def small_scale(store_export='dictionary'):
""" Performs a single simulation of the given system, creates
the following plots:
- population dynamics overview (free naive cells, free memory cells
and GC populations over time)
- for each GC, a clonal composition plot together with its memory
output in a separate panel
- for each GC, the evolution of its largest clone's affinities over
mutation count (this plot contains aritificial noise to increase
visibility!).
If store_export is set 'datafile', the simulation data is stored in a
hdf5 file for future purposes, for 'dictionary' the data is passed
internally and discarded after the run.
Recommended only for small simulation sizes with up to ~5 GCs and ~5k
cells, as otherwise things get crowded and plots get large.
"""
# get runID from current system time
runID = int(time.time())
# run simulation and get filepath or dict
simdata = main(runID, store_export=store_export, evalperday=12)
# import required information for small scale plots
l_times, l_fn, l_fm, l_GCs, LFcurve, Agcurve, evaltimes, freePan, GCPans, \
ms_times, ms_vals, ms_fams, ms_muts, mut_list, \
E_list = import_file(simdata)
# plot population behaviour
population_plot(l_times, l_fn, l_fm, l_GCs, runID)
# plot GC contents and memory output for every GC
for i in range(len(l_GCs)):
GC_dynamics_plot(GCPans[i], ms_times[i], ms_fams[i], ms_vals[i],
ms_muts[i], runID, i)
return (simdata)
def AM_effect_nkey(nkeys=[1, 5, 10, 15], repeats=100, d_export=True):
""" Given a list of values for nkey and a number of individual GC reactions
to be averaged over for each of them, computes and plots the improvement
within single GCs for one infection. Thus, overwrites parameters giving
the infection protocol and duration of the simulation as well as setting
the nubmer of GCs to 1. Other parameters remain untouched. A textfile with
the computed mean results is exported if d_export==True.
"""
# set single infection and single GC for this analysis
cf.endtime = 30*12
cf.tinf = [0*12]
cf.dose = [1]
cf.nGCs = 1
cf.naive_pool = 1000*1 # size of the naive precursor pool
cf.memory_pool = 100*1 # size of the initial unspecific memory pool
# function for calculating mean E_norm from GC panel
def GC_affinity(GCPan):
""" Given a GC panel, gets the mean E_norm for each timepoint."""
energies = []
tList = GCPan.keys()
for tp in range(len(tList)):
energy = GCPan[tList[tp]]['affinity'].dropna().mean()
energies.append(energy)
return tList, energies
topElist = []
for hs in nkeys:
# set binding model parameters accordingly
cf.nkey = hs
cf.lAg = hs
cf.lAb = 220 - hs
eL = [] # list for collecting energies timecurses of all runs
for r in range(repeats):
simdata = main(store_export='dictionary', evalperday=12)
l_times, l_fn, l_fm, l_GCs, LFcurve, Agcurve, evaltimes, freePan, \
GCPans, ms_times, ms_vals, ms_fams, ms_muts, mut_list, E_list\
= import_file(simdata)
tList, energies = GC_affinity(GCPans[0])
eL.append(energies)
# calculate mean and std of all runs and plot
eM = np.nanmean(np.array(eL), axis=0)
eStd = np.nanstd(np.array(eL), axis=0)
topElist.append((hs, tList, eM, eStd))
# write information to file
if d_export:
datafile = open('processed_data/AM_effect_data', 'w')
datafile.write('number of simulation runs per n_key = {} \n'.format(repeats))
datafile.write('n_key, time (days), mean(normalised energies), std(normalised energies) \n')
for i in range(len(nkeys)):
datafile.write('{0}, {1}, {2}, {3}\n \n'.format(topElist[i][0], np.array(topElist[i][1])/12., topElist[i][2], topElist[i][3]))
datafile.close()
# plot
AM_effect_plot(topElist)
def map_params(dose=[1], LFdecay=[10*12], nGCs=[1], nLFs=[25],
naive_pool=[1000], nkey=[1, 2, 10], p_err=[0.003],
tinf=[[0*12]],
p_block=[0.5], repeats=1):
""" Function for mapping out the effects of different parameter
(combinations) on the standard TUCHMI protocol. All arguments are lists,
either containing only the default value or a set of values, in which case
all combinations of list arguments will be executed the given number
of times (repeat).
The paramter set used is stored together with the complete timecourse of
of E_bind, SHM, entropy, memory number (means and std where applicable)
and exported into a .h5 file.
For running on a cluster, accepts an ID argument (e.g. job ID) for easier
handling of errors etc.
"""
# open file stamped with systemtime if no other ID was provided in the call
try:
sys.argv[1]
except IndexError:
filepath = 'map_data/data{}.h5'.format(int(time.time()*100))
else:
filepath = 'map_data/data{}.h5'.format(sys.argv[1])
print(filepath)
datafile = pd.HDFStore(filepath)
# dict for collecting result series
seriesdict = {}
# set endtime and days for evaluation
cf.endtime = 126*12 # run until challenge timepoint
# evaluate pool every day
evaldays = np.arange(126)
# get parameter combinations
paramsets = list(product(dose, LFdecay, nGCs, nLFs, naive_pool, nkey,
p_err, tinf, p_block))
# for every parameter set, run the simulation repeat times and write
# results to the file
for p in paramsets:
# set parameters
cf.dose = [p[0] for i in range(len(p[7]))]
cf.LFdecay = p[1]
cf.nGCs = p[2]
cf.nLFs = p[3]
cf.naive_pool = p[4]*cf.nGCs
cf.memory_pool = 100*cf.nGCs # fixed! (or change manually)
cf.nkey = p[5]
cf.lAg = p[5]
cf.lAb = 220 - p[5]
cf.p_err_FWR = p[6]
cf.p_err_CDR = p[6]
cf.tinf = p[7]
cf.p_block_FWR = p[8]
for r in range(repeats):
# get lists to store individual simulation results
l_mems = []
l_KDs = []
s_KDs = [] # std
l_SHMs = []
s_SHMs = [] # std
l_Entrs = []
# run simulation and get filepath or dict
evaldays, l_mems, l_KDs, s_KDs, l_SHMs, s_SHMs, l_Entrs = \
main(store_export='minimal', evalperday=1)
# write these lists to file together with parameters used.
# Identifier system time.
ID = 'ID_{}'.format(time.time())
series = pd.Series([cf.dose, cf.LFdecay, cf.nGCs, cf.nLFs,
cf.naive_pool, cf.nkey, cf.p_block_FWR,
cf.p_err_CDR, cf.tinf, cf.memory_pool,
evaldays, np.array(l_mems),
np.array(l_KDs), np.array(s_KDs),
np.array(l_SHMs), np.array(s_SHMs),
np.array(l_Entrs)],
index=['dose', 'LFdecay', 'nGCs', 'nLFs',
'naive_pool', 'nkey', 'p_block',
'p_err', 'tinf', 'mem_pool',
'evaldays', 'memcount', 'E_bind',
'E_bind_std', 'SHM', 'SHM_std',
'entropy'])
seriesdict[ID] = series
# make dataframe and store it
df = pd.DataFrame(seriesdict)
df = df.transpose()
datafile['data'] = df
# close datafile
datafile.close()
print('END')
def TUCHMI_sampling(store_export='datafile', d_export=True, subsample=12):
""" Performs a single simulation of a given size using the TUCHMI vaccination
protocol, samples memory from the simulated pool and creates several plots
summarising the information. User settings regarding the protocol are
overwritten.
Plots produced include:
- mean SHMs and clonal expansion (fraction of cells sampled from
clones that appeared more than once within the sample) in samples of
size subsample
- scatter plot of affinity over mutational status in polyclonal samples
at TUCHMI time points I, II and III
- scatter plot of affinity over mutational status at a clonal level,
cells sampled from three TUCHMI time points merged into single plots
(but sampling time point encoded in colouring)
If store_export is set 'datafile', the simulation data is stored in a
hdf5 file for future purposes, for 'dictionary' the data is passed
internally and lost after the run.
If d_export is set True, textfiles containing the sampled data (used for
plotting) are exported for each plot individually.
Subsample gives the number of cells to be sampled at each timepoint in
oder to calculate entropy and unique fraction.
Can be used for all simulation sizes, but is especially useful for larger
simulations (e.g. >=50 GCs, 50k cells).
"""
# give protocol
cf.endtime = 126 * 12
cf.tinf = [0 * 12, 28 * 12, 56 * 12]
cf.dose = [1, 1, 1]
# get runID from current system time
runID = int(time.time())
# run simulation and get filepath or dict
simdata = main(runID, store_export=store_export, evalperday=1)
# import required information
l_times, l_fn, l_fm, l_GCs, LFcurve, Agcurve, evaltimes, freePan, GCPans, \
ms_times, ms_vals, ms_fams, ms_muts, mut_list, E_list = \
import_file(simdata)
# for affinity-mutation scatter plot, downsample for visibility
pick_tp = 35 * 12
samplefrac = 100. / len(freePan.sel(timepoint=pick_tp).dropna("dim_0"))
# get list of lists to catch values at every timepoint
tList = list(freePan["timepoint"].values)
TT = len(tList)
SHM_means = [[] for t in range(TT)]
Entropies = [[] for t in range(TT)]
clusterfracs = [[] for t in range(TT)]
""" Cell pool affinity over time """
# get mean affinity at all time points
Elist = []
for tp in range(len(tList)):
C = freePan.sel(timepoint=tList[tp]).loc[dict(
dim_1="affinity")].dropna("dim_0").values.mean()
Elist.append(C)
# pass energies to plot function
pool_affinity_plot(tList, Elist)
""" Mean SHM and clonal expansion within sample of size subsample """
# sample 100 times to calculate standard deviations
for nn in range(100):
for tp in range(TT):
ttp = 12 * tp
# cellnumber to be sampled is either subsample or, if less cells
# are available (more of a hypothetic case really), all cells
freePan_no_na = freePan.sel(timepoint=ttp).dropna("dim_0")
cellnum = min(subsample, len(freePan_no_na))
if cellnum > 0:
cell_id = np.random.choice(len(freePan_no_na), cellnum)
cells = freePan_no_na[cell_id, :]
c_muts = list(cells.loc[dict(dim_1="mutations")].values)
SHM_means[tp].append(np.nanmean(c_muts))
# evaluate entropies and clusterfractions
CC = Counter(list(cells.loc[dict(dim_1="family")].values))
Entropies[tp].append(
scipy.stats.entropy(list(CC.values()), base=2) /
math.log(cellnum, 2))
# count again to find how many clones have one member only,
# calculate clusterfrac from this
sizedist = list(CC.values())
C2 = Counter(sizedist)
uniquefrac = float(C2[1]) / cellnum
clusterfracs[tp].append(1 - uniquefrac)
else:
SHM_means[tp].append(np.nan)
Entropies[tp].append(np.nan)
uniquefrac[tp].append(np.nan)
clusterfracs[tp].append(np.nan)
# pass information to plotting function
MSHM = np.nanmean(SHM_means, axis=1)
SSHM = np.nanstd(SHM_means, axis=1)
MEntropies = np.nanmean(Entropies, axis=1)
SEntropies = np.nanstd(Entropies, axis=1)
Mclusterfracs = np.nanmean(clusterfracs, axis=1)
Sclusterfracs = np.nanstd(clusterfracs, axis=1)
sample_statistics_plot(subsample, tList, MSHM, SSHM, MEntropies,
SEntropies, Mclusterfracs, Sclusterfracs)
""" Plot of affinity/mutations on tps I, II and III """
# sample cells 7 days post each infection, record SHM, KD and origin
# (memory versus naive first activated ancestor)
timecourse = [7 * 12, 35 * 12, 63 * 12]
SHM_list = [[] for t in timecourse]
KD_list = [[] for t in timecourse]
orglist = [[] for t in timecourse]
for d in range(len(timecourse)):
tp = timecourse[d]
freePan_no_na = freePan.sel(timepoint=tp).dropna("dim_0")
cellnum = int(np.round(len(freePan_no_na) * samplefrac))
if cellnum > 0:
cell_id = np.random.choice(len(freePan_no_na), cellnum)
cells = freePan_no_na[cell_id, :]
kdl = list(cells.loc[dict(dim_1="affinity")].values)
# transform norm E to KD
kdll = np.exp(cf.y0 + np.array(kdl) * cf.m)
KD_list[d] = list(kdll)
# get mutation counts, correct them and origin
SHM_list[d] = list(cells.loc[dict(dim_1="mutations")].values)
orglist[d] = list(cells.loc[dict(dim_1="origin")].values)
# pass information to plot function
sample_scatter_plot(KD_list, SHM_list, orglist)
""" Affinity/mutation plots for individual clusters """
# samples from the memory pool at the given timepoints, split information
# into clusters and plot SHM/KD scatter plots for some of these clusters.
# lists to collect SHM, KD values, families and timepoints for all panels
SHM_list = []
KD_list = []
fam_list = []
tp_list = []
for d in range(len(timecourse)):
tp = timecourse[d]
freePan_no_na = freePan.sel(timepoint=tp).dropna("dim_0")
cellnum = int(len(freePan_no_na) * samplefrac)
if cellnum > 0:
cell_id = np.random.choice(len(freePan_no_na), cellnum)
cells = freePan_no_na[cell_id, :]
kdl = list(cells.loc[dict(dim_1="affinity")].values)
# transform norm E to KD
kdll = np.exp(cf.y0 + np.array(kdl) * cf.m)
KD_list += list(kdll)
SHM_list += list(list(cells.loc[dict(dim_1="mutations")].values))
fam_list += list(cells.loc[dict(dim_1="family")].values)
tp_list += [tp for k in range(cellnum)]
# count into families and find clusters with more than xx members
famcounter = Counter(fam_list)
fams = list(famcounter.keys())
clusters = []
for fam in fams:
if famcounter[fam] > 1:
clusters.append(fam)
# make separate lists for SHM, KD and TP (defining color) within clusters
# and add information to list
iSHMs = [[] for i in clusters]
iKDs = [[] for i in clusters]
iTPs = [[] for i in clusters]
for ff in range(len(fam_list)):
if fam_list[ff] in clusters:
ii = clusters.index(fam_list[ff])
iSHMs[ii].append(SHM_list[ff])
iKDs[ii].append(KD_list[ff])
# give different colors for different timepoints
if tp_list[ff] == timecourse[0]:
iTPs[ii].append('lightcoral')
elif tp_list[ff] == timecourse[1]:
iTPs[ii].append('indianred')
else:
iTPs[ii].append('firebrick')
# pass information to plot function
clonal_scatter_plot(iSHMs, iKDs, iTPs)
# write information to file
if d_export:
datafile = open('processed_data/TUCHMI_sampling_data', 'w')
datafile.write('1) SAMPLE STATISTICS \n \n')
datafile.write('sampled fraction = {} \n \n'.format(samplefrac))
datafile.write('timecourse (days) \n {} \n \n'.format(
np.array(tList) / 12.))
datafile.write(
'SHMs of cells in sample, mean and std \n {} \n {} \n \n'.format(
MSHM, SSHM))
datafile.write(
'normalised Shannon entropy of cells in sample, mean and std \n {} \n {} \n \n'
.format(MEntropies, SEntropies))
datafile.write(
'fraction of non-unique cells in sample, mean and std \n {} \n {} \n \n'
.format(Mclusterfracs, Sclusterfracs))
datafile.close()
return (simdata)
def stacked_mutations(store_export='dictionary', d_export=True, repeats=10):
""" Performs a number of simulation runs, computes histograms for
improved, impaired and unchanged binders at a single given timepoint
and saves the individual as well as the summed values to file. For several
repeats, data is accumulated in the histograms as well.
If store_export is set 'datafile', the simulation data is stored in a
hdf5 file for future purposes, for 'dictionary' the data is passed
internally and lost after the run.
If d_export is set True, textfiles containing the sampled data (used for
plotting) are exported for each plot individually."""
# parameters relevant to this analysis
# evaluation timepoint in days
analysis_time = 29
bins = np.linspace(0.6, 1, 17)
# collect results
sum_zero = np.zeros(len(bins) - 1)
sum_plus = np.zeros(len(bins) - 1)
sum_minus = np.zeros(len(bins) - 1)
list_zero = []
list_plus = []
list_minus = []
for i in range(repeats):
# get runID from current system time
runID = int(time.time())
# run simulation and get filepath or dict
simdata = main(runID, store_export=store_export, evalperday=1)
# import required information for small scale plots
l_times, l_fn, l_fm, l_GCs, LFcurve, Agcurve, evaltimes, freePan, \
GCPans, ms_times, ms_vals, ms_fams, ms_muts, mut_list, E_list = \
import_file(simdata)
# extract the affinities and ancestor affinities at the analysis points
tList = list(freePan["timepoint"].values)
# limit cell number to be drawn in order not to clatter the plot
# possibility of subsampling here
tp = analysis_time
freePan_no_na = freePan.sel(timepoint=tList[tp]).dropna("dim_0")
cellnum = len(freePan_no_na)
cell_id = np.random.choice(len(freePan_no_na), cellnum)
cells = freePan_no_na[cell_id, :]
final_dist = list(
cells.loc[dict(dim_1="affinity")].dropna("dim_0").values)
ancestor_dist = list(
cells.loc[dict(dim_1="affinity0")].dropna("dim_0").values)
# extract counts of unchanged, improved and impaired cells
unchanged_list = np.array(final_dist)[np.where(
np.array(ancestor_dist) == np.array(final_dist))[0]]
improved_list = np.array(final_dist)[np.where(
np.array(ancestor_dist) < np.array(final_dist))[0]]
impaired_list = np.array(final_dist)[np.where(
np.array(ancestor_dist) > np.array(final_dist))[0]]
# make histograms, store information both in list and in sum.
U_counts, _ = np.histogram(unchanged_list, bins=bins)
plus_counts, _ = np.histogram(improved_list, bins=bins)
minus_counts, _ = np.histogram(impaired_list, bins=bins)
# collect results
sum_zero += U_counts
sum_plus += plus_counts
sum_minus += minus_counts
list_zero.append(U_counts)
list_plus.append(plus_counts)
list_minus.append(minus_counts)
cellsum = np.sum(sum_zero) + np.sum(sum_plus) + np.sum(sum_minus)
# plot
stacked_energy_plot(bins, sum_plus, sum_minus, sum_zero, analysis_time)
if d_export:
datafile = open('processed_data/stacked_histogram_data', 'w')
datafile.write('1) day \n \n')
datafile.write('{} \n \n'.format(analysis_time))
datafile.write('2) bins \n \n')
datafile.write('{} \n \n'.format(bins))
datafile.write('3) runs \n \n')
datafile.write('{} \n \n'.format(repeats))
datafile.write('4) sum of counts with unchanged energies\n \n')
datafile.write('{} \n \n'.format(sum_zero))
datafile.write('5) sum of counts with improved energies\n \n')
datafile.write('{} \n \n'.format(sum_plus))
datafile.write('6) sum of counts with impaired energies\n \n')
datafile.write('{} \n \n'.format(sum_minus))
datafile.write('7) list of counts with unchanged energies\n \n')
datafile.write('{} \n \n'.format(list_zero))
datafile.write('8) list of counts with improved energies\n \n')
datafile.write('{} \n \n'.format(list_plus))
datafile.write('9) list of counts with impaired energies\n \n')
datafile.write('{} \n \n'.format(list_minus))
datafile.write('10) percentage of umutated, improved, impaired \n \n')
datafile.write('{}, {}, {}'.format(
np.sum(sum_zero) / cellsum,
np.sum(sum_plus) / cellsum,
np.sum(sum_minus) / cellsum))
datafile.close()
def selection_vs_mutation(store_export='dictionary', d_export=True):
""" Performs a single simulation of a given size using a specified protocol
of vaccination boosters. At specified timepoints, a specified number of
memory cells is sampled and the affinities of their ancestors as well as
their current affinities are written to a list. Also written to list
are the binding energies of the naive cells. These three lists are then
passed on to be plotted as distribution histograms.
Plots produced include a collection of three histograms (unselected,
selected germline energies, actual energies after mutations) for each
queried timepoint and a more complex scatter plot with marginal histograms
for each queried timepoint.
For each timepoint, the fraction of cells with unaltered/improved/impaired
affinity is printed to screen.
If store_export is set 'datafile', the simulation data is stored in a
hdf5 file for future purposes, for 'dictionary' the data is passed
internally and lost after the run.
If d_export is set True, textfiles containing the sampled data (used for
plotting) are exported for each plot individually.
"""
# parameters relevant to this analysis
# evaluation timepoint in days
analysis_times = [29]
# prepare lists
ancestor_dists = []
final_dists = []
# get runID from current system time
runID = int(time.time())
# run simulation and get filepath or dict
simdata = main(runID, store_export=store_export, evalperday=1)
# import required information for small scale plots
l_times, l_fn, l_fm, l_GCs, LFcurve, Agcurve, evaltimes, freePan, GCPans, \
ms_times, ms_vals, ms_fams, ms_muts, mut_list, E_list = \
import_file(simdata)
# extract the affinities and ancestor affinities at the analysis points
tList = list(freePan["timepoint"].values)
for i in range(len(analysis_times)):
# limit cell number to be drawn in order not to clatter the plot
tp = analysis_times[i]
freePan_no_na = freePan.sel(timepoint=tList[tp]).dropna("dim_0")
cellnum = min(2000, len(freePan_no_na))
cellnum = len(freePan_no_na)
cell_id = np.random.choice(len(freePan_no_na), cellnum)
cells = freePan_no_na[cell_id, :]
afflist = list(cells.loc[dict(dim_1="affinity")].values)
final_dists.append(afflist)
aff0list = list(cells.loc[dict(dim_1="affinity0")].values)
ancestor_dists.append(aff0list)
# send energy lists to histogram plot
for i in range(len(analysis_times)):
energy_distributions_plot(E_list, ancestor_dists[i], final_dists[i],
analysis_times[i])
energy_scatter_plot(ancestor_dists[i], final_dists[i],
analysis_times[i])
if d_export:
datafile = open('processed_data/energy_distribution_data', 'w')
datafile.write('1) naive distribution \n \n')
datafile.write('{} \n \n'.format(E_list))
datafile.write('2) analysis days \n \n')
datafile.write('{} \n \n'.format(analysis_times))
datafile.write('3) ancestor distributions per time point \n \n')
datafile.write('{} \n \n'.format(ancestor_dists))
datafile.write('4) memory distributions per time point \n \n')
datafile.write('{} \n \n'.format(final_dists))
datafile.close()
return (simdata)
def oneGC(repeats=100):
""" fig 3C/D, showing clone number, cell number and mutation number per day
in an average GC """
cenL = []
clnL = []
mmL = []
bmL = []
for r in range(repeats):
# get runID from current system time
runID = int(time.time())
# run simulation and get filepath or dict
simdata = main(runID, store_export='datafile', evalperday=12)
# import required information for small scale plots
l_times, l_fn, l_fm, l_GCs, LFcurve, Agcurve, evaltimes, freePan, \
GCPans, ms_times, ms_vals, ms_fams, ms_muts, mut_list, \
E_list = import_file(simdata)
tList, cen, cln, endtime, mm, bm = GC_phases(GCPans[0], mut_list)
cenL.append(cen)
clnL.append(cln)
mmL.append(mm)
bmL.append(bm)
cen = np.nanmean(np.array(cenL), axis=0)
cln = np.nanmean(np.array(clnL), axis=0)
mm = np.nanmean(np.array(mmL), axis=0)
bm = np.nanmean(np.array(bmL), axis=0)
# bin mutation counts into days
mmbin = []
bmbin = []
tend = int(tList[-1]/12)
for i in [12*j for j in range(tend+1)]:
if np.isinf(np.nansum(mm[i:i+12])):
mmbin.append(0)
bmbin.append(0)
else:
mmbin.append(np.nansum(mm[i:i+12]))
bmbin.append(np.nansum(bm[i:i+12]))
""" plot """
fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, figsize=(10, 10))
ax1.plot(np.array(tList)/12., cen, label='cells/GC', color='crimson')
ax1.plot(np.array(tList)/12., cln, label='clones/GC',
color='cornflowerblue')
ax1.set_ylabel('count')
ax1.legend(loc=0)
seaborn.despine()
ax2.plot(range(tend+1), mmbin, '-o', label='all', color='crimson')
ax2.plot(range(tend+1), np.array(bmbin)*10, '-o',
label='beneficial ($\cdot 10$)', color='cornflowerblue')
ax2.legend(loc=0)
ax2.set_ylabel('mutations/(clone$\cdot$day)')
ax2.set_xlabel('time after infection (days)')
seaborn.despine()
pylab.savefig('figures/oneGC.pdf', bbox_inches='tight')
# write the matrices to file
datasave = open('processed_data/datafile_oneGC', 'w')
datasave.write(str(tList)+'\n')
datasave.write(str(cen)+'\n')
datasave.write(str(cln)+'\n')
datasave.write(str(mmbin)+'\n')
datasave.write(str(bmbin)+'\n')
datasave.close()
