conftest.py

+import os
+import runpy
+import sys
+import tempfile
+import warnings
+
+import nbformat
+import pytest
+from nbconvert import PythonExporter
+
+from pystencils.boundaries.createindexlist import *  # NOQA
+# Trigger config file reading / creation once - to avoid race conditions when multiple instances are creating it
+# at the same time
+from pystencils.cpu import cpujit
+
+# trigger cython imports - there seems to be a problem when multiple processes try to compile the same cython file
+# at the same time
+try:
+    import pyximport
+    pyximport.install(language_level=3)
+except ImportError:
+    pass
+
+
+SCRIPT_FOLDER = os.path.dirname(os.path.realpath(__file__))
+sys.path.insert(0, os.path.abspath('pystencils'))
+
+# the Ubuntu pipeline uses an older version of pytest which uses deprecated functionality.
+# This leads to many warinings in the test and coverage pipeline.
+pytest_numeric_version = [int(x, 10) for x in pytest.__version__.split('.')]
+pytest_numeric_version.reverse()
+pytest_version = sum(x * (100 ** i) for i, x in enumerate(pytest_numeric_version))
+
+
+def add_path_to_ignore(path):
+    if not os.path.exists(path):
+        return
+    global collect_ignore
+    collect_ignore += [os.path.join(SCRIPT_FOLDER, path, f) for f in os.listdir(os.path.join(SCRIPT_FOLDER, path))]
+
+
+collect_ignore = [os.path.join(SCRIPT_FOLDER, "doc", "conf.py"),
+                  os.path.join(SCRIPT_FOLDER, "src", "pystencils", "opencl", "opencl.autoinit")]
+add_path_to_ignore('tests/benchmark')
+add_path_to_ignore('_local_tmp')
+
+
+try:
+    import cupy
+except ImportError:
+    collect_ignore += [os.path.join(SCRIPT_FOLDER, "tests/test_gpu.py")]
+    add_path_to_ignore('src/pystencils/gpu')
+
+try:
+    import waLBerla
+except ImportError:
+    collect_ignore += [os.path.join(SCRIPT_FOLDER, "tests/test_aligned_array.py"),
+                       os.path.join(SCRIPT_FOLDER, "tests/test_datahandling_parallel.py"),
+                       os.path.join(SCRIPT_FOLDER, "doc/notebooks/03_tutorial_datahandling.ipynb"),
+                       os.path.join(SCRIPT_FOLDER, "src/pystencils/datahandling/parallel_datahandling.py"),
+                       os.path.join(SCRIPT_FOLDER, "tests/test_small_block_benchmark.ipynb")]
+
+try:
+    import blitzdb
+except ImportError:
+    add_path_to_ignore('src/pystencils/runhelper')
+    collect_ignore += [os.path.join(SCRIPT_FOLDER, "tests/test_parameterstudy.py")]
+    collect_ignore += [os.path.join(SCRIPT_FOLDER, "tests/test_json_serializer.py")]
+
+try:
+    import islpy
+except ImportError:
+    collect_ignore += [os.path.join(SCRIPT_FOLDER, "src/pystencils/integer_set_analysis.py")]
+
+try:
+    import graphviz
+except ImportError:
+    collect_ignore += [os.path.join(SCRIPT_FOLDER, "src/pystencils/backends/dot.py")]
+    collect_ignore += [os.path.join(SCRIPT_FOLDER, "doc/notebooks/01_tutorial_getting_started.ipynb")]
+
+try:
+    import pyevtk
+except ImportError:
+    collect_ignore += [os.path.join(SCRIPT_FOLDER, "src/pystencils/datahandling/vtk.py")]
+
+collect_ignore += [os.path.join(SCRIPT_FOLDER, 'setup.py')]
+
+for root, sub_dirs, files in os.walk('.'):
+    for f in files:
+        if f.endswith(".ipynb") and not any(f.startswith(k) for k in ['demo', 'tutorial', 'test', 'doc']):
+            collect_ignore.append(f)
+
+
+class IPythonMockup:
+    def run_line_magic(self, *args, **kwargs):
+        pass
+
+    def run_cell_magic(self, *args, **kwargs):
+        pass
+
+    def magic(self, *args, **kwargs):
+        pass
+
+    def __bool__(self):
+        return False
+
+
+class IPyNbTest(pytest.Item):
+    def __init__(self, name, parent, code):
+        super(IPyNbTest, self).__init__(name, parent)
+        self.code = code
+        self.add_marker('notebook')
+
+    @pytest.mark.filterwarnings("ignore:IPython.core.inputsplitter is deprecated")
+    def runtest(self):
+        global_dict = {'get_ipython': lambda: IPythonMockup(),
+                       'is_test_run': True}
+
+        # disable matplotlib output
+        exec("import matplotlib.pyplot as p; "
+             "p.switch_backend('Template')", global_dict)
+
+        # in notebooks there is an implicit plt.show() - if this is not called a warning is shown when the next
+        # plot is created. This warning is suppressed here
+        exec("import warnings;"
+             "warnings.filterwarnings('ignore', 'Adding an axes using the same arguments as a previous.*')",
+             global_dict)
+        with tempfile.NamedTemporaryFile() as f:
+            f.write(self.code.encode())
+            f.flush()
+            runpy.run_path(f.name, init_globals=global_dict, run_name=self.name)
+
+
+class IPyNbFile(pytest.File):
+    def collect(self):
+        exporter = PythonExporter()
+        exporter.exclude_markdown = True
+        exporter.exclude_input_prompt = True
+
+        notebook_contents = self.fspath.open(encoding='utf-8')
+
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", "IPython.core.inputsplitter is deprecated")
+            notebook = nbformat.read(notebook_contents, 4)
+            code, _ = exporter.from_notebook_node(notebook)
+        if pytest_version >= 50403:
+            yield IPyNbTest.from_parent(name=self.name, parent=self, code=code)
+        else:
+            yield IPyNbTest(self.name, self, code)
+
+    def teardown(self):
+        pass
+
+
+def pytest_collect_file(path, parent):
+    glob_exprs = ["*demo*.ipynb", "*tutorial*.ipynb", "test_*.ipynb"]
+    if any(path.fnmatch(g) for g in glob_exprs):
+        if pytest_version >= 50403:
+            return IPyNbFile.from_parent(fspath=path, parent=parent)
+        else:
+            return IPyNbFile(path, parent)

cpu/__init__.py

-from pystencils.cpu.kernelcreation import createKernel, addOpenMP
-from pystencils.cpu.cpujit import makePythonFunction
-from pystencils.backends.cbackend import generateC

cpu/cpujit.py

-import os
-import subprocess
-from ctypes import cdll, c_double, c_float, sizeof
-from tempfile import TemporaryDirectory
-from pystencils.backends.cbackend import generateC
-import numpy as np
-import pickle
-import hashlib
-
-from pystencils.transformations import symbolNameToVariableName
-
-CONFIG_GCC = {
-    'compiler': 'g++',
-    'flags': '-Ofast -DNDEBUG -fPIC -shared -march=native -fopenmp',
-}
-CONFIG_INTEL = {
-    'compiler': '/software/intel/2017/bin/icpc',
-    'flags': '-Ofast -DNDEBUG -fPIC -shared -march=native -fopenmp -Wl,-rpath=/software/intel/2017/lib/intel64',
-    'env': {
-        'INTEL_LICENSE_FILE': '1713@license4.rrze.uni-erlangen.de',
-        'LM_PROJECT': 'iwia',
-    }
-}
-CONFIG_INTEL_SUPERMUC = {
-    'compiler': '/lrz/sys/intel/studio2017_u1/compilers_and_libraries_2017.1.132/linux/bin/intel64/icpc',
-    'flags': '-Ofast -DNDEBUG -fPIC -shared -march=native -fopenmp -Wl,'
-             '-rpath=/lrz/sys/intel/studio2016_u4/compilers_and_libraries_2016.4.258/linux/mkl/lib/intel64',
-    'env': {
-        'INTEL_LICENSE_FILE': '/lrz/sys/intel/licenses',
-    }
-}
-CONFIG_CLANG = {
-    'compiler': 'clang++',
-    'flags': '-Ofast -DNDEBUG -fPIC -shared -march=native -fopenmp',
-}
-CONFIG = CONFIG_GCC
-
-
-def ctypeFromString(typename, includePointers=True):
-    import ctypes as ct
-
-    typename = str(typename).replace("*", " * ")
-    typeComponents = typename.split()
-
-    basicTypeMap = {
-        'double': ct.c_double,
-        'float': ct.c_float,
-        'int': ct.c_int,
-        'long': ct.c_long,
-    }
-
-    resultType = None
-    for typeComponent in typeComponents:
-        typeComponent = typeComponent.strip()
-        if typeComponent == "const" or typeComponent == "restrict" or typeComponent == "volatile":
-            continue
-        if typeComponent in basicTypeMap:
-            resultType = basicTypeMap[typeComponent]
-        elif typeComponent == "*" and includePointers:
-            assert resultType is not None
-            resultType = ct.POINTER(resultType)
-
-    return resultType
-
-
-def ctypeFromNumpyType(numpyType):
-    typeMap = {
-        np.dtype('float64'): c_double,
-        np.dtype('float32'): c_float,
-    }
-    return typeMap[numpyType]
-
-
-def compile(code, tmpDir, libFile, createAssemblyCode=False):
-    srcFile = os.path.join(tmpDir, 'source.cpp')
-    with open(srcFile, 'w') as sourceFile:
-        print('#include <iostream>', file=sourceFile)
-        print("#include <cmath>", file=sourceFile)
-        print('extern "C" { ', file=sourceFile)
-        print(code, file=sourceFile)
-        print('}', file=sourceFile)
-
-    compilerCmd = [CONFIG['compiler']] + CONFIG['flags'].split()
-    compilerCmd += [srcFile, '-o', libFile]
-    configEnv = CONFIG['env'] if 'env' in CONFIG else {}
-    env = os.environ.copy()
-    env.update(configEnv)
-    subprocess.call(compilerCmd, env=env)
-
-    assembly = None
-    if createAssemblyCode:
-        assemblyFile = os.path.join(tmpDir, "assembly.s")
-        compilerCmd = [CONFIG['compiler'], '-S', '-o', assemblyFile, srcFile] + CONFIG['flags'].split()
-        subprocess.call(compilerCmd, env=env)
-        assembly = open(assemblyFile, 'r').read()
-    return assembly
-
-
-def compileAndLoad(kernelFunctionNode):
-    with TemporaryDirectory() as tmpDir:
-        libFile = os.path.join(tmpDir, "jit.so")
-        compile(generateC(kernelFunctionNode), tmpDir, libFile)
-        loadedJitLib = cdll.LoadLibrary(libFile)
-
-    return loadedJitLib
-
-
-def buildCTypeArgumentList(parameterSpecification, argumentDict):
-    argumentDict = {symbolNameToVariableName(k): v for k, v in argumentDict.items()}
-    ctArguments = []
-    for arg in parameterSpecification:
-        if arg.isFieldArgument:
-            field = argumentDict[arg.fieldName]
-            if arg.isFieldPtrArgument:
-                ctArguments.append(field.ctypes.data_as(ctypeFromString(arg.dtype)))
-            elif arg.isFieldShapeArgument:
-                dataType = ctypeFromString(arg.dtype, includePointers=False)
-                ctArguments.append(field.ctypes.shape_as(dataType))
-            elif arg.isFieldStrideArgument:
-                dataType = ctypeFromString(arg.dtype, includePointers=False)
-                baseFieldType = ctypeFromNumpyType(field.dtype)
-                strides = field.ctypes.strides_as(dataType)
-                for i in range(len(field.shape)):
-                    assert strides[i] % sizeof(baseFieldType) == 0
-                    strides[i] //= sizeof(baseFieldType)
-                ctArguments.append(strides)
-            else:
-                assert False
-        else:
-            param = argumentDict[arg.name]
-            expectedType = ctypeFromString(arg.dtype)
-            ctArguments.append(expectedType(param))
-    return ctArguments
-
-
-def makePythonFunctionIncompleteParams(kernelFunctionNode, argumentDict):
-    func = compileAndLoad(kernelFunctionNode)[kernelFunctionNode.functionName]
-    func.restype = None
-    parameters = kernelFunctionNode.parameters
-
-    def wrapper(**kwargs):
-        from copy import copy
-        fullArguments = copy(argumentDict)
-        fullArguments.update(kwargs)
-        args = buildCTypeArgumentList(parameters, fullArguments)
-        func(*args)
-    return wrapper
-
-
-def makePythonFunction(kernelFunctionNode, argumentDict={}):
-    """
-    Creates C code from the abstract syntax tree, compiles it and makes it accessible as Python function
-
-    The parameters of the kernel are:
-        - numpy arrays for each field used in the kernel. The keyword argument name is the name of the field
-        - all symbols which are not defined in the kernel itself are expected as parameters
-
-    :param kernelFunctionNode: the abstract syntax tree
-    :param argumentDict: parameters passed here are already fixed. Remaining parameters have to be passed to the
-                        returned kernel functor.
-    :return: kernel functor
-    """
-    # build up list of CType arguments
-    try:
-        args = buildCTypeArgumentList(kernelFunctionNode.parameters, argumentDict)
-    except KeyError:
-        # not all parameters specified yet
-        return makePythonFunctionIncompleteParams(kernelFunctionNode, argumentDict)
-    func = compileAndLoad(kernelFunctionNode)[kernelFunctionNode.functionName]
-    func.restype = None
-    return lambda: func(*args)
-
-
-class CachedKernel:
-    def __init__(self, configDict, ast, parameterValues):
-        self.configDict = configDict
-        self.ast = ast
-        self.parameterValues = parameterValues
-        self.funcPtr = None
-
-    def __compile(self):
-        self.funcPtr = makePythonFunction(self.ast, self.parameterValues)
-
-    def __call__(self, *args, **kwargs):
-        if self.funcPtr is None:
-            self.__compile()
-        self.funcPtr(*args, **kwargs)
-
-
-def hashToFunctionName(h):
-    res = "func_%s" % (h,)
-    return res.replace('-', 'm')
-
-
-def createLibrary(cachedKernels, libraryFile):
-    libraryInfoFile = libraryFile + ".info"
-
-    with TemporaryDirectory() as tmpDir:
-        code = ""
-        infoDict = {}
-        for cachedKernel in cachedKernels:
-            s = repr(sorted(cachedKernel.configDict.items()))
-            configHash = hashlib.sha1(s.encode()).hexdigest()
-            cachedKernel.ast.functionName = hashToFunctionName(configHash)
-            kernelCode = generateC(cachedKernel.ast)
-            code += kernelCode + "\n"
-            infoDict[configHash] = {'code': kernelCode,
-                                    'parameterValues': cachedKernel.parameterValues,
-                                    'configDict': cachedKernel.configDict,
-                                    'parameterSpecification': cachedKernel.ast.parameters}
-
-        compile(code, tmpDir, libraryFile)
-        pickle.dump(infoDict, open(libraryInfoFile, "wb"))
-
-
-def loadLibrary(libraryFile):
-    libraryInfoFile = libraryFile + ".info"
-
-    libraryFile = cdll.LoadLibrary(libraryFile)
-    libraryInfo = pickle.load(open(libraryInfoFile, 'rb'))
-
-    def getKernel(**kwargs):
-        s = repr(sorted(kwargs.items()))
-        configHash = hashlib.sha1(s.encode()).hexdigest()
-        if configHash not in libraryInfo:
-            raise ValueError("No such kernel in library")
-        func = libraryFile[hashToFunctionName(configHash)]
-        func.restype = None
-
-        def wrapper(**kwargs):
-            from copy import copy
-            fullArguments = copy(libraryInfo[configHash]['parameterValues'])
-            fullArguments.update(kwargs)
-            args = buildCTypeArgumentList(libraryInfo[configHash]['parameterSpecification'], fullArguments)
-            func(*args)
-        wrapper.configDict = libraryInfo[configHash]['configDict']
-        return wrapper
-
-    return getKernel

cpu/kerncraft.py

-from pystencils.transformations import makeLoopOverDomain, typingFromSympyInspection, \
-    typeAllEquations, moveConstantsBeforeLoop, getOptimalLoopOrdering
-import pystencils.ast as ast
-from pystencils.backends.cbackend import CBackend, CustomSympyPrinter
-from pystencils import TypedSymbol
-
-
-def createKerncraftCode(listOfEquations, typeForSymbol=None, ghostLayers=None):
-    """
-    Creates an abstract syntax tree for a kernel function, by taking a list of update rules.
-
-    Loops are created according to the field accesses in the equations.
-
-    :param listOfEquations: list of sympy equations, containing accesses to :class:`pystencils.field.Field`.
-           Defining the update rules of the kernel
-    :param typeForSymbol: a map from symbol name to a C type specifier. If not specified all symbols are assumed to
-           be of type 'double' except symbols which occur on the left hand side of equations where the
-           right hand side is a sympy Boolean which are assumed to be 'bool' .
-    :param ghostLayers: a sequence of pairs for each coordinate with lower and upper nr of ghost layers
-                        if None, the number of ghost layers is determined automatically and assumed to be equal for a
-                        all dimensions
-
-    :return: :class:`pystencils.ast.KernelFunction` node
-    """
-    if not typeForSymbol:
-        typeForSymbol = typingFromSympyInspection(listOfEquations, "double")
-
-    fieldsRead, fieldsWritten, assignments = typeAllEquations(listOfEquations, typeForSymbol)
-    allFields = fieldsRead.union(fieldsWritten)
-
-    optimalLoopOrder = getOptimalLoopOrdering(allFields)
-    cstyleLoopOrder = list(range(len(optimalLoopOrder)))
-
-    body = ast.Block(assignments)
-    code = makeLoopOverDomain(body, "kerncraft", ghostLayers=ghostLayers, loopOrder=cstyleLoopOrder)
-    moveConstantsBeforeLoop(code)
-    loopBody = code.body
-
-    printer = CBackend(sympyPrinter=ArraySympyPrinter())
-
-    FIXED_SIZES = ("XS", "YS", "ZS", "E1S", "E2S")
-
-    result = ""
-    for field in allFields:
-        sizesPermutation = [FIXED_SIZES[i] for i in field.layout]
-        suffix = "".join("[%s]" % (size,) for size in sizesPermutation)
-        result += "%s%s;\n" % (field.name, suffix)
-
-    # add parameter definitions
-    for s in loopBody.undefinedSymbols:
-        if isinstance(s, TypedSymbol):
-            result += "%s %s;\n" % (s.dtype, s.name)
-
-    for element in loopBody.args:
-        result += printer(element)
-        result += "\n"
-    return result
-
-
-class ArraySympyPrinter(CustomSympyPrinter):
-
-    def _print_Access(self, fieldAccess):
-        """"""
-        Loop = ast.LoopOverCoordinate
-        coordinateValues = [Loop.getLoopCounterSymbol(i) + offset for i, offset in enumerate(fieldAccess.offsets)]
-        coordinateValues += list(fieldAccess.index)
-        permutedCoordinates = [coordinateValues[i] for i in fieldAccess.field.layout]
-
-        suffix = "".join("[%s]" % (self._print(a)) for a in permutedCoordinates)
-        return "%s%s" % (self._print(fieldAccess.field.name), suffix)

cpu/kernelcreation.py

-import sympy as sp
-from pystencils.transformations import resolveFieldAccesses, makeLoopOverDomain, typingFromSympyInspection, \
-    typeAllEquations, getOptimalLoopOrdering, parseBasePointerInfo, moveConstantsBeforeLoop, splitInnerLoop
-from pystencils.types import TypedSymbol
-from pystencils.field import Field
-import pystencils.ast as ast
-
-
-def createKernel(listOfEquations, functionName="kernel", typeForSymbol=None, splitGroups=(),
-                 iterationSlice=None, ghostLayers=None):
-    """
-    Creates an abstract syntax tree for a kernel function, by taking a list of update rules.
-
-    Loops are created according to the field accesses in the equations.
-
-    :param listOfEquations: list of sympy equations, containing accesses to :class:`pystencils.field.Field`.
-           Defining the update rules of the kernel
-    :param functionName: name of the generated function - only important if generated code is written out
-    :param typeForSymbol: a map from symbol name to a C type specifier. If not specified all symbols are assumed to
-           be of type 'double' except symbols which occur on the left hand side of equations where the
-           right hand side is a sympy Boolean which are assumed to be 'bool' .
-    :param splitGroups: Specification on how to split up inner loop into multiple loops. For details see
-           transformation :func:`pystencils.transformation.splitInnerLoop`
-    :param iterationSlice: if not None, iteration is done only over this slice of the field
-    :param ghostLayers: a sequence of pairs for each coordinate with lower and upper nr of ghost layers
-                        if None, the number of ghost layers is determined automatically and assumed to be equal for a
-                        all dimensions
-
-    :return: :class:`pystencils.ast.KernelFunction` node
-    """
-    if not typeForSymbol or typeForSymbol == 'double':
-        typeForSymbol = typingFromSympyInspection(listOfEquations, "double")
-    elif typeForSymbol == 'float':
-        typeForSymbol = typingFromSympyInspection(listOfEquations, "float")
-
-    def typeSymbol(term):
-        if isinstance(term, Field.Access) or isinstance(term, TypedSymbol):
-            return term
-        elif isinstance(term, sp.Symbol):
-            return TypedSymbol(term.name, typeForSymbol[term.name])
-        else:
-            raise ValueError("Term has to be field access or symbol")
-
-    fieldsRead, fieldsWritten, assignments = typeAllEquations(listOfEquations, typeForSymbol)
-    allFields = fieldsRead.union(fieldsWritten)
-
-    readOnlyFields = set([f.name for f in fieldsRead - fieldsWritten])
-
-    body = ast.Block(assignments)
-    loopOrder = getOptimalLoopOrdering(allFields)
-    code = makeLoopOverDomain(body, functionName, iterationSlice=iterationSlice,
-                              ghostLayers=ghostLayers, loopOrder=loopOrder)
-
-    if splitGroups:
-        typedSplitGroups = [[typeSymbol(s) for s in splitGroup] for splitGroup in splitGroups]
-        splitInnerLoop(code, typedSplitGroups)
-
-    basePointerInfo = [['spatialInner0'], ['spatialInner1']]
-    basePointerInfos = {field.name: parseBasePointerInfo(basePointerInfo, loopOrder, field) for field in allFields}
-
-    resolveFieldAccesses(code, readOnlyFields, fieldToBasePointerInfo=basePointerInfos)
-    moveConstantsBeforeLoop(code)
-
-    return code
-
-
-def addOpenMP(astNode, schedule="static", numThreads=None):
-    """
-    Parallelizes the outer loop with OpenMP
-
-    :param astNode: abstract syntax tree created e.g. by :func:`createKernel`
-    :param schedule: OpenMP scheduling policy e.g. 'static' or 'dynamic'
-    :param numThreads: explicitly specify number of threads
-    """
-    assert type(astNode) is ast.KernelFunction
-    body = astNode.body
-    threadsClause = "" if numThreads is None else " num_threads(%s)" % (numThreads,)
-    wrapperBlock = ast.PragmaBlock('#pragma omp parallel' + threadsClause, body.takeChildNodes())
-    body.append(wrapperBlock)
-
-    outerLoops = [l for l in body.atoms(ast.LoopOverCoordinate) if l.isOutermostLoop]
-    assert outerLoops, "No outer loop found"
-    assert len(outerLoops) <= 1, "More than one outer loop found. Which one should be parallelized?"
-    outerLoops[0].prefixLines.append("#pragma omp for schedule(%s)" % (schedule,))

display_utils.py

-
-
-def toDot(expr, graphStyle={}):
-    """Show a sympy or pystencils AST as dot graph"""
-    from pystencils.ast import Node
-    import graphviz
-    if isinstance(expr, Node):
-        from pystencils.backends.dot import dotprint
-        return graphviz.Source(dotprint(expr, short=True, graph_attr=graphStyle))
-    else:
-        from sympy.printing.dot import dotprint
-        return graphviz.Source(dotprint(expr, graph_attr=graphStyle))
-
-
-def highlightCpp(code):
-    """Highlight the given C/C++ source code with Pygments"""
-    from IPython.display import HTML, display
-    from pygments import highlight
-    from pygments.formatters import HtmlFormatter
-    from pygments.lexers import CppLexer
-
-    display(HTML("""
-            <style>
-            {pygments_css}
-            </style>
-            """.format(pygments_css=HtmlFormatter().get_style_defs('.highlight'))))
-    return HTML(highlight(code, CppLexer(), HtmlFormatter()))
-
-
-# ----------------- Embedding of animations as videos in IPython notebooks ---------------------------------------------
-
-
-# -------   Version 1: Animation is embedded as an HTML5 Video tag  ---------------------------------------
-
-VIDEO_TAG = """<video controls width="100%">
- <source src="data:video/x-m4v;base64,{0}" type="video/mp4">
- Your browser does not support the video tag.
-</video>"""
-
-
-def __anim_to_html(anim, fps):
-    from tempfile import NamedTemporaryFile
-    import base64
-
-    if not hasattr(anim, '_encoded_video'):
-        with NamedTemporaryFile(suffix='.mp4') as f:
-            anim.save(f.name, fps=fps, extra_args=['-vcodec', 'libx264', '-pix_fmt',
-                                                   'yuv420p', '-profile:v', 'baseline', '-level', '3.0'])
-            video = open(f.name, "rb").read()
-        anim._encoded_video = base64.b64encode(video).decode('ascii')
-
-    return VIDEO_TAG.format(anim._encoded_video)
-
-
-def disp_as_video(anim, fps=30, show=True, **kwargs):
-    import matplotlib.pyplot as plt
-    from IPython.display import HTML
-
-    try:
-        plt.close(anim._fig)
-        res = __anim_to_html(anim, fps)
-        if show:
-            return HTML(res)
-        else:
-            return HTML("")
-    except KeyboardInterrupt:
-        pass
-# -------   Version 2: Animation is shown in extra matplotlib window ----------------------------------
-
-
-def disp_extra_window(animation, *args,**kwargs):
-    import matplotlib.pyplot as plt
-
-    fig = plt.gcf()
-    try:
-      fig.canvas.manager.window.raise_()
-    except Exception:
-      pass
-    plt.show()
-
-
-# -------   Version 3: Animation is shown in images that are updated directly in website --------------
-
-def disp_image_update(animation, iterations=10000, *args, **kwargs):
-    from IPython import display
-    import matplotlib.pyplot as plt
-
-    try:
-        fig = plt.gcf()
-        animation._init_draw()
-        for i in range(iterations):
-            display.display(fig)
-            animation._step()
-            display.clear_output(wait=True)
-    except KeyboardInterrupt:
-        pass
-
-
-# Dispatcher
-
-animation_display_mode = 'imageupdate'
-display_animation_func = None
-
-
-def disp(*args, **kwargs):
-    if not display_animation_func:
-        raise Exception("Call set_display_mode first")
-    return display_animation_func(*args, **kwargs)
-
-
-def set_display_mode(mode):
-    from IPython import get_ipython
-    ipython = get_ipython()
-    global animation_display_mode
-    global display_animation_func
-    animation_display_mode = mode
-    if animation_display_mode == 'video':
-        ipython.magic("matplotlib inline")
-        display_animation_func = disp_as_video
-    elif animation_display_mode == 'window':
-        ipython.magic("matplotlib qt")
-        display_animation_func = disp_extra_window
-    elif animation_display_mode == 'imageupdate':
-        ipython.magic("matplotlib inline")
-        display_animation_func = disp_image_update
-    else:
-        raise Exception("Unknown mode. Available modes 'imageupdate', 'video' and 'window' ")
-
-
-set_display_mode('video')
-
-
-# --------------------- Convenience functions --------------------------------------------------------------------------
-
-
-def makeSurfacePlotAnimation(runFunction, frames=90, interval=30):
-    from mpl_toolkits.mplot3d import Axes3D
-    import matplotlib.animation as animation
-    import matplotlib.pyplot as plt
-    from matplotlib import cm
-
-    fig = plt.figure()
-    ax = fig.add_subplot(111, projection='3d')
-    X, Y, data = runFunction(1)
-    ax.plot_surface(X, Y, data, rstride=2, cstride=2, color='b', cmap=cm.coolwarm,)
-    ax.set_zlim(-1.0, 1.0)
-
-    def updatefig(*args):
-        X, Y, data = runFunction(1)
-        ax.clear()
-        plot = ax.plot_surface(X, Y, data, rstride=2, cstride=2, color='b', cmap=cm.coolwarm,)
-        ax.set_zlim(-1.0, 1.0)
-        return plot,
-
-    return animation.FuncAnimation(fig, updatefig, interval=interval, frames=frames, blit=False)

doc/conf.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+#
+import datetime
+import sphinx_rtd_theme
+import os
+import re
+import sys
+
+sys.path.insert(0, os.path.abspath('.'))
+import pystencils
+
+extensions = [
+    'sphinx.ext.autodoc',
+    'sphinx.ext.doctest',
+    'sphinx.ext.intersphinx',
+    'sphinx.ext.mathjax',
+    'sphinx.ext.napoleon',
+    'nbsphinx',
+    'sphinxcontrib.bibtex',
+    'sphinx_autodoc_typehints',
+]
+
+add_module_names = False
+templates_path = ['_templates']
+source_suffix = '.rst'
+master_doc = 'index'
+
+copyright = f'{datetime.datetime.now().year}, Martin Bauer, Markus Holzer, Frederik Hennig'
+author = 'Martin Bauer, Markus Holzer, Frederik Hennig'
+# The short X.Y version (including .devXXXX, rcX, b1 suffixes if present)
+version = re.sub(r'(\d+\.\d+)\.\d+(.*)', r'\1\2', pystencils.__version__)
+version = re.sub(r'(\.dev\d+).*?$', r'\1', version)
+# The full version, including alpha/beta/rc tags.
+release = pystencils.__version__
+language = 'en'
+exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store', '**.ipynb_checkpoints']
+default_role = 'any'
+pygments_style = 'sphinx'
+todo_include_todos = False
+
+# Options for HTML output
+
+html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
+html_theme = 'sphinx_rtd_theme'
+htmlhelp_basename = 'pystencilsdoc'
+html_sidebars = {'**': ['globaltoc.html', 'relations.html', 'sourcelink.html', 'searchbox.html']}
+
+# NbSphinx configuration
+nbsphinx_execute = 'never'
+nbsphinx_codecell_lexer = 'python3'
+
+# Example configuration for intersphinx: refer to the Python standard library.
+intersphinx_mapping = {'python': ('https://docs.python.org/3.8', None),
+                       'numpy': ('https://docs.scipy.org/doc/numpy/', None),
+                       'matplotlib': ('https://matplotlib.org/', None),
+                       'sympy': ('https://docs.sympy.org/latest/', None),
+                       }
+
+autodoc_member_order = 'bysource'
+bibtex_bibfiles = ['sphinx/pystencils.bib']
+
+project = 'pystencils'
+html_logo = 'img/logo.png'

doc/img/logo.svg

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doc/index.rst

+pystencils
+==========
+
+Welcome to the documentation of the pystencils code generation tool for stencil codes.
+pystencils can help you to generate blazingly fast code for image processing, numerical simulations or any other task involving numpy arrays.
+
+
+.. toctree::
+   :maxdepth: 2
+
+   sphinx/tutorials.rst
+   sphinx/api.rst
+
+
+.. image:: /img/pystencils_arch_block_diagram.svg
+    :height: 450px
+    :align: center
+
+

doc/notebooks/02_tutorial_basic_kernels.ipynb

+%% Cell type:code id: tags:
+
+``` python
+from pystencils.session import *
+```
+
+%% Cell type:markdown id: tags:
+
+# Tutorial 02: Basic Kernel generation with *pystencils*
+
+Now that you have an [overview of pystencils](01_tutorial_getting_started.ipynb),
+this tutorial shows in more detail how to formulate, optimize and run stencil kernels.
+
+## 1) Kernel Definition
+
+### a) Defining kernels with assignment lists and the  `kernel` decorator
+
+*pystencils* gets a symbolic formulation of the kernel. This can be either an `Assignment` or a sequence of `Assignment`s that follow a set of restrictions.
+
+Lets first create a kernel that consists of multiple assignments:
+
+%% Cell type:code id: tags:
+
+``` python
+src_arr = np.zeros([20, 30])
+dst_arr = np.zeros_like(src_arr)
+
+dst, src = ps.fields(dst=dst_arr, src=src_arr)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+grad_x, grad_y = sp.symbols("grad_x, grad_y")
+
+symbolic_description = [
+    ps.Assignment(grad_x, (src[1, 0] - src[-1, 0]) / 2),
+    ps.Assignment(grad_y, (src[0, 1] - src[0, -1]) / 2),
+    ps.Assignment(dst[0, 0], grad_x + grad_y),
+]
+kernel = ps.create_kernel(symbolic_description)
+symbolic_description
+```
+
+%% Output
+
+
+    $\displaystyle \left[ grad_{x} \leftarrow_{} \frac{{src}_{(1,0)}}{2} - \frac{{src}_{(-1,0)}}{2}, \  grad_{y} \leftarrow_{} \frac{{src}_{(0,1)}}{2} - \frac{{src}_{(0,-1)}}{2}, \  {dst}_{(0,0)} \leftarrow_{} grad_{x} + grad_{y}\right]$
+    ⎡         src_E   src_W            src_N   src_S                         ⎤
+    ⎢gradₓ := ───── - ─────, grad_y := ───── - ─────, dst_C := gradₓ + grad_y⎥
+    ⎣           2       2                2       2                           ⎦
+
+%% Cell type:markdown id: tags:
+
+We created subexpressions, using standard sympy symbols on the left hand side, to split the kernel into multiple assignments. Defining a kernel using a list of `Assignment`s is quite tedious and hard to read.
+To simplify the formulation of a kernel, *pystencils* offers the `kernel` decorator, that transforms a normal Python function with `@=` assignments into an assignment list that can be passed to `create_kernel`.
+
+%% Cell type:code id: tags:
+
+``` python
+@ps.kernel
+def symbolic_description_using_function():
+    grad_x @= (src[1, 0] - src[-1, 0]) / 2
+    grad_y @= (src[0, 1] - src[0, -1]) / 2
+    dst[0, 0] @= grad_x + grad_y
+symbolic_description_using_function
+```
+
+%% Output
+
+
+    $\displaystyle \left[ grad_{x} \leftarrow_{} \frac{{src}_{(1,0)}}{2} - \frac{{src}_{(-1,0)}}{2}, \  grad_{y} \leftarrow_{} \frac{{src}_{(0,1)}}{2} - \frac{{src}_{(0,-1)}}{2}, \  {dst}_{(0,0)} \leftarrow_{} grad_{x} + grad_{y}\right]$
+    ⎡         src_E   src_W            src_N   src_S                         ⎤
+    ⎢gradₓ := ───── - ─────, grad_y := ───── - ─────, dst_C := gradₓ + grad_y⎥
+    ⎣           2       2                2       2                           ⎦
+
+%% Cell type:markdown id: tags:
+
+The decorated function can contain any Python code, only the `@=` operator, and the ternary inline `if-else` operator have different meaning.
+
+### b) Ternary 'if' with `Piecewise`
+
+The ternary operator maps to `sympy.Piecewise` functions, that can be used to introduce branching into the kernel. Piecewise defined functions must give a value for every input, i.e. there must be a 'otherwise' clause in the end that is indicated by the condition `True`. Piecewise objects are standard sympy terms that can be integrated into bigger expressions:
+
+%% Cell type:code id: tags:
+
+``` python
+sp.Piecewise((1.0, src[0,1] > 0), (0.0, True)) + src[1, 0]
+```
+
+%% Output
+
+
+    $\displaystyle {src}_{(1,0)} + \begin{cases} 1.0 & \text{for}\: {src}_{(0,1)} > 0 \\0.0 & \text{otherwise} \end{cases}$
+            ⎛⎧1.0  for src_N > 0⎞
+    src_E + ⎜⎨                  ⎟
+            ⎝⎩0.0    otherwise  ⎠
+
+%% Cell type:markdown id: tags:
+
+Piecewise objects are created by the `kernel` decorator for ternary if-else statements.
+
+%% Cell type:code id: tags:
+
+``` python
+@ps.kernel
+def kernel_with_piecewise():
+    grad_x @= (src[1, 0] - src[-1, 0]) / 2 if src[-1, 0] > 0 else 0.0
+kernel_with_piecewise
+```
+
+%% Output
+
+
+    $\displaystyle \left[ grad_{x} \leftarrow_{} \begin{cases} \frac{{src}_{(1,0)}}{2} - \frac{{src}_{(-1,0)}}{2} & \text{for}\: {src}_{(-1,0)} > 0 \\0.0 & \text{otherwise} \end{cases}\right]$
+    ⎡         ⎧src_E   src_W               ⎤
+    ⎢         ⎪───── - ─────  for src_W > 0⎥
+    ⎢gradₓ := ⎨  2       2                 ⎥
+    ⎢         ⎪                            ⎥
+    ⎣         ⎩     0.0         otherwise  ⎦
+
+%% Cell type:markdown id: tags:
+
+### c) Assignment level optimizations using `AssignmentCollection`
+
+When the kernels get larger and more complex, it is helpful to organize the list of assignment into a more structured way. The `AssignmentCollection` offers optimizating transformation on a list of assignments. It holds two assignment lists, one for subexpressions and one for the main assignments. Main assignments are typically those that write to an array.
+
+%% Cell type:code id: tags:
+
+``` python
+@ps.kernel
+def somewhat_longer_dummy_kernel(s):
+    s.a @= src[0, 1] + src[-1, 0]
+    s.b @= 2 * src[1, 0] + src[0, -1]
+    s.c @= src[0, 1] + 2 * src[1, 0] + src[-1, 0] + src[0, -1] - src[0,0]
+    dst[0, 0] @= s.a + s.b + s.c
+
+ac = ps.AssignmentCollection(main_assignments=somewhat_longer_dummy_kernel[-1:],
+                             subexpressions=somewhat_longer_dummy_kernel[:-1])
+ac
+```
+
+%% Output
+
+    AssignmentCollection: dst_C, <- f(src_C, src_W, src_S, src_N, src_E)
+
+%% Cell type:code id: tags:
+
+``` python
+ac.operation_count
+```
+
+%% Output
+
+    {'adds': 8,
+     'muls': 2,
+     'divs': 0,
+     'sqrts': 0,
+     'fast_sqrts': 0,
+     'fast_inv_sqrts': 0,
+     'fast_div': 0}
+
+%% Cell type:markdown id: tags:
+
+The `pystencils.simp` submodule offers several functions to optimize a collection of assignments.
+It also offers functionality to group optimization into strategies and evaluate them.
+In this example we reduce the number of operations by reusing existing subexpressions to get rid of two unnecessary floating point additions. For more information about assignment collections and simplifications see the [demo notebook](demo_assignment_collection.ipynb).
+
+%% Cell type:code id: tags:
+
+``` python
+opt_ac = ps.simp.subexpression_substitution_in_existing_subexpressions(ac)
+opt_ac
+```
+
+%% Output
+
+    AssignmentCollection: dst_C, <- f(src_C, src_W, src_S, src_N, src_E)
+
+%% Cell type:code id: tags:
+
+``` python
+opt_ac.operation_count
+```
+
+%% Output
+
+    {'adds': 6,
+     'muls': 1,
+     'divs': 0,
+     'sqrts': 0,
+     'fast_sqrts': 0,
+     'fast_inv_sqrts': 0,
+     'fast_div': 0}
+
+%% Cell type:markdown id: tags:
+
+### d) Ghost layers and iteration region
+
+When creating a kernel with neighbor accesses, *pystencils* automatically restricts the iteration region, such that all accesses are safe.
+
+%% Cell type:code id: tags:
+
+``` python
+kernel = ps.create_kernel(ps.Assignment(dst[0,0], src[2, 0] + src[-1, 0]))
+ps.show_code(kernel)
+```
+
+%% Output
+
+
+
+%% Cell type:markdown id: tags:
+
+When no additional ghost layer information is given, *pystencils* looks at all neighboring field accesses and introduces the required number of ghost layers **for all directions**. In the example above the largest neighbor accesses was ``src[2, 0]``, so theoretically we would need 2 ghost layers only the the end of the x coordinate.
+By default *pystencils* introduces 2 ghost layers at all borders of the domain. The next cell shows how to change this behavior. Be careful with manual ghost layer specification, wrong values may lead to SEGFAULTs.
+
+%% Cell type:code id: tags:
+
+``` python
+gl_spec = [(0, 2),   # 0 ghost layers at the left, 2 at the right border
+           (1, 0)]   # 1 ghost layer at the lower y, one at the upper y coordinate
+kernel = ps.create_kernel(ps.Assignment(dst[0,0], src[2, 0] + src[-1, 0]), ghost_layers=gl_spec)
+ps.show_code(kernel)
+```
+
+%% Output
+
+
+
+%% Cell type:markdown id: tags:
+
+## 2 ) Restrictions
+
+
+### a) Independence Restriction
+
+*pystencils* only works for kernels where each array element can be updated independently from all other elements. This restriction ensures that the kernels can be easily parallelized and also be run on the GPU. Trying to define kernels where the results depends on the iteration order, leads to a ValueError.
+
+%% Cell type:code id: tags:
+
+``` python
+invalid_description = [
+    ps.Assignment(dst[1, 0], src[1, 0] + src[-1, 0]),
+    ps.Assignment(dst[0, 0], src[1, 0] - src[-1, 0]),
+]
+try:
+    invalid_kernel = ps.create_kernel(invalid_description)
+    assert False, "Should never be executed"
+except ValueError as e:
+    print(e)
+```
+
+%% Output
+
+    Field dst is written at two different locations
+
+%% Cell type:markdown id: tags:
+
+The independence restriction makes sure that the kernel can be safely parallelized by checking the following conditions: If a field is modified inside the kernel, it may only be modified at a single spatial position. In that case the field may also only be read at this position. Fields that are not modified may be read at multiple neighboring positions.
+
+Specifically, this rule allows for in-place updates that don't access neighbors.
+
+%% Cell type:code id: tags:
+
+``` python
+valid_kernel = ps.create_kernel(ps.Assignment(src[0,0], 2*src[0,0] + 42))
+```
+
+%% Cell type:markdown id: tags:
+
+If a field stores multiple values per cell, as in the next example, this restriction only applies for accesses with the same index.
+
+%% Cell type:code id: tags:
+
+``` python
+v = ps.fields("v(2): double[2D]")
+valid_kernel = ps.create_kernel([ps.Assignment(v[0,0](1), 2*v[0,0](1) + 42),
+                                 ps.Assignment(v[0,1](0), 2*v[0,1](0) + 42)])
+```
+
+%% Cell type:markdown id: tags:
+
+### b) Static Single Assignment Form
+
+All assignments that don't write to a field must be in SSA form
+1. Each sympy symbol may only occur once as a left-hand-side (fields can be written multiple times)
+2. A symbol has to be defined before it is used. If it is never defined it is introduced as function parameter
+
+The next cell demonstrates the first SSA restriction:
+
+%% Cell type:code id: tags:
+
+``` python
+@ps.kernel
+def not_allowed():
+    a, b = sp.symbols("a b")
+    a @= src[0, 0]
+    b @= a + 3
+    a @= src[-1, 0]
+    dst[0, 0] @= a + b
+try:
+    ps.create_kernel(not_allowed)
+    assert False
+except ValueError as e:
+    print(e)
+```
+
+%% Output
+
+    Assignments not in SSA form, multiple assignments to a
+
+%% Cell type:markdown id: tags:
+
+Also it is not allowed to write a field at the same location
+
+%% Cell type:code id: tags:
+
+``` python
+@ps.kernel
+def not_allowed():
+    dst[0, 0] @= src[0, 1] + src[1, 0]
+    dst[0, 0] @= 2 * dst[0, 0]
+
+try:
+    ps.create_kernel(not_allowed)
+    assert False
+except ValueError as e:
+    print(e)
+```
+
+%% Output
+
+    Field dst is written twice at the same location
+
+%% Cell type:markdown id: tags:
+
+This situation should be resolved by introducing temporary variables
+
+%% Cell type:code id: tags:
+
+``` python
+tmp_var = sp.Symbol("a")
+
+@ps.kernel
+def allowed():
+    tmp_var @= src[0, 1] + src[1, 0]
+    dst[0, 0] @= 2 * tmp_var
+
+
+ast = ps.create_kernel(allowed)
+ps.show_code(ast)
+```
+
+%% Output
+
+