/usr/lib/python2.7/dist-packages/PySPH-1.0a4.dev0-py2.7-linux-x86_64.egg/pysph/base/particle_array.pyx is in python-pysph 0~20160514.git91867dc-4build1.
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"""
A `ParticleArray` represents a collection of particles.
"""
# logging imports
import logging
logger = logging.getLogger()
# numpy imports
cimport numpy
import numpy
# PyZoltan imports
from pyzoltan.core.carray cimport *
# Local imports
from cpython cimport PyObject
from cpython cimport *
from cython cimport *
# Parallel imports
try:
from mpi4py import MPI
except ImportError:
MPI = None
# Maximum value of an unsigned int
cdef extern from "limits.h":
cdef unsigned int UINT_MAX
_UINT_MAX = UINT_MAX
# Declares various tags for particles, and functions to check them.
# Note that these tags are the ones set in the 'tag' property of the
# particles, in a particle array. To define additional discrete
# properties, one can add another integer property to the particles in
# the particle array while creating them.
# These tags could be considered as 'system tags' used internally to
# distinguish among different kinds of particles. If more tags are
# needed for a particular application, add them as mentioned above.
# The is_* functions defined below are to be used in Python for tests
# etc. Cython modules can directly use the enum name.
cpdef bint is_local(int tag):
return tag == Local
cpdef bint is_remote(int tag):
return tag == Remote
cpdef bint is_ghost(int tag):
return tag == Ghost
cpdef int get_local_tag():
return Local
cpdef int get_remote_tag():
return Remote
cpdef int get_ghost_tag():
return Ghost
cdef class ParticleArray:
"""
Class to represent a collection of particles.
Attributes
----------
name : str
name of this particle array.
properties : dict
dictionary of {prop_name:carray}.
constants : dict
dictionary of {const_name: carray}
Examples
--------
There are many ways to create a ParticleArray::
>>> p = ParticleArray(name='fluid', x=[1.,2., 3., 4.])
>>> p.name
'fluid'
>>> p.x, p.tag, p.pid, p.gid
For a full specification of properties with their type etc.::
>>> p = ParticleArray(name='fluid',
... x=dict(data=[1,2], type='int', default=1))
>>> p.get_carray('x').get_c_type()
'int'
The default value is what is used to set the default value when a new
particle is added and the arrays auto-resized.
To create constants that are not resized with added/removed particles::
>>> p = ParticleArray(name='f', x=[1,2], constants={'c':[0.,0.,0.]})
"""
######################################################################
# `object` interface
######################################################################
def __init__(self, str name='', default_particle_tag=Local,
constants=None, **props):
"""Constructor
Parameters
----------
name : str
name of this particle array.
default_particle_tag : int
one of `Local`, `Remote` or `Ghost`
constants : dict
dictionary of constant arrays for the entire particle array. These
must be arrays and are not resized when particles are added or
removed. These are stored as CArrays internally.
props :
any additional keyword arguments are taken to be properties, one
for each property.
"""
self.time = 0.0
self.name = name
self.is_dirty = True
self.indices_invalid = True
self.properties = {}
self.default_values = {'tag':default_particle_tag}
self._initialize(**props)
self.constants = {}
if constants is not None:
for const, data in constants.items():
self.add_constant(name=const, data=data)
# default lb_props are all the arrays
self.lb_props = None
# list of output property arrays
self.output_property_arrays = []
def __getattr__(self, name):
"""Convenience, to access particle property arrays as an attribute
A numpy array is returned. Look at the get() functions documentation for
care on using this numpy array.
"""
if name in self.properties:
return self._get_real_particle_prop(name)
elif name in self.constants:
return self.constants[name].get_npy_array()
else:
msg = "ParticleArray %s has no property/constant %s."\
%(self.name, name)
raise AttributeError(msg)
def __setattr__(self, name, value):
"""Convenience, to set particle property arrays as an attribute """
self.set(**{name:value})
def __reduce__(self):
"""Implemented to facilitate pickling of extension types """
d = {}
d['name'] = self.name
props = {}
default_values = {}
for prop, arr in self.properties.items():
pinfo = {}
pinfo['name'] = prop
pinfo['type'] = arr.get_c_type()
pinfo['data'] = arr.get_npy_array()
pinfo['default'] = self.default_values[prop]
props[prop] = pinfo
d['properties'] = props
props = {}
for prop, arr in self.constants.items():
pinfo = dict(name=prop, data=arr)
props[prop] = pinfo
d['constants'] = props
return (ParticleArray, (), d)
def __setstate__(self, d):
""" Load the particle array object from the saved dictionary """
self.properties = {}
self.constants = {}
self.property_arrays = []
self.default_values = {}
self.is_dirty = True
self.indices_invalid = True
self.num_real_particles = 0
self.name = d['name']
props = d['properties']
for prop in props:
self.add_property(**props[prop])
consts = d['constants']
for prop in consts:
self.add_constant(**consts[prop])
self.num_real_particles = numpy.sum(props['tag']['data']==Local)
def _initialize(self, **props):
"""Initialize the particle array with the given properties.
Parameters
----------
props : dict
dictionary containing various property arrays. All these
arrays are expected to be numpy arrays or objects that can
be converted to numpy arrays.
Notes
-----
- This will clear any existing data.
- As a rule internal arrays will always be either long or double
arrays.
"""
cdef int nprop, nparticles
cdef bint tag_present = False
cdef numpy.ndarray a, arr, npyarr
cdef IntArray tagarr
cdef str name
self.clear()
nprop = len(props)
if nprop == 0:
return
# add the properties
for name, prop in props.items():
if isinstance(prop, dict):
prop_info = prop
prop_info['name'] = name
else:
prop_info = dict(name=name, data=prop)
self.add_property(**prop_info)
self.align_particles()
######################################################################
# `Public` interface
######################################################################
def set_output_arrays(self, list props):
"""Set the list of output arrays for this ParticleArray
Parameters
----------
props : list
The list of property arrays
Notes
-----
In PySPH, the solver obtains the list of property arrays to
output by calling the `ParticleArray.get_property_arrays`
method. If detailed output is not requested, the
`output_property_arrays` attribute is used to determine the
arrays that will be written to file
"""
# first check if the arrays are valid and raise a warning
for prop in props:
self._check_property(prop)
self.output_property_arrays = props
def add_output_arrays(self, list props):
"""Append props to the existing list of output arrays
Parameters
----------
props : list
The additional list of property arrays to save
"""
# first check if the arrays are valid and raise a warning
for prop in props:
self._check_property(prop)
# add to the existing list
self.output_property_arrays.extend(props)
self.output_property_arrays = list( set(self.output_property_arrays) )
def get_property_arrays(self, all=True, only_real=True):
"""Return a dictionary of arrays held by the `ParticleArray` container.
This does not include the constants.
Parameters
----------
all : bint
Flag to select all arrays
only_real : bint
Flag to select Local/Remote particles
Notes
-----
The dictionary returned is keyed on the property name and the
value is the NumPy array representing the data. If `all` is
set to False, the list of arrays is determined by the
`output_property_arrays` data attribute.
"""
# the arrays dictionary to be returned
arrays = {}
# the list of properties
props = self.output_property_arrays
if all or len(props) == 0:
props = list(self.properties.keys())
# number of particles
num_particles = self.get_number_of_particles(only_real)
# add the property arrays
for prop in props:
prop_array = self.properties[ prop ].get_npy_array()[:num_particles]
arrays[prop] = prop_array
return arrays
cpdef set_dirty(self, bint value):
"""Set the is_dirty variable to given value """
self.is_dirty = value
cpdef set_indices_invalid(self, bint value):
"""Set the indices_invalid to the given value """
self.indices_invalid = value
cpdef has_array(self, str arr_name):
"""Returns true if the array arr_name is present """
return self.properties.has_key(arr_name)
def clear(self):
"""Clear all data held by this array """
self.properties = {'tag':IntArray(0), 'pid':IntArray(0), 'gid':UIntArray(0)}
tag_def_values = self.default_values['tag']
self.default_values.clear()
self.default_values = {'tag':tag_def_values, 'pid':0, 'gid':_UINT_MAX}
self.is_dirty = True
self.indices_invalid = True
cpdef set_time(self, double time):
self.time = time
cpdef double get_time(self):
return self.time
cpdef set_name(self, str name):
self.name = name
def set_lb_props(self, list lb_props):
self.lb_props = lb_props
cpdef get_lb_props(self):
"""Return the properties that are to be load balanced. If none are
explicitly set by the user, return all of the properties.
"""
if self.lb_props is None:
return list(self.properties.keys())
else:
return self.lb_props
cpdef int get_number_of_particles(self, bint real=False):
""" Return the number of particles """
if real:
return self.num_real_particles
else:
if len(self.properties) > 0:
prop0 = list(self.properties.values())[0]
return prop0.length
else:
return 0
cpdef remove_particles(self, indices):
""" Remove particles whose indices are given in index_list.
We repeatedly interchange the values of the last element and values from
the index_list and reduce the size of the array by one. This is done for
every property that is being maintained.
Parameters
----------
indices : array
an array of indices, this array can be a list, numpy array
or a LongArray.
Notes
-----
Pseudo-code for the implementation::
if index_list.length > number of particles
raise ValueError
sorted_indices <- index_list sorted in ascending order.
for every every array in property_array
array.remove(sorted_indices)
"""
cdef BaseArray index_list
if isinstance(indices, BaseArray):
index_list = indices
else:
indices = numpy.asarray(indices)
index_list = LongArray(indices.size)
index_list.set_data(indices)
cdef str msg
cdef numpy.ndarray sorted_indices
cdef BaseArray prop_array
cdef int num_arrays, i
cdef list property_arrays
if index_list.length > self.get_number_of_particles():
msg = 'Number of particles to be removed is greater than'
msg += 'number of particles in array'
raise ValueError, msg
sorted_indices = numpy.sort(index_list.get_npy_array())
num_arrays = len(self.properties)
property_arrays = list(self.properties.values())
for i in range(num_arrays):
prop_array = property_arrays[i]
prop_array.remove(sorted_indices, 1)
if index_list.length > 0:
self.align_particles()
self.is_dirty = True
self.indices_invalid = True
cpdef remove_tagged_particles(self, int tag):
""" Remove particles that have the given tag.
Parameters
----------
tag : int
the type of particles that need to be removed.
"""
cdef LongArray indices = LongArray()
cdef IntArray tag_array = self.properties['tag']
cdef int *tagarrptr = tag_array.get_data_ptr()
cdef int i
# find the indices of the particles to be removed.
for i in range(tag_array.length):
if tagarrptr[i] == tag:
indices.append(i)
# remove the particles.
self.remove_particles(indices)
def add_particles(self, **particle_props):
"""
Add particles in particle_array to self.
Parameters
----------
particle_props : dict
a dictionary containing numpy arrays for various particle
properties.
Notes
-----
- all properties should have same length arrays.
- all properties should already be present in this particles array.
if new properties are seen, an exception will be raised.
properties.
"""
cdef int num_extra_particles, old_num_particles, new_num_particles
cdef str prop
cdef BaseArray arr
cdef numpy.ndarray s_arr, nparr
if len(particle_props) == 0:
return 0
# check if the input properties are valid.
for prop in particle_props:
self._check_property(prop)
num_extra_particles = len(list(particle_props.values())[0])
old_num_particles = self.get_number_of_particles()
new_num_particles = num_extra_particles + old_num_particles
for prop in self.properties:
arr = <BaseArray>PyDict_GetItem(self.properties, prop)
if PyDict_Contains(particle_props, prop)== 1:
d_type = arr.get_npy_array().dtype
s_arr = numpy.asarray(particle_props[prop], dtype=d_type)
arr.extend(s_arr)
else:
arr.resize(new_num_particles)
# set the properties of the new particles to the default ones.
nparr = arr.get_npy_array()
nparr[old_num_particles:] = self.default_values[prop]
if num_extra_particles > 0:
# make sure particles are aligned properly.
self.align_particles()
self.is_dirty = True
return 0
cpdef int append_parray(self, ParticleArray parray) except -1:
""" Add particles from a particle array
properties that are not there in self will be added
"""
if parray.get_number_of_particles() == 0:
return 0
cdef int num_extra_particles = parray.get_number_of_particles()
cdef int old_num_particles = self.get_number_of_particles()
cdef int new_num_particles = num_extra_particles + old_num_particles
cdef str prop_name
cdef BaseArray arr, source, dest
cdef numpy.ndarray nparr_dest, nparr_source
# extend current arrays by the required number of particles
self.extend(num_extra_particles)
for prop_name in parray.properties:
if PyDict_Contains(self.properties, prop_name):
arr = <BaseArray>PyDict_GetItem(self.properties, prop_name)
else:
arr = None
if arr is not None:
source = <BaseArray>PyDict_GetItem(parray.properties, prop_name)
nparr_source = source.get_npy_array()
nparr_dest = arr.get_npy_array()
nparr_dest[old_num_particles:] = nparr_source
else:
# meaning this property is not there in self.
self.add_property(name=prop_name,
type=parray.properties[prop_name].get_c_type(),
default=parray.default_values[prop_name]
)
# now add the values to the end of the created array
dest = <BaseArray>PyDict_GetItem(self.properties, prop_name)
nparr_dest = dest.get_npy_array()
source = <BaseArray>PyDict_GetItem(parray.properties, prop_name)
nparr_source = source.get_npy_array()
nparr_dest[old_num_particles:] = nparr_source
for const in parray.constants:
self.constants.setdefault(const, parray.constants[const])
if num_extra_particles > 0:
self.align_particles()
self.is_dirty = True
return 0
cpdef extend(self, int num_particles):
""" Increase the total number of particles by the requested amount
New particles are added at the end of the list, you may have to manually
call align_particles later.
"""
if num_particles <= 0:
return
cdef int old_size = self.get_number_of_particles()
cdef int new_size = old_size + num_particles
cdef BaseArray arr
cdef numpy.ndarray nparr
for key in self.properties:
arr = self.properties[key]
arr.resize(new_size)
nparr = arr.get_npy_array()
nparr[old_size:] = self.default_values[key]
def get_property_index(self, prop_name):
""" Get the index of the property in the property array """
return self.properties.get(prop_name)
cdef numpy.ndarray _get_real_particle_prop(self, str prop_name):
""" get the npy array of property corresponding to only real particles
No checks are performed. Only call this after making sure that the
property required already exists.
"""
cdef BaseArray prop_array
prop_array = self.properties.get(prop_name)
if prop_array is not None:
return prop_array.get_npy_array()[:self.num_real_particles]
else:
return None
def get(self, *args, only_real_particles=True):
""" Return the numpy array/constant for the property names in
the arguments.
Parameters
----------
only_real_particles : bool
indicates if properties of only real particles need to be
returned or all particles to be returned. By default only
real particles will be returned.
args : additional args
a list of property names.
Notes
-----
The returned numpy array does **NOT** own its data. Other
operations may be performed.
Returns
-------
Numpy array.
"""
cdef int nargs = len(args)
cdef list result = []
cdef str arg
cdef int i
cdef BaseArray arg_array
if nargs == 0:
return
if only_real_particles == True:
for i in range(nargs):
arg = args[i]
self._check_property(arg)
if arg in self.properties:
arg_array = self.properties[arg]
result.append(
arg_array.get_npy_array()[:self.num_real_particles])
else:
result.append(self.constants[arg].get_npy_array())
else:
for i in range(nargs):
arg = args[i]
self._check_property(arg)
if arg in self.properties:
arg_array = self.properties[arg]
result.append(arg_array.get_npy_array())
else:
result.append(self.constants[arg].get_npy_array())
if nargs == 1:
return result[0]
else:
return tuple(result)
def set(self, **props):
""" Set properties from numpy arrays like objects
Parameters
----------
props : dict
a dictionary of properties containing the arrays to be set.
Notes
-----
- the properties being set must already be present in the properties
dict.
- the size of the data should match the array already present.
"""
cdef str prop
cdef BaseArray prop_array
cdef int nprops = len(props)
cdef list prop_names = list(props.keys())
cdef int i
for i in range(nprops):
prop = prop_names[i]
self._check_property(prop)
for prop in props:
proparr = numpy.asarray(props[prop])
if self.properties.has_key(prop):
prop_array = self.properties[prop]
prop_array.set_data(proparr)
elif self.constants.has_key(prop):
prop_array = self.constants[prop]
prop_array.set_data(proparr)
# if the tag property is being set, the alignment will have to be
# changed.
if prop == 'tag':
self.align_particles()
if prop == 'x' or prop == 'y' or prop == 'z':
self.set_dirty(True)
cpdef BaseArray get_carray(self, str prop):
"""Return the c-array for the property or constant.
"""
if PyDict_Contains(self.properties, prop) == 1:
return <BaseArray>PyDict_GetItem(self.properties, prop)
elif PyDict_Contains(self.constants, prop) == 1:
return <BaseArray>PyDict_GetItem(self.constants, prop)
else:
return None
cpdef add_constant(self, str name, data):
"""Add a constant property to the particle array.
A constant property is an array but has a fixed size in that it is
never resized as particles are added or removed. These properties
are always stored internally as CArrays.
An example of where this is useful is if you need to calculate the
center of mass of a solid body or the net force on the body.
Parameters
----------
name : str
name of the constant
data : array-like
the value for the data.
"""
if name in self.constants:
raise RuntimeError('Constant called "%s" already exists.'%name)
if name in self.properties:
raise RuntimeError('Property called "%s" already exists.'%name)
array_data = numpy.atleast_1d(data)
self.constants[name] = self._create_c_array_from_npy_array(array_data)
cpdef add_property(self, str name, str type='double', default=None, data=None):
"""Add a new property to the particle array.
If a `default` is not supplied 0 is assumed.
Parameters
----------
name : str
compulsory name of property.
type : str
specifying the data type of this property ('double', 'int'
etc.)
default : value
specifying the default value of this property.
data : ndarray
specifying the data associated with each particle.
Notes
-----
If there are no particles currently in the particle array, and a
new property with some particles is added, all the remaining
properties will be resized to the size of the newly added array.
If there are some particles in the particle array, and a new
property is added without any particles, then this new property will
be resized according to the current size.
If there are some particles in the particle array and a new property
is added with a different number of particles, then an error will be
raised.
Warning
-------
- it is best not to add properties with data when you already have
particles in the particle array. Reason is that, the particles in
the particle array will be stored so that the 'Real' particles are
in the top of the list and later the dummy ones. The data in your
property array should be matched to the particles appropriately.
This may not always be possible when there are particles of
different type in the particle array.
- Properties without any values can be added anytime.
- While initializing particle arrays only using the add_property
function, you will have to call align_particles manually to make
sure particles are aligned properly.
"""
cdef str prop_name=name, data_type=type
cdef bint array_size_proper = False
# make sure the size of the supplied array is consistent.
if (data is None or self.get_number_of_particles() == 0 or
len(data) == 0):
array_size_proper = True
else:
if self.get_number_of_particles() == len(data):
array_size_proper = True
if array_size_proper == False:
logger.error('Array sizes incompatible')
raise ValueError, 'Array sizes incompatible'
# setup the default values
if default is None:
if PyDict_Contains(self.properties, prop_name) != 1:
default = 0
else:
default = <object>PyDict_GetItem(self.default_values, prop_name)
PyDict_SetItem(self.default_values, prop_name, default)
# array sizes are compatible, now resize the required arrays
# appropriately and add.
if self.get_number_of_particles() == 0:
if data is None or len(data) == 0:
# if a property with that name already exists, do not do
# anything.
if PyDict_Contains(self.properties, prop_name) != 1:
# just add the property with a zero array.
self.properties[prop_name] = self._create_carray(
data_type, 0, default)
else:
# new property has been added with some particles, while no
# particles are currently present. First resize the current
# properties to this new length, and then add this new
# property.
for prop in self.properties:
arr = self.properties[prop]
arr.resize(len(data))
arr.get_npy_array()[:] = self.default_values[prop]
if prop_name == 'tag':
arr = numpy.asarray(data)
self.num_real_particles = numpy.sum(arr==Local)
else:
self.num_real_particles = len(data)
if self.properties.has_key(prop_name):
# just add the particles to the already existing array.
d_type = self.properties[prop_name].get_npy_array().dtype
arr = numpy.asarray(data, dtype=d_type)
self.properties[prop_name].set_data(arr)
else:
# now add the new property array
# if a type was specifed create that type of array
if data_type is None:
# get an array for this data
arr = numpy.asarray(data, dtype=numpy.double)
self.properties[prop_name] = (
self._create_c_array_from_npy_array(arr))
else:
arr = self._create_carray(data_type, len(data), default)
np_arr = arr.get_npy_array()
np_arr[:] = numpy.asarray(data)
self.properties[prop_name] = arr
else:
if data is None or len(data) == 0:
# new property is added without any initial data, resize it to
# current particle count.
if not self.properties.has_key(prop_name):
arr = self._create_carray(data_type,
self.get_number_of_particles(),
default)
self.properties[prop_name] = arr
else:
if self.properties.has_key(prop_name):
d_type = self.properties[prop_name].get_npy_array().dtype
arr = numpy.asarray(data, dtype=d_type)
self.properties[prop_name].set_data(arr)
# realign the particles if the tag variable is being set.
else:
if data_type is None:
# just add the property array
arr = numpy.asarray(data, dtype=numpy.double)
self.properties[prop_name] = (
self._create_c_array_from_npy_array(arr))
else:
arr = self._create_carray(data_type, len(data), default)
np_arr = arr.get_npy_array()
arr.get_npy_array()[:] = numpy.asarray(data)
self.properties[prop_name] = arr
######################################################################
# Non-public interface
######################################################################
def _create_carray(self, str data_type, int size, default=0):
"""Create a carray of the requested type, and of requested size
Parameters
----------
data_type : str
string representing the 'c' data type - eg. 'int' for
integers.
size : int
the size of the requested array
default : value
the default value to initialize the array with.
"""
cdef BaseArray arr
if data_type == None:
arr = DoubleArray(size)
elif data_type == 'double':
arr = DoubleArray(size)
elif data_type == 'long':
arr = LongArray(size)
elif data_type == 'float':
arr = FloatArray(size)
elif data_type == 'int':
arr = IntArray(size)
elif data_type == 'unsigned int':
arr = UIntArray(size)
else:
logger.error('Trying to create carray of unknown '
'datatype: %s' %data_type)
if size > 0:
arr.get_npy_array()[:] = default
return arr
cdef _check_property(self, str prop):
"""Check if a property is present or not """
if (PyDict_Contains(self.properties, prop) or
PyDict_Contains(self.constants, prop)):
return
else:
raise AttributeError, 'property %s not present'%(prop)
cdef object _create_c_array_from_npy_array(self, numpy.ndarray np_array):
"""Create and return a carray array from the given numpy array.
Notes
-----
- this function is used only when a C array needs to be
created (in the initialize function).
"""
cdef int np = np_array.size
cdef object a
if np_array.dtype == numpy.int32 or np_array.dtype == numpy.int64:
a = LongArray(np)
a.set_data(np_array)
elif np_array.dtype == numpy.float32:
a = FloatArray(np)
a.set_data(np_array)
elif np_array.dtype == numpy.double:
a = DoubleArray(np)
a.set_data(np_array)
else:
msg = 'unknown numpy data type passed %s'%(np_array.dtype)
raise TypeError, msg
return a
cpdef int align_particles(self) except -1:
"""Moves all 'Local' particles to the beginning of the array
This makes retrieving numpy slices of properties of 'Local'
particles possible. This facility will be required frequently.
Notes
-----
Pseudo-code::
index_arr = LongArray(n)
next_insert = 0
for i from 0 to n
p <- ith particle
if p is Local
if i != next_insert
tmp = index_arr[next_insert]
index_arr[next_insert] = i
index_arr[i] = tmp
next_insert += 1
else
index_arr[i] = i
next_insert += 1
else
index_arr[i] = i
# we now have the new index assignment.
# swap the required values as needed.
for every property array:
for i from 0 to n:
if index_arr[i] != i:
tmp = prop[i]
prop[i] = prop[index_arr[i]]
prop[index_arr[i]] = tmp
"""
cdef size_t i, num_particles
cdef size_t next_insert
cdef size_t num_arrays
cdef int tmp
cdef IntArray tag_arr
cdef LongArray index_array
cdef BaseArray arr
cdef list arrays
cdef long num_real_particles = 0
cdef long num_moves = 0
next_insert = 0
num_particles = self.get_number_of_particles()
tag_arr = self.get_carray('tag')
# malloc the new index array
index_array = LongArray(num_particles)
for i in range(num_particles):
if tag_arr.data[i] == Local:
num_real_particles += 1
if i != next_insert:
tmp = index_array.data[next_insert]
index_array.data[next_insert] = i
index_array.data[i] = tmp
next_insert += 1
num_moves += 1
else:
index_array.data[i] = i
next_insert += 1
else:
index_array.data[i] = i
self.num_real_particles = num_real_particles
# we now have the aligned indices. Rearrage the particles particles
# accordingly.
arrays = list(self.properties.values())
num_arrays = len(arrays)
for i in range(num_arrays):
arr = arrays[i]
arr.c_align_array(index_array)
if num_moves > 0:
self.is_dirty = True
self.indices_invalid = True
cpdef ParticleArray extract_particles(self, indices, list props=None):
"""Create new particle array for particles with indices in index_array
Parameters
----------
indices : list/array/LongArray
indices of particles to be extracted (can be a LongArray or
list/numpy array).
props : list
the list of properties to extract, if None all properties
are extracted.
Notes
-----
The algorithm is as follows:
- create a new particle array with the required properties.
- resize the new array to the desired length (index_array.length)
- copy the properties from the existing array to the new array.
"""
cdef BaseArray index_array
if isinstance(indices, BaseArray):
index_array = indices
else:
indices = numpy.asarray(indices)
index_array = LongArray(indices.size)
index_array.set_data(indices)
cdef ParticleArray result_array = ParticleArray()
cdef list prop_names, output_arrays
cdef BaseArray dst_prop_array, src_prop_array
cdef str prop_type, prop
if props is None:
prop_names = list(self.properties.keys())
else:
prop_names = props
for prop in prop_names:
prop_type = self.properties[prop].get_c_type()
prop_default = self.default_values[prop]
result_array.add_property(name=prop,
type=prop_type,
default=prop_default)
# now we have the result array setup.
# resize it
if index_array.length == 0:
return result_array
result_array.extend(index_array.length)
# copy the required indices for each property.
for prop in prop_names:
src_prop_array = self.get_carray(prop)
dst_prop_array = result_array.get_carray(prop)
src_prop_array.copy_values(index_array, dst_prop_array)
for const in self.constants:
result_array.constants[const] = self.constants[const]
result_array.align_particles()
result_array.name = self.name
if props is None:
output_arrays = list(self.output_property_arrays)
else:
output_arrays = list(
set(props).intersection(
self.output_property_arrays
)
)
result_array.set_output_arrays(output_arrays)
return result_array
cpdef set_tag(self, long tag_value, LongArray indices):
"""Set value of tag to tag_value for the particles in indices """
cdef LongArray tag_array = self.get_carray('tag')
cdef int i
for i in range(indices.length):
tag_array.data[indices.data[i]] = tag_value
cpdef copy_properties(self, ParticleArray source, long start_index=-1, long
end_index=-1):
""" Copy properties from source to self
Parameters
----------
source : ParticleArray
the particle array from where to copy.
start_index : long
the first particle in self which maps to the 0th particle in
source
end_index : long
the index of first particle from start_index that
is not copied
"""
cdef BaseArray src_array, dst_array
for prop_name in source.properties:
src_array = source.get_carray(prop_name)
dst_array = self.get_carray(prop_name)
if src_array != None and dst_array != None:
dst_array.copy_subset(src_array, start_index, end_index)
cpdef copy_over_properties(self, dict props):
""" Copy the properties from one set to another.
Parameters
----------
props : dict
A mapping between the properties to be copied.
Examples
--------
To save the properties 'x' and 'y' to say 'x0' and 'y0'::
>>> pa.copy_over_properties(props = {'x':'x0', 'y':'y0'}
"""
cdef DoubleArray dst, src
cdef str prop
cdef long np = self.get_number_of_particles()
cdef long a
for prop in props:
src = self.get_carray( prop )
dst = self.get_carray( props[prop] )
for a in range( np ):
dst.data[ a ] = src.data[ a ]
cpdef set_to_zero(self, list props):
cdef long np = self.get_number_of_particles()
cdef long a
cdef DoubleArray prop_arr
cdef str prop
for prop in props:
prop_arr = self.get_carray(prop)
for a in range(np):
prop_arr.data[a] = 0.0
cpdef update_property(self, BaseArray a, BaseArray a0,
BaseArray acc, double dt):
"""Update an array from another array and a scalar.
This situation arises often when we want to update an array
from computed accelrations. The scalar in this case will be
the the time step.
"""
cdef size_t npart = self.get_number_of_particles()
cdef size_t i
for i in range(npart):
a.data[i] = a0.data[i] + dt*acc.data[i]
cpdef set_pid(self, int pid):
"""Set the processor id for all particles """
cdef IntArray pid_arr = self.properties['pid']
cdef long a
for a in range(pid_arr.length):
pid_arr.data[a] = pid
cpdef remove_property(self, str prop_name):
"""Removes property prop_name from the particle array """
if self.properties.has_key(prop_name):
self.properties.pop(prop_name)
self.default_values.pop(prop_name)
if prop_name in self.output_property_arrays:
self.output_property_arrays.remove(prop_name)
def update_min_max(self, props=None):
"""Update the min,max values of all properties """
if props:
for prop in props:
array = self.properties[prop]
array.update_min_max()
else:
for array in self.properties.values():
array.update_min_max()
cpdef resize(self, long size):
"""Resize all arrays to the new size"""
for prop, array in self.properties.items():
array.resize(size)
######################################################################
# Parallel interface
######################################################################
def Send(self, int recv_proc, props=None) :
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
nprocs = comm.Get_size()
if rank < 10 :
rank_str = '0' + str(rank)
else :
rank_str = str(rank)
if recv_proc < 10 :
recv_proc_str = '0' + str(recv_proc)
else :
recv_proc_str = str(recv_proc)
pre_tag = str(1) + rank_str + recv_proc_str
tag_count = 0
if recv_proc < nprocs :
datasize = numpy.zeros(1)
for prop in props :
if self.properties.has_key(prop) :
datasize[0] = len(self.properties[prop])
else :
props.remove(prop)
if datasize[0] != 0 and len(props) != 0 :
prop_count = numpy.zeros(1)
prop_count[0] = len(props)
tag = int(pre_tag + str(tag_count))
comm.Send(prop_count, recv_proc, tag=tag)
tag_count+=1
tag = int(pre_tag + str(tag_count))
comm.Send(datasize, recv_proc, tag=tag)
tag_count+=1
prop_len = numpy.zeros(len(props))
i = 0
for prop in props :
prop_len[i] = len(prop)
i+=1
tag = int(pre_tag + str(tag_count))
comm.Send(prop_len, recv_proc, tag=tag)
tag_count+=1
for prop in props :
prop_char = numpy.zeros(len(prop), dtype=numpy.character)
prop_char[0:] = list(prop)
tag = int(pre_tag + str(tag_count))
comm.Send([prop_char, MPI.CHARACTER], recv_proc, tag=tag)
tag_count+=1
for prop in props :
tag = int(pre_tag + str(tag_count))
comm.Send(self.get(prop), recv_proc, tag=tag)
tag_count+=1
def Receive(self, int send_proc) :
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
nprocs = comm.Get_size()
if rank < 10 :
rank_str = '0' + str(rank)
else :
rank_str = str(rank)
if send_proc < 10 :
send_proc_str = '0' + str(send_proc)
else :
send_proc_str = str(send_proc)
pre_tag = str(1) + send_proc_str + rank_str
tag_count = 0
recv_props = {}
props = []
if send_proc < nprocs :
prop_count = numpy.zeros(1)
tag = int(pre_tag + str(tag_count))
comm.Recv(prop_count, send_proc, tag=tag)
tag_count+=1
datasize = numpy.zeros(1)
tag = int(pre_tag + str(tag_count))
comm.Recv(datasize, send_proc, tag=tag)
tag_count+=1
if datasize[0] != 0 and prop_count[0] != 0 :
prop_len = numpy.zeros(int(prop_count[0]))
tag = int(pre_tag + str(tag_count))
comm.Recv(prop_len, send_proc, tag=tag)
tag_count+=1
for i in range(int(prop_count[0])) :
prop_char = numpy.zeros(int(prop_len[i]), dtype=numpy.character)
tag = int(pre_tag + str(tag_count))
comm.Recv([prop_char, MPI.CHARACTER], send_proc, tag=tag)
tag_count+=1
prop = "".join(prop_char)
props.append(prop)
recv_props[prop] = numpy.zeros(int(datasize[0]))
for prop in props :
tag = int(pre_tag + str(tag_count))
comm.Recv(recv_props[prop], send_proc, tag=tag)
tag_count+=1
self.add_particles(**recv_props)
# End of ParticleArray class
##############################################################################
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