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توضیحات

Package to extract binary files into pandas dataframes

ویژگی	مقدار
سیستم عامل	-
نام فایل	AmiAutomation-0.1.4.2
نام	AmiAutomation
نسخه کتابخانه	0.1.4.2
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	AMI
ایمیل نویسنده	luis.castro@amiautomation.com
آدرس صفحه اصلی	-
آدرس اینترنتی	https://pypi.org/project/AmiAutomation/
مجوز	-

# 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

نیازمندی

مقدار	نام
>=1.1.0	pandas

زبان مورد نیاز

مقدار	نام
>=3.6	Python

نحوه نصب

نصب پکیج whl AmiAutomation-0.1.4.2:

pip install AmiAutomation-0.1.4.2.whl

نصب پکیج tar.gz AmiAutomation-0.1.4.2:

pip install AmiAutomation-0.1.4.2.tar.gz