def plot_parent_coding_hist(self, fhand):
fig = Figure()
axes = fig.add_subplot(111)
axes.hist(self.indexes, fill=True, log=True, bins=20, rwidth=1)
axes.axvline(x=self.parent_index_threshold)
axes.set_xlabel('support index for parental coding')
axes.set_ylabel('num. SNPs')
_print_figure(axes, fig, fhand, plot_legend=False)
def plot_parent_coding_hist(self, fhand):
fig = Figure()
axes = fig.add_subplot(111)
axes.hist(self.indexes, fill=True, log=True, bins=20, rwidth=1)
axes.axvline(x=self.parent_index_threshold)
axes.set_xlabel('support index for parental coding')
axes.set_ylabel('num. SNPs')
_print_figure(axes, fig, fhand, plot_legend=False)
def plot_failed_freq_dist(self, fhand):
fig = Figure()
axes = fig.add_subplot(111)
axes.hist(self._failed_freqs, fill=True, log=True, bins=20, rwidth=1)
axes.axvline(x=self.max_failed_freq)
axes.set_xlabel('% of adjacent SNPs segregating differently')
axes.set_ylabel('num. SNPs')
_print_figure(axes, fig, fhand, plot_legend=False)
def plot_failed_freq_dist(self, fhand):
fig = Figure()
axes = fig.add_subplot(111)
axes.hist(self._failed_freqs, fill=True, log=True, bins=20,
rwidth=1)
axes.axvline(x=self.max_failed_freq)
axes.set_xlabel('% of adjacent SNPs segregating differently')
axes.set_ylabel('num. SNPs')
_print_figure(axes, fig, fhand, plot_legend=False)
def draw_hist(self, fhand):
fig = Figure()
axes = fig.add_subplot(111)
axes.hist(self._scores, fill=True, log=True, bins=20,
rwidth=1)
axes.axvline(x=self.threshold)
axes.set_xlabel('% complexity score')
axes.set_ylabel('num. SNPs')
_print_figure(axes, fig, fhand, plot_legend=False)
def draw_hist(self, fhand):
fig = Figure()
axes = fig.add_subplot(111)
axes.hist(self._scores, fill=True, log=True, bins=20,
rwidth=1)
axes.axvline(x=self.threshold)
axes.set_xlabel('% complexity score')
axes.set_ylabel('num. SNPs')
_print_figure(axes, fig, fhand, plot_legend=False)
def _plot_segregation_debug(plot_info, fhand):
greens = ['#2d6e12', '#76b75b', '#164900']
reds = ['#8f0007', '#fb1f2a', '#b90009']
grays = ['0.5', '0.25', '0.75']
fig = Figure(figsize=(10, 4))
axes1 = fig.add_subplot(211)
axes2 = fig.add_subplot(212)
plot_info.sort(key=itemgetter('pos'))
is_close_snp = [geno_info['close_snp'] for geno_info in plot_info]
tested_snp_idx = is_close_snp.index(False)
passed = [geno_info['result'] for geno_info in plot_info]
total_cnts = [
geno_info['AA'] + geno_info['Aa'] + geno_info['aa']
for geno_info in plot_info
]
num_snvs = len(passed)
bottoms = [0] * num_snvs
freq_bottoms = [0] * num_snvs
lefts = range(num_snvs)
for idx, geno in enumerate(('AA', 'Aa', 'aa')):
cnts = [geno_info[geno] for geno_info in plot_info]
backcolors = [greens[idx] if pss else reds[idx] for pss in passed]
edgecolors = [grays[idx] for pss in passed]
backcolors[tested_snp_idx], edgecolors[tested_snp_idx] = \
edgecolors[tested_snp_idx], backcolors[tested_snp_idx]
heights = cnts
axes1.bar(lefts,
heights,
bottom=bottoms,
color=backcolors,
edgecolor=edgecolors)
freq_heights = [
height / total_cnt
for height, total_cnt in zip(heights, total_cnts)
]
axes2.bar(lefts,
freq_heights,
bottom=freq_bottoms,
color=backcolors,
edgecolor=edgecolors)
bottoms = [bot + heig for bot, heig in zip(bottoms, heights)]
freq_bottoms = [
bot + heig for bot, heig in zip(freq_bottoms, freq_heights)
]
axes1.tick_params(axis='y', which='both', left='off', right='off')
axes2.tick_params(axis='y', which='both', left='off', right='off')
canvas = FigureCanvas(fig)
canvas.print_figure(fhand)
fhand.flush()
def plot_haplotypes(vcf_fhand, plot_fhand, genotype_mode=REFERENCE,
filter_alleles_gt=FILTER_ALLELES_GT):
reader = VCFReader(vcf_fhand)
# collect data
genotypes = None
samples = []
for snv in reader.parse_snvs():
if genotypes is None:
genotypes = {}
for call in snv.calls:
sample = call.sample
genotypes[sample] = []
samples.append(sample)
for call in snv.calls:
alleles = _get_alleles(call, filter_alleles_gt=filter_alleles_gt)
genotypes[call.sample].append(alleles)
# draw
n_samples = len(samples)
xsize = len(genotypes[sample]) / 100
if xsize >= 100:
xsize = 100
if xsize <= 8:
xsize = 8
ysize = n_samples * 2
if ysize >= 100:
ysize = 100
# print xsize, ysize
figure_size = (xsize, ysize)
fig = Figure(figsize=figure_size)
for index, sample in enumerate(samples):
axes = fig.add_subplot(n_samples, 1, index)
axes.set_title(sample)
y_data = genotypes[sample]
x_data = [i + 1 for i in range(len(y_data))]
x_data, y_data = _flatten_data(x_data, y_data)
axes.plot(x_data, y_data, marker='o',
linestyle='None', markersize=3.0, markeredgewidth=0,
markerfacecolor='red')
ylim = axes.get_ylim()
ylim = ylim[0] - 0.1, ylim[1] + 0.1
axes.set_ylim(ylim)
axes.tick_params(axis='x', bottom='off', top='off', which='both',
labelbottom='off')
axes.tick_params(axis='y', left='on', right='off', labelleft='off')
axes.set_ylabel(sample)
canvas = FigureCanvas(fig)
canvas.print_figure(plot_fhand, dpi=300)
plot_fhand.flush()
def _plot_hist(self, fhand, values, xlabel, ylabel, min_value=None,
max_value=None):
fig = Figure()
axes = fig.add_subplot(111)
axes.hist(values, fill=True, log=True, bins=20, rwidth=1)
if min_value is not None:
axes.axvline(x=min_value)
if max_value is not None:
axes.axvline(x=max_value)
axes.set_xlabel(xlabel)
axes.set_ylabel(ylabel)
_print_figure(axes, fig, fhand, plot_legend=False)
def plot_smooth_hist(self, fhand):
bins = 20
fig = Figure()
axes2 = fig.add_subplot(111)
x = self._smoothes
y = self._recombs
image = axes2.hist2d(x, y, bins=bins, norm=LogNorm())[-1]
axes2.tick_params(
which='both', # both major and minor ticks are affected
bottom='off', # ticks along the bottom edge are off
top='off', # ticks along the top edge are off
labelbottom='off',
right='off',
left='off',
labelleft='off')
xmin2d, xmax2d = axes2.get_xlim()
ymin2d, ymax2d = axes2.get_ylim()
axes2.vlines(self.smooth_threhsold, ymin2d, ymax2d, color='r')
if self.recomb_threshold is not None:
axes2.hlines(self.recomb_threshold, xmin2d, xmax2d, color='r')
divider = make_axes_locatable(axes2)
cax = divider.append_axes("right", size="5%", pad=0.05)
fig.colorbar(image, cax=cax)
axes = divider.append_axes('bottom', size=2, pad=0.1, sharex=axes2)
#axes = fig.add_subplot(224)
#print axes2.get_position().bounds
axes.hist(x, fill=True, log=True, bins=bins, rwidth=1)
axes.set_xlabel('Smooth index')
axes.set_ylabel('Num. SNPs')
axes.set_xlim(xmin2d, xmax2d)
ymin, ymax = axes.get_ylim()
axes.vlines(self.smooth_threhsold, ymin, ymax, color='r')
axes = divider.append_axes('left', size=2, pad=0.1, sharey=axes2)
#axes = fig.add_subplot(221)
axes.hist(y,
orientation='horizontal',
fill=True,
log=True,
bins=bins,
rwidth=1)
axes.set_ylabel('Num. recombs.')
axes.set_xlabel('Num. SNPs')
_print_figure(axes, fig, fhand, plot_legend=False)
axes.set_ylim(ymin2d, ymax2d)
xmin, xmax = axes.get_xlim()
if self.recomb_threshold is not None:
axes.hlines(self.recomb_threshold, xmin, xmax, color='r')
def plot_recomb_at_0_dist_hist(self, fhand):
fig = Figure()
axes = fig.add_subplot(111)
data = [self.recomb_rates['ok'], self.recomb_rates['ok_conf_is_None'],
self.recomb_rates['not_ok']]
labels = ['OK', 'OK conf. is none', 'Not OK']
colors = [(0.3, 1, 0.3), (0.3, 1, 0.6), (1, 0.3, 0.3)]
some_data = [bool(dataset) for dataset in data]
labels = [label for draw, label in zip(some_data, labels) if draw]
colors = [color for draw, color in zip(some_data, colors) if draw]
data = [dataset for draw, dataset in zip(some_data, data) if draw]
axes.hist(data, stacked=True, fill=True, log=True, bins=20,
label=labels, rwidth=1, color=colors)
_print_figure(axes, fig, fhand)
def get_canvas_and_axes(figure_size=FIGURE_SIZE, left=0.1, right=0.9, top=0.9,
bottom=0.1, plot_type=111):
'It returns a matplotlib canvas and axes instance'
try:
fig = Figure(figsize=FIGURE_SIZE)
canvas = FigureCanvas(fig)
except NameError:
msg = 'Matplotlib module is required to draw graphical histograms'
raise OptionalRequirementError(msg)
axes = fig.add_subplot(plot_type)
fig.subplots_adjust(left=left, right=right, top=top, bottom=bottom)
return canvas, axes
def plot_smooth_hist(self, fhand):
bins = 20
fig = Figure()
axes2 = fig.add_subplot(111)
x = self._smoothes
y = self._recombs
image = axes2.hist2d(x, y, bins=bins,
norm=LogNorm())[-1]
axes2.tick_params(
which='both', # both major and minor ticks are affected
bottom='off', # ticks along the bottom edge are off
top='off', # ticks along the top edge are off
labelbottom='off',
right='off', left='off', labelleft='off')
xmin2d, xmax2d = axes2.get_xlim()
ymin2d, ymax2d = axes2.get_ylim()
axes2.vlines(self.smooth_threhsold, ymin2d, ymax2d, color='r')
if self.recomb_threshold is not None:
axes2.hlines(self.recomb_threshold, xmin2d, xmax2d, color='r')
divider = make_axes_locatable(axes2)
cax = divider.append_axes("right", size="5%", pad=0.05)
fig.colorbar(image, cax=cax)
axes = divider.append_axes('bottom', size=2, pad=0.1, sharex=axes2)
#axes = fig.add_subplot(224)
#print axes2.get_position().bounds
axes.hist(x, fill=True, log=True, bins=bins, rwidth=1)
axes.set_xlabel('Smooth index')
axes.set_ylabel('Num. SNPs')
axes.set_xlim(xmin2d, xmax2d)
ymin, ymax = axes.get_ylim()
axes.vlines(self.smooth_threhsold, ymin, ymax, color='r')
axes = divider.append_axes('left', size=2, pad=0.1, sharey=axes2)
#axes = fig.add_subplot(221)
axes.hist(y, orientation='horizontal', fill=True, log=True, bins=bins,
rwidth=1)
axes.set_ylabel('Num. recombs.')
axes.set_xlabel('Num. SNPs')
_print_figure(axes, fig, fhand, plot_legend=False)
axes.set_ylim(ymin2d, ymax2d)
xmin, xmax = axes.get_xlim()
if self.recomb_threshold is not None:
axes.hlines(self.recomb_threshold, xmin, xmax, color='r')
def _plot_hist(self,
fhand,
values,
xlabel,
ylabel,
min_value=None,
max_value=None):
fig = Figure()
axes = fig.add_subplot(111)
axes.hist(values, fill=True, log=True, bins=20, rwidth=1)
if min_value is not None:
axes.axvline(x=min_value)
if max_value is not None:
axes.axvline(x=max_value)
axes.set_xlabel(xlabel)
axes.set_ylabel(ylabel)
_print_figure(axes, fig, fhand, plot_legend=False)
def get_canvas_and_axes(figure_size=FIGURE_SIZE,
left=0.1,
right=0.9,
top=0.9,
bottom=0.1,
plot_type=111):
'It returns a matplotlib canvas and axes instance'
try:
fig = Figure(figsize=FIGURE_SIZE)
canvas = FigureCanvas(fig)
except NameError:
msg = 'Matplotlib module is required to draw graphical histograms'
raise OptionalRequirementError(msg)
axes = fig.add_subplot(plot_type)
fig.subplots_adjust(left=left, right=right, top=top, bottom=bottom)
return canvas, axes
def _plot_segregation_debug(plot_info, fhand):
greens = ['#2d6e12', '#76b75b', '#164900']
reds = ['#8f0007', '#fb1f2a', '#b90009']
grays = ['0.5', '0.25', '0.75']
fig = Figure(figsize=(10, 4))
axes1 = fig.add_subplot(211)
axes2 = fig.add_subplot(212)
plot_info.sort(key=itemgetter('pos'))
is_close_snp = [geno_info['close_snp'] for geno_info in plot_info]
tested_snp_idx = is_close_snp.index(False)
passed = [geno_info['result'] for geno_info in plot_info]
total_cnts = [geno_info['AA'] + geno_info['Aa'] + geno_info['aa'] for geno_info in plot_info]
num_snvs = len(passed)
bottoms = [0] * num_snvs
freq_bottoms = [0] * num_snvs
lefts = range(num_snvs)
for idx, geno in enumerate(('AA', 'Aa', 'aa')):
cnts = [geno_info[geno] for geno_info in plot_info]
backcolors = [greens[idx] if pss else reds[idx] for pss in passed]
edgecolors = [grays[idx] for pss in passed]
backcolors[tested_snp_idx], edgecolors[tested_snp_idx] = \
edgecolors[tested_snp_idx], backcolors[tested_snp_idx]
heights = cnts
axes1.bar(lefts, heights, bottom=bottoms, color=backcolors,
edgecolor=edgecolors)
freq_heights = [height/total_cnt for height, total_cnt in zip(heights, total_cnts)]
axes2.bar(lefts, freq_heights, bottom=freq_bottoms,
color=backcolors, edgecolor=edgecolors)
bottoms = [bot + heig for bot, heig in zip(bottoms, heights)]
freq_bottoms = [bot + heig for bot, heig in zip(freq_bottoms, freq_heights)]
axes1.tick_params(axis='y', which='both', left='off', right='off')
axes2.tick_params(axis='y', which='both', left='off', right='off')
canvas = FigureCanvas(fig)
canvas.print_figure(fhand)
fhand.flush()
def plot_recomb_at_0_dist_hist(self, fhand):
fig = Figure()
axes = fig.add_subplot(111)
data = [
self.recomb_rates['ok'], self.recomb_rates['ok_conf_is_None'],
self.recomb_rates['not_ok']
]
labels = ['OK', 'OK conf. is none', 'Not OK']
colors = [(0.3, 1, 0.3), (0.3, 1, 0.6), (1, 0.3, 0.3)]
some_data = [bool(dataset) for dataset in data]
labels = [label for draw, label in zip(some_data, labels) if draw]
colors = [color for draw, color in zip(some_data, colors) if draw]
data = [dataset for draw, dataset in zip(some_data, data) if draw]
axes.hist(data,
stacked=True,
fill=True,
log=True,
bins=20,
label=labels,
rwidth=1,
color=colors)
_print_figure(axes, fig, fhand)
def get_fig_and_canvas(num_rows=1, num_cols=1, figsize=None):
if figsize is None:
height = 5.0 * num_rows
width = 7.5 * num_cols
if height > 320.0:
height = 320.0
figsize = (width, height)
try:
fig = Figure(figsize=figsize)
canvas = FigureCanvas(fig)
except NameError:
msg = 'Matplotlib module is required to draw graphical histograms'
raise OptionalRequirementError(msg)
return fig, canvas
def plot_haplotypes(vcf_fhand,
plot_fhand,
genotype_mode=REFERENCE,
filter_alleles_gt=FILTER_ALLELES_GT):
reader = VCFReader(vcf_fhand)
# collect data
genotypes = None
samples = []
for snv in reader.parse_snvs():
if genotypes is None:
genotypes = {}
for call in snv.calls:
sample = call.sample
genotypes[sample] = []
samples.append(sample)
for call in snv.calls:
alleles = _get_alleles(call, filter_alleles_gt=filter_alleles_gt)
genotypes[call.sample].append(alleles)
# draw
n_samples = len(samples)
xsize = len(genotypes[sample]) / 100
if xsize >= 100:
xsize = 100
if xsize <= 8:
xsize = 8
ysize = n_samples * 2
if ysize >= 100:
ysize = 100
# print xsize, ysize
figure_size = (xsize, ysize)
fig = Figure(figsize=figure_size)
for index, sample in enumerate(samples):
axes = fig.add_subplot(n_samples, 1, index)
axes.set_title(sample)
y_data = genotypes[sample]
x_data = [i + 1 for i in range(len(y_data))]
x_data, y_data = _flatten_data(x_data, y_data)
axes.plot(x_data,
y_data,
marker='o',
linestyle='None',
markersize=3.0,
markeredgewidth=0,
markerfacecolor='red')
ylim = axes.get_ylim()
ylim = ylim[0] - 0.1, ylim[1] + 0.1
axes.set_ylim(ylim)
axes.tick_params(axis='x',
bottom='off',
top='off',
which='both',
labelbottom='off')
axes.tick_params(axis='y', left='on', right='off', labelleft='off')
axes.set_ylabel(sample)
canvas = FigureCanvas(fig)
canvas.print_figure(plot_fhand, dpi=300)
plot_fhand.flush()
def _calc_ajusted_recomb(dists, recombs, max_recomb, max_zero_dist_recomb,
alpha_recomb_0, plot_fhand=None):
# first rough interpolation
# we remove the physical distances with high recombination rates because
# they're not very informative. e.g. more than 40 cM will not discriminate
# between false recombination due to hidden segregation in the parents and
# true recombination
if plot_fhand:
fig = Figure()
axes = fig.add_subplot(111)
axes.set_axis_bgcolor((1, 0.6, 0.6))
axes.scatter(dists, recombs, c='r', label='For 1st fit')
else:
axes = None
fig = None
dists = array(dists)
recombs = array(recombs)
recomb_rate = 1e-7
popt, pcov = _fit_kosambi(dists, recombs, init_params=[recomb_rate, 0])
if popt is None:
_print_figure(axes, fig, plot_fhand)
return None, False, {'kosambi_fit_ok': False,
'reason_no_fit': '1st fit failed'}
est_dists = dists
est_recombs = _kosambi(est_dists, popt[0], popt[1])
if fig:
axes.plot(est_dists, est_recombs, label='1st fit', c='r')
# now we perform a second fit but only with those markers that are a
# distance that results in a recombination fraction lower than max_recomb
close_markers = est_recombs < max_recomb
close_recombs = recombs[close_markers]
close_dists = dists[close_markers]
if plot_fhand:
axes.scatter(close_dists, close_recombs, c='b', label='For 2nd fit')
if len(close_dists) < 1:
# This marker is so bad that their closest markers are at a large
# distance
_print_figure(axes, fig, plot_fhand)
return None, False, {'kosambi_fit_ok': False,
'reason_no_fit': 'no close region left'}
if len(close_dists) != len(dists):
# If we've removed any points we fit again
popt, pcov = _fit_kosambi(close_dists, close_recombs, init_params=popt)
if popt is None:
_print_figure(axes, fig, plot_fhand)
return None, False, {'kosambi_fit_ok': False,
'reason_no_fit': '2nd fit failed'}
est_close_recombs = _kosambi(close_dists, popt[0], popt[1])
residuals = close_recombs - est_close_recombs
if fig:
axes.plot(close_dists, est_close_recombs, c='b', label='2nd_fit')
# we exclude the markers with a residual outlier
quartile_25, quartile_75 = percentile(residuals, [25, 75])
iqr = quartile_75 - quartile_25
outlayer_thrld = [quartile_25 - iqr * 1.5, quartile_75 + iqr * 1.5]
ok_markers = [idx for idx, res in enumerate(residuals) if (not isnan(res) and (outlayer_thrld[0] < res < outlayer_thrld[1]))]
ok_recombs = close_recombs[ok_markers]
ok_dists = close_dists[ok_markers]
if fig:
axes.scatter(ok_dists, ok_recombs, c='g', label='For 3rd fit')
if len(ok_dists) != len(close_dists):
# If we've removed any points we fit again
popt, pcov = _fit_kosambi(ok_dists, ok_recombs, init_params=popt)
if popt is None:
_print_figure(axes, fig, plot_fhand)
return None, False, {'kosambi_fit_ok': False,
'reason_no_fit': '3rd fit failed'}
var_recomb_at_dist_0 = pcov[1, 1]
recomb_at_dist_0 = popt[1]
ok_color = (0.3, 1, 0.6)
if isinf(var_recomb_at_dist_0):
conf_interval = None
if abs(recomb_at_dist_0) < 0.01:
# recomb is 0 for all points and the variance is inf
snp_ok = True
else:
snp_ok = False
else:
if alpha_recomb_0 is None:
conf_interval = None
if abs(recomb_at_dist_0) <= max_zero_dist_recomb:
snp_ok = True
ok_color = (0.3, 1, 0.3)
else:
snp_ok = False
else:
num_data_points = len(ok_dists)
num_params = len(popt)
deg_of_freedom = max(0, num_data_points - num_params)
tval = t.ppf(1.0 - alpha_recomb_0 / 2., deg_of_freedom)
std_dev = var_recomb_at_dist_0 ** 0.5
conf_interval = (recomb_at_dist_0 - std_dev * tval,
recomb_at_dist_0 + std_dev * tval)
if abs(recomb_at_dist_0) <= max_zero_dist_recomb:
snp_ok = True
ok_color = (0.3, 1, 0.3)
elif conf_interval[0] < 0 < conf_interval[1]:
snp_ok = True
else:
snp_ok = False
if plot_fhand:
axes.vlines(0, conf_interval[0], conf_interval[1],
label='conf. interval')
if plot_fhand:
color = ok_color if snp_ok else (1, 0.3, 0.3)
axes.set_axis_bgcolor(color)
if popt is None:
_print_figure(axes, fig, plot_fhand)
return None, False, {'kosambi_fit_ok': False,
'reason_no_fit': '3rd fit failed'}
est2_recombs = _kosambi(ok_dists, popt[0], popt[1])
if fig:
axes.plot(ok_dists, est2_recombs, c='g', label='3rd_fit')
_print_figure(axes, fig, plot_fhand)
return recomb_at_dist_0, snp_ok, {'kosambi_fit_ok': True,
'conf_interval': conf_interval}
def _calc_ajusted_recomb(dists,
recombs,
max_recomb,
max_zero_dist_recomb,
alpha_recomb_0,
plot_fhand=None):
# first rough interpolation
# we remove the physical distances with high recombination rates because
# they're not very informative. e.g. more than 40 cM will not discriminate
# between false recombination due to hidden segregation in the parents and
# true recombination
if plot_fhand:
fig = Figure()
axes = fig.add_subplot(111)
axes.set_axis_bgcolor((1, 0.6, 0.6))
axes.scatter(dists, recombs, c='r', label='For 1st fit')
else:
axes = None
fig = None
dists = array(dists)
recombs = array(recombs)
recomb_rate = 1e-7
popt, pcov = _fit_kosambi(dists, recombs, init_params=[recomb_rate, 0])
if popt is None:
_print_figure(axes, fig, plot_fhand)
return None, False, {
'kosambi_fit_ok': False,
'reason_no_fit': '1st fit failed'
}
est_dists = dists
est_recombs = _kosambi(est_dists, popt[0], popt[1])
if fig:
axes.plot(est_dists, est_recombs, label='1st fit', c='r')
# now we perform a second fit but only with those markers that are a
# distance that results in a recombination fraction lower than max_recomb
close_markers = est_recombs < max_recomb
close_recombs = recombs[close_markers]
close_dists = dists[close_markers]
if plot_fhand:
axes.scatter(close_dists, close_recombs, c='b', label='For 2nd fit')
if len(close_dists) < 1:
# This marker is so bad that their closest markers are at a large
# distance
_print_figure(axes, fig, plot_fhand)
return None, False, {
'kosambi_fit_ok': False,
'reason_no_fit': 'no close region left'
}
if len(close_dists) != len(dists):
# If we've removed any points we fit again
popt, pcov = _fit_kosambi(close_dists, close_recombs, init_params=popt)
if popt is None:
_print_figure(axes, fig, plot_fhand)
return None, False, {
'kosambi_fit_ok': False,
'reason_no_fit': '2nd fit failed'
}
est_close_recombs = _kosambi(close_dists, popt[0], popt[1])
residuals = close_recombs - est_close_recombs
if fig:
axes.plot(close_dists, est_close_recombs, c='b', label='2nd_fit')
# we exclude the markers with a residual outlier
quartile_25, quartile_75 = percentile(residuals, [25, 75])
iqr = quartile_75 - quartile_25
outlayer_thrld = [quartile_25 - iqr * 1.5, quartile_75 + iqr * 1.5]
ok_markers = [
idx for idx, res in enumerate(residuals)
if (not isnan(res) and (outlayer_thrld[0] < res < outlayer_thrld[1]))
]
ok_recombs = close_recombs[ok_markers]
ok_dists = close_dists[ok_markers]
if fig:
axes.scatter(ok_dists, ok_recombs, c='g', label='For 3rd fit')
if len(ok_dists) != len(close_dists):
# If we've removed any points we fit again
popt, pcov = _fit_kosambi(ok_dists, ok_recombs, init_params=popt)
if popt is None:
_print_figure(axes, fig, plot_fhand)
return None, False, {
'kosambi_fit_ok': False,
'reason_no_fit': '3rd fit failed'
}
var_recomb_at_dist_0 = pcov[1, 1]
recomb_at_dist_0 = popt[1]
ok_color = (0.3, 1, 0.6)
if isinf(var_recomb_at_dist_0):
conf_interval = None
if abs(recomb_at_dist_0) < 0.01:
# recomb is 0 for all points and the variance is inf
snp_ok = True
else:
snp_ok = False
else:
if alpha_recomb_0 is None:
conf_interval = None
if abs(recomb_at_dist_0) <= max_zero_dist_recomb:
snp_ok = True
ok_color = (0.3, 1, 0.3)
else:
snp_ok = False
else:
num_data_points = len(ok_dists)
num_params = len(popt)
deg_of_freedom = max(0, num_data_points - num_params)
tval = t.ppf(1.0 - alpha_recomb_0 / 2., deg_of_freedom)
std_dev = var_recomb_at_dist_0**0.5
conf_interval = (recomb_at_dist_0 - std_dev * tval,
recomb_at_dist_0 + std_dev * tval)
if abs(recomb_at_dist_0) <= max_zero_dist_recomb:
snp_ok = True
ok_color = (0.3, 1, 0.3)
elif conf_interval[0] < 0 < conf_interval[1]:
snp_ok = True
else:
snp_ok = False
if plot_fhand:
axes.vlines(0,
conf_interval[0],
conf_interval[1],
label='conf. interval')
if plot_fhand:
color = ok_color if snp_ok else (1, 0.3, 0.3)
axes.set_axis_bgcolor(color)
if popt is None:
_print_figure(axes, fig, plot_fhand)
return None, False, {
'kosambi_fit_ok': False,
'reason_no_fit': '3rd fit failed'
}
est2_recombs = _kosambi(ok_dists, popt[0], popt[1])
if fig:
axes.plot(ok_dists, est2_recombs, c='g', label='3rd_fit')
_print_figure(axes, fig, plot_fhand)
return recomb_at_dist_0, snp_ok, {
'kosambi_fit_ok': True,
'conf_interval': conf_interval
}