Package Bio :: Package SubsMat :: Class SeqMat
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Type SeqMat

object --+    
         |    
      dict --+
             |
            SeqMat


A Generic sequence matrix class The key is a 2-tuple containing the letter indices of the matrix. Those should be sorted in the tuple (low, high). Because each matrix is dealt with as a half-matrix.
Method Summary
  __init__(self, data, alphabet, mat_type, mat_name, build_later)
  __mul__(self, other)
returns a matrix for which each entry is the multiplication product of the two matrices passed
  __sub__(self, other)
returns a number which is the subtraction product of the two matrices
  __sum__(self, other)
  all_letters_sum(self)
  letter_sum(self, letter)
  make_entropy(self)
  make_relative_entropy(self, obs_freq_mat)
if this matrix is a log-odds matrix, return its entropy Needs the observed frequency matrix for that
  print_full_mat(self, f, format, topformat, alphabet, factor, non_sym)
  print_mat(self, f, format, bottomformat, alphabet, factor)
Print a nice half-matrix.
  _alphabet_from_matrix(self)
  _correct_matrix(self)
  _full_to_half(self)
Convert a full-matrix to a half-matrix
  _init_zero(self)
    Inherited from dict
  __cmp__(x, y)
x.__cmp__(y) <==> cmp(x,y)
  __contains__(D, k)
D.__contains__(k) -> True if D has a key k, else False
  __delitem__(x, y)
x.__delitem__(y) <==> del x[y]
  __eq__(x, y)
x.__eq__(y) <==> x==y
  __ge__(x, y)
x.__ge__(y) <==> x>=y
  __getattribute__(...)
x.__getattribute__('name') <==> x.name
  __getitem__(x, y)
x.__getitem__(y) <==> x[y]
  __gt__(x, y)
x.__gt__(y) <==> x>y
  __hash__(x)
x.__hash__() <==> hash(x)
  __iter__(x)
x.__iter__() <==> iter(x)
  __le__(x, y)
x.__le__(y) <==> x<=y
  __len__(x)
x.__len__() <==> len(x)
  __lt__(x, y)
x.__lt__(y) <==> x<y
  __ne__(x, y)
x.__ne__(y) <==> x!=y
  __new__(T, S, ...)
T.__new__(S, ...) -> a new object with type S, a subtype of T
  __repr__(x)
x.__repr__() <==> repr(x)
  __setitem__(x, i, y)
x.__setitem__(i, y) <==> x[i]=y
  clear(D)
D.clear() -> None.
  copy(D)
D.copy() -> a shallow copy of D
  get(D, k, d)
D.get(k[,d]) -> D[k] if k in D, else d.
  has_key(D, k)
D.has_key(k) -> True if D has a key k, else False
  items(D)
D.items() -> list of D's (key, value) pairs, as 2-tuples
  iteritems(D)
D.iteritems() -> an iterator over the (key, value) items of D
  iterkeys(D)
D.iterkeys() -> an iterator over the keys of D
  itervalues(D)
D.itervalues() -> an iterator over the values of D
  keys(D)
D.keys() -> list of D's keys
  pop(D, k, d)
If key is not found, d is returned if given, otherwise KeyError is raised
  popitem(D)
2-tuple; but raise KeyError if D is empty
  setdefault(D, k, d)
D.setdefault(k[,d]) -> D.get(k,d), also set D[k]=d if k not in D
  update(...)
D.update(E, **F) -> None.
  values(D)
D.values() -> list of D's values
    Inherited from object
  __delattr__(...)
x.__delattr__('name') <==> del x.name
  __reduce__(...)
helper for pickle
  __reduce_ex__(...)
helper for pickle
  __setattr__(...)
x.__setattr__('name', value) <==> x.name = value
  __str__(x)
x.__str__() <==> str(x)
    Inherited from type
  fromkeys(dict, S, v)
v defaults to None.

Method Details

__mul__(self, other)

returns a matrix for which each entry is the multiplication product of the two matrices passed

__sub__(self, other)
(Subtraction operator)

returns a number which is the subtraction product of the two matrices

make_relative_entropy(self, obs_freq_mat)

if this matrix is a log-odds matrix, return its entropy Needs the observed frequency matrix for that

print_mat(self, f=None, format='%4d', bottomformat='%4s', alphabet=None, factor=1)

Print a nice half-matrix. f=sys.stdout to see on the screen User may pass own alphabet, which should contain all letters in the alphabet of the matrix, but may be in a different order. This order will be the order of the letters on the axes

_full_to_half(self)

Convert a full-matrix to a half-matrix

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