name(cls):\n return cls.NAME","def name(cls) -> str:\n return cls.__name__","def name(self):\n return 'Predict model (classification map)'","def get_name(cls):\n return cls.name or cls.__name__.lower()","def get_name(name, class_name):\n if name:\n return name\n if not class_name:\n raise MLRunInvalidArgumentError(\"name or class_name must be provided\")\n if isinstance(class_name, type):\n return class_name.__name__\n return class_name","def _get_custom_attribute_field_name(self, attribute):\n return 'attribute_{0}'.format(attribute.id)","def getname(self):\n return self.__class__.__name__","def name(self):\n return self.__class__.__name__","def name(self):\n return self.__class__.__name__","def name(self):\n return self.__class__.__name__","def name(self):\n return self.__class__.__name__","def name(self):\n return self.__class__.__name__","def Name(self):\r\n\t\treturn self._get_attribute('name')","def attribute(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"attribute\")","def attribute(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"attribute\")","def attribute(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"attribute\")","def name(self):\n\n return self.__class__.__name__","def attribute_key(self) -> Optional[str]:\n return pulumi.get(self, \"attribute_key\")","def get_label(cls) -> str:\n return cls._meta.label_lower.split('.')[-1]","def get_label(cls):\r\n return cls._type_name(cls.label)","def name(cls) -> str:\n\n return cls.__name__","def name(self) -> str:\n return self.__class__.__name__","def name(self) -> str:\n return self.__class__.__name__","def get_label(cls):\n return cls._type_name(cls.label)","def name(self):\r\n return self.__class__.__name__","def getClassifier(self):\n return self.classify","def name(self):\n return (\n self._raw_data.get(ATTR_NAME_UNICODE)\n or self._raw_data.get(ATTR_NAME)\n or \"\"\n )","def name(self) -> str:\n if self._name is None:\n return 'AutoML Metric'\n else:\n return self._name","def get_name(cls: Type[\"Attribution\"]) -> str:\n return \"\".join(\n [\n char if char.islower() or idx == 0 else \" \" + char\n for idx, char in enumerate(cls.__name__)\n ]\n )","def get_class_name(self, name):\n name_list = name.split('_')\n file_name = ''\n for item in name_list:\n file_name += item.capitalize()\n return file_name","def Name(self):\n return self._get_attribute('name')","def class_key(cls):\n return 5, 0, cls.__name__","def name(self) -> str:\n return str(self.category.value)","def getKssUIDClass(self):\n uid = IUUID(aq_inner(self.context))\n\n return \"kssattr-atuid-%s\" % uid","def getAttrName(self, *args):\n return _libsbml.XMLToken_getAttrName(self, *args)","def get_name(self):\n return self.attributes[\"name\"]","def get_label(self, ):\n return self.attrs.get(self.AttributeNames.LABEL, None)","def name(self) -> str:\n return self.__class__.__name__ # pragma: no cover","def classify(self, residueName, atomName):\n classIndex = freesasa_classifier_class(self._c_classifier, residueName, atomName)\n return freesasa_classifier_class2str(classIndex)","def get_attribute(self, name):\n\n pass","def class_id(self) -> str:\n return self._class_id","def class_id(self) -> str:\n return self._class_id","def get_class_functional_name(name):\n name = _strip_class_name(name)\n return name","def test_class_attribute():\n assert isinstance(ResRNNModel.model_name, str)\n assert ResRNNModel.model_name == 'res-RNN'\n assert ResRNNModel.file_name == 'model-{}.pt'","def DescriptiveName(self):\r\n\t\treturn self._get_attribute('descriptiveName')","def clsname(c):\n return c.__class__.__name__.lower()","def attribute_value(self) -> str:\n return pulumi.get(self, \"attribute_value\")","def get_entity_classification_name(entity_type, entity_classification=None):\n assert entity_type in BlueprintEntity.entity_classification\n if entity_classification is None:\n entity_classification = BlueprintEntity.get_entity_classification_default(entity_type)\n assert entity_classification in BlueprintEntity.entity_classification[entity_type]\n return BlueprintEntity.entity_classification[entity_type][entity_classification]","def classname(class_object):\n return class_object.__class__.__name__","def className(self):\n return _osgAnimation.Bone_className(self)","def derived_name(self):\n return self.__context__.className","def att_uc(self):\n return self._att_uc","def mode_categorical_attribute(nodes: typ.Iterable[vtna.graph.TemporalNode], attribute_name: str) -> str:\n values = [node.get_global_attribute(attribute_name) for node in nodes]\n return max(set(values), key=values.count)"],"string":"[\n \"def name(self) -> str:\\n return self.class_names[self.class_num]\",\n \"def get_attribute_class(self, attr_name):\\n return self.attrs.get_attribute_class(attr_name)\",\n \"def class_name(self) -> str:\\n return pulumi.get(self, \\\"class_name\\\")\",\n \"def get_attribute_class(self):\\n return self._attr_cls\",\n \"def class_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"class_name\\\")\",\n \"def className(self):\\n namevalue = self.__class__.__name__\\n return str(namevalue)\",\n \"def get_attribute_class(self, name):\\n self.validate_attribute_name(name)\\n return self.schema[name].get_attribute_class()\",\n \"def get_class_name(self):\\n\\n if \\\"class\\\" in self._root.attrib:\\n return self._root.attrib['class']\\n else:\\n return self._root.tag\",\n \"def f_get_class_name(self):\\n return self.__class__.__name__\",\n \"def class_attribute(self):\\n\\n return getattr(self.parent.class_, self.key)\",\n \"def name(self):\\n return self._get_device_class_name()\",\n \"def __id_attr_name(self):\\n return self._attr_name()\",\n \"def class_name(self) -> str:\\n return self.__class__.__name__\",\n \"def AttributeString(self) -> str:\",\n \"def AttributeString(self) -> str:\",\n \"def class_name(cls):\\n return cls.__name__\",\n \"def class_name(cls):\\n return cls.__name__\",\n \"def getClassName(self):\\n n = type(self).__name__\\n return n\",\n \"def name_to_label(self, name):\\n\\t\\treturn self.classes[name]\",\n \"def name_to_label(self, name):\\n\\t\\t\\treturn self.classes[name]\",\n \"def get_attr_name(self, instance):\\n if not hasattr(self, '_attr_name'):\\n cls = type(instance)\\n self._attr_name = get_descriptor_attr_name(self, cls)\\n\\n assert self._attr_name is not None, (\\n 'Could not find the attribute for %r on %r' % (self, cls))\\n\\n return self._attr_name\",\n \"def classname(cls):\\n return cls.__name__.lower()\",\n \"def name_to_label(self, name):\\n return self.classes[name]\",\n \"def attribute_key(self) -> str:\\n return pulumi.get(self, \\\"attribute_key\\\")\",\n \"def _get_classname(cls):\\n return cls.__name__\",\n \"def attribute_name(name: str) -> str:\\n return text.snake_case(utils.safe_snake(name))\",\n \"def type_name(attr_type: AttrType) -> str:\\n return attr_type.native_name or class_name(attr_type.name)\",\n \"def classifier(self):\\n return self.config.get('classifier', \\\"general\\\")\",\n \"def getCustomAttribute(self):\\n\\t\\treturn self.Attribute\",\n \"def name(cls) -> str:\\n return cls.__name__ # type: ignore[attr-defined]\",\n \"def classname(self):\\n if not self.has_classname():\\n return \\\"\\\"\\n\\n classname_offset = self.unpack_dword(0x30)\\n classname_length = self.unpack_word(0x4A)\\n\\n offset = self.abs_offset_from_hbin_offset(classname_offset)\\n d = HBINCell(self._buf, offset, self)\\n return struct.unpack_from(str(\\\"<%ds\\\") % (classname_length), self._buf, d.data_offset())[0].decode(\\\"utf-16le\\\").rstrip(\\\"\\\\x00\\\")\",\n \"def get_name(cls):\\n return cls.__name__\",\n \"def get_attribute(self, attribute: str) -> str:\\n pass\",\n \"def get_attr(self):\\n attr = self._bld.FindOrCreateAttribute(self._sobj, self.sname)\\n return attr._narrow(self.stype)\",\n \"def getAttrName(self, context):\\r\\n return self.attr if self.attr is not None else context.attr\",\n \"def attribute(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"attribute\\\")\",\n \"def attribute(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"attribute\\\")\",\n \"def generate_class_name(self):\\n self.class_name = self.profile_id_dict[self.profile_id]\\n return self.class_name\",\n \"def name(self) -> \\\"str\\\":\\n return self._attrs.get(\\\"name\\\")\",\n \"def name(self) -> \\\"str\\\":\\n return self._attrs.get(\\\"name\\\")\",\n \"def name(self) -> \\\"str\\\":\\n return self._attrs.get(\\\"name\\\")\",\n \"def name(cls):\\n return cls._NAME\",\n \"def attributeType(self) -> unicode:\\n ...\",\n \"def stringifier_attribute(self):\\n return self._stringifier_attribute\",\n \"def class_name(name: str) -> str:\\n return text.pascal_case(utils.safe_snake(name, \\\"type\\\"))\",\n \"def get_name(cls):\\n\\t\\treturn '' if cls is SAM3X else cls.__name__\",\n \"def name(cls):\\n return cls.NAME\",\n \"def name(cls):\\n return cls.NAME\",\n \"def name(cls) -> str:\\n return cls.__name__\",\n \"def name(self):\\n return 'Predict model (classification map)'\",\n \"def get_name(cls):\\n return cls.name or cls.__name__.lower()\",\n \"def get_name(name, class_name):\\n if name:\\n return name\\n if not class_name:\\n raise MLRunInvalidArgumentError(\\\"name or class_name must be provided\\\")\\n if isinstance(class_name, type):\\n return class_name.__name__\\n return class_name\",\n \"def _get_custom_attribute_field_name(self, attribute):\\n return 'attribute_{0}'.format(attribute.id)\",\n \"def getname(self):\\n return self.__class__.__name__\",\n \"def name(self):\\n return self.__class__.__name__\",\n \"def name(self):\\n return self.__class__.__name__\",\n \"def name(self):\\n return self.__class__.__name__\",\n \"def name(self):\\n return self.__class__.__name__\",\n \"def name(self):\\n return self.__class__.__name__\",\n \"def Name(self):\\r\\n\\t\\treturn self._get_attribute('name')\",\n \"def attribute(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"attribute\\\")\",\n \"def attribute(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"attribute\\\")\",\n \"def attribute(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"attribute\\\")\",\n \"def name(self):\\n\\n return self.__class__.__name__\",\n \"def attribute_key(self) -> Optional[str]:\\n return pulumi.get(self, \\\"attribute_key\\\")\",\n \"def get_label(cls) -> str:\\n return cls._meta.label_lower.split('.')[-1]\",\n \"def get_label(cls):\\r\\n return cls._type_name(cls.label)\",\n \"def name(cls) -> str:\\n\\n return cls.__name__\",\n \"def name(self) -> str:\\n return self.__class__.__name__\",\n \"def name(self) -> str:\\n return self.__class__.__name__\",\n \"def get_label(cls):\\n return cls._type_name(cls.label)\",\n \"def name(self):\\r\\n return self.__class__.__name__\",\n \"def getClassifier(self):\\n return self.classify\",\n \"def name(self):\\n return (\\n self._raw_data.get(ATTR_NAME_UNICODE)\\n or self._raw_data.get(ATTR_NAME)\\n or \\\"\\\"\\n )\",\n \"def name(self) -> str:\\n if self._name is None:\\n return 'AutoML Metric'\\n else:\\n return self._name\",\n \"def get_name(cls: Type[\\\"Attribution\\\"]) -> str:\\n return \\\"\\\".join(\\n [\\n char if char.islower() or idx == 0 else \\\" \\\" + char\\n for idx, char in enumerate(cls.__name__)\\n ]\\n )\",\n \"def get_class_name(self, name):\\n name_list = name.split('_')\\n file_name = ''\\n for item in name_list:\\n file_name += item.capitalize()\\n return file_name\",\n \"def Name(self):\\n return self._get_attribute('name')\",\n \"def class_key(cls):\\n return 5, 0, cls.__name__\",\n \"def name(self) -> str:\\n return str(self.category.value)\",\n \"def getKssUIDClass(self):\\n uid = IUUID(aq_inner(self.context))\\n\\n return \\\"kssattr-atuid-%s\\\" % uid\",\n \"def getAttrName(self, *args):\\n return _libsbml.XMLToken_getAttrName(self, *args)\",\n \"def get_name(self):\\n return self.attributes[\\\"name\\\"]\",\n \"def get_label(self, ):\\n return self.attrs.get(self.AttributeNames.LABEL, None)\",\n \"def name(self) -> str:\\n return self.__class__.__name__ # pragma: no cover\",\n \"def classify(self, residueName, atomName):\\n classIndex = freesasa_classifier_class(self._c_classifier, residueName, atomName)\\n return freesasa_classifier_class2str(classIndex)\",\n \"def get_attribute(self, name):\\n\\n pass\",\n \"def class_id(self) -> str:\\n return self._class_id\",\n \"def class_id(self) -> str:\\n return self._class_id\",\n \"def get_class_functional_name(name):\\n name = _strip_class_name(name)\\n return name\",\n \"def test_class_attribute():\\n assert isinstance(ResRNNModel.model_name, str)\\n assert ResRNNModel.model_name == 'res-RNN'\\n assert ResRNNModel.file_name == 'model-{}.pt'\",\n \"def DescriptiveName(self):\\r\\n\\t\\treturn self._get_attribute('descriptiveName')\",\n \"def clsname(c):\\n return c.__class__.__name__.lower()\",\n \"def attribute_value(self) -> str:\\n return pulumi.get(self, \\\"attribute_value\\\")\",\n \"def get_entity_classification_name(entity_type, entity_classification=None):\\n assert entity_type in BlueprintEntity.entity_classification\\n if entity_classification is None:\\n entity_classification = BlueprintEntity.get_entity_classification_default(entity_type)\\n assert entity_classification in BlueprintEntity.entity_classification[entity_type]\\n return BlueprintEntity.entity_classification[entity_type][entity_classification]\",\n \"def classname(class_object):\\n return class_object.__class__.__name__\",\n \"def className(self):\\n return _osgAnimation.Bone_className(self)\",\n \"def derived_name(self):\\n return self.__context__.className\",\n \"def att_uc(self):\\n return self._att_uc\",\n \"def mode_categorical_attribute(nodes: typ.Iterable[vtna.graph.TemporalNode], attribute_name: str) -> str:\\n values = [node.get_global_attribute(attribute_name) for node in nodes]\\n return max(set(values), key=values.count)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7220845","0.7191606","0.7166631","0.70328903","0.6854248","0.68273634","0.68061316","0.6639919","0.66164047","0.65259147","0.64209676","0.64048225","0.63896745","0.637577","0.637577","0.6366623","0.6366623","0.63627464","0.63504124","0.63288695","0.63267916","0.63187","0.63071144","0.6285846","0.62599534","0.62588763","0.6222137","0.6205986","0.6203427","0.61956066","0.6156421","0.6155829","0.6153842","0.6151229","0.61238253","0.61237985","0.61237985","0.61217797","0.6058347","0.6058347","0.6058347","0.6049518","0.60472447","0.6023154","0.6020976","0.6019874","0.60080737","0.60080737","0.60001314","0.5997776","0.59963995","0.59747684","0.5972627","0.59713197","0.59662724","0.59662724","0.59662724","0.59662724","0.59662724","0.59654146","0.59622043","0.59622043","0.59622043","0.59528977","0.595181","0.59332","0.592511","0.5903096","0.5893533","0.5893533","0.5889637","0.5879121","0.5871267","0.58681446","0.58541304","0.5838201","0.5835786","0.5830837","0.58257467","0.58198076","0.58179736","0.58118266","0.5800333","0.5791034","0.5767864","0.5764486","0.575707","0.57561946","0.57561946","0.5739236","0.57391655","0.5739044","0.57345384","0.572617","0.570879","0.56988037","0.56984025","0.5695499","0.56903076","0.56686395"],"string":"[\n \"0.7220845\",\n \"0.7191606\",\n \"0.7166631\",\n \"0.70328903\",\n \"0.6854248\",\n \"0.68273634\",\n \"0.68061316\",\n \"0.6639919\",\n \"0.66164047\",\n \"0.65259147\",\n \"0.64209676\",\n \"0.64048225\",\n \"0.63896745\",\n \"0.637577\",\n \"0.637577\",\n \"0.6366623\",\n \"0.6366623\",\n \"0.63627464\",\n \"0.63504124\",\n \"0.63288695\",\n \"0.63267916\",\n \"0.63187\",\n \"0.63071144\",\n \"0.6285846\",\n \"0.62599534\",\n \"0.62588763\",\n \"0.6222137\",\n \"0.6205986\",\n \"0.6203427\",\n \"0.61956066\",\n \"0.6156421\",\n \"0.6155829\",\n \"0.6153842\",\n \"0.6151229\",\n \"0.61238253\",\n \"0.61237985\",\n \"0.61237985\",\n \"0.61217797\",\n \"0.6058347\",\n \"0.6058347\",\n \"0.6058347\",\n \"0.6049518\",\n \"0.60472447\",\n \"0.6023154\",\n \"0.6020976\",\n \"0.6019874\",\n \"0.60080737\",\n \"0.60080737\",\n \"0.60001314\",\n \"0.5997776\",\n \"0.59963995\",\n \"0.59747684\",\n \"0.5972627\",\n \"0.59713197\",\n \"0.59662724\",\n \"0.59662724\",\n \"0.59662724\",\n \"0.59662724\",\n \"0.59662724\",\n \"0.59654146\",\n \"0.59622043\",\n \"0.59622043\",\n \"0.59622043\",\n \"0.59528977\",\n \"0.595181\",\n \"0.59332\",\n \"0.592511\",\n \"0.5903096\",\n \"0.5893533\",\n \"0.5893533\",\n \"0.5889637\",\n \"0.5879121\",\n \"0.5871267\",\n \"0.58681446\",\n \"0.58541304\",\n \"0.5838201\",\n \"0.5835786\",\n \"0.5830837\",\n \"0.58257467\",\n \"0.58198076\",\n \"0.58179736\",\n \"0.58118266\",\n \"0.5800333\",\n \"0.5791034\",\n \"0.5767864\",\n \"0.5764486\",\n \"0.575707\",\n \"0.57561946\",\n \"0.57561946\",\n \"0.5739236\",\n \"0.57391655\",\n \"0.5739044\",\n \"0.57345384\",\n \"0.572617\",\n \"0.570879\",\n \"0.56988037\",\n \"0.56984025\",\n \"0.5695499\",\n \"0.56903076\",\n \"0.56686395\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7351364"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":16891,"cells":{"query":{"kind":"string","value":"Returns the value used in the dataset to indicate the positive classification choice."},"document":{"kind":"string","value":"def get_positive_class_val(self, tag):\n # FIXME this dependence between tags and metadata is bad; don't know how to fix it right now\n if tag == 'numerical-binsensitive':\n return 1\n else:\n return self.positive_class_val"},"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 __value_of(sentiment):\n if sentiment == 'positive': return 1\n if sentiment == 'negative': return -1\n return 0","def get_prediction_from_score(score):\n if(score >= 0.03):\n return 'Positive'\n elif(score <= -0.03):\n return 'Negative'\n else:\n return 'Neutral'","def predict(self,X):\n if (int(self.classifier.predict(self.scaler.transform(X)))==-1):\n return \"popular\"\n else:\n return \"unpopular\"","def classify(self,X):\n return int(self.classifier.predict(self.scaler.transform(X)))","def classify(self,X):\n return int(self.classifier.predict(self.scaler.transform(X)))","def classify(cls, i):\r\n # language_model \r\n if i[2] == None:\r\n return 1\r\n elif (float(i[2])) <= -7.848941176618522:\r\n return 0\r\n else:\r\n return 1","def classify(cls, i):\r\n # language_model \r\n if i[2] == None:\r\n return 1\r\n elif (float(i[2])) <= -8.357419966171143:\r\n return 1\r\n else:\r\n return 0","def get_value(self):\r\n return 0","def classification(self) -> 'outputs.CaseClassificationResponse':\n return pulumi.get(self, \"classification\")","def negative_predictive_value(y_true, y_pred):\n\n cm = confusion_matrix(y_true, y_pred)\n return cm[0,0] / cm[:,0].sum()","def predict(self,X):\n if (int(self.classifier.predict(self.scaler.transform(X)))==1):\n return \"increase\"\n elif (int(self.classifier.predict(self.scaler.transform(X)))==0):\n return \"keep\"\n else:\n return \"decrease\"","def purity_test(self):\n mean = filter_data(self.data,self.ancestors)['Class'].mean()\n if mean == 0:\n return 0\n elif mean == 1:\n return 1\n return None","def get_classification(self, idx):\n if idx in self.min_indices:\n return \"minimum\"\n elif idx in self.max_indices:\n return \"maximum\"\n return \"regular\"","def predict(self, x):\n if(self.score(x) > 0):\n return 1\n return -1","def predict(self, x):\n if(self.score(x) > 0):\n return 1\n return -1","def predict(self, x):\n if(self.score(x) > 0):\n return 1\n return -1","def predict(self, x):\n if(self.score(x) > 0):\n return 1\n return -1","def getDecision(self):\n return self.decision","def getlabel(scores):\n main_score = scores['compound']\n if main_score > 0.1:\n return 'pos'\n elif main_score < -0.1:\n return 'neg'\n else:\n return 'neu'","def classify(some_string, trained_pos, trained_neg):\n pos_probability = get_probability(trained_pos, some_string)\n neg_probability = get_probability(trained_neg, some_string)\n if (pos_probability >= neg_probability):\n return \"positive\"\n elif pos_probability < neg_probability: \n return \"negative\"","def predict(self, X):\n res = self.predict_proba(X)\n positive_mask = res >= 0.5\n negative_mask = res < 0.5\n res[positive_mask] = self.POSITIVE_CLASS\n res[negative_mask] = self.NEGATIVE_CLASS\n return res","def GetValue(self):\n \n return self.choices[self.selected].GetLabel()","def predict(self, X):\r\n \r\n # To speed up, we apply the scoring function to all the instances\r\n # at the same time.\r\n scores = X.dot(self.w)\r\n \r\n # Create the output array.\r\n # At the positions where the score is positive, this will contain\r\n # self.positive class, otherwise self.negative_class.\r\n out = numpy.select([scores>=0.0, scores<0.0], [self.positive_class, \r\n self.negative_class])\r\n return out","def predict_category(self):\n pass","def __neg__(self):\n if self.value == NEG:\n return TRIT_POS\n elif self.value == POS:\n return TRIT_NEG\n else:\n return TRIT_ZERO","def value(self):\n return 0","def sensitivity(self):\n return self.recall","def classification_score(self, x, y):\t\n\t\tpass","def classify_snp_prediction(row):\n\n if row['true_snp'] == True and row['prediction'] == True:\n return 'true_positive'\n elif row['true_snp'] == False and row['prediction'] == True:\n return 'false_positive'\n elif row['true_snp'] == False and row['prediction'] == False:\n return 'true_negative'\n else:\n return 'false_negative' # can occur here because we check against prediction, not calls this time","def predict(self, X):\r\n return 1 if self.predict_prob(X) > 0.5 else 0","def native_value(self):\n if self.is_binary:\n return self.attr[STAT_COUNT]\n if self._state_characteristic in (\n STAT_MIN_AGE,\n STAT_MAX_AGE,\n STAT_QUANTILES,\n ):\n return self.attr[self._state_characteristic]\n if self._precision == 0:\n with contextlib.suppress(TypeError, ValueError):\n return int(self.attr[self._state_characteristic])\n return self.attr[self._state_characteristic]","def predict_class_binary(self):\n return round(logistic(self.input_matrix[len(self.dimens)-1][0]))","def extract_predictions(dataset):\n return dataset.Prediction.apply(lambda x: -1 if x == 'b' else 1)","def predict(self, X):\n if isinstance(self.model, ClassifierMixin):\n scores = self._decision_function(X)\n if len(scores.shape) == 1:\n indices = (scores > 0).astype(np.int)\n else:\n indices = scores.argmax(axis=1)\n return self.classes_[indices]\n else:\n return self._decision_function(X)","def classify(cls, i):\r\n # lda \r\n if i[1] == None:\r\n return 0\r\n elif (float(i[1])) <= 0.1142382568740966:\r\n return 1\r\n else:\r\n return 0","def classify(cls, i):\r\n # lda \r\n if i[1] == None:\r\n return 1\r\n elif (float(i[1])) <= 0.01755814193254369:\r\n return 1\r\n else:\r\n return 0","def classify(cls, i):\r\n # lda \r\n if i[1] == None:\r\n return 1\r\n elif (float(i[1])) <= 0.6215704159296479:\r\n return 0\r\n else:\r\n return 1","def pred_sentiment(self):\n return self._pred_sentiment","def predLabel(self, DataMatrix):\n self.predict(DataMatrix)\n # Calculamos el valor mas alto, y a partir de este obtenemos el nombre de la etiqueta\n tags = [self.classes[np.argmax(elem)] for elem in self.data]\n return tags","def classify_example(self, x):\n\t\tscores = np.zeros(self.num_classes) \n\n\t\tfor c in xrange(self.num_classes):\n\t\t\tPsi = self.psi(x, c)\n\t\t\tscores[c] = np.dot(Psi, self.tempw)\n\n\t\t# Return the label with the max discriminant value.\n\t\treturn np.argmax(scores)","def decision_function(self, user_id=0):\n if user_id != 0:\n cls_scores, class_ids = self.__decision_function\n try:\n index = np.where(class_ids == user_id)\n return cls_scores[index]\n except:\n return -self.__decision_nr_samples\n else:\n return self.__decision_function","def evaluate(labels, predictions):\n positive_count = 0\n positive = 0\n negative_count = 0\n negative = 0\n for i in range(len(labels)):\n if labels[i] == 1:\n positive_count+=1\n if predictions[i] == 1:\n positive +=1\n else:\n negative_count+=1\n if predictions[i] == 0:\n negative +=1\n\n sensitivity = positive / positive_count\n specificity = negative / negative_count\n\n return (sensitivity, specificity)","def score(self, X, label):\n pred_risk = self.predict(X)\n CI = self._metrics_ci(label, pred_risk)\n return CI","def get_prediction(self, data, class_label):\n\t\taccuracy = 0\n\t\thit=0\n\t\tcount=0\n\t\tfor index, row in test.iterrows():\n\t\t\tcount += 1\n\t\t\ttmp = self.get_classLabel(row.tolist(),row[class_label])\n\t\t\t#print (tmp)\n\t\t\tif tmp:\n\t\t\t\thit+=1\n\t\t#print (\"hit \"+ str(hit) )\n\t\taccuracy = hit/count\n\t\t\n\t\treturn accuracy","def best_value(self):\r\n return self._best_value","def classify(cls, i):\r\n # lda \r\n if i[1] == None:\r\n return 1\r\n elif (float(i[1])) <= 0.610257172808176:\r\n return 1\r\n else:\r\n return 0","def get_classification(self, image):\n # Image pre-processing pipeline\n img = cv2.resize(image, None, fx=0.5, fy=0.5)\n img = img.astype(np.float32)\n img = keras.applications.vgg16.preprocess_input(img)\n # Execute prediction\n probs = self.model.predict(np.array([img]), batch_size=1, verbose=1)[0]\n # get label with max probability\n g_x = np.argmax(probs)\n\n # reject if model is not confident\n if probs[g_x] < CONFIDENCE_THRESHOLD:\n return TrafficLight.UNKNOWN\n\n label = self.predictionary[g_x]\n rospy.loginfo(\"label: %d, conf: %f, %f, %f, %f\", g_x, probs[0], probs[1], probs[2], probs[3])\n return label","def classify(cls, i):\r\n # lda \r\n if i[1] == None:\r\n return 0\r\n elif (float(i[1])) <= 0.02728102940334218:\r\n return 1\r\n else:\r\n return 0","def get_prediction(self):\n return self.prediction","def convertclasstoemotion(pred):\n \n label_conversion = {'0': 'neutral',\n '1': 'calm',\n '2': 'happy',\n '3': 'sad',\n '4': 'angry',\n '5': 'fearful',\n '6': 'disgust',\n '7': 'surprised'}\n\n for key, value in label_conversion.items():\n if int(key) == pred:\n label = value\n return label","def predLabel(self, DataMatrix):\n self.predict(DataMatrix)\n # Calculamos el valor mas alto, y a partir de este obtenemos el nombre de la etiqueta\n tags = [[self.classes[np.argmax(subrow)] for subrow in row] for row in self.data]\n return tags","def classify(self, inputValue):\n if self.hardStump:\n return self.hardClassify(inputValue)\n else:\n return self.softClassify(inputValue)","def value(self, state):\n value = self.value_model.predict(state)\n return value[0]","def classify(cls, i):\r\n # statuses_count \r\n if i[7] == None:\r\n return 0\r\n elif (float(i[7])) <= 85216.0:\r\n return 0\r\n else:\r\n return 1","def pos_or_neg() -> int:\n # use random() < 0.5 instead of choice([True, False]) since choice\n # is substantially slower\n return -1 if rand_uni_val() < 0.5 else 1","def get_prediction(self, x):\n \"*** YOUR CODE HERE ***\"\n if (nn.as_scalar(self.run(x)) >= 0.0):\n return 1\n return -1","def predict(self, X):\n scores = self.decision_function(X)\n if self.classes.shape[0] == 2:\n indices = np.array(scores > 0, dtype=np.int)\n else:\n indices = scores.argmax(axis=1)\n return self.classes[np.ravel(indices)]","def classify(cls, i):\r\n # lda \r\n if i[1] == None:\r\n return 1\r\n elif (float(i[1])) <= 0.891599215656381:\r\n return 1\r\n else:\r\n return 0","def predictValue(self, sampleData):\r\n value = self.regressor.predict([sampleData])\r\n return value[0]","def value(self):\n if not self.isscalar():\n raise ValueError(\"value called on a non-scalar tensor.\")\n else:\n return self.defval","def getValue(self):\n choice = int(self.choice.getValue())\n if choice not in self.util:\n self.logger.warning(\n f'Choice is {choice}. List of alternatives '\n f'is {self.util.keys()}'\n )\n return np.nan\n if self.av[choice].getValue() == 0.0:\n return -np.log(0)\n Vchosen = self.util[choice].getValue()\n denom = 0.0\n for i, V in self.util.items():\n if self.av[i].getValue() != 0.0:\n denom += np.exp(V.getValue() - Vchosen)\n return -np.log(denom)","def _get_label(cls, file_name):\n if cls == \"neg\":\n return \"0\"\n else:\n return \"1\"\n # reg = _REGEX_\n # rmtch = reg.match(file_name)\n # if rmtch:\n # return rmtch.groupdict()[\"label\"]\n # else:\n # return \"unknown_positive\"","def classify(cls, i):\r\n # lda \r\n if i[1] == None:\r\n return 1\r\n elif (float(i[1])) <= 0.1142382568740966:\r\n return 1\r\n else:\r\n return 1","def get(self):\n score = self._evaluate(self.y_true, self.y_pred)\n\n return score","def classify_sentiment(sent_index):\n\n\tif sent_index < -0.5:\n\t\treturn 'negative'\n\tif sent_index <= 0.5 and sent_index >= -0.5:\n\t\treturn 'neutral'\n\tif sent_index >= 0.5:\n\t\treturn 'positive'","def predict_one(self):\n return (self.contexted.calc_p(\"\", self.seen + \"1\") /\n float(self.contexted.calc_p(\"\", self.seen)))","def classification(original_training_data):\n\n ''' Storing the dataframe as numpy array '''\n original_training_data_values = original_training_data.values\n\n ''' Storing the values of target attribute for finding out the counts of each recipetype'''\n target_column = original_training_data_values[:, -1]\n\n ''' Recipe_type stores the unique values of target attribute in the form of a list [Muffin Cupcake] \n cupcake_muffin_count stores the count of muffin and cupcakes in the form of a list [451 451]'''\n recipe_type, cupcake_muffin_count = np.unique(target_column, return_counts=True)\n\n ''' cupcake_muffin_count.argmax() returns the index of the highest value. In this case, it will return the index of \n muffin or cupcake count. '''\n majority_class = recipe_type[cupcake_muffin_count.argmax()]\n\n return majority_class","def predict(self,X):\n return np.where(self.net_input(X) >= 0.0, 1, -1)","def est_valide(self):\n\t\treturn self.__valide","def first_value(self):\n return 0","def prediction_a(self):\n return self._prediction_a","def getPredictedResult(self):\n output = self.svclassifier.predict([self.inputData])\n return output[0]","def predict(self, X):\n return np.where(self.net_input(X) >= 0.0, 1, -1)","def predict(self, X):\n return np.where(self.net_input(X) >= 0.0, 1, -1)","def predict(self, X):\n return np.where(self.net_input(X) >= 0.0, 1, -1)","def classify(cls, i):\r\n # lda \r\n if i[1] == None:\r\n return 1\r\n elif (float(i[1])) <= 0.02728102940334218:\r\n return 1\r\n else:\r\n return 1","def get_metrics_class(self):\n return ClassificationMetricsValue","def evaluate(labels, predictions):\n correct_positive = 0\n correct_negative = 0\n total_positive = 0\n total_negative = 0\n\n for i in range(len(labels)):\n if labels[i] == 1:\n total_positive += 1\n if predictions[i] == 1:\n correct_positive += 1\n else:\n total_negative += 1\n if predictions[i] == 0:\n correct_negative += 1\n\n sensitivity = correct_positive / total_positive\n specificity = correct_negative / total_negative\n\n return sensitivity, specificity","def value(self):\n if self._check_:\n f = self.getChecked\n else:\n f = self.getSelected\n return f()","def get_prediction(self, x):\n \"*** YOUR CODE HERE question 1 ***\"\n dProduct = nn.as_scalar(self.run(x))\n\n if dProduct < 0:\n return -1\n else:\n return 1","def get_classLabel(self, dataset, class_label): \n\t\tnode = self.root\n\t\tbroken=0\n\t\t\n\t\t#print(\"BEBE:\" + str(node.get_bebe( dataset)))\n\t\t\n\t\tif (node.get_bebe( dataset) == class_label ):\n\t\t\treturn 1\n\t\telse:\n\t\t\treturn 0\n\n\t\t\tdef junk(data, class_label, seed, ratio):","def lookup_relevant(score):\n category = \"\"\n if score > 2.0:\n category = \"RELEVANT\"\n elif score > 0.0:\n category = \"PARTIALLY RELEVANT\"\n else:\n category = \"NOT RELEVANT\"\n return category","def state(self):\n return round(self.values.primary.value, 2)","def a_value(y_true, y_pred_prob, zero_label=0, one_label=1):\n \n idx = np.isin(y_true, [zero_label, one_label])\n labels = y_true[idx]\n prob = y_pred_prob[idx, zero_label]\n sorted_ranks = labels[np.argsort(prob)]\n \n n0, n1, sum_ranks = 0, 0, 0\n n0 = np.count_nonzero(sorted_ranks==zero_label)\n n1 = np.count_nonzero(sorted_ranks==one_label)\n sum_ranks = np.sum(np.where(sorted_ranks==zero_label)) + n0\n\n return (sum_ranks - (n0*(n0+1)/2.0)) / float(n0 * n1) # Eqn 3","def value(self):\n return self.compute_value()","def evaluate(labels, predictions):\n #labels and predictions\n truePos = 0\n trueNeg = 0\n for data in range(len(labels)):\n if((predictions[data] == 1) and (predictions[data] == labels[data])):\n truePos+=1\n elif((predictions[data] == 0) and (predictions[data] == labels[data])):\n trueNeg+=1\n sensitivity = truePos/(len(labels) + 1)\n specificity = trueNeg/(len(labels) + 1)\n return (sensitivity, specificity)\n \n\n #raise NotImplementedError","def __int__(self):\n\n return self.value","def classifier(self, xvalue):\n # Initialize key variables\n probability = {}\n classes = self.classes()\n\n # Get probability of each class\n probability = self.probability(xvalue)\n\n # Reassign variables for readability\n prob_c0 = probability[classes[0]]\n prob_c1 = probability[classes[1]]\n\n # Evaluate probabilities\n if prob_c0 + prob_c1 == 0:\n selection = None\n else:\n if prob_c0 > prob_c1:\n selection = classes[0]\n elif prob_c0 < prob_c1:\n selection = classes[1]\n else:\n selection = None\n\n # Return\n return selection","def softClassify(self, inputValue):\n result = 1.0 / (1 + math.e ** (-self.beta * (inputValue - self.threshold)))\n logging.debug(\"Classification of %f on value %f for feature %d\" %(result, inputValue, self.feature))\n return result","def _value(self):\n if self.data is None:\n return self._original_value\n elif self.get_label:\n return self.get_label(self.data)\n else:\n return unicode(self.data)","def classify(self, data):\n \"*** YOUR CODE HERE ***\"\n return self.sklearn_classifier.predict(data)","def get_serendipity_val(dic, key):\n # The key was in the training set\n try:\n return dic[key]\n # The key wasn't in the training set, then the serendipity is 1\n except KeyError:\n return 1.0","def confidence(self) -> float:\n return float(self.class_scores[self.class_num])","def predict(self, testData=[]):\n result = []\n for classValue in self._classAttrs:\n #print(f'Computing Label: {classValue}, {self._classLabelMap[classValue]}')\n result.append(self._computeCondProb(testData, classValue))\n return self._classLabelMap[result.index(max(result))]","def decide(self, feature):\n\n if self.class_label is not None:\n return self.class_label\n\n elif self.decision_function(feature):\n return self.left.decide(feature)\n\n else:\n return self.right.decide(feature)","def predict(self, X):\n return np.where(self.activation(self.net_input(X)) >= 0.0, 1, -1)","def get_psm_category(self, row):\n category = 0 # unknown (mix of true positives and false positives)\n self.PSM_count += 1 # for FDR calculation\n sequence = unify_sequence(row[\"Sequence\"])\n psm_FDR = calc_FDR(self.PSM_count, self.decoy_count)\n\n if row_is_decoy(row):\n self.decoy_count += 1\n if psm_FDR <= 0.25 and sequence not in self.shitty_decoy_seqs:\n category = -1 # decoy (false positive hits)\n self.counter[\"negative\"] += 1\n else:\n if not self.decoy_train_prob:\n need_max = self.counter[\"positive\"] * 2\n have = self.counter[\"negative\"]\n still_there = self.counter[\"decoy\"] - have\n prob = need_max / still_there\n if prob < 0.001:\n prob = 0.001\n self.decoy_train_prob = prob\n print()\n print(self.counter)\n print(\"need max:\", need_max)\n print(\"have:\", have)\n print(\"still_there:\", still_there)\n print(\"probability:\", self.decoy_train_prob)\n print()\n if self.decoy_train_prob >= 1.0 or random() <= self.decoy_train_prob:\n category = -1 # decoy (false positive hits)\n self.counter[\"negative\"] += 1\n\n else: # row is target\n if psm_FDR <= self[\"fdr_cutoff\"] and sequence not in self.shitty_decoy_seqs:\n category = 1 # high quality target (almost certainly true positives)\n self.counter[\"positive\"] += 1\n\n if category == 0:\n self.counter[\"unknown\"] += 1\n return (category, psm_FDR)","def classify(cls, i):\r\n # statuses_count \r\n if i[7] == None:\r\n return 1\r\n elif (float(i[7])) <= 85216.0:\r\n return 1\r\n else:\r\n return 1","async def decide(self):\n return 0., 0., 0.","def agility(self):\n return self._getAttribute(Attribute.agility)"],"string":"[\n \"def __value_of(sentiment):\\n if sentiment == 'positive': return 1\\n if sentiment == 'negative': return -1\\n return 0\",\n \"def get_prediction_from_score(score):\\n if(score >= 0.03):\\n return 'Positive'\\n elif(score <= -0.03):\\n return 'Negative'\\n else:\\n return 'Neutral'\",\n \"def predict(self,X):\\n if (int(self.classifier.predict(self.scaler.transform(X)))==-1):\\n return \\\"popular\\\"\\n else:\\n return \\\"unpopular\\\"\",\n \"def classify(self,X):\\n return int(self.classifier.predict(self.scaler.transform(X)))\",\n \"def classify(self,X):\\n return int(self.classifier.predict(self.scaler.transform(X)))\",\n \"def classify(cls, i):\\r\\n # language_model \\r\\n if i[2] == None:\\r\\n return 1\\r\\n elif (float(i[2])) <= -7.848941176618522:\\r\\n return 0\\r\\n else:\\r\\n return 1\",\n \"def classify(cls, i):\\r\\n # language_model \\r\\n if i[2] == None:\\r\\n return 1\\r\\n elif (float(i[2])) <= -8.357419966171143:\\r\\n return 1\\r\\n else:\\r\\n return 0\",\n \"def get_value(self):\\r\\n return 0\",\n \"def classification(self) -> 'outputs.CaseClassificationResponse':\\n return pulumi.get(self, \\\"classification\\\")\",\n \"def negative_predictive_value(y_true, y_pred):\\n\\n cm = confusion_matrix(y_true, y_pred)\\n return cm[0,0] / cm[:,0].sum()\",\n \"def predict(self,X):\\n if (int(self.classifier.predict(self.scaler.transform(X)))==1):\\n return \\\"increase\\\"\\n elif (int(self.classifier.predict(self.scaler.transform(X)))==0):\\n return \\\"keep\\\"\\n else:\\n return \\\"decrease\\\"\",\n \"def purity_test(self):\\n mean = filter_data(self.data,self.ancestors)['Class'].mean()\\n if mean == 0:\\n return 0\\n elif mean == 1:\\n return 1\\n return None\",\n \"def get_classification(self, idx):\\n if idx in self.min_indices:\\n return \\\"minimum\\\"\\n elif idx in self.max_indices:\\n return \\\"maximum\\\"\\n return \\\"regular\\\"\",\n \"def predict(self, x):\\n if(self.score(x) > 0):\\n return 1\\n return -1\",\n \"def predict(self, x):\\n if(self.score(x) > 0):\\n return 1\\n return -1\",\n \"def predict(self, x):\\n if(self.score(x) > 0):\\n return 1\\n return -1\",\n \"def predict(self, x):\\n if(self.score(x) > 0):\\n return 1\\n return -1\",\n \"def getDecision(self):\\n return self.decision\",\n \"def getlabel(scores):\\n main_score = scores['compound']\\n if main_score > 0.1:\\n return 'pos'\\n elif main_score < -0.1:\\n return 'neg'\\n else:\\n return 'neu'\",\n \"def classify(some_string, trained_pos, trained_neg):\\n pos_probability = get_probability(trained_pos, some_string)\\n neg_probability = get_probability(trained_neg, some_string)\\n if (pos_probability >= neg_probability):\\n return \\\"positive\\\"\\n elif pos_probability < neg_probability: \\n return \\\"negative\\\"\",\n \"def predict(self, X):\\n res = self.predict_proba(X)\\n positive_mask = res >= 0.5\\n negative_mask = res < 0.5\\n res[positive_mask] = self.POSITIVE_CLASS\\n res[negative_mask] = self.NEGATIVE_CLASS\\n return res\",\n \"def GetValue(self):\\n \\n return self.choices[self.selected].GetLabel()\",\n \"def predict(self, X):\\r\\n \\r\\n # To speed up, we apply the scoring function to all the instances\\r\\n # at the same time.\\r\\n scores = X.dot(self.w)\\r\\n \\r\\n # Create the output array.\\r\\n # At the positions where the score is positive, this will contain\\r\\n # self.positive class, otherwise self.negative_class.\\r\\n out = numpy.select([scores>=0.0, scores<0.0], [self.positive_class, \\r\\n self.negative_class])\\r\\n return out\",\n \"def predict_category(self):\\n pass\",\n \"def __neg__(self):\\n if self.value == NEG:\\n return TRIT_POS\\n elif self.value == POS:\\n return TRIT_NEG\\n else:\\n return TRIT_ZERO\",\n \"def value(self):\\n return 0\",\n \"def sensitivity(self):\\n return self.recall\",\n \"def classification_score(self, x, y):\\t\\n\\t\\tpass\",\n \"def classify_snp_prediction(row):\\n\\n if row['true_snp'] == True and row['prediction'] == True:\\n return 'true_positive'\\n elif row['true_snp'] == False and row['prediction'] == True:\\n return 'false_positive'\\n elif row['true_snp'] == False and row['prediction'] == False:\\n return 'true_negative'\\n else:\\n return 'false_negative' # can occur here because we check against prediction, not calls this time\",\n \"def predict(self, X):\\r\\n return 1 if self.predict_prob(X) > 0.5 else 0\",\n \"def native_value(self):\\n if self.is_binary:\\n return self.attr[STAT_COUNT]\\n if self._state_characteristic in (\\n STAT_MIN_AGE,\\n STAT_MAX_AGE,\\n STAT_QUANTILES,\\n ):\\n return self.attr[self._state_characteristic]\\n if self._precision == 0:\\n with contextlib.suppress(TypeError, ValueError):\\n return int(self.attr[self._state_characteristic])\\n return self.attr[self._state_characteristic]\",\n \"def predict_class_binary(self):\\n return round(logistic(self.input_matrix[len(self.dimens)-1][0]))\",\n \"def extract_predictions(dataset):\\n return dataset.Prediction.apply(lambda x: -1 if x == 'b' else 1)\",\n \"def predict(self, X):\\n if isinstance(self.model, ClassifierMixin):\\n scores = self._decision_function(X)\\n if len(scores.shape) == 1:\\n indices = (scores > 0).astype(np.int)\\n else:\\n indices = scores.argmax(axis=1)\\n return self.classes_[indices]\\n else:\\n return self._decision_function(X)\",\n \"def classify(cls, i):\\r\\n # lda \\r\\n if i[1] == None:\\r\\n return 0\\r\\n elif (float(i[1])) <= 0.1142382568740966:\\r\\n return 1\\r\\n else:\\r\\n return 0\",\n \"def classify(cls, i):\\r\\n # lda \\r\\n if i[1] == None:\\r\\n return 1\\r\\n elif (float(i[1])) <= 0.01755814193254369:\\r\\n return 1\\r\\n else:\\r\\n return 0\",\n \"def classify(cls, i):\\r\\n # lda \\r\\n if i[1] == None:\\r\\n return 1\\r\\n elif (float(i[1])) <= 0.6215704159296479:\\r\\n return 0\\r\\n else:\\r\\n return 1\",\n \"def pred_sentiment(self):\\n return self._pred_sentiment\",\n \"def predLabel(self, DataMatrix):\\n self.predict(DataMatrix)\\n # Calculamos el valor mas alto, y a partir de este obtenemos el nombre de la etiqueta\\n tags = [self.classes[np.argmax(elem)] for elem in self.data]\\n return tags\",\n \"def classify_example(self, x):\\n\\t\\tscores = np.zeros(self.num_classes) \\n\\n\\t\\tfor c in xrange(self.num_classes):\\n\\t\\t\\tPsi = self.psi(x, c)\\n\\t\\t\\tscores[c] = np.dot(Psi, self.tempw)\\n\\n\\t\\t# Return the label with the max discriminant value.\\n\\t\\treturn np.argmax(scores)\",\n \"def decision_function(self, user_id=0):\\n if user_id != 0:\\n cls_scores, class_ids = self.__decision_function\\n try:\\n index = np.where(class_ids == user_id)\\n return cls_scores[index]\\n except:\\n return -self.__decision_nr_samples\\n else:\\n return self.__decision_function\",\n \"def evaluate(labels, predictions):\\n positive_count = 0\\n positive = 0\\n negative_count = 0\\n negative = 0\\n for i in range(len(labels)):\\n if labels[i] == 1:\\n positive_count+=1\\n if predictions[i] == 1:\\n positive +=1\\n else:\\n negative_count+=1\\n if predictions[i] == 0:\\n negative +=1\\n\\n sensitivity = positive / positive_count\\n specificity = negative / negative_count\\n\\n return (sensitivity, specificity)\",\n \"def score(self, X, label):\\n pred_risk = self.predict(X)\\n CI = self._metrics_ci(label, pred_risk)\\n return CI\",\n \"def get_prediction(self, data, class_label):\\n\\t\\taccuracy = 0\\n\\t\\thit=0\\n\\t\\tcount=0\\n\\t\\tfor index, row in test.iterrows():\\n\\t\\t\\tcount += 1\\n\\t\\t\\ttmp = self.get_classLabel(row.tolist(),row[class_label])\\n\\t\\t\\t#print (tmp)\\n\\t\\t\\tif tmp:\\n\\t\\t\\t\\thit+=1\\n\\t\\t#print (\\\"hit \\\"+ str(hit) )\\n\\t\\taccuracy = hit/count\\n\\t\\t\\n\\t\\treturn accuracy\",\n \"def best_value(self):\\r\\n return self._best_value\",\n \"def classify(cls, i):\\r\\n # lda \\r\\n if i[1] == None:\\r\\n return 1\\r\\n elif (float(i[1])) <= 0.610257172808176:\\r\\n return 1\\r\\n else:\\r\\n return 0\",\n \"def get_classification(self, image):\\n # Image pre-processing pipeline\\n img = cv2.resize(image, None, fx=0.5, fy=0.5)\\n img = img.astype(np.float32)\\n img = keras.applications.vgg16.preprocess_input(img)\\n # Execute prediction\\n probs = self.model.predict(np.array([img]), batch_size=1, verbose=1)[0]\\n # get label with max probability\\n g_x = np.argmax(probs)\\n\\n # reject if model is not confident\\n if probs[g_x] < CONFIDENCE_THRESHOLD:\\n return TrafficLight.UNKNOWN\\n\\n label = self.predictionary[g_x]\\n rospy.loginfo(\\\"label: %d, conf: %f, %f, %f, %f\\\", g_x, probs[0], probs[1], probs[2], probs[3])\\n return label\",\n \"def classify(cls, i):\\r\\n # lda \\r\\n if i[1] == None:\\r\\n return 0\\r\\n elif (float(i[1])) <= 0.02728102940334218:\\r\\n return 1\\r\\n else:\\r\\n return 0\",\n \"def get_prediction(self):\\n return self.prediction\",\n \"def convertclasstoemotion(pred):\\n \\n label_conversion = {'0': 'neutral',\\n '1': 'calm',\\n '2': 'happy',\\n '3': 'sad',\\n '4': 'angry',\\n '5': 'fearful',\\n '6': 'disgust',\\n '7': 'surprised'}\\n\\n for key, value in label_conversion.items():\\n if int(key) == pred:\\n label = value\\n return label\",\n \"def predLabel(self, DataMatrix):\\n self.predict(DataMatrix)\\n # Calculamos el valor mas alto, y a partir de este obtenemos el nombre de la etiqueta\\n tags = [[self.classes[np.argmax(subrow)] for subrow in row] for row in self.data]\\n return tags\",\n \"def classify(self, inputValue):\\n if self.hardStump:\\n return self.hardClassify(inputValue)\\n else:\\n return self.softClassify(inputValue)\",\n \"def value(self, state):\\n value = self.value_model.predict(state)\\n return value[0]\",\n \"def classify(cls, i):\\r\\n # statuses_count \\r\\n if i[7] == None:\\r\\n return 0\\r\\n elif (float(i[7])) <= 85216.0:\\r\\n return 0\\r\\n else:\\r\\n return 1\",\n \"def pos_or_neg() -> int:\\n # use random() < 0.5 instead of choice([True, False]) since choice\\n # is substantially slower\\n return -1 if rand_uni_val() < 0.5 else 1\",\n \"def get_prediction(self, x):\\n \\\"*** YOUR CODE HERE ***\\\"\\n if (nn.as_scalar(self.run(x)) >= 0.0):\\n return 1\\n return -1\",\n \"def predict(self, X):\\n scores = self.decision_function(X)\\n if self.classes.shape[0] == 2:\\n indices = np.array(scores > 0, dtype=np.int)\\n else:\\n indices = scores.argmax(axis=1)\\n return self.classes[np.ravel(indices)]\",\n \"def classify(cls, i):\\r\\n # lda \\r\\n if i[1] == None:\\r\\n return 1\\r\\n elif (float(i[1])) <= 0.891599215656381:\\r\\n return 1\\r\\n else:\\r\\n return 0\",\n \"def predictValue(self, sampleData):\\r\\n value = self.regressor.predict([sampleData])\\r\\n return value[0]\",\n \"def value(self):\\n if not self.isscalar():\\n raise ValueError(\\\"value called on a non-scalar tensor.\\\")\\n else:\\n return self.defval\",\n \"def getValue(self):\\n choice = int(self.choice.getValue())\\n if choice not in self.util:\\n self.logger.warning(\\n f'Choice is {choice}. List of alternatives '\\n f'is {self.util.keys()}'\\n )\\n return np.nan\\n if self.av[choice].getValue() == 0.0:\\n return -np.log(0)\\n Vchosen = self.util[choice].getValue()\\n denom = 0.0\\n for i, V in self.util.items():\\n if self.av[i].getValue() != 0.0:\\n denom += np.exp(V.getValue() - Vchosen)\\n return -np.log(denom)\",\n \"def _get_label(cls, file_name):\\n if cls == \\\"neg\\\":\\n return \\\"0\\\"\\n else:\\n return \\\"1\\\"\\n # reg = _REGEX_\\n # rmtch = reg.match(file_name)\\n # if rmtch:\\n # return rmtch.groupdict()[\\\"label\\\"]\\n # else:\\n # return \\\"unknown_positive\\\"\",\n \"def classify(cls, i):\\r\\n # lda \\r\\n if i[1] == None:\\r\\n return 1\\r\\n elif (float(i[1])) <= 0.1142382568740966:\\r\\n return 1\\r\\n else:\\r\\n return 1\",\n \"def get(self):\\n score = self._evaluate(self.y_true, self.y_pred)\\n\\n return score\",\n \"def classify_sentiment(sent_index):\\n\\n\\tif sent_index < -0.5:\\n\\t\\treturn 'negative'\\n\\tif sent_index <= 0.5 and sent_index >= -0.5:\\n\\t\\treturn 'neutral'\\n\\tif sent_index >= 0.5:\\n\\t\\treturn 'positive'\",\n \"def predict_one(self):\\n return (self.contexted.calc_p(\\\"\\\", self.seen + \\\"1\\\") /\\n float(self.contexted.calc_p(\\\"\\\", self.seen)))\",\n \"def classification(original_training_data):\\n\\n ''' Storing the dataframe as numpy array '''\\n original_training_data_values = original_training_data.values\\n\\n ''' Storing the values of target attribute for finding out the counts of each recipetype'''\\n target_column = original_training_data_values[:, -1]\\n\\n ''' Recipe_type stores the unique values of target attribute in the form of a list [Muffin Cupcake] \\n cupcake_muffin_count stores the count of muffin and cupcakes in the form of a list [451 451]'''\\n recipe_type, cupcake_muffin_count = np.unique(target_column, return_counts=True)\\n\\n ''' cupcake_muffin_count.argmax() returns the index of the highest value. In this case, it will return the index of \\n muffin or cupcake count. '''\\n majority_class = recipe_type[cupcake_muffin_count.argmax()]\\n\\n return majority_class\",\n \"def predict(self,X):\\n return np.where(self.net_input(X) >= 0.0, 1, -1)\",\n \"def est_valide(self):\\n\\t\\treturn self.__valide\",\n \"def first_value(self):\\n return 0\",\n \"def prediction_a(self):\\n return self._prediction_a\",\n \"def getPredictedResult(self):\\n output = self.svclassifier.predict([self.inputData])\\n return output[0]\",\n \"def predict(self, X):\\n return np.where(self.net_input(X) >= 0.0, 1, -1)\",\n \"def predict(self, X):\\n return np.where(self.net_input(X) >= 0.0, 1, -1)\",\n \"def predict(self, X):\\n return np.where(self.net_input(X) >= 0.0, 1, -1)\",\n \"def classify(cls, i):\\r\\n # lda \\r\\n if i[1] == None:\\r\\n return 1\\r\\n elif (float(i[1])) <= 0.02728102940334218:\\r\\n return 1\\r\\n else:\\r\\n return 1\",\n \"def get_metrics_class(self):\\n return ClassificationMetricsValue\",\n \"def evaluate(labels, predictions):\\n correct_positive = 0\\n correct_negative = 0\\n total_positive = 0\\n total_negative = 0\\n\\n for i in range(len(labels)):\\n if labels[i] == 1:\\n total_positive += 1\\n if predictions[i] == 1:\\n correct_positive += 1\\n else:\\n total_negative += 1\\n if predictions[i] == 0:\\n correct_negative += 1\\n\\n sensitivity = correct_positive / total_positive\\n specificity = correct_negative / total_negative\\n\\n return sensitivity, specificity\",\n \"def value(self):\\n if self._check_:\\n f = self.getChecked\\n else:\\n f = self.getSelected\\n return f()\",\n \"def get_prediction(self, x):\\n \\\"*** YOUR CODE HERE question 1 ***\\\"\\n dProduct = nn.as_scalar(self.run(x))\\n\\n if dProduct < 0:\\n return -1\\n else:\\n return 1\",\n \"def get_classLabel(self, dataset, class_label): \\n\\t\\tnode = self.root\\n\\t\\tbroken=0\\n\\t\\t\\n\\t\\t#print(\\\"BEBE:\\\" + str(node.get_bebe( dataset)))\\n\\t\\t\\n\\t\\tif (node.get_bebe( dataset) == class_label ):\\n\\t\\t\\treturn 1\\n\\t\\telse:\\n\\t\\t\\treturn 0\\n\\n\\t\\t\\tdef junk(data, class_label, seed, ratio):\",\n \"def lookup_relevant(score):\\n category = \\\"\\\"\\n if score > 2.0:\\n category = \\\"RELEVANT\\\"\\n elif score > 0.0:\\n category = \\\"PARTIALLY RELEVANT\\\"\\n else:\\n category = \\\"NOT RELEVANT\\\"\\n return category\",\n \"def state(self):\\n return round(self.values.primary.value, 2)\",\n \"def a_value(y_true, y_pred_prob, zero_label=0, one_label=1):\\n \\n idx = np.isin(y_true, [zero_label, one_label])\\n labels = y_true[idx]\\n prob = y_pred_prob[idx, zero_label]\\n sorted_ranks = labels[np.argsort(prob)]\\n \\n n0, n1, sum_ranks = 0, 0, 0\\n n0 = np.count_nonzero(sorted_ranks==zero_label)\\n n1 = np.count_nonzero(sorted_ranks==one_label)\\n sum_ranks = np.sum(np.where(sorted_ranks==zero_label)) + n0\\n\\n return (sum_ranks - (n0*(n0+1)/2.0)) / float(n0 * n1) # Eqn 3\",\n \"def value(self):\\n return self.compute_value()\",\n \"def evaluate(labels, predictions):\\n #labels and predictions\\n truePos = 0\\n trueNeg = 0\\n for data in range(len(labels)):\\n if((predictions[data] == 1) and (predictions[data] == labels[data])):\\n truePos+=1\\n elif((predictions[data] == 0) and (predictions[data] == labels[data])):\\n trueNeg+=1\\n sensitivity = truePos/(len(labels) + 1)\\n specificity = trueNeg/(len(labels) + 1)\\n return (sensitivity, specificity)\\n \\n\\n #raise NotImplementedError\",\n \"def __int__(self):\\n\\n return self.value\",\n \"def classifier(self, xvalue):\\n # Initialize key variables\\n probability = {}\\n classes = self.classes()\\n\\n # Get probability of each class\\n probability = self.probability(xvalue)\\n\\n # Reassign variables for readability\\n prob_c0 = probability[classes[0]]\\n prob_c1 = probability[classes[1]]\\n\\n # Evaluate probabilities\\n if prob_c0 + prob_c1 == 0:\\n selection = None\\n else:\\n if prob_c0 > prob_c1:\\n selection = classes[0]\\n elif prob_c0 < prob_c1:\\n selection = classes[1]\\n else:\\n selection = None\\n\\n # Return\\n return selection\",\n \"def softClassify(self, inputValue):\\n result = 1.0 / (1 + math.e ** (-self.beta * (inputValue - self.threshold)))\\n logging.debug(\\\"Classification of %f on value %f for feature %d\\\" %(result, inputValue, self.feature))\\n return result\",\n \"def _value(self):\\n if self.data is None:\\n return self._original_value\\n elif self.get_label:\\n return self.get_label(self.data)\\n else:\\n return unicode(self.data)\",\n \"def classify(self, data):\\n \\\"*** YOUR CODE HERE ***\\\"\\n return self.sklearn_classifier.predict(data)\",\n \"def get_serendipity_val(dic, key):\\n # The key was in the training set\\n try:\\n return dic[key]\\n # The key wasn't in the training set, then the serendipity is 1\\n except KeyError:\\n return 1.0\",\n \"def confidence(self) -> float:\\n return float(self.class_scores[self.class_num])\",\n \"def predict(self, testData=[]):\\n result = []\\n for classValue in self._classAttrs:\\n #print(f'Computing Label: {classValue}, {self._classLabelMap[classValue]}')\\n result.append(self._computeCondProb(testData, classValue))\\n return self._classLabelMap[result.index(max(result))]\",\n \"def decide(self, feature):\\n\\n if self.class_label is not None:\\n return self.class_label\\n\\n elif self.decision_function(feature):\\n return self.left.decide(feature)\\n\\n else:\\n return self.right.decide(feature)\",\n \"def predict(self, X):\\n return np.where(self.activation(self.net_input(X)) >= 0.0, 1, -1)\",\n \"def get_psm_category(self, row):\\n category = 0 # unknown (mix of true positives and false positives)\\n self.PSM_count += 1 # for FDR calculation\\n sequence = unify_sequence(row[\\\"Sequence\\\"])\\n psm_FDR = calc_FDR(self.PSM_count, self.decoy_count)\\n\\n if row_is_decoy(row):\\n self.decoy_count += 1\\n if psm_FDR <= 0.25 and sequence not in self.shitty_decoy_seqs:\\n category = -1 # decoy (false positive hits)\\n self.counter[\\\"negative\\\"] += 1\\n else:\\n if not self.decoy_train_prob:\\n need_max = self.counter[\\\"positive\\\"] * 2\\n have = self.counter[\\\"negative\\\"]\\n still_there = self.counter[\\\"decoy\\\"] - have\\n prob = need_max / still_there\\n if prob < 0.001:\\n prob = 0.001\\n self.decoy_train_prob = prob\\n print()\\n print(self.counter)\\n print(\\\"need max:\\\", need_max)\\n print(\\\"have:\\\", have)\\n print(\\\"still_there:\\\", still_there)\\n print(\\\"probability:\\\", self.decoy_train_prob)\\n print()\\n if self.decoy_train_prob >= 1.0 or random() <= self.decoy_train_prob:\\n category = -1 # decoy (false positive hits)\\n self.counter[\\\"negative\\\"] += 1\\n\\n else: # row is target\\n if psm_FDR <= self[\\\"fdr_cutoff\\\"] and sequence not in self.shitty_decoy_seqs:\\n category = 1 # high quality target (almost certainly true positives)\\n self.counter[\\\"positive\\\"] += 1\\n\\n if category == 0:\\n self.counter[\\\"unknown\\\"] += 1\\n return (category, psm_FDR)\",\n \"def classify(cls, i):\\r\\n # statuses_count \\r\\n if i[7] == None:\\r\\n return 1\\r\\n elif (float(i[7])) <= 85216.0:\\r\\n return 1\\r\\n else:\\r\\n return 1\",\n \"async def decide(self):\\n return 0., 0., 0.\",\n \"def agility(self):\\n return self._getAttribute(Attribute.agility)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6920214","0.6468132","0.6377437","0.6298673","0.6298673","0.6276346","0.62528133","0.62347436","0.6230235","0.61446166","0.61428374","0.6095599","0.60748625","0.6050616","0.6050616","0.6050616","0.6050616","0.6029829","0.60096","0.5973834","0.5958873","0.5956844","0.59472567","0.5940582","0.5926571","0.5919561","0.590596","0.5897776","0.5891463","0.588618","0.5880356","0.58614385","0.58490866","0.5848971","0.58426845","0.5831801","0.5828595","0.582725","0.5826627","0.5821324","0.5818959","0.5812203","0.5807462","0.58002156","0.5799782","0.57973385","0.5793228","0.5792135","0.5787988","0.57732314","0.5757169","0.57534164","0.5751546","0.5748739","0.574853","0.57434624","0.57402986","0.5732827","0.5727402","0.57264704","0.5722236","0.57206094","0.57196736","0.5718622","0.5708684","0.5700656","0.56918335","0.5686508","0.5682652","0.5674416","0.56683415","0.5667687","0.56669056","0.56669056","0.56669056","0.5665989","0.56620914","0.56544644","0.5653992","0.5651833","0.5648326","0.5646527","0.56458324","0.5645502","0.56448597","0.5634157","0.5631158","0.56270766","0.56259716","0.56248176","0.5620985","0.5618467","0.5617037","0.5606175","0.56048936","0.56016725","0.56015235","0.5597825","0.5596261","0.5592716"],"string":"[\n \"0.6920214\",\n \"0.6468132\",\n \"0.6377437\",\n \"0.6298673\",\n \"0.6298673\",\n \"0.6276346\",\n \"0.62528133\",\n \"0.62347436\",\n \"0.6230235\",\n \"0.61446166\",\n \"0.61428374\",\n \"0.6095599\",\n \"0.60748625\",\n \"0.6050616\",\n \"0.6050616\",\n \"0.6050616\",\n \"0.6050616\",\n \"0.6029829\",\n \"0.60096\",\n \"0.5973834\",\n \"0.5958873\",\n \"0.5956844\",\n \"0.59472567\",\n \"0.5940582\",\n \"0.5926571\",\n \"0.5919561\",\n \"0.590596\",\n \"0.5897776\",\n \"0.5891463\",\n \"0.588618\",\n \"0.5880356\",\n \"0.58614385\",\n \"0.58490866\",\n \"0.5848971\",\n \"0.58426845\",\n \"0.5831801\",\n \"0.5828595\",\n \"0.582725\",\n \"0.5826627\",\n \"0.5821324\",\n \"0.5818959\",\n \"0.5812203\",\n \"0.5807462\",\n \"0.58002156\",\n \"0.5799782\",\n \"0.57973385\",\n \"0.5793228\",\n \"0.5792135\",\n \"0.5787988\",\n \"0.57732314\",\n \"0.5757169\",\n \"0.57534164\",\n \"0.5751546\",\n \"0.5748739\",\n \"0.574853\",\n \"0.57434624\",\n \"0.57402986\",\n \"0.5732827\",\n \"0.5727402\",\n \"0.57264704\",\n \"0.5722236\",\n \"0.57206094\",\n \"0.57196736\",\n \"0.5718622\",\n \"0.5708684\",\n \"0.5700656\",\n \"0.56918335\",\n \"0.5686508\",\n \"0.5682652\",\n \"0.5674416\",\n \"0.56683415\",\n \"0.5667687\",\n \"0.56669056\",\n \"0.56669056\",\n \"0.56669056\",\n \"0.5665989\",\n \"0.56620914\",\n \"0.56544644\",\n \"0.5653992\",\n \"0.5651833\",\n \"0.5648326\",\n \"0.5646527\",\n \"0.56458324\",\n \"0.5645502\",\n \"0.56448597\",\n \"0.5634157\",\n \"0.5631158\",\n \"0.56270766\",\n \"0.56259716\",\n \"0.56248176\",\n \"0.5620985\",\n \"0.5618467\",\n \"0.5617037\",\n \"0.5606175\",\n \"0.56048936\",\n \"0.56016725\",\n \"0.56015235\",\n \"0.5597825\",\n \"0.5596261\",\n \"0.5592716\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.73992556"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":16892,"cells":{"query":{"kind":"string","value":"Returns a list of the names of any sensitive / protected attribute(s) that will be used for a fairness analysis and should not be used to train the model."},"document":{"kind":"string","value":"def get_sensitive_attributes(self):\n return self.sensitive_attrs"},"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 __listAttr(self):\n attr = dir(self) # already sorted\n filter = []\n for name in attr:\n if name[:2] == '__': pass\n elif name[:10] == '_HelpDoc__': pass # used to mask private attr\n elif name in self.__exclude: pass\n else: filter.append(name)\n return filter","def attrs(self):\n return list(name for name in self.__dict__\n if not name.startswith(\"_\"))","def keys(self):\n return list(s.name.lower() for s in self.attributes)","def get_attribute_names(cls):\n return cls._attributes.keys()","def attributes(self):\n attr = super(BaseDataRecord, self).attributes()\n return [a for a in attr if a not in self.excluded()]","def get_attribute_list(self):\n attributes = [attr for attr in vars(self.entries[0]) if not attr.startswith('__')]\n return attributes","def keys(self):\n return [a.name for a in self.__attrs_attrs__]","def _getAttributeNames(self):\n return sorted(self._field_map.keys())","def getDataAttributes(self):\n asRet = [];\n asAttrs = dir(self);\n for sAttr in asAttrs:\n if sAttr[0] == '_' or sAttr[0] == 'k':\n continue;\n if sAttr in self.kasInternalAttributes:\n continue;\n oValue = getattr(self, sAttr);\n if callable(oValue):\n continue;\n asRet.append(sAttr);\n return asRet;","def get_attributes(cls):\r\n return [\r\n Attribute(\"show_correctness\", \"always\"),\r\n Attribute(\"submitted_message\", \"Answer received.\"),\r\n Attribute(\"label\", \"\"),\r\n ]","def get_privileged_class_names(self, tag):\n # FIXME this dependence between tags and privileged class names is bad; don't know how to\n # fix it right now\n if tag == 'numerical-binsensitive':\n return [1 for x in self.get_sensitive_attributes()]\n else:\n return self.privileged_class_names","def attrs(self):\n return [name for name in self.traits() if self.trait_metadata(name, \"attr\")]","def attrs(self):\n return [name for name in self.traits() if self.trait_metadata(name, \"attr\")]","def get_attrs(self):\n attrs = []\n for attribute in self.__dict__.keys():\n attrs.append(attribute)","def attribute_restrictions(self):\n return self._attribute_restrictions","def attributes(self) -> Set[str]:\n return set()","def get_sensitive_attributes_with_joint(self):\n if len(self.get_sensitive_attributes()) > 1:\n return self.get_sensitive_attributes() + ['-'.join(self.get_sensitive_attributes())]\n return self.get_sensitive_attributes()","def keys(self):\n\n keys = list()\n\n for attribute_name, type_instance in inspect.getmembers(self):\n\n if attribute_name.startswith('__') or inspect.ismethod(type_instance):\n # Ignore parameters with __ and if they are methods\n continue\n\n keys.append(attribute_name)\n\n return keys","def all(self):\r\n return self.attr_dict.keys()","def testattributes(self):\n for attr in ('ST', 'DX', 'IQ', 'MA', 'Dam', 'Hit'):\n AttributeAbility([attr,])","def get_attributes(cls):\r\n return []","def getAttributes(self):\n pass","def get_attribute_list(self):\n return self.dp.get_attribute_list()","def attributes(self):\n return self.__dict.keys()","def attributes(self):\n\n attributes = []\n\n for member in self.members:\n if member.attribute:\n attributes.append(member)\n\n return attributes","def attributes(self):\n attrs_ = [\"name\", \"type\"]\n if self.exclusive:\n attrs_.append(\"exclusive\")\n if self.default is not None:\n attrs_.append(\"default\")\n\n return attrs_","def keys(self):\n\n return self._uniforms.keys() + self._attributes.keys()","def _get_all_attributes(self):\n\n attributes= []\n for shader in self._verts:\n attributes.extend(shader.attributes)\n # No attribute in fragment shaders\n attributes = list(set(attributes))\n return attributes","def listglobal(self):\n return list(self.attributes.keys())","def filter_attributes_choices(self): \n filter_attributes = [\n 'no filters',\n 'user_id',\n 'device_id',\n 'device_first_seen_ts',\n 'device_first_view_ts', \n 'platform',\n 'platform_type',\n 'country',\n 'region',\n 'city',\n 'dma',\n 'os',\n 'os_version',\n 'manufacturer',\n 'app_mode',\n 'app_version',\n 'device_language',\n 'content_id',\n 'program_id',\n 'content_type',\n 'tvt_sec' # note: here tvt_sec is treated as an attribute rather than a cumulative metric\n ]\n return filter_attributes","def get_attributes(cls):\r\n return [\r\n Attribute('height', None),\r\n Attribute('width', None),\r\n Attribute('parts', None),\r\n Attribute('analyses', None),\r\n Attribute('initial_value', None),\r\n Attribute('submit_analyses', None),\r\n Attribute('label', ''),\r\n ]","def read_global_attributes(self):\n return self._attrs.keys()","def getattrs(self):\n # ICAT 4.5.0 also lists the meta attributes as attributes in\n # the entity info. Need to remove them here, as they should\n # not be added to InstAttr.\n return self.getfieldnames('ATTRIBUTE') - Entity.MetaAttr","def attributes(self):\n _attrs = [\"type\", \"name\", \"value\"]\n if self.confidence is not None:\n _attrs.append(\"confidence\")\n if self.constant:\n _attrs.append(\"constant\")\n if self.tags:\n _attrs.append(\"tags\")\n\n return _attrs","def attributes(self):\n return [self._ELE_ATTR]","def protected_classifications(self):\n if self.customer is None:\n return None\n return sorted([b.name for b in self._customer.protected_species if b.deployed])","def all_attributes(self):\n\n attributes = []\n for level in self.levels:\n attributes.extend(level.attributes)\n\n return attributes","def attributes(self):\n\n return list(self._attributes.values())","def specified_unchanging_attributes(self) -> List[int]:\n indices = []\n\n for idx, (cpi, epi) in enumerate(zip(self.condition, self.effect)):\n if isinstance(epi, ProbabilityEnhancedAttribute):\n if cpi != self.cfg.classifier_wildcard and \\\n epi.does_contain(cpi):\n indices.append(idx)\n else:\n if cpi != self.cfg.classifier_wildcard and \\\n epi == self.cfg.classifier_wildcard:\n indices.append(idx)\n\n return indices","def _detectors_attrs(self):\n if self._det.get('detectors'):\n return self._det['detectors'].keys()\n else:\n return []","def ListAttributes(self):\n\n print(\"\\n\")\n print(\"Attributes List of: \" + repr(self.__dict__[\"name\"]) + \" - \" + self.__class__.__name__ + \" Instance\\n\")\n self_keys = self.__dict__.keys()\n self_keys.sort()\n for key in self_keys:\n if key != \"name\":\n print(str(key) + \" : \" + repr(self.__dict__[key]))\n # end\n # end\n print(\"\\n\")","def getAttributes(iface):\n return [(name, attr)\n for name, attr in getElements(iface, IAttribute).items()\n if not (IField.providedBy(attr) or IMethod.providedBy(attr))]","def get_attributes(self):\n attrs = list()\n syms = list()\n for item in self.gradual_items:\n gi = item.as_integer()\n attrs.append(gi[0])\n syms.append(gi[1])\n return attrs, syms","def attributes(self):\n _attrs = super(CategoricalAttribute, self).attributes()\n if self.top_k_probs is not None:\n _attrs.append(\"top_k_probs\")\n\n return _attrs","def get_attributes(cls):\r\n return [Attribute('size', '20'),\r\n Attribute('label', ''), ]","def attributes(self) -> pulumi.Input[Sequence[pulumi.Input[str]]]:\n return pulumi.get(self, \"attributes\")","def get_attributes(cls):\r\n return [Attribute('file'),\r\n Attribute('missing', None)]","def getAttributes(self):\n return self.attributes","def getAttributes(self):\n return self.attributes","def listattribute(self, varName):\n fName = \"\"\n if varName in self.statVars:\n fName = self.statVars[varName][0]\n elif varName in self.timeVars:\n fName = self.timeVars[varName][0][0]\n if fName:\n var = cdms2.open(fName, 'r')(varName)\n return var.listattributes()\n else:\n return []","def get_attributes(self) -> Dict[str, str]:\n pass","def get_attributes(cls):\r\n return [\r\n Attribute('size', '20'),\r\n Attribute('inline', False),\r\n Attribute('label', ''),\r\n ]","def keys(self):\n return self.itunesAttributes.keys()","def getRuntimeAttrs(ad):\n \n re_runtime = re.compile('^(.*)Runtime$')\n\n # some attributes should always be ignored\n re_ignore = re.compile('^DC(Socket|Pipe)')\n ignored_attrs = ['SCGetAutoCluster_cchit']\n\n attrs = []\n for key in ad.keys():\n match = re_runtime.match(key)\n if match:\n attr = match.groups()[0]\n if not (re_ignore.match(attr) or (attr in ignored_attrs)):\n attrs.append(attr)\n\n return attrs","def attributes(self) -> typing.Iterator[typing.Tuple[str]]:\n minimize = self.lattice._context._minimize(self._extent, self._intent)\n return (i.members() for i in minimize)","def attributes(self):\n return self.problem.attributes","def attributes(self):\n raise NotImplementedError","def get_all_attributes(dataset):\n return dataset.flatMap(lambda x: x.keys()).distinct().collect()","def test_sim_control_attribute_list(self):\n implemented_attr = helper_module.SIM_CONTROL_ADDITIONAL_IMPLEMENTED_ATTR\n control_attributes = test_sim_test_interface.control_attributes(\n self.expected_model\n )\n attributes = set(self.sim_control_device.get_attribute_list())\n self.assertEqual(attributes - implemented_attr, set(control_attributes))","def ListAttributes(self):\n\n ListAttributes(self)","def extra_state_attributes(self):\n return {attr: getattr(self, '_' + prop)\n for attr, prop in ATTRIBUTES_IRHVAC.items()}","def key_attributes(self):\n\n return [level.key for level in self._levels.values()]","def attributes(self):\n _attrs = []\n if self.name:\n _attrs.append(\"name\")\n if self.label:\n _attrs.append(\"label\")\n if self.confidence:\n _attrs.append(\"confidence\")\n if self.index:\n _attrs.append(\"index\")\n if self.attrs:\n _attrs.append(\"attrs\")\n return _attrs + [\"points\"]","def attribute_summary(self):\n attributes = self.get_attribute_values()\n pairs = [attribute.summary() for attribute in attributes]\n return \", \".join(pairs)","def key_attributes(self):\n\n return [level.key for level in self.levels]","def attributes(self):","def get_attributes(doc):\n\treturn doc.keys()","def attributeInfo(*args, allAttributes: bool=True, bool: bool=True, enumerated: bool=True,\n hidden: bool=True, inherited: bool=True, internal: bool=True, leaf: bool=True,\n logicalAnd: bool=True, multi: bool=True, short: bool=True, userInterface:\n bool=True, writable: bool=True, type: AnyStr=\"\", **kwargs)->List[AnyStr]:\n pass","def get_all_attribute(self):\n for attr, value in self.__dict__.items():\n print(attr, value)","def attributes(self):\n ...","def _filter(self):\n for attr in self:\n short_name_in_ignores = attr.shortName() in self.ignores\n long_name_in_ignores = attr.longName() in self.ignores\n\n if not (short_name_in_ignores or long_name_in_ignores):\n yield attr","def important_attributes(vector):\r\n atts = []\r\n for i in xrange(len(vector)):\r\n if len(atts) < 3:\r\n atts.append((attributes[i], vector[i]))\r\n atts.sort(key=lambda x: x[1], reverse = True)\r\n else:\r\n if vector[i] > atts[2][1]:\r\n if attributes[i] in ('score', 'a_b_v'):\r\n continue\r\n atts[2] = (attributes[i], vector[i])\r\n atts.sort(key=lambda x: x[1], reverse = True)\r\n return atts","def get_list(self):\n categories = []\n for attribut in self.attributes:\n attr = getattr(self, attribut, False)\n if attr is True:\n categories.append(attribut)\n if getattr(self, 'education') is True:\n categories.append(_(u'education'))\n if getattr(self, 'training') is True:\n categories.append(_(u'training'))\n if getattr(self, 'tutoring') is True:\n categories.append(_(u'tutoring'))\n\n return categories","def _attrlist(self,obj, attrs):\n vlist = [obj.__getattribute__(attr) for attr in attrs]\n return vlist","def _get_attrs_symbols():\n return {\n 'True', 'False', 'None', # those are identifiers in Python 2.7\n 'self',\n 'parent',\n 'id',\n 'uid',\n 'context',\n 'context_today',\n 'active_id',\n 'active_ids',\n 'allowed_company_ids',\n 'current_company_id',\n 'active_model',\n 'time',\n 'datetime',\n 'relativedelta',\n 'current_date',\n 'abs',\n 'len',\n 'bool',\n 'float',\n 'str',\n 'unicode',\n }","def get_attributes(self):\n return self.attributes","def attributes(self):\n _attrs = [\"label\"]\n if self.attrs:\n _attrs.append(\"attrs\")\n return _attrs","def get_all_attrs():\n cursor = Biometa.objects.aggregate(*[\n {'$unwind': '$sample_attributes'},\n {\n '$project': {'_id': '$sample_attributes.name'}\n }\n ])\n\n return sorted(list(set([x['_id'] for x in cursor])))","def get_attributes(cls):\r\n return [Attribute('allowed_files', '[]', transform=cls.parse_files),\r\n Attribute('label', ''),\r\n Attribute('required_files', '[]', transform=cls.parse_files), ]","def values(self):\n return self.attrs.values()","def testattributes(self):\n for attr in AmuletAbility.attributes:\n a = AmuletAbility('Attribute', attr=attr)\n self.assert_(attr in str(a))\n self.assertEqual(a.attribute, attr)\n self.assertTrue(isinstance(a.AC, int))\n self.assertTrue(isinstance(a.description(), str))","def _drop_protected_attrs(model_class, values):\n for attr in model_class.__protected_attributes__:\n if attr in values:\n del values[attr]","def get_sensitive_terms(self):\n sensitive_terms_dict = {}\n for attribute in self.__non_redundant_entity_attributes:\n for record_id, sensitive_terms in self.__df[attribute].dropna().iteritems():\n for sensitive_term in sensitive_terms:\n cleaned_sensitive_term = \" \".join([t.lemma_.lower() for t in sensitive_term if not t.is_stop])\n if len(cleaned_sensitive_term) > 0:\n sensitive_terms_dict.setdefault(attribute, {}).setdefault(cleaned_sensitive_term, set()).add(record_id)\n\n # Sort sensitive terms dict alphabetically to have a deterministic order\n sensitive_terms_dict = {el[0]: el[1] for el in sorted(sensitive_terms_dict.items(), key=lambda x: x)}\n\n # Sort sensitive terms dict ascending by number terms per entity type\n sensitive_terms_dict = {el[0]: el[1] for el in sorted(sensitive_terms_dict.items(), key=lambda x: len(x[1]))}\n\n for attribute, sensitive_terms in sensitive_terms_dict.items():\n word = \"terms\"\n if len(sensitive_terms) == 1:\n word = \"term\"\n logger.info(\"Found %d distinct sensitive %s within attribute %s\", len(sensitive_terms), word, attribute)\n return sensitive_terms_dict","def GetAttributes(self):\r\n\r\n return self._attr","def purposes(self):\n\n return ('train', 'enroll', 'probe')","def _columns(cls):\n columns = []\n for name, member in inspect.getmembers(cls):\n if (not name.startswith('_') and\n isinstance(member, InstrumentedAttribute)):\n columns.append(name)\n return columns","def keyable_attributes(pynode, attr_name_list = None):\n if attr_name_list is None:\n attr_name_list = ['tx', 'ty', 'tz', 'rx', 'ry', 'rz', 'sx', 'sy', 'sz', 'v']\n\n _do_attributes_key_lock_hide(pynode, attr_name_list, keyable = True)","def allowed(cls):\n # type: () -> List[Str]\n names = cls.names()\n allowed = names\n allowed.extend([name.lower() for name in names])\n return allowed","def values(self):\n return [getattr(self, a.name) for a in self.__attrs_attrs__]","def nonkeyable_attributes(pynode, attr_name_list = None):\n if attr_name_list is None:\n attr_name_list = ['tx', 'ty', 'tz', 'rx', 'ry', 'rz', 'sx', 'sy', 'sz', 'v']\n\n _do_attributes_key_lock_hide(pynode, attr_name_list, keyable = False)","def cert_readonly_fields():\n if settings.DEBUG:\n return []\n return [\n 'user', 'get_template', 'csr', 'serial', 'subject',\n 'valid_from', 'valid_to', 'x509',\n ]","def describe_account_attributes():\n pass","def _get_active_attributes(self):\n\n count = gl.glGetProgramiv(self.handle, gl.GL_ACTIVE_ATTRIBUTES)\n attributes = []\n\n # This match a name of the form \"name[size]\" (= array)\n regex = re.compile(\"\"\"(?P<name>\\w+)\\s*(\\[(?P<size>\\d+)\\])\"\"\")\n\n for i in range(count):\n name, size, gtype = gl.glGetActiveAttrib(self.handle, i)\n\n # This checks if the attribute is an array\n # Name will be something like xxx[0] instead of xxx\n m = regex.match(name)\n # When attribute is an array, size corresponds to the highest used index\n if m:\n name = m.group('name')\n if size >= 1:\n for i in range(size):\n name = '%s[%d]' % (m.group('name'),i)\n attributes.append((name, gtype))\n else:\n attributes.append((name, gtype))\n return attributes","def names(self):\n return list(item.name for item in self.mechanisms)","def get_cryptomatte_names(self):\n return [self.cryptomattes[x][\"name\"] for x in self.cryptomattes]","def ATTRIBUTE():\n return \"author\", \"title\", \"publisher\", \"shelf\", \"category\", \"subject\"","def _yamlSpeciallyHandledAttributes(self):\n return []","def get_keyable_attribute(node):\n attrs = cmds.listAttr(node, ud=False, k=True)\n\n return attrs","def analyze_textual_attributes(self):\n for attribute in self.__textual_attributes:\n self.__analyze_textual_attribute(attribute)","def test_attractor_list(self):\n assert len(get_attractor_list()) > 130"],"string":"[\n \"def __listAttr(self):\\n attr = dir(self) # already sorted\\n filter = []\\n for name in attr:\\n if name[:2] == '__': pass\\n elif name[:10] == '_HelpDoc__': pass # used to mask private attr\\n elif name in self.__exclude: pass\\n else: filter.append(name)\\n return filter\",\n \"def attrs(self):\\n return list(name for name in self.__dict__\\n if not name.startswith(\\\"_\\\"))\",\n \"def keys(self):\\n return list(s.name.lower() for s in self.attributes)\",\n \"def get_attribute_names(cls):\\n return cls._attributes.keys()\",\n \"def attributes(self):\\n attr = super(BaseDataRecord, self).attributes()\\n return [a for a in attr if a not in self.excluded()]\",\n \"def get_attribute_list(self):\\n attributes = [attr for attr in vars(self.entries[0]) if not attr.startswith('__')]\\n return attributes\",\n \"def keys(self):\\n return [a.name for a in self.__attrs_attrs__]\",\n \"def _getAttributeNames(self):\\n return sorted(self._field_map.keys())\",\n \"def getDataAttributes(self):\\n asRet = [];\\n asAttrs = dir(self);\\n for sAttr in asAttrs:\\n if sAttr[0] == '_' or sAttr[0] == 'k':\\n continue;\\n if sAttr in self.kasInternalAttributes:\\n continue;\\n oValue = getattr(self, sAttr);\\n if callable(oValue):\\n continue;\\n asRet.append(sAttr);\\n return asRet;\",\n \"def get_attributes(cls):\\r\\n return [\\r\\n Attribute(\\\"show_correctness\\\", \\\"always\\\"),\\r\\n Attribute(\\\"submitted_message\\\", \\\"Answer received.\\\"),\\r\\n Attribute(\\\"label\\\", \\\"\\\"),\\r\\n ]\",\n \"def get_privileged_class_names(self, tag):\\n # FIXME this dependence between tags and privileged class names is bad; don't know how to\\n # fix it right now\\n if tag == 'numerical-binsensitive':\\n return [1 for x in self.get_sensitive_attributes()]\\n else:\\n return self.privileged_class_names\",\n \"def attrs(self):\\n return [name for name in self.traits() if self.trait_metadata(name, \\\"attr\\\")]\",\n \"def attrs(self):\\n return [name for name in self.traits() if self.trait_metadata(name, \\\"attr\\\")]\",\n \"def get_attrs(self):\\n attrs = []\\n for attribute in self.__dict__.keys():\\n attrs.append(attribute)\",\n \"def attribute_restrictions(self):\\n return self._attribute_restrictions\",\n \"def attributes(self) -> Set[str]:\\n return set()\",\n \"def get_sensitive_attributes_with_joint(self):\\n if len(self.get_sensitive_attributes()) > 1:\\n return self.get_sensitive_attributes() + ['-'.join(self.get_sensitive_attributes())]\\n return self.get_sensitive_attributes()\",\n \"def keys(self):\\n\\n keys = list()\\n\\n for attribute_name, type_instance in inspect.getmembers(self):\\n\\n if attribute_name.startswith('__') or inspect.ismethod(type_instance):\\n # Ignore parameters with __ and if they are methods\\n continue\\n\\n keys.append(attribute_name)\\n\\n return keys\",\n \"def all(self):\\r\\n return self.attr_dict.keys()\",\n \"def testattributes(self):\\n for attr in ('ST', 'DX', 'IQ', 'MA', 'Dam', 'Hit'):\\n AttributeAbility([attr,])\",\n \"def get_attributes(cls):\\r\\n return []\",\n \"def getAttributes(self):\\n pass\",\n \"def get_attribute_list(self):\\n return self.dp.get_attribute_list()\",\n \"def attributes(self):\\n return self.__dict.keys()\",\n \"def attributes(self):\\n\\n attributes = []\\n\\n for member in self.members:\\n if member.attribute:\\n attributes.append(member)\\n\\n return attributes\",\n \"def attributes(self):\\n attrs_ = [\\\"name\\\", \\\"type\\\"]\\n if self.exclusive:\\n attrs_.append(\\\"exclusive\\\")\\n if self.default is not None:\\n attrs_.append(\\\"default\\\")\\n\\n return attrs_\",\n \"def keys(self):\\n\\n return self._uniforms.keys() + self._attributes.keys()\",\n \"def _get_all_attributes(self):\\n\\n attributes= []\\n for shader in self._verts:\\n attributes.extend(shader.attributes)\\n # No attribute in fragment shaders\\n attributes = list(set(attributes))\\n return attributes\",\n \"def listglobal(self):\\n return list(self.attributes.keys())\",\n \"def filter_attributes_choices(self): \\n filter_attributes = [\\n 'no filters',\\n 'user_id',\\n 'device_id',\\n 'device_first_seen_ts',\\n 'device_first_view_ts', \\n 'platform',\\n 'platform_type',\\n 'country',\\n 'region',\\n 'city',\\n 'dma',\\n 'os',\\n 'os_version',\\n 'manufacturer',\\n 'app_mode',\\n 'app_version',\\n 'device_language',\\n 'content_id',\\n 'program_id',\\n 'content_type',\\n 'tvt_sec' # note: here tvt_sec is treated as an attribute rather than a cumulative metric\\n ]\\n return filter_attributes\",\n \"def get_attributes(cls):\\r\\n return [\\r\\n Attribute('height', None),\\r\\n Attribute('width', None),\\r\\n Attribute('parts', None),\\r\\n Attribute('analyses', None),\\r\\n Attribute('initial_value', None),\\r\\n Attribute('submit_analyses', None),\\r\\n Attribute('label', ''),\\r\\n ]\",\n \"def read_global_attributes(self):\\n return self._attrs.keys()\",\n \"def getattrs(self):\\n # ICAT 4.5.0 also lists the meta attributes as attributes in\\n # the entity info. Need to remove them here, as they should\\n # not be added to InstAttr.\\n return self.getfieldnames('ATTRIBUTE') - Entity.MetaAttr\",\n \"def attributes(self):\\n _attrs = [\\\"type\\\", \\\"name\\\", \\\"value\\\"]\\n if self.confidence is not None:\\n _attrs.append(\\\"confidence\\\")\\n if self.constant:\\n _attrs.append(\\\"constant\\\")\\n if self.tags:\\n _attrs.append(\\\"tags\\\")\\n\\n return _attrs\",\n \"def attributes(self):\\n return [self._ELE_ATTR]\",\n \"def protected_classifications(self):\\n if self.customer is None:\\n return None\\n return sorted([b.name for b in self._customer.protected_species if b.deployed])\",\n \"def all_attributes(self):\\n\\n attributes = []\\n for level in self.levels:\\n attributes.extend(level.attributes)\\n\\n return attributes\",\n \"def attributes(self):\\n\\n return list(self._attributes.values())\",\n \"def specified_unchanging_attributes(self) -> List[int]:\\n indices = []\\n\\n for idx, (cpi, epi) in enumerate(zip(self.condition, self.effect)):\\n if isinstance(epi, ProbabilityEnhancedAttribute):\\n if cpi != self.cfg.classifier_wildcard and \\\\\\n epi.does_contain(cpi):\\n indices.append(idx)\\n else:\\n if cpi != self.cfg.classifier_wildcard and \\\\\\n epi == self.cfg.classifier_wildcard:\\n indices.append(idx)\\n\\n return indices\",\n \"def _detectors_attrs(self):\\n if self._det.get('detectors'):\\n return self._det['detectors'].keys()\\n else:\\n return []\",\n \"def ListAttributes(self):\\n\\n print(\\\"\\\\n\\\")\\n print(\\\"Attributes List of: \\\" + repr(self.__dict__[\\\"name\\\"]) + \\\" - \\\" + self.__class__.__name__ + \\\" Instance\\\\n\\\")\\n self_keys = self.__dict__.keys()\\n self_keys.sort()\\n for key in self_keys:\\n if key != \\\"name\\\":\\n print(str(key) + \\\" : \\\" + repr(self.__dict__[key]))\\n # end\\n # end\\n print(\\\"\\\\n\\\")\",\n \"def getAttributes(iface):\\n return [(name, attr)\\n for name, attr in getElements(iface, IAttribute).items()\\n if not (IField.providedBy(attr) or IMethod.providedBy(attr))]\",\n \"def get_attributes(self):\\n attrs = list()\\n syms = list()\\n for item in self.gradual_items:\\n gi = item.as_integer()\\n attrs.append(gi[0])\\n syms.append(gi[1])\\n return attrs, syms\",\n \"def attributes(self):\\n _attrs = super(CategoricalAttribute, self).attributes()\\n if self.top_k_probs is not None:\\n _attrs.append(\\\"top_k_probs\\\")\\n\\n return _attrs\",\n \"def get_attributes(cls):\\r\\n return [Attribute('size', '20'),\\r\\n Attribute('label', ''), ]\",\n \"def attributes(self) -> pulumi.Input[Sequence[pulumi.Input[str]]]:\\n return pulumi.get(self, \\\"attributes\\\")\",\n \"def get_attributes(cls):\\r\\n return [Attribute('file'),\\r\\n Attribute('missing', None)]\",\n \"def getAttributes(self):\\n return self.attributes\",\n \"def getAttributes(self):\\n return self.attributes\",\n \"def listattribute(self, varName):\\n fName = \\\"\\\"\\n if varName in self.statVars:\\n fName = self.statVars[varName][0]\\n elif varName in self.timeVars:\\n fName = self.timeVars[varName][0][0]\\n if fName:\\n var = cdms2.open(fName, 'r')(varName)\\n return var.listattributes()\\n else:\\n return []\",\n \"def get_attributes(self) -> Dict[str, str]:\\n pass\",\n \"def get_attributes(cls):\\r\\n return [\\r\\n Attribute('size', '20'),\\r\\n Attribute('inline', False),\\r\\n Attribute('label', ''),\\r\\n ]\",\n \"def keys(self):\\n return self.itunesAttributes.keys()\",\n \"def getRuntimeAttrs(ad):\\n \\n re_runtime = re.compile('^(.*)Runtime$')\\n\\n # some attributes should always be ignored\\n re_ignore = re.compile('^DC(Socket|Pipe)')\\n ignored_attrs = ['SCGetAutoCluster_cchit']\\n\\n attrs = []\\n for key in ad.keys():\\n match = re_runtime.match(key)\\n if match:\\n attr = match.groups()[0]\\n if not (re_ignore.match(attr) or (attr in ignored_attrs)):\\n attrs.append(attr)\\n\\n return attrs\",\n \"def attributes(self) -> typing.Iterator[typing.Tuple[str]]:\\n minimize = self.lattice._context._minimize(self._extent, self._intent)\\n return (i.members() for i in minimize)\",\n \"def attributes(self):\\n return self.problem.attributes\",\n \"def attributes(self):\\n raise NotImplementedError\",\n \"def get_all_attributes(dataset):\\n return dataset.flatMap(lambda x: x.keys()).distinct().collect()\",\n \"def test_sim_control_attribute_list(self):\\n implemented_attr = helper_module.SIM_CONTROL_ADDITIONAL_IMPLEMENTED_ATTR\\n control_attributes = test_sim_test_interface.control_attributes(\\n self.expected_model\\n )\\n attributes = set(self.sim_control_device.get_attribute_list())\\n self.assertEqual(attributes - implemented_attr, set(control_attributes))\",\n \"def ListAttributes(self):\\n\\n ListAttributes(self)\",\n \"def extra_state_attributes(self):\\n return {attr: getattr(self, '_' + prop)\\n for attr, prop in ATTRIBUTES_IRHVAC.items()}\",\n \"def key_attributes(self):\\n\\n return [level.key for level in self._levels.values()]\",\n \"def attributes(self):\\n _attrs = []\\n if self.name:\\n _attrs.append(\\\"name\\\")\\n if self.label:\\n _attrs.append(\\\"label\\\")\\n if self.confidence:\\n _attrs.append(\\\"confidence\\\")\\n if self.index:\\n _attrs.append(\\\"index\\\")\\n if self.attrs:\\n _attrs.append(\\\"attrs\\\")\\n return _attrs + [\\\"points\\\"]\",\n \"def attribute_summary(self):\\n attributes = self.get_attribute_values()\\n pairs = [attribute.summary() for attribute in attributes]\\n return \\\", \\\".join(pairs)\",\n \"def key_attributes(self):\\n\\n return [level.key for level in self.levels]\",\n \"def attributes(self):\",\n \"def get_attributes(doc):\\n\\treturn doc.keys()\",\n \"def attributeInfo(*args, allAttributes: bool=True, bool: bool=True, enumerated: bool=True,\\n hidden: bool=True, inherited: bool=True, internal: bool=True, leaf: bool=True,\\n logicalAnd: bool=True, multi: bool=True, short: bool=True, userInterface:\\n bool=True, writable: bool=True, type: AnyStr=\\\"\\\", **kwargs)->List[AnyStr]:\\n pass\",\n \"def get_all_attribute(self):\\n for attr, value in self.__dict__.items():\\n print(attr, value)\",\n \"def attributes(self):\\n ...\",\n \"def _filter(self):\\n for attr in self:\\n short_name_in_ignores = attr.shortName() in self.ignores\\n long_name_in_ignores = attr.longName() in self.ignores\\n\\n if not (short_name_in_ignores or long_name_in_ignores):\\n yield attr\",\n \"def important_attributes(vector):\\r\\n atts = []\\r\\n for i in xrange(len(vector)):\\r\\n if len(atts) < 3:\\r\\n atts.append((attributes[i], vector[i]))\\r\\n atts.sort(key=lambda x: x[1], reverse = True)\\r\\n else:\\r\\n if vector[i] > atts[2][1]:\\r\\n if attributes[i] in ('score', 'a_b_v'):\\r\\n continue\\r\\n atts[2] = (attributes[i], vector[i])\\r\\n atts.sort(key=lambda x: x[1], reverse = True)\\r\\n return atts\",\n \"def get_list(self):\\n categories = []\\n for attribut in self.attributes:\\n attr = getattr(self, attribut, False)\\n if attr is True:\\n categories.append(attribut)\\n if getattr(self, 'education') is True:\\n categories.append(_(u'education'))\\n if getattr(self, 'training') is True:\\n categories.append(_(u'training'))\\n if getattr(self, 'tutoring') is True:\\n categories.append(_(u'tutoring'))\\n\\n return categories\",\n \"def _attrlist(self,obj, attrs):\\n vlist = [obj.__getattribute__(attr) for attr in attrs]\\n return vlist\",\n \"def _get_attrs_symbols():\\n return {\\n 'True', 'False', 'None', # those are identifiers in Python 2.7\\n 'self',\\n 'parent',\\n 'id',\\n 'uid',\\n 'context',\\n 'context_today',\\n 'active_id',\\n 'active_ids',\\n 'allowed_company_ids',\\n 'current_company_id',\\n 'active_model',\\n 'time',\\n 'datetime',\\n 'relativedelta',\\n 'current_date',\\n 'abs',\\n 'len',\\n 'bool',\\n 'float',\\n 'str',\\n 'unicode',\\n }\",\n \"def get_attributes(self):\\n return self.attributes\",\n \"def attributes(self):\\n _attrs = [\\\"label\\\"]\\n if self.attrs:\\n _attrs.append(\\\"attrs\\\")\\n return _attrs\",\n \"def get_all_attrs():\\n cursor = Biometa.objects.aggregate(*[\\n {'$unwind': '$sample_attributes'},\\n {\\n '$project': {'_id': '$sample_attributes.name'}\\n }\\n ])\\n\\n return sorted(list(set([x['_id'] for x in cursor])))\",\n \"def get_attributes(cls):\\r\\n return [Attribute('allowed_files', '[]', transform=cls.parse_files),\\r\\n Attribute('label', ''),\\r\\n Attribute('required_files', '[]', transform=cls.parse_files), ]\",\n \"def values(self):\\n return self.attrs.values()\",\n \"def testattributes(self):\\n for attr in AmuletAbility.attributes:\\n a = AmuletAbility('Attribute', attr=attr)\\n self.assert_(attr in str(a))\\n self.assertEqual(a.attribute, attr)\\n self.assertTrue(isinstance(a.AC, int))\\n self.assertTrue(isinstance(a.description(), str))\",\n \"def _drop_protected_attrs(model_class, values):\\n for attr in model_class.__protected_attributes__:\\n if attr in values:\\n del values[attr]\",\n \"def get_sensitive_terms(self):\\n sensitive_terms_dict = {}\\n for attribute in self.__non_redundant_entity_attributes:\\n for record_id, sensitive_terms in self.__df[attribute].dropna().iteritems():\\n for sensitive_term in sensitive_terms:\\n cleaned_sensitive_term = \\\" \\\".join([t.lemma_.lower() for t in sensitive_term if not t.is_stop])\\n if len(cleaned_sensitive_term) > 0:\\n sensitive_terms_dict.setdefault(attribute, {}).setdefault(cleaned_sensitive_term, set()).add(record_id)\\n\\n # Sort sensitive terms dict alphabetically to have a deterministic order\\n sensitive_terms_dict = {el[0]: el[1] for el in sorted(sensitive_terms_dict.items(), key=lambda x: x)}\\n\\n # Sort sensitive terms dict ascending by number terms per entity type\\n sensitive_terms_dict = {el[0]: el[1] for el in sorted(sensitive_terms_dict.items(), key=lambda x: len(x[1]))}\\n\\n for attribute, sensitive_terms in sensitive_terms_dict.items():\\n word = \\\"terms\\\"\\n if len(sensitive_terms) == 1:\\n word = \\\"term\\\"\\n logger.info(\\\"Found %d distinct sensitive %s within attribute %s\\\", len(sensitive_terms), word, attribute)\\n return sensitive_terms_dict\",\n \"def GetAttributes(self):\\r\\n\\r\\n return self._attr\",\n \"def purposes(self):\\n\\n return ('train', 'enroll', 'probe')\",\n \"def _columns(cls):\\n columns = []\\n for name, member in inspect.getmembers(cls):\\n if (not name.startswith('_') and\\n isinstance(member, InstrumentedAttribute)):\\n columns.append(name)\\n return columns\",\n \"def keyable_attributes(pynode, attr_name_list = None):\\n if attr_name_list is None:\\n attr_name_list = ['tx', 'ty', 'tz', 'rx', 'ry', 'rz', 'sx', 'sy', 'sz', 'v']\\n\\n _do_attributes_key_lock_hide(pynode, attr_name_list, keyable = True)\",\n \"def allowed(cls):\\n # type: () -> List[Str]\\n names = cls.names()\\n allowed = names\\n allowed.extend([name.lower() for name in names])\\n return allowed\",\n \"def values(self):\\n return [getattr(self, a.name) for a in self.__attrs_attrs__]\",\n \"def nonkeyable_attributes(pynode, attr_name_list = None):\\n if attr_name_list is None:\\n attr_name_list = ['tx', 'ty', 'tz', 'rx', 'ry', 'rz', 'sx', 'sy', 'sz', 'v']\\n\\n _do_attributes_key_lock_hide(pynode, attr_name_list, keyable = False)\",\n \"def cert_readonly_fields():\\n if settings.DEBUG:\\n return []\\n return [\\n 'user', 'get_template', 'csr', 'serial', 'subject',\\n 'valid_from', 'valid_to', 'x509',\\n ]\",\n \"def describe_account_attributes():\\n pass\",\n \"def _get_active_attributes(self):\\n\\n count = gl.glGetProgramiv(self.handle, gl.GL_ACTIVE_ATTRIBUTES)\\n attributes = []\\n\\n # This match a name of the form \\\"name[size]\\\" (= array)\\n regex = re.compile(\\\"\\\"\\\"(?P<name>\\\\w+)\\\\s*(\\\\[(?P<size>\\\\d+)\\\\])\\\"\\\"\\\")\\n\\n for i in range(count):\\n name, size, gtype = gl.glGetActiveAttrib(self.handle, i)\\n\\n # This checks if the attribute is an array\\n # Name will be something like xxx[0] instead of xxx\\n m = regex.match(name)\\n # When attribute is an array, size corresponds to the highest used index\\n if m:\\n name = m.group('name')\\n if size >= 1:\\n for i in range(size):\\n name = '%s[%d]' % (m.group('name'),i)\\n attributes.append((name, gtype))\\n else:\\n attributes.append((name, gtype))\\n return attributes\",\n \"def names(self):\\n return list(item.name for item in self.mechanisms)\",\n \"def get_cryptomatte_names(self):\\n return [self.cryptomattes[x][\\\"name\\\"] for x in self.cryptomattes]\",\n \"def ATTRIBUTE():\\n return \\\"author\\\", \\\"title\\\", \\\"publisher\\\", \\\"shelf\\\", \\\"category\\\", \\\"subject\\\"\",\n \"def _yamlSpeciallyHandledAttributes(self):\\n return []\",\n \"def get_keyable_attribute(node):\\n attrs = cmds.listAttr(node, ud=False, k=True)\\n\\n return attrs\",\n \"def analyze_textual_attributes(self):\\n for attribute in self.__textual_attributes:\\n self.__analyze_textual_attribute(attribute)\",\n \"def test_attractor_list(self):\\n assert len(get_attractor_list()) > 130\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.70694345","0.66682184","0.6661344","0.66439575","0.66118485","0.654151","0.6416619","0.6314357","0.63003594","0.626828","0.6239923","0.62366736","0.62366736","0.6221969","0.6175809","0.61643624","0.61223054","0.6114993","0.61009276","0.6096935","0.6096455","0.60956854","0.60580903","0.604152","0.604023","0.603697","0.5994728","0.5976761","0.59743744","0.5970619","0.5968962","0.5956963","0.59399563","0.5928554","0.5883242","0.58552206","0.5842725","0.5817934","0.579079","0.5783436","0.5782305","0.5753595","0.57267183","0.5712004","0.569477","0.56766653","0.56652653","0.56477743","0.56477743","0.564264","0.5628985","0.56283253","0.5625977","0.5619234","0.5615282","0.5599402","0.5572618","0.55567676","0.5547798","0.5533754","0.5529622","0.5519371","0.55173975","0.55092275","0.5505681","0.54916525","0.54908496","0.54844195","0.5476169","0.54749495","0.5457431","0.5456303","0.5447995","0.54424566","0.54418916","0.54282665","0.54129595","0.54054606","0.54022497","0.5397255","0.538653","0.53863966","0.53820246","0.5377798","0.5376863","0.53724915","0.53722394","0.5366407","0.5360684","0.5359708","0.53401387","0.53392166","0.5324522","0.53169626","0.53050613","0.53022665","0.5301592","0.53005534","0.52859247","0.5283002"],"string":"[\n \"0.70694345\",\n \"0.66682184\",\n \"0.6661344\",\n \"0.66439575\",\n \"0.66118485\",\n \"0.654151\",\n \"0.6416619\",\n \"0.6314357\",\n \"0.63003594\",\n \"0.626828\",\n \"0.6239923\",\n \"0.62366736\",\n \"0.62366736\",\n \"0.6221969\",\n \"0.6175809\",\n \"0.61643624\",\n \"0.61223054\",\n \"0.6114993\",\n \"0.61009276\",\n \"0.6096935\",\n \"0.6096455\",\n \"0.60956854\",\n \"0.60580903\",\n \"0.604152\",\n \"0.604023\",\n \"0.603697\",\n \"0.5994728\",\n \"0.5976761\",\n \"0.59743744\",\n \"0.5970619\",\n \"0.5968962\",\n \"0.5956963\",\n \"0.59399563\",\n \"0.5928554\",\n \"0.5883242\",\n \"0.58552206\",\n \"0.5842725\",\n \"0.5817934\",\n \"0.579079\",\n \"0.5783436\",\n \"0.5782305\",\n \"0.5753595\",\n \"0.57267183\",\n \"0.5712004\",\n \"0.569477\",\n \"0.56766653\",\n \"0.56652653\",\n \"0.56477743\",\n \"0.56477743\",\n \"0.564264\",\n \"0.5628985\",\n \"0.56283253\",\n \"0.5625977\",\n \"0.5619234\",\n \"0.5615282\",\n \"0.5599402\",\n \"0.5572618\",\n \"0.55567676\",\n \"0.5547798\",\n \"0.5533754\",\n \"0.5529622\",\n \"0.5519371\",\n \"0.55173975\",\n \"0.55092275\",\n \"0.5505681\",\n \"0.54916525\",\n \"0.54908496\",\n \"0.54844195\",\n \"0.5476169\",\n \"0.54749495\",\n \"0.5457431\",\n \"0.5456303\",\n \"0.5447995\",\n \"0.54424566\",\n \"0.54418916\",\n \"0.54282665\",\n \"0.54129595\",\n \"0.54054606\",\n \"0.54022497\",\n \"0.5397255\",\n \"0.538653\",\n \"0.53863966\",\n \"0.53820246\",\n \"0.5377798\",\n \"0.5376863\",\n \"0.53724915\",\n \"0.53722394\",\n \"0.5366407\",\n \"0.5360684\",\n \"0.5359708\",\n \"0.53401387\",\n \"0.53392166\",\n \"0.5324522\",\n \"0.53169626\",\n \"0.53050613\",\n \"0.53022665\",\n \"0.5301592\",\n \"0.53005534\",\n \"0.52859247\",\n \"0.5283002\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7331742"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":16893,"cells":{"query":{"kind":"string","value":"Same as get_sensitive_attributes, but also includes the joint sensitive attribute if there is more than one sensitive attribute."},"document":{"kind":"string","value":"def get_sensitive_attributes_with_joint(self):\n if len(self.get_sensitive_attributes()) > 1:\n return self.get_sensitive_attributes() + ['-'.join(self.get_sensitive_attributes())]\n return self.get_sensitive_attributes()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_sensitive_attributes(self):\n return self.sensitive_attrs","def get_sensitive_terms(self):\n sensitive_terms_dict = {}\n for attribute in self.__non_redundant_entity_attributes:\n for record_id, sensitive_terms in self.__df[attribute].dropna().iteritems():\n for sensitive_term in sensitive_terms:\n cleaned_sensitive_term = \" \".join([t.lemma_.lower() for t in sensitive_term if not t.is_stop])\n if len(cleaned_sensitive_term) > 0:\n sensitive_terms_dict.setdefault(attribute, {}).setdefault(cleaned_sensitive_term, set()).add(record_id)\n\n # Sort sensitive terms dict alphabetically to have a deterministic order\n sensitive_terms_dict = {el[0]: el[1] for el in sorted(sensitive_terms_dict.items(), key=lambda x: x)}\n\n # Sort sensitive terms dict ascending by number terms per entity type\n sensitive_terms_dict = {el[0]: el[1] for el in sorted(sensitive_terms_dict.items(), key=lambda x: len(x[1]))}\n\n for attribute, sensitive_terms in sensitive_terms_dict.items():\n word = \"terms\"\n if len(sensitive_terms) == 1:\n word = \"term\"\n logger.info(\"Found %d distinct sensitive %s within attribute %s\", len(sensitive_terms), word, attribute)\n return sensitive_terms_dict","def _get_include_attributes(cls, args, extra_attributes=None):\n extra_attributes = extra_attributes or []\n\n include_attributes = []\n\n if extra_attributes:\n include_attributes.extend(extra_attributes)\n\n # If user specifies which attributes to retrieve via CLI --attr / -a argument, take that\n # into account\n\n # Special case for \"all\"\n if \"all\" in args.attr:\n return None\n\n for attr in args.attr:\n include_attributes.append(attr)\n\n if include_attributes:\n return include_attributes\n\n display_attributes = getattr(cls, \"display_attributes\", [])\n\n if display_attributes:\n include_attributes += display_attributes\n\n include_attributes = list(OrderedSet(include_attributes))\n\n return include_attributes","def get_redundant_entity_attributes(self):\n return self.__redundant_entity_attributes","def _filtered_attributes(\n self, required_attributes: Union[Iterable, Literal[\"__all__\"]], dontformat=False\n ) -> Tuple[Dict, Dict]:\n if required_attributes == \"__all__\":\n required_attributes = self.__atomic_fields_set__ | {\"meta\"}\n required_attributes = set(required_attributes)\n errors = []\n attrs = {name: getattr(self, name, None) for name in required_attributes-{\"meta\"}}\n for name in required_attributes - {\"meta\"}:\n if name not in self.__atomic_fields_set__:\n errors.append(f\" Unexpected required attribute: '{name}'.\")\n continue\n if attrs.get(name) is None:\n if not utils.is_an_optional_type_hint(self.__fields_types__[name]):\n errors.append(f\" Missing required attribute: '{name}'.\")\n if errors:\n raise ValueError(\"\\n\" + \"\\n\".join(errors))\n attrs = {\n utils.snake_to_camel_case(k, dontformat): v\n for (k, v) in attrs.items()\n if k in set(required_attributes) - self._identifier_fields\n }\n meta_attrs = {\n utils.snake_to_camel_case(name, dontformat): getattr(self, name)\n for name in self.__meta_attributes__\n if getattr(self, name) is not None\n } if \"meta\" in required_attributes else None\n return attrs, meta_attrs","def attributes(self):\n attr = super(BaseDataRecord, self).attributes()\n return [a for a in attr if a not in self.excluded()]","def extra_state_attributes(self) -> dict[str, Any]:\n return self._attributes","def extra_state_attributes(self):\n return {attr: getattr(self, '_' + prop)\n for attr, prop in ATTRIBUTES_IRHVAC.items()}","def extra_state_attributes(self) -> dict[str, Any]:\n ret = {\n ATTR_SOURCE: self._source_entity_id,\n ATTR_COEFFICIENTS: self._coefficients,\n }\n if self._source_attribute:\n ret[ATTR_SOURCE_ATTRIBUTE] = self._source_attribute\n return ret","def extra_from_record(self, record):\n return {\n attr_name: record.__dict__[attr_name]\n for attr_name in record.__dict__\n if attr_name not in BUILTIN_ATTRS\n }","def attributes(self) -> Optional[Mapping[str, Sequence[str]]]:\n return pulumi.get(self, \"attributes\")","def extra_state_attributes(self):\n return self._attributes","def clean_attributes(self):\n attrs = {}\n\n # Only fetch the fields we need.\n for a in self.attributes.only('name', 'value', 'attribute').iterator():\n if a.attribute.multi:\n if a.name not in attrs:\n attrs[a.name] = []\n attrs[a.name].append(a.value)\n else:\n attrs[a.name] = a.value\n self._attributes_cache = attrs # Cache the attributes\n\n return attrs","def extra_state_attributes(self):\r\n return self._attributes","def localized_attributes(self) -> Optional[Mapping[str, Mapping[str, str]]]:\n return pulumi.get(self, \"localized_attributes\")","def senate_attribs(self):\n return self.senate_votes[4]","def extra_state_attributes(self):\n return self._attributes","def extra_state_attributes(self):\n return {ATTR_DEVICE: \"SKYBEACON\", ATTR_MODEL: 1}","def extra_state_attributes(self):\n return {ATTR_DEVICE: \"SKYBEACON\", ATTR_MODEL: 1}","def get_non_redundant_entity_attributes(self):\n return self.__non_redundant_entity_attributes","def attributes(self) -> Optional[pulumi.Input[Mapping[str, Any]]]:\n return pulumi.get(self, \"attributes\")","def attributes(self) -> Optional[pulumi.Input[Mapping[str, Any]]]:\n return pulumi.get(self, \"attributes\")","def sensitive(self, value: object) -> Union[sensitive, object]:\n return wrap_sensitive_value(value)","def extra_state_attributes(self):\n attr = self._attributes\n if self.tesla_device.has_battery():\n attr[ATTR_BATTERY_LEVEL] = self.tesla_device.battery_level()\n attr[ATTR_BATTERY_CHARGING] = self.tesla_device.battery_charging()\n return attr","def _get_feature_attributes(self) -> dict:\n srs = pd.Series(dir(self))\n srs = srs[\n (~srs.str.startswith('_'))\n & (~srs.str.contains('as_'))\n & (srs != 'putin')\n & (srs != 'takeout')\n & (srs != 'intermediate_accesses')\n & (srs != 'geometry')\n & (srs != 'has_a_point')\n & (srs != 'centroid')\n ]\n srs = srs[srs.apply(lambda p: not hasattr(getattr(self, p), '__call__'))]\n return {key: getattr(self, key) for key in srs}","def extra_state_attributes(self):\n state_attr = {}\n if self.vendor_id is not None:\n state_attr[ATTR_VENDOR_ID] = self.vendor_id\n state_attr[ATTR_VENDOR_NAME] = self.vendor_name\n if self.type_id is not None:\n state_attr[ATTR_TYPE_ID] = self.type_id\n state_attr[ATTR_TYPE] = self.type\n if self.physical_address is not None:\n state_attr[ATTR_PHYSICAL_ADDRESS] = self.physical_address\n return state_attr","def get_attributes(self) -> Dict[str, str]:\n pass","def extra_state_attributes(self):\n return {\n ATTR_SOURCE_ENTITY: self._source_entity,\n ATTR_SOURCE_DOMAIN: self._source_domain,\n }","def key_attributes(self):\n\n return [level.key for level in self.levels]","def extra_state_attributes(self):\n return {ATTR_ATTRIBUTION: ATTRIBUTION}","def remove_vector_fields(attributes, data):\n for attrib in attributes:\n if attrib['similarity'] == 'Semantic USE' or attrib['similarity'] == 'Semantic SBERT' or attrib['similarity'] == 'Array SBERT':\n value = data.get(attrib['name'])\n if value is not None:\n data[attrib['name']] = value['name']\n return data","def get_textual_attribute_mapping(self):\n textual_attributes = self.__config.get_textual_attributes()\n textual_attributes_mapping = {}\n for textual_attribute in textual_attributes:\n textual_attributes_mapping[textual_attribute] = [attribute for attribute in self.__non_redundant_entity_attributes if textual_attribute in attribute]\n return textual_attributes_mapping","def find_own_attributes(cs):\n own_attributes = {}\n for con in cs:\n own_attributes[con] = []\n for attr in con.intent:\n own_attributes[con].append(attr)\n for sub_con in cs:\n if sub_con.intent < con.intent and\\\n attr in sub_con.intent:\n own_attributes[con].pop()\n break\n return own_attributes","def extra_state_attributes(self):\n if self.is_binary:\n return None\n return self.attr","def extra_state_attributes(self):\n return self._state_attributes","def get_attributes(self):\n return self.attributes","def all_attributes(self):\n\n attributes = []\n for level in self.levels:\n attributes.extend(level.attributes)\n\n return attributes","def attrs(*attributes):\n return ';'.join([ str(i) for i in attributes ])","def get_elements_w_same_attributes(dataset):\n\n # Get the keys from the first attribute\n first_att = set(dataset.first().keys())\n return dataset.filter(lambda line: same_att(first_att, line))","def attrib(self) -> Any:\n return self.attributes","def _get_all_attributes(self):\n all_attributes = self.__dict__.copy()\n all_attributes.update(self.class_attributes)\n return all_attributes","def attribute_restrictions(self):\n return self._attribute_restrictions","def merge_attrs(self):\n for aid in self.attrs:\n new_val = self.attrs[aid]\n if aid in self.attributes:\n if ('value' in self.attributes[aid] and\n self.attributes[aid]['value'] != new_val):\n pass\n # print \"Updating attribute %s[%s] %s -> %s\" % (\n # self.name, aid, self.attributes[aid]['value'], new_val)\n else:\n # print \"** Warning: non-declaired attribute %s['%s'] set to:\\n'%s'\" % (\n # self.name, aid, new_val)\n self.remember_custom_attribute(self.name, aid, new_val)\n self.attributes[aid] = {}\n self.attributes[aid]['nv'] = new_val","def getAttributeDict(self):\n result = {}\n dict = self.getDict()\n for key in dict.keys():\n value = dict.get(key)\n if value.__class__.__name__ != 'SpecialDict':\n result[key] = value\n return result","def _get_all_attributes(self) -> Dict[str, Any]:\n all_attributes = self.__dict__.copy()\n all_attributes.update(self.class_attributes)\n return all_attributes","def key_attributes(self):\n\n return [level.key for level in self._levels.values()]","def get_non_null_attributes(self) -> dict:\n return {\n key: getattr(self, key, None)\n for key in sorted(self.attributes)\n if getattr(self, key, None) is not None\n }","def _attrs(self):\n for field in self.model._meta.get_fields():\n if isinstance(field, (models.OneToOneField, models.ManyToOneRel)):\n # Skip non-field attributes\n continue\n if field is self._geom_field:\n # Skip the geometry field, which is not an attribute\n continue\n yield field","def getAttributes(self):\n return self.attributes","def getAttributes(self):\n return self.attributes","def extra_state_attributes(self):\n return dict(\n self._instrument.attributes,\n model=\"{}/{}\".format(\n self._instrument.vehicle_model, self._instrument.vehicle_name\n ),\n model_year=self._instrument.vehicle_model_year,\n model_family=self._instrument.vehicle_model_family,\n title=self._instrument.vehicle_name,\n csid=self._instrument.vehicle_csid,\n vin=self._instrument.vehicle_vin,\n )","def Attributes(self) -> PolicyStatementAttribute:","def _get_inactive_attributes(self):\n\n active_attributes = self.active_attributes\n inactive_attributes = self.all_attributes\n for attribute in active_attributes:\n if attribute in inactive_attributes:\n inacative_attributes.remove(attribute)\n return inactive_attributes","def extra_state_attributes(self) -> dict[str, Any]:\n state_attr = super().extra_state_attributes\n state_attr[ATTR_SOURCE_ENTITY] = self._powercalc_source_entity\n state_attr[ATTR_SOURCE_DOMAIN] = self._powercalc_source_domain\n return state_attr","def custom_attributes(self):\n return self._custom_attributes","def analyze_textual_attributes(self):\n for attribute in self.__textual_attributes:\n self.__analyze_textual_attribute(attribute)","def apply(self, attributes):\n return {\n self.attribute_names[k]: v\n for k, v in attributes.items()\n }","def get_all_attributes(dataset):\n return dataset.flatMap(lambda x: x.keys()).distinct().collect()","def _does_product_contains_given_attributes(self, product, *attrs):\n\n for attribute in list(attrs[0]):\n if not product.get(attribute):\n return False\n\n return True","def _filter_attributes(self, keyset):\n filtered = self._filter_keys(self.to_dict(), keyset)\n return Language.make(**filtered)","def getAttributes(self):\n pass","def __get_attributes(self, config_details):\n attributes = []\n\n if 'attribute' in config_details:\n if type(config_details['attribute']) == dict:\n attributes.append(config_details['attribute'])\n else:\n attributes = config_details['attribute']\n\n return attributes","def _merge_attributes(self, workout):\n keys = self.__table__.columns.keys()\n for key in keys:\n if key in [\"id\",\n \"external_id\",\n \"is_duplicate_with\",\n \"manual_check_required_with\",\n ]:\n continue\n elif getattr(self, key) == None:\n # copy attribute if empty; else keep existing \n setattr(self, key, getattr(workout, key))","def keep_attributes(context, *attributes):\n attr_to_value = preserve_attributes(context, attributes)\n yield\n restore_attributes(context, attr_to_value)","def complementary(self):\n complementary_attributes = ['not ' + self.attributes[i]\n for i in range(len(self.attributes))]\n complementary_table = []\n for i in range(len(self.objects)):\n complementary_table.append([not self.table[i][j]\n for j in range(len(self.attributes))])\n return Context(complementary_table, self.objects, complementary_attributes)","def get_study_user_attrs(self, study_id: int) -> Dict[str, Any]:\n raise NotImplementedError","def capability_attributes(self) -> Mapping[str, Any] | None:\n if state_class := self.state_class:\n return {ATTR_STATE_CLASS: state_class}\n\n if options := self.options:\n return {ATTR_OPTIONS: options}\n\n return None","def build_attrs(self, base_attrs, extra_attrs=None, **kwargs):\n attrs = dict(base_attrs, **kwargs)\n if extra_attrs:\n attrs.update(extra_attrs)\n return attrs","def sensitive(self, sensitive):\n\n self._sensitive = sensitive","def get_attributes(self):\n _attributes = {\n 'condition_id': self.condition_id,\n 'description': self.description,\n 'condition_type': self.cond_type\n }\n\n return _attributes","def _get_attributes(self, limit=None):\n attrs0 = []\n cnt = 0\n seen_attrs = {}\n for rubric in self.rubrics:\n if limit and cnt > limit:\n break\n ancestors = PublicationCharacteristicOrMarkSet._get_attr_ancs_by_rbrc(rubric, self.attribute_mode)\n if ancestors:\n attr0 = ancestors.pop(0)\n prev_attr = attr0\n buffer = []\n for ancestor in ancestors:\n index = seen_attrs.get(ancestor.id)\n if index is None:\n if prev_attr.parent_id != ancestor.id:\n buffer.insert(0, {\n 'attr': ancestor,\n 'rubric': prev_attr.parent\n })\n prev_attr = ancestor\n else:\n if prev_attr.parent_id != ancestor.id:\n attr_info = attrs0[index]\n attr_info.values.append(prev_attr.parent.name)\n if prev_attr.parent.tags:\n attr_info.tags.extend(prev_attr.parent.tags.split())\n break\n for obj in buffer:\n attr = obj['attr']\n seen_attrs[attr.id] = len(attrs0)\n tags = attr.tags.split() if attr.tags else []\n if obj['rubric'].tags:\n tags.extend(obj['rubric'].tags.split())\n attrs0.append(PublicationCharacteristicOrMarkInfo(attr.name, attr.path, [obj['rubric'].name],\n tags,\n getattr(attr, self.tree_opts.tree_id_attr),\n getattr(attr, self.tree_opts.left_attr)))\n cnt += 1\n if rubric.attribute_mode != self.attribute_mode:\n index = seen_attrs.get(attr0.id)\n if index is None:\n seen_attrs[attr0.id] = len(attrs0)\n tags = attr0.tags.split() if attr0.tags else []\n if rubric.tags:\n tags.extend(rubric.tags.split())\n attrs0.append(PublicationCharacteristicOrMarkInfo(attr0.name, attr0.path, [rubric.name],\n tags,\n getattr(attr0, self.tree_opts.tree_id_attr),\n getattr(attr0, self.tree_opts.left_attr)))\n cnt += 1\n else:\n attr_info = attrs0[index]\n attr_info.values.append(rubric.name)\n if rubric.tags:\n attr_info.tags.extend(rubric.tags.split())\n attrs1 = []\n prev_id = None\n cnt = 0\n for additional_attribute in self.additional_characteristics_or_marks.select_related('rubric'):\n if limit and cnt > limit:\n break\n attribute = additional_attribute.rubric\n if attribute.id != prev_id:\n tags = attribute.tags.split() if attribute.tags else []\n if additional_attribute.tags:\n tags.extend(additional_attribute.tags.split())\n attrs1.append(PublicationCharacteristicOrMarkInfo(attribute.name, attribute.path, [additional_attribute.value],\n tags,\n getattr(attribute, self.tree_opts.tree_id_attr),\n getattr(attribute, self.tree_opts.left_attr)))\n cnt += 1\n prev_id = attribute.id\n else:\n attr_info = attrs1[-1]\n attr_info.values.append(additional_attribute.value)\n if additional_attribute.tags:\n attr_info.tags.extend(additional_attribute.tags.split())\n # merge attributes\n attrs = []\n while attrs0 and attrs1:\n if attrs0[0] == attrs1[0]:\n attrs.append(attrs1.pop(0))\n attrs0.pop(0)\n elif attrs0[0] < attrs1[0]:\n attrs.append(attrs0.pop(0))\n else:\n attrs.append(attrs1.pop(0))\n if attrs0:\n attrs.extend(attrs0)\n elif attrs1:\n attrs.extend(attrs1)\n\n # clean not uniq values and tags\n for attr in attrs:\n if len(attr.values) > 1:\n attr.values = uniq(attr.values)\n if len(attr.tags) > 1:\n attr.tags = uniq(attr.tags)\n\n # sort values\n for attr in attrs:\n for v in attr.values:\n if not v.isdigit():\n attr.values.sort()\n break\n else:\n attr.values.sort(key=int)\n return attrs if limit is None else attrs[:limit]","def extra_state_attributes(self) -> dict[str, Any]:\n data = {ATTR_ENTITY_ID: self.tracking, ATTR_ORDER: self._order}\n if not self.user_defined:\n data[ATTR_AUTO] = True\n\n return data","def context_sensitive_sentence(self):\n sentence = self.expanded_sentence\n if self.context_class:\n return utils.str_lreplace(\n \"and \",\n self.context_class.capitalize() + \" \",\n sentence,\n flags=re.IGNORECASE,\n )\n\n return sentence","def extra_state_attributes(self) -> dict[str, Any] | None:\n # Different measuring stations have different sets of sensors. We don't know\n # what data we will get.\n for sensor in SENSOR_MAP:\n if sensor in self.coordinator.data:\n self._attrs[f\"{SENSOR_MAP[sensor]}_index\"] = self.coordinator.data[\n sensor\n ][\"index\"]\n self._attrs[ATTR_STATION] = self.coordinator.gios.station_name\n return self._attrs","def attributes(self):\n _attrs = [\"type\", \"name\", \"value\"]\n if self.confidence is not None:\n _attrs.append(\"confidence\")\n if self.constant:\n _attrs.append(\"constant\")\n if self.tags:\n _attrs.append(\"tags\")\n\n return _attrs","def prepare_attrib_mapping(self, primitive):\n buffer_info = []\n for name, accessor in primitive.attributes.items():\n info = VBOInfo(*accessor.info())\n info.attributes.append((name, info.components))\n\n if buffer_info and buffer_info[-1].buffer_view == info.buffer_view:\n if buffer_info[-1].interleaves(info):\n buffer_info[-1].merge(info)\n continue\n\n buffer_info.append(info)\n\n return buffer_info","def to_json(self, attrs=None):\n d = self.__dict__\n if attrs is None:\n return d\n else:\n filt_d = {}\n for element in attrs:\n for key, val in d.items():\n if element == key:\n filt_d[key] = val\n return filt_d","def attributes(self) -> pulumi.Output[Optional[Mapping[str, Any]]]:\n return pulumi.get(self, \"attributes\")","def get_attributes(self):\n \n retdict = {}\n retdict['s'] = str(self.s)\n if self.t != None:\n retdict['t'] = str(self.t)\n retdict['a'] = str(self.a)\n retdict['b'] = str(self.b)\n retdict['c'] = str(self.c)\n retdict['d'] = str(self.d)\n return retdict","def extra_state_attributes(self):\n return {\n ATTR_ATTRIBUTION: COMPONENT_ATTRIBUTION,\n \"brand\": COMPONENT_BRAND,\n \"friendly_name\": self._name,\n }","def extra_state_attributes(self) -> dict[str, Any] | None:\n attr: dict[str, Any] = {}\n\n if self._device.last_telegram is not None:\n attr[ATTR_SOURCE] = str(self._device.last_telegram.source_address)\n return attr","def test_attributes_weigthed(self):\n fields = Mineral.attributes_weighted()\n self.assertListEqual(fields[:-2], [\n 'group',\n 'formula',\n 'category',\n 'strunz_classification',\n 'crystal_system',\n 'mohs_scale_hardness',\n 'luster',\n 'color',\n 'specific_gravity',\n 'cleavage',\n 'diaphaneity',\n 'crystal_habit',\n 'streak',\n 'optical_properties',\n 'refractive_index', ])\n self.assertSetEqual(set(fields[-2:]), {\n 'unit_cell',\n 'crystal_symmetry',\n })","def get_attributes(self):\n return self._attributes_cache","def attrs(context):\n result = \"\"\n for key, value in context.flatten().items():\n if key not in [\"True\", \"False\", \"None\", \"content\", \"element\"]:\n if \"hx_\" in key:\n key = key.replace(\"_\", \"-\")\n result += f' {key}=\"{value}\"'\n return mark_safe(result)","def attributes(self) -> pulumi.Input[Sequence[pulumi.Input[str]]]:\n return pulumi.get(self, \"attributes\")","def __aggregate(self, series):\n if series.name in self.__non_redundant_entity_attributes or series.name in self.__redundant_entity_attributes: # Textual entities\n merged_sensitive_terms = list()\n for sensitive_terms in series.dropna():\n merged_sensitive_terms = merged_sensitive_terms + sensitive_terms\n return merged_sensitive_terms if len(merged_sensitive_terms) > 0 else None # Return merged result, or None\n else:\n if series.nunique() > 1: # Since there are more values, pack them into a list / frozenset\n if series.name in self.__textual_attributes or series.name in self.__config.get_insensitive_attributes():\n return list(series.array)\n else:\n return frozenset(series.array)\n else:\n return series.unique()[0] # Else return just this single value","def getAttributes(self, keys):\n return self.graph._readExtendedAttributes(dbKeys)","def get_attributes(self):\n return dict(self.attributes) # return the attributes","def get_attributes(self):\n return dict(self.attributes) # return the attributes","def get_attributes(self):\n return dict(self.attributes) # return the attributes","def keyable_attributes(pynode, attr_name_list = None):\n if attr_name_list is None:\n attr_name_list = ['tx', 'ty', 'tz', 'rx', 'ry', 'rz', 'sx', 'sy', 'sz', 'v']\n\n _do_attributes_key_lock_hide(pynode, attr_name_list, keyable = True)","def extract_subcontext_filtered_by_attributes(self, attributes_names,\n mode=\"and\"):\n values = dict( [(attribute, True) for attribute in attributes_names] )\n object_names, subtable = \\\n self._extract_subtable_by_attribute_values(values, mode)\n return Context(subtable,\n object_names,\n self.attributes)","def get_attributes(self):\n _attributes = {\n 'measurement_id': self.measurement_id,\n 'code': self.code,\n 'description': self.description,\n 'measurement_type': self.measurement_type\n }\n\n return _attributes","def _instrument_estimator_attribute(\n self, estimator: BaseEstimator, attributes: Attributes = None\n ):\n attribs = self.recurse_attribs.get(estimator.__class__, [])\n for attrib in attribs:\n attrib_value = getattr(estimator, attrib)\n if isinstance(attrib_value, Sequence):\n for value in attrib_value:\n self.instrument_estimator(\n estimator=value, attributes=attributes\n )\n elif isinstance(attrib_value, MutableMapping):\n for value in attrib_value.values():\n self.instrument_estimator(\n estimator=value, attributes=attributes\n )\n else:\n self.instrument_estimator(\n estimator=attrib_value, attributes=attributes\n )","def process_single_ns_record(target_dict, sensitive_key_names, sharing_consent_value):\r\n return dict_to_minified_json_string(\r\n add_sharing_consent(\r\n sub_sensitive_key(target_dict, sensitive_key_names),\r\n sharing_consent_value\r\n )\r\n )","def prepare_attributes(attributes):\n new_attributes = []\n for attribute in attributes:\n new_attributes.append(\"e_\" + attribute)\n return new_attributes","def extra_state_attributes(self):\n # return self._attributes\n return {\n CONF_WORKDAYS: self._workdays,\n CONF_EXCLUDES: self._excludes,\n CONF_OFFSET: self._days_offset,\n }","def has_attributes(self):\n\n pass","def get_similar_attr(self, attrname) -> list:\n Similar_corpus = self._similar_corpus\n for key in Similar_corpus:\n if key == attrname:\n return [key] + Similar_corpus[key]\n return []","def get_similar_attr(self, attrname) -> list:\n Similar_corpus = self._similar_corpus\n for key in Similar_corpus:\n if key == attrname:\n return [key] + Similar_corpus[key]\n return []"],"string":"[\n \"def get_sensitive_attributes(self):\\n return self.sensitive_attrs\",\n \"def get_sensitive_terms(self):\\n sensitive_terms_dict = {}\\n for attribute in self.__non_redundant_entity_attributes:\\n for record_id, sensitive_terms in self.__df[attribute].dropna().iteritems():\\n for sensitive_term in sensitive_terms:\\n cleaned_sensitive_term = \\\" \\\".join([t.lemma_.lower() for t in sensitive_term if not t.is_stop])\\n if len(cleaned_sensitive_term) > 0:\\n sensitive_terms_dict.setdefault(attribute, {}).setdefault(cleaned_sensitive_term, set()).add(record_id)\\n\\n # Sort sensitive terms dict alphabetically to have a deterministic order\\n sensitive_terms_dict = {el[0]: el[1] for el in sorted(sensitive_terms_dict.items(), key=lambda x: x)}\\n\\n # Sort sensitive terms dict ascending by number terms per entity type\\n sensitive_terms_dict = {el[0]: el[1] for el in sorted(sensitive_terms_dict.items(), key=lambda x: len(x[1]))}\\n\\n for attribute, sensitive_terms in sensitive_terms_dict.items():\\n word = \\\"terms\\\"\\n if len(sensitive_terms) == 1:\\n word = \\\"term\\\"\\n logger.info(\\\"Found %d distinct sensitive %s within attribute %s\\\", len(sensitive_terms), word, attribute)\\n return sensitive_terms_dict\",\n \"def _get_include_attributes(cls, args, extra_attributes=None):\\n extra_attributes = extra_attributes or []\\n\\n include_attributes = []\\n\\n if extra_attributes:\\n include_attributes.extend(extra_attributes)\\n\\n # If user specifies which attributes to retrieve via CLI --attr / -a argument, take that\\n # into account\\n\\n # Special case for \\\"all\\\"\\n if \\\"all\\\" in args.attr:\\n return None\\n\\n for attr in args.attr:\\n include_attributes.append(attr)\\n\\n if include_attributes:\\n return include_attributes\\n\\n display_attributes = getattr(cls, \\\"display_attributes\\\", [])\\n\\n if display_attributes:\\n include_attributes += display_attributes\\n\\n include_attributes = list(OrderedSet(include_attributes))\\n\\n return include_attributes\",\n \"def get_redundant_entity_attributes(self):\\n return self.__redundant_entity_attributes\",\n \"def _filtered_attributes(\\n self, required_attributes: Union[Iterable, Literal[\\\"__all__\\\"]], dontformat=False\\n ) -> Tuple[Dict, Dict]:\\n if required_attributes == \\\"__all__\\\":\\n required_attributes = self.__atomic_fields_set__ | {\\\"meta\\\"}\\n required_attributes = set(required_attributes)\\n errors = []\\n attrs = {name: getattr(self, name, None) for name in required_attributes-{\\\"meta\\\"}}\\n for name in required_attributes - {\\\"meta\\\"}:\\n if name not in self.__atomic_fields_set__:\\n errors.append(f\\\" Unexpected required attribute: '{name}'.\\\")\\n continue\\n if attrs.get(name) is None:\\n if not utils.is_an_optional_type_hint(self.__fields_types__[name]):\\n errors.append(f\\\" Missing required attribute: '{name}'.\\\")\\n if errors:\\n raise ValueError(\\\"\\\\n\\\" + \\\"\\\\n\\\".join(errors))\\n attrs = {\\n utils.snake_to_camel_case(k, dontformat): v\\n for (k, v) in attrs.items()\\n if k in set(required_attributes) - self._identifier_fields\\n }\\n meta_attrs = {\\n utils.snake_to_camel_case(name, dontformat): getattr(self, name)\\n for name in self.__meta_attributes__\\n if getattr(self, name) is not None\\n } if \\\"meta\\\" in required_attributes else None\\n return attrs, meta_attrs\",\n \"def attributes(self):\\n attr = super(BaseDataRecord, self).attributes()\\n return [a for a in attr if a not in self.excluded()]\",\n \"def extra_state_attributes(self) -> dict[str, Any]:\\n return self._attributes\",\n \"def extra_state_attributes(self):\\n return {attr: getattr(self, '_' + prop)\\n for attr, prop in ATTRIBUTES_IRHVAC.items()}\",\n \"def extra_state_attributes(self) -> dict[str, Any]:\\n ret = {\\n ATTR_SOURCE: self._source_entity_id,\\n ATTR_COEFFICIENTS: self._coefficients,\\n }\\n if self._source_attribute:\\n ret[ATTR_SOURCE_ATTRIBUTE] = self._source_attribute\\n return ret\",\n \"def extra_from_record(self, record):\\n return {\\n attr_name: record.__dict__[attr_name]\\n for attr_name in record.__dict__\\n if attr_name not in BUILTIN_ATTRS\\n }\",\n \"def attributes(self) -> Optional[Mapping[str, Sequence[str]]]:\\n return pulumi.get(self, \\\"attributes\\\")\",\n \"def extra_state_attributes(self):\\n return self._attributes\",\n \"def clean_attributes(self):\\n attrs = {}\\n\\n # Only fetch the fields we need.\\n for a in self.attributes.only('name', 'value', 'attribute').iterator():\\n if a.attribute.multi:\\n if a.name not in attrs:\\n attrs[a.name] = []\\n attrs[a.name].append(a.value)\\n else:\\n attrs[a.name] = a.value\\n self._attributes_cache = attrs # Cache the attributes\\n\\n return attrs\",\n \"def extra_state_attributes(self):\\r\\n return self._attributes\",\n \"def localized_attributes(self) -> Optional[Mapping[str, Mapping[str, str]]]:\\n return pulumi.get(self, \\\"localized_attributes\\\")\",\n \"def senate_attribs(self):\\n return self.senate_votes[4]\",\n \"def extra_state_attributes(self):\\n return self._attributes\",\n \"def extra_state_attributes(self):\\n return {ATTR_DEVICE: \\\"SKYBEACON\\\", ATTR_MODEL: 1}\",\n \"def extra_state_attributes(self):\\n return {ATTR_DEVICE: \\\"SKYBEACON\\\", ATTR_MODEL: 1}\",\n \"def get_non_redundant_entity_attributes(self):\\n return self.__non_redundant_entity_attributes\",\n \"def attributes(self) -> Optional[pulumi.Input[Mapping[str, Any]]]:\\n return pulumi.get(self, \\\"attributes\\\")\",\n \"def attributes(self) -> Optional[pulumi.Input[Mapping[str, Any]]]:\\n return pulumi.get(self, \\\"attributes\\\")\",\n \"def sensitive(self, value: object) -> Union[sensitive, object]:\\n return wrap_sensitive_value(value)\",\n \"def extra_state_attributes(self):\\n attr = self._attributes\\n if self.tesla_device.has_battery():\\n attr[ATTR_BATTERY_LEVEL] = self.tesla_device.battery_level()\\n attr[ATTR_BATTERY_CHARGING] = self.tesla_device.battery_charging()\\n return attr\",\n \"def _get_feature_attributes(self) -> dict:\\n srs = pd.Series(dir(self))\\n srs = srs[\\n (~srs.str.startswith('_'))\\n & (~srs.str.contains('as_'))\\n & (srs != 'putin')\\n & (srs != 'takeout')\\n & (srs != 'intermediate_accesses')\\n & (srs != 'geometry')\\n & (srs != 'has_a_point')\\n & (srs != 'centroid')\\n ]\\n srs = srs[srs.apply(lambda p: not hasattr(getattr(self, p), '__call__'))]\\n return {key: getattr(self, key) for key in srs}\",\n \"def extra_state_attributes(self):\\n state_attr = {}\\n if self.vendor_id is not None:\\n state_attr[ATTR_VENDOR_ID] = self.vendor_id\\n state_attr[ATTR_VENDOR_NAME] = self.vendor_name\\n if self.type_id is not None:\\n state_attr[ATTR_TYPE_ID] = self.type_id\\n state_attr[ATTR_TYPE] = self.type\\n if self.physical_address is not None:\\n state_attr[ATTR_PHYSICAL_ADDRESS] = self.physical_address\\n return state_attr\",\n \"def get_attributes(self) -> Dict[str, str]:\\n pass\",\n \"def extra_state_attributes(self):\\n return {\\n ATTR_SOURCE_ENTITY: self._source_entity,\\n ATTR_SOURCE_DOMAIN: self._source_domain,\\n }\",\n \"def key_attributes(self):\\n\\n return [level.key for level in self.levels]\",\n \"def extra_state_attributes(self):\\n return {ATTR_ATTRIBUTION: ATTRIBUTION}\",\n \"def remove_vector_fields(attributes, data):\\n for attrib in attributes:\\n if attrib['similarity'] == 'Semantic USE' or attrib['similarity'] == 'Semantic SBERT' or attrib['similarity'] == 'Array SBERT':\\n value = data.get(attrib['name'])\\n if value is not None:\\n data[attrib['name']] = value['name']\\n return data\",\n \"def get_textual_attribute_mapping(self):\\n textual_attributes = self.__config.get_textual_attributes()\\n textual_attributes_mapping = {}\\n for textual_attribute in textual_attributes:\\n textual_attributes_mapping[textual_attribute] = [attribute for attribute in self.__non_redundant_entity_attributes if textual_attribute in attribute]\\n return textual_attributes_mapping\",\n \"def find_own_attributes(cs):\\n own_attributes = {}\\n for con in cs:\\n own_attributes[con] = []\\n for attr in con.intent:\\n own_attributes[con].append(attr)\\n for sub_con in cs:\\n if sub_con.intent < con.intent and\\\\\\n attr in sub_con.intent:\\n own_attributes[con].pop()\\n break\\n return own_attributes\",\n \"def extra_state_attributes(self):\\n if self.is_binary:\\n return None\\n return self.attr\",\n \"def extra_state_attributes(self):\\n return self._state_attributes\",\n \"def get_attributes(self):\\n return self.attributes\",\n \"def all_attributes(self):\\n\\n attributes = []\\n for level in self.levels:\\n attributes.extend(level.attributes)\\n\\n return attributes\",\n \"def attrs(*attributes):\\n return ';'.join([ str(i) for i in attributes ])\",\n \"def get_elements_w_same_attributes(dataset):\\n\\n # Get the keys from the first attribute\\n first_att = set(dataset.first().keys())\\n return dataset.filter(lambda line: same_att(first_att, line))\",\n \"def attrib(self) -> Any:\\n return self.attributes\",\n \"def _get_all_attributes(self):\\n all_attributes = self.__dict__.copy()\\n all_attributes.update(self.class_attributes)\\n return all_attributes\",\n \"def attribute_restrictions(self):\\n return self._attribute_restrictions\",\n \"def merge_attrs(self):\\n for aid in self.attrs:\\n new_val = self.attrs[aid]\\n if aid in self.attributes:\\n if ('value' in self.attributes[aid] and\\n self.attributes[aid]['value'] != new_val):\\n pass\\n # print \\\"Updating attribute %s[%s] %s -> %s\\\" % (\\n # self.name, aid, self.attributes[aid]['value'], new_val)\\n else:\\n # print \\\"** Warning: non-declaired attribute %s['%s'] set to:\\\\n'%s'\\\" % (\\n # self.name, aid, new_val)\\n self.remember_custom_attribute(self.name, aid, new_val)\\n self.attributes[aid] = {}\\n self.attributes[aid]['nv'] = new_val\",\n \"def getAttributeDict(self):\\n result = {}\\n dict = self.getDict()\\n for key in dict.keys():\\n value = dict.get(key)\\n if value.__class__.__name__ != 'SpecialDict':\\n result[key] = value\\n return result\",\n \"def _get_all_attributes(self) -> Dict[str, Any]:\\n all_attributes = self.__dict__.copy()\\n all_attributes.update(self.class_attributes)\\n return all_attributes\",\n \"def key_attributes(self):\\n\\n return [level.key for level in self._levels.values()]\",\n \"def get_non_null_attributes(self) -> dict:\\n return {\\n key: getattr(self, key, None)\\n for key in sorted(self.attributes)\\n if getattr(self, key, None) is not None\\n }\",\n \"def _attrs(self):\\n for field in self.model._meta.get_fields():\\n if isinstance(field, (models.OneToOneField, models.ManyToOneRel)):\\n # Skip non-field attributes\\n continue\\n if field is self._geom_field:\\n # Skip the geometry field, which is not an attribute\\n continue\\n yield field\",\n \"def getAttributes(self):\\n return self.attributes\",\n \"def getAttributes(self):\\n return self.attributes\",\n \"def extra_state_attributes(self):\\n return dict(\\n self._instrument.attributes,\\n model=\\\"{}/{}\\\".format(\\n self._instrument.vehicle_model, self._instrument.vehicle_name\\n ),\\n model_year=self._instrument.vehicle_model_year,\\n model_family=self._instrument.vehicle_model_family,\\n title=self._instrument.vehicle_name,\\n csid=self._instrument.vehicle_csid,\\n vin=self._instrument.vehicle_vin,\\n )\",\n \"def Attributes(self) -> PolicyStatementAttribute:\",\n \"def _get_inactive_attributes(self):\\n\\n active_attributes = self.active_attributes\\n inactive_attributes = self.all_attributes\\n for attribute in active_attributes:\\n if attribute in inactive_attributes:\\n inacative_attributes.remove(attribute)\\n return inactive_attributes\",\n \"def extra_state_attributes(self) -> dict[str, Any]:\\n state_attr = super().extra_state_attributes\\n state_attr[ATTR_SOURCE_ENTITY] = self._powercalc_source_entity\\n state_attr[ATTR_SOURCE_DOMAIN] = self._powercalc_source_domain\\n return state_attr\",\n \"def custom_attributes(self):\\n return self._custom_attributes\",\n \"def analyze_textual_attributes(self):\\n for attribute in self.__textual_attributes:\\n self.__analyze_textual_attribute(attribute)\",\n \"def apply(self, attributes):\\n return {\\n self.attribute_names[k]: v\\n for k, v in attributes.items()\\n }\",\n \"def get_all_attributes(dataset):\\n return dataset.flatMap(lambda x: x.keys()).distinct().collect()\",\n \"def _does_product_contains_given_attributes(self, product, *attrs):\\n\\n for attribute in list(attrs[0]):\\n if not product.get(attribute):\\n return False\\n\\n return True\",\n \"def _filter_attributes(self, keyset):\\n filtered = self._filter_keys(self.to_dict(), keyset)\\n return Language.make(**filtered)\",\n \"def getAttributes(self):\\n pass\",\n \"def __get_attributes(self, config_details):\\n attributes = []\\n\\n if 'attribute' in config_details:\\n if type(config_details['attribute']) == dict:\\n attributes.append(config_details['attribute'])\\n else:\\n attributes = config_details['attribute']\\n\\n return attributes\",\n \"def _merge_attributes(self, workout):\\n keys = self.__table__.columns.keys()\\n for key in keys:\\n if key in [\\\"id\\\",\\n \\\"external_id\\\",\\n \\\"is_duplicate_with\\\",\\n \\\"manual_check_required_with\\\",\\n ]:\\n continue\\n elif getattr(self, key) == None:\\n # copy attribute if empty; else keep existing \\n setattr(self, key, getattr(workout, key))\",\n \"def keep_attributes(context, *attributes):\\n attr_to_value = preserve_attributes(context, attributes)\\n yield\\n restore_attributes(context, attr_to_value)\",\n \"def complementary(self):\\n complementary_attributes = ['not ' + self.attributes[i]\\n for i in range(len(self.attributes))]\\n complementary_table = []\\n for i in range(len(self.objects)):\\n complementary_table.append([not self.table[i][j]\\n for j in range(len(self.attributes))])\\n return Context(complementary_table, self.objects, complementary_attributes)\",\n \"def get_study_user_attrs(self, study_id: int) -> Dict[str, Any]:\\n raise NotImplementedError\",\n \"def capability_attributes(self) -> Mapping[str, Any] | None:\\n if state_class := self.state_class:\\n return {ATTR_STATE_CLASS: state_class}\\n\\n if options := self.options:\\n return {ATTR_OPTIONS: options}\\n\\n return None\",\n \"def build_attrs(self, base_attrs, extra_attrs=None, **kwargs):\\n attrs = dict(base_attrs, **kwargs)\\n if extra_attrs:\\n attrs.update(extra_attrs)\\n return attrs\",\n \"def sensitive(self, sensitive):\\n\\n self._sensitive = sensitive\",\n \"def get_attributes(self):\\n _attributes = {\\n 'condition_id': self.condition_id,\\n 'description': self.description,\\n 'condition_type': self.cond_type\\n }\\n\\n return _attributes\",\n \"def _get_attributes(self, limit=None):\\n attrs0 = []\\n cnt = 0\\n seen_attrs = {}\\n for rubric in self.rubrics:\\n if limit and cnt > limit:\\n break\\n ancestors = PublicationCharacteristicOrMarkSet._get_attr_ancs_by_rbrc(rubric, self.attribute_mode)\\n if ancestors:\\n attr0 = ancestors.pop(0)\\n prev_attr = attr0\\n buffer = []\\n for ancestor in ancestors:\\n index = seen_attrs.get(ancestor.id)\\n if index is None:\\n if prev_attr.parent_id != ancestor.id:\\n buffer.insert(0, {\\n 'attr': ancestor,\\n 'rubric': prev_attr.parent\\n })\\n prev_attr = ancestor\\n else:\\n if prev_attr.parent_id != ancestor.id:\\n attr_info = attrs0[index]\\n attr_info.values.append(prev_attr.parent.name)\\n if prev_attr.parent.tags:\\n attr_info.tags.extend(prev_attr.parent.tags.split())\\n break\\n for obj in buffer:\\n attr = obj['attr']\\n seen_attrs[attr.id] = len(attrs0)\\n tags = attr.tags.split() if attr.tags else []\\n if obj['rubric'].tags:\\n tags.extend(obj['rubric'].tags.split())\\n attrs0.append(PublicationCharacteristicOrMarkInfo(attr.name, attr.path, [obj['rubric'].name],\\n tags,\\n getattr(attr, self.tree_opts.tree_id_attr),\\n getattr(attr, self.tree_opts.left_attr)))\\n cnt += 1\\n if rubric.attribute_mode != self.attribute_mode:\\n index = seen_attrs.get(attr0.id)\\n if index is None:\\n seen_attrs[attr0.id] = len(attrs0)\\n tags = attr0.tags.split() if attr0.tags else []\\n if rubric.tags:\\n tags.extend(rubric.tags.split())\\n attrs0.append(PublicationCharacteristicOrMarkInfo(attr0.name, attr0.path, [rubric.name],\\n tags,\\n getattr(attr0, self.tree_opts.tree_id_attr),\\n getattr(attr0, self.tree_opts.left_attr)))\\n cnt += 1\\n else:\\n attr_info = attrs0[index]\\n attr_info.values.append(rubric.name)\\n if rubric.tags:\\n attr_info.tags.extend(rubric.tags.split())\\n attrs1 = []\\n prev_id = None\\n cnt = 0\\n for additional_attribute in self.additional_characteristics_or_marks.select_related('rubric'):\\n if limit and cnt > limit:\\n break\\n attribute = additional_attribute.rubric\\n if attribute.id != prev_id:\\n tags = attribute.tags.split() if attribute.tags else []\\n if additional_attribute.tags:\\n tags.extend(additional_attribute.tags.split())\\n attrs1.append(PublicationCharacteristicOrMarkInfo(attribute.name, attribute.path, [additional_attribute.value],\\n tags,\\n getattr(attribute, self.tree_opts.tree_id_attr),\\n getattr(attribute, self.tree_opts.left_attr)))\\n cnt += 1\\n prev_id = attribute.id\\n else:\\n attr_info = attrs1[-1]\\n attr_info.values.append(additional_attribute.value)\\n if additional_attribute.tags:\\n attr_info.tags.extend(additional_attribute.tags.split())\\n # merge attributes\\n attrs = []\\n while attrs0 and attrs1:\\n if attrs0[0] == attrs1[0]:\\n attrs.append(attrs1.pop(0))\\n attrs0.pop(0)\\n elif attrs0[0] < attrs1[0]:\\n attrs.append(attrs0.pop(0))\\n else:\\n attrs.append(attrs1.pop(0))\\n if attrs0:\\n attrs.extend(attrs0)\\n elif attrs1:\\n attrs.extend(attrs1)\\n\\n # clean not uniq values and tags\\n for attr in attrs:\\n if len(attr.values) > 1:\\n attr.values = uniq(attr.values)\\n if len(attr.tags) > 1:\\n attr.tags = uniq(attr.tags)\\n\\n # sort values\\n for attr in attrs:\\n for v in attr.values:\\n if not v.isdigit():\\n attr.values.sort()\\n break\\n else:\\n attr.values.sort(key=int)\\n return attrs if limit is None else attrs[:limit]\",\n \"def extra_state_attributes(self) -> dict[str, Any]:\\n data = {ATTR_ENTITY_ID: self.tracking, ATTR_ORDER: self._order}\\n if not self.user_defined:\\n data[ATTR_AUTO] = True\\n\\n return data\",\n \"def context_sensitive_sentence(self):\\n sentence = self.expanded_sentence\\n if self.context_class:\\n return utils.str_lreplace(\\n \\\"and \\\",\\n self.context_class.capitalize() + \\\" \\\",\\n sentence,\\n flags=re.IGNORECASE,\\n )\\n\\n return sentence\",\n \"def extra_state_attributes(self) -> dict[str, Any] | None:\\n # Different measuring stations have different sets of sensors. We don't know\\n # what data we will get.\\n for sensor in SENSOR_MAP:\\n if sensor in self.coordinator.data:\\n self._attrs[f\\\"{SENSOR_MAP[sensor]}_index\\\"] = self.coordinator.data[\\n sensor\\n ][\\\"index\\\"]\\n self._attrs[ATTR_STATION] = self.coordinator.gios.station_name\\n return self._attrs\",\n \"def attributes(self):\\n _attrs = [\\\"type\\\", \\\"name\\\", \\\"value\\\"]\\n if self.confidence is not None:\\n _attrs.append(\\\"confidence\\\")\\n if self.constant:\\n _attrs.append(\\\"constant\\\")\\n if self.tags:\\n _attrs.append(\\\"tags\\\")\\n\\n return _attrs\",\n \"def prepare_attrib_mapping(self, primitive):\\n buffer_info = []\\n for name, accessor in primitive.attributes.items():\\n info = VBOInfo(*accessor.info())\\n info.attributes.append((name, info.components))\\n\\n if buffer_info and buffer_info[-1].buffer_view == info.buffer_view:\\n if buffer_info[-1].interleaves(info):\\n buffer_info[-1].merge(info)\\n continue\\n\\n buffer_info.append(info)\\n\\n return buffer_info\",\n \"def to_json(self, attrs=None):\\n d = self.__dict__\\n if attrs is None:\\n return d\\n else:\\n filt_d = {}\\n for element in attrs:\\n for key, val in d.items():\\n if element == key:\\n filt_d[key] = val\\n return filt_d\",\n \"def attributes(self) -> pulumi.Output[Optional[Mapping[str, Any]]]:\\n return pulumi.get(self, \\\"attributes\\\")\",\n \"def get_attributes(self):\\n \\n retdict = {}\\n retdict['s'] = str(self.s)\\n if self.t != None:\\n retdict['t'] = str(self.t)\\n retdict['a'] = str(self.a)\\n retdict['b'] = str(self.b)\\n retdict['c'] = str(self.c)\\n retdict['d'] = str(self.d)\\n return retdict\",\n \"def extra_state_attributes(self):\\n return {\\n ATTR_ATTRIBUTION: COMPONENT_ATTRIBUTION,\\n \\\"brand\\\": COMPONENT_BRAND,\\n \\\"friendly_name\\\": self._name,\\n }\",\n \"def extra_state_attributes(self) -> dict[str, Any] | None:\\n attr: dict[str, Any] = {}\\n\\n if self._device.last_telegram is not None:\\n attr[ATTR_SOURCE] = str(self._device.last_telegram.source_address)\\n return attr\",\n \"def test_attributes_weigthed(self):\\n fields = Mineral.attributes_weighted()\\n self.assertListEqual(fields[:-2], [\\n 'group',\\n 'formula',\\n 'category',\\n 'strunz_classification',\\n 'crystal_system',\\n 'mohs_scale_hardness',\\n 'luster',\\n 'color',\\n 'specific_gravity',\\n 'cleavage',\\n 'diaphaneity',\\n 'crystal_habit',\\n 'streak',\\n 'optical_properties',\\n 'refractive_index', ])\\n self.assertSetEqual(set(fields[-2:]), {\\n 'unit_cell',\\n 'crystal_symmetry',\\n })\",\n \"def get_attributes(self):\\n return self._attributes_cache\",\n \"def attrs(context):\\n result = \\\"\\\"\\n for key, value in context.flatten().items():\\n if key not in [\\\"True\\\", \\\"False\\\", \\\"None\\\", \\\"content\\\", \\\"element\\\"]:\\n if \\\"hx_\\\" in key:\\n key = key.replace(\\\"_\\\", \\\"-\\\")\\n result += f' {key}=\\\"{value}\\\"'\\n return mark_safe(result)\",\n \"def attributes(self) -> pulumi.Input[Sequence[pulumi.Input[str]]]:\\n return pulumi.get(self, \\\"attributes\\\")\",\n \"def __aggregate(self, series):\\n if series.name in self.__non_redundant_entity_attributes or series.name in self.__redundant_entity_attributes: # Textual entities\\n merged_sensitive_terms = list()\\n for sensitive_terms in series.dropna():\\n merged_sensitive_terms = merged_sensitive_terms + sensitive_terms\\n return merged_sensitive_terms if len(merged_sensitive_terms) > 0 else None # Return merged result, or None\\n else:\\n if series.nunique() > 1: # Since there are more values, pack them into a list / frozenset\\n if series.name in self.__textual_attributes or series.name in self.__config.get_insensitive_attributes():\\n return list(series.array)\\n else:\\n return frozenset(series.array)\\n else:\\n return series.unique()[0] # Else return just this single value\",\n \"def getAttributes(self, keys):\\n return self.graph._readExtendedAttributes(dbKeys)\",\n \"def get_attributes(self):\\n return dict(self.attributes) # return the attributes\",\n \"def get_attributes(self):\\n return dict(self.attributes) # return the attributes\",\n \"def get_attributes(self):\\n return dict(self.attributes) # return the attributes\",\n \"def keyable_attributes(pynode, attr_name_list = None):\\n if attr_name_list is None:\\n attr_name_list = ['tx', 'ty', 'tz', 'rx', 'ry', 'rz', 'sx', 'sy', 'sz', 'v']\\n\\n _do_attributes_key_lock_hide(pynode, attr_name_list, keyable = True)\",\n \"def extract_subcontext_filtered_by_attributes(self, attributes_names,\\n mode=\\\"and\\\"):\\n values = dict( [(attribute, True) for attribute in attributes_names] )\\n object_names, subtable = \\\\\\n self._extract_subtable_by_attribute_values(values, mode)\\n return Context(subtable,\\n object_names,\\n self.attributes)\",\n \"def get_attributes(self):\\n _attributes = {\\n 'measurement_id': self.measurement_id,\\n 'code': self.code,\\n 'description': self.description,\\n 'measurement_type': self.measurement_type\\n }\\n\\n return _attributes\",\n \"def _instrument_estimator_attribute(\\n self, estimator: BaseEstimator, attributes: Attributes = None\\n ):\\n attribs = self.recurse_attribs.get(estimator.__class__, [])\\n for attrib in attribs:\\n attrib_value = getattr(estimator, attrib)\\n if isinstance(attrib_value, Sequence):\\n for value in attrib_value:\\n self.instrument_estimator(\\n estimator=value, attributes=attributes\\n )\\n elif isinstance(attrib_value, MutableMapping):\\n for value in attrib_value.values():\\n self.instrument_estimator(\\n estimator=value, attributes=attributes\\n )\\n else:\\n self.instrument_estimator(\\n estimator=attrib_value, attributes=attributes\\n )\",\n \"def process_single_ns_record(target_dict, sensitive_key_names, sharing_consent_value):\\r\\n return dict_to_minified_json_string(\\r\\n add_sharing_consent(\\r\\n sub_sensitive_key(target_dict, sensitive_key_names),\\r\\n sharing_consent_value\\r\\n )\\r\\n )\",\n \"def prepare_attributes(attributes):\\n new_attributes = []\\n for attribute in attributes:\\n new_attributes.append(\\\"e_\\\" + attribute)\\n return new_attributes\",\n \"def extra_state_attributes(self):\\n # return self._attributes\\n return {\\n CONF_WORKDAYS: self._workdays,\\n CONF_EXCLUDES: self._excludes,\\n CONF_OFFSET: self._days_offset,\\n }\",\n \"def has_attributes(self):\\n\\n pass\",\n \"def get_similar_attr(self, attrname) -> list:\\n Similar_corpus = self._similar_corpus\\n for key in Similar_corpus:\\n if key == attrname:\\n return [key] + Similar_corpus[key]\\n return []\",\n \"def get_similar_attr(self, attrname) -> list:\\n Similar_corpus = self._similar_corpus\\n for key in Similar_corpus:\\n if key == attrname:\\n return [key] + Similar_corpus[key]\\n return []\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7723388","0.61657095","0.5457752","0.53589123","0.5331123","0.5112113","0.5105332","0.51015556","0.5088783","0.50400466","0.5025615","0.5014114","0.5013227","0.49880865","0.4979075","0.495395","0.4940964","0.49345672","0.49345672","0.49008775","0.48915753","0.48915753","0.4867706","0.48594257","0.48439616","0.48341948","0.47859508","0.4782373","0.47820234","0.47594118","0.4755244","0.47464156","0.47459054","0.47414657","0.47386906","0.47261488","0.47218063","0.471868","0.47166896","0.4712492","0.47100675","0.4693418","0.46863487","0.46858215","0.46837115","0.4674086","0.4665667","0.46555787","0.4646548","0.4646548","0.46459302","0.46457464","0.46425894","0.46279192","0.46269265","0.46204805","0.46156824","0.46142387","0.46102142","0.46064398","0.45959172","0.4591492","0.45903158","0.45832267","0.457912","0.45780832","0.4571677","0.4553354","0.45447466","0.45435026","0.4532449","0.45243645","0.45163256","0.45069137","0.4504691","0.4502551","0.45011726","0.44947788","0.44943467","0.44942448","0.449144","0.44730362","0.44729793","0.44636595","0.44619352","0.44544512","0.444918","0.44454667","0.44454667","0.44454667","0.4444006","0.44421592","0.44410673","0.44401303","0.44392902","0.44389758","0.4438228","0.44373658","0.443686","0.443686"],"string":"[\n \"0.7723388\",\n \"0.61657095\",\n \"0.5457752\",\n \"0.53589123\",\n \"0.5331123\",\n \"0.5112113\",\n \"0.5105332\",\n \"0.51015556\",\n \"0.5088783\",\n \"0.50400466\",\n \"0.5025615\",\n \"0.5014114\",\n \"0.5013227\",\n \"0.49880865\",\n \"0.4979075\",\n \"0.495395\",\n \"0.4940964\",\n \"0.49345672\",\n \"0.49345672\",\n \"0.49008775\",\n \"0.48915753\",\n \"0.48915753\",\n \"0.4867706\",\n \"0.48594257\",\n \"0.48439616\",\n \"0.48341948\",\n \"0.47859508\",\n \"0.4782373\",\n \"0.47820234\",\n \"0.47594118\",\n \"0.4755244\",\n \"0.47464156\",\n \"0.47459054\",\n \"0.47414657\",\n \"0.47386906\",\n \"0.47261488\",\n \"0.47218063\",\n \"0.471868\",\n \"0.47166896\",\n \"0.4712492\",\n \"0.47100675\",\n \"0.4693418\",\n \"0.46863487\",\n \"0.46858215\",\n \"0.46837115\",\n \"0.4674086\",\n \"0.4665667\",\n \"0.46555787\",\n \"0.4646548\",\n \"0.4646548\",\n \"0.46459302\",\n \"0.46457464\",\n \"0.46425894\",\n \"0.46279192\",\n \"0.46269265\",\n \"0.46204805\",\n \"0.46156824\",\n \"0.46142387\",\n \"0.46102142\",\n \"0.46064398\",\n \"0.45959172\",\n \"0.4591492\",\n \"0.45903158\",\n \"0.45832267\",\n \"0.457912\",\n \"0.45780832\",\n \"0.4571677\",\n \"0.4553354\",\n \"0.45447466\",\n \"0.45435026\",\n \"0.4532449\",\n \"0.45243645\",\n \"0.45163256\",\n \"0.45069137\",\n \"0.4504691\",\n \"0.4502551\",\n \"0.45011726\",\n \"0.44947788\",\n \"0.44943467\",\n \"0.44942448\",\n \"0.449144\",\n \"0.44730362\",\n \"0.44729793\",\n \"0.44636595\",\n \"0.44619352\",\n \"0.44544512\",\n \"0.444918\",\n \"0.44454667\",\n \"0.44454667\",\n \"0.44454667\",\n \"0.4444006\",\n \"0.44421592\",\n \"0.44410673\",\n \"0.44401303\",\n \"0.44392902\",\n \"0.44389758\",\n \"0.4438228\",\n \"0.44373658\",\n \"0.443686\",\n \"0.443686\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.85016006"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":16894,"cells":{"query":{"kind":"string","value":"Returns a list in the same order as the sensitive attributes list above of the privileged class name (exactly as it appears in the data) of the associated sensitive attribute."},"document":{"kind":"string","value":"def get_privileged_class_names(self, tag):\n # FIXME this dependence between tags and privileged class names is bad; don't know how to\n # fix it right now\n if tag == 'numerical-binsensitive':\n return [1 for x in self.get_sensitive_attributes()]\n else:\n return self.privileged_class_names"},"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 getDataAttributes(self):\n asRet = [];\n asAttrs = dir(self);\n for sAttr in asAttrs:\n if sAttr[0] == '_' or sAttr[0] == 'k':\n continue;\n if sAttr in self.kasInternalAttributes:\n continue;\n oValue = getattr(self, sAttr);\n if callable(oValue):\n continue;\n asRet.append(sAttr);\n return asRet;","def get_attribute_list(self):\n attributes = [attr for attr in vars(self.entries[0]) if not attr.startswith('__')]\n return attributes","def __listAttr(self):\n attr = dir(self) # already sorted\n filter = []\n for name in attr:\n if name[:2] == '__': pass\n elif name[:10] == '_HelpDoc__': pass # used to mask private attr\n elif name in self.__exclude: pass\n else: filter.append(name)\n return filter","def get_sensitive_attributes(self):\n return self.sensitive_attrs","def attrs(self):\n return list(name for name in self.__dict__\n if not name.startswith(\"_\"))","def keys(self):\n return list(s.name.lower() for s in self.attributes)","def keys(self):\n return [a.name for a in self.__attrs_attrs__]","def get_attrs(self):\n attrs = []\n for attribute in self.__dict__.keys():\n attrs.append(attribute)","def get_attribute_list(self):\n return self.dp.get_attribute_list()","def keys(self):\n\n keys = list()\n\n for attribute_name, type_instance in inspect.getmembers(self):\n\n if attribute_name.startswith('__') or inspect.ismethod(type_instance):\n # Ignore parameters with __ and if they are methods\n continue\n\n keys.append(attribute_name)\n\n return keys","def attributes(self):\n\n attributes = []\n\n for member in self.members:\n if member.attribute:\n attributes.append(member)\n\n return attributes","def get_attributes(cls):\r\n return []","def ListAttributes(self):\n\n print(\"\\n\")\n print(\"Attributes List of: \" + repr(self.__dict__[\"name\"]) + \" - \" + self.__class__.__name__ + \" Instance\\n\")\n self_keys = self.__dict__.keys()\n self_keys.sort()\n for key in self_keys:\n if key != \"name\":\n print(str(key) + \" : \" + repr(self.__dict__[key]))\n # end\n # end\n print(\"\\n\")","def values(self):\n return [getattr(self, a.name) for a in self.__attrs_attrs__]","def attrs(self):\n return [name for name in self.traits() if self.trait_metadata(name, \"attr\")]","def attrs(self):\n return [name for name in self.traits() if self.trait_metadata(name, \"attr\")]","def attributes(self):\n\n return list(self._attributes.values())","def attributes(self):\n attr = super(BaseDataRecord, self).attributes()\n return [a for a in attr if a not in self.excluded()]","def key_attributes(self):\n\n return [level.key for level in self.levels]","def _attrlist(self,obj, attrs):\n vlist = [obj.__getattribute__(attr) for attr in attrs]\n return vlist","def listglobal(self):\n return list(self.attributes.keys())","def _getAttributeNames(self):\n return sorted(self._field_map.keys())","def extractAttrs(data):\n\treturn [instance[1:] for instance in data]","def attributes(self):\n return self.__dict.keys()","def get_attribute_names(cls):\n return cls._attributes.keys()","def get_class_attr_list(self, o):\n alist = None # Attributes to store\n ff = None # Load filter function\n for cl in self.classes:\n if isinstance(o, cl):\n alist = self.classes[cl][0]\n ff = self.classes[cl][1]\n break\n if isinstance(o, Block._ComponentDataClass):\n # If you're here you are trying to serialize an element of an\n # indexed block at the top level. We do want to allow that, so\n # we'll pretend it's a block.\n alist = self.classes[Block][0]\n ff = self.classes[Block][1]\n return (alist, ff)","def key_attributes(self):\n\n return [level.key for level in self._levels.values()]","def get_data_class_attr_list(self, o):\n alist = None # Attributes to store\n ff = None # Load filter function\n for cl in self.data_classes:\n if isinstance(o, cl):\n alist = self.data_classes[cl][0]\n ff = self.data_classes[cl][1]\n break\n return (alist, ff)","def all_attributes(self):\n\n attributes = []\n for level in self.levels:\n attributes.extend(level.attributes)\n\n return attributes","def getAttributes(self):\n pass","def _columns(cls):\n columns = []\n for name, member in inspect.getmembers(cls):\n if (not name.startswith('_') and\n isinstance(member, InstrumentedAttribute)):\n columns.append(name)\n return columns","def thrift_attrs(obj_or_cls) -> List[str]:\n return [v[1] for v in obj_or_cls.thrift_spec.values()]","def __dir__(self):\n dict_keys = []\n for k in self.keys():\n if isinstance(k, str):\n m = self._re_pattern.match(k)\n if m:\n dict_keys.append(m.string)\n\n obj_attrs = list(dir(Dict))\n\n return dict_keys + obj_attrs","def _fix_up_private_attr(clsname, spec):\n out = OrderedDict()\n for k, v in spec.items():\n if k.startswith('__') and not k.endswith('__'):\n k = '_' + clsname + k\n out[k] = v\n return out","def listattribute(self, varName):\n fName = \"\"\n if varName in self.statVars:\n fName = self.statVars[varName][0]\n elif varName in self.timeVars:\n fName = self.timeVars[varName][0][0]\n if fName:\n var = cdms2.open(fName, 'r')(varName)\n return var.listattributes()\n else:\n return []","def get_sensitive_attributes_with_joint(self):\n if len(self.get_sensitive_attributes()) > 1:\n return self.get_sensitive_attributes() + ['-'.join(self.get_sensitive_attributes())]\n return self.get_sensitive_attributes()","def __get_attributes(self, config_details):\n attributes = []\n\n if 'attribute' in config_details:\n if type(config_details['attribute']) == dict:\n attributes.append(config_details['attribute'])\n else:\n attributes = config_details['attribute']\n\n return attributes","def get_privileged_class_names_with_joint(self, tag):\n priv_class_names = self.get_privileged_class_names(tag)\n if len(priv_class_names) > 1:\n return priv_class_names + ['-'.join(str(v) for v in priv_class_names)]\n return priv_class_names","def protected_classifications(self):\n if self.customer is None:\n return None\n return sorted([b.name for b in self._customer.protected_species if b.deployed])","def getAttributes(self):\n return self.attributes","def getAttributes(self):\n return self.attributes","def get_attributes(cls):\r\n return [Attribute('size', '20'),\r\n Attribute('label', ''), ]","def attributes(self):\n return [self._ELE_ATTR]","def values(self):\n return self.attrs.values()","def __fields(self):\n return [self.__class__.__dict__[f] for f in self.__class__._fields]","def read_global_attributes(self):\n return self._attrs.keys()","def get_attributes(self) -> Dict[str, str]:\n pass","def get_attrs(self):\n return dir(self.config)","def get_attrs(self):\n return dir(self.config)","def _detectors_attrs(self):\n if self._det.get('detectors'):\n return self._det['detectors'].keys()\n else:\n return []","def attributes(self):\n attrs_ = [\"name\", \"type\"]\n if self.exclusive:\n attrs_.append(\"exclusive\")\n if self.default is not None:\n attrs_.append(\"default\")\n\n return attrs_","def get_attributes(self):\n return self.attributes","def attributes(self):\n return { k: getattr(self, k) for k in self.__class__.columns().keys() }","def attributes(self):\n return dict(self.__attributes)","def all(self):\r\n return self.attr_dict.keys()","def get_attributes(cls):\r\n return [Attribute('file'),\r\n Attribute('missing', None)]","def get_attributes(cls):\n return cls._attributes","def getAttributes(self, convertToString = False):\n d = self.__dict__\n list = []\n \n # loop through list given return values in proper format\n for item in self.defaultAttributes:\n if d.has_key(item):\n if convertToString:\n list.append(str(d[item]))\n else:\n list.append(d[item])\n return list","def security_entries(self):\n return self._security_entries","def get_cryptomatte_names(self):\n return [self.cryptomattes[x][\"name\"] for x in self.cryptomattes]","def attribute_restrictions(self):\n return self._attribute_restrictions","def get_attributes(self):\n\t\treturn dict(list(self.__element.items()))","def attributes(self):","def _get_all_attributes(self) -> Dict[str, Any]:\n all_attributes = self.__dict__.copy()\n all_attributes.update(self.class_attributes)\n return all_attributes","def get_attributes(cls):\r\n return [Attribute('allowed_files', '[]', transform=cls.parse_files),\r\n Attribute('label', ''),\r\n Attribute('required_files', '[]', transform=cls.parse_files), ]","def data_skill_list(self):\n data_skill_list = []\n for skill in self.data_skill:\n if 'name' in skill.keys():\n data_skill_list.append(skill['name'])\n return data_skill_list","def get_attributes(cls):\r\n return [\r\n Attribute('size', '20'),\r\n Attribute('inline', False),\r\n Attribute('label', ''),\r\n ]","def attrs(self):\n return self.__dict__","def keyable_attributes(pynode, attr_name_list = None):\n if attr_name_list is None:\n attr_name_list = ['tx', 'ty', 'tz', 'rx', 'ry', 'rz', 'sx', 'sy', 'sz', 'v']\n\n _do_attributes_key_lock_hide(pynode, attr_name_list, keyable = True)","def getAttributes(self, name):\r\n ent = self.entities[name]\r\n\r\n attrs = []\r\n while ent != None:\r\n this_ent_attrs = copy.copy(ent[\"attributes\"])\r\n this_ent_attrs.reverse()\r\n attrs.extend(this_ent_attrs)\r\n ent = self.entities.get(ent[\"supertype\"], None)\r\n\r\n attrs.reverse()\r\n return attrs","def getAttributes(iface):\n return [(name, attr)\n for name, attr in getElements(iface, IAttribute).items()\n if not (IField.providedBy(attr) or IMethod.providedBy(attr))]","def getattrnames(instance):\n return tuple(sorted([attr_name for attr_name in vars(instance).keys()\n if not attr_name.startswith('_')]))","def _get_all_attributes(self):\n all_attributes = self.__dict__.copy()\n all_attributes.update(self.class_attributes)\n return all_attributes","def _get_attrs_symbols():\n return {\n 'True', 'False', 'None', # those are identifiers in Python 2.7\n 'self',\n 'parent',\n 'id',\n 'uid',\n 'context',\n 'context_today',\n 'active_id',\n 'active_ids',\n 'allowed_company_ids',\n 'current_company_id',\n 'active_model',\n 'time',\n 'datetime',\n 'relativedelta',\n 'current_date',\n 'abs',\n 'len',\n 'bool',\n 'float',\n 'str',\n 'unicode',\n }","def _read_attributes(root):\n output_list = []\n for _, value in enumerate(root[0][2]):\n attr = Attribute(value)\n output_list.append(attr)\n return output_list","def attributes(self) -> typing.Iterator[typing.Tuple[str]]:\n minimize = self.lattice._context._minimize(self._extent, self._intent)\n return (i.members() for i in minimize)","def getAttributes(self, name):\n ent = self.entities[name]\n\n attrs = []\n while ent != None:\n this_ent_attrs = copy.copy(ent[\"attributes\"])\n this_ent_attrs.reverse()\n attrs.extend(this_ent_attrs)\n ent = self.entities.get(ent[\"supertype\"], None)\n\n attrs.reverse()\n return attrs","def to_list(self) -> List[NumericalAttribute]:\n return self._attributes","def keyValues(self): # real signature unknown; restored from __doc__\n return []","def attributes(self):\n _attrs = []\n if self.name:\n _attrs.append(\"name\")\n if self.label:\n _attrs.append(\"label\")\n if self.confidence:\n _attrs.append(\"confidence\")\n if self.index:\n _attrs.append(\"index\")\n if self.attrs:\n _attrs.append(\"attrs\")\n return _attrs + [\"points\"]","def get_all_attribute(self):\n for attr, value in self.__dict__.items():\n print(attr, value)","def attributes(self):\n raise NotImplementedError","def attributeInfo(*args, allAttributes: bool=True, bool: bool=True, enumerated: bool=True,\n hidden: bool=True, inherited: bool=True, internal: bool=True, leaf: bool=True,\n logicalAnd: bool=True, multi: bool=True, short: bool=True, userInterface:\n bool=True, writable: bool=True, type: AnyStr=\"\", **kwargs)->List[AnyStr]:\n pass","def attributes(self):\n ...","def _get_attrs(self, names):\n assert isinstance(names, str)\n names = names.replace(\",\", \" \").split(\" \")\n res = []\n for n in names:\n if n == \"\":\n continue\n if n not in self.__dict__:\n raise KeyError(\"Unknown name for Container attribute: '{}'\".format(n))\n res.append(getattr(self, n))\n return res","def GetAttributeValuesString(self):\n attributes = []\n for attribute_name, attribute_value in sorted(self.__dict__.items()):\n # Not using startswith to improve performance.\n if attribute_name[0] == '_' or attribute_value is None:\n continue\n\n if isinstance(attribute_value, bytes):\n raise TypeError(\n 'Attribute: {0:s} value of type bytes not supported.'.format(\n attribute_name))\n\n if isinstance(attribute_value, dict):\n raise TypeError(\n 'Attribute: {0:s} value of type dict not supported.'.format(\n attribute_name))\n\n attribute_string = '{0:s}: {1!s}'.format(attribute_name, attribute_value)\n attributes.append(attribute_string)\n\n return ', '.join(attributes)","def getAttributes(clazz):\n return {name: attr for name, attr in clazz.__dict__.items()\n if not name.startswith(\"__\") \n and not callable(attr)\n and not type(attr) is staticmethod\n and not type(attr) is classmethod}","def getattrs(self, attrlist):\n\t\treturn np.array([getattr(self, attr) for attr in attrlist])","def _values(cls):\n assert inspect.isclass(cls)\n vals = tuple(val for val in _public_attributes(cls)\n if not inspect.isclass(val))\n return vals","def get_attributes(cls):\r\n return [\r\n Attribute('height', None),\r\n Attribute('width', None),\r\n Attribute('parts', None),\r\n Attribute('analyses', None),\r\n Attribute('initial_value', None),\r\n Attribute('submit_analyses', None),\r\n Attribute('label', ''),\r\n ]","def getattrs(self):\n # ICAT 4.5.0 also lists the meta attributes as attributes in\n # the entity info. Need to remove them here, as they should\n # not be added to InstAttr.\n return self.getfieldnames('ATTRIBUTE') - Entity.MetaAttr","def list(cls):\n return [cls.__dict__.get(name) for name in dir(cls) if (\n not callable(getattr(cls, name)) and not name.startswith(\"_\")\n )]","def get_attributes(self):\n attrs = list()\n syms = list()\n for item in self.gradual_items:\n gi = item.as_integer()\n attrs.append(gi[0])\n syms.append(gi[1])\n return attrs, syms","def get_attributes(cls):\r\n return [\r\n Attribute('rows', '30'),\r\n Attribute('cols', '80'),\r\n Attribute('hidden', ''),\r\n\r\n # For CodeMirror\r\n Attribute('mode', 'python'),\r\n Attribute('linenumbers', 'true'),\r\n # Template expects tabsize to be an int it can do math with\r\n Attribute('tabsize', 4, transform=int),\r\n ]","def attributes(self) -> Set[str]:\n return set()","def attrib(self) -> Any:\n return self.attributes","def getUserAttributes(self):\n return self._UserAttributes","def keys(self):\n\n return self._uniforms.keys() + self._attributes.keys()","def get_attr_list(query):\r\n attr_list = []\r\n if isinstance(query, dict):\r\n for k, v in query.items():\r\n ref_keys = k.split('__')\r\n attr_name = \".\".join(ref_keys)\r\n attr_list.append({attr_name: v})\r\n return attr_list","def extensible_attributes_list_values():\n return \"extensibleattributedef?\" \\\n \"_return_fields=\" \\\n \"list_values,\" \\\n \"comment,\" \\\n \"name,\" \\\n \"type\""],"string":"[\n \"def getDataAttributes(self):\\n asRet = [];\\n asAttrs = dir(self);\\n for sAttr in asAttrs:\\n if sAttr[0] == '_' or sAttr[0] == 'k':\\n continue;\\n if sAttr in self.kasInternalAttributes:\\n continue;\\n oValue = getattr(self, sAttr);\\n if callable(oValue):\\n continue;\\n asRet.append(sAttr);\\n return asRet;\",\n \"def get_attribute_list(self):\\n attributes = [attr for attr in vars(self.entries[0]) if not attr.startswith('__')]\\n return attributes\",\n \"def __listAttr(self):\\n attr = dir(self) # already sorted\\n filter = []\\n for name in attr:\\n if name[:2] == '__': pass\\n elif name[:10] == '_HelpDoc__': pass # used to mask private attr\\n elif name in self.__exclude: pass\\n else: filter.append(name)\\n return filter\",\n \"def get_sensitive_attributes(self):\\n return self.sensitive_attrs\",\n \"def attrs(self):\\n return list(name for name in self.__dict__\\n if not name.startswith(\\\"_\\\"))\",\n \"def keys(self):\\n return list(s.name.lower() for s in self.attributes)\",\n \"def keys(self):\\n return [a.name for a in self.__attrs_attrs__]\",\n \"def get_attrs(self):\\n attrs = []\\n for attribute in self.__dict__.keys():\\n attrs.append(attribute)\",\n \"def get_attribute_list(self):\\n return self.dp.get_attribute_list()\",\n \"def keys(self):\\n\\n keys = list()\\n\\n for attribute_name, type_instance in inspect.getmembers(self):\\n\\n if attribute_name.startswith('__') or inspect.ismethod(type_instance):\\n # Ignore parameters with __ and if they are methods\\n continue\\n\\n keys.append(attribute_name)\\n\\n return keys\",\n \"def attributes(self):\\n\\n attributes = []\\n\\n for member in self.members:\\n if member.attribute:\\n attributes.append(member)\\n\\n return attributes\",\n \"def get_attributes(cls):\\r\\n return []\",\n \"def ListAttributes(self):\\n\\n print(\\\"\\\\n\\\")\\n print(\\\"Attributes List of: \\\" + repr(self.__dict__[\\\"name\\\"]) + \\\" - \\\" + self.__class__.__name__ + \\\" Instance\\\\n\\\")\\n self_keys = self.__dict__.keys()\\n self_keys.sort()\\n for key in self_keys:\\n if key != \\\"name\\\":\\n print(str(key) + \\\" : \\\" + repr(self.__dict__[key]))\\n # end\\n # end\\n print(\\\"\\\\n\\\")\",\n \"def values(self):\\n return [getattr(self, a.name) for a in self.__attrs_attrs__]\",\n \"def attrs(self):\\n return [name for name in self.traits() if self.trait_metadata(name, \\\"attr\\\")]\",\n \"def attrs(self):\\n return [name for name in self.traits() if self.trait_metadata(name, \\\"attr\\\")]\",\n \"def attributes(self):\\n\\n return list(self._attributes.values())\",\n \"def attributes(self):\\n attr = super(BaseDataRecord, self).attributes()\\n return [a for a in attr if a not in self.excluded()]\",\n \"def key_attributes(self):\\n\\n return [level.key for level in self.levels]\",\n \"def _attrlist(self,obj, attrs):\\n vlist = [obj.__getattribute__(attr) for attr in attrs]\\n return vlist\",\n \"def listglobal(self):\\n return list(self.attributes.keys())\",\n \"def _getAttributeNames(self):\\n return sorted(self._field_map.keys())\",\n \"def extractAttrs(data):\\n\\treturn [instance[1:] for instance in data]\",\n \"def attributes(self):\\n return self.__dict.keys()\",\n \"def get_attribute_names(cls):\\n return cls._attributes.keys()\",\n \"def get_class_attr_list(self, o):\\n alist = None # Attributes to store\\n ff = None # Load filter function\\n for cl in self.classes:\\n if isinstance(o, cl):\\n alist = self.classes[cl][0]\\n ff = self.classes[cl][1]\\n break\\n if isinstance(o, Block._ComponentDataClass):\\n # If you're here you are trying to serialize an element of an\\n # indexed block at the top level. We do want to allow that, so\\n # we'll pretend it's a block.\\n alist = self.classes[Block][0]\\n ff = self.classes[Block][1]\\n return (alist, ff)\",\n \"def key_attributes(self):\\n\\n return [level.key for level in self._levels.values()]\",\n \"def get_data_class_attr_list(self, o):\\n alist = None # Attributes to store\\n ff = None # Load filter function\\n for cl in self.data_classes:\\n if isinstance(o, cl):\\n alist = self.data_classes[cl][0]\\n ff = self.data_classes[cl][1]\\n break\\n return (alist, ff)\",\n \"def all_attributes(self):\\n\\n attributes = []\\n for level in self.levels:\\n attributes.extend(level.attributes)\\n\\n return attributes\",\n \"def getAttributes(self):\\n pass\",\n \"def _columns(cls):\\n columns = []\\n for name, member in inspect.getmembers(cls):\\n if (not name.startswith('_') and\\n isinstance(member, InstrumentedAttribute)):\\n columns.append(name)\\n return columns\",\n \"def thrift_attrs(obj_or_cls) -> List[str]:\\n return [v[1] for v in obj_or_cls.thrift_spec.values()]\",\n \"def __dir__(self):\\n dict_keys = []\\n for k in self.keys():\\n if isinstance(k, str):\\n m = self._re_pattern.match(k)\\n if m:\\n dict_keys.append(m.string)\\n\\n obj_attrs = list(dir(Dict))\\n\\n return dict_keys + obj_attrs\",\n \"def _fix_up_private_attr(clsname, spec):\\n out = OrderedDict()\\n for k, v in spec.items():\\n if k.startswith('__') and not k.endswith('__'):\\n k = '_' + clsname + k\\n out[k] = v\\n return out\",\n \"def listattribute(self, varName):\\n fName = \\\"\\\"\\n if varName in self.statVars:\\n fName = self.statVars[varName][0]\\n elif varName in self.timeVars:\\n fName = self.timeVars[varName][0][0]\\n if fName:\\n var = cdms2.open(fName, 'r')(varName)\\n return var.listattributes()\\n else:\\n return []\",\n \"def get_sensitive_attributes_with_joint(self):\\n if len(self.get_sensitive_attributes()) > 1:\\n return self.get_sensitive_attributes() + ['-'.join(self.get_sensitive_attributes())]\\n return self.get_sensitive_attributes()\",\n \"def __get_attributes(self, config_details):\\n attributes = []\\n\\n if 'attribute' in config_details:\\n if type(config_details['attribute']) == dict:\\n attributes.append(config_details['attribute'])\\n else:\\n attributes = config_details['attribute']\\n\\n return attributes\",\n \"def get_privileged_class_names_with_joint(self, tag):\\n priv_class_names = self.get_privileged_class_names(tag)\\n if len(priv_class_names) > 1:\\n return priv_class_names + ['-'.join(str(v) for v in priv_class_names)]\\n return priv_class_names\",\n \"def protected_classifications(self):\\n if self.customer is None:\\n return None\\n return sorted([b.name for b in self._customer.protected_species if b.deployed])\",\n \"def getAttributes(self):\\n return self.attributes\",\n \"def getAttributes(self):\\n return self.attributes\",\n \"def get_attributes(cls):\\r\\n return [Attribute('size', '20'),\\r\\n Attribute('label', ''), ]\",\n \"def attributes(self):\\n return [self._ELE_ATTR]\",\n \"def values(self):\\n return self.attrs.values()\",\n \"def __fields(self):\\n return [self.__class__.__dict__[f] for f in self.__class__._fields]\",\n \"def read_global_attributes(self):\\n return self._attrs.keys()\",\n \"def get_attributes(self) -> Dict[str, str]:\\n pass\",\n \"def get_attrs(self):\\n return dir(self.config)\",\n \"def get_attrs(self):\\n return dir(self.config)\",\n \"def _detectors_attrs(self):\\n if self._det.get('detectors'):\\n return self._det['detectors'].keys()\\n else:\\n return []\",\n \"def attributes(self):\\n attrs_ = [\\\"name\\\", \\\"type\\\"]\\n if self.exclusive:\\n attrs_.append(\\\"exclusive\\\")\\n if self.default is not None:\\n attrs_.append(\\\"default\\\")\\n\\n return attrs_\",\n \"def get_attributes(self):\\n return self.attributes\",\n \"def attributes(self):\\n return { k: getattr(self, k) for k in self.__class__.columns().keys() }\",\n \"def attributes(self):\\n return dict(self.__attributes)\",\n \"def all(self):\\r\\n return self.attr_dict.keys()\",\n \"def get_attributes(cls):\\r\\n return [Attribute('file'),\\r\\n Attribute('missing', None)]\",\n \"def get_attributes(cls):\\n return cls._attributes\",\n \"def getAttributes(self, convertToString = False):\\n d = self.__dict__\\n list = []\\n \\n # loop through list given return values in proper format\\n for item in self.defaultAttributes:\\n if d.has_key(item):\\n if convertToString:\\n list.append(str(d[item]))\\n else:\\n list.append(d[item])\\n return list\",\n \"def security_entries(self):\\n return self._security_entries\",\n \"def get_cryptomatte_names(self):\\n return [self.cryptomattes[x][\\\"name\\\"] for x in self.cryptomattes]\",\n \"def attribute_restrictions(self):\\n return self._attribute_restrictions\",\n \"def get_attributes(self):\\n\\t\\treturn dict(list(self.__element.items()))\",\n \"def attributes(self):\",\n \"def _get_all_attributes(self) -> Dict[str, Any]:\\n all_attributes = self.__dict__.copy()\\n all_attributes.update(self.class_attributes)\\n return all_attributes\",\n \"def get_attributes(cls):\\r\\n return [Attribute('allowed_files', '[]', transform=cls.parse_files),\\r\\n Attribute('label', ''),\\r\\n Attribute('required_files', '[]', transform=cls.parse_files), ]\",\n \"def data_skill_list(self):\\n data_skill_list = []\\n for skill in self.data_skill:\\n if 'name' in skill.keys():\\n data_skill_list.append(skill['name'])\\n return data_skill_list\",\n \"def get_attributes(cls):\\r\\n return [\\r\\n Attribute('size', '20'),\\r\\n Attribute('inline', False),\\r\\n Attribute('label', ''),\\r\\n ]\",\n \"def attrs(self):\\n return self.__dict__\",\n \"def keyable_attributes(pynode, attr_name_list = None):\\n if attr_name_list is None:\\n attr_name_list = ['tx', 'ty', 'tz', 'rx', 'ry', 'rz', 'sx', 'sy', 'sz', 'v']\\n\\n _do_attributes_key_lock_hide(pynode, attr_name_list, keyable = True)\",\n \"def getAttributes(self, name):\\r\\n ent = self.entities[name]\\r\\n\\r\\n attrs = []\\r\\n while ent != None:\\r\\n this_ent_attrs = copy.copy(ent[\\\"attributes\\\"])\\r\\n this_ent_attrs.reverse()\\r\\n attrs.extend(this_ent_attrs)\\r\\n ent = self.entities.get(ent[\\\"supertype\\\"], None)\\r\\n\\r\\n attrs.reverse()\\r\\n return attrs\",\n \"def getAttributes(iface):\\n return [(name, attr)\\n for name, attr in getElements(iface, IAttribute).items()\\n if not (IField.providedBy(attr) or IMethod.providedBy(attr))]\",\n \"def getattrnames(instance):\\n return tuple(sorted([attr_name for attr_name in vars(instance).keys()\\n if not attr_name.startswith('_')]))\",\n \"def _get_all_attributes(self):\\n all_attributes = self.__dict__.copy()\\n all_attributes.update(self.class_attributes)\\n return all_attributes\",\n \"def _get_attrs_symbols():\\n return {\\n 'True', 'False', 'None', # those are identifiers in Python 2.7\\n 'self',\\n 'parent',\\n 'id',\\n 'uid',\\n 'context',\\n 'context_today',\\n 'active_id',\\n 'active_ids',\\n 'allowed_company_ids',\\n 'current_company_id',\\n 'active_model',\\n 'time',\\n 'datetime',\\n 'relativedelta',\\n 'current_date',\\n 'abs',\\n 'len',\\n 'bool',\\n 'float',\\n 'str',\\n 'unicode',\\n }\",\n \"def _read_attributes(root):\\n output_list = []\\n for _, value in enumerate(root[0][2]):\\n attr = Attribute(value)\\n output_list.append(attr)\\n return output_list\",\n \"def attributes(self) -> typing.Iterator[typing.Tuple[str]]:\\n minimize = self.lattice._context._minimize(self._extent, self._intent)\\n return (i.members() for i in minimize)\",\n \"def getAttributes(self, name):\\n ent = self.entities[name]\\n\\n attrs = []\\n while ent != None:\\n this_ent_attrs = copy.copy(ent[\\\"attributes\\\"])\\n this_ent_attrs.reverse()\\n attrs.extend(this_ent_attrs)\\n ent = self.entities.get(ent[\\\"supertype\\\"], None)\\n\\n attrs.reverse()\\n return attrs\",\n \"def to_list(self) -> List[NumericalAttribute]:\\n return self._attributes\",\n \"def keyValues(self): # real signature unknown; restored from __doc__\\n return []\",\n \"def attributes(self):\\n _attrs = []\\n if self.name:\\n _attrs.append(\\\"name\\\")\\n if self.label:\\n _attrs.append(\\\"label\\\")\\n if self.confidence:\\n _attrs.append(\\\"confidence\\\")\\n if self.index:\\n _attrs.append(\\\"index\\\")\\n if self.attrs:\\n _attrs.append(\\\"attrs\\\")\\n return _attrs + [\\\"points\\\"]\",\n \"def get_all_attribute(self):\\n for attr, value in self.__dict__.items():\\n print(attr, value)\",\n \"def attributes(self):\\n raise NotImplementedError\",\n \"def attributeInfo(*args, allAttributes: bool=True, bool: bool=True, enumerated: bool=True,\\n hidden: bool=True, inherited: bool=True, internal: bool=True, leaf: bool=True,\\n logicalAnd: bool=True, multi: bool=True, short: bool=True, userInterface:\\n bool=True, writable: bool=True, type: AnyStr=\\\"\\\", **kwargs)->List[AnyStr]:\\n pass\",\n \"def attributes(self):\\n ...\",\n \"def _get_attrs(self, names):\\n assert isinstance(names, str)\\n names = names.replace(\\\",\\\", \\\" \\\").split(\\\" \\\")\\n res = []\\n for n in names:\\n if n == \\\"\\\":\\n continue\\n if n not in self.__dict__:\\n raise KeyError(\\\"Unknown name for Container attribute: '{}'\\\".format(n))\\n res.append(getattr(self, n))\\n return res\",\n \"def GetAttributeValuesString(self):\\n attributes = []\\n for attribute_name, attribute_value in sorted(self.__dict__.items()):\\n # Not using startswith to improve performance.\\n if attribute_name[0] == '_' or attribute_value is None:\\n continue\\n\\n if isinstance(attribute_value, bytes):\\n raise TypeError(\\n 'Attribute: {0:s} value of type bytes not supported.'.format(\\n attribute_name))\\n\\n if isinstance(attribute_value, dict):\\n raise TypeError(\\n 'Attribute: {0:s} value of type dict not supported.'.format(\\n attribute_name))\\n\\n attribute_string = '{0:s}: {1!s}'.format(attribute_name, attribute_value)\\n attributes.append(attribute_string)\\n\\n return ', '.join(attributes)\",\n \"def getAttributes(clazz):\\n return {name: attr for name, attr in clazz.__dict__.items()\\n if not name.startswith(\\\"__\\\") \\n and not callable(attr)\\n and not type(attr) is staticmethod\\n and not type(attr) is classmethod}\",\n \"def getattrs(self, attrlist):\\n\\t\\treturn np.array([getattr(self, attr) for attr in attrlist])\",\n \"def _values(cls):\\n assert inspect.isclass(cls)\\n vals = tuple(val for val in _public_attributes(cls)\\n if not inspect.isclass(val))\\n return vals\",\n \"def get_attributes(cls):\\r\\n return [\\r\\n Attribute('height', None),\\r\\n Attribute('width', None),\\r\\n Attribute('parts', None),\\r\\n Attribute('analyses', None),\\r\\n Attribute('initial_value', None),\\r\\n Attribute('submit_analyses', None),\\r\\n Attribute('label', ''),\\r\\n ]\",\n \"def getattrs(self):\\n # ICAT 4.5.0 also lists the meta attributes as attributes in\\n # the entity info. Need to remove them here, as they should\\n # not be added to InstAttr.\\n return self.getfieldnames('ATTRIBUTE') - Entity.MetaAttr\",\n \"def list(cls):\\n return [cls.__dict__.get(name) for name in dir(cls) if (\\n not callable(getattr(cls, name)) and not name.startswith(\\\"_\\\")\\n )]\",\n \"def get_attributes(self):\\n attrs = list()\\n syms = list()\\n for item in self.gradual_items:\\n gi = item.as_integer()\\n attrs.append(gi[0])\\n syms.append(gi[1])\\n return attrs, syms\",\n \"def get_attributes(cls):\\r\\n return [\\r\\n Attribute('rows', '30'),\\r\\n Attribute('cols', '80'),\\r\\n Attribute('hidden', ''),\\r\\n\\r\\n # For CodeMirror\\r\\n Attribute('mode', 'python'),\\r\\n Attribute('linenumbers', 'true'),\\r\\n # Template expects tabsize to be an int it can do math with\\r\\n Attribute('tabsize', 4, transform=int),\\r\\n ]\",\n \"def attributes(self) -> Set[str]:\\n return set()\",\n \"def attrib(self) -> Any:\\n return self.attributes\",\n \"def getUserAttributes(self):\\n return self._UserAttributes\",\n \"def keys(self):\\n\\n return self._uniforms.keys() + self._attributes.keys()\",\n \"def get_attr_list(query):\\r\\n attr_list = []\\r\\n if isinstance(query, dict):\\r\\n for k, v in query.items():\\r\\n ref_keys = k.split('__')\\r\\n attr_name = \\\".\\\".join(ref_keys)\\r\\n attr_list.append({attr_name: v})\\r\\n return attr_list\",\n \"def extensible_attributes_list_values():\\n return \\\"extensibleattributedef?\\\" \\\\\\n \\\"_return_fields=\\\" \\\\\\n \\\"list_values,\\\" \\\\\\n \\\"comment,\\\" \\\\\\n \\\"name,\\\" \\\\\\n \\\"type\\\"\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.70606923","0.7041535","0.69863814","0.6956516","0.6759619","0.66697687","0.66511804","0.6639891","0.64760447","0.638376","0.6333704","0.632314","0.62966156","0.61981976","0.6196645","0.6196645","0.6156528","0.6151096","0.61357003","0.6134827","0.6129417","0.6113867","0.60566044","0.6043393","0.6015978","0.60054904","0.59888685","0.5978978","0.5948422","0.594416","0.58871686","0.58653605","0.57924277","0.57886416","0.5785423","0.5773269","0.57699996","0.57510674","0.57026285","0.5697828","0.5697828","0.56712484","0.5658337","0.5654064","0.5641475","0.5634082","0.5633759","0.5627662","0.5627662","0.561885","0.5604986","0.5594673","0.5593371","0.5589056","0.55811936","0.5579313","0.5577451","0.5565263","0.55574554","0.555343","0.55433303","0.5526554","0.5521545","0.5521438","0.5519263","0.551416","0.55127585","0.5507667","0.5500823","0.5499903","0.5487873","0.54873776","0.5486151","0.5475961","0.54725844","0.5466916","0.5456734","0.54557776","0.54457116","0.54450536","0.544444","0.54433733","0.54408705","0.54381716","0.54334295","0.5427509","0.5426158","0.541503","0.54140127","0.54115325","0.5407704","0.5406031","0.53896344","0.5370238","0.5363013","0.53581","0.53566283","0.53490674","0.5342343","0.5342217"],"string":"[\n \"0.70606923\",\n \"0.7041535\",\n \"0.69863814\",\n \"0.6956516\",\n \"0.6759619\",\n \"0.66697687\",\n \"0.66511804\",\n \"0.6639891\",\n \"0.64760447\",\n \"0.638376\",\n \"0.6333704\",\n \"0.632314\",\n \"0.62966156\",\n \"0.61981976\",\n \"0.6196645\",\n \"0.6196645\",\n \"0.6156528\",\n \"0.6151096\",\n \"0.61357003\",\n \"0.6134827\",\n \"0.6129417\",\n \"0.6113867\",\n \"0.60566044\",\n \"0.6043393\",\n \"0.6015978\",\n \"0.60054904\",\n \"0.59888685\",\n \"0.5978978\",\n \"0.5948422\",\n \"0.594416\",\n \"0.58871686\",\n \"0.58653605\",\n \"0.57924277\",\n \"0.57886416\",\n \"0.5785423\",\n \"0.5773269\",\n \"0.57699996\",\n \"0.57510674\",\n \"0.57026285\",\n \"0.5697828\",\n \"0.5697828\",\n \"0.56712484\",\n \"0.5658337\",\n \"0.5654064\",\n \"0.5641475\",\n \"0.5634082\",\n \"0.5633759\",\n \"0.5627662\",\n \"0.5627662\",\n \"0.561885\",\n \"0.5604986\",\n \"0.5594673\",\n \"0.5593371\",\n \"0.5589056\",\n \"0.55811936\",\n \"0.5579313\",\n \"0.5577451\",\n \"0.5565263\",\n \"0.55574554\",\n \"0.555343\",\n \"0.55433303\",\n \"0.5526554\",\n \"0.5521545\",\n \"0.5521438\",\n \"0.5519263\",\n \"0.551416\",\n \"0.55127585\",\n \"0.5507667\",\n \"0.5500823\",\n \"0.5499903\",\n \"0.5487873\",\n \"0.54873776\",\n \"0.5486151\",\n \"0.5475961\",\n \"0.54725844\",\n \"0.5466916\",\n \"0.5456734\",\n \"0.54557776\",\n \"0.54457116\",\n \"0.54450536\",\n \"0.544444\",\n \"0.54433733\",\n \"0.54408705\",\n \"0.54381716\",\n \"0.54334295\",\n \"0.5427509\",\n \"0.5426158\",\n \"0.541503\",\n \"0.54140127\",\n \"0.54115325\",\n \"0.5407704\",\n \"0.5406031\",\n \"0.53896344\",\n \"0.5370238\",\n \"0.5363013\",\n \"0.53581\",\n \"0.53566283\",\n \"0.53490674\",\n \"0.5342343\",\n \"0.5342217\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7225402"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":16895,"cells":{"query":{"kind":"string","value":"Same as get_privileged_class_names, but also includes the joint sensitive attribute if there is more than one sensitive attribute."},"document":{"kind":"string","value":"def get_privileged_class_names_with_joint(self, tag):\n priv_class_names = self.get_privileged_class_names(tag)\n if len(priv_class_names) > 1:\n return priv_class_names + ['-'.join(str(v) for v in priv_class_names)]\n return priv_class_names"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_privileged_class_names(self, tag):\n # FIXME this dependence between tags and privileged class names is bad; don't know how to\n # fix it right now\n if tag == 'numerical-binsensitive':\n return [1 for x in self.get_sensitive_attributes()]\n else:\n return self.privileged_class_names","def get_sensitive_attributes_with_joint(self):\n if len(self.get_sensitive_attributes()) > 1:\n return self.get_sensitive_attributes() + ['-'.join(self.get_sensitive_attributes())]\n return self.get_sensitive_attributes()","def get_sensitive_attributes(self):\n return self.sensitive_attrs","def protected_classifications(self):\n if self.customer is None:\n return None\n return sorted([b.name for b in self._customer.protected_species if b.deployed])","def class_names(self):\n raise NotImplementedError","def get_attribute_names(cls):\n return cls._attributes.keys()","def _get_include_attributes(cls, args, extra_attributes=None):\n extra_attributes = extra_attributes or []\n\n include_attributes = []\n\n if extra_attributes:\n include_attributes.extend(extra_attributes)\n\n # If user specifies which attributes to retrieve via CLI --attr / -a argument, take that\n # into account\n\n # Special case for \"all\"\n if \"all\" in args.attr:\n return None\n\n for attr in args.attr:\n include_attributes.append(attr)\n\n if include_attributes:\n return include_attributes\n\n display_attributes = getattr(cls, \"display_attributes\", [])\n\n if display_attributes:\n include_attributes += display_attributes\n\n include_attributes = list(OrderedSet(include_attributes))\n\n return include_attributes","def classes(attrs):\n return attrs.get('class', '').split()","def keys(self):\n return list(s.name.lower() for s in self.attributes)","def __listAttr(self):\n attr = dir(self) # already sorted\n filter = []\n for name in attr:\n if name[:2] == '__': pass\n elif name[:10] == '_HelpDoc__': pass # used to mask private attr\n elif name in self.__exclude: pass\n else: filter.append(name)\n return filter","def keys(self):\n return [a.name for a in self.__attrs_attrs__]","def get_feature_names(self):\n return [self.__class__.__name__]","def get_sensitive_terms(self):\n sensitive_terms_dict = {}\n for attribute in self.__non_redundant_entity_attributes:\n for record_id, sensitive_terms in self.__df[attribute].dropna().iteritems():\n for sensitive_term in sensitive_terms:\n cleaned_sensitive_term = \" \".join([t.lemma_.lower() for t in sensitive_term if not t.is_stop])\n if len(cleaned_sensitive_term) > 0:\n sensitive_terms_dict.setdefault(attribute, {}).setdefault(cleaned_sensitive_term, set()).add(record_id)\n\n # Sort sensitive terms dict alphabetically to have a deterministic order\n sensitive_terms_dict = {el[0]: el[1] for el in sorted(sensitive_terms_dict.items(), key=lambda x: x)}\n\n # Sort sensitive terms dict ascending by number terms per entity type\n sensitive_terms_dict = {el[0]: el[1] for el in sorted(sensitive_terms_dict.items(), key=lambda x: len(x[1]))}\n\n for attribute, sensitive_terms in sensitive_terms_dict.items():\n word = \"terms\"\n if len(sensitive_terms) == 1:\n word = \"term\"\n logger.info(\"Found %d distinct sensitive %s within attribute %s\", len(sensitive_terms), word, attribute)\n return sensitive_terms_dict","def attrs(self):\n return list(name for name in self.__dict__\n if not name.startswith(\"_\"))","def check_unused_attributes(self):\n all_attrs_read = collections.defaultdict(set)\n\n def _add_attrs(typ, attr_names_read):\n if typ is None:\n return\n all_attrs_read[typ] |= attr_names_read\n for base_cls in typ.__bases__:\n all_attrs_read[base_cls] |= attr_names_read\n if isinstance(typ, type):\n for child_cls in qcore.inspection.get_subclass_tree(typ):\n all_attrs_read[child_cls] |= attr_names_read\n\n for serialized, attrs_read in six.iteritems(self.attributes_read):\n attr_names_read = {attr_name for attr_name, _, _ in attrs_read}\n _add_attrs(self.unserialize_type(serialized), attr_names_read)\n\n for typ, attrs in self.config.IGNORED_UNUSED_ATTRS_BY_CLASS:\n _add_attrs(typ, attrs)\n\n used_bases = tuple(self.config.USED_BASE_CLASSES)\n\n for typ, attrs_read in sorted(\n six.iteritems(all_attrs_read), key=self._cls_sort\n ):\n if self.serialize_type(typ) not in self.classes_examined or issubclass(\n typ, used_bases\n ):\n continue\n existing_attrs = set(typ.__dict__.keys())\n for attr in existing_attrs - attrs_read - self.config.IGNORED_UNUSED_ATTRS:\n # server calls will always show up as unused here\n if _safe_getattr(_safe_getattr(typ, attr, None), \"server_call\", False):\n continue\n print(\"Unused method: %r.%s\" % (typ, attr))","def allowed_runtime_class_names(self) -> pulumi.Input[Sequence[pulumi.Input[str]]]:\n return pulumi.get(self, \"allowed_runtime_class_names\")","def _get_base_class_names(frame):\n co, lasti = frame.f_code, frame.f_lasti\n code = co.co_code\n i = 0\n extended_arg = 0\n extends = []\n while i <= lasti:\n c = code[i]\n op = ord(c)\n i += 1\n if op >= dis.HAVE_ARGUMENT:\n oparg = ord(code[i]) + ord(code[i + 1]) * 256 + extended_arg\n extended_arg = 0\n i += 2\n if op == dis.EXTENDED_ARG:\n extended_arg = oparg * int(65536)\n if op in dis.hasconst:\n if type(co.co_consts[oparg]) == str:\n extends = []\n elif op in dis.hasname:\n if dis.opname[op] == 'LOAD_NAME':\n extends.append(('name', co.co_names[oparg]))\n if dis.opname[op] == 'LOAD_ATTR':\n extends.append(('attr', co.co_names[oparg]))\n items = []\n previous_item = []\n for t, s in extends:\n if t == 'name':\n if previous_item:\n items.append(previous_item)\n previous_item = [s]\n else:\n previous_item += [s]\n if previous_item:\n items.append(previous_item)\n return items","def get_extra(self):\n\t\tselect = []\n\t\tfor cls in range(1, NCLASSES + 1):\n\t\t\tselect.append(where(self.labels == cls)[0][:self.limits[cls - 1]])\n\t\tfilter = concatenate(select)\n\t\treturn self.data[filter, :], self.labels[filter]","def relevant_classifications(self):\n return self.relevant_classes","def getFeatureClassNames(self):\n return self.featureClasses.keys()","def keys(self):\n\n keys = list()\n\n for attribute_name, type_instance in inspect.getmembers(self):\n\n if attribute_name.startswith('__') or inspect.ismethod(type_instance):\n # Ignore parameters with __ and if they are methods\n continue\n\n keys.append(attribute_name)\n\n return keys","def thrift_attrs(obj_or_cls) -> List[str]:\n return [v[1] for v in obj_or_cls.thrift_spec.values()]","def get_cryptomatte_names(self):\n return [self.cryptomattes[x][\"name\"] for x in self.cryptomattes]","def attributes(self):\n attr = super(BaseDataRecord, self).attributes()\n return [a for a in attr if a not in self.excluded()]","def getDataAttributes(self):\n asRet = [];\n asAttrs = dir(self);\n for sAttr in asAttrs:\n if sAttr[0] == '_' or sAttr[0] == 'k':\n continue;\n if sAttr in self.kasInternalAttributes:\n continue;\n oValue = getattr(self, sAttr);\n if callable(oValue):\n continue;\n asRet.append(sAttr);\n return asRet;","def get_attribute_list(self):\n attributes = [attr for attr in vars(self.entries[0]) if not attr.startswith('__')]\n return attributes","def class_trait_names ( cls, **metadata ):\n return cls.class_traits( **metadata ).keys()","def getAttributes(clazz):\n return {name: attr for name, attr in clazz.__dict__.items()\n if not name.startswith(\"__\") \n and not callable(attr)\n and not type(attr) is staticmethod\n and not type(attr) is classmethod}","def _GetSuspectedCLsWithOnlyCLInfo(suspected_cls):\n simplified_suspected_cls = []\n for cl in suspected_cls:\n simplified_cl = {\n 'repo_name': cl['repo_name'],\n 'revision': cl['revision'],\n 'commit_position': cl['commit_position'],\n 'url': cl['url']\n }\n simplified_suspected_cls.append(simplified_cl)\n return simplified_suspected_cls","def _detectors_attrs(self):\n if self._det.get('detectors'):\n return self._det['detectors'].keys()\n else:\n return []","def _setup_special_names(self):\n special_names = []\n dynamic_params = tuple(set(self._fget_params_list + self._fset_params_list))\n # Check whether class variables of DynamicProperty type are present\n for attr_name, attr in getmembers(self.__class__):\n if isinstance(attr, DynamicProperty):\n special_names += [attr_name + \"_\" + key for key in dynamic_params]\n # Check if special variables are defined at class level\n for attr, value in getmembers(self.__class__):\n if attr in special_names:\n # Copy class special variable at instance level, prefixing reserved_prefix\n setattr(self, self.__reserved_prefix + attr, value)\n return special_names","def get_trial_system_attrs(self, trial_id: int) -> Dict[str, Any]:\n return self.get_trial(trial_id).system_attrs","def get_attributes(cls):\r\n return []","def iter_provided_extras(self) -> Iterable[str]:\n raise NotImplementedError()","def _columns(cls):\n columns = []\n for name, member in inspect.getmembers(cls):\n if (not name.startswith('_') and\n isinstance(member, InstrumentedAttribute)):\n columns.append(name)\n return columns","def extra_state_attributes(self):\n return {attr: getattr(self, '_' + prop)\n for attr, prop in ATTRIBUTES_IRHVAC.items()}","def attrs(self):\n return [name for name in self.traits() if self.trait_metadata(name, \"attr\")]","def attrs(self):\n return [name for name in self.traits() if self.trait_metadata(name, \"attr\")]","def _member_field_names_for_protect(self, protect):\n result = []\n fields = self.MEMBER_DEFAULT_FIELDS.copy()\n fields.update((k,v) for k,v in self.SUPPLEMENTARY_FIELDS.iteritems() if (v['OBJECT'] == 'MEMBER'))\n for (name, spec) in fields.iteritems():\n if spec['PROTECT'] == protect:\n result.append(name)\n return result","def attribute_restrictions(self):\n return self._attribute_restrictions","def capability_attributes(self) -> Mapping[str, Any] | None:\n if state_class := self.state_class:\n return {ATTR_STATE_CLASS: state_class}\n\n if options := self.options:\n return {ATTR_OPTIONS: options}\n\n return None","def native_methods(self):\n base_attributes = {\n *dir(TapiAdapter),\n *dir(TapiClientExecutor),\n *dir(JSONAdapterMixin),\n \"serializer\",\n }\n a = [\n attr\n for attr in dir(self)\n if not attr.startswith(\"_\") and attr not in base_attributes\n ]\n return a","def all(self):\r\n return self.attr_dict.keys()","def _get_all_attributes(self) -> Dict[str, Any]:\n all_attributes = self.__dict__.copy()\n all_attributes.update(self.class_attributes)\n return all_attributes","def copyable_trait_names ( self, **metadata ):\n return self.trait_names( **metadata )","def _fix_up_private_attr(clsname, spec):\n out = OrderedDict()\n for k, v in spec.items():\n if k.startswith('__') and not k.endswith('__'):\n k = '_' + clsname + k\n out[k] = v\n return out","def attributes(self):\n _attrs = super(CategoricalAttribute, self).attributes()\n if self.top_k_probs is not None:\n _attrs.append(\"top_k_probs\")\n\n return _attrs","def _getAttributeNames(self):\n return sorted(self._field_map.keys())","def get_original_names_and_sharded_parameters(self):\n orig_named_parameters = []\n for module_name, m in self.named_modules(): # includes self\n if isinstance(m, XlaFullyShardedDataParallel):\n prefix = \"\" if module_name == \"\" else module_name + \".\"\n for p in self.sharded_params:\n n = prefix + p._orig_name\n n = n.replace(\"_fsdp_wrapped_module.\", \"\").replace(\"_fpw_module.\", \"\")\n orig_named_parameters.append((n, p))\n\n return orig_named_parameters","def exclude_protected(cls):\n cls.decls(pd.access_type_matcher_t(pd.ACCESS_TYPES.PROTECTED),allow_empty=True).exclude()","def function_names(self):\n # Only select classes that extend the base class\n return self._classes.keys()","def class_editable_traits ( cls ):\n names = cls.class_trait_names( type = _is_not_event )\n names.sort()\n return names","def classes(self):\n return self._.d","def allowed(cls):\n # type: () -> List[Str]\n names = cls.names()\n allowed = names\n allowed.extend([name.lower() for name in names])\n return allowed","def allowed_runtime_class_names(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\n return pulumi.get(self, \"allowed_runtime_class_names\")","def col_attr_keys(cls):\n AttrKeys = collections.namedtuple(\n \"AttrKeys\", [\"all\", \"columns\", \"hybrids\", \"primaries\", \"relationships\"]\n )\n inspection = sqlalchemy.inspection.inspect(cls)\n hybrids = [\n k\n for (k, v) in inspection.all_orm_descriptors.items()\n if v.extension_type is sqlalchemy.ext.hybrid.HYBRID_PROPERTY\n ]\n base_attrs = [a for a in inspection.attrs if not a.key.startswith(\"_sa_\")]\n columns = [\n a.key\n for a in base_attrs\n if type(a) is not sqlalchemy.orm.properties.RelationshipProperty\n ]\n relationships = [\n a.key\n for a in base_attrs\n if type(a) is sqlalchemy.orm.properties.RelationshipProperty\n ]\n primaries = columns + hybrids\n all = relationships + primaries\n return AttrKeys(\n all=all,\n columns=columns,\n hybrids=hybrids,\n primaries=primaries,\n relationships=relationships,\n )","def specified_unchanging_attributes(self) -> List[int]:\n indices = []\n\n for idx, (cpi, epi) in enumerate(zip(self.condition, self.effect)):\n if isinstance(epi, ProbabilityEnhancedAttribute):\n if cpi != self.cfg.classifier_wildcard and \\\n epi.does_contain(cpi):\n indices.append(idx)\n else:\n if cpi != self.cfg.classifier_wildcard and \\\n epi == self.cfg.classifier_wildcard:\n indices.append(idx)\n\n return indices","def get_attrs(self):\n attrs = []\n for attribute in self.__dict__.keys():\n attrs.append(attribute)","def classes(self):\n if self.classname:\n return [self.classname]\n return []","def key_attributes(self):\n\n return [level.key for level in self.levels]","def attributes(self):\n return self.__dict.keys()","def _drop_protected_attrs(model_class, values):\n for attr in model_class.__protected_attributes__:\n if attr in values:\n del values[attr]","def _get_all_attributes(self):\n all_attributes = self.__dict__.copy()\n all_attributes.update(self.class_attributes)\n return all_attributes","def __str_additional_info_nvps__(self):\n return []","def get_class_attr_list(self, o):\n alist = None # Attributes to store\n ff = None # Load filter function\n for cl in self.classes:\n if isinstance(o, cl):\n alist = self.classes[cl][0]\n ff = self.classes[cl][1]\n break\n if isinstance(o, Block._ComponentDataClass):\n # If you're here you are trying to serialize an element of an\n # indexed block at the top level. We do want to allow that, so\n # we'll pretend it's a block.\n alist = self.classes[Block][0]\n ff = self.classes[Block][1]\n return (alist, ff)","def sclasses(self, w: Wrapper) -> list[str]:\n return str(w.property(self.style_sclass_property) or '').split()","def getAttributes(iface):\n return [(name, attr)\n for name, attr in getElements(iface, IAttribute).items()\n if not (IField.providedBy(attr) or IMethod.providedBy(attr))]","def attributes(self) -> Set[str]:\n return set()","def get_str_param_names(self):\n # Exclude self.api and self.names from the command string\n return self.get_attribute_names(FormattedParameter)","def get_classes(self):\n out_classes = ()\n classes = super(NamedEntityRecognizerModel, self).get_classes()\n\n for c in classes:\n out_classes += (c[:2],)\n\n return ((self.outside_class, self.outside_class_display),) + out_classes","def class_attr(self, attr, ignore_protected):\n protected = False if not ignore_protected else self.protected_attr(attr)\n return re.match('^(?!__).*', attr) and not callable(getattr(self, attr)) and not protected","def _get_all_classnames(\n module: ModuleType\n) -> List[str]:\n return list(map(lambda x: x[0], inspect.getmembers(module, inspect.isclass)))","def attributeInfo(*args, allAttributes: bool=True, bool: bool=True, enumerated: bool=True,\n hidden: bool=True, inherited: bool=True, internal: bool=True, leaf: bool=True,\n logicalAnd: bool=True, multi: bool=True, short: bool=True, userInterface:\n bool=True, writable: bool=True, type: AnyStr=\"\", **kwargs)->List[AnyStr]:\n pass","def get_skill_levels(self):\n return self.model_class.objects.filter(enforced=self.enforced).order_by('-gte')","def keyable_attributes(pynode, attr_name_list = None):\n if attr_name_list is None:\n attr_name_list = ['tx', 'ty', 'tz', 'rx', 'ry', 'rz', 'sx', 'sy', 'sz', 'v']\n\n _do_attributes_key_lock_hide(pynode, attr_name_list, keyable = True)","def get_trial_user_attrs(self, trial_id: int) -> Dict[str, Any]:\n return self.get_trial(trial_id).user_attrs","def attributes(self):\n attrs_ = [\"name\", \"type\"]\n if self.exclusive:\n attrs_.append(\"exclusive\")\n if self.default is not None:\n attrs_.append(\"default\")\n\n return attrs_","def attrs(*attributes):\n return ';'.join([ str(i) for i in attributes ])","def all_trait_names ( self ):\n return self.__class_traits__.keys()","def extra_tags(self) -> str:\n all_tags = dict.fromkeys(\n self.default_tags.split() + self.custom_tags.strip().split()\n )\n return \" \".join(all_tags.keys())","def Attributes(self) -> PolicyStatementAttribute:","def keys(cls):\n if cls._keys is None:\n cls._keys = tuple(attr for attr in dir(cls) if all((not callable(getattr(cls, attr)),\n not attr.startswith('_'),\n not attr == 'ALL')))\n return cls._keys","def dss_user_classes(self) -> List[str]:\n return Bridge.var_array_function(self.dss_obj.DSSV, 1, None, '')","def find_own_attributes(cs):\n own_attributes = {}\n for con in cs:\n own_attributes[con] = []\n for attr in con.intent:\n own_attributes[con].append(attr)\n for sub_con in cs:\n if sub_con.intent < con.intent and\\\n attr in sub_con.intent:\n own_attributes[con].pop()\n break\n return own_attributes","def getAttributes(self):\n pass","def getAttributeDict(self):\n result = {}\n dict = self.getDict()\n for key in dict.keys():\n value = dict.get(key)\n if value.__class__.__name__ != 'SpecialDict':\n result[key] = value\n return result","def get_all_super_names(value):\n if hasattr(value, '__class__'):\n # old style class\n klass = value.__class__\n class_name = klass.__name__\n bases = get_oldstyle_bases(klass)\n bases_names = [x.__name__ for x in bases]\n else:\n # new style\n t = type(value)\n class_name = t.__name__\n bases_names = [b.__name__ for b in t.mro()]\n return class_name, bases_names","def get_attrs_names_for_entities(cls, entity=None):\n all_entities_cls = (string_utils.convert_to_list(entity) if entity\n else Entity.all_entities_classes())\n all_entities_attrs_names = string_utils.convert_list_elements_to_list(\n [entity_cls().__dict__.keys() for entity_cls in all_entities_cls])\n return list(set(all_entities_attrs_names))","def find_class_names(self) -> [str]:\n if not self.faithful:\n raise AttributeError('The workspace is not faithful')\n\n result = list()\n\n list_possible_class = list_folders_into_directory(join(self.directory_path, \"train-images\"))\n\n for possible_class in list_possible_class:\n if possible_class != \"img\":\n result.append(possible_class)\n\n self.class_names = result\n return result","def classify_class_attrs(cls):\r\n\r\n mro = getmro(cls)\r\n names = dir(cls)\r\n result = []\r\n for name in names:\r\n # Get the object associated with the name.\r\n # Getting an obj from the __dict__ sometimes reveals more than\r\n # using getattr. Static and class methods are dramatic examples.\r\n if name in cls.__dict__:\r\n obj = cls.__dict__[name]\r\n else:\r\n obj = getattr(cls, name)\r\n\r\n # Figure out where it was defined.\r\n homecls = getattr(obj, \"__objclass__\", None)\r\n if homecls is None:\r\n # search the dicts.\r\n for base in mro:\r\n if name in base.__dict__:\r\n homecls = base\r\n break\r\n\r\n # Get the object again, in order to get it from the defining\r\n # __dict__ instead of via getattr (if possible).\r\n if homecls is not None and name in homecls.__dict__:\r\n obj = homecls.__dict__[name]\r\n\r\n # Also get the object via getattr.\r\n obj_via_getattr = getattr(cls, name)\r\n\r\n # Classify the object.\r\n if isinstance(obj, staticmethod):\r\n kind = \"static method\"\r\n elif isinstance(obj, classmethod):\r\n kind = \"class method\"\r\n elif isinstance(obj, property):\r\n kind = \"property\"\r\n elif (ismethod(obj_via_getattr) or\r\n ismethoddescriptor(obj_via_getattr)):\r\n kind = \"method\"\r\n else:\r\n kind = \"data\"\r\n\r\n result.append(Attribute(name, kind, homecls, obj))\r\n\r\n return result","def get_study_user_attrs(self, study_id: int) -> Dict[str, Any]:\n raise NotImplementedError","def get_names(self):\n import tc\n opts_list = []\n for k, v in self.__class__.__dict__.iteritems():\n if isinstance(v, tc.TC):\n opts_list.append(k)\n opts_list = sorted(opts_list)\n return opts_list","def ListAttributes(self):\n\n print(\"\\n\")\n print(\"Attributes List of: \" + repr(self.__dict__[\"name\"]) + \" - \" + self.__class__.__name__ + \" Instance\\n\")\n self_keys = self.__dict__.keys()\n self_keys.sort()\n for key in self_keys:\n if key != \"name\":\n print(str(key) + \" : \" + repr(self.__dict__[key]))\n # end\n # end\n print(\"\\n\")","def key_attributes(self):\n\n return [level.key for level in self._levels.values()]","def cert_readonly_fields():\n if settings.DEBUG:\n return []\n return [\n 'user', 'get_template', 'csr', 'serial', 'subject',\n 'valid_from', 'valid_to', 'x509',\n ]","def _get_privileged_groups(self):\n if self.privileged_groups == \"auto\":\n return []\n else:\n return deepcopy(self.privileged_groups)","def clsnames_affecting_onsets(self):\n # type: () -> Set[str]\n output = set()\n output.update(self.NONGRACE_NOTEHEAD_CLSNAMES)\n output.update(self.REST_CLSNAMES)\n output.update(self.MEASURE_SEPARATOR_CLSNAMES)\n output.update(self.TIME_SIGNATURES)\n output.add('repeat_measure')\n return output","def attributes(self):\n\n attributes = []\n\n for member in self.members:\n if member.attribute:\n attributes.append(member)\n\n return attributes","def get_patients_classes(self):\n patients = self.get_patients()\n classes = self.get_classes()\n return patients, classes","def attributes(self):\n return { k: getattr(self, k) for k in self.__class__.columns().keys() }"],"string":"[\n \"def get_privileged_class_names(self, tag):\\n # FIXME this dependence between tags and privileged class names is bad; don't know how to\\n # fix it right now\\n if tag == 'numerical-binsensitive':\\n return [1 for x in self.get_sensitive_attributes()]\\n else:\\n return self.privileged_class_names\",\n \"def get_sensitive_attributes_with_joint(self):\\n if len(self.get_sensitive_attributes()) > 1:\\n return self.get_sensitive_attributes() + ['-'.join(self.get_sensitive_attributes())]\\n return self.get_sensitive_attributes()\",\n \"def get_sensitive_attributes(self):\\n return self.sensitive_attrs\",\n \"def protected_classifications(self):\\n if self.customer is None:\\n return None\\n return sorted([b.name for b in self._customer.protected_species if b.deployed])\",\n \"def class_names(self):\\n raise NotImplementedError\",\n \"def get_attribute_names(cls):\\n return cls._attributes.keys()\",\n \"def _get_include_attributes(cls, args, extra_attributes=None):\\n extra_attributes = extra_attributes or []\\n\\n include_attributes = []\\n\\n if extra_attributes:\\n include_attributes.extend(extra_attributes)\\n\\n # If user specifies which attributes to retrieve via CLI --attr / -a argument, take that\\n # into account\\n\\n # Special case for \\\"all\\\"\\n if \\\"all\\\" in args.attr:\\n return None\\n\\n for attr in args.attr:\\n include_attributes.append(attr)\\n\\n if include_attributes:\\n return include_attributes\\n\\n display_attributes = getattr(cls, \\\"display_attributes\\\", [])\\n\\n if display_attributes:\\n include_attributes += display_attributes\\n\\n include_attributes = list(OrderedSet(include_attributes))\\n\\n return include_attributes\",\n \"def classes(attrs):\\n return attrs.get('class', '').split()\",\n \"def keys(self):\\n return list(s.name.lower() for s in self.attributes)\",\n \"def __listAttr(self):\\n attr = dir(self) # already sorted\\n filter = []\\n for name in attr:\\n if name[:2] == '__': pass\\n elif name[:10] == '_HelpDoc__': pass # used to mask private attr\\n elif name in self.__exclude: pass\\n else: filter.append(name)\\n return filter\",\n \"def keys(self):\\n return [a.name for a in self.__attrs_attrs__]\",\n \"def get_feature_names(self):\\n return [self.__class__.__name__]\",\n \"def get_sensitive_terms(self):\\n sensitive_terms_dict = {}\\n for attribute in self.__non_redundant_entity_attributes:\\n for record_id, sensitive_terms in self.__df[attribute].dropna().iteritems():\\n for sensitive_term in sensitive_terms:\\n cleaned_sensitive_term = \\\" \\\".join([t.lemma_.lower() for t in sensitive_term if not t.is_stop])\\n if len(cleaned_sensitive_term) > 0:\\n sensitive_terms_dict.setdefault(attribute, {}).setdefault(cleaned_sensitive_term, set()).add(record_id)\\n\\n # Sort sensitive terms dict alphabetically to have a deterministic order\\n sensitive_terms_dict = {el[0]: el[1] for el in sorted(sensitive_terms_dict.items(), key=lambda x: x)}\\n\\n # Sort sensitive terms dict ascending by number terms per entity type\\n sensitive_terms_dict = {el[0]: el[1] for el in sorted(sensitive_terms_dict.items(), key=lambda x: len(x[1]))}\\n\\n for attribute, sensitive_terms in sensitive_terms_dict.items():\\n word = \\\"terms\\\"\\n if len(sensitive_terms) == 1:\\n word = \\\"term\\\"\\n logger.info(\\\"Found %d distinct sensitive %s within attribute %s\\\", len(sensitive_terms), word, attribute)\\n return sensitive_terms_dict\",\n \"def attrs(self):\\n return list(name for name in self.__dict__\\n if not name.startswith(\\\"_\\\"))\",\n \"def check_unused_attributes(self):\\n all_attrs_read = collections.defaultdict(set)\\n\\n def _add_attrs(typ, attr_names_read):\\n if typ is None:\\n return\\n all_attrs_read[typ] |= attr_names_read\\n for base_cls in typ.__bases__:\\n all_attrs_read[base_cls] |= attr_names_read\\n if isinstance(typ, type):\\n for child_cls in qcore.inspection.get_subclass_tree(typ):\\n all_attrs_read[child_cls] |= attr_names_read\\n\\n for serialized, attrs_read in six.iteritems(self.attributes_read):\\n attr_names_read = {attr_name for attr_name, _, _ in attrs_read}\\n _add_attrs(self.unserialize_type(serialized), attr_names_read)\\n\\n for typ, attrs in self.config.IGNORED_UNUSED_ATTRS_BY_CLASS:\\n _add_attrs(typ, attrs)\\n\\n used_bases = tuple(self.config.USED_BASE_CLASSES)\\n\\n for typ, attrs_read in sorted(\\n six.iteritems(all_attrs_read), key=self._cls_sort\\n ):\\n if self.serialize_type(typ) not in self.classes_examined or issubclass(\\n typ, used_bases\\n ):\\n continue\\n existing_attrs = set(typ.__dict__.keys())\\n for attr in existing_attrs - attrs_read - self.config.IGNORED_UNUSED_ATTRS:\\n # server calls will always show up as unused here\\n if _safe_getattr(_safe_getattr(typ, attr, None), \\\"server_call\\\", False):\\n continue\\n print(\\\"Unused method: %r.%s\\\" % (typ, attr))\",\n \"def allowed_runtime_class_names(self) -> pulumi.Input[Sequence[pulumi.Input[str]]]:\\n return pulumi.get(self, \\\"allowed_runtime_class_names\\\")\",\n \"def _get_base_class_names(frame):\\n co, lasti = frame.f_code, frame.f_lasti\\n code = co.co_code\\n i = 0\\n extended_arg = 0\\n extends = []\\n while i <= lasti:\\n c = code[i]\\n op = ord(c)\\n i += 1\\n if op >= dis.HAVE_ARGUMENT:\\n oparg = ord(code[i]) + ord(code[i + 1]) * 256 + extended_arg\\n extended_arg = 0\\n i += 2\\n if op == dis.EXTENDED_ARG:\\n extended_arg = oparg * int(65536)\\n if op in dis.hasconst:\\n if type(co.co_consts[oparg]) == str:\\n extends = []\\n elif op in dis.hasname:\\n if dis.opname[op] == 'LOAD_NAME':\\n extends.append(('name', co.co_names[oparg]))\\n if dis.opname[op] == 'LOAD_ATTR':\\n extends.append(('attr', co.co_names[oparg]))\\n items = []\\n previous_item = []\\n for t, s in extends:\\n if t == 'name':\\n if previous_item:\\n items.append(previous_item)\\n previous_item = [s]\\n else:\\n previous_item += [s]\\n if previous_item:\\n items.append(previous_item)\\n return items\",\n \"def get_extra(self):\\n\\t\\tselect = []\\n\\t\\tfor cls in range(1, NCLASSES + 1):\\n\\t\\t\\tselect.append(where(self.labels == cls)[0][:self.limits[cls - 1]])\\n\\t\\tfilter = concatenate(select)\\n\\t\\treturn self.data[filter, :], self.labels[filter]\",\n \"def relevant_classifications(self):\\n return self.relevant_classes\",\n \"def getFeatureClassNames(self):\\n return self.featureClasses.keys()\",\n \"def keys(self):\\n\\n keys = list()\\n\\n for attribute_name, type_instance in inspect.getmembers(self):\\n\\n if attribute_name.startswith('__') or inspect.ismethod(type_instance):\\n # Ignore parameters with __ and if they are methods\\n continue\\n\\n keys.append(attribute_name)\\n\\n return keys\",\n \"def thrift_attrs(obj_or_cls) -> List[str]:\\n return [v[1] for v in obj_or_cls.thrift_spec.values()]\",\n \"def get_cryptomatte_names(self):\\n return [self.cryptomattes[x][\\\"name\\\"] for x in self.cryptomattes]\",\n \"def attributes(self):\\n attr = super(BaseDataRecord, self).attributes()\\n return [a for a in attr if a not in self.excluded()]\",\n \"def getDataAttributes(self):\\n asRet = [];\\n asAttrs = dir(self);\\n for sAttr in asAttrs:\\n if sAttr[0] == '_' or sAttr[0] == 'k':\\n continue;\\n if sAttr in self.kasInternalAttributes:\\n continue;\\n oValue = getattr(self, sAttr);\\n if callable(oValue):\\n continue;\\n asRet.append(sAttr);\\n return asRet;\",\n \"def get_attribute_list(self):\\n attributes = [attr for attr in vars(self.entries[0]) if not attr.startswith('__')]\\n return attributes\",\n \"def class_trait_names ( cls, **metadata ):\\n return cls.class_traits( **metadata ).keys()\",\n \"def getAttributes(clazz):\\n return {name: attr for name, attr in clazz.__dict__.items()\\n if not name.startswith(\\\"__\\\") \\n and not callable(attr)\\n and not type(attr) is staticmethod\\n and not type(attr) is classmethod}\",\n \"def _GetSuspectedCLsWithOnlyCLInfo(suspected_cls):\\n simplified_suspected_cls = []\\n for cl in suspected_cls:\\n simplified_cl = {\\n 'repo_name': cl['repo_name'],\\n 'revision': cl['revision'],\\n 'commit_position': cl['commit_position'],\\n 'url': cl['url']\\n }\\n simplified_suspected_cls.append(simplified_cl)\\n return simplified_suspected_cls\",\n \"def _detectors_attrs(self):\\n if self._det.get('detectors'):\\n return self._det['detectors'].keys()\\n else:\\n return []\",\n \"def _setup_special_names(self):\\n special_names = []\\n dynamic_params = tuple(set(self._fget_params_list + self._fset_params_list))\\n # Check whether class variables of DynamicProperty type are present\\n for attr_name, attr in getmembers(self.__class__):\\n if isinstance(attr, DynamicProperty):\\n special_names += [attr_name + \\\"_\\\" + key for key in dynamic_params]\\n # Check if special variables are defined at class level\\n for attr, value in getmembers(self.__class__):\\n if attr in special_names:\\n # Copy class special variable at instance level, prefixing reserved_prefix\\n setattr(self, self.__reserved_prefix + attr, value)\\n return special_names\",\n \"def get_trial_system_attrs(self, trial_id: int) -> Dict[str, Any]:\\n return self.get_trial(trial_id).system_attrs\",\n \"def get_attributes(cls):\\r\\n return []\",\n \"def iter_provided_extras(self) -> Iterable[str]:\\n raise NotImplementedError()\",\n \"def _columns(cls):\\n columns = []\\n for name, member in inspect.getmembers(cls):\\n if (not name.startswith('_') and\\n isinstance(member, InstrumentedAttribute)):\\n columns.append(name)\\n return columns\",\n \"def extra_state_attributes(self):\\n return {attr: getattr(self, '_' + prop)\\n for attr, prop in ATTRIBUTES_IRHVAC.items()}\",\n \"def attrs(self):\\n return [name for name in self.traits() if self.trait_metadata(name, \\\"attr\\\")]\",\n \"def attrs(self):\\n return [name for name in self.traits() if self.trait_metadata(name, \\\"attr\\\")]\",\n \"def _member_field_names_for_protect(self, protect):\\n result = []\\n fields = self.MEMBER_DEFAULT_FIELDS.copy()\\n fields.update((k,v) for k,v in self.SUPPLEMENTARY_FIELDS.iteritems() if (v['OBJECT'] == 'MEMBER'))\\n for (name, spec) in fields.iteritems():\\n if spec['PROTECT'] == protect:\\n result.append(name)\\n return result\",\n \"def attribute_restrictions(self):\\n return self._attribute_restrictions\",\n \"def capability_attributes(self) -> Mapping[str, Any] | None:\\n if state_class := self.state_class:\\n return {ATTR_STATE_CLASS: state_class}\\n\\n if options := self.options:\\n return {ATTR_OPTIONS: options}\\n\\n return None\",\n \"def native_methods(self):\\n base_attributes = {\\n *dir(TapiAdapter),\\n *dir(TapiClientExecutor),\\n *dir(JSONAdapterMixin),\\n \\\"serializer\\\",\\n }\\n a = [\\n attr\\n for attr in dir(self)\\n if not attr.startswith(\\\"_\\\") and attr not in base_attributes\\n ]\\n return a\",\n \"def all(self):\\r\\n return self.attr_dict.keys()\",\n \"def _get_all_attributes(self) -> Dict[str, Any]:\\n all_attributes = self.__dict__.copy()\\n all_attributes.update(self.class_attributes)\\n return all_attributes\",\n \"def copyable_trait_names ( self, **metadata ):\\n return self.trait_names( **metadata )\",\n \"def _fix_up_private_attr(clsname, spec):\\n out = OrderedDict()\\n for k, v in spec.items():\\n if k.startswith('__') and not k.endswith('__'):\\n k = '_' + clsname + k\\n out[k] = v\\n return out\",\n \"def attributes(self):\\n _attrs = super(CategoricalAttribute, self).attributes()\\n if self.top_k_probs is not None:\\n _attrs.append(\\\"top_k_probs\\\")\\n\\n return _attrs\",\n \"def _getAttributeNames(self):\\n return sorted(self._field_map.keys())\",\n \"def get_original_names_and_sharded_parameters(self):\\n orig_named_parameters = []\\n for module_name, m in self.named_modules(): # includes self\\n if isinstance(m, XlaFullyShardedDataParallel):\\n prefix = \\\"\\\" if module_name == \\\"\\\" else module_name + \\\".\\\"\\n for p in self.sharded_params:\\n n = prefix + p._orig_name\\n n = n.replace(\\\"_fsdp_wrapped_module.\\\", \\\"\\\").replace(\\\"_fpw_module.\\\", \\\"\\\")\\n orig_named_parameters.append((n, p))\\n\\n return orig_named_parameters\",\n \"def exclude_protected(cls):\\n cls.decls(pd.access_type_matcher_t(pd.ACCESS_TYPES.PROTECTED),allow_empty=True).exclude()\",\n \"def function_names(self):\\n # Only select classes that extend the base class\\n return self._classes.keys()\",\n \"def class_editable_traits ( cls ):\\n names = cls.class_trait_names( type = _is_not_event )\\n names.sort()\\n return names\",\n \"def classes(self):\\n return self._.d\",\n \"def allowed(cls):\\n # type: () -> List[Str]\\n names = cls.names()\\n allowed = names\\n allowed.extend([name.lower() for name in names])\\n return allowed\",\n \"def allowed_runtime_class_names(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\\n return pulumi.get(self, \\\"allowed_runtime_class_names\\\")\",\n \"def col_attr_keys(cls):\\n AttrKeys = collections.namedtuple(\\n \\\"AttrKeys\\\", [\\\"all\\\", \\\"columns\\\", \\\"hybrids\\\", \\\"primaries\\\", \\\"relationships\\\"]\\n )\\n inspection = sqlalchemy.inspection.inspect(cls)\\n hybrids = [\\n k\\n for (k, v) in inspection.all_orm_descriptors.items()\\n if v.extension_type is sqlalchemy.ext.hybrid.HYBRID_PROPERTY\\n ]\\n base_attrs = [a for a in inspection.attrs if not a.key.startswith(\\\"_sa_\\\")]\\n columns = [\\n a.key\\n for a in base_attrs\\n if type(a) is not sqlalchemy.orm.properties.RelationshipProperty\\n ]\\n relationships = [\\n a.key\\n for a in base_attrs\\n if type(a) is sqlalchemy.orm.properties.RelationshipProperty\\n ]\\n primaries = columns + hybrids\\n all = relationships + primaries\\n return AttrKeys(\\n all=all,\\n columns=columns,\\n hybrids=hybrids,\\n primaries=primaries,\\n relationships=relationships,\\n )\",\n \"def specified_unchanging_attributes(self) -> List[int]:\\n indices = []\\n\\n for idx, (cpi, epi) in enumerate(zip(self.condition, self.effect)):\\n if isinstance(epi, ProbabilityEnhancedAttribute):\\n if cpi != self.cfg.classifier_wildcard and \\\\\\n epi.does_contain(cpi):\\n indices.append(idx)\\n else:\\n if cpi != self.cfg.classifier_wildcard and \\\\\\n epi == self.cfg.classifier_wildcard:\\n indices.append(idx)\\n\\n return indices\",\n \"def get_attrs(self):\\n attrs = []\\n for attribute in self.__dict__.keys():\\n attrs.append(attribute)\",\n \"def classes(self):\\n if self.classname:\\n return [self.classname]\\n return []\",\n \"def key_attributes(self):\\n\\n return [level.key for level in self.levels]\",\n \"def attributes(self):\\n return self.__dict.keys()\",\n \"def _drop_protected_attrs(model_class, values):\\n for attr in model_class.__protected_attributes__:\\n if attr in values:\\n del values[attr]\",\n \"def _get_all_attributes(self):\\n all_attributes = self.__dict__.copy()\\n all_attributes.update(self.class_attributes)\\n return all_attributes\",\n \"def __str_additional_info_nvps__(self):\\n return []\",\n \"def get_class_attr_list(self, o):\\n alist = None # Attributes to store\\n ff = None # Load filter function\\n for cl in self.classes:\\n if isinstance(o, cl):\\n alist = self.classes[cl][0]\\n ff = self.classes[cl][1]\\n break\\n if isinstance(o, Block._ComponentDataClass):\\n # If you're here you are trying to serialize an element of an\\n # indexed block at the top level. We do want to allow that, so\\n # we'll pretend it's a block.\\n alist = self.classes[Block][0]\\n ff = self.classes[Block][1]\\n return (alist, ff)\",\n \"def sclasses(self, w: Wrapper) -> list[str]:\\n return str(w.property(self.style_sclass_property) or '').split()\",\n \"def getAttributes(iface):\\n return [(name, attr)\\n for name, attr in getElements(iface, IAttribute).items()\\n if not (IField.providedBy(attr) or IMethod.providedBy(attr))]\",\n \"def attributes(self) -> Set[str]:\\n return set()\",\n \"def get_str_param_names(self):\\n # Exclude self.api and self.names from the command string\\n return self.get_attribute_names(FormattedParameter)\",\n \"def get_classes(self):\\n out_classes = ()\\n classes = super(NamedEntityRecognizerModel, self).get_classes()\\n\\n for c in classes:\\n out_classes += (c[:2],)\\n\\n return ((self.outside_class, self.outside_class_display),) + out_classes\",\n \"def class_attr(self, attr, ignore_protected):\\n protected = False if not ignore_protected else self.protected_attr(attr)\\n return re.match('^(?!__).*', attr) and not callable(getattr(self, attr)) and not protected\",\n \"def _get_all_classnames(\\n module: ModuleType\\n) -> List[str]:\\n return list(map(lambda x: x[0], inspect.getmembers(module, inspect.isclass)))\",\n \"def attributeInfo(*args, allAttributes: bool=True, bool: bool=True, enumerated: bool=True,\\n hidden: bool=True, inherited: bool=True, internal: bool=True, leaf: bool=True,\\n logicalAnd: bool=True, multi: bool=True, short: bool=True, userInterface:\\n bool=True, writable: bool=True, type: AnyStr=\\\"\\\", **kwargs)->List[AnyStr]:\\n pass\",\n \"def get_skill_levels(self):\\n return self.model_class.objects.filter(enforced=self.enforced).order_by('-gte')\",\n \"def keyable_attributes(pynode, attr_name_list = None):\\n if attr_name_list is None:\\n attr_name_list = ['tx', 'ty', 'tz', 'rx', 'ry', 'rz', 'sx', 'sy', 'sz', 'v']\\n\\n _do_attributes_key_lock_hide(pynode, attr_name_list, keyable = True)\",\n \"def get_trial_user_attrs(self, trial_id: int) -> Dict[str, Any]:\\n return self.get_trial(trial_id).user_attrs\",\n \"def attributes(self):\\n attrs_ = [\\\"name\\\", \\\"type\\\"]\\n if self.exclusive:\\n attrs_.append(\\\"exclusive\\\")\\n if self.default is not None:\\n attrs_.append(\\\"default\\\")\\n\\n return attrs_\",\n \"def attrs(*attributes):\\n return ';'.join([ str(i) for i in attributes ])\",\n \"def all_trait_names ( self ):\\n return self.__class_traits__.keys()\",\n \"def extra_tags(self) -> str:\\n all_tags = dict.fromkeys(\\n self.default_tags.split() + self.custom_tags.strip().split()\\n )\\n return \\\" \\\".join(all_tags.keys())\",\n \"def Attributes(self) -> PolicyStatementAttribute:\",\n \"def keys(cls):\\n if cls._keys is None:\\n cls._keys = tuple(attr for attr in dir(cls) if all((not callable(getattr(cls, attr)),\\n not attr.startswith('_'),\\n not attr == 'ALL')))\\n return cls._keys\",\n \"def dss_user_classes(self) -> List[str]:\\n return Bridge.var_array_function(self.dss_obj.DSSV, 1, None, '')\",\n \"def find_own_attributes(cs):\\n own_attributes = {}\\n for con in cs:\\n own_attributes[con] = []\\n for attr in con.intent:\\n own_attributes[con].append(attr)\\n for sub_con in cs:\\n if sub_con.intent < con.intent and\\\\\\n attr in sub_con.intent:\\n own_attributes[con].pop()\\n break\\n return own_attributes\",\n \"def getAttributes(self):\\n pass\",\n \"def getAttributeDict(self):\\n result = {}\\n dict = self.getDict()\\n for key in dict.keys():\\n value = dict.get(key)\\n if value.__class__.__name__ != 'SpecialDict':\\n result[key] = value\\n return result\",\n \"def get_all_super_names(value):\\n if hasattr(value, '__class__'):\\n # old style class\\n klass = value.__class__\\n class_name = klass.__name__\\n bases = get_oldstyle_bases(klass)\\n bases_names = [x.__name__ for x in bases]\\n else:\\n # new style\\n t = type(value)\\n class_name = t.__name__\\n bases_names = [b.__name__ for b in t.mro()]\\n return class_name, bases_names\",\n \"def get_attrs_names_for_entities(cls, entity=None):\\n all_entities_cls = (string_utils.convert_to_list(entity) if entity\\n else Entity.all_entities_classes())\\n all_entities_attrs_names = string_utils.convert_list_elements_to_list(\\n [entity_cls().__dict__.keys() for entity_cls in all_entities_cls])\\n return list(set(all_entities_attrs_names))\",\n \"def find_class_names(self) -> [str]:\\n if not self.faithful:\\n raise AttributeError('The workspace is not faithful')\\n\\n result = list()\\n\\n list_possible_class = list_folders_into_directory(join(self.directory_path, \\\"train-images\\\"))\\n\\n for possible_class in list_possible_class:\\n if possible_class != \\\"img\\\":\\n result.append(possible_class)\\n\\n self.class_names = result\\n return result\",\n \"def classify_class_attrs(cls):\\r\\n\\r\\n mro = getmro(cls)\\r\\n names = dir(cls)\\r\\n result = []\\r\\n for name in names:\\r\\n # Get the object associated with the name.\\r\\n # Getting an obj from the __dict__ sometimes reveals more than\\r\\n # using getattr. Static and class methods are dramatic examples.\\r\\n if name in cls.__dict__:\\r\\n obj = cls.__dict__[name]\\r\\n else:\\r\\n obj = getattr(cls, name)\\r\\n\\r\\n # Figure out where it was defined.\\r\\n homecls = getattr(obj, \\\"__objclass__\\\", None)\\r\\n if homecls is None:\\r\\n # search the dicts.\\r\\n for base in mro:\\r\\n if name in base.__dict__:\\r\\n homecls = base\\r\\n break\\r\\n\\r\\n # Get the object again, in order to get it from the defining\\r\\n # __dict__ instead of via getattr (if possible).\\r\\n if homecls is not None and name in homecls.__dict__:\\r\\n obj = homecls.__dict__[name]\\r\\n\\r\\n # Also get the object via getattr.\\r\\n obj_via_getattr = getattr(cls, name)\\r\\n\\r\\n # Classify the object.\\r\\n if isinstance(obj, staticmethod):\\r\\n kind = \\\"static method\\\"\\r\\n elif isinstance(obj, classmethod):\\r\\n kind = \\\"class method\\\"\\r\\n elif isinstance(obj, property):\\r\\n kind = \\\"property\\\"\\r\\n elif (ismethod(obj_via_getattr) or\\r\\n ismethoddescriptor(obj_via_getattr)):\\r\\n kind = \\\"method\\\"\\r\\n else:\\r\\n kind = \\\"data\\\"\\r\\n\\r\\n result.append(Attribute(name, kind, homecls, obj))\\r\\n\\r\\n return result\",\n \"def get_study_user_attrs(self, study_id: int) -> Dict[str, Any]:\\n raise NotImplementedError\",\n \"def get_names(self):\\n import tc\\n opts_list = []\\n for k, v in self.__class__.__dict__.iteritems():\\n if isinstance(v, tc.TC):\\n opts_list.append(k)\\n opts_list = sorted(opts_list)\\n return opts_list\",\n \"def ListAttributes(self):\\n\\n print(\\\"\\\\n\\\")\\n print(\\\"Attributes List of: \\\" + repr(self.__dict__[\\\"name\\\"]) + \\\" - \\\" + self.__class__.__name__ + \\\" Instance\\\\n\\\")\\n self_keys = self.__dict__.keys()\\n self_keys.sort()\\n for key in self_keys:\\n if key != \\\"name\\\":\\n print(str(key) + \\\" : \\\" + repr(self.__dict__[key]))\\n # end\\n # end\\n print(\\\"\\\\n\\\")\",\n \"def key_attributes(self):\\n\\n return [level.key for level in self._levels.values()]\",\n \"def cert_readonly_fields():\\n if settings.DEBUG:\\n return []\\n return [\\n 'user', 'get_template', 'csr', 'serial', 'subject',\\n 'valid_from', 'valid_to', 'x509',\\n ]\",\n \"def _get_privileged_groups(self):\\n if self.privileged_groups == \\\"auto\\\":\\n return []\\n else:\\n return deepcopy(self.privileged_groups)\",\n \"def clsnames_affecting_onsets(self):\\n # type: () -> Set[str]\\n output = set()\\n output.update(self.NONGRACE_NOTEHEAD_CLSNAMES)\\n output.update(self.REST_CLSNAMES)\\n output.update(self.MEASURE_SEPARATOR_CLSNAMES)\\n output.update(self.TIME_SIGNATURES)\\n output.add('repeat_measure')\\n return output\",\n \"def attributes(self):\\n\\n attributes = []\\n\\n for member in self.members:\\n if member.attribute:\\n attributes.append(member)\\n\\n return attributes\",\n \"def get_patients_classes(self):\\n patients = self.get_patients()\\n classes = self.get_classes()\\n return patients, classes\",\n \"def attributes(self):\\n return { k: getattr(self, k) for k in self.__class__.columns().keys() }\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.73706985","0.6840645","0.6202078","0.594918","0.5039532","0.50019443","0.49859846","0.49820405","0.49799612","0.49191874","0.49018767","0.48560244","0.48521727","0.48503172","0.48184666","0.478812","0.47643054","0.47436982","0.47109863","0.47010607","0.46969378","0.4690221","0.46895146","0.46829548","0.4679855","0.46590713","0.4658538","0.46548977","0.4650458","0.4638773","0.45932546","0.45926654","0.45881307","0.45872527","0.45855778","0.45818445","0.45700693","0.45700693","0.45693034","0.45670193","0.45435506","0.4536649","0.45326108","0.45270205","0.4516101","0.4515708","0.4513538","0.45045495","0.4494719","0.4494226","0.44867077","0.4472497","0.44723195","0.44674963","0.44652668","0.4464226","0.44615337","0.44609892","0.44585922","0.44496292","0.44479796","0.44471908","0.44471434","0.44460103","0.44439065","0.44423282","0.4432545","0.44257304","0.44245043","0.44193858","0.44190776","0.44186583","0.44172814","0.44166124","0.44042903","0.43948522","0.4392347","0.4390814","0.43877596","0.43773627","0.43668935","0.43521494","0.43516788","0.43473673","0.43452498","0.4343158","0.4340409","0.43393224","0.43311346","0.4328599","0.43236223","0.43228012","0.4309851","0.43095925","0.4308156","0.43076017","0.4306027","0.42991295","0.42976642","0.42963588"],"string":"[\n \"0.73706985\",\n \"0.6840645\",\n \"0.6202078\",\n \"0.594918\",\n \"0.5039532\",\n \"0.50019443\",\n \"0.49859846\",\n \"0.49820405\",\n \"0.49799612\",\n \"0.49191874\",\n \"0.49018767\",\n \"0.48560244\",\n \"0.48521727\",\n \"0.48503172\",\n \"0.48184666\",\n \"0.478812\",\n \"0.47643054\",\n \"0.47436982\",\n \"0.47109863\",\n \"0.47010607\",\n \"0.46969378\",\n \"0.4690221\",\n \"0.46895146\",\n \"0.46829548\",\n \"0.4679855\",\n \"0.46590713\",\n \"0.4658538\",\n \"0.46548977\",\n \"0.4650458\",\n \"0.4638773\",\n \"0.45932546\",\n \"0.45926654\",\n \"0.45881307\",\n \"0.45872527\",\n \"0.45855778\",\n \"0.45818445\",\n \"0.45700693\",\n \"0.45700693\",\n \"0.45693034\",\n \"0.45670193\",\n \"0.45435506\",\n \"0.4536649\",\n \"0.45326108\",\n \"0.45270205\",\n \"0.4516101\",\n \"0.4515708\",\n \"0.4513538\",\n \"0.45045495\",\n \"0.4494719\",\n \"0.4494226\",\n \"0.44867077\",\n \"0.4472497\",\n \"0.44723195\",\n \"0.44674963\",\n \"0.44652668\",\n \"0.4464226\",\n \"0.44615337\",\n \"0.44609892\",\n \"0.44585922\",\n \"0.44496292\",\n \"0.44479796\",\n \"0.44471908\",\n \"0.44471434\",\n \"0.44460103\",\n \"0.44439065\",\n \"0.44423282\",\n \"0.4432545\",\n \"0.44257304\",\n \"0.44245043\",\n \"0.44193858\",\n \"0.44190776\",\n \"0.44186583\",\n \"0.44172814\",\n \"0.44166124\",\n \"0.44042903\",\n \"0.43948522\",\n \"0.4392347\",\n \"0.4390814\",\n \"0.43877596\",\n \"0.43773627\",\n \"0.43668935\",\n \"0.43521494\",\n \"0.43516788\",\n \"0.43473673\",\n \"0.43452498\",\n \"0.4343158\",\n \"0.4340409\",\n \"0.43393224\",\n \"0.43311346\",\n \"0.4328599\",\n \"0.43236223\",\n \"0.43228012\",\n \"0.4309851\",\n \"0.43095925\",\n \"0.4308156\",\n \"0.43076017\",\n \"0.4306027\",\n \"0.42991295\",\n \"0.42976642\",\n \"0.42963588\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7298145"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":16896,"cells":{"query":{"kind":"string","value":"Returns a list of features that should be expanded to onehot versions for numericalonly algorithms. This should not include the protected features or the outcome class variable."},"document":{"kind":"string","value":"def get_categorical_features(self):\n return self.categorical_features"},"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 onehot_features(data):\n\n# Binary Features\n columns = ['Weekend', 'Revenue']\n for col in columns:\n data[col] = data[col].apply(lambda x: float(1) if x else float(0))\n\n columns = ['Month', 'OperatingSystems', 'Browser', 'Region', 'TrafficType',\n 'VisitorType']\n for col in columns:\n enc = OneHotEncoder()\n data_array = enc.fit_transform(data[[col]]).toarray()\n enc_data = pd.DataFrame(data_array)\n enc_data.columns = list(enc.get_feature_names([col]))\n data = data.join(enc_data)\n\n data = data.drop(columns={'Month', 'Month_May', 'OperatingSystems',\n 'OperatingSystems_2', 'Browser', 'Browser_2',\n 'Region', 'Region_1.0', 'TrafficType',\n 'TrafficType_2', 'VisitorType',\n 'VisitorType_Returning_Visitor'})\n return data","def features(self):\n other_features = ['listen_type', 'is_context', 'is_context_flow', \n 'is_listened_context', 'is_listened_flow', \n 'is_listened_context_flow']\n \n drop_features = self.categorize_features + self.drop_features + other_features + self.features_bis\n features = np.setdiff1d(self.train.columns.tolist(), drop_features + ['is_listened'], assume_unique=True)\n \n return features","def construct_feature_columns(input_features):\n return set([tf.feature_column.numeric_column(my_feature)\n for my_feature in input_features])","def specialFeatures(self):\r\n return self._specialFeatures","def special_features(self):\r\n return self._special_features","def get_features(self):\n return []","def input_features(self) -> List[str]:\n return self._input_features","def get_feature_names():\n return ['UserID', 'SessionID', 'TaskName', 'Orientation', 'TapType'] + get_numerical_feature_names()","def get_all_features(self) :\n raise NotImplementedError","def used_features(self) -> List[str]:\n mapped = map_pipeline_names(self.input_features, self.output_features)\n return list(set(mapped))","def get_numerical_features(self, categorical_feats):\n common_feats = self.get_common_features()\n return list(set(common_feats).difference(categorical_feats))","def features(self) -> List[np.ndarray]:\n return None","def get_feature_names(self):\n\t\treturn np.array(['nouns', 'adjectives', 'verbs', 'adverbs'])","def get_sparse_feature_columns(self) -> List[FeatureColumn]:\n\n return self._get_numeric_feature_columns(\n ) + self._get_sparse_categorical_feature_columns()","def _get_relevant_features(self, X):\n if self.only_binary_features:\n feature_mask = which_columns_are_binary(X)\n else:\n feature_mask = np.ones(X.shape[1], dtype=bool)\n return feature_mask","def _concatenate_features(features):\n pass","def advanced_features(self):\n return self._advanced_features","def compute_filtered_features(self, features):\n return [\n feature\n for feature in features\n if \"\".join(feature.qualifiers.get(\"is_edit\", \"false\")) != \"true\"\n ]","def dummify_features(df):\n colnames = df.columns\n le_dict = {}\n for col in colnames:\n le_dict[col] = preprocessing.LabelEncoder()\n le_dict[col].fit(df[col])\n df.loc[:, col] = le_dict[col].transform(df[col])\n\n enc = preprocessing.OneHotEncoder()\n enc.fit(df)\n X = enc.transform(df)\n\n dummy_colnames = [cv + '_' + str(modality) for cv in colnames for modality in le_dict[cv].classes_]\n # for cv in colnames:\n # for modality in le_dict[cv].classes_:\n # dummy_colnames.append(cv + '_' + modality)\n\n return X, dummy_colnames, enc","def _get_relevant_features(X):\n # FIXME utilize sklearn.utils.multiclass.type_of_target()\n continuous_cols = X.columns[~which_columns_are_binary(X)]\n return continuous_cols","def _get_frequent_features(self):\n feature_terms = [sub_items for items in self.data['noun_and_np'].values for sub_items in items]\n C1 = apriori.createC1(feature_terms)\n D = map(set, feature_terms)\n L1, support_data = apriori.scanD(D,C1,0.01) # minimum support 0.01\n self.frequent_features = map(lambda x: \"\".join(list(x)), L1)","def training_features(training_data: pd.DataFrame):\n return pd.get_dummies(\n training_data.drop(columns=[\"outstanding_balance\", \"status\", \"account_no\"])\n )","def get_all_features(train_data, test_data):\n #train_wc_matrix, test_wc_matrix = get_word_count_features(train_data, test_data)\n train_idf_matrix, test_idf_matrix = get_idf_features(train_data, test_data)\n train_ngram_matrix, test_ngram_matrix = get_ngram_features(train_data, test_data)\n # train_liwc_matrix, test_liwc_matrix = get_liwc_features(train_data, test_data)\n return sparse.hstack([train_idf_matrix, train_ngram_matrix]), \\\n sparse.hstack([test_idf_matrix, test_ngram_matrix])","def _special_handle_onehot(cls, op, X, W):\n tensor_list = []\n append_inputs = {\n \"depth\": op.depth,\n \"values\": op.values,\n }\n for tmp_name, append_input in append_inputs.items():\n node_name = op.name + \":\" + tmp_name\n tensor_list.append(\n numpy_helper.from_array(np.array(append_input), node_name))\n return tensor_list","def findFeatures(self):\n\t\tpass","def to_numeric_features(features, feature_cols=None):\n\n for col in CATEGORICAL_COLS:\n features = pd.concat(\n [features, pd.get_dummies(features[col[0]], drop_first=True)], axis=1)\n features.drop(col[0], axis=1, inplace=True)\n\n # Remove the unused columns from the dataframe.\n for col in UNUSED_COLUMNS:\n features.pop(col)\n\n # Re-index dataframe (if categories list changed from the previous dataset)\n if feature_cols is not None:\n features = features.T.reindex(feature_cols).T.fillna(0)\n return features","def get_cat1_features(self):\n return self.category1_features","def getFeatureNames(self):\n return [\"f100\", \"f103\", \"f104\"]","def getFeatureNames(self):\n feature_names = super().getFeatureNames()\n feature_names.extend([\"f101\", \"f102\", \"f105\", \"fNum\", \"fCapStart\", \"fCapNoStart\"])\n return feature_names","def features(self) -> List[Feature]:\n return self._features","def get_numerical_features(trn, sub):\n ohe = OneHotEncoder()\n full_csr = ohe.fit_transform(np.vstack((trn.values, sub.values)))\n csr_trn = full_csr[:trn.shape[0]]\n csr_sub = full_csr[trn.shape[0]:]\n del full_csr\n gc.collect()\n\n # Now remove features that don't have enough samples either in train or test\n\n return clean_csr(csr_trn, csr_sub, 3)","def features(self):\n return self._features","def notable_features(self):\n return self._notable_features","def get_feature_names(self):\n if isinstance(self.featurizers, list):\n return [self.feature_name]\n return self.featurizers(\"get feature names\")","def get_feature_labels(self):\n\t\tfeature_labels = []\n\t\tfor feature, i in zip(self.feature_names,self.feature_mask):\n\t\t\tif i == True:\n\t\t\t\tfeature_labels.append(feature)\n\t\treturn feature_labels","def get_selected_features(pipeline, verbose = False):\n\n \n assert isinstance(pipeline, Pipeline), \"Input isn't a Pipeline\"\n assert isinstance(pipeline[0], ColumnTransformer), \"First step isn't a ColumnTransformer\"\n\n features = get_feature_names(pipeline[0], verbose=verbose)\n\n for i, step in enumerate(pipeline.steps[1:]):\n if verbose: print(i, \": \", step[0])\n \n if hasattr(step[1], 'get_support'):\n \n check_is_fitted(step[1])\n\n retained_cols = step[1].get_support()\n if verbose: print(sum(retained_cols), \"of\", len(retained_cols), \"retained, \",\\\n round(sum(retained_cols) / len(retained_cols) * 100, 1), \"%\")\n\n features = [feature for is_retained, feature in zip(retained_cols, features) if is_retained] \n\n return features","def featureNames(self):\n return [feature.name for feature in self.features]","def get_available_features(self) -> list[str]:\n modules = []\n for mdir in [ZeroBot.__path__[0]] + self.config[\"Core\"][\"ModuleDirs\"]:\n mdir = Path(mdir)\n modules += [child.stem for child in mdir.glob(\"feature/*.py\")]\n return modules","def output_features(self) -> List[str]:\n return self._pipeline.features","def getFeatures(self, state, action):\n util.raiseNotDefined()","def getFeatures(self, state, action):\n util.raiseNotDefined()","def getFeatures(self, state, action):\n util.raiseNotDefined()","def get_all_features(config: Config) -> typing.List[str]:\n return [feature.name for feature in config.features]","def getFeatures(self, state, action, thisAgent):\n util.raiseNotDefined()","def getFeatures(self, state, action):\n util.raiseNotDefined()","def irrelevant_features(features):\n irrelevant = []\n for vec in set(features):\n if (features[vec].count(0)/len(indtf_features[vec])) < 0.1:\n irrelevant.append(vec)\n return irrelevant","def one_hot(seq):\n prot_seq = seq\n BASES = 'ARNDCQEGHILKMFPSTWYV'\n bases = np.array([base for base in BASES])\n feat = np.concatenate(\n [[(bases == base.upper()).astype(int)] if str(base).upper() in BASES else np.array([[-1] * len(BASES)]) for base\n in prot_seq])\n return feat","def oneHotEncoding(X, list_indices):\n print('Onehotencoding..')\n X = X.astype(float)\n for i in list_indices:\n onehotencoder = OneHotEncoder(categorical_features=[i])\n X = onehotencoder.fit_transform(X)\n return X[1:]","def _make_features(self, x):\n\t\tx = x.unsqueeze(1)\n\t\treturn torch.cat([x ** i for i in range(1, self._degree+1)], 1)","def _one_hot(self, proba, y):\n hm_1hot = []\n for label in np.unique(y):\n mask_ = y == label\n proba_tmp = proba.copy()\n proba_tmp[~mask_] = 0\n hm_y, auto_gamma = self._adjust_gamma(proba_tmp)\n\n if self.verbose:\n print('Adjusted gamma: ', str(auto_gamma))\n hm_1hot.append(hm_y)\n return hm_1hot","def get_features(self):\n return self._features","def get_features(df, target=[], meta=[]):\n ############################################################\n # Type conversion\n ############################################################\n\n types = df[df.columns[~df.columns.isin(target+meta)]].dtypes\n for col_name, col_type in types.iteritems():\n if col_type == bool:\n df[col_name] = df[col_name].astype(float)\n\n ############################################################\n # Get features by type\n ############################################################\n \n features_cat = filter(lambda x: not np.issubdtype(x[1], np.number), types.iteritems())\n features_cat = sorted(list(map(lambda x: x[0], features_cat)))\n # target and meta should have already been removed. but just to be sure\n features_num = sorted(list(set(types.index) - set(features_cat) - set(target) - set(meta))) \n selected_features = df.columns.to_list()\n features_idx = dict(zip(selected_features, range(len(selected_features))))\n \n return selected_features, features_num, features_cat, features_idx","def GetActions(features):\n featureOP = []\n featureYMore = []\n featureYAcc = []\n featureImprove = []\n count = 0\n for index, row in features[::-1].iterrows():\n \tif count > 7: # Don't recommend more than 8 actions\n \t\tbreak\n if ('poss' not in row['features']) and ('scoring' not in row['features']):\n if '_op' in row['features']:\n if (row['coef'] < 0) and ('accuracy' not in row['features']) and ('accurate' not in row['features']):\n featureOP.append(row['features'][:-3].replace('_', ' ').title())\n featureImprove.append(row['features'])\n count += 1\n else:\n if row['coef'] > 0:\n if 'accuracy' not in row['features']:\n featureYMore.append(row['features'].replace('_', ' ').title())\n featureImprove.append(row['features'])\n count += 1\n else:\n featureYAcc.append(row['features'].replace('_', ' ').title())\n featureImprove.append(row['features'])\n count += 1\n\n # Whether show 2 columns or 3\n useTwoCol = True\n if useTwoCol:\n actions = pd.DataFrame([featureYAcc + featureYMore, featureOP], index = ['Your', 'OP']).T\n\n else:\n actions = pd.DataFrame([featureYAcc, featureYMore, featureOP], index = ['YAcc', 'YMore', 'OP']).T\n nDimActions = actions.shape\n actions = actions.values.tolist()\n\t\n ## Make the actions more readable\n for ii in np.arange(nDimActions[0]):\n for jj in np.arange(nDimActions[1]):\n #print actions[ii][jj]\n if actions[ii][jj] == None:\n actions[ii][jj] = ' '\n else:\n actions[ii][jj] = actions[ii][jj].replace('Att', 'Attempt').replace('Obox', 'Outside the Penalty Box').replace('Ibox', 'Inside the Penalty Box').replace('Total ', '').replace('Fwd', 'Forward').replace('18Yardplus', 'Outside the Penalty Box').replace('18Yard', 'Inside the Penalty Box')\n if 'Accuracy' in actions[ii][jj]:\n actions[ii][jj] = actions[ii][jj][9:] + ' Accuracy'\n else:\n actions[ii][jj] = '# of ' + actions[ii][jj]\n if (\"alls\" not in actions[ii][jj]) and (\"Penalty Box\" not in actions[ii][jj]):\n if \"Won\" in actions[ii][jj]:\n actions[ii][jj] = actions[ii][jj][:-4] + 's Won'\n elif actions[ii][jj][-2:] != 'ss':\n actions[ii][jj] = actions[ii][jj] + 's'\n else:\n actions[ii][jj] = actions[ii][jj] + 'es'\n #print actions\n return actions, featureImprove","def extract_features(X):\n X = X.drop(\"PassengerId\", axis=1)\n X = X.drop(\"Ticket\", axis=1)\n X = X.drop(\"Cabin\", axis=1)\n \n # Adding polynomial features\n X[\"Age2\"] = X[\"Age\"] ** 2\n #X[\"Fare2\"] = X[\"Fare\"] ** 2\n #X[\"Pclass2\"] = X[\"Pclass\"] ** 2\n\n \n male_titles = set([\"Mr\", \"Don\", \"Sir\"])\n female_titles = set([\"Miss\", \"Ms\", \"Mrs\", \"Mme\", \"Mdm\", \"Lady\"])\n professionals = set([\"Dr\", \"Rev\", \"Master\"])\n military = set([\"Col\", \"Major\", \"Capt\"])\n royalty = set([\"the Countess\", \"Jonkheer\"])\n \n names = X[\"Name\"]\n for i in range(len(names)): \n name_tokens = names[i].split(\", \") \n passenger_title = name_tokens[1].split(\".\")[0]\n if passenger_title in male_titles:\n names[i] = 1\n elif passenger_title in female_titles:\n names[i] = 2\n elif passenger_title in professionals:\n names[i] = 3\n #elif passenger_title in royalty:\n # names[i] = 4\n elif passenger_title in military:\n names[i] = 5\n else:\n names[i] = 6\n \n X[\"Name\"].update(names)\n \n # One hot encoding of categorical data\n X = pd.get_dummies(X) \n \n X.fillna(0, inplace=True)\n X['Fam'] = X['SibSp'] + X['Parch'] # assigned to a column\n return X","def getFeatures(self, gameState, action):\n # features = util.Counter()\n # successor = self.getSuccessor(gameState, action)\n # features['successorScore'] = self.getScore(successor)\n # return features\n if self.isOffensive:\n return self.getOffensiveFeatures(gameState, action)\n else:\n return self.getDefensiveFeatures(gameState, action)","def get_feature_names(self):\n ...","def onehot(isTrue):\n if isTrue:\n return [1, 0]\n else:\n return [0, 1]","def features(self):\n\n return self._features","def features(self, state, action, next_state):\n raise NotImplementedError","def get_style1_features(self):\n return self.style1_features","def set_to_features(X_set):\n ext = Extractor()\n features = []\n for i in range(len(X_set)):\n print(i, \" out of \", len(X_set))\n bag_of_features = [ext.extract(X_set[i][j]) for j in range(len(X_set[i]))]\n\n features.append(bag_of_features)\n\n return features","def extract_features(self, inputs):\n pass","def get_feature_names(self, input_features=...):\n ...","def enableAllFeatures(self):\n self.logger.debug('Enabling all features in all feature classes')\n for featureClassName in self.getFeatureClassNames():\n self.enabledFeatures[featureClassName] = []\n self.logger.debug('Enabled features: %s', self.enabledFeatures)","def get_feature_names(self):\n if self.linear_features:\n self.features_ = np.concatenate([self.features, np.array(self.extract_rules(labels=self.features))], 0)[self.feature_mask_]\n else:\n self.features_ = np.array(self.extract_rules(labels=self.features))[self.feature_mask_]\n return self.features_","def extract_features(sentence, vocabulary):\n n_tokens = len(sentence)\n n_features = n_feature_functions + len(vocabulary)\n X = sp.lil_matrix((n_tokens, n_features), dtype=bool)\n\n for i in xrange(n_tokens):\n for j, f in enumerate(FEATURE_FUNCTIONS):\n X[i, j] = f(sentence, i)\n\n # Vocabulary feature\n try:\n X[i, n_feature_functions + vocabulary[sentence[i][0].lower()]] = 1\n except KeyError:\n pass\n\n return X","def listFeatures() :\n global features\n features = [feature.split(\".\")[0] for feature in os.listdir(os.path.abspath(__file__)[:-11])\n if feature.endswith(\".py\") and feature != \"__init__.py\"]","def make_onehot(x,num_labels=7):\n enc = OneHotEncoder(n_values=num_labels)\n return enc.fit_transform(np.array(x).reshape(-1, 1)).toarray()","def featurize(self, tokens):\n features = []\n \n nrc_hashtag_emotion_features = self.nrc_hashtag_emotion(tokens)\n nrc_affect_intensity_features = self.nrc_affect_intensity(tokens)\n nrc_hashtag_sentiment_lexicon_unigrams_features = self.nrc_hashtag_sentiment_lexicon_unigrams(tokens)\n nrc_hashtag_sentiment_lexicon_bigrams_features = self.nrc_hashtag_sentiment_lexicon_bigrams(tokens)\n sentiment140_unigrams_features = self.sentiment140_unigrams(tokens)\n sentiment140_bigrams_features = self.sentiment140_bigrams(tokens)\n senti_wordnet_features = self.senti_wordnet(tokens)\n bing_lui_sentiment_lexicons_features = self.bing_lui_sentiment_lexicons(tokens)\n nrc_expanded_lexicon_features = self.nrc_10_expanded(tokens)\n negating_word_list_features = self.negating_words_list(tokens)\n total_number_of_words_features = self.get_total_number_of_words(tokens)\n mpqa_subjectivity_lexicon_features = self.mpqa_subjectivity_lexicon(tokens)\n afinn_sentiment_features = self.afinn_sentiment_scores(tokens)\n # senti_strength_features = self.get_sentistrength(\" \".join(tokens))\n\n features.extend(nrc_hashtag_emotion_features.values()) # 10 features\n features.extend(nrc_affect_intensity_features.values()) # 10 features\n features.extend(nrc_hashtag_sentiment_lexicon_unigrams_features.values()) # 4 features\n features.extend(nrc_hashtag_sentiment_lexicon_bigrams_features.values()) # 4 features\n features.extend(sentiment140_unigrams_features.values()) # 4 features \n features.extend(sentiment140_bigrams_features.values()) # 4 features\n features.extend(senti_wordnet_features.values()) # 4 features\n features.extend(bing_lui_sentiment_lexicons_features.values()) # 2 features\n features.extend(nrc_expanded_lexicon_features.values()) # 10 features\n features.extend(negating_word_list_features.values()) # 1 feature\n features.extend(total_number_of_words_features.values()) # 1 feature\n features.extend(mpqa_subjectivity_lexicon_features.values()) # 2 features\n features.extend(afinn_sentiment_features.values()) # 2 features\n # features.extend(senti_strength_features.values()) # 2 features\n\n return features","def preprocess_features(features):\r\n rowsum = np.array(features.sum(1))\r\n r_inv = np.power(rowsum, -1).flatten()\r\n r_inv[np.isinf(r_inv)] = 0.\r\n r_mat_inv = np.diag(r_inv)\r\n features = r_mat_inv.dot(features)\r\n return features","def feature_eng2(housing_tr, housing):\n logging.info(\"Adding features.....\")\n housing_tr[\"rooms_per_household\"] = (\n housing_tr[\"total_rooms\"] / housing_tr[\"households\"]\n )\n housing_tr[\"bedrooms_per_room\"] = (\n housing_tr[\"total_bedrooms\"] / housing_tr[\"total_rooms\"]\n )\n housing_tr[\"population_per_household\"] = (\n housing_tr[\"population\"] / housing_tr[\"households\"]\n )\n housing_cat = housing[[\"ocean_proximity\"]]\n housing_prepared = housing_tr.join(\n pd.get_dummies(housing_cat, drop_first=True)\n )\n return housing_prepared","def supported_features(self):\n return self._supported_features","def get_dense_feature_columns(self) -> List[FeatureColumn]:\n\n return self._get_numeric_feature_columns(\n ) + self._get_embedding_feature_columns()","def getFeatures(self, N=None, indexes=False):\n if indexes:\n features = self.softmax2feats(self.feats, sort_values=True)\n else:\n features = self.feats\n if N:\n return features[:N]\n return features","def one_hot_encoding(self):\n \n try: \n\n # Encode dependent variable\n le = LabelEncoder()\n le.fit(self.data[\"consumption\"])\n df_dependent_enc = pd.DataFrame(le.transform(self.data[\"consumption\"]))\n\n # Encode independent variable\n categorical_features = Config.FEATURE_DEFINITION[\"category_cols\"]\n categorical_df = self.data.loc[:, self.data.columns.isin(categorical_features)]\n oe = OrdinalEncoder()\n oe.fit(categorical_df)\n df_catindependent_enc = pd.DataFrame(oe.transform(categorical_df))\n df_catindependent_enc.columns = categorical_df.columns\n\n except KeyError: \n\n st.write(\"Cannot perform one-hot encoding for numerical variables. Please check if variables are properly defined.\")\n st.write(self.data.columns != \"consumption\")\n df_dependent_enc = []\n df_catindependent_enc = []\n\n else:\n \n return df_dependent_enc, df_catindependent_enc","def vectorizer_features(self) -> list:\n if self._vectorizer:\n return self._vectorizer.get_feature_names()\n self.logger.warning('Uninitialized vector. Please call count_vectorizer first.')","def preprocess_features(X):\n\t# Initialize new output DataFrame\n\toutput = pd.DataFrame(index = X.index)\n\n\t# Investigate new output DataFrame\n\tfor col, col_data in X.iteritems():\n\t\t# If data type is categorical, convert to dummy variables\n\t\tif col_data.dtype == object:\n\t\t\tcol_data = pd.get_dummies(col_data, prefix = col)\n\n\t\t\t# Collect the revised columns\n\t\t\toutput - output.join(col_data)\n\treturn output","def get_one_hot_vector(number):\n from sklearn.preprocessing import LabelBinarizer\n encoder = LabelBinarizer()\n seed_vector = np.arange(10)\n encoder.fit(seed_vector)\n labels = encoder.transform(seed_vector)\n return labels.astype(np.float32)","def preprocessing_fn(inputs):\n outputs = {}\n\n for key in ONE_HOT_FEATURES.keys():\n dim = ONE_HOT_FEATURES[key]\n int_value = tft.compute_and_apply_vocabulary(\n fill_in_missing(inputs[key]), top_k=dim + 1\n )\n outputs[transformed_name(key)] = convert_num_to_one_hot(\n int_value, num_labels=dim + 1\n )\n\n for key, bucket_count in BUCKET_FEATURES.items():\n temp_feature = tft.bucketize(\n convert_zip_code(fill_in_missing(inputs[key])),\n bucket_count,\n )\n outputs[transformed_name(key)] = convert_num_to_one_hot(\n temp_feature, num_labels=bucket_count + 1\n )\n\n for key in TEXT_FEATURES.keys():\n outputs[transformed_name(key)] = fill_in_missing(inputs[key])\n\n outputs[transformed_name(LABEL_KEY)] = fill_in_missing(inputs[LABEL_KEY])\n\n return outputs","def all(cls):\n results = []\n for name in FEATURES:\n feat = cls.get_by_name(name)\n if feat is None:\n feat = cls(name=name)\n cls.query.session.add(feat)\n results.append(feat)\n return results","def choose_features(nof_features, max_features):\n features_indices = numpy.arange(nof_features)\n #numpy.random.seed()\n #features_chosen = numpy.random.choice(features_indices, size=max_features, replace = True)\n features_chosen = numpy.random.choice(features_indices,\n size=nof_features,\n replace=False)\n\n #print(features_chosen)\n return features_chosen","def global_class_to_one_hot(global_class):\n if global_class == 'Store':\n return [1, 0, 0, 0, 0]\n elif global_class == 'Home':\n return [0, 1, 0, 0, 0]\n elif global_class == 'Public spaces':\n return [0, 0, 1, 0, 0]\n elif global_class == 'Leisure':\n return [0, 0, 0, 1, 0]\n elif global_class == 'Working place':\n return [0, 0, 0, 0, 1]\n else:\n raise Exception('The global class \"' + global_class + '\" cant be transform to one-hot format')","def concat_non_bleeding_features(X_train, X_test, BOW_feature_list, features_dir, fold):\n feat_list = []\n for feature in BOW_feature_list:\n X_train_part = np.load(\"%s/%s.%s.npy\" % (features_dir, feature, fold))\n last_index = len(X_train[0])\n X_train = np.concatenate([X_train, X_train_part], axis=1)\n feat_list.append((last_index, last_index+len(X_train_part[0]), str(feature)))\n X_test_part = np.load(\"%s/%s.%s.test.npy\" % (features_dir, feature, fold))\n X_test = np.concatenate([X_test, X_test_part], axis=1)\n return X_train, X_test, feat_list","def get_feature_names(self):\n return [self.__class__.__name__]","def get_used_features():\n print(\"filtering used features\")\n # is this user an active user? if any of the features is non-zero take this user as an active user\n used_features = {}\n for row in df_train.values:\n row = list(row)\n id = row.pop(0)\n active = [c[0] for c in zip(df_train.columns[1:], row) if (c[0] in item_cols and c[1] > 0)]\n if id not in used_features:\n used_features[id] = []\n used_features[id].extend(active)\n return used_features","def reduce_features(train_data_lst, test_data_lst, train_anots, data_anots):\r\n \r\n train_data_reduced = []\r\n test_data_reduced = []\r\n new_anots = []\r\n for idx in range(len(train_data_lst)):\r\n \r\n most_important_features = rank_features(train_data_lst[idx], train_anots)\r\n train_data_reduced.append(train_data_lst[idx][:, most_important_features])\r\n test_data_reduced.append(test_data_lst[idx][:, most_important_features])\r\n new_anots.append(data_anots[idx]+'_reduced')\r\n return train_data_reduced, test_data_reduced, new_anots","def get_active_features(summary_df, slots_offered): # prev -> getActiveFeatures\n disc_cols = [col+'_Discount' for col in slots_offered]\n eco_cols = [col+'_Eco' for col in slots_offered]\n gr_cols = [col+'_Eco' for col in slots_offered]\n features = summary_df.loc[:, disc_cols+eco_cols+gr_cols]\n features = features.loc[:, features.sum(axis=0) > 0]\n for i in reversed(['NO_PURCHASE']+slots_offered):\n features.insert(0, i+'_Asc', value=1)\n return features, disc_cols, eco_cols, gr_cols","def featurize(tokens, feature_fns):\n answer = []\n for func in feature_fns:\n feats = defaultdict(lambda: 0)\n func(tokens,feats)\n answer.extend(feats.items())\n return sorted(answer, key= lambda x: x[0])","def one_hot(df):\r\n # One-hot encode into \r\n cols = ['job', 'marital', 'education', 'month', 'day_of_week', 'poutcome']\r\n for each in cols:\r\n dummies = pd.get_dummies(df[each], prefix=each, drop_first=False)\r\n df = pd.concat([df, dummies], axis=1)\r\n df = df.drop(cols,axis=1)\r\n return df","def build_feature_filter(self):\n if self.features == [\"*\"]:\n random_iso = list(self.data.keys())[0]\n self.features = set()\n for lang_features in self.data.values():\n self.features |= set(lang_features.keys())\n self.features = list(self.features)\n if self.exclusions:\n self.features = [f for f in self.features if f not in self.exclusions]\n self.feature_filter = set(self.features)","def hard_swish(features):\n features = tf.convert_to_tensor(features)\n return features * tf.nn.relu6(features + tf.constant(3.)) * (1. / 6.)","def get_feature_names(self):\n raise NotImplementedError()","def _extract_features(self):\n # print(os.getpid())\n return {n:self._extract_feature(f) for (n,f) in self.features.items()}","def add_all_features(df):\n df.reset_index(drop=True, inplace=True)\n df = target_indicators(df)\n df = momentum_indicators(df)\n df = trend_indicators(df)\n df = volatility_indicators(df)\n df = volume_indicators(df)\n df = special_indicators(df)\n return df","def preprocess_features(features):\n rowsum = np.array(features.sum(1))\n r_inv = np.power(rowsum, -1).flatten()\n r_inv[np.isinf(r_inv)] = 0.\n r_mat_inv = sp.diags(r_inv)\n features = r_mat_inv.dot(features)\n return features","def preprocess_features(features):\n rowsum = np.array(features.sum(1))\n r_inv = np.power(rowsum, -1).flatten()\n r_inv[np.isinf(r_inv)] = 0.\n r_mat_inv = sp.diags(r_inv)\n features = r_mat_inv.dot(features)\n return features","def extract_features(self, inputs):\n x = self.conv1(inputs)\n x = self.maxpool1(x)\n x = self.conv2(x)\n x = self.conv3(x)\n x = self.maxpool2(x)\n\n x = self.inception3a(x)\n x = self.inception3b(x)\n x = self.maxpool3(x)\n x = self.inception4a(x)\n\n x = self.inception4b(x)\n x = self.inception4c(x)\n x = self.inception4d(x)\n\n x = self.inception4e(x)\n x = self.maxpool4(x)\n x = self.inception5a(x)\n x = self.inception5b(x)\n x = self.avgpool(x)\n x = torch.flatten(x,1)\n x = self.dropout(x)\n return x","def get_feature_names(self, input_features=None):\n check_is_fitted(self)\n n_features = self.n_features_in_\n if input_features is None:\n input_features = [\"x%d\" % i for i in range(n_features)]\n if self.function_names is None:\n self.function_names = list(\n map(\n lambda i: (lambda *x: \"f\" + str(i) + \"(\" + \",\".join(x) + \")\"),\n range(n_features),\n )\n )\n feature_names = []\n\n # Include constant term\n if self.include_bias:\n feature_names.append(\"1\")\n\n # Include any non-derivative terms\n for i, f in enumerate(self.functions):\n for c in self._combinations(\n n_features, f.__code__.co_argcount, self.interaction_only\n ):\n feature_names.append(\n self.function_names[i](*[input_features[j] for j in c])\n )\n\n if self.grid_ndim != 0:\n\n def derivative_string(multiindex):\n ret = \"\"\n for axis in range(self.ind_range):\n if (axis == self.ind_range - 1) and (\n self.ind_range == self.grid_ndim\n ):\n str_deriv = \"t\"\n else:\n str_deriv = str(axis + 1)\n for i in range(multiindex[axis]):\n ret = ret + str_deriv\n return ret\n\n # Include integral terms\n for k in range(self.num_derivatives):\n for j in range(n_features):\n feature_names.append(\n input_features[j]\n + \"_\"\n + derivative_string(self.multiindices[k])\n )\n # Include mixed non-derivative + integral terms\n if self.include_interaction:\n for k in range(self.num_derivatives):\n for jj in range(n_features):\n for i, f in enumerate(self.functions):\n for c in self._combinations(\n n_features,\n f.__code__.co_argcount,\n self.interaction_only,\n ):\n feature_names.append(\n self.function_names[i](\n *[input_features[j] for j in c]\n )\n + input_features[jj]\n + \"_\"\n + derivative_string(self.multiindices[k])\n )\n return feature_names","def value_head(features):\n with tf.variable_scope('critic', reuse=tf.AUTO_REUSE):\n features = tf.layers.dense(features, units=1, activation=None, name='output')\n return tf.squeeze(features, axis=-1)","def features(self) -> List[Feature]:\n return [field for field in self._fields.values() if isinstance(field, Feature)]"],"string":"[\n \"def onehot_features(data):\\n\\n# Binary Features\\n columns = ['Weekend', 'Revenue']\\n for col in columns:\\n data[col] = data[col].apply(lambda x: float(1) if x else float(0))\\n\\n columns = ['Month', 'OperatingSystems', 'Browser', 'Region', 'TrafficType',\\n 'VisitorType']\\n for col in columns:\\n enc = OneHotEncoder()\\n data_array = enc.fit_transform(data[[col]]).toarray()\\n enc_data = pd.DataFrame(data_array)\\n enc_data.columns = list(enc.get_feature_names([col]))\\n data = data.join(enc_data)\\n\\n data = data.drop(columns={'Month', 'Month_May', 'OperatingSystems',\\n 'OperatingSystems_2', 'Browser', 'Browser_2',\\n 'Region', 'Region_1.0', 'TrafficType',\\n 'TrafficType_2', 'VisitorType',\\n 'VisitorType_Returning_Visitor'})\\n return data\",\n \"def features(self):\\n other_features = ['listen_type', 'is_context', 'is_context_flow', \\n 'is_listened_context', 'is_listened_flow', \\n 'is_listened_context_flow']\\n \\n drop_features = self.categorize_features + self.drop_features + other_features + self.features_bis\\n features = np.setdiff1d(self.train.columns.tolist(), drop_features + ['is_listened'], assume_unique=True)\\n \\n return features\",\n \"def construct_feature_columns(input_features):\\n return set([tf.feature_column.numeric_column(my_feature)\\n for my_feature in input_features])\",\n \"def specialFeatures(self):\\r\\n return self._specialFeatures\",\n \"def special_features(self):\\r\\n return self._special_features\",\n \"def get_features(self):\\n return []\",\n \"def input_features(self) -> List[str]:\\n return self._input_features\",\n \"def get_feature_names():\\n return ['UserID', 'SessionID', 'TaskName', 'Orientation', 'TapType'] + get_numerical_feature_names()\",\n \"def get_all_features(self) :\\n raise NotImplementedError\",\n \"def used_features(self) -> List[str]:\\n mapped = map_pipeline_names(self.input_features, self.output_features)\\n return list(set(mapped))\",\n \"def get_numerical_features(self, categorical_feats):\\n common_feats = self.get_common_features()\\n return list(set(common_feats).difference(categorical_feats))\",\n \"def features(self) -> List[np.ndarray]:\\n return None\",\n \"def get_feature_names(self):\\n\\t\\treturn np.array(['nouns', 'adjectives', 'verbs', 'adverbs'])\",\n \"def get_sparse_feature_columns(self) -> List[FeatureColumn]:\\n\\n return self._get_numeric_feature_columns(\\n ) + self._get_sparse_categorical_feature_columns()\",\n \"def _get_relevant_features(self, X):\\n if self.only_binary_features:\\n feature_mask = which_columns_are_binary(X)\\n else:\\n feature_mask = np.ones(X.shape[1], dtype=bool)\\n return feature_mask\",\n \"def _concatenate_features(features):\\n pass\",\n \"def advanced_features(self):\\n return self._advanced_features\",\n \"def compute_filtered_features(self, features):\\n return [\\n feature\\n for feature in features\\n if \\\"\\\".join(feature.qualifiers.get(\\\"is_edit\\\", \\\"false\\\")) != \\\"true\\\"\\n ]\",\n \"def dummify_features(df):\\n colnames = df.columns\\n le_dict = {}\\n for col in colnames:\\n le_dict[col] = preprocessing.LabelEncoder()\\n le_dict[col].fit(df[col])\\n df.loc[:, col] = le_dict[col].transform(df[col])\\n\\n enc = preprocessing.OneHotEncoder()\\n enc.fit(df)\\n X = enc.transform(df)\\n\\n dummy_colnames = [cv + '_' + str(modality) for cv in colnames for modality in le_dict[cv].classes_]\\n # for cv in colnames:\\n # for modality in le_dict[cv].classes_:\\n # dummy_colnames.append(cv + '_' + modality)\\n\\n return X, dummy_colnames, enc\",\n \"def _get_relevant_features(X):\\n # FIXME utilize sklearn.utils.multiclass.type_of_target()\\n continuous_cols = X.columns[~which_columns_are_binary(X)]\\n return continuous_cols\",\n \"def _get_frequent_features(self):\\n feature_terms = [sub_items for items in self.data['noun_and_np'].values for sub_items in items]\\n C1 = apriori.createC1(feature_terms)\\n D = map(set, feature_terms)\\n L1, support_data = apriori.scanD(D,C1,0.01) # minimum support 0.01\\n self.frequent_features = map(lambda x: \\\"\\\".join(list(x)), L1)\",\n \"def training_features(training_data: pd.DataFrame):\\n return pd.get_dummies(\\n training_data.drop(columns=[\\\"outstanding_balance\\\", \\\"status\\\", \\\"account_no\\\"])\\n )\",\n \"def get_all_features(train_data, test_data):\\n #train_wc_matrix, test_wc_matrix = get_word_count_features(train_data, test_data)\\n train_idf_matrix, test_idf_matrix = get_idf_features(train_data, test_data)\\n train_ngram_matrix, test_ngram_matrix = get_ngram_features(train_data, test_data)\\n # train_liwc_matrix, test_liwc_matrix = get_liwc_features(train_data, test_data)\\n return sparse.hstack([train_idf_matrix, train_ngram_matrix]), \\\\\\n sparse.hstack([test_idf_matrix, test_ngram_matrix])\",\n \"def _special_handle_onehot(cls, op, X, W):\\n tensor_list = []\\n append_inputs = {\\n \\\"depth\\\": op.depth,\\n \\\"values\\\": op.values,\\n }\\n for tmp_name, append_input in append_inputs.items():\\n node_name = op.name + \\\":\\\" + tmp_name\\n tensor_list.append(\\n numpy_helper.from_array(np.array(append_input), node_name))\\n return tensor_list\",\n \"def findFeatures(self):\\n\\t\\tpass\",\n \"def to_numeric_features(features, feature_cols=None):\\n\\n for col in CATEGORICAL_COLS:\\n features = pd.concat(\\n [features, pd.get_dummies(features[col[0]], drop_first=True)], axis=1)\\n features.drop(col[0], axis=1, inplace=True)\\n\\n # Remove the unused columns from the dataframe.\\n for col in UNUSED_COLUMNS:\\n features.pop(col)\\n\\n # Re-index dataframe (if categories list changed from the previous dataset)\\n if feature_cols is not None:\\n features = features.T.reindex(feature_cols).T.fillna(0)\\n return features\",\n \"def get_cat1_features(self):\\n return self.category1_features\",\n \"def getFeatureNames(self):\\n return [\\\"f100\\\", \\\"f103\\\", \\\"f104\\\"]\",\n \"def getFeatureNames(self):\\n feature_names = super().getFeatureNames()\\n feature_names.extend([\\\"f101\\\", \\\"f102\\\", \\\"f105\\\", \\\"fNum\\\", \\\"fCapStart\\\", \\\"fCapNoStart\\\"])\\n return feature_names\",\n \"def features(self) -> List[Feature]:\\n return self._features\",\n \"def get_numerical_features(trn, sub):\\n ohe = OneHotEncoder()\\n full_csr = ohe.fit_transform(np.vstack((trn.values, sub.values)))\\n csr_trn = full_csr[:trn.shape[0]]\\n csr_sub = full_csr[trn.shape[0]:]\\n del full_csr\\n gc.collect()\\n\\n # Now remove features that don't have enough samples either in train or test\\n\\n return clean_csr(csr_trn, csr_sub, 3)\",\n \"def features(self):\\n return self._features\",\n \"def notable_features(self):\\n return self._notable_features\",\n \"def get_feature_names(self):\\n if isinstance(self.featurizers, list):\\n return [self.feature_name]\\n return self.featurizers(\\\"get feature names\\\")\",\n \"def get_feature_labels(self):\\n\\t\\tfeature_labels = []\\n\\t\\tfor feature, i in zip(self.feature_names,self.feature_mask):\\n\\t\\t\\tif i == True:\\n\\t\\t\\t\\tfeature_labels.append(feature)\\n\\t\\treturn feature_labels\",\n \"def get_selected_features(pipeline, verbose = False):\\n\\n \\n assert isinstance(pipeline, Pipeline), \\\"Input isn't a Pipeline\\\"\\n assert isinstance(pipeline[0], ColumnTransformer), \\\"First step isn't a ColumnTransformer\\\"\\n\\n features = get_feature_names(pipeline[0], verbose=verbose)\\n\\n for i, step in enumerate(pipeline.steps[1:]):\\n if verbose: print(i, \\\": \\\", step[0])\\n \\n if hasattr(step[1], 'get_support'):\\n \\n check_is_fitted(step[1])\\n\\n retained_cols = step[1].get_support()\\n if verbose: print(sum(retained_cols), \\\"of\\\", len(retained_cols), \\\"retained, \\\",\\\\\\n round(sum(retained_cols) / len(retained_cols) * 100, 1), \\\"%\\\")\\n\\n features = [feature for is_retained, feature in zip(retained_cols, features) if is_retained] \\n\\n return features\",\n \"def featureNames(self):\\n return [feature.name for feature in self.features]\",\n \"def get_available_features(self) -> list[str]:\\n modules = []\\n for mdir in [ZeroBot.__path__[0]] + self.config[\\\"Core\\\"][\\\"ModuleDirs\\\"]:\\n mdir = Path(mdir)\\n modules += [child.stem for child in mdir.glob(\\\"feature/*.py\\\")]\\n return modules\",\n \"def output_features(self) -> List[str]:\\n return self._pipeline.features\",\n \"def getFeatures(self, state, action):\\n util.raiseNotDefined()\",\n \"def getFeatures(self, state, action):\\n util.raiseNotDefined()\",\n \"def getFeatures(self, state, action):\\n util.raiseNotDefined()\",\n \"def get_all_features(config: Config) -> typing.List[str]:\\n return [feature.name for feature in config.features]\",\n \"def getFeatures(self, state, action, thisAgent):\\n util.raiseNotDefined()\",\n \"def getFeatures(self, state, action):\\n util.raiseNotDefined()\",\n \"def irrelevant_features(features):\\n irrelevant = []\\n for vec in set(features):\\n if (features[vec].count(0)/len(indtf_features[vec])) < 0.1:\\n irrelevant.append(vec)\\n return irrelevant\",\n \"def one_hot(seq):\\n prot_seq = seq\\n BASES = 'ARNDCQEGHILKMFPSTWYV'\\n bases = np.array([base for base in BASES])\\n feat = np.concatenate(\\n [[(bases == base.upper()).astype(int)] if str(base).upper() in BASES else np.array([[-1] * len(BASES)]) for base\\n in prot_seq])\\n return feat\",\n \"def oneHotEncoding(X, list_indices):\\n print('Onehotencoding..')\\n X = X.astype(float)\\n for i in list_indices:\\n onehotencoder = OneHotEncoder(categorical_features=[i])\\n X = onehotencoder.fit_transform(X)\\n return X[1:]\",\n \"def _make_features(self, x):\\n\\t\\tx = x.unsqueeze(1)\\n\\t\\treturn torch.cat([x ** i for i in range(1, self._degree+1)], 1)\",\n \"def _one_hot(self, proba, y):\\n hm_1hot = []\\n for label in np.unique(y):\\n mask_ = y == label\\n proba_tmp = proba.copy()\\n proba_tmp[~mask_] = 0\\n hm_y, auto_gamma = self._adjust_gamma(proba_tmp)\\n\\n if self.verbose:\\n print('Adjusted gamma: ', str(auto_gamma))\\n hm_1hot.append(hm_y)\\n return hm_1hot\",\n \"def get_features(self):\\n return self._features\",\n \"def get_features(df, target=[], meta=[]):\\n ############################################################\\n # Type conversion\\n ############################################################\\n\\n types = df[df.columns[~df.columns.isin(target+meta)]].dtypes\\n for col_name, col_type in types.iteritems():\\n if col_type == bool:\\n df[col_name] = df[col_name].astype(float)\\n\\n ############################################################\\n # Get features by type\\n ############################################################\\n \\n features_cat = filter(lambda x: not np.issubdtype(x[1], np.number), types.iteritems())\\n features_cat = sorted(list(map(lambda x: x[0], features_cat)))\\n # target and meta should have already been removed. but just to be sure\\n features_num = sorted(list(set(types.index) - set(features_cat) - set(target) - set(meta))) \\n selected_features = df.columns.to_list()\\n features_idx = dict(zip(selected_features, range(len(selected_features))))\\n \\n return selected_features, features_num, features_cat, features_idx\",\n \"def GetActions(features):\\n featureOP = []\\n featureYMore = []\\n featureYAcc = []\\n featureImprove = []\\n count = 0\\n for index, row in features[::-1].iterrows():\\n \\tif count > 7: # Don't recommend more than 8 actions\\n \\t\\tbreak\\n if ('poss' not in row['features']) and ('scoring' not in row['features']):\\n if '_op' in row['features']:\\n if (row['coef'] < 0) and ('accuracy' not in row['features']) and ('accurate' not in row['features']):\\n featureOP.append(row['features'][:-3].replace('_', ' ').title())\\n featureImprove.append(row['features'])\\n count += 1\\n else:\\n if row['coef'] > 0:\\n if 'accuracy' not in row['features']:\\n featureYMore.append(row['features'].replace('_', ' ').title())\\n featureImprove.append(row['features'])\\n count += 1\\n else:\\n featureYAcc.append(row['features'].replace('_', ' ').title())\\n featureImprove.append(row['features'])\\n count += 1\\n\\n # Whether show 2 columns or 3\\n useTwoCol = True\\n if useTwoCol:\\n actions = pd.DataFrame([featureYAcc + featureYMore, featureOP], index = ['Your', 'OP']).T\\n\\n else:\\n actions = pd.DataFrame([featureYAcc, featureYMore, featureOP], index = ['YAcc', 'YMore', 'OP']).T\\n nDimActions = actions.shape\\n actions = actions.values.tolist()\\n\\t\\n ## Make the actions more readable\\n for ii in np.arange(nDimActions[0]):\\n for jj in np.arange(nDimActions[1]):\\n #print actions[ii][jj]\\n if actions[ii][jj] == None:\\n actions[ii][jj] = ' '\\n else:\\n actions[ii][jj] = actions[ii][jj].replace('Att', 'Attempt').replace('Obox', 'Outside the Penalty Box').replace('Ibox', 'Inside the Penalty Box').replace('Total ', '').replace('Fwd', 'Forward').replace('18Yardplus', 'Outside the Penalty Box').replace('18Yard', 'Inside the Penalty Box')\\n if 'Accuracy' in actions[ii][jj]:\\n actions[ii][jj] = actions[ii][jj][9:] + ' Accuracy'\\n else:\\n actions[ii][jj] = '# of ' + actions[ii][jj]\\n if (\\\"alls\\\" not in actions[ii][jj]) and (\\\"Penalty Box\\\" not in actions[ii][jj]):\\n if \\\"Won\\\" in actions[ii][jj]:\\n actions[ii][jj] = actions[ii][jj][:-4] + 's Won'\\n elif actions[ii][jj][-2:] != 'ss':\\n actions[ii][jj] = actions[ii][jj] + 's'\\n else:\\n actions[ii][jj] = actions[ii][jj] + 'es'\\n #print actions\\n return actions, featureImprove\",\n \"def extract_features(X):\\n X = X.drop(\\\"PassengerId\\\", axis=1)\\n X = X.drop(\\\"Ticket\\\", axis=1)\\n X = X.drop(\\\"Cabin\\\", axis=1)\\n \\n # Adding polynomial features\\n X[\\\"Age2\\\"] = X[\\\"Age\\\"] ** 2\\n #X[\\\"Fare2\\\"] = X[\\\"Fare\\\"] ** 2\\n #X[\\\"Pclass2\\\"] = X[\\\"Pclass\\\"] ** 2\\n\\n \\n male_titles = set([\\\"Mr\\\", \\\"Don\\\", \\\"Sir\\\"])\\n female_titles = set([\\\"Miss\\\", \\\"Ms\\\", \\\"Mrs\\\", \\\"Mme\\\", \\\"Mdm\\\", \\\"Lady\\\"])\\n professionals = set([\\\"Dr\\\", \\\"Rev\\\", \\\"Master\\\"])\\n military = set([\\\"Col\\\", \\\"Major\\\", \\\"Capt\\\"])\\n royalty = set([\\\"the Countess\\\", \\\"Jonkheer\\\"])\\n \\n names = X[\\\"Name\\\"]\\n for i in range(len(names)): \\n name_tokens = names[i].split(\\\", \\\") \\n passenger_title = name_tokens[1].split(\\\".\\\")[0]\\n if passenger_title in male_titles:\\n names[i] = 1\\n elif passenger_title in female_titles:\\n names[i] = 2\\n elif passenger_title in professionals:\\n names[i] = 3\\n #elif passenger_title in royalty:\\n # names[i] = 4\\n elif passenger_title in military:\\n names[i] = 5\\n else:\\n names[i] = 6\\n \\n X[\\\"Name\\\"].update(names)\\n \\n # One hot encoding of categorical data\\n X = pd.get_dummies(X) \\n \\n X.fillna(0, inplace=True)\\n X['Fam'] = X['SibSp'] + X['Parch'] # assigned to a column\\n return X\",\n \"def getFeatures(self, gameState, action):\\n # features = util.Counter()\\n # successor = self.getSuccessor(gameState, action)\\n # features['successorScore'] = self.getScore(successor)\\n # return features\\n if self.isOffensive:\\n return self.getOffensiveFeatures(gameState, action)\\n else:\\n return self.getDefensiveFeatures(gameState, action)\",\n \"def get_feature_names(self):\\n ...\",\n \"def onehot(isTrue):\\n if isTrue:\\n return [1, 0]\\n else:\\n return [0, 1]\",\n \"def features(self):\\n\\n return self._features\",\n \"def features(self, state, action, next_state):\\n raise NotImplementedError\",\n \"def get_style1_features(self):\\n return self.style1_features\",\n \"def set_to_features(X_set):\\n ext = Extractor()\\n features = []\\n for i in range(len(X_set)):\\n print(i, \\\" out of \\\", len(X_set))\\n bag_of_features = [ext.extract(X_set[i][j]) for j in range(len(X_set[i]))]\\n\\n features.append(bag_of_features)\\n\\n return features\",\n \"def extract_features(self, inputs):\\n pass\",\n \"def get_feature_names(self, input_features=...):\\n ...\",\n \"def enableAllFeatures(self):\\n self.logger.debug('Enabling all features in all feature classes')\\n for featureClassName in self.getFeatureClassNames():\\n self.enabledFeatures[featureClassName] = []\\n self.logger.debug('Enabled features: %s', self.enabledFeatures)\",\n \"def get_feature_names(self):\\n if self.linear_features:\\n self.features_ = np.concatenate([self.features, np.array(self.extract_rules(labels=self.features))], 0)[self.feature_mask_]\\n else:\\n self.features_ = np.array(self.extract_rules(labels=self.features))[self.feature_mask_]\\n return self.features_\",\n \"def extract_features(sentence, vocabulary):\\n n_tokens = len(sentence)\\n n_features = n_feature_functions + len(vocabulary)\\n X = sp.lil_matrix((n_tokens, n_features), dtype=bool)\\n\\n for i in xrange(n_tokens):\\n for j, f in enumerate(FEATURE_FUNCTIONS):\\n X[i, j] = f(sentence, i)\\n\\n # Vocabulary feature\\n try:\\n X[i, n_feature_functions + vocabulary[sentence[i][0].lower()]] = 1\\n except KeyError:\\n pass\\n\\n return X\",\n \"def listFeatures() :\\n global features\\n features = [feature.split(\\\".\\\")[0] for feature in os.listdir(os.path.abspath(__file__)[:-11])\\n if feature.endswith(\\\".py\\\") and feature != \\\"__init__.py\\\"]\",\n \"def make_onehot(x,num_labels=7):\\n enc = OneHotEncoder(n_values=num_labels)\\n return enc.fit_transform(np.array(x).reshape(-1, 1)).toarray()\",\n \"def featurize(self, tokens):\\n features = []\\n \\n nrc_hashtag_emotion_features = self.nrc_hashtag_emotion(tokens)\\n nrc_affect_intensity_features = self.nrc_affect_intensity(tokens)\\n nrc_hashtag_sentiment_lexicon_unigrams_features = self.nrc_hashtag_sentiment_lexicon_unigrams(tokens)\\n nrc_hashtag_sentiment_lexicon_bigrams_features = self.nrc_hashtag_sentiment_lexicon_bigrams(tokens)\\n sentiment140_unigrams_features = self.sentiment140_unigrams(tokens)\\n sentiment140_bigrams_features = self.sentiment140_bigrams(tokens)\\n senti_wordnet_features = self.senti_wordnet(tokens)\\n bing_lui_sentiment_lexicons_features = self.bing_lui_sentiment_lexicons(tokens)\\n nrc_expanded_lexicon_features = self.nrc_10_expanded(tokens)\\n negating_word_list_features = self.negating_words_list(tokens)\\n total_number_of_words_features = self.get_total_number_of_words(tokens)\\n mpqa_subjectivity_lexicon_features = self.mpqa_subjectivity_lexicon(tokens)\\n afinn_sentiment_features = self.afinn_sentiment_scores(tokens)\\n # senti_strength_features = self.get_sentistrength(\\\" \\\".join(tokens))\\n\\n features.extend(nrc_hashtag_emotion_features.values()) # 10 features\\n features.extend(nrc_affect_intensity_features.values()) # 10 features\\n features.extend(nrc_hashtag_sentiment_lexicon_unigrams_features.values()) # 4 features\\n features.extend(nrc_hashtag_sentiment_lexicon_bigrams_features.values()) # 4 features\\n features.extend(sentiment140_unigrams_features.values()) # 4 features \\n features.extend(sentiment140_bigrams_features.values()) # 4 features\\n features.extend(senti_wordnet_features.values()) # 4 features\\n features.extend(bing_lui_sentiment_lexicons_features.values()) # 2 features\\n features.extend(nrc_expanded_lexicon_features.values()) # 10 features\\n features.extend(negating_word_list_features.values()) # 1 feature\\n features.extend(total_number_of_words_features.values()) # 1 feature\\n features.extend(mpqa_subjectivity_lexicon_features.values()) # 2 features\\n features.extend(afinn_sentiment_features.values()) # 2 features\\n # features.extend(senti_strength_features.values()) # 2 features\\n\\n return features\",\n \"def preprocess_features(features):\\r\\n rowsum = np.array(features.sum(1))\\r\\n r_inv = np.power(rowsum, -1).flatten()\\r\\n r_inv[np.isinf(r_inv)] = 0.\\r\\n r_mat_inv = np.diag(r_inv)\\r\\n features = r_mat_inv.dot(features)\\r\\n return features\",\n \"def feature_eng2(housing_tr, housing):\\n logging.info(\\\"Adding features.....\\\")\\n housing_tr[\\\"rooms_per_household\\\"] = (\\n housing_tr[\\\"total_rooms\\\"] / housing_tr[\\\"households\\\"]\\n )\\n housing_tr[\\\"bedrooms_per_room\\\"] = (\\n housing_tr[\\\"total_bedrooms\\\"] / housing_tr[\\\"total_rooms\\\"]\\n )\\n housing_tr[\\\"population_per_household\\\"] = (\\n housing_tr[\\\"population\\\"] / housing_tr[\\\"households\\\"]\\n )\\n housing_cat = housing[[\\\"ocean_proximity\\\"]]\\n housing_prepared = housing_tr.join(\\n pd.get_dummies(housing_cat, drop_first=True)\\n )\\n return housing_prepared\",\n \"def supported_features(self):\\n return self._supported_features\",\n \"def get_dense_feature_columns(self) -> List[FeatureColumn]:\\n\\n return self._get_numeric_feature_columns(\\n ) + self._get_embedding_feature_columns()\",\n \"def getFeatures(self, N=None, indexes=False):\\n if indexes:\\n features = self.softmax2feats(self.feats, sort_values=True)\\n else:\\n features = self.feats\\n if N:\\n return features[:N]\\n return features\",\n \"def one_hot_encoding(self):\\n \\n try: \\n\\n # Encode dependent variable\\n le = LabelEncoder()\\n le.fit(self.data[\\\"consumption\\\"])\\n df_dependent_enc = pd.DataFrame(le.transform(self.data[\\\"consumption\\\"]))\\n\\n # Encode independent variable\\n categorical_features = Config.FEATURE_DEFINITION[\\\"category_cols\\\"]\\n categorical_df = self.data.loc[:, self.data.columns.isin(categorical_features)]\\n oe = OrdinalEncoder()\\n oe.fit(categorical_df)\\n df_catindependent_enc = pd.DataFrame(oe.transform(categorical_df))\\n df_catindependent_enc.columns = categorical_df.columns\\n\\n except KeyError: \\n\\n st.write(\\\"Cannot perform one-hot encoding for numerical variables. Please check if variables are properly defined.\\\")\\n st.write(self.data.columns != \\\"consumption\\\")\\n df_dependent_enc = []\\n df_catindependent_enc = []\\n\\n else:\\n \\n return df_dependent_enc, df_catindependent_enc\",\n \"def vectorizer_features(self) -> list:\\n if self._vectorizer:\\n return self._vectorizer.get_feature_names()\\n self.logger.warning('Uninitialized vector. Please call count_vectorizer first.')\",\n \"def preprocess_features(X):\\n\\t# Initialize new output DataFrame\\n\\toutput = pd.DataFrame(index = X.index)\\n\\n\\t# Investigate new output DataFrame\\n\\tfor col, col_data in X.iteritems():\\n\\t\\t# If data type is categorical, convert to dummy variables\\n\\t\\tif col_data.dtype == object:\\n\\t\\t\\tcol_data = pd.get_dummies(col_data, prefix = col)\\n\\n\\t\\t\\t# Collect the revised columns\\n\\t\\t\\toutput - output.join(col_data)\\n\\treturn output\",\n \"def get_one_hot_vector(number):\\n from sklearn.preprocessing import LabelBinarizer\\n encoder = LabelBinarizer()\\n seed_vector = np.arange(10)\\n encoder.fit(seed_vector)\\n labels = encoder.transform(seed_vector)\\n return labels.astype(np.float32)\",\n \"def preprocessing_fn(inputs):\\n outputs = {}\\n\\n for key in ONE_HOT_FEATURES.keys():\\n dim = ONE_HOT_FEATURES[key]\\n int_value = tft.compute_and_apply_vocabulary(\\n fill_in_missing(inputs[key]), top_k=dim + 1\\n )\\n outputs[transformed_name(key)] = convert_num_to_one_hot(\\n int_value, num_labels=dim + 1\\n )\\n\\n for key, bucket_count in BUCKET_FEATURES.items():\\n temp_feature = tft.bucketize(\\n convert_zip_code(fill_in_missing(inputs[key])),\\n bucket_count,\\n )\\n outputs[transformed_name(key)] = convert_num_to_one_hot(\\n temp_feature, num_labels=bucket_count + 1\\n )\\n\\n for key in TEXT_FEATURES.keys():\\n outputs[transformed_name(key)] = fill_in_missing(inputs[key])\\n\\n outputs[transformed_name(LABEL_KEY)] = fill_in_missing(inputs[LABEL_KEY])\\n\\n return outputs\",\n \"def all(cls):\\n results = []\\n for name in FEATURES:\\n feat = cls.get_by_name(name)\\n if feat is None:\\n feat = cls(name=name)\\n cls.query.session.add(feat)\\n results.append(feat)\\n return results\",\n \"def choose_features(nof_features, max_features):\\n features_indices = numpy.arange(nof_features)\\n #numpy.random.seed()\\n #features_chosen = numpy.random.choice(features_indices, size=max_features, replace = True)\\n features_chosen = numpy.random.choice(features_indices,\\n size=nof_features,\\n replace=False)\\n\\n #print(features_chosen)\\n return features_chosen\",\n \"def global_class_to_one_hot(global_class):\\n if global_class == 'Store':\\n return [1, 0, 0, 0, 0]\\n elif global_class == 'Home':\\n return [0, 1, 0, 0, 0]\\n elif global_class == 'Public spaces':\\n return [0, 0, 1, 0, 0]\\n elif global_class == 'Leisure':\\n return [0, 0, 0, 1, 0]\\n elif global_class == 'Working place':\\n return [0, 0, 0, 0, 1]\\n else:\\n raise Exception('The global class \\\"' + global_class + '\\\" cant be transform to one-hot format')\",\n \"def concat_non_bleeding_features(X_train, X_test, BOW_feature_list, features_dir, fold):\\n feat_list = []\\n for feature in BOW_feature_list:\\n X_train_part = np.load(\\\"%s/%s.%s.npy\\\" % (features_dir, feature, fold))\\n last_index = len(X_train[0])\\n X_train = np.concatenate([X_train, X_train_part], axis=1)\\n feat_list.append((last_index, last_index+len(X_train_part[0]), str(feature)))\\n X_test_part = np.load(\\\"%s/%s.%s.test.npy\\\" % (features_dir, feature, fold))\\n X_test = np.concatenate([X_test, X_test_part], axis=1)\\n return X_train, X_test, feat_list\",\n \"def get_feature_names(self):\\n return [self.__class__.__name__]\",\n \"def get_used_features():\\n print(\\\"filtering used features\\\")\\n # is this user an active user? if any of the features is non-zero take this user as an active user\\n used_features = {}\\n for row in df_train.values:\\n row = list(row)\\n id = row.pop(0)\\n active = [c[0] for c in zip(df_train.columns[1:], row) if (c[0] in item_cols and c[1] > 0)]\\n if id not in used_features:\\n used_features[id] = []\\n used_features[id].extend(active)\\n return used_features\",\n \"def reduce_features(train_data_lst, test_data_lst, train_anots, data_anots):\\r\\n \\r\\n train_data_reduced = []\\r\\n test_data_reduced = []\\r\\n new_anots = []\\r\\n for idx in range(len(train_data_lst)):\\r\\n \\r\\n most_important_features = rank_features(train_data_lst[idx], train_anots)\\r\\n train_data_reduced.append(train_data_lst[idx][:, most_important_features])\\r\\n test_data_reduced.append(test_data_lst[idx][:, most_important_features])\\r\\n new_anots.append(data_anots[idx]+'_reduced')\\r\\n return train_data_reduced, test_data_reduced, new_anots\",\n \"def get_active_features(summary_df, slots_offered): # prev -> getActiveFeatures\\n disc_cols = [col+'_Discount' for col in slots_offered]\\n eco_cols = [col+'_Eco' for col in slots_offered]\\n gr_cols = [col+'_Eco' for col in slots_offered]\\n features = summary_df.loc[:, disc_cols+eco_cols+gr_cols]\\n features = features.loc[:, features.sum(axis=0) > 0]\\n for i in reversed(['NO_PURCHASE']+slots_offered):\\n features.insert(0, i+'_Asc', value=1)\\n return features, disc_cols, eco_cols, gr_cols\",\n \"def featurize(tokens, feature_fns):\\n answer = []\\n for func in feature_fns:\\n feats = defaultdict(lambda: 0)\\n func(tokens,feats)\\n answer.extend(feats.items())\\n return sorted(answer, key= lambda x: x[0])\",\n \"def one_hot(df):\\r\\n # One-hot encode into \\r\\n cols = ['job', 'marital', 'education', 'month', 'day_of_week', 'poutcome']\\r\\n for each in cols:\\r\\n dummies = pd.get_dummies(df[each], prefix=each, drop_first=False)\\r\\n df = pd.concat([df, dummies], axis=1)\\r\\n df = df.drop(cols,axis=1)\\r\\n return df\",\n \"def build_feature_filter(self):\\n if self.features == [\\\"*\\\"]:\\n random_iso = list(self.data.keys())[0]\\n self.features = set()\\n for lang_features in self.data.values():\\n self.features |= set(lang_features.keys())\\n self.features = list(self.features)\\n if self.exclusions:\\n self.features = [f for f in self.features if f not in self.exclusions]\\n self.feature_filter = set(self.features)\",\n \"def hard_swish(features):\\n features = tf.convert_to_tensor(features)\\n return features * tf.nn.relu6(features + tf.constant(3.)) * (1. / 6.)\",\n \"def get_feature_names(self):\\n raise NotImplementedError()\",\n \"def _extract_features(self):\\n # print(os.getpid())\\n return {n:self._extract_feature(f) for (n,f) in self.features.items()}\",\n \"def add_all_features(df):\\n df.reset_index(drop=True, inplace=True)\\n df = target_indicators(df)\\n df = momentum_indicators(df)\\n df = trend_indicators(df)\\n df = volatility_indicators(df)\\n df = volume_indicators(df)\\n df = special_indicators(df)\\n return df\",\n \"def preprocess_features(features):\\n rowsum = np.array(features.sum(1))\\n r_inv = np.power(rowsum, -1).flatten()\\n r_inv[np.isinf(r_inv)] = 0.\\n r_mat_inv = sp.diags(r_inv)\\n features = r_mat_inv.dot(features)\\n return features\",\n \"def preprocess_features(features):\\n rowsum = np.array(features.sum(1))\\n r_inv = np.power(rowsum, -1).flatten()\\n r_inv[np.isinf(r_inv)] = 0.\\n r_mat_inv = sp.diags(r_inv)\\n features = r_mat_inv.dot(features)\\n return features\",\n \"def extract_features(self, inputs):\\n x = self.conv1(inputs)\\n x = self.maxpool1(x)\\n x = self.conv2(x)\\n x = self.conv3(x)\\n x = self.maxpool2(x)\\n\\n x = self.inception3a(x)\\n x = self.inception3b(x)\\n x = self.maxpool3(x)\\n x = self.inception4a(x)\\n\\n x = self.inception4b(x)\\n x = self.inception4c(x)\\n x = self.inception4d(x)\\n\\n x = self.inception4e(x)\\n x = self.maxpool4(x)\\n x = self.inception5a(x)\\n x = self.inception5b(x)\\n x = self.avgpool(x)\\n x = torch.flatten(x,1)\\n x = self.dropout(x)\\n return x\",\n \"def get_feature_names(self, input_features=None):\\n check_is_fitted(self)\\n n_features = self.n_features_in_\\n if input_features is None:\\n input_features = [\\\"x%d\\\" % i for i in range(n_features)]\\n if self.function_names is None:\\n self.function_names = list(\\n map(\\n lambda i: (lambda *x: \\\"f\\\" + str(i) + \\\"(\\\" + \\\",\\\".join(x) + \\\")\\\"),\\n range(n_features),\\n )\\n )\\n feature_names = []\\n\\n # Include constant term\\n if self.include_bias:\\n feature_names.append(\\\"1\\\")\\n\\n # Include any non-derivative terms\\n for i, f in enumerate(self.functions):\\n for c in self._combinations(\\n n_features, f.__code__.co_argcount, self.interaction_only\\n ):\\n feature_names.append(\\n self.function_names[i](*[input_features[j] for j in c])\\n )\\n\\n if self.grid_ndim != 0:\\n\\n def derivative_string(multiindex):\\n ret = \\\"\\\"\\n for axis in range(self.ind_range):\\n if (axis == self.ind_range - 1) and (\\n self.ind_range == self.grid_ndim\\n ):\\n str_deriv = \\\"t\\\"\\n else:\\n str_deriv = str(axis + 1)\\n for i in range(multiindex[axis]):\\n ret = ret + str_deriv\\n return ret\\n\\n # Include integral terms\\n for k in range(self.num_derivatives):\\n for j in range(n_features):\\n feature_names.append(\\n input_features[j]\\n + \\\"_\\\"\\n + derivative_string(self.multiindices[k])\\n )\\n # Include mixed non-derivative + integral terms\\n if self.include_interaction:\\n for k in range(self.num_derivatives):\\n for jj in range(n_features):\\n for i, f in enumerate(self.functions):\\n for c in self._combinations(\\n n_features,\\n f.__code__.co_argcount,\\n self.interaction_only,\\n ):\\n feature_names.append(\\n self.function_names[i](\\n *[input_features[j] for j in c]\\n )\\n + input_features[jj]\\n + \\\"_\\\"\\n + derivative_string(self.multiindices[k])\\n )\\n return feature_names\",\n \"def value_head(features):\\n with tf.variable_scope('critic', reuse=tf.AUTO_REUSE):\\n features = tf.layers.dense(features, units=1, activation=None, name='output')\\n return tf.squeeze(features, axis=-1)\",\n \"def features(self) -> List[Feature]:\\n return [field for field in self._fields.values() if isinstance(field, Feature)]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.66784006","0.64435947","0.6321415","0.6291115","0.6268722","0.6225658","0.61513215","0.6115024","0.6107136","0.6081699","0.6007897","0.600501","0.60009587","0.5978102","0.5973954","0.59635854","0.5962802","0.59291697","0.59200054","0.5908913","0.5896621","0.58603954","0.5831205","0.58308977","0.5824725","0.5823911","0.58235216","0.5821257","0.58189213","0.5807414","0.580064","0.5800519","0.57777894","0.57583517","0.5747596","0.57235146","0.571899","0.5718344","0.5707463","0.57060325","0.57060325","0.57060325","0.5701699","0.5700571","0.56984574","0.568982","0.5684246","0.56800693","0.5678825","0.5673599","0.5647275","0.56471443","0.5643944","0.5641273","0.56397104","0.56352866","0.5614984","0.56133693","0.5601909","0.55970675","0.55953944","0.55846083","0.55777496","0.5572005","0.55602866","0.5558192","0.55531317","0.5551012","0.5546041","0.5529865","0.552797","0.55277234","0.5519832","0.5516046","0.55159545","0.5509934","0.5506595","0.54947054","0.54924256","0.54893345","0.548929","0.548635","0.548427","0.54806","0.54740846","0.5470231","0.54624605","0.545853","0.5456298","0.54556566","0.5455095","0.5454388","0.5446067","0.54418737","0.54411405","0.54411405","0.5439881","0.5428694","0.5428418","0.5424796"],"string":"[\n \"0.66784006\",\n \"0.64435947\",\n \"0.6321415\",\n \"0.6291115\",\n \"0.6268722\",\n \"0.6225658\",\n \"0.61513215\",\n \"0.6115024\",\n \"0.6107136\",\n \"0.6081699\",\n \"0.6007897\",\n \"0.600501\",\n \"0.60009587\",\n \"0.5978102\",\n \"0.5973954\",\n \"0.59635854\",\n \"0.5962802\",\n \"0.59291697\",\n \"0.59200054\",\n \"0.5908913\",\n \"0.5896621\",\n \"0.58603954\",\n \"0.5831205\",\n \"0.58308977\",\n \"0.5824725\",\n \"0.5823911\",\n \"0.58235216\",\n \"0.5821257\",\n \"0.58189213\",\n \"0.5807414\",\n \"0.580064\",\n \"0.5800519\",\n \"0.57777894\",\n \"0.57583517\",\n \"0.5747596\",\n \"0.57235146\",\n \"0.571899\",\n \"0.5718344\",\n \"0.5707463\",\n \"0.57060325\",\n \"0.57060325\",\n \"0.57060325\",\n \"0.5701699\",\n \"0.5700571\",\n \"0.56984574\",\n \"0.568982\",\n \"0.5684246\",\n \"0.56800693\",\n \"0.5678825\",\n \"0.5673599\",\n \"0.5647275\",\n \"0.56471443\",\n \"0.5643944\",\n \"0.5641273\",\n \"0.56397104\",\n \"0.56352866\",\n \"0.5614984\",\n \"0.56133693\",\n \"0.5601909\",\n \"0.55970675\",\n \"0.55953944\",\n \"0.55846083\",\n \"0.55777496\",\n \"0.5572005\",\n \"0.55602866\",\n \"0.5558192\",\n \"0.55531317\",\n \"0.5551012\",\n \"0.5546041\",\n \"0.5529865\",\n \"0.552797\",\n \"0.55277234\",\n \"0.5519832\",\n \"0.5516046\",\n \"0.55159545\",\n \"0.5509934\",\n \"0.5506595\",\n \"0.54947054\",\n \"0.54924256\",\n \"0.54893345\",\n \"0.548929\",\n \"0.548635\",\n \"0.548427\",\n \"0.54806\",\n \"0.54740846\",\n \"0.5470231\",\n \"0.54624605\",\n \"0.545853\",\n \"0.5456298\",\n \"0.54556566\",\n \"0.5455095\",\n \"0.5454388\",\n \"0.5446067\",\n \"0.54418737\",\n \"0.54411405\",\n \"0.54411405\",\n \"0.5439881\",\n \"0.5428694\",\n \"0.5428418\",\n \"0.5424796\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5930906"},"document_rank":{"kind":"string","value":"17"}}},{"rowIdx":16897,"cells":{"query":{"kind":"string","value":"Takes a pandas dataframe and modifies it to do any data specific processing. This should include any ordered categorical replacement by numbers. The resulting pandas dataframe is returned."},"document":{"kind":"string","value":"def data_specific_processing(self, dataframe):\n return dataframe"},"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 clean_data(df):\r\n \r\n # list of categories to use as column names \r\n categories_cols = [names.split('-')[0] for names in df['categories'][0].split(';')]\r\n \r\n # creating 36 individual category columns\r\n for i in range(len(categories_cols)):\r\n df[categories_cols[i]] = [int(row.split(';')[i].split('-')[1]) for row in df['categories']]\r\n \r\n # labels 0 and 2 in 'related' class are similar (refer to notebook)\r\n # change 2s into 0s to make it more simple\r\n df['related'] = df['related'].map({0:0,1:1,2:0})\r\n \r\n # drop 'categories' column\r\n df.drop('categories', axis=1, inplace=True)\r\n \r\n # drop duplicates\r\n df.drop_duplicates(inplace=True)\r\n \r\n return df","def clean_data(df):\n # create a dataframe of the 36 individual category columns\n categories = df.categories.str.split(';', expand=True)\n\n # select the first row of the categories dataframe\n row = categories[:1]\n\n # use this row to extract a list of new column names for categories\n # take beginning up to 2nd to last character of each string with slicing\n category_colnames = [name[:-2] for name in row.values[0]]\n\n # rename the columns of 'categories'\n categories.columns = category_colnames\n\n # Convert category values to numbers 0 or 1\n for column in categories:\n # set each value to be the last character of the string\n categories[column] = [x[-1] for x in categories[column]]\n\n # convert column from string to numeric\n categories[column] = categories[column].astype(int)\n\n # replace value of 2 with a 1\n categories[column] = categories[column].replace(2, 1)\n\n # Replace 'categories' column in df with new category columns\n # drop the original categories column from `df`\n df = df.drop('categories', axis=1)\n\n # concatenate the original dataframe with the new `categories` dataframe\n df = pd.concat([df, categories], axis=1)\n\n # Remove duplicates\n df = df.drop_duplicates()\n\n return df","def clean_data(dataframe):\n # split categories into seperate\n categories = dataframe.categories.str.split(';', expand=True)\n \n # select the first row&col of the categories dataframe\n row&col = categories.iloc[0]\n cate_col = row&col.apply(lambda x: x[:-2])\n cate.columns = cate_colnames\n \n #convert categories values to numeric instead of strings\n for column in categories:\n categories[column] = categories[column].str[-1]\n categories[column] = categories[column].astype(int)\n \n # replace categories column in dataframe \n dataframe.drop(columns = ['categories'], inplace=True)\n # concatenate the original dataframe with the new `categories` dataframe\n dataframe = dataframe.join(categories)\n \n #drop duplicates\n dataframe.drop_duplicates(inplace=True)\n \n return dataframe","def transform(self, dataframe: DataFrame) -> DataFrame:","def _convert_categorical(from_frame: DataFrame) -> DataFrame:\n for col in from_frame:\n ser = from_frame[col]\n if isinstance(ser.dtype, CategoricalDtype):\n cat = ser._values.remove_unused_categories()\n if cat.categories.dtype == object:\n categories = pd.Index._with_infer(cat.categories._values)\n cat = cat.set_categories(categories)\n from_frame[col] = cat\n return from_frame","def pre_process_data(df):\n\n # one-hot encode categorical values\n df = pd.get_dummies(df)\n\n return df","def cat_converter(df):\n \n categoricals = df.columns[df.dtypes == object] \n \n for column in categoricals: \n df[column] = pd.Categorical(df[column])\n new_column = column + '_new'\n df[new_column] = df[column].cat.codes\n df = df.drop([column], axis = 1)\n \n return df","def clean_data(df):\n # Extract column names from values, and rename\n categories = df.categories.str.split(\";\", expand=True)\n row = categories.iloc[0]\n category_colnames = list(map(lambda item: item.split(\"-\")[0], row))\n categories.columns = category_colnames\n\n # Fix values\n for column in categories:\n # set each value to be the last character of the string\n categories[column] = categories[column].str[-1]\n\n # convert column from string to numeric\n categories[column] = categories[column].apply(lambda x: int(x))\n\n df = df.drop(['categories'], axis=1)\n df = pd.concat([df, categories], axis=1)\n df.drop_duplicates(inplace=True)\n\n return df","def clean_data(df):\n # expand categories as new data frame\n new_columns = [re.sub('[^a-zA-Z]', ' ', i).strip() for i in df['categories'][0].split(';')]\n cat_df = df['categories'].str.split(';', expand=True)\n cat_df.columns = new_columns\n \n # remove anything except numerical value\n # change new feature's type\n for column in cat_df:\n cat_df[column] = cat_df[column].apply(lambda x: re.sub('[^0-9]', '', x)).astype('str')\n \n # concatenate old dataframe and new features dataframe\n # remove olf categories column\n new_df = pd.concat([df, cat_df], axis=1)\n new_df = new_df.drop('categories', axis=1).drop_duplicates()\n binary_df = new_df[new_df['related']!='2']\n \n return binary_df","def preprocess_dataframe(self, dataframe):\n return dataframe","def clean_data(df):\n # Split the categories column to get each category into a column\n categories = df['categories'].str.split(pat=';', expand=True)\n\n # Assign category names to df columns\n row = categories.iloc[0]\n category_colnames = [i.split('-')[0] for i in row]\n categories.columns = category_colnames\n\n # Get only the numeric value from each column\n for column in categories:\n categories[column] = categories[column].str.extract('(?P<digit>\\d)', expand=False).replace('2','1')\n categories[column] = categories[column].astype(int)\n\n # Drop categories column\n df.drop('categories', axis=1, inplace=True)\n\n # Concat df with all the categories columns\n df = pd.concat([df, categories], axis=1)\n\n # Remove duplicated rows\n df.drop_duplicates(inplace=True)\n\n return df","def preprocess_feature(df):","def clean_data(df): \n # Now we first determen the column names\n columns = df['categories'].str.replace(\"-0\",\"\").str.replace(\"-1\",\"\").str.replace(\"-2\",\"\").drop_duplicates().str.split(';')[0]\n\n # And split the 'categories' column\n df[columns] = df['categories'].str.split(';',expand=True)\n\n # The columns are still an object/string. Now make it a number\n for column in columns :\n df[column]=df[column].str.replace(column+\"-\",\"\").astype(int)\n\n # We don't need the column 'categories' anymore, so delete it\n df.drop('categories',1,inplace=True)\n \n # There are some duplicate records, so drop them\n df.drop_duplicates(inplace=True)\n \n return df","def clean_data(df):\n # Copy dataframe to local dataframe\n df_clean = df\n # Split category into subcategories\n categories = df.categories.str.split(\";\", expand=True)\n # Label columns according to new label\n categories.columns = categories.iloc[0].str[:-2]\n # Make columns numeric, i.e. remove the label substring from the content\n for label, content in categories.iteritems():\n categories[label] = pd.to_numeric(content.str.replace(f\"{label}-\", \"\"))\n # Clean related category to 0/1 - there are outliers with 2s\n categories[\"related\"] = categories[\"related\"].map(lambda x: 1 if x == 2 else x)\n # Drop original category column\n df_clean = df_clean.drop(labels=\"categories\", axis=1)\n # Add categories to dataframe\n df_clean = df_clean.join(categories)\n\n return df_clean","def clean_data(df):\n categories= pd.Series(df['categories']).str.split(pat = ';', expand= True)\n # select the first row of the categories dataframe\n row = categories.iloc[0]\n# use this row to extract a list of new column names for categories.\n# one way is to apply a lambda function that takes everything \n# up to the second to last character of each string with slicing\n category_colnames = list(row.apply(lambda x:x[0:-2]))\n categories.columns = category_colnames\n \n for column in categories:\n # set each value to be the last character of the string\n categories[column] = categories[column].apply(lambda x:x[-1:])\n categories[column] = categories[column].apply(pd.to_numeric)\n # we have few entries in related column as value 2. we need to convert these to 1.\n a = np.array(categories['related'].values.tolist())\n categories['related'] = np.where(a > 1, 1, a).tolist()\n df = df.drop(['categories'], axis = 1)\n \n frames = [df, categories]\n df = pd.concat(frames, axis =1, sort = False) \n df = df.drop_duplicates()\n return df","def augment_dataframe(self, df: pd.DataFrame) -> pd.DataFrame:","def apply_cats(df, trn):\n for n,c in df.items():\n if (n in trn.columns) and (trn[n].dtype.name=='category'):\n df[n] = pd.Categorical(c, categories=trn[n].cat.categories, ordered=True)","def clean_data(df):\n \n # split categories into separate category columns\n categories = df['categories'].str.split(';', expand = True)\n \n # select the first row of the categories dataframe\n row = categories.loc[0,:]\n \n # use this row tp extract a list of new column names for categories\n # here we apply a lambda function that slices the data and takes first part\n category_colnames = list(row.apply(lambda x: x.split('-')[0]))\n \n # rename the columns of categories series\n categories.columns = category_colnames\n \n # convert category values to just numbers 0 or 1\n for column in categories:\n # set each value to be the last character of the string\n categories[column] = categories[column].apply(lambda x: x.split('-')[1])\n \n # convert column from string to numeric\n categories[column] = categories[column].astype(int)\n \n # replace categories column in df with new category columns\n df.drop(columns = ['categories'], inplace = True)\n \n # concatenate the original dataframe with the new categories dataframe\n df = pd.concat([df, categories], axis = 1)\n \n # remove duplicates\n df = df.drop_duplicates(keep = 'first')\n \n df['related'] = df['related'].replace(2,1)\n \n return df # return the cleaned dataframe","def clean_up_raw(df_raw):\n # exclude_subset = ['well', 'tile', 'cell', 'intensity', 'blob'] # causes issues with later joins, maybe a pandas bug\n import lasagna.utils\n df_raw[CYCLE] = df_raw[CYCLE].astype(int)\n df_raw = df_raw.sort_values([WELL, TILE, CELL, BLOB, CYCLE, CHANNEL])\n return df_raw","def clean_data(df):\n \n # split the categories column on semicolon and expand each split into \n # a column of a new dataframe\n cats_expanded = df.categories.str.split(';', expand=True)\n\n # take the first row of the new df, split on hyphen and grab the first column of the\n # new expanded df. This column should contain the label name\n labels = list(cats_expanded.iloc[0, :].str.split('-', expand=True)[0])\n \n # remove none numerics from cats_expanded df columns - replae label names with '', leaving\n # only numeric values\n cats_expanded = cats_expanded.apply(lambda col: col.str.replace('[^0-9]', ''))\n \n # convert to int from str\n cats_expanded = cats_expanded.astype(int)\n \n # assign label names\n cats_expanded.columns = labels\n\n # make sure all label values are binary\n cats_expanded.where(cats_expanded < 2, 1, inplace=True)\n\n # check to make sure we have at least one instance for each label\n labels_with_no_instance = cats_expanded.columns[~(cats_expanded == 1).any(axis=0)]\n # drop labels that have no instance of positive class\n cats_expanded = cats_expanded.drop(labels_with_no_instance, axis=1)\n\n # merge with orginal dataframe on index and drop now defunct 'categories' column\n clean_cats = df \\\n .merge(cats_expanded, how='inner', left_index=True, right_index=True) \\\n .drop('categories', axis=1)\n\n return clean_cats","def clean_data(df):\n categories = df['categories']\n categories = categories.str.split(';', expand=True)\n row = categories.iloc[0]\n category_colnames = row.apply(lambda x: x[:-2])\n categories.columns = category_colnames\n for column in categories:\n # set each value to be the last character of the string\n categories[column] = categories[column].apply(lambda x: x[-1:])\n\n # convert column from string to numeric\n categories[column] = pd.to_numeric(categories[column])\n \n df = df.drop(['categories'], axis = 1)\n df = pd.concat([df,categories], axis = 1)\n df.drop_duplicates(keep='first',inplace=True)\n return df","def clean_data(df):\n\t# create a dataframe of the 36 individual category columns\n\tcategories = df['categories'].str.split(';', expand=True)\n\t# select the first row of the categories dataframe\n\trow = categories.loc[0]\n\t# extract a list of new column names for categories. Remove unnecessary chars.\n\tcategory_colnames = row.str.replace(r'-\\w','')\n\t# rename the columns of `categories`\n\tcategories.columns = category_colnames\n\t# Convert category values to just numbers 0 or 1.\n\tcategories = categories.applymap(lambda x: int(x.split('-')[1]))\n\t# drop the original categories column from `df`\n\tdf.drop(['categories'],axis=1, inplace=True)\n\t# concatenate the original dataframe with the new `categories` dataframe\n\tdf = pd.concat([df,categories],axis=1)\n\t# find duplicates\n\tdups = df.duplicated(subset=None, keep='first')\n\t# drop duplicates\n\tdf = df[~(dups)]\n\treturn df","def _convert_to_dummies_pandas(df: pd.DataFrame) -> pd.DataFrame:\n df = df.copy()\n print(\"Raw dataset shape = {}\".format(df.shape))\n print(\"Creating one hot encoded categories...\")\n for col in df.columns:\n if col in CATEGORICAL_TRANS and col != \"isFraud\" and not col.startswith(\"card\"):\n print(\"Handling category: {}\".format(col))\n # Convert to categorical type, may not be necessary.\n df[col] = pd.Categorical(df[col])\n one_hot_encoded = pd.get_dummies(df[col], prefix=col)\n df = df.drop(columns=[col]) # remove the original categorical column.\n # Add the one-hot-encoded column.\n df = pd.concat([df, one_hot_encoded], axis=1)\n print(\"One-hot-encoded dataset shape = {}\".format(df.shape))\n return df","def clean(df):","def transform(self, df: DataFrame) -> DataFrame:\n return df","def transform(self, df):\n _df = df.copy()\n categorical = self.find_categorical(df)\n for cat in categorical:\n _df[cat] = pd.Categorical(_df[cat], categories=_test_categories[cat])\n\n return pd.get_dummies(_df[categorical]).as_matrix()","def categorical(df):\n\n # variables which need to be transformed to categorical\n categorical = [\"prop_country_id\", \"visitor_location_country_id\"]\n\n for var in categorical:\n df = pd.concat([df, pd.get_dummies(df[var], prefix=var)], axis=1)\n del df[var]\n\n return df","def preprocess(df): \n \n df.drop_duplicates(subset=df.columns[0], inplace=True) #drop duplicate gene_names. \n df.set_index(keys=df.columns[0], inplace=True)\n df.dropna(how='all', inplace=True) #drop rows with all NAs\n df2 = df.select_dtypes(include=['float64']) + 0.001 #select numbers in DataFrame \n \n return df2","def convert_categorical(df):\n print(\" --- Converting Categories into binary features.\")\n columns = df.columns\n categorical = [x for x in columns if x.startswith('c_')]\n for col in categorical:\n print(\" ---- Converting: {}\".format(col))\n category_binary = pd.get_dummies(df[col], prefix=col)\n df = pd.concat([df, category_binary], axis=1)\n df = df.drop(categorical, axis=1)\n print(\" --- Finished converting Categories into binary features.\")\n return df","def _convert_to_dummies_sklearn(df: pd.DataFrame) -> pd.DataFrame:\n raise NotImplementedError","def transform(self, df):\n df = df.copy()\n \"\"\"\n if self.grouping is not None:\n df = self.hier.transform(df)\n \"\"\"\n # fill NaN\n df = self.fill_na(df)\n\n self.df_index = df.index\n self.df_colnames = df.columns\n # transformations\n for i in sorted(self.transformations.keys()):\n transformation = self.transformations[i]\n df = self.transformers[i].transform(df)\n # convert to DataFrame only if it isn't already\n if not isinstance(df, pd.DataFrame):\n df = pd.DataFrame(df)\n df.index = self.df_index\n df.columns = self.df_colnames\n # update index reference if sliced\n if transformation in ['Slice']:\n self.df_index = df.index\n self.df_colnames = df.columns\n df = df.replace([np.inf, -np.inf], 0) # .fillna(0)\n return df","def pre_process_data_set(df):\n df.replace([np.inf, -np.inf], np.nan)\n df[df == np.inf] = np.nan\n df = remove_bad_columns(df)\n df = fill_na(df)\n df = convert_factorial_to_numerical(df)\n\n # Remove columns only containing 0\n df = df[(df.T != 0).any()]\n return df","def transform(self, data: pd.DataFrame):\n raise NotImplementedError","def _inplace_fix_subset_categorical_obs(subset_adata: AnnData, original_adata: AnnData) -> None:\n obs = subset_adata.obs\n for column in obs.columns:\n is_categorical = pd.api.types.is_categorical_dtype(obs[column])\n if is_categorical:\n c = obs[column].cat.set_categories(original_adata.obs[column].cat.categories)\n obs[column] = c","def _prune_categories(\n df: pd.DataFrame,\n prune_dict: dict,\n fill: object = \"Other\",\n) -> pd.DataFrame:\n df = df.copy()\n for col in df.columns:\n if col in CATEGORICAL_TRANS:\n if col not in prune_dict.keys():\n continue\n n = prune_dict[col]\n if n == -1:\n continue\n # Get most frequent:\n major_categories = list(\n df[col].value_counts()[:n].keys()\n )\n mask = df[col].isin(major_categories)\n df[col][~ mask] = fill\n return df","def transform(self, df):\n\t\tdf = self.__parse_json(df)\n\t\tdf = self.__fillnan(df)\n\t\tdf = self.__parse_dates(df)\n\t\tdf['budget'] = df['budget'].apply(lambda x: self.missing_budget_imputing if int(x) == 0 else x)\n\t\tdf['has_collections'] = df['belongs_to_collection'].isna().astype(int)\n\t\tdf['homepage'] = df['homepage'].isna().astype(int)\n\t\tdf['is_en'] = df['original_language'].apply(lambda x: 1 if x == 'en' else 0)\n\t\tdf = self.__encode_genre_transform(df)\n\t\tdf = self.__top_countries_and_companies_transform(df)\n\t\tdf = self.__bin_columns_transform(df)\n\t\tdf.drop(\n\t\t\t['release_date', 'original_language', 'production_countries', 'production_companies', 'id', 'backdrop_path',\n\t\t\t 'imdb_id', 'poster_path', 'video', 'belongs_to_collection', 'status', 'runtime',\n\t\t\t 'original_title', 'overview', 'tagline', 'title'], axis=1, inplace=True)\n\t\treturn df","def add_category(df):\n df[\"category\"] = df.apply(lambda row: transform_cat(row), axis=1)\n df = drop_cols(df, [\"booking_bool\", \"click_bool\"])\n return df","def dummify_all_categorical(df):\n\n df = pd.get_dummies(df)\n df = dummify(df, \"detailed industry recode\")\n df = dummify(df, \"detailed occupation recode\") ## add some variables that are encoded as int64 but that are in fact categorical\n return df","def preprocess(old_df, label_name, category_features, non_category_features):\n old_df['fraud'] = old_df[label_name].apply(lambda x: x[0] == 'f')\n\n # Creating a new dataframe with a subset of features.\n new_df = old_df[['fraud'] + non_category_features]\n\n # For categorical features, we make dummy variables,\n # and merge them into new_df.\n for feature in category_features:\n dummy_df = pd.get_dummies(old_df[feature], prefix=feature,\n dummy_na=True)\n # Since dummy_na=True, the last column will be for null values.\n dummy_df.drop(dummy_df.columns[-1], axis=1, inplace=True)\n new_df = pd.concat([new_df, dummy_df], axis=1)\n return new_df","def revise_report_df(report_df):\n # quick check to see whether report_df column structure is as expected\n if report_df.columns.tolist() != ['0', '1', 'accuracy', 'macro avg', 'weighted avg']:\n print(\"Warning: Column names aren't as expected. Verify report_df output_dict is correct.\")\n\n report_df.columns = ['0', '1', 'accuracy', 'Macro Avg', 'Micro Avg' ]\n\n dict_columns = ['0', '1', 'Macro Avg', 'Micro Avg']\n keys = ['precision', 'recall', 'f1-score', 'support']\n\n for col in dict_columns:\n # revise key values to personalize to its associated column i.e. from 'precision' to 'precision_0'\n report_df[col] = report_df[col].apply(lambda x: customize_dict_keys(x, col, keys))\n\n # iterate row wise through dataframe to normalize dictionary values into flat tables\n new_dict = {str(classifier) + '_df': normalize_to_flat(classifier, report_df, col) for classifier in report_df.index.values.tolist()}\n\n # concat all classifier flat tables into one dataframe\n dict_df = pd.concat(list(new_dict.values())).reset_index().drop(columns=['index'], axis=1)\n\n # merge on existing report_df dataframe index and on dict_df 'classifier' column value\n report_df = report_df.merge(dict_df, how='left', left_index=True, left_on=None, right_on='classifier').set_index('classifier')\n\n # select only created columns\n report_df = report_df.iloc[:,5:]\n # sort columns and filter out 'support' related columns\n report_df = report_df[sorted([col for col in report_df.columns if 'support' not in col])]\n\n return report_df","def preprocess(self, df):\n print(\"Started Processing....\")\n # binary conversion\n df.replace(to_replace=\"yes\", value=1, inplace=True)\n df.replace(to_replace=\"no\", value=0, inplace=True)\n\n # replace unknowns with nan\n df = df.replace(to_replace=\"unknown\", value=np.nan)\n # getting the list of columns with nan\n ml = df.columns[df.isna().any()].tolist()\n\n for item in ml:\n # getting the ratio of the index labels\n val = pd.DataFrame(df[item].value_counts(normalize=True))\n\n # index labels in a list\n valr = val.index.tolist()\n # drc.index = valr\n # columns values in a list\n valc = val[item].tolist()\n # replacing the nan values with ratio\n df[item] = df[item].fillna(pd.Series(np.random.choice(valr, p=valc, size=len(df))))\n\n # dependent variable\n dfy = df.iloc[:, -1]\n # independent variable\n dfx = df.iloc[:, :-1]\n\n # converting categorical data to numerical\n dfx = pd.get_dummies(dfx)\n\n # normalizing\n dfx = (dfx - dfx.min()) / (dfx.max() - dfx.min())\n\n dxdy = pd.concat([dfx, dfy], axis=1)\n\n # class balancing\n sm = RandomOverSampler(random_state=42)\n dfx, dfy = sm.fit_sample(dxdy.iloc[:, :-1], dxdy.iloc[:, -1])\n\n # converting to dataframe\n dfx = pd.DataFrame(dfx, columns=dxdy.iloc[:, :-1].columns.values)\n\n # dimensionality reduction\n pca = PCA(n_components=33)\n dfx = pca.fit_transform((dfx))\n\n print(\"Processing Done\")\n\n return dfx, dfy","def train_cats(df):\n for n,c in df.items():\n if is_string_dtype(c): df[n] = c.astype('category').cat.as_ordered()","def train_cats(df):\n for n,c in df.items():\n if is_string_dtype(c): df[n] = c.astype('category').cat.as_ordered()","def preprocess_features(X):\n\t# Initialize new output DataFrame\n\toutput = pd.DataFrame(index = X.index)\n\n\t# Investigate new output DataFrame\n\tfor col, col_data in X.iteritems():\n\t\t# If data type is categorical, convert to dummy variables\n\t\tif col_data.dtype == object:\n\t\t\tcol_data = pd.get_dummies(col_data, prefix = col)\n\n\t\t\t# Collect the revised columns\n\t\t\toutput - output.join(col_data)\n\treturn output","def preprocess(self, df: pd.DataFrame) -> pd.DataFrame:\n for transform in self.transforms:\n cols = transform(df)\n\n if isinstance(cols, pd.Series):\n df.loc[:, transform.name] = cols\n\n elif isinstance(cols, list):\n for i, col in enumerate(cols):\n\n if not isinstance(col, pd.Series):\n raise ValueError('Invalid Transform return type:', type(cols))\n\n df.loc[:, f'{transform.name}_{i}'] = col\n\n else:\n raise ValueError('Invalid Transform return type:', type(cols))\n return df","def transform(self, data: pd.DataFrame, columns: list, verbose: int=1) -> pd.DataFrame:","def process_dataframe(df):\n # iterate over all items\n for i in range(len(df.index)):\n # replace policy with a list of integers\n df.loc[i, 'policy'] = list(map(int, df.loc[i, 'policy'].split('-')))\n # get rid of ':' in letter\n df.loc[i, 'letter'] = df.loc[i, 'letter'].split(':')[0]\n\n return df","def categories(df):\n print 'going to code categories'\n print_time()\n\n ethnicities = ['Mexican', 'Italian', 'American']\n df['num_categories'] = 0\n for ethnicity in ethnicities:\n df.loc[df['categories'].str.contains(ethnicity, flags=re.I, na=False, case=False), 'category'] = ethnicity\n df.loc[df['categories'].str.contains(ethnicity, flags=re.I, na=False, case=False), 'num_categories'] += 1\n df.loc[df['category'].isnull(), 'category'] = 'Other'\n df.loc[df['num_categories'] > 1, 'category'] = 'Multiple'\n df = df[df['category'].notnull()]\n return df","def _dataframe_preprocess(self):\n # 1. add baisc feature like date, time in day, ....\n if self.data_type != 'porto':\n self.df['TIMESTAMP'] = self.df.apply(lambda df: df['TIMESTAMPS'][0], axis=1)\n self.df['TIME'] = pd.to_datetime(self.df['TIMESTAMP'], unit='s', utc=True)\n \n self.df.TIME = self.df.TIME.dt.tz_convert(self.timezone)\n # 2. group df for specific driver analysis\n self.grouped_df = self.df.groupby('LABEL')\n if self.count_od_info:\n if 'SD' not in self.df.columns:\n self._add_OD_info()\n self.grouped_od = self.df.groupby('SD')","def transform(self, df):\n _df = df.copy()\n _df['Age'] = _df['YrSold'] - _df['YearBuilt']\n _df['AgeRemod'] = _df['YrSold'] - _df['YearRemodAdd']\n _df['Baths'] = _df['FullBath'] + _df['HalfBath']\n _df['BsmtBaths'] = _df['BsmtFullBath'] + _df['BsmtHalfBath']\n _df['OverallQual_Square'] = _df['OverallQual'] * _df['OverallQual']\n _df['OverallQual_3'] = _df['OverallQual'] * _df['OverallQual'] * _df['OverallQual']\n _df['OverallQual_exp'] = np.exp(_df['OverallQual'])\n _df['GrLivArea_Square'] = _df['GrLivArea'] * _df['GrLivArea']\n _df['GrLivArea_3'] = _df['GrLivArea'] * _df['GrLivArea'] * _df['GrLivArea']\n _df['GrLivArea_exp'] = np.exp(_df['GrLivArea'])\n _df['GrLivArea_log'] = np.log(_df['GrLivArea'])\n _df['TotalBsmtSF_/GrLivArea'] = _df['TotalBsmtSF'] / _df['GrLivArea']\n _df['OverallCond_sqrt'] = np.sqrt(_df['OverallCond'])\n _df['OverallCond_square'] = _df['OverallCond'] * _df['OverallCond']\n _df['LotArea_sqrt'] = np.sqrt(_df['LotArea'])\n _df['1stFlrSF_log'] = np.log1p(_df['1stFlrSF'])\n _df['1stFlrSF'] = np.sqrt(_df['1stFlrSF'])\n _df['TotRmsAbvGrd_sqrt'] = np.sqrt(_df['TotRmsAbvGrd'])\n categorical = self.find_categorical(df)\n numerical = self.find_numerical(df)\n\n for cat in categorical:\n _df[cat] = pd.Categorical(_df[cat], categories=_test_categories[cat])\n\n for key in numerical:\n _df[key].fillna(_df[key].median(), inplace=True)\n\n # #if numerical feature are skewed apply log\n\n skewed_feats = _df[numerical].apply(lambda x: scipy.stats.skew(x.dropna())) # compute skewness\n skewed_feats = skewed_feats[skewed_feats > 0.75]\n skewed_feats = skewed_feats.index\n _df[skewed_feats] = np.log1p(_df[skewed_feats])\n cat_matrix = pd.get_dummies(_df[categorical]).as_matrix()\n num_matrix = StandardScaler().fit_transform(np.asarray(_df[numerical]))\n\n return np.hstack((num_matrix, cat_matrix))","def prepare_data_features(raw=False, round_ratings=False):\n df = prepare_data(raw=False)\n print(\"prepare features\")\n df_categories = df['categories'].str.get_dummies(sep=\", \")\n df_categories = df_categories[df_categories.columns[\n df_categories.sum() > len(df)*0.01]]\n df = pd.concat([df.drop('categories', 1), df_categories], axis=1)\n print(\"end prepare features\")\n return df","def preprocess(data_df, remove_stopwords=False):\n data_cp = data_df.copy()\n for i, row in tqdm(data_cp.iterrows(), total=len(data_cp), desc='Preprocessing dataframe contents'):\n\n article_content = _clean(row.article_content, remove_stopwords)\n row.article_content = _tokenize_stem_lem_join(article_content)\n\n _clean_claim = _clean(row.claim, remove_stopwords)\n row.claim = _tokenize_stem_lem_join(_clean_claim)\n\n data_cp.loc[i] = row\n\n return data_cp","def data_preprocessing(dataframe):\n\n dataframe = dataframe.loc[:, 0:]\n dataframe.columns = [\"url\", \"date\", \"personurl\", \"name\"]\n dataframe[\"date_new\"] = [i.replace(\"Düzenlendi\", \"\").replace(\"•\", \"\").strip() for i in dataframe[\"date\"]]\n dataframe[\"date_new\"] = [str(int(i.replace(\"yıl\", \"\").strip()) * 48) if \"yıl\" in i else i for i in\n dataframe[\"date_new\"]]\n dataframe[\"date_new\"] = [str(int(i.replace(\"ay\", \"\").strip()) * 4) if \"ay\" in i else i for i in\n dataframe[\"date_new\"]]\n dataframe[\"date_new\"] = [i.replace(\"hafta\", \"\").strip() if \"hafta\" in i else i for i in dataframe[\"date_new\"]]\n\n return dataframe","def pre_process(self, frame: pd.DataFrame) -> pd.DataFrame:\n # frame = rows_filtering(frame)\n # frame = feature_dropping(frame)\n # frame = feature_values_fixing(frame)\n\n # frame = extreme_values_handling(frame, [])\n # missing_value_imputation(frame, [])\n\n # data_type_conversion(frame)\n # frame = feature_engineering(frame, self.GENERATE_USER_FEATURES)\n # feature_renaming(frame)\n\n return frame","def clean(dataframe):\n # replace 'unknown' in Pop. density with np.nan\n dataframe = dataframe.replace('unknown', np.nan)\n\n # remove spaces from column names and content\n dataframe.columns = dataframe.columns.str.strip()\n\n # change YYYYMMDD to days of the year\n date_debug = []\n for i in range(1, 366):\n date_debug.append(i)\n\n dataframe2 = {'YYYYMMDD': date_debug}\n dataframe['YYYYMMDD'] = dataframe2['YYYYMMDD']\n\n return dataframe","def preprocess(df):\n \n # drop the following columns - irrelevant now\n DROP_COLUMNS = ['id', 'original_title', 'release_date'\n , 'tmdbId', 'popularity', 'year']\n df.drop(DROP_COLUMNS, axis=1, inplace=True)\n \n # drop all of the language columns\n DROP_COLUMNS = [col for col in df.columns if col[:3]==\"lan\"]\n df.drop(DROP_COLUMNS, axis=1, inplace=True)\n\n # loop through the columns we want to aggregate\n for col_type in [\n \"original_language_\"\n , \"prod_comp_cntry_\"\n , \"prod_comp_names_\"\n , \"writers_\"\n , \"actors_\"\n , \"genres_\"\n , \"director_\"\n ]:\n # create a dictionary of each unique value and its frequency\n val_freq = {}\n for col in df.columns:\n if col.startswith(col_type):\n val_freq[col] = df[col].sum()\n\n # create a dataframe from this dictionary; sort by count\n counts = pd.DataFrame.from_dict(\n val_freq\n , orient='index'\n , columns=['count']\n ).sort_values('count', ascending=False)\n counts['frac'] = counts['count'].apply(lambda x: 100*x / df.shape[0])\n\n # handle special case of production company country\n if col_type == \"prod_comp_cntry_\":\n DROP_COLUMNS = [col for col in counts.index][3:]\n\n # handle special case of directors\n elif col_type == \"director_\":\n DIRECTOR_COLS = [col for col in df.columns\n if col.startswith(\"director_\")\n and col!=\"director_pop\"]\n df['established_director'] = df[DIRECTOR_COLS].max(axis=1)\n DROP_COLUMNS = DIRECTOR_COLS\n\n # handle special case of actors\n elif col_type == \"actors_\":\n ACTORS_COLS = [col for col in df.columns if \"actors\" in col]\n df['num_top_100_actors'] = df[ACTORS_COLS].sum(axis=1)\n DROP_COLUMNS = ACTORS_COLS\n\n # handle all the other cases\n else:\n DROP_COLUMNS = [col for col in counts.query('frac < 2').index]\n\n\n df.drop(DROP_COLUMNS, axis=1, inplace=True)\n \n ##########################################################################\n # adjust the data for inflation\n CPI_tf = df['CPIAUCSL'].max()\n df['budget'] = df[['budget', 'CPIAUCSL']].apply(\n cpi_adjust\n , args=(CPI_tf ,)\n , axis=1\n )\n df['revenue'] = df[['revenue', 'CPIAUCSL']].apply(\n cpi_adjust\n , args=(CPI_tf ,)\n , axis=1\n )\n # no longer need CPI data\n df.drop('CPIAUCSL', axis=1, inplace=True)\n \n ########################################################################## \n # add in useful features about the cast and crew \n df['cast_crew_sum_pop'] = (\n df['director_pop']\n + df['avg_actor_pop']\n + df['avg_writer_pop']\n )\n df['cast_crew_product_pop'] = (\n df['director_pop']\n * df['avg_actor_pop']\n * df['avg_writer_pop']\n )\n df['runtime'].replace(to_replace=0, value=df['runtime'].median(), inplace=True)\n df = df.query('10000 <= revenue').copy()\n df = df.query('100000 <= budget').copy()\n df.drop('sum_actor_pop', axis=1, inplace=True)\n df.drop('min_writer_pop', axis=1, inplace=True)\n\n # code to transform columns\n for col in [\n \"budget\", \"director_pop\", \"avg_writer_pop\"\n , \"max_writer_pop\", \"avg_actor_pop\", \"max_actor_pop\"\n , \"min_actor_pop\", 'cast_crew_sum_pop'\n , 'cast_crew_product_pop'\n ]:\n df['log10_'+col] = df[col].apply(lambda x: math.log10(x))\n df.drop(col, axis=1, inplace=True)\n \n return df","def preprocess(df):\n # there were very few missing records for these columns, thus\n # we drop them to prevent biasing the data with imputation\n # (only 12 rows were removed)\n subset = ['num-of-doors', 'bore', 'stroke', 'horsepower', 'peak-rpm', 'price']\n df = df.dropna(subset = subset)\n\n # normalized-losses, drop due to lots of missing values\n # make drop due to having too many distinct categorical values\n df = df.drop(['normalized-losses', 'make'], axis = 1)\n\n # extract the response/output column\n response_col = 'price'\n y = df[response_col].values\n df = df.drop(response_col, axis = 1)\n\n # categorical variables: one-hot-encode them\n # the numeric variables are the columns excluding\n # the categorical variables and the variables that\n # have been detected to have multicollinearity using\n # VIF (variance inflation factor)\n cat_cols = ['symboling', 'fuel-type', 'aspiration', 'num-of-doors',\n 'body-style', 'drive-wheels', 'engine-location', 'fuel-system',\n 'engine-type', 'num-of-cylinders']\n for col in cat_cols:\n df[col] = df[col].astype('category')\n\n dummied = pd.get_dummies(df[cat_cols], drop_first = True)\n\n # use sorted set to ensure the consistency of the column order\n collinear_cols = SortedSet(['city-mpg', 'curb-weight', 'horsepower', 'length', 'width'])\n num_cols = list(SortedSet(df.columns) - SortedSet(cat_cols) - collinear_cols)\n X = pd.concat([df[num_cols], dummied], axis = 1)\n\n # standardize the numeric columns using the training set\n X_train, X_test, y_train, y_test = train_test_split(X.values, y,\n test_size = 0.2,\n random_state = 4321)\n scaler = StandardScaler()\n X_train[:, :len(num_cols)] = scaler.fit_transform(X_train[:, :len(num_cols)])\n X_test[:, :len(num_cols)] = scaler.transform(X_test[:, :len(num_cols)])\n return X_train, X_test, y_train, y_test, X.columns","def preprocess(data, to_drop=[]):\n \n columns = data.columns.to_list()\n \n # split data to numeric vs categorical\n numeric_features = data.select_dtypes(include=[\n 'int64', 'float64']).columns\n \n if len(to_drop) > 0:\n categorical_features = data.select_dtypes(include=[\n 'object']).drop(to_drop, axis=1).columns\n print(categorical_features)\n else: \n categorical_features = data.select_dtypes(include=[\n 'object']).columns\n \n categorical_transformer = Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent', fill_value='missing'))])\n \n numerical_transformer = Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n # missing_values = np.nan\n \n# Bundle preprocessing for numerical and categorical data\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numeric_features),\n ('cat', categorical_transformer, categorical_features)\n ])\n\n my_pipeline = Pipeline(steps=[('preprocessor', preprocessor) ])\n \n for col in to_drop:\n columns.remove(col)\n print('Hello')\n \n trans_data = my_pipeline.fit_transform(data)\n return trans_data#pd.DataFrame(#, columns=columns) ","def _transform_df(self, data):\n # specify if has FIPS or not\n if self.has_location:\n loc_col_type = \"location\"\n elif not self.has_location:\n loc_col_type = \"location_name\"\n\n out = data.melt(\n id_vars=[\"dt\", loc_col_type], value_vars=self.crename.keys()\n ).dropna()\n out.loc[:, \"value\"] = pd.to_numeric(out[\"value\"])\n out = self.extract_CMU(out, self.crename)\n out[\"vintage\"] = self._retrieve_vintage()\n\n cols_to_keep = [\n \"vintage\",\n \"dt\",\n loc_col_type,\n \"category\",\n \"measurement\",\n \"unit\",\n \"age\",\n \"race\",\n \"ethnicity\",\n \"sex\",\n \"value\",\n ]\n return out.loc[:, cols_to_keep]","def clean_up(df: pd.DataFrame) -> pd.DataFrame:\n\n # Hereafter df is sorted by date, which is helpful as it allows using .iloc[-1]\n # to get current (or most recent known) situation per location\n # (Otherwise we'd have to groupby agg -> min date, and then filter)\n df = df.sort_values(\n [Columns.LOCATION_NAME, Columns.DATE, Columns.CASE_TYPE], ascending=True\n )\n\n return df","def clean_data(df):\n \n # Put in code here to execute all main cleaning steps:\n # convert missing value codes into NaNs, ...\n count_miss = df.isnull().sum(axis=0).values #find number of nans for each column\n count_miss = [val for val in count_miss]\n \n drop_cols = []\n\n for ind, val in enumerate(count_miss):\n if val > 200000:\n drop_cols.append(ind)\n \n df_drop_cols = list(azdias.columns[drop_cols])\n df = df.drop(df_drop_cols, axis=1)\n \n for col in range(df.shape[1]): #loop through columns\n column_name = df.columns[col] #get column name\n missing_list = feat_info.iloc[col,3] #get missing_or_unknown column from feature info\n missing_list = missing_list.replace('[','') #remove left bracket from string\n missing_list = missing_list.replace(']','') #remove right bracket from string\n missing_list = missing_list.split(',') #split into individual strings\n \n #find data that is natually missing and continue loop to omit\n if missing_list == ['']:\n continue\n \n else:\n for dat_type in missing_list: \n if df[column_name].dtype == 'object': #find values that contain x\n df.loc[df[column_name] == dat_type, column_name] = np.nan #replace x with nan\n \n else:\n dat_type = int(dat_type) #if no x, convert to integer and replace with nan\n df.loc[df[column_name] == dat_type, column_name] = np.nan\n \n # select, re-encode, and engineer column values.\n \n # encode OST_WEST_KZ\n df.loc[df['OST_WEST_KZ'] == 'W','OST_WEST_KZ'] = 0\n df.loc[df['OST_WEST_KZ'] == 'O','OST_WEST_KZ'] = 1\n \n # Re-encode categorical variable(s) to be kept in the analysis.\n \n \n #get list of attributes with type categorical\n feat_info[feat_info['type'] == 'categorical']\n \n cat_new_cols = [] #initialize\n for i in feat_info[feat_info['type'] == 'categorical']['attribute']:\n cat_new_cols.append(i)\n \n for cols in df.columns:\n if cols in cat_new_cols:\n if df[cols].nunique(dropna=True) > 2: #if the number of unique values is greater than 2 \n df = df.drop(cols, axis=1) #drop from the analysis\n print(\"more than 2 categories: {}\".format(cols))\n \n else:\n if not df[cols].unique()[0] > 0:\n #if not df[cols].unique()[0] > 0:\n dummies = pd.get_dummies(df[cols], prefix=cols)\n df = df.drop(cols, axis=1) #create dummy variable\n df = df.join(dummies)\n print(\"transformed to dummy variable: {}\".format(cols))\n \n # create variable: MOVEMENT\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([1,3,5,8,10,12,14]),'MOVEMENT'] = 1\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([2,4,6,7,9,11,13,15]),'MOVEMENT'] = 2\n \n #Capture Decade\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([1,2]), 'DECADE'] = 40\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([3,4]), 'DECADE'] = 50\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([5,6,7]), 'DECADE'] = 60\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([8,9]), 'DECADE'] = 70\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([10,11,12,13]), 'DECADE'] = 80\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([14,15]), 'DECADE'] = 90\n \n df['CAMEO_INTL_2015'] = df['CAMEO_INTL_2015'].astype(float)\n\n # create new variable: WEALTH\n df.loc[df['CAMEO_INTL_2015'].isin([51,52,53,54,55]), 'WEALTH'] = 1\n df.loc[df['CAMEO_INTL_2015'].isin([41,42,43,44,45]), 'WEALTH'] = 2\n df.loc[df['CAMEO_INTL_2015'].isin([31,32,33,34,35]), 'WEALTH'] = 3\n df.loc[df['CAMEO_INTL_2015'].isin([21,22,23,24,25]), 'WEALTH'] = 4\n df.loc[df['CAMEO_INTL_2015'].isin([11,12,13,14,15]), 'WEALTH'] = 5\n \n # create new variable: LIFE_STAGE\n df.loc[df['CAMEO_INTL_2015'].isin([11,21,31,41,51]),'LIFE_STAGE'] = 1\n df.loc[df['CAMEO_INTL_2015'].isin([12,22,32,42,52]),'LIFE_STAGE'] = 2\n df.loc[df['CAMEO_INTL_2015'].isin([13,23,33,43,53]),'LIFE_STAGE'] = 3\n df.loc[df['CAMEO_INTL_2015'].isin([14,24,34,44,54]),'LIFE_STAGE'] = 4\n df.loc[df['CAMEO_INTL_2015'].isin([15,25,35,45,55]),'LIFE_STAGE'] = 5\n \n # remove selected columns and rows, ...\n df = df.drop('PRAEGENDE_JUGENDJAHRE', axis=1)\n df = df.drop('CAMEO_INTL_2015',axis=1)\n \n # Return the cleaned dataframe.\n return df","def preprocess_terms(df, dataType='testNames'):\n pass","def classify_df(full_df: pd.DataFrame) -> pd.DataFrame:\n # Use the first 10 rows, and set classification target.\n yield utils.AddClassificationTargetToDataFrame(\n full_df.iloc[range(10), :].copy(), \"amd_tahiti_7970\"\n )","def reconfigures_dataframe(split_list: List, data_frame: pd.DataFrame) -> pd.DataFrame:\n\n # get non-ontology columns\n non_category_columns = [x for x in data_frame.columns if not any(i for i in split_list if i.lower() in x.lower())]\n\n # make sure that all blank variables are converted to NaN\n data_frame = data_frame.replace(r'^\\s*$', np.nan, regex=True)\n\n # identify which columns belong to each of the input ontologies\n category_split_data = []\n for x in split_list:\n # get category-specific columns\n cat_columns = [i for i in data_frame.columns if x.lower() in i.lower()]\n # subset original data to include non-category and specific single category data\n cat_data = data_frame[non_category_columns + cat_columns].drop_duplicates()\n # drop rows where all category columns are NaN and blank string\n cat_data = cat_data.dropna(subset=cat_columns, how='all')\n cat_data.columns = non_category_columns + [col.upper().replace(x.upper(), 'CATEGORY') for col in cat_columns]\n # add category specific column\n cat_data['CATEGORY'] = [x] * len(cat_data)\n category_split_data.append(cat_data)\n\n # concatenate stacked data into single DataFrame\n reconfigured_data = pd.concat(category_split_data)\n\n return reconfigured_data.fillna('').drop_duplicates()","def process_categorical_data(data_df):\n return pd.get_dummies(data_df, columns=Columns.categorical)","def preprocessing(dataframe):\n dataframe.loc[:, \"content\"] = dataframe.loc[:, \"content\"].str.replace(\"&.*?;\", \"\")\n dataframe.loc[:, \"content\"] = dataframe.loc[:, \"content\"].str.replace(\n r\"[^a-zA-Z\\s.:!?/#\\\"]\", \"\"\n )\n dataframe[\"original_content\"] = dataframe[\"content\"]\n dataframe.loc[:, \"content\"] = dataframe.loc[:, \"content\"].str.lower()\n return dataframe","def preprocess(df):\n drop_cols = ['duration_ms', 'key', 'mode', 'time_signature', 'popularity', 'tempo']\n drop_cols += ['track_id', 'track_name', 'artist_name']\n for col in drop_cols:\n if col in list(df.columns):\n df = df.drop(columns=col)\n return df","def encode_category(df, enc, col, method):\n \n if method == 'label':\n # lb = LabelEncoder()\n # lb.fit(df[col])\n df[col] = enc.transform(df[col].astype(str))\n \n elif method == 'onehot':\n # ohe = OneHotEncoder(handle_unknown='ignore')\n # ohe.fit(df[[col]])\n tempdf = enc.transform(df[[col]]).toarray()\n newdf = pd.DataFrame(tempdf, columns = np.array(ohe.categories_).ravel())\n df = pd.concat([df, newdf], axis=1)\n df.drop(columns=[col], inplace = True)\n \n return df","def group_to_others(\n df: pd.DataFrame, to_be_grouped: dict, replace_value: str = \"Other\"\n) -> pd.DataFrame:\n\n for feature, values in to_be_grouped.items():\n df[feature] = [row if row in values else replace_value for row in df[feature]]\n return df","def encode_dtypes(df):\n\n global catn, cato\n\n # Nominal categories\n for name in catn:\n df[name] = df[name].astype(\"category\")\n # Add a None category for missing values\n if \"None\" not in df[name].cat.categories:\n df[name].cat.add_categories(\"None\", inplace=True)\n # Ordinal categories\n for name, levels in cato.items():\n df[name] = df[name].astype(CategoricalDtype(levels,\n ordered=True))\n return df","def normalize_complexity(df: pd.DataFrame, verbose: bool) -> pd.DataFrame:\n before = len(df)\n df[\"complexity\"] = df[\"complexity\"].apply(\n lambda e: \"100\" if e == \"100+\" else e\n )\n df.dropna(subset=['complexity'], inplace=True)\n df[\"complexity\"] = df[\"complexity\"].astype(int)\n after = len(df)\n if verbose:\n print(f\"Complexity normalization: {before} (before) > {after} (after)\")\n return df","def normalize_etpinard_df(df='https://plot.ly/~etpinard/191.csv', columns='x y size text'.split(),\n category_col='category', possible_categories=['Africa', 'Americas', 'Asia', 'Europe', 'Oceania']):\n possible_categories = ['Africa', 'Americas', 'Asia', 'Europe',\n 'Oceania'] if possible_categories is None else possible_categories\n df.columns = clean_columns(df.columns)\n df = pd.read_csv(df) if isinstance(df, str) else df\n columns = clean_columns(list(columns))\n df2 = pd.DataFrame(columns=columns)\n df2[category_col] = np.concatenate([np.array([categ] * len(df)) for categ in possible_categories])\n columns = zip(columns, [[clean_columns(categ + ', ' + column) for categ in possible_categories] for column in columns])\n for col, category_cols in columns:\n df2[col] = np.concatenate([df[label].values for label in category_cols])\n return df2","def clean_up(houses:pd.DataFrame) -> pd.DataFrame:\n houses= delete_columns(houses)\n houses= analyze_missing_values(houses)\n houses= add_seller_house(houses)\n houses= add_underscore(houses)\n houses= create_dummies(houses)\n houses= impute(houses)\n return houses","def de_normalize_data(self, df):\n if len(df) == 0:\n return df\n result = df.copy()\n for feature_name in self.continuous_feature_names:\n max_value = self.permitted_range[feature_name][1]\n min_value = self.permitted_range[feature_name][0]\n result[feature_name] = (\n df[feature_name]*(max_value - min_value)) + min_value\n return result","def prepare_input_df(df: DataFrame) -> DataFrame:\r\n df = df.fillna('') # Fill np.nan values with blanks (\"\").\r\n df = to_upper(df) # Force case to UPPER for all columns.\r\n df = strip_columns(df) # Remove trailing whitespace.\r\n return df","def deal_with_ordinal(df):\n print(\" --- Dealing with Ordinals.\")\n thresh = get_min_filled_threshold(df)\n columns = df.columns\n ordinal = [x for x in columns if x.startswith('o_')]\n\n for col in ordinal:\n filled = get_non_missing_count(df[col])\n if filled < thresh:\n df[col] = df[col].fillna(-10)\n else:\n mode = df[col].value_counts().idxmax()\n df[col] = df[col].fillna(mode)\n median = df[col].median()\n df[col] = df[col].apply(lambda x: sigmoid(x-median))\n \n print(\" --- Finished dealing with Ordinals.\")\n return df","def _preprocess(self):\n\n self.df = self.df[(self.df['days_b_screening_arrest'] <= 30)\n & (self.df['days_b_screening_arrest'] >= -30)\n & (self.df['is_recid'] != -1)\n & (self.df['c_charge_degree'] != 'O')\n & (self.df['score_text'] != 'N/A')]\n\n self.df['c_jail_out'] = pd.to_datetime(self.df['c_jail_out'])\n self.df['c_jail_in'] = pd.to_datetime(self.df['c_jail_in'])\n self.df['length_of_stay'] = (self.df['c_jail_out']\n - self.df['c_jail_in'])\n\n self.df['score_factor'] = np.where(self.df['score_text']\n != 'Low',\n 'HighScore', 'LowScore')\n self.df['y_pred'] = (self.df['score_factor'] == 'HighScore')","def encoding_df(df, cols):\n import pandas as pd\n df = df[cols]\n obj_df = df.select_dtypes(include=['object']).copy()\n num_var = df.select_dtypes(include=['int','float']).copy()\n cat_var = pd.get_dummies(obj_df, columns = obj_df.columns)\n encoded_df = pd.concat([num_var, cat_var], axis=1, sort=False)\n return encoded_df","def preprocess_data(df, cat_columns, cf_columns):\n \n #define cat and cf columns, convert cat to dummies\n df_cat = pd.get_dummies(df[cat_columns], columns = cat_columns, drop_first=True, dtype=float)\n df_cat.rename(mapper= {\"sex_2\": \"sex_female\", \"education_2\":\"education_university\", \"education_3\": \"education_high_school\", \"education_4\":\"education_others\",\"marital_status_2\":\"marital_status_single\", \"marital_status_3\": \"marital_status_others\"}, axis = 1, inplace = True)\n df_cf = df[cf_columns]\n X = pd.concat([df_cf, df_cat], axis = 1)\n y = df[['default_payment_next_month']]\n print(\"dummy variables created\")\n \n #train-test split \n X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n print(\"split done\")\n \n #resample the train sets\n smote = SMOTE(sampling_strategy = \"not majority\", random_state = 42)\n X_train_rs, y_train_rs = smote.fit_sample(X_train, y_train)\n print('original class distribution:')\n print(y[\"default_payment_next_month\"].value_counts())\n print('synthetic sample class distribution:')\n print(pd.Series(y_train_rs).value_counts()) \n return X, X_train_rs, X_test, y_train_rs, y_test","def replace_numeric_values(*, df, colnames=\"all\", lower_limit=\"none\", upper_limit=\"none\", equal=False, replace_with=np.nan, verbose=True): \n\n \n cols_names = colnames\n \n # .. check provided col_names,\n if cols_names==\"all\": \n cols = list(df.columns)\n else: \n cols = cols_names \n\n # .. info, header, \n if verbose==True:\n print(f\"\"\"\\n{\"\".join([\"-\"]*80)} \\n Replacing Numerical Values in {len(cols)} columns\"\"\") \n print(f\" lower filter={lower_limit}, upper filter ={upper_limit}\")\n if equal==True:\n print(f\" Caution, equal=True, ie. values >= and <= then requested limits will be replaced\")\n print(f'{\"\".join([\"-\"]*80)}\\n') \n \n if verbose==False:\n pass\n \n \n # .. intelligent info,\n total_count=[]\n\n # .. count, to limit the number of displayed messages,\n count = 0\n\n # .. replace values and collect examples, \n for i, j in enumerate(cols):\n\n # ..... assume no values were replaced, so the messages work later, \n info_lower_filter = 0\n info_upper_filter = 0 \n \n # ..... test if the column is of the numeric type:\n # from pandas.api.types import is_numeric_dtype\n if is_numeric_dtype(df[j]):\n \n \n # * replace values < or <= lower limit,\n # - ----------------------------------\n if lower_limit!=\"none\": \n if equal == True:\n lower_filter = df.loc[:,j]<=lower_limit\n if equal == False:\n lower_filter = df.loc[:,j]<lower_limit\n \n # info,\n info_lower_filter=lower_filter.sum()\n df.loc[list(lower_filter),j]=replace_with\n \n \n # * replace values > or >= upper limit,\n # - ----------------------------------\n if upper_limit!=\"none\": \n if equal == True:\n upper_filter = df.loc[:,j]>=upper_limit\n if equal == False:\n upper_filter = df.loc[:,j]>upper_limit\n \n # info,\n info_upper_filter=upper_filter.sum()\n df.loc[list(upper_filter),j]=replace_with \n \n # * find how many values were replaced, and add that to the total_count list \n total_count.append(info_upper_filter+info_lower_filter)\n \n # * display examples for 3 first columns with replaced values,\n if verbose==True:\n if info_upper_filter+info_lower_filter>0 and count <4:\n print(f\"eg: {i}, {j} : {info_lower_filter} values <{lower_limit}, ...{info_upper_filter} values <{upper_limit}\")\n else:\n pass\n\n # * add 1 to count, to limit the number of displayed examples,\n count += 1 \n \n else:\n if verbose==True:\n print(f\"{i, j} is not of numeric type, values were not replaced !\")\n else:\n pass\n \n # .. additional message, if more then 2 columns had replaced values, \n if verbose==True:\n if len(total_count)>3 and pd.Series(total_count).sum()>0:\n print(f\". and {len(total_count)-3} other columns had in total {pd.Series(total_count).sum()} replaced values \\n\")\n\n # .. message in case no values vere replaced at all, \n if pd.Series(total_count).sum()==0:\n print(\"No values were replaced in requested columns....\")\n \n else:\n pass\n \n # .. return, \n return df.copy()","def aitchison_transform_part(df):\n df_aitchison = multiplicative_replacement(df)\n #df_aitchison = closure(df)\n df_idx = df.index\n df_col = df.columns\n df_aitchison = pd.DataFrame(df_aitchison, index = df_idx, columns = df_col)\n return df_aitchison","def processXs(self, df, norm_fn = None, encode_n_minus_1 = False):\n xs, ys = self.splitXY(df)\n # encode categorical\n xs = pd.get_dummies(xs, drop_first = encode_n_minus_1)\n # only normalize non-binary df columns\n if norm_fn:\n bin_cols = xs.apply(DataIO.isBinary, axis=0)\n xs.loc[:, ~bin_cols] = norm_fn(xs.loc[:, ~bin_cols])\n return pd.concat([xs, ys], axis=1)","def fit_transform(self, df):\n\t\tdf = self.__parse_json(df)\n\t\tdf = self.__fillnan(df)\n\t\tdf = self.__parse_dates(df)\n\t\tdf['budget'] = df['budget'].apply(lambda x: self.missing_budget_imputing if int(x) == 0 else x)\n\t\tdf['has_collections'] = df['belongs_to_collection'].isna().astype(int)\n\t\tdf['homepage'] = df['homepage'].isna().astype(int)\n\t\tdf['is_en'] = df['original_language'].apply(lambda x: 1 if x == 'en' else 0)\n\t\tdf = self.__encode_genres(df)\n\t\tdf = self.__top_countries_and_companies(df)\n\t\tdf = self.__bin_columns(df)\n\t\tdf.drop(\n\t\t\t['release_date', 'original_language', 'production_countries', 'production_companies', 'id', 'backdrop_path',\n\t\t\t 'imdb_id', 'poster_path', 'video', 'belongs_to_collection', 'status', 'runtime',\n\t\t\t 'original_title', 'overview', 'tagline', 'title'], axis=1, inplace=True)\n\t\treturn df","def to_numeric_features(features, feature_cols=None):\n\n for col in CATEGORICAL_COLS:\n features = pd.concat(\n [features, pd.get_dummies(features[col[0]], drop_first=True)], axis=1)\n features.drop(col[0], axis=1, inplace=True)\n\n # Remove the unused columns from the dataframe.\n for col in UNUSED_COLUMNS:\n features.pop(col)\n\n # Re-index dataframe (if categories list changed from the previous dataset)\n if feature_cols is not None:\n features = features.T.reindex(feature_cols).T.fillna(0)\n return features","def encode_categorical(df):\n cat_cols = df.select_dtypes(\"category\").columns\n for col in cat_cols:\n df[col] = df[col].cat.codes + 1\n unique_no = len(df[col].unique())\n if unique_no < 50:\n df[col] = df[col].astype(\"uint8\")\n elif unique_no < 16000:\n df[col] = df[col].astype(\"int16\")\n else:\n df[col] = df[col].astype(\"int32\")\n return df","def clean_dataframe(df, column_list, length):\n list_of_actual_columns = list(dataframe.columns.values)\n for each_column in column_list:\n if each_column not in list_of_actual_columns:\n return False\n if number_of_rows < 1000:\n return False\n return True\n\n category_value_counts = df['Type'].value_counts()\n category_value_counts.to_csv('category_value_counts')\n cdf = pd.read_csv('category_value_counts')\n df = df.merge(cdf, left_on='Type', right_on='OldCategory')\n df['Datetime'] = pd.to_datetime(df['Datetime'])\n df[(df['Datetime'] > '2019-12-31')]\n df.sort_values(by='Datetime', ascending=False)","def apply(self, df):\n encoded = []\n for feature_name, encoder in zip(self.feature_names, self.encoders):\n column = df[feature_name].to_numpy().reshape(-1, 1)\n encoded.append(pd.DataFrame(\n encoder.transform(column).todense(),\n index=df.index,\n columns=encoder.categories_[0]\n ))\n df = df.drop(columns=self.feature_names)\n df = pd.concat((df, *encoded), axis=1)\n return df","def clean_transaction(df: pd.DataFrame) -> pd.DataFrame:\n # Copy dataframe to local, so that the original dataset is not modified.\n # See /notes/copy_df.py for an example.\n df = df.copy()\n # Drop features with insufficient observations.\n df = _drop_inferior_features_transaction(df, nan_threshold=0.3)\n # Mask missing nan observations left.\n df = _fill_nan(df, categorical_fill=\"Missing\", numerical_fill=-1)\n df = _prune_categories(df, prune_dict=PRUNE_DICT_TRANS)\n df = _convert_to_dummies(df, method=\"pandas\")\n return df","def binarize_string_cols(data_frame, cols_to_replace):\n\n for col in cols_to_replace:\n lb = preprocessing.LabelBinarizer()\n col_values = lb.fit_transform(data_frame[col])\n classes = [col + '_' + x for x in lb.classes_]\n binarized_column = pd.DataFrame(col_values, columns=classes)\n\n # add new columns and remove the old one\n data_frame = pd.concat([data_frame, binarized_column], axis=1)\n data_frame = data_frame.drop([col], axis=1)\n return data_frame","def transform(self, data: Dict) -> Dict:\n\n for c in data.columns:\n if c in self.featBin:\n data[c] = data[c].astype(int)\n if data[c].max() > 1:\n data.loc[data[c] > 1, c] = 1\n elif data[c].min() < 0:\n data.loc[data[c] < 0] = 1\n else:\n pass\n elif c in self.featNum:\n data[c] = np.abs(data[c])\n\n else:\n pass\n\n return data","def own_OneHotColumnCreator(df, columns):\n for col in cat_attribs:\n for value in df[col].unique():\n df[value] = (df[col] == value).astype(int)","def preprocess_data_inplace(df):\n encode(PREPROCESS_LABEL_ENCODERS, df, ['fecha_dato', 'fecha_alta'])","def preprocess_df(df, column, preview=True):\n \n df[column] = df['Attraction'].apply(lambda x: x.lower())\n df[column] = df[column].apply(lambda x: re.sub('[%s]' % re.escape(string.punctuation), '', x))\n df[column] = df[column].apply(lambda x: re.sub('\\w*\\d\\w*','', x))\n \n return df","def transform_and_create_new_features(df):\n # 'GENDER' FEATURE MANAGEMENT\n # Transform 'Gender' feature (categorical) to numerical one\n df['Gender'] = df['Sex'].map({'female': 0, 'male': 1}).astype(int)\n\n # 'EMBARKED' FEATURE MANAGEMENT\n # 1st approach: df['Port'] = df['Embarked'].map({'C': 1, 'S': 2, 'Q': 3}).astype(int)\n # Extract from 'pycon UK Tutorial':\n # \"Replacing {C, S, Q} by {1, 2, 3} would seem to imply the ordering C < S < Q when in fact they are simply arranged\n # alphabetically. To avoid this problem, we create dummy variables. Essentially this involves creating new columns\n # to represent whether the passenger embarked at C with the value 1 if true, 0 otherwise.\"\n dummies_embarked = pd.get_dummies(df['Embarked'], prefix='Embarked')\n df = pd.concat([df, dummies_embarked], axis=1)\n\n # 'AGE' & 'FARE' FEATURES MANAGEMENT\n df = _transform_age_feature(df)\n df = _transform_fare_feature(df)\n\n # CREATION OF A NEW FEATURE: Family size + Alone or not ?\n df['Family'] = df['SibSp'] + df['Parch']\n df['Alone'] = 0\n df.loc[df['Family'] == 0, 'Alone'] = 1\n\n # Drop all columns that are now useless\n df = df.drop(['Sex', 'Age', 'Fare', 'Embarked', 'SibSp', 'Parch'], axis=1)\n print(df.head(10))\n\n return df","def prepare_iris_data(df, encode=True):\n \n # Drop primary/foreign keys\n df = df.drop(columns=[\"measurement_id\", \"species_id\"])\n\n # Rename \"species_name\" to species\n df = df.rename(columns={\"species_name\": \"species\"})\n\n if(encode):\n encoder = LabelEncoder()\n encoder.fit(df.species)\n df.species = encoder.transform(df.species)\n\n return df","def normalize_dataframe(df=None):\n #Drops all the columns that are not 'close'\n col_to_drop = df.columns.levels[1].values\n col_to_drop = np.delete(col_to_drop,np.where(col_to_drop==\"close\"))\n df = df.drop(columns=col_to_drop, level=1)\n #eliminates the double level in columns by deleting the 'close' label. \n #Only ticker label is necessary.\n df.columns = df.columns.droplevel(1)\n return df","def clean_up_df(df):\n df['Age'] = df['Age'].fillna(df['Age'].median())\n df['Gender'] = df['Sex'].map({'female':0, 'male':1}).astype(int)\n df['Family'] = df['Parch'] + df['SibSp']\n df['Fare'] = df['Fare'].fillna(df['Fare'].mean())\n df = df.drop(['SibSp','Parch','Sex','Name','Cabin','Embarked','Ticket'],axis=1)\n return df","def prepare_titanic_data(df):\n\n df.embark_town.fillna('Other', inplace=True)\n\n # Drop deck and embarked_town\n df.drop(columns=['deck', 'embark_town'], inplace=True)\n\n # Encoding: Objects (Categorical Variables) to Numeric\n # Use sklearn's LabelEncoder\n encoder = LabelEncoder()\n\n # Set Unknown and encode Embarked column to numbers\n # 2 == \"S\" == Southampton == 644 people\n # 0 == \"C\" == Cherbourg == 168 people\n # 1 == \"Q\" == Queenstown == 77 people\n # 3 == \"Unknown\" == 2 people\n df.embarked.fillna('Unknown', inplace=True)\n encoder.fit(df.embarked)\n df.embarked = encoder.transform(df.embarked)\n\n # Encode the Class (first class, second, etc...)\n # First class == 0\n # Second class == 1\n # Third class == 2\n encoder.fit(df[\"class\"])\n df[\"class_encoded\"] = encoder.transform(df[\"class\"])\n\n # Encode gender\n # male == 1 == 577 records\n # female == 0 == 314 records\n encoder.fit(df.sex)\n df[\"sex_encoded\"] = encoder.transform(df.sex)\n\n # Handle the 177 records with missing age values\n average_age = df.age.mean()\n df.age.fillna(average_age, inplace=True)\n\n scaler = MinMaxScaler()\n scaler.fit(df[['fare']])\n df[\"fare_scaled\"] = scaler.transform(df[['fare']])\n\n scaler = MinMaxScaler()\n scaler.fit(df[['age']])\n df[\"age_scaled\"] = scaler.transform(df[['age']])\n\n # Set the index to the passenger id\n df = df.set_index(\"passenger_id\")\n return df","def transform_data(data_orig):\n\tif isinstance(data_orig, str):\n\t\tdata_orig = pd.read_csv(data_orig)\n\n\tdata = data_orig\n\n\tnum_rows,num_variables = data.shape\n\tall_columns = data.columns.tolist()\n\tclean_data(data,all_columns,ignore_na=False,fill_mode=\"prob\")\n\texpand_features(data)\n\tvariables = ['Pclass','Sex',\"Fare\",\"Age\",\"SibSp\",\"Parch\",\"Embarked\",\"Fam_size\",\\\n\t\t\t\t \"cabin_no\",\"ticket_no\",\"friend\",\"Fare_person\",\"Child\"]\n\tX = pd.get_dummies(data[variables])\n\n\t## normalise features to zero man and unit variance\n\tscaler = preprocessing.StandardScaler().fit(X)\n\tX_scaled = scaler.transform(X)\n\tX = pd.DataFrame(X_scaled, columns=X.columns)\n\n\tif \"Survived\" in data.columns:\n\t\ty = data['Survived']\n\telse:\n\t\ty = None\n\n\treturn X, y","def inverse_transform(self, df):\n return df"],"string":"[\n \"def clean_data(df):\\r\\n \\r\\n # list of categories to use as column names \\r\\n categories_cols = [names.split('-')[0] for names in df['categories'][0].split(';')]\\r\\n \\r\\n # creating 36 individual category columns\\r\\n for i in range(len(categories_cols)):\\r\\n df[categories_cols[i]] = [int(row.split(';')[i].split('-')[1]) for row in df['categories']]\\r\\n \\r\\n # labels 0 and 2 in 'related' class are similar (refer to notebook)\\r\\n # change 2s into 0s to make it more simple\\r\\n df['related'] = df['related'].map({0:0,1:1,2:0})\\r\\n \\r\\n # drop 'categories' column\\r\\n df.drop('categories', axis=1, inplace=True)\\r\\n \\r\\n # drop duplicates\\r\\n df.drop_duplicates(inplace=True)\\r\\n \\r\\n return df\",\n \"def clean_data(df):\\n # create a dataframe of the 36 individual category columns\\n categories = df.categories.str.split(';', expand=True)\\n\\n # select the first row of the categories dataframe\\n row = categories[:1]\\n\\n # use this row to extract a list of new column names for categories\\n # take beginning up to 2nd to last character of each string with slicing\\n category_colnames = [name[:-2] for name in row.values[0]]\\n\\n # rename the columns of 'categories'\\n categories.columns = category_colnames\\n\\n # Convert category values to numbers 0 or 1\\n for column in categories:\\n # set each value to be the last character of the string\\n categories[column] = [x[-1] for x in categories[column]]\\n\\n # convert column from string to numeric\\n categories[column] = categories[column].astype(int)\\n\\n # replace value of 2 with a 1\\n categories[column] = categories[column].replace(2, 1)\\n\\n # Replace 'categories' column in df with new category columns\\n # drop the original categories column from `df`\\n df = df.drop('categories', axis=1)\\n\\n # concatenate the original dataframe with the new `categories` dataframe\\n df = pd.concat([df, categories], axis=1)\\n\\n # Remove duplicates\\n df = df.drop_duplicates()\\n\\n return df\",\n \"def clean_data(dataframe):\\n # split categories into seperate\\n categories = dataframe.categories.str.split(';', expand=True)\\n \\n # select the first row&col of the categories dataframe\\n row&col = categories.iloc[0]\\n cate_col = row&col.apply(lambda x: x[:-2])\\n cate.columns = cate_colnames\\n \\n #convert categories values to numeric instead of strings\\n for column in categories:\\n categories[column] = categories[column].str[-1]\\n categories[column] = categories[column].astype(int)\\n \\n # replace categories column in dataframe \\n dataframe.drop(columns = ['categories'], inplace=True)\\n # concatenate the original dataframe with the new `categories` dataframe\\n dataframe = dataframe.join(categories)\\n \\n #drop duplicates\\n dataframe.drop_duplicates(inplace=True)\\n \\n return dataframe\",\n \"def transform(self, dataframe: DataFrame) -> DataFrame:\",\n \"def _convert_categorical(from_frame: DataFrame) -> DataFrame:\\n for col in from_frame:\\n ser = from_frame[col]\\n if isinstance(ser.dtype, CategoricalDtype):\\n cat = ser._values.remove_unused_categories()\\n if cat.categories.dtype == object:\\n categories = pd.Index._with_infer(cat.categories._values)\\n cat = cat.set_categories(categories)\\n from_frame[col] = cat\\n return from_frame\",\n \"def pre_process_data(df):\\n\\n # one-hot encode categorical values\\n df = pd.get_dummies(df)\\n\\n return df\",\n \"def cat_converter(df):\\n \\n categoricals = df.columns[df.dtypes == object] \\n \\n for column in categoricals: \\n df[column] = pd.Categorical(df[column])\\n new_column = column + '_new'\\n df[new_column] = df[column].cat.codes\\n df = df.drop([column], axis = 1)\\n \\n return df\",\n \"def clean_data(df):\\n # Extract column names from values, and rename\\n categories = df.categories.str.split(\\\";\\\", expand=True)\\n row = categories.iloc[0]\\n category_colnames = list(map(lambda item: item.split(\\\"-\\\")[0], row))\\n categories.columns = category_colnames\\n\\n # Fix values\\n for column in categories:\\n # set each value to be the last character of the string\\n categories[column] = categories[column].str[-1]\\n\\n # convert column from string to numeric\\n categories[column] = categories[column].apply(lambda x: int(x))\\n\\n df = df.drop(['categories'], axis=1)\\n df = pd.concat([df, categories], axis=1)\\n df.drop_duplicates(inplace=True)\\n\\n return df\",\n \"def clean_data(df):\\n # expand categories as new data frame\\n new_columns = [re.sub('[^a-zA-Z]', ' ', i).strip() for i in df['categories'][0].split(';')]\\n cat_df = df['categories'].str.split(';', expand=True)\\n cat_df.columns = new_columns\\n \\n # remove anything except numerical value\\n # change new feature's type\\n for column in cat_df:\\n cat_df[column] = cat_df[column].apply(lambda x: re.sub('[^0-9]', '', x)).astype('str')\\n \\n # concatenate old dataframe and new features dataframe\\n # remove olf categories column\\n new_df = pd.concat([df, cat_df], axis=1)\\n new_df = new_df.drop('categories', axis=1).drop_duplicates()\\n binary_df = new_df[new_df['related']!='2']\\n \\n return binary_df\",\n \"def preprocess_dataframe(self, dataframe):\\n return dataframe\",\n \"def clean_data(df):\\n # Split the categories column to get each category into a column\\n categories = df['categories'].str.split(pat=';', expand=True)\\n\\n # Assign category names to df columns\\n row = categories.iloc[0]\\n category_colnames = [i.split('-')[0] for i in row]\\n categories.columns = category_colnames\\n\\n # Get only the numeric value from each column\\n for column in categories:\\n categories[column] = categories[column].str.extract('(?P<digit>\\\\d)', expand=False).replace('2','1')\\n categories[column] = categories[column].astype(int)\\n\\n # Drop categories column\\n df.drop('categories', axis=1, inplace=True)\\n\\n # Concat df with all the categories columns\\n df = pd.concat([df, categories], axis=1)\\n\\n # Remove duplicated rows\\n df.drop_duplicates(inplace=True)\\n\\n return df\",\n \"def preprocess_feature(df):\",\n \"def clean_data(df): \\n # Now we first determen the column names\\n columns = df['categories'].str.replace(\\\"-0\\\",\\\"\\\").str.replace(\\\"-1\\\",\\\"\\\").str.replace(\\\"-2\\\",\\\"\\\").drop_duplicates().str.split(';')[0]\\n\\n # And split the 'categories' column\\n df[columns] = df['categories'].str.split(';',expand=True)\\n\\n # The columns are still an object/string. Now make it a number\\n for column in columns :\\n df[column]=df[column].str.replace(column+\\\"-\\\",\\\"\\\").astype(int)\\n\\n # We don't need the column 'categories' anymore, so delete it\\n df.drop('categories',1,inplace=True)\\n \\n # There are some duplicate records, so drop them\\n df.drop_duplicates(inplace=True)\\n \\n return df\",\n \"def clean_data(df):\\n # Copy dataframe to local dataframe\\n df_clean = df\\n # Split category into subcategories\\n categories = df.categories.str.split(\\\";\\\", expand=True)\\n # Label columns according to new label\\n categories.columns = categories.iloc[0].str[:-2]\\n # Make columns numeric, i.e. remove the label substring from the content\\n for label, content in categories.iteritems():\\n categories[label] = pd.to_numeric(content.str.replace(f\\\"{label}-\\\", \\\"\\\"))\\n # Clean related category to 0/1 - there are outliers with 2s\\n categories[\\\"related\\\"] = categories[\\\"related\\\"].map(lambda x: 1 if x == 2 else x)\\n # Drop original category column\\n df_clean = df_clean.drop(labels=\\\"categories\\\", axis=1)\\n # Add categories to dataframe\\n df_clean = df_clean.join(categories)\\n\\n return df_clean\",\n \"def clean_data(df):\\n categories= pd.Series(df['categories']).str.split(pat = ';', expand= True)\\n # select the first row of the categories dataframe\\n row = categories.iloc[0]\\n# use this row to extract a list of new column names for categories.\\n# one way is to apply a lambda function that takes everything \\n# up to the second to last character of each string with slicing\\n category_colnames = list(row.apply(lambda x:x[0:-2]))\\n categories.columns = category_colnames\\n \\n for column in categories:\\n # set each value to be the last character of the string\\n categories[column] = categories[column].apply(lambda x:x[-1:])\\n categories[column] = categories[column].apply(pd.to_numeric)\\n # we have few entries in related column as value 2. we need to convert these to 1.\\n a = np.array(categories['related'].values.tolist())\\n categories['related'] = np.where(a > 1, 1, a).tolist()\\n df = df.drop(['categories'], axis = 1)\\n \\n frames = [df, categories]\\n df = pd.concat(frames, axis =1, sort = False) \\n df = df.drop_duplicates()\\n return df\",\n \"def augment_dataframe(self, df: pd.DataFrame) -> pd.DataFrame:\",\n \"def apply_cats(df, trn):\\n for n,c in df.items():\\n if (n in trn.columns) and (trn[n].dtype.name=='category'):\\n df[n] = pd.Categorical(c, categories=trn[n].cat.categories, ordered=True)\",\n \"def clean_data(df):\\n \\n # split categories into separate category columns\\n categories = df['categories'].str.split(';', expand = True)\\n \\n # select the first row of the categories dataframe\\n row = categories.loc[0,:]\\n \\n # use this row tp extract a list of new column names for categories\\n # here we apply a lambda function that slices the data and takes first part\\n category_colnames = list(row.apply(lambda x: x.split('-')[0]))\\n \\n # rename the columns of categories series\\n categories.columns = category_colnames\\n \\n # convert category values to just numbers 0 or 1\\n for column in categories:\\n # set each value to be the last character of the string\\n categories[column] = categories[column].apply(lambda x: x.split('-')[1])\\n \\n # convert column from string to numeric\\n categories[column] = categories[column].astype(int)\\n \\n # replace categories column in df with new category columns\\n df.drop(columns = ['categories'], inplace = True)\\n \\n # concatenate the original dataframe with the new categories dataframe\\n df = pd.concat([df, categories], axis = 1)\\n \\n # remove duplicates\\n df = df.drop_duplicates(keep = 'first')\\n \\n df['related'] = df['related'].replace(2,1)\\n \\n return df # return the cleaned dataframe\",\n \"def clean_up_raw(df_raw):\\n # exclude_subset = ['well', 'tile', 'cell', 'intensity', 'blob'] # causes issues with later joins, maybe a pandas bug\\n import lasagna.utils\\n df_raw[CYCLE] = df_raw[CYCLE].astype(int)\\n df_raw = df_raw.sort_values([WELL, TILE, CELL, BLOB, CYCLE, CHANNEL])\\n return df_raw\",\n \"def clean_data(df):\\n \\n # split the categories column on semicolon and expand each split into \\n # a column of a new dataframe\\n cats_expanded = df.categories.str.split(';', expand=True)\\n\\n # take the first row of the new df, split on hyphen and grab the first column of the\\n # new expanded df. This column should contain the label name\\n labels = list(cats_expanded.iloc[0, :].str.split('-', expand=True)[0])\\n \\n # remove none numerics from cats_expanded df columns - replae label names with '', leaving\\n # only numeric values\\n cats_expanded = cats_expanded.apply(lambda col: col.str.replace('[^0-9]', ''))\\n \\n # convert to int from str\\n cats_expanded = cats_expanded.astype(int)\\n \\n # assign label names\\n cats_expanded.columns = labels\\n\\n # make sure all label values are binary\\n cats_expanded.where(cats_expanded < 2, 1, inplace=True)\\n\\n # check to make sure we have at least one instance for each label\\n labels_with_no_instance = cats_expanded.columns[~(cats_expanded == 1).any(axis=0)]\\n # drop labels that have no instance of positive class\\n cats_expanded = cats_expanded.drop(labels_with_no_instance, axis=1)\\n\\n # merge with orginal dataframe on index and drop now defunct 'categories' column\\n clean_cats = df \\\\\\n .merge(cats_expanded, how='inner', left_index=True, right_index=True) \\\\\\n .drop('categories', axis=1)\\n\\n return clean_cats\",\n \"def clean_data(df):\\n categories = df['categories']\\n categories = categories.str.split(';', expand=True)\\n row = categories.iloc[0]\\n category_colnames = row.apply(lambda x: x[:-2])\\n categories.columns = category_colnames\\n for column in categories:\\n # set each value to be the last character of the string\\n categories[column] = categories[column].apply(lambda x: x[-1:])\\n\\n # convert column from string to numeric\\n categories[column] = pd.to_numeric(categories[column])\\n \\n df = df.drop(['categories'], axis = 1)\\n df = pd.concat([df,categories], axis = 1)\\n df.drop_duplicates(keep='first',inplace=True)\\n return df\",\n \"def clean_data(df):\\n\\t# create a dataframe of the 36 individual category columns\\n\\tcategories = df['categories'].str.split(';', expand=True)\\n\\t# select the first row of the categories dataframe\\n\\trow = categories.loc[0]\\n\\t# extract a list of new column names for categories. Remove unnecessary chars.\\n\\tcategory_colnames = row.str.replace(r'-\\\\w','')\\n\\t# rename the columns of `categories`\\n\\tcategories.columns = category_colnames\\n\\t# Convert category values to just numbers 0 or 1.\\n\\tcategories = categories.applymap(lambda x: int(x.split('-')[1]))\\n\\t# drop the original categories column from `df`\\n\\tdf.drop(['categories'],axis=1, inplace=True)\\n\\t# concatenate the original dataframe with the new `categories` dataframe\\n\\tdf = pd.concat([df,categories],axis=1)\\n\\t# find duplicates\\n\\tdups = df.duplicated(subset=None, keep='first')\\n\\t# drop duplicates\\n\\tdf = df[~(dups)]\\n\\treturn df\",\n \"def _convert_to_dummies_pandas(df: pd.DataFrame) -> pd.DataFrame:\\n df = df.copy()\\n print(\\\"Raw dataset shape = {}\\\".format(df.shape))\\n print(\\\"Creating one hot encoded categories...\\\")\\n for col in df.columns:\\n if col in CATEGORICAL_TRANS and col != \\\"isFraud\\\" and not col.startswith(\\\"card\\\"):\\n print(\\\"Handling category: {}\\\".format(col))\\n # Convert to categorical type, may not be necessary.\\n df[col] = pd.Categorical(df[col])\\n one_hot_encoded = pd.get_dummies(df[col], prefix=col)\\n df = df.drop(columns=[col]) # remove the original categorical column.\\n # Add the one-hot-encoded column.\\n df = pd.concat([df, one_hot_encoded], axis=1)\\n print(\\\"One-hot-encoded dataset shape = {}\\\".format(df.shape))\\n return df\",\n \"def clean(df):\",\n \"def transform(self, df: DataFrame) -> DataFrame:\\n return df\",\n \"def transform(self, df):\\n _df = df.copy()\\n categorical = self.find_categorical(df)\\n for cat in categorical:\\n _df[cat] = pd.Categorical(_df[cat], categories=_test_categories[cat])\\n\\n return pd.get_dummies(_df[categorical]).as_matrix()\",\n \"def categorical(df):\\n\\n # variables which need to be transformed to categorical\\n categorical = [\\\"prop_country_id\\\", \\\"visitor_location_country_id\\\"]\\n\\n for var in categorical:\\n df = pd.concat([df, pd.get_dummies(df[var], prefix=var)], axis=1)\\n del df[var]\\n\\n return df\",\n \"def preprocess(df): \\n \\n df.drop_duplicates(subset=df.columns[0], inplace=True) #drop duplicate gene_names. \\n df.set_index(keys=df.columns[0], inplace=True)\\n df.dropna(how='all', inplace=True) #drop rows with all NAs\\n df2 = df.select_dtypes(include=['float64']) + 0.001 #select numbers in DataFrame \\n \\n return df2\",\n \"def convert_categorical(df):\\n print(\\\" --- Converting Categories into binary features.\\\")\\n columns = df.columns\\n categorical = [x for x in columns if x.startswith('c_')]\\n for col in categorical:\\n print(\\\" ---- Converting: {}\\\".format(col))\\n category_binary = pd.get_dummies(df[col], prefix=col)\\n df = pd.concat([df, category_binary], axis=1)\\n df = df.drop(categorical, axis=1)\\n print(\\\" --- Finished converting Categories into binary features.\\\")\\n return df\",\n \"def _convert_to_dummies_sklearn(df: pd.DataFrame) -> pd.DataFrame:\\n raise NotImplementedError\",\n \"def transform(self, df):\\n df = df.copy()\\n \\\"\\\"\\\"\\n if self.grouping is not None:\\n df = self.hier.transform(df)\\n \\\"\\\"\\\"\\n # fill NaN\\n df = self.fill_na(df)\\n\\n self.df_index = df.index\\n self.df_colnames = df.columns\\n # transformations\\n for i in sorted(self.transformations.keys()):\\n transformation = self.transformations[i]\\n df = self.transformers[i].transform(df)\\n # convert to DataFrame only if it isn't already\\n if not isinstance(df, pd.DataFrame):\\n df = pd.DataFrame(df)\\n df.index = self.df_index\\n df.columns = self.df_colnames\\n # update index reference if sliced\\n if transformation in ['Slice']:\\n self.df_index = df.index\\n self.df_colnames = df.columns\\n df = df.replace([np.inf, -np.inf], 0) # .fillna(0)\\n return df\",\n \"def pre_process_data_set(df):\\n df.replace([np.inf, -np.inf], np.nan)\\n df[df == np.inf] = np.nan\\n df = remove_bad_columns(df)\\n df = fill_na(df)\\n df = convert_factorial_to_numerical(df)\\n\\n # Remove columns only containing 0\\n df = df[(df.T != 0).any()]\\n return df\",\n \"def transform(self, data: pd.DataFrame):\\n raise NotImplementedError\",\n \"def _inplace_fix_subset_categorical_obs(subset_adata: AnnData, original_adata: AnnData) -> None:\\n obs = subset_adata.obs\\n for column in obs.columns:\\n is_categorical = pd.api.types.is_categorical_dtype(obs[column])\\n if is_categorical:\\n c = obs[column].cat.set_categories(original_adata.obs[column].cat.categories)\\n obs[column] = c\",\n \"def _prune_categories(\\n df: pd.DataFrame,\\n prune_dict: dict,\\n fill: object = \\\"Other\\\",\\n) -> pd.DataFrame:\\n df = df.copy()\\n for col in df.columns:\\n if col in CATEGORICAL_TRANS:\\n if col not in prune_dict.keys():\\n continue\\n n = prune_dict[col]\\n if n == -1:\\n continue\\n # Get most frequent:\\n major_categories = list(\\n df[col].value_counts()[:n].keys()\\n )\\n mask = df[col].isin(major_categories)\\n df[col][~ mask] = fill\\n return df\",\n \"def transform(self, df):\\n\\t\\tdf = self.__parse_json(df)\\n\\t\\tdf = self.__fillnan(df)\\n\\t\\tdf = self.__parse_dates(df)\\n\\t\\tdf['budget'] = df['budget'].apply(lambda x: self.missing_budget_imputing if int(x) == 0 else x)\\n\\t\\tdf['has_collections'] = df['belongs_to_collection'].isna().astype(int)\\n\\t\\tdf['homepage'] = df['homepage'].isna().astype(int)\\n\\t\\tdf['is_en'] = df['original_language'].apply(lambda x: 1 if x == 'en' else 0)\\n\\t\\tdf = self.__encode_genre_transform(df)\\n\\t\\tdf = self.__top_countries_and_companies_transform(df)\\n\\t\\tdf = self.__bin_columns_transform(df)\\n\\t\\tdf.drop(\\n\\t\\t\\t['release_date', 'original_language', 'production_countries', 'production_companies', 'id', 'backdrop_path',\\n\\t\\t\\t 'imdb_id', 'poster_path', 'video', 'belongs_to_collection', 'status', 'runtime',\\n\\t\\t\\t 'original_title', 'overview', 'tagline', 'title'], axis=1, inplace=True)\\n\\t\\treturn df\",\n \"def add_category(df):\\n df[\\\"category\\\"] = df.apply(lambda row: transform_cat(row), axis=1)\\n df = drop_cols(df, [\\\"booking_bool\\\", \\\"click_bool\\\"])\\n return df\",\n \"def dummify_all_categorical(df):\\n\\n df = pd.get_dummies(df)\\n df = dummify(df, \\\"detailed industry recode\\\")\\n df = dummify(df, \\\"detailed occupation recode\\\") ## add some variables that are encoded as int64 but that are in fact categorical\\n return df\",\n \"def preprocess(old_df, label_name, category_features, non_category_features):\\n old_df['fraud'] = old_df[label_name].apply(lambda x: x[0] == 'f')\\n\\n # Creating a new dataframe with a subset of features.\\n new_df = old_df[['fraud'] + non_category_features]\\n\\n # For categorical features, we make dummy variables,\\n # and merge them into new_df.\\n for feature in category_features:\\n dummy_df = pd.get_dummies(old_df[feature], prefix=feature,\\n dummy_na=True)\\n # Since dummy_na=True, the last column will be for null values.\\n dummy_df.drop(dummy_df.columns[-1], axis=1, inplace=True)\\n new_df = pd.concat([new_df, dummy_df], axis=1)\\n return new_df\",\n \"def revise_report_df(report_df):\\n # quick check to see whether report_df column structure is as expected\\n if report_df.columns.tolist() != ['0', '1', 'accuracy', 'macro avg', 'weighted avg']:\\n print(\\\"Warning: Column names aren't as expected. Verify report_df output_dict is correct.\\\")\\n\\n report_df.columns = ['0', '1', 'accuracy', 'Macro Avg', 'Micro Avg' ]\\n\\n dict_columns = ['0', '1', 'Macro Avg', 'Micro Avg']\\n keys = ['precision', 'recall', 'f1-score', 'support']\\n\\n for col in dict_columns:\\n # revise key values to personalize to its associated column i.e. from 'precision' to 'precision_0'\\n report_df[col] = report_df[col].apply(lambda x: customize_dict_keys(x, col, keys))\\n\\n # iterate row wise through dataframe to normalize dictionary values into flat tables\\n new_dict = {str(classifier) + '_df': normalize_to_flat(classifier, report_df, col) for classifier in report_df.index.values.tolist()}\\n\\n # concat all classifier flat tables into one dataframe\\n dict_df = pd.concat(list(new_dict.values())).reset_index().drop(columns=['index'], axis=1)\\n\\n # merge on existing report_df dataframe index and on dict_df 'classifier' column value\\n report_df = report_df.merge(dict_df, how='left', left_index=True, left_on=None, right_on='classifier').set_index('classifier')\\n\\n # select only created columns\\n report_df = report_df.iloc[:,5:]\\n # sort columns and filter out 'support' related columns\\n report_df = report_df[sorted([col for col in report_df.columns if 'support' not in col])]\\n\\n return report_df\",\n \"def preprocess(self, df):\\n print(\\\"Started Processing....\\\")\\n # binary conversion\\n df.replace(to_replace=\\\"yes\\\", value=1, inplace=True)\\n df.replace(to_replace=\\\"no\\\", value=0, inplace=True)\\n\\n # replace unknowns with nan\\n df = df.replace(to_replace=\\\"unknown\\\", value=np.nan)\\n # getting the list of columns with nan\\n ml = df.columns[df.isna().any()].tolist()\\n\\n for item in ml:\\n # getting the ratio of the index labels\\n val = pd.DataFrame(df[item].value_counts(normalize=True))\\n\\n # index labels in a list\\n valr = val.index.tolist()\\n # drc.index = valr\\n # columns values in a list\\n valc = val[item].tolist()\\n # replacing the nan values with ratio\\n df[item] = df[item].fillna(pd.Series(np.random.choice(valr, p=valc, size=len(df))))\\n\\n # dependent variable\\n dfy = df.iloc[:, -1]\\n # independent variable\\n dfx = df.iloc[:, :-1]\\n\\n # converting categorical data to numerical\\n dfx = pd.get_dummies(dfx)\\n\\n # normalizing\\n dfx = (dfx - dfx.min()) / (dfx.max() - dfx.min())\\n\\n dxdy = pd.concat([dfx, dfy], axis=1)\\n\\n # class balancing\\n sm = RandomOverSampler(random_state=42)\\n dfx, dfy = sm.fit_sample(dxdy.iloc[:, :-1], dxdy.iloc[:, -1])\\n\\n # converting to dataframe\\n dfx = pd.DataFrame(dfx, columns=dxdy.iloc[:, :-1].columns.values)\\n\\n # dimensionality reduction\\n pca = PCA(n_components=33)\\n dfx = pca.fit_transform((dfx))\\n\\n print(\\\"Processing Done\\\")\\n\\n return dfx, dfy\",\n \"def train_cats(df):\\n for n,c in df.items():\\n if is_string_dtype(c): df[n] = c.astype('category').cat.as_ordered()\",\n \"def train_cats(df):\\n for n,c in df.items():\\n if is_string_dtype(c): df[n] = c.astype('category').cat.as_ordered()\",\n \"def preprocess_features(X):\\n\\t# Initialize new output DataFrame\\n\\toutput = pd.DataFrame(index = X.index)\\n\\n\\t# Investigate new output DataFrame\\n\\tfor col, col_data in X.iteritems():\\n\\t\\t# If data type is categorical, convert to dummy variables\\n\\t\\tif col_data.dtype == object:\\n\\t\\t\\tcol_data = pd.get_dummies(col_data, prefix = col)\\n\\n\\t\\t\\t# Collect the revised columns\\n\\t\\t\\toutput - output.join(col_data)\\n\\treturn output\",\n \"def preprocess(self, df: pd.DataFrame) -> pd.DataFrame:\\n for transform in self.transforms:\\n cols = transform(df)\\n\\n if isinstance(cols, pd.Series):\\n df.loc[:, transform.name] = cols\\n\\n elif isinstance(cols, list):\\n for i, col in enumerate(cols):\\n\\n if not isinstance(col, pd.Series):\\n raise ValueError('Invalid Transform return type:', type(cols))\\n\\n df.loc[:, f'{transform.name}_{i}'] = col\\n\\n else:\\n raise ValueError('Invalid Transform return type:', type(cols))\\n return df\",\n \"def transform(self, data: pd.DataFrame, columns: list, verbose: int=1) -> pd.DataFrame:\",\n \"def process_dataframe(df):\\n # iterate over all items\\n for i in range(len(df.index)):\\n # replace policy with a list of integers\\n df.loc[i, 'policy'] = list(map(int, df.loc[i, 'policy'].split('-')))\\n # get rid of ':' in letter\\n df.loc[i, 'letter'] = df.loc[i, 'letter'].split(':')[0]\\n\\n return df\",\n \"def categories(df):\\n print 'going to code categories'\\n print_time()\\n\\n ethnicities = ['Mexican', 'Italian', 'American']\\n df['num_categories'] = 0\\n for ethnicity in ethnicities:\\n df.loc[df['categories'].str.contains(ethnicity, flags=re.I, na=False, case=False), 'category'] = ethnicity\\n df.loc[df['categories'].str.contains(ethnicity, flags=re.I, na=False, case=False), 'num_categories'] += 1\\n df.loc[df['category'].isnull(), 'category'] = 'Other'\\n df.loc[df['num_categories'] > 1, 'category'] = 'Multiple'\\n df = df[df['category'].notnull()]\\n return df\",\n \"def _dataframe_preprocess(self):\\n # 1. add baisc feature like date, time in day, ....\\n if self.data_type != 'porto':\\n self.df['TIMESTAMP'] = self.df.apply(lambda df: df['TIMESTAMPS'][0], axis=1)\\n self.df['TIME'] = pd.to_datetime(self.df['TIMESTAMP'], unit='s', utc=True)\\n \\n self.df.TIME = self.df.TIME.dt.tz_convert(self.timezone)\\n # 2. group df for specific driver analysis\\n self.grouped_df = self.df.groupby('LABEL')\\n if self.count_od_info:\\n if 'SD' not in self.df.columns:\\n self._add_OD_info()\\n self.grouped_od = self.df.groupby('SD')\",\n \"def transform(self, df):\\n _df = df.copy()\\n _df['Age'] = _df['YrSold'] - _df['YearBuilt']\\n _df['AgeRemod'] = _df['YrSold'] - _df['YearRemodAdd']\\n _df['Baths'] = _df['FullBath'] + _df['HalfBath']\\n _df['BsmtBaths'] = _df['BsmtFullBath'] + _df['BsmtHalfBath']\\n _df['OverallQual_Square'] = _df['OverallQual'] * _df['OverallQual']\\n _df['OverallQual_3'] = _df['OverallQual'] * _df['OverallQual'] * _df['OverallQual']\\n _df['OverallQual_exp'] = np.exp(_df['OverallQual'])\\n _df['GrLivArea_Square'] = _df['GrLivArea'] * _df['GrLivArea']\\n _df['GrLivArea_3'] = _df['GrLivArea'] * _df['GrLivArea'] * _df['GrLivArea']\\n _df['GrLivArea_exp'] = np.exp(_df['GrLivArea'])\\n _df['GrLivArea_log'] = np.log(_df['GrLivArea'])\\n _df['TotalBsmtSF_/GrLivArea'] = _df['TotalBsmtSF'] / _df['GrLivArea']\\n _df['OverallCond_sqrt'] = np.sqrt(_df['OverallCond'])\\n _df['OverallCond_square'] = _df['OverallCond'] * _df['OverallCond']\\n _df['LotArea_sqrt'] = np.sqrt(_df['LotArea'])\\n _df['1stFlrSF_log'] = np.log1p(_df['1stFlrSF'])\\n _df['1stFlrSF'] = np.sqrt(_df['1stFlrSF'])\\n _df['TotRmsAbvGrd_sqrt'] = np.sqrt(_df['TotRmsAbvGrd'])\\n categorical = self.find_categorical(df)\\n numerical = self.find_numerical(df)\\n\\n for cat in categorical:\\n _df[cat] = pd.Categorical(_df[cat], categories=_test_categories[cat])\\n\\n for key in numerical:\\n _df[key].fillna(_df[key].median(), inplace=True)\\n\\n # #if numerical feature are skewed apply log\\n\\n skewed_feats = _df[numerical].apply(lambda x: scipy.stats.skew(x.dropna())) # compute skewness\\n skewed_feats = skewed_feats[skewed_feats > 0.75]\\n skewed_feats = skewed_feats.index\\n _df[skewed_feats] = np.log1p(_df[skewed_feats])\\n cat_matrix = pd.get_dummies(_df[categorical]).as_matrix()\\n num_matrix = StandardScaler().fit_transform(np.asarray(_df[numerical]))\\n\\n return np.hstack((num_matrix, cat_matrix))\",\n \"def prepare_data_features(raw=False, round_ratings=False):\\n df = prepare_data(raw=False)\\n print(\\\"prepare features\\\")\\n df_categories = df['categories'].str.get_dummies(sep=\\\", \\\")\\n df_categories = df_categories[df_categories.columns[\\n df_categories.sum() > len(df)*0.01]]\\n df = pd.concat([df.drop('categories', 1), df_categories], axis=1)\\n print(\\\"end prepare features\\\")\\n return df\",\n \"def preprocess(data_df, remove_stopwords=False):\\n data_cp = data_df.copy()\\n for i, row in tqdm(data_cp.iterrows(), total=len(data_cp), desc='Preprocessing dataframe contents'):\\n\\n article_content = _clean(row.article_content, remove_stopwords)\\n row.article_content = _tokenize_stem_lem_join(article_content)\\n\\n _clean_claim = _clean(row.claim, remove_stopwords)\\n row.claim = _tokenize_stem_lem_join(_clean_claim)\\n\\n data_cp.loc[i] = row\\n\\n return data_cp\",\n \"def data_preprocessing(dataframe):\\n\\n dataframe = dataframe.loc[:, 0:]\\n dataframe.columns = [\\\"url\\\", \\\"date\\\", \\\"personurl\\\", \\\"name\\\"]\\n dataframe[\\\"date_new\\\"] = [i.replace(\\\"Düzenlendi\\\", \\\"\\\").replace(\\\"•\\\", \\\"\\\").strip() for i in dataframe[\\\"date\\\"]]\\n dataframe[\\\"date_new\\\"] = [str(int(i.replace(\\\"yıl\\\", \\\"\\\").strip()) * 48) if \\\"yıl\\\" in i else i for i in\\n dataframe[\\\"date_new\\\"]]\\n dataframe[\\\"date_new\\\"] = [str(int(i.replace(\\\"ay\\\", \\\"\\\").strip()) * 4) if \\\"ay\\\" in i else i for i in\\n dataframe[\\\"date_new\\\"]]\\n dataframe[\\\"date_new\\\"] = [i.replace(\\\"hafta\\\", \\\"\\\").strip() if \\\"hafta\\\" in i else i for i in dataframe[\\\"date_new\\\"]]\\n\\n return dataframe\",\n \"def pre_process(self, frame: pd.DataFrame) -> pd.DataFrame:\\n # frame = rows_filtering(frame)\\n # frame = feature_dropping(frame)\\n # frame = feature_values_fixing(frame)\\n\\n # frame = extreme_values_handling(frame, [])\\n # missing_value_imputation(frame, [])\\n\\n # data_type_conversion(frame)\\n # frame = feature_engineering(frame, self.GENERATE_USER_FEATURES)\\n # feature_renaming(frame)\\n\\n return frame\",\n \"def clean(dataframe):\\n # replace 'unknown' in Pop. density with np.nan\\n dataframe = dataframe.replace('unknown', np.nan)\\n\\n # remove spaces from column names and content\\n dataframe.columns = dataframe.columns.str.strip()\\n\\n # change YYYYMMDD to days of the year\\n date_debug = []\\n for i in range(1, 366):\\n date_debug.append(i)\\n\\n dataframe2 = {'YYYYMMDD': date_debug}\\n dataframe['YYYYMMDD'] = dataframe2['YYYYMMDD']\\n\\n return dataframe\",\n \"def preprocess(df):\\n \\n # drop the following columns - irrelevant now\\n DROP_COLUMNS = ['id', 'original_title', 'release_date'\\n , 'tmdbId', 'popularity', 'year']\\n df.drop(DROP_COLUMNS, axis=1, inplace=True)\\n \\n # drop all of the language columns\\n DROP_COLUMNS = [col for col in df.columns if col[:3]==\\\"lan\\\"]\\n df.drop(DROP_COLUMNS, axis=1, inplace=True)\\n\\n # loop through the columns we want to aggregate\\n for col_type in [\\n \\\"original_language_\\\"\\n , \\\"prod_comp_cntry_\\\"\\n , \\\"prod_comp_names_\\\"\\n , \\\"writers_\\\"\\n , \\\"actors_\\\"\\n , \\\"genres_\\\"\\n , \\\"director_\\\"\\n ]:\\n # create a dictionary of each unique value and its frequency\\n val_freq = {}\\n for col in df.columns:\\n if col.startswith(col_type):\\n val_freq[col] = df[col].sum()\\n\\n # create a dataframe from this dictionary; sort by count\\n counts = pd.DataFrame.from_dict(\\n val_freq\\n , orient='index'\\n , columns=['count']\\n ).sort_values('count', ascending=False)\\n counts['frac'] = counts['count'].apply(lambda x: 100*x / df.shape[0])\\n\\n # handle special case of production company country\\n if col_type == \\\"prod_comp_cntry_\\\":\\n DROP_COLUMNS = [col for col in counts.index][3:]\\n\\n # handle special case of directors\\n elif col_type == \\\"director_\\\":\\n DIRECTOR_COLS = [col for col in df.columns\\n if col.startswith(\\\"director_\\\")\\n and col!=\\\"director_pop\\\"]\\n df['established_director'] = df[DIRECTOR_COLS].max(axis=1)\\n DROP_COLUMNS = DIRECTOR_COLS\\n\\n # handle special case of actors\\n elif col_type == \\\"actors_\\\":\\n ACTORS_COLS = [col for col in df.columns if \\\"actors\\\" in col]\\n df['num_top_100_actors'] = df[ACTORS_COLS].sum(axis=1)\\n DROP_COLUMNS = ACTORS_COLS\\n\\n # handle all the other cases\\n else:\\n DROP_COLUMNS = [col for col in counts.query('frac < 2').index]\\n\\n\\n df.drop(DROP_COLUMNS, axis=1, inplace=True)\\n \\n ##########################################################################\\n # adjust the data for inflation\\n CPI_tf = df['CPIAUCSL'].max()\\n df['budget'] = df[['budget', 'CPIAUCSL']].apply(\\n cpi_adjust\\n , args=(CPI_tf ,)\\n , axis=1\\n )\\n df['revenue'] = df[['revenue', 'CPIAUCSL']].apply(\\n cpi_adjust\\n , args=(CPI_tf ,)\\n , axis=1\\n )\\n # no longer need CPI data\\n df.drop('CPIAUCSL', axis=1, inplace=True)\\n \\n ########################################################################## \\n # add in useful features about the cast and crew \\n df['cast_crew_sum_pop'] = (\\n df['director_pop']\\n + df['avg_actor_pop']\\n + df['avg_writer_pop']\\n )\\n df['cast_crew_product_pop'] = (\\n df['director_pop']\\n * df['avg_actor_pop']\\n * df['avg_writer_pop']\\n )\\n df['runtime'].replace(to_replace=0, value=df['runtime'].median(), inplace=True)\\n df = df.query('10000 <= revenue').copy()\\n df = df.query('100000 <= budget').copy()\\n df.drop('sum_actor_pop', axis=1, inplace=True)\\n df.drop('min_writer_pop', axis=1, inplace=True)\\n\\n # code to transform columns\\n for col in [\\n \\\"budget\\\", \\\"director_pop\\\", \\\"avg_writer_pop\\\"\\n , \\\"max_writer_pop\\\", \\\"avg_actor_pop\\\", \\\"max_actor_pop\\\"\\n , \\\"min_actor_pop\\\", 'cast_crew_sum_pop'\\n , 'cast_crew_product_pop'\\n ]:\\n df['log10_'+col] = df[col].apply(lambda x: math.log10(x))\\n df.drop(col, axis=1, inplace=True)\\n \\n return df\",\n \"def preprocess(df):\\n # there were very few missing records for these columns, thus\\n # we drop them to prevent biasing the data with imputation\\n # (only 12 rows were removed)\\n subset = ['num-of-doors', 'bore', 'stroke', 'horsepower', 'peak-rpm', 'price']\\n df = df.dropna(subset = subset)\\n\\n # normalized-losses, drop due to lots of missing values\\n # make drop due to having too many distinct categorical values\\n df = df.drop(['normalized-losses', 'make'], axis = 1)\\n\\n # extract the response/output column\\n response_col = 'price'\\n y = df[response_col].values\\n df = df.drop(response_col, axis = 1)\\n\\n # categorical variables: one-hot-encode them\\n # the numeric variables are the columns excluding\\n # the categorical variables and the variables that\\n # have been detected to have multicollinearity using\\n # VIF (variance inflation factor)\\n cat_cols = ['symboling', 'fuel-type', 'aspiration', 'num-of-doors',\\n 'body-style', 'drive-wheels', 'engine-location', 'fuel-system',\\n 'engine-type', 'num-of-cylinders']\\n for col in cat_cols:\\n df[col] = df[col].astype('category')\\n\\n dummied = pd.get_dummies(df[cat_cols], drop_first = True)\\n\\n # use sorted set to ensure the consistency of the column order\\n collinear_cols = SortedSet(['city-mpg', 'curb-weight', 'horsepower', 'length', 'width'])\\n num_cols = list(SortedSet(df.columns) - SortedSet(cat_cols) - collinear_cols)\\n X = pd.concat([df[num_cols], dummied], axis = 1)\\n\\n # standardize the numeric columns using the training set\\n X_train, X_test, y_train, y_test = train_test_split(X.values, y,\\n test_size = 0.2,\\n random_state = 4321)\\n scaler = StandardScaler()\\n X_train[:, :len(num_cols)] = scaler.fit_transform(X_train[:, :len(num_cols)])\\n X_test[:, :len(num_cols)] = scaler.transform(X_test[:, :len(num_cols)])\\n return X_train, X_test, y_train, y_test, X.columns\",\n \"def preprocess(data, to_drop=[]):\\n \\n columns = data.columns.to_list()\\n \\n # split data to numeric vs categorical\\n numeric_features = data.select_dtypes(include=[\\n 'int64', 'float64']).columns\\n \\n if len(to_drop) > 0:\\n categorical_features = data.select_dtypes(include=[\\n 'object']).drop(to_drop, axis=1).columns\\n print(categorical_features)\\n else: \\n categorical_features = data.select_dtypes(include=[\\n 'object']).columns\\n \\n categorical_transformer = Pipeline(steps=[\\n ('imputer', SimpleImputer(strategy='most_frequent', fill_value='missing'))])\\n \\n numerical_transformer = Pipeline(steps=[\\n ('imputer', SimpleImputer(strategy='median')),\\n ('scaler', RobustScaler())\\n ])\\n # missing_values = np.nan\\n \\n# Bundle preprocessing for numerical and categorical data\\n preprocessor = ColumnTransformer(\\n transformers=[\\n ('num', numerical_transformer, numeric_features),\\n ('cat', categorical_transformer, categorical_features)\\n ])\\n\\n my_pipeline = Pipeline(steps=[('preprocessor', preprocessor) ])\\n \\n for col in to_drop:\\n columns.remove(col)\\n print('Hello')\\n \\n trans_data = my_pipeline.fit_transform(data)\\n return trans_data#pd.DataFrame(#, columns=columns) \",\n \"def _transform_df(self, data):\\n # specify if has FIPS or not\\n if self.has_location:\\n loc_col_type = \\\"location\\\"\\n elif not self.has_location:\\n loc_col_type = \\\"location_name\\\"\\n\\n out = data.melt(\\n id_vars=[\\\"dt\\\", loc_col_type], value_vars=self.crename.keys()\\n ).dropna()\\n out.loc[:, \\\"value\\\"] = pd.to_numeric(out[\\\"value\\\"])\\n out = self.extract_CMU(out, self.crename)\\n out[\\\"vintage\\\"] = self._retrieve_vintage()\\n\\n cols_to_keep = [\\n \\\"vintage\\\",\\n \\\"dt\\\",\\n loc_col_type,\\n \\\"category\\\",\\n \\\"measurement\\\",\\n \\\"unit\\\",\\n \\\"age\\\",\\n \\\"race\\\",\\n \\\"ethnicity\\\",\\n \\\"sex\\\",\\n \\\"value\\\",\\n ]\\n return out.loc[:, cols_to_keep]\",\n \"def clean_up(df: pd.DataFrame) -> pd.DataFrame:\\n\\n # Hereafter df is sorted by date, which is helpful as it allows using .iloc[-1]\\n # to get current (or most recent known) situation per location\\n # (Otherwise we'd have to groupby agg -> min date, and then filter)\\n df = df.sort_values(\\n [Columns.LOCATION_NAME, Columns.DATE, Columns.CASE_TYPE], ascending=True\\n )\\n\\n return df\",\n \"def clean_data(df):\\n \\n # Put in code here to execute all main cleaning steps:\\n # convert missing value codes into NaNs, ...\\n count_miss = df.isnull().sum(axis=0).values #find number of nans for each column\\n count_miss = [val for val in count_miss]\\n \\n drop_cols = []\\n\\n for ind, val in enumerate(count_miss):\\n if val > 200000:\\n drop_cols.append(ind)\\n \\n df_drop_cols = list(azdias.columns[drop_cols])\\n df = df.drop(df_drop_cols, axis=1)\\n \\n for col in range(df.shape[1]): #loop through columns\\n column_name = df.columns[col] #get column name\\n missing_list = feat_info.iloc[col,3] #get missing_or_unknown column from feature info\\n missing_list = missing_list.replace('[','') #remove left bracket from string\\n missing_list = missing_list.replace(']','') #remove right bracket from string\\n missing_list = missing_list.split(',') #split into individual strings\\n \\n #find data that is natually missing and continue loop to omit\\n if missing_list == ['']:\\n continue\\n \\n else:\\n for dat_type in missing_list: \\n if df[column_name].dtype == 'object': #find values that contain x\\n df.loc[df[column_name] == dat_type, column_name] = np.nan #replace x with nan\\n \\n else:\\n dat_type = int(dat_type) #if no x, convert to integer and replace with nan\\n df.loc[df[column_name] == dat_type, column_name] = np.nan\\n \\n # select, re-encode, and engineer column values.\\n \\n # encode OST_WEST_KZ\\n df.loc[df['OST_WEST_KZ'] == 'W','OST_WEST_KZ'] = 0\\n df.loc[df['OST_WEST_KZ'] == 'O','OST_WEST_KZ'] = 1\\n \\n # Re-encode categorical variable(s) to be kept in the analysis.\\n \\n \\n #get list of attributes with type categorical\\n feat_info[feat_info['type'] == 'categorical']\\n \\n cat_new_cols = [] #initialize\\n for i in feat_info[feat_info['type'] == 'categorical']['attribute']:\\n cat_new_cols.append(i)\\n \\n for cols in df.columns:\\n if cols in cat_new_cols:\\n if df[cols].nunique(dropna=True) > 2: #if the number of unique values is greater than 2 \\n df = df.drop(cols, axis=1) #drop from the analysis\\n print(\\\"more than 2 categories: {}\\\".format(cols))\\n \\n else:\\n if not df[cols].unique()[0] > 0:\\n #if not df[cols].unique()[0] > 0:\\n dummies = pd.get_dummies(df[cols], prefix=cols)\\n df = df.drop(cols, axis=1) #create dummy variable\\n df = df.join(dummies)\\n print(\\\"transformed to dummy variable: {}\\\".format(cols))\\n \\n # create variable: MOVEMENT\\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([1,3,5,8,10,12,14]),'MOVEMENT'] = 1\\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([2,4,6,7,9,11,13,15]),'MOVEMENT'] = 2\\n \\n #Capture Decade\\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([1,2]), 'DECADE'] = 40\\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([3,4]), 'DECADE'] = 50\\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([5,6,7]), 'DECADE'] = 60\\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([8,9]), 'DECADE'] = 70\\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([10,11,12,13]), 'DECADE'] = 80\\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([14,15]), 'DECADE'] = 90\\n \\n df['CAMEO_INTL_2015'] = df['CAMEO_INTL_2015'].astype(float)\\n\\n # create new variable: WEALTH\\n df.loc[df['CAMEO_INTL_2015'].isin([51,52,53,54,55]), 'WEALTH'] = 1\\n df.loc[df['CAMEO_INTL_2015'].isin([41,42,43,44,45]), 'WEALTH'] = 2\\n df.loc[df['CAMEO_INTL_2015'].isin([31,32,33,34,35]), 'WEALTH'] = 3\\n df.loc[df['CAMEO_INTL_2015'].isin([21,22,23,24,25]), 'WEALTH'] = 4\\n df.loc[df['CAMEO_INTL_2015'].isin([11,12,13,14,15]), 'WEALTH'] = 5\\n \\n # create new variable: LIFE_STAGE\\n df.loc[df['CAMEO_INTL_2015'].isin([11,21,31,41,51]),'LIFE_STAGE'] = 1\\n df.loc[df['CAMEO_INTL_2015'].isin([12,22,32,42,52]),'LIFE_STAGE'] = 2\\n df.loc[df['CAMEO_INTL_2015'].isin([13,23,33,43,53]),'LIFE_STAGE'] = 3\\n df.loc[df['CAMEO_INTL_2015'].isin([14,24,34,44,54]),'LIFE_STAGE'] = 4\\n df.loc[df['CAMEO_INTL_2015'].isin([15,25,35,45,55]),'LIFE_STAGE'] = 5\\n \\n # remove selected columns and rows, ...\\n df = df.drop('PRAEGENDE_JUGENDJAHRE', axis=1)\\n df = df.drop('CAMEO_INTL_2015',axis=1)\\n \\n # Return the cleaned dataframe.\\n return df\",\n \"def preprocess_terms(df, dataType='testNames'):\\n pass\",\n \"def classify_df(full_df: pd.DataFrame) -> pd.DataFrame:\\n # Use the first 10 rows, and set classification target.\\n yield utils.AddClassificationTargetToDataFrame(\\n full_df.iloc[range(10), :].copy(), \\\"amd_tahiti_7970\\\"\\n )\",\n \"def reconfigures_dataframe(split_list: List, data_frame: pd.DataFrame) -> pd.DataFrame:\\n\\n # get non-ontology columns\\n non_category_columns = [x for x in data_frame.columns if not any(i for i in split_list if i.lower() in x.lower())]\\n\\n # make sure that all blank variables are converted to NaN\\n data_frame = data_frame.replace(r'^\\\\s*$', np.nan, regex=True)\\n\\n # identify which columns belong to each of the input ontologies\\n category_split_data = []\\n for x in split_list:\\n # get category-specific columns\\n cat_columns = [i for i in data_frame.columns if x.lower() in i.lower()]\\n # subset original data to include non-category and specific single category data\\n cat_data = data_frame[non_category_columns + cat_columns].drop_duplicates()\\n # drop rows where all category columns are NaN and blank string\\n cat_data = cat_data.dropna(subset=cat_columns, how='all')\\n cat_data.columns = non_category_columns + [col.upper().replace(x.upper(), 'CATEGORY') for col in cat_columns]\\n # add category specific column\\n cat_data['CATEGORY'] = [x] * len(cat_data)\\n category_split_data.append(cat_data)\\n\\n # concatenate stacked data into single DataFrame\\n reconfigured_data = pd.concat(category_split_data)\\n\\n return reconfigured_data.fillna('').drop_duplicates()\",\n \"def process_categorical_data(data_df):\\n return pd.get_dummies(data_df, columns=Columns.categorical)\",\n \"def preprocessing(dataframe):\\n dataframe.loc[:, \\\"content\\\"] = dataframe.loc[:, \\\"content\\\"].str.replace(\\\"&.*?;\\\", \\\"\\\")\\n dataframe.loc[:, \\\"content\\\"] = dataframe.loc[:, \\\"content\\\"].str.replace(\\n r\\\"[^a-zA-Z\\\\s.:!?/#\\\\\\\"]\\\", \\\"\\\"\\n )\\n dataframe[\\\"original_content\\\"] = dataframe[\\\"content\\\"]\\n dataframe.loc[:, \\\"content\\\"] = dataframe.loc[:, \\\"content\\\"].str.lower()\\n return dataframe\",\n \"def preprocess(df):\\n drop_cols = ['duration_ms', 'key', 'mode', 'time_signature', 'popularity', 'tempo']\\n drop_cols += ['track_id', 'track_name', 'artist_name']\\n for col in drop_cols:\\n if col in list(df.columns):\\n df = df.drop(columns=col)\\n return df\",\n \"def encode_category(df, enc, col, method):\\n \\n if method == 'label':\\n # lb = LabelEncoder()\\n # lb.fit(df[col])\\n df[col] = enc.transform(df[col].astype(str))\\n \\n elif method == 'onehot':\\n # ohe = OneHotEncoder(handle_unknown='ignore')\\n # ohe.fit(df[[col]])\\n tempdf = enc.transform(df[[col]]).toarray()\\n newdf = pd.DataFrame(tempdf, columns = np.array(ohe.categories_).ravel())\\n df = pd.concat([df, newdf], axis=1)\\n df.drop(columns=[col], inplace = True)\\n \\n return df\",\n \"def group_to_others(\\n df: pd.DataFrame, to_be_grouped: dict, replace_value: str = \\\"Other\\\"\\n) -> pd.DataFrame:\\n\\n for feature, values in to_be_grouped.items():\\n df[feature] = [row if row in values else replace_value for row in df[feature]]\\n return df\",\n \"def encode_dtypes(df):\\n\\n global catn, cato\\n\\n # Nominal categories\\n for name in catn:\\n df[name] = df[name].astype(\\\"category\\\")\\n # Add a None category for missing values\\n if \\\"None\\\" not in df[name].cat.categories:\\n df[name].cat.add_categories(\\\"None\\\", inplace=True)\\n # Ordinal categories\\n for name, levels in cato.items():\\n df[name] = df[name].astype(CategoricalDtype(levels,\\n ordered=True))\\n return df\",\n \"def normalize_complexity(df: pd.DataFrame, verbose: bool) -> pd.DataFrame:\\n before = len(df)\\n df[\\\"complexity\\\"] = df[\\\"complexity\\\"].apply(\\n lambda e: \\\"100\\\" if e == \\\"100+\\\" else e\\n )\\n df.dropna(subset=['complexity'], inplace=True)\\n df[\\\"complexity\\\"] = df[\\\"complexity\\\"].astype(int)\\n after = len(df)\\n if verbose:\\n print(f\\\"Complexity normalization: {before} (before) > {after} (after)\\\")\\n return df\",\n \"def normalize_etpinard_df(df='https://plot.ly/~etpinard/191.csv', columns='x y size text'.split(),\\n category_col='category', possible_categories=['Africa', 'Americas', 'Asia', 'Europe', 'Oceania']):\\n possible_categories = ['Africa', 'Americas', 'Asia', 'Europe',\\n 'Oceania'] if possible_categories is None else possible_categories\\n df.columns = clean_columns(df.columns)\\n df = pd.read_csv(df) if isinstance(df, str) else df\\n columns = clean_columns(list(columns))\\n df2 = pd.DataFrame(columns=columns)\\n df2[category_col] = np.concatenate([np.array([categ] * len(df)) for categ in possible_categories])\\n columns = zip(columns, [[clean_columns(categ + ', ' + column) for categ in possible_categories] for column in columns])\\n for col, category_cols in columns:\\n df2[col] = np.concatenate([df[label].values for label in category_cols])\\n return df2\",\n \"def clean_up(houses:pd.DataFrame) -> pd.DataFrame:\\n houses= delete_columns(houses)\\n houses= analyze_missing_values(houses)\\n houses= add_seller_house(houses)\\n houses= add_underscore(houses)\\n houses= create_dummies(houses)\\n houses= impute(houses)\\n return houses\",\n \"def de_normalize_data(self, df):\\n if len(df) == 0:\\n return df\\n result = df.copy()\\n for feature_name in self.continuous_feature_names:\\n max_value = self.permitted_range[feature_name][1]\\n min_value = self.permitted_range[feature_name][0]\\n result[feature_name] = (\\n df[feature_name]*(max_value - min_value)) + min_value\\n return result\",\n \"def prepare_input_df(df: DataFrame) -> DataFrame:\\r\\n df = df.fillna('') # Fill np.nan values with blanks (\\\"\\\").\\r\\n df = to_upper(df) # Force case to UPPER for all columns.\\r\\n df = strip_columns(df) # Remove trailing whitespace.\\r\\n return df\",\n \"def deal_with_ordinal(df):\\n print(\\\" --- Dealing with Ordinals.\\\")\\n thresh = get_min_filled_threshold(df)\\n columns = df.columns\\n ordinal = [x for x in columns if x.startswith('o_')]\\n\\n for col in ordinal:\\n filled = get_non_missing_count(df[col])\\n if filled < thresh:\\n df[col] = df[col].fillna(-10)\\n else:\\n mode = df[col].value_counts().idxmax()\\n df[col] = df[col].fillna(mode)\\n median = df[col].median()\\n df[col] = df[col].apply(lambda x: sigmoid(x-median))\\n \\n print(\\\" --- Finished dealing with Ordinals.\\\")\\n return df\",\n \"def _preprocess(self):\\n\\n self.df = self.df[(self.df['days_b_screening_arrest'] <= 30)\\n & (self.df['days_b_screening_arrest'] >= -30)\\n & (self.df['is_recid'] != -1)\\n & (self.df['c_charge_degree'] != 'O')\\n & (self.df['score_text'] != 'N/A')]\\n\\n self.df['c_jail_out'] = pd.to_datetime(self.df['c_jail_out'])\\n self.df['c_jail_in'] = pd.to_datetime(self.df['c_jail_in'])\\n self.df['length_of_stay'] = (self.df['c_jail_out']\\n - self.df['c_jail_in'])\\n\\n self.df['score_factor'] = np.where(self.df['score_text']\\n != 'Low',\\n 'HighScore', 'LowScore')\\n self.df['y_pred'] = (self.df['score_factor'] == 'HighScore')\",\n \"def encoding_df(df, cols):\\n import pandas as pd\\n df = df[cols]\\n obj_df = df.select_dtypes(include=['object']).copy()\\n num_var = df.select_dtypes(include=['int','float']).copy()\\n cat_var = pd.get_dummies(obj_df, columns = obj_df.columns)\\n encoded_df = pd.concat([num_var, cat_var], axis=1, sort=False)\\n return encoded_df\",\n \"def preprocess_data(df, cat_columns, cf_columns):\\n \\n #define cat and cf columns, convert cat to dummies\\n df_cat = pd.get_dummies(df[cat_columns], columns = cat_columns, drop_first=True, dtype=float)\\n df_cat.rename(mapper= {\\\"sex_2\\\": \\\"sex_female\\\", \\\"education_2\\\":\\\"education_university\\\", \\\"education_3\\\": \\\"education_high_school\\\", \\\"education_4\\\":\\\"education_others\\\",\\\"marital_status_2\\\":\\\"marital_status_single\\\", \\\"marital_status_3\\\": \\\"marital_status_others\\\"}, axis = 1, inplace = True)\\n df_cf = df[cf_columns]\\n X = pd.concat([df_cf, df_cat], axis = 1)\\n y = df[['default_payment_next_month']]\\n print(\\\"dummy variables created\\\")\\n \\n #train-test split \\n X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\\n print(\\\"split done\\\")\\n \\n #resample the train sets\\n smote = SMOTE(sampling_strategy = \\\"not majority\\\", random_state = 42)\\n X_train_rs, y_train_rs = smote.fit_sample(X_train, y_train)\\n print('original class distribution:')\\n print(y[\\\"default_payment_next_month\\\"].value_counts())\\n print('synthetic sample class distribution:')\\n print(pd.Series(y_train_rs).value_counts()) \\n return X, X_train_rs, X_test, y_train_rs, y_test\",\n \"def replace_numeric_values(*, df, colnames=\\\"all\\\", lower_limit=\\\"none\\\", upper_limit=\\\"none\\\", equal=False, replace_with=np.nan, verbose=True): \\n\\n \\n cols_names = colnames\\n \\n # .. check provided col_names,\\n if cols_names==\\\"all\\\": \\n cols = list(df.columns)\\n else: \\n cols = cols_names \\n\\n # .. info, header, \\n if verbose==True:\\n print(f\\\"\\\"\\\"\\\\n{\\\"\\\".join([\\\"-\\\"]*80)} \\\\n Replacing Numerical Values in {len(cols)} columns\\\"\\\"\\\") \\n print(f\\\" lower filter={lower_limit}, upper filter ={upper_limit}\\\")\\n if equal==True:\\n print(f\\\" Caution, equal=True, ie. values >= and <= then requested limits will be replaced\\\")\\n print(f'{\\\"\\\".join([\\\"-\\\"]*80)}\\\\n') \\n \\n if verbose==False:\\n pass\\n \\n \\n # .. intelligent info,\\n total_count=[]\\n\\n # .. count, to limit the number of displayed messages,\\n count = 0\\n\\n # .. replace values and collect examples, \\n for i, j in enumerate(cols):\\n\\n # ..... assume no values were replaced, so the messages work later, \\n info_lower_filter = 0\\n info_upper_filter = 0 \\n \\n # ..... test if the column is of the numeric type:\\n # from pandas.api.types import is_numeric_dtype\\n if is_numeric_dtype(df[j]):\\n \\n \\n # * replace values < or <= lower limit,\\n # - ----------------------------------\\n if lower_limit!=\\\"none\\\": \\n if equal == True:\\n lower_filter = df.loc[:,j]<=lower_limit\\n if equal == False:\\n lower_filter = df.loc[:,j]<lower_limit\\n \\n # info,\\n info_lower_filter=lower_filter.sum()\\n df.loc[list(lower_filter),j]=replace_with\\n \\n \\n # * replace values > or >= upper limit,\\n # - ----------------------------------\\n if upper_limit!=\\\"none\\\": \\n if equal == True:\\n upper_filter = df.loc[:,j]>=upper_limit\\n if equal == False:\\n upper_filter = df.loc[:,j]>upper_limit\\n \\n # info,\\n info_upper_filter=upper_filter.sum()\\n df.loc[list(upper_filter),j]=replace_with \\n \\n # * find how many values were replaced, and add that to the total_count list \\n total_count.append(info_upper_filter+info_lower_filter)\\n \\n # * display examples for 3 first columns with replaced values,\\n if verbose==True:\\n if info_upper_filter+info_lower_filter>0 and count <4:\\n print(f\\\"eg: {i}, {j} : {info_lower_filter} values <{lower_limit}, ...{info_upper_filter} values <{upper_limit}\\\")\\n else:\\n pass\\n\\n # * add 1 to count, to limit the number of displayed examples,\\n count += 1 \\n \\n else:\\n if verbose==True:\\n print(f\\\"{i, j} is not of numeric type, values were not replaced !\\\")\\n else:\\n pass\\n \\n # .. additional message, if more then 2 columns had replaced values, \\n if verbose==True:\\n if len(total_count)>3 and pd.Series(total_count).sum()>0:\\n print(f\\\". and {len(total_count)-3} other columns had in total {pd.Series(total_count).sum()} replaced values \\\\n\\\")\\n\\n # .. message in case no values vere replaced at all, \\n if pd.Series(total_count).sum()==0:\\n print(\\\"No values were replaced in requested columns....\\\")\\n \\n else:\\n pass\\n \\n # .. return, \\n return df.copy()\",\n \"def aitchison_transform_part(df):\\n df_aitchison = multiplicative_replacement(df)\\n #df_aitchison = closure(df)\\n df_idx = df.index\\n df_col = df.columns\\n df_aitchison = pd.DataFrame(df_aitchison, index = df_idx, columns = df_col)\\n return df_aitchison\",\n \"def processXs(self, df, norm_fn = None, encode_n_minus_1 = False):\\n xs, ys = self.splitXY(df)\\n # encode categorical\\n xs = pd.get_dummies(xs, drop_first = encode_n_minus_1)\\n # only normalize non-binary df columns\\n if norm_fn:\\n bin_cols = xs.apply(DataIO.isBinary, axis=0)\\n xs.loc[:, ~bin_cols] = norm_fn(xs.loc[:, ~bin_cols])\\n return pd.concat([xs, ys], axis=1)\",\n \"def fit_transform(self, df):\\n\\t\\tdf = self.__parse_json(df)\\n\\t\\tdf = self.__fillnan(df)\\n\\t\\tdf = self.__parse_dates(df)\\n\\t\\tdf['budget'] = df['budget'].apply(lambda x: self.missing_budget_imputing if int(x) == 0 else x)\\n\\t\\tdf['has_collections'] = df['belongs_to_collection'].isna().astype(int)\\n\\t\\tdf['homepage'] = df['homepage'].isna().astype(int)\\n\\t\\tdf['is_en'] = df['original_language'].apply(lambda x: 1 if x == 'en' else 0)\\n\\t\\tdf = self.__encode_genres(df)\\n\\t\\tdf = self.__top_countries_and_companies(df)\\n\\t\\tdf = self.__bin_columns(df)\\n\\t\\tdf.drop(\\n\\t\\t\\t['release_date', 'original_language', 'production_countries', 'production_companies', 'id', 'backdrop_path',\\n\\t\\t\\t 'imdb_id', 'poster_path', 'video', 'belongs_to_collection', 'status', 'runtime',\\n\\t\\t\\t 'original_title', 'overview', 'tagline', 'title'], axis=1, inplace=True)\\n\\t\\treturn df\",\n \"def to_numeric_features(features, feature_cols=None):\\n\\n for col in CATEGORICAL_COLS:\\n features = pd.concat(\\n [features, pd.get_dummies(features[col[0]], drop_first=True)], axis=1)\\n features.drop(col[0], axis=1, inplace=True)\\n\\n # Remove the unused columns from the dataframe.\\n for col in UNUSED_COLUMNS:\\n features.pop(col)\\n\\n # Re-index dataframe (if categories list changed from the previous dataset)\\n if feature_cols is not None:\\n features = features.T.reindex(feature_cols).T.fillna(0)\\n return features\",\n \"def encode_categorical(df):\\n cat_cols = df.select_dtypes(\\\"category\\\").columns\\n for col in cat_cols:\\n df[col] = df[col].cat.codes + 1\\n unique_no = len(df[col].unique())\\n if unique_no < 50:\\n df[col] = df[col].astype(\\\"uint8\\\")\\n elif unique_no < 16000:\\n df[col] = df[col].astype(\\\"int16\\\")\\n else:\\n df[col] = df[col].astype(\\\"int32\\\")\\n return df\",\n \"def clean_dataframe(df, column_list, length):\\n list_of_actual_columns = list(dataframe.columns.values)\\n for each_column in column_list:\\n if each_column not in list_of_actual_columns:\\n return False\\n if number_of_rows < 1000:\\n return False\\n return True\\n\\n category_value_counts = df['Type'].value_counts()\\n category_value_counts.to_csv('category_value_counts')\\n cdf = pd.read_csv('category_value_counts')\\n df = df.merge(cdf, left_on='Type', right_on='OldCategory')\\n df['Datetime'] = pd.to_datetime(df['Datetime'])\\n df[(df['Datetime'] > '2019-12-31')]\\n df.sort_values(by='Datetime', ascending=False)\",\n \"def apply(self, df):\\n encoded = []\\n for feature_name, encoder in zip(self.feature_names, self.encoders):\\n column = df[feature_name].to_numpy().reshape(-1, 1)\\n encoded.append(pd.DataFrame(\\n encoder.transform(column).todense(),\\n index=df.index,\\n columns=encoder.categories_[0]\\n ))\\n df = df.drop(columns=self.feature_names)\\n df = pd.concat((df, *encoded), axis=1)\\n return df\",\n \"def clean_transaction(df: pd.DataFrame) -> pd.DataFrame:\\n # Copy dataframe to local, so that the original dataset is not modified.\\n # See /notes/copy_df.py for an example.\\n df = df.copy()\\n # Drop features with insufficient observations.\\n df = _drop_inferior_features_transaction(df, nan_threshold=0.3)\\n # Mask missing nan observations left.\\n df = _fill_nan(df, categorical_fill=\\\"Missing\\\", numerical_fill=-1)\\n df = _prune_categories(df, prune_dict=PRUNE_DICT_TRANS)\\n df = _convert_to_dummies(df, method=\\\"pandas\\\")\\n return df\",\n \"def binarize_string_cols(data_frame, cols_to_replace):\\n\\n for col in cols_to_replace:\\n lb = preprocessing.LabelBinarizer()\\n col_values = lb.fit_transform(data_frame[col])\\n classes = [col + '_' + x for x in lb.classes_]\\n binarized_column = pd.DataFrame(col_values, columns=classes)\\n\\n # add new columns and remove the old one\\n data_frame = pd.concat([data_frame, binarized_column], axis=1)\\n data_frame = data_frame.drop([col], axis=1)\\n return data_frame\",\n \"def transform(self, data: Dict) -> Dict:\\n\\n for c in data.columns:\\n if c in self.featBin:\\n data[c] = data[c].astype(int)\\n if data[c].max() > 1:\\n data.loc[data[c] > 1, c] = 1\\n elif data[c].min() < 0:\\n data.loc[data[c] < 0] = 1\\n else:\\n pass\\n elif c in self.featNum:\\n data[c] = np.abs(data[c])\\n\\n else:\\n pass\\n\\n return data\",\n \"def own_OneHotColumnCreator(df, columns):\\n for col in cat_attribs:\\n for value in df[col].unique():\\n df[value] = (df[col] == value).astype(int)\",\n \"def preprocess_data_inplace(df):\\n encode(PREPROCESS_LABEL_ENCODERS, df, ['fecha_dato', 'fecha_alta'])\",\n \"def preprocess_df(df, column, preview=True):\\n \\n df[column] = df['Attraction'].apply(lambda x: x.lower())\\n df[column] = df[column].apply(lambda x: re.sub('[%s]' % re.escape(string.punctuation), '', x))\\n df[column] = df[column].apply(lambda x: re.sub('\\\\w*\\\\d\\\\w*','', x))\\n \\n return df\",\n \"def transform_and_create_new_features(df):\\n # 'GENDER' FEATURE MANAGEMENT\\n # Transform 'Gender' feature (categorical) to numerical one\\n df['Gender'] = df['Sex'].map({'female': 0, 'male': 1}).astype(int)\\n\\n # 'EMBARKED' FEATURE MANAGEMENT\\n # 1st approach: df['Port'] = df['Embarked'].map({'C': 1, 'S': 2, 'Q': 3}).astype(int)\\n # Extract from 'pycon UK Tutorial':\\n # \\\"Replacing {C, S, Q} by {1, 2, 3} would seem to imply the ordering C < S < Q when in fact they are simply arranged\\n # alphabetically. To avoid this problem, we create dummy variables. Essentially this involves creating new columns\\n # to represent whether the passenger embarked at C with the value 1 if true, 0 otherwise.\\\"\\n dummies_embarked = pd.get_dummies(df['Embarked'], prefix='Embarked')\\n df = pd.concat([df, dummies_embarked], axis=1)\\n\\n # 'AGE' & 'FARE' FEATURES MANAGEMENT\\n df = _transform_age_feature(df)\\n df = _transform_fare_feature(df)\\n\\n # CREATION OF A NEW FEATURE: Family size + Alone or not ?\\n df['Family'] = df['SibSp'] + df['Parch']\\n df['Alone'] = 0\\n df.loc[df['Family'] == 0, 'Alone'] = 1\\n\\n # Drop all columns that are now useless\\n df = df.drop(['Sex', 'Age', 'Fare', 'Embarked', 'SibSp', 'Parch'], axis=1)\\n print(df.head(10))\\n\\n return df\",\n \"def prepare_iris_data(df, encode=True):\\n \\n # Drop primary/foreign keys\\n df = df.drop(columns=[\\\"measurement_id\\\", \\\"species_id\\\"])\\n\\n # Rename \\\"species_name\\\" to species\\n df = df.rename(columns={\\\"species_name\\\": \\\"species\\\"})\\n\\n if(encode):\\n encoder = LabelEncoder()\\n encoder.fit(df.species)\\n df.species = encoder.transform(df.species)\\n\\n return df\",\n \"def normalize_dataframe(df=None):\\n #Drops all the columns that are not 'close'\\n col_to_drop = df.columns.levels[1].values\\n col_to_drop = np.delete(col_to_drop,np.where(col_to_drop==\\\"close\\\"))\\n df = df.drop(columns=col_to_drop, level=1)\\n #eliminates the double level in columns by deleting the 'close' label. \\n #Only ticker label is necessary.\\n df.columns = df.columns.droplevel(1)\\n return df\",\n \"def clean_up_df(df):\\n df['Age'] = df['Age'].fillna(df['Age'].median())\\n df['Gender'] = df['Sex'].map({'female':0, 'male':1}).astype(int)\\n df['Family'] = df['Parch'] + df['SibSp']\\n df['Fare'] = df['Fare'].fillna(df['Fare'].mean())\\n df = df.drop(['SibSp','Parch','Sex','Name','Cabin','Embarked','Ticket'],axis=1)\\n return df\",\n \"def prepare_titanic_data(df):\\n\\n df.embark_town.fillna('Other', inplace=True)\\n\\n # Drop deck and embarked_town\\n df.drop(columns=['deck', 'embark_town'], inplace=True)\\n\\n # Encoding: Objects (Categorical Variables) to Numeric\\n # Use sklearn's LabelEncoder\\n encoder = LabelEncoder()\\n\\n # Set Unknown and encode Embarked column to numbers\\n # 2 == \\\"S\\\" == Southampton == 644 people\\n # 0 == \\\"C\\\" == Cherbourg == 168 people\\n # 1 == \\\"Q\\\" == Queenstown == 77 people\\n # 3 == \\\"Unknown\\\" == 2 people\\n df.embarked.fillna('Unknown', inplace=True)\\n encoder.fit(df.embarked)\\n df.embarked = encoder.transform(df.embarked)\\n\\n # Encode the Class (first class, second, etc...)\\n # First class == 0\\n # Second class == 1\\n # Third class == 2\\n encoder.fit(df[\\\"class\\\"])\\n df[\\\"class_encoded\\\"] = encoder.transform(df[\\\"class\\\"])\\n\\n # Encode gender\\n # male == 1 == 577 records\\n # female == 0 == 314 records\\n encoder.fit(df.sex)\\n df[\\\"sex_encoded\\\"] = encoder.transform(df.sex)\\n\\n # Handle the 177 records with missing age values\\n average_age = df.age.mean()\\n df.age.fillna(average_age, inplace=True)\\n\\n scaler = MinMaxScaler()\\n scaler.fit(df[['fare']])\\n df[\\\"fare_scaled\\\"] = scaler.transform(df[['fare']])\\n\\n scaler = MinMaxScaler()\\n scaler.fit(df[['age']])\\n df[\\\"age_scaled\\\"] = scaler.transform(df[['age']])\\n\\n # Set the index to the passenger id\\n df = df.set_index(\\\"passenger_id\\\")\\n return df\",\n \"def transform_data(data_orig):\\n\\tif isinstance(data_orig, str):\\n\\t\\tdata_orig = pd.read_csv(data_orig)\\n\\n\\tdata = data_orig\\n\\n\\tnum_rows,num_variables = data.shape\\n\\tall_columns = data.columns.tolist()\\n\\tclean_data(data,all_columns,ignore_na=False,fill_mode=\\\"prob\\\")\\n\\texpand_features(data)\\n\\tvariables = ['Pclass','Sex',\\\"Fare\\\",\\\"Age\\\",\\\"SibSp\\\",\\\"Parch\\\",\\\"Embarked\\\",\\\"Fam_size\\\",\\\\\\n\\t\\t\\t\\t \\\"cabin_no\\\",\\\"ticket_no\\\",\\\"friend\\\",\\\"Fare_person\\\",\\\"Child\\\"]\\n\\tX = pd.get_dummies(data[variables])\\n\\n\\t## normalise features to zero man and unit variance\\n\\tscaler = preprocessing.StandardScaler().fit(X)\\n\\tX_scaled = scaler.transform(X)\\n\\tX = pd.DataFrame(X_scaled, columns=X.columns)\\n\\n\\tif \\\"Survived\\\" in data.columns:\\n\\t\\ty = data['Survived']\\n\\telse:\\n\\t\\ty = None\\n\\n\\treturn X, y\",\n \"def inverse_transform(self, df):\\n return df\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72517514","0.7021752","0.6957841","0.69419897","0.6836155","0.6820173","0.67918307","0.6785911","0.6761052","0.67148185","0.67098016","0.6694147","0.66585565","0.6657991","0.6594431","0.6561653","0.65530825","0.65374434","0.6511466","0.6479127","0.6450024","0.6446714","0.63728374","0.63696223","0.63506633","0.63436127","0.6304701","0.63008505","0.6299268","0.6286119","0.6274437","0.6258278","0.6227062","0.61954385","0.6186877","0.61473256","0.6139235","0.6081554","0.60799444","0.6077946","0.6011271","0.6006143","0.6006143","0.5999969","0.597755","0.59348404","0.5922115","0.58740026","0.5862788","0.5841544","0.5832754","0.58205485","0.5818652","0.5816645","0.5806451","0.57890224","0.5781373","0.5770848","0.5768921","0.57650495","0.5752665","0.57506","0.5747729","0.5733547","0.5721489","0.5718354","0.5710224","0.56931794","0.5687208","0.5683753","0.56804675","0.5679991","0.56717104","0.5660945","0.5656956","0.56518155","0.5651371","0.5647301","0.56391466","0.5638082","0.5638077","0.56375504","0.56372535","0.5630318","0.56169754","0.5610329","0.5605035","0.5601095","0.5591672","0.55833185","0.557946","0.5563002","0.55605745","0.5557835","0.55553246","0.55459","0.5543599","0.5531017","0.5530977","0.5528146"],"string":"[\n \"0.72517514\",\n \"0.7021752\",\n \"0.6957841\",\n \"0.69419897\",\n \"0.6836155\",\n \"0.6820173\",\n \"0.67918307\",\n \"0.6785911\",\n \"0.6761052\",\n \"0.67148185\",\n \"0.67098016\",\n \"0.6694147\",\n \"0.66585565\",\n \"0.6657991\",\n \"0.6594431\",\n \"0.6561653\",\n \"0.65530825\",\n \"0.65374434\",\n \"0.6511466\",\n \"0.6479127\",\n \"0.6450024\",\n \"0.6446714\",\n \"0.63728374\",\n \"0.63696223\",\n \"0.63506633\",\n \"0.63436127\",\n \"0.6304701\",\n \"0.63008505\",\n \"0.6299268\",\n \"0.6286119\",\n \"0.6274437\",\n \"0.6258278\",\n \"0.6227062\",\n \"0.61954385\",\n \"0.6186877\",\n \"0.61473256\",\n \"0.6139235\",\n \"0.6081554\",\n \"0.60799444\",\n \"0.6077946\",\n \"0.6011271\",\n \"0.6006143\",\n \"0.6006143\",\n \"0.5999969\",\n \"0.597755\",\n \"0.59348404\",\n \"0.5922115\",\n \"0.58740026\",\n \"0.5862788\",\n \"0.5841544\",\n \"0.5832754\",\n \"0.58205485\",\n \"0.5818652\",\n \"0.5816645\",\n \"0.5806451\",\n \"0.57890224\",\n \"0.5781373\",\n \"0.5770848\",\n \"0.5768921\",\n \"0.57650495\",\n \"0.5752665\",\n \"0.57506\",\n \"0.5747729\",\n \"0.5733547\",\n \"0.5721489\",\n \"0.5718354\",\n \"0.5710224\",\n \"0.56931794\",\n \"0.5687208\",\n \"0.5683753\",\n \"0.56804675\",\n \"0.5679991\",\n \"0.56717104\",\n \"0.5660945\",\n \"0.5656956\",\n \"0.56518155\",\n \"0.5651371\",\n \"0.5647301\",\n \"0.56391466\",\n \"0.5638082\",\n \"0.5638077\",\n \"0.56375504\",\n \"0.56372535\",\n \"0.5630318\",\n \"0.56169754\",\n \"0.5610329\",\n \"0.5605035\",\n \"0.5601095\",\n \"0.5591672\",\n \"0.55833185\",\n \"0.557946\",\n \"0.5563002\",\n \"0.55605745\",\n \"0.5557835\",\n \"0.55553246\",\n \"0.55459\",\n \"0.5543599\",\n \"0.5531017\",\n \"0.5530977\",\n \"0.5528146\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5775189"},"document_rank":{"kind":"string","value":"57"}}},{"rowIdx":16898,"cells":{"query":{"kind":"string","value":"This method implements any data specific missing data processing. Any missing data not replaced by values in this step will be removed by the general preprocessing script."},"document":{"kind":"string","value":"def handle_missing_data(self, dataframe):\n return dataframe"},"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 _clean(self, dataset):\n # Replace missing values with numpy's NaN. The missing value is\n # usually 1e+20, but values can be like 1.0000002e+20, which is\n # different. Ergo the inequality.\n for var in dataset.data_vars.itervalues():\n if 'missing_value' in var.attrs:\n missing_data_value = var.missing_value\n try:\n var.values[var.values >= missing_data_value] = np.NaN\n except ValueError:\n print \"Encountered ValueError in {0}. Ignoring\".format(var.name)","def data_preparation(self) -> None:\n self.logger.info('data cleaning')\n self.logger.info('num of secs: {}, num of ipo_dates: {}, num of secs with prices: {}'.format(\n len(self.data),\n len(self.ipo_dates),\n len(self.prices)\n ))\n excluded = []\n excluded = [i.lower() for i in excluded]\n self.logger.info(f'number of excluded: {len(excluded)}')\n for i in excluded:\n self.data.pop(i)\n for s in self.data:\n # columns with empty assets sum (empty columns and other situations)\n self.data[s].dropna(axis='columns', how='any', subset=['A_0'], inplace=True)\n # columns with descriptions (polish and english names of values)\n self.data[s].drop(self.data[s].columns[[0, 1]], inplace=True, axis=1)\n\n self.logger.info(f'number of secs after cleaning: {len(self.data)}')\n data_list = [k for k in self.data.values()]\n self.uber_data = pd.concat(data_list, ignore_index=True, axis=1)\n self.uber_data = self.uber_data.transpose()\n self.uber_data = self.uber_data.loc[:, pd.notnull(self.uber_data.columns)]","def unusedFromKDOTDataPreparation():","def _clean_data(self, dataset):\n dataset.dropna(inplace=True)\n # Problem: handle missing data (in a different way), noisy data, inconsistent data","def clean_data(df):\n \n # Put in code here to execute all main cleaning steps:\n # convert missing value codes into NaNs, ...\n count_miss = df.isnull().sum(axis=0).values #find number of nans for each column\n count_miss = [val for val in count_miss]\n \n drop_cols = []\n\n for ind, val in enumerate(count_miss):\n if val > 200000:\n drop_cols.append(ind)\n \n df_drop_cols = list(azdias.columns[drop_cols])\n df = df.drop(df_drop_cols, axis=1)\n \n for col in range(df.shape[1]): #loop through columns\n column_name = df.columns[col] #get column name\n missing_list = feat_info.iloc[col,3] #get missing_or_unknown column from feature info\n missing_list = missing_list.replace('[','') #remove left bracket from string\n missing_list = missing_list.replace(']','') #remove right bracket from string\n missing_list = missing_list.split(',') #split into individual strings\n \n #find data that is natually missing and continue loop to omit\n if missing_list == ['']:\n continue\n \n else:\n for dat_type in missing_list: \n if df[column_name].dtype == 'object': #find values that contain x\n df.loc[df[column_name] == dat_type, column_name] = np.nan #replace x with nan\n \n else:\n dat_type = int(dat_type) #if no x, convert to integer and replace with nan\n df.loc[df[column_name] == dat_type, column_name] = np.nan\n \n # select, re-encode, and engineer column values.\n \n # encode OST_WEST_KZ\n df.loc[df['OST_WEST_KZ'] == 'W','OST_WEST_KZ'] = 0\n df.loc[df['OST_WEST_KZ'] == 'O','OST_WEST_KZ'] = 1\n \n # Re-encode categorical variable(s) to be kept in the analysis.\n \n \n #get list of attributes with type categorical\n feat_info[feat_info['type'] == 'categorical']\n \n cat_new_cols = [] #initialize\n for i in feat_info[feat_info['type'] == 'categorical']['attribute']:\n cat_new_cols.append(i)\n \n for cols in df.columns:\n if cols in cat_new_cols:\n if df[cols].nunique(dropna=True) > 2: #if the number of unique values is greater than 2 \n df = df.drop(cols, axis=1) #drop from the analysis\n print(\"more than 2 categories: {}\".format(cols))\n \n else:\n if not df[cols].unique()[0] > 0:\n #if not df[cols].unique()[0] > 0:\n dummies = pd.get_dummies(df[cols], prefix=cols)\n df = df.drop(cols, axis=1) #create dummy variable\n df = df.join(dummies)\n print(\"transformed to dummy variable: {}\".format(cols))\n \n # create variable: MOVEMENT\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([1,3,5,8,10,12,14]),'MOVEMENT'] = 1\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([2,4,6,7,9,11,13,15]),'MOVEMENT'] = 2\n \n #Capture Decade\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([1,2]), 'DECADE'] = 40\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([3,4]), 'DECADE'] = 50\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([5,6,7]), 'DECADE'] = 60\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([8,9]), 'DECADE'] = 70\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([10,11,12,13]), 'DECADE'] = 80\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([14,15]), 'DECADE'] = 90\n \n df['CAMEO_INTL_2015'] = df['CAMEO_INTL_2015'].astype(float)\n\n # create new variable: WEALTH\n df.loc[df['CAMEO_INTL_2015'].isin([51,52,53,54,55]), 'WEALTH'] = 1\n df.loc[df['CAMEO_INTL_2015'].isin([41,42,43,44,45]), 'WEALTH'] = 2\n df.loc[df['CAMEO_INTL_2015'].isin([31,32,33,34,35]), 'WEALTH'] = 3\n df.loc[df['CAMEO_INTL_2015'].isin([21,22,23,24,25]), 'WEALTH'] = 4\n df.loc[df['CAMEO_INTL_2015'].isin([11,12,13,14,15]), 'WEALTH'] = 5\n \n # create new variable: LIFE_STAGE\n df.loc[df['CAMEO_INTL_2015'].isin([11,21,31,41,51]),'LIFE_STAGE'] = 1\n df.loc[df['CAMEO_INTL_2015'].isin([12,22,32,42,52]),'LIFE_STAGE'] = 2\n df.loc[df['CAMEO_INTL_2015'].isin([13,23,33,43,53]),'LIFE_STAGE'] = 3\n df.loc[df['CAMEO_INTL_2015'].isin([14,24,34,44,54]),'LIFE_STAGE'] = 4\n df.loc[df['CAMEO_INTL_2015'].isin([15,25,35,45,55]),'LIFE_STAGE'] = 5\n \n # remove selected columns and rows, ...\n df = df.drop('PRAEGENDE_JUGENDJAHRE', axis=1)\n df = df.drop('CAMEO_INTL_2015',axis=1)\n \n # Return the cleaned dataframe.\n return df","def parse_missing_fields(data):\n def _get_nan_synonims(k):\n if k == \"extra_security_descriptors\":\n return [pd.NaT, np.nan]\n else:\n return [pd.NaT, '', np.nan]\n\n data = {id_no: {k: (v if v not in _get_nan_synonims(k) and not pd.isna(v) \n else None) for k,v in datapoint.items()} for id_no, datapoint in data.items()}\n return data","def fill_missing(self) -> None:\n\n self.fill_missing_rows()\n self.fill_missing_source_parameters()\n return","def clean_data(df):\n \n # Put in code here to execute all main cleaning steps:\n # convert missing value codes into NaNs, ...\n################################################################################################################ \n missing_data_stats=pd.DataFrame(df.isnull().sum(),columns=['Naturally_Missing_Values'])\n \n # Loop over all the rows/attributes in feat_info\n for i in range(len(feat_info)):\n att=feat_info['attribute'][i] # Get name of attribute\n codes=feat_info['missing_or_unknown'][i].replace('[',\"\").replace(']',\"\").split(',') # Get values from string\n\n # Loop over all the individual values extratcted from '\tmissing_or_unknown' column\n for j in range(len(codes)):\n if codes[j]!='': # skip empty values as nothing needs to be replaced\n try:\n df.loc[df[att]==int(codes[j]),att] = np.nan # replaces numeric values with NaNs\n except:\n df.loc[df[att]==(codes[j]),att] = np.nan # replaces string values with NaNs\n \n # Merge and plot the naturally missing and overall missing values\n missing_data_stats=pd.merge(missing_data_stats,pd.DataFrame(df.isnull().sum(),\n columns=['Overall_Missing_Values']),left_index=True,right_index=True)\n \n################################################################################################################ \n # Perform an assessment of how much missing data there is in each column of the dataset.\n missing_data_stats['Overall_Missing_Percentage']=(missing_data_stats['Overall_Missing_Values']/len(df))*100\n sorted_missing_data_stats=missing_data_stats.sort_values(by='Overall_Missing_Percentage',ascending=False)\n \n # Investigate patterns in the amount of missing data in each column.\n plt.figure(figsize=(15,30))\n sns.barplot(x='Overall_Missing_Percentage', y=sorted_missing_data_stats.index, \n data=sorted_missing_data_stats);\n plt.title('Percentage of Missing Data by Attribute',size=20)\n \n################################################################################################################\n # remove selected columns and rows, ...\n # Define criteria of outliers removal\n outlier=30 # Outlier if % missing more than\n\n #Get outlier columns and drop them from data\n outlier_columns=sorted_missing_data_stats[sorted_missing_data_stats['Overall_Missing_Percentage']>outlier].index\n for i in outlier_columns:\n df.drop(i,axis=1,inplace=True)\n\n################################################################################################################\n # Sum all the missing values by rows\n nan_count_rows=df.isnull().sum(axis=1).sort_values(ascending=False)\n\n # Plot and visualize the distribution of missing rows\n plt.figure(figsize=(20,8))\n sns.countplot(nan_count_rows)\n\n plt.xlabel('Missing Values in each Row')\n plt.ylabel('Count of Rows')\n plt.title('Count of Rows vs Missing Values in each Row')\n \n \n # Define threshold value\n row_nan_threshold=0\n\n # Rows above threshold\n at=df.loc[nan_count_rows>row_nan_threshold]\n\n # Rows below threshold\n bt=df.loc[nan_count_rows<=row_nan_threshold]\n \n################################################################################################################ \n # select, re-encode, and engineer column values.\n\n # Assess categorical variables: which are binary, which are multi-level, and\n # which one needs to be re-encoded?\n\n # Get all categorical features\n cat_att=feat_info[feat_info['type']=='categorical']['attribute']\n\n # Loop through all the categorical features to identify binary numeric, binary non-numeric and multi-level \n #categories\n bin_num=[] #binary and numeric (type1)\n bin_non_num=[] #binary and non_numeric (type2)\n ml_cat=[] #multi-level categories (type3)\n\n for i in cat_att:\n try:\n # get number of unique values\n uv=bt[i].nunique()\n dt=bt[i].dtype\n\n # For binary variable with numeric value\n if uv==2 and dt!='object':\n bin_num.append(i) #append\n\n # For binary variable with non-numeric value\n elif uv==2 and dt=='object':\n bin_non_num.append(i) #append\n\n # For multi-level categories \n elif uv>2:\n ml_cat.append(i) #append\n\n\n # Exception for columns which are already dropped from data\n except Exception as e:\n print('Note=> Column does not exist: ', e)\n\n print('\\nBinary_Numeric:\\n', bin_num , '\\n\\nBinary_Non_Numeric:\\n' , bin_non_num , '\\n\\nMulti-Level:\\n' , ml_cat)\n \n################################################################################################################\n # Import Libraries\n from sklearn.preprocessing import LabelEncoder\n\n # Initialize encoder\n le=LabelEncoder()\n\n # columns list to encode\n col_list=bin_non_num+ml_cat\n\n # Create a dataframe of encoded features\n bt_encoded=bt.copy(deep=True)\n\n # Encode all columns in column list\n for cols in col_list:\n bt_encoded[cols]=le.fit_transform(bt[cols])\n\n################################################################################################################\n # Create a list of all mixed features\n mixed_att=feat_info[feat_info['type']=='mixed']['attribute']\n\n for i in mixed_att:\n # Investigate \"PRAEGENDE_JUGENDJAHRE\" and engineer two new variables.\n if i=='PRAEGENDE_JUGENDJAHRE':\n bt_encoded.loc[(bt_encoded[i]<=2) & (bt_encoded[i]>=1) ,i+'_Decade']=1\n bt_encoded.loc[(bt_encoded[i]<=4) & (bt_encoded[i]>=3),i+'_Decade']=2\n bt_encoded.loc[(bt_encoded[i]<=7) & (bt_encoded[i]>=5),i+'_Decade']=3\n bt_encoded.loc[(bt_encoded[i]<=9) & (bt_encoded[i]>=8),i+'_Decade']=4\n bt_encoded.loc[(bt_encoded[i]<=13) & (bt_encoded[i]>=10),i+'_Decade']=5 \n bt_encoded.loc[(bt_encoded[i]<=15) & (bt_encoded[i]>=14),i+'_Decade']=6\n\n bt_encoded[i+'_Movement']=1\n bt_encoded.loc[(bt_encoded[i]==1) | (bt_encoded[i]==3) |\n (bt_encoded[i]==5) | (bt_encoded[i]==8) |\n (bt_encoded[i]==10) | (bt_encoded[i]==12) |\n (bt_encoded[i]==14) ,i+'_Movement']=2\n\n # Investigate \"CAMEO_INTL_2015\" and engineer two new variables.\n elif i=='CAMEO_INTL_2015':\n bt_encoded[i+'_Wealth']=bt_encoded[i].apply(lambda x:int(x[0]))\n bt_encoded[i+'_Life_Stage']=bt_encoded[i].apply(lambda x:int(x[1]))\n\n # Investigate \"LP_LEBENSPHASE_GROB\" and engineer a new variables (for low,avg,high income groups) \n elif i=='LP_LEBENSPHASE_GROB':\n #low income\n bt_encoded[i+'_income']=1 \n\n #avg income\n bt_encoded.loc[(bt_encoded[i]==3) | (bt_encoded[i]==5) |\n (bt_encoded[i]==8) | (bt_encoded[i]==11) |\n (bt_encoded[i]==12) ,i+'_income']=2 \n #high income\n bt_encoded.loc[(bt_encoded[i]==9) ,i+'_income']=3 \n\n\n # Investigate \"WOHNLAGE\" and engineer a new variables (for Rural and Non Rural Flag) \n elif i=='WOHNLAGE':\n bt_encoded[i+'_Rural']=1\n bt_encoded.loc[(bt_encoded[i]==7) | (bt_encoded[i]==8),i+'_Rural']=2\n\n\n # Investigate \"PLZ8_BAUMAX\" and engineer a new variables (for family and business buildings) \n elif i=='PLZ8_BAUMAX':\n bt_encoded[i+'_Bldg_Type']=1\n bt_encoded.loc[bt_encoded[i]==5,i+'_Bldg_Type']=2\n \n################################################################################################################\n # Return the cleaned dataframe.\n #Remove original mixed columns from final dataset if new columns have been engineered\n for i in mixed_att:\n try:\n bt_encoded.drop(i,axis=1,inplace=True) \n except Exception as e:\n print('Note=> Column does not exist: ', e)\n return bt_encoded","def handle_na(self, data: pd.DataFrame) -> pd.DataFrame:\n return remove_missing(\n data,\n self.params[\"na_rm\"],\n list(self.REQUIRED_AES | self.NON_MISSING_AES),\n self.__class__.__name__,\n )","def clean_up_data(self):\n pass","def _common_preprocess(self, data):\n\n data = data.drop('id', axis=1) \n data = data.drop(['17', '488', 'B01AF', 'H01AB'], axis=1, errors='ignore')\n\n # drop age outliers\n idx = data[(data['age'] > 99)].index\n data = data.drop(idx)\n\n # drop rows with CKD\n idx = data[((data['585'] != 0) | (data['586'] != 0)) &\n (data['ckd'] == 0)].index\n data = data.drop(idx)\n data = data.drop(['585', '586'], axis=1)\n\n return data","def missing_values():\n print('Missings in the train data:', train_data.isnull().sum())","def data_missing(data):\n return type(data)._from_sequence([None, data[0]])","def prepare_process(self, dataset):\n if dataset is not None:\n pass","def preprocess(data):\n raise NotImplementedError","def correct_miss_fill(ds):\n for d in ds.data_vars:\n try:\n ds[d].attrs.update({'missing_value': ds[d]._FillValue})\n except:\n pass\n return xr.decode_cf(ds)","def preprocess_valid_data(self):\r\n print(\"* Preprocessing validation data.\", flush=True)\r\n prep.create_HDF_file(self.C.validation_set)\r\n\r\n self.print_time_elapsed()","def preProcess(self, datum):\n pass","def cleaning (data):","def data_missing_for_sorting() -> NoReturn:\n raise NotImplementedError","def data_missing_for_sorting() -> NoReturn:\n raise NotImplementedError","def Check01_RemoveNoDataValues( DataDF, ReplacedValuesDF ):\n \n #add your code here\n for i in range(0,len(DataDF)-1): #checks for a specific value in each cell, then replaces it with nan if it meets the criteria\n for j in range(0,3):\n if DataDF.iloc[i,j] == -999:\n DataDF.iloc[i,j]=np.nan\n \n ReplacedValuesDF.iloc[0,0]=DataDF['Precip'].isna().sum() #counts the number of \"nan\" values for the referenced variable, then plugs it into the correct cell in the replacedvaluesdf\n ReplacedValuesDF.iloc[0,1]=DataDF['Max Temp'].isna().sum()\n ReplacedValuesDF.iloc[0,2]=DataDF['Min Temp'].isna().sum()\n ReplacedValuesDF.iloc[0,3]=DataDF['Wind Speed'].isna().sum()\n \n return( DataDF, ReplacedValuesDF )","def ImputeMissingValues(self, **kwargs):\n if self._frozen:\n raise Exception(\"Dataset already frozen, no modifications allowed\")\n \n # we should check it there are any duplicated columns in the arguments\n if \"columns\" in kwargs.keys():\n all_columns = [c[\"colname\"] for c in self._columns]\n tmp = kwargs[\"columns\"]\n # check if columns have been validated\n flag = 0\n for c in tmp:\n if c not in all_columns:\n flag = 1\n print(\"Unknown or unvalidated column: '{}'\".format(c))\n if flag>0:\n raise Exception(\"Unknown or unvalidated columns, aborting\")\n if np.unique(tmp).shape[0] != len(tmp):\n raise Exception(\"Duplicated columns, aborting\")\n # check that if a column has been passed as a parameter, all columns for the same group are passed too\n for column in tmp:\n group = [c[\"group\"] for c in self._columns if c[\"colname\"]==column][0]\n columns_in_group = [c[\"colname\"] for c in self._columns if c[\"group\"]==group]\n if all([c in tmp for c in columns_in_group]) != True:\n raise Exception(\"All one-hot encoded mono-label variables of the same group must be included (column: '{}'\".format(column))\n \n columns = tmp\n else:\n # by default : take all columns which are in the X dataset\n columns = [c[\"colname\"] for c in self._columns if c[\"dataset\"]==\"X\"]\n \n df = self._proc_data.copy()\n \n # Remove columns with only missing values\n cols_to_remove = []\n for coln in columns:\n if np.sum(pd.isna(df[coln]))==df.shape[0]:\n print(\" !!!! Column {} (qty) only has missing values, will be removed\".format(coln))\n cols_to_remove.append(coln)\n # Check if 0 variance => would lead to NA values\n for coln in columns:\n if np.nanvar(df[coln])==0:\n print(\" !!!! Column {} (qty) has 0 variance, will be removed\".format(coln))\n cols_to_remove.append(coln)\n if len(cols_to_remove)>0:\n columns = [c for c in columns if c not in cols_to_remove] \n df = df.loc[:,columns]\n mean = np.nanmean(df,axis=0)\n std = np.nanstd(df,axis=0)\n df = (df-mean)/std\n \n print(\" >>>> Total missing values:\\n{}\".format(np.sum(pd.isna(df))))\n \n ar = np.array(df)\n \n # impute\n imputer = KNNImputer(n_neighbors=5, weights=\"distance\")\n imputed_np = imputer.fit_transform(ar)\n \n # unnormalize\n unnorm_imputed_np = imputed_np*std+mean\n unnorm_imputed_df = df.copy()\n # send back to dataframe, with same structure: index, columns...\n unnorm_imputed_df.loc[:,:] = unnorm_imputed_np\n \n # for binary variables : convert to 0/1 and ensure that variables in the same group have only one 1\n # convert by group to make it easier\n # one entry per group\n bin_columns_by_group = {}\n for c in self._columns:\n if c[\"mode\"]==1 and c[\"colname\"] in columns:\n if c[\"group\"] in bin_columns_by_group.keys():\n bin_columns_by_group[c[\"group\"]].append(c[\"colname\"])\n else:\n bin_columns_by_group[c[\"group\"]]=[c[\"colname\"]]\n \n for cols in bin_columns_by_group.values():\n if len(cols)==1: # binary\n unnorm_imputed_df[cols[0]] = np.minimum(1, np.maximum(0, np.round(unnorm_imputed_df[cols[0]])))\n else:\n a = np.argmax(np.array(unnorm_imputed_df[cols]), axis=1)\n b = np.zeros((a.size, a.max()+1))\n b[np.arange(a.size),a] = 1\n unnorm_imputed_df[cols] = b\n \n self._proc_data.loc[:,columns] = unnorm_imputed_df","def preprocess_dataset(dataset=None, remove_missing=60, remove_empty_rows=True):\n print('feature size before dropping:{}'.format(dataset.shape[1]))\n dataset_after_drop = dataset.dropna(thresh=dataset.shape[0]*remove_missing/100, how='all',axis=1)\n print('feature size after dropping:{}'.format(dataset_after_drop.shape[1]))\n print('row size before dropping:{}'.format(dataset_after_drop.shape[0]))\n if remove_empty_rows is True:\n df_final = dataset_after_drop.dropna(inplace=False).reset_index (drop=True)\n print('row size after dropping:{}'.format(df_final.shape[0]))\n print('---------------')\n print('final shape:{}'.format(df_final.shape))\n return df_final\n else:\n return dataset_after_drop","def reprocessSeries(self, tiltseriesdata):\n\t\treturn None","def _pure_data(data, missing_mask):\n missing_rows = np.where(missing_mask)[0]\n pure_data = np.delete(data, missing_rows, axis=0)\n\n return pure_data","def _process_data(self):\r\n # Rename columns to match final feature class\r\n self._rename_columns()\r\n # Add point ID column\r\n self._add_pointid()\r\n # Sort rows by transect id and timestamp\r\n self._sort_rows()\r\n # Fill Null records with a value\r\n self._fill_nulls()\r\n # Set site_code to lower case\r\n self._lower_site_code()\r\n # Create survey_id\r\n self._calc_survey_id()\r\n # Calculate nativesg column if at least one of the veg columns is a Native seagrass type\r\n if set(self.veg_columns).intersection(set(NATIVESG_CODES)) > 0:\r\n self.nativesg_columns = list(set(self.veg_columns).intersection(set(NATIVESG_CODES)))\r\n self._calc_nativesg()\r\n #\r","def fill_missing(self):\n df = self.df\n # Filling with default values\n logger.debug(\"Filling from distributions...\")\n for field in HeatStrokeDataFiller.default_map or field in HeatStrokeDataFiller.positive_default:\n if field not in df.columns:\n logger.warning(\"(%s) missing from data-frame columns\" % field)\n continue\n logger.debug(\"Setting missing in \\\"%s\\\" to default: %s\" % (field, HeatStrokeDataFiller.default_map[field]))\n default_value = HeatStrokeDataFiller.default_map[field]\n\n where = HeatStrokeDataFiller.find_where_missing(df, field, find_nan=True, find_str=False)\n how_many_to_fill = np.sum(where)\n if field in HeatStrokeDataFiller.positive_default:\n # Use default positive dietributions\n distribution = HeatStrokeDataFiller.positive_default[field]\n df[field].loc[where] = distribution(how_many_to_fill)\n else:\n logger.debug(\"Using default %s for field: %s\" % (default_value, field))\n # Use default values\n df[field].loc[where] = np.array([default_value] * how_many_to_fill)\n\n # Filling with Zeros\n logger.debug(\"Fillling with zeros...\")\n for field in HeatStrokeDataFiller.fields_to_fill_with_zero:\n if field not in df.columns:\n logger.warning(\"\\\"%s\\\" missing from columns\" % field)\n continue\n logger.debug(\"Setting missing in \\\"%s\\\" to 0\" % field)\n\n where = HeatStrokeDataFiller.find_where_missing(df, field, find_nan=True, find_str=True)\n how_many_to_fill = np.sum(where)\n df[field].loc[where] = np.zeros(how_many_to_fill)\n\n # Filling in columns with the average from the rest of the column\n logger.debug(\"Filling with agerages...\")\n for field in HeatStrokeDataFiller.fields_to_fill_with_average:\n if field not in df.columns:\n logger.warning(\"\\\"%s\\\" missing from data-frame columns\" % field)\n continue\n\n where = HeatStrokeDataFiller.find_where_missing(df, field, find_nan=True, find_str=True)\n data = df[field][np.invert(where)]\n mean = np.mean(data)\n std = np.std(data)\n if mean == np.nan or std == np.nan:\n mean, std = (0, 0)\n logger.debug(\"Setting missing in \\\"%s\\\" with: %.3f +/- %.3f\" % (field, mean, std))\n how_many_to_fill = np.sum(where)\n df[field].loc[where] = mean + std * np.random.random(how_many_to_fill)\n\n fields_not_modified = set(df.columns) - set(HeatStrokeDataFiller.default_map.keys()) - HeatStrokeDataFiller.fields_to_fill_with_zero - HeatStrokeDataFiller.fields_to_fill_with_zero\n logger.debug(\"Fields not modified: %s\" % fields_not_modified.__str__())\n return df","def clean():\n filter_phase_data()\n combine_phase_data()\n remove_duplicates_phase_data()","def preprocess(self, data, label):\n\t\traise NotImplementedError","def DealWithMissingValues(data_set: pd.DataFrame):\n data_set.fillna(method=\"pad\", inplace=True)","def _clean_results(self):\n\t\tif self.file_type == \"Automobile\":\n\t\t\tcols = [\"Year\", \"Mileage\", \"Price\"]\n\t\t\tself.data.Mileage.replace([',', 'mi.', 'nan', ' '], '', regex=True, inplace=True) # Fix mileage column\n\t\t\tself.data.Price.replace([',', '\\$'], '', regex=True, inplace=True) # Always fix price column (, and $ removed)\n\t\t\tself.data[cols] = self.data[cols].apply(pd.to_numeric, errors='coerce') # Coerces errors into NaN values\n\t\t\tself.data.drop(self.data[self.data.Year < 2000].index, inplace=True) # Remove cars made before 2000\n\t\t\tself.data.drop(self.data[self.data.Price > 30000].index, inplace=True) # Remove cars over $30,000\n\t\t\tself.data.drop(self.data[(self.data.Mileage < 1000) | (self.data.Mileage > 300000)].index, inplace=True) # Remove cars with over 300,000 miles\n\t\t\tself.data['Age'] = 2018 - self.data['Year'] # Change years to Age\n\t\telif self.file_type == \"Apartment\":\n\t\t\tself.data.Area.replace(['ft2'], '', regex=True, inplace=True) # Remove ft2 from square footage column\n\t\t\tself.data.Price.replace([',', '\\$'], '', regex=True, inplace=True) # Always fix price column (, and $ removed)\n\t\telse:\n\t\t\tself.data['Street'], self.data['City'], self.data['State'] = self.data['Address'].str.split(',', 2).str\n\t\t\tdel self.data.Address\n\t\t\tself.data.drop(self.data[self.data.Price > 1000000].index, inplace=True) # Remove houses worth more than $1 million\n\n\t\tself.data.replace('^\\s*$', np.nan, regex=True, inplace=True) # Replace all empty values with np.NaN\n\t\tself.data = self.data.dropna(axis=1, how='all') # Remove Null Columns\n\t\tself.data = self.data.apply(pd.to_numeric, errors='ignore') # Coerces errors into NaN values","def transform(self, data):\n print(\"NA before imputation:\", data[self.field].isna().sum())\n missing_age_df = data[data[self.field].isna()]\n missing_and_na_age_df = missing_age_df[missing_age_df['SibSp'] >= 3]\n data.loc[missing_and_na_age_df.index, \"Age\"] = 11.7\n data[self.field] = data[self.field].fillna(-1)\n print(\"special case imputation:\", len(\n data.loc[missing_and_na_age_df.index, \"Age\"]))\n print(\"-1 after imputation:\", sum(data[self.field] == -1))\n return data","def interpolate_none(self):\n\n # Reset processed data\n self.u_processed_mps = np.copy(self.u_mps)\n self.v_processed_mps = np.copy(self.v_mps)\n self.u_processed_mps[self.valid_data[0, :] == False] = np.nan\n self.v_processed_mps[self.valid_data[0, :] == False] = np.nan","def postprocessData(meta, units, data):\n\n data['time'] = np.arange(0, meta['dt'] * len(data), meta['dt'])\n units['time'] = 's'\n\n meta, units, data = self.calculateForce(meta, units, data)\n\n data['distance'] = np.sqrt(data.xDist**2 + data.yDist**2)\n units['distance'] = 'nm'\n\n return meta, units, data","def preprocess_data(self):\n\n self._preprocess_train_data()\n self._preprocess_test_data()","def minimal_clean_data_inplace(df):\n # There are some 'unknown' users in train dataset only\n unknown_data_lines = df['sexo'].isnull() & df['age'].isnull() & df['ind_empleado'].isnull() & \\\n df['fecha_alta'].isnull() & df['pais_residencia'].isnull()\n\n logging.info(\"- Number of lines with unknown data : %s\" % unknown_data_lines.sum())\n\n # Remove these users as clients\n _clients = df[unknown_data_lines]['ncodpers'].unique()\n bad_lines = df['ncodpers'].isin(_clients)\n df.drop(df[bad_lines].index, inplace=True)\n\n logging.info(\"- Number of columns with nan : %s\" % df.isnull().any().sum())\n\n # Remove accent\n df.loc[df['nomprov'] == \"CORU\\xc3\\x91A, A\", \"nomprov\"] = \"CORUNA\"\n\n unknown_cols = ['sexo',\n 'ind_empleado',\n 'pais_residencia',\n 'ult_fec_cli_1t',\n 'conyuemp',\n 'canal_entrada',\n 'nomprov',\n 'segmento',\n 'tiprel_1mes',\n 'indrel_1mes']\n # Start with cols -> replace nan with UNKNOWN\n for col in unknown_cols:\n df.loc[df[col].isnull(), col] = \"UNKNOWN\"\n\n # Set unknown renta to -99\n df.loc[df['renta'].isnull(), 'renta'] = -99\n\n # Next `fecha_alta` :\n assert df['fecha_alta'].isnull().sum() == 0, \\\n \"Need to replace nan in 'fecha_alta', count=%s\" % df['fecha_alta'].isnull().sum()\n\n # **Remove 'tipodom' and 'cod_prov' columns**\n df.drop([\"tipodom\", \"cod_prov\"], axis=1, inplace=True)\n \n # Convert 'ind_nuevo' to int\n df['ind_nuevo'] = df['ind_nuevo'].astype(int)\n \n # Remove floating point at string indrel_1mes\n df['indrel_1mes'] = df['indrel_1mes'].apply(lambda x: str(int(float(x))) if len(x) == 3 else x)\n\n if \"ind_nomina_ult1\" in df.columns and \"ind_nom_pens_ult1\" in df.columns:\n # Target labels : `ind_nomina_ult1`, `ind_nom_pens_ult1` : nan -> 0\n # I could try to fill in missing values for products by looking at previous months,\n # but since it's such a small number of values for now I'll take the cheap way out.\n df.loc[df.ind_nomina_ult1.isnull(), \"ind_nomina_ult1\"] = 0\n df.loc[df.ind_nom_pens_ult1.isnull(), \"ind_nom_pens_ult1\"] = 0\n\n # replace 'antiguedad' with the number of months between 'fecha_alta' and 'fecha_dato'\n func1 = lambda x: _to_ym_dec(to_yearmonth(x))\n func2 = lambda x: max(_to_nb_months(x), 0) \n\n v1 = df['fecha_dato'].apply(func1)\n v2 = df['fecha_alta'].apply(func1)\n v3 = (v1 - v2).apply(func2)\n df.loc[:, 'antiguedad'] = v3\n \n # Replace 'ult_fec_cli_1t' by current nb of months from fecha_dato, if negative, set to zero\n mask = df['ult_fec_cli_1t'] == 'UNKNOWN'\n df.loc[mask, 'ult_fec_cli_1t'] = df[mask]['fecha_dato']\n v1 = df['fecha_dato'].apply(func1)\n v2 = df['ult_fec_cli_1t'].apply(func1)\n v3 = (v1 - v2).apply(func2)\n df.loc[:, 'ult_fec_cli_1t'] = v3","def fill_nan(data, fm, filename):\r\n print(\"fill Nan begining...\")\r\n\r\n # fill categorical variable\r\n categorical_variable_fill = pd.read_excel(\"./data/VariableFill.xlsx\", dtype={'Name': str, 'Value': float})\r\n categorical_features = categorical_variable_fill['Variable'].values\r\n categorical_variable_fill.set_index('Variable', inplace=True)\r\n for cat_col in categorical_features:\r\n data[cat_col].fillna(categorical_variable_fill.loc[cat_col, 'Fill'], inplace=True)\r\n\r\n # fill continue variable\r\n columns_name = list(data.columns.values)\r\n feature_name = list(fm.keys())\r\n\r\n for col in columns_name:\r\n if col in feature_name:\r\n for i in range(len(data)):\r\n # 填充空值\r\n if np.isnan(data.loc[i, col]):\r\n if len(fm[col]) == 4:\r\n data.loc[i, col] = uniform(fm[col]['low'], fm[col]['up'], 1)\r\n data.loc[i, col+'Grade2'] = fm[col]['grade2']\r\n else:\r\n if data.loc[i, 'Gender'] == 1:\r\n data.loc[i, col] = uniform(fm[col]['F']['low'], fm[col]['F']['up'], 1)\r\n data.loc[i, col+'Grade2'] = fm[col]['F']['grade2']\r\n elif data.loc[i, 'Gender'] == 0:\r\n data.loc[i, col] = uniform(fm[col]['M']['low'], fm[col]['M']['up'], 1)\r\n data.loc[i, col + 'Grade2'] = fm[col]['M']['grade2']\r\n\r\n data.to_csv('./data/'+filename+'processed_data.csv', index=False)\r\n print(\"fill Nan finished! processed data is saved in ./data/processed_data.csv\")\r\n return data","def missing_data(self, plot=True):\n # Set some of the observations to missing\n self.remove_data()\n # Create empty arrays to store values\n F = np.zeros(len(self.y))\n a = np.zeros(len(self.y))\n v = np.zeros(len(self.y))\n P = np.zeros(len(self.y))\n # Initialize at the initial values parsed to the class\n P[0] = self.pardict['P1']\n sigma_eps2 = self.pardict['sigma_eps2']\n sigma_eta2 = self.pardict['sigma_eta2']\n # initialise a for estimated model\n if self.var_name != 'Volume of Nile':\n a[0] = self.y[0]\n # Iterate\n for t in range(0, len(self.y) - 1):\n v[t] = np.nan_to_num(self.y[t] - a[t])\n F[t] = P[t] + sigma_eps2 if np.isfinite(self.y[t]) else np.inf\n # K is defined as ratio of P and F\n Kt = P[t] / F[t] if np.isfinite(self.y[t]) else 0\n a[t + 1] = a[t] + Kt * v[t]\n P[t + 1] = P[t] * (1 - Kt) + sigma_eta2\n v[-1] = self.y[-1] - a[-1]\n F[-1] = P[-1] + sigma_eps2\n # Obtain smoothed state\n # Obtain all time values for L\n L = self.pardict['sigma_eps2'] / F\n index_missing = np.argwhere(np.isnan(self.y))\n L[index_missing] = 1\n\n # Do the recursion for r\n r = np.zeros(len(self.y))\n N = np.zeros(len(self.y))\n V = np.zeros(len(self.y))\n\n for t in np.arange(len(self.y) - 1, 0, -1):\n r[t - 1] = v[t] / F[t] + L[t] * r[t]\n for t in np.arange(len(self.y) - 1, 0, -1):\n N[t - 1] = 1 / F[t] + L[t] ** 2 * N[t]\n for t in np.arange(len(self.y) - 1, 0, -1):\n V[t] = P[t] - P[t] ** 2 * N[t - 1]\n\n # Do the recursion for alpha\n alphas = np.zeros(len(self.y))\n alphas[0] = a[t]\n for t in range(1, len(self.y) - 1):\n alphas[t] = a[t] + P[t] * r[t - 1]\n alphas[-1] = np.nan\n\n if plot:\n fig_name = self.var_name + 'Fig25.pdf'\n plot_fig2_5(self.times, self.y, a, P, alphas, V, fname=fig_name, var_name=self.var_name)\n # Restore data\n self.reset_data()","def pre_process(self, dataset):\n\n # np.empty creates an empty array only. You have to replace this with your code.\n X = np.empty((0,0))\n y = np.empty((0))\n\n if dataset == 0:\n # Implement for the abalone dataset\n df = pd.DataFrame(columns=['sex', 'length', 'diameter', 'height', 'whole_weight', 'shucked_weight', 'viscera_weight', 'shell_weight', 'rings'])\n count = 0\n\n with open('Dataset.data') as file: # reading data from file\n data = file.read()\n\n data = data.split('\\n') # split data into different rows\n data = data[:-1] # last one is empty\n for row in data:\n row = row.split()\n df.loc[count] = row # add in dataframe\n count += 1\n\n df['M'] = np.where(df.sex=='M', 1,0) # genders are turned to a one hot encoding\n df['F'] = np.where(df.sex=='F', 1,0)\n df['I'] = np.where(df.sex=='I', 1,0)\n df = df.drop(['sex'], axis=1)\n df = df.dropna()\n\n df = df.sample(frac=1).reset_index(drop=True) # shuffle dataframe\n\n X = df.drop(['rings'], axis=1)\n X = X.values\n X = X.astype(float)\n y = df['rings'].values\n y = y.astype(float)\n\n elif dataset == 1:\n # Implement for the video game dataset\n df = pd.read_csv('VideoGameDataset - Video_Games_Sales_as_at_22_Dec_2016.csv') # read csv directly into a dataframe\n df1 = df[['Critic_Score', 'User_Score', 'Global_Sales']]\n df1 = df1.dropna()\n df1 = df1[df1.User_Score != 'tbd']\n\n df1 = df1.sample(frac=1).reset_index(drop=True) # shuffle rows\n\n X = df1.drop(['Global_Sales'], axis=1)\n X = X.values\n X = X.astype(float)\n y = df1['Global_Sales'].values\n y = y.astype(float)\n\n elif dataset == 2:\n # Implement for the banknote authentication dataset\n df = pd.DataFrame(columns=['variance', 'skewness', 'curtosis', 'entropy', 'class'])\n count = 0\n\n with open('data_banknote_authentication.txt') as file: # reading file \n data = file.read()\n data = data.split('\\n')\n data = data[:-1]\n for row in data:\n row = row.split(',')\n df.loc[count] = [float(elt) for elt in row[:-1]] + [int(row[-1])] # last column has class so it is int rest are float\n count += 1\n\n df = df.sample(frac=1).reset_index(drop=True) # shuffle dataset\n\n X = df.drop(['class'], axis=1)\n X = X.values\n y = df['class'].values\n y = y.astype(int)\n\n return X, y","def clean_data(data,variables,ignore_na=True,fill_mode=\"mode\"):\n\tnum_rows,num_cols = data.shape\n\tif ignore_na:\n\t\tdata = data.dropna(axis=0,how=\"any\",subset=variables)\n\t\tnum_rows,num_cols = data.shape\n\telse:\n\t\tfor variable in variables:\n\t\t\tif data[variable].isna().sum()>0:\n\t\t\t\tif fill_mode==\"mode\":\n\t\t\t\t\tdata[variable] = data[variable].fillna(data[variable].mode()[0])\n\t\t\t\telse:\n\t\t\t\t\tif variable==\"Age\":\n\t\t\t\t\t\tnan_idx = np.where(data[\"Age\"].isna())[0]\n\t\t\t\t\t\tnorm = [1.,1.]#[8.,3.]\n\t\t\t\t\t\tnew_age = np.zeros((num_rows))\n\t\t\t\t\t\tfor idx in nan_idx:\n\t\t\t\t\t\t\terr = (data[[\"SibSp\",\"Pclass\"]]-data[[\"SibSp\",\"Pclass\"]].iloc[idx])\n\t\t\t\t\t\t\terr /= norm\n\t\t\t\t\t\t\ttotal_err = np.sqrt(np.sum(err**2,axis=1))\n\t\t\t\t\t\t\tif len(data[\"Age\"][total_err==0].mode())>0:\n\t\t\t\t\t\t\t\tnew_age[idx] = data[\"Age\"][total_err==0].mode()[0]\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tnew_age[idx] = data[\"Age\"][total_err<4].mode()[0]\n\t\t\t\t\t\tdata[\"Age\"].fillna(pd.Series(new_age),inplace=True)\n\n\t\t\t\t\tif variable==\"Embarked\":\n\t\t\t\t\t\t# data[variable] = data[variable].fillna(data[variable].mode()[0])\n\t\t\t\t\t\tdata = data.dropna(axis=0,how=\"any\",subset=[\"Embarked\"])\n\t\t\t\t\t\tnum_rows,num_cols = data.shape\n\n\t\t\t\t\tif variable==\"Fare\":\n\t\t\t\t\t\tnan_idx = np.where(data[\"Fare\"].isna())[0]\n\t\t\t\t\t\tnew_fare = np.zeros((num_rows))\n\t\t\t\t\t\tfor idx in nan_idx:\n\t\t\t\t\t\t\ttemp_age = data[\"Age\"].iloc[idx]\n\t\t\t\t\t\t\ttemp_embarked = data[\"Embarked\"].iloc[idx]\n\t\t\t\t\t\t\ttemp_pclass = data[\"Pclass\"].iloc[idx]\n\n\t\t\t\t\t\t\tsimilar_cases = data.loc[(data[\"Embarked\"]==temp_embarked) &\\\n\t\t\t\t\t\t\t\t\t\t\t\t\t (data[\"Pclass\"]==temp_pclass) &\\\n\t\t\t\t\t\t\t\t\t\t\t\t\t (data[\"Age\"]>(temp_age-15)) &\\\n\t\t\t\t\t\t\t\t\t\t\t\t\t (data[\"Age\"]<(temp_age+15)) &\\\n\t\t\t\t\t\t\t\t\t\t\t\t\t (data[\"Fare\"].isna()==False), \"Fare\"]\n\t\t\t\t\t\t\tnew_fare[idx] = similar_cases.median()\n\t\t\t\t\t\tdata[\"Fare\"].fillna(pd.Series(new_fare),inplace=True)\n\n\n\t## format ticket values\n\tduplicates = []\n\tticket_no = np.zeros((num_rows))\n\tfor i,uniq in enumerate(data['Ticket'].unique()):\n\t\tticket_no[data['Ticket']==uniq] = i\n\t## take into account four cases where 'Ticket' is given as LINE\n\tticket_no[data['Ticket']==\"LINE\"] = i + np.arange(np.sum(data['Ticket']==\"LINE\"))\n\tdata[\"ticket_no\"] = pd.Series(ticket_no,dtype=int)\n\n\t## format cabin values, set NaN cabin values to zero\n\tduplicates = []\n\tcabin_no = np.zeros((num_rows))\n\tfor i,uniq in enumerate(data['Cabin'].unique()):\n\t\tif uniq==\"nan\":\n\t\t\tcabin_no[data['Cabin']==uniq] = 0\n\t\tcabin_no[data['Cabin']==uniq] = i + 1\n\tdata[\"cabin_no\"] = pd.Series(cabin_no,dtype=int)","def inject_missing_data(self, data, allocation=None):\n extracted, errors = self.extractData(setErrors=False)\n\n # the extracted fields may contain field values which need to be\n # injected so the defaults are filled - otherwise no value is updated\n # on the disabled field\n for field in ('day', 'start_time', 'end_time'):\n if extracted.get(field) is not None:\n data[field] = extracted[field]\n\n # if the extracted data was not of any help the id of the allocation\n # is our last resort.\n try:\n allocation = allocation or self.allocation(data['id'])\n except DirtyReadOnlySession:\n return\n\n if not allocation:\n return\n\n if data.get('day') is None:\n data['day'] = allocation.display_start.date()\n\n if data.get('start_time') is None:\n data['start_time'] = allocation.display_start.time()\n\n if data.get('end_time') is None:\n data['end_time'] = allocation.display_end.time()","def pre_processing_(data_df , serialized_objects):\n max_recency_acc_dig = serialized_objects['max_recency_acc_dig'] # These values are taken from trained model values\n max_recency_dig_2yr = serialized_objects['max_recency_dig_2yr'] # These values are taken from trained model values\n max_acc_recency_mf = serialized_objects['max_acc_recency_mf'] #These are values imported in training dataset. Same values needs to be used to impute missing values in unseen data\n\n data_df = data_df.na.fill({\n 'recency_acc_dig' : max_recency_acc_dig, # Filling missing values\n 'recency_dig_2yr' : max_recency_dig_2yr,\n 'acc_recency_mf' : max_acc_recency_mf\n })\n\n freq_acc_upg_2yrs_split = [-float('inf'), 0, 1, 2, float('inf')]\n bucketizer_freq_acc_upg_2yrs = Bucketizer(splits=freq_acc_upg_2yrs_split, inputCol='freq_acc_upg_acc_2yrs', outputCol='freq_acc_upg_acc_2yrs_bkt')\n data_df = bucketizer_freq_acc_upg_2yrs.setHandleInvalid('keep').transform(data_df) # Binning the freq_acc_upg_acc_2yrs column\n\n tot_purchase_split = [-float('inf'), 0, 1, 2, 3, float('inf')]\n bucketizer_tot_purchase = Bucketizer(splits=tot_purchase_split, inputCol='tot_accsry_purchse', outputCol='tot_accsry_purchse_bkt')\n data_df = bucketizer_tot_purchase.setHandleInvalid('keep').transform(data_df) # Binning the tot_accsry_purchse column\n\n del_cols_new = ['freq_acc_upg_acc_2yrs', 'tot_accsry_purchse']\n data_df = data_df.drop(*del_cols_new) # Dropping the older continuous columns\n return data_df","def _handle_load_unknown(self, data, original):\n for key, val in original.items():\n if key not in self.fields:\n data[key] = val\n return data","def test_data_preprocessing(raw_data): \r\n\r\n # get data output\r\n data_output = raw_data[['Submitby Date Time', 'Challenge Manager', 'Challenge Copilot', 'Posting Date Date', 'Track',\r\n 'Technology List', 'First Place Prize', 'Num Registrations', 'Total Prize']]\r\n with open('cache/extended_columns.pkl', 'rb') as f:\r\n extended_columns = pickle.load(f)\r\n with open('cache/num_date_columns.pkl', 'rb') as f:\r\n max_date_columns = pickle.load(f)\r\n \r\n data_output = class_binaryzation_for_test(data_output, extended_columns)\r\n try:\r\n data_output = date_separation1(data_output, max_num_columns=NUM_DATE_COLUMNS)\r\n except:\r\n data_output = date_separation2(data_output)\r\n data_output = money_digitalization(data_output)\r\n data_output = get_date_in_days(data_output)\r\n data_output['Days from Posting to Submit'] = data_output['Submitby Date Time Days from 2016'] \\\r\n - data_output['Posting Date Date Days from 2016'] \r\n\r\n return data_output","def preprocess(self, df):\n print(\"Started Processing....\")\n # binary conversion\n df.replace(to_replace=\"yes\", value=1, inplace=True)\n df.replace(to_replace=\"no\", value=0, inplace=True)\n\n # replace unknowns with nan\n df = df.replace(to_replace=\"unknown\", value=np.nan)\n # getting the list of columns with nan\n ml = df.columns[df.isna().any()].tolist()\n\n for item in ml:\n # getting the ratio of the index labels\n val = pd.DataFrame(df[item].value_counts(normalize=True))\n\n # index labels in a list\n valr = val.index.tolist()\n # drc.index = valr\n # columns values in a list\n valc = val[item].tolist()\n # replacing the nan values with ratio\n df[item] = df[item].fillna(pd.Series(np.random.choice(valr, p=valc, size=len(df))))\n\n # dependent variable\n dfy = df.iloc[:, -1]\n # independent variable\n dfx = df.iloc[:, :-1]\n\n # converting categorical data to numerical\n dfx = pd.get_dummies(dfx)\n\n # normalizing\n dfx = (dfx - dfx.min()) / (dfx.max() - dfx.min())\n\n dxdy = pd.concat([dfx, dfy], axis=1)\n\n # class balancing\n sm = RandomOverSampler(random_state=42)\n dfx, dfy = sm.fit_sample(dxdy.iloc[:, :-1], dxdy.iloc[:, -1])\n\n # converting to dataframe\n dfx = pd.DataFrame(dfx, columns=dxdy.iloc[:, :-1].columns.values)\n\n # dimensionality reduction\n pca = PCA(n_components=33)\n dfx = pca.fit_transform((dfx))\n\n print(\"Processing Done\")\n\n return dfx, dfy","def inject_missing_data(self, data, reservation):\n extracted, errors = self.extractData(setErrors=False)\n\n # the extracted fields may contain field values which need to be\n # injected so the defaults are filled - otherwise no value is updated\n # on the disabled field\n for field in ('start_time', 'end_time'):\n if extracted.get(field) is not None:\n data[field] = extracted[field]\n\n if data.get('start_time') is None:\n data['start_time'] = reservation.start.time()\n\n if data.get('end_time') is None:\n data['end_time'] = reservation.end.time()","def preprocess(self):\n \n file_name_list = os.listdir(self.image_dir)\n random.seed(1234)\n random.shuffle(file_name_list)\n \n for i,d in enumerate(self.domains):\n self.attr2idx[d]=i \n\n for i, file_name in enumerate(file_name_list):\n if (file_name.startswith('X_')):\n continue\n \n parts = file_name.split(\"-\")\n label = int(parts[0])\n if label not in self.domains:\n continue\n img_name = file_name\n\n count=self.get_sample_count(label)\n if count<self.valid_set_size:\n # create holdout set on the fly\n utils.copy_file(self.image_dir,self.valid_set_dir,img_name)\n else:\n self.dataset.append([img_name, self.attr2idx[label]])\n \n self.increment_sample_count(label)\n\n print(\"Sample count per domain: \"+str(self.sample_count)+\" (including holdout set, holdout size per domain is: \"+str(self.valid_set_size)+\")\")\n print('Finished preprocessing the dataset...')","def setup_no_data_values(input_dataset, options):\n in_nodata = []\n if options.srcnodata:\n nds = list(map(float, options.srcnodata.split(',')))\n if len(nds) < input_dataset.RasterCount:\n in_nodata = (nds * input_dataset.RasterCount)[:input_dataset.RasterCount]\n else:\n in_nodata = nds\n else:\n for i in range(1, input_dataset.RasterCount+1):\n raster_no_data = input_dataset.GetRasterBand(i).GetNoDataValue()\n if raster_no_data is not None:\n in_nodata.append(raster_no_data)\n\n if options.verbose:\n print(\"NODATA: %s\" % in_nodata)\n\n return in_nodata","def prepare_input_data(self, X):\n X = np.asarray(X)\n if X.dtype != \"f\" and X.dtype != \"d\":\n X = X.astype(float)\n\n self._check_input(X)\n missing_mask = np.isnan(X)\n self._check_missing_value_mask(missing_mask)\n return X, missing_mask","def clean_station_data(station_df):\n # TODO implement data preparation here\n # Fix the datetime field\n\n # Cast to numeric fields where necessary\n\n # Interpolate missing data","def data_missing() -> ExtensionArray:\n data_matrix = np.arange(\n 2 * 10 * 10 * 3,\n dtype=np.float_,\n ).reshape(2, 10, 10, 3)\n data_matrix[0, ...] = np.NaN\n grid_points = [\n np.arange(10),\n np.arange(10) / 10,\n ]\n\n return skfda.FDataGrid(data_matrix, grid_points=grid_points)","def preprocess(self):\n raise RuntimeError(\"please implement this function!\")","def _load_data(self):\n logging.warning('-> loading EMPTY data...')","def fix_data(self, df):\n return df.dropna(axis='columns', how='all').fillna(0.0)","def _process(self, data: np.ndarray) -> np.ndarray:","def _process(self, data: np.ndarray) -> np.ndarray:","def clean_data(self):\n data_clean = []\n for item in self.data:\n if int(item[2]) >= self.seq_length and int(item[2]) <= self.max_frames:# and item[1] in self.classes:\n data_clean.append(item)\n\n return data_clean","def negify_missing(data_path: str) -> None:\n csvs = [x for x in data_path.iterdir() if 'covidtimeseries' in str(x)]\n for csv in tqdm(csvs, desc=\"Regions\"):\n roi = str(csv).split('.')[0].split('_')[-1]\n df = pd.read_csv(csv)\n for kind in ['cases', 'deaths', 'recover']:\n if df['cum_%s' % kind].sum() == 0:\n print(\"Negifying 'new_%s' for %s\" % (kind, roi))\n df['new_%s' % kind] = -1\n out = data_path / (csv.name.split('.')[0]+'.csv')\n df.to_csv(out)","def clean(self):\n # Perform the standard ACE cleaning\n max_status = mm_ace.clean(self)\n\n # Replace bad values with NaN and remove times with no valid data\n ecols = ['eflux_38-53', 'eflux_175-315']\n\n # Evaluate the electron flux data\n self[self.data['status_e'] > max_status, ecols] = np.nan\n\n # Evaluate the proton flux data\n pcols = ['pflux_47-68', 'pflux_115-195', 'pflux_310-580',\n 'pflux_795-1193', 'pflux_1060-1900']\n self[self.data['status_p'] > max_status, pcols] = np.nan\n\n # Include both fluxes and the anisotropy index in the removal eval\n eval_cols = ecols + pcols\n eval_cols.append('anis_ind')\n\n # Remove lines without any good data\n good_cols = (np.isfinite(self.data.loc[:, eval_cols])).sum(axis=1)\n bad_index = good_cols[good_cols == 0].index\n self.data = self.data.drop(index=bad_index)\n\n return","def pre_process_data_set(df):\n df.replace([np.inf, -np.inf], np.nan)\n df[df == np.inf] = np.nan\n df = remove_bad_columns(df)\n df = fill_na(df)\n df = convert_factorial_to_numerical(df)\n\n # Remove columns only containing 0\n df = df[(df.T != 0).any()]\n return df","def Reconstruct_COVID_data(COVID_data):\r\n COVID_data_reconstructed = {}\r\n COVID_data_reconstructed['_Country'] = COVID_data['_Country']\r\n \r\n Countries_list = list(COVID_data.keys())[1:]\r\n \r\n for Country in Countries_list: # For each country...\r\n COVID_data_single_country = list(COVID_data[Country].values()) # Extract the matrix containing the data and transpose it. That way, each element of a single list in the array corresponds to one column (see help of Main_script) and it makes it easier to navigate through each column and recontruct the missing elements\r\n T_COVID_data_single_country = list(map(list, zip(*COVID_data_single_country)))\r\n \r\n for Column_inc in range(len(T_COVID_data_single_country)): # For each column...\r\n Column = T_COVID_data_single_country[Column_inc]\r\n Max_column_inc = len(Column) - 1\r\n \r\n Row_inc = 0\r\n while Column[Row_inc] == None and Row_inc < Max_column_inc: # Recontructing missing data at the beginning is impossible so we just skip the first rows with a None in them\r\n Row_inc += 1\r\n \r\n \r\n if None in Column: # If a None is in the list (meaning there are bits of data missing)...\r\n while Row_inc < Max_column_inc: # Not including this line could prompt an index error\r\n if Column[Row_inc] == None: # When a None in encoutered...\r\n None_interval_start = Row_inc # Recording when the segments of None starts and ends\r\n \r\n while Column[Row_inc] == None and Row_inc < Max_column_inc:\r\n Row_inc += 1\r\n \r\n None_interval_end = Row_inc - 1\r\n Interpolation_interval_length = None_interval_end - None_interval_start + 2\r\n \r\n if Row_inc < Max_column_inc: # Reconstruction of the segment by linear interpolation : Y = mX + b with m = (Y_max - Y_min) / (X_max - X_min)\r\n m = (Column[None_interval_end + 1] - Column[None_interval_start - 1]) / Interpolation_interval_length\r\n b = Column[None_interval_start - 1]\r\n \r\n for Row_inc in range(None_interval_start, Row_inc):\r\n T_COVID_data_single_country[Column_inc][Row_inc] = m * (Row_inc - None_interval_start + 1) + b\r\n \r\n else: # In the case the None segment goes on until the end, the last known value is just copied\r\n for Row_inc in range(None_interval_start, Row_inc + 1):\r\n T_COVID_data_single_country[Column_inc][Row_inc] = T_COVID_data_single_country[Column_inc][None_interval_start - 1]\r\n \r\n Row_inc += 1\r\n \r\n COVID_data_single_country_reconstructed = list(map(list, zip(*T_COVID_data_single_country))) # Retranspose the matrix to get the reconstruted data in the correct format\r\n \r\n Date_list_country = list(COVID_data[Country].keys()) # Add the reconstructed data to the appropriate dictionnary\r\n COVID_data_reconstructed[Country] = {}\r\n for Date_inc in range(len(Date_list_country)):\r\n Date = Date_list_country[Date_inc]\r\n COVID_data_reconstructed[Country][Date] = COVID_data_single_country_reconstructed[Date_inc]\r\n \r\n return COVID_data_reconstructed","def stage_one_preprocessing(data: pd.Series) -> pd.Series:\n data_ = data.dropna()\n print('ascii')\n data_ = remove_non_ascii(data)\n print('lower')\n data_ = to_lowercase(data_)\n print('slash')\n data_ = underscore_and_slash_to_space(data_)\n print('ellipse')\n data_ = remove_ellipses(data_)\n print('white')\n data_ = shrink_whitespace(data_)\n #print('contracts')\n #data_ = remove_contractions(data_)\n return data_","def _preprocess_data_point(self, x: DataPoint) -> DataPoint:\n for preprocessor in self._pre:\n x = preprocessor(x)\n if x is None:\n raise ValueError(\"Preprocessor should not return None\")\n return x","def preprocess(self):\n pass","def preprocess(self):\n pass","def preprocess(self):\n pass","def data_missing_for_sorting(data_for_grouping):\n return type(data_for_grouping)._from_sequence(\n [data_for_grouping[0], data_for_grouping[2], data_for_grouping[4]]\n )","def process_data(data, engine):\n\n def convert(x):\n unit = x['standard_units']\n value = x['standard_value']\n if unit == \"NM\":\n return value * 1e-9\n elif unit == \"-LOG(10) M\":\n return 10 ** (-value)\n else:\n raise RuntimeError\n\n # Filter Na\n data.dropna(how=\"any\", subset=USED_COLS, inplace=True)\n # Only keep measurements that are KD related\n data = data[data.standard_type.isin(KD_TYPES)]\n # Only keep measurements with some defined units\n data = data[data.standard_units.isin(KD_UNITS)]\n # Convert to M valued units\n data['standard_value'] = data.apply(convert, axis=1)\n # Keep only equal relation measurements\n data = data[data.standard_relation == '=']\n # Remove multiple targets measurements\n data = data[~data.target_id.str.contains(',')]\n # Remove (target,compound) pairs with more than one measurement\n key = ['standard_inchi_key', 'target_id']\n grouped = data.groupby(key).size()\n join_condition = grouped[grouped == 1].reset_index()[key]\n data = data.merge(join_condition, on=key, how='inner')\n # Remove outliers measurements\n data = data[(data.standard_value <= 1.7e-3) & (data.standard_value >= 1.e-10)]\n # Convert to PK values\n data['standard_value'] = - np.log10(data['standard_value'])\n # Remove samples for which the protein_id has no sequence\n sequence_loader = SequenceLoader(engine=engine)\n data = sequence_loader.transform(data).dropna(how=\"any\")\n # Remove samples for which the compound_id has no inchi\n inchi_loader = InchiLoader(engine=engine)\n data = inchi_loader.transform(data).dropna(how='any')\n # We will only use the following columns\n return data[[\"standard_inchi_key\", \"target_id\", \"standard_inchi\", \"sequence\", \"standard_value\"]]","def prepare_train_data(self):\r\n ## Impute rlkpis\r\n print(\"Imputing rlKPI df\")\r\n self.rlkpi.add_target_labels(1)\r\n self.rlkpi.impute_rl_kpis()\r\n\r\n print(\"Add 'met-real-station_no' & met-forecast-station_no to rl_kpis_df\")\r\n self.add_met_real_forecast_station_col_to_rlkpis()\r\n print(\"Merge 'met-real-sampled df to rl kps \")\r\n self.merge_met_real_sampled_df_to_rlkpis()\r\n\r\n ## Imputations for met-forecast\r\n print(\"Impute met-forecast\")\r\n met_forecast_obj = self.metfcast\r\n met_forecast_obj.impute_met_forecast()\r\n\r\n #Merge met forecast data to earlier merged data\r\n print(\"Merge Train data with imputed forecast df\")\r\n self.train_data = pd.merge(self.train_data,\r\n met_forecast_obj.imputed_forecast_df,\r\n on=['datetime-station_no'], indicator=True, how='inner')\r\n print(\"Check any imputation needed\", self.train_data.isna().sum().sum())\r\n self.train_data.drop(['_merge'], axis=1, inplace=True)\r\n self.perform_data_under_sampling(self.train_data)","def _clean_data(self, stn, ignore_errors):\n # Confirm that we got some data, and confirm that it's the\n # expected web page by checking the <title> tag contents\n if (not self.data) | (not self.data[0].startswith(\n '<TITLE>Generate WXP 24-Hour Meteogram</TITLE>')):\n if ignore_errors:\n sys.stderr.write('Invalid data returned for '\n '%4i-%02i-%02i\\n'\n % (self.year, self.month, self.day))\n self.data = ''\n return\n else:\n raise UnexpectedPageError\n # Get rid of the <title> and <pre> tag lines\n self.data = self.data[2:]\n # Confirm that data is available for the specified date\n if self.data[0].startswith('No data were found for date'):\n if ignore_errors:\n sys.stderr.write('%4i-%02i-%02i data missing\\n'\n % (self.year, self.month, self.day))\n self.data = ''\n return\n else:\n raise UnexpectedPageError\n #Get rid of the station location and following blank line\n self.data = self.data[2:]\n # Confirm that we got the data for the expected station by\n # checking the \"METAR Data for\" line contents\n if not self.data[0].startswith(\n ' '.join((\"METAR Data for\", stn))):\n if ignore_errors:\n sys.stderr.write('%4i-%02i-%02i data missing '\n 'or incorrect station returned\\n'\n % (self.year, self.month, self.day))\n self.data = ''\n return\n else:\n raise UnexpectedPageError\n # Get rid of the \"METAR Data for\" line and following blank\n # line\n self.data = self.data[2:]\n # Date part of timestamp for each line of data\n datestamp = '%4i %02i %02i' % (self.year, self.month, self.day)\n # Clean up each line\n i = 0\n try:\n while True:\n # Continuations from the previous line start with 5\n # spaces\n if self.data[i].startswith(' '*5):\n # Concatenate continuation to previous line\n self.data[i-1] = ' '.join((self.data[i-1][:-1],\n self.data[i][5:]))\n # Get rid of continuation text that we just consumed\n self.data.pop(i)\n # Get rid of file parse error lines\n if self.data[i].startswith('SFC_parse_file:'):\n self.data.pop(i)\n continue\n # Get rid of SPECI prefix\n if self.data[i].startswith('SPECI'):\n self.data[i] = self.data[i][6:]\n fields = self.data[i].split()\n # Add METAR prefix if it's missing\n if fields[0] != 'METAR':\n fields.insert(0, 'METAR')\n self.data[i] = ' '.join(('METAR', self.data[i]))\n # Add hour to timestamp, and prepend timestamp to line\n self.data[i] = ' '.join((datestamp, fields[2][2:4],\n self.data[i]))\n # Get rid of duplicate data lines\n if self.data[i] == self.data[i-1]:\n self.data.pop(i)\n continue\n i += 1\n except IndexError:\n # No more data lines\n pass","def _preprocess(self, data):\n\n # pipeline: first call the previous statistics:\n if self.previous_statistics is not None:\n data = self.previous_statistics.statistics(data)\n # the first of the statistics need to take list as input, in order to match the API. Then actually the\n # transformations work on np.arrays. In fact the first statistic transforms the list to array. Therefore, the\n # following code needs to be called only if the self statistic is the first, i.e. it does not have a\n # previous_statistic element.\n else:\n data = self._check_and_transform_input(data)\n\n return data","def _prepare_data(self):\n #TODO hardcoded values need to change\n print_info(\"Preprocessing the train data...\")\n self._place_dataset(os.path.join(self._hparams[\"temp-data\"], \"train\"),\n self.TRAIN_OUT_PATH)\n\n print_info(\"Preprocessing the test data...\")\n self._place_dataset(os.path.join(self._hparams[\"temp-data\"], \"test\"),\n self.TEST_OUT_PATH)\n\n print_info(\"Preprocessing the validation data...\")\n self._place_dataset(os.path.join(self._hparams[\"temp-data\"], \"val\"),\n self.VAL_OUT_PATH)","def original_clean():\n # load the data\n dataset = np.genfromtxt(\"wdbc.data\", dtype=np.float, delimiter=',', usecols=(2, 3, 4, 5, 6, 7, 8, 9, 10, 11,\n 12, 13, 14, 15, 16, 17, 18, 19,\n 20, 21, 22, 23, 24, 25, 26, 27,\n 28, 29, 30, 31), encoding=None)\n labels = np.genfromtxt(\"wdbc.data\", dtype=None, delimiter=',', usecols=(1), encoding=None)\n temp_labels = np.zeros(len(labels))\n for i in range(len(labels)):\n if labels[i] == 'B':\n temp_labels[i] = 0\n else:\n temp_labels[i] = 1\n # normalize\n temp_data = normalize(dataset)\n return temp_data, temp_labels","def discard_temp_data(self):\n self.data = None\n self.points = None","def check_Data(self):\r\n \r\n if self._target_data is None:\r\n self.processData()","def test_missing_column(self, example_dataset):\n\n example_dataset = example_dataset.drop([\"Sex\"], axis=1)\n transformer = PreprocessFeatures()\n\n with pytest.raises(ValueError):\n transformer.fit_transform(example_dataset)","def pre_process_data(self, all_labels, all_data):\n\n # [1] Normalizes data\n all_data = self.pre_precess_manager.normalization(all_data)\n\n data_train, data_test, label_train, label_test = train_test_split(all_data, all_labels, test_size=0.1,\n shuffle=True)\n\n return data_train, data_test, label_train, label_test","def test_batch_missing_data():\n\n\n def fx(x, dt):\n F = np.array([[1, dt, 0, 0],\n [0, 1, 0, 0],\n [0, 0, 1, dt],\n [0, 0, 0, 1]], dtype=float)\n\n return np.dot(F, x)\n\n def hx(x):\n return np.array([x[0], x[2]])\n\n\n dt = 0.1\n points = MerweScaledSigmaPoints(4, .1, 2., -1)\n kf = UKF(dim_x=4, dim_z=2, dt=dt, fx=fx, hx=hx, points=points)\n\n\n kf.x = np.array([-1., 1., -1., 1])\n kf.P*=0.0001\n\n zs = []\n for i in range(20):\n z = np.array([i+randn()*0.1, i+randn()*0.1])\n zs.append(z)\n\n zs[2] = None\n Rs = [1]*len(zs)\n Rs[2] = None\n Ms, Ps = kf.batch_filter(zs)","def preprocessing(train_raw_, test_raw_):\n \n undef = np.float64(-999.0)\n pred_dict = {'s':'1','b':'0', '?':'-1'}\n # drop 1st column (Id) and also 1st row with column names (\"[1:,\") \n train_raw = train_raw_[1:, :]\n test_raw = test_raw_[1:, :] \n \n # Change s(signal) and b(background) for s:1 and b:0, and change '?' for -1\n train_raw[:,1] = np.vectorize(pred_dict.get)(train_raw[:,1].astype(str))\n test_raw[:,1] = np.vectorize(pred_dict.get)(test_raw[:,1].astype(str))\n \n # Divide the dataset in four according to PRI_jet_num feature and cast to float\n train_data_jets = divide_dataset_by_jet(train_raw)\n test_data_jets = divide_dataset_by_jet(test_raw)\n \n # Remove columns with nan values or with standard deviation of 0\n test_data_jets, train_data_jets = clean_features(test_data_jets, train_data_jets, undef)\n \n # Standardize train and test sets to have mean=0 and std=1\n train_data_jets, test_data_jets = standardize(train_data_jets, test_data_jets)\n \n # Replace remaining undefined values by mean, median or zero\n train_data_mean, train_data_median, train_data_null = replace_nan(train_data_jets)\n test_data_mean, test_data_median, test_data_null = replace_nan(test_data_jets)\n \n return train_data_mean, train_data_median, train_data_null, test_data_mean, test_data_median, test_data_null","def exclude_empty_feats(self):\n for dataset in self:\n dataset.dropna(axis=1, how=\"all\", inplace=True)","def handle_missing_values(dataset, missing_values_header, missing_label):\n\n return dataset[dataset[missing_values_header] != missing_label]","def process_data_impl(\n self,\n data_dir: Path,\n output_processed_data_dir: Path,\n ) -> NoReturn:\n pass","def import_clean_process():\n # loading the co2 emissions data for the Earth, I'm only interested in the\n # total emissions and the year\n global_co2 = pd.read_csv(\n \"datasets/Global CO2 Emissions.csv\",\n usecols=[\n \"Year\",\n \"Total\"\n ],\n parse_dates=[\"Year\"],\n index_col=\"Year\"\n )\n # creating the global temperature dataframe\n global_temp_data = open(\n \"datasets/CRUTEM.4.6.0.0.global_n+s\",\n \"r\"\n )\n global_temp = pd.DataFrame(\n {\n \"global_temp\": [],\n }\n )\n for line in global_temp_data:\n # each line in the file is an observation for the year, the first\n # column being the year, the second being the temperature measurement\n data = line.split()\n global_temp.at[pd.to_datetime(data[0]), \"global_temp\"] = float(data[1])\n global_temp_data.close()\n # loading the co2 emissions data for the UK\n uk_co2 = pd.read_csv(\n \"datasets/UK carbon dioxide emissions between 1858 to 2017 .csv\",\n parse_dates=[\"Date\"],\n index_col=\"Date\"\n )\n # creating the dataframe for the UK temperature data\n uk_temp = pd.DataFrame(\n {\n \"uk_temp\": [],\n }\n )\n # this file consists of monthly and seasonal averages for the UK surface\n # temperature\n uk_tmean = open(\n \"datasets/UK Mean Temperature (Degrees C)\",\n \"r\"\n )\n for index, line in enumerate(uk_tmean):\n # the data begins on the eigth line in the file\n if index > 7:\n data = line.split()\n # the monthly temperatures are from the 2nd and 13th columns\n month_temps = np.array(data[1:13]).astype(float)\n # the first reading is the year, I've taken the average of all the\n # months to get an annual average\n uk_temp.at[pd.to_datetime(data[0]), \"uk_temp\"] = month_temps.mean()\n uk_tmean.close()\n # removing the temperature reading for 2019 as it isn't averaged over the\n # whole year (this program was written in 06/2019)\n uk_temp = uk_temp[:-1]\n # merging the temperature and co2 emissions dataframes for the Earth\n global_data = pd.merge(\n global_temp,\n global_co2,\n left_index=True,\n right_index=True,\n how=\"outer\"\n )\n # merging the temperature and co2 emissions dataframes for the UK\n uk_data = pd.merge(\n uk_temp,\n uk_co2,\n left_index=True,\n right_index=True,\n how=\"outer\"\n )\n # merging the global and UK dataframes\n df_data = pd.merge(\n global_data,\n uk_data,\n left_index=True,\n right_index=True,\n how=\"outer\"\n )\n # rename some of the columns to make them more clear\n df_data = df_data.rename(\n columns={\n \"Total\": \"global_co2\",\n \"CO2 Emissions\": \"uk_co2\"\n }\n )\n return df_data","def _fillna_meta_cols(self):\n for col_name, fill_value in self._fillna.items():\n if col_name in self._hybrid_meta.columns:\n self._hybrid_meta[col_name].fillna(fill_value, inplace=True)\n else:\n self.__warn_missing_col(col_name, action='fill')\n\n self._hybrid_meta[self.__solar_rpi_n].fillna(-1, inplace=True)\n self._hybrid_meta[self.__wind_rpi_n].fillna(-1, inplace=True)","def fill_missing_data_points(data):\n return data.interpolate()","def clean_features(test_data, train_data, undef):\n \n test_set = test_data\n train_set = train_data\n \n # Preprocessing of the four jet datasets\n for jet in range(4):\n \n # Remove columns full of undefined values (-999.0 in the datase)\n train_set[jet][train_set[jet] <= undef] = np.nan # replace undefined values -999 by NaN\n train_set[jet] = train_set[jet][:, ~np.all(np.isnan(train_set[jet]), axis=0)]\n test_set[jet][test_set[jet] <= undef] = np.nan # replace undefined values -999 by NaN\n test_set[jet] = test_set[jet][:, ~np.all(np.isnan(test_set[jet]), axis=0)]\n\n # Remove columns without standard deviation to remove column full of the same value\n train_id_pred = train_set[jet][:,0:2]\n train_features = train_set[jet][:,2:]\n train_features = train_features[:, np.nanstd(train_features, axis=0) != 0]\n train_set[jet] = np.concatenate((train_id_pred, train_features), axis=1)\n \n # Extract prediction column of test before because it has std=0, and re-insert it after at the begining of the table\n test_id_pred = test_set[jet][:,0:2]\n test_features = test_set[jet][:,2:]\n test_features = test_features[:, np.nanstd(test_features, axis=0) != 0]\n test_set[jet] = np.concatenate((test_id_pred, test_features), axis=1)\n \n return test_set, train_set","def dataset_NER_prepocess(dataset):\n preprocessed = []\n\n try:\n preprocessed = stop_word_remove(dataset)\n\n if not preprocessed:\n preprocessed = dataset\n\n preprocessed = adverb_remove(dataset)\n\n if not preprocessed:\n preprocessed = dataset\n\n preprocessed = verb_remove(dataset)\n\n if not preprocessed:\n preprocessed = dataset\n preprocessed = adjective_remove(dataset)\n\n if not preprocessed:\n preprocessed = dataset\n\n preprocessed = special_symbols_remove(dataset)\n\n except Exception as e:\n print(e)\n return None\n\n return preprocessed","def simulate_missing_data(self):\n num_samples_to_be_removed = round(len(self.original_data) * (self.missing_data_percentage / 100))\n\n new_database = list(self.original_data)\n\n for i in range(0, num_samples_to_be_removed):\n while True:\n rand_num = random.randint(0, len(new_database) - 1)\n\n if not np.isnan(new_database[rand_num]):\n new_database[rand_num] = np.NaN\n break\n\n return new_database","def preprocess_lab_data(self):\n print(\"Processing dataset from the labs ...\")\n self.full_dataset[\"score\"] = self.full_dataset[\"score\"].apply(lambda x: -1 if x==0 else 1)","def preprocess(self):","def inf(self, data):\n self.set_data(data)\n self.infer_post_and_update_samples()","def handel_missing_values(dataset, missing_values_header, missing_label):\n \n return dataset[dataset[missing_values_header] != missing_label]","def preprocess(data, to_drop=[]):\n \n columns = data.columns.to_list()\n \n # split data to numeric vs categorical\n numeric_features = data.select_dtypes(include=[\n 'int64', 'float64']).columns\n \n if len(to_drop) > 0:\n categorical_features = data.select_dtypes(include=[\n 'object']).drop(to_drop, axis=1).columns\n print(categorical_features)\n else: \n categorical_features = data.select_dtypes(include=[\n 'object']).columns\n \n categorical_transformer = Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent', fill_value='missing'))])\n \n numerical_transformer = Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', RobustScaler())\n ])\n # missing_values = np.nan\n \n# Bundle preprocessing for numerical and categorical data\n preprocessor = ColumnTransformer(\n transformers=[\n ('num', numerical_transformer, numeric_features),\n ('cat', categorical_transformer, categorical_features)\n ])\n\n my_pipeline = Pipeline(steps=[('preprocessor', preprocessor) ])\n \n for col in to_drop:\n columns.remove(col)\n print('Hello')\n \n trans_data = my_pipeline.fit_transform(data)\n return trans_data#pd.DataFrame(#, columns=columns) ","def stage_two_preprocessing(data: pd.Series) -> pd.Series:\n # designed to be run after remove_contractions\n data_ = data.dropna()\n data_ = remove_punctuation(data_)\n data_ = numbers_to_words(data_)\n data_ = remove_stopwords(data_)\n return data_","def training_data_preprocessing(raw_data, num_passed_rows=72):\r\n # some samples have errors\r\n raw_data = raw_data[num_passed_rows:].reset_index(drop=True) \r\n \r\n # get data output\r\n data_output = raw_data[['Submitby Date Time', 'Challenge Manager', 'Challenge Copilot', 'Posting Date Date', 'Track',\r\n 'Technology List', 'First Place Prize', 'Num Registrations', 'Total Prize']]\r\n data_output, extended_columns = class_binaryzation(data_output)\r\n \r\n # save extended columns to cache\r\n extended_columns_filepath = 'cache/extended_columns.pkl'\r\n with open(extended_columns_filepath, 'wb') as f:\r\n pickle.dump(extended_columns, f)\r\n\r\n num_date_columns_filepath = 'cache/num_date_columns.pkl'\r\n try:\r\n data_output = date_separation1(data_output) \r\n with open(num_date_columns_filepath, 'wb') as f:\r\n pickle.dump(6, f)\r\n\r\n except:\r\n data_output = date_separation2(data_output)\r\n with open(num_date_columns_filepath, 'wb') as f:\r\n pickle.dump(5, f)\r\n\r\n data_output = money_digitalization(data_output)\r\n data_output = get_date_in_days(data_output)\r\n data_output['Days from Posting to Submit'] = data_output['Submitby Date Time Days from 2016'] \\\r\n - data_output['Posting Date Date Days from 2016'] \r\n \r\n # get other output\r\n label_output = pd.DataFrame(columns=['Success'])\r\n success_output = pd.DataFrame(columns=data_output.columns)\r\n failure_output = pd.DataFrame(columns=data_output.columns)\r\n for i in range(len(raw_data)):\r\n if raw_data.loc[i, 'Num Submissions Passed Review'] >= 1:\r\n label_output.loc[i, 'Success'] = 1\r\n success_output.loc[len(success_output)] = data_output.loc[i]\r\n else:\r\n label_output.loc[i, 'Success'] = 0\r\n failure_output.loc[len(failure_output)] = data_output.loc[i]\r\n\r\n return data_output, label_output, success_output, failure_output, extended_columns","def cleanData(data):\r\n \r\n indexes = data.index[pd.isnull(data[\"release date\"])]\r\n # data = data.drop(data.index[[2172, 3781, 7245, 12475, 14756, 15292, 49295, 93523, 99723]],axis=0,inplace=False)\r\n data = data.drop(indexes,axis=0,inplace=False)\r\n data = data.drop([\"video release date\", 'IMDb URL'],axis=1)\r\n \r\n \r\n data.gender = data.gender.astype(\"category\")\r\n data.occupation = data.occupation.astype(\"category\")\r\n \r\n categorical_variables = [\"gender\", \"occupation\"]\r\n for variable in categorical_variables:\r\n dummies = pd.get_dummies(data[variable], prefix=variable)\r\n data = pd.concat([data, dummies], axis=1)\r\n data.drop([variable], axis=1, inplace=True)\r\n data['movieID'] = data['movieID'].astype('int')\r\n data['userID'] = data['userID'].astype('int')\r\n \r\n\t#data = dateTime(data)\r\n \r\n return data","def process_drought_data():\n raw_data_dir = basedir + '/app/static/data/raw'\n raw_data_files = os.listdir(raw_data_dir)\n\n if raw_data_files:\n print 'Raw data files to process:'\n print raw_data_files\n print ''\n print 'Total to process: %d' % len(raw_data_files)\n\n count = 1\n for raw_file in raw_data_files:\n print 'Processing %d of %d raw files.' % (count, len(raw_data_files))\n\n # opens raw file to read data\n readfile = open(basedir + '/app/static/data/raw/' + raw_file, 'r')\n reader = csv.reader(readfile, delimiter=',')\n\n # sets up processed file for writing\n raw_file_name = raw_file.split('.')[0]\n out_file_name = raw_file_name + '.csv'\n writefile = open(basedir + '/app/static/data/saved/' + out_file_name, 'w')\n writer = csv.writer(writefile, delimiter=',')\n writer.writerow(['id', 'level'])\n\n for line in reader:\n if line[1] == 'FIPS':\n pass\n else:\n fips = int(line[1])\n\n level = 0\n if float(line[9]) > 0:\n level = 5\n elif float(line[8]) > 0:\n level = 4\n elif float(line[7]) > 0:\n level = 3\n elif float(line[6]) > 0:\n level = 2\n elif float(line[5]) > 0:\n level = 1\n\n writer.writerow([fips, level])\n\n writefile.close()\n readfile.close()\n os.remove(basedir + '/app/static/data/raw/' + raw_file)\n count += 1\n\n else:\n print 'All files have been processed.'","def test_null_from_data(self):\n # define prior data that R will use\n data = array([[ 4., 6., 5., 4., 3.],\n [ 5., 3., 11., 17., 2.],\n [ 8., 4., 4., 13., 0.],\n [ 0., 0., 9., 8., 10.],\n [ 19., 8., 3., 8., 1.],\n [ 5., 1., 14., 4., 3.],\n [ 2., 7., 3., 4., 0.],\n [ 5., 5., 5., 14., 6.],\n [ 2., 3., 6., 0., 5.],\n [ 6., 1., 1., 2., 2.]])\n Rseed = 0\n tpk = 10\n actual_out = null_from_data(data, tpk, Rseed=Rseed)\n expected_out = array([[ 18., 1., 0., 3., 0.],\n [ 2., 10., 10., 3., 22.],\n [ 1., 8., 4., 1., 12.],\n [ 10., 1., 5., 1., 2.],\n [ 8., 0., 14., 28., 5.],\n [ 5., 16., 3., 11., 0.],\n [ 0., 2., 8., 2., 0.],\n [ 0., 12., 2., 1., 3.],\n [ 0., 0., 6., 2., 5.],\n [ 8., 2., 0., 0., 3.]])\n self.assertEqual(expected_out, actual_out)","def test_fill_missing_value(self):\n try:\n # test_size need to be a positive float number\n fill_missing_value(DATA, NEEDED, INPUTS, OUTPUT, test_size=-1.0)\n raise Exception(\"Illegal input test failed!\")\n except AssertionError:\n pass\n\n try:\n # n_estimators need to be a positive integer\n fill_missing_value(DATA, NEEDED, INPUTS, OUTPUT, n_estimators=-1)\n except AssertionError:\n pass\n\n try:\n # max_depth need to be a positive integer\n fill_missing_value(DATA, NEEDED, INPUTS, OUTPUT, max_depth=1.2)\n except AssertionError:\n pass\n\n filled_data = fill_missing_value(DATA, NEEDED, INPUTS, OUTPUT)\n\n assert isinstance(filled_data, pd.DataFrame),\\\n \"Type error. pd.DataFrame expected\""],"string":"[\n \"def _clean(self, dataset):\\n # Replace missing values with numpy's NaN. The missing value is\\n # usually 1e+20, but values can be like 1.0000002e+20, which is\\n # different. Ergo the inequality.\\n for var in dataset.data_vars.itervalues():\\n if 'missing_value' in var.attrs:\\n missing_data_value = var.missing_value\\n try:\\n var.values[var.values >= missing_data_value] = np.NaN\\n except ValueError:\\n print \\\"Encountered ValueError in {0}. Ignoring\\\".format(var.name)\",\n \"def data_preparation(self) -> None:\\n self.logger.info('data cleaning')\\n self.logger.info('num of secs: {}, num of ipo_dates: {}, num of secs with prices: {}'.format(\\n len(self.data),\\n len(self.ipo_dates),\\n len(self.prices)\\n ))\\n excluded = []\\n excluded = [i.lower() for i in excluded]\\n self.logger.info(f'number of excluded: {len(excluded)}')\\n for i in excluded:\\n self.data.pop(i)\\n for s in self.data:\\n # columns with empty assets sum (empty columns and other situations)\\n self.data[s].dropna(axis='columns', how='any', subset=['A_0'], inplace=True)\\n # columns with descriptions (polish and english names of values)\\n self.data[s].drop(self.data[s].columns[[0, 1]], inplace=True, axis=1)\\n\\n self.logger.info(f'number of secs after cleaning: {len(self.data)}')\\n data_list = [k for k in self.data.values()]\\n self.uber_data = pd.concat(data_list, ignore_index=True, axis=1)\\n self.uber_data = self.uber_data.transpose()\\n self.uber_data = self.uber_data.loc[:, pd.notnull(self.uber_data.columns)]\",\n \"def unusedFromKDOTDataPreparation():\",\n \"def _clean_data(self, dataset):\\n dataset.dropna(inplace=True)\\n # Problem: handle missing data (in a different way), noisy data, inconsistent data\",\n \"def clean_data(df):\\n \\n # Put in code here to execute all main cleaning steps:\\n # convert missing value codes into NaNs, ...\\n count_miss = df.isnull().sum(axis=0).values #find number of nans for each column\\n count_miss = [val for val in count_miss]\\n \\n drop_cols = []\\n\\n for ind, val in enumerate(count_miss):\\n if val > 200000:\\n drop_cols.append(ind)\\n \\n df_drop_cols = list(azdias.columns[drop_cols])\\n df = df.drop(df_drop_cols, axis=1)\\n \\n for col in range(df.shape[1]): #loop through columns\\n column_name = df.columns[col] #get column name\\n missing_list = feat_info.iloc[col,3] #get missing_or_unknown column from feature info\\n missing_list = missing_list.replace('[','') #remove left bracket from string\\n missing_list = missing_list.replace(']','') #remove right bracket from string\\n missing_list = missing_list.split(',') #split into individual strings\\n \\n #find data that is natually missing and continue loop to omit\\n if missing_list == ['']:\\n continue\\n \\n else:\\n for dat_type in missing_list: \\n if df[column_name].dtype == 'object': #find values that contain x\\n df.loc[df[column_name] == dat_type, column_name] = np.nan #replace x with nan\\n \\n else:\\n dat_type = int(dat_type) #if no x, convert to integer and replace with nan\\n df.loc[df[column_name] == dat_type, column_name] = np.nan\\n \\n # select, re-encode, and engineer column values.\\n \\n # encode OST_WEST_KZ\\n df.loc[df['OST_WEST_KZ'] == 'W','OST_WEST_KZ'] = 0\\n df.loc[df['OST_WEST_KZ'] == 'O','OST_WEST_KZ'] = 1\\n \\n # Re-encode categorical variable(s) to be kept in the analysis.\\n \\n \\n #get list of attributes with type categorical\\n feat_info[feat_info['type'] == 'categorical']\\n \\n cat_new_cols = [] #initialize\\n for i in feat_info[feat_info['type'] == 'categorical']['attribute']:\\n cat_new_cols.append(i)\\n \\n for cols in df.columns:\\n if cols in cat_new_cols:\\n if df[cols].nunique(dropna=True) > 2: #if the number of unique values is greater than 2 \\n df = df.drop(cols, axis=1) #drop from the analysis\\n print(\\\"more than 2 categories: {}\\\".format(cols))\\n \\n else:\\n if not df[cols].unique()[0] > 0:\\n #if not df[cols].unique()[0] > 0:\\n dummies = pd.get_dummies(df[cols], prefix=cols)\\n df = df.drop(cols, axis=1) #create dummy variable\\n df = df.join(dummies)\\n print(\\\"transformed to dummy variable: {}\\\".format(cols))\\n \\n # create variable: MOVEMENT\\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([1,3,5,8,10,12,14]),'MOVEMENT'] = 1\\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([2,4,6,7,9,11,13,15]),'MOVEMENT'] = 2\\n \\n #Capture Decade\\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([1,2]), 'DECADE'] = 40\\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([3,4]), 'DECADE'] = 50\\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([5,6,7]), 'DECADE'] = 60\\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([8,9]), 'DECADE'] = 70\\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([10,11,12,13]), 'DECADE'] = 80\\n df.loc[df['PRAEGENDE_JUGENDJAHRE'].isin([14,15]), 'DECADE'] = 90\\n \\n df['CAMEO_INTL_2015'] = df['CAMEO_INTL_2015'].astype(float)\\n\\n # create new variable: WEALTH\\n df.loc[df['CAMEO_INTL_2015'].isin([51,52,53,54,55]), 'WEALTH'] = 1\\n df.loc[df['CAMEO_INTL_2015'].isin([41,42,43,44,45]), 'WEALTH'] = 2\\n df.loc[df['CAMEO_INTL_2015'].isin([31,32,33,34,35]), 'WEALTH'] = 3\\n df.loc[df['CAMEO_INTL_2015'].isin([21,22,23,24,25]), 'WEALTH'] = 4\\n df.loc[df['CAMEO_INTL_2015'].isin([11,12,13,14,15]), 'WEALTH'] = 5\\n \\n # create new variable: LIFE_STAGE\\n df.loc[df['CAMEO_INTL_2015'].isin([11,21,31,41,51]),'LIFE_STAGE'] = 1\\n df.loc[df['CAMEO_INTL_2015'].isin([12,22,32,42,52]),'LIFE_STAGE'] = 2\\n df.loc[df['CAMEO_INTL_2015'].isin([13,23,33,43,53]),'LIFE_STAGE'] = 3\\n df.loc[df['CAMEO_INTL_2015'].isin([14,24,34,44,54]),'LIFE_STAGE'] = 4\\n df.loc[df['CAMEO_INTL_2015'].isin([15,25,35,45,55]),'LIFE_STAGE'] = 5\\n \\n # remove selected columns and rows, ...\\n df = df.drop('PRAEGENDE_JUGENDJAHRE', axis=1)\\n df = df.drop('CAMEO_INTL_2015',axis=1)\\n \\n # Return the cleaned dataframe.\\n return df\",\n \"def parse_missing_fields(data):\\n def _get_nan_synonims(k):\\n if k == \\\"extra_security_descriptors\\\":\\n return [pd.NaT, np.nan]\\n else:\\n return [pd.NaT, '', np.nan]\\n\\n data = {id_no: {k: (v if v not in _get_nan_synonims(k) and not pd.isna(v) \\n else None) for k,v in datapoint.items()} for id_no, datapoint in data.items()}\\n return data\",\n \"def fill_missing(self) -> None:\\n\\n self.fill_missing_rows()\\n self.fill_missing_source_parameters()\\n return\",\n \"def clean_data(df):\\n \\n # Put in code here to execute all main cleaning steps:\\n # convert missing value codes into NaNs, ...\\n################################################################################################################ \\n missing_data_stats=pd.DataFrame(df.isnull().sum(),columns=['Naturally_Missing_Values'])\\n \\n # Loop over all the rows/attributes in feat_info\\n for i in range(len(feat_info)):\\n att=feat_info['attribute'][i] # Get name of attribute\\n codes=feat_info['missing_or_unknown'][i].replace('[',\\\"\\\").replace(']',\\\"\\\").split(',') # Get values from string\\n\\n # Loop over all the individual values extratcted from '\\tmissing_or_unknown' column\\n for j in range(len(codes)):\\n if codes[j]!='': # skip empty values as nothing needs to be replaced\\n try:\\n df.loc[df[att]==int(codes[j]),att] = np.nan # replaces numeric values with NaNs\\n except:\\n df.loc[df[att]==(codes[j]),att] = np.nan # replaces string values with NaNs\\n \\n # Merge and plot the naturally missing and overall missing values\\n missing_data_stats=pd.merge(missing_data_stats,pd.DataFrame(df.isnull().sum(),\\n columns=['Overall_Missing_Values']),left_index=True,right_index=True)\\n \\n################################################################################################################ \\n # Perform an assessment of how much missing data there is in each column of the dataset.\\n missing_data_stats['Overall_Missing_Percentage']=(missing_data_stats['Overall_Missing_Values']/len(df))*100\\n sorted_missing_data_stats=missing_data_stats.sort_values(by='Overall_Missing_Percentage',ascending=False)\\n \\n # Investigate patterns in the amount of missing data in each column.\\n plt.figure(figsize=(15,30))\\n sns.barplot(x='Overall_Missing_Percentage', y=sorted_missing_data_stats.index, \\n data=sorted_missing_data_stats);\\n plt.title('Percentage of Missing Data by Attribute',size=20)\\n \\n################################################################################################################\\n # remove selected columns and rows, ...\\n # Define criteria of outliers removal\\n outlier=30 # Outlier if % missing more than\\n\\n #Get outlier columns and drop them from data\\n outlier_columns=sorted_missing_data_stats[sorted_missing_data_stats['Overall_Missing_Percentage']>outlier].index\\n for i in outlier_columns:\\n df.drop(i,axis=1,inplace=True)\\n\\n################################################################################################################\\n # Sum all the missing values by rows\\n nan_count_rows=df.isnull().sum(axis=1).sort_values(ascending=False)\\n\\n # Plot and visualize the distribution of missing rows\\n plt.figure(figsize=(20,8))\\n sns.countplot(nan_count_rows)\\n\\n plt.xlabel('Missing Values in each Row')\\n plt.ylabel('Count of Rows')\\n plt.title('Count of Rows vs Missing Values in each Row')\\n \\n \\n # Define threshold value\\n row_nan_threshold=0\\n\\n # Rows above threshold\\n at=df.loc[nan_count_rows>row_nan_threshold]\\n\\n # Rows below threshold\\n bt=df.loc[nan_count_rows<=row_nan_threshold]\\n \\n################################################################################################################ \\n # select, re-encode, and engineer column values.\\n\\n # Assess categorical variables: which are binary, which are multi-level, and\\n # which one needs to be re-encoded?\\n\\n # Get all categorical features\\n cat_att=feat_info[feat_info['type']=='categorical']['attribute']\\n\\n # Loop through all the categorical features to identify binary numeric, binary non-numeric and multi-level \\n #categories\\n bin_num=[] #binary and numeric (type1)\\n bin_non_num=[] #binary and non_numeric (type2)\\n ml_cat=[] #multi-level categories (type3)\\n\\n for i in cat_att:\\n try:\\n # get number of unique values\\n uv=bt[i].nunique()\\n dt=bt[i].dtype\\n\\n # For binary variable with numeric value\\n if uv==2 and dt!='object':\\n bin_num.append(i) #append\\n\\n # For binary variable with non-numeric value\\n elif uv==2 and dt=='object':\\n bin_non_num.append(i) #append\\n\\n # For multi-level categories \\n elif uv>2:\\n ml_cat.append(i) #append\\n\\n\\n # Exception for columns which are already dropped from data\\n except Exception as e:\\n print('Note=> Column does not exist: ', e)\\n\\n print('\\\\nBinary_Numeric:\\\\n', bin_num , '\\\\n\\\\nBinary_Non_Numeric:\\\\n' , bin_non_num , '\\\\n\\\\nMulti-Level:\\\\n' , ml_cat)\\n \\n################################################################################################################\\n # Import Libraries\\n from sklearn.preprocessing import LabelEncoder\\n\\n # Initialize encoder\\n le=LabelEncoder()\\n\\n # columns list to encode\\n col_list=bin_non_num+ml_cat\\n\\n # Create a dataframe of encoded features\\n bt_encoded=bt.copy(deep=True)\\n\\n # Encode all columns in column list\\n for cols in col_list:\\n bt_encoded[cols]=le.fit_transform(bt[cols])\\n\\n################################################################################################################\\n # Create a list of all mixed features\\n mixed_att=feat_info[feat_info['type']=='mixed']['attribute']\\n\\n for i in mixed_att:\\n # Investigate \\\"PRAEGENDE_JUGENDJAHRE\\\" and engineer two new variables.\\n if i=='PRAEGENDE_JUGENDJAHRE':\\n bt_encoded.loc[(bt_encoded[i]<=2) & (bt_encoded[i]>=1) ,i+'_Decade']=1\\n bt_encoded.loc[(bt_encoded[i]<=4) & (bt_encoded[i]>=3),i+'_Decade']=2\\n bt_encoded.loc[(bt_encoded[i]<=7) & (bt_encoded[i]>=5),i+'_Decade']=3\\n bt_encoded.loc[(bt_encoded[i]<=9) & (bt_encoded[i]>=8),i+'_Decade']=4\\n bt_encoded.loc[(bt_encoded[i]<=13) & (bt_encoded[i]>=10),i+'_Decade']=5 \\n bt_encoded.loc[(bt_encoded[i]<=15) & (bt_encoded[i]>=14),i+'_Decade']=6\\n\\n bt_encoded[i+'_Movement']=1\\n bt_encoded.loc[(bt_encoded[i]==1) | (bt_encoded[i]==3) |\\n (bt_encoded[i]==5) | (bt_encoded[i]==8) |\\n (bt_encoded[i]==10) | (bt_encoded[i]==12) |\\n (bt_encoded[i]==14) ,i+'_Movement']=2\\n\\n # Investigate \\\"CAMEO_INTL_2015\\\" and engineer two new variables.\\n elif i=='CAMEO_INTL_2015':\\n bt_encoded[i+'_Wealth']=bt_encoded[i].apply(lambda x:int(x[0]))\\n bt_encoded[i+'_Life_Stage']=bt_encoded[i].apply(lambda x:int(x[1]))\\n\\n # Investigate \\\"LP_LEBENSPHASE_GROB\\\" and engineer a new variables (for low,avg,high income groups) \\n elif i=='LP_LEBENSPHASE_GROB':\\n #low income\\n bt_encoded[i+'_income']=1 \\n\\n #avg income\\n bt_encoded.loc[(bt_encoded[i]==3) | (bt_encoded[i]==5) |\\n (bt_encoded[i]==8) | (bt_encoded[i]==11) |\\n (bt_encoded[i]==12) ,i+'_income']=2 \\n #high income\\n bt_encoded.loc[(bt_encoded[i]==9) ,i+'_income']=3 \\n\\n\\n # Investigate \\\"WOHNLAGE\\\" and engineer a new variables (for Rural and Non Rural Flag) \\n elif i=='WOHNLAGE':\\n bt_encoded[i+'_Rural']=1\\n bt_encoded.loc[(bt_encoded[i]==7) | (bt_encoded[i]==8),i+'_Rural']=2\\n\\n\\n # Investigate \\\"PLZ8_BAUMAX\\\" and engineer a new variables (for family and business buildings) \\n elif i=='PLZ8_BAUMAX':\\n bt_encoded[i+'_Bldg_Type']=1\\n bt_encoded.loc[bt_encoded[i]==5,i+'_Bldg_Type']=2\\n \\n################################################################################################################\\n # Return the cleaned dataframe.\\n #Remove original mixed columns from final dataset if new columns have been engineered\\n for i in mixed_att:\\n try:\\n bt_encoded.drop(i,axis=1,inplace=True) \\n except Exception as e:\\n print('Note=> Column does not exist: ', e)\\n return bt_encoded\",\n \"def handle_na(self, data: pd.DataFrame) -> pd.DataFrame:\\n return remove_missing(\\n data,\\n self.params[\\\"na_rm\\\"],\\n list(self.REQUIRED_AES | self.NON_MISSING_AES),\\n self.__class__.__name__,\\n )\",\n \"def clean_up_data(self):\\n pass\",\n \"def _common_preprocess(self, data):\\n\\n data = data.drop('id', axis=1) \\n data = data.drop(['17', '488', 'B01AF', 'H01AB'], axis=1, errors='ignore')\\n\\n # drop age outliers\\n idx = data[(data['age'] > 99)].index\\n data = data.drop(idx)\\n\\n # drop rows with CKD\\n idx = data[((data['585'] != 0) | (data['586'] != 0)) &\\n (data['ckd'] == 0)].index\\n data = data.drop(idx)\\n data = data.drop(['585', '586'], axis=1)\\n\\n return data\",\n \"def missing_values():\\n print('Missings in the train data:', train_data.isnull().sum())\",\n \"def data_missing(data):\\n return type(data)._from_sequence([None, data[0]])\",\n \"def prepare_process(self, dataset):\\n if dataset is not None:\\n pass\",\n \"def preprocess(data):\\n raise NotImplementedError\",\n \"def correct_miss_fill(ds):\\n for d in ds.data_vars:\\n try:\\n ds[d].attrs.update({'missing_value': ds[d]._FillValue})\\n except:\\n pass\\n return xr.decode_cf(ds)\",\n \"def preprocess_valid_data(self):\\r\\n print(\\\"* Preprocessing validation data.\\\", flush=True)\\r\\n prep.create_HDF_file(self.C.validation_set)\\r\\n\\r\\n self.print_time_elapsed()\",\n \"def preProcess(self, datum):\\n pass\",\n \"def cleaning (data):\",\n \"def data_missing_for_sorting() -> NoReturn:\\n raise NotImplementedError\",\n \"def data_missing_for_sorting() -> NoReturn:\\n raise NotImplementedError\",\n \"def Check01_RemoveNoDataValues( DataDF, ReplacedValuesDF ):\\n \\n #add your code here\\n for i in range(0,len(DataDF)-1): #checks for a specific value in each cell, then replaces it with nan if it meets the criteria\\n for j in range(0,3):\\n if DataDF.iloc[i,j] == -999:\\n DataDF.iloc[i,j]=np.nan\\n \\n ReplacedValuesDF.iloc[0,0]=DataDF['Precip'].isna().sum() #counts the number of \\\"nan\\\" values for the referenced variable, then plugs it into the correct cell in the replacedvaluesdf\\n ReplacedValuesDF.iloc[0,1]=DataDF['Max Temp'].isna().sum()\\n ReplacedValuesDF.iloc[0,2]=DataDF['Min Temp'].isna().sum()\\n ReplacedValuesDF.iloc[0,3]=DataDF['Wind Speed'].isna().sum()\\n \\n return( DataDF, ReplacedValuesDF )\",\n \"def ImputeMissingValues(self, **kwargs):\\n if self._frozen:\\n raise Exception(\\\"Dataset already frozen, no modifications allowed\\\")\\n \\n # we should check it there are any duplicated columns in the arguments\\n if \\\"columns\\\" in kwargs.keys():\\n all_columns = [c[\\\"colname\\\"] for c in self._columns]\\n tmp = kwargs[\\\"columns\\\"]\\n # check if columns have been validated\\n flag = 0\\n for c in tmp:\\n if c not in all_columns:\\n flag = 1\\n print(\\\"Unknown or unvalidated column: '{}'\\\".format(c))\\n if flag>0:\\n raise Exception(\\\"Unknown or unvalidated columns, aborting\\\")\\n if np.unique(tmp).shape[0] != len(tmp):\\n raise Exception(\\\"Duplicated columns, aborting\\\")\\n # check that if a column has been passed as a parameter, all columns for the same group are passed too\\n for column in tmp:\\n group = [c[\\\"group\\\"] for c in self._columns if c[\\\"colname\\\"]==column][0]\\n columns_in_group = [c[\\\"colname\\\"] for c in self._columns if c[\\\"group\\\"]==group]\\n if all([c in tmp for c in columns_in_group]) != True:\\n raise Exception(\\\"All one-hot encoded mono-label variables of the same group must be included (column: '{}'\\\".format(column))\\n \\n columns = tmp\\n else:\\n # by default : take all columns which are in the X dataset\\n columns = [c[\\\"colname\\\"] for c in self._columns if c[\\\"dataset\\\"]==\\\"X\\\"]\\n \\n df = self._proc_data.copy()\\n \\n # Remove columns with only missing values\\n cols_to_remove = []\\n for coln in columns:\\n if np.sum(pd.isna(df[coln]))==df.shape[0]:\\n print(\\\" !!!! Column {} (qty) only has missing values, will be removed\\\".format(coln))\\n cols_to_remove.append(coln)\\n # Check if 0 variance => would lead to NA values\\n for coln in columns:\\n if np.nanvar(df[coln])==0:\\n print(\\\" !!!! Column {} (qty) has 0 variance, will be removed\\\".format(coln))\\n cols_to_remove.append(coln)\\n if len(cols_to_remove)>0:\\n columns = [c for c in columns if c not in cols_to_remove] \\n df = df.loc[:,columns]\\n mean = np.nanmean(df,axis=0)\\n std = np.nanstd(df,axis=0)\\n df = (df-mean)/std\\n \\n print(\\\" >>>> Total missing values:\\\\n{}\\\".format(np.sum(pd.isna(df))))\\n \\n ar = np.array(df)\\n \\n # impute\\n imputer = KNNImputer(n_neighbors=5, weights=\\\"distance\\\")\\n imputed_np = imputer.fit_transform(ar)\\n \\n # unnormalize\\n unnorm_imputed_np = imputed_np*std+mean\\n unnorm_imputed_df = df.copy()\\n # send back to dataframe, with same structure: index, columns...\\n unnorm_imputed_df.loc[:,:] = unnorm_imputed_np\\n \\n # for binary variables : convert to 0/1 and ensure that variables in the same group have only one 1\\n # convert by group to make it easier\\n # one entry per group\\n bin_columns_by_group = {}\\n for c in self._columns:\\n if c[\\\"mode\\\"]==1 and c[\\\"colname\\\"] in columns:\\n if c[\\\"group\\\"] in bin_columns_by_group.keys():\\n bin_columns_by_group[c[\\\"group\\\"]].append(c[\\\"colname\\\"])\\n else:\\n bin_columns_by_group[c[\\\"group\\\"]]=[c[\\\"colname\\\"]]\\n \\n for cols in bin_columns_by_group.values():\\n if len(cols)==1: # binary\\n unnorm_imputed_df[cols[0]] = np.minimum(1, np.maximum(0, np.round(unnorm_imputed_df[cols[0]])))\\n else:\\n a = np.argmax(np.array(unnorm_imputed_df[cols]), axis=1)\\n b = np.zeros((a.size, a.max()+1))\\n b[np.arange(a.size),a] = 1\\n unnorm_imputed_df[cols] = b\\n \\n self._proc_data.loc[:,columns] = unnorm_imputed_df\",\n \"def preprocess_dataset(dataset=None, remove_missing=60, remove_empty_rows=True):\\n print('feature size before dropping:{}'.format(dataset.shape[1]))\\n dataset_after_drop = dataset.dropna(thresh=dataset.shape[0]*remove_missing/100, how='all',axis=1)\\n print('feature size after dropping:{}'.format(dataset_after_drop.shape[1]))\\n print('row size before dropping:{}'.format(dataset_after_drop.shape[0]))\\n if remove_empty_rows is True:\\n df_final = dataset_after_drop.dropna(inplace=False).reset_index (drop=True)\\n print('row size after dropping:{}'.format(df_final.shape[0]))\\n print('---------------')\\n print('final shape:{}'.format(df_final.shape))\\n return df_final\\n else:\\n return dataset_after_drop\",\n \"def reprocessSeries(self, tiltseriesdata):\\n\\t\\treturn None\",\n \"def _pure_data(data, missing_mask):\\n missing_rows = np.where(missing_mask)[0]\\n pure_data = np.delete(data, missing_rows, axis=0)\\n\\n return pure_data\",\n \"def _process_data(self):\\r\\n # Rename columns to match final feature class\\r\\n self._rename_columns()\\r\\n # Add point ID column\\r\\n self._add_pointid()\\r\\n # Sort rows by transect id and timestamp\\r\\n self._sort_rows()\\r\\n # Fill Null records with a value\\r\\n self._fill_nulls()\\r\\n # Set site_code to lower case\\r\\n self._lower_site_code()\\r\\n # Create survey_id\\r\\n self._calc_survey_id()\\r\\n # Calculate nativesg column if at least one of the veg columns is a Native seagrass type\\r\\n if set(self.veg_columns).intersection(set(NATIVESG_CODES)) > 0:\\r\\n self.nativesg_columns = list(set(self.veg_columns).intersection(set(NATIVESG_CODES)))\\r\\n self._calc_nativesg()\\r\\n #\\r\",\n \"def fill_missing(self):\\n df = self.df\\n # Filling with default values\\n logger.debug(\\\"Filling from distributions...\\\")\\n for field in HeatStrokeDataFiller.default_map or field in HeatStrokeDataFiller.positive_default:\\n if field not in df.columns:\\n logger.warning(\\\"(%s) missing from data-frame columns\\\" % field)\\n continue\\n logger.debug(\\\"Setting missing in \\\\\\\"%s\\\\\\\" to default: %s\\\" % (field, HeatStrokeDataFiller.default_map[field]))\\n default_value = HeatStrokeDataFiller.default_map[field]\\n\\n where = HeatStrokeDataFiller.find_where_missing(df, field, find_nan=True, find_str=False)\\n how_many_to_fill = np.sum(where)\\n if field in HeatStrokeDataFiller.positive_default:\\n # Use default positive dietributions\\n distribution = HeatStrokeDataFiller.positive_default[field]\\n df[field].loc[where] = distribution(how_many_to_fill)\\n else:\\n logger.debug(\\\"Using default %s for field: %s\\\" % (default_value, field))\\n # Use default values\\n df[field].loc[where] = np.array([default_value] * how_many_to_fill)\\n\\n # Filling with Zeros\\n logger.debug(\\\"Fillling with zeros...\\\")\\n for field in HeatStrokeDataFiller.fields_to_fill_with_zero:\\n if field not in df.columns:\\n logger.warning(\\\"\\\\\\\"%s\\\\\\\" missing from columns\\\" % field)\\n continue\\n logger.debug(\\\"Setting missing in \\\\\\\"%s\\\\\\\" to 0\\\" % field)\\n\\n where = HeatStrokeDataFiller.find_where_missing(df, field, find_nan=True, find_str=True)\\n how_many_to_fill = np.sum(where)\\n df[field].loc[where] = np.zeros(how_many_to_fill)\\n\\n # Filling in columns with the average from the rest of the column\\n logger.debug(\\\"Filling with agerages...\\\")\\n for field in HeatStrokeDataFiller.fields_to_fill_with_average:\\n if field not in df.columns:\\n logger.warning(\\\"\\\\\\\"%s\\\\\\\" missing from data-frame columns\\\" % field)\\n continue\\n\\n where = HeatStrokeDataFiller.find_where_missing(df, field, find_nan=True, find_str=True)\\n data = df[field][np.invert(where)]\\n mean = np.mean(data)\\n std = np.std(data)\\n if mean == np.nan or std == np.nan:\\n mean, std = (0, 0)\\n logger.debug(\\\"Setting missing in \\\\\\\"%s\\\\\\\" with: %.3f +/- %.3f\\\" % (field, mean, std))\\n how_many_to_fill = np.sum(where)\\n df[field].loc[where] = mean + std * np.random.random(how_many_to_fill)\\n\\n fields_not_modified = set(df.columns) - set(HeatStrokeDataFiller.default_map.keys()) - HeatStrokeDataFiller.fields_to_fill_with_zero - HeatStrokeDataFiller.fields_to_fill_with_zero\\n logger.debug(\\\"Fields not modified: %s\\\" % fields_not_modified.__str__())\\n return df\",\n \"def clean():\\n filter_phase_data()\\n combine_phase_data()\\n remove_duplicates_phase_data()\",\n \"def preprocess(self, data, label):\\n\\t\\traise NotImplementedError\",\n \"def DealWithMissingValues(data_set: pd.DataFrame):\\n data_set.fillna(method=\\\"pad\\\", inplace=True)\",\n \"def _clean_results(self):\\n\\t\\tif self.file_type == \\\"Automobile\\\":\\n\\t\\t\\tcols = [\\\"Year\\\", \\\"Mileage\\\", \\\"Price\\\"]\\n\\t\\t\\tself.data.Mileage.replace([',', 'mi.', 'nan', ' '], '', regex=True, inplace=True) # Fix mileage column\\n\\t\\t\\tself.data.Price.replace([',', '\\\\$'], '', regex=True, inplace=True) # Always fix price column (, and $ removed)\\n\\t\\t\\tself.data[cols] = self.data[cols].apply(pd.to_numeric, errors='coerce') # Coerces errors into NaN values\\n\\t\\t\\tself.data.drop(self.data[self.data.Year < 2000].index, inplace=True) # Remove cars made before 2000\\n\\t\\t\\tself.data.drop(self.data[self.data.Price > 30000].index, inplace=True) # Remove cars over $30,000\\n\\t\\t\\tself.data.drop(self.data[(self.data.Mileage < 1000) | (self.data.Mileage > 300000)].index, inplace=True) # Remove cars with over 300,000 miles\\n\\t\\t\\tself.data['Age'] = 2018 - self.data['Year'] # Change years to Age\\n\\t\\telif self.file_type == \\\"Apartment\\\":\\n\\t\\t\\tself.data.Area.replace(['ft2'], '', regex=True, inplace=True) # Remove ft2 from square footage column\\n\\t\\t\\tself.data.Price.replace([',', '\\\\$'], '', regex=True, inplace=True) # Always fix price column (, and $ removed)\\n\\t\\telse:\\n\\t\\t\\tself.data['Street'], self.data['City'], self.data['State'] = self.data['Address'].str.split(',', 2).str\\n\\t\\t\\tdel self.data.Address\\n\\t\\t\\tself.data.drop(self.data[self.data.Price > 1000000].index, inplace=True) # Remove houses worth more than $1 million\\n\\n\\t\\tself.data.replace('^\\\\s*$', np.nan, regex=True, inplace=True) # Replace all empty values with np.NaN\\n\\t\\tself.data = self.data.dropna(axis=1, how='all') # Remove Null Columns\\n\\t\\tself.data = self.data.apply(pd.to_numeric, errors='ignore') # Coerces errors into NaN values\",\n \"def transform(self, data):\\n print(\\\"NA before imputation:\\\", data[self.field].isna().sum())\\n missing_age_df = data[data[self.field].isna()]\\n missing_and_na_age_df = missing_age_df[missing_age_df['SibSp'] >= 3]\\n data.loc[missing_and_na_age_df.index, \\\"Age\\\"] = 11.7\\n data[self.field] = data[self.field].fillna(-1)\\n print(\\\"special case imputation:\\\", len(\\n data.loc[missing_and_na_age_df.index, \\\"Age\\\"]))\\n print(\\\"-1 after imputation:\\\", sum(data[self.field] == -1))\\n return data\",\n \"def interpolate_none(self):\\n\\n # Reset processed data\\n self.u_processed_mps = np.copy(self.u_mps)\\n self.v_processed_mps = np.copy(self.v_mps)\\n self.u_processed_mps[self.valid_data[0, :] == False] = np.nan\\n self.v_processed_mps[self.valid_data[0, :] == False] = np.nan\",\n \"def postprocessData(meta, units, data):\\n\\n data['time'] = np.arange(0, meta['dt'] * len(data), meta['dt'])\\n units['time'] = 's'\\n\\n meta, units, data = self.calculateForce(meta, units, data)\\n\\n data['distance'] = np.sqrt(data.xDist**2 + data.yDist**2)\\n units['distance'] = 'nm'\\n\\n return meta, units, data\",\n \"def preprocess_data(self):\\n\\n self._preprocess_train_data()\\n self._preprocess_test_data()\",\n \"def minimal_clean_data_inplace(df):\\n # There are some 'unknown' users in train dataset only\\n unknown_data_lines = df['sexo'].isnull() & df['age'].isnull() & df['ind_empleado'].isnull() & \\\\\\n df['fecha_alta'].isnull() & df['pais_residencia'].isnull()\\n\\n logging.info(\\\"- Number of lines with unknown data : %s\\\" % unknown_data_lines.sum())\\n\\n # Remove these users as clients\\n _clients = df[unknown_data_lines]['ncodpers'].unique()\\n bad_lines = df['ncodpers'].isin(_clients)\\n df.drop(df[bad_lines].index, inplace=True)\\n\\n logging.info(\\\"- Number of columns with nan : %s\\\" % df.isnull().any().sum())\\n\\n # Remove accent\\n df.loc[df['nomprov'] == \\\"CORU\\\\xc3\\\\x91A, A\\\", \\\"nomprov\\\"] = \\\"CORUNA\\\"\\n\\n unknown_cols = ['sexo',\\n 'ind_empleado',\\n 'pais_residencia',\\n 'ult_fec_cli_1t',\\n 'conyuemp',\\n 'canal_entrada',\\n 'nomprov',\\n 'segmento',\\n 'tiprel_1mes',\\n 'indrel_1mes']\\n # Start with cols -> replace nan with UNKNOWN\\n for col in unknown_cols:\\n df.loc[df[col].isnull(), col] = \\\"UNKNOWN\\\"\\n\\n # Set unknown renta to -99\\n df.loc[df['renta'].isnull(), 'renta'] = -99\\n\\n # Next `fecha_alta` :\\n assert df['fecha_alta'].isnull().sum() == 0, \\\\\\n \\\"Need to replace nan in 'fecha_alta', count=%s\\\" % df['fecha_alta'].isnull().sum()\\n\\n # **Remove 'tipodom' and 'cod_prov' columns**\\n df.drop([\\\"tipodom\\\", \\\"cod_prov\\\"], axis=1, inplace=True)\\n \\n # Convert 'ind_nuevo' to int\\n df['ind_nuevo'] = df['ind_nuevo'].astype(int)\\n \\n # Remove floating point at string indrel_1mes\\n df['indrel_1mes'] = df['indrel_1mes'].apply(lambda x: str(int(float(x))) if len(x) == 3 else x)\\n\\n if \\\"ind_nomina_ult1\\\" in df.columns and \\\"ind_nom_pens_ult1\\\" in df.columns:\\n # Target labels : `ind_nomina_ult1`, `ind_nom_pens_ult1` : nan -> 0\\n # I could try to fill in missing values for products by looking at previous months,\\n # but since it's such a small number of values for now I'll take the cheap way out.\\n df.loc[df.ind_nomina_ult1.isnull(), \\\"ind_nomina_ult1\\\"] = 0\\n df.loc[df.ind_nom_pens_ult1.isnull(), \\\"ind_nom_pens_ult1\\\"] = 0\\n\\n # replace 'antiguedad' with the number of months between 'fecha_alta' and 'fecha_dato'\\n func1 = lambda x: _to_ym_dec(to_yearmonth(x))\\n func2 = lambda x: max(_to_nb_months(x), 0) \\n\\n v1 = df['fecha_dato'].apply(func1)\\n v2 = df['fecha_alta'].apply(func1)\\n v3 = (v1 - v2).apply(func2)\\n df.loc[:, 'antiguedad'] = v3\\n \\n # Replace 'ult_fec_cli_1t' by current nb of months from fecha_dato, if negative, set to zero\\n mask = df['ult_fec_cli_1t'] == 'UNKNOWN'\\n df.loc[mask, 'ult_fec_cli_1t'] = df[mask]['fecha_dato']\\n v1 = df['fecha_dato'].apply(func1)\\n v2 = df['ult_fec_cli_1t'].apply(func1)\\n v3 = (v1 - v2).apply(func2)\\n df.loc[:, 'ult_fec_cli_1t'] = v3\",\n \"def fill_nan(data, fm, filename):\\r\\n print(\\\"fill Nan begining...\\\")\\r\\n\\r\\n # fill categorical variable\\r\\n categorical_variable_fill = pd.read_excel(\\\"./data/VariableFill.xlsx\\\", dtype={'Name': str, 'Value': float})\\r\\n categorical_features = categorical_variable_fill['Variable'].values\\r\\n categorical_variable_fill.set_index('Variable', inplace=True)\\r\\n for cat_col in categorical_features:\\r\\n data[cat_col].fillna(categorical_variable_fill.loc[cat_col, 'Fill'], inplace=True)\\r\\n\\r\\n # fill continue variable\\r\\n columns_name = list(data.columns.values)\\r\\n feature_name = list(fm.keys())\\r\\n\\r\\n for col in columns_name:\\r\\n if col in feature_name:\\r\\n for i in range(len(data)):\\r\\n # 填充空值\\r\\n if np.isnan(data.loc[i, col]):\\r\\n if len(fm[col]) == 4:\\r\\n data.loc[i, col] = uniform(fm[col]['low'], fm[col]['up'], 1)\\r\\n data.loc[i, col+'Grade2'] = fm[col]['grade2']\\r\\n else:\\r\\n if data.loc[i, 'Gender'] == 1:\\r\\n data.loc[i, col] = uniform(fm[col]['F']['low'], fm[col]['F']['up'], 1)\\r\\n data.loc[i, col+'Grade2'] = fm[col]['F']['grade2']\\r\\n elif data.loc[i, 'Gender'] == 0:\\r\\n data.loc[i, col] = uniform(fm[col]['M']['low'], fm[col]['M']['up'], 1)\\r\\n data.loc[i, col + 'Grade2'] = fm[col]['M']['grade2']\\r\\n\\r\\n data.to_csv('./data/'+filename+'processed_data.csv', index=False)\\r\\n print(\\\"fill Nan finished! processed data is saved in ./data/processed_data.csv\\\")\\r\\n return data\",\n \"def missing_data(self, plot=True):\\n # Set some of the observations to missing\\n self.remove_data()\\n # Create empty arrays to store values\\n F = np.zeros(len(self.y))\\n a = np.zeros(len(self.y))\\n v = np.zeros(len(self.y))\\n P = np.zeros(len(self.y))\\n # Initialize at the initial values parsed to the class\\n P[0] = self.pardict['P1']\\n sigma_eps2 = self.pardict['sigma_eps2']\\n sigma_eta2 = self.pardict['sigma_eta2']\\n # initialise a for estimated model\\n if self.var_name != 'Volume of Nile':\\n a[0] = self.y[0]\\n # Iterate\\n for t in range(0, len(self.y) - 1):\\n v[t] = np.nan_to_num(self.y[t] - a[t])\\n F[t] = P[t] + sigma_eps2 if np.isfinite(self.y[t]) else np.inf\\n # K is defined as ratio of P and F\\n Kt = P[t] / F[t] if np.isfinite(self.y[t]) else 0\\n a[t + 1] = a[t] + Kt * v[t]\\n P[t + 1] = P[t] * (1 - Kt) + sigma_eta2\\n v[-1] = self.y[-1] - a[-1]\\n F[-1] = P[-1] + sigma_eps2\\n # Obtain smoothed state\\n # Obtain all time values for L\\n L = self.pardict['sigma_eps2'] / F\\n index_missing = np.argwhere(np.isnan(self.y))\\n L[index_missing] = 1\\n\\n # Do the recursion for r\\n r = np.zeros(len(self.y))\\n N = np.zeros(len(self.y))\\n V = np.zeros(len(self.y))\\n\\n for t in np.arange(len(self.y) - 1, 0, -1):\\n r[t - 1] = v[t] / F[t] + L[t] * r[t]\\n for t in np.arange(len(self.y) - 1, 0, -1):\\n N[t - 1] = 1 / F[t] + L[t] ** 2 * N[t]\\n for t in np.arange(len(self.y) - 1, 0, -1):\\n V[t] = P[t] - P[t] ** 2 * N[t - 1]\\n\\n # Do the recursion for alpha\\n alphas = np.zeros(len(self.y))\\n alphas[0] = a[t]\\n for t in range(1, len(self.y) - 1):\\n alphas[t] = a[t] + P[t] * r[t - 1]\\n alphas[-1] = np.nan\\n\\n if plot:\\n fig_name = self.var_name + 'Fig25.pdf'\\n plot_fig2_5(self.times, self.y, a, P, alphas, V, fname=fig_name, var_name=self.var_name)\\n # Restore data\\n self.reset_data()\",\n \"def pre_process(self, dataset):\\n\\n # np.empty creates an empty array only. You have to replace this with your code.\\n X = np.empty((0,0))\\n y = np.empty((0))\\n\\n if dataset == 0:\\n # Implement for the abalone dataset\\n df = pd.DataFrame(columns=['sex', 'length', 'diameter', 'height', 'whole_weight', 'shucked_weight', 'viscera_weight', 'shell_weight', 'rings'])\\n count = 0\\n\\n with open('Dataset.data') as file: # reading data from file\\n data = file.read()\\n\\n data = data.split('\\\\n') # split data into different rows\\n data = data[:-1] # last one is empty\\n for row in data:\\n row = row.split()\\n df.loc[count] = row # add in dataframe\\n count += 1\\n\\n df['M'] = np.where(df.sex=='M', 1,0) # genders are turned to a one hot encoding\\n df['F'] = np.where(df.sex=='F', 1,0)\\n df['I'] = np.where(df.sex=='I', 1,0)\\n df = df.drop(['sex'], axis=1)\\n df = df.dropna()\\n\\n df = df.sample(frac=1).reset_index(drop=True) # shuffle dataframe\\n\\n X = df.drop(['rings'], axis=1)\\n X = X.values\\n X = X.astype(float)\\n y = df['rings'].values\\n y = y.astype(float)\\n\\n elif dataset == 1:\\n # Implement for the video game dataset\\n df = pd.read_csv('VideoGameDataset - Video_Games_Sales_as_at_22_Dec_2016.csv') # read csv directly into a dataframe\\n df1 = df[['Critic_Score', 'User_Score', 'Global_Sales']]\\n df1 = df1.dropna()\\n df1 = df1[df1.User_Score != 'tbd']\\n\\n df1 = df1.sample(frac=1).reset_index(drop=True) # shuffle rows\\n\\n X = df1.drop(['Global_Sales'], axis=1)\\n X = X.values\\n X = X.astype(float)\\n y = df1['Global_Sales'].values\\n y = y.astype(float)\\n\\n elif dataset == 2:\\n # Implement for the banknote authentication dataset\\n df = pd.DataFrame(columns=['variance', 'skewness', 'curtosis', 'entropy', 'class'])\\n count = 0\\n\\n with open('data_banknote_authentication.txt') as file: # reading file \\n data = file.read()\\n data = data.split('\\\\n')\\n data = data[:-1]\\n for row in data:\\n row = row.split(',')\\n df.loc[count] = [float(elt) for elt in row[:-1]] + [int(row[-1])] # last column has class so it is int rest are float\\n count += 1\\n\\n df = df.sample(frac=1).reset_index(drop=True) # shuffle dataset\\n\\n X = df.drop(['class'], axis=1)\\n X = X.values\\n y = df['class'].values\\n y = y.astype(int)\\n\\n return X, y\",\n \"def clean_data(data,variables,ignore_na=True,fill_mode=\\\"mode\\\"):\\n\\tnum_rows,num_cols = data.shape\\n\\tif ignore_na:\\n\\t\\tdata = data.dropna(axis=0,how=\\\"any\\\",subset=variables)\\n\\t\\tnum_rows,num_cols = data.shape\\n\\telse:\\n\\t\\tfor variable in variables:\\n\\t\\t\\tif data[variable].isna().sum()>0:\\n\\t\\t\\t\\tif fill_mode==\\\"mode\\\":\\n\\t\\t\\t\\t\\tdata[variable] = data[variable].fillna(data[variable].mode()[0])\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tif variable==\\\"Age\\\":\\n\\t\\t\\t\\t\\t\\tnan_idx = np.where(data[\\\"Age\\\"].isna())[0]\\n\\t\\t\\t\\t\\t\\tnorm = [1.,1.]#[8.,3.]\\n\\t\\t\\t\\t\\t\\tnew_age = np.zeros((num_rows))\\n\\t\\t\\t\\t\\t\\tfor idx in nan_idx:\\n\\t\\t\\t\\t\\t\\t\\terr = (data[[\\\"SibSp\\\",\\\"Pclass\\\"]]-data[[\\\"SibSp\\\",\\\"Pclass\\\"]].iloc[idx])\\n\\t\\t\\t\\t\\t\\t\\terr /= norm\\n\\t\\t\\t\\t\\t\\t\\ttotal_err = np.sqrt(np.sum(err**2,axis=1))\\n\\t\\t\\t\\t\\t\\t\\tif len(data[\\\"Age\\\"][total_err==0].mode())>0:\\n\\t\\t\\t\\t\\t\\t\\t\\tnew_age[idx] = data[\\\"Age\\\"][total_err==0].mode()[0]\\n\\t\\t\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\t\\tnew_age[idx] = data[\\\"Age\\\"][total_err<4].mode()[0]\\n\\t\\t\\t\\t\\t\\tdata[\\\"Age\\\"].fillna(pd.Series(new_age),inplace=True)\\n\\n\\t\\t\\t\\t\\tif variable==\\\"Embarked\\\":\\n\\t\\t\\t\\t\\t\\t# data[variable] = data[variable].fillna(data[variable].mode()[0])\\n\\t\\t\\t\\t\\t\\tdata = data.dropna(axis=0,how=\\\"any\\\",subset=[\\\"Embarked\\\"])\\n\\t\\t\\t\\t\\t\\tnum_rows,num_cols = data.shape\\n\\n\\t\\t\\t\\t\\tif variable==\\\"Fare\\\":\\n\\t\\t\\t\\t\\t\\tnan_idx = np.where(data[\\\"Fare\\\"].isna())[0]\\n\\t\\t\\t\\t\\t\\tnew_fare = np.zeros((num_rows))\\n\\t\\t\\t\\t\\t\\tfor idx in nan_idx:\\n\\t\\t\\t\\t\\t\\t\\ttemp_age = data[\\\"Age\\\"].iloc[idx]\\n\\t\\t\\t\\t\\t\\t\\ttemp_embarked = data[\\\"Embarked\\\"].iloc[idx]\\n\\t\\t\\t\\t\\t\\t\\ttemp_pclass = data[\\\"Pclass\\\"].iloc[idx]\\n\\n\\t\\t\\t\\t\\t\\t\\tsimilar_cases = data.loc[(data[\\\"Embarked\\\"]==temp_embarked) &\\\\\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t (data[\\\"Pclass\\\"]==temp_pclass) &\\\\\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t (data[\\\"Age\\\"]>(temp_age-15)) &\\\\\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t (data[\\\"Age\\\"]<(temp_age+15)) &\\\\\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t (data[\\\"Fare\\\"].isna()==False), \\\"Fare\\\"]\\n\\t\\t\\t\\t\\t\\t\\tnew_fare[idx] = similar_cases.median()\\n\\t\\t\\t\\t\\t\\tdata[\\\"Fare\\\"].fillna(pd.Series(new_fare),inplace=True)\\n\\n\\n\\t## format ticket values\\n\\tduplicates = []\\n\\tticket_no = np.zeros((num_rows))\\n\\tfor i,uniq in enumerate(data['Ticket'].unique()):\\n\\t\\tticket_no[data['Ticket']==uniq] = i\\n\\t## take into account four cases where 'Ticket' is given as LINE\\n\\tticket_no[data['Ticket']==\\\"LINE\\\"] = i + np.arange(np.sum(data['Ticket']==\\\"LINE\\\"))\\n\\tdata[\\\"ticket_no\\\"] = pd.Series(ticket_no,dtype=int)\\n\\n\\t## format cabin values, set NaN cabin values to zero\\n\\tduplicates = []\\n\\tcabin_no = np.zeros((num_rows))\\n\\tfor i,uniq in enumerate(data['Cabin'].unique()):\\n\\t\\tif uniq==\\\"nan\\\":\\n\\t\\t\\tcabin_no[data['Cabin']==uniq] = 0\\n\\t\\tcabin_no[data['Cabin']==uniq] = i + 1\\n\\tdata[\\\"cabin_no\\\"] = pd.Series(cabin_no,dtype=int)\",\n \"def inject_missing_data(self, data, allocation=None):\\n extracted, errors = self.extractData(setErrors=False)\\n\\n # the extracted fields may contain field values which need to be\\n # injected so the defaults are filled - otherwise no value is updated\\n # on the disabled field\\n for field in ('day', 'start_time', 'end_time'):\\n if extracted.get(field) is not None:\\n data[field] = extracted[field]\\n\\n # if the extracted data was not of any help the id of the allocation\\n # is our last resort.\\n try:\\n allocation = allocation or self.allocation(data['id'])\\n except DirtyReadOnlySession:\\n return\\n\\n if not allocation:\\n return\\n\\n if data.get('day') is None:\\n data['day'] = allocation.display_start.date()\\n\\n if data.get('start_time') is None:\\n data['start_time'] = allocation.display_start.time()\\n\\n if data.get('end_time') is None:\\n data['end_time'] = allocation.display_end.time()\",\n \"def pre_processing_(data_df , serialized_objects):\\n max_recency_acc_dig = serialized_objects['max_recency_acc_dig'] # These values are taken from trained model values\\n max_recency_dig_2yr = serialized_objects['max_recency_dig_2yr'] # These values are taken from trained model values\\n max_acc_recency_mf = serialized_objects['max_acc_recency_mf'] #These are values imported in training dataset. Same values needs to be used to impute missing values in unseen data\\n\\n data_df = data_df.na.fill({\\n 'recency_acc_dig' : max_recency_acc_dig, # Filling missing values\\n 'recency_dig_2yr' : max_recency_dig_2yr,\\n 'acc_recency_mf' : max_acc_recency_mf\\n })\\n\\n freq_acc_upg_2yrs_split = [-float('inf'), 0, 1, 2, float('inf')]\\n bucketizer_freq_acc_upg_2yrs = Bucketizer(splits=freq_acc_upg_2yrs_split, inputCol='freq_acc_upg_acc_2yrs', outputCol='freq_acc_upg_acc_2yrs_bkt')\\n data_df = bucketizer_freq_acc_upg_2yrs.setHandleInvalid('keep').transform(data_df) # Binning the freq_acc_upg_acc_2yrs column\\n\\n tot_purchase_split = [-float('inf'), 0, 1, 2, 3, float('inf')]\\n bucketizer_tot_purchase = Bucketizer(splits=tot_purchase_split, inputCol='tot_accsry_purchse', outputCol='tot_accsry_purchse_bkt')\\n data_df = bucketizer_tot_purchase.setHandleInvalid('keep').transform(data_df) # Binning the tot_accsry_purchse column\\n\\n del_cols_new = ['freq_acc_upg_acc_2yrs', 'tot_accsry_purchse']\\n data_df = data_df.drop(*del_cols_new) # Dropping the older continuous columns\\n return data_df\",\n \"def _handle_load_unknown(self, data, original):\\n for key, val in original.items():\\n if key not in self.fields:\\n data[key] = val\\n return data\",\n \"def test_data_preprocessing(raw_data): \\r\\n\\r\\n # get data output\\r\\n data_output = raw_data[['Submitby Date Time', 'Challenge Manager', 'Challenge Copilot', 'Posting Date Date', 'Track',\\r\\n 'Technology List', 'First Place Prize', 'Num Registrations', 'Total Prize']]\\r\\n with open('cache/extended_columns.pkl', 'rb') as f:\\r\\n extended_columns = pickle.load(f)\\r\\n with open('cache/num_date_columns.pkl', 'rb') as f:\\r\\n max_date_columns = pickle.load(f)\\r\\n \\r\\n data_output = class_binaryzation_for_test(data_output, extended_columns)\\r\\n try:\\r\\n data_output = date_separation1(data_output, max_num_columns=NUM_DATE_COLUMNS)\\r\\n except:\\r\\n data_output = date_separation2(data_output)\\r\\n data_output = money_digitalization(data_output)\\r\\n data_output = get_date_in_days(data_output)\\r\\n data_output['Days from Posting to Submit'] = data_output['Submitby Date Time Days from 2016'] \\\\\\r\\n - data_output['Posting Date Date Days from 2016'] \\r\\n\\r\\n return data_output\",\n \"def preprocess(self, df):\\n print(\\\"Started Processing....\\\")\\n # binary conversion\\n df.replace(to_replace=\\\"yes\\\", value=1, inplace=True)\\n df.replace(to_replace=\\\"no\\\", value=0, inplace=True)\\n\\n # replace unknowns with nan\\n df = df.replace(to_replace=\\\"unknown\\\", value=np.nan)\\n # getting the list of columns with nan\\n ml = df.columns[df.isna().any()].tolist()\\n\\n for item in ml:\\n # getting the ratio of the index labels\\n val = pd.DataFrame(df[item].value_counts(normalize=True))\\n\\n # index labels in a list\\n valr = val.index.tolist()\\n # drc.index = valr\\n # columns values in a list\\n valc = val[item].tolist()\\n # replacing the nan values with ratio\\n df[item] = df[item].fillna(pd.Series(np.random.choice(valr, p=valc, size=len(df))))\\n\\n # dependent variable\\n dfy = df.iloc[:, -1]\\n # independent variable\\n dfx = df.iloc[:, :-1]\\n\\n # converting categorical data to numerical\\n dfx = pd.get_dummies(dfx)\\n\\n # normalizing\\n dfx = (dfx - dfx.min()) / (dfx.max() - dfx.min())\\n\\n dxdy = pd.concat([dfx, dfy], axis=1)\\n\\n # class balancing\\n sm = RandomOverSampler(random_state=42)\\n dfx, dfy = sm.fit_sample(dxdy.iloc[:, :-1], dxdy.iloc[:, -1])\\n\\n # converting to dataframe\\n dfx = pd.DataFrame(dfx, columns=dxdy.iloc[:, :-1].columns.values)\\n\\n # dimensionality reduction\\n pca = PCA(n_components=33)\\n dfx = pca.fit_transform((dfx))\\n\\n print(\\\"Processing Done\\\")\\n\\n return dfx, dfy\",\n \"def inject_missing_data(self, data, reservation):\\n extracted, errors = self.extractData(setErrors=False)\\n\\n # the extracted fields may contain field values which need to be\\n # injected so the defaults are filled - otherwise no value is updated\\n # on the disabled field\\n for field in ('start_time', 'end_time'):\\n if extracted.get(field) is not None:\\n data[field] = extracted[field]\\n\\n if data.get('start_time') is None:\\n data['start_time'] = reservation.start.time()\\n\\n if data.get('end_time') is None:\\n data['end_time'] = reservation.end.time()\",\n \"def preprocess(self):\\n \\n file_name_list = os.listdir(self.image_dir)\\n random.seed(1234)\\n random.shuffle(file_name_list)\\n \\n for i,d in enumerate(self.domains):\\n self.attr2idx[d]=i \\n\\n for i, file_name in enumerate(file_name_list):\\n if (file_name.startswith('X_')):\\n continue\\n \\n parts = file_name.split(\\\"-\\\")\\n label = int(parts[0])\\n if label not in self.domains:\\n continue\\n img_name = file_name\\n\\n count=self.get_sample_count(label)\\n if count<self.valid_set_size:\\n # create holdout set on the fly\\n utils.copy_file(self.image_dir,self.valid_set_dir,img_name)\\n else:\\n self.dataset.append([img_name, self.attr2idx[label]])\\n \\n self.increment_sample_count(label)\\n\\n print(\\\"Sample count per domain: \\\"+str(self.sample_count)+\\\" (including holdout set, holdout size per domain is: \\\"+str(self.valid_set_size)+\\\")\\\")\\n print('Finished preprocessing the dataset...')\",\n \"def setup_no_data_values(input_dataset, options):\\n in_nodata = []\\n if options.srcnodata:\\n nds = list(map(float, options.srcnodata.split(',')))\\n if len(nds) < input_dataset.RasterCount:\\n in_nodata = (nds * input_dataset.RasterCount)[:input_dataset.RasterCount]\\n else:\\n in_nodata = nds\\n else:\\n for i in range(1, input_dataset.RasterCount+1):\\n raster_no_data = input_dataset.GetRasterBand(i).GetNoDataValue()\\n if raster_no_data is not None:\\n in_nodata.append(raster_no_data)\\n\\n if options.verbose:\\n print(\\\"NODATA: %s\\\" % in_nodata)\\n\\n return in_nodata\",\n \"def prepare_input_data(self, X):\\n X = np.asarray(X)\\n if X.dtype != \\\"f\\\" and X.dtype != \\\"d\\\":\\n X = X.astype(float)\\n\\n self._check_input(X)\\n missing_mask = np.isnan(X)\\n self._check_missing_value_mask(missing_mask)\\n return X, missing_mask\",\n \"def clean_station_data(station_df):\\n # TODO implement data preparation here\\n # Fix the datetime field\\n\\n # Cast to numeric fields where necessary\\n\\n # Interpolate missing data\",\n \"def data_missing() -> ExtensionArray:\\n data_matrix = np.arange(\\n 2 * 10 * 10 * 3,\\n dtype=np.float_,\\n ).reshape(2, 10, 10, 3)\\n data_matrix[0, ...] = np.NaN\\n grid_points = [\\n np.arange(10),\\n np.arange(10) / 10,\\n ]\\n\\n return skfda.FDataGrid(data_matrix, grid_points=grid_points)\",\n \"def preprocess(self):\\n raise RuntimeError(\\\"please implement this function!\\\")\",\n \"def _load_data(self):\\n logging.warning('-> loading EMPTY data...')\",\n \"def fix_data(self, df):\\n return df.dropna(axis='columns', how='all').fillna(0.0)\",\n \"def _process(self, data: np.ndarray) -> np.ndarray:\",\n \"def _process(self, data: np.ndarray) -> np.ndarray:\",\n \"def clean_data(self):\\n data_clean = []\\n for item in self.data:\\n if int(item[2]) >= self.seq_length and int(item[2]) <= self.max_frames:# and item[1] in self.classes:\\n data_clean.append(item)\\n\\n return data_clean\",\n \"def negify_missing(data_path: str) -> None:\\n csvs = [x for x in data_path.iterdir() if 'covidtimeseries' in str(x)]\\n for csv in tqdm(csvs, desc=\\\"Regions\\\"):\\n roi = str(csv).split('.')[0].split('_')[-1]\\n df = pd.read_csv(csv)\\n for kind in ['cases', 'deaths', 'recover']:\\n if df['cum_%s' % kind].sum() == 0:\\n print(\\\"Negifying 'new_%s' for %s\\\" % (kind, roi))\\n df['new_%s' % kind] = -1\\n out = data_path / (csv.name.split('.')[0]+'.csv')\\n df.to_csv(out)\",\n \"def clean(self):\\n # Perform the standard ACE cleaning\\n max_status = mm_ace.clean(self)\\n\\n # Replace bad values with NaN and remove times with no valid data\\n ecols = ['eflux_38-53', 'eflux_175-315']\\n\\n # Evaluate the electron flux data\\n self[self.data['status_e'] > max_status, ecols] = np.nan\\n\\n # Evaluate the proton flux data\\n pcols = ['pflux_47-68', 'pflux_115-195', 'pflux_310-580',\\n 'pflux_795-1193', 'pflux_1060-1900']\\n self[self.data['status_p'] > max_status, pcols] = np.nan\\n\\n # Include both fluxes and the anisotropy index in the removal eval\\n eval_cols = ecols + pcols\\n eval_cols.append('anis_ind')\\n\\n # Remove lines without any good data\\n good_cols = (np.isfinite(self.data.loc[:, eval_cols])).sum(axis=1)\\n bad_index = good_cols[good_cols == 0].index\\n self.data = self.data.drop(index=bad_index)\\n\\n return\",\n \"def pre_process_data_set(df):\\n df.replace([np.inf, -np.inf], np.nan)\\n df[df == np.inf] = np.nan\\n df = remove_bad_columns(df)\\n df = fill_na(df)\\n df = convert_factorial_to_numerical(df)\\n\\n # Remove columns only containing 0\\n df = df[(df.T != 0).any()]\\n return df\",\n \"def Reconstruct_COVID_data(COVID_data):\\r\\n COVID_data_reconstructed = {}\\r\\n COVID_data_reconstructed['_Country'] = COVID_data['_Country']\\r\\n \\r\\n Countries_list = list(COVID_data.keys())[1:]\\r\\n \\r\\n for Country in Countries_list: # For each country...\\r\\n COVID_data_single_country = list(COVID_data[Country].values()) # Extract the matrix containing the data and transpose it. That way, each element of a single list in the array corresponds to one column (see help of Main_script) and it makes it easier to navigate through each column and recontruct the missing elements\\r\\n T_COVID_data_single_country = list(map(list, zip(*COVID_data_single_country)))\\r\\n \\r\\n for Column_inc in range(len(T_COVID_data_single_country)): # For each column...\\r\\n Column = T_COVID_data_single_country[Column_inc]\\r\\n Max_column_inc = len(Column) - 1\\r\\n \\r\\n Row_inc = 0\\r\\n while Column[Row_inc] == None and Row_inc < Max_column_inc: # Recontructing missing data at the beginning is impossible so we just skip the first rows with a None in them\\r\\n Row_inc += 1\\r\\n \\r\\n \\r\\n if None in Column: # If a None is in the list (meaning there are bits of data missing)...\\r\\n while Row_inc < Max_column_inc: # Not including this line could prompt an index error\\r\\n if Column[Row_inc] == None: # When a None in encoutered...\\r\\n None_interval_start = Row_inc # Recording when the segments of None starts and ends\\r\\n \\r\\n while Column[Row_inc] == None and Row_inc < Max_column_inc:\\r\\n Row_inc += 1\\r\\n \\r\\n None_interval_end = Row_inc - 1\\r\\n Interpolation_interval_length = None_interval_end - None_interval_start + 2\\r\\n \\r\\n if Row_inc < Max_column_inc: # Reconstruction of the segment by linear interpolation : Y = mX + b with m = (Y_max - Y_min) / (X_max - X_min)\\r\\n m = (Column[None_interval_end + 1] - Column[None_interval_start - 1]) / Interpolation_interval_length\\r\\n b = Column[None_interval_start - 1]\\r\\n \\r\\n for Row_inc in range(None_interval_start, Row_inc):\\r\\n T_COVID_data_single_country[Column_inc][Row_inc] = m * (Row_inc - None_interval_start + 1) + b\\r\\n \\r\\n else: # In the case the None segment goes on until the end, the last known value is just copied\\r\\n for Row_inc in range(None_interval_start, Row_inc + 1):\\r\\n T_COVID_data_single_country[Column_inc][Row_inc] = T_COVID_data_single_country[Column_inc][None_interval_start - 1]\\r\\n \\r\\n Row_inc += 1\\r\\n \\r\\n COVID_data_single_country_reconstructed = list(map(list, zip(*T_COVID_data_single_country))) # Retranspose the matrix to get the reconstruted data in the correct format\\r\\n \\r\\n Date_list_country = list(COVID_data[Country].keys()) # Add the reconstructed data to the appropriate dictionnary\\r\\n COVID_data_reconstructed[Country] = {}\\r\\n for Date_inc in range(len(Date_list_country)):\\r\\n Date = Date_list_country[Date_inc]\\r\\n COVID_data_reconstructed[Country][Date] = COVID_data_single_country_reconstructed[Date_inc]\\r\\n \\r\\n return COVID_data_reconstructed\",\n \"def stage_one_preprocessing(data: pd.Series) -> pd.Series:\\n data_ = data.dropna()\\n print('ascii')\\n data_ = remove_non_ascii(data)\\n print('lower')\\n data_ = to_lowercase(data_)\\n print('slash')\\n data_ = underscore_and_slash_to_space(data_)\\n print('ellipse')\\n data_ = remove_ellipses(data_)\\n print('white')\\n data_ = shrink_whitespace(data_)\\n #print('contracts')\\n #data_ = remove_contractions(data_)\\n return data_\",\n \"def _preprocess_data_point(self, x: DataPoint) -> DataPoint:\\n for preprocessor in self._pre:\\n x = preprocessor(x)\\n if x is None:\\n raise ValueError(\\\"Preprocessor should not return None\\\")\\n return x\",\n \"def preprocess(self):\\n pass\",\n \"def preprocess(self):\\n pass\",\n \"def preprocess(self):\\n pass\",\n \"def data_missing_for_sorting(data_for_grouping):\\n return type(data_for_grouping)._from_sequence(\\n [data_for_grouping[0], data_for_grouping[2], data_for_grouping[4]]\\n )\",\n \"def process_data(data, engine):\\n\\n def convert(x):\\n unit = x['standard_units']\\n value = x['standard_value']\\n if unit == \\\"NM\\\":\\n return value * 1e-9\\n elif unit == \\\"-LOG(10) M\\\":\\n return 10 ** (-value)\\n else:\\n raise RuntimeError\\n\\n # Filter Na\\n data.dropna(how=\\\"any\\\", subset=USED_COLS, inplace=True)\\n # Only keep measurements that are KD related\\n data = data[data.standard_type.isin(KD_TYPES)]\\n # Only keep measurements with some defined units\\n data = data[data.standard_units.isin(KD_UNITS)]\\n # Convert to M valued units\\n data['standard_value'] = data.apply(convert, axis=1)\\n # Keep only equal relation measurements\\n data = data[data.standard_relation == '=']\\n # Remove multiple targets measurements\\n data = data[~data.target_id.str.contains(',')]\\n # Remove (target,compound) pairs with more than one measurement\\n key = ['standard_inchi_key', 'target_id']\\n grouped = data.groupby(key).size()\\n join_condition = grouped[grouped == 1].reset_index()[key]\\n data = data.merge(join_condition, on=key, how='inner')\\n # Remove outliers measurements\\n data = data[(data.standard_value <= 1.7e-3) & (data.standard_value >= 1.e-10)]\\n # Convert to PK values\\n data['standard_value'] = - np.log10(data['standard_value'])\\n # Remove samples for which the protein_id has no sequence\\n sequence_loader = SequenceLoader(engine=engine)\\n data = sequence_loader.transform(data).dropna(how=\\\"any\\\")\\n # Remove samples for which the compound_id has no inchi\\n inchi_loader = InchiLoader(engine=engine)\\n data = inchi_loader.transform(data).dropna(how='any')\\n # We will only use the following columns\\n return data[[\\\"standard_inchi_key\\\", \\\"target_id\\\", \\\"standard_inchi\\\", \\\"sequence\\\", \\\"standard_value\\\"]]\",\n \"def prepare_train_data(self):\\r\\n ## Impute rlkpis\\r\\n print(\\\"Imputing rlKPI df\\\")\\r\\n self.rlkpi.add_target_labels(1)\\r\\n self.rlkpi.impute_rl_kpis()\\r\\n\\r\\n print(\\\"Add 'met-real-station_no' & met-forecast-station_no to rl_kpis_df\\\")\\r\\n self.add_met_real_forecast_station_col_to_rlkpis()\\r\\n print(\\\"Merge 'met-real-sampled df to rl kps \\\")\\r\\n self.merge_met_real_sampled_df_to_rlkpis()\\r\\n\\r\\n ## Imputations for met-forecast\\r\\n print(\\\"Impute met-forecast\\\")\\r\\n met_forecast_obj = self.metfcast\\r\\n met_forecast_obj.impute_met_forecast()\\r\\n\\r\\n #Merge met forecast data to earlier merged data\\r\\n print(\\\"Merge Train data with imputed forecast df\\\")\\r\\n self.train_data = pd.merge(self.train_data,\\r\\n met_forecast_obj.imputed_forecast_df,\\r\\n on=['datetime-station_no'], indicator=True, how='inner')\\r\\n print(\\\"Check any imputation needed\\\", self.train_data.isna().sum().sum())\\r\\n self.train_data.drop(['_merge'], axis=1, inplace=True)\\r\\n self.perform_data_under_sampling(self.train_data)\",\n \"def _clean_data(self, stn, ignore_errors):\\n # Confirm that we got some data, and confirm that it's the\\n # expected web page by checking the <title> tag contents\\n if (not self.data) | (not self.data[0].startswith(\\n '<TITLE>Generate WXP 24-Hour Meteogram</TITLE>')):\\n if ignore_errors:\\n sys.stderr.write('Invalid data returned for '\\n '%4i-%02i-%02i\\\\n'\\n % (self.year, self.month, self.day))\\n self.data = ''\\n return\\n else:\\n raise UnexpectedPageError\\n # Get rid of the <title> and <pre> tag lines\\n self.data = self.data[2:]\\n # Confirm that data is available for the specified date\\n if self.data[0].startswith('No data were found for date'):\\n if ignore_errors:\\n sys.stderr.write('%4i-%02i-%02i data missing\\\\n'\\n % (self.year, self.month, self.day))\\n self.data = ''\\n return\\n else:\\n raise UnexpectedPageError\\n #Get rid of the station location and following blank line\\n self.data = self.data[2:]\\n # Confirm that we got the data for the expected station by\\n # checking the \\\"METAR Data for\\\" line contents\\n if not self.data[0].startswith(\\n ' '.join((\\\"METAR Data for\\\", stn))):\\n if ignore_errors:\\n sys.stderr.write('%4i-%02i-%02i data missing '\\n 'or incorrect station returned\\\\n'\\n % (self.year, self.month, self.day))\\n self.data = ''\\n return\\n else:\\n raise UnexpectedPageError\\n # Get rid of the \\\"METAR Data for\\\" line and following blank\\n # line\\n self.data = self.data[2:]\\n # Date part of timestamp for each line of data\\n datestamp = '%4i %02i %02i' % (self.year, self.month, self.day)\\n # Clean up each line\\n i = 0\\n try:\\n while True:\\n # Continuations from the previous line start with 5\\n # spaces\\n if self.data[i].startswith(' '*5):\\n # Concatenate continuation to previous line\\n self.data[i-1] = ' '.join((self.data[i-1][:-1],\\n self.data[i][5:]))\\n # Get rid of continuation text that we just consumed\\n self.data.pop(i)\\n # Get rid of file parse error lines\\n if self.data[i].startswith('SFC_parse_file:'):\\n self.data.pop(i)\\n continue\\n # Get rid of SPECI prefix\\n if self.data[i].startswith('SPECI'):\\n self.data[i] = self.data[i][6:]\\n fields = self.data[i].split()\\n # Add METAR prefix if it's missing\\n if fields[0] != 'METAR':\\n fields.insert(0, 'METAR')\\n self.data[i] = ' '.join(('METAR', self.data[i]))\\n # Add hour to timestamp, and prepend timestamp to line\\n self.data[i] = ' '.join((datestamp, fields[2][2:4],\\n self.data[i]))\\n # Get rid of duplicate data lines\\n if self.data[i] == self.data[i-1]:\\n self.data.pop(i)\\n continue\\n i += 1\\n except IndexError:\\n # No more data lines\\n pass\",\n \"def _preprocess(self, data):\\n\\n # pipeline: first call the previous statistics:\\n if self.previous_statistics is not None:\\n data = self.previous_statistics.statistics(data)\\n # the first of the statistics need to take list as input, in order to match the API. Then actually the\\n # transformations work on np.arrays. In fact the first statistic transforms the list to array. Therefore, the\\n # following code needs to be called only if the self statistic is the first, i.e. it does not have a\\n # previous_statistic element.\\n else:\\n data = self._check_and_transform_input(data)\\n\\n return data\",\n \"def _prepare_data(self):\\n #TODO hardcoded values need to change\\n print_info(\\\"Preprocessing the train data...\\\")\\n self._place_dataset(os.path.join(self._hparams[\\\"temp-data\\\"], \\\"train\\\"),\\n self.TRAIN_OUT_PATH)\\n\\n print_info(\\\"Preprocessing the test data...\\\")\\n self._place_dataset(os.path.join(self._hparams[\\\"temp-data\\\"], \\\"test\\\"),\\n self.TEST_OUT_PATH)\\n\\n print_info(\\\"Preprocessing the validation data...\\\")\\n self._place_dataset(os.path.join(self._hparams[\\\"temp-data\\\"], \\\"val\\\"),\\n self.VAL_OUT_PATH)\",\n \"def original_clean():\\n # load the data\\n dataset = np.genfromtxt(\\\"wdbc.data\\\", dtype=np.float, delimiter=',', usecols=(2, 3, 4, 5, 6, 7, 8, 9, 10, 11,\\n 12, 13, 14, 15, 16, 17, 18, 19,\\n 20, 21, 22, 23, 24, 25, 26, 27,\\n 28, 29, 30, 31), encoding=None)\\n labels = np.genfromtxt(\\\"wdbc.data\\\", dtype=None, delimiter=',', usecols=(1), encoding=None)\\n temp_labels = np.zeros(len(labels))\\n for i in range(len(labels)):\\n if labels[i] == 'B':\\n temp_labels[i] = 0\\n else:\\n temp_labels[i] = 1\\n # normalize\\n temp_data = normalize(dataset)\\n return temp_data, temp_labels\",\n \"def discard_temp_data(self):\\n self.data = None\\n self.points = None\",\n \"def check_Data(self):\\r\\n \\r\\n if self._target_data is None:\\r\\n self.processData()\",\n \"def test_missing_column(self, example_dataset):\\n\\n example_dataset = example_dataset.drop([\\\"Sex\\\"], axis=1)\\n transformer = PreprocessFeatures()\\n\\n with pytest.raises(ValueError):\\n transformer.fit_transform(example_dataset)\",\n \"def pre_process_data(self, all_labels, all_data):\\n\\n # [1] Normalizes data\\n all_data = self.pre_precess_manager.normalization(all_data)\\n\\n data_train, data_test, label_train, label_test = train_test_split(all_data, all_labels, test_size=0.1,\\n shuffle=True)\\n\\n return data_train, data_test, label_train, label_test\",\n \"def test_batch_missing_data():\\n\\n\\n def fx(x, dt):\\n F = np.array([[1, dt, 0, 0],\\n [0, 1, 0, 0],\\n [0, 0, 1, dt],\\n [0, 0, 0, 1]], dtype=float)\\n\\n return np.dot(F, x)\\n\\n def hx(x):\\n return np.array([x[0], x[2]])\\n\\n\\n dt = 0.1\\n points = MerweScaledSigmaPoints(4, .1, 2., -1)\\n kf = UKF(dim_x=4, dim_z=2, dt=dt, fx=fx, hx=hx, points=points)\\n\\n\\n kf.x = np.array([-1., 1., -1., 1])\\n kf.P*=0.0001\\n\\n zs = []\\n for i in range(20):\\n z = np.array([i+randn()*0.1, i+randn()*0.1])\\n zs.append(z)\\n\\n zs[2] = None\\n Rs = [1]*len(zs)\\n Rs[2] = None\\n Ms, Ps = kf.batch_filter(zs)\",\n \"def preprocessing(train_raw_, test_raw_):\\n \\n undef = np.float64(-999.0)\\n pred_dict = {'s':'1','b':'0', '?':'-1'}\\n # drop 1st column (Id) and also 1st row with column names (\\\"[1:,\\\") \\n train_raw = train_raw_[1:, :]\\n test_raw = test_raw_[1:, :] \\n \\n # Change s(signal) and b(background) for s:1 and b:0, and change '?' for -1\\n train_raw[:,1] = np.vectorize(pred_dict.get)(train_raw[:,1].astype(str))\\n test_raw[:,1] = np.vectorize(pred_dict.get)(test_raw[:,1].astype(str))\\n \\n # Divide the dataset in four according to PRI_jet_num feature and cast to float\\n train_data_jets = divide_dataset_by_jet(train_raw)\\n test_data_jets = divide_dataset_by_jet(test_raw)\\n \\n # Remove columns with nan values or with standard deviation of 0\\n test_data_jets, train_data_jets = clean_features(test_data_jets, train_data_jets, undef)\\n \\n # Standardize train and test sets to have mean=0 and std=1\\n train_data_jets, test_data_jets = standardize(train_data_jets, test_data_jets)\\n \\n # Replace remaining undefined values by mean, median or zero\\n train_data_mean, train_data_median, train_data_null = replace_nan(train_data_jets)\\n test_data_mean, test_data_median, test_data_null = replace_nan(test_data_jets)\\n \\n return train_data_mean, train_data_median, train_data_null, test_data_mean, test_data_median, test_data_null\",\n \"def exclude_empty_feats(self):\\n for dataset in self:\\n dataset.dropna(axis=1, how=\\\"all\\\", inplace=True)\",\n \"def handle_missing_values(dataset, missing_values_header, missing_label):\\n\\n return dataset[dataset[missing_values_header] != missing_label]\",\n \"def process_data_impl(\\n self,\\n data_dir: Path,\\n output_processed_data_dir: Path,\\n ) -> NoReturn:\\n pass\",\n \"def import_clean_process():\\n # loading the co2 emissions data for the Earth, I'm only interested in the\\n # total emissions and the year\\n global_co2 = pd.read_csv(\\n \\\"datasets/Global CO2 Emissions.csv\\\",\\n usecols=[\\n \\\"Year\\\",\\n \\\"Total\\\"\\n ],\\n parse_dates=[\\\"Year\\\"],\\n index_col=\\\"Year\\\"\\n )\\n # creating the global temperature dataframe\\n global_temp_data = open(\\n \\\"datasets/CRUTEM.4.6.0.0.global_n+s\\\",\\n \\\"r\\\"\\n )\\n global_temp = pd.DataFrame(\\n {\\n \\\"global_temp\\\": [],\\n }\\n )\\n for line in global_temp_data:\\n # each line in the file is an observation for the year, the first\\n # column being the year, the second being the temperature measurement\\n data = line.split()\\n global_temp.at[pd.to_datetime(data[0]), \\\"global_temp\\\"] = float(data[1])\\n global_temp_data.close()\\n # loading the co2 emissions data for the UK\\n uk_co2 = pd.read_csv(\\n \\\"datasets/UK carbon dioxide emissions between 1858 to 2017 .csv\\\",\\n parse_dates=[\\\"Date\\\"],\\n index_col=\\\"Date\\\"\\n )\\n # creating the dataframe for the UK temperature data\\n uk_temp = pd.DataFrame(\\n {\\n \\\"uk_temp\\\": [],\\n }\\n )\\n # this file consists of monthly and seasonal averages for the UK surface\\n # temperature\\n uk_tmean = open(\\n \\\"datasets/UK Mean Temperature (Degrees C)\\\",\\n \\\"r\\\"\\n )\\n for index, line in enumerate(uk_tmean):\\n # the data begins on the eigth line in the file\\n if index > 7:\\n data = line.split()\\n # the monthly temperatures are from the 2nd and 13th columns\\n month_temps = np.array(data[1:13]).astype(float)\\n # the first reading is the year, I've taken the average of all the\\n # months to get an annual average\\n uk_temp.at[pd.to_datetime(data[0]), \\\"uk_temp\\\"] = month_temps.mean()\\n uk_tmean.close()\\n # removing the temperature reading for 2019 as it isn't averaged over the\\n # whole year (this program was written in 06/2019)\\n uk_temp = uk_temp[:-1]\\n # merging the temperature and co2 emissions dataframes for the Earth\\n global_data = pd.merge(\\n global_temp,\\n global_co2,\\n left_index=True,\\n right_index=True,\\n how=\\\"outer\\\"\\n )\\n # merging the temperature and co2 emissions dataframes for the UK\\n uk_data = pd.merge(\\n uk_temp,\\n uk_co2,\\n left_index=True,\\n right_index=True,\\n how=\\\"outer\\\"\\n )\\n # merging the global and UK dataframes\\n df_data = pd.merge(\\n global_data,\\n uk_data,\\n left_index=True,\\n right_index=True,\\n how=\\\"outer\\\"\\n )\\n # rename some of the columns to make them more clear\\n df_data = df_data.rename(\\n columns={\\n \\\"Total\\\": \\\"global_co2\\\",\\n \\\"CO2 Emissions\\\": \\\"uk_co2\\\"\\n }\\n )\\n return df_data\",\n \"def _fillna_meta_cols(self):\\n for col_name, fill_value in self._fillna.items():\\n if col_name in self._hybrid_meta.columns:\\n self._hybrid_meta[col_name].fillna(fill_value, inplace=True)\\n else:\\n self.__warn_missing_col(col_name, action='fill')\\n\\n self._hybrid_meta[self.__solar_rpi_n].fillna(-1, inplace=True)\\n self._hybrid_meta[self.__wind_rpi_n].fillna(-1, inplace=True)\",\n \"def fill_missing_data_points(data):\\n return data.interpolate()\",\n \"def clean_features(test_data, train_data, undef):\\n \\n test_set = test_data\\n train_set = train_data\\n \\n # Preprocessing of the four jet datasets\\n for jet in range(4):\\n \\n # Remove columns full of undefined values (-999.0 in the datase)\\n train_set[jet][train_set[jet] <= undef] = np.nan # replace undefined values -999 by NaN\\n train_set[jet] = train_set[jet][:, ~np.all(np.isnan(train_set[jet]), axis=0)]\\n test_set[jet][test_set[jet] <= undef] = np.nan # replace undefined values -999 by NaN\\n test_set[jet] = test_set[jet][:, ~np.all(np.isnan(test_set[jet]), axis=0)]\\n\\n # Remove columns without standard deviation to remove column full of the same value\\n train_id_pred = train_set[jet][:,0:2]\\n train_features = train_set[jet][:,2:]\\n train_features = train_features[:, np.nanstd(train_features, axis=0) != 0]\\n train_set[jet] = np.concatenate((train_id_pred, train_features), axis=1)\\n \\n # Extract prediction column of test before because it has std=0, and re-insert it after at the begining of the table\\n test_id_pred = test_set[jet][:,0:2]\\n test_features = test_set[jet][:,2:]\\n test_features = test_features[:, np.nanstd(test_features, axis=0) != 0]\\n test_set[jet] = np.concatenate((test_id_pred, test_features), axis=1)\\n \\n return test_set, train_set\",\n \"def dataset_NER_prepocess(dataset):\\n preprocessed = []\\n\\n try:\\n preprocessed = stop_word_remove(dataset)\\n\\n if not preprocessed:\\n preprocessed = dataset\\n\\n preprocessed = adverb_remove(dataset)\\n\\n if not preprocessed:\\n preprocessed = dataset\\n\\n preprocessed = verb_remove(dataset)\\n\\n if not preprocessed:\\n preprocessed = dataset\\n preprocessed = adjective_remove(dataset)\\n\\n if not preprocessed:\\n preprocessed = dataset\\n\\n preprocessed = special_symbols_remove(dataset)\\n\\n except Exception as e:\\n print(e)\\n return None\\n\\n return preprocessed\",\n \"def simulate_missing_data(self):\\n num_samples_to_be_removed = round(len(self.original_data) * (self.missing_data_percentage / 100))\\n\\n new_database = list(self.original_data)\\n\\n for i in range(0, num_samples_to_be_removed):\\n while True:\\n rand_num = random.randint(0, len(new_database) - 1)\\n\\n if not np.isnan(new_database[rand_num]):\\n new_database[rand_num] = np.NaN\\n break\\n\\n return new_database\",\n \"def preprocess_lab_data(self):\\n print(\\\"Processing dataset from the labs ...\\\")\\n self.full_dataset[\\\"score\\\"] = self.full_dataset[\\\"score\\\"].apply(lambda x: -1 if x==0 else 1)\",\n \"def preprocess(self):\",\n \"def inf(self, data):\\n self.set_data(data)\\n self.infer_post_and_update_samples()\",\n \"def handel_missing_values(dataset, missing_values_header, missing_label):\\n \\n return dataset[dataset[missing_values_header] != missing_label]\",\n \"def preprocess(data, to_drop=[]):\\n \\n columns = data.columns.to_list()\\n \\n # split data to numeric vs categorical\\n numeric_features = data.select_dtypes(include=[\\n 'int64', 'float64']).columns\\n \\n if len(to_drop) > 0:\\n categorical_features = data.select_dtypes(include=[\\n 'object']).drop(to_drop, axis=1).columns\\n print(categorical_features)\\n else: \\n categorical_features = data.select_dtypes(include=[\\n 'object']).columns\\n \\n categorical_transformer = Pipeline(steps=[\\n ('imputer', SimpleImputer(strategy='most_frequent', fill_value='missing'))])\\n \\n numerical_transformer = Pipeline(steps=[\\n ('imputer', SimpleImputer(strategy='median')),\\n ('scaler', RobustScaler())\\n ])\\n # missing_values = np.nan\\n \\n# Bundle preprocessing for numerical and categorical data\\n preprocessor = ColumnTransformer(\\n transformers=[\\n ('num', numerical_transformer, numeric_features),\\n ('cat', categorical_transformer, categorical_features)\\n ])\\n\\n my_pipeline = Pipeline(steps=[('preprocessor', preprocessor) ])\\n \\n for col in to_drop:\\n columns.remove(col)\\n print('Hello')\\n \\n trans_data = my_pipeline.fit_transform(data)\\n return trans_data#pd.DataFrame(#, columns=columns) \",\n \"def stage_two_preprocessing(data: pd.Series) -> pd.Series:\\n # designed to be run after remove_contractions\\n data_ = data.dropna()\\n data_ = remove_punctuation(data_)\\n data_ = numbers_to_words(data_)\\n data_ = remove_stopwords(data_)\\n return data_\",\n \"def training_data_preprocessing(raw_data, num_passed_rows=72):\\r\\n # some samples have errors\\r\\n raw_data = raw_data[num_passed_rows:].reset_index(drop=True) \\r\\n \\r\\n # get data output\\r\\n data_output = raw_data[['Submitby Date Time', 'Challenge Manager', 'Challenge Copilot', 'Posting Date Date', 'Track',\\r\\n 'Technology List', 'First Place Prize', 'Num Registrations', 'Total Prize']]\\r\\n data_output, extended_columns = class_binaryzation(data_output)\\r\\n \\r\\n # save extended columns to cache\\r\\n extended_columns_filepath = 'cache/extended_columns.pkl'\\r\\n with open(extended_columns_filepath, 'wb') as f:\\r\\n pickle.dump(extended_columns, f)\\r\\n\\r\\n num_date_columns_filepath = 'cache/num_date_columns.pkl'\\r\\n try:\\r\\n data_output = date_separation1(data_output) \\r\\n with open(num_date_columns_filepath, 'wb') as f:\\r\\n pickle.dump(6, f)\\r\\n\\r\\n except:\\r\\n data_output = date_separation2(data_output)\\r\\n with open(num_date_columns_filepath, 'wb') as f:\\r\\n pickle.dump(5, f)\\r\\n\\r\\n data_output = money_digitalization(data_output)\\r\\n data_output = get_date_in_days(data_output)\\r\\n data_output['Days from Posting to Submit'] = data_output['Submitby Date Time Days from 2016'] \\\\\\r\\n - data_output['Posting Date Date Days from 2016'] \\r\\n \\r\\n # get other output\\r\\n label_output = pd.DataFrame(columns=['Success'])\\r\\n success_output = pd.DataFrame(columns=data_output.columns)\\r\\n failure_output = pd.DataFrame(columns=data_output.columns)\\r\\n for i in range(len(raw_data)):\\r\\n if raw_data.loc[i, 'Num Submissions Passed Review'] >= 1:\\r\\n label_output.loc[i, 'Success'] = 1\\r\\n success_output.loc[len(success_output)] = data_output.loc[i]\\r\\n else:\\r\\n label_output.loc[i, 'Success'] = 0\\r\\n failure_output.loc[len(failure_output)] = data_output.loc[i]\\r\\n\\r\\n return data_output, label_output, success_output, failure_output, extended_columns\",\n \"def cleanData(data):\\r\\n \\r\\n indexes = data.index[pd.isnull(data[\\\"release date\\\"])]\\r\\n # data = data.drop(data.index[[2172, 3781, 7245, 12475, 14756, 15292, 49295, 93523, 99723]],axis=0,inplace=False)\\r\\n data = data.drop(indexes,axis=0,inplace=False)\\r\\n data = data.drop([\\\"video release date\\\", 'IMDb URL'],axis=1)\\r\\n \\r\\n \\r\\n data.gender = data.gender.astype(\\\"category\\\")\\r\\n data.occupation = data.occupation.astype(\\\"category\\\")\\r\\n \\r\\n categorical_variables = [\\\"gender\\\", \\\"occupation\\\"]\\r\\n for variable in categorical_variables:\\r\\n dummies = pd.get_dummies(data[variable], prefix=variable)\\r\\n data = pd.concat([data, dummies], axis=1)\\r\\n data.drop([variable], axis=1, inplace=True)\\r\\n data['movieID'] = data['movieID'].astype('int')\\r\\n data['userID'] = data['userID'].astype('int')\\r\\n \\r\\n\\t#data = dateTime(data)\\r\\n \\r\\n return data\",\n \"def process_drought_data():\\n raw_data_dir = basedir + '/app/static/data/raw'\\n raw_data_files = os.listdir(raw_data_dir)\\n\\n if raw_data_files:\\n print 'Raw data files to process:'\\n print raw_data_files\\n print ''\\n print 'Total to process: %d' % len(raw_data_files)\\n\\n count = 1\\n for raw_file in raw_data_files:\\n print 'Processing %d of %d raw files.' % (count, len(raw_data_files))\\n\\n # opens raw file to read data\\n readfile = open(basedir + '/app/static/data/raw/' + raw_file, 'r')\\n reader = csv.reader(readfile, delimiter=',')\\n\\n # sets up processed file for writing\\n raw_file_name = raw_file.split('.')[0]\\n out_file_name = raw_file_name + '.csv'\\n writefile = open(basedir + '/app/static/data/saved/' + out_file_name, 'w')\\n writer = csv.writer(writefile, delimiter=',')\\n writer.writerow(['id', 'level'])\\n\\n for line in reader:\\n if line[1] == 'FIPS':\\n pass\\n else:\\n fips = int(line[1])\\n\\n level = 0\\n if float(line[9]) > 0:\\n level = 5\\n elif float(line[8]) > 0:\\n level = 4\\n elif float(line[7]) > 0:\\n level = 3\\n elif float(line[6]) > 0:\\n level = 2\\n elif float(line[5]) > 0:\\n level = 1\\n\\n writer.writerow([fips, level])\\n\\n writefile.close()\\n readfile.close()\\n os.remove(basedir + '/app/static/data/raw/' + raw_file)\\n count += 1\\n\\n else:\\n print 'All files have been processed.'\",\n \"def test_null_from_data(self):\\n # define prior data that R will use\\n data = array([[ 4., 6., 5., 4., 3.],\\n [ 5., 3., 11., 17., 2.],\\n [ 8., 4., 4., 13., 0.],\\n [ 0., 0., 9., 8., 10.],\\n [ 19., 8., 3., 8., 1.],\\n [ 5., 1., 14., 4., 3.],\\n [ 2., 7., 3., 4., 0.],\\n [ 5., 5., 5., 14., 6.],\\n [ 2., 3., 6., 0., 5.],\\n [ 6., 1., 1., 2., 2.]])\\n Rseed = 0\\n tpk = 10\\n actual_out = null_from_data(data, tpk, Rseed=Rseed)\\n expected_out = array([[ 18., 1., 0., 3., 0.],\\n [ 2., 10., 10., 3., 22.],\\n [ 1., 8., 4., 1., 12.],\\n [ 10., 1., 5., 1., 2.],\\n [ 8., 0., 14., 28., 5.],\\n [ 5., 16., 3., 11., 0.],\\n [ 0., 2., 8., 2., 0.],\\n [ 0., 12., 2., 1., 3.],\\n [ 0., 0., 6., 2., 5.],\\n [ 8., 2., 0., 0., 3.]])\\n self.assertEqual(expected_out, actual_out)\",\n \"def test_fill_missing_value(self):\\n try:\\n # test_size need to be a positive float number\\n fill_missing_value(DATA, NEEDED, INPUTS, OUTPUT, test_size=-1.0)\\n raise Exception(\\\"Illegal input test failed!\\\")\\n except AssertionError:\\n pass\\n\\n try:\\n # n_estimators need to be a positive integer\\n fill_missing_value(DATA, NEEDED, INPUTS, OUTPUT, n_estimators=-1)\\n except AssertionError:\\n pass\\n\\n try:\\n # max_depth need to be a positive integer\\n fill_missing_value(DATA, NEEDED, INPUTS, OUTPUT, max_depth=1.2)\\n except AssertionError:\\n pass\\n\\n filled_data = fill_missing_value(DATA, NEEDED, INPUTS, OUTPUT)\\n\\n assert isinstance(filled_data, pd.DataFrame),\\\\\\n \\\"Type error. pd.DataFrame expected\\\"\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6957376","0.6862694","0.6733841","0.6647942","0.6541775","0.6535181","0.64431393","0.6377302","0.63732034","0.6310155","0.62766635","0.6204777","0.6177998","0.6141851","0.6130127","0.6126659","0.60847795","0.6082589","0.6067625","0.6064571","0.6064571","0.6053381","0.6040101","0.60358316","0.6021435","0.6002188","0.5990915","0.59847593","0.5964546","0.5942631","0.59417605","0.5934581","0.59339327","0.59314","0.5898035","0.58967555","0.5895207","0.58784753","0.5867012","0.58639693","0.5852612","0.5847721","0.58381754","0.5822798","0.58168","0.58068395","0.58040535","0.57946306","0.57925874","0.57909554","0.577671","0.57516426","0.5749621","0.5738224","0.57310134","0.57212347","0.57212347","0.57167304","0.57153636","0.5705897","0.57003784","0.5689392","0.5677469","0.5674952","0.56684995","0.56684995","0.56684995","0.5667884","0.566509","0.56643784","0.5660927","0.5659118","0.56533563","0.56405103","0.56360376","0.562305","0.5618987","0.56160474","0.5607064","0.5604651","0.56037265","0.55961597","0.55827975","0.558019","0.55751395","0.55743754","0.55733854","0.5567157","0.5563186","0.55620635","0.5553463","0.55524635","0.55509883","0.55464655","0.55344987","0.5530679","0.5528709","0.55181205","0.55178547","0.55146736"],"string":"[\n \"0.6957376\",\n \"0.6862694\",\n \"0.6733841\",\n \"0.6647942\",\n \"0.6541775\",\n \"0.6535181\",\n \"0.64431393\",\n \"0.6377302\",\n \"0.63732034\",\n \"0.6310155\",\n \"0.62766635\",\n \"0.6204777\",\n \"0.6177998\",\n \"0.6141851\",\n \"0.6130127\",\n \"0.6126659\",\n \"0.60847795\",\n \"0.6082589\",\n \"0.6067625\",\n \"0.6064571\",\n \"0.6064571\",\n \"0.6053381\",\n \"0.6040101\",\n \"0.60358316\",\n \"0.6021435\",\n \"0.6002188\",\n \"0.5990915\",\n \"0.59847593\",\n \"0.5964546\",\n \"0.5942631\",\n \"0.59417605\",\n \"0.5934581\",\n \"0.59339327\",\n \"0.59314\",\n \"0.5898035\",\n \"0.58967555\",\n \"0.5895207\",\n \"0.58784753\",\n \"0.5867012\",\n \"0.58639693\",\n \"0.5852612\",\n \"0.5847721\",\n \"0.58381754\",\n \"0.5822798\",\n \"0.58168\",\n \"0.58068395\",\n \"0.58040535\",\n \"0.57946306\",\n \"0.57925874\",\n \"0.57909554\",\n \"0.577671\",\n \"0.57516426\",\n \"0.5749621\",\n \"0.5738224\",\n \"0.57310134\",\n \"0.57212347\",\n \"0.57212347\",\n \"0.57167304\",\n \"0.57153636\",\n \"0.5705897\",\n \"0.57003784\",\n \"0.5689392\",\n \"0.5677469\",\n \"0.5674952\",\n \"0.56684995\",\n \"0.56684995\",\n \"0.56684995\",\n \"0.5667884\",\n \"0.566509\",\n \"0.56643784\",\n \"0.5660927\",\n \"0.5659118\",\n \"0.56533563\",\n \"0.56405103\",\n \"0.56360376\",\n \"0.562305\",\n \"0.5618987\",\n \"0.56160474\",\n \"0.5607064\",\n \"0.5604651\",\n \"0.56037265\",\n \"0.55961597\",\n \"0.55827975\",\n \"0.558019\",\n \"0.55751395\",\n \"0.55743754\",\n \"0.55733854\",\n \"0.5567157\",\n \"0.5563186\",\n \"0.55620635\",\n \"0.5553463\",\n \"0.55524635\",\n \"0.55509883\",\n \"0.55464655\",\n \"0.55344987\",\n \"0.5530679\",\n \"0.5528709\",\n \"0.55181205\",\n \"0.55178547\",\n \"0.55146736\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5833492"},"document_rank":{"kind":"string","value":"43"}}},{"rowIdx":16899,"cells":{"query":{"kind":"string","value":"A passing grade in the Ricci data is defined as any grade above a 70 in the combined oral and written score. (See Miao 2010.)"},"document":{"kind":"string","value":"def passing_grade(row):\n if row['Combine'] >= 70.0:\n return 1\n else:\n return 0"},"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 grade(self):\n if round(self.numAvg,0) >= 70:\n return round(self.numAvg,0)\n elif self.PassSummer:\n return 70\n elif round(self.numAvg,0) >= 55 and not self.PassSummer:\n return round(self.numAvg,0)\n else:\n return 55","def calc_grade(self, average):\n if 95 <= average:\n return 'S'\n elif 90 <= average:\n return 'A'\n elif 80 <= average:\n return 'B'\n elif 70 <= average:\n return 'C'\n elif 60 <= average:\n return 'D'\n else:\n return 'F'","def computeGrades(e1, e2, a):\n \n a = assignmentScores\n a.sort()\n i=0\n while i<10:\n sum+=sum a[i]\n avg = sum/10\n \n grade = ((e1 + e2) /2) * 0.4 + (avg) * 0.6\n \n return grade\n \n if grade >= 90 and grade <= 100:\n return(\"A\")\n \n elif grade >= 80 and grade < 90:\n return(\"B\")\n \n elif grade >= 70 and grade < 80:\n return(\"C\")\n \n elif grade >= 60 and grade < 70:\n return(\"D\")\n \n elif grade < 60:\n return(\"F\")","def calculate_gpa(score):\n if score < 60:\n return 0\n elif 60 <= score < 70:\n return 1\n elif 70 <= score < 80:\n return 2\n elif 80 <= score < 90:\n return 3\n elif score >= 90:\n return 4","def grade_calculate_grade(self):\n try:\n if int(self.root.ids.grade_input_grade.text) >= 85:\n grade = 'High Distinction'\n elif int(self.root.ids.grade_input_grade.text) >= 75:\n grade = 'Distinction'\n elif int(self.root.ids.grade_input_grade.text) >= 65:\n grade = 'Credit'\n elif int(self.root.ids.grade_input_grade.text) >= 50:\n grade = 'Pass'\n else:\n grade = 'Fail'\n self.root.ids.grade_output_label.text = 'Grade: ' + grade\n except ValueError:\n\n self.root.ids.grade_output_label.text = 'Invalid Grade'","def grade(first, second, third):\n score = round((first + second + third) / 3, 1)\n score_secondary = str(score)\n plusminus = int(score_secondary[1])\n if score > 100:\n return \"Wrong input, friend.\"\n else:\n if score >= 90:\n if plusminus >= 5:\n return \"A+\"\n else:\n return \"A-\"\n elif score >= 80:\n if plusminus >= 5:\n return \"B+\"\n else:\n return \"B-\"\n elif score >= 70:\n if plusminus >= 5:\n return \"C+\"\n else:\n return \"C-\"\n elif score >= 60:\n if plusminus >= 5:\n return \"D+\"\n else:\n return \"D-\"\n else:\n return \"F\"","def calc_score(score):\n if not score:\n return 0\n dbot_score = 1\n if score >= 95:\n dbot_score = 3\n elif score >= 75:\n dbot_score = 2\n return dbot_score","def g_score(self):\n _, _, I_CK = self._entropies()\n return 2.0 * I_CK","def get_grade(self) -> int :\n return self.grade","def test_grade(grade_list):\n num_grade=scale_grade(grade_list)\n trend=[]\n for ind in range(len(num_grade)-1):\n if num_grade[ind+1]>num_grade[ind]:\n trend.append(1)\n elif num_grade[ind+1]<num_grade[ind]:\n trend.append(-1)\n else:\n trend.append(0)\n \n if sum(trend)>0:\n return 1\n elif sum(trend)<0:\n return -1\n else:\n return 0","def test_classify_grade(self):\n\t\ts = Student_Analytics()\n\t\tself.assertEqual(s.classify_grade(5.00),\"A+\")","def test_a_grade(self):\r\n self.basic_setup()\r\n self.submit_question_answer('p1', {'2_1': 'Correct'})\r\n self.submit_question_answer('p2', {'2_1': 'Correct'})\r\n self.submit_question_answer('p3', {'2_1': 'Correct'})\r\n self.check_grade_percent(1.0)\r\n self.assertEqual(self.get_grade_summary()['grade'], 'A')","def grade_for_percentage(grade_cutoffs, percentage):\r\n\r\n letter_grade = None\r\n\r\n # Possible grades, sorted in descending order of score\r\n descending_grades = sorted(grade_cutoffs, key=lambda x: grade_cutoffs[x], reverse=True)\r\n for possible_grade in descending_grades:\r\n if percentage >= grade_cutoffs[possible_grade]:\r\n letter_grade = possible_grade\r\n break\r\n\r\n return letter_grade","def is_good_qualtiative_example(iaa_score, ann1_total, ann2_total):\n return iaa_score > .3 and iaa_score < 1 and ann1_total > 3 and ann2_total > 3","def test_b_grade_above(self):\r\n self.basic_setup()\r\n self.submit_question_answer('p1', {'2_1': 'Correct'})\r\n self.submit_question_answer('p2', {'2_1': 'Correct'})\r\n self.check_grade_percent(0.67)\r\n self.assertEqual(self.get_grade_summary()['grade'], 'B')","def grade(self, points):\n credit = -1\n while credit > points or credit < 0:\n try:\n credit = int(input(\"\\nScore out of \" + str(points) + \": \"))\n except:\n credit = -1\n if credit != points:\n self.feedback += \"\\n\\t\" + str(raw_input(\"Describe problem: \"))\n return credit","def grade_conversion(grade):\n grade_converter = {\"A\": 4.00, \"A-\":3.67, \"B+\": 3.33, \"B\": 3.00, \"B-\": 2.67, \"C+\": 2.33, \"C\": 2.00, \"C-\": 1.67, \"D\": 1.00, \"F\": 0.0}\n while True:\n for val, val2 in grade_converter.items():\n if grade == val:\n return val2","def RScore(x,p,d):\n \n if x <= d[p][0.20]:\n return 5\n elif x <= d[p][0.4]:\n return 4\n elif x <= d[p][0.6]: \n return 3\n elif x <= d[p][0.8]:\n return 2\n else:\n return 1","def RScore(x,p,d):\n \n if x <= d[p][0.20]:\n return 5\n elif x <= d[p][0.4]:\n return 4\n elif x <= d[p][0.6]: \n return 3\n elif x <= d[p][0.8]:\n return 2\n else:\n return 1","def grade_to_gpa(grade):\n\n letter_grade = \"\"\n gpa = 0.0\n\n if type(grade) is str:\n accepted_values = [\"A+\", \"A\", \"A-\", \"B+\", \"B\", \"B-\", \"FZ\"]\n\n # check that the grade is one of the accepted values\n if grade in accepted_values:\n\n # assign grade to letter_grade\n letter_grade = grade\n\n #If grade input is a string, but not an accepted value, raise a ValueError\n else:\n raise ValueError(\"Incorrect value. Grade must be an accepted letter grade.\")\n\n elif type(grade) is int:\n\n # check that grade is in the accepted range 0 to 100\n if 0 <= grade <= 100:\n\n # convert the numeric grade to a letter grade\n mark_to_letter = grade\n\n # assign the value to letter_grade\n # hint: letter_grade = mark_to_letter(grade)\n if mark_to_letter >= 90:\n letter_grade = \"A+\"\n elif mark_to_letter >= 85:\n letter_grade = \"A\"\n elif mark_to_letter >= 80:\n letter_grade = \"A-\"\n elif mark_to_letter >= 77:\n letter_grade = \"B+\"\n elif mark_to_letter >= 73:\n letter_grade = \"B\"\n elif mark_to_letter >= 70:\n letter_grade = \"B-\"\n else:\n letter_grade = \"FZ\"\n\n #If grade input is not in accepted range, raise ValueError\n else:\n raise ValueError(\"Incorrect value. Grade must be in the accepted range of 0 to 100.\")\n else:\n # raise a TypeError exception\n raise TypeError(\"Invalid type passed as parameter\")\n\n # write a long if-statement to convert letter_grade\n # assign the value to gpa\n if letter_grade == \"A+\":\n gpa = 4.0\n if letter_grade == \"A\":\n gpa = 4.0\n if letter_grade == \"A-\":\n gpa = 3.7\n if letter_grade == \"B+\":\n gpa = 3.3\n if letter_grade == \"B\":\n gpa = 3.0\n if letter_grade == \"B-\":\n gpa = 2.7\n if letter_grade == \"FZ\":\n gpa = 0.0\n\n #Return the gpa of the grade\n return gpa","def publish_grade(self):\r\n score = self.lcp.get_score()\r\n self.runtime.publish(\r\n self,\r\n 'grade',\r\n {\r\n 'value': score['score'],\r\n 'max_value': score['total'],\r\n }\r\n )\r\n\r\n return {'grade': score['score'], 'max_grade': score['total']}","def get_score(self, red_score, blue_score):\n if red_score < blue_score:\n return 0\n elif red_score > blue_score:\n return 1\n else:\n return 0.5","def credits_earned(self):\n\n if self.grade() >= 69.5:\n return self.nCredits\n else:\n return 0.0","def test_grade(self):\r\n # Sample variables x and y in the range [-10, 10]\r\n sample_dict = {'x': (-10, 10), 'y': (-10, 10)}\r\n\r\n # The expected solution is numerically equivalent to x+2y\r\n problem = self.build_problem(sample_dict=sample_dict,\r\n num_samples=10,\r\n tolerance=0.01,\r\n answer=\"x+2*y\")\r\n\r\n # Expect an equivalent formula to be marked correct\r\n # 2x - x + y + y = x + 2y\r\n input_formula = \"2*x - x + y + y\"\r\n self.assert_grade(problem, input_formula, \"correct\")\r\n\r\n # Expect an incorrect formula to be marked incorrect\r\n # x + y != x + 2y\r\n input_formula = \"x + y\"\r\n self.assert_grade(problem, input_formula, \"incorrect\")","def testDriver():\n exam1=90\n exam2=85\n assignmentScores = [50, 60, 70, 80, ]\n computeGrades(exam1, exam2, assignmentScores)","def average_grade(self):\n grade_sum = 0\n grades_length = 0\n for c in self.courses_grades:\n if c[1] != \"-\":\n grade_sum += int(c[1])\n grades_length += 1\n average = grade_sum / grades_length\n return average","def get_grade(self):\n return self.__grade_value","def base_contribute_score():\n return 1","def _score_to_decision(self, score):","def grade_opbg(score, breakpoints=[0,5,10,20,50,200], grades=list(range(7))):\n i = bisect.bisect_left(breakpoints, score)\n return grades[i]","def get_grade(course_det):\n return course_det[1]","def level(score):\n user_level = \"\"\n if score < 20:\n user_level = \"elementary\"\n elif score < 30:\n user_level = \"intermediate\"\n elif score < 35:\n user_level = \"upper intermediate\"\n else:\n user_level = \"advanced\"\n return user_level","def main():\n given_scores = []\n num_grades = int(raw_input())\n for i in xrange(num_grades):\n given_scores.append(int(raw_input()))\n for score in grading_students(given_scores):\n print score","def scoring(self):\n pass","def stateQualtityScore(roomba):\n return 0","def get_score_bucket(grade, max_grade):\r\n score_bucket = \"incorrect\"\r\n if(grade > 0 and grade < max_grade):\r\n score_bucket = \"partial\"\r\n elif(grade == max_grade):\r\n score_bucket = \"correct\"\r\n\r\n return score_bucket","def vanilaScore(self,attended,state,W):","def get_score(self, a, b):\n ### FILL IN ###","def f1_score(self):","def letter_grades(adict):\n\n for key in adict:\n\t if adict[key] >= 90:\n\t\t adict[key] = 'A'\n\t elif 80 <= adict[key] < 90:\n\t\t adict[key] = 'B'\n\t elif 70 <= adict[key] < 80:\n\t\t adict[key] = 'C'\n\t elif 60 <= adict[key] < 70:\n\t\t adict[key] = 'D'\n\t else:\n\t\t adict[key] = 'F'","def arsenalResults(dat):\n arsScore = int(dat[0])\n othScore = int(dat[2])\n if arsScore > othScore:\n res = 1\n elif arsScore == othScore:\n res = 2\n else:\n res = 0\n return res","def rating(grade_count):\r\n if grade_count == 0 :\r\n grade = 7\r\n else:\r\n grade = 2\r\n \r\n return grade","def AmOppCr(_cmp, e87482, e87487, e87492, e87497):\n\n \"\"\"\n This function calculates American Opportunity Credit\n for up to four eligible students\n\n \"\"\"\n\n # Expense should not exceed the cap of $4000.\n if _cmp == 1:\n\n c87482 = max(0., min(e87482, 4000.))\n c87487 = max(0., min(e87487, 4000.))\n c87492 = max(0., min(e87492, 4000.))\n c87497 = max(0., min(e87497, 4000.))\n else:\n c87482, c87487, c87492, c87497 = 0., 0., 0., 0.\n\n # Credit calculated as 100% of the first $2000 expense plus\n # 25% of amount exceeding $2000.\n if max(0, c87482 - 2000) == 0:\n c87483 = c87482\n else:\n c87483 = 2000 + 0.25 * max(0, c87482 - 2000)\n\n if max(0, c87487 - 2000) == 0:\n c87488 = c87487\n else:\n c87488 = 2000 + 0.25 * max(0, c87487 - 2000)\n\n if max(0, c87492 - 2000) == 0:\n c87493 = c87492\n else:\n c87493 = 2000 + 0.25 * max(0, c87492 - 2000)\n\n if max(0, c87497 - 2000) == 0:\n c87498 = c87497\n else:\n c87498 = 2000 + 0.25 * max(0, c87497 - 2000)\n\n # Sum of credits of all four students.\n c87521 = c87483 + c87488 + c87493 + c87498\n\n return (c87482, c87487, c87492, c87497, c87483, c87488, c87493, c87498,\n c87521)","def scoring(self):\n return -100 if self.loss_condition() else 0","def _grade(student, request, course, keep_raw_scores):\r\n grading_context = course.grading_context\r\n raw_scores = []\r\n\r\n # Dict of item_ids -> (earned, possible) point tuples. This *only* grabs\r\n # scores that were registered with the submissions API, which for the moment\r\n # means only openassessment (edx-ora2)\r\n submissions_scores = sub_api.get_scores(\r\n course.id.to_deprecated_string(), anonymous_id_for_user(student, course.id)\r\n )\r\n\r\n totaled_scores = {}\r\n # This next complicated loop is just to collect the totaled_scores, which is\r\n # passed to the grader\r\n for section_format, sections in grading_context['graded_sections'].iteritems():\r\n format_scores = []\r\n for section in sections:\r\n section_descriptor = section['section_descriptor']\r\n section_name = section_descriptor.display_name_with_default\r\n\r\n # some problems have state that is updated independently of interaction\r\n # with the LMS, so they need to always be scored. (E.g. foldit.,\r\n # combinedopenended)\r\n should_grade_section = any(\r\n descriptor.always_recalculate_grades for descriptor in section['xmoduledescriptors']\r\n )\r\n\r\n # If there are no problems that always have to be regraded, check to\r\n # see if any of our locations are in the scores from the submissions\r\n # API. If scores exist, we have to calculate grades for this section.\r\n if not should_grade_section:\r\n should_grade_section = any(\r\n descriptor.location.to_deprecated_string() in submissions_scores\r\n for descriptor in section['xmoduledescriptors']\r\n )\r\n\r\n if not should_grade_section:\r\n with manual_transaction():\r\n should_grade_section = StudentModule.objects.filter(\r\n student=student,\r\n module_state_key__in=[\r\n descriptor.location for descriptor in section['xmoduledescriptors']\r\n ]\r\n ).exists()\r\n\r\n # If we haven't seen a single problem in the section, we don't have\r\n # to grade it at all! We can assume 0%\r\n if should_grade_section:\r\n scores = []\r\n\r\n def create_module(descriptor):\r\n '''creates an XModule instance given a descriptor'''\r\n # TODO: We need the request to pass into here. If we could forego that, our arguments\r\n # would be simpler\r\n with manual_transaction():\r\n field_data_cache = FieldDataCache([descriptor], course.id, student)\r\n return get_module_for_descriptor(student, request, descriptor, field_data_cache, course.id)\r\n\r\n for module_descriptor in yield_dynamic_descriptor_descendents(section_descriptor, create_module):\r\n\r\n (correct, total) = get_score(\r\n course.id, student, module_descriptor, create_module, scores_cache=submissions_scores\r\n )\r\n if correct is None and total is None:\r\n continue\r\n\r\n if settings.GENERATE_PROFILE_SCORES: \t# for debugging!\r\n if total > 1:\r\n correct = random.randrange(max(total - 2, 1), total + 1)\r\n else:\r\n correct = total\r\n\r\n graded = module_descriptor.graded\r\n if not total > 0:\r\n #We simply cannot grade a problem that is 12/0, because we might need it as a percentage\r\n graded = False\r\n\r\n scores.append(Score(correct, total, graded, module_descriptor.display_name_with_default))\r\n\r\n _, graded_total = graders.aggregate_scores(scores, section_name)\r\n if keep_raw_scores:\r\n raw_scores += scores\r\n else:\r\n graded_total = Score(0.0, 1.0, True, section_name)\r\n\r\n #Add the graded total to totaled_scores\r\n if graded_total.possible > 0:\r\n format_scores.append(graded_total)\r\n else:\r\n log.exception(\"Unable to grade a section with a total possible score of zero. \" +\r\n str(section_descriptor.location))\r\n\r\n totaled_scores[section_format] = format_scores\r\n\r\n grade_summary = course.grader.grade(totaled_scores, generate_random_scores=settings.GENERATE_PROFILE_SCORES)\r\n\r\n # We round the grade here, to make sure that the grade is an whole percentage and\r\n # doesn't get displayed differently than it gets grades\r\n grade_summary['percent'] = round(grade_summary['percent'] * 100 + 0.05) / 100\r\n\r\n letter_grade = grade_for_percentage(course.grade_cutoffs, grade_summary['percent'])\r\n grade_summary['grade'] = letter_grade\r\n grade_summary['totaled_scores'] = totaled_scores \t# make this available, eg for instructor download & debugging\r\n if keep_raw_scores:\r\n grade_summary['raw_scores'] = raw_scores # way to get all RAW scores out to instructor\r\n # so grader can be double-checked\r\n return grade_summary","def test_grades(grade_list):\n\ti = 0\n\tchange_list = []\n\tfor i in range(len(grade_list)-1):\n\t\tm = ord(grade_list[i])\n\t\tn = ord(grade_list[i+1])\n\t\tif m < n:\n\t\t\tk = -1\n\t\telif m == n:\n\t\t\tk = 0\n\t\telse:\n\t\t\tk = 1\n\t\tchange_list.append(k)\t\n\treturn sum(change_list)","def test_avg_grade(self):\n\t\ts = Student_Analytics()\n\t\tself.assertEqual(s.classify_grade(s.avg_grade(3)),\"B\")","def earned_hw_scores(self):\r\n return [s.earned for s in self.get_grade_summary()['totaled_scores']['Homework']]","def grade_submissions(submissions, roster, deadlines):\n grades = dict([(u, 0) for u in roster.keys()])\n for sub in submissions:\n sub_date = convert_date(sub['Submission date'])\n user = sub['UID'].split('@')[0]\n score = 0\n if user in roster:\n if (sub_date < deadlines[roster[user]] and\n sub['Correct'].upper() == 'TRUE'):\n score = 1\n if grades[user] != 1:\n grades[user] = score\n return grades","def generateROCcurve(junctionBed, ests, stepSize=100, wiggle=0):\n\n goodNames, badNames = findGoodBadReads(junctionBed, ests, wiggle)\n\n rocTable = []\n scores = []\n scoredDict = {}\n countGood = 0\n countBad = 0\n countStacked = 0\n countUnclear = 0\n\n for line in open(junctionBed):\n if line.startswith(\"track\"):\n continue\n [chr, start, stop, name, score] = line.split()[:5]\n score = float(score)\n if goodNames.has_key(name):\n isGood = 1 #True\n elif badNames.has_key(name):\n isGood = 0 #False\n else:\n continue\n\n scores.append(score)\n\n if isGood:\n countGood += 1\n\n else:\n countBad += 1\n\n scoredDict[name] = (score, isGood)\n\n print \"Counted %s good and %s bad\" % (countGood, countBad)\n\n #print scores[:3]\n scores.sort(reverse=True)\n #print scores[:5]\n #use every 100th score\n for x in scores[::stepSize]:\n numGoodAbove = 0\n numBadAbove = 0\n for k, (score, real) in scoredDict.iteritems():\n if score >= x:\n #print real, score, k, pairsDict[k]\n if real:\n numGoodAbove += 1\n else:\n numBadAbove += 1\n\n #print x, numGoodAbove, numBadAbove, numGoodAbove / float(countGood) , numBadAbove / float(countBad)\n rocTable.append((x, numGoodAbove, numBadAbove, numGoodAbove / float(countGood) , numBadAbove / float(countBad)))\n return rocTable,scoredDict","def determine_color(score=be.df[\"score\"].mean()):\n avg_score = int(round(be.df[\"score\"].mean(), 0))\n if score < avg_score * 0.95:\n return \"red\"\n elif score > avg_score * 1.05:\n return \"green\"\n else:\n return \"yellow\"","def calculate(self):\n\n s_sum = 0\n class_num = len(self.scores)\n \n for i in range(class_num):\n s_sum += self.scores[i]\n\n av = float(s_sum)/class_num\n if av >= 90:\n return 'O'\n elif av >= 80:\n return 'E'\n elif av >= 70:\n return 'A'\n elif av >= 55:\n return 'P'\n elif av >= 40:\n return 'D'\n else:\n return 'T'","def test_b_grade_exact(self):\r\n self.basic_setup()\r\n self.submit_question_answer('p1', {'2_1': 'Correct'})\r\n self.check_grade_percent(0.33)\r\n self.assertEqual(self.get_grade_summary()['grade'], 'B')","def day_009_1():\n student_scores = {\n \"Harry\": 81,\n \"Ron\": 78,\n \"Hermione\": 99,\n \"Draco\": 74,\n \"Neville\": 62,\n }\n\n student_grades = {}\n\n for student in student_scores:\n if student_scores[student] <= 70:\n student_grades[student] = \"Fail\"\n elif student_scores[student] <= 80:\n student_grades[student] = \"Acceptable\"\n elif student_scores[student] <= 90:\n student_grades[student] = \"Exceeds Expectations\"\n else:\n student_grades[student] = \"Outstanding\"\n\n print(student_grades)","def ards_score(self):\n if self.ards_wickets == 10:\n var1 = \"All Out\"\n return str('{0} {1}').format(self.ards_runs, var1)\n else:\n var1 = self.ards_wickets\n return str('{0}-{1}').format(self.ards_runs, var1)","def grades_to_number(grade):\n # Conditions\n if grade == \"A\":\n return 1\n elif grade == \"B\":\n return 0.5\n elif grade == \"C\":\n return 0\n elif grade == \"D\":\n return -0.5\n else:\n return -1","def score(self):","def get_adl(adl_score,cog):\n ranges = [(0),(1,2),(3,4),(5,6,7),(8,9),(10,11),(12,13),(14,15,16)]\n modifier = [-2,-1,0,1,2,3,4,5]\n #\n found = True\n while found:\n for i,r in enumerate(ranges):\n print(adl_score,range[i])\n if adl_score in ranges[i]:\n adl_score = adl_score+modifier[i]\n found = False\n break\n return adl_score","def get_score_summary(fname):\n gradedata = {}\n fhandler = open(fname, 'r')\n rest_data = csv.reader(fhandler)\n for row in rest_data:\n if row[10] not in ['P', '', 'GRADE']:\n gradedata[row[0]] = [row[1], row[10]]\n gradedata.update(gradedata)\n fhandler.close()\n\n gradereview = {}\n for value in gradedata.itervalues():\n if value[0] not in gradereview.iterkeys():\n count1 = 1\n count2 = GRADESCL[value[1]]\n else:\n count1 = gradereview[value[0]][0] + 1\n count2 = gradereview[value[0]][1] + GRADESCL[value[1]]\n gradereview[value[0]] = (count1, count2)\n gradereview.update(gradereview)\n grade = {}\n for key in gradereview.iterkeys():\n count1 = gradereview[key][0]\n count2 = gradereview[key][1]/gradereview[key][0]\n grade[key] = (count1, count2)\n return grade","def test_grade_change(self):\n\t\ts = Student_Analytics()\n\t\tself.assertEqual(int(s.grade_change()),0)","def round_grade(grade):\n\t# only round grade if the input is a float\n\tif type(grade) != str:\n\t\trounded_grade = np.floor(grade/10)\n\t\treturn rounded_grade\n\t# otherwise return the original grade -> could be string \"Lunch\" or \"Period\"\n\telse:\n\t\treturn grade","def degrees_to_grade(angle_in_degrees):\n if np.any(angle_in_degrees>90): #np.any works if angle_in_degrees is a single value or array\n raise Exception('Can only convert an angle between 0 to 90 degrees to a percentage grade.\\nSee: https://en.wikipedia.org/wiki/Grade_(slope)')\n angle_in_grade = 100*np.tan(angle_in_degrees*np.pi/180)\n return(angle_in_grade)","def save_grade(self, data):\r\n\r\n required = ['location', 'submission_id', 'submission_key', 'score', 'feedback', 'submission_flagged', 'answer_unknown']\r\n if data.get(\"submission_flagged\", False) in [\"false\", False, \"False\", \"FALSE\"]:\r\n required.append(\"rubric_scores[]\")\r\n success, message = self._check_required(data, set(required))\r\n if not success:\r\n return self._err_response(message)\r\n\r\n success, message = self._check_feedback_length(data)\r\n if not success:\r\n return self._err_response(message)\r\n\r\n data_dict = {k:data.get(k) for k in required}\r\n if 'rubric_scores[]' in required:\r\n data_dict['rubric_scores'] = data.getall('rubric_scores[]')\r\n data_dict['grader_id'] = self.system.anonymous_student_id\r\n\r\n try:\r\n response = self.peer_gs.save_grade(**data_dict)\r\n success, location_data = self.query_data_for_location(data_dict['location'])\r\n #Don't check for success above because the response = statement will raise the same Exception as the one\r\n #that will cause success to be false.\r\n response.update({'required_done' : False})\r\n if 'count_graded' in location_data and 'count_required' in location_data and int(location_data['count_graded'])>=int(location_data['count_required']):\r\n response['required_done'] = True\r\n return response\r\n except GradingServiceError:\r\n # This is a dev_facing_error\r\n log.exception(\"\"\"Error saving grade to open ended grading service. server url: {0}\"\"\"\r\n .format(self.peer_gs.url)\r\n )\r\n # This is a student_facing_error\r\n return {\r\n 'success': False,\r\n 'error': EXTERNAL_GRADER_NO_CONTACT_ERROR\r\n }","def adjusted_grade(clicker_points, midterm_grade):\n if not clicker_points:\n return midterm_grade\n avg_clicker_points = sum(clicker_points.values())/len(clicker_points)\n joint_set = set(clicker_points).union(set(midterm_grade))\n clicker_extra_points = {student: 1 if clicker_points.get(student)\n >= avg_clicker_points else 0 for\n student in clicker_points}\n return {name: midterm_grade.get(name, 0) +\n clicker_extra_points.get(name, 0)\n for name in joint_set}","def disp_score():","def bedroc(y_true, y_score, alpha=..., pos_label=...):\n ...","def abilityScores(self):\n mods = [(self.str -10)/2,\n (self.dex-10)/2,\n (self.con-10)/2,\n (self.int-10)/2,\n (self.wis-10)/2,\n (self.cha-10)/2]\n return \"STR: {0} ({1}) \\nDEX: {2} ({3})\\nCON: {4} ({5})\".format(self.str,\n mods[0],\n self.dex,\n mods[1],\n self.con,\n mods[2])+\"\\n\" \\\n \"INT: {0} ({1})\\nWIS: {2} ({3})\\nCHA: {4} ({5})\".format(self.int,\n mods[3],\n self.wis,\n mods[4],\n self.cha,\n mods[5])","def getAbilityScores(self):\n mods = [(self.str -10)/2,\n (self.dex-10)/2,\n (self.con-10)/2,\n (self.int-10)/2,\n (self.wis-10)/2,\n (self.cha-10)/2]\n print \"STR: {0} ({1}) \\nDEX: {2} ({3})\\nCON: {4} ({5})\".format(self.str,\n mods[0],\n self.dex,\n mods[1],\n self.con,\n mods[2])\n print \"INT: {0} ({1})\\nWIS: {2} ({3})\\nCHA: {4} ({5})\".format(self.int,\n mods[3],\n self.wis,\n mods[4],\n self.cha,\n mods[5])","def letter_grade(grade):\n\t# define dictionary of grading scale\n\t# Check the fillna above was filled w/ -1.0\n\td = {18.0: 'A', 17.0: 'A', 16.0: 'A', 15.0: 'A', 14.0: 'A', 13.0: 'A',\n\t12.0: 'A', 11.0: 'A', 10.0: 'A', 9.0: 'A', 8.0: 'B', \n\t7.0: 'C', 6.0: 'D', 5.0: 'F', 4.0: 'F', 3.0: 'F', 2.0: 'F', \n\t1.0: 'F', 0.0: 'F', -1.0: '-'}\n\t\n\t# get letter grade only if grade is not a string\n\tif type(grade) != str:\n\t\t# get the letter\n\t\tletter = d[grade]\n\t\treturn letter\n\telse:\n\t\treturn grade","def get_result(mishkaScore: int, chrisScore: int) -> bool:\n if mishkaScore > chrisScore:\n return \"M\"\n if mishkaScore < chrisScore:\n return \"C\"\n return \"D\"","def test_compute_grade_for_non_fa(self):\n run3_data = self.user_edx_data.get_run_data(self.run_no_fa.edx_course_key)\n run4_data = self.user_edx_data.get_run_data(self.run_no_fa_with_cert.edx_course_key)\n\n grade3_from_cur_grade = api._compute_grade_for_non_fa(run3_data)\n grade4_from_cert = api._compute_grade_for_non_fa(run4_data)\n\n assert isinstance(grade3_from_cur_grade, api.UserFinalGrade)\n assert isinstance(grade4_from_cert, api.UserFinalGrade)\n\n assert grade3_from_cur_grade.passed is False\n assert grade3_from_cur_grade.grade == self.current_grades.get(\n self.run_no_fa.edx_course_key).data.get('percent')\n # this is true if the enrollment is verified\n assert grade3_from_cur_grade.payed_on_edx is True\n assert grade4_from_cert.passed is True\n assert grade4_from_cert.grade == self.current_grades.get(\n self.run_no_fa_with_cert.edx_course_key).data.get('percent')\n # this is True as long as the certificate is verified\n assert grade4_from_cert.payed_on_edx is True","def get_grade_stats(scores):\r\n # Calculate the arithmetic mean\r\n mean = sum(student_scores) / len(scores)\r\n\r\n # Calculate the standard deviation\r\n tmp = 0\r\n for i in range(len(scores)):\r\n tmp += (scores[i] - mean) ** 2\r\n std_dev = (tmp / len(scores)) ** 0.5\r\n\r\n # Package and return average, standard deviation in a tuple\r\n return mean, std_dev","def test_get_score_with_different_score_value_in_rubric(self):\r\n combinedoe = CombinedOpenEndedV1Module(self.test_system,\r\n self.location,\r\n self.definition,\r\n self.descriptor,\r\n static_data=self.static_data,\r\n metadata=self.metadata,\r\n instance_state=json.loads(INSTANCE_INCONSISTENT_STATE3))\r\n score_dict = combinedoe.get_score()\r\n self.assertEqual(score_dict['score'], 25.0)\r\n self.assertEqual(score_dict['total'], 5.0)","def rads_to_grade(angle_in_radians):\n if np.any(angle_in_radians>0.5*np.pi): #np.any works if angle_in_degrees is a single value or array\n raise Exception('Can only convert an angle between 0 to pi/2 radians to a percentage grade.\\nSee: https://en.wikipedia.org/wiki/Grade_(slope)')\n angle_in_grade = 100*np.tan(angle_in_radians)\n return(angle_in_grade)","def what_is_the_grade(self):\n\t\treturn_dict = {\n\t\t\t'section_title': self.title, \n\t\t\t'section_weight': self.weight,\n\t\t\t'grade_value' : self.current_grade_value,\n\t\t\t'comment_text' : self.current_comment_text,\n\t\t\t'default_comments_text' : self.current_default_comment_text,\n\t\t\t'example_comments_text' : self.current_example_comment_text,\n\t\t\t'is_complete': self.is_complete\n\t\t}\n\n\t\treturn return_dict","def add_percentage(grade):\n\tif type(grade) == float:\n\t\tperc_grade = str(grade) + '%'\n\t\treturn perc_grade\n\telse:\n\t\treturn grade","def enrichment_factor(y_true, y_score, percentage=..., pos_label=..., kind=...):\n ...","def get_strength(self):\n return 10 - self.get_agility()","def passer_rating(pass_attempts_int, pass_completions_int, passing_yards_flt,\n touchdowns_int, interceptions_int):\n # C is the \"completions per pass attempt\" times 100 minus 30, all divided by 20.\n # Y is the \"yards per pass attempt\" minus 3, all divided by 4\n # T is the \"touchdown per pass attempt\" times 20.\n # I the \"interceptions per pass attempt\"\n\n C = (pass_completions_int / pass_attempts_int * 100 - 30) / 20\n Y = (passing_yards_flt / pass_attempts_int - 3) / 4\n T = touchdowns_int / pass_attempts_int * 20\n I = 2.375 - (interceptions_int / pass_attempts_int * 25)\n\n # The PASSER RATING is computed as the summ of C, Y, T and I all divided by 6\n # and then multiplied 100.\n PR = (C + Y + T + I) / 6 * 100\n return PR","def pre_flop_strength(hand):\n highs = {}\n highs[4] = [\n \"AA\", \"AKs\", \"AQs\", \"AJs\", \"ATs\", \"AKo\", \"KK\", \"KQs\", \"KJs\", \"AQo\",\n \"QQ\", \"QJs\", \"JJ\", \"TT\"\n ]\n highs[3] = [\n \"A5s\", \"A4s\", \"A3s\", \"KTs\", \"KQo\", \"QTs\", \"AJo\", \"JTs\", \"T9s\", \"99\",\n \"98s\", \"88\", \"87s\", \"77\", \"66\"\n ]\n highs[2] = [\n \"A9s\", \"A8s\", \"A7s\", \"A6s\", \"A2s\", \"K9s\", \"K8s\", \"Q9s\", \"KJo\", \"QJo\",\n \"J9s\", \"ATo\", \"KTo\", \"QTo\", \"JTo\", \"T8s\", \"A9o\", \"J9o\", \"T9o\", \"97s\",\n \"98o\", \"86s\", \"76s\", \"75s\", \"65s\", \"55\", \"44\", \"33\", \"22\"\n ]\n highs[1] = [\n \"K7s\", \"K6s\", \"K5s\", \"K4s\", \"K3s\", \"Q8s\", \"Q7s\", \"Q6s\", \"Q5s\", \"Q4s\",\n \"J8s\", \"J7s\", \"J6s\", \"J5s\", \"T7s\", \"T6s\", \"K9o\", \"Q9o\", \"96s\", \"A8o\",\n \"K8o\", \"Q8o\", \"J8o\", \"T8o\", \"85s\", \"A7o\", \"K7o\", \"Q7o\", \"T7o\", \"97o\",\n \"87o\", \"74s\", \"A6o\", \"K6o\", \"86o\", \"76o\", \"64s\", \"63s\", \"A5o\", \"75o\",\n \"65o\", \"54s\", \"53s\", \"A4o\", \"43s\", \"A3o\"\n ]\n card0, card1 = hand\n if card0[0] == card1[0]:\n pair = \"\".join([card0[0], card1[0]])\n elif card0[1] == card1[1]:\n pair = \"\".join([card0[0], card1[0], \"s\"])\n else:\n pair = \"\".join([card0[0], card1[0], \"o\"])\n for strenght in highs:\n if pair in highs[strenght]:\n return strenght\n return 0","def worst_score(self):\r\n pass","def solution():\n file = get_source()\n results = []\n for c in range(int(file.readline())):\n grades = sorted([int(v) for v in file.readline().split()][1:])\n average = float(sum(grades))/len(grades)\n first = next((i for i,g in enumerate(grades) if g > average), len(grades))\n people_above_average = len(grades) - first\n results.append(people_above_average * 100.0 / len(grades))\n for r in results:\n print '%.3f%%' % r","def save_grade(request, course_id):\r\n\r\n course_key = SlashSeparatedCourseKey.from_deprecated_string(course_id)\r\n _check_access(request.user, course_key)\r\n\r\n if request.method != 'POST':\r\n raise Http404\r\n p = request.POST\r\n required = set(['score', 'feedback', 'submission_id', 'location', 'submission_flagged'])\r\n skipped = 'skipped' in p\r\n #If the instructor has skipped grading the submission, then there will not be any rubric scores.\r\n #Only add in the rubric scores if the instructor has not skipped.\r\n if not skipped:\r\n required.add('rubric_scores[]')\r\n actual = set(p.keys())\r\n missing = required - actual\r\n if len(missing) > 0:\r\n return _err_response('Missing required keys {0}'.format(\r\n ', '.join(missing)))\r\n\r\n success, message = check_feedback_length(p)\r\n if not success:\r\n return _err_response(message)\r\n\r\n grader_id = unique_id_for_user(request.user)\r\n\r\n location = course_key.make_usage_key_from_deprecated_string(p['location'])\r\n\r\n try:\r\n result = staff_grading_service().save_grade(course_key,\r\n grader_id,\r\n p['submission_id'],\r\n p['score'],\r\n p['feedback'],\r\n skipped,\r\n p.getlist('rubric_scores[]'),\r\n p['submission_flagged'])\r\n except GradingServiceError:\r\n #This is a dev_facing_error\r\n log.exception(\r\n \"Error saving grade in the staff grading interface in open ended grading. Request: {0} Course ID: {1}\".format(\r\n request, course_id))\r\n #This is a staff_facing_error\r\n return _err_response(STAFF_ERROR_MESSAGE)\r\n except ValueError:\r\n #This is a dev_facing_error\r\n log.exception(\r\n \"save_grade returned broken json in the staff grading interface in open ended grading: {0}\".format(\r\n result_json))\r\n #This is a staff_facing_error\r\n return _err_response(STAFF_ERROR_MESSAGE)\r\n\r\n if not result.get('success', False):\r\n #This is a dev_facing_error\r\n log.warning(\r\n 'Got success=False from staff grading service in open ended grading. Response: {0}'.format(result_json))\r\n return _err_response(STAFF_ERROR_MESSAGE)\r\n\r\n # Ok, save_grade seemed to work. Get the next submission to grade.\r\n return HttpResponse(json.dumps(_get_next(course_id, grader_id, location)),\r\n mimetype=\"application/json\")","def scoreR(self) :\n if self.leafR() :\n return self.leafScore(), self\n else :\n games = self.R()\n min_g = games[0]\n min_score = min_g.scoreL()\n for g in games[1:] :\n score = g.scoreL()\n if score[0] < min_score[0] :\n min_g = g\n min_score = score\n return (min_score+(min_g,))","def quality_rating(PR):\n \n if PR <= 85:\n quality = \"poor\"\n elif PR < 90:\n quality = \"mediocre\"\n elif PR < 95:\n quality = \"good\"\n else:\n quality = \"great\"\n\n return quality","def get_prediction(course_grades, train_data, train_grades):\n\n # In the case the student has no grade, return a predicted grade of 0\n if train_data.size == 0:\n return 0,0\n\n model = BayesianRidge()\n model.fit(train_data, train_grades)\n y_mean, y_sd = model.predict(np.array(course_grades).reshape(1, -1), return_std=True)\n\n return y_mean, y_sd","def grade_boro(boroughts,data):\n boro_df=data.iloc[np.where(data.BORO==boroughts)]\n return total_grade(boro_df)","def grade_this_guy(self):\n log.info(\"Function has been called\")\n answer = self._file_storage_path(self.raw_answer['sha1'], self.raw_answer['filename'])\n question = self._question_storage_path(self.raw_question['sha1'], self.raw_question['filename'])\n solution = self._solution_storage_path(self.raw_solution['sha1'], self.raw_solution['filename'])\n\n\n answer = os.path.join(IMAGEDIFF_ROOT, answer)\n question = os.path.join(IMAGEDIFF_ROOT, question)\n solution = os.path.join(IMAGEDIFF_ROOT, solution)\n\n self.score = grade(question, answer, solution)\n if self.score > self.points:\n self.score = self.points\n self.points=float(self.max_score())\n self.save()\n if self.score >= 0: \n self.runtime.publish(self, 'grade',{ 'value': self.score, 'max_value':self.max_score(),})\n log.info(\"runtime.publish-ed\")\n self.save()\n return Response(json_body=self.student_state())","def grade_report(course):\n report = []\n for st in course.get_students():\n try:\n average = sum(course.get_grades(st)) / len(course.get_grades(st))\n report.append(str(st) + '\\'s mean grade is: ' + str(average) + '.')\n except ZeroDivisionError:\n report.append(str(st) + ' has no grades.')\n return '\\n'.join(report)","def __score_by_iceberg_level(self, source_iceberg, iceberg_to_score, iceberg_owner_after_all_groups_arrived):\n if utils.is_me(self.__game, iceberg_owner_after_all_groups_arrived):\n if source_iceberg.level <= iceberg_to_score.level:\n return SOURCE_LEVEL_SMALL_THAN_DESTINATION_SCORE\n if iceberg_to_score.level < iceberg_to_score.upgrade_level_limit:\n return LEVEL_FACTOR_SCORE ** iceberg_to_score.penguins_per_turn\n elif utils.is_neutral(self.__game, iceberg_owner_after_all_groups_arrived):\n if MapChecker.get().is_tricky_map() or MapChecker.get().is_extra_far_treasure() or MapChecker.get().is_extra_far():\n return LEVEL_FACTOR_SCORE * iceberg_to_score.penguins_per_turn\n else:\n return LEVEL_FACTOR_SCORE ** iceberg_to_score.penguins_per_turn\n return iceberg_to_score.penguins_per_turn","def fkg(word, sent, syll):\n flesch_kincaid_grade = (0.39* (word / sent)) + (11.8 * (syll / word)) - 15.59\n return flesch_kincaid_grade","def _get_best_grading():\n possible_main_grading = np.array([10, 20, 25, 40, 50,\n 100, 200, 250, 400,\n 500, 1000])\n total_dist = length * dpp\n # Get the most suitable grading\n num_gradings = total_dist / possible_main_grading\n best_grading = possible_main_grading[num_gradings <= 7][0]\n best_num = num_gradings[num_gradings <= 7][0]\n dist = np.arange(best_num) * best_grading\n pos = (dist / dpp).astype(np.int)\n texts = [\"{0:d}\".format(int(d)) for d in dist]\n return pos, texts","def _add_PERC1(self, w2, row):\n if 'PASS' not in row:\n return False\n if 'EQ' not in row:\n return False\n if row['PASS']:\n row['PERC1'] = 100.0 * row['EQ'] / row['PASS']\n else:\n row['PERC1'] = 0.0\n return True","def test_compute_grade_for_fa(self):\n run1_data = self.user_edx_data.get_run_data(self.run_fa.edx_course_key)\n run2_data = self.user_edx_data.get_run_data(self.run_fa_with_cert.edx_course_key)\n\n grade1_from_cur_grade = api._compute_grade_for_fa(run1_data)\n grade2_from_cert = api._compute_grade_for_fa(run2_data)\n\n assert isinstance(grade1_from_cur_grade, api.UserFinalGrade)\n assert isinstance(grade2_from_cert, api.UserFinalGrade)\n\n assert grade1_from_cur_grade.passed == self.current_grades.get(\n self.run_fa.edx_course_key).data.get('passed')\n assert grade1_from_cur_grade.grade == self.current_grades.get(\n self.run_fa.edx_course_key).data.get('percent')\n assert grade1_from_cur_grade.payed_on_edx == (self.enrollments.get(\n self.run_fa.edx_course_key).data.get('mode') in ['verified', 'honor'])\n\n assert grade2_from_cert.passed is self.current_grades.get(\n self.run_fa_with_cert.edx_course_key).data.get('passed')\n assert grade2_from_cert.grade == self.current_grades.get(\n self.run_fa_with_cert.edx_course_key).data.get('percent')\n # this is True as long as the certificate is verified\n assert grade2_from_cert.payed_on_edx is True","def graham_number(self):\n\n calc_df = self.stock.calculations_df\n\n if calc_df.iloc[9]['Value'] == 'N/A' or calc_df.iloc[10]['Value'] == 'N/A':\n self.stock.append_calc_result('Graham Number less than 22.5 ?', 'N/A', 'N/A', 'Could not obtain 3 Year P/E'\n 'or Current P/B Ratios')\n return\n\n p_to_e_ratio = calc_df.iloc[9]['Value']\n p_to_b_ratio = calc_df.iloc[10]['Value']\n graham_num = round(p_to_e_ratio * p_to_b_ratio, 2)\n\n if graham_num <= 22.5:\n criteria_passed = 'Yes'\n else:\n criteria_passed = 'No'\n\n self.stock.append_calc_result('Graham Number less than 22.5 ?', graham_num, criteria_passed, '')","def test_none_grade(self):\r\n self.basic_setup()\r\n self.check_grade_percent(0)\r\n self.assertEqual(self.get_grade_summary()['grade'], None)","def get_regret(self, df,score_col = 'dk_score',proj_col = 'proj_score',\n driver_col = 'Driver', salary_col = 'Salary'):\n max_score, max_lineup = self.get_max_score(df,score_col = score_col)\n pick_scores, pick_lineups = self.get_max_score(df,score_col = proj_col)\n return max_score-pick_scores, [max_score,pick_scores],[max_lineup,pick_lineups]","def test_get_score(self):\r\n\r\n score_dict = self.get_score(True, 3, 3)\r\n\r\n # Score should be 1.0.\r\n self.assertEqual(score_dict[\"score\"], 1.0)\r\n\r\n # Testing score after data is stored in student_data_for_location in xmodule.\r\n _score_dict = self.peer_grading.get_score()\r\n\r\n # Score should be 1.0.\r\n self.assertEqual(_score_dict[\"score\"], 1.0)","def scoreEducation(self, employee_ed, name):\n\n university = self.university\n schools = [school for school in name if school != \"\"]\n rank = []\n\n for school in schools:\n match_score = []\n for university_name in university['Name']:\n try:\n match_score.append(fuzz.ratio(university_name, school))\n except Exception as e:\n match_score.append(0)\n\n #Case where there is no valid school in the parsed XML, resulting in \"schools\" to be an empty list.\n if not match_score:\n match_score.append(0)\n\n try:\n if max(match_score) > 70:\n rank.append(university['Rank'][match_score.index(max(match_score))])\n else:\n rank.append(1)\n except KeyError:\n rank.append(1)\n\n\n ed_weight = {'Bachelor': 0.1, 'Masters': 0.15, 'doctorate': 0.2, '': 0.05}\n\n employee_ed = list(set(employee_ed).intersection(ed_weight.keys()))\n employee_ed_weight = [ed_weight[i] for i in employee_ed]\n\n try:\n highest_qualification = ed_weight[max(employee_ed, key=lambda x: ed_weight[x])]\n except:\n highest_qualification = 0.05\n\n return sum(i*j for i, j in zip(employee_ed_weight, rank))","def upgrad_one_contribute_score(user_total_click_num):\n return 1/math.log(1 + user_total_click_num)"],"string":"[\n \"def grade(self):\\n if round(self.numAvg,0) >= 70:\\n return round(self.numAvg,0)\\n elif self.PassSummer:\\n return 70\\n elif round(self.numAvg,0) >= 55 and not self.PassSummer:\\n return round(self.numAvg,0)\\n else:\\n return 55\",\n \"def calc_grade(self, average):\\n if 95 <= average:\\n return 'S'\\n elif 90 <= average:\\n return 'A'\\n elif 80 <= average:\\n return 'B'\\n elif 70 <= average:\\n return 'C'\\n elif 60 <= average:\\n return 'D'\\n else:\\n return 'F'\",\n \"def computeGrades(e1, e2, a):\\n \\n a = assignmentScores\\n a.sort()\\n i=0\\n while i<10:\\n sum+=sum a[i]\\n avg = sum/10\\n \\n grade = ((e1 + e2) /2) * 0.4 + (avg) * 0.6\\n \\n return grade\\n \\n if grade >= 90 and grade <= 100:\\n return(\\\"A\\\")\\n \\n elif grade >= 80 and grade < 90:\\n return(\\\"B\\\")\\n \\n elif grade >= 70 and grade < 80:\\n return(\\\"C\\\")\\n \\n elif grade >= 60 and grade < 70:\\n return(\\\"D\\\")\\n \\n elif grade < 60:\\n return(\\\"F\\\")\",\n \"def calculate_gpa(score):\\n if score < 60:\\n return 0\\n elif 60 <= score < 70:\\n return 1\\n elif 70 <= score < 80:\\n return 2\\n elif 80 <= score < 90:\\n return 3\\n elif score >= 90:\\n return 4\",\n \"def grade_calculate_grade(self):\\n try:\\n if int(self.root.ids.grade_input_grade.text) >= 85:\\n grade = 'High Distinction'\\n elif int(self.root.ids.grade_input_grade.text) >= 75:\\n grade = 'Distinction'\\n elif int(self.root.ids.grade_input_grade.text) >= 65:\\n grade = 'Credit'\\n elif int(self.root.ids.grade_input_grade.text) >= 50:\\n grade = 'Pass'\\n else:\\n grade = 'Fail'\\n self.root.ids.grade_output_label.text = 'Grade: ' + grade\\n except ValueError:\\n\\n self.root.ids.grade_output_label.text = 'Invalid Grade'\",\n \"def grade(first, second, third):\\n score = round((first + second + third) / 3, 1)\\n score_secondary = str(score)\\n plusminus = int(score_secondary[1])\\n if score > 100:\\n return \\\"Wrong input, friend.\\\"\\n else:\\n if score >= 90:\\n if plusminus >= 5:\\n return \\\"A+\\\"\\n else:\\n return \\\"A-\\\"\\n elif score >= 80:\\n if plusminus >= 5:\\n return \\\"B+\\\"\\n else:\\n return \\\"B-\\\"\\n elif score >= 70:\\n if plusminus >= 5:\\n return \\\"C+\\\"\\n else:\\n return \\\"C-\\\"\\n elif score >= 60:\\n if plusminus >= 5:\\n return \\\"D+\\\"\\n else:\\n return \\\"D-\\\"\\n else:\\n return \\\"F\\\"\",\n \"def calc_score(score):\\n if not score:\\n return 0\\n dbot_score = 1\\n if score >= 95:\\n dbot_score = 3\\n elif score >= 75:\\n dbot_score = 2\\n return dbot_score\",\n \"def g_score(self):\\n _, _, I_CK = self._entropies()\\n return 2.0 * I_CK\",\n \"def get_grade(self) -> int :\\n return self.grade\",\n \"def test_grade(grade_list):\\n num_grade=scale_grade(grade_list)\\n trend=[]\\n for ind in range(len(num_grade)-1):\\n if num_grade[ind+1]>num_grade[ind]:\\n trend.append(1)\\n elif num_grade[ind+1]<num_grade[ind]:\\n trend.append(-1)\\n else:\\n trend.append(0)\\n \\n if sum(trend)>0:\\n return 1\\n elif sum(trend)<0:\\n return -1\\n else:\\n return 0\",\n \"def test_classify_grade(self):\\n\\t\\ts = Student_Analytics()\\n\\t\\tself.assertEqual(s.classify_grade(5.00),\\\"A+\\\")\",\n \"def test_a_grade(self):\\r\\n self.basic_setup()\\r\\n self.submit_question_answer('p1', {'2_1': 'Correct'})\\r\\n self.submit_question_answer('p2', {'2_1': 'Correct'})\\r\\n self.submit_question_answer('p3', {'2_1': 'Correct'})\\r\\n self.check_grade_percent(1.0)\\r\\n self.assertEqual(self.get_grade_summary()['grade'], 'A')\",\n \"def grade_for_percentage(grade_cutoffs, percentage):\\r\\n\\r\\n letter_grade = None\\r\\n\\r\\n # Possible grades, sorted in descending order of score\\r\\n descending_grades = sorted(grade_cutoffs, key=lambda x: grade_cutoffs[x], reverse=True)\\r\\n for possible_grade in descending_grades:\\r\\n if percentage >= grade_cutoffs[possible_grade]:\\r\\n letter_grade = possible_grade\\r\\n break\\r\\n\\r\\n return letter_grade\",\n \"def is_good_qualtiative_example(iaa_score, ann1_total, ann2_total):\\n return iaa_score > .3 and iaa_score < 1 and ann1_total > 3 and ann2_total > 3\",\n \"def test_b_grade_above(self):\\r\\n self.basic_setup()\\r\\n self.submit_question_answer('p1', {'2_1': 'Correct'})\\r\\n self.submit_question_answer('p2', {'2_1': 'Correct'})\\r\\n self.check_grade_percent(0.67)\\r\\n self.assertEqual(self.get_grade_summary()['grade'], 'B')\",\n \"def grade(self, points):\\n credit = -1\\n while credit > points or credit < 0:\\n try:\\n credit = int(input(\\\"\\\\nScore out of \\\" + str(points) + \\\": \\\"))\\n except:\\n credit = -1\\n if credit != points:\\n self.feedback += \\\"\\\\n\\\\t\\\" + str(raw_input(\\\"Describe problem: \\\"))\\n return credit\",\n \"def grade_conversion(grade):\\n grade_converter = {\\\"A\\\": 4.00, \\\"A-\\\":3.67, \\\"B+\\\": 3.33, \\\"B\\\": 3.00, \\\"B-\\\": 2.67, \\\"C+\\\": 2.33, \\\"C\\\": 2.00, \\\"C-\\\": 1.67, \\\"D\\\": 1.00, \\\"F\\\": 0.0}\\n while True:\\n for val, val2 in grade_converter.items():\\n if grade == val:\\n return val2\",\n \"def RScore(x,p,d):\\n \\n if x <= d[p][0.20]:\\n return 5\\n elif x <= d[p][0.4]:\\n return 4\\n elif x <= d[p][0.6]: \\n return 3\\n elif x <= d[p][0.8]:\\n return 2\\n else:\\n return 1\",\n \"def RScore(x,p,d):\\n \\n if x <= d[p][0.20]:\\n return 5\\n elif x <= d[p][0.4]:\\n return 4\\n elif x <= d[p][0.6]: \\n return 3\\n elif x <= d[p][0.8]:\\n return 2\\n else:\\n return 1\",\n \"def grade_to_gpa(grade):\\n\\n letter_grade = \\\"\\\"\\n gpa = 0.0\\n\\n if type(grade) is str:\\n accepted_values = [\\\"A+\\\", \\\"A\\\", \\\"A-\\\", \\\"B+\\\", \\\"B\\\", \\\"B-\\\", \\\"FZ\\\"]\\n\\n # check that the grade is one of the accepted values\\n if grade in accepted_values:\\n\\n # assign grade to letter_grade\\n letter_grade = grade\\n\\n #If grade input is a string, but not an accepted value, raise a ValueError\\n else:\\n raise ValueError(\\\"Incorrect value. Grade must be an accepted letter grade.\\\")\\n\\n elif type(grade) is int:\\n\\n # check that grade is in the accepted range 0 to 100\\n if 0 <= grade <= 100:\\n\\n # convert the numeric grade to a letter grade\\n mark_to_letter = grade\\n\\n # assign the value to letter_grade\\n # hint: letter_grade = mark_to_letter(grade)\\n if mark_to_letter >= 90:\\n letter_grade = \\\"A+\\\"\\n elif mark_to_letter >= 85:\\n letter_grade = \\\"A\\\"\\n elif mark_to_letter >= 80:\\n letter_grade = \\\"A-\\\"\\n elif mark_to_letter >= 77:\\n letter_grade = \\\"B+\\\"\\n elif mark_to_letter >= 73:\\n letter_grade = \\\"B\\\"\\n elif mark_to_letter >= 70:\\n letter_grade = \\\"B-\\\"\\n else:\\n letter_grade = \\\"FZ\\\"\\n\\n #If grade input is not in accepted range, raise ValueError\\n else:\\n raise ValueError(\\\"Incorrect value. Grade must be in the accepted range of 0 to 100.\\\")\\n else:\\n # raise a TypeError exception\\n raise TypeError(\\\"Invalid type passed as parameter\\\")\\n\\n # write a long if-statement to convert letter_grade\\n # assign the value to gpa\\n if letter_grade == \\\"A+\\\":\\n gpa = 4.0\\n if letter_grade == \\\"A\\\":\\n gpa = 4.0\\n if letter_grade == \\\"A-\\\":\\n gpa = 3.7\\n if letter_grade == \\\"B+\\\":\\n gpa = 3.3\\n if letter_grade == \\\"B\\\":\\n gpa = 3.0\\n if letter_grade == \\\"B-\\\":\\n gpa = 2.7\\n if letter_grade == \\\"FZ\\\":\\n gpa = 0.0\\n\\n #Return the gpa of the grade\\n return gpa\",\n \"def publish_grade(self):\\r\\n score = self.lcp.get_score()\\r\\n self.runtime.publish(\\r\\n self,\\r\\n 'grade',\\r\\n {\\r\\n 'value': score['score'],\\r\\n 'max_value': score['total'],\\r\\n }\\r\\n )\\r\\n\\r\\n return {'grade': score['score'], 'max_grade': score['total']}\",\n \"def get_score(self, red_score, blue_score):\\n if red_score < blue_score:\\n return 0\\n elif red_score > blue_score:\\n return 1\\n else:\\n return 0.5\",\n \"def credits_earned(self):\\n\\n if self.grade() >= 69.5:\\n return self.nCredits\\n else:\\n return 0.0\",\n \"def test_grade(self):\\r\\n # Sample variables x and y in the range [-10, 10]\\r\\n sample_dict = {'x': (-10, 10), 'y': (-10, 10)}\\r\\n\\r\\n # The expected solution is numerically equivalent to x+2y\\r\\n problem = self.build_problem(sample_dict=sample_dict,\\r\\n num_samples=10,\\r\\n tolerance=0.01,\\r\\n answer=\\\"x+2*y\\\")\\r\\n\\r\\n # Expect an equivalent formula to be marked correct\\r\\n # 2x - x + y + y = x + 2y\\r\\n input_formula = \\\"2*x - x + y + y\\\"\\r\\n self.assert_grade(problem, input_formula, \\\"correct\\\")\\r\\n\\r\\n # Expect an incorrect formula to be marked incorrect\\r\\n # x + y != x + 2y\\r\\n input_formula = \\\"x + y\\\"\\r\\n self.assert_grade(problem, input_formula, \\\"incorrect\\\")\",\n \"def testDriver():\\n exam1=90\\n exam2=85\\n assignmentScores = [50, 60, 70, 80, ]\\n computeGrades(exam1, exam2, assignmentScores)\",\n \"def average_grade(self):\\n grade_sum = 0\\n grades_length = 0\\n for c in self.courses_grades:\\n if c[1] != \\\"-\\\":\\n grade_sum += int(c[1])\\n grades_length += 1\\n average = grade_sum / grades_length\\n return average\",\n \"def get_grade(self):\\n return self.__grade_value\",\n \"def base_contribute_score():\\n return 1\",\n \"def _score_to_decision(self, score):\",\n \"def grade_opbg(score, breakpoints=[0,5,10,20,50,200], grades=list(range(7))):\\n i = bisect.bisect_left(breakpoints, score)\\n return grades[i]\",\n \"def get_grade(course_det):\\n return course_det[1]\",\n \"def level(score):\\n user_level = \\\"\\\"\\n if score < 20:\\n user_level = \\\"elementary\\\"\\n elif score < 30:\\n user_level = \\\"intermediate\\\"\\n elif score < 35:\\n user_level = \\\"upper intermediate\\\"\\n else:\\n user_level = \\\"advanced\\\"\\n return user_level\",\n \"def main():\\n given_scores = []\\n num_grades = int(raw_input())\\n for i in xrange(num_grades):\\n given_scores.append(int(raw_input()))\\n for score in grading_students(given_scores):\\n print score\",\n \"def scoring(self):\\n pass\",\n \"def stateQualtityScore(roomba):\\n return 0\",\n \"def get_score_bucket(grade, max_grade):\\r\\n score_bucket = \\\"incorrect\\\"\\r\\n if(grade > 0 and grade < max_grade):\\r\\n score_bucket = \\\"partial\\\"\\r\\n elif(grade == max_grade):\\r\\n score_bucket = \\\"correct\\\"\\r\\n\\r\\n return score_bucket\",\n \"def vanilaScore(self,attended,state,W):\",\n \"def get_score(self, a, b):\\n ### FILL IN ###\",\n \"def f1_score(self):\",\n \"def letter_grades(adict):\\n\\n for key in adict:\\n\\t if adict[key] >= 90:\\n\\t\\t adict[key] = 'A'\\n\\t elif 80 <= adict[key] < 90:\\n\\t\\t adict[key] = 'B'\\n\\t elif 70 <= adict[key] < 80:\\n\\t\\t adict[key] = 'C'\\n\\t elif 60 <= adict[key] < 70:\\n\\t\\t adict[key] = 'D'\\n\\t else:\\n\\t\\t adict[key] = 'F'\",\n \"def arsenalResults(dat):\\n arsScore = int(dat[0])\\n othScore = int(dat[2])\\n if arsScore > othScore:\\n res = 1\\n elif arsScore == othScore:\\n res = 2\\n else:\\n res = 0\\n return res\",\n \"def rating(grade_count):\\r\\n if grade_count == 0 :\\r\\n grade = 7\\r\\n else:\\r\\n grade = 2\\r\\n \\r\\n return grade\",\n \"def AmOppCr(_cmp, e87482, e87487, e87492, e87497):\\n\\n \\\"\\\"\\\"\\n This function calculates American Opportunity Credit\\n for up to four eligible students\\n\\n \\\"\\\"\\\"\\n\\n # Expense should not exceed the cap of $4000.\\n if _cmp == 1:\\n\\n c87482 = max(0., min(e87482, 4000.))\\n c87487 = max(0., min(e87487, 4000.))\\n c87492 = max(0., min(e87492, 4000.))\\n c87497 = max(0., min(e87497, 4000.))\\n else:\\n c87482, c87487, c87492, c87497 = 0., 0., 0., 0.\\n\\n # Credit calculated as 100% of the first $2000 expense plus\\n # 25% of amount exceeding $2000.\\n if max(0, c87482 - 2000) == 0:\\n c87483 = c87482\\n else:\\n c87483 = 2000 + 0.25 * max(0, c87482 - 2000)\\n\\n if max(0, c87487 - 2000) == 0:\\n c87488 = c87487\\n else:\\n c87488 = 2000 + 0.25 * max(0, c87487 - 2000)\\n\\n if max(0, c87492 - 2000) == 0:\\n c87493 = c87492\\n else:\\n c87493 = 2000 + 0.25 * max(0, c87492 - 2000)\\n\\n if max(0, c87497 - 2000) == 0:\\n c87498 = c87497\\n else:\\n c87498 = 2000 + 0.25 * max(0, c87497 - 2000)\\n\\n # Sum of credits of all four students.\\n c87521 = c87483 + c87488 + c87493 + c87498\\n\\n return (c87482, c87487, c87492, c87497, c87483, c87488, c87493, c87498,\\n c87521)\",\n \"def scoring(self):\\n return -100 if self.loss_condition() else 0\",\n \"def _grade(student, request, course, keep_raw_scores):\\r\\n grading_context = course.grading_context\\r\\n raw_scores = []\\r\\n\\r\\n # Dict of item_ids -> (earned, possible) point tuples. This *only* grabs\\r\\n # scores that were registered with the submissions API, which for the moment\\r\\n # means only openassessment (edx-ora2)\\r\\n submissions_scores = sub_api.get_scores(\\r\\n course.id.to_deprecated_string(), anonymous_id_for_user(student, course.id)\\r\\n )\\r\\n\\r\\n totaled_scores = {}\\r\\n # This next complicated loop is just to collect the totaled_scores, which is\\r\\n # passed to the grader\\r\\n for section_format, sections in grading_context['graded_sections'].iteritems():\\r\\n format_scores = []\\r\\n for section in sections:\\r\\n section_descriptor = section['section_descriptor']\\r\\n section_name = section_descriptor.display_name_with_default\\r\\n\\r\\n # some problems have state that is updated independently of interaction\\r\\n # with the LMS, so they need to always be scored. (E.g. foldit.,\\r\\n # combinedopenended)\\r\\n should_grade_section = any(\\r\\n descriptor.always_recalculate_grades for descriptor in section['xmoduledescriptors']\\r\\n )\\r\\n\\r\\n # If there are no problems that always have to be regraded, check to\\r\\n # see if any of our locations are in the scores from the submissions\\r\\n # API. If scores exist, we have to calculate grades for this section.\\r\\n if not should_grade_section:\\r\\n should_grade_section = any(\\r\\n descriptor.location.to_deprecated_string() in submissions_scores\\r\\n for descriptor in section['xmoduledescriptors']\\r\\n )\\r\\n\\r\\n if not should_grade_section:\\r\\n with manual_transaction():\\r\\n should_grade_section = StudentModule.objects.filter(\\r\\n student=student,\\r\\n module_state_key__in=[\\r\\n descriptor.location for descriptor in section['xmoduledescriptors']\\r\\n ]\\r\\n ).exists()\\r\\n\\r\\n # If we haven't seen a single problem in the section, we don't have\\r\\n # to grade it at all! We can assume 0%\\r\\n if should_grade_section:\\r\\n scores = []\\r\\n\\r\\n def create_module(descriptor):\\r\\n '''creates an XModule instance given a descriptor'''\\r\\n # TODO: We need the request to pass into here. If we could forego that, our arguments\\r\\n # would be simpler\\r\\n with manual_transaction():\\r\\n field_data_cache = FieldDataCache([descriptor], course.id, student)\\r\\n return get_module_for_descriptor(student, request, descriptor, field_data_cache, course.id)\\r\\n\\r\\n for module_descriptor in yield_dynamic_descriptor_descendents(section_descriptor, create_module):\\r\\n\\r\\n (correct, total) = get_score(\\r\\n course.id, student, module_descriptor, create_module, scores_cache=submissions_scores\\r\\n )\\r\\n if correct is None and total is None:\\r\\n continue\\r\\n\\r\\n if settings.GENERATE_PROFILE_SCORES: \\t# for debugging!\\r\\n if total > 1:\\r\\n correct = random.randrange(max(total - 2, 1), total + 1)\\r\\n else:\\r\\n correct = total\\r\\n\\r\\n graded = module_descriptor.graded\\r\\n if not total > 0:\\r\\n #We simply cannot grade a problem that is 12/0, because we might need it as a percentage\\r\\n graded = False\\r\\n\\r\\n scores.append(Score(correct, total, graded, module_descriptor.display_name_with_default))\\r\\n\\r\\n _, graded_total = graders.aggregate_scores(scores, section_name)\\r\\n if keep_raw_scores:\\r\\n raw_scores += scores\\r\\n else:\\r\\n graded_total = Score(0.0, 1.0, True, section_name)\\r\\n\\r\\n #Add the graded total to totaled_scores\\r\\n if graded_total.possible > 0:\\r\\n format_scores.append(graded_total)\\r\\n else:\\r\\n log.exception(\\\"Unable to grade a section with a total possible score of zero. \\\" +\\r\\n str(section_descriptor.location))\\r\\n\\r\\n totaled_scores[section_format] = format_scores\\r\\n\\r\\n grade_summary = course.grader.grade(totaled_scores, generate_random_scores=settings.GENERATE_PROFILE_SCORES)\\r\\n\\r\\n # We round the grade here, to make sure that the grade is an whole percentage and\\r\\n # doesn't get displayed differently than it gets grades\\r\\n grade_summary['percent'] = round(grade_summary['percent'] * 100 + 0.05) / 100\\r\\n\\r\\n letter_grade = grade_for_percentage(course.grade_cutoffs, grade_summary['percent'])\\r\\n grade_summary['grade'] = letter_grade\\r\\n grade_summary['totaled_scores'] = totaled_scores \\t# make this available, eg for instructor download & debugging\\r\\n if keep_raw_scores:\\r\\n grade_summary['raw_scores'] = raw_scores # way to get all RAW scores out to instructor\\r\\n # so grader can be double-checked\\r\\n return grade_summary\",\n \"def test_grades(grade_list):\\n\\ti = 0\\n\\tchange_list = []\\n\\tfor i in range(len(grade_list)-1):\\n\\t\\tm = ord(grade_list[i])\\n\\t\\tn = ord(grade_list[i+1])\\n\\t\\tif m < n:\\n\\t\\t\\tk = -1\\n\\t\\telif m == n:\\n\\t\\t\\tk = 0\\n\\t\\telse:\\n\\t\\t\\tk = 1\\n\\t\\tchange_list.append(k)\\t\\n\\treturn sum(change_list)\",\n \"def test_avg_grade(self):\\n\\t\\ts = Student_Analytics()\\n\\t\\tself.assertEqual(s.classify_grade(s.avg_grade(3)),\\\"B\\\")\",\n \"def earned_hw_scores(self):\\r\\n return [s.earned for s in self.get_grade_summary()['totaled_scores']['Homework']]\",\n \"def grade_submissions(submissions, roster, deadlines):\\n grades = dict([(u, 0) for u in roster.keys()])\\n for sub in submissions:\\n sub_date = convert_date(sub['Submission date'])\\n user = sub['UID'].split('@')[0]\\n score = 0\\n if user in roster:\\n if (sub_date < deadlines[roster[user]] and\\n sub['Correct'].upper() == 'TRUE'):\\n score = 1\\n if grades[user] != 1:\\n grades[user] = score\\n return grades\",\n \"def generateROCcurve(junctionBed, ests, stepSize=100, wiggle=0):\\n\\n goodNames, badNames = findGoodBadReads(junctionBed, ests, wiggle)\\n\\n rocTable = []\\n scores = []\\n scoredDict = {}\\n countGood = 0\\n countBad = 0\\n countStacked = 0\\n countUnclear = 0\\n\\n for line in open(junctionBed):\\n if line.startswith(\\\"track\\\"):\\n continue\\n [chr, start, stop, name, score] = line.split()[:5]\\n score = float(score)\\n if goodNames.has_key(name):\\n isGood = 1 #True\\n elif badNames.has_key(name):\\n isGood = 0 #False\\n else:\\n continue\\n\\n scores.append(score)\\n\\n if isGood:\\n countGood += 1\\n\\n else:\\n countBad += 1\\n\\n scoredDict[name] = (score, isGood)\\n\\n print \\\"Counted %s good and %s bad\\\" % (countGood, countBad)\\n\\n #print scores[:3]\\n scores.sort(reverse=True)\\n #print scores[:5]\\n #use every 100th score\\n for x in scores[::stepSize]:\\n numGoodAbove = 0\\n numBadAbove = 0\\n for k, (score, real) in scoredDict.iteritems():\\n if score >= x:\\n #print real, score, k, pairsDict[k]\\n if real:\\n numGoodAbove += 1\\n else:\\n numBadAbove += 1\\n\\n #print x, numGoodAbove, numBadAbove, numGoodAbove / float(countGood) , numBadAbove / float(countBad)\\n rocTable.append((x, numGoodAbove, numBadAbove, numGoodAbove / float(countGood) , numBadAbove / float(countBad)))\\n return rocTable,scoredDict\",\n \"def determine_color(score=be.df[\\\"score\\\"].mean()):\\n avg_score = int(round(be.df[\\\"score\\\"].mean(), 0))\\n if score < avg_score * 0.95:\\n return \\\"red\\\"\\n elif score > avg_score * 1.05:\\n return \\\"green\\\"\\n else:\\n return \\\"yellow\\\"\",\n \"def calculate(self):\\n\\n s_sum = 0\\n class_num = len(self.scores)\\n \\n for i in range(class_num):\\n s_sum += self.scores[i]\\n\\n av = float(s_sum)/class_num\\n if av >= 90:\\n return 'O'\\n elif av >= 80:\\n return 'E'\\n elif av >= 70:\\n return 'A'\\n elif av >= 55:\\n return 'P'\\n elif av >= 40:\\n return 'D'\\n else:\\n return 'T'\",\n \"def test_b_grade_exact(self):\\r\\n self.basic_setup()\\r\\n self.submit_question_answer('p1', {'2_1': 'Correct'})\\r\\n self.check_grade_percent(0.33)\\r\\n self.assertEqual(self.get_grade_summary()['grade'], 'B')\",\n \"def day_009_1():\\n student_scores = {\\n \\\"Harry\\\": 81,\\n \\\"Ron\\\": 78,\\n \\\"Hermione\\\": 99,\\n \\\"Draco\\\": 74,\\n \\\"Neville\\\": 62,\\n }\\n\\n student_grades = {}\\n\\n for student in student_scores:\\n if student_scores[student] <= 70:\\n student_grades[student] = \\\"Fail\\\"\\n elif student_scores[student] <= 80:\\n student_grades[student] = \\\"Acceptable\\\"\\n elif student_scores[student] <= 90:\\n student_grades[student] = \\\"Exceeds Expectations\\\"\\n else:\\n student_grades[student] = \\\"Outstanding\\\"\\n\\n print(student_grades)\",\n \"def ards_score(self):\\n if self.ards_wickets == 10:\\n var1 = \\\"All Out\\\"\\n return str('{0} {1}').format(self.ards_runs, var1)\\n else:\\n var1 = self.ards_wickets\\n return str('{0}-{1}').format(self.ards_runs, var1)\",\n \"def grades_to_number(grade):\\n # Conditions\\n if grade == \\\"A\\\":\\n return 1\\n elif grade == \\\"B\\\":\\n return 0.5\\n elif grade == \\\"C\\\":\\n return 0\\n elif grade == \\\"D\\\":\\n return -0.5\\n else:\\n return -1\",\n \"def score(self):\",\n \"def get_adl(adl_score,cog):\\n ranges = [(0),(1,2),(3,4),(5,6,7),(8,9),(10,11),(12,13),(14,15,16)]\\n modifier = [-2,-1,0,1,2,3,4,5]\\n #\\n found = True\\n while found:\\n for i,r in enumerate(ranges):\\n print(adl_score,range[i])\\n if adl_score in ranges[i]:\\n adl_score = adl_score+modifier[i]\\n found = False\\n break\\n return adl_score\",\n \"def get_score_summary(fname):\\n gradedata = {}\\n fhandler = open(fname, 'r')\\n rest_data = csv.reader(fhandler)\\n for row in rest_data:\\n if row[10] not in ['P', '', 'GRADE']:\\n gradedata[row[0]] = [row[1], row[10]]\\n gradedata.update(gradedata)\\n fhandler.close()\\n\\n gradereview = {}\\n for value in gradedata.itervalues():\\n if value[0] not in gradereview.iterkeys():\\n count1 = 1\\n count2 = GRADESCL[value[1]]\\n else:\\n count1 = gradereview[value[0]][0] + 1\\n count2 = gradereview[value[0]][1] + GRADESCL[value[1]]\\n gradereview[value[0]] = (count1, count2)\\n gradereview.update(gradereview)\\n grade = {}\\n for key in gradereview.iterkeys():\\n count1 = gradereview[key][0]\\n count2 = gradereview[key][1]/gradereview[key][0]\\n grade[key] = (count1, count2)\\n return grade\",\n \"def test_grade_change(self):\\n\\t\\ts = Student_Analytics()\\n\\t\\tself.assertEqual(int(s.grade_change()),0)\",\n \"def round_grade(grade):\\n\\t# only round grade if the input is a float\\n\\tif type(grade) != str:\\n\\t\\trounded_grade = np.floor(grade/10)\\n\\t\\treturn rounded_grade\\n\\t# otherwise return the original grade -> could be string \\\"Lunch\\\" or \\\"Period\\\"\\n\\telse:\\n\\t\\treturn grade\",\n \"def degrees_to_grade(angle_in_degrees):\\n if np.any(angle_in_degrees>90): #np.any works if angle_in_degrees is a single value or array\\n raise Exception('Can only convert an angle between 0 to 90 degrees to a percentage grade.\\\\nSee: https://en.wikipedia.org/wiki/Grade_(slope)')\\n angle_in_grade = 100*np.tan(angle_in_degrees*np.pi/180)\\n return(angle_in_grade)\",\n \"def save_grade(self, data):\\r\\n\\r\\n required = ['location', 'submission_id', 'submission_key', 'score', 'feedback', 'submission_flagged', 'answer_unknown']\\r\\n if data.get(\\\"submission_flagged\\\", False) in [\\\"false\\\", False, \\\"False\\\", \\\"FALSE\\\"]:\\r\\n required.append(\\\"rubric_scores[]\\\")\\r\\n success, message = self._check_required(data, set(required))\\r\\n if not success:\\r\\n return self._err_response(message)\\r\\n\\r\\n success, message = self._check_feedback_length(data)\\r\\n if not success:\\r\\n return self._err_response(message)\\r\\n\\r\\n data_dict = {k:data.get(k) for k in required}\\r\\n if 'rubric_scores[]' in required:\\r\\n data_dict['rubric_scores'] = data.getall('rubric_scores[]')\\r\\n data_dict['grader_id'] = self.system.anonymous_student_id\\r\\n\\r\\n try:\\r\\n response = self.peer_gs.save_grade(**data_dict)\\r\\n success, location_data = self.query_data_for_location(data_dict['location'])\\r\\n #Don't check for success above because the response = statement will raise the same Exception as the one\\r\\n #that will cause success to be false.\\r\\n response.update({'required_done' : False})\\r\\n if 'count_graded' in location_data and 'count_required' in location_data and int(location_data['count_graded'])>=int(location_data['count_required']):\\r\\n response['required_done'] = True\\r\\n return response\\r\\n except GradingServiceError:\\r\\n # This is a dev_facing_error\\r\\n log.exception(\\\"\\\"\\\"Error saving grade to open ended grading service. server url: {0}\\\"\\\"\\\"\\r\\n .format(self.peer_gs.url)\\r\\n )\\r\\n # This is a student_facing_error\\r\\n return {\\r\\n 'success': False,\\r\\n 'error': EXTERNAL_GRADER_NO_CONTACT_ERROR\\r\\n }\",\n \"def adjusted_grade(clicker_points, midterm_grade):\\n if not clicker_points:\\n return midterm_grade\\n avg_clicker_points = sum(clicker_points.values())/len(clicker_points)\\n joint_set = set(clicker_points).union(set(midterm_grade))\\n clicker_extra_points = {student: 1 if clicker_points.get(student)\\n >= avg_clicker_points else 0 for\\n student in clicker_points}\\n return {name: midterm_grade.get(name, 0) +\\n clicker_extra_points.get(name, 0)\\n for name in joint_set}\",\n \"def disp_score():\",\n \"def bedroc(y_true, y_score, alpha=..., pos_label=...):\\n ...\",\n \"def abilityScores(self):\\n mods = [(self.str -10)/2,\\n (self.dex-10)/2,\\n (self.con-10)/2,\\n (self.int-10)/2,\\n (self.wis-10)/2,\\n (self.cha-10)/2]\\n return \\\"STR: {0} ({1}) \\\\nDEX: {2} ({3})\\\\nCON: {4} ({5})\\\".format(self.str,\\n mods[0],\\n self.dex,\\n mods[1],\\n self.con,\\n mods[2])+\\\"\\\\n\\\" \\\\\\n \\\"INT: {0} ({1})\\\\nWIS: {2} ({3})\\\\nCHA: {4} ({5})\\\".format(self.int,\\n mods[3],\\n self.wis,\\n mods[4],\\n self.cha,\\n mods[5])\",\n \"def getAbilityScores(self):\\n mods = [(self.str -10)/2,\\n (self.dex-10)/2,\\n (self.con-10)/2,\\n (self.int-10)/2,\\n (self.wis-10)/2,\\n (self.cha-10)/2]\\n print \\\"STR: {0} ({1}) \\\\nDEX: {2} ({3})\\\\nCON: {4} ({5})\\\".format(self.str,\\n mods[0],\\n self.dex,\\n mods[1],\\n self.con,\\n mods[2])\\n print \\\"INT: {0} ({1})\\\\nWIS: {2} ({3})\\\\nCHA: {4} ({5})\\\".format(self.int,\\n mods[3],\\n self.wis,\\n mods[4],\\n self.cha,\\n mods[5])\",\n \"def letter_grade(grade):\\n\\t# define dictionary of grading scale\\n\\t# Check the fillna above was filled w/ -1.0\\n\\td = {18.0: 'A', 17.0: 'A', 16.0: 'A', 15.0: 'A', 14.0: 'A', 13.0: 'A',\\n\\t12.0: 'A', 11.0: 'A', 10.0: 'A', 9.0: 'A', 8.0: 'B', \\n\\t7.0: 'C', 6.0: 'D', 5.0: 'F', 4.0: 'F', 3.0: 'F', 2.0: 'F', \\n\\t1.0: 'F', 0.0: 'F', -1.0: '-'}\\n\\t\\n\\t# get letter grade only if grade is not a string\\n\\tif type(grade) != str:\\n\\t\\t# get the letter\\n\\t\\tletter = d[grade]\\n\\t\\treturn letter\\n\\telse:\\n\\t\\treturn grade\",\n \"def get_result(mishkaScore: int, chrisScore: int) -> bool:\\n if mishkaScore > chrisScore:\\n return \\\"M\\\"\\n if mishkaScore < chrisScore:\\n return \\\"C\\\"\\n return \\\"D\\\"\",\n \"def test_compute_grade_for_non_fa(self):\\n run3_data = self.user_edx_data.get_run_data(self.run_no_fa.edx_course_key)\\n run4_data = self.user_edx_data.get_run_data(self.run_no_fa_with_cert.edx_course_key)\\n\\n grade3_from_cur_grade = api._compute_grade_for_non_fa(run3_data)\\n grade4_from_cert = api._compute_grade_for_non_fa(run4_data)\\n\\n assert isinstance(grade3_from_cur_grade, api.UserFinalGrade)\\n assert isinstance(grade4_from_cert, api.UserFinalGrade)\\n\\n assert grade3_from_cur_grade.passed is False\\n assert grade3_from_cur_grade.grade == self.current_grades.get(\\n self.run_no_fa.edx_course_key).data.get('percent')\\n # this is true if the enrollment is verified\\n assert grade3_from_cur_grade.payed_on_edx is True\\n assert grade4_from_cert.passed is True\\n assert grade4_from_cert.grade == self.current_grades.get(\\n self.run_no_fa_with_cert.edx_course_key).data.get('percent')\\n # this is True as long as the certificate is verified\\n assert grade4_from_cert.payed_on_edx is True\",\n \"def get_grade_stats(scores):\\r\\n # Calculate the arithmetic mean\\r\\n mean = sum(student_scores) / len(scores)\\r\\n\\r\\n # Calculate the standard deviation\\r\\n tmp = 0\\r\\n for i in range(len(scores)):\\r\\n tmp += (scores[i] - mean) ** 2\\r\\n std_dev = (tmp / len(scores)) ** 0.5\\r\\n\\r\\n # Package and return average, standard deviation in a tuple\\r\\n return mean, std_dev\",\n \"def test_get_score_with_different_score_value_in_rubric(self):\\r\\n combinedoe = CombinedOpenEndedV1Module(self.test_system,\\r\\n self.location,\\r\\n self.definition,\\r\\n self.descriptor,\\r\\n static_data=self.static_data,\\r\\n metadata=self.metadata,\\r\\n instance_state=json.loads(INSTANCE_INCONSISTENT_STATE3))\\r\\n score_dict = combinedoe.get_score()\\r\\n self.assertEqual(score_dict['score'], 25.0)\\r\\n self.assertEqual(score_dict['total'], 5.0)\",\n \"def rads_to_grade(angle_in_radians):\\n if np.any(angle_in_radians>0.5*np.pi): #np.any works if angle_in_degrees is a single value or array\\n raise Exception('Can only convert an angle between 0 to pi/2 radians to a percentage grade.\\\\nSee: https://en.wikipedia.org/wiki/Grade_(slope)')\\n angle_in_grade = 100*np.tan(angle_in_radians)\\n return(angle_in_grade)\",\n \"def what_is_the_grade(self):\\n\\t\\treturn_dict = {\\n\\t\\t\\t'section_title': self.title, \\n\\t\\t\\t'section_weight': self.weight,\\n\\t\\t\\t'grade_value' : self.current_grade_value,\\n\\t\\t\\t'comment_text' : self.current_comment_text,\\n\\t\\t\\t'default_comments_text' : self.current_default_comment_text,\\n\\t\\t\\t'example_comments_text' : self.current_example_comment_text,\\n\\t\\t\\t'is_complete': self.is_complete\\n\\t\\t}\\n\\n\\t\\treturn return_dict\",\n \"def add_percentage(grade):\\n\\tif type(grade) == float:\\n\\t\\tperc_grade = str(grade) + '%'\\n\\t\\treturn perc_grade\\n\\telse:\\n\\t\\treturn grade\",\n \"def enrichment_factor(y_true, y_score, percentage=..., pos_label=..., kind=...):\\n ...\",\n \"def get_strength(self):\\n return 10 - self.get_agility()\",\n \"def passer_rating(pass_attempts_int, pass_completions_int, passing_yards_flt,\\n touchdowns_int, interceptions_int):\\n # C is the \\\"completions per pass attempt\\\" times 100 minus 30, all divided by 20.\\n # Y is the \\\"yards per pass attempt\\\" minus 3, all divided by 4\\n # T is the \\\"touchdown per pass attempt\\\" times 20.\\n # I the \\\"interceptions per pass attempt\\\"\\n\\n C = (pass_completions_int / pass_attempts_int * 100 - 30) / 20\\n Y = (passing_yards_flt / pass_attempts_int - 3) / 4\\n T = touchdowns_int / pass_attempts_int * 20\\n I = 2.375 - (interceptions_int / pass_attempts_int * 25)\\n\\n # The PASSER RATING is computed as the summ of C, Y, T and I all divided by 6\\n # and then multiplied 100.\\n PR = (C + Y + T + I) / 6 * 100\\n return PR\",\n \"def pre_flop_strength(hand):\\n highs = {}\\n highs[4] = [\\n \\\"AA\\\", \\\"AKs\\\", \\\"AQs\\\", \\\"AJs\\\", \\\"ATs\\\", \\\"AKo\\\", \\\"KK\\\", \\\"KQs\\\", \\\"KJs\\\", \\\"AQo\\\",\\n \\\"QQ\\\", \\\"QJs\\\", \\\"JJ\\\", \\\"TT\\\"\\n ]\\n highs[3] = [\\n \\\"A5s\\\", \\\"A4s\\\", \\\"A3s\\\", \\\"KTs\\\", \\\"KQo\\\", \\\"QTs\\\", \\\"AJo\\\", \\\"JTs\\\", \\\"T9s\\\", \\\"99\\\",\\n \\\"98s\\\", \\\"88\\\", \\\"87s\\\", \\\"77\\\", \\\"66\\\"\\n ]\\n highs[2] = [\\n \\\"A9s\\\", \\\"A8s\\\", \\\"A7s\\\", \\\"A6s\\\", \\\"A2s\\\", \\\"K9s\\\", \\\"K8s\\\", \\\"Q9s\\\", \\\"KJo\\\", \\\"QJo\\\",\\n \\\"J9s\\\", \\\"ATo\\\", \\\"KTo\\\", \\\"QTo\\\", \\\"JTo\\\", \\\"T8s\\\", \\\"A9o\\\", \\\"J9o\\\", \\\"T9o\\\", \\\"97s\\\",\\n \\\"98o\\\", \\\"86s\\\", \\\"76s\\\", \\\"75s\\\", \\\"65s\\\", \\\"55\\\", \\\"44\\\", \\\"33\\\", \\\"22\\\"\\n ]\\n highs[1] = [\\n \\\"K7s\\\", \\\"K6s\\\", \\\"K5s\\\", \\\"K4s\\\", \\\"K3s\\\", \\\"Q8s\\\", \\\"Q7s\\\", \\\"Q6s\\\", \\\"Q5s\\\", \\\"Q4s\\\",\\n \\\"J8s\\\", \\\"J7s\\\", \\\"J6s\\\", \\\"J5s\\\", \\\"T7s\\\", \\\"T6s\\\", \\\"K9o\\\", \\\"Q9o\\\", \\\"96s\\\", \\\"A8o\\\",\\n \\\"K8o\\\", \\\"Q8o\\\", \\\"J8o\\\", \\\"T8o\\\", \\\"85s\\\", \\\"A7o\\\", \\\"K7o\\\", \\\"Q7o\\\", \\\"T7o\\\", \\\"97o\\\",\\n \\\"87o\\\", \\\"74s\\\", \\\"A6o\\\", \\\"K6o\\\", \\\"86o\\\", \\\"76o\\\", \\\"64s\\\", \\\"63s\\\", \\\"A5o\\\", \\\"75o\\\",\\n \\\"65o\\\", \\\"54s\\\", \\\"53s\\\", \\\"A4o\\\", \\\"43s\\\", \\\"A3o\\\"\\n ]\\n card0, card1 = hand\\n if card0[0] == card1[0]:\\n pair = \\\"\\\".join([card0[0], card1[0]])\\n elif card0[1] == card1[1]:\\n pair = \\\"\\\".join([card0[0], card1[0], \\\"s\\\"])\\n else:\\n pair = \\\"\\\".join([card0[0], card1[0], \\\"o\\\"])\\n for strenght in highs:\\n if pair in highs[strenght]:\\n return strenght\\n return 0\",\n \"def worst_score(self):\\r\\n pass\",\n \"def solution():\\n file = get_source()\\n results = []\\n for c in range(int(file.readline())):\\n grades = sorted([int(v) for v in file.readline().split()][1:])\\n average = float(sum(grades))/len(grades)\\n first = next((i for i,g in enumerate(grades) if g > average), len(grades))\\n people_above_average = len(grades) - first\\n results.append(people_above_average * 100.0 / len(grades))\\n for r in results:\\n print '%.3f%%' % r\",\n \"def save_grade(request, course_id):\\r\\n\\r\\n course_key = SlashSeparatedCourseKey.from_deprecated_string(course_id)\\r\\n _check_access(request.user, course_key)\\r\\n\\r\\n if request.method != 'POST':\\r\\n raise Http404\\r\\n p = request.POST\\r\\n required = set(['score', 'feedback', 'submission_id', 'location', 'submission_flagged'])\\r\\n skipped = 'skipped' in p\\r\\n #If the instructor has skipped grading the submission, then there will not be any rubric scores.\\r\\n #Only add in the rubric scores if the instructor has not skipped.\\r\\n if not skipped:\\r\\n required.add('rubric_scores[]')\\r\\n actual = set(p.keys())\\r\\n missing = required - actual\\r\\n if len(missing) > 0:\\r\\n return _err_response('Missing required keys {0}'.format(\\r\\n ', '.join(missing)))\\r\\n\\r\\n success, message = check_feedback_length(p)\\r\\n if not success:\\r\\n return _err_response(message)\\r\\n\\r\\n grader_id = unique_id_for_user(request.user)\\r\\n\\r\\n location = course_key.make_usage_key_from_deprecated_string(p['location'])\\r\\n\\r\\n try:\\r\\n result = staff_grading_service().save_grade(course_key,\\r\\n grader_id,\\r\\n p['submission_id'],\\r\\n p['score'],\\r\\n p['feedback'],\\r\\n skipped,\\r\\n p.getlist('rubric_scores[]'),\\r\\n p['submission_flagged'])\\r\\n except GradingServiceError:\\r\\n #This is a dev_facing_error\\r\\n log.exception(\\r\\n \\\"Error saving grade in the staff grading interface in open ended grading. Request: {0} Course ID: {1}\\\".format(\\r\\n request, course_id))\\r\\n #This is a staff_facing_error\\r\\n return _err_response(STAFF_ERROR_MESSAGE)\\r\\n except ValueError:\\r\\n #This is a dev_facing_error\\r\\n log.exception(\\r\\n \\\"save_grade returned broken json in the staff grading interface in open ended grading: {0}\\\".format(\\r\\n result_json))\\r\\n #This is a staff_facing_error\\r\\n return _err_response(STAFF_ERROR_MESSAGE)\\r\\n\\r\\n if not result.get('success', False):\\r\\n #This is a dev_facing_error\\r\\n log.warning(\\r\\n 'Got success=False from staff grading service in open ended grading. Response: {0}'.format(result_json))\\r\\n return _err_response(STAFF_ERROR_MESSAGE)\\r\\n\\r\\n # Ok, save_grade seemed to work. Get the next submission to grade.\\r\\n return HttpResponse(json.dumps(_get_next(course_id, grader_id, location)),\\r\\n mimetype=\\\"application/json\\\")\",\n \"def scoreR(self) :\\n if self.leafR() :\\n return self.leafScore(), self\\n else :\\n games = self.R()\\n min_g = games[0]\\n min_score = min_g.scoreL()\\n for g in games[1:] :\\n score = g.scoreL()\\n if score[0] < min_score[0] :\\n min_g = g\\n min_score = score\\n return (min_score+(min_g,))\",\n \"def quality_rating(PR):\\n \\n if PR <= 85:\\n quality = \\\"poor\\\"\\n elif PR < 90:\\n quality = \\\"mediocre\\\"\\n elif PR < 95:\\n quality = \\\"good\\\"\\n else:\\n quality = \\\"great\\\"\\n\\n return quality\",\n \"def get_prediction(course_grades, train_data, train_grades):\\n\\n # In the case the student has no grade, return a predicted grade of 0\\n if train_data.size == 0:\\n return 0,0\\n\\n model = BayesianRidge()\\n model.fit(train_data, train_grades)\\n y_mean, y_sd = model.predict(np.array(course_grades).reshape(1, -1), return_std=True)\\n\\n return y_mean, y_sd\",\n \"def grade_boro(boroughts,data):\\n boro_df=data.iloc[np.where(data.BORO==boroughts)]\\n return total_grade(boro_df)\",\n \"def grade_this_guy(self):\\n log.info(\\\"Function has been called\\\")\\n answer = self._file_storage_path(self.raw_answer['sha1'], self.raw_answer['filename'])\\n question = self._question_storage_path(self.raw_question['sha1'], self.raw_question['filename'])\\n solution = self._solution_storage_path(self.raw_solution['sha1'], self.raw_solution['filename'])\\n\\n\\n answer = os.path.join(IMAGEDIFF_ROOT, answer)\\n question = os.path.join(IMAGEDIFF_ROOT, question)\\n solution = os.path.join(IMAGEDIFF_ROOT, solution)\\n\\n self.score = grade(question, answer, solution)\\n if self.score > self.points:\\n self.score = self.points\\n self.points=float(self.max_score())\\n self.save()\\n if self.score >= 0: \\n self.runtime.publish(self, 'grade',{ 'value': self.score, 'max_value':self.max_score(),})\\n log.info(\\\"runtime.publish-ed\\\")\\n self.save()\\n return Response(json_body=self.student_state())\",\n \"def grade_report(course):\\n report = []\\n for st in course.get_students():\\n try:\\n average = sum(course.get_grades(st)) / len(course.get_grades(st))\\n report.append(str(st) + '\\\\'s mean grade is: ' + str(average) + '.')\\n except ZeroDivisionError:\\n report.append(str(st) + ' has no grades.')\\n return '\\\\n'.join(report)\",\n \"def __score_by_iceberg_level(self, source_iceberg, iceberg_to_score, iceberg_owner_after_all_groups_arrived):\\n if utils.is_me(self.__game, iceberg_owner_after_all_groups_arrived):\\n if source_iceberg.level <= iceberg_to_score.level:\\n return SOURCE_LEVEL_SMALL_THAN_DESTINATION_SCORE\\n if iceberg_to_score.level < iceberg_to_score.upgrade_level_limit:\\n return LEVEL_FACTOR_SCORE ** iceberg_to_score.penguins_per_turn\\n elif utils.is_neutral(self.__game, iceberg_owner_after_all_groups_arrived):\\n if MapChecker.get().is_tricky_map() or MapChecker.get().is_extra_far_treasure() or MapChecker.get().is_extra_far():\\n return LEVEL_FACTOR_SCORE * iceberg_to_score.penguins_per_turn\\n else:\\n return LEVEL_FACTOR_SCORE ** iceberg_to_score.penguins_per_turn\\n return iceberg_to_score.penguins_per_turn\",\n \"def fkg(word, sent, syll):\\n flesch_kincaid_grade = (0.39* (word / sent)) + (11.8 * (syll / word)) - 15.59\\n return flesch_kincaid_grade\",\n \"def _get_best_grading():\\n possible_main_grading = np.array([10, 20, 25, 40, 50,\\n 100, 200, 250, 400,\\n 500, 1000])\\n total_dist = length * dpp\\n # Get the most suitable grading\\n num_gradings = total_dist / possible_main_grading\\n best_grading = possible_main_grading[num_gradings <= 7][0]\\n best_num = num_gradings[num_gradings <= 7][0]\\n dist = np.arange(best_num) * best_grading\\n pos = (dist / dpp).astype(np.int)\\n texts = [\\\"{0:d}\\\".format(int(d)) for d in dist]\\n return pos, texts\",\n \"def _add_PERC1(self, w2, row):\\n if 'PASS' not in row:\\n return False\\n if 'EQ' not in row:\\n return False\\n if row['PASS']:\\n row['PERC1'] = 100.0 * row['EQ'] / row['PASS']\\n else:\\n row['PERC1'] = 0.0\\n return True\",\n \"def test_compute_grade_for_fa(self):\\n run1_data = self.user_edx_data.get_run_data(self.run_fa.edx_course_key)\\n run2_data = self.user_edx_data.get_run_data(self.run_fa_with_cert.edx_course_key)\\n\\n grade1_from_cur_grade = api._compute_grade_for_fa(run1_data)\\n grade2_from_cert = api._compute_grade_for_fa(run2_data)\\n\\n assert isinstance(grade1_from_cur_grade, api.UserFinalGrade)\\n assert isinstance(grade2_from_cert, api.UserFinalGrade)\\n\\n assert grade1_from_cur_grade.passed == self.current_grades.get(\\n self.run_fa.edx_course_key).data.get('passed')\\n assert grade1_from_cur_grade.grade == self.current_grades.get(\\n self.run_fa.edx_course_key).data.get('percent')\\n assert grade1_from_cur_grade.payed_on_edx == (self.enrollments.get(\\n self.run_fa.edx_course_key).data.get('mode') in ['verified', 'honor'])\\n\\n assert grade2_from_cert.passed is self.current_grades.get(\\n self.run_fa_with_cert.edx_course_key).data.get('passed')\\n assert grade2_from_cert.grade == self.current_grades.get(\\n self.run_fa_with_cert.edx_course_key).data.get('percent')\\n # this is True as long as the certificate is verified\\n assert grade2_from_cert.payed_on_edx is True\",\n \"def graham_number(self):\\n\\n calc_df = self.stock.calculations_df\\n\\n if calc_df.iloc[9]['Value'] == 'N/A' or calc_df.iloc[10]['Value'] == 'N/A':\\n self.stock.append_calc_result('Graham Number less than 22.5 ?', 'N/A', 'N/A', 'Could not obtain 3 Year P/E'\\n 'or Current P/B Ratios')\\n return\\n\\n p_to_e_ratio = calc_df.iloc[9]['Value']\\n p_to_b_ratio = calc_df.iloc[10]['Value']\\n graham_num = round(p_to_e_ratio * p_to_b_ratio, 2)\\n\\n if graham_num <= 22.5:\\n criteria_passed = 'Yes'\\n else:\\n criteria_passed = 'No'\\n\\n self.stock.append_calc_result('Graham Number less than 22.5 ?', graham_num, criteria_passed, '')\",\n \"def test_none_grade(self):\\r\\n self.basic_setup()\\r\\n self.check_grade_percent(0)\\r\\n self.assertEqual(self.get_grade_summary()['grade'], None)\",\n \"def get_regret(self, df,score_col = 'dk_score',proj_col = 'proj_score',\\n driver_col = 'Driver', salary_col = 'Salary'):\\n max_score, max_lineup = self.get_max_score(df,score_col = score_col)\\n pick_scores, pick_lineups = self.get_max_score(df,score_col = proj_col)\\n return max_score-pick_scores, [max_score,pick_scores],[max_lineup,pick_lineups]\",\n \"def test_get_score(self):\\r\\n\\r\\n score_dict = self.get_score(True, 3, 3)\\r\\n\\r\\n # Score should be 1.0.\\r\\n self.assertEqual(score_dict[\\\"score\\\"], 1.0)\\r\\n\\r\\n # Testing score after data is stored in student_data_for_location in xmodule.\\r\\n _score_dict = self.peer_grading.get_score()\\r\\n\\r\\n # Score should be 1.0.\\r\\n self.assertEqual(_score_dict[\\\"score\\\"], 1.0)\",\n \"def scoreEducation(self, employee_ed, name):\\n\\n university = self.university\\n schools = [school for school in name if school != \\\"\\\"]\\n rank = []\\n\\n for school in schools:\\n match_score = []\\n for university_name in university['Name']:\\n try:\\n match_score.append(fuzz.ratio(university_name, school))\\n except Exception as e:\\n match_score.append(0)\\n\\n #Case where there is no valid school in the parsed XML, resulting in \\\"schools\\\" to be an empty list.\\n if not match_score:\\n match_score.append(0)\\n\\n try:\\n if max(match_score) > 70:\\n rank.append(university['Rank'][match_score.index(max(match_score))])\\n else:\\n rank.append(1)\\n except KeyError:\\n rank.append(1)\\n\\n\\n ed_weight = {'Bachelor': 0.1, 'Masters': 0.15, 'doctorate': 0.2, '': 0.05}\\n\\n employee_ed = list(set(employee_ed).intersection(ed_weight.keys()))\\n employee_ed_weight = [ed_weight[i] for i in employee_ed]\\n\\n try:\\n highest_qualification = ed_weight[max(employee_ed, key=lambda x: ed_weight[x])]\\n except:\\n highest_qualification = 0.05\\n\\n return sum(i*j for i, j in zip(employee_ed_weight, rank))\",\n \"def upgrad_one_contribute_score(user_total_click_num):\\n return 1/math.log(1 + user_total_click_num)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7085485","0.6440944","0.63578296","0.63572264","0.6289942","0.6267479","0.60695726","0.59769577","0.5956954","0.59330213","0.5848372","0.5821019","0.58090127","0.58062154","0.57650155","0.57590044","0.5737819","0.5693649","0.5693649","0.5689963","0.56800187","0.5675765","0.56712085","0.56561005","0.5652554","0.5650292","0.56423664","0.5630729","0.5570134","0.5562054","0.5557976","0.5549922","0.5521026","0.55107224","0.5508445","0.5496629","0.54912376","0.5489164","0.54862285","0.54603726","0.5452443","0.5441323","0.54374593","0.54367846","0.5429143","0.5422076","0.54111284","0.5396405","0.53752947","0.5362048","0.53537816","0.5353341","0.5351995","0.5331024","0.5324557","0.5323331","0.5308965","0.53019875","0.53005266","0.5297459","0.5297393","0.5295068","0.5289334","0.52648604","0.52606714","0.5260363","0.52528435","0.5245555","0.5241886","0.52391493","0.5235113","0.52314055","0.522997","0.5229818","0.52294487","0.52168393","0.52148724","0.5213529","0.5200771","0.5200549","0.51970565","0.5193088","0.5187752","0.51842487","0.51792705","0.5178604","0.51732856","0.5165509","0.5164434","0.5160421","0.5159518","0.5156197","0.5154629","0.5153252","0.51531625","0.5145241","0.51425236","0.5136922","0.5136736","0.51356083"],"string":"[\n \"0.7085485\",\n \"0.6440944\",\n \"0.63578296\",\n \"0.63572264\",\n \"0.6289942\",\n \"0.6267479\",\n \"0.60695726\",\n \"0.59769577\",\n \"0.5956954\",\n \"0.59330213\",\n \"0.5848372\",\n \"0.5821019\",\n \"0.58090127\",\n \"0.58062154\",\n \"0.57650155\",\n \"0.57590044\",\n \"0.5737819\",\n \"0.5693649\",\n \"0.5693649\",\n \"0.5689963\",\n \"0.56800187\",\n \"0.5675765\",\n \"0.56712085\",\n \"0.56561005\",\n \"0.5652554\",\n \"0.5650292\",\n \"0.56423664\",\n \"0.5630729\",\n \"0.5570134\",\n \"0.5562054\",\n \"0.5557976\",\n \"0.5549922\",\n \"0.5521026\",\n \"0.55107224\",\n \"0.5508445\",\n \"0.5496629\",\n \"0.54912376\",\n \"0.5489164\",\n \"0.54862285\",\n \"0.54603726\",\n \"0.5452443\",\n \"0.5441323\",\n \"0.54374593\",\n \"0.54367846\",\n \"0.5429143\",\n \"0.5422076\",\n \"0.54111284\",\n \"0.5396405\",\n \"0.53752947\",\n \"0.5362048\",\n \"0.53537816\",\n \"0.5353341\",\n \"0.5351995\",\n \"0.5331024\",\n \"0.5324557\",\n \"0.5323331\",\n \"0.5308965\",\n \"0.53019875\",\n \"0.53005266\",\n \"0.5297459\",\n \"0.5297393\",\n \"0.5295068\",\n \"0.5289334\",\n \"0.52648604\",\n \"0.52606714\",\n \"0.5260363\",\n \"0.52528435\",\n \"0.5245555\",\n \"0.5241886\",\n \"0.52391493\",\n \"0.5235113\",\n \"0.52314055\",\n \"0.522997\",\n \"0.5229818\",\n \"0.52294487\",\n \"0.52168393\",\n \"0.52148724\",\n \"0.5213529\",\n \"0.5200771\",\n \"0.5200549\",\n \"0.51970565\",\n \"0.5193088\",\n \"0.5187752\",\n \"0.51842487\",\n \"0.51792705\",\n \"0.5178604\",\n \"0.51732856\",\n \"0.5165509\",\n \"0.5164434\",\n \"0.5160421\",\n \"0.5159518\",\n \"0.5156197\",\n \"0.5154629\",\n \"0.5153252\",\n \"0.51531625\",\n \"0.5145241\",\n \"0.51425236\",\n \"0.5136922\",\n \"0.5136736\",\n \"0.51356083\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7371235"},"document_rank":{"kind":"string","value":"0"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":168,"numItemsPerPage":100,"numTotalItems":95772,"offset":16800,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjIwMDI4NSwianRpIjoiNTdiMmY1MmMtNGI2OC00OTI5LWEwYzMtNzcxZjlhNjM3ZTUyIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODYyMDM4ODUsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.0ElyRIkDH0zP8zjE73M3h2Txt8ik_EOtfgV0Z6w_1RJCueNeHWCYNmQnJUp7JW3OmAijl3nrFNje9BOcdfNEAw","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 |
|---|---|---|---|---|---|---|
return list of bits in number | def bits_list(number):
# https://wiki.python.org/moin/BitManipulation
if number == 0:
return [0]
else:
# binary_literal string e.g. '0b101'
binary_literal = bin(number)
bits_string = binary_literal.lstrip('0b')
# list comprehension
bits = [int(bit_character) ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def bitlist(n):\n return [n >> i & 1 for i in range(7,-1,-1)]",
"def _bits(num):\r\n return bin(int(num))[2:]",
"def binary_encoding(k: int, bit_number: int=10) -> List[int]:\n return [k>>i & 1 for i in range(bit_number)]",
"def __get_bit_values(self, number, size=32):\n res = list(self._... | [
"0.8127424",
"0.8050236",
"0.7666614",
"0.75274515",
"0.7512719",
"0.7469839",
"0.7446176",
"0.7345393",
"0.7344313",
"0.7332414",
"0.72998476",
"0.7210258",
"0.71295255",
"0.7124242",
"0.70642513",
"0.70642513",
"0.7048847",
"0.7041419",
"0.7011808",
"0.69538057",
"0.688378"... | 0.8186028 | 0 |
return bit in number at location 2 exponent | def bit_at_twos_power(number, exponent):
bits = bits_list(number)
# NOTE: reverse() modifies object, returns None
bits.reverse()
if exponent > (len(bits) - 1):
return 0
else:
return bits[exponent] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_bit(num, position):\n\treturn (num >> position) & 0b1",
"def _get_bit(self, num, bit, mask=1):\n return (int(num) >> bit) & mask",
"def get_bit(num, i):\r\n return 1 if num & 1 << i else 0",
"def power_of_2(c):\n return n & (n - 1) == 0",
"def _find_nearest_power_of_two(x):\n\n retu... | [
"0.74136764",
"0.6971582",
"0.68619984",
"0.68498963",
"0.6782197",
"0.6750494",
"0.6733378",
"0.67329884",
"0.6702873",
"0.6669386",
"0.66691667",
"0.66691667",
"0.6624408",
"0.6621196",
"0.657315",
"0.6546286",
"0.6543188",
"0.65373",
"0.65147185",
"0.6459624",
"0.64361835"... | 0.72224295 | 1 |
return highest power of two in number | def twos_power_max(number):
bits = bits_list(number)
return len(bits) - 1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def next_power2(num):\n return 2 ** int(np.ceil(np.log2(num)))",
"def _find_nearest_power_of_two(x):\n\n return 1 << (x - 1).bit_length()",
"def _nearest_bigger_power_of_two(x: int) -> int:\n y = 2\n while y < x:\n y *= 2\n return y",
"def _next_power_of_two(self, n):\n if n == 0... | [
"0.7722803",
"0.77092505",
"0.7696812",
"0.75224876",
"0.7414126",
"0.7350261",
"0.72898906",
"0.72811985",
"0.7175145",
"0.7173635",
"0.7150411",
"0.71332145",
"0.71239346",
"0.71106535",
"0.7095859",
"0.7025772",
"0.7020954",
"0.69823575",
"0.69276136",
"0.6903265",
"0.6882... | 0.78875387 | 0 |
Expect a dictionary object, produce text in CSV format. | def create_csv_report(cls, instances):
rows = [cls.format_aws_instance_csv(rep) for rep in sorted(instances.items())] # NOQA
fieldnames = ["instance_id", "aws_account", "aws_region", "key_name",
"launch_time", "vpc_id"]
ephemeral_obj = io.BytesIO()
csv_writer = csv... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dict_to_csv(filename, dictionary, paramdict=True):\n if paramdict:\n filename += '_params'\n \n with open(filename + '.csv', 'w') as f:\n for key in dictionary.keys():\n if type(dictionary[key]) == tuple:\n f.write(\"%s,%s, %s \\n\"%(key, dictionary[key][0], dic... | [
"0.7108769",
"0.7102249",
"0.6928045",
"0.6859614",
"0.6837742",
"0.66567266",
"0.66533685",
"0.6644183",
"0.65137273",
"0.644206",
"0.6406594",
"0.63586444",
"0.6356051",
"0.6343163",
"0.6314986",
"0.62683123",
"0.6248978",
"0.6235773",
"0.623546",
"0.62257725",
"0.6212162",... | 0.0 | -1 |
Expect a dictionary object, produce text appropriate for stdout. | def create_stdout_report(cls, instances):
pieces = [cls.format_aws_instance(rep) for rep in sorted(instances.items())] # NOQA
result = "\n----------\n".join(pieces)
return result | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_dict(dictionary, format_=None):\n\n format_ = format_ or DEFAULT\n\n if format_ == TEXT:\n for key, value in iter(sorted(dictionary.items())):\n print(\"%s = %s\" % (key, value))\n elif format_ == DOCKERENV:\n for key, value in iter(sorted(dictionary.items())):\n ... | [
"0.6906092",
"0.66687083",
"0.66687083",
"0.66272444",
"0.6584296",
"0.6557631",
"0.64985704",
"0.6484042",
"0.6484042",
"0.63379157",
"0.6291753",
"0.6272114",
"0.62684363",
"0.62559116",
"0.6220073",
"0.6214857",
"0.61553854",
"0.6144517",
"0.61384547",
"0.613189",
"0.60918... | 0.0 | -1 |
Create a plaintext report for Slack. | def create_slack_reports(cls, channel_reference, default_channel,
routing_rules, instances):
organized = {}
# Group by target Slack channel.
for instance in instances:
channel = Utility.get_channel_for_message(channel_reference,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def send_weekly_report_slack():\n quarterly_text = get_report_text(90)\n annual_text = get_report_text(365)\n text = (\n \"*Monthly Metrics Report*\\n\"\n \"This is an automated monthly report on some of our key metrics.\\n\\n\"\n f\"\\tIn the last 90 days we saw:\\n\\n{quarterly_text... | [
"0.7163552",
"0.63148385",
"0.62569815",
"0.6144792",
"0.59886944",
"0.5957102",
"0.59369105",
"0.5888709",
"0.5854675",
"0.585184",
"0.5844067",
"0.57283854",
"0.57240146",
"0.5716271",
"0.5695551",
"0.5692515",
"0.5688565",
"0.56377584",
"0.5634909",
"0.562721",
"0.56150633... | 0.5431317 | 35 |
Format an AWS instance's metadata for reporting. | def format_aws_instance(cls, aws_instance):
instance_id = "Instance ID: {instance}".format(instance=aws_instance[0]) # NOQA
aws_account = "AWS Account: {account}".format(account=aws_instance[1]["aws_account"]) # NOQA
aws_region = "AWS Region: {region}".format(region=aws_instance[1]["aws_region... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def format_aws_instance_csv(cls, aws_instance):\n result = {\"instance_id\": aws_instance[0],\n \"aws_account\": aws_instance[1][\"aws_account\"],\n \"aws_region\": aws_instance[1][\"aws_region\"],\n \"key_name\": aws_instance[1][\"key_name\"],\n ... | [
"0.6706454",
"0.6214005",
"0.58400214",
"0.57573223",
"0.5682506",
"0.5593217",
"0.5534054",
"0.5494629",
"0.54934895",
"0.5475465",
"0.5431762",
"0.54230183",
"0.5374503",
"0.53482795",
"0.53469235",
"0.5299905",
"0.5294458",
"0.527383",
"0.51930857",
"0.5163742",
"0.5160937... | 0.70394367 | 0 |
Format an AWS instance's metadata for reporting in CSV format. | def format_aws_instance_csv(cls, aws_instance):
result = {"instance_id": aws_instance[0],
"aws_account": aws_instance[1]["aws_account"],
"aws_region": aws_instance[1]["aws_region"],
"key_name": aws_instance[1]["key_name"],
"launch_time": aw... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def format_aws_instance(cls, aws_instance):\n instance_id = \"Instance ID: {instance}\".format(instance=aws_instance[0]) # NOQA\n aws_account = \"AWS Account: {account}\".format(account=aws_instance[1][\"aws_account\"]) # NOQA\n aws_region = \"AWS Region: {region}\".format(region=aws_instanc... | [
"0.6753999",
"0.6415332",
"0.56931126",
"0.5526823",
"0.5469592",
"0.54025054",
"0.5367501",
"0.5292463",
"0.5250131",
"0.52448654",
"0.52434593",
"0.5198777",
"0.51984173",
"0.51540196",
"0.51104605",
"0.51003814",
"0.50938004",
"0.50337195",
"0.5009848",
"0.50069076",
"0.49... | 0.747481 | 0 |
RD Station API use OAuth authentication method. | def __init__(self, client_id, client_secret, refresh_token=None, code=None, callback_url=None):
self.base_url = 'https://api.rd.services'
self.client_id = client_id
self.client_secret = client_secret
self.refresh_token = refresh_token
if code:
self.access_token = sel... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_oauth():\n\n # initial app authorization request - not tied to specific user\n request_token, request_token_secret = goodreads.get_request_token(header_auth=True)\n\n # assign request tokens to session for future use\n session['request_token'] = request_token\n session['request_token_secret'... | [
"0.68430936",
"0.6825009",
"0.68104076",
"0.6695356",
"0.6666678",
"0.66485614",
"0.6634933",
"0.6587592",
"0.65865326",
"0.6556506",
"0.65335166",
"0.65335166",
"0.65196073",
"0.65141773",
"0.65138084",
"0.64944786",
"0.648033",
"0.6461008",
"0.6450203",
"0.64329815",
"0.640... | 0.62075526 | 30 |
It creates a webhook subscription. | def create_webhook(self, webhook_url, event_type='CONVERTED', include_relations=[]):
print('Creating webhook...')
api_url = f'{self.base_url}/integrations/webhooks'
req_data = {
"entity_type": "CONTACT",
"event_type": f"WEBHOOK.{event_type}",
"event_identifi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_webhook():\n\n response = requests.put(KAZOO_SERVER + ':8000/v2/accounts/' + ACC_ID + '/webhooks', headers=HEADERS, data=ws.jsonify(CHANNEL_DESTROY_WEBHOOKS))\n\n return response",
"def create_subscription(self,\n body):\n\n return super().new_api_call_builder.r... | [
"0.7374728",
"0.7267958",
"0.708534",
"0.6968992",
"0.6943122",
"0.6893747",
"0.6887109",
"0.6805869",
"0.67693436",
"0.65960854",
"0.6314443",
"0.62725574",
"0.62692505",
"0.6263543",
"0.62518686",
"0.61873096",
"0.6151583",
"0.6083862",
"0.6073404",
"0.6021803",
"0.6016704"... | 0.6431883 | 10 |
Connect to the PostgreSQL database. Returns a database connection. | def connect():
# returns the connection object to tournament
# database from PostgreSQL
return psycopg2.connect("dbname=tournament") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def connect_to_db():\n return pg.connect(DB_CONN_STRING)",
"def connect():\n conn = None\n try:\n # read connection parameters\n params = config(filename='./configs/database.ini',\n section='postgresql')\n\n # connect to the PostgreSQL server\n logger.i... | [
"0.86190194",
"0.84299386",
"0.8429464",
"0.8308111",
"0.82700765",
"0.82314837",
"0.81962186",
"0.81663454",
"0.8113811",
"0.80987424",
"0.8048103",
"0.79992247",
"0.79880154",
"0.7977557",
"0.7961168",
"0.79450434",
"0.7924049",
"0.78908235",
"0.7873477",
"0.78695047",
"0.7... | 0.74600875 | 66 |
Remove all the match records from the database. | def deleteMatches():
# gets connection to tournament database in conn object
conn = connect()
# gets the cursor to execute queries
c = conn.cursor()
# executes delete query to delete all records in MATCH table
c.execute("DELETE FROM MATCH;")
# commits the changes perform on MATCH table after... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def deleteAll(self):\n self.db.execute(\"DELETE FROM MATCH;\", ())",
"def deleteMatches():\n conn = connect()\n c = conn.cursor()\n # Clears the \"matches\" table, but does not get rid of the table.\n c.execute(\"delete from matches;\")\n conn.commit()\n conn.close()",
"def deleteMatch... | [
"0.7907006",
"0.7667884",
"0.7664419",
"0.7592219",
"0.7570883",
"0.75260276",
"0.7518946",
"0.75174177",
"0.75145423",
"0.75019526",
"0.7491591",
"0.7479197",
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"0.7472819",
"0.7464124",
"0.74493855",
"0.7303164",
"0.7293325",
"0.7290579",
"0.722480... | 0.74734604 | 14 |
Remove all the player records from the database. | def deletePlayers():
# gets connection to tournament database in conn object
conn = connect()
# gets the cursor to execute queries
c = conn.cursor()
# executes delete query to delete all records in PLAYER table
c.execute("DELETE FROM PLAYER;")
# commits the changes perform on PLAYER table af... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def deletePlayers():\n with _connect_db() as (conn, cur):\n cur.execute(\"\"\"DELETE FROM players;\"\"\")\n conn.commit()",
"def deletePlayers():\n conn = connect()\n c = conn.cursor()\n # Clears the \"players\" table, but does not get rid of the table.\n c.execute(\"delete from play... | [
"0.79152656",
"0.7797487",
"0.7717909",
"0.7708946",
"0.7708134",
"0.7673693",
"0.7624109",
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"0.7621035",
"0.7570029",
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"0.7485491",
"0.7446517",
"0.73848397",
"0.7318164",
"0.7262919",
"0.72294337",
"0.720686... | 0.77352023 | 2 |
Returns the number of players currently registered. | def countPlayers():
# gets connection to tournament database in conn object
conn = connect()
# gets the cursor to execute queries
c = conn.cursor()
# executes select with count aggregate function query number of players
# in PLAYER table
c.execute("SELECT COUNT(*) FROM PLAYER;")
# retrei... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def number_of_players(self) -> int:\n return self.param.number_of_players",
"def countPlayers():\n conn = connect()\n cur = conn.cursor()\n cur.execute(\"SELECT COUNT(*) FROM players\")\n players = int(cur.fetchone()[0])\n conn.close()\n return players",
"def countPlayers():\n with ... | [
"0.82761925",
"0.82301927",
"0.81162655",
"0.81077707",
"0.8069197",
"0.8026064",
"0.7994305",
"0.7930107",
"0.79146034",
"0.78450596",
"0.78395754",
"0.7787708",
"0.7766425",
"0.77562374",
"0.77113974",
"0.7687248",
"0.759404",
"0.758135",
"0.7548042",
"0.7539884",
"0.751975... | 0.7104468 | 29 |
Adds a player to the tournament database. The database assigns a unique serial id number for the player. (This should be handled by your SQL database schema, not in your Python code.) | def registerPlayer(name):
# gets connection to tournament database in conn object
conn = connect()
# gets the cursor to execute queries
c = conn.cursor()
# executes insert query which takes the name variable passed in arguments
# of this method and adds a new player record to PLAYER table where ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def registerPlayer(playerName):\n\n tournName = \"Tournament for legacy tests\"\n\n # Connect to database\n conn, c = main.connect()\n\n # Insert a new player with this name\n SQL = \"INSERT INTO player (playerName) values (%s);\"\n data = (playerName, )\n c.execute(SQL, data)\n\n # If the ... | [
"0.7571331",
"0.7488418",
"0.7460844",
"0.7456452",
"0.7349081",
"0.73410213",
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"0.70993054",
"0.707552",
"0.7067138",
"0.7055108",
"0.70531744",
"0.7027579... | 0.7729972 | 0 |
Returns a list of the players and their win records, sorted by wins. The first entry in the list should be the player in first place, or a player tied for first place if there is currently a tie. | def playerStandings():
# gets connection to tournament database in conn object
conn = connect()
# gets the cursor to execute queries
c = conn.cursor()
# executes select statement on STANDING view for getting results in
# descending order of number of wins for each player
c.execute("SELECT * ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def playerStandings():\n # place all players in a dictionary\n player_dict = {}\n conn, c = connect()\n c.execute(\"\"\"SELECT * FROM players;\"\"\")\n for row in c.fetchall():\n player_dict[row[0]] = [row[1], 0, 0]\n\n # count the number of win and matches in for all matches\n c.execut... | [
"0.7002294",
"0.69581795",
"0.68095344",
"0.68075407",
"0.66431624",
"0.6599347",
"0.6576619",
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"0.605928",
"0.6056515",
"0.5985764",
"0.59602445",
"0.59505534",
"0.5936894",... | 0.60500395 | 17 |
Records the outcome of a single match between two players. | def reportMatch(winner, loser, draw):
# gets connection to tournament database in conn object
conn = connect()
# gets the cursor to execute queries
c = conn.cursor()
# sql insert query to add new MATCH record in the MATCH table with
# passing winner or loser player id and true or false for draw
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reportMatch(player1, player2, winner = None):\n #Check for Bye matchup (player1=player2)\n bye_match = False\n if player1 == player2:\n winner = player1\n bye_match = True\n\n #Generate random winner if no winner param is passed in\n if winner is None:\n rand = random.random... | [
"0.75769395",
"0.7318565",
"0.7162485",
"0.715877",
"0.7148469",
"0.70839787",
"0.70600444",
"0.69540495",
"0.6862129",
"0.68571943",
"0.6849386",
"0.68365145",
"0.68264997",
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"0.68064857",
"0.66964144",
"0.66908497",
"0.6688487",
"0.6667407",
"0.66199833",
"0.659... | 0.70029676 | 7 |
Returns a list of pairs of players for the next round of a match. Assuming that there are an even number of players registered, each player appears exactly once in the pairings. Each player is paired with another player with an equal or nearlyequal win record, that is, a player adjacent to him or her in the standings. | def swissPairings():
# retreives player standings i.e. id, player, wins, matches
standings = playerStandings()
# pairs for next round are stored in this array.
next_round = []
# iterates on the standings results. As the results are already in
# descending order, the pairs can be made using adja... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def swissPairings():\n\n # Ok This is where things get interesting, how in the world should i solve this problem\n # A question to the udacity reviewer. Shouldn't standings be passed in to this function since weve already called it in tournament_test.testPairings\n\n #anyways\n\n nextRoundPlayers = []\... | [
"0.8103197",
"0.7787016",
"0.7717939",
"0.76245844",
"0.76115",
"0.7596688",
"0.7552793",
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"0.7043194",
"0.7003615",
"0.69710374",
"0.6970802",
"0.6916647",
"0.6875584",
"... | 0.80501574 | 1 |
Adds two Reco objects, returns a Reco object. | def addReco(obj1,obj2):
px = obj1.px + obj2.px
py = obj1.py + obj2.py
pz = obj1.pz + obj2.pz
E = obj1.E + obj2.E
return Reco(px,py,pz,E) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __add__(self, other):\n new = self.__class__()\n new.extend(self)\n new.extend(other)\n return new",
"def __radd__(self, other):\n return self.runtime.add(self, other)",
"def __add__(self, other):\n return self.add(other)",
"def __radd__(self, other):\n\n ... | [
"0.6708373",
"0.6588393",
"0.6587373",
"0.6527081",
"0.6519195",
"0.6519195",
"0.6518886",
"0.65113086",
"0.6482993",
"0.6482993",
"0.6482993",
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"0.6482993",
"0.6479999",
"0.6457409",
"0.6420922",
"0.6420922",
"0.6419211",
"0.6409279",
... | 0.7727639 | 0 |
Converts a Reco object to a string. | def stringReco(obj):
name = obj.get_name()
name = obj._pid if (name is None) else name
return ("pdg: " + name + " E: " + str(obj._E)
+ " px: " + str(obj._px) + " py: " + str(obj._py)
+ " pz: "+ str(obj._pz) + " mass: " + str(obj._m)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def str_(object_):\n return str(object_)",
"def value_to_string(self, obj):\n value = self.value_from_object(obj)\n return value",
"def _tostr(obj): # pragma: no cover\n return obj if isinstance(obj, str) else obj.decode()",
"def obj_to_string(rental):\n string = rental.id + ';' +... | [
"0.6829037",
"0.6601121",
"0.6591872",
"0.65873903",
"0.6571199",
"0.6557375",
"0.6517904",
"0.6511437",
"0.6509183",
"0.6501627",
"0.64991844",
"0.6456585",
"0.636783",
"0.636783",
"0.636783",
"0.6255697",
"0.6255697",
"0.624381",
"0.62436426",
"0.6181675",
"0.6174107",
"0... | 0.6945018 | 0 |
Switch to another device. | def to(self, dev):
self.weight = self.weight.to(dev)
return self | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def change_device(self):\n if self.state.ser:\n UsbHost.close_port(self.state.ser)\n device = self.CBDevices.currentText()\n if device:\n comport = self.devices[int(device)]\n self.state.ser = UsbHost.open_port(comport)\n if not self.state.ser:\n ... | [
"0.66676337",
"0.6656939",
"0.6473117",
"0.629531",
"0.6137485",
"0.60558873",
"0.6028521",
"0.597831",
"0.5977964",
"0.59667236",
"0.59458566",
"0.59299034",
"0.5924209",
"0.5893226",
"0.5882226",
"0.5856656",
"0.58389336",
"0.5746957",
"0.5674327",
"0.5665239",
"0.56431866"... | 0.0 | -1 |
Creates a password for the user and adds the user to openstack. | def add_user(self, user_email, first_name, last_name, password, role):
course_m = CourseManager(current_user.session)
group_m = GroupManager(current_user.session)
user = self.create(
name=user_email,
domain=course_m.find(name='default').id,
password=password... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_user(username, password):\n return create_user(username, password)",
"def add_user(username, password):\n return create_user(username, password)",
"def add_user(self, user, pw):\n self.db.execute(\"INSERT INTO user_credentials VALUES (?, ?)\", [user, pw])\n self.db.commit()... | [
"0.728109",
"0.728109",
"0.7239141",
"0.70491415",
"0.6990617",
"0.6863269",
"0.68306065",
"0.68255174",
"0.6821471",
"0.6818305",
"0.6790275",
"0.67812306",
"0.67568904",
"0.6725345",
"0.6660526",
"0.66501254",
"0.66493255",
"0.66379577",
"0.6637337",
"0.66206384",
"0.661209... | 0.0 | -1 |
Deletes a user and the network that belongs to that user. The mikrotik configurations are also removed. | def delete_user(self, user, instance_m):
from resela.model.User import authenticate
if user:
mikrotik_m = MikrotikManager()
lab_m = LabManager(current_user.session)
group_m = GroupManager(current_user.session)
user_m = UserManager(current_user.session)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete_user(network, user):\n if user in network:\n del network[user]\n for u in network:\n connections = get_connections(network, u)\n if user in connections:\n i = connections.index(user)\n del connections[i]\n return network",
"def de... | [
"0.7707111",
"0.70516425",
"0.6972116",
"0.67819333",
"0.67293984",
"0.66955173",
"0.66955173",
"0.66955173",
"0.66840297",
"0.667016",
"0.6612195",
"0.6580813",
"0.655327",
"0.6516242",
"0.64975977",
"0.64706296",
"0.64591783",
"0.64546835",
"0.6438795",
"0.6430819",
"0.6407... | 0.6742137 | 4 |
Sends an email to the user who requested a new password or a confirmation email to a user who has reset his or her password. If email and password is set, a mail is sent to a newly registrated user. If email and token is set, a request to reset password is sent to the user with a link and a temporary token. If only the... | def email_user(to_email, password=None, token=None):
try:
if password and token:
raise Exception('No email has been sent. Both token and password is set.')
mail = Mail(APP)
if to_email and password:
message = Message(
'Resel... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def handle_emails():\n email = request.data['email'].strip()\n user = User.query.filter_by(email=email).first()\n option = \\\n request.data['option'].strip() # have a <select> in the frontend\n token = s.dumps(email, salt='email-confirm')\n\n msg = Message('Reset pass... | [
"0.7728737",
"0.7559589",
"0.748966",
"0.7445745",
"0.7434668",
"0.7403786",
"0.7355636",
"0.7344078",
"0.7325047",
"0.7311936",
"0.72110546",
"0.7201742",
"0.71804917",
"0.7156649",
"0.7125851",
"0.7121934",
"0.71122754",
"0.70608777",
"0.7039746",
"0.69806916",
"0.6928046",... | 0.77339584 | 0 |
Returns the most recent transactions by members of government | def get_government_trading(gov_type: str, ticker: str = "") -> pd.DataFrame:
if gov_type == "congress":
if ticker:
url = (
f"https://api.quiverquant.com/beta/historical/congresstrading/{ticker}"
)
else:
url = "https://api.quiverquant.com/beta/live... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def query2(transaction):\n # Part 2a: Obtain ID of most-followed (same as query1)\n top_3_followed = query1(transaction)\n top_1_followed = top_3_followed[0]['personID']\n print(f\"Top most-followed person ID:\\n{top_1_followed}\")\n\n # Part 2b: Use ID of most-followed person and find their city o... | [
"0.5160677",
"0.51275116",
"0.50672626",
"0.5051236",
"0.50158",
"0.5015026",
"0.5006082",
"0.49001202",
"0.48673108",
"0.4849451",
"0.4826606",
"0.4808726",
"0.48059615",
"0.47626555",
"0.47515532",
"0.4711577",
"0.47064406",
"0.4705023",
"0.47003144",
"0.4686806",
"0.464722... | 0.0 | -1 |
Returns a persistent connection to the osgprod database. | def db_connection():
global dbconnection
try:
conn = dbconnection
except:
dbconnection = psycopg2.connect(user = dbuser,
password = dbpass,
host = dbserver,
port = "5432",
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_db_connection():\n db = sqlite3.connect(config.PERSISTENCE_LOCATION, check_same_thread=False)\n db.isolation_level = None\n db.row_factory = sqlite3.Row\n return db",
"def get_db():\n if not hasattr(g, 'db_connection'):\n g.db_connection = connect_db()\n return g.db_connection",
... | [
"0.73980427",
"0.7126742",
"0.70921594",
"0.69822115",
"0.69624156",
"0.6955797",
"0.6952289",
"0.69403976",
"0.6924561",
"0.6893482",
"0.68846476",
"0.6862271",
"0.685671",
"0.6850538",
"0.6845601",
"0.6834621",
"0.6792339",
"0.6782343",
"0.6782343",
"0.6779083",
"0.6768656"... | 0.0 | -1 |
Uploads outfile to the storage element at dst_url under output directory outdir, returns 0 on success, raises an exception on error. | def upload(outfile, outdir):
outpath = outdir + "/" + outfile
my_env = os.environ.copy()
my_env["X509_USER_PROXY"] = dst_cred
for retry in range(0,99):
try:
subprocess.check_output(["globus-url-copy", "-create-dest",
"-rst", "-stall-timeout", "300",
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save(self, url, output):\n\n shutil.copy2(self.get(url), output)",
"def putFile(self, _src, _dst, delExisting = True):\n\n #-------------------- \n # Delete existing _dst from XNAT host.\n #-------------------- \n if delExisting:\n r = self.__... | [
"0.63402486",
"0.6235955",
"0.61301994",
"0.6048785",
"0.6006344",
"0.5978161",
"0.59224904",
"0.58591557",
"0.5799578",
"0.5667479",
"0.56134206",
"0.56105936",
"0.5528729",
"0.54942703",
"0.54917186",
"0.5471971",
"0.5471403",
"0.5466291",
"0.54457706",
"0.5421599",
"0.5386... | 0.72268885 | 0 |
Gets the next output set to bind from the database, unpacks them into a temporary directory, and merges them at the input data file level into files in the output directory tree under the toplevel dir vers. If the bindings db does not already exit, you must manually call create_table_bindings before trying to invoke th... | def next():
iraw = 0
run = 0
seqno = 0
with db_connection() as conn:
with conn.cursor() as curs:
try:
curs.execute("""SELECT rawdata.id,rawdata.run,rawdata.seqno,
slices.block1,slices.block2,
jobs.cluster,jobs.p... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def merge(self, ddb_files, out_ddb, description, cwd=None):\n\n # We work with absolute paths.\n ddb_files = [os.path.abspath(s) for s in list_strings(ddb_files)]\n\n out_ddb = out_ddb if cwd is None else os.path.join(os.path.abspath(cwd), out_ddb)\n\n if self.verbose:\n prin... | [
"0.5135086",
"0.5105571",
"0.5062106",
"0.5051295",
"0.4986725",
"0.49431032",
"0.48690206",
"0.48451462",
"0.4786839",
"0.47686812",
"0.47670457",
"0.47470966",
"0.4744025",
"0.47428954",
"0.47262472",
"0.47167167",
"0.47113854",
"0.4703731",
"0.4658417",
"0.4654385",
"0.464... | 0.49548176 | 5 |
Merge the special events skim files from the individual jobs into a single skim file for each input raw data file. Returns 0 on success, nonzero on error. | def merge_evio_skims(run, seqno, slices):
inset = {"BCAL-LED": "hd_rawdata_{0:06d}_{1:03d}+{2},{3}.BCAL-LED.evio",
"DIRC-LED": "hd_rawdata_{0:06d}_{1:03d}+{2},{3}.DIRC-LED.evio",
"FCAL-LED": "hd_rawdata_{0:06d}_{1:03d}+{2},{3}.FCAL-LED.evio",
"CCAL-LED": "hd_rawdata_{0:06d}_{1:03d... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def process_datasets(self):\n\n with open(self.mappings, \"r+\") as json_file:\n emsl_to_jgi = json.load(json_file)\n emsl_to_jgi_copy = copy.deepcopy(emsl_to_jgi)\n\n contaminant_file_loc = emsl_to_jgi[\"contaminant_file_loc\"]\n # run for each dataset\n ... | [
"0.58434606",
"0.5828776",
"0.5671057",
"0.56308407",
"0.5587917",
"0.55824566",
"0.54915506",
"0.54796594",
"0.5427828",
"0.5424342",
"0.54181063",
"0.53949624",
"0.53836256",
"0.5380785",
"0.53721344",
"0.5371496",
"0.5339565",
"0.53391004",
"0.5329548",
"0.53231126",
"0.53... | 0.5119218 | 44 |
Merge the output root files from the individual jobs into a single root file for each input raw data file. Returns 0 on success, nonzero on error. | def merge_root_histos(run, seqno, slices):
inset = {"hists": "hd_root.root",
"tree_TS_scaler": "tree_TS_scaler.root",
"tree_bcal_hadronic_eff": "tree_bcal_hadronic_eff.root",
"tree_fcal_hadronic_eff": "tree_fcal_hadronic_eff.root",
"tree_tof_eff": "tree_tof_eff.root",
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def merge_root_files(self, force=False):\n self.OutFilePath.parent.mkdir(exist_ok=True)\n cmd = f'hadd{\" -f\" if force else \"\"} {self.proteus_raw_file_path()} {self.Raw.OutFilePath} {self.Ref.OutFilePath} {self.Adc2Vcal.OutFilePath}'\n pinfo(cmd)\n check_call(cmd, shell=True)",
"de... | [
"0.6730804",
"0.6564732",
"0.65505415",
"0.64266163",
"0.60904115",
"0.60902333",
"0.60311466",
"0.60188913",
"0.5925645",
"0.59169954",
"0.59121346",
"0.59091747",
"0.5899683",
"0.58853364",
"0.5863318",
"0.5858405",
"0.584055",
"0.5825858",
"0.5807685",
"0.580593",
"0.57860... | 0.0 | -1 |
Merge the output hddm files from the individual jobs into a single hddm file for each input raw data file. Returns 0 on success, nonzero on error. | def merge_hddm_output(run, seqno, slices):
inset = {"REST": "dana_rest.hddm",
"converted_random": "converted_random.hddm",
}
outset = {"REST": "dana_rest_{0:06d}_{1:03d}.hddm",
"converted_random": "converted_random_{0:06d}_{1:03d}.hddm",
}
badslices = []
slice... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_output_files(self):\n namenode = self.runner.namenode\n for i in range(self.cnt_reducers):\n fname = '%s.%s' % (self.output_dir, reduce_output(self.id, i))\n namenode.create_file(fname)\n self.result_files.append(fname)\n self.open_files.append(f... | [
"0.64265794",
"0.63644433",
"0.6353053",
"0.60704476",
"0.6045988",
"0.60319865",
"0.598607",
"0.59759074",
"0.5901085",
"0.58970237",
"0.5886435",
"0.5850782",
"0.57940143",
"0.5781016",
"0.5780351",
"0.5770798",
"0.576806",
"0.5740822",
"0.57112277",
"0.570429",
"0.56991875... | 0.6682864 | 0 |
Merge the job log hddm files from the individual jobs into a catenated log file for each input raw data file. Returns 0 on success, nonzero on error. | def merge_job_info(run, seqno, slices):
inset = {"job_info": ["workscript.stdout", "workscript.stderr"],
}
outset = {"job_info": ["std_{0:06d}_{1:03d}.out", "std_{0:06d}_{1:03d}.err"],
}
tarset = {"job_info": "job_info_{0:06d}_{1:03d}.tgz",
}
badslices = []
slicepatt = ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _consolidate_mp_logs(self):\n for i, fn in enumerate(self.logfiles):\n with open(fn) as f:\n logger.info(\"Log from thread {0}:\\n{1}\".format(i, f.read()))\n open(fn, \"w\").write(\"\")",
"def merge_all_data(self):\n\n logging.info('***** Starting the m... | [
"0.6514452",
"0.6379347",
"0.61566144",
"0.61549914",
"0.59930986",
"0.59826446",
"0.59685177",
"0.5903456",
"0.5852834",
"0.58354026",
"0.5825984",
"0.56945175",
"0.56898165",
"0.56841546",
"0.56395817",
"0.56382704",
"0.5634285",
"0.56220657",
"0.5616418",
"0.5598884",
"0.5... | 0.59504026 | 7 |
Looks up rawdata id iraw and unpacks the job output tarballs from all jobs that completed successfully into a new directory named iraw under the pwd. If iraw is not known, alternatively one can supply the run number and sequence number of the raw data input file. | def unpack(run=0, seqno=0, iraw=0):
if iraw == 0 and run == 0:
print("Usage: osgprod_bind.unpack(iraw=<iraw>)")
print(" or: osgprod_bind.unpack(run=<run>, seqno=<seqno>)")
return 0
if iraw:
with db_connection() as conn:
with conn.cursor() as curs:
curs.execute("""SEL... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n onlyfiles = [f for f in listdir(RAWDATA_PATH) if isfile(join(RAWDATA_PATH, f))]\n for file in onlyfiles:\n create_RCSB_fastas(file)",
"def initial_processing(subject_dir):\n # get subject name\n subject_name = subject_dir.parts[-1]\n\n # create ${subject_dir}/ASL and ${subject... | [
"0.5513317",
"0.55078816",
"0.5401565",
"0.53775054",
"0.53684473",
"0.53684455",
"0.5358436",
"0.5356014",
"0.5325273",
"0.531172",
"0.53011775",
"0.52965647",
"0.527821",
"0.5257222",
"0.52564025",
"0.5237875",
"0.52120996",
"0.52038366",
"0.5189393",
"0.51522464",
"0.51471... | 0.5015862 | 31 |
Create dummy tensor of longest length to pad for adversary | def collate_adv(self, batch):
dummy_adv_phone_tensor = torch.from_numpy(np.zeros(shape=(15,))) # 15 is max length of transcription sequence including SOS and EOS
dummy_adv_phone_tensor = dummy_adv_phone_tensor.to(torch.float32)
"""Most everything else is the same"""
spectrograms = [item... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pad_zeros(x):\n dim = tf.shape(x)[0]\n log2_dim = tf.math.log(tf.cast(dim, tf.float32)) / tf.math.log(2.0)\n pad_dim = tf.pow(2, tf.cast(tf.math.ceil(log2_dim), tf.int32))\n with tf.control_dependencies([tf.debugging.assert_rank(x, 1)]):\n return tf.pad(x, [[0, tf.maximum(0, pad_dim - dim)]])",
"def s... | [
"0.69676924",
"0.694997",
"0.68974465",
"0.67719644",
"0.67479026",
"0.6743997",
"0.67088985",
"0.67006475",
"0.6668731",
"0.66501755",
"0.66375893",
"0.66347855",
"0.65708846",
"0.6551476",
"0.65336",
"0.6531403",
"0.65311104",
"0.6521792",
"0.6374594",
"0.6359308",
"0.63560... | 0.0 | -1 |
Return true if it's a JavaScript source. | def test_js_source(self):
actual = is_js_source(self.view)
self.assertTrue(actual) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_non_js_source(self):\n self.view.set_syntax_file(\"Packages/Python/Python.tmLanguage\")\n\n actual = is_js_source(self.view)\n\n self.assertFalse(actual)",
"def is_js_file(fname):\r\n return REJS.search(fname) and \\\r\n TEST_INDICATOR not in fname",
"def isJsFile(path):... | [
"0.72429377",
"0.6709106",
"0.66963446",
"0.64444417",
"0.62756056",
"0.6237253",
"0.6226813",
"0.6111022",
"0.5996234",
"0.5935359",
"0.58793366",
"0.5862295",
"0.58062017",
"0.5804725",
"0.5759924",
"0.566558",
"0.56178796",
"0.5614935",
"0.55970407",
"0.55769956",
"0.55665... | 0.7687013 | 0 |
Return false if it's not a JS source. | def test_non_js_source(self):
self.view.set_syntax_file("Packages/Python/Python.tmLanguage")
actual = is_js_source(self.view)
self.assertFalse(actual) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_js_source(self):\n actual = is_js_source(self.view)\n\n self.assertTrue(actual)",
"def is_js_file(fname):\r\n return REJS.search(fname) and \\\r\n TEST_INDICATOR not in fname",
"def isJsFile(path):\n return os.path.splitext(path)[1] == '.js'",
"def has_source_file( self ):... | [
"0.7866086",
"0.6981806",
"0.68132186",
"0.6648081",
"0.63434523",
"0.62751704",
"0.62293243",
"0.61936146",
"0.6170908",
"0.6103844",
"0.60846484",
"0.60547394",
"0.6031727",
"0.6030578",
"0.5997301",
"0.5979819",
"0.5919813",
"0.58965653",
"0.5846535",
"0.58112514",
"0.5780... | 0.7661912 | 1 |
combine all similarity measures into single score | def similarity_score(a,b):
jsc_scaler = 15
ocs_scaler = 5
tcss_scaler = 0.05
jaccard_similarity_coefficient_score = jsc_scaler * jaccard_similarity_coefficient(a,b)
overlap_coefficient_score = ocs_scaler * overlap_coefficient(a,b)
total_char_similarity_score = tcss_scaler * total_char_similarity(a,b)
total_scor... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def similarity_search(self):\n self.ssr = {gene: self.ssw.get_phenotypically_similar_genes(phenotypes, taxon=self.taxon) for gene, phenotypes in self.gene2phenotype_associations.items()}\n self.results = [ssr.get_results() for ssr in self.ssr.values()]\n self.phenogene_score = reduce(lambda x,... | [
"0.65878206",
"0.64811784",
"0.62395096",
"0.6237274",
"0.6205374",
"0.6188678",
"0.61824363",
"0.6181081",
"0.61457",
"0.6135755",
"0.61197",
"0.6090049",
"0.6086925",
"0.6083988",
"0.6074309",
"0.6043134",
"0.60306597",
"0.60225576",
"0.60199016",
"0.6013829",
"0.6005431",
... | 0.6321166 | 3 |
Simple test of applyFunction() function. The function we'll apply is exp(x) so this is equivalent to the test_exp tests above | def test_applyFunction(self):
ptwise_linear = XYs1d(axes=XYs1d.defaultAxes(labelsUnits={
XYs1dModule.yAxisIndex: ('crossSection', 'b'),
XYs1dModule.xAxisIndex: ('energy_in', 'eV')}), data=[[1e-5, 1.0], [20.0e6, 21.0]])
self.assertAlmostEqual(ptwise_linear.evaluate(15.0e6), 16.0... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_exp(self):\n funcs = ['exp', 'exp_']\n for func in funcs:\n tensor = get_random_test_tensor(max_value=2)\n encrypted = SharedTensor(tensor)\n reference = getattr(tensor, func)()\n encrypted_out = getattr(encrypted, func)()\n self._check(... | [
"0.63081706",
"0.6230978",
"0.61486775",
"0.6126683",
"0.6062297",
"0.5937917",
"0.5935332",
"0.58760333",
"0.5758355",
"0.5745007",
"0.5745007",
"0.5663189",
"0.5640548",
"0.5635015",
"0.5623988",
"0.5621284",
"0.55899805",
"0.5588484",
"0.5581032",
"0.55406785",
"0.5523366"... | 0.73073655 | 0 |
Tests of traversing up GNDS hierarchy | def test_parent(self):
self.assertEqual( self.xs_const.ancestor, None )
self.assertEqual( self.xs_const.rootAncestor, self.xs_const )
self.assertEqual( self.xs_const.ancestor, None )
self.xs_const.setAncestor( 'fred' )
self.assertEqual( self.xs_const.ancestor, 'fred' ) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_Tree():",
"def traverseTree(mdsnode,dead_branches=False,depth=float('Nan'),current_depth=0,noisy=False,strict=False,tags=False):\n tagdict={}\n if isinstance(mdsnode,mds.tree.Tree): \n mdsnode=mdsnode.getNode(\"\\\\TOP\")\n \n name = get_mds_shortname(mdsnode) \n me = Branch(... | [
"0.6712216",
"0.6288564",
"0.6222426",
"0.61912704",
"0.6082186",
"0.6045664",
"0.6010632",
"0.6006964",
"0.59376144",
"0.5929815",
"0.59266883",
"0.59031993",
"0.5889081",
"0.5888845",
"0.5888638",
"0.58834285",
"0.58627",
"0.58545256",
"0.58476526",
"0.5847015",
"0.5845282"... | 0.0 | -1 |
Filters list of CRAN packages and metadata for a given array of software mentions | def build_df(packages, software_mentions, cran_links, titles):
df = pd.DataFrame({'CRAN Package' : packages, 'CRAN Link' : cran_links, 'Title' : titles})
df = df[df['CRAN Package'].isin(software_mentions)]
return df | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _filter_installed_packages(self, packages):\n filtered_packages = []\n for package in packages:\n name = package.name\n for installed in self._top_installed_repository.find_packages(name):\n if installed.key == package.key:\n break\n ... | [
"0.61648244",
"0.59064674",
"0.586823",
"0.58534795",
"0.58129144",
"0.57932645",
"0.5780702",
"0.574569",
"0.54876506",
"0.5449792",
"0.54124254",
"0.5338315",
"0.5329355",
"0.5319731",
"0.53164315",
"0.5304868",
"0.5286493",
"0.52616996",
"0.5226314",
"0.5220258",
"0.520920... | 0.57852787 | 6 |
Retrieves links and metadata from the CRAN repository for a list of software mentions. | def get_cran_df(software_mentions, filename = None, save_new = True):
filename_exists = exists(filename)
if filename_exists and not save_new:
print('- Retrieving saved CRAN file:', filename)
return pd.read_csv(filename)
elif not filename_exists and not save_new:
raise Exception('- Sorry, the file', f... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getContributors(auth):\n users = []\n r = requests.get(url='https://gist.github.com/paulmillr/2657075/',\n auth=auth)\n soup = BeautifulSoup(r.text, 'html.parser')\n users = [tr.select_one('a').text for tr in soup('tbody')[0].select('tr')]\n return users",
"def collect_citation_meta... | [
"0.5758305",
"0.56982356",
"0.55455464",
"0.53961664",
"0.53705347",
"0.5366114",
"0.53286135",
"0.5226537",
"0.5211295",
"0.51962847",
"0.5171388",
"0.51701033",
"0.5125818",
"0.50910467",
"0.50589",
"0.5043194",
"0.5025954",
"0.50184816",
"0.5018108",
"0.5009344",
"0.500316... | 0.0 | -1 |
Loads the schedulers state. | def load_state_dict(self, state_dict):
self.__dict__.update(state_dict) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _load_state_dict(self, state: dict):\n for o, dct in zip(self.optimizers, state.get('optimizers', [])):\n o.load_state_dict(dct)\n for s, dct in zip(self.schedulers, state.get('schedulers', [])):\n s.load_state_dict(dct)",
"def _load_state_dict(self, state: dict):\n ... | [
"0.6747899",
"0.6747899",
"0.6669607",
"0.6487705",
"0.64369476",
"0.64369476",
"0.64369476",
"0.64369476",
"0.64369476",
"0.64369476",
"0.64369476",
"0.64369476",
"0.64369476",
"0.6360741",
"0.6294948",
"0.62494737",
"0.6181577",
"0.61719745",
"0.61689407",
"0.61068344",
"0.... | 0.0 | -1 |
Creates all database tables. | def initdb():
db.create_all() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_tables():\n db.create_all()",
"def create_tables():\n db.create_all()",
"def create_all_tables():\n\tcommon_db.create_all_tables()",
"def create_tables():\n db.create_all()",
"def create_db_tables():\n\n try:\n webapp.dbsql.create_all()\n webapp.dbsql.session.co... | [
"0.90490264",
"0.90490264",
"0.90303427",
"0.8878403",
"0.8667726",
"0.8665649",
"0.86038023",
"0.8575255",
"0.85521376",
"0.8430205",
"0.8323267",
"0.8294338",
"0.80128086",
"0.79913557",
"0.79555404",
"0.79508626",
"0.7938032",
"0.7930278",
"0.79032606",
"0.78875494",
"0.78... | 0.743903 | 72 |
Drops all database tables. | def dropdb():
db.drop_all() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def drop_all_tables():\n\tcommon_db.drop_all_tables()",
"def drop_all_tables(args):\n engine = sqlalchemy.create_engine(CONFIG.db_uri)\n print(\"Dropping all tables on {}...\".format(CONFIG.db_uri), end=\" \")\n Base.metadata.drop_all(bind=engine)\n print(\"finished.\")",
"def drop_tables() -> None... | [
"0.8857266",
"0.8710739",
"0.84407437",
"0.84020525",
"0.8315211",
"0.8266745",
"0.800701",
"0.7992689",
"0.79507643",
"0.794851",
"0.7897835",
"0.7893143",
"0.7868288",
"0.7847475",
"0.7834109",
"0.7800142",
"0.77895695",
"0.7765368",
"0.77633756",
"0.77343154",
"0.7734028",... | 0.7677401 | 34 |
Fix a 3D dataset recursively to enforce watertight manifolds, it is copyonly. It does not change the source. | def fix(
resolution: int = typer.Option(
5_000,
help="the number of leaf nodes of octree. The face number increases linearly with the resolution.",
),
simplify: bool = typer.Option(
False, help="if True, tries to simplify the obtained manifold"
),
manifold_check: bool = typer... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fix_data(self, cube: Cube) -> Cube:\n return cube",
"def test_fix_data(self):\n cube = self.fix.fix_data(self.cube)\n np.testing.assert_allclose(cube.data[0], 1.0)\n np.testing.assert_allclose(cube.data[2], 2.0)\n assert not np.ma.is_masked(cube.data[0])\n assert np.... | [
"0.5982272",
"0.5936331",
"0.57506317",
"0.5538353",
"0.55340886",
"0.54715705",
"0.54591495",
"0.5380183",
"0.53772956",
"0.53655994",
"0.5328793",
"0.5279874",
"0.5272796",
"0.5261189",
"0.52566725",
"0.5224833",
"0.5178111",
"0.51780534",
"0.5166307",
"0.5141794",
"0.51339... | 0.5803143 | 2 |
Generates authentication signature and return it in a dictionary | def generate_auth_dict(self) -> Dict[str, str]:
# api.exchange.bitcoin.com uses Basic Authentication https://api.exchange.bitcoin.com/#authentication
message = self.api_key + ":" + self.secret_key
signature = base64.b64encode(bytes(message, "utf8")).decode("utf8")
return {
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _build_signature(self):\n sig_contents = \\\n self.payload + \".\" + \\\n b64encode(b\"application/xml\").decode(\"ascii\") + \".\" + \\\n b64encode(b\"base64url\").decode(\"ascii\") + \".\" + \\\n b64encode(b\"RSA-SHA256\").decode(\"ascii\")\n sig_hash... | [
"0.6962848",
"0.6560777",
"0.63824123",
"0.63468164",
"0.62796557",
"0.62087584",
"0.6205923",
"0.6185844",
"0.61836755",
"0.6151539",
"0.61321473",
"0.61204165",
"0.60580724",
"0.6041897",
"0.60394365",
"0.60393846",
"0.60349697",
"0.6024893",
"0.6023378",
"0.6000974",
"0.59... | 0.7768794 | 0 |
Generates authentication headers required by bitcoin_com | def get_headers(self) -> Dict[str, str]:
header_dict = self.generate_auth_dict()
return {
"Authorization": "Basic " + header_dict["signature"],
"Content-Type": 'application/json',
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getHeaders():\n userid = rhev_settings.USERNAME\n passwd = rhev_settings.PASSWORD\n # base64.encodestring adds trailing \\n. \n auth = base64.encodestring(\"%s:%s\" % (userid, passwd)).rstrip(\"\\n\")\n headers = {\"Content-Type\": \"application/xml\",\n \"Accept\": \"applica... | [
"0.7698527",
"0.7321881",
"0.731975",
"0.7242912",
"0.7227",
"0.7122539",
"0.71201617",
"0.711637",
"0.7112385",
"0.7055011",
"0.70260453",
"0.69531006",
"0.69452506",
"0.6907244",
"0.6905155",
"0.6896297",
"0.6872004",
"0.68131626",
"0.68109024",
"0.6805357",
"0.67865443",
... | 0.7026986 | 10 |
get queries the database on model, starting with key, ordered by order. It receives count + 1 items, returning count and setting a next field to the count + 1 item key. It then reverses the sort, and grabs count objects, returning the last as a the previous. | def get(cls, count=10, q_filters={}, search=None, start=None, model=None, \
order='ASC', order_by='__key__'):
# argument validation
if model == None:
raise ValueError('You must pass a model to query')
# a valid model object will have a gql method.
if callable(mo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _Next(self, count=None):\n if count is not None and (not isinstance(count, (int, long)) or count <= 0):\n raise datastore_errors.BadArgumentError(\n 'Argument to _Next must be an int greater than 0; received %s (a %s)' %\n (count, typename(count)))\n\n if self.__buffer:\n if count... | [
"0.5825054",
"0.57874614",
"0.5762056",
"0.57196856",
"0.5508844",
"0.543483",
"0.54082257",
"0.5403575",
"0.53032744",
"0.5227761",
"0.5183222",
"0.51502746",
"0.51306784",
"0.51216686",
"0.51101565",
"0.5067596",
"0.5039265",
"0.49751845",
"0.49702373",
"0.4947512",
"0.4947... | 0.5722251 | 3 |
Write a JSON dictionary (with formatting) to a file. | def write_json(fd, data, indent=DEFAULT_JSON_INDENT):
print(json.dumps(data, indent=indent), file=fd) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _write_json(fname, dictionary, overwrite=False, verbose=False):\n if op.exists(fname) and not overwrite:\n raise FileExistsError(f'\"{fname}\" already exists. '\n 'Please set overwrite to True.')\n\n json_output = json.dumps(dictionary, indent=4)\n with open(fname, ... | [
"0.8473659",
"0.8349206",
"0.8287237",
"0.8284883",
"0.8232919",
"0.8181084",
"0.8142443",
"0.7959556",
"0.7923507",
"0.7899531",
"0.7847865",
"0.7776783",
"0.7734701",
"0.7602273",
"0.759944",
"0.7568202",
"0.7562128",
"0.7525241",
"0.75060785",
"0.7493676",
"0.74867713",
... | 0.7724335 | 13 |
Write the log list to a file in CSV format. | def write_csv(fd, data):
# df = pd.DataFrame.from_dict(data)
df = pd.io.json.json_normalize(data)
print(df.to_csv(index=False), file=fd) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def export_to_csv(self, log):\n if os.path.isfile(self.GENERATE_FILE):\n os.remove(self.GENERATE_FILE)\n\n with open(self.GENERATE_FILE, \"w\") as f:\n f.write(\"date, time, username, succes, label\\n\")\n\n for entry in log:\n f.write(str(entry[0].date... | [
"0.7428112",
"0.7227314",
"0.72092676",
"0.7194344",
"0.7063981",
"0.7051082",
"0.69845295",
"0.69353986",
"0.69166434",
"0.6862596",
"0.6825977",
"0.6797636",
"0.677685",
"0.67385906",
"0.6696633",
"0.6678054",
"0.66654414",
"0.6664616",
"0.66165817",
"0.6615615",
"0.6590226... | 0.0 | -1 |
Memoize the return value for each call to f(args). Then when called again with same args, we can just look it up. | def memo(f):
# Peter Norvig's
cache = {}
def _f(*args):
try:
return cache[args]
except KeyError:
cache[args] = result = f(*args)
return result
except TypeError:
# some element of args can't be a dict key
return f(*args)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def memoize(f):\r\n cache = {}\r\n\r\n def rval(*args, **kwargs):\r\n kwtup = tuple(kwargs.items())\r\n key = (args, kwtup)\r\n if key not in cache:\r\n val = f(*args, **kwargs)\r\n cache[key] = val\r\n else:\r\n val = cache[key]\r\n return ... | [
"0.8172702",
"0.81722903",
"0.8114816",
"0.8114816",
"0.8114816",
"0.809287",
"0.79903597",
"0.7919817",
"0.7799661",
"0.76765585",
"0.76765585",
"0.75987655",
"0.7522499",
"0.7510138",
"0.750118",
"0.7474494",
"0.740921",
"0.7344802",
"0.73361546",
"0.7307906",
"0.72194403",... | 0.7604618 | 11 |
bspline basis function c = number of control points. n = number of points on the curve. degree = curve degree | def bspline_basis(c, n, degree):
# Create knot vector and a range of samples on the curve
kv = np.array([0] * degree + [i for i in range(c - degree + 1)] +
[c - degree] * degree, dtype='int') # knot vector
u = np.linspace(0, c - degree, n) # samples range
# Cox - DeBoor recursive fu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def bspline(cv, n=100, degree=3, periodic=False):\n cv = np.asarray(cv)\n count = cv.shape[0]\n\n # Closed curve\n if periodic:\n kv = np.arange(-degree,count+degree+1)\n factor, fraction = divmod(count+degree+1, count)\n cv = np.roll(np.concatenate((cv,) * factor + (cv[:fraction],... | [
"0.7293481",
"0.7283197",
"0.7199199",
"0.6930515",
"0.68969107",
"0.68087196",
"0.6772325",
"0.67143494",
"0.6530433",
"0.6447313",
"0.6427295",
"0.63659334",
"0.63659334",
"0.6351063",
"0.63298196",
"0.6279631",
"0.6192802",
"0.61839586",
"0.6183348",
"0.6149349",
"0.609567... | 0.85616636 | 0 |
Converts a string in scientific notation format to a float in regular format | def sci_notation_to_float(n):
if 'e' in n:
exponent = float(n[n.find('e') + 1:])
number = float(n[:n.find('-') - 1])
number *= 10**exponent
return number
return n | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _to_float(self, s: str) -> float:\n return int(s[:-1]) / 1e9 if s.endswith('n') else float(s[:-1])",
"def read_endf_float(string):\n if string.strip() == \"\":\n return 0.0\n if \".\" in string:\n strsplit = string.split('.')\n return float(strsplit[0]+\".\"+strsplit[1].repl... | [
"0.7992028",
"0.7631141",
"0.7486856",
"0.74595267",
"0.7453234",
"0.74226296",
"0.7370093",
"0.7352414",
"0.7284855",
"0.7278769",
"0.72506315",
"0.7243346",
"0.7219503",
"0.7211098",
"0.7196211",
"0.719186",
"0.7183101",
"0.7158389",
"0.71015143",
"0.70598835",
"0.6937231",... | 0.7549512 | 2 |
Parses needed information from HAAR training data in an XML | def haar_parser():
tree = ET.parse('haarcascade_frontalface_alt.xml')
root = tree.getroot()
stage_number = 0
for haar_data in root.findall('haarcascade_frontalface_alt'):
for stage in haar_data.findall('stages'):
for underscore in stage.findall('_'):
for tree in und... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_train_data(training_set, language):\n print \"Reading training set: \" + training_set\n xmldoc = minidom.parse(training_set)\n lex_list = xmldoc.getElementsByTagName('lexelt')\n training_output = {}\n\n print \"Processing training set and training models...\"\n for node in lex_list:\n ... | [
"0.6703193",
"0.60255843",
"0.59246445",
"0.5912362",
"0.5693699",
"0.56034493",
"0.55347985",
"0.5478435",
"0.5460303",
"0.5457903",
"0.54251546",
"0.5424459",
"0.53948337",
"0.5363094",
"0.5358695",
"0.5322238",
"0.5317914",
"0.5302025",
"0.5210451",
"0.5201718",
"0.5198267... | 0.688683 | 0 |
Provide a configuration for testing. | def setUp(self) -> None:
self.config = TMConfiguration(
"q2",
TMTape(
tape="abcdefghij",
blank_symbol=".",
current_position=2,
),
)
self.config2 = MTMConfiguration(
"q1",
(
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setUpConfig(self):\n pass",
"def configuration():",
"def configure_test(self, test, config_json):\n pass",
"def config(self, **kw):\n self.cfg_fixture.config(**kw)",
"def test_config_class():\n assert config is not None",
"def test_configuration(self):\n self.assert... | [
"0.7677674",
"0.7560945",
"0.75177604",
"0.7468991",
"0.727222",
"0.7245467",
"0.7211508",
"0.7211508",
"0.71387666",
"0.71387666",
"0.7129564",
"0.71272254",
"0.70441407",
"0.70441407",
"0.7002823",
"0.698103",
"0.69522583",
"0.68959874",
"0.68959874",
"0.68742955",
"0.68720... | 0.0 | -1 |
Should return a string representation ot the given configuration. | def test_repr_config(self) -> None:
self.assertEqual(
repr(self.config), "TMConfiguration('q2', TMTape('abcdefghij', '.', 2))"
)
self.assertEqual(
repr(self.config2),
"MTMConfiguration('q1', (TMTape('abcdefghij', '.', 2), "
+ "TMTape('klmnopq', '.'... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __str__():\n return str(_config)",
"def __str__(self):\n config_str = 'Configurations\\n'\n config_str += pprint.pformat(self.__dict__)\n return config_str",
"def _config_str(config: Config) -> str:\n _C = config\n\n __C: CN = CN({\"RANDOM_SEED\": _C.random_seed})\n common_... | [
"0.8179262",
"0.7857372",
"0.7363686",
"0.736264",
"0.7243665",
"0.71599424",
"0.71418977",
"0.7087945",
"0.6956395",
"0.69093025",
"0.68716145",
"0.68227834",
"0.67673093",
"0.6715892",
"0.6702617",
"0.6695986",
"0.6667891",
"0.66585165",
"0.6611901",
"0.65899265",
"0.658992... | 0.6567749 | 22 |
Should print the given configuration to stdout. | def test_print_config(self) -> None:
out = io.StringIO()
with contextlib.redirect_stdout(out):
self.config.print()
self.assertEqual(
out.getvalue().rstrip(),
"{}: {}\n{}".format("q2", "abcdefghij", "^".rjust(7)),
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_configuration():\n configlog.info(\"-\" * 50)\n configlog.info(\"Initializing with the following configuration\")\n configlog.info(\"Check constants.py to change any of the following\")\n configlog.info(\"-\" * 50)\n configlog.info(\"COMPANY_NAME: {}\".format(COMPANY_NAME))\n configlog.... | [
"0.7587259",
"0.7523338",
"0.7368167",
"0.7342062",
"0.7209493",
"0.71788865",
"0.7166884",
"0.7094218",
"0.6971661",
"0.6958332",
"0.6929667",
"0.69253343",
"0.6881229",
"0.6872495",
"0.6839328",
"0.68237674",
"0.67180383",
"0.6690289",
"0.6649938",
"0.6621531",
"0.65711033"... | 0.7887999 | 0 |
Should print each machine configuration to stdout. | def test_print_configs(self, print_config: MagicMock) -> None:
tape1 = TMTape(
tape="01010101",
blank_symbol=".",
current_position=0,
)
tape2 = TMTape(
tape="x1010101",
blank_symbol=".",
current_position=-1,
)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_config(self):\n for pod in self.pods:\n for lb in pod.limbs:\n print '%s limb %s ' % (pod.name, lb.name)\n for br in lb.branches:\n br.printInfo()\n sys.stdout.flush()",
"def print_seeds(self):\n for key in self.CONFIG.key... | [
"0.69533426",
"0.6825423",
"0.6796512",
"0.66759825",
"0.66358614",
"0.65241665",
"0.6459319",
"0.63130814",
"0.6197961",
"0.618339",
"0.61394787",
"0.61050266",
"0.60956085",
"0.60902673",
"0.6064124",
"0.60114497",
"0.5990517",
"0.5986336",
"0.5973117",
"0.59216833",
"0.591... | 0.63539267 | 7 |
Should be able to iterate over a Turing machine tape. | def test_tape_iteration(self) -> None:
tape = TMTape(
tape="abcdef",
blank_symbol=".",
current_position=2,
)
self.assertEqual(tuple(tape), ("a", "b", "c", "d", "e", "f")) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __iter__(self) -> Iterator[str]:\n return iter(self.tape)",
"def test_machine_get_tape(self):\n self.machine.add_state('0 ,R, ,R, ,R, a,N,!')\n self.machine.init_tape(' aba caba_caba caba ')\n assert self.machine.get_tape() == 'aba caba caba caba'",
"def test_machine_i... | [
"0.62442267",
"0.58754504",
"0.56811714",
"0.5394346",
"0.5377582",
"0.5257124",
"0.52406955",
"0.52118194",
"0.51803154",
"0.51783246",
"0.5150232",
"0.5150232",
"0.51491994",
"0.51241785",
"0.51049644",
"0.5098543",
"0.5087977",
"0.50546545",
"0.5031224",
"0.50128055",
"0.5... | 0.6964048 | 0 |
Should print tape contents as a string without spaces. | def test_get_symbols_as_str(self) -> None:
tape = TMTape(
tape="abcdef",
blank_symbol=".",
current_position=2,
)
self.assertEqual(tape.get_symbols_as_str(), "abcdef") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def printTape(self):\n print(self.loadedTape.tape)",
"def __str__(self):\n tapeline = self.tape.format(\n self.index - 10, self.index + 11) + ' : state {}'.format(self.state)\n pointline = ' ' * 10 + '^' + ' ' * 11 + \\\n ' : index {}'.format(self.index)\n\n retu... | [
"0.6766568",
"0.6310775",
"0.6097003",
"0.60133976",
"0.60097545",
"0.5993959",
"0.58844167",
"0.57744575",
"0.57032126",
"0.56988627",
"0.56829554",
"0.5672349",
"0.56661415",
"0.56618303",
"0.5640181",
"0.5631739",
"0.56209093",
"0.5618605",
"0.56118405",
"0.55996954",
"0.5... | 0.6144317 | 2 |
Main method that manages supported CLI commands. | def parse():
parser = argparse.ArgumentParser(prog='ebrctl')
parser.add_argument('action', action='store_true')
parent_parser = argparse.ArgumentParser(add_help=False)
parent_parser.add_argument('vnic_mac')
parent_parser.add_argument('device_id')
parent_parser.add_argument('fabric')
parent_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cli():\n config, auth, execute_now = read_command_line_arguments()\n main(config, auth, execute_now)",
"def main_cli():\n pass",
"def cli():\n pass",
"def cli() -> None:",
"def cli() -> None:",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():... | [
"0.8019155",
"0.79849946",
"0.7895816",
"0.7866512",
"0.7866512",
"0.78625804",
"0.78625804",
"0.78625804",
"0.78625804",
"0.78625804",
"0.78625804",
"0.78625804",
"0.78625804",
"0.78625804",
"0.78625804",
"0.78625804",
"0.78625804",
"0.78625804",
"0.78625804",
"0.78625804",
... | 0.0 | -1 |
Opens a cloud manifest file at `path` Converts headers of file to lowercase since partners choose to capitalize arbitrarily returns dictionary | def read_data_from_cloud_manifest(path: str) -> dict:
with open_cloud_file(path, 'r') as csv_file:
def clean_file_header(header: str) -> str:
return header.strip().lower()
data_to_ingest = {'rows': []}
csv_reader = csv.DictReader(csv_file, delimiter=",")
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_manifest(path: Path):\n with open(path, \"rt\") as fin:\n data = json_load(fin)\n return Manifest.schema().load(data, many=True)",
"def get_manifest(path: str):\n base_url = urlparse(path.strip(\"/\"))\n if base_url.scheme != \"s3\":\n raise click.UsageError(\n ... | [
"0.6254436",
"0.5879989",
"0.5814724",
"0.5759604",
"0.5759604",
"0.56664675",
"0.5661104",
"0.56387746",
"0.5431581",
"0.5358934",
"0.5356918",
"0.52470446",
"0.51743126",
"0.5156279",
"0.5142583",
"0.51353735",
"0.50975263",
"0.5095046",
"0.50825524",
"0.50421375",
"0.50267... | 0.591603 | 1 |
Simple check for column names in the model | def validate_columns(self, fieldnames, dao):
unstored_columns = ['blank']
expected_columns = dao.model_type.__table__.columns.keys() + unstored_columns
for column_name in fieldnames:
if column_name not in expected_columns:
raise AttributeError(f"{self.file_path}: {col... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_defined_table_columns(model):\n inst = inspect(model)\n columns_from_model = [c_attr.key for c_attr in inst.mapper.column_attrs]\n _, columns = run_mysql(f\"SELECT * FROM {model.__tablename__} LIMIT 1;\")\n\n assert len(columns) == len(columns_from_model)\n assert set(columns) == set(column... | [
"0.7103625",
"0.71008795",
"0.7066106",
"0.6932352",
"0.68645114",
"0.68028975",
"0.6765049",
"0.67301756",
"0.6637777",
"0.65766287",
"0.6562689",
"0.6500888",
"0.6491967",
"0.64716387",
"0.6445888",
"0.64357543",
"0.64071035",
"0.6393606",
"0.6348869",
"0.6317435",
"0.62963... | 0.6846554 | 5 |
Entrypoint for SMS Workflow execution. Creates a SmsJobController and determines which process to run | def execute_workflow(self):
logging.info(f"called {self.job} with {self.file_type}")
job_params = {
"job": self.job,
"job_run_dao": self.job_run_dao,
"incident_dao": self.incident_dao,
"subprocess": self.file_type
}
with SmsJobController(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def startComponent(self):\n\n # create message service instance\n self.ms = MessageService()\n\n # register\n self.ms.registerAs(\"MergeAccountant\")\n\n # subscribe to messages\n self.ms.subscribeTo(\"MergeAccountant:StartDebug\")\n self.ms.subscribeTo(\"MergeAccou... | [
"0.56484526",
"0.55505526",
"0.5288705",
"0.52483284",
"0.5208785",
"0.5174079",
"0.51654077",
"0.51620835",
"0.5160946",
"0.51558024",
"0.5151097",
"0.51483196",
"0.51433206",
"0.5128541",
"0.51023465",
"0.5086026",
"0.5081889",
"0.50711775",
"0.50601345",
"0.50407666",
"0.5... | 0.68931174 | 0 |
Main method for ingestion jobs. | def job_ingestion(self):
# Map a file type to a DAO
if self.file_type == SmsFileTypes.SAMPLE_LIST:
self.file_dao = SmsSampleDao()
elif self.file_type == SmsFileTypes.N0:
self.file_dao = SmsN0Dao()
else:
self.file_dao = None
if self.file_dao:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n spark = create_spark_session()\n\n input_data = config['STORAGE']['INPUT_DATA']\n output_data = config['STORAGE']['OUTPUT_DATA']\n\n process_song_data(spark, input_data, output_data)\n process_log_data(spark, input_data, output_data)",
"def run_job(self):\n try:\n s... | [
"0.6997714",
"0.68329793",
"0.6822117",
"0.6762344",
"0.6738576",
"0.6704812",
"0.66714764",
"0.66658384",
"0.65523964",
"0.6488315",
"0.64466643",
"0.6445014",
"0.6421937",
"0.6421163",
"0.6390063",
"0.6383545",
"0.63780874",
"0.63768274",
"0.63713306",
"0.63640106",
"0.6357... | 0.6597942 | 8 |
Main method for generation jobs. | def job_generation(self):
# Map a file type to a DAO
if self.file_type == SmsFileTypes.N1_MC1:
self.file_dao = SmsN1Mc1Dao()
else:
self.file_dao = None
if self.file_dao:
source_data = self.file_dao.source_data(recipient=self.recipient)
i... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n # get arguments from command line\n args = parse_arguments()\n\n # checks on the output file\n # if args.stats_only:\n # assert args.output, \"The output file was not provided\"\n if args.output and os.path.exists(args.output):\n warnings.warn(\"Overwriting task file \" +... | [
"0.7010729",
"0.6841828",
"0.67838526",
"0.67346454",
"0.66830397",
"0.6663848",
"0.66300994",
"0.65998673",
"0.6383176",
"0.63787377",
"0.63738227",
"0.6341983",
"0.63118374",
"0.62905675",
"0.62900835",
"0.62722236",
"0.6258422",
"0.62582326",
"0.6255362",
"0.62542695",
"0.... | 0.6215068 | 27 |
Process the prune/clear command. | async def process_prune(
channel, amount, user_id: int, ctx=None, inter=None, allowed_mentions=None
):
user = await User.get(user_id)
if amount not in range(PRUNE_MIN, PRUNE_MAX):
return await send_message(
PRUNE_MIN,
PRUNE_MAX,
key="not_in_range",
use... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main(self, *extra_args):\n self.arguments.message = 'Pruned MediaWiki: %s' % self.arguments.branch\n if not self.arguments.delete:\n self.arguments.message += ' [keeping static files]'\n self.arguments.force = False\n return super(Clean, self).main(*extra_args)",
"def p... | [
"0.6189046",
"0.5942759",
"0.5925492",
"0.58126813",
"0.5764557",
"0.5754827",
"0.57539445",
"0.57497984",
"0.56477517",
"0.5625253",
"0.5578176",
"0.5539403",
"0.54893595",
"0.5482096",
"0.54248",
"0.5415695",
"0.5357811",
"0.5355897",
"0.5350661",
"0.5341999",
"0.5336706",
... | 0.55016893 | 12 |
Add a command prefix to a guild. | async def process_prefix_add_remove(
guild: disnake.Guild,
prefix: str,
ctx: commands.Context = None,
inter: AppCmdInter = None,
allowed_mentions=None,
add=False,
):
await create_guild_model(guild)
guild = await Guild.get(guild.id)
if add:
await guild.add_prefix(prefix)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def prefix(self, ctx, prefix):\n if prefix.strip() == \"\":\n raise exceptions.Warning(\"Prefix cannot be empty.\")\n\n if prefix.startswith(\" \"):\n raise exceptions.Warning(\"Prefix cannot start with a space.\")\n\n if len(prefix) > 32:\n raise excepti... | [
"0.7765609",
"0.7438157",
"0.7337754",
"0.72621065",
"0.71957946",
"0.7084846",
"0.7023182",
"0.7022211",
"0.6995119",
"0.6882482",
"0.68795705",
"0.68495554",
"0.68444985",
"0.6843278",
"0.683003",
"0.6727373",
"0.6712273",
"0.6662263",
"0.66496396",
"0.64985317",
"0.6413639... | 0.7127585 | 5 |
Process the adding of an emoji to a server. | async def process_add_emoji(
emoji,
emoji_name,
user_id,
ctx: commands.Context = None,
inter: AppCmdInter = None,
allowed_mentions=None,
):
response_deferred = await defer_inter(inter)
url = emoji if not isinstance(emoji, disnake.PartialEmoji) else emoji.url
user = await User.get(use... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def add_emoji(\n client,\n event,\n emoji: ('str', 'The emoji to add.'),\n name: ('str', 'Custom name to add the emoji with.') = None\n):\n if not client.is_owner(event.user):\n abort('Owner only!')\n \n emoji = parse_emoji(emoji)\n if emoji is None:\n abort('That\\'s no... | [
"0.67287016",
"0.6683286",
"0.6535552",
"0.63595986",
"0.61546665",
"0.61545706",
"0.6151",
"0.6147468",
"0.60753864",
"0.602852",
"0.5925072",
"0.58887196",
"0.5861602",
"0.58516765",
"0.57721126",
"0.5765197",
"0.57616425",
"0.5761379",
"0.56382954",
"0.55763",
"0.555645",
... | 0.7052691 | 0 |
Send the command prefixes of a guild. | async def process_prefix_list(
guild: disnake.Guild,
ctx: commands.Context = None,
inter: AppCmdInter = None,
allowed_mentions=None,
):
await create_guild_model(guild)
guild = await Guild.get(guild.id)
msg = f"The following are the custom prefixes for {guild.name}:\n" + ", ".join(
gu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def on_guild_join(self, guild: discord.Guild):\n with open(\"./config/prefixes.json\", \"r\") as f:\n prefixes = json.load(f)\n\n prefixes[str(guild.id)] = \".\"\n\n with open(\"./config/prefixes.json\", \"w\") as f:\n json.dump(prefixes, f, indent=4)",
"async def... | [
"0.72532135",
"0.70512486",
"0.6933154",
"0.67259693",
"0.66195005",
"0.65402573",
"0.65276223",
"0.6477757",
"0.6397897",
"0.6349567",
"0.63149333",
"0.6312094",
"0.62954944",
"0.6294326",
"0.62471807",
"0.62218827",
"0.6151493",
"0.61468345",
"0.61232364",
"0.6099351",
"0.6... | 0.74725825 | 0 |
Autocomplete typing for the command prefixes in a guild. | async def auto_complete_type_guild_prefixes(
inter: disnake.AppCmdInter, user_input: str
) -> List[str]:
await create_guild_model(inter.guild)
guild = await Guild.get(inter.guild_id)
return guild.prefixes[:24] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def process_prefix_list(\n guild: disnake.Guild,\n ctx: commands.Context = None,\n inter: AppCmdInter = None,\n allowed_mentions=None,\n):\n await create_guild_model(guild)\n guild = await Guild.get(guild.id)\n msg = f\"The following are the custom prefixes for {guild.name}:\\n\" + \", \... | [
"0.6334425",
"0.61928004",
"0.59438735",
"0.5882108",
"0.58367133",
"0.5759399",
"0.56862473",
"0.566724",
"0.5637605",
"0.5576028",
"0.55751395",
"0.55580425",
"0.5514969",
"0.54804295",
"0.5447843",
"0.54456246",
"0.5445153",
"0.54353315",
"0.54343086",
"0.53711796",
"0.536... | 0.79266447 | 0 |
Add a reaction role. | async def process_add_reaction_role(
user_id, description, ctx=None, inter=None, allowed_mentions=None
):
user = await User.get(user_id)
response_deferred = await defer_inter(inter, ephemeral=True)
view = disnake.ui.View(timeout=None)
view.add_item(RoleDropdown(description))
await send_message(
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def rolemenu_add_role(self,\n interaction: discord.Interaction,\n name: str,\n role: discord.Role,\n emoji: str = None,\n description: str = None):\n ... | [
"0.74878365",
"0.7185206",
"0.7157245",
"0.71171796",
"0.70440525",
"0.7006333",
"0.6889938",
"0.68790966",
"0.6827275",
"0.6738522",
"0.67022955",
"0.6666086",
"0.6641454",
"0.662785",
"0.6625806",
"0.66136146",
"0.6613041",
"0.66006094",
"0.65936184",
"0.6589437",
"0.656451... | 0.6762945 | 9 |
Post the reaction roles message | async def reaction_roles_post(inter: MessageInteraction, description, roles):
view = disnake.ui.View(timeout=None)
for role in roles:
view.add_item(disnake.ui.Button(label=role.name, custom_id=role.id))
messages = await send_message(msg=description, channel=inter.channel, view=view)
for message ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def setroles(self, ctx):\n if not has_permissions(ctx, MOD_ROLE):\n await ctx.send(\"You do not have sufficient permissions to perform this command\", hidden=True)\n return False\n\n def check(m):\n return m.author == ctx.author\n\n roles_dict = {}\n ... | [
"0.70767516",
"0.7048763",
"0.70305854",
"0.67609715",
"0.66043484",
"0.6424783",
"0.63169235",
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"0.6028766",
"0.6005439",
"0.60031676",
"0.5964373",
"0.59568113",
"0.5954478",
"0.59193... | 0.858319 | 0 |
Handles role reaction button presses. A 'on_button_click' listener. | async def handle_role_reaction_press(interaction: disnake.MessageInteraction):
if interaction.message not in await ReactionRoleMessage.get_all():
return
role_id = int(interaction.component.custom_id)
member: disnake.Member = interaction.author
user = await User.get(member.id)
role = member.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def subscribe_command(self, ctx: commands.Context, *_) -> None: # We don't actually care about the args\n view = RoleButtonView(ctx.author, self.assignable_roles)\n await ctx.send(\n \"Click the buttons below to add or remove your roles!\",\n view=view,\n delet... | [
"0.5762249",
"0.5668957",
"0.5659755",
"0.5601932",
"0.5597881",
"0.55494606",
"0.5509019",
"0.550365",
"0.54933995",
"0.54303074",
"0.5379965",
"0.53338444",
"0.5327616",
"0.5321051",
"0.5281076",
"0.52789605",
"0.52783185",
"0.5262219",
"0.52602375",
"0.5247453",
"0.5199621... | 0.6394534 | 0 |
Plateau in refractive index below 330nm for Glass, edge of data artifact | def refractive_index(self):
wd = np.arange(80,820,10)
nd = self.boundary.imat.refractive_index(wd)
plt.plot(wd, nd)
return wd, nd | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_index_of_surface_gate(data, setup={}):\n alts = data['alt']\n return np.argmin(np.abs(alts), 1)",
"def test_no_backg_subt():\n \n test_object = fa.read_in_envision(data_csv=HsHis6_PEX5C_vs_HsPEX5C, platemap_csv=Hs_His6_PEX5C_vs_HsPEX5C_platemap, data_type='plate', size=384)\n test_object.c... | [
"0.5831233",
"0.5830266",
"0.5719483",
"0.563902",
"0.55948025",
"0.55843574",
"0.5583961",
"0.5577517",
"0.55708694",
"0.5549658",
"0.55016595",
"0.5486436",
"0.54773384",
"0.54688746",
"0.5463584",
"0.54448295",
"0.53914726",
"0.5348724",
"0.5341465",
"0.5329872",
"0.531499... | 0.618945 | 0 |
Read h5 format data file | def read_data(path):
with h5py.File(path, 'r') as hf:
data = np.array(hf.get('data'))
return data | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_hdf5(path_to_file):\n\n print(\"\\nReading HDF5 file: \", path_to_file)\n file = h5py.File(path_to_file, 'r')\n\n # List the groups\n groups = list(file.keys())\n print(\"Groups available: \", groups)\n\n # Read Zemax Metadata\n zemax_metadata = {}\n print(\"\\nZemax Metadata:\")\n... | [
"0.7600226",
"0.756424",
"0.7475437",
"0.74319535",
"0.74319535",
"0.7393015",
"0.72876596",
"0.7209565",
"0.71935135",
"0.7171935",
"0.7154797",
"0.7107951",
"0.70604193",
"0.70279646",
"0.70197207",
"0.70096785",
"0.6915676",
"0.68875426",
"0.6883653",
"0.6829923",
"0.67898... | 0.7660385 | 0 |
Preprocess single image file (1) Read original image as YCbCr format (and grayscale as default) (2) Normalize (3) Apply image file with bicubic interpolation | def preprocess(path, scale=3):
image = imread(path, is_grayscale=True)
# Must be normalized
image = (image-127.5 )/ 127.5
input_ = scipy.ndimage.interpolation.zoom(input_, (scale/1.), prefilter=False)
return input_ | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def normalise(image):",
"def preprocess(img):\n \n scaler=StandardScaler() ## scaler object to perform preprocessing\n img=scaler.fit_transform(img) ## zero-center and normalize\n \n return img",
"def preprocess(path, img_w, img_h):\n #print(path)\n img = cv2.imre... | [
"0.67356676",
"0.6674581",
"0.6586979",
"0.6573389",
"0.6537538",
"0.65024185",
"0.64687973",
"0.64650357",
"0.64465904",
"0.6442704",
"0.6418419",
"0.640712",
"0.6392954",
"0.6363671",
"0.6362483",
"0.6359591",
"0.6263613",
"0.6258477",
"0.6247825",
"0.624503",
"0.6207113",
... | 0.6635638 | 2 |
Make input data as h5 file format Depending on 'is_train' (flag value), savepath would be changed. | def make_data(sess, data, data_dir):
if FLAGS.is_train:
#savepath = os.path.join(os.getcwd(), os.path.join('checkpoint',data_dir,'train.h5'))
savepath = os.path.join('.', os.path.join('checkpoint',data_dir,'train.h5'))
if not os.path.exists(os.path.join('.',os.path.join('checkpoint',data_dir))):
o... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_to_hd5(out_file, x_train, y_train, x_val, y_val, x_test, y_test):\n data = h5py.File(out_file, \"w\")\n train_data = data.create_group(\"train_data\")\n train_data.create_dataset(\"x_train\", data=x_train)\n train_data.create_dataset(\"y_train\", data=y_train)\n if x_val is not None:\n ... | [
"0.7217005",
"0.70193493",
"0.684831",
"0.6649944",
"0.6624915",
"0.656537",
"0.6552267",
"0.64465964",
"0.63645554",
"0.6273017",
"0.62496",
"0.61882937",
"0.6150306",
"0.610883",
"0.6098114",
"0.6088304",
"0.60781205",
"0.6075722",
"0.6039973",
"0.6017013",
"0.59959936",
... | 0.71147066 | 1 |
Read image using its path. Default value is grayscale, and image is read by YCbCr format as the paper said. | def imread(path, is_grayscale=True):
if is_grayscale:
return scipy.misc.imread(path, flatten=True, mode='YCbCr').astype(np.float)
else:
return scipy.misc.imread(path, mode='YCbCr').astype(np.float) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def imread(path, is_grayscale=True):\n if is_grayscale:\n #flatten=True 以灰度图的形式读取 \n return scipy.misc.imread(path, flatten=True, mode='YCbCr').astype(np.float)\n else:\n return scipy.misc.imread(path, mode='YCbCr').astype(np.float)",
"def imread(path, is_grayscale=True):\n if is_grayscale:\n ... | [
"0.778447",
"0.77261835",
"0.7638995",
"0.75208247",
"0.74676746",
"0.74573797",
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"0.7026387",
"0.70155853",
"0.6943071",
"0.6942862",
"0.69331473",
"0.689974",
"0.6891293"... | 0.7768587 | 1 |
To scale down and up the original image, first thing to do is to have no remainder while scaling operation. We need to find modulo of height (and width) and scale factor. Then, subtract the modulo from height (and width) of original image size. There would be no remainder even after scaling operation. | def modcrop(image, scale=3):
if len(image.shape) == 3:
h, w, _ = image.shape
h = h - np.mod(h, scale)
w = w - np.mod(w, scale)
image = image[0:h, 0:w, :]
else:
h, w = image.shape
h = h - np.mod(h, scale)
w = w - np.mod(w, scale)
image = image[0:h, 0:w]
return image | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def scale_down(image:np.array)->np.array:\n src = image\n scale_percent = 25\n width = int(src.shape[1] * scale_percent / 100)\n height = int(src.shape[0] * scale_percent / 100)\n dsize = (width, height)\n output = cv2.resize(src, dsize)\n return output",
"def Rescale(self):\r\n picWi... | [
"0.72712576",
"0.7073858",
"0.7063879",
"0.6822762",
"0.6660474",
"0.66532725",
"0.6620047",
"0.6618878",
"0.660835",
"0.65937185",
"0.653122",
"0.65161115",
"0.64969885",
"0.648761",
"0.64868206",
"0.64707977",
"0.64599156",
"0.6456482",
"0.6456482",
"0.64336264",
"0.6403879... | 0.5729484 | 90 |
Read image files and make their subimages and saved them as a h5 file format. | def input_setup_MS(sess,config,data_dir,index=0):
# Load data path
if config.is_train:
data = prepare_data(sess, dataset=data_dir)
sub_input_sequence = []
padding = 0
if config.is_train:
for i in xrange(len(data)):
input_=(imread(data[i])-127.5)/127.5
if len(input_.shape) == 3:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_and_write_output(predictions_path,output_path,inpDir):\n \n filenames= sorted(os.listdir(predictions_path)) \n for filename in filenames:\n \n # read the 3 channel output image from the neural network\n image=cv2.imread(os.path.join(predictions_path,filename))\n \... | [
"0.67509365",
"0.6384123",
"0.635404",
"0.62878686",
"0.6287435",
"0.62700456",
"0.6264196",
"0.62362057",
"0.6180607",
"0.6161816",
"0.6144989",
"0.6094709",
"0.6090776",
"0.6078111",
"0.60713965",
"0.60598326",
"0.6051427",
"0.60020304",
"0.5998716",
"0.5987901",
"0.5978740... | 0.0 | -1 |
Read image files and make their subimages and saved them as a h5 file format. | def input_setup_PAN(sess,config,data_dir,index=0):
if config.is_train:
data = prepare_data(sess, dataset=data_dir)
sub_input_sequence = []
padding = 0
if config.is_train:
for i in xrange(len(data)):
input_=(imread(data[i])-127.5)/127.5
if len(input_.shape) == 3:
h, w, _ = input_.s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_and_write_output(predictions_path,output_path,inpDir):\n \n filenames= sorted(os.listdir(predictions_path)) \n for filename in filenames:\n \n # read the 3 channel output image from the neural network\n image=cv2.imread(os.path.join(predictions_path,filename))\n \... | [
"0.67501724",
"0.6385571",
"0.635447",
"0.6286945",
"0.62869215",
"0.62683004",
"0.62626487",
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"0.617954",
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"0.60599494",
"0.60516787",
"0.60040814",
"0.59981525",
"0.5988912",
"0.597... | 0.0 | -1 |
Dynamically generate options for resource group form field based on the user's selection for Environment. This method requires the user to set the resource_group parameter as dependent on environment. | def generate_options_for_resource_group(control_value=None, **kwargs):
if control_value is None:
return []
env = Environment.objects.get(id=control_value)
if CB_VERSION_93_PLUS:
# Get the Resource Groups as defined on the Environment. The Resource Group is a
# CustomField that is o... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_options_for_cloudbolt_environment(group=None, **kwargs):\n envs = Environment.objects.filter(\n resource_handler__resource_technology__name='Google Cloud Platform') \\\n .select_related('resource_handler')\n if group:\n group_env_ids = [env.id for env in group.get_available_... | [
"0.6358487",
"0.5937777",
"0.55043066",
"0.54691523",
"0.54691523",
"0.54691523",
"0.54691523",
"0.54691523",
"0.54691523",
"0.54691523",
"0.54691523",
"0.54691523",
"0.54691523",
"0.54691523",
"0.54691523",
"0.54691523",
"0.54691523",
"0.54691523",
"0.54691523",
"0.54691523",
... | 0.6859024 | 0 |
Get the client using newer methods from the CloudBolt main repo if this CB is running a version greater than 9.2.1. These internal methods implicitly take care of much of the other features in CloudBolt such as proxy and ssl verification. Otherwise, manually instantiate clients without support for those other CloudBolt... | def _get_client(handler):
if CB_VERSION_93_PLUS:
from resourcehandlers.azure_arm.azure_wrapper import configure_arm_client
wrapper = handler.get_api_wrapper()
sql_client = configure_arm_client(wrapper, sql.SqlManagementClient)
else:
# TODO: Remove once versions <= 9.2.1 are no l... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_client_impl(self):\n api_version = self._get_api_version(None)\n if api_version not in self._client_impls:\n self._create_client_impl(api_version)\n return self._client_impls[api_version]",
"def raw_client(self):\r\n warnings.warn(\"raw_client is deprecated. use se... | [
"0.7103304",
"0.6904759",
"0.65741783",
"0.6360245",
"0.6288746",
"0.62776977",
"0.6226399",
"0.6196101",
"0.6190632",
"0.6187057",
"0.6171933",
"0.61647344",
"0.61564815",
"0.6148251",
"0.61479414",
"0.60850495",
"0.6072552",
"0.60554427",
"0.604616",
"0.6028892",
"0.5996603... | 0.5521968 | 92 |
Deserialize datetime object into string form for JSON processing. | def dump_datetime(value):
if value is None:
return None
return [value.strftime("%Y-%m-%d"), value.strftime("%H:%M:%S")] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def serialize_datetime(self, obj):\r\n if isinstance(obj, datetime.datetime):\r\n return obj.isoformat()\r\n raise TypeError(\"Type not serializable\")",
"def json_datetime_serializer(obj):\n\n if isinstance(obj, datetime):\n serial = obj.isoformat()\n return serial\n ... | [
"0.69946605",
"0.69832784",
"0.6899136",
"0.6865027",
"0.6813981",
"0.6750276",
"0.6649766",
"0.6633779",
"0.65959406",
"0.6547565",
"0.65440494",
"0.6539357",
"0.6486496",
"0.6480947",
"0.64407444",
"0.63945824",
"0.6365423",
"0.63568634",
"0.6337701",
"0.6311433",
"0.629324... | 0.5694063 | 78 |
Run the linter and return the exit code. | def run(args=None):
parser = init_argument_parser()
options = parser.parse_args(args if args is not None else sys.argv[1:])
stdin_filename = None
file_names = set(options.files)
checked_files = set()
# Read input from STDIN
if not sys.stdin.isatty():
with tempfile.NamedTemporaryFil... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def lint_command(argv) -> CommandResult:\n app = FlakeHellApplication(program=NAME, version=VERSION)\n try:\n app.run(argv)\n app.exit()\n except SystemExit as exc:\n return int(exc.code), ''\n raise RuntimeError('unreachable')",
"def run(self):\n success = False\n ... | [
"0.7377753",
"0.70567983",
"0.6740494",
"0.67318183",
"0.6663239",
"0.6513022",
"0.6471138",
"0.6430389",
"0.63975984",
"0.63959485",
"0.63455653",
"0.6329475",
"0.63124686",
"0.6284871",
"0.6266829",
"0.6210904",
"0.6164137",
"0.6136608",
"0.61339283",
"0.6081217",
"0.605742... | 0.53075475 | 88 |
Returns a new initialized argument parser. | def init_argument_parser():
parser = argparse.ArgumentParser(prog=NAME, description=DESCRIPTION)
# The files argument is optional as STDIN is always read
parser.add_argument(dest='files', metavar='FILE', nargs='*', default=[],
help='one or more files or paths')
parser.add_argum... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_parser():\n\n parser = parser.ArgumentParser()\n return parser",
"def get_parser():\n parser = ArgumentParser(\n description=__doc__, formatter_class=ArgumentDefaultsHelpFormatter\n )\n parser.add_argument(\n \"-s\", \"--sentence\", dest=\"sentence\", help=\"sentence,... | [
"0.7757528",
"0.7674221",
"0.766556",
"0.75634253",
"0.7486213",
"0.7486213",
"0.74758685",
"0.74569213",
"0.74259055",
"0.7384082",
"0.737286",
"0.7365048",
"0.7327146",
"0.73229235",
"0.7290896",
"0.7204032",
"0.7203092",
"0.71918964",
"0.71847415",
"0.7169976",
"0.7169976"... | 0.0 | -1 |
Return the initialized output formatter based upon the configuration. | def initialize_formatter(config):
if config.json: # pylint: disable=R1705
return formatters.JsonFormatter()
elif config.severity: # pylint: disable=R1705
return formatters.SeverityFormatter(config.colored)
return formatters.Formatter(config.colored) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_formatter(self):\n return SectionedFormatter(\n sections=self.sections,\n width=self.terminal_width,\n max_width=self.max_content_width,\n )",
"def set_formatter_string(config: dict):\n formatter_str = \"%(levelname)s %(name)s\"\n\n if config.get(\"fo... | [
"0.6507591",
"0.59741753",
"0.5937114",
"0.5926568",
"0.5859191",
"0.58575606",
"0.5845092",
"0.5838448",
"0.5783071",
"0.57651764",
"0.57422423",
"0.5739221",
"0.5724411",
"0.56877214",
"0.56793237",
"0.56695",
"0.5667886",
"0.566292",
"0.56339353",
"0.56309354",
"0.56209934... | 0.74388397 | 0 |
Returns the sorted list of problems. | def sort_problems(problems):
# Note: sort() doesn't return the sorted list; rather, it sorts the list
# in place
problems.sort(
key=lambda problem: (
problem.filename,
problem.linenumber,
problem.rule.id
)
)
return problems | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def problem_list(self):\r\n return [{\r\n 'location': location, 'problem_name': name,\r\n 'num_graded': self.DUMMY_DATA['problem_list_num_graded'],\r\n 'num_pending': self.DUMMY_DATA['problem_list_num_pending'],\r\n 'num_required': self.DUMMY_DATA['problem_list_nu... | [
"0.6703581",
"0.6595729",
"0.62168723",
"0.6204288",
"0.6180413",
"0.61678904",
"0.6021558",
"0.5934097",
"0.5893262",
"0.58796406",
"0.58487594",
"0.5641037",
"0.5635462",
"0.5628445",
"0.5586737",
"0.5583212",
"0.54723084",
"0.5472102",
"0.547204",
"0.5451602",
"0.54358476"... | 0.75329185 | 0 |
This is the stub name that will be used to generate the processed filenames and is the assumed stub for the raw data filename. | def get_dataset_name(self):
return self.dataset_name | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _getfilename(self):\n pass",
"def data_filename(self) -> str: # type: ignore[return-value]\n return os.path.abspath(self.name) # type: ignore",
"def GetFileName(self) -> \"char const *\":\n return _itkVTKPolyDataReaderPython.itkVTKPolyDataReaderMF2_GetFileName(self)",
"def filename... | [
"0.6384839",
"0.63739693",
"0.6017186",
"0.6008499",
"0.5945327",
"0.59224993",
"0.58893055",
"0.5883021",
"0.5876685",
"0.5855248",
"0.58311427",
"0.5830096",
"0.5818306",
"0.5797959",
"0.57899445",
"0.5784805",
"0.5756411",
"0.5752741",
"0.57516646",
"0.57437134",
"0.569571... | 0.0 | -1 |
Returns the name of the class attribute to be used for classification. | def get_class_attribute(self):
return self.class_attr | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def name(self) -> str:\n return self.class_names[self.class_num]",
"def get_attribute_class(self, attr_name):\n return self.attrs.get_attribute_class(attr_name)",
"def class_name(self) -> str:\n return pulumi.get(self, \"class_name\")",
"def get_attribute_class(self):\n return sel... | [
"0.7220845",
"0.7191606",
"0.7166631",
"0.70328903",
"0.6854248",
"0.68273634",
"0.68061316",
"0.6639919",
"0.66164047",
"0.65259147",
"0.64209676",
"0.64048225",
"0.63896745",
"0.637577",
"0.637577",
"0.6366623",
"0.6366623",
"0.63627464",
"0.63504124",
"0.63288695",
"0.6326... | 0.7351364 | 0 |
Returns the value used in the dataset to indicate the positive classification choice. | def get_positive_class_val(self, tag):
# FIXME this dependence between tags and metadata is bad; don't know how to fix it right now
if tag == 'numerical-binsensitive':
return 1
else:
return self.positive_class_val | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __value_of(sentiment):\n if sentiment == 'positive': return 1\n if sentiment == 'negative': return -1\n return 0",
"def get_prediction_from_score(score):\n if(score >= 0.03):\n return 'Positive'\n elif(score <= -0.03):\n return 'Negative'\n else:\n return 'Neutral'",
... | [
"0.6920214",
"0.6468132",
"0.6377437",
"0.6298673",
"0.6298673",
"0.6276346",
"0.62528133",
"0.62347436",
"0.6230235",
"0.61446166",
"0.61428374",
"0.6095599",
"0.60748625",
"0.6050616",
"0.6050616",
"0.6050616",
"0.6050616",
"0.6029829",
"0.60096",
"0.5973834",
"0.5958873",
... | 0.73992556 | 0 |
Returns a list of the names of any sensitive / protected attribute(s) that will be used for a fairness analysis and should not be used to train the model. | def get_sensitive_attributes(self):
return self.sensitive_attrs | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __listAttr(self):\n attr = dir(self) # already sorted\n filter = []\n for name in attr:\n if name[:2] == '__': pass\n elif name[:10] == '_HelpDoc__': pass # used to mask private attr\n elif name in self.__exclude: pass\n else: filter.append(name)... | [
"0.70694345",
"0.66682184",
"0.6661344",
"0.66439575",
"0.66118485",
"0.654151",
"0.6416619",
"0.6314357",
"0.63003594",
"0.626828",
"0.6239923",
"0.62366736",
"0.62366736",
"0.6221969",
"0.6175809",
"0.61643624",
"0.61223054",
"0.6114993",
"0.61009276",
"0.6096935",
"0.60964... | 0.7331742 | 0 |
Same as get_sensitive_attributes, but also includes the joint sensitive attribute if there is more than one sensitive attribute. | def get_sensitive_attributes_with_joint(self):
if len(self.get_sensitive_attributes()) > 1:
return self.get_sensitive_attributes() + ['-'.join(self.get_sensitive_attributes())]
return self.get_sensitive_attributes() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_sensitive_attributes(self):\n return self.sensitive_attrs",
"def get_sensitive_terms(self):\n sensitive_terms_dict = {}\n for attribute in self.__non_redundant_entity_attributes:\n for record_id, sensitive_terms in self.__df[attribute].dropna().iteritems():\n ... | [
"0.7723388",
"0.61657095",
"0.5457752",
"0.53589123",
"0.5331123",
"0.5112113",
"0.5105332",
"0.51015556",
"0.5088783",
"0.50400466",
"0.5025615",
"0.5014114",
"0.5013227",
"0.49880865",
"0.4979075",
"0.495395",
"0.4940964",
"0.49345672",
"0.49345672",
"0.49008775",
"0.489157... | 0.85016006 | 0 |
Returns a list in the same order as the sensitive attributes list above of the privileged class name (exactly as it appears in the data) of the associated sensitive attribute. | def get_privileged_class_names(self, tag):
# FIXME this dependence between tags and privileged class names is bad; don't know how to
# fix it right now
if tag == 'numerical-binsensitive':
return [1 for x in self.get_sensitive_attributes()]
else:
return self.privil... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getDataAttributes(self):\n asRet = [];\n asAttrs = dir(self);\n for sAttr in asAttrs:\n if sAttr[0] == '_' or sAttr[0] == 'k':\n continue;\n if sAttr in self.kasInternalAttributes:\n continue;\n oValue = getattr(self, sAttr);... | [
"0.70606923",
"0.7041535",
"0.69863814",
"0.6956516",
"0.6759619",
"0.66697687",
"0.66511804",
"0.6639891",
"0.64760447",
"0.638376",
"0.6333704",
"0.632314",
"0.62966156",
"0.61981976",
"0.6196645",
"0.6196645",
"0.6156528",
"0.6151096",
"0.61357003",
"0.6134827",
"0.6129417... | 0.7225402 | 0 |
Same as get_privileged_class_names, but also includes the joint sensitive attribute if there is more than one sensitive attribute. | def get_privileged_class_names_with_joint(self, tag):
priv_class_names = self.get_privileged_class_names(tag)
if len(priv_class_names) > 1:
return priv_class_names + ['-'.join(str(v) for v in priv_class_names)]
return priv_class_names | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_privileged_class_names(self, tag):\n # FIXME this dependence between tags and privileged class names is bad; don't know how to\n # fix it right now\n if tag == 'numerical-binsensitive':\n return [1 for x in self.get_sensitive_attributes()]\n else:\n return ... | [
"0.73706985",
"0.6840645",
"0.6202078",
"0.594918",
"0.5039532",
"0.50019443",
"0.49859846",
"0.49820405",
"0.49799612",
"0.49191874",
"0.49018767",
"0.48560244",
"0.48521727",
"0.48503172",
"0.48184666",
"0.478812",
"0.47643054",
"0.47436982",
"0.47109863",
"0.47010607",
"0.... | 0.7298145 | 1 |
Returns a list of features that should be expanded to onehot versions for numericalonly algorithms. This should not include the protected features or the outcome class variable. | def get_categorical_features(self):
return self.categorical_features | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def onehot_features(data):\n\n# Binary Features\n columns = ['Weekend', 'Revenue']\n for col in columns:\n data[col] = data[col].apply(lambda x: float(1) if x else float(0))\n\n columns = ['Month', 'OperatingSystems', 'Browser', 'Region', 'TrafficType',\n 'VisitorType']\n for col... | [
"0.66784006",
"0.64435947",
"0.6321415",
"0.6291115",
"0.6268722",
"0.6225658",
"0.61513215",
"0.6115024",
"0.6107136",
"0.6081699",
"0.6007897",
"0.600501",
"0.60009587",
"0.5978102",
"0.5973954",
"0.59635854",
"0.5962802",
"0.59291697",
"0.59200054",
"0.5908913",
"0.5896621... | 0.5930906 | 17 |
Takes a pandas dataframe and modifies it to do any data specific processing. This should include any ordered categorical replacement by numbers. The resulting pandas dataframe is returned. | def data_specific_processing(self, dataframe):
return dataframe | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clean_data(df):\r\n \r\n # list of categories to use as column names \r\n categories_cols = [names.split('-')[0] for names in df['categories'][0].split(';')]\r\n \r\n # creating 36 individual category columns\r\n for i in range(len(categories_cols)):\r\n df[categories_cols[i]] = [int(r... | [
"0.72517514",
"0.7021752",
"0.6957841",
"0.69419897",
"0.6836155",
"0.6820173",
"0.67918307",
"0.6785911",
"0.6761052",
"0.67148185",
"0.67098016",
"0.6694147",
"0.66585565",
"0.6657991",
"0.6594431",
"0.6561653",
"0.65530825",
"0.65374434",
"0.6511466",
"0.6479127",
"0.64500... | 0.5775189 | 57 |
This method implements any data specific missing data processing. Any missing data not replaced by values in this step will be removed by the general preprocessing script. | def handle_missing_data(self, dataframe):
return dataframe | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _clean(self, dataset):\n # Replace missing values with numpy's NaN. The missing value is\n # usually 1e+20, but values can be like 1.0000002e+20, which is\n # different. Ergo the inequality.\n for var in dataset.data_vars.itervalues():\n if 'missing_value' in var.attrs:... | [
"0.6957376",
"0.6862694",
"0.6733841",
"0.6647942",
"0.6541775",
"0.6535181",
"0.64431393",
"0.6377302",
"0.63732034",
"0.6310155",
"0.62766635",
"0.6204777",
"0.6177998",
"0.6141851",
"0.6130127",
"0.6126659",
"0.60847795",
"0.6082589",
"0.6067625",
"0.6064571",
"0.6064571",... | 0.5833492 | 43 |
A passing grade in the Ricci data is defined as any grade above a 70 in the combined oral and written score. (See Miao 2010.) | def passing_grade(row):
if row['Combine'] >= 70.0:
return 1
else:
return 0 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def grade(self):\n if round(self.numAvg,0) >= 70:\n return round(self.numAvg,0)\n elif self.PassSummer:\n return 70\n elif round(self.numAvg,0) >= 55 and not self.PassSummer:\n return round(self.numAvg,0)\n else:\n return 55",
"def calc_grad... | [
"0.7085485",
"0.6440944",
"0.63578296",
"0.63572264",
"0.6289942",
"0.6267479",
"0.60695726",
"0.59769577",
"0.5956954",
"0.59330213",
"0.5848372",
"0.5821019",
"0.58090127",
"0.58062154",
"0.57650155",
"0.57590044",
"0.5737819",
"0.5693649",
"0.5693649",
"0.5689963",
"0.5680... | 0.7371235 | 0 |
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