AmiAutomation 0.1.4.2

Package to extract binary files into pandas dataframes

RPH extraction

Contains a tool to read a .rph file into a RphData structure.

Usage

A simple example is given below:

from AmiAutomation import RphData

data = RphData.rphToDf(path = "path_to_rph_file")

# Table data inside a dataframe
dataframe = data.dataFrame

Binaries extraction

This package contains the tools to easily extract binary data from PX3's:

• Heat Log
• 2 Second Log
• Wave Log
• Composite
• Histogram

Into a pandas dataframe for further processing

Usage

Importing a function is done the same way as any python package:

from AmiAutomation import PX3_Bin, LogData

From there you can call a method with the module prefix:

dataFrame = PX3_Bin.file_to_df(path = "C:\\Binaries")

or

dataFrame = LogData.binFileToDF(path = "C:\\Binaries")

LogData Methods

You can get Binary log data in a LogData format that contains useful data about the binary file, including samples inside a pandas dataframe

LogData.binFileToDF

Unpacks binary file into LogData

• Parameters:

  • path : str Complete file path

  • extension : str, optional Explicitly enforce file extension. ex: 'bin'

  • null_promoting : dict, optional A dictionary with a .NET Source Type key and a value of either one of the following (default, object, float, Int64, string, error).

    The possible dictionary keys are the .NET simple types:

    • "SByte" : Signed Byte
    • "Byte" : Unsigned Byte
    • "Int16" : 16 bit integer
    • "UInt16" : 16 bit unsigned integer
    • "Int32" : 32 bit integer
    • "UInt32" : 32 bit unsigned integer
    • "Int64" : 64 bit integer
    • "UInt64" : 64 bit unsigned integer
    • "Char" : Character
    • "Single" : Floating point single precision
    • "Double" : Floating point double precision
    • "Boolean" : bit
    • "Decimal" : 16 byte decimal precision
    • "DateTime" : Date time

    This dictionary values determines how null values in deserialization affect the resulting LogData dataframe column:

    • "default" : use pandas automatic inference when dealing with null values on a column
    • "object" : The returned type is the generic python object type
    • "float" : The returned type is the python float type
    • "Int64" : The returned type is the pandas Nullable Integer Int64 type
    • "string" : Values are returned as strings
    • "error" : Raises and exception when null values are encountered

• Returns:

  • LogData
    • Structure containing most file data

Examples

Simple file conversion

from AmiAutomation import LogData

#This returns the whole data
logData = LogData.binFileToDF("bin_file_path.bin")

#To access samples just access the dataframe inside the LogData object
dataFrame = logData.dataFrame 

Conversion with null promoting

from AmiAutomation import LogData

#Adding null promoting to handle missing values in these types of data as object
logData = LogData.binFileToDF("bin_file_path.bin", null_promoting={"Int32":"object", "Int16":"object", "Int64":"object"})

#To access samples just access the dataframe inside the LogData object
dataFrame = logData.dataFrame 

This method can also be used to retrive the data table from inside a ".cpst" or ".hist" file, detection is automatic based on file extension, if none is given, ".bin" is assumed

PX3_Bin Methods

This method returns a single pandas dataframe containing extracted data from the provided file, path or path with constrained dates

• file_to_df ( path, file, start_time, end_time, verbose = False )

• To process a single file you need to provide the absolute path in the file argument

dataFrame = PX3_Bin.file_to_df(file = "C:\\Binaries\\20240403T002821Z$-4038953271967.bin")

• To process several files just provide the directory path where the binaries are (binaries inside sub-directories are also included)

dataFrame = PX3_Bin.file_to_df(path = "C:\\Binaries\\")

• You can constrain the binaries inside a directory (and sub-directories) by also providing a start-date or both a start date and end date as a python datetime.datetime object

import datetime

time = datetime.datetime(2020,2,15,13,30) # February 15th 2020, 1:30 PM

### This returns ALL the data available in the path from the given date to the actual time
dataFrame = PX3_Bin.file_to_df(path = "C:\\Binaries\\", start_time=time)

import datetime

time_start = datetime.datetime(2020,2,15,13,30) # February 15th 2020, 1:30 PM
time_end = datetime.datetime(2020,2,15,13,45) # February 15th 2020, 1:45 PM

### This returns all the data available in the path from the given 15 minutes
dataFrame = PX3_Bin.file_to_df(path = "C:\\Binaries\\", start_time=time_start, end_time=time_end )

Tested with package version

• pythonnet 2.5.1
• pandas 1.1.0