Confuse: Painless Configuration
===============================

Confuse_ is a straightforward, full-featured configuration system for Python.

.. _confuse: https://github.com/beetbox/confuse

Basic Usage
-----------

Set up your Configuration object, which provides unified access to all of your
application's config settings:

.. code-block:: python

    config = confuse.Configuration("MyGreatApp", __name__)

The first parameter is required; it's the name of your application, which will
be used to search the system for a config file named ``config.yaml``. See
:ref:`Search Paths` for the specific locations searched.

The second parameter is optional: it's the name of a module that will guide the
search for a *defaults* file. Use this if you want to include a
``config_default.yaml`` file inside your package. (The included ``example``
package does exactly this.)

Now, you can access your configuration data as if it were a simple structure
consisting of nested dicts and lists—except that you need to call the method
``.get()`` on the leaf of this tree to get the result as a value:

.. code-block:: python

    value = config["foo"][2]["bar"].get()

Under the hood, accessing items in your configuration tree builds up a *view*
into your app's configuration. Then, ``get()`` flattens this view into a value,
performing a search through each configuration data source to find an answer.
(More on views later.)

If you know that a configuration value should have a specific type, just pass
that type to ``get()``:

.. code-block:: python

    int_value = config["number_of_goats"].get(int)

This way, Confuse will either give you an integer or raise a ``ConfigTypeError``
if the user has messed up the configuration. You're safe to assume after this
call that ``int_value`` has the right type. If the key doesn't exist in any
configuration file, Confuse will raise a ``NotFoundError``. Together, catching
these exceptions (both subclasses of ``confuse.ConfigError``) lets you
painlessly validate the user's configuration as you go.

View Theory
-----------

The Confuse API is based on the concept of *views*. You can think of a view as a
*place to look* in a config file: for example, one view might say *get the value
for key ``number_of_goats``*. Another might say *get the value at index 8 inside
the sequence for key ``animal_counts``*. To get the value for a given view, you
*resolve* it by calling the ``get()`` method.

This concept separates the specification of a location from the mechanism for
retrieving data from a location. (In this sense, it's a little like XPath_: you
specify a path to data you want and *then* you retrieve it.)

Using views, you can write ``config['animal_counts'][8]`` and know that no
exceptions will be raised until you call ``get()``, even if the
``animal_counts`` key does not exist. More importantly, it lets you write a
single expression to search many different data sources without preemptively
merging all sources together into a single data structure.

Views also solve an important problem with overriding collections. Imagine, for
example, that you have a dictionary called ``deliciousness`` in your config file
that maps food names to tastiness ratings. If the default configuration gives
carrots a rating of 8 and the user's config rates them a 10, then clearly
``config['deliciousness']['carrots'].get()`` should return 10. But what if the
two data sources have different sets of vegetables? If the user provides a value
for broccoli and zucchini but not carrots, should carrots have a default
deliciousness value of 8 or should Confuse just throw an exception? With
Confuse's views, the application gets to decide.

The above expression, ``config['deliciousness']['carrots'].get()``, returns 8
(falling back on the default). However, you can also write
``config['deliciousness'].get()``. This expression will cause the *entire*
user-specified mapping to override the default one, providing a dict object like
``{'broccoli': 7, 'zucchini': 9}``. As a rule, then, resolve a view at the same
granularity you want config files to override each other.

.. warning::

    It may appear that calling ``config.get()`` would retrieve the entire
    configuration at once. However, this will return only the *highest-priority*
    configuration source, masking any lower-priority values for keys that are
    not present in the top source. This pitfall is especially likely when using
    :ref:`Command-Line Options` or :ref:`Environment Variables`, which may place
    an empty configuration at the top of the stack. A subsequent call to
    ``config.get()`` might then return no configuration at all.

.. _xpath: https://www.w3.org/TR/xpath/

Validation
----------

We saw above that you can easily assert that a configuration value has a certain
type by passing that type to ``get()``. But sometimes you need to do more than
just type checking. For this reason, Confuse provides a few methods on views
that perform fancier validation or even conversion:

- ``as_filename()``: Normalize a filename, substituting tildes and
  absolute-ifying relative paths. For filenames defined in a config file, by
  default the filename is relative to the application's config directory
  (``Configuration.config_dir()``, as described below). However, if the config
  file was loaded with the ``base_for_paths`` parameter set to ``True`` (see
  :ref:`Manually Specifying Config Files`), then a relative path refers to the
  directory containing the config file. A relative path from any other source
  (e.g., command-line options) is relative to the working directory. For full
  control over relative path resolution, use the ``Filename`` template directly
  (see :ref:`Filename`).
- ``as_choice(choices)``: Check that a value is one of the provided choices. The
  argument should be a sequence of possible values. If the sequence is a
  ``dict``, then this method returns the associated value instead of the key.
- ``as_number()``: Raise an exception unless the value is of a numeric type.
- ``as_pairs()``: Get a collection as a list of pairs. The collection should be
  a list of elements that are either pairs (i.e., two-element lists) already or
  single-entry dicts. This can be helpful because, in YAML, lists of
  single-element mappings have a simple syntax (``- key: value``) and, unlike
  real mappings, preserve order.
- ``as_str_seq()``: Given either a string or a list of strings, return a list of
  strings. A single string is split on whitespace.
- ``as_str_expanded()``: Expand any environment variables contained in a string
  using `os.path.expandvars()`_.

.. _os.path.expandvars(): https://docs.python.org/3/library/os.path.html#os.path.expandvars

For example, ``config['path'].as_filename()`` ensures that you get a reasonable
filename string from the configuration. And calling
``config['direction'].as_choice(['up', 'down'])`` will raise a
``ConfigValueError`` unless the ``direction`` value is either "up" or "down".

Command-Line Options
--------------------

Arguments to command-line programs can be seen as just another *source* for
configuration options. Just as options in a user-specific configuration file
should override those from a system-wide config, command-line options should
take priority over all configuration files.

You can use the argparse_ and optparse_ modules from the standard library with
Confuse to accomplish this. Just call the ``set_args`` method on any view and
pass in the object returned by the command-line parsing library. Values from the
command-line option namespace object will be added to the overlay for the view
in question. For example, with argparse:

.. code-block:: python

    args = parser.parse_args()
    config.set_args(args)

Correspondingly, with optparse:

.. code-block:: python

    options, args = parser.parse_args()
    config.set_args(options)

This call will turn all of the command-line options into a top-level source in
your configuration. The key associated with each option in the parser will
become a key available in your configuration. For example, consider this
argparse script:

.. code-block:: python

    config = confuse.Configuration("myapp")
    parser = argparse.ArgumentParser()
    parser.add_argument("--foo", help="a parameter")
    args = parser.parse_args()
    config.set_args(args)
    print(config["foo"].get())

This will allow the user to override the configured value for key ``foo`` by
passing ``--foo <something>`` on the command line.

Overriding nested values can be accomplished by passing `dots=True` and have
dot-delimited properties on the incoming object.

.. code-block:: python

    parser.add_argument("--bar", help="nested parameter", dest="foo.bar")
    args = parser.parse_args()  # args looks like: {'foo.bar': 'value'}
    config.set_args(args, dots=True)
    print(config["foo"]["bar"].get())

`set_args` works with generic dictionaries too.

.. code-block:: python

    args = {"foo": {"bar": 1}}
    config.set_args(args, dots=True)
    print(config["foo"]["bar"].get())

.. _argparse: https://docs.python.org/dev/library/argparse.html

.. _optparse: https://docs.python.org/3/library/optparse.html

.. _parse_args: https://docs.python.org/library/argparse.html#the-parse-args-method

Note that, while you can use the full power of your favorite command-line
parsing library, you'll probably want to avoid specifying defaults in your
argparse or optparse setup. This way, Confuse can use other configuration
sources---possibly your ``config_default.yaml``---to fill in values for
unspecified command-line switches. Otherwise, the argparse/optparse default
value will hide options configured elsewhere.

Environment Variables
---------------------

Confuse supports using environment variables as another source to provide an
additional layer of configuration. The environment variables to include are
identified by a prefix, which defaults to the uppercased name of your
application followed by an underscore. Matching environment variable names are
first stripped of this prefix and then lowercased to determine the corresponding
configuration option. To load the environment variables for your application
using the default prefix, just call ``set_env`` on your ``Configuration``
object. Config values from the environment will then be added as an overlay at
the highest precedence. For example:

.. code-block:: sh

    export MYAPP_FOO=something

.. code-block:: python

    import confuse

    config = confuse.Configuration("myapp", __name__)
    config.set_env()
    print(config["foo"].get())

Nested config values can be overridden by using a separator string in the
environment variable name. By default, double underscores are used as the
separator for nesting, to avoid clashes with config options that contain single
underscores. Note that most shells restrict environment variable names to
alphanumeric and underscore characters, so dots are not a valid separator.

.. code-block:: sh

    export MYAPP_FOO__BAR=something

.. code-block:: python

    import confuse

    config = confuse.Configuration("myapp", __name__)
    config.set_env()
    print(config["foo"]["bar"].get())

Both the prefix and the separator can be customized when using ``set_env``. Note
that prefix matching is done to the environment variables *prior* to
lowercasing, while the separator is matched *after* lowercasing.

.. code-block:: sh

    export APPFOO_NESTED_BAR=something

.. code-block:: python

    import confuse

    config = confuse.Configuration("myapp", __name__)
    config.set_env(prefix="APP", sep="_nested_")
    print(config["foo"]["bar"].get())

For configurations that include lists, use integers starting from 0 as nested
keys to invoke "list conversion." If any of the sibling nested keys are not
integers or the integers are not sequential starting from 0, then conversion
will not be performed. Nested lists and combinations of nested dicts and lists
are supported.

.. code-block:: sh

    export MYAPP_FOO__0=first
    export MYAPP_FOO__1=second
    export MYAPP_FOO__2__BAR__0=nested

.. code-block:: python

    import confuse

    config = confuse.Configuration("myapp", __name__)
    config.set_env()
    print(config["foo"].get())  # ['first', 'second', {'bar': ['nested']}]

For consistency with YAML config files, the values of environment variables are
type converted using the same YAML parser used for file-based configs. This
means that numeric strings will be converted to integers or floats, "true" and
"false" will be converted to booleans, and the empty string or "null" will be
converted to ``None``. Setting an environment variable to the empty string or
"null" allows unsetting a config value from a lower-precedence source.

To change the lowercasing and list handling behaviors when loading environment
variables or to enable full YAML parsing of environment variables, you can
initialize an ``EnvSource`` configuration source directly.

If you use config overlays from both command-line args and environment
variables, the order of calls to ``set_args`` and ``set_env`` will determine the
precedence, with the last call having the highest precedence.

Search Paths
------------

Confuse looks in a number of locations for your application's configurations.
The locations are determined by the platform. For each platform, Confuse has a
list of directories in which it looks for a directory named after the
application. For example, the first search location on Unix-y systems is
``$XDG_CONFIG_HOME/AppName`` for an application called ``AppName``.

Here are the default search paths for each platform:

- macOS: ``~/.config/app`` and ``~/Library/Application Support/app``
- Other Unix: ``~/.config/app`` and ``/etc/app``
- Windows: ``%APPDATA%\app`` where the `APPDATA` environment variable falls back
  to ``%HOME%\AppData\Roaming`` if undefined

Both macOS and other Unix operating sytems also try to use the
``XDG_CONFIG_HOME`` and ``XDG_CONFIG_DIRS`` environment variables if set then
search those directories as well.

Users can also add an override configuration directory with an environment
variable. The variable name is the application name in capitals with "DIR"
appended: for an application named ``AppName``, the environment variable is
``APPNAMEDIR``.

Manually Specifying Config Files
--------------------------------

You may want to leverage Confuse's features without :ref:`Search Paths`. This
can be done by manually specifying the YAML files you want to include, which
also allows changing how relative paths in the file will be resolved:

.. code-block:: python

    import confuse

    # Instantiates config. Confuse searches for a config_default.yaml
    config = confuse.Configuration("MyGreatApp", __name__)
    # Add config items from specified file. Relative path values within the
    # file are resolved relative to the application's configuration directory.
    config.set_file("subdirectory/default_config.yaml")
    # Add config items from a second file. If some items were already defined,
    # they will be overwritten (new file precedes the previous ones). With
    # `base_for_paths` set to True, relative path values in this file will be
    # resolved relative to the config file's directory (i.e., 'subdirectory').
    config.set_file("subdirectory/local_config.yaml", base_for_paths=True)

    val = config["foo"]["bar"].get(int)

Your Application Directory
--------------------------

Confuse provides a simple helper, ``Configuration.config_dir()``, that gives you
a directory used to store your application's configuration. If a configuration
file exists in any of the searched locations, then the highest-priority
directory containing a config file is used. Otherwise, a directory is created
for you and returned. So you can always expect this method to give you a
directory that actually exists.

As an example, you may want to migrate a user's settings to Confuse from an
older configuration system such as ConfigParser_. Just do something like this:

.. code-block:: python

    config_filename = os.path.join(config.config_dir(), confuse.CONFIG_FILENAME)
    with open(config_filename, "w") as f:
        yaml.dump(migrated_config, f)

.. _configparser: https://docs.python.org/3/library/configparser.html

Dynamic Updates
---------------

Occasionally, a program will need to modify its configuration while it's
running. For example, an interactive prompt from the user might cause the
program to change a setting for the current execution only. Or the program might
need to add a *derived* configuration value that the user doesn't specify.

To facilitate this, Confuse lets you *assign* to view objects using ordinary
Python assignment. Assignment will add an overlay source that precedes all other
configuration sources in priority. Here's an example of programmatically setting
a configuration value based on a ``DEBUG`` constant:

.. code-block:: python

    if DEBUG:
        config["verbosity"] = 100
    ...
    my_logger.setLevel(config["verbosity"].get(int))

This example allows the constant to override the default verbosity level, which
would otherwise come from a configuration file.

Assignment works by creating a new "source" for configuration data at the top of
the stack. This new source takes priority over all other, previously-loaded
sources. You can cause this explicitly by calling the ``set()`` method on any
view. A related method, ``add()``, works similarly but instead adds a new
*lowest-priority* source to the bottom of the stack. This can be used to provide
defaults for options that may be overridden by previously-loaded configuration
files.

YAML Tweaks
-----------

Confuse uses the PyYAML_ module to parse YAML configuration files. However, it
deviates very slightly from the official YAML specification to provide a few
niceties suited to human-written configuration files. Those tweaks are:

.. _pyyaml: https://pyyaml.org/

- All strings are returned as Python Unicode objects.
- YAML maps are parsed as Python OrderedDict_ objects. This means that you can
  recover the order that the user wrote down a dictionary.
- Bare strings can begin with the % character. In stock PyYAML, this will throw
  a parse error.

.. _ordereddict: https://docs.python.org/2/library/collections.html#collections.OrderedDict

To produce a YAML string reflecting a configuration, just call
``config.dump()``. This does not cleanly round-trip YAML, but it does play some
tricks to preserve comments and spacing in the original file.

Custom YAML Loaders
~~~~~~~~~~~~~~~~~~~

You can also specify your own PyYAML_ `Loader` object to parse YAML files.
Supply the `loader` parameter to a `Configuration` constructor, like this:

.. code-block:: python

    config = confuse.Configuration("name", loader=yaml.Loaded)

To imbue a loader with Confuse's special parser overrides, use its
`add_constructors` method:

.. code-block:: python

    class MyLoader(yaml.Loader): ...


    confuse.Loader.add_constructors(MyLoader)
    config = confuse.Configuration("name", loader=MyLoader)

Configuring Large Programs
--------------------------

One problem that must be solved by a configuration system is the issue of global
configuration for complex applications. In a large program with many components
and many config options, it can be unwieldy to explicitly pass configuration
values from component to component. You quickly end up with monstrous function
signatures with dozens of keyword arguments, decreasing code legibility and
testability.

In such systems, one option is to pass a single `Configuration` object through
to each component. To avoid even this, however, it's sometimes appropriate to
use a little bit of shared global state. As evil as shared global state usually
is, configuration is (in my opinion) one valid use: since configuration is
mostly read-only, it's relatively unlikely to cause the sorts of problems that
global values sometimes can. And having a global repository for configuration
option can vastly reduce the amount of boilerplate threading-through needed to
explicitly pass configuration from call to call.

To use global configuration, consider creating a configuration object in a
well-known module (say, the root of a package). But since this object will be
initialized at module load time, Confuse provides a `LazyConfig` object that
loads your configuration files on demand instead of when the object is
constructed. (Doing complicated stuff like parsing YAML at module load time is
generally considered a Bad Idea.)

Global state can cause problems for unit testing. To alleviate this, consider
adding code to your test fixtures (e.g., setUp_ in the unittest_ module) that
clears out the global configuration before each test is run. Something like
this:

.. code-block:: python

    config.clear()
    config.read(user=False)

These lines will empty out the current configuration and then re-load the
defaults (but not the user's configuration files). Your tests can then modify
the global configuration values without affecting other tests since these
modifications will be cleared out before the next test runs.

.. _setup: https://docs.python.org/2/library/unittest.html#unittest.TestCase.setUp

.. _unittest: https://docs.python.org/2/library/unittest.html

Redaction
---------

You can also mark certain configuration values as "sensitive" and avoid
including them in output. Just set the `redact` flag:

.. code-block:: python

    config["key"].redact = True

Then flatten or dump the configuration like so:

.. code-block:: python

    config.dump(redact=True)

The resulting YAML will contain "key: REDACTED" instead of the original data.