mirror of
https://github.com/glatterf42/sims_python_files.git
synced 2024-09-19 16:13:45 +02:00
extract methods
this commit shouldn't change any actual code apart from reordering it and extracting parts of it into methods
This commit is contained in:
parent
05e6be9921
commit
5da3f1ad26
1 changed files with 130 additions and 124 deletions
|
@ -41,21 +41,34 @@ def find_coordinates_to_move(minimum, maximum, ratio, x_offset, y_offset, z_offs
|
||||||
return coordinates_to_move
|
return coordinates_to_move
|
||||||
|
|
||||||
|
|
||||||
# file = h5py.File(directory / "auriga6_halo7_8_9.hdf5", "r")
|
@numba.njit()
|
||||||
|
def find_move_candidates(original_data, minimum, maximum, lower_limit_top, upper_limit_bottom):
|
||||||
|
move_candidates = []
|
||||||
|
print("finding move candidates")
|
||||||
|
for particle in original_data:
|
||||||
|
point = particle[0:3]
|
||||||
|
if (
|
||||||
|
minimum <= point[0] <= upper_limit_bottom or
|
||||||
|
lower_limit_top <= point[0] <= maximum or
|
||||||
|
minimum <= point[1] <= upper_limit_bottom or
|
||||||
|
lower_limit_top <= point[1] <= maximum or
|
||||||
|
minimum <= point[2] <= upper_limit_bottom or
|
||||||
|
lower_limit_top <= point[2] <= maximum
|
||||||
|
):
|
||||||
|
move_candidates.append(particle)
|
||||||
|
# print(point)
|
||||||
|
return move_candidates
|
||||||
|
|
||||||
for filename in sys.argv[1:]:
|
|
||||||
filename = Path(filename)
|
def read_file(filename):
|
||||||
print(filename)
|
|
||||||
file = h5py.File(str(filename), "r")
|
file = h5py.File(str(filename), "r")
|
||||||
Header = file['Header']
|
Header = file['Header']
|
||||||
|
|
||||||
highres_coordinates = file["PartType1"]["Coordinates"][:] # for cdm particles
|
highres_coordinates = file["PartType1"]["Coordinates"][:] # for cdm particles
|
||||||
highres_names = file["PartType1"]["ParticleIDs"][:]
|
highres_names = file["PartType1"]["ParticleIDs"][:]
|
||||||
highres_velocities = file["PartType1"]["Velocities"][:]
|
highres_velocities = file["PartType1"]["Velocities"][:]
|
||||||
highres_masses = file['PartType1']['Masses'][:]
|
highres_masses = file['PartType1']['Masses'][:]
|
||||||
highres_group_ids = file['PartType1']['FOFGroupIDs'][:]
|
highres_group_ids = file['PartType1']['FOFGroupIDs'][:]
|
||||||
highres_absolute_velo = np.sqrt(np.sum(highres_velocities ** 2, axis=1))
|
highres_absolute_velo = np.sqrt(np.sum(highres_velocities ** 2, axis=1))
|
||||||
|
|
||||||
if "PartType2" in file:
|
if "PartType2" in file:
|
||||||
lowres_coordinates = file["PartType2"]["Coordinates"][:] # for cdm particles
|
lowres_coordinates = file["PartType2"]["Coordinates"][:] # for cdm particles
|
||||||
lowres_names = file["PartType2"]["ParticleIDs"][:]
|
lowres_names = file["PartType2"]["ParticleIDs"][:]
|
||||||
|
@ -77,7 +90,7 @@ for filename in sys.argv[1:]:
|
||||||
masses = highres_masses
|
masses = highres_masses
|
||||||
group_ids = highres_group_ids
|
group_ids = highres_group_ids
|
||||||
absolute_velo = highres_absolute_velo
|
absolute_velo = highres_absolute_velo
|
||||||
|
file.close()
|
||||||
# if "auriga" in str(filename):
|
# if "auriga" in str(filename):
|
||||||
# original_coordinates /= 1000
|
# original_coordinates /= 1000
|
||||||
# print(original_coordinates.mean())
|
# print(original_coordinates.mean())
|
||||||
|
@ -103,124 +116,117 @@ for filename in sys.argv[1:]:
|
||||||
]).T
|
]).T
|
||||||
print(original_data.shape)
|
print(original_data.shape)
|
||||||
assert (original_coordinates == original_data[::, 0:3]).all()
|
assert (original_coordinates == original_data[::, 0:3]).all()
|
||||||
|
return Header, highres_names, original_data
|
||||||
boundaries = Header.attrs['BoxSize'] # BoxLength for e5 boxes depends on Nres, 2.36438 for 256, 4.72876 for 512.
|
|
||||||
print(boundaries, len(highres_names))
|
|
||||||
if not boundaries.shape:
|
|
||||||
boundaries = np.array([boundaries] * 3)
|
|
||||||
offsets = [-1, 0, 1]
|
|
||||||
transformed_data = original_data[:]
|
|
||||||
number_of_time_that_points_have_been_found = 0
|
|
||||||
|
|
||||||
# assumes cube form and 0.1 as desired ratio to move
|
|
||||||
minimum = 0.0
|
|
||||||
maximum = max(boundaries)
|
|
||||||
ratio = 0.1
|
|
||||||
box_length = maximum - minimum
|
|
||||||
range_to_move = 0.1 * box_length
|
|
||||||
upper_limit_bottom = minimum + range_to_move
|
|
||||||
lower_limit_top = maximum - range_to_move
|
|
||||||
|
|
||||||
print("Find candidates to move...")
|
|
||||||
|
|
||||||
|
|
||||||
@numba.njit()
|
# file = h5py.File(directory / "auriga6_halo7_8_9.hdf5", "r")
|
||||||
def find_move_candidates():
|
def main():
|
||||||
move_candidates = []
|
for filename in sys.argv[1:]:
|
||||||
print("finding move candidates")
|
filename = Path(filename)
|
||||||
for particle in original_data:
|
print(filename)
|
||||||
point = particle[0:3]
|
Header, highres_names, original_data = read_file(filename)
|
||||||
if (
|
|
||||||
minimum <= point[0] <= upper_limit_bottom or
|
boundaries = Header.attrs[
|
||||||
lower_limit_top <= point[0] <= maximum or
|
'BoxSize'] # BoxLength for e5 boxes depends on Nres, 2.36438 for 256, 4.72876 for 512.
|
||||||
minimum <= point[1] <= upper_limit_bottom or
|
print(boundaries, len(highres_names))
|
||||||
lower_limit_top <= point[1] <= maximum or
|
if not boundaries.shape:
|
||||||
minimum <= point[2] <= upper_limit_bottom or
|
boundaries = np.array([boundaries] * 3)
|
||||||
lower_limit_top <= point[2] <= maximum
|
offsets = [-1, 0, 1]
|
||||||
):
|
transformed_data = original_data[:]
|
||||||
move_candidates.append(particle)
|
number_of_time_that_points_have_been_found = 0
|
||||||
# print(point)
|
|
||||||
return move_candidates
|
# assumes cube form and 0.1 as desired ratio to move
|
||||||
|
minimum = 0.0
|
||||||
|
maximum = max(boundaries)
|
||||||
|
ratio = 0.1
|
||||||
|
box_length = maximum - minimum
|
||||||
|
range_to_move = 0.1 * box_length
|
||||||
|
upper_limit_bottom = minimum + range_to_move
|
||||||
|
lower_limit_top = maximum - range_to_move
|
||||||
|
|
||||||
|
print("Find candidates to move...")
|
||||||
|
|
||||||
|
move_candidates = find_move_candidates(original_data, minimum, maximum, lower_limit_top, upper_limit_bottom)
|
||||||
|
move_candidates = np.array(move_candidates)
|
||||||
|
|
||||||
|
print("...done.")
|
||||||
|
for x in offsets:
|
||||||
|
for y in offsets:
|
||||||
|
for z in offsets:
|
||||||
|
if (x, y, z) == (0, 0, 0):
|
||||||
|
continue
|
||||||
|
moved_coordinates = find_coordinates_to_move(minimum, maximum, ratio, x, y, z, move_candidates)
|
||||||
|
# print(moved_coordinates)
|
||||||
|
moved_coordinates = np.array(moved_coordinates)
|
||||||
|
# if not moved_coordinates:
|
||||||
|
# print(f"nothing moved in {(x,y,z)}")
|
||||||
|
# continue
|
||||||
|
moved_coordinates[::, 0] += x * boundaries[0]
|
||||||
|
moved_coordinates[::, 1] += y * boundaries[1]
|
||||||
|
moved_coordinates[::, 2] += z * boundaries[2]
|
||||||
|
transformed_data = np.vstack((transformed_data, moved_coordinates))
|
||||||
|
number_of_time_that_points_have_been_found += 1
|
||||||
|
print(f"Points found: {number_of_time_that_points_have_been_found}/26...")
|
||||||
|
|
||||||
|
# assert coordinates.shape[0] == original_coordinates.shape[0] * 3 ** 3 #check that the new space has the shape we want it to have
|
||||||
|
|
||||||
|
num_nearest_neighbors = 40
|
||||||
|
print("Building 3d-Tree for all particles...")
|
||||||
|
coordinates = transformed_data[::, 0:3]
|
||||||
|
print(coordinates.shape)
|
||||||
|
tree = scipy.spatial.KDTree(coordinates)
|
||||||
|
print(getsizeof(tree) / 1024, "KB")
|
||||||
|
print("...done.")
|
||||||
|
print("Searching neighbours...")
|
||||||
|
a = time.perf_counter_ns()
|
||||||
|
distances, indices = tree.query([coordinates], k=num_nearest_neighbors, workers=6)
|
||||||
|
# shape of closest_neighbours: (1, xxxx, 40)
|
||||||
|
b = time.perf_counter_ns()
|
||||||
|
print("...found neighbours.")
|
||||||
|
print(f"took {(b - a) / 1000 / 1000:.2f} ms")
|
||||||
|
distances = distances[0] # to (xxxx, 40)
|
||||||
|
indices = indices[0] # to (xxxx, 40)
|
||||||
|
print(distances.shape)
|
||||||
|
print(indices.shape)
|
||||||
|
print(indices)
|
||||||
|
mass_array = []
|
||||||
|
print("fetching masses")
|
||||||
|
for subindices in indices: # subindices is (40)
|
||||||
|
# can maybe be optimized to remove loop
|
||||||
|
masses = transformed_data[subindices, 7]
|
||||||
|
mass_array.append(masses)
|
||||||
|
mass_array = np.array(mass_array)
|
||||||
|
print("finished fetching masses")
|
||||||
|
# print(closest_neighbours, indices)
|
||||||
|
# print(indices)
|
||||||
|
|
||||||
|
# densities = num_nearest_neighbors * mass_per_particle / np.mean(closest_neighbours, axis=1) ** 3
|
||||||
|
total_masses = np.sum(mass_array, axis=1)
|
||||||
|
densities = total_masses / np.mean(distances, axis=1) ** 3
|
||||||
|
alt_densities = total_masses / np.max(distances, axis=1) ** 3
|
||||||
|
|
||||||
|
# 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(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")
|
||||||
|
|
||||||
|
|
||||||
move_candidates = find_move_candidates()
|
if __name__ == '__main__':
|
||||||
move_candidates = np.array(move_candidates)
|
main()
|
||||||
|
|
||||||
print("...done.")
|
|
||||||
for x in offsets:
|
|
||||||
for y in offsets:
|
|
||||||
for z in offsets:
|
|
||||||
if (x, y, z) == (0, 0, 0):
|
|
||||||
continue
|
|
||||||
moved_coordinates = find_coordinates_to_move(minimum, maximum, ratio, x, y, z, move_candidates)
|
|
||||||
# print(moved_coordinates)
|
|
||||||
moved_coordinates = np.array(moved_coordinates)
|
|
||||||
# if not moved_coordinates:
|
|
||||||
# print(f"nothing moved in {(x,y,z)}")
|
|
||||||
# continue
|
|
||||||
moved_coordinates[::, 0] += x * boundaries[0]
|
|
||||||
moved_coordinates[::, 1] += y * boundaries[1]
|
|
||||||
moved_coordinates[::, 2] += z * boundaries[2]
|
|
||||||
transformed_data = np.vstack((transformed_data, moved_coordinates))
|
|
||||||
number_of_time_that_points_have_been_found += 1
|
|
||||||
print(f"Points found: {number_of_time_that_points_have_been_found}/26...")
|
|
||||||
|
|
||||||
# assert coordinates.shape[0] == original_coordinates.shape[0] * 3 ** 3 #check that the new space has the shape we want it to have
|
|
||||||
|
|
||||||
num_nearest_neighbors = 40
|
|
||||||
print("Building 3d-Tree for all particles...")
|
|
||||||
coordinates = transformed_data[::, 0:3]
|
|
||||||
print(coordinates.shape)
|
|
||||||
tree = scipy.spatial.KDTree(coordinates)
|
|
||||||
print(getsizeof(tree) / 1024, "KB")
|
|
||||||
print("...done.")
|
|
||||||
print("Searching neighbours...")
|
|
||||||
a = time.perf_counter_ns()
|
|
||||||
distances, indices = tree.query([coordinates], k=num_nearest_neighbors, workers=6)
|
|
||||||
# shape of closest_neighbours: (1, xxxx, 40)
|
|
||||||
b = time.perf_counter_ns()
|
|
||||||
print("...found neighbours.")
|
|
||||||
print(f"took {(b - a) / 1000 / 1000:.2f} ms")
|
|
||||||
distances = distances[0] # to (xxxx, 40)
|
|
||||||
indices = indices[0] # to (xxxx, 40)
|
|
||||||
print(distances.shape)
|
|
||||||
print(indices.shape)
|
|
||||||
print(indices)
|
|
||||||
mass_array = []
|
|
||||||
print("fetching masses")
|
|
||||||
for subindices in indices: # subindices is (40)
|
|
||||||
# can maybe be optimized to remove loop
|
|
||||||
masses = transformed_data[subindices, 7]
|
|
||||||
mass_array.append(masses)
|
|
||||||
mass_array = np.array(mass_array)
|
|
||||||
print("finished fetching masses")
|
|
||||||
# print(closest_neighbours, indices)
|
|
||||||
# print(indices)
|
|
||||||
|
|
||||||
# densities = num_nearest_neighbors * mass_per_particle / np.mean(closest_neighbours, axis=1) ** 3
|
|
||||||
total_masses = np.sum(mass_array, axis=1)
|
|
||||||
densities = total_masses / np.mean(distances, axis=1) ** 3
|
|
||||||
alt_densities = total_masses / np.max(distances, axis=1) ** 3
|
|
||||||
|
|
||||||
# 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(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