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  • Extra characters Extracted with XPath and Python (html)

    - by Nacari
    I have been using XPath with scrapy to extract text from html tags online, but when I do I get extra characters attached. An example is trying to extract a number, like "204" from a <td> tag and getting [u'204']. In some cases its much worse. For instance trying to extract "1 - Mathoverflow" and instead getting [u'\r\n\t\t 1 \u2013 MathOverflow\r\n\t\t ']. Is there a way to prevent this, or trim the strings so that the extra characters arent a part of the string? (using items to store the data). It looks like it has something to do with formatting, so how do I get xpath to not pick up that stuff?

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  • compute mean in python for a generator

    - by nmaxwell
    Hi, I'm doing some statistics work, I have a (large) collection of random numbers to compute the mean of, I'd like to work with generators, because I just need to compute the mean, so I don't need to store the numbers. The problem is that numpy.mean breaks if you pass it a generator. I can write a simple function to do what I want, but I'm wondering if there's a proper, built-in way to do this? It would be nice if I could say "sum(values)/len(values)", but len doesn't work for genetators, and sum already consumed values. here's an example: import numpy def my_mean(values): n = 0 Sum = 0.0 try: while True: Sum += next(values) n += 1 except StopIteration: pass return float(Sum)/n X = [k for k in range(1,7)] Y = (k for k in range(1,7)) print numpy.mean(X) print my_mean(Y) these both give the same, correct, answer, buy my_mean doesn't work for lists, and numpy.mean doesn't work for generators. I really like the idea of working with generators, but details like this seem to spoil things. thanks for any help -nick

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  • I have an Errno 13 Permission denied with subprocess in python

    - by wDroter
    The line with the issue is ret=subprocess.call(shlex.split(cmd)) cmd = /usr/share/java -cp pig-hadoop-conf-Simpsons:lib/pig-0.8.1-cdh3u1-core.jar:lib/hadoop-core-0.20.2-cdh3u1.jar org.apache.pig.Main -param func=cat -param from =foo.txt -x mapreduce fsFunc.pig The error is. File "./run_pig.py", line 157, in process ret=subprocess.call(shlex.split(cmd)) File "/usr/lib/python2.7/subprocess.py", line 493, in call return Popen(*popenargs, **kwargs).wait() File "/usr/lib/python2.7/subprocess.py", line 679, in __init__ errread, errwrite) File "/usr/lib/python2.7/subprocess.py", line 1249, in _execute_child raise child_exception OSError: [Errno 13] Permission denied Let me know if any more info is needed. Any help is appreciated. Thanks.

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  • Dynamic Operator Overloading on dict classes in Python

    - by Ishpeck
    I have a class that dynamically overloads basic arithmetic operators like so... import operator class IshyNum: def __init__(self, n): self.num=n self.buildArith() def arithmetic(self, other, o): return o(self.num, other) def buildArith(self): map(lambda o: setattr(self, "__%s__"%o,lambda f: self.arithmetic(f, getattr(operator, o))), ["add", "sub", "mul", "div"]) if __name__=="__main__": number=IshyNum(5) print number+5 print number/2 print number*3 print number-3 But if I change the class to inherit from the dictionary (class IshyNum(dict):) it doesn't work. I need to explicitly def __add__(self, other) or whatever in order for this to work. Why?

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  • Python unicode search not giving correct answer

    - by user1318912
    I am trying to search hindi words contained one line per file in file-1 and find them in lines in file-2. I have to print the line numbers with the number of words found. This is the code: import codecs hypernyms = codecs.open("hindi_hypernym.txt", "r", "utf-8").readlines() words = codecs.open("hypernyms_en2hi.txt", "r", "utf-8").readlines() count_arr = [] for counter, line in enumerate(hypernyms): count_arr.append(0) for word in words: if line.find(word) >=0: count_arr[counter] +=1 for iterator, count in enumerate(count_arr): if count>0: print iterator, ' ', count This is finding some words, but ignoring some others The input files are: File-1: ???? ??????? File-2: ???????, ????-???? ?????-???, ?????-???, ?????_???, ?????_??? ????_????, ????-????, ???????_???? ????-???? This gives output: 0 1 3 1 Clearly, it is ignoring ??????? and searching for ???? only. I have tried with other inputs as well. It only searches for one word. Any idea how to correct this?

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  • strip spaces in python.

    - by Richard
    ok I know that this should be simple... anyways say: line = "$W5M5A,100527,142500,730301c44892fd1c,2,686.5 4,333.96,0,0,28.6,123,75,-0.4,1.4*49" I want to strip out the spaces. I thought you would just do this line = line.strip() but now line is still '$W5M5A,100527,142500,730301c44892fd1c,2,686.5 4,333.96,0,0,28.6,123,75,-0.4,1.4*49' instead of '$W5M5A,100527,142500,730301c44892fd1c,2,686.54,333.96,0,0,28.6,123,75,-0.4,1.4*49' any thoughts?

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  • Python: Taking an array and break it into subarrays based on some criteria

    - by randombits
    I have an array of files. I'd like to be able to break that array down into one array with multiple subarrays, each subarray contains files that were created on the same day. So right now if the array contains files from March 1 - March 31, I'd like to have an array with 31 subarrays (assuming there is at least 1 file for each day). In the long run, I'm trying to find the file from each day with the latest creation/modification time. If there is a way to bundle that into the iterations that are required above to save some CPU cycles, that would be even more ideal. Then I'd have one flat array with 31 files, one for each day, for the latest file created on each individual day.

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  • python cairoplot store previous readings..

    - by krisdigitx
    hi, i am using cairoplot, to make graphs, however the file from where i am reading the data is growing huge and its taking a long time to process the graph is there any real-time way to produce cairo graph, or at least store the previous readings..like rrd. -krisdigitx

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  • Optimizing python link matching regular expression

    - by Matt
    I have a regular expression, links = re.compile('<a(.+?)href=(?:"|\')?((?:https?://|/)[^\'"]+)(?:"|\')?(.*?)>(.+?)</a>',re.I).findall(data) to find links in some html, it is taking a long time on certain html, any optimization advice? One that it chokes on is http://freeyourmindonline.net/Blog/

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  • Python - pickling fails for numpy.void objects

    - by I82Much
    >>> idmapfile = open("idmap", mode="w") >>> pickle.dump(idMap, idmapfile) >>> idmapfile.close() >>> idmapfile = open("idmap") >>> unpickled = pickle.load(idmapfile) >>> unpickled == idMap False idMap[1] {1537: (552, 1, 1537, 17.793827056884766, 3), 1540: (4220, 1, 1540, 19.31205940246582, 3), 1544: (592, 1, 1544, 18.129131317138672, 3), 1675: (529, 1, 1675, 18.347782135009766, 3), 1550: (4048, 1, 1550, 19.31205940246582, 3), 1424: (1528, 1, 1424, 19.744396209716797, 3), 1681: (1265, 1, 1681, 19.596025466918945, 3), 1560: (3457, 1, 1560, 20.530569076538086, 3), 1690: (477, 1, 1690, 17.395542144775391, 3), 1691: (554, 1, 1691, 13.446117401123047, 3), 1436: (3010, 1, 1436, 19.596025466918945, 3), 1434: (3183, 1, 1434, 19.744396209716797, 3), 1441: (3570, 1, 1441, 20.589576721191406, 3), 1435: (476, 1, 1435, 19.640911102294922, 3), 1444: (527, 1, 1444, 17.98480224609375, 3), 1478: (1897, 1, 1478, 19.596025466918945, 3), 1575: (614, 1, 1575, 19.371648788452148, 3), 1586: (2189, 1, 1586, 19.31205940246582, 3), 1716: (3470, 1, 1716, 19.158674240112305, 3), 1590: (2278, 1, 1590, 19.596025466918945, 3), 1463: (991, 1, 1463, 19.31205940246582, 3), 1594: (1890, 1, 1594, 19.596025466918945, 3), 1467: (1087, 1, 1467, 19.31205940246582, 3), 1596: (3759, 1, 1596, 19.744396209716797, 3), 1602: (3011, 1, 1602, 20.530569076538086, 3), 1547: (490, 1, 1547, 17.994071960449219, 3), 1605: (658, 1, 1605, 19.31205940246582, 3), 1606: (1794, 1, 1606, 16.964881896972656, 3), 1719: (1826, 1, 1719, 19.596025466918945, 3), 1617: (583, 1, 1617, 11.894925117492676, 3), 1492: (3441, 1, 1492, 20.500667572021484, 3), 1622: (3215, 1, 1622, 19.31205940246582, 3), 1628: (2761, 1, 1628, 19.744396209716797, 3), 1502: (1563, 1, 1502, 19.596025466918945, 3), 1632: (1108, 1, 1632, 15.457141876220703, 3), 1468: (3779, 1, 1468, 19.596025466918945, 3), 1642: (3970, 1, 1642, 19.744396209716797, 3), 1518: (612, 1, 1518, 18.570245742797852, 3), 1647: (854, 1, 1647, 16.964881896972656, 3), 1650: (2099, 1, 1650, 20.439058303833008, 3), 1651: (540, 1, 1651, 18.552841186523438, 3), 1653: (613, 1, 1653, 19.237197875976563, 3), 1532: (537, 1, 1532, 18.885730743408203, 3)} >>> unpickled[1] {1537: (64880, 1638, 56700, -1.0808743559293829e+18, 152), 1540: (64904, 1638, 0, 0.0, 0), 1544: (54472, 1490, 0, 0.0, 0), 1675: (6464, 1509, 0, 0.0, 0), 1550: (43592, 1510, 0, 0.0, 0), 1424: (43616, 1510, 0, 0.0, 0), 1681: (0, 0, 0, 0.0, 0), 1560: (400, 152, 400, 2.1299736657737219e-43, 0), 1690: (408, 152, 408, 2.7201111331839077e+26, 34), 1435: (424, 152, 61512, 1.0122952080313192e-39, 0), 1436: (400, 152, 400, 20.250289916992188, 3), 1434: (424, 152, 62080, 1.0122952080313192e-39, 0), 1441: (400, 152, 400, 12.250144958496094, 3), 1691: (424, 152, 42608, 15.813941955566406, 3), 1444: (400, 152, 400, 19.625289916992187, 3), 1606: (424, 152, 42432, 5.2947192852601414e-22, 41), 1575: (400, 152, 400, 6.2537390010262572e-36, 0), 1586: (424, 152, 42488, 1.0122601755697111e-39, 0), 1716: (400, 152, 400, 6.2537390010262572e-36, 0), 1590: (424, 152, 64144, 1.0126357235581501e-39, 0), 1463: (400, 152, 400, 6.2537390010262572e-36, 0), 1594: (424, 152, 32672, 17.002994537353516, 3), 1467: (400, 152, 400, 19.750289916992187, 3), 1596: (424, 152, 7176, 1.0124003054161436e-39, 0), 1602: (400, 152, 400, 18.500289916992188, 3), 1547: (424, 152, 7000, 1.0124003054161436e-39, 0), 1605: (400, 152, 400, 20.500289916992188, 3), 1478: (424, 152, 42256, -6.0222748507426518e+30, 222), 1719: (400, 152, 400, 6.2537390010262572e-36, 0), 1617: (424, 152, 16472, 1.0124283313854301e-39, 0), 1492: (400, 152, 400, 6.2537390010262572e-36, 0), 1622: (424, 152, 35304, 1.0123190301052127e-39, 0), 1628: (400, 152, 400, 6.2537390010262572e-36, 0), 1502: (424, 152, 63152, 19.627988815307617, 3), 1632: (400, 152, 400, 19.375289916992188, 3), 1468: (424, 152, 38088, 1.0124213248931084e-39, 0), 1642: (400, 152, 400, 6.2537390010262572e-36, 0), 1518: (424, 152, 63896, 1.0127436235399031e-39, 0), 1647: (400, 152, 400, 6.2537390010262572e-36, 0), 1650: (424, 152, 53424, 16.752857208251953, 3), 1651: (400, 152, 400, 19.250289916992188, 3), 1653: (424, 152, 50624, 1.0126497365427934e-39, 0), 1532: (400, 152, 400, 6.2537390010262572e-36, 0)} The keys come out fine, the values are screwed up. I tried same thing loading file in binary mode; didn't fix the problem. Any idea what I'm doing wrong? Edit: Here's the code with binary. Note that the values are different in the unpickled object. >>> idmapfile = open("idmap", mode="wb") >>> pickle.dump(idMap, idmapfile) >>> idmapfile.close() >>> idmapfile = open("idmap", mode="rb") >>> unpickled = pickle.load(idmapfile) >>> unpickled==idMap False >>> unpickled[1] {1537: (12176, 2281, 56700, -1.0808743559293829e+18, 152), 1540: (0, 0, 15934, 2.7457842047810522e+26, 108), 1544: (400, 152, 400, 4.9518498821046956e+27, 53), 1675: (408, 152, 408, 2.7201111331839077e+26, 34), 1550: (456, 152, 456, -1.1349175514578289e+18, 152), 1424: (432, 152, 432, 4.5939047815653343e-40, 11), 1681: (408, 152, 408, 2.1299736657737219e-43, 0), 1560: (376, 152, 376, 2.1299736657737219e-43, 0), 1690: (376, 152, 376, 2.1299736657737219e-43, 0), 1435: (376, 152, 376, 2.1299736657737219e-43, 0), 1436: (376, 152, 376, 2.1299736657737219e-43, 0), 1434: (376, 152, 376, 2.1299736657737219e-43, 0), 1441: (376, 152, 376, 2.1299736657737219e-43, 0), 1691: (376, 152, 376, 2.1299736657737219e-43, 0), 1444: (376, 152, 376, 2.1299736657737219e-43, 0), 1606: (25784, 2281, 376, -3.2883343074537754e+26, 34), 1575: (24240, 2281, 376, 2.1299736657737219e-43, 0), 1586: (24240, 2281, 376, 2.1299736657737219e-43, 0), 1716: (24240, 2281, 376, -3.0093091599657311e-35, 26), 1590: (24240, 2281, 376, 2.1299736657737219e-43, 0), 1463: (24240, 2281, 376, 2.1299736657737219e-43, 0), 1594: (24240, 2281, 376, -4123208450048.0, 196), 1467: (25784, 2281, 376, 2.1299736657737219e-43, 0), 1596: (25784, 2281, 376, 2.1299736657737219e-43, 0), 1602: (25784, 2281, 376, -5.9963281433905448e+26, 76), 1547: (25784, 2281, 376, -218106240.0, 139), 1605: (25784, 2281, 376, -3.7138649803377281e+27, 56), 1478: (376, 152, 376, 2.1299736657737219e-43, 0), 1719: (25784, 2281, 376, 2.1299736657737219e-43, 0), 1617: (25784, 2281, 376, -1.4411779941597184e+17, 237), 1492: (25784, 2281, 376, 2.8596493694487798e-30, 80), 1622: (25784, 2281, 376, 184686084096.0, 93), 1628: (1336, 152, 1336, 3.1691839245470052e+29, 179), 1502: (1272, 152, 1272, -5.2042207205116645e-17, 99), 1632: (1208, 152, 1208, 2.1299736657737219e-43, 0), 1468: (1144, 152, 1144, 2.1299736657737219e-43, 0), 1642: (1080, 152, 1080, 2.1299736657737219e-43, 0), 1518: (1016, 152, 1016, 4.0240902787680023e+35, 145), 1647: (952, 152, 952, -985172619034624.0, 237), 1650: (888, 152, 888, 12094787289088.0, 66), 1651: (824, 152, 824, 2.1299736657737219e-43, 0), 1653: (760, 152, 760, 0.00018310768064111471, 238), 1532: (696, 152, 696, 8.8978061885676389e+26, 125)} OK I've isolated the problem, but don't know why it's so. First, apparently what I'm pickling are not tuples (though they look like it), but instead numpy.void types. Here is a series to illustrate the problem. first = run0.detections[0] >>> first (1, 19, 1578, 82.637763977050781, 1) >>> type(first) <type 'numpy.void'> >>> firstTuple = tuple(first) >>> theFile = open("pickleTest", "w") >>> pickle.dump(first, theFile) >>> theTupleFile = open("pickleTupleTest", "w") >>> pickle.dump(firstTuple, theTupleFile) >>> theFile.close() >>> theTupleFile.close() >>> first (1, 19, 1578, 82.637763977050781, 1) >>> firstTuple (1, 19, 1578, 82.637764, 1) >>> theFile = open("pickleTest", "r") >>> theTupleFile = open("pickleTupleTest", "r") >>> unpickledTuple = pickle.load(theTupleFile) >>> unpickledVoid = pickle.load(theFile) >>> type(unpickledVoid) <type 'numpy.void'> >>> type(unpickledTuple) <type 'tuple'> >>> unpickledTuple (1, 19, 1578, 82.637764, 1) >>> unpickledTuple == firstTuple True >>> unpickledVoid == first False >>> unpickledVoid (7936, 1705, 56700, -1.0808743559293829e+18, 152) >>> first (1, 19, 1578, 82.637763977050781, 1)

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  • Caching result of setUp() using Python unittest

    - by dbr
    I currently have a unittest.TestCase that looks like.. class test_appletrailer(unittest.TestCase): def setup(self): self.all_trailers = Trailers(res = "720", verbose = True) def test_has_trailers(self): self.failUnless(len(self.all_trailers) > 1) # ..more tests.. This works fine, but the Trailers() call takes about 2 seconds to run.. Given that setUp() is called before each test is run, the tests now take almost 10 seconds to run (with only 3 test functions) What is the correct way of caching the self.all_trailers variable between tests? Removing the setUp function, and doing.. class test_appletrailer(unittest.TestCase): all_trailers = Trailers(res = "720", verbose = True) ..works, but then it claims "Ran 3 tests in 0.000s" which is incorrect.. The only other way I could think of is to have a cache_trailers global variable (which works correctly, but is rather horrible): cache_trailers = None class test_appletrailer(unittest.TestCase): def setUp(self): global cache_trailers if cache_trailers is None: cache_trailers = self.all_trailers = all_trailers = Trailers(res = "720", verbose = True) else: self.all_trailers = cache_trailers

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  • match strings in python

    - by mesun
    Write a function, called constrainedMatchPair which takes three arguments: a tuple representing starting points for the first substring, a tuple representing starting points for the second substring, and the length of the first substring. The function should return a tuple of all members (call it n) of the first tuple for which there is an element in the second tuple (call it k) such that n+m+1 = k, where m is the length of the first substring. Complete the definition def constrainedMatchPair(firstMatch,secondMatch,length):

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  • Python - Subprocess Popen and Thread error

    - by n0idea
    In both functions record and ftp, i have subprocess.Popen if __name__ == '__main__': try: t1 = threading.Thread(target = record) t1.daemon = True t1.start() t2 = threading.Thread(target = ftp) t2.daemon = True t2.start() except (KeyboardInterrupt, SystemExit): sys.exit() The error I'm receiving is: Exception in thread Thread-1 (most likely raised during interpreter shutdown): Traceback (most recent call last): File "/usr/lib/python2.7/threading.py", line 551, in __bootstrap_inner File "/usr/lib/python2.7/threading.py", line 504, in run File "./in.py", line 20, in recordaudio File "/usr/lib/python2.7/subprocess.py", line 493, in call File "/usr/lib/python2.7/subprocess.py", line 679, in __init__ File "/usr/lib/python2.7/subprocess.py", line 1237, in _execute_child <type 'exceptions.AttributeError'>: 'NoneType' object has no attribute 'close' What might the issue be ?

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  • how can i randomly print an element from a list in python

    - by lm
    So far i have this, which prints out every word in my list, but i am trying to print only one word at random. Any suggestions? def main(): # open a file wordsf = open('words.txt', 'r') word=random.choice('wordsf') words_count=0 for line in wordsf: word= line.rstrip('\n') print(word) words_count+=1 # close the file wordsf.close()

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  • Python metaprogramming help

    - by Timmy
    im looking into mongoengine, and i wanted to make a class an "EmbeddedDocument" dynamically, so i do this def custom(cls): cls = type( cls.__name__, (EmbeddedDocument,), cls.__dict__.copy() ) cls.a = FloatField(required=True) cls.b = FloatField(required=True) return cls A = custom( A ) and tried it on some classes, but its not doing some of the base class's init or sumthing in BaseDocument def __init__(self, **values): self._data = {} # Assign initial values to instance for attr_name, attr_value in self._fields.items(): if attr_name in values: setattr(self, attr_name, values.pop(attr_name)) else: # Use default value if present value = getattr(self, attr_name, None) setattr(self, attr_name, value) but this never gets used, thus never setting ._data, and giving me errors. how do i do this?

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  • Efficient file buffering & scanning methods for large files in python

    - by eblume
    The description of the problem I am having is a bit complicated, and I will err on the side of providing more complete information. For the impatient, here is the briefest way I can summarize it: What is the fastest (least execution time) way to split a text file in to ALL (overlapping) substrings of size N (bound N, eg 36) while throwing out newline characters. I am writing a module which parses files in the FASTA ascii-based genome format. These files comprise what is known as the 'hg18' human reference genome, which you can download from the UCSC genome browser (go slugs!) if you like. As you will notice, the genome files are composed of chr[1..22].fa and chr[XY].fa, as well as a set of other small files which are not used in this module. Several modules already exist for parsing FASTA files, such as BioPython's SeqIO. (Sorry, I'd post a link, but I don't have the points to do so yet.) Unfortunately, every module I've been able to find doesn't do the specific operation I am trying to do. My module needs to split the genome data ('CAGTACGTCAGACTATACGGAGCTA' could be a line, for instance) in to every single overlapping N-length substring. Let me give an example using a very small file (the actual chromosome files are between 355 and 20 million characters long) and N=8 import cStringIO example_file = cStringIO.StringIO("""\ header CAGTcag TFgcACF """) for read in parse(example_file): ... print read ... CAGTCAGTF AGTCAGTFG GTCAGTFGC TCAGTFGCA CAGTFGCAC AGTFGCACF The function that I found had the absolute best performance from the methods I could think of is this: def parse(file): size = 8 # of course in my code this is a function argument file.readline() # skip past the header buffer = '' for line in file: buffer += line.rstrip().upper() while len(buffer) = size: yield buffer[:size] buffer = buffer[1:] This works, but unfortunately it still takes about 1.5 hours (see note below) to parse the human genome this way. Perhaps this is the very best I am going to see with this method (a complete code refactor might be in order, but I'd like to avoid it as this approach has some very specific advantages in other areas of the code), but I thought I would turn this over to the community. Thanks! Note, this time includes a lot of extra calculation, such as computing the opposing strand read and doing hashtable lookups on a hash of approximately 5G in size. Post-answer conclusion: It turns out that using fileobj.read() and then manipulating the resulting string (string.replace(), etc.) took relatively little time and memory compared to the remainder of the program, and so I used that approach. Thanks everyone!

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  • Python/YACC Lexer: Token priority?

    - by Rosarch
    I'm trying to use reserved words in my grammar: reserved = { 'if' : 'IF', 'then' : 'THEN', 'else' : 'ELSE', 'while' : 'WHILE', } tokens = [ 'DEPT_CODE', 'COURSE_NUMBER', 'OR_CONJ', 'ID', ] + list(reserved.values()) t_DEPT_CODE = r'[A-Z]{2,}' t_COURSE_NUMBER = r'[0-9]{4}' t_OR_CONJ = r'or' t_ignore = ' \t' def t_ID(t): r'[a-zA-Z_][a-zA-Z_0-9]*' if t.value in reserved.values(): t.type = reserved[t.value] return t return None However, the t_ID rule somehow swallows up DEPT_CODE and OR_CONJ. How can I get around this? I'd like those two to take higher precedence than the reserved words.

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  • Python: Unpack arbitary length bits for database storage

    - by sberry2A
    I have a binary data format consisting of 18,000+ packed int64s, ints, shorts, bytes and chars. The data is packed to minimize it's size, so they don't always use byte sized chunks. For example, a number whose min and max value are 31, 32 respectively might be stored with a single bit where the actual value is bitvalue + min, so 0 is 31 and 1 is 32. I am looking for the most efficient way to unpack all of these for subsequent processing and database storage. Right now I am able to read any value by using either struct.unpack, or BitBuffer. I use struct.unpack for any data that starts on a bit where (bit-offset % 8 == 0 and data-length % 8 == 0) and I use BitBuffer for anything else. I know the offset and size of every packed piece of data, so what is going to be the fasted way to completely unpack them? Many thanks.

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  • Python module being reloaded for each request with django and mod_wsgi

    - by Vishal
    I have a variable in init of a module which get loaded from the database and takes about 15 seconds. For django development server everything is working fine but looks like with apache2 and mod_wsgi the module is loaded with every request (taking 15 seconds). Any idea about this behavior? Update: I have enabled daemon mode in mod wsgi, looks like its not reloading the modules now! needs more testing and I will update.

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