yumm 0.0.1

yumm

Python library with several tools to work with geometry

Usage

Imports:

import uuid
import numpy as np
from itertools import count
from udbs import db_handler, BaseClass, ObserverDBMeta
from sqlalchemy import Integer, String
from udbs.db_types import (Pandas_DBType,
                           Universal_DBType,
                           List_DBType,
                           DB_List,
                           TrackedList,
                           TrackedDataFrame,
                           Integer_DBType,
                           TrackedInteger,
                           TrackedFloat,
                           DB_Tuple,
                           Tuple_DBType,
                           TrackedTuple,
                           DB_NPNDARRAY,
                           Numpy_DBType,
                           Tracked_NDARRAY
                           )

# do not echo sqlalchemy output
db_handler.echo = False

# do not profile execution
db_handler.profile = False

Create Databases:

• create or connect to a database:

db = db_handler.create_db(name='database_1',
                      dialect='sqlite',
                      database=r'C:\udbs\db_test.db'
                      )

• create or connect to a second database:

db2 = db_handler.create_db(name='database_2',
                           dialect='sqlite',
                           database=r'C:\udbs\db_test_2.db'
                           )

Create a class:

The tracked class must have the metaclass=ObserverDBMeta. The tracked attributes are defined in the _mapped_attributes dictionary. The keys of the dictionary are the names of the attributes to be tracked. The values of the dictionary are the types of the attributes.

Valid types are all sqlalchemy types and the types in sqlalchemy.sql.sqltypes and all types in udbs.db_types. The type Universal_DBType can handle all common data types. Unknown types are pickled. Additional the types in udbs.db_types return a tracked mutable object when queried which can also be referenced.

Valid udbs.db_types:

• Bool_DBType

• Integer_DBType

• Float_DBType

• String_DBType

• Tuple_DBType

• List_DBType

• Pandas_DBType

• Numpy_DBType

• Universal_DBType

The attribute id is automatically added to the class as the unique primary key in the database: (Column(id_type, primary_key=True)).The primary key has to be a UUID.

class MyTest(metaclass=ObserverDBMeta):
    """
    Define which attributes should be saved in the database as a dictionary.

    The keys are the name of the attribute, the value the type of the value.
    In this Example the attribute 'pid', 'name', 'df' and 'unknown' are saved in the database.
    """

    _mapped_attributes = {'pid': Integer,
                          'name': String,
                          'df': Pandas_DBType,
                          'unknown': Universal_DBType}

    new_id = count(0)

    def __init__(self, *args, **kwargs):

        # the attribute 'id' is automatically added to the class as the unique primary key in the database:
        # (Column(id_type, primary_key=True)).The primary key has to be a UUID.
        self.id = kwargs.get('id', uuid.uuid4())
        self.pid = kwargs.get('pid', next(self.new_id))
        self.name = kwargs.get('name', f'MyTest {self.pid}')

        self.df = kwargs.get('df', None)
        self.unknown = kwargs.get('unknown', [1, 2, 3])

Inheritance:

Inheritance is supported.

class MyTest2(MyTest):

    _mapped_attributes = {'test_attr': Integer}

    def __init__(self, *args, **kwargs):

        MyTest.__init__(self, *args, **kwargs)
        self.test_attr = kwargs.get('test_attr', None)

my_test2 = MyTest2(test_attr=7, df=df)
print(MyTest.unknown)

Create class instances:

create instances of MyTest:

fake = Faker()
from faker import Faker

def create_rows(num=100):
    output = [{"name": fake.name(),
               "address": fake.address(),
               "name": fake.name(),
               "email": fake.email(),
               "bs": fake.bs(),
               "address": fake.address(),
               "city": fake.city(),
               "state": fake.state(),
               "date_time": fake.date_time(),
               "paragraph": fake.paragraph(),
               "Conrad": fake.catch_phrase(),
               "randomdata": random.randint(1000, 2000)} for x in range(num)]
    return output

df = create_rows(20)

my_new_test = MyTest(df=df, name='random_name', unknown=2.17)
my_new_test2 = MyTest(df=df, name='some_test_name', unknown=[18, 28.15, {'some_key': np.random.rand(5, 5)}])

create a another instance of MyTest: notice: the database where the object should be stored is given. If None is given the default database is taken.

my_new_test_3 = MyTest(df=df, db=db2)

Reference instances:

Once a object is created, the assigned values can be tracked.

my_new_test_4 = MyTest(df=my_new_test.df, name=my_new_test_2.name, db=db2)

In this case there is no new dataframe saved in the database as it already exists. Instead the reference to the existing dataframe is stored in the database. So if Changes to my_new_test_4.df are made, these also affect my_new_test.df.

Load class instances:

The class instances can be loaded by:

my_new_tests = db.query_all(MyTest)
my_new_tests_2 = db2.query_all(MyTest, return_type=list)

The default return type is a set with all instances. With the key return_type=list this can be changed to a list.

If only one instance with given id should be loaded use:

my_new_test = db.query_single_id(MyTest, id)

You can also do filtered queries:

my_new_test = db.query_filtered(MyTest, 'name', ['some_test_name', 5])

Requirements

Python 3.7+.

Windows Support

Summary: On Windows, use py instead of python3 for many of the examples in this documentation.

This package fully supports Windows, along with Linux and macOS, but Python is typically installed differently on Windows. Windows users typically access Python through the py launcher rather than a python3 link in their PATH. Within a virtual environment, all platforms operate the same and use a python link to access the Python version used in that virtual environment.

Dependencies

Dependencies are defined in:

• requirements.in

• requirements.txt

• dev-requirements.in

• dev-requirements.txt

Virtual Environments

It is best practice during development to create an isolated Python virtual environment using the venv standard library module. This will keep dependant Python packages from interfering with other Python projects on your system.

On *Nix:

$ python3 -m venv venv
$ source venv/bin/activate

On Windows cmd:

> py -m venv venv
> venv\Scripts\activate.bat

Once activated, it is good practice to update core packaging tools (pip, setuptools, and wheel) to the latest versions.

(venv) $ python -m pip install --upgrade pip setuptools wheel

Packaging

This project is designed as a Python package, meaning that it can be bundled up and redistributed as a single compressed file.

Packaging is configured by:

• pyproject.toml

• setup.py

• MANIFEST.in

To package the project as both a source distribution and a wheel:

(venv) $ python setup.py sdist bdist_wheel

This will generate dist/fact-1.0.0.tar.gz and dist/fact-1.0.0-py3-none-any.whl.

Read more about the advantages of wheels to understand why generating wheel distributions are important.

Upload Distributions to PyPI

Source and wheel redistributable packages can be uploaded to PyPI or installed directly from the filesystem using pip.

To upload to PyPI:

(venv) $ python -m pip install twine
(venv) $ twine upload dist/*

Testing

Automated testing is performed using tox. tox will automatically create virtual environments based on tox.ini for unit testing, PEP8 style guide checking, and documentation generation.

# Run all environments.
#   To only run a single environment, specify it like: -e pep8
# Note: tox is installed into the virtual environment automatically by pip-sync command above.
(venv) $ tox

Unit Testing

Unit testing is performed with pytest. pytest has become the defacto Python unit testing framework. Some key advantages over the built in unittest module are:

1. Significantly less boilerplate needed for tests.

2. PEP8 compliant names (e.g. pytest.raises() instead of self.assertRaises()).

3. Vibrant ecosystem of plugins.

pytest will automatically discover and run tests by recursively searching for folders and .py files prefixed with test for any functions prefixed by test.

The tests folder is created as a Python package (i.e. there is an __init__.py file within it) because this helps pytest uniquely namespace the test files. Without this, two test files cannot be named the same, even if they are in different sub-directories.

Code coverage is provided by the pytest-cov plugin.

When running a unit test tox environment (e.g. tox, tox -e py37, etc.), a data file (e.g. .coverage.py37) containing the coverage data is generated. This file is not readable on its own, but when the coverage tox environment is run (e.g. tox or tox -e -coverage), coverage from all unit test environments is combined into a single data file and an HTML report is generated in the htmlcov folder showing each source file and which lines were executed during unit testing. Open htmlcov/index.html in a web browser to view the report. Code coverage reports help identify areas of the project that are currently not tested.

Code coverage is configured in pyproject.toml.

To pass arguments to pytest through tox:

(venv) $ tox -e py37 -- -k invalid_factorial

Code Style Checking

PEP8 is the universally accepted style guide for Python code. PEP8 code compliance is verified using flake8. flake8 is configured in the [flake8] section of tox.ini. Extra flake8 plugins are also included:

• pep8-naming: Ensure functions, classes, and variables are named with correct casing.

Automated Code Formatting

Code is automatically formatted using black. Imports are automatically sorted and grouped using isort.

These tools are configured by:

• pyproject.toml

To automatically format code, run:

(venv) $ tox -e fmt

To verify code has been formatted, such as in a CI job:

(venv) $ tox -e fmt-check

Generated Documentation

Documentation that includes the README.rst and the Python project modules is automatically generated using a Sphinx tox environment. Sphinx is a documentation generation tool that is the defacto tool for Python documentation. Sphinx uses the RST markup language.

This project uses the napoleon plugin for Sphinx, which renders Google-style docstrings. Google-style docstrings provide a good mix of easy-to-read docstrings in code as well as nicely-rendered output.

"""Computes the factorial through a recursive algorithm.

Args:
    n: A positive input value.

Raises:
    InvalidFactorialError: If n is less than 0.

Returns:
    Computed factorial.
"""

The Sphinx project is configured in docs/conf.py.

Build the docs using the docs tox environment (e.g. tox or tox -e docs). Once built, open docs/_build/index.html in a web browser.

Generate a New Sphinx Project

To generate the Sphinx project shown in this project:

# Note: Sphinx is installed into the virtual environment automatically by pip-sync command
# above.
(venv) $ mkdir docs
(venv) $ cd docs
(venv) $ sphinx-quickstart --no-makefile --no-batchfile --extensions sphinx.ext.napoleon
# When prompted, select all defaults.

Modify conf.py appropriately:

# Add the project's Python package to the path so that autodoc can find it.
import os
import sys
sys.path.insert(0, os.path.abspath('../src'))

...

html_theme_options = {
    # Override the default alabaster line wrap, which wraps tightly at 940px.
    'page_width': 'auto',
}

Modify index.rst appropriately:

.. include:: ../README.rst

apidoc/modules.rst

Project Structure

Traditionally, Python projects place the source for their packages in the root of the project structure, like:

fact
├── fact
│   ├── __init__.py
│   ├── cli.py
│   └── lib.py
├── tests
│   ├── __init__.py
│   └── test_fact.py
├── tox.ini
└── setup.py

However, this structure is known to have bad interactions with pytest and tox, two standard tools maintaining Python projects. The fundamental issue is that tox creates an isolated virtual environment for testing. By installing the distribution into the virtual environment, tox ensures that the tests pass even after the distribution has been packaged and installed, thereby catching any errors in packaging and installation scripts, which are common. Having the Python packages in the project root subverts this isolation for two reasons:

1. Calling python in the project root (for example, python -m pytest tests/) causes Python to add the current working directory (the project root) to sys.path, which Python uses to find modules. Because the source package fact is in the project root, it shadows the fact package installed in the tox environment.

2. Calling pytest directly anywhere that it can find the tests will also add the project root to sys.path if the tests folder is a a Python package (that is, it contains a __init__.py file). pytest adds all folders containing packages to sys.path because it imports the tests like regular Python modules.

In order to properly test the project, the source packages must not be on the Python path. To prevent this, there are three possible solutions:

1. Remove the __init__.py file from tests and run pytest directly as a tox command.

2. Remove the __init__.py file from tests and change the working directory of python -m pytest to tests.

3. Move the source packages to a dedicated src folder.

The dedicated src directory is the recommended solution by pytest when using tox and the solution this blueprint promotes because it is the least brittle even though it deviates from the traditional Python project structure. It results is a directory structure like:

fact
├── src
│   └── fact
│       ├── __init__.py
│       ├── cli.py
│       └── lib.py
├── tests
│   ├── __init__.py
│   └── test_fact.py
├── tox.ini
└── setup.py

Type Hinting

Type hinting allows developers to include optional static typing information to Python source code. This allows static analyzers such as PyCharm, mypy, or pytype to check that functions are used with the correct types before runtime.

For PyCharm in particular, the IDE is able to provide much richer auto-completion, refactoring, and type checking while the user types, resulting in increased productivity and correctness.

This project uses the type hinting syntax introduced in Python 3:

def factorial(n: int) -> int:

Type checking is performed by mypy via tox -e mypy. mypy is configured in setup.cfg.

Licensing

Licensing for the project is defined in:

• LICENSE.txt

• setup.py

This project uses a common permissive license, the MIT license.

You may also want to list the licenses of all of the packages that your Python project depends on. To automatically list the licenses for all dependencies in requirements.txt (and their transitive dependencies) using pip-licenses:

(venv) $ tox -e licenses
...
 Name        Version  License
 colorama    0.4.3    BSD License
 exitstatus  1.3.0    MIT License

PyCharm Configuration

To configure PyCharm 2018.3 and newer to align to the code style used in this project:

• Settings | Search “Hard wrap at”

  • Editor | Code Style | General | Hard wrap at: 99

• Settings | Search “Optimize Imports”

  • Editor | Code Style | Python | Imports

    • ☑ Sort import statements

      • ☑ Sort imported names in “from” imports

      • ☐ Sort plain and “from” imports separately within a group

      • ☐ Sort case-insensitively

    • Structure of “from” imports

      • ◎ Leave as is

      • ◉ Join imports with the same source

      • ◎ Always split imports

• Settings | Search “Docstrings”

  • Tools | Python Integrated Tools | Docstrings | Docstring Format: Google

• Settings | Search “Force parentheses”

  • Editor | Code Style | Python | Wrapping and Braces | “From” Import Statements

    • ☑ Force parentheses if multiline

Integrate Code Formatters

To integrate automatic code formatters into PyCharm, reference the following instructions:

• black integration

  • The File Watchers method (step 3) is recommended. This will run black on every save.

• isort integration

  • The File Watchers method (option 1) is recommended. This will run isort on every save.