Add tutorial in the user documentation

docs/api.rst

@@ -115,6 +115,8 @@ algebraic systems that is already built in.
     The label for boson exchange symmetry.
 
 
+.. _problem_drudges:
+
 Direct support of different problems
 ------------------------------------
 

docs/tutorial.rst

@@ -1,3 +1,284 @@
 Drudge tutorial for beginners
 =============================
 
+.. currentmodule:: drudge
+
+
+Get started
+-----------
+
+
+Drudge is a library built on top of the SymPy computer algebra library for
+noncommutative and tensor alegbras.  Usually for these style of problems, the
+symbolic manipulation and simplification of mathematical expressions requires a
+lot of context-dependent information, like the specific commutation rules and
+things like the dummy symbols to be used for different ranges.  So the primary
+entry point for using the library is the :py:class:`Drudge` class, which serves
+as a central repository of all kinds of domain-specific informations.  To
+create a drudge instance, we need to give it a Spark context so that it is
+capable of parallelize things.  For instance, to run things locally with all
+available cores, we can do
+
+.. doctest::
+
+    >>> import pyspark
+    >>> import drudge
+    >>> ctx = pyspark.SparkContext('local[*]', 'drudge-tutorial')
+    >>> dr = drudge.Drudge(ctx)
+
+Then from it, we can create the symbolic expressions as :py:class:`Tensor`
+objects, which are basically mathematical expressions containing noncommutative
+objects and symbolic summations.  For the noncommutativity, in spite of the
+availability of some basic support of it in SymPy, here we have the
+:py:class:`Vec` class to specifically designate the noncommutativity of its
+multiplication.  It can be created with a label and indexed with SymPy
+expressions.
+
+.. doctest::
+
+    >>> v = drudge.Vec('v')
+    >>> import sympy
+    >>> a = sympy.Symbol('a')
+    >>> str(v[a])
+    'v[a]'
+
+For the symbolic summations, we have the :py:class:`Range` class, which denotes
+a symbolic set that a variable could be summed over.  It can be created by just
+a label.
+
+.. doctest::
+
+    >>> l = drudge.Range('L')
+
+With these, we can create tensor objects by using the :py:meth:`Drudge.sum`
+method,
+
+.. doctest::
+
+    >>> x = sympy.IndexedBase('x')
+    >>> tensor = dr.sum((a, l), x[a] * v[a])
+    >>> str(tensor)
+    'sum_{a} x[a] * v[a]'
+
+Now we got a symbolic tensor of a sum of vectors modulated by a SymPy
+IndexedBase.  Actually any type of SymPy expression can be used to modulate the
+noncommutative vectors.
+
+.. doctest::
+
+    >>> tensor = dr.sum((a, l), sympy.sin(a) * v[a])
+    >>> str(tensor)
+    'sum_{a} sin(a) * v[a]'
+
+And we can also have multiple summations and product of the vectors.
+
+.. doctest::
+
+    >>> b = sympy.Symbol('b')
+    >>> tensor = dr.sum((a, l), (b, l), x[a, b] * v[a] * v[b])
+    >>> str(tensor)
+    'sum_{a, b} x[a, b] * v[a] * v[b]'
+
+Of cause the multiplication of the vectors will not be commutative,
+
+.. doctest::
+
+    >>> tensor = dr.sum((a, l), (b, l), x[a, b] * v[b] * v[a])
+    >>> str(tensor)
+    'sum_{a, b} x[a, b] * v[b] * v[a]'
+
+Normally, for each symbolic range, we have some traditional symbols used as
+dummies for summations over them, giving these information to drudge objects
+can be very helpful.  Here in this demonstration, we can use the
+:py:meth:`Drudge.set_dumms` method.
+
+.. doctest::
+
+    >>> dr.set_dumms(l, sympy.symbols('a b c d'))
+    [a, b, c, d]
+    >>> dr.add_resolver_for_dumms()
+
+where the call to the :py:meth:`Drudge.add_resolver_for_dumms` method could
+tell the drudge to interpret all the dummy symbols to be over the range that
+they are set to.  By giving drudge object such domain-specific information, we
+can have a lot convenience.  For instance, now we can use Einstein summation
+convention to create tensor object, without the need to spell all the
+summations out.
+
+.. doctest::
+
+    >>> tensor = dr.einst(x[a, b] * v[a] * v[b])
+    >>> str(tensor)
+    'sum_{a, b} x[a, b] * v[a] * v[b]'
+
+Also the drudge knows what to do when more dummies are needed in mathematical
+operations.  For instance, when we multiply things,
+
+.. doctest::
+
+    >>> tensor = dr.einst(x[a] * v[a])
+    >>> prod = tensor * tensor
+    >>> str(prod)
+    'sum_{a, b} x[a]*x[b] * v[a] * v[b]'
+
+Here the dummy :math:`b` is automatically used since the drudge object knows
+available dummies for its range.  Also the range and the dummies are
+automatically added to the name archive of the drudge, which can be access by
+:py:attr:`Drudge.names`.
+
+.. doctest::
+
+    >>> p = dr.names
+    >>> p.L
+    Range('L')
+    >>> p.L_dumms
+    [a, b, c, d]
+    >>> p.d
+    d
+
+Here in this example, we set the dummies ourselves by
+:py:meth:`Drudge.set_dumms`.  Normally, in subclasses of :py:class:`Drudge` for
+different specific problems, such setting up is already finished within the
+class.  We can just directly get what we need from the names archive.  There is
+also a method :py:meth:`Drudge.inject_names` for the convenience of interactive
+work.
+
+
+Tensor manipulations
+--------------------
+
+
+Now with tensors created by :py:meth:`Drudge.sum` or :py:meth:`Drudge.einst`, a
+lot of mathematical operations are available to them.   In addition to the
+above example of (noncommutative) multiplication, we can also have the linear
+algebraic operations of addition and scalar multiplication.
+
+.. doctest::
+
+    >>> tensor = dr.einst(x[a] * v[a])
+    >>> y = sympy.IndexedBase('y')
+    >>> res = tensor + dr.einst(y[a] * v[a])
+    >>> str(res)
+    'sum_{a} x[a] * v[a]\n + sum_{a} y[a] * v[a]'
+
+    >>> res = 2 * tensor
+    >>> str(res)
+    'sum_{a} 2*x[a] * v[a]'
+
+We can also perform some complex substitutions on either the vector or the
+amplitude part, by using the :py:meth:`Drudge.subst` method.
+
+.. doctest::
+
+    >>> t = sympy.IndexedBase('t')
+    >>> w = drudge.Vec('w')
+    >>> substed = tensor.subst(v[a], dr.einst(t[a, b] * w[b]))
+    >>> str(substed)
+    'sum_{a, b} x[a]*t[a, b] * w[b]'
+
+    >>> substed = tensor.subst(x[a], sympy.sin(a))
+    >>> str(substed)
+    'sum_{a} sin(a) * v[a]'
+
+Note that here the substituted vector does not have to match the left-hand side
+of the substitution exactly, pattern matching is done here.  Other mathematical
+operations are also available, like symbolic differentiation by
+:py:meth:`Tensor.diff` and commutation by ``|`` operator
+:py:meth:`Tensor.__or__`.
+
+Usually for tensorial problems, full simplification requires the utilization of
+some symmetries present on the indexed quantities by permutations among their
+indices.  For instance, an anti-symmetric matrix entry changes sign when we
+transpose the two indices.  Such information can be told to drudge by using the
+:py:meth:`Drudge.set_symm` method, by giving generators of the symmetry group
+by :py:class:`Perm` instances.  For instance, we can do,
+
+.. testcode::
+
+    dr.set_symm(x, drudge.Perm([1, 0], drudge.NEG))
+
+Then the master simplification algorithm in :py:meth:`Tensor.simplify` is able
+to take full advantage of such information.
+
+.. doctest::
+
+    >>> tensor = dr.einst(x[a, b] * v[a] * v[b] + x[b, a] * v[a] * v[b])
+    >>> str(tensor)
+    'sum_{a, b} x[a, b] * v[a] * v[b]\n + sum_{a, b} x[b, a] * v[a] * v[b]'
+    >>> str(tensor.simplify())
+    '0'
+
+Normally, drudge subclasses for specific problems add symmetries for some
+important indexed bases in the problem.  And some drudge subclasses have helper
+methods for the setting of such symmetries, like
+:py:meth:`FockDrudge.set_n_body_base` and :py:meth:`FockDrudge.set_dbbar_base`.
+
+For the simplification of the noncommutative vector parts, the base
+:py:class:`Drudge` class does **not** consider any commutation rules among the
+vectors.  It works on the free algebra, while the subclasses could have the
+specific commutation rules added for the algebraic system.  For instance,
+:py:class:`WickDrudge` add abstract commutation rules where all the commutators
+have scalar values.  Based on it, its special subclass :py:class:`FockDrudge`
+implements the canonical commutation relations for bosons and the canonical
+anti-commutation relations for fermions.
+
+These drudge subclasses only has the mathematical commutation rules implemented,
+for convenience in solving problems, many drudge subclasses are built-in with a
+lot of domain-specific information like the ranges and dummies, which are listed
+in :ref:`problem_drudges`.  For instance, we can easily see the commutativity of
+two particle-hole excitation operators by using the :py:class:`PartHoleDrudge`.
+
+
+.. doctest::
+
+    >>> phdr = drudge.PartHoleDrudge(ctx)
+    >>> t = sympy.IndexedBase('t')
+    >>> u = sympy.IndexedBase('u')
+    >>> p = phdr.names
+    >>> a, i = p.a, p.i
+    >>> excit1 = phdr.einst(t[a, i] * p.c_dag[a] * p.c_[i])
+    >>> excit2 = phdr.einst(u[a, i] * p.c_dag[a] * p.c_[i])
+    >>> comm = excit1 | excit2
+    >>> str(comm)
+    'sum_{i, a, j, b} t[a, i]*u[b, j] * c[CR, a] * c[AN, i] * c[CR, b] * c[AN, j]\n + sum_{i, a, j, b} -t[a, i]*u[b, j] * c[CR, b] * c[AN, j] * c[CR, a] * c[AN, i]'
+    >>> str(comm.simplify())
+    '0'
+
+Note that here basically all things related to the problem, like the vector for
+creation and annihilation operator, the conventional dummies :math:`a` and
+:math:`i` for particle and hole labels, are directly read from the name archive
+of the drudge.  Problem-specific drudges are supposed to give such convenience.
+
+In addition to providing context-dependent information for general tensor
+operations, drudge subclasses could also provide additional operations on
+tensors created from them.  For instance, for the above commutator, we can
+directly compute the expectation value with respect to the Fermi vacuum by
+
+.. doctest::
+
+    >>> str(comm.eval_fermi_vev())
+    '0'
+
+These additional operations are called tensor methods and are documented in the
+drudge subclasses.
+
+
+Examples on real-world applications
+-----------------------------------
+
+In this tutorial, some simple examples are run directly inside a Python
+interpreter.  Actually drudge is designed to work well inside Jupyter
+notebooks.  By calling the :py:meth:`Tensor.display` method, tensor objects can
+be mathematically displayed in Jupyter sessions.  An example of interactive
+usage of drudge, we have a `sample notebook`_ in ``docs/examples/ccsd.ipynb``
+in the project source.  Also included is a `general script`_ ``gencc.py`` for
+the automatic derivation of coupled-cluster theories, mostly to demonstrate
+using drudge programmatically.  And we also have a `script for RCCSD theory`_
+to demonstrate its usage in large-scale spin-explicit coupled-cluster theories.
+
+
+.. _sample notebook: https://github.com/tschijnmo/drudge/blob/master/docs/examples/ccsd.ipynb
+
+.. _general script: https://github.com/tschijnmo/drudge/blob/master/docs/examples/gencc.py
+
+.. _script for RCCSD theory: https://github.com/tschijnmo/drudge/blob/master/docs/examples/rccsd.py