Base CLI class to handle user interactions
More...

Inheritance diagram for Python.Markopy.BaseCLI:

Collaboration diagram for Python.Markopy.BaseCLI:

Public Member Functions
def	__init__ (self, bool add_help=True)
	initialize base CLI More...

def	add_arguments (self)

def	help (self)

def	parse (self)

def	init_post_arguments (self)

def	parse_arguments (self)

def	import_model (self, str filename)
	Import a model file. More...

def	train (self, str dataset, str seperator, str output, bool output_forced=False, bool bulk=False)
	Train a model via CLI parameters. More...

def	export (self, str filename)
	Export model to a file. More...

def	generate (self, str wordlist, bool bulk=False)
	Generate strings from the model. More...

def	process (self)
	Process parameters for operation. More...

Static Public Member Functions
def	check_import_path (str filename)
	check import path for validity More...

def	check_corpus_path (str filename)
	check import path for validity More...

def	check_export_path (str filename)
	check import path for validity More...

Public Attributes
	parser

	print_help

	model

	args

Private Member Functions
def	_generate (self, str wordlist)
	wrapper for generate function. More...

Detailed Description

Base CLI class to handle user interactions

Definition at line 16 of file base.py.

Constructor & Destructor Documentation

◆ init()

def Python.Markopy.BaseCLI.__init__	(		self,
		bool	add_help = `True`
	)

initialize base CLI

Parameters

add_help decide to overload the help function or not

Reimplemented in Python.Markopy.MarkovPasswordsCLI, and Python.Markopy.ModelMatrixCLI.

Definition at line 20 of file base.py.

     def __init__(self, add_help : bool=True):
         """!
         @brief initialize base CLI
         @param add_help decide to overload the help function or not
         """
         self.parser = argparse.ArgumentParser(description="Python wrapper for MarkovPasswords.",
         epilog=f"""{colored("Sample runs:", "yellow")}
         {__file__.split("/")[-1]} train untrained.mdl -d dataset.dat -s "\\t" -o trained.mdl
             Import untrained.mdl, train it with dataset.dat which has tab delimited data, output resulting model to trained.mdl\n
  
         {__file__.split("/")[-1]} generate trained.mdl -n 500 -w output.txt
             Import trained.mdl, and generate 500 lines to output.txt
  
         {__file__.split("/")[-1]} combine untrained.mdl -d dataset.dat -s "\\t" -n 500 -w output.txt
             Train and immediately generate 500 lines to output.txt. Do not export trained model.
  
         {__file__.split("/")[-1]} combine untrained.mdl -d dataset.dat -s "\\t" -n 500 -w output.txt -o trained.mdl
             Train and immediately generate 500 lines to output.txt. Export trained model.
         """, add_help=add_help, formatter_class=argparse.RawTextHelpFormatter)
         self.print_help = self.parser.print_help
         self.model = MarkovModel()
      

Member Function Documentation

◆ _generate()

def Python.Markopy.BaseCLI._generate	(		self,
		str	wordlist
	)

private

wrapper for generate function.

This can be overloaded by other models

Parameters

wordlist filename to generate to

Reimplemented in Python.Markopy.ModelMatrixCLI, and Python.CudaMarkopy.CudaModelMatrixCLI.

Definition at line 161 of file base.py.

     def _generate(self, wordlist : str):
         """!
         @brief wrapper for generate function. This can be overloaded by other models
         @param wordlist filename to generate to
         """
         self.model.Generate(int(self.args.count), wordlist, int(self.args.min), int(self.args.max), int(self.args.threads))
  

References Python.CudaMarkopy.CudaMarkopyCLI.args, Python.Markopy.BaseCLI.args, Python.Markopy.MarkopyCLI.args, Python.CudaMarkopy.CudaModelMatrixCLI.model, Python.Markopy.BaseCLI.model, Python.Markopy.ModelMatrixCLI.model, Python.Markopy.MarkovPasswordsCLI.model, and Markov::GUI::MarkovPasswordsGUI.model().

Referenced by Python.Markopy.BaseCLI.generate().

Here is the call graph for this function:

Here is the caller graph for this function:

◆ add_arguments()

def Python.Markopy.BaseCLI.add_arguments ( self )

Reimplemented in Python.Markopy.ModelMatrixCLI, Python.Markopy.MarkopyCLI, Python.Markopy.AbstractTrainingModelCLI, Python.Markopy.AbstractGenerationModelCLI, and Python.CudaMarkopy.CudaModelMatrixCLI.

Definition at line 43 of file base.py.

     def add_arguments(self):
         "! @brief Add command line arguements to the parser"
         self.parser.add_argument("mode",                             help="Process mode. Either 'Train', 'Generate', or 'Combine'.")
         self.parser.add_argument("-t", "--threads",default=10,       help="Number of lines to generate. Ignored in training mode.")
         self.parser.add_argument("-v", "--verbosity",action="count", help="Output verbosity.")
         self.parser.add_argument("-b", "--bulk",action="store_true", help="Bulk generate or bulk train every corpus/model in the folder.")
  

References Python.Markopy.BaseCLI.parser.

Referenced by Python.Markopy.BaseCLI.parse(), and Python.Markopy.MarkopyCLI.parse().

Here is the caller graph for this function:

◆ check_corpus_path()

def Python.Markopy.BaseCLI.check_corpus_path ( str filename )

static

check import path for validity

Parameters

filename filename to check

Definition at line 181 of file base.py.

     def check_corpus_path(filename : str):
         """!
         @brief check import path for validity
         @param filename filename to check
         """
  
         if(not os.path.isfile(filename)):
             return False
         return True
  

Referenced by Python.Markopy.BaseCLI.train().

Here is the caller graph for this function:

◆ check_export_path()

def Python.Markopy.BaseCLI.check_export_path ( str filename )

static

check import path for validity

Parameters

filename filename to check

Definition at line 192 of file base.py.

     def check_export_path(filename : str):
         """!
         @brief check import path for validity
         @param filename filename to check
         """
  
         if(filename and os.path.isfile(filename)):
             return True
         return True
  

Referenced by Python.Markopy.BaseCLI.train().

Here is the caller graph for this function:

◆ check_import_path()

def Python.Markopy.BaseCLI.check_import_path ( str filename )

static

check import path for validity

Parameters

filename filename to check

Definition at line 169 of file base.py.

     def check_import_path(filename : str):
         """!
         @brief check import path for validity
         @param filename filename to check
         """
         
         if(not os.path.isfile(filename)):
             return False
         else:
             return True
  

Referenced by Python.Markopy.BaseCLI.import_model().

Here is the caller graph for this function:

◆ export()

def Python.Markopy.BaseCLI.export	(		self,
		str	filename
	)

Export model to a file.

Parameters

filename filename to export to

Definition at line 138 of file base.py.

     def export(self, filename : str):
         """! 
         @brief Export model to a file
         @param filename filename to export to
         """
         self.model.Export(filename)
  

References Python.CudaMarkopy.CudaModelMatrixCLI.model, Python.Markopy.BaseCLI.model, Python.Markopy.ModelMatrixCLI.model, Python.Markopy.MarkovPasswordsCLI.model, and Markov::GUI::MarkovPasswordsGUI.model().

Referenced by Python.Markopy.BaseCLI.train().

Here is the call graph for this function:

Here is the caller graph for this function:

◆ generate()

def Python.Markopy.BaseCLI.generate	(		self,
		str	wordlist,
		bool	bulk = `False`
	)

Generate strings from the model.

Parameters

model	model instance
wordlist	wordlist filename
bulk	marks bulk operation with directories

Definition at line 145 of file base.py.

     def generate(self, wordlist : str, bulk : bool=False):
         """! 
             @brief Generate strings from the model
             @param model: model instance
             @param wordlist wordlist filename
             @param bulk marks bulk operation with directories
         """
         if not (wordlist or self.args.count):
             logging.pprint("Generation mode requires -w/--wordlist and -n/--count parameters. Exiting.")
             return False
     
         if(bulk and os.path.isfile(wordlist)):
             logging.pprint(f"{wordlist} exists and will be overwritten.", 1)
         self._generate(wordlist)
  

References Python.CudaMarkopy.CudaModelMatrixCLI._generate(), Python.Markopy.BaseCLI._generate(), Python.Markopy.ModelMatrixCLI._generate(), Python.Markopy.MarkovPasswordsCLI._generate(), Python.CudaMarkopy.CudaMarkopyCLI.args, Python.Markopy.BaseCLI.args, and Python.Markopy.MarkopyCLI.args.

Referenced by Python.Markopy.BaseCLI.process().

Here is the call graph for this function:

Here is the caller graph for this function:

◆ help()

def Python.Markopy.BaseCLI.help ( self )

Reimplemented in Python.Markopy.MarkopyCLI, and Python.CudaMarkopy.CudaMarkopyCLI.

Definition at line 51 of file base.py.

     def help(self):
         "! @brief Handle help strings. Defaults to argparse's help"
         self.print_help()
  

References Python.Markopy.BaseCLI.print_help.

Referenced by Python.Markopy.MarkopyCLI.add_arguments().

Here is the caller graph for this function:

◆ import_model()

def Python.Markopy.BaseCLI.import_model	(		self,
		str	filename
	)

Import a model file.

Parameters

filename filename to import

Definition at line 77 of file base.py.

     def import_model(self, filename : str):
         """! 
         @brief Import a model file
         @param filename filename to import
         """
         logging.pprint("Importing model file.", 1)
  
         if not self.check_import_path(filename):
             logging.pprint(f"Model file at {filename} not found. Check the file path, or working directory")
             return False
  
         self.model.Import(filename)
         logging.pprint("Model imported successfully.", 2)
         return True
  
  
  

References Python.Markopy.BaseCLI.check_import_path(), Python.CudaMarkopy.CudaModelMatrixCLI.model, Python.Markopy.BaseCLI.model, Python.Markopy.ModelMatrixCLI.model, Python.Markopy.MarkovPasswordsCLI.model, and Markov::GUI::MarkovPasswordsGUI.model().

Referenced by Python.Markopy.BaseCLI.process().

Here is the call graph for this function:

Here is the caller graph for this function:

◆ init_post_arguments()

def Python.Markopy.BaseCLI.init_post_arguments ( self )

Reimplemented in Python.Markopy.ModelMatrixCLI, Python.Markopy.MarkopyCLI, Python.CudaMarkopy.CudaModelMatrixCLI, and Python.Markopy.MarkopyCLI.

Definition at line 62 of file base.py.

     def init_post_arguments(self):
         "! @brief set up stuff that is collected from command line arguements"
         logging.VERBOSITY = 0
         try:
             if self.args.verbosity:
                 logging.VERBOSITY = self.args.verbosity
                 logging.pprint(f"Verbosity set to {self.args.verbosity}.", 2)
         except:
             pass
     

References Python.CudaMarkopy.CudaMarkopyCLI.args, Python.Markopy.BaseCLI.args, and Python.Markopy.MarkopyCLI.args.

Referenced by Python.Markopy.BaseCLI.parse(), and Python.Markopy.MarkopyCLI.parse().

Here is the caller graph for this function:

◆ parse()

def Python.Markopy.BaseCLI.parse ( self )

Reimplemented in Python.Markopy.MarkopyCLI, and Python.CudaMarkopy.CudaMarkopyCLI.

Definition at line 55 of file base.py.

     def parse(self):
         "! @brief add, parse and hook arguements"
         self.add_arguments()
         self.parse_arguments()
         self.init_post_arguments()
  

References Python.CudaMarkopy.CudaModelMatrixCLI.add_arguments(), Python.Markopy.BaseCLI.add_arguments(), Python.Markopy.AbstractGenerationModelCLI.add_arguments(), Python.Markopy.AbstractTrainingModelCLI.add_arguments(), Python.Markopy.MarkopyCLI.add_arguments(), Python.Markopy.ModelMatrixCLI.add_arguments(), Python.CudaMarkopy.CudaModelMatrixCLI.init_post_arguments(), Python.Markopy.BaseCLI.init_post_arguments(), Python.Markopy.MarkopyCLI.init_post_arguments(), Python.Markopy.ModelMatrixCLI.init_post_arguments(), and Python.Markopy.BaseCLI.parse_arguments().

Here is the call graph for this function:

◆ parse_arguments()

def Python.Markopy.BaseCLI.parse_arguments ( self )

Definition at line 73 of file base.py.

     def parse_arguments(self):
         "! @brief trigger parser"
         self.args = self.parser.parse_known_args()[0]
  

Referenced by Python.Markopy.BaseCLI.parse(), and Python.Markopy.MarkopyCLI.parse().

Here is the caller graph for this function:

◆ process()

def Python.Markopy.BaseCLI.process ( self )

Process parameters for operation.

Reimplemented in Python.Markopy.MarkopyCLI.

Definition at line 202 of file base.py.

     def process(self):
         """!
         @brief Process parameters for operation
         """
         if(self.args.bulk):
             logging.pprint(f"Bulk mode operation chosen.", 4)
             if (self.args.mode.lower() == "train"):
                 if (os.path.isdir(self.args.output) and not os.path.isfile(self.args.output)) and (os.path.isdir(self.args.dataset) and not os.path.isfile(self.args.dataset)):
                     corpus_list = os.listdir(self.args.dataset)
                     for corpus in corpus_list:
                         self.import_model(self.args.input)
                         logging.pprint(f"Training {self.args.input} with {corpus}", 2)
                         output_file_name = corpus
                         model_extension = ""
                         if "." in self.args.input:
                             model_extension = self.args.input.split(".")[-1]
                         self.train(f"{self.args.dataset}/{corpus}", self.args.seperator, f"{self.args.output}/{corpus}.{model_extension}", output_forced=True, bulk=True)
                 else:
                     logging.pprint("In bulk training, output and dataset should be a directory.")
                     exit(1)
  
             elif (self.args.mode.lower() == "generate"):
                 if (os.path.isdir(self.args.wordlist) and not os.path.isfile(self.args.wordlist)) and (os.path.isdir(self.args.input) and not os.path.isfile(self.args.input)):
                     model_list = os.listdir(self.args.input)
                     print(model_list)
                     for input in model_list:
                         logging.pprint(f"Generating from {self.args.input}/{input} to {self.args.wordlist}/{input}.txt", 2)
                         self.import_model(f"{self.args.input}/{input}")
                         model_base = input
                         if "." in self.args.input:
                             model_base = input.split(".")[1]
                         self.generate(f"{self.args.wordlist}/{model_base}.txt", bulk=True)
                 else:
                     logging.pprint("In bulk generation, input and wordlist should be directory.")
  
         else:
             self.import_model(self.args.input)
             if (self.args.mode.lower() == "generate"):
                 self.generate(self.args.wordlist)
  
  
             elif (self.args.mode.lower() == "train"):
                 self.train(self.args.dataset, self.args.seperator, self.args.output, output_forced=True)
  
  
             elif(self.args.mode.lower() == "combine"):
                 self.train(self.args.dataset, self.args.seperator, self.args.output)
                 self.generate(self.args.wordlist)
  
  
             else:
                 logging.pprint("Invalid mode arguement given.")
                 logging.pprint("Accepted modes: 'Generate', 'Train', 'Combine'")
                 exit(5)
  

References Python.CudaMarkopy.CudaMarkopyCLI.args, Python.Markopy.BaseCLI.args, Python.Markopy.MarkopyCLI.args, Python.Markopy.BaseCLI.generate(), Python.Markopy.BaseCLI.import_model(), Markov::GUI::Generate.train(), Markov::GUI::Train.train(), and Python.Markopy.BaseCLI.train().

Here is the call graph for this function:

◆ train()

def Python.Markopy.BaseCLI.train	(		self,
		str	dataset,
		str	seperator,
		str	output,
		bool	output_forced = `False`,
		bool	bulk = `False`
	)

Train a model via CLI parameters.

Parameters

model	Model instance
dataset	filename for the dataset
seperator	seperator used with the dataset
output	output filename
output_forced	force overwrite
bulk	marks bulk operation with directories

Definition at line 94 of file base.py.

     def train(self, dataset : str, seperator : str, output : str, output_forced : bool=False, bulk : bool=False):
         """! 
             @brief Train a model via CLI parameters 
             @param model Model instance
             @param dataset filename for the dataset
             @param seperator seperator used with the dataset
             @param output output filename
             @param output_forced force overwrite
             @param bulk marks bulk operation with directories
         """
         logging.pprint("Training.")
  
         if not (dataset and seperator and (output or not output_forced)):
             logging.pprint(f"Training mode requires -d/--dataset{', -o/--output' if output_forced else''} and -s/--seperator parameters. Exiting.")
             return False
  
         if not bulk and not self.check_corpus_path(dataset):
             logging.pprint(f"{dataset} doesn't exists. Check the file path, or working directory")
             return False
  
         if not self.check_export_path(output):
             logging.pprint(f"Cannot create output at {output}")
             return False
  
         if(seperator == '\\t'):
             logging.pprint("Escaping seperator.", 3)
             seperator = '\t'
         
         if(len(seperator)!=1):
             logging.pprint(f'Delimiter must be a single character, and "{seperator}" is not accepted.')
             exit(4)
  
         logging.pprint(f'Starting training.', 3)
         self.model.Train(dataset,seperator, int(self.args.threads))
         logging.pprint(f'Training completed.', 2)
  
         if(output):
             logging.pprint(f'Exporting model to {output}', 2)
             self.export(output)
         else:
             logging.pprint(f'Model will not be exported.', 1)
  
         return True
  

References Python.CudaMarkopy.CudaMarkopyCLI.args, Python.Markopy.BaseCLI.args, Python.Markopy.MarkopyCLI.args, Python.Markopy.BaseCLI.check_corpus_path(), Python.Markopy.BaseCLI.check_export_path(), Python.Markopy.BaseCLI.export(), Python.CudaMarkopy.CudaModelMatrixCLI.model, Python.Markopy.BaseCLI.model, Python.Markopy.ModelMatrixCLI.model, Python.Markopy.MarkovPasswordsCLI.model, and Markov::GUI::MarkovPasswordsGUI.model().

Referenced by Python.Markopy.BaseCLI.process().

Here is the call graph for this function:

Here is the caller graph for this function:

Member Data Documentation

◆ args

Python.Markopy.BaseCLI.args

Definition at line 75 of file base.py.

Referenced by Python.CudaMarkopy.CudaModelMatrixCLI._generate(), Python.Markopy.BaseCLI._generate(), Python.Markopy.ModelMatrixCLI._generate(), Python.Markopy.MarkovPasswordsCLI._generate(), Python.Markopy.BaseCLI.generate(), Python.Markopy.MarkopyCLI.help(), Python.Markopy.BaseCLI.init_post_arguments(), Python.Markopy.MarkopyCLI.parse(), Python.CudaMarkopy.CudaMarkopyCLI.parse_fail(), Python.Markopy.BaseCLI.process(), and Python.Markopy.BaseCLI.train().

◆ model

Python.Markopy.BaseCLI.model

Definition at line 40 of file base.py.

Referenced by Python.CudaMarkopy.CudaModelMatrixCLI._generate(), Python.Markopy.BaseCLI._generate(), Python.Markopy.ModelMatrixCLI._generate(), Python.Markopy.MarkovPasswordsCLI._generate(), Python.Markopy.BaseCLI.export(), Python.Markopy.BaseCLI.import_model(), and Python.Markopy.BaseCLI.train().

◆ parser

Python.Markopy.BaseCLI.parser

Definition at line 25 of file base.py.

Referenced by Python.CudaMarkopy.CudaMarkopyCLI.__init__(), Python.CudaMarkopy.CudaModelMatrixCLI.add_arguments(), Python.Markopy.BaseCLI.add_arguments(), Python.Markopy.AbstractGenerationModelCLI.add_arguments(), Python.Markopy.AbstractTrainingModelCLI.add_arguments(), Python.Markopy.MarkopyCLI.add_arguments(), Python.Markopy.ModelMatrixCLI.add_arguments(), Python.CudaMarkopy.CudaMarkopyCLI.help(), and Python.Markopy.MarkopyCLI.help().

◆ print_help

Python.Markopy.BaseCLI.print_help

Definition at line 39 of file base.py.

Referenced by Python.Markopy.BaseCLI.help(), and Python.Markopy.MarkopyCLI.help().

The documentation for this class was generated from the following file:

Markopy/Markopy/src/CLI/base.py

Public Member Functions

Static Public Member Functions

Public Attributes

Private Member Functions

Detailed Description

Constructor & Destructor Documentation

◆ __init__()

Member Function Documentation

◆ _generate()

◆ add_arguments()

◆ check_corpus_path()

◆ check_export_path()

◆ check_import_path()

◆ export()

◆ generate()

◆ help()

◆ import_model()

◆ init_post_arguments()

◆ parse()

◆ parse_arguments()

◆ process()

◆ train()

Member Data Documentation

◆ args

◆ model

◆ parser

◆ print_help

◆ init()