Metadata-Version: 2.1
Name: wagano
Version: 0.5.4
Summary: A package with japanese-related functions
Home-page: UNKNOWN
Author: Ichiro Ishikawa
Author-email: Saser003@Outlook.sa
License: UNKNOWN
Description: Wagano is a Multi-purpose package designed for **japanese**-related functions.
        An extension of its features goes to a calculation.py file, where different features associated with math overlay.
        
        ![Logo](https://github.com/ichirod-japan/waganolib/blob/main/images/wagano.jfif?raw=true)
        
        # Table of Contents
        
        [Requirements](https://github.com/ichirod-japan/waganolib#requirements)
        
        [Installation](https://github.com/ichirod-japan/waganolib#installation)
        
        [Help](https://github.com/ichirod-japan/waganolib#help)
        
        [Build Source Code](https://github.com/ichirod-japan/waganolib#build-source-code)
        
        [License](https://github.com/ichirod-japan/waganolib#license)
        
        [Features](https://github.com/ichirod-japan/waganolib#features)
        
        [Haversine](https://github.com/ichirod-japan/waganolib#Haversine)
        
        [Mean](https://github.com/ichirod-japan/waganolib#mean)
        
        [Median](https://github.com/ichirod-japan/waganolib#median)
        
        [Stdev](https://github.com/ichirod-japan/waganolib#stdev)
        
        [_sum](https://github.com/ichirod-japan/waganolib#_sum)
        
        [Summary](https://github.com/ichirod-japan/waganolib#summary)
        
        
        # Requirements
        
        There is one thing you must have installed (the basics) before installing this module:
        
        - Python 3.6.8
        
        It may work on other versions, however, it has not been tested whether it'll work on them or not.
        
        # Installation
        
        One of the recommended ways to install wagano is by PyPi's pip.
        ```
        pip install wagano
        ```
        ____________
        Another way to get wagano is to git clone.
        
        ```
        git clone https://github.com/ichirod-japan/waganolib.git
        ```
        Then you can run the setup file from there.
        ```
        python setup.py install
        ```
        (or if you're on a Unix-Like OS (such as MacOS or Linux), use `sudo python setup.py install`)
        
        # Help
        
        If you need help or have an issue, do not hesitate to open one at https://github.com/ichirod-japan/waganolib/issues
        Very soon, this module is going to have a documentation to answer some of the basic questions.
        Remember before submitting an issue, check other issues (closed as well) and see if your question has already been answered.
        
        # build-source-code
        
        If you want to use features that are currently in development, or you want to contribute to wagano, you will need to build wagano locally from its source code, rather than pip installing it.
        
        Installing from source is fairly automated. The most work will involve compiling and installing all the wagano dependencies (not much, really). Once that is done, run the setup.py script which will attempt to auto-configure, build, and install wagano.
        
        # License
        
        This library is distributed under the [MIT License](https://github.com/ichirod-japan/waganolib/blob/main/LICENSE) which can be found in the `waganolib/LICENSE` file.
        
        # Features
        
        The following is a content table for all the **features** and only features of this README.md file.
        
        [Haversine](https://github.com/ichirod-japan/waganolib#Haversine)
        
        [Mean](https://github.com/ichirod-japan/waganolib#mean)
        
        [Median](https://github.com/ichirod-japan/waganolib#median)
        
        [Stdev](https://github.com/ichirod-japan/waganolib#stdev)
        
        [_sum](https://github.com/ichirod-japan/waganolib#_sum)
        
        [Summary](https://github.com/ichirod-japan/waganolib#summary)
        
        
        # Haversine
        
        To use one of the basic functions on this module, also known as **haversine** you can copy the following code for testing:
        ```py
        print(calculation.haversine(52.370216, 4.895168, 52.520008,
            13.404954) == 945793.4375088713)
        ```
        Harversine gets you the ability to Calculate the great circle distance between two points on the 
        earth (specified in decimal degrees), returns the distance in
        meters.
        All arguments must be of equal length.
        **:param lon1:** longitude of first place
        **:param lat1:** latitude of first place
        **:param lon2:** longitude of second place
        **:param lat2:** latitude of second place
        **:return:** distance in meters between the two sets of coordinates
        *So far, this is not japanese-related but is included in calculation.py so as to help you in the field*
        *of math and algebra. Other calculation functions will be listed in future updates on here.*
        
        # mean([])
        
        Mean is a default feature that would allow you to Calculate the arithmetic mean ("the average") of data:
        ```py
        >>> mean([-1.0, 2.5, 3.25, 5.75])
        2.625
        ```
        The calculation of the average is a known default, but has been tweaked a little to fit the purpose.
        
        # median([])
        
        Median is a feature that is similar to the one previously shown, mean, and it calculates the standard median of discrete data:
        ```py
        >>> median([2, 3, 4, 5])
        3.5
        ```
        
        Median has an extension which calculates the median, or 50th percentile, of data grouped into class intervals
        centred on the data values provided. E.g. if your data points are rounded to
        the nearest whole number:
        ```py
        >>> median_grouped([2, 2, 3, 3, 3, 4])  #doctest: +ELLIPSIS
        2.8333333333...
        ```
        This should be interpreted in this way: you have two data points in the class
        interval 1.5-2.5, three data points in the class interval 2.5-3.5, and one in
        the class interval 3.5-4.5. The median of these data points is 2.8333...
        Calculating variability or spread
        
        # stdev
        
        stdev is a feature that calculates the standard deviation of sample data:
        ```py
        >>> stdev([2.5, 3.25, 5.5, 11.25, 11.75])  #doctest: +ELLIPSIS
        4.38961843444...
        ```
        If you have previously calculated the mean, you can pass it as the optional
        second argument to the four "spread" functions to avoid recalculating it:
        ```py
        >>> data = [1, 2, 2, 4, 4, 4, 5, 6]
        >>> mu = mean(data)
        >>> pvariance(data, mu)
        2.5
        
        Exceptions
        ```
        
        # _sum
        
        Yet another feature in calculation.py is the _sum.
        the _sum returns a high-precision sum of the given numeric data as a fraction,
            together with the type to be converted to and the count of items.
            If optional argument ``start`` is given, it is added to the total.
            If ``data`` is empty, ``start`` (defaulting to 0) is returned.
            Examples
            --------
        ```py
            >>> _sum([3, 2.25, 4.5, -0.5, 1.0], 0.75)
            (<class 'float'>, Fraction(11, 1), 5)
        ```
            Some sources of round-off error will be avoided:
        ```py
            # Built-in sum returns zero.
            >>> _sum([1e50, 1, -1e50] * 1000)
            (<class 'float'>, Fraction(1000, 1), 3000)
        ```
            Fractions and Decimals are also supported:
        ```py
            >>> from fractions import Fraction as F
            >>> _sum([F(2, 3), F(7, 5), F(1, 4), F(5, 6)])
            (<class 'fractions.Fraction'>, Fraction(63, 20), 4)
            >>> from decimal import Decimal as D
            >>> data = [D("0.1375"), D("0.2108"), D("0.3061"), D("0.0419")]
            >>> _sum(data)
            (<class 'decimal.Decimal'>, Fraction(6963, 10000), 4)
        ```
            Mixed types are currently treated as an error, except that int is
            allowed.
        
        # Summary
        
        I don't think you expected this, but I won't list all features over here, you can find most of the features of calculation.py at https://github.com/python/cpython/blob/master/Lib/statistics.py, where most features reside.
        There is only a calculation.py file for now, since the module is still on 0.5.4, but the japanese-related features will be added very soon, and it is frantically being worked on!
Keywords: python,japan,japanese
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Education
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: Microsoft :: Windows
Description-Content-Type: text/markdown
