side_size = 100.0e+3
thickness = 8.0e+3
n_files = 1
frac = 1.0
units = 'kpc'
part_type = [0, 1, 2.0, 3.0, 4, 5]
grid_size = 600
rand_seed = 1
fig_size = 10
augment = False
legend = True
hex_plot = True
show_plot = False
velocity = False
part_df_1 = rf.read_snap(file_name=file_path, part_types=[1], n_files=1)
part_df_2 = rf.read_snap(file_name=file_path, part_types=[2], n_files=1)
n_resample = int(len(part_df_1) / 8.0)
part_df_1r = part_df_1.sample(n_resample)
part_df = pd.concat([part_df_1r, part_df_2], axis=0)
slab_part_df = t.find_slab(part_df=part_df,
side=side_size,
thick=thickness,
center=center,
reduction_factor=frac,
z_axis=2,
rand_seed=rand_seed)
for col in ['X', 'Y']:
def particles_extract():
'''
Find a series of particles in gadget format files and extract their properties
'''
# IC data
#base_ic_path = '/home/edoardo/CLUES/DATA/ICs/'
base_ic_path = '/z/carlesi/CLUES/ginnungagap/ginnungagap/ICs/'
#base_ic_path = '/z/carlesi/HestiaNoam/RE_SIMS/512/DM_ONLY/'
#00_10/ICs'
n_ic_files = 2; ic_root = 'zoom_cf2_512_100.000_'
# Snapshot data
base_snap_path = '/z/carlesi/CLUES/DATA/512/'
n_snap_files = 1; snap_root = 'snapshot_054'
#n_snap_files = 8; snap_root = 'snapshot_127'
# Save files in PKL format
out_extension = '.pkl'
# Look at snapshots OR ICs?
#snapshot = False
snapshot = True
# Plot properties
velocity = True
n_files_ics = 2
n_files_snap = 1
part_type = [1]
rand_state = 1
reduce_factor = 1.0
# Now loop on all the simulations and gather data
for code in code_run:
for sub in sub_run:
if snapshot == False:
#this_file = base_ic_path + code + '_' + sub + '/ICs/' + ic_root + code + '_' + sub
#this_fout = 'output/ic_hestia_' + code + '_' + sub + out_extension
this_file = base_ic_path + ic_root + code + '_' + sub
this_fout = 'output/ic_' + code + '_' + sub + out_extension
n_files = n_ic_files
# If this is not ICs, try reading a snapshot
else:
this_file = base_snap_path + code + '_' + sub + '/' + snap_root
this_fout = 'output/snap_' + code + '_' + sub + out_extension
n_files = n_snap_files
if n_files > 1:
this_file_test = this_file + '.0'
else:
this_file_test = this_file
# First check if file exists
if os.path.isfile(this_file_test):
part_df = rf.read_snap(file_name=this_file, velocity=velocity, part_types=part_type, n_files=n_files)
# Then compress the data and save only a subset of the total particles
if reduce_factor < 1.0 and len(part_df) > 1000:
part_df = part_df.sample(frac=reduce_factor, random_state=rand_state)
if len(part_df) > 1000:
print('Saving file to: ', this_fout)
part_df.to_pickle(this_fout)
return None
'''
Python Routines for COsmology and Data I/O
PyRCODIO Pandas Version
Edoardo Carlesi 2020
[email protected]
main_particles_extract.py: extract particle data within a high-res region in the ICs or in a snapshot
'''
import read_files as rf
import sys
file_in = sys.argv[1]
part_type = sys.argv[2]
part_df = rf.read_snap(file_name=this_file, velocity=velocity, part_types=part_type, n_files=n_files)
def plot_density():
# Local data path
data_path = '/home/edoardo/CLUES/PyRCODIO/data/'
# Full dataset
#base_path = '/media/edoardo/data1/DATA/'
base_path = '/media/edoardo/Elements/CLUES/DATA/2048/'
snap_path = 'snapshot_054'
mpc2kpc = 1.e+3
# Plot properties
side_size = 3.0 * mpc2kpc
thickness = 1.5 * mpc2kpc
n_files = 1
frac = 1.00
units = 'kpc'
part_types = [4]
grid_size = 500
rand_seed = 1
fig_size = 8
version = ''
show_plot = False
velocity = False
augment = False
shift = False
legend = False
randomize = True
vel_components = ['Vx', 'Vy', 'Vz']
n_min_part = 1000
# Configure the LG model and subpaths
code_run = cfg.simu_runs()
sub_run = cfg.gen_runs(0, 10)
# Now loop on all the simulations and gather data
for code in code_run:
for sub in sub_run:
this_path = base_path + code + '/' + sub + '/'
this_snap = this_path + snap_path
input_all_csv = 'output/clusters_' + code + '_' + sub + '.csv'
try:
data_all = pd.read_csv(input_all_csv)
com = ['Xc(6)', 'Yc(7)', 'Zc(8)']
these_com = data_all[com].values
except:
print('Error, file: ', input_all_csv, ' could not be found.')
# Check that file exists
if os.path.isfile(this_snap) and os.path.isfile(input_all_csv):
print('Found: ', len(these_com), ' clusters in ', this_snap)
# Read the full snapshot here
part_df = rf.read_snap(file_name=this_snap, velocity=velocity, part_types=part_types, n_files=1)
for i, this_com in enumerate(these_com):
for z_axis in range(0, 3):
# Select particles for the plot, do a selection first. z_axis is orthogonal to the plane of the projection
ax0 = (z_axis + 1) % 3
ax1 = (z_axis + 2) % 3
if shift == True:
'Implement this function'
center = t.shift(this_com, side_size * 0.5)
this_fout = 'output/cluster_shift_' + code + '_' + sub + '.' + str(i) + '.'
else:
center = this_com
this_fout = 'output/cluster_' + version + code + '_' + sub + '.' + str(i) + '.'
this_fout_rand = 'output/cluster_' + version + code + '_' + sub + '.' + str(i) + '.rand.'
print('Plot axes, z=', z_axis, ' ax0=', ax0, ' ax1=', ax1, ' center of mass: ', this_com)
try:
# Select a slab around a given axis, this function returns a dataframe
slab_part_df = t.find_slab(part_df=part_df, side=side_size, thick=thickness, center=center, reduction_factor=frac, z_axis=z_axis, rand_seed=rand_seed)
# Do a plot only if there are enough particles
if len(slab_part_df) > n_min_part:
# Feed the previously chosen dataframe and plot its 2D density projection
pu.plot_density(data=slab_part_df, axes_plot=[ax0, ax1], file_name=this_fout, show_plot=show_plot, legend=legend,
grid_size=grid_size, margin=0.1, data_augment=augment, fig_size=fig_size, velocity=velocity, vel=vel_components)
except:
print('Could not generate a plot for: ', this_snap, '. Data read error.')
# Add some scatter to the plot properties
if randomize == True:
print('Randomizing ...')
# The randomization of the center needs to be very small - a small % on tens of kpc might shift the LG out of the frame
for i, c in enumerate(center):
center[i] = c - thickness * 0.25 + 0.5 * np.random.uniform()
this_fout = this_fout_rand
thickness0 = thickness * (0.9 + 0.4 * np.random.uniform())
side_size0 = side_size * (0.9 + 0.4 * np.random.uniform())
grid_size0 = int(grid_size * (0.75 + 0.5 * np.random.uniform()))
fig_size0 = fig_size * (0.8 + 0.5 * np.random.uniform())
frac0 = frac
try:
print('Adding randomized plots around: ', center, ' with size: ', side_size0, ' and frac: ', frac0, ' grid: ', grid_size0, ' fig_size: ', fig_size0)
slab_part_df = t.find_slab(part_df=part_df, side=side_size0, thick=thickness0, center=center,
reduction_factor=frac0, z_axis=z_axis, rand_seed=rand_seed)
if len(slab_part_df > n_min_part):
# Feed the previously chosen dataframe and plot its 2D density projection
pu.plot_density(data=slab_part_df, axes_plot=[ax0, ax1], file_name=this_fout, show_plot=show_plot, legend=legend,
grid_size=grid_size, margin=0.1, data_augment=augment, fig_size=fig_size0, velocity=velocity, vel=vel_components)
except:
print('Could not print slices for file: ', this_fout)
this_com = data_all[(data_all['sub_code'] == sub) & (data_all['simu_code'] == code)][com].values
sub = '%02d' % sub
this_path = base_path + code + '/' + sub + '/'
#this_fout = 'output/lg_' + code + '_' + sub + '_'
this_fout = 'output/lg_fig_' + code + '_' + sub + '_'
this_fout_rand = 'output/lg_fig_' + code + '_' + sub + '_rand_'
this_snap = this_path + snap_path
# Check that file exists
if os.path.isfile(this_snap) and len(this_com) > 0:
center = this_com[0]
print('Found: ', this_snap, ', plotting around: ', center)
# Select a slab around a given axis, this function returns a dataframe
part_df = rf.read_snap(file_name=this_snap, velocity=velocity, part_types=1, n_files=1)
# Select particles for the plot, do a selection first. z_axis is orthogonal to the plane of the projection
for z_axis in range(0, 3):
ax0 = (z_axis + 1) % 3
ax1 = (z_axis + 2) % 3
print('Plot axes, z=', z_axis, ' ax0=', ax0, ' ax1=', ax1)
try:
slab_part_df = t.find_slab(part_df=part_df, side=side_size, thick=thickness, center=center, reduction_factor=frac, z_axis=z_axis, rand_seed=rand_seed)
# Feed the previously chosen dataframe and plot its 2D density projection
pu.plot_density(data=slab_part_df, axes_plot=[ax0, ax1], file_name=this_fout, show_plot=show_plot, legend=legend,
grid_size=grid_size, margin=0.1, data_augment=augment, fig_size=fig_size, velocity=velocity, vel=vel_components)
except: