cleanup of t1, tried integration in t2, units

.gitignore

@@ -1 +1,2 @@
-*.pyc
+*.pyc
+.cache/

Pipfile

@@ -9,7 +9,7 @@ ipython = "*"
 jupyter = "*"
 matplotlib = "*"
 scipy = "*"
-spacepy = "*"
+astropy = "*"
 
 [dev-packages]
 

nbody/checklist.md

@@ -8,7 +8,21 @@
 - [x] compare with the analytical expectation from Newtons 2nd law
 - [ ] compute the relaxation time
 
-### Task 2
+### Task 2 (particle mesh)
+- [ ] Choose reasonable units
+- [ ] Implement force computation on mesh
+- [ ] Find optimal mesh size
+- [ ] Compare with direct nbody simulation
+- [ ] Time integration for direct method AND mesh method
+
+
+### Task 2 (tree code)
+- [ ] Implement force computation with multipole expansion
+    - [ ] Find optimal grouping criterion
+- [ ] Compare with direct nbody simulation
+- [ ] Time integration for direct method AND tree method
+
+
 
 
 
@@ -16,4 +30,8 @@
 
 
 ### Questions
-- Procedure for each time step of a mesh simulation? Potential on mesh -> forces on particles -> update particle positions -> new mesh potential? or skip the creation of particles in each time step?
+- Procedure for each time step of a mesh simulation? Potential on mesh -> forces on particles -> update particle positions -> new mesh potential? or skip the creation of particles in each time step?
+- How to represent the time evolution of the system?
+    - plot total energy vs time
+    - plot particle positions?
+- What is the parameter a of the Hernquist model?

nbody/utils/__init__.py

@@ -1,9 +1,9 @@
 ## Import all functions in all the files in the current directory
 # Basic helpers for interacting with the data 
 from .load import *
-from .mesh import *
 from .model import *
 from .particles import *
+from .units import *
 
 # Helpers for computing the forces
 from .forces_basic import *

nbody/utils/forces_basic.py

@@ -16,7 +16,7 @@ def n_body_forces(particles: np.ndarray, G: float, softening: float = 0):
 
     n = particles.shape[0]
     forces = np.zeros((n, 3))
-    logger.debug(f"Computing forces for {n} particles using n^2 algorithm (using {softening=})")
+    logger.debug(f"Computing forces for {n} particles using n^2 algorithm (using {softening=:.2g})")
 
     for i in range(n):
         # the current particle is at x_current

nbody/utils/forces_mesh.py

@@ -56,19 +56,22 @@ def mesh_forces_v2(particles: np.ndarray, G: float, n_grid: int, mapping: callab
     if particles.shape[1] != 4:
         raise ValueError("Particles array must have 4 columns: x, y, z, m")
 
-    logger.debug(f"Computing forces for {particles.shape[0]} particles using mesh (using mapping={mapping.__name__})")
+    logger.debug(f"Computing forces for {particles.shape[0]} particles using mesh [mapping={mapping.__name__}, {n_grid=}]")
 
     mesh, axis = to_mesh(particles, n_grid, mapping)
-    show_mesh_information(mesh, "Mesh")
+    if logger.level >= logging.DEBUG:
+        show_mesh_information(mesh, "Density mesh")
 
     spacing = axis[1] - axis[0]
     logger.debug(f"Using mesh spacing: {spacing}")
     phi = mesh_poisson_v2(mesh, G)
     logger.debug(f"Got phi with: {phi.shape}, {np.max(phi)}")
     phi_grad = np.stack(np.gradient(phi, spacing), axis=0)
-    show_mesh_information(phi, "Mesh potential")
-    show_mesh_information(phi_grad[0], "Mesh potential grad x")
+    if logger.level >= logging.DEBUG:
+        show_mesh_information(phi, "Potential mesh")
+        show_mesh_information(phi_grad[0], "Potential gradient")
     logger.debug(f"Got phi_grad with: {phi_grad.shape}, {np.max(phi_grad)}")
+    
     forces = np.zeros_like(particles[:, :3])
     for i, p in enumerate(particles):
         ijk = np.digitize(p, axis) - 1
@@ -93,7 +96,7 @@ def mesh_poisson_v2(mesh: np.ndarray, G: float) -> np.ndarray:
     return phi
 
 
-## Helper functions for star mapping
+#### Helper functions for star mapping
 def to_mesh(particles: np.ndarray, n_grid: int, mapping: callable) -> tuple[np.ndarray, np.ndarray]:
     """
     Maps a list of particles to a of mesh of size n_grid x n_grid x n_grid.
@@ -108,7 +111,7 @@ def to_mesh(particles: np.ndarray, n_grid: int, mapping: callable) -> tuple[np.n
 
     for p in particles:
         m = p[-1]
-        if logger.level <= logging.DEBUG and  m <= 0:
+        if logger.level >= logging.DEBUG and  m <= 0:
             logger.warning(f"Particle with negative mass: {p}")
         # spread the star onto cells through the shape function, taking into account the mass
         ijks, weights = mapping(p, axis)
@@ -153,20 +156,20 @@ def particle_to_cells_cic(particle, axis, width):
 
 
 
-## Helper functions for mesh plotting
+#### Helper functions for mesh plotting
 def show_mesh_information(mesh: np.ndarray, name: str):
-    print(f"Mesh information for {name}")
-    print(f"Total mapped mass: {np.sum(mesh):.0f}")
-    print(f"Max cell value: {np.max(mesh)}")
-    print(f"Min cell value: {np.min(mesh)}")
-    print(f"Mean cell value: {np.mean(mesh)}")
+    logger.info(f"Mesh information for {name}")
+    logger.info(f"Total mapped mass: {np.sum(mesh):.0f}")
+    logger.info(f"Max cell value: {np.max(mesh)}")
+    logger.info(f"Min cell value: {np.min(mesh)}")
+    logger.info(f"Mean cell value: {np.mean(mesh)}")
     plot_3d(mesh, name)
     plot_2d(mesh, name)
 
 
 def plot_3d(mesh: np.ndarray, name: str):
     fig = plt.figure()
-    fig.suptitle(name)
+    fig.suptitle(f"{name} - {mesh.shape}")
     ax = fig.add_subplot(111, projection='3d')
     ax.scatter(*np.where(mesh), c=mesh[np.where(mesh)], cmap='viridis')
     plt.show()
@@ -174,7 +177,7 @@ def plot_3d(mesh: np.ndarray, name: str):
 
 def plot_2d(mesh: np.ndarray, name: str):
     fig = plt.figure()
-    fig.suptitle(name)
+    fig.suptitle(f"{name} - {mesh.shape}")
     axs = fig.subplots(1, 3)
     axs[0].imshow(np.sum(mesh, axis=0))
     axs[0].set_title("Flattened in x")
@@ -182,4 +185,4 @@ def plot_2d(mesh: np.ndarray, name: str):
     axs[1].set_title("Flattened in y")
     axs[2].imshow(np.sum(mesh, axis=2))
     axs[2].set_title("Flattened in z")
-    plt.show()
+    plt.show()

nbody/utils/integrate.py

@@ -16,54 +16,53 @@ def ode_setup(particles: np.ndarray, force_function: callable) -> tuple[np.ndarr
     if particles.shape[1] != 7:
         raise ValueError("Particles array must have 7 columns: x, y, z, vx, vy, vz, m")
     
-    n = particles.shape[0]
-    # for scipy integrators we need to flatten the n 3D positions and n 3D velocities
-    y0 = np.zeros(6*n) 
-    y0[:3*n] = particles[:, :3].flatten()
-    y0[3*n:] = particles[:, 3:6].flatten()
-
-    # the masses don't change we can define them once
-    masses = particles[:, 6]
-    logger.debug(f"Reshaped {particles.shape} to y0 with {y0.shape} and masses with {masses.shape}")
-
+    # for scipy integrators we need to flatten array which contains 7 columns for now
+    # we don't really care how we reshape as long as we unflatten consistently afterwards
+    particles = particles.reshape(-1, copy=False, order='A')
+    # this is consistent with the unflattening in to_particles()!
+    logger.debug(f"Reshaped 7 columns into {particles.shape=}")
 
     def f(y, t):
         """
         Computes the right hand side of the ODE system.
         The ODE system is linearized around the current positions and velocities.
         """
-        n = y.size // 6
-        logger.debug(f"y with shape {y.shape}")
-        # unsqueeze and unstack to extract the positions and velocities
-        y = y.reshape((2*n, 3))
-        x = y[:n, ...]
-        v = y[n:, ...]
-        logger.debug(f"Unstacked y into x with shape {x.shape} and v with shape {v.shape}")
+        y = to_particles(y)
+        # now y has shape (n, 7), with columns x, y, z, vx, vy, vz, m
         
-        # compute the forces
-        x_with_m = np.zeros((n, 4))
-        x_with_m[:, :3] = x
-        x_with_m[:, 3] = masses
-        forces = force_function(x_with_m)
+
+        forces = force_function(y[:, [0, 1, 2, -1]])
         
         # compute the accelerations
+        masses = y[:, -1]
         a = forces / masses[:, None]
-        a.flatten()
         # the [:, None] is to force broadcasting in order to divide each row of forces by the corresponding mass
+        # a.flatten()
         
-        # reshape into a 1D array
-        return np.vstack((v, a)).flatten()
-    
-    return y0, f
+        # replace some values in y:
+        # the position columns become the velocities
+        # the velocity columns become the accelerations
+        y[:, 0:3] = y[:, 3:6]
+        y[:, 3:6] = a
+        # the masses remain unchanged
+
+        # flatten the array again
+        y = y.reshape(-1, copy=False, order='A')
+        return y
+
+    return particles, f
 
 
 def to_particles(y: np.ndarray) -> np.ndarray:
     """
-    Converts the 1D array y into a 2D array with the shape (n, 6) where n is the number of particles.
-    The columns are x, y, z, vx, vy, vz
+    Converts the 1D array y into a 2D array IN PLACE
+    The new shape is (n, 7) where n is the number of particles.
+    The columns are x, y, z, vx, vy, vz, m
     """
-    n = y.size // 6
-    y = y.reshape((2*n, 3))
-    x = y[:n, ...]
-    v = y[n:, ...]
-    return np.hstack((x, v))
+    if y.size % 7 != 0:
+        raise ValueError("The array y should be inflatable to 7 columns")
+    
+    n = y.size // 7
+    y = y.reshape((n, 7), copy=False, order='F')
+    logger.debug(f"Unflattened array into {y.shape=}")
+    return y

nbody/utils/logging_config.py

@@ -3,7 +3,7 @@ import logging
 logging.basicConfig(
     ## set logging level
     # level=logging.INFO,
-    level=logging.DEBUG,
+    level=logging.INFO,
     format='%(asctime)s - %(name)s - %(message)s',
     datefmt='%H:%M:%S'
 )
@@ -13,3 +13,4 @@ logging.basicConfig(
 logging.getLogger('matplotlib.font_manager').setLevel(logging.WARNING)
 logging.getLogger('matplotlib.ticker').setLevel(logging.WARNING)
 logging.getLogger('matplotlib.pyplot').setLevel(logging.WARNING)
+logging.getLogger('matplotlib.colorbar').setLevel(logging.WARNING)

nbody/utils/model.py

@@ -1,12 +1,14 @@
 import numpy as np
 
-M = 5
-a = 5
 
-def model_density_distribution(r_bins: np.ndarray):
+def model_density_distribution(r_bins: np.ndarray, M: float = 5, a: float = 5) -> np.ndarray:
     """
     Generate a density distribution for a spherical galaxy model, as per the Hernquist model.
+    Parameters:
+    - r_bins: The radial bins to calculate the density distribution for.
+    - M: The total mass of the galaxy.
+    - a: The scale radius of the galaxy.
     See https://doi.org/10.1086%2F168845 for more information.
     """
     rho = M / (2 * np.pi) * a / (r_bins * (r_bins + a)**3)
-    return rho
+    return rho

nbody/utils/particles.py

@@ -1,5 +1,6 @@
 import numpy as np
 import logging
+from . import forces_basic
 logger = logging.getLogger(__name__)
 
 
@@ -7,20 +8,34 @@ def density_distribution(r_bins: np.ndarray, particles: np.ndarray, ret_error: b
     """
     Computes the radial density distribution of a set of particles.
     Assumes that the particles array has the following columns: x, y, z, m.
+    If ret_error is True, it will return the absolute error of the density.
     """
     if particles.shape[1] != 4:
         raise ValueError("Particles array must have 4 columns: x, y, z, m")
 
     m = particles[:, 3]
     r = np.linalg.norm(particles[:, :3], axis=1)
-    density = [np.sum(m[(r >= r_bins[i]) & (r < r_bins[i + 1])]) for i in range(len(r_bins) - 1)]
+    
+    m_shells = np.zeros_like(r_bins)
+    v_shells = np.zeros_like(r_bins)
+    error_relative = np.zeros_like(r_bins)
+    r_bins = np.insert(r_bins, 0, 0)
+
+    for i in range(len(r_bins) - 1):
+        mask = (r >= r_bins[i]) & (r < r_bins[i + 1])
+        m_shells[i] = np.sum(m[mask])
+        v_shells[i] = 4/3 * np.pi * (r_bins[i + 1]**3 - r_bins[i]**3)
+        if ret_error:
+            count = np.count_nonzero(mask)
+            if count > 0:
+                error_relative[i] = 1 / np.sqrt(count)
+            else:
+                error_relative[i] = 0
+
+    density = m_shells / v_shells
 
-    # add the first volume which  should be wrt 0
-    volume = 4/3 * np.pi * (r_bins[1:]**3 - r_bins[:-1]**3)
-    volume = np.insert(volume, 0, 4/3 * np.pi * r_bins[0]**3)
-    density = r_bins / volume
     if ret_error:
-        return density, density / np.sqrt(r_bins)
+        return density, density * error_relative
     else:
         return density
 
@@ -72,7 +87,10 @@ def mean_interparticle_distance(particles: np.ndarray):
 
     rho = n_half_mass / (4/3 * np.pi * r_half_mass**3)
     # the mean distance between particles is the inverse of the density
-    return (1 / rho)**(1/3)
+
+    epsilon = (1 / rho)**(1/3)
+    logger.info(f"Found mean interparticle distance: {epsilon}")
+    return epsilon
     # TODO: check if this is correct
 
 
@@ -104,21 +122,25 @@ def half_mass_radius(particles: np.ndarray):
 
 
 
-def relaxation_timescale(particles: np.ndarray, G:float) -> float:
+def total_energy(particles: np.ndarray):
     """
-    Computes the relaxation timescale of a set of particles using the velocity at the half mass radius.
-    Assumes that the particles array has the following columns: x, y, z ...
+    Computes the total energy of a set of particles.
+    Assumes that the particles array has the following columns: x, y, z, vx, vy, vz, m.
+    Uses the approximation that the particles are in a central potential as computed in analytical.py
     """
-    m_half = np.sum(particles[:, 3]) / 2 # enclosed mass at half mass radius
-    r_half = half_mass_radius(particles)
-    n_half = np.sum(np.linalg.norm(particles[:, :3], axis=1) < r_half) # number of enclosed particles
-    v_c = np.sqrt(G * m_half / r_half)
+    if particles.shape[1] != 7:
+        raise ValueError("Particles array must have 7 columns: x, y, z, vx, vy, vz, m")
 
-    # the crossing time for the half mass system is
-    t_c = r_half / v_c
-    logger.debug(f"Crossing time for half mass system: {t_c}")
+    # compute the kinetic energy
+    v = particles[:, 3:6]
+    m = particles[:, 6]
+    ke = 0.5 * np.sum(m * np.linalg.norm(v, axis=1)**2)
 
-    # the relaxation timescale is t_c * N/(10 * log(N))
-    t_rel = t_c * n_half / (10 * np.ln(n_half))
-
-    return t_rel
+    # # compute the potential energy
+    # forces = forces_basic.analytical_forces(particles)
+    # r = np.linalg.norm(particles[:, :3], axis=1)
+    # pe_particles = -forces[:, 0] * particles[:, 0] - forces[:, 1] * particles[:, 1] - forces[:, 2] * particles[:, 2]
+    # pe = np.sum(pe_particles)
+    # # TODO: i am pretty sure this is wrong
+    pe = 0
+    return ke + pe

nbody/utils/units.py

@@ -0,0 +1,41 @@
+import astropy.units as u
+import numpy as np
+import logging
+logger = logging.getLogger(__name__)
+
+
+M_SCALE: int = None
+R_SCALE: int = None
+
+def seed_scales(r_scale: u.Quantity, m_scale: u.Quantity):
+    """
+    Set the scales for the given simulation.
+    Parameters:
+    - r_scale: astropy.units.Quantity with units of length - the characteristic length scale of the simulation. Particle positions are expressed in units of this scale.
+    - m_scale: astropy.units.Quantity with units of mass - the characteristic mass scale of the simulation. Particle masses are expressed in units of this scale.
+    """
+    global M_SCALE, R_SCALE
+    M_SCALE = m_scale
+    R_SCALE = r_scale
+    logger.info(f"Set scales: M_SCALE = {M_SCALE}, R_SCALE = {R_SCALE}")
+
+
+def apply_units(columns: np.array, quantity: str):
+    if quantity == "mass":
+        return columns * M_SCALE
+    elif quantity == "position":
+        return columns * R_SCALE
+    elif quantity == "velocity":
+        return columns * R_SCALE / u.s
+    elif quantity == "time":
+        return columns * u.s
+    elif quantity == "acceleration":
+        return columns * R_SCALE / u.s**2
+    elif quantity == "force":
+        return columns * M_SCALE * R_SCALE / u.s**2
+    elif quantity == "volume":
+        return columns * R_SCALE**3
+    elif quantity == "density":
+        return columns * M_SCALE / R_SCALE**3
+    else:
+        raise ValueError(f"Unknown quantity: {quantity}")