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• How to remove commas etc from a matrix in python

  - by robert

  say ive got a matrix that looks like: [[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]] how can i make it on seperate lines: [[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]] and then remove commas etc: 0 0 0 0 0 And also to make it blank instead of 0's, so that numbers can be put in later, so in the end it will be like: _ 1 2 _ 1 _ 1 (spaces not underscores) thanks

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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 python code performance when importing zipped csv to a mongo collection

  - by mark

  I need to import a zipped csv into a mongo collection, but there is a catch - every record contains a timestamp in Pacific Time, which must be converted to the local time corresponding to the (longitude,latitude) pair found in the same record. The code looks like so: def read_csv_zip(path, timezones): with ZipFile(path) as z, z.open(z.namelist()[0]) as input: csv_rows = csv.reader(input) header = csv_rows.next() check,converters = get_aux_stuff(header) for csv_row in csv_rows: if check(csv_row): row = { converter[0]:converter[1](value) for converter, value in zip(converters, csv_row) if allow_field(converter) } ts = row['ts'] lng, lat = row['loc'] found_tz_entry = timezones.find_one(SON({'loc': {'$within': {'$box': [[lng-tz_lookup_radius, lat-tz_lookup_radius],[lng+tz_lookup_radius, lat+tz_lookup_radius]]}}})) if found_tz_entry: tz_name = found_tz_entry['tz'] local_ts = ts.astimezone(timezone(tz_name)).replace(tzinfo=None) row['tz'] = tz_name else: local_ts = (ts.astimezone(utc) + timedelta(hours = int(lng/15))).replace(tzinfo = None) row['local_ts'] = local_ts yield row def insert_documents(collection, source, batch_size): while True: items = list(itertools.islice(source, batch_size)) if len(items) == 0: break; try: collection.insert(items) except: for item in items: try: collection.insert(item) except Exception as exc: print("Failed to insert record {0} - {1}".format(item['_id'], exc)) def main(zip_path): with Connection() as connection: data = connection.mydb.data timezones = connection.timezones.data insert_documents(data, read_csv_zip(zip_path, timezones), 1000) The code proceeds as follows: Every record read from the csv is checked and converted to a dictionary, where some fields may be skipped, some titles be renamed (from those appearing in the csv header), some values may be converted (to datetime, to integers, to floats. etc ...) For each record read from the csv, a lookup is made into the timezones collection to map the record location to the respective time zone. If the mapping is successful - that timezone is used to convert the record timestamp (pacific time) to the respective local timestamp. If no mapping is found - a rough approximation is calculated. The timezones collection is appropriately indexed, of course - calling explain() confirms it. The process is slow. Naturally, having to query the timezones collection for every record kills the performance. I am looking for advises on how to improve it. Thanks. EDIT The timezones collection contains 8176040 records, each containing four values: > db.data.findOne() { "_id" : 3038814, "loc" : [ 1.48333, 42.5 ], "tz" : "Europe/Andorra" } EDIT2 OK, I have compiled a release build of http://toblerity.github.com/rtree/ and configured the rtree package. Then I have created an rtree dat/idx pair of files corresponding to my timezones collection. So, instead of calling collection.find_one I call index.intersection. Surprisingly, not only there is no improvement, but it works even more slowly now! May be rtree could be fine tuned to load the entire dat/idx pair into RAM (704M), but I do not know how to do it. Until then, it is not an alternative. In general, I think the solution should involve parallelization of the task. EDIT3 Profile output when using collection.find_one: >>> p.sort_stats('cumulative').print_stats(10) Tue Apr 10 14:28:39 2012 ImportDataIntoMongo.profile 64549590 function calls (64549180 primitive calls) in 1231.257 seconds Ordered by: cumulative time List reduced from 730 to 10 due to restriction <10> ncalls tottime percall cumtime percall filename:lineno(function) 1 0.012 0.012 1231.257 1231.257 ImportDataIntoMongo.py:1(<module>) 1 0.001 0.001 1230.959 1230.959 ImportDataIntoMongo.py:187(main) 1 853.558 853.558 853.558 853.558 {raw_input} 1 0.598 0.598 370.510 370.510 ImportDataIntoMongo.py:165(insert_documents) 343407 9.965 0.000 359.034 0.001 ImportDataIntoMongo.py:137(read_csv_zip) 343408 2.927 0.000 287.035 0.001 c:\python27\lib\site-packages\pymongo\collection.py:489(find_one) 343408 1.842 0.000 274.803 0.001 c:\python27\lib\site-packages\pymongo\cursor.py:699(next) 343408 2.542 0.000 271.212 0.001 c:\python27\lib\site-packages\pymongo\cursor.py:644(_refresh) 343408 4.512 0.000 253.673 0.001 c:\python27\lib\site-packages\pymongo\cursor.py:605(__send_message) 343408 0.971 0.000 242.078 0.001 c:\python27\lib\site-packages\pymongo\connection.py:871(_send_message_with_response) Profile output when using index.intersection: >>> p.sort_stats('cumulative').print_stats(10) Wed Apr 11 16:21:31 2012 ImportDataIntoMongo.profile 41542960 function calls (41542536 primitive calls) in 2889.164 seconds Ordered by: cumulative time List reduced from 778 to 10 due to restriction <10> ncalls tottime percall cumtime percall filename:lineno(function) 1 0.028 0.028 2889.164 2889.164 ImportDataIntoMongo.py:1(<module>) 1 0.017 0.017 2888.679 2888.679 ImportDataIntoMongo.py:202(main) 1 2365.526 2365.526 2365.526 2365.526 {raw_input} 1 0.766 0.766 502.817 502.817 ImportDataIntoMongo.py:180(insert_documents) 343407 9.147 0.000 491.433 0.001 ImportDataIntoMongo.py:152(read_csv_zip) 343406 0.571 0.000 391.394 0.001 c:\python27\lib\site-packages\rtree-0.7.0-py2.7.egg\rtree\index.py:384(intersection) 343406 379.957 0.001 390.824 0.001 c:\python27\lib\site-packages\rtree-0.7.0-py2.7.egg\rtree\index.py:435(_intersection_obj) 686513 22.616 0.000 38.705 0.000 c:\python27\lib\site-packages\rtree-0.7.0-py2.7.egg\rtree\index.py:451(_get_objects) 343406 6.134 0.000 33.326 0.000 ImportDataIntoMongo.py:162(<dictcomp>) 346 0.396 0.001 30.665 0.089 c:\python27\lib\site-packages\pymongo\collection.py:240(insert) EDIT4 I have parallelized the code, but the results are still not very encouraging. I am convinced it could be done better. See my own answer to this question for details.

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• How to read pdf, ppt, xl, doc files content into a string in php/python

  - by harsha

  Pls suggest me any inbuilt command or package?

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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 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/Tkinter] Grid within a frame?

  - by Sam

  Is it possible to place a grid of buttons in Tkinter inside another frame? I'm wanting to create a tic-tac-toe like game and want to use the grid feature to put gamesquares (that will be buttons). However, I'd like to have other stuff in the GUI other than just the game board so it's not ideal to just have everything in the one grid. To illustrate: O | X | X | ---------- | O | O | X | Player 2 wins! ---------- | X | O | X | The tic tac toe board is in a grid that is made up of all buttons and the 'player 2 wins' is a label inside a frame. This is an oversimplification of what I'm trying to do so bear with me, for the way I've designed the program so far (the board is dynamically created) a grid makes the most sense.

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• Adding a small photo/image to a large graph in Matplotlib/Python

  - by Mark

  I have a large graph that I am generating in matplotlib. I'd like to add a number of icons to this graph at certain (x,y) coordinates. I am wondering if there is any way to do that in matplotlib Thank you

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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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• Convert hex to utf in Python

  - by user260223

  Hi.. I have a hex string and i want to convert it utf8 to insert mysql. (my database is utf8) hex_string = 'kitap ara\xfet\xfdrmas\xfd' .. .. .. result='kitap arastirmasi' How can i do that? Best regards.

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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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• supply inputs to python unittests

  - by zubin71

  I`m relatively new to the concept of unit-testing and have very little experience in the same. I have been looking at lots of articles on how to write unit-tests; however, I still have difficulty in writing tests where conditions like the following arise:- Test user Input. Test input read from a file. Test input read from an environment variable. Itd be great if someone could show me how to approach the above mentioned scenarios; itd still be awesome if you could point me to a few docs/articles/blog posts which I could read.

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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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• 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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• 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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• 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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• 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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• Look for match in a nested list in Python

  - by elfuego1

  Hello everybody, I have two nested lists of different sizes: A = [[1, 7, 3, 5], [5, 5, 14, 10]] B = [[1, 17, 3, 5], [1487, 34, 14, 74], [1487, 34, 3, 87], [141, 25, 14, 10]] I'd like to gather all nested lists from list B if A[2:4] == B[2:4] and put it into list L: L = [[1, 17, 3, 5], [141, 25, 14, 10]] Additionally if the match occurs then I want to change last element of sublist B into first element of sublist A so the final solution would look like this: L1 = [[1, 17, 3, 1], [141, 25, 14, 5]]

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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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• Python - Subprocess Popen and Thread error

  - by n0idea

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

  - by lm

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  - 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 while loop

  - by Bunny Rabbit

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