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Commit d8f0ba70 authored by ultimanet's avatar ultimanet
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Misc changes. Separation of field and space functionalities further improved.... 

Misc changes. Separation of field and space functionalities further improved. The fft of a field now runs purely with a distributed data object as it is now the natural data object of rg_space.
parent dc144ef8
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lm/nifty_lm.py
View file @ d8f0ba70
......@@ -213,6 +213,11 @@ class lm_space(point_space):
"""
return self.paradict['mmax']
def shape(self):
mmax = self.paradict('mmax')
lmax = self.paradict('lmax')
return np.array([(mmax+1)*(lmax+1)-(lmax+1)*(mmax//2)], dtype=int)
def dim(self,split=False):
"""
Computes the dimension of the space, i.e.\ the number of spherical
......@@ -237,9 +242,11 @@ class lm_space(point_space):
"""
## dim = (mmax+1)*(lmax-mmax/2+1)
if(split):
return np.array([(self.para[0]+1)*(self.para[1]+1)-(self.para[1]+1)*self.para[1]//2],dtype=np.int)
return self.shape()
#return np.array([(self.para[0]+1)*(self.para[1]+1)-(self.para[1]+1)*self.para[1]//2],dtype=np.int)
else:
return (self.para[0]+1)*(self.para[1]+1)-(self.para[1]+1)*self.para[1]//2
return np.prod(self.shape())
#return (self.para[0]+1)*(self.para[1]+1)-(self.para[1]+1)*self.para[1]//2
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
......@@ -1035,7 +1042,7 @@ class gl_space(point_space):
self.datatype = datatype
self.discrete = False
self.vol = gl.vol(self.para[0],nlon=self.para[1]).astype(self.datatype)
self.vol = gl.vol(self.paradict['nlat'],nlon=self.paradict['nlon']).astype(self.datatype)
@property
......@@ -1074,6 +1081,9 @@ class gl_space(point_space):
"""
return self.paradict['nlon']
def shape(self):
return np.array([(self.paradict['nlat']*self.paradict['nlon'])], dtype=np.int)
def dim(self,split=False):
"""
Computes the dimension of the space, i.e.\ the number of pixels.
......@@ -1091,9 +1101,11 @@ class gl_space(point_space):
"""
## dim = nlat*nlon
if(split):
return np.array([self.para[0]*self.para[1]],dtype=np.int)
return self.shape()
#return np.array([self.para[0]*self.para[1]],dtype=np.int)
else:
return self.para[0]*self.para[1]
return np.prod(self.shape())
#return self.para[0]*self.para[1]
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
......@@ -1699,7 +1711,7 @@ class hp_space(point_space):
self.datatype = np.float64
self.discrete = False
self.vol = np.array([4*pi/(12*self.para[0]**2)],dtype=self.datatype)
self.vol = np.array([4*pi/(12*self.paradict['nside']**2)],dtype=self.datatype)
@property
def para(self):
......@@ -1725,6 +1737,8 @@ class hp_space(point_space):
"""
return self.paradict['nside']
def shape(self):
return np.array([12*self.paradict['nside']**2], dtype=np.int)
def dim(self,split=False):
"""
......@@ -1743,9 +1757,11 @@ class hp_space(point_space):
"""
## dim = 12*nside**2
if(split):
return np.array([12*self.para[0]**2],dtype=np.int)
return self.shape()
#return np.array([12*self.para[0]**2],dtype=np.int)
else:
return 12*self.para[0]**2
return np.prod(self.shape())
#return 12*self.para[0]**2
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
......
nifty_core.py
View file @ d8f0ba70
......@@ -1011,12 +1011,9 @@ class space(object):
self.discrete = True
self.vol = np.real(np.array([1],dtype=self.datatype))
self.shape = None
@property
def para(self):
return self.paradict['default']
#return self.distributed_val
@para.setter
def para(self, x):
......@@ -1033,12 +1030,12 @@ class space(object):
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
def getitem(self, key):
def getitem(self, data, key):
raise NotImplementedError(about._errors.cstring("ERROR: no generic instance method 'getitem'."))
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
def setitem(self, key):
def setitem(self, data, key):
raise NotImplementedError(about._errors.cstring("ERROR: no generic instance method 'getitem'."))
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
......@@ -1053,6 +1050,8 @@ class space(object):
def norm(self, x, q):
raise NotImplementedError(about._errors.cstring("ERROR: no generic instance method 'norm'."))
def shape(self):
raise NotImplementedError(about._errors.cstring("ERROR: no generic instance method 'shape'."))
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
def dim(self,split=False):
......@@ -1217,8 +1216,30 @@ class space(object):
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
def cast(self, x):
return self.enforce_values(x)
def cast(self, x, verbose=False):
"""
Computes valid field values from a given object, trying
to translate the given data into a valid form. Thereby it is as
benevolent as possible.
Parameters
----------
x : {float, numpy.ndarray, nifty.field}
Object to be transformed into an array of valid field values.
Returns
-------
x : numpy.ndarray, distributed_data_object
Array containing the field values, which are compatible to the
space.
Other parameters
----------------
verbose : bool, *optional*
Whether the method should raise a warning if information is
lost during casting (default: False).
"""
return self.enforce_values(x, extend=True)
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
......@@ -1827,13 +1848,24 @@ class point_space(space):
def para(self):
temp = np.array([self.paradict['num']], dtype=int)
return temp
#return self.distributed_val
@para.setter
def para(self, x):
self.paradict['num'] = x
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
def getitem(self, data, key):
return data[key]
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
def setitem(self, data, update, key):
data[key]=update
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
def unary_operation(self, x, op='None', **kwargs):
"""
x must be a numpy array which is compatible with the space!
......@@ -1947,6 +1979,10 @@ class point_space(space):
"""
return self.para[0]
def shape(self):
return np.array([self.paradict['num']])
def dim(self,split=False):
"""
Computes the dimension of the space, i.e.\ the number of points.
......@@ -1964,9 +2000,11 @@ class point_space(space):
"""
## dim = num
if(split):
return np.array([self.para[0]],dtype=np.int)
return self.shape()
#return np.array([self.para[0]],dtype=np.int)
else:
return self.para[0]
return np.prod(self.shape())
#return self.para[0]
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
......@@ -4711,6 +4749,12 @@ class nested_space(space):
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
def shape(self):
temp = []
for i in range(self.paradict.ndim):
temp = np.append(temp, self.paradict[i])
return temp
def dim(self,split=False):
"""
Computes the dimension of the product space.
......@@ -4729,9 +4773,11 @@ class nested_space(space):
Dimension(s) of the space.
"""
if(split):
return self.para
return self.shape()
#return self.para
else:
return np.prod(self.para,axis=0,dtype=None,out=None)
return np.prod(self.shape())
#return np.prod(self.para,axis=0,dtype=None,out=None)
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
......@@ -5436,7 +5482,10 @@ class field(object):
else:
self.domain.check_codomain(target)
self.target = target
self.val = self.domain.cast(val)
"""
self.distributed_val = distributed_data_object(global_shape=domain.dim(split=True), dtype=domain.datatype)
## check values
......@@ -5454,6 +5503,8 @@ class field(object):
@val.setter
def val(self, x):
return self.distributed_val.set_full_data(x)
"""
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
def dim(self,split=False):
......@@ -5792,7 +5843,7 @@ class field(object):
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
def transform(self,target=None,overwrite=False,**kwargs):
def transform(self, target=None, overwrite=False, **kwargs):
"""
Computes the transform of the field using the appropriate conjugate
transformation.
......@@ -5821,12 +5872,16 @@ class field(object):
target = self.target
else:
self.domain.check_codomain(target) ## a bit pointless
new_val = self.domain.calc_transform(self.val,
codomain=target,
**kwargs)
if(overwrite):
self.val = self.domain.calc_transform(self.val,codomain=target,field_val=self.distributed_val, **kwargs)
self.val = new_val
self.target = self.domain
self.domain = target
else:
return field(target,val=self.domain.calc_transform(self.val,codomain=target, field_val=self.distributed_val, **kwargs),target=self.domain)
return field(target, val=new_val, target=self.domain)
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
......
nifty_mpi_data.py
View file @ d8f0ba70
This diff is collapsed.
nifty_paradict.py
View file @ d8f0ba70
......@@ -29,7 +29,14 @@ class space_paradict(object):
self.parameters = {}
for key in kwargs:
self[key] = kwargs[key]
def __eq__(self, other):
return (isinstance(other, self.__class__)
and self.__dict__ == other.__dict__)
def __ne__(self, other):
return not self.__eq__(other)
def __repr__(self):
return self.parameters.__repr__()
......
rg/fft_rg.py
View file @ d8f0ba70
# -*- coding: utf-8 -*-
import numpy as np
from nifty import nifty_mpi_data
from nifty.nifty_mpi_data import distributed_data_object
# Try to import pyfftw. If this fails fall back to gfft. If this fails fall back to local gfft_rg
......@@ -50,7 +50,7 @@ class fft(object):
----------
None
"""
def transform(self,field_val,domain,codomain,**kwargs):
def transform(self, val, domain, codomain, **kwargs):
"""
A generic ff-transform function.
......@@ -71,9 +71,10 @@ class fft(object):
if fft_machine == 'pyfftw':
## The instances of plan_and_info store the fftw plan and all
## other information needed in order to perform a mpi-fftw transformation
class _fftw_plan_and_info(object):
class _fftw_plan_and_info(fft):
def __init__(self,domain,codomain,fft_fftw_context,**kwargs):
self.compute_plan_and_info(domain,codomain,fft_fftw_context,**kwargs)
self.compute_plan_and_info(domain, codomain, fft_fftw_context,
**kwargs)
def set_plan(self, x):
self.plan=x
......@@ -90,19 +91,21 @@ if fft_machine == 'pyfftw':
def get_codomain_centering_mask(self):
return self.codomain_centering_mask
def compute_plan_and_info(self, domain, codomain,fft_fftw_context,**kwargs):
def compute_plan_and_info(self, domain, codomain, fft_fftw_context,
**kwargs):
self.input_dtype = 'complex128'
self.output_dtype = 'complex128'
self.global_input_shape = domain.dim(split=True)
self.global_output_shape = codomain.dim(split=True)
self.global_input_shape = domain.shape()
self.global_output_shape = codomain.shape()
self.fftw_local_size = pyfftw.local_size(self.global_input_shape)
self.in_zero_centered_dimensions = domain.zerocenter()[::-1]
self.out_zero_centered_dimensions = codomain.zerocenter()[::-1]
self.in_zero_centered_dimensions = domain.paradict['zerocenter']
self.out_zero_centered_dimensions = codomain.paradict['zerocenter']
self.local_node_dimensions = np.append((self.fftw_local_size[1],),self.global_input_shape[1:])
self.local_node_dimensions = np.append((self.fftw_local_size[1],),
self.global_input_shape[1:])
self.offsetQ = self.fftw_local_size[2]%2
if codomain.fourier == True:
......@@ -147,10 +150,12 @@ if fft_machine == 'pyfftw':
## to a certain set of (field_val, domain, codomain) sets.
self.plan_dict = {}
## initialize the dictionary which stores the values from get_centering_mask
## initialize the dictionary which stores the values from
## get_centering_mask
self.centering_mask_dict = {}
def get_centering_mask(self, to_center_input, dimensions_input, offset_input=0):
def get_centering_mask(self, to_center_input, dimensions_input,
offset_input=0):
"""
Computes the mask, used to (de-)zerocenter domain and target
fields.
......@@ -177,7 +182,8 @@ if fft_machine == 'pyfftw':
to_center = np.array(to_center_input)
dimensions = np.array(dimensions_input)
if np.all(dimensions == np.array(1)) or np.all(dimensions == np.array([1])):
if np.all(dimensions == np.array(1)) or \
np.all(dimensions == np.array([1])):
return dimensions
## The dimensions of size 1 must be sorted out for computing the
## centering_mask. The depth of the array will be restored in the
......@@ -199,7 +205,8 @@ if fft_machine == 'pyfftw':
offset[0] = int(offset_input)
## check for dimension match
if to_center.size != dimensions.size:
raise TypeError('The length of the supplied lists does not match.')
raise TypeError(\
'The length of the supplied lists does not match.')
## build up the value memory
## compute an identifier for the parameter set
......@@ -207,7 +214,13 @@ if fft_machine == 'pyfftw':
if not temp_id in self.centering_mask_dict:
## use np.tile in order to stack the core alternation scheme
## until the desired format is constructed.
core = np.fromfunction(lambda *args : (-1)**(np.tensordot(to_center,args+offset.reshape(offset.shape+(1,)*(np.array(args).ndim-1)),1)), (2,)*to_center.size)
core = np.fromfunction(
lambda *args : (-1)**\
(np.tensordot(to_center,args + \
offset.reshape(offset.shape + \
(1,)*(np.array(args).ndim - 1)),1)),\
(2,)*to_center.size)
centering_mask = np.tile(core,dimensions//2)
## for the dimensions of odd size corresponding slices must be added
for i in range(centering_mask.ndim):
......@@ -215,9 +228,11 @@ if fft_machine == 'pyfftw':
if (dimensions%2)[i]==0:
continue
## prepare the slice object
temp_slice=(slice(None),)*i + (slice(-2,-1,1),) + (slice(None),)*(centering_mask.ndim -1 - i)
temp_slice = (slice(None),)*i + (slice(-2,-1,1),) +\
(slice(None),)*(centering_mask.ndim -1 - i)
## append the slice to the centering_mask
centering_mask = np.append(centering_mask,centering_mask[temp_slice],axis=i)
centering_mask = np.append(centering_mask,
centering_mask[temp_slice],axis=i)
## Add depth to the centering_mask where the length of a
## dimension was one
temp_slice = ()
......@@ -236,16 +251,17 @@ if fft_machine == 'pyfftw':
temp_id = (domain.__identifier__(), codomain.__identifier__())
## generate the plan_and_info object if not already there
if not temp_id in self.plan_dict:
self.plan_dict[temp_id]=_fftw_plan_and_info(domain,codomain,self,**kwargs)
self.plan_dict[temp_id]=_fftw_plan_and_info(domain, codomain,
self, **kwargs)
return self.plan_dict[temp_id]
def transform(self,val,domain,codomain, field_val, **kwargs):
def transform(self, val, domain, codomain, **kwargs):
"""
The pyfftw transform function.
Parameters
----------
field_val : distributed_data_object
val : distributed_data_object or numpy.ndarray
The value-array of the field which is supposed to
be transformed.
......@@ -263,32 +279,72 @@ if fft_machine == 'pyfftw':
result : np.ndarray
Fourier-transformed pendant of the input field.
"""
current_plan_and_info=self._get_plan_and_info(domain,codomain,**kwargs)
## Prepare the input data
current_plan_and_info=self._get_plan_and_info(domain, codomain,
**kwargs)
## Prepare the environment variables
local_size = current_plan_and_info.fftw_local_size
local_start = local_size[2]
local_end = local_start + local_size[1]
val = field_val.get_data(slice(local_start,local_end))
val *= current_plan_and_info.get_codomain_centering_mask()
## Prepare the input data
## Case 1: val is a distributed_data_object
if isinstance(val, distributed_data_object):
return_val = val.copy_empty(global_shape =\
tuple(current_plan_and_info.global_output_shape),
dtype = np.complex128)
## If the distribution strategy of the d2o is fftw, extract
## the data directly
if val.distribution_strategy == 'fftw':
local_val = val.get_local_data()
else:
local_val = val.get_data(slice(local_start, local_end))
## Case 2: val is a numpy array carrying the full data
else:
local_val = val[slice(local_start, local_end)]
local_val *= current_plan_and_info.get_codomain_centering_mask()
## Define a abbreviation for the fftw plan
p = current_plan_and_info.get_plan()
## load the field into the plan
if p.has_input:
p.input_array[:] = val
p.input_array[:] = local_val
## execute the plan
p()
result = p.output_array*current_plan_and_info.get_domain_centering_mask()
result = p.output_array * current_plan_and_info.\
get_domain_centering_mask()
## renorm the result according to the convention of gfft
if current_plan_and_info.direction == 'FFTW_FORWARD':
result = result/float(result.size)
else:
result *= float(result.size)
## build a distributed_data_object
data_object = nifty_mpi_data.distributed_data_object(global_shape = tuple(current_plan_and_info.global_output_shape), dtype = np.complex128, distribution_strategy='fftw')
data_object.set_local_data(data=result)
return data_object.get_full_data()
## build the return object according to the input val
try:
if return_val.distribution_strategy == 'fftw':
return_val.set_local_data(data = result)
else:
return_val.set_data(data = result,
key = slice(local_start, local_end))
## If the values living in domain are purely real, the
## result of the fft is hermitian
if domain.paradict['complexity'] == 0:
return_val.hermitian = True
## In case the input val was not a distributed data obect, the try
## will produce a NameError
except(NameError):
return_val = distributed_data_object(
global_shape =\
tuple(current_plan_and_info.global_output_shape),
dtype = np.complex128,
distribution_strategy='fftw')
return_val.set_local_data(data = result)
return_val = return_val.get_full_data()
return return_val
......@@ -309,7 +365,7 @@ elif fft_machine == 'gfft' or 'gfft_fallback':
Parameters
----------
val : numpy.ndarray
val : numpy.ndarray or distributed_data_object
The value-array of the field which is supposed to
be transformed.
......@@ -332,9 +388,43 @@ elif fft_machine == 'gfft' or 'gfft_fallback':
ftmachine = "fft"
else:
ftmachine = "ifft"
## if the input is a distributed_data_object, extract the data
if isinstance(val, distributed_data_object):
d2oQ = True
val = val.get_full_data()
## transform and return
if(domain.datatype==np.float64):
return gfft.gfft(val.astype(np.complex128),in_ax=[],out_ax=[],ftmachine=ftmachine,in_zero_center=domain.para[-naxes:].astype(np.bool).tolist(),out_zero_center=codomain.para[-naxes:].astype(np.bool).tolist(),enforce_hermitian_symmetry=bool(codomain.para[naxes]==1),W=-1,alpha=-1,verbose=False)
temp = gfft.gfft(val.astype(np.complex128),
in_ax=[],
out_ax=[],
ftmachine=ftmachine,
in_zero_center=domain.para[-naxes:].\
astype(np.bool).tolist(),
out_zero_center=codomain.para[-naxes:].\
astype(np.bool).tolist(),
enforce_hermitian_symmetry = \
bool(codomain.para[naxes]==1),
W=-1,
alpha=-1,
verbose=False)
else:
return gfft.gfft(val,in_ax=[],out_ax=[],ftmachine=ftmachine,in_zero_center=domain.para[-naxes:].astype(np.bool).tolist(),out_zero_center=codomain.para[-naxes:].astype(np.bool).tolist(),enforce_hermitian_symmetry=bool(codomain.para[naxes]==1),W=-1,alpha=-1,verbose=False)
\ No newline at end of file
temp = gfft.gfft(val,
in_ax=[],
out_ax=[],
ftmachine=ftmachine,
in_zero_center=domain.para[-naxes:].\
astype(np.bool).tolist(),
out_zero_center=codomain.para[-naxes:].\
astype(np.bool).tolist(),
enforce_hermitian_symmetry = \
bool(codomain.para[naxes]==1),
W=-1,
alpha=-1,
verbose=False)
if d2oQ == True:
val.set_full_data(temp)
else:
val = temp
return val
\ No newline at end of file
rg/nifty_rg.py
View file @ d8f0ba70
This diff is collapsed.
rg/powerspectrum.py
View file @ d8f0ba70
......@@ -438,6 +438,8 @@ class power_indices(object):
else:
k = kindex
dk = np.max(k[2:]-k[1:-1]) ## minimal dk
print ('k', k)
print ('dk', dk)
if(nbin is None):
nbin = int((k[-1]-0.5*(k[2]+k[1]))/dk-0.5) ## maximal nbin
else:
......@@ -446,6 +448,7 @@ class power_indices(object):
binbounds = np.r_[0.5*(3*k[1]-k[2]),0.5*(k[1]+k[2])+dk*np.arange(nbin-2)]
if(log):
binbounds = np.exp(binbounds)
print nbin
## reordering
reorder = np.searchsorted(binbounds,kindex)
rho_ = np.zeros(len(binbounds)+1,dtype=rho.dtype)
......@@ -475,7 +478,7 @@ if __name__ == '__main__':
comm = MPI.COMM_WORLD
rank = comm.rank
size = comm.size
p = power_indices((4,4),(1,1), zerocentered=(True,True), nbin = 4)
p = power_indices((4,4),(1,1), zerocentered=(True,True), nbin = 5)
"""
obj = p.default_indices['nkdict']
for i in np.arange(size):
......@@ -1062,13 +1065,14 @@ def nhermitianize_fast(field,zerocentered,special=False):
index = tuple(ii*maxindex)
field[index] *= np.sqrt(0.5)
else: ## regular case
field = 0.5*(field+dummy)
#field = 0.5*(field+dummy)
field = dummy
## reshift zerocentered axes
if(np.any(zerocentered==True)):
field = np.fft.fftshift(field,axes=shiftaxes(zerocentered))
return field