156 lines
5.0 KiB
Python
156 lines
5.0 KiB
Python
import csv
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import functools
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import operator
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import re
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from numbers import Number
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from typing import Callable, Any
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from typing import Dict, List, Pattern
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import lxml.etree
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from lxml.cssselect import CSSSelector
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from lxml.etree import _Element
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from rapidfuzz import fuzz
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from .utils import _match_record
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import sqlite3
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def exact_match(result, rules) -> float:
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expect = rules["expected"]
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print(result, expect)
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if result == expect:
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return 1.
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else:
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return 0.
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def fuzzy_match(result, rules) -> float:
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expect = rules["expected"]
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return fuzz.ratio(result, expect) / 100.
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def check_csv(result: str, rules: Dict[str, List[Dict[str, str]]]) -> float:
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"""
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Args:
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result (str): path to csv file
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rules (Dict[str, List[Dict[str, str]]]): dict like
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{
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"expect": [{key: value}]
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"unexpect": [{key: value}]
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}
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Returns:
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float
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"""
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if result is None:
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return 0.
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expect_metrics = [False] * len(rules.get("expect", []))
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unexpect_metric = True
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with open(result) as f:
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reader = csv.DictReader(f)
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for rcd in reader:
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for i, r in enumerate(rules.get("expect", [])):
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expect_metrics[i] = expect_metrics[i] or _match_record(r, rcd)
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unexpect_metric = unexpect_metric and not any(_match_record(r, rcd) for r in rules.get("unexpect", []))
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return float(all(expect_metrics) and unexpect_metric)
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def check_list(result: str, rules: Dict[str, List[str]]) -> float:
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"""
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Args:
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result (str): path to list file
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rules (Dict[str, List[str]]): dict like
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{
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"expect": list of str as regexes
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"unexpect": list of str as regexes
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}
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Returns:
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float
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"""
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if result is None:
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return 0.
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expect_patterns: List[Pattern[str]] = [re.compile(ptt) for ptt in rules.get("expect", [])]
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unexpect_patterns: List[Pattern[str]] = [re.compile(ptt) for ptt in rules.get("unexpect", [])]
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expect_metrics = [False] * len(expect_patterns)
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unexpect_metric = True
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with open(result) as f:
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for l in f:
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for i, r in enumerate(expect_patterns):
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expect_metrics[i] = expect_metrics[i] or (r.search(l) is not None)
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unexpect_metric = unexpect_metric and all(r.search(l) is None for r in unexpect_patterns)
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return float(all(expect_metrics) and unexpect_metric)
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_accessibility_ns_map = {"st": "uri:deskat:state.at-spi.gnome.org"
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, "attr": "uri:deskat:attributes.at-spi.gnome.org"
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, "cp": "uri:deskat:component.at-spi.gnome.org"
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, "doc": "uri:deskat:document.at-spi.gnome.org"
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, "docattr": "uri:deskat:attributes.document.at-spi.gnome.org"
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, "txt": "uri:deskat:text.at-spi.gnome.org"
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, "val": "uri:deskat:value.at-spi.gnome.org"
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, "act": "uri:deskat:action.at-spi.gnome.org"
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}
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def check_accessibility_tree(result: str, rules: Dict[str, Any]) -> float:
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"""
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Args:
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result (str): XML of GNOME Accessibility Tree
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rules (Dict[str, Any]): dict like
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{
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"selectors": list of str as CSS selectors, will be connected by ", "
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to form a composite selector. Only one from `selectors` and
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`xpath` is needed. If both are present, `xpath` takes the
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priority.
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"xpath": str as xpath. Only one from `selectors` and `xpath` is
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needed. If both are present, `xpath` takes the priority.
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"text": str as the expected text content of the selected element.
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"exact": bool specifying whether exact match or fuzzy match should
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be performed. defaults to True
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}
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Returns:
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float
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"""
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at: _Element = lxml.etree.fromstring(result)
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if "xpath" in rules:
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elements: List[_Element] = at.xpath(rules["xpath"], namespaces=_accessibility_ns_map)
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elif "selectors" in rules:
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selector = CSSSelector(", ".join(rules["selectors"]), namespaces=_accessibility_ns_map)
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elements: List[_Element] = selector(at)
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else:
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raise ValueError("At least one of xpath and selectors is required")
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if len(elements) == 0:
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return 0.
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if "text" in rules:
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match_func: Callable[[str], Number] = functools.partial( operator.eq if rules["exact"]\
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else (lambda a, b: fuzz.ratio(a, b)/100.)
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, rules["text"]
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)
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match_score: Number = 0
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for elm in elements:
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match_score = max(match_score, match_func(elm.text or None))
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else:
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match_score = 1.
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return float(match_score)
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# def check_existence(result: str, *args) -> float:
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# return 1. - (result is None)
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def run_sqlite3(result: str, rules: Dict[str, Any]) -> float:
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connection: sqlite3.Connection = sqlite3.connect(result)
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cursor: sqlite3.Cursor = connection.execute(rules["sql"])
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return float(cursor.fetchone()[0] or 0)
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