Merge branch 'master' into with_python

1 files changed, 468 insertions, 0 deletions

diff --git a/Python/pywarpx/_libwarpx.py b/Python/pywarpx/_libwarpx.py
index e0a7262be..71e0a6729 100755
--- a/Python/pywarpx/_libwarpx.py
+++ b/Python/pywarpx/_libwarpx.py
@@ -83,18 +83,54 @@ f.restype = LP_LP_c_double
 f = libwarpx.warpx_getEfieldLoVects
 f.restype = LP_c_int
 
+f = libwarpx.warpx_getEfieldCP
+f.restype = LP_LP_c_double
+
+f = libwarpx.warpx_getEfieldCPLoVects
+f.restype = LP_c_int
+
+f = libwarpx.warpx_getEfieldFP
+f.restype = LP_LP_c_double
+
+f = libwarpx.warpx_getEfieldFPLoVects
+f.restype = LP_c_int
+
 f = libwarpx.warpx_getBfield
 f.restype = LP_LP_c_double
 
 f = libwarpx.warpx_getBfieldLoVects
 f.restype = LP_c_int
 
+f = libwarpx.warpx_getBfieldCP
+f.restype = LP_LP_c_double
+
+f = libwarpx.warpx_getBfieldCPLoVects
+f.restype = LP_c_int
+
+f = libwarpx.warpx_getBfieldFP
+f.restype = LP_LP_c_double
+
+f = libwarpx.warpx_getBfieldFPLoVects
+f.restype = LP_c_int
+
 f = libwarpx.warpx_getCurrentDensity
 f.restype = LP_LP_c_double
 
 f = libwarpx.warpx_getCurrentDensityLoVects
 f.restype = LP_c_int
 
+f = libwarpx.warpx_getCurrentDensityCP
+f.restype = LP_LP_c_double
+
+f = libwarpx.warpx_getCurrentDensityCPLoVects
+f.restype = LP_c_int
+
+f = libwarpx.warpx_getCurrentDensityFP
+f.restype = LP_LP_c_double
+
+f = libwarpx.warpx_getCurrentDensityFPLoVects
+f.restype = LP_c_int
+
 #f = libwarpx.warpx_getPMLSigma
 #f.restype = LP_c_double
 #
@@ -550,6 +586,8 @@ def get_mesh_electric_field(level, direction, include_ghosts=True):
     This returns a list of numpy arrays containing the mesh electric field
     data on each grid for this process.
 
+    This version is for the full "auxillary" solution on the given level.
+
     The data for the numpy arrays are not copied, but share the underlying
     memory buffer with WarpX. The numpy arrays are fully writeable.
 
@@ -592,12 +630,112 @@ def get_mesh_electric_field(level, direction, include_ghosts=True):
     return grid_data
 
 
+def get_mesh_electric_field_cp(level, direction, include_ghosts=True):
+    '''
+
+    This returns a list of numpy arrays containing the mesh electric field
+    data on each grid for this process. This version returns the field on
+    the coarse patch for the given level.
+
+    The data for the numpy arrays are not copied, but share the underlying
+    memory buffer with WarpX. The numpy arrays are fully writeable.
+
+    Parameters
+    ----------
+
+        level          : the AMR level to get the data for
+        direction      : the component of the data you want
+        include_ghosts : whether to include ghost zones or not
+
+    Returns
+    -------
+
+        A List of numpy arrays.
+
+    '''
+
+    assert(level == 0)
+
+    shapes = LP_c_int()
+    size = ctypes.c_int(0)
+    ngrow = ctypes.c_int(0)
+    data = libwarpx.warpx_getEfieldCP(level, direction,
+                                      ctypes.byref(size), ctypes.byref(ngrow),
+                                      ctypes.byref(shapes))
+    ng = ngrow.value
+    grid_data = []
+    for i in range(size.value):
+        shape = tuple([shapes[dim*i + d] for d in range(dim)])
+        # --- The data is stored in Fortran order, hence shape is reversed and a transpose is taken.
+        arr = np.ctypeslib.as_array(data[i], shape[::-1]).T
+        arr.setflags(write=1)
+        if include_ghosts:
+            grid_data.append(arr)
+        else:
+            grid_data.append(arr[[slice(ng, -ng) for _ in range(dim)]])
+
+    libc.free(shapes)
+    libc.free(data)
+    return grid_data
+
+
+def get_mesh_electric_field_fp(level, direction, include_ghosts=True):
+    '''
+
+    This returns a list of numpy arrays containing the mesh electric field
+    data on each grid for this process. This version returns the field on
+    the fine patch for the given level.
+
+    The data for the numpy arrays are not copied, but share the underlying
+    memory buffer with WarpX. The numpy arrays are fully writeable.
+
+    Parameters
+    ----------
+
+        level          : the AMR level to get the data for
+        direction      : the component of the data you want
+        include_ghosts : whether to include ghost zones or not
+
+    Returns
+    -------
+
+        A List of numpy arrays.
+
+    '''
+
+    assert(level == 0)
+
+    shapes = LP_c_int()
+    size = ctypes.c_int(0)
+    ngrow = ctypes.c_int(0)
+    data = libwarpx.warpx_getEfieldFP(level, direction,
+                                      ctypes.byref(size), ctypes.byref(ngrow),
+                                      ctypes.byref(shapes))
+    ng = ngrow.value
+    grid_data = []
+    for i in range(size.value):
+        shape = tuple([shapes[dim*i + d] for d in range(dim)])
+        # --- The data is stored in Fortran order, hence shape is reversed and a transpose is taken.
+        arr = np.ctypeslib.as_array(data[i], shape[::-1]).T
+        arr.setflags(write=1)
+        if include_ghosts:
+            grid_data.append(arr)
+        else:
+            grid_data.append(arr[[slice(ng, -ng) for _ in range(dim)]])
+
+    libc.free(shapes)
+    libc.free(data)
+    return grid_data
+
+
 def get_mesh_magnetic_field(level, direction, include_ghosts=True):
     '''
 
     This returns a list of numpy arrays containing the mesh magnetic field
     data on each grid for this process.
 
+    This version is for the full "auxillary" solution on the given level.
+
     The data for the numpy arrays are not copied, but share the underlying
     memory buffer with WarpX. The numpy arrays are fully writeable.
 
@@ -640,6 +778,104 @@ def get_mesh_magnetic_field(level, direction, include_ghosts=True):
     return grid_data
 
 
+def get_mesh_magnetic_field_cp(level, direction, include_ghosts=True):
+    '''
+
+    This returns a list of numpy arrays containing the mesh magnetic field
+    data on each grid for this process. This version returns the field on
+    the coarse patch for the given level.
+
+    The data for the numpy arrays are not copied, but share the underlying
+    memory buffer with WarpX. The numpy arrays are fully writeable.
+
+    Parameters
+    ----------
+
+        level          : the AMR level to get the data for
+        direction      : the component of the data you want
+        include_ghosts : whether to include ghost zones or not
+
+    Returns
+    -------
+
+        A List of numpy arrays.
+
+    '''
+
+    assert(level == 0)
+
+    shapes = LP_c_int()
+    size = ctypes.c_int(0)
+    ngrow = ctypes.c_int(0)
+    data = libwarpx.warpx_getBfieldCP(level, direction,
+                                      ctypes.byref(size), ctypes.byref(ngrow),
+                                      ctypes.byref(shapes))
+    ng = ngrow.value
+    grid_data = []
+    for i in range(size.value):
+        shape = tuple([shapes[dim*i + d] for d in range(dim)])
+        # --- The data is stored in Fortran order, hence shape is reversed and a transpose is taken.
+        arr = np.ctypeslib.as_array(data[i], shape[::-1]).T
+        arr.setflags(write=1)
+        if include_ghosts:
+            grid_data.append(arr)
+        else:
+            grid_data.append(arr[[slice(ng, -ng) for _ in range(dim)]])
+
+    libc.free(shapes)
+    libc.free(data)
+    return grid_data
+
+
+def get_mesh_magnetic_field_fp(level, direction, include_ghosts=True):
+    '''
+
+    This returns a list of numpy arrays containing the mesh magnetic field
+    data on each grid for this process. This version returns the field on
+    the fine patch for the given level.
+
+    The data for the numpy arrays are not copied, but share the underlying
+    memory buffer with WarpX. The numpy arrays are fully writeable.
+
+    Parameters
+    ----------
+
+        level          : the AMR level to get the data for
+        direction      : the component of the data you want
+        include_ghosts : whether to include ghost zones or not
+
+    Returns
+    -------
+
+        A List of numpy arrays.
+
+    '''
+
+    assert(level == 0)
+
+    shapes = LP_c_int()
+    size = ctypes.c_int(0)
+    ngrow = ctypes.c_int(0)
+    data = libwarpx.warpx_getBfieldFP(level, direction,
+                                      ctypes.byref(size), ctypes.byref(ngrow),
+                                      ctypes.byref(shapes))
+    ng = ngrow.value
+    grid_data = []
+    for i in range(size.value):
+        shape = tuple([shapes[dim*i + d] for d in range(dim)])
+        # --- The data is stored in Fortran order, hence shape is reversed and a transpose is taken.
+        arr = np.ctypeslib.as_array(data[i], shape[::-1]).T
+        arr.setflags(write=1)
+        if include_ghosts:
+            grid_data.append(arr)
+        else:
+            grid_data.append(arr[[slice(ng, -ng) for _ in range(dim)]])
+
+    libc.free(shapes)
+    libc.free(data)
+    return grid_data
+
+
 def get_mesh_current_density(level, direction, include_ghosts=True):
     '''
 
@@ -688,6 +924,104 @@ def get_mesh_current_density(level, direction, include_ghosts=True):
     return grid_data
 
 
+def get_mesh_current_density_cp(level, direction, include_ghosts=True):
+    '''
+
+    This returns a list of numpy arrays containing the mesh current density
+    data on each grid for this process. This version returns the density for
+    the coarse patch on the given level.
+
+    The data for the numpy arrays are not copied, but share the underlying
+    memory buffer with WarpX. The numpy arrays are fully writeable.
+
+    Parameters
+    ----------
+
+        level          : the AMR level to get the data for
+        direction      : the component of the data you want
+        include_ghosts : whether to include ghost zones or not
+
+    Returns
+    -------
+
+        A List of numpy arrays.
+
+    '''
+
+    assert(level == 0)
+
+    shapes = LP_c_int()
+    size = ctypes.c_int(0)
+    ngrow = ctypes.c_int(0)
+    data = libwarpx.warpx_getCurrentDensityCP(level, direction,
+                                              ctypes.byref(size), ctypes.byref(ngrow),
+                                              ctypes.byref(shapes))
+    ng = ngrow.value
+    grid_data = []
+    for i in range(size.value):
+        shape = tuple([shapes[dim*i + d] for d in range(dim)])
+        # --- The data is stored in Fortran order, hence shape is reversed and a transpose is taken.
+        arr = np.ctypeslib.as_array(data[i], shape[::-1]).T
+        arr.setflags(write=1)
+        if include_ghosts:
+            grid_data.append(arr)
+        else:
+            grid_data.append(arr[[slice(ng, -ng) for _ in range(dim)]])
+
+    libc.free(shapes)
+    libc.free(data)
+    return grid_data
+
+
+def get_mesh_current_density_fp(level, direction, include_ghosts=True):
+    '''
+
+    This returns a list of numpy arrays containing the mesh current density
+    data on each grid for this process. This version returns the density on
+    the fine patch for the given level.
+
+    The data for the numpy arrays are not copied, but share the underlying
+    memory buffer with WarpX. The numpy arrays are fully writeable.
+
+    Parameters
+    ----------
+
+        level          : the AMR level to get the data for
+        direction      : the component of the data you want
+        include_ghosts : whether to include ghost zones or not
+
+    Returns
+    -------
+
+        A List of numpy arrays.
+
+    '''
+
+    assert(level == 0)
+
+    shapes = LP_c_int()
+    size = ctypes.c_int(0)
+    ngrow = ctypes.c_int(0)
+    data = libwarpx.warpx_getCurrentDensityFP(level, direction,
+                                              ctypes.byref(size), ctypes.byref(ngrow),
+                                              ctypes.byref(shapes))
+    ng = ngrow.value
+    grid_data = []
+    for i in range(size.value):
+        shape = tuple([shapes[dim*i + d] for d in range(dim)])
+        # --- The data is stored in Fortran order, hence shape is reversed and a transpose is taken.
+        arr = np.ctypeslib.as_array(data[i], shape[::-1]).T
+        arr.setflags(write=1)
+        if include_ghosts:
+            grid_data.append(arr)
+        else:
+            grid_data.append(arr[[slice(ng, -ng) for _ in range(dim)]])
+
+    libc.free(shapes)
+    libc.free(data)
+    return grid_data
+
+
 def _get_mesh_array_lovects(level, direction, include_ghosts=True, getarrayfunc=None):
     assert(0 <= level and level <= libwarpx.warpx_finestLevel())
 
@@ -715,6 +1049,8 @@ def get_mesh_electric_field_lovects(level, direction, include_ghosts=True):
     This returns a list of the lo vectors of the arrays containing the mesh electric field
     data on each grid for this process.
 
+    This version is for the full "auxillary" solution on the given level.
+
     Parameters
     ----------
 
@@ -731,12 +1067,58 @@ def get_mesh_electric_field_lovects(level, direction, include_ghosts=True):
     return _get_mesh_array_lovects(level, direction, include_ghosts, libwarpx.warpx_getEfieldLoVects)
 
 
+def get_mesh_electric_field_cp_lovects(level, direction, include_ghosts=True):
+    '''
+
+    This returns a list of the lo vectors of the arrays containing the mesh electric field
+    data on each grid for this process.
+
+    Parameters
+    ----------
+
+        level          : the AMR level to get the data for
+        direction      : the component of the data you want
+        include_ghosts : whether to include ghost zones or not
+
+    Returns
+    -------
+
+        A 2d numpy array of the lo vector for each grid with the shape (dims, number of grids)
+
+    '''
+    return _get_mesh_array_lovects(level, direction, include_ghosts, libwarpx.warpx_getEfieldCPLoVects)
+
+
+def get_mesh_electric_field_fp_lovects(level, direction, include_ghosts=True):
+    '''
+
+    This returns a list of the lo vectors of the arrays containing the mesh electric field
+    data on each grid for this process.
+
+    Parameters
+    ----------
+
+        level          : the AMR level to get the data for
+        direction      : the component of the data you want
+        include_ghosts : whether to include ghost zones or not
+
+    Returns
+    -------
+
+        A 2d numpy array of the lo vector for each grid with the shape (dims, number of grids)
+
+    '''
+    return _get_mesh_array_lovects(level, direction, include_ghosts, libwarpx.warpx_getEfieldFPLoVects)
+
+
 def get_mesh_magnetic_field_lovects(level, direction, include_ghosts=True):
     '''
 
     This returns a list of the lo vectors of the arrays containing the mesh electric field
     data on each grid for this process.
 
+    This version is for the full "auxillary" solution on the given level.
+
     Parameters
     ----------
 
@@ -753,6 +1135,50 @@ def get_mesh_magnetic_field_lovects(level, direction, include_ghosts=True):
     return _get_mesh_array_lovects(level, direction, include_ghosts, libwarpx.warpx_getBfieldLoVects)
 
 
+def get_mesh_magnetic_field_cp_lovects(level, direction, include_ghosts=True):
+    '''
+
+    This returns a list of the lo vectors of the arrays containing the mesh electric field
+    data on each grid for this process.
+
+    Parameters
+    ----------
+
+        level          : the AMR level to get the data for
+        direction      : the component of the data you want
+        include_ghosts : whether to include ghost zones or not
+
+    Returns
+    -------
+
+        A 2d numpy array of the lo vector for each grid with the shape (dims, number of grids)
+
+    '''
+    return _get_mesh_array_lovects(level, direction, include_ghosts, libwarpx.warpx_getBfieldCPLoVects)
+
+
+def get_mesh_magnetic_field_fp_lovects(level, direction, include_ghosts=True):
+    '''
+
+    This returns a list of the lo vectors of the arrays containing the mesh electric field
+    data on each grid for this process.
+
+    Parameters
+    ----------
+
+        level          : the AMR level to get the data for
+        direction      : the component of the data you want
+        include_ghosts : whether to include ghost zones or not
+
+    Returns
+    -------
+
+        A 2d numpy array of the lo vector for each grid with the shape (dims, number of grids)
+
+    '''
+    return _get_mesh_array_lovects(level, direction, include_ghosts, libwarpx.warpx_getBfieldFPLoVects)
+
+
 def get_mesh_current_density_lovects(level, direction, include_ghosts=True):
     '''
 
@@ -775,3 +1201,45 @@ def get_mesh_current_density_lovects(level, direction, include_ghosts=True):
     return _get_mesh_array_lovects(level, direction, include_ghosts, libwarpx.warpx_getCurrentDensityLoVects)
 
 
+def get_mesh_current_density_cp_lovects(level, direction, include_ghosts=True):
+    '''
+
+    This returns a list of the lo vectors of the arrays containing the mesh electric field
+    data on each grid for this process.
+
+    Parameters
+    ----------
+
+        level          : the AMR level to get the data for
+        direction      : the component of the data you want
+        include_ghosts : whether to include ghost zones or not
+
+    Returns
+    -------
+
+        A 2d numpy array of the lo vector for each grid with the shape (dims, number of grids)
+
+    '''
+    return _get_mesh_array_lovects(level, direction, include_ghosts, libwarpx.warpx_getCurrentDensityCPLoVects)
+
+def get_mesh_current_density_fp_lovects(level, direction, include_ghosts=True):
+    '''
+
+    This returns a list of the lo vectors of the arrays containing the mesh electric field
+    data on each grid for this process.
+
+    Parameters
+    ----------
+
+        level          : the AMR level to get the data for
+        direction      : the component of the data you want
+        include_ghosts : whether to include ghost zones or not
+
+    Returns
+    -------
+
+        A 2d numpy array of the lo vector for each grid with the shape (dims, number of grids)
+
+    '''
+    return _get_mesh_array_lovects(level, direction, include_ghosts, libwarpx.warpx_getCurrentDensityFPLoVects)
+