Source code for api.verification

"""
verification.py
====================================
Verification API
"""

import sys, logging, multiprocessing, os
import pandas as pd

from typing import Dict, List, Tuple, Union

sys.path.append("..")

from .verification_case import *
from constrain.run_verification_case import *
from constrain.workflowsteps import *
from constrain.libcases import *


[docs] class Verification: def __init__(self, verifications: VerificationCase = None): self.lib_classes_py_file = None self.preprocessed_data = None self.cases = None self.output_path = None self.lib_items_path = None self.plot_option = None self.fig_size = None self.num_threads = None if verifications is None: logging.error( "A VerificationCase object should be provided to `verifications`." ) else: if isinstance(verifications, VerificationCase): if len(verifications.case_suite) == 0: logging.error("The verification case suite is empty.") return None else: self.cases = verifications.case_suite else: logging.error( f"A VerificationCase should be provided not a {type(verifications)}." ) return None
[docs] def configure( self, output_path: str = None, lib_items_path: str = None, lib_classes_py_file: str = None, plot_option: str = None, fig_size: tuple = (6.4, 4.8), num_threads: int = 1, preprocessed_data: pd.DataFrame = None, ) -> None: """Configure verification environment. Args: output_path (str): Verification results output path. lib_items_path (str, optional): User provided verification item json path (include name of the file with extension). lib_classes_py_file (str, optional): User provided verification item python classes file. plot_option (str, optional): Type of plots to include. It should either be all-compact, all-expand, day-compact, or day-expand. It can also be None, which will plot all types. Default to None. fig_size (tuple, optional): Tuple of integers (length, height) describing the size of the figure to plot. Defaults to (6.4, 4.8). num_threads (int, optional): Number of threads to run verifications in parallel. Defaults to 1. preprocessed_data (pd.DataFrame, optional): Pre-processed data stored in the data frame. Default to None. """ if self.cases is None or len(self.cases) == 0: logging.error( "The verification case suite is empty, there is nothing to configure." ) return None if output_path is None: logging.error("An output_path argument should be specified.") return None elif not os.path.isdir(output_path): logging.error("The specificed output directory does not exist.") return None if lib_items_path is None: logging.error( "A path to the library of verification cases should be provided." ) return None elif not isinstance(lib_items_path, str): logging.error("The path to the library of verification cases is not valid.") return None elif not os.path.isfile(lib_items_path): logging.error("The path to the library of verification cases is not valid.") return None elif "json" != lib_items_path.split(".")[-1].lower(): logging.error("The library should be a JSON file.") return None if not plot_option in [ None, "all-compact", "all-expand", "day-compact", "day-expand", ]: logging.error( f"The plot_option argument should either be all-compact, all-expand, day-compact, day-expand, or None, not {plot_option}." ) return None if isinstance(fig_size, tuple): if not ( (isinstance(fig_size[0], int) or isinstance(fig_size[0], float)) and (isinstance(fig_size[1], int) or isinstance(fig_size[1], float)) ): logging.error( "The fig_size argument should be a tuple of integers or floats." ) return None else: logging.error( f"The fig_size argument should be a tuple of integers or floats. Here is the variable type that was passed {type(fig_size)}." ) return None if (isinstance(num_threads, int) and num_threads < 1) or ( not isinstance(num_threads, int) ): logging.error("The number of threads should be an integer greater than 1.") return None if ( not isinstance(preprocessed_data, pd.DataFrame) and not preprocessed_data is None ): logging.error( f"A Pandas DataFrame should be passed as the `preprocessed_data` argument, not a {type(preprocessed_data)}." ) return None self.output_path = output_path self.lib_items_path = lib_items_path self.lib_classes_py_file = lib_classes_py_file self.plot_option = plot_option self.fig_size = fig_size self.num_threads = num_threads self.preprocessed_data = preprocessed_data
[docs] def run_single_verification(self, case: dict = None) -> None: """Run a single verification and generate a json file containing markdown report string and other results info. Args: case (dict): Verification case dictionary. """ # Input validation if case is None: logging.error("A case must be passed as an argument.") if not isinstance(case, dict): logging.error( f"A case dictionary must be passed as an argument, not a {type(case)}." ) # Run verification items = assemble_verification_items( cases=case, lib_items_path=self.lib_items_path ) results = run_libcase( item_dict=items[0], user_lib_file=self.lib_classes_py_file, plot_option=self.plot_option, output_path=self.output_path, fig_size=self.fig_size, produce_outputs=True, preprocessed_data=self.preprocessed_data, ) # TODO: JXL to make this compatible with reporting API, save md json instead of md files directly. # Output case summary cases_file = f"{self.output_path}/{case['no']}_md.json" with open(cases_file, "w") as fw: json.dump(results, fw)
[docs] def run(self) -> None: """Run all verification cases and generate json files containing results of all cases""" # Input validation if self.output_path is None: self.output_path = "" if self.cases is None or len(self.cases) == 0: logging.error( "The verification case suite is empty, there is nothing to run." ) return None # Run verifications # with multiprocessing.Pool(self.num_threads) as c: # c.map(self.run_single_verification, self.cases.values()) for case in self.cases.values(): self.run_single_verification(case)