code: import re
  import _ctypes
  import inspect
  import numpy.ctypeslib as npct
  from time import time as ostime
  from os import path
  from os import remove
  from sys import platform
  from sys import version_info
  from ast import FunctionDef
  from hashlib import md5
  from parcels.tools.loggers import logger
  import numpy as np
  from numpy import ndarray
  
  try:
      from mpi4py import MPI
  except:
      MPI = None
  
  from parcels.tools.global_statics import get_cache_dir
  
  # === import just necessary field classes to perform setup checks === #
  from parcels.field import Field
  from parcels.field import VectorField
  from parcels.field import NestedField
  from parcels.field import SummedField
  from parcels.grid import GridCode
  from parcels.field import FieldOutOfBoundError
  from parcels.field import FieldOutOfBoundSurfaceError
  from parcels.field import TimeExtrapolationError
  from parcels.tools.statuscodes import StateCode, OperationCode, ErrorCode
  from parcels.application_kernels.advection import AdvectionRK4_3D
  from parcels.application_kernels.advection import AdvectionAnalytical
  
  __all__ = ['BaseKernel']
  
  
  re_indent = re.compile(r"^(\s+)")
  
  
  class BaseKernel(object):
      """Base super class for base Kernel objects that encapsulates auto-generated code.
  
      :arg fieldset: FieldSet object providing the field information (possibly None)
      :arg ptype: PType object for the kernel particle
      :arg pyfunc: (aggregated) Kernel function
      :arg funcname: function name
      :param delete_cfiles: Boolean whether to delete the C-files after compilation in JIT mode (default is True)
  
      Note: A Kernel is either created from a compiled <function ...> object
      or the necessary information (funcname, funccode, funcvars) is provided.
      The py_ast argument may be derived from the code string, but for
      concatenation, the merged AST plus the new header definition is required.
      """
      _pyfunc = None
      _fieldset = None
      _ptype = None
      funcname = None
  
      def __init__(self, fieldset, ptype, pyfunc=None, funcname=None, funccode=None, py_ast=None, funcvars=None,
                   c_include="", delete_cfiles=True):
          self._fieldset = fieldset
          self.field_args = None
          self.const_args = None
          self._ptype = ptype
          self._lib = None
          self.delete_cfiles = delete_cfiles
          self._cleanup_files = None
          self._cleanup_lib = None
          self._c_include = c_include
  
          # Derive meta information from pyfunc, if not given
          self._pyfunc = None
          self.funcname = funcname or pyfunc.__name__
          self.name = "%s%s" % (ptype.name, self.funcname)
          self.ccode = ""
          self.funcvars = funcvars
          self.funccode = funccode
          self.py_ast = py_ast
          self.dyn_srcs = []
          self.static_srcs = []
          self.src_file = None
          self.lib_file = None
          self.log_file = None
  
          # Generate the kernel function and add the outer loop
          if self._ptype.uses_jit:
              src_file_or_files, self.lib_file, self.log_file = self.get_kernel_compile_files()
              if type(src_file_or_files) in (list, dict, tuple, ndarray):
                  self.dyn_srcs = src_file_or_files
              else:
                  self.src_file = src_file_or_files
  
      def __del__(self):
          # Clean-up the in-memory dynamic linked libraries.
          # This is not really necessary, as these programs are not that large, but with the new random
          # naming scheme which is required on Windows OS'es to deal with updates to a Parcels' kernel.
          try:
              self.remove_lib()
          except:
              pass
          self._fieldset = None
          self.field_args = None
          self.const_args = None
          self.funcvars = None
          self.funccode = None
  
      @property
      def ptype(self):
          return self._ptype
  
      @property
      def pyfunc(self):
          return self._pyfunc
  
      @property
      def fieldset(self):
          return self._fieldset
  
      @property
      def c_include(self):
          return self._c_include
  
      @property
      def _cache_key(self):
          field_keys = ""
          if self.field_args is not None:
              field_keys = "-".join(
                  ["%s:%s" % (name, field.units.__class__.__name__) for name, field in self.field_args.items()])
          key = self.name + self.ptype._cache_key + field_keys + ('TIME:%f' % ostime())
          return md5(key.encode('utf-8')).hexdigest()
  
      @staticmethod
      def fix_indentation(string):
          """Fix indentation to allow in-lined kernel definitions"""
          lines = string.split('\n')
          indent = re_indent.match(lines[0])
          if indent:
              lines = [line.replace(indent.groups()[0], '', 1) for line in lines]
          return "\n".join(lines)
  
      def check_fieldsets_in_kernels(self, pyfunc):
          """
          function checks the integrity of the fieldset with the kernels.
          This function is to be called from the derived class when setting up the 'pyfunc'.
          """
          if self.fieldset is not None:
              if pyfunc is AdvectionRK4_3D:
                  warning = False
                  if isinstance(self._fieldset.W, Field) and self._fieldset.W.creation_log != 'from_nemo' and \
                     self._fieldset.W._scaling_factor is not None and self._fieldset.W._scaling_factor > 0:
                      warning = True
                  if type(self._fieldset.W) in [SummedField, NestedField]:
                      for f in self._fieldset.W:
                          if f.creation_log != 'from_nemo' and f._scaling_factor is not None and f._scaling_factor > 0:
                              warning = True
                  if warning:
                      logger.warning_once('Note that in AdvectionRK4_3D, vertical velocity is assumed positive towards increasing z.\n'
                                          '  If z increases downward and w is positive upward you can re-orient it downwards by setting fieldset.W.set_scaling_factor(-1.)')
              elif pyfunc is AdvectionAnalytical:
                  if self._ptype.uses_jit:
                      raise NotImplementedError('Analytical Advection only works in Scipy mode')
                  if self._fieldset.U.interp_method != 'cgrid_velocity':
                      raise NotImplementedError('Analytical Advection only works with C-grids')
                  if self._fieldset.U.grid.gtype not in [GridCode.CurvilinearZGrid, GridCode.RectilinearZGrid]:
                      raise NotImplementedError('Analytical Advection only works with Z-grids in the vertical')
  
      def check_kernel_signature_on_version(self):
          """
          returns numkernelargs
          """
          numkernelargs = 0
          if self._pyfunc is not None:
              if version_info[0] < 3:
                  numkernelargs = len(inspect.getargspec(self._pyfunc).args)
              else:
                  numkernelargs = len(inspect.getfullargspec(self._pyfunc).args)
          return numkernelargs
  
      def remove_lib(self):
          if self._lib is not None:
              BaseKernel.cleanup_unload_lib(self._lib)
              del self._lib
              self._lib = None
  
          all_files_array = []
          if self.src_file is None:
              if self.dyn_srcs is not None:
                  [all_files_array.append(fpath) for fpath in self.dyn_srcs]
          else:
              if self.src_file is not None:
                  all_files_array.append(self.src_file)
          if self.log_file is not None:
              all_files_array.append(self.log_file)
          if self.lib_file is not None and all_files_array is not None and self.delete_cfiles is not None:
              BaseKernel.cleanup_remove_files(self.lib_file, all_files_array, self.delete_cfiles)
  
          # If file already exists, pull new names. This is necessary on a Windows machine, because
          # Python's ctype does not deal in any sort of manner well with dynamic linked libraries on this OS.
          if self._ptype.uses_jit:
              src_file_or_files, self.lib_file, self.log_file = self.get_kernel_compile_files()
              if type(src_file_or_files) in (list, dict, tuple, ndarray):
                  self.dyn_srcs = src_file_or_files
              else:
                  self.src_file = src_file_or_files
  
      def get_kernel_compile_files(self):
          """
          Returns the correct src_file, lib_file, log_file for this kernel
          """
          if MPI:
              mpi_comm = MPI.COMM_WORLD
              mpi_rank = mpi_comm.Get_rank()
              cache_name = self._cache_key  # only required here because loading is done by Kernel class instead of Compiler class
              dyn_dir = get_cache_dir() if mpi_rank == 0 else None
              dyn_dir = mpi_comm.bcast(dyn_dir, root=0)
              basename = cache_name if mpi_rank == 0 else None
              basename = mpi_comm.bcast(basename, root=0)
              basename = basename + "_%d" % mpi_rank
          else:
              cache_name = self._cache_key  # only required here because loading is done by Kernel class instead of Compiler class
              dyn_dir = get_cache_dir()
              basename = "%s_0" % cache_name
          lib_path = "lib" + basename
          src_file_or_files = None
          if type(basename) in (list, dict, tuple, ndarray):
              src_file_or_files = ["", ] * len(basename)
              for i, src_file in enumerate(basename):
                  src_file_or_files[i] = "%s.c" % path.join(dyn_dir, src_file)
          else:
              src_file_or_files = "%s.c" % path.join(dyn_dir, basename)
          lib_file = "%s.%s" % (path.join(dyn_dir, lib_path), 'dll' if platform == 'win32' else 'so')
          log_file = "%s.log" % path.join(dyn_dir, basename)
          return src_file_or_files, lib_file, log_file
  
      def compile(self, compiler):
          """ Writes kernel code to file and compiles it."""
          all_files_array = []
          if self.src_file is None:
              if self.dyn_srcs is not None:
                  for dyn_src in self.dyn_srcs:
                      with open(dyn_src, 'w') as f:
                          f.write(self.ccode)
                      all_files_array.append(dyn_src)
                  compiler.compile(self.dyn_srcs, self.lib_file, self.log_file)
          else:
              if self.src_file is not None:
                  with open(self.src_file, 'w') as f:
                      f.write(self.ccode)
                  if self.src_file is not None:
                      all_files_array.append(self.src_file)
                  compiler.compile(self.src_file, self.lib_file, self.log_file)
          if len(all_files_array) > 0:
              logger.info("Compiled %s ==> %s" % (self.name, self.lib_file))
              if self.log_file is not None:
                  all_files_array.append(self.log_file)
  
      def load_lib(self):
          self._lib = npct.load_library(self.lib_file, '.')
          self._function = self._lib.particle_loop
  
      def merge(self, kernel, kclass):
          funcname = self.funcname + kernel.funcname
          func_ast = None
          if self.py_ast is not None:
              func_ast = FunctionDef(name=funcname, args=self.py_ast.args, body=self.py_ast.body + kernel.py_ast.body,
                                     decorator_list=[], lineno=1, col_offset=0)
          delete_cfiles = self.delete_cfiles and kernel.delete_cfiles
          return kclass(self.fieldset, self.ptype, pyfunc=None,
                        funcname=funcname, funccode=self.funccode + kernel.funccode,
                        py_ast=func_ast, funcvars=self.funcvars + kernel.funcvars,
                        c_include=self._c_include + kernel.c_include,
                        delete_cfiles=delete_cfiles)
  
      def __add__(self, kernel):
          if not isinstance(kernel, BaseKernel):
              kernel = BaseKernel(self.fieldset, self.ptype, pyfunc=kernel)
          return self.merge(kernel, BaseKernel)
  
      def __radd__(self, kernel):
          if not isinstance(kernel, BaseKernel):
              kernel = BaseKernel(self.fieldset, self.ptype, pyfunc=kernel)
          return kernel.merge(self, BaseKernel)
  
      @staticmethod
      def cleanup_remove_files(lib_file, all_files_array, delete_cfiles):
          if lib_file is not None:
              if path.isfile(lib_file):  # and delete_cfiles
                  [remove(s) for s in [lib_file, ] if path is not None and path.exists(s)]
              if delete_cfiles and len(all_files_array) > 0:
                  [remove(s) for s in all_files_array if path is not None and path.exists(s)]
  
      @staticmethod
      def cleanup_unload_lib(lib):
          # Clean-up the in-memory dynamic linked libraries.
          # This is not really necessary, as these programs are not that large, but with the new random
          # naming scheme which is required on Windows OS'es to deal with updates to a Parcels' kernel.
          if lib is not None:
              try:
                  _ctypes.FreeLibrary(lib._handle) if platform == 'win32' else _ctypes.dlclose(lib._handle)
              except:
                  pass
  
      def remove_deleted(self, pset, output_file, endtime):
          """
          Utility to remove all particles that signalled deletion.
  
          This version is generally applicable to all structures and collections
          """
          indices = [i for i, p in enumerate(pset) if p.state == OperationCode.Delete]
          if len(indices) > 0 and output_file is not None:
              output_file.write(pset, endtime, deleted_only=indices)
          pset.remove_indices(indices)
  
      def load_fieldset_jit(self, pset):
          """
          Updates the loaded fields of pset's fieldset according to the chunk information within their grids
          """
          if pset.fieldset is not None:
              for g in pset.fieldset.gridset.grids:
                  g.cstruct = None  # This force to point newly the grids from Python to C
              # Make a copy of the transposed array to enforce
              # C-contiguous memory layout for JIT mode.
              for f in pset.fieldset.get_fields():
                  if type(f) in [VectorField, NestedField, SummedField]:
                      continue
                  if f.data.dtype != np.float32:
                      raise RuntimeError('Field %s data needs to be float32 in JIT mode' % f.name)
                  if f in self.field_args.values():
                      f.chunk_data()
                  else:
                      for block_id in range(len(f.data_chunks)):
                          f.data_chunks[block_id] = None
                          f.c_data_chunks[block_id] = None
  
              for g in pset.fieldset.gridset.grids:
                  g.load_chunk = np.where(g.load_chunk == g.chunk_loading_requested,
                                          g.chunk_loaded_touched, g.load_chunk)
                  if len(g.load_chunk) > g.chunk_not_loaded:  # not the case if a field in not called in the kernel
                      if not g.load_chunk.flags.c_contiguous:
                          g.load_chunk = g.load_chunk.copy()
                  if not g.depth.flags.c_contiguous:
                      g.depth = g.depth.copy()
                  if not g.lon.flags.c_contiguous:
                      g.lon = g.lon.copy()
                  if not g.lat.flags.c_contiguous:
                      g.lat = g.lat.copy()
  
      def evaluate_particle(self, p, endtime, sign_dt, dt, analytical=False):
          """
          Execute the kernel evaluation of for an individual particle.
          :arg p: object of (sub-)type (ScipyParticle, JITParticle) or (sub-)type of BaseParticleAccessor
          :arg fieldset: fieldset of the containing ParticleSet (e.g. pset.fieldset)
          :arg analytical: flag indicating the analytical advector or an iterative advection
          :arg endtime: endtime of this overall kernel evaluation step
          :arg dt: computational integration timestep
          """
          variables = self._ptype.variables
          # back up variables in case of OperationCode.Repeat
          p_var_back = {}
          pdt_prekernels = .0
          # Don't execute particles that aren't started yet
          sign_end_part = np.sign(endtime - p.time)
          # Compute min/max dt for first timestep. Only use endtime-p.time for one timestep
          reset_dt = False
          if abs(endtime - p.time) < abs(p.dt):
              dt_pos = abs(endtime - p.time)
              reset_dt = True
          else:
              dt_pos = abs(p.dt)
              reset_dt = False
  
          # ==== numerically stable; also making sure that continuously-recovered particles do end successfully,
          # as they fulfil the condition here on entering at the final calculation here. ==== #
          if ((sign_end_part != sign_dt) or np.isclose(dt_pos, 0)) and not np.isclose(dt, 0):
              if abs(p.time) >= abs(endtime):
                  p.set_state(StateCode.Success)
              return p
  
          while p.state in [StateCode.Evaluate, OperationCode.Repeat] or np.isclose(dt, 0):
              for var in variables:
                  p_var_back[var.name] = getattr(p, var.name)
              try:
                  pdt_prekernels = sign_dt * dt_pos
                  p.dt = pdt_prekernels
                  state_prev = p.state
                  res = self._pyfunc(p, self._fieldset, p.time)
                  if res is None:
                      res = StateCode.Success
  
                  if res is StateCode.Success and p.state != state_prev:
                      res = p.state
  
                  if not analytical and res == StateCode.Success and not np.isclose(p.dt, pdt_prekernels):
                      res = OperationCode.Repeat
  
              except FieldOutOfBoundError as fse_xy:
                  res = ErrorCode.ErrorOutOfBounds
                  p.exception = fse_xy
              except FieldOutOfBoundSurfaceError as fse_z:
                  res = ErrorCode.ErrorThroughSurface
                  p.exception = fse_z
              except TimeExtrapolationError as fse_t:
                  res = ErrorCode.ErrorTimeExtrapolation
                  p.exception = fse_t
  
              except Exception as e:
                  res = ErrorCode.Error
                  p.exception = e
  
              # Handle particle time and time loop
              if res in [StateCode.Success, OperationCode.Delete]:
                  # Update time and repeat
                  p.time += p.dt
                  if reset_dt and p.dt == pdt_prekernels:
                      p.dt = dt
                  p.update_next_dt()
                  if analytical:
                      p.dt = np.inf
                  if abs(endtime - p.time) < abs(p.dt):
                      dt_pos = abs(endtime - p.time)
                      reset_dt = True
                  else:
                      dt_pos = abs(p.dt)
                      reset_dt = False
  
                  sign_end_part = np.sign(endtime - p.time)
                  if res != OperationCode.Delete and not np.isclose(dt_pos, 0) and (sign_end_part == sign_dt):
                      res = StateCode.Evaluate
                  if sign_end_part != sign_dt:
                      dt_pos = 0
  
                  p.set_state(res)
                  if np.isclose(dt, 0):
                      break
              else:
                  p.set_state(res)
                  # Try again without time update
                  for var in variables:
                      if var.name not in ['dt', 'state']:
                          setattr(p, var.name, p_var_back[var.name])
                  if abs(endtime - p.time) < abs(p.dt):
                      dt_pos = abs(endtime - p.time)
                      reset_dt = True
                  else:
                      dt_pos = abs(p.dt)
                      reset_dt = False
  
                  sign_end_part = np.sign(endtime - p.time)
                  if sign_end_part != sign_dt:
                      dt_pos = 0
                  break
          return p
  
      def execute_jit(self, pset, endtime, dt):
          pass
  
      def execute_python(self, pset, endtime, dt):
          pass
  
      def execute(self, pset, endtime, dt, recovery=None, output_file=None, execute_once=False):
          pass
  
apis: numpy.where numpy.ctypeslib.load_library numpy.isclose numpy.sign