I'm new to python and just wrote this module level function:
def _interval(patt):
""" Converts a string pattern of the form '1y 42d 14h56m'
to a timedelta object.
y - years (365 days), M - months (30 days), w - weeks, d - days,
h - hours, m - minutes, s - seconds"""
m = _re.findall(r'([+-]?\d*(?:\.\d+)?)([yMwdhms])', patt)
args = {'weeks': 0.0,
'days': 0.0,
'hours': 0.0,
'minutes': 0.0,
'seconds': 0.0}
for (n,q) in m:
if q=='y':
args['days'] += float(n)*365
elif q=='M':
args['days'] += float(n)*30
elif q=='w':
args['weeks'] += float(n)
elif q=='d':
args['days'] += float(n)
elif q=='h':
args['hours'] += float(n)
elif q=='m':
args['minutes'] += float(n)
elif q=='s':
args['seconds'] += float(n)
return _dt.timedelta(**args)
My issue is with the for loop here i.e the long if elif block, and was wondering if there is a more pythonic way of doing it.
So I re-wrote the function as:
def _interval2(patt):
m = _re.findall(r'([+-]?\d*(?:\.\d+)?)([yMwdhms])', patt)
args = {'weeks': 0.0,
'days': 0.0,
'hours': 0.0,
'minutes': 0.0,
'seconds': 0.0}
argsmap = {'y': ('days', lambda x: float(x)*365),
'M': ('days', lambda x: float(x)*30),
'w': ('weeks', lambda x: float(x)),
'd': ('days', lambda x: float(x)),
'h': ('hours', lambda x: float(x)),
'm': ('minutes', lambda x: float(x)),
's': ('seconds', lambda x: float(x))}
for (n,q) in m:
args[argsmap[q][0]] += argsmap[q][1](n)
return _dt.timedelta(**args)
I tested the execution times of both the codes using timeit module and found that the second one took about 5-6 seconds longer (for the default number of repeats).
So my question is:
1. Which code is considered more pythonic?
2. Is there still a more pythonic was of writing this function?
3. What about the trade-offs between pythonicity and other aspects (like speed in this case) of programming?
p.s. I kinda have an OCD for elegant code.
EDITED _interval2 after seeing this answer:
argsmap = {'y': ('days', 365),
'M': ('days', 30),
'w': ('weeks', 1),
'd': ('days', 1),
'h': ('hours', 1),
'm': ('minutes', 1),
's': ('seconds', 1)}
for (n,q) in m:
args[argsmap[q][0]] += float(n)*argsmap[q][1]
You seem to create a lot of lambdas every time you parse. You really don't need a lambda, just a multiplier. Try this:
def _factor_for(what):
if what == 'y': return 365
elif what == 'M': return 30
elif what in ('w', 'd', 'h', 's', 'm'): return 1
else raise ValueError("Invalid specifier %r" % what)
for (n,q) in m:
args[argsmap[q][0]] += _factor_for([q][1]) * n
Don't make _factor_for a method's local function or a method, though, to speed things up.
(I have not timed this, but) if you're going to use this function often it might be worth pre-compiling the regex expression.
Here's my take on your function:
re_timestr = re.compile("""
((?P<years>\d+)y)?\s*
((?P<months>\d+)M)?\s*
((?P<weeks>\d+)w)?\s*
((?P<days>\d+)d)?\s*
((?P<hours>\d+)h)?\s*
((?P<minutes>\d+)m)?\s*
((?P<seconds>\d+)s)?
""", re.VERBOSE)
def interval3(patt):
p = {}
match = re_timestr.match(patt)
if not match:
raise ValueError("invalid pattern : %s" % (patt))
for k,v in match.groupdict("0").iteritems():
p[k] = int(v) # cast string to int
p["days"] += p.pop("years") * 365 # convert years to days
p["days"] += p.pop("months") * 30 # convert months to days
return datetime.timedelta(**p)
update
From this question, it looks like precompiling regex patterns does not bring about noticeable performance improvement since Python caches and reuses them anyway. You only save the time it takes to check the cache which, unless you are repeating it numerous times, is negligible.
update2
As you quite rightly pointed out, this solution does not support interval3("1h 30s" + "2h 10m"). However, timedelta supports arithmetic operations which means one you can still express it as interval3("1h 30s") + interval3("2h 10m").
Also, as mentioned by some of the comments on the question, you may want to avoid supporting "years" and "months" in the inputs. There's a reason why timedelta does not support those arguments; it cannot be handled correctly (and incorrect code are almost never elegant).
Here's another version, this time with support for float, negative values, and some error checking.
re_timestr = re.compile("""
^\s*
((?P<weeks>[+-]?\d+(\.\d*)?)w)?\s*
((?P<days>[+-]?\d+(\.\d*)?)d)?\s*
((?P<hours>[+-]?\d+(\.\d*)?)h)?\s*
((?P<minutes>[+-]?\d+(\.\d*)?)m)?\s*
((?P<seconds>[+-]?\d+(\.\d*)?)s)?\s*
$
""", re.VERBOSE)
def interval4(patt):
p = {}
match = re_timestr.match(patt)
if not match:
raise ValueError("invalid pattern : %s" % (patt))
for k,v in match.groupdict("0").iteritems():
p[k] = float(v) # cast string to int
return datetime.timedelta(**p)
Example use cases:
>>> print interval4("1w 2d 3h4m") # basic use
9 days, 3:04:00
>>> print interval4("1w") - interval4("2d 3h 4m") # timedelta arithmetic
4 days, 20:56:00
>>> print interval4("0.3w -2.d +1.01h") # +ve and -ve floats
3:24:36
>>> print interval4("0.3x") # reject invalid input
Traceback (most recent call last):
File "date.py", line 19, in interval4
raise ValueError("invalid pattern : %s" % (patt))
ValueError: invalid pattern : 0.3x
>>> print interval4("1h 2w") # order matters
Traceback (most recent call last):
File "date.py", line 19, in interval4
raise ValueError("invalid pattern : %s" % (patt))
ValueError: invalid pattern : 1h 2w
Yes, there is. Use time.strptime instead:
Parse a string representing a time
according to a format. The return
value is a struct_time as returned
by gmtime() or localtime().
Related
I am trying to write an explicit Successive Overrelaxation Function over a 2D matrix. In this case for an electrostatic potential.
When trying to optimize this in Cython I seem to get an error that I am not quite sure I understand.
%%cython
cimport cython
import numpy as np
cimport numpy as np
from libc.math cimport pi
#SOR function
#cython.boundscheck(False)
#cython.wraparound(False)
#cython.initializedcheck(False)
#cython.nonecheck(False)
def SOR_potential(np.float64_t[:, :] potential, mask, int max_iter, float error_threshold, float alpha):
#the ints
cdef int height = potential.shape[0]
cdef int width = potential.shape[1] #more general non quadratic
cdef int it = 0
#the floats
cdef float error = 0.0
cdef float sor_adjustment
#the copy array we will iterate over and return
cdef np.ndarray[np.float64_t, ndim=2] input_matrix = potential.copy()
#set the ideal alpha if user input is 0.0
if alpha == 0.0:
alpha = 2/(1+(pi/((height+width)*0.5)))
#start the SOR loop. The for loops omit the 0 and -1 index\
#because they are *shadow points* used for neuman boundary conditions\
cdef int row, col
#iteration loop
while True:
#2-stencil loop
for row in range(1, height-1):
for col in range(1, width-1):
if not(mask[row][col]):
potential[row][col] = 0.25*(input_matrix[row-1][col] + \
input_matrix[row+1][col] + \
input_matrix[row][col-1] + \
input_matrix[row][col+1])
sor_adjustment = alpha * (potential[row][col] - input_matrix[row][col])
input_matrix[row][col] = sor_adjustment + input_matrix[row][col]
error += np.abs(input_matrix[row][col] - potential[row][col])
#by the end of this loop input_matrix and potential have diff values
if error<error_threshold:
break
elif it>max_iter:
break
else:
error = 0
it = it + 1
return input_matrix, error, it
and I used a very simple example for an array to see if it would give an error output.
test = [[True, False], [True, False]]
pot = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float64)
SOR_potential(pot, test, 50, 0.1, 0.0)
Gives out this error:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In [30], line 1
----> 1 SOR_potential(pot, test, 50, 0.1, 0.0)
File _cython_magic_6c09a5060df996862b8e35adacc0e25c.pyx:21, in _cython_magic_6c09a5060df996862b8e35adacc0e25c.SOR_potential()
TypeError: Cannot convert _cython_magic_6c09a5060df996862b8e35adacc0e25c._memoryviewslice to numpy.ndarray
But when I delete the np.float64_t[:, :] part from
def SOR_potential(np.float64_t[:, :] potential,...)
the code works. Of course, the simple 2x2 matrix will not converge but it gives no errors. Where is the mistake here?
I also tried importing the modules differently as suggested here
Cython: how to resolve TypeError: Cannot convert memoryviewslice to numpy.ndarray?
but I got 2 errors instead of 1 where there were type mismatches.
Note: I would also like to ask, how would I define a numpy array of booleans to put in front of the "mask" input in the function?
A minimal reproducible example of your error message would look like this:
def foo(np.float64_t[:, :] A):
cdef np.ndarray[np.float64_t, ndim=2] B = A.copy()
# ... do something with B ...
return B
The problem is, that A is a memoryview while B is a np.ndarray. If both A and B are memoryviews, i.e.
def foo(np.float64_t[:, :] A):
cdef np.float64_t[:, :] B = A.copy()
# ... do something with B ...
return np.asarray(B)
your example will compile without errors. Note that you then need to call np.asarray if you want to return a np.ndarray.
Regarding your second question: You could use a memoryview with dtype np.uint8_t
def foo(np.float64_t[:, :] A, np.uint8_t[:, :] mask):
cdef np.float64_t[:, :] B = A.copy()
# ... do something with B and mask ...
return np.asarray(B)
and call it like this from Python:
mask = np.array([[True, True], [False, False]], dtype=bool)
A = np.ones((2,2), dtype=np.float64)
foo(A, mask)
PS: If your array's buffers are guaranteed to be C-Contiguous, you can use contiguous memoryviews for better performance:
def foo(np.float64_t[:, ::1] A, np.uint8_t[:, ::1] mask):
cdef np.float64_t[:, ::1] B = A.copy()
# ... do something with B and mask ...
return np.asarray(B)
This is really similar to Fibonacci Sequence problem. I understand the DP optimization with Fibonacci function using the for loop, but I'm having hard time to connect to this problem.
The recursion function I want to optimize is:
def cal(n):
if n <= 0:
return 1
else:
return cal(n-25)+cal(n-26)
Something like this may help:
(It's inspired by previous post)
from functools import cache
#cache
def cal(n):
if n <= 0:
return 1
else:
return cal(n-25) + cal(n-26)
print(cal(100))
The idea of a "for loop optimization" is that we calculate cal(0), cal(1), cal(2), ... consecutively. And when we want cal(n), we already have cal(n-25) and cal(n-26) stored in an array.
So, the following solution has linear complexity for non-negative n:
def cal(n):
mem = [1] # cal(0) is 1
for i in range(1, n + 1):
num = 1 if i - 25 < 0 else mem[i - 25]
num += 1 if i - 26 < 0 else mem[i - 26]
mem.append (num)
return mem[-1]
One can further optimize to make all the values cal(1), cal(2), ..., cal(n) globally available after calculating the last of them.
Advent of Code Day 1 requires looping, in one form or another, over a long string of parentheses like ((((())(())(((()))(( etc. The idea is that ( goes up one "floor", ) goes down one floor, and the objectives are to print
the first index in the string where the floor number is negative and
the final floor when the end of the string is found.
The imperative solution with a for loop is simple (Python as an example):
def main():
flr = 0
basement = False
for idx, elt in enumerate(text):
flr += {
"(": 1,
")": -1
}.get(elt)
if flr < 0 and not basement:
print("first basement pos:", idx + 1)
basement = True
print("final floor:", flr)
The recursive functional solution is a little more complex, but still not too hard.
def worker(flr, txt, idx, basement):
flr += {"(": 1, ")": -1}[ txt[0] ]
if not (len(txt) - 1): return flr
if flr < 0 and not basement:
print("first basement floor index: ", idx + 1)
basement = True
return worker(flr, txt[1:], idx + 1, basement)
def starter(txt):
flr, basement, idx = 0, False, 0
return worker(flr, txt, idx, basement)
if __name__ == '__main__':
__import__("sys").setrecursionlimit(int(1e5))
print("final floor:", starter(text))
Both of these give the correct output of
first basement floor index: 1795
final floor: 74
when run against my challenge input.
except the second one is dumb because Python doesn't have tail call optimisation but never mind that
How can I implement either of these in Factor? This is something I've been confused by ever since I started using Factor.
We can't just use a for loop because there's no equivalent that allows us to keep mutable state between iterations.
We could use a recursive solution:
: day-1-starter ( string -- final-floor )
[ 0 ] dip 0 f day-1-worker 3drop "final floor: %s" printf ;
: day-1-worker
( floor string index basement? -- floor string index basement? )
day-1-worker ! what goes here?
; recursive
Great, that's a skeleton, but what goes in the body of day-1-worker? Factor doesn't have any way to "early return" from a recursive call because there's no way to run the program in reverse and no concept of return -- that doesn't make any sense.
I get the feeling maybe recursion isn't the answer to this question in Factor. If it is, how do I stop recursing?
First of all, recursion is always the answer :)
Since this is a challenge (and I don't know factor), just a hint:
in your python solution you have used the side effect to print the first basement level. Quite unnecessary! You can use basemet argument to hold the floor number too, like this:
def worker(flr, txt, idx, basement):
flr += {"(": 1, ")": -1}[ txt[0] ]
if not (len(txt) - 1): return [flr, basement] # <- return both
if flr < 0 and not basement:
#print("first basement floor index: ", idx + 1) # side effects go away!
basement = idx+1 # <- a number in not False, so that's all
return worker(flr, txt[1:], idx + 1, basement)
So now you get
final,first_basement = worker(0, txt, 0, False)
Or, alternatively you can write 2 functions, first one seeks the index of first basement floor, the other one just computes the final floor. Having <2000 additional small steps is not a big deal even if you do care about performance.
Good luck!
Edit: as of your question concerning recursion in factor, take a look at the Ackermann Function in Factor and the Fibonacci sequence in Factor and you should get the idea how to "break the loop". Actually the only problem is in thinking (emancipate yourself from the imperative model :)); in functional languages there is no "return", just the final value, and stack-based languages you mention are other computational model of the same thing (instead of thinking of folding a tree one thinks about "pushing and poping to/from the stacks" -- which is btw a common way to implement the former).
Edit: (SPOILER!)
I installed Factor and started playing with it (quite nice), for the first question (computing the final score) a possible solution is
: day-1-worker ( string floor -- floor )
dup length 0 =
[ drop ]
[ dup first 40 =
[ swap 1 + ]
[ swap 1 - ]
if
swap rest
day-1-worker ]
if ;
: day-1-starter ( string -- floor )
0 swap day-1-worker ;
So now you can either write similar one for computing basement's index, or (which would be more cool!) to modify it so that it also manages index and basement... (Probably using cond would be wiser than nesting ifs).
You could use the cum-sum combinator:
: to-ups/downs ( str -- seq )
[ CHAR: ( = 1 -1 ? ] { } map-as ;
: run-elevator ( str -- first-basement final-floor )
to-ups/downs cum-sum [ -1 swap index 1 + ] [ last ] bi ;
IN: scratchpad "((())))(())(())(((()))((" run-elevator
--- Data stack:
7
2
EDIT
I originally misread how your were computing the basement value. I've updated the answers below
Here's a JavaScript solution. Sorry I have no idea how this converts to Factor. reduce is an iterative process
const worker = txt=>
txt.split('').reduce(({floor, basement}, x, i)=> {
if (x === '(')
return {floor: floor + 1, basement}
else if (basement === null && floor === 0)
return {floor: floor - 1, basement: i}
else
return {floor: floor - 1, basement}
}, {floor: 0, basement: null})
let {floor, basement} = worker('((((())(())(((()))((')
console.log(floor) //=> 6
console.log(basement) //=> null; never reaches basement
The answer above relies on some some .split and .reduce which may not be present in your language. Here's another solution using Y-combinator and only the substring built-in (which most languages include). This answer also depends on your language having first-class functions.
const U = f=> f (f)
const Y = U (h=> f=> f (x=> h (h) (f) (x)))
const strhead = s=> s.substring(0,1)
const strtail = s=> s.substring(1)
const worker = Y (f=> ({floor, basement})=> i=> txt=> {
// txt is empty string; return answer
if (txt === '')
return {floor, basement}
// first char in txt is '(', increment the floor
else if (strhead (txt) === '(')
return f ({floor: floor + 1, basement}) (i+1) (strtail (txt))
// if basement isn't set and we're on floor 0, we found the basement
else if (basement === null && floor === 0)
return f ({floor: floor - 1, basement: i}) (i+1) (strtail (txt))
// we're already in the basement, go down another floor
else
return f ({floor: floor - 1, basement}) (i+1) (strtail (txt))
}) ({floor: 0, basement: null}) (0)
{
let {floor, basement} = worker('((((())(())(((()))((')
console.log(floor) //=> 6
console.log(basement) //=> null; never reaches basement
}
{
let {floor, basement} = worker(')(((((')
console.log(floor) //=> 4
console.log(basement) //=> 0
}
{
let {floor, basement} = worker('((())))')
console.log(floor) //=> -1
console.log(basement) //=> 6
}
I found this project on GitHub; it was the only search term returned for "nimrod matrix". I took the bare bones of it and changed it a little bit so that it compiled without errors, and then I added the last two lines to build a simple matrix, and then output a value, but the "getter" function isn't working for some reason. I adapted the instructions for adding properties found here, but something isn't right.
Here is my code so far. I'd like to use the GNU Scientific Library from within Nimrod, and I figured that this was the first logical step.
type
TMatrix*[T] = object
transposed: bool
dataRows: int
dataCols: int
data: seq[T]
proc index[T](x: TMatrix[T], r,c: int): int {.inline.} =
if r<0 or r>(x.rows()-1):
raise newException(EInvalidIndex, "matrix index out of range")
if c<0 or c>(x.cols()-1):
raise newException(EInvalidIndex, "matrix index out of range")
result = if x.transposed: c*x.dataCols+r else: r*x.dataCols+c
proc rows*[T](x: TMatrix[T]): int {.inline.} =
## Returns the number of rows in the matrix `x`.
result = if x.transposed: x.dataCols else: x.dataRows
proc cols*[T](x: TMatrix[T]): int {.inline.} =
## Returns the number of columns in the matrix `x`.
result = if x.transposed: x.dataRows else: x.dataCols
proc matrix*[T](rows, cols: int, d: openarray[T]): TMatrix[T] =
## Constructor. Initializes the matrix by allocating memory
## for the data and setting the number of rows and columns
## and sets the data to the values specified in `d`.
result.dataRows = rows
result.dataCols = cols
newSeq(result.data, rows*cols)
if len(d)>0:
if len(d)<(rows*cols):
raise newException(EInvalidIndex, "insufficient data supplied in matrix constructor")
for i in countup(0,rows*cols-1):
result.data[i] = d[i]
proc `[][]`*[T](x: TMatrix[T], r,c: int): T =
## Element access. Returns the element at row `r` column `c`.
result = x.data[x.index(r,c)]
proc `[][]=`*[T](x: var TMatrix[T], r,c: int, a: T) =
## Sets the value of the element at row `r` column `c` to
## the value supplied in `a`.
x.data[x.index(r,c)] = a
var m = matrix( 2, 2, [1,2,3,4] )
echo( $m[0][0] )
This is the error I get:
c:\program files (x86)\nimrod\config\nimrod.cfg(36, 11) Hint: added path: 'C:\Users\H127\.babel\libs\' [Path]
Hint: used config file 'C:\Program Files (x86)\Nimrod\config\nimrod.cfg' [Conf]
Hint: system [Processing]
Hint: mat [Processing]
mat.nim(48, 9) Error: type mismatch: got (TMatrix[int], int literal(0))
but expected one of:
system.[](a: array[Idx, T], x: TSlice[Idx]): seq[T]
system.[](a: array[Idx, T], x: TSlice[int]): seq[T]
system.[](s: string, x: TSlice[int]): string
system.[](s: seq[T], x: TSlice[int]): seq[T]
Thanks you guys!
I'd like to first point out that the matrix library you refer to is three years old. For a programming language in development that's a lot of time due to changes, and it doesn't compile any more with the current Nimrod git version:
$ nimrod c matrix
...
private/tmp/n/matrix/matrix.nim(97, 8) Error: ']' expected
It fails on the double array accessor, which seems to have changed syntax. I guess your attempt to create a double [][] accessor is problematic, it could be ambiguous: are you accessing the double array accessor of the object or are you accessing the nested array returned by the first brackets? I had to change the proc to the following:
proc `[]`*[T](x: TMatrix[T], r,c: int): T =
After that change you also need to change the way to access the matrix. Here's what I got:
for x in 0 .. <2:
for y in 0 .. <2:
echo "x: ", x, " y: ", y, " = ", m[x,y]
Basically, instead of specifying two bracket accesses you pass all the parameters inside a single bracket. That code generates:
x: 0 y: 0 = 1
x: 0 y: 1 = 2
x: 1 y: 0 = 3
x: 1 y: 1 = 4
With regards to finding software for Nimrod, I would like to recommend you using Nimble, Nimrod's package manager. Once you have it installed you can search available and maintained packages. The command nimble search math shows two potential packages: linagl and extmath. Not sure if they are what you are looking for, but at least they seem more fresh.
I am using MySQLdb to talk to mysql database and I am able to retrieve dynamically all the result sets.
My problem is that once I get the result set, there are a couple columns which are declared as timestamps in mysql but when it is retrieved, it turns to None.
I have two columns, both are declared timestamps but one returns correct data while other returns None. Both utime and enddate are declared timestamps but utime does not return correctly while enddate does.
['utime', 'userstr', 'vstr_client', 'enddate']
((None, '000102030ff43260gg0809000000000004', '7.7.0', '1970-01-01 12:00:00.000000'))
def parse_data_and_description(cursor, data):
res = []
cols = [d[0] for d in cursor.description]
print cols
print data
for i in data:
res.append(OrderedDict(zip(cols, i)))
return res
def call_multi_rs(sp, args):
rs_id=0;
conn = connect()
cursor = conn.cursor()
try:
conn.autocommit(True)
cursor.execute ("CALL %s%s" % (sp, args))
while True:
rs_id+=1
data = cursor.fetchone( )
listout = parse_data_and_description(cursor, data)
print listout
if cursor.nextset( )==None:
# This means no more recordsets available
break
Finally after nobody answered or tried finding more information, I went ahead and looked for more solutions and found that the MySQLdb library converts the datatypes from sql to python and there is a bug which does not convert the timestamp.
I still do not know why one of them is converted and the other is not. If somebody can figure that out, please update this.
But here is the modification that needs to be done when connecting to the mysql database.
MySQLdb can't serialize a python datetime object
try:
import MySQLdb.converters
except ImportError:
_connarg('conv')
def connect(host='abc.dev.local', user='abc', passwd='def', db='myabc', port=3306):
try:
orig_conv = MySQLdb.converters.conversions
conv_iter = iter(orig_conv)
convert = dict(zip(conv_iter, [str,] * len(orig_conv.keys())))
print "Connecting host=%s user=%s db=%s port=%d" % (host, user, db, port)
conn = MySQLdb.connect(host, user, passwd, db, port, conv=convert)
except MySQLdb.Error, e:
print "Error connecting %d: %s" % (e.args[0], e.args[1])
return conn
I stumbled upon the same problem: Retrieving data of the DATETIME(1)-type returns None.
Some research brought up MySQLdb-Bug #325. According to that bug tracker, the issue is still open (has been for over 2 years now), but the comments provide a working solution:
In times.py of the MySQLdb package, you need to insert some lines to handle microseconds like this:
def DateTime_or_None(s):
if ' ' in s:
sep = ' '
elif 'T' in s:
sep = 'T'
else:
return Date_or_None(s)
try:
d, t = s.split(sep, 1)
if '.' in t:
t, ms = t.split('.',1)
ms = ms.ljust(6, '0')
else:
ms = 0
return datetime(*[ int(x) for x in d.split('-')+t.split(':')+[ms] ])
except (SystemExit, KeyboardInterrupt):
raise
except:
return Date_or_None(s)
def TimeDelta_or_None(s):
try:
h, m, s = s.split(':')
if '.' in s:
s, ms = s.split('.')
ms = ms.ljust(6, '0')
else:
ms = 0
h, m, s, ms = int(h), int(m), int(s), int(ms)
td = timedelta(hours=abs(h), minutes=m, seconds=s,
microseconds=ms)
if h < 0:
return -td
else:
return td
except ValueError:
# unpacking or int/float conversion failed
return None
def Time_or_None(s):
try:
h, m, s = s.split(':')
if '.' in s:
s, ms = s.split('.')
ms = ms.ljust(6, '0')
else:
ms = 0
h, m, s, ms = int(h), int(m), int(s), int(ms)
return time(hour=h, minute=m, second=s, microsecond=ms)
except ValueError:
return None
What I cannot explain, though, is your original query working on one column and not on the other.. Maybe, the second does not have any microsecond-information in it?