Skip to content

use pool.map to accomplish parallelism of generating waveform #1

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
zuchengchen wants to merge 1 commit into
base: master
Choose a base branch
from
Open

use pool.map to accomplish parallelism of generating waveform #1

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
72 changes: 72 additions & 0 deletions .gitignore
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,72 @@
### Python ###
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class

# C extensions
*.so

# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
pip-wheel-metadata/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST

# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec

# Installer logs
pip-log.txt
pip-delete-this-directory.txt

# Jupyter Notebook
.ipynb_checkpoints

# IPython
profile_default/
ipython_config.py

# pyenv
.python-version

# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/

.DS_Store


## Core latex/pdflatex auxiliary files:
*.aux
#*.bbl
*.blg
*.fdb_latexmk
*.fls
*.log
*Notes.bib
*.out
*.synctex.gz
35 changes: 25 additions & 10 deletions Code/PyROQ/pyroq.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -11,6 +11,8 @@
import warnings
import random
import multiprocessing as mp
from functools import partial
from datetime import datetime

def howmany_within_range(row, minimum, maximum):
"""Returns how many numbers lie within `maximum` and `minimum` in a given `row`"""
Expand Down Expand Up @@ -146,11 +148,9 @@ def compute_modulus_quad(paramspoint, known_quad_bases, distance, deltaF, f_min,
def least_match_waveform_unnormalized(parallel, nprocesses, paramspoints, known_bases, distance, deltaF, f_min, f_max, waveFlags, approximant):
if parallel == 1:
paramspointslist = paramspoints.tolist()
#pool = mp.Pool(mp.cpu_count())
pool = mp.Pool(processes=nprocesses)
modula = [pool.apply(compute_modulus, args=(paramspoint, known_bases, distance, deltaF, f_min, f_max, approximant)) for paramspoint in paramspointslist]
pool.close()
if parallel == 0:
with mp.Pool(processes=nprocesses) as pool:
modula = pool.map(partial(compute_modulus, known_bases=known_bases, distance=distance, deltaF=deltaF, f_min=f_min, f_max=f_max, approximant=approximant), paramspointslist)
else:
npts = len(paramspoints)
modula = numpy.zeros(npts)
for i in numpy.arange(0,npts):
Expand Down Expand Up @@ -183,9 +183,8 @@ def least_match_waveform_unnormalized(parallel, nprocesses, paramspoints, known_
def least_match_quadratic_waveform_unnormalized(parallel, nprocesses, paramspoints, known_quad_bases, distance, deltaF, f_min, f_max, waveFlags, approximant):
if parallel == 1:
paramspointslist = paramspoints.tolist()
pool = mp.Pool(processes=nprocesses)
modula = [pool.apply(compute_modulus_quad, args=(paramspoint, known_quad_bases, distance, deltaF, f_min, f_max, approximant)) for paramspoint in paramspointslist]
pool.close()
with mp.Pool(processes=nprocesses) as pool:
modula = pool.map(partial(compute_modulus_quad, known_quad_bases=known_quad_bases, distance=distance, deltaF=deltaF, f_min=f_min, f_max=f_max, approximant=approximant), paramspointslist)
if parallel == 0:
npts = len(paramspoints)
modula = numpy.zeros(npts)
Expand Down Expand Up @@ -220,25 +219,41 @@ def bases_searching_results_unnormalized(parallel, nprocesses, npts, nparams, nb
if nparams == 10: print("The parameters are Mc, q, s1(mag, theta, phi), s2(mag, theta, phi), iota, and phiRef\n")
if nparams == 11: print("The parameters are Mc, q, s1(mag, theta, phi), s2(mag, theta, phi), iota, phiRef, and eccentricity\n")
if nparams == 12: print("The parameters are Mc, q, s1(mag, theta, phi), s2(mag, theta, phi), iota, phiRef, lambda1, and lambda2\n")

t_start = datetime.now()
print(str(t_start), "Start linear bases searching...")
for k in numpy.arange(0,nbases-1):
paramspoints = generate_params_points(npts, nparams, params_low, params_high)
basis_new, params_new, rm_new = least_match_waveform_unnormalized(parallel, nprocesses, paramspoints, known_bases, distance, deltaF, f_min, f_max, waveFlags, approximant)
print("Linear Iter: ", k+1, "and new basis waveform", params_new)
print(str(datetime.now()), "Linear Iter:", k+1, "and new basis waveform", params_new)
known_bases= numpy.append(known_bases, numpy.array([basis_new]), axis=0)
params = numpy.append(params, numpy.array([params_new]), axis = 0)
residual_modula = numpy.append(residual_modula, rm_new)

t_end = datetime.now()
print(str(t_start), "Finish linear bases searching...")
print(f"It takes {(t_end - t_start).total_seconds()} seconds.")

numpy.save('./linearbases.npy',known_bases)
numpy.save('./linearbasiswaveformparams.npy',params)
return known_bases, params, residual_modula

def bases_searching_quadratic_results_unnormalized(parallel, nprocesses, npts, nparams, nbases_quad, known_quad_bases, basis_waveforms, params_quad, residual_modula, params_low, params_high, distance, deltaF, f_min, f_max, waveFlags, approximant):

t_start = datetime.now()
print(str(t_start), "Start quadratic bases searching...")
for k in numpy.arange(0,nbases_quad-1):
print("Quadratic Iter: ", k+1)
print(str(datetime.now()), "Quadratic Iter: ", k+1)
paramspoints = generate_params_points(npts, nparams, params_low, params_high)
basis_new, params_new, rm_new= least_match_quadratic_waveform_unnormalized(parallel, nprocesses, paramspoints, known_quad_bases, distance, deltaF, f_min, f_max, waveFlags, approximant)
known_quad_bases= numpy.append(known_quad_bases, numpy.array([basis_new]), axis=0)
params_quad = numpy.append(params_quad, numpy.array([params_new]), axis = 0)
residual_modula = numpy.append(residual_modula, rm_new)

t_end = datetime.now()
print(str(t_start), "Finish quadratic bases searching...")
print(f"It takes {(t_end - t_start).total_seconds()} seconds.")

numpy.save('./quadraticbases.npy',known_quad_bases)
numpy.save('./quadraticbasiswaveformparams.npy',params_quad)
return known_quad_bases, params_quad, residual_modula
Expand Down
Loading