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  • Python: How best to parse a simple grammar?

    - by Rosarch
    Ok, so I've asked a bunch of smaller questions about this project, but I still don't have much confidence in the designs I'm coming up with, so I'm going to ask a question on a broader scale. I am parsing pre-requisite descriptions for a course catalog. The descriptions almost always follow a certain form, which makes me think I can parse most of them. From the text, I would like to generate a graph of course pre-requisite relationships. (That part will be easy, after I have parsed the data.) Some sample inputs and outputs: "CS 2110" => ("CS", 2110) # 0 "CS 2110 and INFO 3300" => [("CS", 2110), ("INFO", 3300)] # 1 "CS 2110, INFO 3300" => [("CS", 2110), ("INFO", 3300)] # 1 "CS 2110, 3300, 3140" => [("CS", 2110), ("CS", 3300), ("CS", 3140)] # 1 "CS 2110 or INFO 3300" => [[("CS", 2110)], [("INFO", 3300)]] # 2 "MATH 2210, 2230, 2310, or 2940" => [[("MATH", 2210), ("MATH", 2230), ("MATH", 2310)], [("MATH", 2940)]] # 3 If the entire description is just a course, it is output directly. If the courses are conjoined ("and"), they are all output in the same list If the course are disjoined ("or"), they are in separate lists Here, we have both "and" and "or". One caveat that makes it easier: it appears that the nesting of "and"/"or" phrases is never greater than as shown in example 3. What is the best way to do this? I started with PLY, but I couldn't figure out how to resolve the reduce/reduce conflicts. The advantage of PLY is that it's easy to manipulate what each parse rule generates: def p_course(p): 'course : DEPT_CODE COURSE_NUMBER' p[0] = (p[1], int(p[2])) With PyParse, it's less clear how to modify the output of parseString(). I was considering building upon @Alex Martelli's idea of keeping state in an object and building up the output from that, but I'm not sure exactly how that is best done. def addCourse(self, str, location, tokens): self.result.append((tokens[0][0], tokens[0][1])) def makeCourseList(self, str, location, tokens): dept = tokens[0][0] new_tokens = [(dept, tokens[0][1])] new_tokens.extend((dept, tok) for tok in tokens[1:]) self.result.append(new_tokens) For instance, to handle "or" cases: def __init__(self): self.result = [] # ... self.statement = (course_data + Optional(OR_CONJ + course_data)).setParseAction(self.disjunctionCourses) def disjunctionCourses(self, str, location, tokens): if len(tokens) == 1: return tokens print "disjunction tokens: %s" % tokens How does disjunctionCourses() know which smaller phrases to disjoin? All it gets is tokens, but what's been parsed so far is stored in result, so how can the function tell which data in result corresponds to which elements of token? I guess I could search through the tokens, then find an element of result with the same data, but that feel convoluted... What's a better way to approach this problem?

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  • Exposing boost::scoped_ptr in boost::python

    - by Rupert Jones
    Hello, I am getting a compile error, saying that the copy constructor of the scoped_ptr is private with the following code snippet: class a {}; struct s { boost::scoped_ptr<a> p; }; BOOST_PYTHON_MODULE( module ) { class_<s>( "s" ); } This example works with a shared_ptr though. It would be nice, if anyone knows the answer. Thanks

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  • Python - werid behavior

    - by orokusaki
    I've done what I shouldn't have done and written 4 modules (6 hours or so) without running any tests along the way. I have a method inside of /mydir/__init__.py called get_hash(), and a class inside of /mydir/utils.py called SpamClass. /mydir/utils.py imports get_hash() from /mydir/__init__. /mydir/__init__.py imports SpamClass from /mydir/utils.py. Both the class and the method work fine on their own but for some reason if I try to import /mydir/, I get an import error saying "Cannot import name get_hash" from /mydir/__init__.py. The only stack trace is the line saying that __init__.py imported SpamClass. The next line is where the error occurs in in SpamClass when trying to import get_hash. Why is this?

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  • Python implementation of avro slow?

    - by lazy1
    I'm reading some data from avro file using the avro library. It takes about a minute to load 33K objects from the file. This seem very slow to me, specially with the Java version reading the same file in about 1sec. Here is the code, am I doing something wrong? import avro.datafile import avro.io from time import time def load(filename): fo = open(filename, "rb") reader = avro.datafile.DataFileReader(fo, avro.io.DatumReader()) for i, record in enumerate(reader): pass return i + 1 def main(argv=None): import sys from argparse import ArgumentParser argv = argv or sys.argv parser = ArgumentParser(description="Read avro file") start = time() num_records = load("events.avro") end = time() print("{0} records in {1} seconds".format(num_records, end - start)) if __name__ == "__main__": main()

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  • Python - Problems using mechanize to log into a difficult website

    - by user1781599
    × 139886 I am trying to log in to betfair.com by using mechanize. I have tried several ways but it always fail. This is the code I have developed so far, can anyone help me to identify what is wrong with it and how I can improve it to log into my betfair account? Thanks, import cookielib import urllib import urllib2 from BeautifulSoup import BeautifulSoup import mechanize from mechanize import Browser import re bf_username_name = "username" bf_password_name = "password" bf_form_name = "loginForm" bf_username = "xxxxx" bf_password = "yyyyy" urlLogIn = "http://www.betfair.com/" accountUrl = "https://myaccount.betfair.com/account/home?rlhm=0&" # This url I will use to verify if log in has been successful br = mechanize.Browser(factory=mechanize.RobustFactory()) br.addheaders = [("User-Agent","Mozilla/5.0 (Macintosh; Intel Mac OS X 10_5_8) AppleWebKit/537.1 (KHTML, like Gecko) Chrome/21.0.1180.90 Safari/537.1")] br.open(urlLogIn) br.select_form(nr=0) print br.form br.form[bf_username_name] = bf_username br.form[bf_password_name] = bf_password print br.form #just to check username and psw have been recorded correctly responseSubmit = br.submit() response = br.open(accountUrl) text_file = open("LogInResponse.html", "w") text_file.write(responseSubmit.read()) #this file should show the home page with me logged in, but it show home page as if I was not logged it text_file.close() text_file = open("Account.html", "w") text_file.write(response.read()) #this file should show my account page, but it should a pop up with an error text_file.close()

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  • Efficient way in Python to remove an element from a comma-separated string

    - by ensnare
    I'm looking for the most efficient way to add an element to a comma-separated string while maintaining alphabetical order for the words: For example: string = 'Apples, Bananas, Grapes, Oranges' subtraction = 'Bananas' result = 'Apples, Grapes, Oranges' Also, a way to do this but while maintaining IDs: string = '1:Apples, 4:Bananas, 6:Grapes, 23:Oranges' subtraction = '4:Bananas' result = '1:Apples, 6:Grapes, 23:Oranges' Sample code is greatly appreciated. Thank you so much.

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  • Unique elements of list within list in python

    - by user2901061
    We are given a list of animals in different zoos and need to find which zoos have animals that are not in any others. The animals of each zoo are separated by spaces, and each zoo is originally separated by a comma. I am currently enumerating over all of the zoos to split each animal and create lists within lists for different zoos as such: for i, zoo in enumerate(zoos): zoos[i] = zoo.split() However, I then do not know how to tell and count how many of the zoos have unique animals. I figure it is something else with enumerate and possibly sets, but cannot get it down exactly. Any help is greatly appreciated. Thanks

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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 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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  • python logparse search specific text

    - by krisdigitx
    hi, I am using this function in my code to return the strings i want from reading the log file, I want to grep the "exim" process and return the results, but running the code gives no error, but the output is limited to three lines, how can i just get the output only related to exim process.. #output: {'date': '13', 'process': 'syslogd', 'time': '06:27:33', 'month': 'May'} {'date': '13', 'process': 'exim[23168]:', 'time': '06:27:33', 'month': 'May'} {'May': ['syslogd']} #function: def generate_log_report(logfile): report_dict = {} for line in logfile: line_dict = dictify_logline(line) print line_dict try: month = line_dict['month'] date = line_dict['date'] time = line_dict['time'] #process = line_dict['process'] if "exim" in line_dict['process']: process = line_dict['process'] break else: process = line_dict['process'] except ValueError: continue report_dict.setdefault(month, []).append(process) return report_dict

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  • Python recursion with list returns None

    - by newman
    def foo(a): a.append(1) if len(a) > 10: print a return a else: foo(a) Why this recursive function returns None (see transcript below)? I can't quite understand what I am doing wrong. In [263]: x = [] In [264]: y = foo(x) [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] In [265]: print y None

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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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  • 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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  • [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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  • Python string formatting too slow

    - by wich
    I use the following code to log a map, it is fast when it only contains zeroes, but as soon as there is actual data in the map it becomes unbearably slow... Is there any way to do this faster? log_file = open('testfile', 'w') for i, x in ((i, start + i * interval) for i in range(length)): log_file.write('%-5d %8.3f %13g %13g %13g %13g %13g %13g\n' % (i, x, map[0][i], map[1][i], map[2][i], map[3][i], map[4][i], map[5][i]))

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  • Find last match with python regular expression

    - by SDD
    I wanto to match the last occurence of a simple pattern in a string, e.g. list = re.findall(r"\w+ AAAA \w+", "foo bar AAAA foo2 AAAA bar2) print "last match: ", list[len(list)-1] however, if the string is very long, a huge list of matches is generated. Is there a more direct way to match the second occurence of "AAAA" or should I use this workaround?

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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) Converting a dictionary to a list?

    - by Daria Egelhoff
    So I have this dictionary: ScoreDict = {"Blue": {'R1': 89, 'R2': 80}, "Brown": {'R1': 61, 'R2': 77}, "Purple": {'R1': 60, 'R2': 98}, "Green": {'R1': 74, 'R2': 91}, "Red": {'R1': 87, 'Lon': 74}} Is there any way how I can convert this dictionary into a list like this: ScoreList = [['Blue', 89, 80], ['Brown', 61, 77], ['Purple', 60, 98], ['Green', 74, 91], ['Red', 87, 74]] I'm not too familiar with dictionaries, so I really need some help here. Thanks in advance!

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  • Python and classes

    - by Artyom
    Hello, i have 2 classes. How i call first.TQ in Second ? Without creating object First in Second. class First: def __init__(self): self.str = "" def TQ(self): pass def main(self): T = Second(self.str) # Called here class Second(): def __init__(self): list = {u"RANDINT":first.TQ} # List of funcs maybe called in first ..... ..... return data

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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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  • 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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