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Added split-up versions of visualisation scripts (one for zooms, one for monofonic tests)
This commit is contained in:
glatterf42
parent
373ddd7151
commit
b87cc4fde1
3 changed files with 415 additions and 2 deletions
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@ -41,11 +41,13 @@ def find_coordinates_to_move(minimum, maximum, ratio, x_offset, y_offset, z_offs
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return coordinates_to_move
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directory = Path(r"/home/ben/sims/swiftsim/examples/zoom_tests/auriga6_halo7_8_10/")
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# directory = Path(r"/home/ben/sims/swiftsim/examples/zoom_tests/auriga6_halo7_8_10/")
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directory = Path(r"/home/ben/sims/data_swift/monofonic_tests/DB2_128_100/")
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# file = h5py.File(directory / "auriga6_halo7_8_9.hdf5", "r")
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for filename in sorted(directory.glob("output_*.hdf5")):
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# for filename in sorted(directory.glob("output_*.hdf5")):
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for filename in sorted(directory.glob("output_0004.hdf5")):
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print(filename)
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file = h5py.File(str(filename), "r")
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Header = file['Header']
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192
visualisation_swift_monofonic_tests.py
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192
visualisation_swift_monofonic_tests.py
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@ -0,0 +1,192 @@
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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# import vtk
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import sys
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import time
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from pathlib import Path
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from sys import getsizeof
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import h5py
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import numba
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import numpy as np
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import scipy.spatial
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@numba.njit()
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def determine_desired_range(offset, minimum, upper_limit_bottom, lower_limit_top, maximum):
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a = minimum
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b = maximum
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if offset < 0:
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a = lower_limit_top
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elif offset > 0:
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b = upper_limit_bottom
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return a, b
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@numba.njit()
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def find_coordinates_to_move(minimum, maximum, x_offset, y_offset, z_offset, move_candidates):
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coordinates_to_move = []
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x_start, x_end = determine_desired_range(x_offset, minimum, upper_limit_bottom, lower_limit_top, maximum)
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y_start, y_end = determine_desired_range(y_offset, minimum, upper_limit_bottom, lower_limit_top, maximum)
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z_start, z_end = determine_desired_range(z_offset, minimum, upper_limit_bottom, lower_limit_top, maximum)
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for particle in move_candidates:
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point = particle[0:3]
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if x_start <= point[0] <= x_end and y_start <= point[1] <= y_end and z_start <= point[2] <= z_end:
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coordinates_to_move.append(particle)
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return coordinates_to_move
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directory = Path(r"/home/ben/sims/data_swift/monofonic_tests/shannon_256_100/")
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for filename in sorted(directory.glob("output_0004.hdf5")):
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print(filename)
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file = h5py.File(str(filename), "r")
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Header = file['Header']
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original_coordinates = file["PartType1"]["Coordinates"][:] # for cdm particles
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names = file["PartType1"]["ParticleIDs"][:]
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velocities = file["PartType1"]["Velocities"][:]
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masses = file['PartType1']['Masses'][:]
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group_ids = file['PartType1']['FOFGroupIDs'][:]
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absolute_velo = np.sqrt(np.sum(velocities ** 2, axis=1))
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original_data = np.vstack([
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original_coordinates[::, 0],
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original_coordinates[::, 1],
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original_coordinates[::, 2],
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names,
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velocities[::, 0],
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velocities[::, 1],
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velocities[::, 2],
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masses,
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group_ids,
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absolute_velo,
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]).T
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print(original_data.shape)
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assert (original_coordinates == original_data[::, 0:3]).all()
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boundaries = Header.attrs['BoxSize'] # BoxLength for e5 boxes depends on Nres, 2.36438 for 256, 4.72876 for 512.
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print(boundaries, len(names))
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if not boundaries.shape:
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boundaries = np.array([boundaries] * 3)
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offsets = [-1, 0, 1]
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transformed_data = original_data[:]
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number_of_time_that_points_have_been_found = 0
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# assumes cube form and 0.1 as desired ratio to move
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minimum = 0.0
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maximum = max(boundaries)
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box_length = maximum - minimum
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# magic number: mean particle separation * 15 in units of box_length
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range_to_move = 0.0586 * box_length # mean particle separation: 0.78125 Mpc for 128, 0.390625 Mpc for 256, etc
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upper_limit_bottom = minimum + range_to_move
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lower_limit_top = maximum - range_to_move
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print("Find candidates to move...")
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@numba.njit()
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def find_move_candidates():
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move_candidates = []
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print("finding move candidates")
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for particle in original_data:
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point = particle[0:3]
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if (
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minimum <= point[0] <= upper_limit_bottom or
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lower_limit_top <= point[0] <= maximum or
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minimum <= point[1] <= upper_limit_bottom or
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lower_limit_top <= point[1] <= maximum or
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minimum <= point[2] <= upper_limit_bottom or
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lower_limit_top <= point[2] <= maximum
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):
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move_candidates.append(particle)
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# print(point)
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return move_candidates
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move_candidates = find_move_candidates()
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move_candidates = np.array(move_candidates)
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print("...done.")
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for x in offsets:
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for y in offsets:
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for z in offsets:
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if (x, y, z) == (0, 0, 0):
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continue
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moved_coordinates = find_coordinates_to_move(minimum, maximum, x, y, z, move_candidates)
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# print(moved_coordinates)
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moved_coordinates = np.array(moved_coordinates)
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# if not moved_coordinates.all():
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# print(f"nothing moved in {(x,y,z)}")
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# continue
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moved_coordinates[::, 0] += x * boundaries[0]
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moved_coordinates[::, 1] += y * boundaries[1]
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moved_coordinates[::, 2] += z * boundaries[2]
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transformed_data = np.vstack((transformed_data, moved_coordinates))
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number_of_time_that_points_have_been_found += 1
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print(f"Points found: {number_of_time_that_points_have_been_found}/26...")
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# assert coordinates.shape[0] == original_coordinates.shape[0] * 3 ** 3 #check that the new space has the shape we want it to have
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num_nearest_neighbors = 40
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print("Building 3d-Tree for all particles...")
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coordinates = transformed_data[::, 0:3]
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print(coordinates.shape)
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tree = scipy.spatial.KDTree(coordinates)
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print(getsizeof(tree) / 1024, "KB")
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print("...done.")
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print("Searching neighbours...")
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a = time.perf_counter_ns()
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distances, indices = tree.query([coordinates], k=num_nearest_neighbors, workers=6)
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# shape of closest_neighbours: (1, xxxx, 40)
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b = time.perf_counter_ns()
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print("...found neighbours.")
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print(f"took {(b - a) / 1000 / 1000:.2f} ms")
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distances = distances[0] # to (xxxx, 40)
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indices = indices[0] # to (xxxx, 40)
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print(distances.shape)
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print(indices.shape)
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print(indices)
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mass_array = []
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print("fetching masses")
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for subindices in indices: # subindices is (40)
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# can maybe be optimized to remove loop
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masses = transformed_data[subindices, 7]
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mass_array.append(masses)
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mass_array = np.array(mass_array)
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print("finished fetching masses")
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# print(closest_neighbours, indices)
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# print(indices)
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# densities = num_nearest_neighbors * mass_per_particle / np.mean(closest_neighbours, axis=1) ** 3
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total_masses = np.sum(mass_array, axis=1)
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densities = total_masses / np.mean(distances, axis=1) ** 3
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alt_densities = total_masses / np.max(distances, axis=1) ** 3
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# print(closest_neighbours.shape)
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# print(densities)
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# print(densities.shape)
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all_data = np.column_stack([list(range(densities.shape[0])), transformed_data, densities, alt_densities])
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# print(all_data.shape)
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# print(original_data.shape[0])
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export_data = all_data[:original_data.shape[0]]
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# print(export_data.shape)
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# all_data = np.append(coordinates, velocities, axis=1)
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# all_data = np.column_stack((all_data, absolute_velo, names, densities))
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# sorted_index = np.argsort(all_data[::, 7], kind="stable")
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# all_data = all_data[sorted_index, :]
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# np.savetxt("out_"+filename.with_suffix(".csv").name, all_data[indices], delimiter=",", fmt="%.3f", header="x,y,z,vx,vy,vz,v,name") #if indices are needed
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np.savetxt(directory / f"visualisation_{filename.with_suffix('.csv').name}",
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export_data,
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delimiter=",",
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fmt="%.3f",
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header="num,x,y,z,name,vx,vy,vz,masse,groupid,v,density,density_alt")
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file.close()
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219
visualisation_swift_zooms.py
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219
visualisation_swift_zooms.py
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@ -0,0 +1,219 @@
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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# import vtk
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import sys
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import time
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from pathlib import Path
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from sys import getsizeof
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import h5py
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import numba
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import numpy as np
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import scipy.spatial
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@numba.njit()
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def determine_desired_range(offset, minimum, upper_limit_bottom, lower_limit_top, maximum):
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a = minimum
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b = maximum
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if offset < 0:
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a = lower_limit_top
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elif offset > 0:
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b = upper_limit_bottom
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return a, b
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@numba.njit()
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def find_coordinates_to_move(minimum, maximum, ratio, x_offset, y_offset, z_offset, move_candidates):
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coordinates_to_move = []
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x_start, x_end = determine_desired_range(x_offset, minimum, upper_limit_bottom, lower_limit_top, maximum)
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y_start, y_end = determine_desired_range(y_offset, minimum, upper_limit_bottom, lower_limit_top, maximum)
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z_start, z_end = determine_desired_range(z_offset, minimum, upper_limit_bottom, lower_limit_top, maximum)
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for particle in move_candidates:
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point = particle[0:3]
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if x_start <= point[0] <= x_end and y_start <= point[1] <= y_end and z_start <= point[2] <= z_end:
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coordinates_to_move.append(particle)
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return coordinates_to_move
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# directory = Path(r"/home/ben/sims/swiftsim/examples/zoom_tests/auriga6_halo7_8_10/")
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directory = Path(r"/home/ben/sims/data_swift/monofonic_tests/DB2_128_100/")
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# file = h5py.File(directory / "auriga6_halo7_8_9.hdf5", "r")
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# for filename in sorted(directory.glob("output_*.hdf5")):
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for filename in sorted(directory.glob("output_0004.hdf5")):
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print(filename)
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file = h5py.File(str(filename), "r")
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Header = file['Header']
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highres_coordinates = file["PartType1"]["Coordinates"][:] # for highres particles
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highres_names = file["PartType1"]["ParticleIDs"][:]
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highres_velocities = file["PartType1"]["Velocities"][:]
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highres_masses = file['PartType1']['Masses'][:]
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highres_group_ids = file['PartType1']['FOFGroupIDs'][:]
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highres_absolute_velo = np.sqrt(np.sum(highres_velocities ** 2, axis=1))
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lowres_coordinates = file["PartType2"]["Coordinates"][:] # for lowres particles
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lowres_names = file["PartType2"]["ParticleIDs"][:]
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lowres_velocities = file["PartType2"]["Velocities"][:]
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lowres_masses = file['PartType2']['Masses'][:]
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lowres_group_ids = file['PartType2']['FOFGroupIDs'][:]
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lowres_absolute_velo = np.sqrt(np.sum(lowres_velocities ** 2, axis=1))
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original_coordinates = np.concatenate((highres_coordinates, lowres_coordinates))
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# if "auriga" in str(filename):
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# original_coordinates /= 1000
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# print(original_coordinates.mean())
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# print(original_coordinates.min())
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# print(original_coordinates.max())
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# print(file['Header'])
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# print(list(file['Units'].attrs.items()))
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# exit()
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names = np.concatenate((highres_names, lowres_names))
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velocities = np.concatenate((highres_velocities, lowres_velocities))
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masses = np.concatenate((highres_masses, lowres_masses))
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group_ids = np.concatenate((highres_group_ids, lowres_group_ids))
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absolute_velo = np.concatenate((highres_absolute_velo, lowres_absolute_velo))
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# for bla in [original_coordinates, names, velocities, masses, absolute_velo]:
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# print(bla.shape)
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original_data = np.vstack([
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original_coordinates[::, 0],
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original_coordinates[::, 1],
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original_coordinates[::, 2],
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names,
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velocities[::, 0],
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velocities[::, 1],
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velocities[::, 2],
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masses,
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group_ids,
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absolute_velo,
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]).T
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print(original_data.shape)
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assert (original_coordinates == original_data[::, 0:3]).all()
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boundaries = Header.attrs['BoxSize'] # BoxLength for e5 boxes depends on Nres, 2.36438 for 256, 4.72876 for 512.
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print(boundaries, len(highres_names))
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if not boundaries.shape:
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boundaries = np.array([boundaries] * 3)
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offsets = [-1, 0, 1]
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transformed_data = original_data[:]
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number_of_time_that_points_have_been_found = 0
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# assumes cube form and 0.1 as desired ratio to move
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minimum = 0.0
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maximum = max(boundaries)
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ratio = 0.1
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box_length = maximum - minimum
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range_to_move = 0.1 * box_length
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upper_limit_bottom = minimum + range_to_move
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lower_limit_top = maximum - range_to_move
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print("Find candidates to move...")
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@numba.njit()
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def find_move_candidates():
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move_candidates = []
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print("finding move candidates")
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for particle in original_data:
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point = particle[0:3]
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if (
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minimum <= point[0] <= upper_limit_bottom or
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lower_limit_top <= point[0] <= maximum or
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minimum <= point[1] <= upper_limit_bottom or
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lower_limit_top <= point[1] <= maximum or
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minimum <= point[2] <= upper_limit_bottom or
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lower_limit_top <= point[2] <= maximum
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):
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move_candidates.append(particle)
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# print(point)
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return move_candidates
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move_candidates = find_move_candidates()
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move_candidates = np.array(move_candidates)
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print("...done.")
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for x in offsets:
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for y in offsets:
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for z in offsets:
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if (x, y, z) == (0, 0, 0):
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continue
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moved_coordinates = find_coordinates_to_move(minimum, maximum, ratio, x, y, z, move_candidates)
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# print(moved_coordinates)
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moved_coordinates = np.array(moved_coordinates)
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# if not moved_coordinates:
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# print(f"nothing moved in {(x,y,z)}")
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# continue
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moved_coordinates[::, 0] += x * boundaries[0]
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moved_coordinates[::, 1] += y * boundaries[1]
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moved_coordinates[::, 2] += z * boundaries[2]
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transformed_data = np.vstack((transformed_data, moved_coordinates))
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number_of_time_that_points_have_been_found += 1
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print(f"Points found: {number_of_time_that_points_have_been_found}/26...")
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# assert coordinates.shape[0] == original_coordinates.shape[0] * 3 ** 3 #check that the new space has the shape we want it to have
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num_nearest_neighbors = 40
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print("Building 3d-Tree for all particles...")
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coordinates = transformed_data[::, 0:3]
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print(coordinates.shape)
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tree = scipy.spatial.KDTree(coordinates)
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print(getsizeof(tree) / 1024, "KB")
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print("...done.")
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print("Searching neighbours...")
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a = time.perf_counter_ns()
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distances, indices = tree.query([coordinates], k=num_nearest_neighbors, workers=6)
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# shape of closest_neighbours: (1, xxxx, 40)
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b = time.perf_counter_ns()
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print("...found neighbours.")
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print(f"took {(b - a) / 1000 / 1000:.2f} ms")
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distances = distances[0] # to (xxxx, 40)
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indices = indices[0] # to (xxxx, 40)
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print(distances.shape)
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print(indices.shape)
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print(indices)
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mass_array = []
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print("fetching masses")
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for subindices in indices: # subindices is (40)
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# can maybe be optimized to remove loop
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masses = transformed_data[subindices, 7]
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mass_array.append(masses)
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mass_array = np.array(mass_array)
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print("finished fetching masses")
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# print(closest_neighbours, indices)
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# print(indices)
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# densities = num_nearest_neighbors * mass_per_particle / np.mean(closest_neighbours, axis=1) ** 3
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total_masses = np.sum(mass_array, axis=1)
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densities = total_masses / np.mean(distances, axis=1) ** 3
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alt_densities = total_masses / np.max(distances, axis=1) ** 3
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# print(closest_neighbours.shape)
|
||||
|
||||
# print(densities)
|
||||
# print(densities.shape)
|
||||
all_data = np.column_stack([list(range(densities.shape[0])), transformed_data, densities, alt_densities])
|
||||
# print(all_data.shape)
|
||||
# print(original_data.shape[0])
|
||||
export_data = all_data[:original_data.shape[0]]
|
||||
# print(export_data.shape)
|
||||
|
||||
# all_data = np.append(coordinates, velocities, axis=1)
|
||||
# all_data = np.column_stack((all_data, absolute_velo, names, densities))
|
||||
# sorted_index = np.argsort(all_data[::, 7], kind="stable")
|
||||
# all_data = all_data[sorted_index, :]
|
||||
|
||||
# np.savetxt("out_"+filename.with_suffix(".csv").name, all_data[indices], delimiter=",", fmt="%.3f", header="x,y,z,vx,vy,vz,v,name") #if indices are needed
|
||||
np.savetxt(directory / f"out_{filename.with_suffix('.csv').name}",
|
||||
export_data,
|
||||
delimiter=",",
|
||||
fmt="%.3f",
|
||||
header="num,x,y,z,name,vx,vy,vz,masse,groupid,v,density,density_alt")
|
||||
file.close()
|
||||
Loading…
Reference in a new issue