diff --git a/README.md b/README.md index ba3ec6a..ff08f59 100644 --- a/README.md +++ b/README.md @@ -1,11 +1,16 @@ -# CSiBORG analysis tools :dart: +# CSiBORG tools -## TODO :scroll: -- [ ] Calculate $M_{\rm vir}, R_{\rm vir}, c$ from $R_s, \rho_0, \ldots$ -- [ ] Calculate $M_{\rm 500c}$ by sphere shrinking -- [ ] Calculate the cross-correlation in CSiBORG. Should see the scale of the constraints? +## :scroll: Short-term TODO +- [x] Calculate $M_{\rm vir}, R_{\rm vir}, c$ from $R_s, \rho_0, \ldots$ +- [x] In `NFWPosterior` correct for the radius in which particles are fitted. +- [x] Calculate $M_{\rm 500c}$ by sphere shrinking +- [x] Change to log10 of the scale factor +- [ ] Calculate uncertainty on $R_{\rm s}$, switch to `JAX` and get gradients. + + +## :hourglass: Long-term TODO - [ ] Improve file naming system +- [ ] Calculate the cross-correlation in CSiBORG. Should see the scale of the constraints? -## Open questions :bulb: -- Get uncertainty on the fitted $R_{\rm s}$? If so get this directly from JAX. \ No newline at end of file +## :bulb: Open questions \ No newline at end of file diff --git a/csiborgtools/fits/halo.py b/csiborgtools/fits/halo.py index 42d09d3..c4abbdf 100644 --- a/csiborgtools/fits/halo.py +++ b/csiborgtools/fits/halo.py @@ -18,6 +18,7 @@ Tools for splitting the particles and a clump object. import numpy +from scipy.optimize import minimize_scalar from os import remove from warnings import warn from os.path import join @@ -235,7 +236,7 @@ def pick_single_clump(n, particles, particle_clumps, clumps): class Clump: - """ + r""" A clump (halo) object to handle the particles and their clump's data. Parameters @@ -254,28 +255,40 @@ class Clump: Clump center coordinate along the y-axis. z0 : float Clump center coordinate along the z-axis. - clump_mass : float - Mass of the clump. - vx : 1-dimensional array - Particle velocity along the x-axis. - vy : 1-dimensional array - Particle velocity along the y-axis. - vz : 1-dimensional array - Particle velocity along the z-axis. + clump_mass : float, optional + Mass of the clump. By default not set. + vx : 1-dimensional array, optional + Particle velocity along the x-axis. By default not set. + vy : 1-dimensional array, optional + Particle velocity along the y-axis. By default not set. + vz : 1-dimensional array, optional + Particle velocity along the z-axis. By default not set. + index : int, optional + The halo finder index of this clump. By default not set. + rhoc : float, optional + The critical density :math:`\rho_c` at this snapshot in box units. By + default not set. """ _r = None + _rmin = None + _rmax = None _pos = None _clump_pos = None _clump_mass = None _vel = None + _Npart = None + _index = None + _rhoc = None def __init__(self, x, y, z, m, x0, y0, z0, clump_mass=None, - vx=None, vy=None, vz=None): + vx=None, vy=None, vz=None, index=None, rhoc=None): self.pos = (x, y, z, x0, y0, z0) self.clump_pos = (x0, y0, z0) self.clump_mass = clump_mass self.vel = (vx, vy, vz) self.m = m + self.index = index + self.rhoc = rhoc @property def pos(self): @@ -294,7 +307,46 @@ class Clump: """Sets `pos` and calculates radial distance.""" x, y, z, x0, y0, z0 = X self._pos = numpy.vstack([x - x0, y - y0, z - z0]).T - self.r = numpy.sum(self.pos**2, axis=1)**0.5 + self._r = numpy.sum(self.pos**2, axis=1)**0.5 + self._rmin = numpy.min(self._r) + self._rmax = numpy.max(self._r) + self._Npart = self._r.size + + @property + def r(self): + """ + Radial distance of the particles from the clump peak. + + Returns + ------- + r : 1-dimensional array + Array of shape `(n_particles, )`. + """ + return self._r + + @property + def rmin(self): + """ + The minimum radial distance of a particle. + + Returns + ------- + rmin : float + The minimum distance. + """ + return self._rmin + + @property + def rmax(self): + """ + The maximum radial distance of a particle. + + Returns + ------- + rmin : float + The maximum distance. + """ + return self._rmax @property def Npart(self): @@ -306,7 +358,7 @@ class Clump: Npart : int Number of particles. """ - return self.r.size + return self._Npart @property def clump_pos(self): @@ -338,12 +390,14 @@ class Clump: mass : float Clump mass. """ + if self._clump_mass is None: + raise ValueError("Clump mass `clump_mass` has not been set.") return self._clump_mass @clump_mass.setter def clump_mass(self, mass): """Sets `clump_mass`, making sure it is a float.""" - if not isinstance(mass, float): + if mass is not None and not isinstance(mass, float): raise ValueError("`clump_mass` must be a float.") self._clump_mass = mass @@ -391,25 +445,46 @@ class Clump: self._m = m @property - def r(self): + def index(self): """ - Radial distance of particles from the clump peak. + The halo finder clump index. Returns ------- - r : 1-dimensional array - Array of shape `(n_particles, )` + hindex : int + The index. """ - return self._r + if self._index is None: + raise ValueError("Halo index `hindex` has not been set.") + return self._index - @r.setter - def r(self, r): - """Sets `r`. Again checks the shape.""" - if not isinstance(r, numpy.ndarray) and r.ndim == 1: - raise TypeError("`r` must be a 1-dimensional array.") - if not numpy.all(r >= 0): - raise ValueError("`r` larger than zero.") - self._r = r + @index.setter + def index(self, n): + """Sets the halo index, making sure it is an integer.""" + if n is not None and not (isinstance(n, (int, numpy.int64)) and n > 0): + raise ValueError("Halo index `index` must be an integer > 0.") + self._index = n + + @property + def rhoc(self): + """ + The critical density :math:`\rho_c` at this snapshot in box units. + + Returns + ------- + rhoc : float + The critical density. + """ + if self._rhoc is None: + raise ValueError("The critical density `rhoc` has not been set.") + return self._rhoc + + @rhoc.setter + def rhoc(self, rhoc): + """Sets the critical density. Makes sure it is > 0.""" + if rhoc is not None and not rhoc > 0: + raise ValueError("Critical density `rho_c` must be > 0.") + self._rhoc = rhoc @property def total_particle_mass(self): @@ -435,8 +510,165 @@ class Clump: """ return numpy.mean(self.pos + self.clump_pos, axis=0) + def enclosed_spherical_mass(self, rmax, rmin=None): + """ + The enclosed spherical mass between two radii. All quantities remain + in the box units. + + Parameters + ---------- + rmax : float + The maximum radial distance. + rmin : float, optional + The minimum radial distance. By default the radial distance of the + closest particle. + + Returns + ------- + M_enclosed : float + The enclosed mass. + """ + rmin = self.rmin if rmin is None else rmin + return numpy.sum(self.m[(self.r >= rmin) & (self.r <= rmax)]) + + def enclosed_spherical_density(self, rmax, rmin=None): + """ + The enclosed spherical density between two radii. All quantities + remain in box units. + + Parameters + ---------- + rmax : float + The maximum radial distance. + rmin : float, optional + The minimum radial distance. By default the radial distance of the + closest particle. + + Returns + ------- + rho_enclosed : float + The enclosed density. + """ + rmin = self.rmin if rmin is None else rmin + M = self.enclosed_spherical_mass(rmax, rmin) + V = 4 * numpy.pi / 3 * (rmax**3 - rmin**3) + return M / V + + def radius_enclosed_overdensity(self, delta): + r""" + Radius of where the mean enclosed spherical density reaches a multiple + of the critical radius at a given redshift `self.rho_c`. Returns + `numpy.nan` if the fit does not converge. Note that `rhoc` must be in + box units! + + Parameters + ---------- + delta : int or float + The :math:`\delta_{\rm x}` parameters where :math:`\mathrm{x}` is + the overdensity multiple. + + Returns + ------- + rx : float + The radius where the enclosed density reaches required value. + """ + # Loss function to minimise + def loss(r): + return abs(self.enclosed_spherical_density(r, self.rmin) + - delta * self.rhoc) + + res = minimize_scalar(loss, bounds=(self.rmin, self.rmax), + method='bounded') + return res.x if res.success else numpy.nan + + @property + def r200(self): + r""" + The radius at which the mean spherical density reaches 200 times + the critical density, :math:`R_{200c}`. Returns `numpy.nan` if the + estimate fails. + + Returns + ------- + r200 : float + The R200c radius + """ + return self.radius_enclosed_overdensity(200) + + @property + def r178(self): + r""" + The radius at which the mean spherical density reaches 178 times + the critical density, :math:`R_{178c}`. Returns `numpy.nan` if the + estimate fails. + + Returns + ------- + r178 : float + The R178c radius + """ + return self.radius_enclosed_overdensity(178) + + @property + def r500(self): + r""" + The radius at which the mean spherical density reaches 500 times + the critical density, :math:`R_{500c}`. Returns `numpy.nan` if the + estimate fails. + + Returns + ------- + r500 : float + The R500c radius + """ + return self.radius_enclosed_overdensity(500) + + @property + def m200(self): + r""" + The mass enclosed within the :math:`R_{200c}` region, obtained from + `self.r200`. Returns `numpy.nan` if the radius estimate fails. + + Returns + ------- + m200 : float + The M200 mass + """ + r200 = self.radius_enclosed_overdensity(200) + return self.enclosed_spherical_mass(r200) + + @property + def m178(self): + r""" + The mass enclosed within the :math:`R_{178c}` region, obtained from + `self.r178`. This is approximately the virial mass, though this notion + depends on the dynamical state of the clump. Returns `numpy.nan` if + the radius estimate fails. + + Returns + ------- + m178 : float + The M178 mass + """ + r178 = self.radius_enclosed_overdensity(178) + return self.enclosed_spherical_mass(r178) + + @property + def m500(self): + r""" + The mass enclosed within the :math:`R_{500c}` region, obtained from + `self.r500`. Returns `numpy.nan` if the radius estimate fails. + + Returns + ------- + m500 : float + The M500 mass + """ + r500 = self.radius_enclosed_overdensity(500) + return self.enclosed_spherical_mass(r500) + @classmethod - def from_arrays(cls, particles, clump): + def from_arrays(cls, particles, clump, rhoc=None): """ Initialises `Halo` from `particles` containing the relevant particle information and its `clump` information. @@ -452,14 +684,15 @@ class Clump: Returns ------- - halo : `Halo` - An initialised halo object. + clump : `Clump` + An initialised clump object. """ x, y, z, m = (particles[p] for p in ["x", "y", "z", "M"]) - x0, y0, z0, cl_mass = ( - clump[p] for p in ["peak_x", "peak_y", "peak_z", "mass_cl"]) + x0, y0, z0, cl_mass, hindex = ( + clump[p] for p in ["peak_x", "peak_y", "peak_z", "mass_cl", + "index"]) try: vx, vy, vz = (particles[p] for p in ["vx", "vy", "vz"]) except ValueError: vx, vy, vz = None, None, None - return cls(x, y, z, m, x0, y0, z0, cl_mass, vx, vy, vz) + return cls(x, y, z, m, x0, y0, z0, cl_mass, vx, vy, vz, hindex, rhoc) diff --git a/csiborgtools/fits/haloprofile.py b/csiborgtools/fits/haloprofile.py index 1a58769..7264f95 100644 --- a/csiborgtools/fits/haloprofile.py +++ b/csiborgtools/fits/haloprofile.py @@ -43,7 +43,8 @@ class NFWProfile: def __init__(self): pass - def profile(self, r, Rs, rho0): + @staticmethod + def profile(r, Rs, rho0): r""" Halo profile evaluated at :math:`r`. @@ -64,7 +65,8 @@ class NFWProfile: x = r / Rs return rho0 / (x * (1 + x)**2) - def logprofile(self, r, Rs, rho0): + @staticmethod + def logprofile(r, Rs, rho0): r""" Natural logarithm of the halo profile evaluated at :math:`r`. @@ -85,7 +87,8 @@ class NFWProfile: x = r / Rs return numpy.log(rho0) - numpy.log(x) - 2 * numpy.log(1 + x) - def enclosed_mass(self, r, Rs, rho0): + @staticmethod + def enclosed_mass(r, Rs, rho0): r""" Enclosed mass of a NFW profile in radius :math:`r`. @@ -198,16 +201,18 @@ class NFWProfile: class NFWPosterior(NFWProfile): r""" - Posterior of for fitting the NFW profile in the range specified by the - closest and further particle. The likelihood is calculated as + Posterior for fitting the NFW profile in the range specified by the + closest particle and the :math:`r_{200c}` radius. The likelihood is + calculated as .. math:: - \frac{4\pi r^2 \rho(r)} {M(r_\min, r_\max)} \frac{m}{M / N} + \frac{4\pi r^2 \rho(r)} {M(r_{\min} r_{200c})} \frac{m}{M / N} - where :math:`M(r_\min, r_\max)` is the enclosed mass between the closest - and further particle as expected from a NFW profile, :math:`m` is the - particle mass, :math:`M` is the sum of the particle masses and :math:`N` - is the number of particles. + where :math:`M(r_{\min} r_{200c}))` is the NFW enclosed mass between the + closest particle and the :math:`r_{200c}` radius, :math:`m` is the particle + mass, :math:`M` is the sum of the particle masses and :math:`N` is the + number of particles. Calculated only using particles within the + above-mentioned range. Paramaters ---------- @@ -215,10 +220,12 @@ class NFWPosterior(NFWProfile): Clump object containing the particles and clump information. """ _clump = None - _N = None + _binsguess = 10 + _r = None + _Npart = None + _m = None _rmin = None _rmax = None - _binsguess = 10 def __init__(self, clump): # Initialise the NFW profile @@ -228,7 +235,8 @@ class NFWPosterior(NFWProfile): @property def clump(self): """ - Clump object. + Clump object containig all particles, i.e. ones beyond :math:`R_{200c}` + as well. Returns ------- @@ -237,44 +245,49 @@ class NFWPosterior(NFWProfile): """ return self._clump - @clump.setter - def clump(self, clump): - """Sets `clump` and precalculates useful things.""" - if not isinstance(clump, Clump): - raise TypeError( - "`clump` must be :py:class:`csiborgtools.fits.Clump` type. " - "Currently `{}`".format(type(clump))) - self._clump = clump - # Set here the rest of the radial info - self._rmax = numpy.max(self.r) - # r_min could be zero if particle in the potential minimum. - # Set it to the closest particle that is at distance > 0 - self._rmin = numpy.sort(self.r[self.r > 0])[0] - self._logrmin = numpy.log(self.rmin) - self._logrmax = numpy.log(self.rmax) - self._logprior_volume = numpy.log(self._logrmax - self._logrmin) - self._N = self.r.size - # Precalculate useful things - self._logMtot = numpy.log(numpy.sum(self.clump.m)) - gamma = 4 * numpy.pi * self.r**2 * self.clump.m * self.N - self._ll0 = numpy.sum(numpy.log(gamma)) - self.N * self._logMtot - @property def r(self): - """ - Radial distance of particles. + r""" + Radial distance of particles used to fit the NFW profile, i.e. the ones + whose radial distance is less than :math:`R_{\rm 200c}`. Returns ------- r : 1-dimensional array - Radial distance of particles. + Array of shape `(n_particles, )`. """ - return self.clump.r + return self._r + + @property + def Npart(self): + r""" + Number of particles used to fit the NFW profile, i.e. the ones + whose radial distance is less than :math:`R_{\rm 200c}`. + + Returns + ------- + Npart : int + Number of particles. + """ + return self._Npart + + @property + def m(self): + r""" + Mass of particles used to fit the NFW profile, i.e. the ones + whose radial distance is less than :math:`R_{\rm 200c}`. + + Returns + ------- + r : 1-dimensional array + Array of shape `(n_particles, )`. + """ + return self._m @property def rmin(self): """ - Minimum radial distance of a particle belonging to this clump. + The minimum radial distance of a particle. Returns ------- @@ -285,30 +298,50 @@ class NFWPosterior(NFWProfile): @property def rmax(self): - """ - Maximum radial distance of a particle belonging to this clump. + r""" + The maximum radial distance used to fit the profile, here takem to be + the :math:`R_{\rm 200c}`. Returns ------- - rmin : float - The maximum distance. + rmax : float + The R200c radius. """ return self._rmax - @property - def N(self): - """ - The number of particles in this clump. - - Returns - ------- - N : int - Number of particles. - """ - return self._N + @clump.setter + def clump(self, clump): + """Sets `clump` and precalculates useful things.""" + if not isinstance(clump, Clump): + raise TypeError( + "`clump` must be :py:class:`csiborgtools.fits.Clump` type. " + "Currently `{}`".format(type(clump))) + self._clump = clump + # The minimum separation + rmin = self.clump.rmin + rmax = self.clump.r200 + # Set the distances + self._rmin = rmin + self._rmax = rmax + # Set particles that will be used to fit the halo + mask_r200 = (self.clump.r >= rmin) & (self.clump.r <= rmax) + self._r = self.clump.r[mask_r200] + self._m = self.clump.m[mask_r200] + self._Npart = self._r.size + # Ensure that the minimum separation is > 0 for finite log + if self.rmin > 0: + self._logrmin = numpy.log10(self.rmin) + else: + self._logrmin = numpy.log10(numpy.min(self.r[self.r > 0])) + self._logrmax = numpy.log10(self.rmax) + self._logprior_volume = numpy.log(self._logrmax - self._logrmin) + # Precalculate useful things + self._logMtot = numpy.log(numpy.sum(self.m)) + gamma = 4 * numpy.pi * self.r**2 * self.m * self.Npart + self._ll0 = numpy.sum(numpy.log(gamma)) - self.Npart * self._logMtot def rho0_from_logRs(self, logRs): - """ + r""" Obtain :math:`\rho_0` of the NFW profile from the integral constraint on total mass. Calculated as the ratio between the total particle mass and the enclosed NFW profile mass. @@ -324,8 +357,8 @@ class NFWPosterior(NFWProfile): The NFW density parameter. """ Mtot = numpy.exp(self._logMtot) - Rs = numpy.exp(logRs) - Mnfw_norm = self.bounded_enclosed_mass(self.rmin, self.rmax, Rs, 1) + Mnfw_norm = self.bounded_enclosed_mass(self.rmin, self.rmax, + 10**logRs, 1) return Mtot / Mnfw_norm def logprior(self, logRs): @@ -360,11 +393,12 @@ class NFWPosterior(NFWProfile): ll : float The logarithmic likelihood. """ - Rs = numpy.exp(logRs) + Rs = 10**logRs # Expected enclosed mass from a NFW - Mnfw = self.bounded_enclosed_mass(self.rmin, self.rmax, Rs, 1) + Mnfw = self.bounded_enclosed_mass(self.rmin, self.rmax, + Rs, 1) ll = self._ll0 + numpy.sum(self.logprofile(self.r, Rs, 1)) - return ll - self.N * numpy.log(Mnfw) + return ll - self.Npart * numpy.log(Mnfw) @property def initlogRs(self): @@ -378,7 +412,8 @@ class NFWPosterior(NFWProfile): initlogRs : float The initial guess of :math:`\log R_{\rm s}`. """ - bins = numpy.linspace(self.rmin, self.rmax, self._binsguess) + bins = numpy.linspace(self.rmin, self.rmax, + self._binsguess) counts, edges = numpy.histogram(self.r, bins) return numpy.log(edges[numpy.argmax(counts)]) @@ -401,61 +436,19 @@ class NFWPosterior(NFWProfile): return - numpy.infty return self.loglikelihood(logRs) + lp - def hamiltonian(self, logRs): - """ - Negative logarithmic posterior (i.e. the Hamiltonian). - - Parameters - ---------- - logRs : float - Logarithmic scale factor in units matching the coordinates. - - Returns - ------- - neg_lpost : float - The Hamiltonian. - """ - return - self(logRs) - def maxpost_logRs(self): r""" Maximum a-posterio estimate of the scale radius :math:`\log R_{\rm s}`. + Returns the scale radius if the fit converged, otherwise `numpy.nan`. Returns ------- - res : `scipy.optimize.OptimizeResult` - Optimisation result. + res : float + The scale radius. """ - bounds = (self._logrmin, self._logrmax) - return minimize_scalar( - self.hamiltonian, bounds=bounds, method='bounded') - - @classmethod - def from_coords(cls, x, y, z, m, x0, y0, z0): - """ - Initiate `NFWPosterior` from a set of Cartesian coordinates. - - Parameters - ---------- - x : 1-dimensional array - Particle coordinates along the x-axis. - y : 1-dimensional array - Particle coordinates along the y-axis. - z : 1-dimensional array - Particle coordinates along the z-axis. - m : 1-dimensional array - Particle masses. - x0 : float - Halo center coordinate along the x-axis. - y0 : float - Halo center coordinate along the y-axis. - z0 : float - Halo center coordinate along the z-axis. - - Returns - ------- - post : `NFWPosterior` - Initiated `NFWPosterior` instance. - """ - r = numpy.sqrt((x - x0)**2 + (y - y0)**2 + (z - z0)**2) - return cls(r, m) + # Loss function to optimize + def loss(logRs): + return - self(logRs) + res = minimize_scalar(loss, bounds=(self._logrmin, self._logrmax), + method='bounded') + return res.x if res.success else numpy.nan diff --git a/csiborgtools/io/__init__.py b/csiborgtools/io/__init__.py index 6bc10b1..806ba0d 100644 --- a/csiborgtools/io/__init__.py +++ b/csiborgtools/io/__init__.py @@ -17,6 +17,7 @@ from .readsim import (get_csiborg_ids, get_sim_path, get_snapshots, # noqa get_snapshot_path, read_info, nparts_to_start_ind, # noqa open_particle, open_unbinding, read_particle, # noqa drop_zero_indx, # noqa - read_clumpid, read_clumps, read_mmain) # noqa + read_clumpid, read_clumps, read_mmain, # noqa + merge_mmain_to_clumps) # noqa from .readobs import (read_planck2015, read_2mpp) # noqa from .outsim import (dump_split, combine_splits) # noqa diff --git a/csiborgtools/io/outsim.py b/csiborgtools/io/outsim.py index 03401de..990263f 100644 --- a/csiborgtools/io/outsim.py +++ b/csiborgtools/io/outsim.py @@ -85,9 +85,6 @@ def combine_splits(Nsplits, Nsim, Nsnap, outdir, cols_add, remove_splits=False, out : structured array Clump array with appended results from the splits. """ - # Will be grabbing these columns from each split - cols_add = [("npart", I64), ("totpartmass", F64), ("logRs", F64), - ("rho0", F64)] # Load clumps to see how many there are and will add to this array simpath = get_sim_path(Nsim) clumps = read_clumps(Nsnap, simpath, cols=None) diff --git a/csiborgtools/io/readsim.py b/csiborgtools/io/readsim.py index efa05f0..65f9e6d 100644 --- a/csiborgtools/io/readsim.py +++ b/csiborgtools/io/readsim.py @@ -23,7 +23,7 @@ from os.path import (join, isfile) from glob import glob from tqdm import tqdm -from ..utils import cols_to_structured +from ..utils import (cols_to_structured, add_columns) F16 = numpy.float16 @@ -495,3 +495,30 @@ def read_mmain(n, srcdir, fname="Mmain_{}.npy"): out[name] = arr[:, i] return out + + +def merge_mmain_to_clumps(clumps, mmain): + """ + Merge columns from the `mmain` files to the `clump` file, matches them + by their halo index while assuming that the indices `index` in both arrays + are sorted. + + Parameters + ---------- + clumps : structured array + Clumps structured array. + mmain : structured array + Parent halo array whose information is to be merged into `clumps`. + + Returns + ------- + out : structured array + Array with added columns. + """ + X = numpy.full((clumps.size, 2), numpy.nan) + # Mask of which clumps have a mmain index + mask = numpy.isin(clumps["index"], mmain["index"]) + + X[mask, 0] = mmain["mass_cl"] + X[mask, 1] = mmain["sub_frac"] + return add_columns(clumps, X, ["mass_mmain", "sub_frac"]) diff --git a/csiborgtools/units/box_units.py b/csiborgtools/units/box_units.py index d64454c..de9432d 100644 --- a/csiborgtools/units/box_units.py +++ b/csiborgtools/units/box_units.py @@ -16,24 +16,16 @@ Simulation box unit transformations. """ - +import numpy from astropy.cosmology import LambdaCDM from astropy import (constants, units) from ..io import read_info -# Conversion factors -MSUNCGS = constants.M_sun.cgs.value -KPC_TO_CM = 3.0856775814913673e+21 -PI = 3.1415926535897932384626433 - - class BoxUnits: r""" Box units class for converting between box and physical units. - TODO: check factors of :math:`a` in mass and density transformations - Paramaters ---------- Nsnap : int @@ -41,6 +33,7 @@ class BoxUnits: simpath : str Path to the simulation where its snapshot index folders are stored. """ + _cosmo = None def __init__(self, Nsnap, simpath): """ @@ -51,21 +44,105 @@ class BoxUnits: "omega_m", "omega_l", "omega_k", "omega_b", "unit_l", "unit_d", "unit_t"] for par in pars: - setattr(self, par, float(info[par])) + setattr(self, "_" + par, float(info[par])) - self.h = self.H0 / 100 - self.cosmo = LambdaCDM(H0=self.H0, Om0=self.omega_m, Ode0=self.omega_l, - Tcmb0=2.725 * units.K, Ob0=self.omega_b) - # Constants in box units - self.G = constants.G.cgs.value * (self.unit_d * self.unit_t ** 2) - self.H0 = self.H0 * 1e5 / (1e3 * KPC_TO_CM) * self.unit_t - self.c = constants.c.cgs.value * self.unit_t / self.unit_l - self.rho_crit = 3 * self.H0 ** 2 / (8 * PI * self.G) + self._cosmo = LambdaCDM(H0=self._H0, Om0=self._omega_m, + Ode0=self._omega_l, Tcmb0=2.725 * units.K, + Ob0=self._omega_b) + self._Msuncgs = constants.M_sun.cgs.value # Solar mass in grams + + @property + def cosmo(self): + """ + The box cosmology. + + Returns + ------- + cosmo : `astropy.cosmology.LambdaCDM` + The CSiBORG cosmology. + """ + return self._cosmo + + @property + def H0(self): + r""" + The Hubble parameter at the time of the snapshot + in :math:`\mathrm{Mpc} / \mathrm{km} / \mathrm{s}`. + + Returns + ------- + H0 : float + Hubble constant. + """ + return self._H0 + + @property + def h(self): + r""" + The little 'h` parameter at the time of the snapshot. + + Returns + ------- + h : float + The little h + """ + return self._H0 / 100 + + @property + def box_G(self): + """ + Gravitational constant :math:`G` in box units. Given everything else + it looks like `self.unit_t` is in seconds. + + Returns + ------- + G : float + The gravitational constant. + """ + return constants.G.cgs.value * (self._unit_d * self._unit_t ** 2) + + @property + def box_H0(self): + """ + Present time Hubble constant :math:`H_0` in box units. + + Returns + ------- + H0 : float + The Hubble constant. + """ + return self.H0 * 1e5 / units.Mpc.to(units.cm) * self._unit_t + + @property + def box_c(self): + """ + Speed of light in box units. + + Returns + ------- + c : float + The speed of light. + """ + return constants.c.cgs.value * self._unit_t / self._unit_l + + @property + def box_rhoc(self): + """ + Critical density in box units. + + Returns + ------- + rhoc : float + The critical density. + """ + + return 3 * self.box_H0 ** 2 / (8 * numpy.pi * self.box_G) def box2kpc(self, length): r""" Convert length from box units to :math:`\mathrm{ckpc}` (with - :math:`h=0.705`). + :math:`h=0.705`). It appears that `self.unit_l` must be in + :math:`\mathrm{cm}`. Parameters ---------- @@ -77,7 +154,7 @@ class BoxUnits: length : foat Length in :math:`\mathrm{ckpc}` """ - return length * self.unit_l / KPC_TO_CM / self.aexp + return length * (self._unit_l / units.kpc.to(units.cm) / self._aexp) def kpc2box(self, length): r""" @@ -94,7 +171,7 @@ class BoxUnits: length : foat Length in box units. """ - return length / self.unit_l * KPC_TO_CM * self.aexp + return length / (self._unit_l / units.kpc.to(units.cm) / self._aexp) def solarmass2box(self, mass): r""" @@ -110,11 +187,13 @@ class BoxUnits: mass : float Mass in box units. """ - return mass / self.unit_d / (self.unit_l**3 / MSUNCGS) + return mass / (self._unit_d * self._unit_l**3) * self._Msuncgs def box2solarmass(self, mass): r""" Convert mass from box units to :math:`M_\odot` (with :math:`h=0.705`). + It appears that `self.unit_d` is density in units of + :math:`\mathrm{g}/\mathrm{cm}^3`. Parameters ---------- @@ -126,7 +205,7 @@ class BoxUnits: mass : float Mass in :math:`M_\odot`. """ - return mass * self.unit_d * self.unit_l**3 / MSUNCGS + return mass * (self._unit_d * self._unit_l**3) / self._Msuncgs def box2dens(self, density): r""" @@ -143,7 +222,8 @@ class BoxUnits: density : float Density in :math:`M_\odot / \mathrm{pc}^3`. """ - return density * self.unit_d / MSUNCGS * (KPC_TO_CM * 1e-3)**3 + return (density * self._unit_d / self._Msuncgs + * (units.pc.to(units.cm))**3) def dens2box(self, density): r""" @@ -160,4 +240,5 @@ class BoxUnits: density : float Density in box units. """ - return density / self.unit_d * MSUNCGS / (KPC_TO_CM * 1e-3)**3 + return (density / self._unit_d * self._Msuncgs + / (units.pc.to(units.cm))**3) diff --git a/scripts/run_fit_halos.py b/scripts/run_fit_halos.py index 26e617b..73e3fa3 100644 --- a/scripts/run_fit_halos.py +++ b/scripts/run_fit_halos.py @@ -44,7 +44,10 @@ nproc = comm.Get_size() dumpdir = utils.dumpdir loaddir = join(utils.dumpdir, "temp") cols_collect = [("npart", I64), ("totpartmass", F64), ("logRs", F64), - ("rho0", F64)] + ("rho0", F64), ("rmin", F64), ("rmax", F64), + ("r200", F64), ("r178", F64), ("r500", F64), + ("m200", F64), ("m178", F64), ("m500", F64)] + # NOTE later loop over sims too Nsim = Nsims[0] @@ -56,7 +59,9 @@ for Nsplit in jobs: N = clumps.size cols = [("index", I64), ("npart", I64), ("totpartmass", F64), - ("logRs", F64), ("rho0", F64)] + ("logRs", F64), ("rho0", F64), ("rmin", F64), ("rmax", F64), + ("r200", F64), ("r178", F64), ("r500", F64), + ("m200", F64), ("m178", F64), ("m500", F64)] out = csiborgtools.utils.cols_to_structured(N, cols) out["index"] = clumps["index"] @@ -65,15 +70,25 @@ for Nsplit in jobs: xs = csiborgtools.fits.pick_single_clump(n, parts, part_clumps, clumps) clump = csiborgtools.fits.Clump.from_arrays(*xs) out["npart"][n] = clump.Npart + out["rmin"][n] = clump.rmin + out["rmax"][n] = clump.rmax out["totpartmass"][n] = clump.total_particle_mass + out["r200"][n] = clump.r200 + out["r178"][n] = clump.r178 + out["r500"][n] = clump.r500 + out["m200"][n] = clump.m200 + out["m178"][n] = clump.m178 + out["m500"][n] = clump.m200 # NFW profile fit - if clump.Npart > 10: + if clump.Npart > 10 and numpy.isfinite(out["r200"][n]): + # NOTE here it calculates the r200 again, but its fast so does not + # matter anyway. nfwpost = csiborgtools.fits.NFWPosterior(clump) logRs = nfwpost.maxpost_logRs() - if logRs.success: - out["logRs"][n] = logRs.x - out["rho0"][n] = nfwpost.rho0_from_logRs(logRs.x) + if not numpy.isnan(logRs): + out["logRs"][n] = logRs + out["rho0"][n] = nfwpost.rho0_from_logRs(logRs) csiborgtools.io.dump_split(out, Nsplit, Nsim, Nsnap, dumpdir)