deepair-dev-utils 0.0.6

Deep Developer Utilities

This package consists of developer utilities specifically used for data operations and handeling within deep air environment.

Package structure

deepair_dev_utils . ├── general │   ├── init.py │   └── tools.py ├── init.py └── loader ├── init.py └── tools.py

2 directories, 5 files

Dependencies

Note: The following python3 packages are necessary for this package to run:

• numpy
• scipy
• pandas
• sklearn
• tqdm

Function Declarations

Here are the signatures for the functions in the package that can be used for deepair-dev.

general.py

Below are the functions that can be accessed by importing this module as from deepair_dev_utils.general.tools import <function_name>.

log:

def log(message):
    '''
        prints message on console
        input :
            message     : msg to print (string)
    '''

get_data:

def get_data(path):
    '''
        Single file loader function
        input :
            path     : abs path to load from (string)
    '''

daterange:

def daterange(s_date, e_date):
    '''
        To return a list of all the dates from
        start date to end date (excluding end date)
        input :
            s_date     : start date (datetime)
            e_date     : end date (datetime)
        returns :
            list of dates
    '''

jsonReader:

def jsonReader(path):
    '''
        JSON File Reader (from absolute path).
        Args:
            path   : absolute path of json file (string)
        Return:
            data   : loaded JSON
    '''

jsonWriter:

def jsonWriter(data, path):
    '''
        JSON File Writer (to absolute path).
        Args:
            data   : data to write (JSON/DICT/STRING)
            path   : absolute path of json file (string)
    '''

ddmmyyyy2datetime:

def ddmmyyyy2datetime(start_date):
    '''
        Convert dd-mm-yyyy to std data time format.
        Args:
            start_date   : date with dd-mm-yyyy (string)
        Return:
            date   : converted format
    '''

Below are the decorators that can be accessed by importing this module as from deepair_dev_utils.general.decorators import <decorator_name>.

function_logger:

def function_logger(orig_func):
    '''
        Create a file with function.log (if possible)
        otherwise with unknown_function.log and record
        the arguments passed for the function

        example:
        @function_logger
        def target_function(...):
            ...
    '''

function_timer:

def function_timer(orig_func):
    '''
        Displays runtime on console

        example:
        @function_timer
        def target_function(...):
            ...
    '''

Loader

This subpackage contains tools for loading data as Handler.

Handler

Below are the functions that can be accessed by importing this module as from deepair_dev_utils.loader.tools import Handler.

Then create an object to access the fuctions. example obj = Handler() and then obj.<function_name>

__init__:

def __init__(self, verbose=True):
    '''
        Handlder (class) constructor.
        inputs:
            verbose: Indicator for log and progress bar (bool)
    '''

loader:

def loader(self, dir_path, start_date, end_date,
           prefix='', postfix='', ext='.csv'):
    '''
        Primary loader function to load the data from start date to
        end date in concatinated (single dataframe) format.
        inputs:
            dir_path    : absolute path to the directory path (series)
            start_date  : load start date in dd-mm-yyyy format (string)
            end_date    : load end date in dd-mm-yyyy format (string)
            prefix      : file prefix [if necessary] (string)
            postfix     : file postfix [if necessary] (string)
            ext         : file extension [default is .csv] (string)
        return:
            df:  loaded concatenated dataframe (pandas df)
    '''

loader_v2:

def loader_v2(self, dir_path, start_date, end_date,
              prefix='', postfix='', ext='.csv'):
    '''
        (VERSION 2)
        Primary loader function to load the data from start date to
        end date in concatinated (single dataframe) format.
        inputs:
            dir_path    : absolute path to the directory path (series)
            start_date  : load start date in yyyy-mm-dd format (string)
            end_date    : load end date in yyyy-mm-dd format (string)
            prefix      : file prefix [if necessary] (string)
            postfix     : file postfix [if necessary] (string)
            ext         : file extension [default is .csv] (string)
        return:
            df:  loaded concatenated dataframe (pandas df)
    '''

single_loader:

def single_loader(self, dir_path, start_date, end_date,
                  prefix='', postfix='', ext='.csv'):
    '''
        Single loader function to load the data from start date to
        end date in individual datewise (each dataframe is of one date)
        format.
        inputs:
            dir_path    : absolute path to the directory path (series)
            start_date  : load start date in dd-mm-yyyy format (string)
            end_date    : load end date in dd-mm-yyyy format (string)
            prefix      : file prefix [if necessary] (string)
            postfix     : file postfix [if necessary] (string)
            ext         : file extension [default is .csv] (string)
        return:
            data:  list of data frames datewise (list)
    '''

batch_loader:

def batch_loader(self, dir_path, start_date, end_date,
                 batch_size=1, prefix='', postfix='', ext='.csv'):
    '''
        Batch loader function to load the data from start date to
        end date in batches (each dataframe is in the form of batch datewise)
        format.
        inputs:
            dir_path    : absolute path to the directory path (series)
            start_date  : load start date in dd-mm-yyyy format (string)
            end_date    : load end date in dd-mm-yyyy format (string)
            batch_size  : batch size (int)
            prefix      : file prefix [if necessary] (string)
            postfix     : file postfix [if necessary] (string)
            ext         : file extension [default is .csv] (string)
        return:
            data:  list of data frames datewise (list)
    '''

_load_action:

def _load_action(self, df):
    '''
        @abstractmethod
        User defined Bottle neck pipeline within load.
        NOTE -> Default job of this function is pass i.e. do nothing
        inputs:
            df:  Dataframe to apply this method on (pandas df)
        return:
            df:  Modified dataframe (pandas df)
    '''