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climatecontrol-0.9.2
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مشاهده بیشترتوضیحات
Python library for loading app configurations from files and/or namespaced environment variables.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | climatecontrol-0.9.2 |
| نام | climatecontrol |
| نسخه کتابخانه | 0.9.2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Davis Kirkendall |
| ایمیل نویسنده | davis.e.kirkendall@gmail.com |
| آدرس صفحه اصلی | https://github.com/daviskirk/climatecontrol |
| آدرس اینترنتی | https://pypi.org/project/climatecontrol/ |
| مجوز | MIT |
|Build Status| |Coverage Status| |PyPi version| |PyPI license| |PyPI pyversions| |Conda version|
|Code Style Black|
.. image:: https://raw.githubusercontent.com/daviskirk/climatecontrol/logo/climatecontrol-text.svg?sanitize=true
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>`__
.. |Build Status| image:: https://img.shields.io/github/workflow/status/daviskirk/climatecontrol/ci?style=flat-square
:target: https://github.com/daviskirk/climatecontrol
.. |Coverage Status| image:: https://img.shields.io/codecov/c/github/daviskirk/climatecontrol/master?style=flat-square
:target: https://codecov.io/gh/daviskirk/climatecontrol
.. |PyPI version| image:: https://img.shields.io/pypi/v/climatecontrol?style=flat-square
:target: https://pypi.python.org/pypi/climatecontrol/
.. |PyPI license| image:: https://img.shields.io/pypi/l/climatecontrol?style=flat-square
:target: https://pypi.python.org/pypi/climatecontrol/
.. |PyPI pyversions| image:: https://img.shields.io/pypi/pyversions/climatecontrol?style=flat-square
:target: https://pypi.python.org/pypi/climatecontrol/
.. |Conda version| image:: https://img.shields.io/conda/vn/conda-forge/climatecontrol?style=flat-square
:target: https://anaconda.org/conda-forge/climatecontrol
.. |Code Style Black| image:: https://img.shields.io/badge/code%20style-black-000000.svg?style=flat-square
:target: https://github.com/psf/black
.. _click: http://click.pocoo.org/
.. _toml: https://github.com/toml-lang/toml
.. _secrets: https://docs.docker.com/engine/swarm/secrets
نیازمندی
| مقدار | نام |
|---|---|
| >=1.12,<2.0 | wrapt |
| >=1.6,<2.0) | dacite |
| >=1.7.4,<2.0.0) | pydantic |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7,<4.0 | Python |
نحوه نصب
نصب پکیج whl climatecontrol-0.9.2:
pip install climatecontrol-0.9.2.whl
نصب پکیج tar.gz climatecontrol-0.9.2:
pip install climatecontrol-0.9.2.tar.gz