def test_to_igraph() :
#Make sure the igraph output has correct same structure
T1 = SuchTree( gopher_tree )
T2 = SuchTree( lice_tree )
links = pd.read_csv( gl_links, index_col=0 )
SLT = SuchLinkedTrees( T1, T2, links )
g = SLT.to_igraph()
# igraph returns an unweighted adjacency matrix,
# so we'll convert SuchLinkedTrees weighted
# adjacency matrix to an unweighted form.
saj = numpy.ceil( SLT.adjacency() )
# For some reason, igraph invented its own Matrix
# class that doesn't implement a standard numpy
# interface. :-/
iaj = numpy.array( list( map( list, g.get_adjacency() ) ) )
# matrixes must be the same shape
assert saj.shape == iaj.shape
# all matrix elements must be equal
assert reduce( lambda a,b:a and b, (saj == iaj).flatten() )
def test_to_igraph():
#Make sure the igraph output has correct same structure
T1 = SuchTree(gopher_tree)
T2 = SuchTree(lice_tree)
links = pd.read_csv(gl_links, index_col=0)
SLT = SuchLinkedTrees(T1, T2, links)
g = SLT.to_igraph()
# igraph returns an unweighted adjacency matrix,
# so we'll convert SuchLinkedTrees weighted
# adjacency matrix to an unweighted form.
saj = numpy.ceil(SLT.adjacency())
# For some reason, igraph invented its own Matrix
# class that doesn't implement a standard numpy
# interface. :-/
iaj = numpy.array(list(map(list, g.get_adjacency())))
# matrixes must be the same shape
assert saj.shape == iaj.shape
# all matrix elements must be equal
assert reduce(lambda a, b: a and b, (saj == iaj).flatten())
def test_get_column_leafs_by_name_as_row_ids() :
T = SuchTree( test_tree )
links = pd.DataFrame( numpy.random.random_integers( 0, 3, size=(N,N)),
columns=list(T.leafs.keys()),
index=list(T.leafs.keys()) )
SLT = SuchLinkedTrees( T, T, links )
for colname in links.columns :
s = links.applymap(bool)[ colname ]
leafs1 = set( map( list(SLT.col_ids).index, map( lambda x : T.leafs[x], s[ s > 0 ].index ) ) )
leafs2 = set( SLT.get_column_leafs( colname, as_row_ids=True ) )
assert leafs1 == leafs2
def test_get_column_leafs():
T = SuchTree(test_tree)
links = pd.DataFrame(numpy.random.random_integers(0, 3, size=(N, N)),
columns=list(T.leafs.keys()),
index=list(T.leafs.keys()))
SLT = SuchLinkedTrees(T, T, links)
for n, colname in enumerate(links.columns):
s = links.applymap(bool)[colname]
leafs1 = set(map(lambda x: T.leafs[x], s[s > 0].index))
leafs2 = set(SLT.get_column_leafs(n))
assert leafs1 == leafs2
def test_get_column_leafs() :
T = SuchTree( test_tree )
links = pd.DataFrame( numpy.random.random_integers( 0, 3, size=(N,N)),
columns=list(T.leafs.keys()),
index=list(T.leafs.keys()) )
SLT = SuchLinkedTrees( T, T, links )
for n,colname in enumerate( links.columns ) :
s = links.applymap(bool)[ colname ]
leafs1 = set( map( lambda x : T.leafs[x], s[ s > 0 ].index ) )
leafs2 = set( SLT.get_column_leafs(n) )
assert leafs1 == leafs2
def test_get_column_links():
T = SuchTree(test_tree)
row_names = list(T.leafs.keys())
numpy.random.shuffle(row_names)
links = pd.DataFrame(numpy.random.random_integers(0, 3, size=(N, N)),
columns=list(T.leafs.keys()),
index=row_names)
SLT = SuchLinkedTrees(T, T, links)
for n, colname in enumerate(links.columns):
s = links.applymap(bool)[colname]
c = SLT.get_column_links(n)
for m, rowname in enumerate(SLT.row_names):
assert s[rowname] == c[m]
def test_get_column_links() :
T = SuchTree( test_tree )
row_names = list(T.leafs.keys())
numpy.random.shuffle(row_names)
links = pd.DataFrame( numpy.random.random_integers( 0, 3, size=(N,N)),
columns=list(T.leafs.keys()),
index=row_names )
SLT = SuchLinkedTrees( T, T, links )
for n,colname in enumerate( links.columns ) :
s = links.applymap(bool)[ colname ]
c = SLT.get_column_links(n)
for m,rowname in enumerate( SLT.row_names ) :
assert s[rowname] == c[m]
def test_get_column_leafs_by_name_as_row_ids():
T = SuchTree(test_tree)
links = pd.DataFrame(numpy.random.random_integers(0, 3, size=(N, N)),
columns=list(T.leafs.keys()),
index=list(T.leafs.keys()))
SLT = SuchLinkedTrees(T, T, links)
for colname in links.columns:
s = links.applymap(bool)[colname]
leafs1 = set(
map(
list(SLT.col_ids).index,
map(lambda x: T.leafs[x], s[s > 0].index)))
leafs2 = set(SLT.get_column_leafs(colname, as_row_ids=True))
assert leafs1 == leafs2
def test_init_both_trees_by_file():
T = SuchTree(test_tree)
links = pd.DataFrame(numpy.random.random_integers(0, 3, size=(N, N)),
columns=list(T.leafs.keys()),
index=list(T.leafs.keys()))
SLT = SuchLinkedTrees(test_tree, test_tree, links)
assert type(SLT) == SuchLinkedTrees
def test_link_identities():
with tempfile.NamedTemporaryFile() as f1:
f1.file.write(b'(A:1,(B:1,(C:1,D:1)E:1)F:1)G:1;')
f1.file.close()
T1 = SuchTree(f1.name)
with tempfile.NamedTemporaryFile() as f2:
f2.file.write(b'((a:1,b:1)e:1,(c:1,d:1)f:1)g:1;')
f2.file.close()
T2 = SuchTree(f2.name)
ll = (('A', 'a'), ('B', 'c'), ('B', 'd'), ('C', 'd'), ('D', 'd'))
links = pd.DataFrame(numpy.zeros((4, 4), dtype=int),
index=list(T1.leafs.keys()),
columns=list(T2.leafs.keys()))
for i, j in ll:
links.at[i, j] = 1
SLT = SuchLinkedTrees(T1, T2, links)
t1_sfeal = dict(zip(T1.leafs.values(), T1.leafs.keys()))
t2_sfeal = dict(zip(T2.leafs.values(), T2.leafs.keys()))
lll = set((t1_sfeal[j], t2_sfeal[i]) for i, j in SLT.linklist.tolist())
assert set(ll) == lll
def test_subset_b() :
T = SuchTree( test_tree )
row_names = list(T.leafs.keys())
numpy.random.shuffle(row_names)
links = pd.DataFrame( numpy.random.random_integers( 0, 3, size=(N,N)),
columns=list(T.leafs.keys()),
index=row_names )
SLT = SuchLinkedTrees( T, T, links )
sfeal = dict( zip( SLT.TreeB.leafs.values(), SLT.TreeB.leafs.keys() ) )
subset_links = links[ list(map( lambda x: sfeal[x], SLT.TreeB.get_leafs(1) )) ]
l = subset_links.unstack()
SLT.subset_b(1)
A = set(map( lambda x : (SLT.TreeB.leafs[x[0]], SLT.TreeA.leafs[x[1]]),
list( l[l>0].index ) ))
B = set(map( lambda x : (x[0], x[1]), SLT.linklist ) )
assert A == B
def test_subset_b():
T = SuchTree(test_tree)
row_names = list(T.leafs.keys())
numpy.random.shuffle(row_names)
links = pd.DataFrame(numpy.random.random_integers(0, 3, size=(N, N)),
columns=list(T.leafs.keys()),
index=row_names)
SLT = SuchLinkedTrees(T, T, links)
sfeal = dict(zip(SLT.TreeB.leafs.values(), SLT.TreeB.leafs.keys()))
subset_links = links[list(map(lambda x: sfeal[x], SLT.TreeB.get_leafs(1)))]
l = subset_links.unstack()
SLT.subset_b(1)
A = set(
map(lambda x: (SLT.TreeB.leafs[x[0]], SLT.TreeA.leafs[x[1]]),
list(l[l > 0].index)))
B = set(map(lambda x: (x[0], x[1]), SLT.linklist))
assert A == B
def test_row_names():
T = SuchTree(test_tree)
links = pd.DataFrame(numpy.random.random_integers(0, 3, size=(N, N)),
columns=list(T.leafs.keys()),
index=list(T.leafs.keys()))
SLT = SuchLinkedTrees(T, T, links)
assert SLT.row_names == list(T.leafs.keys())
def test_col_ids():
T = SuchTree(test_tree)
links = pd.DataFrame(numpy.random.random_integers(0, 3, size=(N, N)),
columns=list(T.leafs.keys()),
index=list(T.leafs.keys()))
SLT = SuchLinkedTrees(T, T, links)
col_ids = SLT.col_ids
leaf_ids = T.leafs.values()
assert len(col_ids) == len(leaf_ids)
for i, j in zip(col_ids, leaf_ids):
assert i == j
def test_linkmatrix_property():
T = SuchTree(test_tree)
row_names = list(T.leafs.keys())
numpy.random.shuffle(row_names)
links = pd.DataFrame(numpy.random.random_integers(0, 3, size=(N, N)),
columns=list(T.leafs.keys()),
index=row_names)
SLT = SuchLinkedTrees(T, T, links)
for col in SLT.col_names:
for row in SLT.row_names:
col_id = SLT.col_names.index(col)
row_id = SLT.row_names.index(row)
assert bool(links.T[row][col]) == SLT.linkmatrix[row_id][col_id]
def test_linklist_property():
T = SuchTree(test_tree)
row_names = list(T.leafs.keys())
numpy.random.shuffle(row_names)
links = pd.DataFrame(numpy.random.random_integers(0, 3, size=(N, N)),
columns=list(T.leafs.keys()),
index=row_names)
SLT = SuchLinkedTrees(T, T, links)
l = links.unstack()
A = set(
map(lambda x: (SLT.TreeB.leafs[x[0]], SLT.TreeA.leafs[x[1]]),
list(l[l > 0].index)))
B = set(map(lambda x: (x[0], x[1]), SLT.linklist))
assert A == B
def simtree(prefix,
birth_rate=0.3,
death_rate=0.1,
min_host_leafs=8,
max_host_leafs=64,
min_guest_leafs=4,
max_guest_leafs=128,
duplication_rate=0.2,
loss_rate=0.1,
switch_rate=0.05,
k=2.0,
theta=0.5):
'''
Time interval is always 1.0 units, and GuestTreeGen stops after 1000
attempts.
'''
max_guest_attempts = 1000
# make output directory
if not exists(prefix):
mkdir(prefix)
# build the host tree
E = subprocess.call(['java'] + java_ops + [
'-jar', 'jprime.jar', 'HostTreeGen', '-bi', '-min',
str(min_host_leafs), '-max',
str(max_host_leafs), '1.0',
str(birth_rate),
str(death_rate), prefix + '/' + 'host'
])
if not E == 0: raise JPrIMEError('HostTreeGen failed.')
E = subprocess.call(['java'] + java_ops + [
'-jar', 'jprime.jar', 'BranchRelaxer', '-o', prefix + '/' +
'host.relaxed.tree', prefix + '/' + 'host.pruned.tree', 'IIDGamma',
str(k),
str(theta)
])
if not E == 0: raise JPrIMEError('BranchRelaxer failed on host tree.')
# build the guest tree
E = subprocess.call(['java'] + java_ops + [
'-jar', 'jprime.jar', 'GuestTreeGen', '--max-attempts',
str(max_guest_attempts), '-min',
str(min_guest_leafs), '-max',
str(max_guest_leafs), prefix + '/' + 'host.pruned.tree',
str(duplication_rate),
str(loss_rate),
str(switch_rate), prefix + '/' + 'guest'
])
if not E == 0: raise JPrIMEError('GuestTreGen failed.')
E = subprocess.call(['java'] + java_ops + [
'-jar', 'jprime.jar', 'BranchRelaxer', '-o', prefix + '/' +
'guest.relaxed.tree', prefix + '/' + 'guest.pruned.tree', 'IIDGamma',
str(k),
str(theta)
])
if not E == 0: raise JPrIMEError('BranchRelaxer failed on guest tree.')
# load the trees
T1 = SuchTree(prefix + '/' + 'host.relaxed.tree')
T2 = SuchTree(prefix + '/' + 'guest.relaxed.tree')
# populate the link matrix using the leaf names
l = zeros((T1.n_leafs, T2.n_leafs), dtype=int)
hostnames = T1.leafs.keys()
guestnames = T2.leafs.keys()
for L in T2.leafs.keys():
guest, host = L.split('_')
#host = 'H' + host
i = hostnames.index(host)
j = guestnames.index(L)
l[i, j] = 1
links = pandas.DataFrame(l, index=hostnames, columns=guestnames)
links.to_csv(prefix + '/' + 'links.csv')
# initialize the SuchLinkedTrees object
SLT = SuchLinkedTrees(T1, T2, links)
# plot the adjacency matrix
aj = SLT.adjacency()
lp_plot = seaborn.heatmap(aj.T,
cmap='viridis',
vmin=0,
vmax=1,
cbar=False,
square=True,
xticklabels=False,
yticklabels=False)
lp_plot.invert_yaxis()
fig = lp_plot.get_figure()
fig.savefig(prefix + '/' + 'adjacency.png', size=6)
fig.clf()
# plot cophylogeny using R
r_code = '''
tr1 <- read.tree( "HOST_TREE" )
tr2 <- read.tree( "GUEST_TREE" )
links <- read.csv( "LINKS", row.names=1, stringsAsFactors = F )
im <- graph_from_incidence_matrix( as.matrix( links ) )
assoc <- as_edgelist( im )
obj <- cophylo( tr1, tr2, assoc=assoc )
pdf( "OUTFILE", width = 10, height = 12 )
plot( obj )
dev.off()
'''
r_code = r_code.replace('HOST_TREE', prefix + '/' + 'host.relaxed.tree')
r_code = r_code.replace('GUEST_TREE', prefix + '/' + 'guest.relaxed.tree')
r_code = r_code.replace('LINKS', prefix + '/' + 'links.csv')
r_code = r_code.replace('OUTFILE', prefix + '/' + 'cophylo.pdf')
robjects.r(r_code)
# calculate spectral densities
lambdas = SLT.spectrum()
a_lambd = eigvalsh(SLT.TreeA.laplacian()['laplacian'])
b_lambd = eigvalsh(SLT.TreeB.laplacian()['laplacian'])
with open(prefix + '/' + 'eigenvalues.csv', 'w') as f:
f.write('graph ' + ','.join(map(str, lambdas)) + '\n')
f.write('TreeA ' + ','.join(map(str, a_lambd)) + '\n')
f.write('TreeB ' + ','.join(map(str, b_lambd)) + '\n')
bandwidth = 0.4
X = linspace(-0.5, 1.5, 200)
density = gaussian_kde(lambdas / max(lambdas), bw_method=bandwidth).pdf(X)
a_dnsty = gaussian_kde(a_lambd / max(a_lambd), bw_method=bandwidth).pdf(X)
b_dnsty = gaussian_kde(b_lambd / max(b_lambd), bw_method=bandwidth).pdf(X)
with open(prefix + '/' + 'densities.txt', 'w') as f:
f.write('graph ' + ','.join(map(str, density)) + '\n')
f.write('TreeA ' + ','.join(map(str, a_dnsty)) + '\n')
f.write('TreeB ' + ','.join(map(str, b_dnsty)) + '\n')
# calculate Hommola correlation
d = SLT.linked_distances()
r, p = pearsonr(d['TreeA'], d['TreeB'])
with open(prefix + '/' + 'distances.txt', 'w') as f:
f.write('TreeA ' + ','.join(map(str, d['TreeA'])) + '\n')
f.write('TreeB ' + ','.join(map(str, d['TreeB'])) + '\n')
# save jointplot of patristic distances
jp = seaborn.jointplot(d['TreeA'], d['TreeB'], size=6)
jp.savefig(prefix + '/' + 'correlation.png')
jp.fig.clf()
# output moment data
moments = {}
moments['eigengap'] = lambdas[-1] - lambdas[-2]
moments['skew'] = skew(density)
moments['kurtosis'] = kurtosis(density)
moments['treedist'] = pdd(a_dnsty, b_dnsty)
moments['occupancy'] = ( 2.0 * SLT.n_links ) \
/ ( SLT.TreeA.n_leafs \
+ SLT.TreeB.n_leafs )
moments['squareness'] = float( SLT.TreeA.n_leafs ) \
/ SLT.TreeB.n_leafs
moments['r'] = r
moments['p'] = p
with open(prefix + '/' + 'moments.csv', 'w') as f:
f.write(','.join(moments.keys()) + '\n')
f.write(','.join(map(str, moments.values())))
# output simulation parameters
data = {}
data['prefix'] = prefix
data['host_leafs'] = T1.n_leafs
data['guest_leafs'] = T2.n_leafs
data['links'] = SLT.n_links
data['birth_rate'] = birth_rate
data['death_rate'] = death_rate
data['min_host_leafs'] = min_host_leafs
data['max_host_leafs'] = max_host_leafs
data['min_guest_leafs'] = min_guest_leafs
data['max_guest_leafs'] = max_guest_leafs
data['duplication_rate'] = duplication_rate
data['loss_rate'] = loss_rate
data['switch_rate'] = switch_rate
data['k'] = k
data['theta'] = theta
with open(prefix + '/' + 'data.csv', 'w') as f:
f.write(','.join(data.keys()) + '\n')
f.write(','.join(map(str, data.values())))
