def mutsummary(self):
"View selection coefficient distributions and mutation type counts"
#calculate number of neutral mutations
neutral = 0
for mut in self.mts.mutations():
if mut.derived_state == '1':
neutral += 1
#record fitness coefficients
coeffs = []
for mut in self.mts.mutations():
mut_list = mut.metadata["mutation_list"]
for i in mut_list:
coeffs.append(i["selection_coeff"])
self.coeffs = coeffs
#altair plot of mutation distrbutions
mean = sum(coeffs) / len(coeffs)
dist = np.histogram(coeffs, 50)
source = pd.DataFrame(
{
'counts': dist[0],
'values': dist[1][:-1],
'mean': mean
},
columns=['values', 'counts', 'mean'])
base = alt.Chart(source)
histo = base.mark_bar(
opacity=0.5, color=alt.ColorName("cornflowerblue")).encode(
alt.X('values:Q', axis=alt.Axis(title='Fitness Effect')),
alt.Y('counts:Q'),
color=alt.condition(
alt.datum.values > 0,
alt.value("lightseagreen"), # The positive color
alt.value("indianred") # The negative color
),
tooltip=[
alt.Tooltip('values', title='Fitness Effect'),
])
mean = base.mark_rule(color=alt.ColorName("goldenrod"),
opacity=0.4).encode(
x='mean:Q',
size=alt.value(3),
tooltip=[
alt.Tooltip('mean(values)',
title='Mean Fitness Effect'),
])
print("Note: this plot does not contain neutral mutations overlaid "
"with `msprime`.")
IPython.display.display_html(histo + mean)
print(f"Total mutations: {self.mts.num_mutations}\n"
f"Neutral mutations: {neutral}\n"
f"Non-neutral mutations: {self.mts.num_mutations - neutral}\n")
def zoomplot(self):
"interactive altair plot - but needs to be opened in vega editor"
chrom = Chromosome(self.genome)
chrom.altair()
ichrom = chrom.ichrom
brush = alt.selection_interval(encodings=['x'],
mark=alt.BrushConfig(fill='red',
fillOpacity=0.700))
fadedchrom = ichrom.mark_rect(opacity=0.4)
mut_pos = pd.DataFrame({'x': self.positions})
mut_positions = alt.Chart(mut_pos).mark_rule(
color=alt.ColorName("mediumblue")).encode(
x='x:Q',
size=alt.value(1),
tooltip=[
alt.Tooltip('x', title='Position'),
])
layered = alt.layer(fadedchrom, mut_positions)
zoomtest = alt.vconcat(
layered.encode(
alt.X('x1:Q', title=None,
scale=alt.Scale(domain=brush))).properties(height=80),
layered.add_selection(brush).properties(height=30))
print("Note: this plot must be opened in the Vega editor for "
"interactive features to work")
IPython.display.display_html(zoomtest)
def plotmuts(self, chromosome=None):
"""
Plots the mutations over the chromosome as interactive altair
plot - but needs to be opened in vega editor (??)
"""
if self.chromosome == None:
if chromosome is not None:
self.chromosome = chromosome
else:
logger.warning(
"PostSim object was not initialised with"
"a chromosome object - please provide one for plot"
"function.")
else:
self.chromosome.inspect()
ichrom = self.chromosome.ichrom
alt.data_transformers.disable_max_rows()
brush = alt.selection_interval(encodings=['x'],
mark=alt.BrushConfig(
fill='red', fillOpacity=0.700))
fadedchrom = ichrom.mark_rect(opacity=0.4)
if len(self.edge_ids) > 1:
mut_pos = pd.DataFrame({
'x': self.positions,
'population': str("pop" + self.popids)
})
mut_positions = alt.Chart(mut_pos).mark_rule().encode(
color=alt.Color('population',
scale=alt.Scale(
domain=self.edge_ids,
range=["mediumblue", "crimson"])),
x='x:Q',
size=alt.value(1),
tooltip=[
alt.Tooltip('x', title='Position'),
])
else:
mut_pos = pd.DataFrame({'x': self.positions})
self.mut_pos = mut_pos
mut_positions = alt.Chart(mut_pos).mark_rule(
color=alt.ColorName("mediumblue")).encode(
x='x:Q',
size=alt.value(1),
tooltip=[
alt.Tooltip('x', title='Position'),
])
layered = alt.layer(fadedchrom, mut_positions)
zoomtest = alt.vconcat(
layered.encode(
alt.X(
'x1:Q', title=None,
scale=alt.Scale(domain=brush))).properties(height=80),
layered.add_selection(brush).properties(height=30))
print("Note: this plot must be opened in the Vega editor for "
"interactive features to work")
IPython.display.display_html(zoomtest)
self.plot = zoomtest
def summary(self):
"View selection coefficient distributions and mutation type counts"
#calculate number of neutral mutations
neutral = 0
for mut in self.tscoal.mutations():
if mut.derived_state == '1':
neutral += 1
#record fitness coefficients
coeffs = []
for mut in self.tscoal.mutations():
mut_list = mut.metadata["mutation_list"]
for i in mut_list:
coeffs.append(i["selection_coeff"])
#altair plot of mutation distrbutions
mean = sum(coeffs) / len(coeffs)
dist = np.histogram(coeffs, 50)
source = pd.DataFrame(
{
'counts': dist[0],
'values': dist[1][:-1],
'mean': mean
},
columns=['values', 'counts', 'mean'])
base = alt.Chart(source)
histo = base.mark_bar(
opacity=0.5, color=alt.ColorName("cornflowerblue")).encode(
alt.X('values:Q', axis=alt.Axis(title='Fitness Effect')),
alt.Y('counts:Q'),
color=alt.condition(
alt.datum.values > 0,
alt.value("lightseagreen"), # The positive color
alt.value("indianred") # The negative color
),
tooltip=[
alt.Tooltip('values', title='Fitness Effect'),
])
mean = base.mark_rule(color=alt.ColorName("goldenrod"),
opacity=0.4).encode(
x='mean:Q',
size=alt.value(3),
tooltip=[
alt.Tooltip('mean(values)',
title='Mean Fitness Effect'),
])
print("Note: this plot does not contain neutral mutations overlaid "
"with `msprime`.")
IPython.display.display_html(histo + mean)
print(
f"Total mutations: {self.tscoal.num_mutations}\n"
f"Neutral mutations: {neutral}\n"
f"Non-neutral mutations: {self.tscoal.num_mutations - neutral}\n")
#static toyplot
print("Mutation positions along chromosome:")
chrom = Chromosome(genome=self.genome)
chrom.toyplot()
self.rectangles = chrom.rectangles
canvas = toyplot.Canvas(width=2500, height=200)
axes = canvas.cartesian()
axes.show = False
#draw the rectangles
for index, row in self.rectangles.iterrows():
axes.rectangle(
row['x1'],
row['x2'],
row['y1'],
row['y2'],
color=row['color'],
style={"opacity": 0.6},
)
#draw the positions
lines = axes.vlines(self.positions,
style={
"stroke": "blue",
"stroke-width": 2
})