# alpha = 0.5,
# edgecolors='none',
# facecolors='black')
for k, indiv in enumerate(samples):
admix = admixture[k, :]
admix /= sum(admix)
draw_pie(ax, locs[indiv, 0], locs[indiv, 1], admix)
return fig
num_gens = 301
treefile = sps.run_slim(script=script,
seed=23,
SIGMA=4.0,
W=50.0,
K=5.0,
NUMGENS=num_gens,
BURNIN=1)
outbase = ".".join(treefile.split(".")[:-1])
num_samples = 200
num_times = 5
# num_gens = 901
# treefile = sps.run_slim(script = script,
# seed = 23,
# SIGMA = 3.0,
# W = 50.0,
# K = 5.0,
# NUMGENS = num_gens,
# BURNIN=1)
x = np.concatenate([x, dists[:, k]])
y = np.concatenate([y, div[:, k]])
po = np.random.choice(len(x), len(x), replace=False)
ax.scatter(x[po], y[po], s=10, alpha=0.5, marker=".", c=colors[po])
ax.set_xlabel("geographic distance")
ax.set_ylabel("genetic distance")
return fig
# for script in ("valleys.slim", "flat_map.slim"):
## pass in script on command-line
num_gens = 1
treefile = sps.run_slim(script=script,
seed=23,
SIGMA=0.4,
W=50.0,
NUMGENS=num_gens,
BURNIN=10000)
outbase = ".".join(treefile.split(".")[:-1])
recapfile = outbase + ".recap.trees"
if os.path.isfile(recapfile):
ts = sps.SpatialSlimTreeSequence(pyslim.load(recapfile), dim=2)
else:
ts = pyslim.load(treefile)
ts = sps.SpatialSlimTreeSequence(ts.recapitate(Ne=1e3,
recombination_rate=1e-9),
dim=2)
ts.dump(recapfile)
num_targets = 4
locs[ips[pair_indices[po], 1], 1] -
locs[ips[pair_indices[po], 0], 1], # dY
color=colors[po],
alpha=0.4,
width=0.15,
units='xy',
scale=1)
return fig
# for script in ("valleys.slim", "flat_map.slim"):
## pass in script on command-line
treefile = sps.run_slim(script=script,
seed=23,
SIGMA=1.5,
W=50.0,
K=5.0,
NUMGENS=30)
outbase = ".".join(treefile.split(".")[:-1])
ts = sps.SpatialSlimTreeSequence(pyslim.load(treefile), dim=2)
# number of time steps to plot the flux for
plot_ngens = 10
xmax = max([ind.location[0] for ind in ts.individuals()])
ymax = max([ind.location[1] for ind in ts.individuals()])
circle_xy = (xmax / 2, ymax / 2)
circle_rad = xmax / 3
def update(frame):
inds = ts.individuals_by_time(frame)
circles.set_offsets(locs[inds, :])
# color based on age so far
circles.set_color(colormap(ts.individuals_age(frame)[inds]))
circles.set_sizes(size_fun(inds)),
return circles
animation = ani.FuncAnimation(fig,
update,
frames=np.linspace(0, num_gens - 1,
num_gens))
return animation
for script in ("flat_map.slim", "valleys.slim"):
num_gens = 300
treefile = sps.run_slim(script=script,
seed=23,
SIGMA=0.4,
W=8.0,
NUMGENS=num_gens)
outbase = ".".join(treefile.split(".")[:-1])
ts = sps.SpatialSlimTreeSequence(pyslim.load(treefile), dim=2)
today = np.where(ts.individual_times == 0)[0]
animation = animate_ancestry(ts, np.random.choice(today, 1), num_gens)
animation.save(outbase + ".ancestry.mp4", writer='ffmpeg')
}
paths = []
pathcolors = []
for u, p in path_dict:
if periodic:
this_paths = break_path(path_dict[(u, p)], W)
else:
this_paths = [path_dict[(u, p)]]
for this_path in this_paths:
paths.append(this_path)
pathcolors.append(treecolors[p])
lc = cs.LineCollection(paths, linewidths=0.5, colors=pathcolors)
ax.add_collection(lc)
treefile = sps.run_slim(script=script, **kwargs)
patchfile = treefile + ".landscape"
outbase = ".".join(treefile.split(".")[:-1])
ts = pyslim.load(treefile)
patchdata = np.loadtxt(patchfile)
patchtimes = patchdata[:, 0]
patchvals = patchdata[:, 1:]
patches = sps.patch_polygons(patchtimes, patchvals, ts.slim_generation)
W = patchdata.shape[1] - 1
today = ts.individuals_alive_at(0)
has_parents = ts.has_individual_parents()
max_time = np.max(ts.individual_times[has_parents])
if len(today) < num_indivs:
raise ValueError(f"Not enough individuals: only {len(today)} alive today!")
import pyslim, msprime
import numpy as np
import spatial_slim as sps
import os, sys
# we have the full pedigree for this long
num_gens = 200
treefile = sps.run_slim(script="flat_map.slim",
seed=23,
SIGMA=1.0,
W=50.0,
K=5.0,
NUMGENS=num_gens,
BURNIN=1)
outbase = ".".join(treefile.split(".")[:-1])
# treefile = "valleys/run_1.0_50.0_5.0_100_23.trees"
ts = sps.SpatialSlimTreeSequence(pyslim.load(treefile), dim=2)
###
# Find effective generation time:
# for each time t in the past,
# take the average over all individuals alive at time t
# of the average of their two parents' age at the time of their birth
# where individuals are chosen proportionally to their genetic contribution to the modern generation
# So, as t increases, this should converge to the effective generation time.
#
# Said another way:
# 1) pick a bit of today's genome
# 2) trace back to the individual, x, from whom it was inherited t time units in the past
# 3) pick a random one of x's two parents, whom we call y.
