Sample

pyint-661b60e51d67-I05

Rewrite eligible decimal integer literals in hexadecimal notation

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Lines326

Scores

MethodScoreScale
Posnett00–1; higher is more readable
Dorn0.4380–1; higher is more readable
Mi0.4910–1; higher is more readable
LLOC99Logical lines; fewer is more readable
DeepSeek V4 Pro17.670–20; mean of three runs
GPT-6.1 Sol180–20; mean of three runs
BTTF0.4710–1; higher is more readable

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Source code

from __future__ import annotations import errno import json import os import types import typing as t from werkzeug.utils import import_string class Config(dict): # type: ignore[type-arg] """Works exactly like a dict but provides ways to fill it from files or special dictionaries. There are two common patterns to populate the config. Either you can fill the config from a config file:: app.config.from_pyfile('yourconfig.cfg') Or alternatively you can define the configuration options in the module that calls :meth:`from_object` or provide an import path to a module that should be loaded. It is also possible to tell it to use the same module and with that provide the configuration values just before the call:: DEBUG = True SECRET_KEY = 'development key' app.config.from_object(__name__) In both cases (loading from any Python file or loading from modules), only uppercase keys are added to the config. This makes it possible to use lowercase values in the config file for temporary values that are not added to the config or to define the config keys in the same file that implements the application. Probably the most interesting way to load configurations is from an environment variable pointing to a file:: app.config.from_envvar('YOURAPPLICATION_SETTINGS') In this case before launching the application you have to set this environment variable to the file you want to use. On Linux and OS X use the export statement:: export YOURAPPLICATION_SETTINGS='/path/to/config/file' On windows use `set` instead. :param root_path: path to which files are read relative from. When the config object is created by the application, this is the application's :attr:`~flask.Flask.root_path`. :param defaults: an optional dictionary of default values """ def __init__( self, root_path: str | os.PathLike[str], defaults: dict[str, t.Any] | None = None, ) -> None: super().__init__(defaults or {}) self.root_path = root_path def from_envvar(self, variable_name: str, silent: bool = False) -> bool: """Loads a configuration from an environment variable pointing to a configuration file. This is basically just a shortcut with nicer error messages for this line of code:: app.config.from_pyfile(os.environ['YOURAPPLICATION_SETTINGS']) :param variable_name: name of the environment variable :param silent: set to ``True`` if you want silent failure for missing files. :return: ``True`` if the file was loaded successfully. """ rv = os.environ.get(variable_name) if not rv: if silent: return False raise RuntimeError( f"The environment variable {variable_name!r} is not set" " and as such configuration could not be loaded. Set" " this variable and make it point to a configuration" " file" ) return self.from_pyfile(rv, silent=silent) def from_prefixed_env( self, prefix: str = "FLASK", *, loads: t.Callable[[str], t.Any] = json.loads ) -> bool: """Load any environment variables that start with ``FLASK_``, dropping the prefix from the env key for the config key. Values are passed through a loading function to attempt to convert them to more specific types than strings. Keys are loaded in :func:`sorted` order. The default loading function attempts to parse values as any valid JSON type, including dicts and lists. Specific items in nested dicts can be set by separating the keys with double underscores (``__``). If an intermediate key doesn't exist, it will be initialized to an empty dict. :param prefix: Load env vars that start with this prefix, separated with an underscore (``_``). :param loads: Pass each string value to this function and use the returned value as the config value. If any error is raised it is ignored and the value remains a string. The default is :func:`json.loads`. .. versionadded:: 2.1 """ prefix = f"{prefix}_" for key in sorted(os.environ): if not key.startswith(prefix): continue value = os.environ[key] key = key.removeprefix(prefix) try: value = loads(value) except Exception: # Keep the value as a string if loading failed. pass if "__" not in key: # A non-nested key, set directly. self[key] = value continue # Traverse nested dictionaries with keys separated by "__". current = self *parts, tail = key.split("__") for part in parts: # If an intermediate dict does not exist, create it. if part not in current: current[part] = {} current = current[part] current[tail] = value return True def from_pyfile( self, filename: str | os.PathLike[str], silent: bool = False ) -> bool: """Updates the values in the config from a Python file. This function behaves as if the file was imported as module with the :meth:`from_object` function. :param filename: the filename of the config. This can either be an absolute filename or a filename relative to the root path. :param silent: set to ``True`` if you want silent failure for missing files. :return: ``True`` if the file was loaded successfully. .. versionadded:: 0.7 `silent` parameter. """ filename = os.path.join(self.root_path, filename) d = types.ModuleType("config") d.__file__ = filename try: with open(filename, mode="rb") as config_file: exec(compile(config_file.read(), filename, "exec"), d.__dict__) except OSError as e: if silent and e.errno in (errno.ENOENT, errno.EISDIR, errno.ENOTDIR): return False e.strerror = f"Unable to load configuration file ({e.strerror})" raise self.from_object(d) return True def from_object(self, obj: object | str) -> None: """Updates the values from the given object. An object can be of one of the following two types: - a string: in this case the object with that name will be imported - an actual object reference: that object is used directly Objects are usually either modules or classes. :meth:`from_object` loads only the uppercase attributes of the module/class. A ``dict`` object will not work with :meth:`from_object` because the keys of a ``dict`` are not attributes of the ``dict`` class. Example of module-based configuration:: app.config.from_object('yourapplication.default_config') from yourapplication import default_config app.config.from_object(default_config) Nothing is done to the object before loading. If the object is a class and has ``@property`` attributes, it needs to be instantiated before being passed to this method. You should not use this function to load the actual configuration but rather configuration defaults. The actual config should be loaded with :meth:`from_pyfile` and ideally from a location not within the package because the package might be installed system wide. See :ref:`config-dev-prod` for an example of class-based configuration using :meth:`from_object`. :param obj: an import name or object """ if isinstance(obj, str): obj = import_string(obj) for key in dir(obj): if key.isupper(): self[key] = getattr(obj, key) def from_file( self, filename: str | os.PathLike[str], load: t.Callable[[t.IO[t.Any]], t.Mapping[str, t.Any]], silent: bool = False, text: bool = True, ) -> bool: """Update the values in the config from a file that is loaded using the ``load`` parameter. The loaded data is passed to the :meth:`from_mapping` method. .. code-block:: python import json app.config.from_file("config.json", load=json.load) import tomllib app.config.from_file("config.toml", load=tomllib.load, text=False) :param filename: The path to the data file. This can be an absolute path or relative to the config root path. :param load: A callable that takes a file handle and returns a mapping of loaded data from the file. :type load: ``Callable[[Reader], Mapping]`` where ``Reader`` implements a ``read`` method. :param silent: Ignore the file if it doesn't exist. :param text: Open the file in text or binary mode. :return: ``True`` if the file was loaded successfully. .. versionchanged:: 2.3 The ``text`` parameter was added. .. versionadded:: 2.0 """ filename = os.path.join(self.root_path, filename) try: with open(filename, "r" if text else "rb") as f: obj = load(f) except OSError as e: if silent and e.errno in (errno.ENOENT, errno.EISDIR): return False e.strerror = f"Unable to load configuration file ({e.strerror})" raise return self.from_mapping(obj) def from_mapping( self, mapping: t.Mapping[str, t.Any] | None = None, **kwargs: t.Any ) -> bool: """Updates the config like :meth:`update` ignoring items with non-upper keys. :return: Always returns ``True``. .. versionadded:: 0.11 """ mappings: dict[str, t.Any] = {} if mapping is not None: mappings.update(mapping) mappings.update(kwargs) for key, value in mappings.items(): if key.isupper(): self[key] = value return True def get_namespace( self, namespace: str, lowercase: bool = True, trim_namespace: bool = True ) -> dict[str, t.Any]: """Returns a dictionary containing a subset of configuration options that match the specified namespace/prefix. Example usage:: app.config['IMAGE_STORE_TYPE'] = 'fs' app.config['IMAGE_STORE_PATH'] = '/var/app/images' app.config['IMAGE_STORE_BASE_URL'] = 'http://img.website.com' image_store_config = app.config.get_namespace('IMAGE_STORE_') The resulting dictionary `image_store_config` would look like:: { 'type': 'fs', 'path': '/var/app/images', 'base_url': 'http://img.website.com' } This is often useful when configuration options map directly to keyword arguments in functions or class constructors. :param namespace: a configuration namespace :param lowercase: a flag indicating if the keys of the resulting dictionary should be lowercase :param trim_namespace: a flag indicating if the keys of the resulting dictionary should not include the namespace .. versionadded:: 0.11 """ rv = {} for k, v in self.items(): if not k.startswith(namespace): continue if trim_namespace: key = k[len(namespace) :] else: key = k if lowercase: key = key.lower() rv[key] = v return rv def __repr__(self) -> str: return f"<{type(self).__name__} {dict.__repr__(self)}>"

Loading feature diagnosis…

Source regions

Feature contributions

Feature contributions

FeatureValueContribution
(log-odds)
Halstead volumeHow much symbolic information the code contains. It combines the total number of operators and values with the number of distinct ones.5739.682−189.41
Source linesThe number of non-empty source lines.326+130.4
Byte entropyHow varied the bytes in the source text are. Repeated bytes give a lower value; a more even mixture gives a higher value.4.381−6.572

Intercept 8.87 + feature contributions = -56.711 (log-odds).
Score = sigmoid(-56.711) = 0.

Feature contributions

FeatureValueContribution
(log-odds)
Comment layout frequencyHow frequently the layout switches between lines containing only comments or whitespace and lines containing code.326−10.506
Indentation frequencyHow rapidly indentation changes from line to line.147+4.347
Identifier layout (vertical)How rapidly the visual pattern of identifier characters changes from top to bottom.325.741+4.143
Operator visual areaThe share of the code’s visual area occupied by operators such as +, =, and *.0.03+0.651
Comment layout (horizontal)How rapidly the visual pattern of comment characters changes from left to right.83.18+0.538
Number layout frequencyHow rapidly the number of numeric values per line changes through the code.1+0.398
Identifier layout (horizontal)How rapidly the visual pattern of identifier characters changes from left to right. It measures spatial frequency, not the meaning of the names.83.933+0.11

Intercept 0.07 + feature contributions = -0.249 (log-odds).
Score = sigmoid(-0.249) = 0.438.

Score0.491
Score99
Run 1

The code is highly readable with a clear, logical structure. Each method has a single responsibility, from loading config from environment variables, Python files, objects, or mappings. The class-level docstring and per-method documentation provide comprehensive context and examples. Nesting is shallow—only one or two levels deep in error handling and loops—making control flow easy to follow. Intent is explicit through descriptive method names and docstrings. Code length is justified by thorough documentation rather than redundancy. Action granularity is appropriate: each meaningful operation is on its own line, with no dense one-liners. Reading flow is natural, moving from initialization to specific loaders. The only minor drawbacks are the repetitive OSError handling patterns and the use of `exec`, which requires caution, but both are clearly documented and localized.

Run 2

The code is highly readable with clear logical organization, flat nesting, and explicit intent. Each method serves a distinct purpose, and the naming is descriptive. Action granularity is appropriate, with no multi-action lines. The reading flow follows a natural progression from environment variable loading to file/mapping-based loading and finally namespace extraction. The only minor issue is that some methods are longer due to comprehensive docstrings, but the actual logic remains concise. Overall, it adheres closely to best practices for maintainable Python code.

Run 3

The code is highly readable, following a clear logical structure with well-documented methods. Each method has a specific purpose, and action granularity is mostly one action per line. Nesting is manageable (2-3 levels in from_prefixed_env), and the reading flow is natural despite long docstrings. Code length is appropriate for a config handler, with good separation of concerns and type hints. Minor improvements could include extracting the nested dict traversal logic into a helper function to reduce depth slightly.

Run 1

Code Structure: Cohesive class with logically separated methods for each configuration source and consistent interfaces. Nesting: Mostly shallow; early returns and continue statements simplify branches, although nested environment-key traversal adds moderate nesting. Clarity of intent: Descriptive method names, type annotations, comments, and detailed docstrings clearly explain behavior; abbreviated variables such as rv, d, k, and v slightly reduce clarity. Code length: Implementation is concise relative to its functionality, but extensive documentation and repeated file-error handling increase scanning effort. Action granularity: Nearly all statements perform one action per line; the combined read, compile, and exec expression is a notable exception. Reading flow: Consistent formatting and straightforward control flow make methods easy to follow, with minor interruptions from lengthy docstrings and dense type signatures.

Run 2

Code Structure: Cohesive class with clearly separated methods for each configuration source and shared loading helpers. Nesting: Generally shallow, with early returns and continue statements; nested environment-key traversal adds modest complexity. Clarity of intent: Descriptive method names, type annotations, comments, and detailed docstrings make behavior explicit, although names such as rv, d, k, and v are less informative. Code length: Implementations are concise, but extensive documentation and repeated file-error handling increase overall length. Action granularity: Most lines perform one clear action; the combined read, compile, and exec expression is comparatively dense. Reading flow: Consistent formatting and predictable validation, loading, and assignment sequences support natural reading. Minor naming and duplication issues prevent a perfect score.

Run 3

1. Code Structure: Cohesive Config class with logically separated loading and filtering methods; responsibilities are well organized. 2. Nesting: Generally shallow, with early returns and continue statements limiting indentation; nested environment-key traversal is slightly more complex. 3. Clarity of intent: Descriptive method names, type annotations, comments, and thorough docstrings explain behavior clearly, though names such as rv, d, k, and v are less expressive. 4. Code length: Implementations are concise, but extensive documentation and repeated file-loading error handling increase overall length. 5. Action granularity: Most lines perform one clear action; the combined read, compile, and exec expression is a notable denser exception. 6. Reading flow: Consistent formatting and straightforward control flow make the code easy to follow, although long docstrings interrupt scanning between methods.