Metadata-Version: 2.1
Name: pbu
Version: 0.4.0
Summary: Basic Utility module for the Python programming language
Home-page: https://github.com/ilfrich/python-basic-utils
Author: Peter Ilfrich
Author-email: das-peter@gmx.de
License: Apache-2.0
Description: # Python Basic Utilities `pbu`
        
        **Table of Contents**
        
        1. [Installation](#installation)
        2. [Usage](#usage)
        3. [Classes](#classes)
            1. [JSON](#json) - a JavaScript-like dictionary access helper
            2. [Logger](#logger) - a wrapper around the Python logging framework
            3. [TimeSeries](#timeseries) - powerful helper class to organise time series
            4. [AbstractMongoStore](#abstractmongostore) - helper and wrapper class for MongoDB access
            5. [AbstractMysqlStore](#abstractmysqlstore) - helper and wrapper class for MySQL access
        
        ## Installation
        
        Install via pip:
        
        ```bash
        pip install pbu
        ```
        
        ## Usage
        
        If you have a requirement.txt file, you can add `pbu` (replace 0.4.0 with latest available version or omit entirely to 
        get the latest):
        
        ```bash
        pbu==0.4.0
        ```
        
        Then, simply import the class / module you need:
        
        ```python
        from pbu import JSON
        
        # and start using it
        obj = JSON({"my": {"obj": "content"}})
        print(obj.my.obj)
        ```
        
        ## Classes
        
        ### JSON
        
        This is an adaptation of the native `dict` class, providing Javascript-like dictionary access using the "dot-notation" 
        (e.g. `person.relations[0].address.street`) rather than the Python-native bracket notation (e.g. 
        `person["relations"][0]["address"]["street"]`). It overrides the basic `__getattr__` and `__setattr__` methods as a 
        shortcut to manage the dictionary content.
        
        **Example**
        
        ```python
        from pbu import JSON
        my_obj = JSON({"initial": "content"})
        print(my_obj.initial)
        # prints out "content"
        
        my_obj.initial = {"a": 5, "b": 3}
        print(my_obj.initial.a + my_obj.initial.b)
        # prints out 8
        my_obj.initial.b = 13
        print(my_obj.initial.a + my_obj.initial.b)
        # prints out 18
        
        my_obj.extension = 10
        print(my_obj.extension)
        # prints out 10
        ```
        
        ### Logger
        
        This is a basic logger allowing to write log files, for `logger.info` it writes a debug.log and for `logger.error` or 
        `logger.exception` it writes an error.log file.
        
        **Example**
        
        ```python
        from pbu import Logger
        
        logger = Logger(name="logger-name")
        logger.debug("Some debug message goes here")
        logger.error("Error executing something")
        
        logger = Logger(name="logger-name", log_folder="./logs")
        logger.debug("This will create the debug.log and error.log in the ./logs folder")
        ```
        
        ### TimeSeries
        
        The time series class is a helper utility, that allows to compile complex time-series, offering functionality to add 
        time series, remove time series and most importantly align time series with timestamps to a previously defined 
        resolution by interpolating missing values and re-aligning measurements within the tolerance of the provided time 
        series. 
        
        It supports 2 different structures:
        
        **List of Dictionary Items**
        
        ```python
        from datetime import datetime, timedelta
        
        list_of_dict = [
            { "date_time": datetime.now(), "measurement_1": 12, "measurement_2": 15 },
            { "date_time": datetime.now() + timedelta(hours=1), "measurement_1": 10, "measurement_2": 16 },
            { "date_time": datetime.now() + timedelta(hours=2), "measurement_1": 9, "measurement_2": 12 },
        ]
        ```
        
        **Dictionary of Lists**
        
        ```python
        from datetime import datetime, timedelta
        
        dict_of_list = {
            "date_time": [datetime.now(), datetime.now() + timedelta(hours=1), datetime + timedelta(hours=2)],
            "measurement_1": [12, 10, 16],
            "measurement_2": [15, 16, 12],
        }
        ```
        
        **Example**
        
        ```python
        from pbu import TimeSeries
        from datetime import datetime, timedelta
        
        # initial time series base data (you can add measurements as well or provide as list of dictionaries
        dict_of_list = {
            "date_time": TimeSeries.create_date_range(datetime.now(), datetime.now() + timedelta(days=1), timedelta(hours=3)),
        }
        
        # init time series
        ts = TimeSeries(input_data=dict_of_list, date_time_key="date_time")
        # add values (ensure same length as date_time series)
        ts.add_values("measurement_1", [12, 10, 16, 10, 5, 8, 12, 9])  
        
        # you can translate into a list of dictionary items (keys are maintained)
        list_of_dict = ts.translate_to_list_of_dicts()
        
        # extract data series from the time series
        measurement_1 = ts.get_values("measurement_1")
        
        # create new series that provides same value for all timestamps
        ts.fill_values("constant_series", 5)
        
        # remove a series from the total data structure
        ts.remove_series("constant_series")
        
        # re-sample data to 5 minute resolution, interpolating values, also pre-pending another day in front of the time series 
        ts.align_to_resolution(resolution=timedelta(minutes=5), start_date=datetime.now() - timedelta(days=1))
        # this will result in "interpolated" values for the first day, using the first value (12) to fill missing values
        print(len(ts.translate_to_list_of_dicts()))  # 12 an hour, 2 days, 48 * 12 = ~576 items
        
        # the same can also be achieved by:
        ts.set_resolution(timedelta(minutes=5))
        # no need to provide resolution now
        ts.align_to_resolution(start_date=datetime.now() - timedelta(days=1))
        ```
        
        ### AbstractMongoStore
        
        Database store with helper functions for accessing MongoDB. Each store instance represents a single collection. This 
        comes with an `AbstractMongoDocument` class, which can be used to model the document types you store within a MongoDB 
        collection.
        
        **Example**
        
        ```python
        from pbu import AbstractMongoStore, AbstractMongoDocument
        
        # this is the object type stored in the mongo store
        class MyObjectType(AbstractMongoDocument):
            def __init__(self, val1, val2):
                # optional: provide id and data model version 
                super().__init__()
                self.attribute = val1
                self.attribute2 = val2,
            def to_json(self):
                # init with version and id
                result = super().to_json()
                # add attributes to dictionary and return
                result["attribute"] = self.attribute
                result["attribute2"] = self.attribute2
                return result
                
            @staticmethod
            def from_json(json):
                result = MyObjectType(json["attribute1"], json["attribute2"])
                # get _id and version attributes
                result.extract_system_fields(json)
                return result
                
        
        class MyObjectStore(AbstractMongoStore):
            def __init__(self, mongo_url, db_name, collection_name, data_model_version):
                # provide object type class as de-serialisation class (providing from_json and to_json)
                super.__init__(mongo_url, db_name, collection_name, MyObjectType, data_model_version)
        
        
        # create instance of store
        store = MyObjectStore("mongodb://localhost:27017", "mydb", "colName", 5)
        
        # create document using a dictionary
        store.create({
            "version": 5,
            "attribute1": "a",
            "attribute2": 16,
        })
        
        # or use the type
        doc = MyObjectType("a", 16)
        doc.version = 5
        doc_id = store.create(doc)
        
        # update single document using helper functions
        store.update(AbstractMongoStore.id_query(doc_id), 
                     AbstractMongoStore.set_update(["attribute1", "attribute2"], ["b", 12]))
        
        
        # returns a list of MyObjectType objects matching the version
        list_of_results = store.query({ "version": 5 })
        ``` 
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
