Source code for pynbody.analysis.profile

"""
Support for creating profiles of various quantities, normally as a function of 2d or 3d radius.

The functions defined in the module represent profiles that can be access from a
:class:`~pynbody.analysis.profile.Profile` object.

For more information and example usage, see the :ref:`profile` tutorial and the documentation for the
:class:`~pynbody.analysis.profile.Profile` class.
"""

import logging
import math
import pickle
import warnings
from time import process_time

import numpy as np

import pynbody

from .. import array, units, util

logger = logging.getLogger('pynbody.analysis.profile')


[docs] class Profile: """Generates profiles of specified quantities as a function of radius or other binning quantity. Any quantity known in the SimSnap can be profiled, meaning that the mean value of that quantity in each bin is calculated. .. seealso:: For more information and example usage, see the :ref:`profile` tutorial. To define profiles of new quantities, see :meth:`profile_property`. **Implicit averaging**: If an array ``ar`` is defined in the underlying ``SimSnap``, then a profile of ``ar`` can be accessed as ``p['ar']`` where ``p`` is a ``Profile`` object. For example, ``p['vr']`` gives the radial velocity profile. Implicitly, this is averaged over all particles in each bin, weighted by mass (unless an alternate weighting scheme is passed to the ``weight_by`` keyword argument of the constructor). **Dispersions**: One may append ``_rms`` or ``_disp`` to the name of a defined array to get the root-mean-square or dispersion profile, respectively. For example, ``p['vr_rms']`` gives the root-mean-square radial velocity profile, while ``p['vr_disp']`` gives the radial velocity dispersion profile. By definition, ``p['vr_disp']**2`` is the same as ``p['vr_rms']**2 - p['vr']**2``. **Derivatives**: One may also prepend ``d_`` to the name of a defined array to get the derivative, e.g. ``p['d_temp']`` gives the radial temperature gradient. **Non-array profiles**: Profiles can be defined that do not directly correspond to the average over an array in the snapshot. Examples include ``density``, ``mass`` and ``mass_enc``. These are implemented as functions in the :mod:`pynbody.analysis.profile` module; you can therefore find a list of available profiles by looking at the functions there. These profiles can be accessed in the same way as array profiles, e.g. ``p['density']``. For profiles that take an argument, such as ``sb``, this is passed in with an underscore e.g. ``p['sb_b']`` for b-band surface brightnesses. **Storing profiles**: Use the :func:`~pynbody.analysis.profile.Profile.write` function to write the current profiles with all the necessary information to a file. Initialize a profile with the ``load_from_file=True`` keyword to automatically load a previously saved profile. The filename is chosen automatically and corresponds to a hash generated from the positions of the particles used in the profile. This is to ensure that you are always looking at the same set of particles, centered in the same way. It also means you *must* use the same centering method if you want to reuse a saved profile. .. versionchanged:: 2.0 The method ``create_particle_array`` has been removed. Its behaviour was poorly defined in v1, and not believed to be widely used. """ _profile_registry = {} def _calculate_x(self, sim): if self._x_calculator is not None: return self._x_calculator(sim) else: return ((sim['pos'][:, 0:self.ndim] ** 2).sum(axis=1)) ** (1, 2)
[docs] def __init__(self, sim, load_from_file=False, ndim=2, type='lin', calc_x=None, weight_by='mass', **kwargs): """Initialise a profile, determining the binning quantity and bin size. The constructor generates the bins without actually calculating any profiles. The profiles are calculated lazily when requested. Parameters ---------- sim : pynbody.snapshot.SimSnap The simulation snapshot to generate a profile for ndim : int, optional: Specifies whether it's a 2D or 3D profile - in the 2D case, the bins are generated in the xy plane type : str, optional: Specifies whether bins should be spaced linearly ('lin', default), logarithmically ('log') or contain equal numbers of particles ('equaln') rmin : float, optional: Minimum value to consider (left-hand-edge of lowest bin). Default is the minimum value of the binning quantity. rmax : float, optional: Maximum value to consider (right-hand-edge of highest bin). Default is the maximum value of the binning quantity. nbins : int, optional: Number of bins to use. Default is 100. bins : array-like, optional: Predefined bin edges in units of the binning quantity. If this keyword is set, the values of the keywords type, nbins, rmin and rmax will be ignored. calc_x : function, optional: Function to use to calculate the value for binning. If None it defaults to the radial distance from origin (in either 2 or 3 dimensions), but you can specify this function to return any value you want for making profiles along arbitrary axes. Depending on your function, the units of certain profiles (such as density) might not make sense. weight_by : str, optional: Name of the array to use for weighting averages across particles in each bin. Default is 'mass'. """ generate_new = True self._x_calculator = calc_x self.sim = sim self.type = type self.ndim = ndim self._weight_by = weight_by self._x = self._calculate_x(sim) x = self._x if load_from_file: filename = self._get_unique_filepath_from_particle_list() try: with open(filename, "rb") as f: data = pickle.load(f) self._properties = data['properties'] self.max = data['max'] self.min = data['min'] self.nbins = data['nbins'] self._profiles = data['profiles'] self.binind = data['binind'] logger.info("Loaded profile from %s" % filename) generate_new = False except FileNotFoundError: logger.warning( "Existing profile not found -- generating one from scratch instead") if generate_new: self._properties = {} # The profile object is initialized given some array of values # and optional keyword parameters if 'max' in kwargs: kwargs['rmax'] = kwargs.pop('max') warnings.warn("Use of max as a keyword argument is deprecated. Use rmax instead.", DeprecationWarning) if 'min' in kwargs: kwargs['rmin'] = kwargs.pop('min') warnings.warn("Use of min as a keyword argument is deprecated. Use rmin instead.", DeprecationWarning) if 'rmax' in kwargs and kwargs['rmax'] is not None: if isinstance(kwargs['rmax'], str): self.max = units.Unit(kwargs['rmax']).ratio(x.units, **sim.conversion_context()) else: self.max = kwargs['rmax'] else: self.max = np.max(x) if 'bins' in kwargs: self.nbins = len(kwargs['bins']) - 1 elif 'nbins' in kwargs: self.nbins = kwargs['nbins'] else: self.nbins = 100 if 'rmin' in kwargs and kwargs['rmin'] is not None: if isinstance(kwargs['rmin'], str): self.min = units.Unit(kwargs['rmin']).ratio(x.units, **sim.conversion_context()) else: self.min = kwargs['rmin'] else: if type == 'log': self.min = np.min(x[x > 0]) else: self.min = np.min(x) if 'bins' in kwargs: self._properties['bin_edges'] = kwargs['bins'] self.min = kwargs['bins'].min() self.max = kwargs['bins'].max() elif type == 'log': self._properties['bin_edges'] = np.logspace( np.log10(self.min), np.log10(self.max), num=self.nbins + 1) elif type == 'lin': self._properties['bin_edges'] = np.linspace( self.min, self.max, num=self.nbins + 1) elif type == 'equaln': self._properties['bin_edges'] = util.equipartition( x, self.nbins, self.min, self.max) else: raise RuntimeError("Bin type must be one of: lin, log, equaln") self['bin_edges'] = array.SimArray(self['bin_edges'], x.units) self['bin_edges'].sim = self.sim n, bins = np.histogram(self._x, self['bin_edges']) self._setup_bins() # set up the empty list of profiles self._profiles = {'n': n}
def _setup_bins(self): # middle of the bins for convenience self._properties['rbins'] = (0.5 * (self['bin_edges'][:-1] + self['bin_edges'][1:])).view(array.SimArray) self._properties['rbins'].units = self['bin_edges'].units self._properties['rbins'].sim = self.sim # important to have this relationship e.g. for comoving unit conversions # Width of the bins self._properties['dr'] = np.gradient(self['rbins']).view(array.SimArray) self._properties['dr'].units = self['rbins'].units self._properties['dr'].sim = self.sim self.binind = [] if len(self._x) > 0: self.partbin = np.digitize(self._x, self['bin_edges']) - 1 else: self.partbin = np.array([]) self._properties['npart_bins'] = np.zeros(self.nbins, dtype=int) assert self.ndim in [2, 3] if self.ndim == 2: self._binsize = np.pi * (self['bin_edges'][1:] ** 2 - self['bin_edges'][:-1] ** 2) else: self._binsize = 4. / 3. * np.pi * (self['bin_edges'][1:] ** 3 - self['bin_edges'][:-1] ** 3) # sort the partbin array from bisect import bisect sortind = self.partbin.argsort() sort_pind = self.partbin[sortind] # create the bin index arrays prev_index = bisect(sort_pind, -1) for i in range(self.nbins): new_index = bisect(sort_pind, i) self.binind.append(np.sort(sortind[prev_index:new_index])) self._properties['npart_bins'][i] = len(self.binind[i]) prev_index = new_index def __len__(self): """Returns the number of bins used in this profile object""" return self.nbins def _get_profile(self, name): """Return the profile of a given kind""" x = name.split(",") if name in self._profiles: return self._profiles[name] elif x[0] in Profile._profile_registry: args = x[1:] self._profiles[name] = Profile._profile_registry[x[0]](self, *args) try: self._profiles[name].sim = self.sim except AttributeError: pass return self._profiles[name] elif name in list(self.sim.keys()) or name in self.sim.all_keys(): self._profiles[name] = self._auto_profile(name) self._profiles[name].sim = self.sim return self._profiles[name] elif name[-5:] == "_disp" and (name[:-5] in list(self.sim.keys()) or name[:-5] in self.sim.all_keys()): logger.info("Auto-deriving %s" % name) self._profiles[name] = self._auto_profile( name[:-5], dispersion=True) self._profiles[name].sim = self.sim return self._profiles[name] elif name[-4:] == "_rms" and (name[:-4] in list(self.sim.keys()) or name[:-4] in self.sim.all_keys()): logger.info("Auto-deriving %s" % name) self._profiles[name] = self._auto_profile(name[:-4], rms=True) self._profiles[name].sim = self.sim return self._profiles[name] elif name[-4:] == "_med" and (name[:-4] in list(self.sim.keys()) or name[:-4] in self.sim.all_keys()): logger.info("Auto-deriving %s" % name) self._profiles[name] = self._auto_profile(name[:-4], median=True) self._profiles[name].sim = self.sim return self._profiles[name] elif name[0:2] == "d_" and (name[2:] in list(self.keys()) or name[2:] in self.derivable_keys() or name[2:] in self.sim.all_keys()): # if np.diff(self['dr']).all() < 1e-13 : logger.info("Auto-deriving %s/dR" % name) self._profiles[name] = np.gradient(self[name[2:]], self['dr'][0]) self._profiles[name] = self._profiles[name] / self['dr'].units return self._profiles[name] # else : # raise RuntimeError, "Derivatives only possible for profiles of fixed bin width." else: raise KeyError(name + " is not a valid profile") def _auto_profile(self, name, dispersion=False, rms=False, median=False): result = np.zeros(self.nbins) # force derivation of array if necessary: self.sim[name] for i in range(self.nbins): subs = self.sim[self.binind[i]] name_array = subs[name].view(np.ndarray) mass_array = subs[self._weight_by].view(np.ndarray) if dispersion: sq_mean = (name_array ** 2 * mass_array).sum() / \ self['weight_fn'][i] mean_sq = ( (name_array * mass_array).sum() / self['weight_fn'][i]) ** 2 try: result[i] = math.sqrt(sq_mean - mean_sq) except ValueError: # sq_mean<mean_sq occasionally from numerical roundoff result[i] = 0 elif rms: result[i] = np.sqrt( (name_array ** 2 * mass_array).sum() / self['weight_fn'][i]) elif median: if len(subs) == 0: result[i] = np.nan else: sorted_name = sorted(name_array) result[i] = sorted_name[int(np.floor(0.5 * len(subs)))] else: result[i] = (name_array * mass_array).sum() / self['weight_fn'][i] result = result.view(array.SimArray) result.units = self.sim[name].units result.sim = self.sim return result def __getitem__(self, name): """Return the profile of a given kind""" if name in self._properties: return self._properties[name] else: return self._get_profile(name) def __setitem__(self, name, item): """Set the profile or property by hand""" if name in self._properties: self._properties[name] = item elif name in self._profiles: self._profiles[name] = item else: raise KeyError(name + " is not a valid profile or property") def __delitem__(self, name): del self._profiles[name] def __repr__(self): return ("<Profile: " + str(self.families()) + " ; " + str(self.ndim) + "D ; " + self.type) + " ; " + str(list(self.keys())) + ">"
[docs] def keys(self): """Returns a listing of available profile types""" return list(self._profiles.keys())
[docs] def derivable_keys(self): """Returns a list of possible profiles""" return list(self._profile_registry.keys())
[docs] def families(self): """Returns the family of particles used""" return self.sim.families()
[docs] def create_particle_array(self, profile_name, particle_name=None, log_x_interpolation = None, log_y_interpolation = None, target_simulation=None): """Interpolate the profile back onto the particles For example, calling ``create_particle_array('density')`` will create a new array in the simulation called `'density'` which is the density of each particle according to the profile. Parameters ---------- profile_name : str The name of the profile to interpolate particle_name : str, optional The name of the new array to create. If not specified, it will be the same as the profile_name. log_x_interpolation : bool, optional If True, interpolate in log space for the x-axis; if False, don't. If None, perform log interpolation if all bin centres are positive. log_y_interpolation : bool, optional If True, interpolate in log space for the y-axis; if False, don't. If None, perform log interpolation if all profile values are positive. target_simulation : pynbody.SimSnap, optional The simulation to create the new array in. If not specified, the array will be created in the current simulation. Specifying another simulation is helpful e.g. if you want to interpolate the profile onto a different set of particles. """ if particle_name is None: particle_name = profile_name if target_simulation is None: target_simulation = self.sim particle_x = self._x else: particle_x = self._calculate_x(target_simulation) binned_x = self['rbins'] binned_y = self[profile_name] # this lets us use a quantile profile with a single quantile return if len(binned_y.shape) == 2 and binned_y.shape[1] == 1: binned_y = binned_y[:,0] if log_x_interpolation is None: log_x_interpolation = np.all(binned_x > 0) if log_y_interpolation is None: log_y_interpolation = np.all(binned_y > 0) if log_x_interpolation: particle_x = np.log(particle_x) binned_x = np.log(binned_x) if log_y_interpolation: binned_y = np.log(binned_y) rep = np.interp(particle_x, binned_x, binned_y) if log_y_interpolation: target_simulation[particle_name] = np.exp(rep) else: target_simulation[particle_name] = rep target_simulation[particle_name].units = self[profile_name].units
def _generate_hash_filename_from_particles(self): """Create a filename for the saved profile from a hash using the binning data""" import hashlib # Changing to the new() method, which will not fail if usedforsecurity is unsupported # in a given system configuration (issue 581) h = hashlib.new('md5') # Reproduce old behaviour, given byte-like data to create the hash. h.update(self._x) return h.hexdigest() def _get_unique_filepath_from_particle_list(self): try: folder_path = self.sim.base.filename except AttributeError: folder_path = self.sim.filename unique_hash = self._generate_hash_filename_from_particles() filename = folder_path + '.profile.' + unique_hash return filename
[docs] def write(self): """ Writes all the vital information of the profile to a file. To recover the profile, initialize a profile with the ``load_from_file=True`` keyword to automatically load a previously saved profile. The filename is chosen automatically and corresponds to a hash generated from the positions of the particles used in the profile. This is to ensure that you are always looking at the same set of particles, centered in the same way. It also means you *must* use the same centering method if you want to reuse a saved profile. """ # record all the important data except for the snapshot itself # use the hash generated from the particle list for the file name # suffix filename = self._get_unique_filepath_from_particle_list() logger.info("Writing profile to %s", filename) with open(filename, "wb") as f: pickle.dump( { 'properties': self._properties, 'max': self.max, 'min': self.min, 'nbins': self.nbins, 'profiles': self._profiles, 'binind': self.binind }, f, )
[docs] @staticmethod def profile_property(fn): """Function decorator to define a new profile property. For example, .. code-block:: python @Profile.profile_property def x_squared(pro): return pro['x']**2 would define a new profile property 'x_squared' which is the square of the 'x' profile. This can then be accessed as ``pro['x_squared']`` for any profile object ``pro``. """ Profile._profile_registry[fn.__name__] = fn return fn
[docs] @Profile.profile_property def weight_fn(pro, weight_by=None): """ Calculate mass in each bin """ if weight_by is None: weight_by = pro._weight_by mass = array.SimArray(np.zeros(pro.nbins), pro.sim[weight_by].units) with pro.sim.immediate_mode: pmass = pro.sim[weight_by].view(np.ndarray) for i in range(pro.nbins): mass[i] = (pmass[pro.binind[i]]).sum() mass.sim = pro.sim mass.units = pro.sim[weight_by].units return mass
[docs] @Profile.profile_property def mass(pro): return weight_fn(pro, 'mass')
[docs] @Profile.profile_property def density(pro): """ Generate a radial density profile for the current type of profile """ return pro['mass'] / pro._binsize
[docs] @Profile.profile_property def fourier(pro, delta_t ="0.1 Myr", phi_bins=100): """ Generate a profile of fourier coefficients, amplitudes and phases """ delta_t = pynbody.units.Unit(delta_t) f = {'c': np.zeros((7, pro.nbins), dtype=complex), 'c_delta': np.zeros((7, pro.nbins), dtype=complex), 'amp': np.zeros((7, pro.nbins)), 'phi': np.zeros((7, pro.nbins)), 'dphi_dt': np.zeros((7, pro.nbins))} for i in range(pro.nbins): if pro._profiles['n'][i] > 100: phi = np.arctan2( pro.sim['y'][pro.binind[i]], pro.sim['x'][pro.binind[i]]) mass = pro.sim['mass'][pro.binind[i]] x1 = pro.sim['x'][pro.binind[i]] + pro.sim['vx'][pro.binind[i]] * delta_t y1 = pro.sim['y'][pro.binind[i]] + pro.sim['vy'][pro.binind[i]] * delta_t phi1 = np.arctan2(y1,x1) hist_range = (-np.pi, np.pi) hist, binphi = np.histogram(phi, weights=mass, bins=phi_bins, range=(hist_range)) hist1, _ = np.histogram(phi1, weights=mass, bins=phi_bins, range=(hist_range)) binphi = binphi[:-1] + .5*np.diff(binphi) for m in range(7): f['c'][m, i] = np.sum(hist * np.exp(-1j * m * binphi)) f['c_delta'][m, i] = np.sum(hist1 * np.exp(-1j * m * binphi)) f['c'][:, pro['mass'] > 0] /= pro['mass'][pro['mass'] > 0] f['amp'] = np.sqrt(np.imag(f['c']) ** 2 + np.real(f['c']) ** 2) f['phi'] = np.arctan2(np.imag(f['c']), np.real(f['c'])) dphi = np.arctan2(np.imag(f['c_delta']), np.real(f['c_delta'])) - f['phi'] dphi[dphi>np.pi] = dphi[dphi>np.pi]-2*np.pi dphi[dphi<-np.pi] = dphi[dphi<-np.pi]+2*np.pi dphi = array.SimArray(dphi,"1") f['dphi_dt'] = (dphi / delta_t).in_units(pro.sim['vx'].units / pro.sim['x'].units) return f
[docs] @Profile.profile_property def pattern_frequency(pro): """Estimate the pattern speed from the m=2 Fourier mode""" return pro['fourier']['dphi_dt'][2,:]/2
[docs] @Profile.profile_property def mass_enc(pro): """ Generate the enclosed mass profile """ return pro['mass'].cumsum()
[docs] @Profile.profile_property def density_enc(pro): """ Generate the mean enclosed density profile """ return pro['mass_enc'] / ((4. * math.pi / 3) * pro['rbins'] ** 3)
[docs] @Profile.profile_property def dyntime(pro): """The dynamical time of the bin, sqrt(R^3/2GM).""" dyntime = (pro['rbins'] ** 3 / (2 * units.G * pro['mass_enc'])) ** (1, 2) return dyntime
[docs] @Profile.profile_property def g_spherical(pro): """The naive gravitational acceleration assuming spherical symmetry = GM_enc/r^2""" return (units.G * pro['mass_enc'] / pro['rbins'] ** 2)
[docs] @Profile.profile_property def rotation_curve_spherical(pro): """ The naive rotation curve assuming spherical symmetry: vc = sqrt(G M_enc/r) """ # .in_units('km s**-1') return ((units.G * pro['mass_enc'] / pro['rbins']) ** (1, 2))
[docs] @Profile.profile_property def j_circ(pro): """Angular momentum of particles on circular orbits.""" return pro['v_circ_total'] * pro['rbins']
[docs] @Profile.profile_property def v_circ(pro, grav_sim=None): """Circular velocity, i.e. rotation curve. Calculated by computing the gravity in the midplane""" from .. import gravity global config grav_sim = grav_sim or pro.sim if str(grav_sim.current_transformation()) != 'faceon': warnings.warn("Profile v_circ -- this routine assumes the disk is in the x-y plane") # If this is a cosmological run, go up to the halo level # if hasattr(grav_sim,'base') and grav_sim.base.properties.has_key("halo_number") : # while hasattr(grav_sim,'base') and grav_sim.base.properties.has_key("halo_number") : # grav_sim = grav_sim.base # elif hasattr(grav_sim,'base') : # grav_sim = grav_sim.base start = process_time() rc = gravity.midplane_rot_curve( grav_sim, pro['rbins']).in_units(pro.sim['vel'].units) end = process_time() logger.info("Rotation curve calculated in %5.3g s" % (end - start)) return rc
[docs] @Profile.profile_property def v_circ_total(pro): """Circular velocity using all particles, not just those in the profile, to source gravity. In reality, only particles out to 3 times the maximum profile radius are used, for speed.""" sim = pro.sim.ancestor[pynbody.filt.Sphere(3 * pro['rbins'].max())] return v_circ(pro, sim)
[docs] @Profile.profile_property def E_circ(pro): """Calculates the energy of particles on circular orbits in the z=0 plane.""" return 0.5 * (pro['v_circ_total'] ** 2) + pro['pot']
[docs] @Profile.profile_property def pot(pro): """Calculates the potential in the z=0 plane""" from .. import gravity grav_sim = pro.sim # Go up to the halo level while hasattr(grav_sim, 'base') and "halo_number" in grav_sim.base.properties: grav_sim = grav_sim.base if str(grav_sim.current_transformation()) != 'faceon': warnings.warn("Profile pot -- this routine assumes the disk is in the x-y plane") start = process_time() pot = gravity.midplane_potential( grav_sim, pro['rbins']).in_units(pro.sim['vel'].units ** 2) end = process_time() logger.info("Potential calculated in %5.3g s" % (end - start)) return pot
[docs] @Profile.profile_property def omega(pro): """Circular frequency Omega = v_circ/radius (see Binney & Tremaine Sect. 3.2) in the z=0 plane""" prof = pro['v_circ'] / pro['rbins'] prof.set_units_like('km s**-1 kpc**-1') return prof
[docs] @Profile.profile_property def kappa(pro): """Radial frequency kappa = sqrt(R dOmega^2/dR + 4 Omega^2) (see Binney & Tremaine Sect. 3.2) in the z=0 plane""" dOmega2dR = np.gradient(pro['omega'] ** 2) / np.gradient(pro['rbins']) return np.sqrt(pro['rbins'] * dOmega2dR + 4 * pro['omega'] ** 2)
[docs] @Profile.profile_property def beta(pro): """3D Anisotropy parameter as defined in Binney and Tremaine""" assert pro.ndim == 3 return 1.5 - (pro['vx_disp'] ** 2 + pro['vy_disp'] ** 2 + pro['vz_disp'] ** 2) / pro['vr_disp'] ** 2 / 2.
[docs] @Profile.profile_property def magnitudes(pro, band='v'): """Calculate magnitudes in each bin When calling this from a profile object, the band can be specified after an underscore, e.g. ``p['magnitudes_b']`` for b-band magnitudes. For important information about the calculation of magnitudes and surface brightnesses, see the module documentation for :mod:`pynbody.analysis.luminosity`. """ from . import luminosity magnitudes = np.zeros(pro.nbins) for i in range(pro.nbins): magnitudes[i] = luminosity.halo_mag( pro.sim[pro.binind[i]], band=band) magnitudes = array.SimArray(magnitudes, units.Unit('1')) magnitudes.sim = pro.sim return magnitudes
[docs] @Profile.profile_property def sb(pro, band='v'): """Calculate surface brightness in each bin When calling this from a profile object, the band can be specified after an underscore, e.g. ``p['sb_b']`` for b-band surface brightnesses. For important information about the calculation of magnitudes and surface brightnesses, see the module documentation for :mod:`pynbody.analysis.luminosity`. """ # At 10 pc (distance for absolute magnitudes), 1 arcsec is 10 AU=1/2.06e4 pc # In [5]: (np.tan(np.pi/180/3600)*10.0)**2 # Out[5]: 2.3504430539466191e-09 # 1 square arcsecond is thus 2.35e-9 pc^2 sqarcsec_in_bin = pro._binsize.in_units('pc^2') / 2.3504430539466191e-09 bin_luminosity = 10.0 ** (-0.4 * pro['magnitudes,' + band]) #import pdb; pdb.set_trace() surfb = -2.5 * np.log10(bin_luminosity / sqarcsec_in_bin) surfb = array.SimArray(surfb, units.Unit('1')) surfb.sim = pro.sim return surfb
[docs] @Profile.profile_property def Q(pro): """Toomre Q parameter""" return (pro['vr_disp'] * pro['kappa'] / (3.36 * pro['density'] * units.G)).in_units("")
[docs] @Profile.profile_property def X(pro): """X parameter defined as kappa^2*R/(2*pi*G*sigma*m), using the rotation curve from the z=0 plane See Binney & Tremaine 2008, eq. 6.77""" lambda_crit = 4. * np.pi ** 2 * units.G * \ pro['density'] / (pro['kappa'] ** 2) kcrit = 2. * np.pi / lambda_crit return (kcrit * pro['rbins'] / 2).in_units("")
[docs] @Profile.profile_property def jtot(pro): """Magnitude of the total angular momentum """ jtot = np.zeros(pro.nbins) for i in range(pro.nbins): subs = pro.sim[pro.binind[i]] jx = (subs['j'][:, 0] * subs['mass']).sum() / pro['mass'][i] jy = (subs['j'][:, 1] * subs['mass']).sum() / pro['mass'][i] jz = (subs['j'][:, 2] * subs['mass']).sum() / pro['mass'][i] jtot[i] = np.sqrt(jx ** 2 + jy ** 2 + jz ** 2) return jtot
[docs] @Profile.profile_property def j_theta(pro): """Angle that the angular momentum vector of the bin makes with respect to the xy-plane.""" return np.arccos(pro['jz'] / pro['jtot'])
[docs] @Profile.profile_property def j_phi(pro): """Angle that the angular momentum vector of the bin makes with the x-axis in the xy plane.""" j_phi = np.zeros(pro.nbins) for i in range(pro.nbins): subs = pro.sim[pro.binind[i]] jx = (subs['j'][:, 0] * subs['mass']).sum() / pro['mass'][i] jy = (subs['j'][:, 1] * subs['mass']).sum() / pro['mass'][i] j_phi[i] = np.arctan2(jy, jx) return j_phi
[docs] class InclinedProfile(Profile): """ A profile object to be used with a snapshot inclined by some known angle to the xy plane. In addition to the SimSnap object, it also requires the angle to initialize. **Example:** >>> s = pynbody.load('sim') >>> pynbody.analysis.angmom.faceon(s) >>> s.rotate_x(60) >>> p = pynbody.profile.InclinedProfile(s, 60) """ def _calculate_x(self, sim): # calculate an ellipsoidal radius return (sim['x'] ** 2 + (sim['y'] / np.cos(np.radians(self.angle))) ** 2) ** (1, 2)
[docs] def __init__(self, sim, angle, load_from_file=False, ndim=2, type='lin', **kwargs): self.angle = angle Profile.__init__( self, sim, load_from_file=load_from_file, ndim=ndim, type=type, **kwargs) # define the minor axis self._properties['rbins_min'] = np.cos( np.radians(self.angle)) * self['rbins']
[docs] class VerticalProfile(Profile): """A profile class that uses the absolute value of the z coordinate for binning instead of a radial coordinate. """ def _calculate_x(self, sim): return array.SimArray(np.abs(sim['z']), sim['z'].units)
[docs] def __init__(self, sim, rmin, rmax, zmax, load_from_file=False, ndim=3, type='lin', **kwargs): """Creates a profile object that uses the absolute value of the z-coordinate for binning. Parameters ---------- sim : pynbody.snapshot.simsnap.SimSnap The snapshot to make a profile from. rmin : str, float or pynbody.units.Unit Minimum radius for particle selection. rmax : str, float or pynbody.units.Unit Maximum radius for particle selection. zmax : str, float or pynbody.units.Unit Maximum height to consider (the upper edge of the binning range) ndim : int, optional If ndim=2, an edge-on projected profile is produced, i.e. density is in units of mass/length^2. If ndim=3 (default) a volume profile is made, i.e. density is in units of mass/length^3. type : str, optional The type of binning to use. Can be 'lin' (default), 'log', or 'equaln'. """ if isinstance(rmin, str): rmin = units.Unit(rmin) if isinstance(rmax, str): rmax = units.Unit(rmax) if isinstance(zmax, str): zmax = units.Unit(zmax) self.rmin = rmin self.rmax = rmax self.zmax = zmax # create a snapshot that only includes the section of disk we're # interested in assert ndim in [2, 3] if ndim == 3: sub_sim = sim[ pynbody.filt.Disc(rmax, zmax) & ~pynbody.filt.Disc(rmin, zmax)] else: sub_sim = sim[(pynbody.filt.BandPass('x', rmin, rmax) | pynbody.filt.BandPass('x', -rmax, -rmin)) & pynbody.filt.BandPass('z', -zmax, zmax)] Profile.__init__( self, sub_sim, load_from_file=load_from_file, ndim=ndim, type=type, **kwargs)
def _setup_bins(self): Profile._setup_bins(self) dr = self.rmax - self.rmin if self.ndim == 2: self._binsize = ( self['bin_edges'][1:] - self['bin_edges'][:-1]) * dr else: area = array.SimArray( np.pi * (self.rmax ** 2 - self.rmin ** 2), "kpc^2") self._binsize = ( self['bin_edges'][1:] - self['bin_edges'][:-1]) * area
[docs] class QuantileProfile(Profile): """A profile object that returns requested quantiles instead of means in each bin. """
[docs] def __init__(self, sim, q=(0.16, 0.50, 0.84), weights=None, load_from_file = False, ndim = 3, type = 'lin', **kwargs): """Creates a profile object that returns the requested quantiles for a given array in a given bin. Parameters ---------- sim : pynbody.snapshot.simsnap.SimSnap The snapshot to make a profile from. q : list of floats, optional The quantiles that will be returned. Default is median with 1-sigma on either side. q can be of arbitrary length allowing the user to select any quantiles they desire. weights : pynbody.array.SimArray, optional What should be used to weight the quantile. A likely possibility is to use particle mass: ``sim['mass']``. The default is to weight by particle number, weights=None. **kwargs : Additional keyword arguments are passed onto the underlying :class:`Profile` constructor. """ # create a snapshot that only includes the section of disk we're # interested in self.quantiles = q self.qweights = weights Profile.__init__( self, sim, load_from_file=load_from_file, ndim=ndim, type=type, **kwargs)
def _get_profile(self, name): """Return the profile of a given kind""" x = name.split(",") if name in self._profiles: return self._profiles[name] elif name in list(self.sim.keys()) or name in self.sim.all_keys(): self._profiles[name] = self._auto_profile(name) self._profiles[name].sim = self.sim return self._profiles[name] else: raise KeyError(name + " is not a valid QuantileProfile") def _auto_profile(self, name, dispersion=False, rms=False, median=False): result = np.zeros((self.nbins, len(self.quantiles))) with self.sim.immediate_mode: source_array = self.sim[name].view(np.ndarray) for i in range(self.nbins): array_this_bin = source_array[self.binind[i]] if self.qweights is None: array_this_bin_sorted = np.sort(array_this_bin) quantiles_this_bin = np.linspace(0, 1, len(array_this_bin)) else: sorter = np.argsort(array_this_bin) array_this_bin_sorted = array_this_bin[sorter] quantiles_this_bin = np.cumsum(self.qweights[self.binind[i]][sorter]) quantiles_this_bin -= quantiles_this_bin[0] quantiles_this_bin /= quantiles_this_bin[-1] result[i] = np.interp(self.quantiles, quantiles_this_bin, array_this_bin_sorted) result = result.view(array.SimArray) result.units = self.sim[name].units result.sim = self.sim return result