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_dfs(clade_b)
_dfs(root)
getTreeFromLinkage(names, linkage)
isinstance(linkage, np.ndarray)
TypeError('linkage must be a numpy.ndarray instance')
LinkageError('linkage must be a 2-dimensional matrix')
LinkageError('linkage must have exactly 4 columns')
len(names)
LinkageError('linkage must have exactly len(names)
Clade(None, name)
clades.append(clade)
heights.append(0.)
int(link[0])
int(link[1])
Clade(None, None)
clade.clades.append(left)
clade.clades.append(right)
clades.append(clade)
heights.append(height)
Tree(clade)
calcTree(names, distance_matrix, method='upgma', linkage=False)
len(names)
len(names)
ValueError("Mismatch between the sizes of matrix and names.")
method.lower()
strip()
hlinkage(squareform(distance_matrix)
getTreeFromLinkage(names, Z)
matrix.append(list(row[:k])
isinstance(names, np.ndarray)
names.tolist()
DistanceMatrix(names, matrix)
DistanceTreeConstructor()
method.strip()
lower()
constructor.nj(dm)
constructor.upgma(dm)
getLinkage(names, tree)
tree.get_nonterminals()
writeTree(filename, tree, format_str='newick')
isinstance(filename, str)
TypeError('filename should be a string')
isinstance(tree, Phylo.BaseTree.Tree)
TypeError('tree should be a Biopython.Phylo Tree object')
isinstance(format_str, str)
TypeError('format_str should be a string')
Phylo.write(tree, filename, format_str)
clusterMatrix(distance_matrix=None, similarity_matrix=None, labels=None, return_linkage=None, **kwargs)
labels (if **labels** are passed)
matrix (if **return_linkage** is **True**)
ValueError('Please provide a distance matrix or a similarity matrix')
kwargs.pop('orientiation', 'right')
kwargs.pop('reversed', False)
kwargs.pop('no_plot', True)
spatial.distance.squareform(distance_matrix)
sch.linkage(formatted_distance_matrix, **kwargs)
sch.dendrogram(linkage_matrix, orientation=orientation, labels=labels, no_plot=no_plot)
return_vals.append(sorted_labels)
return_vals.append(linkage_matrix)
tuple(return_vals)
showLines(*args, **kwargs)
band(s)
band(s)
kwargs.pop('ticklabels', None)
kwargs.pop('dy', None)
kwargs.pop('lower', None)
kwargs.pop('upper', None)
kwargs.pop('alpha', 0.5)
kwargs.pop('beta', 0.25)
kwargs.pop('gap', False)
kwargs.pop('label', None)
gca()
ax.plot(*args, **kwargs)
enumerate(lines)
line.get_color()
line.get_data()
addEnds(x, y)
line.set_data(x_new, y_new)
np.isscalar(labels)
line.set_label(labels)
line.set_label(labels[i])
ValueError('The number of labels ({0})
y ({1})
format(len(labels)
len(line)
sub_array(a, i, tag='a')
np.isscalar(a[0])
list (array)
list (array)
ValueError('The number of {2} ({0})
y ({1})
format(len(miny)
len(line)
len(_a)
len(y)
ValueError('The shapes of {2} ({0})
y ({1})
format(len(_miny)
len(y)
sub_array(miny, i)