binder/start

+#!/bin/bash
+
+python3 setup.py develop
+
+exec "$@"
\ No newline at end of file

cache.py

-import sympy as sp
-import json
-import os
-
-try:
-    from functools import lru_cache as memorycache
-except ImportError:
-    from backports.functools_lru_cache import lru_cache as memorycache
-
-try:
-    from joblib import Memory
-    from appdirs import user_cache_dir
-    if 'PYSTENCILS_CACHE_DIR' in os.environ:
-        cacheDir = os.environ['PYSTENCILS_CACHE_DIR']
-    else:
-        cacheDir = user_cache_dir('pystencils')
-    diskcache = Memory(cachedir=cacheDir, verbose=False).cache
-    diskcacheNoFallback = diskcache
-except ImportError:
-    # fallback to in-memory caching if joblib is not available
-    diskcache = memorycache(maxsize=64)
-    diskcacheNoFallback = lambda o: o
-

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

cpu/cpujit.py

-r"""
-
-*pystencils* looks for a configuration file in JSON format at the following locations in the listed order.
-
-1. at the path specified in the environment variable ``PYSTENCILS_CONFIG``
-2. in the current working direction for a file named ``pystencils.json``
-3. or in your home directory at ``~/.config/pystencils/config.json`` (Linux) or
-   ``%HOMEPATH%\.pystencils\config.json`` (Windows)
-
-If no configuration file is found, a default configuration is created at the above mentioned location in your home.
-So run *pystencils* once, then edit the created configuration file.
-
-
-Compiler Config (Linux)
------------------------
-
-- **'os'**: should be detected automatically as 'linux'
-- **'command'**: path to C++ compiler (defaults to 'g++')
-- **'flags'**: space separated list of compiler flags. Make sure to activate OpenMP in your compiler
-- **'restrictQualifier'**: the restrict qualifier is not standardized accross compilers.
-  For most Linux compilers the qualifier is ``__restrict__``
-
-
-Compiler Config (Windows)
--------------------------
-
-*pystencils* uses the mechanism of *setuptools.msvc* to search for a compilation environment.
-Then 'cl.exe' is used to compile.
-
-- **'os'**: should be detected automatically as 'windows'
-- **'msvcVersion'**:  either a version number, year number, 'auto' or 'latest' for automatic detection of latest
-                      installed version or 'setuptools' for setuptools-based detection. Alternatively path to folder
-                      where Visual Studio is installed. This path has to contain a file called 'vcvarsall.bat'
-- **'arch'**: 'x86' or 'x64'
-- **'flags'**: flags passed to 'cl.exe', make sure OpenMP is activated
-- **'restrictQualifier'**: the restrict qualifier is not standardized across compilers.
-  For Windows compilers the qualifier should be ``__restrict``
-
-
-Cache Config
-------------
-
-*pystencils* uses a directory to store intermediate files like the generated C++ files, compiled object files and
-the shared libraries which are then loaded from Python using ctypes. The file names are SHA hashes of the
-generated code. If the same kernel was already compiled, the existing object file is used - no recompilation is done.
-
-If 'sharedLibrary' is specified, all kernels that are currently in the cache are compiled into a single shared library.
-This mechanism can be used to run *pystencils* on systems where compilation is not possible, e.g. on clusters where
-compilation on the compute nodes is not possible.
-First the script is run on a system where compilation is possible (e.g. the login node) with
-'readFromSharedLibrary=False' and with 'sharedLibrary' set a valid path.
-All kernels generated during the run are put into the cache and at the end
-compiled into the shared library. Then, the same script can be run from the compute nodes, with
-'readFromSharedLibrary=True', such that kernels are taken from the library instead of compiling them.
-
-
-- **'readFromSharedLibrary'**: if true kernels are not compiled but assumed to be in the shared library
-- **'objectCache'**: path to a folder where intermediate files are stored
-- **'clearCacheOnStart'**: when true the cache is cleared on each start of a *pystencils* script
-- **'sharedLibrary'**: path to a shared library file, which is created if `readFromSharedLibrary=false`
-"""
-from __future__ import print_function
-import os
-import subprocess
-import hashlib
-import json
-import platform
-import glob
-import atexit
-import shutil
-from appdirs import user_config_dir, user_cache_dir
-from ctypes import cdll
-from pystencils.backends.cbackend import generateC, getHeaders
-from collections import OrderedDict, Mapping
-from pystencils.transformations import symbolNameToVariableName
-from pystencils.data_types import toCtypes, getBaseType, StructType
-
-
-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
-    func = compileAndLoad(kernelFunctionNode)
-    func.restype = None
-    try:
-        args = buildCTypeArgumentList(kernelFunctionNode.parameters, argumentDict)
-    except KeyError:
-        # not all parameters specified yet
-        return makePythonFunctionIncompleteParams(kernelFunctionNode, argumentDict, func)
-    return lambda: func(*args)
-
-
-def setCompilerConfig(config):
-    """
-    Override the configuration provided in config file
-
-    Configuration of compiler parameters:
-    If this function is not called the configuration is taken from a config file in JSON format which
-    is searched in the following locations in the order specified:
-        - at location provided in environment variable PYSTENCILS_CONFIG (if this variable exists)
-        - a file called ".pystencils.json" in the current working directory
-        - ~/.pystencils.json in your home
-    If none of these files exist a file ~/.pystencils.json is created with a default configuration using
-    the GNU 'g++'
-
-    An example JSON file with all possible keys. If not all keys are specified, default values are used
-    ``
-    {
-        'compiler' :
-        {
-            "command": "/software/intel/2017/bin/icpc",
-            "flags": "-Ofast -DNDEBUG -fPIC -march=native -fopenmp",
-            "env": {
-                "LM_PROJECT": "iwia",
-            }
-        }
-    }
-    ``
-    """
-    global _config
-    _config = config.copy()
-
-
-def _recursiveDictUpdate(d, u):
-    for k, v in u.items():
-        if isinstance(v, Mapping):
-            r = _recursiveDictUpdate(d.get(k, {}), v)
-            d[k] = r
-        else:
-            d[k] = u[k]
-    return d
-
-
-def getConfigurationFilePath():
-    configPathInHome = os.path.join(user_config_dir('pystencils'), 'config.json')
-
-    # 1) Read path from environment variable if found
-    if 'PYSTENCILS_CONFIG' in os.environ:
-        return os.environ['PYSTENCILS_CONFIG'], True
-    # 2) Look in current directory for pystencils.json
-    elif os.path.exists("pystencils.json"):
-        return "pystencils.json", True
-    # 3) Try ~/.pystencils.json
-    elif os.path.exists(configPathInHome):
-        return configPathInHome, True
-    else:
-        return configPathInHome, False
-
-
-def createFolder(path, isFile):
-    if isFile:
-        path = os.path.split(path)[0]
-    try:
-        os.makedirs(path)
-    except os.error:
-        pass
-
-
-def readConfig():
-    if platform.system().lower() == 'linux':
-        defaultCompilerConfig = OrderedDict([
-            ('os', 'linux'),
-            ('command', 'g++'),
-            ('flags', '-Ofast -DNDEBUG -fPIC -march=native -fopenmp -std=c++11'),
-            ('restrictQualifier', '__restrict__')
-        ])
-
-    elif platform.system().lower() == 'windows':
-        defaultCompilerConfig = OrderedDict([
-            ('os', 'windows'),
-            ('msvcVersion', 'latest'),
-            ('arch', 'x64'),
-            ('flags', '/Ox /fp:fast /openmp /arch:avx'),
-            ('restrictQualifier', '__restrict')
-        ])
-    defaultCacheConfig = OrderedDict([
-        ('readFromSharedLibrary', False),
-        ('objectCache', os.path.join(user_cache_dir('pystencils'), 'objectcache')),
-        ('clearCacheOnStart', False),
-        ('sharedLibrary', os.path.join(user_cache_dir('pystencils'), 'cache.so')),
-    ])
-
-    defaultConfig = OrderedDict([('compiler', defaultCompilerConfig),
-                                 ('cache', defaultCacheConfig)])
-
-    configPath, configExists = getConfigurationFilePath()
-    config = defaultConfig.copy()
-    if configExists:
-        with open(configPath, 'r') as jsonConfigFile:
-            loadedConfig = json.load(jsonConfigFile)
-        config = _recursiveDictUpdate(config, loadedConfig)
-    else:
-        createFolder(configPath, True)
-        json.dump(config, open(configPath, 'w'), indent=4)
-
-    config['cache']['sharedLibrary'] = os.path.expanduser(config['cache']['sharedLibrary']).format(pid=os.getpid())
-    config['cache']['objectCache'] = os.path.expanduser(config['cache']['objectCache']).format(pid=os.getpid())
-
-    if config['cache']['clearCacheOnStart']:
-        shutil.rmtree(config['cache']['objectCache'], ignore_errors=True)
-
-    createFolder(config['cache']['objectCache'], False)
-    createFolder(config['cache']['sharedLibrary'], True)
-
-    if 'env' not in config['compiler']:
-        config['compiler']['env'] = {}
-
-    if config['compiler']['os'] == 'windows':
-        from pystencils.cpu.msvc_detection import getEnvironment
-        msvcEnv = getEnvironment(config['compiler']['msvcVersion'], config['compiler']['arch'])
-        config['compiler']['env'].update(msvcEnv)
-
-    return config
-
-
-_config = readConfig()
-
-
-def getCompilerConfig():
-    return _config['compiler']
-
-
-def getCacheConfig():
-    return _config['cache']
-
-
-def hashToFunctionName(h):
-    res = "func_%s" % (h,)
-    return res.replace('-', 'm')
-
-
-def compileObjectCacheToSharedLibrary():
-    compilerConfig = getCompilerConfig()
-    cacheConfig = getCacheConfig()
-
-    sharedLibrary = cacheConfig['sharedLibrary']
-    if len(sharedLibrary) == 0 or cacheConfig['readFromSharedLibrary']:
-        return
-
-    configEnv = compilerConfig['env'] if 'env' in compilerConfig else {}
-    compileEnvironment = os.environ.copy()
-    compileEnvironment.update(configEnv)
-
-    try:
-        if compilerConfig['os'] == 'windows':
-            allObjectFiles = glob.glob(os.path.join(cacheConfig['objectCache'], '*.obj'))
-            linkCmd = ['link.exe',  '/DLL', '/out:' + sharedLibrary]
-        else:
-            allObjectFiles = glob.glob(os.path.join(cacheConfig['objectCache'], '*.o'))
-            linkCmd = [compilerConfig['command'], '-shared', '-o', sharedLibrary]
-
-        linkCmd += allObjectFiles
-        if len(allObjectFiles) > 0:
-            runCompileStep(linkCmd)
-    except subprocess.CalledProcessError as e:
-        print(e.output)
-        raise e
-
-atexit.register(compileObjectCacheToSharedLibrary)
-
-
-def generateCode(ast, restrictQualifier, functionPrefix, targetFile):
-    headers = getHeaders(ast)
-    headers.update(['<cmath>', '<cstdint>'])
-
-    with open(targetFile, 'w') as sourceFile:
-        code = generateC(ast)
-        includes = "\n".join(["#include %s" % (includeFile,) for includeFile in headers])
-        print(includes, file=sourceFile)
-        print("#define RESTRICT %s" % (restrictQualifier,), file=sourceFile)
-        print("#define FUNC_PREFIX %s" % (functionPrefix,), file=sourceFile)
-        print('extern "C" { ', file=sourceFile)
-        print(code, file=sourceFile)
-        print('}', file=sourceFile)
-
-
-def runCompileStep(command):
-    compilerConfig = getCompilerConfig()
-    configEnv = compilerConfig['env'] if 'env' in compilerConfig else {}
-    compileEnvironment = os.environ.copy()
-    compileEnvironment.update(configEnv)
-
-    try:
-        shell = True if compilerConfig['os'].lower() == 'windows' else False
-        subprocess.check_output(command, env=compileEnvironment, stderr=subprocess.STDOUT, shell=shell)
-    except subprocess.CalledProcessError as e:
-        print(" ".join(command))
-        print(e.output.decode('utf8'))
-        raise e
-
-
-def compileLinux(ast, codeHashStr, srcFile, libFile):
-    cacheConfig = getCacheConfig()
-    compilerConfig = getCompilerConfig()
-
-    objectFile = os.path.join(cacheConfig['objectCache'], codeHashStr + '.o')
-    # Compilation
-    if not os.path.exists(objectFile):
-        generateCode(ast, compilerConfig['restrictQualifier'], '', srcFile)
-        compileCmd = [compilerConfig['command'], '-c'] + compilerConfig['flags'].split()
-        compileCmd += ['-o', objectFile, srcFile]
-        runCompileStep(compileCmd)
-
-    # Linking
-    runCompileStep([compilerConfig['command'], '-shared', objectFile, '-o', libFile] + compilerConfig['flags'].split())
-
-
-def compileWindows(ast, codeHashStr, srcFile, libFile):
-    cacheConfig = getCacheConfig()
-    compilerConfig = getCompilerConfig()
-
-    objectFile = os.path.join(cacheConfig['objectCache'], codeHashStr + '.obj')
-    # Compilation
-    if not os.path.exists(objectFile):
-        generateCode(ast, compilerConfig['restrictQualifier'],
-                     '__declspec(dllexport)', srcFile)
-
-        # /c compiles only, /EHsc turns of exception handling in c code
-        compileCmd = ['cl.exe', '/c', '/EHsc'] + compilerConfig['flags'].split()
-        compileCmd += [srcFile, '/Fo' + objectFile]
-        runCompileStep(compileCmd)
-
-    # Linking
-    runCompileStep(['link.exe', '/DLL', '/out:' + libFile, objectFile])
-
-
-def compileAndLoad(ast):
-    cacheConfig = getCacheConfig()
-
-    codeHashStr = hashlib.sha256(generateC(ast).encode()).hexdigest()
-    ast.functionName = hashToFunctionName(codeHashStr)
-
-    srcFile = os.path.join(cacheConfig['objectCache'], codeHashStr + ".cpp")
-
-    if cacheConfig['readFromSharedLibrary']:
-        return cdll.LoadLibrary(cacheConfig['sharedLibrary'])[ast.functionName]
-    else:
-        if getCompilerConfig()['os'].lower() == 'windows':
-            libFile = os.path.join(cacheConfig['objectCache'], codeHashStr + ".dll")
-            if not os.path.exists(libFile):
-                compileWindows(ast, codeHashStr, srcFile, libFile)
-        else:
-            libFile = os.path.join(cacheConfig['objectCache'], codeHashStr + ".so")
-            if not os.path.exists(libFile):
-                compileLinux(ast, codeHashStr, srcFile, libFile)
-        return cdll.LoadLibrary(libFile)[ast.functionName]
-
-
-def buildCTypeArgumentList(parameterSpecification, argumentDict):
-    argumentDict = {symbolNameToVariableName(k): v for k, v in argumentDict.items()}
-    ctArguments = []
-    arrayShapes = set()
-    indexArrShapes = set()
-
-    for arg in parameterSpecification:
-        if arg.isFieldArgument:
-            try:
-                fieldArr = argumentDict[arg.fieldName]
-            except KeyError:
-                raise KeyError("Missing field parameter for kernel call " + arg.fieldName)
-
-            symbolicField = arg.field
-            if arg.isFieldPtrArgument:
-                ctArguments.append(fieldArr.ctypes.data_as(toCtypes(arg.dtype)))
-                if symbolicField.hasFixedShape:
-                    symbolicFieldShape = tuple(int(i) for i in symbolicField.shape)
-                    if isinstance(symbolicField.dtype, StructType):
-                        symbolicFieldShape = symbolicFieldShape[:-1]
-                    if symbolicFieldShape != fieldArr.shape:
-                        raise ValueError("Passed array '%s' has shape %s which does not match expected shape %s" %
-                                         (arg.fieldName, str(fieldArr.shape), str(symbolicField.shape)))
-                if symbolicField.hasFixedShape:
-                    symbolicFieldStrides = tuple(int(i) * fieldArr.itemsize for i in symbolicField.strides)
-                    if isinstance(symbolicField.dtype, StructType):
-                        symbolicFieldStrides = symbolicFieldStrides[:-1]
-                    if symbolicFieldStrides != fieldArr.strides:
-                        raise ValueError("Passed array '%s' has strides %s which does not match expected strides %s" %
-                                         (arg.fieldName, str(fieldArr.strides), str(symbolicFieldStrides)))
-
-                if symbolicField.isIndexField:
-                    indexArrShapes.add(fieldArr.shape[:symbolicField.spatialDimensions])
-                else:
-                    arrayShapes.add(fieldArr.shape[:symbolicField.spatialDimensions])
-
-            elif arg.isFieldShapeArgument:
-                dataType = toCtypes(getBaseType(arg.dtype))
-                ctArguments.append(fieldArr.ctypes.shape_as(dataType))
-            elif arg.isFieldStrideArgument:
-                dataType = toCtypes(getBaseType(arg.dtype))
-                strides = fieldArr.ctypes.strides_as(dataType)
-                for i in range(len(fieldArr.shape)):
-                    assert strides[i] % fieldArr.itemsize == 0
-                    strides[i] //= fieldArr.itemsize
-                ctArguments.append(strides)
-            else:
-                assert False
-        else:
-            try:
-                param = argumentDict[arg.name]
-            except KeyError:
-                raise KeyError("Missing parameter for kernel call " + arg.name)
-            expectedType = toCtypes(arg.dtype)
-            ctArguments.append(expectedType(param))
-
-    if len(arrayShapes) > 1:
-        raise ValueError("All passed arrays have to have the same size " + str(arrayShapes))
-    if len(indexArrShapes) > 1:
-        raise ValueError("All passed index arrays have to have the same size " + str(arrayShapes))
-
-    return ctArguments
-
-
-def makePythonFunctionIncompleteParams(kernelFunctionNode, argumentDict, func):
-    parameters = kernelFunctionNode.parameters
-
-    cache = {}
-    cacheValues = []
-
-    def wrapper(**kwargs):
-        key = hash(tuple((k, id(v)) for k, v in kwargs.items()))
-        try:
-            args = cache[key]
-            func(*args)
-        except KeyError:
-            fullArguments = argumentDict.copy()
-            fullArguments.update(kwargs)
-            args = buildCTypeArgumentList(parameters, fullArguments)
-            cache[key] = args
-            cacheValues.append(kwargs)  # keep objects alive such that ids remain unique
-            func(*args)
-    return wrapper

cpu/kernelcreation.py

-import sympy as sp
-from functools import partial
-
-from collections import defaultdict
-
-from pystencils.astnodes import SympyAssignment, Block, LoopOverCoordinate, KernelFunction
-from pystencils.transformations import resolveFieldAccesses, makeLoopOverDomain, \
-    typeAllEquations, getOptimalLoopOrdering, parseBasePointerInfo, moveConstantsBeforeLoop, splitInnerLoop, \
-    substituteArrayAccessesWithConstants
-from pystencils.data_types import TypedSymbol, BasicType, StructType, createType
-from pystencils.field import Field
-import pystencils.astnodes as ast
-from pystencils.cpu.cpujit import makePythonFunction
-
-
-def createKernel(listOfEquations, functionName="kernel", typeForSymbol='double', 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
-    """
-
-    def typeSymbol(term):
-        if isinstance(term, Field.Access) or isinstance(term, TypedSymbol):
-            return term
-        elif isinstance(term, sp.Symbol):
-            if isinstance(typeForSymbol, str):
-                return TypedSymbol(term.name, createType(typeForSymbol))
-            else:
-                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)
-    code.target = 'cpu'
-
-    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)
-    substituteArrayAccessesWithConstants(code)
-    moveConstantsBeforeLoop(code)
-    code.compile = partial(makePythonFunction, code)
-    return code
-
-
-def createIndexedKernel(listOfEquations, indexFields, functionName="kernel", typeForSymbol=None,
-                        coordinateNames=('x', 'y', 'z')):
-    """
-    Similar to :func:`createKernel`, but here not all cells of a field are updated but only cells with
-    coordinates which are stored in an index field. This traversal method can e.g. be used for boundary handling.
-
-    The coordinates are stored in a separated indexField, which is a one dimensional array with struct data type.
-    This struct has to contain fields named 'x', 'y' and for 3D fields ('z'). These names are configurable with the
-    'coordinateNames' parameter. The struct can have also other fields that can be read and written in the kernel, for
-    example boundary parameters.
-
-    :param listOfEquations: list of update equations or AST nodes
-    :param indexFields: list of index fields, i.e. 1D fields with struct data type
-    :param typeForSymbol: see documentation of :func:`createKernel`
-    :param functionName: see documentation of :func:`createKernel`
-    :param coordinateNames: name of the coordinate fields in the struct data type
-    :return: abstract syntax tree
-    """
-    fieldsRead, fieldsWritten, assignments = typeAllEquations(listOfEquations, typeForSymbol)
-    allFields = fieldsRead.union(fieldsWritten)
-
-    for indexField in indexFields:
-        indexField.isIndexField = True
-        assert indexField.spatialDimensions == 1, "Index fields have to be 1D"
-
-    nonIndexFields = [f for f in allFields if f not in indexFields]
-    spatialCoordinates = {f.spatialDimensions for f in nonIndexFields}
-    assert len(spatialCoordinates) == 1, "Non-index fields do not have the same number of spatial coordinates"
-    spatialCoordinates = list(spatialCoordinates)[0]
-
-    def getCoordinateSymbolAssignment(name):
-        for indexField in indexFields:
-            assert isinstance(indexField.dtype, StructType), "Index fields have to have a struct datatype"
-            dataType = indexField.dtype
-            if dataType.hasElement(name):
-                rhs = indexField[0](name)
-                lhs = TypedSymbol(name, BasicType(dataType.getElementType(name)))
-                return SympyAssignment(lhs, rhs)
-        raise ValueError("Index %s not found in any of the passed index fields" % (name,))
-
-    coordinateSymbolAssignments = [getCoordinateSymbolAssignment(n) for n in coordinateNames[:spatialCoordinates]]
-    coordinateTypedSymbols = [eq.lhs for eq in coordinateSymbolAssignments]
-    assignments = coordinateSymbolAssignments + assignments
-
-    # make 1D loop over index fields
-    loopBody = Block([])
-    loopNode = LoopOverCoordinate(loopBody, coordinateToLoopOver=0, start=0, stop=indexFields[0].shape[0])
-
-    for assignment in assignments:
-        loopBody.append(assignment)
-
-    functionBody = Block([loopNode])
-    ast = KernelFunction(functionBody, "cpu", functionName=functionName)
-
-    fixedCoordinateMapping = {f.name: coordinateTypedSymbols for f in nonIndexFields}
-    resolveFieldAccesses(ast, set(['indexField']), fieldToFixedCoordinates=fixedCoordinateMapping)
-    substituteArrayAccessesWithConstants(ast)
-    moveConstantsBeforeLoop(ast)
-    ast.compile = partial(makePythonFunction, ast)
-    return ast
-
-
-def addOpenMP(astNode, schedule="static", numThreads=True):
-    """
-    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
-    """
-    if not numThreads:
-        return
-
-    assert type(astNode) is ast.KernelFunction
-    body = astNode.body
-    threadsClause = "" if numThreads and isinstance(numThreads,bool) 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?"
-    loopToParallelize = outerLoops[0]
-    try:
-        loopRange = int(loopToParallelize.stop - loopToParallelize.start)
-    except TypeError:
-        loopRange = None
-
-    if numThreads is None:
-        import multiprocessing
-        numThreads = multiprocessing.cpu_count()
-
-    if loopRange is not None and loopRange < numThreads:
-        containedLoops = [l for l in loopToParallelize.body.args if isinstance(l, LoopOverCoordinate)]
-        if len(containedLoops) == 1:
-            containedLoop = containedLoops[0]
-            try:
-                containedLoopRange = int(containedLoop.stop - containedLoop.start)
-                if containedLoopRange > loopRange:
-                    loopToParallelize = containedLoop
-            except TypeError:
-                pass
-
-    loopToParallelize.prefixLines.append("#pragma omp for schedule(%s)" % (schedule,))

data_types.py

-import ctypes
-import sympy as sp
-import numpy as np
-try:
-    import llvmlite.ir as ir
-except ImportError as e:
-    ir = None
-    _ir_importerror = e
-from sympy.core.cache import cacheit
-
-from pystencils.cache import memorycache
-from pystencils.utils import allEqual
-
-
-# to work in conditions of sp.Piecewise castFunc has to be of type Relational as well
-class castFunc(sp.Function, sp.Rel):
-    @property
-    def canonical(self):
-        if hasattr(self.args[0], 'canonical'):
-            return self.args[0].canonical
-        else:
-            raise NotImplementedError()
-
-    @property
-    def is_commutative(self):
-        return self.args[0].is_commutative
-
-
-class pointerArithmeticFunc(sp.Function, sp.Rel):
-
-    @property
-    def canonical(self):
-        if hasattr(self.args[0], 'canonical'):
-            return self.args[0].canonical
-        else:
-            raise NotImplementedError()
-
-
-class TypedSymbol(sp.Symbol):
-    def __new__(cls, *args, **kwds):
-        obj = TypedSymbol.__xnew_cached_(cls, *args, **kwds)
-        return obj
-
-    def __new_stage2__(cls, name, dtype):
-        obj = super(TypedSymbol, cls).__xnew__(cls, name)
-        try:
-            obj._dtype = createType(dtype)
-        except TypeError:
-            # on error keep the string
-            obj._dtype = dtype
-        return obj
-
-    __xnew__ = staticmethod(__new_stage2__)
-    __xnew_cached_ = staticmethod(cacheit(__new_stage2__))
-
-    @property
-    def dtype(self):
-        return self._dtype
-
-    def _hashable_content(self):
-        superClassContents = list(super(TypedSymbol, self)._hashable_content())
-        return tuple(superClassContents + [hash(self._dtype)])
-
-    def __getnewargs__(self):
-        return self.name, self.dtype
-
-
-def createType(specification):
-    """
-    Create a subclass of Type according to a string or an object of subclass Type
-    :param specification: Type object, or a string
-    :return: Type object, or a new Type object parsed from the string
-    """
-    if isinstance(specification, Type):
-        return specification
-    else:
-        npDataType = np.dtype(specification)
-        if npDataType.fields is None:
-            return BasicType(npDataType, const=False)
-        else:
-            return StructType(npDataType, const=False)
-
-
-@memorycache(maxsize=64)
-def createCompositeTypeFromString(specification):
-    """
-    Creates a new Type object from a c-like string specification
-    :param specification: Specification string
-    :return: Type object
-    """
-    specification = specification.lower().split()
-    parts = []
-    current = []
-    for s in specification:
-        if s == '*':
-            parts.append(current)
-            current = [s]
-        else:
-            current.append(s)
-    if len(current) > 0:
-        parts.append(current)
-        # Parse native part
-    basePart = parts.pop(0)
-    const = False
-    if 'const' in basePart:
-        const = True
-        basePart.remove('const')
-    assert len(basePart) == 1
-    if basePart[0][-1] == "*":
-        basePart[0] = basePart[0][:-1]
-        parts.append('*')
-    currentType = BasicType(np.dtype(basePart[0]), const)
-    # Parse pointer parts
-    for part in parts:
-        restrict = False
-        const = False
-        if 'restrict' in part:
-            restrict = True
-            part.remove('restrict')
-        if 'const' in part:
-            const = True
-            part.remove("const")
-        assert len(part) == 1 and part[0] == '*'
-        currentType = PointerType(currentType, const, restrict)
-    return currentType
-
-
-def getBaseType(type):
-    while type.baseType is not None:
-        type = type.baseType
-    return type
-
-
-def toCtypes(dataType):
-    """
-    Transforms a given Type into ctypes
-    :param dataType: Subclass of Type
-    :return: ctypes type object
-    """
-    if isinstance(dataType, PointerType):
-        return ctypes.POINTER(toCtypes(dataType.baseType))
-    elif isinstance(dataType, StructType):
-        return ctypes.POINTER(ctypes.c_uint8)
-    else:
-        return toCtypes.map[dataType.numpyDtype]
-
-toCtypes.map = {
-    np.dtype(np.int8): ctypes.c_int8,
-    np.dtype(np.int16): ctypes.c_int16,
-    np.dtype(np.int32): ctypes.c_int32,
-    np.dtype(np.int64): ctypes.c_int64,
-
-    np.dtype(np.uint8): ctypes.c_uint8,
-    np.dtype(np.uint16): ctypes.c_uint16,
-    np.dtype(np.uint32): ctypes.c_uint32,
-    np.dtype(np.uint64): ctypes.c_uint64,
-
-    np.dtype(np.float32): ctypes.c_float,
-    np.dtype(np.float64): ctypes.c_double,
-}
-
-
-def ctypes_from_llvm(data_type):
-    if not ir:
-        raise _ir_importerror
-    if isinstance(data_type, ir.PointerType):
-        ctype = ctypes_from_llvm(data_type.pointee)
-        if ctype is None:
-            return ctypes.c_void_p
-        else:
-            return ctypes.POINTER(ctype)
-    elif isinstance(data_type, ir.IntType):
-        if data_type.width == 8:
-            return ctypes.c_int8
-        elif data_type.width == 16:
-            return ctypes.c_int16
-        elif data_type.width == 32:
-            return ctypes.c_int32
-        elif data_type.width == 64:
-            return ctypes.c_int64
-        else:
-            raise ValueError("Int width %d is not supported" % data_type.width)
-    elif isinstance(data_type, ir.FloatType):
-        return ctypes.c_float
-    elif isinstance(data_type, ir.DoubleType):
-        return ctypes.c_double
-    elif isinstance(data_type, ir.VoidType):
-        return None  # Void type is not supported by ctypes
-    else:
-        raise NotImplementedError('Data type %s of %s is not supported yet' % (type(data_type), data_type))
-
-
-def to_llvm_type(data_type):
-    """
-    Transforms a given type into ctypes
-    :param data_type: Subclass of Type
-    :return: llvmlite type object
-    """
-    if not ir:
-        raise _ir_importerror
-    if isinstance(data_type, PointerType):
-        return to_llvm_type(data_type.baseType).as_pointer()
-    else:
-        return to_llvm_type.map[data_type.numpyDtype]
-
-if ir:
-    to_llvm_type.map = {
-        np.dtype(np.int8): ir.IntType(8),
-        np.dtype(np.int16): ir.IntType(16),
-        np.dtype(np.int32): ir.IntType(32),
-        np.dtype(np.int64): ir.IntType(64),
-
-        np.dtype(np.uint8): ir.IntType(8),
-        np.dtype(np.uint16): ir.IntType(16),
-        np.dtype(np.uint32): ir.IntType(32),
-        np.dtype(np.uint64): ir.IntType(64),
-
-        np.dtype(np.float32): ir.FloatType(),
-        np.dtype(np.float64): ir.DoubleType(),
-    }
-
-
-def peelOffType(dtype, typeToPeelOff):
-    while type(dtype) is typeToPeelOff:
-        dtype = dtype.baseType
-    return dtype
-
-
-def collateTypes(types):
-    """
-    Takes a sequence of types and returns their "common type" e.g. (float, double, float) -> double
-    Uses the collation rules from numpy.
-    """
-
-    # Pointer arithmetic case i.e. pointer + integer is allowed
-    if any(type(t) is PointerType for t in types):
-        pointerType = None
-        for t in types:
-            if type(t) is PointerType:
-                if pointerType is not None:
-                    raise ValueError("Cannot collate the combination of two pointer types")
-                pointerType = t
-            elif type(t) is BasicType:
-                if not (t.is_int() or t.is_uint()):
-                    raise ValueError("Invalid pointer arithmetic")
-            else:
-                raise ValueError("Invalid pointer arithmetic")
-        return pointerType
-
-    # peel of vector types, if at least one vector type occurred the result will also be the vector type
-    vectorType = [t for t in types if type(t) is VectorType]
-    if not allEqual(t.width for t in vectorType):
-        raise ValueError("Collation failed because of vector types with different width")
-    types = [peelOffType(t, VectorType) for t in types]
-
-    # now we should have a list of basic types - struct types are not yet supported
-    assert all(type(t) is BasicType for t in types)
-
-    # use numpy collation -> create type from numpy type -> and, put vector type around if necessary
-    resultNumpyType = np.result_type(*(t.numpyDtype for t in types))
-    result = BasicType(resultNumpyType)
-    if vectorType:
-        result = VectorType(result, vectorType[0].width)
-    return result
-
-
-@memorycache(maxsize=2048)
-def getTypeOfExpression(expr):
-    from pystencils.astnodes import ResolvedFieldAccess
-    expr = sp.sympify(expr)
-    if isinstance(expr, sp.Integer):
-        return createType("int")
-    elif isinstance(expr, sp.Rational) or isinstance(expr, sp.Float):
-        return createType("double")
-    elif isinstance(expr, ResolvedFieldAccess):
-        return expr.field.dtype
-    elif isinstance(expr, TypedSymbol):
-        return expr.dtype
-    elif isinstance(expr, sp.Symbol):
-        raise ValueError("All symbols inside this expression have to be typed!")
-    elif hasattr(expr, 'func') and expr.func == castFunc:
-        return expr.args[1]
-    elif hasattr(expr, 'func') and expr.func == sp.Piecewise:
-        collatedResultType = collateTypes(tuple(getTypeOfExpression(a[0]) for a in expr.args))
-        collatedConditionType = collateTypes(tuple(getTypeOfExpression(a[1]) for a in expr.args))
-        if type(collatedConditionType) is VectorType and type(collatedResultType) is not VectorType:
-            collatedResultType = VectorType(collatedResultType, width=collatedConditionType.width)
-        return collatedResultType
-    elif isinstance(expr, sp.Indexed):
-        typedSymbol = expr.base.label
-        return typedSymbol.dtype.baseType
-    elif isinstance(expr, sp.boolalg.Boolean) or isinstance(expr, sp.boolalg.BooleanFunction):
-        # if any arg is of vector type return a vector boolean, else return a normal scalar boolean
-        result = createType("bool")
-        vecArgs = [getTypeOfExpression(a) for a in expr.args if isinstance(getTypeOfExpression(a), VectorType)]
-        if vecArgs:
-            result = VectorType(result, width=vecArgs[0].width)
-        return result
-    elif isinstance(expr, sp.Expr):
-        types = tuple(getTypeOfExpression(a) for a in expr.args)
-        return collateTypes(types)
-
-    raise NotImplementedError("Could not determine type for", expr, type(expr))
-
-
-class Type(sp.Basic):
-    def __new__(cls, *args, **kwargs):
-        return sp.Basic.__new__(cls)
-
-    def __lt__(self, other):  # deprecated
-        # Needed for sorting the types inside an expression
-        if isinstance(self, BasicType):
-            if isinstance(other, BasicType):
-                return self.numpyDtype > other.numpyDtype  # TODO const
-            elif isinstance(other, PointerType):
-                return False
-            else:  # isinstance(other, StructType):
-                raise NotImplementedError("Struct type comparison is not yet implemented")
-        elif isinstance(self, PointerType):
-            if isinstance(other, BasicType):
-                return True
-            elif isinstance(other, PointerType):
-                return self.baseType > other.baseType  # TODO const, restrict
-            else:  # isinstance(other, StructType):
-                raise NotImplementedError("Struct type comparison is not yet implemented")
-        elif isinstance(self, StructType):
-            raise NotImplementedError("Struct type comparison is not yet implemented")
-        else:
-            raise NotImplementedError
-
-    def _sympystr(self, *args, **kwargs):
-        return str(self)
-
-    def _sympystr(self, *args, **kwargs):
-        return str(self)
-
-
-class BasicType(Type):
-    @staticmethod
-    def numpyNameToC(name):
-        if name == 'float64':
-            return 'double'
-        elif name == 'float32':
-            return 'float'
-        elif name.startswith('int'):
-            width = int(name[len("int"):])
-            return "int%d_t" % (width,)
-        elif name.startswith('uint'):
-            width = int(name[len("uint"):])
-            return "uint%d_t" % (width,)
-        elif name == 'bool':
-            return 'bool'
-        else:
-            raise NotImplemented("Can map numpy to C name for %s" % (name,))
-
-    def __init__(self, dtype, const=False):
-        self.const = const
-        if isinstance(dtype, Type):
-            self._dtype = dtype.numpyDtype
-        else:
-            self._dtype = np.dtype(dtype)
-        assert self._dtype.fields is None, "Tried to initialize NativeType with a structured type"
-        assert self._dtype.hasobject is False
-        assert self._dtype.subdtype is None
-
-    def __getnewargs__(self):
-        return self.numpyDtype, self.const
-
-    @property
-    def baseType(self):
-        return None
-
-    @property
-    def numpyDtype(self):
-        return self._dtype
-
-    @property
-    def itemSize(self):
-        return 1
-
-    def is_int(self):
-        return self.numpyDtype in np.sctypes['int']
-
-    def is_float(self):
-        return self.numpyDtype in np.sctypes['float']
-
-    def is_uint(self):
-        return self.numpyDtype in np.sctypes['uint']
-
-    def is_comlex(self):
-        return self.numpyDtype in np.sctypes['complex']
-
-    def is_other(self):
-        return self.numpyDtype in np.sctypes['others']
-
-    @property
-    def baseName(self):
-        return BasicType.numpyNameToC(str(self._dtype))
-
-    def __str__(self):
-        result = BasicType.numpyNameToC(str(self._dtype))
-        if self.const:
-            result += " const"
-        return result
-
-    def __repr__(self):
-        return str(self)
-
-    def __eq__(self, other):
-        if not isinstance(other, BasicType):
-            return False
-        else:
-            return (self.numpyDtype, self.const) == (other.numpyDtype, other.const)
-
-    def __hash__(self):
-        return hash(str(self))
-
-
-class VectorType(Type):
-    instructionSet = None
-
-    def __init__(self, baseType, width=4):
-        self._baseType = baseType
-        self.width = width
-
-    @property
-    def baseType(self):
-        return self._baseType
-
-    @property
-    def itemSize(self):
-        return self.width * self.baseType.itemSize
-
-    def __eq__(self, other):
-        if not isinstance(other, VectorType):
-            return False
-        else:
-            return (self.baseType, self.width) == (other.baseType, other.width)
-
-    def __str__(self):
-        if self.instructionSet is None:
-            return "%s[%d]" % (self.baseType, self.width)
-        else:
-            if self.baseType == createType("int64"):
-                return self.instructionSet['int']
-            elif self.baseType == createType("float64"):
-                return self.instructionSet['double']
-            elif self.baseType == createType("float32"):
-                return self.instructionSet['float']
-            elif self.baseType == createType("bool"):
-                return self.instructionSet['bool']
-            else:
-                raise NotImplementedError()
-
-    def __hash__(self):
-        return hash((self.baseType, self.width))
-
-
-class PointerType(Type):
-    def __init__(self, baseType, const=False, restrict=True):
-        self._baseType = baseType
-        self.const = const
-        self.restrict = restrict
-
-    def __getnewargs__(self):
-        return self.baseType, self.const, self.restrict
-
-    @property
-    def alias(self):
-        return not self.restrict
-
-    @property
-    def baseType(self):
-        return self._baseType
-
-    @property
-    def itemSize(self):
-        return self.baseType.itemSize
-
-    def __eq__(self, other):
-        if not isinstance(other, PointerType):
-            return False
-        else:
-            return (self.baseType, self.const, self.restrict) == (other.baseType, other.const, other.restrict)
-
-    def __str__(self):
-        return "%s *%s%s" % (self.baseType, " RESTRICT " if self.restrict else "", " const " if self.const else "")
-
-    def __repr__(self):
-        return str(self)
-
-    def __hash__(self):
-        return hash((self._baseType, self.const, self.restrict))
-
-
-class StructType(object):
-    def __init__(self, numpyType, const=False):
-        self.const = const
-        self._dtype = np.dtype(numpyType)
-
-    def __getnewargs__(self):
-        return self.numpyDtype, self.const
-
-    @property
-    def baseType(self):
-        return None
-
-    @property
-    def numpyDtype(self):
-        return self._dtype
-
-    @property
-    def itemSize(self):
-        return self.numpyDtype.itemsize
-
-    def getElementOffset(self, elementName):
-        return self.numpyDtype.fields[elementName][1]
-
-    def getElementType(self, elementName):
-        npElementType = self.numpyDtype.fields[elementName][0]
-        return BasicType(npElementType, self.const)
-
-    def hasElement(self, elementName):
-        return elementName in self.numpyDtype.fields
-
-    def __eq__(self, other):
-        if not isinstance(other, StructType):
-            return False
-        else:
-            return (self.numpyDtype, self.const) == (other.numpyDtype, other.const)
-
-    def __str__(self):
-        # structs are handled byte-wise
-        result = "uint8_t"
-        if self.const:
-            result += " const"
-        return result
-
-    def __repr__(self):
-        return str(self)
-
-    def __hash__(self):
-        return hash((self.numpyDtype, self.const))
-
-    # TODO this should not work at all!!!
-    def __gt__(self, other):
-        if self.ptr and not other.ptr:
-            return True
-        if self.dtype > other.dtype:
-            return True
-
-
-def get_type_from_sympy(node):
-    """
-    Creates a Type object from a Sympy object
-    :param node: Sympy object
-    :return: Type object
-    """
-    # Rational, NumberSymbol?
-    # Zero, One, NegativeOne )= Integer
-    # Half )= Rational
-    # NAN, Infinity, Negative Inifinity,
-    # Exp1, Imaginary Unit, Pi, EulerGamma, Catalan, Golden Ratio
-    # Pow, Mul, Add, Mod, Relational
-    if not isinstance(node, sp.Number):
-        raise TypeError(node, 'is not a sp.Number')
-
-    if isinstance(node, sp.Float) or isinstance(node, sp.RealNumber):
-        return createType('double'), float(node)
-    elif isinstance(node, sp.Integer):
-        return createType('int'), int(node)
-    elif isinstance(node, sp.Rational):
-        # TODO is it always float?
-        return createType('double'), float(node.p/node.q)
-    else:
-        raise TypeError(node, ' is not a supported type (yet)!')

datahandling.py

-import numpy as np
-from abc import ABC, abstractmethod, abstractproperty
-
-from collections import defaultdict
-from contextlib import contextmanager
-
-from lbmpy.stencils import getStencil
-from pystencils import Field, makeSlice
-from pystencils.parallel.blockiteration import BlockIterationInfo
-from pystencils.slicing import normalizeSlice, removeGhostLayers
-from pystencils.utils import DotDict
-
-try:
-    import pycuda.gpuarray as gpuarray
-except ImportError:
-    gpuarray = None
-
-
-class DataHandling(ABC):
-    """
-    Manages the storage of arrays and maps them to a symbolic field.
-    Two versions are available: a simple, pure Python implementation for single node
-    simulations :py:class:SerialDataHandling and a distributed version using waLBerla in :py:class:ParallelDataHandling
-
-    Keep in mind that the data can be distributed, so use the 'access' method whenever possible and avoid the
-    'gather' function that has collects (parts of the) distributed data on a single process.
-    """
-
-    def __init__(self):
-        self._preAccessFunctions = defaultdict(list)
-        self._postAccessFunctions = defaultdict(list)
-
-    # ---------------------------- Adding and accessing data -----------------------------------------------------------
-
-    @property
-    @abstractmethod
-    def dim(self):
-        """Dimension of the domain, either 2 or 3"""
-
-    @abstractmethod
-    def addArray(self, name, fSize=1, dtype=np.float64, latexName=None, ghostLayers=None, layout=None, cpu=True, gpu=False):
-        """
-        Adds a (possibly distributed) array to the handling that can be accessed using the given name.
-        For each array a symbolic field is available via the 'fields' dictionary
-
-        :param name: unique name that is used to access the field later
-        :param fSize: shape of the dim+1 coordinate. DataHandling supports zero or one index dimensions, i.e. scalar
-                      fields and vector fields. This parameter gives the shape of the index dimensions. The default
-                      value of 1 means no index dimension
-        :param dtype: data type of the array as numpy data type
-        :param latexName: optional, name of the symbolic field, if not given 'name' is used
-        :param ghostLayers: number of ghost layers - if not specified a default value specified in the constructor
-                            is used
-        :param layout: memory layout of array, either structure of arrays 'SoA' or array of structures 'AoS'.
-                       this is only important if fSize > 1
-        :param cpu: allocate field on the CPU
-        :param gpu: allocate field on the GPU
-        """
-
-    @abstractmethod
-    def hasData(self, name):
-        """
-        Returns true if a field or custom data element with this name was added
-        """
-
-    @abstractmethod
-    def addArrayLike(self, name, nameOfTemplateField, latexName=None, cpu=True, gpu=False):
-        """
-        Adds an array with the same parameters (number of ghost layers, fSize, dtype) as existing array
-        :param name: name of new array
-        :param nameOfTemplateField: name of array that is used as template
-        :param latexName: see 'add' method
-        :param cpu: see 'add' method
-        :param gpu: see 'add' method
-        """
-
-    @abstractmethod
-    def addCustomData(self, name, cpuCreationFunction,
-                      gpuCreationFunction=None, cpuToGpuTransferFunc=None, gpuToCpuTransferFunc=None):
-        """
-        Adds custom (non-array) data to domain
-        :param name: name to access data
-        :param cpuCreationFunction: function returning a new instance of the data that should be stored
-        :param gpuCreationFunction: optional, function returning a new instance, stored on GPU
-        :param cpuToGpuTransferFunc: function that transfers cpu to gpu version, getting two parameters (gpuInstance, cpuInstance)
-        :param gpuToCpuTransferFunc: function that transfers gpu to cpu version, getting two parameters (gpuInstance, cpuInstance)
-        :return:
-        """
-
-    @property
-    @abstractmethod
-    def fields(self):
-        """Dictionary mapping data name to symbolic pystencils field - use this to create pystencils kernels"""
-
-    @abstractmethod
-    def accessArray(self, name, sliceObj=None, innerGhostLayers=None, outerGhostLayers=0):
-        """
-        Generator yielding locally stored sub-arrays together with information about their place in the global domain
-
-        :param name: name of data to access
-        :param sliceObj: optional rectangular sub-region to access
-        :param innerGhostLayers: how many inner (not at domain border) ghost layers to include
-        :param outerGhostLayers: how many ghost layers at the domain border to include
-        Yields a numpy array with local part of data and a BlockIterationInfo object containing geometric information
-        """
-
-    @abstractmethod
-    def accessCustomData(self, name):
-        """
-        Similar to accessArray, however for custom data no slicing and ghost layer info is necessary/available
-        """
-
-    @abstractmethod
-    def gatherArray(self, name, sliceObj=None, allGather=False):
-        """
-        Gathers part of the domain on a local process. Whenever possible use 'access' instead, since this method copies
-        the distributed data to a single process which is inefficient and may exhaust the available memory
-
-        :param name: name of the array to gather
-        :param sliceObj: slice expression of the rectangular sub-part that should be gathered
-        :param allGather: if False only the root process receives the result, if True all processes
-        :return: generator expression yielding the gathered field, the gathered field does not include any ghost layers
-        """
-
-    def registerPreAccessFunction(self, name, function):
-        self._preAccessFunctions[name].append(function)
-
-    def registerPostAccessFunction(self, name, function):
-        self._postAccessFunctions[name].append(function)
-
-    @contextmanager
-    def accessWrapper(self, name):
-        for func in self._preAccessFunctions[name]:
-            func()
-        yield
-        for func in self._postAccessFunctions[name]:
-            func()
-    # ------------------------------- CPU/GPU transfer -----------------------------------------------------------------
-
-    @abstractmethod
-    def toCpu(self, name):
-        """Copies GPU data of array with specified name to CPU.
-        Works only if 'cpu=True' and 'gpu=True' has been used in 'add' method"""
-        pass
-
-    @abstractmethod
-    def toGpu(self, name):
-        """Copies GPU data of array with specified name to GPU.
-        Works only if 'cpu=True' and 'gpu=True' has been used in 'add' method"""
-        pass
-
-    @abstractmethod
-    def allToCpu(self, name):
-        """Copies data from GPU to CPU for all arrays that have a CPU and a GPU representation"""
-        pass
-
-    @abstractmethod
-    def allToGpu(self, name):
-        """Copies data from CPU to GPU for all arrays that have a CPU and a GPU representation"""
-        pass
-
-    # ------------------------------- Communication --------------------------------------------------------------------
-
-    def synchronizationFunctionCPU(self, names, stencil=None, **kwargs):
-        """
-        Synchronizes ghost layers for distributed arrays - for serial scenario this has to be called
-        for correct periodicity handling
-        :param names: what data to synchronize: name of array or sequence of names
-        :param stencil: stencil as string defining which neighbors are synchronized e.g. 'D2Q9', 'D3Q19'
-                        if None, a full synchronization (i.e. D2Q9 or D3Q27) is done
-        :param kwargs: implementation specific, optional optimization parameters for communication
-        :return: function object to run the communication
-        """
-
-    def synchronizationFunctionGPU(self, names, stencil=None, **kwargs):
-        """
-        Synchronization of GPU fields, for documentation see CPU version above
-        """
-
-
-class SerialDataHandling(DataHandling):
-
-    class _PassThroughContextManager:
-        def __init__(self, arr):
-            self.arr = arr
-
-        def __enter__(self, *args, **kwargs):
-            return self.arr
-
-    def __init__(self, domainSize, defaultGhostLayers=1, defaultLayout='SoA', periodicity=False):
-        """
-        Creates a data handling for single node simulations
-
-        :param domainSize: size of the spatial domain as tuple
-        :param defaultGhostLayers: nr of ghost layers used if not specified in add() method
-        :param defaultLayout: layout used if no layout is given to add() method
-        """
-        super(SerialDataHandling, self).__init__()
-        self._domainSize = tuple(domainSize)
-        self.defaultGhostLayers = defaultGhostLayers
-        self.defaultLayout = defaultLayout
-        self._fields = DotDict()
-        self.cpuArrays = DotDict()
-        self.gpuArrays = DotDict()
-        self.customDataCpu = DotDict()
-        self.customDataGpu = DotDict()
-        self._customDataTransferFunctions = {}
-
-        if periodicity is None or periodicity is False:
-            periodicity = [False] * self.dim
-        if periodicity is True:
-            periodicity = [True] * self.dim
-
-        self._periodicity = periodicity
-        self._fieldInformation = {}
-
-    @property
-    def dim(self):
-        return len(self._domainSize)
-
-    @property
-    def fields(self):
-        return self._fields
-
-    def addArray(self, name, fSize=1, dtype=np.float64, latexName=None, ghostLayers=None, layout=None, cpu=True, gpu=False):
-        if ghostLayers is None:
-            ghostLayers = self.defaultGhostLayers
-        if layout is None:
-            layout = self.defaultLayout
-        if latexName is None:
-            latexName = name
-
-        assert layout in ('SoA', 'AoS')
-
-        kwargs = {
-            'shape': tuple(s + 2 * ghostLayers for s in self._domainSize),
-            'dtype': dtype,
-            'order': 'C' if layout == 'AoS' else 'F',
-        }
-        self._fieldInformation[name] = {
-            'ghostLayers': ghostLayers,
-            'fSize': fSize,
-            'layout': layout,
-            'dtype': dtype,
-        }
-
-        if fSize > 1:
-            kwargs['shape'] = kwargs['shape'] + (fSize,)
-            indexDimensions = 1
-        else:
-            indexDimensions = 0
-        if cpu:
-            if name in self.cpuArrays:
-                raise ValueError("CPU Field with this name already exists")
-            self.cpuArrays[name] = np.empty(**kwargs)
-        if gpu:
-            if name in self.gpuArrays:
-                raise ValueError("GPU Field with this name already exists")
-
-            self.gpuArrays[name] = gpuarray.empty(**kwargs)
-
-        assert all(f.name != latexName for f in self.fields.values()), "Symbolic field with this name already exists"
-        self.fields[name] = Field.createFixedSize(latexName, shape=kwargs['shape'], indexDimensions=indexDimensions,
-                                                  dtype=kwargs['dtype'], layout=kwargs['order'])
-
-    def addCustomData(self, name, cpuCreationFunction,
-                      gpuCreationFunction=None, cpuToGpuTransferFunc=None, gpuToCpuTransferFunc=None):
-        assert name not in self.cpuArrays
-        assert name not in self.customDataCpu
-        self.customDataCpu[name] = cpuCreationFunction()
-        if gpuCreationFunction:
-            self.customDataGpu[name] = gpuCreationFunction()
-            if cpuToGpuTransferFunc is None or gpuToCpuTransferFunc is None:
-                raise ValueError("For GPU data, both transfer functions have to be specified")
-            self._customDataTransferFunctions[name] = (cpuToGpuTransferFunc, gpuToCpuTransferFunc)
-
-    def hasData(self, name):
-        return name in self.fields
-
-    def addArrayLike(self, name, nameOfTemplateField, latexName=None, cpu=True, gpu=False):
-        self.addArray(name, latexName=latexName, cpu=cpu, gpu=gpu, **self._fieldInformation[nameOfTemplateField])
-
-    def accessArray(self, name, sliceObj=None, outerGhostLayers='all', **kwargs):
-        if outerGhostLayers == 'all':
-            outerGhostLayers = self._fieldInformation[name]['ghostLayers']
-
-        if sliceObj is None:
-            sliceObj = [slice(None, None)] * self.dim
-
-        with self.accessWrapper(name):
-            arr = self.cpuArrays[name]
-            glToRemove = self._fieldInformation[name]['ghostLayers'] - outerGhostLayers
-            assert glToRemove >= 0
-            arr = removeGhostLayers(arr, indexDimensions=self.fields[name].indexDimensions, ghostLayers=glToRemove)
-            sliceObj = normalizeSlice(sliceObj, arr.shape[:self.dim])
-            yield arr[sliceObj], BlockIterationInfo(None, tuple(s.start for s in sliceObj), sliceObj)
-
-    def accessCustomData(self, name):
-        yield self.customDataCpu[name], ((0,0,0)[:self.dim], self._domainSize)
-
-    def gatherArray(self, name, sliceObj=None, **kwargs):
-        with self.accessWrapper(name):
-            gls = self._fieldInformation[name]['ghostLayers']
-            arr = self.cpuArrays[name]
-            arr = removeGhostLayers(arr, indexDimensions=self.fields[name].indexDimensions, ghostLayers=gls)
-
-            if sliceObj is not None:
-                arr = arr[sliceObj]
-            yield arr
-
-    def swap(self, name1, name2, gpu=False):
-        if not gpu:
-            self.cpuArrays[name1], self.cpuArrays[name2] = self.cpuArrays[name2], self.cpuArrays[name1]
-        else:
-            self.gpuArrays[name1], self.gpuArrays[name2] = self.gpuArrays[name2], self.gpuArrays[name1]
-
-    def allToCpu(self):
-        for name in (self.cpuArrays.keys() & self.gpuArrays.keys()) | self._customDataTransferFunctions.keys():
-            self.toCpu(name)
-
-    def allToGpu(self):
-        for name in (self.cpuArrays.keys() & self.gpuArrays.keys()) | self._customDataTransferFunctions.keys():
-            self.toGpu(name)
-
-    def toCpu(self, name):
-        if name in self._customDataTransferFunctions:
-            transferFunc = self._customDataTransferFunctions[name][1]
-            transferFunc(self.customDataGpu[name], self.customDataCpu[name])
-        else:
-            self.gpuArrays[name].get(self.cpuArrays[name])
-
-    def toGpu(self, name):
-        if name in self._customDataTransferFunctions:
-            transferFunc = self._customDataTransferFunctions[name][0]
-            transferFunc(self.customDataGpu[name], self.customDataCpu[name])
-        else:
-            self.gpuArrays[name].set(self.cpuArrays[name])
-
-    def synchronizationFunctionCPU(self, names, stencilName=None, **kwargs):
-        return self._synchronizationFunctor(names, stencilName, 'cpu')
-
-    def synchronizationFunctionGPU(self, names, stencilName=None, **kwargs):
-        return self._synchronizationFunctor(names, stencilName, 'gpu')
-
-    def _synchronizationFunctor(self, names, stencil, target):
-        if stencil is None:
-            stencil = 'D3Q27' if self.dim == 3 else 'D2Q9'
-
-        assert stencil in ("D2Q9", 'D3Q27'), "Serial scenario support only D2Q9 or D3Q27 for periodicity sync"
-
-        assert target in ('cpu', 'gpu')
-        if not hasattr(names, '__len__') or type(names) is str:
-            names = [names]
-
-        filteredStencil = []
-        for direction in getStencil(stencil):
-            useDirection = True
-            if direction == (0, 0) or direction == (0, 0, 0):
-                useDirection = False
-            for component, periodicity in zip(direction, self._periodicity):
-                if not periodicity and component != 0:
-                    useDirection = False
-            if useDirection:
-                filteredStencil.append(direction)
-
-        resultFunctors = []
-        for name in names:
-            gls = self._fieldInformation[name]['ghostLayers']
-            if len(filteredStencil) > 0:
-                if target == 'cpu':
-                    from pystencils.slicing import getPeriodicBoundaryFunctor
-                    resultFunctors.append(getPeriodicBoundaryFunctor(filteredStencil, ghostLayers=gls))
-                else:
-                    from pystencils.gpucuda.periodicity import getPeriodicBoundaryFunctor
-                    resultFunctors.append(getPeriodicBoundaryFunctor(filteredStencil, self._domainSize,
-                                                                     indexDimensions=self.fields[name].indexDimensions,
-                                                                     indexDimShape=self._fieldInformation[name]['fSize'],
-                                                                     dtype=self.fields[name].dtype.numpyDtype,
-                                                                     ghostLayers=gls))
-
-        if target == 'cpu':
-            def resultFunctor():
-                for func in resultFunctors:
-                    func(pdfs=self.cpuArrays[name])
-        else:
-            def resultFunctor():
-                for func in resultFunctors:
-                    func(pdfs=self.gpuArrays[name])
-
-        return resultFunctor

display_utils.py

-
-def toDot(expr, graphStyle={}):
-    """Show a sympy or pystencils AST as dot graph"""
-    from pystencils.astnodes 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()))
-
-
-def showCode(ast):
-    from pystencils.cpu import generateC
-
-    class CodeDisplay:
-        def __init__(self, astInput):
-            self.ast = astInput
-
-        def _repr_html_(self):
-            return highlightCpp(generateC(self.ast)).__html__()
-
-        def __str__(self):
-            return generateC(self.ast)
-
-        def __repr__(self):
-            return generateC(self.ast)
-    return CodeDisplay(ast)
-
-
-
-# ----------------- 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()
-    if ipython is None:
-        return
-    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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