def make_physical(self, refinement=1, pad=5, order=2, clear_obs=True):
r"""Project the maps and weights into physical coordinates
refinement allows the physical bins to be e.g. =2 times finer
pad puts a number of padded pixels on all sides of the physical vol.
the clear_obs flag deletes the obs. coord maps
"""
self.phys_map1 = bh.repackage_kiyo(
pg.physical_grid(self.map1,
refinement=refinement,
pad=pad,
order=order))
self.phys_map2 = bh.repackage_kiyo(
pg.physical_grid(self.map2,
refinement=refinement,
pad=pad,
order=order))
self.phys_noise_inv1 = bh.repackage_kiyo(
pg.physical_grid(self.noise_inv1,
refinement=refinement,
pad=pad,
order=order))
self.phys_noise_inv2 = bh.repackage_kiyo(
pg.physical_grid(self.noise_inv2,
refinement=refinement,
pad=pad,
order=order))
del self.map1, self.map2, self.noise_inv1, self.noise_inv2
gc.collect()
return
def make_physical(self, refinement=1, pad=5, order=2, clear_obs=True):
r"""Project the maps and weights into physical coordinates
refinement allows the physical bins to be e.g. =2 times finer
pad puts a number of padded pixels on all sides of the physical vol.
the clear_obs flag deletes the obs. coord maps
"""
self.phys_map1 = bh.repackage_kiyo(pg.physical_grid(self.map1,
refinement=refinement,
pad=pad, order=order))
self.phys_map2 = bh.repackage_kiyo(pg.physical_grid(self.map2,
refinement=refinement,
pad=pad, order=order))
self.phys_noise_inv1 = bh.repackage_kiyo(pg.physical_grid(
self.noise_inv1,
refinement=refinement,
pad=pad, order=order))
self.phys_noise_inv2 = bh.repackage_kiyo(pg.physical_grid(
self.noise_inv2,
refinement=refinement,
pad=pad, order=order))
del self.map1, self.map2, self.noise_inv1, self.noise_inv2
gc.collect()
return
def find_avg_fsky(map_key, tack_on=None, refinement=2, pad=5, order=1):
"""Take all the pairs that enter the autopower, open their weight files and
find the fsky for each data treatment
In a pipeline
fsky = find_avg_fsky(self.params["map_key"],
tack_on=self.params["tack_on"],
refinement=self.params["refinement"],
pad=self.params["pad"],
order=self.params["order"])
"""
fsky = {}
datapath_db = dp.DataPath()
map_cases = datapath_db.fileset_cases(map_key, "pair;type;treatment")
# This code is essentially verbatim for the permutation in the real
# autopower
unique_pairs = dp.GBTauto_cross_pairs(map_cases['pair'],
map_cases['pair'],
cross_sym="_with_")
treatment_list = map_cases['treatment']
for treatment in treatment_list:
for item in unique_pairs:
dbkeydict = {}
mapset0 = (map_key, item[0], treatment)
mapset1 = (map_key, item[1], treatment)
dbkeydict['noiseinv1_key'] = "%s:%s;noise_inv;%s" % mapset0
dbkeydict['noiseinv2_key'] = "%s:%s;noise_inv;%s" % mapset1
files = dp.convert_dbkeydict_to_filedict(dbkeydict,
datapath_db=datapath_db,
tack_on=tack_on)
print files['noiseinv1_key'], files['noiseinv2_key']
weight1 = algebra.make_vect(algebra.load(files['noiseinv1_key']))
weight2 = algebra.make_vect(algebra.load(files['noiseinv2_key']))
physweight1 = bh.repackage_kiyo(
pg.physical_grid(weight1,
refinement=refinement,
pad=pad,
order=order))
physweight2 = bh.repackage_kiyo(
pg.physical_grid(weight2,
refinement=refinement,
pad=pad,
order=order))
#fsky = np.sum(physweight1 * physweight2)**2
#fsky /= np.sum(physweight1**2 * physweight2**2)
#fsky /= float(physweight1.size)
fsky = np.sum(weight1 * weight2)**2
fsky /= np.sum(weight1**2 * weight2**2)
fsky /= float(weight1.size)
print "volume factor in noise weight: ", fsky
return fsky
def wrap_find_weight(filename, regenerate=False):
if regenerate:
retval = find_weight(filename)
else:
retval = memoize_find_weight(filename)
return batch_handler.repackage_kiyo(retval)
def wrap_find_weight(filename, regenerate=False, calc_diagnal=False):
if not calc_diagnal:
if regenerate:
retval = find_weight(filename)
else:
retval = memoize_find_weight(filename)
else:
if regenerate:
retval = find_weight_re_diagnal(filename)
else:
retval = memoize_find_weight_re_diagnal(filename)
return batch_handler.repackage_kiyo(retval)
def get_cached_physical(filename, refinement=2, pad=5, order=1):
basename = filename.split(".")[0]
phys_cachename = basename + "_physical.npy"
chksum_cachename = basename + ".md5"
print phys_cachename, chksum_cachename
curr_chksum = ft.hashfile(filename)
# ALSO CHECK IF THE PARAMS CHANGED!
# try to get an existing checksum
try:
chkfile = open(chksum_cachename, "r")
old_chksum = chkfile.read()
chkfile.close()
if old_chksum == curr_chksum:
chksum_not_changed = True
except IOError as e:
chksum_not_changed = False
if os.path.isfile(phys_cachename) and chksum_not_changed:
print "using the cached file: " + phys_cachename
ret_data = algebra.make_vect(algebra.load(chksum_cachename))
else:
print "writing a physical cache for: " + filename
# calculate the physical coordinate box
obs_map = algebra.make_vect(algebra.load(filename))
ret_data = bh.repackage_kiyo(pg.physical_grid(obs_map,
refinement=refinement,
pad=pad, order=order))
algebra.save(chksum_cachename, ret_data)
# save the new checksum
chkfile = open(chksum_cachename, "w")
chkfile.write(curr_chksum)
chkfile.close()
return ret_data
def calculate_mixing(weight_file1, weight_file2, bins, xspec_fileout,
mixing_fileout,
unitless=False, refinement=2, pad=5, order=1,
window='blackman', zero_pad=False, identity_test=False):
print "loading the weights and converting to physical coordinates"
weight1_obs = algebra.make_vect(algebra.load(weight_file1))
weight1 = bh.repackage_kiyo(pg.physical_grid(
weight1_obs,
refinement=refinement,
pad=pad, order=order))
weight2_obs = algebra.make_vect(algebra.load(weight_file2))
weight2 = bh.repackage_kiyo(pg.physical_grid(
weight2_obs,
refinement=refinement,
pad=pad, order=order))
if window:
window_function = fftutil.window_nd(weight1.shape, name=window)
weight1 *= window_function
weight2 *= window_function
print "calculating the cross-power of the spatial weighting functions"
arr1 = algebra.ones_like(weight1)
arr2 = algebra.ones_like(weight2)
# no window applied here (applied above)
xspec = pe.cross_power_est(weight1, weight2, arr1, arr2,
window=None, nonorm=True)
# for each point in the cube, find |k|, k_perp, k_parallel
# TODO: speed this up by using one direct numpy call (not limiting)
k_mag_arr = binning.radius_array(xspec)
k_perp_arr = binning.radius_array(xspec, zero_axes=[0])
k_parallel_arr = binning.radius_array(xspec, zero_axes=[1, 2])
if unitless:
xspec = pe.make_unitless(xspec, radius_arr=k_mag_arr)
# NOTE: assuming lowest k bin has only one point in 3D k-space
# could make this floor of dimensions divided by 2 also
center_3d = np.transpose(np.transpose(np.where(k_mag_arr == 0.))[0])
# In the estimator, we devide by 1/sum(w1 * w2) to get most of the effect
# of the weighing. The mixing matrix here can be thought of as a correction
# that that diagonal-only estimate.
leakage_ratio = xspec[center_3d[0], center_3d[1], center_3d[2]] / \
np.sum(weight1 * weight2)
print "power leakage ratio: %10.5g" % leakage_ratio
xspec /= np.sum(weight1 * weight2)
print "partitioning the 3D kspace up into the 2D k bins"
(kflat, ret_indices) = bin_indices_2d(k_perp_arr, k_parallel_arr,
bins, bins)
# perform a test where the window function is a delta function at the
# origin so that the mixing matrix is identity
if identity_test:
xspec = algebra.zeros_like(xspec)
xspec[center_3d[0], center_3d[1], center_3d[2]] = 1.
# now save the window cross-power for downstream pooled users
algebra.save(xspec_fileout, xspec)
runlist = []
for bin_index in range(kflat.shape[0]):
bin_3d = ret_indices[repr(bin_index)]
if bin_3d is not None:
runlist.append((xspec_fileout, bin_index, bins, bin_3d, center_3d))
pool = multiprocessing.Pool(processes=(multiprocessing.cpu_count() - 4))
# the longest runs get pushed to the end; randomize for better job packing
random.shuffle(runlist)
results = pool.map(sum_window, runlist)
#gnuplot_single_slice(runlist[0]) # for troubleshooting
# now save the results for post-processing
params = {"unitless": unitless, "refinement": refinement, "pad": pad,
"order": order, "window": window, "zero_pad": zero_pad,
"identity_test": identity_test, "weight_file1": weight_file1,
"weight_file2": weight_file2, "bins": bins}
outshelve = shelve.open(mixing_fileout, "n")
outshelve["params"] = params # parameters for this run
outshelve["weight1"] = weight1 # weight map 1
outshelve["weight2"] = weight2 # weight map 2
outshelve["xspec"] = xspec # copy of the weight spectra
outshelve["kflat"] = kflat # 2D k bin vector
outshelve["bins_3d"] = ret_indices # indices to k3d for a 2d k bin
outshelve["results"] = results # mixing matrix columns
outshelve.close()
def calculate_mixing(weight_file1,
weight_file2,
bins,
xspec_fileout,
mixing_fileout,
unitless=False,
refinement=2,
pad=5,
order=1,
window='blackman',
zero_pad=False,
identity_test=False):
print "loading the weights and converting to physical coordinates"
weight1_obs = algebra.make_vect(algebra.load(weight_file1))
weight1 = bh.repackage_kiyo(
pg.physical_grid(weight1_obs,
refinement=refinement,
pad=pad,
order=order))
weight2_obs = algebra.make_vect(algebra.load(weight_file2))
weight2 = bh.repackage_kiyo(
pg.physical_grid(weight2_obs,
refinement=refinement,
pad=pad,
order=order))
if window:
window_function = fftutil.window_nd(weight1.shape, name=window)
weight1 *= window_function
weight2 *= window_function
print "calculating the cross-power of the spatial weighting functions"
arr1 = algebra.ones_like(weight1)
arr2 = algebra.ones_like(weight2)
# no window applied here (applied above)
xspec = pe.cross_power_est(weight1,
weight2,
arr1,
arr2,
window=None,
nonorm=True)
# for each point in the cube, find |k|, k_perp, k_parallel
# TODO: speed this up by using one direct numpy call (not limiting)
k_mag_arr = binning.radius_array(xspec)
k_perp_arr = binning.radius_array(xspec, zero_axes=[0])
k_parallel_arr = binning.radius_array(xspec, zero_axes=[1, 2])
if unitless:
xspec = pe.make_unitless(xspec, radius_arr=k_mag_arr)
# NOTE: assuming lowest k bin has only one point in 3D k-space
# could make this floor of dimensions divided by 2 also
center_3d = np.transpose(np.transpose(np.where(k_mag_arr == 0.))[0])
# In the estimator, we devide by 1/sum(w1 * w2) to get most of the effect
# of the weighing. The mixing matrix here can be thought of as a correction
# that that diagonal-only estimate.
leakage_ratio = xspec[center_3d[0], center_3d[1], center_3d[2]] / \
np.sum(weight1 * weight2)
print "power leakage ratio: %10.5g" % leakage_ratio
xspec /= np.sum(weight1 * weight2)
print "partitioning the 3D kspace up into the 2D k bins"
(kflat, ret_indices) = bin_indices_2d(k_perp_arr, k_parallel_arr, bins,
bins)
# perform a test where the window function is a delta function at the
# origin so that the mixing matrix is identity
if identity_test:
xspec = algebra.zeros_like(xspec)
xspec[center_3d[0], center_3d[1], center_3d[2]] = 1.
# now save the window cross-power for downstream pooled users
algebra.save(xspec_fileout, xspec)
runlist = []
for bin_index in range(kflat.shape[0]):
bin_3d = ret_indices[repr(bin_index)]
if bin_3d is not None:
runlist.append((xspec_fileout, bin_index, bins, bin_3d, center_3d))
pool = multiprocessing.Pool(processes=(multiprocessing.cpu_count() - 4))
# the longest runs get pushed to the end; randomize for better job packing
random.shuffle(runlist)
results = pool.map(sum_window, runlist)
#gnuplot_single_slice(runlist[0]) # for troubleshooting
# now save the results for post-processing
params = {
"unitless": unitless,
"refinement": refinement,
"pad": pad,
"order": order,
"window": window,
"zero_pad": zero_pad,
"identity_test": identity_test,
"weight_file1": weight_file1,
"weight_file2": weight_file2,
"bins": bins
}
outshelve = shelve.open(mixing_fileout, "n")
outshelve["params"] = params # parameters for this run
outshelve["weight1"] = weight1 # weight map 1
outshelve["weight2"] = weight2 # weight map 2
outshelve["xspec"] = xspec # copy of the weight spectra
outshelve["kflat"] = kflat # 2D k bin vector
outshelve["bins_3d"] = ret_indices # indices to k3d for a 2d k bin
outshelve["results"] = results # mixing matrix columns
outshelve.close()
