# Source code for brian2.codegen.generators.cython_generator

    import itertools

    from brian2.codegen.cpp_prefs import C99Check
    from brian2.core.functions import DEFAULT_FUNCTIONS, Function
    from brian2.core.variables import (
        AuxiliaryVariable,
        Constant,
        Subexpression,
        Variable,
        get_dtype_str,
    )
    from brian2.devices.device import all_devices
    from brian2.parsing.bast import brian_dtype_from_dtype
    from brian2.parsing.rendering import NodeRenderer
    from brian2.utils.stringtools import deindent, indent, word_substitute

    from .base import CodeGenerator

    __all__ = ["CythonCodeGenerator"]

    # fmt: off
    data_type_conversion_table = [
        # canonical         C++            Numpy
        ('float32',        'float',       'float32'),
        ('float64',        'double',      'float64'),
        ('int32',          'int32_t',     'int32'),
        ('int64',          'int64_t',     'int64'),
        ('bool',           'bool',        'bool'),
        ('uint8',          'char',        'uint8'),
        ('uint64',         'uint64_t',    'uint64'),
        ]
    # fmt: on

    cpp_dtype = {canonical: cpp for canonical, cpp, np in data_type_conversion_table}
    numpy_dtype = {canonical: np for canonical, cpp, np in data_type_conversion_table}

    def get_cpp_dtype(obj):
        return cpp_dtype[get_dtype_str(obj)]

    def get_numpy_dtype(obj):
        return numpy_dtype[get_dtype_str(obj)]

    class CythonNodeRenderer(NodeRenderer):

        def render_NameConstant(self, node):
            return {True: "1", False: "0"}.get(node.value, node.value)

        def render_Name(self, node):
            return {"True": "1", "False": "0"}.get(node.id, node.id)

        def render_BinOp(self, node):
            if node.op.__class__.__name__ == "Mod":
                left = self.render_node(node.left)
                right = self.render_node(node.right)
                return f"((({left})%({right}))+({right}))%({right})"
            else:
                return super().render_BinOp(node)

    class CythonCodeGenerator(CodeGenerator):
        """
        Cython code generator
        """

        class_name = "cython"

        def __init__(self, *args, **kwds):
            self.temporary_vars = set()
            super().__init__(*args, **kwds)

        def translate_expression(self, expr):
            expr = word_substitute(expr, self.func_name_replacements)
            return (
                CythonNodeRenderer(auto_vectorise=self.auto_vectorise)
                .render_expr(expr, self.variables)
                .strip()
            )

        def translate_statement(self, statement):
            var, op, expr, comment = (
                statement.var,
                statement.op,
                statement.expr,
                statement.comment,
            )
            if op == ":=":  # make no distinction in Cython (declaration are done elsewhere)
                op = "="
            # For Cython we replace complex expressions involving boolean variables into a sequence of
            # if/then expressions with simpler expressions. This is provided by the optimise_statements
            # function.
            if (
                statement.used_boolean_variables is not None
                and len(statement.used_boolean_variables)
                # todo: improve dtype analysis so that this isn't necessary
                and brian_dtype_from_dtype(statement.dtype) == "float"
            ):
                used_boolvars = statement.used_boolean_variables
                bool_simp = statement.boolean_simplified_expressions
                codelines = []
                firstline = True
                # bool assigns is a sequence of (var, value) pairs giving the conditions under
                # which the simplified expression simp_expr holds
                for bool_assigns, simp_expr in bool_simp.items():
                    # generate a boolean expression like ``var1 and var2 and not var3``
                    atomics = []
                    for boolvar, boolval in bool_assigns:
                        if boolval:
                            atomics.append(boolvar)
                        else:
                            atomics.append(f"not {boolvar}")
                    # use if/else/elif correctly
                    if firstline:
                        line = f"if {' and '.join(atomics)}:"
                    else:
                        if len(used_boolvars) > 1:
                            line = f"elif {' and '.join(atomics)}:"
                        else:
                            line = "else:"
                    line += "\n    "
                    line += f"{var} {op} {self.translate_expression(simp_expr)}"
                    codelines.append(line)
                    firstline = False
                code = "\n".join(codelines)
            else:
                code = f"{var} {op} {self.translate_expression(expr)}"
            if len(comment):
                code += f" # {comment}"
            return code

        def translate_one_statement_sequence(self, statements, scalar=False):
            # Note that we do not call this function from
            # `translate_statement_sequence` (which has been overwritten)
            # It is nevertheless implemented, so that it can be called explicitly
            # (e.g. from the GSL code generation)
            read, write, indices, conditional_write_vars = self.arrays_helper(statements)
            lines = []
            # index and read arrays (index arrays first)
            lines += self.translate_to_read_arrays(read, indices)
            # the actual code
            lines += self.translate_to_statements(statements, conditional_write_vars)
            # write arrays
            lines += self.translate_to_write_arrays(write)

            return lines

        def translate_to_read_arrays(self, read, indices):
            lines = []
            for varname in itertools.chain(sorted(indices), sorted(read)):
                var = self.variables[varname]
                index = self.variable_indices[varname]
                arrayname = self.get_array_name(var)
                line = f"{varname} = {arrayname}[{index}]"
                lines.append(line)
            return lines

        def translate_to_statements(self, statements, conditional_write_vars):
            lines = []
            for stmt in statements:
                if stmt.op == ":=" and stmt.var not in self.variables:
                    self.temporary_vars.add((stmt.var, stmt.dtype))
                line = self.translate_statement(stmt)
                if stmt.var in conditional_write_vars:
                    condvar = conditional_write_vars[stmt.var]
                    lines.append(f"if {condvar}:")
                    lines.append(indent(line))
                else:
                    lines.append(line)
            return lines

        def translate_to_write_arrays(self, write):
            lines = []
            for varname in sorted(write):
                index_var = self.variable_indices[varname]
                var = self.variables[varname]
                line = (
                    f"{self.get_array_name(var, self.variables)}[{index_var}] = {varname}"
                )
                lines.append(line)
            return lines

        def translate_statement_sequence(self, sc_statements, ve_statements):
            # This function is overwritten, since we do not want to completely
            # separate the code generation for scalar and vector code

            assert set(sc_statements.keys()) == set(ve_statements.keys())

            sc_code = {}
            ve_code = {}

            for block_name in sc_statements:
                sc_block = sc_statements[block_name]
                ve_block = ve_statements[block_name]
                (sc_read, sc_write, sc_indices, sc_cond_write) = self.arrays_helper(
                    sc_block
                )
                (ve_read, ve_write, ve_indices, ve_cond_write) = self.arrays_helper(
                    ve_block
                )
                # We want to read all scalar variables that are needed in the
                # vector code already in the scalar code, if they are not written
                for varname in set(ve_read):
                    var = self.variables[varname]
                    if var.scalar and varname not in ve_write:
                        sc_read.add(varname)
                        ve_read.remove(varname)

                for code, stmts, read, write, indices, cond_write in [
                    (sc_code, sc_block, sc_read, sc_write, sc_indices, sc_cond_write),
                    (ve_code, ve_block, ve_read, ve_write, ve_indices, ve_cond_write),
                ]:
                    lines = []
                    # index and read arrays (index arrays first)
                    lines += self.translate_to_read_arrays(read, indices)
                    # the actual code
                    lines += self.translate_to_statements(stmts, cond_write)
                    # write arrays
                    lines += self.translate_to_write_arrays(write)
                    code[block_name] = "\n".join(lines)

            kwds = self.determine_keywords()
            return sc_code, ve_code, kwds

        def _add_user_function(self, varname, var, added):
            user_functions = []
            load_namespace = []
            support_code = []
            impl = var.implementations[self.codeobj_class]
            if (impl.name, var) in added:
                return  # nothing to do
            else:
                added.add((impl.name, var))
            func_code = impl.get_code(self.owner)
            # Implementation can be None if the function is already
            # available in Cython (possibly under a different name)
            if func_code is not None:
                if isinstance(func_code, str):
                    # Function is provided as Cython code
                    # To make namespace variables available to functions, we
                    # create global variables and assign to them in the main
                    # code
                    user_functions.append((varname, var))
                    func_namespace = impl.get_namespace(self.owner) or {}
                    for ns_key, ns_value in func_namespace.items():
                        load_namespace.append(f"# namespace for function {varname}")
                        if hasattr(ns_value, "dtype"):
                            if ns_value.shape == ():
                                raise NotImplementedError(
                                    "Directly replace scalar values in the function "
                                    "instead of providing them via the namespace"
                                )
                            newlines = [
                                "global _namespace{var_name}",
                                "global _namespace_num{var_name}",
                                (
                                    "cdef _numpy.ndarray[{cpp_dtype}, ndim=1, mode='c']"
                                    " _buf_{var_name} = _namespace['{var_name}']"
                                ),
                                (
                                    "_namespace{var_name} = <{cpp_dtype} *>"
                                    " _buf_{var_name}.data"
                                ),
                                "_namespace_num{var_name} = len(_namespace['{var_name}'])",
                            ]
                            support_code.append(
                                f"cdef {get_cpp_dtype(ns_value.dtype)} *_namespace{ns_key}"
                            )

                        else:  # e.g. a function
                            newlines = ["_namespace{var_name} = namespace['{var_name}']"]
                        for line in newlines:
                            load_namespace.append(
                                line.format(
                                    cpp_dtype=get_cpp_dtype(ns_value.dtype),
                                    numpy_dtype=get_numpy_dtype(ns_value.dtype),
                                    var_name=ns_key,
                                )
                            )
                    # Rename references to any dependencies if necessary
                    for dep_name, dep in impl.dependencies.items():
                        dep_impl = dep.implementations[self.codeobj_class]
                        dep_impl_name = dep_impl.name
                        if dep_impl_name is None:
                            dep_impl_name = dep.pyfunc.__name__
                        if dep_name != dep_impl_name:
                            func_code = word_substitute(
                                func_code, {dep_name: dep_impl_name}
                            )
                    support_code.append(deindent(func_code))
                elif callable(func_code):
                    self.variables[varname] = func_code
                    line = f'{varname} = _namespace["{varname}"]'
                    load_namespace.append(line)
                else:
                    raise TypeError(
                        "Provided function implementation for function "
                        f"'{varname}' is neither a string nor callable (is "
                        f"type {type(func_code)} instead)."
                    )

            dep_support_code = []
            dep_load_namespace = []
            dep_user_functions = []
            if impl.dependencies is not None:
                for dep_name, dep in impl.dependencies.items():
                    if dep_name not in self.variables:
                        self.variables[dep_name] = dep
                        user_func = self._add_user_function(dep_name, dep, added)
                        if user_func is not None:
                            sc, ln, uf = user_func
                            dep_support_code.extend(sc)
                            dep_load_namespace.extend(ln)
                            dep_user_functions.extend(uf)

            return (
                support_code + dep_support_code,
                dep_load_namespace + load_namespace,
                dep_user_functions + user_functions,
            )

        def determine_keywords(self):
            from brian2.devices.device import get_device

            device = get_device()
            # load variables from namespace
            load_namespace = []
            support_code = []
            handled_pointers = set()
            user_functions = []
            added = set()
            for varname, var in sorted(self.variables.items()):
                if isinstance(var, Variable) and not isinstance(
                    var, (Subexpression, AuxiliaryVariable)
                ):
                    load_namespace.append(f'_var_{varname} = _namespace["_var_{varname}"]')
                if isinstance(var, AuxiliaryVariable):
                    line = f"cdef {get_cpp_dtype(var.dtype)} {varname}"
                    load_namespace.append(line)
                elif isinstance(var, Subexpression):
                    dtype = get_cpp_dtype(var.dtype)
                    line = f"cdef {dtype} {varname}"
                    load_namespace.append(line)
                elif isinstance(var, Constant):
                    dtype_name = get_cpp_dtype(var.value)
                    line = f'cdef {dtype_name} {varname} = _namespace["{varname}"]'
                    load_namespace.append(line)
                elif isinstance(var, Variable):
                    if var.dynamic:
                        pointer_name = self.get_array_name(var, False)
                        load_namespace.append(
                            f'{pointer_name} = _namespace["{pointer_name}"]'
                        )

                    # This is the "true" array name, not the restricted pointer.
                    array_name = device.get_array_name(var)
                    pointer_name = self.get_array_name(var)
                    if pointer_name in handled_pointers:
                        continue
                    if getattr(var, "ndim", 1) > 1:
                        continue  # multidimensional (dynamic) arrays have to be treated differently
                    if get_dtype_str(var.dtype) == "bool":
                        newlines = [
                            (
                                "cdef _numpy.ndarray[char, ndim=1, mode='c', cast=True]"
                                " _buf_{array_name} = _namespace['{array_name}']"
                            ),
                            (
                                "cdef {cpp_dtype} * {array_name} = <{cpp_dtype} *>"
                                " _buf_{array_name}.data"
                            ),
                        ]
                    else:
                        newlines = [
                            (
                                "cdef _numpy.ndarray[{cpp_dtype}, ndim=1, mode='c']"
                                " _buf_{array_name} = _namespace['{array_name}']"
                            ),
                            (
                                "cdef {cpp_dtype} * {array_name} = <{cpp_dtype} *>"
                                " _buf_{array_name}.data"
                            ),
                        ]

                    if not var.scalar:
                        newlines += [
                            "cdef size_t _num{array_name} = len(_namespace['{array_name}'])"
                        ]

                    if var.scalar and var.constant:
                        newlines += ['cdef {cpp_dtype} {varname} = _namespace["{varname}"]']
                    else:
                        newlines += ["cdef {cpp_dtype} {varname}"]

                    for line in newlines:
                        line = line.format(
                            cpp_dtype=get_cpp_dtype(var.dtype),
                            numpy_dtype=get_numpy_dtype(var.dtype),
                            pointer_name=pointer_name,
                            array_name=array_name,
                            varname=varname,
                        )
                        load_namespace.append(line)
                    handled_pointers.add(pointer_name)

                elif isinstance(var, Function):
                    user_func = self._add_user_function(varname, var, added)
                    if user_func is not None:
                        sc, ln, uf = user_func
                        support_code.extend(sc)
                        load_namespace.extend(ln)
                        user_functions.extend(uf)
                else:
                    # fallback to Python object
                    load_namespace.append(f'{varname} = _namespace["{varname}"]')

            for varname, dtype in sorted(self.temporary_vars):
                cpp_dtype = get_cpp_dtype(dtype)
                line = f"cdef {cpp_dtype} {varname}"
                load_namespace.append(line)

            return {
                "load_namespace": "\n".join(load_namespace),
                "support_code_lines": support_code,
            }

    ###############################################################################
    # Implement functions
    ################################################################################
    # Functions that exist under the same name in C++
    for func in [
        "sin",
        "cos",
        "tan",
        "sinh",
        "cosh",
        "tanh",
        "exp",
        "log",
        "log10",
        "sqrt",
        "ceil",
        "floor",
        "abs",
    ]:
        DEFAULT_FUNCTIONS[func].implementations.add_implementation(
            CythonCodeGenerator, code=None
        )
    DEFAULT_FUNCTIONS["expm1"].implementations.add_implementation(
        CythonCodeGenerator, code=None, availability_check=C99Check("expm1")
    )
    DEFAULT_FUNCTIONS["log1p"].implementations.add_implementation(
        CythonCodeGenerator, code=None, availability_check=C99Check("log1p")
    )

    # Functions that need a name translation
    for func, func_cpp in [
        ("arcsin", "asin"),
        ("arccos", "acos"),
        ("arctan", "atan"),
        ("int", "int_"),  # from stdint_compat.h
    ]:
        DEFAULT_FUNCTIONS[func].implementations.add_implementation(
            CythonCodeGenerator, code=None, name=func_cpp
        )

    exprel_code = """
    cdef inline double _exprel(double x) nogil:
        if fabs(x) < 1e-16:
            return 1.0
        elif x > 717:  # near log(DBL_MAX)
            return NPY_INFINITY
        else:
            return expm1(x) / x
    """
    DEFAULT_FUNCTIONS["exprel"].implementations.add_implementation(
        CythonCodeGenerator,
        code=exprel_code,
        name="_exprel",
        availability_check=C99Check("exprel"),
    )
    _BUFFER_SIZE = 20000

    rand_code = """
    cdef double _rand(int _idx):
        cdef double **buffer_pointer = <double**>_namespace_rand_buffer
        cdef double *buffer = buffer_pointer[0]
        cdef _numpy.ndarray _new_rand

        if(_namespace_rand_buffer_index[0] == 0):
            if buffer != NULL:
                free(buffer)
            _new_rand = _numpy.random.rand(_BUFFER_SIZE)
            buffer = <double *>_numpy.PyArray_DATA(_new_rand)
            PyArray_CLEARFLAGS(<_numpy.PyArrayObject*>_new_rand, _numpy.NPY_ARRAY_OWNDATA)
            buffer_pointer[0] = buffer

        cdef double val = buffer[_namespace_rand_buffer_index[0]]
        _namespace_rand_buffer_index[0] += 1
        if _namespace_rand_buffer_index[0] == _BUFFER_SIZE:
            _namespace_rand_buffer_index[0] = 0
        return val
    """.replace(
        "_BUFFER_SIZE", str(_BUFFER_SIZE)
    )

    randn_code = rand_code.replace("rand", "randn").replace("randnom", "random")

    poisson_code = """
    cdef double _loggam(double x):
      cdef double x0, x2, xp, gl, gl0
      cdef int32_t k, n

      cdef double a[10]
      a[:] = [8.333333333333333e-02, -2.777777777777778e-03,
              7.936507936507937e-04, -5.952380952380952e-04,
              8.417508417508418e-04, -1.917526917526918e-03,
              6.410256410256410e-03, -2.955065359477124e-02,
              1.796443723688307e-01, -1.39243221690590e+00]
      x0 = x
      n = 0
      if (x == 1.0) or (x == 2.0):
        return 0.0
      elif x <= 7.0:
        n = <int32_t>(7 - x)
        x0 = x + n
      x2 = 1.0 / (x0 * x0)
      xp = 2 * M_PI
      gl0 = a[9]
      for k in range(8, -1, -1):
        gl0 *= x2
        gl0 += a[k]
      gl = gl0 / x0 + 0.5 * log(xp) + (x0 - 0.5) * log(x0) - x0
      if x <= 7.0:
        for k in range(1, n+1):
          gl -= log(x0 - 1.0)
          x0 -= 1.0
      return gl

    cdef int32_t _poisson_mult(double lam, int _vectorisation_idx):
      cdef int32_t X
      cdef double prod, U, enlam

      enlam = exp(-lam)
      X = 0
      prod = 1.0
      while True:
        U = _rand(_vectorisation_idx)
        prod *= U
        if (prod > enlam):
          X += 1
        else:
          return X

    cdef int32_t _poisson_ptrs(double lam, int _vectorisation_idx):
      cdef int32_t k
      cdef double U, V, slam, loglam, a, b, invalpha, vr, us

      slam = sqrt(lam)
      loglam = log(lam)
      b = 0.931 + 2.53 * slam
      a = -0.059 + 0.02483 * b
      invalpha = 1.1239 + 1.1328 / (b - 3.4)
      vr = 0.9277 - 3.6224 / (b - 2)

      while True:
        U = _rand(_vectorisation_idx) - 0.5
        V = _rand(_vectorisation_idx)
        us = 0.5 - abs(U)
        k = <int32_t>floor((2 * a / us + b) * U + lam + 0.43)
        if (us >= 0.07) and (V <= vr):
          return k
        if ((k < 0) or ((us < 0.013) and (V > us))):
          continue
        if ((log(V) + log(invalpha) - log(a / (us * us) + b)) <=
            (-lam + k * loglam - _loggam(k + 1))):
          return k

    cdef int32_t _poisson(double lam, int32_t _idx):
      if lam >= 10:
        return _poisson_ptrs(lam, _idx)
      elif lam == 0:
        return 0
      else:
        return _poisson_mult(lam, _idx)
    """

    device = all_devices["runtime"]
    DEFAULT_FUNCTIONS["rand"].implementations.add_implementation(
        CythonCodeGenerator,
        code=rand_code,
        name="_rand",
        namespace={
            "_rand_buffer": device.rand_buffer,
            "_rand_buffer_index": device.rand_buffer_index,
        },
    )

    DEFAULT_FUNCTIONS["randn"].implementations.add_implementation(
        CythonCodeGenerator,
        code=randn_code,
        name="_randn",
        namespace={
            "_randn_buffer": device.randn_buffer,
            "_randn_buffer_index": device.randn_buffer_index,
        },
    )
    DEFAULT_FUNCTIONS["poisson"].implementations.add_implementation(
        CythonCodeGenerator,
        code=poisson_code,
        name="_poisson",
        dependencies={"_rand": DEFAULT_FUNCTIONS["rand"]},
    )

    sign_code = """
    ctypedef fused _to_sign:
        char
        short
        int
        long
        float
        double

    cdef int _sign(_to_sign x):
        return (0 < x) - (x < 0)
    """
    DEFAULT_FUNCTIONS["sign"].implementations.add_implementation(
        CythonCodeGenerator, code=sign_code, name="_sign"
    )

    clip_code = """
    ctypedef fused _to_clip:
        char
        short
        int
        long
        float
        double

    cdef _to_clip _clip(_to_clip x, double low, double high):
        if x < low:
            return <_to_clip?>low
        if x > high:
            return <_to_clip?>high
        return x
    """
    DEFAULT_FUNCTIONS["clip"].implementations.add_implementation(
        CythonCodeGenerator, code=clip_code, name="_clip"
    )

    timestep_code = """
    cdef int64_t _timestep(double t, double dt):
        return <int64_t>((t + 1e-3*dt)/dt)
    """
    DEFAULT_FUNCTIONS["timestep"].implementations.add_implementation(
        CythonCodeGenerator, code=timestep_code, name="_timestep"
    )