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  • python conditional list creation from 2D lists

    - by dls
    Say I've got a list of lists. Say the inner list of three elements in size and looks like this: ['apple', 'fruit', 1.23] The outer list looks like this data = [['apple', 'fruit', 1.23], ['pear', 'fruit', 2.34], ['lettuce', 'vegetable', 3.45]] I want to iterate through the outer list and cull data for a temporary list only in the case that element 1 matches some keyword (aka: 'fruit'). So, if I'm matching fruit, I would end up with this: tempList = [('apple', 1.23), ('pear', 2.34)] This is one way to accomplish this: tempList = [] for i in data: if i[1] == 'fruit': tempList.append(i[0], i[2]) is there some 'Pythonic' way to do this in fewer lines?

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  • Python: Repeat elements in a list comprehension?

    - by User
    I have the following list comprehension which returns a list of coordinate objects for each location. coordinate_list = [Coordinates(location.latitude, location.longitude) for location in locations] This works. Now suppose the location object has a number_of_times member. I want a list comprehension to generate n Coordinate objects where n is the number_of_times for the particular location. So if a location has number_of_times = 5 then the coordinates for that location will be repeated 5 times in the list. (Maybe this is a case for a for-loop but I'm curious if it can be done via list comprehensions)

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  • Python Game using pyGame with Window Menu elements

    - by Zoja
    Here's the deal. I'm trying to write an arkanoid clone game and the thing is that I need a window menu like you get in pyGTK. For example File-(Open/Save/Exit) .. something like that and opening an "about" context where the author should be written. I'm already using pyGame for writting the game logic. I've tried pgu to write the GUI but that doesn't help me, altough it has those menu elements I'm taking about, you can't include the screen of the game in it's container. Does anybody know how to include such window menus with the usage of pyGame ?

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  • Python - calendar.timegm() vs. time.mktime()

    - by ibz
    I seem to have a hard time getting my head around this. What's the difference between calendar.timegm() and time.mktime()? Say I have a datetime.datetime with no tzinfo attached, shouldn't the two give the same output? Don't they both give the number of seconds between epoch and the date passed as a parameter? And since the date passed has no tzinfo, isn't that number of seconds the same? >>> import calendar >>> import time >>> import datetime >>> d = datetime.datetime(2010, 10, 10) >>> calendar.timegm(d.timetuple()) 1286668800 >>> time.mktime(d.timetuple()) 1286640000.0 >>>

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  • python popen and mysql import

    - by khelll
    I'm doing the following: from subprocess import PIPE from subprocess import Popen file = 'dump.sql.gz' p1 = Popen(["gzip", "-cd" ,file], stdout=PIPE) print "Importing temporary file %s" % file p2 = Popen(["mysql","--default-character-set=utf8", "--user=root" , "--password=something", "--host=localhost", "--port=3306" , 'my_db'],stdin=p1.stdout, stdout=PIPE,stderr=PIPE) err = p1.communicate()[1] if err: print err err = p2.communicate()[1] if err: print err But the db is not being populated. No errors are shown, also I have checked p1.stdout and it has the file contents. Any ideas?

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  • List comprehension from multiple sources in Python?

    - by Noah
    Is it possible to replace the following with a list comprehension? res = [] for a, _, c in myList: for i in c: res.append((a, i)) For example: # Input myList = [("Foo", None, [1, 2, 3]), ("Bar", None, ["i", "j"])] # Output res = [("Foo", 1), ("Foo", 2), ("Foo", 3), ("Bar", "i"), ("Bar", "j")]

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  • python: strip comma end of string

    - by krisdigitx
    how do i strip comma from the end of an string, i tried awk = subprocess.Popen([r"awk", "{print $10}"], stdin=subprocess.PIPE) awk_stdin = awk.communicate(uptime_stdout)[0] print awk_stdin temp = awk_stdin t = temp.strip(",") also tried t = temp.rstrip(","), both don't work.

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  • Python: How do sets work

    - by Guy
    I have a list of objects which I want to turn into a set. My objects contain a few fields that some of which are o.id and o.area. I want two objects to be equal if these two fields are the same. ie: o1==o2 if and only if o1.area==o2.area and o1.id==o2.id. I tried over-writing __eq__ and __cmp__ but I get the error: TypeError: unhashable instance. What should I over-write?

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  • Get the last '/' or '\\' character in Python

    - by wowus
    If I have a string that looks like either ./A/B/c.d OR .\A\B\c.d How do I get just the "./A/B/" part? The direction of the slashes can be the same as they are passed. This problem kinda boils down to: How do I get the last of a specific character in a string? Basically, I want the path of a file without the file part of it.

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  • Replacing python docstrings

    - by tomaz
    I have written a epytext to reST markup converter, and now I want to convert all the docstrings in my entire library from epytext to reST format. Is there a smart way to read the all the docstrings in a module and write back the replacements? ps: ast module perhaps?

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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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  • [Tkinter/Python] Different line widths with canvas.create_line?

    - by Sam
    Does anyone have any idea why I get different line widths on the canvas in the following example? from Tkinter import * bigBoxSize = 150 class cFrame(Frame): def __init__(self, master, cwidth=450, cheight=450): Frame.__init__(self, master, relief=RAISED, height=550, width=600, bg = "grey") self.canvasWidth = cwidth self.canvasHeight = cheight self.canvas = Canvas(self, bg="white", width=cwidth, height=cheight, border =0) self.drawGridLines() self.canvas.pack(side=TOP, pady=20, padx=20) def drawGridLines(self, linewidth = 10): self.canvas.create_line(0, 0, self.canvasWidth, 0, width= linewidth ) self.canvas.create_line(0, 0, 0, self.canvasHeight, width= linewidth ) self.canvas.create_line(0, self.canvasHeight, self.canvasWidth + 2, self.canvasHeight, width= linewidth ) self.canvas.create_line(self.canvasWidth, self.canvasHeight, self.canvasWidth, 1, width= linewidth ) self.canvas.create_line(0, bigBoxSize, self.canvasWidth, bigBoxSize, width= linewidth ) self.canvas.create_line(0, bigBoxSize * 2, self.canvasWidth, bigBoxSize * 2, width= linewidth) root = Tk() C = cFrame(root) C.pack() root.mainloop() It's really frustrating me as I have no idea what's happening. If anyone can help me out then that'd be fantastic. Thanks!

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  • varargs in lambda functions in Python

    - by brain_damage
    Is it possible a lambda function to have variable number of arguments? For example, I want to write a metaclass, which creates a method for every method of some other class and this newly created method returns the opposite value of the original method and has the same number of arguments. And I want to do this with lambda function. How to pass the arguments? Is it possible? class Negate(type): def __new__(mcs, name, bases, _dict): extended_dict = _dict.copy() for (k, v) in _dict.items(): if hasattr(v, '__call__'): extended_dict["not_" + k] = lambda s, *args, **kw: not v(s, *args, **kw) return type.__new__(mcs, name, bases, extended_dict) class P(metaclass=Negate): def __init__(self, a): self.a = a def yes(self): return True def maybe(self, you_can_chose): return you_can_chose But the result is totally wrong: >>>p = P(0) >>>p.yes() True >>>p.not_yes() # should be False Traceback (most recent call last): File "<pyshell#150>", line 1, in <module> p.not_yes() File "C:\Users\Nona\Desktop\p10.py", line 51, in <lambda> extended_dict["not_" + k] = lambda s, *args, **kw: not v(s, *args, **kw) TypeError: __init__() takes exactly 2 positional arguments (1 given) >>>p.maybe(True) True >>>p.not_maybe(True) #should be False True

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  • [Python] OR in regular expression?

    - by www.yegorov-p.ru
    Hello. I have text file with several thousands lines. I want to parse this file into database and decided to write a regexp. Here's part of file: blablabla checked=12 unchecked=1 blablabla unchecked=13 blablabla checked=14 As a result, I would like to get something like (12,1) (0,13) (14,0) Is it possible?

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  • Django models & Python class attributes

    - by Geo
    The tutorial on the django website shows this code for the models: from django.db import models class Poll(models.Model): question = models.CharField(max_length=200) pub_date = models.DateTimeField('date published') class Choice(models.Model): poll = models.ForeignKey(Poll) choice = models.CharField(max_length=200) votes = models.IntegerField() Now, each of those attribute, is a class attribute, right? So, the same attribute should be shared by all instances of the class. A bit later, they present this code: class Poll(models.Model): # ... def __unicode__(self): return self.question class Choice(models.Model): # ... def __unicode__(self): return self.choice How did they turn from class attributes into instance attributes? Did I get class attributes wrong?

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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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  • Python loop | "do-while" over a tree

    - by johannix
    Is there a more Pythonic way to put this loop together?: while True: children = tree.getChildren() if not children: break tree = children[0] UPDATE: I think this syntax is probably what I'm going to go with: while tree.getChildren(): tree = tree.getChildren()[0]

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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 - 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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  • supply inputs to python unittests

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    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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