def convert_2d_to_1d(pwrspec2d_product, logbins=True,
bins=None, transfer=None):
"""if bins is not given, just use the x axis"""
nxbins = len(pwrspec2d_product['bin_x_center'])
bins_kx = np.zeros(nxbins + 1)
bins_kx[0: -1] = pwrspec2d_product['bin_x_left']
bins_kx[-1] = pwrspec2d_product['bin_x_right'][-1]
nybins = len(pwrspec2d_product['bin_y_center'])
bins_ky = np.zeros(nybins + 1)
bins_ky[0: -1] = pwrspec2d_product['bin_y_left']
bins_ky[-1] = pwrspec2d_product['bin_y_right'][-1]
if bins is None:
bins = bins_kx
entry = {}
(entry['counts_histo'], entry['binavg']) = \
convert_2d_to_1d_driver(pwrspec2d_product['binavg'],
pwrspec2d_product['counts_histo'],
pwrspec2d_product['bin_x_center'],
pwrspec2d_product['bin_y_center'],
bins, transfer=transfer)
bin_left, bin_center, bin_right = binning.bin_edges(bins, log=logbins)
entry['bin_left'] = bin_left
entry['bin_center'] = bin_center
entry['bin_right'] = bin_right
return entry
def convert_2d_to_1d(pwrspec2d_product,
logbins=True,
bins=None,
transfer=None):
"""if bins is not given, just use the x axis"""
nxbins = len(pwrspec2d_product['bin_x_center'])
bins_kx = np.zeros(nxbins + 1)
bins_kx[0:-1] = pwrspec2d_product['bin_x_left']
bins_kx[-1] = pwrspec2d_product['bin_x_right'][-1]
nybins = len(pwrspec2d_product['bin_y_center'])
bins_ky = np.zeros(nybins + 1)
bins_ky[0:-1] = pwrspec2d_product['bin_y_left']
bins_ky[-1] = pwrspec2d_product['bin_y_right'][-1]
if bins is None:
bins = bins_kx
entry = {}
(entry['counts_histo'], entry['binavg']) = \
convert_2d_to_1d_driver(pwrspec2d_product['binavg'],
pwrspec2d_product['counts_histo'],
pwrspec2d_product['bin_x_center'],
pwrspec2d_product['bin_y_center'],
bins, transfer=transfer)
bin_left, bin_center, bin_right = binning.bin_edges(bins, log=logbins)
entry['bin_left'] = bin_left
entry['bin_center'] = bin_center
entry['bin_right'] = bin_right
return entry
def convert_2d_to_1d(self, logbins=True,
bins=None, weights_2d=None):
r"""bin the 2D powers onto 1D (the _from_2d variables)
optionally, weights for each treatment class can be given
"""
bins_1d = np.zeros(self.num_k_1d + 1)
bins_1d[0: -1] = self.k_1d["left"]
bins_1d[-1] = self.k_1d["right"][-1]
if bins is None:
bins = bins_1d
if weights_2d is not None:
print "using 2d weights for 2d->1d"
for treatment in self.treatment_cases:
if weights_2d is not None:
weights_2d_treat = weights_2d[treatment]
else:
weights_2d_treat = None
for comb in self.comb_cases:
pwrcase = "%s:%s" % (comb, treatment)
(self.counts_1d_from_2d[pwrcase], \
self.gauss_errors_1d_from_2d[pwrcase], \
self.pwrspec_1d_from_2d[pwrcase]) = \
pe.convert_2d_to_1d_pwrspec(
self.pwrspec_2d[pwrcase],
self.counts_2d[pwrcase],
self.kx_2d["center"],
self.ky_2d["center"],
bins,
weights_2d=weights_2d_treat)
bin_left, bin_center, bin_right = binning.bin_edges(bins, log=logbins)
self.k_1d_from_2d["left"] = bin_left
self.k_1d_from_2d["center"] = bin_center
self.k_1d_from_2d["right"] = bin_right
self.num_k_1d_from_2d = bin_center.shape[0]
def calculate_xspec(cube1, cube2, weight1, weight2,
window="blackman", unitless=True, bins=None,
truncate=False, nbins=40, logbins=True, return_3d=False):
print "finding the signal power spectrum"
pwrspec3d_signal = cross_power_est(cube1, cube2, weight1, weight2,
window=window)
radius_arr = binning.radius_array(pwrspec3d_signal)
# find the k_perp by not including k_nu in the distance
radius_arr_perp = binning.radius_array(pwrspec3d_signal,
zero_axes=[0])
# find the k_perp by not including k_RA,Dec in the distance
radius_arr_parallel = binning.radius_array(pwrspec3d_signal,
zero_axes=[1, 2])
if bins is None:
bins = binning.suggest_bins(pwrspec3d_signal,
truncate=truncate,
logbins=logbins,
nbins=nbins,
radius_arr=radius_arr)
if unitless:
print "making the power spectrum unitless"
pwrspec3d_signal = make_unitless(pwrspec3d_signal,
radius_arr=radius_arr)
print "calculating the 2D histogram"
# TODO: do better independent binning; for now:
bins_x = copy.deepcopy(bins)
bins_y = copy.deepcopy(bins)
counts_histo_2d, binavg_2d = binning.bin_an_array_2d(pwrspec3d_signal,
radius_arr_perp,
radius_arr_parallel,
bins_x, bins_y)
print "calculating the 1D histogram"
counts_histo, binavg = binning.bin_an_array(pwrspec3d_signal, bins,
radius_arr=radius_arr)
bin_left_x, bin_center_x, bin_right_x = binning.bin_edges(bins_x,
log=logbins)
bin_left_y, bin_center_y, bin_right_y = binning.bin_edges(bins_y,
log=logbins)
bin_left, bin_center, bin_right = binning.bin_edges(bins, log=logbins)
pwrspec2d_product = {}
pwrspec2d_product['bin_x_left'] = bin_left_x
pwrspec2d_product['bin_x_center'] = bin_center_x
pwrspec2d_product['bin_x_right'] = bin_right_x
pwrspec2d_product['bin_y_left'] = bin_left_y
pwrspec2d_product['bin_y_center'] = bin_center_y
pwrspec2d_product['bin_y_right'] = bin_right_y
pwrspec2d_product['counts_histo'] = counts_histo_2d
pwrspec2d_product['binavg'] = binavg_2d
pwrspec1d_product = {}
pwrspec1d_product['bin_left'] = bin_left
pwrspec1d_product['bin_center'] = bin_center
pwrspec1d_product['bin_right'] = bin_right
pwrspec1d_product['counts_histo'] = counts_histo
pwrspec1d_product['binavg'] = binavg
if not return_3d:
return pwrspec2d_product, pwrspec1d_product
else:
return pwrspec3d_signal, pwrspec2d_product, pwrspec1d_product
def calculate_xspec(cube1, cube2, weight1, weight2,
window="blackman", unitless=True, bins=None,
truncate=False, nbins=40, logbins=True, return_3d=False):
print "finding the signal power spectrum"
pwrspec3d_signal = cross_power_est(cube1, cube2, weight1, weight2,
window=window)
radius_arr = binning.radius_array(pwrspec3d_signal)
if bins is None:
bins = binning.suggest_bins(pwrspec3d_signal,
truncate=truncate,
logbins=logbins,
nbins=nbins,
radius_arr=radius_arr)
if unitless:
print "making the power spectrum unitless"
pwrspec3d_signal = make_unitless(pwrspec3d_signal,
radius_arr=radius_arr)
print "calculating the 1D histogram"
counts_histo, binavg = binning.bin_an_array(pwrspec3d_signal, bins,
radius_arr=radius_arr)
del radius_arr
gc.collect()
print "calculating the 2D histogram"
# find the k_perp by not including k_nu in the distance
radius_arr_perp = binning.radius_array(pwrspec3d_signal,
zero_axes=[0])
# find the k_perp by not including k_RA,Dec in the distance
radius_arr_parallel = binning.radius_array(pwrspec3d_signal,
zero_axes=[1, 2])
# TODO: do better independent binning; for now:
bins_x = copy.deepcopy(bins)
bins_y = copy.deepcopy(bins)
counts_histo_2d, binavg_2d = binning.bin_an_array_2d(pwrspec3d_signal,
radius_arr_perp,
radius_arr_parallel,
bins_x, bins_y)
del radius_arr_perp
del radius_arr_parallel
gc.collect()
bin_left_x, bin_center_x, bin_right_x = binning.bin_edges(bins_x,
log=logbins)
bin_left_y, bin_center_y, bin_right_y = binning.bin_edges(bins_y,
log=logbins)
bin_left, bin_center, bin_right = binning.bin_edges(bins, log=logbins)
pwrspec2d_product = {}
pwrspec2d_product['bin_x_left'] = bin_left_x
pwrspec2d_product['bin_x_center'] = bin_center_x
pwrspec2d_product['bin_x_right'] = bin_right_x
pwrspec2d_product['bin_y_left'] = bin_left_y
pwrspec2d_product['bin_y_center'] = bin_center_y
pwrspec2d_product['bin_y_right'] = bin_right_y
pwrspec2d_product['counts_histo'] = counts_histo_2d
pwrspec2d_product['binavg'] = binavg_2d
pwrspec1d_product = {}
pwrspec1d_product['bin_left'] = bin_left
pwrspec1d_product['bin_center'] = bin_center
pwrspec1d_product['bin_right'] = bin_right
pwrspec1d_product['counts_histo'] = counts_histo
pwrspec1d_product['binavg'] = binavg
if not return_3d:
return pwrspec2d_product, pwrspec1d_product
else:
return pwrspec3d_signal, pwrspec2d_product, pwrspec1d_product
