<commit_before>"""
    picasso.simulate
    ~~~~~~~~~~~~~~~~

    Simulate single molcule fluorescence data

    :author: <NAME>, 2016
    :copyright: Copyright (c) 2016 Jungmann Lab, Max Planck Institute of Biochemistry
"""
import numpy as _np
from . import io as _io

def saveInfo(filename,info):
    _io.save_info(filename, [info], default_flow_style=True)

def noisy(image,mu,sigma):        #Add gaussian noise to an image.
    row,col= image.shape  #Variance for _np.random is 1
    gauss = sigma*_np.random.normal(0,1,(row,col)) + mu
    gauss = gauss.reshape(row,col)
    noisy = image + gauss
    noisy[noisy<0]=0
    return noisy

def noisy_p(image,mu): #Add poissonian noise to an image
    poiss = _np.random.poisson(mu,image.shape).astype(float)
    noisy = image + poiss
    return noisy


def paintgen( meandark,meanbright,frames,time,photonrate,photonratestd,photonbudget ): #Paint-Generator: Generates on and off-traces for given parameters. Calculates the number of Photons in each frame for a binding site
    meanlocs = int(_np.ceil(frames*time/(meandark+meanbright))) #This is an estimate for the total number of binding events
    if meanlocs < 10:
        meanlocs = meanlocs*5
    else:
        meanlocs = meanlocs*2

    ## GENERATE ON AND OFF-EVENTS
    dark_times = _np.random.exponential(meandark,meanlocs)
    bright_times = _np.random.exponential(meanbright,meanlocs)

    events = _np.vstack((dark_times,bright_times)).reshape((-1,),order='F') # Interweave dark_times and bright_times [dt,bt,dt,bt..]
    simulatedmeandark = _np.mean(events[::2])
    simulatedmeanbright = _np.mean(events[1::2])
    eventsum = _np.cumsum(events)

    maxloc = next(x[0] for x in enumerate(eventsum) if x[1] > frames*time) #Find the first event that exceeds the total integration time

    ## CHECK Trace
    if _np.mod(maxloc,2): #uneven -> ends with an OFF-event
        onevents = int(_np.floor(maxloc/2));
    else: #even -> ends with bright event
        onevents = int(maxloc/2);
    bright_events = _np.floor(maxloc/2); #number of bright_events

    #AN ON-EVENT MIGHT BE LONGER THAN THE MOVIE, SO ALLOCATE MORE MEMORY, AS AN ESTIMATE: MEANBRIGHT/time*10
    photonsinframe = _np.zeros(int(frames+_np.ceil(meanbright/time*20)))

    ## CALCULATE PHOTON NUMBERS
    for i in range(1,maxloc,2):
        photons = _np.round(_np.random.normal(photonrate,photonratestd)*time) #Number of Photons that are emitted in one frame
        if photons < 0:
            photons = 0

        tempFrame = int(_np.floor(eventsum[i-1]/time)); #Get the first frame in which something happens in on-event
        onFrames = int(_np.ceil((eventsum[i]-tempFrame*time)/time)); #Number of frames in which photon emittance happens

        if photons*onFrames > photonbudget:
            onFrames = int(_np.ceil(photonbudget/(photons*onFrames)*onFrames)) #Reduce the number of on-frames once the photonbudget is reached

        for j in range(0,(onFrames)): #LOOP THROUGH ALL ONFRAMES<commit_msg>fixed bug in simulate where in rare cases not enough events were generated<commit_after>"""
    picasso.simulate
    ~~~~~~~~~~~~~~~~

    Simulate single molcule fluorescence data

    :author: <NAME>, 2016
    :copyright: Copyright (c) 2016 Jungmann Lab, Max Planck Institute of Biochemistry
"""
import numpy as _np
from . import io as _io

def saveInfo(filename,info):
    _io.save_info(filename, [info], default_flow_style=True)

def noisy(image,mu,sigma):        #Add gaussian noise to an image.
    row,col= image.shape  #Variance for _np.random is 1
    gauss = sigma*_np.random.normal(0,1,(row,col)) + mu
    gauss = gauss.reshape(row,col)
    noisy = image + gauss
    noisy[noisy<0]=0
    return noisy

def noisy_p(image,mu): #Add poissonian noise to an image
    poiss = _np.random.poisson(mu,image.shape).astype(float)
    noisy = image + poiss
    return noisy


def paintgen( meandark,meanbright,frames,time,photonrate,photonratestd,photonbudget ): #Paint-Generator: Generates on and off-traces for given parameters. Calculates the number of Photons in each frame for a binding site
    meanlocs = 4*int(_np.ceil(frames*time/(meandark+meanbright))) #This is an estimate for the total number of binding events
    if meanlocs < 10:
        meanlocs = meanlocs*10

    ## GENERATE ON AND OFF-EVENTS
    dark_times = _np.random.exponential(meandark,meanlocs)
    bright_times = _np.random.exponential(meanbright,meanlocs)

    events = _np.vstack((dark_times,bright_times)).reshape((-1,),order='F') # Interweave dark_times and bright_times [dt,bt,dt,bt..]
    simulatedmeandark = _np.mean(events[::2])
    simulatedmeanbright = _np.mean(events[1::2])
    eventsum = _np.cumsum(events)

    maxloc = _np.argmax(eventsum>(frames*time)) #Find the first event that exceeds the total integration time
    ## CHECK Trace
    if _np.mod(maxloc,2): #uneven -> ends with an OFF-event
        onevents = int(_np.floor(maxloc/2));
    else: #even -> ends with bright event
        onevents = int(maxloc/2);
    bright_events = _np.floor(maxloc/2); #number of bright_events

    #AN ON-EVENT MIGHT BE LONGER THAN THE MOVIE, SO ALLOCATE MORE MEMORY, AS AN ESTIMATE: MEANBRIGHT/time*10
    photonsinframe = _np.zeros(int(frames+_np.ceil(meanbright/time*20)))

    ## CALCULATE PHOTON NUMBERS
    for i in range(1,maxloc,2):
        photons = _np.round(_np.random.normal(photonrate,photonratestd)*time) #Number of Photons that are emitted in one frame
        if photons < 0:
            photons = 0

        tempFrame = int(_np.floor(eventsum[i-1]/time)); #Get the first frame in which something happens in on-event
        onFrames = int(_np.ceil((eventsum[i]-tempFrame*time)/time)); #Number of frames in which photon emittance happens

        if photons*onFrames > photonbudget:
            onFrames = int(_np.ceil(photonbudget/(photons*onFrames)*onFrames)) #Reduce the number of on-frames once the photonbudget is reached

        for j in range(0,(onFrames)): #LOOP THROUGH ALL ONFRAMES
{'fraction_non_alphanumeric': 0.11053511705685619, 'fraction_numerical': 0.012207357859531772, 'mean_word_length': 3.773343974461293, 'pattern_counts': {'":': 0, '<': 11, '<?xml version=': 0, '>': 13, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '70357', 'n_tokens_mistral': 2080, 'n_tokens_neox': 1922, 'n_words': 541}