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CLIMATECONTROL controls your applications settings and configuration environment. It is a Python library for loading app configurations from files and/or namespaced environment variables.

Features

• Separation of settings and code
• Loading from files (.yaml, .json, .toml)
• Loading multiple files using glob syntax
• Loading from environment variables, including loading of nested values
• Freely reference nested configurations via files or environment variables
• CLI integration
• Validation using the Validation library of your choice
• Logging configuration integration
• Testing integration

Install

::

pip install climatecontrol

Usage

Set some environment variables in your shell

.. code:: sh

export CLIMATECONTROL_VALUE1=test1 export CLIMATECONTROL_VALUE2=test2

Then use them in your python modules:

.. code:: python

from climatecontrol import climate print(climate.settings)

{ 'value1': 'test1', 'value2': 'test2' }

In case you want to update your settings or your environment variables have changed and you want to reload them, the update method will reload your settings:

.. code:: python

import os os.environ['CLIMATECONTROL_VALUE3'] = 'new_env_data' climate.reload() print(climate.settings)

{ 'value1': 'test1', 'value2': 'test2', 'value3': 'new_env_data' }

Now you've noticed that you want more complex configurations and need nested settings. For this situation we can delimit sections using a double underscore:

.. code:: sh

export CLIMATECONTROL_SECTION1__VALUE1=test1 export CLIMATECONTROL_SECTION2__VALUE2=test2 export CLIMATECONTROL_SECTION2__VALUE3=test3 export CLIMATECONTROL_SECTION2__SUB_SECTION__VALUE4=test4

.. code:: python

from climatecontrol import climate print(climate.settings)

{ 'section1': { 'value1': 'test1' }, 'section2': { 'value2': 'test2', 'value3': 'test3', 'sub_section': { 'value4': 'test4' } } }

Settings file support

If you don't want to use an environment variable for every single setting and want to put your settings in a single file instead you can to this as well. Settings files can be yaml files (.yml/ .yaml), json files (.json) or toml_ files (.toml).

.. code-block:: sh

export CLIMATECONTROL_SETTINGS_FILE=./my_settings_file.yml

The file could look like this:

.. code-block:: yaml

./climatecontrol_settings.yaml

section1: subsection1 = test1

section2: subsection2: test2 subsection3: test3

or in toml form:

.. code-block:: sh

./climatecontrol_settings.toml

[section1] subsection1 = "test1"

[section2] subsection2 = "test2" subsection3 = "test3"

In the following documentation examples, yaml files will be used, but any examples will work using the other file syntaxes as well.

See the climatecontrol.core.Climate.inferred_settings_files docstring for further examples of how settings files are loaded and how they can be named. Also note that you can set your own settings files explicitely either by settings an environment variable:

.. code-block:: sh

export CLIMATECONTROL_SETTINGS_FILE="mysettings.yaml, mysettings.toml, override.yml"

or by adding them in code:

.. code-block:: python

climate.settings_files.extend(["mysettings.yaml", "mysettings.toml", "override.yml"]

Advanced Features

Setting variables from values saved in files ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Sometimes we don't want to save values in plain text in environment files or in the settings file itself. Instead we have a file that contains the value of the setting we want. A good example for this behaviour are docker secrets_ that store secrets in temporary files.

To read a variable from a file, simply add a "_from_file" to the variable name and give it the path to the file that contains the variable as a value.

Using a settings file with the contents (in this case yaml):

.. code-block:: yaml

section1: subsection1_from_file: /home/myuser/supersecret.txt

or using an environment variable:

.. code-block:: sh

export CLIMATECONTROL_SECTION1_SUBSECTION1_FROM_FILE="/home/myuser/supersecret.txt"

will both write the content of the file at "/home/myuser/supersecret.txt" into the variable section1 -> subsection1.

Setting variables from values saved in specific environment variables ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Similarly, to read a value from an environment variable, add a "_from_env" to the variable name. For example if we wanted to obtain a value from the variable SPECIFIC_ENV_VAR:

.. code-block:: sh

export SPECIFIC_ENV_VAR="some value"

Using a settings file with the contents (in this case yaml):

.. code-block:: yaml

section1: subsection1_from_env: SPECIFIC_ENV_VAR

or using an environment variable:

.. code-block:: sh

export CLIMATECONTROL_SECTION1_SUBSECTION1_FROM_FILE="/home/myuser/supersecret.txt"

will both write "some value" into the variable section1 -> subsection1.

Settings variables from serialized content ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. code-block:: yaml

section1_from_json_content: '{"subsection1": "test", "subsection2": 2}' section2_from_toml_content: 'subsection1 = "test"\nsubsection2 = 2\n' section3_from_yaml_content: 'subsection1: test\nsubsection2: 2\n'

The equivilant environment variables are also handled correctly:

.. code-block:: sh

CLIMATECONTROL_SECTION1_FROM_JSON_CONTENT='{"subsection1": "test", "subsection2": 2}' CLIMATECONTROL_SECTION2_FROM_TOML_CONTENT='subsection1 = "test"\nsubsection2 = 2\n' CLIMATECONTROL_SECTION3_FROM_YAML_CONTENT='subsection1: test\nsubsection2: 2\n'

Nested settings files ^^^^^^^^^^^^^^^^^^^^^

In addition, file variables can also target other settings files directly. To do this, just make sure the target file is has an extension supported by climate control. A simple example is illustrated here. Given a settings file:

.. code-block:: yaml

value1: "spam" section1_from_file: /home/myuser/nestedfile.yaml

where the content of /home/myuser/nestedfile.yaml is:

.. code-block:: yaml

value2: "cheese" subsection: value3: "parrot"

which would result in a settings structure:

.. code-block:: python

{ "value1": "spam", "section1": { "value2": "cheese", "subsection": { "value3": "parrot" } } }

You can also expand the settings at the root of the document by using only "_from_file" as the key:

.. code-block:: yaml

value1: "spam" _from_file: /home/myuser/nestedfile.yaml

.. code-block:: python

{ "value1": "spam", "value2": "cheese", "subsection": { "value3": "parrot" } }

Extensions

While the default climate object is great for most uses, perhaps you already have a settings object style that you like or use a specific library for validation. In these cases, CLIMATECONTROL can be extended to use these libraries.

Dataclasses ^^^^^^^^^^^

from climatecontrol.ext.dataclasses import Climate from dataclasses import dataclass, field

@dataclass ... class SettingsSubSchema: ... d: int = 4 ... @dataclass ... class SettingsSchema: ... a: str = 'test' ... b: bool = False ... c: SettingsSubSchema = field(default_factory=SettingsSubSchema) ... climate = Climate(dataclass_cls=SettingsSchema)

defaults are initialized automatically:

climate.settings.a 'test' climate.settings.c.d 4

Types are checked if given

climate.update({'c': {'d': 'boom!'}}) Traceback (most recent call last): ... dacite.exceptions.WrongTypeError: wrong type for field "c.d" - should be "int" instead of "str"

Pydantic ^^^^^^^^

Pydantic is a great data validation library: https://github.com/samuelcolvin/pydantic and climatecontrol also provides a simple extension to use pydantic models directly (typing functionality mentioned above works here as well).

from climatecontrol.ext.pydantic import Climate

class SettingsSubSchema(BaseModel): ... d: int = 4 ... class SettingsSchema(BaseModel): ... a: str = 'test' ... b: bool = False ... c: SettingsSubSchema = SettingsSubSchema() ... climate = Climate(model=SettingsSchema)

defaults are initialized automatically:

climate.settings.a 'test' climate.settings.c.d 4

Types are checked if given

climate.update({'c': {'d': 'boom!'}}) Traceback (most recent call last): ... pydantic.error_wrappers.ValidationError: 1 validation error for SettingsSchema c -> d value is not a valid integer (type=type_error.integer)

Integrations

Command line support using click ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

The click_ library is a great tool for creating command line applications. If you don't want to have to use an environment to set your configuration file. Write your command line application like this:

.. code-block:: python

import click

@click.command() @climate.click_settings_file_option() def cli(): print(climate.settings)

save it to a file like "cli.py" and then call it after installing click:

.. code-block:: sh

pip install click python cli.py --settings ./my_settings_file.toml

whithout needing to set any env vars.

Multiple files are supported. They will be automatically recursively merged with the last file overriting any overlapping keys of the first file.

.. code-block:: sh

pip install click python cli.py --settings ./my_settings_file.toml --settings ./my_settings_file.yaml

Logging ^^^^^^^

If you have a "logging" section in your settings files, you can configure python standard library logging using that section directly:

.. code:: yaml

logging: formatters: default: format': "%(levelname)s > %(message)s" root: level: DEBUG

.. code:: python

import logging from climatecontrol import climate

climate.setup_logging() logging.debug('test')

outputs: DEBUG > test

Testing

When testing your application, different behaviours often depend on settings taking on different values. Assuming that you are using a single Settings object accross multiple functions or modules, handling these settings changes in tests can be tricky.

The settings object provides a simple method for modifying your settings object temporarily:

.. code-block:: python

climate.update({'a': 1})

Enter a temporary changes context block:

with climate.temporary_changes(): climate.update({'a': 1}) # Inside the context, the settings can be modified and used as you choose print(climate['a']) # outputs: 2

After the context exits the settings map

print(climate['a']) # outputs: 1

Contributing

See: CONTRIBUTING.md <./CONTRIBUTING.md>__

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