Recherche de tag: python

import csv as L
def readCSV(file):
    with open(file) as csvfile:
        readCSV = L.reader(csvfile, delimiter=";")
        firstRow=True
        columnName=[]
        for row in readCSV:
            if firstRow:
                firstRow=False
                for col in row:
                    columnName.append(col)
            else:
                aRow={}
                i=0
                for col in columnName:
                    aRow[col]=row[i]
                    i+=1
                yield aRow

contentGFF = ["seqid", "source", "type", "start", "end", "score", "strand", "phase", "attributes"]
def gff3parser (filename, dictOrf):
	 """ parser de gff3 """
	with open(filename) as f:
		for line in f:
			if line.startswith("#"):
		                continue
		content = line.strip().split("\t")
		if len(content) == len(contentGFF):
			value = [content[3], content[4], content[6]]
			#print(value)
			#print(content[8])
			numORF = content[8].strip().split(";")
			#print(numORF)
			for product in numORF:
				if re.search(r"^product=ORF\d{0,3}", product):
					keys = product[11:]
					dictOrf[keys] = value

		return {}

# Usage :
orf = {}
gff3parser(gffname,orf) #avec gffname le nom du fichier gff3 à parser

import cProfile

def profileit(func):
    def profiled_func(*args, **kwargs):
        profile = cProfile.Profile()
        print("Profiling " + "\033[95m" + func.__name__ + "\033[0m") # colors
        try:
            profile.enable()
            result = func(*args, **kwargs)
            profile.disable()
            return result
        finally:
            profile.print_stats(sort="time")
    return profiled_func



##########
# Example #
##########

@profileit
def times2():
   for i in range(500000):
       j = i * 2
       j = add_one(j)

def add_one(n):
    return n+1

times2()

import functools
import gc
import itertools
import sys
from timeit import default_timer as _timer


def timeit(_func=None, *, repeat=3, number=1000, file=sys.stdout):
    """Decorator: prints time from best of `repeat` trials.
    Mimics `timeit.repeat()`, but avg. time is printed.
    Returns function result and prints time.
    You can decorate with or without parentheses, as in
    Python's @dataclass class decorator.
    kwargs are passed to `print()`.
    >>> @timeit
    ... def f():
    ...     return "-".join(str(n) for n in range(100))
    ...
    >>> @timeit(number=100000)
    ... def g():
    ...     return "-".join(str(n) for n in range(10))
    ...
    """

    _repeat = functools.partial(itertools.repeat, None)

    def wrap(func):
        @functools.wraps(func)
        def _timeit(*args, **kwargs):
            # Temporarily turn off garbage collection during the timing.
            # Makes independent timings more comparable.
            # If it was originally enabled, switch it back on afterwards.
            gcold = gc.isenabled()
            gc.disable()

            try:
                # Outer loop - the number of repeats.
                trials = []
                for _ in _repeat(repeat):
                    # Inner loop - the number of calls within each repeat.
                    total = 0
                    for _ in _repeat(number):
                        start = _timer()
                        result = func(*args, **kwargs)
                        end = _timer()
                        total += end - start
                    trials.append(total)

                # We want the *average time* from the *best* trial.
                # For more on this methodology, see the docs for
                # Python's `timeit` module.
                #
                # "In a typical case, the lowest value gives a lower bound
                # for how fast your machine can run the given code snippet;
                # higher values in the result vector are typically not
                # caused by variability in Python’s speed, but by other
                # processes interfering with your timing accuracy."
                best = min(trials) / number
                print(
                    "Best of {} trials with {} function"
                    " calls per trial:".format(repeat, number)
                )
                print(
                    "Function `{}` ran in average"
                    " of {:0.3f} seconds.".format(func.__name__, best),
                    end="\n\n",
                    file=file,
                )
            finally:
                if gcold:
                    gc.enable()
            # Result is returned *only once*
            return result

        return _timeit

    # Syntax trick from Python @dataclass
    if _func is None:
        return wrap
    else:
        return wrap(_func)

import Bio
from Bio import SeqIO
from Bio.Seq import Seq
from Bio.Alphabet import generic_dna

#on importe la sequence mystere avec les outils en ligne

def seq_codante_de_phase(phase):
    meilleure_seq=compteur=i=j=start=stop=0
    longueur = len(phase)-1
    for compteur in range (i,longueur):
        if (phase[i] != 'M'):
            i += 1
        else:
            start = i
            j = start
            while (phase[j] != '*' and j < len(phase)-1):
                j += 1
            if (phase[j] == "*"):
                stop = j
            seq_codante = phase[i:j]
            if (len(seq_codante) > meilleure_seq):
                meilleure_seq = len(seq_codante)
                meilleur_start = start
                meilleur_stop = stop
                proteine_de_phase = phase[meilleur_start+1:meilleur_stop]
            i += 1
    return(proteine_de_phase)        

#main

mysterious_sequence = SeqIO.read(open('my_sequence.fasta'),
'fasta',
alphabet=generic_dna).seq


phase_1 = mysterious_sequence[0::]
phase_2 = mysterious_sequence[1::]
phase_3 = mysterious_sequence[2::]

#on renverse la sequence mystere pour creer les 3 dernieres phases 
complement_sequence = mysterious_sequence.complement()
reverse_sequence = complement_sequence[::-1]

#sequence à partir du dernier nucleotide en sens contraire
phase_4 = reverse_sequence[0::]
#sequence à partir de l'avant-dernier nucleotide en sens contraire
phase_5 = reverse_sequence[1::]
#sequence à partir de l'antépénultième nucleotide en sens contraire 
phase_6 = reverse_sequence[2::]

prot_1 = str(phase_1.translate())
prot_2 = str(phase_2.translate())
prot_3 = str(phase_3.translate())
prot_4 = str(phase_4.translate())
prot_5 = str(phase_5.translate())
prot_6 = str(phase_6.translate())

liste=[seq_codante_de_phase(prot_1),seq_codante_de_phase(prot_2),seq_codante_de_phase(prot_3),seq_codante_de_phase(prot_3),seq_codante_de_phase(prot_4),seq_codante_de_phase(prot_5),seq_codante_de_phase(prot_6)]
sorted(liste, key=len)
#la phase vraisemblable est la plus longue, donc la première de la liste triee par longueur
print (liste[1])