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anavar_utils.py
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#!/usr/bin/env python
from __future__ import print_function
import re
import os
from anavar_alpha import alpha
class Snp1ControlFile(object):
def __init__(self):
# create holders
self.alg_opts = ''
self.model_opts = ''
self.dfe_opts = ''
self.constraint_opts = ''
self.sfs = ['SNP']
self.dfe_optional_opts = ''
self.constraints = ['none', 'no_pol_error', 'equal_pol_error', 'equal_mutation_rate']
def _check_sfs_m_in(self, sfs_m):
"""
checks that all required sfs are given in input
:param sfs_m: dict
:return: NA
"""
sfs_keys = sfs_m.keys()
sfs_not_given = []
for key in self.sfs:
if key not in sfs_keys:
sfs_not_given.append(key)
if len(sfs_not_given) > 0:
raise KeyError('Missing the following SFS in input: {}'.format(','.join(sfs_not_given)))
def set_alg_opts(self, alg='NLOPT_LD_LBFGS', maxeval=100000, maxtime=600, search=500, nnoimp=1, maximp=3,
optional=False, size=10000, key=3, epsabs=1e-50, epsrel=1e-10, rftol=1e-10, init=()):
"""
sets algorithm options in control file
:param alg: str
:param maxeval: int
:param maxtime: int
:param search: int
:param nnoimp: int
:param maximp: int
:param optional: bool
:param size: int
:param key: int
:param epsabs: float
:param epsrel: float
:param rftol: float
:param init: tuple
:return: sets algorithm string
"""
algs = {'NLOPT_LD_SLSQP', 'NLOPT_LD_LBFGS', 'NLOPT_LD_VAR1', 'NLOPT_LD_VAR2',
'NLOPT_LN_NELDERMEAD', 'NLOPT_LD_TNEWTON_PRECOND_RESTART'}
if alg not in algs:
raise ValueError('Unsupported algorithm')
alg_string = ('[algorithm_commands]\n'
'search_algorithm: {}\n'
'maxeval: {}\n'
'maxtime: {}\n'
'num_searches: {}\n'
'nnoimp: {}\n'
'maximp: {}\n').format(alg, maxeval, maxtime, search, nnoimp, maximp)
if optional:
# set init string - new in 1.4
if len(init) == 0:
init = 'random'
else:
init = 'given[{}]'.format('\t'.join([str(x) for x in init]))
alg_string += ('optional: true\n'
'size: {}\n'
'key: {}\n'
'epsabs: {}\n'
'epsrel: {}\n'
'rftol: {}\n'
'init: {}\n'
'\n').format(size, key, epsabs, epsrel, rftol, init)
else:
alg_string += 'optional: false\n\n'
self.alg_opts = alg_string
def set_data(self, sfs_m, n, snp_fold=False,
dfe='discrete', c=1,
theta_r=(1e-6, 0.1), gamma_r=(-250, 10), error_r=(0.0, 0.5),
shape_r=(1e-3, 200), scale_r=(0.1, 1e3), r_r=(0.05, 5.0)):
"""
sets model and dfe commands in control file
:param sfs_m: dict{str: tuple(list(float), int), ..}
:param n: int
:param snp_fold: bool
:param dfe: str
:param c: int
:param theta_r: tuple(float, float)
:param gamma_r: tuple(float, float)
:param error_r: tuple(float, float)
:param shape_r: tuple(float, float)
:param scale_r: tuple(float, float)
:param r_r: tuple(float, float)
:return: NA
"""
self._check_sfs_m_in(sfs_m)
model_ctrls = ('[model_commands]\n'
'model: SNP_1\n'
'n: {n}\n'
'm: {m}\n'
'folded: {fold}\n'
'sfs: {sfs}\n'.format(n=n, m=sfs_m['SNP'][1],
fold=str(snp_fold).lower(),
sfs=', '.join([str(x) for x in sfs_m['SNP'][0]])))
self.model_opts = model_ctrls
if dfe == 'discrete':
dfe_param = ('dfe: discrete\n'
'c: {c}\n'
'theta_range: {t1}, {t2}\n'
'gamma_range: {g1}, {g2}\n'
'e_range: {e1}, {e2}\n')
dfe_param = dfe_param.format(c=c,
t1=theta_r[0], t2=theta_r[1],
g1=gamma_r[0], g2=gamma_r[1],
e1=error_r[0], e2=error_r[1])
else:
dfe_param = ('dfe: continuous\n'
'distribution: reflected_gamma\n'
'theta_range: {t1}, {t2}\n'
'shape_range: {sh1}, {sh2}\n'
'scale_range: {sc1}, {sc2}\n'
'e_range: {e1}, {e2}\n')
dfe_param = dfe_param.format(t1=theta_r[0], t2=theta_r[1],
sh1=shape_r[0], sh2=shape_r[1],
sc1=scale_r[0], sc2=scale_r[1],
e1=error_r[0], e2=error_r[1])
if self.dfe_optional_opts == '':
self.set_dfe_optional_opts()
self.dfe_opts = dfe_param
def set_constraint(self, constraint):
"""
sets constraint
:param constraint: str
:return: sets str
"""
if constraint not in self.constraints:
raise ValueError('Not a recognised constraint!')
constraint_str = 'constraint: {}\n'.format(constraint)
self.constraint_opts = constraint_str
def set_dfe_optional_opts(self, optional=False, fraction=0.005, degree=50, delta=1e-5):
opt_string = 'optional: {}\n'.format(str(optional).lower())
if optional is True:
opt_string += ('fraction: {}\n'
'delta: {}\n'
'degree: {}\n'
'').format(fraction, delta, degree)
self.dfe_optional_opts = opt_string
def construct(self):
# check data set
if self.model_opts == '' or self.dfe_opts == '':
raise ValueError('Data not set')
# if data is set then check if other sections are set, if not set to defaults
if self.alg_opts == '':
self.set_alg_opts()
if self.constraint_opts == '':
self.set_constraint('none')
control_contents = (self.alg_opts + self.model_opts +
self.dfe_opts + self.constraint_opts +
self.dfe_optional_opts)
return control_contents
class Indel1ControlFile(Snp1ControlFile):
def __init__(self):
Snp1ControlFile.__init__(self)
self.sfs = ['INS', 'DEL']
self.constraints = ['none', 'neutral']
def set_data(self, sfs_m, n, snp_fold=False,
dfe='discrete', c=1,
theta_r=(1e-6, 0.1), gamma_r=(-250, 10), error_r=(0.0, 0.5),
shape_r=(1e-3, 200), scale_r=(0.1, 1e3), r_r=(0.05, 5.0)):
"""
sets model and dfe commands in control file
:param sfs_m: dict{str: tuple(list(float), int), ..}
:param n: int
:param snp_fold: bool
:param dfe: str
:param c: int
:param theta_r: tuple(float, float)
:param gamma_r: tuple(float, float)
:param error_r: tuple(float, float)
:param shape_r: tuple(float, float)
:param scale_r: tuple(float, float)
:param r_r: tuple(float, float)
:return: NA
"""
self._check_sfs_m_in(sfs_m)
model_ctrls = ('[model_commands]\n'
'model: INDEL_1\n'
'n: {n}\n'
'm: {m1}\n'
'ins_sfs: {sfs1}\n'
'del_sfs: {sfs2}\n').format(n=n, m1=sfs_m['INS'][1],
sfs1=', '.join([str(x) for x in sfs_m['INS'][0]]),
sfs2=', '.join([str(x) for x in sfs_m['DEL'][0]]))
self.model_opts = model_ctrls
if dfe == 'discrete':
dfe_param = ('dfe: discrete\n'
'c: {c}\n'
'ins_theta_range: {t1}, {t2}\n'
'ins_gamma_range: {g1}, {g2}\n'
'ins_e_range: {e1}, {e2}\n'
'del_theta_range: {t1}, {t2}\n'
'del_gamma_range: {g1}, {g2}\n'
'del_e_range: {e1}, {e2}\n')
dfe_param = dfe_param.format(c=c,
t1=theta_r[0], t2=theta_r[1],
g1=gamma_r[0], g2=gamma_r[1],
e1=error_r[0], e2=error_r[1])
else:
dfe_param = ('dfe: continuous\n'
'distribution: reflected_gamma\n'
'ins_theta_range: {t1}, {t2}\n'
'ins_shape_range: {sh1}, {sh2}\n'
'ins_scale_range: {sc1}, {sc2}\n'
'ins_e_range: {e1}, {e2}\n'
'del_theta_range: {t1}, {t2}\n'
'del_shape_range: {sh1}, {sh2}\n'
'del_scale_range: {sc1}, {sc2}\n'
'del_e_range: {e1}, {e2}\n')
dfe_param = dfe_param.format(t1=theta_r[0], t2=theta_r[1],
sh1=shape_r[0], sh2=shape_r[1],
sc1=scale_r[0], sc2=scale_r[1],
e1=error_r[0], e2=error_r[1])
if self.dfe_optional_opts == '':
self.set_dfe_optional_opts()
self.dfe_opts = dfe_param
class GbgcControlFile(Snp1ControlFile):
def __init__(self):
Snp1ControlFile.__init__(self)
self.sfs = ['neutral_SNPs', 'ws_SNPs', 'sw_SNPs']
self.constraints = ['none', 'M0', 'M1', 'M0*', 'M1*']
def set_data(self, sfs_m, n, snp_fold=False,
dfe='discrete', c=1,
theta_r=(1e-6, 0.1), gamma_r=(-250, 10), error_r=(0.0, 0.5),
shape_r=(1e-3, 200), scale_r=(0.1, 1e3), r_r=(0.05, 5.0)):
"""
sets model and dfe commands in control file
:param sfs_m: dict{str: tuple(list(float), int), ..}
:param n: int
:param snp_fold: bool
:param dfe: str
:param c: int
:param theta_r: tuple(float, float)
:param gamma_r: tuple(float, float)
:param error_r: tuple(float, float)
:param shape_r: tuple(float, float)
:param scale_r: tuple(float, float)
:param r_r: tuple(float, float)
:return: NA
"""
self._check_sfs_m_in(sfs_m)
model_ctrls = ('[model_commands]\n'
'model: gBGC_EXTENDED_M1*\n'
'n: {n}\n'
'r_range: {r1}, {r2}\n'
'\n'
'[neutral_SNPs]\n'
'm: {m1}\n'
'sfs: {sfs1}\n'
'theta_range: {t1}, {t2}\n'
'gamma_range: {g1}, {g2}\n'
'e_range: {e1}, {e2}\n'
'\n'
'[ws_SNPs]\n'
'm: {m2}\n'
'sfs: {sfs2}\n'
'theta_range: {t1}, {t2}\n'
'gamma_range: {g1}, {g2}\n'
'e_range: {e1}, {e2}\n'
'\n'
'[sw_SNPs]\n'
'm: {m3}\n'
'sfs: {sfs3}\n'
'theta_range: {t1}, {t2}\n'
'gamma_range: {g1}, {g2}\n'
'e_range: {e1}, {e2}')
model_ctrls = model_ctrls.format(n=n,
r1=r_r[0], r2=r_r[1],
m1=sfs_m['neutral_SNPs'][1],
sfs1=', '.join([str(x) for x in sfs_m['neutral_SNPs'][0]]),
m2=sfs_m['ws_SNPs'][1],
sfs2=', '.join([str(x) for x in sfs_m['ws_SNPs'][0]]),
m3=sfs_m['sw_SNPs'][1],
sfs3=', '.join([str(x) for x in sfs_m['sw_SNPs'][0]]),
t1=theta_r[0], t2=theta_r[1],
g1=gamma_r[0], g2=gamma_r[1],
e1=error_r[0], e2=error_r[1])
self.model_opts = model_ctrls
self.dfe_opts = '\n'
class IndelNeuSelControlFile(Snp1ControlFile):
def __init__(self):
Snp1ControlFile.__init__(self)
self.sfs = ['neutral_INS', 'neutral_DEL', 'selected_INS', 'selected_DEL']
self.constraints = ['none', 'equal_mutation_rate']
def set_data(self, sfs_m, n, snp_fold=False,
dfe='discrete', c=1,
theta_r=(1e-6, 0.1), gamma_r=(-250, 10), error_r=(0.0, 0.5),
shape_r=(1e-3, 200), scale_r=(0.1, 1e3), r_r=(0.05, 5.0)):
"""
sets model and dfe commands in control file
:param sfs_m: dict{str: tuple(list(float), int), ..}
:param n: int
:param snp_fold: bool
:param dfe: str
:param c: int
:param theta_r: tuple(float, float)
:param gamma_r: tuple(float, float)
:param error_r: tuple(float, float)
:param shape_r: tuple(float, float)
:param scale_r: tuple(float, float)
:param r_r: tuple(float, float)
:return: NA
"""
self._check_sfs_m_in(sfs_m)
model_ctrls = ('[model_commands]\n'
'model: neutralINDEL_vs_selectedINDEL\n'
'n: {n}\n'
'r_range: {r1}, {r2}\n'
'neu_indel_m: {m1}\n'
'neu_ins_sfs: {sfs1}\n'
'neu_del_sfs: {sfs2}\n'
'neu_ins_theta_range: {t1}, {t2}\n'
'neu_ins_e_range: {e1}, {e2}\n'
'neu_del_theta_range: {t1}, {t2}\n'
'neu_del_e_range: {e1}, {e2}\n'
'sel_indel_m: {m3}\n'
'sel_ins_sfs: {sfs3}\n'
'sel_del_sfs: {sfs4}\n')
model_ctrls = model_ctrls.format(n=n,
r1=r_r[0], r2=r_r[1],
m1=sfs_m['neutral_INS'][1],
sfs1=', '.join([str(x) for x in sfs_m['neutral_INS'][0]]),
sfs2=', '.join([str(x) for x in sfs_m['neutral_DEL'][0]]),
m3=sfs_m['selected_INS'][1],
sfs3=', '.join([str(x) for x in sfs_m['selected_INS'][0]]),
sfs4=', '.join([str(x) for x in sfs_m['selected_DEL'][0]]),
t1=theta_r[0], t2=theta_r[1],
e1=error_r[0], e2=error_r[1])
self.model_opts = model_ctrls
if dfe == 'discrete':
dfe_param = ('dfe: discrete\n'
'c: {c}\n'
'ins_theta_range: {t1}, {t2}\n'
'ins_gamma_range: {g1}, {g2}\n'
'ins_e_range: {e1}, {e2}\n'
'del_theta_range: {t1}, {t2}\n'
'del_gamma_range: {g1}, {g2}\n'
'del_e_range: {e1}, {e2}\n')
dfe_param = dfe_param.format(c=c,
t1=theta_r[0], t2=theta_r[1],
g1=gamma_r[0], g2=gamma_r[1],
e1=error_r[0], e2=error_r[1])
else:
dfe_param = ('dfe: continuous\n'
'distribution: reflected_gamma\n'
'ins_theta_range: {t1}, {t2}\n'
'ins_shape_range: {sh1}, {sh2}\n'
'ins_scale_range: {sc1}, {sc2}\n'
'ins_e_range: {e1}, {e2}\n'
'del_theta_range: {t1}, {t2}\n'
'del_shape_range: {sh1}, {sh2}\n'
'del_scale_range: {sc1}, {sc2}\n'
'del_e_range: {e1}, {e2}\n')
dfe_param = dfe_param.format(t1=theta_r[0], t2=theta_r[1],
sh1=shape_r[0], sh2=shape_r[1],
sc1=scale_r[0], sc2=scale_r[1],
e1=error_r[0], e2=error_r[1])
if self.dfe_optional_opts == '':
self.set_dfe_optional_opts()
self.dfe_opts = dfe_param
class IndelSnpNeuSelControlFile(Snp1ControlFile):
def __init__(self):
Snp1ControlFile.__init__(self)
self.sfs = ['neutral_SNP', 'selected_INS', 'selected_DEL']
self.constraints = ['none']
def set_data(self, sfs_m, n, snp_fold=False,
dfe='discrete', c=1,
theta_r=(1e-6, 0.1), gamma_r=(-250, 10), error_r=(0.0, 0.5),
shape_r=(1e-3, 200), scale_r=(0.1, 1e3), r_r=(0.05, 5.0)):
"""
sets model and dfe commands in control file
:param sfs_m: dict{str: tuple(list(float), int), ..}
:param n: int
:param snp_fold: bool
:param dfe: str
:param c: int
:param theta_r: tuple(float, float)
:param gamma_r: tuple(float, float)
:param error_r: tuple(float, float)
:param shape_r: tuple(float, float)
:param scale_r: tuple(float, float)
:param r_r: tuple(float, float)
:return: NA
"""
self._check_sfs_m_in(sfs_m)
model_ctrls = ('[model_commands]\n'
'model: neutralSNP_vs_selectedINDEL\n'
'n: {n}\n'
'r_range: {r1}, {r2}\n'
'neu_snp_m: {m1}\n'
'neu_snp_sfs: {sfs1}\n'
'neu_snp_theta_range: {t1}, {t2}\n'
'neu_snp_e_range: {e1}, {e2}\n'
'sel_indel_m: {m3}\n'
'sel_ins_sfs: {sfs3}\n'
'sel_del_sfs: {sfs4}\n')
model_ctrls = model_ctrls.format(n=n,
r1=r_r[0], r2=r_r[1],
m1=sfs_m['neutral_SNP'][1],
sfs1=', '.join([str(x) for x in sfs_m['neutral_SNP'][0]]),
m3=sfs_m['selected_INS'][1],
sfs3=', '.join([str(x) for x in sfs_m['selected_INS'][0]]),
sfs4=', '.join([str(x) for x in sfs_m['selected_DEL'][0]]),
t1=theta_r[0], t2=theta_r[1],
e1=error_r[0], e2=error_r[1])
self.model_opts = model_ctrls
if dfe == 'discrete':
dfe_param = ('dfe: discrete\n'
'c: {c}\n'
'ins_theta_range: {t1}, {t2}\n'
'ins_gamma_range: {g1}, {g2}\n'
'ins_e_range: {e1}, {e2}\n'
'del_theta_range: {t1}, {t2}\n'
'del_gamma_range: {g1}, {g2}\n'
'del_e_range: {e1}, {e2}\n')
dfe_param = dfe_param.format(c=c,
t1=theta_r[0], t2=theta_r[1],
g1=gamma_r[0], g2=gamma_r[1],
e1=error_r[0], e2=error_r[1])
else:
dfe_param = ('dfe: continuous\n'
'distribution: reflected_gamma\n'
'ins_theta_range: {t1}, {t2}\n'
'ins_shape_range: {sh1}, {sh2}\n'
'ins_scale_range: {sc1}, {sc2}\n'
'ins_e_range: {e1}, {e2}\n'
'del_theta_range: {t1}, {t2}\n'
'del_shape_range: {sh1}, {sh2}\n'
'del_scale_range: {sc1}, {sc2}\n'
'del_e_range: {e1}, {e2}\n')
dfe_param = dfe_param.format(t1=theta_r[0], t2=theta_r[1],
sh1=shape_r[0], sh2=shape_r[1],
sc1=scale_r[0], sc2=scale_r[1],
e1=error_r[0], e2=error_r[1])
if self.dfe_optional_opts == '':
self.set_dfe_optional_opts()
self.dfe_opts = dfe_param
class SNPNeuSelControlFile(Snp1ControlFile):
def __init__(self):
Snp1ControlFile.__init__(self)
self.sfs = ['neutral_SNP', 'selected_SNP']
self.constraints = ['none', 'equal_mutation_rate', 'equal_mutation_rate_AND_no_pol_error']
def set_data(self, sfs_m, n, snp_fold=False,
dfe='discrete', c=1,
theta_r=(1e-6, 0.1), gamma_r=(-250, 10), error_r=(0.0, 0.5),
shape_r=(1e-3, 200), scale_r=(0.1, 1e3), r_r=(0.05, 5.0)):
"""
sets model and dfe commands in control file
:param sfs_m: dict{str: tuple(list(float), int), ..}
:param n: int
:param snp_fold: bool
:param dfe: str
:param c: int
:param theta_r: tuple(float, float)
:param gamma_r: tuple(float, float)
:param error_r: tuple(float, float)
:param shape_r: tuple(float, float)
:param scale_r: tuple(float, float)
:param r_r: tuple(float, float)
:return: NA
"""
self._check_sfs_m_in(sfs_m)
model_ctrls = ('[model_commands]\n'
'model: neutralSNP_vs_selectedSNP\n'
'n: {n}\n'
'folded: {fold}\n'
'r_range: {r1}, {r2}\n'
'neu_m: {m1}\n'
'neu_sfs: {sfs1}\n'
'neu_theta_range: {t1}, {t2}\n'
'neu_e_range: {e1}, {e2}\n'
'sel_m: {m3}\n'
'sel_sfs: {sfs3}\n')
model_ctrls = model_ctrls.format(n=n,
r1=r_r[0], r2=r_r[1],
m1=sfs_m['neutral_SNP'][1],
fold=str(snp_fold).lower(),
sfs1=', '.join([str(x) for x in sfs_m['neutral_SNP'][0]]),
m3=sfs_m['selected_SNP'][1],
sfs3=', '.join([str(x) for x in sfs_m['selected_SNP'][0]]),
t1=theta_r[0], t2=theta_r[1],
e1=error_r[0], e2=error_r[1])
self.model_opts = model_ctrls
if dfe == 'discrete':
dfe_param = ('dfe: discrete\n'
'c: {c}\n'
'theta_range: {t1}, {t2}\n'
'gamma_range: {g1}, {g2}\n'
'e_range: {e1}, {e2}\n')
dfe_param = dfe_param.format(c=c,
t1=theta_r[0], t2=theta_r[1],
g1=gamma_r[0], g2=gamma_r[1],
e1=error_r[0], e2=error_r[1])
else:
dfe_param = ('dfe: continuous\n'
'distribution: reflected_gamma\n'
'theta_range: {t1}, {t2}\n'
'shape_range: {sh1}, {sh2}\n'
'scale_range: {sc1}, {sc2}\n'
'e_range: {e1}, {e2}\n')
dfe_param = dfe_param.format(t1=theta_r[0], t2=theta_r[1],
sh1=shape_r[0], sh2=shape_r[1],
sc1=scale_r[0], sc2=scale_r[1],
e1=error_r[0], e2=error_r[1])
if self.dfe_optional_opts == '':
self.set_dfe_optional_opts()
self.dfe_opts = dfe_param
class ResultsFile(object):
def __init__(self, anavar_results_file):
"""
Creates an anavar results file object
:param anavar_results_file: file
"""
contents = anavar_results_file.readlines()
self.head = contents[0:7]
self.control_location = self.head[0].split()[2]
self.param = tuple(self.head[2].split()[4:])
self.columns = tuple(self.head[6].split())
self.data = contents[7:]
def seed(self):
"""
returns the seed anavar was run with
:return: int
"""
seed = int(self.head[4].rstrip().replace('seed = ', ''))
return seed
def free_parameters(self):
"""
returns all free parameters in results file
:return: tuple
"""
return self.param
def control_file(self):
"""
returns control file path
:return: str
"""
return self.control_location
def header(self):
"""
returns the column headers line from the file
:return: tuple
"""
return self.columns
def __results_to_dict(self, line):
"""
converts anavar results line to dict
:param line: str
:return: dict
"""
line = line.split()
line_dict = {}
for i in range(0, len(self.columns)):
col_val = line[i]
if '.' in col_val or 'e' in col_val:
col_val = float(col_val)
else:
col_val = int(col_val)
line_dict[self.columns[i]] = col_val
return line_dict
def estimates(self, as_string=False):
"""
returns generator of all estimates in dictionary form
:param as_string: bool
:return: generator
"""
for line in self.data:
if as_string:
line = line.rstrip()
else:
line = self.__results_to_dict(line)
yield line
def ml_estimate(self, as_string=False):
"""
returns the maximum likelihood estimate
:param as_string: bool
:return: dict
"""
ml = self.data[0]
if as_string:
return ml.rstrip()
else:
return self.__results_to_dict(ml)
def converged(self):
"""
assesses convergence for results file
:return: bool
"""
top_lnls = [abs(x['lnL']) for x in self.estimates()][0:2]
# dif = (top_lnls[0] - top_lnls[1]) / (0.5 * (top_lnls[0] + top_lnls[1]))
dif = top_lnls[0] - top_lnls[1]
if abs(dif) < 0.1:
return True
else:
return False
def bounds_hit(self, theta_r=(1e-6, 0.1), gamma_r=(-250, 10), error_r=(0.0, 0.5),
shape_r=(1e-3, 200), scale_r=(0.1, 1e3), r_r=(0.05, 5.0)):
"""
lists parameters too close to estimate boundaries
:param theta_r: tuple
:param gamma_r: tuple
:param error_r: tuple
:param shape_r: tuple
:param scale_r: tuple
:param r_r: tuple
:return: list
"""
params_hitting_limits = []
skip_params = ['run', 'imp', 'exit_code', 'lnL']
ml_dict = self.ml_estimate()
for param in ml_dict.keys():
if re.search(r'theta', param):
bounds = theta_r
elif re.search(r'gamma', param):
bounds = gamma_r
elif re.search(r'scale', param):
bounds = scale_r
elif re.search(r'shape', param):
bounds = shape_r
elif re.search(r'r_\d{1,2}', param):
bounds = r_r
elif param in skip_params:
continue
else:
bounds = error_r
try:
b_hit = self.__boundary_hit(ml_dict[param], bounds[1], bounds[0], 1e-5)
# catches error = 0
except ZeroDivisionError:
params_hitting_limits.append(param)
continue
if b_hit:
params_hitting_limits.append(param)
return params_hitting_limits
def __boundary_hit(self, mle, upr_b, lwr_b, min_dif):
"""
decides if ml estimate is too close to boundary
:param mle: float
:param upr_b: float
:param lwr_b: float
:param min_dif: float
:return: bool
"""
for b in [abs(upr_b), abs(lwr_b)]:
dif = (abs(mle) - b) / (0.5 * (abs(mle) + b))
if abs(dif) < abs(min_dif):
return True
return False
def data_type(self):
"""
assigns data as either indel, snp or snp_indel
:return: str
"""
param_str = ','.join(self.free_parameters())
if 'ins' in param_str and 'del' in param_str:
if 'snp' in param_str:
return 'snp_indel'
else:
return 'indel'
else:
return 'snp'
def num_class(self):
"""
returns number of classes in results file
:return: int
"""
params = self.free_parameters()
thetas = set([x.split('_')[-1] for x in params if 'theta' in x])
if 'theta' in thetas:
thetas.remove('theta')
if 1 >= len(thetas) >= 0:
return 1
else:
return len(thetas)
def dfe(self):
"""
returns type of distribution
:return: str
"""
param_str = ','.join(self.free_parameters())
if 'scale' in param_str and 'shape' in param_str:
return 'continuous'
else:
return 'discrete'
def num_runs(self):
"""
gives the number of runs listed in the results file
:return: int
"""
return len(self.data)
def get_alpha(self, dn, ds, var_type):
"""
calculates alpha as described in equation 19 Barton and Zeng 2018
:param dn: float
:param ds: float
:param var_type: str
:return: str
"""
if var_type not in ['snp', 'indel', 'ins', 'del']:
raise ValueError('Unrecognised variant type')
# if self.data_type() != 'indel':
# raise NotImplementedError('Only implemented for INDELs')
mle = self.ml_estimate()
theta_keys = [x for x in mle.keys() if 'theta' in x and 'sel' in x]
if self.dfe() == 'continuous':
gamma_keys = [x for x in mle.keys() if 'scale' in x and 'sel' in x] + \
[x for x in mle.keys() if 'shape' in x and 'sel' in x]
else:
gamma_keys = [x for x in mle.keys() if 'gamma' in x and 'sel' in x]
# separate by class
c = self.num_class()
if var_type == 'indel':
variants_to_process = ['ins', 'del']
elif var_type == 'ins':
variants_to_process = ['ins']
elif var_type == 'del':
variants_to_process = ['del']
else:
variants_to_process = ['']
ts = []
gs = []
for var in variants_to_process:
for i in range(1, c+1):
if self.dfe() == 'continuous':
ti_key = [x for x in theta_keys if var in x]
dfei_keys = [x for x in gamma_keys if var in x]
gi = -(mle[dfei_keys[0]] * mle[dfei_keys[1]])
else:
ti_key = [x for x in theta_keys if var in x and str(i) in x]
gi_key = [x for x in gamma_keys if var in x and str(i) in x]
gi = mle[gi_key[0]]
ti = mle[ti_key[0]]
ts.append(ti)
gs.append(gi)
var_alpha = alpha(ts, gs, dn, ds)
return var_alpha
class MultiResultsFile(ResultsFile):
def __init__(self, file_list):
"""
takes a list of anavar results files and merges them into one,
then calls ResultsFile instance on merged file before deleting
merged file
:param file_list: list
"""
merged_name = file_list[0] + '.merged.results.txt'
merge_results(file_list, merged_name)
ResultsFile.__init__(self, open(merged_name))
os.remove(merged_name)
def merge_results(file_list, name):
"""
takes list of results files and merges them
:param file_list: list
:param name: str
:return:
"""
all_estimates = []
merged_file = open(name, 'w')
first = True
for x in file_list:
with open(x) as results:
res = ResultsFile(results)
if first: