mirror of https://github.com/searx/searx
284 lines
9.9 KiB
Python
284 lines
9.9 KiB
Python
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'''
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searx is free software: you can redistribute it and/or modify
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it under the terms of the GNU Affero General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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searx is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU Affero General Public License for more details.
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You should have received a copy of the GNU Affero General Public License
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along with searx. If not, see < http://www.gnu.org/licenses/ >.
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(C) 2013- by Adam Tauber, <asciimoo@gmail.com>
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'''
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from os.path import realpath, dirname, splitext, join
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from imp import load_source
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import grequests
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from itertools import izip_longest, chain
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from operator import itemgetter
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from urlparse import urlparse
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from searx import settings
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from searx.utils import gen_useragent
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import sys
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from datetime import datetime
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engine_dir = dirname(realpath(__file__))
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number_of_searches = 0
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engines = {}
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categories = {'general': []}
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def load_module(filename):
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modname = splitext(filename)[0]
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if modname in sys.modules:
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del sys.modules[modname]
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filepath = join(engine_dir, filename)
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module = load_source(modname, filepath)
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module.name = modname
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return module
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if not 'engines' in settings or not settings['engines']:
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print '[E] Error no engines found. Edit your settings.yml'
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exit(2)
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for engine_data in settings['engines']:
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engine_name = engine_data['engine']
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engine = load_module(engine_name + '.py')
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for param_name in engine_data:
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if param_name == 'engine':
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continue
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if param_name == 'categories':
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if engine_data['categories'] == 'none':
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engine.categories = []
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else:
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engine.categories = map(
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str.strip, engine_data['categories'].split(','))
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continue
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setattr(engine, param_name, engine_data[param_name])
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for engine_attr in dir(engine):
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if engine_attr.startswith('_'):
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continue
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if getattr(engine, engine_attr) == None:
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print '[E] Engine config error: Missing attribute "{0}.{1}"'.format(engine.name, engine_attr) # noqa
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sys.exit(1)
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engines[engine.name] = engine
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engine.stats = {
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'result_count': 0,
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'search_count': 0,
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'page_load_time': 0,
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'score_count': 0,
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'errors': 0
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}
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if hasattr(engine, 'categories'):
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for category_name in engine.categories:
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categories.setdefault(category_name, []).append(engine)
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else:
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categories['general'].append(engine)
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def default_request_params():
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return {
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'method': 'GET', 'headers': {}, 'data': {}, 'url': '', 'cookies': {}}
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def make_callback(engine_name, results, suggestions, callback, params):
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# creating a callback wrapper for the search engine results
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def process_callback(response, **kwargs):
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cb_res = []
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response.search_params = params
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engines[engine_name].stats['page_load_time'] += \
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(datetime.now() - params['started']).total_seconds()
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try:
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search_results = callback(response)
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except Exception, e:
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engines[engine_name].stats['errors'] += 1
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results[engine_name] = cb_res
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print '[E] Error with engine "{0}":\n\t{1}'.format(
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engine_name, str(e))
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return
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for result in search_results:
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result['engine'] = engine_name
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if 'suggestion' in result:
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# TODO type checks
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suggestions.add(result['suggestion'])
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continue
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cb_res.append(result)
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results[engine_name] = cb_res
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return process_callback
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def score_results(results):
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flat_res = filter(
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None, chain.from_iterable(izip_longest(*results.values())))
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flat_len = len(flat_res)
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engines_len = len(results)
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results = []
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# deduplication + scoring
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for i, res in enumerate(flat_res):
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res['parsed_url'] = urlparse(res['url'])
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res['engines'] = [res['engine']]
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weight = 1.0
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if hasattr(engines[res['engine']], 'weight'):
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weight = float(engines[res['engine']].weight)
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score = int((flat_len - i) / engines_len) * weight + 1
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duplicated = False
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for new_res in results:
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p1 = res['parsed_url'].path[:-1] if res['parsed_url'].path.endswith('/') else res['parsed_url'].path # noqa
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p2 = new_res['parsed_url'].path[:-1] if new_res['parsed_url'].path.endswith('/') else new_res['parsed_url'].path # noqa
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if res['parsed_url'].netloc == new_res['parsed_url'].netloc and\
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p1 == p2 and\
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res['parsed_url'].query == new_res['parsed_url'].query and\
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res.get('template') == new_res.get('template'):
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duplicated = new_res
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break
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if duplicated:
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if len(res.get('content', '')) > len(duplicated.get('content', '')): # noqa
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duplicated['content'] = res['content']
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duplicated['score'] += score
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duplicated['engines'].append(res['engine'])
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if duplicated['parsed_url'].scheme == 'https':
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continue
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elif res['parsed_url'].scheme == 'https':
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duplicated['url'] = res['parsed_url'].geturl()
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duplicated['parsed_url'] = res['parsed_url']
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else:
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res['score'] = score
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results.append(res)
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return sorted(results, key=itemgetter('score'), reverse=True)
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def search(query, request, selected_engines):
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global engines, categories, number_of_searches
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requests = []
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results = {}
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suggestions = set()
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number_of_searches += 1
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#user_agent = request.headers.get('User-Agent', '')
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user_agent = gen_useragent()
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for selected_engine in selected_engines:
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if selected_engine['name'] not in engines:
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continue
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engine = engines[selected_engine['name']]
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request_params = default_request_params()
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request_params['headers']['User-Agent'] = user_agent
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request_params['category'] = selected_engine['category']
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request_params['started'] = datetime.now()
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request_params = engine.request(query, request_params)
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callback = make_callback(
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selected_engine['name'],
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results,
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suggestions,
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engine.response,
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request_params
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)
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request_args = dict(
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headers=request_params['headers'],
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hooks=dict(response=callback),
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cookies=request_params['cookies'],
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timeout=settings['server']['request_timeout']
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)
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if request_params['method'] == 'GET':
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req = grequests.get
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else:
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req = grequests.post
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request_args['data'] = request_params['data']
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# ignoring empty urls
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if not request_params['url']:
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continue
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requests.append(req(request_params['url'], **request_args))
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grequests.map(requests)
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for engine_name, engine_results in results.items():
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engines[engine_name].stats['search_count'] += 1
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engines[engine_name].stats['result_count'] += len(engine_results)
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results = score_results(results)
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for result in results:
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for res_engine in result['engines']:
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engines[result['engine']].stats['score_count'] += result['score']
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return results, suggestions
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def get_engines_stats():
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# TODO refactor
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pageloads = []
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results = []
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scores = []
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errors = []
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scores_per_result = []
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max_pageload = max_results = max_score = max_errors = max_score_per_result = 0 # noqa
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for engine in engines.values():
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if engine.stats['search_count'] == 0:
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continue
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results_num = \
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engine.stats['result_count'] / float(engine.stats['search_count'])
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load_times = engine.stats['page_load_time'] / float(engine.stats['search_count']) # noqa
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if results_num:
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score = engine.stats['score_count'] / float(engine.stats['search_count']) # noqa
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score_per_result = score / results_num
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else:
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score = score_per_result = 0.0
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max_results = max(results_num, max_results)
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max_pageload = max(load_times, max_pageload)
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max_score = max(score, max_score)
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max_score_per_result = max(score_per_result, max_score_per_result)
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max_errors = max(max_errors, engine.stats['errors'])
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pageloads.append({'avg': load_times, 'name': engine.name})
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results.append({'avg': results_num, 'name': engine.name})
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scores.append({'avg': score, 'name': engine.name})
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errors.append({'avg': engine.stats['errors'], 'name': engine.name})
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scores_per_result.append({
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'avg': score_per_result,
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'name': engine.name
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})
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for engine in pageloads:
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engine['percentage'] = int(engine['avg'] / max_pageload * 100)
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for engine in results:
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engine['percentage'] = int(engine['avg'] / max_results * 100)
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for engine in scores:
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engine['percentage'] = int(engine['avg'] / max_score * 100)
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for engine in scores_per_result:
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engine['percentage'] = int(engine['avg'] / max_score_per_result * 100)
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for engine in errors:
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if max_errors:
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engine['percentage'] = int(float(engine['avg']) / max_errors * 100)
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else:
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engine['percentage'] = 0
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return [
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('Page loads (sec)', sorted(pageloads, key=itemgetter('avg'))),
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(
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'Number of results',
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sorted(results, key=itemgetter('avg'), reverse=True)
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),
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('Scores', sorted(scores, key=itemgetter('avg'), reverse=True)),
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(
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'Scores per result',
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sorted(scores_per_result, key=itemgetter('avg'), reverse=True)
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),
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('Errors', sorted(errors, key=itemgetter('avg'), reverse=True)),
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]
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