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)