baseimage-tutorial-framework/lib/tfw/components/commands_equal.py

# Copyright (C) 2018 Avatao.com Innovative Learning Kft.
# All Rights Reserved. See LICENSE file for details.

from shlex import split
from re import search

from tfw.decorators.lazy_property import lazy_property


class CommandsEqual:
    """
    This class is useful for comparing executed commands with
    excepted commands (i.e. when triggering a state change when
    the correct command is executed).

    Note that in most cases you should test the changes
    caused by the commands instead of just checking command history
    (stuff can be done in countless ways and preparing for every
    single case is impossible). This should only be used when
    testing the changes would be very difficult, like when
    explaining stuff with cli tools and such.

    This class implicitly converts to bool, use it like
    if CommandsEqual(...): ...

    It tries detecting differing command parameter orders with similar
    semantics and provides fuzzy logic options.
    The rationale behind this is that a few false positives
    are better than only accepting a single version of a command
    (i.e. using ==).
    """
    def __init__(
            self, command_1, command_2,
            fuzzyness=1, begin_similarly=True,
            include_patterns=None, exclude_patterns=None
    ):
        """
        :param command_1: Compared command 1
        :param command_2: Compared command 2
        :param fuzzyness: float between 0 and 1.
                          the percentage of arguments required to
                          match between commands to result in True.
                          i.e 1 means 100% - all arguments need to be
                          present in both commands, while 0.75
                          would mean 75% - in case of 4 arguments
                          1 could differ between the commands.
        :param begin_similarly: bool, the first word of the commands
                                must match
        :param include_patterns: list of regex patterns the commands
                                 must include
        :param exclude_patterns: list of regex patterns the commands
                                 must exclude
        """
        self.command_1 = split(command_1)
        self.command_2 = split(command_2)
        self.fuzzyness = fuzzyness
        self.begin_similarly = begin_similarly
        self.include_patterns = include_patterns
        self.exclude_patterns = exclude_patterns

    def __bool__(self):
        if self.begin_similarly:
            if not self.beginnings_are_equal:
                return False

        if self.include_patterns is not None:
            if not self.commands_contain_include_patterns:
                return False

        if self.exclude_patterns is not None:
            if not self.commands_contain_no_exclude_patterns:
                return False

        print(self.similarity)
        return self.similarity >= self.fuzzyness

    @lazy_property
    def beginnings_are_equal(self):
        return self.command_1[0] == self.command_2[0]

    @lazy_property
    def commands_contain_include_patterns(self):
        return all((
            self.contains_regex_patterns(self.command_1, self.include_patterns),
            self.contains_regex_patterns(self.command_2, self.include_patterns)
        ))

    @lazy_property
    def commands_contain_no_exclude_patterns(self):
        return all((
            not self.contains_regex_patterns(self.command_1, self.exclude_patterns),
            not self.contains_regex_patterns(self.command_2, self.exclude_patterns)
        ))

    @staticmethod
    def contains_regex_patterns(command, regex_parts):
        command = ' '.join(command)
        for pattern in regex_parts:
            if not search(pattern, command):
                return False
        return True

    @lazy_property
    def similarity(self):
        parts_1 = set(self.command_1)
        parts_2 = set(self.command_2)

        difference = parts_1 - parts_2
        deviance = len(difference) / len(max(parts_1, parts_2))
        return 1 - deviance