In [1]:

    

import os
import csv
import sys
import scipy.optimize as opt
import scipy.stats as stat
from operator import itemgetter
import random
import numpy as np
import numpy.ma as ma
import numpy.linalg as la

pi = np.pi
sin = np.sin
cos = np.cos


    

In [28]:

    

def fillin2(data):
    """
    Fills in blanks of arrays without shifting frames by the starting frame.  Compare to fillin.

    Input: trajectory dataset from MOSAIC tracking software read into a numpy array
    Output: modified numpy array with missing frames filled in.
    """

    shap = int(max(data[:, 1])) + 1
    shape1 = int(min(data[:, 1]))
    newshap = shap - shape1
    filledin = np.zeros((newshap, 5))
    filledin[0, :] = data[0, :]

    count = 0
    new = 0
    other = 0
    tot = 0

    for num in range(1, newshap):
        if data[num-new, 1]-data[num-new-1, 1] == 1:
            count = count + 1
            filledin[num, :] = data[num-new, :]
        elif data[num - new, 1]-data[num - new - 1, 1] > 1:
            new = new + 1
            iba = int(data[num - new+1, 1]-data[num - new, 1])
            togoin = data[num - new]
            togoin[1] = togoin[1] + 1
            filledin[num, :] = togoin
            # dataset[2] = np.insert(dataset[2], [num + new - 2], togoin, axis=0)

        else:
            other = other + 1
        tot = count + new + other

    return filledin


def MSD_iteration(folder, name, cut, totvids, conversion, frames):
    """
    Cleans up data for MSD analysis from csv files.  Outputs in form of
    dictionaries.
    """

    trajectory = dict()
    tots = dict()  # Total particles in each video
    newtots = dict()  # Cumulative total particles.
    newtots[0] = 0
    tlen = dict()
    tlength = dict()
    tlength[0] = 0

    for num in range(1, totvids + 1):
        trajectory[num] = np.genfromtxt(folder+'Traj_{}_{}.tif.csv'.format(name, num), delimiter=",")
        trajectory[num] = np.delete(trajectory[num], 0, 1)

        tots[num] = trajectory[num][-1, 0].astype(np.int64)
        newtots[num] = newtots[num-1] + tots[num]

        tlen[num] = trajectory[num].shape[0]
        tlength[num] = tlength[num-1] + tlen[num]

    placeholder = np.zeros((tlength[totvids], 11))

    for num in range(1, totvids + 1):
        placeholder[tlength[num-1]:tlength[num], :] = trajectory[num]
        placeholder[tlength[num-1]:tlength[num], 0] = placeholder[tlength[num-1]:tlength[num], 0] + newtots[num-1]

    dataset = dict()
    rawdataset = np.zeros(placeholder.shape)
    particles = placeholder[:, 0]
    total = int(max(particles))
    total1 = total + 1
    rawdataset = placeholder[:, :]

    fixed = np.zeros(placeholder.shape)
    fixed[:, 0:2] = rawdataset[:, 0:2]
    fixed[:, 2:4] = conversion[0] * rawdataset[:, 2:4]
    fixed[:, 4] = conversion[2] * rawdataset[:, 4]

    x = np.zeros((frames, total1 - 1))
    y = np.zeros((frames, total1 - 1))
    xs = np.zeros((frames, total1 - 1))
    ys = np.zeros((frames, total1 - 1))

    nones = 0
    cutoff = cut
    for num in range(1, total1):

        hold = np.where(particles == num)
        itindex = hold[0]
        min1 = min(itindex)
        max1 = max(itindex)

        if max1 - min1 < cutoff:
            nones = nones + 1
        else:
            holdplease = fillin2(fixed[min1:max1+1, 0:5])
            x[int(holdplease[0, 1]):int(holdplease[-1, 1])+1, num - nones - 1] = holdplease[:, 2]
            y[int(holdplease[0, 1]):int(holdplease[-1, 1])+1, num - nones - 1] = holdplease[:, 3]

            xs[0:int(holdplease[-1, 1])+1-int(holdplease[0, 1]), num - nones - 1] = holdplease[:, 2]
            ys[0:int(holdplease[-1, 1])+1-int(holdplease[0, 1]), num - nones - 1] = holdplease[:, 3]

    total1 = total1 - nones - 1
    x_m = x[:, :total1-1]
    y_m = y[:, :total1-1]
    xs_m = xs[:, :total1-1]
    ys_m = ys[:, :total1-1]

    return total1, xs_m, ys_m, x_m, y_m


def vectorized_MMSD_calcs(frames, total1, xs_m, ys_m, x_m, y_m, frame_m):

    SM1x = np.zeros((frames, total1-1))
    SM1y = np.zeros((frames, total1-1))
    SM2xy = np.zeros((frames, total1-1))

    xs_m = ma.masked_equal(xs_m, 0)
    ys_m = ma.masked_equal(ys_m, 0)

    x_m = ma.masked_equal(x_m, 0)
    y_m = ma.masked_equal(y_m, 0)

    geoM1x = np.zeros(frame_m)
    geoM1y = np.zeros(frame_m)

    for frame in range(1, frame_m):
        bx = xs_m[frame, :]
        cx = xs_m[:-frame, :]
        Mx = (bx - cx)**2

        Mxa = np.mean(Mx, axis=0)
        # Mxab = np.mean(np.log(Mxa), axis=0)

        # geoM1x[frame] = Mxab

        by = ys_m[frame, :]
        cy = ys_m[:-frame, :]
        My = (by - cy)**2

        Mya = np.mean(My, axis=0)
        # Myab = np.mean(np.log(Mya), axis=0)

        # geoM1y[frame] = Myab
        SM1x[frame, :] = Mxa
        SM1y[frame, :] = Mya

    geoM2xy = np.mean(ma.log(SM1x+SM1y), axis=1)
    gSEM = stat.sem(ma.log(SM1x+SM1y), axis=1)
    SM2xy = SM1x + SM1y

    return geoM2xy, gSEM, SM1x, SM1y, SM2xy


    

In [29]:

    

folder = "./"
path = "./geoM2xy_{sample_name}.csv"
frames = 651
SD_frames = [10, 20, 50, 80]
conversion = (0.16, 100.02, 1)  # (0.3, 3.95, 1)
to_frame = 120
dimension = "2D"
time_to_calculate = 1

base = "in_agarose"
base_name = "RED"
test_bins = np.linspace(0, 75, 76)

# name = 'RED_KO_PEG_P1_S1_cortex'
cut = 1
totvids = 2
frame_m = 651  # atm I can't go lower than the actual value.

parameters = {}
parameters["channels"] = ["RED"]
parameters["surface functionalities"] = ["nPEG"]
parameters["slices"] = ["S1", "S2"]
parameters["videos"] = [1, 2]
parameters["replicates"] = [1, 2]


channels = parameters["channels"]
surface_functionalities = parameters["surface functionalities"]
slices = parameters["slices"]
videos = parameters["videos"]
replicates = parameters["replicates"]

geoM2xy = {}
gSEM = {}
SM1x = {}
SM1y = {}
SM2xy = {}


    

In [30]:

    

for channel in channels:
    for surface_functionality in surface_functionalities:
        slice_counter = 0
        for slic in slices:
            for video in videos:
                sample_name = "{}_{}_{}_{}_{}".format(channel, surface_functionality, '37C_pH72', slic, video)
                DIR = folder

                total1, xs, ys, x, y = MSD_iteration(DIR, sample_name, cut, totvids, conversion, frame_m)

                geoM2xy[sample_name], gSEM[sample_name], SM1x[sample_name], SM1y[sample_name],\
                    SM2xy[sample_name] = vectorized_MMSD_calcs(frames, total1, xs, ys, x, y, frame_m)
                np.savetxt(DIR+'geoM2xy_{}.csv'.format(sample_name), geoM2xy[sample_name], delimiter=',')
                np.savetxt(DIR+'gSEM_{}.csv'.format(sample_name), gSEM[sample_name], delimiter=',')
                np.savetxt(DIR+'SM2xy_{}.csv'.format(sample_name), SM2xy[sample_name], delimiter=',')

                slice_counter = slice_counter + 1


    

In [12]:

    

SM2xy1 = SM2xy['RED_nPEG_37C_pH72_S1_1']


    

In [18]:

    

geoM2xy1 = np.mean(ma.log(SM2xy1+SM2xy1), axis=1)


    

In [20]:

    

geoM2xy1 = stat.sem(ma.log(SM2xy1+SM2xy1), axis=1)


    

In [21]:

    

geoM2xy


    

    
Out[21]:





array([ 0.        ,  0.08080702,  0.10318802,  0.09891259,  0.09218002,
        0.08695801,  0.08191538,  0.07787173,  0.07386894,  0.07153525,
        0.06986269,  0.0677853 ,  0.06817925,  0.06713191,  0.06544003,
        0.06434326,  0.06463163,  0.06243887,  0.06439937,  0.06076896,
        0.06281202,  0.06024286,  0.06101672,  0.05546603,  0.05897295,
        0.05390863,  0.0592696 ,  0.05865116,  0.05621035,  0.05509468,
        0.05768897,  0.05565244,  0.05728605,  0.05587126,  0.05649437,
        0.05265252,  0.0555687 ,  0.05354089,  0.05134968,  0.05016551,
        0.0529781 ,  0.04995147,  0.05317292,  0.05213238,  0.05200264,
        0.05096998,  0.05074257,  0.05103048,  0.05128592,  0.04962384,
        0.05090041,  0.04865657,  0.05051277,  0.05031416,  0.05088686,
        0.05134718,  0.0480223 ,  0.04939072,  0.04996471,  0.05048015,
        0.04950887,  0.05086384,  0.04957504,  0.05078168,  0.04972348,
        0.04940347,  0.04997614,  0.04872188,  0.05078497,  0.05017846,
        0.04918097,  0.05056897,  0.05076452,  0.0481482 ,  0.04936432,
        0.04895433,  0.04862314,  0.05039844,  0.051026  ,  0.04825902,
        0.04746805,  0.05029038,  0.04933575,  0.05026547,  0.04914154,
        0.05012216,  0.0483543 ,  0.04963976,  0.04905538,  0.05000016,
        0.04845275,  0.04739605,  0.04741467,  0.04623446,  0.04966757,
        0.04486533,  0.04715156,  0.04666719,  0.04663062,  0.04705951,
        0.04962039,  0.04709277,  0.04951821,  0.0483735 ,  0.04887656,
        0.04931291,  0.04684154,  0.04849986,  0.05016815,  0.05004373,
        0.05033034,  0.0494569 ,  0.05016163,  0.04847539,  0.04825777,
        0.04741339,  0.04963447,  0.04850851,  0.04780471,  0.04854123,
        0.04482608,  0.0495212 ,  0.0487819 ,  0.04675827,  0.04937856,
        0.04867849,  0.04790692,  0.04918639,  0.04940167,  0.04676195,
        0.0480478 ,  0.04792764,  0.04923566,  0.04882225,  0.04889127,
        0.04796756,  0.04764432,  0.04539095,  0.04771806,  0.04773377,
        0.04836379,  0.04800305,  0.04990386,  0.04863025,  0.04562648,
        0.04883655,  0.04867504,  0.04890923,  0.04975684,  0.04860192,
        0.0473076 ,  0.04871942,  0.04695258,  0.04761333,  0.04926467,
        0.04892572,  0.04864182,  0.04811494,  0.04860405,  0.04583895,
        0.049779  ,  0.0478839 ,  0.04863979,  0.0473031 ,  0.04683393,
        0.04969771,  0.0473818 ,  0.04686177,  0.04604629,  0.04659908,
        0.04849276,  0.04819565,  0.04432604,  0.04831374,  0.04833031,
        0.04778104,  0.04840514,  0.04838517,  0.0493097 ,  0.04878637,
        0.04837487,  0.0466132 ,  0.04904537,  0.04925578,  0.04847353,
        0.04771652,  0.04800824,  0.0485428 ,  0.04824305,  0.04619413,
        0.0482783 ,  0.04826666,  0.04660657,  0.04498476,  0.04787337,
        0.04952782,  0.04720161,  0.04555834,  0.04902448,  0.04786976,
        0.04688185,  0.04947422,  0.04708211,  0.04608815,  0.04924668,
        0.04827407,  0.04801299,  0.04920877,  0.04904188,  0.04796435,
        0.04922678,  0.04810965,  0.04871084,  0.04774953,  0.04599528,
        0.04628991,  0.0469925 ,  0.04787667,  0.04888768,  0.04932509,
        0.04911085,  0.04780641,  0.04840206,  0.04780101,  0.04764976,
        0.04833689,  0.04763062,  0.04745301,  0.04901556,  0.04614122,
        0.04785245,  0.04783021,  0.0474632 ,  0.04783711,  0.04868049,
        0.04883382,  0.04959246,  0.04843364,  0.04840869,  0.04845331,
        0.04755539,  0.04871012,  0.04816079,  0.04873833,  0.04741982,
        0.04694922,  0.04821592,  0.04805561,  0.04860907,  0.04771792,
        0.04850507,  0.04811753,  0.04538866,  0.04819361,  0.04873706,
        0.04876828,  0.04563275,  0.04759702,  0.04788825,  0.04729116,
        0.04695638,  0.0495687 ,  0.0466935 ,  0.04838533,  0.04807004,
        0.04847556,  0.04749099,  0.04815141,  0.04869378,  0.04897528,
        0.04845917,  0.0488186 ,  0.04871517,  0.04688467,  0.04951827,
        0.04899225,  0.04774186,  0.04664274,  0.04982864,  0.04915551,
        0.04627114,  0.04885282,  0.0476951 ,  0.04813757,  0.04677178,
        0.04887108,  0.04855768,  0.04534812,  0.04736636,  0.04739552,
        0.04650525,  0.04779428,  0.04624474,  0.04806847,  0.04693198,
        0.04763013,  0.04946792,  0.04869658,  0.04897834,  0.04771522,
        0.04861267,  0.04694853,  0.04831157,  0.04510684,  0.04750778,
        0.04608585,  0.04731651,  0.04804355,  0.04574515,  0.04908274,
        0.04800964,  0.04337534,  0.04555348,  0.04631456,  0.04541533,
        0.04719415,  0.04636745,  0.04802312,  0.04830799,  0.04926084,
        0.04778174,  0.04929539,  0.04751214,  0.04816142,  0.05011882,
        0.04730059,  0.0481005 ,  0.04729436,  0.04901894,  0.04824789,
        0.04824776,  0.04600842,  0.04526048,  0.04814256,  0.04862397,
        0.04306445,  0.04621237,  0.04793617,  0.04460325,  0.04541596,
        0.0488393 ,  0.04642989,  0.04551177,  0.04761237,  0.04786501,
        0.048508  ,  0.04850275,  0.04593411,  0.04871582,  0.04218405,
        0.04549929,  0.04938616,  0.04752146,  0.04780132,  0.04882526,
        0.04304879,  0.04934606,  0.04526917,  0.04889439,  0.04872249,
        0.04799072,  0.04618657,  0.04548243,  0.04670056,  0.04981989,
        0.04945649,  0.04613223,  0.0485873 ,  0.04625866,  0.04733655,
        0.04912149,  0.04924475,  0.04725575,  0.04806932,  0.04723061,
        0.04890364,  0.04712339,  0.04859664,  0.04938015,  0.04912668,
        0.04695663,  0.04885745,  0.04471271,  0.0461976 ,  0.04852519,
        0.04599234,  0.04681828,  0.04937107,  0.04275751,  0.0487833 ,
        0.04715169,  0.0488338 ,  0.0483317 ,  0.04775542,  0.04750452,
        0.046458  ,  0.04533072,  0.04766649,  0.04852831,  0.04465266,
        0.04898198,  0.04867659,  0.04764053,  0.04277224,  0.04714523,
        0.04567109,  0.04845803,  0.0467238 ,  0.04789447,  0.04543226,
        0.04689909,  0.04320032,  0.04693502,  0.04589953,  0.04869742,
        0.04830824,  0.04564928,  0.04377753,  0.04887973,  0.04392249,
        0.04865736,  0.04754703,  0.04912167,  0.04684461,  0.04799693,
        0.04699884,  0.04858953,  0.04447692,  0.04714605,  0.04610744,
        0.04804921,  0.04863639,  0.04748229,  0.04631292,  0.04813008,
        0.0479292 ,  0.04512   ,  0.04517252,  0.04719114,  0.04625684,
        0.045799  ,  0.04779635,  0.04873565,  0.04476152,  0.04558947,
        0.04847948,  0.04646564,  0.04639927,  0.04770351,  0.04499232,
        0.04907966,  0.04518301,  0.04645826,  0.04435244,  0.04928288,
        0.04779867,  0.04927273,  0.04849512,  0.04872859,  0.04510246,
        0.04548961,  0.04759714,  0.04806664,  0.04692112,  0.04698719,
        0.04423797,  0.04822181,  0.04321879,  0.04568006,  0.04714834,
        0.04757959,  0.04636522,  0.04546381,  0.04110437,  0.04696802,
        0.04574883,  0.04850878,  0.04719341,  0.04865981,  0.04610231,
        0.04911328,  0.04531973,  0.04642629,  0.0437148 ,  0.04828445,
        0.04854921,  0.04905263,  0.04806174,  0.04710962,  0.04329796,
        0.04569331,  0.04174566,  0.0490547 ,  0.04688643,  0.04841274,
        0.04862094,  0.04664555,  0.04163047,  0.04587668,  0.04675176,
        0.04894809,  0.04792147,  0.04584206,  0.04541177,  0.0427964 ,
        0.04731078,  0.04606908,  0.04760296,  0.04562409,  0.04894656,
        0.04544754,  0.04513363,  0.0439077 ,  0.04648911,  0.04166009,
        0.04860363,  0.045624  ,  0.04476935,  0.04522902,  0.04810538,
        0.04318798,  0.04697022,  0.04593674,  0.04209679,  0.0449565 ,
        0.04548164,  0.0477799 ,  0.04747732,  0.04464012,  0.0482516 ,
        0.04512753,  0.0464162 ,  0.04338387,  0.04271565,  0.04557774,
        0.04796477,  0.04839774,  0.0472393 ,  0.0443934 ,  0.04145067,
        0.0471688 ,  0.0410203 ,  0.04711422,  0.04755051,  0.04652841,
        0.04563023,  0.04671155,  0.04150835,  0.04413302,  0.04529317,
        0.04760119,  0.0467076 ,  0.0477029 ,  0.0436625 ,  0.0442322 ,
        0.04468641,  0.04826485,  0.04487106,  0.04801513,  0.0423939 ,
        0.04554864,  0.04351345,  0.04416009,  0.04199573,  0.04517073,
        0.04646086,  0.04649482,  0.04307963,  0.04393585,  0.04293633,
        0.04852946,  0.04162693,  0.04630155,  0.04329851,  0.04654206,
        0.04384774,  0.04701393,  0.04179516,  0.04157412,  0.0460927 ,
        0.04809664,  0.04687588,  0.04603996,  0.04412842,  0.04498052,
        0.04371607,  0.04494817,  0.04420567,  0.04435346,  0.04409609,
        0.04790681,  0.04259957,  0.04550533,  0.04557165,  0.0459245 ,
        0.04419097,  0.04700363,  0.04242771,  0.0461407 ,  0.04313022,
        0.04615338,  0.04561195,  0.04503744,  0.04389807,  0.04291907,
        0.04444208,  0.04310773,  0.04702469,  0.04585353,  0.04624881,
        0.04503854,  0.04580158,  0.04225655,  0.04240649,  0.04586058,
        0.04632985,  0.04809472,  0.04591763,  0.04268428,  0.04318644,
        0.04556987,  0.04524685,  0.044603  ,  0.04715148,  0.04677126,
        0.04441741,  0.0447226 ,  0.04357775,  0.04315603,  0.04315739,
        0.04466031,  0.04976869,  0.0442291 ,  0.04221624,  0.04331361,
        0.04670949,  0.04241623,  0.04476296,  0.04470355,  0.04583173,
        0.04365824,  0.04760827,  0.04246756,  0.04482691,  0.04499976,
        0.04517573,  0.04634506,  0.04465674,  0.04053175,  0.04817483,
        0.05056338])

In [31]:

    

geoM2xy[sample_name]


    

    
Out[31]:





masked_array(data = [-- -5.528753793045865 -5.154351076507821 -4.9884488061401955
 -4.8704793432450515 -4.789961056015163 -4.856133493288042
 -4.795382746192625 -4.980337433401773 -5.054798104552319 -5.03000562725889
 -5.107536635763646 -5.291879340082459 -5.3797722708582425
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In [ ]: