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  • OpenMeetings + Python + Suds

    - by user366774
    Trying to integrate openmeetings with django website, but can't understand how properly configure ImportDoctor: (here :// replaced with __ 'cause spam protection) print url http://sovershenstvo.com.ua:5080/openmeetings/services/UserService?wsdl imp = Import('http__schemas.xmlsoap.org/soap/encoding/') imp.filter.add('http__services.axis.openmeetings.org') imp.filter.add('http__basic.beans.hibernate.app.openmeetings.org/xsd') imp.filter.add('http__basic.beans.data.app.openmeetings.org/xsd') imp.filter.add('http__services.axis.openmeetings.org') d = ImportDoctor(imp) client = Client(url, doctor = d) client.service.getSession() Traceback (most recent call last): File "", line 1, in File "/usr/lib/python2.6/site-packages/suds/client.py", line 539, in call return client.invoke(args, kwargs) File "/usr/lib/python2.6/site-packages/suds/client.py", line 598, in invoke result = self.send(msg) File "/usr/lib/python2.6/site-packages/suds/client.py", line 627, in send result = self.succeeded(binding, reply.message) File "/usr/lib/python2.6/site-packages/suds/client.py", line 659, in succeeded r, p = binding.get_reply(self.method, reply) File "/usr/lib/python2.6/site-packages/suds/bindings/binding.py", line 159, in get_reply resolved = rtypes[0].resolve(nobuiltin=True) File "/usr/lib/python2.6/site-packages/suds/xsd/sxbasic.py", line 63, in resolve raise TypeNotFound(qref) suds.TypeNotFound: Type not found: '(Sessiondata, http__basic.beans.hibernate.app.openmeetings.org/xsd, )' what i'm doing wrong? please help and sorry for my english, but you are my last chance to save position :( need webinars at morning (2.26 am now)

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  • Python hash() can't handle long integer?

    - by Xie
    I defined a class: class A: ''' hash test class a = A(9, 1196833379, 1, 1773396906) hash(a) -340004569 This is weird, 12544897317L expected. ''' def __init__(self, a, b, c, d): self.a = a self.b = b self.c = c self.d = d def __hash__(self): return self.a * self.b + self.c * self.d Why, in the doctest, hash() function gives a negative integer?

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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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  • Proper structure for many test cases in Python with unittest

    - by mellort
    I am looking into the unittest package, and I'm not sure of the proper way to structure my test cases when writing a lot of them for the same method. Say I have a fact function which calculates the factorial of a number; would this testing file be OK? import unittest class functions_tester(unittest.TestCase): def test_fact_1(self): self.assertEqual(1, fact(1)) def test_fact_2(self): self.assertEqual(2, fact(2)) def test_fact_3(self): self.assertEqual(6, fact(3)) def test_fact_4(self): self.assertEqual(24, fact(4)) def test_fact_5(self): self.assertFalse(1==fact(5)) def test_fact_6(self): self.assertRaises(RuntimeError, fact, -1) #fact(-1) if __name__ == "__main__": unittest.main() It seems sloppy to have so many test methods for one method. I'd like to just have one testing method and put a ton of basic test cases (ie 4! ==24, 3!==6, 5!==120, and so on), but unittest doesn't let you do that. What is the best way to structure a testing file in this scenario? Thanks in advance for the help.

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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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  • Using __str__ representation for printing objects in containers in Python

    - by BobDobbs
    I've noticed that when an instance with an overloaded str method is passed to the print() function as an argument, it prints as intended. However, when passing a container that contains one of those instances to print(), it uses the repr method instead. That is to say, print(x) displays the correct string representation of x, and print(x, y) works correctly, but print([x]) or print((x, y)) prints the repr representation instead. First off, why does this happen? Secondly, is there a way to correct that behavior of print() in this circumstance?

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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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  • 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 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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  • Restart logging to a new file (Python)

    - by compie
    I'm using the following code to initialize logging in my application. logger = logging.getLogger() logger.setLevel(logging.DEBUG) # log to a file directory = '/reserved/DYPE/logfiles' now = datetime.now().strftime("%Y%m%d_%H%M%S") filename = os.path.join(directory, 'dype_%s.log' % now) file_handler = logging.FileHandler(filename) file_handler.setLevel(logging.DEBUG) formatter = logging.Formatter("%(asctime)s %(filename)s, %(lineno)d, %(funcName)s: %(message)s") file_handler.setFormatter(formatter) logger.addHandler(file_handler) # log to the console console_handler = logging.StreamHandler() level = logging.INFO console_handler.setLevel(level) logger.addHandler(console_handler) logging.debug('logging initialized') How can I close the current logging file and restart logging to a new file? Note: I don't want to use RotatingFileHandler, because I want full control over all the filenames and the moment of rotation.

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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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  • 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 fdb save huge data from database to file

    - by peter
    I have this script SELECT = """ select coalesce (p.ID,'') as id, coalesce (p.name,'') as name, from TABLE as p """ self.cur.execute(SELECT) for row in self.cur.itermap(): xml +=" <item>\n" xml +=" <id>" + id + "</id>\n" xml +=" <name>" + name + "</name>\n" xml +=" </item>\n\n" #save xml to file here f = open... and I need to save data from huge database to file. There are 10 000s (up to 40000) of items in my database and it takes very long time when script runs (1 hour and more) until finish. How can I take data I need from database and save it to file "at once"? (as quick as possible? I don't need xml output because I can process data from output on my server later. I just need to do it as quickly as possible. Any idea?) Many thanks!

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  • Python RegExp exception

    - by Jasie
    How do I split on all nonalphanumeric characters, EXCEPT the apostrophe? re.split('\W+',text) works, but will also split on apostrophes. How do I add an exception to this rule? Thanks!

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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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  • 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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  • Optimizing BeautifulSoup (Python) code

    - by user283405
    I have code that uses the BeautifulSoup library for parsing, but it is very slow. The code is written in such a way that threads cannot be used. Can anyone help me with this? I am using BeautifulSoup for parsing and than save into a DB. If I comment out the save statement, it still takes a long time, so there is no problem with the database. def parse(self,text): soup = BeautifulSoup(text) arr = soup.findAll('tbody') for i in range(0,len(arr)-1): data=Data() soup2 = BeautifulSoup(str(arr[i])) arr2 = soup2.findAll('td') c=0 for j in arr2: if str(j).find("<a href=") > 0: data.sourceURL = self.getAttributeValue(str(j),'<a href="') else: if c == 2: data.Hits=j.renderContents() #and few others... c = c+1 data.save() Any suggestions? Note: I already ask this question here but that was closed due to incomplete information.

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  • Sorting Python list based on the length of the string

    - by prosseek
    I want to sort a list of strings based on the string length. I tried to use sort as follows, but it doesn't seem to give me correct result. xs = ['dddd','a','bb','ccc'] print xs xs.sort(lambda x,y: len(x) < len(y)) print xs ['dddd', 'a', 'bb', 'ccc'] ['dddd', 'a', 'bb', 'ccc'] What might be wrong?

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  • Organizing a random list of objects in Python.

    - by Saebin
    So I have a list that I want to convert to a list that contains a list for each group of objects. ie ['objA.attr1', 'objC', 'objA.attr55', 'objB.attr4'] would return [['objA.attr1', 'objA.attr55'], ['objC'], ['objB.attr4']] currently this is what I use: givenList = ['a.attr1', 'b', 'a.attr55', 'c.attr4'] trgList = [] objNames = [] for val in givenList: obj = val.split('.')[0] if obj in objNames: id = objNames.index(obj) trgList[id].append(val) else: objNames.append(obj) trgList.append([val]) #print trgList It seems to run a decent speed when the original list has around 100,000 ids... but I am curious if there is a better way to do this. Order of the objects or attributes does not matter. Any ideas?

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  • python: problem with dictionary get method default value

    - by goutham
    I'm having a new problem here .. CODE 1: try: urlParams += "%s=%s&"%(val['name'], data.get(val['name'], serverInfo_D.get(val['name']))) except KeyError: print "expected parameter not provided - "+val["name"]+" is missing" exit(0) CODE 2: try: urlParams += "%s=%s&"%(val['name'], data.get(val['name'], serverInfo_D[val['name']])) except KeyError: print "expected parameter not provided - "+val["name"]+" is missing" exit(0) see the diffrence in serverInfo_D[val['name']] & serverInfo_D.get(val['name']) code 2 fails but code 1 works the data serverInfo_D:{'user': 'usr', 'pass': 'pass'} data: {'par1': 9995, 'extraparam1': 22} val: {'par1','user','pass','extraparam1'} exception are raised for for data dict .. and all code in for loop which iterates over val

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