Homec4science
Diffusion PySONIC (old) (master) Browse

R4670/PySONIC/batches.py
8a71066a647cmaster

Last Change
Raw File

batches.py
View Options

# -*- coding: utf-8 -*-
# @Author: Theo Lemaire
# @Date: 2017-08-22 14:33:04
# @Last Modified by: Theo Lemaire
# @Last Modified time: 2018-11-27 16:58:25
''' Utility functions used in simulations '''
import os
import lockfile
import logging
import multiprocessing as mp
import numpy as np
import pandas as pd
from .utils import logger
class Consumer(mp.Process):
''' Generic consumer process, taking tasks from a queue and outputing results in
another queue.
'''
def __init__(self, queue_in, queue_out):
mp.Process.__init__(self)
self.queue_in = queue_in
self.queue_out = queue_out
logger.debug('Starting %s', self.name)
def run(self):
while True:
nextTask = self.queue_in.get()
if nextTask is None:
logger.debug('Exiting %s', self.name)
self.queue_in.task_done()
break
answer = nextTask()
self.queue_in.task_done()
self.queue_out.put(answer)
return
class Worker():
''' Generic worker class calling a specific object's method with a given set of parameters. '''
def __init__(self, wid, obj, method_str, params, loglevel):
''' Worker constructor.
:param wid: worker ID
:param obj: object containing the method to call
:param method_str: name of the method to call
:param params: list of method parameters
:param loglevel: logging level
'''
self.id = wid
self.obj = obj
self.method_str = method_str
self.params = params
self.loglevel = loglevel
def __call__(self):
''' Caller to the specific object method. '''
logger.setLevel(self.loglevel)
return self.id, getattr(self.obj, self.method_str)(*self.params)
def createQueue(dims):
''' Create a serialized 2D array of all parameter combinations for a series of individual
parameter sweeps.
:param dims: list of lists (or 1D arrays) of input parameters
:return: list of parameters (list) for each simulation
'''
ndims = len(dims)
dims_in = [dims[1], dims[0]]
inds_out = [1, 0]
if ndims > 2:
dims_in += dims[2:]
inds_out += list(range(2, ndims))
queue = np.stack(np.meshgrid(*dims_in), -1).reshape(-1, ndims)
queue = queue[:, inds_out]
return queue.tolist()
def createEStimQueue(amps, durations, offsets, PRFs, DCs):
''' Create a serialized 2D array of all parameter combinations for a series of individual
parameter sweeps, while avoiding repetition of CW protocols for a given PRF sweep.
:param amps: list (or 1D-array) of acoustic amplitudes
:param durations: list (or 1D-array) of stimulus durations
:param offsets: list (or 1D-array) of stimulus offsets (paired with durations array)
:param PRFs: list (or 1D-array) of pulse-repetition frequencies
:param DCs: list (or 1D-array) of duty cycle values
:return: list of parameters (list) for each simulation
'''
DCs = np.array(DCs)
queue = []
if 1.0 in DCs:
queue += createQueue((durations, offsets, min(PRFs), 1.0, amps))
if np.any(DCs != 1.0):
queue += createQueue((durations, offsets, PRFs, DCs[DCs != 1.0], amps))
return queue
def createAStimQueue(freqs, amps, durations, offsets, PRFs, DCs, method):
''' Create a serialized 2D array of all parameter combinations for a series of individual
parameter sweeps, while avoiding repetition of CW protocols for a given PRF sweep.
:param freqs: list (or 1D-array) of US frequencies
:param amps: list (or 1D-array) of acoustic amplitudes
:param durations: list (or 1D-array) of stimulus durations
:param offsets: list (or 1D-array) of stimulus offsets (paired with durations array)
:param PRFs: list (or 1D-array) of pulse-repetition frequencies
:param DCs: list (or 1D-array) of duty cycle values
:params method: integration method
:return: list of parameters (list) for each simulation
'''
DCs = np.array(DCs)
queue = []
if 1.0 in DCs:
queue += createQueue((freqs, durations, offsets, min(PRFs), 1.0, amps))
if np.any(DCs != 1.0):
queue += createQueue((freqs, durations, offsets, PRFs, DCs[DCs != 1.0], amps))
for item in queue:
item.append(method)
return queue
def runBatch(obj, method_str, queue, extra_params=[], mpi=False,
loglevel=logging.INFO):
''' Run batch of simulations of a given object for various combinations of stimulation parameters.
:param queue: array of all stimulation parameters combinations
:param mpi: boolean stating whether or not to use multiprocessing
'''
nsims = len(queue)
if mpi:
mp.freeze_support()
tasks = mp.JoinableQueue()
results = mp.Queue()
nconsumers = min(mp.cpu_count(), nsims)
consumers = [Consumer(tasks, results) for i in range(nconsumers)]
for w in consumers:
w.start()
# Run simulations
outputs = []
for i, stim_params in enumerate(queue):
params = extra_params + stim_params
if mpi:
worker = Worker(i, obj, method_str, params, loglevel)
tasks.put(worker, block=False)
else:
outputs.append(getattr(obj, method_str)(*params))
if mpi:
for i in range(nconsumers):
tasks.put(None, block=False)
tasks.join()
idxs = []
for i in range(nsims):
wid, out = results.get()
outputs.append(out)
idxs.append(wid)
outputs = [x for _, x in sorted(zip(idxs, outputs))]
# Close tasks and results queues
tasks.close()
results.close()
return outputs
def xlslog(filepath, logentry, sheetname='Data'):
''' Append log data on a new row to specific sheet of excel workbook, using a lockfile
to avoid read/write errors between concurrent processes.
:param filepath: absolute or relative path to the Excel workbook
:param logentry: log entry (dictionary) to add to log file
:param sheetname: name of the Excel spreadsheet to which data is appended
:return: boolean indicating success (1) or failure (0) of operation
'''
# Parse log dataframe from Excel file if it exists, otherwise create new one
if not os.path.isfile(filepath):
df = pd.DataFrame(columns=logentry.keys())
else:
df = pd.read_excel(filepath, sheet_name=sheetname)
# Add log entry to log dataframe
df = df.append(pd.Series(logentry), ignore_index=True)
# Write log dataframe to Excel file
try:
lock = lockfile.FileLock(filepath)
lock.acquire()
writer = pd.ExcelWriter(filepath)
df.to_excel(writer, sheet_name=sheetname, index=False)
writer.save()
lock.release()
return 1
except PermissionError:
# If file cannot be accessed for writing because already opened
logger.warning('Cannot write to "%s". Close the file and type "Y"', filepath)
user_str = input()
if user_str in ['y', 'Y']:
return xlslog(filepath, logentry, sheetname)
else:
return 0
c4science · Help