def setUp(self):
self.precision = 10
#resource.setrlimit(resource.RLIMIT_STACK, (resource.RLIM_INFINITY, resource.RLIM_INFINITY))
with open(
os.path.join(os.path.dirname(__file__), 'data',
'testseqs.txt')) as f:
self.sequences = f.readlines()
self.sequences = list(
map(lambda s: pyopa.Sequence(s.strip()), self.sequences))
self.darwin_results = []
defaults = pyopa.load_default_environments()
self.alignment_environments = defaults['environments']
self.log_pam1 = defaults['log_pam1']
self.dms = pyopa.MutipleAlEnv(self.alignment_environments,
self.log_pam1)
"""
write_all_env_files(self.alignment_environments)
with open(os.path.dirname(__file__) + '/data/matrices/json/logPAM1.json') as lp:
json_data = json.load(lp)
#json_data["Scores"] = map(lambda l: map(lambda s: s/(2048*2048*2048), l), json_data["Scores"])
logPAM1 = pyopa.read_env_json(json_data, self.alignment_environments[0].columns)
write_env_file(logPAM1, "logPAM1")
"""
with open(
os.path.join(os.path.dirname(__file__), 'data',
'reference_test_results.dat')) as f:
#skip header
next(f)
reader = csv.reader(f, delimiter='\t')
for s1, s2, matrix_nr, pam, threshold, score_d, score_f, score_s,\
score_b, als1, als2, ep_sim, ep_pamn, ep_var, in reader:
curr = DarwinResult()
curr.s1_id = int(s1)
curr.s2_id = int(s2)
curr.matrix_nr = int(matrix_nr)
curr.pam = float(pam)
curr.threshold = float(threshold)
curr.score_double = float(score_d)
curr.score_float = float(score_f)
curr.score_short = float(score_s)
curr.score_byte = float(score_b)
curr.als1 = als1
curr.als2 = als2
curr.ep_sim = float(ep_sim)
curr.ep_pamn = float(ep_pamn)
curr.ep_var = float(ep_var)
self.darwin_results.append(curr)
'''
for i in range(20):
for j in range(20):
qsum[i] += json_data['scores'][j][i]
for i in range(20):
json_data['scores'][i][i] = -qsum[i]
return json_data
json_matrix = convert_to_json(os.path.dirname(os.path.abspath(__file__)) + '/test/data/jtt.dat')
for i in range(20):
print(json_matrix['scores'][i])
log_pam1 = pyopa.read_env_json(
os.path.dirname(os.path.abspath(__file__)) + '/test/data/matrices/json/logPAM1.json')
envs = pyopa.read_all_env_json(
os.path.dirname(os.path.abspath(__file__)) + '/test/data/matrices/json/all_matrices.json')
generated_envs = pyopa.generate_all_env(log_pam1, 1266)
mul1 = pyopa.MutipleAlEnv(envs, log_pam1)
mul2 = pyopa.MutipleAlEnv(generated_envs, log_pam1)
s1 = 'PDVRTQYSRTKTIKLAQVRKCGAWRVLCLDLIPDLTAKNNHMRTKWTEVQYLAFVVSIVKKRPLSHSLVLITTGKAWNGTWRALPRLSNKLIETAFKEIQAEETVYDTKAFVAGKKPRWVSPFICYGLPFVISRFDFAQYRLKDMLILFSDMLLSRICNFYNGNTGPVPNSKTNEDTDLFFDGLSGMLKLNLKRSDAICHVICYEAPIARVKFGREVKDKFSLPKGGKNPSRRISWNILGILIDRTMFIRPRLVARKEAIHLFDLIGENIDAITQRLRAHKTLMVHESQVVEQPLKVKNLDLRPELVGEEEKNRHGRAKQLDRMANGNMAQIKNGHFKQTYLISVFRPQWLQLQGGCLIAEGFHSEVGGTVDGLKGTPCAQGPVVKGLFAVWRRCDTLAGRYYQKAADIDKLGDILLASLYYIPQGAIITLSEEMAKRIGANVLLVGLINVRYSGIGYEACVGDLAPEVSWLNAGHGNIQMVLHTIDGDGCQTPHGLKIYTDKRLLDLYQGAQLKVTVATTGSVKVSKSMGWLQEGGLDYFALAGRFYRADLREIEHPRAMAVSAHLCAVGLNWVFLADIICDPNEAFKFGKDFEPRTLTYGFANEDENPKNGGATTTSFAVAVYKIKTVATLKVIGKALWKGIQMRTQQGSGPTCQWALRKGKNSILLLAQDSRGGIPKNEFTILGDLPEGQTTTCTHTEIKTRLLYGATVFFMRGDLVGLYADGCSHLYRSSNLMSQACAAAKTILCSLDGERANFSNPTDFAMYNAVFRPRLYTVSFGVFDNNVDVLQAALYYLIMMAMKQYWGVKQGGLEGTLYTWSKVSGKKETSDSRNNPSICVSVCKNPLKDVQLRIAALKRFAEAEEIGKPAVVIRALEPGLTLYILLSSHGSEGKKTHNPILVSAFVVTTVADTSKPKVTYHKDQEMAIYQVLGNNPAGYEVELAFLLPTASSKQQSGRTRKFMDTASGELKEMPIQSSHEITQAADINNLRQLPRTYKKESAKVKVAACKQPPAALNTGIEKVPSHPDGLQLIIEDEWKLLEASSMSQYNEQAKEWPFHKGGIFFKGHEQKCIDASELPRGITRDLRVILINEALVLNTFCGERKLQNEATLILLRAYVWGRHLLANYFRAPNEQDGVLVDIPQGRSTLKSDHLRASIPLFLYTTIETCTSNVTIHKRVQPMIILDIAVAGEGVCDMKNGQVFKRRMARSNDRRLPPGARMKIILFRRNHECYPLQKHQEQWILGAIRTPYGLYNLQEKATLTTRYLIKLQINNRNDLVTTLVSLLMHTRESYIRFTKERRTTESPIDVLAATLYQEFTREVRRAGEQRAGIFFSQDTNYEQAIFETKMAAYPPFGANSWNPTLRYEAWTIIKTPNSKGQEFFLEHMQDVGYGKIASSKYQEKDDDEEVARGRIVPAWY'
s2 = 'PPFQPDKKLAGIELVLCNADLPGRSIYLRKVLQANANKRASASKRCTDDDIIKVDSAPDPQRKLVQAGKVPRVLYNGDVSNIISQILICAYVTGASRNFQHVMLLMDKGWGRGFTLMVNYPCPKVLEEFNPTLLTALVIISVYLNSIECERAGVTIAALNVKLEATDRLALLGRQTANTVMRAPLLLLCQGDSAKNTLNWSLEDLAIVFGRAATRVCKNLALLLNSQVFFQKTTGYKSQLGKNVINFDLYKPLVCDLVDATKYMKFYGTNDDSTDIQGRSSEKAAALAAAAMGVVGWHFLAPTGLVGAGSTFSPVFCIKGNAQLCCKRFDIDEWKALLTLQKSKIANIDYLRYRTGAVIEVGANYDGCSGQPKLQCFYDYLIRYPETVLGTNRQERVMTDEGGEHVRDLILRNVLENPTGFIGSGTHPGNISCTLETTNADLIIGSTDYDGVGSYLIIMGTCFMVTGCVVFTYAVMELVRPLKIHIFACAKVILQEADGSQKTNLRGRGKVSSFGDLPVRFRTLDGIATPSTTHAEMGASFDAAVLVIGRTGTAKFRQFATLDNRNLACNINLSSIRRYFNDNNWLEAGAKNAAEILVNHADKSLTPWVVGLGPLLKPGDIACPMIAVSYLVLVIMDMYLASYSDSFAKHLKNKHRTTTSAHKPSNQQLALDGALTAKRSSQAASIIFEAEEWGFLEWAMIGHLQTKMIYDDAFRLNSPEEELLTQATTHKIKPNYLIALQMLHRDFCIGFFHTLIHASVADSIVYASRLKQNAAIIDRGKTARQDLLGIALKLIVSASTKNAASFNRDFKLPVDVMFRFLDKMLNHGVNTIVHGGQDPKNGNPVGAGLPSWAKNIKVELQVTMFQLFESVDCTSELRLLSTAVDTTLHGEVQVMSAKDLFGRFRYRILSAGESLMENGISPKSFVEALKYFIMYYWTDITEPRCRGSALYPITIQPNLYKRTSATSLHPKGERWLPFEETSRTTISTVLMNNALLGICLYKSYQLLDHDFLGDKKQSNKRVSENSFLGIQTLHDPTGYLQKLDHSRLSKFNRDIRWGQGKSPEQWAVTLVPTLFVKKGTNAWRKKNNAEPIIVTTGTNTAPLEELHKAWMQLAHDGIVVSTLTENEKLEFFSFQDGMPSLVLFSIMAETNQLRYIGNKIYASRKWMADAQKASWVYASLPTNSCNWTAVEVAFEPKGECQMAKKFDLHSMAIVMVRLLAQERSDGADGMNNASSVKWLRKEANEKVCKWWFASPKINAMFQTVKIQSSGKYLARNPKAATKDVKKVEQDLLSRIQTQEHGLLWFYVRLIGEISEVPILSCNKALFLTIKLFNKFIRWNIAPLEITSGVDAWHTIFTSSRFSETDTGIEMTALDLTLPQGNWGTMKKKVALAATGFILFLAYSMGTLSKKFEGNHHWTW'
print(mul1.estimate_pam(pyopa.Sequence(s1), pyopa.Sequence(s2)))
print(mul2.estimate_pam(pyopa.Sequence(s1), pyopa.Sequence(s2)))
print('Local byte estimation with threshold %f: %f' % (env.threshold, profile.align_byte(s2, env)))
#since the byte estimation is larger than the threshold, double_max is returned, which indicates that by
# the byte estimation the score is larger than the threshold
#if we wish to change the threshold, we have to recreate the matrices and the profile used for the estimations,
# as follows:
env.threshold = 62.0
#if we forget about this part we will get inaccurate results!
env.create_scaled_matrices()
profile.create_profiles(s1, env)
print('Local short estimation with threshold of %f: %f' % (env.threshold, profile.align_short(s2, env)))
print('Local byte estimation with threshold of %f: %f' % (env.threshold, profile.align_byte(s2, env)))
#to hide the profile creation we can also use the simple align_short/byte function, but since
# it always creates a new profile, it is inefficient
print('Simple short alignment %f' % pyopa.align_short(s1, s2, env))
print('Simple byte alignment %f' % pyopa.align_byte(s1, s2, env))
#to use the EstimatePam function we have to create a data structure that stores the matrices
#loading the logPAM1 matrix, which was used to generate the other matrices
log_pam1 = defaults['log_pam1']
dms = pyopa.MutipleAlEnv(envs, log_pam1)
t = threading.Thread(None, nt_epam, 'EstimatePam Thread', (s1, s2, dms, env))
t.start()
t.join()
#to generate environments instead of reading from a file
generated_envs = pyopa.generate_all_env(log_pam1, 1000)
#---------------------------------------------------------------------------------------------------
#to do the concrete alignment in a new thread
#or alternatively you can increase your stack size on UNIX-based systems:
#'resource.setrlimit(resource.RLIMIT_STACK, (resource.RLIM_INFINITY, resource.RLIM_INFINITY))'
def nt_align(s1, s2, env, is_global, aligned_strs):
print('Concrete %s alignment:' % ('global' if is_global else 'local'))
tmp_aligned_strings = pyopa.align_strings(s1, s2, env, is_global)
print('\taligned_s1: %s' % tmp_aligned_strings[0])
print('\taligned_s2: %s' % tmp_aligned_strings[1])
aligned_strs.extend(tmp_aligned_strings)
s1 = pyopa.Sequence('PISRIDNNKITTTLGNTGIISVTIGVIIFKDLHAKVHGF')
s2 = pyopa.Sequence('PIERIENNKILANTGVISVTIGVIIYQDLHADTVMTSDY')
threading.stack_size(100000000)
# aligned_s1: PISRIDNNKITTTLGNTGIISVTIGVIIFKDLHAKV
# aligned_s2: PIERIENNKI___LANTGVISVTIGVIIYQDLHADT
aligned_strings = []
t = threading.Thread(None, nt_align, 'Aligning Thread',
(s1, s2, generated_env, False, aligned_strings))
t.start()
t.join()
print(aligned_strings[0])
print(aligned_strings[1])
#---------------------------------------------------------------------------------------------------
dms = pyopa.MutipleAlEnv(gen_env_list, log_pam1_env)
#returns an array: [similarity, pam_distance, variance]
print(dms.estimate_pam(aligned_strings[0], aligned_strings[1]))
def test_estimate_pam(self):
dms = pyopa.MutipleAlEnv(self.envs, self.log_pam1)
self.assertRaises(ValueError, dms.estimate_pam, self.s1, self.s2)