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)

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 all_equal
-from sympy.logic.boolalg import Boolean
-
-
-# noinspection PyPep8Naming
-class cast_func(sp.Function):
-    is_Atom = True
-
-    def __new__(cls, *args, **kwargs):
-        # to work in conditions of sp.Piecewise cast_func has to be of type Boolean as well
-        # however, a cast_function should only be a boolean if its argument is a boolean, otherwise this leads
-        # to problems when for example comparing cast_func's for equality
-        #
-        # lhs = bitwise_and(a, cast_func(1, 'int'))
-        # rhs = cast_func(0, 'int')
-        # print( sp.Ne(lhs, rhs) ) # would give true if all cast_funcs are booleans
-        # -> thus a separate class bollean_cast_func is introduced
-        if isinstance(args[0], Boolean):
-            cls = boolean_cast_func
-        return sp.Function.__new__(cls, *args, **kwargs)
-
-    @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
-
-    @property
-    def dtype(self):
-        return self.args[1]
-
-
-# noinspection PyPep8Naming
-class boolean_cast_func(cast_func, Boolean):
-    pass
-
-
-# noinspection PyPep8Naming
-class vector_memory_access(cast_func):
-    nargs = (4,)
-
-
-# noinspection PyPep8Naming
-class pointer_arithmetic_func(sp.Function, Boolean):
-    @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 = create_type(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):
-        return super()._hashable_content(), hash(self._dtype)
-
-    def __getnewargs__(self):
-        return self.name, self.dtype
-
-
-def create_type(specification):
-    """Creates a subclass of Type according to a string or an object of subclass Type.
-
-    Args:
-        specification: Type object, or a string
-
-    Returns:
-        Type object, or a new Type object parsed from the string
-    """
-    if isinstance(specification, Type):
-        return specification
-    else:
-        numpy_dtype = np.dtype(specification)
-        if numpy_dtype.fields is None:
-            return BasicType(numpy_dtype, const=False)
-        else:
-            return StructType(numpy_dtype, const=False)
-
-
-@memorycache(maxsize=64)
-def create_composite_type_from_string(specification):
-    """Creates a new Type object from a c-like string specification.
-
-    Args:
-        specification: Specification string
-
-    Returns:
-        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
-    base_part = parts.pop(0)
-    const = False
-    if 'const' in base_part:
-        const = True
-        base_part.remove('const')
-    assert len(base_part) == 1
-    if base_part[0][-1] == "*":
-        base_part[0] = base_part[0][:-1]
-        parts.append('*')
-    current_type = BasicType(np.dtype(base_part[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] == '*'
-        current_type = PointerType(current_type, const, restrict)
-    return current_type
-
-
-def get_base_type(data_type):
-    while data_type.base_type is not None:
-        data_type = data_type.base_type
-    return data_type
-
-
-def to_ctypes(data_type):
-    """
-    Transforms a given Type into ctypes
-    :param data_type: Subclass of Type
-    :return: ctypes type object
-    """
-    if isinstance(data_type, PointerType):
-        return ctypes.POINTER(to_ctypes(data_type.base_type))
-    elif isinstance(data_type, StructType):
-        return ctypes.POINTER(ctypes.c_uint8)
-    else:
-        return to_ctypes.map[data_type.numpy_dtype]
-
-
-to_ctypes.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.base_type).as_pointer()
-    else:
-        return to_llvm_type.map[data_type.numpy_dtype]
-
-
-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 peel_off_type(dtype, type_to_peel_off):
-    while type(dtype) is type_to_peel_off:
-        dtype = dtype.base_type
-    return dtype
-
-
-def collate_types(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):
-        pointer_type = None
-        for t in types:
-            if type(t) is PointerType:
-                if pointer_type is not None:
-                    raise ValueError("Cannot collate the combination of two pointer types")
-                pointer_type = 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 pointer_type
-
-    # peel of vector types, if at least one vector type occurred the result will also be the vector type
-    vector_type = [t for t in types if type(t) is VectorType]
-    if not all_equal(t.width for t in vector_type):
-        raise ValueError("Collation failed because of vector types with different width")
-    types = [peel_off_type(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)
-
-    if any(t.is_float() for t in types):
-        types = tuple(t for t in types if t.is_float())
-    # use numpy collation -> create type from numpy type -> and, put vector type around if necessary
-    result_numpy_type = np.result_type(*(t.numpy_dtype for t in types))
-    result = BasicType(result_numpy_type)
-    if vector_type:
-        result = VectorType(result, vector_type[0].width)
-    return result
-
-
-@memorycache(maxsize=2048)
-def get_type_of_expression(expr):
-    from pystencils.astnodes import ResolvedFieldAccess
-    expr = sp.sympify(expr)
-    if isinstance(expr, sp.Integer):
-        return create_type("int")
-    elif isinstance(expr, sp.Rational) or isinstance(expr, sp.Float):
-        return create_type("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! ", str(expr))
-    elif isinstance(expr, cast_func):
-        return expr.args[1]
-    elif hasattr(expr, 'func') and expr.func == sp.Piecewise:
-        collated_result_type = collate_types(tuple(get_type_of_expression(a[0]) for a in expr.args))
-        collated_condition_type = collate_types(tuple(get_type_of_expression(a[1]) for a in expr.args))
-        if type(collated_condition_type) is VectorType and type(collated_result_type) is not VectorType:
-            collated_result_type = VectorType(collated_result_type, width=collated_condition_type.width)
-        return collated_result_type
-    elif isinstance(expr, sp.Indexed):
-        typed_symbol = expr.base.label
-        return typed_symbol.dtype.base_type
-    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 = create_type("bool")
-        vec_args = [get_type_of_expression(a) for a in expr.args if isinstance(get_type_of_expression(a), VectorType)]
-        if vec_args:
-            result = VectorType(result, width=vec_args[0].width)
-        return result
-    elif isinstance(expr, sp.Pow):
-        return get_type_of_expression(expr.args[0])
-    elif isinstance(expr, sp.Expr):
-        types = tuple(get_type_of_expression(a) for a in expr.args)
-        return collate_types(types)
-
-    raise NotImplementedError("Could not determine type for", expr, type(expr))
-
-
-class Type(sp.Basic):
-    is_Atom = True
-
-    def __new__(cls, *args, **kwargs):
-        return sp.Basic.__new__(cls)
-
-    def _sympystr(self, *args, **kwargs):
-        return str(self)
-
-
-class BasicType(Type):
-    @staticmethod
-    def numpy_name_to_c(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.numpy_dtype
-        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.numpy_dtype, self.const
-
-    @property
-    def base_type(self):
-        return None
-
-    @property
-    def numpy_dtype(self):
-        return self._dtype
-
-    @property
-    def item_size(self):
-        return 1
-
-    def is_int(self):
-        return self.numpy_dtype in np.sctypes['int']
-
-    def is_float(self):
-        return self.numpy_dtype in np.sctypes['float']
-
-    def is_uint(self):
-        return self.numpy_dtype in np.sctypes['uint']
-
-    def is_complex(self):
-        return self.numpy_dtype in np.sctypes['complex']
-
-    def is_other(self):
-        return self.numpy_dtype in np.sctypes['others']
-
-    @property
-    def base_name(self):
-        return BasicType.numpy_name_to_c(str(self._dtype))
-
-    def __str__(self):
-        result = BasicType.numpy_name_to_c(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.numpy_dtype, self.const) == (other.numpy_dtype, other.const)
-
-    def __hash__(self):
-        return hash(str(self))
-
-
-class VectorType(Type):
-    instruction_set = None
-
-    def __init__(self, base_type, width=4):
-        self._base_type = base_type
-        self.width = width
-
-    @property
-    def base_type(self):
-        return self._base_type
-
-    @property
-    def item_size(self):
-        return self.width * self.base_type.item_size
-
-    def __eq__(self, other):
-        if not isinstance(other, VectorType):
-            return False
-        else:
-            return (self.base_type, self.width) == (other.base_type, other.width)
-
-    def __str__(self):
-        if self.instruction_set is None:
-            return "%s[%d]" % (self.base_type, self.width)
-        else:
-            if self.base_type == create_type("int64"):
-                return self.instruction_set['int']
-            elif self.base_type == create_type("float64"):
-                return self.instruction_set['double']
-            elif self.base_type == create_type("float32"):
-                return self.instruction_set['float']
-            elif self.base_type == create_type("bool"):
-                return self.instruction_set['bool']
-            else:
-                raise NotImplementedError()
-
-    def __hash__(self):
-        return hash((self.base_type, self.width))
-
-    def __getnewargs__(self):
-        return self._base_type, self.width
-
-
-class PointerType(Type):
-    def __init__(self, base_type, const=False, restrict=True):
-        self._base_type = base_type
-        self.const = const
-        self.restrict = restrict
-
-    def __getnewargs__(self):
-        return self.base_type, self.const, self.restrict
-
-    @property
-    def alias(self):
-        return not self.restrict
-
-    @property
-    def base_type(self):
-        return self._base_type
-
-    @property
-    def item_size(self):
-        return self.base_type.item_size
-
-    def __eq__(self, other):
-        if not isinstance(other, PointerType):
-            return False
-        else:
-            return (self.base_type, self.const, self.restrict) == (other.base_type, other.const, other.restrict)
-
-    def __str__(self):
-        components = [str(self.base_type), '*']
-        if self.restrict:
-            components.append('RESTRICT')
-        if self.const:
-            components.append("const")
-        return " ".join(components)
-
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-        return str(self)
-
-    def __hash__(self):
-        return hash((self._base_type, self.const, self.restrict))
-
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-class StructType:
-    def __init__(self, numpy_type, const=False):
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-
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-        return self.numpy_dtype, self.const
-
-    @property
-    def base_type(self):
-        return None
-
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-    def numpy_dtype(self):
-        return self._dtype
-
-    @property
-    def item_size(self):
-        return self.numpy_dtype.itemsize
-
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-
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-
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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'

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