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# Copyright 2009 by Tiago Antao <[email protected]>. All rights reserved.
#
# This file is part of the Biopython distribution and governed by your
# choice of the "Biopython License Agreement" or the "BSD 3-Clause License".
# Please see the LICENSE file that should have been included as part of this
# package.
"""Module to control GenePop."""
import os
import re
import shutil
import tempfile
from Bio.Application import AbstractCommandline, _Argument
def _gp_float(tok):
"""Get a float from a token, if it fails, returns the string (PRIVATE)."""
try:
return float(tok)
except ValueError:
return str(tok)
def _gp_int(tok):
"""Get a int from a token, if it fails, returns the string (PRIVATE)."""
try:
return int(tok)
except ValueError:
return str(tok)
def _read_allele_freq_table(f):
line = f.readline()
while " --" not in line:
if line == "":
raise StopIteration
if "No data" in line:
return None, None
line = f.readline()
alleles = [x for x in f.readline().rstrip().split(" ") if x != ""]
alleles = [_gp_int(x) for x in alleles]
line = f.readline().rstrip()
table = []
while line != "":
parts = [x for x in line.split(" ") if x != ""]
try:
table.append(
(parts[0], [_gp_float(x) for x in parts[1:-1]], _gp_int(parts[-1]))
)
except ValueError:
table.append((parts[0], [None] * len(alleles), 0))
line = f.readline().rstrip()
return alleles, table
def _read_table(f, funs):
table = []
line = f.readline().rstrip()
while "---" not in line:
line = f.readline().rstrip()
line = f.readline().rstrip()
while "===" not in line and "---" not in line and line != "":
toks = [x for x in line.split(" ") if x != ""]
parts = []
for i, tok in enumerate(toks):
try:
parts.append(funs[i](tok))
except ValueError:
parts.append(tok) # Could not cast
table.append(tuple(parts))
line = f.readline().rstrip()
return table
def _read_triangle_matrix(f):
matrix = []
line = f.readline().rstrip()
while line != "":
matrix.append([_gp_float(x) for x in [y for y in line.split(" ") if y != ""]])
line = f.readline().rstrip()
return matrix
def _read_headed_triangle_matrix(f):
matrix = {}
header = f.readline().rstrip()
if "---" in header or "===" in header:
header = f.readline().rstrip()
nlines = len([x for x in header.split(" ") if x != ""]) - 1
for line_pop in range(nlines):
line = f.readline().rstrip()
vals = [x for x in line.split(" ")[1:] if x != ""]
clean_vals = []
for val in vals:
try:
clean_vals.append(_gp_float(val))
except ValueError:
clean_vals.append(None)
for col_pop, clean_val in enumerate(clean_vals):
matrix[(line_pop + 1, col_pop)] = clean_val
return matrix
def _hw_func(stream, is_locus, has_fisher=False):
line = stream.readline()
if is_locus:
hook = "Locus "
else:
hook = "Pop : "
while line != "":
if line.lstrip().startswith(hook):
stream.readline()
stream.readline()
stream.readline()
table = _read_table(
stream, [str, _gp_float, _gp_float, _gp_float, _gp_float, _gp_int, str]
)
# loci might mean pop if hook="Locus "
loci = {}
for entry in table:
if len(entry) < 4:
loci[entry[0]] = None
else:
locus, p, se, fis_wc, fis_rh, steps = entry[:-1]
if se == "-":
se = None
loci[locus] = p, se, fis_wc, fis_rh, steps
return loci
line = stream.readline()
# self.done = True
raise StopIteration
class _FileIterator:
"""Return an iterator which crawls over a stream of lines with a function (PRIVATE).
The generator function is expected to yield a tuple, while
consuming input
"""
def __init__(self, func, fname, handle=None):
self.func = func
if handle is None:
self.stream = open(fname)
else:
# For special cases where calling code wants to
# seek into the file before starting:
self.stream = handle
self.fname = fname
self.done = False
def __iter__(self):
if self.done:
self.done = True
raise StopIteration
return self
def __next__(self):
return self.func(self)
def __del__(self):
self.stream.close()
os.remove(self.fname)
class _GenePopCommandline(AbstractCommandline):
"""Return a Command Line Wrapper for GenePop (PRIVATE)."""
def __init__(self, genepop_dir=None, cmd="Genepop", **kwargs):
self.parameters = [
_Argument(["command"], "GenePop option to be called", is_required=True),
_Argument(["mode"], "Should always be batch", is_required=True),
_Argument(["input"], "Input file", is_required=True),
_Argument(["Dememorization"], "Dememorization step"),
_Argument(["BatchNumber"], "Number of MCMC batches"),
_Argument(["BatchLength"], "Length of MCMC chains"),
_Argument(["HWtests"], "Enumeration or MCMC"),
_Argument(["IsolBDstatistic"], "IBD statistic (a or e)"),
_Argument(["MinimalDistance"], "Minimal IBD distance"),
_Argument(["GeographicScale"], "Log or Linear"),
]
AbstractCommandline.__init__(self, cmd, **kwargs)
self.set_parameter("mode", "Mode=Batch")
def set_menu(self, option_list):
"""Set the menu option.
Example set_menu([6,1]) = get all F statistics (menu 6.1)
"""
self.set_parameter(
"command", "MenuOptions=" + ".".join(str(x) for x in option_list)
)
def set_input(self, fname):
"""Set the input file name."""
self.set_parameter("input", "InputFile=" + fname)
class GenePopController:
"""Define a class to interface with the GenePop program."""
def __init__(self, genepop_dir=None):
"""Initialize the controller.
genepop_dir is the directory where GenePop is.
The binary should be called Genepop (capital G)
"""
self.controller = _GenePopCommandline(genepop_dir)
def _get_opts(self, dememorization, batches, iterations, enum_test=None):
opts = {}
opts["Dememorization"] = dememorization
opts["BatchNumber"] = batches
opts["BatchLength"] = iterations
if enum_test is not None:
if enum_test is True:
opts["HWtests"] = "Enumeration"
else:
opts["HWtests"] = "MCMC"
return opts
def _run_genepop(self, extensions, option, fname, opts=None):
if opts is None:
opts = {}
cwd = os.getcwd()
temp_dir = tempfile.mkdtemp()
os.chdir(temp_dir)
self.controller.set_menu(option)
if os.path.isabs(fname):
self.controller.set_input(fname)
else:
self.controller.set_input(cwd + os.sep + fname)
for opt in opts:
self.controller.set_parameter(opt, opt + "=" + str(opts[opt]))
self.controller() # checks error level is zero
os.chdir(cwd)
shutil.rmtree(temp_dir)
def _test_pop_hz_both(
self,
fname,
type,
ext,
enum_test=True,
dememorization=10000,
batches=20,
iterations=5000,
):
"""Use Hardy-Weinberg test for heterozygote deficiency/excess (PRIVATE).
Returns a population iterator containing a dictionary where
dictionary[locus]=(P-val, SE, Fis-WC, Fis-RH, steps).
Some loci have a None if the info is not available.
SE might be none (for enumerations).
"""
opts = self._get_opts(dememorization, batches, iterations, enum_test)
self._run_genepop([ext], [1, type], fname, opts)
def hw_func(self):
return _hw_func(self.stream, False)
return _FileIterator(hw_func, fname + ext)
def _test_global_hz_both(
self,
fname,
type,
ext,
enum_test=True,
dememorization=10000,
batches=20,
iterations=5000,
):
"""Use Global Hardy-Weinberg test for heterozygote deficiency/excess (PRIVATE).
Returns a triple with:
- A list per population containing (pop_name, P-val, SE, switches).
Some pops have a None if the info is not available.
SE might be none (for enumerations).
- A list per loci containing (locus_name, P-val, SE, switches).
Some loci have a None if the info is not available.
SE might be none (for enumerations).
- Overall results (P-val, SE, switches).
"""
opts = self._get_opts(dememorization, batches, iterations, enum_test)
self._run_genepop([ext], [1, type], fname, opts)
def hw_pop_func(self):
return _read_table(self.stream, [str, _gp_float, _gp_float, _gp_float])
with open(fname + ext) as f1:
line = f1.readline()
while "by population" not in line:
line = f1.readline()
pop_p = _read_table(f1, [str, _gp_float, _gp_float, _gp_float])
with open(fname + ext) as f2:
line = f2.readline()
while "by locus" not in line:
line = f2.readline()
loc_p = _read_table(f2, [str, _gp_float, _gp_float, _gp_float])
with open(fname + ext) as f:
line = f.readline()
while "all locus" not in line:
line = f.readline()
f.readline()
f.readline()
f.readline()
f.readline()
line = f.readline().rstrip()
p, se, switches = tuple(
_gp_float(x) for x in [y for y in line.split(" ") if y != ""]
)
return pop_p, loc_p, (p, se, switches)
# 1.1
def test_pop_hz_deficiency(
self, fname, enum_test=True, dememorization=10000, batches=20, iterations=5000
):
"""Use Hardy-Weinberg test for heterozygote deficiency.
Returns a population iterator containing a dictionary wehre
dictionary[locus]=(P-val, SE, Fis-WC, Fis-RH, steps).
Some loci have a None if the info is not available.
SE might be none (for enumerations).
"""
return self._test_pop_hz_both(
fname, 1, ".D", enum_test, dememorization, batches, iterations
)
# 1.2
def test_pop_hz_excess(
self, fname, enum_test=True, dememorization=10000, batches=20, iterations=5000
):
"""Use Hardy-Weinberg test for heterozygote deficiency.
Returns a population iterator containing a dictionary where
dictionary[locus]=(P-val, SE, Fis-WC, Fis-RH, steps).
Some loci have a None if the info is not available.
SE might be none (for enumerations).
"""
return self._test_pop_hz_both(
fname, 2, ".E", enum_test, dememorization, batches, iterations
)
# 1.3 P file
def test_pop_hz_prob(
self,
fname,
ext,
enum_test=False,
dememorization=10000,
batches=20,
iterations=5000,
):
"""Use Hardy-Weinberg test based on probability.
Returns 2 iterators and a final tuple:
1. Returns a loci iterator containing:
- A dictionary[pop_pos]=(P-val, SE, Fis-WC, Fis-RH, steps).
Some pops have a None if the info is not available.
SE might be none (for enumerations).
- Result of Fisher's test (Chi2, deg freedom, prob).
2. Returns a population iterator containing:
- A dictionary[locus]=(P-val, SE, Fis-WC, Fis-RH, steps).
Some loci have a None if the info is not available.
SE might be none (for enumerations).
- Result of Fisher's test (Chi2, deg freedom, prob).
3. Final tuple (Chi2, deg freedom, prob).
"""
opts = self._get_opts(dememorization, batches, iterations, enum_test)
self._run_genepop([ext], [1, 3], fname, opts)
def hw_prob_loci_func(self):
return _hw_func(self.stream, True, True)
def hw_prob_pop_func(self):
return _hw_func(self.stream, False, True)
shutil.copyfile(fname + ".P", fname + ".P2")
return (
_FileIterator(hw_prob_loci_func, fname + ".P"),
_FileIterator(hw_prob_pop_func, fname + ".P2"),
)
# 1.4
def test_global_hz_deficiency(
self, fname, enum_test=True, dememorization=10000, batches=20, iterations=5000
):
"""Use Global Hardy-Weinberg test for heterozygote deficiency.
Returns a triple with:
- An list per population containing (pop_name, P-val, SE, switches).
Some pops have a None if the info is not available.
SE might be none (for enumerations).
- An list per loci containing (locus_name, P-val, SE, switches).
Some loci have a None if the info is not available.
SE might be none (for enumerations).
- Overall results (P-val, SE, switches).
"""
return self._test_global_hz_both(
fname, 4, ".DG", enum_test, dememorization, batches, iterations
)
# 1.5
def test_global_hz_excess(
self, fname, enum_test=True, dememorization=10000, batches=20, iterations=5000
):
"""Use Global Hardy-Weinberg test for heterozygote excess.
Returns a triple with:
- A list per population containing (pop_name, P-val, SE, switches).
Some pops have a None if the info is not available.
SE might be none (for enumerations).
- A list per loci containing (locus_name, P-val, SE, switches).
Some loci have a None if the info is not available.
SE might be none (for enumerations).
- Overall results (P-val, SE, switches)
"""
return self._test_global_hz_both(
fname, 5, ".EG", enum_test, dememorization, batches, iterations
)
# 2.1
def test_ld(self, fname, dememorization=10000, batches=20, iterations=5000):
"""Test for linkage disequilibrium on each pair of loci in each population."""
opts = self._get_opts(dememorization, batches, iterations)
self._run_genepop([".DIS"], [2, 1], fname, opts)
def ld_pop_func(self):
current_pop = None
line = self.stream.readline().rstrip()
if line == "":
self.done = True
raise StopIteration
toks = [x for x in line.split(" ") if x != ""]
pop, locus1, locus2 = toks[0], toks[1], toks[2]
if not hasattr(self, "start_locus1"):
start_locus1, start_locus2 = locus1, locus2
current_pop = -1
if locus1 == start_locus1 and locus2 == start_locus2:
current_pop += 1
if toks[3] == "No":
return current_pop, pop, (locus1, locus2), None
p, se, switches = _gp_float(toks[3]), _gp_float(toks[4]), _gp_int(toks[5])
return current_pop, pop, (locus1, locus2), (p, se, switches)
def ld_func(self):
line = self.stream.readline().rstrip()
if line == "":
self.done = True
raise StopIteration
toks = [x for x in line.split(" ") if x != ""]
locus1, locus2 = toks[0], toks[2]
try:
chi2, df, p = _gp_float(toks[3]), _gp_int(toks[4]), _gp_float(toks[5])
except ValueError:
return (locus1, locus2), None
return (locus1, locus2), (chi2, df, p)
f1 = open(fname + ".DIS")
line = f1.readline()
while "----" not in line:
line = f1.readline()
shutil.copyfile(fname + ".DIS", fname + ".DI2")
f2 = open(fname + ".DI2")
line = f2.readline()
while "Locus pair" not in line:
line = f2.readline()
while "----" not in line:
line = f2.readline()
return (
_FileIterator(ld_pop_func, fname + ".DIS", f1),
_FileIterator(ld_func, fname + ".DI2", f2),
)
# 2.2
def create_contingency_tables(self, fname):
"""Provision for creating Genotypic contingency tables."""
raise NotImplementedError
# 3.1 PR/GE files
def test_genic_diff_all(
self, fname, dememorization=10000, batches=20, iterations=5000
):
"""Provision for Genic differentiation for all populations."""
raise NotImplementedError
# 3.2 PR2/GE2 files
def test_genic_diff_pair(
self, fname, dememorization=10000, batches=20, iterations=5000
):
"""Provision for Genic differentiation for all population pairs."""
raise NotImplementedError
# 3.3 G files
def test_genotypic_diff_all(
self, fname, dememorization=10000, batches=20, iterations=5000
):
"""Provision for Genotypic differentiation for all populations."""
raise NotImplementedError
# 3.4 2G2 files
def test_genotypic_diff_pair(
self, fname, dememorization=10000, batches=20, iterations=5000
):
"""Provision for Genotypic differentiation for all population pairs."""
raise NotImplementedError
# 4
def estimate_nm(self, fname):
"""Estimate the Number of Migrants.
Parameters:
- fname - file name
Returns
- Mean sample size
- Mean frequency of private alleles
- Number of migrants for Ne=10
- Number of migrants for Ne=25
- Number of migrants for Ne=50
- Number of migrants after correcting for expected size
"""
self._run_genepop(["PRI"], [4], fname)
with open(fname + ".PRI") as f:
lines = f.readlines() # Small file, it is ok
for line in lines:
m = re.search("Mean sample size: ([.0-9]+)", line)
if m is not None:
mean_sample_size = _gp_float(m.group(1))
m = re.search(r"Mean frequency of private alleles p\(1\)= ([.0-9]+)", line)
if m is not None:
mean_priv_alleles = _gp_float(m.group(1))
m = re.search("N=10: ([.0-9]+)", line)
if m is not None:
mig10 = _gp_float(m.group(1))
m = re.search("N=25: ([.0-9]+)", line)
if m is not None:
mig25 = _gp_float(m.group(1))
m = re.search("N=50: ([.0-9]+)", line)
if m is not None:
mig50 = _gp_float(m.group(1))
m = re.search("for size= ([.0-9]+)", line)
if m is not None:
mig_corrected = _gp_float(m.group(1))
os.remove(fname + ".PRI")
return mean_sample_size, mean_priv_alleles, mig10, mig25, mig50, mig_corrected
# 5.1
def calc_allele_genotype_freqs(self, fname):
"""Calculate allele and genotype frequencies per locus and per sample.
Parameters:
- fname - file name
Returns tuple with 2 elements:
- Population iterator with
- population name
- Locus dictionary with key = locus name and content tuple as
Genotype List with
(Allele1, Allele2, observed, expected)
(expected homozygotes, observed hm,
expected heterozygotes, observed ht)
Allele frequency/Fis dictionary with allele as key and
(count, frequency, Fis Weir & Cockerham)
- Totals as a pair
- count
- Fis Weir & Cockerham,
- Fis Robertson & Hill
- Locus iterator with
- Locus name
- allele list
- Population list with a triple
- population name
- list of allele frequencies in the same order as allele list above
- number of genes
Will create a file called fname.INF
"""
self._run_genepop(["INF"], [5, 1], fname)
# First pass, general information
# num_loci = None
# num_pops = None
# with open(fname + ".INF") as f:
# line = f.readline()
# while (num_loci is None or num_pops is None) and line != '':
# m = re.search("Number of populations detected : ([0-9+])", l)
# if m is not None:
# num_pops = _gp_int(m.group(1))
# m = re.search("Number of loci detected : ([0-9+])", l)
# if m is not None:
# num_loci = _gp_int(m.group(1))
# line = f.readline()
def pop_parser(self):
if hasattr(self, "old_line"):
line = self.old_line
del self.old_line
else:
line = self.stream.readline()
loci_content = {}
while line != "":
line = line.rstrip()
if "Tables of allelic frequencies for each locus" in line:
return self.curr_pop, loci_content
match = re.match(".*Pop: (.+) Locus: (.+)", line)
if match is not None:
pop = match.group(1).rstrip()
locus = match.group(2)
if not hasattr(self, "first_locus"):
self.first_locus = locus
if hasattr(self, "curr_pop"):
if self.first_locus == locus:
old_pop = self.curr_pop
# self.curr_pop = pop
self.old_line = line
del self.first_locus
del self.curr_pop
return old_pop, loci_content
self.curr_pop = pop
else:
line = self.stream.readline()
continue
geno_list = []
line = self.stream.readline()
if "No data" in line:
continue
while "Genotypes Obs." not in line:
line = self.stream.readline()
while line != "\n":
m2 = re.match(" +([0-9]+) , ([0-9]+) *([0-9]+) *(.+)", line)
if m2 is not None:
geno_list.append(
(
_gp_int(m2.group(1)),
_gp_int(m2.group(2)),
_gp_int(m2.group(3)),
_gp_float(m2.group(4)),
)
)
else:
line = self.stream.readline()
continue
line = self.stream.readline()
while "Expected number of ho" not in line:
line = self.stream.readline()
expHo = _gp_float(line[38:])
line = self.stream.readline()
obsHo = _gp_int(line[38:])
line = self.stream.readline()
expHe = _gp_float(line[38:])
line = self.stream.readline()
obsHe = _gp_int(line[38:])
line = self.stream.readline()
while "Sample count" not in line:
line = self.stream.readline()
line = self.stream.readline()
freq_fis = {}
overall_fis = None
while "----" not in line:
vals = [x for x in line.rstrip().split(" ") if x != ""]
if vals[0] == "Tot":
overall_fis = (
_gp_int(vals[1]),
_gp_float(vals[2]),
_gp_float(vals[3]),
)
else:
freq_fis[_gp_int(vals[0])] = (
_gp_int(vals[1]),
_gp_float(vals[2]),
_gp_float(vals[3]),
)
line = self.stream.readline()
loci_content[locus] = (
geno_list,
(expHo, obsHo, expHe, obsHe),
freq_fis,
overall_fis,
)
self.done = True
raise StopIteration
def locus_parser(self):
line = self.stream.readline()
while line != "":
line = line.rstrip()
match = re.match(" Locus: (.+)", line)
if match is not None:
locus = match.group(1)
alleles, table = _read_allele_freq_table(self.stream)
return locus, alleles, table
line = self.stream.readline()
self.done = True
raise StopIteration
shutil.copyfile(fname + ".INF", fname + ".IN2")
pop_iter = _FileIterator(pop_parser, fname + ".INF")
locus_iter = _FileIterator(locus_parser, fname + ".IN2")
return (pop_iter, locus_iter)
def _calc_diversities_fis(self, fname, ext):
self._run_genepop([ext], [5, 2], fname)
with open(fname + ext) as f:
line = f.readline()
while line != "":
line = line.rstrip()
if line.startswith(
"Statistics per sample over all loci with at least two individuals typed"
):
avg_fis = _read_table(f, [str, _gp_float, _gp_float, _gp_float])
avg_Qintra = _read_table(f, [str, _gp_float])
line = f.readline()
def fis_func(self):
line = self.stream.readline()
while line != "":
line = line.rstrip()
m = re.search("Locus: (.+)", line)
if m is not None:
locus = m.group(1)
self.stream.readline()
if "No complete" in self.stream.readline():
return locus, None
self.stream.readline()
fis_table = _read_table(
self.stream, [str, _gp_float, _gp_float, _gp_float]
)
self.stream.readline()
avg_qinter, avg_fis = tuple(
_gp_float(x)
for x in [
y for y in self.stream.readline().split(" ") if y != ""
]
)
return locus, fis_table, avg_qinter, avg_fis
line = self.stream.readline()
self.done = True
raise StopIteration
return _FileIterator(fis_func, fname + ext), avg_fis, avg_Qintra
# 5.2
def calc_diversities_fis_with_identity(self, fname):
"""Compute identity-base Gene diversities and Fis."""
return self._calc_diversities_fis(fname, ".DIV")
# 5.3
def calc_diversities_fis_with_size(self, fname):
"""Provision to Computer Allele size-based Gene diversities and Fis."""
raise NotImplementedError
# 6.1 Less genotype frequencies
def calc_fst_all(self, fname):
"""Execute GenePop and gets Fst/Fis/Fit (all populations).
Parameters:
- fname - file name
Returns:
- (multiLocusFis, multiLocusFst, multiLocus Fit),
- Iterator of tuples
(Locus name, Fis, Fst, Fit, Qintra, Qinter)
Will create a file called ``fname.FST``.
This does not return the genotype frequencies.
"""
self._run_genepop([".FST"], [6, 1], fname)
with open(fname + ".FST") as f:
line = f.readline()
while line != "":
if line.startswith(" All:"):
toks = [x for x in line.rstrip().split(" ") if x != ""]
try:
allFis = _gp_float(toks[1])
except ValueError:
allFis = None
try:
allFst = _gp_float(toks[2])
except ValueError:
allFst = None
try:
allFit = _gp_float(toks[3])
except ValueError:
allFit = None
line = f.readline()
def proc(self):
if hasattr(self, "last_line"):
line = self.last_line
del self.last_line
else:
line = self.stream.readline()
locus = None
fis = None
fst = None
fit = None
qintra = None
qinter = None
while line != "":
line = line.rstrip()
if line.startswith(" Locus:"):
if locus is not None:
self.last_line = line
return locus, fis, fst, fit, qintra, qinter
else:
locus = line.split(":")[1].lstrip()
elif line.startswith("Fis^="):
fis = _gp_float(line.split(" ")[1])
elif line.startswith("Fst^="):
fst = _gp_float(line.split(" ")[1])
elif line.startswith("Fit^="):
fit = _gp_float(line.split(" ")[1])
elif line.startswith("1-Qintra^="):
qintra = _gp_float(line.split(" ")[1])
elif line.startswith("1-Qinter^="):
qinter = _gp_float(line.split(" ")[1])
return locus, fis, fst, fit, qintra, qinter
line = self.stream.readline()
if locus is not None:
return locus, fis, fst, fit, qintra, qinter
self.stream.close()
self.done = True
raise StopIteration
return (allFis, allFst, allFit), _FileIterator(proc, fname + ".FST")
# 6.2
def calc_fst_pair(self, fname):
"""Estimate spatial structure from Allele identity for all population pairs."""
self._run_genepop([".ST2", ".MIG"], [6, 2], fname)
with open(fname + ".ST2") as f:
line = f.readline()
while line != "":
line = line.rstrip()
if line.startswith("Estimates for all loci"):
avg_fst = _read_headed_triangle_matrix(f)
line = f.readline()
def loci_func(self):
line = self.stream.readline()
while line != "":
line = line.rstrip()
m = re.search(" Locus: (.+)", line)
if m is not None:
locus = m.group(1)
matrix = _read_headed_triangle_matrix(self.stream)
return locus, matrix
line = self.stream.readline()
self.done = True
raise StopIteration
os.remove(fname + ".MIG")
return _FileIterator(loci_func, fname + ".ST2"), avg_fst
# 6.3
def calc_rho_all(self, fname):
"""Provision for estimating spatial structure from Allele size for all populations."""
raise NotImplementedError
# 6.4
def calc_rho_pair(self, fname):
"""Provision for estimating spatial structure from Allele size for all population pairs."""
raise NotImplementedError
def _calc_ibd(self, fname, sub, stat="a", scale="Log", min_dist=0.00001):
"""Calculate isolation by distance statistics (PRIVATE)."""
self._run_genepop(
[".GRA", ".MIG", ".ISO"],
[6, sub],
fname,
opts={
"MinimalDistance": min_dist,
"GeographicScale": scale,
"IsolBDstatistic": stat,
},
)
with open(fname + ".ISO") as f:
f.readline()
f.readline()
f.readline()
f.readline()
estimate = _read_triangle_matrix(f)
f.readline()
f.readline()
distance = _read_triangle_matrix(f)
f.readline()
match = re.match("a = (.+), b = (.+)", f.readline().rstrip())
a = _gp_float(match.group(1))
b = _gp_float(match.group(2))
f.readline()
f.readline()
match = re.match(" b=(.+)", f.readline().rstrip())
bb = _gp_float(match.group(1))
match = re.match(r".*\[(.+) ; (.+)\]", f.readline().rstrip())
bblow = _gp_float(match.group(1))
bbhigh = _gp_float(match.group(2))
os.remove(fname + ".MIG")
os.remove(fname + ".GRA")
os.remove(fname + ".ISO")
return estimate, distance, (a, b), (bb, bblow, bbhigh)
# 6.5
def calc_ibd_diplo(self, fname, stat="a", scale="Log", min_dist=0.00001):
"""Calculate isolation by distance statistics for diploid data.
See _calc_ibd for parameter details.
Note that each pop can only have a single individual and
the individual name has to be the sample coordinates.
"""
return self._calc_ibd(fname, 5, stat, scale, min_dist)
# 6.6
def calc_ibd_haplo(self, fname, stat="a", scale="Log", min_dist=0.00001):
"""Calculate isolation by distance statistics for haploid data.
See _calc_ibd for parameter details.
Note that each pop can only have a single individual and
the individual name has to be the sample coordinates.
"""
return self._calc_ibd(fname, 6, stat, scale, min_dist)
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