def makeMoveTable(game, states):
header = [['state:', 'moves']]
table = []
for state in states:
row = [str(state) + ':']
moves = game.actions(state)
moveString = str(moves)
row.append(moveString)
table.append(row)
print_table(table, header)
def makeABtable(game, states):
topLeft = str(game)[1:-1]
header = [[str(topLeft)]]
maxChars = len(topLeft)
for i in range(len(AB_searches)):
header[0].append('AB(' + str(i) + ')')
table = []
for state in states:
row = [str(state)[:maxChars]]
maxDepth = len(AB_searches)
if hasattr(state, 'maxDepth'):
maxDepth = min(maxDepth, state.maxDepth + 1)
for abi in range(len(AB_searches)):
if (abi > maxDepth):
row.append(None)
continue
abSearch = AB_searches[abi]
bestMove = abSearch(game, state)
row.append(str(bestMove))
table.append(row)
print_table(table, header, tjust='rjust')
def test_mongo_orm():
def save_user_repo():
json = requests.get(GithubUserUrls('hvnobug').starred_url()).json()
for item in json:
node_id = item['node_id']
name = item['name']
forks = item['forks']
fork = item['fork']
private = item['private']
watchers = item['watchers']
language = item['language']
full_name = item['full_name']
owner = item['owner']['login']
created_at = format_ufc_datetime(item['created_at'])
updated_at = format_ufc_datetime(item['updated_at'])
stars = item['stargazers_count']
open_issues = item['open_issues']
gr = GithubRepository(owner=owner,
name=name,
stars=stars,
forks=forks,
private=private,
watchers=watchers,
language=language,
full_name=full_name,
open_issues=open_issues,
fork=fork,
created_at=created_at,
updated_at=updated_at,
id=item['id'],
node_id=node_id)
gr.create_time = datetime.now()
gr.save()
save_user_repo()
print_table(GithubRepository.objects.all())
help='prints top referred urls in topic')
args = parser.parse_args()
if args.trends:
print '### Trending topics:'
print get_trends()
elif args.listen:
if not args.topic:
print '### Tweets about the trending topics:'
start_trends_stream(prints=True)
else:
print '### Tweets about %s' % args.topic
start_trends_stream(trends=[args.topic], prints=True)
elif args.top and args.topic:
print '### Top URLs about %s' % args.topic
urls = start_trends_stream(trends=[args.topic], prints=True)
urls = sorted(urls, key=urls.get) # ordering urls by counts
table = [(url, urls[url]) for url in urls]
print_table(table)
elif args.top and args.topic:
print '### Summary of %s' % args.topic
else:
parser.print_help()
def lcs(input_a, input_b, codon_length=1, verbose=False):
r"""Longest Common Substring with variable codon length.
>>> print('%r\n%r\n%r' % lcs((0, 1, 2) * 3, (2, 0, 1) * 3))
[None, 0, 1, 2, 0, 1, 2, 0, 1, 2]
[2, 0, 1, 2, 0, 1, 2, 0, 1, None]
8
>>> print('%r\n%r\n%r' % lcs((0, 1, 2) * 3, (2, 0, 1) * 3, 3))
[None, 0, 1, 2, 0, 1, 2, None, None, 0, 1, 2]
[2, 0, 1, 2, 0, 1, 2, 0, 1, None, None, None]
2
>>> print('%r\n%r\n%r' % lcs((0, 1, 2) * 3, (0, 1, 2) * 3, 3))
[0, 1, 2, 0, 1, 2, 0, 1, 2]
[0, 1, 2, 0, 1, 2, 0, 1, 2]
3
>>> print('%r\n%r\n%r' % lcs((0, 1, 2) * 3, (0, 1, 2) * 3))
[0, 1, 2, 0, 1, 2, 0, 1, 2]
[0, 1, 2, 0, 1, 2, 0, 1, 2]
9
>>> print('%r\n%r\n%r' % lcs((0, 1, 2) * 3, (2, 1, 0) * 3))
[None, None, 0, None, 1, None, 2, 0, 1, 2, 0, 1, 2]
[2, 1, 0, 2, 1, 0, 2, None, 1, None, 0, None, None]
5
"""
if verbose:
from util import print_table
print(input_a)
print(input_b)
match_matrix = [[0] * (len(input_b) + 1) for i in range(len(input_a) + 1)]
if verbose:
matching_points = [[''] * (len(input_b) + 1) for i in range(len(input_a) + 1)]
codon_length_less_1 = codon_length - 1
for x in range(codon_length - 1, len(input_a)):
for y in range(codon_length - 1, len(input_b)):
match_matrix[x + 1][y + 1] = max(
((1 if input_a[x - codon_length_less_1:x + 1] == input_b[y - codon_length_less_1:y + 1]
else 0)
+ match_matrix[x - codon_length_less_1][y - codon_length_less_1]),
match_matrix[x][y + 1],
match_matrix[x + 1][y])
if verbose:
if match_matrix[x + 1][y + 1] != match_matrix[x][y + 1] and match_matrix[x + 1][y + 1] != match_matrix[x + 1][y]:
matching_points[x + 1][y + 1] = 'x'
if verbose:
display_matrix = match_matrix[:]
for i in range(len(display_matrix)):
display_matrix[i] = display_matrix[i][:]
display_matrix[0] = [''] + list(input_b)
matching_points[0] = [''] + list(input_b)
for x in range(len(input_a)):
display_matrix[x + 1][0] = input_a[x]
matching_points[x + 1][0] = input_a[x]
print_table(display_matrix)
print_table(matching_points)
x = len(input_a)
y = len(input_b)
result_a = []
result_b = []
while x > codon_length_less_1 and y > codon_length_less_1:
if match_matrix[x][y] == match_matrix[x - 1][y]:
x -= 1
result_a.append(input_a[x])
result_b.append(None)
elif match_matrix[x][y] == match_matrix[x][y - 1]:
y -= 1
result_a.append(None)
result_b.append(input_b[y])
else:
x -= codon_length
y -= codon_length
result_a.extend(reversed(input_a[x:x + codon_length]))
result_b.extend(reversed(input_b[y:y + codon_length]))
while x > 0:
x -= 1
result_a.append(input_a[x])
result_b.append(None)
while y > 0:
y -= 1
result_a.append(None)
result_b.append(input_b[y])
result_a.reverse()
result_b.reverse()
if verbose:
if isinstance(input_a, str):
print(''.join(' ' if c is None else c for c in result_a))
else:
print(result_a)
if isinstance(input_b, str):
print(''.join(' ' if c is None else c for c in result_b))
else:
print(result_b)
return (result_a, result_b, match_matrix[len(input_a)][len(input_b)])
def test_mongo_collect2():
print_table(GithubRepository.objects())
def test_mongo_collect1():
with mongo_collection('github_repository') as github_repository:
print_table(github_repository.find())
def show(boxes, level):
util.clear()
table = convert_to_table(boxes, level)
util.print_table(table)
def list(self):
images = self.session.query(models.Image)\
.filter_by(status="available")
util.print_table(["name", "size", "status"],
map(lambda x: [x.name, x.size, x.status], images))
def summarize(args):
col_order = PredictedSeqInfoKey.get_columns_order()
failed_articles = []
articles_folder = os.path.join(args.data_folder, "text")
transcript_folder = os.path.join(args.data_folder, "transcript")
sections_info_folder = os.path.join(args.data_folder, "sections_info")
section_per_sent_folder = os.path.join(args.data_folder,
"section_per_sent")
article_names = args.article_names
print("number of articles: {}".format(len(article_names)))
predict_enable = not args.no_predict
# log only if we are in predict mode
logging_enable = predict_enable
for article_i, article_name in enumerate(article_names):
if logging_enable:
# set up log file for current article
log_filename = os.path.join(args.log_folder, article_name)
if os.path.isfile(log_filename):
raise Exception(
"log file already exists: {}".format(log_filename))
logger = Logger(log_filename)
sys.stdout = sys.stderr = logger
print("Logging to file: {}\n".format(log_filename))
print("--- paper {}: {}\n".format(article_i, article_name))
article_fname = os.path.join(articles_folder, article_name)
transcript_fname = os.path.join(transcript_folder, article_name)
sections_info_fname = os.path.join(sections_info_folder, article_name)
section_per_sent_fname = os.path.join(section_per_sent_folder,
article_name)
# remove the ".txt" extension and add numpy extension
similarity_fname = article_name[:-4] + '.npy'
similarity_fname = os.path.join(args.similarity_folder,
similarity_fname)
try:
article_data_sample = ArticleDataSample(transcript_fname,
article_fname,
sections_info_fname,
section_per_sent_fname)
# prepare configuration
cfg = HmmArticleConfig(args.word_embed_path,
labeled_data_mode=False)
cfg.similarity_fname = similarity_fname
cfg.print_configuration()
print("")
durations_folder = os.path.join(args.base_summaries_folder,
"durations")
os.makedirs(durations_folder, mode=0o775, exist_ok=True)
durations_fname = os.path.join(durations_folder, article_name)
alignment_folder = os.path.join(args.base_summaries_folder,
"alignment")
os.makedirs(alignment_folder, mode=0o775, exist_ok=True)
alignment_fname = os.path.join(alignment_folder, article_name)
top_scored_sents_folder = os.path.join(
args.base_summaries_folder,
"top_scored_sents.num_sents_{}_thresh_{}".format(
args.num_sents, args.thresh))
os.makedirs(top_scored_sents_folder, mode=0o775, exist_ok=True)
top_scored_sents_fname = os.path.join(top_scored_sents_folder,
article_name)
if predict_enable:
hmm_article = HmmArticle(article_data_sample, cfg)
predicted_seq_info, log_prob = hmm_article.predict()
print("log_prob = {}".format(log_prob))
print("predicted sequence info:\n")
alignment_str = print_table(predicted_seq_info, col_order)
with open(alignment_fname, 'w') as out_file:
out_file.write(alignment_str + "\n")
print("\n")
hmm_article.create_durations_file(durations_fname)
summary_creator = SummaryCreator(article_data_sample,
durations_fname=durations_fname)
if os.path.isfile(top_scored_sents_fname):
print("file exists: {}".format(top_scored_sents_fname))
else:
summary_creator.create_top_scored_sents_file(
args.num_sents, args.thresh, top_scored_sents_fname)
if predict_enable:
warnings = hmm_article.get_warnings()
if len(warnings) > 0:
for warning in warnings:
print("- {}".format(warning))
except Exception as ex:
print("EXCEPTION WAS CAUGHT FOR PAPER: {}".format(article_name))
print(ex)
failed_articles.append(article_name)
return failed_articles
def lcs(input_a, input_b, codon_length=1, verbose=False):
r"""Longest Common Substring with variable codon length.
>>> print '%r\n%r\n%r' % lcs((0, 1, 2) * 3, (2, 0, 1) * 3)
[None, 0, 1, 2, 0, 1, 2, 0, 1, 2]
[2, 0, 1, 2, 0, 1, 2, 0, 1, None]
8
>>> print '%r\n%r\n%r' % lcs((0, 1, 2) * 3, (2, 0, 1) * 3, 3)
[None, 0, 1, 2, 0, 1, 2, None, None, 0, 1, 2]
[2, 0, 1, 2, 0, 1, 2, 0, 1, None, None, None]
2
>>> print '%r\n%r\n%r' % lcs((0, 1, 2) * 3, (0, 1, 2) * 3, 3)
[0, 1, 2, 0, 1, 2, 0, 1, 2]
[0, 1, 2, 0, 1, 2, 0, 1, 2]
3
>>> print '%r\n%r\n%r' % lcs((0, 1, 2) * 3, (0, 1, 2) * 3)
[0, 1, 2, 0, 1, 2, 0, 1, 2]
[0, 1, 2, 0, 1, 2, 0, 1, 2]
9
>>> print '%r\n%r\n%r' % lcs((0, 1, 2) * 3, (2, 1, 0) * 3)
[None, None, 0, None, 1, None, 2, 0, 1, 2, 0, 1, 2]
[2, 1, 0, 2, 1, 0, 2, None, 1, None, 0, None, None]
5
"""
if verbose:
from util import print_table
print input_a
print input_b
match_matrix = [[0] * (len(input_b) + 1) for i in xrange(len(input_a) + 1)]
if verbose:
matching_points = [[""] * (len(input_b) + 1) for i in xrange(len(input_a) + 1)]
codon_length_less_1 = codon_length - 1
for x in xrange(codon_length - 1, len(input_a)):
for y in xrange(codon_length - 1, len(input_b)):
match_matrix[x + 1][y + 1] = max(
(
(1 if input_a[x - codon_length_less_1 : x + 1] == input_b[y - codon_length_less_1 : y + 1] else 0)
+ match_matrix[x - codon_length_less_1][y - codon_length_less_1]
),
match_matrix[x][y + 1],
match_matrix[x + 1][y],
)
if verbose:
if (
match_matrix[x + 1][y + 1] != match_matrix[x][y + 1]
and match_matrix[x + 1][y + 1] != match_matrix[x + 1][y]
):
matching_points[x + 1][y + 1] = "x"
if verbose:
display_matrix = match_matrix[:]
for i in xrange(len(display_matrix)):
display_matrix[i] = display_matrix[i][:]
display_matrix[0] = [""] + list(input_b)
matching_points[0] = [""] + list(input_b)
for x in xrange(len(input_a)):
display_matrix[x + 1][0] = input_a[x]
matching_points[x + 1][0] = input_a[x]
print_table(display_matrix)
print_table(matching_points)
x = len(input_a)
y = len(input_b)
result_a = []
result_b = []
while x > codon_length_less_1 and y > codon_length_less_1:
if match_matrix[x][y] == match_matrix[x - 1][y]:
x -= 1
result_a.append(input_a[x])
result_b.append(None)
elif match_matrix[x][y] == match_matrix[x][y - 1]:
y -= 1
result_a.append(None)
result_b.append(input_b[y])
else:
x -= codon_length
y -= codon_length
result_a.extend(reversed(input_a[x : x + codon_length]))
result_b.extend(reversed(input_b[y : y + codon_length]))
while x > 0:
x -= 1
result_a.append(input_a[x])
result_b.append(None)
while y > 0:
y -= 1
result_a.append(None)
result_b.append(input_b[y])
result_a.reverse()
result_b.reverse()
if verbose:
if isinstance(input_a, str):
print "".join(" " if c is None else c for c in result_a)
else:
print result_a
if isinstance(input_b, str):
print "".join(" " if c is None else c for c in result_b)
else:
print result_b
return (result_a, result_b, match_matrix[len(input_a)][len(input_b)])