if sum(1 for l in aa if l == 'X') < 10:
prots.append(aa)
names.append(name)
# <codecell>
seq_df = DataFrame({
'Tat':Series(prots, index = names)
})
# <codecell>
from SeqProcessTools import align_seq_data_frame
out_align = align_seq_data_frame(seq_df, '/home/will/HIVReportGen/Data/BlastDB/ConBseqs.txt')
# <codecell>
cohort_data = read_csv('Texas_Cohort_Data.txt', sep = '\t')
nout_align = out_align.reset_index()
nout_align['short_ind'] = nout_align['index'].map(lambda x: x.split('-')[0])
nout = merge(nout_align, cohort_data,
left_index = 'short_ind', right_index = 'Patient ID')
nout = nout.drop(nout['NeuroCog'] == 'Not Tested', axis = 0)
print nout
# <codecell>
os.chdir('/home/will/Dropbox/HIVseqs/')
sys.path.append('/home/will/HIVReportGen/AnalysisCode/')
from SeqProcessTools import read_pat_seq_data, load_training_seq_data, align_seq_data_frame
# <codecell>
import glob
pat_files = glob.glob('/home/will/HIVReportGen/Data/PatientFasta/*.fasta')
pat_seq = read_pat_seq_data(pat_files, '/home/will/HIVReportGen/Data/BlastDB/ConBseqs.txt')
training_files = glob.glob('/home/will/HIVReportGen/Data/TrainingSequences/*.fasta')
training_data = load_training_seq_data(training_files)
align_lanl = align_seq_data_frame(training_data, '/home/will/HIVReportGen/Data/BlastDB/ConBseqs.txt')
all_seqs = concat([pat_seq, align_lanl])
# <codecell>
def get_pairwise_distances(seq_series, tree_file = None, seq_file = None):
if seq_file is None:
fasta_handle = NTF()
if tree_file is None:
tree_handle = NTF()
else:
for ind, trop in zip(trops.index, trops["Tropism"].values):
trop_dict[ind] = trop
# <codecell>
grouped_seq_df.dropna(subset=["gp120"])[["score"]].to_excel("NewPSSMScores.xlsx")
# <codecell>
wanted_seq_data = grouped_seq_df.dropna(subset=["gp120"])
# <codecell>
print "aligning"
align_data = align_seq_data_frame(wanted_seq_data, "/home/will/HIVReportGen/Data/BlastDB/ConBseqs.txt")
# <codecell>
align_data["Tropism"] = align_data["score"].map(decide_tropism)
# <codecell>
wanted_data = align_data.dropna(subset=["Tropism"])
# <codecell>
from itertools import product
def yield_regions(trop_dict):
