def test_search(expected_nb_results, conn, query):
"""
Executes a search on the given connection, and check the result against
expected data.
"""
nb_results, cur = core.search(conn, query, expected_nb_results, 0)
assert_eq(expected_nb_results, nb_results)
for relev, freq, avg_idx, id, type_, loc, title in cur:
break
else:
assert_fail("At least one result should be available")
def search(query: str, ignore_done_results=True):
"""."""
results = core.search(query)
# remove tasks on the 'deleted' list
updated_results = [
result for result in results
if result['metadata'][TO_DONT_METADTA_KEY]['list'] != 'deleted'
]
if ignore_done_results:
# remove tasks on the 'done' list
updated_results = [
result for result in updated_results
if result['metadata'][TO_DONT_METADTA_KEY]['list'] != 'done'
]
return updated_results
def search_weather(self):
city = self.CityEdit.text()
if not city:
self.statusBar().setStyleSheet("color: red;")
self.statusBar().showMessage("请输入想要查询的城市名称!")
return False
city_code = get_city_code(city)
if not city_code:
self.statusBar().setStyleSheet("color: red;")
self.statusBar().showMessage("没有找到您想要查询的城市名称,请检查名称是否错误!")
return False
try:
response = search(city_code)
data = parse(response)
self.WeatherResult.setText("\n".join(data))
except Exception as error:
self.statusBar().setStyleSheet("color: red;")
self.statusBar().showMessage(str(error))
def coauthors(name, depth=1, author_repr=None):
"""Build a network of co-authors based on the name provided,
to a level depth as provided.
Inputs
------
name : str
The author name to focus on.
depth : int
The number of depth levels to progress from `name`.
author_repr : callable function
A function that formats the author name (generally LastName, Firstname I.)
max_papers : int
The maximum number of papers to query for a given author.
"""
try:
depth = int(depth)
except TypeError:
raise TypeError("depth must be an integer-like type")
if depth < 1:
raise ValueError("depth must be a positive integer")
if author_repr is None:
author_repr = lambda x: x
all_articles = []
level_authors = [name]
for i in xrange(depth):
next_level_authors = []
for j, author in enumerate(level_authors):
# If this is a "Collaboration" or "Team",
# we should ignore it and just deal with
# *real* people.
if "collaboration" in author.lower() \
or " team" in author.lower():
continue
# Get 5 top articles by this author
articles = search(u'"{author}"'.format(author=author),
fl="author,citation_count", filter="property:refereed", rows="all",
order="desc", sort="citations")
# Add these articles to the list
all_articles.extend(articles)
# Put these authors into the next level
next_level_authors.extend(sum([article.author for article in articles], []))
level_authors = []
level_authors.extend(next_level_authors)
# Initialise a group with the name input
links = []
groups = [0]
values = []
nodes = [author_repr(name)]
# Remove article double-ups?
# Go through all articles
for group_number, article in enumerate(all_articles, start=1):
# Make sure each co-author has a node
for co_author in article.author:
co_author = author_repr(co_author)
if co_author not in nodes:
# Create the node for this author
groups.append(group_number)
nodes.append(author_repr(co_author))
# Links should be drawn between all these article.author's,
# since they all published together.
for (author_one, author_two) in itertools.combinations(article.author, 2):
if author_repr(author_one) == author_repr(author_two):
continue
source = nodes.index(author_repr(author_one))
target = nodes.index(author_repr(author_two))
link = (source, target)
knil = (target, source)
if link not in links and knil not in links:
links.append(link)
values.append(1)
else:
try:
index = links.index(link)
except:
index = links.index(knil)
values[index] += 1
# Build formatted nodes and links
formatted_nodes = []
for author, group in zip(nodes, groups):
formatted_nodes.append({"name": author, "group": group})
formatted_links = []
for (source, target), value in zip(links, values):
formatted_links.append({
"source": source,
"target": target,
"value": value
})
output = {
"nodes": formatted_nodes,
"links": formatted_links
}
return output
exit
elif o.update_themes:
update("themes", o.limit)
message("Update themes. Done." ,core.M_UPDATE,"OK")
exit
if o.url != None:
site_version = detect_version(o.url)
message("Posible version " + str(site_version),core.M_VERSION, "WARNING")
message("\t" + o.url + "/CHANGELOG.txt",core.M_FILE, "WARNING")
if o.modules:
search ( o.url, "modules" , version = site_version )
if o.themes:
search ( o.url, "themes" , version = site_version )
if o.users:
if o.limit != 3:
ulimit = o.limit
else:
ulimit = 20
search_users(o.url, ulimit)
if o.paths:
search_urls(o.url)
def __twistdl__():
session = requests.Session()
parser = argparse.ArgumentParser(
description="twistdl is a downloader for anime from https://twist.moe."
)
parser.add_argument('-s',
'--search',
required=False,
default='',
help="Query for searching from Twist APIs.")
parser.add_argument(
'--iafl',
action="store_true",
help=
"'I am feeling lucky' mode, that is, the first result (if search is used) will be enqueued for download."
)
parser.add_argument('-dl',
'--download',
required=False,
default='',
help="Download an anime using slug name.")
parser.add_argument('-r',
'--range',
required=False,
default='1',
help="Range for downloading episodes from.")
parser.add_argument('-g',
'--grab',
required=False,
default='',
help="Grab the stream link(s) without downloading.")
parser.add_argument('-q',
'--quiet',
action="store_true",
help="Quiet mode, basically for disabling tqdm.")
parsed = parser.parse_args()
dl_check = range_check_parser(parsed.range)
if parsed.search:
print("Searching for '%s' on Twist:" % parsed.search)
results = [*core.search(session, parsed.search)]
if not results:
print("Could not find anything from that identifier.")
for index, content in enumerate(results, 1):
print(("{0:02d}: {title}" + (" ({alt_title})" if content.get('alt_title') else "") + \
" \x1b[33m{slug[slug]}\x1b[0m").format(index, **content))
if not parsed.iafl:
return
if not results:
return print(
"'I am feeling lucky' cannot continue since there are no results."
)
first_result = results.pop(0)
slug = first_result.get('slug', {}).get('slug', '')
for episode, content in enumerate(get_episodes(session, slug), 1):
if dl_check(episode):
cli_download(session, content.get('source'), episode, slug,
parsed.quiet)
if parsed.download:
slug = slug_converter(parsed.download)
for episode, content in enumerate(get_episodes(session, slug), 1):
if dl_check(episode):
cli_download(session, content.get('source'), episode, slug,
parsed.quiet)
if parsed.grab:
slug = slug_converter(parsed.grab)
for episode, content in enumerate(get_episodes(session, slug), 1):
if dl_check(episode):
print("Episode {0:02d}: {1}".format(episode,
content.get('source')))
def coauthors(name, depth=1, author_repr=None):
"""Build a network of co-authors based on the name provided,
to a level depth as provided.
Inputs
------
name : str
The author name to focus on.
depth : int
The number of depth levels to progress from `name`.
author_repr : callable function
A function that formats the author name (generally LastName, Firstname I.)
max_papers : int
The maximum number of papers to query for a given author.
"""
try:
depth = int(depth)
except TypeError:
raise TypeError("depth must be an integer-like type")
if depth < 1:
raise ValueError("depth must be a positive integer")
if author_repr is None:
author_repr = lambda x: x
all_articles = []
level_authors = [name]
for i in xrange(depth):
next_level_authors = []
for j, author in enumerate(level_authors):
# If this is a "Collaboration" or "Team",
# we should ignore it and just deal with
# *real* people.
if "collaboration" in author.lower() \
or " team" in author.lower():
continue
# Get 5 top articles by this author
articles = search(u'"{author}"'.format(author=author),
fl="author,citation_count", filter="property:refereed", rows="all",
order="desc", sort="citations")
# Add these articles to the list
all_articles.extend(articles)
# Put these authors into the next level
next_level_authors.extend(sum([article.author for article in articles], []))
level_authors = []
level_authors.extend(next_level_authors)
# Initialise a group with the name input
links = []
groups = [0]
values = []
nodes = [author_repr(name)]
# Remove article double-ups?
# Go through all articles
for group_number, article in enumerate(all_articles, start=1):
# Make sure each co-author has a node
for co_author in article.author:
co_author = author_repr(co_author)
if co_author not in nodes:
# Create the node for this author
groups.append(group_number)
nodes.append(author_repr(co_author))
# Links should be drawn between all these article.author's,
# since they all published together.
for (author_one, author_two) in itertools.combinations(article.author, 2):
if author_repr(author_one) == author_repr(author_two):
continue
source = nodes.index(author_repr(author_one))
target = nodes.index(author_repr(author_two))
link = (source, target)
knil = (target, source)
if link not in links and knil not in links:
links.append(link)
values.append(1)
else:
try:
index = links.index(link)
except:
index = links.index(knil)
values[index] += 1
# Build formatted nodes and links
formatted_nodes = []
for author, group in zip(nodes, groups):
formatted_nodes.append({"name": author, "group": group})
formatted_links = []
for (source, target), value in zip(links, values):
formatted_links.append({
"source": source,
"target": target,
"value": value
})
output = {
"nodes": formatted_nodes,
"links": formatted_links
}
return output