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def diff_prettyHtml(self, diffs):
"""Convert a diff array into a pretty HTML report. Args: diffs: Array of diff tuples. Returns: HTML representation. """ |
html = []
for (op, data) in diffs:
text = (data.replace("&", "&").replace("<", "<")
.replace(">", ">").replace("\n", "¶<br>"))
if op == self.DIFF_INSERT:
html.append("<ins style=\"background:#e6ffe6;\">%s</ins>" % text)
elif op == self.DIFF_DELETE:
html.append("<del style=\"background:#ffe6e6;\">%s</del>" % text)
elif op == self.DIFF_EQUAL:
html.append("<span>%s</span>" % text)
return "".join(html) |
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def diff_levenshtein(self, diffs):
"""Compute the Levenshtein distance; the number of inserted, deleted or substituted characters. Args: diffs: Array of diff tuples. Returns: Number of changes. """ |
levenshtein = 0
insertions = 0
deletions = 0
for (op, data) in diffs:
if op == self.DIFF_INSERT:
insertions += len(data)
elif op == self.DIFF_DELETE:
deletions += len(data)
elif op == self.DIFF_EQUAL:
# A deletion and an insertion is one substitution.
levenshtein += max(insertions, deletions)
insertions = 0
deletions = 0
levenshtein += max(insertions, deletions)
return levenshtein |
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def diff_fromDelta(self, text1, delta):
"""Given the original text1, and an encoded string which describes the operations required to transform text1 into text2, compute the full diff. Args: text1: Source string for the diff. delta: Delta text. Returns: Array of diff tuples. Raises: ValueError: If invalid input. """ |
diffs = []
pointer = 0 # Cursor in text1
tokens = delta.split("\t")
for token in tokens:
if token == "":
# Blank tokens are ok (from a trailing \t).
continue
# Each token begins with a one character parameter which specifies the
# operation of this token (delete, insert, equality).
param = token[1:]
if token[0] == "+":
param = urllib.parse.unquote(param)
diffs.append((self.DIFF_INSERT, param))
elif token[0] == "-" or token[0] == "=":
try:
n = int(param)
except ValueError:
raise ValueError("Invalid number in diff_fromDelta: " + param)
if n < 0:
raise ValueError("Negative number in diff_fromDelta: " + param)
text = text1[pointer : pointer + n]
pointer += n
if token[0] == "=":
diffs.append((self.DIFF_EQUAL, text))
else:
diffs.append((self.DIFF_DELETE, text))
else:
# Anything else is an error.
raise ValueError("Invalid diff operation in diff_fromDelta: " +
token[0])
if pointer != len(text1):
raise ValueError(
"Delta length (%d) does not equal source text length (%d)." %
(pointer, len(text1)))
return diffs |
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def match_main(self, text, pattern, loc):
"""Locate the best instance of 'pattern' in 'text' near 'loc'. Args: text: The text to search. pattern: The pattern to search for. loc: The location to search around. Returns: Best match index or -1. """ |
# Check for null inputs.
if text == None or pattern == None:
raise ValueError("Null inputs. (match_main)")
loc = max(0, min(loc, len(text)))
if text == pattern:
# Shortcut (potentially not guaranteed by the algorithm)
return 0
elif not text:
# Nothing to match.
return -1
elif text[loc:loc + len(pattern)] == pattern:
# Perfect match at the perfect spot! (Includes case of null pattern)
return loc
else:
# Do a fuzzy compare.
match = self.match_bitap(text, pattern, loc)
return match |
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def match_bitap(self, text, pattern, loc):
"""Locate the best instance of 'pattern' in 'text' near 'loc' using the Bitap algorithm. Args: text: The text to search. pattern: The pattern to search for. loc: The location to search around. Returns: Best match index or -1. """ |
# Python doesn't have a maxint limit, so ignore this check.
#if self.Match_MaxBits != 0 and len(pattern) > self.Match_MaxBits:
# raise ValueError("Pattern too long for this application.")
# Initialise the alphabet.
s = self.match_alphabet(pattern)
def match_bitapScore(e, x):
"""Compute and return the score for a match with e errors and x location.
Accesses loc and pattern through being a closure.
Args:
e: Number of errors in match.
x: Location of match.
Returns:
Overall score for match (0.0 = good, 1.0 = bad).
"""
accuracy = float(e) / len(pattern)
proximity = abs(loc - x)
if not self.Match_Distance:
# Dodge divide by zero error.
return proximity and 1.0 or accuracy
return accuracy + (proximity / float(self.Match_Distance))
# Highest score beyond which we give up.
score_threshold = self.Match_Threshold
# Is there a nearby exact match? (speedup)
best_loc = text.find(pattern, loc)
if best_loc != -1:
score_threshold = min(match_bitapScore(0, best_loc), score_threshold)
# What about in the other direction? (speedup)
best_loc = text.rfind(pattern, loc + len(pattern))
if best_loc != -1:
score_threshold = min(match_bitapScore(0, best_loc), score_threshold)
# Initialise the bit arrays.
matchmask = 1 << (len(pattern) - 1)
best_loc = -1
bin_max = len(pattern) + len(text)
# Empty initialization added to appease pychecker.
last_rd = None
for d in range(len(pattern)):
# Scan for the best match each iteration allows for one more error.
# Run a binary search to determine how far from 'loc' we can stray at
# this error level.
bin_min = 0
bin_mid = bin_max
while bin_min < bin_mid:
if match_bitapScore(d, loc + bin_mid) <= score_threshold:
bin_min = bin_mid
else:
bin_max = bin_mid
bin_mid = (bin_max - bin_min) // 2 + bin_min
# Use the result from this iteration as the maximum for the next.
bin_max = bin_mid
start = max(1, loc - bin_mid + 1)
finish = min(loc + bin_mid, len(text)) + len(pattern)
rd = [0] * (finish + 2)
rd[finish + 1] = (1 << d) - 1
for j in range(finish, start - 1, -1):
if len(text) <= j - 1:
# Out of range.
charMatch = 0
else:
charMatch = s.get(text[j - 1], 0)
if d == 0: # First pass: exact match.
rd[j] = ((rd[j + 1] << 1) | 1) & charMatch
else: # Subsequent passes: fuzzy match.
rd[j] = (((rd[j + 1] << 1) | 1) & charMatch) | (
((last_rd[j + 1] | last_rd[j]) << 1) | 1) | last_rd[j + 1]
if rd[j] & matchmask:
score = match_bitapScore(d, j - 1)
# This match will almost certainly be better than any existing match.
# But check anyway.
if score <= score_threshold:
# Told you so.
score_threshold = score
best_loc = j - 1
if best_loc > loc:
# When passing loc, don't exceed our current distance from loc.
start = max(1, 2 * loc - best_loc)
else:
# Already passed loc, downhill from here on in.
break
# No hope for a (better) match at greater error levels.
if match_bitapScore(d + 1, loc) > score_threshold:
break
last_rd = rd
return best_loc |
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def patch_addContext(self, patch, text):
"""Increase the context until it is unique, but don't let the pattern expand beyond Match_MaxBits. Args: patch: The patch to grow. text: Source text. """ |
if len(text) == 0:
return
pattern = text[patch.start2 : patch.start2 + patch.length1]
padding = 0
# Look for the first and last matches of pattern in text. If two different
# matches are found, increase the pattern length.
while (text.find(pattern) != text.rfind(pattern) and (self.Match_MaxBits ==
0 or len(pattern) < self.Match_MaxBits - self.Patch_Margin -
self.Patch_Margin)):
padding += self.Patch_Margin
pattern = text[max(0, patch.start2 - padding) :
patch.start2 + patch.length1 + padding]
# Add one chunk for good luck.
padding += self.Patch_Margin
# Add the prefix.
prefix = text[max(0, patch.start2 - padding) : patch.start2]
if prefix:
patch.diffs[:0] = [(self.DIFF_EQUAL, prefix)]
# Add the suffix.
suffix = text[patch.start2 + patch.length1 :
patch.start2 + patch.length1 + padding]
if suffix:
patch.diffs.append((self.DIFF_EQUAL, suffix))
# Roll back the start points.
patch.start1 -= len(prefix)
patch.start2 -= len(prefix)
# Extend lengths.
patch.length1 += len(prefix) + len(suffix)
patch.length2 += len(prefix) + len(suffix) |
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def patch_deepCopy(self, patches):
"""Given an array of patches, return another array that is identical. Args: patches: Array of Patch objects. Returns: Array of Patch objects. """ |
patchesCopy = []
for patch in patches:
patchCopy = patch_obj()
# No need to deep copy the tuples since they are immutable.
patchCopy.diffs = patch.diffs[:]
patchCopy.start1 = patch.start1
patchCopy.start2 = patch.start2
patchCopy.length1 = patch.length1
patchCopy.length2 = patch.length2
patchesCopy.append(patchCopy)
return patchesCopy |
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def patch_addPadding(self, patches):
"""Add some padding on text start and end so that edges can match something. Intended to be called only from within patch_apply. Args: patches: Array of Patch objects. Returns: The padding string added to each side. """ |
paddingLength = self.Patch_Margin
nullPadding = ""
for x in range(1, paddingLength + 1):
nullPadding += chr(x)
# Bump all the patches forward.
for patch in patches:
patch.start1 += paddingLength
patch.start2 += paddingLength
# Add some padding on start of first diff.
patch = patches[0]
diffs = patch.diffs
if not diffs or diffs[0][0] != self.DIFF_EQUAL:
# Add nullPadding equality.
diffs.insert(0, (self.DIFF_EQUAL, nullPadding))
patch.start1 -= paddingLength # Should be 0.
patch.start2 -= paddingLength # Should be 0.
patch.length1 += paddingLength
patch.length2 += paddingLength
elif paddingLength > len(diffs[0][1]):
# Grow first equality.
extraLength = paddingLength - len(diffs[0][1])
newText = nullPadding[len(diffs[0][1]):] + diffs[0][1]
diffs[0] = (diffs[0][0], newText)
patch.start1 -= extraLength
patch.start2 -= extraLength
patch.length1 += extraLength
patch.length2 += extraLength
# Add some padding on end of last diff.
patch = patches[-1]
diffs = patch.diffs
if not diffs or diffs[-1][0] != self.DIFF_EQUAL:
# Add nullPadding equality.
diffs.append((self.DIFF_EQUAL, nullPadding))
patch.length1 += paddingLength
patch.length2 += paddingLength
elif paddingLength > len(diffs[-1][1]):
# Grow last equality.
extraLength = paddingLength - len(diffs[-1][1])
newText = diffs[-1][1] + nullPadding[:extraLength]
diffs[-1] = (diffs[-1][0], newText)
patch.length1 += extraLength
patch.length2 += extraLength
return nullPadding |
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def patch_toText(self, patches):
"""Take a list of patches and return a textual representation. Args: patches: Array of Patch objects. Returns: Text representation of patches. """ |
text = []
for patch in patches:
text.append(str(patch))
return "".join(text) |
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def diff_toDelta(self, diffs):
"""Crush the diff into an encoded string which describes the operations required to transform text1 into text2. E.g. =3\t-2\t+ing -> Keep 3 chars, delete 2 chars, insert 'ing'. Operations are tab-separated. Inserted text is escaped using %xx notation. Args: diffs: Array of diff tuples. Returns: Delta text. """ |
text = []
for (op, data) in diffs:
if op == self.DIFF_INSERT:
# High ascii will raise UnicodeDecodeError. Use Unicode instead.
data = data.encode("utf-8")
text.append("+" + urllib.quote(data, "!~*'();/?:@&=+$,# "))
elif op == self.DIFF_DELETE:
text.append("-%d" % len(data))
elif op == self.DIFF_EQUAL:
text.append("=%d" % len(data))
return "\t".join(text) |
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def patch_fromText(self, textline):
"""Parse a textual representation of patches and return a list of patch objects. Args: textline: Text representation of patches. Returns: Array of Patch objects. Raises: ValueError: If invalid input. """ |
if type(textline) == unicode:
# Patches should be composed of a subset of ascii chars, Unicode not
# required. If this encode raises UnicodeEncodeError, patch is invalid.
textline = textline.encode("ascii")
patches = []
if not textline:
return patches
text = textline.split('\n')
while len(text) != 0:
m = re.match("^@@ -(\d+),?(\d*) \+(\d+),?(\d*) @@$", text[0])
if not m:
raise ValueError("Invalid patch string: " + text[0])
patch = patch_obj()
patches.append(patch)
patch.start1 = int(m.group(1))
if m.group(2) == '':
patch.start1 -= 1
patch.length1 = 1
elif m.group(2) == '0':
patch.length1 = 0
else:
patch.start1 -= 1
patch.length1 = int(m.group(2))
patch.start2 = int(m.group(3))
if m.group(4) == '':
patch.start2 -= 1
patch.length2 = 1
elif m.group(4) == '0':
patch.length2 = 0
else:
patch.start2 -= 1
patch.length2 = int(m.group(4))
del text[0]
while len(text) != 0:
if text[0]:
sign = text[0][0]
else:
sign = ''
line = urllib.unquote(text[0][1:])
line = line.decode("utf-8")
if sign == '+':
# Insertion.
patch.diffs.append((self.DIFF_INSERT, line))
elif sign == '-':
# Deletion.
patch.diffs.append((self.DIFF_DELETE, line))
elif sign == ' ':
# Minor equality.
patch.diffs.append((self.DIFF_EQUAL, line))
elif sign == '@':
# Start of next patch.
break
elif sign == '':
# Blank line? Whatever.
pass
else:
# WTF?
raise ValueError("Invalid patch mode: '%s'\n%s" % (sign, line))
del text[0]
return patches |
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def diff_trees(left, right, diff_options=None, formatter=None):
"""Takes two lxml root elements or element trees""" |
if formatter is not None:
formatter.prepare(left, right)
if diff_options is None:
diff_options = {}
differ = diff.Differ(**diff_options)
diffs = differ.diff(left, right)
if formatter is None:
return list(diffs)
return formatter.format(diffs, left) |
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def diff_texts(left, right, diff_options=None, formatter=None):
"""Takes two Unicode strings containing XML""" |
return _diff(etree.fromstring, left, right,
diff_options=diff_options, formatter=formatter) |
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def diff_files(left, right, diff_options=None, formatter=None):
"""Takes two filenames or streams, and diffs the XML in those files""" |
return _diff(etree.parse, left, right,
diff_options=diff_options, formatter=formatter) |
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def patch_tree(actions, tree):
"""Takes an lxml root element or element tree, and a list of actions""" |
patcher = patch.Patcher()
return patcher.patch(actions, tree) |
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def patch_text(actions, tree):
"""Takes a string with XML and a string with actions""" |
tree = etree.fromstring(tree)
actions = patch.DiffParser().parse(actions)
tree = patch_tree(actions, tree)
return etree.tounicode(tree) |
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def patch_file(actions, tree):
"""Takes two filenames or streams, one with XML the other a diff""" |
tree = etree.parse(tree)
if isinstance(actions, six.string_types):
# It's a string, so it's a filename
with open(actions) as f:
actions = f.read()
else:
# We assume it's a stream
actions = actions.read()
actions = patch.DiffParser().parse(actions)
tree = patch_tree(actions, tree)
return etree.tounicode(tree) |
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| def url_to_text(self, url):
'''
Download PDF file and transform its document to string.
Args:
url: PDF url.
Returns:
string.
'''
path, headers = urllib.request.urlretrieve(url)
return self.path_to_text(path) |
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| def path_to_text(self, path):
'''
Transform local PDF file to string.
Args:
path: path to PDF file.
Returns:
string.
'''
rsrcmgr = PDFResourceManager()
retstr = StringIO()
codec = 'utf-8'
laparams = LAParams()
device = TextConverter(rsrcmgr, retstr, codec=codec, laparams=laparams)
fp = open(path, 'rb')
interpreter = PDFPageInterpreter(rsrcmgr, device)
password = ""
maxpages = 0
caching = True
pagenos = set()
pages_data = PDFPage.get_pages(
fp,
pagenos,
maxpages=maxpages,
password=password,
caching=caching,
check_extractable=True
)
for page in pages_data:
interpreter.process_page(page)
text = retstr.getvalue()
text = text.replace("\n", "")
fp.close()
device.close()
retstr.close()
return text |
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| def listup_sentence(self, data, counter=0):
'''
Divide string into sentence list.
Args:
data: string.
counter: recursive counter.
Returns:
List of sentences.
'''
delimiter = self.delimiter_list[counter]
sentence_list = []
[sentence_list.append(sentence + delimiter) for sentence in data.split(delimiter) if sentence != ""]
if counter + 1 < len(self.delimiter_list):
sentence_list_r = []
[sentence_list_r.extend(self.listup_sentence(sentence, counter+1)) for sentence in sentence_list]
sentence_list = sentence_list_r
return sentence_list |
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| def observe(self, success, failure):
'''
Observation data.
Args:
success: The number of success.
failure: The number of failure.
'''
if isinstance(success, int) is False:
if isinstance(success, float) is False:
raise TypeError()
if isinstance(failure, int) is False:
if isinstance(failure, float) is False:
raise TypeError()
if success <= 0:
raise ValueError()
if failure <= 0:
raise ValueError()
self.__success += success
self.__failure += failure |
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| def likelihood(self):
'''
Compute likelihood.
Returns:
likelihood.
'''
try:
likelihood = self.__success / (self.__success + self.__failure)
except ZeroDivisionError:
likelihood = 0.0
return likelihood |
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| def expected_value(self):
'''
Compute expected value.
Returns:
Expected value.
'''
alpha = self.__success + self.__default_alpha
beta = self.__failure + self.__default_beta
try:
expected_value = alpha / (alpha + beta)
except ZeroDivisionError:
expected_value = 0.0
return expected_value |
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| def variance(self):
'''
Compute variance.
Returns:
variance.
'''
alpha = self.__success + self.__default_alpha
beta = self.__failure + self.__default_beta
try:
variance = alpha * beta / ((alpha + beta) ** 2) * (alpha + beta + 1)
except ZeroDivisionError:
variance = 0.0
return variance |
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| def __move(self, current_pos):
'''
Move in the feature map.
Args:
current_pos: The now position.
Returns:
The next position.
'''
if self.__move_range is not None:
next_pos = np.random.randint(current_pos - self.__move_range, current_pos + self.__move_range)
if next_pos < 0:
next_pos = 0
elif next_pos >= self.var_arr.shape[0] - 1:
next_pos = self.var_arr.shape[0] - 1
return next_pos
else:
next_pos = np.random.randint(self.var_arr.shape[0] - 1)
return next_pos |
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| def summarize(self, document, Abstractor, similarity_filter=None):
'''
Execute summarization.
Args:
document: The target document.
Abstractor: The object of AbstractableDoc.
similarity_filter The object of SimilarityFilter.
Returns:
dict data.
- "summarize_result": The list of summarized sentences.,
- "scoring_data": The list of scores.
'''
if isinstance(document, str) is False:
raise TypeError("The type of document must be str.")
if isinstance(Abstractor, AbstractableDoc) is False:
raise TypeError("The type of Abstractor must be AbstractableDoc.")
if isinstance(similarity_filter, SimilarityFilter) is False and similarity_filter is not None:
raise TypeError("The type of similarity_filter must be SimilarityFilter.")
normalized_sentences = self.listup_sentence(document)
# for filtering similar sentences.
if similarity_filter is not None:
normalized_sentences = similarity_filter.similar_filter_r(normalized_sentences)
self.tokenize(document)
words = self.token
fdist = nltk.FreqDist(words)
top_n_words = [w[0] for w in fdist.items()][:self.target_n]
scored_list = self.__closely_associated_score(normalized_sentences, top_n_words)
filtered_list = Abstractor.filter(scored_list)
result_list = [normalized_sentences[idx] for (idx, score) in filtered_list]
result_dict = {
"summarize_result": result_list,
"scoring_data": filtered_list
}
return result_dict |
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| def __closely_associated_score(self, normalized_sentences, top_n_words):
'''
Scoring the sentence with closely associations.
Args:
normalized_sentences: The list of sentences.
top_n_words: Important sentences.
Returns:
The list of scores.
'''
scores_list = []
sentence_idx = -1
for sentence in normalized_sentences:
self.tokenize(sentence)
sentence = self.token
sentence_idx += 1
word_idx = []
for w in top_n_words:
try:
word_idx.append(sentence.index(w))
except ValueError:
pass
word_idx.sort()
if len(word_idx) == 0:
continue
clusters = []
cluster = [word_idx[0]]
i = 1
while i < len(word_idx):
if word_idx[i] - word_idx[i - 1] < self.cluster_threshold:
cluster.append(word_idx[i])
else:
clusters.append(cluster[:])
cluster = [word_idx[i]]
i += 1
clusters.append(cluster)
max_cluster_score = 0
for c in clusters:
significant_words_in_cluster = len(c)
total_words_in_cluster = c[-1] - c[0] + 1
score = 1.0 * significant_words_in_cluster \
* significant_words_in_cluster / total_words_in_cluster
if score > max_cluster_score:
max_cluster_score = score
scores_list.append((sentence_idx, score))
return scores_list |
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Description:
| def learn(self, initial_state_key, limit=1000, game_n=1):
'''
Multi-Agent Learning.
Override.
Args:
initial_state_key: Initial state.
limit: Limit of the number of learning.
game_n: The number of games.
'''
end_flag = False
state_key_list = [None] * len(self.q_learning_list)
action_key_list = [None] * len(self.q_learning_list)
next_action_key_list = [None] * len(self.q_learning_list)
for game in range(game_n):
state_key = initial_state_key
self.t = 1
while self.t <= limit:
for i in range(len(self.q_learning_list)):
state_key_list[i] = state_key
if game + 1 == game_n:
self.state_key_list.append(tuple(i, state_key_list))
self.q_learning_list[i].t = self.t
next_action_list = self.q_learning_list[i].extract_possible_actions(tuple(i, state_key_list))
if len(next_action_list):
action_key = self.q_learning_list[i].select_action(
state_key=tuple(i, state_key_list),
next_action_list=next_action_list
)
action_key_list[i] = action_key
reward_value = self.q_learning_list[i].observe_reward_value(
tuple(i, state_key_list),
tuple(i, action_key_list)
)
# Check.
if self.q_learning_list[i].check_the_end_flag(tuple(i, state_key_list)) is True:
end_flag = True
# Max-Q-Value in next action time.
next_next_action_list = self.q_learning_list[i].extract_possible_actions(
tuple(i, action_key_list)
)
if len(next_next_action_list):
next_action_key = self.q_learning_list[i].predict_next_action(
tuple(i, action_key_list),
next_next_action_list
)
next_action_key_list[i] = next_action_key
next_max_q = self.q_learning_list[i].extract_q_df(
tuple(i, action_key_list),
next_action_key
)
# Update Q-Value.
self.q_learning_list[i].update_q(
state_key=tuple(i, state_key_list),
action_key=tuple(i, action_key_list),
reward_value=reward_value,
next_max_q=next_max_q
)
# Update State.
state_key = self.q_learning_list[i].update_state(
state_key=tuple(i, state_key_list),
action_key=tuple(i, action_key_list)
)
state_key_list[i] = state_key
# Epsode.
self.t += 1
self.q_learning_list[i].t = self.t
if end_flag is True:
break |
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Description:
| def get_model(self):
'''
`object` of model as a function approximator,
which has `cnn` whose type is
`pydbm.cnn.pydbm.cnn.convolutional_neural_network.ConvolutionalNeuralNetwork`.
'''
class Model(object):
def __init__(self, cnn):
self.cnn = cnn
return Model(self.__cnn) |
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| def update_state(self, state_arr, action_arr):
'''
Update state.
Override.
Args:
state_arr: `np.ndarray` of state in `self.t`.
action_arr: `np.ndarray` of action in `self.t`.
Returns:
`np.ndarray` of state in `self.t+1`.
'''
x, y = np.where(action_arr[-1] == 1)
self.__agent_pos = (x[0], y[0])
self.__route_memory_list.append((x[0], y[0]))
self.__route_long_memory_list.append((x[0], y[0]))
self.__route_long_memory_list = list(set(self.__route_long_memory_list))
while len(self.__route_memory_list) > self.__memory_num:
self.__route_memory_list = self.__route_memory_list[1:]
return self.extract_now_state() |
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| def initialize(self, map_arr, start_point_label="S", end_point_label="G", wall_label="#", agent_label="@"):
'''
Initialize map of maze and setup reward value.
Args:
map_arr: Map. the 2d- `np.ndarray`.
start_point_label: Label of start point.
end_point_label: Label of end point.
wall_label: Label of wall.
agent_label: Label of agent.
'''
np.set_printoptions(threshold=np.inf)
self.__agent_label = agent_label
self.__map_arr = map_arr
self.__start_point_label = start_point_label
start_arr_tuple = np.where(self.__map_arr == self.__start_point_label)
x_arr, y_arr = start_arr_tuple
self.__start_point_tuple = (x_arr[0], y_arr[0])
end_arr_tuple = np.where(self.__map_arr == self.__end_point_label)
x_arr, y_arr = end_arr_tuple
self.__end_point_tuple = (x_arr[0], y_arr[0])
self.__wall_label = wall_label
for x in range(self.__map_arr.shape[1]):
for y in range(self.__map_arr.shape[0]):
if (x, y) == self.__start_point_tuple or (x, y) == self.__end_point_tuple:
continue
arr_value = self.__map_arr[y][x]
if arr_value == self.__wall_label:
continue
self.save_r_df((x, y), float(arr_value)) |
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| def visualize_learning_result(self, state_key):
'''
Visualize learning result.
'''
x, y = state_key
map_arr = copy.deepcopy(self.__map_arr)
goal_point_tuple = np.where(map_arr == self.__end_point_label)
goal_x, goal_y = goal_point_tuple
map_arr[y][x] = "@"
self.__map_arr_list.append(map_arr)
if goal_x == x and goal_y == y:
for i in range(10):
key = len(self.__map_arr_list) - (10 - i)
print("Number of searches: " + str(key))
print(self.__map_arr_list[key])
print("Total number of searches: " + str(self.t))
print(self.__map_arr_list[-1])
print("Goal !!") |
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| def normalize_r_value(self):
'''
Normalize r-value.
Override.
This method is called in each learning steps.
For example:
self.r_df = self.r_df.r_value / self.r_df.r_value.sum()
'''
if self.r_df is not None and self.r_df.shape[0]:
# z-score normalization.
self.r_df.r_value = (self.r_df.r_value - self.r_df.r_value.mean()) / self.r_df.r_value.std() |
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| def get_alpha_value(self):
'''
getter
Learning rate.
'''
if isinstance(self.__alpha_value, float) is False:
raise TypeError("The type of __alpha_value must be float.")
return self.__alpha_value |
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| def set_alpha_value(self, value):
'''
setter
Learning rate.
'''
if isinstance(value, float) is False:
raise TypeError("The type of __alpha_value must be float.")
self.__alpha_value = value |
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| def get_gamma_value(self):
'''
getter
Gamma value.
'''
if isinstance(self.__gamma_value, float) is False:
raise TypeError("The type of __gamma_value must be float.")
return self.__gamma_value |
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| def set_gamma_value(self, value):
'''
setter
Gamma value.
'''
if isinstance(value, float) is False:
raise TypeError("The type of __gamma_value must be float.")
self.__gamma_value = value |
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| def filter(self, scored_list):
'''
Filtering with top-n ranking.
Args:
scored_list: The list of scoring.
Retruns:
The list of filtered result.
'''
top_n_key = -1 * self.top_n
top_n_list = sorted(scored_list, key=lambda x: x[1])[top_n_key:]
result_list = sorted(top_n_list, key=lambda x: x[0])
return result_list |
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| def tokenize(self, data):
'''
Tokenize sentence.
Args:
[n-gram, n-gram, n-gram, ...]
'''
super().tokenize(data)
token_tuple_zip = self.n_gram.generate_tuple_zip(self.token, self.n)
token_list = []
self.token = ["".join(list(token_tuple)) for token_tuple in token_tuple_zip] |
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| def extract_q_df(self, state_key, action_key):
'''
Extract Q-Value from `self.q_df`.
Args:
state_key: The key of state.
action_key: The key of action.
Returns:
Q-Value.
'''
q = 0.0
if self.q_df is None:
self.save_q_df(state_key, action_key, q)
return q
q_df = self.q_df[self.q_df.state_key == state_key]
q_df = q_df[q_df.action_key == action_key]
if q_df.shape[0]:
q = float(q_df["q_value"])
else:
self.save_q_df(state_key, action_key, q)
return q |
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| def save_q_df(self, state_key, action_key, q_value):
'''
Insert or update Q-Value in `self.q_df`.
Args:
state_key: State.
action_key: Action.
q_value: Q-Value.
Exceptions:
TypeError: If the type of `q_value` is not float.
'''
if isinstance(q_value, float) is False:
raise TypeError("The type of q_value must be float.")
new_q_df = pd.DataFrame([(state_key, action_key, q_value)], columns=["state_key", "action_key", "q_value"])
if self.q_df is not None:
self.q_df = pd.concat([new_q_df, self.q_df])
self.q_df = self.q_df.drop_duplicates(["state_key", "action_key"])
else:
self.q_df = new_q_df |
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| def get_t(self):
'''
getter
Time.
'''
if isinstance(self.__t, int) is False:
raise TypeError("The type of __t must be int.")
return self.__t |
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| def set_t(self, value):
'''
setter
Time.
'''
if isinstance(value, int) is False:
raise TypeError("The type of __t must be int.")
self.__t = value |
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| def update_q(self, state_key, action_key, reward_value, next_max_q):
'''
Update Q-Value.
Args:
state_key: The key of state.
action_key: The key of action.
reward_value: R-Value(Reward).
next_max_q: Maximum Q-Value.
'''
# Now Q-Value.
q = self.extract_q_df(state_key, action_key)
# Update Q-Value.
new_q = q + self.alpha_value * (reward_value + (self.gamma_value * next_max_q) - q)
# Save updated Q-Value.
self.save_q_df(state_key, action_key, new_q) |
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| def predict_next_action(self, state_key, next_action_list):
'''
Predict next action by Q-Learning.
Args:
state_key: The key of state in `self.t+1`.
next_action_list: The possible action in `self.t+1`.
Returns:
The key of action.
'''
if self.q_df is not None:
next_action_q_df = self.q_df[self.q_df.state_key == state_key]
next_action_q_df = next_action_q_df[next_action_q_df.action_key.isin(next_action_list)]
if next_action_q_df.shape[0] == 0:
return random.choice(next_action_list)
else:
if next_action_q_df.shape[0] == 1:
max_q_action = next_action_q_df["action_key"].values[0]
else:
next_action_q_df = next_action_q_df.sort_values(by=["q_value"], ascending=False)
max_q_action = next_action_q_df.iloc[0, :]["action_key"]
return max_q_action
else:
return random.choice(next_action_list) |
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| def pull(self, arm_id, success, failure):
'''
Pull arms.
Args:
arm_id: Arms master id.
success: The number of success.
failure: The number of failure.
'''
self.__beta_dist_dict[arm_id].observe(success, failure) |
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| def recommend(self, limit=10):
'''
Listup arms and expected value.
Args:
limit: Length of the list.
Returns:
[Tuple(`Arms master id`, `expected value`)]
'''
expected_list = [(arm_id, beta_dist.expected_value()) for arm_id, beta_dist in self.__beta_dist_dict.items()]
expected_list = sorted(expected_list, key=lambda x: x[1], reverse=True)
return expected_list[:limit] |
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| def get_time_rate(self):
'''
getter
Time rate.
'''
if isinstance(self.__time_rate, float) is False:
raise TypeError("The type of __time_rate must be float.")
if self.__time_rate <= 0.0:
raise ValueError("The value of __time_rate must be greater than 0.0")
return self.__time_rate |
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| def set_time_rate(self, value):
'''
setter
Time rate.
'''
if isinstance(value, float) is False:
raise TypeError("The type of __time_rate must be float.")
if value <= 0.0:
raise ValueError("The value of __time_rate must be greater than 0.0")
self.__time_rate = value |
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| def __calculate_sigmoid(self):
'''
Function of temperature.
Returns:
Sigmoid.
'''
sigmoid = 1 / np.log(self.t * self.time_rate + 1.1)
return sigmoid |
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| def __calculate_boltzmann_factor(self, state_key, next_action_list):
'''
Calculate boltzmann factor.
Args:
state_key: The key of state.
next_action_list: The possible action in `self.t+1`.
If the length of this list is 0, all action should be possible.
Returns:
[(`The key of action`, `boltzmann probability`)]
'''
sigmoid = self.__calculate_sigmoid()
q_df = self.q_df[self.q_df.state_key == state_key]
q_df = q_df[q_df.isin(next_action_list)]
q_df["boltzmann_factor"] = q_df["q_value"] / sigmoid
q_df["boltzmann_factor"] = q_df["boltzmann_factor"].apply(np.exp)
q_df["boltzmann_factor"] = q_df["boltzmann_factor"] / q_df["boltzmann_factor"].sum()
return q_df |
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| def get_model(self):
'''
`object` of model as a function approximator,
which has `lstm_model` whose type is `pydbm.rnn.lstm_model.LSTMModel`.
'''
class Model(object):
def __init__(self, lstm_model):
self.lstm_model = lstm_model
return Model(self.__lstm_model) |
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| def filter(self, scored_list):
'''
Filtering with std.
Args:
scored_list: The list of scoring.
Retruns:
The list of filtered result.
'''
if len(scored_list) > 0:
avg = np.mean([s[1] for s in scored_list])
std = np.std([s[1] for s in scored_list])
else:
avg = 0
std = 0
limiter = avg + 0.5 * std
mean_scored = [(sent_idx, score) for (sent_idx, score) in scored_list if score > limiter]
return mean_scored |
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def search(self, query, fields=None, page=1, max_records=None, flatten=True):
"""returns iterator over all records that match the given query""" |
if fields is None:
fields = []
page = int(page)
pages = float('inf')
data = {
"query": query,
"page": page,
"fields": fields,
"flatten": flatten
}
count = 0
while page <= pages:
payload = self._post(self.search_path, data=data)
pages = payload['metadata']['pages']
page += 1
data["page"] = page
for result in payload["results"]:
yield result
count += 1
if max_records and count >= max_records:
return |
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def adjustColors(self, mode='dark'):
""" Change a few colors depending on the mode to use. The default mode doesn't assume anything and avoid using white & black colors. The dark mode use white and avoid dark blue while the light mode use black and avoid yellow, to give a few examples. """ |
rp = Game.__color_modes.get(mode, {})
for k, color in self.__colors.items():
self.__colors[k] = rp.get(color, color) |
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def loadBestScore(self):
""" load local best score from the default file """ |
try:
with open(self.scores_file, 'r') as f:
self.best_score = int(f.readline(), 10)
except:
return False
return True |
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def saveBestScore(self):
""" save current best score in the default file """ |
if self.score > self.best_score:
self.best_score = self.score
try:
with open(self.scores_file, 'w') as f:
f.write(str(self.best_score))
except:
return False
return True |
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def incScore(self, pts):
""" update the current score by adding it the specified number of points """ |
self.score += pts
if self.score > self.best_score:
self.best_score = self.score |
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Description:
def store(self):
""" save the current game session's score and data for further use """ |
size = self.board.SIZE
cells = []
for i in range(size):
for j in range(size):
cells.append(str(self.board.getCell(j, i)))
score_str = "%s\n%d" % (' '.join(cells), self.score)
try:
with open(self.store_file, 'w') as f:
f.write(score_str)
except:
return False
return True |
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def restore(self):
""" restore the saved game score and data """ |
size = self.board.SIZE
try:
with open(self.store_file, 'r') as f:
lines = f.readlines()
score_str = lines[0]
self.score = int(lines[1])
except:
return False
score_str_list = score_str.split(' ')
count = 0
for i in range(size):
for j in range(size):
value = score_str_list[count]
self.board.setCell(j, i, int(value))
count += 1
return True |
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def loop(self):
""" main game loop. returns the final score. """ |
pause_key = self.board.PAUSE
margins = {'left': 4, 'top': 4, 'bottom': 4}
atexit.register(self.showCursor)
try:
self.hideCursor()
while True:
self.clearScreen()
print(self.__str__(margins=margins))
if self.board.won() or not self.board.canMove():
break
m = self.readMove()
if m == pause_key:
self.saveBestScore()
if self.store():
print("Game successfully saved. "
"Resume it with `term2048 --resume`.")
return self.score
print("An error ocurred while saving your game.")
return None
self.incScore(self.board.move(m))
except KeyboardInterrupt:
self.saveBestScore()
return None
self.saveBestScore()
print('You won!' if self.board.won() else 'Game Over')
return self.score |
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def getCellStr(self, x, y):
# TODO: refactor regarding issue #11 """ return a string representation of the cell located at x,y. """ |
c = self.board.getCell(x, y)
if c == 0:
return '.' if self.__azmode else ' .'
elif self.__azmode:
az = {}
for i in range(1, int(math.log(self.board.goal(), 2))):
az[2 ** i] = chr(i + 96)
if c not in az:
return '?'
s = az[c]
elif c == 1024:
s = ' 1k'
elif c == 2048:
s = ' 2k'
else:
s = '%3d' % c
return self.__colors.get(c, Fore.RESET) + s + Style.RESET_ALL |
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def boardToString(self, margins=None):
""" return a string representation of the current board. """ |
if margins is None:
margins = {}
b = self.board
rg = range(b.size())
left = ' '*margins.get('left', 0)
s = '\n'.join(
[left + ' '.join([self.getCellStr(x, y) for x in rg]) for y in rg])
return s |
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def canMove(self):
""" test if a move is possible """ |
if not self.filled():
return True
for y in self.__size_range:
for x in self.__size_range:
c = self.getCell(x, y)
if (x < self.__size-1 and c == self.getCell(x+1, y)) \
or (y < self.__size-1 and c == self.getCell(x, y+1)):
return True
return False |
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def setCell(self, x, y, v):
"""set the cell value at x,y""" |
self.cells[y][x] = v |
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def getCol(self, x):
"""return the x-th column, starting at 0""" |
return [self.getCell(x, i) for i in self.__size_range] |
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def setCol(self, x, l):
"""set the x-th column, starting at 0""" |
for i in xrange(0, self.__size):
self.setCell(x, i, l[i]) |
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def __collapseLineOrCol(self, line, d):
""" Merge tiles in a line or column according to a direction and return a tuple with the new line and the score for the move on this line """ |
if (d == Board.LEFT or d == Board.UP):
inc = 1
rg = xrange(0, self.__size-1, inc)
else:
inc = -1
rg = xrange(self.__size-1, 0, inc)
pts = 0
for i in rg:
if line[i] == 0:
continue
if line[i] == line[i+inc]:
v = line[i]*2
if v == self.__goal:
self.__won = True
line[i] = v
line[i+inc] = 0
pts += v
return (line, pts) |
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def move(self, d, add_tile=True):
""" move and return the move score """ |
if d == Board.LEFT or d == Board.RIGHT:
chg, get = self.setLine, self.getLine
elif d == Board.UP or d == Board.DOWN:
chg, get = self.setCol, self.getCol
else:
return 0
moved = False
score = 0
for i in self.__size_range:
# save the original line/col
origin = get(i)
# move it
line = self.__moveLineOrCol(origin, d)
# merge adjacent tiles
collapsed, pts = self.__collapseLineOrCol(line, d)
# move it again (for when tiles are merged, because empty cells are
# inserted in the middle of the line/col)
new = self.__moveLineOrCol(collapsed, d)
# set it back in the board
chg(i, new)
# did it change?
if origin != new:
moved = True
score += pts
# don't add a new tile if nothing changed
if moved and add_tile:
self.addTile()
return score |
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def parse_cli_args():
"""parse args from the CLI and return a dict""" |
parser = argparse.ArgumentParser(description='2048 in your terminal')
parser.add_argument('--mode', dest='mode', type=str,
default=None, help='colors mode (dark or light)')
parser.add_argument('--az', dest='azmode', action='store_true',
help='Use the letters a-z instead of numbers')
parser.add_argument('--resume', dest='resume', action='store_true',
help='restart the game from where you left')
parser.add_argument('-v', '--version', action='store_true')
parser.add_argument('-r', '--rules', action='store_true')
return vars(parser.parse_args()) |
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def start_game(debug=False):
""" Start a new game. If ``debug`` is set to ``True``, the game object is returned and the game loop isn't fired. """ |
args = parse_cli_args()
if args['version']:
print_version_and_exit()
if args['rules']:
print_rules_and_exit()
game = Game(**args)
if args['resume']:
game.restore()
if debug:
return game
return game.loop() |
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def on_message(self, message):
"""Handshake with livereload.js 1. client send 'hello' 2. server reply 'hello' 3. client send 'info' """ |
message = ObjectDict(escape.json_decode(message))
if message.command == 'hello':
handshake = {
'command': 'hello',
'protocols': [
'http://livereload.com/protocols/official-7',
],
'serverName': 'livereload-tornado',
}
self.send_message(handshake)
if message.command == 'info' and 'url' in message:
logger.info('Browser Connected: %s' % message.url)
LiveReloadHandler.waiters.add(self) |
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def get_content_modified_time(cls, abspath):
"""Returns the time that ``abspath`` was last modified. May be overridden in subclasses. Should return a `~datetime.datetime` object or None. """ |
stat_result = os.stat(abspath)
modified = datetime.datetime.utcfromtimestamp(
stat_result[stat.ST_MTIME])
return modified |
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def ignore(self, filename):
"""Ignore a given filename or not.""" |
_, ext = os.path.splitext(filename)
return ext in ['.pyc', '.pyo', '.o', '.swp'] |
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def watch(self, path, func=None, delay=0, ignore=None):
"""Add a task to watcher. :param path: a filepath or directory path or glob pattern :param func: the function to be executed when file changed :param delay: Delay sending the reload message. Use 'forever' to not send it. This is useful to compile sass files to css, but reload on changed css files then only. :param ignore: A function return True to ignore a certain pattern of filepath. """ |
self._tasks[path] = {
'func': func,
'delay': delay,
'ignore': ignore,
} |
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def already_coords(self, address):
"""test used to see if we have coordinates or address""" |
m = re.search(self.COORD_MATCH, address)
return (m != None) |
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def coords_string_parser(self, coords):
"""Pareses the address string into coordinates to match address_to_coords return object""" |
lat, lon = coords.split(',')
return {"lat": lat.strip(), "lon": lon.strip(), "bounds": {}} |
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def address_to_coords(self, address):
"""Convert address to coordinates""" |
base_coords = self.BASE_COORDS[self.region]
get_cord = self.COORD_SERVERS[self.region]
url_options = {
"q": address,
"lang": "eng",
"origin": "livemap",
"lat": base_coords["lat"],
"lon": base_coords["lon"]
}
response = requests.get(self.WAZE_URL + get_cord, params=url_options, headers=self.HEADERS)
for response_json in response.json():
if response_json.get('city'):
lat = response_json['location']['lat']
lon = response_json['location']['lon']
bounds = response_json['bounds'] # sometimes the coords don't match up
if bounds is not None:
bounds['top'], bounds['bottom'] = max(bounds['top'], bounds['bottom']), min(bounds['top'], bounds['bottom'])
bounds['left'], bounds['right'] = min(bounds['left'], bounds['right']), max(bounds['left'], bounds['right'])
else:
bounds = {}
return {"lat": lat, "lon": lon, "bounds": bounds}
raise WRCError("Cannot get coords for %s" % address) |
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def get_route(self, npaths=1, time_delta=0):
"""Get route data from waze""" |
routing_server = self.ROUTING_SERVERS[self.region]
url_options = {
"from": "x:%s y:%s" % (self.start_coords["lon"], self.start_coords["lat"]),
"to": "x:%s y:%s" % (self.end_coords["lon"], self.end_coords["lat"]),
"at": time_delta,
"returnJSON": "true",
"returnGeometries": "true",
"returnInstructions": "true",
"timeout": 60000,
"nPaths": npaths,
"options": "AVOID_TRAILS:t",
}
if self.vehicle_type:
url_options["vehicleType"] = self.vehicle_type
response = requests.get(self.WAZE_URL + routing_server, params=url_options, headers=self.HEADERS)
response.encoding = 'utf-8'
response_json = self._check_response(response)
if response_json:
if 'error' in response_json:
raise WRCError(response_json.get("error"))
else:
if response_json.get("alternatives"):
return [alt['response'] for alt in response_json['alternatives']]
if npaths > 1:
return [response_json['response']]
return response_json['response']
else:
raise WRCError("empty response") |
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def _add_up_route(self, results, real_time=True, stop_at_bounds=False):
"""Calculate route time and distance.""" |
start_bounds = self.start_coords['bounds']
end_bounds = self.end_coords['bounds']
def between(target, min, max):
return target > min and target < max
time = 0
distance = 0
for segment in results:
if stop_at_bounds and segment.get('path'):
x = segment['path']['x']
y = segment['path']['y']
if (
between(x, start_bounds.get('left', 0), start_bounds.get('right', 0)) or
between(x, end_bounds.get('left', 0), end_bounds.get('right', 0))
) and (
between(y, start_bounds.get('bottom', 0), start_bounds.get('top', 0)) or
between(y, end_bounds.get('bottom', 0), end_bounds.get('top', 0))
):
continue
time += segment['crossTime' if real_time else 'crossTimeWithoutRealTime']
distance += segment['length']
route_time = time / 60.0
route_distance = distance / 1000.0
return route_time, route_distance |
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def calc_route_info(self, real_time=True, stop_at_bounds=False, time_delta=0):
"""Calculate best route info.""" |
route = self.get_route(1, time_delta)
results = route['results']
route_time, route_distance = self._add_up_route(results, real_time=real_time, stop_at_bounds=stop_at_bounds)
self.log.info('Time %.2f minutes, distance %.2f km.', route_time, route_distance)
return route_time, route_distance |
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def calc_all_routes_info(self, npaths=3, real_time=True, stop_at_bounds=False, time_delta=0):
"""Calculate all route infos.""" |
routes = self.get_route(npaths, time_delta)
results = {route['routeName']: self._add_up_route(route['results'], real_time=real_time, stop_at_bounds=stop_at_bounds) for route in routes}
route_time = [route[0] for route in results.values()]
route_distance = [route[1] for route in results.values()]
self.log.info('Time %.2f - %.2f minutes, distance %.2f - %.2f km.', min(route_time), max(route_time), min(route_distance), max(route_distance))
return results |
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def _initialize(self):
"""Read the SharQ configuration and set appropriate variables. Open a redis connection pool and load all the Lua scripts. """ |
self._key_prefix = self._config.get('redis', 'key_prefix')
self._job_expire_interval = int(
self._config.get('sharq', 'job_expire_interval')
)
self._default_job_requeue_limit = int(
self._config.get('sharq', 'default_job_requeue_limit')
)
# initalize redis
redis_connection_type = self._config.get('redis', 'conn_type')
db = self._config.get('redis', 'db')
if redis_connection_type == 'unix_sock':
self._r = redis.StrictRedis(
db=db,
unix_socket_path=self._config.get('redis', 'unix_socket_path')
)
elif redis_connection_type == 'tcp_sock':
self._r = redis.StrictRedis(
db=db,
host=self._config.get('redis', 'host'),
port=self._config.get('redis', 'port')
)
self._load_lua_scripts() |
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def _load_config(self):
"""Read the configuration file and load it into memory.""" |
self._config = ConfigParser.SafeConfigParser()
self._config.read(self.config_path) |
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def _load_lua_scripts(self):
"""Loads all lua scripts required by SharQ.""" |
# load lua scripts
lua_script_path = os.path.join(
os.path.dirname(os.path.abspath(__file__)),
'scripts/lua'
)
with open(os.path.join(
lua_script_path,
'enqueue.lua'), 'r') as enqueue_file:
self._lua_enqueue_script = enqueue_file.read()
self._lua_enqueue = self._r.register_script(
self._lua_enqueue_script)
with open(os.path.join(
lua_script_path,
'dequeue.lua'), 'r') as dequeue_file:
self._lua_dequeue_script = dequeue_file.read()
self._lua_dequeue = self._r.register_script(
self._lua_dequeue_script)
with open(os.path.join(
lua_script_path,
'finish.lua'), 'r') as finish_file:
self._lua_finish_script = finish_file.read()
self._lua_finish = self._r.register_script(self._lua_finish_script)
with open(os.path.join(
lua_script_path,
'interval.lua'), 'r') as interval_file:
self._lua_interval_script = interval_file.read()
self._lua_interval = self._r.register_script(
self._lua_interval_script)
with open(os.path.join(
lua_script_path,
'requeue.lua'), 'r') as requeue_file:
self._lua_requeue_script = requeue_file.read()
self._lua_requeue = self._r.register_script(
self._lua_requeue_script)
with open(os.path.join(
lua_script_path,
'metrics.lua'), 'r') as metrics_file:
self._lua_metrics_script = metrics_file.read()
self._lua_metrics = self._r.register_script(
self._lua_metrics_script) |
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def enqueue(self, payload, interval, job_id, queue_id, queue_type='default', requeue_limit=None):
"""Enqueues the job into the specified queue_id of a particular queue_type """ |
# validate all the input
if not is_valid_interval(interval):
raise BadArgumentException('`interval` has an invalid value.')
if not is_valid_identifier(job_id):
raise BadArgumentException('`job_id` has an invalid value.')
if not is_valid_identifier(queue_id):
raise BadArgumentException('`queue_id` has an invalid value.')
if not is_valid_identifier(queue_type):
raise BadArgumentException('`queue_type` has an invalid value.')
if requeue_limit is None:
requeue_limit = self._default_job_requeue_limit
if not is_valid_requeue_limit(requeue_limit):
raise BadArgumentException('`requeue_limit` has an invalid value.')
try:
serialized_payload = serialize_payload(payload)
except TypeError as e:
raise BadArgumentException(e.message)
timestamp = str(generate_epoch())
keys = [
self._key_prefix,
queue_type
]
args = [
timestamp,
queue_id,
job_id,
'"%s"' % serialized_payload,
interval,
requeue_limit
]
self._lua_enqueue(keys=keys, args=args)
response = {
'status': 'queued'
}
return response |
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def dequeue(self, queue_type='default'):
"""Dequeues a job from any of the ready queues based on the queue_type. If no job is ready, returns a failure status. """ |
if not is_valid_identifier(queue_type):
raise BadArgumentException('`queue_type` has an invalid value.')
timestamp = str(generate_epoch())
keys = [
self._key_prefix,
queue_type
]
args = [
timestamp,
self._job_expire_interval
]
dequeue_response = self._lua_dequeue(keys=keys, args=args)
if len(dequeue_response) < 4:
response = {
'status': 'failure'
}
return response
queue_id, job_id, payload, requeues_remaining = dequeue_response
payload = deserialize_payload(payload[1:-1])
response = {
'status': 'success',
'queue_id': queue_id,
'job_id': job_id,
'payload': payload,
'requeues_remaining': int(requeues_remaining)
}
return response |
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def interval(self, interval, queue_id, queue_type='default'):
"""Updates the interval for a specific queue_id of a particular queue type. """ |
# validate all the input
if not is_valid_interval(interval):
raise BadArgumentException('`interval` has an invalid value.')
if not is_valid_identifier(queue_id):
raise BadArgumentException('`queue_id` has an invalid value.')
if not is_valid_identifier(queue_type):
raise BadArgumentException('`queue_type` has an invalid value.')
# generate the interval key
interval_hmap_key = '%s:interval' % self._key_prefix
interval_queue_key = '%s:%s' % (queue_type, queue_id)
keys = [
interval_hmap_key,
interval_queue_key
]
args = [
interval
]
interval_response = self._lua_interval(keys=keys, args=args)
if interval_response == 0:
# the queue with the id and type does not exist.
response = {
'status': 'failure'
}
else:
response = {
'status': 'success'
}
return response |
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def is_valid_identifier(identifier):
"""Checks if the given identifier is valid or not. A valid identifier may consists of the following characters with a maximum length of 100 characters, minimum of 1 character. Valid characters for an identifier, - A to Z - a to z - 0 to 9 - _ (underscore) - - (hypen) """ |
if not isinstance(identifier, basestring):
return False
if len(identifier) > 100 or len(identifier) < 1:
return False
condensed_form = set(list(identifier.lower()))
return condensed_form.issubset(VALID_IDENTIFIER_SET) |
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def is_valid_interval(interval):
"""Checks if the given interval is valid. A valid interval is always a positive, non-zero integer value. """ |
if not isinstance(interval, (int, long)):
return False
if interval <= 0:
return False
return True |
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def is_valid_requeue_limit(requeue_limit):
"""Checks if the given requeue limit is valid. A valid requeue limit is always greater than or equal to -1. """ |
if not isinstance(requeue_limit, (int, long)):
return False
if requeue_limit <= -2:
return False
return True |
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def get_search_names(name):
"""Return a list of values to search on when we are looking for a package with the given name. This is required to search on both pyramid_debugtoolbar and pyramid-debugtoolbar. """ |
parts = re.split('[-_.]', name)
if len(parts) == 1:
return parts
result = set()
for i in range(len(parts) - 1, 0, -1):
for s1 in '-_.':
prefix = s1.join(parts[:i])
for s2 in '-_.':
suffix = s2.join(parts[i:])
for s3 in '-_.':
result.add(s3.join([prefix, suffix]))
return list(result) |
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def alter_old_distutils_request(request: WSGIRequest):
"""Alter the request body for compatibility with older distutils clients Due to a bug in the Python distutils library, the request post is sent using \n as a separator instead of the \r\n that the HTTP spec demands. This breaks the Django form parser and therefore we have to write a custom parser. This bug was fixed in the Python 2.7.4 and 3.4: http://bugs.python.org/issue10510 """ |
# We first need to retrieve the body before accessing POST or FILES since
# it can only be read once.
body = request.body
if request.POST or request.FILES:
return
new_body = BytesIO()
# Split the response in the various parts based on the boundary string
content_type, opts = parse_header(request.META['CONTENT_TYPE'].encode('ascii'))
parts = body.split(b'\n--' + opts['boundary'] + b'\n')
for part in parts:
if b'\n\n' not in part:
continue
headers, content = part.split(b'\n\n', 1)
if not headers:
continue
new_body.write(b'--' + opts['boundary'] + b'\r\n')
new_body.write(headers.replace(b'\n', b'\r\n'))
new_body.write(b'\r\n\r\n')
new_body.write(content)
new_body.write(b'\r\n')
new_body.write(b'--' + opts['boundary'] + b'--\r\n')
request._body = new_body.getvalue()
request.META['CONTENT_LENGTH'] = len(request._body)
# Clear out _files and _post so that the request object re-parses the body
if hasattr(request, '_files'):
delattr(request, '_files')
if hasattr(request, '_post'):
delattr(request, '_post') |
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def delete_files(sender, **kwargs):
"""Signal callback for deleting old files when database item is deleted""" |
instance = kwargs['instance']
if not hasattr(instance.distribution, 'path'):
return
if not os.path.exists(instance.distribution.path):
return
# Check if there are other instances which reference this fle
is_referenced = (
instance.__class__.objects
.filter(distribution=instance.distribution)
.exclude(pk=instance._get_pk_val())
.exists())
if is_referenced:
return
try:
instance.distribution.storage.delete(instance.distribution.path)
except Exception:
logger.exception(
'Error when trying to delete file %s of package %s:' % (
instance.pk, instance.distribution.path)) |
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def md5_hash_file(fh):
"""Return the md5 hash of the given file-object""" |
md5 = hashlib.md5()
while True:
data = fh.read(8192)
if not data:
break
md5.update(data)
return md5.hexdigest() |
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def get_versio_versioning_scheme(full_class_path):
"""Return a class based on it's full path""" |
module_path = '.'.join(full_class_path.split('.')[0:-1])
class_name = full_class_path.split('.')[-1]
try:
module = importlib.import_module(module_path)
except ImportError:
raise RuntimeError('Invalid specified Versio schema {}'.format(full_class_path))
try:
return getattr(module, class_name)
except AttributeError:
raise RuntimeError(
'Could not find Versio schema class {!r} inside {!r} module.'.format(
class_name, module_path)) |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def search(spec, operator='and'):
"""Implement xmlrpc search command. This only searches through the mirrored and private packages """ |
field_map = {
'name': 'name__icontains',
'summary': 'releases__summary__icontains',
}
query_filter = None
for field, values in spec.items():
for value in values:
if field not in field_map:
continue
field_filter = Q(**{field_map[field]: value})
if not query_filter:
query_filter = field_filter
continue
if operator == 'and':
query_filter &= field_filter
else:
query_filter |= field_filter
result = []
packages = models.Package.objects.filter(query_filter).all()[:20]
for package in packages:
release = package.releases.all()[0]
result.append({
'name': package.name,
'summary': release.summary,
'version': release.version,
'_pypi_ordering': 0,
})
return result |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def credentials_required(view_func):
""" This decorator should be used with views that need simple authentication against Django's authentication framework. """ |
@wraps(view_func, assigned=available_attrs(view_func))
def decorator(request, *args, **kwargs):
if settings.LOCALSHOP_USE_PROXIED_IP:
try:
ip_addr = request.META['HTTP_X_FORWARDED_FOR']
except KeyError:
return HttpResponseForbidden('No permission')
else:
# HTTP_X_FORWARDED_FOR can be a comma-separated list of IPs.
# The client's IP will be the first one.
ip_addr = ip_addr.split(",")[0].strip()
else:
ip_addr = request.META['REMOTE_ADDR']
if CIDR.objects.has_access(ip_addr, with_credentials=False):
return view_func(request, *args, **kwargs)
if not CIDR.objects.has_access(ip_addr, with_credentials=True):
return HttpResponseForbidden('No permission')
# Just return the original view because already logged in
if request.user.is_authenticated():
return view_func(request, *args, **kwargs)
user = authenticate_user(request)
if user is not None:
login(request, user)
return view_func(request, *args, **kwargs)
return HttpResponseUnauthorized(content='Authorization Required')
return decorator |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def no_duplicates(function, *args, **kwargs):
""" Makes sure that no duplicated tasks are enqueued. """ |
@wraps(function)
def wrapper(self, *args, **kwargs):
key = generate_key(function, *args, **kwargs)
try:
function(self, *args, **kwargs)
finally:
logging.info('Removing key %s', key)
cache.delete(key)
return wrapper |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
def download_file(pk):
"""Download the file reference in `models.ReleaseFile` with the given pk. """ |
release_file = models.ReleaseFile.objects.get(pk=pk)
logging.info("Downloading %s", release_file.url)
proxies = None
if settings.LOCALSHOP_HTTP_PROXY:
proxies = settings.LOCALSHOP_HTTP_PROXY
response = requests.get(release_file.url, stream=True, proxies=proxies)
# Write the file to the django file field
filename = os.path.basename(release_file.url)
# Setting the size manually since Django can't figure it our from
# the raw HTTPResponse
if 'content-length' in response.headers:
size = int(response.headers['content-length'])
else:
size = len(response.content)
# Setting the content type by first looking at the response header
# and falling back to guessing it from the filename
default_content_type = 'application/octet-stream'
content_type = response.headers.get('content-type')
if content_type is None or content_type == default_content_type:
content_type = mimetypes.guess_type(filename)[0] or default_content_type
# Using Django's temporary file upload system to not risk memory
# overflows
with TemporaryUploadedFile(name=filename, size=size, charset='utf-8',
content_type=content_type) as temp_file:
temp_file.write(response.content)
temp_file.seek(0)
# Validate the md5 hash of the downloaded file
md5_hash = md5_hash_file(temp_file)
if md5_hash != release_file.md5_digest:
logging.error("MD5 hash mismatch: %s (expected: %s)" % (
md5_hash, release_file.md5_digest))
return
release_file.distribution.save(filename, temp_file)
release_file.save()
logging.info("Complete") |
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