def non_iter_ls_inv_stft(stft_object):
stft_data = stft_object['stft']
origSigSize = stft_object['origSigSize']
num_rows, _, _ = origSigSize
shift_length = stft_object['shift_length']
len_each_section, num_rows_overlap, _, _ = stft_data.shape
# TODO: Isn't this just num_rows in the very beginning?
# total_new_elements = (num_rows_overlap - 1) * shift_length + len_each_section
win_info = stft_object['win_info']
wVec = win_info(len_each_section)
wVecSq = wVec**2
vecC = np_arange(1, num_rows_overlap * shift_length, step=shift_length)
# vecC = range(0, num_rows_overlap*shift_length-1, shift_length)
DlsArr = np_zeros((num_rows, ))
for j in vecC:
tmpArr = np_arange(j - 1, len_each_section + j - 1)
# tmpArr = np_arange(j, len_each_section+j)
DlsArr[tmpArr] += wVecSq
# DlsArrInv = 1/DlsArr
invFT = math_sqrt(len_each_section) * np_ifft(stft_data, axis=0)
invFT_real = invFT.real
invFT *= wVec[:, np_newaxis, np_newaxis, np_newaxis]
yEst = np_zeros(origSigSize)
for index, j in enumerate(vecC):
tmpArr = np_arange(j - 1, len_each_section + j - 1)
yEst[tmpArr, :] += invFT_real[:, index, :]
# sigOut = yEst * DlsArrInv[:, np_newaxis, np_newaxis]
sigOut = yEst / DlsArr[:, np_newaxis, np_newaxis]
return sigOut
def _modeCheck(Ser1):
c = np_bincount(Ser1)
# 返回众数
i = np_argmax(c)
rule = (i - 2 > Ser1) | (i + 2 < Ser1)
index = np_arange(Ser1.shape[0])[rule]
return index
def parse_matrix_part(matrix, szSub, ovSub):
assert matrix.ndim == 3
assert np_ndim(szSub) == 1
assert len(szSub) == 3
assert np_ndim(ovSub) == 1
assert len(ovSub) == 3
matrix_shape = np_asarray(matrix.shape, dtype=int)
len_each_section, _, _ = szSub
shift_length, _, _ = ovSub
len_each_section_range = np_arange(len_each_section)
matrix_shape = np_ceil((matrix_shape - szSub + 1)/ovSub).astype(int)
num_rows_overlap, num_elements, num_beams = matrix_shape
result_matrix = np_zeros((np_prod(szSub), np_prod(matrix_shape)))
cnt = 0
for i in range(num_beams):
for j in range(num_elements):
for k in range(num_rows_overlap):
index_1 = len_each_section_range + k * shift_length
index_2 = j
index_3 = i
tmp = matrix[index_1, index_2, index_3]
result_matrix[:, cnt] = tmp
cnt += 1
return result_matrix
def _two_sigma(Ser1):
'''
Ser1：表示传入DataFrame的某一列。
'''
rule = (Ser1.mean() - 2 * Ser1.std() >
Ser1) | (Ser1.mean() + 2 * Ser1.std() < Ser1)
index = np_arange(Ser1.shape[0])[rule]
return index
def invert_index_list(indexes, length):
'''
Inverts indexes list
indexes: List[Int] of Ndarray flat numpy array
length: Int. Length of the base list
'''
mask = np_ones(length, dtype='bool')
mask[indexes] = False
inverted_indexes = np_arange(length)[mask]
return inverted_indexes
def fan_regular_pols(np_verts,
np_pols,
np_distances,
np_faces_id,
custom_normals,
index_offset=0,
use_custom_normals=False,
output_old_v_id=True,
output_old_face_id=True,
output_pols_groups=True):
pols_number = np_pols.shape[0]
pol_sides = np_pols.shape[1]
v_pols = np_verts[np_pols] #shape [num_pols, num_corners, 3]
if (len(np_distances) > 1
and np.any(np_distances != 0)) or np_distances != 0:
if use_custom_normals:
normals = custom_normals
else:
normals = np_faces_normals(v_pols)
average = np.sum(
v_pols, axis=1
) / pol_sides + normals * np_distances[:,
np_newaxis] #shape [num_pols, 3]
else:
average = np.sum(v_pols, axis=1) / pol_sides
idx_offset = len(np_verts) + index_offset
new_idx = np_arange(idx_offset, pols_number + idx_offset)
new_pols = np.zeros([pols_number, pol_sides, 3], dtype=int)
new_pols[:, :, 0] = np_pols
new_pols[:, :, 1] = np_roll(np_pols, -1, axis=1)
new_pols[:, :, 2] = new_idx[:, np_newaxis]
old_vert_id = np_pols[:, 0].tolist() if output_old_v_id else []
if output_old_face_id:
old_face_id = np_repeat(np_faces_id[:, np_newaxis], pol_sides,
axis=1).tolist()
else:
old_face_id = []
if output_pols_groups:
pols_groups = np_repeat(1, len(new_pols) * pol_sides).tolist()
else:
pols_groups = []
return (
average.tolist(),
new_pols.reshape(-1, 3).tolist(),
old_vert_id,
old_face_id,
pols_groups,
)
def getBreakpointsByCardinality(self, cardinality):
if cardinality not in self.breakpointsByCardinality:
frac = 1.0 / cardinality
list_percent = []
for i_fl in np_arange(frac, 1.0, frac):
list_percent.append(i_fl)
self.breakpointsByCardinality[cardinality] = (
np_array(norm.ppf(list_percent)) * self.std + self.mean)
return self.breakpointsByCardinality[cardinality]
def stft(signal, len_each_section, frac_overlap, padding, win_info=boxcar):
shift_length = round(len_each_section *
(1. - frac_overlap)) # shift_length = 2
_, num_elements, num_beams = signal.shape
zeroCrct = 0
wVec_rectwin = win_info(len_each_section + zeroCrct)
wVec = wVec_rectwin[zeroCrct // 2:len(wVec_rectwin) - zeroCrct // 2]
allOvrlp = parse_matrix_part(signal, [len_each_section, 1, 1],
[shift_length, 1, 1])
num_rows_overlap = allOvrlp.shape[1] // (num_elements * num_beams)
newShape = [len_each_section, num_rows_overlap, num_elements, num_beams]
subOvrlp = allOvrlp.reshape(newShape,
order="F") # Matlab defaults to Fortran
startLocs = np_arange(num_rows_overlap * shift_length, step=shift_length)
winOvrlp = subOvrlp * wVec[:, np_newaxis, np_newaxis, np_newaxis]
stft_array = np_fft(winOvrlp, padding, axis=0)
freq = np_arange(padding) / padding
out = {
'stft': stft_array,
'freqs': freq,
'startOffsets': startLocs,
'len_each_section': len_each_section,
'padding': padding,
'win_info': win_info,
'frac_overlap': frac_overlap,
'shift_length': shift_length,
}
return out
def naive_search_with_np(n):
prime_numbers = np_array([2])
i = prime_numbers[0]
while len(prime_numbers) != n:
i += 1
for d in np_arange(2, i):
i_is_prime = True
if i%d == 0:
i_is_prime = False
break
if i_is_prime:
prime_numbers = np_append(prime_numbers, i)
print('Байт:', getsizeof(prime_numbers))
def centers(c, side, edges, catan):
"""
Input: (center,length of the inner side, number of edges, type of CATAN)
Output: numpy array of "many" points
"""
alpha = 2.0 * pi / edges
if catan == "CATAN_ext":
i_y = int(1 * small_side / (3 * sqrt(3) - 1.0) + 0.5) * 1.3
i_x = int(i_y * cos(alpha) + 0.5) * 2.3
#i_y = int(1*small_side/(3*sqrt(3)-1.0)+0.5)*1.4
#i_x = int(i_y*cos(alpha)+0.5)*2.4
many, j, j_x = 30, 7, 3
else:
i_y = int(1.5 * small_side / (3 * sqrt(3) - 1.0) + 0.5) * 1.05
i_x = int(i_y * cos(alpha) + 0.5) * 2.3
many, j, j_x = 19, 5, 2
centers = np_zeros((many, 2))
raw_centers = np_zeros((many, 3))
k = 0
for j_y in np_arange(j) - j_x:
if j == 5:
j += 1
for j_j in np_arange(j - abs(j_y) - 1):
g = j - abs(j_y) - 1
f = 0.5 * (g % 2 == 0)
j_k = j_j - g / 2 + f
#print j-abs(j_y)-1,(j_j,j_y),j_k
centers[k, :] = [
int(c[0] + i_x * j_k + 0.5),
int(c[1] + i_y * j_y + 0.5)
]
raw_centers[k, :] = [j - abs(j_y) - 1, j_j, j_y]
k += 1
#print centers
return centers, raw_centers
def process_impropers(atom,
dihe_list,
func="imp",
verbose=False,
forzmatrix=False,
dihed_count=None):
alist = [
atom,
atom.get_atom_list()[0],
atom.get_atom_list()[1],
atom.get_atom_list()[2]
]
looked_up_param, centeratom_name, dihed_identifier = return_params(
func, alist)
if looked_up_param is None: # dihed was not specified
pass
else: # was specified. Now, check whether it was given double
params = looked_up_param.split(",")
if len(params) == 1:
d = Dihedral(*alist,
func=func,
param=params[0],
was_input=False,
is_multiparam=False,
has_siblings=False,
dihed_idx=dihed_count)
dihed_count += 1
dihe_list.append(d)
elif len(params) > 1:
siblings = np_arange(start=dihed_count,
stop=dihed_count + len(params))
if verbose:
print_verbose_dihedral_message(func, alist, params)
for param in params:
d = Dihedral(
*alist,
func=func,
param=param,
was_input=True,
is_multiparam=True,
around_center_atom=atom.get_atomtype() == centeratom_name,
siblings=siblings,
dihed_idx=dihed_count)
dihed_count += 1
dihe_list.append(d)
if not forzmatrix:
pass
return dihe_list, dihed_count
def Skew(x,y,dat,noise=3):
interp=sp_interp2d(x,y,dat)
dx=x[1]-x[0]
ySkew=np_arange(np_amin(y)-dx*x.size,np_amax(y),dx)
DAT=np_empty((ySkew.size,x.size))
yMax=np_amax(y); yMin=np_amin(y)
for i in range(ySkew.size):
for j in range(x.size):
if ySkew[i]+j*dx > yMax or ySkew[i]+j*dx < yMin:
DAT[i,j]=(np_rand(1)-0.5)*noise
else:
DAT[i,j]=interp(x[j],ySkew[i]+j*dx)
return ySkew,DAT
def plot(self, unchanged, passive, active, xticklabels, ylabel):
"""Create stacked bar plot."""
self.fig.clear()
self.fig.set_size_inches(self.options.width, self.options.height)
axis = self.fig.add_subplot(111)
ind = np_arange(len(unchanged))
width = 0.7
unchanged = np_array(unchanged)
passive = np_array(passive)
active = np_array(active)
p1 = axis.bar(ind, unchanged, width, color='#80b1d3')
p2 = axis.bar(ind, passive, width, bottom=unchanged, color='#fdae6b')
p3 = axis.bar(ind,
active,
width,
bottom=unchanged + passive,
color='#b3de69')
axis.set_ylim([0, 100])
axis.set_yticks(range(0, 101, 10))
axis.set_ylabel(ylabel)
axis.set_xticks(ind)
axis.set_xticklabels(xticklabels)
axis.yaxis.grid(True,
linestyle='-',
which='major',
color='lightgrey',
alpha=0.7,
zorder=1)
axis.set_axisbelow(True)
self.prettify(axis)
axis.legend((p3[0], p2[0], p1[0]),
('Active change', 'Passive change', 'Unchanged'),
fontsize=self.options.tick_font_size,
loc='upper left',
bbox_to_anchor=(1, 1),
frameon=False)
#self.fig.tight_layout(pad=1.0, w_pad=0.1, h_pad=0.1)
self.draw()
def __init_matches(self):
for match_type, var in [['qm', 'qualification_matches'], ['qf', 'quarter_final_matches'],
['sf', 'semi_final_matches'], ['f', 'final_matches']]:
num_matches = self.__count_matches(self.raw_matches, match_type)
if num_matches is not 0:
# zero = range(num_matches)
red_teams = np_zeros((num_matches,), np_object)
blue_teams = np_zeros((num_matches,), np_object)
blue_scores = np_zeros((num_matches,), np_object)
red_scores = np_zeros((num_matches,), np_object)
match_code = np_zeros((num_matches,), np_object)
match_numbers = np_arange(1, num_matches + 1, 1)
for match in self.raw_matches:
if match['comp_level'] == match_type:
match_num = match['match_number'] - 1
red_teams[match_num] = [np_int(match['alliances']['red']['teams'][0][3:]),
np_int(match['alliances']['red']['teams'][1][3:]),
np_int(match['alliances']['red']['teams'][2][3:])]
red_scores[match_num] = [-1 if match['alliances']['red']['score'] is None
else match['alliances']['red']['score'],
-1 if match['score_breakdown']['red']['auto'] is None
else match['score_breakdown']['red']['auto'],
-1 if match['score_breakdown']['red']['foul'] is None
else match['score_breakdown']['red']['foul']]
blue_teams[match_num] = [np_int(match['alliances']['blue']['teams'][0][3:]),
np_int(match['alliances']['blue']['teams'][1][3:]),
np_int(match['alliances']['blue']['teams'][2][3:])]
blue_scores[match_num] = [-1 if match['alliances']['blue']['score'] is None
else match['alliances']['blue']['score'],
-1 if match['score_breakdown']['blue']['auto'] is None
else match['score_breakdown']['blue']['auto'],
-1 if match['score_breakdown']['blue']['foul'] is None
else match['score_breakdown']['blue']['foul']]
match_code[match_num] = match['key']
red_win = np_array(red_scores.tolist())[:, 0] > np_array(blue_scores.tolist())[:, 0]
winner = np_array(['blue'] * len(red_win))
winner[red_win] = 'red'
self.__setattr__(var,
np_rot90(np_array([[match_type] * num_matches, match_numbers, red_teams, blue_teams,
red_scores, blue_scores, winner, match_code], np_object))[::-1])
def plot(self, both, isolate, env, xticklabels):
"""Create stacked bar plot."""
self.fig.clear()
self.fig.set_size_inches(self.options.width, self.options.height)
axis = self.fig.add_subplot(111)
ind = np_arange(len(both))
width = 0.7
both = np_array(both)
isolate = np_array(isolate)
env = np_array(env)
p1 = axis.bar(ind, both, width, color='#80b1d3')
p2 = axis.bar(ind, isolate, width, bottom=both, color='#fdae6b')
p3 = axis.bar(ind, env, width, bottom=both+isolate, color='#b3de69')
axis.set_ylim([0, 100])
axis.set_yticks(range(0, 101, 10))
axis.set_ylabel('Taxa (%)')
axis.set_xticks(ind)
axis.set_xticklabels(xticklabels)
axis.yaxis.grid(True,
linestyle='-',
which='major',
color='lightgrey',
alpha=0.7,
zorder=1)
axis.set_axisbelow(True)
self.prettify(axis)
axis.legend((p3[0], p2[0], p1[0]), ('Exclusively MAGs and/or SAGs',
'Exclusively isolates',
'Isolate and environmental genomes'),
fontsize=self.options.tick_font_size,
loc='upper left',
bbox_to_anchor=(1, 1),
frameon=False)
#self.fig.tight_layout(pad=1.0, w_pad=0.1, h_pad=0.1)
self.draw()
def plot(self, plot_latinized, plot_placeholder, xticklabels):
"""Create stacked bar plot."""
self.fig.clear()
self.fig.set_size_inches(self.options.width, self.options.height)
axis = self.fig.add_subplot(111)
ind = np_arange(len(plot_latinized))
width = 0.7
plot_latinized = np_array(plot_latinized)
plot_placeholder = np_array(plot_placeholder)
p1 = axis.bar(ind, plot_latinized, width, color='#80b1d3')
p2 = axis.bar(ind,
plot_placeholder,
width,
bottom=plot_latinized,
color='#fdae6b')
axis.set_ylim([0, 100])
axis.set_yticks(range(0, 101, 10))
axis.set_ylabel('Taxa (%)')
axis.set_xticks(ind)
axis.set_xticklabels(xticklabels)
axis.yaxis.grid(True,
linestyle='-',
which='major',
color='lightgrey',
alpha=0.7,
zorder=1)
axis.set_axisbelow(True)
self.prettify(axis)
axis.legend((p2[0], p1[0]), ('Placeholder', 'Latinized'),
fontsize=self.options.tick_font_size,
loc='upper left',
bbox_to_anchor=(1, 1),
frameon=False)
#self.fig.tight_layout(pad=1.0, w_pad=0.1, h_pad=0.1)
self.draw()
def split_examples(X, y, percent_valid=PERCENT_VALID, percent_test=PERCENT_TEST):
'''
# Split by target after selecting a single frequency. So X.shape is probably
[24, 2377, 65, 2]
'''
# TODO: prove that the split indices for X match those for y.
# ## Train, Valid, Test split
try:
assert X.ndim == 4
except:
raise ValueError('X.ndim should be 4, but X.shape is {}'.format(X.shape))
num_examples_all = len(X)
assert len(y) == num_examples_all
nums_splits = [int((1-percent_valid-percent_test)*num_examples_all), \
int((1-percent_test)*num_examples_all)]
indices_original = np_arange(num_examples_all, dtype=int)
indices_shuffled = np_random_permutation(indices_original)
indices_train, indices_valid, indices_test = np_split(indices_shuffled, nums_splits) # pylint: disable=W0632
X_train, X_valid, X_test = X[indices_train, :, :], X[indices_valid, :, :], X[indices_test, :, :]
y_train, y_valid, y_test = y[indices_train, :, :], y[indices_valid, :, :], y[indices_test, :, :]
return (X_train, X_valid, X_test), (y_train, y_valid, y_test)
def setUpClass(cls):
super(TestTsManagement, cls).setUpClass()
# ts_one=
# [[ 0 1]
# [ 2 3]
# [ 4 5]
# ...
# [92 93]
# [94 95]
# [96 97]
# [98 99]]
cls.ts_one = np_arange(100).reshape(50, 2)
# ts_two=
# [[198 199]
# [196 197]
# [194 195]
# [192 193]
# ...
# [104 105]
# [102 103]
# [100 101]]
#
cls.ts_two = cls.ts_one[50::-1] + 100
# reverse sort of ts_two:
#
# ts_inverse=
# [[100 101]
# [102 103]
# [104 105]
# ...
# [194 195]
# [196 197]
# [198 199]]
#
cls.ts_inverse = cls.ts_two[::-1]
def deConvolve(self,G_w,noise_dT=3,noise_avg=3,fMax=2.4):
self.reGrid(noise_dT=noise_dT,noise_avg=noise_avg)
self.tPumpDeconv=np_arange(np_amin(self.tPump),np_amax(self.tPump),
self.tTHz[1]-self.tTHz[0])
loc=np_amin(np_where(self.f >= fMax))
for i in range(self.tPumpSkew.size):
self.dTSkewFFT[i,:loc]=self.dTSkewFFT[i,:loc]/G_w[:loc]
self.avgSkewFFT[i,:loc]=self.avgSkewFFT[i,:loc]/G_w[:loc]
self.dTskew=np_irfft(self.dTSkewFFT,axis=1)
self.avgSkewFFT=np_irfft(self.avgSkewFFT,axis=1)
self.dTdeconv=unSkew(self.tTHz,self.tPump,self.tPumpSkew,self.dTskew)
self.avgDeconv=unSkew(self.tTHz,self.tPump
,self.tPumpSkew,self.avgSkewFFT)
self.refDeconv=self.avgDeconv-self.dTdeconv
self.pumpDeconv=self.avgDeconv+self.dTdeconv
self.refFFTdeconv=np_rfft(self.refDeconv,axis=1)
self.pumpFFTdeconv=np_rfft(self.pumpDeconv,axis=1)
self.transDeconv=self.pumpFFTdeconv/self.refFFTdeconv
return
def run(self, rank, input_tree_dir, full_tree_file, derep_tree_file,
taxonomy_file, output_prefix, min_children, title):
# determine named clades in full tree
named_clades = set()
tree = dendropy.Tree.get_from_path(full_tree_file,
schema='newick',
rooting='force-rooted',
preserve_underscores=True)
for node in tree.preorder_node_iter():
if node.label:
taxonomy = node.label.split(';')
named_clades.add(taxonomy[-1].strip().split(':')[-1])
print 'Identified %d named clades in full tree.' % len(named_clades)
# determine named groups with at least the specified number of children
print 'Determining taxa with sufficient named children lineages.'
taxon_children = defaultdict(set)
groups = defaultdict(list)
print taxonomy_file
for line in open(taxonomy_file):
line_split = line.replace('; ', ';').split()
genome_id = line_split[0]
taxonomy = [x.strip() for x in line_split[1].split(';')]
if len(taxonomy) > rank + 1:
taxon_children[taxonomy[rank]].add(taxonomy[rank + 1])
if len(taxonomy) > rank:
groups[taxonomy[rank]].append(genome_id)
groups_to_consider = set()
for taxon, children_taxa in taxon_children.iteritems():
if len(children_taxa) >= min_children and taxon in named_clades:
groups_to_consider.add(taxon)
print 'Assessing distribution over %d groups.' % len(
groups_to_consider)
# calculate RED for full tree
print ''
print 'Calculating RED over full tree.'
tree = dendropy.Tree.get_from_path(full_tree_file,
schema='newick',
rooting='force-rooted',
preserve_underscores=True)
full_rel_dist, _full_dist_components, polyphyletic = self.rel_dist_to_specified_groups(
tree, groups_to_consider, groups)
if len(polyphyletic) > 0:
print ''
print '[Warning] Full tree contains polyphyletic groups.'
# calculate RED for dereplicated tree
print ''
print 'Calculating RED over dereplicated tree.'
tree = dendropy.Tree.get_from_path(derep_tree_file,
schema='newick',
rooting='force-rooted',
preserve_underscores=True)
derep_rel_dist, derep_dist_components, polyphyletic = self.rel_dist_to_specified_groups(
tree, groups_to_consider, groups)
groups_to_consider = groups_to_consider - polyphyletic
print 'Assessing distriubtion over %d groups after removing polyphyletic groups in original trees.' % len(
groups_to_consider)
# calculate RED to each group in each tree
print ''
rel_dists = defaultdict(list)
dist_components = defaultdict(list)
for f in os.listdir(input_tree_dir):
if not f.endswith('.rooted.tree'):
continue
print f
tree_file = os.path.join(input_tree_dir, f)
tree = dendropy.Tree.get_from_path(tree_file,
schema='newick',
rooting='force-rooted',
preserve_underscores=True)
# calculate relative distance to named taxa
rel_dist, components, _polyphyletic = self.rel_dist_to_specified_groups(
tree, groups_to_consider, groups)
for taxon, dist in rel_dist.iteritems():
rel_dists[taxon].append(dist)
dist_components[taxon].append(components[taxon])
# create scatter plot
x = []
y = []
xDerep = []
yDerep = []
xFull = []
yFull = []
perc10 = []
perc90 = []
labels = []
fout = open(output_prefix + '.tsv', 'w')
fout.write(
'Taxon\tP10\tP90\tP90-P10\tMean RED\tMean dist to parent\tMean dist to leaves\tOriginal RED\tOrigial dist to parent\tOriginal dist to leaves\n'
)
for i, taxon in enumerate(sorted(rel_dists.keys(), reverse=True)):
labels.append(taxon + ' (%d)' % (len(rel_dists[taxon])))
rd = rel_dists[taxon]
for d in rd:
x.append(d)
y.append(i + 0.2)
p10, p90 = np_percentile(rd, [10, 90])
perc10.append(p10)
perc90.append(p90)
print taxon, p90 - p10
mean_x, mean_a, mean_b = np_mean(dist_components[taxon], axis=0)
derep_x, derep_a, derep_b = derep_dist_components[taxon]
fout.write(
'%s\t%.2f\t%.2f\t%.3f\t%.3f\t%.3f\t%.3f\t%.3f\t%.3f\t%.3f\n' %
(taxon, p10, p90, p90 - p10, mean_x, mean_a, mean_b, derep_x,
derep_a, derep_b))
xDerep.append(derep_rel_dist[taxon])
yDerep.append(i)
xFull.append(full_rel_dist[taxon])
yFull.append(i)
fout.close()
self.fig.clear()
self.fig.set_size_inches(8, len(rel_dists) * 0.4)
ax = self.fig.add_subplot(111)
ax.scatter(x, y, alpha=0.5, s=24, c=(0.5, 0.5, 0.5), marker='s')
ax.scatter(xDerep,
yDerep,
alpha=1.0,
s=24,
c=(1.0, 0.0, 0.0),
marker='s')
ax.scatter(xFull,
yFull,
alpha=1.0,
s=24,
c=(0.0, 0.0, 1.0),
marker='*')
for i in xrange(len(labels)):
ax.plot((perc10[i], perc10[i]), (i, i + 0.4), 'r-')
ax.plot((perc90[i], perc90[i]), (i, i + 0.4), 'r-')
# set plot elements
ax.grid(color=(0.8, 0.8, 0.8), linestyle='dashed')
if title:
ax.set_title(title, size=12)
ax.set_xlabel('relative distance')
ax.set_xticks(np_arange(0, 1.05, 0.1))
ax.set_xlim([-0.05, 1.05])
ax.set_ylabel('taxa')
ax.set_yticks(xrange(0, len(rel_dists)))
ax.set_ylim([-0.2, len(rel_dists) - 0.01])
ax.set_yticklabels(labels)
self.prettify(ax)
# make plot interactive
# mpld3.plugins.connect(fig, mpld3.plugins.PointLabelTooltip(scatter, labels=labels))
# mpld3.plugins.connect(fig, mpld3.plugins.MousePosition(fontsize=12))
# mpld3.save_html(fig, output_prefix + '.html')
self.fig.tight_layout(pad=1)
self.fig.savefig(output_prefix + '.png', dpi=300)
def __init_alliances(self):
alliances = [[team[3:] for team in alliance['picks']] for alliance in self.raw_event['alliances']]
alliances = np_array(alliances, np_int)
numbers = np_vstack(np_arange(1, 9, 1))
self.alliances = np_concatenate((numbers, alliances), 1)
def _distribution_summary_plot(self, phylum_rel_dists,
taxa_for_dist_inference, plot_file):
"""Summary plot showing the distribution of taxa at each taxonomic rank under different rootings.
Parameters
----------
phylum_rel_dists: phylum_rel_dists[phylum][rank_index][taxon] -> relative divergences
Relative divergence of taxon at each rank for different phylum-level rootings.
taxa_for_dist_inference : iterable
Taxa to considered when inferring distributions.
plot_file : str
Desired name of output plot.
"""
self.fig.clear()
self.fig.set_size_inches(12, 6)
ax = self.fig.add_subplot(111)
# determine median relative distance for each taxa
medians_for_taxa = self.taxa_median_rd(phylum_rel_dists)
# create percentile and classification boundary lines
percentiles = {}
for i, rank in enumerate(sorted(medians_for_taxa.keys())):
v = [
np_median(dists)
for taxon, dists in medians_for_taxa[rank].items()
if taxon in taxa_for_dist_inference
]
if not v:
# not taxa at rank suitable for creating classification
# boundaries
continue
p10, p50, p90 = np_percentile(v, [10, 50, 90])
ax.plot((p10, p10), (i, i + 0.25),
c=(0.3, 0.3, 0.3),
lw=2,
zorder=2)
ax.plot((p50, p50), (i, i + 0.5),
c=(0.3, 0.3, 0.3),
lw=2,
zorder=2)
ax.plot((p90, p90), (i, i + 0.25),
c=(0.3, 0.3, 0.3),
lw=2,
zorder=2)
for b in [-0.2, -0.1, 0.1, 0.2]:
boundary = p50 + b
if 1.0 > boundary > 0.0:
if abs(b) == 0.1:
c = (1.0, 0.65, 0.0) # orange
else:
c = (1.0, 0.0, 0.0)
ax.plot((boundary, boundary), (i, i + 0.5),
c=c,
lw=2,
zorder=2)
percentiles[i] = [p10, p50, p90]
# create scatter plot and results table
x = []
y = []
c = []
labels = []
rank_labels = []
for i, rank in enumerate(sorted(medians_for_taxa.keys())):
rank_label = Taxonomy.rank_labels[rank]
rank_labels.append(rank_label +
' (%d)' % len(medians_for_taxa[rank]))
mono = []
poly = []
no_inference = []
for clade_label, dists in medians_for_taxa[rank].items():
md = np_median(dists)
x.append(md)
y.append(i)
labels.append(clade_label)
if self._is_integer(clade_label.split('^')[-1]):
# taxa with a numerical suffix after a caret indicate
# polyphyletic groups when decorated with tax2tree
c.append((1.0, 0.0, 0.0))
poly.append(md)
elif clade_label not in taxa_for_dist_inference:
c.append((0.3, 0.3, 0.3))
no_inference.append(md)
else:
c.append((0.0, 0.0, 1.0))
mono.append(md)
# histogram for each rank
n = 0
if len(mono) > 0:
mono = np_array(mono)
no_inference = np_array(no_inference)
poly = np_array(poly)
binwidth = 0.025
bins = np_arange(0, 1.0 + binwidth, binwidth)
mono_max_count = max(np_histogram(mono, bins=bins)[0])
mono_weights = np_ones_like(mono) * (1.0 / mono_max_count)
w = float(
len(mono)) / (len(mono) + len(poly) + len(no_inference))
n, b, p = ax.hist(mono,
bins=bins,
color=(0.0, 0.0, 1.0),
alpha=0.25,
weights=0.9 * w * mono_weights,
bottom=i,
lw=0,
zorder=0)
if len(no_inference) > 0:
no_inference_max_count = max(
np_histogram(no_inference, bins=bins)[0])
no_inference_weights = np_ones_like(no_inference) * (
1.0 / no_inference_max_count)
ax.hist(no_inference,
bins=bins,
color=(0.3, 0.3, 0.3),
alpha=0.25,
weights=0.9 * (1.0 - w) * no_inference_weights,
bottom=i + n,
lw=0,
zorder=0)
if len(poly) > 0:
poly_max_count = max(np_histogram(poly, bins=bins)[0])
poly_weights = np_ones_like(poly) * (1.0 / poly_max_count)
ax.hist(poly,
bins=bins,
color=(1.0, 0.0, 0.0),
alpha=0.25,
weights=0.9 * (1.0 - w) * poly_weights,
bottom=i + n,
lw=0,
zorder=0)
scatter = ax.scatter(x, y, alpha=0.5, s=48, c=c, zorder=1)
# set plot elements
ax.grid(color=(0.8, 0.8, 0.8), linestyle='dashed')
ax.set_xlabel('relative distance')
ax.set_xticks(np_arange(0, 1.05, 0.1))
ax.set_xlim([-0.01, 1.01])
ax.set_ylabel('rank (no. taxa)')
ax.set_yticks(list(range(0, len(medians_for_taxa))))
ax.set_ylim([-0.2, len(medians_for_taxa) - 0.01])
ax.set_yticklabels(rank_labels)
self.prettify(ax)
# make plot interactive
mpld3.plugins.clear(self.fig)
mpld3.plugins.connect(
self.fig, mpld3.plugins.PointLabelTooltip(scatter, labels=labels))
mpld3.plugins.connect(self.fig,
mpld3.plugins.MousePosition(fontsize=10))
mpld3.save_html(self.fig, plot_file[0:plot_file.rfind('.')] + '.html')
self.fig.tight_layout(pad=1)
self.fig.savefig(plot_file, dpi=self.dpi)
def clean_meshes(vertices,
edges,
faces,
remove_unreferenced_edges=False,
remove_unreferenced_faces=False,
remove_duplicated_edges=False,
remove_duplicated_faces=False,
remove_degenerated_edges=False,
remove_degenerated_faces=False,
remove_loose_verts=False,
calc_verts_idx=False,
calc_edges_idx=False,
calc_faces_idx=False):
'''
Cleans a group of meshes using different routines.
Returs Clened meshes and removed items indexes
'''
verts_out, edges_out, faces_out = [], [], []
verts_removed_out, edges_removed_out, faces_removed_out = [], [], []
for verts_original, edges_original, faces_original in zip(
vertices, edges, faces):
verts_changed, edges_changed, faces_changed = False, False, False
preserved_edges_idx = []
preserved_faces_idx = []
if remove_unreferenced_edges:
edges, preserved_edges_mask = remove_unreferenced_topology(
edges_original, len(verts_original))
preserved_edges_idx = np_arange(
len(edges_original))[preserved_edges_mask]
edges_changed = True
if remove_unreferenced_faces:
faces, preserved_faces_mask = remove_unreferenced_topology(
faces_original, len(verts_original))
preserved_faces_idx = np_arange(
len(faces_original))[preserved_faces_mask]
faces_changed = True
if remove_duplicated_edges:
if edges_changed:
edges, unique_edges_mask = get_unique_topology(edges)
preserved_edges_idx = preserved_edges_idx[unique_edges_mask]
else:
edges, unique_edges_mask = get_unique_topology(edges_original)
preserved_edges_idx = np_arange(
len(edges_original))[unique_edges_mask]
edges_changed = True
if remove_duplicated_faces:
if faces_changed:
faces, unique_faces_mask = get_unique_topology(faces)
preserved_faces_idx = preserved_faces_idx[unique_faces_mask]
else:
faces, unique_faces_mask = get_unique_topology(faces_original)
preserved_faces_idx = np_arange(
len(faces_original))[unique_faces_mask]
faces_changed = True
if remove_degenerated_edges:
if edges_changed:
edges, non_coincident_mask = non_coincident_edges(edges)
preserved_edges_idx = preserved_edges_idx[non_coincident_mask]
else:
edges, non_coincident_mask = non_coincident_edges(
edges_original)
preserved_edges_idx = np_arange(
len(edges_original))[non_coincident_mask]
edges_changed = True
if remove_degenerated_faces:
if faces_changed:
faces, non_redundant_mask = non_redundant_faces_indices(faces)
preserved_faces_idx = preserved_faces_idx[non_redundant_mask]
else:
faces, non_redundant_mask = non_redundant_faces_indices(
faces_original)
preserved_faces_idx = np_arange(
len(faces_original))[non_redundant_mask]
faces_changed = True
if remove_loose_verts:
verts, edges, faces, removed_verts_idx = remove_unreferenced_verts(
verts_original, edges if edges_changed else edges_original,
faces if faces_changed else faces_original)
verts_changed = True
edges_changed = True
faces_changed = True
if verts_changed:
verts_out.append(verts)
if calc_verts_idx:
verts_removed_out.append(removed_verts_idx)
else:
verts_removed_out.append([])
else:
verts_out.append(verts_original)
verts_removed_out.append([])
if edges_changed:
edges_out.append(edges)
if calc_edges_idx and len(preserved_edges_idx) > 0:
edges_removed_out.append(
invert_index_list(preserved_edges_idx,
len(edges_original)).tolist())
else:
edges_removed_out.append([])
else:
edges_out.append(edges_original)
edges_removed_out.append([])
if faces_changed:
faces_out.append(faces)
if calc_faces_idx and len(preserved_faces_idx) > 0:
faces_removed_out.append(
invert_index_list(preserved_faces_idx,
len(faces_original)).tolist())
else:
faces_removed_out.append([])
else:
faces_out.append(faces_original)
faces_removed_out.append([])
return verts_out, edges_out, faces_out, verts_removed_out, edges_removed_out, faces_removed_out
def process_torsionals(atom,
dihe_list,
func="prop",
verbose=False,
forzmatrix=False,
dihed_count=None):
vizinho1 = atom.get_atom_list()[0]
vizinho2 = atom.get_atom_list()[1]
for vizinhoTemp in vizinho1.get_atom_list():
alist = [vizinhoTemp, vizinho1, atom, vizinho2]
if not (vizinhoTemp is None) and (vizinhoTemp != atom):
looked_up_param, centeratom_name, dihed_identifier = return_params(
func, alist)
if looked_up_param is None:
if forzmatrix: # dihed was not specified
d = Dihedral(*alist, func, None, dihed_idx=dihed_count)
dihed_count += 1
dihe_list.append(d)
else: # was specified. Now, check whether it was given double
params = looked_up_param.split(",")
if len(params) == 1:
d = Dihedral(*alist,
func=func,
param=params[0],
was_input=False,
is_multiparam=False,
has_siblings=False,
dihed_idx=dihed_count)
dihed_count += 1
dihe_list.append(d)
if not forzmatrix:
break
elif len(params) > 1:
# dihedrals who belong to the same parameter set:
siblings = np_arange(start=dihed_count,
stop=dihed_count + len(params))
if verbose:
print_verbose_dihedral_message(func, alist, params)
for param in params:
if atom.get_was_central_atom():
if not (dihed_identifier
in atom.get_was_central_atom_for()):
d = Dihedral(
*alist,
func=func,
param=param,
was_input=True,
isDetermined=True,
is_multiparam=True,
around_center_atom=atom.get_atomtype() ==
centeratom_name,
has_siblings=True,
siblings=siblings,
dihed_idx=dihed_count)
dihed_count += 1
dihe_list.append(d)
else:
d = Dihedral(
*alist,
func=func,
param=param,
was_input=True,
isDetermined=True,
is_multiparam=True,
around_center_atom=atom.get_atomtype() ==
centeratom_name,
has_siblings=True,
siblings=siblings,
dihed_idx=dihed_count)
dihed_count += 1
dihe_list.append(d)
if not forzmatrix:
atom.set_was_central_atom(
True
) # was used as central atom for multiparam dihed
atom.append_was_central_atom_for(dihed_identifier)
break
for vizinhoTemp in vizinho2.get_atom_list():
alist = [vizinhoTemp, vizinho2, atom, vizinho1]
if not (vizinhoTemp is None) and vizinhoTemp != atom:
looked_up_param, centeratom_name, dihed_identifier = return_params(
func, alist)
if looked_up_param is None:
if forzmatrix: # dihed was not specified
d = Dihedral(*alist, func, None, dihed_idx=dihed_count)
dihed_count += 1
dihe_list.append(d)
else: # was specified. Now, check whether it was given double
params = looked_up_param.split(",")
if len(params) == 1:
d = Dihedral(*alist,
func=func,
param=params[0],
was_input=False,
is_multiparam=False,
has_siblings=False,
dihed_idx=dihed_count)
dihed_count += 1
dihe_list.append(d)
if not forzmatrix:
break
elif len(params) > 1:
# dihedrals who belong to the same parameter set:
siblings = np_arange(start=dihed_count,
stop=dihed_count + len(params))
if verbose:
print_verbose_dihedral_message(func, alist, params)
for param in params:
if atom.get_was_central_atom():
if not (dihed_identifier
in atom.get_was_central_atom_for()):
d = Dihedral(
*alist,
func=func,
param=param,
was_input=True,
isDetermined=True,
is_multiparam=True,
around_center_atom=atom.get_atomtype() ==
centeratom_name,
has_siblings=True,
siblings=siblings,
dihed_idx=dihed_count)
dihed_count += 1
dihe_list.append(d)
else:
d = Dihedral(
*alist,
func=func,
param=param,
was_input=True,
isDetermined=False,
is_multiparam=True,
around_center_atom=atom.get_atomtype() ==
centeratom_name,
has_siblings=True,
siblings=siblings,
dihed_idx=dihed_count)
dihed_count += 1
dihe_list.append(d)
if not forzmatrix:
atom.set_was_central_atom(
True
) # was used as central atom for multiparam dihed
atom.append_was_central_atom_for(dihed_identifier)
break
return dihe_list, dihed_count
def _distribution_plot(self, rel_dists, taxa_for_dist_inference, distribution_table, plot_file):
"""Create plot showing the distribution of taxa at each taxonomic rank.
Parameters
----------
rel_dists: d[rank_index][taxon] -> relative divergence
Relative divergence of taxa at each rank.
taxa_for_dist_inference : iterable
Taxa to considered when inferring distributions.
distribution_table : str
Desired name of output table with distribution information.
plot_file : str
Desired name of output plot.
"""
self.fig.clear()
self.fig.set_size_inches(12, 6)
ax = self.fig.add_subplot(111)
# create normal distributions
for i, rank in enumerate(sorted(rel_dists.keys())):
v = [dist for taxa, dist in rel_dists[rank].iteritems() if taxa in taxa_for_dist_inference]
if len(v) < 2:
continue
u = np_mean(v)
rv = norm(loc=u, scale=np_std(v))
x = np_linspace(rv.ppf(0.001), rv.ppf(0.999), 1000)
nd = rv.pdf(x)
# ax.plot(x, 0.75 * (nd / max(nd)) + i, 'b-', alpha=0.6, zorder=2)
# ax.plot((u, u), (i, i + 0.5), 'b-', zorder=2)
# create percentile and classifciation boundary lines
percentiles = {}
for i, rank in enumerate(sorted(rel_dists.keys())):
v = [dist for taxa, dist in rel_dists[rank].iteritems() if taxa in taxa_for_dist_inference]
if len(v) == 0:
continue
p10, p50, p90 = np_percentile(v, [10, 50, 90])
ax.plot((p10, p10), (i, i + 0.25), c=(0.3, 0.3, 0.3), lw=2, zorder=2)
ax.plot((p50, p50), (i, i + 0.5), c=(0.3, 0.3, 0.3), lw=2, zorder=2)
ax.plot((p90, p90), (i, i + 0.25), c=(0.3, 0.3, 0.3), lw=2, zorder=2)
for b in [-0.2, -0.1, 0.1, 0.2]:
boundary = p50 + b
if boundary < 1.0 and boundary > 0.0:
if abs(b) == 0.1:
c = (1.0, 0.65, 0.0) # orange
else:
c = (1.0, 0.0, 0.0)
ax.plot((boundary, boundary), (i, i + 0.5), c=c, lw=2, zorder=2)
percentiles[i] = [p10, p50, p90]
# create scatter plot and results table
fout = open(distribution_table, 'w')
fout.write('Taxa\tRelative Distance\tP10\tMedian\tP90\tPercentile outlier\n')
x = []
y = []
c = []
labels = []
rank_labels = []
for i, rank in enumerate(sorted(rel_dists.keys())):
rank_label = Taxonomy.rank_labels[rank]
rank_labels.append(rank_label + ' (%d)' % len(rel_dists[rank]))
mono = []
poly = []
no_inference = []
for clade_label, dist in rel_dists[rank].iteritems():
x.append(dist)
y.append(i)
labels.append(clade_label)
if is_integer(clade_label.split('^')[-1]):
# taxa with a numerical suffix after a caret indicate
# polyphyletic groups when decorated with tax2tree
c.append((1.0, 0.0, 0.0))
poly.append(dist)
elif clade_label not in taxa_for_dist_inference:
c.append((0.3, 0.3, 0.3))
no_inference.append(dist)
else:
c.append((0.0, 0.0, 1.0))
mono.append(dist)
# report results
v = [clade_label, dist]
if i in percentiles:
p10, p50, p90 = percentiles[i]
percentile_outlier = not (dist >= p10 and dist <= p90)
v += percentiles[i] + [str(percentile_outlier)]
else:
percentile_outlier = 'Insufficent data to calculate percentiles'
v += [-1,-1,-1] + [str(percentile_outlier)]
fout.write('%s\t%.2f\t%.2f\t%.2f\t%.2f\t%s\n' % tuple(v))
# histogram for each rank
mono = np_array(mono)
no_inference = np_array(no_inference)
poly = np_array(poly)
binwidth = 0.025
bins = np_arange(0, 1.0 + binwidth, binwidth)
w = float(len(mono)) / (len(mono) + len(poly) + len(no_inference))
n = 0
if len(mono) > 0:
mono_max_count = max(np_histogram(mono, bins=bins)[0])
mono_weights = np_ones_like(mono) * (1.0 / mono_max_count)
n, b, p = ax.hist(mono, bins=bins,
color=(0.0, 0.0, 1.0),
alpha=0.25,
weights=0.9 * w * mono_weights,
bottom=i,
lw=0,
zorder=0)
if len(no_inference) > 0:
no_inference_max_count = max(np_histogram(no_inference, bins=bins)[0])
no_inference_weights = np_ones_like(no_inference) * (1.0 / no_inference_max_count)
ax.hist(no_inference, bins=bins,
color=(0.3, 0.3, 0.3),
alpha=0.25,
weights=0.9 * (1.0 - w) * no_inference_weights,
bottom=i + n,
lw=0,
zorder=0)
if len(poly) > 0:
poly_max_count = max(np_histogram(poly, bins=bins)[0])
poly_weights = np_ones_like(poly) * (1.0 / poly_max_count)
ax.hist(poly, bins=bins,
color=(1.0, 0.0, 0.0),
alpha=0.25,
weights=0.9 * (1.0 - w) * poly_weights,
bottom=i + n,
lw=0,
zorder=0)
fout.close()
# overlay scatter plot elements
scatter = ax.scatter(x, y, alpha=0.5, s=48, c=c, zorder=1)
# set plot elements
ax.grid(color=(0.8, 0.8, 0.8), linestyle='dashed')
ax.set_xlabel('relative distance')
ax.set_xticks(np_arange(0, 1.05, 0.1))
ax.set_xlim([-0.05, 1.05])
ax.set_ylabel('rank (no. taxa)')
ax.set_yticks(xrange(0, len(rel_dists)))
ax.set_ylim([-0.2, len(rel_dists) - 0.01])
ax.set_yticklabels(rank_labels)
self.prettify(ax)
# make plot interactive
mpld3.plugins.clear(self.fig)
mpld3.plugins.connect(self.fig, mpld3.plugins.PointLabelTooltip(scatter, labels=labels))
mpld3.plugins.connect(self.fig, mpld3.plugins.MousePosition(fontsize=10))
mpld3.save_html(self.fig, plot_file[0:plot_file.rfind('.')] + '.html')
self.fig.tight_layout(pad=1)
self.fig.savefig(plot_file, dpi=self.dpi)
def _distribution_plot(self, rel_dists, taxa_for_dist_inference, distribution_table, plot_file):
"""Create plot showing the distribution of taxa at each taxonomic rank.
Parameters
----------
rel_dists: d[rank_index][taxon] -> relative divergence
Relative divergence of taxa at each rank.
taxa_for_dist_inference : iterable
Taxa to considered when inferring distributions.
distribution_table : str
Desired name of output table with distribution information.
plot_file : str
Desired name of output plot.
"""
self.fig.clear()
self.fig.set_size_inches(12, 6)
ax = self.fig.add_subplot(111)
# create normal distributions
for i, rank in enumerate(sorted(rel_dists.keys())):
v = [dist for taxa, dist in rel_dists[rank].items() if taxa in taxa_for_dist_inference]
if len(v) < 2:
continue
u = np_mean(v)
rv = norm(loc=u, scale=np_std(v))
x = np_linspace(rv.ppf(0.001), rv.ppf(0.999), 1000)
nd = rv.pdf(x)
# ax.plot(x, 0.75 * (nd / max(nd)) + i, 'b-', alpha=0.6, zorder=2)
# ax.plot((u, u), (i, i + 0.5), 'b-', zorder=2)
# create percentile and classifciation boundary lines
percentiles = {}
for i, rank in enumerate(sorted(rel_dists.keys())):
v = [dist for taxa, dist in rel_dists[rank].items() if taxa in taxa_for_dist_inference]
if len(v) == 0:
continue
p10, p50, p90 = np_percentile(v, [10, 50, 90])
ax.plot((p10, p10), (i, i + 0.25), c=(0.3, 0.3, 0.3), lw=2, zorder=2)
ax.plot((p50, p50), (i, i + 0.5), c=(0.3, 0.3, 0.3), lw=2, zorder=2)
ax.plot((p90, p90), (i, i + 0.25), c=(0.3, 0.3, 0.3), lw=2, zorder=2)
for b in [-0.2, -0.1, 0.1, 0.2]:
boundary = p50 + b
if boundary < 1.0 and boundary > 0.0:
if abs(b) == 0.1:
c = (1.0, 0.65, 0.0) # orange
else:
c = (1.0, 0.0, 0.0)
ax.plot((boundary, boundary), (i, i + 0.5), c=c, lw=2, zorder=2)
percentiles[i] = [p10, p50, p90]
# create scatter plot and results table
fout = open(distribution_table, 'w')
fout.write('Taxa\tRelative Distance\tP10\tMedian\tP90\tPercentile outlier\n')
x = []
y = []
c = []
labels = []
rank_labels = []
for i, rank in enumerate(sorted(rel_dists.keys())):
rank_label = Taxonomy.rank_labels[rank]
rank_labels.append(rank_label + ' (%d)' % len(rel_dists[rank]))
mono = []
poly = []
no_inference = []
for clade_label, dist in rel_dists[rank].items():
x.append(dist)
y.append(i)
labels.append(clade_label)
if is_integer(clade_label.split('^')[-1]):
# taxa with a numerical suffix after a caret indicate
# polyphyletic groups when decorated with tax2tree
c.append((1.0, 0.0, 0.0))
poly.append(dist)
elif clade_label not in taxa_for_dist_inference:
c.append((0.3, 0.3, 0.3))
no_inference.append(dist)
else:
c.append((0.0, 0.0, 1.0))
mono.append(dist)
# report results
v = [clade_label, dist]
if i in percentiles:
p10, p50, p90 = percentiles[i]
percentile_outlier = not (dist >= p10 and dist <= p90)
v += percentiles[i] + [str(percentile_outlier)]
else:
percentile_outlier = 'Insufficent data to calculate percentiles'
v += [-1,-1,-1] + [str(percentile_outlier)]
fout.write('%s\t%.2f\t%.2f\t%.2f\t%.2f\t%s\n' % tuple(v))
# histogram for each rank
mono = np_array(mono)
no_inference = np_array(no_inference)
poly = np_array(poly)
binwidth = 0.025
bins = np_arange(0, 1.0 + binwidth, binwidth)
d = len(mono) + len(poly) + len(no_inference)
if d == 0:
break
w = float(len(mono)) / d
n = 0
if len(mono) > 0:
mono_max_count = max(np_histogram(mono, bins=bins)[0])
mono_weights = np_ones_like(mono) * (1.0 / mono_max_count)
n, b, p = ax.hist(mono, bins=bins,
color=(0.0, 0.0, 1.0),
alpha=0.25,
weights=0.9 * w * mono_weights,
bottom=i,
lw=0,
zorder=0)
if len(no_inference) > 0:
no_inference_max_count = max(np_histogram(no_inference, bins=bins)[0])
no_inference_weights = np_ones_like(no_inference) * (1.0 / no_inference_max_count)
ax.hist(no_inference, bins=bins,
color=(0.3, 0.3, 0.3),
alpha=0.25,
weights=0.9 * (1.0 - w) * no_inference_weights,
bottom=i + n,
lw=0,
zorder=0)
if len(poly) > 0:
poly_max_count = max(np_histogram(poly, bins=bins)[0])
poly_weights = np_ones_like(poly) * (1.0 / poly_max_count)
ax.hist(poly, bins=bins,
color=(1.0, 0.0, 0.0),
alpha=0.25,
weights=0.9 * (1.0 - w) * poly_weights,
bottom=i + n,
lw=0,
zorder=0)
fout.close()
# overlay scatter plot elements
scatter = ax.scatter(x, y, alpha=0.5, s=48, c=c, zorder=1)
# set plot elements
ax.grid(color=(0.8, 0.8, 0.8), linestyle='dashed')
ax.set_xlabel('relative distance')
ax.set_xticks(np_arange(0, 1.05, 0.1))
ax.set_xlim([-0.05, 1.05])
ax.set_ylabel('rank (no. taxa)')
ax.set_yticks(range(0, len(rel_dists)))
ax.set_ylim([-0.2, len(rel_dists) - 0.01])
ax.set_yticklabels(rank_labels)
self.prettify(ax)
# make plot interactive
mpld3.plugins.clear(self.fig)
mpld3.plugins.connect(self.fig, mpld3.plugins.PointLabelTooltip(scatter, labels=labels))
mpld3.plugins.connect(self.fig, mpld3.plugins.MousePosition(fontsize=10))
mpld3.save_html(self.fig, plot_file[0:plot_file.rfind('.')] + '.html')
self.fig.tight_layout(pad=1)
self.fig.savefig(plot_file, dpi=self.dpi)
def table(self, input_tree, taxon_category_file, bl_step_size,
output_table):
"""Produce table with number of lineage for increasing mean branch lengths
Parameters
----------
input_tree : str
Name of input tree.
taxon_category_file : str
File indicating category for each taxon in the tree.
bl_step_size : float
Step size in table for mean branch length criterion.
output_table : str
Name of output table.
"""
# get category for each taxon
taxon_category = {}
for line in open(taxon_category_file):
line_split = line.strip().split('\t')
taxon_category[line_split[0]] = line_split[1]
# read tree
tree = dendropy.Tree.get_from_path(input_tree,
schema='newick',
rooting='force-rooted',
preserve_underscores=True)
# determine mean distance to leaves and taxon categories for each node
all_categories = set()
node_info = {}
parent_mean_dist_to_leafs = {}
max_bl_threshold = None
for i, node in enumerate(tree.seed_node.preorder_iter()):
node.id = i
if node.is_leaf():
mean_dist_to_leafs = 0.0
categories = set()
for c in taxon_category[node.taxon.label].split('/'):
categories.add(c)
else:
dist_to_leafs = []
categories = set()
for t in node.leaf_iter():
dist_to_leafs.append(self._dist_to_ancestor(t, node))
for c in taxon_category[t.taxon.label].split('/'):
categories.add(c)
mean_dist_to_leafs = np_mean(dist_to_leafs)
if node.parent_node:
p = parent_mean_dist_to_leafs[node.parent_node.id]
else:
p = mean_dist_to_leafs + 1e-6
category = '/'.join(sorted(list(categories), reverse=True))
all_categories.add(category)
node_info[node.id] = [mean_dist_to_leafs, p, category]
parent_mean_dist_to_leafs[node.id] = mean_dist_to_leafs
if mean_dist_to_leafs > max_bl_threshold:
max_bl_threshold = mean_dist_to_leafs
# write table
fout = open(output_table, 'w')
fout.write('Threshold')
for c in all_categories:
fout.write('\t%s' % c)
fout.write('\n')
for bl_threshold in np_arange(0, max_bl_threshold + bl_step_size,
bl_step_size):
category_count = defaultdict(int)
stack = [tree.seed_node]
while stack:
node = stack.pop()
mean_dist_to_leafs, _, category = node_info[node.id]
if mean_dist_to_leafs > bl_threshold:
for c in node.child_node_iter():
stack.append(c)
else:
category_count[category] += 1
# check if node meets mean branch length criterion
if sum(category_count.values()) > 0:
fout.write('%.3f' % bl_threshold)
for c in all_categories:
fout.write('\t%d' % category_count[c])
fout.write('\n')
fout.close()
if False:
node_info.sort()
for bl_threshold in np_arange(0, node_info[-1][0] + bl_step_size,
bl_step_size):
category_count = defaultdict(int)
for mean_bl_dist, parent_mean_bl_dist, category in node_info:
if bl_threshold >= mean_bl_dist and bl_threshold < parent_mean_bl_dist:
category_count[category] += 1
if sum(category_count.values()) > 0:
fout.write('%.3f' % bl_threshold)
for c in all_categories:
fout.write('\t%d' % category_count[c])
fout.write('\n')
def optimal(self, input_tree, rank, min_dist, max_dist, step_size,
output_table):
"""Determine branch length for best congruency with existing taxonomy.
Parameters
----------
input_tree : str
Name of input tree.
rank : int
Taxonomic rank to consider (1=Phylum, ..., 6=Species).
output_table : str
Name of output table.
"""
# read tree
self.logger.info('Reading tree.')
tree = dendropy.Tree.get_from_path(input_tree,
schema='newick',
rooting='force-rooted',
preserve_underscores=True)
# get mean distance to terminal taxa for each node along with
# other stats needed to determine classification
self.logger.info('Determining MDTT for each node.')
rank_prefix = Taxonomy.rank_prefixes[rank]
child_rank_prefix = Taxonomy.rank_prefixes[rank + 1]
rank_info = []
rank_dists = set()
for node in tree.seed_node.preorder_internal_node_iter():
if node == tree.seed_node:
continue
# check if node is at the specified rank
node_taxon = None
if node.label:
support, taxon_name, _auxiliary_info = parse_label(node.label)
if taxon_name:
for taxon in [x.strip() for x in taxon_name.split(';')]:
if taxon.startswith(rank_prefix):
node_taxon = taxon
if not node_taxon:
continue
# check that node has two descendants at the next rank
child_rank_taxa = []
for c in node.levelorder_iter():
if c.label:
support, taxon_name, _auxiliary_info = parse_label(c.label)
if taxon_name:
for taxon in [
x.strip() for x in taxon_name.split(';')
]:
if taxon.startswith(child_rank_prefix):
child_rank_taxa.append(taxon)
if len(child_rank_taxa) >= 2:
break
if len(child_rank_taxa) < 2:
continue
# get mean branch length to terminal taxa
dists_to_tips = []
for t in node.leaf_iter():
dists_to_tips.append(self._dist_to_ancestor(t, node))
node_dist = np_mean(dists_to_tips)
# get mean branch length to terminal taxa for first ancestor spanning multiple phyla
ancestor = self._ancestor_multiple_taxa_at_rank(node, rank_prefix)
ancestor_dists_to_tips = []
for t in ancestor.leaf_iter():
ancestor_dists_to_tips.append(
self._dist_to_ancestor(t, ancestor))
ancestor_dist = np_mean(ancestor_dists_to_tips)
rank_info.append([node_dist, ancestor_dist, node_taxon])
rank_dists.add(node_dist)
self.logger.info(
'Calculating threshold from %d taxa with specified rank resolution.'
% len(rank_info))
fout = open('bl_optimal_taxa_dists.tsv', 'w')
fout.write('Taxon\tNode MDTT\tMulti-phyla Ancestor MDTT\n')
for node_dist, ancestor_dist, node_taxon in rank_info:
fout.write('%s\t%.3f\t%.3f\n' %
(node_taxon, node_dist, ancestor_dist))
fout.close()
# report number of correct and incorrect taxa for each threshold
fout = open(output_table, 'w')
header = 'Threshold\tCorrect\tIncorrect\tPrecision\tNo. Lineages\tNo. Multiple Taxa Lineages\tNo. Terminal Lineages'
fout.write(header + '\n')
print(header)
top_correct = 0
top_incorrect = 0
top_precision = 0
for d in np_arange(min_dist, max_dist + step_size, step_size):
rank_dists.add(d)
for dist_threshold in sorted(rank_dists, reverse=True):
correct = 0
incorrect = 0
for node_dist, ancestor_dist, node_taxon in rank_info:
# check if node/edge would be collapsed at the given threshold
if node_dist <= dist_threshold and ancestor_dist > dist_threshold:
correct += 1
elif node_dist > dist_threshold:
incorrect += 1
else:
incorrect += 1 # above ancestor with multiple taxa
denominator = correct + incorrect
if denominator:
precision = float(correct) / denominator
else:
precision = 0
num_lineages, num_terminal_lineages = self._num_lineages(
tree, dist_threshold)
row = '%f\t%d\t%d\t%.3f\t%d\t%d\t%d' % (
dist_threshold, correct, incorrect, precision, num_lineages +
num_terminal_lineages, num_lineages, num_terminal_lineages)
fout.write(row + '\n')
print(row)
if precision > top_precision:
top_correct = correct
top_incorrect = incorrect
top_precision = precision
top_threshold = dist_threshold
return top_threshold, top_correct, top_incorrect
def optimal(self, input_tree,
rank,
min_dist,
max_dist,
step_size,
output_table):
"""Determine branch length for best congruency with existing taxonomy.
Parameters
----------
input_tree : str
Name of input tree.
rank : int
Taxonomic rank to consider (1=Phylum, ..., 6=Species).
output_table : str
Name of output table.
"""
# read tree
self.logger.info('Reading tree.')
tree = dendropy.Tree.get_from_path(input_tree,
schema='newick',
rooting='force-rooted',
preserve_underscores=True)
# get mean distance to terminal taxa for each node along with
# other stats needed to determine classification
self.logger.info('Determining MDTT for each node.')
rank_prefix = Taxonomy.rank_prefixes[rank]
child_rank_prefix = Taxonomy.rank_prefixes[rank+1]
rank_info = []
rank_dists = set()
for node in tree.seed_node.preorder_internal_node_iter():
if node == tree.seed_node:
continue
# check if node is at the specified rank
node_taxon = None
if node.label:
support, taxon_name, _auxiliary_info = parse_label(node.label)
if taxon_name:
for taxon in [x.strip() for x in taxon_name.split(';')]:
if taxon.startswith(rank_prefix):
node_taxon = taxon
if not node_taxon:
continue
# check that node has two descendants at the next rank
child_rank_taxa = []
for c in node.levelorder_iter():
if c.label:
support, taxon_name, _auxiliary_info = parse_label(c.label)
if taxon_name:
for taxon in [x.strip() for x in taxon_name.split(';')]:
if taxon.startswith(child_rank_prefix):
child_rank_taxa.append(taxon)
if len(child_rank_taxa) >= 2:
break
if len(child_rank_taxa) < 2:
continue
# get mean branch length to terminal taxa
dists_to_tips = []
for t in node.leaf_iter():
dists_to_tips.append(self._dist_to_ancestor(t, node))
node_dist = np_mean(dists_to_tips)
# get mean branch length to terminal taxa for first ancestor spanning multiple phyla
ancestor = self._ancestor_multiple_taxa_at_rank(node, rank_prefix)
ancestor_dists_to_tips = []
for t in ancestor.leaf_iter():
ancestor_dists_to_tips.append(self._dist_to_ancestor(t, ancestor))
ancestor_dist = np_mean(ancestor_dists_to_tips)
rank_info.append([node_dist, ancestor_dist, node_taxon])
rank_dists.add(node_dist)
self.logger.info('Calculating threshold from %d taxa with specified rank resolution.' % len(rank_info))
fout = open('bl_optimal_taxa_dists.tsv' , 'w')
fout.write('Taxon\tNode MDTT\tMulti-phyla Ancestor MDTT\n')
for node_dist, ancestor_dist, node_taxon in rank_info:
fout.write('%s\t%.3f\t%.3f\n' % (node_taxon, node_dist, ancestor_dist))
fout.close()
# report number of correct and incorrect taxa for each threshold
fout = open(output_table, 'w')
header = 'Threshold\tCorrect\tIncorrect\tPrecision\tNo. Lineages\tNo. Multiple Taxa Lineages\tNo. Terminal Lineages'
fout.write(header + '\n')
print header
top_correct = 0
top_incorrect = 0
top_precision = 0
for d in np_arange(min_dist, max_dist+step_size, step_size):
rank_dists.add(d)
for dist_threshold in sorted(rank_dists, reverse=True):
correct = 0
incorrect = 0
for node_dist, ancestor_dist, node_taxon in rank_info:
# check if node/edge would be collapsed at the given threshold
if node_dist <= dist_threshold and ancestor_dist > dist_threshold:
correct += 1
elif node_dist > dist_threshold:
incorrect += 1
else:
incorrect += 1 # above ancestor with multiple taxa
denominator = correct + incorrect
if denominator:
precision = float(correct) / denominator
else:
precision = 0
num_lineages, num_terminal_lineages = self._num_lineages(tree, dist_threshold)
row = '%f\t%d\t%d\t%.3f\t%d\t%d\t%d' % (dist_threshold,
correct,
incorrect,
precision,
num_lineages + num_terminal_lineages,
num_lineages,
num_terminal_lineages)
fout.write(row + '\n')
print row
if precision > top_precision:
top_correct = correct
top_incorrect = incorrect
top_precision = precision
top_threshold = dist_threshold
return top_threshold, top_correct, top_incorrect
from mpi4py import MPI
from numpy import \
arange as np_arange, \
zeros as np_zeros
comm = MPI.COMM_WORLD
myrank = comm.Get_rank()
nproc = comm.Get_size()
if myrank == 0:
fulldata = np_arange(3 * nproc, dtype='i')
print("I'm {0} fulldata is: {1}".format(myrank, fulldata))
else:
fulldata = None
count = 3
mydata = np_zeros(count, dtype='i')
comm.Scatter([fulldata, count, MPI.INT], [mydata, count, MPI.INT], root=0)
print("After Scatter, I'm {0} and mydata is: {1}".format(myrank, mydata))
def shuffleBAMs(self):
"""Make the data transformation deterministic by reordering the bams"""
# first we should make a subset of the total data
# we'd like to take it down to about 1500 or so RI's
# but we'd like to do this in a repeatable way
ideal_contig_num = 1500
sub_cons = range(len(self.indices))
while len(sub_cons) > ideal_contig_num:
# select every second contig when sorted by norm cov
cov_sorted = np_argsort(self.normCoverages[sub_cons])
sub_cons = np_array([sub_cons[cov_sorted[i*2]] for i in np_arange(int(len(sub_cons)/2))])
if len(sub_cons) > ideal_contig_num:
# select every second contig when sorted by mer PC1
mer_sorted = np_argsort(self.kmerNormPC1[sub_cons])
sub_cons = np_array([sub_cons[mer_sorted[i*2]] for i in np_arange(int(len(sub_cons)/2))])
# now that we have a subset, calculate the distance between each of the untransformed vectors
num_sc = len(sub_cons)
# log shift the coverages towards the origin
sub_covs = np_transpose([self.covProfiles[i]*(np_log10(self.normCoverages[i])/self.normCoverages[i]) for i in sub_cons])
sq_dists = cdist(sub_covs,sub_covs,'cityblock')
dists = squareform(sq_dists)
# initialise a list of left, right neighbours
lr_dict = {}
for i in range(self.numStoits):
lr_dict[i] = []
too_big = 10000
while True:
closest = np_argmin(dists)
if dists[closest] == too_big:
break
(i,j) = self.small2indices(closest, self.numStoits-1)
lr_dict[j].append(i)
lr_dict[i].append(j)
# mark these guys as neighbours
if len(lr_dict[i]) == 2:
# no more than 2 neighbours
sq_dists[i,:] = too_big
sq_dists[:,i] = too_big
sq_dists[i,i] = 0.0
if len(lr_dict[j]) == 2:
# no more than 2 neighbours
sq_dists[j,:] = too_big
sq_dists[:,j] = too_big
sq_dists[j,j] = 0.0
# fix the dist matrix
sq_dists[j,i] = too_big
sq_dists[i,j] = too_big
dists = squareform(sq_dists)
# now make the ordering
ordering = [0, lr_dict[0][0]]
done = 2
while done < self.numStoits:
last = ordering[done-1]
if lr_dict[last][0] == ordering[done-2]:
ordering.append(lr_dict[last][1])
last = lr_dict[last][1]
else:
ordering.append(lr_dict[last][0])
last = lr_dict[last][0]
done+=1
# reshuffle the contig order!
# yay for bubble sort!
working = np_arange(self.numStoits)
for i in range(1, self.numStoits):
# where is this guy in the list
loc = list(working).index(ordering[i])
if loc != i:
# swap the columns
self.covProfiles[:,[i,loc]] = self.covProfiles[:,[loc,i]]
self.stoitColNames[[i,loc]] = self.stoitColNames[[loc,i]]
working[[i,loc]] = working[[loc,i]]
def shuffleBAMs(self):
"""Make the data transformation deterministic by reordering the bams"""
# first we should make a subset of the total data
# we'd like to take it down to about 1500 or so RI's
# but we'd like to do this in a repeatable way
ideal_contig_num = 1500
sub_cons = range(len(self.indices))
while len(sub_cons) > ideal_contig_num:
# select every second contig when sorted by norm cov
cov_sorted = np_argsort(self.normCoverages[sub_cons])
sub_cons = np_array([
sub_cons[cov_sorted[i * 2]]
for i in np_arange(int(len(sub_cons) / 2))
])
if len(sub_cons) > ideal_contig_num:
# select every second contig when sorted by mer PC1
mer_sorted = np_argsort(self.kmerNormPC1[sub_cons])
sub_cons = np_array([
sub_cons[mer_sorted[i * 2]]
for i in np_arange(int(len(sub_cons) / 2))
])
# now that we have a subset, calculate the distance between each of the untransformed vectors
num_sc = len(sub_cons)
# log shift the coverages towards the origin
sub_covs = np_transpose([
self.covProfiles[i] *
(np_log10(self.normCoverages[i]) / self.normCoverages[i])
for i in sub_cons
])
sq_dists = cdist(sub_covs, sub_covs, 'cityblock')
dists = squareform(sq_dists)
# initialise a list of left, right neighbours
lr_dict = {}
for i in range(self.numStoits):
lr_dict[i] = []
too_big = 10000
while True:
closest = np_argmin(dists)
if dists[closest] == too_big:
break
(i, j) = self.small2indices(closest, self.numStoits - 1)
lr_dict[j].append(i)
lr_dict[i].append(j)
# mark these guys as neighbours
if len(lr_dict[i]) == 2:
# no more than 2 neighbours
sq_dists[i, :] = too_big
sq_dists[:, i] = too_big
sq_dists[i, i] = 0.0
if len(lr_dict[j]) == 2:
# no more than 2 neighbours
sq_dists[j, :] = too_big
sq_dists[:, j] = too_big
sq_dists[j, j] = 0.0
# fix the dist matrix
sq_dists[j, i] = too_big
sq_dists[i, j] = too_big
dists = squareform(sq_dists)
# now make the ordering
ordering = [0, lr_dict[0][0]]
done = 2
while done < self.numStoits:
last = ordering[done - 1]
if lr_dict[last][0] == ordering[done - 2]:
ordering.append(lr_dict[last][1])
last = lr_dict[last][1]
else:
ordering.append(lr_dict[last][0])
last = lr_dict[last][0]
done += 1
# reshuffle the contig order!
# yay for bubble sort!
working = np_arange(self.numStoits)
for i in range(1, self.numStoits):
# where is this guy in the list
loc = list(working).index(ordering[i])
if loc != i:
# swap the columns
self.covProfiles[:, [i, loc]] = self.covProfiles[:, [loc, i]]
self.stoitColNames[[i, loc]] = self.stoitColNames[[loc, i]]
working[[i, loc]] = working[[loc, i]]
def run(self, rank, input_tree_dir, full_tree_file, derep_tree_file, taxonomy_file, output_prefix, min_children, title):
# determine named clades in full tree
named_clades = set()
tree = dendropy.Tree.get_from_path(full_tree_file,
schema='newick',
rooting='force-rooted',
preserve_underscores=True)
for node in tree.preorder_node_iter():
if node.label:
taxonomy = node.label.split(';')
named_clades.add(taxonomy[-1].strip().split(':')[-1])
print 'Identified %d named clades in full tree.' % len(named_clades)
# determine named groups with at least the specified number of children
print 'Determining taxa with sufficient named children lineages.'
taxon_children = defaultdict(set)
groups = defaultdict(list)
print taxonomy_file
for line in open(taxonomy_file):
line_split = line.replace('; ', ';').split()
genome_id = line_split[0]
taxonomy = [x.strip() for x in line_split[1].split(';')]
if len(taxonomy) > rank + 1:
taxon_children[taxonomy[rank]].add(taxonomy[rank + 1])
if len(taxonomy) > rank:
groups[taxonomy[rank]].append(genome_id)
groups_to_consider = set()
for taxon, children_taxa in taxon_children.iteritems():
if len(children_taxa) >= min_children and taxon in named_clades:
groups_to_consider.add(taxon)
print 'Assessing distribution over %d groups.' % len(groups_to_consider)
# calculate relative distance for full tree
print ''
print 'Calculating relative distance over full tree.'
tree = dendropy.Tree.get_from_path(full_tree_file,
schema='newick',
rooting='force-rooted',
preserve_underscores=True)
full_rel_dist, _full_dist_components, polyphyletic = self.rel_dist_to_specified_groups(tree, groups_to_consider, groups)
if len(polyphyletic) > 0:
print ''
print '[Warning] Full tree contains polyphyletic groups.'
# calculate relative distance for dereplicated tree
print ''
print 'Calculating relative distance over dereplicated tree.'
tree = dendropy.Tree.get_from_path(derep_tree_file,
schema='newick',
rooting='force-rooted',
preserve_underscores=True)
derep_rel_dist, derep_dist_components, polyphyletic = self.rel_dist_to_specified_groups(tree, groups_to_consider, groups)
groups_to_consider = groups_to_consider - polyphyletic
print 'Assessing distriubtion over %d groups after removing polyphyletic groups in original trees.' % len(groups_to_consider)
# calculate relative distance to each group in each tree
print ''
rel_dists = defaultdict(list)
dist_components = defaultdict(list)
for f in os.listdir(input_tree_dir):
if not f.endswith('.rooted.tree'):
continue
print f
tree_file = os.path.join(input_tree_dir, f)
tree = dendropy.Tree.get_from_path(tree_file,
schema='newick',
rooting='force-rooted',
preserve_underscores=True)
# calculate relative distance to named taxa
rel_dist, components, _polyphyletic = self.rel_dist_to_specified_groups(tree, groups_to_consider, groups)
for taxon, dist in rel_dist.iteritems():
rel_dists[taxon].append(dist)
dist_components[taxon].append(components[taxon])
# create scatter plot
x = []
y = []
xDerep = []
yDerep = []
xFull = []
yFull = []
perc10 = []
perc90 = []
labels = []
fout = open(output_prefix + '.tsv', 'w')
fout.write('Taxon\tP10\tP90\tP90-P10\tMean rel. dist\tMean dist to parent\tMean dist to leaves\tOriginal rel. dist.\tOrigial dist to parent\tOriginal dist to leaves\n')
for i, taxon in enumerate(sorted(rel_dists.keys(), reverse=True)):
labels.append(taxon + ' (%d)' % (len(rel_dists[taxon])))
rd = rel_dists[taxon]
for d in rd:
x.append(d)
y.append(i + 0.2)
p10, p90 = np_percentile(rd, [10, 90])
perc10.append(p10)
perc90.append(p90)
print taxon, p90 - p10
mean_x, mean_a, mean_b = np_mean(dist_components[taxon], axis=0)
derep_x, derep_a, derep_b = derep_dist_components[taxon]
fout.write('%s\t%.2f\t%.2f\t%.3f\t%.3f\t%.3f\t%.3f\t%.3f\t%.3f\t%.3f\n' % (taxon, p10, p90, p90 - p10, mean_x, mean_a, mean_b, derep_x, derep_a, derep_b))
xDerep.append(derep_rel_dist[taxon])
yDerep.append(i)
xFull.append(full_rel_dist[taxon])
yFull.append(i)
fout.close()
self.fig.clear()
self.fig.set_size_inches(8, len(rel_dists) * 0.4)
ax = self.fig.add_subplot(111)
ax.scatter(x, y, alpha=0.5, s=24, c=(0.5, 0.5, 0.5), marker='s')
ax.scatter(xDerep, yDerep, alpha=1.0, s=24, c=(1.0, 0.0, 0.0), marker='s')
ax.scatter(xFull, yFull, alpha=1.0, s=24, c=(0.0, 0.0, 1.0), marker='*')
for i in xrange(len(labels)):
ax.plot((perc10[i], perc10[i]), (i, i + 0.4), 'r-')
ax.plot((perc90[i], perc90[i]), (i, i + 0.4), 'r-')
# set plot elements
ax.grid(color=(0.8, 0.8, 0.8), linestyle='dashed')
if title:
ax.set_title(title, size=12)
ax.set_xlabel('relative distance')
ax.set_xticks(np_arange(0, 1.05, 0.1))
ax.set_xlim([-0.05, 1.05])
ax.set_ylabel('taxa')
ax.set_yticks(xrange(0, len(rel_dists)))
ax.set_ylim([-0.2, len(rel_dists) - 0.01])
ax.set_yticklabels(labels)
self.prettify(ax)
# make plot interactive
# mpld3.plugins.connect(fig, mpld3.plugins.PointLabelTooltip(scatter, labels=labels))
# mpld3.plugins.connect(fig, mpld3.plugins.MousePosition(fontsize=12))
# mpld3.save_html(fig, output_prefix + '.html')
self.fig.tight_layout(pad=1)
self.fig.savefig(output_prefix + '.png', dpi=300)
def _distribution_summary_plot(self, phylum_rel_dists, taxa_for_dist_inference, plot_file):
"""Summary plot showing the distribution of taxa at each taxonomic rank under different rootings.
Parameters
----------
phylum_rel_dists: phylum_rel_dists[phylum][rank_index][taxon] -> relative divergences
Relative divergence of taxon at each rank for different phylum-level rootings.
taxa_for_dist_inference : iterable
Taxa to considered when inferring distributions.
plot_file : str
Desired name of output plot.
"""
self.fig.clear()
self.fig.set_size_inches(12, 6)
ax = self.fig.add_subplot(111)
# determine median relative distance for each taxa
medians_for_taxa = self.taxa_median_rd(phylum_rel_dists)
# create percentile and classification boundary lines
percentiles = {}
for i, rank in enumerate(sorted(medians_for_taxa.keys())):
v = [np_median(dists) for taxon, dists in medians_for_taxa[rank].iteritems() if taxon in taxa_for_dist_inference]
p10, p50, p90 = np_percentile(v, [10, 50, 90])
ax.plot((p10, p10), (i, i + 0.25), c=(0.3, 0.3, 0.3), lw=2, zorder=2)
ax.plot((p50, p50), (i, i + 0.5), c=(0.3, 0.3, 0.3), lw=2, zorder=2)
ax.plot((p90, p90), (i, i + 0.25), c=(0.3, 0.3, 0.3), lw=2, zorder=2)
for b in [-0.2, -0.1, 0.1, 0.2]:
boundary = p50 + b
if boundary < 1.0 and boundary > 0.0:
if abs(b) == 0.1:
c = (1.0, 0.65, 0.0) # orange
else:
c = (1.0, 0.0, 0.0)
ax.plot((boundary, boundary), (i, i + 0.5), c=c, lw=2, zorder=2)
percentiles[i] = [p10, p50, p90]
# create scatter plot and results table
x = []
y = []
c = []
labels = []
rank_labels = []
for i, rank in enumerate(sorted(medians_for_taxa.keys())):
rank_label = Taxonomy.rank_labels[rank]
rank_labels.append(rank_label + ' (%d)' % len(medians_for_taxa[rank]))
mono = []
poly = []
no_inference = []
for clade_label, dists in medians_for_taxa[rank].iteritems():
md = np_median(dists)
x.append(md)
y.append(i)
labels.append(clade_label)
if is_integer(clade_label.split('^')[-1]):
# taxa with a numerical suffix after a caret indicate
# polyphyletic groups when decorated with tax2tree
c.append((1.0, 0.0, 0.0))
poly.append(md)
elif clade_label not in taxa_for_dist_inference:
c.append((0.3, 0.3, 0.3))
no_inference.append(md)
else:
c.append((0.0, 0.0, 1.0))
mono.append(md)
# histogram for each rank
mono = np_array(mono)
no_inference = np_array(no_inference)
poly = np_array(poly)
binwidth = 0.025
bins = np_arange(0, 1.0 + binwidth, binwidth)
mono_max_count = max(np_histogram(mono, bins=bins)[0])
mono_weights = np_ones_like(mono) * (1.0 / mono_max_count)
w = float(len(mono)) / (len(mono) + len(poly) + len(no_inference))
n, b, p = ax.hist(mono, bins=bins,
color=(0.0, 0.0, 1.0),
alpha=0.25,
weights=0.9 * w * mono_weights,
bottom=i,
lw=0,
zorder=0)
if len(no_inference) > 0:
no_inference_max_count = max(np_histogram(no_inference, bins=bins)[0])
no_inference_weights = np_ones_like(no_inference) * (1.0 / no_inference_max_count)
ax.hist(no_inference, bins=bins,
color=(0.3, 0.3, 0.3),
alpha=0.25,
weights=0.9 * (1.0 - w) * no_inference_weights,
bottom=i + n,
lw=0,
zorder=0)
if len(poly) > 0:
poly_max_count = max(np_histogram(poly, bins=bins)[0])
poly_weights = np_ones_like(poly) * (1.0 / poly_max_count)
ax.hist(poly, bins=bins,
color=(1.0, 0.0, 0.0),
alpha=0.25,
weights=0.9 * (1.0 - w) * poly_weights,
bottom=i + n,
lw=0,
zorder=0)
scatter = ax.scatter(x, y, alpha=0.5, s=48, c=c, zorder=1)
# set plot elements
ax.grid(color=(0.8, 0.8, 0.8), linestyle='dashed')
ax.set_xlabel('relative distance')
ax.set_xticks(np_arange(0, 1.05, 0.1))
ax.set_xlim([-0.01, 1.01])
ax.set_ylabel('rank (no. taxa)')
ax.set_yticks(xrange(0, len(medians_for_taxa)))
ax.set_ylim([-0.2, len(medians_for_taxa) - 0.01])
ax.set_yticklabels(rank_labels)
self.prettify(ax)
# make plot interactive
mpld3.plugins.clear(self.fig)
mpld3.plugins.connect(self.fig, mpld3.plugins.PointLabelTooltip(scatter, labels=labels))
mpld3.plugins.connect(self.fig, mpld3.plugins.MousePosition(fontsize=10))
mpld3.save_html(self.fig, plot_file[0:plot_file.rfind('.')] + '.html')
self.fig.tight_layout(pad=1)
self.fig.savefig(plot_file, dpi=self.dpi)
def inset_regular_pols(np_verts,
np_pols,
np_distances,
np_inset_rate,
np_make_inners,
np_faces_id,
custom_normals,
matrices,
offset_mode='CENTER',
proportional=False,
concave_support=True,
index_offset=0,
use_custom_normals=False,
output_old_face_id=True,
output_old_v_id=True,
output_pols_groups=True):
pols_number = np_pols.shape[0]
pol_sides = np_pols.shape[1]
v_pols = np_verts[np_pols] #shape [num_pols, num_corners, 3]
if offset_mode == 'SIDES':
inner_points = sides_mode_inset(v_pols, np_inset_rate, np_distances,
concave_support, proportional,
use_custom_normals, custom_normals)
elif offset_mode == 'MATRIX':
inner_points = matrix_mode_inset(v_pols, matrices, use_custom_normals,
custom_normals)
else:
if any(np_distances != 0):
if use_custom_normals:
normals = custom_normals
else:
normals = np_faces_normals(v_pols)
average = np.sum(
v_pols, axis=1
) / pol_sides #+ normals*np_distances[:, np_newaxis] #shape [num_pols, 3]
inner_points = average[:, np_newaxis, :] + (
v_pols - average[:, np_newaxis, :]
) * np_inset_rate[:, np_newaxis,
np_newaxis] + normals[:,
np_newaxis, :] * np_distances[:,
np_newaxis,
np_newaxis]
else:
average = np.sum(v_pols, axis=1) / pol_sides #shape [num_pols, 3]
inner_points = average[:, np_newaxis, :] + (
v_pols - average[:, np_newaxis, :]
) * np_inset_rate[:, np_newaxis, np_newaxis]
idx_offset = len(np_verts) + index_offset
new_v_idx = np_arange(idx_offset,
pols_number * pol_sides + idx_offset).reshape(
pols_number, pol_sides)
side_pols = np.zeros([pols_number, pol_sides, 4], dtype=int)
side_pols[:, :, 0] = np_pols
side_pols[:, :, 1] = np_roll(np_pols, -1, axis=1)
side_pols[:, :, 2] = np_roll(new_v_idx, -1, axis=1)
side_pols[:, :, 3] = new_v_idx
side_faces = side_pols.reshape(-1, 4)
new_insets = new_v_idx[np_make_inners]
if pol_sides == 4:
new_faces = np_concatenate([side_faces, new_insets]).tolist()
else:
new_faces = side_faces.tolist() + new_insets.tolist()
old_v_id = np_pols.flatten().tolist() if output_old_v_id else []
if output_old_face_id:
side_ids = np.repeat(np_faces_id[:, np_newaxis], pol_sides, axis=1)
inset_ids = np_faces_id[np_make_inners]
old_face_id = np.concatenate((side_ids.flatten(), inset_ids)).tolist()
else:
old_face_id = []
if output_pols_groups:
pols_groups = np_repeat(
[1, 2], [len(side_faces), len(new_insets)]).tolist()
else:
pols_groups = []
return (inner_points.reshape(-1, 3).tolist(), new_faces,
new_insets.tolist(), old_v_id, old_face_id, pols_groups)
from mpi4py import MPI
import sys
from numpy import \
arange as np_arange, \
zeros as np_zeros, \
uint32 as np_uint32
comm = MPI.COMM_WORLD
myrank = comm.Get_rank()
nproc = comm.Get_size()
size = int(sys.argv[1])
partial_sum = np_arange(size)
if (myrank != 0):
comm.Send([partial_sum, size, MPI.INT], dest=0, tag=7)
else:
tmp_sum = np_zeros(size, dtype=np_uint32)
for i in range(1, nproc):
comm.Recv([tmp_sum, size, MPI.INT], source=i, tag=7)
print("received data")
def table(self, input_tree, taxon_category_file, bl_step_size, output_table):
"""Produce table with number of lineage for increasing mean branch lengths
Parameters
----------
input_tree : str
Name of input tree.
taxon_category_file : str
File indicating category for each taxon in the tree.
bl_step_size : float
Step size in table for mean branch length criterion.
output_table : str
Name of output table.
"""
# get category for each taxon
taxon_category = {}
for line in open(taxon_category_file):
line_split = line.strip().split('\t')
taxon_category[line_split[0]] = line_split[1]
# read tree
tree = dendropy.Tree.get_from_path(input_tree,
schema='newick',
rooting='force-rooted',
preserve_underscores=True)
# determine mean distance to leaves and taxon categories for each node
all_categories = set()
node_info = {}
parent_mean_dist_to_leafs = {}
max_bl_threshold = None
for i, node in enumerate(tree.seed_node.preorder_iter()):
node.id = i
if node.is_leaf():
mean_dist_to_leafs = 0.0
categories = set()
for c in taxon_category[node.taxon.label].split('/'):
categories.add(c)
else:
dist_to_leafs = []
categories = set()
for t in node.leaf_iter():
dist_to_leafs.append(self._dist_to_ancestor(t, node))
for c in taxon_category[t.taxon.label].split('/'):
categories.add(c)
mean_dist_to_leafs = np_mean(dist_to_leafs)
if node.parent_node:
p = parent_mean_dist_to_leafs[node.parent_node.id]
else:
p = mean_dist_to_leafs + 1e-6
category = '/'.join(sorted(list(categories), reverse=True))
all_categories.add(category)
node_info[node.id] = [mean_dist_to_leafs, p, category]
parent_mean_dist_to_leafs[node.id] = mean_dist_to_leafs
if mean_dist_to_leafs > max_bl_threshold:
max_bl_threshold = mean_dist_to_leafs
# write table
fout = open(output_table, 'w')
fout.write('Threshold')
for c in all_categories:
fout.write('\t%s' % c)
fout.write('\n')
for bl_threshold in np_arange(0, max_bl_threshold + bl_step_size, bl_step_size):
category_count = defaultdict(int)
stack = [tree.seed_node]
while stack:
node = stack.pop()
mean_dist_to_leafs, _, category = node_info[node.id]
if mean_dist_to_leafs > bl_threshold:
for c in node.child_node_iter():
stack.append(c)
else:
category_count[category] += 1
# check if node meets mean branch length criterion
if sum(category_count.values()) > 0:
fout.write('%.3f' % bl_threshold)
for c in all_categories:
fout.write('\t%d' % category_count[c])
fout.write('\n')
fout.close()
if False:
node_info.sort()
for bl_threshold in np_arange(0, node_info[-1][0] + bl_step_size, bl_step_size):
category_count = defaultdict(int)
for mean_bl_dist, parent_mean_bl_dist, category in node_info:
if bl_threshold >= mean_bl_dist and bl_threshold < parent_mean_bl_dist:
category_count[category] += 1
if sum(category_count.values()) > 0:
fout.write('%.3f' % bl_threshold)
for c in all_categories:
fout.write('\t%d' % category_count[c])
fout.write('\n')
def _distribution_plot(self, rel_dists, rel_dist_thresholds,
taxa_for_dist_inference, distribution_table,
plot_file):
"""Create plot showing the distribution of taxa at each taxonomic rank.
Parameters
----------
rel_dists: d[rank_index][taxon] -> relative divergence
Relative divergence of taxa at each rank.
rel_dist_thresholds: list
Relative distances cutoffs for defining ranks.
taxa_for_dist_inference : iterable
Taxa to considered when inferring distributions.
distribution_table : str
Desired name of output table with distribution information.
plot_file : str
Desired name of output plot.
"""
self.fig.clear()
self.fig.set_size_inches(12, 6)
ax = self.fig.add_subplot(111)
# create normal distributions
for i, rank in enumerate(sorted(rel_dists.keys())):
v = [
dist for taxa, dist in rel_dists[rank].iteritems()
if taxa in taxa_for_dist_inference
]
u = np_mean(v)
rv = norm(loc=u, scale=np_std(v))
x = np_linspace(rv.ppf(0.001), rv.ppf(0.999), 1000)
nd = rv.pdf(x)
ax.plot(x, 0.75 * (nd / max(nd)) + i, 'b-', alpha=0.6, zorder=2)
ax.plot((u, u), (i, i + 0.5), 'b-', zorder=2)
# create percentile lines
percentiles = {}
for i, rank in enumerate(sorted(rel_dists.keys())):
v = [
dist for taxa, dist in rel_dists[rank].iteritems()
if taxa in taxa_for_dist_inference
]
p10, p50, p90 = np_percentile(v, [10, 50, 90])
ax.plot((p10, p10), (i, i + 0.5), 'r-', zorder=2)
ax.plot((p50, p50), (i, i + 0.5), 'r-', zorder=2)
ax.plot((p90, p90), (i, i + 0.5), 'r-', zorder=2)
percentiles[i] = [p10, p50, p90]
# create scatter plot and results table
fout = open(distribution_table, 'w')
fout.write(
'Taxa\tRelative Distance\tRank cutoff\tRank outlier\tP10\tMedian\tP90\tPercentile outlier\n'
)
x = []
y = []
c = []
labels = []
rank_labels = []
rel_dist_thresholds += [1.0] # append boundry for species
for i, rank in enumerate(sorted(rel_dists.keys())):
rank_label = Taxonomy.rank_labels[rank]
rank_labels.append(rank_label + ' (%d)' % len(rel_dists[rank]))
for clade_label, dist in rel_dists[rank].iteritems():
x.append(dist)
y.append(i)
labels.append(clade_label)
if clade_label in taxa_for_dist_inference:
c.append((0.0, 0.0, 0.5))
else:
c.append((0.5, 0.5, 0.5))
p10, p50, p90 = percentiles[i]
percentile_outlier = not (dist >= p10 and dist <= p90)
if i == 0:
rank_cutoff = rel_dist_thresholds[i]
rank_outlier = dist > rank_cutoff
else:
rank_cutoff = rel_dist_thresholds[i]
upper_rank_cutoff = rel_dist_thresholds[i - 1]
rank_outlier = not (dist >= upper_rank_cutoff
and dist <= rank_cutoff)
v = [clade_label, dist, rank_cutoff, str(rank_outlier)]
v += percentiles[i] + [str(percentile_outlier)]
fout.write('%s\t%.2f\t%.2f\t%s\t%.2f\t%.2f\t%.2f\t%s\n' %
tuple(v))
fout.close()
scatter = ax.scatter(x, y, alpha=0.5, s=48, c=c, zorder=1)
# set plot elements
ax.grid(color=(0.8, 0.8, 0.8), linestyle='dashed')
ax.set_xlabel('relative distance')
ax.set_xticks(np_arange(0, 1.05, 0.1))
ax.set_xlim([-0.05, 1.05])
ax.set_ylabel('rank (no. taxa)')
ax.set_yticks(xrange(0, len(rel_dists)))
ax.set_ylim([-0.2, len(rel_dists) - 0.01])
ax.set_yticklabels(rank_labels)
self.prettify(ax)
# plot relative divergence threshold lines
y_min, y_max = ax.get_ylim()
for threshold in rel_dist_thresholds[
0:-1]: # don't draw species boundary
ax.plot((threshold, threshold), (y_min, y_max), color='r', ls='--')
ax.text(threshold + 0.001,
y_max,
'%.3f' % threshold,
horizontalalignment='center')
# make plot interactive
mpld3.plugins.connect(
self.fig, mpld3.plugins.PointLabelTooltip(scatter, labels=labels))
mpld3.plugins.connect(self.fig,
mpld3.plugins.MousePosition(fontsize=10))
mpld3.save_html(self.fig, plot_file[0:plot_file.rfind('.')] + '.html')
self.fig.tight_layout(pad=1)
self.fig.savefig(plot_file, dpi=96)
def _distribution_plot(self, rel_dists, rel_dist_thresholds, taxa_for_dist_inference, distribution_table, plot_file):
"""Create plot showing the distribution of taxa at each taxonomic rank.
Parameters
----------
rel_dists: d[rank_index][taxon] -> relative divergence
Relative divergence of taxa at each rank.
rel_dist_thresholds: list
Relative distances cutoffs for defining ranks.
taxa_for_dist_inference : iterable
Taxa to considered when inferring distributions.
distribution_table : str
Desired name of output table with distribution information.
plot_file : str
Desired name of output plot.
"""
self.fig.clear()
self.fig.set_size_inches(12, 6)
ax = self.fig.add_subplot(111)
# create normal distributions
for i, rank in enumerate(sorted(rel_dists.keys())):
v = [dist for taxa, dist in rel_dists[rank].iteritems() if taxa in taxa_for_dist_inference]
u = np_mean(v)
rv = norm(loc=u, scale=np_std(v))
x = np_linspace(rv.ppf(0.001), rv.ppf(0.999), 1000)
nd = rv.pdf(x)
ax.plot(x, 0.75 * (nd / max(nd)) + i, 'b-', alpha=0.6, zorder=2)
ax.plot((u, u), (i, i + 0.5), 'b-', zorder=2)
# create percentile lines
percentiles = {}
for i, rank in enumerate(sorted(rel_dists.keys())):
v = [dist for taxa, dist in rel_dists[rank].iteritems() if taxa in taxa_for_dist_inference]
p10, p50, p90 = np_percentile(v, [10, 50, 90])
ax.plot((p10, p10), (i, i + 0.5), 'r-', zorder=2)
ax.plot((p50, p50), (i, i + 0.5), 'r-', zorder=2)
ax.plot((p90, p90), (i, i + 0.5), 'r-', zorder=2)
percentiles[i] = [p10, p50, p90]
# create scatter plot and results table
fout = open(distribution_table, 'w')
fout.write('Taxa\tRelative Distance\tRank cutoff\tRank outlier\tP10\tMedian\tP90\tPercentile outlier\n')
x = []
y = []
c = []
labels = []
rank_labels = []
rel_dist_thresholds += [1.0] # append boundry for species
for i, rank in enumerate(sorted(rel_dists.keys())):
rank_label = Taxonomy.rank_labels[rank]
rank_labels.append(rank_label + ' (%d)' % len(rel_dists[rank]))
for clade_label, dist in rel_dists[rank].iteritems():
x.append(dist)
y.append(i)
labels.append(clade_label)
if clade_label in taxa_for_dist_inference:
c.append((0.0, 0.0, 0.5))
else:
c.append((0.5, 0.5, 0.5))
p10, p50, p90 = percentiles[i]
percentile_outlier = not (dist >= p10 and dist <= p90)
if i == 0:
rank_cutoff = rel_dist_thresholds[i]
rank_outlier = dist > rank_cutoff
else:
rank_cutoff = rel_dist_thresholds[i]
upper_rank_cutoff = rel_dist_thresholds[i - 1]
rank_outlier = not (dist >= upper_rank_cutoff and dist <= rank_cutoff)
v = [clade_label, dist, rank_cutoff, str(rank_outlier)]
v += percentiles[i] + [str(percentile_outlier)]
fout.write('%s\t%.2f\t%.2f\t%s\t%.2f\t%.2f\t%.2f\t%s\n' % tuple(v))
fout.close()
scatter = ax.scatter(x, y, alpha=0.5, s=48, c=c, zorder=1)
# set plot elements
ax.grid(color=(0.8, 0.8, 0.8), linestyle='dashed')
ax.set_xlabel('relative distance')
ax.set_xticks(np_arange(0, 1.05, 0.1))
ax.set_xlim([-0.05, 1.05])
ax.set_ylabel('rank (no. taxa)')
ax.set_yticks(xrange(0, len(rel_dists)))
ax.set_ylim([-0.2, len(rel_dists) - 0.01])
ax.set_yticklabels(rank_labels)
self.prettify(ax)
# plot relative divergence threshold lines
y_min, y_max = ax.get_ylim()
for threshold in rel_dist_thresholds[0:-1]: # don't draw species boundary
ax.plot((threshold, threshold), (y_min, y_max), color='r', ls='--')
ax.text(threshold + 0.001, y_max, '%.3f' % threshold, horizontalalignment='center')
# make plot interactive
mpld3.plugins.connect(self.fig, mpld3.plugins.PointLabelTooltip(scatter, labels=labels))
mpld3.plugins.connect(self.fig, mpld3.plugins.MousePosition(fontsize=10))
mpld3.save_html(self.fig, plot_file[0:plot_file.rfind('.')] + '.html')
self.fig.tight_layout(pad=1)
self.fig.savefig(plot_file, dpi=96)
