def _split_data(self, precision=6):
"""precision is to get same results as in the original perl script"""
#FIXME::
mask = self.gs.Flag == 0
self.user_data_clean = self.user_data[mask].copy()
print(
'Splitting the user data set and removing flagged data (%s out of %s)'
% (self.gs.shape[0] - mask.sum(), self.gs.shape[0]))
self.gs_clean = self.gs[mask].copy()
# local aliases
gs = self.gs_clean
user_data = self.user_data_clean
pb = progress_bar(self.Ntf, interval=1)
for tf_index in range(1, self.Ntf + 1):
this_tf = 'TF_%s' % tf_index
tf_gs = gs.query("Id == @this_tf").Answer
tf_user = user_data.query("TF_Id == @this_tf").Signal_Mean
df = pd.concat([tf_gs, tf_user], axis=1)
df.to_csv(self._setfile(tf_index, 'Data'),
index=False,
sep='\t',
header=False,
float_format="%f")
pb.animate(tf_index)
def compute_statistics(self):
"""Returns final results of the user predcition
:return: a dataframe with various metrics for each transcription factor.
Must call :meth:`score` before.
"""
data = {
'Pearson': [],
'Spearman': [],
'Pearson_Log': [],
"AUROC_8mer": [],
"AUPR_8mer": [],
"AUROC_probe": [],
"AUPR_probe": []
}
pb = progress_bar(self.Ntf, interval=1)
for tf_index in range(1, self.Ntf + 1):
dfdata = pd.read_csv(self._setfile(tf_index, "Data"),
sep='\t',
header=None)
pearson = dfdata.corr('pearson').ix[0, 1]
spearman = dfdata.corr('spearman').ix[0, 1]
pearsonLog = np.log10(dfdata).corr('pearson').ix[0, 1]
data['Pearson'].append(pearson)
data['Pearson_Log'].append(pearsonLog)
data['Spearman'].append(spearman)
dvdata = self._dvs[tf_index]
r = ROCDiscovery(dvdata.values)
rocdata = r.get_statistics()
auroc = r.compute_auc(roc=rocdata)
aupr = r.compute_aupr(roc=rocdata)
data['AUROC_8mer'].append(auroc)
data['AUPR_8mer'].append(aupr)
dvdata = self._dvps[tf_index]
r = ROCDiscovery(dvdata.values)
rocdata = r.get_statistics()
auroc = r.compute_auc(roc=rocdata)
aupr = r.compute_aupr(roc=rocdata)
data['AUROC_probe'].append(auroc)
data['AUPR_probe'].append(aupr)
pb.animate(tf_index)
df = pd.DataFrame(data)
df = df[[
'Pearson', u'Spearman', u'Pearson_Log', u'AUROC_8mer',
u'AUPR_8mer', u'AUROC_probe', u'AUPR_probe'
]]
return df
def _preprocessing(self):
"""Create temporary files for before further processing
:return: nothing
"""
# Read file octomers gold standard
filename = self.directory + os.sep + '8mers_gs.txt'
self.octomers_gs = pd.read_csv(filename, sep='\t', header=None)
# Read file octomers
filename = self.directory + os.sep + 'all_8mers.txt'
self.octomers = pd.read_csv(filename, sep='\t',
header=None) # contains reverse complemtn
self.octomers.columns = ['octomer', 'octomerRC']
# Read probes gs
filename = self.directory + os.sep + 'probe35_gs.txt'
self.probes_gs = pd.read_csv(filename, header=None, sep='\t')
self.probes_gs.columns = ['Id', 'Sequence']
# reads probes (sequences)
print('Reading probes')
filename = self.directory + os.sep + 'probes35.txt'
# just one column so no need for a separator
probes = pd.read_csv(filename)
# Extract information (first and third column of pred.txt)
df = self.user_data[['TF_Id', 'Signal_Mean']].copy()
df['Signal_Mean'] = df['Signal_Mean'].map(lambda x: round(x, 6))
# data.txt is paste of probes35.txt and val.txt
data = pd.concat([probes, df], axis=1)
# Creates probes/TF_1.dat that contains the sequence from the GS and the answer from the user
# for each TF
print('Creating probes/TF_1.csv + sorting')
pb = progress_bar(self.Ntf, interval=1)
for i in range(1, self.Ntf + 1):
# could use a groupby here ? faster maybe
tag = 'TF_%s' % i
sequence = data[['Sequence']].ix[self.gs.Id == tag]
answer = data.Signal_Mean[data.TF_Id == tag]
df = pd.concat([sequence, answer], axis=1)
try:
df.sort_values(by=['Signal_Mean', 'Sequence'],
ascending=[False, False],
inplace=True)
except:
df.sort(columns=['Signal_Mean', 'Sequence'],
ascending=[False, False],
inplace=True)
df['Signal_Mean'] = df['Signal_Mean'].map(lambda x: round(x, 6))
self._probes[i] = df
pb.animate(i)
def get_jaccard(self, progress=True):
import sklearn.metrics
N = len(self.df)
J = np.zeros((N,N))
from easydev import progress_bar
pb = progress_bar(N)
for ic, i in enumerate(self.df.index):
for jc, j in enumerate(self.df.index):
J[ic][jc] = sklearn.metrics.jaccard_similarity_score(self.df.ix[i], self.df.ix[j])
pb.animate(1+ic)
return J
def _preprocessing(self):
"""Create temporary files for before further processing
:return: nothing
"""
# Read file octomers gold standard
filename = self.directory + os.sep + '8mers_gs.txt'
self.octomers_gs = pd.read_csv(filename, sep='\t', header=None)
# Read file octomers
filename = self.directory + os.sep + 'all_8mers.txt'
self.octomers = pd.read_csv(filename, sep='\t', header=None) # contains reverse complemtn
self.octomers.columns = ['octomer','octomerRC']
# Read probes gs
filename = self.directory + os.sep + 'probe35_gs.txt'
self.probes_gs = pd.read_csv(filename, header=None, sep='\t')
self.probes_gs.columns = ['Id', 'Sequence']
# reads probes (sequences)
print('Reading probes')
filename = self.directory + os.sep + 'probes35.txt'
# just one column so no need for a separator
probes = pd.read_csv(filename)
# Extract information (first and third column of pred.txt)
df = self.user_data[['TF_Id', 'Signal_Mean']].copy()
df['Signal_Mean'] = df['Signal_Mean'].map(lambda x: round(x,6))
# data.txt is paste of probes35.txt and val.txt
data = pd.concat([probes, df], axis=1)
# Creates probes/TF_1.dat that contains the sequence from the GS and the answer from the user
# for each TF
print('Creating probes/TF_1.csv + sorting')
pb = progress_bar(self.Ntf, interval=1)
for i in range(1, self.Ntf+1):
# could use a groupby here ? faster maybe
tag = 'TF_%s' % i
sequence = data[['Sequence']].ix[self.gs.Id==tag]
answer = data.Signal_Mean[data.TF_Id == tag]
df = pd.concat([sequence, answer], axis=1)
try:
df.sort_values(by=['Signal_Mean', 'Sequence'],
ascending=[False, False], inplace=True)
except:
df.sort(columns=['Signal_Mean', 'Sequence'],
ascending=[False, False], inplace=True)
df['Signal_Mean'] = df['Signal_Mean'].map(lambda x: round(x,6))
self._probes[i] = df
pb.animate(i)
def get_null_timecourse_model1(self, N=10000):
data = self._get_random_timecourse_model1(N=N)
distances = []
from easydev import progress_bar
pb = progress_bar(N)
for i in xrange(0,N):
df = data[:,:,i]
# FIXME those values 10,39 should not be hardcoded
distance = self._compute_score_timecourse_model1(df, 10,39)
distances.append(distance)
pb.animate(i)
return distances
def analyse(self):
models = self.simulator.results.models
self.truth_tables = {}
from easydev import progress_bar
pb = progress_bar(len(models.df))
for i, index in enumerate(models.df.index):
reactions = models.df.ix[index][models.df.ix[index]==1]
reactions = list(reactions.index)
self.simulator.simulate(reactions=reactions)
tt = self.simulator.simulated[self.simulator.time].flatten()
self.truth_tables[index] = tt
pb.animate(i+1)
def _load_complexes(self, show_progress=True):
from easydev import progress_bar
import time
pb = progress_bar(len(self.df.complexAC))
complexes = {}
self.logging.info("Loading all details from the IntactComplex database")
for i, identifier in enumerate(self.df.complexAC):
res = self.webserv.details(identifier)
complexes[identifier] = res
if show_progress:
pb.animate(i+1, time.time()-pb.start)
self._complexes = complexes
def get_null_parameters_model1(self, N=10000):
"""Returns score distribution (parameter model1)"""
df = self._get_random_parameters_model1(N=N)
distances =[]
from easydev import progress_bar
pb = progress_bar(N)
for i in xrange(0, N):
df1 = df.ix[i].to_frame(name='values')
distance = self._compute_score_model1_parameters(df1)
distances.append(distance)
pb.animate(i+1)
return distances
def compute_statistics(self):
"""Returns final results of the user predcition
:return: a dataframe with various metrics for each transcription factor.
Must call :meth:`score` before.
"""
data = {'Pearson': [],
'Spearman': [],
'Pearson_Log': [],
"AUROC_8mer": [],
"AUPR_8mer": [],
"AUROC_probe": [],
"AUPR_probe": []}
pb = progress_bar(self.Ntf, interval=1)
for tf_index in range(1, self.Ntf + 1):
dfdata = pd.read_csv(self._setfile(tf_index, "Data"), sep='\t', header=None)
pearson = dfdata.corr('pearson').ix[0,1]
spearman = dfdata.corr('spearman').ix[0,1]
pearsonLog = np.log10(dfdata).corr('pearson').ix[0,1]
data['Pearson'].append(pearson)
data['Pearson_Log'].append(pearsonLog)
data['Spearman'].append(spearman)
dvdata = self._dvs[tf_index]
r = ROCDiscovery(dvdata.values)
rocdata = r.get_statistics()
auroc = r.compute_auc(roc=rocdata)
aupr = r.compute_aupr(roc=rocdata)
data['AUROC_8mer'].append(auroc)
data['AUPR_8mer'].append(aupr)
dvdata = self._dvps[tf_index]
r = ROCDiscovery(dvdata.values)
rocdata = r.get_statistics()
auroc = r.compute_auc(roc=rocdata)
aupr = r.compute_aupr(roc=rocdata)
data['AUROC_probe'].append(auroc)
data['AUPR_probe'].append(aupr)
pb.animate(tf_index)
df = pd.DataFrame(data)
df = df[['Pearson', u'Spearman', u'Pearson_Log', u'AUROC_8mer',
u'AUPR_8mer', u'AUROC_probe', u'AUPR_probe']]
return df
def compute_distances(self, N=100, show=True, progress=True):
self.init()
from easydev import progress_bar
distances = []
pb = progress_bar(N)
for i in range(0,N):
self.swap(1, inplace=True)
dist = self.get_distance(self.graph)
distances.append(dist)
if progress:pb.animate(i)
if show is True:
import pylab
pylab.plot(distances)
pylab.grid(True)
return distances
def exhaustive(self):
from cno.optimisers.binary_tools import permutations
# create all
scores = []
sizes = []
from easydev import progress_bar
N = len(self.model.reactions)
pb = progress_bar(2**N)
for i,this in enumerate(permutations(N)):
self.simulate(self.parameters2reactions(this))
scores.append(self.score())
pb.animate(i)
sizes.append(sum(this))
#self._fill_results()
self.scores = scores
self.sizes = sizes
return scores
def compute_gtts(self):
print("init R library")
self._init()
N = len(self.models)
from easydev import progress_bar
b = progress_bar(N)
d = {}
for i in range(0, N):
res = np.array(self._get_sim(self.models.df.ix[i].values))
b.animate(i)
d[i] = res
df = pd.DataFrame(d).transpose()
grouped = df.groupby(list(df.columns))
pylab.hist([len(this) for this in grouped.groups.values()], 100)
res = {'df':df, 'simulation': d, 'grouped':grouped}#
self.gtts = res
return self.gtts
def _split_data(self, precision=6):
"""precision is to get same results as in the original perl script"""
mask = self.gs.Flag == 0
self.user_data_clean = self.user_data[mask].copy()
print('Splitting the user data set and removing flagged data (%s out of %s)' % (self.gs.shape[0] - mask.sum(), self.gs.shape[0]))
self.gs_clean = self.gs[mask].copy()
# local aliases
gs = self.gs_clean
user_data = self.user_data_clean
pb = progress_bar(self.Ntf, interval=1)
for tf_index in range(1, self.Ntf + 1):
this_tf = 'TF_%s' % tf_index
tf_gs = gs.query("Id == @this_tf").Answer
tf_user = user_data.query("TF_Id == @this_tf").Signal_Mean
df = pd.concat([tf_gs, tf_user], axis=1)
df.to_csv(self._setfile(tf_index, 'Data'), index=False, sep='\t',
header=False, float_format="%f")
pb.animate(tf_index)
def run(self, N=10, nswap=20, verbose=True, maxstallgen=50, maxtime=60):
self.sim = steady.Steady(self.real.cnograph, self.real.midas)
# creates the model, preprocessed
self.sim.preprocessing()
from easydev import progress_bar
pb = progress_bar(N)
for i in xrange(0,N):
self.sim = steady.Steady(self.real.cnograph, self.real.midas)
self.sim.preprocessing()
self.sim.model.swap_edges(nswap)
self.sim.preprocessing()
self.sim.optimise(verbose=verbose, reuse_best=False,
maxstallgen=maxstallgen, maxtime=maxtime)
score = self.sim.results.results.best_score[-1]
self.best_scores.append(score)
pb.animate(i+1)
def clean_models(self, tolerance=0.1):
models = self.results.models.copy()
models.midas = self.midas
print("Found %s models within the tolerance" % len(models.df))
models.drop_duplicates()
print("Removing duplicates found %s" % len(models.df))
models.drop_scores_above(tolerance=tolerance)
print("Keeping within tolerance, found %s" % len(models.df))
from easydev import progress_bar
pb = progress_bar(len(models))
count = 0
changed = 0
for index in models.df.index:
count +=1
reactions = list(models.df.columns[models.df.ix[index]==1])
self.simulate(reactions)
score = self.score()
#if models.scores[index] != score:
# print(index, models.scores[index], score)
# compute essentiality to simplify models
dummy, newr = self.essentiality(reactions, show=False)
self.simulate(newr)
new_score = self.score()
#print score, new_score, len(reactions), len(newr)
if new_score <= score:
# keep that pruned model
models.df.ix[index] = self.reactions2parameters(newr)
models.scores.ix[index] = new_score
changed += 1
else:
# keep original
pass
pb.animate(count)
print('Simplified %s %% of the model' % float(changed/float(len(models.df))))
models.drop_duplicates()
print("Removing duplicaes found %s" % len(models.df))
models.drop_scores_above(tolerance=tolerance)
print("Keeping within tolerance, found %s" % len(models.df))
return models
def plot_average_distance(self, repeat=10, N=100):
import pandas as pd
import pylab
distances = []
from easydev import progress_bar
pb = progress_bar(repeat)
for i in range(0, repeat):
distance = self.compute_distances(N=N, show=False, progress=False)
distances.append(distance)
pb.animate(i+1)
df = pd.DataFrame(distances)
pylab.clf()
pylab.fill_between(range(0,N), df.mean()+df.std(),
y2=df.mean()-df.std());
pylab.plot(df.mean(), 'r', lw=2)
pylab.grid(True)
pylab.ylim([0,1])
pylab.ylabel('similarity')
return distances
def download_all_data(self):
"""Download all large data sets from Synapse"""
pb = progress_bar(5)
# load the large gold standard file from D5C2 synapse main page
filename = self._download_data('DREAM5_GoldStandard_probes.zip',
'syn2898469')
pb.animate(1)
z = ZIP()
z.loadZIPFile(filename)
data = z.read('Answers.txt')
self.gs = pd.read_csv(StringIO.StringIO(data), sep='\t')
# download 4 other filenames from dreamtools synapse project
self._download_data('all_8mers.txt', 'syn4483185')
pb.animate(2)
self._download_data('8mers_gs.txt', 'syn4483187')
pb.animate(3)
self._download_data('probe35_gs.txt', 'syn4483184')
pb.animate(4)
self._download_data('probes35.txt', 'syn4483183')
pb.animate(5)
def download_all_data(self):
"""Download all large data sets from Synapse"""
pb = progress_bar(5)
# load the large gold standard file from D5C2 synapse main page
filename = self._download_data('DREAM5_GoldStandard_probes.zip',
'syn2898469')
pb.animate(1)
z = ZIP()
z.loadZIPFile(filename)
data = z.read('Answers.txt')
self.gs = pd.read_csv(BytesIO(data), sep='\t')
# download 4 other filenames from dreamtools synapse project
self._download_data('all_8mers.txt', 'syn4483185')
pb.animate(2)
self._download_data('8mers_gs.txt', 'syn4483187')
pb.animate(3)
self._download_data('probe35_gs.txt', 'syn4483184')
pb.animate(4)
self._download_data('probes35.txt', 'syn4483183')
pb.animate(5)
def _processing(self):
"""
:return:
"""
######################################## 1 Create the Out/TF_XX.dat files
octomers = self.octomers.octomer
octomersRC = self.octomers.octomerRC
mapping1 = dict([(k,v) for k,v in zip(octomers.values, octomersRC.values)])
mapping2 = dict([(k,v) for k,v in zip(octomersRC.values, octomers.values)])
keys = tuple(sorted(octomers.values))
lm = set(octomers.values)
pb = progress_bar(self.Ntf, interval=1)
pb.animate(0)
for tf_index in range(1, self.Ntf + 1):
tf = self._probes[tf_index]
tf.columns = ['Sequence', 'Score']
ids = collections.defaultdict(list)
###### TODO: most of the time is spent in the "for curR in generator" loop
for seq, score in zip(tf.Sequence, tf.Score):
# scan the sequence by chunk of octomers using a generator
# for speed (although gain is small)
generator = (seq[i:i+8] for i in xrange(0,28))
for curR in generator:
if mapping1.has_key(curR) is False:
curR = mapping2[curR]
ids[curR].append(score)
# Using a set does not help speeding up the code
#for curR in generator:
# if curR not in lm:
# curR = mapping2[curR]
# ids[curR].append(score)
# now let us build the new dataframe for the indices found
df = pd.DataFrame({0:[k for k in ids.keys()],
1:[np.median(v) for v in ids.values()]})
df.sort(columns=[1,0], ascending=[False, False], inplace=True)
df[1] = df[1].map(lambda x: round(x,6))
df.to_csv(self._setfile(tf_index, 'Out'), sep=' ', index=False, header=None, float_format="%.6f")
pb.animate(tf_index)
print("ooooooooooo")
################################################# 2 create the DVP
pb = progress_bar(self.Ntf, interval=1)
for tf_index in range(1,self.Ntf+1):
tag = 'TF_%s' % tf_index
tf_probes = list(self.probes_gs.ix[self.probes_gs.groupby('Id').groups[tag]].Sequence)
tf = self._probes[tf_index]
dv = tf.Sequence.apply(lambda x: x in tf_probes).astype(int)
self._dvps[tf_index] = dv
pb.animate(tf_index)
print("")
########################################################## DV
gs_octomers = self.octomers_gs.copy()
gs_octomers.columns = ['id', 'octomer']
pb = progress_bar(self.Ntf, interval=1)
for tf_index in range(1,self.Ntf+1):
tag = 'TF_%s' % tf_index
tf_octomers = list(gs_octomers.ix[gs_octomers.groupby('id').groups[tag]].octomer)
tf = pd.read_csv(self._setfile(tf_index, "Out"), sep=" ",
header=None)
tf.columns = ['Octomer', 'Score']
dv = tf.Octomer.apply(lambda x: x in tf_octomers).astype(int)
# Stores the dataframe
self._dvs[tf_index] = dv
pb.animate(tf_index)
def run(self, eval_func, N, nswap=3, proposal=None):
self.Nparameters = N
results = Results(N=N, step=1)
t1 = time.time()
self.alpha = []
if proposal is None:
proposal_parameter = [1] * self.Nparameters
else:
proposal_parameter = proposal[:]
init_bs = proposal_parameter[:]
prev_score = eval_func(proposal_parameter) # compute the score for the initial bitstring
prev_bs = init_bs[:]
best_score = prev_score
results['best_score'] = best_score
best_parameters = init_bs[:]
# store the initial values
results['scores'].append(prev_score)
results['parameters'].append(prev_bs)
from easydev import progress_bar
pb = progress_bar(self.N)
for i in range(0, self.N):
proposal_parameter = self.swaps(best_parameters, nswap)
#tup_param = tuple(proposal_parameter)
#if tup_param in self._buffer.keys():
# proposal_score = self._buffer[tup_param]
#else:
# proposal_score = eval_func(proposal_parameter)
# self._buffer[tup_param] = proposal_score
proposal_score = eval_func(proposal_parameter)
alpha = prev_score / proposal_score # best score is the smallests one
# so alpha >1 means new proposal is better
self.alpha.append(alpha)
if alpha >=1:
prev_score = proposal_score
score = proposal_score
results['parameters'].append(proposal_parameter)
prev_bs = proposal_parameter[:]
accepted = 1
else:
r = random.uniform(0,1)
if r <= alpha:
prev_score = proposal_score
score = proposal_score
# storing results
results['parameters'].append(proposal_parameter)
prev_bs = proposal_parameter[:]
accepted = 1
else:
prev_score = prev_score
# storing results
score = prev_score
results['parameters'].append(prev_bs)
accepted = 0
self.acceptance.append(accepted)
results['scores'].append(score)
if score < best_score:
best_score = score
best_parameters = proposal_parameter[:]
results['best_score'] = best_score
results['best_scores'].append(best_score)
results['best_score'] = best_score # just for the progres
pb.animate(i)
#print best_parameters
del results['scores'][0] # remove first element to have a length of N value
del results['parameters'][0] # remove first element to have a length of N value
results['best_parameters'] = best_parameters[:]
results['min_index'] = numpy.argmin(results['best_scores']) # store the index of the minimum score from the best_scores list
t2 = time.time()
print "simulation took", t2-t1, "seconds."
self.results = results.copy()
def _processing(self):
"""
:return:
"""
######################################## 1 Create the Out/TF_XX.dat files
octomers = self.octomers.octomer
octomersRC = self.octomers.octomerRC
mapping1 = dict([(k, v)
for k, v in zip(octomers.values, octomersRC.values)])
mapping2 = dict([(k, v)
for k, v in zip(octomersRC.values, octomers.values)])
keys = tuple(sorted(octomers.values))
lm = set(octomers.values)
pb = progress_bar(self.Ntf, interval=1)
pb.animate(0)
for tf_index in range(1, self.Ntf + 1):
tf = self._probes[tf_index]
tf.columns = ['Sequence', 'Score']
ids = collections.defaultdict(list)
## TODO: most of the time is spent in "for curR in generator" loop
for seq, score in zip(tf.Sequence, tf.Score):
# scan the sequence by chunk of octomers using a generator
# for speed (although gain is small)
generator = (seq[i:i + 8] for i in range(0, 28))
#for curR in generator:
# if curR not in mapping1.keys():
# curR = mapping2[curR]
# ids[curR].append(score)
# Using a set does help speeding up the code
for curR in generator:
if curR not in lm:
curR = mapping2[curR]
ids[curR].append(score)
# now let us build the new dataframe for the indices found
df = pd.DataFrame({
0: [k for k in ids.keys()],
1: [np.median(v) for v in ids.values()]
})
try:
df.sort_values(by=[1, 0],
ascending=[False, False],
inplace=True)
except:
df.sort(columns=[1, 0], ascending=[False, False], inplace=True)
df[1] = df[1].map(lambda x: round(x, 6))
df.to_csv(self._setfile(tf_index, 'Out'),
sep=' ',
index=False,
header=None,
float_format="%.6f")
pb.animate(tf_index)
################################################# 2 create the DVP
pb = progress_bar(self.Ntf, interval=1)
for tf_index in range(1, self.Ntf + 1):
tag = 'TF_%s' % tf_index
tf_probes = list(self.probes_gs.ix[self.probes_gs.groupby(
'Id').groups[tag]].Sequence)
tf = self._probes[tf_index]
dv = tf.Sequence.apply(lambda x: x in tf_probes).astype(int)
self._dvps[tf_index] = dv
pb.animate(tf_index)
print("")
########################################################## DV
gs_octomers = self.octomers_gs.copy()
gs_octomers.columns = ['id', 'octomer']
pb = progress_bar(self.Ntf, interval=1)
for tf_index in range(1, self.Ntf + 1):
tag = 'TF_%s' % tf_index
tf_octomers = list(
gs_octomers.ix[gs_octomers.groupby('id').groups[tag]].octomer)
tf = pd.read_csv(self._setfile(tf_index, "Out"),
sep=" ",
header=None)
tf.columns = ['Octomer', 'Score']
dv = tf.Octomer.apply(lambda x: x in tf_octomers).astype(int)
# Stores the dataframe
self._dvs[tf_index] = dv
pb.animate(tf_index)
