lparkourer10/starcoder-python5b · Datasets at Hugging Face

7943a81206a66e9aa6bdf1d7f3ebb7117688a94c

82,500

py

Python

rootfs/usr/lib/python3.2/difflib.py

kappaIO-Dev/kappaIO-sdk-armhf-crosscompile

66fc5fc21e6235f7a3be72a7ccac68e2224b7fb2

[ "MIT" ]

null

rootfs/usr/lib/python3.2/difflib.py

kappaIO-Dev/kappaIO-sdk-armhf-crosscompile

66fc5fc21e6235f7a3be72a7ccac68e2224b7fb2

[ "MIT" ]

null

rootfs/usr/lib/python3.2/difflib.py

kappaIO-Dev/kappaIO-sdk-armhf-crosscompile

66fc5fc21e6235f7a3be72a7ccac68e2224b7fb2

[ "MIT" ]

null

#! /usr/bin/python3.2 """ Module difflib -- helpers for computing deltas between objects. Function get_close_matches(word, possibilities, n=3, cutoff=0.6): Use SequenceMatcher to return list of the best "good enough" matches. Function context_diff(a, b): For two lists of strings, return a delta in context diff format. Function ndiff(a, b): Return a delta: the difference between `a` and `b` (lists of strings). Function restore(delta, which): Return one of the two sequences that generated an ndiff delta. Function unified_diff(a, b): For two lists of strings, return a delta in unified diff format. Class SequenceMatcher: A flexible class for comparing pairs of sequences of any type. Class Differ: For producing human-readable deltas from sequences of lines of text. Class HtmlDiff: For producing HTML side by side comparison with change highlights. """ __all__ = ['get_close_matches', 'ndiff', 'restore', 'SequenceMatcher', 'Differ','IS_CHARACTER_JUNK', 'IS_LINE_JUNK', 'context_diff', 'unified_diff', 'HtmlDiff', 'Match'] import warnings import heapq from collections import namedtuple as _namedtuple Match = _namedtuple('Match', 'a b size') def _calculate_ratio(matches, length): if length: return 2.0 * matches / length return 1.0 class SequenceMatcher: """ SequenceMatcher is a flexible class for comparing pairs of sequences of any type, so long as the sequence elements are hashable. The basic algorithm predates, and is a little fancier than, an algorithm published in the late 1980's by Ratcliff and Obershelp under the hyperbolic name "gestalt pattern matching". The basic idea is to find the longest contiguous matching subsequence that contains no "junk" elements (R-O doesn't address junk). The same idea is then applied recursively to the pieces of the sequences to the left and to the right of the matching subsequence. This does not yield minimal edit sequences, but does tend to yield matches that "look right" to people. SequenceMatcher tries to compute a "human-friendly diff" between two sequences. Unlike e.g. UNIX(tm) diff, the fundamental notion is the longest *contiguous* & junk-free matching subsequence. That's what catches peoples' eyes. The Windows(tm) windiff has another interesting notion, pairing up elements that appear uniquely in each sequence. That, and the method here, appear to yield more intuitive difference reports than does diff. This method appears to be the least vulnerable to synching up on blocks of "junk lines", though (like blank lines in ordinary text files, or maybe "<P>" lines in HTML files). That may be because this is the only method of the 3 that has a *concept* of "junk" <wink>. Example, comparing two strings, and considering blanks to be "junk": >>> s = SequenceMatcher(lambda x: x == " ", ... "private Thread currentThread;", ... "private volatile Thread currentThread;") >>> .ratio() returns a float in [0, 1], measuring the "similarity" of the sequences. As a rule of thumb, a .ratio() value over 0.6 means the sequences are close matches: >>> print(round(s.ratio(), 3)) 0.866 >>> If you're only interested in where the sequences match, .get_matching_blocks() is handy: >>> for block in s.get_matching_blocks(): ... print("a[%d] and b[%d] match for %d elements" % block) a[0] and b[0] match for 8 elements a[8] and b[17] match for 21 elements a[29] and b[38] match for 0 elements Note that the last tuple returned by .get_matching_blocks() is always a dummy, (len(a), len(b), 0), and this is the only case in which the last tuple element (number of elements matched) is 0. If you want to know how to change the first sequence into the second, use .get_opcodes(): >>> for opcode in s.get_opcodes(): ... print("%6s a[%d:%d] b[%d:%d]" % opcode) equal a[0:8] b[0:8] insert a[8:8] b[8:17] equal a[8:29] b[17:38] See the Differ class for a fancy human-friendly file differencer, which uses SequenceMatcher both to compare sequences of lines, and to compare sequences of characters within similar (near-matching) lines. See also function get_close_matches() in this module, which shows how simple code building on SequenceMatcher can be used to do useful work. Timing: Basic R-O is cubic time worst case and quadratic time expected case. SequenceMatcher is quadratic time for the worst case and has expected-case behavior dependent in a complicated way on how many elements the sequences have in common; best case time is linear. Methods: __init__(isjunk=None, a='', b='') Construct a SequenceMatcher. set_seqs(a, b) Set the two sequences to be compared. set_seq1(a) Set the first sequence to be compared. set_seq2(b) Set the second sequence to be compared. find_longest_match(alo, ahi, blo, bhi) Find longest matching block in a[alo:ahi] and b[blo:bhi]. get_matching_blocks() Return list of triples describing matching subsequences. get_opcodes() Return list of 5-tuples describing how to turn a into b. ratio() Return a measure of the sequences' similarity (float in [0,1]). quick_ratio() Return an upper bound on .ratio() relatively quickly. real_quick_ratio() Return an upper bound on ratio() very quickly. """ def __init__(self, isjunk=None, a='', b='', autojunk=True): """Construct a SequenceMatcher. Optional arg isjunk is None (the default), or a one-argument function that takes a sequence element and returns true iff the element is junk. None is equivalent to passing "lambda x: 0", i.e. no elements are considered to be junk. For example, pass lambda x: x in " \\t" if you're comparing lines as sequences of characters, and don't want to synch up on blanks or hard tabs. Optional arg a is the first of two sequences to be compared. By default, an empty string. The elements of a must be hashable. See also .set_seqs() and .set_seq1(). Optional arg b is the second of two sequences to be compared. By default, an empty string. The elements of b must be hashable. See also .set_seqs() and .set_seq2(). Optional arg autojunk should be set to False to disable the "automatic junk heuristic" that treats popular elements as junk (see module documentation for more information). """ # Members: # a # first sequence # b # second sequence; differences are computed as "what do # we need to do to 'a' to change it into 'b'?" # b2j # for x in b, b2j[x] is a list of the indices (into b) # at which x appears; junk and popular elements do not appear # fullbcount # for x in b, fullbcount[x] == the number of times x # appears in b; only materialized if really needed (used # only for computing quick_ratio()) # matching_blocks # a list of (i, j, k) triples, where a[i:i+k] == b[j:j+k]; # ascending & non-overlapping in i and in j; terminated by # a dummy (len(a), len(b), 0) sentinel # opcodes # a list of (tag, i1, i2, j1, j2) tuples, where tag is # one of # 'replace' a[i1:i2] should be replaced by b[j1:j2] # 'delete' a[i1:i2] should be deleted # 'insert' b[j1:j2] should be inserted # 'equal' a[i1:i2] == b[j1:j2] # isjunk # a user-supplied function taking a sequence element and # returning true iff the element is "junk" -- this has # subtle but helpful effects on the algorithm, which I'll # get around to writing up someday <0.9 wink>. # DON'T USE! Only __chain_b uses this. Use isbjunk. # bjunk # the items in b for which isjunk is True. # bpopular # nonjunk items in b treated as junk by the heuristic (if used). self.isjunk = isjunk self.a = self.b = None self.autojunk = autojunk self.set_seqs(a, b) def set_seqs(self, a, b): """Set the two sequences to be compared. >>> s = SequenceMatcher() >>> s.set_seqs("abcd", "bcde") >>> s.ratio() 0.75 """ self.set_seq1(a) self.set_seq2(b) def set_seq1(self, a): """Set the first sequence to be compared. The second sequence to be compared is not changed. >>> s = SequenceMatcher(None, "abcd", "bcde") >>> s.ratio() 0.75 >>> s.set_seq1("bcde") >>> s.ratio() 1.0 >>> SequenceMatcher computes and caches detailed information about the second sequence, so if you want to compare one sequence S against many sequences, use .set_seq2(S) once and call .set_seq1(x) repeatedly for each of the other sequences. See also set_seqs() and set_seq2(). """ if a is self.a: return self.a = a self.matching_blocks = self.opcodes = None def set_seq2(self, b): """Set the second sequence to be compared. The first sequence to be compared is not changed. >>> s = SequenceMatcher(None, "abcd", "bcde") >>> s.ratio() 0.75 >>> s.set_seq2("abcd") >>> s.ratio() 1.0 >>> SequenceMatcher computes and caches detailed information about the second sequence, so if you want to compare one sequence S against many sequences, use .set_seq2(S) once and call .set_seq1(x) repeatedly for each of the other sequences. See also set_seqs() and set_seq1(). """ if b is self.b: return self.b = b self.matching_blocks = self.opcodes = None self.fullbcount = None self.__chain_b() # For each element x in b, set b2j[x] to a list of the indices in # b where x appears; the indices are in increasing order; note that # the number of times x appears in b is len(b2j[x]) ... # when self.isjunk is defined, junk elements don't show up in this # map at all, which stops the central find_longest_match method # from starting any matching block at a junk element ... # also creates the fast isbjunk function ... # b2j also does not contain entries for "popular" elements, meaning # elements that account for more than 1 + 1% of the total elements, and # when the sequence is reasonably large (>= 200 elements); this can # be viewed as an adaptive notion of semi-junk, and yields an enormous # speedup when, e.g., comparing program files with hundreds of # instances of "return NULL;" ... # note that this is only called when b changes; so for cross-product # kinds of matches, it's best to call set_seq2 once, then set_seq1 # repeatedly def __chain_b(self): # Because isjunk is a user-defined (not C) function, and we test # for junk a LOT, it's important to minimize the number of calls. # Before the tricks described here, __chain_b was by far the most # time-consuming routine in the whole module! If anyone sees # Jim Roskind, thank him again for profile.py -- I never would # have guessed that. # The first trick is to build b2j ignoring the possibility # of junk. I.e., we don't call isjunk at all yet. Throwing # out the junk later is much cheaper than building b2j "right" # from the start. b = self.b self.b2j = b2j = {} for i, elt in enumerate(b): indices = b2j.setdefault(elt, []) indices.append(i) # Purge junk elements self.bjunk = junk = set() isjunk = self.isjunk if isjunk: for elt in b2j.keys(): if isjunk(elt): junk.add(elt) for elt in junk: # separate loop avoids separate list of keys del b2j[elt] # Purge popular elements that are not junk self.bpopular = popular = set() n = len(b) if self.autojunk and n >= 200: ntest = n // 100 + 1 for elt, idxs in b2j.items(): if len(idxs) > ntest: popular.add(elt) for elt in popular: # ditto; as fast for 1% deletion del b2j[elt] def isbjunk(self, item): "Deprecated; use 'item in SequenceMatcher().bjunk'." warnings.warn("'SequenceMatcher().isbjunk(item)' is deprecated;\n" "use 'item in SMinstance.bjunk' instead.", DeprecationWarning, 2) return item in self.bjunk def isbpopular(self, item): "Deprecated; use 'item in SequenceMatcher().bpopular'." warnings.warn("'SequenceMatcher().isbpopular(item)' is deprecated;\n" "use 'item in SMinstance.bpopular' instead.", DeprecationWarning, 2) return item in self.bpopular def find_longest_match(self, alo, ahi, blo, bhi): """Find longest matching block in a[alo:ahi] and b[blo:bhi]. If isjunk is not defined: Return (i,j,k) such that a[i:i+k] is equal to b[j:j+k], where alo <= i <= i+k <= ahi blo <= j <= j+k <= bhi and for all (i',j',k') meeting those conditions, k >= k' i <= i' and if i == i', j <= j' In other words, of all maximal matching blocks, return one that starts earliest in a, and of all those maximal matching blocks that start earliest in a, return the one that starts earliest in b. >>> s = SequenceMatcher(None, " abcd", "abcd abcd") >>> s.find_longest_match(0, 5, 0, 9) Match(a=0, b=4, size=5) If isjunk is defined, first the longest matching block is determined as above, but with the additional restriction that no junk element appears in the block. Then that block is extended as far as possible by matching (only) junk elements on both sides. So the resulting block never matches on junk except as identical junk happens to be adjacent to an "interesting" match. Here's the same example as before, but considering blanks to be junk. That prevents " abcd" from matching the " abcd" at the tail end of the second sequence directly. Instead only the "abcd" can match, and matches the leftmost "abcd" in the second sequence: >>> s = SequenceMatcher(lambda x: x==" ", " abcd", "abcd abcd") >>> s.find_longest_match(0, 5, 0, 9) Match(a=1, b=0, size=4) If no blocks match, return (alo, blo, 0). >>> s = SequenceMatcher(None, "ab", "c") >>> s.find_longest_match(0, 2, 0, 1) Match(a=0, b=0, size=0) """ # CAUTION: stripping common prefix or suffix would be incorrect. # E.g., # ab # acab # Longest matching block is "ab", but if common prefix is # stripped, it's "a" (tied with "b"). UNIX(tm) diff does so # strip, so ends up claiming that ab is changed to acab by # inserting "ca" in the middle. That's minimal but unintuitive: # "it's obvious" that someone inserted "ac" at the front. # Windiff ends up at the same place as diff, but by pairing up # the unique 'b's and then matching the first two 'a's. a, b, b2j, isbjunk = self.a, self.b, self.b2j, self.bjunk.__contains__ besti, bestj, bestsize = alo, blo, 0 # find longest junk-free match # during an iteration of the loop, j2len[j] = length of longest # junk-free match ending with a[i-1] and b[j] j2len = {} nothing = [] for i in range(alo, ahi): # look at all instances of a[i] in b; note that because # b2j has no junk keys, the loop is skipped if a[i] is junk j2lenget = j2len.get newj2len = {} for j in b2j.get(a[i], nothing): # a[i] matches b[j] if j < blo: continue if j >= bhi: break k = newj2len[j] = j2lenget(j-1, 0) + 1 if k > bestsize: besti, bestj, bestsize = i-k+1, j-k+1, k j2len = newj2len # Extend the best by non-junk elements on each end. In particular, # "popular" non-junk elements aren't in b2j, which greatly speeds # the inner loop above, but also means "the best" match so far # doesn't contain any junk *or* popular non-junk elements. while besti > alo and bestj > blo and \ not isbjunk(b[bestj-1]) and \ a[besti-1] == b[bestj-1]: besti, bestj, bestsize = besti-1, bestj-1, bestsize+1 while besti+bestsize < ahi and bestj+bestsize < bhi and \ not isbjunk(b[bestj+bestsize]) and \ a[besti+bestsize] == b[bestj+bestsize]: bestsize += 1 # Now that we have a wholly interesting match (albeit possibly # empty!), we may as well suck up the matching junk on each # side of it too. Can't think of a good reason not to, and it # saves post-processing the (possibly considerable) expense of # figuring out what to do with it. In the case of an empty # interesting match, this is clearly the right thing to do, # because no other kind of match is possible in the regions. while besti > alo and bestj > blo and \ isbjunk(b[bestj-1]) and \ a[besti-1] == b[bestj-1]: besti, bestj, bestsize = besti-1, bestj-1, bestsize+1 while besti+bestsize < ahi and bestj+bestsize < bhi and \ isbjunk(b[bestj+bestsize]) and \ a[besti+bestsize] == b[bestj+bestsize]: bestsize = bestsize + 1 return Match(besti, bestj, bestsize) def get_matching_blocks(self): """Return list of triples describing matching subsequences. Each triple is of the form (i, j, n), and means that a[i:i+n] == b[j:j+n]. The triples are monotonically increasing in i and in j. New in Python 2.5, it's also guaranteed that if (i, j, n) and (i', j', n') are adjacent triples in the list, and the second is not the last triple in the list, then i+n != i' or j+n != j'. IOW, adjacent triples never describe adjacent equal blocks. The last triple is a dummy, (len(a), len(b), 0), and is the only triple with n==0. >>> s = SequenceMatcher(None, "abxcd", "abcd") >>> list(s.get_matching_blocks()) [Match(a=0, b=0, size=2), Match(a=3, b=2, size=2), Match(a=5, b=4, size=0)] """ if self.matching_blocks is not None: return self.matching_blocks la, lb = len(self.a), len(self.b) # This is most naturally expressed as a recursive algorithm, but # at least one user bumped into extreme use cases that exceeded # the recursion limit on their box. So, now we maintain a list # ('queue`) of blocks we still need to look at, and append partial # results to `matching_blocks` in a loop; the matches are sorted # at the end. queue = [(0, la, 0, lb)] matching_blocks = [] while queue: alo, ahi, blo, bhi = queue.pop() i, j, k = x = self.find_longest_match(alo, ahi, blo, bhi) # a[alo:i] vs b[blo:j] unknown # a[i:i+k] same as b[j:j+k] # a[i+k:ahi] vs b[j+k:bhi] unknown if k: # if k is 0, there was no matching block matching_blocks.append(x) if alo < i and blo < j: queue.append((alo, i, blo, j)) if i+k < ahi and j+k < bhi: queue.append((i+k, ahi, j+k, bhi)) matching_blocks.sort() # It's possible that we have adjacent equal blocks in the # matching_blocks list now. Starting with 2.5, this code was added # to collapse them. i1 = j1 = k1 = 0 non_adjacent = [] for i2, j2, k2 in matching_blocks: # Is this block adjacent to i1, j1, k1? if i1 + k1 == i2 and j1 + k1 == j2: # Yes, so collapse them -- this just increases the length of # the first block by the length of the second, and the first # block so lengthened remains the block to compare against. k1 += k2 else: # Not adjacent. Remember the first block (k1==0 means it's # the dummy we started with), and make the second block the # new block to compare against. if k1: non_adjacent.append((i1, j1, k1)) i1, j1, k1 = i2, j2, k2 if k1: non_adjacent.append((i1, j1, k1)) non_adjacent.append( (la, lb, 0) ) self.matching_blocks = non_adjacent return map(Match._make, self.matching_blocks) def get_opcodes(self): """Return list of 5-tuples describing how to turn a into b. Each tuple is of the form (tag, i1, i2, j1, j2). The first tuple has i1 == j1 == 0, and remaining tuples have i1 == the i2 from the tuple preceding it, and likewise for j1 == the previous j2. The tags are strings, with these meanings: 'replace': a[i1:i2] should be replaced by b[j1:j2] 'delete': a[i1:i2] should be deleted. Note that j1==j2 in this case. 'insert': b[j1:j2] should be inserted at a[i1:i1]. Note that i1==i2 in this case. 'equal': a[i1:i2] == b[j1:j2] >>> a = "qabxcd" >>> b = "abycdf" >>> s = SequenceMatcher(None, a, b) >>> for tag, i1, i2, j1, j2 in s.get_opcodes(): ... print(("%7s a[%d:%d] (%s) b[%d:%d] (%s)" % ... (tag, i1, i2, a[i1:i2], j1, j2, b[j1:j2]))) delete a[0:1] (q) b[0:0] () equal a[1:3] (ab) b[0:2] (ab) replace a[3:4] (x) b[2:3] (y) equal a[4:6] (cd) b[3:5] (cd) insert a[6:6] () b[5:6] (f) """ if self.opcodes is not None: return self.opcodes i = j = 0 self.opcodes = answer = [] for ai, bj, size in self.get_matching_blocks(): # invariant: we've pumped out correct diffs to change # a[:i] into b[:j], and the next matching block is # a[ai:ai+size] == b[bj:bj+size]. So we need to pump # out a diff to change a[i:ai] into b[j:bj], pump out # the matching block, and move (i,j) beyond the match tag = '' if i < ai and j < bj: tag = 'replace' elif i < ai: tag = 'delete' elif j < bj: tag = 'insert' if tag: answer.append( (tag, i, ai, j, bj) ) i, j = ai+size, bj+size # the list of matching blocks is terminated by a # sentinel with size 0 if size: answer.append( ('equal', ai, i, bj, j) ) return answer def get_grouped_opcodes(self, n=3): """ Isolate change clusters by eliminating ranges with no changes. Return a generator of groups with upto n lines of context. Each group is in the same format as returned by get_opcodes(). >>> from pprint import pprint >>> a = list(map(str, range(1,40))) >>> b = a[:] >>> b[8:8] = ['i'] # Make an insertion >>> b[20] += 'x' # Make a replacement >>> b[23:28] = [] # Make a deletion >>> b[30] += 'y' # Make another replacement >>> pprint(list(SequenceMatcher(None,a,b).get_grouped_opcodes())) [[('equal', 5, 8, 5, 8), ('insert', 8, 8, 8, 9), ('equal', 8, 11, 9, 12)], [('equal', 16, 19, 17, 20), ('replace', 19, 20, 20, 21), ('equal', 20, 22, 21, 23), ('delete', 22, 27, 23, 23), ('equal', 27, 30, 23, 26)], [('equal', 31, 34, 27, 30), ('replace', 34, 35, 30, 31), ('equal', 35, 38, 31, 34)]] """ codes = self.get_opcodes() if not codes: codes = [("equal", 0, 1, 0, 1)] # Fixup leading and trailing groups if they show no changes. if codes[0][0] == 'equal': tag, i1, i2, j1, j2 = codes[0] codes[0] = tag, max(i1, i2-n), i2, max(j1, j2-n), j2 if codes[-1][0] == 'equal': tag, i1, i2, j1, j2 = codes[-1] codes[-1] = tag, i1, min(i2, i1+n), j1, min(j2, j1+n) nn = n + n group = [] for tag, i1, i2, j1, j2 in codes: # End the current group and start a new one whenever # there is a large range with no changes. if tag == 'equal' and i2-i1 > nn: group.append((tag, i1, min(i2, i1+n), j1, min(j2, j1+n))) yield group group = [] i1, j1 = max(i1, i2-n), max(j1, j2-n) group.append((tag, i1, i2, j1 ,j2)) if group and not (len(group)==1 and group[0][0] == 'equal'): yield group def ratio(self): """Return a measure of the sequences' similarity (float in [0,1]). Where T is the total number of elements in both sequences, and M is the number of matches, this is 2.0*M / T. Note that this is 1 if the sequences are identical, and 0 if they have nothing in common. .ratio() is expensive to compute if you haven't already computed .get_matching_blocks() or .get_opcodes(), in which case you may want to try .quick_ratio() or .real_quick_ratio() first to get an upper bound. >>> s = SequenceMatcher(None, "abcd", "bcde") >>> s.ratio() 0.75 >>> s.quick_ratio() 0.75 >>> s.real_quick_ratio() 1.0 """ matches = sum(triple[-1] for triple in self.get_matching_blocks()) return _calculate_ratio(matches, len(self.a) + len(self.b)) def quick_ratio(self): """Return an upper bound on ratio() relatively quickly. This isn't defined beyond that it is an upper bound on .ratio(), and is faster to compute. """ # viewing a and b as multisets, set matches to the cardinality # of their intersection; this counts the number of matches # without regard to order, so is clearly an upper bound if self.fullbcount is None: self.fullbcount = fullbcount = {} for elt in self.b: fullbcount[elt] = fullbcount.get(elt, 0) + 1 fullbcount = self.fullbcount # avail[x] is the number of times x appears in 'b' less the # number of times we've seen it in 'a' so far ... kinda avail = {} availhas, matches = avail.__contains__, 0 for elt in self.a: if availhas(elt): numb = avail[elt] else: numb = fullbcount.get(elt, 0) avail[elt] = numb - 1 if numb > 0: matches = matches + 1 return _calculate_ratio(matches, len(self.a) + len(self.b)) def real_quick_ratio(self): """Return an upper bound on ratio() very quickly. This isn't defined beyond that it is an upper bound on .ratio(), and is faster to compute than either .ratio() or .quick_ratio(). """ la, lb = len(self.a), len(self.b) # can't have more matches than the number of elements in the # shorter sequence return _calculate_ratio(min(la, lb), la + lb) def get_close_matches(word, possibilities, n=3, cutoff=0.6): """Use SequenceMatcher to return list of the best "good enough" matches. word is a sequence for which close matches are desired (typically a string). possibilities is a list of sequences against which to match word (typically a list of strings). Optional arg n (default 3) is the maximum number of close matches to return. n must be > 0. Optional arg cutoff (default 0.6) is a float in [0, 1]. Possibilities that don't score at least that similar to word are ignored. The best (no more than n) matches among the possibilities are returned in a list, sorted by similarity score, most similar first. >>> get_close_matches("appel", ["ape", "apple", "peach", "puppy"]) ['apple', 'ape'] >>> import keyword as _keyword >>> get_close_matches("wheel", _keyword.kwlist) ['while'] >>> get_close_matches("Apple", _keyword.kwlist) [] >>> get_close_matches("accept", _keyword.kwlist) ['except'] """ if not n > 0: raise ValueError("n must be > 0: %r" % (n,)) if not 0.0 <= cutoff <= 1.0: raise ValueError("cutoff must be in [0.0, 1.0]: %r" % (cutoff,)) result = [] s = SequenceMatcher() s.set_seq2(word) for x in possibilities: s.set_seq1(x) if s.real_quick_ratio() >= cutoff and \ s.quick_ratio() >= cutoff and \ s.ratio() >= cutoff: result.append((s.ratio(), x)) # Move the best scorers to head of list result = heapq.nlargest(n, result) # Strip scores for the best n matches return [x for score, x in result] def _count_leading(line, ch): """ Return number of `ch` characters at the start of `line`. Example: >>> _count_leading(' abc', ' ') 3 """ i, n = 0, len(line) while i < n and line[i] == ch: i += 1 return i class Differ: r""" Differ is a class for comparing sequences of lines of text, and producing human-readable differences or deltas. Differ uses SequenceMatcher both to compare sequences of lines, and to compare sequences of characters within similar (near-matching) lines. Each line of a Differ delta begins with a two-letter code: '- ' line unique to sequence 1 '+ ' line unique to sequence 2 ' ' line common to both sequences '? ' line not present in either input sequence Lines beginning with '? ' attempt to guide the eye to intraline differences, and were not present in either input sequence. These lines can be confusing if the sequences contain tab characters. Note that Differ makes no claim to produce a *minimal* diff. To the contrary, minimal diffs are often counter-intuitive, because they synch up anywhere possible, sometimes accidental matches 100 pages apart. Restricting synch points to contiguous matches preserves some notion of locality, at the occasional cost of producing a longer diff. Example: Comparing two texts. First we set up the texts, sequences of individual single-line strings ending with newlines (such sequences can also be obtained from the `readlines()` method of file-like objects): >>> text1 = ''' 1. Beautiful is better than ugly. ... 2. Explicit is better than implicit. ... 3. Simple is better than complex. ... 4. Complex is better than complicated. ... '''.splitlines(1) >>> len(text1) 4 >>> text1[0][-1] '\n' >>> text2 = ''' 1. Beautiful is better than ugly. ... 3. Simple is better than complex. ... 4. Complicated is better than complex. ... 5. Flat is better than nested. ... '''.splitlines(1) Next we instantiate a Differ object: >>> d = Differ() Note that when instantiating a Differ object we may pass functions to filter out line and character 'junk'. See Differ.__init__ for details. Finally, we compare the two: >>> result = list(d.compare(text1, text2)) 'result' is a list of strings, so let's pretty-print it: >>> from pprint import pprint as _pprint >>> _pprint(result) [' 1. Beautiful is better than ugly.\n', '- 2. Explicit is better than implicit.\n', '- 3. Simple is better than complex.\n', '+ 3. Simple is better than complex.\n', '? ++\n', '- 4. Complex is better than complicated.\n', '? ^ ---- ^\n', '+ 4. Complicated is better than complex.\n', '? ++++ ^ ^\n', '+ 5. Flat is better than nested.\n'] As a single multi-line string it looks like this: >>> print(''.join(result), end="") 1. Beautiful is better than ugly. - 2. Explicit is better than implicit. - 3. Simple is better than complex. + 3. Simple is better than complex. ? ++ - 4. Complex is better than complicated. ? ^ ---- ^ + 4. Complicated is better than complex. ? ++++ ^ ^ + 5. Flat is better than nested. Methods: __init__(linejunk=None, charjunk=None) Construct a text differencer, with optional filters. compare(a, b) Compare two sequences of lines; generate the resulting delta. """ def __init__(self, linejunk=None, charjunk=None): """ Construct a text differencer, with optional filters. The two optional keyword parameters are for filter functions: - `linejunk`: A function that should accept a single string argument, and return true iff the string is junk. The module-level function `IS_LINE_JUNK` may be used to filter out lines without visible characters, except for at most one splat ('#'). It is recommended to leave linejunk None; as of Python 2.3, the underlying SequenceMatcher class has grown an adaptive notion of "noise" lines that's better than any static definition the author has ever been able to craft. - `charjunk`: A function that should accept a string of length 1. The module-level function `IS_CHARACTER_JUNK` may be used to filter out whitespace characters (a blank or tab; **note**: bad idea to include newline in this!). Use of IS_CHARACTER_JUNK is recommended. """ self.linejunk = linejunk self.charjunk = charjunk def compare(self, a, b): r""" Compare two sequences of lines; generate the resulting delta. Each sequence must contain individual single-line strings ending with newlines. Such sequences can be obtained from the `readlines()` method of file-like objects. The delta generated also consists of newline- terminated strings, ready to be printed as-is via the writeline() method of a file-like object. Example: >>> print(''.join(Differ().compare('one\ntwo\nthree\n'.splitlines(1), ... 'ore\ntree\nemu\n'.splitlines(1))), ... end="") - one ? ^ + ore ? ^ - two - three ? - + tree + emu """ cruncher = SequenceMatcher(self.linejunk, a, b) for tag, alo, ahi, blo, bhi in cruncher.get_opcodes(): if tag == 'replace': g = self._fancy_replace(a, alo, ahi, b, blo, bhi) elif tag == 'delete': g = self._dump('-', a, alo, ahi) elif tag == 'insert': g = self._dump('+', b, blo, bhi) elif tag == 'equal': g = self._dump(' ', a, alo, ahi) else: raise ValueError('unknown tag %r' % (tag,)) for line in g: yield line def _dump(self, tag, x, lo, hi): """Generate comparison results for a same-tagged range.""" for i in range(lo, hi): yield '%s %s' % (tag, x[i]) def _plain_replace(self, a, alo, ahi, b, blo, bhi): assert alo < ahi and blo < bhi # dump the shorter block first -- reduces the burden on short-term # memory if the blocks are of very different sizes if bhi - blo < ahi - alo: first = self._dump('+', b, blo, bhi) second = self._dump('-', a, alo, ahi) else: first = self._dump('-', a, alo, ahi) second = self._dump('+', b, blo, bhi) for g in first, second: for line in g: yield line def _fancy_replace(self, a, alo, ahi, b, blo, bhi): r""" When replacing one block of lines with another, search the blocks for *similar* lines; the best-matching pair (if any) is used as a synch point, and intraline difference marking is done on the similar pair. Lots of work, but often worth it. Example: >>> d = Differ() >>> results = d._fancy_replace(['abcDefghiJkl\n'], 0, 1, ... ['abcdefGhijkl\n'], 0, 1) >>> print(''.join(results), end="") - abcDefghiJkl ? ^ ^ ^ + abcdefGhijkl ? ^ ^ ^ """ # don't synch up unless the lines have a similarity score of at # least cutoff; best_ratio tracks the best score seen so far best_ratio, cutoff = 0.74, 0.75 cruncher = SequenceMatcher(self.charjunk) eqi, eqj = None, None # 1st indices of equal lines (if any) # search for the pair that matches best without being identical # (identical lines must be junk lines, & we don't want to synch up # on junk -- unless we have to) for j in range(blo, bhi): bj = b[j] cruncher.set_seq2(bj) for i in range(alo, ahi): ai = a[i] if ai == bj: if eqi is None: eqi, eqj = i, j continue cruncher.set_seq1(ai) # computing similarity is expensive, so use the quick # upper bounds first -- have seen this speed up messy # compares by a factor of 3. # note that ratio() is only expensive to compute the first # time it's called on a sequence pair; the expensive part # of the computation is cached by cruncher if cruncher.real_quick_ratio() > best_ratio and \ cruncher.quick_ratio() > best_ratio and \ cruncher.ratio() > best_ratio: best_ratio, best_i, best_j = cruncher.ratio(), i, j if best_ratio < cutoff: # no non-identical "pretty close" pair if eqi is None: # no identical pair either -- treat it as a straight replace for line in self._plain_replace(a, alo, ahi, b, blo, bhi): yield line return # no close pair, but an identical pair -- synch up on that best_i, best_j, best_ratio = eqi, eqj, 1.0 else: # there's a close pair, so forget the identical pair (if any) eqi = None # a[best_i] very similar to b[best_j]; eqi is None iff they're not # identical # pump out diffs from before the synch point for line in self._fancy_helper(a, alo, best_i, b, blo, best_j): yield line # do intraline marking on the synch pair aelt, belt = a[best_i], b[best_j] if eqi is None: # pump out a '-', '?', '+', '?' quad for the synched lines atags = btags = "" cruncher.set_seqs(aelt, belt) for tag, ai1, ai2, bj1, bj2 in cruncher.get_opcodes(): la, lb = ai2 - ai1, bj2 - bj1 if tag == 'replace': atags += '^' * la btags += '^' * lb elif tag == 'delete': atags += '-' * la elif tag == 'insert': btags += '+' * lb elif tag == 'equal': atags += ' ' * la btags += ' ' * lb else: raise ValueError('unknown tag %r' % (tag,)) for line in self._qformat(aelt, belt, atags, btags): yield line else: # the synch pair is identical yield ' ' + aelt # pump out diffs from after the synch point for line in self._fancy_helper(a, best_i+1, ahi, b, best_j+1, bhi): yield line def _fancy_helper(self, a, alo, ahi, b, blo, bhi): g = [] if alo < ahi: if blo < bhi: g = self._fancy_replace(a, alo, ahi, b, blo, bhi) else: g = self._dump('-', a, alo, ahi) elif blo < bhi: g = self._dump('+', b, blo, bhi) for line in g: yield line def _qformat(self, aline, bline, atags, btags): r""" Format "?" output and deal with leading tabs. Example: >>> d = Differ() >>> results = d._qformat('\tabcDefghiJkl\n', '\tabcdefGhijkl\n', ... ' ^ ^ ^ ', ' ^ ^ ^ ') >>> for line in results: print(repr(line)) ... '- \tabcDefghiJkl\n' '? \t ^ ^ ^\n' '+ \tabcdefGhijkl\n' '? \t ^ ^ ^\n' """ # Can hurt, but will probably help most of the time. common = min(_count_leading(aline, "\t"), _count_leading(bline, "\t")) common = min(common, _count_leading(atags[:common], " ")) common = min(common, _count_leading(btags[:common], " ")) atags = atags[common:].rstrip() btags = btags[common:].rstrip() yield "- " + aline if atags: yield "? %s%s\n" % ("\t" * common, atags) yield "+ " + bline if btags: yield "? %s%s\n" % ("\t" * common, btags) # With respect to junk, an earlier version of ndiff simply refused to # *start* a match with a junk element. The result was cases like this: # before: private Thread currentThread; # after: private volatile Thread currentThread; # If you consider whitespace to be junk, the longest contiguous match # not starting with junk is "e Thread currentThread". So ndiff reported # that "e volatil" was inserted between the 't' and the 'e' in "private". # While an accurate view, to people that's absurd. The current version # looks for matching blocks that are entirely junk-free, then extends the # longest one of those as far as possible but only with matching junk. # So now "currentThread" is matched, then extended to suck up the # preceding blank; then "private" is matched, and extended to suck up the # following blank; then "Thread" is matched; and finally ndiff reports # that "volatile " was inserted before "Thread". The only quibble # remaining is that perhaps it was really the case that " volatile" # was inserted after "private". I can live with that <wink>. import re def IS_LINE_JUNK(line, pat=re.compile(r"\s*#?\s*$").match): r""" Return 1 for ignorable line: iff `line` is blank or contains a single '#'. Examples: >>> IS_LINE_JUNK('\n') True >>> IS_LINE_JUNK(' # \n') True >>> IS_LINE_JUNK('hello\n') False """ return pat(line) is not None def IS_CHARACTER_JUNK(ch, ws=" \t"): r""" Return 1 for ignorable character: iff `ch` is a space or tab. Examples: >>> IS_CHARACTER_JUNK(' ') True >>> IS_CHARACTER_JUNK('\t') True >>> IS_CHARACTER_JUNK('\n') False >>> IS_CHARACTER_JUNK('x') False """ return ch in ws ######################################################################## ### Unified Diff ######################################################################## def _format_range_unified(start, stop): 'Convert range to the "ed" format' # Per the diff spec at http://www.unix.org/single_unix_specification/ beginning = start + 1 # lines start numbering with one length = stop - start if length == 1: return '{}'.format(beginning) if not length: beginning -= 1 # empty ranges begin at line just before the range return '{},{}'.format(beginning, length) def unified_diff(a, b, fromfile='', tofile='', fromfiledate='', tofiledate='', n=3, lineterm='\n'): r""" Compare two sequences of lines; generate the delta as a unified diff. Unified diffs are a compact way of showing line changes and a few lines of context. The number of context lines is set by 'n' which defaults to three. By default, the diff control lines (those with ---, +++, or @@) are created with a trailing newline. This is helpful so that inputs created from file.readlines() result in diffs that are suitable for file.writelines() since both the inputs and outputs have trailing newlines. For inputs that do not have trailing newlines, set the lineterm argument to "" so that the output will be uniformly newline free. The unidiff format normally has a header for filenames and modification times. Any or all of these may be specified using strings for 'fromfile', 'tofile', 'fromfiledate', and 'tofiledate'. The modification times are normally expressed in the ISO 8601 format. Example: >>> for line in unified_diff('one two three four'.split(), ... 'zero one tree four'.split(), 'Original', 'Current', ... '2005-01-26 23:30:50', '2010-04-02 10:20:52', ... lineterm=''): ... print(line) # doctest: +NORMALIZE_WHITESPACE --- Original 2005-01-26 23:30:50 +++ Current 2010-04-02 10:20:52 @@ -1,4 +1,4 @@ +zero one -two -three +tree four """ started = False for group in SequenceMatcher(None,a,b).get_grouped_opcodes(n): if not started: started = True fromdate = '\t{}'.format(fromfiledate) if fromfiledate else '' todate = '\t{}'.format(tofiledate) if tofiledate else '' yield '--- {}{}{}'.format(fromfile, fromdate, lineterm) yield '+++ {}{}{}'.format(tofile, todate, lineterm) first, last = group[0], group[-1] file1_range = _format_range_unified(first[1], last[2]) file2_range = _format_range_unified(first[3], last[4]) yield '@@ -{} +{} @@{}'.format(file1_range, file2_range, lineterm) for tag, i1, i2, j1, j2 in group: if tag == 'equal': for line in a[i1:i2]: yield ' ' + line continue if tag in {'replace', 'delete'}: for line in a[i1:i2]: yield '-' + line if tag in {'replace', 'insert'}: for line in b[j1:j2]: yield '+' + line ######################################################################## ### Context Diff ######################################################################## def _format_range_context(start, stop): 'Convert range to the "ed" format' # Per the diff spec at http://www.unix.org/single_unix_specification/ beginning = start + 1 # lines start numbering with one length = stop - start if not length: beginning -= 1 # empty ranges begin at line just before the range if length <= 1: return '{}'.format(beginning) return '{},{}'.format(beginning, beginning + length - 1) # See http://www.unix.org/single_unix_specification/ def context_diff(a, b, fromfile='', tofile='', fromfiledate='', tofiledate='', n=3, lineterm='\n'): r""" Compare two sequences of lines; generate the delta as a context diff. Context diffs are a compact way of showing line changes and a few lines of context. The number of context lines is set by 'n' which defaults to three. By default, the diff control lines (those with *** or ---) are created with a trailing newline. This is helpful so that inputs created from file.readlines() result in diffs that are suitable for file.writelines() since both the inputs and outputs have trailing newlines. For inputs that do not have trailing newlines, set the lineterm argument to "" so that the output will be uniformly newline free. The context diff format normally has a header for filenames and modification times. Any or all of these may be specified using strings for 'fromfile', 'tofile', 'fromfiledate', and 'tofiledate'. The modification times are normally expressed in the ISO 8601 format. If not specified, the strings default to blanks. Example: >>> print(''.join(context_diff('one\ntwo\nthree\nfour\n'.splitlines(1), ... 'zero\none\ntree\nfour\n'.splitlines(1), 'Original', 'Current')), ... end="") *** Original --- Current *************** *** 1,4 **** one ! two ! three four --- 1,4 ---- + zero one ! tree four """ prefix = dict(insert='+ ', delete='- ', replace='! ', equal=' ') started = False for group in SequenceMatcher(None,a,b).get_grouped_opcodes(n): if not started: started = True fromdate = '\t{}'.format(fromfiledate) if fromfiledate else '' todate = '\t{}'.format(tofiledate) if tofiledate else '' yield '*** {}{}{}'.format(fromfile, fromdate, lineterm) yield '--- {}{}{}'.format(tofile, todate, lineterm) first, last = group[0], group[-1] yield '***************' + lineterm file1_range = _format_range_context(first[1], last[2]) yield '*** {} ****{}'.format(file1_range, lineterm) if any(tag in {'replace', 'delete'} for tag, _, _, _, _ in group): for tag, i1, i2, _, _ in group: if tag != 'insert': for line in a[i1:i2]: yield prefix[tag] + line file2_range = _format_range_context(first[3], last[4]) yield '--- {} ----{}'.format(file2_range, lineterm) if any(tag in {'replace', 'insert'} for tag, _, _, _, _ in group): for tag, _, _, j1, j2 in group: if tag != 'delete': for line in b[j1:j2]: yield prefix[tag] + line def ndiff(a, b, linejunk=None, charjunk=IS_CHARACTER_JUNK): r""" Compare `a` and `b` (lists of strings); return a `Differ`-style delta. Optional keyword parameters `linejunk` and `charjunk` are for filter functions (or None): - linejunk: A function that should accept a single string argument, and return true iff the string is junk. The default is None, and is recommended; as of Python 2.3, an adaptive notion of "noise" lines is used that does a good job on its own. - charjunk: A function that should accept a string of length 1. The default is module-level function IS_CHARACTER_JUNK, which filters out whitespace characters (a blank or tab; note: bad idea to include newline in this!). Tools/scripts/ndiff.py is a command-line front-end to this function. Example: >>> diff = ndiff('one\ntwo\nthree\n'.splitlines(1), ... 'ore\ntree\nemu\n'.splitlines(1)) >>> print(''.join(diff), end="") - one ? ^ + ore ? ^ - two - three ? - + tree + emu """ return Differ(linejunk, charjunk).compare(a, b) def _mdiff(fromlines, tolines, context=None, linejunk=None, charjunk=IS_CHARACTER_JUNK): r"""Returns generator yielding marked up from/to side by side differences. Arguments: fromlines -- list of text lines to compared to tolines tolines -- list of text lines to be compared to fromlines context -- number of context lines to display on each side of difference, if None, all from/to text lines will be generated. linejunk -- passed on to ndiff (see ndiff documentation) charjunk -- passed on to ndiff (see ndiff documentation) This function returns an interator which returns a tuple: (from line tuple, to line tuple, boolean flag) from/to line tuple -- (line num, line text) line num -- integer or None (to indicate a context separation) line text -- original line text with following markers inserted: '\0+' -- marks start of added text '\0-' -- marks start of deleted text '\0^' -- marks start of changed text '\1' -- marks end of added/deleted/changed text boolean flag -- None indicates context separation, True indicates either "from" or "to" line contains a change, otherwise False. This function/iterator was originally developed to generate side by side file difference for making HTML pages (see HtmlDiff class for example usage). Note, this function utilizes the ndiff function to generate the side by side difference markup. Optional ndiff arguments may be passed to this function and they in turn will be passed to ndiff. """ import re # regular expression for finding intraline change indices change_re = re.compile('(\++|\-+|\^+)') # create the difference iterator to generate the differences diff_lines_iterator = ndiff(fromlines,tolines,linejunk,charjunk) def _make_line(lines, format_key, side, num_lines=[0,0]): """Returns line of text with user's change markup and line formatting. lines -- list of lines from the ndiff generator to produce a line of text from. When producing the line of text to return, the lines used are removed from this list. format_key -- '+' return first line in list with "add" markup around the entire line. '-' return first line in list with "delete" markup around the entire line. '?' return first line in list with add/delete/change intraline markup (indices obtained from second line) None return first line in list with no markup side -- indice into the num_lines list (0=from,1=to) num_lines -- from/to current line number. This is NOT intended to be a passed parameter. It is present as a keyword argument to maintain memory of the current line numbers between calls of this function. Note, this function is purposefully not defined at the module scope so that data it needs from its parent function (within whose context it is defined) does not need to be of module scope. """ num_lines[side] += 1 # Handle case where no user markup is to be added, just return line of # text with user's line format to allow for usage of the line number. if format_key is None: return (num_lines[side],lines.pop(0)[2:]) # Handle case of intraline changes if format_key == '?': text, markers = lines.pop(0), lines.pop(0) # find intraline changes (store change type and indices in tuples) sub_info = [] def record_sub_info(match_object,sub_info=sub_info): sub_info.append([match_object.group(1)[0],match_object.span()]) return match_object.group(1) change_re.sub(record_sub_info,markers) # process each tuple inserting our special marks that won't be # noticed by an xml/html escaper. for key,(begin,end) in sub_info[::-1]: text = text[0:begin]+'\0'+key+text[begin:end]+'\1'+text[end:] text = text[2:] # Handle case of add/delete entire line else: text = lines.pop(0)[2:] # if line of text is just a newline, insert a space so there is # something for the user to highlight and see. if not text: text = ' ' # insert marks that won't be noticed by an xml/html escaper. text = '\0' + format_key + text + '\1' # Return line of text, first allow user's line formatter to do its # thing (such as adding the line number) then replace the special # marks with what the user's change markup. return (num_lines[side],text) def _line_iterator(): """Yields from/to lines of text with a change indication. This function is an iterator. It itself pulls lines from a differencing iterator, processes them and yields them. When it can it yields both a "from" and a "to" line, otherwise it will yield one or the other. In addition to yielding the lines of from/to text, a boolean flag is yielded to indicate if the text line(s) have differences in them. Note, this function is purposefully not defined at the module scope so that data it needs from its parent function (within whose context it is defined) does not need to be of module scope. """ lines = [] num_blanks_pending, num_blanks_to_yield = 0, 0 while True: # Load up next 4 lines so we can look ahead, create strings which # are a concatenation of the first character of each of the 4 lines # so we can do some very readable comparisons. while len(lines) < 4: try: lines.append(next(diff_lines_iterator)) except StopIteration: lines.append('X') s = ''.join([line[0] for line in lines]) if s.startswith('X'): # When no more lines, pump out any remaining blank lines so the # corresponding add/delete lines get a matching blank line so # all line pairs get yielded at the next level. num_blanks_to_yield = num_blanks_pending elif s.startswith('-?+?'): # simple intraline change yield _make_line(lines,'?',0), _make_line(lines,'?',1), True continue elif s.startswith('--++'): # in delete block, add block coming: we do NOT want to get # caught up on blank lines yet, just process the delete line num_blanks_pending -= 1 yield _make_line(lines,'-',0), None, True continue elif s.startswith(('--?+', '--+', '- ')): # in delete block and see a intraline change or unchanged line # coming: yield the delete line and then blanks from_line,to_line = _make_line(lines,'-',0), None num_blanks_to_yield,num_blanks_pending = num_blanks_pending-1,0 elif s.startswith('-+?'): # intraline change yield _make_line(lines,None,0), _make_line(lines,'?',1), True continue elif s.startswith('-?+'): # intraline change yield _make_line(lines,'?',0), _make_line(lines,None,1), True continue elif s.startswith('-'): # delete FROM line num_blanks_pending -= 1 yield _make_line(lines,'-',0), None, True continue elif s.startswith('+--'): # in add block, delete block coming: we do NOT want to get # caught up on blank lines yet, just process the add line num_blanks_pending += 1 yield None, _make_line(lines,'+',1), True continue elif s.startswith(('+ ', '+-')): # will be leaving an add block: yield blanks then add line from_line, to_line = None, _make_line(lines,'+',1) num_blanks_to_yield,num_blanks_pending = num_blanks_pending+1,0 elif s.startswith('+'): # inside an add block, yield the add line num_blanks_pending += 1 yield None, _make_line(lines,'+',1), True continue elif s.startswith(' '): # unchanged text, yield it to both sides yield _make_line(lines[:],None,0),_make_line(lines,None,1),False continue # Catch up on the blank lines so when we yield the next from/to # pair, they are lined up. while(num_blanks_to_yield < 0): num_blanks_to_yield += 1 yield None,('','\n'),True while(num_blanks_to_yield > 0): num_blanks_to_yield -= 1 yield ('','\n'),None,True if s.startswith('X'): raise StopIteration else: yield from_line,to_line,True def _line_pair_iterator(): """Yields from/to lines of text with a change indication. This function is an iterator. It itself pulls lines from the line iterator. Its difference from that iterator is that this function always yields a pair of from/to text lines (with the change indication). If necessary it will collect single from/to lines until it has a matching pair from/to pair to yield. Note, this function is purposefully not defined at the module scope so that data it needs from its parent function (within whose context it is defined) does not need to be of module scope. """ line_iterator = _line_iterator() fromlines,tolines=[],[] while True: # Collecting lines of text until we have a from/to pair while (len(fromlines)==0 or len(tolines)==0): from_line, to_line, found_diff = next(line_iterator) if from_line is not None: fromlines.append((from_line,found_diff)) if to_line is not None: tolines.append((to_line,found_diff)) # Once we have a pair, remove them from the collection and yield it from_line, fromDiff = fromlines.pop(0) to_line, to_diff = tolines.pop(0) yield (from_line,to_line,fromDiff or to_diff) # Handle case where user does not want context differencing, just yield # them up without doing anything else with them. line_pair_iterator = _line_pair_iterator() if context is None: while True: yield next(line_pair_iterator) # Handle case where user wants context differencing. We must do some # storage of lines until we know for sure that they are to be yielded. else: context += 1 lines_to_write = 0 while True: # Store lines up until we find a difference, note use of a # circular queue because we only need to keep around what # we need for context. index, contextLines = 0, [None]*(context) found_diff = False while(found_diff is False): from_line, to_line, found_diff = next(line_pair_iterator) i = index % context contextLines[i] = (from_line, to_line, found_diff) index += 1 # Yield lines that we have collected so far, but first yield # the user's separator. if index > context: yield None, None, None lines_to_write = context else: lines_to_write = index index = 0 while(lines_to_write): i = index % context index += 1 yield contextLines[i] lines_to_write -= 1 # Now yield the context lines after the change lines_to_write = context-1 while(lines_to_write): from_line, to_line, found_diff = next(line_pair_iterator) # If another change within the context, extend the context if found_diff: lines_to_write = context-1 else: lines_to_write -= 1 yield from_line, to_line, found_diff _file_template = """ <!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> <html> <head> <meta http-equiv="Content-Type" content="text/html; charset=ISO-8859-1" /> <title></title> <style type="text/css">%(styles)s </style> </head> <body> %(table)s%(legend)s </body> </html>""" _styles = """ table.diff {font-family:Courier; border:medium;} .diff_header {background-color:#e0e0e0} td.diff_header {text-align:right} .diff_next {background-color:#c0c0c0} .diff_add {background-color:#aaffaa} .diff_chg {background-color:#ffff77} .diff_sub {background-color:#ffaaaa}""" _table_template = """ <table class="diff" id="difflib_chg_%(prefix)s_top" cellspacing="0" cellpadding="0" rules="groups" > <colgroup></colgroup> <colgroup></colgroup> <colgroup></colgroup> <colgroup></colgroup> <colgroup></colgroup> <colgroup></colgroup> %(header_row)s <tbody> %(data_rows)s </tbody> </table>""" _legend = """ <table class="diff" summary="Legends"> <tr> <th colspan="2"> Legends </th> </tr> <tr> <td> <table border="" summary="Colors"> <tr><th> Colors </th> </tr> <tr><td class="diff_add"> Added </td></tr> <tr><td class="diff_chg">Changed</td> </tr> <tr><td class="diff_sub">Deleted</td> </tr> </table></td> <td> <table border="" summary="Links"> <tr><th colspan="2"> Links </th> </tr> <tr><td>(f)irst change</td> </tr> <tr><td>(n)ext change</td> </tr> <tr><td>(t)op</td> </tr> </table></td> </tr> </table>""" class HtmlDiff(object): """For producing HTML side by side comparison with change highlights. This class can be used to create an HTML table (or a complete HTML file containing the table) showing a side by side, line by line comparison of text with inter-line and intra-line change highlights. The table can be generated in either full or contextual difference mode. The following methods are provided for HTML generation: make_table -- generates HTML for a single side by side table make_file -- generates complete HTML file with a single side by side table See tools/scripts/diff.py for an example usage of this class. """ _file_template = _file_template _styles = _styles _table_template = _table_template _legend = _legend _default_prefix = 0 def __init__(self,tabsize=8,wrapcolumn=None,linejunk=None, charjunk=IS_CHARACTER_JUNK): """HtmlDiff instance initializer Arguments: tabsize -- tab stop spacing, defaults to 8. wrapcolumn -- column number where lines are broken and wrapped, defaults to None where lines are not wrapped. linejunk,charjunk -- keyword arguments passed into ndiff() (used to by HtmlDiff() to generate the side by side HTML differences). See ndiff() documentation for argument default values and descriptions. """ self._tabsize = tabsize self._wrapcolumn = wrapcolumn self._linejunk = linejunk self._charjunk = charjunk def make_file(self,fromlines,tolines,fromdesc='',todesc='',context=False, numlines=5): """Returns HTML file of side by side comparison with change highlights Arguments: fromlines -- list of "from" lines tolines -- list of "to" lines fromdesc -- "from" file column header string todesc -- "to" file column header string context -- set to True for contextual differences (defaults to False which shows full differences). numlines -- number of context lines. When context is set True, controls number of lines displayed before and after the change. When context is False, controls the number of lines to place the "next" link anchors before the next change (so click of "next" link jumps to just before the change). """ return self._file_template % dict( styles = self._styles, legend = self._legend, table = self.make_table(fromlines,tolines,fromdesc,todesc, context=context,numlines=numlines)) def _tab_newline_replace(self,fromlines,tolines): """Returns from/to line lists with tabs expanded and newlines removed. Instead of tab characters being replaced by the number of spaces needed to fill in to the next tab stop, this function will fill the space with tab characters. This is done so that the difference algorithms can identify changes in a file when tabs are replaced by spaces and vice versa. At the end of the HTML generation, the tab characters will be replaced with a nonbreakable space. """ def expand_tabs(line): # hide real spaces line = line.replace(' ','\0') # expand tabs into spaces line = line.expandtabs(self._tabsize) # replace spaces from expanded tabs back into tab characters # (we'll replace them with markup after we do differencing) line = line.replace(' ','\t') return line.replace('\0',' ').rstrip('\n') fromlines = [expand_tabs(line) for line in fromlines] tolines = [expand_tabs(line) for line in tolines] return fromlines,tolines def _split_line(self,data_list,line_num,text): """Builds list of text lines by splitting text lines at wrap point This function will determine if the input text line needs to be wrapped (split) into separate lines. If so, the first wrap point will be determined and the first line appended to the output text line list. This function is used recursively to handle the second part of the split line to further split it. """ # if blank line or context separator, just add it to the output list if not line_num: data_list.append((line_num,text)) return # if line text doesn't need wrapping, just add it to the output list size = len(text) max = self._wrapcolumn if (size <= max) or ((size -(text.count('\0')*3)) <= max): data_list.append((line_num,text)) return # scan text looking for the wrap point, keeping track if the wrap # point is inside markers i = 0 n = 0 mark = '' while n < max and i < size: if text[i] == '\0': i += 1 mark = text[i] i += 1 elif text[i] == '\1': i += 1 mark = '' else: i += 1 n += 1 # wrap point is inside text, break it up into separate lines line1 = text[:i] line2 = text[i:] # if wrap point is inside markers, place end marker at end of first # line and start marker at beginning of second line because each # line will have its own table tag markup around it. if mark: line1 = line1 + '\1' line2 = '\0' + mark + line2 # tack on first line onto the output list data_list.append((line_num,line1)) # use this routine again to wrap the remaining text self._split_line(data_list,'>',line2) def _line_wrapper(self,diffs): """Returns iterator that splits (wraps) mdiff text lines""" # pull from/to data and flags from mdiff iterator for fromdata,todata,flag in diffs: # check for context separators and pass them through if flag is None: yield fromdata,todata,flag continue (fromline,fromtext),(toline,totext) = fromdata,todata # for each from/to line split it at the wrap column to form # list of text lines. fromlist,tolist = [],[] self._split_line(fromlist,fromline,fromtext) self._split_line(tolist,toline,totext) # yield from/to line in pairs inserting blank lines as # necessary when one side has more wrapped lines while fromlist or tolist: if fromlist: fromdata = fromlist.pop(0) else: fromdata = ('',' ') if tolist: todata = tolist.pop(0) else: todata = ('',' ') yield fromdata,todata,flag def _collect_lines(self,diffs): """Collects mdiff output into separate lists Before storing the mdiff from/to data into a list, it is converted into a single line of text with HTML markup. """ fromlist,tolist,flaglist = [],[],[] # pull from/to data and flags from mdiff style iterator for fromdata,todata,flag in diffs: try: # store HTML markup of the lines into the lists fromlist.append(self._format_line(0,flag,*fromdata)) tolist.append(self._format_line(1,flag,*todata)) except TypeError: # exceptions occur for lines where context separators go fromlist.append(None) tolist.append(None) flaglist.append(flag) return fromlist,tolist,flaglist def _format_line(self,side,flag,linenum,text): """Returns HTML markup of "from" / "to" text lines side -- 0 or 1 indicating "from" or "to" text flag -- indicates if difference on line linenum -- line number (used for line number column) text -- line text to be marked up """ try: linenum = '%d' % linenum id = ' id="%s%s"' % (self._prefix[side],linenum) except TypeError: # handle blank lines where linenum is '>' or '' id = '' # replace those things that would get confused with HTML symbols text=text.replace("&","&").replace(">",">").replace("<","<") # make space non-breakable so they don't get compressed or line wrapped text = text.replace(' ',' ').rstrip() return '<td class="diff_header"%s>%s</td><td nowrap="nowrap">%s</td>' \ % (id,linenum,text) def _make_prefix(self): """Create unique anchor prefixes""" # Generate a unique anchor prefix so multiple tables # can exist on the same HTML page without conflicts. fromprefix = "from%d_" % HtmlDiff._default_prefix toprefix = "to%d_" % HtmlDiff._default_prefix HtmlDiff._default_prefix += 1 # store prefixes so line format method has access self._prefix = [fromprefix,toprefix] def _convert_flags(self,fromlist,tolist,flaglist,context,numlines): """Makes list of "next" links""" # all anchor names will be generated using the unique "to" prefix toprefix = self._prefix[1] # process change flags, generating middle column of next anchors/links next_id = ['']*len(flaglist) next_href = ['']*len(flaglist) num_chg, in_change = 0, False last = 0 for i,flag in enumerate(flaglist): if flag: if not in_change: in_change = True last = i # at the beginning of a change, drop an anchor a few lines # (the context lines) before the change for the previous # link i = max([0,i-numlines]) next_id[i] = ' id="difflib_chg_%s_%d"' % (toprefix,num_chg) # at the beginning of a change, drop a link to the next # change num_chg += 1 next_href[last] = '<a href="#difflib_chg_%s_%d">n</a>' % ( toprefix,num_chg) else: in_change = False # check for cases where there is no content to avoid exceptions if not flaglist: flaglist = [False] next_id = [''] next_href = [''] last = 0 if context: fromlist = ['<td></td><td> No Differences Found </td>'] tolist = fromlist else: fromlist = tolist = ['<td></td><td> Empty File </td>'] # if not a change on first line, drop a link if not flaglist[0]: next_href[0] = '<a href="#difflib_chg_%s_0">f</a>' % toprefix # redo the last link to link to the top next_href[last] = '<a href="#difflib_chg_%s_top">t</a>' % (toprefix) return fromlist,tolist,flaglist,next_href,next_id def make_table(self,fromlines,tolines,fromdesc='',todesc='',context=False, numlines=5): """Returns HTML table of side by side comparison with change highlights Arguments: fromlines -- list of "from" lines tolines -- list of "to" lines fromdesc -- "from" file column header string todesc -- "to" file column header string context -- set to True for contextual differences (defaults to False which shows full differences). numlines -- number of context lines. When context is set True, controls number of lines displayed before and after the change. When context is False, controls the number of lines to place the "next" link anchors before the next change (so click of "next" link jumps to just before the change). """ # make unique anchor prefixes so that multiple tables may exist # on the same page without conflict. self._make_prefix() # change tabs to spaces before it gets more difficult after we insert # markkup fromlines,tolines = self._tab_newline_replace(fromlines,tolines) # create diffs iterator which generates side by side from/to data if context: context_lines = numlines else: context_lines = None diffs = _mdiff(fromlines,tolines,context_lines,linejunk=self._linejunk, charjunk=self._charjunk) # set up iterator to wrap lines that exceed desired width if self._wrapcolumn: diffs = self._line_wrapper(diffs) # collect up from/to lines and flags into lists (also format the lines) fromlist,tolist,flaglist = self._collect_lines(diffs) # process change flags, generating middle column of next anchors/links fromlist,tolist,flaglist,next_href,next_id = self._convert_flags( fromlist,tolist,flaglist,context,numlines) s = [] fmt = ' <tr><td class="diff_next"%s>%s</td>%s' + \ '<td class="diff_next">%s</td>%s</tr>\n' for i in range(len(flaglist)): if flaglist[i] is None: # mdiff yields None on separator lines skip the bogus ones # generated for the first line if i > 0: s.append(' </tbody> \n <tbody>\n') else: s.append( fmt % (next_id[i],next_href[i],fromlist[i], next_href[i],tolist[i])) if fromdesc or todesc: header_row = '<thead><tr>%s%s%s%s</tr></thead>' % ( '<th class="diff_next"><br /></th>', '<th colspan="2" class="diff_header">%s</th>' % fromdesc, '<th class="diff_next"><br /></th>', '<th colspan="2" class="diff_header">%s</th>' % todesc) else: header_row = '' table = self._table_template % dict( data_rows=''.join(s), header_row=header_row, prefix=self._prefix[1]) return table.replace('\0+','<span class="diff_add">'). \ replace('\0-','<span class="diff_sub">'). \ replace('\0^','<span class="diff_chg">'). \ replace('\1','</span>'). \ replace('\t',' ') del re def restore(delta, which): r""" Generate one of the two sequences that generated a delta. Given a `delta` produced by `Differ.compare()` or `ndiff()`, extract lines originating from file 1 or 2 (parameter `which`), stripping off line prefixes. Examples: >>> diff = ndiff('one\ntwo\nthree\n'.splitlines(1), ... 'ore\ntree\nemu\n'.splitlines(1)) >>> diff = list(diff) >>> print(''.join(restore(diff, 1)), end="") one two three >>> print(''.join(restore(diff, 2)), end="") ore tree emu """ try: tag = {1: "- ", 2: "+ "}[int(which)] except KeyError: raise ValueError('unknown delta choice (must be 1 or 2): %r' % which) prefixes = (" ", tag) for line in delta: if line[:2] in prefixes: yield line[2:] def _test(): import doctest, difflib return doctest.testmod(difflib) if __name__ == "__main__": _test()

39.951574

83

0.573721

7943a95a823275ae303fce38cbc724040630f5a7

2,622

py

Python

virtual/lib/python3.6/site-packages/dns/rdataclass.py

amoskipz/pitch

477599a56958bc677e22764d7e0cc14d34510e8c

[ "Unlicense", "MIT" ]

2

2021-07-26T15:04:07.000Z

2021-07-26T17:23:08.000Z

virtual/lib/python3.6/site-packages/dns/rdataclass.py

amoskipz/pitch

477599a56958bc677e22764d7e0cc14d34510e8c

[ "Unlicense", "MIT" ]

30

2020-07-31T05:23:33.000Z

2022-03-25T11:04:00.000Z

virtual/lib/python3.6/site-packages/dns/rdataclass.py

amoskipz/pitch

477599a56958bc677e22764d7e0cc14d34510e8c

[ "Unlicense", "MIT" ]

2

2021-02-26T16:25:00.000Z

2021-03-06T15:45:56.000Z

# Copyright (C) Dnspython Contributors, see LICENSE for text of ISC license # Copyright (C) 2001-2017 Nominum, Inc. # # Permission to use, copy, modify, and distribute this software and its # documentation for any purpose with or without fee is hereby granted, # provided that the above copyright notice and this permission notice # appear in all copies. # # THE SOFTWARE IS PROVIDED "AS IS" AND NOMINUM DISCLAIMS ALL WARRANTIES # WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF # MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL NOMINUM BE LIABLE FOR # ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES # WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN # ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT # OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. """DNS Rdata Classes.""" import dns.enum import dns.exception class RdataClass(dns.enum.IntEnum): """DNS Rdata Class""" RESERVED0 = 0 IN = 1 INTERNET = IN CH = 3 CHAOS = CH HS = 4 HESIOD = HS NONE = 254 ANY = 255 @classmethod def _maximum(cls): return 65535 @classmethod def _short_name(cls): return "class" @classmethod def _prefix(cls): return "CLASS" @classmethod def _unknown_exception_class(cls): return UnknownRdataclass globals().update(RdataClass.__members__) _metaclasses = {RdataClass.NONE, RdataClass.ANY} class UnknownRdataclass(dns.exception.DNSException): """A DNS class is unknown.""" def from_text(text): """Convert text into a DNS rdata class value. The input text can be a defined DNS RR class mnemonic or instance of the DNS generic class syntax. For example, "IN" and "CLASS1" will both result in a value of 1. Raises ``dns.rdatatype.UnknownRdataclass`` if the class is unknown. Raises ``ValueError`` if the rdata class value is not >= 0 and <= 65535. Returns an ``int``. """ return RdataClass.from_text(text) def to_text(value): """Convert a DNS rdata class value to text. If the value has a known mnemonic, it will be used, otherwise the DNS generic class syntax will be used. Raises ``ValueError`` if the rdata class value is not >= 0 and <= 65535. Returns a ``str``. """ return RdataClass.to_text(value) def is_metaclass(rdclass): """True if the specified class is a metaclass. The currently defined metaclasses are ANY and NONE. *rdclass* is an ``int``. """ if rdclass in _metaclasses: return True return False

25.456311

76

0.694508

7943ac674aea293c6ed0ebfff43d53877a99ba13

774

py

Python

Aula 02/[Exercicio 05] Testing information.py

IsaacPSilva/LetsCode

64396ee9fd0ad395598c74c3727a614261e5dd50

[ "MIT" ]

null

Aula 02/[Exercicio 05] Testing information.py

IsaacPSilva/LetsCode

64396ee9fd0ad395598c74c3727a614261e5dd50

[ "MIT" ]

null

Aula 02/[Exercicio 05] Testing information.py

IsaacPSilva/LetsCode

64396ee9fd0ad395598c74c3727a614261e5dd50

[ "MIT" ]

null

'''5. Faça um programa que leia a validade das informações: a. Idade: entre 0 e 150; b. Salário: maior que 0; c. Sexo: M, F ou Outro; O programa deve imprimir uma mensagem de erro para cada informação inválida.''' print('TESTING INFORMATION') print('-'*30) #Input of data age = int(input('Insert the age: ')) if age>=0 and age<=150: print('Valid age') print('-'*30) else: print('Invalid value') print('-'*30) salary = float(input('Insert the salary: R$ ')) if salary>0: print('Valid salary') print('-'*30) else: print('Invalid value') print('-'*30) sex = input('Insert the sex [M,F, Others] : ').lower() if sex=='m' or sex=='f' or sex=='others': print('Valid sex') print('-'*30) else: print('Invalid value') print('-'*30)

22.114286

59

0.618863

7943ac76a4c7c6ee26fbc1631d05309a6b7fa719

685

py

Python

venv/Lib/site-packages/torch/_VF.py

Westlanderz/AI-Plat1

1187c22819e5135e8e8189c99b86a93a0d66b8d8

[ "MIT" ]

1

2022-01-08T12:30:44.000Z

venv/Lib/site-packages/torch/_VF.py

Westlanderz/AI-Plat1

1187c22819e5135e8e8189c99b86a93a0d66b8d8

[ "MIT" ]

null

venv/Lib/site-packages/torch/_VF.py

Westlanderz/AI-Plat1

1187c22819e5135e8e8189c99b86a93a0d66b8d8

[ "MIT" ]

null

""" This makes the functions in torch._C._VariableFunctions available as torch._VF.<funcname> without mypy being able to find them. A subset of those functions are mapped to ATen functions in torch/jit/_builtins.py See https://github.com/pytorch/pytorch/issues/21478 for the reason for introducing torch._VF """ import torch import sys import types class VFModule(types.ModuleType): vf: types.ModuleType def __init__(self, name): super(VFModule, self).__init__(name) self.vf = torch._C._VariableFunctions def __getattr__(self, attr): return getattr(self.vf, attr) sys.modules[__name__] = VFModule(__name__)

22.833333

71

0.705109

7943ad89bc9c903ed683fa5d41eebdd28fd3c0de

5,546

py

Python

molanet/run/base_architecture.py

exaV/molanet

e12e24d8e4788f839a74a2e7310c42180b2419dd

[ "Apache-2.0" ]

null

molanet/run/base_architecture.py

exaV/molanet

e12e24d8e4788f839a74a2e7310c42180b2419dd

[ "Apache-2.0" ]

3

2017-12-08T18:12:37.000Z

2018-02-25T18:39:41.000Z

molanet/run/base_architecture.py

michaelaerni/ip6-molanet

cb226d3866f86b030fa3951eefdba0da85f0dd92

[ "Apache-2.0" ]

null

import argparse import logging import os import shutil from datetime import datetime import tensorflow as tf from molanet.base import NetworkTrainer, TrainingOptions from molanet.input import TrainingPipeline, \ EvaluationPipeline, random_rotate_flip_rgb, random_contrast_rgb, random_brightness_rgb from molanet.models.pix2pix import Pix2PixFactory from molanet.models.wgan_gp import WassersteinGradientPenaltyFactory def create_arg_parser() -> argparse.ArgumentParser: parser = argparse.ArgumentParser("Molanet PoC script") parser.add_argument("--sampledir", type=str, help="Root sample directory, containing set directories and meta files") parser.add_argument("--test-set", type=str, help="Name of the test set") parser.add_argument("--cv-set", type=str, help="Name of the cv set") parser.add_argument("--train-set", type=str, help="Name of the training set") parser.add_argument("--logdir", type=str, help="Directory into which summaries and checkpoints are written") parser.add_argument("--restore", type=int, help="If set, restores the model from logdir with the given iteration") parser.add_argument("--debug-placement", action="store_true", help="Output device placement") parser.add_argument("--no-gpu", action="store_true", help="Run everything on CPU") parser.add_argument("--logsubdir", action="store_true", help="Create a subdirectory in logdir for each new run") parser.add_argument("--nchw", action="store_true", help="Uses NCHW format for training and inference") parser.add_argument("--cv-interval", type=int, default=200, help="Cross-validation interval") parser.add_argument("--max-iterations", type=int, help="Maximum number of iterations before training stops") parser.add_argument("--xla", action="store_true", help="Enable XLA JIT compilation (GPU only)") return parser if __name__ == "__main__": parser = create_arg_parser() args = parser.parse_args() logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s [%(name)s]: %(message)s") log = logging.getLogger(__name__) if args.logsubdir and args.restore is None: now = datetime.now() subdirname = f"run_{now.month:02}{now.day:02}_{now.hour:02}{now.minute:02}_base_architecture" logdir = os.path.join(args.logdir, subdirname) else: logdir = args.logdir if args.restore is None: shutil.rmtree(logdir, ignore_errors=True) os.makedirs(logdir) data_format = "NCHW" if args.nchw else "NHWC" tf.reset_default_graph() AUGMENTATION_FUNCTIONS = [ lambda image, segmentation: random_rotate_flip_rgb(image, segmentation), lambda image, segmentation: (random_contrast_rgb(image, 0.8, 1.2), segmentation), lambda image, segmentation: (random_brightness_rgb(image, -0.3, 0.3), segmentation) ] # No color conversion color_converter = None # Create input pipelines training_pipeline = TrainingPipeline(args.sampledir, args.train_set, image_size=512, color_converter=color_converter, data_format=data_format, batch_size=1, read_thread_count=4, batch_thread_count=1, augmentation_functions=AUGMENTATION_FUNCTIONS, name="training") cv_pipeline = EvaluationPipeline(args.sampledir, args.cv_set, image_size=512, color_converter=color_converter, data_format=data_format, batch_size=1, batch_thread_count=1, name="cv") log.info("Input pipelines created") log.info(f"Training set size: {training_pipeline.sample_count}") log.info(f"CV set size: {cv_pipeline.sample_count}") if args.debug_placement: log.info("Enabled device placement logging") config = tf.ConfigProto(log_device_placement=args.debug_placement) if args.xla: config.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1 log.info("Enabled JIT XLA compilation") network_factory = Pix2PixFactory( spatial_extent=512, min_generator_features=64, max_generator_features=1024, min_discriminator_features=64, max_discriminator_features=1024, dropout_keep_probability=0.5, dropout_layer_count=2, use_batchnorm=True, weight_initializer=tf.truncated_normal_initializer(stddev=0.02) ) trainer = NetworkTrainer( training_pipeline, cv_pipeline, network_factory, WassersteinGradientPenaltyFactory(gradient_lambda=10, network_factory=network_factory, l1_lambda=0.0), training_options=TrainingOptions( cv_summary_interval=args.cv_interval, summary_directory=logdir, discriminator_iterations=5, max_iterations=args.max_iterations, session_configuration=config, use_gpu=not args.no_gpu, data_format=data_format), learning_rate=0.0001, beta1=0.5, beta2=0.9) log.info("Trainer created") with trainer: log.info("Session started") if args.restore is not None: trainer.restore(args.restore) log.info(f"Iteration {args.restore} restored") log.info("Starting training") trainer.train() log.info("Shutting down...")

42.335878

118

0.674721

7943af335298b418a57d74be68e9f74197fc1b47

6,086

py

Python

tf_agents/bandits/agents/examples/v2/train_eval_stationary_linear.py

Veluga/agents

e436726df11fa5c1c01637b730c7fa6a8fdda1c5

[ "Apache-2.0" ]

2

2021-07-25T11:06:56.000Z

2021-07-25T11:07:02.000Z

tf_agents/bandits/agents/examples/v2/train_eval_stationary_linear.py

shuvoxcd01/agents

c9c690841cd188a2d2d10a4e586a990c075e887d

[ "Apache-2.0" ]

null

tf_agents/bandits/agents/examples/v2/train_eval_stationary_linear.py

shuvoxcd01/agents

c9c690841cd188a2d2d10a4e586a990c075e887d

[ "Apache-2.0" ]

null

# coding=utf-8 # Copyright 2020 The TF-Agents Authors. # # 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 # # https://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. """End-to-end test for bandit training under stationary linear environments.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import functools import os from absl import app from absl import flags import tensorflow as tf # pylint: disable=g-explicit-tensorflow-version-import from tf_agents.bandits.agents import exp3_mixture_agent from tf_agents.bandits.agents import lin_ucb_agent from tf_agents.bandits.agents import linear_thompson_sampling_agent as lin_ts_agent from tf_agents.bandits.agents import neural_epsilon_greedy_agent from tf_agents.bandits.agents.examples.v2 import trainer from tf_agents.bandits.environments import environment_utilities from tf_agents.bandits.environments import stationary_stochastic_py_environment as sspe from tf_agents.bandits.metrics import tf_metrics as tf_bandit_metrics from tf_agents.environments import tf_py_environment from tf_agents.networks import q_network from tf_agents.policies import utils as policy_utilities flags.DEFINE_string('root_dir', os.getenv('TEST_UNDECLARED_OUTPUTS_DIR'), 'Root directory for writing logs/summaries/checkpoints.') flags.DEFINE_enum( 'agent', 'LinUCB', ['LinUCB', 'LinTS', 'epsGreedy', 'Mix'], 'Which agent to use. Possible values are `LinUCB` and `LinTS`, `epsGreedy`,' ' and `Mix`.' ) flags.DEFINE_bool('normalize_reward_fns', False, 'Whether to normalize the ' 'reward functions so that rewards are close to being in ' '[0, 1].') FLAGS = flags.FLAGS BATCH_SIZE = 8 CONTEXT_DIM = 15 NUM_ACTIONS = 5 REWARD_NOISE_VARIANCE = 0.01 TRAINING_LOOPS = 2000 STEPS_PER_LOOP = 2 AGENT_ALPHA = 0.1 EPSILON = 0.05 LAYERS = (50, 50, 50) LR = 0.001 def main(unused_argv): tf.compat.v1.enable_v2_behavior() # The trainer only runs with V2 enabled. with tf.device('/CPU:0'): # due to b/128333994 if FLAGS.normalize_reward_fns: action_reward_fns = ( environment_utilities.normalized_sliding_linear_reward_fn_generator( CONTEXT_DIM, NUM_ACTIONS, REWARD_NOISE_VARIANCE)) else: action_reward_fns = ( environment_utilities.sliding_linear_reward_fn_generator( CONTEXT_DIM, NUM_ACTIONS, REWARD_NOISE_VARIANCE)) env = sspe.StationaryStochasticPyEnvironment( functools.partial( environment_utilities.context_sampling_fn, batch_size=BATCH_SIZE, context_dim=CONTEXT_DIM), action_reward_fns, batch_size=BATCH_SIZE) environment = tf_py_environment.TFPyEnvironment(env) optimal_reward_fn = functools.partial( environment_utilities.tf_compute_optimal_reward, per_action_reward_fns=action_reward_fns) optimal_action_fn = functools.partial( environment_utilities.tf_compute_optimal_action, per_action_reward_fns=action_reward_fns) network = q_network.QNetwork( input_tensor_spec=environment.time_step_spec().observation, action_spec=environment.action_spec(), fc_layer_params=LAYERS) if FLAGS.agent == 'LinUCB': agent = lin_ucb_agent.LinearUCBAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), alpha=AGENT_ALPHA, dtype=tf.float32) elif FLAGS.agent == 'LinTS': agent = lin_ts_agent.LinearThompsonSamplingAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), alpha=AGENT_ALPHA, dtype=tf.float32) elif FLAGS.agent == 'epsGreedy': agent = neural_epsilon_greedy_agent.NeuralEpsilonGreedyAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), reward_network=network, optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=LR), epsilon=EPSILON) elif FLAGS.agent == 'Mix': emit_policy_info = policy_utilities.InfoFields.PREDICTED_REWARDS_MEAN agent_linucb = lin_ucb_agent.LinearUCBAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), emit_policy_info=emit_policy_info, alpha=AGENT_ALPHA, dtype=tf.float32) agent_lints = lin_ts_agent.LinearThompsonSamplingAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), emit_policy_info=emit_policy_info, alpha=AGENT_ALPHA, dtype=tf.float32) agent_epsgreedy = neural_epsilon_greedy_agent.NeuralEpsilonGreedyAgent( time_step_spec=environment.time_step_spec(), action_spec=environment.action_spec(), reward_network=network, optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=LR), emit_policy_info=emit_policy_info, epsilon=EPSILON) agent = exp3_mixture_agent.Exp3MixtureAgent( (agent_linucb, agent_lints, agent_epsgreedy)) regret_metric = tf_bandit_metrics.RegretMetric(optimal_reward_fn) suboptimal_arms_metric = tf_bandit_metrics.SuboptimalArmsMetric( optimal_action_fn) trainer.train( root_dir=FLAGS.root_dir, agent=agent, environment=environment, training_loops=TRAINING_LOOPS, steps_per_loop=STEPS_PER_LOOP, additional_metrics=[regret_metric, suboptimal_arms_metric]) if __name__ == '__main__': app.run(main)

38.27673

87

0.733158

7943af4e7b8de138dce98ba3461ae11796d489bb

771

py

Python

src/mission_report/helpers.py

Flyingfox646/flyingfox

fc936ca1c2631a5f9d842d0deae1e197d50e7a96

[ "MIT" ]

null

src/mission_report/helpers.py

Flyingfox646/flyingfox

fc936ca1c2631a5f9d842d0deae1e197d50e7a96

[ "MIT" ]

null

src/mission_report/helpers.py

Flyingfox646/flyingfox

fc936ca1c2631a5f9d842d0deae1e197d50e7a96

[ "MIT" ]

null

import math # http://www.ariel.com.au/a/python-point-int-poly.html def point_in_polygon(point, polygon): x, y = point['x'], point['z'] n = len(polygon) inside = False p1x, _, p1y = polygon[0] for i in range(n + 1): p2x, _, p2y = polygon[i % n] if min(p1y, p2y) < y <= max(p1y, p2y) and x <= max(p1x, p2x): if p1y != p2y: xinters = (y - p1y) * (p2x - p1x) / (p2y - p1y) + p1x if p1x == p2x or x <= xinters: inside = not inside p1x, p1y = p2x, p2y return inside def distance(p1, p2): return math.hypot(p2['x'] - p1['x'], p2['z'] - p1['z']) def is_pos_correct(pos): if not pos or pos == {'x': 0.0, 'y': 0.0, 'z': 0.0}: return False return True

26.586207

69

0.507134

7943af648e5beebdc3766920a5d9f634375e724f

2,700

py

Python

abusehelper/bots/cleanmx/cleanmxbot.py

heikipikker/abusehelper

c451e36e9927b95ba82253accf170121fce61f4f

[ "MIT" ]

1

2020-04-07T07:01:12.000Z

abusehelper/bots/cleanmx/cleanmxbot.py

heikipikker/abusehelper

c451e36e9927b95ba82253accf170121fce61f4f

[ "MIT" ]

null

abusehelper/bots/cleanmx/cleanmxbot.py

heikipikker/abusehelper

c451e36e9927b95ba82253accf170121fce61f4f

[ "MIT" ]

1

2020-04-07T07:01:13.000Z

# -*- coding: utf-8 -*- # In the runtime config: # yield Source("cleanmxbot", csv_url="http://support.clean-mx.de/clean-mx/xmlphishing?response=alive&format=csv&domain=") # yield Source("cleanmxbot", csv_url="http://support.clean-mx.de/clean-mx/xmlviruses?response=alive&format=csv&domain=", csv_name="xmlvirii") """ CleanMX bot Maintainer: Codenomicon <[email protected]> """ import re import idiokit import urlparse from xml.sax.saxutils import unescape as _unescape from abusehelper.core import bot, events, utils cdata = re.compile("(.*?)\<\!\[CDATA\[(.*?)\]\]\>") def unescape(string): """ >>> unescape("one <![CDATA[two ]]>three") 'one two three' """ result = list() for index, data in enumerate(cdata.split(string)): if index % 3 != 2: data = _unescape(data, {" ": " "}) result.append(data) return "".join(result) class CleanMXBot(bot.PollingBot): def feed_keys(self, csv_url, csv_name=None, **keys): if csv_name is None: csv_name = urlparse.urlparse(csv_url)[2].split("/")[-1] yield (csv_url, csv_name) @idiokit.stream def poll(self, url, name): try: self.log.info("Downloading page from: %r", url) info, fileobj = yield utils.fetch_url(url) except utils.FetchUrlFailed, e: self.log.error("Failed to download page %r: %r", url, e) return charset = info.get_param("charset", None) lines = (line.strip() for line in fileobj if line.strip()) yield utils.csv_to_events(lines, charset=charset) | self.normalize(name) @idiokit.stream def normalize(self, name): while True: event = yield idiokit.next() # A dict telling how to rename raw event keys. # A key is not renamed by default. # Mapping a key to None removes the key. key_mappings = { "time": "source time", "id": "cleanmx id", "phishtank": "phishtank id", "line": None, "firsttime": "first seen", "lasttime": "last seen" } new = events.Event() for key, value in event.items(): key = key_mappings.get(key, key) if key is None: continue value = unescape(value).strip() if not value: continue new.add(key, value) if name: new.add("feed", name) yield idiokit.send(new) if __name__ == "__main__": CleanMXBot.from_command_line().execute()

28.723404

141

0.561852

7943afbd4ce30a106a343db08e25fe296363d9a7

144

py

Python

bot_api/__init__.py

wqzai/QQ-official-guild-bot

19f7ad42cb0e6c3c9541ac6a9ee73116727f7cb2

[ "MIT" ]

null

bot_api/__init__.py

wqzai/QQ-official-guild-bot

19f7ad42cb0e6c3c9541ac6a9ee73116727f7cb2

[ "MIT" ]

null

bot_api/__init__.py

wqzai/QQ-official-guild-bot

19f7ad42cb0e6c3c9541ac6a9ee73116727f7cb2

[ "MIT" ]

null

from .sdk_main import * from . import api from . import models from . import structs from . import utils from .models import BotCallingAPIError

20.571429

38

0.784722

7943afd78364951e40c67dec58adf129e2fd997f

1,493

py

Python

tests/MedianBlurTest.py

spongezhang/maskgen

7284e300d1cb326a5349879de0bace9cfa8788a8

[ "BSD-3-Clause" ]

null

tests/MedianBlurTest.py

spongezhang/maskgen

7284e300d1cb326a5349879de0bace9cfa8788a8

[ "BSD-3-Clause" ]

null

tests/MedianBlurTest.py

spongezhang/maskgen

7284e300d1cb326a5349879de0bace9cfa8788a8

[ "BSD-3-Clause" ]

null

import unittest import os from maskgen import plugins, image_wrap import numpy import tempfile class MedianBlurTestCase(unittest.TestCase): filesToKill = [] def setUp(self): plugins.loadPlugins() def test_something(self): img = numpy.random.randint(0, 255, (500, 500, 3), dtype='uint8') wrapper = image_wrap.ImageWrapper(img) filename = tempfile.mktemp(prefix='mstc',suffix='.png',dir='.') filename_output = tempfile.mktemp(prefix='mstcr', suffix='.png', dir='.') self.filesToKill.append(filename) wrapper.save(filename) self.filesToKill.append(filename_output) image_wrap.ImageWrapper(img).save(filename_output) args,error = plugins.callPlugin('MedianBlur', wrapper, filename, filename_output, percentageChange = 0.5) wrapper = image_wrap.openImageFile(filename_output) output = wrapper.to_array() self.assertEqual(output.shape, img.shape) diff = abs(output - img) finaldiff = numpy.zeros((500,500)) for i in range(3): finaldiff = finaldiff + diff[:,:,i] finaldiff[finaldiff > 0] = 1 self.assertTrue(abs(sum(sum(finaldiff))-62500) < 100) def tearDown(self): for f in self.filesToKill: if os.path.exists(f): os.remove(f) if __name__ == '__main__': unittest.main()

33.177778

81

0.596115

7943b0277758cdcbd0714ce3020cb7a32f47fe34

623

py

Python

nand2vm/seq/bit.py

mlouielu/nand2vm

0c24f49efde8303a157ab658e75f0c081e442ae2

[ "BSD-3-Clause" ]

13

2017-07-04T10:52:12.000Z

2021-07-19T16:13:32.000Z

nand2vm/seq/bit.py

mlouielu/nand2vm

0c24f49efde8303a157ab658e75f0c081e442ae2

[ "BSD-3-Clause" ]

2

2017-07-03T15:53:52.000Z

2017-07-04T03:48:24.000Z

nand2vm/seq/bit.py

mlouielu/nand2vm

0c24f49efde8303a157ab658e75f0c081e442ae2

[ "BSD-3-Clause" ]

1

2021-06-25T17:15:35.000Z

# # Copyright (c) 2017 Louie Lu. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. # from .clock import ClockPhase from .dff import DFF from .. import gate class Bit(object): def __init__(self): self.d = DFF() self.state = self.d.state self.clock = ClockPhase.POSITIVE_EDGE def update(self, source: bool, load: bool, clock: ClockPhase=None) -> bool: if clock: self.clock = clock mux = gate.Mux(self.d.state, source, load) out = self.d.update(mux, self.clock) return out

25.958333

79

0.638844

7943b0a7a2b9fb76d3754eba48e5dfb549293033

177

py

Python

routelift_api/roles_and_permissions/urls.py

BitMask-Technologies/route-lift-api

7ac78c6cce523fc5a3852dca7b289fe3a5f3afa8

[ "MIT" ]

null

routelift_api/roles_and_permissions/urls.py

BitMask-Technologies/route-lift-api

7ac78c6cce523fc5a3852dca7b289fe3a5f3afa8

[ "MIT" ]

7

2021-06-24T16:12:09.000Z

2021-08-05T16:09:22.000Z

routelift_api/roles_and_permissions/urls.py

BitMask-Technologies/route-lift-api

7ac78c6cce523fc5a3852dca7b289fe3a5f3afa8

[ "MIT" ]

null

from django.urls import path from . import routers urlpatterns = [ path('', routers.staff_role_router), path('/<int:staffRoleId>', routers.single_staff_role_router) ]

19.666667

64

0.728814

7943b1cc78ebeda86c2c12b072674488383c0f15

2,517

py

Python

neo4j/_conf.py

matilda-me/neo4j-python-driver

4fb25a266841bf2a861f00d5dcf257bd5ae5c686

[ "Apache-2.0" ]

null

neo4j/_conf.py

matilda-me/neo4j-python-driver

4fb25a266841bf2a861f00d5dcf257bd5ae5c686

[ "Apache-2.0" ]

null

neo4j/_conf.py

matilda-me/neo4j-python-driver

4fb25a266841bf2a861f00d5dcf257bd5ae5c686

[ "Apache-2.0" ]

null

# Copyright (c) "Neo4j" # Neo4j Sweden AB [http://neo4j.com] # # This file is part of Neo4j. # # 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. class TrustStore: # Base class for trust stores. For internal type-checking only. pass class TrustSystemCAs(TrustStore): """Used to configure the driver to trust system CAs (default). Trust server certificates that can be verified against the system certificate authority. This option is primarily intended for use with full certificates. For example:: driver = neo4j.GraphDatabase.driver( url, auth=auth, trusted_certificates=neo4j.TrustSystemCAs() ) """ pass class TrustAll(TrustStore): """Used to configure the driver to trust all certificates. Trust any server certificate. This ensures that communication is encrypted but does not verify the server certificate against a certificate authority. This option is primarily intended for use with the default auto-generated server certificate. For example:: driver = neo4j.GraphDatabase.driver( url, auth=auth, trusted_certificates=neo4j.TrustAll() ) """ pass class TrustCustomCAs(TrustStore): """Used to configure the driver to trust custom CAs. Trust server certificates that can be verified against the certificate authority at the specified paths. This option is primarily intended for self-signed and custom certificates. :param certificates (str): paths to the certificates to trust. Those are not the certificates you expect to see from the server but the CA certificates you expect to be used to sign the server's certificate. For example:: driver = neo4j.GraphDatabase.driver( url, auth=auth, trusted_certificates=neo4j.TrustCustomCAs( "/path/to/ca1.crt", "/path/to/ca2.crt", ) ) """ def __init__(self, *certificates): self.certs = certificates

31.074074

76

0.699245

7943b2997f7dc993f8481d53e35128582a4d28c6

3,739

py

Python

salt/runners/jobs.py

mitsuhiko/salt

3b211f87e307936f43c937edc62207dd1b887e19

[ "Apache-2.0" ]

4

2015-10-06T22:20:27.000Z

2017-09-04T08:03:44.000Z

salt/runners/jobs.py

mitsuhiko/salt

3b211f87e307936f43c937edc62207dd1b887e19

[ "Apache-2.0" ]

null

salt/runners/jobs.py

mitsuhiko/salt

3b211f87e307936f43c937edc62207dd1b887e19

[ "Apache-2.0" ]

null

''' A conveniance system to manage jobs, both active and already run ''' # Import Python Modules import os # Import Salt Modules import salt.client import salt.payload import salt.utils from salt._compat import string_types from salt.exceptions import SaltException # Import Third party libs import yaml def active(): ''' Return a report on all actively running jobs from a job id centric perspective ''' ret = {} job_dir = os.path.join(__opts__['cachedir'], 'jobs') client = salt.client.LocalClient(__opts__['conf_file']) active_ = client.cmd('*', 'saltutil.running', timeout=__opts__['timeout']) for minion, data in active_.items(): if not isinstance(data, list): continue for job in data: if not job['jid'] in ret: ret[job['jid']] = {'Running': [], 'Returned': [], 'Function': job['fun'], 'Arguments': list(job['arg']), 'Target': job['tgt'], 'Target-type': job['tgt_type']} else: ret[job['jid']]['Running'].append({minion: job['pid']}) for jid in ret: jid_dir = salt.utils.jid_dir( jid, __opts__['cachedir'], __opts__['hash_type'] ) if not os.path.isdir(jid_dir): continue for minion in os.listdir(jid_dir): if minion.startswith('.'): continue if os.path.exists(os.path.join(jid_dir, minion)): ret[jid]['Returned'].append(minion) print(yaml.dump(ret)) return ret def lookup_jid(jid): ''' Return the printout from a previousely executed job ''' def _format_ret(full_ret): ''' Take the full return data and format it to simple output ''' out = None ret = {} for key, data in full_ret.items(): ret[key] = data['ret'] if 'out' in data: out = data['out'] return ret, out client = salt.client.LocalClient(__opts__['conf_file']) full_ret = client.get_full_returns(jid, [], 0) formatted = _format_ret(full_ret) if formatted: ret = formatted[0] out = formatted[1] else: ret = SaltException('Job {0} hasn\'t finished. No data yet :('.format(jid)) out = '' # Determine the proper output method and run it get_outputter = salt.output.get_outputter if isinstance(ret, (list, dict, string_types)) and out: printout = get_outputter(out) # Pretty print any salt exceptions elif isinstance(ret, SaltException): printout = get_outputter("txt") else: printout = get_outputter(None) printout(ret) return ret def list_jobs(): ''' List all detectable jobs and associated functions ''' serial = salt.payload.Serial(__opts__) ret = {} job_dir = os.path.join(__opts__['cachedir'], 'jobs') for top in os.listdir(job_dir): t_path = os.path.join(job_dir, top) for final in os.listdir(t_path): loadpath = os.path.join(t_path, final, '.load.p') if not os.path.isfile(loadpath): continue load = serial.load(open(loadpath, 'rb')) jid = load['jid'] ret[jid] = {'Start Time': salt.utils.jid_to_time(jid), 'Function': load['fun'], 'Arguments': list(load['arg']), 'Target': load['tgt'], 'Target-type': load['tgt_type']} print(yaml.dump(ret)) return ret

31.158333

83

0.543193

7943b33eb4380ae0f7977a68eaef8facfa66e0c0

7,223

py

Python

rtg/decode_pro.py

XuezheMax/rtg

a4bfc81dc1874c6f43765eb588d1026a2296aa2f

[ "Apache-2.0" ]

15

2019-06-28T21:22:46.000Z

2022-02-03T06:36:43.000Z

rtg/decode_pro.py

XuezheMax/rtg

a4bfc81dc1874c6f43765eb588d1026a2296aa2f

[ "Apache-2.0" ]

23

2019-06-14T19:12:26.000Z

2022-03-15T23:22:14.000Z

rtg/decode_pro.py

XuezheMax/rtg

a4bfc81dc1874c6f43765eb588d1026a2296aa2f

[ "Apache-2.0" ]

8

2019-06-11T19:03:39.000Z

2022-01-09T06:58:23.000Z

# CLI interface to decode task import argparse import sys from argparse import ArgumentDefaultsHelpFormatter as ArgFormatter import torch from pathlib import Path from rtg import TranslationExperiment as Experiment, log, yaml from rtg.module.decoder import Decoder, ReloadEvent from rtg.utils import IO def parse_args(): parser = argparse.ArgumentParser(prog="rtg.decode", description="Decode using NMT model", formatter_class=ArgFormatter) parser.add_argument("work_dir", help="Working directory", type=str) parser.add_argument("model_path", type=str, nargs='*', help="Path to model's checkpoint. " "If not specified, a best model (based on the score on validation set)" " from the experiment directory will be used." " If multiple paths are specified, then an ensembling is performed by" " averaging the param weights") parser.add_argument("-if", '--input', default=sys.stdin, type=argparse.FileType('r', encoding='utf-8', errors='ignore'), help='Input file path. default is STDIN') parser.add_argument("-of", '--output', default=sys.stdout, type=argparse.FileType('w', encoding='utf-8', errors='ignore'), help='Output File path. default is STDOUT') parser.add_argument("-bs", '--beam-size', type=int, default=5, help='Beam size. beam_size=1 is greedy, ' 'In theory: higher beam is better approximation but expensive. ' 'But in practice, higher beam doesnt always increase.') parser.add_argument("-bc", '--batch-size', type=int, default=1, help='Number of source tokens in a batch, approximately. ' 'tries to fit in atleast one sentence => so even if you set 0 or 1, ' 'there will be atleast one sentence in batch. ' '1 sentence seems better in CPU but larger number is better on GPUs') parser.add_argument("-lp", '--lp-alpha', type=float, default=0.6, help='Length penalty alpha. to disable set <= 0.0 ' 'Ideally in the range [0.0, 1.0] but you are allowed to ' 'experiment beyond > 1.0 but not less than 0.0') parser.add_argument("-ml", '--max-len', type=int, default=60, help='Maximum output sequence length. ' 'Example: if max_len=10 and if source_len is 50, ' 'then decoder goes up to 50+10 time steps in search of EOS token.') parser.add_argument("-msl", '--max-src-len', type=int, help='max source len; longer seqs will be truncated') parser.add_argument("-nh", '--num-hyp', type=int, default=1, help='Number of hypothesis to output. This should be smaller than beam_size') parser.add_argument("--prepared", dest="prepared", action='store_true', default=None, help='Each token is a valid integer which is an index to embedding,' ' so skip indexifying again') parser.add_argument("-bp", '--binmt-path', type=str, default=None, choices=['E1D1', 'E2D2', 'E1D2E2D1', 'E2D2E1D2', 'E1D2', 'E2D1'], help='Sub module path inside BiNMT. applicable only when model is BiNMT') parser.add_argument("-it", '--interactive', action='store_true', help='Open interactive shell with decoder') parser.add_argument("-sc", '--skip-check', action='store_true', help='Skip Checking whether the experiment dir is prepared and trained') parser.add_argument("-en", '--ensemble', type=int, default=1, help='Ensemble best --ensemble models by averaging them') parser.add_argument("-cb", '--sys-comb', type=Path, help='System combine models at the softmax layer using the weights' ' specified in this file. When this argument is supplied, model_path ' 'argument is ignored.') args = vars(parser.parse_args()) return args def validate_args(args, exp: Experiment): if not args.pop('skip_check'): # if --skip-check is not requested assert exp.has_prepared(), \ f'Experiment dir {exp.work_dir} is not ready to train. Please run "prep" sub task' assert exp.has_trained(), \ f'Experiment dir {exp.work_dir} is not ready to decode.' \ f' Please run "train" sub task or --skip-check to ignore this' weights_file = exp.work_dir / 'combo-weights.yml' if not args.get('sys_comb') and weights_file.exists(): log.warning("Found default combo weights, switching to combo mode") args['sys_comb'] = weights_file if args.get("sys_comb"): with IO.reader(args['sys_comb']) as fh: weights = yaml.load(fh)['weights'] args['model_path'], args['weights'] = zip(*weights.items()) for model in args['model_path']: assert Path(model).exists(), model assert abs(sum(args['weights']) - 1) < 1e-3, \ f'Weights from --sys-comb file should sum to 1.0, given={args["weights"]}' def main(): # No grads required torch.set_grad_enabled(False) args = parse_args() gen_args = {} exp = Experiment(args.pop('work_dir'), read_only=True) validate_args(args, exp) if exp.model_type == 'binmt': if not args.get('path'): Exception('--binmt-path argument is needed for BiNMT model.') gen_args['path'] = args.pop('binmt_path') weights = args.get('weights') if weights: decoder = Decoder.combo_new(exp, model_paths=args.pop('model_path'), weights=weights) else: decoder = Decoder.new(exp, gen_args=gen_args, model_paths=args.pop('model_path', None), ensemble=args.pop('ensemble', 1)) if args.pop('interactive'): if weights: log.warning("Interactive shell not reloadable for combo mode. FIXME: TODO:") if args['input'] != sys.stdin or args['output'] != sys.stdout: log.warning('--input and --output args are not applicable in --interactive mode') args.pop('input') args.pop('output') while True: try: # an hacky way to unload and reload model when user tries to switch models decoder.decode_interactive(**args) break # exit loop if there is no request for reload except ReloadEvent as re: decoder = Decoder.new(exp, gen_args=gen_args, model_paths=re.model_paths) args = re.state # go back to loop and redo interactive shell else: return decoder.decode_file(args.pop('input'), args.pop('output'), **args) if __name__ == '__main__': main()

52.34058

101

0.581476

7943b39fea167258135ce86450f8c8157613d9ff

1,832

py

Python

scripts/05_modules/colorchooser/colorswatch_creatematerials_r19.py

PluginCafe/cinema4d_py_sdk_extended

aea195b47c15e1c94443292e489afe6779b68550

[ "Apache-2.0" ]

85

2019-09-06T22:53:15.000Z

2022-03-27T01:33:09.000Z

scripts/05_modules/colorchooser/colorswatch_creatematerials_r19.py

PluginCafe/cinema4d_py_sdk_extended

aea195b47c15e1c94443292e489afe6779b68550

[ "Apache-2.0" ]

11

2019-09-03T22:59:19.000Z

2022-02-27T03:42:52.000Z

scripts/05_modules/colorchooser/colorswatch_creatematerials_r19.py

PluginCafe/cinema4d_py_sdk_extended

aea195b47c15e1c94443292e489afe6779b68550

[ "Apache-2.0" ]

31

2019-09-09T09:35:35.000Z

2022-03-28T09:08:47.000Z

""" Copyright: MAXON Computer GmbH Author: Maxime Adam Description: - Reads all the colors from the first swatch group in the active document and creates material for each one. Class/method highlighted: - c4d.modules.colorchooser.SwatchData - c4d.modules.colorchooser.SwatchGroup """ import c4d def main(): # Creates a swatch data swatchData = c4d.modules.colorchooser.ColorSwatchData() if swatchData is None: raise MemoryError("Failed to create a ColorSwatchData.") # Loads the swatches data from the active document if not swatchData.Load(doc): raise RuntimeError("Failed to load the ColorSwatchData.") # Makes sure the document contains at least a swatch group if swatchData.GetGroupCount(c4d.SWATCH_CATEGORY_DOCUMENT) == 0: raise RuntimeError("There is no color swatch stored in the document.") # Retrieves the first swatch group group = swatchData.GetGroupAtIndex(0, c4d.SWATCH_CATEGORY_DOCUMENT) if group is None: raise RuntimeError("Failed to retrieve the first Group of the color swatch.") groupName = group.GetName() colorCount = group.GetColorCount() for colorIndex in range(colorCount): # Gets the current color color = group.GetColor(colorIndex)[0] # Creates a material for the current color mat = c4d.BaseMaterial(c4d.Mmaterial) # Sets the name with the group name and color index mat.SetName(groupName + str(colorIndex)) # Converts maxon.ColorA to c4d.Vector to set the material color mat[c4d.MATERIAL_COLOR_COLOR] = c4d.Vector(color.r, color.g, color.b) # Inserts the material into the active document doc.InsertMaterial(mat) # Pushes an update event to Cinema 4D c4d.EventAdd() if __name__ == '__main__': main()

31.050847

112

0.702511

7943b55ebcd4a7581b260d2ae0be079fff46cbb2

1,006

py

Python

models/bare/conv4.py

sdamadi/image-classification

2f38dc3c64c733f1ce820d09fe9d5f6dbf988f97

[ "MIT" ]

3

2021-12-13T13:05:58.000Z

2022-03-23T09:14:13.000Z

models/bare/conv4.py

sdamadi/image-classification

2f38dc3c64c733f1ce820d09fe9d5f6dbf988f97

[ "MIT" ]

null

models/bare/conv4.py

sdamadi/image-classification

2f38dc3c64c733f1ce820d09fe9d5f6dbf988f97

[ "MIT" ]

1

2021-12-31T04:00:45.000Z

import torch import torch.nn as nn class Conv4(nn.Module): def __init__(self, in_ch, imgsz, num_classes=10): super(Conv4, self).__init__() self.conv1 = nn.Conv2d(in_ch, 64, kernel_size=(3, 3), stride=1, padding=1) self.conv2 = nn.Conv2d(64, 64, kernel_size=(3, 3), stride=1, padding=1) self.conv3 = nn.Conv2d(64, 128, kernel_size=(3, 3), stride=1, padding=1) self.conv4 = nn.Conv2d(128, 128, kernel_size=(3, 3), stride=1, padding=1) self.maxpool = nn.MaxPool2d(kernel_size=2) self.relu = nn.ReLU(inplace=True) self.fc1 = nn.Linear(128*(imgsz//4)*(imgsz//4), 256) self.fc2 = nn.Linear(256, 256) self.fc3 = nn.Linear(256, num_classes) def forward(self, x): x = self.relu(self.conv1(x)) x = self.relu(self.conv2(x)) x = self.maxpool(x) x = self.relu(self.conv3(x)) x = self.relu(self.conv4(x)) x = self.maxpool(x) x = x.view( x.size(0), -1) x = self.relu(self.fc1(x)) x = self.relu(self.fc2(x)) x = self.fc3(x) return x

35.928571

78

0.626243

7943b59a44a70785844a266fa6cdc7373bff767e

117,318

py

Python

tests/migrations/test_operations.py

bak1an/django

98bcc5d81bca578f3a5b4d47907ba4ac40446887

[ "PSF-2.0", "BSD-3-Clause" ]

1

2018-09-22T20:35:14.000Z

tests/migrations/test_operations.py

seanfagan/django

66bbde6819586cc3a75630e12e569dc8ae72f211

[ "PSF-2.0", "BSD-3-Clause" ]

null

tests/migrations/test_operations.py

seanfagan/django

66bbde6819586cc3a75630e12e569dc8ae72f211

[ "PSF-2.0", "BSD-3-Clause" ]

1

2017-02-28T17:05:19.000Z

import unittest from django.db import connection, migrations, models, transaction from django.db.migrations.migration import Migration from django.db.migrations.operations import CreateModel from django.db.migrations.state import ModelState, ProjectState from django.db.models.fields import NOT_PROVIDED from django.db.transaction import atomic from django.db.utils import IntegrityError from django.test import SimpleTestCase, override_settings, skipUnlessDBFeature from .models import FoodManager, FoodQuerySet, UnicodeModel from .test_base import MigrationTestBase try: import sqlparse except ImportError: sqlparse = None class Mixin: pass class OperationTestBase(MigrationTestBase): """ Common functions to help test operations. """ def apply_operations(self, app_label, project_state, operations): migration = Migration('name', app_label) migration.operations = operations with connection.schema_editor() as editor: return migration.apply(project_state, editor) def unapply_operations(self, app_label, project_state, operations): migration = Migration('name', app_label) migration.operations = operations with connection.schema_editor() as editor: return migration.unapply(project_state, editor) def make_test_state(self, app_label, operation, **kwargs): """ Makes a test state using set_up_test_model and returns the original state and the state after the migration is applied. """ project_state = self.set_up_test_model(app_label, **kwargs) new_state = project_state.clone() operation.state_forwards(app_label, new_state) return project_state, new_state def set_up_test_model( self, app_label, second_model=False, third_model=False, index=False, multicol_index=False, related_model=False, mti_model=False, proxy_model=False, manager_model=False, unique_together=False, options=False, db_table=None, index_together=False): """ Creates a test model state and database table. """ # Delete the tables if they already exist table_names = [ # Start with ManyToMany tables '_pony_stables', '_pony_vans', # Then standard model tables '_pony', '_stable', '_van', ] tables = [(app_label + table_name) for table_name in table_names] with connection.cursor() as cursor: table_names = connection.introspection.table_names(cursor) connection.disable_constraint_checking() sql_delete_table = connection.schema_editor().sql_delete_table with transaction.atomic(): for table in tables: if table in table_names: cursor.execute(sql_delete_table % { "table": connection.ops.quote_name(table), }) connection.enable_constraint_checking() # Make the "current" state model_options = { "swappable": "TEST_SWAP_MODEL", "index_together": [["weight", "pink"]] if index_together else [], "unique_together": [["pink", "weight"]] if unique_together else [], } if options: model_options["permissions"] = [("can_groom", "Can groom")] if db_table: model_options["db_table"] = db_table operations = [migrations.CreateModel( "Pony", [ ("id", models.AutoField(primary_key=True)), ("pink", models.IntegerField(default=3)), ("weight", models.FloatField()), ], options=model_options, )] if index: operations.append(migrations.AddIndex( "Pony", models.Index(fields=["pink"], name="pony_pink_idx") )) if multicol_index: operations.append(migrations.AddIndex( "Pony", models.Index(fields=["pink", "weight"], name="pony_test_idx") )) if second_model: operations.append(migrations.CreateModel( "Stable", [ ("id", models.AutoField(primary_key=True)), ] )) if third_model: operations.append(migrations.CreateModel( "Van", [ ("id", models.AutoField(primary_key=True)), ] )) if related_model: operations.append(migrations.CreateModel( "Rider", [ ("id", models.AutoField(primary_key=True)), ("pony", models.ForeignKey("Pony", models.CASCADE)), ("friend", models.ForeignKey("self", models.CASCADE)) ], )) if mti_model: operations.append(migrations.CreateModel( "ShetlandPony", fields=[ ('pony_ptr', models.OneToOneField( 'Pony', models.CASCADE, auto_created=True, parent_link=True, primary_key=True, to_field='id', serialize=False, )), ("cuteness", models.IntegerField(default=1)), ], bases=['%s.Pony' % app_label], )) if proxy_model: operations.append(migrations.CreateModel( "ProxyPony", fields=[], options={"proxy": True}, bases=['%s.Pony' % app_label], )) if manager_model: operations.append(migrations.CreateModel( "Food", fields=[ ("id", models.AutoField(primary_key=True)), ], managers=[ ("food_qs", FoodQuerySet.as_manager()), ("food_mgr", FoodManager("a", "b")), ("food_mgr_kwargs", FoodManager("x", "y", 3, 4)), ] )) return self.apply_operations(app_label, ProjectState(), operations) class OperationTests(OperationTestBase): """ Tests running the operations and making sure they do what they say they do. Each test looks at their state changing, and then their database operation - both forwards and backwards. """ def test_create_model(self): """ Tests the CreateModel operation. Most other tests use this operation as part of setup, so check failures here first. """ operation = migrations.CreateModel( "Pony", [ ("id", models.AutoField(primary_key=True)), ("pink", models.IntegerField(default=1)), ], ) self.assertEqual(operation.describe(), "Create model Pony") # Test the state alteration project_state = ProjectState() new_state = project_state.clone() operation.state_forwards("test_crmo", new_state) self.assertEqual(new_state.models["test_crmo", "pony"].name, "Pony") self.assertEqual(len(new_state.models["test_crmo", "pony"].fields), 2) # Test the database alteration self.assertTableNotExists("test_crmo_pony") with connection.schema_editor() as editor: operation.database_forwards("test_crmo", editor, project_state, new_state) self.assertTableExists("test_crmo_pony") # And test reversal with connection.schema_editor() as editor: operation.database_backwards("test_crmo", editor, new_state, project_state) self.assertTableNotExists("test_crmo_pony") # And deconstruction definition = operation.deconstruct() self.assertEqual(definition[0], "CreateModel") self.assertEqual(definition[1], []) self.assertEqual(sorted(definition[2].keys()), ["fields", "name"]) # And default manager not in set operation = migrations.CreateModel("Foo", fields=[], managers=[("objects", models.Manager())]) definition = operation.deconstruct() self.assertNotIn('managers', definition[2]) def test_create_model_with_duplicate_field_name(self): with self.assertRaisesMessage(ValueError, 'Found duplicate value pink in CreateModel fields argument.'): migrations.CreateModel( "Pony", [ ("id", models.AutoField(primary_key=True)), ("pink", models.TextField()), ("pink", models.IntegerField(default=1)), ], ) def test_create_model_with_duplicate_base(self): message = 'Found duplicate value test_crmo.pony in CreateModel bases argument.' with self.assertRaisesMessage(ValueError, message): migrations.CreateModel( "Pony", fields=[], bases=("test_crmo.Pony", "test_crmo.Pony",), ) with self.assertRaisesMessage(ValueError, message): migrations.CreateModel( "Pony", fields=[], bases=("test_crmo.Pony", "test_crmo.pony",), ) message = 'Found duplicate value migrations.unicodemodel in CreateModel bases argument.' with self.assertRaisesMessage(ValueError, message): migrations.CreateModel( "Pony", fields=[], bases=(UnicodeModel, UnicodeModel,), ) with self.assertRaisesMessage(ValueError, message): migrations.CreateModel( "Pony", fields=[], bases=(UnicodeModel, 'migrations.unicodemodel',), ) with self.assertRaisesMessage(ValueError, message): migrations.CreateModel( "Pony", fields=[], bases=(UnicodeModel, 'migrations.UnicodeModel',), ) message = "Found duplicate value <class 'django.db.models.base.Model'> in CreateModel bases argument." with self.assertRaisesMessage(ValueError, message): migrations.CreateModel( "Pony", fields=[], bases=(models.Model, models.Model,), ) message = "Found duplicate value <class 'migrations.test_operations.Mixin'> in CreateModel bases argument." with self.assertRaisesMessage(ValueError, message): migrations.CreateModel( "Pony", fields=[], bases=(Mixin, Mixin,), ) def test_create_model_with_duplicate_manager_name(self): with self.assertRaisesMessage(ValueError, 'Found duplicate value objects in CreateModel managers argument.'): migrations.CreateModel( "Pony", fields=[], managers=[ ("objects", models.Manager()), ("objects", models.Manager()), ], ) def test_create_model_with_unique_after(self): """ Tests the CreateModel operation directly followed by an AlterUniqueTogether (bug #22844 - sqlite remake issues) """ operation1 = migrations.CreateModel( "Pony", [ ("id", models.AutoField(primary_key=True)), ("pink", models.IntegerField(default=1)), ], ) operation2 = migrations.CreateModel( "Rider", [ ("id", models.AutoField(primary_key=True)), ("number", models.IntegerField(default=1)), ("pony", models.ForeignKey("test_crmoua.Pony", models.CASCADE)), ], ) operation3 = migrations.AlterUniqueTogether( "Rider", [ ("number", "pony"), ], ) # Test the database alteration project_state = ProjectState() self.assertTableNotExists("test_crmoua_pony") self.assertTableNotExists("test_crmoua_rider") with connection.schema_editor() as editor: new_state = project_state.clone() operation1.state_forwards("test_crmoua", new_state) operation1.database_forwards("test_crmoua", editor, project_state, new_state) project_state, new_state = new_state, new_state.clone() operation2.state_forwards("test_crmoua", new_state) operation2.database_forwards("test_crmoua", editor, project_state, new_state) project_state, new_state = new_state, new_state.clone() operation3.state_forwards("test_crmoua", new_state) operation3.database_forwards("test_crmoua", editor, project_state, new_state) self.assertTableExists("test_crmoua_pony") self.assertTableExists("test_crmoua_rider") def test_create_model_m2m(self): """ Test the creation of a model with a ManyToMany field and the auto-created "through" model. """ project_state = self.set_up_test_model("test_crmomm") operation = migrations.CreateModel( "Stable", [ ("id", models.AutoField(primary_key=True)), ("ponies", models.ManyToManyField("Pony", related_name="stables")) ] ) # Test the state alteration new_state = project_state.clone() operation.state_forwards("test_crmomm", new_state) # Test the database alteration self.assertTableNotExists("test_crmomm_stable_ponies") with connection.schema_editor() as editor: operation.database_forwards("test_crmomm", editor, project_state, new_state) self.assertTableExists("test_crmomm_stable") self.assertTableExists("test_crmomm_stable_ponies") self.assertColumnNotExists("test_crmomm_stable", "ponies") # Make sure the M2M field actually works with atomic(): Pony = new_state.apps.get_model("test_crmomm", "Pony") Stable = new_state.apps.get_model("test_crmomm", "Stable") stable = Stable.objects.create() p1 = Pony.objects.create(pink=False, weight=4.55) p2 = Pony.objects.create(pink=True, weight=5.43) stable.ponies.add(p1, p2) self.assertEqual(stable.ponies.count(), 2) stable.ponies.all().delete() # And test reversal with connection.schema_editor() as editor: operation.database_backwards("test_crmomm", editor, new_state, project_state) self.assertTableNotExists("test_crmomm_stable") self.assertTableNotExists("test_crmomm_stable_ponies") def test_create_model_inheritance(self): """ Tests the CreateModel operation on a multi-table inheritance setup. """ project_state = self.set_up_test_model("test_crmoih") # Test the state alteration operation = migrations.CreateModel( "ShetlandPony", [ ('pony_ptr', models.OneToOneField( 'test_crmoih.Pony', models.CASCADE, auto_created=True, primary_key=True, to_field='id', serialize=False, )), ("cuteness", models.IntegerField(default=1)), ], ) new_state = project_state.clone() operation.state_forwards("test_crmoih", new_state) self.assertIn(("test_crmoih", "shetlandpony"), new_state.models) # Test the database alteration self.assertTableNotExists("test_crmoih_shetlandpony") with connection.schema_editor() as editor: operation.database_forwards("test_crmoih", editor, project_state, new_state) self.assertTableExists("test_crmoih_shetlandpony") # And test reversal with connection.schema_editor() as editor: operation.database_backwards("test_crmoih", editor, new_state, project_state) self.assertTableNotExists("test_crmoih_shetlandpony") def test_create_proxy_model(self): """ CreateModel ignores proxy models. """ project_state = self.set_up_test_model("test_crprmo") # Test the state alteration operation = migrations.CreateModel( "ProxyPony", [], options={"proxy": True}, bases=("test_crprmo.Pony", ), ) self.assertEqual(operation.describe(), "Create proxy model ProxyPony") new_state = project_state.clone() operation.state_forwards("test_crprmo", new_state) self.assertIn(("test_crprmo", "proxypony"), new_state.models) # Test the database alteration self.assertTableNotExists("test_crprmo_proxypony") self.assertTableExists("test_crprmo_pony") with connection.schema_editor() as editor: operation.database_forwards("test_crprmo", editor, project_state, new_state) self.assertTableNotExists("test_crprmo_proxypony") self.assertTableExists("test_crprmo_pony") # And test reversal with connection.schema_editor() as editor: operation.database_backwards("test_crprmo", editor, new_state, project_state) self.assertTableNotExists("test_crprmo_proxypony") self.assertTableExists("test_crprmo_pony") # And deconstruction definition = operation.deconstruct() self.assertEqual(definition[0], "CreateModel") self.assertEqual(definition[1], []) self.assertEqual(sorted(definition[2].keys()), ["bases", "fields", "name", "options"]) def test_create_unmanaged_model(self): """ CreateModel ignores unmanaged models. """ project_state = self.set_up_test_model("test_crummo") # Test the state alteration operation = migrations.CreateModel( "UnmanagedPony", [], options={"proxy": True}, bases=("test_crummo.Pony", ), ) self.assertEqual(operation.describe(), "Create proxy model UnmanagedPony") new_state = project_state.clone() operation.state_forwards("test_crummo", new_state) self.assertIn(("test_crummo", "unmanagedpony"), new_state.models) # Test the database alteration self.assertTableNotExists("test_crummo_unmanagedpony") self.assertTableExists("test_crummo_pony") with connection.schema_editor() as editor: operation.database_forwards("test_crummo", editor, project_state, new_state) self.assertTableNotExists("test_crummo_unmanagedpony") self.assertTableExists("test_crummo_pony") # And test reversal with connection.schema_editor() as editor: operation.database_backwards("test_crummo", editor, new_state, project_state) self.assertTableNotExists("test_crummo_unmanagedpony") self.assertTableExists("test_crummo_pony") def test_create_model_managers(self): """ The managers on a model are set. """ project_state = self.set_up_test_model("test_cmoma") # Test the state alteration operation = migrations.CreateModel( "Food", fields=[ ("id", models.AutoField(primary_key=True)), ], managers=[ ("food_qs", FoodQuerySet.as_manager()), ("food_mgr", FoodManager("a", "b")), ("food_mgr_kwargs", FoodManager("x", "y", 3, 4)), ] ) self.assertEqual(operation.describe(), "Create model Food") new_state = project_state.clone() operation.state_forwards("test_cmoma", new_state) self.assertIn(("test_cmoma", "food"), new_state.models) managers = new_state.models["test_cmoma", "food"].managers self.assertEqual(managers[0][0], "food_qs") self.assertIsInstance(managers[0][1], models.Manager) self.assertEqual(managers[1][0], "food_mgr") self.assertIsInstance(managers[1][1], FoodManager) self.assertEqual(managers[1][1].args, ("a", "b", 1, 2)) self.assertEqual(managers[2][0], "food_mgr_kwargs") self.assertIsInstance(managers[2][1], FoodManager) self.assertEqual(managers[2][1].args, ("x", "y", 3, 4)) def test_delete_model(self): """ Tests the DeleteModel operation. """ project_state = self.set_up_test_model("test_dlmo") # Test the state alteration operation = migrations.DeleteModel("Pony") self.assertEqual(operation.describe(), "Delete model Pony") new_state = project_state.clone() operation.state_forwards("test_dlmo", new_state) self.assertNotIn(("test_dlmo", "pony"), new_state.models) # Test the database alteration self.assertTableExists("test_dlmo_pony") with connection.schema_editor() as editor: operation.database_forwards("test_dlmo", editor, project_state, new_state) self.assertTableNotExists("test_dlmo_pony") # And test reversal with connection.schema_editor() as editor: operation.database_backwards("test_dlmo", editor, new_state, project_state) self.assertTableExists("test_dlmo_pony") # And deconstruction definition = operation.deconstruct() self.assertEqual(definition[0], "DeleteModel") self.assertEqual(definition[1], []) self.assertEqual(list(definition[2]), ["name"]) def test_delete_proxy_model(self): """ Tests the DeleteModel operation ignores proxy models. """ project_state = self.set_up_test_model("test_dlprmo", proxy_model=True) # Test the state alteration operation = migrations.DeleteModel("ProxyPony") new_state = project_state.clone() operation.state_forwards("test_dlprmo", new_state) self.assertIn(("test_dlprmo", "proxypony"), project_state.models) self.assertNotIn(("test_dlprmo", "proxypony"), new_state.models) # Test the database alteration self.assertTableExists("test_dlprmo_pony") self.assertTableNotExists("test_dlprmo_proxypony") with connection.schema_editor() as editor: operation.database_forwards("test_dlprmo", editor, project_state, new_state) self.assertTableExists("test_dlprmo_pony") self.assertTableNotExists("test_dlprmo_proxypony") # And test reversal with connection.schema_editor() as editor: operation.database_backwards("test_dlprmo", editor, new_state, project_state) self.assertTableExists("test_dlprmo_pony") self.assertTableNotExists("test_dlprmo_proxypony") def test_rename_model(self): """ Tests the RenameModel operation. """ project_state = self.set_up_test_model("test_rnmo", related_model=True) # Test the state alteration operation = migrations.RenameModel("Pony", "Horse") self.assertEqual(operation.describe(), "Rename model Pony to Horse") # Test initial state and database self.assertIn(("test_rnmo", "pony"), project_state.models) self.assertNotIn(("test_rnmo", "horse"), project_state.models) self.assertTableExists("test_rnmo_pony") self.assertTableNotExists("test_rnmo_horse") if connection.features.supports_foreign_keys: self.assertFKExists("test_rnmo_rider", ["pony_id"], ("test_rnmo_pony", "id")) self.assertFKNotExists("test_rnmo_rider", ["pony_id"], ("test_rnmo_horse", "id")) # Migrate forwards new_state = project_state.clone() new_state = self.apply_operations("test_rnmo", new_state, [operation]) # Test new state and database self.assertNotIn(("test_rnmo", "pony"), new_state.models) self.assertIn(("test_rnmo", "horse"), new_state.models) # RenameModel also repoints all incoming FKs and M2Ms self.assertEqual("test_rnmo.Horse", new_state.models["test_rnmo", "rider"].fields[1][1].remote_field.model) self.assertTableNotExists("test_rnmo_pony") self.assertTableExists("test_rnmo_horse") if connection.features.supports_foreign_keys: self.assertFKNotExists("test_rnmo_rider", ["pony_id"], ("test_rnmo_pony", "id")) self.assertFKExists("test_rnmo_rider", ["pony_id"], ("test_rnmo_horse", "id")) # Migrate backwards original_state = self.unapply_operations("test_rnmo", project_state, [operation]) # Test original state and database self.assertIn(("test_rnmo", "pony"), original_state.models) self.assertNotIn(("test_rnmo", "horse"), original_state.models) self.assertEqual("Pony", original_state.models["test_rnmo", "rider"].fields[1][1].remote_field.model) self.assertTableExists("test_rnmo_pony") self.assertTableNotExists("test_rnmo_horse") if connection.features.supports_foreign_keys: self.assertFKExists("test_rnmo_rider", ["pony_id"], ("test_rnmo_pony", "id")) self.assertFKNotExists("test_rnmo_rider", ["pony_id"], ("test_rnmo_horse", "id")) # And deconstruction definition = operation.deconstruct() self.assertEqual(definition[0], "RenameModel") self.assertEqual(definition[1], []) self.assertEqual(definition[2], {'old_name': "Pony", 'new_name': "Horse"}) def test_rename_model_state_forwards(self): """ RenameModel operations shouldn't trigger the caching of rendered apps on state without prior apps. """ state = ProjectState() state.add_model(ModelState('migrations', 'Foo', [])) operation = migrations.RenameModel('Foo', 'Bar') operation.state_forwards('migrations', state) self.assertNotIn('apps', state.__dict__) self.assertNotIn(('migrations', 'foo'), state.models) self.assertIn(('migrations', 'bar'), state.models) # Now with apps cached. apps = state.apps operation = migrations.RenameModel('Bar', 'Foo') operation.state_forwards('migrations', state) self.assertIs(state.apps, apps) self.assertNotIn(('migrations', 'bar'), state.models) self.assertIn(('migrations', 'foo'), state.models) def test_rename_model_with_self_referential_fk(self): """ Tests the RenameModel operation on model with self referential FK. """ project_state = self.set_up_test_model("test_rmwsrf", related_model=True) # Test the state alteration operation = migrations.RenameModel("Rider", "HorseRider") self.assertEqual(operation.describe(), "Rename model Rider to HorseRider") new_state = project_state.clone() operation.state_forwards("test_rmwsrf", new_state) self.assertNotIn(("test_rmwsrf", "rider"), new_state.models) self.assertIn(("test_rmwsrf", "horserider"), new_state.models) # Remember, RenameModel also repoints all incoming FKs and M2Ms self.assertEqual( 'self', new_state.models["test_rmwsrf", "horserider"].fields[2][1].remote_field.model ) HorseRider = new_state.apps.get_model('test_rmwsrf', 'horserider') self.assertIs(HorseRider._meta.get_field('horserider').remote_field.model, HorseRider) # Test the database alteration self.assertTableExists("test_rmwsrf_rider") self.assertTableNotExists("test_rmwsrf_horserider") if connection.features.supports_foreign_keys: self.assertFKExists("test_rmwsrf_rider", ["friend_id"], ("test_rmwsrf_rider", "id")) self.assertFKNotExists("test_rmwsrf_rider", ["friend_id"], ("test_rmwsrf_horserider", "id")) with connection.schema_editor() as editor: operation.database_forwards("test_rmwsrf", editor, project_state, new_state) self.assertTableNotExists("test_rmwsrf_rider") self.assertTableExists("test_rmwsrf_horserider") if connection.features.supports_foreign_keys: self.assertFKNotExists("test_rmwsrf_horserider", ["friend_id"], ("test_rmwsrf_rider", "id")) self.assertFKExists("test_rmwsrf_horserider", ["friend_id"], ("test_rmwsrf_horserider", "id")) # And test reversal with connection.schema_editor() as editor: operation.database_backwards("test_rmwsrf", editor, new_state, project_state) self.assertTableExists("test_rmwsrf_rider") self.assertTableNotExists("test_rmwsrf_horserider") if connection.features.supports_foreign_keys: self.assertFKExists("test_rmwsrf_rider", ["friend_id"], ("test_rmwsrf_rider", "id")) self.assertFKNotExists("test_rmwsrf_rider", ["friend_id"], ("test_rmwsrf_horserider", "id")) def test_rename_model_with_superclass_fk(self): """ Tests the RenameModel operation on a model which has a superclass that has a foreign key. """ project_state = self.set_up_test_model("test_rmwsc", related_model=True, mti_model=True) # Test the state alteration operation = migrations.RenameModel("ShetlandPony", "LittleHorse") self.assertEqual(operation.describe(), "Rename model ShetlandPony to LittleHorse") new_state = project_state.clone() operation.state_forwards("test_rmwsc", new_state) self.assertNotIn(("test_rmwsc", "shetlandpony"), new_state.models) self.assertIn(("test_rmwsc", "littlehorse"), new_state.models) # RenameModel shouldn't repoint the superclass's relations, only local ones self.assertEqual( project_state.models["test_rmwsc", "rider"].fields[1][1].remote_field.model, new_state.models["test_rmwsc", "rider"].fields[1][1].remote_field.model ) # Before running the migration we have a table for Shetland Pony, not Little Horse self.assertTableExists("test_rmwsc_shetlandpony") self.assertTableNotExists("test_rmwsc_littlehorse") if connection.features.supports_foreign_keys: # and the foreign key on rider points to pony, not shetland pony self.assertFKExists("test_rmwsc_rider", ["pony_id"], ("test_rmwsc_pony", "id")) self.assertFKNotExists("test_rmwsc_rider", ["pony_id"], ("test_rmwsc_shetlandpony", "id")) with connection.schema_editor() as editor: operation.database_forwards("test_rmwsc", editor, project_state, new_state) # Now we have a little horse table, not shetland pony self.assertTableNotExists("test_rmwsc_shetlandpony") self.assertTableExists("test_rmwsc_littlehorse") if connection.features.supports_foreign_keys: # but the Foreign keys still point at pony, not little horse self.assertFKExists("test_rmwsc_rider", ["pony_id"], ("test_rmwsc_pony", "id")) self.assertFKNotExists("test_rmwsc_rider", ["pony_id"], ("test_rmwsc_littlehorse", "id")) def test_rename_model_with_self_referential_m2m(self): app_label = "test_rename_model_with_self_referential_m2m" project_state = self.apply_operations(app_label, ProjectState(), operations=[ migrations.CreateModel("ReflexivePony", fields=[ ("id", models.AutoField(primary_key=True)), ("ponies", models.ManyToManyField("self")), ]), ]) project_state = self.apply_operations(app_label, project_state, operations=[ migrations.RenameModel("ReflexivePony", "ReflexivePony2"), ]) Pony = project_state.apps.get_model(app_label, "ReflexivePony2") pony = Pony.objects.create() pony.ponies.add(pony) def test_rename_model_with_m2m(self): app_label = "test_rename_model_with_m2m" project_state = self.apply_operations(app_label, ProjectState(), operations=[ migrations.CreateModel("Rider", fields=[ ("id", models.AutoField(primary_key=True)), ]), migrations.CreateModel("Pony", fields=[ ("id", models.AutoField(primary_key=True)), ("riders", models.ManyToManyField("Rider")), ]), ]) Pony = project_state.apps.get_model(app_label, "Pony") Rider = project_state.apps.get_model(app_label, "Rider") pony = Pony.objects.create() rider = Rider.objects.create() pony.riders.add(rider) project_state = self.apply_operations(app_label, project_state, operations=[ migrations.RenameModel("Pony", "Pony2"), ]) Pony = project_state.apps.get_model(app_label, "Pony2") Rider = project_state.apps.get_model(app_label, "Rider") pony = Pony.objects.create() rider = Rider.objects.create() pony.riders.add(rider) self.assertEqual(Pony.objects.count(), 2) self.assertEqual(Rider.objects.count(), 2) self.assertEqual(Pony._meta.get_field('riders').remote_field.through.objects.count(), 2) def test_rename_m2m_target_model(self): app_label = "test_rename_m2m_target_model" project_state = self.apply_operations(app_label, ProjectState(), operations=[ migrations.CreateModel("Rider", fields=[ ("id", models.AutoField(primary_key=True)), ]), migrations.CreateModel("Pony", fields=[ ("id", models.AutoField(primary_key=True)), ("riders", models.ManyToManyField("Rider")), ]), ]) Pony = project_state.apps.get_model(app_label, "Pony") Rider = project_state.apps.get_model(app_label, "Rider") pony = Pony.objects.create() rider = Rider.objects.create() pony.riders.add(rider) project_state = self.apply_operations(app_label, project_state, operations=[ migrations.RenameModel("Rider", "Rider2"), ]) Pony = project_state.apps.get_model(app_label, "Pony") Rider = project_state.apps.get_model(app_label, "Rider2") pony = Pony.objects.create() rider = Rider.objects.create() pony.riders.add(rider) self.assertEqual(Pony.objects.count(), 2) self.assertEqual(Rider.objects.count(), 2) self.assertEqual(Pony._meta.get_field('riders').remote_field.through.objects.count(), 2) def test_rename_m2m_through_model(self): app_label = "test_rename_through" project_state = self.apply_operations(app_label, ProjectState(), operations=[ migrations.CreateModel("Rider", fields=[ ("id", models.AutoField(primary_key=True)), ]), migrations.CreateModel("Pony", fields=[ ("id", models.AutoField(primary_key=True)), ]), migrations.CreateModel("PonyRider", fields=[ ("id", models.AutoField(primary_key=True)), ("rider", models.ForeignKey("test_rename_through.Rider", models.CASCADE)), ("pony", models.ForeignKey("test_rename_through.Pony", models.CASCADE)), ]), migrations.AddField( "Pony", "riders", models.ManyToManyField("test_rename_through.Rider", through="test_rename_through.PonyRider"), ), ]) Pony = project_state.apps.get_model(app_label, "Pony") Rider = project_state.apps.get_model(app_label, "Rider") PonyRider = project_state.apps.get_model(app_label, "PonyRider") pony = Pony.objects.create() rider = Rider.objects.create() PonyRider.objects.create(pony=pony, rider=rider) project_state = self.apply_operations(app_label, project_state, operations=[ migrations.RenameModel("PonyRider", "PonyRider2"), ]) Pony = project_state.apps.get_model(app_label, "Pony") Rider = project_state.apps.get_model(app_label, "Rider") PonyRider = project_state.apps.get_model(app_label, "PonyRider2") pony = Pony.objects.first() rider = Rider.objects.create() PonyRider.objects.create(pony=pony, rider=rider) self.assertEqual(Pony.objects.count(), 1) self.assertEqual(Rider.objects.count(), 2) self.assertEqual(PonyRider.objects.count(), 2) self.assertEqual(pony.riders.count(), 2) def test_add_field(self): """ Tests the AddField operation. """ # Test the state alteration operation = migrations.AddField( "Pony", "height", models.FloatField(null=True, default=5), ) self.assertEqual(operation.describe(), "Add field height to Pony") project_state, new_state = self.make_test_state("test_adfl", operation) self.assertEqual(len(new_state.models["test_adfl", "pony"].fields), 4) field = [ f for n, f in new_state.models["test_adfl", "pony"].fields if n == "height" ][0] self.assertEqual(field.default, 5) # Test the database alteration self.assertColumnNotExists("test_adfl_pony", "height") with connection.schema_editor() as editor: operation.database_forwards("test_adfl", editor, project_state, new_state) self.assertColumnExists("test_adfl_pony", "height") # And test reversal with connection.schema_editor() as editor: operation.database_backwards("test_adfl", editor, new_state, project_state) self.assertColumnNotExists("test_adfl_pony", "height") # And deconstruction definition = operation.deconstruct() self.assertEqual(definition[0], "AddField") self.assertEqual(definition[1], []) self.assertEqual(sorted(definition[2]), ["field", "model_name", "name"]) def test_add_charfield(self): """ Tests the AddField operation on TextField. """ project_state = self.set_up_test_model("test_adchfl") Pony = project_state.apps.get_model("test_adchfl", "Pony") pony = Pony.objects.create(weight=42) new_state = self.apply_operations("test_adchfl", project_state, [ migrations.AddField( "Pony", "text", models.CharField(max_length=10, default="some text"), ), migrations.AddField( "Pony", "empty", models.CharField(max_length=10, default=""), ), # If not properly quoted digits would be interpreted as an int. migrations.AddField( "Pony", "digits", models.CharField(max_length=10, default="42"), ), # Manual quoting is fragile and could trip on quotes. Refs #xyz. migrations.AddField( "Pony", "quotes", models.CharField(max_length=10, default='"\'"'), ), ]) Pony = new_state.apps.get_model("test_adchfl", "Pony") pony = Pony.objects.get(pk=pony.pk) self.assertEqual(pony.text, "some text") self.assertEqual(pony.empty, "") self.assertEqual(pony.digits, "42") self.assertEqual(pony.quotes, '"\'"') def test_add_textfield(self): """ Tests the AddField operation on TextField. """ project_state = self.set_up_test_model("test_adtxtfl") Pony = project_state.apps.get_model("test_adtxtfl", "Pony") pony = Pony.objects.create(weight=42) new_state = self.apply_operations("test_adtxtfl", project_state, [ migrations.AddField( "Pony", "text", models.TextField(default="some text"), ), migrations.AddField( "Pony", "empty", models.TextField(default=""), ), # If not properly quoted digits would be interpreted as an int. migrations.AddField( "Pony", "digits", models.TextField(default="42"), ), # Manual quoting is fragile and could trip on quotes. Refs #xyz. migrations.AddField( "Pony", "quotes", models.TextField(default='"\'"'), ), ]) Pony = new_state.apps.get_model("test_adtxtfl", "Pony") pony = Pony.objects.get(pk=pony.pk) self.assertEqual(pony.text, "some text") self.assertEqual(pony.empty, "") self.assertEqual(pony.digits, "42") self.assertEqual(pony.quotes, '"\'"') def test_add_binaryfield(self): """ Tests the AddField operation on TextField/BinaryField. """ project_state = self.set_up_test_model("test_adbinfl") Pony = project_state.apps.get_model("test_adbinfl", "Pony") pony = Pony.objects.create(weight=42) new_state = self.apply_operations("test_adbinfl", project_state, [ migrations.AddField( "Pony", "blob", models.BinaryField(default=b"some text"), ), migrations.AddField( "Pony", "empty", models.BinaryField(default=b""), ), # If not properly quoted digits would be interpreted as an int. migrations.AddField( "Pony", "digits", models.BinaryField(default=b"42"), ), # Manual quoting is fragile and could trip on quotes. Refs #xyz. migrations.AddField( "Pony", "quotes", models.BinaryField(default=b'"\'"'), ), ]) Pony = new_state.apps.get_model("test_adbinfl", "Pony") pony = Pony.objects.get(pk=pony.pk) # SQLite returns buffer/memoryview, cast to bytes for checking. self.assertEqual(bytes(pony.blob), b"some text") self.assertEqual(bytes(pony.empty), b"") self.assertEqual(bytes(pony.digits), b"42") self.assertEqual(bytes(pony.quotes), b'"\'"') def test_column_name_quoting(self): """ Column names that are SQL keywords shouldn't cause problems when used in migrations (#22168). """ project_state = self.set_up_test_model("test_regr22168") operation = migrations.AddField( "Pony", "order", models.IntegerField(default=0), ) new_state = project_state.clone() operation.state_forwards("test_regr22168", new_state) with connection.schema_editor() as editor: operation.database_forwards("test_regr22168", editor, project_state, new_state) self.assertColumnExists("test_regr22168_pony", "order") def test_add_field_preserve_default(self): """ Tests the AddField operation's state alteration when preserve_default = False. """ project_state = self.set_up_test_model("test_adflpd") # Test the state alteration operation = migrations.AddField( "Pony", "height", models.FloatField(null=True, default=4), preserve_default=False, ) new_state = project_state.clone() operation.state_forwards("test_adflpd", new_state) self.assertEqual(len(new_state.models["test_adflpd", "pony"].fields), 4) field = [ f for n, f in new_state.models["test_adflpd", "pony"].fields if n == "height" ][0] self.assertEqual(field.default, NOT_PROVIDED) # Test the database alteration project_state.apps.get_model("test_adflpd", "pony").objects.create( weight=4, ) self.assertColumnNotExists("test_adflpd_pony", "height") with connection.schema_editor() as editor: operation.database_forwards("test_adflpd", editor, project_state, new_state) self.assertColumnExists("test_adflpd_pony", "height") # And deconstruction definition = operation.deconstruct() self.assertEqual(definition[0], "AddField") self.assertEqual(definition[1], []) self.assertEqual(sorted(definition[2]), ["field", "model_name", "name", "preserve_default"]) def test_add_field_m2m(self): """ Tests the AddField operation with a ManyToManyField. """ project_state = self.set_up_test_model("test_adflmm", second_model=True) # Test the state alteration operation = migrations.AddField("Pony", "stables", models.ManyToManyField("Stable", related_name="ponies")) new_state = project_state.clone() operation.state_forwards("test_adflmm", new_state) self.assertEqual(len(new_state.models["test_adflmm", "pony"].fields), 4) # Test the database alteration self.assertTableNotExists("test_adflmm_pony_stables") with connection.schema_editor() as editor: operation.database_forwards("test_adflmm", editor, project_state, new_state) self.assertTableExists("test_adflmm_pony_stables") self.assertColumnNotExists("test_adflmm_pony", "stables") # Make sure the M2M field actually works with atomic(): Pony = new_state.apps.get_model("test_adflmm", "Pony") p = Pony.objects.create(pink=False, weight=4.55) p.stables.create() self.assertEqual(p.stables.count(), 1) p.stables.all().delete() # And test reversal with connection.schema_editor() as editor: operation.database_backwards("test_adflmm", editor, new_state, project_state) self.assertTableNotExists("test_adflmm_pony_stables") def test_alter_field_m2m(self): project_state = self.set_up_test_model("test_alflmm", second_model=True) project_state = self.apply_operations("test_alflmm", project_state, operations=[ migrations.AddField("Pony", "stables", models.ManyToManyField("Stable", related_name="ponies")) ]) Pony = project_state.apps.get_model("test_alflmm", "Pony") self.assertFalse(Pony._meta.get_field('stables').blank) project_state = self.apply_operations("test_alflmm", project_state, operations=[ migrations.AlterField( "Pony", "stables", models.ManyToManyField(to="Stable", related_name="ponies", blank=True) ) ]) Pony = project_state.apps.get_model("test_alflmm", "Pony") self.assertTrue(Pony._meta.get_field('stables').blank) def test_repoint_field_m2m(self): project_state = self.set_up_test_model("test_alflmm", second_model=True, third_model=True) project_state = self.apply_operations("test_alflmm", project_state, operations=[ migrations.AddField("Pony", "places", models.ManyToManyField("Stable", related_name="ponies")) ]) Pony = project_state.apps.get_model("test_alflmm", "Pony") project_state = self.apply_operations("test_alflmm", project_state, operations=[ migrations.AlterField("Pony", "places", models.ManyToManyField(to="Van", related_name="ponies")) ]) # Ensure the new field actually works Pony = project_state.apps.get_model("test_alflmm", "Pony") p = Pony.objects.create(pink=False, weight=4.55) p.places.create() self.assertEqual(p.places.count(), 1) p.places.all().delete() def test_remove_field_m2m(self): project_state = self.set_up_test_model("test_rmflmm", second_model=True) project_state = self.apply_operations("test_rmflmm", project_state, operations=[ migrations.AddField("Pony", "stables", models.ManyToManyField("Stable", related_name="ponies")) ]) self.assertTableExists("test_rmflmm_pony_stables") with_field_state = project_state.clone() operations = [migrations.RemoveField("Pony", "stables")] project_state = self.apply_operations("test_rmflmm", project_state, operations=operations) self.assertTableNotExists("test_rmflmm_pony_stables") # And test reversal self.unapply_operations("test_rmflmm", with_field_state, operations=operations) self.assertTableExists("test_rmflmm_pony_stables") def test_remove_field_m2m_with_through(self): project_state = self.set_up_test_model("test_rmflmmwt", second_model=True) self.assertTableNotExists("test_rmflmmwt_ponystables") project_state = self.apply_operations("test_rmflmmwt", project_state, operations=[ migrations.CreateModel("PonyStables", fields=[ ("pony", models.ForeignKey('test_rmflmmwt.Pony', models.CASCADE)), ("stable", models.ForeignKey('test_rmflmmwt.Stable', models.CASCADE)), ]), migrations.AddField( "Pony", "stables", models.ManyToManyField("Stable", related_name="ponies", through='test_rmflmmwt.PonyStables') ) ]) self.assertTableExists("test_rmflmmwt_ponystables") operations = [migrations.RemoveField("Pony", "stables")] self.apply_operations("test_rmflmmwt", project_state, operations=operations) def test_remove_field(self): """ Tests the RemoveField operation. """ project_state = self.set_up_test_model("test_rmfl") # Test the state alteration operation = migrations.RemoveField("Pony", "pink") self.assertEqual(operation.describe(), "Remove field pink from Pony") new_state = project_state.clone() operation.state_forwards("test_rmfl", new_state) self.assertEqual(len(new_state.models["test_rmfl", "pony"].fields), 2) # Test the database alteration self.assertColumnExists("test_rmfl_pony", "pink") with connection.schema_editor() as editor: operation.database_forwards("test_rmfl", editor, project_state, new_state) self.assertColumnNotExists("test_rmfl_pony", "pink") # And test reversal with connection.schema_editor() as editor: operation.database_backwards("test_rmfl", editor, new_state, project_state) self.assertColumnExists("test_rmfl_pony", "pink") # And deconstruction definition = operation.deconstruct() self.assertEqual(definition[0], "RemoveField") self.assertEqual(definition[1], []) self.assertEqual(definition[2], {'model_name': "Pony", 'name': 'pink'}) def test_remove_fk(self): """ Tests the RemoveField operation on a foreign key. """ project_state = self.set_up_test_model("test_rfk", related_model=True) self.assertColumnExists("test_rfk_rider", "pony_id") operation = migrations.RemoveField("Rider", "pony") new_state = project_state.clone() operation.state_forwards("test_rfk", new_state) with connection.schema_editor() as editor: operation.database_forwards("test_rfk", editor, project_state, new_state) self.assertColumnNotExists("test_rfk_rider", "pony_id") with connection.schema_editor() as editor: operation.database_backwards("test_rfk", editor, new_state, project_state) self.assertColumnExists("test_rfk_rider", "pony_id") def test_alter_model_table(self): """ Tests the AlterModelTable operation. """ project_state = self.set_up_test_model("test_almota") # Test the state alteration operation = migrations.AlterModelTable("Pony", "test_almota_pony_2") self.assertEqual(operation.describe(), "Rename table for Pony to test_almota_pony_2") new_state = project_state.clone() operation.state_forwards("test_almota", new_state) self.assertEqual(new_state.models["test_almota", "pony"].options["db_table"], "test_almota_pony_2") # Test the database alteration self.assertTableExists("test_almota_pony") self.assertTableNotExists("test_almota_pony_2") with connection.schema_editor() as editor: operation.database_forwards("test_almota", editor, project_state, new_state) self.assertTableNotExists("test_almota_pony") self.assertTableExists("test_almota_pony_2") # And test reversal with connection.schema_editor() as editor: operation.database_backwards("test_almota", editor, new_state, project_state) self.assertTableExists("test_almota_pony") self.assertTableNotExists("test_almota_pony_2") # And deconstruction definition = operation.deconstruct() self.assertEqual(definition[0], "AlterModelTable") self.assertEqual(definition[1], []) self.assertEqual(definition[2], {'name': "Pony", 'table': "test_almota_pony_2"}) def test_alter_model_table_none(self): """ Tests the AlterModelTable operation if the table name is set to None. """ operation = migrations.AlterModelTable("Pony", None) self.assertEqual(operation.describe(), "Rename table for Pony to (default)") def test_alter_model_table_noop(self): """ Tests the AlterModelTable operation if the table name is not changed. """ project_state = self.set_up_test_model("test_almota") # Test the state alteration operation = migrations.AlterModelTable("Pony", "test_almota_pony") new_state = project_state.clone() operation.state_forwards("test_almota", new_state) self.assertEqual(new_state.models["test_almota", "pony"].options["db_table"], "test_almota_pony") # Test the database alteration self.assertTableExists("test_almota_pony") with connection.schema_editor() as editor: operation.database_forwards("test_almota", editor, project_state, new_state) self.assertTableExists("test_almota_pony") # And test reversal with connection.schema_editor() as editor: operation.database_backwards("test_almota", editor, new_state, project_state) self.assertTableExists("test_almota_pony") def test_alter_model_table_m2m(self): """ AlterModelTable should rename auto-generated M2M tables. """ app_label = "test_talflmltlm2m" pony_db_table = 'pony_foo' project_state = self.set_up_test_model(app_label, second_model=True, db_table=pony_db_table) # Add the M2M field first_state = project_state.clone() operation = migrations.AddField("Pony", "stables", models.ManyToManyField("Stable")) operation.state_forwards(app_label, first_state) with connection.schema_editor() as editor: operation.database_forwards(app_label, editor, project_state, first_state) original_m2m_table = "%s_%s" % (pony_db_table, "stables") new_m2m_table = "%s_%s" % (app_label, "pony_stables") self.assertTableExists(original_m2m_table) self.assertTableNotExists(new_m2m_table) # Rename the Pony db_table which should also rename the m2m table. second_state = first_state.clone() operation = migrations.AlterModelTable(name='pony', table=None) operation.state_forwards(app_label, second_state) with connection.schema_editor() as editor: operation.database_forwards(app_label, editor, first_state, second_state) self.assertTableExists(new_m2m_table) self.assertTableNotExists(original_m2m_table) # And test reversal with connection.schema_editor() as editor: operation.database_backwards(app_label, editor, second_state, first_state) self.assertTableExists(original_m2m_table) self.assertTableNotExists(new_m2m_table) def test_alter_field(self): """ Tests the AlterField operation. """ project_state = self.set_up_test_model("test_alfl") # Test the state alteration operation = migrations.AlterField("Pony", "pink", models.IntegerField(null=True)) self.assertEqual(operation.describe(), "Alter field pink on Pony") new_state = project_state.clone() operation.state_forwards("test_alfl", new_state) self.assertIs(project_state.models["test_alfl", "pony"].get_field_by_name("pink").null, False) self.assertIs(new_state.models["test_alfl", "pony"].get_field_by_name("pink").null, True) # Test the database alteration self.assertColumnNotNull("test_alfl_pony", "pink") with connection.schema_editor() as editor: operation.database_forwards("test_alfl", editor, project_state, new_state) self.assertColumnNull("test_alfl_pony", "pink") # And test reversal with connection.schema_editor() as editor: operation.database_backwards("test_alfl", editor, new_state, project_state) self.assertColumnNotNull("test_alfl_pony", "pink") # And deconstruction definition = operation.deconstruct() self.assertEqual(definition[0], "AlterField") self.assertEqual(definition[1], []) self.assertEqual(sorted(definition[2]), ["field", "model_name", "name"]) def test_alter_field_pk(self): """ Tests the AlterField operation on primary keys (for things like PostgreSQL's SERIAL weirdness) """ project_state = self.set_up_test_model("test_alflpk") # Test the state alteration operation = migrations.AlterField("Pony", "id", models.IntegerField(primary_key=True)) new_state = project_state.clone() operation.state_forwards("test_alflpk", new_state) self.assertIsInstance(project_state.models["test_alflpk", "pony"].get_field_by_name("id"), models.AutoField) self.assertIsInstance(new_state.models["test_alflpk", "pony"].get_field_by_name("id"), models.IntegerField) # Test the database alteration with connection.schema_editor() as editor: operation.database_forwards("test_alflpk", editor, project_state, new_state) # And test reversal with connection.schema_editor() as editor: operation.database_backwards("test_alflpk", editor, new_state, project_state) @skipUnlessDBFeature('supports_foreign_keys') def test_alter_field_pk_fk(self): """ Tests the AlterField operation on primary keys changes any FKs pointing to it. """ project_state = self.set_up_test_model("test_alflpkfk", related_model=True) # Test the state alteration operation = migrations.AlterField("Pony", "id", models.FloatField(primary_key=True)) new_state = project_state.clone() operation.state_forwards("test_alflpkfk", new_state) self.assertIsInstance(project_state.models["test_alflpkfk", "pony"].get_field_by_name("id"), models.AutoField) self.assertIsInstance(new_state.models["test_alflpkfk", "pony"].get_field_by_name("id"), models.FloatField) def assertIdTypeEqualsFkType(): with connection.cursor() as cursor: id_type, id_null = [ (c.type_code, c.null_ok) for c in connection.introspection.get_table_description(cursor, "test_alflpkfk_pony") if c.name == "id" ][0] fk_type, fk_null = [ (c.type_code, c.null_ok) for c in connection.introspection.get_table_description(cursor, "test_alflpkfk_rider") if c.name == "pony_id" ][0] self.assertEqual(id_type, fk_type) self.assertEqual(id_null, fk_null) assertIdTypeEqualsFkType() # Test the database alteration with connection.schema_editor() as editor: operation.database_forwards("test_alflpkfk", editor, project_state, new_state) assertIdTypeEqualsFkType() # And test reversal with connection.schema_editor() as editor: operation.database_backwards("test_alflpkfk", editor, new_state, project_state) assertIdTypeEqualsFkType() def test_rename_field(self): """ Tests the RenameField operation. """ project_state = self.set_up_test_model("test_rnfl", unique_together=True, index_together=True) # Test the state alteration operation = migrations.RenameField("Pony", "pink", "blue") self.assertEqual(operation.describe(), "Rename field pink on Pony to blue") new_state = project_state.clone() operation.state_forwards("test_rnfl", new_state) self.assertIn("blue", [n for n, f in new_state.models["test_rnfl", "pony"].fields]) self.assertNotIn("pink", [n for n, f in new_state.models["test_rnfl", "pony"].fields]) # Make sure the unique_together has the renamed column too self.assertIn("blue", new_state.models["test_rnfl", "pony"].options['unique_together'][0]) self.assertNotIn("pink", new_state.models["test_rnfl", "pony"].options['unique_together'][0]) # Make sure the index_together has the renamed column too self.assertIn("blue", new_state.models["test_rnfl", "pony"].options['index_together'][0]) self.assertNotIn("pink", new_state.models["test_rnfl", "pony"].options['index_together'][0]) # Test the database alteration self.assertColumnExists("test_rnfl_pony", "pink") self.assertColumnNotExists("test_rnfl_pony", "blue") with connection.schema_editor() as editor: operation.database_forwards("test_rnfl", editor, project_state, new_state) self.assertColumnExists("test_rnfl_pony", "blue") self.assertColumnNotExists("test_rnfl_pony", "pink") # Ensure the unique constraint has been ported over with connection.cursor() as cursor: cursor.execute("INSERT INTO test_rnfl_pony (blue, weight) VALUES (1, 1)") with self.assertRaises(IntegrityError): with atomic(): cursor.execute("INSERT INTO test_rnfl_pony (blue, weight) VALUES (1, 1)") cursor.execute("DELETE FROM test_rnfl_pony") # Ensure the index constraint has been ported over self.assertIndexExists("test_rnfl_pony", ["weight", "blue"]) # And test reversal with connection.schema_editor() as editor: operation.database_backwards("test_rnfl", editor, new_state, project_state) self.assertColumnExists("test_rnfl_pony", "pink") self.assertColumnNotExists("test_rnfl_pony", "blue") # Ensure the index constraint has been reset self.assertIndexExists("test_rnfl_pony", ["weight", "pink"]) # And deconstruction definition = operation.deconstruct() self.assertEqual(definition[0], "RenameField") self.assertEqual(definition[1], []) self.assertEqual(definition[2], {'model_name': "Pony", 'old_name': "pink", 'new_name': "blue"}) def test_alter_unique_together(self): """ Tests the AlterUniqueTogether operation. """ project_state = self.set_up_test_model("test_alunto") # Test the state alteration operation = migrations.AlterUniqueTogether("Pony", [("pink", "weight")]) self.assertEqual(operation.describe(), "Alter unique_together for Pony (1 constraint(s))") new_state = project_state.clone() operation.state_forwards("test_alunto", new_state) self.assertEqual(len(project_state.models["test_alunto", "pony"].options.get("unique_together", set())), 0) self.assertEqual(len(new_state.models["test_alunto", "pony"].options.get("unique_together", set())), 1) # Make sure we can insert duplicate rows with connection.cursor() as cursor: cursor.execute("INSERT INTO test_alunto_pony (pink, weight) VALUES (1, 1)") cursor.execute("INSERT INTO test_alunto_pony (pink, weight) VALUES (1, 1)") cursor.execute("DELETE FROM test_alunto_pony") # Test the database alteration with connection.schema_editor() as editor: operation.database_forwards("test_alunto", editor, project_state, new_state) cursor.execute("INSERT INTO test_alunto_pony (pink, weight) VALUES (1, 1)") with self.assertRaises(IntegrityError): with atomic(): cursor.execute("INSERT INTO test_alunto_pony (pink, weight) VALUES (1, 1)") cursor.execute("DELETE FROM test_alunto_pony") # And test reversal with connection.schema_editor() as editor: operation.database_backwards("test_alunto", editor, new_state, project_state) cursor.execute("INSERT INTO test_alunto_pony (pink, weight) VALUES (1, 1)") cursor.execute("INSERT INTO test_alunto_pony (pink, weight) VALUES (1, 1)") cursor.execute("DELETE FROM test_alunto_pony") # Test flat unique_together operation = migrations.AlterUniqueTogether("Pony", ("pink", "weight")) operation.state_forwards("test_alunto", new_state) self.assertEqual(len(new_state.models["test_alunto", "pony"].options.get("unique_together", set())), 1) # And deconstruction definition = operation.deconstruct() self.assertEqual(definition[0], "AlterUniqueTogether") self.assertEqual(definition[1], []) self.assertEqual(definition[2], {'name': "Pony", 'unique_together': {("pink", "weight")}}) def test_alter_unique_together_remove(self): operation = migrations.AlterUniqueTogether("Pony", None) self.assertEqual(operation.describe(), "Alter unique_together for Pony (0 constraint(s))") def test_add_index(self): """ Test the AddIndex operation. """ project_state = self.set_up_test_model("test_adin") msg = ( "Indexes passed to AddIndex operations require a name argument. " "<Index: fields='pink'> doesn't have one." ) with self.assertRaisesMessage(ValueError, msg): migrations.AddIndex("Pony", models.Index(fields=["pink"])) index = models.Index(fields=["pink"], name="test_adin_pony_pink_idx") operation = migrations.AddIndex("Pony", index) self.assertEqual(operation.describe(), "Create index test_adin_pony_pink_idx on field(s) pink of model Pony") new_state = project_state.clone() operation.state_forwards("test_adin", new_state) # Test the database alteration self.assertEqual(len(new_state.models["test_adin", "pony"].options['indexes']), 1) self.assertIndexNotExists("test_adin_pony", ["pink"]) with connection.schema_editor() as editor: operation.database_forwards("test_adin", editor, project_state, new_state) self.assertIndexExists("test_adin_pony", ["pink"]) # And test reversal with connection.schema_editor() as editor: operation.database_backwards("test_adin", editor, new_state, project_state) self.assertIndexNotExists("test_adin_pony", ["pink"]) # And deconstruction definition = operation.deconstruct() self.assertEqual(definition[0], "AddIndex") self.assertEqual(definition[1], []) self.assertEqual(definition[2], {'model_name': "Pony", 'index': index}) def test_remove_index(self): """ Test the RemoveIndex operation. """ project_state = self.set_up_test_model("test_rmin", multicol_index=True) self.assertTableExists("test_rmin_pony") self.assertIndexExists("test_rmin_pony", ["pink", "weight"]) operation = migrations.RemoveIndex("Pony", "pony_test_idx") self.assertEqual(operation.describe(), "Remove index pony_test_idx from Pony") new_state = project_state.clone() operation.state_forwards("test_rmin", new_state) # Test the state alteration self.assertEqual(len(new_state.models["test_rmin", "pony"].options['indexes']), 0) self.assertIndexExists("test_rmin_pony", ["pink", "weight"]) # Test the database alteration with connection.schema_editor() as editor: operation.database_forwards("test_rmin", editor, project_state, new_state) self.assertIndexNotExists("test_rmin_pony", ["pink", "weight"]) # And test reversal with connection.schema_editor() as editor: operation.database_backwards("test_rmin", editor, new_state, project_state) self.assertIndexExists("test_rmin_pony", ["pink", "weight"]) # And deconstruction definition = operation.deconstruct() self.assertEqual(definition[0], "RemoveIndex") self.assertEqual(definition[1], []) self.assertEqual(definition[2], {'model_name': "Pony", 'name': "pony_test_idx"}) # Also test a field dropped with index - sqlite remake issue operations = [ migrations.RemoveIndex("Pony", "pony_test_idx"), migrations.RemoveField("Pony", "pink"), ] self.assertColumnExists("test_rmin_pony", "pink") self.assertIndexExists("test_rmin_pony", ["pink", "weight"]) # Test database alteration new_state = project_state.clone() self.apply_operations('test_rmin', new_state, operations=operations) self.assertColumnNotExists("test_rmin_pony", "pink") self.assertIndexNotExists("test_rmin_pony", ["pink", "weight"]) # And test reversal self.unapply_operations("test_rmin", project_state, operations=operations) self.assertIndexExists("test_rmin_pony", ["pink", "weight"]) def test_alter_field_with_index(self): """ Test AlterField operation with an index to ensure indexes created via Meta.indexes don't get dropped with sqlite3 remake. """ project_state = self.set_up_test_model("test_alflin", index=True) operation = migrations.AlterField("Pony", "pink", models.IntegerField(null=True)) new_state = project_state.clone() operation.state_forwards("test_alflin", new_state) # Test the database alteration self.assertColumnNotNull("test_alflin_pony", "pink") with connection.schema_editor() as editor: operation.database_forwards("test_alflin", editor, project_state, new_state) # Index hasn't been dropped self.assertIndexExists("test_alflin_pony", ["pink"]) # And test reversal with connection.schema_editor() as editor: operation.database_backwards("test_alflin", editor, new_state, project_state) # Ensure the index is still there self.assertIndexExists("test_alflin_pony", ["pink"]) def test_alter_index_together(self): """ Tests the AlterIndexTogether operation. """ project_state = self.set_up_test_model("test_alinto") # Test the state alteration operation = migrations.AlterIndexTogether("Pony", [("pink", "weight")]) self.assertEqual(operation.describe(), "Alter index_together for Pony (1 constraint(s))") new_state = project_state.clone() operation.state_forwards("test_alinto", new_state) self.assertEqual(len(project_state.models["test_alinto", "pony"].options.get("index_together", set())), 0) self.assertEqual(len(new_state.models["test_alinto", "pony"].options.get("index_together", set())), 1) # Make sure there's no matching index self.assertIndexNotExists("test_alinto_pony", ["pink", "weight"]) # Test the database alteration with connection.schema_editor() as editor: operation.database_forwards("test_alinto", editor, project_state, new_state) self.assertIndexExists("test_alinto_pony", ["pink", "weight"]) # And test reversal with connection.schema_editor() as editor: operation.database_backwards("test_alinto", editor, new_state, project_state) self.assertIndexNotExists("test_alinto_pony", ["pink", "weight"]) # And deconstruction definition = operation.deconstruct() self.assertEqual(definition[0], "AlterIndexTogether") self.assertEqual(definition[1], []) self.assertEqual(definition[2], {'name': "Pony", 'index_together': {("pink", "weight")}}) def test_alter_index_together_remove(self): operation = migrations.AlterIndexTogether("Pony", None) self.assertEqual(operation.describe(), "Alter index_together for Pony (0 constraint(s))") def test_alter_model_options(self): """ Tests the AlterModelOptions operation. """ project_state = self.set_up_test_model("test_almoop") # Test the state alteration (no DB alteration to test) operation = migrations.AlterModelOptions("Pony", {"permissions": [("can_groom", "Can groom")]}) self.assertEqual(operation.describe(), "Change Meta options on Pony") new_state = project_state.clone() operation.state_forwards("test_almoop", new_state) self.assertEqual(len(project_state.models["test_almoop", "pony"].options.get("permissions", [])), 0) self.assertEqual(len(new_state.models["test_almoop", "pony"].options.get("permissions", [])), 1) self.assertEqual(new_state.models["test_almoop", "pony"].options["permissions"][0][0], "can_groom") # And deconstruction definition = operation.deconstruct() self.assertEqual(definition[0], "AlterModelOptions") self.assertEqual(definition[1], []) self.assertEqual(definition[2], {'name': "Pony", 'options': {"permissions": [("can_groom", "Can groom")]}}) def test_alter_model_options_emptying(self): """ The AlterModelOptions operation removes keys from the dict (#23121) """ project_state = self.set_up_test_model("test_almoop", options=True) # Test the state alteration (no DB alteration to test) operation = migrations.AlterModelOptions("Pony", {}) self.assertEqual(operation.describe(), "Change Meta options on Pony") new_state = project_state.clone() operation.state_forwards("test_almoop", new_state) self.assertEqual(len(project_state.models["test_almoop", "pony"].options.get("permissions", [])), 1) self.assertEqual(len(new_state.models["test_almoop", "pony"].options.get("permissions", [])), 0) # And deconstruction definition = operation.deconstruct() self.assertEqual(definition[0], "AlterModelOptions") self.assertEqual(definition[1], []) self.assertEqual(definition[2], {'name': "Pony", 'options': {}}) def test_alter_order_with_respect_to(self): """ Tests the AlterOrderWithRespectTo operation. """ project_state = self.set_up_test_model("test_alorwrtto", related_model=True) # Test the state alteration operation = migrations.AlterOrderWithRespectTo("Rider", "pony") self.assertEqual(operation.describe(), "Set order_with_respect_to on Rider to pony") new_state = project_state.clone() operation.state_forwards("test_alorwrtto", new_state) self.assertIsNone( project_state.models["test_alorwrtto", "rider"].options.get("order_with_respect_to", None) ) self.assertEqual( new_state.models["test_alorwrtto", "rider"].options.get("order_with_respect_to", None), "pony" ) # Make sure there's no matching index self.assertColumnNotExists("test_alorwrtto_rider", "_order") # Create some rows before alteration rendered_state = project_state.apps pony = rendered_state.get_model("test_alorwrtto", "Pony").objects.create(weight=50) rendered_state.get_model("test_alorwrtto", "Rider").objects.create(pony=pony, friend_id=1) rendered_state.get_model("test_alorwrtto", "Rider").objects.create(pony=pony, friend_id=2) # Test the database alteration with connection.schema_editor() as editor: operation.database_forwards("test_alorwrtto", editor, project_state, new_state) self.assertColumnExists("test_alorwrtto_rider", "_order") # Check for correct value in rows updated_riders = new_state.apps.get_model("test_alorwrtto", "Rider").objects.all() self.assertEqual(updated_riders[0]._order, 0) self.assertEqual(updated_riders[1]._order, 0) # And test reversal with connection.schema_editor() as editor: operation.database_backwards("test_alorwrtto", editor, new_state, project_state) self.assertColumnNotExists("test_alorwrtto_rider", "_order") # And deconstruction definition = operation.deconstruct() self.assertEqual(definition[0], "AlterOrderWithRespectTo") self.assertEqual(definition[1], []) self.assertEqual(definition[2], {'name': "Rider", 'order_with_respect_to': "pony"}) def test_alter_model_managers(self): """ The managers on a model are set. """ project_state = self.set_up_test_model("test_almoma") # Test the state alteration operation = migrations.AlterModelManagers( "Pony", managers=[ ("food_qs", FoodQuerySet.as_manager()), ("food_mgr", FoodManager("a", "b")), ("food_mgr_kwargs", FoodManager("x", "y", 3, 4)), ] ) self.assertEqual(operation.describe(), "Change managers on Pony") managers = project_state.models["test_almoma", "pony"].managers self.assertEqual(managers, []) new_state = project_state.clone() operation.state_forwards("test_almoma", new_state) self.assertIn(("test_almoma", "pony"), new_state.models) managers = new_state.models["test_almoma", "pony"].managers self.assertEqual(managers[0][0], "food_qs") self.assertIsInstance(managers[0][1], models.Manager) self.assertEqual(managers[1][0], "food_mgr") self.assertIsInstance(managers[1][1], FoodManager) self.assertEqual(managers[1][1].args, ("a", "b", 1, 2)) self.assertEqual(managers[2][0], "food_mgr_kwargs") self.assertIsInstance(managers[2][1], FoodManager) self.assertEqual(managers[2][1].args, ("x", "y", 3, 4)) rendered_state = new_state.apps model = rendered_state.get_model('test_almoma', 'pony') self.assertIsInstance(model.food_qs, models.Manager) self.assertIsInstance(model.food_mgr, FoodManager) self.assertIsInstance(model.food_mgr_kwargs, FoodManager) def test_alter_model_managers_emptying(self): """ The managers on a model are set. """ project_state = self.set_up_test_model("test_almomae", manager_model=True) # Test the state alteration operation = migrations.AlterModelManagers("Food", managers=[]) self.assertEqual(operation.describe(), "Change managers on Food") self.assertIn(("test_almomae", "food"), project_state.models) managers = project_state.models["test_almomae", "food"].managers self.assertEqual(managers[0][0], "food_qs") self.assertIsInstance(managers[0][1], models.Manager) self.assertEqual(managers[1][0], "food_mgr") self.assertIsInstance(managers[1][1], FoodManager) self.assertEqual(managers[1][1].args, ("a", "b", 1, 2)) self.assertEqual(managers[2][0], "food_mgr_kwargs") self.assertIsInstance(managers[2][1], FoodManager) self.assertEqual(managers[2][1].args, ("x", "y", 3, 4)) new_state = project_state.clone() operation.state_forwards("test_almomae", new_state) managers = new_state.models["test_almomae", "food"].managers self.assertEqual(managers, []) def test_alter_fk(self): """ Creating and then altering an FK works correctly and deals with the pending SQL (#23091) """ project_state = self.set_up_test_model("test_alfk") # Test adding and then altering the FK in one go create_operation = migrations.CreateModel( name="Rider", fields=[ ("id", models.AutoField(primary_key=True)), ("pony", models.ForeignKey("Pony", models.CASCADE)), ], ) create_state = project_state.clone() create_operation.state_forwards("test_alfk", create_state) alter_operation = migrations.AlterField( model_name='Rider', name='pony', field=models.ForeignKey("Pony", models.CASCADE, editable=False), ) alter_state = create_state.clone() alter_operation.state_forwards("test_alfk", alter_state) with connection.schema_editor() as editor: create_operation.database_forwards("test_alfk", editor, project_state, create_state) alter_operation.database_forwards("test_alfk", editor, create_state, alter_state) def test_alter_fk_non_fk(self): """ Altering an FK to a non-FK works (#23244) """ # Test the state alteration operation = migrations.AlterField( model_name="Rider", name="pony", field=models.FloatField(), ) project_state, new_state = self.make_test_state("test_afknfk", operation, related_model=True) # Test the database alteration self.assertColumnExists("test_afknfk_rider", "pony_id") self.assertColumnNotExists("test_afknfk_rider", "pony") with connection.schema_editor() as editor: operation.database_forwards("test_afknfk", editor, project_state, new_state) self.assertColumnExists("test_afknfk_rider", "pony") self.assertColumnNotExists("test_afknfk_rider", "pony_id") # And test reversal with connection.schema_editor() as editor: operation.database_backwards("test_afknfk", editor, new_state, project_state) self.assertColumnExists("test_afknfk_rider", "pony_id") self.assertColumnNotExists("test_afknfk_rider", "pony") @unittest.skipIf(sqlparse is None and connection.features.requires_sqlparse_for_splitting, "Missing sqlparse") def test_run_sql(self): """ Tests the RunSQL operation. """ project_state = self.set_up_test_model("test_runsql") # Create the operation operation = migrations.RunSQL( # Use a multi-line string with a comment to test splitting on SQLite and MySQL respectively "CREATE TABLE i_love_ponies (id int, special_thing varchar(15));\n" "INSERT INTO i_love_ponies (id, special_thing) VALUES (1, 'i love ponies'); -- this is magic!\n" "INSERT INTO i_love_ponies (id, special_thing) VALUES (2, 'i love django');\n" "UPDATE i_love_ponies SET special_thing = 'Ponies' WHERE special_thing LIKE '%%ponies';" "UPDATE i_love_ponies SET special_thing = 'Django' WHERE special_thing LIKE '%django';", # Run delete queries to test for parameter substitution failure # reported in #23426 "DELETE FROM i_love_ponies WHERE special_thing LIKE '%Django%';" "DELETE FROM i_love_ponies WHERE special_thing LIKE '%%Ponies%%';" "DROP TABLE i_love_ponies", state_operations=[migrations.CreateModel("SomethingElse", [("id", models.AutoField(primary_key=True))])], ) self.assertEqual(operation.describe(), "Raw SQL operation") # Test the state alteration new_state = project_state.clone() operation.state_forwards("test_runsql", new_state) self.assertEqual(len(new_state.models["test_runsql", "somethingelse"].fields), 1) # Make sure there's no table self.assertTableNotExists("i_love_ponies") # Test SQL collection with connection.schema_editor(collect_sql=True) as editor: operation.database_forwards("test_runsql", editor, project_state, new_state) self.assertIn("LIKE '%%ponies';", "\n".join(editor.collected_sql)) operation.database_backwards("test_runsql", editor, project_state, new_state) self.assertIn("LIKE '%%Ponies%%';", "\n".join(editor.collected_sql)) # Test the database alteration with connection.schema_editor() as editor: operation.database_forwards("test_runsql", editor, project_state, new_state) self.assertTableExists("i_love_ponies") # Make sure all the SQL was processed with connection.cursor() as cursor: cursor.execute("SELECT COUNT(*) FROM i_love_ponies") self.assertEqual(cursor.fetchall()[0][0], 2) cursor.execute("SELECT COUNT(*) FROM i_love_ponies WHERE special_thing = 'Django'") self.assertEqual(cursor.fetchall()[0][0], 1) cursor.execute("SELECT COUNT(*) FROM i_love_ponies WHERE special_thing = 'Ponies'") self.assertEqual(cursor.fetchall()[0][0], 1) # And test reversal self.assertTrue(operation.reversible) with connection.schema_editor() as editor: operation.database_backwards("test_runsql", editor, new_state, project_state) self.assertTableNotExists("i_love_ponies") # And deconstruction definition = operation.deconstruct() self.assertEqual(definition[0], "RunSQL") self.assertEqual(definition[1], []) self.assertEqual(sorted(definition[2]), ["reverse_sql", "sql", "state_operations"]) # And elidable reduction self.assertIs(False, operation.reduce(operation, [])) elidable_operation = migrations.RunSQL('SELECT 1 FROM void;', elidable=True) self.assertEqual(elidable_operation.reduce(operation, []), [operation]) def test_run_sql_params(self): """ #23426 - RunSQL should accept parameters. """ project_state = self.set_up_test_model("test_runsql") # Create the operation operation = migrations.RunSQL( ["CREATE TABLE i_love_ponies (id int, special_thing varchar(15));"], ["DROP TABLE i_love_ponies"], ) param_operation = migrations.RunSQL( # forwards ( "INSERT INTO i_love_ponies (id, special_thing) VALUES (1, 'Django');", ["INSERT INTO i_love_ponies (id, special_thing) VALUES (2, %s);", ['Ponies']], ("INSERT INTO i_love_ponies (id, special_thing) VALUES (%s, %s);", (3, 'Python',)), ), # backwards [ "DELETE FROM i_love_ponies WHERE special_thing = 'Django';", ["DELETE FROM i_love_ponies WHERE special_thing = 'Ponies';", None], ("DELETE FROM i_love_ponies WHERE id = %s OR special_thing = %s;", [3, 'Python']), ] ) # Make sure there's no table self.assertTableNotExists("i_love_ponies") new_state = project_state.clone() # Test the database alteration with connection.schema_editor() as editor: operation.database_forwards("test_runsql", editor, project_state, new_state) # Test parameter passing with connection.schema_editor() as editor: param_operation.database_forwards("test_runsql", editor, project_state, new_state) # Make sure all the SQL was processed with connection.cursor() as cursor: cursor.execute("SELECT COUNT(*) FROM i_love_ponies") self.assertEqual(cursor.fetchall()[0][0], 3) with connection.schema_editor() as editor: param_operation.database_backwards("test_runsql", editor, new_state, project_state) with connection.cursor() as cursor: cursor.execute("SELECT COUNT(*) FROM i_love_ponies") self.assertEqual(cursor.fetchall()[0][0], 0) # And test reversal with connection.schema_editor() as editor: operation.database_backwards("test_runsql", editor, new_state, project_state) self.assertTableNotExists("i_love_ponies") def test_run_sql_params_invalid(self): """ #23426 - RunSQL should fail when a list of statements with an incorrect number of tuples is given. """ project_state = self.set_up_test_model("test_runsql") new_state = project_state.clone() operation = migrations.RunSQL( # forwards [ ["INSERT INTO foo (bar) VALUES ('buz');"] ], # backwards ( ("DELETE FROM foo WHERE bar = 'buz';", 'invalid', 'parameter count'), ), ) with connection.schema_editor() as editor: with self.assertRaisesMessage(ValueError, "Expected a 2-tuple but got 1"): operation.database_forwards("test_runsql", editor, project_state, new_state) with connection.schema_editor() as editor: with self.assertRaisesMessage(ValueError, "Expected a 2-tuple but got 3"): operation.database_backwards("test_runsql", editor, new_state, project_state) def test_run_sql_noop(self): """ #24098 - Tests no-op RunSQL operations. """ operation = migrations.RunSQL(migrations.RunSQL.noop, migrations.RunSQL.noop) with connection.schema_editor() as editor: operation.database_forwards("test_runsql", editor, None, None) operation.database_backwards("test_runsql", editor, None, None) def test_run_python(self): """ Tests the RunPython operation """ project_state = self.set_up_test_model("test_runpython", mti_model=True) # Create the operation def inner_method(models, schema_editor): Pony = models.get_model("test_runpython", "Pony") Pony.objects.create(pink=1, weight=3.55) Pony.objects.create(weight=5) def inner_method_reverse(models, schema_editor): Pony = models.get_model("test_runpython", "Pony") Pony.objects.filter(pink=1, weight=3.55).delete() Pony.objects.filter(weight=5).delete() operation = migrations.RunPython(inner_method, reverse_code=inner_method_reverse) self.assertEqual(operation.describe(), "Raw Python operation") # Test the state alteration does nothing new_state = project_state.clone() operation.state_forwards("test_runpython", new_state) self.assertEqual(new_state, project_state) # Test the database alteration self.assertEqual(project_state.apps.get_model("test_runpython", "Pony").objects.count(), 0) with connection.schema_editor() as editor: operation.database_forwards("test_runpython", editor, project_state, new_state) self.assertEqual(project_state.apps.get_model("test_runpython", "Pony").objects.count(), 2) # Now test reversal self.assertTrue(operation.reversible) with connection.schema_editor() as editor: operation.database_backwards("test_runpython", editor, project_state, new_state) self.assertEqual(project_state.apps.get_model("test_runpython", "Pony").objects.count(), 0) # Now test we can't use a string with self.assertRaises(ValueError): migrations.RunPython("print 'ahahaha'") # And deconstruction definition = operation.deconstruct() self.assertEqual(definition[0], "RunPython") self.assertEqual(definition[1], []) self.assertEqual(sorted(definition[2]), ["code", "reverse_code"]) # Also test reversal fails, with an operation identical to above but without reverse_code set no_reverse_operation = migrations.RunPython(inner_method) self.assertFalse(no_reverse_operation.reversible) with connection.schema_editor() as editor: no_reverse_operation.database_forwards("test_runpython", editor, project_state, new_state) with self.assertRaises(NotImplementedError): no_reverse_operation.database_backwards("test_runpython", editor, new_state, project_state) self.assertEqual(project_state.apps.get_model("test_runpython", "Pony").objects.count(), 2) def create_ponies(models, schema_editor): Pony = models.get_model("test_runpython", "Pony") pony1 = Pony.objects.create(pink=1, weight=3.55) self.assertIsNot(pony1.pk, None) pony2 = Pony.objects.create(weight=5) self.assertIsNot(pony2.pk, None) self.assertNotEqual(pony1.pk, pony2.pk) operation = migrations.RunPython(create_ponies) with connection.schema_editor() as editor: operation.database_forwards("test_runpython", editor, project_state, new_state) self.assertEqual(project_state.apps.get_model("test_runpython", "Pony").objects.count(), 4) # And deconstruction definition = operation.deconstruct() self.assertEqual(definition[0], "RunPython") self.assertEqual(definition[1], []) self.assertEqual(sorted(definition[2]), ["code"]) def create_shetlandponies(models, schema_editor): ShetlandPony = models.get_model("test_runpython", "ShetlandPony") pony1 = ShetlandPony.objects.create(weight=4.0) self.assertIsNot(pony1.pk, None) pony2 = ShetlandPony.objects.create(weight=5.0) self.assertIsNot(pony2.pk, None) self.assertNotEqual(pony1.pk, pony2.pk) operation = migrations.RunPython(create_shetlandponies) with connection.schema_editor() as editor: operation.database_forwards("test_runpython", editor, project_state, new_state) self.assertEqual(project_state.apps.get_model("test_runpython", "Pony").objects.count(), 6) self.assertEqual(project_state.apps.get_model("test_runpython", "ShetlandPony").objects.count(), 2) # And elidable reduction self.assertIs(False, operation.reduce(operation, [])) elidable_operation = migrations.RunPython(inner_method, elidable=True) self.assertEqual(elidable_operation.reduce(operation, []), [operation]) def test_run_python_atomic(self): """ Tests the RunPython operation correctly handles the "atomic" keyword """ project_state = self.set_up_test_model("test_runpythonatomic", mti_model=True) def inner_method(models, schema_editor): Pony = models.get_model("test_runpythonatomic", "Pony") Pony.objects.create(pink=1, weight=3.55) raise ValueError("Adrian hates ponies.") atomic_migration = Migration("test", "test_runpythonatomic") atomic_migration.operations = [migrations.RunPython(inner_method)] non_atomic_migration = Migration("test", "test_runpythonatomic") non_atomic_migration.operations = [migrations.RunPython(inner_method, atomic=False)] # If we're a fully-transactional database, both versions should rollback if connection.features.can_rollback_ddl: self.assertEqual(project_state.apps.get_model("test_runpythonatomic", "Pony").objects.count(), 0) with self.assertRaises(ValueError): with connection.schema_editor() as editor: atomic_migration.apply(project_state, editor) self.assertEqual(project_state.apps.get_model("test_runpythonatomic", "Pony").objects.count(), 0) with self.assertRaises(ValueError): with connection.schema_editor() as editor: non_atomic_migration.apply(project_state, editor) self.assertEqual(project_state.apps.get_model("test_runpythonatomic", "Pony").objects.count(), 0) # Otherwise, the non-atomic operation should leave a row there else: self.assertEqual(project_state.apps.get_model("test_runpythonatomic", "Pony").objects.count(), 0) with self.assertRaises(ValueError): with connection.schema_editor() as editor: atomic_migration.apply(project_state, editor) self.assertEqual(project_state.apps.get_model("test_runpythonatomic", "Pony").objects.count(), 0) with self.assertRaises(ValueError): with connection.schema_editor() as editor: non_atomic_migration.apply(project_state, editor) self.assertEqual(project_state.apps.get_model("test_runpythonatomic", "Pony").objects.count(), 1) # And deconstruction definition = non_atomic_migration.operations[0].deconstruct() self.assertEqual(definition[0], "RunPython") self.assertEqual(definition[1], []) self.assertEqual(sorted(definition[2]), ["atomic", "code"]) def test_run_python_related_assignment(self): """ #24282 - Model changes to a FK reverse side update the model on the FK side as well. """ def inner_method(models, schema_editor): Author = models.get_model("test_authors", "Author") Book = models.get_model("test_books", "Book") author = Author.objects.create(name="Hemingway") Book.objects.create(title="Old Man and The Sea", author=author) create_author = migrations.CreateModel( "Author", [ ("id", models.AutoField(primary_key=True)), ("name", models.CharField(max_length=100)), ], options={}, ) create_book = migrations.CreateModel( "Book", [ ("id", models.AutoField(primary_key=True)), ("title", models.CharField(max_length=100)), ("author", models.ForeignKey("test_authors.Author", models.CASCADE)) ], options={}, ) add_hometown = migrations.AddField( "Author", "hometown", models.CharField(max_length=100), ) create_old_man = migrations.RunPython(inner_method, inner_method) project_state = ProjectState() new_state = project_state.clone() with connection.schema_editor() as editor: create_author.state_forwards("test_authors", new_state) create_author.database_forwards("test_authors", editor, project_state, new_state) project_state = new_state new_state = new_state.clone() with connection.schema_editor() as editor: create_book.state_forwards("test_books", new_state) create_book.database_forwards("test_books", editor, project_state, new_state) project_state = new_state new_state = new_state.clone() with connection.schema_editor() as editor: add_hometown.state_forwards("test_authors", new_state) add_hometown.database_forwards("test_authors", editor, project_state, new_state) project_state = new_state new_state = new_state.clone() with connection.schema_editor() as editor: create_old_man.state_forwards("test_books", new_state) create_old_man.database_forwards("test_books", editor, project_state, new_state) def test_model_with_bigautofield(self): """ A model with BigAutoField can be created. """ def create_data(models, schema_editor): Author = models.get_model("test_author", "Author") Book = models.get_model("test_book", "Book") author1 = Author.objects.create(name="Hemingway") Book.objects.create(title="Old Man and The Sea", author=author1) Book.objects.create(id=2 ** 33, title="A farewell to arms", author=author1) author2 = Author.objects.create(id=2 ** 33, name="Remarque") Book.objects.create(title="All quiet on the western front", author=author2) Book.objects.create(title="Arc de Triomphe", author=author2) create_author = migrations.CreateModel( "Author", [ ("id", models.BigAutoField(primary_key=True)), ("name", models.CharField(max_length=100)), ], options={}, ) create_book = migrations.CreateModel( "Book", [ ("id", models.BigAutoField(primary_key=True)), ("title", models.CharField(max_length=100)), ("author", models.ForeignKey(to="test_author.Author", on_delete=models.CASCADE)) ], options={}, ) fill_data = migrations.RunPython(create_data) project_state = ProjectState() new_state = project_state.clone() with connection.schema_editor() as editor: create_author.state_forwards("test_author", new_state) create_author.database_forwards("test_author", editor, project_state, new_state) project_state = new_state new_state = new_state.clone() with connection.schema_editor() as editor: create_book.state_forwards("test_book", new_state) create_book.database_forwards("test_book", editor, project_state, new_state) project_state = new_state new_state = new_state.clone() with connection.schema_editor() as editor: fill_data.state_forwards("fill_data", new_state) fill_data.database_forwards("fill_data", editor, project_state, new_state) def test_autofield_foreignfield_growth(self): """ A field may be migrated from AutoField to BigAutoField. """ def create_initial_data(models, schema_editor): Article = models.get_model("test_article", "Article") Blog = models.get_model("test_blog", "Blog") blog = Blog.objects.create(name="web development done right") Article.objects.create(name="Frameworks", blog=blog) Article.objects.create(name="Programming Languages", blog=blog) def create_big_data(models, schema_editor): Article = models.get_model("test_article", "Article") Blog = models.get_model("test_blog", "Blog") blog2 = Blog.objects.create(name="Frameworks", id=2 ** 33) Article.objects.create(name="Django", blog=blog2) Article.objects.create(id=2 ** 33, name="Django2", blog=blog2) create_blog = migrations.CreateModel( "Blog", [ ("id", models.AutoField(primary_key=True)), ("name", models.CharField(max_length=100)), ], options={}, ) create_article = migrations.CreateModel( "Article", [ ("id", models.AutoField(primary_key=True)), ("blog", models.ForeignKey(to="test_blog.Blog", on_delete=models.CASCADE)), ("name", models.CharField(max_length=100)), ("data", models.TextField(default="")), ], options={}, ) fill_initial_data = migrations.RunPython(create_initial_data, create_initial_data) fill_big_data = migrations.RunPython(create_big_data, create_big_data) grow_article_id = migrations.AlterField("Article", "id", models.BigAutoField(primary_key=True)) grow_blog_id = migrations.AlterField("Blog", "id", models.BigAutoField(primary_key=True)) project_state = ProjectState() new_state = project_state.clone() with connection.schema_editor() as editor: create_blog.state_forwards("test_blog", new_state) create_blog.database_forwards("test_blog", editor, project_state, new_state) project_state = new_state new_state = new_state.clone() with connection.schema_editor() as editor: create_article.state_forwards("test_article", new_state) create_article.database_forwards("test_article", editor, project_state, new_state) project_state = new_state new_state = new_state.clone() with connection.schema_editor() as editor: fill_initial_data.state_forwards("fill_initial_data", new_state) fill_initial_data.database_forwards("fill_initial_data", editor, project_state, new_state) project_state = new_state new_state = new_state.clone() with connection.schema_editor() as editor: grow_article_id.state_forwards("test_article", new_state) grow_article_id.database_forwards("test_article", editor, project_state, new_state) state = new_state.clone() article = state.apps.get_model("test_article.Article") self.assertIsInstance(article._meta.pk, models.BigAutoField) project_state = new_state new_state = new_state.clone() with connection.schema_editor() as editor: grow_blog_id.state_forwards("test_blog", new_state) grow_blog_id.database_forwards("test_blog", editor, project_state, new_state) state = new_state.clone() blog = state.apps.get_model("test_blog.Blog") self.assertIsInstance(blog._meta.pk, models.BigAutoField) project_state = new_state new_state = new_state.clone() with connection.schema_editor() as editor: fill_big_data.state_forwards("fill_big_data", new_state) fill_big_data.database_forwards("fill_big_data", editor, project_state, new_state) def test_run_python_noop(self): """ #24098 - Tests no-op RunPython operations. """ project_state = ProjectState() new_state = project_state.clone() operation = migrations.RunPython(migrations.RunPython.noop, migrations.RunPython.noop) with connection.schema_editor() as editor: operation.database_forwards("test_runpython", editor, project_state, new_state) operation.database_backwards("test_runpython", editor, new_state, project_state) @unittest.skipIf(sqlparse is None and connection.features.requires_sqlparse_for_splitting, "Missing sqlparse") def test_separate_database_and_state(self): """ Tests the SeparateDatabaseAndState operation. """ project_state = self.set_up_test_model("test_separatedatabaseandstate") # Create the operation database_operation = migrations.RunSQL( "CREATE TABLE i_love_ponies (id int, special_thing int);", "DROP TABLE i_love_ponies;" ) state_operation = migrations.CreateModel("SomethingElse", [("id", models.AutoField(primary_key=True))]) operation = migrations.SeparateDatabaseAndState( state_operations=[state_operation], database_operations=[database_operation] ) self.assertEqual(operation.describe(), "Custom state/database change combination") # Test the state alteration new_state = project_state.clone() operation.state_forwards("test_separatedatabaseandstate", new_state) self.assertEqual(len(new_state.models["test_separatedatabaseandstate", "somethingelse"].fields), 1) # Make sure there's no table self.assertTableNotExists("i_love_ponies") # Test the database alteration with connection.schema_editor() as editor: operation.database_forwards("test_separatedatabaseandstate", editor, project_state, new_state) self.assertTableExists("i_love_ponies") # And test reversal self.assertTrue(operation.reversible) with connection.schema_editor() as editor: operation.database_backwards("test_separatedatabaseandstate", editor, new_state, project_state) self.assertTableNotExists("i_love_ponies") # And deconstruction definition = operation.deconstruct() self.assertEqual(definition[0], "SeparateDatabaseAndState") self.assertEqual(definition[1], []) self.assertEqual(sorted(definition[2]), ["database_operations", "state_operations"]) def test_separate_database_and_state2(self): """ A complex SeparateDatabaseAndState operation: Multiple operations both for state and database. Verify the state dependencies within each list and that state ops don't affect the database. """ app_label = "test_separatedatabaseandstate2" project_state = self.set_up_test_model(app_label) # Create the operation database_operations = [ migrations.CreateModel( "ILovePonies", [("id", models.AutoField(primary_key=True))], options={"db_table": "iloveponies"}, ), migrations.CreateModel( "ILoveMorePonies", # We use IntegerField and not AutoField because # the model is going to be deleted immediately # and with an AutoField this fails on Oracle [("id", models.IntegerField(primary_key=True))], options={"db_table": "ilovemoreponies"}, ), migrations.DeleteModel("ILoveMorePonies"), migrations.CreateModel( "ILoveEvenMorePonies", [("id", models.AutoField(primary_key=True))], options={"db_table": "iloveevenmoreponies"}, ), ] state_operations = [ migrations.CreateModel( "SomethingElse", [("id", models.AutoField(primary_key=True))], options={"db_table": "somethingelse"}, ), migrations.DeleteModel("SomethingElse"), migrations.CreateModel( "SomethingCompletelyDifferent", [("id", models.AutoField(primary_key=True))], options={"db_table": "somethingcompletelydifferent"}, ), ] operation = migrations.SeparateDatabaseAndState( state_operations=state_operations, database_operations=database_operations, ) # Test the state alteration new_state = project_state.clone() operation.state_forwards(app_label, new_state) def assertModelsAndTables(after_db): # Tables and models exist, or don't, as they should: self.assertNotIn((app_label, "somethingelse"), new_state.models) self.assertEqual(len(new_state.models[app_label, "somethingcompletelydifferent"].fields), 1) self.assertNotIn((app_label, "iloveponiesonies"), new_state.models) self.assertNotIn((app_label, "ilovemoreponies"), new_state.models) self.assertNotIn((app_label, "iloveevenmoreponies"), new_state.models) self.assertTableNotExists("somethingelse") self.assertTableNotExists("somethingcompletelydifferent") self.assertTableNotExists("ilovemoreponies") if after_db: self.assertTableExists("iloveponies") self.assertTableExists("iloveevenmoreponies") else: self.assertTableNotExists("iloveponies") self.assertTableNotExists("iloveevenmoreponies") assertModelsAndTables(after_db=False) # Test the database alteration with connection.schema_editor() as editor: operation.database_forwards(app_label, editor, project_state, new_state) assertModelsAndTables(after_db=True) # And test reversal self.assertTrue(operation.reversible) with connection.schema_editor() as editor: operation.database_backwards(app_label, editor, new_state, project_state) assertModelsAndTables(after_db=False) class SwappableOperationTests(OperationTestBase): """ Key operations ignore swappable models (we don't want to replicate all of them here, as the functionality is in a common base class anyway) """ available_apps = ['migrations'] @override_settings(TEST_SWAP_MODEL="migrations.SomeFakeModel") def test_create_ignore_swapped(self): """ The CreateTable operation ignores swapped models. """ operation = migrations.CreateModel( "Pony", [ ("id", models.AutoField(primary_key=True)), ("pink", models.IntegerField(default=1)), ], options={ "swappable": "TEST_SWAP_MODEL", }, ) # Test the state alteration (it should still be there!) project_state = ProjectState() new_state = project_state.clone() operation.state_forwards("test_crigsw", new_state) self.assertEqual(new_state.models["test_crigsw", "pony"].name, "Pony") self.assertEqual(len(new_state.models["test_crigsw", "pony"].fields), 2) # Test the database alteration self.assertTableNotExists("test_crigsw_pony") with connection.schema_editor() as editor: operation.database_forwards("test_crigsw", editor, project_state, new_state) self.assertTableNotExists("test_crigsw_pony") # And test reversal with connection.schema_editor() as editor: operation.database_backwards("test_crigsw", editor, new_state, project_state) self.assertTableNotExists("test_crigsw_pony") @override_settings(TEST_SWAP_MODEL="migrations.SomeFakeModel") def test_delete_ignore_swapped(self): """ Tests the DeleteModel operation ignores swapped models. """ operation = migrations.DeleteModel("Pony") project_state, new_state = self.make_test_state("test_dligsw", operation) # Test the database alteration self.assertTableNotExists("test_dligsw_pony") with connection.schema_editor() as editor: operation.database_forwards("test_dligsw", editor, project_state, new_state) self.assertTableNotExists("test_dligsw_pony") # And test reversal with connection.schema_editor() as editor: operation.database_backwards("test_dligsw", editor, new_state, project_state) self.assertTableNotExists("test_dligsw_pony") @override_settings(TEST_SWAP_MODEL="migrations.SomeFakeModel") def test_add_field_ignore_swapped(self): """ Tests the AddField operation. """ # Test the state alteration operation = migrations.AddField( "Pony", "height", models.FloatField(null=True, default=5), ) project_state, new_state = self.make_test_state("test_adfligsw", operation) # Test the database alteration self.assertTableNotExists("test_adfligsw_pony") with connection.schema_editor() as editor: operation.database_forwards("test_adfligsw", editor, project_state, new_state) self.assertTableNotExists("test_adfligsw_pony") # And test reversal with connection.schema_editor() as editor: operation.database_backwards("test_adfligsw", editor, new_state, project_state) self.assertTableNotExists("test_adfligsw_pony") @override_settings(TEST_SWAP_MODEL='migrations.SomeFakeModel') def test_indexes_ignore_swapped(self): """ Add/RemoveIndex operations ignore swapped models. """ operation = migrations.AddIndex('Pony', models.Index(fields=['pink'], name='my_name_idx')) project_state, new_state = self.make_test_state('test_adinigsw', operation) with connection.schema_editor() as editor: # No database queries should be run for swapped models operation.database_forwards('test_adinigsw', editor, project_state, new_state) operation.database_backwards('test_adinigsw', editor, new_state, project_state) operation = migrations.RemoveIndex('Pony', models.Index(fields=['pink'], name='my_name_idx')) project_state, new_state = self.make_test_state("test_rminigsw", operation) with connection.schema_editor() as editor: operation.database_forwards('test_rminigsw', editor, project_state, new_state) operation.database_backwards('test_rminigsw', editor, new_state, project_state) class TestCreateModel(SimpleTestCase): def test_references_model_mixin(self): CreateModel('name', [], bases=(Mixin, models.Model)).references_model('other_model')

48.081148

118

0.640814

7943b59eb1c4ae9effe4dc644bf84349fad04ce8

4,268

py

Python

keystone/policy/core.py

jamielennox/keystone

b33cbef7841f00aa6bd103f2a76409bda6f86169

[ "Apache-2.0" ]

null

keystone/policy/core.py

jamielennox/keystone

b33cbef7841f00aa6bd103f2a76409bda6f86169

[ "Apache-2.0" ]

null

keystone/policy/core.py

jamielennox/keystone

b33cbef7841f00aa6bd103f2a76409bda6f86169

[ "Apache-2.0" ]

null

# Copyright 2012 OpenStack Foundation # # 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. """Main entry point into the Policy service.""" import abc from oslo_config import cfg import six from keystone.common import dependency from keystone.common import manager from keystone import exception from keystone import notifications CONF = cfg.CONF @dependency.provider('policy_api') class Manager(manager.Manager): """Default pivot point for the Policy backend. See :mod:`keystone.common.manager.Manager` for more details on how this dynamically calls the backend. """ driver_namespace = 'keystone.policy' _POLICY = 'policy' def __init__(self): super(Manager, self).__init__(CONF.policy.driver) def create_policy(self, policy_id, policy, initiator=None): ref = self.driver.create_policy(policy_id, policy) notifications.Audit.created(self._POLICY, policy_id, initiator) return ref def get_policy(self, policy_id): try: return self.driver.get_policy(policy_id) except exception.NotFound: raise exception.PolicyNotFound(policy_id=policy_id) def update_policy(self, policy_id, policy, initiator=None): if 'id' in policy and policy_id != policy['id']: raise exception.ValidationError('Cannot change policy ID') try: ref = self.driver.update_policy(policy_id, policy) except exception.NotFound: raise exception.PolicyNotFound(policy_id=policy_id) notifications.Audit.updated(self._POLICY, policy_id, initiator) return ref @manager.response_truncated def list_policies(self, hints=None): # NOTE(henry-nash): Since the advantage of filtering or list limiting # of policies at the driver level is minimal, we leave this to the # caller. return self.driver.list_policies() def delete_policy(self, policy_id, initiator=None): try: ret = self.driver.delete_policy(policy_id) except exception.NotFound: raise exception.PolicyNotFound(policy_id=policy_id) notifications.Audit.deleted(self._POLICY, policy_id, initiator) return ret @six.add_metaclass(abc.ABCMeta) class Driver(object): def _get_list_limit(self): return CONF.policy.list_limit or CONF.list_limit @abc.abstractmethod def enforce(self, context, credentials, action, target): """Verify that a user is authorized to perform action. For more information on a full implementation of this see: `keystone.policy.backends.rules.Policy.enforce` """ raise exception.NotImplemented() # pragma: no cover @abc.abstractmethod def create_policy(self, policy_id, policy): """Store a policy blob. :raises: keystone.exception.Conflict """ raise exception.NotImplemented() # pragma: no cover @abc.abstractmethod def list_policies(self): """List all policies.""" raise exception.NotImplemented() # pragma: no cover @abc.abstractmethod def get_policy(self, policy_id): """Retrieve a specific policy blob. :raises: keystone.exception.PolicyNotFound """ raise exception.NotImplemented() # pragma: no cover @abc.abstractmethod def update_policy(self, policy_id, policy): """Update a policy blob. :raises: keystone.exception.PolicyNotFound """ raise exception.NotImplemented() # pragma: no cover @abc.abstractmethod def delete_policy(self, policy_id): """Remove a policy blob. :raises: keystone.exception.PolicyNotFound """ raise exception.NotImplemented() # pragma: no cover

30.705036

77

0.687441

7943b778654752a944d718966f86db64ecbf4134

575

py

Python

bench-scripts/bench_histogram_nominal.py

holmes-anonymous-submission/holmes-library

35d01400c6694681ed61b2b3e1f6c7eb6b660e90

[ "MIT" ]

null

bench-scripts/bench_histogram_nominal.py

holmes-anonymous-submission/holmes-library

35d01400c6694681ed61b2b3e1f6c7eb6b660e90

[ "MIT" ]

null

bench-scripts/bench_histogram_nominal.py

holmes-anonymous-submission/holmes-library

35d01400c6694681ed61b2b3e1f6c7eb6b660e90

[ "MIT" ]

null

#!/usr/bin/env python from common import * configure_network() subprocess.run(["cmake", "."]) subprocess.run(["make"]) print("running the bench_histogram_nominal") num_records = 100000 print("running the case for " + str(num_records) + " entries") time.sleep(5) for i in range(5): num_groups = 10 + 10 * i write_configure_info(str(num_records) + " " + str(num_groups)) subprocess.run(["bin/bench_histogram_nominal", os.getenv("EMP_MY_PARTY_ID"), "5000"]) copy_benchmark_result_to_log("bench_histogram_nominal " + str(num_records) + " " + str(num_groups))

30.263158

103

0.711304

7943b86116fd32742e340520d94c0306403b6cdd

2,470

pyw

Python

venv/Lib/site-packages/PyQt4/examples/script/helloscript.pyw

prateekfxtd/ns_Startup

095a62b3a8c7bf0ff7b767355d57d993bbd2423d

[ "MIT" ]

1

2022-03-16T02:10:30.000Z

venv/Lib/site-packages/PyQt4/examples/script/helloscript.pyw

prateekfxtd/ns_Startup

095a62b3a8c7bf0ff7b767355d57d993bbd2423d

[ "MIT" ]

null

venv/Lib/site-packages/PyQt4/examples/script/helloscript.pyw

prateekfxtd/ns_Startup

095a62b3a8c7bf0ff7b767355d57d993bbd2423d

[ "MIT" ]

2

2019-05-28T11:58:59.000Z

2020-09-23T17:21:19.000Z

#!/usr/bin/env python ############################################################################# ## ## Copyright (C) 2010 Riverbank Computing Limited. ## Copyright (C) 2010 Nokia Corporation and/or its subsidiary(-ies). ## All rights reserved. ## ## This file is part of the examples of PyQt. ## ## $QT_BEGIN_LICENSE:BSD$ ## You may use this file under the terms of the BSD license as follows: ## ## "Redistribution and use in source and binary forms, with or without ## modification, are permitted provided that the following conditions are ## met: ## * Redistributions of source code must retain the above copyright ## notice, this list of conditions and the following disclaimer. ## * Redistributions in binary form must reproduce the above copyright ## notice, this list of conditions and the following disclaimer in ## the documentation and/or other materials provided with the ## distribution. ## * Neither the name of Nokia Corporation and its Subsidiary(-ies) nor ## the names of its contributors may be used to endorse or promote ## products derived from this software without specific prior written ## permission. ## ## THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS ## "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT ## LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR ## A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT ## OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, ## SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT ## LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, ## DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY ## THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT ## (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE ## OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE." ## $QT_END_LICENSE$ ## ############################################################################# import sys from PyQt4 import QtGui, QtScript app = QtGui.QApplication(sys.argv) engine = QtScript.QScriptEngine() button = QtGui.QPushButton() scriptButton = engine.newQObject(button) engine.globalObject().setProperty('button', scriptButton) engine.evaluate("button.text = 'Hello World!'") engine.evaluate("button.styleSheet = 'font-style: italic'") engine.evaluate("button.show()") sys.exit(app.exec_())

39.206349

77

0.703239

7943b96017a0e082994b3d97986a96569ac29b43

1,248

py

Python

restexpress/setup.py

oberhamsi/FrameworkBenchmarks

660a66d51a9aad10b43c0660208fb13c098121af

[ "BSD-3-Clause" ]

4

2015-01-22T02:13:03.000Z

2018-06-13T12:02:46.000Z

frameworks/Java/restexpress/setup.py

ratpack/FrameworkBenchmarks

81604309e46e382fe2ffb7970a87d728f20c8be6

[ "BSD-3-Clause" ]

null

frameworks/Java/restexpress/setup.py

ratpack/FrameworkBenchmarks

81604309e46e382fe2ffb7970a87d728f20c8be6

[ "BSD-3-Clause" ]

null

import subprocess import sys import setup_util import os def start(args, logfile, errfile): setup_util.replace_text("restexpress/config/dev/environment.properties", "mongodb:\/\/.*\/hello_world", "mongodb://" + args.database_host + "/hello_world") setup_util.replace_text("restexpress/config/dev/environment.properties", "mysql:\/\/.*:3306", "mysql://" + args.database_host + ":3306") try: subprocess.check_call("mvn clean package", shell=True, cwd="restexpress", stderr=errfile, stdout=logfile) subprocess.check_call("mvn assembly:single", shell=True, cwd="restexpress", stderr=errfile, stdout=logfile) subprocess.check_call("unzip world-1.0-SNAPSHOT-zip-with-dependencies.zip", shell=True, cwd="restexpress/target", stderr=errfile, stdout=logfile) subprocess.Popen("java -jar world-1.0-SNAPSHOT.jar".rsplit(" "), cwd="restexpress/target/world-1.0-SNAPSHOT", stderr=errfile, stdout=logfile) return 0 except subprocess.CalledProcessError: return 1 def stop(logfile, errfile): p = subprocess.Popen(['ps', 'aux'], stdout=subprocess.PIPE) out, err = p.communicate() for line in out.splitlines(): if 'world-1.0-SNAPSHOT.jar' in line: pid = int(line.split(None, 2)[1]) os.kill(pid, 15) return 0

48

157

0.721955

7943ba4a92e516a09a5d8885736604a7fcda8127

4,640

py

Python

formulaic/rules.py

Govexec/django-formulaic

b9516a963673cb21279a2ecdf62a199b9203d538

[ "MIT" ]

1

2021-08-20T04:21:20.000Z

formulaic/rules.py

Govexec/django-formulaic

b9516a963673cb21279a2ecdf62a199b9203d538

[ "MIT" ]

4

2020-12-05T00:31:40.000Z

2021-09-22T20:06:53.000Z

formulaic/rules.py

Govexec/django-formulaic

b9516a963673cb21279a2ecdf62a199b9203d538

[ "MIT" ]

1

2020-12-04T19:16:36.000Z

from six import iteritems from formulaic import exceptions class RuleAssessor(object): """ Mimics the Rule implementation found in JavaScript to toggle validation on and off for fields depending on if they're supposed to be visible to the user. """ def __init__(self, rules_data, form_fields, submission_data={}): self.fields = {} for slug, form_field in form_fields.items(): value = submission_data.get(slug, None) self.fields[slug] = Field(slug, form_field, value) self.rules = [] for rule_data in rules_data: self.rules.append(Rule(self, rule_data)) self.visible_fields = [] self.invisible_fields = [] for slug, field in iteritems(self.fields): field.evaluate_observed_rules() if field.visible: self.visible_fields.append(slug) else: self.invisible_fields.append(slug) def is_field_visible(self, slug): return slug in self.visible_fields class FieldStatus(object): def __init__(self): self.visible = True class Field(object): def __init__(self, slug, form_field, value): self.slug = slug self.form_field = form_field self.value = value self.observed_rules = [] self._visible = None def add_observed_rule(self, rule): self.observed_rules.append(rule) def evaluate_observed_rules(self): new_field_status = FieldStatus() for rule in self.observed_rules: rule.evaluate(self, new_field_status) # apply field status self._visible = new_field_status.visible def has_value(self, value): from django.forms import CheckboxInput if type(self.form_field) is CheckboxInput: return self.value elif type(self.value) is list: return value in self.value else: return self.value == value @property def visible(self): if self._visible is None: raise exceptions.NotSetException("Field hasn't been set active/inactive, yet") else: return self._visible class Rule(object): OPERATOR_AND = "and" OPERATOR_OR = "or" def __init__(self, assessor, rule_data): self.assessor = assessor self.operator = rule_data["operator"] self.conditions = [] for condition_data in rule_data["conditions"]: self.conditions.append(RuleCondition(self, condition_data)) self.results = [] for result_data in rule_data["results"]: self.results.append(RuleResult(self, result_data)) def conditions_met(self): require_all = self.operator == Rule.OPERATOR_AND all_true = True any_true = False for condition in self.conditions: if condition.is_met(): any_true = True else: all_true = False if all_true: return True elif not require_all and any_true: return True else: return False def evaluate(self, field, display_status): if self.conditions_met(): for result in self.results: result.update_result(field, display_status) class RuleCondition(object): def __init__(self, rule, condition_data): self.rule = rule self.field = self.rule.assessor.fields[condition_data["field_slug"]] self.operator = condition_data["operator"] self.value = condition_data["value"] def is_met(self): if self.operator == "is": return self.field.has_value(self.value) else: return not self.field.has_value(self.value) class RuleResult(object): ACTION_SHOW = "show" ACTION_HIDE = "hide" ACTION_CHANGE_OPTION_GROUP = "change-option-group" def __init__(self, rule, result_data): self.rule = rule self.action = result_data["action"] self.field = self.rule.assessor.fields[result_data["field_slug"]] self.field.add_observed_rule(self.rule) def update_result(self, field, display_status): if self.field == field: if self.action == RuleResult.ACTION_SHOW: display_status.visible = True elif self.action == RuleResult.ACTION_HIDE: display_status.visible = False elif self.action == RuleResult.ACTION_CHANGE_OPTION_GROUP: pass else: raise exceptions.InvalidParamException("Invalid RuleResult action: {}".format(self.action))

29.18239

107

0.619828

7943bb5ed91f6a8c196c8a93ab0867332e3ee6c2

5,608

py

Python

tests/test_4_check_word.py

Duckbilled/ebook-reader-dict

0a9131db89d353b686bb25459a62cb6c3c4ffb2c

[ "MIT" ]

null

tests/test_4_check_word.py

Duckbilled/ebook-reader-dict

0a9131db89d353b686bb25459a62cb6c3c4ffb2c

[ "MIT" ]

null

tests/test_4_check_word.py

Duckbilled/ebook-reader-dict

0a9131db89d353b686bb25459a62cb6c3c4ffb2c

[ "MIT" ]

null

from unittest.mock import patch from wikidict import check_word def test_simple(): assert check_word.main("fr", "vendre") == 0 def test_word_of_the_day(): assert check_word.main("fr", "") == 0 def test_etymology_list(): assert check_word.main("fr", "bath") == 0 def test_sublist(): assert check_word.main("fr", "éperon") == 0 def test_subsublist(): assert check_word.main("fr", "vache") == 0 def test_error(): with patch.object(check_word, "contains", return_value=False): assert check_word.main("fr", "42") > 0 def test_no_definition_nor_etymology(): assert check_word.main("es", "42") == 0 def test_filter_obsolete_tpl(): orig = check_word.filter_html def new_filter_html(html: str, locale: str) -> str: html += "<span id='FormattingError'>bouh !</span>" return orig(html, locale) with patch.object(check_word, "filter_html", new=new_filter_html): assert check_word.main("fr", "42") == 0 def test_filter_math_chem(): orig = check_word.filter_html def new_filter_html(html: str, locale: str) -> str: html += '<span class="mwe-math-element"></span>' return orig(html, locale) with patch.object(check_word, "filter_html", new=new_filter_html): assert check_word.main("fr", "42") == 0 def test_filter_anchors(): orig = check_word.filter_html def new_filter_html(html: str, locale: str) -> str: html += '<a href="#cite">[1]</a>' return orig(html, locale) with patch.object(check_word, "filter_html", new=new_filter_html): assert check_word.main("fr", "42") == 0 def test_filter_es(): orig = check_word.filter_html def new_filter_html(html: str, locale: str) -> str: html += "<dl><dt>1 Finanzas.</dt></dl>" return orig(html, locale) with patch.object(check_word, "filter_html", new=new_filter_html): assert check_word.main("es", "cartel") == 0 def test_filter_es_2(): orig = check_word.filter_html def new_filter_html(html: str, locale: str) -> str: html += "<dl><dt>2 Coloquial</dt></dl>" return orig(html, locale) with patch.object(check_word, "filter_html", new=new_filter_html): assert check_word.main("es", "buena") == 0 def test_filter_fr_refnec(): orig = check_word.filter_html def new_filter_html(html: str, locale: str) -> str: html += '<span><sup><i><b>Référence nécessaire</b></i></sup></span><span id="refnec"></span>' return orig(html, locale) with patch.object(check_word, "filter_html", new=new_filter_html): assert check_word.main("fr", "42") == 0 def test_filter_fr_sources(): orig = check_word.filter_html def new_filter_html(html: str, locale: str) -> str: html += ( '<span class="sources"><span class="tiret">— </span>(<i>Ordonnance de Louis XI pour la formation d' "un port et château fort à la Hague</i>)</span>" ) return orig(html, locale) with patch.object(check_word, "filter_html", new=new_filter_html): assert check_word.main("fr", "42") == 0 def test_filter_fr_external_autonumber(): orig = check_word.filter_html def new_filter_html(html: str, locale: str) -> str: html += '<a rel="nofollow" class="external autonumber" href="http://www.iupac.org/publications/pac/1994/pdf/6612x2419.pdf">[2]</a>' # noqa return orig(html, locale) with patch.object(check_word, "filter_html", new=new_filter_html): assert check_word.main("fr", "42") == 0 def test_filter_fr_attention(): orig = check_word.filter_html def new_filter_html(html: str, locale: str) -> str: html += '<a href="/wiki/Fichier:Twemoji12_26a0.svg" class="image" title="alt = attention"><img alt="alt = attention" src="//26a0.svg.png"></a>' # noqa return orig(html, locale) with patch.object(check_word, "filter_html", new=new_filter_html): assert check_word.main("fr", "42") == 0 def test_filter_fr_wikispecies(): orig = check_word.filter_html def new_filter_html(html: str, locale: str) -> str: html += '<i><a href="https://species.wikimedia.org/wiki/Panthera_leo" class="extiw" title="wikispecies:Panthera leo">Panthera leo</a></i> sur Wikispecies' # noqa return orig(html, locale) with patch.object(check_word, "filter_html", new=new_filter_html): assert check_word.main("fr", "42") == 0 def test_filter_fr_lien_rouge_trad(): orig = check_word.filter_html def new_filter_html(html: str, locale: str) -> str: html += '<a href="https://en.wiktionary.org/wiki/Reconstruction:Proto-Indo-European/wemh%E2%82%81-" class="extiw" title="en:Reconstruction:Proto-Indo-European/wemh₁-"><span style="font-family:monospace;font-weight:bold;font-size:small;font-style:normal;" title="Équivalent de l’article « Reconstruction:indo-européen commun/*wem- » dans une autre langue">(en)</span></a>' # noqa return orig(html, locale) with patch.object(check_word, "filter_html", new=new_filter_html): assert check_word.main("fr", "42") == 0 def test_filter_fr_a_preciser(): orig = check_word.filter_html def new_filter_html(html: str, locale: str) -> str: html += '<span title="Cette information a besoin d’être précisée"><small> <span style="color:red">(information <i>à préciser ou à vérifier</i>)</span></small></span>' # noqa return orig(html, locale) with patch.object(check_word, "filter_html", new=new_filter_html): assert check_word.main("fr", "42") == 0

33.380952

387

0.660128

7943bbc2b516fe0274409cfa4ba3e5bd4c33cdac

6,495

py

Python

admin/deadeye/models.py

strykeforce/deadeye

09d791e5fe0677f4d1890c7fc86ab2075725cca7

[ "MIT" ]

1

2020-01-30T05:21:37.000Z

admin/deadeye/models.py

strykeforce/deadeye

09d791e5fe0677f4d1890c7fc86ab2075725cca7

[ "MIT" ]

31

2019-10-14T10:11:02.000Z

2022-03-27T20:05:45.000Z

admin/deadeye/models.py

strykeforce/deadeye

09d791e5fe0677f4d1890c7fc86ab2075725cca7

[ "MIT" ]

null

# pylint: disable=no-member import json import time from urllib.parse import urlparse from flask import current_app from networktables import NetworkTables, NetworkTablesInstance class Unit: units = {} api = None def __init__(self, unit_id, camera_inums): self.cameras = {} self.id = unit_id for inum in camera_inums: try: cam = Camera(unit_id, inum) self.cameras[inum] = cam except Exception as e: current_app.logger.error( "error loading Camera %s%s: %s", unit_id, inum, e ) @classmethod def init(cls, api): Unit.api = api deadeye_table = NetworkTables.getTable("/Deadeye") unit_ids = deadeye_table.getSubTables() for unit_id in unit_ids: unit_table = NetworkTables.getTable(f"/Deadeye/{unit_id}") camera_inums = unit_table.getSubTables() cls.units[unit_id] = Unit(unit_id, camera_inums) def __repr__(self): return f"Unit({self.id})" class Camera: def __init__(self, unit_id, inum): self.unit = unit_id self.inum = int(inum) self.id = f"{unit_id}{inum}" self.light = Light(self) self.on = self.table().getBoolean("On", False) self.error = self.table().getBoolean("Error", False) self.capture = self.load_json("Capture") self.pipeline = self.load_json("Pipeline") self.stream = self.load_json("Stream") self.info = self.load_json("Info") self.table().addEntryListenerEx( self.entry_listener, NetworkTablesInstance.NotifyFlags.UPDATE ) def load_json(self, key): try: return json.loads(self.table().getString(key, "")) except json.JSONDecodeError as err: raise ValueError(f"Camera {key}: {err}") def enable(self, enabled): if self.on == enabled: return control_table = self.table() control_table.putBoolean("On", enabled) control_table.putBoolean("Off", not enabled) self.on = enabled Unit.api.refresh_units = True def set_capture(self, capture): capture_entry = self.table().getEntry("Capture") capture_entry.setString(json.dumps(capture)) self.capture = capture Unit.api.refresh_units = True def set_pipeline(self, pipeline): pipeline_entry = self.table().getEntry("Pipeline") pipeline_entry.setString(json.dumps(pipeline)) self.pipeline = pipeline Unit.api.refresh_units = True def set_stream(self, stream): r = urlparse(stream["url"]) r = r._replace(query=f"s={int(time.monotonic() * 1000)}") stream["url"] = r.geturl() stream_entry = self.table().getEntry("Stream") stream_entry.setString(json.dumps(stream)) self.stream = stream Unit.api.refresh_units = True def table(self): return NetworkTables.getTable(f"/Deadeye/{self.unit}/{self.inum}") def entry_listener(self, table, key, value, is_new): del is_new, table # unused if not value: return if key == "On": self.on = value self.error = False elif key == "Off": self.on = not value self.error = False elif key == "Error": self.error = value elif key == "Stream": self.stream = json.loads(value) elif key == "Pipeline": self.pipeline = json.loads(value) elif key == "Capture": self.capture = json.loads(value) elif key == "Info": self.info = json.loads(value) else: current_app.logger.error("unrecognized key: %s", key) Unit.api.refresh_units = True def __repr__(self): return f"Camera({self.unit}, {self.inum}" def __str__(self): on = self.on error = self.error return f"Camera {self.unit}{self.inum}: on={on} error={error}" class Light: # properties other than "camera" will be stored in "__dict__" and therefore serialized __slots__ = ["camera", "__dict__"] def __init__(self, camera): control_table = camera.table().getSubTable("Light") self.camera = camera self.on = control_table.getBoolean("On", False) self.blink = control_table.getBoolean("Blink", False) control_table.addEntryListenerEx( self.entry_listener, NetworkTablesInstance.NotifyFlags.UPDATE ) def enable(self, enabled): if self.on == enabled: return control_table = self.camera.table().getSubTable("Light") control_table.putBoolean("On", enabled) control_table.putBoolean("Off", not enabled) self.on = enabled Unit.api.refresh_units = True def entry_listener(self, table, key, value, is_new): del is_new, table # unused if not value: return if key == "On": self.on = value self.blink = False elif key == "Off": self.on = not value self.blink = False elif key == "Blink": self.blink = value else: current_app.logger.error("unrecognized key: %s", key) Unit.api.refresh_units = True def __repr__(self): return f"Light({self.on}, {self.blink}" def __str__(self): return f"Light: on={self.on} blink={self.blink}" class Link: def __init__(self, api): self.api = api deadeye_table = NetworkTables.getTable("/Deadeye") entry = deadeye_table.getString("Link", "[]") print(f"Link entry = {entry}") self.entries = json.loads(entry) deadeye_table.addEntryListenerEx( self.entry_listener, NetworkTablesInstance.NotifyFlags.UPDATE ) def entry_listener(self, table, key, value, is_new): del is_new, table # unused if not value: return if key == "Link": self.entries = json.loads(value) else: current_app.logger.error("unrecognized key: %s", key) self.api.refresh_link = True def set_entries(self, message): deadeye_table = NetworkTables.getTable("/Deadeye") entry = deadeye_table.getEntry("Link") entry.setString(json.dumps(message)) self.entries = message self.api.refresh_link = True

31.225962

90

0.589376

7943bc3f0544b57846d37b79f75e0994cdd5ca5f

9,175

py

Python

spinupImplementation/utils/plot.py

PeterChauYEG/spinningupImplementations

d521d27df17bf77b087da09adff2a49fc1588a9f

[ "MIT" ]

null

spinupImplementation/utils/plot.py

PeterChauYEG/spinningupImplementations

d521d27df17bf77b087da09adff2a49fc1588a9f

[ "MIT" ]

null

spinupImplementation/utils/plot.py

PeterChauYEG/spinningupImplementations

d521d27df17bf77b087da09adff2a49fc1588a9f

[ "MIT" ]

null

import seaborn as sns import pandas as pd import matplotlib.pyplot as plt import json import os import os.path as osp import numpy as np DIV_LINE_WIDTH = 50 # Global vars for tracking and labeling data at load time. exp_idx = 0 units = dict() def plot_data(data, xaxis='Epoch', value="AverageEpRet", condition="Condition1", smooth=1, **kwargs): if smooth > 1: """ smooth data with moving window average. that is, smoothed_y[t] = average(y[t-k], y[t-k+1], ..., y[t+k-1], y[t+k]) where the "smooth" param is width of that window (2k+1) """ y = np.ones(smooth) for datum in data: x = np.asarray(datum[value]) z = np.ones(len(x)) smoothed_x = np.convolve(x,y,'same') / np.convolve(z,y,'same') datum[value] = smoothed_x if isinstance(data, list): data = pd.concat(data, ignore_index=True) sns.set(style="darkgrid", font_scale=1.5) sns.tsplot(data=data, time=xaxis, value=value, unit="Unit", condition=condition, ci='sd', **kwargs) """ If you upgrade to any version of Seaborn greater than 0.8.1, switch from tsplot to lineplot replacing L29 with: sns.lineplot(data=data, x=xaxis, y=value, hue=condition, ci='sd', **kwargs) Changes the colorscheme and the default legend style, though. """ plt.legend(loc='best').draggable() """ For the version of the legend used in the Spinning Up benchmarking page, swap L38 with: plt.legend(loc='upper center', ncol=6, handlelength=1, mode="expand", borderaxespad=0., prop={'size': 13}) """ xscale = np.max(np.asarray(data[xaxis])) > 5e3 if xscale: # Just some formatting niceness: x-axis scale in scientific notation if max x is large plt.ticklabel_format(style='sci', axis='x', scilimits=(0,0)) plt.tight_layout(pad=0.5) def get_datasets(logdir, condition=None): """ Recursively look through logdir for output files produced by spinupImplementation.logx.Logger. Assumes that any file "progress.txt" is a valid hit. """ global exp_idx global units datasets = [] for root, _, files in os.walk(logdir): if 'progress.txt' in files: exp_name = None try: config_path = open(os.path.join(root,'config.json')) config = json.load(config_path) if 'exp_name' in config: exp_name = config['exp_name'] except: print('No file named config.json') condition1 = condition or exp_name or 'exp' condition2 = condition1 + '-' + str(exp_idx) exp_idx += 1 if condition1 not in units: units[condition1] = 0 unit = units[condition1] units[condition1] += 1 exp_data = pd.read_table(os.path.join(root,'progress.txt')) performance = 'AverageTestEpRet' if 'AverageTestEpRet' in exp_data else 'AverageEpRet' exp_data.insert(len(exp_data.columns),'Unit',unit) exp_data.insert(len(exp_data.columns),'Condition1',condition1) exp_data.insert(len(exp_data.columns),'Condition2',condition2) exp_data.insert(len(exp_data.columns),'Performance',exp_data[performance]) datasets.append(exp_data) return datasets def get_all_datasets(all_logdirs, legend=None, select=None, exclude=None): """ For every entry in all_logdirs, 1) check if the entry is a real directory and if it is, pull data from it; 2) if not, check to see if the entry is a prefix for a real directory, and pull data from that. """ logdirs = [] for logdir in all_logdirs: if osp.isdir(logdir) and logdir[-1]=='/': logdirs += [logdir] else: basedir = osp.dirname(logdir) fulldir = lambda x : osp.join(basedir, x) prefix = logdir.split('/')[-1] listdir= os.listdir(basedir) logdirs += sorted([fulldir(x) for x in listdir if prefix in x]) """ Enforce selection rules, which check logdirs for certain substrings. Makes it easier to look at graphs from particular ablations, if you launch many jobs at once with similar names. """ if select is not None: logdirs = [log for log in logdirs if all(x in log for x in select)] if exclude is not None: logdirs = [log for log in logdirs if all(not(x in log) for x in exclude)] # Verify logdirs print('Plotting from...\n' + '='*DIV_LINE_WIDTH + '\n') for logdir in logdirs: print(logdir) print('\n' + '='*DIV_LINE_WIDTH) # Make sure the legend is compatible with the logdirs assert not(legend) or (len(legend) == len(logdirs)), \ "Must give a legend title for each set of experiments." # Load data from logdirs data = [] if legend: for log, leg in zip(logdirs, legend): data += get_datasets(log, leg) else: for log in logdirs: data += get_datasets(log) return data def make_plots(all_logdirs, legend=None, xaxis=None, values=None, count=False, font_scale=1.5, smooth=1, select=None, exclude=None, estimator='mean'): data = get_all_datasets(all_logdirs, legend, select, exclude) values = values if isinstance(values, list) else [values] condition = 'Condition2' if count else 'Condition1' estimator = getattr(np, estimator) # choose what to show on main curve: mean? max? min? for value in values: plt.figure() plot_data(data, xaxis=xaxis, value=value, condition=condition, smooth=smooth, estimator=estimator) plt.show() def main(): import argparse parser = argparse.ArgumentParser() parser.add_argument('logdir', nargs='*') parser.add_argument('--legend', '-l', nargs='*') parser.add_argument('--xaxis', '-x', default='TotalEnvInteracts') parser.add_argument('--value', '-y', default='Performance', nargs='*') parser.add_argument('--count', action='store_true') parser.add_argument('--smooth', '-s', type=int, default=1) parser.add_argument('--select', nargs='*') parser.add_argument('--exclude', nargs='*') parser.add_argument('--est', default='mean') args = parser.parse_args() """ Args: logdir (strings): As many log directories (or prefixes to log directories, which the plotter will autocomplete internally) as you'd like to plot from. legend (strings): Optional way to specify legend for the plot. The plotter legend will automatically use the ``exp_name`` from the config.json file, unless you tell it otherwise through this flag. This only works if you provide a name for each directory that will get plotted. (Note: this may not be the same as the number of logdir args you provide! Recall that the plotter looks for autocompletes of the logdir args: there may be more than one match for a given logdir prefix, and you will need to provide a legend string for each one of those matches---unless you have removed some of them as candidates via selection or exclusion rules (below).) xaxis (string): Pick what column from data is used for the x-axis. Defaults to ``TotalEnvInteracts``. value (strings): Pick what columns from data to graph on the y-axis. Submitting multiple values will produce multiple graphs. Defaults to ``Performance``, which is not an actual output of any algorithm. Instead, ``Performance`` refers to either ``AverageEpRet``, the correct performance measure for the on-policy algorithms, or ``AverageTestEpRet``, the correct performance measure for the off-policy algorithms. The plotter will automatically figure out which of ``AverageEpRet`` or ``AverageTestEpRet`` to report for each separate logdir. count: Optional flag. By default, the plotter shows y-values which are averaged across all results that share an ``exp_name``, which is typically a set of identical experiments that only vary in random seed. But if you'd like to see all of those curves separately, use the ``--count`` flag. smooth (int): Smooth data by averaging it over a fixed window. This parameter says how wide the averaging window will be. select (strings): Optional selection rule: the plotter will only show curves from logdirs that contain all of these substrings. exclude (strings): Optional exclusion rule: plotter will only show curves from logdirs that do not contain these substrings. """ make_plots(args.logdir, args.legend, args.xaxis, args.value, args.count, smooth=args.smooth, select=args.select, exclude=args.exclude, estimator=args.est) if __name__ == "__main__": main()

40.418502

106

0.629101

7943bcf77f15ae76020dff23302530d5c3775880

407

py

Python

step9/src/data/version1/BeaconTypeV1.py

pip-services-samples/sample-beacons-python

a117054b1029a6284b9eb304f232088bb997456c

[ "MIT" ]

null

step9/src/data/version1/BeaconTypeV1.py

pip-services-samples/sample-beacons-python

a117054b1029a6284b9eb304f232088bb997456c

[ "MIT" ]

null

step9/src/data/version1/BeaconTypeV1.py

pip-services-samples/sample-beacons-python

a117054b1029a6284b9eb304f232088bb997456c

[ "MIT" ]

1

2020-08-03T13:12:26.000Z

# -*- coding: utf-8 -*- """ step9.data.version1.BeaconTypeV1 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Beacon Type class :copyright: Conceptual Vision Consulting LLC 2018-2019, see AUTHORS for more details. :license: MIT, see LICENSE for more details. """ class BeaconTypeV1: Unknown = "unknown" AltBeacon = "altbeacon" iBeacon = "ibeacons" EddyStoneUdi = "eddistoneudi"

22.611111

89

0.604423

7943bd4dd996d3876298eb810f9261fe24193ac8

1,780

py

Python

test_service.py

cmput401-fall2018/web-app-ci-cd-with-travis-ci-dcam4

10e19f7cdf87fcd799c48a53eea1c988574f57ce

[ "MIT" ]

null

test_service.py

cmput401-fall2018/web-app-ci-cd-with-travis-ci-dcam4

10e19f7cdf87fcd799c48a53eea1c988574f57ce

[ "MIT" ]

1

2018-10-20T05:13:48.000Z

test_service.py

cmput401-fall2018/web-app-ci-cd-with-travis-ci-dcam4

10e19f7cdf87fcd799c48a53eea1c988574f57ce

[ "MIT" ]

null

import unittest from service import Service from mock import patch class TestService(unittest.TestCase): def setUp(self): self.service = Service() @patch('service.Service.bad_random') def test_bad_random(self, mock_bad_random): mock_bad_random.return_value = 20 #check to see if mock method works self.assertEqual(self.service.bad_random(), 20) @patch('service.Service.bad_random') def test_divide(self, mock_bad_random): mock_bad_random.return_value = 20 #positive self.assertEqual(self.service.divide(5), 4) #zero with self.assertRaises(ZeroDivisionError): self.service.divide(0) #negative self.assertEqual(self.service.divide(-2), -10) def test_abs_plus(self): #negative self.assertEqual(self.service.abs_plus(-5), 6) #zero self.assertEqual(self.service.abs_plus(0), 1) #positive self.assertEqual(self.service.abs_plus(5), 6) @patch('service.Service.bad_random') def test_complicated_function(self, mock_bad_random): mock_bad_random.return_value = 20 #test mod with a 0 result self.assertEqual(self.service.complicated_function(5), (4,0)) mock_bad_random.return_value = 9 #test mod with a non zero result self.assertEqual(self.service.complicated_function(3), (3,1)) if __name__ == '__main__': unittest.main()

22.820513

69

0.543258

7943bdb77bb69352f6c8b0760c6f14d11eb7c0b9

19,763

py

Python

g2p_seq2seq/g2p_problem.py

b00f/g2p-seq2seq

96c3e36754b9a4571d6416446a24c5eef2171128

[ "Apache-2.0" ]

661

2016-04-27T05:47:54.000Z

2022-03-20T08:42:43.000Z

g2p_seq2seq/g2p_problem.py

b00f/g2p-seq2seq

96c3e36754b9a4571d6416446a24c5eef2171128

[ "Apache-2.0" ]

189

2016-04-28T20:10:18.000Z

2021-08-19T02:04:34.000Z

g2p_seq2seq/g2p_problem.py

b00f/g2p-seq2seq

96c3e36754b9a4571d6416446a24c5eef2171128

[ "Apache-2.0" ]

219

2016-04-30T23:58:45.000Z

2022-03-09T16:05:37.000Z

# Copyright 2016 AC Technologies LLC. 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. # ============================================================================== """Module for registering custom Grapheme-to-Phoneme problem in tensor2tensor. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import random import re from collections import OrderedDict import tensorflow as tf from tensorflow.python.data.ops import dataset_ops as dataset_ops from g2p_seq2seq import g2p_encoder from tensor2tensor.data_generators import problem from tensor2tensor.data_generators import text_encoder from tensor2tensor.utils import registry from tensor2tensor.data_generators import generator_utils from tensor2tensor.data_generators import text_problems EOS = text_encoder.EOS_ID @registry.register_problem class GraphemeToPhonemeProblem(text_problems.Text2TextProblem): """Problem spec for cmudict PRONALSYL Grapheme-to-Phoneme translation.""" def __init__(self, model_dir, train_path=None, dev_path=None, test_path=None, cleanup=False, p2g_mode=False): """Create a Problem. Args: was_reversed: bool, whether to reverse inputs and targets. was_copy: bool, whether to copy inputs to targets. Can be composed with was_reversed so that if both are true, the targets become the inputs, which are then copied to targets so that the task is targets->targets. """ super(GraphemeToPhonemeProblem, self).__init__() self._encoders = None self._hparams = None self._feature_info = None self._model_dir = model_dir self.train_path, self.dev_path, self.test_path = train_path, dev_path,\ test_path self.cleanup = cleanup self.p2g_mode = p2g_mode vocab_filename = os.path.join(self._model_dir, "vocab.g2p") if train_path: self.train_path, self.dev_path, self.test_path = create_data_files( init_train_path=train_path, init_dev_path=dev_path, init_test_path=test_path, cleanup=self.cleanup, p2g_mode=self.p2g_mode) self.source_vocab, self.target_vocab = g2p_encoder.load_create_vocabs( vocab_filename, train_path=self.train_path, dev_path=self.dev_path, test_path=self.test_path, p2g_mode=self.p2g_mode) elif not os.path.exists(os.path.join(self._model_dir, "checkpoint")): raise Exception("Model not found in {}".format(self._model_dir)) else: self.source_vocab, self.target_vocab = g2p_encoder.load_create_vocabs( vocab_filename, p2g_mode=self.p2g_mode) def generator(self, data_path, source_vocab, target_vocab): """Generator for the training and evaluation data. Generate source and target data from a single file. Args: data_path: The path to data file. source_vocab: the object of GraphemePhonemeEncoder class with encode and decode functions for symbols from source file. target_vocab: the object of GraphemePhonemeEncoder class with encode and decode functions for symbols from target file. Yields: dicts with keys "inputs" and "targets", with values being lists of token ids. """ return self.tabbed_generator(data_path, source_vocab, target_vocab, EOS) def filepattern(self, data_dir, dataset_split, shard=None): if not (".preprocessed" in dataset_split): return os.path.join(self._model_dir, dataset_split + ".preprocessed") return os.path.join(data_dir, dataset_split) @property def input_space_id(self): return 0 @property def target_space_id(self): return 0 @property def num_shards(self): return 1 @property def use_subword_tokenizer(self): return False @property def is_character_level(self): return False @property def targeted_vocab_size(self): return None @property def vocab_name(self): return None def generate_preprocess_data(self): """Generate and save preprocessed data as TFRecord files. Args: train_path: the path to the train data file. eval_path: the path to the evaluation data file. Returns: train_preprocess_path: the path where the preprocessed train data was saved. eval_preprocess_path: the path where the preprocessed evaluation data was saved. """ train_preprocess_path = os.path.join(self._model_dir, "train.preprocessed") eval_preprocess_path = os.path.join(self._model_dir, "eval.preprocessed") train_gen = self.generator(self.train_path, self.source_vocab, self.target_vocab) eval_gen = self.generator(self.dev_path, self.source_vocab, self.target_vocab) generate_preprocess_files(train_gen, eval_gen, train_preprocess_path, eval_preprocess_path) return train_preprocess_path, eval_preprocess_path def get_feature_encoders(self, data_dir=None): if self._encoders is None: self._encoders = self.feature_encoders() return self._encoders def feature_encoders(self): targets_encoder = self.target_vocab if self.has_inputs: inputs_encoder = self.source_vocab return {"inputs": inputs_encoder, "targets": targets_encoder} return {"targets": targets_encoder} def tabbed_generator(self, source_path, source_vocab, target_vocab, eos=None): r"""Generator for sequence-to-sequence tasks using tabbed files. Tokens are derived from text files where each line contains both a source and a target string. The two strings are separated by a tab character ('\t'). It yields dictionaries of "inputs" and "targets" where inputs are characters from the source lines converted to integers, and targets are characters from the target lines, also converted to integers. Args: source_path: path to the file with source and target sentences. source_vocab: a SubwordTextEncoder to encode the source string. target_vocab: a SubwordTextEncoder to encode the target string. eos: integer to append at the end of each sequence (default: None). Yields: A dictionary {"inputs": source-line, "targets": target-line} where the lines are integer lists converted from characters in the file lines. """ eos_list = [] if eos is None else [eos] with tf.gfile.GFile(source_path, mode="r") as source_file: for line_idx, line in enumerate(source_file): if line: source, target = split_graphemes_phonemes(line, cleanup=self.cleanup, p2g_mode=self.p2g_mode) if not (source and target): tf.logging.warning("Invalid data format in line {} in {}:\n" "{}\nGraphemes and phonemes should be separated by white space." .format(line_idx, source_path, line)) continue source_ints = source_vocab.encode(source) + eos_list target_ints = target_vocab.encode(target) + eos_list yield {"inputs": source_ints, "targets": target_ints} def dataset(self, mode, data_dir=None, num_threads=None, output_buffer_size=None, shuffle_files=None, hparams=None, preprocess=True, dataset_split=None, shard=None, partition_id=0, num_partitions=1): """Build a Dataset for this problem. Args: mode: tf.estimator.ModeKeys; determines which files to read from. data_dir: directory that contains data files. num_threads: int, number of threads to use for decode and preprocess Dataset.map calls. output_buffer_size: int, how many elements to prefetch in Dataset.map calls. shuffle_files: whether to shuffle input files. Default behavior (i.e. when shuffle_files=None) is to shuffle if mode == TRAIN. hparams: tf.contrib.training.HParams; hparams to be passed to Problem.preprocess_example and Problem.hparams. If None, will use a default set that is a no-op. preprocess: bool, whether to map the Dataset through Problem.preprocess_example. dataset_split: tf.estimator.ModeKeys + ["test"], which split to read data from (TRAIN:"-train", EVAL:"-dev", "test":"-test"). Defaults to mode. shard: int, if provided, will only read data from the specified shard. Returns: Dataset containing dict<feature name, Tensor>. """ if dataset_split or (mode in ["train", "eval"]): # In case when pathes to preprocessed files pointed out or if train mode # launched, we save preprocessed data first, and then create dataset from # that files. dataset_split = dataset_split or mode assert data_dir if not hasattr(hparams, "data_dir"): hparams.add_hparam("data_dir", data_dir) if not hparams.data_dir: hparams.data_dir = data_dir # Construct the Problem's hparams so that items within it are accessible _ = self.get_hparams(hparams) data_fields, data_items_to_decoders = self.example_reading_spec() if data_items_to_decoders is None: data_items_to_decoders = { field: tf.contrib.slim.tfexample_decoder.Tensor(field) for field in data_fields} is_training = mode == tf.estimator.ModeKeys.TRAIN data_filepattern = self.filepattern(data_dir, dataset_split, shard=shard) tf.logging.info("Reading data files from %s", data_filepattern) data_files = tf.contrib.slim.parallel_reader.get_data_files( data_filepattern) if shuffle_files or shuffle_files is None and is_training: random.shuffle(data_files) else: # In case when pathes to preprocessed files not pointed out, we create # dataset from generator object. eos_list = [] if EOS is None else [EOS] data_list = [] with tf.gfile.GFile(self.test_path, mode="r") as source_file: for line in source_file: if line: if "\t" in line: parts = line.split("\t", 1) source, target = parts[0].strip(), parts[1].strip() source_ints = self.source_vocab.encode(source) + eos_list target_ints = self.target_vocab.encode(target) + eos_list data_list.append({"inputs":source_ints, "targets":target_ints}) else: source_ints = self.source_vocab.encode(line) + eos_list data_list.append(generator_utils.to_example( {"inputs":source_ints})) gen = Gen(self.generator(self.test_path, self.source_vocab, self.target_vocab)) dataset = dataset_ops.Dataset.from_generator(gen, tf.string) preprocess = False def decode_record(record): """Serialized Example to dict of <feature name, Tensor>.""" decoder = tf.contrib.slim.tfexample_decoder.TFExampleDecoder( data_fields, data_items_to_decoders) decode_items = list(data_items_to_decoders) decoded = decoder.decode(record, items=decode_items) return dict(zip(decode_items, decoded)) def _preprocess(example): """Whether preprocess data into required format.""" example = self.preprocess_example(example, mode, hparams) self.maybe_reverse_features(example) self.maybe_copy_features(example) return example dataset = (tf.data.Dataset.from_tensor_slices(data_files) .interleave(lambda x: tf.data.TFRecordDataset(x).map(decode_record, num_parallel_calls=4), cycle_length=4, block_length=16)) if preprocess: dataset = dataset.map(_preprocess, num_parallel_calls=4) return dataset class Gen: """Generator class for dataset creation. Function dataset_ops.Dataset.from_generator() required callable generator object.""" def __init__(self, gen): """ Initialize generator.""" self._gen = gen def __call__(self): for case in self._gen: source_ints = case["inputs"] target_ints = case["targets"] yield generator_utils.to_example({"inputs":source_ints, "targets":target_ints}) def generate_preprocess_files(train_gen, dev_gen, train_preprocess_path, dev_preprocess_path): """Generate cases from a generators and save as TFRecord files. Generated cases are transformed to tf.Example protos and saved as TFRecords in sharded files named output_dir/output_name-00..N-of-00..M=num_shards. Args: train_gen: a generator yielding (string -> int/float/str list) train data. dev_gen: a generator yielding development data. train_preprocess_path: path to the file where preprocessed train data will be saved. dev_preprocess_path: path to the file where preprocessed development data will be saved. """ if dev_gen: gen_file(train_gen, train_preprocess_path) gen_file(dev_gen, dev_preprocess_path) else: # In case when development generator was not given, we create development # preprocess file from train generator. train_writer = tf.python_io.TFRecordWriter(train_preprocess_path) dev_writer = tf.python_io.TFRecordWriter(dev_preprocess_path) line_counter = 1 for case in train_gen: sequence_example = generator_utils.to_example(case) if line_counter % 20 == 0: dev_writer.write(sequence_example.SerializeToString()) else: train_writer.write(sequence_example.SerializeToString()) line_counter += 1 train_writer.close() dev_writer.close() def gen_file(generator, output_file_path): """Generate cases from generator and save as TFRecord file. Args: generator: a generator yielding (string -> int/float/str list) data. output_file_path: path to the file where preprocessed data will be saved. """ writer = tf.python_io.TFRecordWriter(output_file_path) for case in generator: sequence_example = generator_utils.to_example(case) writer.write(sequence_example.SerializeToString()) writer.close() def create_data_files(init_train_path, init_dev_path, init_test_path, cleanup=False, p2g_mode=False): """Create train, development and test data files from initial data files in case when not provided development or test data files or active cleanup flag. Args: init_train_path: path to the train data file. init_dev_path: path to the development data file. init_test_path: path to the test data file. cleanup: flag indicating whether to cleanup datasets from stress and comments. Returns: train_path: path to the new train data file generated from initially provided data. dev_path: path to the new development data file generated from initially provided data. test_path: path to the new test data file generated from initially provided data. """ train_path, dev_path, test_path = init_train_path, init_dev_path,\ init_test_path if (init_dev_path and init_test_path and os.path.exists(init_dev_path) and os.path.exists(init_test_path)): if not cleanup: return init_train_path, init_dev_path, init_test_path else: train_path = init_train_path + ".part.train" if init_dev_path: if not os.path.exists(init_dev_path): raise IOError("File {} not found.".format(init_dev_path)) else: dev_path = init_train_path + ".part.dev" if init_test_path: if not os.path.exists(init_test_path): raise IOError("File {} not found.".format(init_test_path)) else: test_path = init_train_path + ".part.test" if cleanup: train_path += ".cleanup" dev_path += ".cleanup" test_path += ".cleanup" train_dic, dev_dic, test_dic = OrderedDict(), OrderedDict(), OrderedDict() source_dic = collect_pronunciations(source_path=init_train_path, cleanup=cleanup, p2g_mode=p2g_mode) if init_dev_path: dev_dic = collect_pronunciations(source_path=init_dev_path, cleanup=cleanup, p2g_mode=p2g_mode) if init_test_path: test_dic = collect_pronunciations(source_path=init_test_path, cleanup=cleanup, p2g_mode=p2g_mode) #Split dictionary to train, validation and test (if not assigned). for word_counter, (word, pronunciations) in enumerate(source_dic.items()): if word_counter % 20 == 19 and not init_dev_path: dev_dic[word] = pronunciations elif ((word_counter % 20 == 18 or word_counter % 20 == 17) and not init_test_path): test_dic[word] = pronunciations else: train_dic[word] = pronunciations save_dic(train_dic, train_path) if not init_dev_path or cleanup: save_dic(dev_dic, dev_path) if not init_test_path or cleanup: save_dic(test_dic, test_path) return train_path, dev_path, test_path def collect_pronunciations(source_path, cleanup=False, p2g_mode=False): """Create dictionary mapping word to its different pronounciations. Args: source_path: path to the data file; cleanup: flag indicating whether to cleanup datasets from stress and comments. Returns: dic: dictionary mapping word to its pronunciations. """ dic = OrderedDict() with tf.gfile.GFile(source_path, mode="r") as source_file: word_counter = 0 for line in source_file: if line: source, target = split_graphemes_phonemes(line, cleanup=cleanup, p2g_mode=p2g_mode) if not (source, target): tf.logging.warning("Invalid data format in line {} in {}:\n" "{}\nGraphemes and phonemes should be separated by white space." .format(line_idx, source_path, line)) continue if source in dic: dic[source].append(target) else: dic[source] = [target] return dic def split_graphemes_phonemes(input_line, cleanup=False, p2g_mode=False): """Split line into graphemes and phonemes. Args: input_line: raw input line; cleanup: flag indicating whether to cleanup datasets from stress and comments. Returns: graphemes: graphemes string; phonemes: phonemes string. """ line = input_line if cleanup: clean_pattern = re.compile(r"(\[.*\]|\{.*\}|$.*$|#.*)") stress_pattern = re.compile(r"(?<=[a-zA-Z])\d+") line = re.sub(clean_pattern, r"", line) line = re.sub(stress_pattern, r"", line) items = line.split() source, target = None, None if len(items) > 1: if not p2g_mode: source, target = items[0].strip(), " ".join(items[1:]).strip() else: source, target = " ".join(items[:-1]).strip(), items[-1].strip() return source, target def save_dic(dic, save_path): with tf.gfile.GFile(save_path, mode="w") as save_file: for word, pronunciations in dic.items(): for pron in pronunciations: save_file.write(word + " " + pron + "\n")

37.932821

80

0.681071

7943bdd0f4b5e55f8c3c1ee2f3e60e57f423024b

57

py

Python

kerascv/examples/voc_12_download.py

tanzhenyu/keras-cv

b7208ee25735c492ccc171874e34076111dcf637

[ "Apache-2.0" ]

null

kerascv/examples/voc_12_download.py

tanzhenyu/keras-cv

b7208ee25735c492ccc171874e34076111dcf637

[ "Apache-2.0" ]

null

kerascv/examples/voc_12_download.py

tanzhenyu/keras-cv

b7208ee25735c492ccc171874e34076111dcf637

[ "Apache-2.0" ]

null

import tensorflow_datasets as tfds tfds.load('voc/2012')

19

34

0.807018

7943c192fe8b15cc93391b3595562a1114a7117d

2,697

py

Python

src/signer/eth/signer.py

Leibniz137/EthereumBridge

4b82a68cdc09e5ea79ec2fbf87aa065a2a3a5ffa

[ "MIT" ]

null

src/signer/eth/signer.py

Leibniz137/EthereumBridge

4b82a68cdc09e5ea79ec2fbf87aa065a2a3a5ffa

[ "MIT" ]

null

src/signer/eth/signer.py

Leibniz137/EthereumBridge

4b82a68cdc09e5ea79ec2fbf87aa065a2a3a5ffa

[ "MIT" ]

null

from threading import Thread, Event from time import sleep from src.contracts.ethereum.event_listener import EthEventListener from src.contracts.ethereum.multisig_wallet import MultisigWallet from src.signer.eth.impl import EthSignerImpl from src.util.config import Config from src.util.crypto_store.crypto_manager import CryptoManagerBase from src.util.logger import get_logger class EtherSigner(Thread): """ secretETH --> Swap TX --> ETH On Ethereum the leader monitors the sETH Secret Contract. When it sees a new swap, it will broadcast a submit transaction on-chain. On detecting a submit transaction from the event listener, the signer signs and broadcasts a confirm transaction on-chain. The multisig contract, after receiving a number of confirmations greater than the set threshold will trigger the transfer of funds Will first attempt to catch up with unsigned transactions by scanning past events, and only then will start monitoring new transactions. The account set here must have enough ETH for all the transactions you're planning on doing """ def __init__( self, contract: MultisigWallet, signer: CryptoManagerBase, dst_network: str, config: Config, **kwargs ): self.account = signer.address # self.private_key = private_key self.event_listener = EthEventListener(contract, config) self.stop_event = Event() self.logger = get_logger( db_name=config['db_name'], logger_name=config.get('logger_name', f"{self.__class__.__name__}-{self.account[0:5]}") ) self.config = config self.signer = EthSignerImpl(contract, signer, dst_network, config) super().__init__(group=None, name=f"{self.__class__.__name__}-{self.account[0:5]}", target=self.run, **kwargs) self.setDaemon(True) # so tests don't hang def run(self): self.logger.info("Starting..") from_block = self.choose_starting_block() self.event_listener.register(self.signer.sign, ['Submission'], from_block=from_block) self.event_listener.start() while not self.stop_event.is_set(): if not self.event_listener.is_alive(): self.logger.critical("Event listener stopped - stopping signer") self.stop() sleep(10) def stop(self): self.logger.info("Stopping..") self.event_listener.stop() self.stop_event.set() def choose_starting_block(self) -> int: """Returns the block from which we start scanning Ethereum for new tx""" return int(self.config.get('eth_start_block', 0))

38.528571

118

0.687801

7943c1ba5c70403f8e97dfc777520fd4b6abca25

201

py

Python

Coursera/CICCP1/primalidade.py

marcelomiky/python_code

4b843c78e16c37e981e4adfe47ae974ee0f2ad81

[ "MIT" ]

2

2020-10-19T13:53:59.000Z

2021-08-05T19:48:07.000Z

Coursera/CICCP1/primalidade.py

marcelomiky/PythonCodes

07f0db8019805b3b9567b7b57ddb49b4333a3aa2

[ "MIT" ]

null

Coursera/CICCP1/primalidade.py

marcelomiky/PythonCodes

07f0db8019805b3b9567b7b57ddb49b4333a3aa2

[ "MIT" ]

null

x = int(input('Insira um número inteiro positivo:')) divisores = 0 for i in range(1, x): if x % i == 0: divisores += 1 if divisores >= 2: print('não primo') else: print('primo')

15.461538

52

0.572139

7943c306dcd128b0eb78bacb2a294f5f1a071b7f

9,765

py

Python

docs/conf.py

hthompson6/a10-neutron-lbaas

f1639758cd3abcc6c86c8e6b64dcb0397c359621

[ "Apache-2.0" ]

10

2015-09-15T05:16:15.000Z

2020-03-18T02:34:39.000Z

docs/conf.py

hthompson6/a10-neutron-lbaas

f1639758cd3abcc6c86c8e6b64dcb0397c359621

[ "Apache-2.0" ]

334

2015-02-11T23:45:00.000Z

2020-02-28T08:58:51.000Z

docs/conf.py

hthompson6/a10-neutron-lbaas

f1639758cd3abcc6c86c8e6b64dcb0397c359621

[ "Apache-2.0" ]

24

2015-01-13T21:14:45.000Z

2021-06-02T17:22:14.000Z

# -*- coding: utf-8 -*- # # A10 Openstack documentation build configuration file, created by # sphinx-quickstart on Wed Apr 20 19:30:55 2016. # # This file is execfile()d with the current directory set to its # containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. import sys import os import sphinx_rtd_theme # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. #sys.path.insert(0, os.path.abspath('.')) # -- General configuration ------------------------------------------------ # If your documentation needs a minimal Sphinx version, state it here. #needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ 'sphinx.ext.intersphinx', 'sphinxcontrib.httpdomain' ] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: # source_suffix = ['.rst', '.md'] source_suffix = '.rst' # The encoding of source files. #source_encoding = 'utf-8-sig' # The master toctree document. master_doc = 'index' # General information about the project. project = u'A10 Openstack' copyright = u'2016, A10 Networks' author = u'A10 Networks' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = u'git this from the version file' # The full version, including alpha/beta/rc tags. release = u'git this from the version file' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: #today = '' # Else, today_fmt is used as the format for a strftime call. #today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This patterns also effect to html_static_path and html_extra_path exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] # The reST default role (used for this markup: `text`) to use for all # documents. #default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. #add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). #add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. #show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # A list of ignored prefixes for module index sorting. #modindex_common_prefix = [] # If true, keep warnings as "system message" paragraphs in the built documents. #keep_warnings = False # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = False # -- Options for HTML output ---------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. html_theme = 'sphinx_rtd_theme' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. #html_theme_options = {} # Add any paths that contain custom themes here, relative to this directory. html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] # The name for this set of Sphinx documents. # "<project> v<release> documentation" by default. #html_title = u'A10 Openstack vgit this from the version file' # A shorter title for the navigation bar. Default is the same as html_title. #html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. #html_logo = None # The name of an image file (relative to this directory) to use as a favicon of # the docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. #html_favicon = None # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # Add any extra paths that contain custom files (such as robots.txt or # .htaccess) here, relative to this directory. These files are copied # directly to the root of the documentation. #html_extra_path = [] # If not None, a 'Last updated on:' timestamp is inserted at every page # bottom, using the given strftime format. # The empty string is equivalent to '%b %d, %Y'. #html_last_updated_fmt = None # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. #html_use_smartypants = True # Custom sidebar templates, maps document names to template names. #html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. #html_additional_pages = {} # If false, no module index is generated. #html_domain_indices = True # If false, no index is generated. #html_use_index = True # If true, the index is split into individual pages for each letter. #html_split_index = False # If true, links to the reST sources are added to the pages. #html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. #html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. #html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a <link> tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. #html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). #html_file_suffix = None # Language to be used for generating the HTML full-text search index. # Sphinx supports the following languages: # 'da', 'de', 'en', 'es', 'fi', 'fr', 'hu', 'it', 'ja' # 'nl', 'no', 'pt', 'ro', 'ru', 'sv', 'tr', 'zh' #html_search_language = 'en' # A dictionary with options for the search language support, empty by default. # 'ja' uses this config value. # 'zh' user can custom change `jieba` dictionary path. #html_search_options = {'type': 'default'} # The name of a javascript file (relative to the configuration directory) that # implements a search results scorer. If empty, the default will be used. #html_search_scorer = 'scorer.js' # Output file base name for HTML help builder. htmlhelp_basename = 'A10Openstackdoc' # -- Options for LaTeX output --------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). #'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). #'pointsize': '10pt', # Additional stuff for the LaTeX preamble. #'preamble': '', # Latex figure (float) alignment #'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ (master_doc, 'A10Openstack.tex', u'A10 Openstack Documentation', u'A10 Networks', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. #latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. #latex_use_parts = False # If true, show page references after internal links. #latex_show_pagerefs = False # If true, show URL addresses after external links. #latex_show_urls = False # Documents to append as an appendix to all manuals. #latex_appendices = [] # If false, no module index is generated. #latex_domain_indices = True # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ (master_doc, 'a10openstack', u'A10 Openstack Documentation', [author], 1) ] # If true, show URL addresses after external links. #man_show_urls = False # -- Options for Texinfo output ------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ (master_doc, 'A10Openstack', u'A10 Openstack Documentation', author, 'A10Openstack', 'One line description of project.', 'Miscellaneous'), ] # Documents to append as an appendix to all manuals. #texinfo_appendices = [] # If false, no module index is generated. #texinfo_domain_indices = True # How to display URL addresses: 'footnote', 'no', or 'inline'. #texinfo_show_urls = 'footnote' # If true, do not generate a @detailmenu in the "Top" node's menu. #texinfo_no_detailmenu = False # Example configuration for intersphinx: refer to the Python standard library. intersphinx_mapping = {'https://docs.python.org/': None}

32.989865

80

0.722171

7943c34d5f4d8ec022c72eb4522fb59aef9e7d29

6,284

py

Python

landmark/models/mobilefacenet.py

greedpejo/FER_SPRING

622d093da579d17e167f2771da4fb3365824cdd3

[ "MIT" ]

433

2020-04-02T04:24:50.000Z

2022-03-21T12:57:53.000Z

landmark/models/mobilefacenet.py

greedpejo/FER_SPRING

622d093da579d17e167f2771da4fb3365824cdd3

[ "MIT" ]

35

2020-04-09T02:13:52.000Z

2022-03-07T07:48:10.000Z

landmark/models/mobilefacenet.py

greedpejo/FER_SPRING

622d093da579d17e167f2771da4fb3365824cdd3

[ "MIT" ]

72

2020-04-02T21:57:37.000Z

2022-01-10T02:50:33.000Z

from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, ReLU, Sigmoid, Dropout2d, Dropout, AvgPool2d, MaxPool2d, AdaptiveAvgPool2d, Sequential, Module, Parameter import torch.nn.functional as F import torch import torch.nn as nn from collections import namedtuple import math import pdb ################################## Original Arcface Model ############################################################# class Flatten(Module): def forward(self, input): return input.view(input.size(0), -1) ################################## MobileFaceNet ############################################################# class Conv_block(Module): def __init__(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1): super(Conv_block, self).__init__() self.conv = Conv2d(in_c, out_channels=out_c, kernel_size=kernel, groups=groups, stride=stride, padding=padding, bias=False) self.bn = BatchNorm2d(out_c) self.prelu = PReLU(out_c) def forward(self, x): x = self.conv(x) x = self.bn(x) x = self.prelu(x) return x class Linear_block(Module): def __init__(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1): super(Linear_block, self).__init__() self.conv = Conv2d(in_c, out_channels=out_c, kernel_size=kernel, groups=groups, stride=stride, padding=padding, bias=False) self.bn = BatchNorm2d(out_c) def forward(self, x): x = self.conv(x) x = self.bn(x) return x class Depth_Wise(Module): def __init__(self, in_c, out_c, residual = False, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=1): super(Depth_Wise, self).__init__() self.conv = Conv_block(in_c, out_c=groups, kernel=(1, 1), padding=(0, 0), stride=(1, 1)) self.conv_dw = Conv_block(groups, groups, groups=groups, kernel=kernel, padding=padding, stride=stride) self.project = Linear_block(groups, out_c, kernel=(1, 1), padding=(0, 0), stride=(1, 1)) self.residual = residual def forward(self, x): if self.residual: short_cut = x x = self.conv(x) x = self.conv_dw(x) x = self.project(x) if self.residual: output = short_cut + x else: output = x return output class Residual(Module): def __init__(self, c, num_block, groups, kernel=(3, 3), stride=(1, 1), padding=(1, 1)): super(Residual, self).__init__() modules = [] for _ in range(num_block): modules.append(Depth_Wise(c, c, residual=True, kernel=kernel, padding=padding, stride=stride, groups=groups)) self.model = Sequential(*modules) def forward(self, x): return self.model(x) class GNAP(Module): def __init__(self, embedding_size): super(GNAP, self).__init__() assert embedding_size == 512 self.bn1 = BatchNorm2d(512, affine=False) self.pool = nn.AdaptiveAvgPool2d((1, 1)) self.bn2 = BatchNorm1d(512, affine=False) def forward(self, x): x = self.bn1(x) x_norm = torch.norm(x, 2, 1, True) x_norm_mean = torch.mean(x_norm) weight = x_norm_mean / x_norm x = x * weight x = self.pool(x) x = x.view(x.shape[0], -1) feature = self.bn2(x) return feature class GDC(Module): def __init__(self, embedding_size): super(GDC, self).__init__() self.conv_6_dw = Linear_block(512, 512, groups=512, kernel=(7,7), stride=(1, 1), padding=(0, 0)) self.conv_6_flatten = Flatten() self.linear = Linear(512, embedding_size, bias=False) #self.bn = BatchNorm1d(embedding_size, affine=False) self.bn = BatchNorm1d(embedding_size) def forward(self, x): x = self.conv_6_dw(x) x = self.conv_6_flatten(x) x = self.linear(x) x = self.bn(x) return x class MobileFaceNet(Module): def __init__(self, input_size, embedding_size = 512, output_name = "GDC"): super(MobileFaceNet, self).__init__() assert output_name in ["GNAP", 'GDC'] assert input_size[0] in [112] self.conv1 = Conv_block(3, 64, kernel=(3, 3), stride=(2, 2), padding=(1, 1)) self.conv2_dw = Conv_block(64, 64, kernel=(3, 3), stride=(1, 1), padding=(1, 1), groups=64) self.conv_23 = Depth_Wise(64, 64, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=128) self.conv_3 = Residual(64, num_block=4, groups=128, kernel=(3, 3), stride=(1, 1), padding=(1, 1)) self.conv_34 = Depth_Wise(64, 128, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=256) self.conv_4 = Residual(128, num_block=6, groups=256, kernel=(3, 3), stride=(1, 1), padding=(1, 1)) self.conv_45 = Depth_Wise(128, 128, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=512) self.conv_5 = Residual(128, num_block=2, groups=256, kernel=(3, 3), stride=(1, 1), padding=(1, 1)) self.conv_6_sep = Conv_block(128, 512, kernel=(1, 1), stride=(1, 1), padding=(0, 0)) if output_name == "GNAP": self.output_layer = GNAP(512) else: self.output_layer = GDC(embedding_size) self._initialize_weights() def _initialize_weights(self): for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() elif isinstance(m, nn.Linear): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') if m.bias is not None: m.bias.data.zero_() def forward(self, x): out = self.conv1(x) out = self.conv2_dw(out) out = self.conv_23(out) out = self.conv_3(out) out = self.conv_34(out) out = self.conv_4(out) out = self.conv_45(out) out = self.conv_5(out) conv_features = self.conv_6_sep(out) out = self.output_layer(conv_features) return out, conv_features

39.275

175

0.58275

7943c67c61599df8b5c24b600e482e73be5973fd

274

py

Python

users/permissions.py

tgamauf/spritstat

849526ec8dec46c57194d50ff3b32c16d0cb684a

[ "MIT" ]

1

2022-01-30T10:50:14.000Z

users/permissions.py

tgamauf/spritstat

849526ec8dec46c57194d50ff3b32c16d0cb684a

[ "MIT" ]

47

2022-02-02T22:07:28.000Z

2022-03-30T13:53:37.000Z

users/permissions.py

tgamauf/spritstat

849526ec8dec46c57194d50ff3b32c16d0cb684a

[ "MIT" ]

null

from rest_framework import permissions class IsOwner(permissions.BasePermission): """ Custom permission to only allow owners of an object to view or edit it. """ def has_object_permission(self, request, view, obj): return obj.user == request.user

24.909091

75

0.711679

7943c6ce3e9ed2545d9a0ab661efa5022c630c05

1,066

py

Python

pushcast/pcfilter.py

anotherpyr/pushmac

44e87cf6642c59465fc84c8e564829d33a2006ca

[ "Apache-2.0" ]

null

pushcast/pcfilter.py

anotherpyr/pushmac

44e87cf6642c59465fc84c8e564829d33a2006ca

[ "Apache-2.0" ]

null

pushcast/pcfilter.py

anotherpyr/pushmac

44e87cf6642c59465fc84c8e564829d33a2006ca

[ "Apache-2.0" ]

null

''' Created on Aug 24, 2015 @author: anotherpyr ''' class SimpleDescription(): def filter(self, lines): output = u"" append = False for k in range(0, len(lines)): line = lines[k] # Remove excess URLs from descriptions if line.find("://") < 0: if append: output += u" " else: append = True output += line # if this removed all of the lines if len(output) < 1: #Then remove just URLs (could be humorous for k in range(0, len(lines)): if append: output += u" " else: append = True output += lines[k] output = output.replace("<p>", "") output = output.replace("</p>", "") output = output.replace("<span>", "") output = output.replace("</span>", "") return output

27.333333

53

0.407129

7943c7f8c30524dbca6c55e6c847544a19bd29ba

6,803

py

Python

lit_nlp/lib/wsgi_app.py

bigprince97/lit

cd221e2e6fa02518ccd2fda17d43074939d952a3

[ "Apache-2.0" ]

2

2020-08-27T06:12:30.000Z

2020-08-27T06:13:08.000Z

lit_nlp/lib/wsgi_app.py

screwdriver66/lit

a40ef90b514383fb78b3f8742aea31135445693e

[ "Apache-2.0" ]

null

lit_nlp/lib/wsgi_app.py

screwdriver66/lit

a40ef90b514383fb78b3f8742aea31135445693e

[ "Apache-2.0" ]

null

# Copyright 2020 Google LLC # # 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. # ============================================================================== # Lint as: python3 """Simple WSGI app implementation. This takes a list of handlers, and creates a WSGI application that can be served through a variety of methods. Why not use Flask or something? Historical reasons, and if it ain't broke, don't fix it. """ import mimetypes import os import time import traceback import wsgiref.handlers from absl import logging import six from six.moves.urllib.parse import urlparse from werkzeug import wrappers def _LoadResource(path): """Load the resource at given path. Args: path: a string resource path. Returns: The contents of that resource. Raises: ValueError: If the path is not set up correctly. IOError: If the path is not found, or the resource can't be opened. """ try: with open(path,"rb") as f: return f.read() except IOError as e: logging.warning('IOError %s on path %s', e, path) raise e class App(object): """Standalone WSGI app that can serve files, etc.""" _TEXTUAL_MIMETYPES = set([ 'application/javascript', 'application/json', 'application/json+protobuf', 'image/svg+xml', 'text/css', 'text/csv', 'text/html', 'text/json', 'text/plain', 'text/tab-separated-values', 'text/x-protobuf', ]) def __init__(self, handlers, project_root, index_file='index.html'): self._handlers = handlers self._project_root = project_root self._index_file = index_file def respond( # pylint: disable=invalid-name self, request, content, content_type, code=200, expires=0, content_encoding=None): """Construct a werkzeug WSGI response object. Args: request: A werkzeug Request object. Used mostly to check the Accept-Encoding header. content: Payload data as bytes or unicode string (will be UTF-8 encoded). content_type: Media type only - "charset=utf-8" will be added for text. code: Numeric HTTP status code to use. expires: Second duration for browser caching, default 0. content_encoding: Encoding if content is already encoded, e.g. 'gzip'. Returns: A werkzeug Response object (a WSGI application). """ if isinstance(content, six.text_type): content = content.encode('utf-8') if content_type in self._TEXTUAL_MIMETYPES: content_type += '; charset=utf-8' headers = [] headers.append(('Content-Length', str(len(content)))) if content_encoding: headers.append(('Content-Encoding', content_encoding)) if expires > 0: e = wsgiref.handlers.format_date_time(time.time() + float(expires)) headers.append(('Expires', e)) headers.append(('Cache-Control', 'private, max-age=%d' % expires)) else: headers.append(('Expires', '0')) headers.append(('Cache-Control', 'no-cache, must-revalidate')) if request.method == 'HEAD': content = None return wrappers.Response( response=content, status=code, headers=headers, content_type=content_type) def _ServeStaticFile(self, request, path): """Serves the static file located at the given path. Args: request: A Werkzeug Request object. path: The path of the static file, relative to the current directory. Returns: A Werkzeug Response object. """ if not self._PathIsSafe(path): logging.info('path %s not safe, sending 400', path) # Traversal attack, so 400. return self.respond(request, 'Path not safe', 'text/plain', 400) # Open the file and read it. try: contents = _LoadResource(path) except IOError: logging.info('path %s not found, sending 404', path) return self.respond(request, 'Not found', 'text/plain', code=404) mimetype, content_encoding = mimetypes.guess_type(path) mimetype = mimetype or 'application/octet-stream' return self.respond( request, contents, mimetype, expires=3600, content_encoding=content_encoding) def _PathIsSafe(self, path): """Check path is safe (stays within current directory). This is for preventing directory-traversal attacks. Args: path: The path to check for safety. Returns: True if the given path stays within the current directory, and false if it would escape to a higher directory. E.g. _path_is_safe('index.html') returns true, but _path_is_safe('../../../etc/password') returns false. """ base = os.path.abspath(os.curdir) absolute_path = os.path.abspath(path) prefix = os.path.commonprefix([base, absolute_path]) return prefix == base def _ServeCustomHandler(self, request, clean_path): return self._handlers[clean_path](self, request) def __call__(self, environ, start_response): """Implementation of the WSGI interface.""" request = wrappers.Request(environ) try: parsed_url = urlparse(request.path) # Remove a trailing slash, if present. clean_path = parsed_url.path if clean_path.endswith('/'): clean_path = clean_path[:-1] if clean_path in self._handlers: return self._ServeCustomHandler(request, clean_path)(environ, start_response) else: is_index = not clean_path or clean_path == '/index.html' if is_index: clean_path = os.path.join(self._project_root, self._index_file) else: # Strip off the leading forward slash. Don't do it for index because # in the vulcanized version we use an absolute path. clean_path = os.path.join(self._project_root, clean_path.lstrip('/')) response = self._ServeStaticFile(request, clean_path) except Exception as e: # pylint: disable=broad-except errors = (str(e), str(traceback.format_exc())) html_response = ( '<code>Uncaught error: %s <br><br> <code>%s</code></code>' % errors) logging.error('Uncaught error: %s \n\n %s', *errors) response = self.respond(request, html_response, 'text/html', 500) return response(environ, start_response)

32.089623

80

0.657504

7943c83a843e00548170e79563c3645c1a2669e9

2,140

py

Python

binho/binhoHostAdapter.py

pixelfelon/binho-python-package

92f4898722f0578973cb672d1387fbea6cb7bb3d

[ "BSD-3-Clause" ]

4

2021-03-11T12:40:27.000Z

2022-02-01T10:08:20.000Z

binho/binhoHostAdapter.py

pixelfelon/binho-python-package

92f4898722f0578973cb672d1387fbea6cb7bb3d

[ "BSD-3-Clause" ]

1

2021-11-26T10:20:18.000Z

2021-11-30T14:25:56.000Z

binho/binhoHostAdapter.py

pixelfelon/binho-python-package

92f4898722f0578973cb672d1387fbea6cb7bb3d

[ "BSD-3-Clause" ]

2

2021-02-28T00:39:39.000Z

2021-04-05T12:45:56.000Z

from .device import binhoDevice from .devices import nova # pylint: disable=unused-import # Ensure that we have access to all Binho host adapter devices. Normally, we'd avoid # importing an entire namespace, but in this case, this allows us to ensure # that all board modules are loaded for autoidentification. from .errors import DeviceNotFoundError active_connections = {} def binhoHostAdapter(**board_identifiers): """ Attempts to create a new instance of binhoHostAdapter board (sub)class most applicable to the given device. For example, if the attached board is a Binho Nova, this will automatically create a binhoNova object. Accepts the same arguments as pyusb's usb.find() method, allowing narrowing to a more specific binhoHostAdapter by e.g. serial number. Like usb.find(), providing find_all will return a list of all found devices. Throws a DeviceNotFoundError if no device is avaiable and find_all is not set. """ if not board_identifiers: board_identifiers["index"] = 0 return binhoDevice.autodetect(board_identifiers) if ( "port" in board_identifiers and board_identifiers["port"] or "deviceID" in board_identifiers and board_identifiers["deviceID"] or "index" in board_identifiers ): return binhoDevice.autodetect(board_identifiers) if "find_all" in board_identifiers and board_identifiers["find_all"]: return binhoDevice.autodetect_all(board_identifiers) raise DeviceNotFoundError def binhoHostAdapterSingleton(serial=None): """ Returns a binhoHostAdapter object, re-using an existing object if we already have a connection to the given binhoHostAdapter. """ # If we already have a binhoHostAdapter with the given serial, if serial in active_connections: device = active_connections[serial] if device.comms.still_connected(): return device # Otherwise, try to create a new binhoHostAdapter instance. hostAdapter = binhoHostAdapter(serial_number=serial) active_connections[serial] = hostAdapter return hostAdapter

37.54386

115

0.734579

7943c84e63e15045ad36ea2542a48f3fd21ce1d5

4,118

py

Python

demo/demo/settings.py

mwj9446/MWJ_python

48a152803249a62cbedff1a4e7f8367ea861ede5

[ "MIT" ]

null

demo/demo/settings.py

mwj9446/MWJ_python

48a152803249a62cbedff1a4e7f8367ea861ede5

[ "MIT" ]

null

demo/demo/settings.py

mwj9446/MWJ_python

48a152803249a62cbedff1a4e7f8367ea861ede5

[ "MIT" ]

null

""" Django settings for demo project. Generated by 'django-admin startproject' using Django 1.11.11. For more information on this file, see https://docs.djangoproject.com/en/1.11/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/1.11/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/1.11/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! # 加密解密 SECRET_KEY = 'tw9tiw+0=wm=fhy#h5$*1^qz5a!8$oynz-@fql-^$mql4!mm%q' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = ['106.12.192.168', 'localhost', '0.0.0.0', '127.0.0.1'] # Application definition # users子应用。apps。子应用的apps.py中的类名 INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'users.apps.UsersConfig', 'login.apps.LoginConfig', 'index.apps.IndexConfig', 'booktest.apps.BooktestConfig' ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', # 'django.middleware.csrf.CsrfViewMiddleware', # POST 请求 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'demo.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [os.path.join(BASE_DIR, 'template')], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'demo.wsgi.application' # Database # https://docs.djangoproject.com/en/1.11/ref/settings/#databases # DATABASES = { # 'default': { # 'ENGINE': 'django.db.backends.sqlite3', # 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), # } # } #mysql配置 DATABASES = { 'default': { 'ENGINE': 'django.db.backends.mysql', 'HOST': '106.12.192.168', # 数据库主机 'PORT': 3306, # 数据库端口 'USER': 'root', # 数据库用户名 'PASSWORD': 'maiweijie', # 数据库用户密码 'NAME': 'demo' # 数据库名字 } } # Password validation # https://docs.djangoproject.com/en/1.11/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/1.11/topics/i18n/ LANGUAGE_CODE = 'zh-hans' TIME_ZONE = 'Asia/Shanghai' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/1.11/howto/static-files/ STATIC_URL = '/static/' STATICFILES_DIRS = [os.path.join(BASE_DIR, 'static')] # SESSION_ENGINE = 'django.contrib.sessions.backends.db' # redis 数据库配置 CACHES = { "default": { "BACKEND": "django_redis.cache.RedisCache", "LOCATION": "redis://:[email protected]:6379/1", "OPTIONS": { "CLIENT_CLASS": "django_redis.client.DefaultClient", } } } SESSION_ENGINE = "django.contrib.sessions.backends.cache" SESSION_CACHE_ALIAS = "default"

27.637584

91

0.673871

7943c929266ff7cf0baff3e98fb9bc8a61155826

987

py

Python

hwtHls/platform/opRealizationMeta.py

Nic30/hwtHls

1fac6ed128318e698d51e15e9871249ddf243e1c

[ "MIT" ]

8

2018-09-25T03:28:11.000Z

2021-12-15T07:44:38.000Z

hwtHls/platform/opRealizationMeta.py

Nic30/hwtHls

1fac6ed128318e698d51e15e9871249ddf243e1c

[ "MIT" ]

1

2020-12-21T10:56:44.000Z

hwtHls/platform/opRealizationMeta.py

Nic30/hwtHls

1fac6ed128318e698d51e15e9871249ddf243e1c

[ "MIT" ]

2

2018-09-25T03:28:18.000Z

2021-12-15T10:28:35.000Z

class OpRealizationMeta(): """ :ivar in_cycles_offset: number of cycles from component first cycle when the input is accepted :ivar latency_pre: minimal amount of time until next clock cycle :ivar latency_post: time required to stabilize output value after clock cycle :ivar cycles_latency: number of clock cycles required for data to reach output :ivar cycles_delay: number of cycles required until input can process other data """ def __init__(self, cycles_in_offset=0, latency_pre=0.0, latency_post=0.0, cycles_latency=0, cycles_delay=0): self.in_cycles_offset = cycles_in_offset self.latency_pre = latency_pre self.latency_post = latency_post self.cycles_latency = cycles_latency self.cycles_delay = cycles_delay EMPTY_OP_REALIZATION = OpRealizationMeta() UNSPECIFIED_OP_REALIZATION = OpRealizationMeta( latency_pre=None, latency_post=None, cycles_latency=None, cycles_delay=None)

39.48

98

0.742655

7943cae5f0b20dd8f424d31b0b38bc6da0464e63

108

py

Python

yt_dlp/version.py

git-anony-mouse/yt-dlp

6da22e7d4f1ffcda3f802da3e56ac6e171095388

[ "Unlicense" ]

1

2021-12-25T10:22:59.000Z

yt_dlp/version.py

git-anony-mouse/yt-dlp

6da22e7d4f1ffcda3f802da3e56ac6e171095388

[ "Unlicense" ]

null

yt_dlp/version.py

git-anony-mouse/yt-dlp

6da22e7d4f1ffcda3f802da3e56ac6e171095388

[ "Unlicense" ]

null

# Autogenerated by devscripts/update-version.py __version__ = '2021.12.25' RELEASE_GIT_HEAD = '87e049962'

18

47

0.777778

7943cd0717675a774dbe1e28d49b4461b94902a5

332

py

Python

AppPkg/Applications/Python/Python-2.7.2/Lib/test/infinite_reload.py

CEOALT1/RefindPlusUDK

116b957ad735f96fbb6d80a0ba582046960ba164

[ "BSD-2-Clause" ]

2,757

2018-04-28T21:41:36.000Z

2022-03-29T06:33:36.000Z

AppPkg/Applications/Python/Python-2.7.2/Lib/test/infinite_reload.py

CEOALT1/RefindPlusUDK

116b957ad735f96fbb6d80a0ba582046960ba164

[ "BSD-2-Clause" ]

20

2019-07-23T15:29:32.000Z

2022-01-21T12:53:04.000Z

AppPkg/Applications/Python/Python-2.7.2/Lib/test/infinite_reload.py

CEOALT1/RefindPlusUDK

116b957ad735f96fbb6d80a0ba582046960ba164

[ "BSD-2-Clause" ]

449

2018-05-09T05:54:05.000Z

2022-03-30T14:54:18.000Z

# For testing http://python.org/sf/742342, which reports that Python # segfaults (infinite recursion in C) in the presence of infinite # reload()ing. This module is imported by test_import.py:test_infinite_reload # to make sure this doesn't happen any more. import imp import infinite_reload imp.reload(infinite_reload)

36.888889

79

0.771084

7943cd2e5d949d4f95cac104cd86bdde812e6cb3

1,043

py

Python

src/config/middleware.py

vaporyorg/safe-config-service

b90d0dce0a528738859d42e90766f621ca37e89c

[ "MIT" ]

null

src/config/middleware.py

vaporyorg/safe-config-service

b90d0dce0a528738859d42e90766f621ca37e89c

[ "MIT" ]

40

2021-06-14T06:56:59.000Z

2022-03-21T10:07:27.000Z

src/config/middleware.py

vaporyorg/safe-config-service

b90d0dce0a528738859d42e90766f621ca37e89c

[ "MIT" ]

null

import logging import time from django.http import HttpRequest class LoggingMiddleware: def __init__(self, get_response): self.get_response = get_response self.logger = logging.getLogger("LoggingMiddleware") @staticmethod def get_milliseconds_now(): return int(time.time() * 1000) def __call__(self, request: HttpRequest): # before view (and other middleware) are called milliseconds = self.get_milliseconds_now() response = self.get_response(request) # after view is called if request.resolver_match: route = ( request.resolver_match.route[1:] if request.resolver_match else request.path ) self.logger.info( "MT::%s::%s::%s::%d::%s", request.method, route, self.get_milliseconds_now() - milliseconds, response.status_code, request.path, ) return response

27.447368

60

0.571429

7943cd72a638ac648c144e3cfa0a08f8b2a8ed55

1,594

py

Python

tests/test_execution.py

bpannier/TahomaProtocol

be78f68be8776134a2923389aa3e31cce445b676

[ "Apache-2.0" ]

3

2016-08-30T09:02:08.000Z

2018-01-09T09:35:55.000Z

tests/test_execution.py

bpannier/TahomaProtocol

be78f68be8776134a2923389aa3e31cce445b676

[ "Apache-2.0" ]

null

tests/test_execution.py

bpannier/TahomaProtocol

be78f68be8776134a2923389aa3e31cce445b676

[ "Apache-2.0" ]

null

import unittest from tahoma.execution import Execution from tahoma.eventState import EventState class TestExecution(unittest.TestCase): def test_parse(self): data = { "startTime": 1448329821365, "owner": "[email protected]", "actionGroup": { "label": "DeviceName - Action - AppDevice", "shortcut": False, "actions": [{ "deviceURL": "io://1234-1234-1234/12345678", "commands": [{ "type": 1, "name": "setClosure", "parameters": [21] }] }] }, "id": "12345678-1234-1234-1234-1234567890", "executionType": "Immediate execution", "executionSubType": "MANUAL_CONTROL", "state": "NOT_TRANSMITTED" } exe = Execution(data) self.assertEqual(exe.startTime, 1448329821365) self.assertEqual(exe.id, "12345678-1234-1234-1234-1234567890") self.assertEqual(exe.state, EventState.NotTransmitted) self.assertEqual(exe.name, "DeviceName - Action - AppDevice") self.assertEqual(len(exe.actions), 1 ) self.assertEqual(exe.actions[0].deviceURL, "io://1234-1234-1234/12345678") self.assertEqual(len(exe.actions[0].commands), 1) self.assertEqual(exe.actions[0].commands[0].name, "setClosure") self.assertEqual(len(exe.actions[0].commands[0].parameter), 1) self.assertEqual(exe.actions[0].commands[0].parameter[0], 21)

36.227273

82

0.556462

7943cda13881dc8f37bbbdde6b0f2c3c81b4c748

515

py

Python

env/lib/python3.8/site-packages/plotly/validators/parcoords/line/colorbar/_ticklen.py

acrucetta/Chicago_COVI_WebApp

a37c9f492a20dcd625f8647067394617988de913

[ "MIT", "Unlicense" ]

76

2020-07-06T14:44:05.000Z

2022-02-14T15:30:21.000Z

env/lib/python3.8/site-packages/plotly/validators/parcoords/line/colorbar/_ticklen.py

acrucetta/Chicago_COVI_WebApp

a37c9f492a20dcd625f8647067394617988de913

[ "MIT", "Unlicense" ]

11

2020-08-09T02:30:14.000Z

2022-03-12T00:50:14.000Z

env/lib/python3.8/site-packages/plotly/validators/parcoords/line/colorbar/_ticklen.py

acrucetta/Chicago_COVI_WebApp

a37c9f492a20dcd625f8647067394617988de913

[ "MIT", "Unlicense" ]

11

2020-07-12T16:18:07.000Z

2022-02-05T16:48:35.000Z

import _plotly_utils.basevalidators class TicklenValidator(_plotly_utils.basevalidators.NumberValidator): def __init__( self, plotly_name="ticklen", parent_name="parcoords.line.colorbar", **kwargs ): super(TicklenValidator, self).__init__( plotly_name=plotly_name, parent_name=parent_name, edit_type=kwargs.pop("edit_type", "colorbars"), min=kwargs.pop("min", 0), role=kwargs.pop("role", "style"), **kwargs )

32.1875

84

0.629126

7943ce3232027e9c0568463f9b27b52d0c14aca9

2,493

py

Python

jax/abstract_arrays.py

aldragan0/jax

3ddd3905a4c8f59480c43c24bb1f9374238bb2a0

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

1

2021-07-08T16:58:56.000Z

jax/abstract_arrays.py

aldragan0/jax

3ddd3905a4c8f59480c43c24bb1f9374238bb2a0

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

null

jax/abstract_arrays.py

aldragan0/jax

3ddd3905a4c8f59480c43c24bb1f9374238bb2a0

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

1

2020-07-17T18:17:31.000Z

# Copyright 2018 Google LLC # # 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 # # https://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 functools import partial import numpy as np from . import ad_util from . import core from . import dtypes _DIMENSION_TYPES = core._DIMENSION_TYPES UnshapedArray = core.UnshapedArray ShapedArray = core.ShapedArray ConcreteArray = core.ConcreteArray AbstractToken = core.AbstractToken abstract_token = core.abstract_token canonicalize_shape = core.canonicalize_shape raise_to_shaped = core.raise_to_shaped def make_shaped_array(x): dtype = dtypes.canonicalize_dtype(dtypes.result_type(x)) return ShapedArray(np.shape(x), dtype) def zeros_like_array(x): dtype = dtypes.canonicalize_dtype(dtypes.result_type(x)) return zeros_like_shaped_array(ShapedArray(np.shape(x), dtype)) array_types = {np.ndarray, np.bool_, np.int8, np.int16, np.int32, np.int64, np.uint8, np.uint16, np.uint32, np.uint64, dtypes.bfloat16, np.float16, np.float32, np.float64, np.complex64, np.complex128, np.longlong} for t in array_types: core.pytype_aval_mappings[t] = ConcreteArray ad_util.jaxval_zeros_likers[t] = zeros_like_array def zeros_like_shaped_array(aval): assert isinstance(aval, ShapedArray) if aval.dtype == dtypes.float0: return np.zeros(aval.shape, dtypes.float0) return np.broadcast_to(np.array(0, aval.dtype), aval.shape) ad_util.aval_zeros_likers[ShapedArray] = zeros_like_shaped_array core.literalable_types.update(array_types) def _zeros_like_python_scalar(t, x): return np.array(0, dtypes.python_scalar_dtypes[t]) def _make_concrete_python_scalar(t, x): return ConcreteArray( np.array(x, dtype=dtypes.python_scalar_dtypes[t]), weak_type=True) for t in dtypes.python_scalar_dtypes: core.pytype_aval_mappings[t] = partial(_make_concrete_python_scalar, t) ad_util.jaxval_zeros_likers[t] = partial(_zeros_like_python_scalar, t) core.literalable_types.update(dtypes.python_scalar_dtypes.keys())

32.376623

74

0.764541

7943ce3f07cb07214649b36ee85a6c003703d9cc

123

py

Python

ukb/weak_supervision/numbskull/numbskull/__init__.py

wi905252/ukb-cardiac-mri

3177dde898a65b1d7f385b78e4f134de3852bea5

[ "Apache-2.0" ]

19

2018-05-30T22:13:17.000Z

2022-01-18T14:04:40.000Z

ukb/weak_supervision/numbskull/numbskull/__init__.py

wi905252/ukb-cardiac-mri

3177dde898a65b1d7f385b78e4f134de3852bea5

[ "Apache-2.0" ]

1

2019-08-07T07:29:07.000Z

2019-08-07T08:54:10.000Z

ukb/weak_supervision/numbskull/numbskull/__init__.py

wi905252/ukb-cardiac-mri

3177dde898a65b1d7f385b78e4f134de3852bea5

[ "Apache-2.0" ]

8

2019-07-03T23:19:43.000Z

2021-11-15T17:09:24.000Z

"""TODO.""" from .numbskull import NumbSkull from .numbskull import main __all__ = ('numbskull', 'factorgraph', 'timer')

17.571429

47

0.707317

7943cfe26e97bc043278d8f170a11cae053eb661

5,332

py

Python

models/pointnet_seg_svm.py

OmidPoursaeed/Self_supervised_Learning_Point_Clouds

4f684cc761347f329eb967823f80522a8a3aedc0

[ "MIT" ]

11

2020-12-16T16:27:36.000Z

2021-12-01T04:07:56.000Z

models/pointnet_seg_svm.py

OmidPoursaeed/Self_supervised_Learning_Point_Clouds

4f684cc761347f329eb967823f80522a8a3aedc0

[ "MIT" ]

2

2021-02-09T11:35:01.000Z

2021-08-06T01:39:42.000Z

models/pointnet_seg_svm.py

OmidPoursaeed/Self_supervised_Learning_Point_Clouds

4f684cc761347f329eb967823f80522a8a3aedc0

[ "MIT" ]

1

2021-08-05T14:07:51.000Z

import tensorflow as tf import numpy as np import math import sys import os BASE_DIR = os.path.dirname(os.path.abspath(__file__)) sys.path.append(BASE_DIR) sys.path.append(os.path.join(BASE_DIR, '../utils')) import tf_util from transform_nets import input_transform_net, feature_transform_net def placeholder_inputs(batch_size, num_point): pointclouds_pl = tf.placeholder(tf.float32, shape=(batch_size, num_point, 3)) labels_pl = tf.placeholder(tf.int32, shape=(batch_size, num_point)) return pointclouds_pl, labels_pl def get_model(point_cloud, is_training, bn_decay=None, num_classes=50, use_input_trans=True, use_feature_trans=True): """ Classification PointNet, input is BxNx3, output BxNx50 """ batch_size = point_cloud.get_shape()[0].value num_point = point_cloud.get_shape()[1].value end_points = {} if use_input_trans: with tf.variable_scope('transform_net1') as sc: transform = input_transform_net(point_cloud, is_training, bn_decay, K=3) point_cloud_transformed = tf.matmul(point_cloud, transform) else: point_cloud_transformed = point_cloud input_image = tf.expand_dims(point_cloud_transformed, -1) with tf.variable_scope('pointnet_cls_rotation'): net = tf_util.conv2d(input_image, 64, [1,3], padding='VALID', stride=[1,1], bn=True, is_training=is_training, scope='conv1', bn_decay=bn_decay) net = tf_util.conv2d(net, 64, [1,1], padding='VALID', stride=[1,1], bn=True, is_training=is_training, scope='conv2', bn_decay=bn_decay) if use_feature_trans: with tf.variable_scope('transform_net2') as sc: transform = feature_transform_net(net, is_training, bn_decay, K=64) end_points['transform'] = transform net_transformed = tf.matmul(tf.squeeze(net, axis=[2]), transform) point_feat = tf.expand_dims(net_transformed, [2]) else: point_feat = tf.expand_dims(net, [2]) with tf.variable_scope('pointnet_cls_rotation'): net = tf_util.conv2d(point_feat, 64, [1,1], padding='VALID', stride=[1,1], bn=True, is_training=is_training, scope='conv3', bn_decay=bn_decay) net = tf_util.conv2d(net, 128, [1,1], padding='VALID', stride=[1,1], bn=True, is_training=is_training, scope='conv4', bn_decay=bn_decay) net = tf_util.conv2d(net, 1024, [1,1], padding='VALID', stride=[1,1], bn=True, is_training=is_training, scope='conv5', bn_decay=bn_decay) global_feat = tf_util.max_pool2d(net, [num_point,1], padding='VALID', scope='maxpool') print(global_feat) global_feat_expand = tf.tile(global_feat, [1, num_point, 1, 1]) concat_feat = tf.concat([point_feat, global_feat_expand], axis=3) print(concat_feat) net = tf_util.conv2d(concat_feat, 512, [1,1], padding='VALID', stride=[1,1], bn=True, is_training=is_training, scope='conv6', bn_decay=bn_decay) net = tf_util.conv2d(net, 256, [1,1], padding='VALID', stride=[1,1], bn=True, is_training=is_training, scope='conv7', bn_decay=bn_decay) net = tf_util.conv2d(net, 128, [1,1], padding='VALID', stride=[1,1], bn=True, is_training=is_training, scope='conv8', bn_decay=bn_decay) net = tf_util.conv2d(net, 128, [1,1], padding='VALID', stride=[1,1], bn=True, is_training=is_training, scope='conv9', bn_decay=bn_decay) net = tf_util.conv2d(net, num_classes, [1,1], padding='VALID', stride=[1,1], activation_fn=None, scope='conv10') net = tf.squeeze(net, [2]) # BxNxC return net, end_points def get_loss(pred, label, end_points, reg_weight=0.001, use_trans_loss=True, use_angle_loss=False): """ pred: BxNxC, label: BxN, """ loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=pred, labels=label) classify_loss = tf.reduce_mean(loss) tf.summary.scalar('classify loss', classify_loss) if not use_trans_loss: return classify_loss # Enforce the transformation as orthogonal matrix transform = end_points['transform'] # BxKxK K = transform.get_shape()[1].value mat_diff = tf.matmul(transform, tf.transpose(transform, perm=[0,2,1])) mat_diff -= tf.constant(np.eye(K), dtype=tf.float32) mat_diff_loss = tf.nn.l2_loss(mat_diff) tf.summary.scalar('mat_loss', mat_diff_loss) return classify_loss + mat_diff_loss * reg_weight if __name__=='__main__': with tf.Graph().as_default(): inputs = tf.zeros((32,1024,3)) outputs = get_model(inputs, tf.constant(True)) print(outputs)

43

117

0.589085

7943d003750fc6b2887903922762b0e1e8f657fb

3,508

py

Python

middlewear/parallel_proces.py

astronaut71/KU-TII

f46e493bb1ffe83dbcc9aa21deb5a8fb219e088b

[ "MIT" ]

null

middlewear/parallel_proces.py

astronaut71/KU-TII

f46e493bb1ffe83dbcc9aa21deb5a8fb219e088b

[ "MIT" ]

null

middlewear/parallel_proces.py

astronaut71/KU-TII

f46e493bb1ffe83dbcc9aa21deb5a8fb219e088b

[ "MIT" ]

null

#!/usr/bin/env python3 from sys import ps1 import rospy import numpy as np from os import system import time import threading import Microcontroller_Manager_Serial as Serial import IMU_Functions as IMU import Pressure_Functions as Pressure import Modem_Functions as Modem import threading import time from time import sleep from std_msgs.msg import Float32 from std_msgs.msg import String from __future__ import print_function from beginner_tutorials.srv import AddTwoInts,AddTwoIntsResponse class RepeatedTimer(object): def __init__(self, interval, function, *args, **kwargs): self._timer = None self.interval = interval self.function = function self.args = args self.kwargs = kwargs self.is_running = False self.next_call = time.time() self.start() def _run(self): self.is_running = False self.start() self.function(*self.args, **self.kwargs) def start(self): if not self.is_running: self.next_call += self.interval self._timer = threading.Timer(self.next_call - time.time(), self._run) self._timer.start() self.is_running = True def stop(self): self._timer.cancel() self.is_running = False mutex = threading.Lock() Communication_Mode_ = 0 pub_pressure = rospy.Publisher('depth',Float32,queue_size=1) pub_modem = rospy.Publisher('modem_data',Float32,queue_size=1) P0 = 1.01325 #Default Pressure def callback(data): global P0 rospy.init_node('talker', anonymous=True) mutex.acquire() while not rospy.is_shutdown(): try: data_received_pressure = Pressure.Pressure_Get_Final_Values(1,1) data_received_imu = IMU.IMU_Get_Values(1, 1) P1 = (np.int16((data_received_pressure[6]<<24) | (data_received_pressure[7]<<16) | (data_received_pressure[8]<<8) | (data_received_pressure[9])))/10000 P0 = (np.int16((data_received_pressure[6]<<24) | (data_received_pressure[7]<<16) | (data_received_pressure[8]<<8) | (data_received_pressure[9])))/10000 P = P1 - P0 # Relative Measured Pressure pressure = P pub_pressure.publish(pressure) except: print ("pressure not obtained") def callback_modem(data_in): data_in = Serial.Serial_Port_Receive_Data(20,0.2) if (data_in[0] == 91) # Received data from acoustic modem rt = RepeatedTimer(1, data_in, "Recieved Data") # it auto-starts, no need of rt.start() modem_data= data_in pub_modem.publish(modem_data) try: sleep(0.3) # your long-running job goes here... finally: rt.stop() # better in a try/finally block to make sure the program ends! def talker(): pub = rospy.Publisher('chatter', String, queue_size=10) rospy.init_node('talker', anonymous=True) rate = rospy.Rate(10) # 10hz while not rospy.is_shutdown(): hello_str = "hello world %s" % rospy.get_time() rospy.loginfo(hello_str) pub.publish(hello_str) rate.sleep() def handle_add_two_ints(req): print("Returning [%s + %s = %s]"%(req.a, req.b, (req.a + req.b))) return AddTwoIntsResponse(req.a + req.b) def add_two_ints_server(): rospy.init_node('add_two_ints_server') s = rospy.Service('add_two_ints', AddTwoInts, handle_add_two_ints) print("Ready to add two ints.") rospy.spin() if __name__ == "__main__": add_two_ints_server()

28.991736

163

0.663056

7943d1498bbbf0e253d96160d9480e4d052d6cb8

17,166

py

Python

tests/test_attributes_managers.py

srama2512/habitat-sim

1a0a36451bc0a94c98d9f9fa497b4c3cfc095638

[ "MIT" ]

1

2022-01-22T09:04:44.000Z

tests/test_attributes_managers.py

Shubodh/habitat-sim

83b97f42199855e63407d898e20e99780dfc79c0

[ "MIT" ]

null

tests/test_attributes_managers.py

Shubodh/habitat-sim

83b97f42199855e63407d898e20e99780dfc79c0

[ "MIT" ]

null

#!/usr/bin/env python3 import magnum as mn import examples.settings import habitat_sim def perform_general_tests(attr_mgr, search_string): # get size of template library orig_num_templates = attr_mgr.get_num_templates() # make sure this is same value as size of template handles list assert orig_num_templates > 0 # search for attributes with expected string template_handles = attr_mgr.get_template_handles(search_string) # verify handle exists assert len(template_handles) > 0 template_handle = template_handles[0] # get id from handle template_id = attr_mgr.get_template_id_by_handle(template_handle) assert search_string in template_handle # verify both handle and id exist in the manager assert attr_mgr.get_library_has_handle(template_handle) assert attr_mgr.get_library_has_id(template_id) # verify that access is the same for ID and handle lookup template0 = attr_mgr.get_template_by_handle(template_handle) template1 = attr_mgr.get_template_by_id(template_id) # verify template 0 and template 1 are copies of the same template assert template0.handle == template1.handle assert template0.template_id == template1.template_id # modify template, register, then verify that # retrieved template is not same as previous template template0.set("test_key", "template0_test") template1.set("test_key", "template1_test") # save modified template attr_mgr.register_template(template0, template_handle) # retrieve registered template template2 = attr_mgr.get_template_by_handle(template_handle) # verify templates have the same identifiers assert template1.handle == template2.handle assert template1.template_id == template2.template_id # verify the templates hold different data and are not the # same object assert template1.get_string("test_key") != template2.get_string("test_key") # verify 0 and 2 hold same user-set value assert template0.get_string("test_key") == template2.get_string("test_key") # change retrieved template, verify it is not same object as template0 template2.set("test_key", "template2_test") # verify the templates hold different data and are not the # same object assert template0.get_string("test_key") != template2.get_string("test_key") # add new template with specified handle new_template_handle = "new_template_0" attr_mgr.register_template(template0, new_template_handle) # register new template and verify size is greater than original curr_num_templates = attr_mgr.get_num_templates() assert curr_num_templates != orig_num_templates # lock template attr_mgr.set_template_lock(new_template_handle, True) # attempt to delete template3 = attr_mgr.remove_template_by_handle(new_template_handle) # verify that template was not deleted assert template3 == None # unlock template attr_mgr.set_template_lock(new_template_handle, False) # remove template that has been unlocked; retrieves removed template template3 = attr_mgr.remove_template_by_handle(new_template_handle) # verify not NONE assert template3 != None # verify has expected handle assert template3.handle == new_template_handle # get new size of library after remove and verify same as original curr_num_templates = attr_mgr.get_num_templates() assert curr_num_templates == orig_num_templates # add many templates with specified handles, then remove them new_handle_stub = "new_template_" num_to_add = 10 for i in range(num_to_add): new_iter_handle = new_handle_stub + str(i) tmplt_id = attr_mgr.register_template(template3, new_iter_handle) assert tmplt_id != -1 # lock all added templates locked_template_handles = attr_mgr.set_lock_by_substring( True, new_handle_stub, True ) # verify that the number of added and locked templates are equal assert num_to_add == len(locked_template_handles) # attempt to remove templates that are locked removed_templates = attr_mgr.remove_templates_by_str(new_handle_stub, True) # verify that no templates were removed that have the new handle stub assert len(removed_templates) == 0 # unlock all added templates unlocked_template_handles = attr_mgr.set_lock_by_substring( False, new_handle_stub, True ) # verify that the number of added and unlocked templates are equal assert num_to_add == len(unlocked_template_handles) # verify lists of names are same assert sorted(unlocked_template_handles) == sorted(locked_template_handles) # now delete all templates with handle stub removed_templates = attr_mgr.remove_templates_by_str(new_handle_stub, True) # verify that the number of added and removed templates are equal assert num_to_add == len(removed_templates) # get new size of library after remove and verify same as original curr_num_templates = attr_mgr.get_num_templates() assert curr_num_templates == orig_num_templates return template0, template3 def perform_add_blank_template_test(attr_mgr, valid_render_handle=None): # get size of template library orig_num_templates = attr_mgr.get_num_templates() # add new blank template new_template_handle = "new template" # create new default template, do not register it new_template0 = attr_mgr.create_new_template(new_template_handle, False) # change new template field new_template0.set("test_key", "new_template_test") # give new template valid render asset handle, otherwise registration might fail if valid_render_handle is not None: new_template0.render_asset_handle = valid_render_handle # add new template attr_mgr.register_template(new_template0, new_template_handle) # get new size of library after remove and verify not same as original curr_num_templates = attr_mgr.get_num_templates() assert curr_num_templates > orig_num_templates # verify new template added properly new_template1 = attr_mgr.get_template_by_handle(new_template_handle) # verify template 0 and template 1 are copies of the same template assert new_template0.handle == new_template1.handle assert new_template0.template_id == new_template1.template_id assert new_template0.get_string("test_key") == new_template1.get_string("test_key") # remove newly added default template new_template2 = attr_mgr.remove_template_by_handle(new_template_handle) # verify added template was one removed assert new_template0.handle == new_template2.handle assert new_template0.template_id == new_template2.template_id assert new_template0.get_string("test_key") == new_template2.get_string("test_key") # test addition of user-configurations and verify values # create new template, do not register it new_template_usr = attr_mgr.create_new_template(new_template_handle, False) # give new template valid render asset handle, otherwise registration might fail if valid_render_handle is not None: new_template_usr.render_asset_handle = valid_render_handle usr_template_handle = "new_usr_cfg_handle" new_template_usr.handle = usr_template_handle # get user configs and set key new_template_usr.set_user_config_val("my_custom_key0", "my_custom_string") assert ( new_template_usr.get_user_config_string("my_custom_key0") == "my_custom_string" ) new_template_usr.set_user_config_val("my_custom_key1", True) assert new_template_usr.get_user_config_bool("my_custom_key1") == True new_template_usr.set_user_config_val("my_custom_key2", 10) assert new_template_usr.get_user_config_int("my_custom_key2") == 10 new_template_usr.set_user_config_val("my_custom_key3", 5.8) assert new_template_usr.get_user_config_double("my_custom_key3") == 5.8 new_template_usr.set_user_config_val("my_custom_key4", mn.Vector3(1.0, -2.8, 3.0)) assert new_template_usr.get_user_config_vec3("my_custom_key4") == mn.Vector3( 1.0, -2.8, 3.0 ) quat_val = mn.Quaternion.rotation(mn.Deg(-115), mn.Vector3.y_axis()) new_template_usr.set_user_config_val("my_custom_key5", quat_val) assert new_template_usr.num_user_configs == 6 # add new template - should use template-specified name as handle usr_tmplt_ID = attr_mgr.register_template(new_template_usr, "") assert usr_tmplt_ID != -1 # reg_template_usr = attr_mgr.get_template_by_handle(usr_template_handle) assert reg_template_usr != None assert reg_template_usr.num_user_configs == new_template_usr.num_user_configs assert ( reg_template_usr.get_user_config_string("my_custom_key0") == "my_custom_string" ) assert reg_template_usr.get_user_config_bool("my_custom_key1") == True assert reg_template_usr.get_user_config_int("my_custom_key2") == 10 assert reg_template_usr.get_user_config_double("my_custom_key3") == 5.8 assert reg_template_usr.get_user_config_vec3("my_custom_key4") == mn.Vector3( 1.0, -2.8, 3.0 ) assert reg_template_usr.get_user_config_quat("my_custom_key5") == quat_val rmv_template_usr = attr_mgr.remove_template_by_handle(usr_template_handle) assert rmv_template_usr != None assert new_template_usr.handle == rmv_template_usr.handle assert new_template_usr.template_id == rmv_template_usr.template_id # get new size of library after remove and verify same as original curr_num_templates = attr_mgr.get_num_templates() assert curr_num_templates == orig_num_templates def test_physics_attributes_managers(): cfg_settings = examples.settings.default_sim_settings.copy() cfg_settings["scene"] = "data/scene_datasets/habitat-test-scenes/van-gogh-room.glb" cfg_settings["enable_physics"] = True hab_cfg = examples.settings.make_cfg(cfg_settings) with habitat_sim.Simulator(hab_cfg) as sim: # get attribute managers phys_attr_mgr = sim.get_physics_template_manager() # perform general tests for this attributes manager template0, _ = perform_general_tests( phys_attr_mgr, cfg_settings["physics_config_file"] ) # verify that physics template matches expected values in file assert template0.timestep == 0.008 assert template0.simulator == "bullet" # verify creating new template perform_add_blank_template_test(phys_attr_mgr) def test_stage_attributes_managers(): cfg_settings = examples.settings.default_sim_settings.copy() cfg_settings["scene"] = "data/scene_datasets/habitat-test-scenes/van-gogh-room.glb" cfg_settings["enable_physics"] = True hab_cfg = examples.settings.make_cfg(cfg_settings) with habitat_sim.Simulator(hab_cfg) as sim: stage_name = cfg_settings["scene"] # get attribute managers stage_mgr = sim.get_stage_template_manager() # perform general tests for this attributes manager template0, _ = perform_general_tests(stage_mgr, stage_name) # verify gravity in template is as expected assert template0.gravity == mn.Vector3(0.0, -9.8, 0.0) # verify creating new template perform_add_blank_template_test(stage_mgr, template0.render_asset_handle) def test_object_attributes_managers(): cfg_settings = examples.settings.default_sim_settings.copy() cfg_settings["scene"] = "data/scene_datasets/habitat-test-scenes/van-gogh-room.glb" cfg_settings["enable_physics"] = True hab_cfg = examples.settings.make_cfg(cfg_settings) with habitat_sim.Simulator(hab_cfg) as sim: # get object attribute managers obj_mgr = sim.get_object_template_manager() # get object template random handle rand_obj_handle = obj_mgr.get_random_template_handle() # perform general tests for object attribute manager template0, _ = perform_general_tests(obj_mgr, rand_obj_handle) # verify creating new template perform_add_blank_template_test(obj_mgr, template0.render_asset_handle) def perform_asset_attrib_mgr_tests( attr_mgr, default_attribs, ctor_mod_field, legalVal, illegalVal ): # get size of template library orig_num_templates = attr_mgr.get_num_templates() # make sure this is same value as size of template handles list assert orig_num_templates > 0 # get old handle - based on how default_attribs was constructed old_handle = default_attribs.handle # verify that default_attribs is valid assert default_attribs.is_valid_template # modify field values that impact primitive construction, if exist if "none" != ctor_mod_field: default_attribs.set(ctor_mod_field, illegalVal) # verify that this is now an illegal template assert not (default_attribs.is_valid_template) # modify to hold legal value default_attribs.set(ctor_mod_field, legalVal) # verify that default_attribs is valid assert default_attribs.is_valid_template # build new handle reflected in modified template. # This is only necessary because we are bypassing setters default_attribs.build_handle() # verify that new handle is different from old handle new_handle = default_attribs.handle assert new_handle != old_handle # register new template attr_mgr.register_template(default_attribs) # verify that both templates now exist in library assert attr_mgr.get_library_has_handle(new_handle) assert attr_mgr.get_library_has_handle(old_handle) # get new and old templates old_template = attr_mgr.get_template_by_handle(old_handle) new_template = attr_mgr.get_template_by_handle(new_handle) # verify they do not hold the same values in the important fields assert old_template.get_int(ctor_mod_field) != new_template.get_int(ctor_mod_field) # verify we have more templates than when we started assert orig_num_templates != attr_mgr.get_num_templates() # delete new template deleted_template = attr_mgr.remove_template_by_handle(new_handle) assert deleted_template != None # verify we are back where we started assert orig_num_templates == attr_mgr.get_num_templates() def test_asset_attributes_managers(): cfg_settings = examples.settings.default_sim_settings.copy() cfg_settings["scene"] = "data/scene_datasets/habitat-test-scenes/van-gogh-room.glb" cfg_settings["enable_physics"] = True hab_cfg = examples.settings.make_cfg(cfg_settings) with habitat_sim.Simulator(hab_cfg) as sim: # legal and illegal vals for primitives based on wireframe or solid legal_mod_val_wf = 64 illegal_mod_val_wf = 25 legal_mod_val_solid = 5 illegal_mod_val_solid = 0 # get object attribute managers attr_mgr = sim.get_asset_template_manager() # capsule print("Test Capsule attributes construction, modification, saving and removal.") dflt_solid_attribs = attr_mgr.get_default_capsule_template(False) perform_asset_attrib_mgr_tests( attr_mgr, dflt_solid_attribs, "segments", legal_mod_val_solid, illegal_mod_val_solid, ) dflt_wf_attribs = attr_mgr.get_default_capsule_template(True) perform_asset_attrib_mgr_tests( attr_mgr, dflt_wf_attribs, "segments", legal_mod_val_wf, illegal_mod_val_wf ) # cone print("Test Cone attributes construction, modification, saving and removal.") dflt_solid_attribs = attr_mgr.get_default_cone_template(False) perform_asset_attrib_mgr_tests( attr_mgr, dflt_solid_attribs, "segments", legal_mod_val_solid, illegal_mod_val_solid, ) dflt_wf_attribs = attr_mgr.get_default_cone_template(True) perform_asset_attrib_mgr_tests( attr_mgr, dflt_wf_attribs, "segments", legal_mod_val_wf, illegal_mod_val_wf ) # cylinder print( "Test Cylinder attributes construction, modification, saving and removal." ) dflt_solid_attribs = attr_mgr.get_default_cylinder_template(False) perform_asset_attrib_mgr_tests( attr_mgr, dflt_solid_attribs, "segments", 5, illegal_mod_val_solid ) dflt_wf_attribs = attr_mgr.get_default_cylinder_template(True) perform_asset_attrib_mgr_tests( attr_mgr, dflt_wf_attribs, "segments", legal_mod_val_wf, illegal_mod_val_wf ) # UVSphere print( "Test UVSphere attributes construction, modification, saving and removal." ) dflt_solid_attribs = attr_mgr.get_default_UVsphere_template(False) perform_asset_attrib_mgr_tests( attr_mgr, dflt_solid_attribs, "segments", 5, illegal_mod_val_solid ) dflt_wf_attribs = attr_mgr.get_default_UVsphere_template(True) perform_asset_attrib_mgr_tests( attr_mgr, dflt_wf_attribs, "segments", legal_mod_val_wf, illegal_mod_val_wf )

40.390588

88

0.743446

7943d16639e24add06156081e9fee532c520fc82

9,523

py

Python

geokit/test/test_03_geom.py

ENSYSTRA/geokit

510ec5c3fe3c034f1dff776c813eb28c6cd07c40

[ "MIT" ]

null

geokit/test/test_03_geom.py

ENSYSTRA/geokit

510ec5c3fe3c034f1dff776c813eb28c6cd07c40

[ "MIT" ]

null

geokit/test/test_03_geom.py

ENSYSTRA/geokit

510ec5c3fe3c034f1dff776c813eb28c6cd07c40

[ "MIT" ]

null

from .helpers import MASK_DATA, np, pointInAachen3035, pointsInAachen4326, EPSG3035, EPSG4326, POLY, GEOM, SUB_GEOMS, SUB_GEOM, result from geokit import geom import matplotlib.pyplot as plt import pytest import pandas as pd # box def test_box(): # fun func b1 = geom.box(0, 0, 5, 10, srs=EPSG3035) assert np.isclose(b1.Area(), 50) def test_tile(): # fun func t1 = geom.tile(xi=4250, yi=2775, zoom=13) envelope = t1.GetEnvelope() assert np.isclose(envelope[0], 753363.3507786973) assert np.isclose(envelope[1], 758255.3205889486) assert np.isclose(envelope[2], 6457400.14953169) assert np.isclose(envelope[3], 6462292.119341941) def test_subTiles(): tiles = list(geom.subTiles(GEOM, zoom=5, checkIntersect=False, asGeom=False)) assert len(tiles) == 4 assert tiles[0] == (16, 12, 5) assert tiles[1] == (16, 13, 5) assert tiles[2] == (17, 12, 5) assert tiles[3] == (17, 13, 5) tiles = list(geom.subTiles(GEOM, zoom=7, checkIntersect=True, asGeom=False)) assert len(tiles) == 7 assert tiles[0] == (67, 50, 7) assert tiles[1] == (67, 51, 7) assert tiles[2] == (67, 52, 7) assert tiles[3] == (68, 49, 7) assert tiles[4] == (68, 50, 7) assert tiles[5] == (68, 51, 7) assert tiles[6] == (69, 49, 7) def test_tileize(): geoms = list(geom.tileize(GEOM, zoom=7)) assert np.isclose(geoms[0].Area(), 6185440214.480698) assert np.isclose(geoms[1].Area(), 22669806295.02369) assert np.isclose(geoms[2].Area(), 4971343426.690063) assert np.isclose(geoms[3].Area(), 11085156736.902699) assert np.isclose(geoms[4].Area(), 60694504952.24364) assert np.isclose(geoms[5].Area(), 8127832949.697159) assert np.isclose(geoms[6].Area(), 4469553269.708176) def test_point(): x, y = pointInAachen3035 # test separate input p1 = geom.point(x, y, srs=EPSG3035) assert np.isclose(p1.GetX(), x) assert np.isclose(p1.GetY(), y) assert p1.GetSpatialReference().IsSame(EPSG3035) # test tuple input p2 = geom.point((x, y), srs=EPSG3035) assert np.isclose(p2.GetX(), x) assert np.isclose(p2.GetY(), y) assert p2.GetSpatialReference().IsSame(EPSG3035) @pytest.mark.skip("No test implemented for: geom.empty") def test_empty(): assert False def test_convertWKT(): g1 = geom.convertWKT(POLY, srs=EPSG4326) assert np.isclose(g1.Area(), 7.8149999999999995) assert g1.GetSpatialReference().IsSame(EPSG4326) def test_polygonizeMatrix(): # test a simple box boxmatrix = np.array([[0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 0, 1, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 0]], dtype=np.int) g1 = geom.polygonizeMatrix(boxmatrix, shrink=None) assert np.isclose(g1.geom[0].Area(), 8.0) # polygonizeMatrix: simple area # polygonizeMatrix: empty srs assert g1.geom[0].GetSpatialReference() is None assert g1.value[0] == 1 # polygonizeMatrix: Value retention # test shrink g1b = geom.polygonizeMatrix(boxmatrix, shrink=0.0001) # polygonizeMatrix: shrunk area assert np.isclose(g1b.geom[0].Area(), 7.99984000) # test a more complex area complexmatrix = np.array([[0, 2, 0, 0, 0], [2, 2, 0, 1, 0], [0, 0, 0, 1, 1], [1, 1, 0, 1, 0], [3, 1, 0, 0, 0]], dtype=np.int) g2 = geom.polygonizeMatrix(complexmatrix, shrink=None) assert np.isclose(g2.shape[0], 4) # polygonizeMatrix: geometry count assert np.isclose(sum([g.Area() for g in g2.geom]), 11.0) # polygonizeMatrix: area" assert np.isclose(g2.value[0], 2) # polygonizeMatrix: Value retention # flatten the complex area g3 = geom.polygonizeMatrix(complexmatrix, flat=True, shrink=None) assert np.isclose(g3.shape[0], 3) # polygonizeMatrix: geometry count # polygonizeMatrix: flattened area assert np.isclose(g3.geom[0].Area(), 7.0) # set a boundary and srs context g4 = geom.polygonizeMatrix( complexmatrix, bounds=(-3, 10, 22, 35), srs=EPSG3035, flat=True, shrink=None) # polygonizeMatrix: contexted area assert np.isclose(g4.geom[0].Area(), 175.0) assert g4.geom[0].GetSpatialReference().IsSame( EPSG3035) # polygonizeMatrix: contexted srs def test_polygonizeMask(): # test a simple box boxmask = np.array([[0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 0, 1, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 0]], dtype=np.bool) g1 = geom.polygonizeMask(boxmask, shrink=None) assert np.isclose(g1.Area(), 8.0) # polygonizeMask: simple area assert g1.GetSpatialReference() is None # polygonizeMask: empty srs # test shrink g1b = geom.polygonizeMask(boxmask, shrink=0.0001) assert np.isclose(g1b.Area(), 7.99984000) # polygonizeMask: shrunk area # test a more complex area complexmask = np.array([[0, 1, 0, 0, 0], [1, 1, 0, 1, 0], [0, 0, 0, 1, 1], [1, 1, 0, 1, 0], [0, 1, 0, 0, 0]], dtype=np.bool) g2 = geom.polygonizeMask(complexmask, shrink=None, flat=False) assert np.isclose(len(g2), 3) # polygonizeMask: geometry count assert np.isclose(sum([g.Area() for g in g2]), 10.0) # polygonizeMask: area # flatten the complex area g3 = geom.polygonizeMask(complexmask, flat=True, shrink=None) assert np.isclose(g3.Area(), 10.0) # polygonizeMask: flattened area # set a boundary and srs context g4 = geom.polygonizeMask( complexmask, bounds=(-3, 10, 22, 35), srs=EPSG3035, flat=True, shrink=None) assert np.isclose(g4.Area(), 250.0) # polygonizeMask: contexted area assert g4.GetSpatialReference().IsSame( EPSG3035) # error("polygonizeMask: contexted srs def test_flatten(): # Overlapping polygons bounds = [(i, i, i + 2, i + 2) for i in range(5)] # test basic combination geomList = [geom.box(b, srs=EPSG4326) for b in bounds] f1 = geom.flatten(geomList) assert np.isclose(f1.Area(), 16.0) # flattened area env = f1.GetEnvelope() assert np.isclose(env[0], 0) assert np.isclose(env[1], 6) assert np.isclose(env[2], 0) assert np.isclose(env[3], 6) assert f1.GetSpatialReference().IsSame(EPSG4326) # flattened srs def test_transform(): # test a single point pt = geom.point(7, 48, srs=EPSG4326) t1 = geom.transform(pt, toSRS=EPSG3035) assert np.isclose(t1.GetX(), 4097075.016) assert np.isclose(t1.GetY(), 2769703.15423898) # make a collection of polygons using polygonizeMask complexmask = np.array([[0, 1, 0, 0, 0], [1, 1, 0, 1, 0], [0, 0, 0, 1, 1], [1, 1, 0, 1, 0], [0, 1, 0, 0, 0]], dtype=np.bool) polygons = geom.polygonizeMask(complexmask, bounds=( 6, 45, 11, 50), flat=False, srs=EPSG4326, shrink=None) t2 = geom.transform(polygons, toSRS='europe_m', segment=0.1) assert (len(t2) == 3) # "Transform Count assert t2[0].GetSpatialReference().IsSame(EPSG3035) # "Transform srs assert np.isclose(sum([t.Area() for t in t2]), 83747886418.48529) # "Transform Area def test_extractVerticies(): # Test polygon pts1 = geom.extractVerticies(GEOM) assert np.isclose(pts1[5, 1], 35.1) assert pts1.shape == (10, 2) # Test multipolygon pts2 = geom.extractVerticies(geom.flatten(SUB_GEOMS)) assert pts2.shape == (12, 2) # Test linestring pts3 = geom.extractVerticies(GEOM.Boundary()) assert np.isclose(pts3[5, 1], 35.1) assert pts3.shape == (10, 2) # Test multilinestring assert np.isclose(pts3[5, 1], 35.1) assert pts3.shape == (10, 2) # Test Point pts5 = geom.extractVerticies(geom.point(5, 20)) assert np.isclose(pts5[0, 0], 5) assert pts5.shape == (1, 2) def test_drawGeoms(): # Draw single polygon r = geom.drawGeoms(SUB_GEOM) plt.savefig(result("drawGeoms-1.png"), dpi=100) # Draw single linestring r = geom.drawGeoms(SUB_GEOM.Boundary()) plt.savefig(result("drawGeoms-2.png"), dpi=100) # Draw a multipolygon r = geom.drawGeoms(geom.flatten(SUB_GEOMS)) plt.savefig(result("drawGeoms-3.png"), dpi=100) # Draw a list of polygons and set an MPL argument r = geom.drawGeoms(SUB_GEOMS, fc='b') plt.savefig(result("drawGeoms-4.png"), dpi=100) # Change projection systems r = geom.drawGeoms(SUB_GEOMS, fc='r', srs=3035) plt.savefig(result("drawGeoms-5.png"), dpi=100) # Draw from a dataframe df = pd.DataFrame(dict(geom=SUB_GEOMS, hats=[1, 2, 3])) r = geom.drawGeoms(df, srs=3035) plt.savefig(result("drawGeoms-6.png"), dpi=100) # Set individual mpl args df["MPL:hatch"] = ["//", "+", None] r = geom.drawGeoms(df, srs=3035) plt.savefig(result("drawGeoms-7.png"), dpi=100) # Test colorby r = geom.drawGeoms(df, srs=3035, colorBy="hats") plt.savefig(result("drawGeoms-8.png"), dpi=100) assert True

33.297203

134

0.59393

7943d166bc52544970d236e9d18710ad17117dec

807

bzl

Python

bazel/cc_fuzz_target.bzl

bianpengyuan/opencensus-cpp

4ffcd168f165d9fc44b4581337275a071ff7a0da

[ "Apache-2.0" ]

139

2017-08-15T01:03:37.000Z

2022-02-10T11:53:30.000Z

bazel/cc_fuzz_target.bzl

bianpengyuan/opencensus-cpp

4ffcd168f165d9fc44b4581337275a071ff7a0da

[ "Apache-2.0" ]

235

2017-12-12T09:28:12.000Z

2022-03-30T06:36:14.000Z

bazel/cc_fuzz_target.bzl

bianpengyuan/opencensus-cpp

4ffcd168f165d9fc44b4581337275a071ff7a0da

[ "Apache-2.0" ]

68

2017-11-08T02:35:56.000Z

2022-03-28T21:27:21.000Z

# Copyright 2019, OpenCensus Authors # # 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. """ Placeholder rule for fuzzing. Currently, fuzzing is done using the CMake build system. Please refer to ../opencensus/doc/fuzzing.md """ def cc_fuzz_target(name, srcs, corpus, deps): """TODO: Implement.""" pass

33.625

74

0.748451

7943d1f2c2c0a40a25e16c3b82d6ba25b405da03

632

py

Python

edd/rest/paginators.py

TeselaGen/jbei-edd

92792fb30bbd504143b2f75bf08d05b141a7ef6f

[ "BSD-3-Clause-LBNL" ]

null

edd/rest/paginators.py

TeselaGen/jbei-edd

92792fb30bbd504143b2f75bf08d05b141a7ef6f

[ "BSD-3-Clause-LBNL" ]

null

edd/rest/paginators.py

TeselaGen/jbei-edd

92792fb30bbd504143b2f75bf08d05b141a7ef6f

[ "BSD-3-Clause-LBNL" ]

null

from rest_framework.pagination import PageNumberPagination from jbei.rest.clients.edd.api import ( DEFAULT_PAGE_SIZE, PAGE_NUMBER_URL_PARAM, PAGE_SIZE_QUERY_PARAM, ) class ClientConfigurablePagination(PageNumberPagination): """ Overrides defaults to enable client-configurable control (up to a limit) of result pagination by EDD's REST API. Note that specific REST views may override this behavior. See REST_FRAMEWORK setting in edd.settings.py. """ page_size = DEFAULT_PAGE_SIZE page_size_query_param = PAGE_SIZE_QUERY_PARAM page_query_param = PAGE_NUMBER_URL_PARAM max_page_size = 10000

35.111111

99

0.787975

7943d3054d3bd7e3d2dd13662f10719a0c4b996a

99

py

Python

test/arena1/generator.py

da-x/crumb

ff2bd8a38949c50a4bdb590ea98804b386402a53

[ "BSD-3-Clause" ]

1

2015-01-22T18:45:32.000Z

test/arena1/generator.py

da-x/crumb

ff2bd8a38949c50a4bdb590ea98804b386402a53

[ "BSD-3-Clause" ]

null

test/arena1/generator.py

da-x/crumb

ff2bd8a38949c50a4bdb590ea98804b386402a53

[ "BSD-3-Clause" ]

null

import someimport import sys someimport.knows_how_to_generate(sys.argv[1], "and-another-dep.txt")

19.8

68

0.808081

7943d3bcf77eb7a09f1ac2b821d5e95cc36b4f25

1,332

py

Python

market_maker/lion_fx/update.py

teacup123123/fx-predictor

5c25b6f0cb5435a2294262f4ac2681b38abb1dac

[ "MIT" ]

null

market_maker/lion_fx/update.py

teacup123123/fx-predictor

5c25b6f0cb5435a2294262f4ac2681b38abb1dac

[ "MIT" ]

null

market_maker/lion_fx/update.py

teacup123123/fx-predictor

5c25b6f0cb5435a2294262f4ac2681b38abb1dac

[ "MIT" ]

null

import requests from quick_query.xe import grab import os _dir, _ = os.path.split(__file__) file = r'data_lionfx_swap.csv' # manually downloaded from https://hirose-fx.co.jp/swap/lionfx_swap.csv def update_swap_data(): got = requests.get(r'https://hirose-fx.co.jp/swap/lionfx_swap.csv') string = got.content.decode('shift-jis') lines = string.splitlines() lines = [line.strip() for line in lines] header = lines[0] header = header.split(',') header[0] = 'date' for i in range(len(header) - 1, 0, -1): if i % 2 == 0: header[i] = header[i - 1] for i, (word, type) in enumerate(zip(header, lines[2].split(','))): if type.endswith('売り'): header[i] += '_sell' elif type.endswith('買い'): header[i] += '_buy' del lines[1:3] lines[0] = ','.join(header) with open('data_lionfx_swap.csv', 'w') as f: f.write('\n'.join(lines)) def update(): rates = {} with open(rf'{_dir}/data_available_currencies.txt', 'r') as f: currencies = f.read(-1).split() for c in currencies: rate = grab(c, 'JPY') rates[c] = rate with open(rf'{_dir}/data_currency_now.txt', 'w') as f: f.write('\n'.join(f'{k} {v}' for k, v in rates.items())) if __name__ == '__main__': update_swap_data()

27.75

103

0.585586

7943d56193072cf119970d9c0e719d7caa7e3e22

2,165

py

Python

test/test_matmul.py

vishalbelsare/pytorch_sparse

efc98089dde782f3600a60265c1013e0f002465d

[ "MIT" ]

623

2018-07-29T10:45:05.000Z

2022-03-29T04:35:08.000Z

test/test_matmul.py

vishalbelsare/pytorch_sparse

efc98089dde782f3600a60265c1013e0f002465d

[ "MIT" ]

201

2018-08-15T14:11:03.000Z

2022-03-31T14:14:01.000Z

test/test_matmul.py

vishalbelsare/pytorch_sparse

efc98089dde782f3600a60265c1013e0f002465d

[ "MIT" ]

101

2018-10-14T04:08:11.000Z

2022-03-23T21:33:37.000Z

from itertools import product import pytest import torch from torch_sparse.matmul import matmul from torch_sparse.tensor import SparseTensor import torch_scatter from .utils import reductions, devices, grad_dtypes @pytest.mark.parametrize('dtype,device,reduce', product(grad_dtypes, devices, reductions)) def test_spmm(dtype, device, reduce): src = torch.randn((10, 8), dtype=dtype, device=device) src[2:4, :] = 0 # Remove multiple rows. src[:, 2:4] = 0 # Remove multiple columns. src = SparseTensor.from_dense(src).requires_grad_() row, col, value = src.coo() other = torch.randn((2, 8, 2), dtype=dtype, device=device, requires_grad=True) src_col = other.index_select(-2, col) * value.unsqueeze(-1) expected = torch_scatter.scatter(src_col, row, dim=-2, reduce=reduce) if reduce == 'min': expected[expected > 1000] = 0 if reduce == 'max': expected[expected < -1000] = 0 grad_out = torch.randn_like(expected) expected.backward(grad_out) expected_grad_value = value.grad value.grad = None expected_grad_other = other.grad other.grad = None out = matmul(src, other, reduce) out.backward(grad_out) assert torch.allclose(expected, out, atol=1e-2) assert torch.allclose(expected_grad_value, value.grad, atol=1e-2) assert torch.allclose(expected_grad_other, other.grad, atol=1e-2) @pytest.mark.parametrize('dtype,device', product(grad_dtypes, devices)) def test_spspmm(dtype, device): src = torch.tensor([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=dtype, device=device) src = SparseTensor.from_dense(src) out = matmul(src, src) assert out.sizes() == [3, 3] assert out.has_value() rowptr, col, value = out.csr() assert rowptr.tolist() == [0, 1, 2, 3] assert col.tolist() == [0, 1, 2] assert value.tolist() == [1, 1, 1] src.set_value_(None) out = matmul(src, src) assert out.sizes() == [3, 3] assert not out.has_value() rowptr, col, value = out.csr() assert rowptr.tolist() == [0, 1, 2, 3] assert col.tolist() == [0, 1, 2]

31.376812

73

0.642032

7943d6af152456334b359a441712f2d2fd8afda1

3,241

py

Python

monai/config/deviceconfig.py

ntenenz/MONAI

e7bdea0934ad08ef47de183eaa558942962fbaf4

[ "Apache-2.0" ]

2

2020-06-23T16:03:45.000Z

2020-06-25T05:30:45.000Z

monai/config/deviceconfig.py

ntenenz/MONAI

e7bdea0934ad08ef47de183eaa558942962fbaf4

[ "Apache-2.0" ]

null

monai/config/deviceconfig.py

ntenenz/MONAI

e7bdea0934ad08ef47de183eaa558942962fbaf4

[ "Apache-2.0" ]

1

2020-09-14T13:16:01.000Z

# Copyright 2020 MONAI Consortium # 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 sys from collections import OrderedDict import monai import numpy as np import torch try: import ignite ignite_version = ignite.__version__ del ignite except (ImportError, AttributeError): ignite_version = "NOT INSTALLED or UNKNOWN VERSION." try: import nibabel nibabel_version = nibabel.__version__ del nibabel except (ImportError, AttributeError): nibabel_version = "NOT INSTALLED or UNKNOWN VERSION." try: import skimage skimage_version = skimage.__version__ del skimage except (ImportError, AttributeError): skimage_version = "NOT INSTALLED or UNKNOWN VERSION." try: import PIL PIL_version = PIL.__version__ del PIL except (ImportError, AttributeError): PIL_version = "NOT INSTALLED or UNKNOWN VERSION." try: import tensorboard tensorboard_version = tensorboard.__version__ del tensorboard except (ImportError, AttributeError): tensorboard_version = "NOT INSTALLED or UNKNOWN VERSION." def get_config_values(): """ Read the package versions into a dictionary. """ output = OrderedDict() output["MONAI"] = monai.__version__ output["Python"] = sys.version.replace("\n", " ") output["Numpy"] = np.version.full_version output["Pytorch"] = torch.__version__ return output def get_optional_config_values(): """ Read the optional package versions into a dictionary. """ output = OrderedDict() output["Pytorch Ignite"] = ignite_version output["Nibabel"] = nibabel_version output["scikit-image"] = skimage_version output["Pillow"] = PIL_version output["Tensorboard"] = tensorboard_version return output def print_config(file=sys.stdout): """ Print the package versions to `file`. Defaults to `sys.stdout`. """ for k, v in get_config_values().items(): print(f"{k} version: {v}", file=file, flush=True) print("\nOptional dependencies:", file=file, flush=True) for k, v in get_optional_config_values().items(): print(f"{k} version: {v}", file=file, flush=True) print("\nFor details about installing the optional dependencies, please visit:", file=file, flush=True) print( " http://monai.rtfd.io/en/latest/installation.html#installing-the-recommended-dependencies", file=file, flush=True, ) def set_visible_devices(*dev_inds): os.environ["CUDA_VISIBLE_DEVICES"] = ",".join(map(str, dev_inds)) def get_torch_version_tuple(): """ Returns: tuple of ints represents the pytorch major/minor version. """ return tuple([int(x) for x in torch.__version__.split(".")[:2]])

27.008333

107

0.703178

7943d6e96742628e6817aa56c3cfda2560ecde1a

7,037

py

Python

mailchimp_marketing_asyncio/models/rss_options3.py

john-parton/mailchimp-asyncio

3865ca0867bec8f537dc1e3256aa3a160c00f8a2

[ "Apache-2.0" ]

null

mailchimp_marketing_asyncio/models/rss_options3.py

john-parton/mailchimp-asyncio

3865ca0867bec8f537dc1e3256aa3a160c00f8a2

[ "Apache-2.0" ]

null

mailchimp_marketing_asyncio/models/rss_options3.py

john-parton/mailchimp-asyncio

3865ca0867bec8f537dc1e3256aa3a160c00f8a2

[ "Apache-2.0" ]

1

2022-03-09T14:52:22.000Z

# coding: utf-8 """ Mailchimp Marketing API No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) # noqa: E501 OpenAPI spec version: 3.0.74 Contact: [email protected] Generated by: https://github.com/swagger-api/swagger-codegen.git """ import pprint import re # noqa: F401 import six class RSSOptions3(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 = { 'feed_url': 'str', 'frequency': 'str', 'schedule': 'SendingSchedule1', 'last_sent': 'datetime', 'constrain_rss_img': 'bool' } attribute_map = { 'feed_url': 'feed_url', 'frequency': 'frequency', 'schedule': 'schedule', 'last_sent': 'last_sent', 'constrain_rss_img': 'constrain_rss_img' } def __init__(self, feed_url=None, frequency=None, schedule=None, last_sent=None, constrain_rss_img=None): # noqa: E501 """RSSOptions3 - a model defined in Swagger""" # noqa: E501 self._feed_url = None self._frequency = None self._schedule = None self._last_sent = None self._constrain_rss_img = None self.discriminator = None self.feed_url = feed_url self.frequency = frequency if schedule is not None: self.schedule = schedule if last_sent is not None: self.last_sent = last_sent if constrain_rss_img is not None: self.constrain_rss_img = constrain_rss_img @property def feed_url(self): """Gets the feed_url of this RSSOptions3. # noqa: E501 The URL for the RSS feed. # noqa: E501 :return: The feed_url of this RSSOptions3. # noqa: E501 :rtype: str """ return self._feed_url @feed_url.setter def feed_url(self, feed_url): """Sets the feed_url of this RSSOptions3. The URL for the RSS feed. # noqa: E501 :param feed_url: The feed_url of this RSSOptions3. # noqa: E501 :type: str """ if feed_url is None: raise ValueError("Invalid value for `feed_url`, must not be `None`") # noqa: E501 self._feed_url = feed_url @property def frequency(self): """Gets the frequency of this RSSOptions3. # noqa: E501 The frequency of the RSS Campaign. # noqa: E501 :return: The frequency of this RSSOptions3. # noqa: E501 :rtype: str """ return self._frequency @frequency.setter def frequency(self, frequency): """Sets the frequency of this RSSOptions3. The frequency of the RSS Campaign. # noqa: E501 :param frequency: The frequency of this RSSOptions3. # noqa: E501 :type: str """ if frequency is None: raise ValueError("Invalid value for `frequency`, must not be `None`") # noqa: E501 allowed_values = ["daily", "weekly", "monthly"] # noqa: E501 if frequency not in allowed_values: raise ValueError( "Invalid value for `frequency` ({0}), must be one of {1}" # noqa: E501 .format(frequency, allowed_values) ) self._frequency = frequency @property def schedule(self): """Gets the schedule of this RSSOptions3. # noqa: E501 :return: The schedule of this RSSOptions3. # noqa: E501 :rtype: SendingSchedule1 """ return self._schedule @schedule.setter def schedule(self, schedule): """Sets the schedule of this RSSOptions3. :param schedule: The schedule of this RSSOptions3. # noqa: E501 :type: SendingSchedule1 """ self._schedule = schedule @property def last_sent(self): """Gets the last_sent of this RSSOptions3. # noqa: E501 The date the campaign was last sent. # noqa: E501 :return: The last_sent of this RSSOptions3. # noqa: E501 :rtype: datetime """ return self._last_sent @last_sent.setter def last_sent(self, last_sent): """Sets the last_sent of this RSSOptions3. The date the campaign was last sent. # noqa: E501 :param last_sent: The last_sent of this RSSOptions3. # noqa: E501 :type: datetime """ self._last_sent = last_sent @property def constrain_rss_img(self): """Gets the constrain_rss_img of this RSSOptions3. # noqa: E501 Whether to add CSS to images in the RSS feed to constrain their width in campaigns. # noqa: E501 :return: The constrain_rss_img of this RSSOptions3. # noqa: E501 :rtype: bool """ return self._constrain_rss_img @constrain_rss_img.setter def constrain_rss_img(self, constrain_rss_img): """Sets the constrain_rss_img of this RSSOptions3. Whether to add CSS to images in the RSS feed to constrain their width in campaigns. # noqa: E501 :param constrain_rss_img: The constrain_rss_img of this RSSOptions3. # noqa: E501 :type: bool """ self._constrain_rss_img = constrain_rss_img 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(RSSOptions3, 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, RSSOptions3): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

29.817797

123

0.589314

7943d713c8b00e74e1599bc3662dc50b21ce2ffe

162

py

Python

bin/twigs/polytwigs-1234-hex-2.py

tiwo/puzzler

7ad3d9a792f0635f7ec59ffa85fb46b54fd77a7e

[ "Intel" ]

null

bin/twigs/polytwigs-1234-hex-2.py

tiwo/puzzler

7ad3d9a792f0635f7ec59ffa85fb46b54fd77a7e

[ "Intel" ]

null

bin/twigs/polytwigs-1234-hex-2.py

tiwo/puzzler

7ad3d9a792f0635f7ec59ffa85fb46b54fd77a7e

[ "Intel" ]

1

2022-01-02T16:54:14.000Z

#!/usr/bin/env python # $Id$ """ 1506 solutions. """ import puzzler from puzzler.puzzles.polytwigs1234 import Polytwigs1234Hex2 puzzler.run(Polytwigs1234Hex2)

13.5

59

0.765432

7943d75a3a6ed5dad7eab7d082799dd15265651d

2,285

py

Python

api/tests/opentrons/hardware_control/test_execution_manager.py

anuwrag/opentrons

28c8d76a19e367c6bd38f5290faaa32abf378715

[ "Apache-2.0" ]

null

api/tests/opentrons/hardware_control/test_execution_manager.py

anuwrag/opentrons

28c8d76a19e367c6bd38f5290faaa32abf378715

[ "Apache-2.0" ]

null

api/tests/opentrons/hardware_control/test_execution_manager.py

anuwrag/opentrons

28c8d76a19e367c6bd38f5290faaa32abf378715

[ "Apache-2.0" ]

null

import asyncio import pytest from opentrons.hardware_control import ( ExecutionManager, ExecutionState, ExecutionCancelledError, ) async def test_state_machine(loop): """ Test that an execution manager's state is RUNNING on init and PAUSE when it when pause is called, unless CANCELLED """ exec_mgr = ExecutionManager() assert await exec_mgr.get_state() == ExecutionState.RUNNING # passes through on wait_for_is_running if state is RUNNING await asyncio.wait_for(exec_mgr.wait_for_is_running(), timeout=0.2) await exec_mgr.pause() assert await exec_mgr.get_state() == ExecutionState.PAUSED with pytest.raises(asyncio.TimeoutError): # should stall on wait_for_is_running when state is PAUSED await asyncio.wait_for(exec_mgr.wait_for_is_running(), timeout=0.2) await exec_mgr.resume() assert await exec_mgr.get_state() == ExecutionState.RUNNING await exec_mgr.cancel() assert await exec_mgr.get_state() == ExecutionState.CANCELLED with pytest.raises(ExecutionCancelledError): # attempting to pause when CANCELLED should raise await exec_mgr.pause() with pytest.raises(ExecutionCancelledError): # should raise on wait_for_is_running when state is CANCELLED await asyncio.wait_for(exec_mgr.wait_for_is_running(), timeout=0.2) await exec_mgr.reset() assert await exec_mgr.get_state() == ExecutionState.RUNNING async def test_cancel_tasks(loop): """ Test that an execution manager cancels all un-protected running asyncio Tasks when cancel is called """ async def fake_task(): while True: await asyncio.sleep(1) exec_mgr = ExecutionManager() cancellable_task = loop.create_task(fake_task()) await exec_mgr.register_cancellable_task(cancellable_task) other_task = loop.create_task(fake_task()) # current, cancellable, and other assert len(asyncio.all_tasks(loop)) == 3 assert len([t for t in asyncio.all_tasks(loop) if t.cancelled()]) == 0 await exec_mgr.cancel() await asyncio.sleep(0.1) all_tasks = asyncio.all_tasks(loop) assert len(all_tasks) == 2 # current and other assert other_task in all_tasks assert cancellable_task not in all_tasks

30.878378

75

0.721663

7943d7faa6320938f4176c6398be9f9f098c8c85

3,334

py

Python

python/ray/serve/handle.py

carlos-aguayo/ray

fedbdd5dc6a47aa9cba170816f8c0950193b4fd6

[ "Apache-2.0" ]

null

python/ray/serve/handle.py

carlos-aguayo/ray

fedbdd5dc6a47aa9cba170816f8c0950193b4fd6

[ "Apache-2.0" ]

null

python/ray/serve/handle.py

carlos-aguayo/ray

fedbdd5dc6a47aa9cba170816f8c0950193b4fd6

[ "Apache-2.0" ]

null

from typing import Optional, Dict, Any, Union from ray.serve.context import TaskContext from ray.serve.router import RequestMetadata class RayServeHandle: """A handle to a service endpoint. Invoking this endpoint with .remote is equivalent to pinging an HTTP endpoint. Example: >>> handle = serve.get_handle("my_endpoint") >>> handle RayServeHandle( Endpoint="my_endpoint", Traffic=... ) >>> handle.remote(my_request_content) ObjectRef(...) >>> ray.get(handle.remote(...)) # result >>> ray.get(handle.remote(let_it_crash_request)) # raises RayTaskError Exception """ def __init__( self, router_handle, endpoint_name, *, method_name=None, shard_key=None, http_method=None, http_headers=None, ): self.router_handle = router_handle self.endpoint_name = endpoint_name self.method_name = method_name self.shard_key = shard_key self.http_method = http_method self.http_headers = http_headers def remote(self, request_data: Optional[Union[Dict, Any]] = None, **kwargs): """Issue an asynchrounous request to the endpoint. Returns a Ray ObjectRef whose results can be waited for or retrieved using ray.wait or ray.get, respectively. Returns: ray.ObjectRef Input: request_data(dict, Any): If it's a dictionary, the data will be available in ``request.json()`` or ``request.form()``. Otherwise, it will be available in ``request.data``. ``**kwargs``: All keyword arguments will be available in ``request.args``. """ request_metadata = RequestMetadata( self.endpoint_name, TaskContext.Python, call_method=self.method_name or "__call__", shard_key=self.shard_key, http_method=self.http_method or "GET", http_headers=self.http_headers or dict(), ) return self.router_handle.enqueue_request.remote( request_metadata, request_data, **kwargs) def options(self, method_name: Optional[str] = None, *, shard_key: Optional[str] = None, http_method: Optional[str] = None, http_headers: Optional[Dict[str, str]] = None): """Set options for this handle. Args: method_name(str): The method to invoke on the backend. http_method(str): The HTTP method to use for the request. shard_key(str): A string to use to deterministically map this request to a backend if there are multiple for this endpoint. """ return RayServeHandle( self.router_handle, self.endpoint_name, # Don't override existing method method_name=self.method_name or method_name, shard_key=self.shard_key or shard_key, http_method=self.http_method or http_method, http_headers=self.http_headers or http_headers, ) def __repr__(self): return f"RayServeHandle(endpoint='{self.endpoint_name}')"

33.676768

79

0.592981

7943da2f12d911349a210448f76eb686389570b6

7,518

py

Python

fawkes/BoundaryConditions.py

bdevl/PGMCPC

cac2fe4304ae42ef2a0d94219b4349d51e86ab2d

[ "MIT" ]

3

2020-10-23T13:40:56.000Z

2022-02-10T03:42:52.000Z

fawkes/BoundaryConditions.py

pkmtum/generative-physics-informed-pde

63ec383da0f2dbf0d8ffbbb44a670e90d07c132e

[ "MIT" ]

null

fawkes/BoundaryConditions.py

pkmtum/generative-physics-informed-pde

63ec383da0f2dbf0d8ffbbb44a670e90d07c132e

[ "MIT" ]

null

import numpy as np from dolfin import DirichletBC import dolfin as df class BoundaryEncodingEnsemble(object): def __init__(self, boundary_encodings): self._boundary_encodings = boundary_encodings def __getitem__(self, item): return self._boundary_encodings[item] def __iter__(self): yield from self._boundary_encodings class BoundaryEncoding(object): def __init__(self, dirichlet_encoding, neumann_encoding): assert isinstance(dirichlet_encoding, DirichletBoundaryEncoding) assert isinstance(neumann_encoding, NeumannBoundaryEncoding) self.dirichlet_encoding = dirichlet_encoding self.neumann_encoding = neumann_encoding def reconstruct(self): raise NotImplementedError class DirichletBoundaryEncoding(object): def __init__(self, type, data = None): self.type = type if data is None: self._data = dict() else: assert isinstance(data, dict) self._data = data @property def data(self): return self._data def __getitem__(self, item): try: return self._data[item] except KeyError: raise KeyError def __setitem__(self, key, value): self._data[key] = value def reconstruct(self, factory): return factory.reconstruct_dirichlet(self) class NeumannBoundaryEncoding(object): def __init__(self, type, data = None): self.type = type if data is None: self._data = dict() else: assert isinstance(data, dict) self._data = data @property def data(self): return self._data def __getitem__(self, item): try: return self._data[item] except KeyError: raise KeyError def __setitem__(self, key, value): self._data[key] = value def reconstruct(self, factory): return factory.reconstruct_neumann(self) class DirichletSpecification(object): def __init__(self, expression, domain, component=None, pointwise =False): self.expression = expression self.domain = domain self.component = component self.pointwise = pointwise class DirichletBoundaryCondition(object): def __init__(self, bcs, encoding = None, encoding_type = None, encoding_data = None): if isinstance(bcs, list): for bc in bcs: if not isinstance(bc, DirichletSpecification): raise TypeError elif isinstance(bcs, DirichletSpecification): bcs = [bcs] else: raise TypeError self._bcs = bcs if encoding is not None: assert encoding_type is None assert encoding_data is None if encoding is not None: assert isinstance(encoding, DirichletBoundaryEncoding) self._encoding = encoding if encoding_data is not None or encoding_type is not None: assert encoding_data is not None and encoding_type is not None assert isinstance(encoding_type, str) assert isinstance(encoding_data, dict) self._encoding = DirichletBoundaryEncoding(encoding_type, encoding_data) def encode(self): if self._encoding is None: raise NotImplementedError return self._encoding def extract(self, V, ReturnFreeDofs = False): # slow and clumsy fbcs = self.transfer(V) dofs = np.array([dof for bc in fbcs for dof in bc.get_boundary_values().keys()], dtype=int) vals = np.array([val for bc in fbcs for val in bc.get_boundary_values().values()], dtype=float) dofs, index = np.unique(dofs, return_index=True) values = vals[index] if ReturnFreeDofs: all_dofs = set(V.dofmap().dofs()) free_dofs = np.array(list(all_dofs - set(dofs)), dtype=np.int) return dofs, values, free_dofs return dofs, values def is_homogeneous(self, V): dofs, values = self.extract(V) return not any(values) def transfer(self, V): fenics_bcs = list() for bc in self._bcs: if bc.component is not None: if not bc.pointwise: fenics_bcs.append(DirichletBC(V.sub(bc.component), bc.expression, bc.domain)) else: fenics_bcs.append(DirichletBC(V.sub(bc.component), bc.expression, bc.domain, method='pointwise')) else: if not bc.pointwise: fenics_bcs.append(DirichletBC(V, bc.expression, bc.domain)) else: fenics_bcs.append(DirichletBC(V, bc.expression, bc.domain, method='pointwise')) return fenics_bcs def mark_facets(self, mesh): facetfct = df.MeshFunction('size_t', mesh, mesh.topology().dim() - 1) facetfct.set_all(0) for bc in self._bcs: bc.domain.mark(facetfct, 1) return facetfct def apply(self, X): raise NotImplementedError class NeumannSpecification(object): def __init__(self, type, expression, subdomain = None): if type not in ['ds', 'dx']: raise ValueError('Type must either be "ds" or "dx') self._type = type # e.g. ds self._subdomain = subdomain self._expression = expression @property def type(self): return self._type @property def subdomain(self): return self._subdomain @property def expression(self): return self._expression class NeumannBoundaryCondition(object): def __init__(self, NeumannSpecifications, encoding = None, encoding_type = None, encoding_data = None): self._neumman_specifications = NeumannSpecifications if encoding is not None: assert isinstance(encoding, NeumannBoundaryEncoding) self._encoding = encoding if encoding_data is not None or encoding_type is not None: assert encoding_data is not None and encoding_type is not None assert isinstance(encoding_type, str) assert isinstance(encoding_data, dict) self._encoding = NeumannBoundaryEncoding(encoding_type, encoding_data) def encode(self): if self._encoding is None: raise NotImplementedError return self._encoding def __getitem__(self, ind): return self._neumman_specifications[ind] def compile_form(self, V): mesh = V.mesh() v = df.TestFunction(V) form = None for ns in self._neumman_specifications: if ns.type == 'dx': meshfct = df.MeshFunction('size_t', mesh, mesh.topology().dim() , 0) elif ns.type == 'ds': meshfct = df.MeshFunction('size_t', mesh, mesh.topology().dim() - 1 , 0) else: raise NotImplementedError meshfct.set_all(0) if ns.subdomain is None: ID = 0 else: ns.subdomain.mark(meshfct, 1) ID = 1 measure = df.Measure(ns.type, domain=mesh, subdomain_data = meshfct, subdomain_id=ID) form_ = ns.expression * v * measure if form is None: form = form_ else: form = form + form_ return form def assemble_flux(self, V): return df.assemble(self.compile_form(V)).get_local()

27.947955

117

0.610801

7943da73c6636fd9b1de8a31129fd6920c8aeb71

4,224

py

Python

test/test_relationships.py

chalkchisel/neomodel

5aeef4afaefd8fb52f1541abeb279663b985dfd0

[ "MIT" ]

1

2019-06-27T13:05:08.000Z

test/test_relationships.py

techdragon/neomodel

7408a608287402138cf0082a29818f6bc28bc5d4

[ "MIT" ]

null

test/test_relationships.py

techdragon/neomodel

7408a608287402138cf0082a29818f6bc28bc5d4

[ "MIT" ]

null

from neomodel import (StructuredNode, RelationshipTo, RelationshipFrom, Relationship, StringProperty, IntegerProperty, One) class Person(StructuredNode): name = StringProperty(unique_index=True) age = IntegerProperty(index=True) is_from = RelationshipTo('Country', 'IS_FROM') knows = Relationship('Person', 'KNOWS') @property def special_name(self): return self.name def special_power(self): return "I have no powers" class Country(StructuredNode): code = StringProperty(unique_index=True) inhabitant = RelationshipFrom(Person, 'IS_FROM') president = RelationshipTo(Person, 'PRESIDENT', cardinality=One) class SuperHero(Person): power = StringProperty(index=True) def special_power(self): return "I have powers" def test_actions_on_deleted_node(): u = Person(name='Jim2', age=3).save() u.delete() try: u.is_from.connect(None) except ValueError: assert True else: assert False try: u.is_from.get() except ValueError: assert True else: assert False try: u.save() except ValueError: assert True else: assert False def test_bidirectional_relationships(): u = Person(name='Jim', age=3).save() assert u de = Country(code='DE').save() assert de assert len(u.is_from) == 0 assert not u.is_from assert u.is_from.__class__.__name__ == 'ZeroOrMore' u.is_from.connect(de) assert len(u.is_from) == 1 assert u.is_from assert u.is_from.is_connected(de) b = u.is_from.all()[0] assert b.__class__.__name__ == 'Country' assert b.code == 'DE' s = b.inhabitant.all()[0] assert s.name == 'Jim' u.is_from.disconnect(b) assert not u.is_from.is_connected(b) def test_either_direction_connect(): rey = Person(name='Rey', age=3).save() sakis = Person(name='Sakis', age=3).save() rey.knows.connect(sakis) assert rey.knows.is_connected(sakis) assert sakis.knows.is_connected(rey) sakis.knows.connect(rey) result, meta = sakis.cypher("""START us=node({self}), them=node({them}) MATCH (us)-[r:KNOWS]-(them) RETURN COUNT(r)""", {'them': rey.__node__._id}) assert int(result[0][0]) == 1 def test_search(): fred = Person(name='Fred', age=13).save() zz = Country(code='ZZ').save() zx = Country(code='ZX').save() zt = Country(code='ZY').save() fred.is_from.connect(zz) fred.is_from.connect(zx) fred.is_from.connect(zt) result = fred.is_from.search(code='ZX') assert result[0].code == 'ZX' def test_custom_methods(): u = Person(name='Joe90', age=13).save() assert u.special_power() == "I have no powers" u = SuperHero(name='Joe91', age=13, power='xxx').save() assert u.special_power() == "I have powers" assert u.special_name == 'Joe91' def test_valid_reconnection(): p = Person(name='ElPresidente', age=93).save() assert p pp = Person(name='TheAdversary', age=33).save() assert pp c = Country(code='CU').save() assert c c.president.connect(p) assert c.president.is_connected(p) # the coup d'etat c.president.reconnect(p, pp) assert c.president.is_connected(pp) # reelection time c.president.reconnect(pp, pp) assert c.president.is_connected(pp) def test_props_relationship(): u = Person(name='Mar', age=20).save() assert u c = Country(code='AT').save() assert c c2 = Country(code='LA').save() assert c2 c.inhabitant.connect(u, properties={'city': 'Thessaloniki'}) assert c.inhabitant.is_connected(u) # Check if properties were inserted result, meta = u.cypher('START root=node:Person(name={name})' + ' MATCH root-[r:IS_FROM]->() RETURN r.city', {'name': u.name}) assert result and result[0][0] == 'Thessaloniki' u.is_from.reconnect(c, c2) assert u.is_from.is_connected(c2) # Check if properties are transferred correctly result, meta = u.cypher('START root=node:Person(name={name})' + ' MATCH root-[r:IS_FROM]->() RETURN r.city', {'name': u.name}) assert result and result[0][0] == 'Thessaloniki'

25.142857

75

0.638258

7943dccc97d4f13a7dccbfa4e9488cbda8e12d9b

975

py

Python

setup.py

HFOI/napalm-extreme-netiron

6630cfe8644f302c8a3b2824d33101257e526b55

[ "Apache-2.0" ]

1

2021-01-24T00:35:42.000Z

setup.py

HFOI/napalm-extreme-netiron

6630cfe8644f302c8a3b2824d33101257e526b55

[ "Apache-2.0" ]

null

setup.py

HFOI/napalm-extreme-netiron

6630cfe8644f302c8a3b2824d33101257e526b55

[ "Apache-2.0" ]

1

2019-02-15T09:18:49.000Z

"""setup.py file.""" import uuid from setuptools import setup, find_packages from pip.req import parse_requirements __author__ = 'Carles Kishimoto <[email protected]>' install_reqs = parse_requirements('requirements.txt', session=uuid.uuid1()) reqs = [str(ir.req) for ir in install_reqs] setup( name="napalm-extreme-netiron", version="0.1.0", packages=find_packages(), author="Carles Kishimoto", author_email="[email protected]", description="Network Automation and Programmability Abstraction Layer with Multivendor support", classifiers=[ 'Topic :: Utilities', 'Programming Language :: Python', 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.7', 'Operating System :: POSIX :: Linux', 'Operating System :: MacOS', ], url="https://github.com/ckishimo/napalm-extreme-netiron", include_package_data=True, install_requires=reqs, )

30.46875

100

0.687179

7943dd58269a428bd4ba2aafc3f9541503ea8dbb

1,858

py

Python

setup.py

vertiond/vertcoinhash-python

679d7191490311b1c2561c19ae45275047f37709

[ "MIT" ]

2

2022-01-15T22:14:40.000Z

2022-01-16T03:26:43.000Z

setup.py

vertiond/vertcoinhash-python

679d7191490311b1c2561c19ae45275047f37709

[ "MIT" ]

null

setup.py

vertiond/vertcoinhash-python

679d7191490311b1c2561c19ae45275047f37709

[ "MIT" ]

null

from setuptools import setup, Extension vertcoinsources = [ 'scrypt.c', 'Lyra2RE.c', 'Sponge.c', 'Lyra2.c', 'sha3/blake.c', 'sha3/groestl.c', 'sha3/keccak.c', 'sha3/cubehash.c', 'sha3/bmw.c', 'sha3/skein.c', 'h2.c', 'tiny_sha3/sha3.c' ] vertcoinincludes = [ '.', './sha3', './tiny_sha3' ] vtc_scrypt_hash_module = Extension('vtc_scrypt_new', sources = vertcoinsources + ['scryptmodule.c'], extra_compile_args=['-O3', '-msse3'], include_dirs=vertcoinincludes) lyra2re_hash_module = Extension('lyra2re_hash', sources = vertcoinsources + ['lyra2remodule.c'], include_dirs=vertcoinincludes) lyra2re2_hash_module = Extension('lyra2re2_hash', sources = vertcoinsources + ['lyra2re2module.c'], include_dirs=vertcoinincludes) lyra2re3_hash_module = Extension('lyra2re3_hash', sources = vertcoinsources + ['lyra2re3module.c'], include_dirs=vertcoinincludes) verthash_module = Extension('verthash', sources = vertcoinsources + ['verthashmodule.c'], extra_compile_args=['-std=c99'], include_dirs=vertcoinincludes) setup (name = 'vertcoinhash', version = '1.0.1', author_email = '[email protected]', author = 'vertion', url = 'https://github.com/vertcoin-project/vertcoinhash-python', description = 'Bindings for proof of work used by Vertcoin', ext_modules = [verthash_module, lyra2re3_hash_module, lyra2re2_hash_module, lyra2re_hash_module, vtc_scrypt_hash_module])

33.781818

128

0.559203

7943dd7e89d341ea7ee291b25a307783c66e26f1

1,201

py

Python

pages/index.py

AmyBeisel/Olympics-Data

ea11f429fbfb9a5554e5e9f1dfffaf89007985da

[ "MIT" ]

null

pages/index.py

AmyBeisel/Olympics-Data

ea11f429fbfb9a5554e5e9f1dfffaf89007985da

[ "MIT" ]

null

pages/index.py

AmyBeisel/Olympics-Data

ea11f429fbfb9a5554e5e9f1dfffaf89007985da

[ "MIT" ]

null

# Imports from 3rd party libraries import dash import dash_bootstrap_components as dbc import dash_core_components as dcc import dash_html_components as html from dash.dependencies import Input, Output import plotly.express as px # Imports from this application from app import app # 2 column layout. 1st column width = 4/12 # https://dash-bootstrap-components.opensource.faculty.ai/l/components/layout column1 = dbc.Col( [ dcc.Markdown( """ ## What sport would you medal in the Olympics? What if you wanted to compete in the olympics? You can use this app and enter your gender, age, weight and height, to see what sport you would do best in, compared to real olympians stats. This is soley for educational purposes, as most do not train day in and day out, but fun to see!! """ ), dcc.Link(dbc.Button('Predict Your Sport!', color='primary'), href='/predictions') ], md=4, ) column2 = dbc.Col([html.Img(src = 'assets/olympic_rings.jpg', className = 'Olympic Rings') ], align = 'center' ) layout = dbc.Row([column1, column2])

27.295455

92

0.647794

7943dffdfefb027361412439c748a391eb4fe91b

943

py

Python

language_formatters_pre_commit_hooks/__init__.py

greggiacovelli/language-formatters-pre-commit-hooks

f6b82c7eae7b930d613fd20a2fcded0daa60cf3c

[ "Apache-2.0" ]

null

language_formatters_pre_commit_hooks/__init__.py

greggiacovelli/language-formatters-pre-commit-hooks

f6b82c7eae7b930d613fd20a2fcded0daa60cf3c

[ "Apache-2.0" ]

null

language_formatters_pre_commit_hooks/__init__.py

greggiacovelli/language-formatters-pre-commit-hooks

f6b82c7eae7b930d613fd20a2fcded0daa60cf3c

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- from __future__ import absolute_import from __future__ import print_function from __future__ import unicode_literals import typing import pkg_resources __version__ = pkg_resources.get_distribution("language_formatters_pre_commit_hooks").version def _get_default_version(tool_name): # pragma: no cover # type: (typing.Text) -> typing.Text """ Read tool_name default version. The method is intended to be used only from language_formatters_pre_commit_hooks modules """ try: with open( pkg_resources.resource_filename( "language_formatters_pre_commit_hooks", "{tool_name}.version".format(tool_name=tool_name), ) ) as f: return f.readline().split()[0] except: # noqa: E722 (allow usage of bare 'except') raise RuntimeError("No default version found for {tool_name}".format(tool_name=tool_name))

31.433333

98

0.693531

7943e04260b0d410eef59a5288687601141893e4

6,680

py

Python

src/ui/ui_cfg_lpspinor.py

ben-github/NXP-MCUBootUtility

3ff9fa203d667844f83a08c855fef85723d2612e

[ "Apache-2.0" ]

null

src/ui/ui_cfg_lpspinor.py

ben-github/NXP-MCUBootUtility

3ff9fa203d667844f83a08c855fef85723d2612e

[ "Apache-2.0" ]

null

src/ui/ui_cfg_lpspinor.py

ben-github/NXP-MCUBootUtility

3ff9fa203d667844f83a08c855fef85723d2612e

[ "Apache-2.0" ]

null

#! /usr/bin/env python # -*- coding: utf-8 -*- import wx import sys import os import math import RTyyyy_uidef import uidef import uivar import uilang sys.path.append(os.path.abspath("..")) from win import bootDeviceWin_LpspiNor from utils import sound class secBootUiCfgLpspiNor(bootDeviceWin_LpspiNor.bootDeviceWin_LpspiNor): def __init__(self, parent): bootDeviceWin_LpspiNor.bootDeviceWin_LpspiNor.__init__(self, parent) self._setLanguage() lpspiNorOpt0, lpspiNorOpt1 = uivar.getBootDeviceConfiguration(RTyyyy_uidef.kBootDevice_LpspiNor) #1. Prepare SPI NOR/EEPROM option block # bit [31:28] tag, fixed to 0x0c # bit [27:24] Size, (bytes/4) - 1 # bit [23:20] SPI instance # bit [19:16] PCS index # bit [15:12] Flash type, 0-SPI NOR, 1-SPI EEPROM # bit [11:08] Flash size(Bytes) 0 - 512K, 1-1M, 2-2M, 3-4M, 4-8M # 13-64K, 14-128K, 15-256K, etc. # bit [07:04] Sector size (Bytes), 0-4K, 1-8K, 2-32K, 3-64K, # 4-128K, 5-256K # bit [03:00] Page size (Bytes) 0-256, 1-512 self.lpspiNorOpt0 = lpspiNorOpt0 self.lpspiNorOpt1 = lpspiNorOpt1 self._recoverLastSettings() def _setLanguage( self ): runtimeSettings = uivar.getRuntimeSettings() langIndex = runtimeSettings[3] self.m_notebook_memOpt.SetPageText(0, uilang.kSubLanguageContentDict['panel_memOpt'][langIndex]) self.m_staticText_deviceType.SetLabel(uilang.kSubLanguageContentDict['sText_deviceType'][langIndex]) self.m_staticText_pageSize.SetLabel(uilang.kSubLanguageContentDict['sText_pageSize'][langIndex]) self.m_staticText_sectorSize.SetLabel(uilang.kSubLanguageContentDict['sText_sectorSize'][langIndex]) self.m_staticText_totalSize.SetLabel(uilang.kSubLanguageContentDict['sText_totalSize'][langIndex]) self.m_notebook_spiOpt.SetPageText(0, uilang.kSubLanguageContentDict['panel_spiOpt'][langIndex]) self.m_staticText_spiIndex.SetLabel(uilang.kSubLanguageContentDict['sText_spiIndex'][langIndex]) self.m_staticText_spiPcs.SetLabel(uilang.kSubLanguageContentDict['sText_spiPcs'][langIndex]) self.m_staticText_spiSpeed.SetLabel(uilang.kSubLanguageContentDict['sText_spiSpeed'][langIndex]) self.m_button_ok.SetLabel(uilang.kSubLanguageContentDict['button_lpspinor_ok'][langIndex]) self.m_button_cancel.SetLabel(uilang.kSubLanguageContentDict['button_lpspinor_cancel'][langIndex]) def _recoverLastSettings ( self ): deviceType = (self.lpspiNorOpt0 & 0x0000F000) >> 12 self.m_choice_deviceType.SetSelection(deviceType) pageSize = (self.lpspiNorOpt0 & 0x0000000F) >> 0 if pageSize <= 2: self.m_choice_pageSize.SetSelection(pageSize + 3) else: self.m_choice_pageSize.SetSelection(pageSize - 3) sectorSize = (self.lpspiNorOpt0 & 0x000000F0) >> 4 self.m_choice_sectorSize.SetSelection(sectorSize) totalSize = (self.lpspiNorOpt0 & 0x00000F00) >> 8 if totalSize <= 11: self.m_choice_totalSize.SetSelection(totalSize + 4) else: self.m_choice_totalSize.SetSelection(totalSize - 12) spiIndex = (self.lpspiNorOpt0 & 0x00F00000) >> 20 self.m_choice_spiIndex.SetSelection(spiIndex - 1) spiPcs = (self.lpspiNorOpt0 & 0x000F0000) >> 16 self.m_choice_spiPcs.SetSelection(spiPcs) spiSpeed = (self.lpspiNorOpt1 & 0x0000000F) >> 0 self.m_choice_spiSpeed.SetSelection(spiSpeed) def _getDeviceType( self ): txt = self.m_choice_deviceType.GetString(self.m_choice_deviceType.GetSelection()) if txt == '1bit NOR Flash': val = 0x0 elif txt == 'EEPROM': val = 0x1 else: pass self.lpspiNorOpt0 = (self.lpspiNorOpt0 & 0xFFFF0FFF) | (val << 12) def _getPageSize( self ): val = int(self.m_choice_pageSize.GetString(self.m_choice_pageSize.GetSelection())) val = int(math.log(val, 2)) if val >= 8: val -= 8 elif val >= 5: val -= 2 else: pass self.lpspiNorOpt0 = (self.lpspiNorOpt0 & 0xFFFFFFF0) | (val << 0) def _getSectorSize( self ): val = int(self.m_choice_sectorSize.GetString(self.m_choice_sectorSize.GetSelection())) val = int(math.log(val, 2)) if val <= 3: val -= 2 else: val -= 3 self.lpspiNorOpt0 = (self.lpspiNorOpt0 & 0xFFFFFF0F) | (val << 4) def _getTotalSize( self ): val = int(self.m_choice_totalSize.GetString(self.m_choice_totalSize.GetSelection())) val = int(math.log(val, 2)) if val >= 9: val -= 9 elif val >= 5: val += 7 else: pass self.lpspiNorOpt0 = (self.lpspiNorOpt0 & 0xFFFFF0FF) | (val << 8) def _getSpiIndex( self ): val = int(self.m_choice_spiIndex.GetString(self.m_choice_spiIndex.GetSelection())) self.lpspiNorOpt0 = (self.lpspiNorOpt0 & 0xFF0FFFFF) | (val << 20) def _getSpiPcs( self ): val = int(self.m_choice_spiPcs.GetString(self.m_choice_spiPcs.GetSelection())) self.lpspiNorOpt0 = (self.lpspiNorOpt0 & 0xFFF0FFFF) | (val << 16) def _getSpiSpeed( self ): txt = self.m_choice_spiSpeed.GetString(self.m_choice_spiSpeed.GetSelection()) if txt == '20MHz': val = 0x0 elif txt == '10MHz': val = 0x1 elif txt == '5MHz': val = 0x2 elif txt == '2MHz': val = 0x3 else: pass self.lpspiNorOpt1 = (self.lpspiNorOpt1 & 0xFFFFFFF0) | (val << 0) def callbackOk(self, event): self._getDeviceType() self._getPageSize() self._getSectorSize() self._getTotalSize() self._getSpiIndex() self._getSpiPcs() self._getSpiSpeed() uivar.setBootDeviceConfiguration(RTyyyy_uidef.kBootDevice_LpspiNor, self.lpspiNorOpt0, self.lpspiNorOpt1) uivar.setRuntimeSettings(False) self.Show(False) runtimeSettings = uivar.getRuntimeSettings() sound.playSoundEffect(runtimeSettings[1], runtimeSettings[2], uidef.kSoundEffectFilename_Progress) def callbackCancel(self, event): uivar.setRuntimeSettings(False) self.Show(False) def callbackClose( self, event ): uivar.setRuntimeSettings(False) self.Show(False)

40.981595

114

0.633683

7943e2c85e6c542d0c71aa336a259b42d68dd735

5,031

py

Python

Python Code/Double_Pendulum_v1.py

yingranbc/Double-Pendulum-Motion-Animation

22895c835e023b51be7d8c3e2f82e484e642469c

[ "MIT" ]

26

2018-07-02T20:47:53.000Z

2022-03-24T08:10:41.000Z

Python Code/Double_Pendulum_v1.py

yingranbc/Double-Pendulum-Motion-Animation

22895c835e023b51be7d8c3e2f82e484e642469c

[ "MIT" ]

null

Python Code/Double_Pendulum_v1.py

yingranbc/Double-Pendulum-Motion-Animation

22895c835e023b51be7d8c3e2f82e484e642469c

[ "MIT" ]

12

2019-04-06T08:18:29.000Z

2022-03-24T01:47:13.000Z

# -*- coding: utf-8 -*- """ Created on Mon Apr 13 20:40:06 2020 @author: Mohammad Asif Zaman Double pendulum motion animation using FuncAnimation() """ from __future__ import print_function from scipy.integrate import odeint import time import math import numpy as np import pylab as py #import matplotlib.pyplot as plt from matplotlib import animation, rc from IPython.display import HTML from matplotlib import pyplot as plt m1 = 2 # mass of pendulum 1 (in kg) m2 = 1 # mass of pendulum 2 (in kg) L1 = 1.4 # length of pendulum 1 (in meter) L2 = 1 # length of pendulum 2 (in meter) g = 9.8 # gravitatioanl acceleration constant (m/s^2) u0 = [-np.pi/2.2, 0, np.pi/1.8, 0] # initial conditions. # u[0] = angle of the first pendulum # u[1] = angular velocity of the first pendulum # u[2] = angle of the second pendulum # u[3] = angular velocity of the second pendulum tfinal = 25.0 # Final time. Simulation time = 0 to tfinal. Nt = 751 t = np.linspace(0, tfinal, Nt) # Differential equations describing the system def double_pendulum(u,t,m1,m2,L1,L2,g): # du = derivatives # u = variables # p = parameters # t = time variable du = np.zeros(4) c = np.cos(u[0]-u[2]) # intermediate variables s = np.sin(u[0]-u[2]) # intermediate variables du[0] = u[1] # d(theta 1) du[1] = ( m2*g*np.sin(u[2])*c - m2*s*(L1*c*u[1]**2 + L2*u[3]**2) - (m1+m2)*g*np.sin(u[0]) ) /( L1 *(m1+m2*s**2) ) du[2] = u[3] # d(theta 2) du[3] = ((m1+m2)*(L1*u[1]**2*s - g*np.sin(u[2]) + g*np.sin(u[0])*c) + m2*L2*u[3]**2*s*c) / (L2 * (m1 + m2*s**2)) return du sol = odeint(double_pendulum, u0, t, args=(m1,m2,L1,L2,g)) #sol[:,0] = u1 = Θ_1 #sol[:,1] = u2 = ω_1 #sol[:,2] = u3 = Θ_2 #sol[:,3] = u4 = ω_2 u0 = sol[:,0] # theta_1 u1 = sol[:,1] # omega 1 u2 = sol[:,2] # theta_2 u3 = sol[:,3] # omega_2 # Mapping from polar to Cartesian x1 = L1*np.sin(u0); # First Pendulum y1 = -L1*np.cos(u0); x2 = x1 + L2*np.sin(u2); # Second Pendulum y2 = y1 - L2*np.cos(u2); py.close('all') py.figure(1) #py.plot(t,x1) #py.plot(t,y1) py.plot(x1,y1,'.',color = '#0077BE',label = 'mass 1') py.plot(x2,y2,'.',color = '#f66338',label = 'mass 2' ) py.legend() py.xlabel('x (m)') py.ylabel('y (m)') #py.figure(2) #py.plot(t,x2) #py.plot(t,y2) fig = plt.figure() ax = plt.axes(xlim=(-L1-L2-0.5, L1+L2+0.5), ylim=(-2.5, 1.5)) #line, = ax.plot([], [], lw=2,,markersize = 9, markerfacecolor = "#FDB813",markeredgecolor ="#FD7813") line1, = ax.plot([], [], 'o-',color = '#d2eeff',markersize = 12, markerfacecolor = '#0077BE',lw=2, markevery=10000, markeredgecolor = 'k') # line for Earth line2, = ax.plot([], [], 'o-',color = '#ffebd8',markersize = 12, markerfacecolor = '#f66338',lw=2, markevery=10000, markeredgecolor = 'k') # line for Jupiter line3, = ax.plot([], [], color='k', linestyle='-', linewidth=2) line4, = ax.plot([], [], color='k', linestyle='-', linewidth=2) line5, = ax.plot([], [], 'o', color='k', markersize = 10) time_template = 'Time = %.1f s' time_string = ax.text(0.05, 0.9, '', transform=ax.transAxes) ax.get_xaxis().set_ticks([]) # enable this to hide x axis ticks ax.get_yaxis().set_ticks([]) # enable this to hide y axis ticks # initialization function: plot the background of each frame def init(): line1.set_data([], []) line2.set_data([], []) line3.set_data([], []) line4.set_data([], []) line5.set_data([], []) time_string.set_text('') return line3,line4, line5, line1, line2, time_string # animation function. This is called sequentially def animate(i): # Motion trail sizes. Defined in terms of indices. Length will vary with the time step, dt. E.g. 5 indices will span a lower distance if the time step is reduced. trail1 = 6 # length of motion trail of weight 1 trail2 = 8 # length of motion trail of weight 2 dt = t[2]-t[1] # time step line1.set_data(x1[i:max(1,i-trail1):-1], y1[i:max(1,i-trail1):-1]) # marker + line of first weight line2.set_data(x2[i:max(1,i-trail2):-1], y2[i:max(1,i-trail2):-1]) # marker + line of the second weight line3.set_data([x1[i], x2[i]], [y1[i], y2[i]]) # line connecting weight 2 to weight 1 line4.set_data([x1[i], 0], [y1[i],0]) # line connecting origin to weight 1 line5.set_data([0, 0], [0, 0]) time_string.set_text(time_template % (i*dt)) return line3, line4,line5,line1, line2, time_string anim = animation.FuncAnimation(fig, animate, init_func=init, frames=Nt, interval=1000*(t[2]-t[1])*0.8, blit=True) # Comment out the following lines if you do not want to save the animation to file #anim.save('double_pendulum_animation.mp4', fps=30, extra_args=['-vcodec', 'libx264']) anim.save('double_pendulum_animation.gif', fps=1.0/(t[2]-t[1]), writer = 'imagemagick') plt.show()

32.044586

166

0.601272

7943e3313fc8d6a8742f2f6043fdc63a65d3cad9

2,812

py

Python

astore.py

anton-muravev/ased

16ddb70ac3e46556cf49569915df0165a6fb7d16

[ "Apache-2.0" ]

null

astore.py

anton-muravev/ased

16ddb70ac3e46556cf49569915df0165a6fb7d16

[ "Apache-2.0" ]

null

astore.py

anton-muravev/ased

16ddb70ac3e46556cf49569915df0165a6fb7d16

[ "Apache-2.0" ]

1

2021-12-06T08:42:59.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- import pickle from collections.abc import MutableMapping class Astore(MutableMapping): """Class to store the data arrays. Extends the standard dictionary, but offers additional file operations. Needs to be initialized before use.""" def __init__(self): """Initialize with an empty dictionary.""" self.dicty = dict() def __getitem__(self, key): return self.dicty.__getitem__(key) def __setitem__(self, key, value): return self.dicty.__setitem__(key, value) def __delitem__(self, key): return self.dicty.__delitem__(key) def keys(self): return self.dicty.keys() def __iter__(self): yield from self.dicty def __len__(self): return self.dicty.__len__() def extract(self, key): """Return the safe copy of the given contained variable. Arguments ---------- key: string The key of the target variable. Returns ---------- A memory copy of the given variable, safe for modification.""" return self.dicty[key].copy() def load(self, filename, names=None): """Fill the store with the contents of a given file. This function can operate in two modes. If the names argument is not provided, the new file format is implied (pickled file containing only a single dictionary). Otherwise the contents of the names argument are used to deserialize the file contents in the given order. Arguments ---------- filename: string Full name of the file to load from. names: list of strings, optional Indicates the variable names to be loaded from the file (in the given order). Returns ---------- Nothing. """ if names is None: with open(filename, 'rb') as f: self.dicty = pickle.load(f) else: with open(filename, 'rb') as f: for k in names: self.dicty[k] = pickle.load(f) def get_names(self): """Return the list of value names.""" return list(self.dicty.keys()) def dump(self, filename): """Save the store contents to the given file. This operation just pickles the internal dictionary. Arguments ---------- filename: string Full name of the file to save to. Returns ---------- Nothing.""" with open(filename, 'wb') as f: pickle.dump(self.dicty, f)

28.989691

78

0.54303

7943e338e4307eb990a8f6cb96621b0a546a6801

635

py

Python

app/artworks/migrations/0013_auto_20211230_0601.py

Vadee-art/backend

9b068d6ed11c1ffeccc13c4be67f1bb87a12d6ad

[ "MIT" ]

null

app/artworks/migrations/0013_auto_20211230_0601.py

Vadee-art/backend

9b068d6ed11c1ffeccc13c4be67f1bb87a12d6ad

[ "MIT" ]

null

app/artworks/migrations/0013_auto_20211230_0601.py

Vadee-art/backend

9b068d6ed11c1ffeccc13c4be67f1bb87a12d6ad

[ "MIT" ]

null

# Generated by Django 3.1.7 on 2021-12-30 02:31 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('artworks', '0012_auto_20211230_0559'), ] operations = [ migrations.RemoveField( model_name='thetoken', name='artwork', ), migrations.AddField( model_name='thetoken', name='artwork', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='token_artwork', to='artworks.artwork'), ), ]

26.458333

158

0.626772

7943e36631b7aecf2f0c102c5ec7849a40c62b40

5,524

py

Python

tests/unit/acquisition/test_interface.py

clinton0313/trieste

1e4351144c6cf6853c5cbd4dd7b74714aed569b2

[ "Apache-2.0" ]

null

tests/unit/acquisition/test_interface.py

clinton0313/trieste

1e4351144c6cf6853c5cbd4dd7b74714aed569b2

[ "Apache-2.0" ]

2

2022-03-11T18:42:45.000Z

2022-03-19T17:00:12.000Z

tests/unit/acquisition/test_interface.py

clinton0313/trieste

1e4351144c6cf6853c5cbd4dd7b74714aed569b2

[ "Apache-2.0" ]

null

# Copyright 2020 The Trieste Contributors # # 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. from __future__ import annotations from typing import List, Optional, Tuple, cast import pytest from tests.util.misc import empty_dataset, raise_exc from tests.util.models.gpflow.models import QuadraticMeanAndRBFKernel from trieste.acquisition import ( AugmentedExpectedImprovement, BatchMonteCarloExpectedImprovement, ExpectedConstrainedHypervolumeImprovement, ExpectedConstrainedImprovement, ExpectedHypervolumeImprovement, ExpectedImprovement, NegativeLowerConfidenceBound, NegativePredictiveMean, PredictiveVariance, ProbabilityOfFeasibility, ) from trieste.acquisition.interface import ( AcquisitionFunction, SingleModelAcquisitionBuilder, SingleModelGreedyAcquisitionBuilder, ) from trieste.data import Dataset from trieste.models import ProbabilisticModel from trieste.models.interfaces import SupportsPredictJoint from trieste.types import TensorType from trieste.utils import DEFAULTS class _ArbitrarySingleBuilder(SingleModelAcquisitionBuilder[ProbabilisticModel]): def prepare_acquisition_function( self, model: ProbabilisticModel, dataset: Optional[Dataset] = None, ) -> AcquisitionFunction: return raise_exc class _ArbitraryGreedySingleBuilder(SingleModelGreedyAcquisitionBuilder[ProbabilisticModel]): def prepare_acquisition_function( self, model: ProbabilisticModel, dataset: Optional[Dataset] = None, pending_points: Optional[TensorType] = None, ) -> AcquisitionFunction: return raise_exc def test_single_model_acquisition_builder_raises_immediately_for_wrong_key() -> None: builder = _ArbitrarySingleBuilder().using("foo") with pytest.raises(KeyError): builder.prepare_acquisition_function( {"bar": QuadraticMeanAndRBFKernel()}, datasets={"bar": empty_dataset([1], [1])} ) def test_single_model_acquisition_builder_repr_includes_class_name() -> None: builder = _ArbitrarySingleBuilder() assert type(builder).__name__ in repr(builder) def test_single_model_acquisition_builder_using_passes_on_correct_dataset_and_model() -> None: class Builder(SingleModelAcquisitionBuilder[ProbabilisticModel]): def prepare_acquisition_function( self, model: ProbabilisticModel, dataset: Optional[Dataset] = None, ) -> AcquisitionFunction: assert dataset is data["foo"] assert model is models["foo"] return raise_exc data = {"foo": empty_dataset([1], [1]), "bar": empty_dataset([1], [1])} models = {"foo": QuadraticMeanAndRBFKernel(), "bar": QuadraticMeanAndRBFKernel()} Builder().using("foo").prepare_acquisition_function(models, datasets=data) def test_single_model_greedy_acquisition_builder_raises_immediately_for_wrong_key() -> None: builder = _ArbitraryGreedySingleBuilder().using("foo") with pytest.raises(KeyError): builder.prepare_acquisition_function( {"bar": QuadraticMeanAndRBFKernel()}, {"bar": empty_dataset([1], [1])}, None ) def test_single_model_greedy_acquisition_builder_repr_includes_class_name() -> None: builder = _ArbitraryGreedySingleBuilder() assert type(builder).__name__ in repr(builder) @pytest.mark.parametrize( "function, function_repr", cast( List[Tuple[SingleModelAcquisitionBuilder[SupportsPredictJoint]]], [ (ExpectedImprovement(), "ExpectedImprovement()"), (AugmentedExpectedImprovement(), "AugmentedExpectedImprovement()"), (NegativeLowerConfidenceBound(1.96), "NegativeLowerConfidenceBound(1.96)"), (NegativePredictiveMean(), "NegativePredictiveMean()"), (ProbabilityOfFeasibility(0.5), "ProbabilityOfFeasibility(0.5)"), ( ExpectedHypervolumeImprovement(), "ExpectedHypervolumeImprovement(get_reference_point)", ), ( BatchMonteCarloExpectedImprovement(10_000), f"BatchMonteCarloExpectedImprovement(10000, jitter={DEFAULTS.JITTER})", ), (PredictiveVariance(), f"PredictiveVariance(jitter={DEFAULTS.JITTER})"), ], ), ) def test_single_model_acquisition_function_builder_reprs( function: SingleModelAcquisitionBuilder[SupportsPredictJoint], function_repr: str ) -> None: assert repr(function) == function_repr assert repr(function.using("TAG")) == f"{function_repr} using tag 'TAG'" assert ( repr(ExpectedConstrainedImprovement("TAG", function.using("TAG"), 0.0)) == f"ExpectedConstrainedImprovement('TAG', {function_repr} using tag 'TAG', 0.0)" ) assert ( repr(ExpectedConstrainedHypervolumeImprovement("TAG", function.using("TAG"), 0.0)) == f"ExpectedConstrainedHypervolumeImprovement('TAG', " f"{function_repr} using tag 'TAG', 0.0, get_reference_point)" )

38.096552

94

0.722303

7943e3bde0c682af9627d7d997c5ef206ef49e0a

1,580

py

Python

tests/unit/altimeter/aws/resource/elbv1/test_load_balancer.py

elliotsegler/altimeter

c3924524938b4bae86b1acda2a4fc3f79ac523ff

[ "MIT" ]

48

2019-11-06T03:20:53.000Z

2022-02-22T21:10:45.000Z

tests/unit/altimeter/aws/resource/elbv1/test_load_balancer.py

elliotsegler/altimeter

c3924524938b4bae86b1acda2a4fc3f79ac523ff

[ "MIT" ]

27

2020-01-07T23:48:30.000Z

2022-02-26T00:24:04.000Z

tests/unit/altimeter/aws/resource/elbv1/test_load_balancer.py

elliotsegler/altimeter

c3924524938b4bae86b1acda2a4fc3f79ac523ff

[ "MIT" ]

21

2019-12-20T03:06:35.000Z

2021-12-15T23:26:00.000Z

import boto3 from botocore.exceptions import ClientError from unittest import TestCase from moto import mock_elb from unittest.mock import patch from altimeter.aws.resource.elbv1.load_balancer import ClassicLoadBalancerResourceSpec from altimeter.aws.scan.aws_accessor import AWSAccessor class TestLB(TestCase): @mock_elb def test_disappearing_elb_race_condition(self): account_id = "123456789012" region_name = "us-east-1" lb_name = "foo" session = boto3.Session() client = session.client("elb", region_name=region_name) client.create_load_balancer( LoadBalancerName=lb_name, Listeners=[{"Protocol": "HTTP", "LoadBalancerPort": 80, "InstancePort": 80}], Tags=[{"Key": "Name", "Value": lb_name}], ) scan_accessor = AWSAccessor(session=session, account_id=account_id, region_name=region_name) with patch( "altimeter.aws.resource.elbv1.load_balancer.ClassicLoadBalancerResourceSpec.get_lb_attrs" ) as mock_get_lb_attrs: mock_get_lb_attrs.side_effect = ClientError( operation_name="DescribeLoadBalancerAttributes", error_response={ "Error": { "Code": "LoadBalancerNotFound", "Message": f"There is no ACTIVE Load Balancer named '{lb_name}'", } }, ) resources = ClassicLoadBalancerResourceSpec.scan(scan_accessor=scan_accessor) self.assertEqual(resources, [])

38.536585

101

0.643671

7943e4181e82b07c2a880c24bcbfa643f489a57c

1,917

py

Python

repos/system_upgrade/el7toel8/actors/checkosrelease/tests/test_checkosrelease.py

t184256/leapp-repository

b0d8c46eb9bd967692e8cef271587739dced38e8

[ "Apache-2.0" ]

null

repos/system_upgrade/el7toel8/actors/checkosrelease/tests/test_checkosrelease.py

t184256/leapp-repository

b0d8c46eb9bd967692e8cef271587739dced38e8

[ "Apache-2.0" ]

null

repos/system_upgrade/el7toel8/actors/checkosrelease/tests/test_checkosrelease.py

t184256/leapp-repository

b0d8c46eb9bd967692e8cef271587739dced38e8

[ "Apache-2.0" ]

null

import os import pytest from leapp import reporting from leapp.exceptions import StopActorExecution, StopActorExecutionError from leapp.libraries.actor import library from leapp.libraries.common.config import version from leapp.libraries.common.testutils import (create_report_mocked, produce_mocked) from leapp.libraries.stdlib import api from leapp.models import OSReleaseFacts def test_skip_check(monkeypatch): monkeypatch.setattr(os, "getenv", lambda _unused: True) monkeypatch.setattr(reporting, "create_report", create_report_mocked()) assert library.skip_check() assert reporting.create_report.called == 1 assert 'Skipped OS release check' in reporting.create_report.report_fields['title'] assert reporting.create_report.report_fields['severity'] == 'high' assert 'flags' not in reporting.create_report.report_fields def test_no_skip_check(monkeypatch): monkeypatch.setattr(os, "getenv", lambda _unused: False) monkeypatch.setattr(reporting, "create_report", create_report_mocked()) assert not library.skip_check() assert reporting.create_report.called == 0 def test_not_supported_release(monkeypatch): monkeypatch.setattr(version, "is_supported_version", lambda: False) monkeypatch.setattr(reporting, "create_report", create_report_mocked()) library.check_os_version() assert reporting.create_report.called == 1 assert 'Unsupported OS' in reporting.create_report.report_fields['title'] assert 'flags' in reporting.create_report.report_fields assert 'inhibitor' in reporting.create_report.report_fields['flags'] def test_supported_release(monkeypatch): monkeypatch.setattr(version, "is_supported_version", lambda: True) monkeypatch.setattr(reporting, "create_report", create_report_mocked()) library.check_os_version() assert reporting.create_report.called == 0

37.588235

87

0.771518

7943e57c189544849f99576e224a9fe2e59a0f3f

7,780

py

Python

modules/music_race/cog.py

kevslinger/bot-be-named

a476598044a06e446e5d0a52da66fff1d7b69a27

[ "MIT" ]

4

2021-10-30T15:42:33.000Z

2021-11-29T20:02:45.000Z

modules/music_race/cog.py

kevslinger/bot-be-named

a476598044a06e446e5d0a52da66fff1d7b69a27

[ "MIT" ]

97

2021-10-30T14:40:34.000Z

2022-03-01T15:19:29.000Z

modules/music_race/cog.py

kevslinger/bot-be-named

a476598044a06e446e5d0a52da66fff1d7b69a27

[ "MIT" ]

1

2022-03-05T11:53:27.000Z

import constants import discord import re from discord.ext import commands from utils import discord_utils, logging_utils from modules.music_race import music_race_constants import os import numpy as np import string def get_partition_mapping(): map = {} for answer in music_race_constants.ANSWERS: for idx, letter in enumerate(answer): if letter in map: map[letter].append(f"{answer}_part_{idx}") else: map[letter] = [f"{answer}_part_{idx}"] for letter in filter(lambda x: x not in map, string.ascii_uppercase): map[letter] = ["silence"] return map class MusicRace(commands.Cog, name="Music Race"): """Puzzle for Arithmancy June 2020! Identify each of the movies based on their theme songs!""" def __init__(self, bot): self.bot = bot self.partition_map = get_partition_mapping() @commands.command(name="hint") async def hint(self, ctx): """Hint Usage: `~hint` """ logging_utils.log_command("hint", ctx.guild, ctx.channel, ctx.author) embed = discord_utils.create_embed() embed.add_field( name="This is not a hint", value="*Hints will always be given at Hogwarts to those who ask for it.*", ) await ctx.send(embed=embed) @commands.command(name="notesaw", aliases=["musicpuzzleinfo"]) async def musicpuzzleinfo(self, ctx): """Give the users everything they need to know about the puzzle Usage: `~notesaw` """ logging_utils.log_command("notesaw", ctx.guild, ctx.channel, ctx.author) embed = discord_utils.create_embed() embed.add_field( name=f"Welcome to Notesaw!", value=f"To start the puzzle, use `{ctx.prefix}guesstune`. " f"For example, try `{ctx.prefix}guesstune PIANO`. Have fun!", inline=False, ) embed.add_field( name=f"Notice", value=f"Headphone users! We recommend turning the volume down a bit. Some minor glitches might " f"hurt your ear", inline=False, ) await ctx.send(embed=embed) @commands.command(name="guesstune") async def guesstune(self, ctx, *args): """Take a user's guess and give them a response based on what letters they provided Usage: `~guesstune (WORD)` """ logging_utils.log_command("guesstune", ctx.guild, ctx.channel, ctx.author) embed = discord_utils.create_embed() if len(args) < 1: embed = discord_utils.create_no_argument_embed("word") await ctx.send(embed=embed) return # Replace any non-letters word = re.sub("[^A-Z]+", "", "".join(args).upper()) if len(word) < 1 or len(word) > 20: embed.add_field( name=f"{constants.FAILED}!", value=f"Word provided `{word}` is not between 1-20 letters", ) await ctx.send(embed=embed) return if word in music_race_constants.ANSWERS: final_song_path = os.path.join( music_race_constants.PUZZLE_OUTPUTS_DIR, word + f"_final{music_race_constants.MP3_EXTENSION}", ) if not os.path.exists(final_song_path): delay = ( music_race_constants.ANSWERS[word][music_race_constants.DELAY] * 1000 ) os.system( f"ffmpeg -y -hide_banner -loglevel error -i {os.path.join(music_race_constants.PUZZLE_FULL_SONGS_DIR, word + music_race_constants.MP3_EXTENSION)} -filter_complex 'adelay={delay}|{delay}' {final_song_path}" ) # TODO: ffmpeg-normalize is too slow for now. Try to optimize later. # os.system( # f"ffmpeg-normalize -f -c:a libmp3lame {output_path} -o {output_path}" # ) embed.add_field( name=f"Well done! You guessed {word}!", value=f"\nTo play this tune yourself, use this command. (See {ctx.prefix}playtunehelp for more help)" f"\n\n`{music_race_constants.ANSWERS[word][music_race_constants.TUNE]}`", ) await ctx.send(embed=embed) await ctx.send( file=discord.File( final_song_path, filename=f"{list(music_race_constants.ANSWERS).index(word)+1} of {len(music_race_constants.ANSWERS)}.mp3", ) ) return # We need to figure out the longest substring that is part of the answer # Add that duration of the song. # Afterwards, we need to find all the remaining letters # and add random partitions, or silence. finalanswer = [] delay = 0 # For each letter we match, we can add that to the start of our output song for i in range(len(word)): found_song = False x = word[0 : (i + 1)] # Matches one of the songs for answer in music_race_constants.ANSWERS: if answer.startswith(x): finalanswer.append((f"{answer}_part_{i}", delay)) found_song = True break # Newly added character is not the start to a song if not found_song: # Get a random clip with that letter if word[i] in self.partition_map: finalanswer.append( (np.random.choice(self.partition_map[word[i]]), delay) ) # Increments delay += 3 # debug_output_msg = "" # for ans in finalanswer: # debug_output_msg += f"{ans[1]}-{ans[1]+3}: {ans[0]}\n" # TODO: Remove once we are more certain about how this works. It ruins the puzzle, obviously # await ctx.send(debug_output_msg) # print(word) # print(debug_output_msg) inputs = "".join( [ f"-i {os.path.join(music_race_constants.PUZZLE_PARTIAL_SONGS_DIR, finalanswer[idx][0] + '.mp3')} " for idx in range(len(finalanswer)) ] ) # Otherwise, we just chop each song into 3s bits, with 0.5s between them filter_complex = "".join( [ f"[{idx}]atrim=0:{music_race_constants.SONG_SNIPPET_LENGTH},adelay={finalanswer[idx][1]*1000+500*idx}|{finalanswer[idx][1]*1000+500*idx},volume={music_race_constants.VOLUME/2}[{letter}];" for idx, letter in zip(range(len(finalanswer)), string.ascii_lowercase) ] ) mix = "".join( [ f"[{letter}]" for _, letter in zip(finalanswer, list(string.ascii_lowercase)) ] ) output_dir = os.path.join( music_race_constants.PUZZLE_OUTPUTS_DIR, ctx.channel.name ) if not os.path.exists(output_dir): os.mkdir(output_dir) output_path = os.path.join(output_dir, f"{word}.mp3") os.system( f"ffmpeg -y -hide_banner -loglevel error {inputs} -preset veryfast " + f"-filter_complex '{filter_complex}{mix}amix=inputs={len(finalanswer)}:dropout_transition=1000,volume={music_race_constants.VOLUME/2},loudnorm' " f"{output_path}" ) # TODO: ffmpeg-normalize is too slow for now. Try to optimize later. # os.system( # f"ffmpeg-normalize -f -c:a libmp3lame {output_path} -o {output_path}" # ) await ctx.send(file=discord.File(output_path)) def setup(bot): bot.add_cog(MusicRace(bot))

38.9

225

0.574165

7943e581c7b4cb8342d4adc829fca8ec929a1b99

78,418

py

Python

libs/mpfit.py

richardseifert/Hydra_pipeline

a31d782219359bae7fa82fa9b081fb72bef69fce

[ "MIT" ]

1

2017-11-04T15:08:42.000Z

libs/mpfit.py

richardseifert/Hydra_pipeline

a31d782219359bae7fa82fa9b081fb72bef69fce

[ "MIT" ]

1

2018-11-05T17:28:58.000Z

2018-11-05T18:20:00.000Z

libs/mpfit.py

richardseifert/Hydra_pipeline

a31d782219359bae7fa82fa9b081fb72bef69fce

[ "MIT" ]

null

""" Perform Levenberg-Marquardt least-squares minimization, based on MINPACK-1. AUTHORS The original version of this software, called LMFIT, was written in FORTRAN as part of the MINPACK-1 package by XXX. Craig Markwardt converted the FORTRAN code to IDL. The information for the IDL version is: Craig B. Markwardt, NASA/GSFC Code 662, Greenbelt, MD 20770 [email protected] UPDATED VERSIONs can be found on my WEB PAGE: http://cow.physics.wisc.edu/~craigm/idl/idl.html Mark Rivers created this Python version from Craig's IDL version. Mark Rivers, University of Chicago Building 434A, Argonne National Laboratory 9700 South Cass Avenue, Argonne, IL 60439 [email protected] Updated versions can be found at http://cars.uchicago.edu/software Sergey Koposov converted the Mark's Python version from Numeric to numpy Sergey Koposov, University of Cambridge, Institute of Astronomy, Madingley road, CB3 0HA, Cambridge, UK [email protected] Updated versions can be found at http://code.google.com/p/astrolibpy/source/browse/trunk/ DESCRIPTION MPFIT uses the Levenberg-Marquardt technique to solve the least-squares problem. In its typical use, MPFIT will be used to fit a user-supplied function (the "model") to user-supplied data points (the "data") by adjusting a set of parameters. MPFIT is based upon MINPACK-1 (LMDIF.F) by More' and collaborators. For example, a researcher may think that a set of observed data points is best modelled with a Gaussian curve. A Gaussian curve is parameterized by its mean, standard deviation and normalization. MPFIT will, within certain constraints, find the set of parameters which best fits the data. The fit is "best" in the least-squares sense; that is, the sum of the weighted squared differences between the model and data is minimized. The Levenberg-Marquardt technique is a particular strategy for iteratively searching for the best fit. This particular implementation is drawn from MINPACK-1 (see NETLIB), and is much faster and more accurate than the version provided in the Scientific Python package in Scientific.Functions.LeastSquares. This version allows upper and lower bounding constraints to be placed on each parameter, or the parameter can be held fixed. The user-supplied Python function should return an array of weighted deviations between model and data. In a typical scientific problem the residuals should be weighted so that each deviate has a gaussian sigma of 1.0. If X represents values of the independent variable, Y represents a measurement for each value of X, and ERR represents the error in the measurements, then the deviates could be calculated as follows: DEVIATES = (Y - F(X)) / ERR where F is the analytical function representing the model. You are recommended to use the convenience functions MPFITFUN and MPFITEXPR, which are driver functions that calculate the deviates for you. If ERR are the 1-sigma uncertainties in Y, then TOTAL( DEVIATES^2 ) will be the total chi-squared value. MPFIT will minimize the chi-square value. The values of X, Y and ERR are passed through MPFIT to the user-supplied function via the FUNCTKW keyword. Simple constraints can be placed on parameter values by using the PARINFO keyword to MPFIT. See below for a description of this keyword. MPFIT does not perform more general optimization tasks. See TNMIN instead. MPFIT is customized, based on MINPACK-1, to the least-squares minimization problem. USER FUNCTION The user must define a function which returns the appropriate values as specified above. The function should return the weighted deviations between the model and the data. It should also return a status flag and an optional partial derivative array. For applications which use finite-difference derivatives -- the default -- the user function should be declared in the following way: def myfunct(p, fjac=None, x=None, y=None, err=None) # Parameter values are passed in "p" # If fjac==None then partial derivatives should not be # computed. It will always be None if MPFIT is called with default # flag. model = F(x, p) # Non-negative status value means MPFIT should continue, negative means # stop the calculation. status = 0 return([status, (y-model)/err] See below for applications with analytical derivatives. The keyword parameters X, Y, and ERR in the example above are suggestive but not required. Any parameters can be passed to MYFUNCT by using the functkw keyword to MPFIT. Use MPFITFUN and MPFITEXPR if you need ideas on how to do that. The function *must* accept a parameter list, P. In general there are no restrictions on the number of dimensions in X, Y or ERR. However the deviates *must* be returned in a one-dimensional Numeric array of type Float. User functions may also indicate a fatal error condition using the status return described above. If status is set to a number between -15 and -1 then MPFIT will stop the calculation and return to the caller. ANALYTIC DERIVATIVES In the search for the best-fit solution, MPFIT by default calculates derivatives numerically via a finite difference approximation. The user-supplied function need not calculate the derivatives explicitly. However, if you desire to compute them analytically, then the AUTODERIVATIVE=0 keyword must be passed to MPFIT. As a practical matter, it is often sufficient and even faster to allow MPFIT to calculate the derivatives numerically, and so AUTODERIVATIVE=0 is not necessary. If AUTODERIVATIVE=0 is used then the user function must check the parameter FJAC, and if FJAC!=None then return the partial derivative array in the return list. def myfunct(p, fjac=None, x=None, y=None, err=None) # Parameter values are passed in "p" # If FJAC!=None then partial derivatives must be comptuer. # FJAC contains an array of len(p), where each entry # is 1 if that parameter is free and 0 if it is fixed. model = F(x, p) Non-negative status value means MPFIT should continue, negative means # stop the calculation. status = 0 if (dojac): pderiv = zeros([len(x), len(p)], Float) for j in range(len(p)): pderiv[:,j] = FGRAD(x, p, j) else: pderiv = None return([status, (y-model)/err, pderiv] where FGRAD(x, p, i) is a user function which must compute the derivative of the model with respect to parameter P[i] at X. When finite differencing is used for computing derivatives (ie, when AUTODERIVATIVE=1), or when MPFIT needs only the errors but not the derivatives the parameter FJAC=None. Derivatives should be returned in the PDERIV array. PDERIV should be an m x n array, where m is the number of data points and n is the number of parameters. dp[i,j] is the derivative at the ith point with respect to the jth parameter. The derivatives with respect to fixed parameters are ignored; zero is an appropriate value to insert for those derivatives. Upon input to the user function, FJAC is set to a vector with the same length as P, with a value of 1 for a parameter which is free, and a value of zero for a parameter which is fixed (and hence no derivative needs to be calculated). If the data is higher than one dimensional, then the *last* dimension should be the parameter dimension. Example: fitting a 50x50 image, "dp" should be 50x50xNPAR. CONSTRAINING PARAMETER VALUES WITH THE PARINFO KEYWORD The behavior of MPFIT can be modified with respect to each parameter to be fitted. A parameter value can be fixed; simple boundary constraints can be imposed; limitations on the parameter changes can be imposed; properties of the automatic derivative can be modified; and parameters can be tied to one another. These properties are governed by the PARINFO structure, which is passed as a keyword parameter to MPFIT. PARINFO should be a list of dictionaries, one list entry for each parameter. Each parameter is associated with one element of the array, in numerical order. The dictionary can have the following keys (none are required, keys are case insensitive): 'value' - the starting parameter value (but see the START_PARAMS parameter for more information). 'fixed' - a boolean value, whether the parameter is to be held fixed or not. Fixed parameters are not varied by MPFIT, but are passed on to MYFUNCT for evaluation. 'limited' - a two-element boolean array. If the first/second element is set, then the parameter is bounded on the lower/upper side. A parameter can be bounded on both sides. Both LIMITED and LIMITS must be given together. 'limits' - a two-element float array. Gives the parameter limits on the lower and upper sides, respectively. Zero, one or two of these values can be set, depending on the values of LIMITED. Both LIMITED and LIMITS must be given together. 'parname' - a string, giving the name of the parameter. The fitting code of MPFIT does not use this tag in any way. However, the default iterfunct will print the parameter name if available. 'step' - the step size to be used in calculating the numerical derivatives. If set to zero, then the step size is computed automatically. Ignored when AUTODERIVATIVE=0. 'mpside' - the sidedness of the finite difference when computing numerical derivatives. This field can take four values: 0 - one-sided derivative computed automatically 1 - one-sided derivative (f(x+h) - f(x) )/h -1 - one-sided derivative (f(x) - f(x-h))/h 2 - two-sided derivative (f(x+h) - f(x-h))/(2*h) Where H is the STEP parameter described above. The "automatic" one-sided derivative method will chose a direction for the finite difference which does not violate any constraints. The other methods do not perform this check. The two-sided method is in principle more precise, but requires twice as many function evaluations. Default: 0. 'mpmaxstep' - the maximum change to be made in the parameter value. During the fitting process, the parameter will never be changed by more than this value in one iteration. A value of 0 indicates no maximum. Default: 0. 'tied' - a string expression which "ties" the parameter to other free or fixed parameters. Any expression involving constants and the parameter array P are permitted. Example: if parameter 2 is always to be twice parameter 1 then use the following: parinfo(2).tied = '2 * p(1)'. Since they are totally constrained, tied parameters are considered to be fixed; no errors are computed for them. [ NOTE: the PARNAME can't be used in expressions. ] 'mpprint' - if set to 1, then the default iterfunct will print the parameter value. If set to 0, the parameter value will not be printed. This tag can be used to selectively print only a few parameter values out of many. Default: 1 (all parameters printed) Future modifications to the PARINFO structure, if any, will involve adding dictionary tags beginning with the two letters "MP". Therefore programmers are urged to avoid using tags starting with the same letters; otherwise they are free to include their own fields within the PARINFO structure, and they will be ignored. PARINFO Example: parinfo = [{'value':0., 'fixed':0, 'limited':[0,0], 'limits':[0.,0.]} for i in range(5)] parinfo[0]['fixed'] = 1 parinfo[4]['limited'][0] = 1 parinfo[4]['limits'][0] = 50. values = [5.7, 2.2, 500., 1.5, 2000.] for i in range(5): parinfo[i]['value']=values[i] A total of 5 parameters, with starting values of 5.7, 2.2, 500, 1.5, and 2000 are given. The first parameter is fixed at a value of 5.7, and the last parameter is constrained to be above 50. EXAMPLE import mpfit import numpy.oldnumeric as Numeric x = arange(100, float) p0 = [5.7, 2.2, 500., 1.5, 2000.] y = ( p[0] + p[1]*[x] + p[2]*[x**2] + p[3]*sqrt(x) + p[4]*log(x)) fa = {'x':x, 'y':y, 'err':err} m = mpfit('myfunct', p0, functkw=fa) print 'status = ', m.status if (m.status <= 0): print 'error message = ', m.errmsg print 'parameters = ', m.params Minimizes sum of squares of MYFUNCT. MYFUNCT is called with the X, Y, and ERR keyword parameters that are given by FUNCTKW. The results can be obtained from the returned object m. THEORY OF OPERATION There are many specific strategies for function minimization. One very popular technique is to use function gradient information to realize the local structure of the function. Near a local minimum the function value can be taylor expanded about x0 as follows: f(x) = f(x0) + f'(x0) . (x-x0) + (1/2) (x-x0) . f''(x0) . (x-x0) ----- --------------- ------------------------------- (1) Order 0th 1st 2nd Here f'(x) is the gradient vector of f at x, and f''(x) is the Hessian matrix of second derivatives of f at x. The vector x is the set of function parameters, not the measured data vector. One can find the minimum of f, f(xm) using Newton's method, and arrives at the following linear equation: f''(x0) . (xm-x0) = - f'(x0) (2) If an inverse can be found for f''(x0) then one can solve for (xm-x0), the step vector from the current position x0 to the new projected minimum. Here the problem has been linearized (ie, the gradient information is known to first order). f''(x0) is symmetric n x n matrix, and should be positive definite. The Levenberg - Marquardt technique is a variation on this theme. It adds an additional diagonal term to the equation which may aid the convergence properties: (f''(x0) + nu I) . (xm-x0) = -f'(x0) (2a) where I is the identity matrix. When nu is large, the overall matrix is diagonally dominant, and the iterations follow steepest descent. When nu is small, the iterations are quadratically convergent. In principle, if f''(x0) and f'(x0) are known then xm-x0 can be determined. However the Hessian matrix is often difficult or impossible to compute. The gradient f'(x0) may be easier to compute, if even by finite difference techniques. So-called quasi-Newton techniques attempt to successively estimate f''(x0) by building up gradient information as the iterations proceed. In the least squares problem there are further simplifications which assist in solving eqn (2). The function to be minimized is a sum of squares: f = Sum(hi^2) (3) where hi is the ith residual out of m residuals as described above. This can be substituted back into eqn (2) after computing the derivatives: f' = 2 Sum(hi hi') f'' = 2 Sum(hi' hj') + 2 Sum(hi hi'') (4) If one assumes that the parameters are already close enough to a minimum, then one typically finds that the second term in f'' is negligible [or, in any case, is too difficult to compute]. Thus, equation (2) can be solved, at least approximately, using only gradient information. In matrix notation, the combination of eqns (2) and (4) becomes: hT' . h' . dx = - hT' . h (5) Where h is the residual vector (length m), hT is its transpose, h' is the Jacobian matrix (dimensions n x m), and dx is (xm-x0). The user function supplies the residual vector h, and in some cases h' when it is not found by finite differences (see MPFIT_FDJAC2, which finds h and hT'). Even if dx is not the best absolute step to take, it does provide a good estimate of the best *direction*, so often a line minimization will occur along the dx vector direction. The method of solution employed by MINPACK is to form the Q . R factorization of h', where Q is an orthogonal matrix such that QT . Q = I, and R is upper right triangular. Using h' = Q . R and the ortogonality of Q, eqn (5) becomes (RT . QT) . (Q . R) . dx = - (RT . QT) . h RT . R . dx = - RT . QT . h (6) R . dx = - QT . h where the last statement follows because R is upper triangular. Here, R, QT and h are known so this is a matter of solving for dx. The routine MPFIT_QRFAC provides the QR factorization of h, with pivoting, and MPFIT_QRSOLV provides the solution for dx. REFERENCES MINPACK-1, Jorge More', available from netlib (www.netlib.org). "Optimization Software Guide," Jorge More' and Stephen Wright, SIAM, *Frontiers in Applied Mathematics*, Number 14. More', Jorge J., "The Levenberg-Marquardt Algorithm: Implementation and Theory," in *Numerical Analysis*, ed. Watson, G. A., Lecture Notes in Mathematics 630, Springer-Verlag, 1977. MODIFICATION HISTORY Translated from MINPACK-1 in FORTRAN, Apr-Jul 1998, CM Copyright (C) 1997-2002, Craig Markwardt This software is provided as is without any warranty whatsoever. Permission to use, copy, modify, and distribute modified or unmodified copies is granted, provided this copyright and disclaimer are included unchanged. Translated from MPFIT (Craig Markwardt's IDL package) to Python, August, 2002. Mark Rivers Converted from Numeric to numpy (Sergey Koposov, July 2008) """ import numpy import types import scipy.linalg.blas # Original FORTRAN documentation # ********** # # subroutine lmdif # # the purpose of lmdif is to minimize the sum of the squares of # m nonlinear functions in n variables by a modification of # the levenberg-marquardt algorithm. the user must provide a # subroutine which calculates the functions. the jacobian is # then calculated by a forward-difference approximation. # # the subroutine statement is # # subroutine lmdif(fcn,m,n,x,fvec,ftol,xtol,gtol,maxfev,epsfcn, # diag,mode,factor,nprint,info,nfev,fjac, # ldfjac,ipvt,qtf,wa1,wa2,wa3,wa4) # # where # # fcn is the name of the user-supplied subroutine which # calculates the functions. fcn must be declared # in an external statement in the user calling # program, and should be written as follows. # # subroutine fcn(m,n,x,fvec,iflag) # integer m,n,iflag # double precision x(n),fvec(m) # ---------- # calculate the functions at x and # return this vector in fvec. # ---------- # return # end # # the value of iflag should not be changed by fcn unless # the user wants to terminate execution of lmdif. # in this case set iflag to a negative integer. # # m is a positive integer input variable set to the number # of functions. # # n is a positive integer input variable set to the number # of variables. n must not exceed m. # # x is an array of length n. on input x must contain # an initial estimate of the solution vector. on output x # contains the final estimate of the solution vector. # # fvec is an output array of length m which contains # the functions evaluated at the output x. # # ftol is a nonnegative input variable. termination # occurs when both the actual and predicted relative # reductions in the sum of squares are at most ftol. # therefore, ftol measures the relative error desired # in the sum of squares. # # xtol is a nonnegative input variable. termination # occurs when the relative error between two consecutive # iterates is at most xtol. therefore, xtol measures the # relative error desired in the approximate solution. # # gtol is a nonnegative input variable. termination # occurs when the cosine of the angle between fvec and # any column of the jacobian is at most gtol in absolute # value. therefore, gtol measures the orthogonality # desired between the function vector and the columns # of the jacobian. # # maxfev is a positive integer input variable. termination # occurs when the number of calls to fcn is at least # maxfev by the end of an iteration. # # epsfcn is an input variable used in determining a suitable # step length for the forward-difference approximation. this # approximation assumes that the relative errors in the # functions are of the order of epsfcn. if epsfcn is less # than the machine precision, it is assumed that the relative # errors in the functions are of the order of the machine # precision. # # diag is an array of length n. if mode = 1 (see # below), diag is internally set. if mode = 2, diag # must contain positive entries that serve as # multiplicative scale factors for the variables. # # mode is an integer input variable. if mode = 1, the # variables will be scaled internally. if mode = 2, # the scaling is specified by the input diag. other # values of mode are equivalent to mode = 1. # # factor is a positive input variable used in determining the # initial step bound. this bound is set to the product of # factor and the euclidean norm of diag*x if nonzero, or else # to factor itself. in most cases factor should lie in the # interval (.1,100.). 100. is a generally recommended value. # # nprint is an integer input variable that enables controlled # printing of iterates if it is positive. in this case, # fcn is called with iflag = 0 at the beginning of the first # iteration and every nprint iterations thereafter and # immediately prior to return, with x and fvec available # for printing. if nprint is not positive, no special calls # of fcn with iflag = 0 are made. # # info is an integer output variable. if the user has # terminated execution, info is set to the (negative) # value of iflag. see description of fcn. otherwise, # info is set as follows. # # info = 0 improper input parameters. # # info = 1 both actual and predicted relative reductions # in the sum of squares are at most ftol. # # info = 2 relative error between two consecutive iterates # is at most xtol. # # info = 3 conditions for info = 1 and info = 2 both hold. # # info = 4 the cosine of the angle between fvec and any # column of the jacobian is at most gtol in # absolute value. # # info = 5 number of calls to fcn has reached or # exceeded maxfev. # # info = 6 ftol is too small. no further reduction in # the sum of squares is possible. # # info = 7 xtol is too small. no further improvement in # the approximate solution x is possible. # # info = 8 gtol is too small. fvec is orthogonal to the # columns of the jacobian to machine precision. # # nfev is an integer output variable set to the number of # calls to fcn. # # fjac is an output m by n array. the upper n by n submatrix # of fjac contains an upper triangular matrix r with # diagonal elements of nonincreasing magnitude such that # # t t t # p *(jac *jac)*p = r *r, # # where p is a permutation matrix and jac is the final # calculated jacobian. column j of p is column ipvt(j) # (see below) of the identity matrix. the lower trapezoidal # part of fjac contains information generated during # the computation of r. # # ldfjac is a positive integer input variable not less than m # which specifies the leading dimension of the array fjac. # # ipvt is an integer output array of length n. ipvt # defines a permutation matrix p such that jac*p = q*r, # where jac is the final calculated jacobian, q is # orthogonal (not stored), and r is upper triangular # with diagonal elements of nonincreasing magnitude. # column j of p is column ipvt(j) of the identity matrix. # # qtf is an output array of length n which contains # the first n elements of the vector (q transpose)*fvec. # # wa1, wa2, and wa3 are work arrays of length n. # # wa4 is a work array of length m. # # subprograms called # # user-supplied ...... fcn # # minpack-supplied ... dpmpar,enorm,fdjac2,,qrfac # # fortran-supplied ... dabs,dmax1,dmin1,dsqrt,mod # # argonne national laboratory. minpack project. march 1980. # burton s. garbow, kenneth e. hillstrom, jorge j. more # # ********** class mpfit: blas_enorm32, = scipy.linalg.blas.get_blas_funcs(['nrm2'],numpy.array([0],dtype=numpy.float32)) blas_enorm64, = scipy.linalg.blas.get_blas_funcs(['nrm2'],numpy.array([0],dtype=numpy.float64)) def __init__(self, fcn, xall=None, functkw={}, parinfo=None, ftol=1.e-10, xtol=1.e-10, gtol=1.e-10, damp=0., maxiter=200, factor=100., nprint=1, iterfunct='default', iterkw={}, nocovar=0, rescale=0, autoderivative=1, quiet=0, diag=None, epsfcn=None, debug=0): """ Inputs: fcn: The function to be minimized. The function should return the weighted deviations between the model and the data, as described above. xall: An array of starting values for each of the parameters of the model. The number of parameters should be fewer than the number of measurements. This parameter is optional if the parinfo keyword is used (but see parinfo). The parinfo keyword provides a mechanism to fix or constrain individual parameters. Keywords: autoderivative: If this is set, derivatives of the function will be computed automatically via a finite differencing procedure. If not set, then fcn must provide the (analytical) derivatives. Default: set (=1) NOTE: to supply your own analytical derivatives, explicitly pass autoderivative=0 ftol: A nonnegative input variable. Termination occurs when both the actual and predicted relative reductions in the sum of squares are at most ftol (and status is accordingly set to 1 or 3). Therefore, ftol measures the relative error desired in the sum of squares. Default: 1E-10 functkw: A dictionary which contains the parameters to be passed to the user-supplied function specified by fcn via the standard Python keyword dictionary mechanism. This is the way you can pass additional data to your user-supplied function without using global variables. Consider the following example: if functkw = {'xval':[1.,2.,3.], 'yval':[1.,4.,9.], 'errval':[1.,1.,1.] } then the user supplied function should be declared like this: def myfunct(p, fjac=None, xval=None, yval=None, errval=None): Default: {} No extra parameters are passed to the user-supplied function. gtol: A nonnegative input variable. Termination occurs when the cosine of the angle between fvec and any column of the jacobian is at most gtol in absolute value (and status is accordingly set to 4). Therefore, gtol measures the orthogonality desired between the function vector and the columns of the jacobian. Default: 1e-10 iterkw: The keyword arguments to be passed to iterfunct via the dictionary keyword mechanism. This should be a dictionary and is similar in operation to FUNCTKW. Default: {} No arguments are passed. iterfunct: The name of a function to be called upon each NPRINT iteration of the MPFIT routine. It should be declared in the following way: def iterfunct(myfunct, p, iter, fnorm, functkw=None, parinfo=None, quiet=0, dof=None, [iterkw keywords here]) # perform custom iteration update iterfunct must accept all three keyword parameters (FUNCTKW, PARINFO and QUIET). myfunct: The user-supplied function to be minimized, p: The current set of model parameters iter: The iteration number functkw: The arguments to be passed to myfunct. fnorm: The chi-squared value. quiet: Set when no textual output should be printed. dof: The number of degrees of freedom, normally the number of points less the number of free parameters. See below for documentation of parinfo. In implementation, iterfunct can perform updates to the terminal or graphical user interface, to provide feedback while the fit proceeds. If the fit is to be stopped for any reason, then iterfunct should return a a status value between -15 and -1. Otherwise it should return None (e.g. no return statement) or 0. In principle, iterfunct should probably not modify the parameter values, because it may interfere with the algorithm's stability. In practice it is allowed. Default: an internal routine is used to print the parameter values. Set iterfunct=None if there is no user-defined routine and you don't want the internal default routine be called. maxiter: The maximum number of iterations to perform. If the number is exceeded, then the status value is set to 5 and MPFIT returns. Default: 200 iterations nocovar: Set this keyword to prevent the calculation of the covariance matrix before returning (see COVAR) Default: clear (=0) The covariance matrix is returned nprint: The frequency with which iterfunct is called. A value of 1 indicates that iterfunct is called with every iteration, while 2 indicates every other iteration, etc. Note that several Levenberg-Marquardt attempts can be made in a single iteration. Default value: 1 parinfo Provides a mechanism for more sophisticated constraints to be placed on parameter values. When parinfo is not passed, then it is assumed that all parameters are free and unconstrained. Values in parinfo are never modified during a call to MPFIT. See description above for the structure of PARINFO. Default value: None All parameters are free and unconstrained. quiet: Set this keyword when no textual output should be printed by MPFIT damp: A scalar number, indicating the cut-off value of residuals where "damping" will occur. Residuals with magnitudes greater than this number will be replaced by their hyperbolic tangent. This partially mitigates the so-called large residual problem inherent in least-squares solvers (as for the test problem CURVI, http://www.maxthis.com/curviex.htm). A value of 0 indicates no damping. Default: 0 Note: DAMP doesn't work with autoderivative=0 xtol: A nonnegative input variable. Termination occurs when the relative error between two consecutive iterates is at most xtol (and status is accordingly set to 2 or 3). Therefore, xtol measures the relative error desired in the approximate solution. Default: 1E-10 Outputs: Returns an object of type mpfit. The results are attributes of this class, e.g. mpfit.status, mpfit.errmsg, mpfit.params, npfit.niter, mpfit.covar. .status An integer status code is returned. All values greater than zero can represent success (however .status == 5 may indicate failure to converge). It can have one of the following values: -16 A parameter or function value has become infinite or an undefined number. This is usually a consequence of numerical overflow in the user's model function, which must be avoided. -15 to -1 These are error codes that either MYFUNCT or iterfunct may return to terminate the fitting process. Values from -15 to -1 are reserved for the user functions and will not clash with MPFIT. 0 Improper input parameters. 1 Both actual and predicted relative reductions in the sum of squares are at most ftol. 2 Relative error between two consecutive iterates is at most xtol 3 Conditions for status = 1 and status = 2 both hold. 4 The cosine of the angle between fvec and any column of the jacobian is at most gtol in absolute value. 5 The maximum number of iterations has been reached. 6 ftol is too small. No further reduction in the sum of squares is possible. 7 xtol is too small. No further improvement in the approximate solution x is possible. 8 gtol is too small. fvec is orthogonal to the columns of the jacobian to machine precision. .fnorm The value of the summed squared residuals for the returned parameter values. .covar The covariance matrix for the set of parameters returned by MPFIT. The matrix is NxN where N is the number of parameters. The square root of the diagonal elements gives the formal 1-sigma statistical errors on the parameters if errors were treated "properly" in fcn. Parameter errors are also returned in .perror. To compute the correlation matrix, pcor, use this example: cov = mpfit.covar pcor = cov * 0. for i in range(n): for j in range(n): pcor[i,j] = cov[i,j]/sqrt(cov[i,i]*cov[j,j]) If nocovar is set or MPFIT terminated abnormally, then .covar is set to a scalar with value None. .errmsg A string error or warning message is returned. .nfev The number of calls to MYFUNCT performed. .niter The number of iterations completed. .perror The formal 1-sigma errors in each parameter, computed from the covariance matrix. If a parameter is held fixed, or if it touches a boundary, then the error is reported as zero. If the fit is unweighted (i.e. no errors were given, or the weights were uniformly set to unity), then .perror will probably not represent the true parameter uncertainties. *If* you can assume that the true reduced chi-squared value is unity -- meaning that the fit is implicitly assumed to be of good quality -- then the estimated parameter uncertainties can be computed by scaling .perror by the measured chi-squared value. dof = len(x) - len(mpfit.params) # deg of freedom # scaled uncertainties pcerror = mpfit.perror * sqrt(mpfit.fnorm / dof) """ self.niter = 0 self.params = None self.covar = None self.perror = None self.status = 0 # Invalid input flag set while we check inputs self.debug = debug self.errmsg = '' self.nfev = 0 self.damp = damp self.dof=0 if fcn==None: self.errmsg = "Usage: parms = mpfit('myfunt', ... )" return if iterfunct == 'default': iterfunct = self.defiter # Parameter damping doesn't work when user is providing their own # gradients. if (self.damp != 0) and (autoderivative == 0): self.errmsg = 'ERROR: keywords DAMP and AUTODERIVATIVE are mutually exclusive' return # Parameters can either be stored in parinfo, or x. x takes precedence if it exists if (xall is None) and (parinfo is None): self.errmsg = 'ERROR: must pass parameters in P or PARINFO' return # Be sure that PARINFO is of the right type if parinfo is not None: if type(parinfo) != types.ListType: self.errmsg = 'ERROR: PARINFO must be a list of dictionaries.' return else: if type(parinfo[0]) != types.DictionaryType: self.errmsg = 'ERROR: PARINFO must be a list of dictionaries.' return if ((xall is not None) and (len(xall) != len(parinfo))): self.errmsg = 'ERROR: number of elements in PARINFO and P must agree' return # If the parameters were not specified at the command line, then # extract them from PARINFO if xall is None: xall = self.parinfo(parinfo, 'value') if xall is None: self.errmsg = 'ERROR: either P or PARINFO(*)["value"] must be supplied.' return # Make sure parameters are numpy arrays xall = numpy.asarray(xall) # In the case if the xall is not float or if is float but has less # than 64 bits we do convert it into double if xall.dtype.kind != 'f' or xall.dtype.itemsize<=4: xall = xall.astype(numpy.float) npar = len(xall) self.fnorm = -1. fnorm1 = -1. # TIED parameters? ptied = self.parinfo(parinfo, 'tied', default='', n=npar) self.qanytied = 0 for i in range(npar): ptied[i] = ptied[i].strip() if ptied[i] != '': self.qanytied = 1 self.ptied = ptied # FIXED parameters ? pfixed = self.parinfo(parinfo, 'fixed', default=0, n=npar) pfixed = (pfixed == 1) for i in range(npar): pfixed[i] = pfixed[i] or (ptied[i] != '') # Tied parameters are also effectively fixed # Finite differencing step, absolute and relative, and sidedness of deriv. step = self.parinfo(parinfo, 'step', default=0., n=npar) dstep = self.parinfo(parinfo, 'relstep', default=0., n=npar) dside = self.parinfo(parinfo, 'mpside', default=0, n=npar) # Maximum and minimum steps allowed to be taken in one iteration maxstep = self.parinfo(parinfo, 'mpmaxstep', default=0., n=npar) minstep = self.parinfo(parinfo, 'mpminstep', default=0., n=npar) qmin = minstep != 0 qmin[:] = False # Remove minstep for now!! qmax = maxstep != 0 if numpy.any(qmin & qmax & (maxstep<minstep)): self.errmsg = 'ERROR: MPMINSTEP is greater than MPMAXSTEP' return wh = (numpy.nonzero((qmin!=0.) | (qmax!=0.)))[0] qminmax = len(wh > 0) # Finish up the free parameters ifree = (numpy.nonzero(pfixed != 1))[0] nfree = len(ifree) if nfree == 0: self.errmsg = 'ERROR: no free parameters' return # Compose only VARYING parameters self.params = xall.copy() # self.params is the set of parameters to be returned x = self.params[ifree] # x is the set of free parameters # LIMITED parameters ? limited = self.parinfo(parinfo, 'limited', default=[0,0], n=npar) limits = self.parinfo(parinfo, 'limits', default=[0.,0.], n=npar) if (limited is not None) and (limits is not None): # Error checking on limits in parinfo if numpy.any((limited[:,0] & (xall < limits[:,0])) | (limited[:,1] & (xall > limits[:,1]))): self.errmsg = 'ERROR: parameters are not within PARINFO limits' return if numpy.any((limited[:,0] & limited[:,1]) & (limits[:,0] >= limits[:,1]) & (pfixed == 0)): self.errmsg = 'ERROR: PARINFO parameter limits are not consistent' return # Transfer structure values to local variables qulim = (limited[:,1])[ifree] ulim = (limits [:,1])[ifree] qllim = (limited[:,0])[ifree] llim = (limits [:,0])[ifree] if numpy.any((qulim!=0.) | (qllim!=0.)): qanylim = 1 else: qanylim = 0 else: # Fill in local variables with dummy values qulim = numpy.zeros(nfree) ulim = x * 0. qllim = qulim llim = x * 0. qanylim = 0 n = len(x) # Check input parameters for errors if (n < 0) or (ftol <= 0) or (xtol <= 0) or (gtol <= 0) \ or (maxiter < 0) or (factor <= 0): self.errmsg = 'ERROR: input keywords are inconsistent' return if rescale != 0: self.errmsg = 'ERROR: DIAG parameter scales are inconsistent' if len(diag) < n: return if numpy.any(diag <= 0): return self.errmsg = '' [self.status, fvec] = self.call(fcn, self.params, functkw) if self.status < 0: self.errmsg = 'ERROR: first call to "'+str(fcn)+'" failed' return # If the returned fvec has more than four bits I assume that we have # double precision # It is important that the machar is determined by the precision of # the returned value, not by the precision of the input array if numpy.array([fvec]).dtype.itemsize>4: self.machar = machar(double=1) self.blas_enorm = mpfit.blas_enorm64 else: self.machar = machar(double=0) self.blas_enorm = mpfit.blas_enorm32 machep = self.machar.machep m = len(fvec) if m < n: self.errmsg = 'ERROR: number of parameters must not exceed data' return self.dof = m-nfree self.fnorm = self.enorm(fvec) # Initialize Levelberg-Marquardt parameter and iteration counter par = 0. self.niter = 1 qtf = x * 0. self.status = 0 # Beginning of the outer loop while(1): # If requested, call fcn to enable printing of iterates self.params[ifree] = x if self.qanytied: self.params = self.tie(self.params, ptied) if (nprint > 0) and (iterfunct is not None): if ((self.niter-1) % nprint) == 0: mperr = 0 xnew0 = self.params.copy() dof = numpy.max([len(fvec) - len(x), 0]) status = iterfunct(fcn, self.params, self.niter, self.fnorm**2, functkw=functkw, parinfo=parinfo, quiet=quiet, dof=dof, **iterkw) if status is not None: self.status = status # Check for user termination if self.status < 0: self.errmsg = 'WARNING: premature termination by ' + str(iterfunct) return # If parameters were changed (grrr..) then re-tie if numpy.max(numpy.abs(xnew0-self.params)) > 0: if self.qanytied: self.params = self.tie(self.params, ptied) x = self.params[ifree] # Calculate the jacobian matrix self.status = 2 catch_msg = 'calling MPFIT_FDJAC2' fjac = self.fdjac2(fcn, x, fvec, step, qulim, ulim, dside, epsfcn=epsfcn, autoderivative=autoderivative, dstep=dstep, functkw=functkw, ifree=ifree, xall=self.params) if fjac is None: self.errmsg = 'WARNING: premature termination by FDJAC2' return # Determine if any of the parameters are pegged at the limits if qanylim: catch_msg = 'zeroing derivatives of pegged parameters' whlpeg = (numpy.nonzero(qllim & (x == llim)))[0] nlpeg = len(whlpeg) whupeg = (numpy.nonzero(qulim & (x == ulim)))[0] nupeg = len(whupeg) # See if any "pegged" values should keep their derivatives if nlpeg > 0: # Total derivative of sum wrt lower pegged parameters for i in range(nlpeg): sum0 = sum(fvec * fjac[:,whlpeg[i]]) if sum0 > 0: fjac[:,whlpeg[i]] = 0 if nupeg > 0: # Total derivative of sum wrt upper pegged parameters for i in range(nupeg): sum0 = sum(fvec * fjac[:,whupeg[i]]) if sum0 < 0: fjac[:,whupeg[i]] = 0 # Compute the QR factorization of the jacobian [fjac, ipvt, wa1, wa2] = self.qrfac(fjac, pivot=1) # On the first iteration if "diag" is unspecified, scale # according to the norms of the columns of the initial jacobian catch_msg = 'rescaling diagonal elements' if self.niter == 1: if (rescale==0) or (len(diag) < n): diag = wa2.copy() diag[diag == 0] = 1. # On the first iteration, calculate the norm of the scaled x # and initialize the step bound delta wa3 = diag * x xnorm = self.enorm(wa3) delta = factor*xnorm if delta == 0.: delta = factor # Form (q transpose)*fvec and store the first n components in qtf catch_msg = 'forming (q transpose)*fvec' wa4 = fvec.copy() for j in range(n): lj = ipvt[j] temp3 = fjac[j,lj] if temp3 != 0: fj = fjac[j:,lj] wj = wa4[j:] # *** optimization wa4(j:*) wa4[j:] = wj - fj * sum(fj*wj) / temp3 fjac[j,lj] = wa1[j] qtf[j] = wa4[j] # From this point on, only the square matrix, consisting of the # triangle of R, is needed. fjac = fjac[0:n, 0:n] fjac.shape = [n, n] temp = fjac.copy() for i in range(n): temp[:,i] = fjac[:, ipvt[i]] fjac = temp.copy() # Check for overflow. This should be a cheap test here since FJAC # has been reduced to a (small) square matrix, and the test is # O(N^2). #wh = where(finite(fjac) EQ 0, ct) #if ct GT 0 then goto, FAIL_OVERFLOW # Compute the norm of the scaled gradient catch_msg = 'computing the scaled gradient' gnorm = 0. if self.fnorm != 0: for j in range(n): l = ipvt[j] if wa2[l] != 0: sum0 = sum(fjac[0:j+1,j]*qtf[0:j+1])/self.fnorm gnorm = numpy.max([gnorm,numpy.abs(sum0/wa2[l])]) # Test for convergence of the gradient norm if gnorm <= gtol: self.status = 4 break if maxiter == 0: self.status = 5 break # Rescale if necessary if rescale == 0: diag = numpy.choose(diag>wa2, (wa2, diag)) # Beginning of the inner loop while(1): # Determine the levenberg-marquardt parameter catch_msg = 'calculating LM parameter (MPFIT_)' [fjac, par, wa1, wa2] = self.lmpar(fjac, ipvt, diag, qtf, delta, wa1, wa2, par=par) # Store the direction p and x+p. Calculate the norm of p wa1 = -wa1 if (qanylim == 0) and (qminmax == 0): # No parameter limits, so just move to new position WA2 alpha = 1. wa2 = x + wa1 else: # Respect the limits. If a step were to go out of bounds, then # we should take a step in the same direction but shorter distance. # The step should take us right to the limit in that case. alpha = 1. if qanylim: # Do not allow any steps out of bounds catch_msg = 'checking for a step out of bounds' if nlpeg > 0: wa1[whlpeg] = numpy.clip( wa1[whlpeg], 0., numpy.max(wa1)) if nupeg > 0: wa1[whupeg] = numpy.clip(wa1[whupeg], numpy.min(wa1), 0.) dwa1 = numpy.abs(wa1) > machep whl = (numpy.nonzero(((dwa1!=0.) & qllim) & ((x + wa1) < llim)))[0] if len(whl) > 0: t = ((llim[whl] - x[whl]) / wa1[whl]) alpha = numpy.min([alpha, numpy.min(t)]) whu = (numpy.nonzero(((dwa1!=0.) & qulim) & ((x + wa1) > ulim)))[0] if len(whu) > 0: t = ((ulim[whu] - x[whu]) / wa1[whu]) alpha = numpy.min([alpha, numpy.min(t)]) # Obey any max step values. if qminmax: nwa1 = wa1 * alpha whmax = (numpy.nonzero((qmax != 0.) & (maxstep > 0)))[0] if len(whmax) > 0: mrat = numpy.max(numpy.abs(nwa1[whmax]) / numpy.abs(maxstep[ifree[whmax]])) if mrat > 1: alpha = alpha / mrat # Scale the resulting vector wa1 = wa1 * alpha wa2 = x + wa1 # Adjust the final output values. If the step put us exactly # on a boundary, make sure it is exact. sgnu = (ulim >= 0) * 2. - 1. sgnl = (llim >= 0) * 2. - 1. # Handles case of # ... nonzero *LIM ... ...zero * LIM ulim1 = ulim * (1 - sgnu * machep) - (ulim == 0) * machep llim1 = llim * (1 + sgnl * machep) + (llim == 0) * machep wh = (numpy.nonzero((qulim!=0) & (wa2 >= ulim1)))[0] if len(wh) > 0: wa2[wh] = ulim[wh] wh = (numpy.nonzero((qllim!=0.) & (wa2 <= llim1)))[0] if len(wh) > 0: wa2[wh] = llim[wh] # endelse wa3 = diag * wa1 pnorm = self.enorm(wa3) # On the first iteration, adjust the initial step bound if self.niter == 1: delta = numpy.min([delta,pnorm]) self.params[ifree] = wa2 # Evaluate the function at x+p and calculate its norm mperr = 0 catch_msg = 'calling '+str(fcn) [self.status, wa4] = self.call(fcn, self.params, functkw) if self.status < 0: self.errmsg = 'WARNING: premature termination by "'+fcn+'"' return fnorm1 = self.enorm(wa4) # Compute the scaled actual reduction catch_msg = 'computing convergence criteria' actred = -1. if (0.1 * fnorm1) < self.fnorm: actred = - (fnorm1/self.fnorm)**2 + 1. # Compute the scaled predicted reduction and the scaled directional # derivative for j in range(n): wa3[j] = 0 wa3[0:j+1] = wa3[0:j+1] + fjac[0:j+1,j]*wa1[ipvt[j]] # Remember, alpha is the fraction of the full LM step actually # taken temp1 = self.enorm(alpha*wa3)/self.fnorm temp2 = (numpy.sqrt(alpha*par)*pnorm)/self.fnorm prered = temp1*temp1 + (temp2*temp2)/0.5 dirder = -(temp1*temp1 + temp2*temp2) # Compute the ratio of the actual to the predicted reduction. ratio = 0. if prered != 0: ratio = actred/prered # Update the step bound if ratio <= 0.25: if actred >= 0: temp = .5 else: temp = .5*dirder/(dirder + .5*actred) if ((0.1*fnorm1) >= self.fnorm) or (temp < 0.1): temp = 0.1 delta = temp*numpy.min([delta,pnorm/0.1]) par = par/temp else: if (par == 0) or (ratio >= 0.75): delta = pnorm/.5 par = .5*par # Test for successful iteration if ratio >= 0.0001: # Successful iteration. Update x, fvec, and their norms x = wa2 wa2 = diag * x fvec = wa4 xnorm = self.enorm(wa2) self.fnorm = fnorm1 self.niter = self.niter + 1 # Tests for convergence if (numpy.abs(actred) <= ftol) and (prered <= ftol) \ and (0.5 * ratio <= 1): self.status = 1 if delta <= xtol*xnorm: self.status = 2 if (numpy.abs(actred) <= ftol) and (prered <= ftol) \ and (0.5 * ratio <= 1) and (self.status == 2): self.status = 3 if self.status != 0: break # Tests for termination and stringent tolerances if self.niter >= maxiter: self.status = 5 if (numpy.abs(actred) <= machep) and (prered <= machep) \ and (0.5*ratio <= 1): self.status = 6 if delta <= machep*xnorm: self.status = 7 if gnorm <= machep: self.status = 8 if self.status != 0: break # End of inner loop. Repeat if iteration unsuccessful if ratio >= 0.0001: break # Check for over/underflow if ~numpy.all(numpy.isfinite(wa1) & numpy.isfinite(wa2) & \ numpy.isfinite(x)) or ~numpy.isfinite(ratio): errmsg = ('''ERROR: parameter or function value(s) have become 'infinite; check model function for over- 'and underflow''') self.status = -16 break #wh = where(finite(wa1) EQ 0 OR finite(wa2) EQ 0 OR finite(x) EQ 0, ct) #if ct GT 0 OR finite(ratio) EQ 0 then begin if self.status != 0: break; # End of outer loop. catch_msg = 'in the termination phase' # Termination, either normal or user imposed. if len(self.params) == 0: return if nfree == 0: self.params = xall.copy() else: self.params[ifree] = x if (nprint > 0) and (self.status > 0): catch_msg = 'calling ' + str(fcn) [status, fvec] = self.call(fcn, self.params, functkw) catch_msg = 'in the termination phase' self.fnorm = self.enorm(fvec) if (self.fnorm is not None) and (fnorm1 is not None): self.fnorm = numpy.max([self.fnorm, fnorm1]) self.fnorm = self.fnorm**2. self.covar = None self.perror = None # (very carefully) set the covariance matrix COVAR if (self.status > 0) and (nocovar==0) and (n is not None) \ and (fjac is not None) and (ipvt is not None): sz = fjac.shape if (n > 0) and (sz[0] >= n) and (sz[1] >= n) \ and (len(ipvt) >= n): catch_msg = 'computing the covariance matrix' cv = self.calc_covar(fjac[0:n,0:n], ipvt[0:n]) cv.shape = [n, n] nn = len(xall) # Fill in actual covariance matrix, accounting for fixed # parameters. self.covar = numpy.zeros([nn, nn], dtype=float) for i in range(n): self.covar[ifree,ifree[i]] = cv[:,i] # Compute errors in parameters catch_msg = 'computing parameter errors' self.perror = numpy.zeros(nn, dtype=float) d = numpy.diagonal(self.covar) wh = (numpy.nonzero(d >= 0))[0] if len(wh) > 0: self.perror[wh] = numpy.sqrt(d[wh]) return def __str__(self): return {'params': self.params, 'niter': self.niter, 'params': self.params, 'covar': self.covar, 'perror': self.perror, 'status': self.status, 'debug': self.debug, 'errmsg': self.errmsg, 'nfev': self.nfev, 'damp': self.damp #,'machar':self.machar }.__str__() # Default procedure to be called every iteration. It simply prints # the parameter values. def defiter(self, fcn, x, iter, fnorm=None, functkw=None, quiet=0, iterstop=None, parinfo=None, format=None, pformat='%.10g', dof=1): if self.debug: print 'Entering defiter...' if quiet: return if fnorm is None: [status, fvec] = self.call(fcn, x, functkw) fnorm = self.enorm(fvec)**2 # Determine which parameters to print nprint = len(x) print "Iter ", ('%6i' % iter)," CHI-SQUARE = ",('%.10g' % fnorm)," DOF = ", ('%i' % dof) for i in range(nprint): if (parinfo is not None) and (parinfo[i].has_key('parname')): p = ' ' + parinfo[i]['parname'] + ' = ' else: p = ' P' + str(i) + ' = ' if (parinfo is not None) and (parinfo[i].has_key('mpprint')): iprint = parinfo[i]['mpprint'] else: iprint = 1 if iprint: print p + (pformat % x[i]) + ' ' return 0 # DO_ITERSTOP: # if keyword_set(iterstop) then begin # k = get_kbrd(0) # if k EQ string(byte(7)) then begin # message, 'WARNING: minimization not complete', /info # print, 'Do you want to terminate this procedure? (y/n)', $ # format='(A,$)' # k = '' # read, k # if strupcase(strmid(k,0,1)) EQ 'Y' then begin # message, 'WARNING: Procedure is terminating.', /info # mperr = -1 # endif # endif # endif # Procedure to parse the parameter values in PARINFO, which is a list of dictionaries def parinfo(self, parinfo=None, key='a', default=None, n=0): if self.debug: print 'Entering parinfo...' if (n == 0) and (parinfo is not None): n = len(parinfo) if n == 0: values = default return values values = [] for i in range(n): if (parinfo is not None) and (parinfo[i].has_key(key)): values.append(parinfo[i][key]) else: values.append(default) # Convert to numeric arrays if possible test = default if type(default) == types.ListType: test=default[0] if isinstance(test, types.IntType): values = numpy.asarray(values, int) elif isinstance(test, types.FloatType): values = numpy.asarray(values, float) return values # Call user function or procedure, with _EXTRA or not, with # derivatives or not. def call(self, fcn, x, functkw, fjac=None): if self.debug: print 'Entering call...' if self.qanytied: x = self.tie(x, self.ptied) self.nfev = self.nfev + 1 if fjac is None: [status, f] = fcn(x, fjac=fjac, **functkw) if self.damp > 0: # Apply the damping if requested. This replaces the residuals # with their hyperbolic tangent. Thus residuals larger than # DAMP are essentially clipped. f = numpy.tanh(f/self.damp) return [status, f] else: return fcn(x, fjac=fjac, **functkw) def enorm(self, vec): ans = self.blas_enorm(vec) return ans def fdjac2(self, fcn, x, fvec, step=None, ulimited=None, ulimit=None, dside=None, epsfcn=None, autoderivative=1, functkw=None, xall=None, ifree=None, dstep=None): if self.debug: print 'Entering fdjac2...' machep = self.machar.machep if epsfcn is None: epsfcn = machep if xall is None: xall = x if ifree is None: ifree = numpy.arange(len(xall)) if step is None: step = x * 0. nall = len(xall) eps = numpy.sqrt(numpy.max([epsfcn, machep])) m = len(fvec) n = len(x) # Compute analytical derivative if requested if autoderivative == 0: mperr = 0 fjac = numpy.zeros(nall, dtype=float) fjac[ifree] = 1.0 # Specify which parameters need derivatives [status, fp] = self.call(fcn, xall, functkw, fjac=fjac) if len(fjac) != m*nall: print 'ERROR: Derivative matrix was not computed properly.' return None # This definition is consistent with CURVEFIT # Sign error found (thanks Jesus Fernandez <[email protected]>) fjac.shape = [m,nall] fjac = -fjac # Select only the free parameters if len(ifree) < nall: fjac = fjac[:,ifree] fjac.shape = [m, n] return fjac fjac = numpy.zeros([m, n], dtype=float) h = eps * numpy.abs(x) # if STEP is given, use that # STEP includes the fixed parameters if step is not None: stepi = step[ifree] wh = (numpy.nonzero(stepi > 0))[0] if len(wh) > 0: h[wh] = stepi[wh] # if relative step is given, use that # DSTEP includes the fixed parameters if len(dstep) > 0: dstepi = dstep[ifree] wh = (numpy.nonzero(dstepi > 0))[0] if len(wh) > 0: h[wh] = numpy.abs(dstepi[wh]*x[wh]) # In case any of the step values are zero h[h == 0] = eps # Reverse the sign of the step if we are up against the parameter # limit, or if the user requested it. # DSIDE includes the fixed parameters (ULIMITED/ULIMIT have only # varying ones) mask = dside[ifree] == -1 if len(ulimited) > 0 and len(ulimit) > 0: mask = (mask | ((ulimited!=0) & (x > ulimit-h))) wh = (numpy.nonzero(mask))[0] if len(wh) > 0: h[wh] = - h[wh] # Loop through parameters, computing the derivative for each for j in range(n): xp = xall.copy() xp[ifree[j]] = xp[ifree[j]] + h[j] [status, fp] = self.call(fcn, xp, functkw) if status < 0: return None if numpy.abs(dside[ifree[j]]) <= 1: # COMPUTE THE ONE-SIDED DERIVATIVE # Note optimization fjac(0:*,j) fjac[0:,j] = (fp-fvec)/h[j] else: # COMPUTE THE TWO-SIDED DERIVATIVE xp[ifree[j]] = xall[ifree[j]] - h[j] mperr = 0 [status, fm] = self.call(fcn, xp, functkw) if status < 0: return None # Note optimization fjac(0:*,j) fjac[0:,j] = (fp-fm)/(2*h[j]) return fjac # Original FORTRAN documentation # ********** # # subroutine qrfac # # this subroutine uses householder transformations with column # pivoting (optional) to compute a qr factorization of the # m by n matrix a. that is, qrfac determines an orthogonal # matrix q, a permutation matrix p, and an upper trapezoidal # matrix r with diagonal elements of nonincreasing magnitude, # such that a*p = q*r. the householder transformation for # column k, k = 1,2,...,min(m,n), is of the form # # t # i - (1/u(k))*u*u # # where u has zeros in the first k-1 positions. the form of # this transformation and the method of pivoting first # appeared in the corresponding linpack subroutine. # # the subroutine statement is # # subroutine qrfac(m,n,a,lda,pivot,ipvt,lipvt,rdiag,acnorm,wa) # # where # # m is a positive integer input variable set to the number # of rows of a. # # n is a positive integer input variable set to the number # of columns of a. # # a is an m by n array. on input a contains the matrix for # which the qr factorization is to be computed. on output # the strict upper trapezoidal part of a contains the strict # upper trapezoidal part of r, and the lower trapezoidal # part of a contains a factored form of q (the non-trivial # elements of the u vectors described above). # # lda is a positive integer input variable not less than m # which specifies the leading dimension of the array a. # # pivot is a logical input variable. if pivot is set true, # then column pivoting is enforced. if pivot is set false, # then no column pivoting is done. # # ipvt is an integer output array of length lipvt. ipvt # defines the permutation matrix p such that a*p = q*r. # column j of p is column ipvt(j) of the identity matrix. # if pivot is false, ipvt is not referenced. # # lipvt is a positive integer input variable. if pivot is false, # then lipvt may be as small as 1. if pivot is true, then # lipvt must be at least n. # # rdiag is an output array of length n which contains the # diagonal elements of r. # # acnorm is an output array of length n which contains the # norms of the corresponding columns of the input matrix a. # if this information is not needed, then acnorm can coincide # with rdiag. # # wa is a work array of length n. if pivot is false, then wa # can coincide with rdiag. # # subprograms called # # minpack-supplied ... dpmpar,enorm # # fortran-supplied ... dmax1,dsqrt,min0 # # argonne national laboratory. minpack project. march 1980. # burton s. garbow, kenneth e. hillstrom, jorge j. more # # ********** # # PIVOTING / PERMUTING: # # Upon return, A(*,*) is in standard parameter order, A(*,IPVT) is in # permuted order. # # RDIAG is in permuted order. # ACNORM is in standard parameter order. # # # NOTE: in IDL the factors appear slightly differently than described # above. The matrix A is still m x n where m >= n. # # The "upper" triangular matrix R is actually stored in the strict # lower left triangle of A under the standard notation of IDL. # # The reflectors that generate Q are in the upper trapezoid of A upon # output. # # EXAMPLE: decompose the matrix [[9.,2.,6.],[4.,8.,7.]] # aa = [[9.,2.,6.],[4.,8.,7.]] # mpfit_qrfac, aa, aapvt, rdiag, aanorm # IDL> print, aa # 1.81818* 0.181818* 0.545455* # -8.54545+ 1.90160* 0.432573* # IDL> print, rdiag # -11.0000+ -7.48166+ # # The components marked with a * are the components of the # reflectors, and those marked with a + are components of R. # # To reconstruct Q and R we proceed as follows. First R. # r = fltarr(m, n) # for i = 0, n-1 do r(0:i,i) = aa(0:i,i) # fill in lower diag # r(lindgen(n)*(m+1)) = rdiag # # Next, Q, which are composed from the reflectors. Each reflector v # is taken from the upper trapezoid of aa, and converted to a matrix # via (I - 2 vT . v / (v . vT)). # # hh = ident # identity matrix # for i = 0, n-1 do begin # v = aa(*,i) & if i GT 0 then v(0:i-1) = 0 # extract reflector # hh = hh # (ident - 2*(v # v)/total(v * v)) # generate matrix # endfor # # Test the result: # IDL> print, hh # transpose(r) # 9.00000 4.00000 # 2.00000 8.00000 # 6.00000 7.00000 # # Note that it is usually never necessary to form the Q matrix # explicitly, and MPFIT does not. def qrfac(self, a, pivot=0): if self.debug: print 'Entering qrfac...' machep = self.machar.machep sz = a.shape m = sz[0] n = sz[1] # Compute the initial column norms and initialize arrays acnorm = numpy.zeros(n, dtype=float) for j in range(n): acnorm[j] = self.enorm(a[:,j]) rdiag = acnorm.copy() wa = rdiag.copy() ipvt = numpy.arange(n) # Reduce a to r with householder transformations minmn = numpy.min([m,n]) for j in range(minmn): if pivot != 0: # Bring the column of largest norm into the pivot position rmax = numpy.max(rdiag[j:]) kmax = (numpy.nonzero(rdiag[j:] == rmax))[0] ct = len(kmax) kmax = kmax + j if ct > 0: kmax = kmax[0] # Exchange rows via the pivot only. Avoid actually exchanging # the rows, in case there is lots of memory transfer. The # exchange occurs later, within the body of MPFIT, after the # extraneous columns of the matrix have been shed. if kmax != j: temp = ipvt[j] ; ipvt[j] = ipvt[kmax] ; ipvt[kmax] = temp rdiag[kmax] = rdiag[j] wa[kmax] = wa[j] # Compute the householder transformation to reduce the jth # column of A to a multiple of the jth unit vector lj = ipvt[j] ajj = a[j:,lj] ajnorm = self.enorm(ajj) if ajnorm == 0: break if a[j,lj] < 0: ajnorm = -ajnorm ajj = ajj / ajnorm ajj[0] = ajj[0] + 1 # *** Note optimization a(j:*,j) a[j:,lj] = ajj # Apply the transformation to the remaining columns # and update the norms # NOTE to SELF: tried to optimize this by removing the loop, # but it actually got slower. Reverted to "for" loop to keep # it simple. if j+1 < n: for k in range(j+1, n): lk = ipvt[k] ajk = a[j:,lk] # *** Note optimization a(j:*,lk) # (corrected 20 Jul 2000) if a[j,lj] != 0: a[j:,lk] = ajk - ajj * sum(ajk*ajj)/a[j,lj] if (pivot != 0) and (rdiag[k] != 0): temp = a[j,lk]/rdiag[k] rdiag[k] = rdiag[k] * numpy.sqrt(numpy.max([(1.-temp**2), 0.])) temp = rdiag[k]/wa[k] if (0.05*temp*temp) <= machep: rdiag[k] = self.enorm(a[j+1:,lk]) wa[k] = rdiag[k] rdiag[j] = -ajnorm return [a, ipvt, rdiag, acnorm] # Original FORTRAN documentation # ********** # # subroutine qrsolv # # given an m by n matrix a, an n by n diagonal matrix d, # and an m-vector b, the problem is to determine an x which # solves the system # # a*x = b , d*x = 0 , # # in the least squares sense. # # this subroutine completes the solution of the problem # if it is provided with the necessary information from the # factorization, with column pivoting, of a. that is, if # a*p = q*r, where p is a permutation matrix, q has orthogonal # columns, and r is an upper triangular matrix with diagonal # elements of nonincreasing magnitude, then qrsolv expects # the full upper triangle of r, the permutation matrix p, # and the first n components of (q transpose)*b. the system # a*x = b, d*x = 0, is then equivalent to # # t t # r*z = q *b , p *d*p*z = 0 , # # where x = p*z. if this system does not have full rank, # then a least squares solution is obtained. on output qrsolv # also provides an upper triangular matrix s such that # # t t t # p *(a *a + d*d)*p = s *s . # # s is computed within qrsolv and may be of separate interest. # # the subroutine statement is # # subroutine qrsolv(n,r,ldr,ipvt,diag,qtb,x,sdiag,wa) # # where # # n is a positive integer input variable set to the order of r. # # r is an n by n array. on input the full upper triangle # must contain the full upper triangle of the matrix r. # on output the full upper triangle is unaltered, and the # strict lower triangle contains the strict upper triangle # (transposed) of the upper triangular matrix s. # # ldr is a positive integer input variable not less than n # which specifies the leading dimension of the array r. # # ipvt is an integer input array of length n which defines the # permutation matrix p such that a*p = q*r. column j of p # is column ipvt(j) of the identity matrix. # # diag is an input array of length n which must contain the # diagonal elements of the matrix d. # # qtb is an input array of length n which must contain the first # n elements of the vector (q transpose)*b. # # x is an output array of length n which contains the least # squares solution of the system a*x = b, d*x = 0. # # sdiag is an output array of length n which contains the # diagonal elements of the upper triangular matrix s. # # wa is a work array of length n. # # subprograms called # # fortran-supplied ... dabs,dsqrt # # argonne national laboratory. minpack project. march 1980. # burton s. garbow, kenneth e. hillstrom, jorge j. more # def qrsolv(self, r, ipvt, diag, qtb, sdiag): if self.debug: print 'Entering qrsolv...' sz = r.shape m = sz[0] n = sz[1] # copy r and (q transpose)*b to preserve input and initialize s. # in particular, save the diagonal elements of r in x. for j in range(n): r[j:n,j] = r[j,j:n] x = numpy.diagonal(r).copy() wa = qtb.copy() # Eliminate the diagonal matrix d using a givens rotation for j in range(n): l = ipvt[j] if diag[l] == 0: break sdiag[j:] = 0 sdiag[j] = diag[l] # The transformations to eliminate the row of d modify only a # single element of (q transpose)*b beyond the first n, which # is initially zero. qtbpj = 0. for k in range(j,n): if sdiag[k] == 0: break if numpy.abs(r[k,k]) < numpy.abs(sdiag[k]): cotan = r[k,k]/sdiag[k] sine = 0.5/numpy.sqrt(.25 + .25*cotan*cotan) cosine = sine*cotan else: tang = sdiag[k]/r[k,k] cosine = 0.5/numpy.sqrt(.25 + .25*tang*tang) sine = cosine*tang # Compute the modified diagonal element of r and the # modified element of ((q transpose)*b,0). r[k,k] = cosine*r[k,k] + sine*sdiag[k] temp = cosine*wa[k] + sine*qtbpj qtbpj = -sine*wa[k] + cosine*qtbpj wa[k] = temp # Accumulate the transformation in the row of s if n > k+1: temp = cosine*r[k+1:n,k] + sine*sdiag[k+1:n] sdiag[k+1:n] = -sine*r[k+1:n,k] + cosine*sdiag[k+1:n] r[k+1:n,k] = temp sdiag[j] = r[j,j] r[j,j] = x[j] # Solve the triangular system for z. If the system is singular # then obtain a least squares solution nsing = n wh = (numpy.nonzero(sdiag == 0))[0] if len(wh) > 0: nsing = wh[0] wa[nsing:] = 0 if nsing >= 1: wa[nsing-1] = wa[nsing-1]/sdiag[nsing-1] # Degenerate case # *** Reverse loop *** for j in range(nsing-2,-1,-1): sum0 = sum(r[j+1:nsing,j]*wa[j+1:nsing]) wa[j] = (wa[j]-sum0)/sdiag[j] # Permute the components of z back to components of x x[ipvt] = wa return (r, x, sdiag) # Original FORTRAN documentation # # subroutine lmpar # # given an m by n matrix a, an n by n nonsingular diagonal # matrix d, an m-vector b, and a positive number delta, # the problem is to determine a value for the parameter # par such that if x solves the system # # a*x = b , sqrt(par)*d*x = 0 , # # in the least squares sense, and dxnorm is the euclidean # norm of d*x, then either par is zero and # # (dxnorm-delta) .le. 0.1*delta , # # or par is positive and # # abs(dxnorm-delta) .le. 0.1*delta . # # this subroutine completes the solution of the problem # if it is provided with the necessary information from the # qr factorization, with column pivoting, of a. that is, if # a*p = q*r, where p is a permutation matrix, q has orthogonal # columns, and r is an upper triangular matrix with diagonal # elements of nonincreasing magnitude, then lmpar expects # the full upper triangle of r, the permutation matrix p, # and the first n components of (q transpose)*b. on output # lmpar also provides an upper triangular matrix s such that # # t t t # p *(a *a + par*d*d)*p = s *s . # # s is employed within lmpar and may be of separate interest. # # only a few iterations are generally needed for convergence # of the algorithm. if, however, the limit of 10 iterations # is reached, then the output par will contain the best # value obtained so far. # # the subroutine statement is # # subroutine lmpar(n,r,ldr,ipvt,diag,qtb,delta,par,x,sdiag, # wa1,wa2) # # where # # n is a positive integer input variable set to the order of r. # # r is an n by n array. on input the full upper triangle # must contain the full upper triangle of the matrix r. # on output the full upper triangle is unaltered, and the # strict lower triangle contains the strict upper triangle # (transposed) of the upper triangular matrix s. # # ldr is a positive integer input variable not less than n # which specifies the leading dimension of the array r. # # ipvt is an integer input array of length n which defines the # permutation matrix p such that a*p = q*r. column j of p # is column ipvt(j) of the identity matrix. # # diag is an input array of length n which must contain the # diagonal elements of the matrix d. # # qtb is an input array of length n which must contain the first # n elements of the vector (q transpose)*b. # # delta is a positive input variable which specifies an upper # bound on the euclidean norm of d*x. # # par is a nonnegative variable. on input par contains an # initial estimate of the levenberg-marquardt parameter. # on output par contains the final estimate. # # x is an output array of length n which contains the least # squares solution of the system a*x = b, sqrt(par)*d*x = 0, # for the output par. # # sdiag is an output array of length n which contains the # diagonal elements of the upper triangular matrix s. # # wa1 and wa2 are work arrays of length n. # # subprograms called # # minpack-supplied ... dpmpar,enorm,qrsolv # # fortran-supplied ... dabs,dmax1,dmin1,dsqrt # # argonne national laboratory. minpack project. march 1980. # burton s. garbow, kenneth e. hillstrom, jorge j. more # def lmpar(self, r, ipvt, diag, qtb, delta, x, sdiag, par=None): if self.debug: print 'Entering lmpar...' dwarf = self.machar.minnum machep = self.machar.machep sz = r.shape m = sz[0] n = sz[1] # Compute and store in x the gauss-newton direction. If the # jacobian is rank-deficient, obtain a least-squares solution nsing = n wa1 = qtb.copy() rthresh = numpy.max(numpy.abs(numpy.diagonal(r))) * machep wh = (numpy.nonzero(numpy.abs(numpy.diagonal(r)) < rthresh))[0] if len(wh) > 0: nsing = wh[0] wa1[wh[0]:] = 0 if nsing >= 1: # *** Reverse loop *** for j in range(nsing-1,-1,-1): wa1[j] = wa1[j]/r[j,j] if j-1 >= 0: wa1[0:j] = wa1[0:j] - r[0:j,j]*wa1[j] # Note: ipvt here is a permutation array x[ipvt] = wa1 # Initialize the iteration counter. Evaluate the function at the # origin, and test for acceptance of the gauss-newton direction iter = 0 wa2 = diag * x dxnorm = self.enorm(wa2) fp = dxnorm - delta if fp <= 0.1*delta: return [r, 0., x, sdiag] # If the jacobian is not rank deficient, the newton step provides a # lower bound, parl, for the zero of the function. Otherwise set # this bound to zero. parl = 0. if nsing >= n: wa1 = diag[ipvt] * wa2[ipvt] / dxnorm wa1[0] = wa1[0] / r[0,0] # Degenerate case for j in range(1,n): # Note "1" here, not zero sum0 = sum(r[0:j,j]*wa1[0:j]) wa1[j] = (wa1[j] - sum0)/r[j,j] temp = self.enorm(wa1) parl = ((fp/delta)/temp)/temp # Calculate an upper bound, paru, for the zero of the function for j in range(n): sum0 = sum(r[0:j+1,j]*qtb[0:j+1]) wa1[j] = sum0/diag[ipvt[j]] gnorm = self.enorm(wa1) paru = gnorm/delta if paru == 0: paru = dwarf/numpy.min([delta,0.1]) # If the input par lies outside of the interval (parl,paru), set # par to the closer endpoint par = numpy.max([par,parl]) par = numpy.min([par,paru]) if par == 0: par = gnorm/dxnorm # Beginning of an interation while(1): iter = iter + 1 # Evaluate the function at the current value of par if par == 0: par = numpy.max([dwarf, paru*0.001]) temp = numpy.sqrt(par) wa1 = temp * diag [r, x, sdiag] = self.qrsolv(r, ipvt, wa1, qtb, sdiag) wa2 = diag*x dxnorm = self.enorm(wa2) temp = fp fp = dxnorm - delta if (numpy.abs(fp) <= 0.1*delta) or \ ((parl == 0) and (fp <= temp) and (temp < 0)) or \ (iter == 10): break; # Compute the newton correction wa1 = diag[ipvt] * wa2[ipvt] / dxnorm for j in range(n-1): wa1[j] = wa1[j]/sdiag[j] wa1[j+1:n] = wa1[j+1:n] - r[j+1:n,j]*wa1[j] wa1[n-1] = wa1[n-1]/sdiag[n-1] # Degenerate case temp = self.enorm(wa1) parc = ((fp/delta)/temp)/temp # Depending on the sign of the function, update parl or paru if fp > 0: parl = numpy.max([parl,par]) if fp < 0: paru = numpy.min([paru,par]) # Compute an improved estimate for par par = numpy.max([parl, par+parc]) # End of an iteration # Termination return [r, par, x, sdiag] # Procedure to tie one parameter to another. def tie(self, p, ptied=None): if self.debug: print 'Entering tie...' if ptied is None: return for i in range(len(ptied)): if ptied[i] == '': continue cmd = 'p[' + str(i) + '] = ' + ptied[i] exec(cmd) return p # Original FORTRAN documentation # ********** # # subroutine covar # # given an m by n matrix a, the problem is to determine # the covariance matrix corresponding to a, defined as # # t # inverse(a *a) . # # this subroutine completes the solution of the problem # if it is provided with the necessary information from the # qr factorization, with column pivoting, of a. that is, if # a*p = q*r, where p is a permutation matrix, q has orthogonal # columns, and r is an upper triangular matrix with diagonal # elements of nonincreasing magnitude, then covar expects # the full upper triangle of r and the permutation matrix p. # the covariance matrix is then computed as # # t t # p*inverse(r *r)*p . # # if a is nearly rank deficient, it may be desirable to compute # the covariance matrix corresponding to the linearly independent # columns of a. to define the numerical rank of a, covar uses # the tolerance tol. if l is the largest integer such that # # abs(r(l,l)) .gt. tol*abs(r(1,1)) , # # then covar computes the covariance matrix corresponding to # the first l columns of r. for k greater than l, column # and row ipvt(k) of the covariance matrix are set to zero. # # the subroutine statement is # # subroutine covar(n,r,ldr,ipvt,tol,wa) # # where # # n is a positive integer input variable set to the order of r. # # r is an n by n array. on input the full upper triangle must # contain the full upper triangle of the matrix r. on output # r contains the square symmetric covariance matrix. # # ldr is a positive integer input variable not less than n # which specifies the leading dimension of the array r. # # ipvt is an integer input array of length n which defines the # permutation matrix p such that a*p = q*r. column j of p # is column ipvt(j) of the identity matrix. # # tol is a nonnegative input variable used to define the # numerical rank of a in the manner described above. # # wa is a work array of length n. # # subprograms called # # fortran-supplied ... dabs # # argonne national laboratory. minpack project. august 1980. # burton s. garbow, kenneth e. hillstrom, jorge j. more # # ********** def calc_covar(self, rr, ipvt=None, tol=1.e-14): if self.debug: print 'Entering calc_covar...' if numpy.rank(rr) != 2: print 'ERROR: r must be a two-dimensional matrix' return -1 s = rr.shape n = s[0] if s[0] != s[1]: print 'ERROR: r must be a square matrix' return -1 if ipvt is None: ipvt = numpy.arange(n) r = rr.copy() r.shape = [n,n] # For the inverse of r in the full upper triangle of r l = -1 tolr = tol * numpy.abs(r[0,0]) for k in range(n): if numpy.abs(r[k,k]) <= tolr: break r[k,k] = 1./r[k,k] for j in range(k): temp = r[k,k] * r[j,k] r[j,k] = 0. r[0:j+1,k] = r[0:j+1,k] - temp*r[0:j+1,j] l = k # Form the full upper triangle of the inverse of (r transpose)*r # in the full upper triangle of r if l >= 0: for k in range(l+1): for j in range(k): temp = r[j,k] r[0:j+1,j] = r[0:j+1,j] + temp*r[0:j+1,k] temp = r[k,k] r[0:k+1,k] = temp * r[0:k+1,k] # For the full lower triangle of the covariance matrix # in the strict lower triangle or and in wa wa = numpy.repeat([r[0,0]], n) for j in range(n): jj = ipvt[j] sing = j > l for i in range(j+1): if sing: r[i,j] = 0. ii = ipvt[i] if ii > jj: r[ii,jj] = r[i,j] if ii < jj: r[jj,ii] = r[i,j] wa[jj] = r[j,j] # Symmetrize the covariance matrix in r for j in range(n): r[0:j+1,j] = r[j,0:j+1] r[j,j] = wa[j] return r class machar: def __init__(self, double=1): if double == 0: info = numpy.finfo(numpy.float32) else: info = numpy.finfo(numpy.float64) self.machep = info.eps self.maxnum = info.max self.minnum = info.tiny self.maxlog = numpy.log(self.maxnum) self.minlog = numpy.log(self.minnum) self.rdwarf = numpy.sqrt(self.minnum*1.5) * 10 self.rgiant = numpy.sqrt(self.maxnum) * 0.1

33.483348

96

0.666926

7943e794740abd67cbe3fa09614cde67eb5ed75e

381

py

Python

bugtests/test254c.py

doom38/jython_v2.2.1

0803a0c953c294e6d14f9fc7d08edf6a3e630a15

[ "CNRI-Jython" ]

null

bugtests/test254c.py

doom38/jython_v2.2.1

0803a0c953c294e6d14f9fc7d08edf6a3e630a15

[ "CNRI-Jython" ]

null

bugtests/test254c.py

doom38/jython_v2.2.1

0803a0c953c294e6d14f9fc7d08edf6a3e630a15

[ "CNRI-Jython" ]

null

from java import awt, applet import java print "Hi! One stacktrace expected:" try: raise java.lang.Exception() except java.lang.Exception,e: e.printStackTrace() class test254c(applet.Applet): def paint(self, g): g.setColor(awt.Color.black) g.fill3DRect(5,5,590,100,0) g.setFont(awt.Font('Arial', 0, 80)) g.setColor(awt.Color.blue) g.drawString('Hello World', 90, 80)

21.166667

36

0.721785

7943e83d836c398e977030c34aff38281093b0a8

3,940

py

Python

wagtail_transfer/serializers.py

KalobTaulien/wagtail-transfer

c4ec94ac8a18df354462e2528070feaccd65c493

[ "BSD-3-Clause" ]

3

2020-11-06T12:35:47.000Z

2021-03-26T08:13:54.000Z

wagtail_transfer/serializers.py

KalobTaulien/wagtail-transfer

c4ec94ac8a18df354462e2528070feaccd65c493

[ "BSD-3-Clause" ]

null

wagtail_transfer/serializers.py

KalobTaulien/wagtail-transfer

c4ec94ac8a18df354462e2528070feaccd65c493

[ "BSD-3-Clause" ]

null

from functools import lru_cache from django.db import models from modelcluster.fields import ParentalKey from treebeard.mp_tree import MP_Node from wagtail.core.models import Page from .field_adapters import get_field_adapter from .models import get_base_model class ModelSerializer: ignored_fields = [] def __init__(self, model): self.model = model self.base_model = get_base_model(model) self.field_adapters = [] for field in self.model._meta.get_fields(): if field.name in self.ignored_fields: continue if isinstance(field, models.Field): # this is a genuine field rather than a reverse relation # ignore primary keys (including MTI parent pointers) if field.primary_key: continue else: # this is probably a reverse relation, so fetch its related field try: related_field = field.field except AttributeError: # we don't know what sort of pseudo-field this is, so skip it continue # ignore relations other than ParentalKey if not isinstance(related_field, ParentalKey): continue self.field_adapters.append(get_field_adapter(field)) def get_objects_by_ids(self, ids): """ Given a list of IDs, return a queryset of model instances that we can run serialize and get_object_references on """ return self.model.objects.filter(pk__in=ids) def serialize_fields(self, instance): return { field_adapter.name: field_adapter.serialize(instance) for field_adapter in self.field_adapters } def serialize(self, instance): return { 'model': self.model._meta.label_lower, 'pk': instance.pk, 'fields': self.serialize_fields(instance) } def get_object_references(self, instance): refs = { # always include the primary key as an object reference (self.base_model, instance.pk) } for f in self.field_adapters: refs.update(f.get_object_references(instance)) return refs class TreeModelSerializer(ModelSerializer): ignored_fields = ['path', 'depth', 'numchild'] def serialize(self, instance): result = super().serialize(instance) if instance.is_root(): result['parent_id'] = None else: result['parent_id'] = instance.get_parent().pk return result def get_object_references(self, instance): refs = super().get_object_references(instance) if not instance.is_root(): # add a reference for the parent ID refs.add( (self.base_model, instance.get_parent().pk) ) return refs class PageSerializer(TreeModelSerializer): ignored_fields = TreeModelSerializer.ignored_fields + [ 'url_path', 'content_type', 'draft_title', 'has_unpublished_changes', 'owner', 'go_live_at', 'expire_at', 'expired', 'locked', 'first_published_at', 'last_published_at', 'latest_revision_created_at', 'live_revision', ] def get_objects_by_ids(self, ids): # serialize method needs the instance in its specific form return super().get_objects_by_ids(ids).specific() SERIALIZERS_BY_MODEL_CLASS = { models.Model: ModelSerializer, MP_Node: TreeModelSerializer, Page: PageSerializer, } @lru_cache(maxsize=None) def get_model_serializer(model): # find the serializer class for the most specific class in the model's inheritance tree for cls in model.__mro__: if cls in SERIALIZERS_BY_MODEL_CLASS: serializer_class = SERIALIZERS_BY_MODEL_CLASS[cls] return serializer_class(model)

32.295082

98

0.633756

7943e8fafc672c179d403f3d8e4ea2ba7d0840b2

16,208

py

Python

tradingsystems/marketdata.py

GBERESEARCH/tradingsystems

5158d41d32b48d35db34a6e132c7fa2f259987c1

[ "MIT" ]

1

2021-09-10T04:28:37.000Z

tradingsystems/marketdata.py

GBERESEARCH/tradingsystems

5158d41d32b48d35db34a6e132c7fa2f259987c1

[ "MIT" ]

null

tradingsystems/marketdata.py

GBERESEARCH/tradingsystems

5158d41d32b48d35db34a6e132c7fa2f259987c1

[ "MIT" ]

1

2021-09-10T04:28:38.000Z

""" Market Data functions """ import os import norgatedata import numpy as np import pandas as pd import requests from yahoofinancials import YahooFinancials class Markets(): """ Methods for collecting data from Norgate Data, Yahoo Finance and AlphaVantage and extracting the long names of the Norgate Data tickers. """ @classmethod def create_base_data( cls, ticker=None, source=None, params=None): """ Create DataFrame of OHLC prices from NorgateData or Yahoo Finance Parameters ---------- ticker : Str, optional Underlying to return. The default '$SPX'. ccy_1 : Str, optional Primary currency of pair to return. The default 'GBP'. ccy_2 : Str, optional Secondary currency of pair to return. The default 'USD'. start_date : Str, optional Date to begin backtest. Format is 'YYYY-MM-DD'. end_date : Str, optional Date to end backtest. Format is 'YYYY-MM-DD'. source : Str, optional The data source to use, either 'norgate' or 'yahoo'. The default is 'norgate'. api_key : Str AlphaVantage API key. If not provided will look for 'ALPHAVANTAGE_API_KEY' in the environment variables. Returns ------- prices : DataFrame Returns OHLC DataFrame. """ # If a valid source has been provided #try: # Extract data from Norgate if source == 'norgate': prices = cls.return_norgate_data( ticker=ticker, params=params) params['asset_type'] = 'commodity' # Extract data from Yahoo Finance elif source == 'yahoo': prices = cls.return_yahoo_data( ticker=ticker, params=params) params['asset_type'] = 'equity' # Extract data from AlphaVantage elif source == 'alpha': prices = cls.return_alphavantage_data( ticker=ticker, params=params) else: raise ValueError( 'Select a data source from yahoo, norgate or alpha') return prices, params @classmethod def reset_data(cls, tables, params): """ Reset price table to initial data Parameters ---------- tables : Dict Dictionary of key tables. params : Dict Dictionary of key parameters. Returns ------- table_reset : Dict Dictionary with prices table reset. params : Dict Dictionary of key parameters with contract data updated. """ tables_reset = {} tables_reset['prices'] = tables['prices'][ ['Open', 'High', 'Low', 'Close']] tables_reset['benchmark'] = tables['benchmark'][ ['Open', 'High', 'Low', 'Close']] params = cls.contract_data( ticker=params['ticker'], prices=tables['prices'], params=params) return tables_reset, params @staticmethod def return_norgate_data(ticker, params): """ Create DataFrame of historic prices for specified ticker using Norgate Data as the source. Parameters ---------- ticker : Str Norgate data ticker. params : Dict start_date : Str, optional Date to begin backtest. Format is 'YYYY-MM-DD'. end_date : Str, optional Date to end backtest. Format is 'YYYY-MM-DD'. Returns ------- prices : DataFrame DataFrame of historic prices for given ticker. """ timeseriesformat = 'pandas-dataframe' prices = norgatedata.price_timeseries( symbol=ticker, start_date=params['start_date'], end_date=params['end_date'], format=timeseriesformat) return prices @staticmethod def return_yahoo_data(ticker, params): """ Create DataFrame of historic prices for specified ticker using Yahoo Finance as the source. Parameters ---------- ticker : Int Stock to be returned in the form of Reuters RIC code as a string. params : Dict start_date : Str, optional Date to begin backtest. Format is 'YYYY-MM-DD'. end_date : Str, optional Date to end backtest. Format is 'YYYY-MM-DD'. freq : Int Frequency of data - set to 'daily'. Returns ------- prices : DataFrame DataFrame of historic prices for given ticker. """ # Initialise data class yahoo_financials = YahooFinancials(ticker) freq='daily' # Extract historic prices prices = yahoo_financials.get_historical_price_data( params['start_date'], params['end_date'], freq) # Reformat columns prices = pd.DataFrame( prices[ticker]['prices']).drop(['date'], axis=1) \ .rename(columns={'formatted_date':'Date', 'open': 'Open', 'high': 'High', 'low': 'Low', 'close': 'Close', 'volume': 'Volume'}) \ .loc[:, ['Date','Open','High','Low','Close','Volume']] \ .set_index('Date') # Set Index to Datetime prices.index = pd.to_datetime(prices.index) return prices @classmethod def return_alphavantage_data(cls, params, ticker=None): """ Create DataFrame of historic prices for specified ticker using AlphaVantage as the source. Parameters ---------- ticker : Str Underlying to return. The default '$SPX'. ccy_1 : Str Primary currency of pair to return. The default 'GBP'. ccy_2 : Str Secondary currency of pair to return. The default 'USD'. asset_type : Str The alphavantage asset class type. The default is 'fx'. start_date : Str, optional Date to begin backtest. Format is 'YYYY-MM-DD'. end_date : Str, optional Date to end backtest. Format is 'YYYY-MM-DD'. api_key : Str AlphaVantage API key. If not provided will look for 'ALPHAVANTAGE_API_KEY' in the environment variables. Returns ------- prices : DataFrame DataFrame of historic prices for given ticker. """ # Set API key if params['api_key'] == '': params['api_key'] = os.getenv('ALPHAVANTAGE_API_KEY') # FX pair if params['asset_type'] == 'fx': prices = cls._alphavantage_fx( ccy_1=params['ccy_1'], ccy_2=params['ccy_2'], api_key=params['api_key']) # Cryptocurrency elif params['asset_type'] == 'crypto': prices = cls._alphavantage_crypto( ccy_1=params['ccy_1'], ccy_2=params['ccy_2'], api_key=params['api_key']) # Equity Single stock or Index elif params['asset_type'] == 'equity': prices = cls._alphavantage_equity( ticker=ticker, api_key=params['api_key']) # Otherwise raise an error else: raise ValueError("Please enter a valid asset type") # Set Index to Datetime prices.index = pd.to_datetime(prices.index) # Sort data in ascending order prices = prices[::-1] # If a start date has been provided if params['start_date'] is not None: # Set the start variable to this, converting to datetime format start = pd.to_datetime(params['start_date']) # If no start date is provided else: # Set the start variable to the first row in the DataFrame start = prices.index[0] # If an end date has been provided if params['end_date'] is not None: # Set the end variable to this, converting to datetime format end = pd.to_datetime(params['end_date']) # If no end date is provided else: # Set the end variable to the last row in the DataFrame end = prices.index[-1] # Trim data to specified dates prices = prices.loc[start:end] return prices @staticmethod def _alphavantage_fx(ccy_1, ccy_2, api_key): """ Create DataFrame of historic prices for an fx pair using AlphaVantage as the source. Parameters ---------- ccy_1 : Str Primary currency of pair to return. The default 'GBP'. ccy_2 : Str Secondary currency of pair to return. The default 'USD'. api_key : Str AlphaVantage API key. If not provided will look for 'ALPHAVANTAGE_API_KEY' in the environment variables. Returns ------- prices : DataFrame DataFrame of historic prices for given ticker. """ # Set url to extract prices from base_url = 'https://www.alphavantage.co/query?' # Set fx params params = {'function': 'FX_DAILY', 'from_symbol': ccy_1, 'to_symbol': ccy_2, 'outputsize':'full', 'apikey': api_key} response = requests.get(base_url, params=params) response_dict = response.json() _, header = response.json() #Convert to pandas dataframe prices = pd.DataFrame.from_dict( response_dict[header], orient='index') #Clean up column names df_cols = [i.split(' ')[1].title() for i in prices.columns] prices.columns = df_cols # Set datatype to float prices = prices.astype(float) return prices @staticmethod def _alphavantage_crypto(ccy_1, ccy_2, api_key): """ Create DataFrame of historic prices for a cryptocurrency pair using AlphaVantage as the source. Parameters ---------- ccy_1 : Str Primary currency of pair to return. ccy_2 : Str Secondary currency of pair to return. api_key : Str AlphaVantage API key. If not provided will look for 'ALPHAVANTAGE_API_KEY' in the environment variables. Returns ------- prices : DataFrame DataFrame of historic prices for given ticker. """ # Set url to extract prices from base_url = 'https://www.alphavantage.co/query?' # Set crypto params params = {'function': 'DIGITAL_CURRENCY_DAILY', 'symbol': ccy_1, 'market': ccy_2, 'apikey': api_key} response = requests.get(base_url, params=params) response_dict = response.json() _, header = response.json() #Convert to pandas dataframe prices = pd.DataFrame.from_dict( response_dict[header], orient='index') # Select the USD OHLC columns prices = prices[ [prices.columns[1], prices.columns[3], prices.columns[5], prices.columns[7]]] # Set column names prices.columns = ['Open', 'High', 'Low', 'Close'] # Set datatype to float prices = prices.astype(float) return prices @staticmethod def _alphavantage_equity(ticker, api_key): """ Create DataFrame of historic prices for an equity ticker using AlphaVantage as the source. Parameters ---------- ticker : Str Underlying to return. The default '$SPX'. api_key : Str AlphaVantage API key. If not provided will look for 'ALPHAVANTAGE_API_KEY' in the environment variables. Returns ------- prices : DataFrame DataFrame of historic prices for given ticker. """ # Set url to extract prices from base_url = 'https://www.alphavantage.co/query?' # Set equity params params = {'function': 'TIME_SERIES_DAILY_ADJUSTED', 'symbol': ticker, 'outputsize':'full', 'apikey': api_key} response = requests.get(base_url, params=params) response_dict = response.json() _, header = response.json() #Convert to pandas dataframe prices = pd.DataFrame.from_dict( response_dict[header], orient='index') #Clean up column names df_cols = [i.split(' ')[1].title() for i in prices.columns] prices.columns = df_cols # Set datatype to float prices = prices.astype(float) # Calculate stock split multiplier prices['split_mult'] = np.array([1.0]*len(prices)) for row in range(1, len(prices)): if prices['Split'][row] == 1: prices['split_mult'][row] = prices['split_mult'][row-1] else: prices['split_mult'][row] = (prices['split_mult'][row-1] * prices['Split'][row]) # Adjust OHLC prices for splits prices['O'] = np.round(prices['Open'] / prices['split_mult'], 2) prices['H'] = np.round(prices['High'] / prices['split_mult'], 2) prices['L'] = np.round(prices['Low'] / prices['split_mult'], 2) prices['C'] = np.round(prices['Close'] / prices['split_mult'], 2) # Select only OHLC columns prices = prices[['O', 'H', 'L', 'C']] # Set column names prices.columns = ['Open', 'High', 'Low', 'Close'] return prices @staticmethod def contract_data(ticker, prices, params): if ticker[0] == '&': if ticker[-4:] == '_CCB': ticker = ticker[:-4] params['front_ticker'] = ( ticker[1:] +'-' +str(prices['Delivery Month'][-1])[:4] +params['contract_months'][ str(prices['Delivery Month'][-1])[4:6]]) params['per_contract_margin'] = norgatedata.margin( params['front_ticker']) params['contract_point_value'] = norgatedata.point_value( params['front_ticker']) else: params['contract_point_value'] = 1 return params @staticmethod def norgate_name_dict(): """ Create a dictionary of the long names of the Norgate tickers. Returns ------- norgate_name_dict : Dict Dictionary lookup of Norgate tickers to long names. """ # Get list of the available databases alldatabasenames = norgatedata.databases() # Create empty dictionary to store names norgate_name_dict = {} # For each of the available databases for database in alldatabasenames: # Extract dictionary of dictionaries, one for each ticker in the # database databasecontents = norgatedata.database(database) # For each ticker in the database for dicto in databasecontents: # Set the key-value pair of the new dictionary to the ticker # and long name respectively key = dicto['symbol'] value = dicto['securityname'] # Whether to include backadjusted / regular continuous futures if database == 'Continuous Futures': #if '_CCB' in key: norgate_name_dict[key] = value # Don't include the individual futures contracts elif database == 'Futures': pass # Store the values in the dictionary else: norgate_name_dict[key] = value return norgate_name_dict

30.466165

78

0.547878

7943e9f41fe97ec417c49d925aa77dd02ea99b64

5,912

py

Python

raisimGymTorch/raisimGymTorch/env/envs/rsg_anymal/runner.py

gsiddhant/raisimLib

ad844774595189d5d432a5631c05884af4adebdb

[ "Apache-2.0" ]

null

raisimGymTorch/raisimGymTorch/env/envs/rsg_anymal/runner.py

gsiddhant/raisimLib

ad844774595189d5d432a5631c05884af4adebdb

[ "Apache-2.0" ]

null

raisimGymTorch/raisimGymTorch/env/envs/rsg_anymal/runner.py

gsiddhant/raisimLib

ad844774595189d5d432a5631c05884af4adebdb

[ "Apache-2.0" ]

null

from ruamel.yaml import YAML, dump, RoundTripDumper from raisimGymTorch.env.bin import rsg_anymal from raisimGymTorch.env.RaisimGymVecEnv import RaisimGymVecEnv as VecEnv from raisimGymTorch.helper.raisim_gym_helper import ConfigurationSaver, load_param, tensorboard_launcher import os import math import time import raisimGymTorch.algo.ppo.module as ppo_module import raisimGymTorch.algo.ppo.ppo as PPO import torch.nn as nn import numpy as np import torch import datetime import argparse # task specification task_name = "anymal_locomotion" # configuration parser = argparse.ArgumentParser() parser.add_argument('-m', '--mode', help='set mode either train or test', type=str, default='train') parser.add_argument('-w', '--weight', help='pre-trained weight path', type=str, default='') args = parser.parse_args() mode = args.mode weight_path = args.weight # check if gpu is available device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # directories task_path = os.path.dirname(os.path.realpath(__file__)) home_path = task_path + "/../../../../.." # config cfg = YAML().load(open(task_path + "/cfg.yaml", 'r')) # create environment from the configuration file env = VecEnv(rsg_anymal.RaisimGymEnv(home_path + "/rsc", dump(cfg['environment'], Dumper=RoundTripDumper)), cfg['environment']) # shortcuts ob_dim = env.num_obs act_dim = env.num_acts # Training n_steps = math.floor(cfg['environment']['max_time'] / cfg['environment']['control_dt']) total_steps = n_steps * env.num_envs avg_rewards = [] actor = ppo_module.Actor(ppo_module.MLP(cfg['architecture']['policy_net'], nn.LeakyReLU, ob_dim, act_dim), ppo_module.MultivariateGaussianDiagonalCovariance(act_dim, 1.0), device) critic = ppo_module.Critic(ppo_module.MLP(cfg['architecture']['value_net'], nn.LeakyReLU, ob_dim, 1), device) saver = ConfigurationSaver(log_dir=home_path + "/raisimGymTorch/data/"+task_name, save_items=[task_path + "/cfg.yaml", task_path + "/Environment.hpp"]) tensorboard_launcher(saver.data_dir+"/..") # press refresh (F5) after the first ppo update ppo = PPO.PPO(actor=actor, critic=critic, num_envs=cfg['environment']['num_envs'], num_transitions_per_env=n_steps, num_learning_epochs=4, gamma=0.996, lam=0.95, num_mini_batches=4, device=device, log_dir=saver.data_dir, shuffle_batch=False, ) if mode == 'retrain': load_param(weight_path, env, actor, critic, ppo.optimizer, saver.data_dir) for update in range(1000000): start = time.time() env.reset() reward_ll_sum = 0 done_sum = 0 average_dones = 0. if update % cfg['environment']['eval_every_n'] == 0: print("Visualizing and evaluating the current policy") torch.save({ 'actor_architecture_state_dict': actor.architecture.state_dict(), 'actor_distribution_state_dict': actor.distribution.state_dict(), 'critic_architecture_state_dict': critic.architecture.state_dict(), 'optimizer_state_dict': ppo.optimizer.state_dict(), }, saver.data_dir+"/full_"+str(update)+'.pt') # we create another graph just to demonstrate the save/load method loaded_graph = ppo_module.MLP(cfg['architecture']['policy_net'], nn.LeakyReLU, ob_dim, act_dim) loaded_graph.load_state_dict(torch.load(saver.data_dir+"/full_"+str(update)+'.pt')['actor_architecture_state_dict']) env.turn_on_visualization() env.start_video_recording(datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") + "policy_"+str(update)+'.mp4') for step in range(n_steps*2): frame_start = time.time() obs = env.observe(False) action_ll = loaded_graph.architecture(torch.from_numpy(obs).cpu()) reward_ll, dones = env.step(action_ll.cpu().detach().numpy()) frame_end = time.time() wait_time = cfg['environment']['control_dt'] - (frame_end-frame_start) if wait_time > 0.: time.sleep(wait_time) env.stop_video_recording() env.turn_off_visualization() env.reset() env.save_scaling(saver.data_dir, str(update)) # actual training for step in range(n_steps): obs = env.observe() action = ppo.observe(obs) reward, dones = env.step(action) ppo.step(value_obs=obs, rews=reward, dones=dones) done_sum = done_sum + sum(dones) reward_ll_sum = reward_ll_sum + sum(reward) # take st step to get value obs obs = env.observe() ppo.update(actor_obs=obs, value_obs=obs, log_this_iteration=update % 10 == 0, update=update) average_ll_performance = reward_ll_sum / total_steps average_dones = done_sum / total_steps avg_rewards.append(average_ll_performance) actor.distribution.enforce_minimum_std((torch.ones(12)*0.2).to(device)) end = time.time() print('----------------------------------------------------') print('{:>6}th iteration'.format(update)) print('{:<40} {:>6}'.format("average ll reward: ", '{:0.10f}'.format(average_ll_performance))) print('{:<40} {:>6}'.format("dones: ", '{:0.6f}'.format(average_dones))) print('{:<40} {:>6}'.format("time elapsed in this iteration: ", '{:6.4f}'.format(end - start))) print('{:<40} {:>6}'.format("fps: ", '{:6.0f}'.format(total_steps / (end - start)))) print('{:<40} {:>6}'.format("real time factor: ", '{:6.0f}'.format(total_steps / (end - start) * cfg['environment']['control_dt']))) print('std: ') print(np.exp(actor.distribution.std.cpu().detach().numpy())) print('----------------------------------------------------\n')

40.493151

127

0.640054

7943e9f94a73a729cdde3b56a4080f3c7fcf5516

2,514

py

Python

drklauns/timetable/export.py

Ameriks/drklauns

bc8febd72ed6d3f685cf9ad48b487d5c9bb4170e

[ "MIT" ]

null

drklauns/timetable/export.py

Ameriks/drklauns

bc8febd72ed6d3f685cf9ad48b487d5c9bb4170e

[ "MIT" ]

null

drklauns/timetable/export.py

Ameriks/drklauns

bc8febd72ed6d3f685cf9ad48b487d5c9bb4170e

[ "MIT" ]

null

import datetime import pytz import xlwt from io import BytesIO from django.conf import settings from slugify import slugify from drklauns.timetable.models import Summary, Work riga_tz = pytz.timezone(settings.TIME_ZONE) def monthly_excel(year: int, month: int): output = BytesIO() items = Summary.objects.filter(date=datetime.date(year, month, 1)).order_by('employee__first_name', 'employee__last_name', ) wbk = xlwt.Workbook() sheet = wbk.add_sheet("Kopsavilkums %s-%s" % (year, month)) sheet.write(0, 0, "Kopsavilkums par %s - %s" % (year, month)) row = 1 header_row = ( '#', 'ID', 'Vārds', 'Uzvārds', 'Pers.kods', 'Līguma NR', 'Likme', 'Stundas', 'Alga', 'Procedūru skaits', 'Kontaktu skaits') for col, value in enumerate(header_row): sheet.write(row, col, value) row = 2 for index, item in enumerate(items, start=1): salary = round(float(item.employee.contract_rate) * item.hours_worked, 2) row_values = ( index, item.employee_id, item.employee.first_name, item.employee.last_name, item.employee.ssn, item.employee.contract_no, item.employee.contract_rate, item.hours_worked, salary, item.total_procedures, item.total_contacts,) for col, value in enumerate(row_values): sheet.write(row, col, value) row += 1 for item in items: sheet = wbk.add_sheet(slugify(str(item))) sheet.write(0, 0, "Darbinieka %s visi darbi par %s - %s" % (item.employee, year, month)) row = 1 header_row = ('#', 'ID', 'No', 'Līdz', 'Stundu skaits', 'Slimnīca', 'Nodaļa', 'Procedūru skaits', 'Kontaktu skaits', 'Komentāri', 'Pievienots') for col, value in enumerate(header_row): sheet.write(row, col, value) works = Work.objects.filter(employee=item.employee, start__year=item.date.year, start__month=item.date.month) row = 2 for index, work in enumerate(works, start=1): row_values = ( index, work.id, work.start.astimezone(riga_tz).strftime("%Y-%m-%d %H:%M"), work.end.astimezone(riga_tz).strftime("%Y-%m-%d %H:%M"), work.hours_worked, work.department.hospital.name, work.department.name, work.number_of_procedures, work.number_of_contacts, work.comments, work.created.astimezone(riga_tz).strftime("%Y-%m-%d %H:%M")) for col, value in enumerate(row_values): sheet.write(row, col, value) row += 1 wbk.save(output) return output

41.9

219

0.647971

7943ea11eb37f0a21419c25dc79a6e006b173dd0

10,839

py

Python

train-net.py

CrispyHarder/deep-weight-prior

b87e61d6ad590c61b90e188ec86bfb956073be65

[ "MIT" ]

null

train-net.py

CrispyHarder/deep-weight-prior

b87e61d6ad590c61b90e188ec86bfb956073be65

[ "MIT" ]

null

train-net.py

CrispyHarder/deep-weight-prior

b87e61d6ad590c61b90e188ec86bfb956073be65

[ "MIT" ]

null

import torch from torch import nn import utils import numpy as np import os import time from models.lenet import FConvMNIST from models.cifarnet import CIFARNet, CIFARNetNew from models.cifar import ResNet import utils from logger import Logger from utils import tonp from torch.optim.lr_scheduler import MultiStepLR import myexman from torch.nn import functional as F from torch.utils.tensorboard import SummaryWriter from my_utils import MultistepMultiGammaLR def adjust_learning_rate(optimizer, lr): for param_group in optimizer.param_groups: param_group['lr'] = lr def lr_linear(epoch): lr = args.lr * np.minimum((args.decrease_from - epoch) * 1. / (args.epochs - args.decrease_from) + 1, 1.) return max(0, lr) def predict(data, net): pred = [] l = [] for x, y in data: l.append(y.numpy()) x = x.to(device) p = F.log_softmax(net(x), dim=1) pred.append(p.data.cpu().numpy()) return np.concatenate(pred), np.concatenate(l) parser = myexman.ExParser(file=__file__) #general settings parser.add_argument('--name', default='') parser.add_argument('--data', default='cifar') parser.add_argument('--gpu_id', default='0') parser.add_argument('--num_examples', default=None, type=int) parser.add_argument('--data_split_seed', default=456, type=int) parser.add_argument('--seed', default=5743, type=int) parser.add_argument('--resume', default='') parser.add_argument('--epochs', default=120, type=int, help='Number of epochs') parser.add_argument('--bs', default=128, type=int, help='Batch size') parser.add_argument('--test_bs', default=500, type=int, help='Batch size for test dataloader') #model settings parser.add_argument('--model', default='resnet20') parser.add_argument('--model_size', default=1., type=float) parser.add_argument('--net_cfg', default='E') parser.add_argument('--hid_dim', default=[32, 64], type=int, nargs='+') parser.add_argument('--n_classes', default=10, type=int) parser.add_argument('--do', default=[], type=float, nargs='*') #model init settings SINGLE init parser.add_argument('--pretrained', default='') parser.add_argument('--filters_list', default=[], nargs='*', type=str) parser.add_argument('--init', default='xavier') parser.add_argument('--init_list', type=str, nargs='*', default=[]) parser.add_argument('--vae', default='') parser.add_argument('--vae_list', type=str, nargs='*', default=[]) #model init settings MULTI init (if used, single init is ignored) parser.add_argument('--mult_init', default= 1, type = int) parser.add_argument('--mult_init_mode', default= 'xavier', type = str, help = '''such as vqvae1.3''') parser.add_argument('--mult_init_root', type=str, default=os.path.join('data','resnet20','3x3')) parser.add_argument('--mult_init_prior', type=str, default='', help='''such as pixelcnn0''') #optimizer settings parser.add_argument('--lr', default=0.1, type=float, help='Initial learning rate') parser.add_argument('--weight_decay', default=1e-4, type=float) parser.add_argument('--momentum', default=0.9, type=float) parser.add_argument('--milestones', type=int, nargs='*', default=[80,100]) parser.add_argument('--gammas', default=[0.5,0.2], nargs='*', type=float) parser.add_argument('--decrease_from', default=0, type=int) #unused # loss function settings parser.add_argument('--l2', default=0., type=float) parser.add_argument('--dwp_reg', default=0., type=float) #evaluation and leftovers parser.add_argument('--eval_freq', default=1, type=int) parser.add_argument('--dwp_samples', default=1, type=int) parser.add_argument('--rfe', default=0, type=int) parser.add_argument('--fastconv', default=0, type=int) parser.add_argument('--aug', default=0, type=int) args = parser.parse_args() os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_id use_cuda = torch.cuda.is_available() device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") torch.cuda.manual_seed_all(args.seed) torch.manual_seed(args.seed) np.random.seed(args.seed) fmt = { 'lr': '.5f', 'sec': '.0f', } logger = Logger('logs', base=args.root, fmt=fmt) # Load Datasets trainloader, testloader = utils.load_dataset(data=args.data, train_bs=args.bs, test_bs=args.test_bs, num_examples=args.num_examples, seed=args.data_split_seed, augmentation=(args.aug == 1)) if args.model == 'fconv': net = FConvMNIST(args.net_cfg, device=device, hid_dim=args.hid_dim, do=args.do) elif args.model == 'cifarnet': net = CIFARNet(args.net_cfg, device=device, n_classes=args.n_classes, do=args.do, k=args.model_size) elif args.model == 'cifarnetnew': net = CIFARNetNew(args.net_cfg, device=device, n_classes=args.n_classes, do=args.do, k=args.model_size, vae_list=args.vae_list) elif args.model == 'resnet20': net = ResNet([3,3,3],num_classes=args.n_classes).to(device) else: raise NotImplementedError # Initialization if args.mult_init == 1 : net.mult_weights_init(args.mult_init_mode, args.mult_init_root, device=device, prior=args.mult_init_prior) else: if hasattr(net, 'weights_init'): net.weights_init(args.init_list, args.vae_list, pretrained=args.pretrained, filters_list=args.filters_list) else: utils.net_init(net, args.init, args.vae) if args.dwp_reg != 0: net.set_dwp_regularizer(args.vae_list) # Optimizer train_params = [] if args.rfe == 0: train_params = net.parameters() elif args.rfe == 1: tr_modeules = [net.classifier] train_params = list(net.classifier.parameters()) for m in net.features.modules(): if isinstance(m, nn.BatchNorm2d): train_params += list(m.parameters()) tr_modeules += [m] print('==> Random Feature Extration mode') print(*tr_modeules) else: raise NotImplementedError opt = torch.optim.SGD(net.parameters(), args.lr, momentum=args.momentum, weight_decay=args.weight_decay) lrscheduler = MultistepMultiGammaLR(opt, milestones=args.milestones, gamma=args.gammas) # Load params if fine-tuning if args.resume: net.load_state_dict(torch.load(os.path.join(args.resume, 'model.torch'))) opt.load_state_dict(torch.load(os.path.join(args.resume, 'opt.torch'))) N = len(trainloader.dataset) t0 = time.time() it = 0 #add a tensorboard writer writer = SummaryWriter(args.root) best_acc = 0. for e in range(1, args.epochs + 1): if args.milestones: lrscheduler.step() else: adjust_learning_rate(opt, lr_linear(e - 1)) net.train() train_acc = utils.MovingMetric() train_nll = utils.MovingMetric() train_loss = utils.MovingMetric() opt.zero_grad() for x, y in trainloader: opt.zero_grad() it += 1 x = x.to(device) y = y.to(device) p = net(x) data_term = F.cross_entropy(p, y) l2_norm = torch.FloatTensor([0.]).to(device) if args.l2 != 0: l2_norm = torch.sum(torch.stack([torch.sum(p**2) for p in net.features.parameters()])) dwp_reg = 0. if args.dwp_reg != 0.: dwp_reg = net.get_dwp_reg(backward=True, n_tries=args.dwp_samples, weight=args.dwp_reg) loss = data_term + args.l2 * l2_norm loss.backward() opt.step() loss += args.dwp_reg * dwp_reg acc = torch.sum(p.max(1)[1] == y) train_acc.add(acc.item(), p.size(0)) train_nll.add(data_term.item() * x.size(0), x.size(0)) train_loss.add(loss.item() * x.size(0), x.size(0)) if args.fastconv == 1: if (it % args.eval_freq) == 0 or it == 1: net.eval() logp_test, labels = predict(testloader, net) test_acc = np.mean(logp_test.argmax(1) == labels) test_nll = -logp_test[np.arange(len(labels)), labels].mean() logger.add_scalar(it, 'loss', train_loss.get_val()) logger.add_scalar(it, 'train_nll', train_nll.get_val()) logger.add_scalar(it, 'test_nll', test_nll) logger.add_scalar(it, 'train_acc', train_acc.get_val()) logger.add_scalar(it, 'test_acc', test_acc) logger.add_scalar(it, 'lr', opt.param_groups[0]['lr']) logger.add_scalar(it, 'sec', time.time() - t0) logger.add_scalar(it, 'l2_norm', l2_norm.item()) logger.iter_info() logger.save() torch.save(net.state_dict(), os.path.join(args.root, 'model.torch')) torch.save(opt.state_dict(), os.path.join(args.root, 'opt.torch')) t0 = time.time() net.train() if ((e % args.eval_freq) == 0 or e == 1) and (args.fastconv == 0): net.eval() logp_test, labels = predict(testloader, net) test_acc = np.mean(logp_test.argmax(1) == labels) test_nll = -logp_test[np.arange(len(labels)), labels].mean() logger.add_scalar(e, 'loss', train_loss.get_val()) logger.add_scalar(e, 'train_nll', train_nll.get_val()) logger.add_scalar(e, 'test_nll', test_nll) logger.add_scalar(e, 'train_acc', train_acc.get_val()) logger.add_scalar(e, 'test_acc', test_acc) logger.add_scalar(e, 'lr', opt.param_groups[0]['lr']) logger.add_scalar(e, 'sec', time.time() - t0) logger.add_scalar(e, 'l2_norm', l2_norm.item()) logger.add_scalar(e, 'dwp_reg', dwp_reg) logger.iter_info() logger.save() writer.add_scalar( 'train/loss', train_loss.get_val(),e) writer.add_scalar( 'train/nll', train_nll.get_val(),e) writer.add_scalar( 'test/nll', test_nll,e) writer.add_scalar( 'train/acc', train_acc.get_val(),e) writer.add_scalar( 'test/acc', test_acc,e) writer.add_scalar( 'lr', opt.param_groups[0]['lr'],e) writer.add_scalar( 'sec', time.time() - t0,e) writer.add_scalar( 'l2_norm', l2_norm.item(),e) writer.add_scalar( 'dwp_reg', dwp_reg,e) epoch = e if (epoch-1) % 10 == 0: torch.save(net.state_dict() , os.path.join(args.root, 'net_params_epoch_{}.torch'.format(epoch))) torch.save(opt.state_dict(), os.path.join(args.root, 'opt_params_epoch_{}.torch'.format(epoch))) is_best = best_acc < test_acc if is_best: best_acc = test_acc torch.save(net.state_dict(), os.path.join(args.root, 'net_params.torch')) t0 = time.time() torch.save(net.state_dict(), os.path.join(args.root, 'vae_params_lastepoch.torch')) torch.save(opt.state_dict(), os.path.join(args.root, 'opt_params_lastepoch.torch')) writer.flush()

37.247423

115

0.646739

7943ea70400a1f8b54cc5d8e364a96d18ba41819

563

py

Python

nfvparser/toscaparser/tests/__init__.py

onap/modeling-toscaparsers

803b8d4ee8cc38e941cbc1ec26af2336f02fd20c

[ "Apache-2.0", "CC-BY-4.0" ]

null

nfvparser/toscaparser/tests/__init__.py

onap/modeling-toscaparsers

803b8d4ee8cc38e941cbc1ec26af2336f02fd20c

[ "Apache-2.0", "CC-BY-4.0" ]

null

nfvparser/toscaparser/tests/__init__.py

onap/modeling-toscaparsers

803b8d4ee8cc38e941cbc1ec26af2336f02fd20c

[ "Apache-2.0", "CC-BY-4.0" ]

1

2020-06-16T14:47:06.000Z

# 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. description = ""

40.214286

75

0.758437

7943eb4a186791bbcb9bd7613e3addb5dd92a350

3,238

py

Python

programs/django/mysite/mysite/settings.py

Dilmuratjan/MyProject

26f4ee708eb4a7ceef780842ad737fef64a39d7e

[ "WTFPL" ]

2

2017-02-19T15:11:06.000Z

2017-02-22T18:34:10.000Z

programs/django/mysite/mysite/settings.py

Dilmuratjan/MyProject

26f4ee708eb4a7ceef780842ad737fef64a39d7e

[ "WTFPL" ]

null

programs/django/mysite/mysite/settings.py

Dilmuratjan/MyProject

26f4ee708eb4a7ceef780842ad737fef64a39d7e

[ "WTFPL" ]

4

2017-02-26T08:10:30.000Z

2017-05-02T10:02:03.000Z

""" Django settings for mysite project. Generated by 'django-admin startproject' using Django 2.0.3. For more information on this file, see https://docs.djangoproject.com/en/2.0/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.0/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/2.0/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'lr@(%1^4_775&s#s(h=5+zrs=@)tf1=iyrsd=%4=^&yc)rc_vn' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'polls.apps.PollsConfig', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'mysite.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [os.path.join(BASE_DIR, 'templates')], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'mysite.wsgi.application' # Database # https://docs.djangoproject.com/en/2.0/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.mysql', 'NAME': 'mysql', 'USER': 'root', 'PASSWORD': 'di.1995', 'HOST': '127.0.0.1', 'PORT': '3306', } } # Password validation # https://docs.djangoproject.com/en/2.0/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.0/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.0/howto/static-files/ STATIC_URL = '/static/'

25.698413

91

0.680976

7943ebb9b5efbb867df3965e81233b7d1f5d5018

2,861

py

Python

torchdyn/layers/utility.py

willw625731/torchdyn

7a13ff3e7d2314ab98c41ebdd8654ac2ca48fca0

[ "Apache-2.0" ]

null

torchdyn/layers/utility.py

willw625731/torchdyn

7a13ff3e7d2314ab98c41ebdd8654ac2ca48fca0

[ "Apache-2.0" ]

null

torchdyn/layers/utility.py

willw625731/torchdyn

7a13ff3e7d2314ab98c41ebdd8654ac2ca48fca0

[ "Apache-2.0" ]

null

import torch import torch.nn as nn class Augmenter(nn.Module): """Augmentation class. Can handle several types of augmentation strategies for Neural DEs. :param augment_dims: number of augmented dimensions to initialize :type augment_dims: int :param augment_idx: index of dimension to augment :type augment_idx: int :param augment_func: nn.Module applied to the input data of dimension `d` to determine the augmented initial condition of dimension `d + a`. `a` is defined implicitly in `augment_func` e.g. augment_func=nn.Linear(2, 5) augments a 2 dimensional input with 3 additional dimensions. :type augment_func: nn.Module :param order: whether to augment before data [augmentation, x] or after [x, augmentation] along dimension `augment_idx`. Options: ('first', 'last') :type order: str """ def __init__(self, augment_idx:int=1, augment_dims:int=5, augment_func=None, order='first'): super().__init__() self.augment_dims, self.augment_idx, self.augment_func = augment_dims, augment_idx, augment_func self.order = order def forward(self, x: torch.Tensor): if not self.augment_func: new_dims = list(x.shape) new_dims[self.augment_idx] = self.augment_dims # if-else check for augmentation order if self.order == 'first': x = torch.cat([torch.zeros(new_dims).to(x), x], self.augment_idx) else: x = torch.cat([x, torch.zeros(new_dims).to(x)], self.augment_idx) else: # if-else check for augmentation order if self.order == 'first': x = torch.cat([self.augment_func(x).to(x), x], self.augment_idx) else: x = torch.cat([x, self.augment_func(x).to(x)], self.augment_idx) return x class DepthCat(nn.Module): """Depth variable `s` concatenation module. Allows for easy concatenation of `s` each call of the numerical solver, at specified layers of the DEFunc. :param idx_cat: index of the data dimension to concatenate `s` to. :type idx_cat: int """ def __init__(self, idx_cat=1): super().__init__() self.idx_cat = idx_cat ; self.s = None def forward(self, x): s_shape = list(x.shape); s_shape[self.idx_cat] = 1 self.s = self.s * torch.ones(s_shape).to(x) return torch.cat([x, self.s], self.idx_cat).to(x) class DataControl(nn.Module): """Data-control module. Allows for data-control inputs at arbitrary points of the DEFunc """ def __init__(self): super().__init__() self.u = None def forward(self, x): return torch.cat([x, self.u], 1).to(x)

40.871429

162

0.613422

7943ec211d50f4e4b206759f1d4a51ddfbe7281b

654

py

Python

lib/third_party/concurrent/futures/_base.py

kustodian/google-cloud-sdk

b6bae4137d4b58030adb3dcb1271216dfb19f96d

[ "Apache-2.0" ]

2

2019-11-10T09:17:07.000Z

2019-12-18T13:44:08.000Z

lib/third_party/concurrent/futures/_base.py

kustodian/google-cloud-sdk

b6bae4137d4b58030adb3dcb1271216dfb19f96d

[ "Apache-2.0" ]

11

2020-02-29T02:51:12.000Z

2022-03-30T23:20:08.000Z

lib/third_party/concurrent/futures/_base.py

kustodian/google-cloud-sdk

b6bae4137d4b58030adb3dcb1271216dfb19f96d

[ "Apache-2.0" ]

1

2020-07-24T18:47:35.000Z

# Copyright 2018 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. from concurrent.python2.concurrent.futures._base import *

40.875

74

0.770642

7943ec6e7d49784b642fd3dfcdbd0da705a8dbb1

587

py

Python

qulab/drivers/PSG_SignalGenerator.py

ParanoiaSYT/Qulab-backup

09ec5457145b3789d4c1ac02c43dd3e6dfafc96f

[ "MIT" ]

null

qulab/drivers/PSG_SignalGenerator.py

ParanoiaSYT/Qulab-backup

09ec5457145b3789d4c1ac02c43dd3e6dfafc96f

[ "MIT" ]

null

qulab/drivers/PSG_SignalGenerator.py

ParanoiaSYT/Qulab-backup

09ec5457145b3789d4c1ac02c43dd3e6dfafc96f

[ "MIT" ]

null

# -*- coding: utf-8 -*- import numpy as np from qulab import BaseDriver, QInteger, QOption, QReal, QString, QVector class Driver(BaseDriver): support_models = ['E8257D', 'SMF100A', 'SMB100A','SGS100A'] quants = [ QReal('Frequency', unit='Hz', set_cmd=':FREQ %(value).13e%(unit)s', get_cmd=':FREQ?'), QReal('Power', unit='dBm', set_cmd=':POWER %(value).8e%(unit)s', get_cmd=':POWER?'), QOption('Output', set_cmd=':OUTP %(option)s', options=[('OFF', 'OFF'), ('ON', 'ON')]), ]

26.681818

79

0.524702

7943ed68bec567fb50840e9d65b3936c244c365f

2,302

py

Python

Graphly_v1.py

souviksn7/Graphly

3055be4d11e74b8ef2156afdd93ca4cc4859643f

[ "MIT" ]

null

Graphly_v1.py

souviksn7/Graphly

3055be4d11e74b8ef2156afdd93ca4cc4859643f

[ "MIT" ]

null

Graphly_v1.py

souviksn7/Graphly

3055be4d11e74b8ef2156afdd93ca4cc4859643f

[ "MIT" ]

null

from tkinter import* import numpy as np import matplotlib.pyplot as plt from matplotlib.figure import Figure from matplotlib.backends.backend_tkagg import (FigureCanvasTkAgg, NavigationToolbar2Tk) root=Tk() root.title('Graphly') root.geometry("700x850") def eq_maker(ls1,ls2): fin='y= ' for i in range(len(ls1)): if i>0 and ls1[i]>0: fin+='+' if ls1[i]==1: if ls2[i]==0: fin+='1' elif ls2[i]==1: fin+='x' else: fin+='x^'+str(ls2[i]) else: if ls2[i]==0: fin+=str(ls1[i]) elif ls2[i]==1: fin+=str(ls1[i])+'x' else: fin+=str(ls1[i])+'x^'+str(ls2[i]) return fin def coef(): lst1=e1.get().split(",") for i in range(len(lst1)): lst1[i]=int(lst1[i]) return lst1 def power(): lst2=e2.get().split(",") for i in range(len(lst2)): lst2[i]=int(lst2[i]) return lst2 def restart(): e1.delete(0,END) def plot(): a1=coef() a2=power() e1.delete(0,END) e2.delete(0,END) if len(a1)!=len(a2): e1.inser(0,'Error \n click on restart') else: y=np.zeros(20) x=np.arange(1,21,1) for i in range(len(a1)): m=np.ones(20) m=np.power(x,a2[i]) m=np.multiply(m,a1[i]) y=np.add(y,m) string=eq_maker(a1,a2) fig=Figure(figsize=(5,5)) plot1=fig.add_subplot(111) plot1.plot(x,y) plot1.title.set_text("Plot of : "+string) canvas=FigureCanvasTkAgg(fig,master=root) canvas.draw() canvas.get_tk_widget().place(x=50,y=250) toolbar=NavigationToolbar2Tk(canvas, window) toolbar.update() l1=Label(text='Enter the coeffecients of powers of x \nseparating with commas') l2=Label(text='Enter the powers of x \nseparating with commas') e1=Entry(width=50) e2=Entry(width=50) b1=Button(master=root,height=2,width=10,text='Plot',command=plot) b2=Button(master=root,height=2,width=10,text='Restart',command=restart) l1.place(x=30,y=10) l2.place(x=70,y=70) e1.place(x=240,y=10) e2.place(x=240,y=70) b1.place(x=100,y=130) b2.place(x=100,y=170) root.mainloop()

25.577778

79

0.552129

7943edaa51fa29e82e80c2eda18b696ea5b92fed

55,456

py

Python

qa/rpc-tests/test_framework/mininode.py

anhtienlk/bluecoin-new

669ec70192718187913f364cd8d1234edd7b8964

[ "MIT" ]

3

2017-12-09T16:04:29.000Z

2018-05-20T21:46:30.000Z

qa/rpc-tests/test_framework/mininode.py

JUDOKICK/bluecoin-new

669ec70192718187913f364cd8d1234edd7b8964

[ "MIT" ]

2

2018-01-01T06:07:41.000Z

2019-12-20T17:39:29.000Z

qa/rpc-tests/test_framework/mininode.py

JUDOKICK/bluecoin-new

669ec70192718187913f364cd8d1234edd7b8964

[ "MIT" ]

3

2017-12-09T17:06:50.000Z

2019-05-09T09:42:02.000Z

#!/usr/bin/env python3 # Copyright (c) 2010 ArtForz -- public domain half-a-node # Copyright (c) 2012 Jeff Garzik # Copyright (c) 2010-2016 The Bitcoin Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. # # mininode.py - Bitcoin P2P network half-a-node # # This python code was modified from ArtForz' public domain half-a-node, as # found in the mini-node branch of http://github.com/jgarzik/pynode. # # NodeConn: an object which manages p2p connectivity to a bitcoin node # NodeConnCB: a base class that describes the interface for receiving # callbacks with network messages from a NodeConn # CBlock, CTransaction, CBlockHeader, CTxIn, CTxOut, etc....: # data structures that should map to corresponding structures in # bitcoin/primitives # msg_block, msg_tx, msg_headers, etc.: # data structures that represent network messages # ser_*, deser_*: functions that handle serialization/deserialization import struct import socket import asyncore import time import sys import random from .util import hex_str_to_bytes, bytes_to_hex_str from io import BytesIO from codecs import encode import hashlib from threading import RLock from threading import Thread import logging import copy import bluecoin_scrypt from test_framework.siphash import siphash256 BIP0031_VERSION = 60000 MY_VERSION = 80014 # past bip-31 for ping/pong MY_SUBVERSION = b"/python-mininode-tester:0.0.3/" MY_RELAY = 1 # from version 70001 onwards, fRelay should be appended to version messages (BIP37) MAX_INV_SZ = 50000 MAX_BLOCK_BASE_SIZE = 1000000 COIN = 100000000 # 1 btc in satoshis NODE_NETWORK = (1 << 0) NODE_GETUTXO = (1 << 1) NODE_BLOOM = (1 << 2) NODE_WITNESS = (1 << 3) # Keep our own socket map for asyncore, so that we can track disconnects # ourselves (to workaround an issue with closing an asyncore socket when # using select) mininode_socket_map = dict() # One lock for synchronizing all data access between the networking thread (see # NetworkThread below) and the thread running the test logic. For simplicity, # NodeConn acquires this lock whenever delivering a message to to a NodeConnCB, # and whenever adding anything to the send buffer (in send_message()). This # lock should be acquired in the thread running the test logic to synchronize # access to any data shared with the NodeConnCB or NodeConn. mininode_lock = RLock() # Serialization/deserialization tools def sha256(s): return hashlib.new('sha256', s).digest() def ripemd160(s): return hashlib.new('ripemd160', s).digest() def hash256(s): return sha256(sha256(s)) def ser_compact_size(l): r = b"" if l < 253: r = struct.pack("B", l) elif l < 0x10000: r = struct.pack("<BH", 253, l) elif l < 0x100000000: r = struct.pack("<BI", 254, l) else: r = struct.pack("<BQ", 255, l) return r def deser_compact_size(f): nit = struct.unpack("<B", f.read(1))[0] if nit == 253: nit = struct.unpack("<H", f.read(2))[0] elif nit == 254: nit = struct.unpack("<I", f.read(4))[0] elif nit == 255: nit = struct.unpack("<Q", f.read(8))[0] return nit def deser_string(f): nit = deser_compact_size(f) return f.read(nit) def ser_string(s): return ser_compact_size(len(s)) + s def deser_uint256(f): r = 0 for i in range(8): t = struct.unpack("<I", f.read(4))[0] r += t << (i * 32) return r def ser_uint256(u): rs = b"" for i in range(8): rs += struct.pack("<I", u & 0xFFFFFFFF) u >>= 32 return rs def uint256_from_str(s): r = 0 t = struct.unpack("<IIIIIIII", s[:32]) for i in range(8): r += t[i] << (i * 32) return r def uint256_from_compact(c): nbytes = (c >> 24) & 0xFF v = (c & 0xFFFFFF) << (8 * (nbytes - 3)) return v def deser_vector(f, c): nit = deser_compact_size(f) r = [] for i in range(nit): t = c() t.deserialize(f) r.append(t) return r # ser_function_name: Allow for an alternate serialization function on the # entries in the vector (we use this for serializing the vector of transactions # for a witness block). def ser_vector(l, ser_function_name=None): r = ser_compact_size(len(l)) for i in l: if ser_function_name: r += getattr(i, ser_function_name)() else: r += i.serialize() return r def deser_uint256_vector(f): nit = deser_compact_size(f) r = [] for i in range(nit): t = deser_uint256(f) r.append(t) return r def ser_uint256_vector(l): r = ser_compact_size(len(l)) for i in l: r += ser_uint256(i) return r def deser_string_vector(f): nit = deser_compact_size(f) r = [] for i in range(nit): t = deser_string(f) r.append(t) return r def ser_string_vector(l): r = ser_compact_size(len(l)) for sv in l: r += ser_string(sv) return r def deser_int_vector(f): nit = deser_compact_size(f) r = [] for i in range(nit): t = struct.unpack("<i", f.read(4))[0] r.append(t) return r def ser_int_vector(l): r = ser_compact_size(len(l)) for i in l: r += struct.pack("<i", i) return r # Deserialize from a hex string representation (eg from RPC) def FromHex(obj, hex_string): obj.deserialize(BytesIO(hex_str_to_bytes(hex_string))) return obj # Convert a binary-serializable object to hex (eg for submission via RPC) def ToHex(obj): return bytes_to_hex_str(obj.serialize()) # Objects that map to bitcoind objects, which can be serialized/deserialized class CAddress(object): def __init__(self): self.nServices = 1 self.pchReserved = b"\x00" * 10 + b"\xff" * 2 self.ip = "0.0.0.0" self.port = 0 def deserialize(self, f): self.nServices = struct.unpack("<Q", f.read(8))[0] self.pchReserved = f.read(12) self.ip = socket.inet_ntoa(f.read(4)) self.port = struct.unpack(">H", f.read(2))[0] def serialize(self): r = b"" r += struct.pack("<Q", self.nServices) r += self.pchReserved r += socket.inet_aton(self.ip) r += struct.pack(">H", self.port) return r def __repr__(self): return "CAddress(nServices=%i ip=%s port=%i)" % (self.nServices, self.ip, self.port) MSG_WITNESS_FLAG = 1<<30 class CInv(object): typemap = { 0: "Error", 1: "TX", 2: "Block", 1|MSG_WITNESS_FLAG: "WitnessTx", 2|MSG_WITNESS_FLAG : "WitnessBlock", 4: "CompactBlock" } def __init__(self, t=0, h=0): self.type = t self.hash = h def deserialize(self, f): self.type = struct.unpack("<i", f.read(4))[0] self.hash = deser_uint256(f) def serialize(self): r = b"" r += struct.pack("<i", self.type) r += ser_uint256(self.hash) return r def __repr__(self): return "CInv(type=%s hash=%064x)" \ % (self.typemap[self.type], self.hash) class CBlockLocator(object): def __init__(self): self.nVersion = MY_VERSION self.vHave = [] def deserialize(self, f): self.nVersion = struct.unpack("<i", f.read(4))[0] self.vHave = deser_uint256_vector(f) def serialize(self): r = b"" r += struct.pack("<i", self.nVersion) r += ser_uint256_vector(self.vHave) return r def __repr__(self): return "CBlockLocator(nVersion=%i vHave=%s)" \ % (self.nVersion, repr(self.vHave)) class COutPoint(object): def __init__(self, hash=0, n=0): self.hash = hash self.n = n def deserialize(self, f): self.hash = deser_uint256(f) self.n = struct.unpack("<I", f.read(4))[0] def serialize(self): r = b"" r += ser_uint256(self.hash) r += struct.pack("<I", self.n) return r def __repr__(self): return "COutPoint(hash=%064x n=%i)" % (self.hash, self.n) class CTxIn(object): def __init__(self, outpoint=None, scriptSig=b"", nSequence=0): if outpoint is None: self.prevout = COutPoint() else: self.prevout = outpoint self.scriptSig = scriptSig self.nSequence = nSequence def deserialize(self, f): self.prevout = COutPoint() self.prevout.deserialize(f) self.scriptSig = deser_string(f) self.nSequence = struct.unpack("<I", f.read(4))[0] def serialize(self): r = b"" r += self.prevout.serialize() r += ser_string(self.scriptSig) r += struct.pack("<I", self.nSequence) return r def __repr__(self): return "CTxIn(prevout=%s scriptSig=%s nSequence=%i)" \ % (repr(self.prevout), bytes_to_hex_str(self.scriptSig), self.nSequence) class CTxOut(object): def __init__(self, nValue=0, scriptPubKey=b""): self.nValue = nValue self.scriptPubKey = scriptPubKey def deserialize(self, f): self.nValue = struct.unpack("<q", f.read(8))[0] self.scriptPubKey = deser_string(f) def serialize(self): r = b"" r += struct.pack("<q", self.nValue) r += ser_string(self.scriptPubKey) return r def __repr__(self): return "CTxOut(nValue=%i.%08i scriptPubKey=%s)" \ % (self.nValue // COIN, self.nValue % COIN, bytes_to_hex_str(self.scriptPubKey)) class CScriptWitness(object): def __init__(self): # stack is a vector of strings self.stack = [] def __repr__(self): return "CScriptWitness(%s)" % \ (",".join([bytes_to_hex_str(x) for x in self.stack])) def is_null(self): if self.stack: return False return True class CTxInWitness(object): def __init__(self): self.scriptWitness = CScriptWitness() def deserialize(self, f): self.scriptWitness.stack = deser_string_vector(f) def serialize(self): return ser_string_vector(self.scriptWitness.stack) def __repr__(self): return repr(self.scriptWitness) def is_null(self): return self.scriptWitness.is_null() class CTxWitness(object): def __init__(self): self.vtxinwit = [] def deserialize(self, f): for i in range(len(self.vtxinwit)): self.vtxinwit[i].deserialize(f) def serialize(self): r = b"" # This is different than the usual vector serialization -- # we omit the length of the vector, which is required to be # the same length as the transaction's vin vector. for x in self.vtxinwit: r += x.serialize() return r def __repr__(self): return "CTxWitness(%s)" % \ (';'.join([repr(x) for x in self.vtxinwit])) def is_null(self): for x in self.vtxinwit: if not x.is_null(): return False return True class CTransaction(object): def __init__(self, tx=None): if tx is None: self.nVersion = 1 self.vin = [] self.vout = [] self.wit = CTxWitness() self.nLockTime = 0 self.sha256 = None self.hash = None else: self.nVersion = tx.nVersion self.vin = copy.deepcopy(tx.vin) self.vout = copy.deepcopy(tx.vout) self.nLockTime = tx.nLockTime self.sha256 = tx.sha256 self.hash = tx.hash self.wit = copy.deepcopy(tx.wit) def deserialize(self, f): self.nVersion = struct.unpack("<i", f.read(4))[0] self.vin = deser_vector(f, CTxIn) flags = 0 if len(self.vin) == 0: flags = struct.unpack("<B", f.read(1))[0] # Not sure why flags can't be zero, but this # matches the implementation in bitcoind if (flags != 0): self.vin = deser_vector(f, CTxIn) self.vout = deser_vector(f, CTxOut) else: self.vout = deser_vector(f, CTxOut) if flags != 0: self.wit.vtxinwit = [CTxInWitness() for i in range(len(self.vin))] self.wit.deserialize(f) self.nLockTime = struct.unpack("<I", f.read(4))[0] self.sha256 = None self.hash = None def serialize_without_witness(self): r = b"" r += struct.pack("<i", self.nVersion) r += ser_vector(self.vin) r += ser_vector(self.vout) r += struct.pack("<I", self.nLockTime) return r # Only serialize with witness when explicitly called for def serialize_with_witness(self): flags = 0 if not self.wit.is_null(): flags |= 1 r = b"" r += struct.pack("<i", self.nVersion) if flags: dummy = [] r += ser_vector(dummy) r += struct.pack("<B", flags) r += ser_vector(self.vin) r += ser_vector(self.vout) if flags & 1: if (len(self.wit.vtxinwit) != len(self.vin)): # vtxinwit must have the same length as vin self.wit.vtxinwit = self.wit.vtxinwit[:len(self.vin)] for i in range(len(self.wit.vtxinwit), len(self.vin)): self.wit.vtxinwit.append(CTxInWitness()) r += self.wit.serialize() r += struct.pack("<I", self.nLockTime) return r # Regular serialization is without witness -- must explicitly # call serialize_with_witness to include witness data. def serialize(self): return self.serialize_without_witness() # Recalculate the txid (transaction hash without witness) def rehash(self): self.sha256 = None self.calc_sha256() # We will only cache the serialization without witness in # self.sha256 and self.hash -- those are expected to be the txid. def calc_sha256(self, with_witness=False): if with_witness: # Don't cache the result, just return it return uint256_from_str(hash256(self.serialize_with_witness())) if self.sha256 is None: self.sha256 = uint256_from_str(hash256(self.serialize_without_witness())) self.hash = encode(hash256(self.serialize())[::-1], 'hex_codec').decode('ascii') def is_valid(self): self.calc_sha256() for tout in self.vout: if tout.nValue < 0 or tout.nValue > 21000000 * COIN: return False return True def __repr__(self): return "CTransaction(nVersion=%i vin=%s vout=%s wit=%s nLockTime=%i)" \ % (self.nVersion, repr(self.vin), repr(self.vout), repr(self.wit), self.nLockTime) class CBlockHeader(object): def __init__(self, header=None): if header is None: self.set_null() else: self.nVersion = header.nVersion self.hashPrevBlock = header.hashPrevBlock self.hashMerkleRoot = header.hashMerkleRoot self.nTime = header.nTime self.nBits = header.nBits self.nNonce = header.nNonce self.sha256 = header.sha256 self.hash = header.hash self.scrypt256 = header.scrypt256 self.calc_sha256() def set_null(self): self.nVersion = 1 self.hashPrevBlock = 0 self.hashMerkleRoot = 0 self.nTime = 0 self.nBits = 0 self.nNonce = 0 self.sha256 = None self.hash = None self.scrypt256 = None def deserialize(self, f): self.nVersion = struct.unpack("<i", f.read(4))[0] self.hashPrevBlock = deser_uint256(f) self.hashMerkleRoot = deser_uint256(f) self.nTime = struct.unpack("<I", f.read(4))[0] self.nBits = struct.unpack("<I", f.read(4))[0] self.nNonce = struct.unpack("<I", f.read(4))[0] self.sha256 = None self.hash = None self.scrypt256 = None def serialize(self): r = b"" r += struct.pack("<i", self.nVersion) r += ser_uint256(self.hashPrevBlock) r += ser_uint256(self.hashMerkleRoot) r += struct.pack("<I", self.nTime) r += struct.pack("<I", self.nBits) r += struct.pack("<I", self.nNonce) return r def calc_sha256(self): if self.sha256 is None: r = b"" r += struct.pack("<i", self.nVersion) r += ser_uint256(self.hashPrevBlock) r += ser_uint256(self.hashMerkleRoot) r += struct.pack("<I", self.nTime) r += struct.pack("<I", self.nBits) r += struct.pack("<I", self.nNonce) self.sha256 = uint256_from_str(hash256(r)) self.hash = encode(hash256(r)[::-1], 'hex_codec').decode('ascii') self.scrypt256 = uint256_from_str(bluecoin_scrypt.getPoWHash(r)) def rehash(self): self.sha256 = None self.scrypt256 = None self.calc_sha256() return self.sha256 def __repr__(self): return "CBlockHeader(nVersion=%i hashPrevBlock=%064x hashMerkleRoot=%064x nTime=%s nBits=%08x nNonce=%08x)" \ % (self.nVersion, self.hashPrevBlock, self.hashMerkleRoot, time.ctime(self.nTime), self.nBits, self.nNonce) class CBlock(CBlockHeader): def __init__(self, header=None): super(CBlock, self).__init__(header) self.vtx = [] def deserialize(self, f): super(CBlock, self).deserialize(f) self.vtx = deser_vector(f, CTransaction) def serialize(self, with_witness=False): r = b"" r += super(CBlock, self).serialize() if with_witness: r += ser_vector(self.vtx, "serialize_with_witness") else: r += ser_vector(self.vtx) return r # Calculate the merkle root given a vector of transaction hashes def get_merkle_root(self, hashes): while len(hashes) > 1: newhashes = [] for i in range(0, len(hashes), 2): i2 = min(i+1, len(hashes)-1) newhashes.append(hash256(hashes[i] + hashes[i2])) hashes = newhashes return uint256_from_str(hashes[0]) def calc_merkle_root(self): hashes = [] for tx in self.vtx: tx.calc_sha256() hashes.append(ser_uint256(tx.sha256)) return self.get_merkle_root(hashes) def calc_witness_merkle_root(self): # For witness root purposes, the hash of the # coinbase, with witness, is defined to be 0...0 hashes = [ser_uint256(0)] for tx in self.vtx[1:]: # Calculate the hashes with witness data hashes.append(ser_uint256(tx.calc_sha256(True))) return self.get_merkle_root(hashes) def is_valid(self): self.calc_sha256() target = uint256_from_compact(self.nBits) if self.scrypt256 > target: return False for tx in self.vtx: if not tx.is_valid(): return False if self.calc_merkle_root() != self.hashMerkleRoot: return False return True def solve(self): self.rehash() target = uint256_from_compact(self.nBits) while self.scrypt256 > target: self.nNonce += 1 self.rehash() def __repr__(self): return "CBlock(nVersion=%i hashPrevBlock=%064x hashMerkleRoot=%064x nTime=%s nBits=%08x nNonce=%08x vtx=%s)" \ % (self.nVersion, self.hashPrevBlock, self.hashMerkleRoot, time.ctime(self.nTime), self.nBits, self.nNonce, repr(self.vtx)) class CUnsignedAlert(object): def __init__(self): self.nVersion = 1 self.nRelayUntil = 0 self.nExpiration = 0 self.nID = 0 self.nCancel = 0 self.setCancel = [] self.nMinVer = 0 self.nMaxVer = 0 self.setSubVer = [] self.nPriority = 0 self.strComment = b"" self.strStatusBar = b"" self.strReserved = b"" def deserialize(self, f): self.nVersion = struct.unpack("<i", f.read(4))[0] self.nRelayUntil = struct.unpack("<q", f.read(8))[0] self.nExpiration = struct.unpack("<q", f.read(8))[0] self.nID = struct.unpack("<i", f.read(4))[0] self.nCancel = struct.unpack("<i", f.read(4))[0] self.setCancel = deser_int_vector(f) self.nMinVer = struct.unpack("<i", f.read(4))[0] self.nMaxVer = struct.unpack("<i", f.read(4))[0] self.setSubVer = deser_string_vector(f) self.nPriority = struct.unpack("<i", f.read(4))[0] self.strComment = deser_string(f) self.strStatusBar = deser_string(f) self.strReserved = deser_string(f) def serialize(self): r = b"" r += struct.pack("<i", self.nVersion) r += struct.pack("<q", self.nRelayUntil) r += struct.pack("<q", self.nExpiration) r += struct.pack("<i", self.nID) r += struct.pack("<i", self.nCancel) r += ser_int_vector(self.setCancel) r += struct.pack("<i", self.nMinVer) r += struct.pack("<i", self.nMaxVer) r += ser_string_vector(self.setSubVer) r += struct.pack("<i", self.nPriority) r += ser_string(self.strComment) r += ser_string(self.strStatusBar) r += ser_string(self.strReserved) return r def __repr__(self): return "CUnsignedAlert(nVersion %d, nRelayUntil %d, nExpiration %d, nID %d, nCancel %d, nMinVer %d, nMaxVer %d, nPriority %d, strComment %s, strStatusBar %s, strReserved %s)" \ % (self.nVersion, self.nRelayUntil, self.nExpiration, self.nID, self.nCancel, self.nMinVer, self.nMaxVer, self.nPriority, self.strComment, self.strStatusBar, self.strReserved) class CAlert(object): def __init__(self): self.vchMsg = b"" self.vchSig = b"" def deserialize(self, f): self.vchMsg = deser_string(f) self.vchSig = deser_string(f) def serialize(self): r = b"" r += ser_string(self.vchMsg) r += ser_string(self.vchSig) return r def __repr__(self): return "CAlert(vchMsg.sz %d, vchSig.sz %d)" \ % (len(self.vchMsg), len(self.vchSig)) class PrefilledTransaction(object): def __init__(self, index=0, tx = None): self.index = index self.tx = tx def deserialize(self, f): self.index = deser_compact_size(f) self.tx = CTransaction() self.tx.deserialize(f) def serialize(self, with_witness=False): r = b"" r += ser_compact_size(self.index) if with_witness: r += self.tx.serialize_with_witness() else: r += self.tx.serialize_without_witness() return r def serialize_with_witness(self): return self.serialize(with_witness=True) def __repr__(self): return "PrefilledTransaction(index=%d, tx=%s)" % (self.index, repr(self.tx)) # This is what we send on the wire, in a cmpctblock message. class P2PHeaderAndShortIDs(object): def __init__(self): self.header = CBlockHeader() self.nonce = 0 self.shortids_length = 0 self.shortids = [] self.prefilled_txn_length = 0 self.prefilled_txn = [] def deserialize(self, f): self.header.deserialize(f) self.nonce = struct.unpack("<Q", f.read(8))[0] self.shortids_length = deser_compact_size(f) for i in range(self.shortids_length): # shortids are defined to be 6 bytes in the spec, so append # two zero bytes and read it in as an 8-byte number self.shortids.append(struct.unpack("<Q", f.read(6) + b'\x00\x00')[0]) self.prefilled_txn = deser_vector(f, PrefilledTransaction) self.prefilled_txn_length = len(self.prefilled_txn) # When using version 2 compact blocks, we must serialize with_witness. def serialize(self, with_witness=False): r = b"" r += self.header.serialize() r += struct.pack("<Q", self.nonce) r += ser_compact_size(self.shortids_length) for x in self.shortids: # We only want the first 6 bytes r += struct.pack("<Q", x)[0:6] if with_witness: r += ser_vector(self.prefilled_txn, "serialize_with_witness") else: r += ser_vector(self.prefilled_txn) return r def __repr__(self): return "P2PHeaderAndShortIDs(header=%s, nonce=%d, shortids_length=%d, shortids=%s, prefilled_txn_length=%d, prefilledtxn=%s" % (repr(self.header), self.nonce, self.shortids_length, repr(self.shortids), self.prefilled_txn_length, repr(self.prefilled_txn)) # P2P version of the above that will use witness serialization (for compact # block version 2) class P2PHeaderAndShortWitnessIDs(P2PHeaderAndShortIDs): def serialize(self): return super(P2PHeaderAndShortWitnessIDs, self).serialize(with_witness=True) # Calculate the BIP 152-compact blocks shortid for a given transaction hash def calculate_shortid(k0, k1, tx_hash): expected_shortid = siphash256(k0, k1, tx_hash) expected_shortid &= 0x0000ffffffffffff return expected_shortid # This version gets rid of the array lengths, and reinterprets the differential # encoding into indices that can be used for lookup. class HeaderAndShortIDs(object): def __init__(self, p2pheaders_and_shortids = None): self.header = CBlockHeader() self.nonce = 0 self.shortids = [] self.prefilled_txn = [] self.use_witness = False if p2pheaders_and_shortids != None: self.header = p2pheaders_and_shortids.header self.nonce = p2pheaders_and_shortids.nonce self.shortids = p2pheaders_and_shortids.shortids last_index = -1 for x in p2pheaders_and_shortids.prefilled_txn: self.prefilled_txn.append(PrefilledTransaction(x.index + last_index + 1, x.tx)) last_index = self.prefilled_txn[-1].index def to_p2p(self): if self.use_witness: ret = P2PHeaderAndShortWitnessIDs() else: ret = P2PHeaderAndShortIDs() ret.header = self.header ret.nonce = self.nonce ret.shortids_length = len(self.shortids) ret.shortids = self.shortids ret.prefilled_txn_length = len(self.prefilled_txn) ret.prefilled_txn = [] last_index = -1 for x in self.prefilled_txn: ret.prefilled_txn.append(PrefilledTransaction(x.index - last_index - 1, x.tx)) last_index = x.index return ret def get_siphash_keys(self): header_nonce = self.header.serialize() header_nonce += struct.pack("<Q", self.nonce) hash_header_nonce_as_str = sha256(header_nonce) key0 = struct.unpack("<Q", hash_header_nonce_as_str[0:8])[0] key1 = struct.unpack("<Q", hash_header_nonce_as_str[8:16])[0] return [ key0, key1 ] # Version 2 compact blocks use wtxid in shortids (rather than txid) def initialize_from_block(self, block, nonce=0, prefill_list = [0], use_witness = False): self.header = CBlockHeader(block) self.nonce = nonce self.prefilled_txn = [ PrefilledTransaction(i, block.vtx[i]) for i in prefill_list ] self.shortids = [] self.use_witness = use_witness [k0, k1] = self.get_siphash_keys() for i in range(len(block.vtx)): if i not in prefill_list: tx_hash = block.vtx[i].sha256 if use_witness: tx_hash = block.vtx[i].calc_sha256(with_witness=True) self.shortids.append(calculate_shortid(k0, k1, tx_hash)) def __repr__(self): return "HeaderAndShortIDs(header=%s, nonce=%d, shortids=%s, prefilledtxn=%s" % (repr(self.header), self.nonce, repr(self.shortids), repr(self.prefilled_txn)) class BlockTransactionsRequest(object): def __init__(self, blockhash=0, indexes = None): self.blockhash = blockhash self.indexes = indexes if indexes != None else [] def deserialize(self, f): self.blockhash = deser_uint256(f) indexes_length = deser_compact_size(f) for i in range(indexes_length): self.indexes.append(deser_compact_size(f)) def serialize(self): r = b"" r += ser_uint256(self.blockhash) r += ser_compact_size(len(self.indexes)) for x in self.indexes: r += ser_compact_size(x) return r # helper to set the differentially encoded indexes from absolute ones def from_absolute(self, absolute_indexes): self.indexes = [] last_index = -1 for x in absolute_indexes: self.indexes.append(x-last_index-1) last_index = x def to_absolute(self): absolute_indexes = [] last_index = -1 for x in self.indexes: absolute_indexes.append(x+last_index+1) last_index = absolute_indexes[-1] return absolute_indexes def __repr__(self): return "BlockTransactionsRequest(hash=%064x indexes=%s)" % (self.blockhash, repr(self.indexes)) class BlockTransactions(object): def __init__(self, blockhash=0, transactions = None): self.blockhash = blockhash self.transactions = transactions if transactions != None else [] def deserialize(self, f): self.blockhash = deser_uint256(f) self.transactions = deser_vector(f, CTransaction) def serialize(self, with_witness=False): r = b"" r += ser_uint256(self.blockhash) if with_witness: r += ser_vector(self.transactions, "serialize_with_witness") else: r += ser_vector(self.transactions) return r def __repr__(self): return "BlockTransactions(hash=%064x transactions=%s)" % (self.blockhash, repr(self.transactions)) # Objects that correspond to messages on the wire class msg_version(object): command = b"version" def __init__(self): self.nVersion = MY_VERSION self.nServices = 1 self.nTime = int(time.time()) self.addrTo = CAddress() self.addrFrom = CAddress() self.nNonce = random.getrandbits(64) self.strSubVer = MY_SUBVERSION self.nStartingHeight = -1 self.nRelay = MY_RELAY def deserialize(self, f): self.nVersion = struct.unpack("<i", f.read(4))[0] if self.nVersion == 10300: self.nVersion = 300 self.nServices = struct.unpack("<Q", f.read(8))[0] self.nTime = struct.unpack("<q", f.read(8))[0] self.addrTo = CAddress() self.addrTo.deserialize(f) if self.nVersion >= 106: self.addrFrom = CAddress() self.addrFrom.deserialize(f) self.nNonce = struct.unpack("<Q", f.read(8))[0] self.strSubVer = deser_string(f) else: self.addrFrom = None self.nNonce = None self.strSubVer = None self.nStartingHeight = None if self.nVersion >= 209: self.nStartingHeight = struct.unpack("<i", f.read(4))[0] else: self.nStartingHeight = None if self.nVersion >= 70001: # Relay field is optional for version 70001 onwards try: self.nRelay = struct.unpack("<b", f.read(1))[0] except: self.nRelay = 0 else: self.nRelay = 0 def serialize(self): r = b"" r += struct.pack("<i", self.nVersion) r += struct.pack("<Q", self.nServices) r += struct.pack("<q", self.nTime) r += self.addrTo.serialize() r += self.addrFrom.serialize() r += struct.pack("<Q", self.nNonce) r += ser_string(self.strSubVer) r += struct.pack("<i", self.nStartingHeight) r += struct.pack("<b", self.nRelay) return r def __repr__(self): return 'msg_version(nVersion=%i nServices=%i nTime=%s addrTo=%s addrFrom=%s nNonce=0x%016X strSubVer=%s nStartingHeight=%i nRelay=%i)' \ % (self.nVersion, self.nServices, time.ctime(self.nTime), repr(self.addrTo), repr(self.addrFrom), self.nNonce, self.strSubVer, self.nStartingHeight, self.nRelay) class msg_verack(object): command = b"verack" def __init__(self): pass def deserialize(self, f): pass def serialize(self): return b"" def __repr__(self): return "msg_verack()" class msg_addr(object): command = b"addr" def __init__(self): self.addrs = [] def deserialize(self, f): self.addrs = deser_vector(f, CAddress) def serialize(self): return ser_vector(self.addrs) def __repr__(self): return "msg_addr(addrs=%s)" % (repr(self.addrs)) class msg_alert(object): command = b"alert" def __init__(self): self.alert = CAlert() def deserialize(self, f): self.alert = CAlert() self.alert.deserialize(f) def serialize(self): r = b"" r += self.alert.serialize() return r def __repr__(self): return "msg_alert(alert=%s)" % (repr(self.alert), ) class msg_inv(object): command = b"inv" def __init__(self, inv=None): if inv is None: self.inv = [] else: self.inv = inv def deserialize(self, f): self.inv = deser_vector(f, CInv) def serialize(self): return ser_vector(self.inv) def __repr__(self): return "msg_inv(inv=%s)" % (repr(self.inv)) class msg_getdata(object): command = b"getdata" def __init__(self, inv=None): self.inv = inv if inv != None else [] def deserialize(self, f): self.inv = deser_vector(f, CInv) def serialize(self): return ser_vector(self.inv) def __repr__(self): return "msg_getdata(inv=%s)" % (repr(self.inv)) class msg_getblocks(object): command = b"getblocks" def __init__(self): self.locator = CBlockLocator() self.hashstop = 0 def deserialize(self, f): self.locator = CBlockLocator() self.locator.deserialize(f) self.hashstop = deser_uint256(f) def serialize(self): r = b"" r += self.locator.serialize() r += ser_uint256(self.hashstop) return r def __repr__(self): return "msg_getblocks(locator=%s hashstop=%064x)" \ % (repr(self.locator), self.hashstop) class msg_tx(object): command = b"tx" def __init__(self, tx=CTransaction()): self.tx = tx def deserialize(self, f): self.tx.deserialize(f) def serialize(self): return self.tx.serialize_without_witness() def __repr__(self): return "msg_tx(tx=%s)" % (repr(self.tx)) class msg_witness_tx(msg_tx): def serialize(self): return self.tx.serialize_with_witness() class msg_block(object): command = b"block" def __init__(self, block=None): if block is None: self.block = CBlock() else: self.block = block def deserialize(self, f): self.block.deserialize(f) def serialize(self): return self.block.serialize() def __repr__(self): return "msg_block(block=%s)" % (repr(self.block)) # for cases where a user needs tighter control over what is sent over the wire # note that the user must supply the name of the command, and the data class msg_generic(object): def __init__(self, command, data=None): self.command = command self.data = data def serialize(self): return self.data def __repr__(self): return "msg_generic()" class msg_witness_block(msg_block): def serialize(self): r = self.block.serialize(with_witness=True) return r class msg_getaddr(object): command = b"getaddr" def __init__(self): pass def deserialize(self, f): pass def serialize(self): return b"" def __repr__(self): return "msg_getaddr()" class msg_ping_prebip31(object): command = b"ping" def __init__(self): pass def deserialize(self, f): pass def serialize(self): return b"" def __repr__(self): return "msg_ping() (pre-bip31)" class msg_ping(object): command = b"ping" def __init__(self, nonce=0): self.nonce = nonce def deserialize(self, f): self.nonce = struct.unpack("<Q", f.read(8))[0] def serialize(self): r = b"" r += struct.pack("<Q", self.nonce) return r def __repr__(self): return "msg_ping(nonce=%08x)" % self.nonce class msg_pong(object): command = b"pong" def __init__(self, nonce=0): self.nonce = nonce def deserialize(self, f): self.nonce = struct.unpack("<Q", f.read(8))[0] def serialize(self): r = b"" r += struct.pack("<Q", self.nonce) return r def __repr__(self): return "msg_pong(nonce=%08x)" % self.nonce class msg_mempool(object): command = b"mempool" def __init__(self): pass def deserialize(self, f): pass def serialize(self): return b"" def __repr__(self): return "msg_mempool()" class msg_sendheaders(object): command = b"sendheaders" def __init__(self): pass def deserialize(self, f): pass def serialize(self): return b"" def __repr__(self): return "msg_sendheaders()" # getheaders message has # number of entries # vector of hashes # hash_stop (hash of last desired block header, 0 to get as many as possible) class msg_getheaders(object): command = b"getheaders" def __init__(self): self.locator = CBlockLocator() self.hashstop = 0 def deserialize(self, f): self.locator = CBlockLocator() self.locator.deserialize(f) self.hashstop = deser_uint256(f) def serialize(self): r = b"" r += self.locator.serialize() r += ser_uint256(self.hashstop) return r def __repr__(self): return "msg_getheaders(locator=%s, stop=%064x)" \ % (repr(self.locator), self.hashstop) # headers message has # <count> <vector of block headers> class msg_headers(object): command = b"headers" def __init__(self): self.headers = [] def deserialize(self, f): # comment in bitcoind indicates these should be deserialized as blocks blocks = deser_vector(f, CBlock) for x in blocks: self.headers.append(CBlockHeader(x)) def serialize(self): blocks = [CBlock(x) for x in self.headers] return ser_vector(blocks) def __repr__(self): return "msg_headers(headers=%s)" % repr(self.headers) class msg_reject(object): command = b"reject" REJECT_MALFORMED = 1 def __init__(self): self.message = b"" self.code = 0 self.reason = b"" self.data = 0 def deserialize(self, f): self.message = deser_string(f) self.code = struct.unpack("<B", f.read(1))[0] self.reason = deser_string(f) if (self.code != self.REJECT_MALFORMED and (self.message == b"block" or self.message == b"tx")): self.data = deser_uint256(f) def serialize(self): r = ser_string(self.message) r += struct.pack("<B", self.code) r += ser_string(self.reason) if (self.code != self.REJECT_MALFORMED and (self.message == b"block" or self.message == b"tx")): r += ser_uint256(self.data) return r def __repr__(self): return "msg_reject: %s %d %s [%064x]" \ % (self.message, self.code, self.reason, self.data) # Helper function def wait_until(predicate, *, attempts=float('inf'), timeout=float('inf')): attempt = 0 elapsed = 0 while attempt < attempts and elapsed < timeout: with mininode_lock: if predicate(): return True attempt += 1 elapsed += 0.05 time.sleep(0.05) return False class msg_feefilter(object): command = b"feefilter" def __init__(self, feerate=0): self.feerate = feerate def deserialize(self, f): self.feerate = struct.unpack("<Q", f.read(8))[0] def serialize(self): r = b"" r += struct.pack("<Q", self.feerate) return r def __repr__(self): return "msg_feefilter(feerate=%08x)" % self.feerate class msg_sendcmpct(object): command = b"sendcmpct" def __init__(self): self.announce = False self.version = 1 def deserialize(self, f): self.announce = struct.unpack("<?", f.read(1))[0] self.version = struct.unpack("<Q", f.read(8))[0] def serialize(self): r = b"" r += struct.pack("<?", self.announce) r += struct.pack("<Q", self.version) return r def __repr__(self): return "msg_sendcmpct(announce=%s, version=%lu)" % (self.announce, self.version) class msg_cmpctblock(object): command = b"cmpctblock" def __init__(self, header_and_shortids = None): self.header_and_shortids = header_and_shortids def deserialize(self, f): self.header_and_shortids = P2PHeaderAndShortIDs() self.header_and_shortids.deserialize(f) def serialize(self): r = b"" r += self.header_and_shortids.serialize() return r def __repr__(self): return "msg_cmpctblock(HeaderAndShortIDs=%s)" % repr(self.header_and_shortids) class msg_getblocktxn(object): command = b"getblocktxn" def __init__(self): self.block_txn_request = None def deserialize(self, f): self.block_txn_request = BlockTransactionsRequest() self.block_txn_request.deserialize(f) def serialize(self): r = b"" r += self.block_txn_request.serialize() return r def __repr__(self): return "msg_getblocktxn(block_txn_request=%s)" % (repr(self.block_txn_request)) class msg_blocktxn(object): command = b"blocktxn" def __init__(self): self.block_transactions = BlockTransactions() def deserialize(self, f): self.block_transactions.deserialize(f) def serialize(self): r = b"" r += self.block_transactions.serialize() return r def __repr__(self): return "msg_blocktxn(block_transactions=%s)" % (repr(self.block_transactions)) class msg_witness_blocktxn(msg_blocktxn): def serialize(self): r = b"" r += self.block_transactions.serialize(with_witness=True) return r # This is what a callback should look like for NodeConn # Reimplement the on_* functions to provide handling for events class NodeConnCB(object): def __init__(self): self.verack_received = False # deliver_sleep_time is helpful for debugging race conditions in p2p # tests; it causes message delivery to sleep for the specified time # before acquiring the global lock and delivering the next message. self.deliver_sleep_time = None # Remember the services our peer has advertised self.peer_services = None def set_deliver_sleep_time(self, value): with mininode_lock: self.deliver_sleep_time = value def get_deliver_sleep_time(self): with mininode_lock: return self.deliver_sleep_time # Spin until verack message is received from the node. # Tests may want to use this as a signal that the test can begin. # This can be called from the testing thread, so it needs to acquire the # global lock. def wait_for_verack(self): while True: with mininode_lock: if self.verack_received: return time.sleep(0.05) def deliver(self, conn, message): deliver_sleep = self.get_deliver_sleep_time() if deliver_sleep is not None: time.sleep(deliver_sleep) with mininode_lock: try: getattr(self, 'on_' + message.command.decode('ascii'))(conn, message) except: print("ERROR delivering %s (%s)" % (repr(message), sys.exc_info()[0])) def on_version(self, conn, message): if message.nVersion >= 209: conn.send_message(msg_verack()) conn.ver_send = min(MY_VERSION, message.nVersion) if message.nVersion < 209: conn.ver_recv = conn.ver_send conn.nServices = message.nServices def on_verack(self, conn, message): conn.ver_recv = conn.ver_send self.verack_received = True def on_inv(self, conn, message): want = msg_getdata() for i in message.inv: if i.type != 0: want.inv.append(i) if len(want.inv): conn.send_message(want) def on_addr(self, conn, message): pass def on_alert(self, conn, message): pass def on_getdata(self, conn, message): pass def on_getblocks(self, conn, message): pass def on_tx(self, conn, message): pass def on_block(self, conn, message): pass def on_getaddr(self, conn, message): pass def on_headers(self, conn, message): pass def on_getheaders(self, conn, message): pass def on_ping(self, conn, message): if conn.ver_send > BIP0031_VERSION: conn.send_message(msg_pong(message.nonce)) def on_reject(self, conn, message): pass def on_open(self, conn): pass def on_close(self, conn): pass def on_mempool(self, conn): pass def on_pong(self, conn, message): pass def on_feefilter(self, conn, message): pass def on_sendheaders(self, conn, message): pass def on_sendcmpct(self, conn, message): pass def on_cmpctblock(self, conn, message): pass def on_getblocktxn(self, conn, message): pass def on_blocktxn(self, conn, message): pass # More useful callbacks and functions for NodeConnCB's which have a single NodeConn class SingleNodeConnCB(NodeConnCB): def __init__(self): NodeConnCB.__init__(self) self.connection = None self.ping_counter = 1 self.last_pong = msg_pong() def add_connection(self, conn): self.connection = conn # Wrapper for the NodeConn's send_message function def send_message(self, message): self.connection.send_message(message) def send_and_ping(self, message): self.send_message(message) self.sync_with_ping() def on_pong(self, conn, message): self.last_pong = message # Sync up with the node def sync_with_ping(self, timeout=30): def received_pong(): return (self.last_pong.nonce == self.ping_counter) self.send_message(msg_ping(nonce=self.ping_counter)) success = wait_until(received_pong, timeout=timeout) self.ping_counter += 1 return success # The actual NodeConn class # This class provides an interface for a p2p connection to a specified node class NodeConn(asyncore.dispatcher): messagemap = { b"version": msg_version, b"verack": msg_verack, b"addr": msg_addr, b"alert": msg_alert, b"inv": msg_inv, b"getdata": msg_getdata, b"getblocks": msg_getblocks, b"tx": msg_tx, b"block": msg_block, b"getaddr": msg_getaddr, b"ping": msg_ping, b"pong": msg_pong, b"headers": msg_headers, b"getheaders": msg_getheaders, b"reject": msg_reject, b"mempool": msg_mempool, b"feefilter": msg_feefilter, b"sendheaders": msg_sendheaders, b"sendcmpct": msg_sendcmpct, b"cmpctblock": msg_cmpctblock, b"getblocktxn": msg_getblocktxn, b"blocktxn": msg_blocktxn } MAGIC_BYTES = { "mainnet": b"\xfb\xc0\xb6\xdb", # mainnet "testnet3": b"\xfc\xc1\xb7\xdc", # testnet3 "regtest": b"\xfa\xbf\xb5\xda", # regtest } def __init__(self, dstaddr, dstport, rpc, callback, net="regtest", services=NODE_NETWORK, send_version=True): asyncore.dispatcher.__init__(self, map=mininode_socket_map) self.log = logging.getLogger("NodeConn(%s:%d)" % (dstaddr, dstport)) self.dstaddr = dstaddr self.dstport = dstport self.create_socket(socket.AF_INET, socket.SOCK_STREAM) self.sendbuf = b"" self.recvbuf = b"" self.ver_send = 209 self.ver_recv = 209 self.last_sent = 0 self.state = "connecting" self.network = net self.cb = callback self.disconnect = False self.nServices = 0 if send_version: # stuff version msg into sendbuf vt = msg_version() vt.nServices = services vt.addrTo.ip = self.dstaddr vt.addrTo.port = self.dstport vt.addrFrom.ip = "0.0.0.0" vt.addrFrom.port = 0 self.send_message(vt, True) print('MiniNode: Connecting to Bluecoin Node IP # ' + dstaddr + ':' \ + str(dstport)) try: self.connect((dstaddr, dstport)) except: self.handle_close() self.rpc = rpc def show_debug_msg(self, msg): self.log.debug(msg) def handle_connect(self): if self.state != "connected": self.show_debug_msg("MiniNode: Connected & Listening: \n") self.state = "connected" self.cb.on_open(self) def handle_close(self): self.show_debug_msg("MiniNode: Closing Connection to %s:%d... " % (self.dstaddr, self.dstport)) self.state = "closed" self.recvbuf = b"" self.sendbuf = b"" try: self.close() except: pass self.cb.on_close(self) def handle_read(self): try: t = self.recv(8192) if len(t) > 0: self.recvbuf += t self.got_data() except: pass def readable(self): return True def writable(self): with mininode_lock: pre_connection = self.state == "connecting" length = len(self.sendbuf) return (length > 0 or pre_connection) def handle_write(self): with mininode_lock: # asyncore does not expose socket connection, only the first read/write # event, thus we must check connection manually here to know when we # actually connect if self.state == "connecting": self.handle_connect() if not self.writable(): return try: sent = self.send(self.sendbuf) except: self.handle_close() return self.sendbuf = self.sendbuf[sent:] def got_data(self): try: while True: if len(self.recvbuf) < 4: return if self.recvbuf[:4] != self.MAGIC_BYTES[self.network]: raise ValueError("got garbage %s" % repr(self.recvbuf)) if self.ver_recv < 209: if len(self.recvbuf) < 4 + 12 + 4: return command = self.recvbuf[4:4+12].split(b"\x00", 1)[0] msglen = struct.unpack("<i", self.recvbuf[4+12:4+12+4])[0] checksum = None if len(self.recvbuf) < 4 + 12 + 4 + msglen: return msg = self.recvbuf[4+12+4:4+12+4+msglen] self.recvbuf = self.recvbuf[4+12+4+msglen:] else: if len(self.recvbuf) < 4 + 12 + 4 + 4: return command = self.recvbuf[4:4+12].split(b"\x00", 1)[0] msglen = struct.unpack("<i", self.recvbuf[4+12:4+12+4])[0] checksum = self.recvbuf[4+12+4:4+12+4+4] if len(self.recvbuf) < 4 + 12 + 4 + 4 + msglen: return msg = self.recvbuf[4+12+4+4:4+12+4+4+msglen] th = sha256(msg) h = sha256(th) if checksum != h[:4]: raise ValueError("got bad checksum " + repr(self.recvbuf)) self.recvbuf = self.recvbuf[4+12+4+4+msglen:] if command in self.messagemap: f = BytesIO(msg) t = self.messagemap[command]() t.deserialize(f) self.got_message(t) else: self.show_debug_msg("Unknown command: '" + command + "' " + repr(msg)) except Exception as e: print('got_data:', repr(e)) # import traceback # traceback.print_tb(sys.exc_info()[2]) def send_message(self, message, pushbuf=False): if self.state != "connected" and not pushbuf: raise IOError('Not connected, no pushbuf') self.show_debug_msg("Send %s" % repr(message)) command = message.command data = message.serialize() tmsg = self.MAGIC_BYTES[self.network] tmsg += command tmsg += b"\x00" * (12 - len(command)) tmsg += struct.pack("<I", len(data)) if self.ver_send >= 209: th = sha256(data) h = sha256(th) tmsg += h[:4] tmsg += data with mininode_lock: self.sendbuf += tmsg self.last_sent = time.time() def got_message(self, message): if message.command == b"version": if message.nVersion <= BIP0031_VERSION: self.messagemap[b'ping'] = msg_ping_prebip31 if self.last_sent + 30 * 60 < time.time(): self.send_message(self.messagemap[b'ping']()) self.show_debug_msg("Recv %s" % repr(message)) self.cb.deliver(self, message) def disconnect_node(self): self.disconnect = True class NetworkThread(Thread): def run(self): while mininode_socket_map: # We check for whether to disconnect outside of the asyncore # loop to workaround the behavior of asyncore when using # select disconnected = [] for fd, obj in mininode_socket_map.items(): if obj.disconnect: disconnected.append(obj) [ obj.handle_close() for obj in disconnected ] asyncore.loop(0.1, use_poll=True, map=mininode_socket_map, count=1) # An exception we can raise if we detect a potential disconnect # (p2p or rpc) before the test is complete class EarlyDisconnectError(Exception): def __init__(self, value): self.value = value def __str__(self): return repr(self.value)

30.571114

262

0.593137

7943effab02ce25e8966e086149e31761a4b9d12

1,827

py

Python

telethon/tl/custom/forward.py

pgjones/Telethon

1be54035e81ee2e7f869fc2a8c20a16760924895

[ "MIT" ]

1

2019-06-21T19:19:50.000Z

telethon/tl/custom/forward.py

pgjones/Telethon

1be54035e81ee2e7f869fc2a8c20a16760924895

[ "MIT" ]

1

2020-06-30T20:56:35.000Z

telethon/tl/custom/forward.py

SlavikMIPT/Telethon

fece5660f46b1f5b8f464c162914cc51bff6550f

[ "MIT" ]

null

from .chatgetter import ChatGetter from .sendergetter import SenderGetter from ... import utils from ...tl import types class Forward(ChatGetter, SenderGetter): """ Custom class that encapsulates a :tl:`MessageFwdHeader` providing an abstraction to easily access information like the original sender. Remember that this class implements `ChatGetter <telethon.tl.custom.chatgetter.ChatGetter>` and `SenderGetter <telethon.tl.custom.sendergetter.SenderGetter>` which means you have access to all their sender and chat properties and methods. Attributes: original_fwd (:tl:`MessageFwdHeader`): The original :tl:`MessageFwdHeader` instance. Any other attribute: Attributes not described here are the same as those available in the original :tl:`MessageFwdHeader`. """ def __init__(self, client, original, entities): # Copy all the fields, not reference! It would cause memory cycles: # self.original_fwd.original_fwd.original_fwd.original_fwd # ...would be valid if we referenced. self.__dict__ = dict(original.__dict__) self._client = client self.original_fwd = original sender, input_sender = utils._get_entity_pair( original.from_id, entities, client._entity_cache) if not original.channel_id: peer = chat = input_chat = None else: peer = types.PeerChannel(original.channel_id) chat, input_chat = utils._get_entity_pair( utils.get_peer_id(peer), entities, client._entity_cache) ChatGetter.__init__(self, peer, chat=chat, input_chat=input_chat) SenderGetter.__init__(self, original.from_id, sender=sender, input_sender=input_sender) # TODO We could reload the message

38.0625

95

0.692392

7943f075104ea1e2be24449516b1c7491458e92e

16,782

py

Python

Explorer/widgets.py

steveknipmeyer/ModelRelief

a3d067e0ed39a3a8ca78896c21eaa3e7293b15a2

[ "MIT" ]

null

Explorer/widgets.py

steveknipmeyer/ModelRelief

a3d067e0ed39a3a8ca78896c21eaa3e7293b15a2

[ "MIT" ]

null

Explorer/widgets.py

steveknipmeyer/ModelRelief

a3d067e0ed39a3a8ca78896c21eaa3e7293b15a2

[ "MIT" ]

null

#!/usr/bin/env python """ .. module:: widgets :synopsis: QT UI controls for Explorer. .. moduleauthor:: Steve Knipmeyer <[email protected]> """ import os # First, and before importing any Enthought packages, set the ETS_TOOLKIT environment variable to qt4 to tell Traits that we will use Qt. # https://github.com/enthought/traitsui/issues/407 os.environ['ETS_TOOLKIT'] = 'qt4' # By default, mayavi uses the PySide bindings. For the PyQt bindings, set the QT_API environment variable to 'pyqt5' os.environ['QT_API'] = 'pyqt5' # To be able to use PySide or PyQt4 and not run in conflicts with traits, we need to import QtGui and QtCore from pyface.qt from PyQt5 import QtGui, QtCore, QtWidgets import matplotlib matplotlib.use('Qt5Agg') import matplotlib.pyplot as plt from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas from matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as NavigationToolbar from matplotlib.figure import Figure from matplotlib.transforms import Bbox from traits.api import HasTraits, Instance, on_trait_change from traitsui.api import View, Item from mayavi.core.ui.api import MayaviScene, MlabSceneModel, SceneEditor from mayavi import mlab import numpy as np from numpy import pi, sin, cos, mgrid from enum import Enum from typing import Any, Callable, Dict, Optional from results import Results, DataSource # ------------------------------------------# # Images # # ------------------------------------------# class ImageType(Enum): """ A class representing the various UI image view types. """ DepthBuffer = 1, Relief = 2, BackgroundMask = 3, GradientX = 4, GradientXMask = 5, GradientY = 6, GradientYMask = 7, CompositeMask = 8, GradientXUnsharp = 9, GradientYUnsharp = 10, # workbench Image1 = 11, Image2 = 12, Image3 = 13, Image4 = 14, Image5 = 15, Image6 = 16, Image7 = 17, Image8 = 18, class ImageTab(): """ A UI tab of an image view. """ def __init__(self, widget: QtWidgets.QWidget, image_type: ImageType, cmap: str, content_ctor: Callable[[Figure, plt.Axes, np.ndarray, str, str], Figure], source: DataSource) -> None: """ A UI image tab in the Explorer. Parameters ---------- widget QWidget of the tab. image_type The type of the image. cmap The matplotlib colormap. content_ctor The content constructor function that populates the given figure. data The Numpy array holding the image data. """ self.widget = widget self.image_type = image_type self.cmap = cmap self.figure: Figure = None self.canvas: FigureCanvas = None self.scroll: QtWidgets.QScrollArea = None self.nav: NavigationToolbar = None self.content_ctor = content_ctor self.source = source def get_view_extents(self, figure: Figure)->Bbox: """ Returns the bounding box extents of a figure Parameters --------- figure The Figure to query. """ # N.B. get_axes() returns a List; axes[0] = data axes; axes[1] = normalized axes? axes = figure.get_axes()[0] return axes.viewLim def set_view_extents(self, figure: Figure, limits: Bbox)->Bbox: """ Sets the bounding box extents of a figure Parameters --------- figure The Figure to update. limits The new bounding box. """ axes = figure.get_axes()[0] # points: a 2x2 numpy array of the form [[x0, y0], [x1, y1]] points = limits.get_points() axes.set_xlim(points[0][0], points[1][0]) axes.set_ylim(points[0][1], points[1][1]) def construct (self): """ Constructs the UI tab with the image content. Regenerates the matplotlib Figure. """ figure_exists = self.figure != None if figure_exists: viewLim = self.get_view_extents(self.figure) plt.close(self.figure) # construct image figure data = self.source.data self.figure = self.construct_subplot_figures ([data.image], 1, [data.title], [self.cmap]) # restore extents if figure_exists: self.set_view_extents(self.figure, viewLim) self.canvas = FigureCanvas(self.figure) self.canvas.draw() # navigation toolbar if (self.nav is None): self.nav = NavigationToolbar(self.canvas, self.widget) self.widget.layout().addWidget(self.nav) # scroll area if (self.scroll is None): self.scroll = QtWidgets.QScrollArea(self.canvas) self.widget.layout().addWidget(self.scroll) # update associated controls self.scroll.setWidget(self.canvas) self.nav.canvas = self.canvas def update (self): """ Updates the UI tab with the image content. """ if self.source.dirty: self.construct() self.source.dirty = False @staticmethod def add_image(figure: Figure, subplot: plt.Axes, image: np.ndarray, title: str, cmap: str) -> plt.Figure: """ Adds an image to the given Figure. Parameters --------- figure The Figure to which the image will be added. subplot The subplot Axes of the Figure. image The image array. title The title of the image Figure. cmap The colormap to be used. Returns ------- A Figure. """ # flip; first row is at minimum Y image = np.flipud(image) plot = plt.imshow(image, cmap) # title title_obj = subplot.set_title(title) plt.setp(title_obj, color='w') # set the color of title to white # axes plt.setp(plt.getp(subplot, 'yticklabels'), color='w') # set yticklabels color plt.setp(plt.getp(subplot, 'xticklabels'), color='w') # set xticklabels color # colorbar # https://matplotlib.org/examples/images_contours_and_fields/pcolormesh_levels.html colorbar = figure.colorbar(plot, ax=subplot, drawedges=True) plt.setp(plt.getp(colorbar.ax.axes, 'yticklabels'), color='w') # set colorbar to match Y labels # yticklabels color colorbar.outline.set_edgecolor('w') # set colorbar box color colorbar.outline.set_linewidth(2) colorbar.ax.yaxis.set_tick_params(color='w') # set colorbar ticks color colorbar.dividers.set_linewidth(0) return figure def size_figure(self, figure: Figure, n_subplots: int) -> None: """ Sizes a figure to fit the aspect ratio and dimensions of the parent tab. Parameters ---------- figure The Figure to resize. n_subplots Number of subplots. """ xdpi = self.widget.logicalDpiX() ydpi = self.widget.logicalDpiY() dpi = max(xdpi, ydpi) widget_height = self.widget.parent().height() / dpi widget_width = self.widget.parent().width() / dpi widget_aspect_ratio = widget_height / widget_width baseline_height, baseline_width = figure.get_size_inches() # add height of navigation bar if self.nav is not None: nav_height = self.nav.height() / dpi baseline_height += nav_height figure_aspect_ratio = baseline_height / baseline_width # widget is "flatter" than figure widget_aspect_ratio_smaller = widget_aspect_ratio < figure_aspect_ratio display_height = widget_height if widget_aspect_ratio_smaller else widget_width * figure_aspect_ratio display_width = display_height / figure_aspect_ratio # if (self.widget.objectName() == "depthBufferTab"): # print (f"Widget: AR = {widget_aspect_ratio}, height = {widget_height}, width = {widget_width}") # print (f"Figure: AR = {figure_aspect_ratio}, height = {baseline_height}, width = {baseline_width}") # print (f"Display: height = {display_height}, width = {display_width}") # print () figure.set_size_inches(n_subplots * display_width, display_height) try: figure.tight_layout() except ValueError: pass def construct_subplot_figures(self, data, rows, titles = None, cmaps = None) -> plt.Figure: """Display a list of subplots in a single figure with matplotlib. https://gist.github.com/soply/f3eec2e79c165e39c9d540e916142ae1 https://stackoverflow.com/questions/9662995/matplotlib-change-title-and-colorbar-text-and-tick-colors Parameters --------- data List of np.arrays holding the data. rows (Default = 1) Number of rows in figure (number of columns is set to np.ceil(n_subplots/float(rows))). titles List of titles corresponding to each subplot. Must have the same length as data. cmaps List of color maps corresponding to each figure. Must have the same length as data. Returns ------- A Figure. """ assert((titles is None) or (len(data) == len(titles))) n_subplots = len(data) if titles is None: titles = ['Figure (%d)' % i for i in range(1, n_subplots + 1)] figure = plt.figure(facecolor='black') columns = int(np.ceil(n_subplots/float(rows))) for n, (data_array, title, cmap) in enumerate(zip(data, titles, cmaps)): # make a subplot active subplot = figure.add_subplot(rows, columns, n + 1) figure = self.content_ctor(figure, subplot, data_array, title, cmap) self.size_figure(figure, n_subplots) return figure # ------------------------------------------# # Meshes # # ------------------------------------------# class MeshType(Enum): """ A class representing the various UI mesh view types. """ Model = 1, ModelScaled = 2, Relief = 3 class Camera: """ A class representing the Mayavi scene camera. """ def __init__(self, figure) ->None: """ Initialization """ self.figure = figure self.azimuth, self.elevation, self.distance, self.focalpoint = mlab.view(figure=self.figure) self.roll = mlab.roll(figure=self.figure) def apply (self, figure=None) -> None: """ Apply the camera settings to the given figure. """ figure = self.figure if figure is None else figure mlab.view(azimuth=self.azimuth, elevation=self.elevation, distance=self.distance, focalpoint=self.focalpoint, roll=self.roll, figure=figure) class MeshContent(HasTraits): """ Holds an instance of a 3D Mesh """ def __init__ (self, source: DataSource, mesh_type: MeshType) -> None: """ Initialization. Parameters ---------- source The Solver data source for the mesh. mesh_type The type of the mesh. """ super().__init__() self.source = source self.mesh_type = mesh_type self.camera: Optional[Camera] = None def update(self, preserve_camera:bool = True): """ Update the mesh if necessary. Parameters ---------- preserve_camera Preserve the existing camera settings in the view. """ if self.source.dirty: self.construct(self.scene) if preserve_camera and self.camera is not None: self.camera.apply() self.source.dirty = False def construct(self, scene): # This function is called when the view is opened. We don't populate the scene # when the view is not yet open, as some VTK features require a GLContext. shape = self.source.data.image.shape width = shape[1] height = shape[0] X = np.arange(0, width, 1.0) Y = np.arange(0, height, 1.0) X, Y = np.meshgrid(X, Y) Z = self.source.data.image colors = np.empty(X.shape, dtype=str) colors.fill('b') # figure for this MeshContent current_figure = scene.mayavi_scene # get active camera self.camera = Camera(figure=current_figure) # clear figure mlab.clf(figure=current_figure) mlab.figure(figure=current_figure, bgcolor=(0, 0, 0)) # create new figure cyan = (0.25, 0.95, 0.92) mlab.mesh(X, Y, Z, figure=current_figure, color=cyan) #mlab.surf(Z, figure=current_figure, warp_scale="auto") class ModelMeshContent(MeshContent, HasTraits): """ Holds an instance of a Model Mesh """ # N.B. These must be class variables to maintain scene independence. scene = Instance(MlabSceneModel, ()) view = View(Item('scene', editor=SceneEditor(scene_class=MayaviScene), show_label=False), resizable=True # We need this to resize with the parent widget ) @on_trait_change('scene.activated') def update_content(self): super().construct(self.scene) class ModelMeshScaledContent(MeshContent, HasTraits): """ Holds an instance of a Model Mesh that has been only scaled (not transformed).""" # N.B. These must be class variables to maintain scene independence. scene = Instance(MlabSceneModel, ()) view = View(Item('scene', editor=SceneEditor(scene_class=MayaviScene), show_label=False), resizable=True # We need this to resize with the parent widget ) @on_trait_change('scene.activated') def update_content(self): super().construct(self.scene) class ReliefMeshContent(MeshContent, HasTraits): """ Holds an instance of a Relief Mesh """ # N.B. These must be class variables to maintain scene independence. scene = Instance(MlabSceneModel, ()) view = View(Item('scene', editor=SceneEditor(scene_class=MayaviScene), show_label=False), resizable=True # We need this to resize with the parent widget ) @on_trait_change('scene.activated') def update_content(self): super().construct(self.scene) class MeshWidget(QtWidgets.QWidget): """ The QWidget containing the visualization, this is pure PyQt5 code. """ def __init__(self, source: DataSource, mesh_type: MeshType, parent=None) -> None: """ Initialization. Parameters ---------- source The Solver data source for the mesh. mesh_type The type of the mesh. """ super().__init__(parent) self.mesh_type = mesh_type layout = QtWidgets.QVBoxLayout(self) layout.setContentsMargins(0,0,0,0) layout.setSpacing(0) if self.mesh_type == MeshType.Model: self.mesh_content = ModelMeshContent(source, self.mesh_type) if self.mesh_type == MeshType.ModelScaled: self.mesh_content = ModelMeshScaledContent(source, self.mesh_type) if self.mesh_type == MeshType.Relief: self.mesh_content = ReliefMeshContent(source, self.mesh_type) # If you want to debug, beware that you need to remove the Qt input hook. #QtCore.pyqtRemoveInputHook() #import pdb ; pdb.set_trace() #QtCore.pyqtRestoreInputHook() # The edit_traits call will generate the widget to embed. self.ui = self.mesh_content.edit_traits(parent=self, kind='subpanel').control layout.addWidget(self.ui) self.ui.setParent(self) class MeshTab(): """ A UI tab of a mesh view. """ def __init__(self, widget: QtWidgets.QWidget, mesh_type: MeshType, title: str, cmap: str, source: DataSource) -> None: """ A UI mesh tab in the Explorer. Parameters ---------- widget QWidget of the tab. mesh_type The type of the mesh. title The title of the view. cmap The matplotlib colormap. source The Solver data source for the mesh. """ self.widget = widget self.mesh_type = mesh_type self.title = title self.cmap = cmap self.source = source self.mesh_widget = MeshWidget(source, self.mesh_type) self.widget.layout().addWidget(self.mesh_widget)

34.602062

186

0.605649

7943f0de87113eeae537abcbce8087366c778f19

4,582

py

Python

test/test_report_structured.py

laundmo/tidypy

3d08b4f95ed7c8827789222c9670a131cdf965b7

[ "MIT" ]

null

test/test_report_structured.py

laundmo/tidypy

3d08b4f95ed7c8827789222c9670a131cdf965b7

[ "MIT" ]

null

test/test_report_structured.py

laundmo/tidypy

3d08b4f95ed7c8827789222c9670a131cdf965b7

[ "MIT" ]

null

import sys from tidypy import execute_reports, get_default_config, Collector, TidyPyIssue ISSUES = [ TidyPyIssue( 'code1', 'Message 1', u'someproject/foo.py', 5, 23, ), TidyPyIssue( 'code2', 'Message 2', u'someproject/foo.py', 2, ), TidyPyIssue( 'code1', 'Message 1', 'someproject/blah/bar.py', 28, ), TidyPyIssue( 'code3', 'Message 3', 'someproject/subdir/foobar.json', 5, 23, ), ] EXPECTED_JSON = '''{ "tidypy": "0.21.0", "issues": { "blah/bar.py": [ { "line": 28, "character": 0, "code": "code1", "tool": "tidypy", "message": "Message 1" } ], "foo.py": [ { "line": 2, "character": 0, "code": "code2", "tool": "tidypy", "message": "Message 2" }, { "line": 5, "character": 23, "code": "code1", "tool": "tidypy", "message": "Message 1" } ], "subdir/foobar.json": [ { "line": 5, "character": 23, "code": "code3", "tool": "tidypy", "message": "Message 3" } ] } } ''' def test_json_execute(capsys): cfg = get_default_config() cfg['requested_reports'] = [{'type': 'json'}] collector = Collector(cfg) collector.add_issues(ISSUES) execute_reports(cfg, 'someproject', collector) out, err = capsys.readouterr() assert EXPECTED_JSON == out.replace('\r\n', '\n') assert err == '' EXPECTED_TOML = '''tidypy = "0.21.0" [issues] [[issues."blah/bar.py"]] line = 28 character = 0 code = "code1" tool = "tidypy" message = "Message 1" [[issues."foo.py"]] line = 2 character = 0 code = "code2" tool = "tidypy" message = "Message 2" [[issues."foo.py"]] line = 5 character = 23 code = "code1" tool = "tidypy" message = "Message 1" [[issues."subdir/foobar.json"]] line = 5 character = 23 code = "code3" tool = "tidypy" message = "Message 3" ''' def test_toml_execute(capsys): cfg = get_default_config() cfg['requested_reports'] = [{'type': 'toml'}] collector = Collector(cfg) collector.add_issues(ISSUES) execute_reports(cfg, 'someproject', collector) out, err = capsys.readouterr() assert EXPECTED_TOML == out.replace('\r\n', '\n') assert err == '' EXPECTED_YAML = '''tidypy: 0.21.0 issues: blah/bar.py: - line: 28 character: 0 code: code1 tool: tidypy message: Message 1 foo.py: - line: 2 character: 0 code: code2 tool: tidypy message: Message 2 - line: 5 character: 23 code: code1 tool: tidypy message: Message 1 subdir/foobar.json: - line: 5 character: 23 code: code3 tool: tidypy message: Message 3 ''' def test_yaml_execute(capsys): cfg = get_default_config() cfg['requested_reports'] = [{'type': 'yaml'}] collector = Collector(cfg) collector.add_issues(ISSUES) execute_reports(cfg, 'someproject', collector) out, err = capsys.readouterr() assert EXPECTED_YAML == out.replace('\r\n', '\n') assert err == '' EXPECTED_CSV = '''filename,line,character,tool,code,message blah/bar.py,28,0,tidypy,code1,Message 1 foo.py,2,0,tidypy,code2,Message 2 foo.py,5,23,tidypy,code1,Message 1 subdir/foobar.json,5,23,tidypy,code3,Message 3 ''' def test_csv_execute(capsys): cfg = get_default_config() cfg['requested_reports'] = [{'type': 'csv'}] collector = Collector(cfg) collector.add_issues(ISSUES) execute_reports(cfg, 'someproject', collector) out, err = capsys.readouterr() assert EXPECTED_CSV == out.replace('\r\n', '\n') assert err == '' def test_csv_file_output(capsys, tmpdir): target_dir = tmpdir.mkdir('reports') cfg = get_default_config() cfg['requested_reports'] = [{'type': 'csv'}] collector = Collector(cfg) collector.add_issues(ISSUES) test_file = str(target_dir) + 'test1' with open(test_file, 'w') as fp: execute_reports(cfg, 'someproject', collector, output_file=fp) out, err = capsys.readouterr() assert out == '' assert err == '' assert EXPECTED_CSV == open(test_file, 'r').read() test_file = str(target_dir) + 'test2' cfg['requested_reports'] = [{'type': 'csv', 'file': test_file}] execute_reports(cfg, 'someproject', collector) out, err = capsys.readouterr() assert out == '' assert err == '' assert EXPECTED_CSV == open(test_file, 'r').read()

19.75

78

0.576604

7943f11928fc738272093e7e5d8c03261f088667

2,421

py

Python

src/spaceone/identity/model/domain_model.py

spaceone-dev/identity

63a3a8db1d8d7d1e2c17d53fb3dc7aad35aef917

[ "Apache-2.0" ]

13

2020-05-20T13:14:33.000Z

2021-12-23T12:02:40.000Z

src/spaceone/identity/model/domain_model.py

whdalsrnt/identity

6aeaf7ea405d9e2f7c4f24c7518445b955cebec6

[ "Apache-2.0" ]

7

2020-06-02T08:11:18.000Z

2022-03-15T02:26:07.000Z

src/spaceone/identity/model/domain_model.py

whdalsrnt/identity

6aeaf7ea405d9e2f7c4f24c7518445b955cebec6

[ "Apache-2.0" ]

9

2020-06-01T10:08:05.000Z

2021-03-19T06:34:57.000Z

from mongoengine import * from datetime import datetime from spaceone.core.error import * from spaceone.core.model.mongo_model import MongoModel class PluginInfo(EmbeddedDocument): plugin_id = StringField(max_length=40) version = StringField(max_length=255) options = DictField(default={}) metadata = DictField(default={}) secret_id = StringField(max_length=40, null=True, default=None) schema = StringField(max_length=255, null=True, default=None) upgrade_mode = StringField(max_length=20, default='AUTO', choices=('AUTO', 'MANUAL')) def to_dict(self): return dict(self.to_mongo()) class DomainTag(EmbeddedDocument): key = StringField(max_length=255) value = StringField(max_length=255) class Domain(MongoModel): domain_id = StringField(max_length=40, generate_id='domain', unique=True) name = StringField(max_length=255) state = StringField(max_length=20, default='ENABLED') plugin_info = EmbeddedDocumentField(PluginInfo, default=None, null=True) config = DictField() tags = ListField(EmbeddedDocumentField(DomainTag)) created_at = DateTimeField(auto_now_add=True) deleted_at = DateTimeField(default=None, null=True) meta = { 'updatable_fields': [ 'name', 'state', 'plugin_info', 'config', 'tags', 'deleted_at' ], 'minimal_fields': [ 'domain_id', 'name', 'state', ], 'ordering': ['name'], 'indexes': [ 'domain_id', 'state', ('tags.key', 'tags.value') ] } @queryset_manager def objects(doc_cls, queryset): return queryset.filter(state__ne='DELETED') @classmethod def create(cls, data): domain_vos = cls.filter(name=data['name']) if domain_vos.count() > 0: raise ERROR_NOT_UNIQUE(key='name', value=data['name']) return super().create(data) def update(self, data): if 'name' in data: domain_vos = self.filter(name=data['name'], domain_id__ne=self.domain_id) if domain_vos.count() > 0: raise ERROR_NOT_UNIQUE(key='name', value=data['name']) return super().update(data) def delete(self): self.update({ 'state': 'DELETED', 'deleted_at': datetime.utcnow() })

29.52439

89

0.611318

7943f23f7aefc7fd50cdb8b5739084d25b2fc8e9

1,141

py

Python

get_power.py

FraserTooth/echonet_interface

e009a6371543a65ac8c5ed895e7258fb072612f8

[ "MIT" ]

1

2021-09-22T10:25:32.000Z

get_power.py

FraserTooth/echonet_interface

e009a6371543a65ac8c5ed895e7258fb072612f8

[ "MIT" ]

null

get_power.py

FraserTooth/echonet_interface

e009a6371543a65ac8c5ed895e7258fb072612f8

[ "MIT" ]

null

import meter.echonet as echonet from meter.common import byte2str, hex2int import meter.b_route as b_route from meter.serial_connection import connect_to_serial_port import time import logging.handlers import meter.influx as db # ロガー取得 logger = logging.getLogger("main") fmt = "%(asctime)s %(levelname)s %(name)s :%(message)s" logging.basicConfig(level=10, format=fmt) ser = connect_to_serial_port() ipv6_address = b_route.connect_to_broute(ser) # スマートメーターがインスタンスリスト通知を投げてくる # (ECHONET-Lite_Ver.1.12_02.pdf p.4-16) logger.info(byte2str(ser.readline())) while True: command = echonet.get_serial_command( [echonet.SmartMeterActions.NOW_POWER], ipv6_address ) # Send Command ser.write(command) line = "" # Find the line we care about while line.startswith("ERXUDP") is False: line = byte2str(ser.readline()) if len(line) == 0: # Serial Connection has Hung print("Looks like we've hung...") break if len(line) > 0: data = echonet.handle_line(ser, line) if data is not None: db.write_to_influx(data) time.sleep(3)

25.931818

59

0.683611

7943f31f3067d044097556d01e85bfb664d12e5f

6,511

py

Python

sources/simulators/ray_based_simulator/ray_simulate.py

M4rukku/impact_of_non_iid_data_in_federated_learning

c818db03699c82e42217d56f8ddd4cc2081c8bb1

[ "MIT" ]

null

sources/simulators/ray_based_simulator/ray_simulate.py

M4rukku/impact_of_non_iid_data_in_federated_learning

c818db03699c82e42217d56f8ddd4cc2081c8bb1

[ "MIT" ]

null

sources/simulators/ray_based_simulator/ray_simulate.py

M4rukku/impact_of_non_iid_data_in_federated_learning

c818db03699c82e42217d56f8ddd4cc2081c8bb1

[ "MIT" ]

null

# Modified from Adap # Copyright 2020 Adap GmbH. 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. # ============================================================================== """Flower simulation app.""" import sys from logging import ERROR, INFO from typing import Any, Callable, Dict, List, Optional import ray from flwr.client.client import Client from flwr.common.logger import log from flwr.server import Server from flwr.server.app import _fl, _init_defaults from flwr.server.strategy import Strategy from flwr.simulation.ray_transport.ray_client_proxy import RayClientProxy INVALID_ARGUMENTS_START_SIMULATION = """ INVALID ARGUMENTS ERROR Invalid Arguments in method: `start_simulation( *, client_fn: Callable[[str], Client], num_clients: Optional[int] = None, clients_ids: Optional[List[str]] = None, client_resources: Optional[Dict[str, int]] = None, num_rounds: int = 1, strategy: Optional[Strategy] = None, ray_init_args: Optional[Dict[str, Any]] = None, ) -> None:` REASON: Method requires: - Either `num_clients`[int] or `clients_ids`[List[str]] to be set exclusively. OR - `len(clients_ids)` == `num_clients` """ def start_simulation( # pylint: disable=too-many-arguments *, client_fn: Callable[[str], Client], num_clients: Optional[int] = None, clients_ids: Optional[List[str]] = None, client_resources: Optional[Dict[str, int]] = None, num_rounds: int = 1, strategy: Optional[Strategy] = None, ray_init_args: Optional[Dict[str, Any]] = None, server: Optional[Server] = None, ray_callbacks: Optional[List[Callable[[], None]]]=None ) -> None: """Start a Ray-based Flower simulation server. Parameters ---------- client_fn : Callable[[str], Client] A function creating client instances. The function must take a single str argument called `cid`. It should return a single client instance. Note that the created client instances are ephemeral and will often be destroyed after a single method invocation. Since client instances are not long-lived, they should not attempt to carry state over method invocations. Any state required by the instance (model, dataset, hyperparameters, ...) should be (re-)created in either the call to `client_fn` or the call to any of the client methods (e.g., load evaluation data in the `evaluate` method itself). num_clients : Optional[int] The total number of clients in this simulation. This must be set if `clients_ids` is not set and vice-versa. clients_ids : Optional[List[str]] List `client_id`s for each client. This is only required if `num_clients` is not set. Setting both `num_clients` and `clients_ids` with `len(clients_ids)` not equal to `num_clients` generates an error. client_resources : Optional[Dict[str, int]] (default: None) CPU and GPU resources for a single client. Supported keys are `num_cpus` and `num_gpus`. Example: `{"num_cpus": 4, "num_gpus": 1}`. To understand the GPU utilization caused by `num_gpus`, consult the Ray documentation on GPU support. num_rounds : int (default: 1) The number of rounds to train. strategy : Optional[flwr.server.Strategy] (default: None) An implementation of the abstract base class `flwr.server.Strategy`. If no strategy is provided, then `start_server` will use `flwr.server.strategy.FedAvg`. ray_init_args : Optional[Dict[str, Any]] (default: None) Optional dictionary containing arguments for the call to `ray.init`. If ray_init_args is None (the default), Ray will be initialized with the following default args: { "ignore_reinit_error": True, "include_dashboard": False, } An empty dictionary can be used (ray_init_args={}) to prevent any arguments from being passed to ray.init. """ cids: List[str] # clients_ids takes precedence if clients_ids is not None: if (num_clients is not None) and (len(clients_ids) != num_clients): log(ERROR, INVALID_ARGUMENTS_START_SIMULATION) sys.exit() else: cids = clients_ids else: if num_clients is None: log(ERROR, INVALID_ARGUMENTS_START_SIMULATION) sys.exit() else: cids = [str(x) for x in range(num_clients)] # Default arguments for Ray initialization if not ray_init_args: ray_init_args = { "ignore_reinit_error": True, "include_dashboard": False, } # Shut down Ray if it has already been initialized if ray.is_initialized(): ray_init_args = {**ray_init_args} ray_init_args["address"] = "auto" # Initialize Ray ray.init(**ray_init_args) log( INFO, "Ray initialized with resources: %s", ray.cluster_resources(), ) if ray_callbacks is not None: for callback in ray_callbacks: callback() # Initialize server and server config config = {"num_rounds": num_rounds} initialized_server, initialized_config = _init_defaults(server, config, strategy) log( INFO, "Starting Flower simulation running: %s", initialized_config, ) # Register one RayClientProxy object for each client with the ClientManager resources = client_resources if client_resources is not None else {} for cid in cids: client_proxy = RayClientProxy( client_fn=client_fn, cid=cid, resources=resources, ) initialized_server.client_manager().register(client=client_proxy) # Start training _fl( server=initialized_server, config=initialized_config, force_final_distributed_eval=False, )

36.172222

85

0.658424

7943f3c6c44c7bf86810740444594d5429abe8f5

170

py

Python

app/api/index.py

benranderson/fmeca

aedbde0ec20417cace3df01e09f193dd525ba0e2

[ "MIT" ]

null

app/api/index.py

benranderson/fmeca

aedbde0ec20417cace3df01e09f193dd525ba0e2

[ "MIT" ]

11

2017-11-09T22:23:50.000Z

2017-11-30T16:40:22.000Z

app/api/index.py

benranderson/fmeca

aedbde0ec20417cace3df01e09f193dd525ba0e2

[ "MIT" ]

3

2017-11-10T09:52:07.000Z

2022-01-28T11:00:17.000Z

from flask import jsonify from . import api @api.route('/', methods=['GET']) def index(): return jsonify("Welcome to the fmeca API. Check out '/api/facilities/'.")

21.25

77

0.676471

7943f43c477717e8563af684c11c3de16306bca5

11,918

py

Python

odps/df/backends/analyzer.py

walker83/aliyun-odps-python-sdk

f69c2520d346554131f4129360cb7ae1211699ce

[ "Apache-2.0" ]

null

odps/df/backends/analyzer.py

walker83/aliyun-odps-python-sdk

f69c2520d346554131f4129360cb7ae1211699ce

[ "Apache-2.0" ]

null

odps/df/backends/analyzer.py

walker83/aliyun-odps-python-sdk

f69c2520d346554131f4129360cb7ae1211699ce

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright 1999-2017 Alibaba Group Holding Ltd. # # 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 itertools from .core import Backend from ..utils import traverse_until_source from ..expr.expressions import Scalar, SequenceExpr, CollectionExpr from ..expr.reduction import GroupedSequenceReduction from ..expr.element import Switch from .. import output from ... import compat from ...models import Schema from .utils import refresh_dynamic from ..types import DynamicSchema from ...compat import six class BaseAnalyzer(Backend): """ Analyzer is used before optimzing, which analyze some operation that is not supported for this execution backend. """ def __init__(self, expr_dag, traversed=None, on_sub=None): self._dag = expr_dag self._indexer = itertools.count(0) self._traversed = traversed or set() self._on_sub = on_sub def analyze(self): for node in self._iter(): self._traversed.add(id(node)) self._visit_node(node) return self._dag.root def _iter(self): for node in traverse_until_source(self._dag, top_down=True, traversed=self._traversed): yield node while True: all_traversed = True for node in traverse_until_source(self._dag, top_down=True): if id(node) not in self._traversed: all_traversed = False yield node if all_traversed: break def _visit_node(self, node): try: node.accept(self) except NotImplementedError: return def _sub(self, expr, sub, parents=None): self._dag.substitute(expr, sub, parents=parents) if self._on_sub: self._on_sub(expr, sub) @staticmethod def _get_moment_sub_expr(expr, _input, order, center): def _group_mean(e): m = e.mean() if isinstance(expr, GroupedSequenceReduction): m = m.to_grouped_reduction(expr._grouped) return m def _order(e, o): if o == 1: return e else: return e ** o if not center: if order == 0: sub = Scalar(1) else: sub = _group_mean(_input ** order) else: if order == 0: sub = Scalar(1) elif order == 1: sub = Scalar(0) else: sub = _group_mean(_input ** order) divided = 1 divisor = 1 for o in compat.irange(1, order): divided *= order - o + 1 divisor *= o part_item = divided // divisor * _group_mean(_order(_input, order - o)) \ * (_order(_group_mean(_input), o)) if o & 1: sub -= part_item else: sub += part_item part_item = _group_mean(_input) ** order if order & 1: sub -= part_item else: sub += part_item return sub @classmethod def _get_cut_sub_expr(cls, expr): is_seq = isinstance(expr, SequenceExpr) kw = dict() if is_seq: kw['_data_type'] = expr.dtype else: kw['_value_type'] = expr.dtype conditions = [] thens = [] if expr.include_under: bin = expr.bins[0] if expr.right and not expr.include_lowest: conditions.append(expr.input <= bin) else: conditions.append(expr.input < bin) thens.append(expr.labels[0]) for i, bin in enumerate(expr.bins[1:]): lower_bin = expr.bins[i] if not expr.right or (i == 0 and expr.include_lowest): condition = lower_bin <= expr.input else: condition = lower_bin < expr.input if expr.right: condition = (condition & (expr.input <= bin)) else: condition = (condition & (expr.input < bin)) conditions.append(condition) if expr.include_under: thens.append(expr.labels[i + 1]) else: thens.append(expr.labels[i]) if expr.include_over: bin = expr.bins[-1] if expr.right: conditions.append(bin < expr.input) else: conditions.append(bin <= expr.input) thens.append(expr.labels[-1]) return Switch(_conditions=conditions, _thens=thens, _default=None, _input=None, **kw) @classmethod def _get_value_counts_sub_expr(cls, expr): collection = expr.input by = expr._by sort = expr._sort.value ascending = expr._ascending.value dropna = expr._dropna.value sub = collection.groupby(by).agg(count=by.count()) if sort: sub = sub.sort('count', ascending=ascending) if dropna: sub = sub.filter(sub[by.name].notnull()) return sub def _get_pivot_sub_expr(self, expr): columns_expr = expr.input.distinct([c.copy() for c in expr._columns]) group_names = [g.name for g in expr._group] group_types = [g.dtype for g in expr._group] exprs = [expr] def callback(result, new_expr): expr = exprs[0] columns = [r[0] for r in result] if len(expr._values) > 1: names = group_names + \ ['{0}_{1}'.format(v.name, c) for v in expr._values for c in columns] types = group_types + \ list(itertools.chain(*[[n.dtype] * len(columns) for n in expr._values])) else: names = group_names + columns types = group_types + [expr._values[0].dtype] * len(columns) new_expr._schema = Schema.from_lists(names, types) column_name = expr._columns[0].name # column's size can only be 1 values_names = [v.name for v in expr._values] @output(names, types) def reducer(keys): values = [None] * len(columns) * len(values_names) def h(row, done): col = getattr(row, column_name) for val_idx, value_name in enumerate(values_names): val = getattr(row, value_name) idx = len(columns) * val_idx + columns.index(col) if values[idx] is not None: raise ValueError( 'Row contains duplicate entries, rows: {0}, column: {1}'.format(keys, col)) values[idx] = val if done: yield keys + tuple(values) return h fields = expr._group + expr._columns + expr._values pivoted = expr.input.select(fields).map_reduce(reducer=reducer, group=group_names) self._sub(new_expr, pivoted) # trigger refresh of dynamic operations refresh_dynamic(pivoted, self._dag) return CollectionExpr(_schema=DynamicSchema.from_lists(group_names, group_types), _deps=[(columns_expr, callback)]) def _get_pivot_table_sub_expr_without_columns(self, expr): def get_agg(field, agg_func, agg_func_name, fill_value): if isinstance(agg_func, six.string_types): aggregated = getattr(field, agg_func)() else: aggregated = field.agg(agg_func) if fill_value is not None: aggregated.fillna(fill_value) return aggregated.rename('{0}_{1}'.format(field.name, agg_func_name)) grouped = expr.input.groupby(expr._group) aggs = [] for agg_func, agg_func_name in zip(expr._agg_func, expr._agg_func_names): for value in expr._values: agg = get_agg(value, agg_func, agg_func_name, expr.fill_value) aggs.append(agg) return grouped.aggregate(aggs, sort_by_name=False) def _get_pivot_table_sub_expr_with_columns(self, expr): columns_expr = expr.input.distinct([c.copy() for c in expr._columns]) group_names = [g.name for g in expr._group] group_types = [g.dtype for g in expr._group] exprs = [expr] def callback(result, new_expr): expr = exprs[0] columns = [r[0] for r in result] names = list(group_names) tps = list(group_types) aggs = [] for agg_func_name, agg_func in zip(expr._agg_func_names, expr._agg_func): for value_col in expr._values: for col in columns: base = '{0}_'.format(col) if col is not None else '' name = '{0}{1}_{2}'.format(base, value_col.name, agg_func_name) names.append(name) tps.append(value_col.dtype) col = col.item() if hasattr(col, 'item') else col field = (expr._columns[0] == col).ifelse( value_col, Scalar(_value_type=value_col.dtype)) if isinstance(agg_func, six.string_types): agg = getattr(field, agg_func)() else: func = agg_func() class ActualAgg(object): def buffer(self): return func.buffer() def __call__(self, buffer, value): if value is None: return func(buffer, value) def merge(self, buffer, pbuffer): func.merge(buffer, pbuffer) def getvalue(self, buffer): return func.getvalue(buffer) agg = field.agg(ActualAgg) if expr.fill_value is not None: agg = agg.fillna(expr.fill_value) agg = agg.rename(name) aggs.append(agg) new_expr._schema = Schema.from_lists(names, tps) pivoted = expr.input.groupby(expr._group).aggregate(aggs, sort_by_name=False) self._sub(new_expr, pivoted) # trigger refresh of dynamic operations refresh_dynamic(pivoted, self._dag) return CollectionExpr(_schema=DynamicSchema.from_lists(group_names, group_types), _deps=[(columns_expr, callback)]) def _get_pivot_table_sub_expr(self, expr): if expr._columns is None: return self._get_pivot_table_sub_expr_without_columns(expr) else: return self._get_pivot_table_sub_expr_with_columns(expr)

37.012422

107

0.529116

7943f595c674438a1cfec4698c62343f1a8c742b

656

py

Python

infrastructure/crypto_ml/utils/_utils.py

ATCUWgithub/CryptoML

6010c5daf7d985217fa76197b29331457a60a306

[ "MIT" ]

1

2020-02-18T00:38:16.000Z

infrastructure/crypto_ml/utils/_utils.py

ATCUWgithub/CryptoML

6010c5daf7d985217fa76197b29331457a60a306

[ "MIT" ]

null

infrastructure/crypto_ml/utils/_utils.py

ATCUWgithub/CryptoML

6010c5daf7d985217fa76197b29331457a60a306

[ "MIT" ]

1

2020-02-18T00:39:12.000Z

import json as _json import datetime as _datetime def parse_timestamp(dataset, time_format="%Y-%m-%dT%H:%M:%S.000Z"): for d in dataset: d["timestamp"] = _datetime.datetime.strptime(d["timestamp"], time_format) return dataset def load_json(filename, time_format="%Y-%m-%dT%H:%M:%S.000Z"): dictionary = dict() with open(filename) as f: dictionary = _json.load(f) return parse_timestamp(dictionary, time_format) def generate_config(dataset): start_idx = 0 end_idx = len(dataset) - 1 return { "test_start": dataset[start_idx]["timestamp"], "test_end": dataset[end_idx]["timestamp"] }

29.818182

81

0.660061

7943f67fbcf1be196d4617675369352657e99f99

5,806

py

Python

houston/plugin/gcp.py

datasparq-intelligent-products/houston-python

c9248c1f121366ad258e8434caa6d2462d765059

[ "MIT" ]

7

2020-03-16T13:17:50.000Z

2020-12-10T14:46:37.000Z

houston/plugin/gcp.py

datasparq-intelligent-products/houston-python

c9248c1f121366ad258e8434caa6d2462d765059

[ "MIT" ]

null

houston/plugin/gcp.py

datasparq-intelligent-products/houston-python

c9248c1f121366ad258e8434caa6d2462d765059

[ "MIT" ]

null

"""Houston Utilities for Google Cloud Platform PubSub utils: Allows user to create Google Cloud Pub/Sub message according to the plan stage options, e.g.: h.project = "my-project-1234" # set the Google Cloud project name in the client res = h.end_stage("load-data", mission_id) for next_stage in res['next']: h.pubsub_trigger({'stage': next_stage, 'mission_id': mission_id}, topic=h.get_params(next_stage)['topic']) --> sends a Pub/Sub message to the next tasks' topics. This assumes we have given each stage a 'topic' parameter. Note: The topic name can either be provided as an argument or can be set as a parameter for the stage as 'topic' or 'psq', in which case it will be found automatically. or: h.project = "my-project-1234" # set the Google Cloud project name in the client response = h.end_stage("load-data", mission_id) h.call_stage_via_pubsub(response, mission_id) # assumes each stage has a 'psq' parameter which gives the topic name """ import base64 import json import os from google.cloud import pubsub_v1 from houston.client import Houston class GCPHouston(Houston): project = os.getenv("GCP_PROJECT", None) topic = None def pubsub_trigger(self, data, topic=None): """Sends a message to the provided Pub/Sub topic with the provided data payload. :param dict data: content of the message to be sent. Should contain 'stage' and 'mission_id'. Can contain any additional JSON serializable information. :param string topic: Google Pub/Sub topic name, e.g. 'topic-for-stage'. This can either be provided here or be set as a parameter for the stage as 'topic' or 'psq'. """ publisher_client = pubsub_v1.PublisherClient() if self.project is None: raise ValueError( "Project is not set. Use GCPHouston.project = '[PROJECT]' " "or set 'GCP_PROJECT' environment variable" ) if 'plan' not in data: data['plan'] = self.plan['name'] # try to find the topic name in the stage parameters if topic is None: if 'stage' in data: stage_params = self.get_params(data['stage']) if stage_params: if stage_params['topic']: topic = stage_params['topic'] elif stage_params['psq']: topic = stage_params['psq'] if topic is None: raise ValueError("Pub/Sub could not be determined. It can either be provided as an argument to " "pubsub_trigger, or be a stage parameter with name 'topic' or 'psq'") full_topic = "projects/{project}/topics/{topic}".format( project=self.project, topic=topic ) future = publisher_client.publish(topic=full_topic, data=json.dumps(data).encode("utf-8")) future.result() def call_stage_via_pubsub(self, response, mission_id): """Send stage details to Google Cloud Platform PubSub. Sends stage, mission_id, plan name as json in message body, parameters as attributes Message parameter must contain "psq" (PubSub Queue) key, this informs the function which topic is relevant to the task Blocks until PubSub message has been sent :param dict response: response from Houston.end_stage :param string mission_id: unique identifier of mission currently being completed """ publisher_client = pubsub_v1.PublisherClient() if self.project is None: raise ValueError( "Project is not set. Use GCPHouston.project = '[PROJECT]' " "or set 'GCP_PROJECT' environment variable" ) # for all available tasks - trigger qs for next_task in response["next"]: if next_task not in response["params"]: print( "task: {next_task} does not have parameters, skipping".format( next_task=next_task ) ) continue if "psq" not in response["params"][next_task].keys() and "topic" not in response["params"][next_task].keys(): print( "task: {next_task} does not have psq topic set, skipping".format( next_task=next_task ) ) continue task_parameters = response["params"][next_task] if "psq" in task_parameters: target_psq = task_parameters.pop("psq") else: target_psq = task_parameters.pop("topic") data = json.dumps( {"stage": next_task, "mission_id": mission_id, "plan": self.plan} ).encode("utf-8") # make topic string topic = "projects/{project}/topics/{topic}".format( project=self.project, topic=target_psq ) # json encode task param values # useful for decoding in PubSub subscriber for key, value in task_parameters.items(): task_parameters[key] = json.dumps(value) if not task_parameters: future = publisher_client.publish(topic=topic, data=data) future.result() else: future = publisher_client.publish( topic=topic, data=data, **task_parameters ) future.result() @staticmethod def extract_stage_information(data): """Static method to extract stage information from sent PubSub message""" return json.loads(base64.b64decode(data))

38.450331

121

0.59628

7943f76bf464889fd4c6f26155e3653298ba9583

5,235

py

Python

src/bin/shipyard_airflow/shipyard_airflow/control/action/actions_steps_id_logs_api.py

openvdro/airship-shipyard

bae15294c534cf321f5c7ca37592dfa74c4ad7c2

[ "Apache-2.0" ]

12

2018-05-18T18:59:23.000Z

2019-05-10T12:31:44.000Z

src/bin/shipyard_airflow/shipyard_airflow/control/action/actions_steps_id_logs_api.py

openvdro/airship-shipyard

bae15294c534cf321f5c7ca37592dfa74c4ad7c2

[ "Apache-2.0" ]

4

2021-07-28T14:36:57.000Z

2022-03-22T16:39:23.000Z

src/bin/shipyard_airflow/shipyard_airflow/control/action/actions_steps_id_logs_api.py

openvdro/airship-shipyard

bae15294c534cf321f5c7ca37592dfa74c4ad7c2

[ "Apache-2.0" ]

9

2018-05-18T16:42:41.000Z

2019-04-18T20:12:14.000Z

# Copyright 2018 AT&T Intellectual Property. All other 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. import falcon import logging import os import requests from oslo_config import cfg from shipyard_airflow import policy from shipyard_airflow.control.base import BaseResource from shipyard_airflow.control.helpers.action_helper import ActionsHelper from shipyard_airflow.errors import ApiError CONF = cfg.CONF LOG = logging.getLogger(__name__) class ActionsStepsLogsResource(BaseResource): """ The actions steps logs resource retrieves the logs for a particular step of an action. By default, it will retrieve the logs from the last attempt. Note that a workflow step can retry multiple times with the names of the logs as 1.log, 2.log, 3.log, etc. """ @policy.ApiEnforcer(policy.GET_ACTION_STEP_LOGS) def on_get(self, req, resp, **kwargs): """ Returns the logs of an action step :returns: logs of an action step """ # We will set the kwarg to 'try_number' as 'try' is a # reserved keyword try_number = req.get_param_as_int('try', required=False) # Parse kwargs action_id = ActionsHelper.parse_action_id(**kwargs) step_id = ActionsHelper.parse_step_id(**kwargs) # Retrieve logs for the action step resp.body = self.get_action_step_logs(action_id, step_id, try_number) resp.status = falcon.HTTP_200 def get_action_step_logs(self, action_id, step_id, try_number=None): """ Retrieve Airflow Logs """ # Set up actions helper self.actions_helper = ActionsHelper(action_id=action_id) # Retrieve step step = self.actions_helper.get_step(step_id, try_number) # Retrieve Dag ID dag_id = step['dag_id'] # Generate Log Endpoint log_endpoint = self.generate_log_endpoint(step, dag_id, step_id, try_number) LOG.debug("Log endpoint url is: %s", log_endpoint) return self.retrieve_logs(log_endpoint) def generate_log_endpoint(self, step, dag_id, step_id, try_number): """ Retrieve Log Endpoint """ # Construct worker pod URL scheme = CONF.airflow.worker_endpoint_scheme worker_pod_fqdn = step['hostname'] worker_pod_port = CONF.airflow.worker_port worker_pod_url = "{}://{}:{}".format(scheme, worker_pod_fqdn, str(worker_pod_port)) # Define log_file if try_number: log_file = str(try_number) + '.log' else: log_file = str(step['try_number']) + '.log' # Define dag_execution_date dag_execution_date = ( self.actions_helper.get_formatted_dag_execution_date(step)) # Form logs query endpoint log_endpoint = os.path.join(worker_pod_url, 'log', dag_id, step_id, dag_execution_date, log_file) return log_endpoint @staticmethod def retrieve_logs(log_endpoint): """ Retrieve Logs """ LOG.debug("Retrieving Airflow logs...") try: response = requests.get( log_endpoint, timeout=( CONF.requests_config.airflow_log_connect_timeout, CONF.requests_config.airflow_log_read_timeout)) except requests.exceptions.RequestException as e: LOG.exception(e) raise ApiError( title='Log retrieval error', description='Exception happened during Airflow API request', status=falcon.HTTP_500) if response.status_code >= 400: LOG.info('Airflow endpoint returned error status code %s, ' 'content %s. Response code will be bubbled up', response.status_code, response.text) raise ApiError( title='Log retrieval error', description='Airflow endpoint returned error status code', status=getattr( falcon, 'HTTP_%d' % response.status_code, falcon.HTTP_500)) return response.text

35.856164

76

0.580516