fumiyau/python_no_comments_test100000 · Datasets at Hugging Face

f7fbbe4d7825e1ffaab8a2bb0ef632c92d7b5af3

58,990

py

Python

neural-navigation-with-lstm/MARCO/nltk/parser/chunk.py

ronaldahmed/SLAM-for-ugv

52e3241b8b737a0cfe5682c0aa87ec8c27d6a33d

[ "MIT" ]

14

2016-04-03T19:25:13.000Z

2022-01-05T07:03:07.000Z

neural-navigation-with-lstm/MARCO/nltk/parser/chunk.py

ronaldahmed/SLAM-for-ugv

52e3241b8b737a0cfe5682c0aa87ec8c27d6a33d

[ "MIT" ]

null

neural-navigation-with-lstm/MARCO/nltk/parser/chunk.py

ronaldahmed/SLAM-for-ugv

52e3241b8b737a0cfe5682c0aa87ec8c27d6a33d

[ "MIT" ]

5

2018-06-21T12:58:58.000Z

2020-02-15T05:33:39.000Z

# Natural Language Toolkit: A Chunk Parser # # Copyright (C) 2001 University of Pennsylvania # Author: Steven Bird <sb@ldc.upenn.edu> # URL: <http://nltk.sf.net> # For license information, see LICENSE.TXT # # $Id: chunk.py,v 1.1.1.2 2004/09/29 21:58:22 adastra Exp $ """ Classes and interfaces for identifying non-overlapping linguistic groups (such as base noun phrases) in unrestricted text. This task is called X{chunk parsing} or X{chunking}, and the identified groups are called X{chunks}. The chunked text is represented using a shallow tree called a "chunk structure." A X{chunk structure} is a tree containing tokens and chunks, where each chunk is a subtree containing only tokens. For example, the chunk structure for base noun phrase chunks in the sentence "I saw the big dog on the hill" is:: (SENTENCE: (NP: <I>) <saw> (NP: <the> <big> <dog>) <on> (NP: <the> <hill>)) To convert a chunk structure back to a list of tokens, simply use the chunk structure's L{leaves<Tree.leaves>} method. The C{parser.chunk} module defines L{ChunkParserI}, a standard interface for chunking texts; and L{RegexpChunkParser}, a regular-expression based implementation of that interface. It also defines the L{ChunkedTaggedTokenizer} and L{ConllChunkedTokenizer} classes, which tokenize strings containing chunked and tagged texts; and L{ChunkScore}, a utility class for scoring chunk parsers. RegexpChunkParser ============= C{RegexpChunkParser} is an implementation of the chunk parser interface that uses regular-expressions over tags to chunk a text. Its C{parse} method first constructs a C{ChunkString}, which encodes a particular chunking of the input text. Initially, nothing is chunked. C{RegexpChunkParser} then applies a sequence of C{RegexpChunkParserRule}s to the C{ChunkString}, each of which modifies the chunking that it encodes. Finally, the C{ChunkString} is transformed back into a chunk structure, which is returned. C{RegexpChunkParser} can only be used to chunk a single kind of phrase. For example, you can use an C{RegexpChunkParser} to chunk the noun phrases in a text, or the verb phrases in a text; but you can not use it to simultaneously chunk both noun phrases and verb phrases in the same text. (This is a limitation of C{RegexpChunkParser}, not of chunk parsers in general.) RegexpChunkParserRules ------------------ C{RegexpChunkParserRule}s are transformational rules that update the chunking of a text by modifying its C{ChunkString}. Each C{RegexpChunkParserRule} defines the C{apply} method, which modifies the chunking encoded by a C{ChunkString}. The L{RegexpChunkParserRule} class itself can be used to implement any transformational rule based on regular expressions. There are also a number of subclasses, which can be used to implement simpler types of rules: - L{ChunkRule} chunks anything that matches a given regular expression. - L{ChinkRule} chinks anything that matches a given regular expression. - L{UnChunkRule} will un-chunk any chunk that matches a given regular expression. - L{MergeRule} can be used to merge two contiguous chunks. - L{SplitRule} can be used to split a single chunk into two smaller chunks. Tag Patterns ~~~~~~~~~~~~ C{RegexpChunkParserRule}s use a modified version of regular expression patterns, called X{tag patterns}. Tag patterns are used to match sequences of tags. Examples of tag patterns are:: r'(<DT>|<JJ>|<NN>)+' r'<NN>+' r'<NN.*>' The differences between regular expression patterns and tag patterns are: - In tag patterns, C{'<'} and C{'>'} act as parenthases; so C{'<NN>+'} matches one or more repetitions of C{'<NN>'}, not C{'<NN'} followed by one or more repetitions of C{'>'}. - Whitespace in tag patterns is ignored. So C{'<DT> | <NN>'} is equivalant to C{'<DT>|<NN>'} - In tag patterns, C{'.'} is equivalant to C{'[^{}<>]'}; so C{'<NN.*>'} matches any single tag starting with C{'NN'}. The function L{tag_pattern2re_pattern} can be used to transform a tag pattern to an equivalent regular expression pattern. Efficiency ---------- Preliminary tests indicate that C{RegexpChunkParser} can chunk at a rate of about 300 tokens/second, with a moderately complex rule set. There may be problems if C{RegexpChunkParser} is used with more than 5,000 tokens at a time. In particular, evaluation of some regular expressions may cause the Python regular expression engine to exceed its maximum recursion depth. We have attempted to minimize these problems, but it is impossible to avoid them completely. We therefore recommend that you apply the chunk parser to a single sentence at a time. Emacs Tip --------- If you evaluate the following elisp expression in emacs, it will colorize C{ChunkString}s when you use an interactive python shell with emacs or xemacs ("C-c !"):: (let () (defconst comint-mode-font-lock-keywords '(("<[^>]+>" 0 'font-lock-reference-face) ("[{}]" 0 'font-lock-function-name-face))) (add-hook 'comint-mode-hook (lambda () (turn-on-font-lock)))) You can evaluate this code by copying it to a temporary buffer, placing the cursor after the last close parenthasis, and typing "C{C-x C-e}". You should evaluate it before running the interactive session. The change will last until you close emacs. Unresolved Issues ----------------- If we use the C{re} module for regular expressions, Python's regular expression engine generates "maximum recursion depth exceeded" errors when processing very large texts, even for regular expressions that should not require any recursion. We therefore use the C{pre} module instead. But note that C{pre} does not include Unicode support, so this module will not work with unicode strings. Note also that C{pre} regular expressions are not quite as advanced as C{re} ones (e.g., no leftward zero-length assertions). @type _VALID_CHUNK_STRING: C{regexp} @var _VALID_CHUNK_STRING: A regular expression to test whether a chunk string is valid. @type _VALID_TAG_PATTERN: C{regexp} @var _VALID_TAG_PATTERN: A regular expression to test whether a tag pattern is valid. @group Interfaces: ChunkParserI @group Chunk Parsers: RegexpChunkParser @group Chunk Parser Rules: RegexpChunkParserRule, ChunkRule, ChinkRule, MergeRule, SplitRule, UnChunkRule, ChunkString @group Evaluation: ChunkScore @group Tokenizers: ChunkedTaggedTokenizer @sort: ChunkParserI, RegexpChunkParser, RegexpChunkParserRule, ChunkRule, ChinkRule, MergeRule, SplitRule, UnChunkRule, ChunkString, ChunkScore, ChunkedTaggedTokenizer, demo, demo_eval, tag_pattern2re_pattern """ from nltk import TaskI, PropertyIndirectionMixIn from nltk.parser import ParserI, AbstractParser from nltk.tree import Tree from nltk.tokenizer import TokenizerI, AbstractTokenizer from nltk.tokenizer import LineTokenizer, RegexpTokenizer, WhitespaceTokenizer from nltk.token import Token, CharSpanLocation, SubtokenContextPointer from nltk.chktype import chktype from sets import Set import types, re ##////////////////////////////////////////////////////// ## Chunk Parser Interface ##////////////////////////////////////////////////////// class ChunkParserI(ParserI): """ A processing interface for identifying non-overlapping groups in unrestricted text. Typically, chunk parsers are used to find base syntactic constituants, such as base noun phrases. Unlike L{ParserI}, C{ChunkParserI} guarantees that the C{parse} method will always generate a parse. @inprop: C{SUBTOKENS}: The list of subtokens to be parsed. @outprop: C{TREE}: The parse tree. I{(generated by L{parse})} @outprop: C{TREES}: A list of possible parse trees. I{(generated by L{parse_n})} """ def parse(self, token): """ Find the best chunk structure for the given token's C{subtoknes}, and output it to the token's C{TREE} property. @param token: The token whose subtokens should be parsed. @type token: L{Token} """ assert 0, "ChunkParserI is an abstract interface" def parse_n(self, token, n=None): """ Find a list of the C{n} most likely chunk structures for the given token's C{subtokens}, and output it to the token's C{TREES} property. If the given token has fewer than C{n} chunk structures, then find all chunk structures. The chunk structures should be stored in descending order of estimated likelihood. @type n: C{int} @param n: The number of chunk structures to generate. At most C{n} chunk structures will be generated. If C{n} is not specified, generate all chunk structures. @type token: L{Token} @param token: The token whose subtokens should be chunked. """ assert 0, "ChunkParserI is an abstract interface" ##////////////////////////////////////////////////////// ## Evaluation Helper ##////////////////////////////////////////////////////// class ChunkScore: """ A utility class for scoring chunk parsers. C{ChunkScore} can evaluate a chunk parser's output, based on a number of statistics (precision, recall, f-measure, misssed chunks, incorrect chunks). It can also combine the scores from the parsing of multiple texts; this makes it signifigantly easier to evaluate a chunk parser that operates one sentence at a time. Texts are evaluated with the C{score} method. The results of evaluation can be accessed via a number of accessor methods, such as C{precision} and C{f_measure}. A typical use of the C{ChunkScore} class is:: >>> chunkscore = ChunkScore() >>> for correct in correct_sentences: ... guess = chunkparser.parse(correct.leaves()) ... chunkscore.score(correct, guess) >>> print 'F Measure:', chunkscore.f_measure() F Measure: 0.823 @ivar kwargs: Keyword arguments: - max_tp_examples: The maximum number actual examples of true positives to record. This affects the C{correct} member function: C{correct} will not return more than this number of true positive examples. This does *not* affect any of the numerical metrics (precision, recall, or f-measure) - max_fp_examples: The maximum number actual examples of false positives to record. This affects the C{incorrect} member function and the C{guessed} member function: C{incorrect} will not return more than this number of examples, and C{guessed} will not return more than this number of true positive examples. This does *not* affect any of the numerical metrics (precision, recall, or f-measure) - max_fn_examples: The maximum number actual examples of false negatives to record. This affects the C{missed} member function and the C{correct} member function: C{missed} will not return more than this number of examples, and C{correct} will not return more than this number of true negative examples. This does *not* affect any of the numerical metrics (precision, recall, or f-measure) @type _tp: C{list} of C{Token} @ivar _tp: List of true positives @type _fp: C{list} of C{Token} @ivar _fp: List of false positives @type _fn: C{list} of C{Token} @ivar _fn: List of false negatives @type _tp_num: C{int} @ivar _tp_num: Number of true positives @type _fp_num: C{int} @ivar _fp_num: Number of false positives @type _fn_num: C{int} @ivar _fn_num: Number of false negatives. """ def __init__(self, **kwargs): self._correct = Set() self._guessed = Set() self._tp = Set() self._fp = Set() self._fn = Set() self._max_tp = kwargs.get('max_tp_examples', 100) self._max_fp = kwargs.get('max_fp_examples', 100) self._max_fn = kwargs.get('max_fn_examples', 100) self._tp_num = 0 self._fp_num = 0 self._fn_num = 0 def _childtuple(self, t): return tuple([c.freeze() for c in t]) def score(self, correct, guessed): """ Given a correctly chunked text, score another chunked text. Merge the results with all previous scorings. Note that when the score() function is used repeatedly, each token I{must} have a unique location. For sentence-at-a-time chunking, it is recommended that you use locations like C{@12w@3s} (the word at index 12 of the sentence at index 3). @type correct: chunk structure @param correct: The known-correct ("gold standard") chunked sentence. @type guessed: chunk structure @param guessed: The chunked sentence to be scored. """ assert chktype(1, correct, Tree) assert chktype(2, guessed, Tree) self._correct |= Set([self._childtuple(t) for t in correct if isinstance(t, Tree)]) self._guessed |= Set([self._childtuple(t) for t in guessed if isinstance(t, Tree)]) self._tp = self._guessed & self._correct self._fn = self._correct - self._guessed self._fp = self._guessed - self._correct self._tp_num = len(self._tp) self._fp_num = len(self._fp) self._fn_num = len(self._fn) def precision(self): """ @return: the overall precision for all texts that have been scored by this C{ChunkScore}. @rtype: C{float} """ div = self._tp_num + self._fp_num if div == 0: return 0 else: return float(self._tp_num) / div def recall(self): """ @return: the overall recall for all texts that have been scored by this C{ChunkScore}. @rtype: C{float} """ div = self._tp_num + self._fn_num if div == 0: return 0 else: return float(self._tp_num) / div def f_measure(self, alpha=0.5): """ @return: the overall F measure for all texts that have been scored by this C{ChunkScore}. @rtype: C{float} @param alpha: the relative weighting of precision and recall. Larger alpha biases the score towards the precision value, while smaller alpha biases the score towards the recall value. C{alpha} should have a value in the range [0,1]. @type alpha: C{float} """ assert chktype(1, alpha, types.FloatType, types.IntType) p = self.precision() r = self.recall() if p == 0 or r == 0: # what if alpha is 0 or 1? return 0 return 1/(alpha/p + (1-alpha)/r) def missed(self): """ @rtype: C{Set} of C{Token} @return: the set of chunks which were included in the correct chunk structures, but not in the guessed chunk structures. Each chunk is encoded as a single token, spanning the chunk. This encoding makes it easier to examine the missed chunks. """ return list(self._fn) def incorrect(self): """ @rtype: C{Set} of C{Token} @return: the set of chunks which were included in the guessed chunk structures, but not in the correct chunk structures. Each chunk is encoded as a single token, spanning the chunk. This encoding makes it easier to examine the incorrect chunks. """ return list(self._fp) def correct(self): """ @rtype: C{Set} of C{Token} @return: the set of chunks which were included in the correct chunk structures. Each chunk is encoded as a single token, spanning the chunk. This encoding makes it easier to examine the correct chunks. """ return list(self._correct) def guessed(self): """ @rtype: C{Set} of C{Token} @return: the set of chunks which were included in the guessed chunk structures. Each chunk is encoded as a single token, spanning the chunk. This encoding makes it easier to examine the guessed chunks. """ return list(self._guessed) def __len__(self): return self._tp_num + self._fn_num def __repr__(self): """ @rtype: C{String} @return: a concise representation of this C{ChunkScoring}. """ return '<ChunkScoring of '+`len(self)`+' chunks>' def __str__(self): """ @rtype: C{String} @return: a verbose representation of this C{ChunkScoring}. This representation includes the precision, recall, and f-measure scores. For other information about the score, use the accessor methods (e.g., C{missed()} and C{incorrect()}). """ return ("ChunkParser score:\n" + (" Precision: %5.1f%%\n" % (self.precision()*100)) + (" Recall: %5.1f%%\n" % (self.recall()*100))+ (" F-Measure: %5.1f%%\n" % (self.f_measure()*100))) def _chunk_toks(self, text): """ @return: The list of tokens contained in C{text}. """ return [tok for tok in text if isinstance(tok, AbstractTree)] ##////////////////////////////////////////////////////// ## Precompiled regular expressions ##////////////////////////////////////////////////////// _TAGCHAR = r'[^\{\}<>]' _TAG = r'(<%s+?>)' % _TAGCHAR _VALID_TAG_PATTERN = re.compile(r'^((%s|<%s>)*)$' % ('[^\{\}<>]+', '[^\{\}<>]+')) ##////////////////////////////////////////////////////// ## ChunkString ##////////////////////////////////////////////////////// class ChunkString(PropertyIndirectionMixIn): """ A string-based encoding of a particular chunking of a text. Internally, the C{ChunkString} class uses a single string to encode the chunking of the input text. This string contains a sequence of angle-bracket delimited tags, with chunking indicated by braces. An example of this encoding is:: {<DT><JJ><NN>}<VBN><IN>{<DT><NN>}<.>{<DT><NN>}<VBD><.> C{ChunkString} are created from tagged texts (i.e., C{list}s of C{tokens} whose type is C{TaggedType}). Initially, nothing is chunked. The chunking of a C{ChunkString} can be modified with the C{xform} method, which uses a regular expression to transform the string representation. These transformations should only add and remove braces; they should I{not} modify the sequence of angle-bracket delimited tags. @type _str: C{string} @ivar _str: The internal string representation of the text's encoding. This string representation contains a sequence of angle-bracket delimited tags, with chunking indicated by braces. An example of this encoding is:: {<DT><JJ><NN>}<VBN><IN>{<DT><NN>}<.>{<DT><NN>}<VBD><.> @type _ttoks: C{list} of C{Token} @ivar _ttoks: The text whose chunking is encoded by this C{ChunkString}. @ivar _debug: The debug level. See the constructor docs. @cvar IN_CHUNK_PATTERN: A zero-width regexp pattern string that will only match positions that are in chunks. @cvar IN_CHINK_PATTERN: A zero-width regexp pattern string that will only match positions that are in chinks. """ IN_CHUNK_PATTERN = r'(?=[^\{]*\})' IN_CHINK_PATTERN = r'(?=[^\}]*(\{|$))' # These are used by _verify _CHUNK = r'(\{%s+?\})+?' % _TAG _CHINK = r'(%s+?)+?' % _TAG _VALID = re.compile(r'(\{?%s\}?)*?' % _TAG) _BRACKETS = re.compile('[^\{\}]+') _BALANCED_BRACKETS = re.compile(r'(\{\})*$') def __init__(self, tagged_tokens, debug_level=3, **property_names): """ Construct a new C{ChunkString} that encodes the chunking of the text C{tagged_tokens}. @type tagged_tokens: C{list} of C{Token} with C{TaggedType}s @param tagged_tokens: The text whose chunking is encoded by this C{ChunkString}. @type debug_level: int @param debug_level: The level of debugging which should be applied to transformations on the C{ChunkString}. The valid levels are: - 0: no checks - 1: full check on to_chunkstruct - 2: full check on to_chunkstruct and cursory check after each transformation. - 3: full check on to_chunkstruct and full check after each transformation. We recommend you use at least level 1. You should probably use level 3 if you use any non-standard subclasses of C{RegexpChunkParserRule}. """ assert chktype(1, tagged_tokens, [Token, Tree], (Token, Tree), Tree) assert chktype(2, debug_level, types.IntType) PropertyIndirectionMixIn.__init__(self, **property_names) self._ttoks = tagged_tokens tags = [self._tag(tok) for tok in tagged_tokens] self._str = '<' + '><'.join(tags) + '>' self._debug = debug_level def _tag(self, tok): if isinstance(tok, Token): return tok[self.property('TAG')] elif isinstance(tok, Tree): return tok.node else: raise ValueError, 'tagged_tokens must contain tokens and trees' def _verify(self, verify_tags): """ Check to make sure that C{_str} still corresponds to some chunked version of C{_ttoks}. @type verify_tags: C{boolean} @param verify_tags: Whether the individual tags should be checked. If this is false, C{_verify} will check to make sure that C{_str} encodes a chunked version of I{some} list of tokens. If this is true, then C{_verify} will check to make sure that the tags in C{_str} match those in C{_ttoks}. @raise ValueError: if this C{ChunkString}'s internal string representation is invalid or not consistant with _ttoks. """ # Check overall form if not ChunkString._VALID.match(self._str): raise ValueError('Transformation generated invalid chunkstring') # Check that parens are balanced. If the string is long, we # have to do this in pieces, to avoid a maximum recursion # depth limit for regular expressions. brackets = ChunkString._BRACKETS.sub('', self._str) for i in range(1+len(brackets)/5000): substr = brackets[i*5000:i*5000+5000] if not ChunkString._BALANCED_BRACKETS.match(substr): raise ValueError('Transformation generated invalid '+ 'chunkstring') if verify_tags<=0: return tags1 = (re.split(r'[\{\}<>]+', self._str))[1:-1] tags2 = [self._tag(tok) for tok in self._ttoks] if tags1 != tags2: raise ValueError('Transformation generated invalid chunkstring') def to_chunkstruct(self, chunk_node='CHUNK', top_node='TEXT'): """ @return: the chunk structure encoded by this C{ChunkString}. A chunk structure is a C{list} containing tagged tokens and sublists of tagged tokens, where each sublist represents a single chunk. @rtype: chunk structure @raise ValueError: If a transformation has generated an invalid chunkstring. """ if self._debug > 0: self._verify(1) # Extract a list of alternating chinks & chunks pieces = re.split('[{}]', self._str) # Use this alternating list to create the chunkstruct. chunkstruct = [] index = 0 piece_in_chunk = 0 for piece in pieces: # Find the list of tokens contained in this piece. length = piece.count('<') subsequence = self._ttoks[index:index+length] # Add this list of tokens to our chunkstruct. if piece_in_chunk: chunkstruct.append(Tree(chunk_node, subsequence)) else: chunkstruct += subsequence # Update index, piece_in_chunk index += length piece_in_chunk = not piece_in_chunk return Tree(top_node, chunkstruct) def xform(self, regexp, repl): """ Apply the given transformation to this C{ChunkString}'s string encoding. In particular, find all occurances that match C{regexp}, and replace them using C{repl} (as done by C{re.sub}). This transformation should only add and remove braces; it should I{not} modify the sequence of angle-bracket delimited tags. Furthermore, this transformation may not result in improper bracketing. Note, in particular, that bracketing may not be nested. @type regexp: C{string} or C{regexp} @param regexp: A regular expression matching the substring that should be replaced. This will typically include a named group, which can be used by C{repl}. @type repl: C{string} @param repl: An expression specifying what should replace the matched substring. Typically, this will include a named replacement group, specified by C{regexp}. @rtype: C{None} @raise ValueError: If this transformation generateds an invalid chunkstring. """ if type(regexp).__name__ != 'SRE_Pattern': assert chktype(1, regexp, types.StringType) assert chktype(2, repl, types.StringType) # Do the actual substitution self._str = re.sub(regexp, repl, self._str) # The substitution might have generated "empty chunks" # (substrings of the form "{}"). Remove them, so they don't # interfere with other transformations. self._str = re.sub('\{\}', '', self._str) # Make sure that the transformation was legal. if self._debug > 1: self._verify(self._debug-2) def xform_chunk(self, pattern, repl): # Docstring adopted from xform's docstring. """ Apply the given transformation to the chunks in this C{ChunkString}'s string encoding. In particular, find all occurances within chunks that match C{regexp}, and replace them using C{repl} (as done by C{re.sub}). This transformation should only add and remove braces; it should I{not} modify the sequence of angle-bracket delimited tags. Furthermore, this transformation may not result in improper bracketing. Note, in particular, that bracketing may not be nested. @type pattern: C{string} @param pattern: A regular expression pattern matching the substring that should be replaced. This will typically include a named group, which can be used by C{repl}. @type repl: C{string} @param repl: An expression specifying what should replace the matched substring. Typically, this will include a named replacement group, specified by C{regexp}. @rtype: C{None} @raise ValueError: If this transformation generateds an invalid chunkstring. """ if type(pattern).__name__ == 'SRE_Pattern': pattern = pattern.pattern assert chktype(1, pattern, types.StringType) assert chktype(2, repl, types.StringType) self.xform(pattern+ChunkString.IN_CHUNK_PATTERN, repl) def xform_chink(self, pattern, repl): # Docstring adopted from xform's docstring. """ Apply the given transformation to the chinks in this C{ChinkString}'s string encoding. In particular, find all occurances within chinks that match C{regexp}, and replace them using C{repl} (as done by C{re.sub}). This transformation should only add and remove braces; it should I{not} modify the sequence of angle-bracket delimited tags. Furthermore, this transformation may not result in improper bracketing. Note, in particular, that bracketing may not be nested. @type pattern: C{string} or C{regexp} @param pattern: A regular expression pattern matching the substring that should be replaced. This will typically include a named group, which can be used by C{repl}. @type repl: C{string} @param repl: An expression specifying what should replace the matched substring. Typically, this will include a named replacement group, specified by C{regexp}. @rtype: C{None} @raise ValueError: If this transformation generateds an invalid chunkstring. """ if type(pattern).__name__ == 'SRE_Pattern': pattern = pattern.pattern assert chktype(1, pattern, types.StringType) assert chktype(2, repl, types.StringType) self.xform(pattern+ChunkString.IN_CHINK_PATTERN, repl) def __repr__(self): """ @rtype: C{string} @return: A string representation of this C{ChunkString}. This string representation has the form:: <ChunkString: '{<DT><JJ><NN>}<VBN><IN>{<DT><NN>}'> """ return '<ChunkString: %s>' % `self._str` def __str__(self): """ @rtype: C{string} @return: A formatted representation of this C{ChunkString}'s string encoding. This representation will include extra spaces to ensure that tags will line up with the representation of other C{ChunkStrings} for the same text, regardless of the chunking. """ # Add spaces to make everything line up. str = re.sub(r'>(?!\})', r'> ', self._str) str = re.sub(r'([^\{])<', r'\1 <', str) if str[0] == '<': str = ' ' + str return str ##////////////////////////////////////////////////////// ## Rules ##////////////////////////////////////////////////////// def tag_pattern2re_pattern(tag_pattern): """ Convert a tag pattern to a regular expression pattern. A X{tag pattern} is a modified verison of a regular expression, designed for matching sequences of tags. The differences between regular expression patterns and tag patterns are: - In tag patterns, C{'<'} and C{'>'} act as parenthases; so C{'<NN>+'} matches one or more repetitions of C{'<NN>'}, not C{'<NN'} followed by one or more repetitions of C{'>'}. - Whitespace in tag patterns is ignored. So C{'<DT> | <NN>'} is equivalant to C{'<DT>|<NN>'} - In tag patterns, C{'.'} is equivalant to C{'[^{}<>]'}; so C{'<NN.*>'} matches any single tag starting with C{'NN'}. In particular, C{tag_pattern2re_pattern} performs the following transformations on the given pattern: - Replace '.' with '[^<>{}]' - Remove any whitespace - Add extra parens around '<' and '>', to make '<' and '>' act like parenthases. E.g., so that in '<NN>+', the '+' has scope over the entire '<NN>'; and so that in '<NN|IN>', the '|' has scope over 'NN' and 'IN', but not '<' or '>'. - Check to make sure the resulting pattern is valid. @type tag_pattern: C{string} @param tag_pattern: The tag pattern to convert to a regular expression pattern. @raise ValueError: If C{tag_pattern} is not a valid tag pattern. In particular, C{tag_pattern} should not include braces; and it should not contain nested or mismatched angle-brackets. @rtype: C{string} @return: A regular expression pattern corresponding to C{tag_pattern}. """ assert chktype(1, tag_pattern, types.StringType) # Clean up the regular expression tag_pattern = re.sub(r'\s', '', tag_pattern) tag_pattern = re.sub(r'<', '(<(', tag_pattern) tag_pattern = re.sub(r'>', ')>)', tag_pattern) # Check the regular expression if not _VALID_TAG_PATTERN.match(tag_pattern): raise ValueError('Bad tag pattern: %s' % tag_pattern) # Replace "." with _TAGCHAR. # We have to do this after, since it adds {}[]<>s, which would # confuse _VALID_TAG_PATTERN. # PRE doesn't have lookback assertions, so reverse twice, and do # the pattern backwards (with lookahead assertions). This can be # made much cleaner once we can switch back to SRE. def reverse_str(str): lst = list(str) lst.reverse() return ''.join(lst) tc_rev = reverse_str(_TAGCHAR) reversed = reverse_str(tag_pattern) reversed = re.sub(r'\.(?!\$\\\$*($|[^\\]))', tc_rev, reversed) tag_pattern = reverse_str(reversed) return tag_pattern class RegexpChunkParserRule: """ A rule specifying how to modify the chunking in a C{ChunkString}, using a transformational regular expression. The C{RegexpChunkParserRule} class itself can be used to implement any transformational rule based on regular expressions. There are also a number of subclasses, which can be used to implement simpler types of rules, based on matching regular expressions. Each C{RegexpChunkParserRule} has a regular expression and a replacement expression. When a C{RegexpChunkParserRule} is X{applied} to a C{ChunkString}, it searches the C{ChunkString} for any substring that matches the regular expression, and replaces it using the replacement expression. This search/replace operation has the same semantics as C{re.sub}. Each C{RegexpChunkParserRule} also has a description string, which gives a short (typically less than 75 characters) description of the purpose of the rule. This transformation defined by this C{RegexpChunkParserRule} should only add and remove braces; it should I{not} modify the sequence of angle-bracket delimited tags. Furthermore, this transformation may not result in nested or mismatched bracketing. """ def __init__(self, regexp, repl, descr): """ Construct a new RegexpChunkParserRule. @type regexp: C{regexp} or C{string} @param regexp: This C{RegexpChunkParserRule}'s regular expression. When this rule is applied to a C{ChunkString}, any substring that matches C{regexp} will be replaced using the replacement string C{repl}. Note that this must be a normal regular expression, not a tag pattern. @type repl: C{string} @param repl: This C{RegexpChunkParserRule}'s replacement expression. When this rule is applied to a C{ChunkString}, any substring that matches C{regexp} will be replaced using C{repl}. @type descr: C{string} @param descr: A short description of the purpose and/or effect of this rule. """ if type(regexp).__name__ == 'SRE_Pattern': regexp = regexp.pattern assert chktype(1, regexp, types.StringType) assert chktype(2, repl, types.StringType) assert chktype(3, descr, types.StringType) self._repl = repl self._descr = descr if type(regexp) == types.StringType: self._regexp = re.compile(regexp) else: self._regexp = regexp def apply(self, chunkstr): # Keep docstring generic so we can inherit it. """ Apply this rule to the given C{ChunkString}. See the class reference documentation for a description of what it means to apply a rule. @type chunkstr: C{ChunkString} @param chunkstr: The chunkstring to which this rule is applied. @rtype: C{None} @raise ValueError: If this transformation generateds an invalid chunkstring. """ assert chktype(1, chunkstr, ChunkString) chunkstr.xform(self._regexp, self._repl) def descr(self): """ @rtype: C{string} @return: a short description of the purpose and/or effect of this rule. """ return self._descr def __repr__(self): """ @rtype: C{string} @return: A string representation of this rule. This string representation has the form:: <RegexpChunkParserRule: '{<IN|VB.*>}'->'<IN>'> Note that this representation does not include the description string; that string can be accessed separately with the C{descr} method. """ return ('<RegexpChunkParserRule: '+`self._regexp.pattern`+ '->'+`self._repl`+'>') class ChunkRule(RegexpChunkParserRule): """ A rule specifying how to add chunks to a C{ChunkString}, using a matching tag pattern. When applied to a C{ChunkString}, it will find any substring that matches this tag pattern and that is not already part of a chunk, and create a new chunk containing that substring. """ def __init__(self, tag_pattern, descr): """ Construct a new C{ChunkRule}. @type tag_pattern: C{string} @param tag_pattern: This rule's tag pattern. When applied to a C{ChunkString}, this rule will chunk any substring that matches this tag pattern and that is not already part of a chunk. @type descr: C{string} @param descr: A short description of the purpose and/or effect of this rule. """ assert chktype(1, tag_pattern, types.StringType) assert chktype(2, descr, types.StringType) self._pattern = tag_pattern regexp = re.compile('(?P<chunk>%s)%s' % (tag_pattern2re_pattern(tag_pattern), ChunkString.IN_CHINK_PATTERN)) RegexpChunkParserRule.__init__(self, regexp, '{\g<chunk>}', descr) def __repr__(self): """ @rtype: C{string} @return: A string representation of this rule. This string representation has the form:: <ChunkRule: '<IN|VB.*>'> Note that this representation does not include the description string; that string can be accessed separately with the C{descr} method. """ return '<ChunkRule: '+`self._pattern`+'>' class ChinkRule(RegexpChunkParserRule): """ A rule specifying how to remove chinks to a C{ChunkString}, using a matching tag pattern. When applied to a C{ChunkString}, it will find any substring that matches this tag pattern and that is contained in a chunk, and remove it from that chunk, thus creating two new chunks. """ def __init__(self, tag_pattern, descr): """ Construct a new C{ChinkRule}. @type tag_pattern: C{string} @param tag_pattern: This rule's tag pattern. When applied to a C{ChunkString}, this rule will find any substring that matches this tag pattern and that is contained in a chunk, and remove it from that chunk, thus creating two new chunks. @type descr: C{string} @param descr: A short description of the purpose and/or effect of this rule. """ assert chktype(1, tag_pattern, types.StringType) assert chktype(2, descr, types.StringType) self._pattern = tag_pattern regexp = re.compile('(?P<chink>%s)%s' % (tag_pattern2re_pattern(tag_pattern), ChunkString.IN_CHUNK_PATTERN)) RegexpChunkParserRule.__init__(self, regexp, '}\g<chink>{', descr) def __repr__(self): """ @rtype: C{string} @return: A string representation of this rule. This string representation has the form:: <ChinkRule: '<IN|VB.*>'> Note that this representation does not include the description string; that string can be accessed separately with the C{descr} method. """ return '<ChinkRule: '+`self._pattern`+'>' class UnChunkRule(RegexpChunkParserRule): """ A rule specifying how to remove chunks to a C{ChunkString}, using a matching tag pattern. When applied to a C{ChunkString}, it will find any complete chunk that matches this tag pattern, and un-chunk it. """ def __init__(self, tag_pattern, descr): """ Construct a new C{UnChunkRule}. @type tag_pattern: C{string} @param tag_pattern: This rule's tag pattern. When applied to a C{ChunkString}, this rule will find any complete chunk that matches this tag pattern, and un-chunk it. @type descr: C{string} @param descr: A short description of the purpose and/or effect of this rule. """ assert chktype(1, tag_pattern, types.StringType) assert chktype(2, descr, types.StringType) self._pattern = tag_pattern regexp = re.compile('\{(?P<chunk>%s)\}' % tag_pattern2re_pattern(tag_pattern)) RegexpChunkParserRule.__init__(self, regexp, '\g<chunk>', descr) def __repr__(self): """ @rtype: C{string} @return: A string representation of this rule. This string representation has the form:: <UnChunkRule: '<IN|VB.*>'> Note that this representation does not include the description string; that string can be accessed separately with the C{descr} method. """ return '<UnChunkRule: '+`self._pattern`+'>' class MergeRule(RegexpChunkParserRule): """ A rule specifying how to merge chunks in a C{ChunkString}, using two matching tag patterns: a left pattern, and a right pattern. When applied to a C{ChunkString}, it will find any chunk whose end matches left pattern, and immediately followed by a chunk whose beginning matches right pattern. It will then merge those two chunks into a single chunk. """ def __init__(self, left_tag_pattern, right_tag_pattern, descr): """ Construct a new C{MergeRule}. @type right_tag_pattern: C{string} @param right_tag_pattern: This rule's right tag pattern. When applied to a C{ChunkString}, this rule will find any chunk whose end matches C{left_tag_pattern}, and immediately followed by a chunk whose beginning matches this pattern. It will then merge those two chunks into a single chunk. @type left_tag_pattern: C{string} @param left_tag_pattern: This rule's left tag pattern. When applied to a C{ChunkString}, this rule will find any chunk whose end matches this pattern, and immediately followed by a chunk whose beginning matches C{right_tag_pattern}. It will then merge those two chunks into a single chunk. @type descr: C{string} @param descr: A short description of the purpose and/or effect of this rule. """ assert chktype(1, left_tag_pattern, types.StringType) assert chktype(2, right_tag_pattern, types.StringType) assert chktype(3, descr, types.StringType) self._left_tag_pattern = left_tag_pattern self._right_tag_pattern = right_tag_pattern regexp = re.compile('(?P<left>%s)}{(?=%s)' % (tag_pattern2re_pattern(left_tag_pattern), tag_pattern2re_pattern(right_tag_pattern))) RegexpChunkParserRule.__init__(self, regexp, '\g<left>', descr) def __repr__(self): """ @rtype: C{string} @return: A string representation of this rule. This string representation has the form:: <MergeRule: '<NN|DT|JJ>', '<NN|JJ>'> Note that this representation does not include the description string; that string can be accessed separately with the C{descr} method. """ return ('<MergeRule: '+`self._left_tag_pattern`+', '+ `self._right_tag_pattern`+'>') class SplitRule(RegexpChunkParserRule): """ A rule specifying how to split chunks in a C{ChunkString}, using two matching tag patterns: a left pattern, and a right pattern. When applied to a C{ChunkString}, it will find any chunk that matches the left pattern followed by the right pattern. It will then split the chunk into two new chunks, at the point between the two pattern matches. """ def __init__(self, left_tag_pattern, right_tag_pattern, descr): """ Construct a new C{SplitRule}. @type right_tag_pattern: C{string} @param right_tag_pattern: This rule's right tag pattern. When applied to a C{ChunkString}, this rule will find any chunk containing a substring that matches C{left_tag_pattern} followed by this pattern. It will then split the chunk into two new chunks at the point between these two matching patterns. @type left_tag_pattern: C{string} @param left_tag_pattern: This rule's left tag pattern. When applied to a C{ChunkString}, this rule will find any chunk containing a substring that matches this pattern followed by C{right_tag_pattern}. It will then split the chunk into two new chunks at the point between these two matching patterns. @type descr: C{string} @param descr: A short description of the purpose and/or effect of this rule. """ assert chktype(1, left_tag_pattern, types.StringType) assert chktype(2, right_tag_pattern, types.StringType) assert chktype(3, descr, types.StringType) self._left_tag_pattern = left_tag_pattern self._right_tag_pattern = right_tag_pattern regexp = re.compile('(?P<left>%s)(?=%s)' % (tag_pattern2re_pattern(left_tag_pattern), tag_pattern2re_pattern(right_tag_pattern))) RegexpChunkParserRule.__init__(self, regexp, r'\g<left>}{', descr) def __repr__(self): """ @rtype: C{string} @return: A string representation of this rule. This string representation has the form:: <SplitRule: '<NN>', '<DT>'> Note that this representation does not include the description string; that string can be accessed separately with the C{descr} method. """ return ('<SplitRule: '+`self._left_tag_pattern`+', '+ `self._right_tag_pattern`+'>') ##////////////////////////////////////////////////////// ## RegexpChunkParser ##////////////////////////////////////////////////////// class RegexpChunkParser(ChunkParserI, AbstractParser): """ A regular expression based chunk parser. C{RegexpChunkParser} uses a sequence X{rules} to find chunks within a text. The chunking of the text is encoded using a C{ChunkString}, and each rule acts by modifying the chunking in the C{ChunkString}. The rules are all implemented using regular expression matching and substitution. The C{RegexpChunkParserRule} class and its subclasses (C{ChunkRule}, C{ChinkRule}, C{UnChunkRule}, C{MergeRule}, and C{SplitRule}) define the rules that are used by C{RegexpChunkParser}. Each rule defines an C{apply} method, which modifies the chunking encoded by a given C{ChunkString}. @type _rules: C{list} of C{RegexpChunkParserRule} @ivar _rules: The list of rules that should be applied to a text. @type _trace: C{int} @ivar _trace: The default level of tracing. @inprop: C{SUBTOKENS}: The list of subtokens to be chunked. @inprop: C{LEAF}: The string content of the subtokens. @outprop: C{TREE}: The chunk structure. I{(generated by L{parse})} @outprop: C{TREES}: A list of possible chunk structures. I{(generated by L{parse_n})} """ def __init__(self, rules, chunk_node='CHUNK', top_node='TEXT', trace=0, **property_names): """ Construct a new C{RegexpChunkParser}. @type rules: C{list} of C{RegexpChunkParserRule} @param rules: The sequence of rules that should be used to generate the chunking for a tagged text. @type chunk_node: C{string} @param chunk_node: The node value that should be used for chunk subtrees. This is typically a short string describing the type of information contained by the chunk, such as C{"NP"} for base noun phrases. @type top_node: C{string} @param top_node: The node value that should be used for the top node of the chunk structure. @type trace: C{int} @param trace: The level of tracing that should be used when parsing a text. C{0} will generate no tracing output; C{1} will generate normal tracing output; and C{2} or highter will generate verbose tracing output. """ assert chktype(1, rules, [RegexpChunkParserRule], (RegexpChunkParserRule,)) assert chktype(4, trace, types.IntType) self._rules = rules self._trace = trace self._chunk_node = chunk_node self._top_node = top_node AbstractParser.__init__(self, **property_names) def _trace_apply(self, chunkstr, verbose): """ Apply each of this C{RegexpChunkParser}'s rules to C{chunkstr}, in turn. Generate trace output between each rule. If C{verbose} is true, then generate verbose output. @type chunkstr: C{ChunkString} @param chunkstr: The chunk string to which each rule should be applied. @type verbose: C{boolean} @param verbose: Whether output should be verbose. @rtype: C{None} """ indent = ' '*(35-len(str(chunkstr))/2) print 'Input:' print indent, chunkstr for rule in self._rules: rule.apply(chunkstr) if verbose: print rule.descr()+' ('+`rule`+'):' else: print rule.descr()+':' print indent, chunkstr print def _notrace_apply(self, chunkstr): """ Apply each of this C{RegexpChunkParser}'s rules to C{chunkstr}, in turn. @param chunkstr: The chunk string to which each rule should be applied. @type chunkstr: C{ChunkString} @rtype: C{None} """ for rule in self._rules: rule.apply(chunkstr) def parse(self, token, trace=None): """ @rtype: chunk structure @return: a chunk structure that encodes the chunks in a given tagged sentence. A chunk is a non-overlapping linguistic group, such as a noun phrase. The set of chunks identified in the chunk structure depends on the rules used to define this C{RegexpChunkParser}. @type trace: C{int} @param trace: The level of tracing that should be used when parsing a text. C{0} will generate no tracing output; C{1} will generate normal tracing output; and C{2} or highter will generate verbose tracing output. This value overrides the trace level value that was given to the constructor. """ assert chktype(1, token, Token) assert chktype(2, trace, types.NoneType, types.IntType) SUBTOKENS = self.property('SUBTOKENS') TREE = self.property('TREE') if len(token[SUBTOKENS]) == 0: print 'Warning: parsing empty text' token[TREE] = Tree(self._top_node, []) return # Use the default trace value? if trace == None: trace = self._trace # Create the chunkstring, using the same properties as the parser chunkstr = ChunkString(token[SUBTOKENS], **self.property_names()) # Apply the sequence of rules to the chunkstring. if trace: verbose = (trace>1) self._trace_apply(chunkstr, verbose) else: self._notrace_apply(chunkstr) # Use the chunkstring to create a chunk structure. tree = chunkstr.to_chunkstruct(self._chunk_node, self._top_node) token[TREE] = tree def rules(self): """ @return: the sequence of rules used by this C{ChunkParser}. @rtype: C{list} of C{RegexpChunkParserRule} """ return self._rules def __repr__(self): """ @return: a concise string representation of this C{RegexpChunkParser}. @rtype: C{string} """ return "<RegexpChunkParser with %d rules>" % len(self._rules) def __str__(self): """ @return: a verbose string representation of this C{RegexpChunkParser}. @rtype: C{string} """ s = "RegexpChunkParser with %d rules:\n" % len(self._rules) margin = 0 for rule in self._rules: margin = max(margin, len(rule.descr())) if margin < 35: format = " %" + `-(margin+3)` + "s%s\n" else: format = " %s\n %s\n" for rule in self._rules: s += format % (rule.descr(), `rule`) return s[:-1] ##////////////////////////////////////////////////////// ## Demonstration code ##////////////////////////////////////////////////////// def demo_eval(chunkparser, text): """ Demonstration code for evaluating a chunk parser, using a C{ChunkScore}. This function assumes that C{text} contains one sentence per line, and that each sentence has the form expected by C{ChunkedTaggedTokenizer}. It runs the given chunk parser on each sentence in the text, and scores the result. It prints the final score (precision, recall, and f-measure); and reports the set of chunks that were missed and the set of chunks that were incorrect. (At most 10 missing chunks and 10 incorrect chunks are reported). @param chunkparser: The chunkparser to be tested @type chunkparser: C{ChunkParserI} @param text: The chunked tagged text that should be used for evaluation. @type text: C{string} """ assert chktype(1, chunkparser, ChunkParserI) assert chktype(1, text, types.StringType) # Evaluate our chunk parser. chunkscore = ChunkScore() from nltk.tokenreader import ChunkedTaggedTokenReader ctt = ChunkedTaggedTokenReader(chunk_node='NP', SUBTOKENS='WORDS') for sentence in text.split('\n'): sentence = sentence.strip() if not sentence: continue gold = ctt.read_token(sentence) test = Token(WORDS=gold['WORDS'])#, LOC=sentence['LOC']) chunkparser.parse(test) chunkscore.score(gold['TREE'], test['TREE']) print '/'+('='*75)+'\\' print 'Scoring', chunkparser print ('-'*77) print 'Precision: %5.1f%%' % (chunkscore.precision()*100), ' '*4, print 'Recall: %5.1f%%' % (chunkscore.recall()*100), ' '*6, print 'F-Measure: %5.1f%%' % (chunkscore.f_measure()*100) # Missed chunks. if chunkscore.missed(): print 'Missed:' missed = chunkscore.missed() # sort, so they'll always be listed in the same order. missed.sort() for chunk in missed[:10]: print ' ', chunk if len(chunkscore.missed()) > 10: print ' ...' # Incorrect chunks. if chunkscore.incorrect(): print 'Incorrect:' incorrect = chunkscore.incorrect() incorrect.sort() # sort, so they'll always be listed in the same order. for chunk in incorrect[:10]: print ' ', chunk if len(chunkscore.incorrect()) > 10: print ' ...' print '\\'+('='*75)+'/' def demo(): """ A demonstration for the C{RegexpChunkParser} class. A single text is parsed with four different chunk parsers, using a variety of rules and strategies. """ text = """\ [ the/DT little/JJ cat/NN ] sat/VBD on/IN [ the/DT mat/NN ] ./. [ The/DT cats/NNS ] ./. [ John/NNP ] saw/VBD [the/DT cat/NN] [the/DT dog/NN] liked/VBD ./.""" print '*'*75 print 'Evaluation text:' print text print '*'*75 # Use a simple regexp to define regular expressions. r1 = ChunkRule(r'<DT>?<JJ>*<NN.*>', 'Chunk NPs') cp = RegexpChunkParser([r1], chunk_node='NP', top_node='S', trace=1, SUBTOKENS='WORDS') demo_eval(cp, text) print # Use a chink rule to remove everything that's *not* an NP r1 = ChunkRule(r'<.*>+', 'Chunk everything') r2 = ChinkRule(r'<VB.*>|<IN>|<\.>', 'Unchunk VB and IN and .') cp = RegexpChunkParser([r1, r2], chunk_node='NP', top_node='S', trace=1, SUBTOKENS='WORDS') demo_eval(cp, text) print # Unchunk non-NP words, and then merge consecutive NPs r1 = ChunkRule(r'(<.*>)', 'Chunk each tag') r2 = UnChunkRule(r'<VB.*>|<IN>|<.>', 'Unchunk VB? and IN and .') r3 = MergeRule(r'<DT|JJ|NN.*>', r'<DT|JJ|NN.*>', 'Merge NPs') cp = RegexpChunkParser([r1,r2,r3], chunk_node='NP', top_node='S', trace=1, SUBTOKENS='WORDS') demo_eval(cp, text) print # Chunk sequences of NP words, and split them at determiners r1 = ChunkRule(r'(<DT|JJ|NN.*>+)', 'Chunk sequences of DT&JJ&NN') r2 = SplitRule('', r'<DT>', 'Split before DT') cp = RegexpChunkParser([r1,r2], chunk_node='NP', top_node='S', trace=1, SUBTOKENS='WORDS') demo_eval(cp, text) print if __name__ == '__main__': demo()

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""" Classes and interfaces for identifying non-overlapping linguistic groups (such as base noun phrases) in unrestricted text. This task is called X{chunk parsing} or X{chunking}, and the identified groups are called X{chunks}. The chunked text is represented using a shallow tree called a "chunk structure." A X{chunk structure} is a tree containing tokens and chunks, where each chunk is a subtree containing only tokens. For example, the chunk structure for base noun phrase chunks in the sentence "I saw the big dog on the hill" is:: (SENTENCE: (NP: <I>) <saw> (NP: <the> <big> <dog>) <on> (NP: <the> <hill>)) To convert a chunk structure back to a list of tokens, simply use the chunk structure's L{leaves<Tree.leaves>} method. The C{parser.chunk} module defines L{ChunkParserI}, a standard interface for chunking texts; and L{RegexpChunkParser}, a regular-expression based implementation of that interface. It also defines the L{ChunkedTaggedTokenizer} and L{ConllChunkedTokenizer} classes, which tokenize strings containing chunked and tagged texts; and L{ChunkScore}, a utility class for scoring chunk parsers. RegexpChunkParser ============= C{RegexpChunkParser} is an implementation of the chunk parser interface that uses regular-expressions over tags to chunk a text. Its C{parse} method first constructs a C{ChunkString}, which encodes a particular chunking of the input text. Initially, nothing is chunked. C{RegexpChunkParser} then applies a sequence of C{RegexpChunkParserRule}s to the C{ChunkString}, each of which modifies the chunking that it encodes. Finally, the C{ChunkString} is transformed back into a chunk structure, which is returned. C{RegexpChunkParser} can only be used to chunk a single kind of phrase. For example, you can use an C{RegexpChunkParser} to chunk the noun phrases in a text, or the verb phrases in a text; but you can not use it to simultaneously chunk both noun phrases and verb phrases in the same text. (This is a limitation of C{RegexpChunkParser}, not of chunk parsers in general.) RegexpChunkParserRules ------------------ C{RegexpChunkParserRule}s are transformational rules that update the chunking of a text by modifying its C{ChunkString}. Each C{RegexpChunkParserRule} defines the C{apply} method, which modifies the chunking encoded by a C{ChunkString}. The L{RegexpChunkParserRule} class itself can be used to implement any transformational rule based on regular expressions. There are also a number of subclasses, which can be used to implement simpler types of rules: - L{ChunkRule} chunks anything that matches a given regular expression. - L{ChinkRule} chinks anything that matches a given regular expression. - L{UnChunkRule} will un-chunk any chunk that matches a given regular expression. - L{MergeRule} can be used to merge two contiguous chunks. - L{SplitRule} can be used to split a single chunk into two smaller chunks. Tag Patterns ~~~~~~~~~~~~ C{RegexpChunkParserRule}s use a modified version of regular expression patterns, called X{tag patterns}. Tag patterns are used to match sequences of tags. Examples of tag patterns are:: r'(<DT>|<JJ>|<NN>)+' r'<NN>+' r'<NN.*>' The differences between regular expression patterns and tag patterns are: - In tag patterns, C{'<'} and C{'>'} act as parenthases; so C{'<NN>+'} matches one or more repetitions of C{'<NN>'}, not C{'<NN'} followed by one or more repetitions of C{'>'}. - Whitespace in tag patterns is ignored. So C{'<DT> | <NN>'} is equivalant to C{'<DT>|<NN>'} - In tag patterns, C{'.'} is equivalant to C{'[^{}<>]'}; so C{'<NN.*>'} matches any single tag starting with C{'NN'}. The function L{tag_pattern2re_pattern} can be used to transform a tag pattern to an equivalent regular expression pattern. Efficiency ---------- Preliminary tests indicate that C{RegexpChunkParser} can chunk at a rate of about 300 tokens/second, with a moderately complex rule set. There may be problems if C{RegexpChunkParser} is used with more than 5,000 tokens at a time. In particular, evaluation of some regular expressions may cause the Python regular expression engine to exceed its maximum recursion depth. We have attempted to minimize these problems, but it is impossible to avoid them completely. We therefore recommend that you apply the chunk parser to a single sentence at a time. Emacs Tip --------- If you evaluate the following elisp expression in emacs, it will colorize C{ChunkString}s when you use an interactive python shell with emacs or xemacs ("C-c !"):: (let () (defconst comint-mode-font-lock-keywords '(("<[^>]+>" 0 'font-lock-reference-face) ("[{}]" 0 'font-lock-function-name-face))) (add-hook 'comint-mode-hook (lambda () (turn-on-font-lock)))) You can evaluate this code by copying it to a temporary buffer, placing the cursor after the last close parenthasis, and typing "C{C-x C-e}". You should evaluate it before running the interactive session. The change will last until you close emacs. Unresolved Issues ----------------- If we use the C{re} module for regular expressions, Python's regular expression engine generates "maximum recursion depth exceeded" errors when processing very large texts, even for regular expressions that should not require any recursion. We therefore use the C{pre} module instead. But note that C{pre} does not include Unicode support, so this module will not work with unicode strings. Note also that C{pre} regular expressions are not quite as advanced as C{re} ones (e.g., no leftward zero-length assertions). @type _VALID_CHUNK_STRING: C{regexp} @var _VALID_CHUNK_STRING: A regular expression to test whether a chunk string is valid. @type _VALID_TAG_PATTERN: C{regexp} @var _VALID_TAG_PATTERN: A regular expression to test whether a tag pattern is valid. @group Interfaces: ChunkParserI @group Chunk Parsers: RegexpChunkParser @group Chunk Parser Rules: RegexpChunkParserRule, ChunkRule, ChinkRule, MergeRule, SplitRule, UnChunkRule, ChunkString @group Evaluation: ChunkScore @group Tokenizers: ChunkedTaggedTokenizer @sort: ChunkParserI, RegexpChunkParser, RegexpChunkParserRule, ChunkRule, ChinkRule, MergeRule, SplitRule, UnChunkRule, ChunkString, ChunkScore, ChunkedTaggedTokenizer, demo, demo_eval, tag_pattern2re_pattern """ from nltk import TaskI, PropertyIndirectionMixIn from nltk.parser import ParserI, AbstractParser from nltk.tree import Tree from nltk.tokenizer import TokenizerI, AbstractTokenizer from nltk.tokenizer import LineTokenizer, RegexpTokenizer, WhitespaceTokenizer from nltk.token import Token, CharSpanLocation, SubtokenContextPointer from nltk.chktype import chktype from sets import Set import types, re ypically, chunk parsers are used to find base syntactic constituants, such as base noun phrases. Unlike L{ParserI}, C{ChunkParserI} guarantees that the C{parse} method will always generate a parse. @inprop: C{SUBTOKENS}: The list of subtokens to be parsed. @outprop: C{TREE}: The parse tree. I{(generated by L{parse})} @outprop: C{TREES}: A list of possible parse trees. I{(generated by L{parse_n})} """ def parse(self, token): """ Find the best chunk structure for the given token's C{subtoknes}, and output it to the token's C{TREE} property. @param token: The token whose subtokens should be parsed. @type token: L{Token} """ assert 0, "ChunkParserI is an abstract interface" def parse_n(self, token, n=None): """ Find a list of the C{n} most likely chunk structures for the given token's C{subtokens}, and output it to the token's C{TREES} property. If the given token has fewer than C{n} chunk structures, then find all chunk structures. The chunk structures should be stored in descending order of estimated likelihood. @type n: C{int} @param n: The number of chunk structures to generate. At most C{n} chunk structures will be generated. If C{n} is not specified, generate all chunk structures. @type token: L{Token} @param token: The token whose subtokens should be chunked. """ assert 0, "ChunkParserI is an abstract interface" ut, based on a number of statistics (precision, recall, f-measure, misssed chunks, incorrect chunks). It can also combine the scores from the parsing of multiple texts; this makes it signifigantly easier to evaluate a chunk parser that operates one sentence at a time. Texts are evaluated with the C{score} method. The results of evaluation can be accessed via a number of accessor methods, such as C{precision} and C{f_measure}. A typical use of the C{ChunkScore} class is:: >>> chunkscore = ChunkScore() >>> for correct in correct_sentences: ... guess = chunkparser.parse(correct.leaves()) ... chunkscore.score(correct, guess) >>> print 'F Measure:', chunkscore.f_measure() F Measure: 0.823 @ivar kwargs: Keyword arguments: - max_tp_examples: The maximum number actual examples of true positives to record. This affects the C{correct} member function: C{correct} will not return more than this number of true positive examples. This does *not* affect any of the numerical metrics (precision, recall, or f-measure) - max_fp_examples: The maximum number actual examples of false positives to record. This affects the C{incorrect} member function and the C{guessed} member function: C{incorrect} will not return more than this number of examples, and C{guessed} will not return more than this number of true positive examples. This does *not* affect any of the numerical metrics (precision, recall, or f-measure) - max_fn_examples: The maximum number actual examples of false negatives to record. This affects the C{missed} member function and the C{correct} member function: C{missed} will not return more than this number of examples, and C{correct} will not return more than this number of true negative examples. This does *not* affect any of the numerical metrics (precision, recall, or f-measure) @type _tp: C{list} of C{Token} @ivar _tp: List of true positives @type _fp: C{list} of C{Token} @ivar _fp: List of false positives @type _fn: C{list} of C{Token} @ivar _fn: List of false negatives @type _tp_num: C{int} @ivar _tp_num: Number of true positives @type _fp_num: C{int} @ivar _fp_num: Number of false positives @type _fn_num: C{int} @ivar _fn_num: Number of false negatives. """ def __init__(self, **kwargs): self._correct = Set() self._guessed = Set() self._tp = Set() self._fp = Set() self._fn = Set() self._max_tp = kwargs.get('max_tp_examples', 100) self._max_fp = kwargs.get('max_fp_examples', 100) self._max_fn = kwargs.get('max_fn_examples', 100) self._tp_num = 0 self._fp_num = 0 self._fn_num = 0 def _childtuple(self, t): return tuple([c.freeze() for c in t]) def score(self, correct, guessed): """ Given a correctly chunked text, score another chunked text. Merge the results with all previous scorings. Note that when the score() function is used repeatedly, each token I{must} have a unique location. For sentence-at-a-time chunking, it is recommended that you use locations like C{@12w@3s} (the word at index 12 of the sentence at index 3). @type correct: chunk structure @param correct: The known-correct ("gold standard") chunked sentence. @type guessed: chunk structure @param guessed: The chunked sentence to be scored. """ assert chktype(1, correct, Tree) assert chktype(2, guessed, Tree) self._correct |= Set([self._childtuple(t) for t in correct if isinstance(t, Tree)]) self._guessed |= Set([self._childtuple(t) for t in guessed if isinstance(t, Tree)]) self._tp = self._guessed & self._correct self._fn = self._correct - self._guessed self._fp = self._guessed - self._correct self._tp_num = len(self._tp) self._fp_num = len(self._fp) self._fn_num = len(self._fn) def precision(self): """ @return: the overall precision for all texts that have been scored by this C{ChunkScore}. @rtype: C{float} """ div = self._tp_num + self._fp_num if div == 0: return 0 else: return float(self._tp_num) / div def recall(self): """ @return: the overall recall for all texts that have been scored by this C{ChunkScore}. @rtype: C{float} """ div = self._tp_num + self._fn_num if div == 0: return 0 else: return float(self._tp_num) / div def f_measure(self, alpha=0.5): """ @return: the overall F measure for all texts that have been scored by this C{ChunkScore}. @rtype: C{float} @param alpha: the relative weighting of precision and recall. Larger alpha biases the score towards the precision value, while smaller alpha biases the score towards the recall value. C{alpha} should have a value in the range [0,1]. @type alpha: C{float} """ assert chktype(1, alpha, types.FloatType, types.IntType) p = self.precision() r = self.recall() if p == 0 or r == 0: # what if alpha is 0 or 1? return 0 return 1/(alpha/p + (1-alpha)/r) def missed(self): """ @rtype: C{Set} of C{Token} @return: the set of chunks which were included in the correct chunk structures, but not in the guessed chunk structures. Each chunk is encoded as a single token, spanning the chunk. This encoding makes it easier to examine the missed chunks. """ return list(self._fn) def incorrect(self): """ @rtype: C{Set} of C{Token} @return: the set of chunks which were included in the guessed chunk structures, but not in the correct chunk structures. Each chunk is encoded as a single token, spanning the chunk. This encoding makes it easier to examine the incorrect chunks. """ return list(self._fp) def correct(self): """ @rtype: C{Set} of C{Token} @return: the set of chunks which were included in the correct chunk structures. Each chunk is encoded as a single token, spanning the chunk. This encoding makes it easier to examine the correct chunks. """ return list(self._correct) def guessed(self): """ @rtype: C{Set} of C{Token} @return: the set of chunks which were included in the guessed chunk structures. Each chunk is encoded as a single token, spanning the chunk. This encoding makes it easier to examine the guessed chunks. """ return list(self._guessed) def __len__(self): return self._tp_num + self._fn_num def __repr__(self): """ @rtype: C{String} @return: a concise representation of this C{ChunkScoring}. """ return '<ChunkScoring of '+`len(self)`+' chunks>' def __str__(self): """ @rtype: C{String} @return: a verbose representation of this C{ChunkScoring}. This representation includes the precision, recall, and f-measure scores. For other information about the score, use the accessor methods (e.g., C{missed()} and C{incorrect()}). """ return ("ChunkParser score:\n" + (" Precision: %5.1f%%\n" % (self.precision()*100)) + (" Recall: %5.1f%%\n" % (self.recall()*100))+ (" F-Measure: %5.1f%%\n" % (self.f_measure()*100))) def _chunk_toks(self, text): """ @return: The list of tokens contained in C{text}. """ return [tok for tok in text if isinstance(tok, AbstractTree)] ##////////////////////////////////////////////////////// ## Precompiled regular expressions ##////////////////////////////////////////////////////// _TAGCHAR = r'[^\{\}<>]' _TAG = r'(<%s+?>)' % _TAGCHAR _VALID_TAG_PATTERN = re.compile(r'^((%s|<%s>)*)$' % ('[^\{\}<>]+', '[^\{\}<>]+')) ##////////////////////////////////////////////////////// ## ChunkString ##////////////////////////////////////////////////////// class ChunkString(PropertyIndirectionMixIn): """ A string-based encoding of a particular chunking of a text. Internally, the C{ChunkString} class uses a single string to encode the chunking of the input text. This string contains a sequence of angle-bracket delimited tags, with chunking indicated by braces. An example of this encoding is:: {<DT><JJ><NN>}<VBN><IN>{<DT><NN>}<.>{<DT><NN>}<VBD><.> C{ChunkString} are created from tagged texts (i.e., C{list}s of C{tokens} whose type is C{TaggedType}). Initially, nothing is chunked. The chunking of a C{ChunkString} can be modified with the C{xform} method, which uses a regular expression to transform the string representation. These transformations should only add and remove braces; they should I{not} modify the sequence of angle-bracket delimited tags. @type _str: C{string} @ivar _str: The internal string representation of the text's encoding. This string representation contains a sequence of angle-bracket delimited tags, with chunking indicated by braces. An example of this encoding is:: {<DT><JJ><NN>}<VBN><IN>{<DT><NN>}<.>{<DT><NN>}<VBD><.> @type _ttoks: C{list} of C{Token} @ivar _ttoks: The text whose chunking is encoded by this C{ChunkString}. @ivar _debug: The debug level. See the constructor docs. @cvar IN_CHUNK_PATTERN: A zero-width regexp pattern string that will only match positions that are in chunks. @cvar IN_CHINK_PATTERN: A zero-width regexp pattern string that will only match positions that are in chinks. """ IN_CHUNK_PATTERN = r'(?=[^\{]*\})' IN_CHINK_PATTERN = r'(?=[^\}]*(\{|$))' _CHUNK = r'(\{%s+?\})+?' % _TAG _CHINK = r'(%s+?)+?' % _TAG _VALID = re.compile(r'(\{?%s\}?)*?' % _TAG) _BRACKETS = re.compile('[^\{\}]+') _BALANCED_BRACKETS = re.compile(r'(\{\})*$') def __init__(self, tagged_tokens, debug_level=3, **property_names): """ Construct a new C{ChunkString} that encodes the chunking of the text C{tagged_tokens}. @type tagged_tokens: C{list} of C{Token} with C{TaggedType}s @param tagged_tokens: The text whose chunking is encoded by this C{ChunkString}. @type debug_level: int @param debug_level: The level of debugging which should be applied to transformations on the C{ChunkString}. The valid levels are: - 0: no checks - 1: full check on to_chunkstruct - 2: full check on to_chunkstruct and cursory check after each transformation. - 3: full check on to_chunkstruct and full check after each transformation. We recommend you use at least level 1. You should probably use level 3 if you use any non-standard subclasses of C{RegexpChunkParserRule}. """ assert chktype(1, tagged_tokens, [Token, Tree], (Token, Tree), Tree) assert chktype(2, debug_level, types.IntType) PropertyIndirectionMixIn.__init__(self, **property_names) self._ttoks = tagged_tokens tags = [self._tag(tok) for tok in tagged_tokens] self._str = '<' + '><'.join(tags) + '>' self._debug = debug_level def _tag(self, tok): if isinstance(tok, Token): return tok[self.property('TAG')] elif isinstance(tok, Tree): return tok.node else: raise ValueError, 'tagged_tokens must contain tokens and trees' def _verify(self, verify_tags): """ Check to make sure that C{_str} still corresponds to some chunked version of C{_ttoks}. @type verify_tags: C{boolean} @param verify_tags: Whether the individual tags should be checked. If this is false, C{_verify} will check to make sure that C{_str} encodes a chunked version of I{some} list of tokens. If this is true, then C{_verify} will check to make sure that the tags in C{_str} match those in C{_ttoks}. @raise ValueError: if this C{ChunkString}'s internal string representation is invalid or not consistant with _ttoks. """ # Check overall form if not ChunkString._VALID.match(self._str): raise ValueError('Transformation generated invalid chunkstring') # Check that parens are balanced. If the string is long, we # have to do this in pieces, to avoid a maximum recursion # depth limit for regular expressions. brackets = ChunkString._BRACKETS.sub('', self._str) for i in range(1+len(brackets)/5000): substr = brackets[i*5000:i*5000+5000] if not ChunkString._BALANCED_BRACKETS.match(substr): raise ValueError('Transformation generated invalid '+ 'chunkstring') if verify_tags<=0: return tags1 = (re.split(r'[\{\}<>]+', self._str))[1:-1] tags2 = [self._tag(tok) for tok in self._ttoks] if tags1 != tags2: raise ValueError('Transformation generated invalid chunkstring') def to_chunkstruct(self, chunk_node='CHUNK', top_node='TEXT'): """ @return: the chunk structure encoded by this C{ChunkString}. A chunk structure is a C{list} containing tagged tokens and sublists of tagged tokens, where each sublist represents a single chunk. @rtype: chunk structure @raise ValueError: If a transformation has generated an invalid chunkstring. """ if self._debug > 0: self._verify(1) # Extract a list of alternating chinks & chunks pieces = re.split('[{}]', self._str) # Use this alternating list to create the chunkstruct. chunkstruct = [] index = 0 piece_in_chunk = 0 for piece in pieces: # Find the list of tokens contained in this piece. length = piece.count('<') subsequence = self._ttoks[index:index+length] # Add this list of tokens to our chunkstruct. if piece_in_chunk: chunkstruct.append(Tree(chunk_node, subsequence)) else: chunkstruct += subsequence # Update index, piece_in_chunk index += length piece_in_chunk = not piece_in_chunk return Tree(top_node, chunkstruct) def xform(self, regexp, repl): """ Apply the given transformation to this C{ChunkString}'s string encoding. In particular, find all occurances that match C{regexp}, and replace them using C{repl} (as done by C{re.sub}). This transformation should only add and remove braces; it should I{not} modify the sequence of angle-bracket delimited tags. Furthermore, this transformation may not result in improper bracketing. Note, in particular, that bracketing may not be nested. @type regexp: C{string} or C{regexp} @param regexp: A regular expression matching the substring that should be replaced. This will typically include a named group, which can be used by C{repl}. @type repl: C{string} @param repl: An expression specifying what should replace the matched substring. Typically, this will include a named replacement group, specified by C{regexp}. @rtype: C{None} @raise ValueError: If this transformation generateds an invalid chunkstring. """ if type(regexp).__name__ != 'SRE_Pattern': assert chktype(1, regexp, types.StringType) assert chktype(2, repl, types.StringType) self._str = re.sub(regexp, repl, self._str) # interfere with other transformations. self._str = re.sub('\{\}', '', self._str) # Make sure that the transformation was legal. if self._debug > 1: self._verify(self._debug-2) def xform_chunk(self, pattern, repl): # Docstring adopted from xform's docstring. """ Apply the given transformation to the chunks in this C{ChunkString}'s string encoding. In particular, find all occurances within chunks that match C{regexp}, and replace them using C{repl} (as done by C{re.sub}). This transformation should only add and remove braces; it should I{not} modify the sequence of angle-bracket delimited tags. Furthermore, this transformation may not result in improper bracketing. Note, in particular, that bracketing may not be nested. @type pattern: C{string} @param pattern: A regular expression pattern matching the substring that should be replaced. This will typically include a named group, which can be used by C{repl}. @type repl: C{string} @param repl: An expression specifying what should replace the matched substring. Typically, this will include a named replacement group, specified by C{regexp}. @rtype: C{None} @raise ValueError: If this transformation generateds an invalid chunkstring. """ if type(pattern).__name__ == 'SRE_Pattern': pattern = pattern.pattern assert chktype(1, pattern, types.StringType) assert chktype(2, repl, types.StringType) self.xform(pattern+ChunkString.IN_CHUNK_PATTERN, repl) def xform_chink(self, pattern, repl): # Docstring adopted from xform's docstring. """ Apply the given transformation to the chinks in this C{ChinkString}'s string encoding. In particular, find all occurances within chinks that match C{regexp}, and replace them using C{repl} (as done by C{re.sub}). This transformation should only add and remove braces; it should I{not} modify the sequence of angle-bracket delimited tags. Furthermore, this transformation may not result in improper bracketing. Note, in particular, that bracketing may not be nested. @type pattern: C{string} or C{regexp} @param pattern: A regular expression pattern matching the substring that should be replaced. This will typically include a named group, which can be used by C{repl}. @type repl: C{string} @param repl: An expression specifying what should replace the matched substring. Typically, this will include a named replacement group, specified by C{regexp}. @rtype: C{None} @raise ValueError: If this transformation generateds an invalid chunkstring. """ if type(pattern).__name__ == 'SRE_Pattern': pattern = pattern.pattern assert chktype(1, pattern, types.StringType) assert chktype(2, repl, types.StringType) self.xform(pattern+ChunkString.IN_CHINK_PATTERN, repl) def __repr__(self): """ @rtype: C{string} @return: A string representation of this C{ChunkString}. This string representation has the form:: <ChunkString: '{<DT><JJ><NN>}<VBN><IN>{<DT><NN>}'> """ return '<ChunkString: %s>' % `self._str` def __str__(self): """ @rtype: C{string} @return: A formatted representation of this C{ChunkString}'s string encoding. This representation will include extra spaces to ensure that tags will line up with the representation of other C{ChunkStrings} for the same text, regardless of the chunking. """ str = re.sub(r'>(?!\})', r'> ', self._str) str = re.sub(r'([^\{])<', r'\1 <', str) if str[0] == '<': str = ' ' + str return str ag pattern} is a modified verison of a regular expression, designed for matching sequences of tags. The differences between regular expression patterns and tag patterns are: - In tag patterns, C{'<'} and C{'>'} act as parenthases; so C{'<NN>+'} matches one or more repetitions of C{'<NN>'}, not C{'<NN'} followed by one or more repetitions of C{'>'}. - Whitespace in tag patterns is ignored. So C{'<DT> | <NN>'} is equivalant to C{'<DT>|<NN>'} - In tag patterns, C{'.'} is equivalant to C{'[^{}<>]'}; so C{'<NN.*>'} matches any single tag starting with C{'NN'}. In particular, C{tag_pattern2re_pattern} performs the following transformations on the given pattern: - Replace '.' with '[^<>{}]' - Remove any whitespace - Add extra parens around '<' and '>', to make '<' and '>' act like parenthases. E.g., so that in '<NN>+', the '+' has scope over the entire '<NN>'; and so that in '<NN|IN>', the '|' has scope over 'NN' and 'IN', but not '<' or '>'. - Check to make sure the resulting pattern is valid. @type tag_pattern: C{string} @param tag_pattern: The tag pattern to convert to a regular expression pattern. @raise ValueError: If C{tag_pattern} is not a valid tag pattern. In particular, C{tag_pattern} should not include braces; and it should not contain nested or mismatched angle-brackets. @rtype: C{string} @return: A regular expression pattern corresponding to C{tag_pattern}. """ assert chktype(1, tag_pattern, types.StringType) tag_pattern = re.sub(r'\s', '', tag_pattern) tag_pattern = re.sub(r'<', '(<(', tag_pattern) tag_pattern = re.sub(r'>', ')>)', tag_pattern) if not _VALID_TAG_PATTERN.match(tag_pattern): raise ValueError('Bad tag pattern: %s' % tag_pattern) # the pattern backwards (with lookahead assertions). This can be # made much cleaner once we can switch back to SRE. def reverse_str(str): lst = list(str) lst.reverse() return ''.join(lst) tc_rev = reverse_str(_TAGCHAR) reversed = reverse_str(tag_pattern) reversed = re.sub(r'\.(?!\$\\\$*($|[^\\]))', tc_rev, reversed) tag_pattern = reverse_str(reversed) return tag_pattern class RegexpChunkParserRule: """ A rule specifying how to modify the chunking in a C{ChunkString}, using a transformational regular expression. The C{RegexpChunkParserRule} class itself can be used to implement any transformational rule based on regular expressions. There are also a number of subclasses, which can be used to implement simpler types of rules, based on matching regular expressions. Each C{RegexpChunkParserRule} has a regular expression and a replacement expression. When a C{RegexpChunkParserRule} is X{applied} to a C{ChunkString}, it searches the C{ChunkString} for any substring that matches the regular expression, and replaces it using the replacement expression. This search/replace operation has the same semantics as C{re.sub}. Each C{RegexpChunkParserRule} also has a description string, which gives a short (typically less than 75 characters) description of the purpose of the rule. This transformation defined by this C{RegexpChunkParserRule} should only add and remove braces; it should I{not} modify the sequence of angle-bracket delimited tags. Furthermore, this transformation may not result in nested or mismatched bracketing. """ def __init__(self, regexp, repl, descr): """ Construct a new RegexpChunkParserRule. @type regexp: C{regexp} or C{string} @param regexp: This C{RegexpChunkParserRule}'s regular expression. When this rule is applied to a C{ChunkString}, any substring that matches C{regexp} will be replaced using the replacement string C{repl}. Note that this must be a normal regular expression, not a tag pattern. @type repl: C{string} @param repl: This C{RegexpChunkParserRule}'s replacement expression. When this rule is applied to a C{ChunkString}, any substring that matches C{regexp} will be replaced using C{repl}. @type descr: C{string} @param descr: A short description of the purpose and/or effect of this rule. """ if type(regexp).__name__ == 'SRE_Pattern': regexp = regexp.pattern assert chktype(1, regexp, types.StringType) assert chktype(2, repl, types.StringType) assert chktype(3, descr, types.StringType) self._repl = repl self._descr = descr if type(regexp) == types.StringType: self._regexp = re.compile(regexp) else: self._regexp = regexp def apply(self, chunkstr): # Keep docstring generic so we can inherit it. """ Apply this rule to the given C{ChunkString}. See the class reference documentation for a description of what it means to apply a rule. @type chunkstr: C{ChunkString} @param chunkstr: The chunkstring to which this rule is applied. @rtype: C{None} @raise ValueError: If this transformation generateds an invalid chunkstring. """ assert chktype(1, chunkstr, ChunkString) chunkstr.xform(self._regexp, self._repl) def descr(self): """ @rtype: C{string} @return: a short description of the purpose and/or effect of this rule. """ return self._descr def __repr__(self): """ @rtype: C{string} @return: A string representation of this rule. This string representation has the form:: <RegexpChunkParserRule: '{<IN|VB.*>}'->'<IN>'> Note that this representation does not include the description string; that string can be accessed separately with the C{descr} method. """ return ('<RegexpChunkParserRule: '+`self._regexp.pattern`+ '->'+`self._repl`+'>') class ChunkRule(RegexpChunkParserRule): """ A rule specifying how to add chunks to a C{ChunkString}, using a matching tag pattern. When applied to a C{ChunkString}, it will find any substring that matches this tag pattern and that is not already part of a chunk, and create a new chunk containing that substring. """ def __init__(self, tag_pattern, descr): """ Construct a new C{ChunkRule}. @type tag_pattern: C{string} @param tag_pattern: This rule's tag pattern. When applied to a C{ChunkString}, this rule will chunk any substring that matches this tag pattern and that is not already part of a chunk. @type descr: C{string} @param descr: A short description of the purpose and/or effect of this rule. """ assert chktype(1, tag_pattern, types.StringType) assert chktype(2, descr, types.StringType) self._pattern = tag_pattern regexp = re.compile('(?P<chunk>%s)%s' % (tag_pattern2re_pattern(tag_pattern), ChunkString.IN_CHINK_PATTERN)) RegexpChunkParserRule.__init__(self, regexp, '{\g<chunk>}', descr) def __repr__(self): """ @rtype: C{string} @return: A string representation of this rule. This string representation has the form:: <ChunkRule: '<IN|VB.*>'> Note that this representation does not include the description string; that string can be accessed separately with the C{descr} method. """ return '<ChunkRule: '+`self._pattern`+'>' class ChinkRule(RegexpChunkParserRule): """ A rule specifying how to remove chinks to a C{ChunkString}, using a matching tag pattern. When applied to a C{ChunkString}, it will find any substring that matches this tag pattern and that is contained in a chunk, and remove it from that chunk, thus creating two new chunks. """ def __init__(self, tag_pattern, descr): """ Construct a new C{ChinkRule}. @type tag_pattern: C{string} @param tag_pattern: This rule's tag pattern. When applied to a C{ChunkString}, this rule will find any substring that matches this tag pattern and that is contained in a chunk, and remove it from that chunk, thus creating two new chunks. @type descr: C{string} @param descr: A short description of the purpose and/or effect of this rule. """ assert chktype(1, tag_pattern, types.StringType) assert chktype(2, descr, types.StringType) self._pattern = tag_pattern regexp = re.compile('(?P<chink>%s)%s' % (tag_pattern2re_pattern(tag_pattern), ChunkString.IN_CHUNK_PATTERN)) RegexpChunkParserRule.__init__(self, regexp, '}\g<chink>{', descr) def __repr__(self): """ @rtype: C{string} @return: A string representation of this rule. This string representation has the form:: <ChinkRule: '<IN|VB.*>'> Note that this representation does not include the description string; that string can be accessed separately with the C{descr} method. """ return '<ChinkRule: '+`self._pattern`+'>' class UnChunkRule(RegexpChunkParserRule): """ A rule specifying how to remove chunks to a C{ChunkString}, using a matching tag pattern. When applied to a C{ChunkString}, it will find any complete chunk that matches this tag pattern, and un-chunk it. """ def __init__(self, tag_pattern, descr): """ Construct a new C{UnChunkRule}. @type tag_pattern: C{string} @param tag_pattern: This rule's tag pattern. When applied to a C{ChunkString}, this rule will find any complete chunk that matches this tag pattern, and un-chunk it. @type descr: C{string} @param descr: A short description of the purpose and/or effect of this rule. """ assert chktype(1, tag_pattern, types.StringType) assert chktype(2, descr, types.StringType) self._pattern = tag_pattern regexp = re.compile('\{(?P<chunk>%s)\}' % tag_pattern2re_pattern(tag_pattern)) RegexpChunkParserRule.__init__(self, regexp, '\g<chunk>', descr) def __repr__(self): """ @rtype: C{string} @return: A string representation of this rule. This string representation has the form:: <UnChunkRule: '<IN|VB.*>'> Note that this representation does not include the description string; that string can be accessed separately with the C{descr} method. """ return '<UnChunkRule: '+`self._pattern`+'>' class MergeRule(RegexpChunkParserRule): """ A rule specifying how to merge chunks in a C{ChunkString}, using two matching tag patterns: a left pattern, and a right pattern. When applied to a C{ChunkString}, it will find any chunk whose end matches left pattern, and immediately followed by a chunk whose beginning matches right pattern. It will then merge those two chunks into a single chunk. """ def __init__(self, left_tag_pattern, right_tag_pattern, descr): """ Construct a new C{MergeRule}. @type right_tag_pattern: C{string} @param right_tag_pattern: This rule's right tag pattern. When applied to a C{ChunkString}, this rule will find any chunk whose end matches C{left_tag_pattern}, and immediately followed by a chunk whose beginning matches this pattern. It will then merge those two chunks into a single chunk. @type left_tag_pattern: C{string} @param left_tag_pattern: This rule's left tag pattern. When applied to a C{ChunkString}, this rule will find any chunk whose end matches this pattern, and immediately followed by a chunk whose beginning matches C{right_tag_pattern}. It will then merge those two chunks into a single chunk. @type descr: C{string} @param descr: A short description of the purpose and/or effect of this rule. """ assert chktype(1, left_tag_pattern, types.StringType) assert chktype(2, right_tag_pattern, types.StringType) assert chktype(3, descr, types.StringType) self._left_tag_pattern = left_tag_pattern self._right_tag_pattern = right_tag_pattern regexp = re.compile('(?P<left>%s)}{(?=%s)' % (tag_pattern2re_pattern(left_tag_pattern), tag_pattern2re_pattern(right_tag_pattern))) RegexpChunkParserRule.__init__(self, regexp, '\g<left>', descr) def __repr__(self): """ @rtype: C{string} @return: A string representation of this rule. This string representation has the form:: <MergeRule: '<NN|DT|JJ>', '<NN|JJ>'> Note that this representation does not include the description string; that string can be accessed separately with the C{descr} method. """ return ('<MergeRule: '+`self._left_tag_pattern`+', '+ `self._right_tag_pattern`+'>') class SplitRule(RegexpChunkParserRule): """ A rule specifying how to split chunks in a C{ChunkString}, using two matching tag patterns: a left pattern, and a right pattern. When applied to a C{ChunkString}, it will find any chunk that matches the left pattern followed by the right pattern. It will then split the chunk into two new chunks, at the point between the two pattern matches. """ def __init__(self, left_tag_pattern, right_tag_pattern, descr): """ Construct a new C{SplitRule}. @type right_tag_pattern: C{string} @param right_tag_pattern: This rule's right tag pattern. When applied to a C{ChunkString}, this rule will find any chunk containing a substring that matches C{left_tag_pattern} followed by this pattern. It will then split the chunk into two new chunks at the point between these two matching patterns. @type left_tag_pattern: C{string} @param left_tag_pattern: This rule's left tag pattern. When applied to a C{ChunkString}, this rule will find any chunk containing a substring that matches this pattern followed by C{right_tag_pattern}. It will then split the chunk into two new chunks at the point between these two matching patterns. @type descr: C{string} @param descr: A short description of the purpose and/or effect of this rule. """ assert chktype(1, left_tag_pattern, types.StringType) assert chktype(2, right_tag_pattern, types.StringType) assert chktype(3, descr, types.StringType) self._left_tag_pattern = left_tag_pattern self._right_tag_pattern = right_tag_pattern regexp = re.compile('(?P<left>%s)(?=%s)' % (tag_pattern2re_pattern(left_tag_pattern), tag_pattern2re_pattern(right_tag_pattern))) RegexpChunkParserRule.__init__(self, regexp, r'\g<left>}{', descr) def __repr__(self): """ @rtype: C{string} @return: A string representation of this rule. This string representation has the form:: <SplitRule: '<NN>', '<DT>'> Note that this representation does not include the description string; that string can be accessed separately with the C{descr} method. """ return ('<SplitRule: '+`self._left_tag_pattern`+', '+ `self._right_tag_pattern`+'>') er} uses a sequence X{rules} to find chunks within a text. The chunking of the text is encoded using a C{ChunkString}, and each rule acts by modifying the chunking in the C{ChunkString}. The rules are all implemented using regular expression matching and substitution. The C{RegexpChunkParserRule} class and its subclasses (C{ChunkRule}, C{ChinkRule}, C{UnChunkRule}, C{MergeRule}, and C{SplitRule}) define the rules that are used by C{RegexpChunkParser}. Each rule defines an C{apply} method, which modifies the chunking encoded by a given C{ChunkString}. @type _rules: C{list} of C{RegexpChunkParserRule} @ivar _rules: The list of rules that should be applied to a text. @type _trace: C{int} @ivar _trace: The default level of tracing. @inprop: C{SUBTOKENS}: The list of subtokens to be chunked. @inprop: C{LEAF}: The string content of the subtokens. @outprop: C{TREE}: The chunk structure. I{(generated by L{parse})} @outprop: C{TREES}: A list of possible chunk structures. I{(generated by L{parse_n})} """ def __init__(self, rules, chunk_node='CHUNK', top_node='TEXT', trace=0, **property_names): """ Construct a new C{RegexpChunkParser}. @type rules: C{list} of C{RegexpChunkParserRule} @param rules: The sequence of rules that should be used to generate the chunking for a tagged text. @type chunk_node: C{string} @param chunk_node: The node value that should be used for chunk subtrees. This is typically a short string describing the type of information contained by the chunk, such as C{"NP"} for base noun phrases. @type top_node: C{string} @param top_node: The node value that should be used for the top node of the chunk structure. @type trace: C{int} @param trace: The level of tracing that should be used when parsing a text. C{0} will generate no tracing output; C{1} will generate normal tracing output; and C{2} or highter will generate verbose tracing output. """ assert chktype(1, rules, [RegexpChunkParserRule], (RegexpChunkParserRule,)) assert chktype(4, trace, types.IntType) self._rules = rules self._trace = trace self._chunk_node = chunk_node self._top_node = top_node AbstractParser.__init__(self, **property_names) def _trace_apply(self, chunkstr, verbose): """ Apply each of this C{RegexpChunkParser}'s rules to C{chunkstr}, in turn. Generate trace output between each rule. If C{verbose} is true, then generate verbose output. @type chunkstr: C{ChunkString} @param chunkstr: The chunk string to which each rule should be applied. @type verbose: C{boolean} @param verbose: Whether output should be verbose. @rtype: C{None} """ indent = ' '*(35-len(str(chunkstr))/2) print 'Input:' print indent, chunkstr for rule in self._rules: rule.apply(chunkstr) if verbose: print rule.descr()+' ('+`rule`+'):' else: print rule.descr()+':' print indent, chunkstr print def _notrace_apply(self, chunkstr): """ Apply each of this C{RegexpChunkParser}'s rules to C{chunkstr}, in turn. @param chunkstr: The chunk string to which each rule should be applied. @type chunkstr: C{ChunkString} @rtype: C{None} """ for rule in self._rules: rule.apply(chunkstr) def parse(self, token, trace=None): """ @rtype: chunk structure @return: a chunk structure that encodes the chunks in a given tagged sentence. A chunk is a non-overlapping linguistic group, such as a noun phrase. The set of chunks identified in the chunk structure depends on the rules used to define this C{RegexpChunkParser}. @type trace: C{int} @param trace: The level of tracing that should be used when parsing a text. C{0} will generate no tracing output; C{1} will generate normal tracing output; and C{2} or highter will generate verbose tracing output. This value overrides the trace level value that was given to the constructor. """ assert chktype(1, token, Token) assert chktype(2, trace, types.NoneType, types.IntType) SUBTOKENS = self.property('SUBTOKENS') TREE = self.property('TREE') if len(token[SUBTOKENS]) == 0: print 'Warning: parsing empty text' token[TREE] = Tree(self._top_node, []) return if trace == None: trace = self._trace chunkstr = ChunkString(token[SUBTOKENS], **self.property_names()) if trace: verbose = (trace>1) self._trace_apply(chunkstr, verbose) else: self._notrace_apply(chunkstr) tree = chunkstr.to_chunkstruct(self._chunk_node, self._top_node) token[TREE] = tree def rules(self): """ @return: the sequence of rules used by this C{ChunkParser}. @rtype: C{list} of C{RegexpChunkParserRule} """ return self._rules def __repr__(self): """ @return: a concise string representation of this C{RegexpChunkParser}. @rtype: C{string} """ return "<RegexpChunkParser with %d rules>" % len(self._rules) def __str__(self): """ @return: a verbose string representation of this C{RegexpChunkParser}. @rtype: C{string} """ s = "RegexpChunkParser with %d rules:\n" % len(self._rules) margin = 0 for rule in self._rules: margin = max(margin, len(rule.descr())) if margin < 35: format = " %" + `-(margin+3)` + "s%s\n" else: format = " %s\n %s\n" for rule in self._rules: s += format % (rule.descr(), `rule`) return s[:-1] s function assumes that C{text} contains one sentence per line, and that each sentence has the form expected by C{ChunkedTaggedTokenizer}. It runs the given chunk parser on each sentence in the text, and scores the result. It prints the final score (precision, recall, and f-measure); and reports the set of chunks that were missed and the set of chunks that were incorrect. (At most 10 missing chunks and 10 incorrect chunks are reported). @param chunkparser: The chunkparser to be tested @type chunkparser: C{ChunkParserI} @param text: The chunked tagged text that should be used for evaluation. @type text: C{string} """ assert chktype(1, chunkparser, ChunkParserI) assert chktype(1, text, types.StringType) chunkscore = ChunkScore() from nltk.tokenreader import ChunkedTaggedTokenReader ctt = ChunkedTaggedTokenReader(chunk_node='NP', SUBTOKENS='WORDS') for sentence in text.split('\n'): sentence = sentence.strip() if not sentence: continue gold = ctt.read_token(sentence) test = Token(WORDS=gold['WORDS']) chunkparser.parse(test) chunkscore.score(gold['TREE'], test['TREE']) print '/'+('='*75)+'\\' print 'Scoring', chunkparser print ('-'*77) print 'Precision: %5.1f%%' % (chunkscore.precision()*100), ' '*4, print 'Recall: %5.1f%%' % (chunkscore.recall()*100), ' '*6, print 'F-Measure: %5.1f%%' % (chunkscore.f_measure()*100) if chunkscore.missed(): print 'Missed:' missed = chunkscore.missed() missed.sort() for chunk in missed[:10]: print ' ', chunk if len(chunkscore.missed()) > 10: print ' ...' # Incorrect chunks. if chunkscore.incorrect(): print 'Incorrect:' incorrect = chunkscore.incorrect() incorrect.sort() # sort, so they'll always be listed in the same order. for chunk in incorrect[:10]: print ' ', chunk if len(chunkscore.incorrect()) > 10: print ' ...' print '\\'+('='*75)+'/' def demo(): """ A demonstration for the C{RegexpChunkParser} class. A single text is parsed with four different chunk parsers, using a variety of rules and strategies. """ text = """\ [ the/DT little/JJ cat/NN ] sat/VBD on/IN [ the/DT mat/NN ] ./. [ The/DT cats/NNS ] ./. [ John/NNP ] saw/VBD [the/DT cat/NN] [the/DT dog/NN] liked/VBD ./.""" print '*'*75 print 'Evaluation text:' print text print '*'*75 r1 = ChunkRule(r'<DT>?<JJ>*<NN.*>', 'Chunk NPs') cp = RegexpChunkParser([r1], chunk_node='NP', top_node='S', trace=1, SUBTOKENS='WORDS') demo_eval(cp, text) print r1 = ChunkRule(r'<.*>+', 'Chunk everything') r2 = ChinkRule(r'<VB.*>|<IN>|<\.>', 'Unchunk VB and IN and .') cp = RegexpChunkParser([r1, r2], chunk_node='NP', top_node='S', trace=1, SUBTOKENS='WORDS') demo_eval(cp, text) print # Unchunk non-NP words, and then merge consecutive NPs r1 = ChunkRule(r'(<.*>)', 'Chunk each tag') r2 = UnChunkRule(r'<VB.*>|<IN>|<.>', 'Unchunk VB? and IN and .') r3 = MergeRule(r'<DT|JJ|NN.*>', r'<DT|JJ|NN.*>', 'Merge NPs') cp = RegexpChunkParser([r1,r2,r3], chunk_node='NP', top_node='S', trace=1, SUBTOKENS='WORDS') demo_eval(cp, text) print # Chunk sequences of NP words, and split them at determiners r1 = ChunkRule(r'(<DT|JJ|NN.*>+)', 'Chunk sequences of DT&JJ&NN') r2 = SplitRule('', r'<DT>', 'Split before DT') cp = RegexpChunkParser([r1,r2], chunk_node='NP', top_node='S', trace=1, SUBTOKENS='WORDS') demo_eval(cp, text) print if __name__ == '__main__': demo()

false

true

f7fbbf62f2943cc79ec76501fd55c8862927df08

511

py

Python

Python/Collections.OrderedDict.py

WinrichSy/HackerRank-Solutions

ed928de50cbbbdf0aee471630f6c04f9a0f69a1f

[ "Apache-2.0" ]

null

Python/Collections.OrderedDict.py

WinrichSy/HackerRank-Solutions

ed928de50cbbbdf0aee471630f6c04f9a0f69a1f

[ "Apache-2.0" ]

null

Python/Collections.OrderedDict.py

WinrichSy/HackerRank-Solutions

ed928de50cbbbdf0aee471630f6c04f9a0f69a1f

[ "Apache-2.0" ]

null

#Collections.OrderedDict() #https://www.hackerrank.com/challenges/py-collections-ordereddict/problem from collections import OrderedDict items = int(input()) ordered_dict = OrderedDict() for i in range(items): item_price = input().split() item = ' '.join(item_price[0:-1]) price = int(item_price[-1]) if item not in ordered_dict.keys(): ordered_dict[item] = price else: ordered_dict[item] += price for key, val in ordered_dict.items(): print('{} {}'.format(key, val))

26.894737

73

0.675147

from collections import OrderedDict items = int(input()) ordered_dict = OrderedDict() for i in range(items): item_price = input().split() item = ' '.join(item_price[0:-1]) price = int(item_price[-1]) if item not in ordered_dict.keys(): ordered_dict[item] = price else: ordered_dict[item] += price for key, val in ordered_dict.items(): print('{} {}'.format(key, val))

true

f7fbbfc00c62087ff680b4e49b6e3bdf810186d5

6,625

py

Python

ck/repo/module/user/module.py

santosh653/ck

f09b836df48598aff4db241b52c37899a73eb569

[ "BSD-3-Clause" ]

37

2015-02-04T16:03:33.000Z

2021-01-03T13:24:29.000Z

ck/repo/module/user/module.py

santosh653/ck

f09b836df48598aff4db241b52c37899a73eb569

[ "BSD-3-Clause" ]

3

2018-10-25T13:03:05.000Z

2018-11-13T05:14:57.000Z

ck/repo/module/user/module.py

santosh653/ck

f09b836df48598aff4db241b52c37899a73eb569

[ "BSD-3-Clause" ]

4

2017-11-21T01:39:41.000Z

2020-08-09T19:22:43.000Z

# # Collective Knowledge (web-based user auth) # # # # # Developer: # cfg={} # Will be updated by CK (meta description of this module) work={} # Will be updated by CK (temporal data) ck=None # Will be updated by CK (initialized CK kernel) # Local settings ############################################################################## # Initialize module def init(i): """ Input: {} Output: { return - return code = 0, if successful > 0, if error (error) - error text if return > 0 } """ return {'return':0} ############################################################################## # authentication of a user def auth(i): """ Input: { (web_vars_post) (web_vars_get) (web_vars_session) } Output: { return - return code = 0, if successful > 0, if error (error) - error text if return > 0 user_id session_id } """ import re import hashlib rr={'return':0} wvp=i.get('web_vars_post',{}) # Check username username=wvp.get('username','').strip() if username=='': return {'return':1, 'error':'username is empty'} if not re.match("^[A-Za-z0-9.@_-]*$", username): return {'return':1, 'error':'username contains forbidden characters'} # Check password password=wvp.get('password','').strip() if password=='': return {'return':1, 'error':'password is empty'} password_md5=hashlib.md5(password.encode('utf8')).hexdigest() # Check if entry exists default_repo=ck.cfg.get('default_repo_to_write','') if default_repo=='': default_repo='local' r=ck.access({'action':'load', 'module_uoa':work['self_module_uid'], 'repo_uoa':default_repo, 'data_uoa':username}) if r['return']>0: if r['return']==16: return {'return':16, 'error':'Username not registered'} return r d=r['dict'] load_password_md5=d.get('password_md5','') if password_md5!=load_password_md5: return {'return':1, 'error':'password did not match'} # Generate random token for the session r=ck.gen_uid({}) if r['return']>0: return r rr['session_id']=r['data_uid'] rr['user_id']=username return rr ############################################################################## # create account def create(i): """ Input: { (web_vars_post) (web_vars_get) (web_vars_session) } Output: { return - return code = 0, if successful > 0, if error (error) - error text if return > 0 user_id session_id } """ import re import hashlib rr={'return':0} wvp=i.get('web_vars_post',{}) # Check username username=wvp.get('username','').strip() if username=='': return {'return':1, 'error':'username is empty'} if not re.match("^[A-Za-z0-9.@_-]*$", username): return {'return':1, 'error':'username contains forbidden characters'} # Check password password=wvp.get('password','').strip() if password=='': return {'return':1, 'error':'password is empty'} password_md5=hashlib.md5(password.encode('utf8')).hexdigest() # Check email email=wvp.get('email','').strip() if email=='': return {'return':1, 'error':'email is empty'} realname=wvp.get('realname','').strip() # Check if entry exists default_repo=ck.cfg.get('default_repo_to_write','') if default_repo=='': default_repo='local' r=ck.access({'action':'load', 'module_uoa':work['self_module_uid'], 'repo_uoa':default_repo, 'data_uoa':username}) if r['return']==0: return {'return':32, 'error':'Username already registered'} if r['return']!=16: return r # Create entry d={'password_md5':password_md5, 'email':email, 'realname':realname} r=ck.access({'action':'add', 'module_uoa':work['self_module_uid'], 'repo_uoa':default_repo, 'data_uoa':username, 'dict':d, 'sort_key':'yes'}) if r['return']>0: return r # Generate random token for the session r=ck.gen_uid({}) if r['return']>0: return r rr['session_id']=r['data_uid'] rr['user_id']=username return rr ############################################################################## # renew user def renew(i): """ Input: { (web_vars_post) (web_vars_get) (web_vars_session) } Output: { return - return code = 0, if successful > 0, if error (error) - error text if return > 0 user_id session_id } """ import re import hashlib rr={'return':0} wvp=i.get('web_vars_post',{}) # Check username username=wvp.get('username','').strip() if username=='': return {'return':1, 'error':'username is empty'} if not re.match("^[A-Za-z0-9.@_-]*$", username): return {'return':1, 'error':'username contains forbidden characters'} # Load user default_repo=ck.cfg.get('default_repo_to_write','') if default_repo=='': default_repo='local' r=ck.access({'action':'load', 'module_uoa':work['self_module_uid'], 'repo_uoa':default_repo, 'data_uoa':username}) if r['return']>0: return r d=r['dict'] # Check password password=wvp.get('password','').strip() if password!='': password_md5=hashlib.md5(password.encode('utf8')).hexdigest() d['password_md5']=password_md5 # Check email email=wvp.get('email','').strip() if email!='': d['email']=email realname=wvp.get('realname','').strip() if realname!='': d['realname']=realname # Update entry r=ck.access({'action':'update', 'module_uoa':work['self_module_uid'], 'repo_uoa':default_repo, 'data_uoa':username, 'dict':d, 'substitute':'yes', 'sort_key':'yes'}) if r['return']>0: return r return {'return':0}

25

82

0.497358

cfg={} work={} ck=None

true

f7fbc09d21dc9d20beb5a5fc6a3538c481e012c2

1,580

py

Python

frappe/setup_logging.py

pawaranand/phr_frappe

d997ae7d6fbade4b2c4a2491603d988876dfd67e

[ "MIT" ]

1

2022-03-05T16:02:39.000Z

frappe/setup_logging.py

pawaranand/phr_frappe

d997ae7d6fbade4b2c4a2491603d988876dfd67e

[ "MIT" ]

1

2015-07-11T20:52:38.000Z

2019-12-06T15:00:58.000Z

frappe/setup_logging.py

pawaranand/phr_frappe

d997ae7d6fbade4b2c4a2491603d988876dfd67e

[ "MIT" ]

2

2015-09-05T05:30:23.000Z

2018-03-21T19:45:10.000Z

import frappe import logging import logging.config import os import json from pprint import pformat class ContextFilter(logging.Filter): """ This is a filter which injects request information (if available) into the log. """ def filter(self, record): record.form_dict = pformat(getattr(frappe.local, 'form_dict', None)) record.site = getattr(frappe.local, 'site', None) record.tb = frappe.utils.get_traceback() return True def setup_logging(): conf = frappe.get_site_config(sites_path=os.environ.get('SITES_PATH', '.')) if conf.logging_conf: logging_conf = conf.logging_conf else: logging_conf = { "version": 1, "disable_existing_loggers": True, "filters": { "context_filter": { "()": "frappe.setup_logging.ContextFilter" } }, "formatters": { "site_wise": { "format": "\n%(asctime)s %(message)s \n site: %(site)s\n form: %(form_dict)s\n\n%(tb)s\n--------------" } }, "loggers": { "frappe": { "level": "INFO", "propagate": False, "filters": ["context_filter"], "handlers": ["request_exception"] } }, "handlers": { "request_exception": { "level": "ERROR", "formatter": "site_wise", "class": "logging.StreamHandler", } } } if conf.request_exception_log_file: logging_conf.update({ "handlers": { "request_exception": { "level": "ERROR", "formatter": "site_wise", "class": "logging.handlers.WatchedFileHandler", "filename": conf.request_exception_log_file } } }) logging.config.dictConfig(logging_conf)

24.307692

108

0.633544

import frappe import logging import logging.config import os import json from pprint import pformat class ContextFilter(logging.Filter): def filter(self, record): record.form_dict = pformat(getattr(frappe.local, 'form_dict', None)) record.site = getattr(frappe.local, 'site', None) record.tb = frappe.utils.get_traceback() return True def setup_logging(): conf = frappe.get_site_config(sites_path=os.environ.get('SITES_PATH', '.')) if conf.logging_conf: logging_conf = conf.logging_conf else: logging_conf = { "version": 1, "disable_existing_loggers": True, "filters": { "context_filter": { "()": "frappe.setup_logging.ContextFilter" } }, "formatters": { "site_wise": { "format": "\n%(asctime)s %(message)s \n site: %(site)s\n form: %(form_dict)s\n\n%(tb)s\n--------------" } }, "loggers": { "frappe": { "level": "INFO", "propagate": False, "filters": ["context_filter"], "handlers": ["request_exception"] } }, "handlers": { "request_exception": { "level": "ERROR", "formatter": "site_wise", "class": "logging.StreamHandler", } } } if conf.request_exception_log_file: logging_conf.update({ "handlers": { "request_exception": { "level": "ERROR", "formatter": "site_wise", "class": "logging.handlers.WatchedFileHandler", "filename": conf.request_exception_log_file } } }) logging.config.dictConfig(logging_conf)

true

f7fbc0c8571bb84f8156dfaed15a35423276590d

5,459

py

Python

mitmproxy/addons/cut.py

dotnes/mitmproxy

5eb17bbf6d47c8d703763bfa41cf1ff3f98a632f

[ "MIT" ]

1

2017-12-27T09:05:23.000Z

mitmproxy/addons/cut.py

dotnes/mitmproxy

5eb17bbf6d47c8d703763bfa41cf1ff3f98a632f

[ "MIT" ]

null

mitmproxy/addons/cut.py

dotnes/mitmproxy

5eb17bbf6d47c8d703763bfa41cf1ff3f98a632f

[ "MIT" ]

null

import io import csv import typing from mitmproxy import command from mitmproxy import exceptions from mitmproxy import flow from mitmproxy import ctx from mitmproxy import certs from mitmproxy.utils import strutils import mitmproxy.types import pyperclip def headername(spec: str): if not (spec.startswith("header[") and spec.endswith("]")): raise exceptions.CommandError("Invalid header spec: %s" % spec) return spec[len("header["):-1].strip() def is_addr(v): return isinstance(v, tuple) and len(v) > 1 def extract(cut: str, f: flow.Flow) -> typing.Union[str, bytes]: path = cut.split(".") current = f # type: typing.Any for i, spec in enumerate(path): if spec.startswith("_"): raise exceptions.CommandError("Can't access internal attribute %s" % spec) part = getattr(current, spec, None) if i == len(path) - 1: if spec == "port" and is_addr(current): return str(current[1]) if spec == "host" and is_addr(current): return str(current[0]) elif spec.startswith("header["): if not current: return "" return current.headers.get(headername(spec), "") elif isinstance(part, bytes): return part elif isinstance(part, bool): return "true" if part else "false" elif isinstance(part, certs.Cert): return part.to_pem().decode("ascii") current = part return str(current or "") class Cut: @command.command("cut") def cut( self, flows: typing.Sequence[flow.Flow], cuts: mitmproxy.types.CutSpec, ) -> mitmproxy.types.Data: """ Cut data from a set of flows. Cut specifications are attribute paths from the base of the flow object, with a few conveniences - "port" and "host" retrieve parts of an address tuple, ".header[key]" retrieves a header value. Return values converted to strings or bytes: SSL certificates are converted to PEM format, bools are "true" or "false", "bytes" are preserved, and all other values are converted to strings. """ ret = [] # type:typing.List[typing.List[typing.Union[str, bytes]]] for f in flows: ret.append([extract(c, f) for c in cuts]) return ret # type: ignore @command.command("cut.save") def save( self, flows: typing.Sequence[flow.Flow], cuts: mitmproxy.types.CutSpec, path: mitmproxy.types.Path ) -> None: """ Save cuts to file. If there are multiple flows or cuts, the format is UTF-8 encoded CSV. If there is exactly one row and one column, the data is written to file as-is, with raw bytes preserved. If the path is prefixed with a "+", values are appended if there is an existing file. """ append = False if path.startswith("+"): append = True path = mitmproxy.types.Path(path[1:]) try: if len(cuts) == 1 and len(flows) == 1: with open(path, "ab" if append else "wb") as fp: if fp.tell() > 0: # We're appending to a file that already exists and has content fp.write(b"\n") v = extract(cuts[0], flows[0]) if isinstance(v, bytes): fp.write(v) else: fp.write(v.encode("utf8")) ctx.log.alert("Saved single cut.") else: with open(path, "a" if append else "w", newline='', encoding="utf8") as fp: writer = csv.writer(fp) for f in flows: vals = [extract(c, f) for c in cuts] writer.writerow( [strutils.always_str(x) or "" for x in vals] # type: ignore ) ctx.log.alert("Saved %s cuts over %d flows as CSV." % (len(cuts), len(flows))) except IOError as e: ctx.log.error(str(e)) @command.command("cut.clip") def clip( self, flows: typing.Sequence[flow.Flow], cuts: mitmproxy.types.CutSpec, ) -> None: """ Send cuts to the clipboard. If there are multiple flows or cuts, the format is UTF-8 encoded CSV. If there is exactly one row and one column, the data is written to file as-is, with raw bytes preserved. """ fp = io.StringIO(newline="") if len(cuts) == 1 and len(flows) == 1: v = extract(cuts[0], flows[0]) if isinstance(v, bytes): fp.write(strutils.always_str(v)) else: fp.write(v) ctx.log.alert("Clipped single cut.") else: writer = csv.writer(fp) for f in flows: vals = [extract(c, f) for c in cuts] writer.writerow( [strutils.always_str(v) or "" for v in vals] # type: ignore ) ctx.log.alert("Clipped %s cuts as CSV." % len(cuts)) try: pyperclip.copy(fp.getvalue()) except pyperclip.PyperclipException as e: ctx.log.error(str(e))

37.390411

94

0.535446

import io import csv import typing from mitmproxy import command from mitmproxy import exceptions from mitmproxy import flow from mitmproxy import ctx from mitmproxy import certs from mitmproxy.utils import strutils import mitmproxy.types import pyperclip def headername(spec: str): if not (spec.startswith("header[") and spec.endswith("]")): raise exceptions.CommandError("Invalid header spec: %s" % spec) return spec[len("header["):-1].strip() def is_addr(v): return isinstance(v, tuple) and len(v) > 1 def extract(cut: str, f: flow.Flow) -> typing.Union[str, bytes]: path = cut.split(".") current = f for i, spec in enumerate(path): if spec.startswith("_"): raise exceptions.CommandError("Can't access internal attribute %s" % spec) part = getattr(current, spec, None) if i == len(path) - 1: if spec == "port" and is_addr(current): return str(current[1]) if spec == "host" and is_addr(current): return str(current[0]) elif spec.startswith("header["): if not current: return "" return current.headers.get(headername(spec), "") elif isinstance(part, bytes): return part elif isinstance(part, bool): return "true" if part else "false" elif isinstance(part, certs.Cert): return part.to_pem().decode("ascii") current = part return str(current or "") class Cut: @command.command("cut") def cut( self, flows: typing.Sequence[flow.Flow], cuts: mitmproxy.types.CutSpec, ) -> mitmproxy.types.Data: ret = [] # type:typing.List[typing.List[typing.Union[str, bytes]]] for f in flows: ret.append([extract(c, f) for c in cuts]) return ret # type: ignore @command.command("cut.save") def save( self, flows: typing.Sequence[flow.Flow], cuts: mitmproxy.types.CutSpec, path: mitmproxy.types.Path ) -> None: append = False if path.startswith("+"): append = True path = mitmproxy.types.Path(path[1:]) try: if len(cuts) == 1 and len(flows) == 1: with open(path, "ab" if append else "wb") as fp: if fp.tell() > 0: # We're appending to a file that already exists and has content fp.write(b"\n") v = extract(cuts[0], flows[0]) if isinstance(v, bytes): fp.write(v) else: fp.write(v.encode("utf8")) ctx.log.alert("Saved single cut.") else: with open(path, "a" if append else "w", newline='', encoding="utf8") as fp: writer = csv.writer(fp) for f in flows: vals = [extract(c, f) for c in cuts] writer.writerow( [strutils.always_str(x) or "" for x in vals] ) ctx.log.alert("Saved %s cuts over %d flows as CSV." % (len(cuts), len(flows))) except IOError as e: ctx.log.error(str(e)) @command.command("cut.clip") def clip( self, flows: typing.Sequence[flow.Flow], cuts: mitmproxy.types.CutSpec, ) -> None: fp = io.StringIO(newline="") if len(cuts) == 1 and len(flows) == 1: v = extract(cuts[0], flows[0]) if isinstance(v, bytes): fp.write(strutils.always_str(v)) else: fp.write(v) ctx.log.alert("Clipped single cut.") else: writer = csv.writer(fp) for f in flows: vals = [extract(c, f) for c in cuts] writer.writerow( [strutils.always_str(v) or "" for v in vals] ) ctx.log.alert("Clipped %s cuts as CSV." % len(cuts)) try: pyperclip.copy(fp.getvalue()) except pyperclip.PyperclipException as e: ctx.log.error(str(e))

true

f7fbc1b08bdb0f523b71727d92cf25327e2fc11a

936

py

Python

kubernetes_asyncio/test/test_v1_scale_spec.py

PidgeyBE/kubernetes_asyncio

14d15dc309890253c26b6274a022e84441e05217

[ "Apache-2.0" ]

null

kubernetes_asyncio/test/test_v1_scale_spec.py

PidgeyBE/kubernetes_asyncio

14d15dc309890253c26b6274a022e84441e05217

[ "Apache-2.0" ]

null

kubernetes_asyncio/test/test_v1_scale_spec.py

PidgeyBE/kubernetes_asyncio

14d15dc309890253c26b6274a022e84441e05217

[ "Apache-2.0" ]

null

# coding: utf-8 """ Kubernetes No description provided (generated by Openapi Generator https://github.com/openapitools/openapi-generator) # noqa: E501 OpenAPI spec version: v1.13.5 Generated by: https://openapi-generator.tech """ from __future__ import absolute_import import unittest import kubernetes_asyncio.client from kubernetes_asyncio.client.models.v1_scale_spec import V1ScaleSpec # noqa: E501 from kubernetes_asyncio.client.rest import ApiException class TestV1ScaleSpec(unittest.TestCase): """V1ScaleSpec unit test stubs""" def setUp(self): pass def tearDown(self): pass def testV1ScaleSpec(self): """Test V1ScaleSpec""" # FIXME: construct object with mandatory attributes with example values # model = kubernetes_asyncio.client.models.v1_scale_spec.V1ScaleSpec() # noqa: E501 pass if __name__ == '__main__': unittest.main()

23.4

124

0.71688

from __future__ import absolute_import import unittest import kubernetes_asyncio.client from kubernetes_asyncio.client.models.v1_scale_spec import V1ScaleSpec from kubernetes_asyncio.client.rest import ApiException class TestV1ScaleSpec(unittest.TestCase): def setUp(self): pass def tearDown(self): pass def testV1ScaleSpec(self): s if __name__ == '__main__': unittest.main()

true

f7fbc2dd55385ce727b6db9a0cf7aa762a44f47c

3,579

py

Python

var/spack/repos/builtin/packages/roctracer-dev/package.py

LiamBindle/spack

e90d5ad6cfff2ba3de7b537d6511adccd9d5fcf1

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

2,360

2017-11-06T08:47:01.000Z

2022-03-31T14:45:33.000Z

var/spack/repos/builtin/packages/roctracer-dev/package.py

LiamBindle/spack

e90d5ad6cfff2ba3de7b537d6511adccd9d5fcf1

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

13,838

2017-11-04T07:49:45.000Z

2022-03-31T23:38:39.000Z

var/spack/repos/builtin/packages/roctracer-dev/package.py

LiamBindle/spack

e90d5ad6cfff2ba3de7b537d6511adccd9d5fcf1

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

1,793

2017-11-04T07:45:50.000Z

2022-03-30T14:31:53.000Z

# Copyright 2013-2021 Lawrence Livermore National Security, LLC and other # Spack Project Developers. See the top-level COPYRIGHT file for details. # # SPDX-License-Identifier: (Apache-2.0 OR MIT) from spack import * class RoctracerDev(CMakePackage): """ROC-tracer library: Runtimes Generic Callback/Activity APIs. The goal of the implementation is to provide a generic independent from specific runtime profiler to trace API and asyncronous activity.""" homepage = "https://github.com/ROCm-Developer-Tools/roctracer" git = "https://github.com/ROCm-Developer-Tools/roctracer.git" url = "https://github.com/ROCm-Developer-Tools/roctracer/archive/rocm-4.3.0.tar.gz" maintainers = ['srekolam', 'arjun-raj-kuppala'] version('4.3.1', sha256='88ada5f256a570792d1326a305663e94cf2c3b0cbd99f7e745326923882dafd2') version('4.3.0', sha256='c3d9f408df8d4dc0e9c0026217b8c684f68e775da80b215fecb3cd24419ee6d3') version('4.2.0', sha256='62a9c0cb1ba50b1c39a0636c886ac86e75a1a71cbf5fec05801517ceb0e67a37') version('4.1.0', sha256='5d93de4e92895b6eb5f9d098f5dbd182d33923bd9b2ab69cf5a1abbf91d70695') version('4.0.0', sha256='f47859a46173228b597c463eda850b870e810534af5efd5f2a746067ef04edee') version('3.10.0', sha256='ac4a1d059fc34377e906071fd0e56f5434a7e0e4ded9db8faf9217a115239dec') version('3.9.0', sha256='0678f9faf45058b16923948c66d77ba2c072283c975d167899caef969169b292') version('3.8.0', sha256='5154a84ce7568cd5dba756e9508c34ae9fc62f4b0b5731f93c2ad68b21537ed1') version('3.7.0', sha256='6fa5b771e990f09c242237ab334b9f01039ec7d54ccde993e719c5d6577d1518') version('3.5.0', sha256='7af5326c9ca695642b4265232ec12864a61fd6b6056aa7c4ecd9e19c817f209e') variant('build_type', default='Release', values=("Release", "Debug", "RelWithDebInfo"), description='CMake build type') depends_on('cmake@3:', type='build') depends_on('python@:2', type='build', when='@:4.1.0') depends_on('python@3:', type='build', when='@4.2.0:') depends_on('py-cppheaderparser', type='build') for ver in ['3.5.0', '3.7.0', '3.8.0', '3.9.0', '3.10.0', '4.0.0', '4.1.0', '4.2.0', '4.3.0', '4.3.1']: depends_on('hsakmt-roct@' + ver, when='@' + ver) depends_on('hsa-rocr-dev@' + ver, when='@' + ver) depends_on('rocminfo@' + ver, when='@' + ver) depends_on('hip@' + ver, when='@' + ver) for ver in ['4.2.0', '4.3.0', '4.3.1']: depends_on('rocprofiler-dev@' + ver, when='@' + ver) def setup_build_environment(self, build_env): spec = self.spec build_env.set("HIP_PATH", spec['hip'].prefix) def patch(self): filter_file('${CMAKE_PREFIX_PATH}/hsa', '${HSA_RUNTIME_INC_PATH}', 'src/CMakeLists.txt', string=True) kwargs = {'ignore_absent': False, 'backup': False, 'string': False} with working_dir('script'): match = '^#!/usr/bin/python[23]' python = self.spec['python'].command.path substitute = "#!{python}".format(python=python) files = [ 'check_trace.py', 'gen_ostream_ops.py', 'hsaap.py', 'kfdap.py' ] filter_file(match, substitute, *files, **kwargs) def cmake_args(self): args = ['-DHIP_VDI=1', '-DCMAKE_MODULE_PATH={0}/cmake_modules'.format( self.stage.source_path), '-DHSA_RUNTIME_HSA_INC_PATH={0}/include'.format( self.spec['hsa-rocr-dev'].prefix) ] return args

48.364865

123

0.655211

from spack import * class RoctracerDev(CMakePackage): homepage = "https://github.com/ROCm-Developer-Tools/roctracer" git = "https://github.com/ROCm-Developer-Tools/roctracer.git" url = "https://github.com/ROCm-Developer-Tools/roctracer/archive/rocm-4.3.0.tar.gz" maintainers = ['srekolam', 'arjun-raj-kuppala'] version('4.3.1', sha256='88ada5f256a570792d1326a305663e94cf2c3b0cbd99f7e745326923882dafd2') version('4.3.0', sha256='c3d9f408df8d4dc0e9c0026217b8c684f68e775da80b215fecb3cd24419ee6d3') version('4.2.0', sha256='62a9c0cb1ba50b1c39a0636c886ac86e75a1a71cbf5fec05801517ceb0e67a37') version('4.1.0', sha256='5d93de4e92895b6eb5f9d098f5dbd182d33923bd9b2ab69cf5a1abbf91d70695') version('4.0.0', sha256='f47859a46173228b597c463eda850b870e810534af5efd5f2a746067ef04edee') version('3.10.0', sha256='ac4a1d059fc34377e906071fd0e56f5434a7e0e4ded9db8faf9217a115239dec') version('3.9.0', sha256='0678f9faf45058b16923948c66d77ba2c072283c975d167899caef969169b292') version('3.8.0', sha256='5154a84ce7568cd5dba756e9508c34ae9fc62f4b0b5731f93c2ad68b21537ed1') version('3.7.0', sha256='6fa5b771e990f09c242237ab334b9f01039ec7d54ccde993e719c5d6577d1518') version('3.5.0', sha256='7af5326c9ca695642b4265232ec12864a61fd6b6056aa7c4ecd9e19c817f209e') variant('build_type', default='Release', values=("Release", "Debug", "RelWithDebInfo"), description='CMake build type') depends_on('cmake@3:', type='build') depends_on('python@:2', type='build', when='@:4.1.0') depends_on('python@3:', type='build', when='@4.2.0:') depends_on('py-cppheaderparser', type='build') for ver in ['3.5.0', '3.7.0', '3.8.0', '3.9.0', '3.10.0', '4.0.0', '4.1.0', '4.2.0', '4.3.0', '4.3.1']: depends_on('hsakmt-roct@' + ver, when='@' + ver) depends_on('hsa-rocr-dev@' + ver, when='@' + ver) depends_on('rocminfo@' + ver, when='@' + ver) depends_on('hip@' + ver, when='@' + ver) for ver in ['4.2.0', '4.3.0', '4.3.1']: depends_on('rocprofiler-dev@' + ver, when='@' + ver) def setup_build_environment(self, build_env): spec = self.spec build_env.set("HIP_PATH", spec['hip'].prefix) def patch(self): filter_file('${CMAKE_PREFIX_PATH}/hsa', '${HSA_RUNTIME_INC_PATH}', 'src/CMakeLists.txt', string=True) kwargs = {'ignore_absent': False, 'backup': False, 'string': False} with working_dir('script'): match = '^#!/usr/bin/python[23]' python = self.spec['python'].command.path substitute = "#!{python}".format(python=python) files = [ 'check_trace.py', 'gen_ostream_ops.py', 'hsaap.py', 'kfdap.py' ] filter_file(match, substitute, *files, **kwargs) def cmake_args(self): args = ['-DHIP_VDI=1', '-DCMAKE_MODULE_PATH={0}/cmake_modules'.format( self.stage.source_path), '-DHSA_RUNTIME_HSA_INC_PATH={0}/include'.format( self.spec['hsa-rocr-dev'].prefix) ] return args

true

f7fbc5228971a5c65f10bb5e37b1f626858d1332

3,871

py

Python

analysis/video.py

MorrisHuang-skipper/Serial-MD

48356dc88cdc47a832fa02bc61a03d8583bb4a79

[ "MIT" ]

null

analysis/video.py

MorrisHuang-skipper/Serial-MD

48356dc88cdc47a832fa02bc61a03d8583bb4a79

[ "MIT" ]

null

analysis/video.py

MorrisHuang-skipper/Serial-MD

48356dc88cdc47a832fa02bc61a03d8583bb4a79

[ "MIT" ]

null

import matplotlib.pyplot as plt import matplotlib as mpl import numpy as np from pylab import cm import math from mpl_toolkits.mplot3d import Axes3D import os import sys import matplotlib.gridspec as gridspec mpl.rcParams['font.family'] = 'STIXGeneral' plt.rcParams['xtick.labelsize'] = 16 plt.rcParams['ytick.labelsize'] = 16 plt.rcParams['font.size'] = 16 plt.rcParams['figure.figsize'] = [5.4*4, 5.2] plt.rcParams['axes.titlesize'] = 16 plt.rcParams['axes.labelsize'] = 16 plt.rcParams['lines.linewidth'] = 2 plt.rcParams['lines.markersize'] = 6 plt.rcParams['legend.fontsize'] = 13 plt.rcParams['mathtext.fontset'] = 'stix' plt.rcParams['axes.linewidth'] = 1 colors = cm.get_cmap('Set1', 10) fig = plt.figure(constrained_layout=True) spec = gridspec.GridSpec(ncols=4, nrows=1, figure=fig) ax = fig.add_subplot(spec[0, 0]) ax2 = fig.add_subplot(spec[0, 1]) ax3 = fig.add_subplot(spec[0, 2]) ax4 = fig.add_subplot(spec[0, 3]) # ax = fig.add_subplot(1, 3, 1, projection='3d') ax.xaxis.set_tick_params(which='major', size=5, width=1, direction='in', top='on') ax.xaxis.set_tick_params(which='minor', size=3, width=1, direction='in', top='on') ax.yaxis.set_tick_params(which='major', size=5, width=1, direction='in', right='on') ax.yaxis.set_tick_params(which='minor', size=3, width=1, direction='in', right='on') fname = 'ext' rx = np.loadtxt('../../data/'+fname+'/rx.dat') ry = np.loadtxt('../../data/'+fname+'/ry.dat') rz = np.loadtxt('../../data/'+fname+'/rz.dat') vx = np.loadtxt('../../data/'+fname+'/vx.dat') vy = np.loadtxt('../../data/'+fname+'/vy.dat') vz = np.loadtxt('../../data/'+fname+'/vz.dat') t, Ki, Ke, K, U, Te, Ti, within = np.loadtxt('../../data/'+fname+'/info.dat', unpack=True) x1 = np.arange(-10, 10, 0.00001) yy1 = (40-x1**2)**0.5 yy2 = -(40-x1**2)**0.5 step = rx.shape[0] NP = rx.shape[1] e = 1.60217e-19 kt = e/38.9 nth = 50 # plot phase space for i in range(0, step, 1): num = '{0:04}'.format(i) # ax.set_zlim(0, 10) # 2d ax.plot(rx[i, :-(NP//2)]*1e9, ry[i, :-(NP//2)]*1e9, '.', color=colors(0), label='$H^+$', markersize=3) ax.plot(rx[i, (NP//2):]*1e9, ry[i, (NP//2):]*1e9, '.', color=colors(1), label='$e^{-}$', markersize=3) ax.plot(rx[:i, nth]*1e9, ry[:i, nth]*1e9, '-', color=colors(2), label='tracking') # for j in range(50): # ax.plot(rx[:i, nth+j]*1e9, ry[:i, nth+j]*1e9, '.') ax.set_xlabel('$x \ [nm]$') ax.set_ylabel('$y \ [nm]$') ax.set_xlim(0, 40) ax.set_ylim(0, 40) # diag ax2.plot(t[:i]*1e15, (Ki[:i]+Ke[:i])/e/NP, '-', label=r'$K_{avg}$', color=colors(2)) ax2.plot(t[:i]*1e15, U[:i]/e/NP, '-', label=r'$U_{avg}$', color=colors(3)) ax2.plot(t[:i]*1e15, Ki[:i]/e/NP+Ke[:i]/e/NP+U[:i]/e/NP, '-', label=r'$E_{avg}$', color=colors(4)) ax2.set_xlabel('$time \ [fs]$') # ax2.set_ylabel('$E \ [ev]$') ax2.set_xlim(0, 100) ax3.plot(t[:i]*1e15, Te[:i]/e, '-', label=r'$T_e$', color=colors(6)) ax3.plot(t[:i]*1e15, Ti[:i]/e, '-', label=r'$T_i$', color=colors(0)) ax3.set_xlabel('$time \ [fs]$') ax3.set_ylabel('$Temperature \ [eV]$') # ax3.set_ylabel('$Temperature \ [k_BT]$') ax3.set_xlim(0, 100) ax4.hist(vx[i, 1:NP//2], histtype='step') ax4.hist(vx[i, NP//2+1:NP], histtype='step') # ax4.set_xlabel('$time \ [fs]$') # ax4.set_ylabel('$Temperature \ [eV]$') # ax4.set_xlim(0, 100) ax.legend(loc=1) ax2.legend(loc=1) ax3.legend(loc=1) ax4.legend(loc=1) plt.tight_layout() plt.show(block=False) plt.pause(.01) # plt.savefig('../figures/'+fname+'/'+str(num)+'.png', dpi=300) ax.clear() ax2.clear() ax3.clear() ax4.clear() # os.system('ffmpeg -i ../figures/'+fname+'/%04d.png -c:v ffv1 -qscale:v 0 ../figures/animate.mp4') # plt.tight_layout()

32.258333

106

0.58667

import matplotlib.pyplot as plt import matplotlib as mpl import numpy as np from pylab import cm import math from mpl_toolkits.mplot3d import Axes3D import os import sys import matplotlib.gridspec as gridspec mpl.rcParams['font.family'] = 'STIXGeneral' plt.rcParams['xtick.labelsize'] = 16 plt.rcParams['ytick.labelsize'] = 16 plt.rcParams['font.size'] = 16 plt.rcParams['figure.figsize'] = [5.4*4, 5.2] plt.rcParams['axes.titlesize'] = 16 plt.rcParams['axes.labelsize'] = 16 plt.rcParams['lines.linewidth'] = 2 plt.rcParams['lines.markersize'] = 6 plt.rcParams['legend.fontsize'] = 13 plt.rcParams['mathtext.fontset'] = 'stix' plt.rcParams['axes.linewidth'] = 1 colors = cm.get_cmap('Set1', 10) fig = plt.figure(constrained_layout=True) spec = gridspec.GridSpec(ncols=4, nrows=1, figure=fig) ax = fig.add_subplot(spec[0, 0]) ax2 = fig.add_subplot(spec[0, 1]) ax3 = fig.add_subplot(spec[0, 2]) ax4 = fig.add_subplot(spec[0, 3]) ax.xaxis.set_tick_params(which='major', size=5, width=1, direction='in', top='on') ax.xaxis.set_tick_params(which='minor', size=3, width=1, direction='in', top='on') ax.yaxis.set_tick_params(which='major', size=5, width=1, direction='in', right='on') ax.yaxis.set_tick_params(which='minor', size=3, width=1, direction='in', right='on') fname = 'ext' rx = np.loadtxt('../../data/'+fname+'/rx.dat') ry = np.loadtxt('../../data/'+fname+'/ry.dat') rz = np.loadtxt('../../data/'+fname+'/rz.dat') vx = np.loadtxt('../../data/'+fname+'/vx.dat') vy = np.loadtxt('../../data/'+fname+'/vy.dat') vz = np.loadtxt('../../data/'+fname+'/vz.dat') t, Ki, Ke, K, U, Te, Ti, within = np.loadtxt('../../data/'+fname+'/info.dat', unpack=True) x1 = np.arange(-10, 10, 0.00001) yy1 = (40-x1**2)**0.5 yy2 = -(40-x1**2)**0.5 step = rx.shape[0] NP = rx.shape[1] e = 1.60217e-19 kt = e/38.9 nth = 50 for i in range(0, step, 1): num = '{0:04}'.format(i) ax.plot(rx[i, :-(NP//2)]*1e9, ry[i, :-(NP//2)]*1e9, '.', color=colors(0), label='$H^+$', markersize=3) ax.plot(rx[i, (NP//2):]*1e9, ry[i, (NP//2):]*1e9, '.', color=colors(1), label='$e^{-}$', markersize=3) ax.plot(rx[:i, nth]*1e9, ry[:i, nth]*1e9, '-', color=colors(2), label='tracking') ax.set_xlabel('$x \ [nm]$') ax.set_ylabel('$y \ [nm]$') ax.set_xlim(0, 40) ax.set_ylim(0, 40) ax2.plot(t[:i]*1e15, (Ki[:i]+Ke[:i])/e/NP, '-', label=r'$K_{avg}$', color=colors(2)) ax2.plot(t[:i]*1e15, U[:i]/e/NP, '-', label=r'$U_{avg}$', color=colors(3)) ax2.plot(t[:i]*1e15, Ki[:i]/e/NP+Ke[:i]/e/NP+U[:i]/e/NP, '-', label=r'$E_{avg}$', color=colors(4)) ax2.set_xlabel('$time \ [fs]$') ax2.set_xlim(0, 100) ax3.plot(t[:i]*1e15, Te[:i]/e, '-', label=r'$T_e$', color=colors(6)) ax3.plot(t[:i]*1e15, Ti[:i]/e, '-', label=r'$T_i$', color=colors(0)) ax3.set_xlabel('$time \ [fs]$') ax3.set_ylabel('$Temperature \ [eV]$') ax3.set_xlim(0, 100) ax4.hist(vx[i, 1:NP//2], histtype='step') ax4.hist(vx[i, NP//2+1:NP], histtype='step') ax.legend(loc=1) ax2.legend(loc=1) ax3.legend(loc=1) ax4.legend(loc=1) plt.tight_layout() plt.show(block=False) plt.pause(.01) ax.clear() ax2.clear() ax3.clear() ax4.clear()

true

f7fbc648a9b7bedcd218e285211ce2787a1458ef

1,210

py

Python

docker/server_setup.py

maxpark/dive

5dce25822d9b53d96ff0c2c8fb02265e4b43911e

[ "Apache-2.0" ]

28

2021-02-15T13:46:58.000Z

2022-03-27T14:20:37.000Z

docker/server_setup.py

maxpark/dive

5dce25822d9b53d96ff0c2c8fb02265e4b43911e

[ "Apache-2.0" ]

506

2021-02-12T22:33:10.000Z

2022-03-30T17:46:36.000Z

docker/server_setup.py

maxpark/dive

5dce25822d9b53d96ff0c2c8fb02265e4b43911e

[ "Apache-2.0" ]

14

2021-02-21T10:18:48.000Z

2022-02-23T15:32:40.000Z

import os import cherrypy from girder.exceptions import ValidationException from girder.models.assetstore import Assetstore from girder.models.setting import Setting from girder.models.user import User cherrypy.config["database"]["uri"] = os.getenv("GIRDER_MONGO_URI") ADMIN_USER = os.getenv("GIRDER_ADMIN_USER", "admin") ADMIN_PASS = os.getenv("GIRDER_ADMIN_PASS", "letmein") def createInitialUser(): try: User().createUser( ADMIN_USER, ADMIN_PASS, ADMIN_USER, ADMIN_USER, "admin@admin.com", admin=True, public=True, ) except ValidationException: print("Admin user already exists, skipping...") def createAssetstore(): try: Assetstore().createFilesystemAssetstore("assetstore", "/home/assetstore") except ValidationException: print("Assetstore already exists, skipping...") def configure(): Setting().set( "core.cors.allow_origin", os.environ.get( "CORS_ALLOWED_ORIGINS", "http://localhost:8080, http://localhost:8010" ), ) if __name__ == '__main__': createInitialUser() createAssetstore() configure()

24.2

82

0.649587

import os import cherrypy from girder.exceptions import ValidationException from girder.models.assetstore import Assetstore from girder.models.setting import Setting from girder.models.user import User cherrypy.config["database"]["uri"] = os.getenv("GIRDER_MONGO_URI") ADMIN_USER = os.getenv("GIRDER_ADMIN_USER", "admin") ADMIN_PASS = os.getenv("GIRDER_ADMIN_PASS", "letmein") def createInitialUser(): try: User().createUser( ADMIN_USER, ADMIN_PASS, ADMIN_USER, ADMIN_USER, "admin@admin.com", admin=True, public=True, ) except ValidationException: print("Admin user already exists, skipping...") def createAssetstore(): try: Assetstore().createFilesystemAssetstore("assetstore", "/home/assetstore") except ValidationException: print("Assetstore already exists, skipping...") def configure(): Setting().set( "core.cors.allow_origin", os.environ.get( "CORS_ALLOWED_ORIGINS", "http://localhost:8080, http://localhost:8010" ), ) if __name__ == '__main__': createInitialUser() createAssetstore() configure()

true

f7fbc72dfaa2722fa757ff4575e23e08699ce449

9,147

py

Python

fas2ipa/groups.py

markobrien1/fas2ipa

df8696b4a25f29f4de0f093c12806c5b1b45db16

[ "MIT" ]

1

2020-09-22T17:13:39.000Z

fas2ipa/groups.py

markobrien1/fas2ipa

df8696b4a25f29f4de0f093c12806c5b1b45db16

[ "MIT" ]

18

2020-02-25T11:38:47.000Z

2021-03-19T18:12:44.000Z

fas2ipa/groups.py

markobrien1/fas2ipa

df8696b4a25f29f4de0f093c12806c5b1b45db16

[ "MIT" ]

7

2020-02-24T15:11:52.000Z

2021-03-30T14:34:00.000Z

import click import progressbar import python_freeipa from collections import defaultdict from typing import Any, Dict, List from .status import Status, print_status from .utils import ObjectManager class Groups(ObjectManager): def __init__(self, *args, agreements, **kwargs): super().__init__(*args, **kwargs) self.agreements = agreements def pull_from_fas(self) -> Dict[str, List[Dict]]: fas_groups = {} for fas_name, fas_inst in self.fas_instances.items(): click.echo(f"Pulling group information from FAS ({fas_name})...") fas_conf = self.config["fas"][fas_name] groups = fas_inst.send_request( "/group/list", req_params={"search": fas_conf["groups"]["search"]}, auth=True, timeout=240, )["groups"] groups.sort(key=lambda g: g["name"]) click.echo(f"Got {len(groups)} groups!") fas_groups[fas_name] = groups return fas_groups def push_to_ipa( self, groups: Dict[str, List[Dict]], conflicts: Dict[str, List[Dict[str, Any]]], ) -> dict: added = 0 edited = 0 counter = 0 if not conflicts: conflicts = {} skip_conflicts = set(self.config["groups"].get("skip_conflicts", ())) for fas_name, fas_groups in groups.items(): click.echo(f"Pushing {fas_name} group information to IPA...") fas_conf = self.config["fas"][fas_name] # Start by creating the umbrella group, if any umbrella_group = fas_conf["groups"].get("umbrella") if umbrella_group: click.echo(f"Ensuring umbrella group {umbrella_group['name']} exists...") name_max_length = max((len(g["name"]) for g in fas_groups)) click.echo(umbrella_group["name"].ljust(name_max_length + 2), nl=False) status = self._write_group_to_ipa(fas_name, umbrella_group, from_fas=False) print_status(status) if status == Status.ADDED: added += 1 elif status == Status.UPDATED: edited += 1 umbrella_members = set() # Start by creating groups fas_groups = [ g for g in fas_groups if g["name"] not in fas_conf["groups"].get("ignore", ()) ] name_max_length = max((len(g["name"]) for g in fas_groups)) for group in progressbar.progressbar(fas_groups, redirect_stdout=True): counter += 1 group_conflicts = set(conflicts.get(group["name"], ())) group_skip_conflicts = skip_conflicts & group_conflicts if group_skip_conflicts: print_status( Status.FAILED, f"[{fas_name}: Skipping group '{group['name']}' because of conflicts:" f" {', '.join(group_skip_conflicts)}", ) continue self.check_reauth(counter) click.echo(group["name"].ljust(name_max_length + 2), nl=False) status = self._write_group_to_ipa(fas_name, group) print_status(status) if status == Status.ADDED: added += 1 elif status == Status.UPDATED: edited += 1 if umbrella_group and status in (Status.ADDED, Status.UPDATED, Status.UNMODIFIED): umbrella_members.add( fas_conf["groups"].get("prefix", "") + group["name"].lower() ) if umbrella_group: ipa_group = self.ipa.group_show(umbrella_group["name"]) existing_umbrella_members = set(ipa_group.get("member_group", [])) new_umbrella_members = umbrella_members - existing_umbrella_members if not new_umbrella_members: click.echo(f"No new members to add to umbrella group {umbrella_group['name']}") else: click.echo( f"Adding {len(new_umbrella_members)} new groups to umbrella group" f" {umbrella_group['name']}" ) self.ipa.group_add_member( umbrella_group["name"], groups=list(new_umbrella_members) ) click.echo(f"Done with {fas_name}") # add groups to agreements click.echo("Recording group requirements in IPA...") self.agreements.record_group_requirements(groups) click.echo("Done.") return dict(groups_added=added, groups_edited=edited, groups_counter=counter,) def _write_group_to_ipa(self, fas_name: str, group: dict, from_fas: bool = True): if from_fas: # transform FAS group info into what IPA expects name = ( self.config["fas"][fas_name]["groups"].get("prefix", "") + group["name"].lower() ) # calculate the IRC channel (FAS has 2 fields, freeipa-fas has a single one ) # if we have an irc channel defined. try to generate the irc:// uri # there are a handful of groups that have an IRC server defined (freenode), but # no channel, which is kind of useless, so we don't handle that case. irc_channel = group.get("irc_channel") irc_string = None if irc_channel: if irc_channel[0] == "#": irc_channel = irc_channel[1:] irc_network = group.get("irc_network").lower() if "gimp" in irc_network: irc_string = f"irc://irc.gimp.org/#{irc_channel}" elif "oftc" in irc_network: irc_string = f"irc://irc.oftc.net/#{irc_channel}" else: # the remainder of the entries here are either blank or # freenode, so we freenode them all. irc_string = f"irc://irc.freenode.net/#{irc_channel}" url = group.get("url") if not url: url = None else: url = url.strip() mailing_list = group.get("mailing_list") if not mailing_list: mailing_list = None else: if "@" not in mailing_list: mailing_list = f"{mailing_list}@lists.fedoraproject.org" mailing_list = mailing_list.strip() mailing_list = mailing_list.rstrip(".") mailing_list = mailing_list.lower() group_args = { "description": group["display_name"].strip(), "fasurl": url, "fasmailinglist": mailing_list, "fasircchannel": irc_string, } else: name = group["name"] group_args = { k: v for k, v in group.items() if k in ( "description", "fasurl", "fasmailinglist", "fasircchannel" ) } group["fasgroup"] = True try: self.ipa.group_add(name, **group_args) return Status.ADDED except python_freeipa.exceptions.FreeIPAError as e: if e.message == 'group with name "%s" already exists' % name: try: self.ipa.group_mod(name, **group_args) except python_freeipa.exceptions.FreeIPAError as e: if e.message != "no modifications to be performed": raise return Status.UNMODIFIED else: print(e.message) print(e) print(url, mailing_list, irc_string) return Status.FAILED except Exception as e: print(e) print(url, mailing_list, irc_string) return Status.FAILED def find_group_conflicts( self, fas_groups: Dict[str, List[Dict]] ) -> Dict[str, List[str]]: """Compare groups from different FAS instances and flag conflicts.""" click.echo("Checking for conflicts between groups from different FAS instances") groups_to_conflicts = {} groupnames_to_fas = defaultdict(set) for fas_name, group_objs in fas_groups.items(): for group_obj in group_objs: groupnames_to_fas[group_obj["name"]].add(fas_name) for group_name, fas_names in sorted( groupnames_to_fas.items(), key=lambda x: x[0] ): if len(fas_names) == 1: continue groups_to_conflicts[group_name] = group_conflicts = defaultdict(list) group_conflicts["same_group_name"] = {"fas_names": fas_names} click.echo("Done checking group conflicts.") click.echo(f"Found {len(groups_to_conflicts)} groups with conflicts.") return groups_to_conflicts

39.769565

99

0.539521

import click import progressbar import python_freeipa from collections import defaultdict from typing import Any, Dict, List from .status import Status, print_status from .utils import ObjectManager class Groups(ObjectManager): def __init__(self, *args, agreements, **kwargs): super().__init__(*args, **kwargs) self.agreements = agreements def pull_from_fas(self) -> Dict[str, List[Dict]]: fas_groups = {} for fas_name, fas_inst in self.fas_instances.items(): click.echo(f"Pulling group information from FAS ({fas_name})...") fas_conf = self.config["fas"][fas_name] groups = fas_inst.send_request( "/group/list", req_params={"search": fas_conf["groups"]["search"]}, auth=True, timeout=240, )["groups"] groups.sort(key=lambda g: g["name"]) click.echo(f"Got {len(groups)} groups!") fas_groups[fas_name] = groups return fas_groups def push_to_ipa( self, groups: Dict[str, List[Dict]], conflicts: Dict[str, List[Dict[str, Any]]], ) -> dict: added = 0 edited = 0 counter = 0 if not conflicts: conflicts = {} skip_conflicts = set(self.config["groups"].get("skip_conflicts", ())) for fas_name, fas_groups in groups.items(): click.echo(f"Pushing {fas_name} group information to IPA...") fas_conf = self.config["fas"][fas_name] umbrella_group = fas_conf["groups"].get("umbrella") if umbrella_group: click.echo(f"Ensuring umbrella group {umbrella_group['name']} exists...") name_max_length = max((len(g["name"]) for g in fas_groups)) click.echo(umbrella_group["name"].ljust(name_max_length + 2), nl=False) status = self._write_group_to_ipa(fas_name, umbrella_group, from_fas=False) print_status(status) if status == Status.ADDED: added += 1 elif status == Status.UPDATED: edited += 1 umbrella_members = set() fas_groups = [ g for g in fas_groups if g["name"] not in fas_conf["groups"].get("ignore", ()) ] name_max_length = max((len(g["name"]) for g in fas_groups)) for group in progressbar.progressbar(fas_groups, redirect_stdout=True): counter += 1 group_conflicts = set(conflicts.get(group["name"], ())) group_skip_conflicts = skip_conflicts & group_conflicts if group_skip_conflicts: print_status( Status.FAILED, f"[{fas_name}: Skipping group '{group['name']}' because of conflicts:" f" {', '.join(group_skip_conflicts)}", ) continue self.check_reauth(counter) click.echo(group["name"].ljust(name_max_length + 2), nl=False) status = self._write_group_to_ipa(fas_name, group) print_status(status) if status == Status.ADDED: added += 1 elif status == Status.UPDATED: edited += 1 if umbrella_group and status in (Status.ADDED, Status.UPDATED, Status.UNMODIFIED): umbrella_members.add( fas_conf["groups"].get("prefix", "") + group["name"].lower() ) if umbrella_group: ipa_group = self.ipa.group_show(umbrella_group["name"]) existing_umbrella_members = set(ipa_group.get("member_group", [])) new_umbrella_members = umbrella_members - existing_umbrella_members if not new_umbrella_members: click.echo(f"No new members to add to umbrella group {umbrella_group['name']}") else: click.echo( f"Adding {len(new_umbrella_members)} new groups to umbrella group" f" {umbrella_group['name']}" ) self.ipa.group_add_member( umbrella_group["name"], groups=list(new_umbrella_members) ) click.echo(f"Done with {fas_name}") click.echo("Recording group requirements in IPA...") self.agreements.record_group_requirements(groups) click.echo("Done.") return dict(groups_added=added, groups_edited=edited, groups_counter=counter,) def _write_group_to_ipa(self, fas_name: str, group: dict, from_fas: bool = True): if from_fas: name = ( self.config["fas"][fas_name]["groups"].get("prefix", "") + group["name"].lower() ) irc_channel = group.get("irc_channel") irc_string = None if irc_channel: if irc_channel[0] == "#": irc_channel = irc_channel[1:] irc_network = group.get("irc_network").lower() if "gimp" in irc_network: irc_string = f"irc://irc.gimp.org/#{irc_channel}" elif "oftc" in irc_network: irc_string = f"irc://irc.oftc.net/#{irc_channel}" else: # the remainder of the entries here are either blank or # freenode, so we freenode them all. irc_string = f"irc://irc.freenode.net/#{irc_channel}" url = group.get("url") if not url: url = None else: url = url.strip() mailing_list = group.get("mailing_list") if not mailing_list: mailing_list = None else: if "@" not in mailing_list: mailing_list = f"{mailing_list}@lists.fedoraproject.org" mailing_list = mailing_list.strip() mailing_list = mailing_list.rstrip(".") mailing_list = mailing_list.lower() group_args = { "description": group["display_name"].strip(), "fasurl": url, "fasmailinglist": mailing_list, "fasircchannel": irc_string, } else: name = group["name"] group_args = { k: v for k, v in group.items() if k in ( "description", "fasurl", "fasmailinglist", "fasircchannel" ) } group["fasgroup"] = True try: self.ipa.group_add(name, **group_args) return Status.ADDED except python_freeipa.exceptions.FreeIPAError as e: if e.message == 'group with name "%s" already exists' % name: try: self.ipa.group_mod(name, **group_args) except python_freeipa.exceptions.FreeIPAError as e: if e.message != "no modifications to be performed": raise return Status.UNMODIFIED else: print(e.message) print(e) print(url, mailing_list, irc_string) return Status.FAILED except Exception as e: print(e) print(url, mailing_list, irc_string) return Status.FAILED def find_group_conflicts( self, fas_groups: Dict[str, List[Dict]] ) -> Dict[str, List[str]]: click.echo("Checking for conflicts between groups from different FAS instances") groups_to_conflicts = {} groupnames_to_fas = defaultdict(set) for fas_name, group_objs in fas_groups.items(): for group_obj in group_objs: groupnames_to_fas[group_obj["name"]].add(fas_name) for group_name, fas_names in sorted( groupnames_to_fas.items(), key=lambda x: x[0] ): if len(fas_names) == 1: continue groups_to_conflicts[group_name] = group_conflicts = defaultdict(list) group_conflicts["same_group_name"] = {"fas_names": fas_names} click.echo("Done checking group conflicts.") click.echo(f"Found {len(groups_to_conflicts)} groups with conflicts.") return groups_to_conflicts

true

f7fbc7a9eba15a1c42b2f4cbc34f30de2a9613c5

53

py

Python

cacreader/swig-4.0.2/Examples/test-suite/python/global_ns_arg_runme.py

kyletanyag/LL-Smartcard

02abea9de5a13f8bae4d7832ab34cb7f0d9514c9

[ "BSD-3-Clause" ]

1,031

2015-01-02T14:08:47.000Z

2022-03-29T02:25:27.000Z

cacreader/swig-4.0.2/Examples/test-suite/python/global_ns_arg_runme.py

kyletanyag/LL-Smartcard

02abea9de5a13f8bae4d7832ab34cb7f0d9514c9

[ "BSD-3-Clause" ]

240

2015-01-11T04:27:19.000Z

2022-03-30T00:35:57.000Z

cacreader/swig-4.0.2/Examples/test-suite/python/global_ns_arg_runme.py

kyletanyag/LL-Smartcard

02abea9de5a13f8bae4d7832ab34cb7f0d9514c9

[ "BSD-3-Clause" ]

224

2015-01-05T06:13:54.000Z

2022-02-25T14:39:51.000Z

from global_ns_arg import * a = foo(1) b = bar_fn()

10.6

27

0.660377

from global_ns_arg import * a = foo(1) b = bar_fn()

true

f7fbc8b13652297abf6e73d85bc19be2333ab0b8

4,238

py

Python

src/common/utils.py

DmitryBurnaev/podcast

48c7c60e2a46378f36635dc58222e5e7682f977f

[ "MIT" ]

1

2020-09-05T10:37:55.000Z

src/common/utils.py

DmitryBurnaev/podcast

48c7c60e2a46378f36635dc58222e5e7682f977f

[ "MIT" ]

null

src/common/utils.py

DmitryBurnaev/podcast

48c7c60e2a46378f36635dc58222e5e7682f977f

[ "MIT" ]

null

import logging import logging.config import aiohttp from aiohttp import web import settings from common.excpetions import SendRequestError def get_logger(name: str = None): """ Getting configured logger """ logging.config.dictConfig(settings.LOGGING) return logging.getLogger(name or "app") def redirect( request, router_name: str, *, permanent=False, url: str = None, reason="HttpFound", **kwargs ): """ Redirect to given URL name :param request: current request for web app :param router_name: router name for creating redirect url (used if url not specified) :param permanent: use permanent redirect :param url: directly set url for redirecting :param reason: Http reason for redirect moving ('HttpFound' by default) :param kwargs: url-kwargs for forming redirecting url :raises `aiohttp.web.HTTPMovedPermanently` `aiohttp.web.HTTPFound` """ if not url: for key in kwargs.keys(): kwargs[key] = str(kwargs[key]) url = request.app.router[router_name].url_for(**kwargs) if permanent: raise web.HTTPMovedPermanently(url, reason=reason) raise web.HTTPFound(url, reason=reason) def add_message(request, message: str, title: str = None, kind: str = "info"): """ Put message into session """ messages = request.session.get("messages", []) kind = "danger" if kind == "error" else kind message = {"title": title or "Podcasts informer", "message": message} messages.append((kind, message)) request.session["messages"] = messages async def get_object_or_404(request, model, **kwargs): """ Get object or raise HttpNotFound """ try: return await request.app.objects.get(model, **kwargs) except model.DoesNotExist: raise web.HTTPNotFound() def is_mobile_app(request): """ Detects requests from mobile application (It will change some view)""" user_agent = request.headers.get("User-Agent") return "mobile-app-web-view" in user_agent def database_init(db): db.init( settings.DATABASE["name"], host=settings.DATABASE["host"], port=settings.DATABASE["port"], user=settings.DATABASE["username"], password=settings.DATABASE["password"], ) return db async def send_email(recipient_email: str, subject: str, html_content: str): """ Allows to send email via Sendgrid API """ request_url = f"https://api.sendgrid.com/{settings.SENDGRID_API_VERSION}/mail/send" request_data = { "personalizations": [{"to": [{"email": recipient_email}], "subject": subject}], "from": {"email": settings.EMAIL_FROM}, "content": [{"type": "text/html", "value": html_content}], } request_header = {"Authorization": f"Bearer {settings.SENDGRID_API_KEY}"} request_logger = get_logger(__name__) request_logger.info("Send request to %s. Data: %s", request_url, request_data) async with aiohttp.ClientSession() as session: async with session.post(request_url, json=request_data, headers=request_header) as response: if response.status > 299: response_text = await response.text() raise SendRequestError( f"Couldn't send email to {recipient_email}", f"Got status code: {response.status}; response text: {response_text}", response_status=response.status, request_url=request_url, ) else: request_logger.info( "Email sent to %s. Status code: %s", recipient_email, response.status ) def cut_string(source_string: str, max_length: int, finish_seq: str = "...") -> str: """ Allows to limit source_string and append required sequence >>> cut_string('Some long string', max_length=13) 'Some long ...' >>> cut_string('Some long string', max_length=8, finish_seq="***") 'Some ***' >>> cut_string('Some long string', max_length=1) '' """ if len(source_string) > max_length: slice_length = max_length - len(finish_seq) return source_string[:slice_length] + finish_seq if (slice_length > 0) else "" return source_string

35.024793

100

0.651958

import logging import logging.config import aiohttp from aiohttp import web import settings from common.excpetions import SendRequestError def get_logger(name: str = None): logging.config.dictConfig(settings.LOGGING) return logging.getLogger(name or "app") def redirect( request, router_name: str, *, permanent=False, url: str = None, reason="HttpFound", **kwargs ): if not url: for key in kwargs.keys(): kwargs[key] = str(kwargs[key]) url = request.app.router[router_name].url_for(**kwargs) if permanent: raise web.HTTPMovedPermanently(url, reason=reason) raise web.HTTPFound(url, reason=reason) def add_message(request, message: str, title: str = None, kind: str = "info"): messages = request.session.get("messages", []) kind = "danger" if kind == "error" else kind message = {"title": title or "Podcasts informer", "message": message} messages.append((kind, message)) request.session["messages"] = messages async def get_object_or_404(request, model, **kwargs): try: return await request.app.objects.get(model, **kwargs) except model.DoesNotExist: raise web.HTTPNotFound() def is_mobile_app(request): user_agent = request.headers.get("User-Agent") return "mobile-app-web-view" in user_agent def database_init(db): db.init( settings.DATABASE["name"], host=settings.DATABASE["host"], port=settings.DATABASE["port"], user=settings.DATABASE["username"], password=settings.DATABASE["password"], ) return db async def send_email(recipient_email: str, subject: str, html_content: str): request_url = f"https://api.sendgrid.com/{settings.SENDGRID_API_VERSION}/mail/send" request_data = { "personalizations": [{"to": [{"email": recipient_email}], "subject": subject}], "from": {"email": settings.EMAIL_FROM}, "content": [{"type": "text/html", "value": html_content}], } request_header = {"Authorization": f"Bearer {settings.SENDGRID_API_KEY}"} request_logger = get_logger(__name__) request_logger.info("Send request to %s. Data: %s", request_url, request_data) async with aiohttp.ClientSession() as session: async with session.post(request_url, json=request_data, headers=request_header) as response: if response.status > 299: response_text = await response.text() raise SendRequestError( f"Couldn't send email to {recipient_email}", f"Got status code: {response.status}; response text: {response_text}", response_status=response.status, request_url=request_url, ) else: request_logger.info( "Email sent to %s. Status code: %s", recipient_email, response.status ) def cut_string(source_string: str, max_length: int, finish_seq: str = "...") -> str: if len(source_string) > max_length: slice_length = max_length - len(finish_seq) return source_string[:slice_length] + finish_seq if (slice_length > 0) else "" return source_string

true

f7fbc8f88b244ccead27235fc29111b1a56ae99a

4,164

py

Python

src/afancontrol/fans.py

X0rg/afancontrol

91fd8b3bf1fa759651b991ffc5e0ae551e6e4907

[ "MIT" ]

1

2021-07-28T21:16:43.000Z

src/afancontrol/fans.py

X0rg/afancontrol

91fd8b3bf1fa759651b991ffc5e0ae551e6e4907

[ "MIT" ]

null

src/afancontrol/fans.py

X0rg/afancontrol

91fd8b3bf1fa759651b991ffc5e0ae551e6e4907

[ "MIT" ]

null

from contextlib import ExitStack from typing import Mapping, MutableSet, Optional from afancontrol.config import FanName from afancontrol.logger import logger from afancontrol.pwmfan import PWMFanNorm, PWMValueNorm from afancontrol.report import Report class Fans: def __init__(self, fans: Mapping[FanName, PWMFanNorm], *, report: Report) -> None: self.fans = fans self.report = report self._stack = None # type: Optional[ExitStack] # Set of fans marked as failing (which speed is 0) self._failed_fans = set() # type: MutableSet[FanName] # Set of fans that will be skipped on speed check self._stopped_fans = set() # type: MutableSet[FanName] def is_fan_failing(self, fan_name: FanName) -> bool: return fan_name in self._failed_fans def is_fan_stopped(self, fan_name: FanName) -> bool: return fan_name in self._stopped_fans def __enter__(self): # reusable self._stack = ExitStack() logger.info("Enabling PWM on fans...") try: for pwmfan in self.fans.values(): self._stack.enter_context(pwmfan) except Exception: self._stack.close() raise return self def __exit__(self, exc_type, exc_value, exc_tb): assert self._stack is not None logger.info("Disabling PWM on fans...") self._stack.close() logger.info("Done. Fans should be returned to full speed") return None def check_speeds(self) -> None: for name, fan in self.fans.items(): if name in self._stopped_fans: continue try: if fan.get_speed() <= 0: raise RuntimeError("Fan speed is 0") except Exception as e: self._ensure_fan_is_failing(name, e) else: self._ensure_fan_is_not_failing(name) def set_all_to_full_speed(self) -> None: for name, fan in self.fans.items(): if name in self._failed_fans: continue try: fan.set_full_speed() except Exception as e: logger.warning("Unable to set the fan '%s' to full speed:\n%s", name, e) def set_fan_speeds(self, speeds: Mapping[FanName, PWMValueNorm]) -> None: assert speeds.keys() == self.fans.keys() self._stopped_fans.clear() for name, pwm_norm in speeds.items(): fan = self.fans[name] assert 0.0 <= pwm_norm <= 1.0 if name in self._failed_fans: continue try: pwm = fan.set(pwm_norm) except Exception as e: logger.warning( "Unable to set the fan '%s' to speed %s:\n%s", name, pwm_norm, e ) else: logger.debug( "Fan status [%s]: speed: %.3f, pwm: %s", name, pwm_norm, pwm ) if fan.is_pwm_stopped(pwm): self._stopped_fans.add(name) def _ensure_fan_is_failing(self, name: FanName, get_speed_exc: Exception) -> None: if name in self._failed_fans: return self._failed_fans.add(name) fan = self.fans[name] try: # Perhaps it had jammed, so setting it to full speed might # recover it? fan.set_full_speed() except Exception as e: full_speed_result = "Setting fan speed to full has failed:\n%s" % e else: full_speed_result = "Fan has been set to full speed" self.report.report( "fan stopped: %s" % name, "Looks like the fan '%s' is failing:\n%s\n\n%s" % (name, get_speed_exc, full_speed_result), ) def _ensure_fan_is_not_failing(self, name: FanName) -> None: if name not in self._failed_fans: return self.report.report( "fan started: %s" % name, "Fan '%s' which had previously been reported as failing has just started." % name, ) self._failed_fans.remove(name)

34.991597

88

0.571085

from contextlib import ExitStack from typing import Mapping, MutableSet, Optional from afancontrol.config import FanName from afancontrol.logger import logger from afancontrol.pwmfan import PWMFanNorm, PWMValueNorm from afancontrol.report import Report class Fans: def __init__(self, fans: Mapping[FanName, PWMFanNorm], *, report: Report) -> None: self.fans = fans self.report = report self._stack = None self._failed_fans = set() self._stopped_fans = set() def is_fan_failing(self, fan_name: FanName) -> bool: return fan_name in self._failed_fans def is_fan_stopped(self, fan_name: FanName) -> bool: return fan_name in self._stopped_fans def __enter__(self): self._stack = ExitStack() logger.info("Enabling PWM on fans...") try: for pwmfan in self.fans.values(): self._stack.enter_context(pwmfan) except Exception: self._stack.close() raise return self def __exit__(self, exc_type, exc_value, exc_tb): assert self._stack is not None logger.info("Disabling PWM on fans...") self._stack.close() logger.info("Done. Fans should be returned to full speed") return None def check_speeds(self) -> None: for name, fan in self.fans.items(): if name in self._stopped_fans: continue try: if fan.get_speed() <= 0: raise RuntimeError("Fan speed is 0") except Exception as e: self._ensure_fan_is_failing(name, e) else: self._ensure_fan_is_not_failing(name) def set_all_to_full_speed(self) -> None: for name, fan in self.fans.items(): if name in self._failed_fans: continue try: fan.set_full_speed() except Exception as e: logger.warning("Unable to set the fan '%s' to full speed:\n%s", name, e) def set_fan_speeds(self, speeds: Mapping[FanName, PWMValueNorm]) -> None: assert speeds.keys() == self.fans.keys() self._stopped_fans.clear() for name, pwm_norm in speeds.items(): fan = self.fans[name] assert 0.0 <= pwm_norm <= 1.0 if name in self._failed_fans: continue try: pwm = fan.set(pwm_norm) except Exception as e: logger.warning( "Unable to set the fan '%s' to speed %s:\n%s", name, pwm_norm, e ) else: logger.debug( "Fan status [%s]: speed: %.3f, pwm: %s", name, pwm_norm, pwm ) if fan.is_pwm_stopped(pwm): self._stopped_fans.add(name) def _ensure_fan_is_failing(self, name: FanName, get_speed_exc: Exception) -> None: if name in self._failed_fans: return self._failed_fans.add(name) fan = self.fans[name] try: fan.set_full_speed() except Exception as e: full_speed_result = "Setting fan speed to full has failed:\n%s" % e else: full_speed_result = "Fan has been set to full speed" self.report.report( "fan stopped: %s" % name, "Looks like the fan '%s' is failing:\n%s\n\n%s" % (name, get_speed_exc, full_speed_result), ) def _ensure_fan_is_not_failing(self, name: FanName) -> None: if name not in self._failed_fans: return self.report.report( "fan started: %s" % name, "Fan '%s' which had previously been reported as failing has just started." % name, ) self._failed_fans.remove(name)

true

f7fbc9150969f438fbd8f4e7994322f560e87fe5

9,894

py

Python

tests/fixtures/test_signup.py

AusDTO/dto-digitalmarketplace-api

937843c9c01a71518cf4688b4daa55bbe7df1965

[ "MIT" ]

6

2017-06-09T03:38:53.000Z

2021-12-22T02:42:15.000Z

tests/fixtures/test_signup.py

AusDTO/dto-digitalmarketplace-api

937843c9c01a71518cf4688b4daa55bbe7df1965

[ "MIT" ]

47

2016-08-02T05:21:31.000Z

2022-03-28T01:14:17.000Z

tests/fixtures/test_signup.py

AusDTO/dto-digitalmarketplace-api

937843c9c01a71518cf4688b4daa55bbe7df1965

[ "MIT" ]

7

2016-09-13T13:07:18.000Z

2021-02-17T10:16:21.000Z

import json import pytest import mock import copy from app.models import db, Agency, AgencyDomain from app.api.services import user_claims_service from nose.tools import assert_equal test_seller = { 'name': 'matt', 'email_address': 'email+s@company.com', 'user_type': 'seller', 'abn': '123456' } gov_au_buyer = { 'name': 'Indiana Jones', 'email_address': 'indy+b@adventure.gov.au', 'user_type': 'buyer' } whitelisted_non_gov_au_buyer = { 'name': 'Han Solo', 'email_address': 'solo+b@falcon.net.au', 'user_type': 'buyer' } non_whitelisted_buyer_in_agency = { 'name': 'Luke Skywalker', 'email_address': 'luke+b@jedi.edu.au', 'user_type': 'buyer' } non_whitelisted_buyer = { 'name': 'Rick Deckard', 'email_address': 'deckard+b@runner.com.au', 'user_type': 'buyer' } @pytest.fixture() def agencies(app, request): with app.app_context(): db.session.add(Agency( id=1, name='Department of Adventure', domain='adventure.gov.au', category='Commonwealth', state='ACT', whitelisted=True, domains=[AgencyDomain( domain='adventure.gov.au', active=True )] )) db.session.add(Agency( id=2, name='Department of Millenium Falcons', domain='falcon.net.au', category='Commonwealth', state='NSW', whitelisted=True, domains=[AgencyDomain( domain='falcon.net.au', active=True )] )) db.session.add(Agency( id=3, name='Jedi Temple', domain='jedi.edu.au', category='State', state='NSW', whitelisted=False, domains=[AgencyDomain( domain='jedi.edu.au', active=True )] )) db.session.commit() yield Agency.query.all() @mock.patch('app.api.views.users.send_account_activation_email') @mock.patch('app.tasks.publish_tasks.user_claim') def test_send_seller_type_signup_invite_email(user_claim, mock_send_email, client): response = client.post( '/2/signup', data=json.dumps(test_seller), content_type='application/json') assert response.status_code == 200 mock_send_email.assert_called() @mock.patch('app.tasks.publish_tasks.user_claim') def test_seller_signup_fail_missing_abn(user_claim, client): test_seller_no_abn = copy.copy(test_seller) del test_seller_no_abn['abn'] response = client.post( '/2/signup', data=json.dumps(test_seller_no_abn), content_type='application/json') assert response.status_code == 400 @mock.patch('app.api.views.users.send_account_activation_email') @mock.patch('app.tasks.publish_tasks.user_claim') def test_send_buyer_type_signup_invite_email(user_claim, mock_send_email, client): response = client.post( '/2/signup', data=json.dumps({ 'email_address': 'm@digital.gov.au', 'name': 'Jeff Labowski', 'user_type': 'buyer', 'employment_status': 'employee' }), content_type='application/json') assert response.status_code == 200 data = json.loads(response.data) assert data['message'] == 'Email invite sent successfully' mock_send_email.assert_called() @mock.patch('app.api.views.users.send_account_activation_manager_email') @mock.patch('app.tasks.publish_tasks.user_claim') def test_send_contractor_buyer_type_signup_invite_email(user_claim, mock_send_email, client): response = client.post( '/2/signup', data=json.dumps({ 'line_manager_email': 'm@danger.gov.au', 'line_manager_name': 'Jeff Labowski', 'employment_status': 'contractor', 'user_type': 'buyer', 'name': 'Royal', 'email_address': 'rtenenabaum@mouse.gov.au' }), content_type='application/json') assert response.status_code == 200 data = json.loads(response.data) assert data['message'] == 'Email invite sent successfully' mock_send_email.assert_called() @mock.patch('app.tasks.publish_tasks.user_claim') def test_invalid_employment_status(user_claim, client): response = client.post( '/2/signup', data=json.dumps({ 'line_manager_email': 'm@danger.gov.au', 'line_manager_name': 'Jeff Labowski', 'employment_status': 'nope', 'user_type': 'buyer', 'name': 'Royal', 'email_address': 'rtenenabaum@mouse.gov.au' }), content_type='application/json') assert response.status_code == 400 @mock.patch('app.tasks.publish_tasks.user_claim') def test_missing_name(user_claim, client): response = client.post( '/2/signup', data=json.dumps({ 'email_address': 'm@goolag.com', 'user_type': 'seller' }), content_type='application/json') assert response.status_code == 400 @mock.patch('app.tasks.publish_tasks.user_claim') def test_signup_fails_without_required_fields(user_claim, client, supplier_user): response = client.post( '/2/signup', data=json.dumps({ 'email_address': 'm@goolag.com', 'name': 'Jeff Labowski' }), content_type='application/json') assert response.status_code == 400 data = json.loads(response.data) assert_equal(data['message'], "'user_type' is a required property") @mock.patch('app.tasks.publish_tasks.user_claim') def test_supplier_with_same_domain(user_claim, client, supplier_user): response = client.post( '/2/signup', data=json.dumps({ 'email_address': 'm@examplecompany.biz', 'name': 'Jeff Labowski', 'user_type': 'seller', 'abn': '56789' }), content_type='application/json') assert response.status_code == 200 @mock.patch('app.tasks.publish_tasks.user_claim') def test_duplicate_supplier_with_same_abn(user_claim, client, suppliers, supplier_user): response = client.post( '/2/signup', data=json.dumps({ 'email_address': 'm@examplecompany.biz', 'name': 'Jeff Labowski', 'user_type': 'seller', 'abn': '1' }), content_type='application/json') assert response.status_code == 409 data = json.loads(response.data) assert_equal(data['message'], 'There is already a seller account with ABN 1') @mock.patch('app.tasks.publish_tasks.user_claim') def test_duplicate_application_with_same_abn(user_claim, client, application_user): response = client.post( '/2/signup', data=json.dumps({ 'email_address': 'm@don.com', 'name': 'Jeff Labowski', 'user_type': 'seller', 'abn': '123456' }), content_type='application/json') assert response.status_code == 409 data = json.loads(response.data) assert_equal(data['message'], 'There is already a seller account with ABN 123456') @mock.patch('app.tasks.publish_tasks.user_claim') def test_generic_domain(user_claim, client): response = client.post( '/2/signup', data=json.dumps({ 'email_address': 'm@gmail.com', 'name': 'Jeff Labowski', 'user_type': 'seller', 'url': '/2', 'abn': '56789' }), content_type='application/json') assert response._status_code == 200 data = json.loads(response.data) assert_equal(data['message'], 'Email invite sent successfully') @mock.patch('app.api.views.users.send_account_activation_email') @mock.patch('app.tasks.publish_tasks.user_claim') @pytest.mark.parametrize('user', [gov_au_buyer, whitelisted_non_gov_au_buyer]) def test_buyer_can_signup_with_whitelisted_email(user_claim, mock_send_email, client, agencies, user): response = client.post( '/2/signup', data=json.dumps({ 'name': user['name'], 'email_address': user['email_address'], 'employment_status': 'employee', 'user_type': user['user_type'] }), content_type='application/json') assert response.status_code == 200 data = json.loads(response.data) assert data['message'] == 'Email invite sent successfully' mock_send_email.assert_called() @mock.patch('app.tasks.publish_tasks.user_claim') @pytest.mark.parametrize('user', [non_whitelisted_buyer_in_agency, non_whitelisted_buyer]) def test_buyer_can_not_signup_with_non_whitelisted_email(user_claim, client, agencies, user): response = client.post( '/2/signup', data=json.dumps({ 'name': user['name'], 'email_address': user['email_address'], 'employment_status': 'employee', 'user_type': user['user_type'] }), content_type='application/json') assert response.status_code == 403 data = json.loads(response.data) assert data['message'] == 'A buyer account must have a valid government entity email domain' @mock.patch('app.tasks.publish_tasks.user_claim') def test_signup_creates_signup_claim_token(user_claim, app, client): response = client.post( '/2/signup', data=json.dumps({ 'email_address': 'm@digital.gov.au', 'name': 'Jeff Labowski', 'user_type': 'buyer', 'employment_status': 'employee' }), content_type='application/json') assert response.status_code == 200 with app.app_context(): claim = user_claims_service.find(email_address='m@digital.gov.au', type='signup', claimed=False).one_or_none() assert claim.email_address == 'm@digital.gov.au'

31.509554

118

0.624419

import json import pytest import mock import copy from app.models import db, Agency, AgencyDomain from app.api.services import user_claims_service from nose.tools import assert_equal test_seller = { 'name': 'matt', 'email_address': 'email+s@company.com', 'user_type': 'seller', 'abn': '123456' } gov_au_buyer = { 'name': 'Indiana Jones', 'email_address': 'indy+b@adventure.gov.au', 'user_type': 'buyer' } whitelisted_non_gov_au_buyer = { 'name': 'Han Solo', 'email_address': 'solo+b@falcon.net.au', 'user_type': 'buyer' } non_whitelisted_buyer_in_agency = { 'name': 'Luke Skywalker', 'email_address': 'luke+b@jedi.edu.au', 'user_type': 'buyer' } non_whitelisted_buyer = { 'name': 'Rick Deckard', 'email_address': 'deckard+b@runner.com.au', 'user_type': 'buyer' } @pytest.fixture() def agencies(app, request): with app.app_context(): db.session.add(Agency( id=1, name='Department of Adventure', domain='adventure.gov.au', category='Commonwealth', state='ACT', whitelisted=True, domains=[AgencyDomain( domain='adventure.gov.au', active=True )] )) db.session.add(Agency( id=2, name='Department of Millenium Falcons', domain='falcon.net.au', category='Commonwealth', state='NSW', whitelisted=True, domains=[AgencyDomain( domain='falcon.net.au', active=True )] )) db.session.add(Agency( id=3, name='Jedi Temple', domain='jedi.edu.au', category='State', state='NSW', whitelisted=False, domains=[AgencyDomain( domain='jedi.edu.au', active=True )] )) db.session.commit() yield Agency.query.all() @mock.patch('app.api.views.users.send_account_activation_email') @mock.patch('app.tasks.publish_tasks.user_claim') def test_send_seller_type_signup_invite_email(user_claim, mock_send_email, client): response = client.post( '/2/signup', data=json.dumps(test_seller), content_type='application/json') assert response.status_code == 200 mock_send_email.assert_called() @mock.patch('app.tasks.publish_tasks.user_claim') def test_seller_signup_fail_missing_abn(user_claim, client): test_seller_no_abn = copy.copy(test_seller) del test_seller_no_abn['abn'] response = client.post( '/2/signup', data=json.dumps(test_seller_no_abn), content_type='application/json') assert response.status_code == 400 @mock.patch('app.api.views.users.send_account_activation_email') @mock.patch('app.tasks.publish_tasks.user_claim') def test_send_buyer_type_signup_invite_email(user_claim, mock_send_email, client): response = client.post( '/2/signup', data=json.dumps({ 'email_address': 'm@digital.gov.au', 'name': 'Jeff Labowski', 'user_type': 'buyer', 'employment_status': 'employee' }), content_type='application/json') assert response.status_code == 200 data = json.loads(response.data) assert data['message'] == 'Email invite sent successfully' mock_send_email.assert_called() @mock.patch('app.api.views.users.send_account_activation_manager_email') @mock.patch('app.tasks.publish_tasks.user_claim') def test_send_contractor_buyer_type_signup_invite_email(user_claim, mock_send_email, client): response = client.post( '/2/signup', data=json.dumps({ 'line_manager_email': 'm@danger.gov.au', 'line_manager_name': 'Jeff Labowski', 'employment_status': 'contractor', 'user_type': 'buyer', 'name': 'Royal', 'email_address': 'rtenenabaum@mouse.gov.au' }), content_type='application/json') assert response.status_code == 200 data = json.loads(response.data) assert data['message'] == 'Email invite sent successfully' mock_send_email.assert_called() @mock.patch('app.tasks.publish_tasks.user_claim') def test_invalid_employment_status(user_claim, client): response = client.post( '/2/signup', data=json.dumps({ 'line_manager_email': 'm@danger.gov.au', 'line_manager_name': 'Jeff Labowski', 'employment_status': 'nope', 'user_type': 'buyer', 'name': 'Royal', 'email_address': 'rtenenabaum@mouse.gov.au' }), content_type='application/json') assert response.status_code == 400 @mock.patch('app.tasks.publish_tasks.user_claim') def test_missing_name(user_claim, client): response = client.post( '/2/signup', data=json.dumps({ 'email_address': 'm@goolag.com', 'user_type': 'seller' }), content_type='application/json') assert response.status_code == 400 @mock.patch('app.tasks.publish_tasks.user_claim') def test_signup_fails_without_required_fields(user_claim, client, supplier_user): response = client.post( '/2/signup', data=json.dumps({ 'email_address': 'm@goolag.com', 'name': 'Jeff Labowski' }), content_type='application/json') assert response.status_code == 400 data = json.loads(response.data) assert_equal(data['message'], "'user_type' is a required property") @mock.patch('app.tasks.publish_tasks.user_claim') def test_supplier_with_same_domain(user_claim, client, supplier_user): response = client.post( '/2/signup', data=json.dumps({ 'email_address': 'm@examplecompany.biz', 'name': 'Jeff Labowski', 'user_type': 'seller', 'abn': '56789' }), content_type='application/json') assert response.status_code == 200 @mock.patch('app.tasks.publish_tasks.user_claim') def test_duplicate_supplier_with_same_abn(user_claim, client, suppliers, supplier_user): response = client.post( '/2/signup', data=json.dumps({ 'email_address': 'm@examplecompany.biz', 'name': 'Jeff Labowski', 'user_type': 'seller', 'abn': '1' }), content_type='application/json') assert response.status_code == 409 data = json.loads(response.data) assert_equal(data['message'], 'There is already a seller account with ABN 1') @mock.patch('app.tasks.publish_tasks.user_claim') def test_duplicate_application_with_same_abn(user_claim, client, application_user): response = client.post( '/2/signup', data=json.dumps({ 'email_address': 'm@don.com', 'name': 'Jeff Labowski', 'user_type': 'seller', 'abn': '123456' }), content_type='application/json') assert response.status_code == 409 data = json.loads(response.data) assert_equal(data['message'], 'There is already a seller account with ABN 123456') @mock.patch('app.tasks.publish_tasks.user_claim') def test_generic_domain(user_claim, client): response = client.post( '/2/signup', data=json.dumps({ 'email_address': 'm@gmail.com', 'name': 'Jeff Labowski', 'user_type': 'seller', 'url': '/2', 'abn': '56789' }), content_type='application/json') assert response._status_code == 200 data = json.loads(response.data) assert_equal(data['message'], 'Email invite sent successfully') @mock.patch('app.api.views.users.send_account_activation_email') @mock.patch('app.tasks.publish_tasks.user_claim') @pytest.mark.parametrize('user', [gov_au_buyer, whitelisted_non_gov_au_buyer]) def test_buyer_can_signup_with_whitelisted_email(user_claim, mock_send_email, client, agencies, user): response = client.post( '/2/signup', data=json.dumps({ 'name': user['name'], 'email_address': user['email_address'], 'employment_status': 'employee', 'user_type': user['user_type'] }), content_type='application/json') assert response.status_code == 200 data = json.loads(response.data) assert data['message'] == 'Email invite sent successfully' mock_send_email.assert_called() @mock.patch('app.tasks.publish_tasks.user_claim') @pytest.mark.parametrize('user', [non_whitelisted_buyer_in_agency, non_whitelisted_buyer]) def test_buyer_can_not_signup_with_non_whitelisted_email(user_claim, client, agencies, user): response = client.post( '/2/signup', data=json.dumps({ 'name': user['name'], 'email_address': user['email_address'], 'employment_status': 'employee', 'user_type': user['user_type'] }), content_type='application/json') assert response.status_code == 403 data = json.loads(response.data) assert data['message'] == 'A buyer account must have a valid government entity email domain' @mock.patch('app.tasks.publish_tasks.user_claim') def test_signup_creates_signup_claim_token(user_claim, app, client): response = client.post( '/2/signup', data=json.dumps({ 'email_address': 'm@digital.gov.au', 'name': 'Jeff Labowski', 'user_type': 'buyer', 'employment_status': 'employee' }), content_type='application/json') assert response.status_code == 200 with app.app_context(): claim = user_claims_service.find(email_address='m@digital.gov.au', type='signup', claimed=False).one_or_none() assert claim.email_address == 'm@digital.gov.au'

true

f7fbc96245e2f0ea302afae850c53c5b30f0d66a

166

py

Python

src/ralph/lib/transitions/apps.py

andrzej-jankowski/ralph

68ec18a66b8fe47ddf1c082c3ce2d82b2cd430dc

[ "Apache-2.0" ]

null

src/ralph/lib/transitions/apps.py

andrzej-jankowski/ralph

68ec18a66b8fe47ddf1c082c3ce2d82b2cd430dc

[ "Apache-2.0" ]

null

src/ralph/lib/transitions/apps.py

andrzej-jankowski/ralph

68ec18a66b8fe47ddf1c082c3ce2d82b2cd430dc

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- from django.apps import AppConfig class TransitionAppConfig(AppConfig): name = 'ralph.lib.transitions' verbose_name = 'Transitions'

20.75

37

0.710843

from django.apps import AppConfig class TransitionAppConfig(AppConfig): name = 'ralph.lib.transitions' verbose_name = 'Transitions'

true

f7fbca6c2148ac0fc50fbec1cae5c9a0710cdce0

493

py

Python

hazelcast/protocol/codec/map_clear_codec.py

tonytheonlypony/hazelcast-python-client

3aafeaf2ebc05aee4f2386c62c079db496a7c81f

[ "Apache-2.0" ]

98

2015-12-08T14:26:27.000Z

2022-03-23T17:44:11.000Z

hazelcast/protocol/codec/map_clear_codec.py

tonytheonlypony/hazelcast-python-client

3aafeaf2ebc05aee4f2386c62c079db496a7c81f

[ "Apache-2.0" ]

396

2016-02-23T11:07:55.000Z

2022-03-31T14:26:34.000Z

hazelcast/protocol/codec/map_clear_codec.py

tonytheonlypony/hazelcast-python-client

3aafeaf2ebc05aee4f2386c62c079db496a7c81f

[ "Apache-2.0" ]

62

2015-12-09T11:20:53.000Z

2022-01-28T01:30:54.000Z

from hazelcast.protocol.client_message import OutboundMessage, REQUEST_HEADER_SIZE, create_initial_buffer from hazelcast.protocol.builtin import StringCodec # hex: 0x012D00 _REQUEST_MESSAGE_TYPE = 77056 # hex: 0x012D01 _RESPONSE_MESSAGE_TYPE = 77057 _REQUEST_INITIAL_FRAME_SIZE = REQUEST_HEADER_SIZE def encode_request(name): buf = create_initial_buffer(_REQUEST_INITIAL_FRAME_SIZE, _REQUEST_MESSAGE_TYPE) StringCodec.encode(buf, name, True) return OutboundMessage(buf, False)

30.8125

105

0.8357

from hazelcast.protocol.client_message import OutboundMessage, REQUEST_HEADER_SIZE, create_initial_buffer from hazelcast.protocol.builtin import StringCodec _REQUEST_MESSAGE_TYPE = 77056 _RESPONSE_MESSAGE_TYPE = 77057 _REQUEST_INITIAL_FRAME_SIZE = REQUEST_HEADER_SIZE def encode_request(name): buf = create_initial_buffer(_REQUEST_INITIAL_FRAME_SIZE, _REQUEST_MESSAGE_TYPE) StringCodec.encode(buf, name, True) return OutboundMessage(buf, False)

true

f7fbcacf21058ebd2add82f562467cc65c7e70fa

668

py

Python

core/models.py

danielqiang/CombineSearch

d24b49c4ef0cc0bf4470383f293a6d625765f3d2

[ "MIT" ]

null

core/models.py

danielqiang/CombineSearch

d24b49c4ef0cc0bf4470383f293a6d625765f3d2

[ "MIT" ]

null

core/models.py

danielqiang/CombineSearch

d24b49c4ef0cc0bf4470383f293a6d625765f3d2

[ "MIT" ]

1

2020-03-20T20:41:30.000Z

from django.db import models class Author(models.Model): name = models.CharField(max_length=30) def __str__(self): return self.name class Category(models.Model): name = models.CharField(max_length=20) def __str__(self): return self.name class Journal(models.Model): title = models.CharField(max_length=120) author = models.ForeignKey(Author, on_delete=models.CASCADE) categories = models.ManyToManyField(Category) publish_date = models.DateTimeField(auto_now_add=True) views = models.IntegerField(default=0) reviewed = models.BooleanField(default=False) def __str__(self): return self.title

23.857143

64

0.715569

from django.db import models class Author(models.Model): name = models.CharField(max_length=30) def __str__(self): return self.name class Category(models.Model): name = models.CharField(max_length=20) def __str__(self): return self.name class Journal(models.Model): title = models.CharField(max_length=120) author = models.ForeignKey(Author, on_delete=models.CASCADE) categories = models.ManyToManyField(Category) publish_date = models.DateTimeField(auto_now_add=True) views = models.IntegerField(default=0) reviewed = models.BooleanField(default=False) def __str__(self): return self.title

true

f7fbcaf2144a99a1427daa079bab87f73f035547

2,099

py

Python

002-pyopengl/PyOpenGL-Demo-3.0.1b1/PyOpenGL-Demo/proesch/colorCube/colorCube.py

lhl/vrdev

fc1a9af2b51d159c99c8779349ef3392a70ed9ed

[ "Apache-2.0" ]

12

2015-12-02T02:36:36.000Z

2020-09-20T17:14:24.000Z

002-pyopengl/PyOpenGL-Demo-3.0.1b1/PyOpenGL-Demo/proesch/colorCube/colorCube.py

lhl/vrdev

fc1a9af2b51d159c99c8779349ef3392a70ed9ed

[ "Apache-2.0" ]

null

002-pyopengl/PyOpenGL-Demo-3.0.1b1/PyOpenGL-Demo/proesch/colorCube/colorCube.py

lhl/vrdev

fc1a9af2b51d159c99c8779349ef3392a70ed9ed

[ "Apache-2.0" ]

8

2016-11-02T11:17:04.000Z

2021-10-21T07:42:19.000Z

#!/usr/bin/python # rotating color cube # Copyright (C) 2007 "Peter Roesch" <Peter.Roesch@fh-augsburg.de> # # This code is licensed under the PyOpenGL License. # Details are given in the file license.txt included in this distribution. import sys try: from OpenGL.GLUT import * from OpenGL.GL import * from OpenGL.GLU import * except: print ''' Error: PyOpenGL not installed properly ''' sys.exit( ) import array vertices = array.array('f', [ -1,-1,1, -1,1,1, 1,1,1, 1,-1,1,\ -1,-1,-1, -1,1,-1, 1,1,-1, 1,-1,-1 ] ) colors = array.array('f', [ 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, \ 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1] ) cIndices = array.array('B', [0, 3, 2, 1, 2, 3, 7, 6, 0, 4, 7, 3, \ 1, 2, 6, 5, 4, 5, 6, 7, 0, 1, 5, 4 ] ) animationAngle = 0.0 frameRate = 25 from time import sleep def animationStep( ): global animationAngle global frameRate animationAngle += 2 while animationAngle > 360: animationAngle -= 360 sleep( 1 / float( frameRate ) ) glutPostRedisplay( ) def display( ): glClear( GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT ) glMatrixMode( GL_PROJECTION ) glLoadIdentity( ) glOrtho( -2, 2, -2, 2, -2, 2 ) glMatrixMode( GL_MODELVIEW ) glLoadIdentity( ) glRotatef( animationAngle, 1, 1, 1 ) glEnableClientState( GL_COLOR_ARRAY ) glEnableClientState( GL_VERTEX_ARRAY ) glColorPointer( 3, GL_FLOAT, 0, colors.tostring( ) ) glVertexPointer( 3, GL_FLOAT, 0, vertices.tostring( ) ) glDrawElements( GL_QUADS, 24, GL_UNSIGNED_BYTE, cIndices.tostring( ) ) glDisableClientState( GL_COLOR_ARRAY ) glDisableClientState( GL_VERTEX_ARRAY ) glutSwapBuffers( ) def init( ): if not (glColorPointer and glVertexPointer and glDrawElements): print ''' Error: no vertex array support''' sys.exit( ) glClearColor ( 0, 0, 0, 0 ) glEnable(GL_DEPTH_TEST) glShadeModel( GL_SMOOTH ) glutInit( sys.argv ) glutInitDisplayMode( GLUT_DOUBLE | GLUT_RGB | GLUT_DEPTH ) glutInitWindowSize( 250, 250 ) glutInitWindowPosition( 100, 100 ) glutCreateWindow( sys.argv[0] ) init( ) glutDisplayFunc( display ) glutIdleFunc( animationStep ) glutMainLoop( )

28.364865

74

0.685565

import sys try: from OpenGL.GLUT import * from OpenGL.GL import * from OpenGL.GLU import * except: print ''' Error: PyOpenGL not installed properly ''' sys.exit( ) import array vertices = array.array('f', [ -1,-1,1, -1,1,1, 1,1,1, 1,-1,1,\ -1,-1,-1, -1,1,-1, 1,1,-1, 1,-1,-1 ] ) colors = array.array('f', [ 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, \ 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1] ) cIndices = array.array('B', [0, 3, 2, 1, 2, 3, 7, 6, 0, 4, 7, 3, \ 1, 2, 6, 5, 4, 5, 6, 7, 0, 1, 5, 4 ] ) animationAngle = 0.0 frameRate = 25 from time import sleep def animationStep( ): global animationAngle global frameRate animationAngle += 2 while animationAngle > 360: animationAngle -= 360 sleep( 1 / float( frameRate ) ) glutPostRedisplay( ) def display( ): glClear( GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT ) glMatrixMode( GL_PROJECTION ) glLoadIdentity( ) glOrtho( -2, 2, -2, 2, -2, 2 ) glMatrixMode( GL_MODELVIEW ) glLoadIdentity( ) glRotatef( animationAngle, 1, 1, 1 ) glEnableClientState( GL_COLOR_ARRAY ) glEnableClientState( GL_VERTEX_ARRAY ) glColorPointer( 3, GL_FLOAT, 0, colors.tostring( ) ) glVertexPointer( 3, GL_FLOAT, 0, vertices.tostring( ) ) glDrawElements( GL_QUADS, 24, GL_UNSIGNED_BYTE, cIndices.tostring( ) ) glDisableClientState( GL_COLOR_ARRAY ) glDisableClientState( GL_VERTEX_ARRAY ) glutSwapBuffers( ) def init( ): if not (glColorPointer and glVertexPointer and glDrawElements): print ''' Error: no vertex array support''' sys.exit( ) glClearColor ( 0, 0, 0, 0 ) glEnable(GL_DEPTH_TEST) glShadeModel( GL_SMOOTH ) glutInit( sys.argv ) glutInitDisplayMode( GLUT_DOUBLE | GLUT_RGB | GLUT_DEPTH ) glutInitWindowSize( 250, 250 ) glutInitWindowPosition( 100, 100 ) glutCreateWindow( sys.argv[0] ) init( ) glutDisplayFunc( display ) glutIdleFunc( animationStep ) glutMainLoop( )

false

true

f7fbccd65eb14ab564d2f902ca145e5f22769a61

294

py

Python

sympy/integrals/tests/test_lineintegrals.py

sn6uv/sympy

5b149c2f72847e4785c65358b09d99b29f101dd5

[ "BSD-3-Clause" ]

2

2015-05-11T12:26:38.000Z

2016-08-19T00:11:03.000Z

sympy/integrals/tests/test_lineintegrals.py

goodok/sympy

de84ed2139125a755ea7b6ba91d945d9fbbe5ed9

[ "BSD-3-Clause" ]

1

2016-06-13T01:29:51.000Z

2016-06-14T00:38:27.000Z

sympy/integrals/tests/test_lineintegrals.py

goodok/sympy

de84ed2139125a755ea7b6ba91d945d9fbbe5ed9

[ "BSD-3-Clause" ]

1

2022-03-21T09:07:27.000Z

from sympy import (symbols, integrate, Integral, diff, sin, cos, pi, E, ln, sympify, Curve, line_integrate, sqrt) s, t, x, y, z = symbols('s,t,x,y,z') def test_lineintegral(): c = Curve([E**t + 1, E**t - 1], (t, 0, ln(2))) assert line_integrate(x + y, c, [x, y]) == 3*sqrt(2)

32.666667

75

0.57483

from sympy import (symbols, integrate, Integral, diff, sin, cos, pi, E, ln, sympify, Curve, line_integrate, sqrt) s, t, x, y, z = symbols('s,t,x,y,z') def test_lineintegral(): c = Curve([E**t + 1, E**t - 1], (t, 0, ln(2))) assert line_integrate(x + y, c, [x, y]) == 3*sqrt(2)

true

f7fbcd2dab63894d0039ebaed84bcaa13266d9e1

7,042

py

Python

src/transformers/models/deberta_v2/configuration_deberta_v2.py

kct22aws/transformers

04cddaf402591e9f5bdb5f116a111d829a0ce4f4

[ "Apache-2.0" ]

31

2022-02-02T13:13:41.000Z

2022-03-29T08:37:20.000Z

src/transformers/models/deberta_v2/configuration_deberta_v2.py

guang7400613/transformers

28e091430eea9e0d40839e56fd0d57aec262f5f9

[ "Apache-2.0" ]

2

2022-03-14T10:13:16.000Z

2022-03-14T11:50:27.000Z

src/transformers/models/deberta_v2/configuration_deberta_v2.py

guang7400613/transformers

28e091430eea9e0d40839e56fd0d57aec262f5f9

[ "Apache-2.0" ]

2

2022-03-21T04:32:39.000Z

2022-03-22T01:02:49.000Z

# coding=utf-8 # Copyright 2020, Microsoft and the HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ DeBERTa-v2 model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP = { "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json", "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json", "microsoft/deberta-v2-xlarge-mnli": "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json", "microsoft/deberta-v2-xxlarge-mnli": "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json", } class DebertaV2Config(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`DebertaV2Model`]. It is used to instantiate a DeBERTa-v2 model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the DeBERTa [microsoft/deberta-v2-xlarge](https://huggingface.co/microsoft/deberta-base) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Arguments: vocab_size (`int`, *optional*, defaults to 128100): Vocabulary size of the DeBERTa-v2 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`DebertaV2Model`]. hidden_size (`int`, *optional*, defaults to 1536): Dimensionality of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 24): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 24): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 6144): Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"`, `"gelu"`, `"tanh"`, `"gelu_fast"`, `"mish"`, `"linear"`, `"sigmoid"` and `"gelu_new"` are supported. hidden_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention probabilities. max_position_embeddings (`int`, *optional*, defaults to 512): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). type_vocab_size (`int`, *optional*, defaults to 0): The vocabulary size of the `token_type_ids` passed when calling [`DebertaModel`] or [`TFDebertaModel`]. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-7): The epsilon used by the layer normalization layers. relative_attention (`bool`, *optional*, defaults to `True`): Whether use relative position encoding. max_relative_positions (`int`, *optional*, defaults to -1): The range of relative positions `[-max_position_embeddings, max_position_embeddings]`. Use the same value as `max_position_embeddings`. pad_token_id (`int`, *optional*, defaults to 0): The value used to pad input_ids. position_biased_input (`bool`, *optional*, defaults to `False`): Whether add absolute position embedding to content embedding. pos_att_type (`List[str]`, *optional*): The type of relative position attention, it can be a combination of `["p2c", "c2p", "p2p"]`, e.g. `["p2c"]`, `["p2c", "c2p"]`, `["p2c", "c2p", 'p2p"]`. layer_norm_eps (`float`, optional, defaults to 1e-12): The epsilon used by the layer normalization layers. """ model_type = "deberta-v2" def __init__( self, vocab_size=128100, hidden_size=1536, num_hidden_layers=24, num_attention_heads=24, intermediate_size=6144, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=0, initializer_range=0.02, layer_norm_eps=1e-7, relative_attention=False, max_relative_positions=-1, pad_token_id=0, position_biased_input=True, pos_att_type=None, pooler_dropout=0, pooler_hidden_act="gelu", **kwargs ): super().__init__(**kwargs) self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.initializer_range = initializer_range self.relative_attention = relative_attention self.max_relative_positions = max_relative_positions self.pad_token_id = pad_token_id self.position_biased_input = position_biased_input # Backwards compatibility if type(pos_att_type) == str: pos_att_type = [x.strip() for x in pos_att_type.lower().split("|")] self.pos_att_type = pos_att_type self.vocab_size = vocab_size self.layer_norm_eps = layer_norm_eps self.pooler_hidden_size = kwargs.get("pooler_hidden_size", hidden_size) self.pooler_dropout = pooler_dropout self.pooler_hidden_act = pooler_hidden_act

51.028986

125

0.690571

from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP = { "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json", "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json", "microsoft/deberta-v2-xlarge-mnli": "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json", "microsoft/deberta-v2-xxlarge-mnli": "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json", } class DebertaV2Config(PretrainedConfig): model_type = "deberta-v2" def __init__( self, vocab_size=128100, hidden_size=1536, num_hidden_layers=24, num_attention_heads=24, intermediate_size=6144, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=0, initializer_range=0.02, layer_norm_eps=1e-7, relative_attention=False, max_relative_positions=-1, pad_token_id=0, position_biased_input=True, pos_att_type=None, pooler_dropout=0, pooler_hidden_act="gelu", **kwargs ): super().__init__(**kwargs) self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.initializer_range = initializer_range self.relative_attention = relative_attention self.max_relative_positions = max_relative_positions self.pad_token_id = pad_token_id self.position_biased_input = position_biased_input if type(pos_att_type) == str: pos_att_type = [x.strip() for x in pos_att_type.lower().split("|")] self.pos_att_type = pos_att_type self.vocab_size = vocab_size self.layer_norm_eps = layer_norm_eps self.pooler_hidden_size = kwargs.get("pooler_hidden_size", hidden_size) self.pooler_dropout = pooler_dropout self.pooler_hidden_act = pooler_hidden_act

true

f7fbcdcafadd48f6c9d1e843eefee400b2e44500

411

py

Python

utility_belt/__init__.py

ajsilveira/utility_belt

b593400bdbe1bcda89117615175045f6eefda74a

[ "MIT" ]

null

utility_belt/__init__.py

ajsilveira/utility_belt

b593400bdbe1bcda89117615175045f6eefda74a

[ "MIT" ]

null

utility_belt/__init__.py

ajsilveira/utility_belt

b593400bdbe1bcda89117615175045f6eefda74a

[ "MIT" ]

null

""" utility_belt A set of tools for my work """ # Make Python 2 and 3 imports work the same # Safe to remove with Python 3-only code from __future__ import absolute_import # Add imports here from .utility_belt import * # Handle versioneer from ._version import get_versions versions = get_versions() __version__ = versions['version'] __git_revision__ = versions['full-revisionid'] del get_versions, versions

21.631579

46

0.778589

from __future__ import absolute_import from .utility_belt import * from ._version import get_versions versions = get_versions() __version__ = versions['version'] __git_revision__ = versions['full-revisionid'] del get_versions, versions

true

f7fbce9552f151899983ca30fa2163e5439036cc

16,110

py

Python

intel/k8s.py

hanyin-intel/CPU-Manager-for-Kubernetes

c9848d62f0d9c1d7a3de0745ea172df9a4ccaf7b

[ "Apache-2.0" ]

152

2018-05-24T02:19:41.000Z

2022-03-23T08:39:11.000Z

intel/k8s.py

hanyin-intel/CPU-Manager-for-Kubernetes

c9848d62f0d9c1d7a3de0745ea172df9a4ccaf7b

[ "Apache-2.0" ]

64

2018-05-30T13:09:40.000Z

2021-12-29T14:00:25.000Z

intel/k8s.py

isabella232/CPU-Manager-for-Kubernetes

b92d994fedc734898f9852bb65fcd4cd2be55384

[ "Apache-2.0" ]

78

2018-05-22T13:26:54.000Z

2022-02-20T22:33:07.000Z

# Copyright (c) 2017 Intel Corporation # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging from intel import util from kubernetes import client as k8sclient, config as k8sconfig from kubernetes.client import V1Namespace, V1DeleteOptions VERSION_NAME = "v1.9.0" # Only set up the Volume Mounts necessary for the container CONTAINER_VOLUME_MOUNTS = { "init": { "volumeMounts": [ { "mountPath": "/host/proc", "name": "host-proc", "readOnly": True } ], "securityContext": { "readOnlyRootFilesystem": True, "runAsNonRoot": True, "runAsUser": 1000, "runAsGroup": 3000, "fsGroup": 2000 } }, "install": { "volumeMounts": [ { "mountPath": "/opt/bin", "name": "cmk-install-dir", } ], "securityContext": { "privileged": True } }, "discover": { "volumeMounts": [ ], "securityContext": { "readOnlyRootFilesystem": True, "runAsNonRoot": True, "runAsUser": 1000, "runAsGroup": 3000, "fsGroup": 2000 } }, "reconcile": { "volumeMounts": [ { "mountPath": "/host/proc", "name": "host-proc", "readOnly": True }, { "mountPath": "/opt/bin", "name": "cmk-install-dir", "readOnly": True } ], "securityContext": { "readOnlyRootFilesystem": True, "runAsNonRoot": True, "runAsUser": 1000, "runAsGroup": 3000, "fsGroup": 2000 } }, "nodereport": { "volumeMounts": [ { "mountPath": "/host/proc", "name": "host-proc", "readOnly": True }, { "mountPath": "/opt/bin", "name": "cmk-install-dir", "readOnly": True } ], "securityContext": { "readOnlyRootFilesystem": True, "runAsNonRoot": True, "runAsUser": 1000, "runAsGroup": 3000, "fsGroup": 2000 } }, "webhook": { "volumeMounts": [ { "mountPath": "/host/proc", "name": "host-proc", "readOnly": True }, { "mountPath": "/opt/bin", "name": "cmk-install-dir", "readOnly": True } ], "securityContext": { "runAsNonRoot": True, "runAsUser": 1000, "runAsGroup": 3000, "fsGroup": 2000 } }, "reconfigure": { "volumeMounts": [ { "mountPath": "/host/proc", "name": "host-proc", "readOnly": True } ], "securityContext": { "readOnlyRootFilesystem": True, "runAsNonRoot": True, "runAsUser": 1000, "runAsGroup": 3000, "fsGroup": 2000 } } } def get_pod_template(saname="cmk-serviceaccount"): pod_template = { "apiVersion": "v1", "kind": "Pod", "metadata": { "name": "PODNAME", "annotations": { } }, "spec": { "serviceAccount": saname, "nodeName": "NODENAME", "containers": [ ], "restartPolicy": "Never", "volumes": [ { "hostPath": { "path": "/proc" }, "name": "host-proc" }, { "hostPath": { "path": "/opt/bin" }, "name": "cmk-install-dir" } ] } } return pod_template def ds_from(pod, version): ds_template = {} if version >= util.parse_version(VERSION_NAME): ds_template = { "apiVersion": "apps/v1", "kind": "DaemonSet", "metadata": { "name": pod["metadata"]["name"].replace("pod", "ds") }, "spec": { "selector": { "matchLabels": { "app": pod["metadata"]["name"].replace("pod", "ds") } }, "template": { "metadata": { "labels": { "app": pod["metadata"]["name"].replace("pod", "ds") } }, "spec": pod["spec"] } } } # for k8s versions older than 1.9.0 use extensions/v1beta1 API else: ds_template = { "apiVersion": "extensions/v1beta1", "kind": "DaemonSet", "metadata": { "name": pod["metadata"]["name"].replace("pod", "ds") }, "spec": { "template": { "metadata": { "labels": { "app": pod["metadata"]["name"].replace("pod", "ds") } }, "spec": pod["spec"] } } } return ds_template def deployment_from(pod): deployment_template = { "apiVersion": "apps/v1", "kind": "Deployment", "metadata": { "labels": pod["metadata"]["labels"], "name": pod["metadata"]["name"].replace("pod", "deployment") }, "spec": { "replicas": 1, "selector": { "matchLabels": pod["metadata"]["labels"] }, "template": { "metadata": { "labels": pod["metadata"]["labels"] }, "spec": pod["spec"] } } } return deployment_template def client_from_config(config): if config is None: k8sconfig.load_incluster_config() return k8sclient.CoreV1Api() else: client = k8sclient.ApiClient(configuration=config) return k8sclient.CoreV1Api(api_client=client) def apps_api_client_from_config(config): if config is None: k8sconfig.load_incluster_config() return k8sclient.AppsV1Api() else: client = k8sclient.ApiClient(configuration=config) return k8sclient.AppsV1Api(api_client=client) def extensions_client_from_config(config): if config is None: k8sconfig.load_incluster_config() return k8sclient.ExtensionsV1beta1Api() else: client = k8sclient.ApiClient(configuration=config) return k8sclient.ExtensionsV1beta1Api(api_client=client) def version_api_client_from_config(config): if config is None: k8sconfig.load_incluster_config() return k8sclient.VersionApi() else: client = k8sclient.ApiClient(configuration=config) return k8sclient.VersionApi(api_client=client) def admissionregistartion_api_client_from_config(config): if config is None: k8sconfig.load_incluster_config() return k8sclient.AdmissionregistrationV1beta1Api() else: client = k8sclient.ApiClient(configuration=config) return k8sclient.AdmissionregistrationV1beta1Api(api_client=client) def get_container_template(cmd): container_template = { "args": [ "ARGS" ], "command": ["/bin/bash", "-c"], "env": [ { "name": "CMK_PROC_FS", "value": "/host/proc" }, { "name": "NODE_NAME", "valueFrom": { "fieldRef": { "fieldPath": "spec.nodeName" } } } ], "image": "IMAGENAME", "name": "NAME", "securityContext": CONTAINER_VOLUME_MOUNTS[cmd]["securityContext"], "volumeMounts": CONTAINER_VOLUME_MOUNTS[cmd]["volumeMounts"], "imagePullPolicy": "Never" } return container_template # get_node_list() returns the node list in the current Kubernetes cluster. def get_node_list(config, label_selector=None): k8s_api = client_from_config(config) if label_selector: nodes = k8s_api.list_node(label_selector=label_selector).to_dict() else: nodes = k8s_api.list_node().to_dict() return nodes["items"] # get_node_from_pod returns the node that a given pod is running on def get_node_from_pod(config, pod_name): pods = get_pod_list(config) for p in pods["items"]: if p["metadata"]["name"] == pod_name: return p["spec"]["node_name"] # get_pod_list() returns the pod list in the current Kubernetes cluster. def get_pod_list(config): k8s_api = client_from_config(config) return k8s_api.list_pod_for_all_namespaces().to_dict() # create_pod() sends a request to the Kubernetes API server to create a # pod based on podspec. def create_pod(config, podspec, ns_name): k8s_api = client_from_config(config) return k8s_api.create_namespaced_pod(ns_name, podspec) # create_legacy_ds() sends a request to the Kubernetes API server to create a # ds based on deamonset spec. def create_ds(config, spec, ns_name, version): if version >= util.parse_version(VERSION_NAME): k8s_api = apps_api_client_from_config(config) return k8s_api.create_namespaced_daemon_set(ns_name, spec) else: k8s_api = extensions_client_from_config(config) return k8s_api.create_namespaced_daemon_set(ns_name, spec) def create_service(config, spec, ns_name): k8s_api = client_from_config(config) return k8s_api.create_namespaced_service(ns_name, spec) def create_config_map(config, spec, ns_name): k8s_api = client_from_config(config) return k8s_api.create_namespaced_config_map(ns_name, spec) def patch_config_map(config, cm_name, spec, ns_name): k8s_api = client_from_config(config) return k8s_api.patch_namespaced_config_map(cm_name, ns_name, spec) def create_secret(config, spec, ns_name): k8s_api = client_from_config(config) return k8s_api.create_namespaced_secret(ns_name, spec) def create_mutating_webhook_configuration(config, spec): k8s_api = admissionregistartion_api_client_from_config(config) return k8s_api.create_mutating_webhook_configuration(spec) def create_deployment(config, spec, ns_name): k8s_api = apps_api_client_from_config(config) return k8s_api.create_namespaced_deployment(ns_name, spec) # Create list of schedulable nodes. def get_compute_nodes(config, label_selector=None): compute_nodes = [] for node in get_node_list(config, label_selector): if "unschedulable" in node["spec"] and \ node["spec"]["unschedulable"]: continue compute_nodes.append(node) return compute_nodes # Set label to selected node. def set_node_label(config, node, label, label_value): patch_body = [{ "op": "add", "path": "/metadata/labels/%s" % label, "value": label_value, }] k8s_api = client_from_config(config) k8s_api.patch_node(node, patch_body) # Unset label from node. def unset_node_label(config, node, label): patch_body = [{ "op": "remove", "path": "/metadata/labels/%s" % label, }] k8s_api = client_from_config(config) k8s_api.patch_node(node, patch_body) # Create namespace with generated namespace name. def create_namespace(config, ns_name): metadata = {'name': ns_name} namespace = V1Namespace(metadata=metadata) k8s_api = client_from_config(config) k8s_api.create_namespace(namespace) # Get available namespaces. def get_namespaces(config): k8s_api = client_from_config(config) return k8s_api.list_namespace().to_dict() # Get k8s version def get_kube_version(config): k8s_api = version_api_client_from_config(config) version_info = k8s_api.get_code() return version_info.git_version # Get named configmap def get_config_map(config, name, ns_name): k8s_api = client_from_config(config) configmaps = k8s_api.list_namespaced_config_map(ns_name) for cm in configmaps.items: if cm.metadata.name == name: return cm # Delete namespace by name. def delete_namespace(config, ns_name, delete_options=V1DeleteOptions()): k8s_api = client_from_config(config) k8s_api.delete_namespace(ns_name) # Delete pod from namespace. def delete_pod(config, name, ns_name="default", body=V1DeleteOptions()): k8s_api = client_from_config(config) k8s_api.delete_namespaced_pod(name, ns_name) # Delete ds from namespace. # Due to problem with orphan_dependents flag and changes # in cascade deletion in k8s, first delete the ds, then the pod # https://github.com/kubernetes-incubator/client-python/issues/162 # https://github.com/kubernetes/kubernetes/issues/44046 def delete_ds(config, version, ds_name, ns_name="default", body=V1DeleteOptions()): k8s_api_core = client_from_config(config) if version >= util.parse_version(VERSION_NAME): k8s_api_apps = apps_api_client_from_config(config) k8s_api_apps.delete_namespaced_daemon_set(ds_name, ns_name, grace_period_seconds=0, orphan_dependents=False) else: k8s_api_ext = extensions_client_from_config(config) k8s_api_ext.delete_namespaced_daemon_set(ds_name, ns_name, grace_period_seconds=0, orphan_dependents=False) # Pod in ds has fixed label so we use label selector data = k8s_api_core.list_namespaced_pod( ns_name, label_selector="app={}".format(ds_name)).to_dict() # There should be only one pod for pod in data["items"]: logging.debug("Removing pod \"{}\"".format(pod["metadata"]["name"])) delete_pod(None, pod["metadata"]["name"], ns_name) def delete_service(config, name, ns_name="default", body=V1DeleteOptions()): k8s_api = client_from_config(config) return k8s_api.delete_namespaced_service(name, ns_name) def delete_config_map(config, name, ns_name="default", body=V1DeleteOptions()): k8s_api = client_from_config(config) return k8s_api.delete_namespaced_config_map(name, ns_name) def delete_secret(config, name, ns_name="default", body=V1DeleteOptions()): k8s_api = client_from_config(config) return k8s_api.delete_namespaced_secret(name, ns_name) def delete_mutating_webhook_configuration(config, name, body=V1DeleteOptions()): k8s_api = admissionregistartion_api_client_from_config(config) return k8s_api.delete_mutating_webhook_configuration(name) def delete_deployment(config, name, ns_name="default", body=V1DeleteOptions()): k8s_api = apps_api_client_from_config(config) return k8s_api.delete_namespaced_deployment(name, ns_name)

30.56926

79

0.566046

import logging from intel import util from kubernetes import client as k8sclient, config as k8sconfig from kubernetes.client import V1Namespace, V1DeleteOptions VERSION_NAME = "v1.9.0" CONTAINER_VOLUME_MOUNTS = { "init": { "volumeMounts": [ { "mountPath": "/host/proc", "name": "host-proc", "readOnly": True } ], "securityContext": { "readOnlyRootFilesystem": True, "runAsNonRoot": True, "runAsUser": 1000, "runAsGroup": 3000, "fsGroup": 2000 } }, "install": { "volumeMounts": [ { "mountPath": "/opt/bin", "name": "cmk-install-dir", } ], "securityContext": { "privileged": True } }, "discover": { "volumeMounts": [ ], "securityContext": { "readOnlyRootFilesystem": True, "runAsNonRoot": True, "runAsUser": 1000, "runAsGroup": 3000, "fsGroup": 2000 } }, "reconcile": { "volumeMounts": [ { "mountPath": "/host/proc", "name": "host-proc", "readOnly": True }, { "mountPath": "/opt/bin", "name": "cmk-install-dir", "readOnly": True } ], "securityContext": { "readOnlyRootFilesystem": True, "runAsNonRoot": True, "runAsUser": 1000, "runAsGroup": 3000, "fsGroup": 2000 } }, "nodereport": { "volumeMounts": [ { "mountPath": "/host/proc", "name": "host-proc", "readOnly": True }, { "mountPath": "/opt/bin", "name": "cmk-install-dir", "readOnly": True } ], "securityContext": { "readOnlyRootFilesystem": True, "runAsNonRoot": True, "runAsUser": 1000, "runAsGroup": 3000, "fsGroup": 2000 } }, "webhook": { "volumeMounts": [ { "mountPath": "/host/proc", "name": "host-proc", "readOnly": True }, { "mountPath": "/opt/bin", "name": "cmk-install-dir", "readOnly": True } ], "securityContext": { "runAsNonRoot": True, "runAsUser": 1000, "runAsGroup": 3000, "fsGroup": 2000 } }, "reconfigure": { "volumeMounts": [ { "mountPath": "/host/proc", "name": "host-proc", "readOnly": True } ], "securityContext": { "readOnlyRootFilesystem": True, "runAsNonRoot": True, "runAsUser": 1000, "runAsGroup": 3000, "fsGroup": 2000 } } } def get_pod_template(saname="cmk-serviceaccount"): pod_template = { "apiVersion": "v1", "kind": "Pod", "metadata": { "name": "PODNAME", "annotations": { } }, "spec": { "serviceAccount": saname, "nodeName": "NODENAME", "containers": [ ], "restartPolicy": "Never", "volumes": [ { "hostPath": { "path": "/proc" }, "name": "host-proc" }, { "hostPath": { "path": "/opt/bin" }, "name": "cmk-install-dir" } ] } } return pod_template def ds_from(pod, version): ds_template = {} if version >= util.parse_version(VERSION_NAME): ds_template = { "apiVersion": "apps/v1", "kind": "DaemonSet", "metadata": { "name": pod["metadata"]["name"].replace("pod", "ds") }, "spec": { "selector": { "matchLabels": { "app": pod["metadata"]["name"].replace("pod", "ds") } }, "template": { "metadata": { "labels": { "app": pod["metadata"]["name"].replace("pod", "ds") } }, "spec": pod["spec"] } } } else: ds_template = { "apiVersion": "extensions/v1beta1", "kind": "DaemonSet", "metadata": { "name": pod["metadata"]["name"].replace("pod", "ds") }, "spec": { "template": { "metadata": { "labels": { "app": pod["metadata"]["name"].replace("pod", "ds") } }, "spec": pod["spec"] } } } return ds_template def deployment_from(pod): deployment_template = { "apiVersion": "apps/v1", "kind": "Deployment", "metadata": { "labels": pod["metadata"]["labels"], "name": pod["metadata"]["name"].replace("pod", "deployment") }, "spec": { "replicas": 1, "selector": { "matchLabels": pod["metadata"]["labels"] }, "template": { "metadata": { "labels": pod["metadata"]["labels"] }, "spec": pod["spec"] } } } return deployment_template def client_from_config(config): if config is None: k8sconfig.load_incluster_config() return k8sclient.CoreV1Api() else: client = k8sclient.ApiClient(configuration=config) return k8sclient.CoreV1Api(api_client=client) def apps_api_client_from_config(config): if config is None: k8sconfig.load_incluster_config() return k8sclient.AppsV1Api() else: client = k8sclient.ApiClient(configuration=config) return k8sclient.AppsV1Api(api_client=client) def extensions_client_from_config(config): if config is None: k8sconfig.load_incluster_config() return k8sclient.ExtensionsV1beta1Api() else: client = k8sclient.ApiClient(configuration=config) return k8sclient.ExtensionsV1beta1Api(api_client=client) def version_api_client_from_config(config): if config is None: k8sconfig.load_incluster_config() return k8sclient.VersionApi() else: client = k8sclient.ApiClient(configuration=config) return k8sclient.VersionApi(api_client=client) def admissionregistartion_api_client_from_config(config): if config is None: k8sconfig.load_incluster_config() return k8sclient.AdmissionregistrationV1beta1Api() else: client = k8sclient.ApiClient(configuration=config) return k8sclient.AdmissionregistrationV1beta1Api(api_client=client) def get_container_template(cmd): container_template = { "args": [ "ARGS" ], "command": ["/bin/bash", "-c"], "env": [ { "name": "CMK_PROC_FS", "value": "/host/proc" }, { "name": "NODE_NAME", "valueFrom": { "fieldRef": { "fieldPath": "spec.nodeName" } } } ], "image": "IMAGENAME", "name": "NAME", "securityContext": CONTAINER_VOLUME_MOUNTS[cmd]["securityContext"], "volumeMounts": CONTAINER_VOLUME_MOUNTS[cmd]["volumeMounts"], "imagePullPolicy": "Never" } return container_template def get_node_list(config, label_selector=None): k8s_api = client_from_config(config) if label_selector: nodes = k8s_api.list_node(label_selector=label_selector).to_dict() else: nodes = k8s_api.list_node().to_dict() return nodes["items"] def get_node_from_pod(config, pod_name): pods = get_pod_list(config) for p in pods["items"]: if p["metadata"]["name"] == pod_name: return p["spec"]["node_name"] def get_pod_list(config): k8s_api = client_from_config(config) return k8s_api.list_pod_for_all_namespaces().to_dict() def create_pod(config, podspec, ns_name): k8s_api = client_from_config(config) return k8s_api.create_namespaced_pod(ns_name, podspec) def create_ds(config, spec, ns_name, version): if version >= util.parse_version(VERSION_NAME): k8s_api = apps_api_client_from_config(config) return k8s_api.create_namespaced_daemon_set(ns_name, spec) else: k8s_api = extensions_client_from_config(config) return k8s_api.create_namespaced_daemon_set(ns_name, spec) def create_service(config, spec, ns_name): k8s_api = client_from_config(config) return k8s_api.create_namespaced_service(ns_name, spec) def create_config_map(config, spec, ns_name): k8s_api = client_from_config(config) return k8s_api.create_namespaced_config_map(ns_name, spec) def patch_config_map(config, cm_name, spec, ns_name): k8s_api = client_from_config(config) return k8s_api.patch_namespaced_config_map(cm_name, ns_name, spec) def create_secret(config, spec, ns_name): k8s_api = client_from_config(config) return k8s_api.create_namespaced_secret(ns_name, spec) def create_mutating_webhook_configuration(config, spec): k8s_api = admissionregistartion_api_client_from_config(config) return k8s_api.create_mutating_webhook_configuration(spec) def create_deployment(config, spec, ns_name): k8s_api = apps_api_client_from_config(config) return k8s_api.create_namespaced_deployment(ns_name, spec) def get_compute_nodes(config, label_selector=None): compute_nodes = [] for node in get_node_list(config, label_selector): if "unschedulable" in node["spec"] and \ node["spec"]["unschedulable"]: continue compute_nodes.append(node) return compute_nodes def set_node_label(config, node, label, label_value): patch_body = [{ "op": "add", "path": "/metadata/labels/%s" % label, "value": label_value, }] k8s_api = client_from_config(config) k8s_api.patch_node(node, patch_body) def unset_node_label(config, node, label): patch_body = [{ "op": "remove", "path": "/metadata/labels/%s" % label, }] k8s_api = client_from_config(config) k8s_api.patch_node(node, patch_body) def create_namespace(config, ns_name): metadata = {'name': ns_name} namespace = V1Namespace(metadata=metadata) k8s_api = client_from_config(config) k8s_api.create_namespace(namespace) def get_namespaces(config): k8s_api = client_from_config(config) return k8s_api.list_namespace().to_dict() def get_kube_version(config): k8s_api = version_api_client_from_config(config) version_info = k8s_api.get_code() return version_info.git_version def get_config_map(config, name, ns_name): k8s_api = client_from_config(config) configmaps = k8s_api.list_namespaced_config_map(ns_name) for cm in configmaps.items: if cm.metadata.name == name: return cm def delete_namespace(config, ns_name, delete_options=V1DeleteOptions()): k8s_api = client_from_config(config) k8s_api.delete_namespace(ns_name) def delete_pod(config, name, ns_name="default", body=V1DeleteOptions()): k8s_api = client_from_config(config) k8s_api.delete_namespaced_pod(name, ns_name) def delete_ds(config, version, ds_name, ns_name="default", body=V1DeleteOptions()): k8s_api_core = client_from_config(config) if version >= util.parse_version(VERSION_NAME): k8s_api_apps = apps_api_client_from_config(config) k8s_api_apps.delete_namespaced_daemon_set(ds_name, ns_name, grace_period_seconds=0, orphan_dependents=False) else: k8s_api_ext = extensions_client_from_config(config) k8s_api_ext.delete_namespaced_daemon_set(ds_name, ns_name, grace_period_seconds=0, orphan_dependents=False) data = k8s_api_core.list_namespaced_pod( ns_name, label_selector="app={}".format(ds_name)).to_dict() for pod in data["items"]: logging.debug("Removing pod \"{}\"".format(pod["metadata"]["name"])) delete_pod(None, pod["metadata"]["name"], ns_name) def delete_service(config, name, ns_name="default", body=V1DeleteOptions()): k8s_api = client_from_config(config) return k8s_api.delete_namespaced_service(name, ns_name) def delete_config_map(config, name, ns_name="default", body=V1DeleteOptions()): k8s_api = client_from_config(config) return k8s_api.delete_namespaced_config_map(name, ns_name) def delete_secret(config, name, ns_name="default", body=V1DeleteOptions()): k8s_api = client_from_config(config) return k8s_api.delete_namespaced_secret(name, ns_name) def delete_mutating_webhook_configuration(config, name, body=V1DeleteOptions()): k8s_api = admissionregistartion_api_client_from_config(config) return k8s_api.delete_mutating_webhook_configuration(name) def delete_deployment(config, name, ns_name="default", body=V1DeleteOptions()): k8s_api = apps_api_client_from_config(config) return k8s_api.delete_namespaced_deployment(name, ns_name)

true

f7fbcf3f077ecc368c9cd1e42d6381cbf2728225

2,505

py

Python

tests/store/artifact/test_dbfs_artifact_repo_delegation.py

PeterSulcs/mlflow

14c48e7bb1ca6cd6a3c1b249a486cd98bd5e7051

[ "Apache-2.0" ]

10,351

2018-07-31T02:52:49.000Z

2022-03-31T23:33:13.000Z

tests/store/artifact/test_dbfs_artifact_repo_delegation.py

PeterSulcs/mlflow

14c48e7bb1ca6cd6a3c1b249a486cd98bd5e7051

[ "Apache-2.0" ]

3,733

2018-07-31T01:38:51.000Z

2022-03-31T23:56:25.000Z

tests/store/artifact/test_dbfs_artifact_repo_delegation.py

PeterSulcs/mlflow

14c48e7bb1ca6cd6a3c1b249a486cd98bd5e7051

[ "Apache-2.0" ]

2,596

2018-07-31T06:38:39.000Z

2022-03-31T23:56:32.000Z

import os import pytest from unittest import mock from mlflow.store.artifact.artifact_repository_registry import get_artifact_repository from mlflow.store.artifact.local_artifact_repo import LocalArtifactRepository from mlflow.store.artifact.dbfs_artifact_repo import DbfsRestArtifactRepository from mlflow.store.artifact.dbfs_artifact_repo import DatabricksArtifactRepository from mlflow.utils.rest_utils import MlflowHostCreds @pytest.fixture() def host_creds_mock(): with mock.patch( "mlflow.store.artifact.dbfs_artifact_repo._get_host_creds_from_default_store" ) as get_creds_mock: get_creds_mock.return_value = lambda: MlflowHostCreds("http://host") yield @mock.patch("mlflow.utils.databricks_utils.is_dbfs_fuse_available") def test_dbfs_artifact_repo_delegates_to_correct_repo( is_dbfs_fuse_available, host_creds_mock ): # pylint: disable=unused-argument # fuse available is_dbfs_fuse_available.return_value = True artifact_uri = "dbfs:/databricks/my/absolute/dbfs/path" repo = get_artifact_repository(artifact_uri) assert isinstance(repo, LocalArtifactRepository) assert repo.artifact_dir == os.path.join( os.path.sep, "dbfs", "databricks", "my", "absolute", "dbfs", "path" ) # fuse available but a model repository DBFS location repo = get_artifact_repository("dbfs:/databricks/mlflow-registry/version12345/models") assert isinstance(repo, DbfsRestArtifactRepository) # fuse not available with mock.patch.dict(os.environ, {"MLFLOW_ENABLE_DBFS_FUSE_ARTIFACT_REPO": "false"}): fuse_disabled_repo = get_artifact_repository(artifact_uri) assert isinstance(fuse_disabled_repo, DbfsRestArtifactRepository) assert fuse_disabled_repo.artifact_uri == artifact_uri is_dbfs_fuse_available.return_value = False rest_repo = get_artifact_repository(artifact_uri) assert isinstance(rest_repo, DbfsRestArtifactRepository) assert rest_repo.artifact_uri == artifact_uri with mock.patch( "mlflow.store.artifact.databricks_artifact_repo" + ".DatabricksArtifactRepository._get_run_artifact_root" ) as get_run_artifact_root_mock: mock_uri = "dbfs:/databricks/mlflow-tracking/MOCK-EXP/MOCK-RUN-ID/artifacts" get_run_artifact_root_mock.return_value = mock_uri databricks_repo = get_artifact_repository(mock_uri) assert isinstance(databricks_repo, DatabricksArtifactRepository) assert databricks_repo.artifact_uri == mock_uri

44.732143

90

0.786427

import os import pytest from unittest import mock from mlflow.store.artifact.artifact_repository_registry import get_artifact_repository from mlflow.store.artifact.local_artifact_repo import LocalArtifactRepository from mlflow.store.artifact.dbfs_artifact_repo import DbfsRestArtifactRepository from mlflow.store.artifact.dbfs_artifact_repo import DatabricksArtifactRepository from mlflow.utils.rest_utils import MlflowHostCreds @pytest.fixture() def host_creds_mock(): with mock.patch( "mlflow.store.artifact.dbfs_artifact_repo._get_host_creds_from_default_store" ) as get_creds_mock: get_creds_mock.return_value = lambda: MlflowHostCreds("http://host") yield @mock.patch("mlflow.utils.databricks_utils.is_dbfs_fuse_available") def test_dbfs_artifact_repo_delegates_to_correct_repo( is_dbfs_fuse_available, host_creds_mock ): is_dbfs_fuse_available.return_value = True artifact_uri = "dbfs:/databricks/my/absolute/dbfs/path" repo = get_artifact_repository(artifact_uri) assert isinstance(repo, LocalArtifactRepository) assert repo.artifact_dir == os.path.join( os.path.sep, "dbfs", "databricks", "my", "absolute", "dbfs", "path" ) repo = get_artifact_repository("dbfs:/databricks/mlflow-registry/version12345/models") assert isinstance(repo, DbfsRestArtifactRepository) with mock.patch.dict(os.environ, {"MLFLOW_ENABLE_DBFS_FUSE_ARTIFACT_REPO": "false"}): fuse_disabled_repo = get_artifact_repository(artifact_uri) assert isinstance(fuse_disabled_repo, DbfsRestArtifactRepository) assert fuse_disabled_repo.artifact_uri == artifact_uri is_dbfs_fuse_available.return_value = False rest_repo = get_artifact_repository(artifact_uri) assert isinstance(rest_repo, DbfsRestArtifactRepository) assert rest_repo.artifact_uri == artifact_uri with mock.patch( "mlflow.store.artifact.databricks_artifact_repo" + ".DatabricksArtifactRepository._get_run_artifact_root" ) as get_run_artifact_root_mock: mock_uri = "dbfs:/databricks/mlflow-tracking/MOCK-EXP/MOCK-RUN-ID/artifacts" get_run_artifact_root_mock.return_value = mock_uri databricks_repo = get_artifact_repository(mock_uri) assert isinstance(databricks_repo, DatabricksArtifactRepository) assert databricks_repo.artifact_uri == mock_uri

true

f7fbd05bd7299fa0aaeb5b1d3712e2cf9cef053e

2,472

py

Python

python/seldon_deploy_sdk/models/capability.py

adriangonz/seldon-deploy-sdk

c5504838630a87053387cec57ec2e1e7251971e2

[ "Apache-2.0" ]

6

2021-02-18T14:37:54.000Z

2022-01-13T13:27:43.000Z

python/seldon_deploy_sdk/models/capability.py

adriangonz/seldon-deploy-sdk

c5504838630a87053387cec57ec2e1e7251971e2

[ "Apache-2.0" ]

14

2021-01-04T16:32:03.000Z

2021-12-13T17:53:59.000Z

python/seldon_deploy_sdk/models/capability.py

adriangonz/seldon-deploy-sdk

c5504838630a87053387cec57ec2e1e7251971e2

[ "Apache-2.0" ]

7

2021-03-17T09:05:55.000Z

2022-01-05T10:39:56.000Z

# coding: utf-8 """ Seldon Deploy API API to interact and manage the lifecycle of your machine learning models deployed through Seldon Deploy. # noqa: E501 OpenAPI spec version: v1alpha1 Contact: hello@seldon.io Generated by: https://github.com/swagger-api/swagger-codegen.git """ import pprint import re # noqa: F401 import six class Capability(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ """ Attributes: swagger_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ swagger_types = { } attribute_map = { } def __init__(self): # noqa: E501 """Capability - a model defined in Swagger""" # noqa: E501 self.discriminator = None def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value if issubclass(Capability, dict): for key, value in self.items(): result[key] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, Capability): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

28.090909

122

0.55623

import pprint import re import six class Capability(object): swagger_types = { } attribute_map = { } def __init__(self): self.discriminator = None def to_dict(self): result = {} for attr, _ in six.iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value if issubclass(Capability, dict): for key, value in self.items(): result[key] = value return result def to_str(self): return pprint.pformat(self.to_dict()) def __repr__(self): return self.to_str() def __eq__(self, other): if not isinstance(other, Capability): return False return self.__dict__ == other.__dict__ def __ne__(self, other): return not self == other

true

f7fbd071cb019beda3409456c1d66e1b9eeacdd4

2,249

py

Python

day-32-automated-email/main.py

jskolnicki/100-Days-of-Python

146af2b73914a525121f1c91737abd4857dc2f89

[ "CNRI-Python" ]

null

day-32-automated-email/main.py

jskolnicki/100-Days-of-Python

146af2b73914a525121f1c91737abd4857dc2f89

[ "CNRI-Python" ]

null

day-32-automated-email/main.py

jskolnicki/100-Days-of-Python

146af2b73914a525121f1c91737abd4857dc2f89

[ "CNRI-Python" ]

null

##################### Extra Hard Starting Project ###################### from email import message from re import template import pandas as pd import os import datetime import random import smtplib from csv import reader os.chdir(os.path.dirname(__file__)) my_email = "jareds.automated.email@gmail.com" password = "abcdefg123()" # 1. Update the birthdays.csv # 2. Check if today matches a birthday in the birthdays.csv df = pd.read_csv("birthdays.csv") today_string = datetime.date.today().strftime('%m-%d') with open('birthdays.csv', 'r') as read_obj: csv_reader = reader(read_obj) header = next(csv_reader) # Check file as empty if header != None: # Iterate over each row after the header in the csv for row in csv_reader: # row variable is a list that represents a row in csv birthday = f'{row[4].zfill(2)}-{row[5].zfill(2)}' if birthday == today_string: # 3. If step 2 is true, pick a random letter from letter templates and replace the [NAME] with the person's actual name from birthdays.csv number_of_letter_options = 0 for letter in os.listdir('letter_templates'): if row[1] in letter: number_of_letter_options += 1 with open(f'C:/Users/jared/GitHub/continued-ed/100-Days-of-Python/day-32-email/letter_templates/{row[1]}_{random.randint(1,number_of_letter_options)}.txt') as file: message = file.readlines() email_message = "" for line in message: email_message = email_message + line email_message = email_message.replace("[NAME]",row[0]) # 4. Send the letter generated in step 3 to that person's email address. with smtplib.SMTP("smtp.gmail.com", port=587) as connection: connection.starttls() connection.login(user=my_email, password=password) connection.sendmail(from_addr=my_email, to_addrs=row[2], msg= f'Subject:Happy Birthday!\n\n{email_message}')

38.775862

180

0.586483

connection.starttls() connection.login(user=my_email, password=password) connection.sendmail(from_addr=my_email, to_addrs=row[2], msg= f'Subject:Happy Birthday!\n\n{email_message}')

true

f7fbd073906bc9f5fcfc8f06b63d0dfb6db3baaa

8,418

py

Python

verifiable_mpc/ac20/compressed_pivot.py

marcusmjh/verifiable_mpc

8f9b734c28e98057cb3928870cb504ab045c3a83

[ "MIT" ]

null

verifiable_mpc/ac20/compressed_pivot.py

marcusmjh/verifiable_mpc

8f9b734c28e98057cb3928870cb504ab045c3a83

[ "MIT" ]

null

verifiable_mpc/ac20/compressed_pivot.py

marcusmjh/verifiable_mpc

8f9b734c28e98057cb3928870cb504ab045c3a83

[ "MIT" ]

null

""" Implementation of https://eprint.iacr.org/2020/152 ``Compressed Σ-Protocol Theory and Practical Application to Plug & Play Secure Algorithmics'' Protocols: * Compressed Σ-protocol Π_c for relation R, page 15, protocol 5 """ import os import sys import logging from random import SystemRandom project_root = sys.path.append(os.path.abspath("..")) if project_root not in sys.path: sys.path.insert(0, project_root) import verifiable_mpc.ac20.pivot as pivot from sec_groups.fingroups import EllipticCurveElement prng = SystemRandom() logger_cp = logging.getLogger("compressed_pivot") logger_cp.setLevel(logging.INFO) logger_cp_hin = logging.getLogger("compressed_pivot_hash_inputs") logger_cp_hin.setLevel(logging.INFO) logger_cp_hout = logging.getLogger("compressed_pivot_hash_outputs") logger_cp_hout.setLevel(logging.INFO) def protocol_4_prover(g_hat, k, Q, L_tilde, z_hat, gf, proof={}, round_i=0): """ Non-interactive version of Protocol 4 from Section 4.2: Prover's side """ # Step 5: Prover calculates A, B half = len(g_hat) // 2 g_hat_l = g_hat[:half] g_hat_r = g_hat[half:] z_hat_l = z_hat[:half] z_hat_r = z_hat[half:] logger_cp.debug("Calculate A_i, B_i.") A = pivot.vector_commitment(z_hat_l, int(L_tilde([0] * half + z_hat_l)), g_hat_r, k) B = pivot.vector_commitment(z_hat_r, int(L_tilde(z_hat_r + [0] * half)), g_hat_l, k) proof["A" + str(round_i)] = A proof["B" + str(round_i)] = B # Step 6: Prover calculates challenge order = k.order # Hash A, B and all public elements of Protocol 4 # input_list = [A, B, g_hat, k, Q, L_tilde] if isinstance(A, EllipticCurveElement): input_list = [A.to_affine(), B.to_affine(), g_hat, k, Q.to_affine(), L_tilde] else: input_list = [A, B, g_hat, k, Q, L_tilde] logger_cp_hin.debug( f"Method protocol_4_prover: Before fiat_shamir_hash, input_list=\n{input_list}" ) c = pivot.fiat_shamir_hash(input_list, order) logger_cp_hout.debug(f"After hash, hash=\n{c}") # Step 7: Prover calculates following public values logger_cp.debug("Calculate g_prime.") g_prime = [(g_hat_l[i] ** c) * g_hat_r[i] for i in range(half)] logger_cp.debug("Calculate Q_prime.") Q_prime = A * (Q ** c) * (B ** (c ** 2)) assert ( L_tilde.constant == 0 ), "Next line assumes L_tilde is a linear form, not affine form." c_L_tilde_l_coeffs = [coeff * gf(c) for coeff in L_tilde.coeffs[:half]] L_prime = pivot.LinearForm(c_L_tilde_l_coeffs) + pivot.LinearForm( L_tilde.coeffs[half:] ) # Step 8: Prover calculates z_prime and tests if recursion is required (if z' in Z or Z^2) z_prime = [z_hat_l[i] + c * z_hat_r[i] for i in range(half)] if len(z_prime) <= 2: proof["z_prime"] = z_prime return proof else: # Step 9: Prover sends z_prime and starts recursion of protocol 4 round_i += 1 proof = protocol_4_prover( g_prime, k, Q_prime, L_prime, z_prime, gf, proof, round_i ) return proof def protocol_5_prover(generators, P, L, y, x, gamma, gf): g = generators["g"] h = generators["h"] k = generators["k"] proof = {} # Public parameters n = len(x) L, y = pivot.affine_to_linear(L, y, n) assert ( bin(n + 1).count("1") == 1 ), "This implementation requires n+1 to be power of 2 (else, use padding with zeros)." # Non-interactive proof # Step 1: Prover calculates ("announcement") t, A order = gf.order r = list(prng.randrange(order) for i in range(n)) rho = prng.randrange(order) logger_cp.debug("Calculate t.") t = L(r) logger_cp.debug("Calculate A.") A = pivot.vector_commitment(r, rho, g, h) proof["t"] = t proof["A"] = A # Step 2: Prover computes challenge # input_list = [t, A, generators, P, L, y] if isinstance(A, EllipticCurveElement): input_list = [t, A.to_affine(), generators, P.to_affine(), L, y] else: input_list = [t, A, generators, P, L, y] logger_cp_hin.debug( f"Method protocol_5_prover: Before fiat_shamir_hash, input_list=\n{input_list}" ) c0 = pivot.fiat_shamir_hash( input_list + [0] + ["First hash of compressed pivot"], order ) c1 = pivot.fiat_shamir_hash( input_list + [1] + ["First hash of compressed pivot"], order ) logger_cp_hout.debug(f"After hash, hash=\n{c0}, {c1}") # Step 3: Prover calculates z = [c0 * x_i + r[i] for i, x_i in enumerate(x)] phi = gf(c0 * gamma + rho) z_hat = z + [phi] # Step 4: Prover calculates following public variables g_hat = g + [h] logger_cp.debug("Calculate Q.") Q = A * (P ** c0) * (k ** int(c1 * (c0 * y + t))) L_tilde = pivot.LinearForm(L.coeffs + [0]) * c1 assert L(z) * c1 == L_tilde(z_hat) proof = protocol_4_prover(g_hat, k, Q, L_tilde, z_hat, gf, proof) return proof def protocol_4_verifier(g_hat, k, Q, L_tilde, gf, proof, round_i=0): """ Non-interactive version of Protocol 4 from Section 4.2: Verifier's side """ # Step 5 half = len(g_hat) // 2 g_hat_l = g_hat[:half] g_hat_r = g_hat[half:] logger_cp.debug("Load from proof: A_i, B_i.") A = proof["A" + str(round_i)] B = proof["B" + str(round_i)] # Step 6 order = k.order # Hash A, B and all public elements of Protocol 4 # input_list = [A, B, g_hat, k, Q, L_tilde] if isinstance(A, EllipticCurveElement): input_list = [A.to_affine(), B.to_affine(), g_hat, k, Q.to_affine(), L_tilde] else: input_list = [A, B, g_hat, k, Q, L_tilde] logger_cp_hin.debug( f"Method protocol_4_verifier: Before fiat_shamir_hash, input_list=\n{input_list}" ) c = pivot.fiat_shamir_hash(input_list, order) logger_cp_hout.debug(f"After hash, hash=\n{c}") # Step 7 logger_cp.debug("Calculate g_prime.") g_prime = [(g_hat_l[i] ** c) * g_hat_r[i] for i in range(half)] logger_cp.debug("Calculate Q_prime.") Q_prime = A * (Q ** c) * (B ** (c ** 2)) assert ( L_tilde.constant == 0 ), "Next line assumes L_tilde is a linear form, not affine form." c_L_tilde_l_coeffs = [coeff * gf(c) for coeff in L_tilde.coeffs[:half]] L_prime = pivot.LinearForm(c_L_tilde_l_coeffs) + pivot.LinearForm( L_tilde.coeffs[half:] ) # Step 8 if len(g_prime) <= 2: z_prime = proof["z_prime"] Q_check = pivot.vector_commitment(z_prime, int(L_prime(z_prime)), g_prime, k) logger_cp.debug("Arrived in final step of protocol_4_verifier.") logger_cp.debug(f"Q_check= {Q_check}") logger_cp.debug(f"Q_prime= {Q_prime}") verification = Q_check == Q_prime return verification else: # Step 9 round_i += 1 return protocol_4_verifier(g_prime, k, Q_prime, L_prime, gf, proof, round_i) def protocol_5_verifier(generators, P, L, y, proof, gf): g = generators["g"] h = generators["h"] k = generators["k"] order = gf.order n = len(g) L, y = pivot.affine_to_linear(L, y, n) logger_cp.debug("Load from proof: t, A.") t = proof["t"] A = proof["A"] # input_list = [t, A, generators, P, L, y] if isinstance(A, EllipticCurveElement): input_list = [t, A.to_affine(), generators, P.to_affine(), L, y] else: input_list = [t, A, generators, P, L, y] logger_cp_hin.debug( f"Method protocol_5_verifier: Before fiat_shamir_hash, input_list=\n{input_list}" ) c0 = pivot.fiat_shamir_hash( input_list + [0] + ["First hash of compressed pivot"], order ) c1 = pivot.fiat_shamir_hash( input_list + [1] + ["First hash of compressed pivot"], order ) logger_cp_hout.debug(f"After hash, hash=\n{c0}, {c1}") g_hat = g + [h] logger_cp.debug("Calculate Q.") Q = A * (P ** c0) * (k ** int(c1 * (c0 * y + t))) L_tilde = pivot.LinearForm(L.coeffs + [0]) * c1 verification = protocol_4_verifier(g_hat, k, Q, L_tilde, gf, proof) return verification # TODO-list # TODO1: Input y can be removed from inputs to protocol5 method.

33.807229

95

0.614517

import os import sys import logging from random import SystemRandom project_root = sys.path.append(os.path.abspath("..")) if project_root not in sys.path: sys.path.insert(0, project_root) import verifiable_mpc.ac20.pivot as pivot from sec_groups.fingroups import EllipticCurveElement prng = SystemRandom() logger_cp = logging.getLogger("compressed_pivot") logger_cp.setLevel(logging.INFO) logger_cp_hin = logging.getLogger("compressed_pivot_hash_inputs") logger_cp_hin.setLevel(logging.INFO) logger_cp_hout = logging.getLogger("compressed_pivot_hash_outputs") logger_cp_hout.setLevel(logging.INFO) def protocol_4_prover(g_hat, k, Q, L_tilde, z_hat, gf, proof={}, round_i=0): half = len(g_hat) // 2 g_hat_l = g_hat[:half] g_hat_r = g_hat[half:] z_hat_l = z_hat[:half] z_hat_r = z_hat[half:] logger_cp.debug("Calculate A_i, B_i.") A = pivot.vector_commitment(z_hat_l, int(L_tilde([0] * half + z_hat_l)), g_hat_r, k) B = pivot.vector_commitment(z_hat_r, int(L_tilde(z_hat_r + [0] * half)), g_hat_l, k) proof["A" + str(round_i)] = A proof["B" + str(round_i)] = B order = k.order if isinstance(A, EllipticCurveElement): input_list = [A.to_affine(), B.to_affine(), g_hat, k, Q.to_affine(), L_tilde] else: input_list = [A, B, g_hat, k, Q, L_tilde] logger_cp_hin.debug( f"Method protocol_4_prover: Before fiat_shamir_hash, input_list=\n{input_list}" ) c = pivot.fiat_shamir_hash(input_list, order) logger_cp_hout.debug(f"After hash, hash=\n{c}") logger_cp.debug("Calculate g_prime.") g_prime = [(g_hat_l[i] ** c) * g_hat_r[i] for i in range(half)] logger_cp.debug("Calculate Q_prime.") Q_prime = A * (Q ** c) * (B ** (c ** 2)) assert ( L_tilde.constant == 0 ), "Next line assumes L_tilde is a linear form, not affine form." c_L_tilde_l_coeffs = [coeff * gf(c) for coeff in L_tilde.coeffs[:half]] L_prime = pivot.LinearForm(c_L_tilde_l_coeffs) + pivot.LinearForm( L_tilde.coeffs[half:] ) z_prime = [z_hat_l[i] + c * z_hat_r[i] for i in range(half)] if len(z_prime) <= 2: proof["z_prime"] = z_prime return proof else: # Step 9: Prover sends z_prime and starts recursion of protocol 4 round_i += 1 proof = protocol_4_prover( g_prime, k, Q_prime, L_prime, z_prime, gf, proof, round_i ) return proof def protocol_5_prover(generators, P, L, y, x, gamma, gf): g = generators["g"] h = generators["h"] k = generators["k"] proof = {} # Public parameters n = len(x) L, y = pivot.affine_to_linear(L, y, n) assert ( bin(n + 1).count("1") == 1 ), "This implementation requires n+1 to be power of 2 (else, use padding with zeros)." # Non-interactive proof # Step 1: Prover calculates ("announcement") t, A order = gf.order r = list(prng.randrange(order) for i in range(n)) rho = prng.randrange(order) logger_cp.debug("Calculate t.") t = L(r) logger_cp.debug("Calculate A.") A = pivot.vector_commitment(r, rho, g, h) proof["t"] = t proof["A"] = A # Step 2: Prover computes challenge # input_list = [t, A, generators, P, L, y] if isinstance(A, EllipticCurveElement): input_list = [t, A.to_affine(), generators, P.to_affine(), L, y] else: input_list = [t, A, generators, P, L, y] logger_cp_hin.debug( f"Method protocol_5_prover: Before fiat_shamir_hash, input_list=\n{input_list}" ) c0 = pivot.fiat_shamir_hash( input_list + [0] + ["First hash of compressed pivot"], order ) c1 = pivot.fiat_shamir_hash( input_list + [1] + ["First hash of compressed pivot"], order ) logger_cp_hout.debug(f"After hash, hash=\n{c0}, {c1}") # Step 3: Prover calculates z = [c0 * x_i + r[i] for i, x_i in enumerate(x)] phi = gf(c0 * gamma + rho) z_hat = z + [phi] # Step 4: Prover calculates following public variables g_hat = g + [h] logger_cp.debug("Calculate Q.") Q = A * (P ** c0) * (k ** int(c1 * (c0 * y + t))) L_tilde = pivot.LinearForm(L.coeffs + [0]) * c1 assert L(z) * c1 == L_tilde(z_hat) proof = protocol_4_prover(g_hat, k, Q, L_tilde, z_hat, gf, proof) return proof def protocol_4_verifier(g_hat, k, Q, L_tilde, gf, proof, round_i=0): # Step 5 half = len(g_hat) // 2 g_hat_l = g_hat[:half] g_hat_r = g_hat[half:] logger_cp.debug("Load from proof: A_i, B_i.") A = proof["A" + str(round_i)] B = proof["B" + str(round_i)] # Step 6 order = k.order # Hash A, B and all public elements of Protocol 4 # input_list = [A, B, g_hat, k, Q, L_tilde] if isinstance(A, EllipticCurveElement): input_list = [A.to_affine(), B.to_affine(), g_hat, k, Q.to_affine(), L_tilde] else: input_list = [A, B, g_hat, k, Q, L_tilde] logger_cp_hin.debug( f"Method protocol_4_verifier: Before fiat_shamir_hash, input_list=\n{input_list}" ) c = pivot.fiat_shamir_hash(input_list, order) logger_cp_hout.debug(f"After hash, hash=\n{c}") # Step 7 logger_cp.debug("Calculate g_prime.") g_prime = [(g_hat_l[i] ** c) * g_hat_r[i] for i in range(half)] logger_cp.debug("Calculate Q_prime.") Q_prime = A * (Q ** c) * (B ** (c ** 2)) assert ( L_tilde.constant == 0 ), "Next line assumes L_tilde is a linear form, not affine form." c_L_tilde_l_coeffs = [coeff * gf(c) for coeff in L_tilde.coeffs[:half]] L_prime = pivot.LinearForm(c_L_tilde_l_coeffs) + pivot.LinearForm( L_tilde.coeffs[half:] ) # Step 8 if len(g_prime) <= 2: z_prime = proof["z_prime"] Q_check = pivot.vector_commitment(z_prime, int(L_prime(z_prime)), g_prime, k) logger_cp.debug("Arrived in final step of protocol_4_verifier.") logger_cp.debug(f"Q_check= {Q_check}") logger_cp.debug(f"Q_prime= {Q_prime}") verification = Q_check == Q_prime return verification else: # Step 9 round_i += 1 return protocol_4_verifier(g_prime, k, Q_prime, L_prime, gf, proof, round_i) def protocol_5_verifier(generators, P, L, y, proof, gf): g = generators["g"] h = generators["h"] k = generators["k"] order = gf.order n = len(g) L, y = pivot.affine_to_linear(L, y, n) logger_cp.debug("Load from proof: t, A.") t = proof["t"] A = proof["A"] # input_list = [t, A, generators, P, L, y] if isinstance(A, EllipticCurveElement): input_list = [t, A.to_affine(), generators, P.to_affine(), L, y] else: input_list = [t, A, generators, P, L, y] logger_cp_hin.debug( f"Method protocol_5_verifier: Before fiat_shamir_hash, input_list=\n{input_list}" ) c0 = pivot.fiat_shamir_hash( input_list + [0] + ["First hash of compressed pivot"], order ) c1 = pivot.fiat_shamir_hash( input_list + [1] + ["First hash of compressed pivot"], order ) logger_cp_hout.debug(f"After hash, hash=\n{c0}, {c1}") g_hat = g + [h] logger_cp.debug("Calculate Q.") Q = A * (P ** c0) * (k ** int(c1 * (c0 * y + t))) L_tilde = pivot.LinearForm(L.coeffs + [0]) * c1 verification = protocol_4_verifier(g_hat, k, Q, L_tilde, gf, proof) return verification # TODO-list # TODO1: Input y can be removed from inputs to protocol5 method.

true

f7fbd0f6b49a9995ee4760d8ee58541fa28e487c

720

py

Python

config.py

hackersandslackers/flask-blueprint-tutorial

a082799815d7e19c6d7e8b3c85e109bbd32ca713

[ "MIT" ]

315

2020-02-18T10:41:38.000Z

2022-03-24T04:01:07.000Z

config.py

Diego-MP/flask-blueprint-tutorial

a082799815d7e19c6d7e8b3c85e109bbd32ca713

[ "MIT" ]

138

2020-05-07T10:11:57.000Z

2022-03-31T10:04:15.000Z

config.py

Diego-MP/flask-blueprint-tutorial

a082799815d7e19c6d7e8b3c85e109bbd32ca713

[ "MIT" ]

47

2020-02-18T22:29:54.000Z

2022-03-09T05:58:41.000Z

"""Class-based Flask app configuration.""" from os import environ, path from dotenv import load_dotenv basedir = path.abspath(path.dirname(__file__)) load_dotenv(path.join(basedir, ".env")) class Config: """Configuration from environment variables.""" SECRET_KEY = environ.get("SECRET_KEY") FLASK_ENV = environ.get("FLASK_ENV") FLASK_APP = "wsgi.py" # Flask-Assets LESS_BIN = environ.get("LESS_BIN") ASSETS_DEBUG = True LESS_RUN_IN_DEBUG = True # Static Assets STATIC_FOLDER = "static" TEMPLATES_FOLDER = "templates" COMPRESSOR_DEBUG = True # Datadog DD_SERVICE = environ.get("DD_SERVICE") # API BEST_BUY_API_KEY = environ.get("BEST_BUY_API_KEY")

22.5

54

0.694444

from os import environ, path from dotenv import load_dotenv basedir = path.abspath(path.dirname(__file__)) load_dotenv(path.join(basedir, ".env")) class Config: SECRET_KEY = environ.get("SECRET_KEY") FLASK_ENV = environ.get("FLASK_ENV") FLASK_APP = "wsgi.py" LESS_BIN = environ.get("LESS_BIN") ASSETS_DEBUG = True LESS_RUN_IN_DEBUG = True STATIC_FOLDER = "static" TEMPLATES_FOLDER = "templates" COMPRESSOR_DEBUG = True DD_SERVICE = environ.get("DD_SERVICE") BEST_BUY_API_KEY = environ.get("BEST_BUY_API_KEY")

true

f7fbd1e9b6412b7b6ae78cddd74c9f47b792fa4e

127

py

Python

traffic/core/logging.py

xabbu137/traffic

c82e859700a226ffaf04d7d782800e24fa4fc290

[ "MIT" ]

1

2020-01-30T08:21:07.000Z

traffic/core/logging.py

xabbu137/traffic

c82e859700a226ffaf04d7d782800e24fa4fc290

[ "MIT" ]

null

traffic/core/logging.py

xabbu137/traffic

c82e859700a226ffaf04d7d782800e24fa4fc290

[ "MIT" ]

1

2020-03-13T00:31:03.000Z

import logging def loglevel(mode: str) -> None: logger = logging.getLogger() logger.setLevel(getattr(logging, mode))

18.142857

43

0.700787

import logging def loglevel(mode: str) -> None: logger = logging.getLogger() logger.setLevel(getattr(logging, mode))

true

f7fbd28978c17fad6f5e3ae95968055986a7a08b

2,068

py

Python

dfirtrack_config/tests/filter_forms/test_filter_forms.py

thomas-kropeit/dfirtrack

b1e0e659af7bc8085cfe2d269ddc651f9f4ba585

[ "Apache-2.0" ]

null

dfirtrack_config/tests/filter_forms/test_filter_forms.py

thomas-kropeit/dfirtrack

b1e0e659af7bc8085cfe2d269ddc651f9f4ba585

[ "Apache-2.0" ]

6

2022-03-16T12:30:51.000Z

2022-03-28T01:34:45.000Z

dfirtrack_config/tests/filter_forms/test_filter_forms.py

thomas-kropeit/dfirtrack

b1e0e659af7bc8085cfe2d269ddc651f9f4ba585

[ "Apache-2.0" ]

null

from django.test import TestCase from dfirtrack_config.filter_forms import AssignmentFilterForm class AssignmentFilterFormTestCase(TestCase): """assignment filter form tests""" def test_case_form_label(self): """test form label""" # get object form = AssignmentFilterForm() # compare self.assertEqual(form.fields['case'].label, 'Filter for case') def test_case_form_empty_label(self): """test form label""" # get object form = AssignmentFilterForm() # compare self.assertEqual(form.fields['case'].empty_label, 'Filter for case') def test_tag_form_label(self): """test form label""" # get object form = AssignmentFilterForm() # compare self.assertEqual(form.fields['tag'].label, 'Filter for tag') def test_tag_form_empty_label(self): """test form label""" # get object form = AssignmentFilterForm() # compare self.assertEqual(form.fields['tag'].empty_label, 'Filter for tag') def test_user_form_label(self): """test form label""" # get object form = AssignmentFilterForm() # compare self.assertEqual(form.fields['user'].label, 'Filter for user') def test_user_form_empty_label(self): """test form label""" # get object form = AssignmentFilterForm() # compare self.assertEqual(form.fields['user'].empty_label, 'No user assigned') def test_filter_assignment_view_keep_form_label(self): """test form label""" # get object form = AssignmentFilterForm() # compare self.assertEqual( form.fields['filter_assignment_view_keep'].label, 'Remember filter settings (confirm by applying)', ) def test_assignment_filter_form_empty(self): """test minimum form requirements / VALID""" # get object form = AssignmentFilterForm(data={}) # compare self.assertTrue(form.is_valid())

27.573333

77

0.621857

from django.test import TestCase from dfirtrack_config.filter_forms import AssignmentFilterForm class AssignmentFilterFormTestCase(TestCase): def test_case_form_label(self): form = AssignmentFilterForm() self.assertEqual(form.fields['case'].label, 'Filter for case') def test_case_form_empty_label(self): form = AssignmentFilterForm() self.assertEqual(form.fields['case'].empty_label, 'Filter for case') def test_tag_form_label(self): form = AssignmentFilterForm() self.assertEqual(form.fields['tag'].label, 'Filter for tag') def test_tag_form_empty_label(self): form = AssignmentFilterForm() self.assertEqual(form.fields['tag'].empty_label, 'Filter for tag') def test_user_form_label(self): form = AssignmentFilterForm() self.assertEqual(form.fields['user'].label, 'Filter for user') def test_user_form_empty_label(self): form = AssignmentFilterForm() self.assertEqual(form.fields['user'].empty_label, 'No user assigned') def test_filter_assignment_view_keep_form_label(self): form = AssignmentFilterForm() self.assertEqual( form.fields['filter_assignment_view_keep'].label, 'Remember filter settings (confirm by applying)', ) def test_assignment_filter_form_empty(self): form = AssignmentFilterForm(data={}) self.assertTrue(form.is_valid())

true

f7fbd3776ed1e1f6443976d5b489ca6a36d0f901

321

py

Python

test/benchmark/api_v2_0/__init__.py

nucklehead/RackHD

c21876e1138c19caeeaf5ef2e2a6c0236e780896

[ "Apache-2.0" ]

null

test/benchmark/api_v2_0/__init__.py

nucklehead/RackHD

c21876e1138c19caeeaf5ef2e2a6c0236e780896

[ "Apache-2.0" ]

null

test/benchmark/api_v2_0/__init__.py

nucklehead/RackHD

c21876e1138c19caeeaf5ef2e2a6c0236e780896

[ "Apache-2.0" ]

null

from poller_tests import BenchmarkPollerTests from discovery_tests import BenchmarkDiscoveryTests from bootstrap_tests import BenchmarkBootstrapTests benchmark_poller_tests = [ 'benchmark.poller' ] benchmark_discovery_tests = [ 'benchmark.discovery' ] benchmark_bootstrap_tests = [ 'benchmark.bootstrap' ]

20.0625

51

0.813084

from poller_tests import BenchmarkPollerTests from discovery_tests import BenchmarkDiscoveryTests from bootstrap_tests import BenchmarkBootstrapTests benchmark_poller_tests = [ 'benchmark.poller' ] benchmark_discovery_tests = [ 'benchmark.discovery' ] benchmark_bootstrap_tests = [ 'benchmark.bootstrap' ]

true

f7fbd39c2503053d6e0e62a5d448563647642992

9,116

py

Python

tests/test_auth.py

anlcnydn/graceful

d4678cb6349a5c843a5e58002fc80140821609e4

[ "BSD-3-Clause" ]

83

2015-06-18T18:08:50.000Z

2021-05-21T06:12:46.000Z

tests/test_auth.py

anlcnydn/graceful

d4678cb6349a5c843a5e58002fc80140821609e4

[ "BSD-3-Clause" ]

59

2015-07-02T12:28:50.000Z

2019-01-21T18:32:47.000Z

tests/test_auth.py

anlcnydn/graceful

d4678cb6349a5c843a5e58002fc80140821609e4

[ "BSD-3-Clause" ]

14

2015-08-26T22:43:47.000Z

2020-04-17T11:00:37.000Z

# -*- coding: utf-8 -*- import base64 import pytest import hashlib from falcon.testing import TestBase from falcon import API from falcon import status_codes from graceful.resources.base import BaseResource from graceful import authentication from graceful import authorization @authorization.authentication_required class ExampleResource(BaseResource, with_context=True): def on_get(self, req, resp, **kwargs): assert 'user' in req.context class ExampleKVUserStorage(authentication.KeyValueUserStorage): class SimpleKVStore(dict): def set(self, key, value): self[key] = value def __init__(self, data=None): super().__init__(self.SimpleKVStore(data or {})) def clear(self): self.kv_store.clear() @ExampleKVUserStorage.hash_identifier.register(authentication.Basic) def _(identified_with, identifier): return ":".join([ identifier[0], hashlib.sha1(identifier[1].encode()).hexdigest() ]) def test_default_kv_hashes_only_strings(): with pytest.raises(TypeError): ExampleKVUserStorage.hash_identifier(None, [1, 2, 3, 4]) def test_invalid_basic_auth_realm(): with pytest.raises(ValueError): authentication.Basic(realm="Impro=per realm%%% &") @pytest.mark.parametrize( "auth_class", [ authentication.Basic, authentication.Token, authentication.XAPIKey, ] ) def test_auth_requires_storage(auth_class): with pytest.raises(ValueError): auth_class() class AuthTestsMixin: """ Test mixin that defines common routine for testing auth classes. """ class SkipTest(Exception): """Raised when given tests is marked to be skipped Note: we use this exception instead of self.skipTest() method because this has slightly different semantics. We simply don't want to report these tests as skipped. """ route = '/foo/' user = { "username": "foo", "details": "bar", "password": "secretP4ssw0rd", "allowed_ip": "127.100.100.1", "allowed_remote": "127.0.0.1", "token": "s3cr3t70ken", 'allowed_ip_range': ['127.100.100.1'], } ident_keys = ['password'] auth_storage = ExampleKVUserStorage() auth_middleware = [authentication.Anonymous(user)] def get_authorized_headers(self): raise NotImplementedError def get_invalid_headers(self): raise NotImplementedError def get_unauthorized_headers(self): return {} def setUp(self): super().setUp() self.api = API(middleware=self.auth_middleware) self.api.add_route(self.route, ExampleResource()) self.auth_storage.clear() identity = [self.user[key] for key in self.ident_keys] self.auth_storage.register( self.auth_middleware[0], identity[0] if len(identity) == 1 else identity, self.user ) def test_unauthorized(self): try: self.simulate_request( self.route, decode='utf-8', method='GET', headers=self.get_unauthorized_headers() ) assert self.srmock.status == status_codes.HTTP_UNAUTHORIZED except self.SkipTest: pass def test_authorized(self): try: self.simulate_request( self.route, decode='utf-8', method='GET', headers=self.get_authorized_headers() ) assert self.srmock.status == status_codes.HTTP_OK except self.SkipTest: pass def test_bad_request(self): try: maybe_multiple_headers_sets = self.get_invalid_headers() if isinstance(maybe_multiple_headers_sets, tuple): header_sets = maybe_multiple_headers_sets else: header_sets = (maybe_multiple_headers_sets,) for headers in header_sets: self.simulate_request( self.route, decode='utf-8', method='GET', headers=headers ) assert self.srmock.status == status_codes.HTTP_BAD_REQUEST except self.SkipTest: pass class AnonymousAuthTestCase(AuthTestsMixin, TestBase): auth_middleware = [authentication.Anonymous(...)] def get_authorized_headers(self): return {} def get_unauthorized_headers(self): # note: Anonymous always authenticates the user. raise self.SkipTest def get_invalid_headers(self): # note: it is not possible to have invalid header for this auth. raise self.SkipTest class BasicAuthTestCase(AuthTestsMixin, TestBase): auth_middleware = [authentication.Basic(AuthTestsMixin.auth_storage)] ident_keys = ['username', 'password'] def get_authorized_headers(self): return { "Authorization": "Basic " + base64.b64encode( ":".join( [self.user['username'], self.user['password']] ).encode() ).decode() } def get_invalid_headers(self): return ( # to many header tokens {"Authorization": "Basic Basic Basic"}, # non base64 decoded {"Authorization": "Basic nonbase64decoded"} ) class TokenAuthTestCase(AuthTestsMixin, TestBase): auth_middleware = [authentication.Token(AuthTestsMixin.auth_storage)] ident_keys = ['token'] def get_authorized_headers(self): return {"Authorization": "Token " + self.user['token']} def get_invalid_headers(self): return {"Authorization": "Token Token Token"} class XAPIKeyAuthTestCase(AuthTestsMixin, TestBase): auth_middleware = [authentication.XAPIKey(AuthTestsMixin.auth_storage)] ident_keys = ['token'] def get_authorized_headers(self): return {"X-Api-Key": self.user['token']} def get_invalid_headers(self): # note: it is not possible to have invalid header for this auth. raise self.SkipTest class XForwardedForAuthTestCase(AuthTestsMixin, TestBase): auth_middleware = [ authentication.XForwardedFor(AuthTestsMixin.auth_storage) ] ident_keys = ['allowed_ip'] def get_authorized_headers(self): return {"X-Forwarded-For": self.user['allowed_ip']} def get_invalid_headers(self): # note: it is not possible to have invalid header for this auth. raise self.SkipTest class XForwardedForWithoutStorageAuthTestCase(AuthTestsMixin, TestBase): auth_middleware = [authentication.XForwardedFor()] ident_keys = ['allowed_ip'] def get_authorized_headers(self): return {"X-Forwarded-For": self.user['allowed_ip']} def get_invalid_headers(self): # note: it is not possible to have invalid header for this auth. raise self.SkipTest class XForwardedForWithFallbackAuthTestCase(AuthTestsMixin, TestBase): auth_middleware = [ authentication.XForwardedFor(remote_address_fallback=True) ] ident_keys = ['allowed_remote'] def get_authorized_headers(self): return {} def get_unauthorized_headers(self): raise self.SkipTest def get_invalid_headers(self): # note: it is not possible to have invalid header for this auth. raise self.SkipTest class IPRangeXForwardedForAuthTestCase(AuthTestsMixin, TestBase): class IPRangeWhitelistStorage(authentication.IPRangeWhitelistStorage): """Test compatible implementation of IPRangeWhitelistStorage. This implementation simply extends the base class with tests-compatible ``register()`` and ``clear()`` methods. """ def register(self, identified_with, identity, user): self.ip_range = identity self.user = user def clear(self): self.ip_range = [] self.user = None auth_storage = IPRangeWhitelistStorage([], None) auth_middleware = [ authentication.XForwardedFor(auth_storage) ] ident_keys = ['allowed_ip_range'] def get_authorized_headers(self): return {'X-Forwarded-For': self.user['allowed_ip_range'][0]} def get_unauthorized_headers(self): raise self.SkipTest def get_invalid_headers(self): # note: it is not possible to have invalid header for this auth. raise self.SkipTest class MultipleAuthTestCase(AuthTestsMixin, TestBase): auth_middleware = [ authentication.Token(AuthTestsMixin.auth_storage), authentication.Anonymous(...), authentication.Basic(AuthTestsMixin.auth_storage), ] ident_keys = ["token"] def get_unauthorized_headers(self): # note: Anonymous will always authenticate the user as a fallback auth raise self.SkipTest def get_invalid_headers(self): # this is invalid header for basic authentication return {"Authorization": "Token Basic Basic"} def get_authorized_headers(self): return {"Authorization": "Token " + self.user['password']}

29.501618

78

0.65204

import base64 import pytest import hashlib from falcon.testing import TestBase from falcon import API from falcon import status_codes from graceful.resources.base import BaseResource from graceful import authentication from graceful import authorization @authorization.authentication_required class ExampleResource(BaseResource, with_context=True): def on_get(self, req, resp, **kwargs): assert 'user' in req.context class ExampleKVUserStorage(authentication.KeyValueUserStorage): class SimpleKVStore(dict): def set(self, key, value): self[key] = value def __init__(self, data=None): super().__init__(self.SimpleKVStore(data or {})) def clear(self): self.kv_store.clear() @ExampleKVUserStorage.hash_identifier.register(authentication.Basic) def _(identified_with, identifier): return ":".join([ identifier[0], hashlib.sha1(identifier[1].encode()).hexdigest() ]) def test_default_kv_hashes_only_strings(): with pytest.raises(TypeError): ExampleKVUserStorage.hash_identifier(None, [1, 2, 3, 4]) def test_invalid_basic_auth_realm(): with pytest.raises(ValueError): authentication.Basic(realm="Impro=per realm%%% &") @pytest.mark.parametrize( "auth_class", [ authentication.Basic, authentication.Token, authentication.XAPIKey, ] ) def test_auth_requires_storage(auth_class): with pytest.raises(ValueError): auth_class() class AuthTestsMixin: class SkipTest(Exception): route = '/foo/' user = { "username": "foo", "details": "bar", "password": "secretP4ssw0rd", "allowed_ip": "127.100.100.1", "allowed_remote": "127.0.0.1", "token": "s3cr3t70ken", 'allowed_ip_range': ['127.100.100.1'], } ident_keys = ['password'] auth_storage = ExampleKVUserStorage() auth_middleware = [authentication.Anonymous(user)] def get_authorized_headers(self): raise NotImplementedError def get_invalid_headers(self): raise NotImplementedError def get_unauthorized_headers(self): return {} def setUp(self): super().setUp() self.api = API(middleware=self.auth_middleware) self.api.add_route(self.route, ExampleResource()) self.auth_storage.clear() identity = [self.user[key] for key in self.ident_keys] self.auth_storage.register( self.auth_middleware[0], identity[0] if len(identity) == 1 else identity, self.user ) def test_unauthorized(self): try: self.simulate_request( self.route, decode='utf-8', method='GET', headers=self.get_unauthorized_headers() ) assert self.srmock.status == status_codes.HTTP_UNAUTHORIZED except self.SkipTest: pass def test_authorized(self): try: self.simulate_request( self.route, decode='utf-8', method='GET', headers=self.get_authorized_headers() ) assert self.srmock.status == status_codes.HTTP_OK except self.SkipTest: pass def test_bad_request(self): try: maybe_multiple_headers_sets = self.get_invalid_headers() if isinstance(maybe_multiple_headers_sets, tuple): header_sets = maybe_multiple_headers_sets else: header_sets = (maybe_multiple_headers_sets,) for headers in header_sets: self.simulate_request( self.route, decode='utf-8', method='GET', headers=headers ) assert self.srmock.status == status_codes.HTTP_BAD_REQUEST except self.SkipTest: pass class AnonymousAuthTestCase(AuthTestsMixin, TestBase): auth_middleware = [authentication.Anonymous(...)] def get_authorized_headers(self): return {} def get_unauthorized_headers(self): raise self.SkipTest def get_invalid_headers(self): raise self.SkipTest class BasicAuthTestCase(AuthTestsMixin, TestBase): auth_middleware = [authentication.Basic(AuthTestsMixin.auth_storage)] ident_keys = ['username', 'password'] def get_authorized_headers(self): return { "Authorization": "Basic " + base64.b64encode( ":".join( [self.user['username'], self.user['password']] ).encode() ).decode() } def get_invalid_headers(self): return ( {"Authorization": "Basic Basic Basic"}, {"Authorization": "Basic nonbase64decoded"} ) class TokenAuthTestCase(AuthTestsMixin, TestBase): auth_middleware = [authentication.Token(AuthTestsMixin.auth_storage)] ident_keys = ['token'] def get_authorized_headers(self): return {"Authorization": "Token " + self.user['token']} def get_invalid_headers(self): return {"Authorization": "Token Token Token"} class XAPIKeyAuthTestCase(AuthTestsMixin, TestBase): auth_middleware = [authentication.XAPIKey(AuthTestsMixin.auth_storage)] ident_keys = ['token'] def get_authorized_headers(self): return {"X-Api-Key": self.user['token']} def get_invalid_headers(self): raise self.SkipTest class XForwardedForAuthTestCase(AuthTestsMixin, TestBase): auth_middleware = [ authentication.XForwardedFor(AuthTestsMixin.auth_storage) ] ident_keys = ['allowed_ip'] def get_authorized_headers(self): return {"X-Forwarded-For": self.user['allowed_ip']} def get_invalid_headers(self): raise self.SkipTest class XForwardedForWithoutStorageAuthTestCase(AuthTestsMixin, TestBase): auth_middleware = [authentication.XForwardedFor()] ident_keys = ['allowed_ip'] def get_authorized_headers(self): return {"X-Forwarded-For": self.user['allowed_ip']} def get_invalid_headers(self): raise self.SkipTest class XForwardedForWithFallbackAuthTestCase(AuthTestsMixin, TestBase): auth_middleware = [ authentication.XForwardedFor(remote_address_fallback=True) ] ident_keys = ['allowed_remote'] def get_authorized_headers(self): return {} def get_unauthorized_headers(self): raise self.SkipTest def get_invalid_headers(self): raise self.SkipTest class IPRangeXForwardedForAuthTestCase(AuthTestsMixin, TestBase): class IPRangeWhitelistStorage(authentication.IPRangeWhitelistStorage): def register(self, identified_with, identity, user): self.ip_range = identity self.user = user def clear(self): self.ip_range = [] self.user = None auth_storage = IPRangeWhitelistStorage([], None) auth_middleware = [ authentication.XForwardedFor(auth_storage) ] ident_keys = ['allowed_ip_range'] def get_authorized_headers(self): return {'X-Forwarded-For': self.user['allowed_ip_range'][0]} def get_unauthorized_headers(self): raise self.SkipTest def get_invalid_headers(self): raise self.SkipTest class MultipleAuthTestCase(AuthTestsMixin, TestBase): auth_middleware = [ authentication.Token(AuthTestsMixin.auth_storage), authentication.Anonymous(...), authentication.Basic(AuthTestsMixin.auth_storage), ] ident_keys = ["token"] def get_unauthorized_headers(self): raise self.SkipTest def get_invalid_headers(self): return {"Authorization": "Token Basic Basic"} def get_authorized_headers(self): return {"Authorization": "Token " + self.user['password']}

true

f7fbd472d8bcfc731ae2f04f592f284801beae5f

411

py

Python

test/face_eye_detection.py

jeffskinnerbox/people-counter

f4381ee82df11f30d98673d0bba403c3350a8463

[ "Unlicense", "MIT" ]

64

2017-10-19T11:46:14.000Z

2021-11-10T03:40:38.000Z

test/face_eye_detection.py

mathewspjacob/people-counter

f4381ee82df11f30d98673d0bba403c3350a8463

[ "Unlicense", "MIT" ]

1

2020-05-14T07:56:48.000Z

test/face_eye_detection.py

jeffskinnerbox/people-counter

f4381ee82df11f30d98673d0bba403c3350a8463

[ "Unlicense", "MIT" ]

27

2018-03-12T15:50:12.000Z

2021-03-29T16:32:53.000Z

#!/usr/bin/python3 # # Maintainer: jeffskinnerbox@yahoo.com / www.jeffskinnerbox.me # Version: 0.3.0 # # Haar Cascade Object Detection Face & Eye OpenCV Python Tutorial - https://pythonprogramming.net/haar-cascade-face-eye-detection-python-opencv-tutorial/ # Creating your own Haar Cascade OpenCV Python Tutorial - https://pythonprogramming.net/haar-cascade-object-detection-python-opencv-tutorial/

31.615385

153

0.768856

true

f7fbd5747006c092c841e0ff277182958285676a

21,374

py

Python

One-GORD/Ours-o/model.py

zju-vipa/One-GORD

9f3846949d9d7de5b21caaad1f8696f4ce257b55

[ "Apache-2.0" ]

3

2020-12-23T08:54:00.000Z

2021-04-12T08:39:12.000Z

One-GORD/Ours-o/model.py

HymEric/One-GORD

a63ecaf2b9538789ca0e761d55608a28d7194c4d

[ "Apache-2.0" ]

null

One-GORD/Ours-o/model.py

HymEric/One-GORD

a63ecaf2b9538789ca0e761d55608a28d7194c4d

[ "Apache-2.0" ]

2

2021-01-03T06:26:31.000Z

2021-04-12T08:39:15.000Z

import os, sys import time import re import numpy as np import tensorflow as tf import sys sys.path.append('../') import lib.models as lib from lib.models import params_with_name from lib.models.save_images import save_images from lib.models.distributions import Bernoulli, Gaussian, Product from lib.models.nets_32x32_small import NetsRetreiver, NetsRetreiverWithClassifier TINY = 1e-8 SEED = 123 ch=1 CRITIC_ITERS = 5 # For WGAN and WGAN-GP, number of critic iters per gen iter class DIAE(object): def __init__(self, session, arch,lr,alpha,beta,latent_dim,latent_num,class_net_unit_num,output_dim, batch_size, image_shape, exp_name, dirs, vis_reconst): """ :type output_dist: Distribution :type z_dist: Gaussian """ self.session = session self.arch = arch self.lr=lr self.alpha=alpha self.beta=beta self.latent_dim=latent_dim self.latent_num=latent_num self.class_net_unit_num=class_net_unit_num self.output_dim=output_dim self.batch_size = batch_size self.image_shape = image_shape self.exp_name = exp_name self.dirs = dirs self.vis_reconst = vis_reconst self.__build_graph() def __build_graph(self): tf.set_random_seed(SEED) np.random.seed(SEED) self.is_training = tf.placeholder(tf.bool) self.x1 = tf.placeholder(tf.float32, shape=[None] + list(self.image_shape)) self.aux1_mask = tf.placeholder(tf.float32, shape=[None] + list(self.image_shape)) # auxilary dataset self.aux1 = tf.placeholder(tf.float32, shape=[None] + list(self.image_shape)) self.aux2 = tf.placeholder(tf.float32, shape=[None] + list(self.image_shape)) self.aux_GT1 = tf.placeholder(tf.float32, shape=[None] + list(self.image_shape)) self.aux_GT2 = tf.placeholder(tf.float32, shape=[None] + list(self.image_shape)) self.class_gt0 = tf.placeholder(tf.float32, shape=[None] + list([self.latent_num+9])) self.class_gt1 = tf.placeholder(tf.float32, shape=[None] + list([self.latent_num+9])) self.class_gt2 = tf.placeholder(tf.float32, shape=[None] + list([self.latent_num+9])) self.class_gt3 = tf.placeholder(tf.float32, shape=[None] + list([self.latent_num+9])) self.class_gt4 = tf.placeholder(tf.float32, shape=[None] + list([self.latent_num + 9])) self.class_gt5 = tf.placeholder(tf.float32, shape=[None] + list([self.latent_num + 9])) self.class_gt6 = tf.placeholder(tf.float32, shape=[None] + list([self.latent_num + 9])) self.class_gt7 = tf.placeholder(tf.float32, shape=[None] + list([self.latent_num + 9])) self.class_gt8 = tf.placeholder(tf.float32, shape=[None] + list([self.latent_num + 9])) self.class_gt9 = tf.placeholder(tf.float32, shape=[None] + list([self.latent_num + 9])) self.class_gt10 = tf.placeholder(tf.float32, shape=[None] + list([self.latent_num + 9])) # onesample labels self.aux_class_gt=tf.placeholder(tf.float32,shape=[None]+list([self.latent_num+9])) # Normalize + reshape 'real' input data norm_x1 = 2*(tf.cast(self.x1, tf.float32)-.5) norm_aux1 = 2*(tf.cast(self.aux1, tf.float32)-.5) norm_aux2 = 2*(tf.cast(self.aux2, tf.float32)-.5) norm_aux_GT1 = 2*(tf.cast(self.aux_GT1, tf.float32)-.5) norm_aux_GT2 = 2*(tf.cast(self.aux_GT2, tf.float32)-.5) # Set Encoder and Decoder archs self.Encoder, self.Decoder,self.Classifier,self.gan_discriminator = NetsRetreiverWithClassifier(self.arch) # Encode and decode self.z1 = self.__Enc(norm_x1) self.x1_out = self.__Dec(self.z1) # aux data self.aux_z1 = self.__Enc(norm_aux1) self.aux1_out = self.__Dec(self.aux_z1) self.aux_z2 = self.__Enc(norm_aux2) self.aux2_out = self.__Dec(self.aux_z2) aux1_head,aux1_bg=tf.split(self.aux_z1,2,axis=1) aux2_head,aux2_bg=tf.split(self.aux_z2,2,axis=1) GT1_z=tf.concat([aux2_head,aux1_bg],axis=1) GT2_z=tf.concat([aux1_head,aux2_bg],axis=1) self.GT1_out = self.__Dec(GT1_z) self.GT2_out = self.__Dec(GT2_z) #dual swap x1_head,x1_bg=tf.split(self.z1,2,axis=1) self.mix_head_out=self.__Dec(tf.concat([aux1_head,x1_bg],axis=1)) mix_head,mix_bg=tf.split(self.__Enc(self.mix_head_out),2,axis=1) x1_dual_out=self.__Dec(tf.concat([x1_head,mix_bg],axis=1)) self.aux1_mix_head_out=self.__Dec(tf.concat([x1_head,aux1_bg],axis=1)) # classification loss ## for x1 r_part1, r_part2 = tf.split(self.z1, 2, axis=1) c_p0 = self.__Classifier(r_part1) c_p1 = self.__Classifier(r_part2) ## for aux1 aux1_r_part1, aux1_r_part2 = tf.split(self.aux_z1, 2, axis=1) aux1_c_p0 = self.__Classifier(aux1_r_part1) aux1_c_p1 = self.__Classifier(aux1_r_part2) ## for aux2 aux2_r_part1, aux2_r_part2 = tf.split(self.aux_z2, 2, axis=1) aux2_c_p0 = self.__Classifier(aux2_r_part1) aux2_c_p1 = self.__Classifier(aux2_r_part2) # Loss and optimizer self.__prep_loss_optimizer(norm_x1,norm_aux1,norm_aux2,norm_aux_GT1,norm_aux_GT2,x1_dual_out,c_p0,c_p1,aux1_c_p0,aux1_c_p1,aux2_c_p0,aux2_c_p1) def __Enc(self, x): #resnet_encoder(name, inputs, n_channels, latent_dim, is_training, mode=None, nonlinearity=tf.nn.relu): z= self.Encoder('Encoder', x, self.image_shape[0], self.latent_dim,self.is_training) return z def __Dec(self, z): x_out_logit = self.Decoder('Decoder', z, self.image_shape[0], self.is_training) x_out = tf.tanh(x_out_logit) return x_out def __Classifier(self,z): x_out= self.Classifier('Classifier', z, self.class_net_unit_num,self.latent_num+9, self.is_training) x_out = tf.nn.softmax(x_out) return x_out def __prep_loss_optimizer(self,norm_x1,norm_aux1,norm_aux2,norm_aux_GT1,norm_aux_GT2,x1_dual_out,c_p0,c_p1,aux1_c_p0,aux1_c_p1,aux2_c_p0,aux2_c_p1): norm_x1= tf.reshape(norm_x1, [-1, self.output_dim]) norm_aux1= tf.reshape(norm_aux1, [-1, self.output_dim]) norm_aux2= tf.reshape(norm_aux2, [-1, self.output_dim]) norm_aux_GT1= tf.reshape(norm_aux_GT1, [-1, self.output_dim]) norm_aux_GT2= tf.reshape(norm_aux_GT2, [-1, self.output_dim]) #[Loss1]dual unsupervised img reconstruction loss self.rec_img_loss1 = tf.reduce_mean(tf.reduce_sum(tf.square(norm_x1 -self.x1_out), axis=1)) self.rec_aux1_loss2 = tf.reduce_mean(tf.reduce_sum(tf.square(norm_aux1 -self.aux1_out), axis=1)) self.rec_aux2_loss3 = tf.reduce_mean(tf.reduce_sum(tf.square(norm_aux2 -self.aux2_out), axis=1)) #swap loss self.rec_aux1_swap_loss4 = tf.reduce_mean(tf.reduce_sum(tf.square(norm_aux_GT1 -self.GT1_out), axis=1)) self.rec_aux2_swap_loss5 = tf.reduce_mean(tf.reduce_sum(tf.square(norm_aux_GT2 -self.GT2_out), axis=1)) # dual swap loss self.rec_dual_loss6 = tf.reduce_mean(tf.reduce_sum(tf.square(x1_dual_out -self.x1_out), axis=1)) # # head loss # # segment head and do head loss with mask # x1_out_img = tf.reshape(self.mix_head_out, shape=[-1] + self.image_shape) # to img tensor # x1_out_head = tf.multiply(x1_out_img, self.aux1_mask) # norm_aux1_img = tf.reshape(norm_aux1, shape=[-1] + self.image_shape) # to img tensor # aux1_head = tf.multiply(norm_aux1_img, self.aux1_mask) # self.head_loss = tf.reduce_mean(tf.reduce_sum(tf.square(x1_out_head - aux1_head), axis=1)) # classification loss # temp_1=self.vec_gt-tf.reduce_sum((self.class_gt1-self.class_gt1*c_p1),1)*tf.reduce_sum((self.class_gt2-self.class_gt2*c_p1),1) # self.class1_loss=-tf.reduce_mean(tf.log(temp_1)) temp=1-tf.reduce_sum((self.class_gt0-self.class_gt0*c_p0),1)*tf.reduce_sum((self.class_gt1-self.class_gt1*c_p0),1)*tf.reduce_sum((self.class_gt2-self.class_gt2*c_p0),1)*tf.reduce_sum((self.class_gt3-self.class_gt3*c_p0),1)*\ tf.reduce_sum((self.class_gt4-self.class_gt4*c_p0),1)*tf.reduce_sum((self.class_gt5-self.class_gt5*c_p0),1)*tf.reduce_sum((self.class_gt6-self.class_gt6*c_p0),1)*tf.reduce_sum((self.class_gt7-self.class_gt7*c_p0),1)*tf.reduce_sum((self.class_gt8-self.class_gt8*c_p0),1)*\ tf.reduce_sum((self.class_gt9-self.class_gt9*c_p0),1) self.fuzzy_class_loss = -tf.reduce_mean(tf.log(temp)) self.fuzzy_bg_class_loss= -tf.reduce_mean(self.class_gt10 * tf.log(c_p1)) # class_loss1 = -tf.reduce_mean(self.class_gt1 * tf.log(c_p1)) self.aux1_class_loss = -tf.reduce_mean(self.aux_class_gt * tf.log(aux1_c_p0)) self.aux1_bg_class_loss = -tf.reduce_mean(self.class_gt10 * tf.log(aux1_c_p1)) # [0,0,0,1] self.aux2_class_loss = -tf.reduce_mean(self.aux_class_gt * tf.log(aux2_c_p0)) self.aux2_bg_class_loss = - tf.reduce_mean(self.class_gt10 * tf.log(aux2_c_p1)) self.class_loss = self.fuzzy_class_loss+self.fuzzy_bg_class_loss+self.aux1_class_loss+ self.aux1_bg_class_loss+self.aux2_class_loss+ self.aux2_bg_class_loss self.loss=2*self.rec_img_loss1+self.rec_aux1_loss2+self.rec_aux2_loss3+2*self.rec_aux1_swap_loss4+2*self.rec_aux2_swap_loss5+self.rec_dual_loss6+5*self.class_loss lr=self.lr self.optimizer = tf.train.AdamOptimizer(learning_rate=lr, beta1=0., beta2=0.9).minimize(self.loss) print('Learning rate=') print(lr) def load(self): #self.saver = tf.train.Saver() self.saver = tf.train.Saver(max_to_keep=3760) ckpt = tf.train.get_checkpoint_state(self.dirs['ckpt']) if ckpt and ckpt.model_checkpoint_path: ckpt_name = ckpt.model_checkpoint_path self.saver.restore(self.session, ckpt_name) print("Checkpoint restored: {0}".format(ckpt_name)) prev_step = int(next(re.finditer("(\d+)(?!.*\d)",ckpt_name)).group(0)) print('prev_step=') print(prev_step) else: print("Failed to find checkpoint.") prev_step = 0 sys.stdout.flush() return prev_step + 1 def load_fixedNum(self,inter_num): #self.saver = tf.train.Saver() self.saver = tf.train.Saver(max_to_keep=3760) ckpt = tf.train.get_checkpoint_state(self.dirs['ckpt']) if ckpt and ckpt.model_checkpoint_path: ckpt_name = ckpt.model_checkpoint_path ckpt_name_prefix=ckpt_name.split('-')[0] ckpt_name_new=ckpt_name_prefix+'-'+str(inter_num) self.saver.restore(self.session, ckpt_name_new) print("Checkpoint restored: {0}".format(ckpt_name_new)) prev_step = int(next(re.finditer("(\d+)(?!.*\d)",ckpt_name_new)).group(0)) print('prev_step=') print(prev_step) else: print("Failed to find checkpoint.") prev_step = 0 sys.stdout.flush() return prev_step + 1 def train(self, n_iters, n_iters_per_epoch, stats_iters, ckpt_interval): # for save loss count=0 self.session.run(tf.global_variables_initializer()) # Fixed GT samples - save fixed_x1,fixed_mask_1,_= next(self.train_iter1) fixed_x2,fixed_mask_2,_= next(self.train_iter2) fixed_aux1,fixed_GT1,_= next(self.train_iter3) fixed_aux2,fixed_GT2,_= next(self.train_iter4) fixed_x1= self.session.run(tf.constant(fixed_x1)) fixed_mask_1= self.session.run(tf.constant(fixed_mask_1)) #fixed_x1 = ((fixed_x1+1.)*(255.99/2)).astype('int32').reshape([-1] + self.image_shape) save_images(fixed_x1, os.path.join(self.dirs['samples'], 'samples_1_groundtruth.png')) ##save_images(fixed_mask_1, os.path.join(self.dirs['samples'], 'mask_1_groundtruth.png')) fixed_aux1= self.session.run(tf.constant(fixed_aux1)) fixed_aux2= self.session.run(tf.constant(fixed_aux2)) #fixed_aux1 = ((fixed_aux1+1.)*(255.99/2)).astype('int32').reshape([-1] + self.image_shape) #fixed_aux2 = ((fixed_aux2+1.)*(255.99/2)).astype('int32').reshape([-1] + self.image_shape) save_images(fixed_aux1, os.path.join(self.dirs['samples'], 'aux_1_groundtruth.png')) save_images(fixed_aux2, os.path.join(self.dirs['samples'], 'aux_2_groundtruth.png')) # start_iter = self.load() running_cost = 0. class_gt0, class_gt1,class_gt2,class_gt3,class_gt4,class_gt5, class_gt6, class_gt7, class_gt8,class_gt9,class_gt10 = self.generateClassificationLabel(self.batch_size) _gan_data=fixed_x1 logs=open('loss_records.txt','w') for iteration in range(start_iter, n_iters): start_time = time.time() _data1,_mask1, _ = next(self.train_iter1) _aux_label, _, _ = next(self.train_iter2) _aux1,_gt1, _ = next(self.train_iter3) _aux2,_gt2, _ = next(self.train_iter4) _, cost = self.session.run((self.optimizer, self.loss),feed_dict={self.x1:_data1,self.aux_class_gt:_aux_label,self.aux1_mask:_mask1,self.aux1:_aux1,self.aux2:_aux2,self.aux_GT1:_gt1,self.aux_GT2:_gt2,self.is_training:True,self.class_gt0:class_gt0,self.class_gt1:class_gt1,self.class_gt2:class_gt2,self.class_gt3:class_gt3, self.class_gt4:class_gt4, self.class_gt5:class_gt5, self.class_gt6:class_gt6,self.class_gt7:class_gt7, self.class_gt8:class_gt8, self.class_gt9:class_gt9, self.class_gt10:class_gt10}) running_cost += cost if iteration % n_iters_per_epoch == 1: print("Epoch: {0}".format(iteration // n_iters_per_epoch)) # Print avg stats and dev set stats if (iteration < start_iter + 4) or iteration % stats_iters == 0: t = time.time() dev_data1,dev_mask1, _ = next(self.dev_iter1) dev_aux1,dev_gt1, _ = next(self.dev_iter3) dev_aux2,dev_gt2, _ = next(self.dev_iter4) dev_loss,dev_rec_img_loss1,dev_rec_aux1_loss2,dev_rec_aux2_loss3,dev_rec_aux1_swap_loss4,dev_rec_aux2_swap_loss5,dev_rec_dual_loss6,class_loss,fuzzy_class,aux1_class_loss,aux2_class_loss,fuzzy_bg_class_loss,aux1_bg_class_loss,aux2_bg_class_loss= \ self.session.run([self.loss,self.rec_img_loss1,self.rec_aux1_loss2,self.rec_aux2_loss3,self.rec_aux1_swap_loss4,self.rec_aux2_swap_loss5,self.rec_dual_loss6,self.class_loss,self.fuzzy_class_loss,self.aux1_class_loss,self.aux2_class_loss,self.fuzzy_bg_class_loss,self.aux1_bg_class_loss,self.aux2_bg_class_loss], feed_dict={self.x1:dev_data1,self.aux1_mask:dev_mask1,self.aux1:dev_aux1,self.aux2:dev_aux2,self.aux_GT1:dev_gt1,self.aux_GT2:dev_gt2,self.is_training:False, self.class_gt0: class_gt0, self.class_gt1: class_gt1,self.class_gt2: class_gt2,self.class_gt3:class_gt3,self.aux_class_gt:_aux_label,self.class_gt4:class_gt4, self.class_gt5:class_gt5, self.class_gt6:class_gt6,self.class_gt7:class_gt7, self.class_gt8:class_gt8, self.class_gt9:class_gt9, self.class_gt10:class_gt10}) n_samples = 1. if (iteration < start_iter + 4) else float(stats_iters) avg_cost = running_cost / n_samples running_cost = 0. print("Iteration:{0} \t| Train cost:{1:.1f} \t| Dev cost: {2:.1f}(img1_loss:{3:.1f},aux1_loss2:{4:.1f},aux2_loss3:{5:.1f},aux1_swap_loss:{6:.1f},aux2_swap_loss:{7:.1f},dual_swap_loss:{8:.1f},class loss:{9:.1f}(fuzzy_class:{10:.1f},aux1_class_loss:{11:.1f},aux2_class_loss:{12:.1f},fuzzy_bg_class_loss:{13:.1f},aux1_bg_class_loss:{14:.1f},aux2_bg_class_loss:{15:.1f}))". format(iteration, avg_cost, dev_loss,dev_rec_img_loss1,dev_rec_aux1_loss2,dev_rec_aux2_loss3,dev_rec_aux1_swap_loss4,dev_rec_aux2_swap_loss5,dev_rec_dual_loss6,class_loss,fuzzy_class,aux1_class_loss,aux2_class_loss,fuzzy_bg_class_loss,aux1_bg_class_loss,aux2_bg_class_loss)) logs.writelines("Iteration:{0} \t| Train cost:{1:.1f} \t| Dev cost: {2:.1f}(img1_loss:{3:.1f},aux1_loss2:{4:.1f},aux2_loss3:{5:.1f},aux1_swap_loss:{6:.1f},aux2_swap_loss:{7:.1f},dual_swap_loss:{8:.1f},class loss:{9:.1f}(fuzzy_class:{10:.1f},aux1_class_loss:{11:.1f},aux2_class_loss:{12:.1f},fuzzy_bg_class_loss:{13:.1f},aux1_bg_class_loss:{14:.1f},aux2_bg_class_loss:{15:.1f}))\n". format(iteration, avg_cost, dev_loss,dev_rec_img_loss1,dev_rec_aux1_loss2,dev_rec_aux2_loss3,dev_rec_aux1_swap_loss4,dev_rec_aux2_swap_loss5,dev_rec_dual_loss6,class_loss,fuzzy_class,aux1_class_loss,aux2_class_loss,fuzzy_bg_class_loss,aux1_bg_class_loss,aux2_bg_class_loss)) #print(avg_cost) #print(dev_loss) #print("Iteration:{0} \t| Train cost:{1:.1f} \t| Dev cost: {2:.1f}".format(iteration, avg_cost, dev_loss)) count=count+1 if self.vis_reconst: self.visualise_reconstruction(fixed_x1,fixed_aux1,fixed_aux2,iteration) #self.visualise_reconstruction(img_zero,fixed_mk1,iteration) if np.any(np.isnan(avg_cost)): raise ValueError("NaN detected!") # save checkpoint if (iteration > start_iter) and iteration % (ckpt_interval) == 0: self.saver.save(self.session, os.path.join(self.dirs['ckpt'], self.exp_name), global_step=iteration) _gan_data=_data1 logs.close() # for save loss #np.save('logArray.npy',logArray) def reconstruct(self, X1, aux1,aux2, is_training=False): """ Reconstruct data. """ return self.session.run([self.x1_out,self.mix_head_out,self.aux1_mix_head_out], feed_dict={self.x1: X1,self.aux1:aux1,self.aux2:aux2,self.is_training: is_training}) def visualise_reconstruction(self, X1, aux1,aux2,iteration): X_out1,mix_head_out,aux1_mix_head_out= self.reconstruct(X1, aux1,aux2) # print(X_out1.shape) X1 = ((X_out1+1.)*(255.99/2)).astype('int32').reshape([-1] + self.image_shape) mix_head_out = ((mix_head_out+1.)*(255.99/2)).astype('int32').reshape([-1] + self.image_shape) aux1_mix_head_out = ((aux1_mix_head_out+1.)*(255.99/2)).astype('int32').reshape([-1] + self.image_shape) save_images(X1, os.path.join(self.dirs['samples'], str(iteration)+'samples_1_rec.png')) save_images(mix_head_out, os.path.join(self.dirs['samples'], str(iteration)+'X1bg_aux1head.png')) save_images(aux1_mix_head_out, os.path.join(self.dirs['samples'], str(iteration)+'X1head_aux1bg.png')) def generateClassificationLabel(self, batch_size): # ==============get mask============== class_num = 11 class_gt1 = np.zeros((batch_size, class_num)) class_gt2 = np.zeros((batch_size, class_num)) class_gt3 = np.zeros((batch_size, class_num)) class_gt4 = np.zeros((batch_size, class_num)) class_gt5 = np.zeros((batch_size, class_num)) class_gt6 = np.zeros((batch_size, class_num)) class_gt7 = np.zeros((batch_size, class_num)) class_gt8 = np.zeros((batch_size, class_num)) class_gt9 = np.zeros((batch_size, class_num)) class_gt10 = np.zeros((batch_size, class_num)) class_gt11 = np.zeros((batch_size, class_num)) for i in range(batch_size): class_gt1[i, 0] = 1 class_gt2[i, 1] = 1 class_gt3[i, 2] = 1 class_gt4[i, 3] = 1 class_gt5[i, 4] = 1 class_gt6[i, 5] = 1 class_gt7[i, 6] = 1 class_gt8[i, 7] = 1 class_gt9[i, 8] = 1 class_gt10[i, 9] = 1 class_gt11[i, 10] = 1 return class_gt1, class_gt2, class_gt3, class_gt4,class_gt5, class_gt6, class_gt7, class_gt8,class_gt9, class_gt10,class_gt11 def getCodesAndImgs(self, pathForSave, X1, k, is_training=False): z1, X_r0 = self.session.run([self.z1, self.x1_out], feed_dict={self.x1: X1, self.is_training: is_training}) ImageNorm0_1 = ((X_r0 + 1.) * (1.00 / 2)).astype('double').reshape( [-1, self.image_shape[1], self.image_shape[2], self.image_shape[0]]) # for visual the first result to valide it effectiveness if k == 1: X_save = ((X_r0 + 1.) * (255.99 / 2)).astype('int32').reshape([-1] + self.image_shape) save_images(X_save, os.path.join(pathForSave, 'iter' + str(k) + '_samples_reconstructed.png')) return z1, ImageNorm0_1

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import os, sys import time import re import numpy as np import tensorflow as tf import sys sys.path.append('../') import lib.models as lib from lib.models import params_with_name from lib.models.save_images import save_images from lib.models.distributions import Bernoulli, Gaussian, Product from lib.models.nets_32x32_small import NetsRetreiver, NetsRetreiverWithClassifier TINY = 1e-8 SEED = 123 ch=1 CRITIC_ITERS = 5 class DIAE(object): def __init__(self, session, arch,lr,alpha,beta,latent_dim,latent_num,class_net_unit_num,output_dim, batch_size, image_shape, exp_name, dirs, vis_reconst): self.session = session self.arch = arch self.lr=lr self.alpha=alpha self.beta=beta self.latent_dim=latent_dim self.latent_num=latent_num self.class_net_unit_num=class_net_unit_num self.output_dim=output_dim self.batch_size = batch_size self.image_shape = image_shape self.exp_name = exp_name self.dirs = dirs self.vis_reconst = vis_reconst self.__build_graph() def __build_graph(self): tf.set_random_seed(SEED) np.random.seed(SEED) self.is_training = tf.placeholder(tf.bool) self.x1 = tf.placeholder(tf.float32, shape=[None] + list(self.image_shape)) self.aux1_mask = tf.placeholder(tf.float32, shape=[None] + list(self.image_shape)) self.aux1 = tf.placeholder(tf.float32, shape=[None] + list(self.image_shape)) self.aux2 = tf.placeholder(tf.float32, shape=[None] + list(self.image_shape)) self.aux_GT1 = tf.placeholder(tf.float32, shape=[None] + list(self.image_shape)) self.aux_GT2 = tf.placeholder(tf.float32, shape=[None] + list(self.image_shape)) self.class_gt0 = tf.placeholder(tf.float32, shape=[None] + list([self.latent_num+9])) self.class_gt1 = tf.placeholder(tf.float32, shape=[None] + list([self.latent_num+9])) self.class_gt2 = tf.placeholder(tf.float32, shape=[None] + list([self.latent_num+9])) self.class_gt3 = tf.placeholder(tf.float32, shape=[None] + list([self.latent_num+9])) self.class_gt4 = tf.placeholder(tf.float32, shape=[None] + list([self.latent_num + 9])) self.class_gt5 = tf.placeholder(tf.float32, shape=[None] + list([self.latent_num + 9])) self.class_gt6 = tf.placeholder(tf.float32, shape=[None] + list([self.latent_num + 9])) self.class_gt7 = tf.placeholder(tf.float32, shape=[None] + list([self.latent_num + 9])) self.class_gt8 = tf.placeholder(tf.float32, shape=[None] + list([self.latent_num + 9])) self.class_gt9 = tf.placeholder(tf.float32, shape=[None] + list([self.latent_num + 9])) self.class_gt10 = tf.placeholder(tf.float32, shape=[None] + list([self.latent_num + 9])) self.aux_class_gt=tf.placeholder(tf.float32,shape=[None]+list([self.latent_num+9])) norm_x1 = 2*(tf.cast(self.x1, tf.float32)-.5) norm_aux1 = 2*(tf.cast(self.aux1, tf.float32)-.5) norm_aux2 = 2*(tf.cast(self.aux2, tf.float32)-.5) norm_aux_GT1 = 2*(tf.cast(self.aux_GT1, tf.float32)-.5) norm_aux_GT2 = 2*(tf.cast(self.aux_GT2, tf.float32)-.5) self.Encoder, self.Decoder,self.Classifier,self.gan_discriminator = NetsRetreiverWithClassifier(self.arch) self.z1 = self.__Enc(norm_x1) self.x1_out = self.__Dec(self.z1) self.aux_z1 = self.__Enc(norm_aux1) self.aux1_out = self.__Dec(self.aux_z1) self.aux_z2 = self.__Enc(norm_aux2) self.aux2_out = self.__Dec(self.aux_z2) aux1_head,aux1_bg=tf.split(self.aux_z1,2,axis=1) aux2_head,aux2_bg=tf.split(self.aux_z2,2,axis=1) GT1_z=tf.concat([aux2_head,aux1_bg],axis=1) GT2_z=tf.concat([aux1_head,aux2_bg],axis=1) self.GT1_out = self.__Dec(GT1_z) self.GT2_out = self.__Dec(GT2_z) x1_head,x1_bg=tf.split(self.z1,2,axis=1) self.mix_head_out=self.__Dec(tf.concat([aux1_head,x1_bg],axis=1)) mix_head,mix_bg=tf.split(self.__Enc(self.mix_head_out),2,axis=1) x1_dual_out=self.__Dec(tf.concat([x1_head,mix_bg],axis=1)) self.aux1_mix_head_out=self.__Dec(tf.concat([x1_head,aux1_bg],axis=1)) r_part1, r_part2 = tf.split(self.z1, 2, axis=1) c_p0 = self.__Classifier(r_part1) c_p1 = self.__Classifier(r_part2) x1_r_part1, aux1_r_part2 = tf.split(self.aux_z1, 2, axis=1) aux1_c_p0 = self.__Classifier(aux1_r_part1) aux1_c_p1 = self.__Classifier(aux1_r_part2) x2_r_part1, aux2_r_part2 = tf.split(self.aux_z2, 2, axis=1) aux2_c_p0 = self.__Classifier(aux2_r_part1) aux2_c_p1 = self.__Classifier(aux2_r_part2) self.__prep_loss_optimizer(norm_x1,norm_aux1,norm_aux2,norm_aux_GT1,norm_aux_GT2,x1_dual_out,c_p0,c_p1,aux1_c_p0,aux1_c_p1,aux2_c_p0,aux2_c_p1) def __Enc(self, x): z= self.Encoder('Encoder', x, self.image_shape[0], self.latent_dim,self.is_training) return z def __Dec(self, z): x_out_logit = self.Decoder('Decoder', z, self.image_shape[0], self.is_training) x_out = tf.tanh(x_out_logit) return x_out def __Classifier(self,z): x_out= self.Classifier('Classifier', z, self.class_net_unit_num,self.latent_num+9, self.is_training) x_out = tf.nn.softmax(x_out) return x_out def __prep_loss_optimizer(self,norm_x1,norm_aux1,norm_aux2,norm_aux_GT1,norm_aux_GT2,x1_dual_out,c_p0,c_p1,aux1_c_p0,aux1_c_p1,aux2_c_p0,aux2_c_p1): norm_x1= tf.reshape(norm_x1, [-1, self.output_dim]) norm_aux1= tf.reshape(norm_aux1, [-1, self.output_dim]) norm_aux2= tf.reshape(norm_aux2, [-1, self.output_dim]) norm_aux_GT1= tf.reshape(norm_aux_GT1, [-1, self.output_dim]) norm_aux_GT2= tf.reshape(norm_aux_GT2, [-1, self.output_dim]) self.rec_img_loss1 = tf.reduce_mean(tf.reduce_sum(tf.square(norm_x1 -self.x1_out), axis=1)) self.rec_aux1_loss2 = tf.reduce_mean(tf.reduce_sum(tf.square(norm_aux1 -self.aux1_out), axis=1)) self.rec_aux2_loss3 = tf.reduce_mean(tf.reduce_sum(tf.square(norm_aux2 -self.aux2_out), axis=1)) self.rec_aux1_swap_loss4 = tf.reduce_mean(tf.reduce_sum(tf.square(norm_aux_GT1 -self.GT1_out), axis=1)) self.rec_aux2_swap_loss5 = tf.reduce_mean(tf.reduce_sum(tf.square(norm_aux_GT2 -self.GT2_out), axis=1)) self.rec_dual_loss6 = tf.reduce_mean(tf.reduce_sum(tf.square(x1_dual_out -self.x1_out), axis=1)) temp=1-tf.reduce_sum((self.class_gt0-self.class_gt0*c_p0),1)*tf.reduce_sum((self.class_gt1-self.class_gt1*c_p0),1)*tf.reduce_sum((self.class_gt2-self.class_gt2*c_p0),1)*tf.reduce_sum((self.class_gt3-self.class_gt3*c_p0),1)*\ tf.reduce_sum((self.class_gt4-self.class_gt4*c_p0),1)*tf.reduce_sum((self.class_gt5-self.class_gt5*c_p0),1)*tf.reduce_sum((self.class_gt6-self.class_gt6*c_p0),1)*tf.reduce_sum((self.class_gt7-self.class_gt7*c_p0),1)*tf.reduce_sum((self.class_gt8-self.class_gt8*c_p0),1)*\ tf.reduce_sum((self.class_gt9-self.class_gt9*c_p0),1) self.fuzzy_class_loss = -tf.reduce_mean(tf.log(temp)) self.fuzzy_bg_class_loss= -tf.reduce_mean(self.class_gt10 * tf.log(c_p1)) self.aux1_class_loss = -tf.reduce_mean(self.aux_class_gt * tf.log(aux1_c_p0)) self.aux1_bg_class_loss = -tf.reduce_mean(self.class_gt10 * tf.log(aux1_c_p1)) self.aux2_class_loss = -tf.reduce_mean(self.aux_class_gt * tf.log(aux2_c_p0)) self.aux2_bg_class_loss = - tf.reduce_mean(self.class_gt10 * tf.log(aux2_c_p1)) self.class_loss = self.fuzzy_class_loss+self.fuzzy_bg_class_loss+self.aux1_class_loss+ self.aux1_bg_class_loss+self.aux2_class_loss+ self.aux2_bg_class_loss self.loss=2*self.rec_img_loss1+self.rec_aux1_loss2+self.rec_aux2_loss3+2*self.rec_aux1_swap_loss4+2*self.rec_aux2_swap_loss5+self.rec_dual_loss6+5*self.class_loss lr=self.lr self.optimizer = tf.train.AdamOptimizer(learning_rate=lr, beta1=0., beta2=0.9).minimize(self.loss) print('Learning rate=') print(lr) def load(self): self.saver = tf.train.Saver(max_to_keep=3760) ckpt = tf.train.get_checkpoint_state(self.dirs['ckpt']) if ckpt and ckpt.model_checkpoint_path: ckpt_name = ckpt.model_checkpoint_path self.saver.restore(self.session, ckpt_name) print("Checkpoint restored: {0}".format(ckpt_name)) prev_step = int(next(re.finditer("(\d+)(?!.*\d)",ckpt_name)).group(0)) print('prev_step=') print(prev_step) else: print("Failed to find checkpoint.") prev_step = 0 sys.stdout.flush() return prev_step + 1 def load_fixedNum(self,inter_num): self.saver = tf.train.Saver(max_to_keep=3760) ckpt = tf.train.get_checkpoint_state(self.dirs['ckpt']) if ckpt and ckpt.model_checkpoint_path: ckpt_name = ckpt.model_checkpoint_path ckpt_name_prefix=ckpt_name.split('-')[0] ckpt_name_new=ckpt_name_prefix+'-'+str(inter_num) self.saver.restore(self.session, ckpt_name_new) print("Checkpoint restored: {0}".format(ckpt_name_new)) prev_step = int(next(re.finditer("(\d+)(?!.*\d)",ckpt_name_new)).group(0)) print('prev_step=') print(prev_step) else: print("Failed to find checkpoint.") prev_step = 0 sys.stdout.flush() return prev_step + 1 def train(self, n_iters, n_iters_per_epoch, stats_iters, ckpt_interval): count=0 self.session.run(tf.global_variables_initializer()) fixed_x1,fixed_mask_1,_= next(self.train_iter1) fixed_x2,fixed_mask_2,_= next(self.train_iter2) fixed_aux1,fixed_GT1,_= next(self.train_iter3) fixed_aux2,fixed_GT2,_= next(self.train_iter4) fixed_x1= self.session.run(tf.constant(fixed_x1)) fixed_mask_1= self.session.run(tf.constant(fixed_mask_1)) save_images(fixed_x1, os.path.join(self.dirs['samples'], 'samples_1_groundtruth.png')) ux2= self.session.run(tf.constant(fixed_aux2)) save_images(fixed_aux1, os.path.join(self.dirs['samples'], 'aux_1_groundtruth.png')) save_images(fixed_aux2, os.path.join(self.dirs['samples'], 'aux_2_groundtruth.png')) start_iter = self.load() running_cost = 0. class_gt0, class_gt1,class_gt2,class_gt3,class_gt4,class_gt5, class_gt6, class_gt7, class_gt8,class_gt9,class_gt10 = self.generateClassificationLabel(self.batch_size) _gan_data=fixed_x1 logs=open('loss_records.txt','w') for iteration in range(start_iter, n_iters): start_time = time.time() _data1,_mask1, _ = next(self.train_iter1) _aux_label, _, _ = next(self.train_iter2) _aux1,_gt1, _ = next(self.train_iter3) _aux2,_gt2, _ = next(self.train_iter4) _, cost = self.session.run((self.optimizer, self.loss),feed_dict={self.x1:_data1,self.aux_class_gt:_aux_label,self.aux1_mask:_mask1,self.aux1:_aux1,self.aux2:_aux2,self.aux_GT1:_gt1,self.aux_GT2:_gt2,self.is_training:True,self.class_gt0:class_gt0,self.class_gt1:class_gt1,self.class_gt2:class_gt2,self.class_gt3:class_gt3, self.class_gt4:class_gt4, self.class_gt5:class_gt5, self.class_gt6:class_gt6,self.class_gt7:class_gt7, self.class_gt8:class_gt8, self.class_gt9:class_gt9, self.class_gt10:class_gt10}) running_cost += cost if iteration % n_iters_per_epoch == 1: print("Epoch: {0}".format(iteration // n_iters_per_epoch)) if (iteration < start_iter + 4) or iteration % stats_iters == 0: t = time.time() dev_data1,dev_mask1, _ = next(self.dev_iter1) dev_aux1,dev_gt1, _ = next(self.dev_iter3) dev_aux2,dev_gt2, _ = next(self.dev_iter4) dev_loss,dev_rec_img_loss1,dev_rec_aux1_loss2,dev_rec_aux2_loss3,dev_rec_aux1_swap_loss4,dev_rec_aux2_swap_loss5,dev_rec_dual_loss6,class_loss,fuzzy_class,aux1_class_loss,aux2_class_loss,fuzzy_bg_class_loss,aux1_bg_class_loss,aux2_bg_class_loss= \ self.session.run([self.loss,self.rec_img_loss1,self.rec_aux1_loss2,self.rec_aux2_loss3,self.rec_aux1_swap_loss4,self.rec_aux2_swap_loss5,self.rec_dual_loss6,self.class_loss,self.fuzzy_class_loss,self.aux1_class_loss,self.aux2_class_loss,self.fuzzy_bg_class_loss,self.aux1_bg_class_loss,self.aux2_bg_class_loss], feed_dict={self.x1:dev_data1,self.aux1_mask:dev_mask1,self.aux1:dev_aux1,self.aux2:dev_aux2,self.aux_GT1:dev_gt1,self.aux_GT2:dev_gt2,self.is_training:False, self.class_gt0: class_gt0, self.class_gt1: class_gt1,self.class_gt2: class_gt2,self.class_gt3:class_gt3,self.aux_class_gt:_aux_label,self.class_gt4:class_gt4, self.class_gt5:class_gt5, self.class_gt6:class_gt6,self.class_gt7:class_gt7, self.class_gt8:class_gt8, self.class_gt9:class_gt9, self.class_gt10:class_gt10}) n_samples = 1. if (iteration < start_iter + 4) else float(stats_iters) avg_cost = running_cost / n_samples running_cost = 0. print("Iteration:{0} \t| Train cost:{1:.1f} \t| Dev cost: {2:.1f}(img1_loss:{3:.1f},aux1_loss2:{4:.1f},aux2_loss3:{5:.1f},aux1_swap_loss:{6:.1f},aux2_swap_loss:{7:.1f},dual_swap_loss:{8:.1f},class loss:{9:.1f}(fuzzy_class:{10:.1f},aux1_class_loss:{11:.1f},aux2_class_loss:{12:.1f},fuzzy_bg_class_loss:{13:.1f},aux1_bg_class_loss:{14:.1f},aux2_bg_class_loss:{15:.1f}))". format(iteration, avg_cost, dev_loss,dev_rec_img_loss1,dev_rec_aux1_loss2,dev_rec_aux2_loss3,dev_rec_aux1_swap_loss4,dev_rec_aux2_swap_loss5,dev_rec_dual_loss6,class_loss,fuzzy_class,aux1_class_loss,aux2_class_loss,fuzzy_bg_class_loss,aux1_bg_class_loss,aux2_bg_class_loss)) logs.writelines("Iteration:{0} \t| Train cost:{1:.1f} \t| Dev cost: {2:.1f}(img1_loss:{3:.1f},aux1_loss2:{4:.1f},aux2_loss3:{5:.1f},aux1_swap_loss:{6:.1f},aux2_swap_loss:{7:.1f},dual_swap_loss:{8:.1f},class loss:{9:.1f}(fuzzy_class:{10:.1f},aux1_class_loss:{11:.1f},aux2_class_loss:{12:.1f},fuzzy_bg_class_loss:{13:.1f},aux1_bg_class_loss:{14:.1f},aux2_bg_class_loss:{15:.1f}))\n". format(iteration, avg_cost, dev_loss,dev_rec_img_loss1,dev_rec_aux1_loss2,dev_rec_aux2_loss3,dev_rec_aux1_swap_loss4,dev_rec_aux2_swap_loss5,dev_rec_dual_loss6,class_loss,fuzzy_class,aux1_class_loss,aux2_class_loss,fuzzy_bg_class_loss,aux1_bg_class_loss,aux2_bg_class_loss)) count=count+1 if self.vis_reconst: self.visualise_reconstruction(fixed_x1,fixed_aux1,fixed_aux2,iteration) if np.any(np.isnan(avg_cost)): raise ValueError("NaN detected!") if (iteration > start_iter) and iteration % (ckpt_interval) == 0: self.saver.save(self.session, os.path.join(self.dirs['ckpt'], self.exp_name), global_step=iteration) _gan_data=_data1 logs.close() def reconstruct(self, X1, aux1,aux2, is_training=False): return self.session.run([self.x1_out,self.mix_head_out,self.aux1_mix_head_out], feed_dict={self.x1: X1,self.aux1:aux1,self.aux2:aux2,self.is_training: is_training}) def visualise_reconstruction(self, X1, aux1,aux2,iteration): X_out1,mix_head_out,aux1_mix_head_out= self.reconstruct(X1, aux1,aux2) X1 = ((X_out1+1.)*(255.99/2)).astype('int32').reshape([-1] + self.image_shape) mix_head_out = ((mix_head_out+1.)*(255.99/2)).astype('int32').reshape([-1] + self.image_shape) aux1_mix_head_out = ((aux1_mix_head_out+1.)*(255.99/2)).astype('int32').reshape([-1] + self.image_shape) save_images(X1, os.path.join(self.dirs['samples'], str(iteration)+'samples_1_rec.png')) save_images(mix_head_out, os.path.join(self.dirs['samples'], str(iteration)+'X1bg_aux1head.png')) save_images(aux1_mix_head_out, os.path.join(self.dirs['samples'], str(iteration)+'X1head_aux1bg.png')) def generateClassificationLabel(self, batch_size): class_num = 11 class_gt1 = np.zeros((batch_size, class_num)) class_gt2 = np.zeros((batch_size, class_num)) class_gt3 = np.zeros((batch_size, class_num)) class_gt4 = np.zeros((batch_size, class_num)) class_gt5 = np.zeros((batch_size, class_num)) class_gt6 = np.zeros((batch_size, class_num)) class_gt7 = np.zeros((batch_size, class_num)) class_gt8 = np.zeros((batch_size, class_num)) class_gt9 = np.zeros((batch_size, class_num)) class_gt10 = np.zeros((batch_size, class_num)) class_gt11 = np.zeros((batch_size, class_num)) for i in range(batch_size): class_gt1[i, 0] = 1 class_gt2[i, 1] = 1 class_gt3[i, 2] = 1 class_gt4[i, 3] = 1 class_gt5[i, 4] = 1 class_gt6[i, 5] = 1 class_gt7[i, 6] = 1 class_gt8[i, 7] = 1 class_gt9[i, 8] = 1 class_gt10[i, 9] = 1 class_gt11[i, 10] = 1 return class_gt1, class_gt2, class_gt3, class_gt4,class_gt5, class_gt6, class_gt7, class_gt8,class_gt9, class_gt10,class_gt11 def getCodesAndImgs(self, pathForSave, X1, k, is_training=False): z1, X_r0 = self.session.run([self.z1, self.x1_out], feed_dict={self.x1: X1, self.is_training: is_training}) ImageNorm0_1 = ((X_r0 + 1.) * (1.00 / 2)).astype('double').reshape( [-1, self.image_shape[1], self.image_shape[2], self.image_shape[0]]) if k == 1: X_save = ((X_r0 + 1.) * (255.99 / 2)).astype('int32').reshape([-1] + self.image_shape) save_images(X_save, os.path.join(pathForSave, 'iter' + str(k) + '_samples_reconstructed.png')) return z1, ImageNorm0_1

true

f7fbd5bd1cef121fc4393bb804be64d14bfbae49

1,963

py

Python

aliyun-python-sdk-hbr/aliyunsdkhbr/request/v20170908/RenewClientTokenRequest.py

yndu13/aliyun-openapi-python-sdk

12ace4fb39fe2fb0e3927a4b1b43ee4872da43f5

[ "Apache-2.0" ]

1,001

2015-07-24T01:32:41.000Z

2022-03-25T01:28:18.000Z

aliyun-python-sdk-hbr/aliyunsdkhbr/request/v20170908/RenewClientTokenRequest.py

yndu13/aliyun-openapi-python-sdk

12ace4fb39fe2fb0e3927a4b1b43ee4872da43f5

[ "Apache-2.0" ]

363

2015-10-20T03:15:00.000Z

2022-03-08T12:26:19.000Z

aliyun-python-sdk-hbr/aliyunsdkhbr/request/v20170908/RenewClientTokenRequest.py

yndu13/aliyun-openapi-python-sdk

12ace4fb39fe2fb0e3927a4b1b43ee4872da43f5

[ "Apache-2.0" ]

682

2015-09-22T07:19:02.000Z

2022-03-22T09:51:46.000Z

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # # http://www.apache.org/licenses/LICENSE-2.0 # # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. from aliyunsdkcore.request import RpcRequest from aliyunsdkhbr.endpoint import endpoint_data class RenewClientTokenRequest(RpcRequest): def __init__(self): RpcRequest.__init__(self, 'hbr', '2017-09-08', 'RenewClientToken','hbr') self.set_protocol_type('https') self.set_method('POST') if hasattr(self, "endpoint_map"): setattr(self, "endpoint_map", endpoint_data.getEndpointMap()) if hasattr(self, "endpoint_regional"): setattr(self, "endpoint_regional", endpoint_data.getEndpointRegional()) def get_ClientId(self): return self.get_query_params().get('ClientId') def set_ClientId(self,ClientId): self.add_query_param('ClientId',ClientId) def get_VaultId(self): return self.get_query_params().get('VaultId') def set_VaultId(self,VaultId): self.add_query_param('VaultId',VaultId) def get_Token(self): return self.get_query_params().get('Token') def set_Token(self,Token): self.add_query_param('Token',Token) def get_ExpireInSeconds(self): return self.get_query_params().get('ExpireInSeconds') def set_ExpireInSeconds(self,ExpireInSeconds): self.add_query_param('ExpireInSeconds',ExpireInSeconds)

34.438596

75

0.76108

from aliyunsdkcore.request import RpcRequest from aliyunsdkhbr.endpoint import endpoint_data class RenewClientTokenRequest(RpcRequest): def __init__(self): RpcRequest.__init__(self, 'hbr', '2017-09-08', 'RenewClientToken','hbr') self.set_protocol_type('https') self.set_method('POST') if hasattr(self, "endpoint_map"): setattr(self, "endpoint_map", endpoint_data.getEndpointMap()) if hasattr(self, "endpoint_regional"): setattr(self, "endpoint_regional", endpoint_data.getEndpointRegional()) def get_ClientId(self): return self.get_query_params().get('ClientId') def set_ClientId(self,ClientId): self.add_query_param('ClientId',ClientId) def get_VaultId(self): return self.get_query_params().get('VaultId') def set_VaultId(self,VaultId): self.add_query_param('VaultId',VaultId) def get_Token(self): return self.get_query_params().get('Token') def set_Token(self,Token): self.add_query_param('Token',Token) def get_ExpireInSeconds(self): return self.get_query_params().get('ExpireInSeconds') def set_ExpireInSeconds(self,ExpireInSeconds): self.add_query_param('ExpireInSeconds',ExpireInSeconds)

true

f7fbd67754e0880e81c219bdc08f1400c9178289

13,271

py

Python

lib/spack/spack/cmd/uninstall.py

NERSC/spack

cf8de02e7387727f02ff5d695405fca8b4cad400

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

11

2015-10-04T02:17:46.000Z

2018-02-07T18:23:00.000Z

lib/spack/spack/cmd/uninstall.py

NERSC/spack

cf8de02e7387727f02ff5d695405fca8b4cad400

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

22

2017-08-01T22:45:10.000Z

2022-03-10T07:46:31.000Z

lib/spack/spack/cmd/uninstall.py

NERSC/spack

cf8de02e7387727f02ff5d695405fca8b4cad400

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

4

2016-06-10T17:57:39.000Z

2018-09-11T04:59:38.000Z

# Copyright 2013-2022 Lawrence Livermore National Security, LLC and other # Spack Project Developers. See the top-level COPYRIGHT file for details. # # SPDX-License-Identifier: (Apache-2.0 OR MIT) from __future__ import print_function import itertools import sys from llnl.util import tty from llnl.util.tty.colify import colify import spack.cmd import spack.cmd.common.arguments as arguments import spack.environment as ev import spack.error import spack.package import spack.repo import spack.store from spack.database import InstallStatuses description = "remove installed packages" section = "build" level = "short" error_message = """You can either: a) use a more specific spec, or b) specify the spec by its hash (e.g. `spack uninstall /hash`), or c) use `spack uninstall --all` to uninstall ALL matching specs. """ # Arguments for display_specs when we find ambiguity display_args = { 'long': True, 'show_flags': False, 'variants': False, 'indent': 4, } def setup_parser(subparser): epilog_msg = ("Specs to be uninstalled are specified using the spec syntax" " (`spack help --spec`) and can be identified by their " "hashes. To remove packages that are needed only at build " "time and were not explicitly installed see `spack gc -h`." "\n\nWhen using the --all option ALL packages matching the " "supplied specs will be uninstalled. For instance, " "`spack uninstall --all libelf` uninstalls all the versions " "of `libelf` currently present in Spack's store. If no spec " "is supplied, all installed packages will be uninstalled. " "If used in an environment, all packages in the environment " "will be uninstalled.") subparser.epilog = epilog_msg subparser.add_argument( '-f', '--force', action='store_true', dest='force', help="remove regardless of whether other packages or environments " "depend on this one") arguments.add_common_arguments( subparser, ['recurse_dependents', 'yes_to_all', 'installed_specs']) subparser.add_argument( '-a', '--all', action='store_true', dest='all', help="remove ALL installed packages that match each supplied spec" ) subparser.add_argument( '--origin', dest='origin', help="only remove DB records with the specified origin" ) def find_matching_specs(env, specs, allow_multiple_matches=False, force=False, origin=None): """Returns a list of specs matching the not necessarily concretized specs given from cli Args: env (spack.environment.Environment): active environment, or ``None`` if there is not one specs (list): list of specs to be matched against installed packages allow_multiple_matches (bool): if True multiple matches are admitted Return: list: list of specs """ # constrain uninstall resolution to current environment if one is active hashes = env.all_hashes() if env else None # List of specs that match expressions given via command line specs_from_cli = [] has_errors = False for spec in specs: install_query = [InstallStatuses.INSTALLED, InstallStatuses.DEPRECATED] matching = spack.store.db.query_local( spec, hashes=hashes, installed=install_query, origin=origin) # For each spec provided, make sure it refers to only one package. # Fail and ask user to be unambiguous if it doesn't if not allow_multiple_matches and len(matching) > 1: tty.error('{0} matches multiple packages:'.format(spec)) sys.stderr.write('\n') spack.cmd.display_specs(matching, output=sys.stderr, **display_args) sys.stderr.write('\n') sys.stderr.flush() has_errors = True # No installed package matches the query if len(matching) == 0 and spec is not any: if env: pkg_type = "packages in environment '%s'" % env.name else: pkg_type = 'installed packages' tty.die('{0} does not match any {1}.'.format(spec, pkg_type)) specs_from_cli.extend(matching) if has_errors: tty.die(error_message) return specs_from_cli def installed_dependents(specs, env): """Map each spec to a list of its installed dependents. Args: specs (list): list of Specs env (spack.environment.Environment or None): the active environment, or None Returns: tuple: two mappings: one from specs to their dependent environments in the active environment (or global scope if there is no environment), and one from specs to their dependents in *inactive* environments (empty if there is no environment """ active_dpts = {} inactive_dpts = {} env_hashes = set(env.all_hashes()) if env else set() all_specs_in_db = spack.store.db.query() for spec in specs: installed = [x for x in all_specs_in_db if spec in x] # separate installed dependents into dpts in this environment and # dpts that are outside this environment for dpt in installed: if dpt not in specs: if not env or dpt.dag_hash() in env_hashes: active_dpts.setdefault(spec, set()).add(dpt) else: inactive_dpts.setdefault(spec, set()).add(dpt) return active_dpts, inactive_dpts def dependent_environments(specs): """Map each spec to environments that depend on it. Args: specs (list): list of Specs Returns: dict: mapping from spec to lists of dependent Environments """ dependents = {} for env in ev.all_environments(): hashes = set(env.all_hashes()) for spec in specs: if spec.dag_hash() in hashes: dependents.setdefault(spec, []).append(env) return dependents def inactive_dependent_environments(spec_envs): """Strip the active environment from a dependent map. Take the output of ``dependent_environment()`` and remove the active environment from all mappings. Remove any specs in the map that now have no dependent environments. Return the result. Args: spec_envs (dict): mapping from spec to lists of dependent Environments Returns: dict: mapping from spec to lists of *inactive* dependent Environments """ spec_inactive_envs = {} for spec, de_list in spec_envs.items(): inactive = [de for de in de_list if not de.active] if inactive: spec_inactive_envs[spec] = inactive return spec_inactive_envs def _remove_from_env(spec, env): """Remove a spec from an environment if it is a root.""" try: # try removing the spec from the current active # environment. this will fail if the spec is not a root env.remove(spec, force=True) except ev.SpackEnvironmentError: pass # ignore non-root specs def do_uninstall(env, specs, force): """Uninstalls all the specs in a list. Args: env (spack.environment.Environment or None): active environment, or ``None`` if there is not one specs (list): list of specs to be uninstalled force (bool): force uninstallation (boolean) """ packages = [] for item in specs: try: # should work if package is known to spack packages.append(item.package) except spack.repo.UnknownEntityError: # The package.py file has gone away -- but still # want to uninstall. spack.package.Package.uninstall_by_spec(item, force=True) # A package is ready to be uninstalled when nothing else references it, # unless we are requested to force uninstall it. def is_ready(dag_hash): if force: return True _, record = spack.store.db.query_by_spec_hash(dag_hash) if not record.ref_count: return True # If this spec is only used as a build dependency, we can uninstall return all( dspec.deptypes == ("build",) for dspec in record.spec.edges_from_dependents() ) while packages: ready = [x for x in packages if is_ready(x.spec.dag_hash())] if not ready: msg = 'unexpected error [cannot proceed uninstalling specs with' \ ' remaining link or run dependents {0}]' msg = msg.format(', '.join(x.name for x in packages)) raise spack.error.SpackError(msg) packages = [x for x in packages if x not in ready] for item in ready: item.do_uninstall(force=force) def get_uninstall_list(args, specs, env): # Gets the list of installed specs that match the ones give via cli # args.all takes care of the case where '-a' is given in the cli uninstall_list = find_matching_specs(env, specs, args.all, args.force, args.origin) # Takes care of '-R' active_dpts, inactive_dpts = installed_dependents(uninstall_list, env) # if we are in the global scope, we complain if you try to remove a # spec that's in an environment. If we're in an environment, we'll # just *remove* it from the environment, so we ignore this # error when *in* an environment spec_envs = dependent_environments(uninstall_list) spec_envs = inactive_dependent_environments(spec_envs) # Process spec_dependents and update uninstall_list has_error = not args.force and ( (active_dpts and not args.dependents) # dependents in the current env or (not env and spec_envs) # there are environments that need specs ) # say why each problem spec is needed if has_error: specs = set(active_dpts) if not env: specs.update(set(spec_envs)) # environments depend on this for i, spec in enumerate(sorted(specs)): # space out blocks of reasons if i > 0: print() spec_format = '{name}{@version}{%compiler}{/hash:7}' tty.info("Will not uninstall %s" % spec.cformat(spec_format), format='*r') dependents = active_dpts.get(spec) if dependents: print('The following packages depend on it:') spack.cmd.display_specs(dependents, **display_args) if not env: envs = spec_envs.get(spec) if envs: print('It is used by the following environments:') colify([e.name for e in envs], indent=4) msgs = [] if active_dpts: msgs.append( 'use `spack uninstall --dependents` to remove dependents too') if spec_envs: msgs.append('use `spack env remove` to remove from environments') print() tty.die('There are still dependents.', *msgs) elif args.dependents: for spec, lst in active_dpts.items(): uninstall_list.extend(lst) uninstall_list = list(set(uninstall_list)) # only force-remove (don't completely uninstall) specs that still # have external dependent envs or pkgs removes = set(inactive_dpts) if env: removes.update(spec_envs) # remove anything in removes from the uninstall list uninstall_list = set(uninstall_list) - removes return uninstall_list, removes def uninstall_specs(args, specs): env = ev.active_environment() uninstall_list, remove_list = get_uninstall_list(args, specs, env) anything_to_do = set(uninstall_list).union(set(remove_list)) if not anything_to_do: tty.warn('There are no package to uninstall.') return if not args.yes_to_all: confirm_removal(anything_to_do) if env: # Remove all the specs that are supposed to be uninstalled or just # removed. with env.write_transaction(): for spec in itertools.chain(remove_list, uninstall_list): _remove_from_env(spec, env) env.write() # Uninstall everything on the list do_uninstall(env, uninstall_list, args.force) def confirm_removal(specs): """Display the list of specs to be removed and ask for confirmation. Args: specs (list): specs to be removed """ tty.msg('The following packages will be uninstalled:\n') spack.cmd.display_specs(specs, **display_args) print('') answer = tty.get_yes_or_no('Do you want to proceed?', default=False) if not answer: tty.msg('Aborting uninstallation') sys.exit(0) def uninstall(parser, args): if not args.specs and not args.all: tty.die('uninstall requires at least one package argument.', ' Use `spack uninstall --all` to uninstall ALL packages.') # [any] here handles the --all case by forcing all specs to be returned specs = spack.cmd.parse_specs(args.specs) if args.specs else [any] uninstall_specs(args, specs)

35.201592

85

0.638686

from __future__ import print_function import itertools import sys from llnl.util import tty from llnl.util.tty.colify import colify import spack.cmd import spack.cmd.common.arguments as arguments import spack.environment as ev import spack.error import spack.package import spack.repo import spack.store from spack.database import InstallStatuses description = "remove installed packages" section = "build" level = "short" error_message = """You can either: a) use a more specific spec, or b) specify the spec by its hash (e.g. `spack uninstall /hash`), or c) use `spack uninstall --all` to uninstall ALL matching specs. """ display_args = { 'long': True, 'show_flags': False, 'variants': False, 'indent': 4, } def setup_parser(subparser): epilog_msg = ("Specs to be uninstalled are specified using the spec syntax" " (`spack help --spec`) and can be identified by their " "hashes. To remove packages that are needed only at build " "time and were not explicitly installed see `spack gc -h`." "\n\nWhen using the --all option ALL packages matching the " "supplied specs will be uninstalled. For instance, " "`spack uninstall --all libelf` uninstalls all the versions " "of `libelf` currently present in Spack's store. If no spec " "is supplied, all installed packages will be uninstalled. " "If used in an environment, all packages in the environment " "will be uninstalled.") subparser.epilog = epilog_msg subparser.add_argument( '-f', '--force', action='store_true', dest='force', help="remove regardless of whether other packages or environments " "depend on this one") arguments.add_common_arguments( subparser, ['recurse_dependents', 'yes_to_all', 'installed_specs']) subparser.add_argument( '-a', '--all', action='store_true', dest='all', help="remove ALL installed packages that match each supplied spec" ) subparser.add_argument( '--origin', dest='origin', help="only remove DB records with the specified origin" ) def find_matching_specs(env, specs, allow_multiple_matches=False, force=False, origin=None): # constrain uninstall resolution to current environment if one is active hashes = env.all_hashes() if env else None # List of specs that match expressions given via command line specs_from_cli = [] has_errors = False for spec in specs: install_query = [InstallStatuses.INSTALLED, InstallStatuses.DEPRECATED] matching = spack.store.db.query_local( spec, hashes=hashes, installed=install_query, origin=origin) # For each spec provided, make sure it refers to only one package. # Fail and ask user to be unambiguous if it doesn't if not allow_multiple_matches and len(matching) > 1: tty.error('{0} matches multiple packages:'.format(spec)) sys.stderr.write('\n') spack.cmd.display_specs(matching, output=sys.stderr, **display_args) sys.stderr.write('\n') sys.stderr.flush() has_errors = True if len(matching) == 0 and spec is not any: if env: pkg_type = "packages in environment '%s'" % env.name else: pkg_type = 'installed packages' tty.die('{0} does not match any {1}.'.format(spec, pkg_type)) specs_from_cli.extend(matching) if has_errors: tty.die(error_message) return specs_from_cli def installed_dependents(specs, env): active_dpts = {} inactive_dpts = {} env_hashes = set(env.all_hashes()) if env else set() all_specs_in_db = spack.store.db.query() for spec in specs: installed = [x for x in all_specs_in_db if spec in x] for dpt in installed: if dpt not in specs: if not env or dpt.dag_hash() in env_hashes: active_dpts.setdefault(spec, set()).add(dpt) else: inactive_dpts.setdefault(spec, set()).add(dpt) return active_dpts, inactive_dpts def dependent_environments(specs): dependents = {} for env in ev.all_environments(): hashes = set(env.all_hashes()) for spec in specs: if spec.dag_hash() in hashes: dependents.setdefault(spec, []).append(env) return dependents def inactive_dependent_environments(spec_envs): spec_inactive_envs = {} for spec, de_list in spec_envs.items(): inactive = [de for de in de_list if not de.active] if inactive: spec_inactive_envs[spec] = inactive return spec_inactive_envs def _remove_from_env(spec, env): try: env.remove(spec, force=True) except ev.SpackEnvironmentError: pass def do_uninstall(env, specs, force): packages = [] for item in specs: try: packages.append(item.package) except spack.repo.UnknownEntityError: spack.package.Package.uninstall_by_spec(item, force=True) def is_ready(dag_hash): if force: return True _, record = spack.store.db.query_by_spec_hash(dag_hash) if not record.ref_count: return True return all( dspec.deptypes == ("build",) for dspec in record.spec.edges_from_dependents() ) while packages: ready = [x for x in packages if is_ready(x.spec.dag_hash())] if not ready: msg = 'unexpected error [cannot proceed uninstalling specs with' \ ' remaining link or run dependents {0}]' msg = msg.format(', '.join(x.name for x in packages)) raise spack.error.SpackError(msg) packages = [x for x in packages if x not in ready] for item in ready: item.do_uninstall(force=force) def get_uninstall_list(args, specs, env): uninstall_list = find_matching_specs(env, specs, args.all, args.force, args.origin) active_dpts, inactive_dpts = installed_dependents(uninstall_list, env) # just *remove* it from the environment, so we ignore this # error when *in* an environment spec_envs = dependent_environments(uninstall_list) spec_envs = inactive_dependent_environments(spec_envs) # Process spec_dependents and update uninstall_list has_error = not args.force and ( (active_dpts and not args.dependents) # dependents in the current env or (not env and spec_envs) # there are environments that need specs ) # say why each problem spec is needed if has_error: specs = set(active_dpts) if not env: specs.update(set(spec_envs)) # environments depend on this for i, spec in enumerate(sorted(specs)): # space out blocks of reasons if i > 0: print() spec_format = '{name}{@version}{%compiler}{/hash:7}' tty.info("Will not uninstall %s" % spec.cformat(spec_format), format='*r') dependents = active_dpts.get(spec) if dependents: print('The following packages depend on it:') spack.cmd.display_specs(dependents, **display_args) if not env: envs = spec_envs.get(spec) if envs: print('It is used by the following environments:') colify([e.name for e in envs], indent=4) msgs = [] if active_dpts: msgs.append( 'use `spack uninstall --dependents` to remove dependents too') if spec_envs: msgs.append('use `spack env remove` to remove from environments') print() tty.die('There are still dependents.', *msgs) elif args.dependents: for spec, lst in active_dpts.items(): uninstall_list.extend(lst) uninstall_list = list(set(uninstall_list)) # only force-remove (don't completely uninstall) specs that still removes = set(inactive_dpts) if env: removes.update(spec_envs) uninstall_list = set(uninstall_list) - removes return uninstall_list, removes def uninstall_specs(args, specs): env = ev.active_environment() uninstall_list, remove_list = get_uninstall_list(args, specs, env) anything_to_do = set(uninstall_list).union(set(remove_list)) if not anything_to_do: tty.warn('There are no package to uninstall.') return if not args.yes_to_all: confirm_removal(anything_to_do) if env: with env.write_transaction(): for spec in itertools.chain(remove_list, uninstall_list): _remove_from_env(spec, env) env.write() do_uninstall(env, uninstall_list, args.force) def confirm_removal(specs): tty.msg('The following packages will be uninstalled:\n') spack.cmd.display_specs(specs, **display_args) print('') answer = tty.get_yes_or_no('Do you want to proceed?', default=False) if not answer: tty.msg('Aborting uninstallation') sys.exit(0) def uninstall(parser, args): if not args.specs and not args.all: tty.die('uninstall requires at least one package argument.', ' Use `spack uninstall --all` to uninstall ALL packages.') specs = spack.cmd.parse_specs(args.specs) if args.specs else [any] uninstall_specs(args, specs)

true

f7fbd84bd7dc834c667a2f4c61326629d1896354

2,687

py

Python

setup.py

legaultmarc/geneparse

5a844df77ded5adc765a086a8d346fce6ba01f3d

[ "MIT" ]

4

2018-11-09T11:10:24.000Z

2021-07-23T22:17:58.000Z

setup.py

legaultmarc/geneparse

5a844df77ded5adc765a086a8d346fce6ba01f3d

[ "MIT" ]

5

2017-05-02T15:28:01.000Z

2018-04-16T18:29:15.000Z

setup.py

legaultmarc/geneparse

5a844df77ded5adc765a086a8d346fce6ba01f3d

[ "MIT" ]

1

2017-05-12T17:58:32.000Z

#!/usr/bin/env python # How to build source distribution # - python setup.py sdist --format bztar # - python setup.py sdist --format gztar # - python setup.py sdist --format zip # - python setup.py bdist_wheel import os import sys from setuptools import setup, find_packages MAJOR = 0 MINOR = 8 MICRO = 1 VERSION = "{0}.{1}.{2}".format(MAJOR, MINOR, MICRO) def check_python_version(): """Checks the python version, exits if < 3.4.""" python_major, python_minor = sys.version_info[:2] if python_major != 3 or python_minor < 4: sys.stderr.write("geneparse requires python 3 " "(version 3.4 or higher)\n") sys.exit(1) def write_version_file(fn=None): if fn is None: fn = os.path.join( os.path.dirname(os.path.abspath(__file__)), os.path.join("geneparse", "version.py"), ) content = ("\n# THIS FILE WAS GENERATED AUTOMATICALLY\n" 'geneparse_version = "{version}"\n') a = open(fn, "w") try: a.write(content.format(version=VERSION)) finally: a.close() def setup_package(): # Checking the python version prior to installation check_python_version() # Saving the version into a file write_version_file() setup( name="geneparse", version=VERSION, description="A suite of parse for genotype formats.", url="https://github.com/pgxcentre/geneparse", license="MIT", test_suite="geneparse.tests.test_suite", zip_safe=False, install_requires=["numpy >= 1.11.0", "pandas >= 0.19.0", "pyplink >= 1.3.4", "setuptools >= 26.1.0", "biopython >= 1.68", "pybgen >= 0.7.0"], packages=find_packages(), package_data={"geneparse.tests": ["data/*", "data/*/*"]}, classifiers=["Development Status :: 4 - Beta", "Intended Audience :: Science/Research", "License :: Free for non-commercial use", "Operating System :: Unix", "Operating System :: POSIX :: Linux", "Operating System :: MacOS :: MacOS X", "Operating System :: Microsoft", "Programming Language :: Python", "Programming Language :: Python :: 3.4", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Topic :: Scientific/Engineering :: Bio-Informatics"], keywords="bioinformatics genetics statistics", ) if __name__ == "__main__": setup_package()

30.885057

75

0.560849

import os import sys from setuptools import setup, find_packages MAJOR = 0 MINOR = 8 MICRO = 1 VERSION = "{0}.{1}.{2}".format(MAJOR, MINOR, MICRO) def check_python_version(): python_major, python_minor = sys.version_info[:2] if python_major != 3 or python_minor < 4: sys.stderr.write("geneparse requires python 3 " "(version 3.4 or higher)\n") sys.exit(1) def write_version_file(fn=None): if fn is None: fn = os.path.join( os.path.dirname(os.path.abspath(__file__)), os.path.join("geneparse", "version.py"), ) content = ("\n# THIS FILE WAS GENERATED AUTOMATICALLY\n" 'geneparse_version = "{version}"\n') a = open(fn, "w") try: a.write(content.format(version=VERSION)) finally: a.close() def setup_package(): check_python_version() write_version_file() setup( name="geneparse", version=VERSION, description="A suite of parse for genotype formats.", url="https://github.com/pgxcentre/geneparse", license="MIT", test_suite="geneparse.tests.test_suite", zip_safe=False, install_requires=["numpy >= 1.11.0", "pandas >= 0.19.0", "pyplink >= 1.3.4", "setuptools >= 26.1.0", "biopython >= 1.68", "pybgen >= 0.7.0"], packages=find_packages(), package_data={"geneparse.tests": ["data/*", "data/*/*"]}, classifiers=["Development Status :: 4 - Beta", "Intended Audience :: Science/Research", "License :: Free for non-commercial use", "Operating System :: Unix", "Operating System :: POSIX :: Linux", "Operating System :: MacOS :: MacOS X", "Operating System :: Microsoft", "Programming Language :: Python", "Programming Language :: Python :: 3.4", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Topic :: Scientific/Engineering :: Bio-Informatics"], keywords="bioinformatics genetics statistics", ) if __name__ == "__main__": setup_package()

true

f7fbd980831ccec066261d37e528035e5f2d7c7a

12,278

py

Python

open-hackathon-client/src/client/config_sample.py

overbest/open-hackathon

62e085fbe603bcb00ca56d2b96cfc43bf44c710b

[ "MIT" ]

null

open-hackathon-client/src/client/config_sample.py

overbest/open-hackathon

62e085fbe603bcb00ca56d2b96cfc43bf44c710b

[ "MIT" ]

null

open-hackathon-client/src/client/config_sample.py

overbest/open-hackathon

62e085fbe603bcb00ca56d2b96cfc43bf44c710b

[ "MIT" ]

null

# -*- coding: utf-8 -*- # # ----------------------------------------------------------------------------------- # Copyright (c) Microsoft Open Technologies (Shanghai) Co. Ltd. All rights reserved. # # The MIT License (MIT) # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. # ----------------------------------------------------------------------------------- # "javascript" section for javascript. see @app.route('/config.js') in app/views.py # NOTE: all following key/secrets for test purpose. HOSTNAME = "http://localhost" # host name of the UI site # hacking.kaiyuanshe.cn is used for wechat oauth login # HOSTNAME = "http://hacking.kaiyuanshe.cn" # HOSTNAME = "http://open-hackathon-dev.chinacloudapp.cn" # host name of the UI site # HOSTNAME = "http://hacking.kaiyuanshe.cn" QQ_OAUTH_STATE = "openhackathon" # todo state should be constant. Actually it should be unguessable to prevent CSFA HACKATHON_API_ENDPOINT = "http://localhost:15000" # HACKATHON_API_ENDPOINT = "http://open-hackathon-dev.chinacloudapp.cn:15000" # HACKATHON_API_ENDPOINT = "http://hacking.kaiyuanshe.cn:15000" # github key for `localhost` GITHUB_CLIENT_ID = "b44f3d47bdeb26b9c4e6" GITHUB_CLIENT_SECRET = "98de14161c4b2ed3ea7a19787d62cda73b8e292c" # github oauth key for `open-hackathon-dev.chinacloudapp.cn` # GITHUB_CLIENT_ID = "b8e407813350f26bf537" # GITHUB_CLIENT_SECRET = "daa78ae27e13c9f5b4a884bd774cadf2f75a199f" QQ_CLIENT_ID = "101200890" QQ_CLIENT_SECRET = "88ad67bd4521c4cc47136854781cb9b5" QQ_META_CONTENT = "274307566465013314076545663016134754100636" WECHAT_APP_ID = "wxe75b8aef71c2059f" WECHAT_SECRET = "4532b90750f4c7bc70fcfbc42d881622" WECHAT_OAUTH_STATE = "openhackathon" # NOTE: may be should be same as QQ_OAUTH_STATE? WEIBO_CLIENT_ID = "479757037" WEIBO_CLIENT_SECRET = "efc5e75ff8891be37d90b4eaec5c02de" WEIBO_META_CONTENT = "ae884e09bc02b700" LIVE_CLIENT_ID = "000000004414E0A6" LIVE_CLIENT_SECRET = "b4mkfVqjtwHY2wJh0T4tj74lxM5LgAT2" ALAUDA_CLIENT_ID = "4VR9kzNZVyWcnk9OnAwMuSus7xOOcozJIpic6W6y" ALAUDA_CLIENT_SECRET = "E5PUL5h9feLlEirec5HQhjIzYecv7vVbEBjWLBkRMoCoFXdvS1PzNmd4AAeNgu4M2AJ87uGnnJaoDLCcDuVxkBoHRWCn6LmfB4SKK1Dty1SkGukkTcZPEk9wpHLSiRQ3" Config = { "environment": "local", "app": { "secret_key": "secret_key" }, "login": { "github": { "client_id": GITHUB_CLIENT_ID, "access_token_url": 'https://github.com/login/oauth/access_token?client_id=%s&client_secret=%s&redirect_uri=%s/github&code=' % ( GITHUB_CLIENT_ID, GITHUB_CLIENT_SECRET, HOSTNAME), "user_info_url": 'https://api.github.com/user?access_token=', "emails_info_url": 'https://api.github.com/user/emails?access_token=' }, "qq": { "client_id": QQ_CLIENT_ID, "meta_content": QQ_META_CONTENT, "access_token_url": 'https://graph.qq.com/oauth2.0/token?grant_type=authorization_code&client_id=%s&client_secret=%s&redirect_uri=%s/qq&code=' % ( QQ_CLIENT_ID, QQ_CLIENT_SECRET, HOSTNAME), "openid_url": 'https://graph.qq.com/oauth2.0/me?access_token=', "user_info_url": 'https://graph.qq.com/user/get_user_info?access_token=%s&oauth_consumer_key=%s&openid=%s' }, "wechat": { "client_id": WECHAT_APP_ID, "access_token_url": "https://api.weixin.qq.com/sns/oauth2/access_token?appid=%s&secret=%s&code=%%s&grant_type=authorization_code" % ( WECHAT_APP_ID, WECHAT_SECRET), "user_info_url": "https://api.weixin.qq.com/sns/userinfo?access_token=%s&openid=%s" }, "weibo": { "client_id": WEIBO_CLIENT_ID, "meta_content": WEIBO_META_CONTENT, "user_info_url": 'https://api.weibo.com/2/users/show.json?access_token=', "email_info_url": 'https://api.weibo.com/2/account/profile/email.json?access_token=', "access_token_url": 'https://api.weibo.com/oauth2/access_token?client_id=%s&client_secret=%s&grant_type=authorization_code&redirect_uri=%s/weibo&code=' % ( WEIBO_CLIENT_ID, WEIBO_CLIENT_SECRET, HOSTNAME) }, "live": { "client_id": LIVE_CLIENT_ID, "client_secret": LIVE_CLIENT_SECRET, "redirect_uri": '%s/live' % HOSTNAME, "access_token_url": 'https://login.live.com/oauth20_token.srf', "user_info_url": 'https://apis.live.net/v5.0/me?access_token=' }, "alauda": { "client_id": ALAUDA_CLIENT_ID, "client_secret": ALAUDA_CLIENT_SECRET, "redirect_uri": '%s/alauda' % HOSTNAME, "access_token_url": 'http://console.int.alauda.io/oauth/token' }, "provider_enabled": ["github", "wechat"], "session_valid_time_minutes": 60 }, "hackathon-api": { "endpoint": HACKATHON_API_ENDPOINT }, "javascript": { "github": { "authorize_url": "https://github.com/login/oauth/authorize?client_id=%s&redirect_uri=%s/github&scope=user" % ( GITHUB_CLIENT_ID, HOSTNAME) }, "weibo": { "authorize_url": "https://api.weibo.com/oauth2/authorize?client_id=%s&redirect_uri=%s/weibo&scope=all" % ( WEIBO_CLIENT_ID, HOSTNAME) }, "qq": { "authorize_url": "https://graph.qq.com/oauth2.0/authorize?client_id=%s&redirect_uri=%s/qq&scope=get_user_info&state=%s&response_type=code" % ( QQ_CLIENT_ID, HOSTNAME, QQ_OAUTH_STATE) }, "wechat": { "authorize_url": "https://open.weixin.qq.com/connect/qrconnect?appid=%s&redirect_uri=%s/wechat&response_type=code&scope=snsapi_login&state=%s#wechat_redirect" % ( WECHAT_APP_ID, HOSTNAME, WECHAT_OAUTH_STATE) }, "live": { "authorize_url": "https://login.live.com/oauth20_authorize.srf?client_id=%s&scope=wl.basic+,wl.emails&response_type=code&redirect_uri=%s/live" % ( LIVE_CLIENT_ID, HOSTNAME) }, "alauda": { "authorize_url": "http://console.int.alauda.io/oauth/authorize?response_type=code&client_id=%s&state=state&redirect_uri=%s/alauda" % ( ALAUDA_CLIENT_ID, HOSTNAME) }, "hackathon": { "endpoint": HACKATHON_API_ENDPOINT }, "apiconfig": { "proxy": HACKATHON_API_ENDPOINT, "api": { "admin": { "hackathon": { "": ["get", "post", "put", "delete"], "checkname": ["get"], "list": ["get"], "online": ["post"], "applyonline": ["post"], "offline": ["post"], "tags": ["get", "post", "put", "delete"], "config": ["get", "post", "put", "delete"], "administrator": { "": ["put", "post", "delete"], "list": ["get"] }, "template": { "": ["post", "delete"], "list": ["get"], "check": ["get"] }, "organizer": { "": ["get", "post", "put", "delete"] }, "award": { "": ["get", "post", "put", "delete"], "list": ["get"] }, "notice": { "": ["get", "post", "put", "delete"] } }, "registration": { "": ["get", "post", "delete", "put"], "list": ["get"] }, "azure": { "": ["get", "post", "delete", "put"], "checksubid": ["post"] }, "experiment": { "list": ["get"], "": ["post", "put"] }, "team": { "list": ["get"], "score": { "list": ["get"] }, "award": ["get", "post", "delete"] }, "user": { "list": ["get"] }, "hostserver": { "": ["get", "post", "delete", "put"], "list": ["get"] } }, "template": { "": ["get", "post", "delete", "put"], "file": ["post"], "list": ["get"], "check": ["get"] }, "user": { "": ["get"], "login": ["post", "delete"], "experiment": { "": ["get", "post", "delete", "put"] }, "registration": { "": ["put", "post", "get"], "checkemail": ["get"], "list": ["get"] }, "profile": { "": ["post", "put"] }, "picture": { "": ["put"] }, "team": { "member": ["get"] }, "hackathon": { "like": ["get", "post", "delete"] }, "notice": { "read": ["put"] }, "show": { "list": ["get"] }, "file": { "": ["post"] } }, "hackathon": { "": ["get"], "list": ["get"], "stat": ["get"], "template": ["get"], "team": { "list": ["get"] }, "registration": { "list": ["get"] }, "show": { "list": ["get"] }, "grantedawards": ["get"], "notice": { "list": ["get"] } }, "team": { "": ["get", "post", "put", "delete"], "score": ["get", "post", "put"], "member": { "": ["post", "put", "delete"], "list": ["get"] }, "show": ["get", "post", "delete"], "template": ["post", "delete"] }, "talent": { "list": ["get"] }, "grantedawards": ["get"] } } } }

42.93007

174

0.476136

HOSTNAME = "http://localhost" ackathon" HACKATHON_API_ENDPOINT = "http://localhost:15000" GITHUB_CLIENT_ID = "b44f3d47bdeb26b9c4e6" GITHUB_CLIENT_SECRET = "98de14161c4b2ed3ea7a19787d62cda73b8e292c" QQ_CLIENT_ID = "101200890" QQ_CLIENT_SECRET = "88ad67bd4521c4cc47136854781cb9b5" QQ_META_CONTENT = "274307566465013314076545663016134754100636" WECHAT_APP_ID = "wxe75b8aef71c2059f" WECHAT_SECRET = "4532b90750f4c7bc70fcfbc42d881622" WECHAT_OAUTH_STATE = "openhackathon" WEIBO_CLIENT_ID = "479757037" WEIBO_CLIENT_SECRET = "efc5e75ff8891be37d90b4eaec5c02de" WEIBO_META_CONTENT = "ae884e09bc02b700" LIVE_CLIENT_ID = "000000004414E0A6" LIVE_CLIENT_SECRET = "b4mkfVqjtwHY2wJh0T4tj74lxM5LgAT2" ALAUDA_CLIENT_ID = "4VR9kzNZVyWcnk9OnAwMuSus7xOOcozJIpic6W6y" ALAUDA_CLIENT_SECRET = "E5PUL5h9feLlEirec5HQhjIzYecv7vVbEBjWLBkRMoCoFXdvS1PzNmd4AAeNgu4M2AJ87uGnnJaoDLCcDuVxkBoHRWCn6LmfB4SKK1Dty1SkGukkTcZPEk9wpHLSiRQ3" Config = { "environment": "local", "app": { "secret_key": "secret_key" }, "login": { "github": { "client_id": GITHUB_CLIENT_ID, "access_token_url": 'https://github.com/login/oauth/access_token?client_id=%s&client_secret=%s&redirect_uri=%s/github&code=' % ( GITHUB_CLIENT_ID, GITHUB_CLIENT_SECRET, HOSTNAME), "user_info_url": 'https://api.github.com/user?access_token=', "emails_info_url": 'https://api.github.com/user/emails?access_token=' }, "qq": { "client_id": QQ_CLIENT_ID, "meta_content": QQ_META_CONTENT, "access_token_url": 'https://graph.qq.com/oauth2.0/token?grant_type=authorization_code&client_id=%s&client_secret=%s&redirect_uri=%s/qq&code=' % ( QQ_CLIENT_ID, QQ_CLIENT_SECRET, HOSTNAME), "openid_url": 'https://graph.qq.com/oauth2.0/me?access_token=', "user_info_url": 'https://graph.qq.com/user/get_user_info?access_token=%s&oauth_consumer_key=%s&openid=%s' }, "wechat": { "client_id": WECHAT_APP_ID, "access_token_url": "https://api.weixin.qq.com/sns/oauth2/access_token?appid=%s&secret=%s&code=%%s&grant_type=authorization_code" % ( WECHAT_APP_ID, WECHAT_SECRET), "user_info_url": "https://api.weixin.qq.com/sns/userinfo?access_token=%s&openid=%s" }, "weibo": { "client_id": WEIBO_CLIENT_ID, "meta_content": WEIBO_META_CONTENT, "user_info_url": 'https://api.weibo.com/2/users/show.json?access_token=', "email_info_url": 'https://api.weibo.com/2/account/profile/email.json?access_token=', "access_token_url": 'https://api.weibo.com/oauth2/access_token?client_id=%s&client_secret=%s&grant_type=authorization_code&redirect_uri=%s/weibo&code=' % ( WEIBO_CLIENT_ID, WEIBO_CLIENT_SECRET, HOSTNAME) }, "live": { "client_id": LIVE_CLIENT_ID, "client_secret": LIVE_CLIENT_SECRET, "redirect_uri": '%s/live' % HOSTNAME, "access_token_url": 'https://login.live.com/oauth20_token.srf', "user_info_url": 'https://apis.live.net/v5.0/me?access_token=' }, "alauda": { "client_id": ALAUDA_CLIENT_ID, "client_secret": ALAUDA_CLIENT_SECRET, "redirect_uri": '%s/alauda' % HOSTNAME, "access_token_url": 'http://console.int.alauda.io/oauth/token' }, "provider_enabled": ["github", "wechat"], "session_valid_time_minutes": 60 }, "hackathon-api": { "endpoint": HACKATHON_API_ENDPOINT }, "javascript": { "github": { "authorize_url": "https://github.com/login/oauth/authorize?client_id=%s&redirect_uri=%s/github&scope=user" % ( GITHUB_CLIENT_ID, HOSTNAME) }, "weibo": { "authorize_url": "https://api.weibo.com/oauth2/authorize?client_id=%s&redirect_uri=%s/weibo&scope=all" % ( WEIBO_CLIENT_ID, HOSTNAME) }, "qq": { "authorize_url": "https://graph.qq.com/oauth2.0/authorize?client_id=%s&redirect_uri=%s/qq&scope=get_user_info&state=%s&response_type=code" % ( QQ_CLIENT_ID, HOSTNAME, QQ_OAUTH_STATE) }, "wechat": { "authorize_url": "https://open.weixin.qq.com/connect/qrconnect?appid=%s&redirect_uri=%s/wechat&response_type=code&scope=snsapi_login&state=%s#wechat_redirect" % ( WECHAT_APP_ID, HOSTNAME, WECHAT_OAUTH_STATE) }, "live": { "authorize_url": "https://login.live.com/oauth20_authorize.srf?client_id=%s&scope=wl.basic+,wl.emails&response_type=code&redirect_uri=%s/live" % ( LIVE_CLIENT_ID, HOSTNAME) }, "alauda": { "authorize_url": "http://console.int.alauda.io/oauth/authorize?response_type=code&client_id=%s&state=state&redirect_uri=%s/alauda" % ( ALAUDA_CLIENT_ID, HOSTNAME) }, "hackathon": { "endpoint": HACKATHON_API_ENDPOINT }, "apiconfig": { "proxy": HACKATHON_API_ENDPOINT, "api": { "admin": { "hackathon": { "": ["get", "post", "put", "delete"], "checkname": ["get"], "list": ["get"], "online": ["post"], "applyonline": ["post"], "offline": ["post"], "tags": ["get", "post", "put", "delete"], "config": ["get", "post", "put", "delete"], "administrator": { "": ["put", "post", "delete"], "list": ["get"] }, "template": { "": ["post", "delete"], "list": ["get"], "check": ["get"] }, "organizer": { "": ["get", "post", "put", "delete"] }, "award": { "": ["get", "post", "put", "delete"], "list": ["get"] }, "notice": { "": ["get", "post", "put", "delete"] } }, "registration": { "": ["get", "post", "delete", "put"], "list": ["get"] }, "azure": { "": ["get", "post", "delete", "put"], "checksubid": ["post"] }, "experiment": { "list": ["get"], "": ["post", "put"] }, "team": { "list": ["get"], "score": { "list": ["get"] }, "award": ["get", "post", "delete"] }, "user": { "list": ["get"] }, "hostserver": { "": ["get", "post", "delete", "put"], "list": ["get"] } }, "template": { "": ["get", "post", "delete", "put"], "file": ["post"], "list": ["get"], "check": ["get"] }, "user": { "": ["get"], "login": ["post", "delete"], "experiment": { "": ["get", "post", "delete", "put"] }, "registration": { "": ["put", "post", "get"], "checkemail": ["get"], "list": ["get"] }, "profile": { "": ["post", "put"] }, "picture": { "": ["put"] }, "team": { "member": ["get"] }, "hackathon": { "like": ["get", "post", "delete"] }, "notice": { "read": ["put"] }, "show": { "list": ["get"] }, "file": { "": ["post"] } }, "hackathon": { "": ["get"], "list": ["get"], "stat": ["get"], "template": ["get"], "team": { "list": ["get"] }, "registration": { "list": ["get"] }, "show": { "list": ["get"] }, "grantedawards": ["get"], "notice": { "list": ["get"] } }, "team": { "": ["get", "post", "put", "delete"], "score": ["get", "post", "put"], "member": { "": ["post", "put", "delete"], "list": ["get"] }, "show": ["get", "post", "delete"], "template": ["post", "delete"] }, "talent": { "list": ["get"] }, "grantedawards": ["get"] } } } }

true

f7fbda38fbc7b3f196dad37b9ad073f1cfcd3845

9,350

py

Python

cqlengine/tests/statements/test_assignment_clauses.py

dokai/cqlengine

a080aff3a73351d37126b14eef606061b445aa37

[ "BSD-3-Clause" ]

null

cqlengine/tests/statements/test_assignment_clauses.py

dokai/cqlengine

a080aff3a73351d37126b14eef606061b445aa37

[ "BSD-3-Clause" ]

null

cqlengine/tests/statements/test_assignment_clauses.py

dokai/cqlengine

a080aff3a73351d37126b14eef606061b445aa37

[ "BSD-3-Clause" ]

null

from unittest import TestCase from cqlengine.statements import AssignmentClause, SetUpdateClause, ListUpdateClause, MapUpdateClause, MapDeleteClause, FieldDeleteClause, CounterUpdateClause class AssignmentClauseTests(TestCase): def test_rendering(self): pass def test_insert_tuple(self): ac = AssignmentClause('a', 'b') ac.set_context_id(10) self.assertEqual(ac.insert_tuple(), ('a', 10)) class SetUpdateClauseTests(TestCase): def test_update_from_none(self): c = SetUpdateClause('s', {1, 2}, None) c._analyze() c.set_context_id(0) self.assertEqual(c._assignments, {1, 2}) self.assertIsNone(c._additions) self.assertIsNone(c._removals) self.assertEqual(c.get_context_size(), 1) self.assertEqual(str(c), '"s" = :0') ctx = {} c.update_context(ctx) self.assertEqual(ctx, {'0': {1, 2}}) def test_null_update(self): """ tests setting a set to None creates an empty update statement """ c = SetUpdateClause('s', None, {1, 2}) c._analyze() c.set_context_id(0) self.assertIsNone(c._assignments) self.assertIsNone(c._additions) self.assertIsNone(c._removals) self.assertEqual(c.get_context_size(), 0) self.assertEqual(str(c), '') ctx = {} c.update_context(ctx) self.assertEqual(ctx, {}) def test_no_update(self): """ tests an unchanged value creates an empty update statement """ c = SetUpdateClause('s', {1, 2}, {1, 2}) c._analyze() c.set_context_id(0) self.assertIsNone(c._assignments) self.assertIsNone(c._additions) self.assertIsNone(c._removals) self.assertEqual(c.get_context_size(), 0) self.assertEqual(str(c), '') ctx = {} c.update_context(ctx) self.assertEqual(ctx, {}) def test_additions(self): c = SetUpdateClause('s', {1, 2, 3}, {1, 2}) c._analyze() c.set_context_id(0) self.assertIsNone(c._assignments) self.assertEqual(c._additions, {3}) self.assertIsNone(c._removals) self.assertEqual(c.get_context_size(), 1) self.assertEqual(str(c), '"s" = "s" + :0') ctx = {} c.update_context(ctx) self.assertEqual(ctx, {'0': {3}}) def test_removals(self): c = SetUpdateClause('s', {1, 2}, {1, 2, 3}) c._analyze() c.set_context_id(0) self.assertIsNone(c._assignments) self.assertIsNone(c._additions) self.assertEqual(c._removals, {3}) self.assertEqual(c.get_context_size(), 1) self.assertEqual(str(c), '"s" = "s" - :0') ctx = {} c.update_context(ctx) self.assertEqual(ctx, {'0': {3}}) def test_additions_and_removals(self): c = SetUpdateClause('s', {2, 3}, {1, 2}) c._analyze() c.set_context_id(0) self.assertIsNone(c._assignments) self.assertEqual(c._additions, {3}) self.assertEqual(c._removals, {1}) self.assertEqual(c.get_context_size(), 2) self.assertEqual(str(c), '"s" = "s" + :0, "s" = "s" - :1') ctx = {} c.update_context(ctx) self.assertEqual(ctx, {'0': {3}, '1': {1}}) class ListUpdateClauseTests(TestCase): def test_update_from_none(self): c = ListUpdateClause('s', [1, 2, 3]) c._analyze() c.set_context_id(0) self.assertEqual(c._assignments, [1, 2, 3]) self.assertIsNone(c._append) self.assertIsNone(c._prepend) self.assertEqual(c.get_context_size(), 1) self.assertEqual(str(c), '"s" = :0') ctx = {} c.update_context(ctx) self.assertEqual(ctx, {'0': [1, 2, 3]}) def test_update_from_empty(self): c = ListUpdateClause('s', [1, 2, 3], previous=[]) c._analyze() c.set_context_id(0) self.assertEqual(c._assignments, [1, 2, 3]) self.assertIsNone(c._append) self.assertIsNone(c._prepend) self.assertEqual(c.get_context_size(), 1) self.assertEqual(str(c), '"s" = :0') ctx = {} c.update_context(ctx) self.assertEqual(ctx, {'0': [1, 2, 3]}) def test_update_from_different_list(self): c = ListUpdateClause('s', [1, 2, 3], previous=[3, 2, 1]) c._analyze() c.set_context_id(0) self.assertEqual(c._assignments, [1, 2, 3]) self.assertIsNone(c._append) self.assertIsNone(c._prepend) self.assertEqual(c.get_context_size(), 1) self.assertEqual(str(c), '"s" = :0') ctx = {} c.update_context(ctx) self.assertEqual(ctx, {'0': [1, 2, 3]}) def test_append(self): c = ListUpdateClause('s', [1, 2, 3, 4], previous=[1, 2]) c._analyze() c.set_context_id(0) self.assertIsNone(c._assignments) self.assertEqual(c._append, [3, 4]) self.assertIsNone(c._prepend) self.assertEqual(c.get_context_size(), 1) self.assertEqual(str(c), '"s" = "s" + :0') ctx = {} c.update_context(ctx) self.assertEqual(ctx, {'0': [3, 4]}) def test_prepend(self): c = ListUpdateClause('s', [1, 2, 3, 4], previous=[3, 4]) c._analyze() c.set_context_id(0) self.assertIsNone(c._assignments) self.assertIsNone(c._append) self.assertEqual(c._prepend, [1, 2]) self.assertEqual(c.get_context_size(), 1) self.assertEqual(str(c), '"s" = :0 + "s"') ctx = {} c.update_context(ctx) # test context list reversal self.assertEqual(ctx, {'0': [2, 1]}) def test_append_and_prepend(self): c = ListUpdateClause('s', [1, 2, 3, 4, 5, 6], previous=[3, 4]) c._analyze() c.set_context_id(0) self.assertIsNone(c._assignments) self.assertEqual(c._append, [5, 6]) self.assertEqual(c._prepend, [1, 2]) self.assertEqual(c.get_context_size(), 2) self.assertEqual(str(c), '"s" = :0 + "s", "s" = "s" + :1') ctx = {} c.update_context(ctx) # test context list reversal self.assertEqual(ctx, {'0': [2, 1], '1': [5, 6]}) def test_shrinking_list_update(self): """ tests that updating to a smaller list results in an insert statement """ c = ListUpdateClause('s', [1, 2, 3], previous=[1, 2, 3, 4]) c._analyze() c.set_context_id(0) self.assertEqual(c._assignments, [1, 2, 3]) self.assertIsNone(c._append) self.assertIsNone(c._prepend) self.assertEqual(c.get_context_size(), 1) self.assertEqual(str(c), '"s" = :0') ctx = {} c.update_context(ctx) self.assertEqual(ctx, {'0': [1, 2, 3]}) class MapUpdateTests(TestCase): def test_update(self): c = MapUpdateClause('s', {3: 0, 5: 6}, {5: 0, 3: 4}) c._analyze() c.set_context_id(0) self.assertEqual(c._updates, [3, 5]) self.assertEqual(c.get_context_size(), 4) self.assertEqual(str(c), '"s"[:0] = :1, "s"[:2] = :3') ctx = {} c.update_context(ctx) self.assertEqual(ctx, {'0': 3, "1": 0, '2': 5, '3': 6}) def test_update_from_null(self): c = MapUpdateClause('s', {3: 0, 5: 6}) c._analyze() c.set_context_id(0) self.assertEqual(c._updates, [3, 5]) self.assertEqual(c.get_context_size(), 4) self.assertEqual(str(c), '"s"[:0] = :1, "s"[:2] = :3') ctx = {} c.update_context(ctx) self.assertEqual(ctx, {'0': 3, "1": 0, '2': 5, '3': 6}) def test_nulled_columns_arent_included(self): c = MapUpdateClause('s', {3: 0}, {1: 2, 3: 4}) c._analyze() c.set_context_id(0) self.assertNotIn(1, c._updates) class CounterUpdateTests(TestCase): def test_positive_update(self): c = CounterUpdateClause('a', 5, 3) c.set_context_id(5) self.assertEqual(c.get_context_size(), 1) self.assertEqual(str(c), '"a" = "a" + :5') ctx = {} c.update_context(ctx) self.assertEqual(ctx, {'5': 2}) def test_negative_update(self): c = CounterUpdateClause('a', 4, 7) c.set_context_id(3) self.assertEqual(c.get_context_size(), 1) self.assertEqual(str(c), '"a" = "a" - :3') ctx = {} c.update_context(ctx) self.assertEqual(ctx, {'3': 3}) def noop_update(self): c = CounterUpdateClause('a', 5, 5) c.set_context_id(5) self.assertEqual(c.get_context_size(), 1) self.assertEqual(str(c), '"a" = "a" + :5') ctx = {} c.update_context(ctx) self.assertEqual(ctx, {'5': 0}) class MapDeleteTests(TestCase): def test_update(self): c = MapDeleteClause('s', {3: 0}, {1: 2, 3: 4, 5: 6}) c._analyze() c.set_context_id(0) self.assertEqual(c._removals, [1, 5]) self.assertEqual(c.get_context_size(), 2) self.assertEqual(str(c), '"s"[:0], "s"[:1]') ctx = {} c.update_context(ctx) self.assertEqual(ctx, {'0': 1, '1': 5}) class FieldDeleteTests(TestCase): def test_str(self): f = FieldDeleteClause("blake") assert str(f) == '"blake"'

28.506098

158

0.563316

from unittest import TestCase from cqlengine.statements import AssignmentClause, SetUpdateClause, ListUpdateClause, MapUpdateClause, MapDeleteClause, FieldDeleteClause, CounterUpdateClause class AssignmentClauseTests(TestCase): def test_rendering(self): pass def test_insert_tuple(self): ac = AssignmentClause('a', 'b') ac.set_context_id(10) self.assertEqual(ac.insert_tuple(), ('a', 10)) class SetUpdateClauseTests(TestCase): def test_update_from_none(self): c = SetUpdateClause('s', {1, 2}, None) c._analyze() c.set_context_id(0) self.assertEqual(c._assignments, {1, 2}) self.assertIsNone(c._additions) self.assertIsNone(c._removals) self.assertEqual(c.get_context_size(), 1) self.assertEqual(str(c), '"s" = :0') ctx = {} c.update_context(ctx) self.assertEqual(ctx, {'0': {1, 2}}) def test_null_update(self): c = SetUpdateClause('s', None, {1, 2}) c._analyze() c.set_context_id(0) self.assertIsNone(c._assignments) self.assertIsNone(c._additions) self.assertIsNone(c._removals) self.assertEqual(c.get_context_size(), 0) self.assertEqual(str(c), '') ctx = {} c.update_context(ctx) self.assertEqual(ctx, {}) def test_no_update(self): c = SetUpdateClause('s', {1, 2}, {1, 2}) c._analyze() c.set_context_id(0) self.assertIsNone(c._assignments) self.assertIsNone(c._additions) self.assertIsNone(c._removals) self.assertEqual(c.get_context_size(), 0) self.assertEqual(str(c), '') ctx = {} c.update_context(ctx) self.assertEqual(ctx, {}) def test_additions(self): c = SetUpdateClause('s', {1, 2, 3}, {1, 2}) c._analyze() c.set_context_id(0) self.assertIsNone(c._assignments) self.assertEqual(c._additions, {3}) self.assertIsNone(c._removals) self.assertEqual(c.get_context_size(), 1) self.assertEqual(str(c), '"s" = "s" + :0') ctx = {} c.update_context(ctx) self.assertEqual(ctx, {'0': {3}}) def test_removals(self): c = SetUpdateClause('s', {1, 2}, {1, 2, 3}) c._analyze() c.set_context_id(0) self.assertIsNone(c._assignments) self.assertIsNone(c._additions) self.assertEqual(c._removals, {3}) self.assertEqual(c.get_context_size(), 1) self.assertEqual(str(c), '"s" = "s" - :0') ctx = {} c.update_context(ctx) self.assertEqual(ctx, {'0': {3}}) def test_additions_and_removals(self): c = SetUpdateClause('s', {2, 3}, {1, 2}) c._analyze() c.set_context_id(0) self.assertIsNone(c._assignments) self.assertEqual(c._additions, {3}) self.assertEqual(c._removals, {1}) self.assertEqual(c.get_context_size(), 2) self.assertEqual(str(c), '"s" = "s" + :0, "s" = "s" - :1') ctx = {} c.update_context(ctx) self.assertEqual(ctx, {'0': {3}, '1': {1}}) class ListUpdateClauseTests(TestCase): def test_update_from_none(self): c = ListUpdateClause('s', [1, 2, 3]) c._analyze() c.set_context_id(0) self.assertEqual(c._assignments, [1, 2, 3]) self.assertIsNone(c._append) self.assertIsNone(c._prepend) self.assertEqual(c.get_context_size(), 1) self.assertEqual(str(c), '"s" = :0') ctx = {} c.update_context(ctx) self.assertEqual(ctx, {'0': [1, 2, 3]}) def test_update_from_empty(self): c = ListUpdateClause('s', [1, 2, 3], previous=[]) c._analyze() c.set_context_id(0) self.assertEqual(c._assignments, [1, 2, 3]) self.assertIsNone(c._append) self.assertIsNone(c._prepend) self.assertEqual(c.get_context_size(), 1) self.assertEqual(str(c), '"s" = :0') ctx = {} c.update_context(ctx) self.assertEqual(ctx, {'0': [1, 2, 3]}) def test_update_from_different_list(self): c = ListUpdateClause('s', [1, 2, 3], previous=[3, 2, 1]) c._analyze() c.set_context_id(0) self.assertEqual(c._assignments, [1, 2, 3]) self.assertIsNone(c._append) self.assertIsNone(c._prepend) self.assertEqual(c.get_context_size(), 1) self.assertEqual(str(c), '"s" = :0') ctx = {} c.update_context(ctx) self.assertEqual(ctx, {'0': [1, 2, 3]}) def test_append(self): c = ListUpdateClause('s', [1, 2, 3, 4], previous=[1, 2]) c._analyze() c.set_context_id(0) self.assertIsNone(c._assignments) self.assertEqual(c._append, [3, 4]) self.assertIsNone(c._prepend) self.assertEqual(c.get_context_size(), 1) self.assertEqual(str(c), '"s" = "s" + :0') ctx = {} c.update_context(ctx) self.assertEqual(ctx, {'0': [3, 4]}) def test_prepend(self): c = ListUpdateClause('s', [1, 2, 3, 4], previous=[3, 4]) c._analyze() c.set_context_id(0) self.assertIsNone(c._assignments) self.assertIsNone(c._append) self.assertEqual(c._prepend, [1, 2]) self.assertEqual(c.get_context_size(), 1) self.assertEqual(str(c), '"s" = :0 + "s"') ctx = {} c.update_context(ctx) self.assertEqual(ctx, {'0': [2, 1]}) def test_append_and_prepend(self): c = ListUpdateClause('s', [1, 2, 3, 4, 5, 6], previous=[3, 4]) c._analyze() c.set_context_id(0) self.assertIsNone(c._assignments) self.assertEqual(c._append, [5, 6]) self.assertEqual(c._prepend, [1, 2]) self.assertEqual(c.get_context_size(), 2) self.assertEqual(str(c), '"s" = :0 + "s", "s" = "s" + :1') ctx = {} c.update_context(ctx) self.assertEqual(ctx, {'0': [2, 1], '1': [5, 6]}) def test_shrinking_list_update(self): c = ListUpdateClause('s', [1, 2, 3], previous=[1, 2, 3, 4]) c._analyze() c.set_context_id(0) self.assertEqual(c._assignments, [1, 2, 3]) self.assertIsNone(c._append) self.assertIsNone(c._prepend) self.assertEqual(c.get_context_size(), 1) self.assertEqual(str(c), '"s" = :0') ctx = {} c.update_context(ctx) self.assertEqual(ctx, {'0': [1, 2, 3]}) class MapUpdateTests(TestCase): def test_update(self): c = MapUpdateClause('s', {3: 0, 5: 6}, {5: 0, 3: 4}) c._analyze() c.set_context_id(0) self.assertEqual(c._updates, [3, 5]) self.assertEqual(c.get_context_size(), 4) self.assertEqual(str(c), '"s"[:0] = :1, "s"[:2] = :3') ctx = {} c.update_context(ctx) self.assertEqual(ctx, {'0': 3, "1": 0, '2': 5, '3': 6}) def test_update_from_null(self): c = MapUpdateClause('s', {3: 0, 5: 6}) c._analyze() c.set_context_id(0) self.assertEqual(c._updates, [3, 5]) self.assertEqual(c.get_context_size(), 4) self.assertEqual(str(c), '"s"[:0] = :1, "s"[:2] = :3') ctx = {} c.update_context(ctx) self.assertEqual(ctx, {'0': 3, "1": 0, '2': 5, '3': 6}) def test_nulled_columns_arent_included(self): c = MapUpdateClause('s', {3: 0}, {1: 2, 3: 4}) c._analyze() c.set_context_id(0) self.assertNotIn(1, c._updates) class CounterUpdateTests(TestCase): def test_positive_update(self): c = CounterUpdateClause('a', 5, 3) c.set_context_id(5) self.assertEqual(c.get_context_size(), 1) self.assertEqual(str(c), '"a" = "a" + :5') ctx = {} c.update_context(ctx) self.assertEqual(ctx, {'5': 2}) def test_negative_update(self): c = CounterUpdateClause('a', 4, 7) c.set_context_id(3) self.assertEqual(c.get_context_size(), 1) self.assertEqual(str(c), '"a" = "a" - :3') ctx = {} c.update_context(ctx) self.assertEqual(ctx, {'3': 3}) def noop_update(self): c = CounterUpdateClause('a', 5, 5) c.set_context_id(5) self.assertEqual(c.get_context_size(), 1) self.assertEqual(str(c), '"a" = "a" + :5') ctx = {} c.update_context(ctx) self.assertEqual(ctx, {'5': 0}) class MapDeleteTests(TestCase): def test_update(self): c = MapDeleteClause('s', {3: 0}, {1: 2, 3: 4, 5: 6}) c._analyze() c.set_context_id(0) self.assertEqual(c._removals, [1, 5]) self.assertEqual(c.get_context_size(), 2) self.assertEqual(str(c), '"s"[:0], "s"[:1]') ctx = {} c.update_context(ctx) self.assertEqual(ctx, {'0': 1, '1': 5}) class FieldDeleteTests(TestCase): def test_str(self): f = FieldDeleteClause("blake") assert str(f) == '"blake"'

true

f7fbda7490aea884559b813a79978c1c6ea5a0cb

2,092

py

Python

CNN/utils/activations.py

shafzhr/SimpleConvNet

89b669a59099743f0e115526cbc156aa22f453c3

[ "MIT" ]

2

2020-06-29T16:40:59.000Z

2020-11-12T18:24:01.000Z

CNN/utils/activations.py

shafzhr/SimpleConvNet

89b669a59099743f0e115526cbc156aa22f453c3

[ "MIT" ]

1

2021-02-24T16:28:15.000Z

CNN/utils/activations.py

shafzhr/SimpleConvNet

89b669a59099743f0e115526cbc156aa22f453c3

[ "MIT" ]

null

import numpy as np import abc class Activation(metaclass=abc.ABCMeta): """ Activation abstract class """ @abc.abstractmethod def apply(self, x, is_training): """ Applying the activation function over `x` """ pass @abc.abstractmethod def backprop(self, dA_prev): """ Back Propagation in an activation function """ pass class ReLU(Activation): """ ReLU activation function """ def __init__(self): self.X = None def apply(self, x, is_training=True): """ Applying ReLU over `x` :param x: input (numpy array) :param is_training: a boolean indicating whether training or not """ if is_training: self.X = x.copy() x[x < 0] = 0 return x def backprop(self, dA_prev): """ Back Propagation in ReLU :param dA_prev: derivative of the cost function with respect to the previous layer(when going backwards) :return: the derivative of the cost layer with respect to the current layer """ return dA_prev * np.where(self.X > 0, 1, 0) class Softmax(Activation): """ Softmax activation """ def __init__(self): self.X = None def apply(self, x, is_training=True): """ Applying Softmax over `x` :param is_training: a boolean indicating whether training or not :param x: input (numpy array) """ if is_training: self.X = x.copy() shiftx = x - np.max(x, axis=1, keepdims=True) exps = np.exp(shiftx) probs = exps / np.sum(exps, axis=1, keepdims=True) return probs def backprop(self, dA_prev): """ Back Propagation in Softmax :param dA_prev: derivative of the cost function with respect to the previous layer(when going backwards) :return: the derivative of the cost layer with respect to the current layer """ return dA_prev ACTIVATION_FUNCTIONS = {'relu': ReLU, 'softmax': Softmax}

24.904762

112

0.585564

import numpy as np import abc class Activation(metaclass=abc.ABCMeta): @abc.abstractmethod def apply(self, x, is_training): pass @abc.abstractmethod def backprop(self, dA_prev): pass class ReLU(Activation): def __init__(self): self.X = None def apply(self, x, is_training=True): if is_training: self.X = x.copy() x[x < 0] = 0 return x def backprop(self, dA_prev): return dA_prev * np.where(self.X > 0, 1, 0) class Softmax(Activation): def __init__(self): self.X = None def apply(self, x, is_training=True): if is_training: self.X = x.copy() shiftx = x - np.max(x, axis=1, keepdims=True) exps = np.exp(shiftx) probs = exps / np.sum(exps, axis=1, keepdims=True) return probs def backprop(self, dA_prev): return dA_prev ACTIVATION_FUNCTIONS = {'relu': ReLU, 'softmax': Softmax}

true

f7fbda9b7a677ee3832e4b52f14ded305adcce8d

62,448

py

Python

addon_common/common/updater_core.py

senjacob/retopoflow

7817bb7d68f98e5ae2c7835f28eeafe76367789e

[ "OML" ]

1

2021-08-04T07:02:57.000Z

addon_common/common/updater_core.py

Varelshen/retopoflow

5e9fd7ff65e7a5a64bf3078c78fb71cc270fdb71

[ "OML" ]

null

addon_common/common/updater_core.py

Varelshen/retopoflow

5e9fd7ff65e7a5a64bf3078c78fb71cc270fdb71

[ "OML" ]

null

# ##### BEGIN GPL LICENSE BLOCK ##### # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software Foundation, # Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. # # ##### END GPL LICENSE BLOCK ##### """ See documentation for usage https://github.com/CGCookie/blender-addon-updater """ import errno import ssl import urllib.request import urllib import os import json import zipfile import shutil import threading import fnmatch from datetime import datetime, timedelta # blender imports, used in limited cases import bpy import addon_utils # ----------------------------------------------------------------------------- # Define error messages/notices & hard coded globals # ----------------------------------------------------------------------------- # currently not used DEFAULT_TIMEOUT = 10 DEFAULT_PER_PAGE = 30 # ----------------------------------------------------------------------------- # The main class # ----------------------------------------------------------------------------- class Singleton_updater(object): """ This is the singleton class to reference a copy from, it is the shared module level class """ def __init__(self): self._engine = GithubEngine() self._user = None self._repo = None self._website = None self._current_version = None self._subfolder_path = None self._tags = [] self._tag_latest = None self._tag_names = [] self._latest_release = None self._use_releases = False self._include_branches = False self._include_branch_list = ['master'] self._include_branch_autocheck = False self._manual_only = False self._version_min_update = None self._version_max_update = None # by default, backup current addon if new is being loaded self._backup_current = True self._backup_ignore_patterns = None # set patterns for what files to overwrite on update self._overwrite_patterns = ["*.py","*.pyc"] self._remove_pre_update_patterns = [] # by default, don't auto enable/disable the addon on update # as it is slightly less stable/won't always fully reload module self._auto_reload_post_update = False # settings relating to frequency and whether to enable auto background check self._check_interval_enable = False self._check_interval_months = 0 self._check_interval_days = 7 self._check_interval_hours = 0 self._check_interval_minutes = 0 # runtime variables, initial conditions self._verbose = False self._fake_install = False self._async_checking = False # only true when async daemon started self._update_ready = None self._update_link = None self._update_version = None self._source_zip = None self._check_thread = None self._select_link = None self.skip_tag = None # get from module data self._addon = __package__.split('.')[0].lower() self._addon_package = __package__.split('.')[0] # must be set to addon's package! self._updater_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..', 'updater_tmp')) self._addon_root = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..')) self._json = {} self._error = None self._error_msg = None self._prefiltered_tag_count = 0 # UI code only, ie not used within this module but still useful # properties to have # to verify a valid import, in place of placeholder import self.showpopups = True # used in UI to show or not show update popups self.invalidupdater = False # pre-assign basic select-link function def select_link_function(self, tag): return tag["zipball_url"] self._select_link = select_link_function # ------------------------------------------------------------------------- # Getters and setters # ------------------------------------------------------------------------- @property def addon(self): return self._addon @addon.setter def addon(self, value): self._addon = str(value) @property def addon_package(self): return self._addon_package @addon_package.setter def addon_package(self, value): self._addon_package = value @property def api_url(self): return self._engine.api_url @api_url.setter def api_url(self, value): if self.check_is_url(value) == False: raise ValueError("Not a valid URL: " + value) self._engine.api_url = value @property def async_checking(self): return self._async_checking @property def auto_reload_post_update(self): return self._auto_reload_post_update @auto_reload_post_update.setter def auto_reload_post_update(self, value): try: self._auto_reload_post_update = bool(value) except: raise ValueError("Must be a boolean value") @property def backup_current(self): return self._backup_current @backup_current.setter def backup_current(self, value): if value == None: self._backup_current = False return else: self._backup_current = value @property def backup_ignore_patterns(self): return self._backup_ignore_patterns @backup_ignore_patterns.setter def backup_ignore_patterns(self, value): if value == None: self._backup_ignore_patterns = None return elif type(value) != type(['list']): raise ValueError("Backup pattern must be in list format") else: self._backup_ignore_patterns = value @property def check_interval(self): return (self._check_interval_enable, self._check_interval_months, self._check_interval_days, self._check_interval_hours, self._check_interval_minutes) @property def current_version(self): return self._current_version @current_version.setter def current_version(self, tuple_values): if tuple_values==None: self._current_version = None return elif type(tuple_values) is not tuple: try: tuple(tuple_values) except: raise ValueError( "Not a tuple! current_version must be a tuple of integers") for i in tuple_values: if type(i) is not int: raise ValueError( "Not an integer! current_version must be a tuple of integers") self._current_version = tuple(tuple_values) @property def engine(self): return self._engine.name @engine.setter def engine(self, value): if value.lower()=="github": self._engine = GithubEngine() elif value.lower()=="gitlab": self._engine = GitlabEngine() elif value.lower()=="bitbucket": self._engine = BitbucketEngine() else: raise ValueError("Invalid engine selection") @property def error(self): return self._error @property def error_msg(self): return self._error_msg @property def fake_install(self): return self._fake_install @fake_install.setter def fake_install(self, value): if type(value) != type(False): raise ValueError("fake_install must be a boolean value") self._fake_install = bool(value) # not currently used @property def include_branch_autocheck(self): return self._include_branch_autocheck @include_branch_autocheck.setter def include_branch_autocheck(self, value): try: self._include_branch_autocheck = bool(value) except: raise ValueError("include_branch_autocheck must be a boolean value") @property def include_branch_list(self): return self._include_branch_list @include_branch_list.setter def include_branch_list(self, value): try: if value == None: self._include_branch_list = ['master'] elif type(value) != type(['master']) or value==[]: raise ValueError("include_branch_list should be a list of valid branches") else: self._include_branch_list = value except: raise ValueError("include_branch_list should be a list of valid branches") @property def include_branches(self): return self._include_branches @include_branches.setter def include_branches(self, value): try: self._include_branches = bool(value) except: raise ValueError("include_branches must be a boolean value") @property def json(self): if self._json == {}: self.set_updater_json() return self._json @property def latest_release(self): if self._latest_release == None: return None return self._latest_release @property def manual_only(self): return self._manual_only @manual_only.setter def manual_only(self, value): try: self._manual_only = bool(value) except: raise ValueError("manual_only must be a boolean value") @property def overwrite_patterns(self): return self._overwrite_patterns @overwrite_patterns.setter def overwrite_patterns(self, value): if value == None: self._overwrite_patterns = ["*.py","*.pyc"] elif type(value) != type(['']): raise ValueError("overwrite_patterns needs to be in a list format") else: self._overwrite_patterns = value @property def private_token(self): return self._engine.token @private_token.setter def private_token(self, value): if value==None: self._engine.token = None else: self._engine.token = str(value) @property def remove_pre_update_patterns(self): return self._remove_pre_update_patterns @remove_pre_update_patterns.setter def remove_pre_update_patterns(self, value): if value == None: self._remove_pre_update_patterns = [] elif type(value) != type(['']): raise ValueError("remove_pre_update_patterns needs to be in a list format") else: self._remove_pre_update_patterns = value @property def repo(self): return self._repo @repo.setter def repo(self, value): try: self._repo = str(value) except: raise ValueError("User must be a string") @property def select_link(self): return self._select_link @select_link.setter def select_link(self, value): # ensure it is a function assignment, with signature: # input self, tag; returns link name if not hasattr(value, "__call__"): raise ValueError("select_link must be a function") self._select_link = value @property def stage_path(self): return self._updater_path @stage_path.setter def stage_path(self, value): if value == None: if self._verbose: print("Aborting assigning stage_path, it's null") return elif value != None and not os.path.exists(value): try: os.makedirs(value) except: if self._verbose: print("Error trying to staging path") return self._updater_path = value @property def subfolder_path(self): return self._subfolder_path @subfolder_path.setter def subfolder_path(self, value): self._subfolder_path = value @property def tags(self): if self._tags == []: return [] tag_names = [] for tag in self._tags: tag_names.append(tag["name"]) return tag_names @property def tag_latest(self): if self._tag_latest == None: return None return self._tag_latest["name"] @property def update_link(self): return self._update_link @property def update_ready(self): return self._update_ready @property def update_version(self): return self._update_version @property def use_releases(self): return self._use_releases @use_releases.setter def use_releases(self, value): try: self._use_releases = bool(value) except: raise ValueError("use_releases must be a boolean value") @property def user(self): return self._user @user.setter def user(self, value): try: self._user = str(value) except: raise ValueError("User must be a string value") @property def verbose(self): return self._verbose @verbose.setter def verbose(self, value): try: self._verbose = bool(value) if self._verbose == True: print(self._addon+" updater verbose is enabled") except: raise ValueError("Verbose must be a boolean value") @property def version_max_update(self): return self._version_max_update @version_max_update.setter def version_max_update(self, value): if value == None: self._version_max_update = None return if type(value) != type((1,2,3)): raise ValueError("Version maximum must be a tuple") for subvalue in value: if type(subvalue) != int: raise ValueError("Version elements must be integers") self._version_max_update = value @property def version_min_update(self): return self._version_min_update @version_min_update.setter def version_min_update(self, value): if value == None: self._version_min_update = None return if type(value) != type((1,2,3)): raise ValueError("Version minimum must be a tuple") for subvalue in value: if type(subvalue) != int: raise ValueError("Version elements must be integers") self._version_min_update = value @property def website(self): return self._website @website.setter def website(self, value): if self.check_is_url(value) == False: raise ValueError("Not a valid URL: " + value) self._website = value # ------------------------------------------------------------------------- # Parameter validation related functions # ------------------------------------------------------------------------- def check_is_url(self, url): if not ("http://" in url or "https://" in url): return False if "." not in url: return False return True def get_tag_names(self): tag_names = [] self.get_tags() for tag in self._tags: tag_names.append(tag["name"]) return tag_names def set_check_interval(self,enable=False,months=0,days=14,hours=0,minutes=0): # enabled = False, default initially will not check against frequency # if enabled, default is then 2 weeks if type(enable) is not bool: raise ValueError("Enable must be a boolean value") if type(months) is not int: raise ValueError("Months must be an integer value") if type(days) is not int: raise ValueError("Days must be an integer value") if type(hours) is not int: raise ValueError("Hours must be an integer value") if type(minutes) is not int: raise ValueError("Minutes must be an integer value") if enable==False: self._check_interval_enable = False else: self._check_interval_enable = True self._check_interval_months = months self._check_interval_days = days self._check_interval_hours = hours self._check_interval_minutes = minutes # declare how the class gets printed def __repr__(self): return "<Module updater from {a}>".format(a=__file__) def __str__(self): return "Updater, with user: {a}, repository: {b}, url: {c}".format( a=self._user, b=self._repo, c=self.form_repo_url()) # ------------------------------------------------------------------------- # API-related functions # ------------------------------------------------------------------------- def form_repo_url(self): return self._engine.form_repo_url(self) def form_tags_url(self): return self._engine.form_tags_url(self) def form_branch_url(self, branch): return self._engine.form_branch_url(branch, self) def get_tags(self): request = self.form_tags_url() if self._verbose: print("Getting tags from server") # get all tags, internet call all_tags = self._engine.parse_tags(self.get_api(request), self) if all_tags is not None: self._prefiltered_tag_count = len(all_tags) else: self._prefiltered_tag_count = 0 all_tags = [] # pre-process to skip tags if self.skip_tag != None: self._tags = [tg for tg in all_tags if self.skip_tag(self, tg)==False] else: self._tags = all_tags # get additional branches too, if needed, and place in front # Does NO checking here whether branch is valid if self._include_branches == True: temp_branches = self._include_branch_list.copy() temp_branches.reverse() for branch in temp_branches: request = self.form_branch_url(branch) include = { "name":branch.title(), "zipball_url":request } self._tags = [include] + self._tags # append to front if self._tags == None: # some error occurred self._tag_latest = None self._tags = [] return elif self._prefiltered_tag_count == 0 and self._include_branches == False: self._tag_latest = None if self._error == None: # if not None, could have had no internet self._error = "No releases found" self._error_msg = "No releases or tags found on this repository" if self._verbose: print("No releases or tags found on this repository") elif self._prefiltered_tag_count == 0 and self._include_branches == True: if not self._error: self._tag_latest = self._tags[0] if self._verbose: branch = self._include_branch_list[0] print("{} branch found, no releases".format(branch), self._tags[0]) elif (len(self._tags)-len(self._include_branch_list)==0 and self._include_branches==True) \ or (len(self._tags)==0 and self._include_branches==False) \ and self._prefiltered_tag_count > 0: self._tag_latest = None self._error = "No releases available" self._error_msg = "No versions found within compatible version range" if self._verbose: print("No versions found within compatible version range") else: if self._include_branches == False: self._tag_latest = self._tags[0] if self._verbose: print("Most recent tag found:",self._tags[0]['name']) else: # don't return branch if in list n = len(self._include_branch_list) self._tag_latest = self._tags[n] # guaranteed at least len()=n+1 if self._verbose: print("Most recent tag found:",self._tags[n]['name']) # all API calls to base url def get_raw(self, url): # print("Raw request:", url) request = urllib.request.Request(url) try: context = ssl._create_unverified_context() except: # some blender packaged python versions don't have this, largely # useful for local network setups otherwise minimal impact context = None # setup private request headers if appropriate if self._engine.token != None: if self._engine.name == "gitlab": request.add_header('PRIVATE-TOKEN',self._engine.token) else: if self._verbose: print("Tokens not setup for engine yet") # run the request try: if context: result = urllib.request.urlopen(request, context=context) else: result = urllib.request.urlopen(request) except urllib.error.HTTPError as e: if str(e.code) == "403": self._error = "HTTP error (access denied)" self._error_msg = str(e.code) + " - server error response" print(self._error, self._error_msg) else: self._error = "HTTP error" self._error_msg = str(e.code) print(self._error, self._error_msg) self._update_ready = None except urllib.error.URLError as e: reason = str(e.reason) if "TLSV1_ALERT" in reason or "SSL" in reason.upper(): self._error = "Connection rejected, download manually" self._error_msg = reason print(self._error, self._error_msg) else: self._error = "URL error, check internet connection" self._error_msg = reason print(self._error, self._error_msg) self._update_ready = None return None else: result_string = result.read() result.close() return result_string.decode() # result of all api calls, decoded into json format def get_api(self, url): # return the json version get = None get = self.get_raw(url) if get != None: try: return json.JSONDecoder().decode(get) except Exception as e: self._error = "API response has invalid JSON format" self._error_msg = str(e.reason) self._update_ready = None print(self._error, self._error_msg) return None else: return None # create a working directory and download the new files def stage_repository(self, url): local = os.path.join(self._updater_path,"update_staging") error = None # make/clear the staging folder # ensure the folder is always "clean" if self._verbose: print("Preparing staging folder for download:\n",local) if os.path.isdir(local) == True: try: shutil.rmtree(local) os.makedirs(local) except: error = "failed to remove existing staging directory. check permissions for staging directory" else: try: os.makedirs(local) except: error = "failed to create staging directory. check permissions for staging directory" if error != None: if self._verbose: print("Error: Aborting update, "+error) self._error = "Update aborted, staging path error" self._error_msg = "Error: {}".format(error) return False if self._backup_current==True: self.create_backup() if self._verbose: print("Now retrieving the new source zip") self._source_zip = os.path.join(local,"source.zip") if self._verbose: print("Starting download update zip") try: request = urllib.request.Request(url) context = ssl._create_unverified_context() # setup private token if appropriate if self._engine.token != None: if self._engine.name == "gitlab": request.add_header('PRIVATE-TOKEN',self._engine.token) else: if self._verbose: print("Tokens not setup for selected engine yet") self.urlretrieve(urllib.request.urlopen(request,context=context), self._source_zip) # add additional checks on file size being non-zero if self._verbose: print("Successfully downloaded update zip") return True except Exception as e: self._error = "Error retrieving download, bad link?" self._error_msg = "Error: {}".format(e) if self._verbose: print("Error retrieving download, bad link?") print("Error: {}".format(e)) return False def create_backup(self): if self._verbose: print("Backing up current addon folder") local = os.path.join(self._updater_path,"backup") tempdest = os.path.join(self._addon_root, os.pardir, self._addon+"_updater_backup_temp") if self._verbose: print("Backup destination path: ",local) if os.path.isdir(local): try: shutil.rmtree(local) except: if self._verbose:print("Failed to removed previous backup folder, contininuing") # remove the temp folder; shouldn't exist but could if previously interrupted if os.path.isdir(tempdest): try: shutil.rmtree(tempdest) except: if self._verbose:print("Failed to remove existing temp folder, contininuing") # make the full addon copy, which temporarily places outside the addon folder if self._backup_ignore_patterns != None: shutil.copytree( self._addon_root,tempdest, ignore=shutil.ignore_patterns(*self._backup_ignore_patterns)) else: shutil.copytree(self._addon_root,tempdest) shutil.move(tempdest,local) # save the date for future ref now = datetime.now() self._json["backup_date"] = "{m}-{d}-{yr}".format( m=now.strftime("%B"),d=now.day,yr=now.year) self.save_updater_json() def restore_backup(self): if self._verbose: print("Restoring backup") if self._verbose: print("Backing up current addon folder") backuploc = os.path.join(self._updater_path,"backup") tempdest = os.path.join(self._addon_root, os.pardir, self._addon+"_updater_backup_temp") tempdest = os.path.abspath(tempdest) # make the copy shutil.move(backuploc,tempdest) shutil.rmtree(self._addon_root) os.rename(tempdest,self._addon_root) self._json["backup_date"] = "" self._json["just_restored"] = True self._json["just_updated"] = True self.save_updater_json() self.reload_addon() def unpack_staged_zip(self,clean=False): """Unzip the downloaded file, and validate contents""" if os.path.isfile(self._source_zip) == False: if self._verbose: print("Error, update zip not found") self._error = "Install failed" self._error_msg = "Downloaded zip not found" return -1 # clear the existing source folder in case previous files remain outdir = os.path.join(self._updater_path, "source") if os.path.exists(outdir): if self._verbose: print("Old source folder exists; clearing") try: shutil.rmtree(outdir) if self._verbose: print("Old source folder cleared") except: print("Error occurred while clearing old extract dir:") print(str(err)) self._error = "Install failed" self._error_msg = "Failed to clear old extract directory" return -1 # Create parent directories if needed, would not be relevant unless # installing addon into another location or via an addon manager try: os.makedirs(outdir) if self._verbose: print("Source folder cleared and recreated") except Exception as err: print("Error occurred while making extract dir:") print(str(err)) self._error = "Install failed" self._error_msg = "Failed to make extract directory" return -1 if not os.path.isdir(outdir): print("Failed to create source directory") self._error = "Install failed" self._error_msg = "Failed to create extract directory" return -1 if self._verbose: print("Begin extracting source from zip:", self._source_zip) zfile = zipfile.ZipFile(self._source_zip, "r") if not zfile: if self._verbose: print("Resulting file is not a zip, cannot extract") self._error = "Install failed" self._error_msg = "Resulting file is not a zip, cannot extract" return -1 # Now extract directly from the first subfolder (not root) # this avoids adding the first subfolder to the path length, # which can be too long if the download has the SHA in the name zsep = '/' #os.sep # might just always be / even on windows for name in zfile.namelist(): if zsep not in name: continue top_folder = name[:name.index(zsep)+1] if name == top_folder + zsep: continue # skip top level folder subpath = name[name.index(zsep)+1:] if name.endswith(zsep): try: os.mkdir(os.path.join(outdir, subpath)) if self._verbose: print("Extract - mkdir: ", os.path.join(outdir, subpath)) except OSError as exc: if exc.errno != errno.EEXIST: self._error = "Install failed" self._error_msg = "Could not create folder from zip" return -1 else: with open(os.path.join(outdir, subpath), "wb") as outfile: data = zfile.read(name) outfile.write(data) if self._verbose: print("Extract - create:", os.path.join(outdir, subpath)) if self._verbose: print("Extracted source") unpath = os.path.join(self._updater_path, "source") if not os.path.isdir(unpath): self._error = "Install failed" self._error_msg = "Extracted path does not exist" print("Extracted path does not exist: ", unpath) return -1 if self._subfolder_path: self._subfolder_path.replace('/', os.path.sep) self._subfolder_path.replace('\\', os.path.sep) # either directly in root of zip/one subfolder, or use specified path if os.path.isfile(os.path.join(unpath,"__init__.py")) == False: dirlist = os.listdir(unpath) if len(dirlist)>0: if self._subfolder_path == "" or self._subfolder_path == None: unpath = os.path.join(unpath, dirlist[0]) else: unpath = os.path.join(unpath, self._subfolder_path) # smarter check for additional sub folders for a single folder # containing __init__.py if os.path.isfile(os.path.join(unpath,"__init__.py")) == False: if self._verbose: print("not a valid addon found") print("Paths:") print(dirlist) self._error = "Install failed" self._error_msg = "No __init__ file found in new source" return -1 # merge code with running addon directory, using blender default behavior # plus any modifiers indicated by user (e.g. force remove/keep) self.deepMergeDirectory(self._addon_root, unpath, clean) # Now save the json state # Change to True, to trigger the handler on other side # if allowing reloading within same blender instance self._json["just_updated"] = True self.save_updater_json() self.reload_addon() self._update_ready = False return 0 def deepMergeDirectory(self,base,merger,clean=False): """Merge folder 'merger' into folder 'base' without deleting existing""" if not os.path.exists(base): if self._verbose: print("Base path does not exist:", base) return -1 elif not os.path.exists(merger): if self._verbose: print("Merger path does not exist") return -1 # paths to be aware of and not overwrite/remove/etc staging_path = os.path.join(self._updater_path,"update_staging") backup_path = os.path.join(self._updater_path,"backup") # If clean install is enabled, clear existing files ahead of time # note: will not delete the update.json, update folder, staging, or staging # but will delete all other folders/files in addon directory error = None if clean==True: try: # implement clearing of all folders/files, except the # updater folder and updater json # Careful, this deletes entire subdirectories recursively... # make sure that base is not a high level shared folder, but # is dedicated just to the addon itself if self._verbose: print("clean=True, clearing addon folder to fresh install state") # remove root files and folders (except update folder) files = [f for f in os.listdir(base) if os.path.isfile(os.path.join(base,f))] folders = [f for f in os.listdir(base) if os.path.isdir(os.path.join(base,f))] for f in files: os.remove(os.path.join(base,f)) print("Clean removing file {}".format(os.path.join(base,f))) for f in folders: if os.path.join(base,f)==self._updater_path: continue shutil.rmtree(os.path.join(base,f)) print("Clean removing folder and contents {}".format(os.path.join(base,f))) except Exception as err: error = "failed to create clean existing addon folder" print(error, str(err)) # Walk through the base addon folder for rules on pre-removing # but avoid removing/altering backup and updater file for path, dirs, files in os.walk(base): # prune ie skip updater folder dirs[:] = [d for d in dirs if os.path.join(path,d) not in [self._updater_path]] for file in files: for ptrn in self.remove_pre_update_patterns: if fnmatch.filter([file],ptrn): try: fl = os.path.join(path,file) os.remove(fl) if self._verbose: print("Pre-removed file "+file) except OSError: print("Failed to pre-remove "+file) # Walk through the temp addon sub folder for replacements # this implements the overwrite rules, which apply after # the above pre-removal rules. This also performs the # actual file copying/replacements for path, dirs, files in os.walk(merger): # verify this structure works to prune updater sub folder overwriting dirs[:] = [d for d in dirs if os.path.join(path,d) not in [self._updater_path]] relPath = os.path.relpath(path, merger) destPath = os.path.join(base, relPath) if not os.path.exists(destPath): os.makedirs(destPath) for file in files: # bring in additional logic around copying/replacing # Blender default: overwrite .py's, don't overwrite the rest destFile = os.path.join(destPath, file) srcFile = os.path.join(path, file) # decide whether to replace if file already exists, and copy new over if os.path.isfile(destFile): # otherwise, check each file to see if matches an overwrite pattern replaced=False for ptrn in self._overwrite_patterns: if fnmatch.filter([destFile],ptrn): replaced=True break if replaced: os.remove(destFile) os.rename(srcFile, destFile) if self._verbose: print("Overwrote file "+os.path.basename(destFile)) else: if self._verbose: print("Pattern not matched to "+os.path.basename(destFile)+", not overwritten") else: # file did not previously exist, simply move it over os.rename(srcFile, destFile) if self._verbose: print("New file "+os.path.basename(destFile)) # now remove the temp staging folder and downloaded zip try: shutil.rmtree(staging_path) except: error = "Error: Failed to remove existing staging directory, consider manually removing "+staging_path if self._verbose: print(error) def reload_addon(self): # if post_update false, skip this function # else, unload/reload addon & trigger popup if self._auto_reload_post_update == False: print("Restart blender to reload addon and complete update") return if self._verbose: print("Reloading addon...") addon_utils.modules(refresh=True) bpy.utils.refresh_script_paths() # not allowed in restricted context, such as register module # toggle to refresh bpy.ops.wm.addon_disable(module=self._addon_package) bpy.ops.wm.addon_refresh() bpy.ops.wm.addon_enable(module=self._addon_package) # ------------------------------------------------------------------------- # Other non-api functions and setups # ------------------------------------------------------------------------- def clear_state(self): self._update_ready = None self._update_link = None self._update_version = None self._source_zip = None self._error = None self._error_msg = None # custom urlretrieve implementation def urlretrieve(self, urlfile, filepath): chunk = 1024*8 f = open(filepath, "wb") while 1: data = urlfile.read(chunk) if not data: #print("done.") break f.write(data) #print("Read %s bytes"%len(data)) f.close() def version_tuple_from_text(self,text): if text == None: return () # should go through string and remove all non-integers, # and for any given break split into a different section segments = [] tmp = '' for l in str(text): if l.isdigit()==False: if len(tmp)>0: segments.append(int(tmp)) tmp = '' else: tmp+=l if len(tmp)>0: segments.append(int(tmp)) if len(segments)==0: if self._verbose: print("No version strings found text: ",text) if self._include_branches == False: return () else: return (text) return tuple(segments) # called for running check in a background thread def check_for_update_async(self, callback=None): if self._json != None and "update_ready" in self._json and self._json["version_text"]!={}: if self._json["update_ready"] == True: self._update_ready = True self._update_link = self._json["version_text"]["link"] self._update_version = str(self._json["version_text"]["version"]) # cached update callback(True) return # do the check if self._check_interval_enable == False: return elif self._async_checking == True: if self._verbose: print("Skipping async check, already started") return # already running the bg thread elif self._update_ready == None: self.start_async_check_update(False, callback) def check_for_update_now(self, callback=None): self._error = None self._error_msg = None if self._verbose: print("Check update pressed, first getting current status") if self._async_checking == True: if self._verbose: print("Skipping async check, already started") return # already running the bg thread elif self._update_ready == None: self.start_async_check_update(True, callback) else: self._update_ready = None self.start_async_check_update(True, callback) # this function is not async, will always return in sequential fashion # but should have a parent which calls it in another thread def check_for_update(self, now=False): if self._verbose: print("Checking for update function") # clear the errors if any self._error = None self._error_msg = None # avoid running again in, just return past result if found # but if force now check, then still do it if self._update_ready != None and now == False: return (self._update_ready,self._update_version,self._update_link) if self._current_version == None: raise ValueError("current_version not yet defined") if self._repo == None: raise ValueError("repo not yet defined") if self._user == None: raise ValueError("username not yet defined") self.set_updater_json() # self._json if now == False and self.past_interval_timestamp()==False: if self._verbose: print("Aborting check for updated, check interval not reached") return (False, None, None) # check if using tags or releases # note that if called the first time, this will pull tags from online if self._fake_install == True: if self._verbose: print("fake_install = True, setting fake version as ready") self._update_ready = True self._update_version = "(999,999,999)" self._update_link = "http://127.0.0.1" return (self._update_ready, self._update_version, self._update_link) # primary internet call self.get_tags() # sets self._tags and self._tag_latest self._json["last_check"] = str(datetime.now()) self.save_updater_json() # can be () or ('master') in addition to branches, and version tag new_version = self.version_tuple_from_text(self.tag_latest) if len(self._tags)==0: self._update_ready = False self._update_version = None self._update_link = None return (False, None, None) if self._include_branches == False: link = self.select_link(self, self._tags[0]) else: n = len(self._include_branch_list) if len(self._tags)==n: # effectively means no tags found on repo # so provide the first one as default link = self.select_link(self, self._tags[0]) else: link = self.select_link(self, self._tags[n]) if new_version == (): self._update_ready = False self._update_version = None self._update_link = None return (False, None, None) elif str(new_version).lower() in self._include_branch_list: # handle situation where master/whichever branch is included # however, this code effectively is not triggered now # as new_version will only be tag names, not branch names if self._include_branch_autocheck == False: # don't offer update as ready, # but set the link for the default # branch for installing self._update_ready = False self._update_version = new_version self._update_link = link self.save_updater_json() return (True, new_version, link) else: raise ValueError("include_branch_autocheck: NOT YET DEVELOPED") # bypass releases and look at timestamp of last update # from a branch compared to now, see if commit values # match or not. else: # situation where branches not included if new_version > self._current_version: self._update_ready = True self._update_version = new_version self._update_link = link self.save_updater_json() return (True, new_version, link) # elif new_version != self._current_version: # self._update_ready = False # self._update_version = new_version # self._update_link = link # self.save_updater_json() # return (True, new_version, link) # if no update, set ready to False from None self._update_ready = False self._update_version = None self._update_link = None return (False, None, None) def set_tag(self, name): """Assign the tag name and url to update to""" tg = None for tag in self._tags: if name == tag["name"]: tg = tag break if tg: new_version = self.version_tuple_from_text(self.tag_latest) self._update_version = new_version self._update_link = self.select_link(self, tg) elif self._include_branches and name in self._include_branch_list: # scenario if reverting to a specific branch name instead of tag tg = name link = self.form_branch_url(tg) self._update_version = name # this will break things self._update_link = link if not tg: raise ValueError("Version tag not found: "+name) def run_update(self,force=False,revert_tag=None,clean=False,callback=None): """Runs an install, update, or reversion of an addon from online source Arguments: force: Install assigned link, even if self.update_ready is False revert_tag: Version to install, if none uses detected update link clean: not used, but in future could use to totally refresh addon callback: used to run function on update completion """ self._json["update_ready"] = False self._json["ignore"] = False # clear ignore flag self._json["version_text"] = {} if revert_tag != None: self.set_tag(revert_tag) self._update_ready = True # clear the errors if any self._error = None self._error_msg = None if self._verbose: print("Running update") if self._fake_install == True: # change to True, to trigger the reload/"update installed" handler if self._verbose: print("fake_install=True") print("Just reloading and running any handler triggers") self._json["just_updated"] = True self.save_updater_json() if self._backup_current == True: self.create_backup() self.reload_addon() self._update_ready = False res = True # fake "success" zip download flag elif force==False: if self._update_ready != True: if self._verbose: print("Update stopped, new version not ready") if callback: callback( self._addon_package, "Update stopped, new version not ready") return "Update stopped, new version not ready" elif self._update_link == None: # this shouldn't happen if update is ready if self._verbose: print("Update stopped, update link unavailable") if callback: callback( self._addon_package, "Update stopped, update link unavailable") return "Update stopped, update link unavailable" if self._verbose and revert_tag==None: print("Staging update") elif self._verbose: print("Staging install") res = self.stage_repository(self._update_link) if res !=True: print("Error in staging repository: "+str(res)) if callback != None: callback(self._addon_package, self._error_msg) return self._error_msg res = self.unpack_staged_zip(clean) if res<0: if callback: callback(self._addon_package, self._error_msg) return res else: if self._update_link == None: if self._verbose: print("Update stopped, could not get link") return "Update stopped, could not get link" if self._verbose: print("Forcing update") res = self.stage_repository(self._update_link) if res !=True: print("Error in staging repository: "+str(res)) if callback: callback(self._addon_package, self._error_msg) return self._error_msg res = self.unpack_staged_zip(clean) if res<0: return res # would need to compare against other versions held in tags # run the front-end's callback if provided if callback: callback(self._addon_package) # return something meaningful, 0 means it worked return 0 def past_interval_timestamp(self): if self._check_interval_enable == False: return True # ie this exact feature is disabled if "last_check" not in self._json or self._json["last_check"] == "": return True else: now = datetime.now() last_check = datetime.strptime(self._json["last_check"], "%Y-%m-%d %H:%M:%S.%f") next_check = last_check offset = timedelta( days=self._check_interval_days + 30*self._check_interval_months, hours=self._check_interval_hours, minutes=self._check_interval_minutes ) delta = (now - offset) - last_check if delta.total_seconds() > 0: if self._verbose: print("{} Updater: Time to check for updates!".format(self._addon)) return True else: if self._verbose: print("{} Updater: Determined it's not yet time to check for updates".format(self._addon)) return False def get_json_path(self): """Returns the full path to the JSON state file used by this updater. Will also rename old file paths to addon-specific path if found """ json_path = os.path.join(self._updater_path, "{}_updater_status.json".format(self._addon_package)) old_json_path = os.path.join(self._updater_path, "updater_status.json") # rename old file if it exists try: os.rename(old_json_path, json_path) except FileNotFoundError: pass except Exception as err: print("Other OS error occurred while trying to rename old JSON") print(err) return json_path def set_updater_json(self): """Load or initialize JSON dictionary data for updater state""" if self._updater_path == None: raise ValueError("updater_path is not defined") elif os.path.isdir(self._updater_path) == False: os.makedirs(self._updater_path) jpath = self.get_json_path() if os.path.isfile(jpath): with open(jpath) as data_file: self._json = json.load(data_file) if self._verbose: print("{} Updater: Read in JSON settings from file".format( self._addon)) else: # set data structure self._json = { "last_check":"", "backup_date":"", "update_ready":False, "ignore":False, "just_restored":False, "just_updated":False, "version_text":{} } self.save_updater_json() def save_updater_json(self): # first save the state if self._update_ready == True: if type(self._update_version) == type((0,0,0)): self._json["update_ready"] = True self._json["version_text"]["link"]=self._update_link self._json["version_text"]["version"]=self._update_version else: self._json["update_ready"] = False self._json["version_text"] = {} else: self._json["update_ready"] = False self._json["version_text"] = {} jpath = self.get_json_path() outf = open(jpath,'w') data_out = json.dumps(self._json, indent=4) outf.write(data_out) outf.close() if self._verbose: print(self._addon+": Wrote out updater JSON settings to file, with the contents:") print(self._json) def json_reset_postupdate(self): self._json["just_updated"] = False self._json["update_ready"] = False self._json["version_text"] = {} self.save_updater_json() def json_reset_restore(self): self._json["just_restored"] = False self._json["update_ready"] = False self._json["version_text"] = {} self.save_updater_json() self._update_ready = None # reset so you could check update again def ignore_update(self): self._json["ignore"] = True self.save_updater_json() # ------------------------------------------------------------------------- # ASYNC stuff # ------------------------------------------------------------------------- def start_async_check_update(self, now=False, callback=None): """Start a background thread which will check for updates""" if self._async_checking is True: return if self._verbose: print("{} updater: Starting background checking thread".format( self._addon)) check_thread = threading.Thread(target=self.async_check_update, args=(now,callback,)) check_thread.daemon = True self._check_thread = check_thread check_thread.start() def async_check_update(self, now, callback=None): """Perform update check, run as target of background thread""" self._async_checking = True if self._verbose: print("{} BG thread: Checking for update now in background".format( self._addon)) try: self.check_for_update(now=now) except Exception as exception: print("Checking for update error:") print(exception) if not self._error: self._update_ready = False self._update_version = None self._update_link = None self._error = "Error occurred" self._error_msg = "Encountered an error while checking for updates" self._async_checking = False self._check_thread = None if self._verbose: print("{} BG thread: Finished checking for update, doing callback".format(self._addon)) if callback: callback(self._update_ready) def stop_async_check_update(self): """Method to give impression of stopping check for update. Currently does nothing but allows user to retry/stop blocking UI from hitting a refresh button. This does not actually stop the thread, as it will complete after the connection timeout regardless. If the thread does complete with a successful response, this will be still displayed on next UI refresh (ie no update, or update available). """ if self._check_thread != None: if self._verbose: print("Thread will end in normal course.") # however, "There is no direct kill method on a thread object." # better to let it run its course #self._check_thread.stop() self._async_checking = False self._error = None self._error_msg = None # ----------------------------------------------------------------------------- # Updater Engines # ----------------------------------------------------------------------------- class BitbucketEngine(object): """Integration to Bitbucket API for git-formatted repositories""" def __init__(self): self.api_url = 'https://api.bitbucket.org' self.token = None self.name = "bitbucket" def form_repo_url(self, updater): return self.api_url+"/2.0/repositories/"+updater.user+"/"+updater.repo def form_tags_url(self, updater): return self.form_repo_url(updater) + "/refs/tags?sort=-name" def form_branch_url(self, branch, updater): return self.get_zip_url(branch, updater) def get_zip_url(self, name, updater): return "https://bitbucket.org/{user}/{repo}/get/{name}.zip".format( user=updater.user, repo=updater.repo, name=name) def parse_tags(self, response, updater): if response == None: return [] return [{"name": tag["name"], "zipball_url": self.get_zip_url(tag["name"], updater)} for tag in response["values"]] class GithubEngine(object): """Integration to Github API""" def __init__(self): self.api_url = 'https://api.github.com' self.token = None self.name = "github" def form_repo_url(self, updater): return "{}{}{}{}{}".format(self.api_url,"/repos/",updater.user, "/",updater.repo) def form_tags_url(self, updater): if updater.use_releases: return "{}{}".format(self.form_repo_url(updater),"/releases") else: return "{}{}".format(self.form_repo_url(updater),"/tags") def form_branch_list_url(self, updater): return "{}{}".format(self.form_repo_url(updater),"/branches") def form_branch_url(self, branch, updater): return "{}{}{}".format(self.form_repo_url(updater), "/zipball/",branch) def parse_tags(self, response, updater): if response == None: return [] return response class GitlabEngine(object): """Integration to GitLab API""" def __init__(self): self.api_url = 'https://gitlab.com' self.token = None self.name = "gitlab" def form_repo_url(self, updater): return "{}{}{}".format(self.api_url,"/api/v4/projects/",updater.repo) def form_tags_url(self, updater): return "{}{}".format(self.form_repo_url(updater),"/repository/tags") def form_branch_list_url(self, updater): # does not validate branch name. return "{}{}".format( self.form_repo_url(updater), "/repository/branches") def form_branch_url(self, branch, updater): # Could clash with tag names and if it does, it will # download TAG zip instead of branch zip to get # direct path, would need. return "{}{}{}".format( self.form_repo_url(updater), "/repository/archive.zip?sha=", branch) def get_zip_url(self, sha, updater): return "{base}/repository/archive.zip?sha={sha}".format( base=self.form_repo_url(updater), sha=sha) # def get_commit_zip(self, id, updater): # return self.form_repo_url(updater)+"/repository/archive.zip?sha:"+id def parse_tags(self, response, updater): if response == None: return [] return [{"name": tag["name"], "zipball_url": self.get_zip_url(tag["commit"]["id"], updater)} for tag in response] # ----------------------------------------------------------------------------- # The module-shared class instance, # should be what's imported to other files # ----------------------------------------------------------------------------- Updater = Singleton_updater()

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self._engine = GithubEngine() self._user = None self._repo = None self._website = None self._current_version = None self._subfolder_path = None self._tags = [] self._tag_latest = None self._tag_names = [] self._latest_release = None self._use_releases = False self._include_branches = False self._include_branch_list = ['master'] self._include_branch_autocheck = False self._manual_only = False self._version_min_update = None self._version_max_update = None self._backup_current = True self._backup_ignore_patterns = None self._overwrite_patterns = ["*.py","*.pyc"] self._remove_pre_update_patterns = [] # as it is slightly less stable/won't always fully reload module self._auto_reload_post_update = False self._check_interval_enable = False self._check_interval_months = 0 self._check_interval_days = 7 self._check_interval_hours = 0 self._check_interval_minutes = 0 self._verbose = False self._fake_install = False self._async_checking = False self._update_ready = None self._update_link = None self._update_version = None self._source_zip = None self._check_thread = None self._select_link = None self.skip_tag = None self._addon = __package__.split('.')[0].lower() self._addon_package = __package__.split('.')[0] self._updater_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..', 'updater_tmp')) self._addon_root = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..')) self._json = {} self._error = None self._error_msg = None self._prefiltered_tag_count = 0 # UI code only, ie not used within this module but still useful # properties to have # to verify a valid import, in place of placeholder import self.showpopups = True # used in UI to show or not show update popups self.invalidupdater = False # pre-assign basic select-link function def select_link_function(self, tag): return tag["zipball_url"] self._select_link = select_link_function # ------------------------------------------------------------------------- # Getters and setters # ------------------------------------------------------------------------- @property def addon(self): return self._addon @addon.setter def addon(self, value): self._addon = str(value) @property def addon_package(self): return self._addon_package @addon_package.setter def addon_package(self, value): self._addon_package = value @property def api_url(self): return self._engine.api_url @api_url.setter def api_url(self, value): if self.check_is_url(value) == False: raise ValueError("Not a valid URL: " + value) self._engine.api_url = value @property def async_checking(self): return self._async_checking @property def auto_reload_post_update(self): return self._auto_reload_post_update @auto_reload_post_update.setter def auto_reload_post_update(self, value): try: self._auto_reload_post_update = bool(value) except: raise ValueError("Must be a boolean value") @property def backup_current(self): return self._backup_current @backup_current.setter def backup_current(self, value): if value == None: self._backup_current = False return else: self._backup_current = value @property def backup_ignore_patterns(self): return self._backup_ignore_patterns @backup_ignore_patterns.setter def backup_ignore_patterns(self, value): if value == None: self._backup_ignore_patterns = None return elif type(value) != type(['list']): raise ValueError("Backup pattern must be in list format") else: self._backup_ignore_patterns = value @property def check_interval(self): return (self._check_interval_enable, self._check_interval_months, self._check_interval_days, self._check_interval_hours, self._check_interval_minutes) @property def current_version(self): return self._current_version @current_version.setter def current_version(self, tuple_values): if tuple_values==None: self._current_version = None return elif type(tuple_values) is not tuple: try: tuple(tuple_values) except: raise ValueError( "Not a tuple! current_version must be a tuple of integers") for i in tuple_values: if type(i) is not int: raise ValueError( "Not an integer! current_version must be a tuple of integers") self._current_version = tuple(tuple_values) @property def engine(self): return self._engine.name @engine.setter def engine(self, value): if value.lower()=="github": self._engine = GithubEngine() elif value.lower()=="gitlab": self._engine = GitlabEngine() elif value.lower()=="bitbucket": self._engine = BitbucketEngine() else: raise ValueError("Invalid engine selection") @property def error(self): return self._error @property def error_msg(self): return self._error_msg @property def fake_install(self): return self._fake_install @fake_install.setter def fake_install(self, value): if type(value) != type(False): raise ValueError("fake_install must be a boolean value") self._fake_install = bool(value) # not currently used @property def include_branch_autocheck(self): return self._include_branch_autocheck @include_branch_autocheck.setter def include_branch_autocheck(self, value): try: self._include_branch_autocheck = bool(value) except: raise ValueError("include_branch_autocheck must be a boolean value") @property def include_branch_list(self): return self._include_branch_list @include_branch_list.setter def include_branch_list(self, value): try: if value == None: self._include_branch_list = ['master'] elif type(value) != type(['master']) or value==[]: raise ValueError("include_branch_list should be a list of valid branches") else: self._include_branch_list = value except: raise ValueError("include_branch_list should be a list of valid branches") @property def include_branches(self): return self._include_branches @include_branches.setter def include_branches(self, value): try: self._include_branches = bool(value) except: raise ValueError("include_branches must be a boolean value") @property def json(self): if self._json == {}: self.set_updater_json() return self._json @property def latest_release(self): if self._latest_release == None: return None return self._latest_release @property def manual_only(self): return self._manual_only @manual_only.setter def manual_only(self, value): try: self._manual_only = bool(value) except: raise ValueError("manual_only must be a boolean value") @property def overwrite_patterns(self): return self._overwrite_patterns @overwrite_patterns.setter def overwrite_patterns(self, value): if value == None: self._overwrite_patterns = ["*.py","*.pyc"] elif type(value) != type(['']): raise ValueError("overwrite_patterns needs to be in a list format") else: self._overwrite_patterns = value @property def private_token(self): return self._engine.token @private_token.setter def private_token(self, value): if value==None: self._engine.token = None else: self._engine.token = str(value) @property def remove_pre_update_patterns(self): return self._remove_pre_update_patterns @remove_pre_update_patterns.setter def remove_pre_update_patterns(self, value): if value == None: self._remove_pre_update_patterns = [] elif type(value) != type(['']): raise ValueError("remove_pre_update_patterns needs to be in a list format") else: self._remove_pre_update_patterns = value @property def repo(self): return self._repo @repo.setter def repo(self, value): try: self._repo = str(value) except: raise ValueError("User must be a string") @property def select_link(self): return self._select_link @select_link.setter def select_link(self, value): # ensure it is a function assignment, with signature: # input self, tag; returns link name if not hasattr(value, "__call__"): raise ValueError("select_link must be a function") self._select_link = value @property def stage_path(self): return self._updater_path @stage_path.setter def stage_path(self, value): if value == None: if self._verbose: print("Aborting assigning stage_path, it's null") return elif value != None and not os.path.exists(value): try: os.makedirs(value) except: if self._verbose: print("Error trying to staging path") return self._updater_path = value @property def subfolder_path(self): return self._subfolder_path @subfolder_path.setter def subfolder_path(self, value): self._subfolder_path = value @property def tags(self): if self._tags == []: return [] tag_names = [] for tag in self._tags: tag_names.append(tag["name"]) return tag_names @property def tag_latest(self): if self._tag_latest == None: return None return self._tag_latest["name"] @property def update_link(self): return self._update_link @property def update_ready(self): return self._update_ready @property def update_version(self): return self._update_version @property def use_releases(self): return self._use_releases @use_releases.setter def use_releases(self, value): try: self._use_releases = bool(value) except: raise ValueError("use_releases must be a boolean value") @property def user(self): return self._user @user.setter def user(self, value): try: self._user = str(value) except: raise ValueError("User must be a string value") @property def verbose(self): return self._verbose @verbose.setter def verbose(self, value): try: self._verbose = bool(value) if self._verbose == True: print(self._addon+" updater verbose is enabled") except: raise ValueError("Verbose must be a boolean value") @property def version_max_update(self): return self._version_max_update @version_max_update.setter def version_max_update(self, value): if value == None: self._version_max_update = None return if type(value) != type((1,2,3)): raise ValueError("Version maximum must be a tuple") for subvalue in value: if type(subvalue) != int: raise ValueError("Version elements must be integers") self._version_max_update = value @property def version_min_update(self): return self._version_min_update @version_min_update.setter def version_min_update(self, value): if value == None: self._version_min_update = None return if type(value) != type((1,2,3)): raise ValueError("Version minimum must be a tuple") for subvalue in value: if type(subvalue) != int: raise ValueError("Version elements must be integers") self._version_min_update = value @property def website(self): return self._website @website.setter def website(self, value): if self.check_is_url(value) == False: raise ValueError("Not a valid URL: " + value) self._website = value def check_is_url(self, url): if not ("http://" in url or "https://" in url): return False if "." not in url: return False return True def get_tag_names(self): tag_names = [] self.get_tags() for tag in self._tags: tag_names.append(tag["name"]) return tag_names def set_check_interval(self,enable=False,months=0,days=14,hours=0,minutes=0): if type(enable) is not bool: raise ValueError("Enable must be a boolean value") if type(months) is not int: raise ValueError("Months must be an integer value") if type(days) is not int: raise ValueError("Days must be an integer value") if type(hours) is not int: raise ValueError("Hours must be an integer value") if type(minutes) is not int: raise ValueError("Minutes must be an integer value") if enable==False: self._check_interval_enable = False else: self._check_interval_enable = True self._check_interval_months = months self._check_interval_days = days self._check_interval_hours = hours self._check_interval_minutes = minutes def __repr__(self): return "<Module updater from {a}>".format(a=__file__) def __str__(self): return "Updater, with user: {a}, repository: {b}, url: {c}".format( a=self._user, b=self._repo, c=self.form_repo_url()) def form_repo_url(self): return self._engine.form_repo_url(self) def form_tags_url(self): return self._engine.form_tags_url(self) def form_branch_url(self, branch): return self._engine.form_branch_url(branch, self) def get_tags(self): request = self.form_tags_url() if self._verbose: print("Getting tags from server") all_tags = self._engine.parse_tags(self.get_api(request), self) if all_tags is not None: self._prefiltered_tag_count = len(all_tags) else: self._prefiltered_tag_count = 0 all_tags = [] if self.skip_tag != None: self._tags = [tg for tg in all_tags if self.skip_tag(self, tg)==False] else: self._tags = all_tags if self._include_branches == True: temp_branches = self._include_branch_list.copy() temp_branches.reverse() for branch in temp_branches: request = self.form_branch_url(branch) include = { "name":branch.title(), "zipball_url":request } self._tags = [include] + self._tags if self._tags == None: self._tag_latest = None self._tags = [] return elif self._prefiltered_tag_count == 0 and self._include_branches == False: self._tag_latest = None if self._error == None: self._error = "No releases found" self._error_msg = "No releases or tags found on this repository" if self._verbose: print("No releases or tags found on this repository") elif self._prefiltered_tag_count == 0 and self._include_branches == True: if not self._error: self._tag_latest = self._tags[0] if self._verbose: branch = self._include_branch_list[0] print("{} branch found, no releases".format(branch), self._tags[0]) elif (len(self._tags)-len(self._include_branch_list)==0 and self._include_branches==True) \ or (len(self._tags)==0 and self._include_branches==False) \ and self._prefiltered_tag_count > 0: self._tag_latest = None self._error = "No releases available" self._error_msg = "No versions found within compatible version range" if self._verbose: print("No versions found within compatible version range") else: if self._include_branches == False: self._tag_latest = self._tags[0] if self._verbose: print("Most recent tag found:",self._tags[0]['name']) else: n = len(self._include_branch_list) self._tag_latest = self._tags[n] # guaranteed at least len()=n+1 if self._verbose: print("Most recent tag found:",self._tags[n]['name']) # all API calls to base url def get_raw(self, url): # print("Raw request:", url) request = urllib.request.Request(url) try: context = ssl._create_unverified_context() except: # some blender packaged python versions don't have this, largely context = None if self._engine.token != None: if self._engine.name == "gitlab": request.add_header('PRIVATE-TOKEN',self._engine.token) else: if self._verbose: print("Tokens not setup for engine yet") try: if context: result = urllib.request.urlopen(request, context=context) else: result = urllib.request.urlopen(request) except urllib.error.HTTPError as e: if str(e.code) == "403": self._error = "HTTP error (access denied)" self._error_msg = str(e.code) + " - server error response" print(self._error, self._error_msg) else: self._error = "HTTP error" self._error_msg = str(e.code) print(self._error, self._error_msg) self._update_ready = None except urllib.error.URLError as e: reason = str(e.reason) if "TLSV1_ALERT" in reason or "SSL" in reason.upper(): self._error = "Connection rejected, download manually" self._error_msg = reason print(self._error, self._error_msg) else: self._error = "URL error, check internet connection" self._error_msg = reason print(self._error, self._error_msg) self._update_ready = None return None else: result_string = result.read() result.close() return result_string.decode() def get_api(self, url): get = None get = self.get_raw(url) if get != None: try: return json.JSONDecoder().decode(get) except Exception as e: self._error = "API response has invalid JSON format" self._error_msg = str(e.reason) self._update_ready = None print(self._error, self._error_msg) return None else: return None def stage_repository(self, url): local = os.path.join(self._updater_path,"update_staging") error = None if self._verbose: print("Preparing staging folder for download:\n",local) if os.path.isdir(local) == True: try: shutil.rmtree(local) os.makedirs(local) except: error = "failed to remove existing staging directory. check permissions for staging directory" else: try: os.makedirs(local) except: error = "failed to create staging directory. check permissions for staging directory" if error != None: if self._verbose: print("Error: Aborting update, "+error) self._error = "Update aborted, staging path error" self._error_msg = "Error: {}".format(error) return False if self._backup_current==True: self.create_backup() if self._verbose: print("Now retrieving the new source zip") self._source_zip = os.path.join(local,"source.zip") if self._verbose: print("Starting download update zip") try: request = urllib.request.Request(url) context = ssl._create_unverified_context() if self._engine.token != None: if self._engine.name == "gitlab": request.add_header('PRIVATE-TOKEN',self._engine.token) else: if self._verbose: print("Tokens not setup for selected engine yet") self.urlretrieve(urllib.request.urlopen(request,context=context), self._source_zip) if self._verbose: print("Successfully downloaded update zip") return True except Exception as e: self._error = "Error retrieving download, bad link?" self._error_msg = "Error: {}".format(e) if self._verbose: print("Error retrieving download, bad link?") print("Error: {}".format(e)) return False def create_backup(self): if self._verbose: print("Backing up current addon folder") local = os.path.join(self._updater_path,"backup") tempdest = os.path.join(self._addon_root, os.pardir, self._addon+"_updater_backup_temp") if self._verbose: print("Backup destination path: ",local) if os.path.isdir(local): try: shutil.rmtree(local) except: if self._verbose:print("Failed to removed previous backup folder, contininuing") if os.path.isdir(tempdest): try: shutil.rmtree(tempdest) except: if self._verbose:print("Failed to remove existing temp folder, contininuing") # make the full addon copy, which temporarily places outside the addon folder if self._backup_ignore_patterns != None: shutil.copytree( self._addon_root,tempdest, ignore=shutil.ignore_patterns(*self._backup_ignore_patterns)) else: shutil.copytree(self._addon_root,tempdest) shutil.move(tempdest,local) # save the date for future ref now = datetime.now() self._json["backup_date"] = "{m}-{d}-{yr}".format( m=now.strftime("%B"),d=now.day,yr=now.year) self.save_updater_json() def restore_backup(self): if self._verbose: print("Restoring backup") if self._verbose: print("Backing up current addon folder") backuploc = os.path.join(self._updater_path,"backup") tempdest = os.path.join(self._addon_root, os.pardir, self._addon+"_updater_backup_temp") tempdest = os.path.abspath(tempdest) # make the copy shutil.move(backuploc,tempdest) shutil.rmtree(self._addon_root) os.rename(tempdest,self._addon_root) self._json["backup_date"] = "" self._json["just_restored"] = True self._json["just_updated"] = True self.save_updater_json() self.reload_addon() def unpack_staged_zip(self,clean=False): if os.path.isfile(self._source_zip) == False: if self._verbose: print("Error, update zip not found") self._error = "Install failed" self._error_msg = "Downloaded zip not found" return -1 # clear the existing source folder in case previous files remain outdir = os.path.join(self._updater_path, "source") if os.path.exists(outdir): if self._verbose: print("Old source folder exists; clearing") try: shutil.rmtree(outdir) if self._verbose: print("Old source folder cleared") except: print("Error occurred while clearing old extract dir:") print(str(err)) self._error = "Install failed" self._error_msg = "Failed to clear old extract directory" return -1 # Create parent directories if needed, would not be relevant unless # installing addon into another location or via an addon manager try: os.makedirs(outdir) if self._verbose: print("Source folder cleared and recreated") except Exception as err: print("Error occurred while making extract dir:") print(str(err)) self._error = "Install failed" self._error_msg = "Failed to make extract directory" return -1 if not os.path.isdir(outdir): print("Failed to create source directory") self._error = "Install failed" self._error_msg = "Failed to create extract directory" return -1 if self._verbose: print("Begin extracting source from zip:", self._source_zip) zfile = zipfile.ZipFile(self._source_zip, "r") if not zfile: if self._verbose: print("Resulting file is not a zip, cannot extract") self._error = "Install failed" self._error_msg = "Resulting file is not a zip, cannot extract" return -1 # Now extract directly from the first subfolder (not root) # this avoids adding the first subfolder to the path length, # which can be too long if the download has the SHA in the name zsep = '/' #os.sep # might just always be / even on windows for name in zfile.namelist(): if zsep not in name: continue top_folder = name[:name.index(zsep)+1] if name == top_folder + zsep: continue # skip top level folder subpath = name[name.index(zsep)+1:] if name.endswith(zsep): try: os.mkdir(os.path.join(outdir, subpath)) if self._verbose: print("Extract - mkdir: ", os.path.join(outdir, subpath)) except OSError as exc: if exc.errno != errno.EEXIST: self._error = "Install failed" self._error_msg = "Could not create folder from zip" return -1 else: with open(os.path.join(outdir, subpath), "wb") as outfile: data = zfile.read(name) outfile.write(data) if self._verbose: print("Extract - create:", os.path.join(outdir, subpath)) if self._verbose: print("Extracted source") unpath = os.path.join(self._updater_path, "source") if not os.path.isdir(unpath): self._error = "Install failed" self._error_msg = "Extracted path does not exist" print("Extracted path does not exist: ", unpath) return -1 if self._subfolder_path: self._subfolder_path.replace('/', os.path.sep) self._subfolder_path.replace('\\', os.path.sep) # either directly in root of zip/one subfolder, or use specified path if os.path.isfile(os.path.join(unpath,"__init__.py")) == False: dirlist = os.listdir(unpath) if len(dirlist)>0: if self._subfolder_path == "" or self._subfolder_path == None: unpath = os.path.join(unpath, dirlist[0]) else: unpath = os.path.join(unpath, self._subfolder_path) # smarter check for additional sub folders for a single folder # containing __init__.py if os.path.isfile(os.path.join(unpath,"__init__.py")) == False: if self._verbose: print("not a valid addon found") print("Paths:") print(dirlist) self._error = "Install failed" self._error_msg = "No __init__ file found in new source" return -1 # merge code with running addon directory, using blender default behavior # plus any modifiers indicated by user (e.g. force remove/keep) self.deepMergeDirectory(self._addon_root, unpath, clean) # Now save the json state # Change to True, to trigger the handler on other side # if allowing reloading within same blender instance self._json["just_updated"] = True self.save_updater_json() self.reload_addon() self._update_ready = False return 0 def deepMergeDirectory(self,base,merger,clean=False): if not os.path.exists(base): if self._verbose: print("Base path does not exist:", base) return -1 elif not os.path.exists(merger): if self._verbose: print("Merger path does not exist") return -1 # paths to be aware of and not overwrite/remove/etc staging_path = os.path.join(self._updater_path,"update_staging") backup_path = os.path.join(self._updater_path,"backup") # If clean install is enabled, clear existing files ahead of time # note: will not delete the update.json, update folder, staging, or staging # but will delete all other folders/files in addon directory error = None if clean==True: try: # implement clearing of all folders/files, except the # updater folder and updater json # Careful, this deletes entire subdirectories recursively... # make sure that base is not a high level shared folder, but # is dedicated just to the addon itself if self._verbose: print("clean=True, clearing addon folder to fresh install state") # remove root files and folders (except update folder) files = [f for f in os.listdir(base) if os.path.isfile(os.path.join(base,f))] folders = [f for f in os.listdir(base) if os.path.isdir(os.path.join(base,f))] for f in files: os.remove(os.path.join(base,f)) print("Clean removing file {}".format(os.path.join(base,f))) for f in folders: if os.path.join(base,f)==self._updater_path: continue shutil.rmtree(os.path.join(base,f)) print("Clean removing folder and contents {}".format(os.path.join(base,f))) except Exception as err: error = "failed to create clean existing addon folder" print(error, str(err)) # Walk through the base addon folder for rules on pre-removing # but avoid removing/altering backup and updater file for path, dirs, files in os.walk(base): # prune ie skip updater folder dirs[:] = [d for d in dirs if os.path.join(path,d) not in [self._updater_path]] for file in files: for ptrn in self.remove_pre_update_patterns: if fnmatch.filter([file],ptrn): try: fl = os.path.join(path,file) os.remove(fl) if self._verbose: print("Pre-removed file "+file) except OSError: print("Failed to pre-remove "+file) # Walk through the temp addon sub folder for replacements # this implements the overwrite rules, which apply after # the above pre-removal rules. This also performs the # actual file copying/replacements for path, dirs, files in os.walk(merger): # verify this structure works to prune updater sub folder overwriting dirs[:] = [d for d in dirs if os.path.join(path,d) not in [self._updater_path]] relPath = os.path.relpath(path, merger) destPath = os.path.join(base, relPath) if not os.path.exists(destPath): os.makedirs(destPath) for file in files: # bring in additional logic around copying/replacing # Blender default: overwrite .py's, don't overwrite the rest destFile = os.path.join(destPath, file) srcFile = os.path.join(path, file) # decide whether to replace if file already exists, and copy new over if os.path.isfile(destFile): # otherwise, check each file to see if matches an overwrite pattern replaced=False for ptrn in self._overwrite_patterns: if fnmatch.filter([destFile],ptrn): replaced=True break if replaced: os.remove(destFile) os.rename(srcFile, destFile) if self._verbose: print("Overwrote file "+os.path.basename(destFile)) else: if self._verbose: print("Pattern not matched to "+os.path.basename(destFile)+", not overwritten") else: # file did not previously exist, simply move it over os.rename(srcFile, destFile) if self._verbose: print("New file "+os.path.basename(destFile)) # now remove the temp staging folder and downloaded zip try: shutil.rmtree(staging_path) except: error = "Error: Failed to remove existing staging directory, consider manually removing "+staging_path if self._verbose: print(error) def reload_addon(self): # if post_update false, skip this function # else, unload/reload addon & trigger popup if self._auto_reload_post_update == False: print("Restart blender to reload addon and complete update") return if self._verbose: print("Reloading addon...") addon_utils.modules(refresh=True) bpy.utils.refresh_script_paths() # not allowed in restricted context, such as register module # toggle to refresh bpy.ops.wm.addon_disable(module=self._addon_package) bpy.ops.wm.addon_refresh() bpy.ops.wm.addon_enable(module=self._addon_package) # ------------------------------------------------------------------------- # Other non-api functions and setups # ------------------------------------------------------------------------- def clear_state(self): self._update_ready = None self._update_link = None self._update_version = None self._source_zip = None self._error = None self._error_msg = None # custom urlretrieve implementation def urlretrieve(self, urlfile, filepath): chunk = 1024*8 f = open(filepath, "wb") while 1: data = urlfile.read(chunk) if not data: #print("done.") break f.write(data) #print("Read %s bytes"%len(data)) f.close() def version_tuple_from_text(self,text): if text == None: return () # should go through string and remove all non-integers, # and for any given break split into a different section segments = [] tmp = '' for l in str(text): if l.isdigit()==False: if len(tmp)>0: segments.append(int(tmp)) tmp = '' else: tmp+=l if len(tmp)>0: segments.append(int(tmp)) if len(segments)==0: if self._verbose: print("No version strings found text: ",text) if self._include_branches == False: return () else: return (text) return tuple(segments) # called for running check in a background thread def check_for_update_async(self, callback=None): if self._json != None and "update_ready" in self._json and self._json["version_text"]!={}: if self._json["update_ready"] == True: self._update_ready = True self._update_link = self._json["version_text"]["link"] self._update_version = str(self._json["version_text"]["version"]) # cached update callback(True) return # do the check if self._check_interval_enable == False: return elif self._async_checking == True: if self._verbose: print("Skipping async check, already started") return # already running the bg thread elif self._update_ready == None: self.start_async_check_update(False, callback) def check_for_update_now(self, callback=None): self._error = None self._error_msg = None if self._verbose: print("Check update pressed, first getting current status") if self._async_checking == True: if self._verbose: print("Skipping async check, already started") return # already running the bg thread elif self._update_ready == None: self.start_async_check_update(True, callback) else: self._update_ready = None self.start_async_check_update(True, callback) # this function is not async, will always return in sequential fashion # but should have a parent which calls it in another thread def check_for_update(self, now=False): if self._verbose: print("Checking for update function") # clear the errors if any self._error = None self._error_msg = None # avoid running again in, just return past result if found # but if force now check, then still do it if self._update_ready != None and now == False: return (self._update_ready,self._update_version,self._update_link) if self._current_version == None: raise ValueError("current_version not yet defined") if self._repo == None: raise ValueError("repo not yet defined") if self._user == None: raise ValueError("username not yet defined") self.set_updater_json() # self._json if now == False and self.past_interval_timestamp()==False: if self._verbose: print("Aborting check for updated, check interval not reached") return (False, None, None) # check if using tags or releases # note that if called the first time, this will pull tags from online if self._fake_install == True: if self._verbose: print("fake_install = True, setting fake version as ready") self._update_ready = True self._update_version = "(999,999,999)" self._update_link = "http://127.0.0.1" return (self._update_ready, self._update_version, self._update_link) # primary internet call self.get_tags() # sets self._tags and self._tag_latest self._json["last_check"] = str(datetime.now()) self.save_updater_json() # can be () or ('master') in addition to branches, and version tag new_version = self.version_tuple_from_text(self.tag_latest) if len(self._tags)==0: self._update_ready = False self._update_version = None self._update_link = None return (False, None, None) if self._include_branches == False: link = self.select_link(self, self._tags[0]) else: n = len(self._include_branch_list) if len(self._tags)==n: # effectively means no tags found on repo # so provide the first one as default link = self.select_link(self, self._tags[0]) else: link = self.select_link(self, self._tags[n]) if new_version == (): self._update_ready = False self._update_version = None self._update_link = None return (False, None, None) elif str(new_version).lower() in self._include_branch_list: # handle situation where master/whichever branch is included # however, this code effectively is not triggered now # as new_version will only be tag names, not branch names if self._include_branch_autocheck == False: # don't offer update as ready, self._update_ready = False self._update_version = new_version self._update_link = link self.save_updater_json() return (True, new_version, link) else: raise ValueError("include_branch_autocheck: NOT YET DEVELOPED") else: if new_version > self._current_version: self._update_ready = True self._update_version = new_version self._update_link = link self.save_updater_json() return (True, new_version, link) self._update_ready = False self._update_version = None self._update_link = None return (False, None, None) def set_tag(self, name): tg = None for tag in self._tags: if name == tag["name"]: tg = tag break if tg: new_version = self.version_tuple_from_text(self.tag_latest) self._update_version = new_version self._update_link = self.select_link(self, tg) elif self._include_branches and name in self._include_branch_list: tg = name link = self.form_branch_url(tg) self._update_version = name self._update_link = link if not tg: raise ValueError("Version tag not found: "+name) def run_update(self,force=False,revert_tag=None,clean=False,callback=None): self._json["update_ready"] = False self._json["ignore"] = False self._json["version_text"] = {} if revert_tag != None: self.set_tag(revert_tag) self._update_ready = True self._error = None self._error_msg = None if self._verbose: print("Running update") if self._fake_install == True: if self._verbose: print("fake_install=True") print("Just reloading and running any handler triggers") self._json["just_updated"] = True self.save_updater_json() if self._backup_current == True: self.create_backup() self.reload_addon() self._update_ready = False res = True elif force==False: if self._update_ready != True: if self._verbose: print("Update stopped, new version not ready") if callback: callback( self._addon_package, "Update stopped, new version not ready") return "Update stopped, new version not ready" elif self._update_link == None: if self._verbose: print("Update stopped, update link unavailable") if callback: callback( self._addon_package, "Update stopped, update link unavailable") return "Update stopped, update link unavailable" if self._verbose and revert_tag==None: print("Staging update") elif self._verbose: print("Staging install") res = self.stage_repository(self._update_link) if res !=True: print("Error in staging repository: "+str(res)) if callback != None: callback(self._addon_package, self._error_msg) return self._error_msg res = self.unpack_staged_zip(clean) if res<0: if callback: callback(self._addon_package, self._error_msg) return res else: if self._update_link == None: if self._verbose: print("Update stopped, could not get link") return "Update stopped, could not get link" if self._verbose: print("Forcing update") res = self.stage_repository(self._update_link) if res !=True: print("Error in staging repository: "+str(res)) if callback: callback(self._addon_package, self._error_msg) return self._error_msg res = self.unpack_staged_zip(clean) if res<0: return res # would need to compare against other versions held in tags # run the front-end's callback if provided if callback: callback(self._addon_package) return 0 def past_interval_timestamp(self): if self._check_interval_enable == False: return True if "last_check" not in self._json or self._json["last_check"] == "": return True else: now = datetime.now() last_check = datetime.strptime(self._json["last_check"], "%Y-%m-%d %H:%M:%S.%f") next_check = last_check offset = timedelta( days=self._check_interval_days + 30*self._check_interval_months, hours=self._check_interval_hours, minutes=self._check_interval_minutes ) delta = (now - offset) - last_check if delta.total_seconds() > 0: if self._verbose: print("{} Updater: Time to check for updates!".format(self._addon)) return True else: if self._verbose: print("{} Updater: Determined it's not yet time to check for updates".format(self._addon)) return False def get_json_path(self): json_path = os.path.join(self._updater_path, "{}_updater_status.json".format(self._addon_package)) old_json_path = os.path.join(self._updater_path, "updater_status.json") # rename old file if it exists try: os.rename(old_json_path, json_path) except FileNotFoundError: pass except Exception as err: print("Other OS error occurred while trying to rename old JSON") print(err) return json_path def set_updater_json(self): if self._updater_path == None: raise ValueError("updater_path is not defined") elif os.path.isdir(self._updater_path) == False: os.makedirs(self._updater_path) jpath = self.get_json_path() if os.path.isfile(jpath): with open(jpath) as data_file: self._json = json.load(data_file) if self._verbose: print("{} Updater: Read in JSON settings from file".format( self._addon)) else: # set data structure self._json = { "last_check":"", "backup_date":"", "update_ready":False, "ignore":False, "just_restored":False, "just_updated":False, "version_text":{} } self.save_updater_json() def save_updater_json(self): # first save the state if self._update_ready == True: if type(self._update_version) == type((0,0,0)): self._json["update_ready"] = True self._json["version_text"]["link"]=self._update_link self._json["version_text"]["version"]=self._update_version else: self._json["update_ready"] = False self._json["version_text"] = {} else: self._json["update_ready"] = False self._json["version_text"] = {} jpath = self.get_json_path() outf = open(jpath,'w') data_out = json.dumps(self._json, indent=4) outf.write(data_out) outf.close() if self._verbose: print(self._addon+": Wrote out updater JSON settings to file, with the contents:") print(self._json) def json_reset_postupdate(self): self._json["just_updated"] = False self._json["update_ready"] = False self._json["version_text"] = {} self.save_updater_json() def json_reset_restore(self): self._json["just_restored"] = False self._json["update_ready"] = False self._json["version_text"] = {} self.save_updater_json() self._update_ready = None # reset so you could check update again def ignore_update(self): self._json["ignore"] = True self.save_updater_json() # ------------------------------------------------------------------------- # ASYNC stuff # ------------------------------------------------------------------------- def start_async_check_update(self, now=False, callback=None): if self._async_checking is True: return if self._verbose: print("{} updater: Starting background checking thread".format( self._addon)) check_thread = threading.Thread(target=self.async_check_update, args=(now,callback,)) check_thread.daemon = True self._check_thread = check_thread check_thread.start() def async_check_update(self, now, callback=None): self._async_checking = True if self._verbose: print("{} BG thread: Checking for update now in background".format( self._addon)) try: self.check_for_update(now=now) except Exception as exception: print("Checking for update error:") print(exception) if not self._error: self._update_ready = False self._update_version = None self._update_link = None self._error = "Error occurred" self._error_msg = "Encountered an error while checking for updates" self._async_checking = False self._check_thread = None if self._verbose: print("{} BG thread: Finished checking for update, doing callback".format(self._addon)) if callback: callback(self._update_ready) def stop_async_check_update(self): if self._check_thread != None: if self._verbose: print("Thread will end in normal course.") # however, "There is no direct kill method on a thread object." # better to let it run its course #self._check_thread.stop() self._async_checking = False self._error = None self._error_msg = None # ----------------------------------------------------------------------------- # Updater Engines # ----------------------------------------------------------------------------- class BitbucketEngine(object): def __init__(self): self.api_url = 'https://api.bitbucket.org' self.token = None self.name = "bitbucket" def form_repo_url(self, updater): return self.api_url+"/2.0/repositories/"+updater.user+"/"+updater.repo def form_tags_url(self, updater): return self.form_repo_url(updater) + "/refs/tags?sort=-name" def form_branch_url(self, branch, updater): return self.get_zip_url(branch, updater) def get_zip_url(self, name, updater): return "https://bitbucket.org/{user}/{repo}/get/{name}.zip".format( user=updater.user, repo=updater.repo, name=name) def parse_tags(self, response, updater): if response == None: return [] return [{"name": tag["name"], "zipball_url": self.get_zip_url(tag["name"], updater)} for tag in response["values"]] class GithubEngine(object): def __init__(self): self.api_url = 'https://api.github.com' self.token = None self.name = "github" def form_repo_url(self, updater): return "{}{}{}{}{}".format(self.api_url,"/repos/",updater.user, "/",updater.repo) def form_tags_url(self, updater): if updater.use_releases: return "{}{}".format(self.form_repo_url(updater),"/releases") else: return "{}{}".format(self.form_repo_url(updater),"/tags") def form_branch_list_url(self, updater): return "{}{}".format(self.form_repo_url(updater),"/branches") def form_branch_url(self, branch, updater): return "{}{}{}".format(self.form_repo_url(updater), "/zipball/",branch) def parse_tags(self, response, updater): if response == None: return [] return response class GitlabEngine(object): def __init__(self): self.api_url = 'https://gitlab.com' self.token = None self.name = "gitlab" def form_repo_url(self, updater): return "{}{}{}".format(self.api_url,"/api/v4/projects/",updater.repo) def form_tags_url(self, updater): return "{}{}".format(self.form_repo_url(updater),"/repository/tags") def form_branch_list_url(self, updater): # does not validate branch name. return "{}{}".format( self.form_repo_url(updater), "/repository/branches") def form_branch_url(self, branch, updater): # Could clash with tag names and if it does, it will # download TAG zip instead of branch zip to get # direct path, would need. return "{}{}{}".format( self.form_repo_url(updater), "/repository/archive.zip?sha=", branch) def get_zip_url(self, sha, updater): return "{base}/repository/archive.zip?sha={sha}".format( base=self.form_repo_url(updater), sha=sha) # def get_commit_zip(self, id, updater): # return self.form_repo_url(updater)+"/repository/archive.zip?sha:"+id def parse_tags(self, response, updater): if response == None: return [] return [{"name": tag["name"], "zipball_url": self.get_zip_url(tag["commit"]["id"], updater)} for tag in response] # ----------------------------------------------------------------------------- # The module-shared class instance, # should be what's imported to other files Updater = Singleton_updater()

true

f7fbdb885279ab0c23eadba10893de95e20c379e

11,746

py

Python

wpcmd/base.py

zrong/wpcmd

f755bedba8cd44af62f59cf6d9d8bace46245e92

[ "BSD-3-Clause" ]

17

2015-11-22T04:28:59.000Z

2020-02-15T11:29:39.000Z

wpcmd/base.py

zrong/wpcmd

f755bedba8cd44af62f59cf6d9d8bace46245e92

[ "BSD-3-Clause" ]

1

2019-04-02T04:07:41.000Z

2019-04-02T08:26:05.000Z

wpcmd/base.py

zrong/wpcmd

f755bedba8cd44af62f59cf6d9d8bace46245e92

[ "BSD-3-Clause" ]

8

2015-11-21T02:04:45.000Z

2019-10-06T12:27:09.000Z

######################################### # base.py # # Author zrong(zengrong.net) # Creation 2014-12-04 # Modification 2015-11-22 ######################################### import os import re import platform import shutil from xmlrpc.client import Fault from string import Template from datetime import (datetime, timedelta) import configparser from wordpress_xmlrpc import (Client, WordPressPost, WordPressPage, WordPressTerm, WordPressMedia) from wordpress_xmlrpc.exceptions import InvalidCredentialsError from wordpress_xmlrpc.methods.taxonomies import (GetTerms) from pkg_resources import (resource_filename, resource_string) from rookout import slog from rookout.base import (list_dir, read_file, write_file) from rookout.conf import PYConf class Conf(object): TPL_FILE = 'wpcmd.ini.tpl' PRE_NAME = '_' if platform.system() == 'Windows' else '.' INI_FILE = PRE_NAME+'wpcmd.ini' CACHE_FILE = PRE_NAME+'wpcmd.cache.py' ARTICLE_TYPES = ('post', 'page', 'draft') def __init__(self, conffile): self.conffile = conffile self.ini = configparser.ConfigParser() self.cache = None def init(self, workdir): if os.path.exists(self.conffile): self.read_from_file() return True tplstr = read_file(resource_filename('wpcmd', Conf.TPL_FILE)) inistr = Template(tplstr).substitute({ 'CACHEFILE':Conf.CACHE_FILE, 'WORK':workdir, }) self.save_to_file(inistr) self.read_from_file() slog.info('Please modify %s !'%self.conffile) return False def init_cache(self, site_name): self.__site_section = site_name self.cache = TermCache(self.get_site('cachefile')) self.cache.init() def __missing__(self, key): return None def __getattr__(self, name): return self.ini[name] def get(self, section, option): return self.ini.get(section, option, raw=True, fallback=None) def get_site(self, option): return self.get(self.__site_section, option) def get_user(self): return self.get_site('user') def get_password(self): return self.get_site('password') def get_url(self, only_site=False): url = self.get_site('url') site = None if url.endswith('/xmlrpc.php'): site = url[:-11] elif url.endswith('/'): site = url[:-1] url = url + 'xmlrpc.php' else: site = url url = url + '/xmlrpc.php' if only_site: return site return url def save_to_file(self, inistr): write_file(self.conffile, inistr) def read_from_file(self): self.ini.read(self.conffile) def is_article(self, posttype): return posttype in Conf.ARTICLE_TYPES def get_draft(self, name): """ There are two kind of draft file in draft directory. One has published to wordpress and in draft status; One has not been published to wordpress yet. """ draftname = (self.get_site('draftfmt') % str(name))+self.get_site('ext') return self.get_work_path('draft', draftname), draftname def get_new_draft(self, name=None): draftdir = self.get_work_path('draft') draftnames = list(list_dir(draftdir)) draftfile, draftname = None, None if name: draftfile, draftname = self.get_draft(name) if draftname in draftnames: raise WPError('The draft file "%s" is already existence!'% draftname) else: name = 1 draftfile, draftname = self.get_draft(name) while os.path.exists(draftfile): name += 1 draftfile, draftname = self.get_draft(name) return draftfile, draftname def get_article(self, name, posttype): postname = name+self.get_site('ext') if self.is_article(posttype): return self.get_work_path(posttype, postname), postname return None, None def get_path(self, name, *path): workdir = os.path.join(self.get_site('work'), name) if path: return os.path.abspath(os.path.join(workdir, *path)) return workdir def get_work_path(self, dirname, *path): workpath = self.get_path(self.get_site(dirname)) if not os.path.exists(workpath): os.makedirs(workpath) if path: return os.path.join(workpath, *path) return workpath def get_mdfiles(self, posttype): workpath = self.get_work_path(posttype) for afile in os.listdir(workpath): if afile.endswith(self.get_site('ext')): name = afile.split('.')[0] filepath = os.path.join(workpath, afile) yield (posttype, name, filepath) class Action(object): def __init__(self, gconf, gtermcache, gargs, gparser): self.conf = gconf self.conf.site = gargs.site self.cache = gtermcache self.args = gargs self.parser = gparser self._wp = None def get_postid(self, as_list=False): if not self.args.query: return None if as_list: postids = [] for postid in self.args.query: match = re.match(r'^(\d+)-(\d+)$', postid) if match: a = int(match.group(1)) b = int(match.group(2)) for i in range(a,b+1): postids.append(str(i)) else: postids.append(postid) return postids return self.args.query[0] def get_dict_from_query(self, query): if query: d = {} for v in query: value = v.split('=') d[value[0]] = value[1] return d return None def get_term_query(self): typ = self.args.type q = self.args.query query = [] if typ == 'term': query = q else: if typ == 'tag': typ = 'post_tag' query.append(typ) if q and len(q)>0: query.append(q[0]) return query def get_terms_from_wp(self, query, force=False): if not query or len(query)== 0: slog.error('Please provide a taxonomy name! You can use ' '"show -t tax" to get one.') return None taxname = query[0] slug = query[1] if len(query)>1 else None terms = self.cache[taxname] if not terms or force: results = self.wpcall(GetTerms(taxname)) if results: self.cache.save_terms(results, taxname) if terms and slug: return terms[slug] return terms def print_result(self, result): if isinstance(result, WordPressTerm): slog.info('id=%s, group=%s, ' 'taxnomy_id=%s, name=%s, slug=%s, ' 'parent=%s, count=%d', result.id, result.group, result.taxonomy_id, result.name, result.slug, result.parent, result.count) elif isinstance(result, WordPressPost): slog.info('id=%s, date=%s, date_modified=%s, ' 'slug=%s, title=%s, post_status=%s, post_type=%s', result.id, str(result.date), str(result.date_modified), result.slug, result.title, result.post_status, result.post_type) elif isinstance(result, WordPressMedia): slog.info('id=%s, parent=%s, title=%s, ' 'description=%s, caption=%s, date_created=%s, link=%s, ' 'thumbnail=%s, metadata=%s', result.id, result.parent, result.title, result.description, result.caption, str(result.date_created), result.link, result.thumbnail, result.metadata) else: slog.info(result) def print_results(self, results): if isinstance(results, list): for result in results: self.print_result(result) elif isinstance(results, dict): for k,v in results.items(): slog.info('%s %s'%(k, str(v))) else: self.print_result(results) def get_datetime(self, datestring): dt = datetime.strptime(datestring, '%Y-%m-%d %H:%M:%S') return dt - timedelta(hours=8) def wpcall(self, method): if not self._wp: self._wp = Client(self.conf.get_url(), self.conf.get_user(), self.conf.get_password()) try: results = self._wp.call(method) except InvalidCredentialsError as e: slog.error(e) return None except Fault as e: slog.error(e) return None return results def go(self): pass def build(self): if self.args.type: self.go() elif self.parser: self.parser.print_help() class TermCache(PYConf): """ A cache for terms. """ def __init__(self, filepath): self.cachefile = filepath def init(self): if os.path.exists(self.cachefile): super().read_from_file(self.cachefile) def save_to_file(self): super().save_to_file(self.cachefile) def save_terms(self, terms, taxname): termdict = PYConf() for term in terms: self.save_term(term, taxname, termdict) self[taxname] = termdict self.save_to_file() def save_term(self, term, taxname, termdict=None): if termdict == None: termdict = self[taxname] if termdict == None: termdict = PYConf() self[taxname] = termdict termdict[term.slug] = PYConf({ 'id':term.id, 'group':term.group, 'taxonomy':term.taxonomy, 'taxonomy_id':term.taxonomy_id, 'name':term.name, 'slug':term.slug, 'description':term.description, 'parent':term.parent, 'count':term.count, }) def get_term(self, taxname, slug): if not self[taxname]: return None if not self[taxname][slug]: return None termdict = self[taxname][slug] term = WordPressTerm() term.id = termdict['id'] term.group = termdict['group'] term.taxonomy = termdict['taxonomy'] term.taxonomy_id = termdict['taxonomy_id'] term.name = termdict['name'] term.slug = termdict['slug'] term.description = termdict['description'] term.parent = termdict['parent'] term.count = termdict['count'] return term def get_terms_from_meta(self, categories, tags): terms = [] if categories: for cat in categories: term = self.get_term('category', cat) if not term: slog.error('The category "%s" is not in wordpress.' ' Please create it first.'%cat) return None terms.append(term) if tags: for tag in tags: term = self.get_term('post_tag', tag) if not term: slog.error('The tag "%s" is not in wordpress.' 'Please create it first'%tag) return None terms.append(term) return terms

32.269231

82

0.548187

lf, key): return None def __getattr__(self, name): return self.ini[name] def get(self, section, option): return self.ini.get(section, option, raw=True, fallback=None) def get_site(self, option): return self.get(self.__site_section, option) def get_user(self): return self.get_site('user') def get_password(self): return self.get_site('password') def get_url(self, only_site=False): url = self.get_site('url') site = None if url.endswith('/xmlrpc.php'): site = url[:-11] elif url.endswith('/'): site = url[:-1] url = url + 'xmlrpc.php' else: site = url url = url + '/xmlrpc.php' if only_site: return site return url def save_to_file(self, inistr): write_file(self.conffile, inistr) def read_from_file(self): self.ini.read(self.conffile) def is_article(self, posttype): return posttype in Conf.ARTICLE_TYPES def get_draft(self, name): draftname = (self.get_site('draftfmt') % str(name))+self.get_site('ext') return self.get_work_path('draft', draftname), draftname def get_new_draft(self, name=None): draftdir = self.get_work_path('draft') draftnames = list(list_dir(draftdir)) draftfile, draftname = None, None if name: draftfile, draftname = self.get_draft(name) if draftname in draftnames: raise WPError('The draft file "%s" is already existence!'% draftname) else: name = 1 draftfile, draftname = self.get_draft(name) while os.path.exists(draftfile): name += 1 draftfile, draftname = self.get_draft(name) return draftfile, draftname def get_article(self, name, posttype): postname = name+self.get_site('ext') if self.is_article(posttype): return self.get_work_path(posttype, postname), postname return None, None def get_path(self, name, *path): workdir = os.path.join(self.get_site('work'), name) if path: return os.path.abspath(os.path.join(workdir, *path)) return workdir def get_work_path(self, dirname, *path): workpath = self.get_path(self.get_site(dirname)) if not os.path.exists(workpath): os.makedirs(workpath) if path: return os.path.join(workpath, *path) return workpath def get_mdfiles(self, posttype): workpath = self.get_work_path(posttype) for afile in os.listdir(workpath): if afile.endswith(self.get_site('ext')): name = afile.split('.')[0] filepath = os.path.join(workpath, afile) yield (posttype, name, filepath) class Action(object): def __init__(self, gconf, gtermcache, gargs, gparser): self.conf = gconf self.conf.site = gargs.site self.cache = gtermcache self.args = gargs self.parser = gparser self._wp = None def get_postid(self, as_list=False): if not self.args.query: return None if as_list: postids = [] for postid in self.args.query: match = re.match(r'^(\d+)-(\d+)$', postid) if match: a = int(match.group(1)) b = int(match.group(2)) for i in range(a,b+1): postids.append(str(i)) else: postids.append(postid) return postids return self.args.query[0] def get_dict_from_query(self, query): if query: d = {} for v in query: value = v.split('=') d[value[0]] = value[1] return d return None def get_term_query(self): typ = self.args.type q = self.args.query query = [] if typ == 'term': query = q else: if typ == 'tag': typ = 'post_tag' query.append(typ) if q and len(q)>0: query.append(q[0]) return query def get_terms_from_wp(self, query, force=False): if not query or len(query)== 0: slog.error('Please provide a taxonomy name! You can use ' '"show -t tax" to get one.') return None taxname = query[0] slug = query[1] if len(query)>1 else None terms = self.cache[taxname] if not terms or force: results = self.wpcall(GetTerms(taxname)) if results: self.cache.save_terms(results, taxname) if terms and slug: return terms[slug] return terms def print_result(self, result): if isinstance(result, WordPressTerm): slog.info('id=%s, group=%s, ' 'taxnomy_id=%s, name=%s, slug=%s, ' 'parent=%s, count=%d', result.id, result.group, result.taxonomy_id, result.name, result.slug, result.parent, result.count) elif isinstance(result, WordPressPost): slog.info('id=%s, date=%s, date_modified=%s, ' 'slug=%s, title=%s, post_status=%s, post_type=%s', result.id, str(result.date), str(result.date_modified), result.slug, result.title, result.post_status, result.post_type) elif isinstance(result, WordPressMedia): slog.info('id=%s, parent=%s, title=%s, ' 'description=%s, caption=%s, date_created=%s, link=%s, ' 'thumbnail=%s, metadata=%s', result.id, result.parent, result.title, result.description, result.caption, str(result.date_created), result.link, result.thumbnail, result.metadata) else: slog.info(result) def print_results(self, results): if isinstance(results, list): for result in results: self.print_result(result) elif isinstance(results, dict): for k,v in results.items(): slog.info('%s %s'%(k, str(v))) else: self.print_result(results) def get_datetime(self, datestring): dt = datetime.strptime(datestring, '%Y-%m-%d %H:%M:%S') return dt - timedelta(hours=8) def wpcall(self, method): if not self._wp: self._wp = Client(self.conf.get_url(), self.conf.get_user(), self.conf.get_password()) try: results = self._wp.call(method) except InvalidCredentialsError as e: slog.error(e) return None except Fault as e: slog.error(e) return None return results def go(self): pass def build(self): if self.args.type: self.go() elif self.parser: self.parser.print_help() class TermCache(PYConf): def __init__(self, filepath): self.cachefile = filepath def init(self): if os.path.exists(self.cachefile): super().read_from_file(self.cachefile) def save_to_file(self): super().save_to_file(self.cachefile) def save_terms(self, terms, taxname): termdict = PYConf() for term in terms: self.save_term(term, taxname, termdict) self[taxname] = termdict self.save_to_file() def save_term(self, term, taxname, termdict=None): if termdict == None: termdict = self[taxname] if termdict == None: termdict = PYConf() self[taxname] = termdict termdict[term.slug] = PYConf({ 'id':term.id, 'group':term.group, 'taxonomy':term.taxonomy, 'taxonomy_id':term.taxonomy_id, 'name':term.name, 'slug':term.slug, 'description':term.description, 'parent':term.parent, 'count':term.count, }) def get_term(self, taxname, slug): if not self[taxname]: return None if not self[taxname][slug]: return None termdict = self[taxname][slug] term = WordPressTerm() term.id = termdict['id'] term.group = termdict['group'] term.taxonomy = termdict['taxonomy'] term.taxonomy_id = termdict['taxonomy_id'] term.name = termdict['name'] term.slug = termdict['slug'] term.description = termdict['description'] term.parent = termdict['parent'] term.count = termdict['count'] return term def get_terms_from_meta(self, categories, tags): terms = [] if categories: for cat in categories: term = self.get_term('category', cat) if not term: slog.error('The category "%s" is not in wordpress.' ' Please create it first.'%cat) return None terms.append(term) if tags: for tag in tags: term = self.get_term('post_tag', tag) if not term: slog.error('The tag "%s" is not in wordpress.' 'Please create it first'%tag) return None terms.append(term) return terms

true

f7fbdd0928e0dd0fb1e4cd34a710e45de480bda2

2,604

py

Python

allennlp/tests/data/dataset_readers/semantic_parsing/atis_test.py

threefoldo/allennlp

983db284cb46fd18c898dd3b0e04eed6cb932768

[ "Apache-2.0" ]

null

allennlp/tests/data/dataset_readers/semantic_parsing/atis_test.py

threefoldo/allennlp

983db284cb46fd18c898dd3b0e04eed6cb932768

[ "Apache-2.0" ]

null

allennlp/tests/data/dataset_readers/semantic_parsing/atis_test.py

threefoldo/allennlp

983db284cb46fd18c898dd3b0e04eed6cb932768

[ "Apache-2.0" ]

null

# pylint: disable=no-self-use,invalid-name from allennlp.common.file_utils import cached_path from allennlp.data.dataset_readers import AtisDatasetReader from allennlp.common.testing import AllenNlpTestCase from allennlp.semparse.worlds import AtisWorld class TestAtisReader(AllenNlpTestCase): def test_atis_read_from_file(self): data_path = AllenNlpTestCase.FIXTURES_ROOT / "data" / "atis" / "sample.json" database_file = cached_path("https://s3-us-west-2.amazonaws.com/allennlp/datasets/atis/atis.db") reader = AtisDatasetReader(database_file=database_file) instances = list(reader.read(str(data_path))) assert len(instances) == 13 instance = instances[0] assert set(instance.fields.keys()) == \ {'utterance', 'actions', 'world', 'sql_queries', 'target_action_sequence', 'linking_scores'} assert [t.text for t in instance.fields["utterance"].tokens] == \ ['show', 'me', 'the', 'one', 'way', 'flights', 'from', 'detroit', 'to', 'westchester', 'county'] assert isinstance(instance.fields['world'].as_tensor({}), AtisWorld) world = instance.fields['world'].metadata assert set(world.valid_actions['number']) == \ {'number -> ["1"]', 'number -> ["0"]', 'number -> ["41"]', 'number -> ["60"]'} assert world.linked_entities['string']['airport_airport_code_string -> ["\'DTW\'"]'][2] == \ [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0] # ``detroit`` -> ``DTW`` assert world.linked_entities['string']['flight_stop_stop_airport_string -> ["\'DTW\'"]'][2] == \ [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0] # ``detroit`` -> ``DTW`` assert world.linked_entities['string']['city_city_code_string -> ["\'DDTT\'"]'][2] == \ [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0] # ``detroit`` -> ``DDTT`` assert world.linked_entities['string']['fare_basis_economy_string -> ["\'NO\'"]'][2] == \ [0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0] # ``one way`` -> ``NO`` assert world.linked_entities['string']['city_city_name_string -> ["\'WESTCHESTER COUNTY\'"]'][2] == \ [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1] # ``westchester county`` -> ``WESTCHESTER COUNTY`` assert world.linked_entities['string']['city_city_code_string -> ["\'HHPN\'"]'][2] == \ [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1] # ``westchester county`` -> ``HHPN``

49.132075

109

0.544547

from allennlp.common.file_utils import cached_path from allennlp.data.dataset_readers import AtisDatasetReader from allennlp.common.testing import AllenNlpTestCase from allennlp.semparse.worlds import AtisWorld class TestAtisReader(AllenNlpTestCase): def test_atis_read_from_file(self): data_path = AllenNlpTestCase.FIXTURES_ROOT / "data" / "atis" / "sample.json" database_file = cached_path("https://s3-us-west-2.amazonaws.com/allennlp/datasets/atis/atis.db") reader = AtisDatasetReader(database_file=database_file) instances = list(reader.read(str(data_path))) assert len(instances) == 13 instance = instances[0] assert set(instance.fields.keys()) == \ {'utterance', 'actions', 'world', 'sql_queries', 'target_action_sequence', 'linking_scores'} assert [t.text for t in instance.fields["utterance"].tokens] == \ ['show', 'me', 'the', 'one', 'way', 'flights', 'from', 'detroit', 'to', 'westchester', 'county'] assert isinstance(instance.fields['world'].as_tensor({}), AtisWorld) world = instance.fields['world'].metadata assert set(world.valid_actions['number']) == \ {'number -> ["1"]', 'number -> ["0"]', 'number -> ["41"]', 'number -> ["60"]'} assert world.linked_entities['string']['airport_airport_code_string -> ["\'DTW\'"]'][2] == \ [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0] assert world.linked_entities['string']['flight_stop_stop_airport_string -> ["\'DTW\'"]'][2] == \ [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0] assert world.linked_entities['string']['city_city_code_string -> ["\'DDTT\'"]'][2] == \ [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0] assert world.linked_entities['string']['fare_basis_economy_string -> ["\'NO\'"]'][2] == \ [0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0] assert world.linked_entities['string']['city_city_name_string -> ["\'WESTCHESTER COUNTY\'"]'][2] == \ [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1] assert world.linked_entities['string']['city_city_code_string -> ["\'HHPN\'"]'][2] == \ [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1]

true

f7fbde329e3743fcc1be07306b336fa20800b168

809

py

Python

src/project/utils/xmodels.py

tgrx/obliviscor

31f9a4476892460c931b9a8fc5403c3afcc47607

[ "Apache-2.0" ]

null

src/project/utils/xmodels.py

tgrx/obliviscor

31f9a4476892460c931b9a8fc5403c3afcc47607

[ "Apache-2.0" ]

20

2020-04-16T23:45:50.000Z

2020-05-05T14:22:03.000Z

src/project/utils/xmodels.py

tgrx/obliviscor

31f9a4476892460c931b9a8fc5403c3afcc47607

[ "Apache-2.0" ]

null

from functools import singledispatch from typing import Text import django from django.db.models.base import ModelBase from django.db.models.fields.related_descriptors import ForwardManyToOneDescriptor from django.db.models.fields.related_descriptors import ManyToManyDescriptor from django.db.models.query_utils import DeferredAttribute @singledispatch def a(obj) -> Text: return str(obj) @a.register def _(obj: DeferredAttribute) -> Text: if django.VERSION[0] < 3: # pragma: nocover return obj.field_name return obj.field.get_attname() @a.register def _(obj: ForwardManyToOneDescriptor) -> Text: return obj.field.name @a.register def _(obj: ManyToManyDescriptor) -> Text: return obj.field.name @a.register def _(obj: ModelBase) -> Text: return obj._meta.db_table

22.472222

82

0.765142

from functools import singledispatch from typing import Text import django from django.db.models.base import ModelBase from django.db.models.fields.related_descriptors import ForwardManyToOneDescriptor from django.db.models.fields.related_descriptors import ManyToManyDescriptor from django.db.models.query_utils import DeferredAttribute @singledispatch def a(obj) -> Text: return str(obj) @a.register def _(obj: DeferredAttribute) -> Text: if django.VERSION[0] < 3: return obj.field_name return obj.field.get_attname() @a.register def _(obj: ForwardManyToOneDescriptor) -> Text: return obj.field.name @a.register def _(obj: ManyToManyDescriptor) -> Text: return obj.field.name @a.register def _(obj: ModelBase) -> Text: return obj._meta.db_table

true

f7fbdf22581f47bc1202559017c47381d8bda634

394

py

Python

scripts/jenkins_win32.py

mbits-os/JiraDesktop

eb9b66b9c11b2fba1079f03a6f90aea425ce6138

[ "MIT" ]

null

scripts/jenkins_win32.py

mbits-os/JiraDesktop

eb9b66b9c11b2fba1079f03a6f90aea425ce6138

[ "MIT" ]

null

scripts/jenkins_win32.py

mbits-os/JiraDesktop

eb9b66b9c11b2fba1079f03a6f90aea425ce6138

[ "MIT" ]

null

import sys, os, glob, zipfile ROOT = 'apps/Tasks/Release/' FILES = [ 'tasks.exe', 'locale/Tasks.*' ] def paths(): for FILE in FILES: for name in glob.iglob(os.path.join(ROOT, FILE)): yield (name, os.path.join('bin', os.path.relpath(name, ROOT))) with zipfile.ZipFile(sys.argv[1], 'w') as zip: for src, dst in paths(): zip.write(src, dst) zip.write('msbuild.log')

21.888889

66

0.626904

import sys, os, glob, zipfile ROOT = 'apps/Tasks/Release/' FILES = [ 'tasks.exe', 'locale/Tasks.*' ] def paths(): for FILE in FILES: for name in glob.iglob(os.path.join(ROOT, FILE)): yield (name, os.path.join('bin', os.path.relpath(name, ROOT))) with zipfile.ZipFile(sys.argv[1], 'w') as zip: for src, dst in paths(): zip.write(src, dst) zip.write('msbuild.log')

true

f7fbdfa1d83c3367452e2b32485d78c99766b1f7

1,812

py

Python

test/image_sampler_test.py

Enigmatisms/NeRF

870abd83c9679e0735284a1641ffc0d13f0cf1d4

[ "Apache-2.0" ]

1

2022-03-26T16:56:41.000Z

test/image_sampler_test.py

Enigmatisms/NeRF

870abd83c9679e0735284a1641ffc0d13f0cf1d4

[ "Apache-2.0" ]

null

test/image_sampler_test.py

Enigmatisms/NeRF

870abd83c9679e0735284a1641ffc0d13f0cf1d4

[ "Apache-2.0" ]

null

#-*-coding:utf-8-*- """ @author Enigmatisms @date 2022.3.23 Last testing module for image sampler """ import torch import numpy as np from time import time from nerf_helper import imageSampling from mpl_toolkits.mplot3d import axes3d import matplotlib.pyplot as plt from instances import BLENDER_FOV, Rs, ts from torchvision import transforms import sys sys.path.append("..") from py.utils import fov2Focal from py.dataset import CustomDataSet POINT_TO_SAMPLE = 16 IMAGE_SIZE = 50 NEAR_T, FAR_T = 2., 6. INITIAL_SKIP = 0 if __name__ == "__main__": torch.set_default_tensor_type(torch.FloatTensor) # tf = np.concatenate((Rs[-1], ts[-1]), axis = -1) # tf = torch.from_numpy(tf).float().cuda() dataset = CustomDataSet("../../dataset/nerf_synthetic/%s/"%("lego"), transforms.ToTensor(), True, use_alpha = True) cam_fov_train, train_cam_tf, _ = dataset.get_dataset(to_cuda = True) focal = fov2Focal(cam_fov_train, IMAGE_SIZE) axis = plt.axes(projection='3d') colors = ('r', 'g', 'b', 'y', 'p') for k in range(4): output = torch.zeros(IMAGE_SIZE, IMAGE_SIZE, POINT_TO_SAMPLE, 6, dtype = torch.float32).cuda() lengths = torch.zeros(IMAGE_SIZE, IMAGE_SIZE, POINT_TO_SAMPLE, dtype = torch.float32).cuda() imageSampling(train_cam_tf[k], output, lengths, IMAGE_SIZE, IMAGE_SIZE, POINT_TO_SAMPLE, focal, NEAR_T, FAR_T) for i in range(IMAGE_SIZE): for j in range(IMAGE_SIZE): point_list = output[i, j].cpu().numpy() axis.plot3D(point_list[:, 0], point_list[:, 1], point_list[:, 2], c = colors[k], alpha = 0.7) # axis.scatter(point_list[:, 0], point_list[:, 1], point_list[:, 2], c = 'r', s = 2) axis.set_zlabel('Z') axis.set_ylabel('Y') axis.set_xlabel('X') plt.show()

38.553191

119

0.666115

import torch import numpy as np from time import time from nerf_helper import imageSampling from mpl_toolkits.mplot3d import axes3d import matplotlib.pyplot as plt from instances import BLENDER_FOV, Rs, ts from torchvision import transforms import sys sys.path.append("..") from py.utils import fov2Focal from py.dataset import CustomDataSet POINT_TO_SAMPLE = 16 IMAGE_SIZE = 50 NEAR_T, FAR_T = 2., 6. INITIAL_SKIP = 0 if __name__ == "__main__": torch.set_default_tensor_type(torch.FloatTensor) dataset = CustomDataSet("../../dataset/nerf_synthetic/%s/"%("lego"), transforms.ToTensor(), True, use_alpha = True) cam_fov_train, train_cam_tf, _ = dataset.get_dataset(to_cuda = True) focal = fov2Focal(cam_fov_train, IMAGE_SIZE) axis = plt.axes(projection='3d') colors = ('r', 'g', 'b', 'y', 'p') for k in range(4): output = torch.zeros(IMAGE_SIZE, IMAGE_SIZE, POINT_TO_SAMPLE, 6, dtype = torch.float32).cuda() lengths = torch.zeros(IMAGE_SIZE, IMAGE_SIZE, POINT_TO_SAMPLE, dtype = torch.float32).cuda() imageSampling(train_cam_tf[k], output, lengths, IMAGE_SIZE, IMAGE_SIZE, POINT_TO_SAMPLE, focal, NEAR_T, FAR_T) for i in range(IMAGE_SIZE): for j in range(IMAGE_SIZE): point_list = output[i, j].cpu().numpy() axis.plot3D(point_list[:, 0], point_list[:, 1], point_list[:, 2], c = colors[k], alpha = 0.7) axis.set_zlabel('Z') axis.set_ylabel('Y') axis.set_xlabel('X') plt.show()

true

f7fbe0513fe16af12b96d800b2b62c560b4bcd2d

509

py

Python

setup.py

what-digital/aldryn-simple-glossary

af0e9bd56f55a0773ac528dccd2393182c3b3c53

[ "MIT" ]

null

setup.py

what-digital/aldryn-simple-glossary

af0e9bd56f55a0773ac528dccd2393182c3b3c53

[ "MIT" ]

null

setup.py

what-digital/aldryn-simple-glossary

af0e9bd56f55a0773ac528dccd2393182c3b3c53

[ "MIT" ]

null

# -*- coding: utf-8 -*- from setuptools import setup, find_packages from aldryn_simple_glossary import __version__ REQUIREMENTS = [ 'Django>=1.7', 'djangocms-text-ckeditor', ] setup( name='aldryn-simple-glossary', version=__version__, description=open('README.rst').read(), author='Divio AG', author_email='info@divio.com', packages=find_packages(), platforms=['OS Independent'], install_requires=REQUIREMENTS, include_package_data=True, zip_safe=False, )

20.36

46

0.691552

from setuptools import setup, find_packages from aldryn_simple_glossary import __version__ REQUIREMENTS = [ 'Django>=1.7', 'djangocms-text-ckeditor', ] setup( name='aldryn-simple-glossary', version=__version__, description=open('README.rst').read(), author='Divio AG', author_email='info@divio.com', packages=find_packages(), platforms=['OS Independent'], install_requires=REQUIREMENTS, include_package_data=True, zip_safe=False, )

true

f7fbe09243f22bf796b7740f20cc26f56e877667

5,055

py

Python

markote/onenote_html_mapper.py

Frederick-S/one-mark

b2a85dce82f56fc0d08df7519178b4a7e4f89b70

[ "MIT" ]

8

2018-07-12T02:33:04.000Z

2020-11-12T20:28:49.000Z

markote/onenote_html_mapper.py

Frederick-S/one-mark

b2a85dce82f56fc0d08df7519178b4a7e4f89b70

[ "MIT" ]

353

2018-05-21T03:34:36.000Z

2022-03-28T21:06:43.000Z

markote/onenote_html_mapper.py

Frederick-S/one-mark

b2a85dce82f56fc0d08df7519178b4a7e4f89b70

[ "MIT" ]

2

2019-08-04T23:39:09.000Z

2020-04-14T12:22:20.000Z

import io import uuid from PIL import Image from pyquery import PyQuery from markote.oauth import oauth from markote.resource import Resource from markote.util import convert_svg_to_png class OneNoteHtmlMapper(object): """ Converts standard html to OneNote supported html. """ def __init__(self, document): self.document = document self.resources = [] def convert(self): self._convert_svg_to_resources() self._convert_local_image_to_resources() self._move_inline_images_to_table() if self._only_contains_table(): self._table_only_content_hack() def get_html(self): return self.document.outer_html() def _convert_local_image_to_resources(self): images = [PyQuery(image) for image in self.document.find('img')] for image in images: src = image.attr('src') if src.lower().startswith('http') or src.startswith('name:'): continue try: file_name = src.split('/')[-2] oauth_client = oauth.microsoft_graph response = oauth_client.get( 'me/drive/special/approot:/{0}:/content'.format(file_name)) if response.status_code == 200: name = uuid.uuid4().hex image.attr('src', 'name:{0}'.format(name)) content_type = response.headers['Content-Type'] self.resources.append( Resource(name, response.content, content_type)) except Exception as e: print(e) def _convert_svg_to_resources(self): self.resources = [self._convert_svg_to_resource(svg_element) for svg_element in self.document.find('svg')] def _convert_svg_to_resource(self, svg_element): """ Converts svg element to image. """ name = uuid.uuid4().hex element = PyQuery(svg_element) svg = element.outer_html().replace('viewbox', 'viewBox') element.replace_with(PyQuery('<img src="name:{0}" />'.format(name))) return Resource(name, convert_svg_to_png(svg), 'image/png') def _move_inline_images_to_table(self): """ OneNote doesn't support inline images, so we put inline images to a table row. """ images = self.document.find('img') parents = [tuple(PyQuery(image).parent()) for image in images] parents = list(map(lambda x: PyQuery(x[0]), list(set(parents)))) parents = list(filter(self._has_inline_images, parents)) for parent in parents: table = PyQuery('<table></table>') table.append(self._create_table_row_with_inline_images( parent.contents())) parent.replace_with(table) def _has_inline_images(self, element): """ Check if the element contains inline images. The element is a parent element which already contains images, so we only need to check if the length of all children is greater than the size of all images. """ return element.contents().length > element.find('img').length def _create_table_cell_with_elements(self, elements): cell = PyQuery('<td></td>') for element in elements: cell.append(element) return cell def _create_table_row_with_inline_images(self, contents): """ Move inline images and other elements into a table row. """ children_so_far = [] row = PyQuery('<tr></tr>') for content in contents: if hasattr(content, 'tag') and content.tag == 'img': if len(children_so_far) != 0: row.append(self._create_table_cell_with_elements( children_so_far)) children_so_far = [] row.append( '<td>{0}</td>'.format(PyQuery(content).outer_html())) else: children_so_far.append(content) if len(children_so_far) != 0: row.append(self._create_table_cell_with_elements( children_so_far)) return row def _only_contains_table(self): """ Check if the document only contains tables. """ return all(element.tag == 'table' for element in self.document.children()) def _table_only_content_hack(self): """ Hack for https://stackoverflow.com/questions/50789978. If the document only contains a table element, then create a 1*1 image element and append it to the document. """ file = io.BytesIO() image = Image.new('RGB', (1, 1), color='white') image.save(file, 'png') file.seek(0) name = uuid.uuid4().hex element = PyQuery('<img src="name:{0}" />'.format(name)) self.document.append(element) self.resources.append(Resource(name, file, 'image/png'))

32.403846

79

0.58912

import io import uuid from PIL import Image from pyquery import PyQuery from markote.oauth import oauth from markote.resource import Resource from markote.util import convert_svg_to_png class OneNoteHtmlMapper(object): def __init__(self, document): self.document = document self.resources = [] def convert(self): self._convert_svg_to_resources() self._convert_local_image_to_resources() self._move_inline_images_to_table() if self._only_contains_table(): self._table_only_content_hack() def get_html(self): return self.document.outer_html() def _convert_local_image_to_resources(self): images = [PyQuery(image) for image in self.document.find('img')] for image in images: src = image.attr('src') if src.lower().startswith('http') or src.startswith('name:'): continue try: file_name = src.split('/')[-2] oauth_client = oauth.microsoft_graph response = oauth_client.get( 'me/drive/special/approot:/{0}:/content'.format(file_name)) if response.status_code == 200: name = uuid.uuid4().hex image.attr('src', 'name:{0}'.format(name)) content_type = response.headers['Content-Type'] self.resources.append( Resource(name, response.content, content_type)) except Exception as e: print(e) def _convert_svg_to_resources(self): self.resources = [self._convert_svg_to_resource(svg_element) for svg_element in self.document.find('svg')] def _convert_svg_to_resource(self, svg_element): name = uuid.uuid4().hex element = PyQuery(svg_element) svg = element.outer_html().replace('viewbox', 'viewBox') element.replace_with(PyQuery('<img src="name:{0}" />'.format(name))) return Resource(name, convert_svg_to_png(svg), 'image/png') def _move_inline_images_to_table(self): images = self.document.find('img') parents = [tuple(PyQuery(image).parent()) for image in images] parents = list(map(lambda x: PyQuery(x[0]), list(set(parents)))) parents = list(filter(self._has_inline_images, parents)) for parent in parents: table = PyQuery('<table></table>') table.append(self._create_table_row_with_inline_images( parent.contents())) parent.replace_with(table) def _has_inline_images(self, element): return element.contents().length > element.find('img').length def _create_table_cell_with_elements(self, elements): cell = PyQuery('<td></td>') for element in elements: cell.append(element) return cell def _create_table_row_with_inline_images(self, contents): children_so_far = [] row = PyQuery('<tr></tr>') for content in contents: if hasattr(content, 'tag') and content.tag == 'img': if len(children_so_far) != 0: row.append(self._create_table_cell_with_elements( children_so_far)) children_so_far = [] row.append( '<td>{0}</td>'.format(PyQuery(content).outer_html())) else: children_so_far.append(content) if len(children_so_far) != 0: row.append(self._create_table_cell_with_elements( children_so_far)) return row def _only_contains_table(self): return all(element.tag == 'table' for element in self.document.children()) def _table_only_content_hack(self): file = io.BytesIO() image = Image.new('RGB', (1, 1), color='white') image.save(file, 'png') file.seek(0) name = uuid.uuid4().hex element = PyQuery('<img src="name:{0}" />'.format(name)) self.document.append(element) self.resources.append(Resource(name, file, 'image/png'))

true

f7fbe2c78a9888a6d2b82ad7266d559f4c9c00ba

10,485

py

Python

dace/fpga_testing.py

jdahm/dace

d4f32d97cd1fafd9ad8e52ca7ad5283461c64e78

[ "BSD-3-Clause" ]

227

2019-03-15T23:39:06.000Z

2022-03-30T07:49:08.000Z

dace/fpga_testing.py

jdahm/dace

d4f32d97cd1fafd9ad8e52ca7ad5283461c64e78

[ "BSD-3-Clause" ]

834

2019-07-31T22:49:31.000Z

2022-03-28T14:01:32.000Z

dace/fpga_testing.py

jdahm/dace

d4f32d97cd1fafd9ad8e52ca7ad5283461c64e78

[ "BSD-3-Clause" ]

64

2019-03-19T05:40:37.000Z

2022-03-11T15:02:42.000Z

# Copyright 2019-2021 ETH Zurich and the DaCe authors. All rights reserved. from datetime import datetime import importlib.util import inspect import os import multiprocessing as mp from pathlib import Path import pytest import re import subprocess as sp from typing import Callable, Iterable, Optional, Tuple, Union from dace import SDFG from dace.config import Config, temporary_config TEST_TIMEOUT = 600 # Timeout tests after 10 minutes class Colors: SUCCESS = "\033[92m" STATUS = "\033[94m" ERROR = "\033[91m" BOLD = "\033[1m" UNDERLINE = "\033[4m" END = "\033[0m" def print_status(message): timestamp = datetime.now().strftime("%H:%M:%S") print(f"{Colors.STATUS}{Colors.BOLD}[{timestamp}]{Colors.END} {message}") def print_success(message): timestamp = datetime.now().strftime("%H:%M:%S") print(f"{Colors.SUCCESS}{Colors.BOLD}[{timestamp}]{Colors.END} {message}") def print_error(message): timestamp = datetime.now().strftime("%H:%M:%S") print(f"{Colors.ERROR}{Colors.BOLD}[{timestamp}]{Colors.END} {message}") def dump_logs(proc_or_logs: Union[sp.CompletedProcess, Tuple[str, str]]): if isinstance(proc_or_logs, tuple): log_out, log_err = proc_or_logs else: proc_or_logs.terminate() proc_or_logs.kill() try: log_out, log_err = proc_or_logs.communicate(timeout=10) except sp.TimeoutExpired: return None # Failed to even kill the process if log_out: print(log_out) if log_err: print(log_err) return log_out, log_err # https://stackoverflow.com/a/33599967/2949968 class FPGATestProcess(mp.Process): def __init__(self, *args, **kwargs): mp.Process.__init__(self, *args, **kwargs) self._pconn, self._cconn = mp.Pipe() self._exception = None def run(self): try: ret = mp.Process.run(self) self._cconn.send(ret) except Exception as e: self._cconn.send(e) raise e @property def exception(self): if self._pconn.poll(): self._exception = self._pconn.recv() return self._exception class TestFailed(Exception): pass def raise_error(message): print_error(message) raise TestFailed(message) def _run_fpga_test(vendor: str, test_function: Callable, run_synthesis: bool = True, assert_ii_1: bool = True): path = Path(inspect.getfile(test_function)) base_name = f"{path.stem}::{Colors.UNDERLINE}{test_function.__name__}{Colors.END}" with temporary_config(): Config.set("compiler", "use_cache", value=False) Config.set("cache", value="unique") Config.set("optimizer", "transform_on_call", value=False) Config.set("optimizer", "interface", value=None) Config.set("optimizer", "autooptimize", value=False) if vendor == "xilinx": Config.set("compiler", "fpga_vendor", value="xilinx") Config.set("compiler", "xilinx", "mode", value="simulation") # Simulation in software print_status(f"{base_name} [Xilinx]: Running simulation.") if "rtl" in path.parts: Config.set("compiler", "xilinx", "mode", value="hardware_emulation") if "LIBRARY_PATH" not in os.environ: os.environ["LIBRARY_PATH"] = "" library_path_backup = None else: library_path_backup = os.environ["LIBRARY_PATH"] os.environ["LIBRARY_PATH"] += ":/usr/lib/x86_64-linux-gnu" sdfgs = test_function() if "rtl" in path.parts: if library_path_backup is None: del os.environ["LIBRARY_PATH"] else: os.environ["LIBRARY_PATH"] = library_path_backup if sdfgs is None: raise_error("No SDFG(s) returned by FPGA test.") elif isinstance(sdfgs, SDFG): sdfgs = [sdfgs] print_success(f"{base_name} [Xilinx]: " "Simulation successful.") for sdfg in sdfgs: build_folder = Path(sdfg.build_folder) / "build" if not build_folder.exists(): raise_error(f"Build folder {build_folder} " f"not found for {base_name}.") # High-level synthesis if run_synthesis: print_status(f"{base_name} [Xilinx]: Running high-level " f"synthesis for {sdfg.name}.") try: proc = sp.Popen(["make", "synthesis"], cwd=build_folder, stdout=sp.PIPE, stderr=sp.PIPE, encoding="utf=8") syn_out, syn_err = proc.communicate( timeout=TEST_TIMEOUT) except sp.TimeoutExpired: dump_logs(proc) raise_error(f"{base_name} [Xilinx]: High-level " f"synthesis timed out after " f"{TEST_TIMEOUT} seconds.") if proc.returncode != 0: dump_logs(proc) raise_error(f"{base_name} [Xilinx]: High-level " f"synthesis failed.") print_success(f"{base_name} [Xilinx]: High-level " f"synthesis successful for " f"{sdfg.name}.") open(build_folder / "synthesis.out", "w").write(syn_out) open(build_folder / "synthesis.err", "w").write(syn_err) # Check if loops were pipelined with II=1 if assert_ii_1: loops_found = False for f in build_folder.iterdir(): if "hls.log" in f.name: hls_log = f break else: raise_error(f"{base_name} [Xilinx]: HLS " f"log file not found.") hls_log = open(hls_log, "r").read() for m in re.finditer(r"Final II = ([0-9]+)", hls_log): loops_found = True if int(m.group(1)) != 1: dump_logs((syn_out, syn_err)) raise_error(f"{base_name} [Xilinx]: " f"Failed to achieve II=1.") if not loops_found: dump_logs((syn_out, syn_err)) raise_error(f"{base_name} [Xilinx]: No " f"pipelined loops found.") print_success(f"{base_name} [Xilinx]: II=1 " f"achieved.") elif vendor == "intel_fpga": # Set environment variables Config.set("compiler", "fpga_vendor", value="intel_fpga") Config.set("compiler", "default_data_types", value="C") Config.set("compiler", "intel_fpga", "mode", value="emulator") # Simulation in software print_status(f"{base_name} [Intel FPGA]: Running " f"emulation.") test_function() print_success(f"{base_name} [Intel FPGA]: Emulation " f"successful.") else: raise ValueError(f"Unrecognized vendor {vendor}.") def fpga_test(run_synthesis: bool = True, assert_ii_1: bool = True, xilinx: bool = True, intel: bool = True): """ Decorator to run an FPGA test with pytest, setting the appropriate variables and performing additional checks, such as running HLS and asserting II=1. The test function must return an SDFG or a list of SDFGs that will be used for this check. :param run_synthesis: Whether to run HLS for Xilinx tests (Intel tests will always run synthesis). :param assert_ii_1: Assert that all loops have been fully pipelined (currently only implemented for Xilinx). :param xilinx: Run as a Xilinx test. :param intel: Run as an Intel test. """ # Check arguments if not xilinx and not intel: raise ValueError("FPGA test must be run for Xilinx, Intel, or both.") pytest_params = [] if xilinx: pytest_params.append("xilinx") if intel: pytest_params.append("intel_fpga") def decorator(test_function: Callable): @pytest.mark.fpga @pytest.mark.parametrize("vendor", pytest_params) def wrapper(vendor: Optional[str]): if vendor == None: vendor = Config.get("compiler", "fpga_vendor") p = FPGATestProcess(target=_run_fpga_test, args=(vendor, test_function, run_synthesis, assert_ii_1)) p.start() p.join(timeout=TEST_TIMEOUT) if p.is_alive(): p.kill() raise_error(f"Test {Colors.UNDERLINE}{test_function.__name__}" f"{Colors.END} timed out.") if p.exception: raise p.exception return wrapper return decorator def xilinx_test(*args, **kwargs): return fpga_test(*args, xilinx=True, intel=False, **kwargs) def intel_fpga_test(*args, **kwargs): return fpga_test(*args, xilinx=False, intel=True, **kwargs) def import_sample(path: Union[Path, str]): """ Import a Python file from the samples directory as a module so it can be used in a test. :param path: Path relative to the DaCe samples directory. """ path = Path(__file__).parent.parent / "samples" / Path(path) if not path.exists(): raise ValueError(f"Sample {path} not found.") name = path.stem spec = importlib.util.spec_from_file_location(name, path) loaded_module = importlib.util.module_from_spec(spec) spec.loader.exec_module(loaded_module) return loaded_module

38.127273

112

0.539628

from datetime import datetime import importlib.util import inspect import os import multiprocessing as mp from pathlib import Path import pytest import re import subprocess as sp from typing import Callable, Iterable, Optional, Tuple, Union from dace import SDFG from dace.config import Config, temporary_config TEST_TIMEOUT = 600 class Colors: SUCCESS = "\033[92m" STATUS = "\033[94m" ERROR = "\033[91m" BOLD = "\033[1m" UNDERLINE = "\033[4m" END = "\033[0m" def print_status(message): timestamp = datetime.now().strftime("%H:%M:%S") print(f"{Colors.STATUS}{Colors.BOLD}[{timestamp}]{Colors.END} {message}") def print_success(message): timestamp = datetime.now().strftime("%H:%M:%S") print(f"{Colors.SUCCESS}{Colors.BOLD}[{timestamp}]{Colors.END} {message}") def print_error(message): timestamp = datetime.now().strftime("%H:%M:%S") print(f"{Colors.ERROR}{Colors.BOLD}[{timestamp}]{Colors.END} {message}") def dump_logs(proc_or_logs: Union[sp.CompletedProcess, Tuple[str, str]]): if isinstance(proc_or_logs, tuple): log_out, log_err = proc_or_logs else: proc_or_logs.terminate() proc_or_logs.kill() try: log_out, log_err = proc_or_logs.communicate(timeout=10) except sp.TimeoutExpired: return None if log_out: print(log_out) if log_err: print(log_err) return log_out, log_err class FPGATestProcess(mp.Process): def __init__(self, *args, **kwargs): mp.Process.__init__(self, *args, **kwargs) self._pconn, self._cconn = mp.Pipe() self._exception = None def run(self): try: ret = mp.Process.run(self) self._cconn.send(ret) except Exception as e: self._cconn.send(e) raise e @property def exception(self): if self._pconn.poll(): self._exception = self._pconn.recv() return self._exception class TestFailed(Exception): pass def raise_error(message): print_error(message) raise TestFailed(message) def _run_fpga_test(vendor: str, test_function: Callable, run_synthesis: bool = True, assert_ii_1: bool = True): path = Path(inspect.getfile(test_function)) base_name = f"{path.stem}::{Colors.UNDERLINE}{test_function.__name__}{Colors.END}" with temporary_config(): Config.set("compiler", "use_cache", value=False) Config.set("cache", value="unique") Config.set("optimizer", "transform_on_call", value=False) Config.set("optimizer", "interface", value=None) Config.set("optimizer", "autooptimize", value=False) if vendor == "xilinx": Config.set("compiler", "fpga_vendor", value="xilinx") Config.set("compiler", "xilinx", "mode", value="simulation") print_status(f"{base_name} [Xilinx]: Running simulation.") if "rtl" in path.parts: Config.set("compiler", "xilinx", "mode", value="hardware_emulation") if "LIBRARY_PATH" not in os.environ: os.environ["LIBRARY_PATH"] = "" library_path_backup = None else: library_path_backup = os.environ["LIBRARY_PATH"] os.environ["LIBRARY_PATH"] += ":/usr/lib/x86_64-linux-gnu" sdfgs = test_function() if "rtl" in path.parts: if library_path_backup is None: del os.environ["LIBRARY_PATH"] else: os.environ["LIBRARY_PATH"] = library_path_backup if sdfgs is None: raise_error("No SDFG(s) returned by FPGA test.") elif isinstance(sdfgs, SDFG): sdfgs = [sdfgs] print_success(f"{base_name} [Xilinx]: " "Simulation successful.") for sdfg in sdfgs: build_folder = Path(sdfg.build_folder) / "build" if not build_folder.exists(): raise_error(f"Build folder {build_folder} " f"not found for {base_name}.") if run_synthesis: print_status(f"{base_name} [Xilinx]: Running high-level " f"synthesis for {sdfg.name}.") try: proc = sp.Popen(["make", "synthesis"], cwd=build_folder, stdout=sp.PIPE, stderr=sp.PIPE, encoding="utf=8") syn_out, syn_err = proc.communicate( timeout=TEST_TIMEOUT) except sp.TimeoutExpired: dump_logs(proc) raise_error(f"{base_name} [Xilinx]: High-level " f"synthesis timed out after " f"{TEST_TIMEOUT} seconds.") if proc.returncode != 0: dump_logs(proc) raise_error(f"{base_name} [Xilinx]: High-level " f"synthesis failed.") print_success(f"{base_name} [Xilinx]: High-level " f"synthesis successful for " f"{sdfg.name}.") open(build_folder / "synthesis.out", "w").write(syn_out) open(build_folder / "synthesis.err", "w").write(syn_err) if assert_ii_1: loops_found = False for f in build_folder.iterdir(): if "hls.log" in f.name: hls_log = f break else: raise_error(f"{base_name} [Xilinx]: HLS " f"log file not found.") hls_log = open(hls_log, "r").read() for m in re.finditer(r"Final II = ([0-9]+)", hls_log): loops_found = True if int(m.group(1)) != 1: dump_logs((syn_out, syn_err)) raise_error(f"{base_name} [Xilinx]: " f"Failed to achieve II=1.") if not loops_found: dump_logs((syn_out, syn_err)) raise_error(f"{base_name} [Xilinx]: No " f"pipelined loops found.") print_success(f"{base_name} [Xilinx]: II=1 " f"achieved.") elif vendor == "intel_fpga": Config.set("compiler", "fpga_vendor", value="intel_fpga") Config.set("compiler", "default_data_types", value="C") Config.set("compiler", "intel_fpga", "mode", value="emulator") print_status(f"{base_name} [Intel FPGA]: Running " f"emulation.") test_function() print_success(f"{base_name} [Intel FPGA]: Emulation " f"successful.") else: raise ValueError(f"Unrecognized vendor {vendor}.") def fpga_test(run_synthesis: bool = True, assert_ii_1: bool = True, xilinx: bool = True, intel: bool = True): if not xilinx and not intel: raise ValueError("FPGA test must be run for Xilinx, Intel, or both.") pytest_params = [] if xilinx: pytest_params.append("xilinx") if intel: pytest_params.append("intel_fpga") def decorator(test_function: Callable): @pytest.mark.fpga @pytest.mark.parametrize("vendor", pytest_params) def wrapper(vendor: Optional[str]): if vendor == None: vendor = Config.get("compiler", "fpga_vendor") p = FPGATestProcess(target=_run_fpga_test, args=(vendor, test_function, run_synthesis, assert_ii_1)) p.start() p.join(timeout=TEST_TIMEOUT) if p.is_alive(): p.kill() raise_error(f"Test {Colors.UNDERLINE}{test_function.__name__}" f"{Colors.END} timed out.") if p.exception: raise p.exception return wrapper return decorator def xilinx_test(*args, **kwargs): return fpga_test(*args, xilinx=True, intel=False, **kwargs) def intel_fpga_test(*args, **kwargs): return fpga_test(*args, xilinx=False, intel=True, **kwargs) def import_sample(path: Union[Path, str]): path = Path(__file__).parent.parent / "samples" / Path(path) if not path.exists(): raise ValueError(f"Sample {path} not found.") name = path.stem spec = importlib.util.spec_from_file_location(name, path) loaded_module = importlib.util.module_from_spec(spec) spec.loader.exec_module(loaded_module) return loaded_module

true

f7fbe2dd98394448bba6ae61ada6dc2211884eec

7,972

py

Python

assignments/tutorialSpaceInvaders/tutorialSpaceInvaders.py

noelleclement/pythoncourse

37d6b26aaf7c8d4537f13fd1b92bfa2d5e4d5128

[ "MIT" ]

null

assignments/tutorialSpaceInvaders/tutorialSpaceInvaders.py

noelleclement/pythoncourse

37d6b26aaf7c8d4537f13fd1b92bfa2d5e4d5128

[ "MIT" ]

null

assignments/tutorialSpaceInvaders/tutorialSpaceInvaders.py

noelleclement/pythoncourse

37d6b26aaf7c8d4537f13fd1b92bfa2d5e4d5128

[ "MIT" ]

null

import pyglet from pyglet.window import key, FPSDisplay import numpy as np def preload_image(image): img = pyglet.image.load('resources/sprites/'+image) return img class GameWindow(pyglet.window.Window): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.set_location(150,50) self.frame_rate = 1/60.0 #self.fps_display = FPSDisplay(self) #self.fps_display.label.font_size = 50 #self.main_batch = pyglet.graphics.Batch() self.right = False self.left = False self.player_speed = 300 #self.fire = False #self.player_fire_rate = 0 self.player_img = preload_image('PlayerShip.png') self.player_spr = pyglet.sprite.Sprite(self.player_img) self.player = GameAttribute(500, 100, 0, 0, self.player_spr) #why can you put the image as a later parameter (otherwise it gives an error) ''' self.player_laser_list = [] #niet zo'n goede datastructuur? is wel redelijk opgelost met if statement bij update_player_laser self.player_laser_img = preload_image('laser.png') self.player_laser_sprite = pyglet.sprite.Sprite(self.player_laser_img) self.player_laser = GameAttribute(self.player.pos_x+(self.player.width/2.0-(self.player_laser_img.width/2.0)), self.player.pos_y+self.player.height, 0, 0, self.player_laser_sprite) ''' #rest van player_laser_list staat in def player_fire self.space_img = preload_image('space.jpg') self.space_list = [] self.space_sprite = pyglet.sprite.Sprite(self.space_img) #initiate scrolling background for spc in range(2): self.space_list.append(GameAttribute(0, spc*1200, 0, -100, self.space_sprite)) ''' self.enemy_ship = preload_image('enemyShip_Sh01.png') self.enemy_ship_seq = pyglet.image.ImageGrid(self.enemy_ship, 1, 15, item_width=100, item_height=100) self.enemy_ship_anim = pyglet.image.Animation.from_image_sequence(self.enemy_ship_seq[0:], 0.1, loop=True) self.enemy_ship_sprite = pyglet.sprite.Sprite(self.enemy_ship_anim, np.random.randint(200,600), np.random.randint(500,800), batch=self.main_batch) self.enemy_ship_list = [] for enship in range(5): #miss variabele met hoeveel enemies self.enemy_ship_list.append(self.enemy_ship_sprite) ''' def on_key_press(self, symbol, modifiers): if symbol == key.RIGHT: self.right = True if symbol == key.LEFT: self.left = True #if symbol == key.SPACE: # self.fire = True if symbol == key.ESCAPE: pyglet.app.exit() def on_key_release(self, symbol, modifiers): if symbol == key.RIGHT: #hoe kan dit, waarom moet het niet != key.Right zijn? (geldt ook voor left) self.right = False if symbol == key.LEFT: self.left = False #if symbol == key.SPACE: # self.fire = False def on_draw(self): self.clear() for spc in self.space_list: spc.draw() self.player.draw() #later make an encompassing class with all sprites? #for lsr in self.player_laser_list: # lsr.draw() #self.main_batch.draw() #self.fps_display.draw() def update_player(self, dt): self.player.update(dt) if self.right and self.player.pos_x < 1000 - self.player.width: #hier integreren grootte beeldscherm (hij stopt nu 200 pixels vd rechterkant, relatief tot linkerkant afbeelding) self.player.pos_x += self.player_speed * dt if self.left and self.player.pos_x > 100: #hier integreren grootte beeldscherm + grootte afbeelding (hij stopt nu 100 pixels vd linkerkant, relatief tot linkerkant afbeelding) self.player.pos_x -= self.player_speed * dt ''' def update_player_laser(self, dt): for lsr in self.player_laser_list: lsr.update(dt) lsr.pos_y += 400 * dt if lsr.pos_y > 900: #hier iets toevoegen van beeldschermgrootte self.player_laser_list.remove(lsr) def player_fire(self, dt): self.player_fire_rate -= dt if self.player_fire_rate <= 0: self.player_laser_list.append(self.player_laser) self.player_fire_rate += 0.2 # waarom precies 0.2? kan dit relatief tot iets zijn? ''' def update_space(self,dt): for spc in self.space_list: spc.update(dt) if spc.pos_y <= -1300: #windowsize add self.space_list.remove(spc) #if bottom image is below screen, remove and create new image and add to list (see below) self.space_list.append(GameAttribute(0, 1100, 0, -100, self.space_sprite)) #is this an elegant way to solve the scrolling (creating new background), is the list just growing? def update(self, dt): self.update_player(dt) #if self.fire: # self.player_fire(dt) #self.update_player_laser(dt) self.update_space(dt) class GameAttribute: def __init__(self, pos_x, pos_y, vel_x, vel_y, sprite=None): #ik snap nog niet helemaal waarom de sprite sprite heet (heette eerst image, maar is nu een apart functie voor refactoring) self.pos_x = pos_x #start position xcoor based on given parameter self.pos_y = pos_y #start position xcoor based on given parameter self.vel_x = vel_x #velocity x self.vel_y = vel_y #velocity y if sprite is not None: self.sprite = sprite self.sprite.x = self.pos_x self.sprite.y = self.pos_y self.width = self.sprite.width self.height = self.sprite.height def draw(self): self.sprite.draw() def update(self, dt): #dt = deltatime self.pos_x += self.vel_x*dt self.pos_y += self.vel_y*dt self.sprite.x = self.pos_x self.sprite.y = self.pos_y if __name__ == "__main__": window = GameWindow(1200, 800, "Space Invaders", resizable=False) pyglet.clock.schedule_interval(window.update, window.frame_rate) pyglet.app.run() """ to do: - window relatief tot beeldscherm maken (full screen of x-*iets* etc) (let op background, die is y1200 > lijkt niet uit te maken want heeft meer te maken met image) - ik heb nu sprite functies in het algemene gameattribute staan, dit is niet de bedoeling - framerate meer realtime (zie assignment) - alles door pushen (in algemene attributes een update, maar ook in elke attribute zelf? en dan link je die allemaal en dan hoef je in update alleen maar die ene attribute aan te roepen?) - player als onderdeel van een grotere groep sprites - space aparte attribute - naam: geen window, maar wat je ziet - niet van window erven ( proberen niet te veel afhankelijk van pyglet.window.Window, let op uitbreidbaarheid + vernieuwbaarheid, maar alleen als nog tijd over) - centrale key handler - kijken of def preload_image op een betere plek kan (en als binnen een classe, dan nog bruikbaar erbuiten?) - kijken of bepaalde variabelen centraal bepaald kunnen worden (makkelijk aan te passen) - misschien kan ik de velocity meegeven met de update (dus in gameattribute in de update wordt de velocity dan meegerekend) > doet ie nu al maar dat kan voor player bijv handiger zijn want nu wordt dat allemaal in update player gedaan """

45.039548

234

0.622303

import pyglet from pyglet.window import key, FPSDisplay import numpy as np def preload_image(image): img = pyglet.image.load('resources/sprites/'+image) return img class GameWindow(pyglet.window.Window): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.set_location(150,50) self.frame_rate = 1/60.0 self.right = False self.left = False self.player_speed = 300 self.player_img = preload_image('PlayerShip.png') self.player_spr = pyglet.sprite.Sprite(self.player_img) self.player = GameAttribute(500, 100, 0, 0, self.player_spr) self.space_img = preload_image('space.jpg') self.space_list = [] self.space_sprite = pyglet.sprite.Sprite(self.space_img) for spc in range(2): self.space_list.append(GameAttribute(0, spc*1200, 0, -100, self.space_sprite)) def on_key_press(self, symbol, modifiers): if symbol == key.RIGHT: self.right = True if symbol == key.LEFT: self.left = True if symbol == key.ESCAPE: pyglet.app.exit() def on_key_release(self, symbol, modifiers): if symbol == key.RIGHT: self.right = False if symbol == key.LEFT: self.left = False def on_draw(self): self.clear() for spc in self.space_list: spc.draw() self.player.draw() def update_player(self, dt): self.player.update(dt) if self.right and self.player.pos_x < 1000 - self.player.width: self.player.pos_x += self.player_speed * dt if self.left and self.player.pos_x > 100: self.player.pos_x -= self.player_speed * dt def update_space(self,dt): for spc in self.space_list: spc.update(dt) if spc.pos_y <= -1300: self.space_list.remove(spc) self.space_list.append(GameAttribute(0, 1100, 0, -100, self.space_sprite)) def update(self, dt): self.update_player(dt) self.update_space(dt) class GameAttribute: def __init__(self, pos_x, pos_y, vel_x, vel_y, sprite=None): self.pos_x = pos_x self.pos_y = pos_y self.vel_x = vel_x self.vel_y = vel_y if sprite is not None: self.sprite = sprite self.sprite.x = self.pos_x self.sprite.y = self.pos_y self.width = self.sprite.width self.height = self.sprite.height def draw(self): self.sprite.draw() def update(self, dt): self.pos_x += self.vel_x*dt self.pos_y += self.vel_y*dt self.sprite.x = self.pos_x self.sprite.y = self.pos_y if __name__ == "__main__": window = GameWindow(1200, 800, "Space Invaders", resizable=False) pyglet.clock.schedule_interval(window.update, window.frame_rate) pyglet.app.run()

true

f7fbe2e15e3f55c65d5589bf082424b9e93dbe23

13,708

py

Python

homeassistant/components/emulated_hue/hue_api.py

robin13/home-assistant

4976569e304c23975d34ec88e2dfb94e84ab1f1c

[ "Apache-2.0" ]

7

2018-08-03T10:15:36.000Z

2019-03-25T13:31:55.000Z

homeassistant/components/emulated_hue/hue_api.py

robin13/home-assistant

4976569e304c23975d34ec88e2dfb94e84ab1f1c

[ "Apache-2.0" ]

6

2021-02-08T20:25:50.000Z

2022-03-11T23:27:53.000Z

homeassistant/components/emulated_hue/hue_api.py

robin13/home-assistant

4976569e304c23975d34ec88e2dfb94e84ab1f1c

[ "Apache-2.0" ]

3

2018-09-14T07:34:09.000Z

2018-09-29T12:57:10.000Z

"""Provides a Hue API to control Home Assistant.""" import asyncio import logging from aiohttp import web from homeassistant import core from homeassistant.const import ( ATTR_ENTITY_ID, SERVICE_TURN_OFF, SERVICE_TURN_ON, SERVICE_VOLUME_SET, SERVICE_OPEN_COVER, SERVICE_CLOSE_COVER, STATE_ON, STATE_OFF, HTTP_BAD_REQUEST, HTTP_NOT_FOUND, ATTR_SUPPORTED_FEATURES, ) from homeassistant.components.light import ( ATTR_BRIGHTNESS, SUPPORT_BRIGHTNESS ) from homeassistant.components.media_player import ( ATTR_MEDIA_VOLUME_LEVEL, SUPPORT_VOLUME_SET, ) from homeassistant.components.fan import ( ATTR_SPEED, SUPPORT_SET_SPEED, SPEED_OFF, SPEED_LOW, SPEED_MEDIUM, SPEED_HIGH ) from homeassistant.components.http import HomeAssistantView _LOGGER = logging.getLogger(__name__) HUE_API_STATE_ON = 'on' HUE_API_STATE_BRI = 'bri' class HueUsernameView(HomeAssistantView): """Handle requests to create a username for the emulated hue bridge.""" url = '/api' name = 'emulated_hue:api:create_username' extra_urls = ['/api/'] requires_auth = False @asyncio.coroutine def post(self, request): """Handle a POST request.""" try: data = yield from request.json() except ValueError: return self.json_message('Invalid JSON', HTTP_BAD_REQUEST) if 'devicetype' not in data: return self.json_message('devicetype not specified', HTTP_BAD_REQUEST) return self.json([{'success': {'username': '12345678901234567890'}}]) class HueGroupView(HomeAssistantView): """Group handler to get Logitech Pop working.""" url = '/api/{username}/groups/0/action' name = 'emulated_hue:groups:state' requires_auth = False def __init__(self, config): """Initialize the instance of the view.""" self.config = config @core.callback def put(self, request, username): """Process a request to make the Logitech Pop working.""" return self.json([{ 'error': { 'address': '/groups/0/action/scene', 'type': 7, 'description': 'invalid value, dummy for parameter, scene' } }]) class HueAllLightsStateView(HomeAssistantView): """Handle requests for getting and setting info about entities.""" url = '/api/{username}/lights' name = 'emulated_hue:lights:state' requires_auth = False def __init__(self, config): """Initialize the instance of the view.""" self.config = config @core.callback def get(self, request, username): """Process a request to get the list of available lights.""" hass = request.app['hass'] json_response = {} for entity in hass.states.async_all(): if self.config.is_entity_exposed(entity): state, brightness = get_entity_state(self.config, entity) number = self.config.entity_id_to_number(entity.entity_id) json_response[number] = entity_to_json( self.config, entity, state, brightness) return self.json(json_response) class HueOneLightStateView(HomeAssistantView): """Handle requests for getting and setting info about entities.""" url = '/api/{username}/lights/{entity_id}' name = 'emulated_hue:light:state' requires_auth = False def __init__(self, config): """Initialize the instance of the view.""" self.config = config @core.callback def get(self, request, username, entity_id): """Process a request to get the state of an individual light.""" hass = request.app['hass'] entity_id = self.config.number_to_entity_id(entity_id) entity = hass.states.get(entity_id) if entity is None: _LOGGER.error('Entity not found: %s', entity_id) return web.Response(text="Entity not found", status=404) if not self.config.is_entity_exposed(entity): _LOGGER.error('Entity not exposed: %s', entity_id) return web.Response(text="Entity not exposed", status=404) state, brightness = get_entity_state(self.config, entity) json_response = entity_to_json(self.config, entity, state, brightness) return self.json(json_response) class HueOneLightChangeView(HomeAssistantView): """Handle requests for getting and setting info about entities.""" url = '/api/{username}/lights/{entity_number}/state' name = 'emulated_hue:light:state' requires_auth = False def __init__(self, config): """Initialize the instance of the view.""" self.config = config @asyncio.coroutine def put(self, request, username, entity_number): """Process a request to set the state of an individual light.""" config = self.config hass = request.app['hass'] entity_id = config.number_to_entity_id(entity_number) if entity_id is None: _LOGGER.error('Unknown entity number: %s', entity_number) return self.json_message('Entity not found', HTTP_NOT_FOUND) entity = hass.states.get(entity_id) if entity is None: _LOGGER.error('Entity not found: %s', entity_id) return self.json_message('Entity not found', HTTP_NOT_FOUND) if not config.is_entity_exposed(entity): _LOGGER.error('Entity not exposed: %s', entity_id) return web.Response(text="Entity not exposed", status=404) try: request_json = yield from request.json() except ValueError: _LOGGER.error('Received invalid json') return self.json_message('Invalid JSON', HTTP_BAD_REQUEST) # Parse the request into requested "on" status and brightness parsed = parse_hue_api_put_light_body(request_json, entity) if parsed is None: _LOGGER.error('Unable to parse data: %s', request_json) return web.Response(text="Bad request", status=400) result, brightness = parsed # Choose general HA domain domain = core.DOMAIN # Entity needs separate call to turn on turn_on_needed = False # Convert the resulting "on" status into the service we need to call service = SERVICE_TURN_ON if result else SERVICE_TURN_OFF # Construct what we need to send to the service data = {ATTR_ENTITY_ID: entity_id} # Make sure the entity actually supports brightness entity_features = entity.attributes.get(ATTR_SUPPORTED_FEATURES, 0) if entity.domain == "light": if entity_features & SUPPORT_BRIGHTNESS: if brightness is not None: data[ATTR_BRIGHTNESS] = brightness # If the requested entity is a script add some variables elif entity.domain == "script": data['variables'] = { 'requested_state': STATE_ON if result else STATE_OFF } if brightness is not None: data['variables']['requested_level'] = brightness # If the requested entity is a media player, convert to volume elif entity.domain == "media_player": if entity_features & SUPPORT_VOLUME_SET: if brightness is not None: turn_on_needed = True domain = entity.domain service = SERVICE_VOLUME_SET # Convert 0-100 to 0.0-1.0 data[ATTR_MEDIA_VOLUME_LEVEL] = brightness / 100.0 # If the requested entity is a cover, convert to open_cover/close_cover elif entity.domain == "cover": domain = entity.domain if service == SERVICE_TURN_ON: service = SERVICE_OPEN_COVER else: service = SERVICE_CLOSE_COVER # If the requested entity is a fan, convert to speed elif entity.domain == "fan": if entity_features & SUPPORT_SET_SPEED: if brightness is not None: domain = entity.domain # Convert 0-100 to a fan speed if brightness == 0: data[ATTR_SPEED] = SPEED_OFF elif 0 < brightness <= 33.3: data[ATTR_SPEED] = SPEED_LOW elif 33.3 < brightness <= 66.6: data[ATTR_SPEED] = SPEED_MEDIUM elif 66.6 < brightness <= 100: data[ATTR_SPEED] = SPEED_HIGH if entity.domain in config.off_maps_to_on_domains: # Map the off command to on service = SERVICE_TURN_ON # Caching is required because things like scripts and scenes won't # report as "off" to Alexa if an "off" command is received, because # they'll map to "on". Thus, instead of reporting its actual # status, we report what Alexa will want to see, which is the same # as the actual requested command. config.cached_states[entity_id] = (result, brightness) # Separate call to turn on needed if turn_on_needed: hass.async_add_job(hass.services.async_call( core.DOMAIN, SERVICE_TURN_ON, {ATTR_ENTITY_ID: entity_id}, blocking=True)) hass.async_add_job(hass.services.async_call( domain, service, data, blocking=True)) json_response = \ [create_hue_success_response(entity_id, HUE_API_STATE_ON, result)] if brightness is not None: json_response.append(create_hue_success_response( entity_id, HUE_API_STATE_BRI, brightness)) return self.json(json_response) def parse_hue_api_put_light_body(request_json, entity): """Parse the body of a request to change the state of a light.""" if HUE_API_STATE_ON in request_json: if not isinstance(request_json[HUE_API_STATE_ON], bool): return None if request_json['on']: # Echo requested device be turned on brightness = None report_brightness = False result = True else: # Echo requested device be turned off brightness = None report_brightness = False result = False if HUE_API_STATE_BRI in request_json: try: # Clamp brightness from 0 to 255 brightness = \ max(0, min(int(request_json[HUE_API_STATE_BRI]), 255)) except ValueError: return None # Make sure the entity actually supports brightness entity_features = entity.attributes.get(ATTR_SUPPORTED_FEATURES, 0) if entity.domain == "light": if entity_features & SUPPORT_BRIGHTNESS: report_brightness = True result = (brightness > 0) elif entity.domain == "scene": brightness = None report_brightness = False result = True elif (entity.domain == "script" or entity.domain == "media_player" or entity.domain == "fan"): # Convert 0-255 to 0-100 level = brightness / 255 * 100 brightness = round(level) report_brightness = True result = True return (result, brightness) if report_brightness else (result, None) def get_entity_state(config, entity): """Retrieve and convert state and brightness values for an entity.""" cached_state = config.cached_states.get(entity.entity_id, None) if cached_state is None: final_state = entity.state != STATE_OFF final_brightness = entity.attributes.get( ATTR_BRIGHTNESS, 255 if final_state else 0) # Make sure the entity actually supports brightness entity_features = entity.attributes.get(ATTR_SUPPORTED_FEATURES, 0) if entity.domain == "light": if entity_features & SUPPORT_BRIGHTNESS: pass elif entity.domain == "media_player": level = entity.attributes.get( ATTR_MEDIA_VOLUME_LEVEL, 1.0 if final_state else 0.0) # Convert 0.0-1.0 to 0-255 final_brightness = round(min(1.0, level) * 255) elif entity.domain == "fan": speed = entity.attributes.get(ATTR_SPEED, 0) # Convert 0.0-1.0 to 0-255 final_brightness = 0 if speed == SPEED_LOW: final_brightness = 85 elif speed == SPEED_MEDIUM: final_brightness = 170 elif speed == SPEED_HIGH: final_brightness = 255 else: final_state, final_brightness = cached_state # Make sure brightness is valid if final_brightness is None: final_brightness = 255 if final_state else 0 return (final_state, final_brightness) def entity_to_json(config, entity, is_on=None, brightness=None): """Convert an entity to its Hue bridge JSON representation.""" return { 'state': { HUE_API_STATE_ON: is_on, HUE_API_STATE_BRI: brightness, 'reachable': True }, 'type': 'Dimmable light', 'name': config.get_entity_name(entity), 'modelid': 'HASS123', 'uniqueid': entity.entity_id, 'swversion': '123' } def create_hue_success_response(entity_id, attr, value): """Create a success response for an attribute set on a light.""" success_key = '/lights/{}/state/{}'.format(entity_id, attr) return {'success': {success_key: value}}

35.329897

79

0.618471

import asyncio import logging from aiohttp import web from homeassistant import core from homeassistant.const import ( ATTR_ENTITY_ID, SERVICE_TURN_OFF, SERVICE_TURN_ON, SERVICE_VOLUME_SET, SERVICE_OPEN_COVER, SERVICE_CLOSE_COVER, STATE_ON, STATE_OFF, HTTP_BAD_REQUEST, HTTP_NOT_FOUND, ATTR_SUPPORTED_FEATURES, ) from homeassistant.components.light import ( ATTR_BRIGHTNESS, SUPPORT_BRIGHTNESS ) from homeassistant.components.media_player import ( ATTR_MEDIA_VOLUME_LEVEL, SUPPORT_VOLUME_SET, ) from homeassistant.components.fan import ( ATTR_SPEED, SUPPORT_SET_SPEED, SPEED_OFF, SPEED_LOW, SPEED_MEDIUM, SPEED_HIGH ) from homeassistant.components.http import HomeAssistantView _LOGGER = logging.getLogger(__name__) HUE_API_STATE_ON = 'on' HUE_API_STATE_BRI = 'bri' class HueUsernameView(HomeAssistantView): url = '/api' name = 'emulated_hue:api:create_username' extra_urls = ['/api/'] requires_auth = False @asyncio.coroutine def post(self, request): try: data = yield from request.json() except ValueError: return self.json_message('Invalid JSON', HTTP_BAD_REQUEST) if 'devicetype' not in data: return self.json_message('devicetype not specified', HTTP_BAD_REQUEST) return self.json([{'success': {'username': '12345678901234567890'}}]) class HueGroupView(HomeAssistantView): url = '/api/{username}/groups/0/action' name = 'emulated_hue:groups:state' requires_auth = False def __init__(self, config): self.config = config @core.callback def put(self, request, username): return self.json([{ 'error': { 'address': '/groups/0/action/scene', 'type': 7, 'description': 'invalid value, dummy for parameter, scene' } }]) class HueAllLightsStateView(HomeAssistantView): url = '/api/{username}/lights' name = 'emulated_hue:lights:state' requires_auth = False def __init__(self, config): self.config = config @core.callback def get(self, request, username): hass = request.app['hass'] json_response = {} for entity in hass.states.async_all(): if self.config.is_entity_exposed(entity): state, brightness = get_entity_state(self.config, entity) number = self.config.entity_id_to_number(entity.entity_id) json_response[number] = entity_to_json( self.config, entity, state, brightness) return self.json(json_response) class HueOneLightStateView(HomeAssistantView): url = '/api/{username}/lights/{entity_id}' name = 'emulated_hue:light:state' requires_auth = False def __init__(self, config): self.config = config @core.callback def get(self, request, username, entity_id): hass = request.app['hass'] entity_id = self.config.number_to_entity_id(entity_id) entity = hass.states.get(entity_id) if entity is None: _LOGGER.error('Entity not found: %s', entity_id) return web.Response(text="Entity not found", status=404) if not self.config.is_entity_exposed(entity): _LOGGER.error('Entity not exposed: %s', entity_id) return web.Response(text="Entity not exposed", status=404) state, brightness = get_entity_state(self.config, entity) json_response = entity_to_json(self.config, entity, state, brightness) return self.json(json_response) class HueOneLightChangeView(HomeAssistantView): url = '/api/{username}/lights/{entity_number}/state' name = 'emulated_hue:light:state' requires_auth = False def __init__(self, config): self.config = config @asyncio.coroutine def put(self, request, username, entity_number): config = self.config hass = request.app['hass'] entity_id = config.number_to_entity_id(entity_number) if entity_id is None: _LOGGER.error('Unknown entity number: %s', entity_number) return self.json_message('Entity not found', HTTP_NOT_FOUND) entity = hass.states.get(entity_id) if entity is None: _LOGGER.error('Entity not found: %s', entity_id) return self.json_message('Entity not found', HTTP_NOT_FOUND) if not config.is_entity_exposed(entity): _LOGGER.error('Entity not exposed: %s', entity_id) return web.Response(text="Entity not exposed", status=404) try: request_json = yield from request.json() except ValueError: _LOGGER.error('Received invalid json') return self.json_message('Invalid JSON', HTTP_BAD_REQUEST) parsed = parse_hue_api_put_light_body(request_json, entity) if parsed is None: _LOGGER.error('Unable to parse data: %s', request_json) return web.Response(text="Bad request", status=400) result, brightness = parsed domain = core.DOMAIN turn_on_needed = False service = SERVICE_TURN_ON if result else SERVICE_TURN_OFF data = {ATTR_ENTITY_ID: entity_id} entity_features = entity.attributes.get(ATTR_SUPPORTED_FEATURES, 0) if entity.domain == "light": if entity_features & SUPPORT_BRIGHTNESS: if brightness is not None: data[ATTR_BRIGHTNESS] = brightness elif entity.domain == "script": data['variables'] = { 'requested_state': STATE_ON if result else STATE_OFF } if brightness is not None: data['variables']['requested_level'] = brightness elif entity.domain == "media_player": if entity_features & SUPPORT_VOLUME_SET: if brightness is not None: turn_on_needed = True domain = entity.domain service = SERVICE_VOLUME_SET data[ATTR_MEDIA_VOLUME_LEVEL] = brightness / 100.0 elif entity.domain == "cover": domain = entity.domain if service == SERVICE_TURN_ON: service = SERVICE_OPEN_COVER else: service = SERVICE_CLOSE_COVER elif entity.domain == "fan": if entity_features & SUPPORT_SET_SPEED: if brightness is not None: domain = entity.domain if brightness == 0: data[ATTR_SPEED] = SPEED_OFF elif 0 < brightness <= 33.3: data[ATTR_SPEED] = SPEED_LOW elif 33.3 < brightness <= 66.6: data[ATTR_SPEED] = SPEED_MEDIUM elif 66.6 < brightness <= 100: data[ATTR_SPEED] = SPEED_HIGH if entity.domain in config.off_maps_to_on_domains: service = SERVICE_TURN_ON # report as "off" to Alexa if an "off" command is received, because # they'll map to "on". Thus, instead of reporting its actual config.cached_states[entity_id] = (result, brightness) if turn_on_needed: hass.async_add_job(hass.services.async_call( core.DOMAIN, SERVICE_TURN_ON, {ATTR_ENTITY_ID: entity_id}, blocking=True)) hass.async_add_job(hass.services.async_call( domain, service, data, blocking=True)) json_response = \ [create_hue_success_response(entity_id, HUE_API_STATE_ON, result)] if brightness is not None: json_response.append(create_hue_success_response( entity_id, HUE_API_STATE_BRI, brightness)) return self.json(json_response) def parse_hue_api_put_light_body(request_json, entity): if HUE_API_STATE_ON in request_json: if not isinstance(request_json[HUE_API_STATE_ON], bool): return None if request_json['on']: brightness = None report_brightness = False result = True else: brightness = None report_brightness = False result = False if HUE_API_STATE_BRI in request_json: try: brightness = \ max(0, min(int(request_json[HUE_API_STATE_BRI]), 255)) except ValueError: return None entity_features = entity.attributes.get(ATTR_SUPPORTED_FEATURES, 0) if entity.domain == "light": if entity_features & SUPPORT_BRIGHTNESS: report_brightness = True result = (brightness > 0) elif entity.domain == "scene": brightness = None report_brightness = False result = True elif (entity.domain == "script" or entity.domain == "media_player" or entity.domain == "fan"): level = brightness / 255 * 100 brightness = round(level) report_brightness = True result = True return (result, brightness) if report_brightness else (result, None) def get_entity_state(config, entity): cached_state = config.cached_states.get(entity.entity_id, None) if cached_state is None: final_state = entity.state != STATE_OFF final_brightness = entity.attributes.get( ATTR_BRIGHTNESS, 255 if final_state else 0) entity_features = entity.attributes.get(ATTR_SUPPORTED_FEATURES, 0) if entity.domain == "light": if entity_features & SUPPORT_BRIGHTNESS: pass elif entity.domain == "media_player": level = entity.attributes.get( ATTR_MEDIA_VOLUME_LEVEL, 1.0 if final_state else 0.0) final_brightness = round(min(1.0, level) * 255) elif entity.domain == "fan": speed = entity.attributes.get(ATTR_SPEED, 0) final_brightness = 0 if speed == SPEED_LOW: final_brightness = 85 elif speed == SPEED_MEDIUM: final_brightness = 170 elif speed == SPEED_HIGH: final_brightness = 255 else: final_state, final_brightness = cached_state if final_brightness is None: final_brightness = 255 if final_state else 0 return (final_state, final_brightness) def entity_to_json(config, entity, is_on=None, brightness=None): return { 'state': { HUE_API_STATE_ON: is_on, HUE_API_STATE_BRI: brightness, 'reachable': True }, 'type': 'Dimmable light', 'name': config.get_entity_name(entity), 'modelid': 'HASS123', 'uniqueid': entity.entity_id, 'swversion': '123' } def create_hue_success_response(entity_id, attr, value): success_key = '/lights/{}/state/{}'.format(entity_id, attr) return {'success': {success_key: value}}

true

f7fbe2f0a69c1aae8f05d0d0cc30c8c7142db7e2

965

py

Python

tests/core/test_synonyms.py

IgorZyktin/MediaStorageSystem

df8d260581cb806eb54f320d63aa674c6175c17e

[ "MIT" ]

2

2021-03-06T16:07:30.000Z

2021-03-17T10:27:25.000Z

tests/core/test_synonyms.py

IgorZyktin/MediaStorageSystem

df8d260581cb806eb54f320d63aa674c6175c17e

[ "MIT" ]

null

tests/core/test_synonyms.py

IgorZyktin/MediaStorageSystem

df8d260581cb806eb54f320d63aa674c6175c17e

[ "MIT" ]

null

# -*- coding: utf-8 -*- """Tests. """ import operator from functools import partial, reduce from mss.core.class_synonyms import Synonyms def test_synonyms_creation(synonyms): assert str(synonyms) == 'Synonyms<2 groups>' def test_synonyms_iteration(synonyms): assert list(synonyms) == [frozenset({'b', 'a'}), frozenset({'d', 'c'})] def test_synonyms_from_dict(): inst = Synonyms.from_dict({ 'first comment': ['one', 'two'], 'second_comment': ['three', 'four'], }) assert list(inst) == [frozenset({'one', 'two'}), frozenset({'three', 'four'})] def test_synonyms_sum(): inst1 = Synonyms([['a', 'b'], ['c', 'd']]) inst2 = Synonyms([['c', 'd'], ['e', 'f']]) ref = Synonyms([['a', 'b'], ['c', 'd'], ['e', 'f']]) assert inst1 + inst2 == ref assert inst2 + inst1 == ref _sum = partial(reduce, operator.add) assert _sum([inst1, inst2]) == ref

25.394737

56

0.553368

import operator from functools import partial, reduce from mss.core.class_synonyms import Synonyms def test_synonyms_creation(synonyms): assert str(synonyms) == 'Synonyms<2 groups>' def test_synonyms_iteration(synonyms): assert list(synonyms) == [frozenset({'b', 'a'}), frozenset({'d', 'c'})] def test_synonyms_from_dict(): inst = Synonyms.from_dict({ 'first comment': ['one', 'two'], 'second_comment': ['three', 'four'], }) assert list(inst) == [frozenset({'one', 'two'}), frozenset({'three', 'four'})] def test_synonyms_sum(): inst1 = Synonyms([['a', 'b'], ['c', 'd']]) inst2 = Synonyms([['c', 'd'], ['e', 'f']]) ref = Synonyms([['a', 'b'], ['c', 'd'], ['e', 'f']]) assert inst1 + inst2 == ref assert inst2 + inst1 == ref _sum = partial(reduce, operator.add) assert _sum([inst1, inst2]) == ref

true

f7fbe35fc05575866e02a6b7537f1be5c906cc69

14,642

py

Python

src/train_cf.py

mattiafrak/Processes-Predictions-with-MP-A-Priori-Knowledge

7e1bb94bb2fc535972a351f543be4f0ad8475275

[ "MIT" ]

null

src/train_cf.py

mattiafrak/Processes-Predictions-with-MP-A-Priori-Knowledge

7e1bb94bb2fc535972a351f543be4f0ad8475275

[ "MIT" ]

null

src/train_cf.py

mattiafrak/Processes-Predictions-with-MP-A-Priori-Knowledge

7e1bb94bb2fc535972a351f543be4f0ad8475275

[ "MIT" ]

null

""" this script trains an LSTM model on one of the data files in the data folder of this repository. the input file can be changed to another file from the data folder by changing its name in line 46. it is recommended to run this script on GPU, as recurrent networks are quite computationally intensive. Author: Niek Tax """ from __future__ import print_function, division import copy import csv import os import time from datetime import datetime from itertools import izip import numpy as np from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau from keras.layers import Input, BatchNormalization, LeakyReLU, Dropout from keras.layers.core import Dense from keras.layers.recurrent import LSTM from keras.models import Model from keras.optimizers import Nadam from shared_variables import get_unicode_from_int class TrainCF: def __init__(self): pass @staticmethod def _build_model(max_len, num_features, target_chars): print('Build model...') main_input = Input(shape=(max_len, num_features), name='main_input') processed = main_input processed = Dense(32)(processed) processed = BatchNormalization()(processed) processed = LeakyReLU()(processed) processed = Dropout(0.5)(processed) processed = LSTM(64, return_sequences=False, recurrent_dropout=0.5)(processed) processed = Dense(32)(processed) processed = BatchNormalization()(processed) processed = LeakyReLU()(processed) processed = Dropout(0.5)(processed) act_output = Dense(len(target_chars), activation='softmax', name='act_output')(processed) time_output = Dense(1, activation='sigmoid', name='time_output')(processed) model = Model(main_input, [act_output, time_output]) model.compile(loss={'act_output': 'categorical_crossentropy', 'time_output': 'mse'}, loss_weights=[1.0, 0.0], optimizer='adam') return model @staticmethod def _create_checkpoints_path(log_name, models_folder, fold): folder_path = 'output_files/' + models_folder + '/' + str(fold) + '/models/CF/' + log_name + '/' if not os.path.exists(folder_path): os.makedirs(folder_path) checkpoint_name = folder_path + 'model_{epoch:03d}-{val_loss:.3f}.h5' return checkpoint_name @staticmethod def _train_model(model, checkpoint_name, X, y_a, y_t): model_checkpoint = ModelCheckpoint(checkpoint_name, save_best_only=True) # lr_reducer = ReduceLROnPlateau(factor=0.5, patience=10, verbose=1) early_stopping = EarlyStopping(monitor='val_loss', patience=5) model.fit(X, {'act_output': y_a, 'time_output': y_t}, validation_split=0.2, verbose=2, callbacks=[early_stopping, model_checkpoint], epochs=10) @staticmethod def _load_dataset(log_name): dataset = [] current_case = [] current_case_id = None current_case_start_time = None last_event_time = None csvfile = open('../data2/final_experiments/%s.csv' % log_name, 'r') csv_reader = csv.reader(csvfile, delimiter=',', quotechar='|') next(csv_reader, None) for row in csv_reader: # CaseID, ActivityID, Timestamp, ResourceID case_id = row[0] activity_id = int(row[1]) timestamp = datetime.strptime(row[2], '%Y-%m-%d %H:%M:%S') resource_id = int(row[3]) if case_id != current_case_id: if len(current_case) > 0: dataset.append(current_case) current_case = [] current_case_id = case_id current_case_start_time = timestamp last_event_time = timestamp time_since_case_start = int((timestamp - current_case_start_time).total_seconds()) time_since_last_event = int((timestamp - last_event_time).total_seconds()) time_since_midnight = int( (timestamp - timestamp.replace(hour=0, minute=0, second=0, microsecond=0)).total_seconds()) day_of_week = timestamp.weekday() current_case.append( (activity_id, time_since_case_start, time_since_last_event, time_since_midnight, day_of_week)) print(len(dataset)) @staticmethod def train(log_name, models_folder, folds): # TrainCF._load_dataset(log_name) lines = [] # list of all the activity sequences timeseqs = [] # time sequences (differences between two events) timeseqs2 = [] # time sequences (differences between the current and first) # helper variables last_case = '' line = '' # sequence of activities for one case first_line = True times = [] times2 = [] num_lines = 0 case_start_time = None last_event_time = None csvfile = open('../data2/final_experiments/%s.csv' % log_name, 'r') csv_reader = csv.reader(csvfile, delimiter=',', quotechar='|') next(csv_reader, None) # skip the headers for row in csv_reader: # the rows are "CaseID,ActivityID,CompleteTimestamp" t = time.strptime(row[2], "%Y-%m-%d %H:%M:%S") # creates a datetime object from row[2] if row[0] != last_case: # 'last_case' is to save the last executed case for the loop case_start_time = t last_event_time = t last_case = row[0] if not first_line: # here we actually add the sequences to the lists lines.append(line) timeseqs.append(times) timeseqs2.append(times2) line = '' times = [] times2 = [] num_lines += 1 line += get_unicode_from_int(row[1]) time_since_last_event = datetime.fromtimestamp(time.mktime(t)) - datetime.fromtimestamp( time.mktime(last_event_time)) time_since_case_start = datetime.fromtimestamp(time.mktime(t)) - datetime.fromtimestamp( time.mktime(case_start_time)) time_diff = 86400 * time_since_last_event.days + time_since_last_event.seconds time_diff2 = 86400 * time_since_case_start.days + time_since_case_start.seconds times.append(time_diff) times2.append(time_diff2) last_event_time = t first_line = False # add last case lines.append(line) timeseqs.append(times) timeseqs2.append(times2) num_lines += 1 divisor = 1.0 * np.max([item for sublist in timeseqs for item in sublist]) # average time between events print('divisor: {}'.format(divisor)) divisor2 = 1.0 * np.max([item for sublist in timeseqs2 for item in sublist]) # average time between current and # first events print('divisor2: {}'.format(divisor2)) # separate training data into 2(out of 3) parts elements_per_fold = int(round(num_lines / 3)) many = 0 for i in range(len(lines)): many = many + len(lines[i]) print("average length of the trace: ", many / len(lines)) print("number of traces: ", len(lines)) fold1 = lines[:elements_per_fold] fold2 = lines[elements_per_fold:2 * elements_per_fold] lines = fold1 + fold2 lines = map(lambda x: x + '!', lines) # put delimiter symbol maxlen = max(map(lambda x: len(x), lines)) # find maximum line size # next lines here to get all possible characters for events and annotate them with numbers chars = map(lambda x: set(x), lines) # remove duplicate activities from each separate case chars = list(set().union(*chars)) # creates a list of all the unique activities in the data set chars.sort() # sorts the chars in alphabetical order target_chars = copy.copy(chars) chars.remove('!') print('total chars: {}, target chars: {}'.format(len(chars), len(target_chars))) char_indices = dict((c, i) for i, c in enumerate(chars)) target_char_indices = dict((c, i) for i, c in enumerate(target_chars)) csvfile = open('../data2/final_experiments/%s.csv' % log_name, 'r') csv_reader = csv.reader(csvfile, delimiter=',', quotechar='|') next(csv_reader, None) # skip the headers last_case = '' line = '' first_line = True lines = [] timeseqs = [] timeseqs2 = [] timeseqs3 = [] timeseqs4 = [] times = [] times2 = [] times3 = [] times4 = [] num_lines = 0 case_start_time = None last_event_time = None for row in csv_reader: t = time.strptime(row[2], "%Y-%m-%d %H:%M:%S") # new case starts if row[0] != last_case: case_start_time = t last_event_time = t last_case = row[0] if not first_line: lines.append(line) timeseqs.append(times) timeseqs2.append(times2) timeseqs3.append(times3) timeseqs4.append(times4) line = '' times = [] times2 = [] times3 = [] times4 = [] num_lines += 1 line += get_unicode_from_int(row[1]) time_since_last_event = datetime.fromtimestamp(time.mktime(t)) - datetime.fromtimestamp( time.mktime(last_event_time)) time_since_case_start = datetime.fromtimestamp(time.mktime(t)) - datetime.fromtimestamp( time.mktime(case_start_time)) midnight = datetime.fromtimestamp(time.mktime(t)).replace(hour=0, minute=0, second=0, microsecond=0) timesincemidnight = datetime.fromtimestamp(time.mktime(t)) - midnight time_diff = 86400 * time_since_last_event.days + time_since_last_event.seconds time_diff2 = 86400 * time_since_case_start.days + time_since_case_start.seconds timediff3 = timesincemidnight.seconds # this leaves only time even occurred after midnight timediff4 = datetime.fromtimestamp(time.mktime(t)).weekday() # day of the week times.append(time_diff) times2.append(time_diff2) times3.append(timediff3) times4.append(timediff4) last_event_time = t first_line = False # add last case lines.append(line) timeseqs.append(times) timeseqs2.append(times2) timeseqs3.append(times3) timeseqs4.append(times4) num_lines += 1 elements_per_fold = int(round(num_lines / 3)) lines = lines[:-elements_per_fold] lines_t = timeseqs[:-elements_per_fold] lines_t2 = timeseqs2[:-elements_per_fold] lines_t3 = timeseqs3[:-elements_per_fold] lines_t4 = timeseqs4[:-elements_per_fold] step = 1 sentences = [] softness = 0 next_chars = [] lines = map(lambda x: x + '!', lines) sentences_t = [] sentences_t2 = [] sentences_t3 = [] sentences_t4 = [] next_chars_t = [] next_chars_t2 = [] next_chars_t3 = [] next_chars_t4 = [] for line, line_t, line_t2, line_t3, line_t4 in izip(lines, lines_t, lines_t2, lines_t3, lines_t4): for i in range(0, len(line), step): if i == 0: continue # we add iteratively, first symbol of the line, then two first, three... sentences.append(line[0: i]) sentences_t.append(line_t[0:i]) sentences_t2.append(line_t2[0:i]) sentences_t3.append(line_t3[0:i]) sentences_t4.append(line_t4[0:i]) next_chars.append(line[i]) if i == len(line) - 1: # special case to deal time of end character next_chars_t.append(0) next_chars_t2.append(0) next_chars_t3.append(0) next_chars_t4.append(0) else: next_chars_t.append(line_t[i]) next_chars_t2.append(line_t2[i]) next_chars_t3.append(line_t3[i]) next_chars_t4.append(line_t4[i]) print('nb sequences:', len(sentences)) print('Vectorization...') num_features = len(chars) + 5 print('num features: {}'.format(num_features)) X = np.zeros((len(sentences), maxlen, num_features), dtype=np.float32) y_a = np.zeros((len(sentences), len(target_chars)), dtype=np.float32) y_t = np.zeros((len(sentences)), dtype=np.float32) for i, sentence in enumerate(sentences): leftpad = maxlen - len(sentence) next_t = next_chars_t[i] sentence_t = sentences_t[i] sentence_t2 = sentences_t2[i] sentence_t3 = sentences_t3[i] sentence_t4 = sentences_t4[i] for t, char in enumerate(sentence): # multiset_abstraction = Counter(sentence[:t+1]) for c in chars: if c == char: # this will encode present events to the right places X[i, t + leftpad, char_indices[c]] = 1 X[i, t + leftpad, len(chars)] = t + 1 X[i, t + leftpad, len(chars) + 1] = sentence_t[t] / divisor X[i, t + leftpad, len(chars) + 2] = sentence_t2[t] / divisor2 X[i, t + leftpad, len(chars) + 3] = sentence_t3[t] / 86400 X[i, t + leftpad, len(chars) + 4] = sentence_t4[t] / 7 for c in target_chars: if c == next_chars[i]: y_a[i, target_char_indices[c]] = 1 - softness else: y_a[i, target_char_indices[c]] = softness / (len(target_chars) - 1) y_t[i] = next_t / divisor for fold in range(folds): # model = build_model(max_length, num_features, max_activity_id) model = TrainCF._build_model(maxlen, num_features, target_chars) checkpoint_name = TrainCF._create_checkpoints_path(log_name, models_folder, fold) TrainCF._train_model(model, checkpoint_name, X, y_a, y_t)

41.361582

120

0.588922

from __future__ import print_function, division import copy import csv import os import time from datetime import datetime from itertools import izip import numpy as np from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau from keras.layers import Input, BatchNormalization, LeakyReLU, Dropout from keras.layers.core import Dense from keras.layers.recurrent import LSTM from keras.models import Model from keras.optimizers import Nadam from shared_variables import get_unicode_from_int class TrainCF: def __init__(self): pass @staticmethod def _build_model(max_len, num_features, target_chars): print('Build model...') main_input = Input(shape=(max_len, num_features), name='main_input') processed = main_input processed = Dense(32)(processed) processed = BatchNormalization()(processed) processed = LeakyReLU()(processed) processed = Dropout(0.5)(processed) processed = LSTM(64, return_sequences=False, recurrent_dropout=0.5)(processed) processed = Dense(32)(processed) processed = BatchNormalization()(processed) processed = LeakyReLU()(processed) processed = Dropout(0.5)(processed) act_output = Dense(len(target_chars), activation='softmax', name='act_output')(processed) time_output = Dense(1, activation='sigmoid', name='time_output')(processed) model = Model(main_input, [act_output, time_output]) model.compile(loss={'act_output': 'categorical_crossentropy', 'time_output': 'mse'}, loss_weights=[1.0, 0.0], optimizer='adam') return model @staticmethod def _create_checkpoints_path(log_name, models_folder, fold): folder_path = 'output_files/' + models_folder + '/' + str(fold) + '/models/CF/' + log_name + '/' if not os.path.exists(folder_path): os.makedirs(folder_path) checkpoint_name = folder_path + 'model_{epoch:03d}-{val_loss:.3f}.h5' return checkpoint_name @staticmethod def _train_model(model, checkpoint_name, X, y_a, y_t): model_checkpoint = ModelCheckpoint(checkpoint_name, save_best_only=True) early_stopping = EarlyStopping(monitor='val_loss', patience=5) model.fit(X, {'act_output': y_a, 'time_output': y_t}, validation_split=0.2, verbose=2, callbacks=[early_stopping, model_checkpoint], epochs=10) @staticmethod def _load_dataset(log_name): dataset = [] current_case = [] current_case_id = None current_case_start_time = None last_event_time = None csvfile = open('../data2/final_experiments/%s.csv' % log_name, 'r') csv_reader = csv.reader(csvfile, delimiter=',', quotechar='|') next(csv_reader, None) for row in csv_reader: case_id = row[0] activity_id = int(row[1]) timestamp = datetime.strptime(row[2], '%Y-%m-%d %H:%M:%S') resource_id = int(row[3]) if case_id != current_case_id: if len(current_case) > 0: dataset.append(current_case) current_case = [] current_case_id = case_id current_case_start_time = timestamp last_event_time = timestamp time_since_case_start = int((timestamp - current_case_start_time).total_seconds()) time_since_last_event = int((timestamp - last_event_time).total_seconds()) time_since_midnight = int( (timestamp - timestamp.replace(hour=0, minute=0, second=0, microsecond=0)).total_seconds()) day_of_week = timestamp.weekday() current_case.append( (activity_id, time_since_case_start, time_since_last_event, time_since_midnight, day_of_week)) print(len(dataset)) @staticmethod def train(log_name, models_folder, folds): lines = [] timeseqs = [] timeseqs2 = [] last_case = '' line = '' first_line = True times = [] times2 = [] num_lines = 0 case_start_time = None last_event_time = None csvfile = open('../data2/final_experiments/%s.csv' % log_name, 'r') csv_reader = csv.reader(csvfile, delimiter=',', quotechar='|') next(csv_reader, None) for row in csv_reader: t = time.strptime(row[2], "%Y-%m-%d %H:%M:%S") if row[0] != last_case: case_start_time = t last_event_time = t last_case = row[0] if not first_line: lines.append(line) timeseqs.append(times) timeseqs2.append(times2) line = '' times = [] times2 = [] num_lines += 1 line += get_unicode_from_int(row[1]) time_since_last_event = datetime.fromtimestamp(time.mktime(t)) - datetime.fromtimestamp( time.mktime(last_event_time)) time_since_case_start = datetime.fromtimestamp(time.mktime(t)) - datetime.fromtimestamp( time.mktime(case_start_time)) time_diff = 86400 * time_since_last_event.days + time_since_last_event.seconds time_diff2 = 86400 * time_since_case_start.days + time_since_case_start.seconds times.append(time_diff) times2.append(time_diff2) last_event_time = t first_line = False lines.append(line) timeseqs.append(times) timeseqs2.append(times2) num_lines += 1 divisor = 1.0 * np.max([item for sublist in timeseqs for item in sublist]) print('divisor: {}'.format(divisor)) divisor2 = 1.0 * np.max([item for sublist in timeseqs2 for item in sublist]) print('divisor2: {}'.format(divisor2)) elements_per_fold = int(round(num_lines / 3)) many = 0 for i in range(len(lines)): many = many + len(lines[i]) print("average length of the trace: ", many / len(lines)) print("number of traces: ", len(lines)) fold1 = lines[:elements_per_fold] fold2 = lines[elements_per_fold:2 * elements_per_fold] lines = fold1 + fold2 lines = map(lambda x: x + '!', lines) maxlen = max(map(lambda x: len(x), lines)) chars = map(lambda x: set(x), lines) chars = list(set().union(*chars)) chars.sort() target_chars = copy.copy(chars) chars.remove('!') print('total chars: {}, target chars: {}'.format(len(chars), len(target_chars))) char_indices = dict((c, i) for i, c in enumerate(chars)) target_char_indices = dict((c, i) for i, c in enumerate(target_chars)) csvfile = open('../data2/final_experiments/%s.csv' % log_name, 'r') csv_reader = csv.reader(csvfile, delimiter=',', quotechar='|') next(csv_reader, None) last_case = '' line = '' first_line = True lines = [] timeseqs = [] timeseqs2 = [] timeseqs3 = [] timeseqs4 = [] times = [] times2 = [] times3 = [] times4 = [] num_lines = 0 case_start_time = None last_event_time = None for row in csv_reader: t = time.strptime(row[2], "%Y-%m-%d %H:%M:%S") if row[0] != last_case: case_start_time = t last_event_time = t last_case = row[0] if not first_line: lines.append(line) timeseqs.append(times) timeseqs2.append(times2) timeseqs3.append(times3) timeseqs4.append(times4) line = '' times = [] times2 = [] times3 = [] times4 = [] num_lines += 1 line += get_unicode_from_int(row[1]) time_since_last_event = datetime.fromtimestamp(time.mktime(t)) - datetime.fromtimestamp( time.mktime(last_event_time)) time_since_case_start = datetime.fromtimestamp(time.mktime(t)) - datetime.fromtimestamp( time.mktime(case_start_time)) midnight = datetime.fromtimestamp(time.mktime(t)).replace(hour=0, minute=0, second=0, microsecond=0) timesincemidnight = datetime.fromtimestamp(time.mktime(t)) - midnight time_diff = 86400 * time_since_last_event.days + time_since_last_event.seconds time_diff2 = 86400 * time_since_case_start.days + time_since_case_start.seconds timediff3 = timesincemidnight.seconds timediff4 = datetime.fromtimestamp(time.mktime(t)).weekday() times.append(time_diff) times2.append(time_diff2) times3.append(timediff3) times4.append(timediff4) last_event_time = t first_line = False lines.append(line) timeseqs.append(times) timeseqs2.append(times2) timeseqs3.append(times3) timeseqs4.append(times4) num_lines += 1 elements_per_fold = int(round(num_lines / 3)) lines = lines[:-elements_per_fold] lines_t = timeseqs[:-elements_per_fold] lines_t2 = timeseqs2[:-elements_per_fold] lines_t3 = timeseqs3[:-elements_per_fold] lines_t4 = timeseqs4[:-elements_per_fold] step = 1 sentences = [] softness = 0 next_chars = [] lines = map(lambda x: x + '!', lines) sentences_t = [] sentences_t2 = [] sentences_t3 = [] sentences_t4 = [] next_chars_t = [] next_chars_t2 = [] next_chars_t3 = [] next_chars_t4 = [] for line, line_t, line_t2, line_t3, line_t4 in izip(lines, lines_t, lines_t2, lines_t3, lines_t4): for i in range(0, len(line), step): if i == 0: continue sentences.append(line[0: i]) sentences_t.append(line_t[0:i]) sentences_t2.append(line_t2[0:i]) sentences_t3.append(line_t3[0:i]) sentences_t4.append(line_t4[0:i]) next_chars.append(line[i]) if i == len(line) - 1: next_chars_t.append(0) next_chars_t2.append(0) next_chars_t3.append(0) next_chars_t4.append(0) else: next_chars_t.append(line_t[i]) next_chars_t2.append(line_t2[i]) next_chars_t3.append(line_t3[i]) next_chars_t4.append(line_t4[i]) print('nb sequences:', len(sentences)) print('Vectorization...') num_features = len(chars) + 5 print('num features: {}'.format(num_features)) X = np.zeros((len(sentences), maxlen, num_features), dtype=np.float32) y_a = np.zeros((len(sentences), len(target_chars)), dtype=np.float32) y_t = np.zeros((len(sentences)), dtype=np.float32) for i, sentence in enumerate(sentences): leftpad = maxlen - len(sentence) next_t = next_chars_t[i] sentence_t = sentences_t[i] sentence_t2 = sentences_t2[i] sentence_t3 = sentences_t3[i] sentence_t4 = sentences_t4[i] for t, char in enumerate(sentence): for c in chars: if c == char: X[i, t + leftpad, char_indices[c]] = 1 X[i, t + leftpad, len(chars)] = t + 1 X[i, t + leftpad, len(chars) + 1] = sentence_t[t] / divisor X[i, t + leftpad, len(chars) + 2] = sentence_t2[t] / divisor2 X[i, t + leftpad, len(chars) + 3] = sentence_t3[t] / 86400 X[i, t + leftpad, len(chars) + 4] = sentence_t4[t] / 7 for c in target_chars: if c == next_chars[i]: y_a[i, target_char_indices[c]] = 1 - softness else: y_a[i, target_char_indices[c]] = softness / (len(target_chars) - 1) y_t[i] = next_t / divisor for fold in range(folds): model = TrainCF._build_model(maxlen, num_features, target_chars) checkpoint_name = TrainCF._create_checkpoints_path(log_name, models_folder, fold) TrainCF._train_model(model, checkpoint_name, X, y_a, y_t)

true

f7fbe627150d8e8a0f60ccd57ac8c6f63ee82bee

15,817

py

Python

MayaIntegration/PAIO.py

MontyThibault/Force-Sensor-Integration

9f949439ea0c1116c00af89735c034cda423ae7e

[ "MIT" ]

null

MayaIntegration/PAIO.py

MontyThibault/Force-Sensor-Integration

9f949439ea0c1116c00af89735c034cda423ae7e

[ "MIT" ]

null

MayaIntegration/PAIO.py

MontyThibault/Force-Sensor-Integration

9f949439ea0c1116c00af89735c034cda423ae7e

[ "MIT" ]

null

from ctypes import * import unittest class Singleton(object): """Ensures only one instance of subtypes exist at a time.""" _instance = None def __new__(class_, *args, **kwargs): if not isinstance(class_._instance, class_): class_._instance = object.__new__(class_, *args, **kwargs) return class_._instance class AIO(Singleton): """ This class overrides the default attribute lookup of the aio DLL library in to implement automated error catching for all functions, assuming the common interface is a zero-valued return. Alternatively, the original functions can still be called via `aio._raw.f()""" # Should be on system path if device drivers were installed properly _raw = cdll.LoadLibrary('CAIO.dll') class ErrorWrapper: def __init__(self, f): self.f = f def __call__(self, *args, **kwargs): errorCode = self.f(*args, **kwargs) if errorCode != 0: # Max error string length of 200 characters errorStringBuffer = create_string_buffer(200) # Copy error code into buffer AIO()._raw.AioGetErrorString(errorCode, errorStringBuffer) print("aio.%s = %s : %s" % (self.f.__name__, errorCode, errorStringBuffer.value.decode("utf-8"))) return errorCode def __getattr__(self, key): if key in consts: # For constants (defined below) return consts[key] else: # For functions return self.ErrorWrapper(getattr(self._raw, key)) class AIODevice(object): """ Sensor instance with single device name and device ID (each device has up to 32 channels). """ aio = AIO() def __init__(self, name = b"AIO000"): self.deviceName = c_char_p(name) self.deviceID = c_short() def Init(self): """ This is the only function in the AIO library that does not follow the convention of passing DeviceID as the first argument; this is the function that generates the ID and sets the value via the pointer in the second argument. It gets a special name without a `Aio` prefix. """ self.aio.AioInit(self.deviceName, byref(self.deviceID)) def _callableWithID(self, f): def composite(*args, **kwargs): f(self.deviceID, *args, **kwargs) return composite def __getattr__(self, key): """ Eliminates "deviceID" syntax from all AIO functions. Note: C function usage & examples are specified in the Contec help files """ return self._callableWithID(getattr(self.aio, key)) class Tests(object): def test_paio_singleton_interface(self): x = AIO() x.test = 10 y = AIO() assert y.test == 10 def test_access_PAIO_functions(self): assert hasattr(AIO(), 'AioInit') assert hasattr(AIO(), 'AioGetErrorString') def test_access_PAIO_constants(self): assert hasattr(AIO(), 'PM10') assert hasattr(AIO(), 'AIOM_CNTM_CARRY_BORROW') def test_PAIO_error_wrapping(self): def alwaysFails(): return 10101 AIO()._raw.bad = alwaysFails assert AIO().bad() == 10101 del AIO()._raw.bad def test_PAIO_device_argument_elision(self): def noArguments(deviceID): assert deviceID == 123 AIO()._raw.noargs = noArguments d = AIODevice('name') d.deviceID = 123 d.noargs() del AIO()._raw.noargs consts = { # # ---------------------------------------------------------------------------------------------- # # External Control Signal # # ---------------------------------------------------------------------------------------------- 'AIO_AIF_CLOCK': 0, # Analog input external clock 'AIO_AIF_START': 1, # Analog input external start trigger 'AIO_AIF_STOP': 2, # Analog input external stop trigger 'AIO_AOF_CLOCK': 3, # Analog output external clock 'AIO_AOF_START': 4, # Analog output external start trigger 'AIO_AOF_STOP': 5, # Analog output external stop trigger # # ---------------------------------------------------------------------------------------------- # # Input/Output Range # # ---------------------------------------------------------------------------------------------- 'PM10': 0, # +/-10V 'PM5': 1, # +/-5V 'PM25': 2, # +/-2.5V 'PM125': 3, # +/-1.25V 'PM1': 4, # +/-1V 'PM0625': 5, # +/-0.625V 'PM05': 6, # +/-0.5V 'PM03125': 7, # +/-0.3125V 'PM025': 8, # +/-0.25V 'PM0125': 9, # +/-0.125V 'PM01': 10, # +/-0.1V 'PM005': 11, # +/-0.05V 'PM0025': 12, # +/-0.025V 'PM00125': 13, # +/-0.0125V 'PM001': 14, # +/-0.01V 'P10': 50, # 0 ~ 10V 'P5': 51, # 0 ~ 5V 'P4095': 52, # 0 ~ 4.095V 'P25': 53, # 0 ~ 2.5V 'P125': 54, # 0 ~ 1.25V 'P1': 55, # 0 ~ 1V 'P05': 56, # 0 ~ 0.5V 'P025': 57, # 0 ~ 0.25V 'P01': 58, # 0 ~ 0.1V 'P005': 59, # 0 ~ 0.05V 'P0025': 60, # 0 ~ 0.025V 'P00125': 61, # 0 ~ 0.0125V 'P001': 62, # 0 ~ 0.01V 'P20MA': 100, # 0 ~ 20mA 'P4TO20MA': 101, # 4 ~ 20mA 'P1TO5': 150, # 1 ~ 5V # # ---------------------------------------------------------------------------------------------- # # Analog Input Event # # ---------------------------------------------------------------------------------------------- 'AIE_START': 0x00000002, # Event that AD converting start conditions are satisfied 'AIE_RPTEND': 0x00000010, # Event of repeat end 'AIE_END': 0x00000020, # Event of device operation end 'AIE_DATA_NUM': 0x00000080, # Event that data of the specified sampling times are stored 'AIE_DATA_TSF': 0x00000100, # Event that data of the specified number are transferred 'AIE_OFERR': 0x00010000, # Event of Overflow 'AIE_SCERR': 0x00020000, # Event of sampling clock error 'AIE_ADERR': 0x00040000, # Event of AD converting error # # ---------------------------------------------------------------------------------------------- # # Analog Output Event # # ---------------------------------------------------------------------------------------------- 'AOE_START': 0x00000002, # Event that DA converting start conditions are satisfied 'AOE_RPTEND': 0x00000010, # Event of repeat end 'AOE_END': 0x00000020, # Event of device operation end 'AOE_DATA_NUM': 0x00000080, # Event that data of the specified sampling times are output 'AOE_DATA_TSF': 0x00000100, # Event that data of the specified number are transferred 'AOE_SCERR': 0x00020000, # Event of sampling clock error 'AOE_DAERR': 0x00040000, # Event of DA converting error # # ---------------------------------------------------------------------------------------------- # # Counter Event # # ---------------------------------------------------------------------------------------------- 'CNTE_DATA_NUM': 0x00000010, # Event of count comparison match 'CNTE_ORERR': 0x00010000, # Event of count overrun 'CNTE_ERR': 0x00020000, # Counter operating error # # ---------------------------------------------------------------------------------------------- # # Timer Event # # ---------------------------------------------------------------------------------------------- 'TME_INT': 0x00000001, # Event that interval is satisfied # # ---------------------------------------------------------------------------------------------- # # Analog Input Status # # ---------------------------------------------------------------------------------------------- 'AIS_BUSY': 0x00000001, # Device is working 'AIS_START_TRG': 0x00000002, # Wait the start trigger 'AIS_DATA_NUM': 0x00000010, # Store the data of the specified number of samplings 'AIS_OFERR': 0x00010000, # Overflow 'AIS_SCERR': 0x00020000, # Sampling clock error 'AIS_AIERR': 0x00040000, # AD converting error 'AIS_DRVERR': 0x00080000, # Driver spec error # # ---------------------------------------------------------------------------------------------- # # Analog Output Status # # ---------------------------------------------------------------------------------------------- 'AOS_BUSY': 0x00000001, # Device is working 'AOS_START_TRG': 0x00000002, # /Wait the start trigger 'AOS_DATA_NUM': 0x00000010, # Output the data of the specified number of samplings 'AOS_SCERR': 0x00020000, # Sampling clock error 'AOS_AOERR': 0x00040000, # DA converting error 'AOS_DRVERR': 0x00080000, # Driver spec error # # ---------------------------------------------------------------------------------------------- # # Counter Status # # ---------------------------------------------------------------------------------------------- 'CNTS_BUSY': 0x00000001, # Counter is working 'CNTS_DATA_NUM': 0x00000010, # Count comparison match 'CNTS_ORERR': 0x00010000, # Overrun 'CNTS_ERR': 0x00020000, # Count operating error # # ---------------------------------------------------------------------------------------------- # # Analog Input Message # # ---------------------------------------------------------------------------------------------- 'AIOM_AIE_START': 0x1000, # Event that AD converting start condition are satisfied 'AIOM_AIE_RPTEND': 0x1001, # Event of repeat end 'AIOM_AIE_END': 0x1002, # Event of device operation end 'AIOM_AIE_DATA_NUM': 0x1003, # Event that data of the specified sampling times are stored 'AIOM_AIE_DATA_TSF': 0x1007, # Event that data of the specified number are transferred 'AIOM_AIE_OFERR': 0x1004, # Event of Overflow 'AIOM_AIE_SCERR': 0x1005, # Event of sampling clock error 'AIOM_AIE_ADERR': 0x1006, # Event of AD converting error # # ---------------------------------------------------------------------------------------------- # # Analog Output Message # # ---------------------------------------------------------------------------------------------- 'AIOM_AOE_START': 0x1020, # Event that DA converting start conditions are satisfied 'AIOM_AOE_RPTEND': 0x1021, # Event of repeat end 'AIOM_AOE_END': 0x1022, # Event of device operation end 'AIOM_AOE_DATA_NUM': 0x1023, # Event that data of the specified sampling times are output 'AIOM_AOE_DATA_TSF': 0x1027, # Event that data of the specified number are transferred 'AIOM_AOE_SCERR': 0x1025, # Event of sampling clock error 'AIOM_AOE_DAERR': 0x1026, # Event of DA converting error # # ---------------------------------------------------------------------------------------------- # # Counter Message # # ---------------------------------------------------------------------------------------------- 'AIOM_CNTE_DATA_NUM': 0x1042, # Event of count comparison match 'AIOM_CNTE_ORERR': 0x1043, # Event of count overrun 'AIOM_CNTE_ERR': 0x1044, # Event of counter operating error # # ---------------------------------------------------------------------------------------------- # # Timer Message # # ---------------------------------------------------------------------------------------------- 'AIOM_TME_INT': 0x1060, # Event that interval is satisfied # # ---------------------------------------------------------------------------------------------- # # Analog Input Attached Data # # ---------------------------------------------------------------------------------------------- 'AIAT_AI': 0x00000001, # Analog input attached information 'AIAT_AO0': 0x00000100, # Analong output data 'AIAT_DIO0': 0x00010000, # Digital input and output data 'AIAT_CNT0': 0x01000000, # Data of counter channel 0 'AIAT_CNT1': 0x02000000, # Data of counter channel 1 # # ---------------------------------------------------------------------------------------------- # # Counter Action Mode # # ---------------------------------------------------------------------------------------------- 'CNT_LOADPRESET': 0x0000001, # Load the preset count value 'CNT_LOADCOMP': 0x0000002, # Load the count comparison value # # ---------------------------------------------------------------------------------------------- # # Event Controller Destination Signal # # ---------------------------------------------------------------------------------------------- 'AIOECU_DEST_AI_CLK': 4, # Analog input sampling clock 'AIOECU_DEST_AI_START': 0, # Analog input converting start signal 'AIOECU_DEST_AI_STOP': 2, # Analog input converting stop signal 'AIOECU_DEST_AO_CLK': 36, # Analog output sampling clock 'AIOECU_DEST_AO_START': 32, # Analog output converting start signal 'AIOECU_DEST_AO_STOP': 34, # Analog output converting stop signal 'AIOECU_DEST_CNT0_UPCLK': 134, # Up clock signal of counter 0 'AIOECU_DEST_CNT1_UPCLK': 135, # Up clock signal of counter 1 'AIOECU_DEST_CNT0_START': 128, # Start signal of counter 0, timer 0 'AIOECU_DEST_CNT1_START': 129, # Start signal of counter 1, timer 1 'AIOECU_DEST_CNT0_STOP': 130, # Stop signal of counter 0, timer 0 'AIOECU_DEST_CNT1_STOP': 131, # Stop signal of counter 1, timer 1 'AIOECU_DEST_MASTER1': 104, # Synchronization bus master signal 1 'AIOECU_DEST_MASTER2': 105, # Synchronization bus master signal 2 'AIOECU_DEST_MASTER3': 106, # Synchronization bus master signal 3 # # ---------------------------------------------------------------------------------------------- # # Event Controller Source Signal # # ---------------------------------------------------------------------------------------------- 'AIOECU_SRC_OPEN': -1, # Not connect 'AIOECU_SRC_AI_CLK': 4, # Analog input internal clock signal 'AIOECU_SRC_AI_EXTCLK': 146, # Analog input external clock signal 'AIOECU_SRC_AI_TRGSTART': 144, # Analog input external trigger start signal 'AIOECU_SRC_AI_LVSTART': 28, # Analog input level trigger start signal 'AIOECU_SRC_AI_STOP': 17, # Analog input signal that data of the specified sampling times have been converted (No delay) 'AIOECU_SRC_AI_STOP_DELAY': 18, # Analog input signal that data of the specified sampling times have been converted (delay) 'AIOECU_SRC_AI_LVSTOP': 29, # Analog input level trigger stop signal 'AIOECU_SRC_AI_TRGSTOP': 145, # Analog input external trigger stop signal 'AIOECU_SRC_AO_CLK': 66, # Analog output internal clock signal 'AIOECU_SRC_AO_EXTCLK': 149, # Analog output external clock signal 'AIOECU_SRC_AO_TRGSTART': 147, # Analog output external trigger start signal 'AIOECU_SRC_AO_STOP_FIFO': 352, # Analog output signal that data of the specified sampling times have been output (FIFO) 'AIOECU_SRC_AO_STOP_RING': 80, # Analog output signal that data of the specified sampling times have been output (RING) 'AIOECU_SRC_AO_TRGSTOP': 148, # Analog output external trigger stop signal 'AIOECU_SRC_CNT0_UPCLK': 150, # Up clock signal of counter 0 'AIOECU_SRC_CNT1_UPCLK': 152, # Up clock signal of counter 1 'AIOECU_SRC_CNT0_CMP': 288, # Count comparison match of counter 0 'AIOECU_SRC_CNT1_CMP': 289, # Count comparison match of counter 1 'AIOECU_SRC_SLAVE1': 136, # Synchronization bus master signal 1 'AIOECU_SRC_SLAVE2': 137, # Synchronization bus master signal 2 'AIOECU_SRC_SLAVE3': 138, # Synchronization bus master signal 3 'AIOECU_SRC_START': 384, # Ai, Ao, Cnt, Tm software start signal 'AIOECU_SRC_STOP': 385, # Ai, Ao, Cnt, Tm software stop signal # # ---------------------------------------------------------------------------------------------- # # Mdevice counter Message # # ---------------------------------------------------------------------------------------------- 'AIOM_CNTM_COUNTUP_CH0': 0x1070, # COUNTUP, CHANNEL No.0 'AIOM_CNTM_COUNTUP_CH1': 0x1071, # " 1 'AIOM_CNTM_TIME_UP': 0x1090, # Timer 'AIOM_CNTM_COUNTER_ERROR': 0x1091, # Counter error 'AIOM_CNTM_CARRY_BORROW': 0x1092 # Carry/Borrow }

45.979651

124

0.52981

from ctypes import * import unittest class Singleton(object): _instance = None def __new__(class_, *args, **kwargs): if not isinstance(class_._instance, class_): class_._instance = object.__new__(class_, *args, **kwargs) return class_._instance class AIO(Singleton): _raw = cdll.LoadLibrary('CAIO.dll') class ErrorWrapper: def __init__(self, f): self.f = f def __call__(self, *args, **kwargs): errorCode = self.f(*args, **kwargs) if errorCode != 0: errorStringBuffer = create_string_buffer(200) AIO()._raw.AioGetErrorString(errorCode, errorStringBuffer) print("aio.%s = %s : %s" % (self.f.__name__, errorCode, errorStringBuffer.value.decode("utf-8"))) return errorCode def __getattr__(self, key): if key in consts: return consts[key] else: return self.ErrorWrapper(getattr(self._raw, key)) class AIODevice(object): aio = AIO() def __init__(self, name = b"AIO000"): self.deviceName = c_char_p(name) self.deviceID = c_short() def Init(self): self.aio.AioInit(self.deviceName, byref(self.deviceID)) def _callableWithID(self, f): def composite(*args, **kwargs): f(self.deviceID, *args, **kwargs) return composite def __getattr__(self, key): return self._callableWithID(getattr(self.aio, key)) class Tests(object): def test_paio_singleton_interface(self): x = AIO() x.test = 10 y = AIO() assert y.test == 10 def test_access_PAIO_functions(self): assert hasattr(AIO(), 'AioInit') assert hasattr(AIO(), 'AioGetErrorString') def test_access_PAIO_constants(self): assert hasattr(AIO(), 'PM10') assert hasattr(AIO(), 'AIOM_CNTM_CARRY_BORROW') def test_PAIO_error_wrapping(self): def alwaysFails(): return 10101 AIO()._raw.bad = alwaysFails assert AIO().bad() == 10101 del AIO()._raw.bad def test_PAIO_device_argument_elision(self): def noArguments(deviceID): assert deviceID == 123 AIO()._raw.noargs = noArguments d = AIODevice('name') d.deviceID = 123 d.noargs() del AIO()._raw.noargs consts = { 'P5': 51, 'P4095': 52, 'P25': 53, 'P125': 54, 'P1': 55, 'P05': 56, 'P025': 57, 'P01': 58, 'P005': 59, 'P0025': 60, 'P00125': 61, 'P001': 62, 'P20MA': 100, 'P4TO20MA': 101, 'P1TO5': 150, x00040000,

true

f7fbe6a159e62b037f1ac3d3c3112fb41bc6e8c5

2,395

py

Python

Dataload/dataload_SST_binary.py

TTdeveloping/text_classification

b8e5ae22de6441479151efcd79a8d4899a1b1fd5

[ "Apache-2.0" ]

3

2019-04-13T08:29:09.000Z

2021-11-11T02:55:24.000Z

Dataload/dataload_SST_binary.py

TTdeveloping/text_classification

b8e5ae22de6441479151efcd79a8d4899a1b1fd5

[ "Apache-2.0" ]

null

Dataload/dataload_SST_binary.py

TTdeveloping/text_classification

b8e5ae22de6441479151efcd79a8d4899a1b1fd5

[ "Apache-2.0" ]

null

import random import sys from Dataload.Instance import Instance class DataLoader(): def __init__(self, path, shuffle, config): """ :param path: :param shuffle: :param config: :return: """ print("Loading data:......") self.data_list = [] self.max_count = config.max_count self.path = path self.shuffle = shuffle def dataload(self): """ :return: """ path = self.path shuffle = self.shuffle assert isinstance(path,list,), "path must be in list"#instanec()指定对象是某种特定的形式 print('data path{}'.format(path)) for data_id in range(len(path)): print("load data form{}".format(path[data_id])) insts = self._Load_Each_Data(path=path[data_id], shuffle=shuffle) if shuffle is True and data_id == 0: print("shuffle train data......") random.shuffle(insts) self.data_list.append(insts)#把三个文件的标签列表和单词列表都放在里边 # return train/dev/test data if len(self.data_list) == 3: return self.data_list[0], self.data_list[1], self.data_list[2] elif len(self.data_list) == 2: return self.data_list[0], self.data_list[1] def _Load_Each_Data(self,path=None,shuffle=False): """ :param path: :param shuffle: :return: """ assert path is not None, "the data is not allow empty" insts=[] now_lines = 0 with open(path,encoding="utf-8") as f: for line in f.readlines(): line = line.strip() line = line.split() inst = Instance() now_lines += 1 if now_lines % 20 == 0: # print("just only handle {} datas".format(now_lines)) sys.stdout.write("\rreading the {} line\t".format(now_lines)) label=line[0] word=line[1:] if label not in ["0", "1"]: print("Error line:", "".join(line)) continue inst.words=word inst.labels.append(label) inst.words_size = len(inst.words) insts.append(inst) if len(insts) == self.max_count: break return insts

30.705128

84

0.508977

import random import sys from Dataload.Instance import Instance class DataLoader(): def __init__(self, path, shuffle, config): print("Loading data:......") self.data_list = [] self.max_count = config.max_count self.path = path self.shuffle = shuffle def dataload(self): path = self.path shuffle = self.shuffle assert isinstance(path,list,), "path must be in list" print('data path{}'.format(path)) for data_id in range(len(path)): print("load data form{}".format(path[data_id])) insts = self._Load_Each_Data(path=path[data_id], shuffle=shuffle) if shuffle is True and data_id == 0: print("shuffle train data......") random.shuffle(insts) self.data_list.append(insts) if len(self.data_list) == 3: return self.data_list[0], self.data_list[1], self.data_list[2] elif len(self.data_list) == 2: return self.data_list[0], self.data_list[1] def _Load_Each_Data(self,path=None,shuffle=False): assert path is not None, "the data is not allow empty" insts=[] now_lines = 0 with open(path,encoding="utf-8") as f: for line in f.readlines(): line = line.strip() line = line.split() inst = Instance() now_lines += 1 if now_lines % 20 == 0: sys.stdout.write("\rreading the {} line\t".format(now_lines)) label=line[0] word=line[1:] if label not in ["0", "1"]: print("Error line:", "".join(line)) continue inst.words=word inst.labels.append(label) inst.words_size = len(inst.words) insts.append(inst) if len(insts) == self.max_count: break return insts

true

f7fbe8a4d328c05c5e4599deeee2b1ff3d5f0605

461

py

Python

tcutils/pkgs/syn_ack_test/syn_server.py

vkolli/contrail-test-perf

db04b8924a2c330baabe3059788b149d957a7d67

[ "Apache-2.0" ]

1

2017-06-13T04:42:34.000Z

tcutils/pkgs/syn_ack_test/syn_server.py

vkolli/contrail-test-perf

db04b8924a2c330baabe3059788b149d957a7d67

[ "Apache-2.0" ]

null

tcutils/pkgs/syn_ack_test/syn_server.py

vkolli/contrail-test-perf

db04b8924a2c330baabe3059788b149d957a7d67

[ "Apache-2.0" ]

null

#! /usr/bin/env python from scapy.all import * from time import sleep import sys ip_remote = sys.argv[1] ip_local = sys.argv[2] os.system( 'iptables -A OUTPUT -p tcp --tcp-flags RST RST -s %s -j DROP' % ip_local) server = conf.L3socket(filter='host %s' % ip_remote) ip=IP(dst=ip_remote) SYN = server.recv() sleep(182) SYNACK = ip/TCP(sport=SYN.dport, dport=SYN.sport, flags="SA", seq=1001, ack=SYN.seq + 1) sr1(SYNACK) print "SUCCESS"

20.954545

88

0.665944

from scapy.all import * from time import sleep import sys ip_remote = sys.argv[1] ip_local = sys.argv[2] os.system( 'iptables -A OUTPUT -p tcp --tcp-flags RST RST -s %s -j DROP' % ip_local) server = conf.L3socket(filter='host %s' % ip_remote) ip=IP(dst=ip_remote) SYN = server.recv() sleep(182) SYNACK = ip/TCP(sport=SYN.dport, dport=SYN.sport, flags="SA", seq=1001, ack=SYN.seq + 1) sr1(SYNACK) print "SUCCESS"

false

true

f7fbe93ae2eb83e5f74d3936c436a08e047b8741

3,944

py

Python

runner/datacker_runner/tests/test_runner.py

tsptoni/datacker

e2989aa9103121e5fd35a38ca061d03e298a43b9

[ "BSD-3-Clause" ]

4

2021-01-26T21:45:28.000Z

2021-02-02T11:45:15.000Z

runner/datacker_runner/tests/test_runner.py

tsptoni/datacker

e2989aa9103121e5fd35a38ca061d03e298a43b9

[ "BSD-3-Clause" ]

1

2021-02-02T11:16:32.000Z

runner/datacker_runner/tests/test_runner.py

tsptoni/datacker

e2989aa9103121e5fd35a38ca061d03e298a43b9

[ "BSD-3-Clause" ]

1

2021-02-02T10:37:29.000Z

import sys import io import unittest from unittest.mock import patch, mock_open from pathlib import Path from ..run import get_config, get_parameters, run class TestGetConfig(unittest.TestCase): def test_get_config_defaults(self): environment = {} expected = { "NOTEBOOK_NAME": "main", "NOTEBOOKS_PATH": Path("/notebooks"), "OUTPUT_PATH": Path("/output"), "OUTPUT_PREFIX": "%Y%m%d%H%M%S%f_", "OUTPUT_STDOUT": False, } result = get_config(environment) self.assertEqual(result, expected) def test_get_config(self): environment = { "NOTEBOOK_NAME": "test_notebook", "NOTEBOOKS_PATH": "/test_notebooks", "OUTPUT_PATH": "/test_output", "OUTPUT_PREFIX": "%Y%m%d_", "OUTPUT_STDOUT": "true", } expected = { "NOTEBOOK_NAME": "test_notebook", "NOTEBOOKS_PATH": Path("/test_notebooks"), "OUTPUT_PATH": Path("/test_output"), "OUTPUT_PREFIX": "%Y%m%d_", "OUTPUT_STDOUT": True, } result = get_config(environment) self.assertEqual(result, expected) class TestGetParameters(unittest.TestCase): def test_get_json_param(self): environment = {"PARAMETERS": '{"var1": 3.1415, "var2": "Test value"}'} expected = {"var1": 3.1415, "var2": "Test value"} result = get_parameters(environment) self.assertEqual(result, expected) def test_get_param(self): environment = {"PARAM_var1": "3.1415", "PARAM_var2": '"Test value"'} expected = {"var1": 3.1415, "var2": "Test value"} result = get_parameters(environment) self.assertEqual(result, expected) def test_get_param_priority(self): environment = { "PARAMETERS": '{"var1": 3.1415, "var2": "Test value"}', "PARAM_var1": "3", "PARAM_var2": '"Another value"', } expected = {"var1": 3, "var2": "Another value"} result = get_parameters(environment) self.assertEqual(result, expected) class TestRun(unittest.TestCase): @patch("datacker_runner.run.pm.execute_notebook") def test_run(self, mock_pm_execute_notebook): run( parameters={"param1": 1.1, "param2": "A string"}, config={ "NOTEBOOK_NAME": "test_notebook", "NOTEBOOKS_PATH": Path("/test_notebooks"), "OUTPUT_PATH": Path("/test_output"), "OUTPUT_PREFIX": "prefix_", "OUTPUT_STDOUT": False, }, ) mock_pm_execute_notebook.assert_called_with( Path("/test_notebooks/test_notebook.ipynb"), Path("/test_output/prefix_test_notebook.ipynb"), parameters={"param1": 1.1, "param2": "A string"}, ) @patch("datacker_runner.run.pm.execute_notebook") def test_output_stdout(self, mock_pm_execute_notebook): saved_stdout = sys.stdout try: output = io.StringIO() sys.stdout = output mock_open_notebook = mock_open(read_data="test_output") with patch("builtins.open", mock_open_notebook): run( parameters={"param1": 1.1, "param2": "A string"}, config={ "NOTEBOOK_NAME": "test_notebook", "NOTEBOOKS_PATH": Path("/test_notebooks"), "OUTPUT_PATH": Path("/test_output"), "OUTPUT_PREFIX": "prefix_", "OUTPUT_STDOUT": True, }, ) mock_open_notebook.assert_called_once_with( Path("/test_output/prefix_test_notebook.ipynb") ) self.assertEqual(output.getvalue(), "test_output\n") finally: sys.stdout = saved_stdout

36.183486

78

0.559331

import sys import io import unittest from unittest.mock import patch, mock_open from pathlib import Path from ..run import get_config, get_parameters, run class TestGetConfig(unittest.TestCase): def test_get_config_defaults(self): environment = {} expected = { "NOTEBOOK_NAME": "main", "NOTEBOOKS_PATH": Path("/notebooks"), "OUTPUT_PATH": Path("/output"), "OUTPUT_PREFIX": "%Y%m%d%H%M%S%f_", "OUTPUT_STDOUT": False, } result = get_config(environment) self.assertEqual(result, expected) def test_get_config(self): environment = { "NOTEBOOK_NAME": "test_notebook", "NOTEBOOKS_PATH": "/test_notebooks", "OUTPUT_PATH": "/test_output", "OUTPUT_PREFIX": "%Y%m%d_", "OUTPUT_STDOUT": "true", } expected = { "NOTEBOOK_NAME": "test_notebook", "NOTEBOOKS_PATH": Path("/test_notebooks"), "OUTPUT_PATH": Path("/test_output"), "OUTPUT_PREFIX": "%Y%m%d_", "OUTPUT_STDOUT": True, } result = get_config(environment) self.assertEqual(result, expected) class TestGetParameters(unittest.TestCase): def test_get_json_param(self): environment = {"PARAMETERS": '{"var1": 3.1415, "var2": "Test value"}'} expected = {"var1": 3.1415, "var2": "Test value"} result = get_parameters(environment) self.assertEqual(result, expected) def test_get_param(self): environment = {"PARAM_var1": "3.1415", "PARAM_var2": '"Test value"'} expected = {"var1": 3.1415, "var2": "Test value"} result = get_parameters(environment) self.assertEqual(result, expected) def test_get_param_priority(self): environment = { "PARAMETERS": '{"var1": 3.1415, "var2": "Test value"}', "PARAM_var1": "3", "PARAM_var2": '"Another value"', } expected = {"var1": 3, "var2": "Another value"} result = get_parameters(environment) self.assertEqual(result, expected) class TestRun(unittest.TestCase): @patch("datacker_runner.run.pm.execute_notebook") def test_run(self, mock_pm_execute_notebook): run( parameters={"param1": 1.1, "param2": "A string"}, config={ "NOTEBOOK_NAME": "test_notebook", "NOTEBOOKS_PATH": Path("/test_notebooks"), "OUTPUT_PATH": Path("/test_output"), "OUTPUT_PREFIX": "prefix_", "OUTPUT_STDOUT": False, }, ) mock_pm_execute_notebook.assert_called_with( Path("/test_notebooks/test_notebook.ipynb"), Path("/test_output/prefix_test_notebook.ipynb"), parameters={"param1": 1.1, "param2": "A string"}, ) @patch("datacker_runner.run.pm.execute_notebook") def test_output_stdout(self, mock_pm_execute_notebook): saved_stdout = sys.stdout try: output = io.StringIO() sys.stdout = output mock_open_notebook = mock_open(read_data="test_output") with patch("builtins.open", mock_open_notebook): run( parameters={"param1": 1.1, "param2": "A string"}, config={ "NOTEBOOK_NAME": "test_notebook", "NOTEBOOKS_PATH": Path("/test_notebooks"), "OUTPUT_PATH": Path("/test_output"), "OUTPUT_PREFIX": "prefix_", "OUTPUT_STDOUT": True, }, ) mock_open_notebook.assert_called_once_with( Path("/test_output/prefix_test_notebook.ipynb") ) self.assertEqual(output.getvalue(), "test_output\n") finally: sys.stdout = saved_stdout

true

f7fbe99146e8b5da4bec7e1aa27780397afc4434

5,397

py

Python

test/end_2_end/test_multiple_events_in_tracker_different_events.py

ryomahan/read-tracardi-api

d0a012fb097ca81daf046b314000301eb54bfad8

[ "MIT" ]

3

2021-11-27T18:03:31.000Z

2022-02-06T21:47:59.000Z

test/end_2_end/test_multiple_events_in_tracker_different_events.py

ryomahan/read-tracardi-api

d0a012fb097ca81daf046b314000301eb54bfad8

[ "MIT" ]

13

2021-11-03T18:15:06.000Z

2022-03-27T22:28:38.000Z

test/end_2_end/test_multiple_events_in_tracker_different_events.py

ryomahan/read-tracardi-api

d0a012fb097ca81daf046b314000301eb54bfad8

[ "MIT" ]

8

2021-11-16T04:07:41.000Z

2022-03-14T14:51:34.000Z

from uuid import uuid4 from tracardi.process_engine.action.v1.flow.start.start_action import StartAction from tracardi.process_engine.action.v1.increase_views_action import IncreaseViewsAction from tracardi.domain.flow import Flow from tracardi.process_engine.action.v1.end_action import EndAction from tracardi.process_engine.action.v1.debug_payload_action import DebugPayloadAction from ..api.test_source import create_event_source from tracardi.service.wf.service.builders import action from ..utils import Endpoint endpoint = Endpoint() def test_source_rule_and_flow(): source_id = str(uuid4()) profile_id = str(uuid4()) flow_id = str(uuid4()) rule_id = str(uuid4()) event_type = 'my-event' session_id = str(uuid4()) try: # Delete profile assert endpoint.delete(f'/profile/{profile_id}').status_code in [200, 404] assert endpoint.delete(f'/rule/{rule_id}').status_code in [200, 404] assert endpoint.delete(f'/flow/{flow_id}').status_code in [200, 404] # Create resource assert create_event_source(source_id, type='javascript', name="End2End test").status_code == 200 assert endpoint.get('/event-sources/refresh').status_code == 200 response = endpoint.post('/rule', data={ "id": rule_id, "name": "Multiple events in one track", "event": { "type": event_type }, "flow": { "id": flow_id, "name": "Multiple events in one track" }, "source": { "id": source_id, "name": "my test source" }, "enabled": True }) assert response.status_code == 200 assert endpoint.get('/rules/refresh').status_code == 200 # Create flows debug = action(DebugPayloadAction, init={"event": {"type": event_type}}) start = action(StartAction) increase_views = action(IncreaseViewsAction) end = action(EndAction) flow = Flow.build("Profile quick update - test", id=flow_id) flow += debug('event') >> start('payload') flow += start('payload') >> increase_views('payload') flow += increase_views('payload') >> end('payload') assert endpoint.post('/flow/production', data=flow.dict()).status_code == 200 assert endpoint.get('/flows/refresh').status_code == 200 # Send event payload = { "source": { "id": source_id }, "session": { "id": session_id }, "profile": { "id": profile_id }, "events": [ {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": event_type}, ], "options": {"profile": True} } response = endpoint.post("/track", data=payload) assert endpoint.get(f'/profiles/refresh').status_code == 200 assert endpoint.get(f'/sessions/refresh').status_code == 200 assert response.status_code == 200 result = response.json() profile_id = result['profile']['id'] assert endpoint.delete(f'/profile/{profile_id}').status_code in [200, 404] assert result['profile']['stats']['views'] == 1 finally: # Delete assert endpoint.get(f'/profiles/refresh').status_code == 200 assert endpoint.get(f'/sessions/refresh').status_code == 200 assert endpoint.get(f'/rules/refresh').status_code == 200 assert endpoint.get(f'/flows/refresh').status_code == 200 assert endpoint.get(f'/event-sources/refresh').status_code == 200 assert endpoint.delete(f'/event-source/{source_id}').status_code in [200, 404] assert endpoint.delete(f'/profile/{profile_id}').status_code in [200, 404] assert endpoint.delete(f'/flow/{flow_id}').status_code in [200, 404] assert endpoint.delete(f'/rule/{rule_id}').status_code in [200, 404] assert endpoint.delete(f'/session/{session_id}').status_code in [200, 404]

38.276596

104

0.519918

from uuid import uuid4 from tracardi.process_engine.action.v1.flow.start.start_action import StartAction from tracardi.process_engine.action.v1.increase_views_action import IncreaseViewsAction from tracardi.domain.flow import Flow from tracardi.process_engine.action.v1.end_action import EndAction from tracardi.process_engine.action.v1.debug_payload_action import DebugPayloadAction from ..api.test_source import create_event_source from tracardi.service.wf.service.builders import action from ..utils import Endpoint endpoint = Endpoint() def test_source_rule_and_flow(): source_id = str(uuid4()) profile_id = str(uuid4()) flow_id = str(uuid4()) rule_id = str(uuid4()) event_type = 'my-event' session_id = str(uuid4()) try: assert endpoint.delete(f'/profile/{profile_id}').status_code in [200, 404] assert endpoint.delete(f'/rule/{rule_id}').status_code in [200, 404] assert endpoint.delete(f'/flow/{flow_id}').status_code in [200, 404] assert create_event_source(source_id, type='javascript', name="End2End test").status_code == 200 assert endpoint.get('/event-sources/refresh').status_code == 200 response = endpoint.post('/rule', data={ "id": rule_id, "name": "Multiple events in one track", "event": { "type": event_type }, "flow": { "id": flow_id, "name": "Multiple events in one track" }, "source": { "id": source_id, "name": "my test source" }, "enabled": True }) assert response.status_code == 200 assert endpoint.get('/rules/refresh').status_code == 200 debug = action(DebugPayloadAction, init={"event": {"type": event_type}}) start = action(StartAction) increase_views = action(IncreaseViewsAction) end = action(EndAction) flow = Flow.build("Profile quick update - test", id=flow_id) flow += debug('event') >> start('payload') flow += start('payload') >> increase_views('payload') flow += increase_views('payload') >> end('payload') assert endpoint.post('/flow/production', data=flow.dict()).status_code == 200 assert endpoint.get('/flows/refresh').status_code == 200 payload = { "source": { "id": source_id }, "session": { "id": session_id }, "profile": { "id": profile_id }, "events": [ {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": str(uuid4())}, {"type": event_type}, ], "options": {"profile": True} } response = endpoint.post("/track", data=payload) assert endpoint.get(f'/profiles/refresh').status_code == 200 assert endpoint.get(f'/sessions/refresh').status_code == 200 assert response.status_code == 200 result = response.json() profile_id = result['profile']['id'] assert endpoint.delete(f'/profile/{profile_id}').status_code in [200, 404] assert result['profile']['stats']['views'] == 1 finally: assert endpoint.get(f'/profiles/refresh').status_code == 200 assert endpoint.get(f'/sessions/refresh').status_code == 200 assert endpoint.get(f'/rules/refresh').status_code == 200 assert endpoint.get(f'/flows/refresh').status_code == 200 assert endpoint.get(f'/event-sources/refresh').status_code == 200 assert endpoint.delete(f'/event-source/{source_id}').status_code in [200, 404] assert endpoint.delete(f'/profile/{profile_id}').status_code in [200, 404] assert endpoint.delete(f'/flow/{flow_id}').status_code in [200, 404] assert endpoint.delete(f'/rule/{rule_id}').status_code in [200, 404] assert endpoint.delete(f'/session/{session_id}').status_code in [200, 404]

true

f7fbe9a3708b617da4237bfbe9e18984e7a255e8

445

py

Python

mmdeploy/apis/openvino/__init__.py

Kayce001/mmdeploy

59470fef0b28e0b760c72269e0696bbdf57db7f1

[ "Apache-2.0" ]

1

2022-03-08T12:22:34.000Z

mmdeploy/apis/openvino/__init__.py

Kayce001/mmdeploy

59470fef0b28e0b760c72269e0696bbdf57db7f1

[ "Apache-2.0" ]

null

mmdeploy/apis/openvino/__init__.py

Kayce001/mmdeploy

59470fef0b28e0b760c72269e0696bbdf57db7f1

[ "Apache-2.0" ]

null

# Copyright (c) OpenMMLab. All rights reserved. from mmdeploy.backend.openvino import is_available __all__ = ['is_available'] if is_available(): from mmdeploy.backend.openvino.onnx2openvino import (get_output_model_file, onnx2openvino) from .utils import get_input_info_from_cfg __all__ += [ 'onnx2openvino', 'get_output_model_file', 'get_input_info_from_cfg' ]

34.230769

79

0.665169

from mmdeploy.backend.openvino import is_available __all__ = ['is_available'] if is_available(): from mmdeploy.backend.openvino.onnx2openvino import (get_output_model_file, onnx2openvino) from .utils import get_input_info_from_cfg __all__ += [ 'onnx2openvino', 'get_output_model_file', 'get_input_info_from_cfg' ]

true

f7fbe9e61649859d1ef656fa555216e15edb3f9f

57

py

Python

simp_py_examples/m5stack/ex029_jpg.py

kcfkwok2003/Simp_py

f75e66da01b45dc8688dda602f8b33d4258f0c31

[ "MIT" ]

null

simp_py_examples/m5stack/ex029_jpg.py

kcfkwok2003/Simp_py

f75e66da01b45dc8688dda602f8b33d4258f0c31

[ "MIT" ]

null

simp_py_examples/m5stack/ex029_jpg.py

kcfkwok2003/Simp_py

f75e66da01b45dc8688dda602f8b33d4258f0c31

[ "MIT" ]

null

from simp_py import tft tft.tft.image(0,0,'kyoco.jpg')

11.4

30

0.719298

from simp_py import tft tft.tft.image(0,0,'kyoco.jpg')

true

f7fbea52e6bc74951f2e6a5fcfdd21016b8c96fb

942

py

Python

test/test_update_schedule_response.py

Logicworks/opsgenie-python-sdk

244c4c40ddcc25e70df5ba4425ab8d7c8da59c18

[ "Apache-2.0" ]

null

test/test_update_schedule_response.py

Logicworks/opsgenie-python-sdk

244c4c40ddcc25e70df5ba4425ab8d7c8da59c18

[ "Apache-2.0" ]

null

test/test_update_schedule_response.py

Logicworks/opsgenie-python-sdk

244c4c40ddcc25e70df5ba4425ab8d7c8da59c18

[ "Apache-2.0" ]

1

2020-11-07T11:27:13.000Z

# coding: utf-8 """ OpsGenie REST API OpsGenie OpenAPI Specification # noqa: E501 OpenAPI spec version: 2.0.0 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import unittest import opsgenie_swagger from opsgenie_swagger.models.update_schedule_response import UpdateScheduleResponse # noqa: E501 from opsgenie_swagger.rest import ApiException class TestUpdateScheduleResponse(unittest.TestCase): """UpdateScheduleResponse unit test stubs""" def setUp(self): pass def tearDown(self): pass def testUpdateScheduleResponse(self): """Test UpdateScheduleResponse""" # FIXME: construct object with mandatory attributes with example values # model = opsgenie_swagger.models.update_schedule_response.UpdateScheduleResponse() # noqa: E501 pass if __name__ == '__main__': unittest.main()

22.97561

105

0.726115

from __future__ import absolute_import import unittest import opsgenie_swagger from opsgenie_swagger.models.update_schedule_response import UpdateScheduleResponse from opsgenie_swagger.rest import ApiException class TestUpdateScheduleResponse(unittest.TestCase): def setUp(self): pass def tearDown(self): pass def testUpdateScheduleResponse(self): s if __name__ == '__main__': unittest.main()

true

f7fbead5fe0e61025c73eb9af7f67c2cb20b3107

867

py

Python

scripts/web_cleaner.py

mgarrettm/graphlet-counting

edbd61d5d3399d901c15f9ccc51016828b6cc4d3

[ "MIT" ]

null

scripts/web_cleaner.py

mgarrettm/graphlet-counting

edbd61d5d3399d901c15f9ccc51016828b6cc4d3

[ "MIT" ]

null

scripts/web_cleaner.py

mgarrettm/graphlet-counting

edbd61d5d3399d901c15f9ccc51016828b6cc4d3

[ "MIT" ]

null

import sys edges = {} seen = {} offsets = {} max_vertex = 0 s = open(sys.argv[2], "w+") with open(sys.argv[1]) as f: for line in f: arr = line.split("\t") u = int(arr[0]) v = int(arr[1]) seen[u] = True seen[v] = True if u not in edges: edges[u] = {} if v not in edges: edges[v] = {} edges[u][v] = True edges[v][u] = True if max_vertex < u: max_vertex = u if max_vertex < v: max_vertex = v offset = 0 for u in range(max_vertex + 1): if u in seen: offsets[u] = offset else: offset += 1 for u in sorted(edges.keys()): for v in sorted(edges[u].keys()): if u != v and u in edges and v in edges[u] and u < v: s.write(str(u - offsets[u] + 1) + "\t" + str(v - offsets[v] + 1) + "\n")

23.432432

84

0.468281

import sys edges = {} seen = {} offsets = {} max_vertex = 0 s = open(sys.argv[2], "w+") with open(sys.argv[1]) as f: for line in f: arr = line.split("\t") u = int(arr[0]) v = int(arr[1]) seen[u] = True seen[v] = True if u not in edges: edges[u] = {} if v not in edges: edges[v] = {} edges[u][v] = True edges[v][u] = True if max_vertex < u: max_vertex = u if max_vertex < v: max_vertex = v offset = 0 for u in range(max_vertex + 1): if u in seen: offsets[u] = offset else: offset += 1 for u in sorted(edges.keys()): for v in sorted(edges[u].keys()): if u != v and u in edges and v in edges[u] and u < v: s.write(str(u - offsets[u] + 1) + "\t" + str(v - offsets[v] + 1) + "\n")

true

f7fbeae393b51337883694aefe65ee83138499c4

2,280

py

Python

convert_vg_images.py

robotics-upo/og-sgg

106c56919428ce927a1cae494932c00a5f58c37d

[ "MIT" ]

null

convert_vg_images.py

robotics-upo/og-sgg

106c56919428ce927a1cae494932c00a5f58c37d

[ "MIT" ]

null

convert_vg_images.py

robotics-upo/og-sgg

106c56919428ce927a1cae494932c00a5f58c37d

[ "MIT" ]

null

import tensorflow as tf #import tensorflow_hub as hub import numpy as np #import cv2 import zipfile import json import lzma import os import telenet.dataset_data as tn_data from telenet.utils import load_image_for_vrd_yolo, mdl_yolo, parse_yolo_results from telenet.config import get as tn_config from tqdm import tqdm VG_PATH = tn_config('paths.vg') imgcnvdata = tn_data.load_json_xz('vg-imgcnvdata') zf1 = zipfile.ZipFile(os.path.join(VG_PATH, 'images.zip'), 'r') zf2 = zipfile.ZipFile(os.path.join(VG_PATH, 'images2.zip'), 'r') train_imgs = [] test_imgs = [] for obj in imgcnvdata: (train_imgs,test_imgs)[obj['split']].append(obj) def load_image(db, index): obj = db[index] if obj['dir'] == 1: imgdata = zf1.read(f"VG_100K/{obj['file']}") elif obj['dir'] == 2: imgdata = zf2.read(f"VG_100K_2/{obj['file']}") else: raise "Bad dir" img, w, h = load_image_for_vrd_yolo(imgdata) return obj['id'], img, w, h def load_train_image(index): return load_image(train_imgs, index) def load_test_image(index): return load_image(test_imgs, index) train_dataset = tf.data.Dataset.from_tensor_slices(list(range(len(train_imgs)))).map( lambda x: tf.py_function(func=load_train_image, inp=[x], Tout=[tf.string, tf.float32, tf.float32, tf.float32]), num_parallel_calls=tf.data.AUTOTUNE).batch(1) test_dataset = tf.data.Dataset.from_tensor_slices(list(range(len(test_imgs)))).map( lambda x: tf.py_function(func=load_test_image, inp=[x], Tout=[tf.string, tf.float32, tf.float32, tf.float32]), num_parallel_calls=tf.data.AUTOTUNE).batch(1) def convert_dataset(dataset, outfile, outfile2): res = {} with zipfile.ZipFile(tn_data.path(outfile), 'w') as zfo: for names,img,widths,heights in tqdm(dataset): names = names.numpy() features,yolodata = mdl_yolo(img) for imnm,imft,imyl,imw,imh in zip(names,features,yolodata,widths,heights): imnm = imnm.decode('utf-8') res[imnm] = parse_yolo_results(np.expand_dims(imyl, axis=0), imw, imh) with zfo.open(f'{imnm}.npy','w') as f: np.save(f, imft) with lzma.open(tn_data.path(outfile2), 'wt', encoding='utf-8') as f: json.dump(res, f) convert_dataset(train_dataset, 'vg-yolo-train.zip', 'vg-yolo-train-objs.json.xz') convert_dataset(test_dataset, 'vg-yolo-test.zip', 'vg-yolo-test-objs.json.xz')

34.029851

112

0.732018

import tensorflow as tf import numpy as np import zipfile import json import lzma import os import telenet.dataset_data as tn_data from telenet.utils import load_image_for_vrd_yolo, mdl_yolo, parse_yolo_results from telenet.config import get as tn_config from tqdm import tqdm VG_PATH = tn_config('paths.vg') imgcnvdata = tn_data.load_json_xz('vg-imgcnvdata') zf1 = zipfile.ZipFile(os.path.join(VG_PATH, 'images.zip'), 'r') zf2 = zipfile.ZipFile(os.path.join(VG_PATH, 'images2.zip'), 'r') train_imgs = [] test_imgs = [] for obj in imgcnvdata: (train_imgs,test_imgs)[obj['split']].append(obj) def load_image(db, index): obj = db[index] if obj['dir'] == 1: imgdata = zf1.read(f"VG_100K/{obj['file']}") elif obj['dir'] == 2: imgdata = zf2.read(f"VG_100K_2/{obj['file']}") else: raise "Bad dir" img, w, h = load_image_for_vrd_yolo(imgdata) return obj['id'], img, w, h def load_train_image(index): return load_image(train_imgs, index) def load_test_image(index): return load_image(test_imgs, index) train_dataset = tf.data.Dataset.from_tensor_slices(list(range(len(train_imgs)))).map( lambda x: tf.py_function(func=load_train_image, inp=[x], Tout=[tf.string, tf.float32, tf.float32, tf.float32]), num_parallel_calls=tf.data.AUTOTUNE).batch(1) test_dataset = tf.data.Dataset.from_tensor_slices(list(range(len(test_imgs)))).map( lambda x: tf.py_function(func=load_test_image, inp=[x], Tout=[tf.string, tf.float32, tf.float32, tf.float32]), num_parallel_calls=tf.data.AUTOTUNE).batch(1) def convert_dataset(dataset, outfile, outfile2): res = {} with zipfile.ZipFile(tn_data.path(outfile), 'w') as zfo: for names,img,widths,heights in tqdm(dataset): names = names.numpy() features,yolodata = mdl_yolo(img) for imnm,imft,imyl,imw,imh in zip(names,features,yolodata,widths,heights): imnm = imnm.decode('utf-8') res[imnm] = parse_yolo_results(np.expand_dims(imyl, axis=0), imw, imh) with zfo.open(f'{imnm}.npy','w') as f: np.save(f, imft) with lzma.open(tn_data.path(outfile2), 'wt', encoding='utf-8') as f: json.dump(res, f) convert_dataset(train_dataset, 'vg-yolo-train.zip', 'vg-yolo-train-objs.json.xz') convert_dataset(test_dataset, 'vg-yolo-test.zip', 'vg-yolo-test-objs.json.xz')

true

f7fbebe3442090849c044d81cc48620b11f473b9

1,627

py

Python

dali/lagrange1d.py

dlouk/DALI

d2de8841123aaf7178b7e5f82908302345dfa586

[ "BSD-3-Clause" ]

1

2021-11-13T15:22:44.000Z

dali/lagrange1d.py

dlouk/DALI

d2de8841123aaf7178b7e5f82908302345dfa586

[ "BSD-3-Clause" ]

null

dali/lagrange1d.py

dlouk/DALI

d2de8841123aaf7178b7e5f82908302345dfa586

[ "BSD-3-Clause" ]

1

2022-01-29T00:33:06.000Z

# -*- coding: utf-8 -*- """ Created on Mon Feb 12 21:09:10 2018 @author: Dimitris Loukrezis Univariate Lagrange and hierarchical polynomials. """ import numpy as np class Lagrange1d: """Univariate Lagrange nodal basis polynomial""" def __init__(self, current_knot, knots): self.current_knot = current_knot self.knots = np.array(knots) self.other_knots = np.setdiff1d(knots, current_knot) # compute denominator once and re-use self.denoms_prod = (self.current_knot - self.other_knots).prod() def evaluate(self, non_grid_knots): """Evaluate polynomial on specific non-grid knots""" non_grid_knots = np.array(non_grid_knots).flatten() L = map(lambda x: np.prod(x-self.other_knots)/self.denoms_prod, non_grid_knots) return L class Hierarchical1d(Lagrange1d): """Univariate Lagrange hierarchical basis polynomial""" def __init__(self, knots): self.knots = np.array(knots) self.current_knot = self.knots[-1] self.other_knots = self.knots[:-1] # compute denominator once and re-use self.denoms_prod = (self.current_knot - self.other_knots).prod() def lagrange1d_eval(current_knot, other_knots, non_grid_knots): """Evaluate on NON_GRID_KNOTS a univariate Lagrange polynomial, defined for CURRENT_KNOT and OTHER_KNOTS""" other_knots = np.array(other_knots) non_grid_knots = np.array(non_grid_knots) denoms = current_knot - other_knots denoms_prod = denoms.prod() L = map(lambda x: np.prod(x - other_knots) / denoms_prod, non_grid_knots) return L

33.895833

77

0.688998

import numpy as np class Lagrange1d: def __init__(self, current_knot, knots): self.current_knot = current_knot self.knots = np.array(knots) self.other_knots = np.setdiff1d(knots, current_knot) self.denoms_prod = (self.current_knot - self.other_knots).prod() def evaluate(self, non_grid_knots): non_grid_knots = np.array(non_grid_knots).flatten() L = map(lambda x: np.prod(x-self.other_knots)/self.denoms_prod, non_grid_knots) return L class Hierarchical1d(Lagrange1d): def __init__(self, knots): self.knots = np.array(knots) self.current_knot = self.knots[-1] self.other_knots = self.knots[:-1] self.denoms_prod = (self.current_knot - self.other_knots).prod() def lagrange1d_eval(current_knot, other_knots, non_grid_knots): other_knots = np.array(other_knots) non_grid_knots = np.array(non_grid_knots) denoms = current_knot - other_knots denoms_prod = denoms.prod() L = map(lambda x: np.prod(x - other_knots) / denoms_prod, non_grid_knots) return L

true

f7fbed4e144dd563190b35303ecb4106f7ef4b94

23,422

py

Python

train_emosup.py

swidi/poemo-generation

3a349ac3a6fc3e82b24410013bced60a24c2d8bf

[ "MIT" ]

15

2020-10-18T08:52:19.000Z

2022-03-22T10:25:26.000Z

train_emosup.py

swidi/poemo-generation

3a349ac3a6fc3e82b24410013bced60a24c2d8bf

[ "MIT" ]

9

2021-02-08T16:40:45.000Z

2021-11-29T09:30:14.000Z

train_emosup.py

swidi/poemo-generation

3a349ac3a6fc3e82b24410013bced60a24c2d8bf

[ "MIT" ]

4

2020-10-29T08:12:20.000Z

2022-01-01T11:10:37.000Z

# Copyright 2019 The Texar Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Example of fine-tuning OpenAI GPT-2 language model. Use this for base model and emosup. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import importlib import numpy as np import tensorflow as tf import texar as tx from data_utils import model_utils, processor, utils # pylint: disable=invalid-name, too-many-locals, too-many-statements, no-member # pylint: disable=invalid-name, too-many-locals, too-many-statements, no-member # pylint: disable=too-many-branches flags = tf.flags FLAGS = flags.FLAGS flags.DEFINE_string("checkpoint", None, "Model checkpoint to resume training or for test.") flags.DEFINE_string("pretrain_checkpoint", "gpt2_pretrained_models/model_117M/model.ckpt", "OpenAI pretrained model checkpoint. Ignored if " "'--checkpoint' is specified.") flags.DEFINE_string("pretrained_model_dir", "gpt2_pretrained_models/model_117M", "The directory of pretrained model, for loading vocabuary, " "etc.") flags.DEFINE_float("temperature", 0.7, "Softmax temperature for top-k sample decoding. Must be " "strictly greater than 0. Defaults to 0.7.") flags.DEFINE_integer("top_k", 40, "The number of top most likely candidates from a vocab " "distribution.") flags.DEFINE_string("config_train", "configs.config_train", "Configurations of GPT-2 training, including data and " "optimization hyperparameters.") flags.DEFINE_string("config_type", "texar", "The configuration file type. Set to 'json' if the GPT-2 " "config file is in the same type of the official GPT-2 " "config file. Set to 'texar' if GPT-2 config file is in " "Texar type.") flags.DEFINE_string("config_model", "configs.config_model", "The model configuration file to configure the model. " "The config file type is define by the 'config_type'," "it be of texar type or json type." "For '--config_type=json', set the json config file path" "like: '--config_model gpt2_pretrained_models/model_117M/" "hparams.json';" "For '--config_type=texar', set the texar config file " "like: '--config_model configs.config_model'.") flags.DEFINE_string("output_dir", "output/remove_space/", "The output directory where the model checkpoints will be " "written.") flags.DEFINE_bool("do_train", False, "Whether to run training.") flags.DEFINE_bool("do_eval", False, "Whether to run eval on the dev set.") flags.DEFINE_bool("do_test", False, "Whether to run test on the test set.") flags.DEFINE_bool("distributed", False, "Whether to run in distributed mode.") flags.DEFINE_bool("finetune", False, "Whether to test on finetune mode.") flags.DEFINE_bool("beam", False, "Whether to do a beam serach for inference?") flags.DEFINE_bool("bpe_loss", False, "Whether to report loss bpe base or word base?") config_train = importlib.import_module(FLAGS.config_train) def _log(msg, log_fn=None): tf.logging.info(msg) if log_fn is None: log_fn = os.path.join(FLAGS.output_dir, config_train.name, 'log.txt') with open(log_fn, 'a') as flog: flog.write(msg + '\n') def _ids_to_text(ids, proc): eos_token_id = proc.encoder['<|endoftext|>'] if ids[0] == eos_token_id: ids = ids[1:] text = proc.decode(ids) return text def main(_): """ Builds the model and runs """ if FLAGS.distributed: import horovod.tensorflow as hvd hvd.init() tf.logging.set_verbosity(tf.logging.INFO) if len(config_train.name) > 0: output_dir = os.path.join(FLAGS.output_dir, config_train.name) else: output_dir = FLAGS.output_dir tx.utils.maybe_create_dir(output_dir) ## Loads GPT-2 model configuration if FLAGS.config_type == "json": gpt2_config = model_utils.transform_gpt2_to_texar_config( FLAGS.config_model) elif FLAGS.config_type == 'texar': gpt2_config = importlib.import_module( FLAGS.config_model) else: raise ValueError('Unknown config_type.') # Creates a data pre-processor for, e.g., BPE encoding proc = processor.get_encoder(FLAGS.pretrained_model_dir) max_decoding_length = config_train.max_decoding_length assert max_decoding_length <= gpt2_config.position_size, ( "max_decoding_length should not be greater than position_size. " "{}>{}".format(max_decoding_length, gpt2_config.position_size)) ## Loads data # Configures training data shard in distribued mode if FLAGS.distributed: config_train.train_hparam["dataset"]["num_shards"] = hvd.size() config_train.train_hparam["dataset"]["shard_id"] = hvd.rank() config_train.train_hparam["batch_size"] //= hvd.size() datasets = {} #if FLAGS.do_train: train_dataset = tx.data.TFRecordData(hparams=config_train.train_hparam) datasets['train'] = train_dataset #if FLAGS.do_eval: dev_dataset = tx.data.TFRecordData(hparams=config_train.dev_hparam) datasets['dev'] = dev_dataset #if FLAGS.do_test: test_dataset = tx.data.TFRecordData(hparams=config_train.test_hparam) datasets['test'] = test_dataset iterator = tx.data.FeedableDataIterator(datasets) batch = iterator.get_next() batch_size = tf.shape(batch['x1x4_ids'])[0] ## Builds the GPT-2 model vocab_size = gpt2_config.vocab_size word_embedder = tx.modules.WordEmbedder( vocab_size=vocab_size, hparams=gpt2_config.embed) pos_embedder = tx.modules.PositionEmbedder( position_size=gpt2_config.position_size, hparams=gpt2_config.pos_embed) # Ties output layer with input word embedding output_layer = tf.transpose(word_embedder.embedding, (1, 0)) decoder = tx.modules.TransformerDecoder( vocab_size=vocab_size, output_layer=output_layer, hparams=gpt2_config.decoder) # For training def _get_recon_loss(ids, full_len, prefix_len, mask_prefix=True, do_print=False): ids = ids[:,:tf.reduce_max(full_len)] batch_size__ = tf.shape(ids)[0] seq_len = tf.fill([batch_size__], tf.shape(ids)[1]) pos_embeds = pos_embedder(sequence_length=seq_len) input_embeds = word_embedder(ids) + pos_embeds outputs = decoder(inputs=input_embeds, decoding_strategy='train_greedy') max_full_len = tf.reduce_max(full_len) ids = ids[:, :max_full_len] logits = outputs.logits[:, :max_full_len] if mask_prefix: loss_recon = tx.losses.sequence_sparse_softmax_cross_entropy( labels=ids[:, 1:], logits=logits[:, :-1, :], sequence_length=full_len-1, average_across_timesteps=False, sum_over_timesteps=False, average_across_batch=False, sum_over_batch=False) mask_recon = tf.sequence_mask( full_len-1, dtype=tf.float32) mask_recon_prefix = 1 - tf.sequence_mask( prefix_len-1, maxlen=max_full_len-1,#max_decoding_length-1, dtype=tf.float32) mask_recon = mask_recon * mask_recon_prefix if do_print: print_op_1 = tf.print(mask_recon) loss_recon_flat = tx.utils.reduce_with_weights( tensor=loss_recon, weights=mask_recon, average_across_remaining=False, sum_over_remaining=False, average_across_batch=False) print_op_2 = tf.print(loss_recon_flat) with tf.control_dependencies([print_op_1, print_op_2]): loss_recon = tx.utils.reduce_with_weights( tensor=loss_recon, weights=mask_recon, average_across_remaining=True, sum_over_remaining=False) return loss_recon, mask_recon, loss_recon_flat else: loss_recon = tx.utils.reduce_with_weights( tensor=loss_recon, weights=mask_recon, average_across_remaining=True, sum_over_remaining=False) else: loss_recon = tx.losses.sequence_sparse_softmax_cross_entropy( labels=ids[:, 1:], logits=logits[:, :-1, :], sequence_length=full_len-1, average_across_timesteps=True, sum_over_timesteps=False, average_across_batch=True, sum_over_batch=False) return loss_recon ## ROC Loss-1: ML loss x1_len = tf.placeholder(tf.int32, shape=[None], name='x1_len') x1x4_ids = tf.placeholder(tf.int32, shape=[None, None], name='x1x4_ids') x1x4_len = tf.placeholder(tf.int32, shape=[None], name='x1x4_len') loss_fine = _get_recon_loss(x1x4_ids, x1x4_len, x1_len) tau = tf.placeholder(tf.float32, shape=[], name='tau') end_token = proc.encoder['<|endoftext|>'] loss = config_train.w_fine * loss_fine loss_dict = { 'loss': loss, 'loss_fine': config_train.w_fine * loss_fine, } ## Inference def _embedding_fn(ids, times): return word_embedder(ids) + pos_embedder(times) def _infer(context_name, target_name): helper = tx.modules.TopKSampleEmbeddingHelper( embedding=_embedding_fn, start_tokens=batch['%s_ids' % context_name][:, 0], end_token=end_token, top_k=FLAGS.top_k, softmax_temperature=FLAGS.temperature) outputs_infer, len_infer = decoder( context=batch['%s_ids' % context_name], context_sequence_length=batch['%s_len' % context_name], max_decoding_length=max_decoding_length, helper=helper) yy_ids = tx.utils.varlength_roll( outputs_infer.sample_id, -batch['%s_len' % context_name]) yy_len = len_infer - batch['%s_len' % context_name] yy_ids = yy_ids[:, :tf.reduce_max(yy_len)] yy_logits = outputs_infer.logits yy_loss = _evaluate_loss_test(yy_logits, target_name, context_name) return yy_ids, yy_len, yy_loss def _evaluate_loss_test(logits, target_name, context_name, bpe_loss=FLAGS.bpe_loss): ids = batch['%s_ids' % target_name] full_len = batch['%s_len' % target_name] ids = ids[:, :tf.reduce_max(full_len)] # new code max_full_len = tf.reduce_max(full_len) logits = logits[:, :max_full_len] test_loss = tx.losses.sequence_sparse_softmax_cross_entropy( labels=ids[:, 1:], logits=logits[:, :-1, :], sequence_length=full_len - 1, average_across_timesteps=False, sum_over_timesteps=not bpe_loss, # True, average_across_batch=False, sum_over_batch=False) mask_recon = tf.sequence_mask( full_len - 1, dtype=tf.float32) mask_recon_prefix = 1 - tf.sequence_mask( batch['%s_len' % context_name] - 1, maxlen=max_full_len - 1, # max_decoding_length-1, dtype=tf.float32) mask_recon = mask_recon * mask_recon_prefix test_loss = tx.utils.reduce_with_weights( tensor=test_loss, weights=mask_recon, average_across_batch=bpe_loss, average_across_remaining=bpe_loss, sum_over_remaining=not bpe_loss) return test_loss # [bs,] ? x4_ids_fine, x4_len_fine, x4_loss_fine = _infer('x1', 'x1x4') ## Optimization def _get_beam_ids(context_name): # beam-search predictions = decoder( beam_width=5, length_penalty=config_train.length_penalty, embedding=_embedding_fn, context=batch['%s_ids' % context_name], context_sequence_length=batch['%s_len' % context_name], max_decoding_length=max_decoding_length, end_token=end_token, mode=tf.estimator.ModeKeys.PREDICT) beam_output_ids = tx.utils.varlength_roll(predictions["sample_id"][:, :, 0], -batch['%s_len' % context_name]) return beam_output_ids beam_search_ids = _get_beam_ids('x1') ## Optimization trainable_variables = tx.utils.collect_trainable_variables( [word_embedder, pos_embedder, decoder]) global_step = tf.Variable(0, trainable=False) opt = tx.core.get_optimizer( global_step=global_step, hparams=config_train.opt) if FLAGS.distributed: opt = hvd.DistributedOptimizer(opt) train_op = tf.contrib.layers.optimize_loss( loss=loss, global_step=global_step, learning_rate=None, optimizer=opt, variables=trainable_variables) ## Train/eval/test routine saver = tf.train.Saver() saver_best = tf.train.Saver(max_to_keep=1) dev_best = { 'loss': 1e8, 'loss_fine': 1e8} def _log_losses(losses, step=None): loss_str = 'loss: %.4f, loss_fine: %.4f' % \ (losses['loss'], losses['loss_fine']) if step is not None: loss_str = 'step: %d, %s' % (step, loss_str) _log(loss_str) def _is_head(): if not FLAGS.distributed: return True else: return hvd.rank() == 0 def _train_epoch(sess, initial=False): """Trains on the training set, and evaluates on the dev set periodically. """ iterator.restart_dataset(sess, 'train') while True: try: # (1) Get data and yy sample fetches_data = { 'batch': batch, 'batch_size': batch_size, } feed_dict_data = { iterator.handle: iterator.get_handle(sess, 'train'), tx.global_mode(): tf.estimator.ModeKeys.PREDICT, } rets_data = sess.run(fetches_data, feed_dict_data) # (2) Optimize loss feed_dict = { #x1_ids: rets_data['batch']['x1_ids'], x1_len: rets_data['batch']['x1_len'], x1x4_ids: rets_data['batch']['x1x4_ids'], x1x4_len: rets_data['batch']['x1x4_len'], tau: config_train.tau, tx.global_mode(): tf.estimator.ModeKeys.TRAIN, } fetches = { 'train_op': train_op, 'step': global_step, } fetches.update(loss_dict) rets = sess.run(fetches, feed_dict) step = rets['step'] dis_steps = config_train.display_steps if _is_head() and dis_steps > 0 and step % dis_steps == 0: _log_losses(rets, step) eval_steps = config_train.eval_steps if _is_head() and eval_steps > 0 and step % eval_steps == 0: _dev_epoch(sess) sample_steps = config_train.sample_steps if _is_head() and sample_steps > 0 and step % sample_steps == 0: print('-----------testing-----------------') _test_epoch(sess, step=step) ckpt_steps = config_train.checkpoint_steps if _is_head() and ckpt_steps > 0 and step % ckpt_steps == 0: ckpt_fn = os.path.join(output_dir, 'model.ckpt') ckpt_fn = saver.save(sess, ckpt_fn, global_step=step) _log('Checkpoint to {}'.format(ckpt_fn)) except tf.errors.OutOfRangeError: break def _dev_epoch(sess): """Evaluates on the dev set. """ iterator.restart_dataset(sess, 'dev') results = tx.utils.AverageRecorder() nsamples = 0 fetches = {} fetches.update(loss_dict) # i = 0 while True: try: # (1) Get data and yy sample fetches_data = { 'batch': batch, 'batch_size': batch_size, } feed_dict_data = { iterator.handle: iterator.get_handle(sess, 'dev'), tx.global_mode(): tf.estimator.ModeKeys.PREDICT, } rets_data = sess.run(fetches_data, feed_dict_data) # (2) eval loss feed_dict = { #x1_ids: rets_data['batch']['x1_ids'], x1_len: rets_data['batch']['x1_len'], x1x4_ids: rets_data['batch']['x1x4_ids'], x1x4_len: rets_data['batch']['x1x4_len'], tau: config_train.tau, tx.global_mode(): tf.estimator.ModeKeys.PREDICT, } rets = sess.run(fetches, feed_dict) results.add(rets, weight=rets_data['batch_size']) nsamples += rets_data['batch_size'] except tf.errors.OutOfRangeError: break _log_losses(results.avg()) _log('nsamples: %d' % nsamples) avg_loss = results.avg('loss') if FLAGS.do_train and avg_loss < dev_best['loss']: dev_best.update(results.avg()) ckpt_fn = os.path.join(output_dir, 'model_best.ckpt') ckpt_fn = saver_best.save(sess, ckpt_fn) _log('Checkpoint best to {}'.format(ckpt_fn)) def _test_epoch(sess, step=None): """Generates samples on the test set. """ iterator.restart_dataset(sess, 'test') _all_inputs = [] _all_samples = [] _all_loss = [] if FLAGS.finetune: _log('Generation input: x1') fetches = { 'inputs': batch['x1_ids'], 'length': batch['x1_len'], 'samples_length': x4_len_fine, 'samples': x4_ids_fine, 'sample_loss': x4_loss_fine, 'outputs': batch['x1x4_ids'], 'out_length': batch['x1x4_len'] } res_fn_appendix = "x1" while True: try: feed_dict = { iterator.handle: iterator.get_handle(sess, 'test'), tx.context.global_mode(): tf.estimator.ModeKeys.PREDICT, } rets = sess.run(fetches, feed_dict=feed_dict) _inputs = [] for i, l in zip(rets['inputs'], rets['length']): # Delete padding _inputs.append(i[:l].tolist()) _all_inputs.extend(_inputs) _samples = [] _loss = [] if not FLAGS.beam: for s, l in zip(rets['samples'], rets['samples_length']): _samples.append(s[:l].tolist()) else: _samples.extend(h.tolist() for h in rets['samples']) _samples = utils.list_strip_eos(_samples, eos_token=proc.encoder['<|endoftext|>']) _all_samples.extend(_samples) except tf.errors.OutOfRangeError: break # Parse samples and write to file eos_token_id = proc.encoder['<|endoftext|>'] _all_input_text = [] for i in _all_inputs: if i[0] == eos_token_id: i = i[1:] i_text = proc.decode(i) _all_input_text.append(i_text) _all_input_text = tx.utils.strip_eos(_all_input_text, eos_token='<|endoftext|>') _all_samples_text = [] for j, (i, s) in enumerate(zip(_all_inputs, _all_samples)): s_text = proc.decode(s) s_text = s_text.replace('\n', ' ') # print(s_text) _all_samples_text.append(s_text) if j % 1000 == 0: print("{} stories is process of total {}".format(j, len(_all_inputs))) _all_samples_text = tx.utils.strip_eos(_all_samples_text, eos_token='<|endoftext|>') if step is None: fn = "test_samples_%s_sample40.tsv" % res_fn_appendix else: fn = "test_samples_%s_%d.tsv" % (res_fn_appendix, step) output_file = os.path.join(output_dir, fn) _log('Write samples to {}'.format(output_file)) if not FLAGS.beam: tx.utils.write_paired_text( _all_input_text, _all_samples_text, output_file) else: with open(output_file, 'w') as f: for item in _all_samples_text: f.write("%s\n" % item) # Broadcasts global variables from rank-0 process if FLAGS.distributed: bcast = hvd.broadcast_global_variables(0) session_config = tf.ConfigProto() if FLAGS.distributed: session_config.gpu_options.visible_device_list = str(hvd.local_rank()) with tf.Session(config=session_config) as sess: sess.run(tf.global_variables_initializer()) sess.run(tf.local_variables_initializer()) sess.run(tf.tables_initializer()) # smry_writer = tf.summary.FileWriter(FLAGS.output_dir, graph=sess.graph) if FLAGS.distributed: bcast.run() #Restores trained model if specified if FLAGS.checkpoint: _log('Restore from {}'.format(FLAGS.checkpoint)) saver.restore(sess, FLAGS.checkpoint) elif FLAGS.pretrain_checkpoint: _log('Restore from {}'.format(FLAGS.pretrain_checkpoint)) model_utils.init_gpt2_checkpoint(sess, FLAGS.pretrain_checkpoint) print("\nFinished loading\n") saver.save(sess, output_dir + '/gpt2_model.ckpt') iterator.initialize_dataset(sess) if FLAGS.do_train: for epoch in range(config_train.max_train_epoch): print("Training epoch {}".format(epoch)) _train_epoch(sess, epoch==0) saver.save(sess, output_dir + '/model.ckpt') if FLAGS.do_eval: _dev_epoch(sess) if FLAGS.do_test: _test_epoch(sess) if __name__ == "__main__": tf.app.run()

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from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import importlib import numpy as np import tensorflow as tf import texar as tx from data_utils import model_utils, processor, utils flags = tf.flags FLAGS = flags.FLAGS flags.DEFINE_string("checkpoint", None, "Model checkpoint to resume training or for test.") flags.DEFINE_string("pretrain_checkpoint", "gpt2_pretrained_models/model_117M/model.ckpt", "OpenAI pretrained model checkpoint. Ignored if " "'--checkpoint' is specified.") flags.DEFINE_string("pretrained_model_dir", "gpt2_pretrained_models/model_117M", "The directory of pretrained model, for loading vocabuary, " "etc.") flags.DEFINE_float("temperature", 0.7, "Softmax temperature for top-k sample decoding. Must be " "strictly greater than 0. Defaults to 0.7.") flags.DEFINE_integer("top_k", 40, "The number of top most likely candidates from a vocab " "distribution.") flags.DEFINE_string("config_train", "configs.config_train", "Configurations of GPT-2 training, including data and " "optimization hyperparameters.") flags.DEFINE_string("config_type", "texar", "The configuration file type. Set to 'json' if the GPT-2 " "config file is in the same type of the official GPT-2 " "config file. Set to 'texar' if GPT-2 config file is in " "Texar type.") flags.DEFINE_string("config_model", "configs.config_model", "The model configuration file to configure the model. " "The config file type is define by the 'config_type'," "it be of texar type or json type." "For '--config_type=json', set the json config file path" "like: '--config_model gpt2_pretrained_models/model_117M/" "hparams.json';" "For '--config_type=texar', set the texar config file " "like: '--config_model configs.config_model'.") flags.DEFINE_string("output_dir", "output/remove_space/", "The output directory where the model checkpoints will be " "written.") flags.DEFINE_bool("do_train", False, "Whether to run training.") flags.DEFINE_bool("do_eval", False, "Whether to run eval on the dev set.") flags.DEFINE_bool("do_test", False, "Whether to run test on the test set.") flags.DEFINE_bool("distributed", False, "Whether to run in distributed mode.") flags.DEFINE_bool("finetune", False, "Whether to test on finetune mode.") flags.DEFINE_bool("beam", False, "Whether to do a beam serach for inference?") flags.DEFINE_bool("bpe_loss", False, "Whether to report loss bpe base or word base?") config_train = importlib.import_module(FLAGS.config_train) def _log(msg, log_fn=None): tf.logging.info(msg) if log_fn is None: log_fn = os.path.join(FLAGS.output_dir, config_train.name, 'log.txt') with open(log_fn, 'a') as flog: flog.write(msg + '\n') def _ids_to_text(ids, proc): eos_token_id = proc.encoder['<|endoftext|>'] if ids[0] == eos_token_id: ids = ids[1:] text = proc.decode(ids) return text def main(_): if FLAGS.distributed: import horovod.tensorflow as hvd hvd.init() tf.logging.set_verbosity(tf.logging.INFO) if len(config_train.name) > 0: output_dir = os.path.join(FLAGS.output_dir, config_train.name) else: output_dir = FLAGS.output_dir tx.utils.maybe_create_dir(output_dir) on": gpt2_config = model_utils.transform_gpt2_to_texar_config( FLAGS.config_model) elif FLAGS.config_type == 'texar': gpt2_config = importlib.import_module( FLAGS.config_model) else: raise ValueError('Unknown config_type.') proc = processor.get_encoder(FLAGS.pretrained_model_dir) max_decoding_length = config_train.max_decoding_length assert max_decoding_length <= gpt2_config.position_size, ( "max_decoding_length should not be greater than position_size. " "{}>{}".format(max_decoding_length, gpt2_config.position_size)) f FLAGS.distributed: config_train.train_hparam["dataset"]["num_shards"] = hvd.size() config_train.train_hparam["dataset"]["shard_id"] = hvd.rank() config_train.train_hparam["batch_size"] //= hvd.size() datasets = {} train_dataset = tx.data.TFRecordData(hparams=config_train.train_hparam) datasets['train'] = train_dataset dev_dataset = tx.data.TFRecordData(hparams=config_train.dev_hparam) datasets['dev'] = dev_dataset test_dataset = tx.data.TFRecordData(hparams=config_train.test_hparam) datasets['test'] = test_dataset iterator = tx.data.FeedableDataIterator(datasets) batch = iterator.get_next() batch_size = tf.shape(batch['x1x4_ids'])[0] onfig.vocab_size word_embedder = tx.modules.WordEmbedder( vocab_size=vocab_size, hparams=gpt2_config.embed) pos_embedder = tx.modules.PositionEmbedder( position_size=gpt2_config.position_size, hparams=gpt2_config.pos_embed) output_layer = tf.transpose(word_embedder.embedding, (1, 0)) decoder = tx.modules.TransformerDecoder( vocab_size=vocab_size, output_layer=output_layer, hparams=gpt2_config.decoder) def _get_recon_loss(ids, full_len, prefix_len, mask_prefix=True, do_print=False): ids = ids[:,:tf.reduce_max(full_len)] batch_size__ = tf.shape(ids)[0] seq_len = tf.fill([batch_size__], tf.shape(ids)[1]) pos_embeds = pos_embedder(sequence_length=seq_len) input_embeds = word_embedder(ids) + pos_embeds outputs = decoder(inputs=input_embeds, decoding_strategy='train_greedy') max_full_len = tf.reduce_max(full_len) ids = ids[:, :max_full_len] logits = outputs.logits[:, :max_full_len] if mask_prefix: loss_recon = tx.losses.sequence_sparse_softmax_cross_entropy( labels=ids[:, 1:], logits=logits[:, :-1, :], sequence_length=full_len-1, average_across_timesteps=False, sum_over_timesteps=False, average_across_batch=False, sum_over_batch=False) mask_recon = tf.sequence_mask( full_len-1, dtype=tf.float32) mask_recon_prefix = 1 - tf.sequence_mask( prefix_len-1, maxlen=max_full_len-1, dtype=tf.float32) mask_recon = mask_recon * mask_recon_prefix if do_print: print_op_1 = tf.print(mask_recon) loss_recon_flat = tx.utils.reduce_with_weights( tensor=loss_recon, weights=mask_recon, average_across_remaining=False, sum_over_remaining=False, average_across_batch=False) print_op_2 = tf.print(loss_recon_flat) with tf.control_dependencies([print_op_1, print_op_2]): loss_recon = tx.utils.reduce_with_weights( tensor=loss_recon, weights=mask_recon, average_across_remaining=True, sum_over_remaining=False) return loss_recon, mask_recon, loss_recon_flat else: loss_recon = tx.utils.reduce_with_weights( tensor=loss_recon, weights=mask_recon, average_across_remaining=True, sum_over_remaining=False) else: loss_recon = tx.losses.sequence_sparse_softmax_cross_entropy( labels=ids[:, 1:], logits=logits[:, :-1, :], sequence_length=full_len-1, average_across_timesteps=True, sum_over_timesteps=False, average_across_batch=True, sum_over_batch=False) return loss_recon eholder(tf.int32, shape=[None], name='x1_len') x1x4_ids = tf.placeholder(tf.int32, shape=[None, None], name='x1x4_ids') x1x4_len = tf.placeholder(tf.int32, shape=[None], name='x1x4_len') loss_fine = _get_recon_loss(x1x4_ids, x1x4_len, x1_len) tau = tf.placeholder(tf.float32, shape=[], name='tau') end_token = proc.encoder['<|endoftext|>'] loss = config_train.w_fine * loss_fine loss_dict = { 'loss': loss, 'loss_fine': config_train.w_fine * loss_fine, } mbedding_fn(ids, times): return word_embedder(ids) + pos_embedder(times) def _infer(context_name, target_name): helper = tx.modules.TopKSampleEmbeddingHelper( embedding=_embedding_fn, start_tokens=batch['%s_ids' % context_name][:, 0], end_token=end_token, top_k=FLAGS.top_k, softmax_temperature=FLAGS.temperature) outputs_infer, len_infer = decoder( context=batch['%s_ids' % context_name], context_sequence_length=batch['%s_len' % context_name], max_decoding_length=max_decoding_length, helper=helper) yy_ids = tx.utils.varlength_roll( outputs_infer.sample_id, -batch['%s_len' % context_name]) yy_len = len_infer - batch['%s_len' % context_name] yy_ids = yy_ids[:, :tf.reduce_max(yy_len)] yy_logits = outputs_infer.logits yy_loss = _evaluate_loss_test(yy_logits, target_name, context_name) return yy_ids, yy_len, yy_loss def _evaluate_loss_test(logits, target_name, context_name, bpe_loss=FLAGS.bpe_loss): ids = batch['%s_ids' % target_name] full_len = batch['%s_len' % target_name] ids = ids[:, :tf.reduce_max(full_len)] max_full_len = tf.reduce_max(full_len) logits = logits[:, :max_full_len] test_loss = tx.losses.sequence_sparse_softmax_cross_entropy( labels=ids[:, 1:], logits=logits[:, :-1, :], sequence_length=full_len - 1, average_across_timesteps=False, sum_over_timesteps=not bpe_loss, average_across_batch=False, sum_over_batch=False) mask_recon = tf.sequence_mask( full_len - 1, dtype=tf.float32) mask_recon_prefix = 1 - tf.sequence_mask( batch['%s_len' % context_name] - 1, maxlen=max_full_len - 1, dtype=tf.float32) mask_recon = mask_recon * mask_recon_prefix test_loss = tx.utils.reduce_with_weights( tensor=test_loss, weights=mask_recon, average_across_batch=bpe_loss, average_across_remaining=bpe_loss, sum_over_remaining=not bpe_loss) return test_loss x4_ids_fine, x4_len_fine, x4_loss_fine = _infer('x1', 'x1x4') _beam_ids(context_name): predictions = decoder( beam_width=5, length_penalty=config_train.length_penalty, embedding=_embedding_fn, context=batch['%s_ids' % context_name], context_sequence_length=batch['%s_len' % context_name], max_decoding_length=max_decoding_length, end_token=end_token, mode=tf.estimator.ModeKeys.PREDICT) beam_output_ids = tx.utils.varlength_roll(predictions["sample_id"][:, :, 0], -batch['%s_len' % context_name]) return beam_output_ids beam_search_ids = _get_beam_ids('x1') _variables = tx.utils.collect_trainable_variables( [word_embedder, pos_embedder, decoder]) global_step = tf.Variable(0, trainable=False) opt = tx.core.get_optimizer( global_step=global_step, hparams=config_train.opt) if FLAGS.distributed: opt = hvd.DistributedOptimizer(opt) train_op = tf.contrib.layers.optimize_loss( loss=loss, global_step=global_step, learning_rate=None, optimizer=opt, variables=trainable_variables) er() saver_best = tf.train.Saver(max_to_keep=1) dev_best = { 'loss': 1e8, 'loss_fine': 1e8} def _log_losses(losses, step=None): loss_str = 'loss: %.4f, loss_fine: %.4f' % \ (losses['loss'], losses['loss_fine']) if step is not None: loss_str = 'step: %d, %s' % (step, loss_str) _log(loss_str) def _is_head(): if not FLAGS.distributed: return True else: return hvd.rank() == 0 def _train_epoch(sess, initial=False): iterator.restart_dataset(sess, 'train') while True: try: fetches_data = { 'batch': batch, 'batch_size': batch_size, } feed_dict_data = { iterator.handle: iterator.get_handle(sess, 'train'), tx.global_mode(): tf.estimator.ModeKeys.PREDICT, } rets_data = sess.run(fetches_data, feed_dict_data) feed_dict = { x1_len: rets_data['batch']['x1_len'], x1x4_ids: rets_data['batch']['x1x4_ids'], x1x4_len: rets_data['batch']['x1x4_len'], tau: config_train.tau, tx.global_mode(): tf.estimator.ModeKeys.TRAIN, } fetches = { 'train_op': train_op, 'step': global_step, } fetches.update(loss_dict) rets = sess.run(fetches, feed_dict) step = rets['step'] dis_steps = config_train.display_steps if _is_head() and dis_steps > 0 and step % dis_steps == 0: _log_losses(rets, step) eval_steps = config_train.eval_steps if _is_head() and eval_steps > 0 and step % eval_steps == 0: _dev_epoch(sess) sample_steps = config_train.sample_steps if _is_head() and sample_steps > 0 and step % sample_steps == 0: print('-----------testing-----------------') _test_epoch(sess, step=step) ckpt_steps = config_train.checkpoint_steps if _is_head() and ckpt_steps > 0 and step % ckpt_steps == 0: ckpt_fn = os.path.join(output_dir, 'model.ckpt') ckpt_fn = saver.save(sess, ckpt_fn, global_step=step) _log('Checkpoint to {}'.format(ckpt_fn)) except tf.errors.OutOfRangeError: break def _dev_epoch(sess): iterator.restart_dataset(sess, 'dev') results = tx.utils.AverageRecorder() nsamples = 0 fetches = {} fetches.update(loss_dict) while True: try: fetches_data = { 'batch': batch, 'batch_size': batch_size, } feed_dict_data = { iterator.handle: iterator.get_handle(sess, 'dev'), tx.global_mode(): tf.estimator.ModeKeys.PREDICT, } rets_data = sess.run(fetches_data, feed_dict_data) feed_dict = { x1_len: rets_data['batch']['x1_len'], x1x4_ids: rets_data['batch']['x1x4_ids'], x1x4_len: rets_data['batch']['x1x4_len'], tau: config_train.tau, tx.global_mode(): tf.estimator.ModeKeys.PREDICT, } rets = sess.run(fetches, feed_dict) results.add(rets, weight=rets_data['batch_size']) nsamples += rets_data['batch_size'] except tf.errors.OutOfRangeError: break _log_losses(results.avg()) _log('nsamples: %d' % nsamples) avg_loss = results.avg('loss') if FLAGS.do_train and avg_loss < dev_best['loss']: dev_best.update(results.avg()) ckpt_fn = os.path.join(output_dir, 'model_best.ckpt') ckpt_fn = saver_best.save(sess, ckpt_fn) _log('Checkpoint best to {}'.format(ckpt_fn)) def _test_epoch(sess, step=None): iterator.restart_dataset(sess, 'test') _all_inputs = [] _all_samples = [] _all_loss = [] if FLAGS.finetune: _log('Generation input: x1') fetches = { 'inputs': batch['x1_ids'], 'length': batch['x1_len'], 'samples_length': x4_len_fine, 'samples': x4_ids_fine, 'sample_loss': x4_loss_fine, 'outputs': batch['x1x4_ids'], 'out_length': batch['x1x4_len'] } res_fn_appendix = "x1" while True: try: feed_dict = { iterator.handle: iterator.get_handle(sess, 'test'), tx.context.global_mode(): tf.estimator.ModeKeys.PREDICT, } rets = sess.run(fetches, feed_dict=feed_dict) _inputs = [] for i, l in zip(rets['inputs'], rets['length']): _inputs.append(i[:l].tolist()) _all_inputs.extend(_inputs) _samples = [] _loss = [] if not FLAGS.beam: for s, l in zip(rets['samples'], rets['samples_length']): _samples.append(s[:l].tolist()) else: _samples.extend(h.tolist() for h in rets['samples']) _samples = utils.list_strip_eos(_samples, eos_token=proc.encoder['<|endoftext|>']) _all_samples.extend(_samples) except tf.errors.OutOfRangeError: break eos_token_id = proc.encoder['<|endoftext|>'] _all_input_text = [] for i in _all_inputs: if i[0] == eos_token_id: i = i[1:] i_text = proc.decode(i) _all_input_text.append(i_text) _all_input_text = tx.utils.strip_eos(_all_input_text, eos_token='<|endoftext|>') _all_samples_text = [] for j, (i, s) in enumerate(zip(_all_inputs, _all_samples)): s_text = proc.decode(s) s_text = s_text.replace('\n', ' ') _all_samples_text.append(s_text) if j % 1000 == 0: print("{} stories is process of total {}".format(j, len(_all_inputs))) _all_samples_text = tx.utils.strip_eos(_all_samples_text, eos_token='<|endoftext|>') if step is None: fn = "test_samples_%s_sample40.tsv" % res_fn_appendix else: fn = "test_samples_%s_%d.tsv" % (res_fn_appendix, step) output_file = os.path.join(output_dir, fn) _log('Write samples to {}'.format(output_file)) if not FLAGS.beam: tx.utils.write_paired_text( _all_input_text, _all_samples_text, output_file) else: with open(output_file, 'w') as f: for item in _all_samples_text: f.write("%s\n" % item) if FLAGS.distributed: bcast = hvd.broadcast_global_variables(0) session_config = tf.ConfigProto() if FLAGS.distributed: session_config.gpu_options.visible_device_list = str(hvd.local_rank()) with tf.Session(config=session_config) as sess: sess.run(tf.global_variables_initializer()) sess.run(tf.local_variables_initializer()) sess.run(tf.tables_initializer()) if FLAGS.distributed: bcast.run() if FLAGS.checkpoint: _log('Restore from {}'.format(FLAGS.checkpoint)) saver.restore(sess, FLAGS.checkpoint) elif FLAGS.pretrain_checkpoint: _log('Restore from {}'.format(FLAGS.pretrain_checkpoint)) model_utils.init_gpt2_checkpoint(sess, FLAGS.pretrain_checkpoint) print("\nFinished loading\n") saver.save(sess, output_dir + '/gpt2_model.ckpt') iterator.initialize_dataset(sess) if FLAGS.do_train: for epoch in range(config_train.max_train_epoch): print("Training epoch {}".format(epoch)) _train_epoch(sess, epoch==0) saver.save(sess, output_dir + '/model.ckpt') if FLAGS.do_eval: _dev_epoch(sess) if FLAGS.do_test: _test_epoch(sess) if __name__ == "__main__": tf.app.run()

true

f7fbee4ccc9422d35bd6f57d12073fe1989f3c2e

32,393

py

Python

qiskit/algorithms/minimum_eigen_solvers/vqe.py

MaxKelsen/qiskit-terra

1ec2f5db5fea8df907cace3a0e0c92b6deac7fd3

[ "Apache-2.0" ]

1

2022-03-10T03:07:30.000Z

qiskit/algorithms/minimum_eigen_solvers/vqe.py

MaxKelsen/qiskit-terra

1ec2f5db5fea8df907cace3a0e0c92b6deac7fd3

[ "Apache-2.0" ]

null

qiskit/algorithms/minimum_eigen_solvers/vqe.py

MaxKelsen/qiskit-terra

1ec2f5db5fea8df907cace3a0e0c92b6deac7fd3

[ "Apache-2.0" ]

null

# This code is part of Qiskit. # # (C) Copyright IBM 2018, 2020. # # This code is licensed under the Apache License, Version 2.0. You may # obtain a copy of this license in the LICENSE.txt file in the root directory # of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. # # Any modifications or derivative works of this code must retain this # copyright notice, and modified files need to carry a notice indicating # that they have been altered from the originals. """The Variational Quantum Eigensolver algorithm. See https://arxiv.org/abs/1304.3061 """ from typing import Optional, List, Callable, Union, Dict, Tuple import logging import warnings from time import time import numpy as np from qiskit.circuit import QuantumCircuit, Parameter from qiskit.circuit.library import RealAmplitudes from qiskit.providers import BaseBackend from qiskit.providers import Backend from qiskit.opflow import ( OperatorBase, ExpectationBase, ExpectationFactory, StateFn, CircuitStateFn, ListOp, CircuitSampler, PauliSumOp, ) from qiskit.opflow.gradients import GradientBase from qiskit.utils.validation import validate_min from qiskit.utils.backend_utils import is_aer_provider from qiskit.utils.deprecation import deprecate_function from qiskit.utils import QuantumInstance, algorithm_globals from ..optimizers import Optimizer, SLSQP from ..variational_algorithm import VariationalAlgorithm, VariationalResult from .minimum_eigen_solver import MinimumEigensolver, MinimumEigensolverResult, ListOrDict from ..exceptions import AlgorithmError logger = logging.getLogger(__name__) class VQE(VariationalAlgorithm, MinimumEigensolver): r"""The Variational Quantum Eigensolver algorithm. `VQE <https://arxiv.org/abs/1304.3061>`__ is a quantum algorithm that uses a variational technique to find the minimum eigenvalue of the Hamiltonian :math:`H` of a given system. An instance of VQE requires defining two algorithmic sub-components: a trial state (a.k.a. ansatz) which is a :class:`QuantumCircuit`, and one of the classical :mod:`~qiskit.algorithms.optimizers`. The ansatz is varied, via its set of parameters, by the optimizer, such that it works towards a state, as determined by the parameters applied to the ansatz, that will result in the minimum expectation value being measured of the input operator (Hamiltonian). An optional array of parameter values, via the *initial_point*, may be provided as the starting point for the search of the minimum eigenvalue. This feature is particularly useful such as when there are reasons to believe that the solution point is close to a particular point. As an example, when building the dissociation profile of a molecule, it is likely that using the previous computed optimal solution as the starting initial point for the next interatomic distance is going to reduce the number of iterations necessary for the variational algorithm to converge. It provides an `initial point tutorial <https://github.com/Qiskit/qiskit-tutorials-community/blob/master /chemistry/h2_vqe_initial_point.ipynb>`__ detailing this use case. The length of the *initial_point* list value must match the number of the parameters expected by the ansatz being used. If the *initial_point* is left at the default of ``None``, then VQE will look to the ansatz for a preferred value, based on its given initial state. If the ansatz returns ``None``, then a random point will be generated within the parameter bounds set, as per above. If the ansatz provides ``None`` as the lower bound, then VQE will default it to :math:`-2\pi`; similarly, if the ansatz returns ``None`` as the upper bound, the default value will be :math:`2\pi`. """ def __init__( self, ansatz: Optional[QuantumCircuit] = None, optimizer: Optional[Optimizer] = None, initial_point: Optional[np.ndarray] = None, gradient: Optional[Union[GradientBase, Callable]] = None, expectation: Optional[ExpectationBase] = None, include_custom: bool = False, max_evals_grouped: int = 1, callback: Optional[Callable[[int, np.ndarray, float, float], None]] = None, quantum_instance: Optional[Union[QuantumInstance, BaseBackend, Backend]] = None, ) -> None: """ Args: ansatz: A parameterized circuit used as Ansatz for the wave function. optimizer: A classical optimizer. initial_point: An optional initial point (i.e. initial parameter values) for the optimizer. If ``None`` then VQE will look to the ansatz for a preferred point and if not will simply compute a random one. gradient: An optional gradient function or operator for optimizer. expectation: The Expectation converter for taking the average value of the Observable over the ansatz state function. When ``None`` (the default) an :class:`~qiskit.opflow.expectations.ExpectationFactory` is used to select an appropriate expectation based on the operator and backend. When using Aer qasm_simulator backend, with paulis, it is however much faster to leverage custom Aer function for the computation but, although VQE performs much faster with it, the outcome is ideal, with no shot noise, like using a state vector simulator. If you are just looking for the quickest performance when choosing Aer qasm_simulator and the lack of shot noise is not an issue then set `include_custom` parameter here to ``True`` (defaults to ``False``). include_custom: When `expectation` parameter here is None setting this to ``True`` will allow the factory to include the custom Aer pauli expectation. max_evals_grouped: Max number of evaluations performed simultaneously. Signals the given optimizer that more than one set of parameters can be supplied so that potentially the expectation values can be computed in parallel. Typically this is possible when a finite difference gradient is used by the optimizer such that multiple points to compute the gradient can be passed and if computed in parallel improve overall execution time. Deprecated if a gradient operator or function is given. callback: a callback that can access the intermediate data during the optimization. Four parameter values are passed to the callback as follows during each evaluation by the optimizer for its current set of parameters as it works towards the minimum. These are: the evaluation count, the optimizer parameters for the ansatz, the evaluated mean and the evaluated standard deviation.` quantum_instance: Quantum Instance or Backend """ validate_min("max_evals_grouped", max_evals_grouped, 1) super().__init__() self._max_evals_grouped = max_evals_grouped self._circuit_sampler = None # type: Optional[CircuitSampler] self._expectation = None self.expectation = expectation self._include_custom = include_custom # set ansatz -- still supporting pre 0.18.0 sorting self._ansatz_params = None self._ansatz = None self.ansatz = ansatz self._optimizer = None self.optimizer = optimizer self._initial_point = None self.initial_point = initial_point self._gradient = None self.gradient = gradient self._quantum_instance = None if quantum_instance is not None: self.quantum_instance = quantum_instance self._eval_time = None self._eval_count = 0 self._callback = None self.callback = callback logger.info(self.print_settings()) # TODO remove this once the stateful methods are deleted self._ret = None @property def ansatz(self) -> QuantumCircuit: """Returns the ansatz.""" return self._ansatz @ansatz.setter def ansatz(self, ansatz: Optional[QuantumCircuit]): """Sets the ansatz. Args: ansatz: The parameterized circuit used as an ansatz. If None is passed, RealAmplitudes is used by default. """ if ansatz is None: ansatz = RealAmplitudes() self._ansatz = ansatz self._ansatz_params = list(ansatz.parameters) @property def gradient(self) -> Optional[Union[GradientBase, Callable]]: """Returns the gradient.""" return self._gradient @gradient.setter def gradient(self, gradient: Optional[Union[GradientBase, Callable]]): """Sets the gradient.""" self._gradient = gradient @property def quantum_instance(self) -> Optional[QuantumInstance]: """Returns quantum instance.""" return self._quantum_instance @quantum_instance.setter def quantum_instance( self, quantum_instance: Union[QuantumInstance, BaseBackend, Backend] ) -> None: """Sets quantum_instance""" if not isinstance(quantum_instance, QuantumInstance): quantum_instance = QuantumInstance(quantum_instance) self._quantum_instance = quantum_instance self._circuit_sampler = CircuitSampler( quantum_instance, param_qobj=is_aer_provider(quantum_instance.backend) ) @property def initial_point(self) -> Optional[np.ndarray]: """Returns initial point""" return self._initial_point @initial_point.setter def initial_point(self, initial_point: np.ndarray): """Sets initial point""" self._initial_point = initial_point @property def max_evals_grouped(self) -> int: """Returns max_evals_grouped""" return self._max_evals_grouped @max_evals_grouped.setter def max_evals_grouped(self, max_evals_grouped: int): """Sets max_evals_grouped""" self._max_evals_grouped = max_evals_grouped self.optimizer.set_max_evals_grouped(max_evals_grouped) @property def include_custom(self) -> bool: """Returns include_custom""" return self._include_custom @include_custom.setter def include_custom(self, include_custom: bool): """Sets include_custom. If set to another value than the one that was previsously set, the expectation attribute is reset to None. """ if include_custom != self._include_custom: self._include_custom = include_custom self.expectation = None @property def callback(self) -> Optional[Callable[[int, np.ndarray, float, float], None]]: """Returns callback""" return self._callback @callback.setter def callback(self, callback: Optional[Callable[[int, np.ndarray, float, float], None]]): """Sets callback""" self._callback = callback @property def expectation(self) -> Optional[ExpectationBase]: """The expectation value algorithm used to construct the expectation measurement from the observable.""" return self._expectation @expectation.setter def expectation(self, exp: Optional[ExpectationBase]) -> None: self._expectation = exp def _check_operator_ansatz(self, operator: OperatorBase): """Check that the number of qubits of operator and ansatz match.""" if operator is not None and self.ansatz is not None: if operator.num_qubits != self.ansatz.num_qubits: # try to set the number of qubits on the ansatz, if possible try: self.ansatz.num_qubits = operator.num_qubits self._ansatz_params = sorted(self.ansatz.parameters, key=lambda p: p.name) except AttributeError as ex: raise AlgorithmError( "The number of qubits of the ansatz does not match the " "operator, and the ansatz does not allow setting the " "number of qubits using `num_qubits`." ) from ex @property def optimizer(self) -> Optimizer: """Returns optimizer""" return self._optimizer @optimizer.setter def optimizer(self, optimizer: Optional[Optimizer]): """Sets the optimizer attribute. Args: optimizer: The optimizer to be used. If None is passed, SLSQP is used by default. """ if optimizer is None: optimizer = SLSQP() optimizer.set_max_evals_grouped(self.max_evals_grouped) self._optimizer = optimizer @property def setting(self): """Prepare the setting of VQE as a string.""" ret = f"Algorithm: {self.__class__.__name__}\n" params = "" for key, value in self.__dict__.items(): if key[0] == "_": if "initial_point" in key and value is None: params += "-- {}: {}\n".format(key[1:], "Random seed") else: params += f"-- {key[1:]}: {value}\n" ret += f"{params}" return ret def print_settings(self): """ Preparing the setting of VQE into a string. Returns: str: the formatted setting of VQE """ ret = "\n" ret += "==================== Setting of {} ============================\n".format( self.__class__.__name__ ) ret += f"{self.setting}" ret += "===============================================================\n" if self.ansatz is not None: ret += "{}".format(self.ansatz.draw(output="text")) else: ret += "ansatz has not been set" ret += "===============================================================\n" ret += f"{self._optimizer.setting}" ret += "===============================================================\n" return ret def construct_expectation( self, parameter: Union[List[float], List[Parameter], np.ndarray], operator: OperatorBase, return_expectation: bool = False, ) -> Union[OperatorBase, Tuple[OperatorBase, ExpectationBase]]: r""" Generate the ansatz circuit and expectation value measurement, and return their runnable composition. Args: parameter: Parameters for the ansatz circuit. operator: Qubit operator of the Observable return_expectation: If True, return the ``ExpectationBase`` expectation converter used in the construction of the expectation value. Useful e.g. to compute the standard deviation of the expectation value. Returns: The Operator equalling the measurement of the ansatz :class:`StateFn` by the Observable's expectation :class:`StateFn`, and, optionally, the expectation converter. Raises: AlgorithmError: If no operator has been provided. AlgorithmError: If no expectation is passed and None could be inferred via the ExpectationFactory. """ if operator is None: raise AlgorithmError("The operator was never provided.") self._check_operator_ansatz(operator) # if expectation was never created, try to create one if self.expectation is None: expectation = ExpectationFactory.build( operator=operator, backend=self.quantum_instance, include_custom=self._include_custom, ) else: expectation = self.expectation param_dict = dict(zip(self._ansatz_params, parameter)) # type: Dict wave_function = self.ansatz.assign_parameters(param_dict) observable_meas = expectation.convert(StateFn(operator, is_measurement=True)) ansatz_circuit_op = CircuitStateFn(wave_function) expect_op = observable_meas.compose(ansatz_circuit_op).reduce() if return_expectation: return expect_op, expectation return expect_op def construct_circuit( self, parameter: Union[List[float], List[Parameter], np.ndarray], operator: OperatorBase, ) -> List[QuantumCircuit]: """Return the circuits used to compute the expectation value. Args: parameter: Parameters for the ansatz circuit. operator: Qubit operator of the Observable Returns: A list of the circuits used to compute the expectation value. """ expect_op = self.construct_expectation(parameter, operator).to_circuit_op() circuits = [] # recursively extract circuits def extract_circuits(op): if isinstance(op, CircuitStateFn): circuits.append(op.primitive) elif isinstance(op, ListOp): for op_i in op.oplist: extract_circuits(op_i) extract_circuits(expect_op) return circuits @classmethod def supports_aux_operators(cls) -> bool: return True def _eval_aux_ops( self, parameters: np.ndarray, aux_operators: ListOrDict[OperatorBase], expectation: ExpectationBase, threshold: float = 1e-12, ) -> ListOrDict[Tuple[complex, complex]]: # Create new CircuitSampler to avoid breaking existing one's caches. sampler = CircuitSampler(self.quantum_instance) if isinstance(aux_operators, dict): list_op = ListOp(list(aux_operators.values())) else: list_op = ListOp(aux_operators) aux_op_expect = expectation.convert( StateFn(list_op, is_measurement=True).compose( CircuitStateFn(self.ansatz.bind_parameters(parameters)) ) ) aux_op_expect_sampled = sampler.convert(aux_op_expect) # compute means values = np.real(aux_op_expect_sampled.eval()) # compute standard deviations variances = np.real(expectation.compute_variance(aux_op_expect_sampled)) if not isinstance(variances, np.ndarray) and variances == 0.0: # when `variances` is a single value equal to 0., our expectation value is exact and we # manually ensure the variances to be a list of the correct length variances = np.zeros(len(aux_operators), dtype=float) std_devs = np.sqrt(variances / self.quantum_instance.run_config.shots) # Discard values below threshold aux_op_means = values * (np.abs(values) > threshold) # zip means and standard deviations into tuples aux_op_results = zip(aux_op_means, std_devs) # Return None eigenvalues for None operators if aux_operators is a list. # None operators are already dropped in compute_minimum_eigenvalue if aux_operators is a dict. if isinstance(aux_operators, list): aux_operator_eigenvalues = [None] * len(aux_operators) key_value_iterator = enumerate(aux_op_results) else: aux_operator_eigenvalues = {} key_value_iterator = zip(aux_operators.keys(), aux_op_results) for key, value in key_value_iterator: if aux_operators[key] is not None: aux_operator_eigenvalues[key] = value return aux_operator_eigenvalues def compute_minimum_eigenvalue( self, operator: OperatorBase, aux_operators: Optional[ListOrDict[OperatorBase]] = None ) -> MinimumEigensolverResult: super().compute_minimum_eigenvalue(operator, aux_operators) if self.quantum_instance is None: raise AlgorithmError( "A QuantumInstance or Backend must be supplied to run the quantum algorithm." ) self.quantum_instance.circuit_summary = True # this sets the size of the ansatz, so it must be called before the initial point # validation self._check_operator_ansatz(operator) # set an expectation for this algorithm run (will be reset to None at the end) initial_point = _validate_initial_point(self.initial_point, self.ansatz) bounds = _validate_bounds(self.ansatz) # We need to handle the array entries being zero or Optional i.e. having value None if aux_operators: zero_op = PauliSumOp.from_list([("I" * self.ansatz.num_qubits, 0)]) # Convert the None and zero values when aux_operators is a list. # Drop None and convert zero values when aux_operators is a dict. if isinstance(aux_operators, list): key_op_iterator = enumerate(aux_operators) converted = [zero_op] * len(aux_operators) else: key_op_iterator = aux_operators.items() converted = {} for key, op in key_op_iterator: if op is not None: converted[key] = zero_op if op == 0 else op aux_operators = converted else: aux_operators = None # Convert the gradient operator into a callable function that is compatible with the # optimization routine. if isinstance(self._gradient, GradientBase): gradient = self._gradient.gradient_wrapper( ~StateFn(operator) @ StateFn(self._ansatz), bind_params=self._ansatz_params, backend=self._quantum_instance, ) else: gradient = self._gradient self._eval_count = 0 energy_evaluation, expectation = self.get_energy_evaluation( operator, return_expectation=True ) start_time = time() # keep this until Optimizer.optimize is removed try: opt_result = self.optimizer.minimize( fun=energy_evaluation, x0=initial_point, jac=gradient, bounds=bounds ) except AttributeError: # self.optimizer is an optimizer with the deprecated interface that uses # ``optimize`` instead of ``minimize``` warnings.warn( "Using an optimizer that is run with the ``optimize`` method is " "deprecated as of Qiskit Terra 0.19.0 and will be unsupported no " "sooner than 3 months after the release date. Instead use an optimizer " "providing ``minimize`` (see qiskit.algorithms.optimizers.Optimizer).", DeprecationWarning, stacklevel=2, ) opt_result = self.optimizer.optimize( len(initial_point), energy_evaluation, gradient, bounds, initial_point ) eval_time = time() - start_time result = VQEResult() result.optimal_point = opt_result.x result.optimal_parameters = dict(zip(self._ansatz_params, opt_result.x)) result.optimal_value = opt_result.fun result.cost_function_evals = opt_result.nfev result.optimizer_time = eval_time result.eigenvalue = opt_result.fun + 0j result.eigenstate = self._get_eigenstate(result.optimal_parameters) logger.info( "Optimization complete in %s seconds.\nFound opt_params %s in %s evals", eval_time, result.optimal_point, self._eval_count, ) # TODO delete as soon as get_optimal_vector etc are removed self._ret = result if aux_operators is not None: aux_values = self._eval_aux_ops(opt_result.x, aux_operators, expectation=expectation) result.aux_operator_eigenvalues = aux_values return result def get_energy_evaluation( self, operator: OperatorBase, return_expectation: bool = False, ) -> Callable[[np.ndarray], Union[float, List[float]]]: """Returns a function handle to evaluates the energy at given parameters for the ansatz. This is the objective function to be passed to the optimizer that is used for evaluation. Args: operator: The operator whose energy to evaluate. return_expectation: If True, return the ``ExpectationBase`` expectation converter used in the construction of the expectation value. Useful e.g. to evaluate other operators with the same expectation value converter. Returns: Energy of the hamiltonian of each parameter, and, optionally, the expectation converter. Raises: RuntimeError: If the circuit is not parameterized (i.e. has 0 free parameters). """ num_parameters = self.ansatz.num_parameters if num_parameters == 0: raise RuntimeError("The ansatz must be parameterized, but has 0 free parameters.") expect_op, expectation = self.construct_expectation( self._ansatz_params, operator, return_expectation=True ) def energy_evaluation(parameters): parameter_sets = np.reshape(parameters, (-1, num_parameters)) # Create dict associating each parameter with the lists of parameterization values for it param_bindings = dict(zip(self._ansatz_params, parameter_sets.transpose().tolist())) start_time = time() sampled_expect_op = self._circuit_sampler.convert(expect_op, params=param_bindings) means = np.real(sampled_expect_op.eval()) if self._callback is not None: variance = np.real(expectation.compute_variance(sampled_expect_op)) estimator_error = np.sqrt(variance / self.quantum_instance.run_config.shots) for i, param_set in enumerate(parameter_sets): self._eval_count += 1 self._callback(self._eval_count, param_set, means[i], estimator_error[i]) else: self._eval_count += len(means) end_time = time() logger.info( "Energy evaluation returned %s - %.5f (ms), eval count: %s", means, (end_time - start_time) * 1000, self._eval_count, ) return means if len(means) > 1 else means[0] if return_expectation: return energy_evaluation, expectation return energy_evaluation @deprecate_function( """ The VQE.get_optimal_cost method is deprecated as of Qiskit Terra 0.18.0 and will be removed no sooner than 3 months after the releasedate. This information is part of the returned result object and can be queried as VQEResult.eigenvalue.""" ) def get_optimal_cost(self) -> float: """Get the minimal cost or energy found by the VQE.""" if self._ret.optimal_point is None: raise AlgorithmError( "Cannot return optimal cost before running the algorithm to find optimal params." ) return self._ret.optimal_value @deprecate_function( """ The VQE.get_optimal_circuit method is deprecated as of Qiskit Terra 0.18.0 and will be removed no sooner than 3 months after the releasedate. This information is part of the returned result object and can be queried as VQEResult.ansatz.bind_parameters(VQEResult.optimal_point).""" ) def get_optimal_circuit(self) -> QuantumCircuit: """Get the circuit with the optimal parameters.""" if self._ret.optimal_point is None: raise AlgorithmError( "Cannot find optimal circuit before running the algorithm to find optimal params." ) return self.ansatz.assign_parameters(self._ret.optimal_parameters) @deprecate_function( """ The VQE.get_optimal_vector method is deprecated as of Qiskit Terra 0.18.0 and will be removed no sooner than 3 months after the releasedate. This information is part of the returned result object and can be queried as VQEResult.eigenvector.""" ) def get_optimal_vector(self) -> Union[List[float], Dict[str, int]]: """Get the simulation outcome of the optimal circuit.""" if self._ret.optimal_parameters is None: raise AlgorithmError( "Cannot find optimal circuit before running the algorithm to find optimal vector." ) return self._get_eigenstate(self._ret.optimal_parameters) def _get_eigenstate(self, optimal_parameters) -> Union[List[float], Dict[str, int]]: """Get the simulation outcome of the ansatz, provided with parameters.""" optimal_circuit = self.ansatz.bind_parameters(optimal_parameters) state_fn = self._circuit_sampler.convert(StateFn(optimal_circuit)).eval() if self.quantum_instance.is_statevector: state = state_fn.primitive.data # VectorStateFn -> Statevector -> np.array else: state = state_fn.to_dict_fn().primitive # SparseVectorStateFn -> DictStateFn -> dict return state @property @deprecate_function( """ The VQE.optimal_params property is deprecated as of Qiskit Terra 0.18.0 and will be removed no sooner than 3 months after the releasedate. This information is part of the returned result object and can be queried as VQEResult.optimal_point.""" ) def optimal_params(self) -> np.ndarray: """The optimal parameters for the ansatz.""" if self._ret.optimal_point is None: raise AlgorithmError("Cannot find optimal params before running the algorithm.") return self._ret.optimal_point class VQEResult(VariationalResult, MinimumEigensolverResult): """VQE Result.""" def __init__(self) -> None: super().__init__() self._cost_function_evals = None @property def cost_function_evals(self) -> Optional[int]: """Returns number of cost optimizer evaluations""" return self._cost_function_evals @cost_function_evals.setter def cost_function_evals(self, value: int) -> None: """Sets number of cost function evaluations""" self._cost_function_evals = value @property def eigenstate(self) -> Optional[np.ndarray]: """return eigen state""" return self._eigenstate @eigenstate.setter def eigenstate(self, value: np.ndarray) -> None: """set eigen state""" self._eigenstate = value def _validate_initial_point(point, ansatz): expected_size = ansatz.num_parameters # try getting the initial point from the ansatz if point is None and hasattr(ansatz, "preferred_init_points"): point = ansatz.preferred_init_points # if the point is None choose a random initial point if point is None: # get bounds if ansatz has them set, otherwise use [-2pi, 2pi] for each parameter bounds = getattr(ansatz, "parameter_bounds", None) if not bounds: bounds = [(-2 * np.pi, 2 * np.pi)] * expected_size # replace all Nones by [-2pi, 2pi] lower_bounds = [] upper_bounds = [] for lower, upper in bounds: lower_bounds.append(lower if lower is not None else -2 * np.pi) upper_bounds.append(upper if upper is not None else 2 * np.pi) # sample from within bounds point = algorithm_globals.random.uniform(lower_bounds, upper_bounds) elif len(point) != expected_size: raise ValueError( f"The dimension of the initial point ({len(point)}) does not match the " f"number of parameters in the circuit ({expected_size})." ) return point def _validate_bounds(ansatz): if hasattr(ansatz, "parameter_bounds") and ansatz.parameter_bounds is not None: bounds = ansatz.parameter_bounds if len(bounds) != ansatz.num_parameters: raise ValueError( f"The number of bounds ({len(bounds)}) does not match the number of " f"parameters in the circuit ({ansatz.num_parameters})." ) else: bounds = [(None, None)] * ansatz.num_parameters return bounds

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from typing import Optional, List, Callable, Union, Dict, Tuple import logging import warnings from time import time import numpy as np from qiskit.circuit import QuantumCircuit, Parameter from qiskit.circuit.library import RealAmplitudes from qiskit.providers import BaseBackend from qiskit.providers import Backend from qiskit.opflow import ( OperatorBase, ExpectationBase, ExpectationFactory, StateFn, CircuitStateFn, ListOp, CircuitSampler, PauliSumOp, ) from qiskit.opflow.gradients import GradientBase from qiskit.utils.validation import validate_min from qiskit.utils.backend_utils import is_aer_provider from qiskit.utils.deprecation import deprecate_function from qiskit.utils import QuantumInstance, algorithm_globals from ..optimizers import Optimizer, SLSQP from ..variational_algorithm import VariationalAlgorithm, VariationalResult from .minimum_eigen_solver import MinimumEigensolver, MinimumEigensolverResult, ListOrDict from ..exceptions import AlgorithmError logger = logging.getLogger(__name__) class VQE(VariationalAlgorithm, MinimumEigensolver): def __init__( self, ansatz: Optional[QuantumCircuit] = None, optimizer: Optional[Optimizer] = None, initial_point: Optional[np.ndarray] = None, gradient: Optional[Union[GradientBase, Callable]] = None, expectation: Optional[ExpectationBase] = None, include_custom: bool = False, max_evals_grouped: int = 1, callback: Optional[Callable[[int, np.ndarray, float, float], None]] = None, quantum_instance: Optional[Union[QuantumInstance, BaseBackend, Backend]] = None, ) -> None: validate_min("max_evals_grouped", max_evals_grouped, 1) super().__init__() self._max_evals_grouped = max_evals_grouped self._circuit_sampler = None self._expectation = None self.expectation = expectation self._include_custom = include_custom self._ansatz_params = None self._ansatz = None self.ansatz = ansatz self._optimizer = None self.optimizer = optimizer self._initial_point = None self.initial_point = initial_point self._gradient = None self.gradient = gradient self._quantum_instance = None if quantum_instance is not None: self.quantum_instance = quantum_instance self._eval_time = None self._eval_count = 0 self._callback = None self.callback = callback logger.info(self.print_settings()) self._ret = None @property def ansatz(self) -> QuantumCircuit: return self._ansatz @ansatz.setter def ansatz(self, ansatz: Optional[QuantumCircuit]): if ansatz is None: ansatz = RealAmplitudes() self._ansatz = ansatz self._ansatz_params = list(ansatz.parameters) @property def gradient(self) -> Optional[Union[GradientBase, Callable]]: return self._gradient @gradient.setter def gradient(self, gradient: Optional[Union[GradientBase, Callable]]): self._gradient = gradient @property def quantum_instance(self) -> Optional[QuantumInstance]: return self._quantum_instance @quantum_instance.setter def quantum_instance( self, quantum_instance: Union[QuantumInstance, BaseBackend, Backend] ) -> None: if not isinstance(quantum_instance, QuantumInstance): quantum_instance = QuantumInstance(quantum_instance) self._quantum_instance = quantum_instance self._circuit_sampler = CircuitSampler( quantum_instance, param_qobj=is_aer_provider(quantum_instance.backend) ) @property def initial_point(self) -> Optional[np.ndarray]: return self._initial_point @initial_point.setter def initial_point(self, initial_point: np.ndarray): self._initial_point = initial_point @property def max_evals_grouped(self) -> int: return self._max_evals_grouped @max_evals_grouped.setter def max_evals_grouped(self, max_evals_grouped: int): self._max_evals_grouped = max_evals_grouped self.optimizer.set_max_evals_grouped(max_evals_grouped) @property def include_custom(self) -> bool: return self._include_custom @include_custom.setter def include_custom(self, include_custom: bool): if include_custom != self._include_custom: self._include_custom = include_custom self.expectation = None @property def callback(self) -> Optional[Callable[[int, np.ndarray, float, float], None]]: return self._callback @callback.setter def callback(self, callback: Optional[Callable[[int, np.ndarray, float, float], None]]): self._callback = callback @property def expectation(self) -> Optional[ExpectationBase]: return self._expectation @expectation.setter def expectation(self, exp: Optional[ExpectationBase]) -> None: self._expectation = exp def _check_operator_ansatz(self, operator: OperatorBase): if operator is not None and self.ansatz is not None: if operator.num_qubits != self.ansatz.num_qubits: try: self.ansatz.num_qubits = operator.num_qubits self._ansatz_params = sorted(self.ansatz.parameters, key=lambda p: p.name) except AttributeError as ex: raise AlgorithmError( "The number of qubits of the ansatz does not match the " "operator, and the ansatz does not allow setting the " "number of qubits using `num_qubits`." ) from ex @property def optimizer(self) -> Optimizer: return self._optimizer @optimizer.setter def optimizer(self, optimizer: Optional[Optimizer]): if optimizer is None: optimizer = SLSQP() optimizer.set_max_evals_grouped(self.max_evals_grouped) self._optimizer = optimizer @property def setting(self): ret = f"Algorithm: {self.__class__.__name__}\n" params = "" for key, value in self.__dict__.items(): if key[0] == "_": if "initial_point" in key and value is None: params += "-- {}: {}\n".format(key[1:], "Random seed") else: params += f"-- {key[1:]}: {value}\n" ret += f"{params}" return ret def print_settings(self): ret = "\n" ret += "==================== Setting of {} ============================\n".format( self.__class__.__name__ ) ret += f"{self.setting}" ret += "===============================================================\n" if self.ansatz is not None: ret += "{}".format(self.ansatz.draw(output="text")) else: ret += "ansatz has not been set" ret += "===============================================================\n" ret += f"{self._optimizer.setting}" ret += "===============================================================\n" return ret def construct_expectation( self, parameter: Union[List[float], List[Parameter], np.ndarray], operator: OperatorBase, return_expectation: bool = False, ) -> Union[OperatorBase, Tuple[OperatorBase, ExpectationBase]]: if operator is None: raise AlgorithmError("The operator was never provided.") self._check_operator_ansatz(operator) if self.expectation is None: expectation = ExpectationFactory.build( operator=operator, backend=self.quantum_instance, include_custom=self._include_custom, ) else: expectation = self.expectation param_dict = dict(zip(self._ansatz_params, parameter)) wave_function = self.ansatz.assign_parameters(param_dict) observable_meas = expectation.convert(StateFn(operator, is_measurement=True)) ansatz_circuit_op = CircuitStateFn(wave_function) expect_op = observable_meas.compose(ansatz_circuit_op).reduce() if return_expectation: return expect_op, expectation return expect_op def construct_circuit( self, parameter: Union[List[float], List[Parameter], np.ndarray], operator: OperatorBase, ) -> List[QuantumCircuit]: expect_op = self.construct_expectation(parameter, operator).to_circuit_op() circuits = [] def extract_circuits(op): if isinstance(op, CircuitStateFn): circuits.append(op.primitive) elif isinstance(op, ListOp): for op_i in op.oplist: extract_circuits(op_i) extract_circuits(expect_op) return circuits @classmethod def supports_aux_operators(cls) -> bool: return True def _eval_aux_ops( self, parameters: np.ndarray, aux_operators: ListOrDict[OperatorBase], expectation: ExpectationBase, threshold: float = 1e-12, ) -> ListOrDict[Tuple[complex, complex]]: sampler = CircuitSampler(self.quantum_instance) if isinstance(aux_operators, dict): list_op = ListOp(list(aux_operators.values())) else: list_op = ListOp(aux_operators) aux_op_expect = expectation.convert( StateFn(list_op, is_measurement=True).compose( CircuitStateFn(self.ansatz.bind_parameters(parameters)) ) ) aux_op_expect_sampled = sampler.convert(aux_op_expect) # compute means values = np.real(aux_op_expect_sampled.eval()) # compute standard deviations variances = np.real(expectation.compute_variance(aux_op_expect_sampled)) if not isinstance(variances, np.ndarray) and variances == 0.0: # when `variances` is a single value equal to 0., our expectation value is exact and we # manually ensure the variances to be a list of the correct length variances = np.zeros(len(aux_operators), dtype=float) std_devs = np.sqrt(variances / self.quantum_instance.run_config.shots) # Discard values below threshold aux_op_means = values * (np.abs(values) > threshold) # zip means and standard deviations into tuples aux_op_results = zip(aux_op_means, std_devs) # Return None eigenvalues for None operators if aux_operators is a list. # None operators are already dropped in compute_minimum_eigenvalue if aux_operators is a dict. if isinstance(aux_operators, list): aux_operator_eigenvalues = [None] * len(aux_operators) key_value_iterator = enumerate(aux_op_results) else: aux_operator_eigenvalues = {} key_value_iterator = zip(aux_operators.keys(), aux_op_results) for key, value in key_value_iterator: if aux_operators[key] is not None: aux_operator_eigenvalues[key] = value return aux_operator_eigenvalues def compute_minimum_eigenvalue( self, operator: OperatorBase, aux_operators: Optional[ListOrDict[OperatorBase]] = None ) -> MinimumEigensolverResult: super().compute_minimum_eigenvalue(operator, aux_operators) if self.quantum_instance is None: raise AlgorithmError( "A QuantumInstance or Backend must be supplied to run the quantum algorithm." ) self.quantum_instance.circuit_summary = True # this sets the size of the ansatz, so it must be called before the initial point # validation self._check_operator_ansatz(operator) # set an expectation for this algorithm run (will be reset to None at the end) initial_point = _validate_initial_point(self.initial_point, self.ansatz) bounds = _validate_bounds(self.ansatz) # We need to handle the array entries being zero or Optional i.e. having value None if aux_operators: zero_op = PauliSumOp.from_list([("I" * self.ansatz.num_qubits, 0)]) # Convert the None and zero values when aux_operators is a list. # Drop None and convert zero values when aux_operators is a dict. if isinstance(aux_operators, list): key_op_iterator = enumerate(aux_operators) converted = [zero_op] * len(aux_operators) else: key_op_iterator = aux_operators.items() converted = {} for key, op in key_op_iterator: if op is not None: converted[key] = zero_op if op == 0 else op aux_operators = converted else: aux_operators = None # Convert the gradient operator into a callable function that is compatible with the # optimization routine. if isinstance(self._gradient, GradientBase): gradient = self._gradient.gradient_wrapper( ~StateFn(operator) @ StateFn(self._ansatz), bind_params=self._ansatz_params, backend=self._quantum_instance, ) else: gradient = self._gradient self._eval_count = 0 energy_evaluation, expectation = self.get_energy_evaluation( operator, return_expectation=True ) start_time = time() # keep this until Optimizer.optimize is removed try: opt_result = self.optimizer.minimize( fun=energy_evaluation, x0=initial_point, jac=gradient, bounds=bounds ) except AttributeError: # self.optimizer is an optimizer with the deprecated interface that uses # ``optimize`` instead of ``minimize``` warnings.warn( "Using an optimizer that is run with the ``optimize`` method is " "deprecated as of Qiskit Terra 0.19.0 and will be unsupported no " "sooner than 3 months after the release date. Instead use an optimizer " "providing ``minimize`` (see qiskit.algorithms.optimizers.Optimizer).", DeprecationWarning, stacklevel=2, ) opt_result = self.optimizer.optimize( len(initial_point), energy_evaluation, gradient, bounds, initial_point ) eval_time = time() - start_time result = VQEResult() result.optimal_point = opt_result.x result.optimal_parameters = dict(zip(self._ansatz_params, opt_result.x)) result.optimal_value = opt_result.fun result.cost_function_evals = opt_result.nfev result.optimizer_time = eval_time result.eigenvalue = opt_result.fun + 0j result.eigenstate = self._get_eigenstate(result.optimal_parameters) logger.info( "Optimization complete in %s seconds.\nFound opt_params %s in %s evals", eval_time, result.optimal_point, self._eval_count, ) # TODO delete as soon as get_optimal_vector etc are removed self._ret = result if aux_operators is not None: aux_values = self._eval_aux_ops(opt_result.x, aux_operators, expectation=expectation) result.aux_operator_eigenvalues = aux_values return result def get_energy_evaluation( self, operator: OperatorBase, return_expectation: bool = False, ) -> Callable[[np.ndarray], Union[float, List[float]]]: num_parameters = self.ansatz.num_parameters if num_parameters == 0: raise RuntimeError("The ansatz must be parameterized, but has 0 free parameters.") expect_op, expectation = self.construct_expectation( self._ansatz_params, operator, return_expectation=True ) def energy_evaluation(parameters): parameter_sets = np.reshape(parameters, (-1, num_parameters)) # Create dict associating each parameter with the lists of parameterization values for it param_bindings = dict(zip(self._ansatz_params, parameter_sets.transpose().tolist())) start_time = time() sampled_expect_op = self._circuit_sampler.convert(expect_op, params=param_bindings) means = np.real(sampled_expect_op.eval()) if self._callback is not None: variance = np.real(expectation.compute_variance(sampled_expect_op)) estimator_error = np.sqrt(variance / self.quantum_instance.run_config.shots) for i, param_set in enumerate(parameter_sets): self._eval_count += 1 self._callback(self._eval_count, param_set, means[i], estimator_error[i]) else: self._eval_count += len(means) end_time = time() logger.info( "Energy evaluation returned %s - %.5f (ms), eval count: %s", means, (end_time - start_time) * 1000, self._eval_count, ) return means if len(means) > 1 else means[0] if return_expectation: return energy_evaluation, expectation return energy_evaluation @deprecate_function( """ The VQE.get_optimal_cost method is deprecated as of Qiskit Terra 0.18.0 and will be removed no sooner than 3 months after the releasedate. This information is part of the returned result object and can be queried as VQEResult.eigenvalue.""" ) def get_optimal_cost(self) -> float: if self._ret.optimal_point is None: raise AlgorithmError( "Cannot return optimal cost before running the algorithm to find optimal params." ) return self._ret.optimal_value @deprecate_function( """ The VQE.get_optimal_circuit method is deprecated as of Qiskit Terra 0.18.0 and will be removed no sooner than 3 months after the releasedate. This information is part of the returned result object and can be queried as VQEResult.ansatz.bind_parameters(VQEResult.optimal_point).""" ) def get_optimal_circuit(self) -> QuantumCircuit: if self._ret.optimal_point is None: raise AlgorithmError( "Cannot find optimal circuit before running the algorithm to find optimal params." ) return self.ansatz.assign_parameters(self._ret.optimal_parameters) @deprecate_function( """ The VQE.get_optimal_vector method is deprecated as of Qiskit Terra 0.18.0 and will be removed no sooner than 3 months after the releasedate. This information is part of the returned result object and can be queried as VQEResult.eigenvector.""" ) def get_optimal_vector(self) -> Union[List[float], Dict[str, int]]: if self._ret.optimal_parameters is None: raise AlgorithmError( "Cannot find optimal circuit before running the algorithm to find optimal vector." ) return self._get_eigenstate(self._ret.optimal_parameters) def _get_eigenstate(self, optimal_parameters) -> Union[List[float], Dict[str, int]]: optimal_circuit = self.ansatz.bind_parameters(optimal_parameters) state_fn = self._circuit_sampler.convert(StateFn(optimal_circuit)).eval() if self.quantum_instance.is_statevector: state = state_fn.primitive.data # VectorStateFn -> Statevector -> np.array else: state = state_fn.to_dict_fn().primitive # SparseVectorStateFn -> DictStateFn -> dict return state @property @deprecate_function( """ The VQE.optimal_params property is deprecated as of Qiskit Terra 0.18.0 and will be removed no sooner than 3 months after the releasedate. This information is part of the returned result object and can be queried as VQEResult.optimal_point.""" ) def optimal_params(self) -> np.ndarray: if self._ret.optimal_point is None: raise AlgorithmError("Cannot find optimal params before running the algorithm.") return self._ret.optimal_point class VQEResult(VariationalResult, MinimumEigensolverResult): def __init__(self) -> None: super().__init__() self._cost_function_evals = None @property def cost_function_evals(self) -> Optional[int]: return self._cost_function_evals @cost_function_evals.setter def cost_function_evals(self, value: int) -> None: self._cost_function_evals = value @property def eigenstate(self) -> Optional[np.ndarray]: return self._eigenstate @eigenstate.setter def eigenstate(self, value: np.ndarray) -> None: self._eigenstate = value def _validate_initial_point(point, ansatz): expected_size = ansatz.num_parameters # try getting the initial point from the ansatz if point is None and hasattr(ansatz, "preferred_init_points"): point = ansatz.preferred_init_points # if the point is None choose a random initial point if point is None: # get bounds if ansatz has them set, otherwise use [-2pi, 2pi] for each parameter bounds = getattr(ansatz, "parameter_bounds", None) if not bounds: bounds = [(-2 * np.pi, 2 * np.pi)] * expected_size # replace all Nones by [-2pi, 2pi] lower_bounds = [] upper_bounds = [] for lower, upper in bounds: lower_bounds.append(lower if lower is not None else -2 * np.pi) upper_bounds.append(upper if upper is not None else 2 * np.pi) # sample from within bounds point = algorithm_globals.random.uniform(lower_bounds, upper_bounds) elif len(point) != expected_size: raise ValueError( f"The dimension of the initial point ({len(point)}) does not match the " f"number of parameters in the circuit ({expected_size})." ) return point def _validate_bounds(ansatz): if hasattr(ansatz, "parameter_bounds") and ansatz.parameter_bounds is not None: bounds = ansatz.parameter_bounds if len(bounds) != ansatz.num_parameters: raise ValueError( f"The number of bounds ({len(bounds)}) does not match the number of " f"parameters in the circuit ({ansatz.num_parameters})." ) else: bounds = [(None, None)] * ansatz.num_parameters return bounds

true

f7fbef0f15e368ac2e21ea80661d64ecef173c7f

1,599

py

Python

cvxpy/problems/param_prob.py

rpradal/cvxpy

222ae345d2df67040346267f55369d8433d2e50f

[ "ECL-2.0", "Apache-2.0" ]

null

cvxpy/problems/param_prob.py

rpradal/cvxpy

222ae345d2df67040346267f55369d8433d2e50f

[ "ECL-2.0", "Apache-2.0" ]

null

cvxpy/problems/param_prob.py

rpradal/cvxpy

222ae345d2df67040346267f55369d8433d2e50f

[ "ECL-2.0", "Apache-2.0" ]

null

""" Copyright 2013 Steven Diamond, 2017 Akshay Agrawal Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import abc class ParamProb: """An abstract base class for parameterized problems. Parameterized problems are produced during the first canonicalization and allow canonicalization to be short-circuited for future solves. """ __metaclass__ = abc.ABCMeta @abc.abstractproperty def is_mixed_integer(self) -> bool: """Is the problem mixed-integer?""" raise NotImplementedError() @abc.abstractproperty def apply_parameters(self, id_to_param_value=None, zero_offset=False, keep_zeros=False): """Returns A, b after applying parameters (and reshaping). Args: id_to_param_value: (optional) dict mapping parameter ids to values zero_offset: (optional) if True, zero out the constant offset in the parameter vector keep_zeros: (optional) if True, store explicit zeros in A where parameters are affected """ raise NotImplementedError()

35.533333

78

0.702314

import abc class ParamProb: __metaclass__ = abc.ABCMeta @abc.abstractproperty def is_mixed_integer(self) -> bool: raise NotImplementedError() @abc.abstractproperty def apply_parameters(self, id_to_param_value=None, zero_offset=False, keep_zeros=False): raise NotImplementedError()

true

f7fbf093307f47a893fa7863d53d2b6cc57e8a86

2,627

py

Python

girder_larger_image/create_tiff.py

cj-abcs/larger_image

6f7af6382c2b4ca7379bdb11c0d130d04e9524af

[ "Apache-2.0" ]

1

2020-12-17T18:22:08.000Z

girder_larger_image/create_tiff.py

abcsFrederick/larger_image

6f7af6382c2b4ca7379bdb11c0d130d04e9524af

[ "Apache-2.0" ]

1

2019-09-11T17:52:12.000Z

girder_larger_image/create_tiff.py

cj-abcs/larger_image

6f7af6382c2b4ca7379bdb11c0d130d04e9524af

[ "Apache-2.0" ]

null

#!/usr/bin/env python ############################################################################### # Girder, large_image plugin framework and tests adapted from Kitware Inc. # source and documentation by the Imaging and Visualization Group, Advanced # Biomedical Computational Science, Frederick National Laboratory for Cancer # Research. # # Copyright Kitware Inc. # # Licensed under the Apache License, Version 2.0 ( the "License" ); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ############################################################################### import os import subprocess def create_tiff(in_path, compression, quality, tile_size, out_path): convert_command = ( 'vips', # Additional vips options can be added to aid debugging. For instance, # '--vips-concurrency', '1', # '--vips-progress', # can show how vips is processing a file. 'tiffsave', in_path, out_path, '--compression', compression, '--Q', str(quality), '--tile', '--tile-width', str(tile_size), '--tile-height', str(tile_size), '--pyramid', '--bigtiff' ) try: import six.moves print('Command: %s' % ( ' '.join([six.moves.shlex_quote(arg) for arg in convert_command]))) except ImportError: pass proc = subprocess.Popen(convert_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE) out, err = proc.communicate() if out.strip(): print('stdout: ' + out) if err.strip(): print('stderr: ' + err) if proc.returncode: raise Exception('VIPS command failed (rc=%d): %s' % ( proc.returncode, ' '.join(convert_command))) try: # Define Girder Worker globals for the style checker _tempdir = _tempdir # noqa in_path = in_path # noqa compression = compression # noqa quality = quality # noqa tile_size = tile_size # noqa out_filename = out_filename # noqa out_path = os.path.join(_tempdir, out_filename) create_tiff(in_path, compression, quality, tile_size, out_path) except NameError: pass

32.8375

79

0.606776

true

f7fbf0ff1f7bdc600f9d34c1be2f349301a80f22

1,016

py

Python

py/py_0083_path_sum_four_ways.py

lcsm29/project-euler

fab794ece5aa7a11fc7c2177f26250f40a5b1447

[ "MIT" ]

null

py/py_0083_path_sum_four_ways.py

lcsm29/project-euler

fab794ece5aa7a11fc7c2177f26250f40a5b1447

[ "MIT" ]

null

py/py_0083_path_sum_four_ways.py

lcsm29/project-euler

fab794ece5aa7a11fc7c2177f26250f40a5b1447

[ "MIT" ]

null

# Solution of; # Project Euler Problem 82: Path sum: four ways # https://projecteuler.net/problem=83 # # NOTE: This problem is a significantly more challenging version of Problem 81. # In the 5 by 5 matrix below, the minimal path sum from the top left to the # bottom right, by moving left, right, up, and down, # is indicated in asterisk(*) and is equal to 2297. # # 131(*) 673(.) 234(*) 103(*) 018(*) # 201(*) 096(*) 342(*) 965(.) 150(*) # 630(.) 803(.) 746(.) 422(*) 111(*) # 537(.) 699(.) 497(.) 121(*) 956(.) # 805(.) 732(.) 524(.) 037(*) 331(*) # # Find the minimal path sum from the top left to the bottom right by moving # left, right, up, and down in matrix.txt, # https://projecteuler.net/project/resources/p083_matrix.txt # a 31K text file containing an 80 by 80 matrix. # # by lcsm29 http://github.com/lcsm29/project-euler import timed def dummy(n): pass if __name__ == '__main__': n = 1000 i = 10000 prob_id = 83 timed.caller(dummy, n, i, prob_id)

29.882353

79

0.633858

import timed def dummy(n): pass if __name__ == '__main__': n = 1000 i = 10000 prob_id = 83 timed.caller(dummy, n, i, prob_id)

true

f7fbf1dd797b49d4d282a5cd229eb70fb2a5a9f8

2,843

py

Python

drop-boxes/register-wf-individualizedproteome/register-individualizedproteome-dropbox.py

qbicsoftware/etl-scripts

e1cea11b5f55fb218e7d4c8d49bdd3c5fe6c62c6

[ "MIT" ]

2

2018-04-20T15:48:02.000Z

2021-11-30T17:39:28.000Z

drop-boxes/register-wf-individualizedproteome/register-individualizedproteome-dropbox.py

qbicsoftware/etl-scripts

e1cea11b5f55fb218e7d4c8d49bdd3c5fe6c62c6

[ "MIT" ]

41

2017-07-19T11:17:26.000Z

2021-09-28T12:10:49.000Z

drop-boxes/register-wf-individualizedproteome/register-individualizedproteome-dropbox.py

qbicsoftware/etl-scripts

e1cea11b5f55fb218e7d4c8d49bdd3c5fe6c62c6

[ "MIT" ]

2

2017-04-27T10:32:33.000Z

2018-02-20T09:26:12.000Z

''' Note: print statements go to: ~openbis/servers/datastore_server/log/startup_log.txt ''' import sys sys.path.append('/home-link/qeana10/bin/') import re import os import time import datetime import shutil import subprocess import ch.systemsx.cisd.etlserver.registrator.api.v2 from java.io import File from org.apache.commons.io import FileUtils from ch.systemsx.cisd.openbis.generic.shared.api.v1.dto import SearchCriteria from ch.systemsx.cisd.openbis.generic.shared.api.v1.dto import SearchSubCriteria # Data import and registration # expected: # *Q[Project Code]^4[Sample No.]^3[Sample Type][Checksum]*.* ePattern = re.compile('Q\w{4}E[0-9]+') pPattern = re.compile('Q\w{4}') def process(transaction): context = transaction.getRegistrationContext().getPersistentMap() # Get the incoming path of the transaction incomingPath = transaction.getIncoming().getAbsolutePath() key = context.get("RETRY_COUNT") if (key == None): key = 1 # Get the name of the incoming file name = transaction.getIncoming().getName() nameSplit = name.split("-") space = nameSplit[0] project = pPattern.findall(nameSplit[1])[0] experiment_id = ePattern.findall(nameSplit[2])[0] sampleCode = nameSplit[-1] sample_id = "/"+space+"/"+sampleCode if not experiment_id: print "The identifier matching the pattern Q\w{4}E\[0-9]+ was not found in the fileName "+name sample = transaction.getSampleForUpdate(sample_id) parents = sample.getParentSampleIdentifiers() parentcodes = [] for parent in parents: parentcodes.append(parent.split("/")[-1]) parentInfos = "_".join(parentcodes) experiment = transaction.getExperimentForUpdate("/"+space+"/"+project+"/"+experiment_id) experiment.setPropertyValue("Q_WF_STATUS", "FINISHED") endpoint = datetime.datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d %H:%M:%S') experiment.setPropertyValue("Q_WF_FINISHED_AT", endpoint) sample.setExperiment(experiment) #Register files dataSetRes = transaction.createNewDataSet('Q_WF_MS_INDIVIDUALIZED_PROTEOME_RESULTS') dataSetRes.setMeasuredData(False) dataSetLogs = transaction.createNewDataSet('Q_WF_MS_INDIVIDUALIZED_PROTEOME_LOGS') dataSetLogs.setMeasuredData(False) dataSetRes.setSample(sample) dataSetLogs.setSample(sample) resultsname = incomingPath+"/"+parentInfos+"_workflow_results" logname = incomingPath+"/"+parentInfos+"_workflow_logs" os.rename(incomingPath+"/logs", logname) os.rename(incomingPath+"/result", resultsname) transaction.moveFile(resultsname, dataSetRes) transaction.moveFile(logname, dataSetLogs)

35.5375

110

0.689061

''' Note: print statements go to: ~openbis/servers/datastore_server/log/startup_log.txt ''' import sys sys.path.append('/home-link/qeana10/bin/') import re import os import time import datetime import shutil import subprocess import ch.systemsx.cisd.etlserver.registrator.api.v2 from java.io import File from org.apache.commons.io import FileUtils from ch.systemsx.cisd.openbis.generic.shared.api.v1.dto import SearchCriteria from ch.systemsx.cisd.openbis.generic.shared.api.v1.dto import SearchSubCriteria ePattern = re.compile('Q\w{4}E[0-9]+') pPattern = re.compile('Q\w{4}') def process(transaction): context = transaction.getRegistrationContext().getPersistentMap() incomingPath = transaction.getIncoming().getAbsolutePath() key = context.get("RETRY_COUNT") if (key == None): key = 1 name = transaction.getIncoming().getName() nameSplit = name.split("-") space = nameSplit[0] project = pPattern.findall(nameSplit[1])[0] experiment_id = ePattern.findall(nameSplit[2])[0] sampleCode = nameSplit[-1] sample_id = "/"+space+"/"+sampleCode if not experiment_id: print "The identifier matching the pattern Q\w{4}E\[0-9]+ was not found in the fileName "+name sample = transaction.getSampleForUpdate(sample_id) parents = sample.getParentSampleIdentifiers() parentcodes = [] for parent in parents: parentcodes.append(parent.split("/")[-1]) parentInfos = "_".join(parentcodes) experiment = transaction.getExperimentForUpdate("/"+space+"/"+project+"/"+experiment_id) experiment.setPropertyValue("Q_WF_STATUS", "FINISHED") endpoint = datetime.datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d %H:%M:%S') experiment.setPropertyValue("Q_WF_FINISHED_AT", endpoint) sample.setExperiment(experiment) dataSetRes = transaction.createNewDataSet('Q_WF_MS_INDIVIDUALIZED_PROTEOME_RESULTS') dataSetRes.setMeasuredData(False) dataSetLogs = transaction.createNewDataSet('Q_WF_MS_INDIVIDUALIZED_PROTEOME_LOGS') dataSetLogs.setMeasuredData(False) dataSetRes.setSample(sample) dataSetLogs.setSample(sample) resultsname = incomingPath+"/"+parentInfos+"_workflow_results" logname = incomingPath+"/"+parentInfos+"_workflow_logs" os.rename(incomingPath+"/logs", logname) os.rename(incomingPath+"/result", resultsname) transaction.moveFile(resultsname, dataSetRes) transaction.moveFile(logname, dataSetLogs)

false

true

f7fbf298d5b64ba1cefa46a4a5d2823c2fa8cf17

417

py

Python

configs/_base_/models/hrnet/hrnet-w18.py

YuxinZou/mmclassification

2037260ea6c98a3b115e97727e1151a1c2c32f7a

[ "Apache-2.0" ]

1

2022-03-15T07:36:04.000Z

configs/_base_/models/hrnet/hrnet-w18.py

YuxinZou/mmclassification

2037260ea6c98a3b115e97727e1151a1c2c32f7a

[ "Apache-2.0" ]

null

configs/_base_/models/hrnet/hrnet-w18.py

YuxinZou/mmclassification

2037260ea6c98a3b115e97727e1151a1c2c32f7a

[ "Apache-2.0" ]

1

2022-03-25T08:40:07.000Z

# model settings model = dict( type='ImageClassifier', backbone=dict(type='HRNet', arch='w18'), neck=[ dict(type='HRFuseScales', in_channels=(18, 36, 72, 144)), dict(type='GlobalAveragePooling'), ], head=dict( type='LinearClsHead', in_channels=2048, num_classes=1000, loss=dict(type='CrossEntropyLoss', loss_weight=1.0), topk=(1, 5), ))

26.0625

65

0.582734

model = dict( type='ImageClassifier', backbone=dict(type='HRNet', arch='w18'), neck=[ dict(type='HRFuseScales', in_channels=(18, 36, 72, 144)), dict(type='GlobalAveragePooling'), ], head=dict( type='LinearClsHead', in_channels=2048, num_classes=1000, loss=dict(type='CrossEntropyLoss', loss_weight=1.0), topk=(1, 5), ))

true

f7fbf2d9bef66db0c629235703060bf818cc2326

8,142

py

Python

rules/check_section_names.py

deptofdefense/SalSA

7ef771398e3d59597bade95d0a23540de0842e2a

[ "MIT" ]

84

2018-01-07T19:43:45.000Z

2021-12-23T14:17:44.000Z

rules/check_section_names.py

deptofdefense/SalSA

7ef771398e3d59597bade95d0a23540de0842e2a

[ "MIT" ]

7

2018-04-02T20:24:28.000Z

2019-06-07T21:48:04.000Z

rules/check_section_names.py

deptofdefense/SalSA

7ef771398e3d59597bade95d0a23540de0842e2a

[ "MIT" ]

18

2017-12-26T19:44:46.000Z

2021-09-13T12:21:02.000Z

""" check sction names for common packers/uncommon names """ # list of common packer section names packers = { '.aspack': 'Aspack packer', '.adata': 'Aspack packer/Armadillo packer', 'ASPack': 'Aspack packer', '.ASPack': 'ASPAck Protector', '.boom': 'The Boomerang List Builder (config+exe xored with a single byte key 0x77)', '.ccg': 'CCG Packer (Chinese Packer)', '.charmve': 'Added by the PIN tool', 'BitArts': 'Crunch 2.0 Packer', 'DAStub': 'DAStub Dragon Armor protector', '!EPack': 'Epack packer', 'FSG!': 'FSG packer (not a section name, but a good identifier)', '.gentee': 'Gentee installer', 'kkrunchy': 'kkrunchy Packer', '.mackt': 'ImpRec-created section', '.MaskPE': 'MaskPE Packer', 'MEW': 'MEW packer', '.MPRESS1': 'Mpress Packer', '.MPRESS2': 'Mpress Packer', '.neolite': 'Neolite Packer', '.neolit': 'Neolite Packer', '.nsp1': 'NsPack packer', '.nsp0': 'NsPack packer', '.nsp2': 'NsPack packer', 'nsp1': 'NsPack packer', 'nsp0': 'NsPack packer', 'nsp2': 'NsPack packer', '.packed': 'RLPack Packer (first section)', 'pebundle': 'PEBundle Packer', 'PEBundle': 'PEBundle Packer', 'PEC2TO': 'PECompact packer', 'PECompact2': 'PECompact packer (not a section name, but a good identifier)', 'PEC2': 'PECompact packer', 'pec1': 'PECompact packer', 'pec2': 'PECompact packer', 'PEC2MO': 'PECompact packer', 'PELOCKnt': 'PELock Protector', '.perplex': 'Perplex PE-Protector', 'PESHiELD': 'PEShield Packer', '.petite': 'Petite Packer', '.pinclie': 'Added by the PIN tool', 'ProCrypt': 'ProCrypt Packer', '.RLPack': 'RLPack Packer (second section)', '.rmnet': 'Ramnit virus marker', 'RCryptor': 'RPCrypt Packer', '.RPCrypt': 'RPCrypt Packer', '.seau': 'SeauSFX Packer', '.sforce3': 'StarForce Protection', '.spack': 'Simple Pack (by bagie)', '.svkp': 'SVKP packer', 'Themida': 'Themida Packer', '.Themida': 'Themida Packer', '.taz': 'Some version os PESpin', '.tsuarch': 'TSULoader', '.tsustub': 'TSULoader', '.packed': 'Unknown Packer', 'PEPACK!!': 'Pepack', '.Upack': 'Upack packer', '.ByDwing': 'Upack Packer', 'UPX0': 'UPX packer', 'UPX1': 'UPX packer', 'UPX2': 'UPX packer', 'UPX!': 'UPX packer', '.UPX0': 'UPX Packer', '.UPX1': 'UPX Packer', '.UPX2': 'UPX Packer', '.vmp0': 'VMProtect packer', '.vmp1': 'VMProtect packer', '.vmp2': 'VMProtect packer', 'VProtect': 'Vprotect Packer', '.winapi': 'Added by API Override tool', 'WinLicen': 'WinLicense (Themida) Protector', '_winzip_': 'WinZip Self-Extractor', '.WWPACK': 'WWPACK Packer', '.yP': 'Y0da Protector', '.y0da': 'Y0da Protector', } # list of common section names common = { '.00cfg': 'Control Flow Guard (CFG) section (added by newer versions of Visual Studio)', '.arch': 'Alpha-architecture section', '.autoload_text': 'cygwin/gcc; the Cygwin DLL uses a section to avoid copying certain data on fork.', '.bindat': 'Binary data (also used by one of the downware installers based on LUA)', '.bootdat': 'section that can be found inside Visual Studio files; contains palette entries', '.bss': 'Uninitialized Data Section', '.BSS': 'Uninitialized Data Section', '.buildid': 'gcc/cygwin; Contains debug information (if overlaps with debug directory)', '.CLR_UEF': '.CLR Unhandled Exception Handler section', '.code': 'Code Section', '.cormeta': '.CLR Metadata Section', '.complua': 'Binary data, most likely compiled LUA (also used by one of the downware installers based on LUA)', '.CRT': 'Initialized Data Section (C RunTime)', '.cygwin_dll_common': 'cygwin section containing flags representing Cygwin capabilities', '.data': 'Data Section', '.DATA': 'Data Section', '.data1': 'Data Section', '.data2': 'Data Section', '.data3': 'Data Section', '.debug': 'Debug info Section', '.debug$F': 'Debug info Section (Visual C++ version <7.0)', '.debug$P': 'Debug info Section (Visual C++ debug information: precompiled information', '.debug$S': 'Debug info Section (Visual C++ debug information: symbolic information)', '.debug$T': 'Debug info Section (Visual C++ debug information: type information)', '.drectve ': 'directive section (temporary, linker removes it after processing it; should not appear in a final PE image)', '.didat': 'Delay Import Section', '.didata': 'Delay Import Section', '.edata': 'Export Data Section', '.eh_fram': 'gcc/cygwin; Exception Handler Frame section', '.export': 'Alternative Export Data Section', '.fasm': 'FASM flat Section', '.flat': 'FASM flat Section', '.gfids': 'section added by new Visual Studio (14.0); purpose unknown', '.giats': 'section added by new Visual Studio (14.0); purpose unknown', '.gljmp': 'section added by new Visual Studio (14.0); purpose unknown', '.glue_7t': 'ARMv7 core glue functions (thumb mode)', '.glue_7': 'ARMv7 core glue functions (32-bit ARM mode)', '.idata': 'Initialized Data Section (Borland Compiler)', '.idlsym': 'IDL Attributes (registered SEH)', '.impdata': 'Alternative Import data section', '.itext': 'Code Section (Borland Compiler)', '.ndata': 'Nullsoft Installer section', '.orpc': 'Code section inside rpcrt4.dll', '.pdata': 'Exception Handling Functions Section (PDATA records)', '.rdata': 'Read-only initialized Data Section (Microsoft and Borland Compiler)', '.reloc': 'Relocations Section', '.rodata': 'Read-only Data Section', '.rsrc': 'Resource section', '.sbss': 'GP-relative Uninitialized Data Section', '.script': 'Section containing script', '.shared': 'Shared section', '.sdata': 'GP-relative Initialized Data Section', '.srdata': 'GP-relative Read-only Data Section', '.stab': 'Created by Haskell compiler (GHC)', '.stabstr': 'Created by Haskell compiler (GHC)', '.sxdata': 'Registered Exception Handlers Section', '.text': 'Code Section', '.text0': 'Alternative Code Section', '.text1': 'Alternative Code Section', '.text2': 'Alternative Code Section', '.text3': 'Alternative Code Section', '.textbss': 'Section used by incremental linking', '.tls': 'Thread Local Storage Section', '.tls$': 'Thread Local Storage Section', '.udata': 'Uninitialized Data Section', '.vsdata': 'GP-relative Initialized Data', '.xdata': 'Exception Information Section', '.wixburn': 'Wix section', 'BSS': 'Uninitialized Data Section (Borland Compiler)', 'CODE': 'Code Section (Borland Compiler)', 'DATA': 'Data Section (Borland Compiler)', 'DGROUP': 'Legacy data group section', 'edata': 'Export Data Section', 'idata': 'Initialized Data Section (C RunTime)', 'INIT': 'INIT section (drivers)', 'minATL': 'Section that can be found inside some ARM PE files; purpose unknown', 'PAGE': 'PAGE section (drivers)', 'rdata': 'Read-only Data Section', 'sdata': 'Initialized Data Section', 'shared': 'Shared section', 'Shared': 'Shared section', 'testdata': 'section containing test data (can be found inside Visual Studio files)', 'text': 'Alternative Code Section', } def run(peobject): alerts = [] unknown = [] known_bad = [] known_common = [] # loop through each section d = peobject.dict() if 'SECTIONS' in d: for s in d['SECTIONS']: # check for known/unknown sections if (s['Name'] in packers): known_bad.append(''.join([s['Name'], ' : ', packers[s['Name']]])) elif (s['Name'] in common): known_common.append(''.join([s['Name'], ' : ', common[s['Name']]])) else: unknown.append(''.join([s['Name'], ' : ???'])) # parse each section if known_common: alerts.append({ 'title': 'Known Common Section Names', 'description': 'These can indicate executable functionality.', 'data': known_common, 'code': '', }) if known_bad: alerts.append({ 'title': 'Known Bad Section Names', 'description': 'These can indicate packer usage.', 'data': known_bad, 'code': '', }) if unknown: alerts.append({ 'title': 'Unknown Section Names', 'description': 'These can indicate unknown packers.', 'data': unknown, 'code': '', }) return alerts

38.40566

125

0.652174

packers = { '.aspack': 'Aspack packer', '.adata': 'Aspack packer/Armadillo packer', 'ASPack': 'Aspack packer', '.ASPack': 'ASPAck Protector', '.boom': 'The Boomerang List Builder (config+exe xored with a single byte key 0x77)', '.ccg': 'CCG Packer (Chinese Packer)', '.charmve': 'Added by the PIN tool', 'BitArts': 'Crunch 2.0 Packer', 'DAStub': 'DAStub Dragon Armor protector', '!EPack': 'Epack packer', 'FSG!': 'FSG packer (not a section name, but a good identifier)', '.gentee': 'Gentee installer', 'kkrunchy': 'kkrunchy Packer', '.mackt': 'ImpRec-created section', '.MaskPE': 'MaskPE Packer', 'MEW': 'MEW packer', '.MPRESS1': 'Mpress Packer', '.MPRESS2': 'Mpress Packer', '.neolite': 'Neolite Packer', '.neolit': 'Neolite Packer', '.nsp1': 'NsPack packer', '.nsp0': 'NsPack packer', '.nsp2': 'NsPack packer', 'nsp1': 'NsPack packer', 'nsp0': 'NsPack packer', 'nsp2': 'NsPack packer', '.packed': 'RLPack Packer (first section)', 'pebundle': 'PEBundle Packer', 'PEBundle': 'PEBundle Packer', 'PEC2TO': 'PECompact packer', 'PECompact2': 'PECompact packer (not a section name, but a good identifier)', 'PEC2': 'PECompact packer', 'pec1': 'PECompact packer', 'pec2': 'PECompact packer', 'PEC2MO': 'PECompact packer', 'PELOCKnt': 'PELock Protector', '.perplex': 'Perplex PE-Protector', 'PESHiELD': 'PEShield Packer', '.petite': 'Petite Packer', '.pinclie': 'Added by the PIN tool', 'ProCrypt': 'ProCrypt Packer', '.RLPack': 'RLPack Packer (second section)', '.rmnet': 'Ramnit virus marker', 'RCryptor': 'RPCrypt Packer', '.RPCrypt': 'RPCrypt Packer', '.seau': 'SeauSFX Packer', '.sforce3': 'StarForce Protection', '.spack': 'Simple Pack (by bagie)', '.svkp': 'SVKP packer', 'Themida': 'Themida Packer', '.Themida': 'Themida Packer', '.taz': 'Some version os PESpin', '.tsuarch': 'TSULoader', '.tsustub': 'TSULoader', '.packed': 'Unknown Packer', 'PEPACK!!': 'Pepack', '.Upack': 'Upack packer', '.ByDwing': 'Upack Packer', 'UPX0': 'UPX packer', 'UPX1': 'UPX packer', 'UPX2': 'UPX packer', 'UPX!': 'UPX packer', '.UPX0': 'UPX Packer', '.UPX1': 'UPX Packer', '.UPX2': 'UPX Packer', '.vmp0': 'VMProtect packer', '.vmp1': 'VMProtect packer', '.vmp2': 'VMProtect packer', 'VProtect': 'Vprotect Packer', '.winapi': 'Added by API Override tool', 'WinLicen': 'WinLicense (Themida) Protector', '_winzip_': 'WinZip Self-Extractor', '.WWPACK': 'WWPACK Packer', '.yP': 'Y0da Protector', '.y0da': 'Y0da Protector', } common = { '.00cfg': 'Control Flow Guard (CFG) section (added by newer versions of Visual Studio)', '.arch': 'Alpha-architecture section', '.autoload_text': 'cygwin/gcc; the Cygwin DLL uses a section to avoid copying certain data on fork.', '.bindat': 'Binary data (also used by one of the downware installers based on LUA)', '.bootdat': 'section that can be found inside Visual Studio files; contains palette entries', '.bss': 'Uninitialized Data Section', '.BSS': 'Uninitialized Data Section', '.buildid': 'gcc/cygwin; Contains debug information (if overlaps with debug directory)', '.CLR_UEF': '.CLR Unhandled Exception Handler section', '.code': 'Code Section', '.cormeta': '.CLR Metadata Section', '.complua': 'Binary data, most likely compiled LUA (also used by one of the downware installers based on LUA)', '.CRT': 'Initialized Data Section (C RunTime)', '.cygwin_dll_common': 'cygwin section containing flags representing Cygwin capabilities', '.data': 'Data Section', '.DATA': 'Data Section', '.data1': 'Data Section', '.data2': 'Data Section', '.data3': 'Data Section', '.debug': 'Debug info Section', '.debug$F': 'Debug info Section (Visual C++ version <7.0)', '.debug$P': 'Debug info Section (Visual C++ debug information: precompiled information', '.debug$S': 'Debug info Section (Visual C++ debug information: symbolic information)', '.debug$T': 'Debug info Section (Visual C++ debug information: type information)', '.drectve ': 'directive section (temporary, linker removes it after processing it; should not appear in a final PE image)', '.didat': 'Delay Import Section', '.didata': 'Delay Import Section', '.edata': 'Export Data Section', '.eh_fram': 'gcc/cygwin; Exception Handler Frame section', '.export': 'Alternative Export Data Section', '.fasm': 'FASM flat Section', '.flat': 'FASM flat Section', '.gfids': 'section added by new Visual Studio (14.0); purpose unknown', '.giats': 'section added by new Visual Studio (14.0); purpose unknown', '.gljmp': 'section added by new Visual Studio (14.0); purpose unknown', '.glue_7t': 'ARMv7 core glue functions (thumb mode)', '.glue_7': 'ARMv7 core glue functions (32-bit ARM mode)', '.idata': 'Initialized Data Section (Borland Compiler)', '.idlsym': 'IDL Attributes (registered SEH)', '.impdata': 'Alternative Import data section', '.itext': 'Code Section (Borland Compiler)', '.ndata': 'Nullsoft Installer section', '.orpc': 'Code section inside rpcrt4.dll', '.pdata': 'Exception Handling Functions Section (PDATA records)', '.rdata': 'Read-only initialized Data Section (Microsoft and Borland Compiler)', '.reloc': 'Relocations Section', '.rodata': 'Read-only Data Section', '.rsrc': 'Resource section', '.sbss': 'GP-relative Uninitialized Data Section', '.script': 'Section containing script', '.shared': 'Shared section', '.sdata': 'GP-relative Initialized Data Section', '.srdata': 'GP-relative Read-only Data Section', '.stab': 'Created by Haskell compiler (GHC)', '.stabstr': 'Created by Haskell compiler (GHC)', '.sxdata': 'Registered Exception Handlers Section', '.text': 'Code Section', '.text0': 'Alternative Code Section', '.text1': 'Alternative Code Section', '.text2': 'Alternative Code Section', '.text3': 'Alternative Code Section', '.textbss': 'Section used by incremental linking', '.tls': 'Thread Local Storage Section', '.tls$': 'Thread Local Storage Section', '.udata': 'Uninitialized Data Section', '.vsdata': 'GP-relative Initialized Data', '.xdata': 'Exception Information Section', '.wixburn': 'Wix section', 'BSS': 'Uninitialized Data Section (Borland Compiler)', 'CODE': 'Code Section (Borland Compiler)', 'DATA': 'Data Section (Borland Compiler)', 'DGROUP': 'Legacy data group section', 'edata': 'Export Data Section', 'idata': 'Initialized Data Section (C RunTime)', 'INIT': 'INIT section (drivers)', 'minATL': 'Section that can be found inside some ARM PE files; purpose unknown', 'PAGE': 'PAGE section (drivers)', 'rdata': 'Read-only Data Section', 'sdata': 'Initialized Data Section', 'shared': 'Shared section', 'Shared': 'Shared section', 'testdata': 'section containing test data (can be found inside Visual Studio files)', 'text': 'Alternative Code Section', } def run(peobject): alerts = [] unknown = [] known_bad = [] known_common = [] d = peobject.dict() if 'SECTIONS' in d: for s in d['SECTIONS']: if (s['Name'] in packers): known_bad.append(''.join([s['Name'], ' : ', packers[s['Name']]])) elif (s['Name'] in common): known_common.append(''.join([s['Name'], ' : ', common[s['Name']]])) else: unknown.append(''.join([s['Name'], ' : ???'])) if known_common: alerts.append({ 'title': 'Known Common Section Names', 'description': 'These can indicate executable functionality.', 'data': known_common, 'code': '', }) if known_bad: alerts.append({ 'title': 'Known Bad Section Names', 'description': 'These can indicate packer usage.', 'data': known_bad, 'code': '', }) if unknown: alerts.append({ 'title': 'Unknown Section Names', 'description': 'These can indicate unknown packers.', 'data': unknown, 'code': '', }) return alerts

true

f7fbf3e0539de519d4dd1b4bb1d7b37b825d2dd1

16,363

py

Python

python/ccxt/braziliex.py

KaceyBolman/ccxt

d34a0651b209ac77453f05c4ce31883f0cd2d6b8

[ "MIT" ]

1

2018-07-06T08:14:13.000Z

python/ccxt/braziliex.py

LeapM/ccxt

aeb07fc86991ce38a218c1199c6ae3bb5cfdf228

[ "MIT" ]

null

python/ccxt/braziliex.py

LeapM/ccxt

aeb07fc86991ce38a218c1199c6ae3bb5cfdf228

[ "MIT" ]

2

2019-03-14T15:17:46.000Z

2019-09-08T19:26:04.000Z

# -*- coding: utf-8 -*- # PLEASE DO NOT EDIT THIS FILE, IT IS GENERATED AND WILL BE OVERWRITTEN: # https://github.com/ccxt/ccxt/blob/master/CONTRIBUTING.md#how-to-contribute-code from ccxt.base.exchange import Exchange import hashlib import math from ccxt.base.errors import ExchangeError from ccxt.base.errors import AuthenticationError from ccxt.base.errors import InvalidOrder class braziliex (Exchange): def describe(self): return self.deep_extend(super(braziliex, self).describe(), { 'id': 'braziliex', 'name': 'Braziliex', 'countries': ['BR'], 'rateLimit': 1000, 'has': { 'fetchCurrencies': True, 'fetchTickers': True, 'fetchOpenOrders': True, 'fetchMyTrades': True, 'fetchDepositAddress': True, }, 'urls': { 'logo': 'https://user-images.githubusercontent.com/1294454/34703593-c4498674-f504-11e7-8d14-ff8e44fb78c1.jpg', 'api': 'https://braziliex.com/api/v1', 'www': 'https://braziliex.com/', 'doc': 'https://braziliex.com/exchange/api.php', 'fees': 'https://braziliex.com/exchange/fees.php', }, 'api': { 'public': { 'get': [ 'currencies', 'ticker', 'ticker/{market}', 'orderbook/{market}', 'tradehistory/{market}', ], }, 'private': { 'post': [ 'balance', 'complete_balance', 'open_orders', 'trade_history', 'deposit_address', 'sell', 'buy', 'cancel_order', ], }, }, 'commonCurrencies': { 'EPC': 'Epacoin', }, 'fees': { 'trading': { 'maker': 0.005, 'taker': 0.005, }, }, 'precision': { 'amount': 8, 'price': 8, }, }) def fetch_currencies(self, params={}): currencies = self.publicGetCurrencies(params) ids = list(currencies.keys()) result = {} for i in range(0, len(ids)): id = ids[i] currency = currencies[id] precision = self.safe_integer(currency, 'decimal') uppercase = id.upper() code = self.common_currency_code(uppercase) active = self.safe_integer(currency, 'active') == 1 maintenance = self.safe_integer(currency, 'under_maintenance') if maintenance != 0: active = False canWithdraw = self.safe_integer(currency, 'is_withdrawal_active') == 1 canDeposit = self.safe_integer(currency, 'is_deposit_active') == 1 if not canWithdraw or not canDeposit: active = False result[code] = { 'id': id, 'code': code, 'name': currency['name'], 'active': active, 'precision': precision, 'funding': { 'withdraw': { 'active': canWithdraw, 'fee': currency['txWithdrawalFee'], }, 'deposit': { 'active': canDeposit, 'fee': currency['txDepositFee'], }, }, 'limits': { 'amount': { 'min': currency['minAmountTrade'], 'max': math.pow(10, precision), }, 'price': { 'min': math.pow(10, -precision), 'max': math.pow(10, precision), }, 'cost': { 'min': None, 'max': None, }, 'withdraw': { 'min': currency['MinWithdrawal'], 'max': math.pow(10, precision), }, 'deposit': { 'min': currency['minDeposit'], 'max': None, }, }, 'info': currency, } return result def fetch_markets(self): markets = self.publicGetTicker() ids = list(markets.keys()) result = [] for i in range(0, len(ids)): id = ids[i] market = markets[id] baseId, quoteId = id.split('_') base = baseId.upper() quote = quoteId.upper() base = self.common_currency_code(base) quote = self.common_currency_code(quote) symbol = base + '/' + quote active = self.safe_integer(market, 'active') == 1 precision = { 'amount': 8, 'price': 8, } lot = math.pow(10, -precision['amount']) result.append({ 'id': id, 'symbol': symbol.upper(), 'base': base, 'quote': quote, 'baseId': baseId, 'quoteId': quoteId, 'active': active, 'lot': lot, 'precision': precision, 'limits': { 'amount': { 'min': lot, 'max': math.pow(10, precision['amount']), }, 'price': { 'min': math.pow(10, -precision['price']), 'max': math.pow(10, precision['price']), }, 'cost': { 'min': None, 'max': None, }, }, 'info': market, }) return result def parse_ticker(self, ticker, market=None): symbol = market['symbol'] timestamp = ticker['date'] ticker = ticker['ticker'] last = self.safe_float(ticker, 'last') return { 'symbol': symbol, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'high': self.safe_float(ticker, 'highestBid24'), 'low': self.safe_float(ticker, 'lowestAsk24'), 'bid': self.safe_float(ticker, 'highestBid'), 'bidVolume': None, 'ask': self.safe_float(ticker, 'lowestAsk'), 'askVolume': None, 'vwap': None, 'open': None, 'close': last, 'last': last, 'previousClose': None, 'change': self.safe_float(ticker, 'percentChange'), 'percentage': None, 'average': None, 'baseVolume': self.safe_float(ticker, 'baseVolume24'), 'quoteVolume': self.safe_float(ticker, 'quoteVolume24'), 'info': ticker, } def fetch_ticker(self, symbol, params={}): self.load_markets() market = self.market(symbol) ticker = self.publicGetTickerMarket(self.extend({ 'market': market['id'], }, params)) ticker = { 'date': self.milliseconds(), 'ticker': ticker, } return self.parse_ticker(ticker, market) def fetch_tickers(self, symbols=None, params={}): self.load_markets() tickers = self.publicGetTicker(params) result = {} timestamp = self.milliseconds() ids = list(tickers.keys()) for i in range(0, len(ids)): id = ids[i] market = self.markets_by_id[id] symbol = market['symbol'] ticker = { 'date': timestamp, 'ticker': tickers[id], } result[symbol] = self.parse_ticker(ticker, market) return result def fetch_order_book(self, symbol, limit=None, params={}): self.load_markets() orderbook = self.publicGetOrderbookMarket(self.extend({ 'market': self.market_id(symbol), }, params)) return self.parse_order_book(orderbook, None, 'bids', 'asks', 'price', 'amount') def parse_trade(self, trade, market=None): timestamp = None if 'date_exec' in trade: timestamp = self.parse8601(trade['date_exec']) else: timestamp = self.parse8601(trade['date']) price = self.safe_float(trade, 'price') amount = self.safe_float(trade, 'amount') symbol = market['symbol'] cost = self.safe_float(trade, 'total') orderId = self.safe_string(trade, 'order_number') return { 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'symbol': symbol, 'id': self.safe_string(trade, '_id'), 'order': orderId, 'type': 'limit', 'side': trade['type'], 'price': price, 'amount': amount, 'cost': cost, 'fee': None, 'info': trade, } def fetch_trades(self, symbol, since=None, limit=None, params={}): self.load_markets() market = self.market(symbol) trades = self.publicGetTradehistoryMarket(self.extend({ 'market': market['id'], }, params)) return self.parse_trades(trades, market, since, limit) def fetch_balance(self, params={}): self.load_markets() balances = self.privatePostCompleteBalance(params) result = {'info': balances} currencies = list(balances.keys()) for i in range(0, len(currencies)): id = currencies[i] balance = balances[id] currency = self.common_currency_code(id) account = { 'free': float(balance['available']), 'used': 0.0, 'total': float(balance['total']), } account['used'] = account['total'] - account['free'] result[currency] = account return self.parse_balance(result) def parse_order(self, order, market=None): symbol = None if market is None: marketId = self.safe_string(order, 'market') if marketId: if marketId in self.markets_by_id: market = self.markets_by_id[marketId] if market: symbol = market['symbol'] timestamp = self.safe_value(order, 'timestamp') if not timestamp: timestamp = self.parse8601(order['date']) price = self.safe_float(order, 'price') cost = self.safe_float(order, 'total', 0.0) amount = self.safe_float(order, 'amount') filledPercentage = self.safe_float(order, 'progress') filled = amount * filledPercentage remaining = self.amount_to_precision(symbol, amount - filled) info = order if 'info' in info: info = order['info'] return { 'id': order['order_number'], 'datetime': self.iso8601(timestamp), 'timestamp': timestamp, 'lastTradeTimestamp': None, 'status': 'open', 'symbol': symbol, 'type': 'limit', 'side': order['type'], 'price': price, 'cost': cost, 'amount': amount, 'filled': filled, 'remaining': remaining, 'trades': None, 'fee': self.safe_value(order, 'fee'), 'info': info, } def create_order(self, symbol, type, side, amount, price=None, params={}): self.load_markets() market = self.market(symbol) method = 'privatePost' + self.capitalize(side) response = getattr(self, method)(self.extend({ 'market': market['id'], # 'price': self.price_to_precision(symbol, price), # 'amount': self.amount_to_precision(symbol, amount), 'price': price, 'amount': amount, }, params)) success = self.safe_integer(response, 'success') if success != 1: raise InvalidOrder(self.id + ' ' + self.json(response)) parts = response['message'].split(' / ') parts = parts[1:] feeParts = parts[5].split(' ') order = self.parse_order({ 'timestamp': self.milliseconds(), 'order_number': response['order_number'], 'type': parts[0].lower(), 'market': parts[0].lower(), 'amount': parts[2].split(' ')[1], 'price': parts[3].split(' ')[1], 'total': parts[4].split(' ')[1], 'fee': { 'cost': float(feeParts[1]), 'currency': feeParts[2], }, 'progress': '0.0', 'info': response, }, market) id = order['id'] self.orders[id] = order return order def cancel_order(self, id, symbol=None, params={}): self.load_markets() market = self.market(symbol) result = self.privatePostCancelOrder(self.extend({ 'order_number': id, 'market': market['id'], }, params)) return result def fetch_open_orders(self, symbol=None, since=None, limit=None, params={}): self.load_markets() market = self.market(symbol) orders = self.privatePostOpenOrders(self.extend({ 'market': market['id'], }, params)) return self.parse_orders(orders['order_open'], market, since, limit) def fetch_my_trades(self, symbol=None, since=None, limit=None, params={}): self.load_markets() market = self.market(symbol) trades = self.privatePostTradeHistory(self.extend({ 'market': market['id'], }, params)) return self.parse_trades(trades['trade_history'], market, since, limit) def fetch_deposit_address(self, code, params={}): self.load_markets() currency = self.currency(code) response = self.privatePostDepositAddress(self.extend({ 'currency': currency['id'], }, params)) address = self.safe_string(response, 'deposit_address') self.check_address(address) tag = self.safe_string(response, 'payment_id') return { 'currency': code, 'address': address, 'tag': tag, 'info': response, } def sign(self, path, api='public', method='GET', params={}, headers=None, body=None): url = self.urls['api'] + '/' + api query = self.omit(params, self.extract_params(path)) if api == 'public': url += '/' + self.implode_params(path, params) if query: url += '?' + self.urlencode(query) else: self.check_required_credentials() query = self.extend({ 'command': path, 'nonce': self.nonce(), }, query) body = self.urlencode(query) signature = self.hmac(self.encode(body), self.encode(self.secret), hashlib.sha512) headers = { 'Content-type': 'application/x-www-form-urlencoded', 'Key': self.apiKey, 'Sign': self.decode(signature), } return {'url': url, 'method': method, 'body': body, 'headers': headers} def request(self, path, api='public', method='GET', params={}, headers=None, body=None): response = self.fetch2(path, api, method, params, headers, body) if 'success' in response: success = self.safe_integer(response, 'success') if success == 0: message = self.safe_string(response, 'message') if message == 'Invalid APIKey': raise AuthenticationError(message) raise ExchangeError(message) return response

36.688341

126

0.478885

ge import Exchange import hashlib import math from ccxt.base.errors import ExchangeError from ccxt.base.errors import AuthenticationError from ccxt.base.errors import InvalidOrder class braziliex (Exchange): def describe(self): return self.deep_extend(super(braziliex, self).describe(), { 'id': 'braziliex', 'name': 'Braziliex', 'countries': ['BR'], 'rateLimit': 1000, 'has': { 'fetchCurrencies': True, 'fetchTickers': True, 'fetchOpenOrders': True, 'fetchMyTrades': True, 'fetchDepositAddress': True, }, 'urls': { 'logo': 'https://user-images.githubusercontent.com/1294454/34703593-c4498674-f504-11e7-8d14-ff8e44fb78c1.jpg', 'api': 'https://braziliex.com/api/v1', 'www': 'https://braziliex.com/', 'doc': 'https://braziliex.com/exchange/api.php', 'fees': 'https://braziliex.com/exchange/fees.php', }, 'api': { 'public': { 'get': [ 'currencies', 'ticker', 'ticker/{market}', 'orderbook/{market}', 'tradehistory/{market}', ], }, 'private': { 'post': [ 'balance', 'complete_balance', 'open_orders', 'trade_history', 'deposit_address', 'sell', 'buy', 'cancel_order', ], }, }, 'commonCurrencies': { 'EPC': 'Epacoin', }, 'fees': { 'trading': { 'maker': 0.005, 'taker': 0.005, }, }, 'precision': { 'amount': 8, 'price': 8, }, }) def fetch_currencies(self, params={}): currencies = self.publicGetCurrencies(params) ids = list(currencies.keys()) result = {} for i in range(0, len(ids)): id = ids[i] currency = currencies[id] precision = self.safe_integer(currency, 'decimal') uppercase = id.upper() code = self.common_currency_code(uppercase) active = self.safe_integer(currency, 'active') == 1 maintenance = self.safe_integer(currency, 'under_maintenance') if maintenance != 0: active = False canWithdraw = self.safe_integer(currency, 'is_withdrawal_active') == 1 canDeposit = self.safe_integer(currency, 'is_deposit_active') == 1 if not canWithdraw or not canDeposit: active = False result[code] = { 'id': id, 'code': code, 'name': currency['name'], 'active': active, 'precision': precision, 'funding': { 'withdraw': { 'active': canWithdraw, 'fee': currency['txWithdrawalFee'], }, 'deposit': { 'active': canDeposit, 'fee': currency['txDepositFee'], }, }, 'limits': { 'amount': { 'min': currency['minAmountTrade'], 'max': math.pow(10, precision), }, 'price': { 'min': math.pow(10, -precision), 'max': math.pow(10, precision), }, 'cost': { 'min': None, 'max': None, }, 'withdraw': { 'min': currency['MinWithdrawal'], 'max': math.pow(10, precision), }, 'deposit': { 'min': currency['minDeposit'], 'max': None, }, }, 'info': currency, } return result def fetch_markets(self): markets = self.publicGetTicker() ids = list(markets.keys()) result = [] for i in range(0, len(ids)): id = ids[i] market = markets[id] baseId, quoteId = id.split('_') base = baseId.upper() quote = quoteId.upper() base = self.common_currency_code(base) quote = self.common_currency_code(quote) symbol = base + '/' + quote active = self.safe_integer(market, 'active') == 1 precision = { 'amount': 8, 'price': 8, } lot = math.pow(10, -precision['amount']) result.append({ 'id': id, 'symbol': symbol.upper(), 'base': base, 'quote': quote, 'baseId': baseId, 'quoteId': quoteId, 'active': active, 'lot': lot, 'precision': precision, 'limits': { 'amount': { 'min': lot, 'max': math.pow(10, precision['amount']), }, 'price': { 'min': math.pow(10, -precision['price']), 'max': math.pow(10, precision['price']), }, 'cost': { 'min': None, 'max': None, }, }, 'info': market, }) return result def parse_ticker(self, ticker, market=None): symbol = market['symbol'] timestamp = ticker['date'] ticker = ticker['ticker'] last = self.safe_float(ticker, 'last') return { 'symbol': symbol, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'high': self.safe_float(ticker, 'highestBid24'), 'low': self.safe_float(ticker, 'lowestAsk24'), 'bid': self.safe_float(ticker, 'highestBid'), 'bidVolume': None, 'ask': self.safe_float(ticker, 'lowestAsk'), 'askVolume': None, 'vwap': None, 'open': None, 'close': last, 'last': last, 'previousClose': None, 'change': self.safe_float(ticker, 'percentChange'), 'percentage': None, 'average': None, 'baseVolume': self.safe_float(ticker, 'baseVolume24'), 'quoteVolume': self.safe_float(ticker, 'quoteVolume24'), 'info': ticker, } def fetch_ticker(self, symbol, params={}): self.load_markets() market = self.market(symbol) ticker = self.publicGetTickerMarket(self.extend({ 'market': market['id'], }, params)) ticker = { 'date': self.milliseconds(), 'ticker': ticker, } return self.parse_ticker(ticker, market) def fetch_tickers(self, symbols=None, params={}): self.load_markets() tickers = self.publicGetTicker(params) result = {} timestamp = self.milliseconds() ids = list(tickers.keys()) for i in range(0, len(ids)): id = ids[i] market = self.markets_by_id[id] symbol = market['symbol'] ticker = { 'date': timestamp, 'ticker': tickers[id], } result[symbol] = self.parse_ticker(ticker, market) return result def fetch_order_book(self, symbol, limit=None, params={}): self.load_markets() orderbook = self.publicGetOrderbookMarket(self.extend({ 'market': self.market_id(symbol), }, params)) return self.parse_order_book(orderbook, None, 'bids', 'asks', 'price', 'amount') def parse_trade(self, trade, market=None): timestamp = None if 'date_exec' in trade: timestamp = self.parse8601(trade['date_exec']) else: timestamp = self.parse8601(trade['date']) price = self.safe_float(trade, 'price') amount = self.safe_float(trade, 'amount') symbol = market['symbol'] cost = self.safe_float(trade, 'total') orderId = self.safe_string(trade, 'order_number') return { 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'symbol': symbol, 'id': self.safe_string(trade, '_id'), 'order': orderId, 'type': 'limit', 'side': trade['type'], 'price': price, 'amount': amount, 'cost': cost, 'fee': None, 'info': trade, } def fetch_trades(self, symbol, since=None, limit=None, params={}): self.load_markets() market = self.market(symbol) trades = self.publicGetTradehistoryMarket(self.extend({ 'market': market['id'], }, params)) return self.parse_trades(trades, market, since, limit) def fetch_balance(self, params={}): self.load_markets() balances = self.privatePostCompleteBalance(params) result = {'info': balances} currencies = list(balances.keys()) for i in range(0, len(currencies)): id = currencies[i] balance = balances[id] currency = self.common_currency_code(id) account = { 'free': float(balance['available']), 'used': 0.0, 'total': float(balance['total']), } account['used'] = account['total'] - account['free'] result[currency] = account return self.parse_balance(result) def parse_order(self, order, market=None): symbol = None if market is None: marketId = self.safe_string(order, 'market') if marketId: if marketId in self.markets_by_id: market = self.markets_by_id[marketId] if market: symbol = market['symbol'] timestamp = self.safe_value(order, 'timestamp') if not timestamp: timestamp = self.parse8601(order['date']) price = self.safe_float(order, 'price') cost = self.safe_float(order, 'total', 0.0) amount = self.safe_float(order, 'amount') filledPercentage = self.safe_float(order, 'progress') filled = amount * filledPercentage remaining = self.amount_to_precision(symbol, amount - filled) info = order if 'info' in info: info = order['info'] return { 'id': order['order_number'], 'datetime': self.iso8601(timestamp), 'timestamp': timestamp, 'lastTradeTimestamp': None, 'status': 'open', 'symbol': symbol, 'type': 'limit', 'side': order['type'], 'price': price, 'cost': cost, 'amount': amount, 'filled': filled, 'remaining': remaining, 'trades': None, 'fee': self.safe_value(order, 'fee'), 'info': info, } def create_order(self, symbol, type, side, amount, price=None, params={}): self.load_markets() market = self.market(symbol) method = 'privatePost' + self.capitalize(side) response = getattr(self, method)(self.extend({ 'market': market['id'], 'price': price, 'amount': amount, }, params)) success = self.safe_integer(response, 'success') if success != 1: raise InvalidOrder(self.id + ' ' + self.json(response)) parts = response['message'].split(' / ') parts = parts[1:] feeParts = parts[5].split(' ') order = self.parse_order({ 'timestamp': self.milliseconds(), 'order_number': response['order_number'], 'type': parts[0].lower(), 'market': parts[0].lower(), 'amount': parts[2].split(' ')[1], 'price': parts[3].split(' ')[1], 'total': parts[4].split(' ')[1], 'fee': { 'cost': float(feeParts[1]), 'currency': feeParts[2], }, 'progress': '0.0', 'info': response, }, market) id = order['id'] self.orders[id] = order return order def cancel_order(self, id, symbol=None, params={}): self.load_markets() market = self.market(symbol) result = self.privatePostCancelOrder(self.extend({ 'order_number': id, 'market': market['id'], }, params)) return result def fetch_open_orders(self, symbol=None, since=None, limit=None, params={}): self.load_markets() market = self.market(symbol) orders = self.privatePostOpenOrders(self.extend({ 'market': market['id'], }, params)) return self.parse_orders(orders['order_open'], market, since, limit) def fetch_my_trades(self, symbol=None, since=None, limit=None, params={}): self.load_markets() market = self.market(symbol) trades = self.privatePostTradeHistory(self.extend({ 'market': market['id'], }, params)) return self.parse_trades(trades['trade_history'], market, since, limit) def fetch_deposit_address(self, code, params={}): self.load_markets() currency = self.currency(code) response = self.privatePostDepositAddress(self.extend({ 'currency': currency['id'], }, params)) address = self.safe_string(response, 'deposit_address') self.check_address(address) tag = self.safe_string(response, 'payment_id') return { 'currency': code, 'address': address, 'tag': tag, 'info': response, } def sign(self, path, api='public', method='GET', params={}, headers=None, body=None): url = self.urls['api'] + '/' + api query = self.omit(params, self.extract_params(path)) if api == 'public': url += '/' + self.implode_params(path, params) if query: url += '?' + self.urlencode(query) else: self.check_required_credentials() query = self.extend({ 'command': path, 'nonce': self.nonce(), }, query) body = self.urlencode(query) signature = self.hmac(self.encode(body), self.encode(self.secret), hashlib.sha512) headers = { 'Content-type': 'application/x-www-form-urlencoded', 'Key': self.apiKey, 'Sign': self.decode(signature), } return {'url': url, 'method': method, 'body': body, 'headers': headers} def request(self, path, api='public', method='GET', params={}, headers=None, body=None): response = self.fetch2(path, api, method, params, headers, body) if 'success' in response: success = self.safe_integer(response, 'success') if success == 0: message = self.safe_string(response, 'message') if message == 'Invalid APIKey': raise AuthenticationError(message) raise ExchangeError(message) return response

true

f7fbf44db0fbb7cca4e38afe57fb7a389fcd8330

9,358

py

Python

src/Algorithms/PPO.py

fightZero/fightZero

84c2f76c7dda31837d002e47cd74936044251079

[ "MIT" ]

1

2021-12-13T14:05:05.000Z

src/Algorithms/PPO.py

FightZero/FightZero

84c2f76c7dda31837d002e47cd74936044251079

[ "MIT" ]

null

src/Algorithms/PPO.py

FightZero/FightZero

84c2f76c7dda31837d002e47cd74936044251079

[ "MIT" ]

null

# an implementation of PPO algorithm # reference to: https://github.com/nikhilbarhate99/PPO-PyTorch import torch import torch.nn as nn from torch.optim import Adam, RMSprop from torch.distributions import Categorical from torch.utils.tensorboard.writer import SummaryWriter from torch.utils.data import BatchSampler, RandomSampler from typing import Tuple # this class implements an actor critic model with linear networks class ActorCritic(nn.Module): def __init__(self, state_dimension, action_dimension): super().__init__() # save dimensions self.d_state = state_dimension self.d_action = action_dimension # create actor network self.actor = nn.Sequential( nn.Linear(self.d_state, 1024), nn.LeakyReLU(), nn.Linear(1024, 512), nn.LeakyReLU(), nn.Linear(512, 256), nn.LeakyReLU(), nn.Linear(256, 128), nn.LeakyReLU(), nn.Linear(128, self.d_action), nn.Softmax(dim=1) ) # create critic network self.critic = nn.Sequential( nn.Linear(self.d_state, 1024), nn.LeakyReLU(), nn.Linear(1024, 512), nn.LeakyReLU(), nn.Linear(512, 256), nn.LeakyReLU(), nn.Linear(256, 128), nn.LeakyReLU(), nn.Linear(128, 64), nn.LeakyReLU(), nn.Linear(64, 1) ) def forward(self, x): """ Empty forward function """ return x def action(self, state) -> Tuple[torch.Tensor, torch.Tensor]: """ Get action and log probs """ # get probabilities of actions probs = self.actor(state) dist = Categorical(probs=probs) # sample an action action = dist.sample() logprob = dist.log_prob(action) return action.detach(), logprob.detach() def evaluate(self, state, action) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: """ Evaluates an action """ # get probabilities of actions probs = self.actor(state) dist = Categorical(probs=probs) # get distribution entropy and log probs of chosen action entropy = dist.entropy() logprob = dist.log_prob(action).diagonal().view(action.shape) # get critic value critics = self.critic(state) return entropy, logprob, critics # this structure stores buffer info for PPO class PPOBuffer: def __init__(self): self.actions = [] self.states = [] self.logprobs = [] self.rewards = [] self.is_terminals = [] def reset(self): del self.actions[:] del self.states[:] del self.logprobs[:] del self.rewards[:] del self.is_terminals[:] self.actions = [] self.states = [] self.logprobs = [] self.rewards = [] self.is_terminals = [] def isEmpty(self): return len(self.actions) <= 0 # this class implements PPO model # with actor and critic updated individually class PPO(object): def __init__(self, state_dimension, action_dimension, lr_actor, lr_critic, num_epochs, discount, eps_clip, batch_size, max_grad_norm, train ): self.discount = discount self.num_epochs = num_epochs self.eps_clip = eps_clip self.lr_actor = lr_actor self.lr_critic = lr_critic self.batch_size = batch_size self.max_grad_norm = max_grad_norm self.training = train self.iter_count = 0 # create buffer self.buffer = PPOBuffer() # select running environment for train self.device = "cuda" if torch.cuda.is_available() else "cpu" # create actor critic model self.AC = ActorCritic(state_dimension, action_dimension).to(self.device) # set optimizer self.optim_actor = Adam(self.AC.actor.parameters(), lr_actor) self.optim_critic = Adam(self.AC.critic.parameters(), lr_critic) # set saved model self.AC_saved = ActorCritic(state_dimension, action_dimension).to(self.device) self.AC_saved.load_state_dict(self.AC.state_dict()) self.AC_saved.eval() # set loss function self.loss = nn.MSELoss() def action(self, state): """ Choose next action """ with torch.no_grad(): # get new action from actor state = torch.FloatTensor(state).to(self.device) action, logprob = self.AC_saved.action(state) # store into buffer if self.training: self.buffer.states.append(state) self.buffer.actions.append(action) self.buffer.logprobs.append(logprob) return action.cpu().item() def save(self, filename): """ Save current network to file path """ torch.save(( self.AC_saved.state_dict(), self.optim_actor.state_dict(), self.optim_critic.state_dict(), ), filename) def load(self, filename): """ Load network from file path """ states, opt1, opt2 = torch.load(filename, map_location=lambda storage, _: storage) self.AC.load_state_dict(states) self.AC_saved.load_state_dict(states) self.optim_actor.load_state_dict(opt1) self.optim_critic.load_state_dict(opt2) def train(self, writer : SummaryWriter): """ Update policy """ if not self.training: return if self.buffer.isEmpty(): return rewards = [] reward_disc = 0.0 for reward, is_terminal in zip(reversed(self.buffer.rewards), reversed(self.buffer.is_terminals)): # if is terminal state, set reward to 0 if is_terminal: reward_disc = 0.0 reward_disc = reward + (self.discount * reward_disc) rewards.insert(0, reward_disc) length = len(rewards) # normalize the rewards target_values = torch.FloatTensor(rewards) target_values = (target_values - target_values.mean()) / (target_values.std() + 1e-8) target_values = target_values.view(-1, 1) # convert list to tensor old_states = torch.squeeze(torch.stack(self.buffer.states[:length], dim=0)).detach() old_actions = torch.squeeze(torch.stack(self.buffer.actions[:length], dim=0)).view(-1, 1).detach() old_logprobs = torch.squeeze(torch.stack(self.buffer.logprobs[:length], dim=0)).view(-1, 1).detach() # start training self.AC.train() torch.cuda.empty_cache() for _ in range(self.num_epochs): for indices in BatchSampler(RandomSampler(range(length)), batch_size=self.batch_size, drop_last=False): target_values_gpu = target_values[indices].to(self.device) old_states_gpu = old_states[indices].to(self.device) old_actions_gpu = old_actions[indices].to(self.device) old_logprobs_gpu = old_logprobs[indices].to(self.device) # get critics _, logprob, state_values = self.AC.evaluate(old_states_gpu, old_actions_gpu) # state_values = torch.squeeze(critics) # compute advantages advantages = (target_values_gpu - state_values).detach() # find the ratio (pi_theta / pi_theta__old) ratios = torch.exp((logprob - old_logprobs_gpu)) # find Surrogate Loss (Clipped Surrogate Objective) surr1 = ratios * advantages surr2 = torch.clamp(ratios, 1 - self.eps_clip, 1 + self.eps_clip) * advantages # compute actor loss loss_actor = -torch.min(surr1, surr2).mean() # optimize actor self.optim_actor.zero_grad() loss_actor.backward() torch.nn.utils.clip_grad.clip_grad_norm_(self.AC.actor.parameters(), max_norm=self.max_grad_norm) self.optim_actor.step() # compute critic loss loss_critic = self.loss(state_values, target_values_gpu).mean() self.optim_critic.zero_grad() loss_critic.backward() torch.nn.utils.clip_grad.clip_grad_norm_(self.AC.critic.parameters(), max_norm=self.max_grad_norm) self.optim_critic.step() # log in tensorboard writer.add_scalar("PPO/Loss Actor", loss_actor.cpu().detach().item(), self.iter_count) writer.add_scalar("PPO/Loss Critic", loss_critic.cpu().detach().item(), self.iter_count) writer.add_scalar("PPO/Advantage", advantages.cpu().detach().mean().item(), self.iter_count) self.iter_count += 1 self.AC.eval() # save weights after training self.AC_saved.load_state_dict(self.AC.state_dict()) self.AC_saved.eval() # clear buffer self.buffer.reset() def update(self, reward, is_terminal): """ Update buffer """ if not self.training: return self.buffer.rewards.append(reward) self.buffer.is_terminals.append(is_terminal)

37.88664

115

0.596495

import torch import torch.nn as nn from torch.optim import Adam, RMSprop from torch.distributions import Categorical from torch.utils.tensorboard.writer import SummaryWriter from torch.utils.data import BatchSampler, RandomSampler from typing import Tuple class ActorCritic(nn.Module): def __init__(self, state_dimension, action_dimension): super().__init__() self.d_state = state_dimension self.d_action = action_dimension self.actor = nn.Sequential( nn.Linear(self.d_state, 1024), nn.LeakyReLU(), nn.Linear(1024, 512), nn.LeakyReLU(), nn.Linear(512, 256), nn.LeakyReLU(), nn.Linear(256, 128), nn.LeakyReLU(), nn.Linear(128, self.d_action), nn.Softmax(dim=1) ) self.critic = nn.Sequential( nn.Linear(self.d_state, 1024), nn.LeakyReLU(), nn.Linear(1024, 512), nn.LeakyReLU(), nn.Linear(512, 256), nn.LeakyReLU(), nn.Linear(256, 128), nn.LeakyReLU(), nn.Linear(128, 64), nn.LeakyReLU(), nn.Linear(64, 1) ) def forward(self, x): return x def action(self, state) -> Tuple[torch.Tensor, torch.Tensor]: probs = self.actor(state) dist = Categorical(probs=probs) action = dist.sample() logprob = dist.log_prob(action) return action.detach(), logprob.detach() def evaluate(self, state, action) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: probs = self.actor(state) dist = Categorical(probs=probs) entropy = dist.entropy() logprob = dist.log_prob(action).diagonal().view(action.shape) critics = self.critic(state) return entropy, logprob, critics class PPOBuffer: def __init__(self): self.actions = [] self.states = [] self.logprobs = [] self.rewards = [] self.is_terminals = [] def reset(self): del self.actions[:] del self.states[:] del self.logprobs[:] del self.rewards[:] del self.is_terminals[:] self.actions = [] self.states = [] self.logprobs = [] self.rewards = [] self.is_terminals = [] def isEmpty(self): return len(self.actions) <= 0 class PPO(object): def __init__(self, state_dimension, action_dimension, lr_actor, lr_critic, num_epochs, discount, eps_clip, batch_size, max_grad_norm, train ): self.discount = discount self.num_epochs = num_epochs self.eps_clip = eps_clip self.lr_actor = lr_actor self.lr_critic = lr_critic self.batch_size = batch_size self.max_grad_norm = max_grad_norm self.training = train self.iter_count = 0 self.buffer = PPOBuffer() self.device = "cuda" if torch.cuda.is_available() else "cpu" self.AC = ActorCritic(state_dimension, action_dimension).to(self.device) self.optim_actor = Adam(self.AC.actor.parameters(), lr_actor) self.optim_critic = Adam(self.AC.critic.parameters(), lr_critic) self.AC_saved = ActorCritic(state_dimension, action_dimension).to(self.device) self.AC_saved.load_state_dict(self.AC.state_dict()) self.AC_saved.eval() self.loss = nn.MSELoss() def action(self, state): with torch.no_grad(): state = torch.FloatTensor(state).to(self.device) action, logprob = self.AC_saved.action(state) if self.training: self.buffer.states.append(state) self.buffer.actions.append(action) self.buffer.logprobs.append(logprob) return action.cpu().item() def save(self, filename): torch.save(( self.AC_saved.state_dict(), self.optim_actor.state_dict(), self.optim_critic.state_dict(), ), filename) def load(self, filename): states, opt1, opt2 = torch.load(filename, map_location=lambda storage, _: storage) self.AC.load_state_dict(states) self.AC_saved.load_state_dict(states) self.optim_actor.load_state_dict(opt1) self.optim_critic.load_state_dict(opt2) def train(self, writer : SummaryWriter): if not self.training: return if self.buffer.isEmpty(): return rewards = [] reward_disc = 0.0 for reward, is_terminal in zip(reversed(self.buffer.rewards), reversed(self.buffer.is_terminals)): if is_terminal: reward_disc = 0.0 reward_disc = reward + (self.discount * reward_disc) rewards.insert(0, reward_disc) length = len(rewards) target_values = torch.FloatTensor(rewards) target_values = (target_values - target_values.mean()) / (target_values.std() + 1e-8) target_values = target_values.view(-1, 1) old_states = torch.squeeze(torch.stack(self.buffer.states[:length], dim=0)).detach() old_actions = torch.squeeze(torch.stack(self.buffer.actions[:length], dim=0)).view(-1, 1).detach() old_logprobs = torch.squeeze(torch.stack(self.buffer.logprobs[:length], dim=0)).view(-1, 1).detach() self.AC.train() torch.cuda.empty_cache() for _ in range(self.num_epochs): for indices in BatchSampler(RandomSampler(range(length)), batch_size=self.batch_size, drop_last=False): target_values_gpu = target_values[indices].to(self.device) old_states_gpu = old_states[indices].to(self.device) old_actions_gpu = old_actions[indices].to(self.device) old_logprobs_gpu = old_logprobs[indices].to(self.device) _, logprob, state_values = self.AC.evaluate(old_states_gpu, old_actions_gpu) advantages = (target_values_gpu - state_values).detach() ratios = torch.exp((logprob - old_logprobs_gpu)) surr1 = ratios * advantages surr2 = torch.clamp(ratios, 1 - self.eps_clip, 1 + self.eps_clip) * advantages loss_actor = -torch.min(surr1, surr2).mean() self.optim_actor.zero_grad() loss_actor.backward() torch.nn.utils.clip_grad.clip_grad_norm_(self.AC.actor.parameters(), max_norm=self.max_grad_norm) self.optim_actor.step() loss_critic = self.loss(state_values, target_values_gpu).mean() self.optim_critic.zero_grad() loss_critic.backward() torch.nn.utils.clip_grad.clip_grad_norm_(self.AC.critic.parameters(), max_norm=self.max_grad_norm) self.optim_critic.step() writer.add_scalar("PPO/Loss Actor", loss_actor.cpu().detach().item(), self.iter_count) writer.add_scalar("PPO/Loss Critic", loss_critic.cpu().detach().item(), self.iter_count) writer.add_scalar("PPO/Advantage", advantages.cpu().detach().mean().item(), self.iter_count) self.iter_count += 1 self.AC.eval() self.AC_saved.load_state_dict(self.AC.state_dict()) self.AC_saved.eval() self.buffer.reset() def update(self, reward, is_terminal): if not self.training: return self.buffer.rewards.append(reward) self.buffer.is_terminals.append(is_terminal)

true

f7fbf451f7ab0b316753c8ad61a542b73cbff82d

14,904

py

Python

processing_provider/Rast_fillRasterwithPatches.py

geodourados/lftools

4b9d703513bd3d49ac7952014575bf95492a2d90

[ "MIT" ]

1

2022-03-28T22:18:09.000Z

processing_provider/Rast_fillRasterwithPatches.py

geodourados/lftools

4b9d703513bd3d49ac7952014575bf95492a2d90

[ "MIT" ]

null

processing_provider/Rast_fillRasterwithPatches.py

geodourados/lftools

4b9d703513bd3d49ac7952014575bf95492a2d90

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ fillRasterwithPatches.py *************************************************************************** * * * This program is free software; you can redistribute it and/or modify * * it under the terms of the GNU General Public License as published by * * the Free Software Foundation; either version 2 of the License, or * * (at your option) any later version. * * * *************************************************************************** """ __author__ = 'Leandro França' __date__ = '2020-09-01' __copyright__ = '(C) 2020, Leandro França' from PyQt5.QtCore import QCoreApplication, QVariant from qgis.core import (QgsProcessing, QgsFeatureSink, QgsWkbTypes, QgsFields, QgsField, QgsFeature, QgsPointXY, QgsGeometry, QgsProcessingException, QgsProcessingAlgorithm, QgsProcessingParameterString, QgsProcessingParameterField, QgsProcessingParameterBoolean, QgsProcessingParameterCrs, QgsProcessingParameterEnum, QgsFeatureRequest, QgsExpression, QgsProcessingParameterFeatureSource, QgsProcessingParameterFeatureSink, QgsProcessingParameterFileDestination, QgsProcessingParameterMultipleLayers, QgsProcessingParameterRasterLayer, QgsProcessingParameterRasterDestination, QgsApplication, QgsProject, QgsRasterLayer, QgsCoordinateTransform, QgsCoordinateReferenceSystem) from osgeo import osr, gdal_array, gdal #https://gdal.org/python/ from math import floor, ceil import numpy as np from lftools.geocapt.dip import Interpolar from lftools.geocapt.imgs import Imgs import os from qgis.PyQt.QtGui import QIcon class FillRasterwithPatches(QgsProcessingAlgorithm): LOC = QgsApplication.locale()[:2] def translate(self, string): return QCoreApplication.translate('Processing', string) def tr(self, *string): # Traduzir para o portugês: arg[0] - english (translate), arg[1] - português if self.LOC == 'pt': if len(string) == 2: return string[1] else: return self.translate(string[0]) else: return self.translate(string[0]) def createInstance(self): return FillRasterwithPatches() def name(self): return 'fillrasterwithpatches' def displayName(self): return self.tr('Fill with patches', 'Remendar vazios de raster') def group(self): return self.tr('Raster') def groupId(self): return 'raster' def tags(self): return self.tr('fill,hole,raster,cloud,remove,drone,patch').split(',') def icon(self): return QIcon(os.path.join(os.path.dirname(os.path.dirname(__file__)), 'images/raster.png')) txt_en = 'Fills Raster null pixels (no data) with data obtained from other smaller raster layers (Patches).' txt_pt = 'Preenche vazios de Raster (pixels nulos) com dados obtidos de outras camadas raster menores (Remendos).' figure = 'images/tutorial/raster_fill_holes.jpg' def shortHelpString(self): social_BW = Imgs().social_BW footer = '''<div align="center"> <img src="'''+ os.path.join(os.path.dirname(os.path.dirname(__file__)), self.figure) +'''"> </div> <div align="right"> <p align="right"> <b>'''+self.tr('Author: Leandro Franca', 'Autor: Leandro França')+'''</b> </p>'''+ social_BW + '''</div> </div>''' return self.tr(self.txt_en, self.txt_pt) + footer RasterIN ='RasterIN' PATCHES = 'PATCHES' RESAMPLING = 'RESAMPLING' RasterOUT = 'RasterOUT' OPEN = 'OPEN' def initAlgorithm(self, config=None): # INPUT self.addParameter( QgsProcessingParameterRasterLayer( self.RasterIN, self.tr('Input Raster', 'Raster de Entrada'), [QgsProcessing.TypeRaster] ) ) self.addParameter( QgsProcessingParameterMultipleLayers( self.PATCHES, self.tr('Patch Layers', 'Rasters de Remendo'), layerType = QgsProcessing.TypeRaster ) ) interp = [self.tr('Nearest neighbor', 'Vizinho mais próximo'), self.tr('Bilinear'), self.tr('Bicubic', 'Bicúbica')] self.addParameter( QgsProcessingParameterEnum( self.RESAMPLING, self.tr('Interpolation', 'Interpolação'), options = interp, defaultValue= 0 ) ) # OUTPUT self.addParameter( QgsProcessingParameterFileDestination( self.RasterOUT, self.tr('Patched Image', 'Imagem Remendada'), fileFilter = 'GeoTIFF (*.tif)' ) ) self.addParameter( QgsProcessingParameterBoolean( self.OPEN, self.tr('Load patched Image', 'Carregar Imagem Remendada'), defaultValue= True ) ) def processAlgorithm(self, parameters, context, feedback): RasterIN = self.parameterAsRasterLayer( parameters, self.RasterIN, context ) if RasterIN is None: raise QgsProcessingException(self.invalidSourceError(parameters, self.RasterIN)) RasterIN = RasterIN.dataProvider().dataSourceUri() PatchesLayers = self.parameterAsLayerList( parameters, self.PATCHES, context ) reamostragem = self.parameterAsEnum( parameters, self.RESAMPLING, context ) reamostragem = ['nearest','bilinear','bicubic'][reamostragem] RGB_Output = self.parameterAsFileOutput( parameters, self.RasterOUT, context ) Carregar = self.parameterAsBool( parameters, self.OPEN, context ) limiar = 240 # Abrir Raster layer como array image = gdal.Open(RasterIN) prj=image.GetProjection() CRS=osr.SpatialReference(wkt=prj) geotransform = image.GetGeoTransform() n_bands = image.RasterCount # Número de bandas cols = image.RasterXSize # Number of columns rows = image.RasterYSize # Number of rows # Origem e resolucao da imagem ulx, xres, xskew, uly, yskew, yres = geotransform origem = (ulx, uly) resol_X = abs(xres) resol_Y = abs(yres) if n_bands ==1: feedback.pushInfo(self.tr('Opening raster band...', 'Abrindo banda do raster...')) band1 = image.GetRasterBand(1).ReadAsArray() if n_bands >=3: feedback.pushInfo(self.tr('Opening Band R...', 'Abrindo Banda R...')) band1 = image.GetRasterBand(1).ReadAsArray() feedback.pushInfo(self.tr('Opening Band G...', 'Abrindo Banda G...')) band2 = image.GetRasterBand(2).ReadAsArray() feedback.pushInfo(self.tr('Opening Band B...', 'Abrindo Banda B...')) band3 = image.GetRasterBand(3).ReadAsArray() # Transparência if n_bands == 4: feedback.pushInfo(self.tr('Opening Band Alpha...', 'Abrindo Banda Alfa...')) band4 = image.GetRasterBand(4).ReadAsArray() Pixel_Nulo = image.GetRasterBand(1).GetNoDataValue() if Pixel_Nulo == None: Pixel_Nulo = 0 image=None # Fechar imagem # Número de pixels para processamento TAM = 0 for Remendo in PatchesLayers: Rem_Path = Remendo.dataProvider().dataSourceUri() Rem = gdal.Open(Rem_Path) # Rem_cols = Rem.RasterXSize # Number of columns Rem_rows = Rem.RasterYSize # Number of rows TAM += Rem_rows # Remendos total = 100.0 / TAM cont = 0 for Remendo in PatchesLayers: feedback.pushInfo((self.tr('Processing Layer: {}', 'Processando Camada: {}')).format(Remendo)) Rem_Path = Remendo.dataProvider().dataSourceUri() Rem = gdal.Open(Rem_Path) ulx, xres, xskew, uly, yskew, yres = Rem.GetGeoTransform() Rem_origem = (ulx, uly) Rem_resol_X = abs(xres) Rem_resol_Y = abs(yres) Rem_cols = Rem.RasterXSize # Number of columns Rem_rows = Rem.RasterYSize # Number of rows lrx = ulx + (Rem_cols * xres) lry = uly + (Rem_rows * yres) bbox = [ulx, lrx, lry, uly] Rem_nulo = Rem.GetRasterBand(1).GetNoDataValue() if Rem_nulo == None: Rem_nulo = 0 Rem_band1 = Rem.GetRasterBand(1).ReadAsArray() if n_bands >1: Rem_band2 = Rem.GetRasterBand(2).ReadAsArray() Rem_band3 = Rem.GetRasterBand(3).ReadAsArray() # Limites de Varredura row_ini = int(round((origem[1]-uly)/resol_Y - 0.5)) row_fim = int(round((origem[1]-lry)/resol_Y - 0.5)) col_ini = int(round((ulx - origem[0])/resol_X - 0.5)) col_fim = int(round((lrx - origem[0])/resol_X - 0.5)) # Varrer Raster if n_bands == 4: for lin in range(row_ini, row_fim): for col in range(col_ini, col_fim): px_value = band4[lin][col] if px_value == 0 or band1[lin][col] > limiar: # Verificar Limiar X = origem[0] + resol_X*(col + 0.5) Y = origem[1] - resol_Y*(lin + 0.5) band1[lin][col] = Interpolar(X, Y, Rem_band1, Rem_origem, Rem_resol_X, Rem_resol_Y, reamostragem, Rem_nulo) band2[lin][col] = Interpolar(X, Y, Rem_band2, Rem_origem, Rem_resol_X, Rem_resol_Y, reamostragem, Rem_nulo) band3[lin][col] = Interpolar(X, Y, Rem_band3, Rem_origem, Rem_resol_X, Rem_resol_Y, reamostragem, Rem_nulo) cont += 1 feedback.setProgress(int(cont * total)) if feedback.isCanceled(): break elif n_bands == 3: for lin in range(row_ini, row_fim): for col in range(col_ini, col_fim): px_value = band1[lin][col] if px_value == Pixel_Nulo or band1[lin][col] > limiar: # Verificar Limiar X = origem[0] + resol_X*(col + 0.5) Y = origem[1] - resol_Y*(lin + 0.5) band1[lin][col] = Interpolar(X, Y, Rem_band1, Rem_origem, Rem_resol_X, Rem_resol_Y, reamostragem, Rem_nulo) band2[lin][col] = Interpolar(X, Y, Rem_band2, Rem_origem, Rem_resol_X, Rem_resol_Y, reamostragem, Rem_nulo) band3[lin][col] = Interpolar(X, Y, Rem_band3, Rem_origem, Rem_resol_X, Rem_resol_Y, reamostragem, Rem_nulo) cont += 1 feedback.setProgress(int(cont * total)) if feedback.isCanceled(): break elif n_bands == 1: for lin in range(row_ini, row_fim): for col in range(col_ini, col_fim): px_value = band1[lin][col] if px_value == Pixel_Nulo or band1[lin][col] > limiar: # Verificar Limiar X = origem[0] + resol_X*(col + 0.5) Y = origem[1] - resol_Y*(lin + 0.5) band1[lin][col] = Interpolar(X, Y, Rem_band1, Rem_origem, Rem_resol_X, Rem_resol_Y, reamostragem, Rem_nulo) cont += 1 feedback.setProgress(int(cont * total)) if feedback.isCanceled(): break Rem = None # Fechar imagem # Criar imagem RGB feedback.pushInfo(self.tr('Saving Raster...', 'Salvando Raster...')) GDT = gdal_array.NumericTypeCodeToGDALTypeCode(band1.dtype) if n_bands ==1: RASTER = gdal.GetDriverByName('GTiff').Create(RGB_Output, cols, rows, 1, GDT) else: RASTER = gdal.GetDriverByName('GTiff').Create(RGB_Output, cols, rows, 3, GDT) RASTER.SetGeoTransform(geotransform) # specify coords RASTER.SetProjection(CRS.ExportToWkt()) # export coords to file if n_bands ==1: feedback.pushInfo(self.tr('Writing rater band...', 'Escrevendo banda do raster...')) banda = RASTER.GetRasterBand(1) banda.WriteArray(band1) banda.SetNoDataValue(Pixel_Nulo) else: feedback.pushInfo(self.tr('Writing Band R...', 'Escrevendo Banda R...')) bandaR = RASTER.GetRasterBand(1) bandaR.WriteArray(band1) feedback.pushInfo(self.tr('Writing Band G...', 'Escrevendo Banda G...')) bandaG = RASTER.GetRasterBand(2) bandaG.WriteArray(band2) feedback.pushInfo(self.tr('Writing Band B...', 'Escrevendo Banda B...')) bandaB = RASTER.GetRasterBand(3) bandaB.WriteArray(band3) feedback.pushInfo(self.tr('Saving raster...', 'Salvando raster...')) RASTER.FlushCache() # Escrever no disco RASTER = None # Salvar e fechar feedback.pushInfo(self.tr('Operation completed successfully!', 'Operação finalizada com sucesso!')) feedback.pushInfo(self.tr('Leandro Franca - Cartographic Engineer', 'Leandro França - Eng Cart')) self.CAMINHO = RGB_Output self.CARREGAR = Carregar return {self.RasterOUT: RGB_Output} # Carregamento de arquivo de saída CAMINHO = '' CARREGAR = True def postProcessAlgorithm(self, context, feedback): if self.CARREGAR: rlayer = QgsRasterLayer(self.CAMINHO, self.tr('Patched Image', 'Imagem Remendada')) QgsProject.instance().addMapLayer(rlayer) return {}

41.51532

135

0.541063

__author__ = 'Leandro França' __date__ = '2020-09-01' __copyright__ = '(C) 2020, Leandro França' from PyQt5.QtCore import QCoreApplication, QVariant from qgis.core import (QgsProcessing, QgsFeatureSink, QgsWkbTypes, QgsFields, QgsField, QgsFeature, QgsPointXY, QgsGeometry, QgsProcessingException, QgsProcessingAlgorithm, QgsProcessingParameterString, QgsProcessingParameterField, QgsProcessingParameterBoolean, QgsProcessingParameterCrs, QgsProcessingParameterEnum, QgsFeatureRequest, QgsExpression, QgsProcessingParameterFeatureSource, QgsProcessingParameterFeatureSink, QgsProcessingParameterFileDestination, QgsProcessingParameterMultipleLayers, QgsProcessingParameterRasterLayer, QgsProcessingParameterRasterDestination, QgsApplication, QgsProject, QgsRasterLayer, QgsCoordinateTransform, QgsCoordinateReferenceSystem) from osgeo import osr, gdal_array, gdal from math import floor, ceil import numpy as np from lftools.geocapt.dip import Interpolar from lftools.geocapt.imgs import Imgs import os from qgis.PyQt.QtGui import QIcon class FillRasterwithPatches(QgsProcessingAlgorithm): LOC = QgsApplication.locale()[:2] def translate(self, string): return QCoreApplication.translate('Processing', string) def tr(self, *string): if self.LOC == 'pt': if len(string) == 2: return string[1] else: return self.translate(string[0]) else: return self.translate(string[0]) def createInstance(self): return FillRasterwithPatches() def name(self): return 'fillrasterwithpatches' def displayName(self): return self.tr('Fill with patches', 'Remendar vazios de raster') def group(self): return self.tr('Raster') def groupId(self): return 'raster' def tags(self): return self.tr('fill,hole,raster,cloud,remove,drone,patch').split(',') def icon(self): return QIcon(os.path.join(os.path.dirname(os.path.dirname(__file__)), 'images/raster.png')) txt_en = 'Fills Raster null pixels (no data) with data obtained from other smaller raster layers (Patches).' txt_pt = 'Preenche vazios de Raster (pixels nulos) com dados obtidos de outras camadas raster menores (Remendos).' figure = 'images/tutorial/raster_fill_holes.jpg' def shortHelpString(self): social_BW = Imgs().social_BW footer = '''<div align="center"> <img src="'''+ os.path.join(os.path.dirname(os.path.dirname(__file__)), self.figure) +'''"> </div> <div align="right"> <p align="right"> <b>'''+self.tr('Author: Leandro Franca', 'Autor: Leandro França')+'''</b> </p>'''+ social_BW + '''</div> </div>''' return self.tr(self.txt_en, self.txt_pt) + footer RasterIN ='RasterIN' PATCHES = 'PATCHES' RESAMPLING = 'RESAMPLING' RasterOUT = 'RasterOUT' OPEN = 'OPEN' def initAlgorithm(self, config=None): self.addParameter( QgsProcessingParameterRasterLayer( self.RasterIN, self.tr('Input Raster', 'Raster de Entrada'), [QgsProcessing.TypeRaster] ) ) self.addParameter( QgsProcessingParameterMultipleLayers( self.PATCHES, self.tr('Patch Layers', 'Rasters de Remendo'), layerType = QgsProcessing.TypeRaster ) ) interp = [self.tr('Nearest neighbor', 'Vizinho mais próximo'), self.tr('Bilinear'), self.tr('Bicubic', 'Bicúbica')] self.addParameter( QgsProcessingParameterEnum( self.RESAMPLING, self.tr('Interpolation', 'Interpolação'), options = interp, defaultValue= 0 ) ) self.addParameter( QgsProcessingParameterFileDestination( self.RasterOUT, self.tr('Patched Image', 'Imagem Remendada'), fileFilter = 'GeoTIFF (*.tif)' ) ) self.addParameter( QgsProcessingParameterBoolean( self.OPEN, self.tr('Load patched Image', 'Carregar Imagem Remendada'), defaultValue= True ) ) def processAlgorithm(self, parameters, context, feedback): RasterIN = self.parameterAsRasterLayer( parameters, self.RasterIN, context ) if RasterIN is None: raise QgsProcessingException(self.invalidSourceError(parameters, self.RasterIN)) RasterIN = RasterIN.dataProvider().dataSourceUri() PatchesLayers = self.parameterAsLayerList( parameters, self.PATCHES, context ) reamostragem = self.parameterAsEnum( parameters, self.RESAMPLING, context ) reamostragem = ['nearest','bilinear','bicubic'][reamostragem] RGB_Output = self.parameterAsFileOutput( parameters, self.RasterOUT, context ) Carregar = self.parameterAsBool( parameters, self.OPEN, context ) limiar = 240 image = gdal.Open(RasterIN) prj=image.GetProjection() CRS=osr.SpatialReference(wkt=prj) geotransform = image.GetGeoTransform() n_bands = image.RasterCount cols = image.RasterXSize rows = image.RasterYSize ulx, xres, xskew, uly, yskew, yres = geotransform origem = (ulx, uly) resol_X = abs(xres) resol_Y = abs(yres) if n_bands ==1: feedback.pushInfo(self.tr('Opening raster band...', 'Abrindo banda do raster...')) band1 = image.GetRasterBand(1).ReadAsArray() if n_bands >=3: feedback.pushInfo(self.tr('Opening Band R...', 'Abrindo Banda R...')) band1 = image.GetRasterBand(1).ReadAsArray() feedback.pushInfo(self.tr('Opening Band G...', 'Abrindo Banda G...')) band2 = image.GetRasterBand(2).ReadAsArray() feedback.pushInfo(self.tr('Opening Band B...', 'Abrindo Banda B...')) band3 = image.GetRasterBand(3).ReadAsArray() if n_bands == 4: feedback.pushInfo(self.tr('Opening Band Alpha...', 'Abrindo Banda Alfa...')) band4 = image.GetRasterBand(4).ReadAsArray() Pixel_Nulo = image.GetRasterBand(1).GetNoDataValue() if Pixel_Nulo == None: Pixel_Nulo = 0 image=None TAM = 0 for Remendo in PatchesLayers: Rem_Path = Remendo.dataProvider().dataSourceUri() Rem = gdal.Open(Rem_Path) ws = Rem.RasterYSize TAM += Rem_rows total = 100.0 / TAM cont = 0 for Remendo in PatchesLayers: feedback.pushInfo((self.tr('Processing Layer: {}', 'Processando Camada: {}')).format(Remendo)) Rem_Path = Remendo.dataProvider().dataSourceUri() Rem = gdal.Open(Rem_Path) ulx, xres, xskew, uly, yskew, yres = Rem.GetGeoTransform() Rem_origem = (ulx, uly) Rem_resol_X = abs(xres) Rem_resol_Y = abs(yres) Rem_cols = Rem.RasterXSize Rem_rows = Rem.RasterYSize lrx = ulx + (Rem_cols * xres) lry = uly + (Rem_rows * yres) bbox = [ulx, lrx, lry, uly] Rem_nulo = Rem.GetRasterBand(1).GetNoDataValue() if Rem_nulo == None: Rem_nulo = 0 Rem_band1 = Rem.GetRasterBand(1).ReadAsArray() if n_bands >1: Rem_band2 = Rem.GetRasterBand(2).ReadAsArray() Rem_band3 = Rem.GetRasterBand(3).ReadAsArray() row_ini = int(round((origem[1]-uly)/resol_Y - 0.5)) row_fim = int(round((origem[1]-lry)/resol_Y - 0.5)) col_ini = int(round((ulx - origem[0])/resol_X - 0.5)) col_fim = int(round((lrx - origem[0])/resol_X - 0.5)) if n_bands == 4: for lin in range(row_ini, row_fim): for col in range(col_ini, col_fim): px_value = band4[lin][col] if px_value == 0 or band1[lin][col] > limiar: X = origem[0] + resol_X*(col + 0.5) Y = origem[1] - resol_Y*(lin + 0.5) band1[lin][col] = Interpolar(X, Y, Rem_band1, Rem_origem, Rem_resol_X, Rem_resol_Y, reamostragem, Rem_nulo) band2[lin][col] = Interpolar(X, Y, Rem_band2, Rem_origem, Rem_resol_X, Rem_resol_Y, reamostragem, Rem_nulo) band3[lin][col] = Interpolar(X, Y, Rem_band3, Rem_origem, Rem_resol_X, Rem_resol_Y, reamostragem, Rem_nulo) cont += 1 feedback.setProgress(int(cont * total)) if feedback.isCanceled(): break elif n_bands == 3: for lin in range(row_ini, row_fim): for col in range(col_ini, col_fim): px_value = band1[lin][col] if px_value == Pixel_Nulo or band1[lin][col] > limiar: X = origem[0] + resol_X*(col + 0.5) Y = origem[1] - resol_Y*(lin + 0.5) band1[lin][col] = Interpolar(X, Y, Rem_band1, Rem_origem, Rem_resol_X, Rem_resol_Y, reamostragem, Rem_nulo) band2[lin][col] = Interpolar(X, Y, Rem_band2, Rem_origem, Rem_resol_X, Rem_resol_Y, reamostragem, Rem_nulo) band3[lin][col] = Interpolar(X, Y, Rem_band3, Rem_origem, Rem_resol_X, Rem_resol_Y, reamostragem, Rem_nulo) cont += 1 feedback.setProgress(int(cont * total)) if feedback.isCanceled(): break elif n_bands == 1: for lin in range(row_ini, row_fim): for col in range(col_ini, col_fim): px_value = band1[lin][col] if px_value == Pixel_Nulo or band1[lin][col] > limiar: X = origem[0] + resol_X*(col + 0.5) Y = origem[1] - resol_Y*(lin + 0.5) band1[lin][col] = Interpolar(X, Y, Rem_band1, Rem_origem, Rem_resol_X, Rem_resol_Y, reamostragem, Rem_nulo) cont += 1 feedback.setProgress(int(cont * total)) if feedback.isCanceled(): break Rem = None feedback.pushInfo(self.tr('Saving Raster...', 'Salvando Raster...')) GDT = gdal_array.NumericTypeCodeToGDALTypeCode(band1.dtype) if n_bands ==1: RASTER = gdal.GetDriverByName('GTiff').Create(RGB_Output, cols, rows, 1, GDT) else: RASTER = gdal.GetDriverByName('GTiff').Create(RGB_Output, cols, rows, 3, GDT) RASTER.SetGeoTransform(geotransform) RASTER.SetProjection(CRS.ExportToWkt()) if n_bands ==1: feedback.pushInfo(self.tr('Writing rater band...', 'Escrevendo banda do raster...')) banda = RASTER.GetRasterBand(1) banda.WriteArray(band1) banda.SetNoDataValue(Pixel_Nulo) else: feedback.pushInfo(self.tr('Writing Band R...', 'Escrevendo Banda R...')) bandaR = RASTER.GetRasterBand(1) bandaR.WriteArray(band1) feedback.pushInfo(self.tr('Writing Band G...', 'Escrevendo Banda G...')) bandaG = RASTER.GetRasterBand(2) bandaG.WriteArray(band2) feedback.pushInfo(self.tr('Writing Band B...', 'Escrevendo Banda B...')) bandaB = RASTER.GetRasterBand(3) bandaB.WriteArray(band3) feedback.pushInfo(self.tr('Saving raster...', 'Salvando raster...')) RASTER.FlushCache() RASTER = None feedback.pushInfo(self.tr('Operation completed successfully!', 'Operação finalizada com sucesso!')) feedback.pushInfo(self.tr('Leandro Franca - Cartographic Engineer', 'Leandro França - Eng Cart')) self.CAMINHO = RGB_Output self.CARREGAR = Carregar return {self.RasterOUT: RGB_Output} CAMINHO = '' CARREGAR = True def postProcessAlgorithm(self, context, feedback): if self.CARREGAR: rlayer = QgsRasterLayer(self.CAMINHO, self.tr('Patched Image', 'Imagem Remendada')) QgsProject.instance().addMapLayer(rlayer) return {}

true

f7fbf45acb2ddec84cefcc472333d7218aec52d7

4,101

py

Python

examples/sprite_bullets.py

jgrigonis/arcade

9b624da7da52e3909f6e82c552446b90249041f1

[ "MIT" ]

1

2021-05-23T20:30:46.000Z

examples/sprite_bullets.py

jgrigonis/arcade

9b624da7da52e3909f6e82c552446b90249041f1

[ "MIT" ]

null

examples/sprite_bullets.py

jgrigonis/arcade

9b624da7da52e3909f6e82c552446b90249041f1

[ "MIT" ]

null

""" Sprite Bullets Simple program to show basic sprite usage. Artwork from http://kenney.nl """ import random import arcade SPRITE_SCALING = 0.5 SCREEN_WIDTH = 800 SCREEN_HEIGHT = 600 BULLET_SPEED = 5 class Bullet(arcade.Sprite): def update(self): self.center_y += BULLET_SPEED class MyAppWindow(arcade.Window): """ Main application class. """ def __init__(self): super().__init__(SCREEN_WIDTH, SCREEN_HEIGHT, "Sprites and Bullets Demo") """ Set up the game and initialize the variables. """ # Sprite lists self.all_sprites_list = arcade.SpriteList() self.coin_list = arcade.SpriteList() self.bullet_list = arcade.SpriteList() # Set up the player self.score = 0 self.player_sprite = arcade.Sprite("images/character.png", SPRITE_SCALING) self.player_sprite.center_x = 50 self.player_sprite.center_y = 70 self.all_sprites_list.append(self.player_sprite) # Load sounds self.gun_sound = arcade.sound.load_sound("sounds/laser1.ogg") self.hit_sound = arcade.sound.load_sound("sounds/phaseJump1.ogg") for i in range(50): # Create the coin instance coin = arcade.Sprite("images/coin_01.png", SPRITE_SCALING / 3) # Position the coin coin.center_x = random.randrange(SCREEN_WIDTH) coin.center_y = random.randrange(120, SCREEN_HEIGHT) # Add the coin to the lists self.all_sprites_list.append(coin) self.coin_list.append(coin) # Don't show the mouse cursor self.set_mouse_visible(False) # Set the background color arcade.set_background_color(arcade.color.AMAZON) def on_draw(self): """ Render the screen. """ # This command has to happen before we start drawing arcade.start_render() # Draw all the sprites. self.all_sprites_list.draw() # Put the text on the screen. output = "Score: {}".format(self.score) arcade.draw_text(output, 10, 20, arcade.color.WHITE, 14) def on_mouse_motion(self, x, y, dx, dy): """ Called whenever the mouse moves. """ self.player_sprite.center_x = x def on_mouse_press(self, x, y, button, modifiers): """ Called whenever the mouse button is clicked. """ # Gunshot sound arcade.sound.play_sound(self.gun_sound) # Create a bullet bullet = Bullet("images/laserBlue01.png", SPRITE_SCALING * 1.5) # The image points to the right, and we want it to point up. So # rotate it. bullet.angle = 90 # Position the bullet bullet.center_x = self.player_sprite.center_x bullet.bottom = self.player_sprite.top # Add the bullet to the appropriate lists self.all_sprites_list.append(bullet) self.bullet_list.append(bullet) def update(self, delta_time): """ Movement and game logic """ # Call update on all sprites (The sprites don't do much in this # example though.) self.all_sprites_list.update() # Loop through each bullet for bullet in self.bullet_list: # Check this bullet to see if it hit a coin hit_list = arcade.check_for_collision_with_list(bullet, self.coin_list) # If it did, get rid of the bullet if len(hit_list) > 0: bullet.kill() # For every coin we hit, add to the score and remove the coin for coin in hit_list: coin.kill() self.score += 1 # Hit Sound arcade.sound.play_sound(self.hit_sound) # If the bullet flies off-screen, remove it. if bullet.bottom > SCREEN_HEIGHT: bullet.kill() def main(): MyAppWindow() arcade.run() if __name__ == "__main__": main()

27.709459

81

0.592295

import random import arcade SPRITE_SCALING = 0.5 SCREEN_WIDTH = 800 SCREEN_HEIGHT = 600 BULLET_SPEED = 5 class Bullet(arcade.Sprite): def update(self): self.center_y += BULLET_SPEED class MyAppWindow(arcade.Window): def __init__(self): super().__init__(SCREEN_WIDTH, SCREEN_HEIGHT, "Sprites and Bullets Demo") self.all_sprites_list = arcade.SpriteList() self.coin_list = arcade.SpriteList() self.bullet_list = arcade.SpriteList() self.score = 0 self.player_sprite = arcade.Sprite("images/character.png", SPRITE_SCALING) self.player_sprite.center_x = 50 self.player_sprite.center_y = 70 self.all_sprites_list.append(self.player_sprite) self.gun_sound = arcade.sound.load_sound("sounds/laser1.ogg") self.hit_sound = arcade.sound.load_sound("sounds/phaseJump1.ogg") for i in range(50): coin = arcade.Sprite("images/coin_01.png", SPRITE_SCALING / 3) coin.center_x = random.randrange(SCREEN_WIDTH) coin.center_y = random.randrange(120, SCREEN_HEIGHT) self.all_sprites_list.append(coin) self.coin_list.append(coin) self.set_mouse_visible(False) # Set the background color arcade.set_background_color(arcade.color.AMAZON) def on_draw(self): # This command has to happen before we start drawing arcade.start_render() # Draw all the sprites. self.all_sprites_list.draw() # Put the text on the screen. output = "Score: {}".format(self.score) arcade.draw_text(output, 10, 20, arcade.color.WHITE, 14) def on_mouse_motion(self, x, y, dx, dy): self.player_sprite.center_x = x def on_mouse_press(self, x, y, button, modifiers): # Gunshot sound arcade.sound.play_sound(self.gun_sound) # Create a bullet bullet = Bullet("images/laserBlue01.png", SPRITE_SCALING * 1.5) # The image points to the right, and we want it to point up. So # rotate it. bullet.angle = 90 # Position the bullet bullet.center_x = self.player_sprite.center_x bullet.bottom = self.player_sprite.top # Add the bullet to the appropriate lists self.all_sprites_list.append(bullet) self.bullet_list.append(bullet) def update(self, delta_time): # Call update on all sprites (The sprites don't do much in this self.all_sprites_list.update() for bullet in self.bullet_list: hit_list = arcade.check_for_collision_with_list(bullet, self.coin_list) if len(hit_list) > 0: bullet.kill() for coin in hit_list: coin.kill() self.score += 1 arcade.sound.play_sound(self.hit_sound) if bullet.bottom > SCREEN_HEIGHT: bullet.kill() def main(): MyAppWindow() arcade.run() if __name__ == "__main__": main()

true

f7fbf4646ad2c439ae2469d60113cbf6f253c938

353

py

Python

Chapter_6/3.py

sai29/Python-John-Zelle-book

b6c54120cfc4742fa651a051b39fc94bf221543d

[ "MIT" ]

14

2015-01-20T15:02:13.000Z

2020-05-20T08:05:37.000Z

Chapter_6/3.py

sai29/Python-John-Zelle-book

b6c54120cfc4742fa651a051b39fc94bf221543d

[ "MIT" ]

1

2016-11-10T16:45:21.000Z

2017-04-17T03:36:46.000Z

Chapter_6/3.py

sai29/Python-John-Zelle-book

b6c54120cfc4742fa651a051b39fc94bf221543d

[ "MIT" ]

11

2015-01-06T18:46:15.000Z

2020-04-02T03:45:53.000Z

def sphereArea(radius): area = 4 * 3.14 * (radius * radius) return area def sphereVolume(radius): vol = (4 / 3) * (3.14 * (radius * radius)) return vol def main(): rad = float(raw_input("Enter the value of the radius")) area1 = sphereArea(rad) volume = sphereVolume(rad) print "The area is", area1 print "The volume is", volume main()

16.045455

56

0.654391

def sphereArea(radius): area = 4 * 3.14 * (radius * radius) return area def sphereVolume(radius): vol = (4 / 3) * (3.14 * (radius * radius)) return vol def main(): rad = float(raw_input("Enter the value of the radius")) area1 = sphereArea(rad) volume = sphereVolume(rad) print "The area is", area1 print "The volume is", volume main()

false

true

f7fbf537fa4a15e63fc1e463b4d6a8cdb7f53b03

3,380

py

Python

revscoring/languages/tests/test_estonian.py

mariushoch/revscoring

5ecd54d31c4088b6f142c0ef54116cc5bdce0ff2

[ "MIT" ]

null

revscoring/languages/tests/test_estonian.py

mariushoch/revscoring

5ecd54d31c4088b6f142c0ef54116cc5bdce0ff2

[ "MIT" ]

null

revscoring/languages/tests/test_estonian.py

mariushoch/revscoring

5ecd54d31c4088b6f142c0ef54116cc5bdce0ff2

[ "MIT" ]

null

import pickle from .. import estonian from ...datasources import revision_oriented from ...dependencies import solve from .util import compare_extraction BAD = [ "butt", "butthole", "crap", "cock", "fag", "faggot", "phaggot", "faggg", "fuck", "fucking", "fucker", "homo", "homod", "homokas", "homokad", "idiots", "idiot", "idioooot", "jobu", "jobud", "kaka", "kakajunn", "kepp", "keppis", "keppi", "keppida", "lits", "litsid", "loll", "lollakas", "lollid", "motherfucker", "munn", "munni", "munnid", "munne", "nahhui", "nahhhui", "nigga", "niggas", "niggaerh", "noku", "noks", "pask", "pede", "peded", "pedekas", "pederast", "pederastid", "perse", "perses", "persesse", "pig", "pigs", "pussy", "puts", "putsi", "sitt", "sitta", "sita", "sitane", "sitajunn", "sitahunnik", "stoopid", "stupid", "taun", "türa", "tuss", "tussu", "vitt", "vittu", "vitupea" ] INFORMAL = [ "animal", "cool", "cooler", "coolest", "kool", "kooler", "koolest", "kewl", "kewler", "kewlest", "fakking", "gangsta", "gängsta", "haha", "hahaa", "hahaha", "hmm", "hmmmmmm", "ilge", "ime", "imege", "jou", "junn", "junni", "kill", "kuradi", "lahe", "lohh", "lol", "loll", "lolz", "neeger", "noob", "pihku", "raisk", "räme", "sakib", "suck", "sucks", "sucking", "sucker", "suht", "tatt", "tatid", "tegelt", "tere", "tsau", "tsmir", "tšmir", "yolo" ] OTHER = [ """ Friedrich Wilhelm Rembert von Berg sündis Beļava ja Sangaste mõisniku Friedrich Georg von Bergi ja Gerdruta Wilhelmine von Ermesi vanima pojana. Tal olid nooremad vennad Gustav, Magnus ja Alexander. Friedrich Wilhelmi ja tema vendade koduõpetaja oli hilisem tuntud astronoom Wilhelm Struve. """ ] r_text = revision_oriented.revision.text def test_badwords(): compare_extraction(estonian.badwords.revision.datasources.matches, BAD, OTHER) assert estonian.badwords == pickle.loads(pickle.dumps(estonian.badwords)) def test_informals(): compare_extraction(estonian.informals.revision.datasources.matches, INFORMAL, OTHER) assert estonian.informals == pickle.loads(pickle.dumps(estonian.informals)) def test_dictionary(): cache = {r_text: "Tal olid nooremad, vennad worngly. <td>"} assert solve(estonian.dictionary.revision.datasources.dict_words, cache=cache) == ["Tal", "olid", "nooremad", "vennad"] assert solve(estonian.dictionary.revision.datasources.non_dict_words, cache=cache) == ["worngly"] assert estonian.dictionary == pickle.loads( pickle.dumps(estonian.dictionary)) def test_stopwords(): cache = {revision_oriented.revision.text: "Bergi ja Gerdruta Wilhelmine " + "von Ermesi vanima pojana."} assert (solve(estonian.stopwords.revision.datasources.stopwords, cache=cache) == ["von"]) assert (solve(estonian.stopwords.revision.datasources.non_stopwords, cache=cache) == ["Bergi", "ja", "Gerdruta", "Wilhelmine", "Ermesi", "vanima", "pojana"]) assert estonian.stopwords == pickle.loads(pickle.dumps(estonian.stopwords))

27.258065

84

0.593195

import pickle from .. import estonian from ...datasources import revision_oriented from ...dependencies import solve from .util import compare_extraction BAD = [ "butt", "butthole", "crap", "cock", "fag", "faggot", "phaggot", "faggg", "fuck", "fucking", "fucker", "homo", "homod", "homokas", "homokad", "idiots", "idiot", "idioooot", "jobu", "jobud", "kaka", "kakajunn", "kepp", "keppis", "keppi", "keppida", "lits", "litsid", "loll", "lollakas", "lollid", "motherfucker", "munn", "munni", "munnid", "munne", "nahhui", "nahhhui", "nigga", "niggas", "niggaerh", "noku", "noks", "pask", "pede", "peded", "pedekas", "pederast", "pederastid", "perse", "perses", "persesse", "pig", "pigs", "pussy", "puts", "putsi", "sitt", "sitta", "sita", "sitane", "sitajunn", "sitahunnik", "stoopid", "stupid", "taun", "türa", "tuss", "tussu", "vitt", "vittu", "vitupea" ] INFORMAL = [ "animal", "cool", "cooler", "coolest", "kool", "kooler", "koolest", "kewl", "kewler", "kewlest", "fakking", "gangsta", "gängsta", "haha", "hahaa", "hahaha", "hmm", "hmmmmmm", "ilge", "ime", "imege", "jou", "junn", "junni", "kill", "kuradi", "lahe", "lohh", "lol", "loll", "lolz", "neeger", "noob", "pihku", "raisk", "räme", "sakib", "suck", "sucks", "sucking", "sucker", "suht", "tatt", "tatid", "tegelt", "tere", "tsau", "tsmir", "tšmir", "yolo" ] OTHER = [ """ Friedrich Wilhelm Rembert von Berg sündis Beļava ja Sangaste mõisniku Friedrich Georg von Bergi ja Gerdruta Wilhelmine von Ermesi vanima pojana. Tal olid nooremad vennad Gustav, Magnus ja Alexander. Friedrich Wilhelmi ja tema vendade koduõpetaja oli hilisem tuntud astronoom Wilhelm Struve. """ ] r_text = revision_oriented.revision.text def test_badwords(): compare_extraction(estonian.badwords.revision.datasources.matches, BAD, OTHER) assert estonian.badwords == pickle.loads(pickle.dumps(estonian.badwords)) def test_informals(): compare_extraction(estonian.informals.revision.datasources.matches, INFORMAL, OTHER) assert estonian.informals == pickle.loads(pickle.dumps(estonian.informals)) def test_dictionary(): cache = {r_text: "Tal olid nooremad, vennad worngly. <td>"} assert solve(estonian.dictionary.revision.datasources.dict_words, cache=cache) == ["Tal", "olid", "nooremad", "vennad"] assert solve(estonian.dictionary.revision.datasources.non_dict_words, cache=cache) == ["worngly"] assert estonian.dictionary == pickle.loads( pickle.dumps(estonian.dictionary)) def test_stopwords(): cache = {revision_oriented.revision.text: "Bergi ja Gerdruta Wilhelmine " + "von Ermesi vanima pojana."} assert (solve(estonian.stopwords.revision.datasources.stopwords, cache=cache) == ["von"]) assert (solve(estonian.stopwords.revision.datasources.non_stopwords, cache=cache) == ["Bergi", "ja", "Gerdruta", "Wilhelmine", "Ermesi", "vanima", "pojana"]) assert estonian.stopwords == pickle.loads(pickle.dumps(estonian.stopwords))

true

f7fbf5c782016ffdc3a2611a220b499a8311f833

225

py

Python

lifepo4weredPyController/helpers/SetHelper.py

fredericklussier/myLifepo4weredPy

a31479a50b630fd94105020159fd269a329de2bd

[ "MIT" ]

null

lifepo4weredPyController/helpers/SetHelper.py

fredericklussier/myLifepo4weredPy

a31479a50b630fd94105020159fd269a329de2bd

[ "MIT" ]

null

lifepo4weredPyController/helpers/SetHelper.py

fredericklussier/myLifepo4weredPy

a31479a50b630fd94105020159fd269a329de2bd

[ "MIT" ]

null

#!/usr/bin/python3 # -*- coding: utf-8 -*- def areSame(dict, dictToCompare): if len(dict) != len(dictToCompare): return False else: return (set() == (set(dict.items()) - set(dictToCompare.items())))

22.5

74

0.586667

def areSame(dict, dictToCompare): if len(dict) != len(dictToCompare): return False else: return (set() == (set(dict.items()) - set(dictToCompare.items())))

true

f7fbf5febaadcdf8549d6bccd8d324dfbc2150a5

4,951

py

Python

sdk/python/pulumi_aws_native/customerprofiles/get_integration.py

pulumi/pulumi-aws-native

1ae4a4d9c2256b2a79ca536f8d8497b28d10e4c3

[ "Apache-2.0" ]

29

2021-09-30T19:32:07.000Z

2022-03-22T21:06:08.000Z

sdk/python/pulumi_aws_native/customerprofiles/get_integration.py

pulumi/pulumi-aws-native

1ae4a4d9c2256b2a79ca536f8d8497b28d10e4c3

[ "Apache-2.0" ]

232

2021-09-30T19:26:26.000Z

2022-03-31T23:22:06.000Z

sdk/python/pulumi_aws_native/customerprofiles/get_integration.py

pulumi/pulumi-aws-native

1ae4a4d9c2256b2a79ca536f8d8497b28d10e4c3

[ "Apache-2.0" ]

4

2021-11-10T19:42:01.000Z

2022-02-05T10:15:49.000Z

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from .. import _utilities from . import outputs __all__ = [ 'GetIntegrationResult', 'AwaitableGetIntegrationResult', 'get_integration', 'get_integration_output', ] @pulumi.output_type class GetIntegrationResult: def __init__(__self__, created_at=None, last_updated_at=None, object_type_name=None, object_type_names=None, tags=None): if created_at and not isinstance(created_at, str): raise TypeError("Expected argument 'created_at' to be a str") pulumi.set(__self__, "created_at", created_at) if last_updated_at and not isinstance(last_updated_at, str): raise TypeError("Expected argument 'last_updated_at' to be a str") pulumi.set(__self__, "last_updated_at", last_updated_at) if object_type_name and not isinstance(object_type_name, str): raise TypeError("Expected argument 'object_type_name' to be a str") pulumi.set(__self__, "object_type_name", object_type_name) if object_type_names and not isinstance(object_type_names, list): raise TypeError("Expected argument 'object_type_names' to be a list") pulumi.set(__self__, "object_type_names", object_type_names) if tags and not isinstance(tags, list): raise TypeError("Expected argument 'tags' to be a list") pulumi.set(__self__, "tags", tags) @property @pulumi.getter(name="createdAt") def created_at(self) -> Optional[str]: """ The time of this integration got created """ return pulumi.get(self, "created_at") @property @pulumi.getter(name="lastUpdatedAt") def last_updated_at(self) -> Optional[str]: """ The time of this integration got last updated at """ return pulumi.get(self, "last_updated_at") @property @pulumi.getter(name="objectTypeName") def object_type_name(self) -> Optional[str]: """ The name of the ObjectType defined for the 3rd party data in Profile Service """ return pulumi.get(self, "object_type_name") @property @pulumi.getter(name="objectTypeNames") def object_type_names(self) -> Optional[Sequence['outputs.IntegrationObjectTypeMapping']]: """ The mapping between 3rd party event types and ObjectType names """ return pulumi.get(self, "object_type_names") @property @pulumi.getter def tags(self) -> Optional[Sequence['outputs.IntegrationTag']]: """ The tags (keys and values) associated with the integration """ return pulumi.get(self, "tags") class AwaitableGetIntegrationResult(GetIntegrationResult): # pylint: disable=using-constant-test def __await__(self): if False: yield self return GetIntegrationResult( created_at=self.created_at, last_updated_at=self.last_updated_at, object_type_name=self.object_type_name, object_type_names=self.object_type_names, tags=self.tags) def get_integration(domain_name: Optional[str] = None, uri: Optional[str] = None, opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetIntegrationResult: """ The resource schema for creating an Amazon Connect Customer Profiles Integration. :param str domain_name: The unique name of the domain. :param str uri: The URI of the S3 bucket or any other type of data source. """ __args__ = dict() __args__['domainName'] = domain_name __args__['uri'] = uri if opts is None: opts = pulumi.InvokeOptions() if opts.version is None: opts.version = _utilities.get_version() __ret__ = pulumi.runtime.invoke('aws-native:customerprofiles:getIntegration', __args__, opts=opts, typ=GetIntegrationResult).value return AwaitableGetIntegrationResult( created_at=__ret__.created_at, last_updated_at=__ret__.last_updated_at, object_type_name=__ret__.object_type_name, object_type_names=__ret__.object_type_names, tags=__ret__.tags) @_utilities.lift_output_func(get_integration) def get_integration_output(domain_name: Optional[pulumi.Input[str]] = None, uri: Optional[pulumi.Input[str]] = None, opts: Optional[pulumi.InvokeOptions] = None) -> pulumi.Output[GetIntegrationResult]: """ The resource schema for creating an Amazon Connect Customer Profiles Integration. :param str domain_name: The unique name of the domain. :param str uri: The URI of the S3 bucket or any other type of data source. """ ...

37.793893

134

0.677641

import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from .. import _utilities from . import outputs __all__ = [ 'GetIntegrationResult', 'AwaitableGetIntegrationResult', 'get_integration', 'get_integration_output', ] @pulumi.output_type class GetIntegrationResult: def __init__(__self__, created_at=None, last_updated_at=None, object_type_name=None, object_type_names=None, tags=None): if created_at and not isinstance(created_at, str): raise TypeError("Expected argument 'created_at' to be a str") pulumi.set(__self__, "created_at", created_at) if last_updated_at and not isinstance(last_updated_at, str): raise TypeError("Expected argument 'last_updated_at' to be a str") pulumi.set(__self__, "last_updated_at", last_updated_at) if object_type_name and not isinstance(object_type_name, str): raise TypeError("Expected argument 'object_type_name' to be a str") pulumi.set(__self__, "object_type_name", object_type_name) if object_type_names and not isinstance(object_type_names, list): raise TypeError("Expected argument 'object_type_names' to be a list") pulumi.set(__self__, "object_type_names", object_type_names) if tags and not isinstance(tags, list): raise TypeError("Expected argument 'tags' to be a list") pulumi.set(__self__, "tags", tags) @property @pulumi.getter(name="createdAt") def created_at(self) -> Optional[str]: return pulumi.get(self, "created_at") @property @pulumi.getter(name="lastUpdatedAt") def last_updated_at(self) -> Optional[str]: return pulumi.get(self, "last_updated_at") @property @pulumi.getter(name="objectTypeName") def object_type_name(self) -> Optional[str]: return pulumi.get(self, "object_type_name") @property @pulumi.getter(name="objectTypeNames") def object_type_names(self) -> Optional[Sequence['outputs.IntegrationObjectTypeMapping']]: return pulumi.get(self, "object_type_names") @property @pulumi.getter def tags(self) -> Optional[Sequence['outputs.IntegrationTag']]: return pulumi.get(self, "tags") class AwaitableGetIntegrationResult(GetIntegrationResult): # pylint: disable=using-constant-test def __await__(self): if False: yield self return GetIntegrationResult( created_at=self.created_at, last_updated_at=self.last_updated_at, object_type_name=self.object_type_name, object_type_names=self.object_type_names, tags=self.tags) def get_integration(domain_name: Optional[str] = None, uri: Optional[str] = None, opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetIntegrationResult: __args__ = dict() __args__['domainName'] = domain_name __args__['uri'] = uri if opts is None: opts = pulumi.InvokeOptions() if opts.version is None: opts.version = _utilities.get_version() __ret__ = pulumi.runtime.invoke('aws-native:customerprofiles:getIntegration', __args__, opts=opts, typ=GetIntegrationResult).value return AwaitableGetIntegrationResult( created_at=__ret__.created_at, last_updated_at=__ret__.last_updated_at, object_type_name=__ret__.object_type_name, object_type_names=__ret__.object_type_names, tags=__ret__.tags) @_utilities.lift_output_func(get_integration) def get_integration_output(domain_name: Optional[pulumi.Input[str]] = None, uri: Optional[pulumi.Input[str]] = None, opts: Optional[pulumi.InvokeOptions] = None) -> pulumi.Output[GetIntegrationResult]: ...

true

f7fbf6f7dd6fbdb6d7896e22e918b16db8e7adef

1,255

py

Python

src/basic_classification/basic.py

asaladino/tf-playground

c188d29077334038b2ae3afefed950f1ebb94477

[ "MIT" ]

null

src/basic_classification/basic.py

asaladino/tf-playground

c188d29077334038b2ae3afefed950f1ebb94477

[ "MIT" ]

null

src/basic_classification/basic.py

asaladino/tf-playground

c188d29077334038b2ae3afefed950f1ebb94477

[ "MIT" ]

null

from __future__ import absolute_import, division, print_function # TensorFlow and tf.keras import tensorflow as tf from tensorflow import keras from basic_classification.helper import prediction_for_one_image fashion_mnist = keras.datasets.fashion_mnist # Load the data (train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data() class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot'] # Scale the data train_images = train_images / 255.0 test_images = test_images / 255.0 # Build the model model = keras.Sequential([ keras.layers.Flatten(input_shape=(28, 28)), keras.layers.Dense(128, activation=tf.nn.relu), keras.layers.Dense(128, activation=tf.nn.relu), keras.layers.Dense(10, activation=tf.nn.softmax) ]) # Compile the model model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) # Train the model model.fit(train_images, train_labels, epochs=5) # Evaluate the model test_loss, test_acc = model.evaluate(test_images, test_labels) print('Test accuracy:', test_acc) # Predict what one image will be. prediction_for_one_image(test_images[0], model, test_labels, test_images, class_names)

35.857143

93

0.759363

from __future__ import absolute_import, division, print_function import tensorflow as tf from tensorflow import keras from basic_classification.helper import prediction_for_one_image fashion_mnist = keras.datasets.fashion_mnist (train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data() class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot'] train_images = train_images / 255.0 test_images = test_images / 255.0 model = keras.Sequential([ keras.layers.Flatten(input_shape=(28, 28)), keras.layers.Dense(128, activation=tf.nn.relu), keras.layers.Dense(128, activation=tf.nn.relu), keras.layers.Dense(10, activation=tf.nn.softmax) ]) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) model.fit(train_images, train_labels, epochs=5) test_loss, test_acc = model.evaluate(test_images, test_labels) print('Test accuracy:', test_acc) prediction_for_one_image(test_images[0], model, test_labels, test_images, class_names)

true

f7fbf76eba006d0a341e5e20f2bcdbdcb5a4ddde

4,004

py

Python

retrieve_news_data.py

aarshpatel/RecapMyDay

7e6e148104ac425747123131a0dd4993d4220545

[ "MIT" ]

null

retrieve_news_data.py

aarshpatel/RecapMyDay

7e6e148104ac425747123131a0dd4993d4220545

[ "MIT" ]

null

retrieve_news_data.py

aarshpatel/RecapMyDay

7e6e148104ac425747123131a0dd4993d4220545

[ "MIT" ]

null

import aylien_news_api from aylien_news_api.rest import ApiException import time import pickle from utils import * import json params = { 'language': ['en'], 'source_locations_country': ['US'], 'published_at_start': 'NOW-1DAYS', 'published_at_end': 'NOW', 'categories_taxonomy': 'iab-qag', 'sort_by': 'relevance', 'categories_id': ['IAB17', 'IAB19', 'IAB11-4', 'IAB1-2', 'IAB3'], 'cursor': '*', 'per_page': 16 } id_to_category = { 'IAB17': 'Sports', 'IAB19': 'Tech', 'IAB11-4': 'Politics', 'IAB1-2': 'Celebrity Fan/Gossip', 'IAB3': 'Business' } def fetch_stories(params={}): fetched_stories = [] stories = None while (stories is None or len(stories) > 0) and (len(fetched_stories) < 200): try: response = api_instance.list_stories(**params) except ApiException as e: if (e.status == 429): print('Usage limit are exceeded. Wating for 60 seconds...') time.sleep(60) continue stories = response.stories params['cursor'] = response.next_page_cursor fetched_stories += stories print("Fetched %d stories. Total story count so far: %d" % (len(stories), len(fetched_stories))) return fetched_stories # Configure API key authorization: app_id, application_key aylien_news_api.configuration.api_key['X-AYLIEN-NewsAPI-Application-ID'] = 'cfa4787e' aylien_news_api.configuration.api_key['X-AYLIEN-NewsAPI-Application-Key'] = '4fd1a82e4716ecccfb9ee352139d6ca9' # create an instance of the API class api_instance = aylien_news_api.DefaultApi() def get_title_and_location_of_stories(stories): """ Preprocess the raw stories from the API to only include the title, link, and location TODO: Need some way to filter out places that aren't part of the United States or aren't states of the US """ all_story_titles = [] stories_and_location = [] for story in stories: title = story.title if title not in all_story_titles: all_story_titles.append(title) all_entities = [] geo_coordinates = [] for entities in story.entities.body: if "Place" in entities.types: coords = convert_location_to_lat_lng(entities.text) if coords is not None and entities.text.isalpha(): geo_coordinates.append(coords) # append the geo_coordinates of the place all_entities.append(entities.text) # append the text of the place print entities.text, coords if len(all_entities) == 0 or len(all_entities) > 2: continue else: # get the category of the story all_ids = [cat.id for cat in story.categories] for story_id in all_ids: if story_id in id_to_category: id_category = id_to_category[story_id] break # get the image of the story url = None if len(story.media) > 0: url = story.media[0].url # get the summary of the story summary = story.summary.sentences stories_and_location.append((title, all_entities, story.links.permalink, geo_coordinates, id_category, url, summary)) return stories_and_location def create_json_from_stories(stories_and_location): """ Convert the preprocessed stories into a list of dictionaries. This will help us with making the 3d visualizations """ stories = [] for story, location, link, geo_coordinates, category, img_url, summary in stories_and_location: story_dict = {} story_dict["title"] = story story_dict["locations"] = location story_dict["link"] = link story_dict["geo_coordinates"] = geo_coordinates story_dict["category"] = category story_dict["img_url"] = img_url story_dict["summary"] = summary stories.append(story_dict) return stories stories = fetch_stories(params) stories = get_title_and_location_of_stories(stories) stories = create_json_from_stories(stories) with open("stories.json", "w") as f: json.dump(stories, f) print stories[:5]

29.659259

125

0.68032

import aylien_news_api from aylien_news_api.rest import ApiException import time import pickle from utils import * import json params = { 'language': ['en'], 'source_locations_country': ['US'], 'published_at_start': 'NOW-1DAYS', 'published_at_end': 'NOW', 'categories_taxonomy': 'iab-qag', 'sort_by': 'relevance', 'categories_id': ['IAB17', 'IAB19', 'IAB11-4', 'IAB1-2', 'IAB3'], 'cursor': '*', 'per_page': 16 } id_to_category = { 'IAB17': 'Sports', 'IAB19': 'Tech', 'IAB11-4': 'Politics', 'IAB1-2': 'Celebrity Fan/Gossip', 'IAB3': 'Business' } def fetch_stories(params={}): fetched_stories = [] stories = None while (stories is None or len(stories) > 0) and (len(fetched_stories) < 200): try: response = api_instance.list_stories(**params) except ApiException as e: if (e.status == 429): print('Usage limit are exceeded. Wating for 60 seconds...') time.sleep(60) continue stories = response.stories params['cursor'] = response.next_page_cursor fetched_stories += stories print("Fetched %d stories. Total story count so far: %d" % (len(stories), len(fetched_stories))) return fetched_stories aylien_news_api.configuration.api_key['X-AYLIEN-NewsAPI-Application-ID'] = 'cfa4787e' aylien_news_api.configuration.api_key['X-AYLIEN-NewsAPI-Application-Key'] = '4fd1a82e4716ecccfb9ee352139d6ca9' api_instance = aylien_news_api.DefaultApi() def get_title_and_location_of_stories(stories): """ Preprocess the raw stories from the API to only include the title, link, and location TODO: Need some way to filter out places that aren't part of the United States or aren't states of the US """ all_story_titles = [] stories_and_location = [] for story in stories: title = story.title if title not in all_story_titles: all_story_titles.append(title) all_entities = [] geo_coordinates = [] for entities in story.entities.body: if "Place" in entities.types: coords = convert_location_to_lat_lng(entities.text) if coords is not None and entities.text.isalpha(): geo_coordinates.append(coords) all_entities.append(entities.text) print entities.text, coords if len(all_entities) == 0 or len(all_entities) > 2: continue else: all_ids = [cat.id for cat in story.categories] for story_id in all_ids: if story_id in id_to_category: id_category = id_to_category[story_id] break url = None if len(story.media) > 0: url = story.media[0].url summary = story.summary.sentences stories_and_location.append((title, all_entities, story.links.permalink, geo_coordinates, id_category, url, summary)) return stories_and_location def create_json_from_stories(stories_and_location): """ Convert the preprocessed stories into a list of dictionaries. This will help us with making the 3d visualizations """ stories = [] for story, location, link, geo_coordinates, category, img_url, summary in stories_and_location: story_dict = {} story_dict["title"] = story story_dict["locations"] = location story_dict["link"] = link story_dict["geo_coordinates"] = geo_coordinates story_dict["category"] = category story_dict["img_url"] = img_url story_dict["summary"] = summary stories.append(story_dict) return stories stories = fetch_stories(params) stories = get_title_and_location_of_stories(stories) stories = create_json_from_stories(stories) with open("stories.json", "w") as f: json.dump(stories, f) print stories[:5]

false

true

f7fbf85d06c907ff650c79d8d3f87dab917a39b8

5,964

py

Python

train.py

free-city-of-ulm/Hashbrowns

586e665b94a2d2221009f2602a10cc862a362895

[ "MIT" ]

null

train.py

free-city-of-ulm/Hashbrowns

586e665b94a2d2221009f2602a10cc862a362895

[ "MIT" ]

null

train.py

free-city-of-ulm/Hashbrowns

586e665b94a2d2221009f2602a10cc862a362895

[ "MIT" ]

null

#!/bin/env python from __future__ import print_function import numpy as np import tensorflow as tf import argparse import time import os from six.moves import cPickle from rnn.utils import TextLoader from rnn.model import Model def main(): parser = argparse.ArgumentParser() parser.add_argument('--data_dir', type=str, default='input', help='data directory containing input.txt') parser.add_argument('--save_dir', type=str, default='output', help='directory to store checkpointed models') parser.add_argument('--rnn_size', type=int, default=256, help='size of RNN hidden state') parser.add_argument('--num_layers', type=int, default=2, help='number of layers in the RNN') parser.add_argument('--model', type=str, default='lstm', help='rnn, gru, or lstm') parser.add_argument('--batch_size', type=int, default=50, help='minibatch size') parser.add_argument('--seq_length', type=int, default=25, help='RNN sequence length') parser.add_argument('--num_epochs', type=int, default=50, help='number of epochs') parser.add_argument('--save_every', type=int, default=1000, help='save frequency') parser.add_argument('--grad_clip', type=float, default=5., help='clip gradients at this value') parser.add_argument('--learning_rate', type=float, default=0.002, help='learning rate') parser.add_argument('--decay_rate', type=float, default=0.97, help='decay rate for rmsprop') parser.add_argument('--init_from', type=str, default=None, help="""continue training from saved model at this path. Path must contain files saved by previous training process: 'config.pkl' : configuration; 'words_vocab.pkl' : vocabulary definitions; 'checkpoint' : paths to model file(s) (created by tf). Note: this file contains absolute paths, be careful when moving files around; 'model.ckpt-*' : file(s) with model definition (created by tf) """) args = parser.parse_args() train(args) def train(args): data_loader = TextLoader(args.data_dir, args.batch_size, args.seq_length) args.vocab_size = data_loader.vocab_size # check compatibility if training is continued from previously saved model if args.init_from is not None: # check if all necessary files exist assert os.path.isdir(args.init_from)," %s must be a a path" % args.init_from assert os.path.isfile(os.path.join(args.init_from,"config.pkl")),"config.pkl file does not exist in path %s"%args.init_from assert os.path.isfile(os.path.join(args.init_from,"words_vocab.pkl")),"words_vocab.pkl.pkl file does not exist in path %s" % args.init_from ckpt = tf.train.get_checkpoint_state(args.init_from) assert ckpt,"No checkpoint found" assert ckpt.model_checkpoint_path,"No model path found in checkpoint" # open old config and check if models are compatible with open(os.path.join(args.init_from, 'config.pkl'), 'rb') as f: saved_model_args = cPickle.load(f) need_be_same=["model","rnn_size","num_layers","seq_length"] for checkme in need_be_same: assert vars(saved_model_args)[checkme]==vars(args)[checkme],"Command line argument and saved model disagree on '%s' "%checkme # open saved vocab/dict and check if vocabs/dicts are compatible with open(os.path.join(args.init_from, 'words_vocab.pkl'), 'rb') as f: saved_words, saved_vocab = cPickle.load(f) assert saved_words==data_loader.words, "Data and loaded model disagreee on word set!" assert saved_vocab==data_loader.vocab, "Data and loaded model disagreee on dictionary mappings!" with open(os.path.join(args.save_dir, 'config.pkl'), 'wb') as f: cPickle.dump(args, f) with open(os.path.join(args.save_dir, 'words_vocab.pkl'), 'wb') as f: cPickle.dump((data_loader.words, data_loader.vocab), f) model = Model(args) with tf.Session() as sess: tf.initialize_all_variables().run() saver = tf.train.Saver(tf.all_variables()) # restore model if args.init_from is not None: saver.restore(sess, ckpt.model_checkpoint_path) for e in range(args.num_epochs): sess.run(tf.assign(model.lr, args.learning_rate * (args.decay_rate ** e))) data_loader.reset_batch_pointer() state = model.initial_state.eval() for b in range(data_loader.num_batches): start = time.time() x, y = data_loader.next_batch() feed = {model.input_data: x, model.targets: y, model.initial_state: state} train_loss, state, _ = sess.run([model.cost, model.final_state, model.train_op], feed) end = time.time() print("{}/{} (epoch {}), train_loss = {:.3f}, time/batch = {:.3f}" \ .format(e * data_loader.num_batches + b, args.num_epochs * data_loader.num_batches, e, train_loss, end - start)) if (e * data_loader.num_batches + b) % args.save_every == 0 \ or (e==args.num_epochs-1 and b == data_loader.num_batches-1): # save for the last result checkpoint_path = os.path.join(args.save_dir, 'model.ckpt') saver.save(sess, checkpoint_path, global_step = e * data_loader.num_batches + b) print("model saved to {}".format(checkpoint_path)) if __name__ == '__main__': main()

52.315789

147

0.611167

from __future__ import print_function import numpy as np import tensorflow as tf import argparse import time import os from six.moves import cPickle from rnn.utils import TextLoader from rnn.model import Model def main(): parser = argparse.ArgumentParser() parser.add_argument('--data_dir', type=str, default='input', help='data directory containing input.txt') parser.add_argument('--save_dir', type=str, default='output', help='directory to store checkpointed models') parser.add_argument('--rnn_size', type=int, default=256, help='size of RNN hidden state') parser.add_argument('--num_layers', type=int, default=2, help='number of layers in the RNN') parser.add_argument('--model', type=str, default='lstm', help='rnn, gru, or lstm') parser.add_argument('--batch_size', type=int, default=50, help='minibatch size') parser.add_argument('--seq_length', type=int, default=25, help='RNN sequence length') parser.add_argument('--num_epochs', type=int, default=50, help='number of epochs') parser.add_argument('--save_every', type=int, default=1000, help='save frequency') parser.add_argument('--grad_clip', type=float, default=5., help='clip gradients at this value') parser.add_argument('--learning_rate', type=float, default=0.002, help='learning rate') parser.add_argument('--decay_rate', type=float, default=0.97, help='decay rate for rmsprop') parser.add_argument('--init_from', type=str, default=None, help="""continue training from saved model at this path. Path must contain files saved by previous training process: 'config.pkl' : configuration; 'words_vocab.pkl' : vocabulary definitions; 'checkpoint' : paths to model file(s) (created by tf). Note: this file contains absolute paths, be careful when moving files around; 'model.ckpt-*' : file(s) with model definition (created by tf) """) args = parser.parse_args() train(args) def train(args): data_loader = TextLoader(args.data_dir, args.batch_size, args.seq_length) args.vocab_size = data_loader.vocab_size if args.init_from is not None: assert os.path.isdir(args.init_from)," %s must be a a path" % args.init_from assert os.path.isfile(os.path.join(args.init_from,"config.pkl")),"config.pkl file does not exist in path %s"%args.init_from assert os.path.isfile(os.path.join(args.init_from,"words_vocab.pkl")),"words_vocab.pkl.pkl file does not exist in path %s" % args.init_from ckpt = tf.train.get_checkpoint_state(args.init_from) assert ckpt,"No checkpoint found" assert ckpt.model_checkpoint_path,"No model path found in checkpoint" with open(os.path.join(args.init_from, 'config.pkl'), 'rb') as f: saved_model_args = cPickle.load(f) need_be_same=["model","rnn_size","num_layers","seq_length"] for checkme in need_be_same: assert vars(saved_model_args)[checkme]==vars(args)[checkme],"Command line argument and saved model disagree on '%s' "%checkme with open(os.path.join(args.init_from, 'words_vocab.pkl'), 'rb') as f: saved_words, saved_vocab = cPickle.load(f) assert saved_words==data_loader.words, "Data and loaded model disagreee on word set!" assert saved_vocab==data_loader.vocab, "Data and loaded model disagreee on dictionary mappings!" with open(os.path.join(args.save_dir, 'config.pkl'), 'wb') as f: cPickle.dump(args, f) with open(os.path.join(args.save_dir, 'words_vocab.pkl'), 'wb') as f: cPickle.dump((data_loader.words, data_loader.vocab), f) model = Model(args) with tf.Session() as sess: tf.initialize_all_variables().run() saver = tf.train.Saver(tf.all_variables()) if args.init_from is not None: saver.restore(sess, ckpt.model_checkpoint_path) for e in range(args.num_epochs): sess.run(tf.assign(model.lr, args.learning_rate * (args.decay_rate ** e))) data_loader.reset_batch_pointer() state = model.initial_state.eval() for b in range(data_loader.num_batches): start = time.time() x, y = data_loader.next_batch() feed = {model.input_data: x, model.targets: y, model.initial_state: state} train_loss, state, _ = sess.run([model.cost, model.final_state, model.train_op], feed) end = time.time() print("{}/{} (epoch {}), train_loss = {:.3f}, time/batch = {:.3f}" \ .format(e * data_loader.num_batches + b, args.num_epochs * data_loader.num_batches, e, train_loss, end - start)) if (e * data_loader.num_batches + b) % args.save_every == 0 \ or (e==args.num_epochs-1 and b == data_loader.num_batches-1): checkpoint_path = os.path.join(args.save_dir, 'model.ckpt') saver.save(sess, checkpoint_path, global_step = e * data_loader.num_batches + b) print("model saved to {}".format(checkpoint_path)) if __name__ == '__main__': main()

true

f7fbf897b66372ba08a4e061fd84692a3927efff

2,831

py

Python

hypernets/dispatchers/grpc/proto/predict_pb2_grpc.py

qigj/Hypernets

534150e682f09df11ac3363e6aa7cd5e4a294c1d

[ "Apache-2.0" ]

1

2020-11-06T07:52:14.000Z

hypernets/dispatchers/grpc/proto/predict_pb2_grpc.py

frankbaul/Hypernets

534150e682f09df11ac3363e6aa7cd5e4a294c1d

[ "Apache-2.0" ]

null

hypernets/dispatchers/grpc/proto/predict_pb2_grpc.py

frankbaul/Hypernets

534150e682f09df11ac3363e6aa7cd5e4a294c1d

[ "Apache-2.0" ]

null

# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT! """Client and server classes corresponding to protobuf-defined services.""" import grpc from hypernets.dispatchers.grpc.proto import predict_pb2 as hypernets_dot_dispatchers_dot_grpc_dot_proto_dot_predict__pb2 class PredictServiceStub(object): """Missing associated documentation comment in .proto file.""" def __init__(self, channel): """Constructor. Args: channel: A grpc.Channel. """ self.predict = channel.unary_unary( '/hypernets.dispatchers.proto.PredictService/predict', request_serializer=hypernets_dot_dispatchers_dot_grpc_dot_proto_dot_predict__pb2.PredictRequest.SerializeToString, response_deserializer=hypernets_dot_dispatchers_dot_grpc_dot_proto_dot_predict__pb2.PredictResponse.FromString, ) class PredictServiceServicer(object): """Missing associated documentation comment in .proto file.""" def predict(self, request, context): """Missing associated documentation comment in .proto file.""" context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def add_PredictServiceServicer_to_server(servicer, server): rpc_method_handlers = { 'predict': grpc.unary_unary_rpc_method_handler( servicer.predict, request_deserializer=hypernets_dot_dispatchers_dot_grpc_dot_proto_dot_predict__pb2.PredictRequest.FromString, response_serializer=hypernets_dot_dispatchers_dot_grpc_dot_proto_dot_predict__pb2.PredictResponse.SerializeToString, ), } generic_handler = grpc.method_handlers_generic_handler( 'hypernets.dispatchers.proto.PredictService', rpc_method_handlers) server.add_generic_rpc_handlers((generic_handler,)) # This class is part of an EXPERIMENTAL API. class PredictService(object): """Missing associated documentation comment in .proto file.""" @staticmethod def predict(request, target, options=(), channel_credentials=None, call_credentials=None, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/hypernets.dispatchers.proto.PredictService/predict', hypernets_dot_dispatchers_dot_grpc_dot_proto_dot_predict__pb2.PredictRequest.SerializeToString, hypernets_dot_dispatchers_dot_grpc_dot_proto_dot_predict__pb2.PredictResponse.FromString, options, channel_credentials, call_credentials, compression, wait_for_ready, timeout, metadata)

42.893939

136

0.7213

import grpc from hypernets.dispatchers.grpc.proto import predict_pb2 as hypernets_dot_dispatchers_dot_grpc_dot_proto_dot_predict__pb2 class PredictServiceStub(object): def __init__(self, channel): self.predict = channel.unary_unary( '/hypernets.dispatchers.proto.PredictService/predict', request_serializer=hypernets_dot_dispatchers_dot_grpc_dot_proto_dot_predict__pb2.PredictRequest.SerializeToString, response_deserializer=hypernets_dot_dispatchers_dot_grpc_dot_proto_dot_predict__pb2.PredictResponse.FromString, ) class PredictServiceServicer(object): def predict(self, request, context): context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def add_PredictServiceServicer_to_server(servicer, server): rpc_method_handlers = { 'predict': grpc.unary_unary_rpc_method_handler( servicer.predict, request_deserializer=hypernets_dot_dispatchers_dot_grpc_dot_proto_dot_predict__pb2.PredictRequest.FromString, response_serializer=hypernets_dot_dispatchers_dot_grpc_dot_proto_dot_predict__pb2.PredictResponse.SerializeToString, ), } generic_handler = grpc.method_handlers_generic_handler( 'hypernets.dispatchers.proto.PredictService', rpc_method_handlers) server.add_generic_rpc_handlers((generic_handler,)) class PredictService(object): @staticmethod def predict(request, target, options=(), channel_credentials=None, call_credentials=None, compression=None, wait_for_ready=None, timeout=None, metadata=None): return grpc.experimental.unary_unary(request, target, '/hypernets.dispatchers.proto.PredictService/predict', hypernets_dot_dispatchers_dot_grpc_dot_proto_dot_predict__pb2.PredictRequest.SerializeToString, hypernets_dot_dispatchers_dot_grpc_dot_proto_dot_predict__pb2.PredictResponse.FromString, options, channel_credentials, call_credentials, compression, wait_for_ready, timeout, metadata)

true

f7fbf96348c87d410ce9ecef02e1e3092f5c23d9

2,916

py

Python

tests/utilities/test_command_runner.py

cmontesano/xappt

66f83fe53006dfd9c33b9d583dc1f0afcd2a8d26

[ "MIT" ]

2

2020-11-27T14:52:04.000Z

2022-03-08T18:33:14.000Z

tests/utilities/test_command_runner.py

cmontesano/xappt

66f83fe53006dfd9c33b9d583dc1f0afcd2a8d26

[ "MIT" ]

11

2021-07-04T00:37:45.000Z

2021-10-04T17:07:56.000Z

tests/utilities/test_command_runner.py

cmontesano/xappt

66f83fe53006dfd9c33b9d583dc1f0afcd2a8d26

[ "MIT" ]

null

import os import re import unittest from xappt.utilities import CommandRunner from xappt.utilities import temporary_path class TestCommandRunner(unittest.TestCase): def test_basic_command(self): with temporary_path() as tmp: cmd = CommandRunner(cwd=tmp) if os.name == "nt": cmd.run(("mkdir", "test"), capture_output=False, shell=True) else: cmd.run(("mkdir", "test"), capture_output=False) self.assertTrue(tmp.joinpath("test").is_dir()) def test_basic_command_silent(self): with temporary_path() as tmp: cmd = CommandRunner(cwd=tmp) if os.name == "nt": cmd.run(("mkdir", "test"), capture_output=True, shell=True) else: cmd.run(("mkdir", "test"), capture_output=True) self.assertTrue(tmp.joinpath("test").is_dir()) def test_basic_command_output(self): with temporary_path() as tmp: cmd = CommandRunner(cwd=tmp) if os.name == "nt": cmd.run(("mkdir", "test"), capture_output=True, shell=True) self.assertTrue(tmp.joinpath("test").is_dir()) output = cmd.run("dir", capture_output=True, shell=True) match_pattern = r"^.*?<DIR>\s+test$" else: cmd.run(("mkdir", "test"), capture_output=True) self.assertTrue(tmp.joinpath("test").is_dir()) output = cmd.run(("ls", "-l"), capture_output=True) match_pattern = r"^[d].*\s(?:test)$" matched = False for line in output.stdout.split("\n"): line = line.strip() if re.match(match_pattern, line): matched = True if not matched: self.fail(f"No matches in '{output.stdout}'") def test_environment_add(self): cmd = CommandRunner(env={}) cmd.env_var_set("TEST", "12345") self.assertIn("TEST", cmd.env) self.assertEqual(cmd.env["TEST"], "12345") cmd.env_var_set("TEST", "abcde") self.assertEqual(cmd.env["TEST"], "abcde") def test_environment_rem(self): cmd = CommandRunner(env={}) cmd.env_var_set("TEST", "12345") self.assertIn("TEST", cmd.env) cmd.env_var_remove("TEST") self.assertNotIn("TEST", cmd.env) try: cmd.env_var_remove("INVALID") except KeyError: self.fail("A KeyError should not have been raised.") def test_environment_path(self): cmd = CommandRunner(env={}) cmd.env_var_set("TESTPATH", "3") cmd.env_path_append("TESTPATH", "4") cmd.env_path_prepend("TESTPATH", "2") cmd.env_path_append("TESTPATH", "5") cmd.env_path_prepend("TESTPATH", "1") self.assertEqual(cmd.env["TESTPATH"], os.pathsep.join("12345"))

38.368421

76

0.564129

import os import re import unittest from xappt.utilities import CommandRunner from xappt.utilities import temporary_path class TestCommandRunner(unittest.TestCase): def test_basic_command(self): with temporary_path() as tmp: cmd = CommandRunner(cwd=tmp) if os.name == "nt": cmd.run(("mkdir", "test"), capture_output=False, shell=True) else: cmd.run(("mkdir", "test"), capture_output=False) self.assertTrue(tmp.joinpath("test").is_dir()) def test_basic_command_silent(self): with temporary_path() as tmp: cmd = CommandRunner(cwd=tmp) if os.name == "nt": cmd.run(("mkdir", "test"), capture_output=True, shell=True) else: cmd.run(("mkdir", "test"), capture_output=True) self.assertTrue(tmp.joinpath("test").is_dir()) def test_basic_command_output(self): with temporary_path() as tmp: cmd = CommandRunner(cwd=tmp) if os.name == "nt": cmd.run(("mkdir", "test"), capture_output=True, shell=True) self.assertTrue(tmp.joinpath("test").is_dir()) output = cmd.run("dir", capture_output=True, shell=True) match_pattern = r"^.*?<DIR>\s+test$" else: cmd.run(("mkdir", "test"), capture_output=True) self.assertTrue(tmp.joinpath("test").is_dir()) output = cmd.run(("ls", "-l"), capture_output=True) match_pattern = r"^[d].*\s(?:test)$" matched = False for line in output.stdout.split("\n"): line = line.strip() if re.match(match_pattern, line): matched = True if not matched: self.fail(f"No matches in '{output.stdout}'") def test_environment_add(self): cmd = CommandRunner(env={}) cmd.env_var_set("TEST", "12345") self.assertIn("TEST", cmd.env) self.assertEqual(cmd.env["TEST"], "12345") cmd.env_var_set("TEST", "abcde") self.assertEqual(cmd.env["TEST"], "abcde") def test_environment_rem(self): cmd = CommandRunner(env={}) cmd.env_var_set("TEST", "12345") self.assertIn("TEST", cmd.env) cmd.env_var_remove("TEST") self.assertNotIn("TEST", cmd.env) try: cmd.env_var_remove("INVALID") except KeyError: self.fail("A KeyError should not have been raised.") def test_environment_path(self): cmd = CommandRunner(env={}) cmd.env_var_set("TESTPATH", "3") cmd.env_path_append("TESTPATH", "4") cmd.env_path_prepend("TESTPATH", "2") cmd.env_path_append("TESTPATH", "5") cmd.env_path_prepend("TESTPATH", "1") self.assertEqual(cmd.env["TESTPATH"], os.pathsep.join("12345"))

true

f7fbfa42fc9f6923adfc89509e8e26b4247e7489

4,367

py

Python

test/test_download_espa_order.py

cgaustin/bulk-downloader

7c8bd16240ca65ac909e13638a7dc7afb4c0c3de

[ "Unlicense" ]

null

test/test_download_espa_order.py

cgaustin/bulk-downloader

7c8bd16240ca65ac909e13638a7dc7afb4c0c3de

[ "Unlicense" ]

null

test/test_download_espa_order.py

cgaustin/bulk-downloader

7c8bd16240ca65ac909e13638a7dc7afb4c0c3de

[ "Unlicense" ]

null

import os import re import unittest from mock import patch import download_espa_order as deo import urllib import requests import requests_mock class TestDownloadEspaOrder(unittest.TestCase): def test_get_version(self): ver = deo.get_version() self.assertTrue(re.match("[0-9]{1}.[0-9]{1,2}.[0-9]{1,2}", ver)) class TestRequestsHandler(unittest.TestCase): def setUp(self): rh = deo.RequestsHandler(host='http://foo.gov') self.handler = rh def tearDown(self): self.handler = None if os.path.exists("bar.tar.part"): os.remove("bar.tar.part") def test_auth(self): self.handler.auth('foo', 'bar') self.assertEqual(('foo', 'bar'), self.handler.creds) def test_get(self): with requests_mock.mock() as m: m.get('http://foo.gov/bar', json={'bilbo': 'baggins'}) resp = self.handler.get('/bar') self.assertEqual(resp, {'bilbo': 'baggins'}) def test_download(self): with requests_mock.mock() as m: m.head('http://foo.gov/bar.tar', headers={'Content-Length': '99'}) m.get('http://foo.gov/bar.tar', text="guacamole") resp = self.handler.download('/bar.tar', 'bar.tar') self.assertEqual(resp, "bar.tar") class TestApi(unittest.TestCase): def setUp(self): self.api = deo.Api('bilbo', 'baggins', 'http://foo.gov') def tearDown(self): self.api = None def test_api_request(self): with requests_mock.mock() as m: m.get('http://foo.gov/morefoo', json={'thing1': 'thing2', 'messages': {'value': 'true'}}) resp = self.api.api_request('/morefoo') self.assertEqual(resp, {'thing1': 'thing2'}) def test_get_completed_scenes(self): with requests_mock.mock() as m: m.get('http://foo.gov/api/v1/item-status/anespaorderid', json={'anespaorderid': [{'product_dload_url': 'a.gov'}, {'product_dload_url': 'b.gov'}]}) resp = self.api.get_completed_scenes('anespaorderid') self.assertEqual(resp, ['a.gov', 'b.gov']) def test_retrieve_all_orders(self): with requests_mock.mock() as m: m.get('http://foo.gov/api/v1/list-orders/ausersemail@gmail.com', json={'orderjson': {'status': 'complete'}}) resp = self.api.retrieve_all_orders('ausersemail@gmail.com') self.assertEqual(resp, {'orderjson': {'status': 'complete'}}) class TestScene(unittest.TestCase): def setUp(self): self.test_url = "https://downloads.usgs.gov/orders/user@contractor.gov-030/LC080070592016082701T1-SC20200423155912.tar.gz" def test_init(self): scene = deo.Scene(self.test_url) self.assertEqual(scene.srcurl, self.test_url) self.assertEqual(scene.orderid, 'user@contractor.gov-030') self.assertEqual(scene.filename, 'LC080070592016082701T1-SC20200423155912.tar.gz') self.assertEqual(scene.name, 'LC080070592016082701T1-SC20200423155912') self.assertEqual(scene.cksum_url, 'https://downloads.usgs.gov/orders/user@contractor.gov-030/LC080070592016082701T1-SC20200423155912.md5') self.assertEqual(scene.cksum_file, 'LC080070592016082701T1-SC20200423155912.md5') self.assertEqual(scene.cksum_name, 'LC080070592016082701T1-SC20200423155912 MD5 checksum') class TestLocalStorage(unittest.TestCase): def setUp(self): self.test_url = "https://downloads.usgs.gov/orders/user@contractor.gov-030/LC080070592016082701T1-SC20200423155912.tar.gz" self.scene = deo.Scene(self.test_url) self.local_storage = deo.LocalStorage('') def tearDown(self): path = "user@contractor.gov-030" if os.path.exists(path): os.rmdir(path) def test_directory_path(self): path = self.local_storage.directory_path(self.scene) self.assertTrue(os.path.exists(path)) def test_scene_path(self): path = self.local_storage.scene_path(self.scene) self.assertEqual(path, "user@contractor.gov-030/LC080070592016082701T1-SC20200423155912.tar.gz") @patch('os.path.exists', lambda i: type(i) == str) def test_is_stored(self): self.assertTrue(self.local_storage.is_stored(self.scene))

38.646018

146

0.640485

import os import re import unittest from mock import patch import download_espa_order as deo import urllib import requests import requests_mock class TestDownloadEspaOrder(unittest.TestCase): def test_get_version(self): ver = deo.get_version() self.assertTrue(re.match("[0-9]{1}.[0-9]{1,2}.[0-9]{1,2}", ver)) class TestRequestsHandler(unittest.TestCase): def setUp(self): rh = deo.RequestsHandler(host='http://foo.gov') self.handler = rh def tearDown(self): self.handler = None if os.path.exists("bar.tar.part"): os.remove("bar.tar.part") def test_auth(self): self.handler.auth('foo', 'bar') self.assertEqual(('foo', 'bar'), self.handler.creds) def test_get(self): with requests_mock.mock() as m: m.get('http://foo.gov/bar', json={'bilbo': 'baggins'}) resp = self.handler.get('/bar') self.assertEqual(resp, {'bilbo': 'baggins'}) def test_download(self): with requests_mock.mock() as m: m.head('http://foo.gov/bar.tar', headers={'Content-Length': '99'}) m.get('http://foo.gov/bar.tar', text="guacamole") resp = self.handler.download('/bar.tar', 'bar.tar') self.assertEqual(resp, "bar.tar") class TestApi(unittest.TestCase): def setUp(self): self.api = deo.Api('bilbo', 'baggins', 'http://foo.gov') def tearDown(self): self.api = None def test_api_request(self): with requests_mock.mock() as m: m.get('http://foo.gov/morefoo', json={'thing1': 'thing2', 'messages': {'value': 'true'}}) resp = self.api.api_request('/morefoo') self.assertEqual(resp, {'thing1': 'thing2'}) def test_get_completed_scenes(self): with requests_mock.mock() as m: m.get('http://foo.gov/api/v1/item-status/anespaorderid', json={'anespaorderid': [{'product_dload_url': 'a.gov'}, {'product_dload_url': 'b.gov'}]}) resp = self.api.get_completed_scenes('anespaorderid') self.assertEqual(resp, ['a.gov', 'b.gov']) def test_retrieve_all_orders(self): with requests_mock.mock() as m: m.get('http://foo.gov/api/v1/list-orders/ausersemail@gmail.com', json={'orderjson': {'status': 'complete'}}) resp = self.api.retrieve_all_orders('ausersemail@gmail.com') self.assertEqual(resp, {'orderjson': {'status': 'complete'}}) class TestScene(unittest.TestCase): def setUp(self): self.test_url = "https://downloads.usgs.gov/orders/user@contractor.gov-030/LC080070592016082701T1-SC20200423155912.tar.gz" def test_init(self): scene = deo.Scene(self.test_url) self.assertEqual(scene.srcurl, self.test_url) self.assertEqual(scene.orderid, 'user@contractor.gov-030') self.assertEqual(scene.filename, 'LC080070592016082701T1-SC20200423155912.tar.gz') self.assertEqual(scene.name, 'LC080070592016082701T1-SC20200423155912') self.assertEqual(scene.cksum_url, 'https://downloads.usgs.gov/orders/user@contractor.gov-030/LC080070592016082701T1-SC20200423155912.md5') self.assertEqual(scene.cksum_file, 'LC080070592016082701T1-SC20200423155912.md5') self.assertEqual(scene.cksum_name, 'LC080070592016082701T1-SC20200423155912 MD5 checksum') class TestLocalStorage(unittest.TestCase): def setUp(self): self.test_url = "https://downloads.usgs.gov/orders/user@contractor.gov-030/LC080070592016082701T1-SC20200423155912.tar.gz" self.scene = deo.Scene(self.test_url) self.local_storage = deo.LocalStorage('') def tearDown(self): path = "user@contractor.gov-030" if os.path.exists(path): os.rmdir(path) def test_directory_path(self): path = self.local_storage.directory_path(self.scene) self.assertTrue(os.path.exists(path)) def test_scene_path(self): path = self.local_storage.scene_path(self.scene) self.assertEqual(path, "user@contractor.gov-030/LC080070592016082701T1-SC20200423155912.tar.gz") @patch('os.path.exists', lambda i: type(i) == str) def test_is_stored(self): self.assertTrue(self.local_storage.is_stored(self.scene))

true

f7fbfaf1e833620b5f09f7ca98374722712475f1

47,007

py

Python

src/twisted/mail/test/test_pop3.py

clokep/twisted

79a26b0aa4b1b81b46cc64d203644b35e455e46b

[ "Unlicense", "MIT" ]

null

src/twisted/mail/test/test_pop3.py

clokep/twisted

79a26b0aa4b1b81b46cc64d203644b35e455e46b

[ "Unlicense", "MIT" ]

null

src/twisted/mail/test/test_pop3.py

clokep/twisted

79a26b0aa4b1b81b46cc64d203644b35e455e46b

[ "Unlicense", "MIT" ]

1

2021-12-13T10:46:13.000Z

# Copyright (c) Twisted Matrix Laboratories. # See LICENSE for details. """ Test cases for Ltwisted.mail.pop3} module. """ import hmac import base64 import itertools from hashlib import md5 from collections import OrderedDict from io import BytesIO from zope.interface import implementer from twisted import cred from twisted import internet from twisted import mail from twisted.internet import defer from twisted.mail import pop3 from twisted.protocols import loopback from twisted.python import failure from twisted.python.compat import intToBytes from twisted.test.proto_helpers import LineSendingProtocol from twisted.trial import unittest, util import twisted.cred.checkers import twisted.cred.credentials import twisted.cred.portal import twisted.internet.protocol import twisted.mail.pop3 import twisted.mail.protocols class UtilityTests(unittest.TestCase): """ Test the various helper functions and classes used by the POP3 server protocol implementation. """ def test_LineBuffering(self): """ Test creating a LineBuffer and feeding it some lines. The lines should build up in its internal buffer for a while and then get spat out to the writer. """ output = [] input = iter(itertools.cycle(['012', '345', '6', '7', '8', '9'])) c = pop3._IteratorBuffer(output.extend, input, 6) i = iter(c) self.assertEqual(output, []) # Nothing is buffer next(i) self.assertEqual(output, []) # '012' is buffered next(i) self.assertEqual(output, []) # '012345' is buffered next(i) self.assertEqual(output, ['012', '345', '6']) # Nothing is buffered for n in range(5): next(i) self.assertEqual(output, ['012', '345', '6', '7', '8', '9', '012', '345']) def test_FinishLineBuffering(self): """ Test that a LineBuffer flushes everything when its iterator is exhausted, and itself raises StopIteration. """ output = [] input = iter(['a', 'b', 'c']) c = pop3._IteratorBuffer(output.extend, input, 5) for i in c: pass self.assertEqual(output, ['a', 'b', 'c']) def test_SuccessResponseFormatter(self): """ Test that the thing that spits out POP3 'success responses' works right. """ self.assertEqual( pop3.successResponse(b'Great.'), b'+OK Great.\r\n') def test_StatLineFormatter(self): """ Test that the function which formats stat lines does so appropriately. """ statLine = list(pop3.formatStatResponse([]))[-1] self.assertEqual(statLine, b'+OK 0 0\r\n') statLine = list(pop3.formatStatResponse([10, 31, 0, 10101]))[-1] self.assertEqual(statLine, b'+OK 4 10142\r\n') def test_ListLineFormatter(self): """ Test that the function which formats the lines in response to a LIST command does so appropriately. """ listLines = list(pop3.formatListResponse([])) self.assertEqual( listLines, [b'+OK 0\r\n', b'.\r\n']) listLines = list(pop3.formatListResponse([1, 2, 3, 100])) self.assertEqual( listLines, [b'+OK 4\r\n', b'1 1\r\n', b'2 2\r\n', b'3 3\r\n', b'4 100\r\n', b'.\r\n']) def test_UIDListLineFormatter(self): """ Test that the function which formats lines in response to a UIDL command does so appropriately. """ uids = ['abc', 'def', 'ghi'] listLines = list(pop3.formatUIDListResponse([], uids.__getitem__)) self.assertEqual( listLines, [b'+OK \r\n', b'.\r\n']) listLines = list(pop3.formatUIDListResponse([123, 431, 591], uids.__getitem__)) self.assertEqual( listLines, [b'+OK \r\n', b'1 abc\r\n', b'2 def\r\n', b'3 ghi\r\n', b'.\r\n']) listLines = list(pop3.formatUIDListResponse([0, None, 591], uids.__getitem__)) self.assertEqual( listLines, [b'+OK \r\n', b'1 abc\r\n', b'3 ghi\r\n', b'.\r\n']) class MyVirtualPOP3(mail.protocols.VirtualPOP3): """ A virtual-domain-supporting POP3 server. """ magic = b'<moshez>' def authenticateUserAPOP(self, user, digest): """ Authenticate against a user against a virtual domain. @param user: The username. @param digest: The digested password. @return: A three-tuple like the one returned by L{IRealm.requestAvatar}. The mailbox will be for the user given by C{user}. """ user, domain = self.lookupDomain(user) return self.service.domains[b'baz.com'].authenticateUserAPOP( user, digest, self.magic, domain) class DummyDomain: """ A virtual domain for a POP3 server. """ def __init__(self): self.users = {} def addUser(self, name): """ Create a mailbox for a new user. @param name: The username. """ self.users[name] = [] def addMessage(self, name, message): """ Add a message to the mailbox of the named user. @param name: The username. @param message: The contents of the message. """ self.users[name].append(message) def authenticateUserAPOP(self, name, digest, magic, domain): """ Succeed with a L{ListMailbox}. @param name: The name of the user authenticating. @param digest: ignored @param magic: ignored @param domain: ignored @return: A three-tuple like the one returned by L{IRealm.requestAvatar}. The mailbox will be for the user given by C{name}. """ return pop3.IMailbox, ListMailbox(self.users[name]), lambda: None class ListMailbox: """ A simple in-memory list implementation of L{IMailbox}. """ def __init__(self, list): """ @param list: The messages. """ self.list = list def listMessages(self, i=None): """ Get some message information. @param i: See L{pop3.IMailbox.listMessages}. @return: See L{pop3.IMailbox.listMessages}. """ if i is None: return [len(l) for l in self.list] return len(self.list[i]) def getMessage(self, i): """ Get the message content. @param i: See L{pop3.IMailbox.getMessage}. @return: See L{pop3.IMailbox.getMessage}. """ return BytesIO(self.list[i]) def getUidl(self, i): """ Construct a UID by using the given index value. @param i: See L{pop3.IMailbox.getUidl}. @return: See L{pop3.IMailbox.getUidl}. """ return i def deleteMessage(self, i): """ Wipe the message at the given index. @param i: See L{pop3.IMailbox.deleteMessage}. """ self.list[i] = b'' def sync(self): """ No-op. @see: L{pop3.IMailbox.sync} """ class MyPOP3Downloader(pop3.POP3Client): """ A POP3 client which downloads all messages from the server. """ def handle_WELCOME(self, line): """ Authenticate. @param line: The welcome response. """ pop3.POP3Client.handle_WELCOME(self, line) self.apop(b'hello@baz.com', b'world') def handle_APOP(self, line): """ Require an I{OK} response to I{APOP}. @param line: The I{APOP} response. """ parts = line.split() code = parts[0] if code != b'+OK': raise AssertionError('code is: %s , parts is: %s ' % (code, parts)) self.lines = [] self.retr(1) def handle_RETR_continue(self, line): """ Record one line of message information. @param line: A I{RETR} response line. """ self.lines.append(line) def handle_RETR_end(self): """ Record the received message information. """ self.message = b'\n'.join(self.lines) + b'\n' self.quit() def handle_QUIT(self, line): """ Require an I{OK} response to I{QUIT}. @param line: The I{QUIT} response. """ if line[:3] != b'+OK': raise AssertionError(b'code is ' + line) class POP3Tests(unittest.TestCase): """ Tests for L{pop3.POP3}. """ message = b'''\ Subject: urgent Someone set up us the bomb! ''' expectedOutput = (b'''\ +OK <moshez>\015 +OK Authentication succeeded\015 +OK \015 1 0\015 .\015 +OK ''' + intToBytes(len(message)) + b'''\015 Subject: urgent\015 \015 Someone set up us the bomb!\015 .\015 +OK \015 ''') def setUp(self): """ Set up a POP3 server with virtual domain support. """ self.factory = internet.protocol.Factory() self.factory.domains = {} self.factory.domains[b'baz.com'] = DummyDomain() self.factory.domains[b'baz.com'].addUser(b'hello') self.factory.domains[b'baz.com'].addMessage(b'hello', self.message) def test_messages(self): """ Messages can be downloaded over a loopback TCP connection. """ client = LineSendingProtocol([ b'APOP hello@baz.com world', b'UIDL', b'RETR 1', b'QUIT', ]) server = MyVirtualPOP3() server.service = self.factory def check(ignored): output = b'\r\n'.join(client.response) + b'\r\n' self.assertEqual(output, self.expectedOutput) return loopback.loopbackTCP(server, client).addCallback(check) def test_loopback(self): """ Messages can be downloaded over a loopback connection. """ protocol = MyVirtualPOP3() protocol.service = self.factory clientProtocol = MyPOP3Downloader() def check(ignored): self.assertEqual(clientProtocol.message, self.message) protocol.connectionLost( failure.Failure(Exception("Test harness disconnect"))) d = loopback.loopbackAsync(protocol, clientProtocol) return d.addCallback(check) test_loopback.suppress = [util.suppress( message="twisted.mail.pop3.POP3Client is deprecated")] def test_incorrectDomain(self): """ Look up a user in a domain which this server does not support. """ factory = internet.protocol.Factory() factory.domains = {} factory.domains[b'twistedmatrix.com'] = DummyDomain() server = MyVirtualPOP3() server.service = factory exc = self.assertRaises(pop3.POP3Error, server.authenticateUserAPOP, b'nobody@baz.com', b'password') self.assertEqual(exc.args[0], 'no such domain baz.com') class DummyPOP3(pop3.POP3): """ A simple POP3 server with a hard-coded mailbox for any user. """ magic = b'<moshez>' def authenticateUserAPOP(self, user, password): """ Succeed with a L{DummyMailbox}. @param user: ignored @param password: ignored @return: A three-tuple like the one returned by L{IRealm.requestAvatar}. """ return pop3.IMailbox, DummyMailbox(ValueError), lambda: None class DummyPOP3Auth(DummyPOP3): """ Class to test successful authentication in twisted.mail.pop3.POP3. """ def __init__(self, user, password): self.portal = cred.portal.Portal(TestRealm()) ch = cred.checkers.InMemoryUsernamePasswordDatabaseDontUse() ch.addUser(user, password) self.portal.registerChecker(ch) class DummyMailbox(pop3.Mailbox): """ An in-memory L{pop3.IMailbox} implementation. @ivar messages: A sequence of L{bytes} defining the messages in this mailbox. @ivar exceptionType: The type of exception to raise when an out-of-bounds index is addressed. """ messages = [b'From: moshe\nTo: moshe\n\nHow are you, friend?\n'] def __init__(self, exceptionType): self.messages = DummyMailbox.messages[:] self.exceptionType = exceptionType def listMessages(self, i=None): """ Get some message information. @param i: See L{pop3.IMailbox.listMessages}. @return: See L{pop3.IMailbox.listMessages}. """ if i is None: return [len(m) for m in self.messages] if i >= len(self.messages): raise self.exceptionType() return len(self.messages[i]) def getMessage(self, i): """ Get the message content. @param i: See L{pop3.IMailbox.getMessage}. @return: See L{pop3.IMailbox.getMessage}. """ return BytesIO(self.messages[i]) def getUidl(self, i): """ Construct a UID which is simply the string representation of the given index. @param i: See L{pop3.IMailbox.getUidl}. @return: See L{pop3.IMailbox.getUidl}. """ if i >= len(self.messages): raise self.exceptionType() return intToBytes(i) def deleteMessage(self, i): """ Wipe the message at the given index. @param i: See L{pop3.IMailbox.deleteMessage}. """ self.messages[i] = b'' class AnotherPOP3Tests(unittest.TestCase): """ Additional L{pop3.POP3} tests. """ def runTest(self, lines, expectedOutput, protocolInstance=None): """ Assert that when C{lines} are delivered to L{pop3.POP3} it responds with C{expectedOutput}. @param lines: A sequence of L{bytes} representing lines to deliver to the server. @param expectedOutput: A sequence of L{bytes} representing the expected response from the server. @param protocolInstance: Instance of L{twisted.mail.pop3.POP3} or L{None}. If L{None}, a new DummyPOP3 will be used. @return: A L{Deferred} that fires when the lines have been delivered and the output checked. """ dummy = protocolInstance if protocolInstance else DummyPOP3() client = LineSendingProtocol(lines) d = loopback.loopbackAsync(dummy, client) return d.addCallback(self._cbRunTest, client, dummy, expectedOutput) def _cbRunTest(self, ignored, client, dummy, expectedOutput): self.assertEqual(b'\r\n'.join(expectedOutput), b'\r\n'.join(client.response)) dummy.connectionLost(failure.Failure( Exception("Test harness disconnect"))) return ignored def test_buffer(self): """ Test a lot of different POP3 commands in an extremely pipelined scenario. This test may cover legitimate behavior, but the intent and granularity are not very good. It would likely be an improvement to split it into a number of smaller, more focused tests. """ return self.runTest( [b"APOP moshez dummy", b"LIST", b"UIDL", b"RETR 1", b"RETR 2", b"DELE 1", b"RETR 1", b"QUIT"], [b'+OK <moshez>', b'+OK Authentication succeeded', b'+OK 1', b'1 44', b'.', b'+OK ', b'1 0', b'.', b'+OK 44', b'From: moshe', b'To: moshe', b'', b'How are you, friend?', b'.', b'-ERR Bad message number argument', b'+OK ', b'-ERR message deleted', b'+OK ']) def test_noop(self): """ Test the no-op command. """ return self.runTest( [b'APOP spiv dummy', b'NOOP', b'QUIT'], [b'+OK <moshez>', b'+OK Authentication succeeded', b'+OK ', b'+OK ']) def test_badUTF8CharactersInCommand(self): """ Sending a command with invalid UTF-8 characters will raise a L{pop3.POP3Error}. """ error = b'not authenticated yet: cannot do \x81PASS' d = self.runTest( [b'\x81PASS', b'QUIT'], [b'+OK <moshez>', b"-ERR bad protocol or server: POP3Error: " + error, b'+OK ']) errors = self.flushLoggedErrors(pop3.POP3Error) self.assertEqual(len(errors), 1) return d def test_authListing(self): """ L{pop3.POP3} responds to an I{AUTH} command with a list of supported authentication types based on its factory's C{challengers}. """ p = DummyPOP3() p.factory = internet.protocol.Factory() p.factory.challengers = {b'Auth1': None, b'secondAuth': None, b'authLast': None} client = LineSendingProtocol([ b"AUTH", b"QUIT", ]) d = loopback.loopbackAsync(p, client) return d.addCallback(self._cbTestAuthListing, client) def _cbTestAuthListing(self, ignored, client): self.assertTrue(client.response[1].startswith(b'+OK')) self.assertEqual(sorted(client.response[2:5]), [b"AUTH1", b"AUTHLAST", b"SECONDAUTH"]) self.assertEqual(client.response[5], b".") def run_PASS(self, real_user, real_password, tried_user=None, tried_password=None, after_auth_input=[], after_auth_output=[]): """ Test a login with PASS. If L{real_user} matches L{tried_user} and L{real_password} matches L{tried_password}, a successful login will be expected. Otherwise an unsuccessful login will be expected. @type real_user: L{bytes} @param real_user: The user to test. @type real_password: L{bytes} @param real_password: The password of the test user. @type tried_user: L{bytes} or L{None} @param tried_user: The user to call USER with. If None, real_user will be used. @type tried_password: L{bytes} or L{None} @param tried_password: The password to call PASS with. If None, real_password will be used. @type after_auth_input: L{list} of l{bytes} @param after_auth_input: Extra protocol input after authentication. @type after_auth_output: L{list} of l{bytes} @param after_auth_output: Extra protocol output after authentication. """ if not tried_user: tried_user = real_user if not tried_password: tried_password = real_password response = [b'+OK <moshez>', b'+OK USER accepted, send PASS', b'-ERR Authentication failed'] if real_user == tried_user and real_password == tried_password: response = [b'+OK <moshez>', b'+OK USER accepted, send PASS', b'+OK Authentication succeeded'] fullInput = [b' '.join([b'USER', tried_user]), b' '.join([b'PASS', tried_password])] fullInput += after_auth_input + [b'QUIT'] response += after_auth_output + [b'+OK '] return self.runTest( fullInput, response, protocolInstance=DummyPOP3Auth(real_user, real_password)) def run_PASS_before_USER(self, password): """ Test protocol violation produced by calling PASS before USER. @type password: L{bytes} @param password: A password to test. """ return self.runTest( [b' '.join([b'PASS', password]), b'QUIT'], [b'+OK <moshez>', b'-ERR USER required before PASS', b'+OK ']) def test_illegal_PASS_before_USER(self): """ Test PASS before USER with a wrong password. """ return self.run_PASS_before_USER(b'fooz') def test_empty_PASS_before_USER(self): """ Test PASS before USER with an empty password. """ return self.run_PASS_before_USER(b'') def test_one_space_PASS_before_USER(self): """ Test PASS before USER with an password that is a space. """ return self.run_PASS_before_USER(b' ') def test_space_PASS_before_USER(self): """ Test PASS before USER with a password containing a space. """ return self.run_PASS_before_USER(b'fooz barz') def test_multiple_spaces_PASS_before_USER(self): """ Test PASS before USER with a password containing multiple spaces. """ return self.run_PASS_before_USER(b'fooz barz asdf') def test_other_whitespace_PASS_before_USER(self): """ Test PASS before USER with a password containing tabs and spaces. """ return self.run_PASS_before_USER(b'fooz barz\tcrazy@! \t ') def test_good_PASS(self): """ Test PASS with a good password. """ return self.run_PASS(b'testuser', b'fooz') def test_space_PASS(self): """ Test PASS with a password containing a space. """ return self.run_PASS(b'testuser', b'fooz barz') def test_multiple_spaces_PASS(self): """ Test PASS with a password containing a space. """ return self.run_PASS(b'testuser', b'fooz barz asdf') def test_other_whitespace_PASS(self): """ Test PASS with a password containing tabs and spaces. """ return self.run_PASS(b'testuser', b'fooz barz\tcrazy@! \t ') def test_pass_wrong_user(self): """ Test PASS with a wrong user. """ return self.run_PASS(b'testuser', b'fooz', tried_user=b'wronguser') def test_wrong_PASS(self): """ Test PASS with a wrong password. """ return self.run_PASS(b'testuser', b'fooz', tried_password=b'barz') def test_wrong_space_PASS(self): """ Test PASS with a password containing a space. """ return self.run_PASS(b'testuser', b'fooz barz', tried_password=b'foozbarz ') def test_wrong_multiple_spaces_PASS(self): """ Test PASS with a password containing a space. """ return self.run_PASS(b'testuser', b'fooz barz asdf', tried_password=b'foozbarz ') def test_wrong_other_whitespace_PASS(self): """ Test PASS with a password containing tabs and spaces. """ return self.run_PASS(b'testuser', b'fooz barz\tcrazy@! \t ') def test_wrong_command(self): """ After logging in, test a dummy command that is not defined. """ extra_input = [b'DUMMY COMMAND'] extra_output = [b' '.join([b'-ERR bad protocol or server: POP3Error:', b'Unknown protocol command: DUMMY'])] return self.run_PASS(b'testuser', b'testpassword', after_auth_input=extra_input, after_auth_output=extra_output, ).addCallback(self.flushLoggedErrors, pop3.POP3Error) @implementer(pop3.IServerFactory) class TestServerFactory: """ A L{pop3.IServerFactory} implementation, for use by the test suite, with some behavior controlled by the values of (settable) public attributes and other behavior based on values hard-coded both here and in some test methods. """ def cap_IMPLEMENTATION(self): """ Return the hard-coded value. @return: L{pop3.IServerFactory} """ return "Test Implementation String" def cap_EXPIRE(self): """ Return the hard-coded value. @return: L{pop3.IServerFactory} """ return 60 challengers = OrderedDict([(b"SCHEME_1", None), (b"SCHEME_2", None)]) def cap_LOGIN_DELAY(self): """ Return the hard-coded value. @return: L{pop3.IServerFactory} """ return 120 pue = True def perUserExpiration(self): """ Return the hard-coded value. @return: L{pop3.IServerFactory} """ return self.pue puld = True def perUserLoginDelay(self): """ Return the hard-coded value. @return: L{pop3.IServerFactory} """ return self.puld class TestMailbox: """ An incomplete L{IMailbox} implementation with certain per-user values hard-coded and known by tests in this module. This is useful for testing the server's per-user capability implementation. """ loginDelay = 100 messageExpiration = 25 def contained(testcase, s, *caps): """ Assert that the given capability is included in all of the capability sets. @param testcase: A L{unittest.TestCase} to use to make assertions. @param s: The capability for which to check. @type s: L{bytes} @param caps: The capability sets in which to check. @type caps: L{tuple} of iterable """ for c in caps: testcase.assertIn(s, c) class CapabilityTests(unittest.TestCase): """ Tests for L{pop3.POP3}'s per-user capability handling. """ def setUp(self): """ Create a POP3 server with some capabilities. """ s = BytesIO() p = pop3.POP3() p.factory = TestServerFactory() p.transport = internet.protocol.FileWrapper(s) p.connectionMade() p.do_CAPA() self.caps = p.listCapabilities() self.pcaps = s.getvalue().splitlines() s = BytesIO() p.mbox = TestMailbox() p.transport = internet.protocol.FileWrapper(s) p.do_CAPA() self.lpcaps = s.getvalue().splitlines() p.connectionLost(failure.Failure(Exception("Test harness disconnect"))) def test_UIDL(self): """ The server can advertise the I{UIDL} capability. """ contained(self, b"UIDL", self.caps, self.pcaps, self.lpcaps) def test_TOP(self): """ The server can advertise the I{TOP} capability. """ contained(self, b"TOP", self.caps, self.pcaps, self.lpcaps) def test_USER(self): """ The server can advertise the I{USER} capability. """ contained(self, b"USER", self.caps, self.pcaps, self.lpcaps) def test_EXPIRE(self): """ The server can advertise its per-user expiration as well as a global expiration. """ contained(self, b"EXPIRE 60 USER", self.caps, self.pcaps) contained(self, b"EXPIRE 25", self.lpcaps) def test_IMPLEMENTATION(self): """ The server can advertise its implementation string. """ contained( self, b"IMPLEMENTATION Test Implementation String", self.caps, self.pcaps, self.lpcaps ) def test_SASL(self): """ The server can advertise the SASL schemes it supports. """ contained( self, b"SASL SCHEME_1 SCHEME_2", self.caps, self.pcaps, self.lpcaps ) def test_LOGIN_DELAY(self): """ The can advertise a per-user login delay as well as a global login delay. """ contained(self, b"LOGIN-DELAY 120 USER", self.caps, self.pcaps) self.assertIn(b"LOGIN-DELAY 100", self.lpcaps) class GlobalCapabilitiesTests(unittest.TestCase): """ Tests for L{pop3.POP3}'s global capability handling. """ def setUp(self): """ Create a POP3 server with some capabilities. """ s = BytesIO() p = pop3.POP3() p.factory = TestServerFactory() p.factory.pue = p.factory.puld = False p.transport = internet.protocol.FileWrapper(s) p.connectionMade() p.do_CAPA() self.caps = p.listCapabilities() self.pcaps = s.getvalue().splitlines() s = BytesIO() p.mbox = TestMailbox() p.transport = internet.protocol.FileWrapper(s) p.do_CAPA() self.lpcaps = s.getvalue().splitlines() p.connectionLost(failure.Failure(Exception("Test harness disconnect"))) def test_EXPIRE(self): """ I{EXPIRE} is in the server's advertised capabilities. """ contained(self, b"EXPIRE 60", self.caps, self.pcaps, self.lpcaps) def test_LOGIN_DELAY(self): """ I{LOGIN-DELAY} is in the server's advertised capabilities. """ contained(self, b"LOGIN-DELAY 120", self.caps, self.pcaps, self.lpcaps) class TestRealm: """ An L{IRealm} which knows about a single test account's mailbox. """ def requestAvatar(self, avatarId, mind, *interfaces): """ Retrieve a mailbox for I{testuser} or fail. @param avatarId: See L{IRealm.requestAvatar}. @param mind: See L{IRealm.requestAvatar}. @param interfaces: See L{IRealm.requestAvatar}. @raises: L{AssertionError} when requesting an C{avatarId} other than I{testuser}. """ if avatarId == b'testuser': return pop3.IMailbox, DummyMailbox(ValueError), lambda: None assert False class SASLTests(unittest.TestCase): """ Tests for L{pop3.POP3}'s SASL implementation. """ def test_ValidLogin(self): """ A CRAM-MD5-based SASL login attempt succeeds if it uses a username and a hashed password known to the server's credentials checker. """ p = pop3.POP3() p.factory = TestServerFactory() p.factory.challengers = {b'CRAM-MD5': cred.credentials.CramMD5Credentials} p.portal = cred.portal.Portal(TestRealm()) ch = cred.checkers.InMemoryUsernamePasswordDatabaseDontUse() ch.addUser(b'testuser', b'testpassword') p.portal.registerChecker(ch) s = BytesIO() p.transport = internet.protocol.FileWrapper(s) p.connectionMade() p.lineReceived(b"CAPA") self.assertTrue(s.getvalue().find(b"SASL CRAM-MD5") >= 0) p.lineReceived(b"AUTH CRAM-MD5") chal = s.getvalue().splitlines()[-1][2:] chal = base64.decodestring(chal) response = hmac.HMAC(b'testpassword', chal, digestmod=md5).hexdigest().encode("ascii") p.lineReceived( base64.encodestring(b'testuser ' + response).rstrip(b'\n')) self.assertTrue(p.mbox) self.assertTrue(s.getvalue().splitlines()[-1].find(b"+OK") >= 0) p.connectionLost(failure.Failure(Exception("Test harness disconnect"))) class CommandMixin: """ Tests for all the commands a POP3 server is allowed to receive. """ extraMessage = b'''\ From: guy To: fellow More message text for you. ''' def setUp(self): """ Make a POP3 server protocol instance hooked up to a simple mailbox and a transport that buffers output to a BytesIO. """ p = pop3.POP3() p.mbox = self.mailboxType(self.exceptionType) p.schedule = list self.pop3Server = p s = BytesIO() p.transport = internet.protocol.FileWrapper(s) p.connectionMade() s.seek(0) s.truncate(0) self.pop3Transport = s def tearDown(self): """ Disconnect the server protocol so it can clean up anything it might need to clean up. """ self.pop3Server.connectionLost(failure.Failure( Exception("Test harness disconnect"))) def _flush(self): """ Do some of the things that the reactor would take care of, if the reactor were actually running. """ # Oh man FileWrapper is pooh. self.pop3Server.transport._checkProducer() def test_LIST(self): """ Test the two forms of list: with a message index number, which should return a short-form response, and without a message index number, which should return a long-form response, one line per message. """ p = self.pop3Server s = self.pop3Transport p.lineReceived(b"LIST 1") self._flush() self.assertEqual(s.getvalue(), b"+OK 1 44\r\n") s.seek(0) s.truncate(0) p.lineReceived(b"LIST") self._flush() self.assertEqual(s.getvalue(), b"+OK 1\r\n1 44\r\n.\r\n") def test_LISTWithBadArgument(self): """ Test that non-integers and out-of-bound integers produce appropriate error responses. """ p = self.pop3Server s = self.pop3Transport p.lineReceived(b"LIST a") self.assertEqual( s.getvalue(), b"-ERR Invalid message-number: a\r\n") s.seek(0) s.truncate(0) p.lineReceived(b"LIST 0") self.assertEqual( s.getvalue(), b"-ERR Invalid message-number: 0\r\n") s.seek(0) s.truncate(0) p.lineReceived(b"LIST 2") self.assertEqual( s.getvalue(), b"-ERR Invalid message-number: 2\r\n") s.seek(0) s.truncate(0) def test_UIDL(self): """ Test the two forms of the UIDL command. These are just like the two forms of the LIST command. """ p = self.pop3Server s = self.pop3Transport p.lineReceived(b"UIDL 1") self.assertEqual(s.getvalue(), b"+OK 0\r\n") s.seek(0) s.truncate(0) p.lineReceived(b"UIDL") self._flush() self.assertEqual(s.getvalue(), b"+OK \r\n1 0\r\n.\r\n") def test_UIDLWithBadArgument(self): """ Test that UIDL with a non-integer or an out-of-bounds integer produces the appropriate error response. """ p = self.pop3Server s = self.pop3Transport p.lineReceived(b"UIDL a") self.assertEqual( s.getvalue(), b"-ERR Bad message number argument\r\n") s.seek(0) s.truncate(0) p.lineReceived(b"UIDL 0") self.assertEqual( s.getvalue(), b"-ERR Bad message number argument\r\n") s.seek(0) s.truncate(0) p.lineReceived(b"UIDL 2") self.assertEqual( s.getvalue(), b"-ERR Bad message number argument\r\n") s.seek(0) s.truncate(0) def test_STAT(self): """ Test the single form of the STAT command, which returns a short-form response of the number of messages in the mailbox and their total size. """ p = self.pop3Server s = self.pop3Transport p.lineReceived(b"STAT") self._flush() self.assertEqual(s.getvalue(), b"+OK 1 44\r\n") def test_RETR(self): """ Test downloading a message. """ p = self.pop3Server s = self.pop3Transport p.lineReceived(b"RETR 1") self._flush() self.assertEqual( s.getvalue(), b"+OK 44\r\n" b"From: moshe\r\n" b"To: moshe\r\n" b"\r\n" b"How are you, friend?\r\n" b".\r\n") s.seek(0) s.truncate(0) def test_RETRWithBadArgument(self): """ Test that trying to download a message with a bad argument, either not an integer or an out-of-bounds integer, fails with the appropriate error response. """ p = self.pop3Server s = self.pop3Transport p.lineReceived(b"RETR a") self.assertEqual( s.getvalue(), b"-ERR Bad message number argument\r\n") s.seek(0) s.truncate(0) p.lineReceived(b"RETR 0") self.assertEqual( s.getvalue(), b"-ERR Bad message number argument\r\n") s.seek(0) s.truncate(0) p.lineReceived(b"RETR 2") self.assertEqual( s.getvalue(), b"-ERR Bad message number argument\r\n") s.seek(0) s.truncate(0) def test_TOP(self): """ Test downloading the headers and part of the body of a message. """ p = self.pop3Server s = self.pop3Transport p.mbox.messages.append(self.extraMessage) p.lineReceived(b"TOP 1 0") self._flush() self.assertEqual( s.getvalue(), b"+OK Top of message follows\r\n" b"From: moshe\r\n" b"To: moshe\r\n" b"\r\n" b".\r\n") def test_TOPWithBadArgument(self): """ Test that trying to download a message with a bad argument, either a message number which isn't an integer or is an out-of-bounds integer or a number of lines which isn't an integer or is a negative integer, fails with the appropriate error response. """ p = self.pop3Server s = self.pop3Transport p.mbox.messages.append(self.extraMessage) p.lineReceived(b"TOP 1 a") self.assertEqual( s.getvalue(), b"-ERR Bad line count argument\r\n") s.seek(0) s.truncate(0) p.lineReceived(b"TOP 1 -1") self.assertEqual( s.getvalue(), b"-ERR Bad line count argument\r\n") s.seek(0) s.truncate(0) p.lineReceived(b"TOP a 1") self.assertEqual( s.getvalue(), b"-ERR Bad message number argument\r\n") s.seek(0) s.truncate(0) p.lineReceived(b"TOP 0 1") self.assertEqual( s.getvalue(), b"-ERR Bad message number argument\r\n") s.seek(0) s.truncate(0) p.lineReceived(b"TOP 3 1") self.assertEqual( s.getvalue(), b"-ERR Bad message number argument\r\n") s.seek(0) s.truncate(0) def test_LAST(self): """ Test the exceedingly pointless LAST command, which tells you the highest message index which you have already downloaded. """ p = self.pop3Server s = self.pop3Transport p.mbox.messages.append(self.extraMessage) p.lineReceived(b'LAST') self.assertEqual( s.getvalue(), b"+OK 0\r\n") s.seek(0) s.truncate(0) def test_RetrieveUpdatesHighest(self): """ Test that issuing a RETR command updates the LAST response. """ p = self.pop3Server s = self.pop3Transport p.mbox.messages.append(self.extraMessage) p.lineReceived(b'RETR 2') self._flush() s.seek(0) s.truncate(0) p.lineReceived(b'LAST') self.assertEqual( s.getvalue(), b'+OK 2\r\n') s.seek(0) s.truncate(0) def test_TopUpdatesHighest(self): """ Test that issuing a TOP command updates the LAST response. """ p = self.pop3Server s = self.pop3Transport p.mbox.messages.append(self.extraMessage) p.lineReceived(b'TOP 2 10') self._flush() s.seek(0) s.truncate(0) p.lineReceived(b'LAST') self.assertEqual( s.getvalue(), b'+OK 2\r\n') def test_HighestOnlyProgresses(self): """ Test that downloading a message with a smaller index than the current LAST response doesn't change the LAST response. """ p = self.pop3Server s = self.pop3Transport p.mbox.messages.append(self.extraMessage) p.lineReceived(b'RETR 2') self._flush() p.lineReceived(b'TOP 1 10') self._flush() s.seek(0) s.truncate(0) p.lineReceived(b'LAST') self.assertEqual( s.getvalue(), b'+OK 2\r\n') def test_ResetClearsHighest(self): """ Test that issuing RSET changes the LAST response to 0. """ p = self.pop3Server s = self.pop3Transport p.mbox.messages.append(self.extraMessage) p.lineReceived(b'RETR 2') self._flush() p.lineReceived(b'RSET') s.seek(0) s.truncate(0) p.lineReceived(b'LAST') self.assertEqual( s.getvalue(), b'+OK 0\r\n') _listMessageDeprecation = ( "twisted.mail.pop3.IMailbox.listMessages may not " "raise IndexError for out-of-bounds message numbers: " "raise ValueError instead.") _listMessageSuppression = util.suppress( message=_listMessageDeprecation, category=PendingDeprecationWarning) _getUidlDeprecation = ( "twisted.mail.pop3.IMailbox.getUidl may not " "raise IndexError for out-of-bounds message numbers: " "raise ValueError instead.") _getUidlSuppression = util.suppress( message=_getUidlDeprecation, category=PendingDeprecationWarning) class IndexErrorCommandTests(CommandMixin, unittest.TestCase): """ Run all of the command tests against a mailbox which raises IndexError when an out of bounds request is made. This behavior will be deprecated shortly and then removed. """ exceptionType = IndexError mailboxType = DummyMailbox def test_LISTWithBadArgument(self): """ An attempt to get metadata about a message with a bad argument fails with an I{ERR} response even if the mailbox implementation raises L{IndexError}. """ return CommandMixin.test_LISTWithBadArgument(self) test_LISTWithBadArgument.suppress = [_listMessageSuppression] def test_UIDLWithBadArgument(self): """ An attempt to look up the UID of a message with a bad argument fails with an I{ERR} response even if the mailbox implementation raises L{IndexError}. """ return CommandMixin.test_UIDLWithBadArgument(self) test_UIDLWithBadArgument.suppress = [_getUidlSuppression] def test_TOPWithBadArgument(self): """ An attempt to download some of a message with a bad argument fails with an I{ERR} response even if the mailbox implementation raises L{IndexError}. """ return CommandMixin.test_TOPWithBadArgument(self) test_TOPWithBadArgument.suppress = [_listMessageSuppression] def test_RETRWithBadArgument(self): """ An attempt to download a message with a bad argument fails with an I{ERR} response even if the mailbox implementation raises L{IndexError}. """ return CommandMixin.test_RETRWithBadArgument(self) test_RETRWithBadArgument.suppress = [_listMessageSuppression] class ValueErrorCommandTests(CommandMixin, unittest.TestCase): """ Run all of the command tests against a mailbox which raises ValueError when an out of bounds request is made. This is the correct behavior and after support for mailboxes which raise IndexError is removed, this will become just C{CommandTestCase}. """ exceptionType = ValueError mailboxType = DummyMailbox class SyncDeferredMailbox(DummyMailbox): """ Mailbox which has a listMessages implementation which returns a Deferred which has already fired. """ def listMessages(self, n=None): """ Synchronously list messages. @type n: L{int} or L{None} @param n: The 0-based index of the message. @return: A L{Deferred} which already has a message list result. """ return defer.succeed(DummyMailbox.listMessages(self, n)) class IndexErrorSyncDeferredCommandTests(IndexErrorCommandTests): """ Run all of the L{IndexErrorCommandTests} tests with a synchronous-Deferred returning IMailbox implementation. """ mailboxType = SyncDeferredMailbox class ValueErrorSyncDeferredCommandTests(ValueErrorCommandTests): """ Run all of the L{ValueErrorCommandTests} tests with a synchronous-Deferred returning IMailbox implementation. """ mailboxType = SyncDeferredMailbox class AsyncDeferredMailbox(DummyMailbox): """ Mailbox which has a listMessages implementation which returns a Deferred which has not yet fired. """ def __init__(self, *a, **kw): self.waiting = [] DummyMailbox.__init__(self, *a, **kw) def listMessages(self, n=None): """ Record a new unfired L{Deferred} in C{self.waiting} and return it. @type n: L{int} or L{None} @param n: The 0-based index of the message. @return: The L{Deferred} """ d = defer.Deferred() # See AsyncDeferredMailbox._flush self.waiting.append((d, DummyMailbox.listMessages(self, n))) return d class IndexErrorAsyncDeferredCommandTests(IndexErrorCommandTests): """ Run all of the L{IndexErrorCommandTests} tests with an asynchronous-Deferred returning IMailbox implementation. """ mailboxType = AsyncDeferredMailbox def _flush(self): """ Fire whatever Deferreds we've built up in our mailbox. """ while self.pop3Server.mbox.waiting: d, a = self.pop3Server.mbox.waiting.pop() d.callback(a) IndexErrorCommandTests._flush(self) class ValueErrorAsyncDeferredCommandTests(ValueErrorCommandTests): """ Run all of the L{IndexErrorCommandTests} tests with an asynchronous-Deferred returning IMailbox implementation. """ mailboxType = AsyncDeferredMailbox def _flush(self): """ Fire whatever Deferreds we've built up in our mailbox. """ while self.pop3Server.mbox.waiting: d, a = self.pop3Server.mbox.waiting.pop() d.callback(a) ValueErrorCommandTests._flush(self) class POP3MiscTests(unittest.TestCase): """ Miscellaneous tests more to do with module/package structure than anything to do with the Post Office Protocol. """ def test_all(self): """ This test checks that all names listed in twisted.mail.pop3.__all__ are actually present in the module. """ mod = twisted.mail.pop3 for attr in mod.__all__: self.assertTrue(hasattr(mod, attr))

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79

0.583381

import hmac import base64 import itertools from hashlib import md5 from collections import OrderedDict from io import BytesIO from zope.interface import implementer from twisted import cred from twisted import internet from twisted import mail from twisted.internet import defer from twisted.mail import pop3 from twisted.protocols import loopback from twisted.python import failure from twisted.python.compat import intToBytes from twisted.test.proto_helpers import LineSendingProtocol from twisted.trial import unittest, util import twisted.cred.checkers import twisted.cred.credentials import twisted.cred.portal import twisted.internet.protocol import twisted.mail.pop3 import twisted.mail.protocols class UtilityTests(unittest.TestCase): def test_LineBuffering(self): output = [] input = iter(itertools.cycle(['012', '345', '6', '7', '8', '9'])) c = pop3._IteratorBuffer(output.extend, input, 6) i = iter(c) self.assertEqual(output, []) next(i) self.assertEqual(output, []) next(i) self.assertEqual(output, []) next(i) self.assertEqual(output, ['012', '345', '6']) for n in range(5): next(i) self.assertEqual(output, ['012', '345', '6', '7', '8', '9', '012', '345']) def test_FinishLineBuffering(self): output = [] input = iter(['a', 'b', 'c']) c = pop3._IteratorBuffer(output.extend, input, 5) for i in c: pass self.assertEqual(output, ['a', 'b', 'c']) def test_SuccessResponseFormatter(self): self.assertEqual( pop3.successResponse(b'Great.'), b'+OK Great.\r\n') def test_StatLineFormatter(self): statLine = list(pop3.formatStatResponse([]))[-1] self.assertEqual(statLine, b'+OK 0 0\r\n') statLine = list(pop3.formatStatResponse([10, 31, 0, 10101]))[-1] self.assertEqual(statLine, b'+OK 4 10142\r\n') def test_ListLineFormatter(self): listLines = list(pop3.formatListResponse([])) self.assertEqual( listLines, [b'+OK 0\r\n', b'.\r\n']) listLines = list(pop3.formatListResponse([1, 2, 3, 100])) self.assertEqual( listLines, [b'+OK 4\r\n', b'1 1\r\n', b'2 2\r\n', b'3 3\r\n', b'4 100\r\n', b'.\r\n']) def test_UIDListLineFormatter(self): uids = ['abc', 'def', 'ghi'] listLines = list(pop3.formatUIDListResponse([], uids.__getitem__)) self.assertEqual( listLines, [b'+OK \r\n', b'.\r\n']) listLines = list(pop3.formatUIDListResponse([123, 431, 591], uids.__getitem__)) self.assertEqual( listLines, [b'+OK \r\n', b'1 abc\r\n', b'2 def\r\n', b'3 ghi\r\n', b'.\r\n']) listLines = list(pop3.formatUIDListResponse([0, None, 591], uids.__getitem__)) self.assertEqual( listLines, [b'+OK \r\n', b'1 abc\r\n', b'3 ghi\r\n', b'.\r\n']) class MyVirtualPOP3(mail.protocols.VirtualPOP3): magic = b'<moshez>' def authenticateUserAPOP(self, user, digest): user, domain = self.lookupDomain(user) return self.service.domains[b'baz.com'].authenticateUserAPOP( user, digest, self.magic, domain) class DummyDomain: def __init__(self): self.users = {} def addUser(self, name): self.users[name] = [] def addMessage(self, name, message): self.users[name].append(message) def authenticateUserAPOP(self, name, digest, magic, domain): return pop3.IMailbox, ListMailbox(self.users[name]), lambda: None class ListMailbox: def __init__(self, list): self.list = list def listMessages(self, i=None): if i is None: return [len(l) for l in self.list] return len(self.list[i]) def getMessage(self, i): return BytesIO(self.list[i]) def getUidl(self, i): return i def deleteMessage(self, i): self.list[i] = b'' def sync(self): class MyPOP3Downloader(pop3.POP3Client): def handle_WELCOME(self, line): pop3.POP3Client.handle_WELCOME(self, line) self.apop(b'hello@baz.com', b'world') def handle_APOP(self, line): parts = line.split() code = parts[0] if code != b'+OK': raise AssertionError('code is: %s , parts is: %s ' % (code, parts)) self.lines = [] self.retr(1) def handle_RETR_continue(self, line): self.lines.append(line) def handle_RETR_end(self): self.message = b'\n'.join(self.lines) + b'\n' self.quit() def handle_QUIT(self, line): if line[:3] != b'+OK': raise AssertionError(b'code is ' + line) class POP3Tests(unittest.TestCase): message = b'''\ Subject: urgent Someone set up us the bomb! ''' expectedOutput = (b'''\ +OK <moshez>\015 +OK Authentication succeeded\015 +OK \015 1 0\015 .\015 +OK ''' + intToBytes(len(message)) + b'''\015 Subject: urgent\015 \015 Someone set up us the bomb!\015 .\015 +OK \015 ''') def setUp(self): self.factory = internet.protocol.Factory() self.factory.domains = {} self.factory.domains[b'baz.com'] = DummyDomain() self.factory.domains[b'baz.com'].addUser(b'hello') self.factory.domains[b'baz.com'].addMessage(b'hello', self.message) def test_messages(self): client = LineSendingProtocol([ b'APOP hello@baz.com world', b'UIDL', b'RETR 1', b'QUIT', ]) server = MyVirtualPOP3() server.service = self.factory def check(ignored): output = b'\r\n'.join(client.response) + b'\r\n' self.assertEqual(output, self.expectedOutput) return loopback.loopbackTCP(server, client).addCallback(check) def test_loopback(self): protocol = MyVirtualPOP3() protocol.service = self.factory clientProtocol = MyPOP3Downloader() def check(ignored): self.assertEqual(clientProtocol.message, self.message) protocol.connectionLost( failure.Failure(Exception("Test harness disconnect"))) d = loopback.loopbackAsync(protocol, clientProtocol) return d.addCallback(check) test_loopback.suppress = [util.suppress( message="twisted.mail.pop3.POP3Client is deprecated")] def test_incorrectDomain(self): factory = internet.protocol.Factory() factory.domains = {} factory.domains[b'twistedmatrix.com'] = DummyDomain() server = MyVirtualPOP3() server.service = factory exc = self.assertRaises(pop3.POP3Error, server.authenticateUserAPOP, b'nobody@baz.com', b'password') self.assertEqual(exc.args[0], 'no such domain baz.com') class DummyPOP3(pop3.POP3): magic = b'<moshez>' def authenticateUserAPOP(self, user, password): return pop3.IMailbox, DummyMailbox(ValueError), lambda: None class DummyPOP3Auth(DummyPOP3): def __init__(self, user, password): self.portal = cred.portal.Portal(TestRealm()) ch = cred.checkers.InMemoryUsernamePasswordDatabaseDontUse() ch.addUser(user, password) self.portal.registerChecker(ch) class DummyMailbox(pop3.Mailbox): messages = [b'From: moshe\nTo: moshe\n\nHow are you, friend?\n'] def __init__(self, exceptionType): self.messages = DummyMailbox.messages[:] self.exceptionType = exceptionType def listMessages(self, i=None): if i is None: return [len(m) for m in self.messages] if i >= len(self.messages): raise self.exceptionType() return len(self.messages[i]) def getMessage(self, i): return BytesIO(self.messages[i]) def getUidl(self, i): if i >= len(self.messages): raise self.exceptionType() return intToBytes(i) def deleteMessage(self, i): self.messages[i] = b'' class AnotherPOP3Tests(unittest.TestCase): def runTest(self, lines, expectedOutput, protocolInstance=None): dummy = protocolInstance if protocolInstance else DummyPOP3() client = LineSendingProtocol(lines) d = loopback.loopbackAsync(dummy, client) return d.addCallback(self._cbRunTest, client, dummy, expectedOutput) def _cbRunTest(self, ignored, client, dummy, expectedOutput): self.assertEqual(b'\r\n'.join(expectedOutput), b'\r\n'.join(client.response)) dummy.connectionLost(failure.Failure( Exception("Test harness disconnect"))) return ignored def test_buffer(self): return self.runTest( [b"APOP moshez dummy", b"LIST", b"UIDL", b"RETR 1", b"RETR 2", b"DELE 1", b"RETR 1", b"QUIT"], [b'+OK <moshez>', b'+OK Authentication succeeded', b'+OK 1', b'1 44', b'.', b'+OK ', b'1 0', b'.', b'+OK 44', b'From: moshe', b'To: moshe', b'', b'How are you, friend?', b'.', b'-ERR Bad message number argument', b'+OK ', b'-ERR message deleted', b'+OK ']) def test_noop(self): return self.runTest( [b'APOP spiv dummy', b'NOOP', b'QUIT'], [b'+OK <moshez>', b'+OK Authentication succeeded', b'+OK ', b'+OK ']) def test_badUTF8CharactersInCommand(self): error = b'not authenticated yet: cannot do \x81PASS' d = self.runTest( [b'\x81PASS', b'QUIT'], [b'+OK <moshez>', b"-ERR bad protocol or server: POP3Error: " + error, b'+OK ']) errors = self.flushLoggedErrors(pop3.POP3Error) self.assertEqual(len(errors), 1) return d def test_authListing(self): p = DummyPOP3() p.factory = internet.protocol.Factory() p.factory.challengers = {b'Auth1': None, b'secondAuth': None, b'authLast': None} client = LineSendingProtocol([ b"AUTH", b"QUIT", ]) d = loopback.loopbackAsync(p, client) return d.addCallback(self._cbTestAuthListing, client) def _cbTestAuthListing(self, ignored, client): self.assertTrue(client.response[1].startswith(b'+OK')) self.assertEqual(sorted(client.response[2:5]), [b"AUTH1", b"AUTHLAST", b"SECONDAUTH"]) self.assertEqual(client.response[5], b".") def run_PASS(self, real_user, real_password, tried_user=None, tried_password=None, after_auth_input=[], after_auth_output=[]): if not tried_user: tried_user = real_user if not tried_password: tried_password = real_password response = [b'+OK <moshez>', b'+OK USER accepted, send PASS', b'-ERR Authentication failed'] if real_user == tried_user and real_password == tried_password: response = [b'+OK <moshez>', b'+OK USER accepted, send PASS', b'+OK Authentication succeeded'] fullInput = [b' '.join([b'USER', tried_user]), b' '.join([b'PASS', tried_password])] fullInput += after_auth_input + [b'QUIT'] response += after_auth_output + [b'+OK '] return self.runTest( fullInput, response, protocolInstance=DummyPOP3Auth(real_user, real_password)) def run_PASS_before_USER(self, password): return self.runTest( [b' '.join([b'PASS', password]), b'QUIT'], [b'+OK <moshez>', b'-ERR USER required before PASS', b'+OK ']) def test_illegal_PASS_before_USER(self): return self.run_PASS_before_USER(b'fooz') def test_empty_PASS_before_USER(self): return self.run_PASS_before_USER(b'') def test_one_space_PASS_before_USER(self): return self.run_PASS_before_USER(b' ') def test_space_PASS_before_USER(self): return self.run_PASS_before_USER(b'fooz barz') def test_multiple_spaces_PASS_before_USER(self): return self.run_PASS_before_USER(b'fooz barz asdf') def test_other_whitespace_PASS_before_USER(self): return self.run_PASS_before_USER(b'fooz barz\tcrazy@! \t ') def test_good_PASS(self): return self.run_PASS(b'testuser', b'fooz') def test_space_PASS(self): return self.run_PASS(b'testuser', b'fooz barz') def test_multiple_spaces_PASS(self): return self.run_PASS(b'testuser', b'fooz barz asdf') def test_other_whitespace_PASS(self): return self.run_PASS(b'testuser', b'fooz barz\tcrazy@! \t ') def test_pass_wrong_user(self): return self.run_PASS(b'testuser', b'fooz', tried_user=b'wronguser') def test_wrong_PASS(self): return self.run_PASS(b'testuser', b'fooz', tried_password=b'barz') def test_wrong_space_PASS(self): return self.run_PASS(b'testuser', b'fooz barz', tried_password=b'foozbarz ') def test_wrong_multiple_spaces_PASS(self): return self.run_PASS(b'testuser', b'fooz barz asdf', tried_password=b'foozbarz ') def test_wrong_other_whitespace_PASS(self): return self.run_PASS(b'testuser', b'fooz barz\tcrazy@! \t ') def test_wrong_command(self): extra_input = [b'DUMMY COMMAND'] extra_output = [b' '.join([b'-ERR bad protocol or server: POP3Error:', b'Unknown protocol command: DUMMY'])] return self.run_PASS(b'testuser', b'testpassword', after_auth_input=extra_input, after_auth_output=extra_output, ).addCallback(self.flushLoggedErrors, pop3.POP3Error) @implementer(pop3.IServerFactory) class TestServerFactory: def cap_IMPLEMENTATION(self): return "Test Implementation String" def cap_EXPIRE(self): return 60 challengers = OrderedDict([(b"SCHEME_1", None), (b"SCHEME_2", None)]) def cap_LOGIN_DELAY(self): return 120 pue = True def perUserExpiration(self): return self.pue puld = True def perUserLoginDelay(self): return self.puld class TestMailbox: loginDelay = 100 messageExpiration = 25 def contained(testcase, s, *caps): for c in caps: testcase.assertIn(s, c) class CapabilityTests(unittest.TestCase): def setUp(self): s = BytesIO() p = pop3.POP3() p.factory = TestServerFactory() p.transport = internet.protocol.FileWrapper(s) p.connectionMade() p.do_CAPA() self.caps = p.listCapabilities() self.pcaps = s.getvalue().splitlines() s = BytesIO() p.mbox = TestMailbox() p.transport = internet.protocol.FileWrapper(s) p.do_CAPA() self.lpcaps = s.getvalue().splitlines() p.connectionLost(failure.Failure(Exception("Test harness disconnect"))) def test_UIDL(self): contained(self, b"UIDL", self.caps, self.pcaps, self.lpcaps) def test_TOP(self): contained(self, b"TOP", self.caps, self.pcaps, self.lpcaps) def test_USER(self): contained(self, b"USER", self.caps, self.pcaps, self.lpcaps) def test_EXPIRE(self): contained(self, b"EXPIRE 60 USER", self.caps, self.pcaps) contained(self, b"EXPIRE 25", self.lpcaps) def test_IMPLEMENTATION(self): contained( self, b"IMPLEMENTATION Test Implementation String", self.caps, self.pcaps, self.lpcaps ) def test_SASL(self): contained( self, b"SASL SCHEME_1 SCHEME_2", self.caps, self.pcaps, self.lpcaps ) def test_LOGIN_DELAY(self): contained(self, b"LOGIN-DELAY 120 USER", self.caps, self.pcaps) self.assertIn(b"LOGIN-DELAY 100", self.lpcaps) class GlobalCapabilitiesTests(unittest.TestCase): def setUp(self): s = BytesIO() p = pop3.POP3() p.factory = TestServerFactory() p.factory.pue = p.factory.puld = False p.transport = internet.protocol.FileWrapper(s) p.connectionMade() p.do_CAPA() self.caps = p.listCapabilities() self.pcaps = s.getvalue().splitlines() s = BytesIO() p.mbox = TestMailbox() p.transport = internet.protocol.FileWrapper(s) p.do_CAPA() self.lpcaps = s.getvalue().splitlines() p.connectionLost(failure.Failure(Exception("Test harness disconnect"))) def test_EXPIRE(self): contained(self, b"EXPIRE 60", self.caps, self.pcaps, self.lpcaps) def test_LOGIN_DELAY(self): contained(self, b"LOGIN-DELAY 120", self.caps, self.pcaps, self.lpcaps) class TestRealm: def requestAvatar(self, avatarId, mind, *interfaces): if avatarId == b'testuser': return pop3.IMailbox, DummyMailbox(ValueError), lambda: None assert False class SASLTests(unittest.TestCase): def test_ValidLogin(self): p = pop3.POP3() p.factory = TestServerFactory() p.factory.challengers = {b'CRAM-MD5': cred.credentials.CramMD5Credentials} p.portal = cred.portal.Portal(TestRealm()) ch = cred.checkers.InMemoryUsernamePasswordDatabaseDontUse() ch.addUser(b'testuser', b'testpassword') p.portal.registerChecker(ch) s = BytesIO() p.transport = internet.protocol.FileWrapper(s) p.connectionMade() p.lineReceived(b"CAPA") self.assertTrue(s.getvalue().find(b"SASL CRAM-MD5") >= 0) p.lineReceived(b"AUTH CRAM-MD5") chal = s.getvalue().splitlines()[-1][2:] chal = base64.decodestring(chal) response = hmac.HMAC(b'testpassword', chal, digestmod=md5).hexdigest().encode("ascii") p.lineReceived( base64.encodestring(b'testuser ' + response).rstrip(b'\n')) self.assertTrue(p.mbox) self.assertTrue(s.getvalue().splitlines()[-1].find(b"+OK") >= 0) p.connectionLost(failure.Failure(Exception("Test harness disconnect"))) class CommandMixin: extraMessage = b'''\ From: guy To: fellow More message text for you. ''' def setUp(self): p = pop3.POP3() p.mbox = self.mailboxType(self.exceptionType) p.schedule = list self.pop3Server = p s = BytesIO() p.transport = internet.protocol.FileWrapper(s) p.connectionMade() s.seek(0) s.truncate(0) self.pop3Transport = s def tearDown(self): self.pop3Server.connectionLost(failure.Failure( Exception("Test harness disconnect"))) def _flush(self): self.pop3Server.transport._checkProducer() def test_LIST(self): p = self.pop3Server s = self.pop3Transport p.lineReceived(b"LIST 1") self._flush() self.assertEqual(s.getvalue(), b"+OK 1 44\r\n") s.seek(0) s.truncate(0) p.lineReceived(b"LIST") self._flush() self.assertEqual(s.getvalue(), b"+OK 1\r\n1 44\r\n.\r\n") def test_LISTWithBadArgument(self): p = self.pop3Server s = self.pop3Transport p.lineReceived(b"LIST a") self.assertEqual( s.getvalue(), b"-ERR Invalid message-number: a\r\n") s.seek(0) s.truncate(0) p.lineReceived(b"LIST 0") self.assertEqual( s.getvalue(), b"-ERR Invalid message-number: 0\r\n") s.seek(0) s.truncate(0) p.lineReceived(b"LIST 2") self.assertEqual( s.getvalue(), b"-ERR Invalid message-number: 2\r\n") s.seek(0) s.truncate(0) def test_UIDL(self): p = self.pop3Server s = self.pop3Transport p.lineReceived(b"UIDL 1") self.assertEqual(s.getvalue(), b"+OK 0\r\n") s.seek(0) s.truncate(0) p.lineReceived(b"UIDL") self._flush() self.assertEqual(s.getvalue(), b"+OK \r\n1 0\r\n.\r\n") def test_UIDLWithBadArgument(self): p = self.pop3Server s = self.pop3Transport p.lineReceived(b"UIDL a") self.assertEqual( s.getvalue(), b"-ERR Bad message number argument\r\n") s.seek(0) s.truncate(0) p.lineReceived(b"UIDL 0") self.assertEqual( s.getvalue(), b"-ERR Bad message number argument\r\n") s.seek(0) s.truncate(0) p.lineReceived(b"UIDL 2") self.assertEqual( s.getvalue(), b"-ERR Bad message number argument\r\n") s.seek(0) s.truncate(0) def test_STAT(self): p = self.pop3Server s = self.pop3Transport p.lineReceived(b"STAT") self._flush() self.assertEqual(s.getvalue(), b"+OK 1 44\r\n") def test_RETR(self): p = self.pop3Server s = self.pop3Transport p.lineReceived(b"RETR 1") self._flush() self.assertEqual( s.getvalue(), b"+OK 44\r\n" b"From: moshe\r\n" b"To: moshe\r\n" b"\r\n" b"How are you, friend?\r\n" b".\r\n") s.seek(0) s.truncate(0) def test_RETRWithBadArgument(self): p = self.pop3Server s = self.pop3Transport p.lineReceived(b"RETR a") self.assertEqual( s.getvalue(), b"-ERR Bad message number argument\r\n") s.seek(0) s.truncate(0) p.lineReceived(b"RETR 0") self.assertEqual( s.getvalue(), b"-ERR Bad message number argument\r\n") s.seek(0) s.truncate(0) p.lineReceived(b"RETR 2") self.assertEqual( s.getvalue(), b"-ERR Bad message number argument\r\n") s.seek(0) s.truncate(0) def test_TOP(self): p = self.pop3Server s = self.pop3Transport p.mbox.messages.append(self.extraMessage) p.lineReceived(b"TOP 1 0") self._flush() self.assertEqual( s.getvalue(), b"+OK Top of message follows\r\n" b"From: moshe\r\n" b"To: moshe\r\n" b"\r\n" b".\r\n") def test_TOPWithBadArgument(self): p = self.pop3Server s = self.pop3Transport p.mbox.messages.append(self.extraMessage) p.lineReceived(b"TOP 1 a") self.assertEqual( s.getvalue(), b"-ERR Bad line count argument\r\n") s.seek(0) s.truncate(0) p.lineReceived(b"TOP 1 -1") self.assertEqual( s.getvalue(), b"-ERR Bad line count argument\r\n") s.seek(0) s.truncate(0) p.lineReceived(b"TOP a 1") self.assertEqual( s.getvalue(), b"-ERR Bad message number argument\r\n") s.seek(0) s.truncate(0) p.lineReceived(b"TOP 0 1") self.assertEqual( s.getvalue(), b"-ERR Bad message number argument\r\n") s.seek(0) s.truncate(0) p.lineReceived(b"TOP 3 1") self.assertEqual( s.getvalue(), b"-ERR Bad message number argument\r\n") s.seek(0) s.truncate(0) def test_LAST(self): p = self.pop3Server s = self.pop3Transport p.mbox.messages.append(self.extraMessage) p.lineReceived(b'LAST') self.assertEqual( s.getvalue(), b"+OK 0\r\n") s.seek(0) s.truncate(0) def test_RetrieveUpdatesHighest(self): p = self.pop3Server s = self.pop3Transport p.mbox.messages.append(self.extraMessage) p.lineReceived(b'RETR 2') self._flush() s.seek(0) s.truncate(0) p.lineReceived(b'LAST') self.assertEqual( s.getvalue(), b'+OK 2\r\n') s.seek(0) s.truncate(0) def test_TopUpdatesHighest(self): p = self.pop3Server s = self.pop3Transport p.mbox.messages.append(self.extraMessage) p.lineReceived(b'TOP 2 10') self._flush() s.seek(0) s.truncate(0) p.lineReceived(b'LAST') self.assertEqual( s.getvalue(), b'+OK 2\r\n') def test_HighestOnlyProgresses(self): p = self.pop3Server s = self.pop3Transport p.mbox.messages.append(self.extraMessage) p.lineReceived(b'RETR 2') self._flush() p.lineReceived(b'TOP 1 10') self._flush() s.seek(0) s.truncate(0) p.lineReceived(b'LAST') self.assertEqual( s.getvalue(), b'+OK 2\r\n') def test_ResetClearsHighest(self): p = self.pop3Server s = self.pop3Transport p.mbox.messages.append(self.extraMessage) p.lineReceived(b'RETR 2') self._flush() p.lineReceived(b'RSET') s.seek(0) s.truncate(0) p.lineReceived(b'LAST') self.assertEqual( s.getvalue(), b'+OK 0\r\n') _listMessageDeprecation = ( "twisted.mail.pop3.IMailbox.listMessages may not " "raise IndexError for out-of-bounds message numbers: " "raise ValueError instead.") _listMessageSuppression = util.suppress( message=_listMessageDeprecation, category=PendingDeprecationWarning) _getUidlDeprecation = ( "twisted.mail.pop3.IMailbox.getUidl may not " "raise IndexError for out-of-bounds message numbers: " "raise ValueError instead.") _getUidlSuppression = util.suppress( message=_getUidlDeprecation, category=PendingDeprecationWarning) class IndexErrorCommandTests(CommandMixin, unittest.TestCase): exceptionType = IndexError mailboxType = DummyMailbox def test_LISTWithBadArgument(self): return CommandMixin.test_LISTWithBadArgument(self) test_LISTWithBadArgument.suppress = [_listMessageSuppression] def test_UIDLWithBadArgument(self): return CommandMixin.test_UIDLWithBadArgument(self) test_UIDLWithBadArgument.suppress = [_getUidlSuppression] def test_TOPWithBadArgument(self): return CommandMixin.test_TOPWithBadArgument(self) test_TOPWithBadArgument.suppress = [_listMessageSuppression] def test_RETRWithBadArgument(self): return CommandMixin.test_RETRWithBadArgument(self) test_RETRWithBadArgument.suppress = [_listMessageSuppression] class ValueErrorCommandTests(CommandMixin, unittest.TestCase): exceptionType = ValueError mailboxType = DummyMailbox class SyncDeferredMailbox(DummyMailbox): def listMessages(self, n=None): return defer.succeed(DummyMailbox.listMessages(self, n)) class IndexErrorSyncDeferredCommandTests(IndexErrorCommandTests): mailboxType = SyncDeferredMailbox class ValueErrorSyncDeferredCommandTests(ValueErrorCommandTests): mailboxType = SyncDeferredMailbox class AsyncDeferredMailbox(DummyMailbox): def __init__(self, *a, **kw): self.waiting = [] DummyMailbox.__init__(self, *a, **kw) def listMessages(self, n=None): d = defer.Deferred() self.waiting.append((d, DummyMailbox.listMessages(self, n))) return d class IndexErrorAsyncDeferredCommandTests(IndexErrorCommandTests): mailboxType = AsyncDeferredMailbox def _flush(self): while self.pop3Server.mbox.waiting: d, a = self.pop3Server.mbox.waiting.pop() d.callback(a) IndexErrorCommandTests._flush(self) class ValueErrorAsyncDeferredCommandTests(ValueErrorCommandTests): mailboxType = AsyncDeferredMailbox def _flush(self): while self.pop3Server.mbox.waiting: d, a = self.pop3Server.mbox.waiting.pop() d.callback(a) ValueErrorCommandTests._flush(self) class POP3MiscTests(unittest.TestCase): def test_all(self): mod = twisted.mail.pop3 for attr in mod.__all__: self.assertTrue(hasattr(mod, attr))

true

f7fbfb511166364af3d606200d30680cfd63cfc2

675

py

Python

nova/network/quantum/__init__.py

armaan/nova

22859fccb95502efcb73ecf2bd827c45c0886bd3

[ "Apache-2.0" ]

1

2021-11-08T10:11:44.000Z

nova/network/quantum/__init__.py

armaan/nova

22859fccb95502efcb73ecf2bd827c45c0886bd3

[ "Apache-2.0" ]

1

2020-07-24T14:14:13.000Z

nova/network/quantum/__init__.py

armaan/nova

22859fccb95502efcb73ecf2bd827c45c0886bd3

[ "Apache-2.0" ]

2

2019-06-12T00:52:15.000Z

2020-07-24T10:35:29.000Z

# vim: tabstop=4 shiftwidth=4 softtabstop=4 # Copyright 2011 Nicira Networks # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License.

39.705882

78

0.731852

true

f7fbfccc6f21e4a7f82e7690279110bd1d673d81

3,462

py

Python

codes/utils/logger.py

kingsj0405/Explorable-Super-Resolution

6582477ec1e2b0c6f4bd781552ac880fabdb4496

[ "Apache-2.0" ]

54

2020-06-16T07:44:19.000Z

2022-03-01T06:23:38.000Z

codes/utils/logger.py

kingsj0405/Explorable-Super-Resolution

6582477ec1e2b0c6f4bd781552ac880fabdb4496

[ "Apache-2.0" ]

6

2020-07-01T08:59:24.000Z

2021-02-22T19:58:23.000Z

codes/utils/logger.py

kingsj0405/Explorable-Super-Resolution

6582477ec1e2b0c6f4bd781552ac880fabdb4496

[ "Apache-2.0" ]

11

2020-06-16T21:28:00.000Z

2022-01-06T12:28:58.000Z

import os import sys from utils.util import get_timestamp # print to file and std_out simultaneously class PrintLogger(object): def __init__(self, log_path): self.terminal = sys.stdout self.log = open(os.path.join(log_path, 'print_log.txt'), 'a') def write(self, message): self.terminal.write(message) self.log.write(message) def flush(self): pass class Logger(object): def __init__(self, opt,tb_logger_suffix=''): self.exp_name = opt['name'] self.use_tb_logger = opt['use_tb_logger'] self.opt = opt['logger'] self.log_dir = opt['path']['log'] if not os.path.isdir(self.log_dir): os.mkdir(self.log_dir) # loss log file self.loss_log_path = os.path.join(self.log_dir, 'loss_log.txt') with open(self.loss_log_path, 'a') as log_file: log_file.write('=============== Time: ' + get_timestamp() + ' =============\n') log_file.write('================ Training Losses ================\n') # val results log file self.val_log_path = os.path.join(self.log_dir, 'val_log.txt') with open(self.val_log_path, 'a') as log_file: log_file.write('================ Time: ' + get_timestamp() + ' ===============\n') log_file.write('================ Validation Results ================\n') if self.use_tb_logger:# and 'debug' not in self.exp_name: from tensorboard_logger import Logger as TensorboardLogger logger_dir_num = 0 tb_logger_dir = self.log_dir.replace('experiments', 'logs') if not os.path.isdir(tb_logger_dir): os.mkdir(tb_logger_dir) existing_dirs = sorted([dir.split('_')[0] for dir in os.listdir(tb_logger_dir) if os.path.isdir(os.path.join(tb_logger_dir,dir))],key=lambda x:int(x.split('_')[0])) if len(existing_dirs)>0: logger_dir_num = int(existing_dirs[-1])+1 self.tb_logger = TensorboardLogger(os.path.join(tb_logger_dir,str(logger_dir_num)+tb_logger_suffix)) def print_format_results(self, mode, rlt,dont_print=False,keys_ignore_list=[]): epoch = rlt.pop('epoch') iters = rlt.pop('iters') time = rlt.pop('time') model = rlt.pop('model') if 'lr' in rlt: lr = rlt.pop('lr') message = '<epoch:{:3d}, iter:{:8,d}, time:{:.2f}, lr:{:.1e}> '.format( epoch, iters, time, lr) else: message = '<epoch:{:3d}, iter:{:8,d}, time:{:.2f}> '.format(epoch, iters, time) for label, value in rlt.items(): if label in keys_ignore_list or '_baseline' in label: continue if mode == 'train': message += '{:s}: {:.4e} '.format(label, value) elif mode == 'val': message += '{:s}: {:.4e} '.format(label, value) # tensorboard logger if self.use_tb_logger:# and 'debug' not in self.exp_name: self.tb_logger.log_value(label, value, iters) # print in console if not dont_print: print(message) # write in log file if mode == 'train': with open(self.loss_log_path, 'a') as log_file: log_file.write(message + '\n') elif mode == 'val': with open(self.val_log_path, 'a') as log_file: log_file.write(message + '\n')

42.219512

176

0.554015

import os import sys from utils.util import get_timestamp class PrintLogger(object): def __init__(self, log_path): self.terminal = sys.stdout self.log = open(os.path.join(log_path, 'print_log.txt'), 'a') def write(self, message): self.terminal.write(message) self.log.write(message) def flush(self): pass class Logger(object): def __init__(self, opt,tb_logger_suffix=''): self.exp_name = opt['name'] self.use_tb_logger = opt['use_tb_logger'] self.opt = opt['logger'] self.log_dir = opt['path']['log'] if not os.path.isdir(self.log_dir): os.mkdir(self.log_dir) self.loss_log_path = os.path.join(self.log_dir, 'loss_log.txt') with open(self.loss_log_path, 'a') as log_file: log_file.write('=============== Time: ' + get_timestamp() + ' =============\n') log_file.write('================ Training Losses ================\n') self.val_log_path = os.path.join(self.log_dir, 'val_log.txt') with open(self.val_log_path, 'a') as log_file: log_file.write('================ Time: ' + get_timestamp() + ' ===============\n') log_file.write('================ Validation Results ================\n') if self.use_tb_logger: from tensorboard_logger import Logger as TensorboardLogger logger_dir_num = 0 tb_logger_dir = self.log_dir.replace('experiments', 'logs') if not os.path.isdir(tb_logger_dir): os.mkdir(tb_logger_dir) existing_dirs = sorted([dir.split('_')[0] for dir in os.listdir(tb_logger_dir) if os.path.isdir(os.path.join(tb_logger_dir,dir))],key=lambda x:int(x.split('_')[0])) if len(existing_dirs)>0: logger_dir_num = int(existing_dirs[-1])+1 self.tb_logger = TensorboardLogger(os.path.join(tb_logger_dir,str(logger_dir_num)+tb_logger_suffix)) def print_format_results(self, mode, rlt,dont_print=False,keys_ignore_list=[]): epoch = rlt.pop('epoch') iters = rlt.pop('iters') time = rlt.pop('time') model = rlt.pop('model') if 'lr' in rlt: lr = rlt.pop('lr') message = '<epoch:{:3d}, iter:{:8,d}, time:{:.2f}, lr:{:.1e}> '.format( epoch, iters, time, lr) else: message = '<epoch:{:3d}, iter:{:8,d}, time:{:.2f}> '.format(epoch, iters, time) for label, value in rlt.items(): if label in keys_ignore_list or '_baseline' in label: continue if mode == 'train': message += '{:s}: {:.4e} '.format(label, value) elif mode == 'val': message += '{:s}: {:.4e} '.format(label, value) if self.use_tb_logger: self.tb_logger.log_value(label, value, iters) if not dont_print: print(message) if mode == 'train': with open(self.loss_log_path, 'a') as log_file: log_file.write(message + '\n') elif mode == 'val': with open(self.val_log_path, 'a') as log_file: log_file.write(message + '\n')

true

f7fbfd64a6f56e1f41ed05c6bb641e2db24f156a

7,108

py

Python

var/spack/repos/builtin/packages/hip-rocclr/package.py

jeanbez/spack

f4e51ce8f366c85bf5aa0eafe078677b42dae1ba

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

null

var/spack/repos/builtin/packages/hip-rocclr/package.py

jeanbez/spack

f4e51ce8f366c85bf5aa0eafe078677b42dae1ba

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

null

var/spack/repos/builtin/packages/hip-rocclr/package.py

jeanbez/spack

f4e51ce8f366c85bf5aa0eafe078677b42dae1ba

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

2

2019-02-08T20:37:20.000Z

2019-03-31T15:19:26.000Z

# Copyright 2013-2022 Lawrence Livermore National Security, LLC and other # Spack Project Developers. See the top-level COPYRIGHT file for details. # # SPDX-License-Identifier: (Apache-2.0 OR MIT) from spack.package import * class HipRocclr(CMakePackage): """Hip-ROCclr is a virtual device interface that compute runtimes interact with to different backends such as ROCr or PAL This abstraction allows runtimes to work on Windows as well as on Linux without much effort.""" homepage = "https://github.com/ROCm-Developer-Tools/ROCclr" git = "https://github.com/ROCm-Developer-Tools/ROCclr.git" maintainers = ['srekolam', 'arjun-raj-kuppala'] def url_for_version(self, version): # Fix up a typo in the 3.5.0 release. if version == Version('3.5.0'): return "https://github.com/ROCm-Developer-Tools/ROCclr/archive/roc-3.5.0.tar.gz" url = "https://github.com/ROCm-Developer-Tools/ROCclr/archive/rocm-{0}.tar.gz" return url.format(version) version('master', branch='main') version('5.1.3', sha256='ddee63cdc6515c90bab89572b13e1627b145916cb8ede075ef8446cbb83f0a48') version('5.1.0', sha256='f4f265604b534795a275af902b2c814f416434d9c9e16db81b3ed5d062187dfa') version('5.0.2', sha256='34decd84652268dde865f38e66f8fb4750a08c2457fea52ad962bced82a03e5e') version('5.0.0', sha256='6b72faf8819628a5c109b2ade515ab9009606d10f11316f0d7e4c4c998d7f724') version('4.5.2', sha256='6581916a3303a31f76454f12f86e020fb5e5c019f3dbb0780436a8f73792c4d1') version('4.5.0', sha256='ca8d6305ff0e620d9cb69ff7ac3898917db9e9b6996a7320244b48ab6511dd8e') version('4.3.1', sha256='bda52c65f03a69a9d8ab1a118d45646d76843249fb975d67e5141e63fa3acc79', deprecated=True) version('4.3.0', sha256='8a86b4f2a1b1c7ac628262e5b11b07ff42a224e62e594a4e0683aeb616062538', deprecated=True) version('4.2.0', sha256='c57525af32c59becf56fd83cdd61f5320a95024d9baa7fb729a01e7a9fcdfd78', deprecated=True) version('4.1.0', sha256='9eb1d88cfc9474979aaf29b99bcf9d3769a0f7f1f8f10660941aabf83d9eeb0c', deprecated=True) version('4.0.0', sha256='8db502d0f607834e3b882f939d33e8abe2f9b55ddafaf1b0c2cd29a0425ed76a', deprecated=True) version('3.10.0', sha256='d1ac02840c2dcb3d5fa3008fe9e313767ebe6d1dcf978a924341834ec96ebfe2', deprecated=True) version('3.9.0', sha256='d248958672ae35ab7f9fbd83827ccf352e2756dfa7819f6b614ace2e1a9a064e', deprecated=True) version('3.8.0', sha256='10d8aa6f5af7b51813015da603c4e75edc863c3530793f6ed9769ca345c08ed6', deprecated=True) version('3.7.0', sha256='a49f464bb2eab6317e87e3cc249aba3b2517a34fbdfe50175f0437f69a219adc', deprecated=True) version('3.5.0', sha256='87c1ee9f02b8aa487b628c543f058198767c474cec3d21700596a73c028959e1', deprecated=True) variant('build_type', default='Release', values=("Release", "Debug", "RelWithDebInfo"), description='CMake build type') depends_on('cmake@3:', type='build') depends_on('gl@4.5:', type='link') depends_on('libelf', type='link', when="@3.7.0:3.8.0") depends_on('numactl', type='link', when="@3.7.0:") for ver in ['3.5.0', '3.7.0', '3.8.0', '3.9.0', '3.10.0', '4.0.0', '4.1.0', '4.2.0', '4.3.0', '4.3.1', '4.5.0', '4.5.2', '5.0.0', '5.0.2', '5.1.0', '5.1.3', 'master']: depends_on('hsakmt-roct@' + ver, when='@' + ver) depends_on('hsa-rocr-dev@' + ver, when='@' + ver) depends_on('comgr@' + ver, when='@' + ver) # See: https://github.com/ROCm-Developer-Tools/ROCclr/pull/16 # In 3.7.0 the find opengl things have changed slightly. patch('opengl.patch', when='@3.5.0') resource(name='opencl-on-vdi', url='https://github.com/RadeonOpenCompute/ROCm-OpenCL-Runtime/archive/roc-3.5.0.tar.gz', sha256='511b617d5192f2d4893603c1a02402b2ac9556e9806ff09dd2a91d398abf39a0', expand=True, destination='', placement='opencl-on-vdi', when='@3.5.0') # Add opencl sources thru the below for d_version, d_shasum in [ ('5.1.3', '44a7fac721abcd93470e1a7e466bdea0c668c253dee93e4f1ea9a72dbce4ba31'), ('5.1.0', '362d81303048cf7ed5d2f69fb65ed65425bc3da4734fff83e3b8fbdda51b0927'), ('5.0.2', '3edb1992ba28b4a7f82dd66fbd121f62bd859c1afb7ceb47fa856bd68feedc95'), ('5.0.0', '2aa3a628b336461f83866c4e76225ef5338359e31f802987699d6308515ae1be'), ('4.5.2', '96b43f314899707810db92149caf518bdb7cf39f7c0ad86e98ad687ffb0d396d'), ('4.5.0', '3a163aed24619b3faf5e8ba17325bdcedd1667a904ea20914ac6bdd33fcdbca8'), ('4.3.1', '7f98f7d4707b4392f8aa7017aaca9e27cb20263428a1a81fb7ec7c552e60c4ca'), ('4.3.0', 'd37bddcc6835b6c0fecdf4d02c204ac1d312076f3eef2b1faded1c4c1bc651e9'), ('4.2.0', '18133451948a83055ca5ebfb5ba1bd536ed0bcb611df98829f1251a98a38f730'), ('4.1.0', '0729e6c2adf1e3cf649dc6e679f9cb936f4f423f4954ad9852857c0a53ef799c'), ('4.0.0', 'd43ea5898c6b9e730b5efabe8367cc136a9260afeac5d0fe85b481d625dd7df1'), ('3.10.0', '3aa9dc5a5f570320b04b35ee129ce9ff21062d2770df934c6c307913f975e93d'), ('3.9.0', '286ff64304905384ce524cd8794c28aee216befd6c9267d4187a12e5a21e2daf'), ('3.8.0', '7f75dd1abf3d771d554b0e7b0a7d915ab5f11a74962c92b013ee044a23c1270a'), ('3.7.0', '283e1dfe4c3d2e8af4d677ed3c20e975393cdb0856e3ccd77b9c7ed2a151650b') ]: resource( name='opencl-on-vdi', url='https://github.com/RadeonOpenCompute/ROCm-OpenCL-Runtime/archive/rocm-{0}.tar.gz'.format(d_version), sha256=d_shasum, expand=True, destination='', placement='opencl-on-vdi', when='@{0}'.format(d_version) ) resource( name='opencl-on-vdi', git='https://github.com/RadeonOpenCompute/ROCm-OpenCL-Runtime.git', destination='', placement='opencl-on-vdi', branch='main', when='@master' ) @property def install_targets(self): if self.spec.satisfies('@4.5.0:'): return [] return ['install'] @run_after('install') def deploy_missing_files(self): if '@3.5.0' in self.spec: # the amdrocclr_staticTargets.cmake file is generated but not # installed and when we install it by hand, we have to fix the # path to the static library libamdrocclr_static.a from build # dir to prefix lib dir. cmakefile = join_path(self.build_directory, 'amdrocclr_staticTargets.cmake') filter_file(self.build_directory, self.prefix.lib, cmakefile) install(cmakefile, self.prefix.lib) elif self.spec.satisfies('@3.7.0:4.3.2'): path = join_path(self.prefix.lib, 'cmake/rocclr/ROCclrConfig.cmake') filter_file(self.build_directory, self.prefix, path) def cmake_args(self): args = [ '-DUSE_COMGR_LIBRARY=yes', '-DOPENCL_DIR={0}/opencl-on-vdi'.format(self.stage.source_path) ] return args

51.883212

123

0.68444

from spack.package import * class HipRocclr(CMakePackage): homepage = "https://github.com/ROCm-Developer-Tools/ROCclr" git = "https://github.com/ROCm-Developer-Tools/ROCclr.git" maintainers = ['srekolam', 'arjun-raj-kuppala'] def url_for_version(self, version): if version == Version('3.5.0'): return "https://github.com/ROCm-Developer-Tools/ROCclr/archive/roc-3.5.0.tar.gz" url = "https://github.com/ROCm-Developer-Tools/ROCclr/archive/rocm-{0}.tar.gz" return url.format(version) version('master', branch='main') version('5.1.3', sha256='ddee63cdc6515c90bab89572b13e1627b145916cb8ede075ef8446cbb83f0a48') version('5.1.0', sha256='f4f265604b534795a275af902b2c814f416434d9c9e16db81b3ed5d062187dfa') version('5.0.2', sha256='34decd84652268dde865f38e66f8fb4750a08c2457fea52ad962bced82a03e5e') version('5.0.0', sha256='6b72faf8819628a5c109b2ade515ab9009606d10f11316f0d7e4c4c998d7f724') version('4.5.2', sha256='6581916a3303a31f76454f12f86e020fb5e5c019f3dbb0780436a8f73792c4d1') version('4.5.0', sha256='ca8d6305ff0e620d9cb69ff7ac3898917db9e9b6996a7320244b48ab6511dd8e') version('4.3.1', sha256='bda52c65f03a69a9d8ab1a118d45646d76843249fb975d67e5141e63fa3acc79', deprecated=True) version('4.3.0', sha256='8a86b4f2a1b1c7ac628262e5b11b07ff42a224e62e594a4e0683aeb616062538', deprecated=True) version('4.2.0', sha256='c57525af32c59becf56fd83cdd61f5320a95024d9baa7fb729a01e7a9fcdfd78', deprecated=True) version('4.1.0', sha256='9eb1d88cfc9474979aaf29b99bcf9d3769a0f7f1f8f10660941aabf83d9eeb0c', deprecated=True) version('4.0.0', sha256='8db502d0f607834e3b882f939d33e8abe2f9b55ddafaf1b0c2cd29a0425ed76a', deprecated=True) version('3.10.0', sha256='d1ac02840c2dcb3d5fa3008fe9e313767ebe6d1dcf978a924341834ec96ebfe2', deprecated=True) version('3.9.0', sha256='d248958672ae35ab7f9fbd83827ccf352e2756dfa7819f6b614ace2e1a9a064e', deprecated=True) version('3.8.0', sha256='10d8aa6f5af7b51813015da603c4e75edc863c3530793f6ed9769ca345c08ed6', deprecated=True) version('3.7.0', sha256='a49f464bb2eab6317e87e3cc249aba3b2517a34fbdfe50175f0437f69a219adc', deprecated=True) version('3.5.0', sha256='87c1ee9f02b8aa487b628c543f058198767c474cec3d21700596a73c028959e1', deprecated=True) variant('build_type', default='Release', values=("Release", "Debug", "RelWithDebInfo"), description='CMake build type') depends_on('cmake@3:', type='build') depends_on('gl@4.5:', type='link') depends_on('libelf', type='link', when="@3.7.0:3.8.0") depends_on('numactl', type='link', when="@3.7.0:") for ver in ['3.5.0', '3.7.0', '3.8.0', '3.9.0', '3.10.0', '4.0.0', '4.1.0', '4.2.0', '4.3.0', '4.3.1', '4.5.0', '4.5.2', '5.0.0', '5.0.2', '5.1.0', '5.1.3', 'master']: depends_on('hsakmt-roct@' + ver, when='@' + ver) depends_on('hsa-rocr-dev@' + ver, when='@' + ver) depends_on('comgr@' + ver, when='@' + ver) patch('opengl.patch', when='@3.5.0') resource(name='opencl-on-vdi', url='https://github.com/RadeonOpenCompute/ROCm-OpenCL-Runtime/archive/roc-3.5.0.tar.gz', sha256='511b617d5192f2d4893603c1a02402b2ac9556e9806ff09dd2a91d398abf39a0', expand=True, destination='', placement='opencl-on-vdi', when='@3.5.0') for d_version, d_shasum in [ ('5.1.3', '44a7fac721abcd93470e1a7e466bdea0c668c253dee93e4f1ea9a72dbce4ba31'), ('5.1.0', '362d81303048cf7ed5d2f69fb65ed65425bc3da4734fff83e3b8fbdda51b0927'), ('5.0.2', '3edb1992ba28b4a7f82dd66fbd121f62bd859c1afb7ceb47fa856bd68feedc95'), ('5.0.0', '2aa3a628b336461f83866c4e76225ef5338359e31f802987699d6308515ae1be'), ('4.5.2', '96b43f314899707810db92149caf518bdb7cf39f7c0ad86e98ad687ffb0d396d'), ('4.5.0', '3a163aed24619b3faf5e8ba17325bdcedd1667a904ea20914ac6bdd33fcdbca8'), ('4.3.1', '7f98f7d4707b4392f8aa7017aaca9e27cb20263428a1a81fb7ec7c552e60c4ca'), ('4.3.0', 'd37bddcc6835b6c0fecdf4d02c204ac1d312076f3eef2b1faded1c4c1bc651e9'), ('4.2.0', '18133451948a83055ca5ebfb5ba1bd536ed0bcb611df98829f1251a98a38f730'), ('4.1.0', '0729e6c2adf1e3cf649dc6e679f9cb936f4f423f4954ad9852857c0a53ef799c'), ('4.0.0', 'd43ea5898c6b9e730b5efabe8367cc136a9260afeac5d0fe85b481d625dd7df1'), ('3.10.0', '3aa9dc5a5f570320b04b35ee129ce9ff21062d2770df934c6c307913f975e93d'), ('3.9.0', '286ff64304905384ce524cd8794c28aee216befd6c9267d4187a12e5a21e2daf'), ('3.8.0', '7f75dd1abf3d771d554b0e7b0a7d915ab5f11a74962c92b013ee044a23c1270a'), ('3.7.0', '283e1dfe4c3d2e8af4d677ed3c20e975393cdb0856e3ccd77b9c7ed2a151650b') ]: resource( name='opencl-on-vdi', url='https://github.com/RadeonOpenCompute/ROCm-OpenCL-Runtime/archive/rocm-{0}.tar.gz'.format(d_version), sha256=d_shasum, expand=True, destination='', placement='opencl-on-vdi', when='@{0}'.format(d_version) ) resource( name='opencl-on-vdi', git='https://github.com/RadeonOpenCompute/ROCm-OpenCL-Runtime.git', destination='', placement='opencl-on-vdi', branch='main', when='@master' ) @property def install_targets(self): if self.spec.satisfies('@4.5.0:'): return [] return ['install'] @run_after('install') def deploy_missing_files(self): if '@3.5.0' in self.spec: cmakefile = join_path(self.build_directory, 'amdrocclr_staticTargets.cmake') filter_file(self.build_directory, self.prefix.lib, cmakefile) install(cmakefile, self.prefix.lib) elif self.spec.satisfies('@3.7.0:4.3.2'): path = join_path(self.prefix.lib, 'cmake/rocclr/ROCclrConfig.cmake') filter_file(self.build_directory, self.prefix, path) def cmake_args(self): args = [ '-DUSE_COMGR_LIBRARY=yes', '-DOPENCL_DIR={0}/opencl-on-vdi'.format(self.stage.source_path) ] return args

true

f7fbfde5f32671eeca378684439614939666ff7c

6,849

py

Python

python/smqtk/representation/key_value/__init__.py

cdeepakroy/SMQTK

ef5cec7399e9766f95ff06fe122471a51fc4b2d8

[ "BSD-3-Clause" ]

null

python/smqtk/representation/key_value/__init__.py

cdeepakroy/SMQTK

ef5cec7399e9766f95ff06fe122471a51fc4b2d8

[ "BSD-3-Clause" ]

null

python/smqtk/representation/key_value/__init__.py

cdeepakroy/SMQTK

ef5cec7399e9766f95ff06fe122471a51fc4b2d8

[ "BSD-3-Clause" ]

null

""" Data abstraction interface for general key-value storage. """ import abc import collections import os from smqtk.exceptions import ReadOnlyError from smqtk.representation import SmqtkRepresentation from smqtk.utils.plugin import Pluggable NO_DEFAULT_VALUE = type("KeyValueStoreNoDefaultValueType", (object,), {})() class KeyValueStore (SmqtkRepresentation, Pluggable): """ Interface for general key/value storage. Implementations may impose restrictions on what types keys or values may be due to backend used. Data access and manipulation should be thread-safe. """ # Mutable storage container is not hashable. __hash__ = None def __len__(self): return self.count() def __contains__(self, item): return self.has(item) @abc.abstractmethod def __repr__(self): """ Return representative string for this class. *NOTE:* **This abstract super-method returns a template string to add to sub-class specific information to. The returned string should be formatted using the ``%`` operator and expects a single string argument.** :return: Representative string for this class. :rtype: str """ return '<' + self.__class__.__name__ + " %s>" @abc.abstractmethod def count(self): """ :return: The number of key-value relationships in this store. :rtype: int | long """ @abc.abstractmethod def keys(self): """ :return: Iterator over keys in this store. :rtype: collections.Iterator[collections.Hashable] """ def values(self): """ :return: Iterator over values in this store. Values are not guaranteed to be in any particular order. :rtype: collections.Iterator[object] """ for k in self.keys(): yield self.get(k) @abc.abstractmethod def is_read_only(self): """ :return: True if this instance is read-only and False if it is not. :rtype: bool """ @abc.abstractmethod def has(self, key): """ Check if this store has a value for the given key. :param key: Key to check for a value for. :type key: collections.Hashable :return: If this store has a value for the given key. :rtype: bool """ @abc.abstractmethod def add(self, key, value): """ Add a key-value pair to this store. *NOTE:* **Implementing sub-classes should call this super-method. This super method should not be considered a critical section for thread safety unless ``is_read_only`` is not thread-safe.** :param key: Key for the value. Must be hashable. :type key: collections.Hashable :param value: Python object to store. :type value: object :raises ReadOnlyError: If this instance is marked as read-only. :return: Self. :rtype: KeyValueStore """ if not isinstance(key, collections.Hashable): raise ValueError("Key is not a hashable type.") if self.is_read_only(): raise ReadOnlyError("Cannot add to read-only instance %s." % self) @abc.abstractmethod def add_many(self, d): """ Add multiple key-value pairs at a time into this store as represented in the provided dictionary `d`. :param d: Dictionary of key-value pairs to add to this store. :type d: dict[collections.Hashable, object] :return: Self. :rtype: KeyValueStore """ @abc.abstractmethod def get(self, key, default=NO_DEFAULT_VALUE): """ Get the value for the given key. *NOTE:* **Implementing sub-classes are responsible for raising a ``KeyError`` where appropriate.** :param key: Key to get the value of. :type key: collections.Hashable :param default: Optional default value if the given key is not present in this store. This may be any value except for the ``NO_DEFAULT_VALUE`` constant (custom anonymous class instance). :type default: object :raises KeyError: The given key is not present in this store and no default value given. :return: Deserialized python object stored for the given key. :rtype: object """ # TODO: get_many(self, keys, default=NO_DEFAULT_VALUE) @abc.abstractmethod def clear(self): """ Clear this key-value store. *NOTE:* **Implementing sub-classes should call this super-method. This super method should not be considered a critical section for thread safety.** :raises ReadOnlyError: If this instance is marked as read-only. """ if self.is_read_only(): raise ReadOnlyError("Cannot clear a read-only %s instance." % self.__class__.__name__) def get_key_value_store_impls(reload_modules=False): """ Discover and return discovered ``KeyValueStore`` classes. Keys in the returned map are the names of the discovered classes, and the paired values are the actual class type objects. We search for implementation classes in: - modules next to this file this function is defined in (ones that begin with an alphanumeric character), - python modules listed in the environment variable ``KEY_VALUE_STORE_PATH`` - This variable should contain a sequence of python module specifications, separated by the platform specific PATH separator character (``;`` for Windows, ``:`` for unix) Within a module we first look for a helper variable by the name ``KEY_VALUE_STORE_CLASS``, which can either be a single class object or an iterable of class objects, to be specifically exported. If the variable is set to None, we skip that module and do not import anything. If the variable is not present, we look at attributes defined in that module for classes that descend from the given base class type. If none of the above are found, or if an exception occurs, the module is skipped. :param reload_modules: Explicitly reload discovered modules from source. :type reload_modules: bool :return: Map of discovered class object of type ``KeyValueStore`` whose keys are the string names of the classes. :rtype: dict[str, type] """ from smqtk.utils.plugin import get_plugins this_dir = os.path.abspath(os.path.dirname(__file__)) env_var = "KEY_VALUE_STORE_PATH" helper_var = "KEY_VALUE_STORE_CLASS" return get_plugins(__name__, this_dir, env_var, helper_var, KeyValueStore, reload_modules=reload_modules)

32.004673

80

0.64681

import abc import collections import os from smqtk.exceptions import ReadOnlyError from smqtk.representation import SmqtkRepresentation from smqtk.utils.plugin import Pluggable NO_DEFAULT_VALUE = type("KeyValueStoreNoDefaultValueType", (object,), {})() class KeyValueStore (SmqtkRepresentation, Pluggable): __hash__ = None def __len__(self): return self.count() def __contains__(self, item): return self.has(item) @abc.abstractmethod def __repr__(self): return '<' + self.__class__.__name__ + " %s>" @abc.abstractmethod def count(self): @abc.abstractmethod def keys(self): def values(self): for k in self.keys(): yield self.get(k) @abc.abstractmethod def is_read_only(self): @abc.abstractmethod def has(self, key): @abc.abstractmethod def add(self, key, value): if not isinstance(key, collections.Hashable): raise ValueError("Key is not a hashable type.") if self.is_read_only(): raise ReadOnlyError("Cannot add to read-only instance %s." % self) @abc.abstractmethod def add_many(self, d): @abc.abstractmethod def get(self, key, default=NO_DEFAULT_VALUE): @abc.abstractmethod def clear(self): if self.is_read_only(): raise ReadOnlyError("Cannot clear a read-only %s instance." % self.__class__.__name__) def get_key_value_store_impls(reload_modules=False): from smqtk.utils.plugin import get_plugins this_dir = os.path.abspath(os.path.dirname(__file__)) env_var = "KEY_VALUE_STORE_PATH" helper_var = "KEY_VALUE_STORE_CLASS" return get_plugins(__name__, this_dir, env_var, helper_var, KeyValueStore, reload_modules=reload_modules)

true

f7fc002869dcb55f0f4ad40430039ad2d1a1ba1e

2,180

py

Python

tests/run_basic_tests.py

ThibHlln/unifhy

4105932ed7dfec34d428c1f2d2f85ec25ea522ed

[ "BSD-3-Clause" ]

null

tests/run_basic_tests.py

ThibHlln/unifhy

4105932ed7dfec34d428c1f2d2f85ec25ea522ed

[ "BSD-3-Clause" ]

2

2021-12-03T10:49:42.000Z

2022-03-26T11:24:00.000Z

tests/run_basic_tests.py

unifhy-org/unifhy

4105932ed7dfec34d428c1f2d2f85ec25ea522ed

[ "BSD-3-Clause" ]

null

import unittest import doctest from tests.test_model import BasicTestModel from tests.test_space import ( TestLatLonGridAPI, TestGridComparison ) from tests.test_time import ( TestTimeDomainAPI, TestTimeDomainComparison ) from tests.test_utils.test_clock import TestClock import unifhy class TestModelSameTimeSameSpace(BasicTestModel, unittest.TestCase): # flag to specify that components are to run at same temporal resolutions t = 'same_t' # flag to specify that components are to run at same spatial resolution s = 'same_s' class TestModelDiffTimeDiffSpace(BasicTestModel, unittest.TestCase): # flag to specify that components are to run at different temporal resolutions t = 'diff_t' # flag to specify that components are to run at different spatial resolutions s = 'diff_s' if __name__ == '__main__': test_loader = unittest.TestLoader() test_suite = unittest.TestSuite() test_suite.addTests( test_loader.loadTestsFromTestCase(TestModelSameTimeSameSpace) ) test_suite.addTests( test_loader.loadTestsFromTestCase(TestModelDiffTimeDiffSpace) ) test_suite.addTests( test_loader.loadTestsFromTestCase(TestLatLonGridAPI) ) test_suite.addTests( test_loader.loadTestsFromTestCase(TestGridComparison) ) test_suite.addTests( test_loader.loadTestsFromTestCase(TestTimeDomainAPI) ) test_suite.addTests( test_loader.loadTestsFromTestCase(TestTimeDomainComparison) ) test_suite.addTests( test_loader.loadTestsFromTestCase(TestClock) ) test_suite.addTests(doctest.DocTestSuite(unifhy.data)) test_suite.addTests(doctest.DocTestSuite(unifhy.time)) test_suite.addTests(doctest.DocTestSuite(unifhy.space)) test_suite.addTests(doctest.DocTestSuite(unifhy.model)) test_suite.addTests(doctest.DocTestSuite(unifhy._utils.clock)) test_suite.addTests(doctest.DocTestSuite(unifhy._utils.exchanger)) test_suite.addTests(doctest.DocTestSuite(unifhy._utils.compass)) runner = unittest.TextTestRunner(verbosity=2) result = runner.run(test_suite) if not result.wasSuccessful(): exit(1)

31.594203

82

0.759174

import unittest import doctest from tests.test_model import BasicTestModel from tests.test_space import ( TestLatLonGridAPI, TestGridComparison ) from tests.test_time import ( TestTimeDomainAPI, TestTimeDomainComparison ) from tests.test_utils.test_clock import TestClock import unifhy class TestModelSameTimeSameSpace(BasicTestModel, unittest.TestCase): t = 'same_t' s = 'same_s' class TestModelDiffTimeDiffSpace(BasicTestModel, unittest.TestCase): t = 'diff_t' s = 'diff_s' if __name__ == '__main__': test_loader = unittest.TestLoader() test_suite = unittest.TestSuite() test_suite.addTests( test_loader.loadTestsFromTestCase(TestModelSameTimeSameSpace) ) test_suite.addTests( test_loader.loadTestsFromTestCase(TestModelDiffTimeDiffSpace) ) test_suite.addTests( test_loader.loadTestsFromTestCase(TestLatLonGridAPI) ) test_suite.addTests( test_loader.loadTestsFromTestCase(TestGridComparison) ) test_suite.addTests( test_loader.loadTestsFromTestCase(TestTimeDomainAPI) ) test_suite.addTests( test_loader.loadTestsFromTestCase(TestTimeDomainComparison) ) test_suite.addTests( test_loader.loadTestsFromTestCase(TestClock) ) test_suite.addTests(doctest.DocTestSuite(unifhy.data)) test_suite.addTests(doctest.DocTestSuite(unifhy.time)) test_suite.addTests(doctest.DocTestSuite(unifhy.space)) test_suite.addTests(doctest.DocTestSuite(unifhy.model)) test_suite.addTests(doctest.DocTestSuite(unifhy._utils.clock)) test_suite.addTests(doctest.DocTestSuite(unifhy._utils.exchanger)) test_suite.addTests(doctest.DocTestSuite(unifhy._utils.compass)) runner = unittest.TextTestRunner(verbosity=2) result = runner.run(test_suite) if not result.wasSuccessful(): exit(1)

true

f7fc003ccfc18d1cf5992acacf7a94974efb90c7

2,479

py

Python

examples/ad_manager/v201905/activity_group_service/create_activity_groups.py

ale180192/googleads-python-lib

783a2d40a49956fb16ed73280708f6f9e322aa09

[ "Apache-2.0" ]

1

2019-09-30T06:36:07.000Z

examples/ad_manager/v201905/activity_group_service/create_activity_groups.py

ale180192/googleads-python-lib

783a2d40a49956fb16ed73280708f6f9e322aa09

[ "Apache-2.0" ]

null

examples/ad_manager/v201905/activity_group_service/create_activity_groups.py

ale180192/googleads-python-lib

783a2d40a49956fb16ed73280708f6f9e322aa09

[ "Apache-2.0" ]

null

#!/usr/bin/env python # # Copyright 2015 Google Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """This code example creates new activity groups. To determine which activity groups exist, run get_all_activity_groups.py. The LoadFromStorage method is pulling credentials and properties from a "googleads.yaml" file. By default, it looks for this file in your home directory. For more information, see the "Caching authentication information" section of our README. """ import uuid # Import appropriate modules from the client library. from googleads import ad_manager # Set the ID of the advertiser company this activity group is associated with. ADVERTISER_COMPANY_ID = 'INSERT_ADVERTISER_COMPANY_ID_HERE' def main(client, advertiser_company_id): # Initialize appropriate service. activity_group_service = client.GetService('ActivityGroupService', version='v201905') # Create a short-term activity group. short_term_activity_group = { 'name': 'Short-term activity group #%s' % uuid.uuid4(), 'companyIds': [advertiser_company_id], 'clicksLookback': '1', 'impressionsLookback': '1' } # Create a long-term activity group. long_term_activity_group = { 'name': 'Long-term activity group #%s' % uuid.uuid4(), 'companyIds': [advertiser_company_id], 'clicksLookback': '30', 'impressionsLookback': '30' } # Create the activity groups on the server. activity_groups = activity_group_service.createActivityGroups([ short_term_activity_group, long_term_activity_group]) # Display results. for activity_group in activity_groups: print('Activity group with ID "%s" and name "%s" was created.' % (activity_group['id'], activity_group['name'])) if __name__ == '__main__': # Initialize client object. ad_manager_client = ad_manager.AdManagerClient.LoadFromStorage() main(ad_manager_client, ADVERTISER_COMPANY_ID)

34.430556

78

0.734167

import uuid from googleads import ad_manager ADVERTISER_COMPANY_ID = 'INSERT_ADVERTISER_COMPANY_ID_HERE' def main(client, advertiser_company_id): activity_group_service = client.GetService('ActivityGroupService', version='v201905') short_term_activity_group = { 'name': 'Short-term activity group #%s' % uuid.uuid4(), 'companyIds': [advertiser_company_id], 'clicksLookback': '1', 'impressionsLookback': '1' } long_term_activity_group = { 'name': 'Long-term activity group #%s' % uuid.uuid4(), 'companyIds': [advertiser_company_id], 'clicksLookback': '30', 'impressionsLookback': '30' } activity_groups = activity_group_service.createActivityGroups([ short_term_activity_group, long_term_activity_group]) for activity_group in activity_groups: print('Activity group with ID "%s" and name "%s" was created.' % (activity_group['id'], activity_group['name'])) if __name__ == '__main__': ad_manager_client = ad_manager.AdManagerClient.LoadFromStorage() main(ad_manager_client, ADVERTISER_COMPANY_ID)

true

f7fc00cad765d312ab428e5d68895bb8a38dd4bc

2,702

py

Python

scons/scons-local-2.3.3/SCons/Tool/latex.py

pedrishi/pdb2pqr_pypka

74f64948658d021a8bfc8fd78936ce4186ffc88e

[ "BSD-3-Clause" ]

null

scons/scons-local-2.3.3/SCons/Tool/latex.py

pedrishi/pdb2pqr_pypka

74f64948658d021a8bfc8fd78936ce4186ffc88e

[ "BSD-3-Clause" ]

null

scons/scons-local-2.3.3/SCons/Tool/latex.py

pedrishi/pdb2pqr_pypka

74f64948658d021a8bfc8fd78936ce4186ffc88e

[ "BSD-3-Clause" ]

null

"""SCons.Tool.latex Tool-specific initialization for LaTeX. Generates .dvi files from .latex or .ltx files There normally shouldn't be any need to import this module directly. It will usually be imported through the generic SCons.Tool.Tool() selection method. """ # # Copyright (c) 2001 - 2014 The SCons Foundation # # Permission is hereby granted, free of charge, to any person obtaining # a copy of this software and associated documentation files (the # "Software"), to deal in the Software without restriction, including # without limitation the rights to use, copy, modify, merge, publish, # distribute, sublicense, and/or sell copies of the Software, and to # permit persons to whom the Software is furnished to do so, subject to # the following conditions: # # The above copyright notice and this permission notice shall be included # in all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY # KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE # WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND # NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE # LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION # OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION # WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # __revision__ = "src/engine/SCons/Tool/latex.py 2014/08/24 12:12:31 garyo" import SCons.Action import SCons.Defaults import SCons.Scanner.LaTeX import SCons.Util import SCons.Tool import SCons.Tool.tex def LaTeXAuxFunction(target = None, source= None, env=None): result = SCons.Tool.tex.InternalLaTeXAuxAction( SCons.Tool.tex.LaTeXAction, target, source, env ) if result != 0: SCons.Tool.tex.check_file_error_message(env['LATEX']) return result LaTeXAuxAction = SCons.Action.Action(LaTeXAuxFunction, strfunction=SCons.Tool.tex.TeXLaTeXStrFunction) def generate(env): """Add Builders and construction variables for LaTeX to an Environment.""" env.AppendUnique(LATEXSUFFIXES=SCons.Tool.LaTeXSuffixes) import dvi dvi.generate(env) import pdf pdf.generate(env) bld = env['BUILDERS']['DVI'] bld.add_action('.ltx', LaTeXAuxAction) bld.add_action('.latex', LaTeXAuxAction) bld.add_emitter('.ltx', SCons.Tool.tex.tex_eps_emitter) bld.add_emitter('.latex', SCons.Tool.tex.tex_eps_emitter) SCons.Tool.tex.generate_common(env) def exists(env): SCons.Tool.tex.generate_darwin(env) return env.Detect('latex') # Local Variables: # tab-width:4 # indent-tabs-mode:nil # End: # vim: set expandtab tabstop=4 shiftwidth=4:

33.358025

101

0.746484

__revision__ = "src/engine/SCons/Tool/latex.py 2014/08/24 12:12:31 garyo" import SCons.Action import SCons.Defaults import SCons.Scanner.LaTeX import SCons.Util import SCons.Tool import SCons.Tool.tex def LaTeXAuxFunction(target = None, source= None, env=None): result = SCons.Tool.tex.InternalLaTeXAuxAction( SCons.Tool.tex.LaTeXAction, target, source, env ) if result != 0: SCons.Tool.tex.check_file_error_message(env['LATEX']) return result LaTeXAuxAction = SCons.Action.Action(LaTeXAuxFunction, strfunction=SCons.Tool.tex.TeXLaTeXStrFunction) def generate(env): env.AppendUnique(LATEXSUFFIXES=SCons.Tool.LaTeXSuffixes) import dvi dvi.generate(env) import pdf pdf.generate(env) bld = env['BUILDERS']['DVI'] bld.add_action('.ltx', LaTeXAuxAction) bld.add_action('.latex', LaTeXAuxAction) bld.add_emitter('.ltx', SCons.Tool.tex.tex_eps_emitter) bld.add_emitter('.latex', SCons.Tool.tex.tex_eps_emitter) SCons.Tool.tex.generate_common(env) def exists(env): SCons.Tool.tex.generate_darwin(env) return env.Detect('latex')

true

f7fc01ffda0a4346bc1fd593b9c49641adc3b38c

159

py

Python

python/testData/quickdoc/ParamDescriptionEpytext.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/quickdoc/ParamDescriptionEpytext.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/quickdoc/ParamDescriptionEpytext.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

def fu<the_ref>nc(param1, param2): """ @param param1: param1 description @param param2: param2 description @return: return description """

22.714286

37

0.660377

def fu<the_ref>nc(param1, param2): """ @param param1: param1 description @param param2: param2 description @return: return description """

false

true

f7fc02aace6b8758a9c0c84593adbea465bf3abc

25,325

py

Python

scripts/sct_compute_hausdorff_distance.py

valosekj/spinalcordtoolbox

266bfc88d6eb6e96a2c2f1ec88c2e185c6f88e09

[ "MIT" ]

1

2020-05-17T00:39:47.000Z

scripts/sct_compute_hausdorff_distance.py

valosekj/spinalcordtoolbox

266bfc88d6eb6e96a2c2f1ec88c2e185c6f88e09

[ "MIT" ]

null

scripts/sct_compute_hausdorff_distance.py

valosekj/spinalcordtoolbox

266bfc88d6eb6e96a2c2f1ec88c2e185c6f88e09

[ "MIT" ]

null

#!/usr/bin/env python # # Thinning with the Zhang-Suen algorithm (1984) --> code taken from https://github.com/linbojin/Skeletonization-by-Zhang-Suen-Thinning-Algorithm # Computation of the distances between two skeleton # --------------------------------------------------------------------------------------- # Copyright (c) 2013 Polytechnique Montreal <www.neuro.polymtl.ca> # Authors: Sara Dupont # CREATED: 2015-07-15 # # About the license: see the file LICENSE.TXT ######################################################################################### from __future__ import absolute_import, division import sys, io, os, time, shutil, argparse import numpy as np import sct_utils as sct import spinalcordtoolbox.image as msct_image from spinalcordtoolbox.image import Image from spinalcordtoolbox.utils import Metavar, SmartFormatter # TODO: display results ==> not only max : with a violin plot of h1 and h2 distribution ? see dev/straightening --> seaborn.violinplot # TODO: add the option Hyberbolic Hausdorff's distance : see choi and seidel paper # ---------------------------------------------------------------------------------------------------------------------- # PARAM ---------------------------------------------------------------------------------------------------------------- class Param: def __init__(self): self.debug = 0 self.thinning = True self.verbose = 1 # ---------------------------------------------------------------------------------------------------------------------- # THINNING ------------------------------------------------------------------------------------------------------------- class Thinning: def __init__(self, im, v=1): sct.printv('Thinning ... ', v, 'normal') self.image = im self.image.data = bin_data(self.image.data) self.dim_im = len(self.image.data.shape) if self.dim_im == 2: self.thinned_image = msct_image.empty_like(self.image) self.thinned_image.data = self.zhang_suen(self.image.data) self.thinned_image.absolutepath = sct.add_suffix(self.image.absolutepath, "_thinned") elif self.dim_im == 3: if not self.image.orientation == 'IRP': sct.printv('-- changing orientation ...') self.image.change_orientation('IRP') thinned_data = np.asarray([self.zhang_suen(im_slice) for im_slice in self.image.data]) self.thinned_image = msct_image.empty_like(self.image) self.thinned_image.data = thinned_data self.thinned_image.absolutepath = sct.add_suffix(self.image.absolutepath, "_thinned") # ------------------------------------------------------------------------------------------------------------------ def get_neighbours(self, x, y, image): """ Return 8-neighbours of image point P1(x,y), in a clockwise order code from https://github.com/linbojin/Skeletonization-by-Zhang-Suen-Thinning-Algorithm :param x: :param y: :param image: :return: """ # now = time.time() x_1, y_1, x1, y1 = x - 1, y - 1, x + 1, y + 1 neighbours = [image[x_1][y], image[x_1][y1], image[x][y1], image[x1][y1], # P2,P3,P4,P5 image[x1][y], image[x1][y_1], image[x][y_1], image[x_1][y_1]] # P6,P7,P8,P9 # t = time.time() - now # sct.printv('t neighbours: ', t) return neighbours # ------------------------------------------------------------------------------------------------------------------ def transitions(self, neighbours): """ No. of 0,1 patterns (transitions from 0 to 1) in the ordered sequence code from https://github.com/linbojin/Skeletonization-by-Zhang-Suen-Thinning-Algorithm :param neighbours: :return: """ # now = time.time() n = neighbours + neighbours[0:1] # P2, P3, ... , P8, P9, P2 s = np.sum((n1, n2) == (0, 1) for n1, n2 in zip(n, n[1:])) # (P2,P3), (P3,P4), ... , (P8,P9), (P9,P2) # t = time.time() - now # sct.printv('t transitions sum: ', t) return s # ------------------------------------------------------------------------------------------------------------------ def zhang_suen(self, image): """ the Zhang-Suen Thinning Algorithm code from https://github.com/linbojin/Skeletonization-by-Zhang-Suen-Thinning-Algorithm :param image: :return: """ # now = time.time() image_thinned = image.copy() # deepcopy to protect the original image changing1 = changing2 = 1 # the points to be removed (set as 0) while changing1 or changing2: # iterates until no further changes occur in the image # Step 1 changing1 = [] max = len(image_thinned) - 1 pass_list = [1, max] # rows, columns = image_thinned.shape # x for rows, y for columns # for x in range(1, rows - 1): # No. of rows # for y in range(1, columns - 1): # No. of columns for x, y in non_zero_coord(image_thinned): if x not in pass_list and y not in pass_list: # if image_thinned[x][y] == 1: # Condition 0: Point P1 in the object regions P2, P3, P4, P5, P6, P7, P8, P9 = n = self.get_neighbours(x, y, image_thinned) if (2 <= sum(n) <= 6 and # Condition 1: 2<= N(P1) <= 6 P2 * P4 * P6 == 0 and # Condition 3 P4 * P6 * P8 == 0 and # Condition 4 self.transitions(n) == 1): # Condition 2: S(P1)=1 changing1.append((x, y)) for x, y in changing1: image_thinned[x][y] = 0 # Step 2 changing2 = [] # for x in range(1, rows - 1): # for y in range(1, columns - 1): for x, y in non_zero_coord(image_thinned): if x not in pass_list and y not in pass_list: # if image_thinned[x][y] == 1: # Condition 0 P2, P3, P4, P5, P6, P7, P8, P9 = n = self.get_neighbours(x, y, image_thinned) if (2 <= sum(n) <= 6 and # Condition 1 P2 * P4 * P8 == 0 and # Condition 3 P2 * P6 * P8 == 0 and # Condition 4 self.transitions(n) == 1): # Condition 2 changing2.append((x, y)) for x, y in changing2: image_thinned[x][y] = 0 # t = time.time() - now # sct.printv('t thinning: ', t) return image_thinned # ---------------------------------------------------------------------------------------------------------------------- # HAUSDORFF'S DISTANCE ------------------------------------------------------------------------------------------------- class HausdorffDistance: def __init__(self, data1, data2, v=1): """ the hausdorff distance between two sets is the maximum of the distances from a point in any of the sets to the nearest point in the other set :return: """ # now = time.time() sct.printv('Computing 2D Hausdorff\'s distance ... ', v, 'normal') self.data1 = bin_data(data1) self.data2 = bin_data(data2) self.min_distances_1 = self.relative_hausdorff_dist(self.data1, self.data2, v) self.min_distances_2 = self.relative_hausdorff_dist(self.data2, self.data1, v) # relatives hausdorff's distances in pixel self.h1 = np.max(self.min_distances_1) self.h2 = np.max(self.min_distances_2) # Hausdorff's distance in pixel self.H = max(self.h1, self.h2) # t = time.time() - now # sct.printv('Hausdorff dist time :', t) # ------------------------------------------------------------------------------------------------------------------ def relative_hausdorff_dist(self, dat1, dat2, v=1): h = np.zeros(dat1.shape) nz_coord_1 = non_zero_coord(dat1) nz_coord_2 = non_zero_coord(dat2) if len(nz_coord_1) != 0 and len(nz_coord_2) != 0 : for x1, y1 in nz_coord_1: # for x1 in range(dat1.shape[0]): # for y1 in range(dat1.shape[1]): # if dat1[x1, y1] == 1: d_p1_dat2 = [] p1 = np.asarray([x1, y1]) for x2, y2 in nz_coord_2: # for x2 in range(dat2.shape[0]): # for y2 in range(dat2.shape[1]): # if dat2[x2, y2] == 1: p2 = np.asarray([x2, y2]) d_p1_dat2.append(np.linalg.norm(p1 - p2)) # Euclidean distance between p1 and p2 h[x1, y1] = min(d_p1_dat2) else: sct.printv('Warning: an image is empty', v, 'warning') return h # ---------------------------------------------------------------------------------------------------------------------- # COMPUTE DISTANCES ---------------------------------------------------------------------------------------------------- class ComputeDistances: def __init__(self, im1, im2=None, param=None): self.im1 = im1 self.im2 = im2 self.dim_im = len(self.im1.data.shape) self.dim_pix = 0 self.distances = None self.res = '' self.param = param self.dist1_distribution = None self.dist2_distribution = None if self.dim_im == 3: self.orientation1 = self.im1.orientation if self.orientation1 != 'IRP': self.im1.change_orientation('IRP', generate_path=True) if self.im2 is not None: self.orientation2 = self.im2.orientation if self.orientation2 != 'IRP': self.im2.change_orientation('IRP', generate_path=True) if self.param.thinning: self.thinning1 = Thinning(self.im1, self.param.verbose) self.thinning1.thinned_image.save() if self.im2 is not None: self.thinning2 = Thinning(self.im2, self.param.verbose) self.thinning2.thinned_image.save() if self.dim_im == 2 and self.im2 is not None: self.compute_dist_2im_2d() if self.dim_im == 3: if self.im2 is None: self.compute_dist_1im_3d() else: self.compute_dist_2im_3d() if self.dim_im == 2 and self.distances is not None: self.dist1_distribution = self.distances.min_distances_1[np.nonzero(self.distances.min_distances_1)] self.dist2_distribution = self.distances.min_distances_2[np.nonzero(self.distances.min_distances_2)] if self.dim_im == 3: self.dist1_distribution = [] self.dist2_distribution = [] for d in self.distances: if np.nonzero(d.min_distances_1)[0].size: # Exist non zero values self.dist1_distribution.append(d.min_distances_1[np.nonzero(d.min_distances_1)]) else: # all values are zero self.dist1_distribution.append(0) if np.nonzero(d.min_distances_2)[0].size: # Exist non zero values self.dist2_distribution.append(d.min_distances_2[np.nonzero(d.min_distances_2)]) else: # all values are zero self.dist2_distribution.append(0) self.res = 'Hausdorff\'s distance - First relative Hausdorff\'s distance median - Second relative Hausdorff\'s distance median(all in mm)\n' for i, d in enumerate(self.distances): med1 = np.median(self.dist1_distribution[i]) med2 = np.median(self.dist2_distribution[i]) if self.im2 is None: self.res += 'Slice ' + str(i) + ' - slice ' + str(i + 1) + ': ' + str(d.H * self.dim_pix) + ' - ' + str(med1 * self.dim_pix) + ' - ' + str(med2 * self.dim_pix) + ' \n' else: self.res += 'Slice ' + str(i) + ': ' + str(d.H * self.dim_pix) + ' - ' + str(med1 * self.dim_pix) + ' - ' + str(med2 * self.dim_pix) + ' \n' sct.printv('-----------------------------------------------------------------------------\n' + self.res, self.param.verbose, 'normal') if self.param.verbose == 2: self.show_results() # ------------------------------------------------------------------------------------------------------------------ def compute_dist_2im_2d(self): nx1, ny1, nz1, nt1, px1, py1, pz1, pt1 = self.im1.dim nx2, ny2, nz2, nt2, px2, py2, pz2, pt2 = self.im2.dim assert np.isclose(px1, px2) and np.isclose(py1, py2) and np.isclose(px1, py1) self.dim_pix = py1 if self.param.thinning: dat1 = self.thinning1.thinned_image.data dat2 = self.thinning2.thinned_image.data else: dat1 = bin_data(self.im1.data) dat2 = bin_data(self.im2.data) self.distances = HausdorffDistance(dat1, dat2, self.param.verbose) self.res = 'Hausdorff\'s distance : ' + str(self.distances.H * self.dim_pix) + ' mm\n\n' \ 'First relative Hausdorff\'s distance : ' + str(self.distances.h1 * self.dim_pix) + ' mm\n' \ 'Second relative Hausdorff\'s distance : ' + str(self.distances.h2 * self.dim_pix) + ' mm' # ------------------------------------------------------------------------------------------------------------------ def compute_dist_1im_3d(self): nx1, ny1, nz1, nt1, px1, py1, pz1, pt1 = self.im1.dim self.dim_pix = py1 if self.param.thinning: dat1 = self.thinning1.thinned_image.data else: dat1 = bin_data(self.im1.data) self.distances = [] for i, dat_slice in enumerate(dat1[:-1]): self.distances.append(HausdorffDistance(bin_data(dat_slice), bin_data(dat1[i + 1]), self.param.verbose)) # ------------------------------------------------------------------------------------------------------------------ def compute_dist_2im_3d(self): nx1, ny1, nz1, nt1, px1, py1, pz1, pt1 = self.im1.dim nx2, ny2, nz2, nt2, px2, py2, pz2, pt2 = self.im2.dim # assert np.round(pz1, 5) == np.round(pz2, 5) and np.round(py1, 5) == np.round(py2, 5) assert nx1 == nx2 self.dim_pix = py1 if self.param.thinning: dat1 = self.thinning1.thinned_image.data dat2 = self.thinning2.thinned_image.data else: dat1 = bin_data(self.im1.data) dat2 = bin_data(self.im2.data) self.distances = [] for slice1, slice2 in zip(dat1, dat2): self.distances.append(HausdorffDistance(slice1, slice2, self.param.verbose)) # ------------------------------------------------------------------------------------------------------------------ def show_results(self): import seaborn as sns import matplotlib.pyplot as plt import pandas as pd plt.hold(True) sns.set(style="whitegrid", palette="pastel", color_codes=True) plt.figure(figsize=(35, 20)) data_dist = {"distances": [], "image": [], "slice": []} if self.dim_im == 2: data_dist["distances"].append([dist * self.dim_pix for dist in self.dist1_distribution]) data_dist["image"].append(len(self.dist1_distribution) * [1]) data_dist["slice"].append(len(self.dist1_distribution) * [0]) data_dist["distances"].append([dist * self.dim_pix for dist in self.dist2_distribution]) data_dist["image"].append(len(self.dist2_distribution) * [2]) data_dist["slice"].append(len(self.dist2_distribution) * [0]) if self.dim_im == 3: for i in range(len(self.distances)): data_dist["distances"].append([dist * self.dim_pix for dist in self.dist1_distribution[i]]) data_dist["image"].append(len(self.dist1_distribution[i]) * [1]) data_dist["slice"].append(len(self.dist1_distribution[i]) * [i]) data_dist["distances"].append([dist * self.dim_pix for dist in self.dist2_distribution[i]]) data_dist["image"].append(len(self.dist2_distribution[i]) * [2]) data_dist["slice"].append(len(self.dist2_distribution[i]) * [i]) for k in data_dist.keys(): # flatten the lists in data_dist data_dist[k] = [item for sublist in data_dist[k] for item in sublist] data_dist = pd.DataFrame(data_dist) sns.violinplot(x="slice", y="distances", hue="image", data=data_dist, split=True, inner="point", cut=0) plt.savefig('violin_plot.png') # plt.show() # ---------------------------------------------------------------------------------------------------------------------- def bin_data(data): return np.asarray((data > 0).astype(int)) # ---------------------------------------------------------------------------------------------------------------------- def resample_image(fname, suffix='_resampled.nii.gz', binary=False, npx=0.3, npy=0.3, thr=0.0, interpolation='spline'): """ Resampling function: add a padding, resample, crop the padding :param fname: name of the image file to be resampled :param suffix: suffix added to the original fname after resampling :param binary: boolean, image is binary or not :param npx: new pixel size in the x direction :param npy: new pixel size in the y direction :param thr: if the image is binary, it will be thresholded at thr (default=0) after the resampling :param interpolation: type of interpolation used for the resampling :return: file name after resampling (or original fname if it was already in the correct resolution) """ im_in = Image(fname) orientation = im_in.orientation if orientation != 'RPI': fname = im_in.change_orientation(im_in, 'RPI', generate_path=True).save().absolutepath nx, ny, nz, nt, px, py, pz, pt = im_in.dim if np.round(px, 2) != np.round(npx, 2) or np.round(py, 2) != np.round(npy, 2): name_resample = sct.extract_fname(fname)[1] + suffix if binary: interpolation = 'nn' if nz == 1: # when data is 2d: we convert it to a 3d image in order to avoid conversion problem with 2d data # TODO: check if this above problem is still present (now that we are using nibabel instead of nipy) sct.run(['sct_image', '-i', ','.join([fname, fname]), '-concat', 'z', '-o', fname]) sct.run(['sct_resample', '-i', fname, '-mm', str(npx) + 'x' + str(npy) + 'x' + str(pz), '-o', name_resample, '-x', interpolation]) if nz == 1: # when input data was 2d: re-convert data 3d-->2d sct.run(['sct_image', '-i', name_resample, '-split', 'z']) im_split = Image(name_resample.split('.nii.gz')[0] + '_Z0000.nii.gz') im_split.save(name_resample) if binary: sct.run(['sct_maths', '-i', name_resample, '-bin', str(thr), '-o', name_resample]) if orientation != 'RPI': name_resample = Image(name_resample) \ .change_orientation(orientation, generate_path=True) \ .save() \ .absolutepath return name_resample else: if orientation != 'RPI': fname = sct.add_suffix(fname, "_RPI") im_in = msct_image.change_orientation(im_in, orientation).save(fname) sct.printv('Image resolution already ' + str(npx) + 'x' + str(npy) + 'xpz') return fname # ---------------------------------------------------------------------------------------------------------------------- def non_zero_coord(data): dim = len(data.shape) if dim == 3: X, Y, Z = (data > 0).nonzero() list_coordinates = [(X[i], Y[i], Z[i]) for i in range(0, len(X))] elif dim == 2: X, Y = (data > 0).nonzero() list_coordinates = [(X[i], Y[i]) for i in range(0, len(X))] return list_coordinates def get_parser(): # Initialize the parser parser = argparse.ArgumentParser( description='Compute the Hausdorff\'s distance between two binary images which can be thinned (ie skeletonized).' ' If only one image is inputted, it will be only thinned', add_help=None, formatter_class=SmartFormatter, prog=os.path.basename(__file__).strip(".py") ) mandatoryArguments = parser.add_argument_group("\nMANDATORY ARGUMENTS") mandatoryArguments.add_argument( "-i", required=True, help='First Image on which you want to find the skeleton Example: t2star_manual_gmseg.nii.gz', metavar=Metavar.file, ) optional = parser.add_argument_group("\nOPTIONAL ARGUMENTS") optional.add_argument( "-h", "--help", action="help", help="show this help message and exit") optional.add_argument( "-d", help='Second Image on which you want to find the skeleton Example: t2star_manual_gmseg.nii.gz', metavar=Metavar.file, required=False, default=None) optional.add_argument( "-thinning", type=int, help="Thinning : find the skeleton of the binary images using the Zhang-Suen algorithm (1984) and use it to " "compute the hausdorff's distance", required=False, default=1, choices=(0, 1)) optional.add_argument( "-resampling", type=float, help="pixel size in mm to resample to Example: 0.5", metavar=Metavar.float, required=False, default=0.1) optional.add_argument( "-o", help='Name of the output file Example: my_hausdorff_dist.txt', metavar=Metavar.str, required=False, default='hausdorff_distance.txt') optional.add_argument( "-v", type=int, help="Verbose. 0: nothing, 1: basic, 2: extended.", required=False, choices=(0, 1, 2), default = 1) return parser ######################################################################################################################## # ------------------------------------------------------ MAIN ------------------------------------------------------- # ######################################################################################################################## if __name__ == "__main__": sct.init_sct() param = Param() input_fname = None if param.debug: sct.printv('\n*** WARNING: DEBUG MODE ON ***\n') else: param_default = Param() parser = get_parser() arguments = parser.parse_args(args=None if sys.argv[1:] else ['--help']) input_fname = arguments.i input_second_fname = '' output_fname = 'hausdorff_distance.txt' resample_to = 0.1 if arguments.d is not None: input_second_fname = arguments.d if arguments.thinning is not None: param.thinning = bool(arguments.thinning) if arguments.resampling is not None: resample_to = arguments.resampling if arguments.o is not None: output_fname = arguments.o param.verbose = arguments.v sct.init_sct(log_level=param.verbose, update=True) # Update log level tmp_dir = sct.tmp_create() im1_name = "im1.nii.gz" sct.copy(input_fname, os.path.join(tmp_dir, im1_name)) if input_second_fname != '': im2_name = 'im2.nii.gz' sct.copy(input_second_fname, os.path.join(tmp_dir, im2_name)) else: im2_name = None curdir = os.getcwd() os.chdir(tmp_dir) # now = time.time() input_im1 = Image(resample_image(im1_name, binary=True, thr=0.5, npx=resample_to, npy=resample_to)) input_im1.absolutepath = os.path.basename(input_fname) if im2_name is not None: input_im2 = Image(resample_image(im2_name, binary=True, thr=0.5, npx=resample_to, npy=resample_to)) input_im2.absolutepath = os.path.basename(input_second_fname) else: input_im2 = None computation = ComputeDistances(input_im1, im2=input_im2, param=param) # TODO change back the orientatin of the thinned image if param.thinning: computation.thinning1.thinned_image.save( os.path.join(curdir, sct.add_suffix(os.path.basename(input_fname), '_thinned'))) if im2_name is not None: computation.thinning2.thinned_image.save( os.path.join(curdir, sct.add_suffix(os.path.basename(input_second_fname), '_thinned'))) os.chdir(curdir) res_fic = open(output_fname, 'w') res_fic.write(computation.res) res_fic.write('\n\nInput 1: ' + input_fname) res_fic.write('\nInput 2: ' + input_second_fname) res_fic.close() # sct.printv('Total time: ', time.time() - now)

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4, P5, P6, P7, P8, P9 = n = self.get_neighbours(x, y, image_thinned) if (2 <= sum(n) <= 6 and # Condition 1: 2<= N(P1) <= 6 P2 * P4 * P6 == 0 and # Condition 3 P4 * P6 * P8 == 0 and # Condition 4 self.transitions(n) == 1): # Condition 2: S(P1)=1 changing1.append((x, y)) for x, y in changing1: image_thinned[x][y] = 0 # Step 2 changing2 = [] # for x in range(1, rows - 1): # for y in range(1, columns - 1): for x, y in non_zero_coord(image_thinned): if x not in pass_list and y not in pass_list: # if image_thinned[x][y] == 1: # Condition 0 P2, P3, P4, P5, P6, P7, P8, P9 = n = self.get_neighbours(x, y, image_thinned) if (2 <= sum(n) <= 6 and # Condition 1 P2 * P4 * P8 == 0 and # Condition 3 P2 * P6 * P8 == 0 and # Condition 4 self.transitions(n) == 1): # Condition 2 changing2.append((x, y)) for x, y in changing2: image_thinned[x][y] = 0 # t = time.time() - now # sct.printv('t thinning: ', t) return image_thinned # ---------------------------------------------------------------------------------------------------------------------- # HAUSDORFF'S DISTANCE ------------------------------------------------------------------------------------------------- class HausdorffDistance: def __init__(self, data1, data2, v=1): sct.printv('Computing 2D Hausdorff\'s distance ... ', v, 'normal') self.data1 = bin_data(data1) self.data2 = bin_data(data2) self.min_distances_1 = self.relative_hausdorff_dist(self.data1, self.data2, v) self.min_distances_2 = self.relative_hausdorff_dist(self.data2, self.data1, v) # relatives hausdorff's distances in pixel self.h1 = np.max(self.min_distances_1) self.h2 = np.max(self.min_distances_2) self.H = max(self.h1, self.h2) # t = time.time() - now # sct.printv('Hausdorff dist time :', t) # ------------------------------------------------------------------------------------------------------------------ def relative_hausdorff_dist(self, dat1, dat2, v=1): h = np.zeros(dat1.shape) nz_coord_1 = non_zero_coord(dat1) nz_coord_2 = non_zero_coord(dat2) if len(nz_coord_1) != 0 and len(nz_coord_2) != 0 : for x1, y1 in nz_coord_1: # for x1 in range(dat1.shape[0]): # for y1 in range(dat1.shape[1]): # if dat1[x1, y1] == 1: d_p1_dat2 = [] p1 = np.asarray([x1, y1]) for x2, y2 in nz_coord_2: # for x2 in range(dat2.shape[0]): # for y2 in range(dat2.shape[1]): # if dat2[x2, y2] == 1: p2 = np.asarray([x2, y2]) d_p1_dat2.append(np.linalg.norm(p1 - p2)) # Euclidean distance between p1 and p2 h[x1, y1] = min(d_p1_dat2) else: sct.printv('Warning: an image is empty', v, 'warning') return h # ---------------------------------------------------------------------------------------------------------------------- # COMPUTE DISTANCES ---------------------------------------------------------------------------------------------------- class ComputeDistances: def __init__(self, im1, im2=None, param=None): self.im1 = im1 self.im2 = im2 self.dim_im = len(self.im1.data.shape) self.dim_pix = 0 self.distances = None self.res = '' self.param = param self.dist1_distribution = None self.dist2_distribution = None if self.dim_im == 3: self.orientation1 = self.im1.orientation if self.orientation1 != 'IRP': self.im1.change_orientation('IRP', generate_path=True) if self.im2 is not None: self.orientation2 = self.im2.orientation if self.orientation2 != 'IRP': self.im2.change_orientation('IRP', generate_path=True) if self.param.thinning: self.thinning1 = Thinning(self.im1, self.param.verbose) self.thinning1.thinned_image.save() if self.im2 is not None: self.thinning2 = Thinning(self.im2, self.param.verbose) self.thinning2.thinned_image.save() if self.dim_im == 2 and self.im2 is not None: self.compute_dist_2im_2d() if self.dim_im == 3: if self.im2 is None: self.compute_dist_1im_3d() else: self.compute_dist_2im_3d() if self.dim_im == 2 and self.distances is not None: self.dist1_distribution = self.distances.min_distances_1[np.nonzero(self.distances.min_distances_1)] self.dist2_distribution = self.distances.min_distances_2[np.nonzero(self.distances.min_distances_2)] if self.dim_im == 3: self.dist1_distribution = [] self.dist2_distribution = [] for d in self.distances: if np.nonzero(d.min_distances_1)[0].size: # Exist non zero values self.dist1_distribution.append(d.min_distances_1[np.nonzero(d.min_distances_1)]) else: # all values are zero self.dist1_distribution.append(0) if np.nonzero(d.min_distances_2)[0].size: # Exist non zero values self.dist2_distribution.append(d.min_distances_2[np.nonzero(d.min_distances_2)]) else: # all values are zero self.dist2_distribution.append(0) self.res = 'Hausdorff\'s distance - First relative Hausdorff\'s distance median - Second relative Hausdorff\'s distance median(all in mm)\n' for i, d in enumerate(self.distances): med1 = np.median(self.dist1_distribution[i]) med2 = np.median(self.dist2_distribution[i]) if self.im2 is None: self.res += 'Slice ' + str(i) + ' - slice ' + str(i + 1) + ': ' + str(d.H * self.dim_pix) + ' - ' + str(med1 * self.dim_pix) + ' - ' + str(med2 * self.dim_pix) + ' \n' else: self.res += 'Slice ' + str(i) + ': ' + str(d.H * self.dim_pix) + ' - ' + str(med1 * self.dim_pix) + ' - ' + str(med2 * self.dim_pix) + ' \n' sct.printv('-----------------------------------------------------------------------------\n' + self.res, self.param.verbose, 'normal') if self.param.verbose == 2: self.show_results() def compute_dist_2im_2d(self): nx1, ny1, nz1, nt1, px1, py1, pz1, pt1 = self.im1.dim nx2, ny2, nz2, nt2, px2, py2, pz2, pt2 = self.im2.dim assert np.isclose(px1, px2) and np.isclose(py1, py2) and np.isclose(px1, py1) self.dim_pix = py1 if self.param.thinning: dat1 = self.thinning1.thinned_image.data dat2 = self.thinning2.thinned_image.data else: dat1 = bin_data(self.im1.data) dat2 = bin_data(self.im2.data) self.distances = HausdorffDistance(dat1, dat2, self.param.verbose) self.res = 'Hausdorff\'s distance : ' + str(self.distances.H * self.dim_pix) + ' mm\n\n' \ 'First relative Hausdorff\'s distance : ' + str(self.distances.h1 * self.dim_pix) + ' mm\n' \ 'Second relative Hausdorff\'s distance : ' + str(self.distances.h2 * self.dim_pix) + ' mm' # ------------------------------------------------------------------------------------------------------------------ def compute_dist_1im_3d(self): nx1, ny1, nz1, nt1, px1, py1, pz1, pt1 = self.im1.dim self.dim_pix = py1 if self.param.thinning: dat1 = self.thinning1.thinned_image.data else: dat1 = bin_data(self.im1.data) self.distances = [] for i, dat_slice in enumerate(dat1[:-1]): self.distances.append(HausdorffDistance(bin_data(dat_slice), bin_data(dat1[i + 1]), self.param.verbose)) # ------------------------------------------------------------------------------------------------------------------ def compute_dist_2im_3d(self): nx1, ny1, nz1, nt1, px1, py1, pz1, pt1 = self.im1.dim nx2, ny2, nz2, nt2, px2, py2, pz2, pt2 = self.im2.dim # assert np.round(pz1, 5) == np.round(pz2, 5) and np.round(py1, 5) == np.round(py2, 5) assert nx1 == nx2 self.dim_pix = py1 if self.param.thinning: dat1 = self.thinning1.thinned_image.data dat2 = self.thinning2.thinned_image.data else: dat1 = bin_data(self.im1.data) dat2 = bin_data(self.im2.data) self.distances = [] for slice1, slice2 in zip(dat1, dat2): self.distances.append(HausdorffDistance(slice1, slice2, self.param.verbose)) # ------------------------------------------------------------------------------------------------------------------ def show_results(self): import seaborn as sns import matplotlib.pyplot as plt import pandas as pd plt.hold(True) sns.set(style="whitegrid", palette="pastel", color_codes=True) plt.figure(figsize=(35, 20)) data_dist = {"distances": [], "image": [], "slice": []} if self.dim_im == 2: data_dist["distances"].append([dist * self.dim_pix for dist in self.dist1_distribution]) data_dist["image"].append(len(self.dist1_distribution) * [1]) data_dist["slice"].append(len(self.dist1_distribution) * [0]) data_dist["distances"].append([dist * self.dim_pix for dist in self.dist2_distribution]) data_dist["image"].append(len(self.dist2_distribution) * [2]) data_dist["slice"].append(len(self.dist2_distribution) * [0]) if self.dim_im == 3: for i in range(len(self.distances)): data_dist["distances"].append([dist * self.dim_pix for dist in self.dist1_distribution[i]]) data_dist["image"].append(len(self.dist1_distribution[i]) * [1]) data_dist["slice"].append(len(self.dist1_distribution[i]) * [i]) data_dist["distances"].append([dist * self.dim_pix for dist in self.dist2_distribution[i]]) data_dist["image"].append(len(self.dist2_distribution[i]) * [2]) data_dist["slice"].append(len(self.dist2_distribution[i]) * [i]) for k in data_dist.keys(): # flatten the lists in data_dist data_dist[k] = [item for sublist in data_dist[k] for item in sublist] data_dist = pd.DataFrame(data_dist) sns.violinplot(x="slice", y="distances", hue="image", data=data_dist, split=True, inner="point", cut=0) plt.savefig('violin_plot.png') # plt.show() # ---------------------------------------------------------------------------------------------------------------------- def bin_data(data): return np.asarray((data > 0).astype(int)) # ---------------------------------------------------------------------------------------------------------------------- def resample_image(fname, suffix='_resampled.nii.gz', binary=False, npx=0.3, npy=0.3, thr=0.0, interpolation='spline'): im_in = Image(fname) orientation = im_in.orientation if orientation != 'RPI': fname = im_in.change_orientation(im_in, 'RPI', generate_path=True).save().absolutepath nx, ny, nz, nt, px, py, pz, pt = im_in.dim if np.round(px, 2) != np.round(npx, 2) or np.round(py, 2) != np.round(npy, 2): name_resample = sct.extract_fname(fname)[1] + suffix if binary: interpolation = 'nn' if nz == 1: # when data is 2d: we convert it to a 3d image in order to avoid conversion problem with 2d data # TODO: check if this above problem is still present (now that we are using nibabel instead of nipy) sct.run(['sct_image', '-i', ','.join([fname, fname]), '-concat', 'z', '-o', fname]) sct.run(['sct_resample', '-i', fname, '-mm', str(npx) + 'x' + str(npy) + 'x' + str(pz), '-o', name_resample, '-x', interpolation]) if nz == 1: # when input data was 2d: re-convert data 3d-->2d sct.run(['sct_image', '-i', name_resample, '-split', 'z']) im_split = Image(name_resample.split('.nii.gz')[0] + '_Z0000.nii.gz') im_split.save(name_resample) if binary: sct.run(['sct_maths', '-i', name_resample, '-bin', str(thr), '-o', name_resample]) if orientation != 'RPI': name_resample = Image(name_resample) \ .change_orientation(orientation, generate_path=True) \ .save() \ .absolutepath return name_resample else: if orientation != 'RPI': fname = sct.add_suffix(fname, "_RPI") im_in = msct_image.change_orientation(im_in, orientation).save(fname) sct.printv('Image resolution already ' + str(npx) + 'x' + str(npy) + 'xpz') return fname # ---------------------------------------------------------------------------------------------------------------------- def non_zero_coord(data): dim = len(data.shape) if dim == 3: X, Y, Z = (data > 0).nonzero() list_coordinates = [(X[i], Y[i], Z[i]) for i in range(0, len(X))] elif dim == 2: X, Y = (data > 0).nonzero() list_coordinates = [(X[i], Y[i]) for i in range(0, len(X))] return list_coordinates def get_parser(): # Initialize the parser parser = argparse.ArgumentParser( description='Compute the Hausdorff\'s distance between two binary images which can be thinned (ie skeletonized).' ' If only one image is inputted, it will be only thinned', add_help=None, formatter_class=SmartFormatter, prog=os.path.basename(__file__).strip(".py") ) mandatoryArguments = parser.add_argument_group("\nMANDATORY ARGUMENTS") mandatoryArguments.add_argument( "-i", required=True, help='First Image on which you want to find the skeleton Example: t2star_manual_gmseg.nii.gz', metavar=Metavar.file, ) optional = parser.add_argument_group("\nOPTIONAL ARGUMENTS") optional.add_argument( "-h", "--help", action="help", help="show this help message and exit") optional.add_argument( "-d", help='Second Image on which you want to find the skeleton Example: t2star_manual_gmseg.nii.gz', metavar=Metavar.file, required=False, default=None) optional.add_argument( "-thinning", type=int, help="Thinning : find the skeleton of the binary images using the Zhang-Suen algorithm (1984) and use it to " "compute the hausdorff's distance", required=False, default=1, choices=(0, 1)) optional.add_argument( "-resampling", type=float, help="pixel size in mm to resample to Example: 0.5", metavar=Metavar.float, required=False, default=0.1) optional.add_argument( "-o", help='Name of the output file Example: my_hausdorff_dist.txt', metavar=Metavar.str, required=False, default='hausdorff_distance.txt') optional.add_argument( "-v", type=int, help="Verbose. 0: nothing, 1: basic, 2: extended.", required=False, choices=(0, 1, 2), default = 1) return parser ######################################################################################################################## # ------------------------------------------------------ MAIN ------------------------------------------------------- # ######################################################################################################################## if __name__ == "__main__": sct.init_sct() param = Param() input_fname = None if param.debug: sct.printv('\n*** WARNING: DEBUG MODE ON ***\n') else: param_default = Param() parser = get_parser() arguments = parser.parse_args(args=None if sys.argv[1:] else ['--help']) input_fname = arguments.i input_second_fname = '' output_fname = 'hausdorff_distance.txt' resample_to = 0.1 if arguments.d is not None: input_second_fname = arguments.d if arguments.thinning is not None: param.thinning = bool(arguments.thinning) if arguments.resampling is not None: resample_to = arguments.resampling if arguments.o is not None: output_fname = arguments.o param.verbose = arguments.v sct.init_sct(log_level=param.verbose, update=True) # Update log level tmp_dir = sct.tmp_create() im1_name = "im1.nii.gz" sct.copy(input_fname, os.path.join(tmp_dir, im1_name)) if input_second_fname != '': im2_name = 'im2.nii.gz' sct.copy(input_second_fname, os.path.join(tmp_dir, im2_name)) else: im2_name = None curdir = os.getcwd() os.chdir(tmp_dir) # now = time.time() input_im1 = Image(resample_image(im1_name, binary=True, thr=0.5, npx=resample_to, npy=resample_to)) input_im1.absolutepath = os.path.basename(input_fname) if im2_name is not None: input_im2 = Image(resample_image(im2_name, binary=True, thr=0.5, npx=resample_to, npy=resample_to)) input_im2.absolutepath = os.path.basename(input_second_fname) else: input_im2 = None computation = ComputeDistances(input_im1, im2=input_im2, param=param) # TODO change back the orientatin of the thinned image if param.thinning: computation.thinning1.thinned_image.save( os.path.join(curdir, sct.add_suffix(os.path.basename(input_fname), '_thinned'))) if im2_name is not None: computation.thinning2.thinned_image.save( os.path.join(curdir, sct.add_suffix(os.path.basename(input_second_fname), '_thinned'))) os.chdir(curdir) res_fic = open(output_fname, 'w') res_fic.write(computation.res) res_fic.write('\n\nInput 1: ' + input_fname) res_fic.write('\nInput 2: ' + input_second_fname) res_fic.close() # sct.printv('Total time: ', time.time() - now)

true

f7fc04972085df58b6a79a56d269a65b319308c8

1,962

py

Python

pypy/translator/backendopt/finalizer.py

benoitc/pypy

a3e1b12d1d01dc29056b7badc051ffc034297658

[ "MIT" ]

1

2020-01-21T11:10:51.000Z

pypy/translator/backendopt/finalizer.py

benoitc/pypy

a3e1b12d1d01dc29056b7badc051ffc034297658

[ "MIT" ]

null

pypy/translator/backendopt/finalizer.py

benoitc/pypy

a3e1b12d1d01dc29056b7badc051ffc034297658

[ "MIT" ]

null

from pypy.translator.backendopt import graphanalyze from pypy.rpython.lltypesystem import lltype class FinalizerError(Exception): """ __del__ marked as lightweight finalizer, but the analyzer did not agree """ class FinalizerAnalyzer(graphanalyze.BoolGraphAnalyzer): """ Analyzer that determines whether a finalizer is lightweight enough so it can be called without all the complicated logic in the garbage collector. The set of operations here is restrictive for a good reason - it's better to be safe. Specifically disallowed operations: * anything that escapes self * anything that can allocate """ ok_operations = ['ptr_nonzero', 'ptr_eq', 'ptr_ne', 'free', 'same_as', 'direct_ptradd', 'force_cast', 'track_alloc_stop', 'raw_free'] def analyze_light_finalizer(self, graph): result = self.analyze_direct_call(graph) if (result is self.top_result() and getattr(graph.func, '_must_be_light_finalizer_', False)): raise FinalizerError(FinalizerError.__doc__, graph) return result def analyze_simple_operation(self, op, graphinfo): if op.opname in self.ok_operations: return self.bottom_result() if (op.opname.startswith('int_') or op.opname.startswith('float_') or op.opname.startswith('cast_')): return self.bottom_result() if op.opname == 'setfield' or op.opname == 'bare_setfield': TP = op.args[2].concretetype if not isinstance(TP, lltype.Ptr) or TP.TO._gckind == 'raw': # primitive type return self.bottom_result() if op.opname == 'getfield': TP = op.result.concretetype if not isinstance(TP, lltype.Ptr) or TP.TO._gckind == 'raw': # primitive type return self.bottom_result() return self.top_result()

41.744681

74

0.639144

from pypy.translator.backendopt import graphanalyze from pypy.rpython.lltypesystem import lltype class FinalizerError(Exception): class FinalizerAnalyzer(graphanalyze.BoolGraphAnalyzer): ok_operations = ['ptr_nonzero', 'ptr_eq', 'ptr_ne', 'free', 'same_as', 'direct_ptradd', 'force_cast', 'track_alloc_stop', 'raw_free'] def analyze_light_finalizer(self, graph): result = self.analyze_direct_call(graph) if (result is self.top_result() and getattr(graph.func, '_must_be_light_finalizer_', False)): raise FinalizerError(FinalizerError.__doc__, graph) return result def analyze_simple_operation(self, op, graphinfo): if op.opname in self.ok_operations: return self.bottom_result() if (op.opname.startswith('int_') or op.opname.startswith('float_') or op.opname.startswith('cast_')): return self.bottom_result() if op.opname == 'setfield' or op.opname == 'bare_setfield': TP = op.args[2].concretetype if not isinstance(TP, lltype.Ptr) or TP.TO._gckind == 'raw': return self.bottom_result() if op.opname == 'getfield': TP = op.result.concretetype if not isinstance(TP, lltype.Ptr) or TP.TO._gckind == 'raw': return self.bottom_result() return self.top_result()

true

f7fc04a584104a02421e2372f31da54c51ab2304

5,333

py

Python

text_utils/segmenter/state.py

numb3r3/text_utils

5ec4d0f28117c30a65e516381b4f182aa6add663

[ "Apache-2.0" ]

4

2020-02-22T08:13:41.000Z

2021-06-18T06:52:30.000Z

text_utils/segmenter/state.py

numb3r3/text_utils

5ec4d0f28117c30a65e516381b4f182aa6add663

[ "Apache-2.0" ]

null

text_utils/segmenter/state.py

numb3r3/text_utils

5ec4d0f28117c30a65e516381b4f182aa6add663

[ "Apache-2.0" ]

null

# coding=utf-8 from .sign import SPLIT_SIGN, SOFTEN_SPLIT_SIGN from .context import Context from ..utils import is_chinese class ContextState(object): def execute(self, context): pass class CharCheckContextState(ContextState): def execute(self, context): global CONTEXT_STATE_MANAGER current_char = context.current_char if is_chinese(current_char): context.char_num += 1 context.token_num = int(context.char_num / 1.5) if (not context.is_pair_sign_opened) and ( current_char in context.pair_signs): context.state = CONTEXT_STATE_MANAGER["PAIR_SIGN_CONTEXT_STATE"] elif context.is_too_long() and current_char in SOFTEN_SPLIT_SIGN: context.current_sentence_builder.append(current_char) context.state = CONTEXT_STATE_MANAGER["SPLIT_CONTEXT_STATE"] elif current_char in context.split_signs: context.current_sentence_builder.append(current_char) context.state = CONTEXT_STATE_MANAGER["SPLIT_CONTEXT_STATE"] elif context.is_pair_sign_opened and current_char in context.back_pair_sign: context.state = CONTEXT_STATE_MANAGER[ "PAIR_SIGN_CLOSE_CONTEXT_STATE"] else: context.current_sentence_builder.append(current_char) context.state = CONTEXT_STATE_MANAGER["FINISH_CHECK_CONTEXT_STATE"] class FinishCheckContextState(ContextState): def execute(self, context): if context.current_index + 1 == len(context.text): if len(context.current_sentence_builder) != 0: sen = ''.join(context.current_sentence_builder) context.sentences.append(sen) # context.current_sentence_builder = [] context.clear_local_state() context.finish() return else: context.current_index += 1 context.state = CONTEXT_STATE_MANAGER["CHAR_CHECK_CONTEXT_STATE"] class PairSignContextState(ContextState): def execute(self, context): if context.current_index + 1 == len(context.text): context.current_sentence_builder.append(context.current_char) else: pair_sign_context = Context( context.text, CONTEXT_STATE_MANAGER["CHAR_CHECK_CONTEXT_STATE"], context.split_signs, context.pair_signs, context.soften_split_signs, context.token_limits) pair_sign_context.sentences.append(context.current_char) pair_sign_context.current_index = context.current_index + 1 pair_sign_context.pair_sign = context.current_char pair_sign_context.back_pair_sign = context.pair_signs[ pair_sign_context.pair_sign] pair_sign_context.is_pair_sign_opened = True pair_sign_context.execute() res = pair_sign_context.sentences if len(res) == 3 and \ res[2] in pair_sign_context.back_pair_sign and \ res[0] == pair_sign_context.pair_sign and \ ((res[1] and res[1][-1] not in SPLIT_SIGN) or (len(res[1]) < 20)): context.current_sentence_builder.append(''.join(res)) else: if len(context.current_sentence_builder) != 0: sen = ''.join(context.current_sentence_builder) context.sentences.append(sen) # context.current_sentence_builder = [] context.clear_local_state() context.sentences += res context.current_index = pair_sign_context.current_index context.state = CONTEXT_STATE_MANAGER["FINISH_CHECK_CONTEXT_STATE"] class PairSignCloseContextState(ContextState): def execute(self, context): if len(context.current_sentence_builder) != 0: sen = ''.join(context.current_sentence_builder) context.sentences.append(sen) # context.current_sentence_builder = [] context.clear_local_state() context.sentences.append(context.current_char) context.finish() return class SplitContextState(ContextState): def execute(self, context): context.has_split_sign = True while context.current_index < len(context.text) - 1: context.current_index += 1 tmp = context.text[context.current_index] if tmp in context.split_signs or tmp in context.soften_split_signs: context.current_sentence_builder.append(tmp) else: context.current_index -= 1 break if len(context.current_sentence_builder) != 0: sen = ''.join(context.current_sentence_builder) context.sentences.append(sen) # context.current_sentence_builder = [] context.clear_local_state() context.state = CONTEXT_STATE_MANAGER["FINISH_CHECK_CONTEXT_STATE"] CONTEXT_STATE_MANAGER = { "SPLIT_CONTEXT_STATE": SplitContextState(), "PAIR_SIGN_CONTEXT_STATE": PairSignContextState(), "CHAR_CHECK_CONTEXT_STATE": CharCheckContextState(), "FINISH_CHECK_CONTEXT_STATE": FinishCheckContextState(), "PAIR_SIGN_CLOSE_CONTEXT_STATE": PairSignCloseContextState() }

37.034722

86

0.651791

from .sign import SPLIT_SIGN, SOFTEN_SPLIT_SIGN from .context import Context from ..utils import is_chinese class ContextState(object): def execute(self, context): pass class CharCheckContextState(ContextState): def execute(self, context): global CONTEXT_STATE_MANAGER current_char = context.current_char if is_chinese(current_char): context.char_num += 1 context.token_num = int(context.char_num / 1.5) if (not context.is_pair_sign_opened) and ( current_char in context.pair_signs): context.state = CONTEXT_STATE_MANAGER["PAIR_SIGN_CONTEXT_STATE"] elif context.is_too_long() and current_char in SOFTEN_SPLIT_SIGN: context.current_sentence_builder.append(current_char) context.state = CONTEXT_STATE_MANAGER["SPLIT_CONTEXT_STATE"] elif current_char in context.split_signs: context.current_sentence_builder.append(current_char) context.state = CONTEXT_STATE_MANAGER["SPLIT_CONTEXT_STATE"] elif context.is_pair_sign_opened and current_char in context.back_pair_sign: context.state = CONTEXT_STATE_MANAGER[ "PAIR_SIGN_CLOSE_CONTEXT_STATE"] else: context.current_sentence_builder.append(current_char) context.state = CONTEXT_STATE_MANAGER["FINISH_CHECK_CONTEXT_STATE"] class FinishCheckContextState(ContextState): def execute(self, context): if context.current_index + 1 == len(context.text): if len(context.current_sentence_builder) != 0: sen = ''.join(context.current_sentence_builder) context.sentences.append(sen) context.clear_local_state() context.finish() return else: context.current_index += 1 context.state = CONTEXT_STATE_MANAGER["CHAR_CHECK_CONTEXT_STATE"] class PairSignContextState(ContextState): def execute(self, context): if context.current_index + 1 == len(context.text): context.current_sentence_builder.append(context.current_char) else: pair_sign_context = Context( context.text, CONTEXT_STATE_MANAGER["CHAR_CHECK_CONTEXT_STATE"], context.split_signs, context.pair_signs, context.soften_split_signs, context.token_limits) pair_sign_context.sentences.append(context.current_char) pair_sign_context.current_index = context.current_index + 1 pair_sign_context.pair_sign = context.current_char pair_sign_context.back_pair_sign = context.pair_signs[ pair_sign_context.pair_sign] pair_sign_context.is_pair_sign_opened = True pair_sign_context.execute() res = pair_sign_context.sentences if len(res) == 3 and \ res[2] in pair_sign_context.back_pair_sign and \ res[0] == pair_sign_context.pair_sign and \ ((res[1] and res[1][-1] not in SPLIT_SIGN) or (len(res[1]) < 20)): context.current_sentence_builder.append(''.join(res)) else: if len(context.current_sentence_builder) != 0: sen = ''.join(context.current_sentence_builder) context.sentences.append(sen) context.clear_local_state() context.sentences += res context.current_index = pair_sign_context.current_index context.state = CONTEXT_STATE_MANAGER["FINISH_CHECK_CONTEXT_STATE"] class PairSignCloseContextState(ContextState): def execute(self, context): if len(context.current_sentence_builder) != 0: sen = ''.join(context.current_sentence_builder) context.sentences.append(sen) context.clear_local_state() context.sentences.append(context.current_char) context.finish() return class SplitContextState(ContextState): def execute(self, context): context.has_split_sign = True while context.current_index < len(context.text) - 1: context.current_index += 1 tmp = context.text[context.current_index] if tmp in context.split_signs or tmp in context.soften_split_signs: context.current_sentence_builder.append(tmp) else: context.current_index -= 1 break if len(context.current_sentence_builder) != 0: sen = ''.join(context.current_sentence_builder) context.sentences.append(sen) context.clear_local_state() context.state = CONTEXT_STATE_MANAGER["FINISH_CHECK_CONTEXT_STATE"] CONTEXT_STATE_MANAGER = { "SPLIT_CONTEXT_STATE": SplitContextState(), "PAIR_SIGN_CONTEXT_STATE": PairSignContextState(), "CHAR_CHECK_CONTEXT_STATE": CharCheckContextState(), "FINISH_CHECK_CONTEXT_STATE": FinishCheckContextState(), "PAIR_SIGN_CLOSE_CONTEXT_STATE": PairSignCloseContextState() }

true

f7fc050606a7847918cef647e3efe9ceab83e11e

619

py

Python

site/brooks/migrations/__init__.py

ivco19/dashboard

f4f6ff6002295556007220660224c5d2ab4f0c06

[ "BSD-3-Clause" ]

4

2020-03-31T21:35:59.000Z

2020-10-13T00:44:53.000Z

site/brooks/migrations/__init__.py

ivco19/dashboard

f4f6ff6002295556007220660224c5d2ab4f0c06

[ "BSD-3-Clause" ]

8

2020-03-21T18:19:17.000Z

2020-04-29T18:04:54.000Z

site/brooks/migrations/__init__.py

ivco19/dashboard

f4f6ff6002295556007220660224c5d2ab4f0c06

[ "BSD-3-Clause" ]

1

2020-04-18T15:58:07.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # This file is part of Arcovid-19 Brooks. # Copyright (c) 2020, Juan B Cabral, Vanessa Daza, Diego García Lambas, # Marcelo Lares, Nadia Luczywo, Dante Paz, Rodrigo Quiroga, # Bruno Sanchez, Federico Stasyszyn. # License: BSD-3-Clause # Full Text: https://github.com/ivco19/brooks/blob/master/LICENSE # ============================================================================= # DOCS # ============================================================================= """Automatically created files for brooks migrations. """

32.578947

79

0.484653

true

f7fc05070cb07528f70cc3e3289796e7da95ca69

1,094

py

Python

FrequencyAnalysis.py

adeloucas/GSoC-Akkadian

65dc1ccc215ef353533c4d3f361910e489b89346

[ "MIT" ]

5

2018-04-25T00:00:24.000Z

2020-07-16T08:06:54.000Z

FrequencyAnalysis.py

adeloucas/GSoC-Akkadian

65dc1ccc215ef353533c4d3f361910e489b89346

[ "MIT" ]

44

2018-06-08T18:43:13.000Z

2019-07-29T18:42:12.000Z

FrequencyAnalysis.py

adeloucas/GSoC-Akkadian

65dc1ccc215ef353533c4d3f361910e489b89346

[ "MIT" ]

null

from collections import Counter from Importer.file_importer import FileImport from Importer.cdli_corpus import CDLICorpus from ATFConverter.tokenizer import Tokenizer from ATFConverter.atf_converter import ATFConverter fi = FileImport('texts/Akkadian.txt') fi.read_file() cc = CDLICorpus() cc.parse_file(fi.file_lines) tk = Tokenizer() atf = ATFConverter() stopwords = ['a-na', 'u3', 'sza', '[...]', 'i-na', '=', 'ARM', '01,', 'lang', 'akk', 'um-ma', 'la', 'u2-ul', 'mesz_', 'asz-szum', '0.1', 'broken', 'isz-tu', '_lu2_', 'ki-a-am', '1(disz)', 'ki-ma', 'x', 'sza-a-ti', 'the', '_lu2', '...]', 'lu-u2', 'sza#', 'a-na#', '_u4', 'beginning', 'of', '2(disz)', '[a-na', 'szum-ma', 'hi-a_', 'ana', 'a-di'] bag_of_words = [] for lines in cc.catalog['P249253']['transliteration']: for word in tk.word_tokenizer(lines): if word[0] not in stopwords: bag_of_words.append('-'.join(atf.process(word[0].split('-')))) frequency_analysis = Counter(bag_of_words).most_common(11) print(frequency_analysis)

37.724138

74

0.606033

from collections import Counter from Importer.file_importer import FileImport from Importer.cdli_corpus import CDLICorpus from ATFConverter.tokenizer import Tokenizer from ATFConverter.atf_converter import ATFConverter fi = FileImport('texts/Akkadian.txt') fi.read_file() cc = CDLICorpus() cc.parse_file(fi.file_lines) tk = Tokenizer() atf = ATFConverter() stopwords = ['a-na', 'u3', 'sza', '[...]', 'i-na', '=', 'ARM', '01,', 'lang', 'akk', 'um-ma', 'la', 'u2-ul', 'mesz_', 'asz-szum', '0.1', 'broken', 'isz-tu', '_lu2_', 'ki-a-am', '1(disz)', 'ki-ma', 'x', 'sza-a-ti', 'the', '_lu2', '...]', 'lu-u2', 'sza#', 'a-na#', '_u4', 'beginning', 'of', '2(disz)', '[a-na', 'szum-ma', 'hi-a_', 'ana', 'a-di'] bag_of_words = [] for lines in cc.catalog['P249253']['transliteration']: for word in tk.word_tokenizer(lines): if word[0] not in stopwords: bag_of_words.append('-'.join(atf.process(word[0].split('-')))) frequency_analysis = Counter(bag_of_words).most_common(11) print(frequency_analysis)

true

f7fc05141269060be2d12868ee0e12989775825b

2,075

py

Python

tests/test_articles.py

JECINTA534521/News-Highligts-project

00634f8eb0fc972d28f1ec911b83fbdea838ebd5

[ "Unlicense" ]

null

tests/test_articles.py

JECINTA534521/News-Highligts-project

00634f8eb0fc972d28f1ec911b83fbdea838ebd5

[ "Unlicense" ]

null

tests/test_articles.py

JECINTA534521/News-Highligts-project

00634f8eb0fc972d28f1ec911b83fbdea838ebd5

[ "Unlicense" ]

null

import unittest from app.models import Articles class ArticleTest(unittest.TestCase): ''' Test class to test behaviours of the Article class Args: unittest.TestCase : Test case class that helps create test cases ''' def setUp(self): ''' Set up method to run before each test case ''' self.new_article = Articles('BBC News','EU acts against Poland judiciary reforms', 'Unprecedented disciplinary measures are taken as the EU says the reforms threaten the rule of law.', 'https://ichef.bbci.co.uk/news/1024/cpsprodpb/F046/production/_98901516_2efffed4-d4a6-486a-8a78-112232b92faa.jpg','http://www.bbc.co.uk/news/world-europe-42420150', '2017-12-20T13:36:14Z') def test_instance(self): ''' Test case to check if self.new_article is an instance of Article ''' self.assertTrue( isinstance( self.new_article, Articles ) ) def test_init(self): ''' Test case to check if the Article class is initialised ''' self.assertEqual( self.new_article.author, 'BBC News') self.assertEqual( self.new_article.title, 'EU acts against Poland judiciary reforms') self.assertEqual( self.new_article.description, 'Unprecedented disciplinary measures are taken as the EU says the reforms threaten the rule of law.') self.assertEqual( self.new_article.urlToImage, 'https://ichef.bbci.co.uk/news/1024/cpsprodpb/F046/production/_98901516_2efffed4-d4a6-486a-8a78-112232b92faa.jpg') self.assertEqual( self.new_article.url, 'http://www.bbc.co.uk/news/world-europe-42420150') self.assertEqual( self.new_article.publishedAt, '2017-12-20T13:36:14Z') def test_publish_date_format(self): ''' Test case to check if UTC date format is converted to a display-friendly format ''' display_friendly_format = self.new_article.publish_date_format(self.new_article.publishedAt) self.assertEqual( display_friendly_format, '2017-12-20') if __name__ == '__main__': unittest.main(verbosity=2)

44.148936

382

0.701205

import unittest from app.models import Articles class ArticleTest(unittest.TestCase): ''' Test class to test behaviours of the Article class Args: unittest.TestCase : Test case class that helps create test cases ''' def setUp(self): ''' Set up method to run before each test case ''' self.new_article = Articles('BBC News','EU acts against Poland judiciary reforms', 'Unprecedented disciplinary measures are taken as the EU says the reforms threaten the rule of law.', 'https://ichef.bbci.co.uk/news/1024/cpsprodpb/F046/production/_98901516_2efffed4-d4a6-486a-8a78-112232b92faa.jpg','http://www.bbc.co.uk/news/world-europe-42420150', '2017-12-20T13:36:14Z') def test_instance(self): ''' Test case to check if self.new_article is an instance of Article ''' self.assertTrue( isinstance( self.new_article, Articles ) ) def test_init(self): ''' Test case to check if the Article class is initialised ''' self.assertEqual( self.new_article.author, 'BBC News') self.assertEqual( self.new_article.title, 'EU acts against Poland judiciary reforms') self.assertEqual( self.new_article.description, 'Unprecedented disciplinary measures are taken as the EU says the reforms threaten the rule of law.') self.assertEqual( self.new_article.urlToImage, 'https://ichef.bbci.co.uk/news/1024/cpsprodpb/F046/production/_98901516_2efffed4-d4a6-486a-8a78-112232b92faa.jpg') self.assertEqual( self.new_article.url, 'http://www.bbc.co.uk/news/world-europe-42420150') self.assertEqual( self.new_article.publishedAt, '2017-12-20T13:36:14Z') def test_publish_date_format(self): ''' Test case to check if UTC date format is converted to a display-friendly format ''' display_friendly_format = self.new_article.publish_date_format(self.new_article.publishedAt) self.assertEqual( display_friendly_format, '2017-12-20') if __name__ == '__main__': unittest.main(verbosity=2)

false

true