From f2f9b4552f654d1f0e89003d4defb7c3bdd8eb3d Mon Sep 17 00:00:00 2001
From: Francis Russell <francis@unchartedbackwaters.co.uk>
Date: Mon, 8 Jul 2013 18:57:32 +0100
Subject: [PATCH] Initial work on second attempt at linear transform analyser.

---
 .gitignore |   1 +
 block.py   | 133 +++++++++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 134 insertions(+)
 create mode 100644 .gitignore
 create mode 100644 block.py

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..0d20b64
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
+*.pyc
diff --git a/block.py b/block.py
new file mode 100644
index 0000000..8dbb0e8
--- /dev/null
+++ b/block.py
@@ -0,0 +1,133 @@
+import sympy
+import numpy
+import itertools
+import copy
+from sets import Set
+
+
+class Block:
+    def __init__(self, shape):
+        self.shape = shape
+        self.indices = map(sympy.Dummy,
+                           map(chr, range(ord('i'), ord('z'))[:len(shape)]))
+        self.blocks = numpy.empty([1] * len(shape), dtype=numpy.object)
+        self.blocks.flat[0] = DenseBlock(shape, 0)
+
+    def __setitem__(self, key, value):
+        print str(key)
+
+    @staticmethod
+    def from_block_shapes(block_shapes):
+        block = Block(map(sum, block_shapes))
+        block.blocks = numpy.empty(map(len, block_shapes), dtype=numpy.object)
+
+        for idx, block_shape in enumerate(itertools.product(*block_shapes)):
+            block.blocks.flat[idx] = DenseBlock(block_shape, 0)
+
+        return block
+
+    def block_shape(self):
+        """Return the shape of the subblocks of this block"""
+        return self.blocks.shape
+
+    def block_sizes(self):
+        """Return the sizes of the subblocks of this block"""
+        sizes = []
+        for dimension in range(0, len(self.shape)):
+            slice_sizes = []
+            slice_index = ([0] * dimension +
+                           [slice(None, None, None)] +
+                           [0] * (len(self.shape) - dimension - 1))
+            for block in self.blocks[slice_index]:
+                slice_sizes.append(block.shape[dimension])
+
+            sizes.append(slice_sizes)
+        return sizes
+
+    def split(self, dimension, offset):
+        assert(dimension < len(self.shape))
+        assert(offset < self.shape[dimension])
+
+        result = copy.deepcopy(self)
+
+        dimension_sizes = self.block_sizes()[dimension]
+
+        block_offset = 0
+        local_offset = offset
+        block_index = 0
+        while(local_offset - dimension_sizes[block_index] >= 0):
+            block_index += 1
+            block_offset += dimension_sizes[block_index]
+            local_offset -= dimension_sizes[block_index]
+
+        if (local_offset == 0):
+            return self
+
+        slice_index = ([slice(None, None, None)] * dimension +
+                       [block_index] +
+                       [slice(None, None, None)] *
+                         (len(self.shape) - dimension - 1))
+
+        split_blocks = []
+        for block in self.blocks[slice_index].flat:
+            split_blocks.append(block.split(dimension, local_offset))
+
+        result.blocks = numpy.delete(result.blocks, block_index, dimension)
+        result.blocks = numpy.insert(result.blocks, block_index, split_blocks,
+                      dimension)
+
+        return result
+
+    def compute_splits(self):
+        splits = []
+        for dimension in range(0, len(self.shape)):
+            offsets = Set()
+            offset = 0
+            for block_index in range(0, self.block_shape()[dimension]):
+                slice_index = ([slice(None, None, None)] * dimension +
+                              [block_index] +
+                              [slice(None, None, None)] *
+                                (len(self.shape) - dimension - 1))
+
+                for block in self.blocks[slice_index].flat:
+                    for local_offset in block.compute_splits()[dimension]:
+                        offsets.add(offset + local_offset)
+
+                offset += self.block_sizes()[dimension][block_index]
+            splits.append(sorted(list(offsets)))
+
+        return splits
+
+    def flatten(self):
+        raise NotImplementedError("Not yet done.")
+
+
+class DenseBlock:
+    def __init__(self, shape, expr):
+        self.expr = expr
+        self.shape = shape
+
+    def block_sizes(self):
+        return self.shape
+
+    def compute_splits(self):
+        return [[size] for size in self.shape]
+
+    def split(self, dimension, offset):
+        assert(dimension < len(self.shape))
+        assert(offset < self.shape[dimension])
+
+        before_shape = (self.shape[0:dimension] + [offset] +
+                        self.shape[dimension + 1:])
+        after_shape = (self.shape[0:dimension] + [self.shape[dimension] - offset] +
+                       self.shape[dimension + 1:])
+
+        before = DenseBlock(before_shape, self.expr)
+        after = DenseBlock(after_shape, self.expr)
+
+        result = Block(self.shape)
+        result.blocks = numpy.empty([1] * dimension + [2] + [1] * (len(self.shape) - dimension - 1), dtype=numpy.object)
+        result.blocks.flat[0] = before
+        result.blocks.flat[1] = after
+
+        return result
-- 
2.47.3