WAVE

Workflow Automation and Versatile Engine

Owned by Lucas Lourenço

Maintained by Lucas Lourenço

Translation to Portuguese

Getting Started on WAVE

To use WAVE, execute the following command in your terminal

`pip install -U wave-flow`

Examples

Practical Examples

Class Use Case Example

DataHandler
Builder
To
Transmitter

How To Use

| DataHandler

The DataHandler class is the first step when you're using WAVE. Using it, you become able to use several features, including the unique way to reach Archive class (as shown in the next topic). Below are the methods and it's explication.

DType

As Pandas, you can pass a DType as parameter. The KEYS on this dict must have to be one of the Headers at the column that you want to perform the read as the VALUE data.

e.g.:

handler = DataHandler(r'example.xlsx')
handler.getArchive().setDelimiter('==') # important part of the code that will be shown below.
handler.setDtype({"ID":str, "DATE":str})

Acess Archive

To acess the class Archive, the unique way to reach it is using the code below.

e.g.:

handler = DataHandler(r'example.xlsx')
handler.getArchive() # and it's methods as shown

Read File

After informate the Delimiter, and if you think it's necessary, inform the Dtype, you have to read the file.
You can only read xlsx, csv and json

e.g.:

from WaveFlow import (PreRequisitesWave, To, DataHandler, Builder, Transmitter)

handler = DataHandler(r'example.xlsx')
handler.getArchive().setDelimiter('==')
handler.setDtype({"ID":str, "DATE":str})
handler.readFile()

print(handler.getArchive().getData()) # -> dict

| Archive

After being accessed by the method in DataHandler (Acess Archive), you can manage a lot of data informations. Which them gonna be expressed below.

Delimiter

One of the most important part of the orchestra. It's necessary and primordial to identify where the placeholders are.

e.g.:

from WaveFlow import (PreRequisitesWave, To, DataHandler, Builder, Transmitter)

handler = DataHandler(r'example.xlsx')
handler.getArchive().setDelimiter('==')

[...]

Transform Data

This method cooperate with To class. To handle data, To has a lot of management about it. You can read more about it clicking here. Following the harmony of the structure, it is appropriate that you use this method to process any type of data.

e.g.:

handler.getArchive().transformData("HOUR", To.Hour().to_hh_mm)
handler.getArchive().transformData("DATE", To.Date().to_dd_mm_yyyy)
handler.getArchive().transformData("FINALDATE", lambda x: To.Date().to_personalizedFormat(x, '%d de %B de %Y'))

Additional Parameter

setAdditionalParameters

Using this method, you can personalize formatting configurations to each info which will be placed. Assuming those obrigatory parameters keyColumn, parameterToChange, newValueToParameter, pay attention to the required data below.

possibilities	appropriate data type
bold	bool
italic	bool
font	string
size	int

keyColumn: Place here the header which you want to operate.

parameterToChange: Select one of the four possibility.

newValueToParameter: place the proper data to what you wanna format.

e.g.:

from WaveFlow import (PreRequisitesWave, To, DataHandler, Builder, Transmitter)

    handler = DataHandler(r'example.xlsx')
    
    handler.getArchive().setDelimiter('==')
    handler.readFile()
    
    handler.getArchive().setAdditionalParameters("NAME", "size", 12)
    handler.getArchive().setAdditionalParameters("NAME", "bold", True)
    handler.getArchive().setAdditionalParameters("COUNTRY", 'italic', True)
    handler.getArchive().setAdditionalParameters("DATE", "font", 'Times New Roman')
    
    [...]

OR setAdditionalParametersForAll

This method implements for all headers the same configuration.

e.g.:

from WaveFlow import (PreRequisitesWave, To, DataHandler, Builder, Transmitter)

    handler = DataHandler(r'example.xlsx')
    
    handler.getArchive().setDelimiter('==')
    handler.readFile()
    
    handler.getArchive().setAdditionalParametersForAll("centralize", True)
    
    [...]

Getters

Method Name	Return Type	Description
`getData()`	`dict`	Returns the data dictionary, raises `ReferenceError` if no data is available. (maybe it's what you're looking for)
`getFileType()`	`str`	Returns the file type of the archive.
`getMetaData()`	`list[str]`	Returns a list of metadata associated with the file.
`getFilename()`	`str`	Returns the name of the file.
`getDesignatedFile()`	`docx.Document`	Returns the designated file object.
`getDataFrame()`	`pd.DataFrame`	Returns the data as a DataFrame, raises `ReferenceError` if no DataFrame exists.
`getDelimiter()`	`str`	Returns the current delimiter used for key formatting.
`getFilesGenerated()`	`list[docx.Document]`	Returns a list of files generated by the system.

| Builder

The Builder class requires only two obrigatory parameters. Are them the instance of Archive, it means you HAVE to informate "handler.getArchive()" as first parameter(archive parameter). As second, you have to informate the base document(baseDocx parameter) which you want to deal.

e.g.:

from WaveFlow import (PreRequisitesWave, To, DataHandler, Builder, Transmitter)


handler = DataHandler(r'example.xlsx')
handler.getArchive().setDelimiter('==')
handler.readFile()

build = Builder(handler.getArchive(), r'example.docx')

[...]

Generation

.generate()

Method used to generate the documents. There's no parameters, just need to run it.

e.g.:

from WaveFlow import (PreRequisitesWave, To, DataHandler, Builder, Transmitter)


handler = DataHandler(r'example.xlsx')
handler.getArchive().setDelimiter('==')
handler.readFile()

build = Builder(handler.getArchive(), r'example.docx')
build.generate()

[...]

Saving Generated Files

.saveAs()

To save in a zip file or locally, first you have to generate as shown above.

This method has a lot of personalization ways to do. Below you gonna find informations.

Parameter	Type Needed
`textAtFile`	str
`keyColumn`	list[str]
`ZipFile`	bool -> False as default
`saveLocally`	bool -> True as default

Explication

textAtFile

That's the pattern name of the file that will be generated.
As e.g.: " {} - Document Generated - {}".
The "{}" in string is present because you can personalize the output with the next paramenter — keyColumn

keyColumn

With the Headers (Keys) which you informate in this list as string, you can provide the current

ZipFile & saveLocally

It'll build a ZipFile content (in case of ZipFile receives True) and create locally files (in case of saveLocally receives True)

e.g.:

from WaveFlow import (PreRequisitesWave, To, DataHandler, Builder, Transmitter)


handler = DataHandler(r'example.xlsx')
handler.getArchive().setDelimiter('==')
handler.readFile()

build = Builder(handler.getArchive(), r'example.docx')
build.generate()

build.saveAs(textAtFile="DOCS/{}/{} - Document",
                    keyColumn=['Date', 'Name'], 
                    ZipFile=True, 
                    saveLocally=True)

Getters

Method	Output
`getTimeToGenerate`	the current time neeeded to generate
`getIndexSequence`	the actual sequence of all documents

| To

The To class provides multiple utilities for transforming dates, times, and monetary values into different formats. Below are the available methods and their usage examples.

1. Language Configuration

Before using the Date, Hour, or Money utilities, you can set the language using the To.languageTo() method.

Languages Supported

'pt_BR' - Portuguese (Brazil)
'es_ES' - Spanish
'en_US' - English
'fr_FR' - French
You can use another language which locale can handle.

Example

from WaveFlow import (PreRequisitesWave, To, DataHandler, Builder, Transmitter)

[...]

To.languageTo('pt_BR')  # Set language to Portuguese
handler.getArchive().transformData("HOUR", To.Hour().to_hh_mm)

[...]

2. Date Manipulation

The To.Date() provides various methods to handle and transform date objects.

Available Methods

Method	Output Format	Example Input	Example Output
`to_dd_mm`	`dd/m`	`Timestamp('2024-01-01')`	`01/1`
`to_dd_MM`	`dd/M`	`Timestamp('2024-01-01')`	`01/January`
`to_MM_yy`	`MM/y`	`Timestamp('2024-01-01')`	`January/24`
`to_MM_yyyy`	`MM/Y`	`Timestamp('2024-01-01')`	`January/2024`
`to_dd_mm_yy`	`dd/mm/yy`	`Timestamp('2024-01-01')`	`01/01/24`
`to_dd_mm_yy_periodSep`	`dd.mm.yy`	`Timestamp('2024-01-01')`	`01.01.24`
`to_dd_MM_yyyy`	`dd/MM/yyyy`	`Timestamp('2024-01-01')`	`01/January/2024`
`to_dd_mm_yyyy`	`dd/mm/yyyy`	`Timestamp('2024-01-01')`	`01/01/2024`
`to_mm_dd_yyyy`	`mm/dd/yyyy`	`Timestamp('2024-01-01')`	`01/01/2024`
`to_yyyy_mm_dd`	`yyyy-mm-dd`	`Timestamp('2024-01-01')`	`2024-01-01`
`to_full_date`	Full Date String	`Timestamp('2024-01-01')`	`Monday, 01 January 2024`
`to_dd_MM_yyyy_in_full`	Full Date String	`Timestamp('2024-01-01')`	`01 January 2024`
`to_personalizedFormat`	Custom Format	`Timestamp('2024-01-01')`	Based on format provided

Input type must be Timestamp class

Example

pattern method use:

[...]

handler.getArchive().transformData("DATE", To.Date().to_dd_mm_yy_periodSep)

[...]

to_personalizedFormat:

It's an exclusive method in < To > class that provides a personalization about data been treated. You can use the same .strftime() as used on pandas.

[...]

handler.getArchive().transformData("DATE",lambda x:To.Date().to_personalizedFormat(x,'%d de %B de %Y'))

[...]

3. Time Manipulation

The To.Hour() provides methods for transforming time objects into desired formats.

Available Methods

Method	Output Format	Example Input	Example Output
`to_hh_mm_ss`	`HH:MM:SS`	`datetime(11, 30)`	`11:30:00`
`to_hh_mm`	`HH:MM`	`datetime(11, 30)`	`11:30`
`to_12_hour_format`	`HH:MM AM/PM`	`datetime(23, 30)`	`11:30 PM`
`to_24_hour_format`	`HH:MM`	`datetime(23, 30)`	`23:30`

Input type must be datetime class

Example

pattern method use:

[...]

To.languageTo('pt_BR')
handler.getArchive().transformData("HOUR", To.Hour().to_hh_mm)

[...]

4. Money Formatting Manipulation

The To.Money() provides methods for formatting monetary values into various currencies.

Available Methods

Method	Output Format	Example Input	Example Output
`to_dollars`	`$ {value}`	`1234.56`	`$ 1,234.56`
`to_euros`	`€ {value}`	`1234.56`	`€ 1.234,56`
`to_pounds`	`£ {value}`	`1234.56`	`£ 1,234.56`
`to_brl`	`R$ {value}`	`1234.56`	`R$ 1.234,56`

Input type must be float or int

Example

pattern method use:

[...]

To.languageTo('pt_BR')
handler.getArchive().transformData("VALUE", To.Money().to_brl)

[...]

| Transmitter

This class could be the first step for your usage on WAVE, because it can analyse a .docx and return a .xlsx file with all headers which where defined at docx.

Everything you need to do is informate the document (it must have to be a list) and pass the delimiter. After that, just use the method export. Follow the example:

from WaveFlow import Transmitter

transmitter = Transmitter(['example.xlsx'], '==')
transmitter.export("exampleExport.xlsx")

Name		Name	Last commit message	Last commit date
Latest commit History 80 Commits
RDM_components		RDM_components
WaveFlow		WaveFlow
e.g		e.g
venv		venv
.gitattributes		.gitattributes
.gitignore		.gitignore
README.md		README.md
setup.py		setup.py

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WAVE

Workflow Automation and Versatile Engine

Getting Started on WAVE

pip install -U wave-flow

Examples

How To Use

| DataHandler

DType

Acess Archive

Read File

| Archive

Delimiter

Transform Data

Additional Parameter

Getters

| Builder

Generation

Saving Generated Files

Explication

Getters

| To

1. Language Configuration

Languages Supported

Example

2. Date Manipulation

Available Methods

Example

3. Time Manipulation

Available Methods

Example

4. Money Formatting Manipulation

Available Methods

Example

| Transmitter

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`pip install -U wave-flow`

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