diff --git a/abacusnbody/analysis/power_spectrum.py b/abacusnbody/analysis/power_spectrum.py index c44b73d1..918162cc 100644 --- a/abacusnbody/analysis/power_spectrum.py +++ b/abacusnbody/analysis/power_spectrum.py @@ -9,7 +9,6 @@ import numpy as np import numba -import asdf from astropy.table import Table from scipy.fft import rfftn, irfftn, fftfreq @@ -17,11 +16,11 @@ from .cic import cic_serial -__all__ = ['pk_to_xi', - 'calc_power', +__all__ = ['calc_power', + 'calc_pk_from_deltak', + 'pk_to_xi', 'project_3d_to_poles', 'get_k_mu_edges', - 'calc_pk_from_deltak', ] MAX_THREADS = numba.config.NUMBA_NUM_THREADS @@ -129,8 +128,8 @@ def P_n(x, n, dtype=np.float32): @numba.njit(parallel=True, fastmath=True) -def bin_kmu(n1d, L, kedges, Nmu, weights, poles=np.empty(0, 'i8'), dtype=np.float32, - space='fourier', nthread=MAX_THREADS, +def bin_kmu(n1d, L, kedges, muedges, weights, poles=np.empty(0, 'i8'), dtype=np.float32, + fourier=True, nthread=MAX_THREADS, ): r""" Compute mean and count modes in (k,mu) bins for a 3D rfft mesh of shape (n1d, n1d, n1d//2+1) @@ -150,17 +149,17 @@ def bin_kmu(n1d, L, kedges, Nmu, weights, poles=np.empty(0, 'i8'), dtype=np.floa box size of the simulation. kedges : array_like edges of the k wavenumbers or r separation bins. - Nmu : int - number of bins of mu, which ranges from 0 to 1. + muedges : array_like + edges of the mu bins. mu ranges from 0 to 1. weights : array_like array of shape (n1d, n1d, n1d//2+1) containing the power spectrum modes. poles : array_like Legendre multipoles of the power spectrum or correlation function. dtype : np.dtype float type (32 or 64) to use in calculations. - space : str - options are Fourier space, `fourier`, which computes power spectrum, - or configuration-space, `real`, which computes the correlation function. + fourier : bool + options are Fourier space, True, which computes power spectrum, + or configuration-space, False, which computes the correlation function. nthread : int, optional Number of numba threads to use @@ -174,18 +173,21 @@ def bin_kmu(n1d, L, kedges, Nmu, weights, poles=np.empty(0, 'i8'), dtype=np.floa mean power spectrum per k for each Legendre multipole. counts_poles : ndarray of int number of modes per k. + weighted_counts_k : ndarray of float + mean wavenumber per (k, mu) wedge. """ numba.set_num_threads(nthread) kzlen = n1d//2 + 1 Nk = len(kedges) - 1 - if space == 'fourier': + Nmu = len(muedges) - 1 + if fourier: dk = 2.*np.pi / L - elif space == 'real': + else: dk = L / n1d kedges2 = ((kedges/dk)**2).astype(dtype) - muedges2 = (np.linspace(0., 1., Nmu+1)**2).astype(dtype) + muedges2 = (muedges**2).astype(dtype) nthread = numba.get_num_threads() counts = np.zeros((nthread, Nk, Nmu), dtype=np.int64) @@ -196,6 +198,7 @@ def bin_kmu(n1d, L, kedges, Nmu, weights, poles=np.empty(0, 'i8'), dtype=np.floa else: poles = poles.astype(np.int64) weighted_counts_poles = np.zeros((nthread, len(poles), Nk), dtype=dtype) + weighted_counts_k = np.zeros((nthread, Nk, Nmu), dtype=dtype) # Loop over all k vectors for i in numba.prange(n1d): @@ -226,20 +229,24 @@ def bin_kmu(n1d, L, kedges, Nmu, weights, poles=np.empty(0, 'i8'), dtype=np.floa counts[tid, bk, bmu] += 1 if k == 0 else 2 weighted_counts[tid, bk, bmu] += weights[i, j, k] if k == 0 else dtype(2.)*weights[i, j, k] + weighted_counts_k[tid, bk, bmu] += np.sqrt(kmag2)*dk if k == 0 else dtype(2.)*np.sqrt(kmag2)*dk if Np > 0: for ip in range(len(poles)): pole = poles[ip] - if pole == 0: - weighted_counts_poles[tid, ip, bk] += weights[i, j, k] if k == 0 else dtype(2.)*weights[i, j, k] - else: + if pole != 0: pw = dtype(2*pole + 1)*P_n(mu2, pole) weighted_counts_poles[tid, ip, bk] += weights[i, j, k]*pw if k == 0 else dtype(2.)*weights[i, j, k]*pw counts = counts.sum(axis=0) weighted_counts = weighted_counts.sum(axis=0) weighted_counts_poles = weighted_counts_poles.sum(axis=0) + weighted_counts_k = weighted_counts_k.sum(axis=0) counts_poles = counts.sum(axis=1) + for ip,pole in enumerate(poles): + if pole == 0: + weighted_counts_poles[ip] = weighted_counts.sum(axis=1) + for i in range(Nk): if Np > 0: if counts_poles[i] != 0: @@ -247,8 +254,110 @@ def bin_kmu(n1d, L, kedges, Nmu, weights, poles=np.empty(0, 'i8'), dtype=np.floa for j in range(Nmu): if counts[i, j] != 0: weighted_counts[i, j] /= dtype(counts[i, j]) - return weighted_counts, counts, weighted_counts_poles, counts_poles + weighted_counts_k[i, j] /= dtype(counts[i, j]) + return weighted_counts, counts, weighted_counts_poles, counts_poles, weighted_counts_k + + +@numba.njit(parallel=True, fastmath=True) +def bin_kppi(n1d, L, kedges, pimax, Npi, weights, dtype=np.float32, + fourier=True, nthread=MAX_THREADS, + ): + r""" + Compute mean and count modes in (kp, pi) bins for a 3D rfft mesh of shape (n1d, n1d, n1d//2+1) + or a real mesh of shape (n1d, n1d, n1d). + The kp and pi values are constructed on the fly. We use the monotonicity of + pi with respect to kz to accelerate the bin search. The same can be used + in real space where we only need to count the positive rz modes and double them. + This works because we construct the `Xi(vec r)` by inverse Fourier transforming + `P(vec k)`, so we preserve the symmetry. Note that Xi has dimensions of + (nmesh, nmesh, nmesh). + + Parameters + ---------- + n1d : int + size of the 3d array along x and y dimension. + L : float + box size of the simulation. + kedges : array_like + edges of the k wavenumbers or r separation bins. + pimax : float + maximum value along the los for which we consider separations. + Npi : int + number of bins of pi, which ranges from 0 to pimax. + weights : array_like + array of shape (n1d, n1d, n1d//2+1) containing the power spectrum modes. + dtype : np.dtype + float type (32 or 64) to use in calculations. + fourier : bool + options are Fourier space, True, which computes power spectrum, + or configuration-space, False, which computes the correlation function. + nthread : int, optional + Number of numba threads to use + + Returns + ------- + weighted_counts : ndarray of float + mean power spectrum per (kp, pi) bin. + counts : ndarray of int + number of modes per (kp, pi) bin. + """ + + numba.set_num_threads(nthread) + + kzlen = n1d//2 + 1 + Nk = len(kedges) - 1 + if fourier: + dk = 2.*np.pi / L + else: + dk = L / n1d + kedges2 = ((kedges/dk)**2).astype(dtype) + piedges2 = ((np.linspace(0., pimax, Npi+1)/dk)**2).astype(dtype) + + nthread = numba.get_num_threads() + counts = np.zeros((nthread, Nk, Npi), dtype=np.int64) + weighted_counts = np.zeros((nthread, Nk, Npi), dtype=dtype) + + # Loop over all k vectors + for i in numba.prange(n1d): + tid = numba.get_thread_id() + i2 = i**2 if i < n1d//2 else (i - n1d)**2 + for j in range(n1d): + bk,bpi = 0,0 # kp not monotonic, but pi is monotonic + j2 = j**2 if j < n1d//2 else (j - n1d)**2 + kmag2 = dtype(i2 + j2) + + # skip until we reach bin of interest + if kmag2 < kedges2[0]: + continue + + # if we are out of bounds, no need to keep searching + if kmag2 >= kedges2[-1]: + break + + while kmag2 > kedges2[bk+1]: + bk += 1 + + for k in range(kzlen): + kz2 = k**2 + + while kz2 > piedges2[bpi+1]: + bpi += 1 + + if kz2 >= piedges2[-1]: + break + + counts[tid, bk, bpi] += 1 if k == 0 else 2 + weighted_counts[tid, bk, bpi] += weights[i, j, k] if k == 0 else dtype(2.)*weights[i, j, k] + + counts = counts.sum(axis=0) + weighted_counts = weighted_counts.sum(axis=0) + + for i in range(Nk): + for j in range(Npi): + if counts[i, j] != 0: + weighted_counts[i, j] /= dtype(counts[i, j]) + return weighted_counts, counts def project_3d_to_poles(k_bin_edges, raw_p3d, Lbox, poles): r""" @@ -276,7 +385,8 @@ def project_3d_to_poles(k_bin_edges, raw_p3d, Lbox, poles): nmesh = raw_p3d.shape[0] poles = np.asarray(poles) raw_p3d = np.asarray(raw_p3d) - binned_p3d, N3d, binned_poles, Npoles = bin_kmu(nmesh, Lbox, k_bin_edges, Nmu=1, weights=raw_p3d, poles=poles) + muedges = np.array([0., 1.]) + binned_p3d, N3d, binned_poles, Npoles, k_avg = bin_kmu(nmesh, Lbox, k_bin_edges, muedges=muedges, weights=raw_p3d, poles=poles) binned_poles *= Lbox**3 return binned_poles, Npoles @@ -447,23 +557,20 @@ def get_delta_mu2(delta, n1d, dtype_c=np.complex64, dtype_f=np.float32): return delta_mu2 -def pk_to_xi(pk_fn, Lbox, r_bins, poles=[0, 2, 4], key='P_k3D_tr_tr'): +def pk_to_xi(Pk, Lbox, r_bins, poles=[0, 2, 4]): r""" Transform 3D power spectrum into correlation function multipoles. - Reads ASDF file locally to save memory. Parameters ---------- - pk_fn : str - Name of ASDF file from which to read power spectrum. + Pk : array_like + 3D power spectrum. Lbox : float box size of the simulation. r_bins : array_like r separation bins. poles : array_like Legendre multipoles of the power spectrum or correlation function. - key : str - key name in the data structure of the ASDF file containing 3D power spectrum. Returns ------- @@ -475,7 +582,6 @@ def pk_to_xi(pk_fn, Lbox, r_bins, poles=[0, 2, 4], key='P_k3D_tr_tr'): number of modes per r bin. """ # apply fourier transform to get 3D correlation - Pk = asdf.open(pk_fn)['data'][key] # open inside function to save space Xi = irfftn(Pk, workers=-1).real del Pk; gc.collect() # noqa: E702 @@ -485,7 +591,8 @@ def pk_to_xi(pk_fn, Lbox, r_bins, poles=[0, 2, 4], key='P_k3D_tr_tr'): # bin into xi_ell(r) nmesh = Xi.shape[0] poles = np.asarray(poles) - _, _, binned_poles, Npoles = bin_kmu(nmesh, Lbox, r_bins, Nmu=1, weights=Xi, poles=poles, space='real') + muedges = np.array([0., 1.]) + _, _, binned_poles, Npoles, r_avg = bin_kmu(nmesh, Lbox, r_bins, muedges=muedges, weights=Xi, poles=poles, space='real') binned_poles *= nmesh**3 return r_binc, binned_poles, Npoles @@ -533,9 +640,32 @@ def get_k_mu_edges(Lbox, k_max, kbins, mubins, logk): return kbins, mubins - @numba.njit(parallel=True, fastmath=True) -def calc_pk_from_deltak(field_fft, Lbox, k_bin_edges, mu_bin_edges, field2_fft=None, poles=np.empty(0, 'i8'), nthread=MAX_THREADS): +def get_raw_power(field_fft, field2_fft=None): + r""" + Calculate the 3D power spectrum of a given Fourier field. + + Parameters + ---------- + field_fft : array_like + Fourier 3D field. + + Returns + ------- + raw_p3d : array_like + raw 3D power spectrum. + """ + # calculate + if field2_fft is not None: + raw_p3d = (np.conj(field_fft)*field2_fft).real + else: + raw_p3d = (np.abs(field_fft)**2) + return raw_p3d + + +def calc_pk_from_deltak(field_fft, Lbox, k_bin_edges, mu_bin_edges, + field2_fft=None, poles=np.empty(0, 'i8'), squeeze_mu_axis=True, + nthread=MAX_THREADS): r""" Calculate the power spectrum of a given Fourier field, with binning in (k,mu). Optionally computes Legendre multipoles in k bins. @@ -555,41 +685,48 @@ def calc_pk_from_deltak(field_fft, Lbox, k_bin_edges, mu_bin_edges, field2_fft=N poles : np.ndarray, optional Legendre multipoles of the power spectrum or correlation function. Probably has to be a Numpy array, or Numba will complain. + squeeze_mu_axis : bool, optional + Remove the mu axis from the output arrays if it has length 1. + Default: True nthread : int, optional Number of numba threads to use Returns ------- - binned_pk : array_like + power : array_like mean power spectrum per (k, mu) wedge. - Nmode : array_like + N_mode : array_like number of modes per (k, mu) wedge. binned_poles : array_like mean power spectrum per k for each Legendre multipole. - Npoles : array_like + N_mode_poles : array_like number of modes per k. + k_avg : array_like + mean wavenumber per (k, mu) wedge. """ numba.set_num_threads(nthread) # get raw power - if field2_fft is not None: - raw_p3d = (np.conj(field_fft)*field2_fft).real - else: - raw_p3d = (np.abs(field_fft)**2) + raw_p3d = get_raw_power(field_fft, field2_fft) # power spectrum nmesh = raw_p3d.shape[0] - Nmu = len(mu_bin_edges) - 1 - binned_pk, Nmode, binned_poles, N_mode_poles = bin_kmu(nmesh, Lbox, k_bin_edges, Nmu, raw_p3d, poles, nthread=nthread) + power, N_mode, binned_poles, N_mode_poles, k_avg = bin_kmu(nmesh, Lbox, k_bin_edges, mu_bin_edges, raw_p3d, poles, nthread=nthread) # quantity above is dimensionless, multiply by box size (in Mpc/h) - binned_pk *= Lbox**3 + power *= Lbox**3 if len(poles) > 0: binned_poles *= Lbox**3 - return binned_pk, Nmode, binned_poles, N_mode_poles + + if squeeze_mu_axis and len(mu_bin_edges) == 2: + power = power[:, 0] + N_mode = N_mode[:, 0] + k_avg = k_avg[:, 0] + + return dict(power=power, N_mode=N_mode, binned_poles=binned_poles, N_mode_poles=N_mode_poles, k_avg=k_avg) -def get_field(pos, Lbox, nmesh, paste, w=None, d=0., nthread=MAX_THREADS): +def get_field(pos, Lbox, nmesh, paste, w=None, d=0., nthread=MAX_THREADS, dtype=np.float32): r""" Construct real-space 3D field given particle positions. @@ -609,6 +746,8 @@ def get_field(pos, Lbox, nmesh, paste, w=None, d=0., nthread=MAX_THREADS): uniform shift to particle positions. nthread : int, optional Number of numba threads to use + dtype : np.dtype, optional + Data type of the field Returns ------- @@ -618,21 +757,19 @@ def get_field(pos, Lbox, nmesh, paste, w=None, d=0., nthread=MAX_THREADS): # check if weights are requested if w is not None: assert pos.shape[0] == len(w) - pos = pos.astype(np.float32, copy=False) + field = np.zeros((nmesh, nmesh, nmesh), dtype=dtype) paste = paste.upper() if paste == 'TSC': - if d != 0.: - field = tsc_parallel(pos, nmesh, Lbox, weights=w, nthread=nthread, offset=d) - else: - field = tsc_parallel(pos, nmesh, Lbox, weights=w, nthread=nthread) + tsc_parallel(pos, field, Lbox, weights=w, nthread=nthread, offset=d) elif paste == 'CIC': - field = np.zeros((nmesh, nmesh, nmesh), dtype=np.float32) warnings.warn("Note that currently CIC pasting, unlike TSC, supports only a non-parallel implementation.") if d != 0.: cic_serial(pos + d, field, Lbox, weights=w) else: cic_serial(pos, field, Lbox, weights=w) + else: + raise ValueError(f"Unknown pasting method: {paste}") if w is None: # in the zcv code the weights are already normalized, so don't normalize here # TODO assuming normalized weights is fragile # same as passing "Value" to nbodykit (1+delta)(x) V(x) @@ -777,7 +914,7 @@ def get_interlaced_field_fft(pos, Lbox, nmesh, paste, w, nthread=MAX_THREADS, ve return field_fft -def get_field_fft(pos, Lbox, nmesh, paste, w, W, compensated, interlaced, nthread=MAX_THREADS, verbose=False): +def get_field_fft(pos, Lbox, nmesh, paste, w, W, compensated, interlaced, nthread=MAX_THREADS, verbose=False, dtype=np.float32): r""" Calculate field from particle positions and return 3D Fourier field. @@ -799,6 +936,10 @@ def get_field_fft(pos, Lbox, nmesh, paste, w, W, compensated, interlaced, nthrea want to apply interlacing? nthread : int, optional Number of numba threads to use + verbose : bool, optional + Print out debugging info + dtype : np.dtype, optional + Data type of the field Returns ------- @@ -806,19 +947,17 @@ def get_field_fft(pos, Lbox, nmesh, paste, w, W, compensated, interlaced, nthrea interlaced 3D Fourier field. """ - # get field in real space - field = get_field(pos, Lbox, nmesh, paste, w, nthread=nthread) - if verbose: - print("field, pos", field.dtype, pos.dtype) if interlaced: # get interlaced field field_fft = get_interlaced_field_fft(pos, Lbox, nmesh, paste, w, nthread=nthread) else: # get field in real space - field = get_field(pos, Lbox, nmesh, paste, w, nthread=nthread) + field = get_field(pos, Lbox, nmesh, paste, w, nthread=nthread, dtype=dtype) + if verbose: + print("field, pos", field.dtype, pos.dtype) # get Fourier modes from skewers grid - inv_size = np.float32(1 / field.size) + inv_size = dtype(1 / field.size) field_fft = rfftn(field, overwrite_x=True, workers=nthread) _normalize(field_fft, inv_size, nthread=nthread) @@ -868,11 +1007,14 @@ def get_W_compensated(Lbox, nmesh, paste, interlaced): k = (fftfreq(nmesh, d=d) * 2. * np.pi).astype(np.float32) # h/Mpc # apply deconvolution + paste = paste.upper() if interlaced: if paste == 'TSC': p = 3. elif paste == 'CIC': p = 2. + else: + raise ValueError(f"Unknown pasting method {paste}") W = np.sinc(0.5*k/kN)**p # sinc def else: # first order correction of interlacing (aka aliasing) s = np.sin(0.5 * np.pi * k/kN)**2 @@ -883,14 +1025,13 @@ def get_W_compensated(Lbox, nmesh, paste, interlaced): del s return W - def calc_power(pos, Lbox, kbins = None, mubins = None, k_max = None, logk = False, - paste = 'tsc', + paste = 'TSC', nmesh = 128, compensated = True, interlaced = True, @@ -898,7 +1039,9 @@ def calc_power(pos, pos2 = None, w2 = None, poles = None, + squeeze_mu_axis = True, nthread = MAX_THREADS, + dtype = np.float32, ): r""" Compute the 3D power spectrum given particle positions by first painting them on a @@ -915,8 +1058,8 @@ def calc_power(pos, Or an array-like, which will be used as-is. Default is None, which sets `kbins` to `nmesh`. mubins : int, None, or array_like, optional - An int indicating the number of bins of mu, which ranges from 0 to 1. - Or an array-like, which will be used as-is. + An int indicating the number of bins of mu. mu ranges from 0 to 1. + Or an array-like of bin edges, which will be used as-is. Default of None sets `mubins` to 1. k_max : float, optional maximum k wavenumber. @@ -925,7 +1068,7 @@ def calc_power(pos, Logarithmic or linear k bins. Ignored if `kbins` is array-like. Default is False. paste : str, optional - particle pasting approach (CIC or TSC). Default is 'tsc'. + particle pasting approach (CIC or TSC). Default is 'TSC'. nmesh : int, optional size of the 3d array along x and y dimension. Default is 128. compensated : bool, optional @@ -934,30 +1077,33 @@ def calc_power(pos, want to apply interlacing? Default is True. w : array_like, optional weights for each particle. - x2 : array_like, optional - second set of particle positions in the x dimension. - y2 : array_like, optional - second set of particle positions in the y dimension. - z2 : array_like, optional - second set of particle positions in the z dimension. + pos2 : array_like, optional + second set of particle positions, shape (N,3) poles : None or list of int, optional Legendre multipoles of the power spectrum or correlation function. Default of None gives the monopole. + squeeze_mu_axis : bool, optional + Remove the mu axis from the output arrays if it has length 1. + Default: True nthread : int, optional Number of numba threads to use + dtype : np.dtype, optional + Data type of the field Returns ------- power : astropy.Table - The power spectrum in an astropy Table of length ``nbins_k``. - The columns are: + The power spectrum in an astropy Table of length ``nbins_k``. The columns are: + - ``k_mid``: arithmetic bin centers of the k wavenumbers, shape ``(nbins_k,)`` + - ``k_avg``: mean wavenumber per (k, mu) wedge, shape ``(nbins_k,nbins_mu)`` - ``mu_mid``: arithmetic bin centers of the mu angles, shape ``(nbins_k,nbins_mu)`` - ``power``: mean power spectrum per (k, mu) wedge, shape ``(nbins_k,nbins_mu)`` - ``N_mode``: number of modes per (k, mu) wedge, shape ``(nbins_k,nbins_mu)`` If multipoles are requested via ``poles``, the table includes: - - ``poles``: mean Legendre multipole coefficients, shape ``(nbins_k,nbins_mu)`` + + - ``poles``: mean Legendre multipole coefficients, shape ``(nbins_k,len(poles))`` - ``N_mode_poles``: number of modes per pole, shape ``(nbins_k,len(poles))`` The ``meta`` field of the table will have metadata about the power spectrum. @@ -978,7 +1124,14 @@ def calc_power(pos, interlaced=interlaced, poles=poles, nthread=nthread, + N_pos=len(pos), + is_weighted=w is not None, + field_dtype=dtype, + squeeze_mu_axis=squeeze_mu_axis, ) + if pos2 is not None: + meta['N_pos2'] = len(pos2) + meta['is_weighted2'] = w2 is not None # get the window function if compensated: @@ -987,12 +1140,12 @@ def calc_power(pos, W = None # convert to fourier space - field_fft = get_field_fft(pos, Lbox, nmesh, paste, w, W, compensated, interlaced, nthread=nthread) + field_fft = get_field_fft(pos, Lbox, nmesh, paste, w, W, compensated, interlaced, nthread=nthread, dtype=dtype) # if second field provided if pos2 is not None: # convert to fourier space - field2_fft = get_field_fft(pos2, Lbox, nmesh, paste, w2, W, compensated, interlaced, nthread=nthread) + field2_fft = get_field_fft(pos2, Lbox, nmesh, paste, w2, W, compensated, interlaced, nthread=nthread, dtype=dtype) else: field2_fft = None @@ -1000,7 +1153,10 @@ def calc_power(pos, # calculate power spectrum kbins, mubins = get_k_mu_edges(Lbox, k_max, kbins, mubins, logk) - pk, N_mode, binned_poles, N_mode_poles = calc_pk_from_deltak(field_fft, Lbox, kbins, mubins, field2_fft=field2_fft, poles=poles, nthread=nthread) + P = calc_pk_from_deltak(field_fft, Lbox, kbins, mubins, + field2_fft=field2_fft, poles=poles, squeeze_mu_axis=squeeze_mu_axis, + nthread=nthread, + ) # define bin centers k_binc = (kbins[1:] + kbins[:-1])*.5 @@ -1010,19 +1166,20 @@ def calc_power(pos, k_min=kbins[:-1], k_max=kbins[1:], k_mid=k_binc, - power=pk, - N_mode=N_mode, + k_avg=P['k_avg'], + power=P['power'], + N_mode=P['N_mode'], ) - if return_mubins: + if len(poles) > 0: res.update( - mu_min=np.broadcast_to(mubins[:-1], pk.shape), - mu_max=np.broadcast_to(mubins[1:], pk.shape), - mu_mid=np.broadcast_to(mu_binc, pk.shape), + poles=P['binned_poles'].T, + N_mode_poles=P['N_mode_poles'], ) - if len(poles) > 0: + if return_mubins: res.update( - poles=binned_poles.T, - N_mode_poles=N_mode_poles, + mu_min=np.broadcast_to(mubins[:-1], res['power'].shape), + mu_max=np.broadcast_to(mubins[1:], res['power'].shape), + mu_mid=np.broadcast_to(mu_binc, res['power'].shape), ) res = Table(res, meta=meta) diff --git a/abacusnbody/analysis/shear.py b/abacusnbody/analysis/shear.py new file mode 100644 index 00000000..05115598 --- /dev/null +++ b/abacusnbody/analysis/shear.py @@ -0,0 +1,125 @@ +import time +import gc + +import numpy as np +import numpy.linalg as la +import numba +from scipy.fft import irfftn, rfftn +from scipy.ndimage import gaussian_filter + +""" +Code still under construction. Originally written by Boryana Hadzhiyska for the ancient: https://arxiv.org/abs/1512.03402. +""" + +def smooth_density(D, R, N_dim, Lbox): + # cell size + cell = Lbox/N_dim + # smoothing scale + R /= cell + D_smooth = gaussian_filter(D, R) + return D_smooth + +# tophat +@numba.njit +def Wth(ksq, r): + k = np.sqrt(ksq) + w = 3*(np.sin(k*r)-k*r*np.cos(k*r))/(k*r)**3 + return w + +# gaussian +@numba.njit +def Wg(k, r): + return np.exp(-k*r*r/2.) + +@numba.njit(parallel=False, fastmath=True) # parallel=True gives seg fault +def get_tidal(dfour, karr, N_dim, R, dtype=np.float32): + + # initialize array + tfour = np.zeros((N_dim, N_dim, N_dim//2 + 1, 6), dtype=np.complex64) + + + # computing tidal tensor + for a in range(N_dim): + for b in range(N_dim): + for c in numba.prange(N_dim//2 + 1): + if a * b * c == 0: + continue + + ksq = dtype(karr[a]**2 + karr[b]**2 + karr[c]**2) + dok2 = dfour[a, b, c]/ksq + + # smoothed density Gauss fourier + #dksmo[a, b, c] = Wg(ksq)*dfour[a, b, c] + # smoothed density TH fourier + #dkth[a, b, c] = Wth(ksq)*dfour[a, b, c] + # 0,0 is 0; 0,1 is 1; 0,2 is 2; 1,1 is 3; 1,2 is 4; 2,2 is 5 + tfour[a, b, c, 0] = karr[a]*karr[a]*dok2 + tfour[a, b, c, 3] = karr[b]*karr[b]*dok2 + tfour[a, b, c, 5] = karr[c]*karr[c]*dok2 + tfour[a, b, c, 1] = karr[a]*karr[b]*dok2 + tfour[a, b, c, 2] = karr[a]*karr[c]*dok2 + tfour[a, b, c, 4] = karr[b]*karr[c]*dok2 + if R is not None: + tfour[a, b, c, :] *= Wth(ksq, R) + return tfour + +@numba.njit(parallel=False, fastmath=True) +def get_shear_nb(tidr, N_dim): + shear = np.zeros(shape=(N_dim, N_dim, N_dim), dtype=np.float32) + tensor = np.zeros((3, 3), dtype=np.float32) + for a in range(N_dim): + for b in range(N_dim): + for c in range(N_dim): + t = tidr[a, b, c, :] + tensor[0, 0] = t[0] + tensor[0, 1] = t[1] + tensor[0, 2] = t[2] + tensor[1, 0] = t[1] + tensor[1, 1] = t[3] + tensor[1, 2] = t[4] + tensor[2, 0] = t[2] + tensor[2, 1] = t[4] + tensor[2, 2] = t[5] + evals = la.eigvals(tensor) + l1 = evals[0] + l2 = evals[1] + l3 = evals[2] + shear[a, b, c] = np.sqrt(0.5*((l2-l1)**2 + (l3-l1)**2 + (l3-l2)**2)) + return shear + +def get_shear(dsmo, N_dim, Lbox, R=None, dtype=np.float32): + # user can also pass string + if isinstance(dsmo, str): + dsmo = np.load(dsmo) + + # fourier transform the density field + dsmo = dsmo.astype(dtype) + dfour = rfftn(dsmo, overwrite_x=True, workers=-1) + del dsmo + gc.collect() + + # k values + karr = np.fft.fftfreq(N_dim, d=Lbox/(2*np.pi*N_dim)).astype(dtype) + + # compute fourier tidal + start = time.time() + tfour = get_tidal(dfour, karr, N_dim, R) + del dfour + gc.collect() + print("finished fourier tidal, took time", time.time() - start) + + # compute real tidal + start = time.time() + tidr = irfftn(tfour, axes = (0, 1, 2), workers=-1).real + del tfour + gc.collect() + print("finished tidal, took time", time.time() - start) + + # compute shear + start = time.time() + shear = get_shear_nb(tidr, N_dim) + del tidr + gc.collect() + print("finished shear, took time", time.time() - start) + + return shear diff --git a/abacusnbody/analysis/tpcf_corrfunc.py b/abacusnbody/analysis/tpcf_corrfunc.py index 19bf26a8..9a88719b 100644 --- a/abacusnbody/analysis/tpcf_corrfunc.py +++ b/abacusnbody/analysis/tpcf_corrfunc.py @@ -38,24 +38,32 @@ def tpcf_multipole(s_mu_tcpf_result, mu_bins, order=0): Examples -------- For demonstration purposes we create a randomly distributed set of points within a - periodic cube of length 250 Mpc/h. - >>> Npts = 100 - >>> Lbox = 250. - >>> x = np.random.uniform(0, Lbox, Npts) - >>> y = np.random.uniform(0, Lbox, Npts) - >>> z = np.random.uniform(0, Lbox, Npts) + periodic cube of length 250 Mpc/h.:: + + >>> Npts = 100 + >>> Lbox = 250. + >>> x = np.random.uniform(0, Lbox, Npts) + >>> y = np.random.uniform(0, Lbox, Npts) + >>> z = np.random.uniform(0, Lbox, Npts) + We transform our *x, y, z* points into the array shape used by the pair-counter by taking the transpose of the result of `numpy.vstack`. This boilerplate transformation - is used throughout the `~halotools.mock_observables` sub-package: - >>> sample1 = np.vstack((x,y,z)).T + is used throughout the `~halotools.mock_observables` sub-package: :: + + >>> sample1 = np.vstack((x,y,z)).T + First, we calculate the correlation function using - `~halotools.mock_observables.s_mu_tpcf`. - >>> from halotools.mock_observables import s_mu_tpcf - >>> s_bins = np.linspace(0.01, 25, 10) - >>> mu_bins = np.linspace(0, 1, 15) - >>> xi_s_mu = s_mu_tpcf(sample1, s_bins, mu_bins, period=Lbox) - Then, we can calculate the quadrapole of the correlation function: - >>> xi_2 = tpcf_multipole(xi_s_mu, mu_bins, order=2) + `~halotools.mock_observables.s_mu_tpcf`.:: + + >>> from halotools.mock_observables import s_mu_tpcf + >>> s_bins = np.linspace(0.01, 25, 10) + >>> mu_bins = np.linspace(0, 1, 15) + >>> xi_s_mu = s_mu_tpcf(sample1, s_bins, mu_bins, period=Lbox) + + Then, we can calculate the quadrapole of the correlation function: :: + + >>> xi_2 = tpcf_multipole(xi_s_mu, mu_bins, order=2) + """ # process inputs diff --git a/abacusnbody/hod/abacus_hod.py b/abacusnbody/hod/abacus_hod.py index 89927e71..06a03a18 100644 --- a/abacusnbody/hod/abacus_hod.py +++ b/abacusnbody/hod/abacus_hod.py @@ -42,7 +42,7 @@ class AbacusHOD: """ A highly efficient multi-tracer HOD code for the AbacusSummmit simulations. """ - def __init__(self, sim_params, HOD_params, clustering_params = None, chunk=-1, n_chunks=1, skip_staging=False): # TESTING + def __init__(self, sim_params, HOD_params, clustering_params = None, chunk=-1, n_chunks=1, skip_staging=False): """ Loads simulation. The ``sim_params`` dictionary specifies which simulation volume to load. The ``HOD_params`` specifies the HOD parameters and tracer @@ -1109,8 +1109,8 @@ def apply_zcv_xi(self, mock_dict, config, load_presaved=False): r_bins = np.linspace(0., 200., 201) pk_rsd_tr_fns = [save_z_dir / f"power{rsd_str}_tr_tr_nmesh{config['zcv_params']['nmesh']:d}.asdf"] # TODO: same as other (could check that we have this if presaved) power_cv_tr_fn = save_z_dir / f"power{rsd_str}_ZCV_tr_nmesh{config['zcv_params']['nmesh']:d}.asdf" # TODO: should be an output (could check that we have this if presaved; run_zcv too) - r_binc, binned_poles_zcv, Npoles = pk_to_xi(power_cv_tr_fn, self.lbox, r_bins, poles=config['power_params']['poles'], key='P_k3D_tr_tr_zcv') - r_binc, binned_poles, Npoles = pk_to_xi(pk_rsd_tr_fns[0], self.lbox, r_bins, poles=config['power_params']['poles'], key='P_k3D_tr_tr') + r_binc, binned_poles_zcv, Npoles = pk_to_xi(asdf.open(power_cv_tr_fn)['data']['P_k3D_tr_tr_zcv'], self.lbox, r_bins, poles=config['power_params']['poles']) + r_binc, binned_poles, Npoles = pk_to_xi(asdf.open(pk_rsd_tr_fns[0])['data']['P_k3D_tr_tr'], self.lbox, r_bins, poles=config['power_params']['poles']) zcv_dict['Xi_tr_tr_ell_zcv'] = binned_poles_zcv zcv_dict['Xi_tr_tr_ell'] = binned_poles zcv_dict['Np_tr_tr_ell'] = Npoles diff --git a/abacusnbody/hod/prepare_sim.py b/abacusnbody/hod/prepare_sim.py index 202bc2bf..9320d6bc 100644 --- a/abacusnbody/hod/prepare_sim.py +++ b/abacusnbody/hod/prepare_sim.py @@ -26,7 +26,7 @@ from abacusnbody.data.compaso_halo_catalog import CompaSOHaloCatalog from abacusnbody.data.read_abacus import read_asdf -from .shear import smooth_density, get_shear +from ..analysis.shear import smooth_density, get_shear from ..analysis.tsc import tsc_parallel DEFAULTS = {} diff --git a/abacusnbody/hod/shear.py b/abacusnbody/hod/shear.py deleted file mode 100644 index b09f3007..00000000 --- a/abacusnbody/hod/shear.py +++ /dev/null @@ -1,256 +0,0 @@ -import time - -import numpy as np -import numpy.linalg as la -from numba import njit -from scipy.ndimage import gaussian_filter - - -# from nbodykit.lab import ArrayCatalog, FieldMesh -# from nbodykit.base.mesh import MeshFilter - - -# @numba.vectorize -# def rightwrap(x, L): -# if x >= L: -# return x - L -# return x - -@njit -def dist(pos1, pos2, L=None): - ''' - Calculate L2 norm distance between a set of points - and either a reference point or another set of points. - Optionally includes periodicity. - Parameters - ---------- - pos1: ndarray of shape (N,m) - A set of points - pos2: ndarray of shape (N,m) or (m,) or (1,m) - A single point or set of points - L: float, optional - The box size. Will do a periodic wrap if given. - Returns - ------- - dist: ndarray of shape (N,) - The distances between pos1 and pos2 - ''' - - # read dimension of data - N, nd = pos1.shape - - # allow pos2 to be a single point - pos2 = np.atleast_2d(pos2) - assert pos2.shape[-1] == nd - broadcast = len(pos2) == 1 - - dist = np.empty(N, dtype=pos1.dtype) - - i2 = 0 - for i in range(N): - delta = 0. - for j in range(nd): - dx = pos1[i][j] - pos2[i2][j] - if L is not None: - if dx >= L/2: - dx -= L - elif dx < -L/2: - dx += L - delta += dx*dx - dist[i] = np.sqrt(delta) - if not broadcast: - i2 += 1 - return dist - -# @njit(nopython=True, nogil=True) -# def numba_tsc_3D(positions, density, boxsize, weights=np.empty(0)): -# gx = np.uint32(density.shape[0]) -# gy = np.uint32(density.shape[1]) -# gz = np.uint32(density.shape[2]) -# threeD = gz != 1 -# W = 1. -# Nw = len(weights) -# for n in range(len(positions)): -# # broadcast scalar weights -# if Nw == 1: -# W = weights[0] -# elif Nw > 1: -# W = weights[n] - -# # convert to a position in the grid -# px = (positions[n,0]/boxsize)*gx # used to say boxsize+0.5 -# py = (positions[n,1]/boxsize)*gy # used to say boxsize+0.5 -# if threeD: -# pz = (positions[n,2]/boxsize)*gz # used to say boxsize+0.5 - -# # round to nearest cell center -# ix = np.int32(round(px)) -# iy = np.int32(round(py)) -# if threeD: -# iz = np.int32(round(pz)) - -# # calculate distance to cell center -# dx = ix - px -# dy = iy - py -# if threeD: -# dz = iz - pz - -# # find the tsc weights for each dimension -# wx = .75 - dx**2 -# wxm1 = .5*(.5 + dx)**2 -# wxp1 = .5*(.5 - dx)**2 -# wy = .75 - dy**2 -# wym1 = .5*(.5 + dy)**2 -# wyp1 = .5*(.5 - dy)**2 -# if threeD: -# wz = .75 - dz**2 -# wzm1 = .5*(.5 + dz)**2 -# wzp1 = .5*(.5 - dz)**2 -# else: -# wz = 1. - -# # find the wrapped x,y,z grid locations of the points we need to change -# # negative indices will be automatically wrapped -# ixm1 = (ix - 1) -# ixw = rightwrap(ix , gx) -# ixp1 = rightwrap(ix + 1, gx) -# iym1 = (iy - 1) -# iyw = rightwrap(iy , gy) -# iyp1 = rightwrap(iy + 1, gy) -# if threeD: -# izm1 = (iz - 1) -# izw = rightwrap(iz , gz) -# izp1 = rightwrap(iz + 1, gz) -# else: -# izw = np.uint32(0) - -# # change the 9 or 27 cells that the cloud touches -# density[ixm1, iym1, izw ] += wxm1*wym1*wz *W -# density[ixm1, iyw , izw ] += wxm1*wy *wz *W -# density[ixm1, iyp1, izw ] += wxm1*wyp1*wz *W -# density[ixw , iym1, izw ] += wx *wym1*wz *W -# density[ixw , iyw , izw ] += wx *wy *wz *W -# density[ixw , iyp1, izw ] += wx *wyp1*wz *W -# density[ixp1, iym1, izw ] += wxp1*wym1*wz *W -# density[ixp1, iyw , izw ] += wxp1*wy *wz *W -# density[ixp1, iyp1, izw ] += wxp1*wyp1*wz *W - -# if threeD: -# density[ixm1, iym1, izm1] += wxm1*wym1*wzm1*W -# density[ixm1, iym1, izp1] += wxm1*wym1*wzp1*W - -# density[ixm1, iyw , izm1] += wxm1*wy *wzm1*W -# density[ixm1, iyw , izp1] += wxm1*wy *wzp1*W - -# density[ixm1, iyp1, izm1] += wxm1*wyp1*wzm1*W -# density[ixm1, iyp1, izp1] += wxm1*wyp1*wzp1*W - -# density[ixw , iym1, izm1] += wx *wym1*wzm1*W -# density[ixw , iym1, izp1] += wx *wym1*wzp1*W - -# density[ixw , iyw , izm1] += wx *wy *wzm1*W -# density[ixw , iyw , izp1] += wx *wy *wzp1*W - -# density[ixw , iyp1, izm1] += wx *wyp1*wzm1*W -# density[ixw , iyp1, izp1] += wx *wyp1*wzp1*W - -# density[ixp1, iym1, izm1] += wxp1*wym1*wzm1*W -# density[ixp1, iym1, izp1] += wxp1*wym1*wzp1*W - -# density[ixp1, iyw , izm1] += wxp1*wy *wzm1*W -# density[ixp1, iyw , izp1] += wxp1*wy *wzp1*W - -# density[ixp1, iyp1, izm1] += wxp1*wyp1*wzm1*W -# density[ixp1, iyp1, izp1] += wxp1*wyp1*wzp1*W - -def smooth_density(D, R, N_dim, Lbox): - # cell size - cell = Lbox/N_dim - # smoothing scale - R /= cell - D_smooth = gaussian_filter(D, R) - return D_smooth - -# tophat -@njit(nopython=True) -def Wth(ksq, r): - k = np.sqrt(ksq) - w = 3*(np.sin(k*r)-k*r*np.cos(k*r))/(k*r)**3 - return w - -# gaussian -@njit(nopython=True) -def Wg(k, r): - return np.exp(-k*r*r/2.) - - -@njit(nopython=True) -def get_tidal(dfour, karr, N_dim, R): - - # initiate array - tfour = np.zeros(shape=(N_dim, N_dim, N_dim, 3, 3),dtype=np.complex128)#complex) - - # computing tidal tensor - for a in range(N_dim): - for b in range(N_dim): - for c in range(N_dim): - if (a, b, c) == (0, 0, 0): - continue - - ksq = karr[a]**2 + karr[b]**2 + karr[c]**2 - # smoothed density Gauss fourier - #dksmo[a, b, c] = Wg(ksq)*dfour[a, b, c] - # smoothed density TH fourier - #dkth[a, b, c] = Wth(ksq)*dfour[a, b, c] - # all 9 components - tfour[a, b, c, 0, 0] = karr[a]*karr[a]*dfour[a, b, c]/ksq - tfour[a, b, c, 1, 1] = karr[b]*karr[b]*dfour[a, b, c]/ksq - tfour[a, b, c, 2, 2] = karr[c]*karr[c]*dfour[a, b, c]/ksq - tfour[a, b, c, 1, 0] = karr[a]*karr[b]*dfour[a, b, c]/ksq - tfour[a, b, c, 0, 1] = tfour[a, b, c, 1, 0] - tfour[a, b, c, 2, 0] = karr[a]*karr[c]*dfour[a, b, c]/ksq - tfour[a, b, c, 0, 2] = tfour[a, b, c, 2, 0] - tfour[a, b, c, 1, 2] = karr[b]*karr[c]*dfour[a, b, c]/ksq - tfour[a, b, c, 2, 1] = tfour[a, b, c, 1, 2] - if R is not None: - tfour[a, b, c, :, :] *= Wth(ksq, R) - return tfour - -@njit(nopython=True) -def get_shear_nb(tidr, N_dim): - shear = np.zeros(shape=(N_dim, N_dim, N_dim), dtype=np.float64) - for a in range(N_dim): - for b in range(N_dim): - for c in range(N_dim): - t = tidr[a, b, c] - evals, evects = la.eig(t) - # ascending - idx = evals.argsort() - evals = evals[idx] - evects = evects[:, idx] - l1 = evals[0] - l2 = evals[1] - l3 = evals[2] - shear[a, b, c] = 0.5*((l2-l1)**2 + (l3-l1)**2 + (l3-l2)**2) - return shear - -def get_shear(dsmo, N_dim, Lbox, R=None): - - # fourier transform the density field - dfour = np.fft.fftn(dsmo) - - # k values - karr = np.fft.fftfreq(N_dim, d=Lbox/(2*np.pi*N_dim)) - - # creating empty arrays for future use - start = time.time() - tfour = get_tidal(dfour, karr, N_dim, R) - tidr = np.real(np.fft.ifftn(tfour, axes = (0, 1, 2))) - print("finished tidal, took time", time.time() - start) - - # compute shear - start = time.time() - shear = np.sqrt(get_shear_nb(tidr, N_dim)) - print("finished shear, took time", time.time() - start) - - return shear diff --git a/abacusnbody/hod/zcv/advect_fields.py b/abacusnbody/hod/zcv/advect_fields.py index 522357b1..f28beca0 100644 --- a/abacusnbody/hod/zcv/advect_fields.py +++ b/abacusnbody/hod/zcv/advect_fields.py @@ -290,14 +290,14 @@ def main(path2config, want_rsd=False, alt_simname=None, save_3D_power=False, onl del field_fft_i, field_fft_j; gc.collect() # noqa: E702 else: # compute power spectrum - pk3d, N3d, binned_poles, Npoles = calc_pk_from_deltak(field_fft_i[f'{keynames[i]}_Re']+1j*field_fft_i[f'{keynames[i]}_Im'], Lbox, k_bin_edges, mu_bin_edges, field2_fft=field_fft_j[f'{keynames[j]}_Re']+1j*field_fft_j[f'{keynames[j]}_Im'], poles=np.asarray(poles)) - pk3d *= field_D[i]*field_D[j] - binned_poles *= field_D[i]*field_D[j] - pk_auto.append(pk3d) - pk_ij_dict[f'P_kmu_{keynames[i]}_{keynames[j]}'] = pk3d - pk_ij_dict[f'N_kmu_{keynames[i]}_{keynames[j]}'] = N3d - pk_ij_dict[f'P_ell_{keynames[i]}_{keynames[j]}'] = binned_poles - pk_ij_dict[f'N_ell_{keynames[i]}_{keynames[j]}'] = Npoles + P = calc_pk_from_deltak(field_fft_i[f'{keynames[i]}_Re']+1j*field_fft_i[f'{keynames[i]}_Im'], Lbox, k_bin_edges, mu_bin_edges, field2_fft=field_fft_j[f'{keynames[j]}_Re']+1j*field_fft_j[f'{keynames[j]}_Im'], poles=np.asarray(poles)) + P['power'] *= field_D[i]*field_D[j] + P['binned_poles'] *= field_D[i]*field_D[j] + pk_auto.append(P['power']) + pk_ij_dict[f'P_kmu_{keynames[i]}_{keynames[j]}'] = P['power'] + pk_ij_dict[f'N_kmu_{keynames[i]}_{keynames[j]}'] = P['N_modes'] + pk_ij_dict[f'P_ell_{keynames[i]}_{keynames[j]}'] = P['binned_poles'] + pk_ij_dict[f'N_ell_{keynames[i]}_{keynames[j]}'] = P['N_mode_poles'] del field_fft_i, field_fft_j; gc.collect() # noqa: E702 if not save_3D_power: diff --git a/abacusnbody/hod/zcv/linear_fields.py b/abacusnbody/hod/zcv/linear_fields.py index 48b3e4a0..827ae36e 100644 --- a/abacusnbody/hod/zcv/linear_fields.py +++ b/abacusnbody/hod/zcv/linear_fields.py @@ -138,11 +138,11 @@ def main(path2config, alt_simname=None, save_3D_power=False): else: # compute power spectrum - pk3d, N3d, binned_poles, Npoles = calc_pk_from_deltak(fields_fft[keynames[i]], Lbox, k_bin_edges, mu_bin_edges, field2_fft=fields_fft[keynames[j]], poles=np.asarray(poles)) - pk_lin_dict[f'P_kmu_{keynames[i]}_{keynames[j]}'] = pk3d - pk_lin_dict[f'N_kmu_{keynames[i]}_{keynames[j]}'] = N3d - pk_lin_dict[f'P_ell_{keynames[i]}_{keynames[j]}'] = binned_poles - pk_lin_dict[f'N_ell_{keynames[i]}_{keynames[j]}'] = Npoles + P = calc_pk_from_deltak(fields_fft[keynames[i]], Lbox, k_bin_edges, mu_bin_edges, field2_fft=fields_fft[keynames[j]], poles=np.asarray(poles)) + pk_lin_dict[f'P_kmu_{keynames[i]}_{keynames[j]}'] = P['power'] + pk_lin_dict[f'N_kmu_{keynames[i]}_{keynames[j]}'] = P['N_mode'] + pk_lin_dict[f'P_ell_{keynames[i]}_{keynames[j]}'] = P['binned_poles'] + pk_lin_dict[f'N_ell_{keynames[i]}_{keynames[j]}'] = P['N_mode_poles'] # record power spectra header = {} diff --git a/abacusnbody/hod/zcv/tracer_power.py b/abacusnbody/hod/zcv/tracer_power.py index 67ab8ba2..513c0b54 100644 --- a/abacusnbody/hod/zcv/tracer_power.py +++ b/abacusnbody/hod/zcv/tracer_power.py @@ -186,11 +186,11 @@ def get_tracer_power(tracer_pos, want_rsd, config, want_save=True, save_3D_power k_bin_edges, mu_bin_edges = get_k_mu_edges(Lbox, k_hMpc_max, n_k_bins, n_mu_bins, logk) # compute the galaxy auto rsd poles - pk3d, N3d, binned_poles, Npoles = calc_pk_from_deltak(tr_field_fft, Lbox, k_bin_edges, mu_bin_edges, field2_fft=None, poles=np.asarray(poles)) - pk_tr_dict['P_kmu_tr_tr'] = pk3d - pk_tr_dict['N_kmu_tr_tr'] = N3d - pk_tr_dict['P_ell_tr_tr'] = binned_poles - pk_tr_dict['N_ell_tr_tr'] = Npoles + P = calc_pk_from_deltak(tr_field_fft, Lbox, k_bin_edges, mu_bin_edges, field2_fft=None, poles=np.asarray(poles)) + pk_tr_dict['P_kmu_tr_tr'] = P['power'] + pk_tr_dict['N_kmu_tr_tr'] = P['N_mode'] + pk_tr_dict['P_ell_tr_tr'] = P['binned_poles'] + pk_tr_dict['N_ell_tr_tr'] = P['N_mode_poles'] # loop over fields for i in range(len(keynames)): @@ -222,13 +222,13 @@ def get_tracer_power(tracer_pos, want_rsd, config, want_save=True, save_3D_power else: # compute power spectrum - pk3d, N3d, binned_poles, Npoles = calc_pk_from_deltak(field_fft_i[f'{keynames[i]}_Re']+1j*field_fft_i[f'{keynames[i]}_Im'], Lbox, k_bin_edges, mu_bin_edges, field2_fft=tr_field_fft, poles=np.asarray(poles)) - pk3d *= field_D[i] - binned_poles *= field_D[i] - pk_tr_dict[f'P_kmu_{keynames[i]}_tr'] = pk3d - pk_tr_dict[f'N_kmu_{keynames[i]}_tr'] = N3d - pk_tr_dict[f'P_ell_{keynames[i]}_tr'] = binned_poles - pk_tr_dict[f'N_ell_{keynames[i]}_tr'] = Npoles + P = calc_pk_from_deltak(field_fft_i[f'{keynames[i]}_Re']+1j*field_fft_i[f'{keynames[i]}_Im'], Lbox, k_bin_edges, mu_bin_edges, field2_fft=tr_field_fft, poles=np.asarray(poles)) + P['power'] *= field_D[i] + P['binned_poles'] *= field_D[i] + pk_tr_dict[f'P_kmu_{keynames[i]}_tr'] = P['power'] + pk_tr_dict[f'N_kmu_{keynames[i]}_tr'] = P['N_mode'] + pk_tr_dict[f'P_ell_{keynames[i]}_tr'] = P['binned_poles'] + pk_tr_dict[f'N_ell_{keynames[i]}_tr'] = P['N_mode_poles'] del field_fft_i; gc.collect() # noqa: E702 if save_3D_power: @@ -401,11 +401,11 @@ def get_recon_power(tracer_pos, random_pos, want_rsd, config, want_save=True, sa power_tr_fns.append(power_tr_fn) compress_asdf(str(power_tr_fn), pk_tr_dict, header) else: - pk3d, N3d, binned_poles, Npoles = calc_pk_from_deltak(tr_field_fft, Lbox, k_bin_edges, mu_bin_edges, field2_fft=None, poles=np.asarray(poles)) - pk_tr_dict['P_kmu_tr_tr'] = pk3d - pk_tr_dict['N_kmu_tr_tr'] = N3d - pk_tr_dict['P_ell_tr_tr'] = binned_poles - pk_tr_dict['N_ell_tr_tr'] = Npoles + P = calc_pk_from_deltak(tr_field_fft, Lbox, k_bin_edges, mu_bin_edges, field2_fft=None, poles=np.asarray(poles)) + pk_tr_dict['P_kmu_tr_tr'] = P['power'] + pk_tr_dict['N_kmu_tr_tr'] = P['N_mode'] + pk_tr_dict['P_ell_tr_tr'] = P['binned_poles'] + pk_tr_dict['N_ell_tr_tr'] = P['N_mode_poles'] # initiate final arrays for i in range(len(keynames)): @@ -428,11 +428,11 @@ def get_recon_power(tracer_pos, random_pos, want_rsd, config, want_save=True, sa compress_asdf(str(power_tr_fn), pk_tr_dict, header) else: # compute power spectrum - pk3d, N3d, binned_poles, Npoles = calc_pk_from_deltak(fields[keynames[i]], Lbox, k_bin_edges, mu_bin_edges, field2_fft=tr_field_fft, poles=np.asarray(poles)) - pk_tr_dict[f'P_kmu_{keynames[i]}_tr'] = pk3d - pk_tr_dict[f'N_kmu_{keynames[i]}_tr'] = N3d - pk_tr_dict[f'P_ell_{keynames[i]}_tr'] = binned_poles - pk_tr_dict[f'N_ell_{keynames[i]}_tr'] = Npoles + P = calc_pk_from_deltak(fields[keynames[i]], Lbox, k_bin_edges, mu_bin_edges, field2_fft=tr_field_fft, poles=np.asarray(poles)) + pk_tr_dict[f'P_kmu_{keynames[i]}_tr'] = P['power'] + pk_tr_dict[f'N_kmu_{keynames[i]}_tr'] = P['N_mode'] + pk_tr_dict[f'P_ell_{keynames[i]}_tr'] = P['binned_poles'] + pk_tr_dict[f'N_ell_{keynames[i]}_tr'] = P['N_mode_poles'] if save_3D_power: diff --git a/abacusnbody/metadata/__init__.py b/abacusnbody/metadata/__init__.py index 8fd38bb7..7a73a568 100644 --- a/abacusnbody/metadata/__init__.py +++ b/abacusnbody/metadata/__init__.py @@ -29,7 +29,7 @@ def get_meta(simname, redshift=None): the time-dependent state values. ''' - if simname.startswith('AbacusSummit'): + if simname.startswith('Abacus'): return abacussummit.get_meta(simname, redshift=redshift) raise ValueError(f'It is unknown what simulation set "{simname}" belongs to ' diff --git a/abacusnbody/metadata/abacusdesi2_headers_compressed.asdf b/abacusnbody/metadata/abacusdesi2_headers_compressed.asdf new file mode 100644 index 00000000..b35f3a41 Binary files /dev/null and b/abacusnbody/metadata/abacusdesi2_headers_compressed.asdf differ diff --git a/abacusnbody/metadata/abacussummit.py b/abacusnbody/metadata/abacussummit.py index 9ebbf6bb..2e507d67 100644 --- a/abacusnbody/metadata/abacussummit.py +++ b/abacusnbody/metadata/abacussummit.py @@ -9,7 +9,7 @@ import msgpack metadata = None -metadata_fn = 'abacussummit_headers_compressed.asdf' +metadata_fns = ['abacussummit_headers_compressed.asdf', 'abacusdesi2_headers_compressed.asdf'] def get_meta(simname, redshift=None): ''' @@ -31,21 +31,23 @@ def get_meta(simname, redshift=None): the time-dependent state values. ''' - if not simname.startswith('AbacusSummit_'): - simname = 'AbacusSummit_' + simname - global metadata if metadata is None: - with importlib.resources.open_binary('abacusnbody.metadata', metadata_fn) as fp, asdf.open(fp) as af: - metadata = dict(af.tree) - del metadata['asdf_library'], metadata['history'] - for sim in metadata: - metadata[sim]['param'] = msgpack.loads(metadata[sim]['param'].data, strict_map_key=False) - metadata[sim]['state'] = msgpack.loads(metadata[sim]['state'].data, strict_map_key=False) - + metadata = {} + for metadata_fn in metadata_fns: + with importlib.resources.open_binary('abacusnbody.metadata', metadata_fn) as fp, asdf.open(fp) as af: + af_tree = dict(af.tree) + del af_tree['asdf_library'], af_tree['history'] + for sim in af_tree: + metadata[sim] = {} + metadata[sim]['param'] = msgpack.loads(af_tree[sim]['param'].data, strict_map_key=False) + metadata[sim]['state'] = msgpack.loads(af_tree[sim]['state'].data, strict_map_key=False) + if 'CLASS_power_spectrum' in af_tree[sim]: + metadata[sim]['CLASS_power_spectrum'] = af_tree[sim]['CLASS_power_spectrum'] if simname not in metadata: raise ValueError(f'Simulation "{simname}" is not in metadata file "{metadata_fn}"') + res = dict(metadata[simname]['param']) if 'CLASS_power_spectrum' in metadata[simname]: res['CLASS_power_spectrum'] = metadata[simname]['CLASS_power_spectrum'] diff --git a/docs/tutorials/analysis/power_spectrum.ipynb b/docs/tutorials/analysis/power_spectrum.ipynb index a5439086..849ea8f8 100644 --- a/docs/tutorials/analysis/power_spectrum.ipynb +++ b/docs/tutorials/analysis/power_spectrum.ipynb @@ -8,6 +8,22 @@ "# Power Spectrum Computation" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "The main entry point to the power spectrum module is the `calc_power()` function, which takes a set of points, deposits them onto a mesh (optionally with interlacing), computes the mode powers through an FFT, and then computes bandpowers and multipoles. This notebook is a quick demonstration of that function, with a similarly quick sanity check against nbodykit." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -17,40 +33,40 @@ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", - "from abacusnbody.analysis.power_spectrum import calc_power\n", - "\n", - "# load data\n", + "from abacusnbody.analysis.power_spectrum import calc_power" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ "power_test_data = dict(**np.load(\"../../../tests/data_power/test_pos.npz\"))\n", "Lbox = 1000.\n", "pos = power_test_data['pos']" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Specifications of the power spectrum computation. The only required args are `pos` and `Lbox`." + ] + }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/mnt/home/lgarrison/abacusutils/abacusnbody/analysis/power_spectrum.py:623: UserWarning: npartition 36 not large enough to use all 24 threads; should be 2*nthread\n", - " field = tsc_parallel(pos, nmesh, Lbox, weights=w, nthread=nthread)\n", - "/mnt/home/lgarrison/abacusutils/abacusnbody/analysis/power_spectrum.py:621: UserWarning: npartition 36 not large enough to use all 24 threads; should be 2*nthread\n", - " field = tsc_parallel(pos + d, nmesh, Lbox, weights=w, nthread=nthread)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "shift float32 float32\n", - "field fft complex64\n" - ] - } - ], + "outputs": [], "source": [ - "# specifications of the power spectrum computation\n", "interlaced = True\n", "compensated = True\n", "paste = 'TSC'\n", @@ -59,20 +75,163 @@ "logk = False\n", "k_hMpc_max = np.pi*nmesh/Lbox + 1.e-6\n", "nbins_k = nmesh//2\n", - "poles = [0, 2, 4]\n", - "\n", - "# compute power\n", + "poles = [0, 2, 4]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute the power spectrum, including bandpowers and multipoles:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ "power = calc_power(pos, Lbox, nbins_k, nbins_mu, k_hMpc_max, logk, \\\n", " paste, nmesh, compensated, interlaced, poles=poles)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result is an Astropy Table. The shape of the `k_avg`, `power`, etc, columns is `(nbins_k,nbins_mu)`. The `poles` column is shape `(nbins_k,len(poles))`." + ] + }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Table length=36\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
k_mink_maxk_midk_avgpowerN_modepolesN_mode_polesmu_minmu_maxmu_mid
float64float64float64float32[4]float32[4]int64[4]float32[3]int64float64[4]float64[4]float64[4]
0.00.0062832130849573640.0031416065424786820.005026548 .. 0.00628318547568.9023 .. 6049.02545 .. 27134.6523 .. 33801.09870.0 .. 0.750.25 .. 1.00.125 .. 0.875
0.0062832130849573640.0125664261699147280.0094248196274360470.010726068 .. 0.0125663712942.2803 .. 49420.0748 .. 212721.525 .. 8581.41260.0 .. 0.750.25 .. 1.00.125 .. 0.875
0.0125664261699147280.0188496392548720940.0157080327123934120.016180087 .. 0.015178949128.091 .. 26119.80516 .. 1817876.928 .. -4302.0957900.0 .. 0.750.25 .. 1.00.125 .. 0.875
0.0188496392548720940.0251328523398294570.0219912457973507750.022035956 .. 0.0222218549138.691 .. 20882.1420 .. 4216421.23 .. -2728.31471340.0 .. 0.750.25 .. 1.00.125 .. 0.875
0.0251328523398294570.0314160654247868240.028274458882308140.0278653 .. 0.028731079497.041 .. 21889.50880 .. 5814460.294 .. 1471.57342580.0 .. 0.750.25 .. 1.00.125 .. 0.875
0.0314160654247868240.037699278509744190.03455767196726550.03427213 .. 0.03448248653.626 .. 17725.803112 .. 9013018.891 .. -5607.49764100.0 .. 0.750.25 .. 1.00.125 .. 0.875
0.037699278509744190.043982491594701550.040840885052222870.04038116 .. 0.040898978631.141 .. 15932.965108 .. 13811582.084 .. 430.214024940.0 .. 0.750.25 .. 1.00.125 .. 0.875
0.043982491594701550.0502657046796589140.047124098137180230.046560723 .. 0.047365518191.2383 .. 14180.858144 .. 17810757.994 .. -2565.64876900.0 .. 0.750.25 .. 1.00.125 .. 0.875
0.0502657046796589140.056548917764616280.05340731122213760.053127706 .. 0.0535847478125.8447 .. 13207.899280 .. 2269751.573 .. -19.1544349620.0 .. 0.750.25 .. 1.00.125 .. 0.875
0.056548917764616280.062832130849573650.059690524307094960.05940248 .. 0.059612997919.7124 .. 12426.76280 .. 2749381.509 .. 696.677610980.0 .. 0.750.25 .. 1.00.125 .. 0.875
.................................
0.163363540208891460.169646753293848850.166505146751370150.16650459 .. 0.166508783508.4248 .. 4512.5692208 .. 22343929.0388 .. -230.0423689940.0 .. 0.750.25 .. 1.00.125 .. 0.875
0.169646753293848850.17592996637880620.17278835983632750.17277633 .. 0.172908683562.9634 .. 4224.7242212 .. 24183855.428 .. 27.3119594460.0 .. 0.750.25 .. 1.00.125 .. 0.875
0.17592996637880620.182213179463763560.17907157292128490.17895347 .. 0.179099193525.7412 .. 4377.74562600 .. 24583806.5596 .. 139.7558799780.0 .. 0.750.25 .. 1.00.125 .. 0.875
0.182213179463763560.188496392548720910.185354786006242220.185251 .. 0.18528613623.513 .. 4494.1072864 .. 27623875.3442 .. 257.82697111380.0 .. 0.750.25 .. 1.00.125 .. 0.875
0.188496392548720910.19477960563367830.19163799909119960.19156447 .. 0.191528663384.806 .. 4229.8432836 .. 28903696.4075 .. 195.4255114060.0 .. 0.750.25 .. 1.00.125 .. 0.875
0.19477960563367830.201062818718635660.1979212121761570.19781813 .. 0.197858663382.7285 .. 4218.51862960 .. 31863763.8347 .. -332.63657125780.0 .. 0.750.25 .. 1.00.125 .. 0.875
0.201062818718635660.2073460318035930.204204425261114320.20413305 .. 0.204237853378.4102 .. 4038.11183584 .. 33463576.4487 .. 61.47841134900.0 .. 0.750.25 .. 1.00.125 .. 0.875
0.2073460318035930.21362924488855040.21048763834607170.21046124 .. 0.210466743222.9612 .. 3837.1593504 .. 34663501.6152 .. -58.982677139620.0 .. 0.750.25 .. 1.00.125 .. 0.875
0.21362924488855040.219912457973507750.21677085143102910.21672955 .. 0.216717473282.833 .. 3728.51863748 .. 37703434.2856 .. 90.5686150620.0 .. 0.750.25 .. 1.00.125 .. 0.875
0.219912457973507750.22619567105846510.223054064515986420.22298157 .. 0.223080563189.86 .. 3806.17583702 .. 40023485.2192 .. 1.8451325156880.0 .. 0.750.25 .. 1.00.125 .. 0.875
" + ], + "text/plain": [ + "\n", + " k_min k_max ... mu_max mu_mid \n", + " float64 float64 ... float64[4] float64[4] \n", + "-------------------- -------------------- ... ----------- --------------\n", + " 0.0 0.006283213084957364 ... 0.25 .. 1.0 0.125 .. 0.875\n", + "0.006283213084957364 0.012566426169914728 ... 0.25 .. 1.0 0.125 .. 0.875\n", + "0.012566426169914728 0.018849639254872094 ... 0.25 .. 1.0 0.125 .. 0.875\n", + "0.018849639254872094 0.025132852339829457 ... 0.25 .. 1.0 0.125 .. 0.875\n", + "0.025132852339829457 0.031416065424786824 ... 0.25 .. 1.0 0.125 .. 0.875\n", + "0.031416065424786824 0.03769927850974419 ... 0.25 .. 1.0 0.125 .. 0.875\n", + " 0.03769927850974419 0.04398249159470155 ... 0.25 .. 1.0 0.125 .. 0.875\n", + " 0.04398249159470155 0.050265704679658914 ... 0.25 .. 1.0 0.125 .. 0.875\n", + "0.050265704679658914 0.05654891776461628 ... 0.25 .. 1.0 0.125 .. 0.875\n", + " 0.05654891776461628 0.06283213084957365 ... 0.25 .. 1.0 0.125 .. 0.875\n", + " ... ... ... ... ...\n", + " 0.16336354020889146 0.16964675329384885 ... 0.25 .. 1.0 0.125 .. 0.875\n", + " 0.16964675329384885 0.1759299663788062 ... 0.25 .. 1.0 0.125 .. 0.875\n", + " 0.1759299663788062 0.18221317946376356 ... 0.25 .. 1.0 0.125 .. 0.875\n", + " 0.18221317946376356 0.18849639254872091 ... 0.25 .. 1.0 0.125 .. 0.875\n", + " 0.18849639254872091 0.1947796056336783 ... 0.25 .. 1.0 0.125 .. 0.875\n", + " 0.1947796056336783 0.20106281871863566 ... 0.25 .. 1.0 0.125 .. 0.875\n", + " 0.20106281871863566 0.207346031803593 ... 0.25 .. 1.0 0.125 .. 0.875\n", + " 0.207346031803593 0.2136292448885504 ... 0.25 .. 1.0 0.125 .. 0.875\n", + " 0.2136292448885504 0.21991245797350775 ... 0.25 .. 1.0 0.125 .. 0.875\n", + " 0.21991245797350775 0.2261956710584651 ... 0.25 .. 1.0 0.125 .. 0.875" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "power" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `meta` field saves some information about how the power spectrum was calculated:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Lbox': 1000.0,\n", + " 'logk': False,\n", + " 'paste': 'TSC',\n", + " 'nmesh': 72,\n", + " 'compensated': True,\n", + " 'interlaced': True,\n", + " 'poles': [0, 2, 4],\n", + " 'nthread': 24,\n", + " 'N_pos': 421791,\n", + " 'is_weighted': False,\n", + " 'field_dtype': numpy.float32,\n", + " 'squeeze_mu_axis': True}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "power.meta" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compare with nbodykit" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load presaved nbodykit computation:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "# load presaved nbodykit computation\n", "comp_str = \"_compensated\" if compensated else \"\"\n", "int_str = \"_interlaced\" if interlaced else \"\"\n", "fn = f\"../../../tests/data_power/nbody_{paste}{comp_str}{int_str}.npz\"\n", @@ -82,24 +241,21 @@ "Pell_nbody = data['power_ell'].real" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot and compare:" + ] + }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -109,7 +265,6 @@ } ], "source": [ - "# plot and compare\n", "colors = ['k', 'c', 'm', 'y']\n", "plt.figure(figsize=(9, 7))\n", "for i in range(nbins_mu):\n", @@ -122,27 +277,17 @@ " plt.plot(power['k_mid'], power['power'][:, i] * power['k_mid'], c=colors[i], ls='--', label=label2)\n", "plt.ylabel(r\"$k P(k)$\")\n", "plt.xlabel(r\"$k \\ [h/{\\rm Mpc}]$\")\n", - "plt.legend()" + "plt.legend();" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", 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" ] @@ -164,15 +309,8 @@ " plt.plot(power['k_mid'], power['poles'][:,i] * power['k_mid'], c=colors[i], ls='--', label=label2)\n", "plt.ylabel(r\"$k P_\\ell(k)$\")\n", "plt.xlabel(r\"$k \\ [h/{\\rm Mpc}]$\")\n", - "plt.legend()" + "plt.legend();" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/scripts/power/bench.py b/scripts/power/bench.py index ae3d0463..34a2ba14 100755 --- a/scripts/power/bench.py +++ b/scripts/power/bench.py @@ -51,7 +51,7 @@ def main(**kwargs): for _ in range(nrep): # field_fft = get_field_fft(pos, Lbox, nmesh, paste, None, None, compensated, interlaced, nthread=nthread) # k_bin_edges, mu_bin_edges = get_k_mu_edges(Lbox, kmax, nbins_k, nbins_mu, logk) - # p3d, N3d, binned_poles, Npoles = calc_pk_from_deltak(field_fft, Lbox, k_bin_edges, mu_bin_edges, None, None, nthread=nthread) + # p3d, N3d, binned_poles, Npoles, k_avg = calc_pk_from_deltak(field_fft, Lbox, k_bin_edges, mu_bin_edges, None, None, nthread=nthread) calc_power(pos, Lbox, nbins_k, nbins_mu, kmax, logk, paste, nmesh, compensated, interlaced, nthread=nthread diff --git a/tests/test_power.py b/tests/test_power.py index 6472678f..133d5243 100644 --- a/tests/test_power.py +++ b/tests/test_power.py @@ -32,12 +32,18 @@ def test_power(power_test_data, interlaced, compensated, paste): logk = False k_hMpc_max = np.pi*nmesh/Lbox + 1.e-6 # so that the first bin includes +/- 2pi/L which nbodykit does for this choice of nmesh nbins_k = nmesh//2 + poles = (0,2,4) # compute power res = calc_power(pos, Lbox, nbins_k, nbins_mu, k_hMpc_max, logk, - paste, nmesh, compensated, interlaced, + paste, nmesh, compensated, interlaced, poles=poles, ) + # check that the monopole and bandpower are equal + assert np.allclose(res['poles'][:,0], + (res['power'] * res['N_mode']).sum(axis=1) / res['N_mode'].sum(axis=1), + ) + # load presaved nbodykit computation comp_str = "_compensated" if compensated else "" int_str = "_interlaced" if interlaced else ""