I love pydub. It is simple to understand. But when it comes to detecting non-silent chunks, librosa seems much faster. So I want to try using librosa in a project to speed my code up.
So far, I have been using pydub like this (segment is an AudioSegment):
thresh = segment.dBFS - (segment.max_dBFS - segment.dBFS)
non_silent_ranges = pydub.silence.detect_nonsilent(segment, min_silence_len=1000, silence_thresh=thresh)
The thresh formula works mostly well, and when it does not, moving it a 5 or so dbs up or down does the trick.
Using librosa, I am trying this (y is a numpy array loaded with librosa.load(), with an sr of 22050)
non_silent_ranges = librosa.effects.split(y, frame_length=sr, top_db=mistery)
To get similar results to pydub I tried setting mistery to the following:
mistery = y.mean() - (y.max() - y.mean())
and the same after converting y to dbs:
ydbs = librosa.amplitude_to_db(y)
mistery = ydbs.mean() - (ydbs.max() - ydbs.mean())
In both cases, the results are very different from what get from pydub.
I have no background in audio processing and although I read about rms, dbFS, etc, I just don't get it--I guess I am getting old:)
Could somebody point me in the right direction? What would be the equivalent of my pydub solution in librosa? Or at least, explain to me how to get the max_dBFS and dBFS values of pydub in librosa (I am aware of how to convert and AudioSegment to the equivalent librosa numpy array thanks to the excellent answer here)?
max_dBFS is always 0 by it's nature. dBFS is how much "quieter" the sound is than the max possible signal.
I suspect another part of your issue is that ydbs.max() is the maximum value among data in ydbs, not the maximum possible value that can be stored (i.e., the highest integer or float possible)
Another difference from pydub is your use of ydbs.mean(), pydub uses RMS when computing dBFS.
You can convert ydbs.mean() to dbfs like so:
from numpy import mean, sqrt, square, iinfo
max_sample_value = iinfo(ydbs.dtype).max
ydbs_rms = sqrt(mean(square(ydbs))
ydbs_dbfs = 20 * log(ydbs_rms) / max_sample_value, 10)
Related
I've been attempting to use the MAFFT command line tool as a means to identify coding regions within a genome. My general process is to align the amino acid consensus sequence of a gene to a translated reading frame of a target sequence. My method has been largely successful. However, I've noticed some peculiar alignments which will unfortunately impede my annotation method. The following is one such example (Note - I've also included a pairwise alignment from the Pairwise2 Biopython module to demonstrate my desired output. Unfortunately, the computation time for Pairwise2 is nearly 20 times slower than MAFFT command line):
from time import *
from Bio.SubsMat import MatrixInfo as matlist
from Bio import pairwise2
from Bio.pairwise2 import format_alignment
from Bio.Align.Applications import MafftCommandline
startTime = time()
sample_tList = [['>Frame 1', 'RIGVGSIPRHLYCQELPLAQPKTCCAETPFRDSPLQGRLGVCPHLASGVALLYGLSTPLTMSGILDRCTCTPNARVFMAEGQVYCTRCLSARSLLPLNLQVPELGVLGLFYRPEEPLRWTLPRAFPTVECSPAGACWLSAIFPIARMTSGNLNFQQRMVRVAAEIYRAGQLTPAVLKVLQVYERGCRWYPIVGPVPGVGVYANSLHVSDKPFPGATHVLTNLPLPQRPKPEDFCPFECAMADVYDIGHGAVMFVAGGKVSWAPRGGDEVRFETVPEELKLIANRLHISFPPHHLVDMSKFAFIVPGSGVSLRVEHQHGCLPADIVPKGNCWWCLFDLLPPGVQNREIRYANQFGYQTKHGVSGKYLQRRLQINGLRAVTDTHGPIVVQYFSVKESWIRHFRLAGEPSLPGFEDLLRIRVESNTSPLADKDEKIFRFGSHKWYGAGKRARKARSGATTTVAHRASSARETRQAKKHEGVDANNAAHLEHYSPPAEGNCGWHCISAIVNRMVNSNFETTLPERVRPSDDWATDEDFVNTIQILRLPAALDRNGACKSAKYVLKLEGEHWTVSVAPGMSPSLLPLECVQGCCEHKGGLGSPDAVEVSGFDPTCLDRLAEVMHLPSSVIPAALAEMSNNSDRPASLVNTAWTVSQFYARHTGGNHRDQVRLGKIISLCQVIEECCCHQNKTNRATPEEVAAKIDQYLRGATSLEECLIKLERVSPPSAADTSFDWNVVLPGVEAAGPTTEQPHANQCCAPVPVVTQEPLDKDSVPLTAFSLSNCYYPAQGDEVRHRERLNSVLSKLEEVVLEEYGLMPTGLGPRPVLPSGLDELKDQMEEDLLKLANAQATSEMMALAAEQVDLKAWVKSYPRWIPPPPPPKVQPRRMKPVKSLPENKPVPAPRRKVRSDPGKSILAVGGPLNFSTPSELVTPLGEPVLMPASQHVSRPVTPLSEPAPVPAPRRIVSRPMTPLSEPTFVFAPWRKSQQVEEANPAAATLTCQDEPLDLSASSQTEYEAYPLAPLENIGVLEAGGQEAEEVLSGISDILDNTNPAPVSSSSSLSSVKITRPKYSAQAIIDSGGPCSGHLQKEKEACLRIMREACDAARLGDPATQEWLSHMWDRVDVLTWRNTSVYQAFRTLDGRFGFLPKMILETPPPYPCGFVMLPHTPTPSVSAESDLTIGSVATEDVPRILGKTENTGNVLNQKPLALFEEEPVCDQPAKDSRTLSRESGDSTTAPPVGTGGAGLPTDLPPLDGVDADGGGLLRTAKGKAERFFDQLSRQVFNIVSHLPVFFSHLFKSDSGYSPGDWGFAAFTLFCLFLCYSYPFFGFAPLLGVFSGSSRRVRMGVFGCWLAFAVGLFKPVSDPVGAACEFDSPECRNILHSFELLKPWDPVRSLVVGPVGLGLAILGRLLGGARYIWHFLLRLGIVADCILAGAYVLSQGRCKKCWGSCIRTAPNEIAFNVFPFTRATRSSLIDLCDRFCAPKGMDPIFLATGWRGCWTGQSPIEQPSEKPIAFAQLDEKRITARTVVSQPYDPNQAVKCLRVLQAGGAMVAEAVPKVVKVSAIPFRAPFFPTGVKVDPECRIVVDPDTFTTALRSGYSTTNLVLGVGDFAQLNGLKIRQISKPSGGGPHLIAALHVACSMVLHMLAGVYVTAVGSCGTGTSDPWCANPFAVPGYGPGSLCTSRLCISQHGLTLPLTALVAGFGLQEIALVVLIFVSIGGMAHRLSCKADMLCILLAIASYVWVPLTWLLCVFPCWLRWFSLHPLTILWLVFFLISVNMPSGILAVVLLVSLWLLGRYTNIAGLVTPYDIHHYTSGPRGVAALATAPDGTYLAAVRRAALTGRTMLFTPSQLGSLLEGAFRTRKPSLNTVNVVGSSMGSGGVFTIDGRIKCVTAAHVLTGNSARVSGVGFNQMLDFDVKGDFAIADCPNWQGVAPKTQFCGDGWTGRAYWLTSSGVEPGVIGDGFAFCFTACGDSGSPVITEAGELVGVHTGSNKQGGGIVTRPSGQFCNVTPIKLSELSEFFAGPKVPLGDVKVGSHIIKDTSEVPSDLCALLAAKPELEGGLSTVQLLCVFFLLWRMMGHAWTPLVAVGFFILNEVLPAVLVRSVFSFGMFALSWLTPWSAQVLMIRLLTAALNRNRVSLIFYSLGAVTGFVADLATTQGHPLQAVMNLSTYAFLPRMMVVTSPVPAIACGVVHLLAIILYLFKYRCLHHVLVGDGAFSAAFFLRYFAEGKLREGVSQSCGMSHESLTGALAIKLSDEDLDFLTKWTDFKCFVSASNMRNAAGQFIEAAYAKALRIELAQLVQVDKVRGTLAKLEAFADTVAPQLSPGDIVVALGHTPVGSIFDLKVGSTKHTLQAIETRVLAGSKMTVARVVDPTPAPPPAPVPIPLPPKVLENGPNAWGGEDRLNKRKRRRMEAVGIFVMDGKKYQKFWDKNSGDVFYEEVHNSTDEWECLRAGDPADFDPETGIQCGHVTIEDKVYNVFTSPSGRRFLVPANPENRRIQWEAARLSVEQALGMMNVDGELTAKELEKLKRIIDKLQGLTKEQCLNCPPVAPAVVAAAWLLLRQRKNFTTGPSPDLTKWPVRLSRTRSSTTNIRLPNRLMVVLCSCAPLFLRLMSSPALMHLLSYLPATGRETLGLMARFGILRPRPPKRKSHLVRKYRLVTLGAVTHLKLVSLISCTLLGATLSGKEFYRIQGLETYLTEPPVTLEAQCMRLPASRPMLLRLMGVPSWPQPCPPVLSCMYRPFQRPSLIILILGLTALNSQSTVVRMLLGTSPNTICPPKALFCLEFFALCGSTCLPMWVSARPFIGLPLTLPRILWLEMGTDFQPRIFRASLKSTFCAHRLCEKTGKLLLLVPSRSSIVGRRRLGQYLALITLRWPTGQRVVLPRASKRHSTRPSPSEKTNLRNYILQFAGALKLILHPAIDPHLQLSAGSLPIFFMNSPVLKSIYRRTCLTAVTTYWLRSPARLREAACRLATRLPPCQTPFTAYMHSTWCSVTLKVVTLMAFCFCKTSSLRTCSRFNPSSIQTTSCCMPSLPPCQITTGGLNITLCVSKRTQRRQPQTRHHFVAGMGVSSLTVTGFLRPSPTIRQAMSLNTTPRRLQYLWTAVLVSMILSGLKSSWLVRSAPARTVTASQARRSSCPCGKNSGPIMKGRSPECAGTAEPRLRTPLPVASTSVLTTPISTSIVLSSGVATRRVLALVVSVNLPWEKAQVLWMRCNKSRISLRGLSCMWSRVSPLLTQVDTKLAADSPLGVASGETKLTCQTVIMPVPPCSPLVKRSTWSLSPPTCCAAGSSSVPPALGKHTGSSNRSRMVMSFTRQLTRPCLTLGLWGCAGSTSQRVRRCNSLPPLVPARGFASWPAVGVLVRIPFWTKQRIAITLMSGFLAKPPLPAEISNNSTRWVLTLIAMFLTSCLRPNRPSGDSDRISVMPSNQITGTNLCPWSTQPVPRWTNLSGMGKSSPPTTGTERTAPSLSTPVKVPHLMWLHCICPLKIHSTGNEPLLLSPGQDMQSSCMTHTGNCRACLIFLRKAHPSTSQCSVTSSSYIEITKNARLLRLAMEINSGLQTSALILSAPFVQIWKGRAPRSPKLHITWGSISHLIHSLLNSQQNSHPTGPWQPRTMKSGLIGWLPAFAPSINIAARALVQAIWWAPRCFAPQGLCHTTSQNLLGARLKCFLRQSSAPAELRIAGSTSMIGSEKLLSPSHMPSLATSKALPVGDVITSPPDTFRASFLRNQLRSGFLAPEKLQRQFAHQMCTSQILKRTSTQRPSPSAGKCWILEKSDWSGKTRRPIFNLKAAISPGINLQATPHTSEFLLILQCIWTPAWALPFATGGLLGPPIGELTSRSPLMITVPKSFCLVHTMVKCLQGTKFWRARSSRLTTQGTNTLGDLNRIQRICTSLLGMVRTGRIIMKRFGRARKGKFIRLLPPASFIFPRALSLNQLATEMKWGLCRASLTKLVNFLWMLSRNFWCPLLISSYFWPFCLASPSPAGWWSFASDWFAPRYSVRALPFTLSNYRRSYEAFLSQCQVDIPTWGVKHPLGILWHHKVSTLIDEMVSRRMYRIMEKAGQAAWKQVVSEATLSRISNLDVVAHFQHLAAIEAETYKYLASRLPMLHNLRMTGSNVTIVYNSTLNQVFAIFPTSGSRPRLHDSQQWLIAVHSSIFSSVVASCTLFVVLWLRIPMLRSVFGFRWLGAIFLLNSRITRCVRLASPGRPLLRSMNPVGLFGAGGMTDAVRTTMTNGSWFRLASAKATPVFTPGWRSCHSATRPSSIPRYLGGTVKFMLTSRTNSFAPSTTGRTPPCLAMTTFQPYFRPTTNIRSTAVIGFTNGCAPSFPLGWFMFRGFSGVRLQAMFQFKSFRHQDQHYRSIRLCCPPGHQLPVWRLAPSDGSQELSVPHGDRDTRVHHHHSQCHRELFTFFSPHAFLLPFLCFDEKGIQSGIWQCVRHRGCVCLYQLRPTCQGVHPTLLGSRSCATASFHDTDHEVGNRFSLSFCHPTGNLNVQVCWGNAPRAVTRNCFLCGVSCRSVLLCSSTPAATAALIFSFITRYVSMAQIGWQKDLTGQWRLLSFFLCLTLFPMEHSPPAIFLTRLVSLCPPPGSITGGMSVVSMRSVLWLRFASSLGLRRTACPGATLVLDTPTSFWTLRADSIVGGRPLLRKGVRLKSRVTSTSKELCLMVPWQPLPEFQRNNGVVSRRLLPHGSTKGAFGVFHYLYASDDICSKGKSRPTARASAPFDLPELCFYLRVHDIRALSEHKGRAHYGGSSCTSLGGVLSHRNLEIHHLQMPFVLARPQVHSGPCPPRRKCRGLSSDCGKPRICRPASRLHYGRHIGARVEKPRVGWQKSCTGSGKPCQICQITTASSKRERRGTASQSISCARCWVRSSPNKTSPEARDRGRKIIREARRSPIFLRLKKMSGTTSPLVSGNCVCRRSRLPLTRAPGHVPCQIQGGVTLWSLVCRRIILCASASQHHPQHDELAFFGHLGVMIGRMCGEWHLTLCLVTYSIRATVWGSLIGENHAAAIKKKKKKKK'], ['>ORF2_GP2', 'MKWGLCKASLTKLANFLWMLSRSFWCPLLISSYFWPFCLASQSPVGWWSFASDWFAPRYSVRALPFTLSNYRRSYEAFLSQCQVDIPTWGVKHPLGVLWHHKVSTLIDEMVSRRMYRIMEKAGQAAWKQVVSEATLSRISGLDVVAHFQHLAAIEAETCKYLASRLPMLHNLRLTGSNVTIVYNSTLDQVFAIFPTPGSRPKLHDFQQWLIAVHSSIFSSVAASCTLFVVLWLRIPMLRSVFGFRWLGATFLLNSW']]
ex_file = open("newTempFile112233.fasta", "w")
for items in sample_tList:
ex_file.write(items[0] + "\n")
ex_file.write(items[1] + "\n")
ex_file.close()
in_file = '.../msa_example.fasta'
mafft_exe = '/usr/local/bin/mafft'
mafft_cline = MafftCommandline(mafft_exe, input=in_file) #have to change file path
#mafft_cline = MafftCommandline(mafft_exe, input=in_file, localpair=True, lexp=-1.5, lop=0.5)
stdout, stderr = mafft_cline()
print(stdout)
test_align = AlignIO.read(io.StringIO(stdout), "fasta")
#print(test_align)
os.remove("newTempFile112233.fasta")
print('Total time = ' + str(time() - startTime))
startTime = time()
matrix = matlist.blosum62
pWise_align = pairwise2.align.localds(sample_tList[0][1], sample_tList[1][1], matrix, -6, -1)
print(format_alignment(*pWise_align[0]))
print('Total time = ' + str(time() - startTime))
I've attempted to change the MAFFT command line alignment algorithm by referencing the help document (http://mafft.cbrc.jp/alignment/software/manual/manual.html). I don't get any error messages, but the alignment output does not change. I'm unsure what adjustments need to be made. I believe that by increasing the gap extension penalty (which is zero by default), the alignment will be improved. I haven't been able to find many documentation examples where custom variables are used when using MAFFT command line on this forum or through Google search. Help is much appreciated. For reference, documentation on the Pairwise2 alignment parameters can be found here: http://biopython.org/DIST/docs/api/Bio.pairwise2-module.html
Managed to figure out a possible solution. The alignment of the example sequences provided results in a long terminal/end gap which should not be present. Changing the MAFFT alignment algorithm using localpair, lexp, and lop had no effect (causing me a good deal of confusion). However, I have noticed differences in the alignment output when each input sequence is reversed. Oddly, the only way I was able to remove the terminal/end gap was to set the lop (gap opening penalty) to a lesser amount relative to lexp (gap extension penalty). I suspect my solution is niche and may not be applicable to other similar occurrences of terminal gaps. Changing the alignment settings also likely reduces the optimal alignment.
Going forward, I plan to use an automated process to run alignments of consensus sequences to raw sequences. In the event I detect irregularities with the alignment output (specifically terminal gaps), I'll attempt to reverse the input sequences and apply custom alignment settings. I suppose if that isn't a consistent solution, I'll figure out a way to refine the alignment output directly.
For anyone curious, I used a lexp value of -1.5 and lop value of 0.5 (now included in a hashed out line in my example code).
I have a Raspberry on which I want to create a timelapse movie.
All examples I see in the internet FIRST save a bunch of images and THEN converts them into a movie all at once.
I want to create a movie over a long period of time so I can't save thousands of images. What I need is a tool that adds an image to a movie right after the image is captured.
Is there a chance to do that?
There's a flaw in your logic, I think - by adding each image to the movie, you would necessarily be adding a full-frame, rather than only a diff frame. This will result in higher quality, sure - but it will also not save you anything in terms of space as compared to saving the entire image. The space savings you see in adding things to movies is all about that diff, rather than storing a full frame.
Doing a partial diff with check-frames at increments might work, but I'm not sure what format you're targeting, nor what codexes would be needed in order to arbitrarily tack on either a diff frame or a full frame, depending on some external condition - encoding usually takes place as a series of operations rather than singly.
An answer but it isn't finished!
I need your help making this perfect!
Running in python2
import os, cv2
from picamera import PiCamera
from picamera.array import PiRGBArray
from datetime import datetime
from time import sleep
now = datetime.now()
x = now.strftime("%Y")+"-"+now.strftime("%m")+"-"+now.strftime("%d")+"-"+now.strftime("%H")+"-"+now.strftime("%M") #string of dateandtimestart
def main():
imagenum = 100 #how many images
period = 1 #seconds between images
os.chdir ("/home/pi/t_lapse")
os.mkdir(x)
os.chdir(x)
filename = x + ".avi"
camera = PiCamera()
camera.resolution=(1920,1088)
camera.vflip = True
camera.hflip = True
camera.color_effects = (128,128) #makes a black and white image for IR camera
sleep(0.1)
out = cv2.VideoWriter(filename, cv2.cv.CV_FOURCC(*'XVID'), 30, (1920,1088))
for c in range(imagenum):
with PiRGBArray(camera, size=(1920,1088)) as output:
camera.capture(output, 'bgr')
imagec = output.array
out.write(imagec)
output.truncate(0) #trying to get more than 300mb files..
pass
sleep(period-0.5)
camera.close()
out.release()
if __name__ == '__main__':
main()
I've got this configured with with a few buttons and an OLED to select time spacing and frame numbers displayed on a OLED (code not shown above for simplicity but it is also here: https://github.com/gchennell/RPi-PiLapse )
This doesn't make videos larger than 366Mb which is some sort of limit I've reached and I don't know why - if anyone has a good suggestion I would appreciate it
I have written a Python 2.7 script that reads a CSV file and then does some standard deviation calculations . It works absolutely fine however it is very very slow. A CSV I tried with 100 million lines took around 28 hours to complete. I did some googling and it appears that maybe using the pandas module might makes this quicker .
I have posted part of the code below, since i am a pretty novice when it comes to python , i am unsure if using pandas would actually help at all and if it did would the function need to be completely re-written.
Just some context for the CSV file, it has 3 columns, first column is an IP address, second is a url and the third is a timestamp.
def parseCsvToDict(filepath):
with open(csv_file_path) as f:
ip_dict = dict()
csv_data = csv.reader(f)
f.next() # skip header line
for row in csv_data:
if len(row) == 3: #Some lines in the csv have more/less than the 3 fields they should have so this is a cheat to get the script working ignoring an wrong data
current_ip, URI, current_timestamp = row
epoch_time = convert_time(current_timestamp) # convert each time to epoch
if current_ip not in ip_dict.keys():
ip_dict[current_ip] = dict()
if URI not in ip_dict[current_ip].keys():
ip_dict[current_ip][URI] = list()
ip_dict[current_ip][URI].append(epoch_time)
return(ip_dict)
Once the above function has finished the data is parsed to another function that calculates the standard deviation for each IP/URL pair (using numpy.std).
Do you think that using pandas may increase the speed and would it require a complete rewrite or is it easy to modify the above code?
The following should work:
import pandas as pd
colnames = ["current_IP", "URI", "current_timestamp", "dummy"]
df = pd.read_csv(filepath, names=colnames)
# Remove incomplete and redundant rows:
df = df[~df.current_timestamp.isnull() & df.dummy.isnull()]
Notice this assumes you have enough RAM. In your code, you are already assuming you have enough memory for the dictionary, but the latter may be significatively smaller than the memory used by the above, for two reasons.
If it is because most lines are dropped, then just parse the csv by chunks: arguments skiprows and nrows are your friends, and then pd.concat
If it is because IPs/URLs are repeated, then you will want to transform IPs and URLs from normal columns to indices: parse by chunks as above, and on each chunk do
indexed = df.set_index(["current_IP", "URI"]).sort_index()
I expect this will indeed give you a performance boost.
EDIT: ... including a performance boost to the calculation of the standard deviation (hint: df.groupby())
I will not be able to give you an exact solution, but here are a couple of ideas.
Based on your data, you read 100000000. / 28 / 60 / 60 approximately 1000 lines per second. Not really slow, but I believe that just reading such a big file can cause a problem.
So take a look at this performance comparison of how to read a huge file. Basically a guy suggests that doing this:
file = open("sample.txt")
while 1:
lines = file.readlines(100000)
if not lines:
break
for line in lines:
pass # do something
can give you like 3x read boost. I also suggest you to try defaultdict instead of your if k in dict create [] otherwise append.
And last, not related to python: working in data-analysis, I have found an amazing tool for working with csv/json. It is csvkit, which allows to manipulate csv data with ease.
In addition to what Salvador Dali said in his answer: If you want to keep as much of the current code of your script, you may find that PyPy can speed up your program:
“If you want your code to run faster, you should probably just use PyPy.” — Guido van Rossum (creator of Python)
my code is taking serial data from an arduino, processing it, and then plotting it. I am using matplotlib as the graphics interface. Every time it 'draws' though it forces attention to it, and a user won't be able to look at anything besides that. What is the best way to get this to stop? (The code works fine except for the stealing focus). I tried to use the matplotlib.use('Agg') method after reading that on another post, but it did not work. (Using a MAC OS X).
The Code shown below is a super simple graph of updating data, with which I have the same problem. I'm not showing my code because it is not copy-pastable without the right inputs
Here is my code:
import matplotlib
from matplotlib import *
from pylab import *
# import math
x=[]
y=[]
def function(iteration):
xValue=iteration#Assigns current x value
yValue=(1./iteration)*34#Assigns current y value
x.extend([xValue]) #adds the current x value to the x list
y.extend([yValue]) #adds the current y value to the y list
clf() #clears the plot
plot(x,y,color='green') #tells the plot what to do
draw() #forces a draw
def main():
for i in range(1,25): #run my function 25 times (24 I think actually)
function(i)
pause(.1)
main()
Have you tried using the interactive mode of matplotlib?
You can switch it on using ion() (see Documentation)
If you use interactive mode you do not need to call draw() but you might need to clear your figures using clf() depending on your desired output
I find that using the Tkagg backend works
import matplotlib
matplotlib.use('Tkagg')
credit to 457290092
Still using bloody OpenOffice Writer to customize my sale_order.rml report.
In my sale order I have 6 order lines with 6 different lead time to delivery. I need to show the maximum out of the six values.
After many attempt I have abandoned using the reduce function as it works erratically or not at all most of the time. I have never seen anything like this.
So I thought I'd give a try using max encapsulating a loop such as:
[[ max(repeatIn(so.order_line.delay,'d')) ]]
My maximum lead time being 20, I would expect to see 20 (yes well that would be too easy, wouldn't it!).
It returns
{'d': 20.0}
At least it contains the value I am after.
But; if I try and manipulate this result, it disappears altogether.
I have tried:
int(re.findall(r'[0-9]+', max(repeatIn(so.order_line.delay,'d')))[0])
which works great from the python window, but returns absolutely nothing in OpenERP.
I import the re from my sale_order.py file, which I have recompiled into sale_order.pyo:
import time
import re
from datetime import datetime, timedelta
from report import report_sxw
class order(report_sxw.rml_parse):
def __init__(self, cr, uid, name, context=None):
super(order, self).__init__(cr, uid, name, context=context)
self.localcontext.update({
'time': time,
'datetime': datetime,
'timedelta': timedelta,
're': re,
})
I have of course restarted the server many times. My test install sits on windows.
So can anyone tell me what I am doing wrong, because I can make it work from Python but not from OpenOffice Writer!
Thanks for your help!
EDIT 1:
The format
{'d': 20.0}
is, according to python, a dictionary. Still in Python, to extract the integer from a dictionary it is possible to do it like so:
>>> dict={'d': 20.0}
>>> print(dict['d'])
20.0
But how can I transpose this to OpenERP writer???
I have manage to get the result I wanted by importing functools and declaring the reduce function within the parameters of the sale_order.py file.
I then simply used a combination of reduce and max function and it works exactly as expected.
The correct syntax is as follow:
repeatIn(objects,'o')
reduce(lambda x, y: max(x, y.delay), o.order_line, 0)
Nothing else is required.
Enjoy!