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@@ -0,0 +1 @@ +/AmiAutomation-0.1.4.2.tar.gz diff --git a/python-amiautomation.spec b/python-amiautomation.spec new file mode 100644 index 0000000..2582fcf --- /dev/null +++ b/python-amiautomation.spec @@ -0,0 +1,538 @@ +%global _empty_manifest_terminate_build 0 +Name: python-AmiAutomation +Version: 0.1.4.2 +Release: 1 +Summary: Package to extract binary files into pandas dataframes +License: MIT License +URL: https://pypi.org/project/AmiAutomation/ +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/6c/b5/1a9152dd7bb2e02b011f50ad0c5e57c7686ac57ff6598942d4fa1948d1d5/AmiAutomation-0.1.4.2.tar.gz +BuildArch: noarch + +Requires: python3-pandas + +%description +# 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 + + + +%package -n python3-AmiAutomation +Summary: Package to extract binary files into pandas dataframes +Provides: python-AmiAutomation +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-AmiAutomation +# 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 + + + +%package help +Summary: Development documents and examples for AmiAutomation +Provides: python3-AmiAutomation-doc +%description help +# 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 + + + +%prep +%autosetup -n AmiAutomation-0.1.4.2 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-AmiAutomation -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.4.2-1 +- Package Spec generated @@ -0,0 +1 @@ +fbbc09bdb6bac7ca48ced3619247c310 AmiAutomation-0.1.4.2.tar.gz |