def segmentTesting(thisModel,
Ysample,
Lnum,
verbose,
label,
serialMode=False,
optimise=100,
calibrate=False):
"""
Method to test multiple samples at a time.
Args:
thisModel : SAMObject model to recall from.
Ysample : Novel feature vector to test.
Lnum : Ground truth labels to compare with.
verbose : Enable or disable logging to stdout.
label : Label for the current segments being tested.
serialMode : Boolean to test serially or in parallel.
optimise : Number of optimisation iterations to perform during recall.
calibrate : Indicate calibration mode when True which requires a different return.
Returns:
labelList, confMatrix, ret, variancesKnown, variancesUnknown if calibrate is `True`.
labelList, confMatrix, labelComparisonDict if calibrate is `False`.
labelList : List of classification labels
confMatrix : Numpy array with the confusion matrix
ret : Classification object
variancesKnown : Variances returned during calibration for known training instances
variancesUnknown : Variances returned during calibration for unknown training instances
labelComparisonDict : Dictionary with two items `'original'` and `'results'`.
"""
def testFunc(data, lab):
d = testSegment(thisModel,
data,
verbose,
visualiseInfo=None,
optimise=optimise)
if verbose:
if lab == d[0]:
res = True
else:
res = False
logging.info('Actual ' + str(lab).ljust(11) + ' Classification: ' + str(d[0]).ljust(11) + ' with ' + \
str(d[1])[:6] + ' confidence: ' + str(res) + '\n')
return d
logging.info('')
if type(Lnum).__module__ == np.__name__:
useModelLabels = True
else:
useModelLabels = False
if len(thisModel) > 1:
labelList = copy.deepcopy(thisModel[0].textLabels)
labelList.append('unknown')
else:
labelList = copy.deepcopy(thisModel[0].textLabels)
labelList.append('unknown')
confMatrix = np.zeros((len(labelList), len(labelList)))
numItems = len(Ysample)
off1 = 11
off2 = 8
off3 = len(str(numItems))
if useModelLabels:
Lsample = [
thisModel[0].textLabels[int(Lnum[i])] for i in range(len(Lnum))
]
else:
Lsample = Lnum
if numItems < 1500:
serialMode = True
c = None
logging.info('serialMode: ' + str(serialMode))
if not serialMode and thisModel[0].parallelOperation:
try:
logging.info('Trying engines ...')
c = ipp.Client()
numWorkers = len(c._engines)
logging.info('Number of engines: ' + str(numWorkers))
except:
logging.error("Parallel workers not found")
thisModel[0].parallelOperation = False
numWorkers = 1
else:
logging.info(str(serialMode) + '= True')
thisModel[0].parallelOperation = False
numWorkers = 1
logging.info('Number of engines: ' + str(numWorkers))
# average 5 classifications before providing this time
vTemp = copy.deepcopy(verbose)
verbose = False
if len(Lsample) < 400:
numTrials = len(Lsample) * 0.1
numTrials = max(1, int(numTrials))
else:
numTrials = 20
t0 = time.time()
for j in range(numTrials):
testFunc(Ysample[j], Lsample[j])
t1 = time.time()
verbose = vTemp
thisModel[0].avgClassTime = (t1 - t0) / numTrials
logging.info('classification rate: ' +
str(1.0 / thisModel[0].avgClassTime) + 'fps')
logging.info('estimated time: ' +
str(thisModel[0].avgClassTime * numItems /
(60 * numWorkers)) + 'mins for ' + str(numItems) +
' items with ' + str(numWorkers) + ' workers')
t0 = time.time()
logging.info(t0)
# check size of model
# modelSize is size in megabytes
modelSize = deep_getsizeof(thisModel, set()) / 1024.0 / 1024.0
logging.info("modelSize: " + str(modelSize))
logging.warning("required testing size: " +
str((modelSize * numWorkers * 2) + 400) + " MB")
# check available system memory in megabytes
freeSystemMem = float(psutil.virtual_memory()[4]) / 1024.0 / 1024.0
logging.info("free memory: " + str(freeSystemMem) + " MB")
if modelSize > 100 or not thisModel[0].parallelOperation or serialMode:
# serial testing
logging.warning('Testing serially')
ret = []
for j in range(len(Lsample)):
logging.info(str(j) + '/' + str(len(Lsample)))
ret.append(testFunc(Ysample[j], Lsample[j]))
else:
# parallel testing
logging.info('Testing in parallel')
dview = c[:] # not load balanced
lb = c.load_balanced_view() # load balanced
# with dview.sync_imports():
# from SAM.SAM_Core import utils
# if not thisModel[0].modelLoaded :
dview.push({'thisModel': thisModel})
dview.push({'verbose': verbose})
dview.push({'optimise': optimise})
# thisModel[0].modelLoaded = True
syn = lb.map_async(testFunc, Ysample, Lsample)
wait_watching_stdout(syn, dt=1, truncate=1000)
ret = syn.get()
# maybe these are upsetting the ipcluster
# dview.clear()
# dview.purge_results('all')
t1 = time.time()
logging.info(t1)
logging.info('Actual time taken = ' + str(t1 - t0))
if calibrate:
variancesKnown = []
variancesUnknown = []
for i in range(len(ret)):
currLabel = Lsample[i]
if verbose:
if currLabel == ret[i][0]:
result = True
else:
result = False
logging.info(
str(i).rjust(off3) + '/' + str(numItems) + ' Truth: ' +
currLabel.ljust(off1) + ' Model: ' +
ret[i][0].ljust(off1) + ' with ' +
str(ret[i][1])[:6].ljust(off2) + ' confidence: ' +
str(result))
if currLabel in thisModel[0].textLabels and currLabel != "unknown":
knownLabel = True
else:
knownLabel = False
currLabel = 'unknown'
if knownLabel:
variancesKnown.append(ret[i][1])
else:
variancesUnknown.append(ret[i][1])
confMatrix[labelList.index(currLabel),
labelList.index(ret[i][0])] += 1
return labelList, confMatrix, ret, variancesKnown, variancesUnknown
else:
labelComparisonDict = dict()
labelComparisonDict['original'] = []
labelComparisonDict['results'] = []
for i in range(len(ret)):
currLabel = Lsample[i]
retLabel = ret[i][0]
if currLabel not in thisModel[0].textLabels:
currLabel = 'unknown'
if verbose:
if currLabel == retLabel:
result = True
else:
result = False
logging.info(
str(i).rjust(off3) + '/' + str(numItems) + ' Truth: ' +
currLabel.ljust(off1) + ' Model: ' + retLabel.ljust(off1) +
' with ' + str(ret[i][1])[:6].ljust(off2) +
' confidence: ' + str(result))
labelComparisonDict['original'].append(Lsample[i])
labelComparisonDict['results'].append(retLabel)
confMatrix[labelList.index(currLabel),
labelList.index(retLabel)] += 1
return labelList, confMatrix, labelComparisonDict
import ipyparallel as ipp
client = ipp.Client()
# this will only run on machines that can import numpy:
@ipp.require('numpy')
def norm(A):
from numpy.linalg import norm
return norm(A, 2)
def checkpid(pid):
"""return the pid of the engine"""
import os
return os.getpid() == pid
def checkhostname(host):
import socket
return socket.gethostname() == host
def getpid():
import os
return os.getpid()
pid0 = client[0].apply_sync(getpid)
#!/usr/bin/env python3
try:
if (directview):
pass
except:
import ipyparallel
c = ipyparallel.Client(profile="mpi_slurm", cluster_id="Azure_cluster_0")
directview = c[:]
directview.block = True
with directview.sync_imports():
import numpy
import mpi4py
from mpi4py import MPI
class rankinfo(object):
'''This holds a few "global" values of our problem,
like stencil width and problem size.
Parameters
----------
sizes : tuple of numbers of lattice points without ghost
points along the coordinate axes (in x,y,z order)
Attributes
----------
rank : the rank of this class in MPI.COMM_WORLD
size : the number of ranks in MPI.COMM_WORLD
ndim : how many physical dimensions does our lattice have
if arguments.timeseries_name is None:
directory_full_path = arguments.directory
else:
directory_full_path = os.path.join(arguments.directory, arguments.timeseries_name)
if not os.path.exists(directory_full_path):
raise Exception("Directory %s does not exists." % directory_full_path)
if arguments.cluster_sample_count < 1:
raise Exception("Cluster sample count must be greater than zero.")
_numSamples = arguments.cluster_sample_count
try:
pool = ipyparallel.Client(profile=arguments.profile)[:]
except:
raise Exception("A running IPython parallel cluster is required to run this script.")
def tardir(path):
# ziph is zipfile handle
with tarfile.open(os.path.join(path, 'slycat-timeseries.tar.gz'), 'w:gz') as tarh:
for root, dirs, files in os.walk(path):
for file in files:
if file != 'slycat-timeseries.tar.gz':
tarh.add(os.path.join(root, file), arcname=file)
# Compute the model.
try:
print("Examining and verifying data.")
"""
Find number of timeseries and accurate cluster sample count before starting model
"""
def __init__(self,
wrapper,
backend='multiprocessing',
n_cpus=-1,
verbosity=10,
dask_args=None):
# -1 cpus means all available cpus - 1 for the scheduler
if n_cpus == -1:
import multiprocessing
n_cpus = multiprocessing.cpu_count() - 1
self.n_cpus = n_cpus
self.wrapper = wrapper
self.verbosity = verbosity
self.dask_args = dask_args
# This configures how to run single point simulations on the model:
self._exec = self.wrapper
ot.OpenTURNSPythonFunction.__init__(self,
self.wrapper.getInputDimension(),
self.wrapper.getOutputDimension())
self.setInputDescription(self.wrapper.getInputDescription())
self.setOutputDescription(self.wrapper.getOutputDescription())
assert backend in [
'ipython', 'ipyparallel', 'multiprocessing', 'pathos', 'joblib',
'dask'
], "Unknown backend"
# This configures how to run samples on the model :
if self.n_cpus == 1:
self._exec_sample = self.wrapper
elif (backend == 'ipython') or (backend == 'ipyparallel'):
# Check that ipyparallel is installed
try:
import ipyparallel as ipp
# If it is, see if there is a cluster running
try:
rc = ipp.Client()
ipy_backend = True
except (ipp.error.TimeoutError, IOError) as e:
ipy_backend = False
import logging
logging.warning('Unable to connect to an ipython cluster.')
except ImportError:
ipy_backend = False
import logging
logging.warning('ipyparallel package missing.')
if ipy_backend:
self._exec_sample = _exec_sample_ipyparallel(
self.wrapper, self.getInputDimension(),
self.getOutputDimension())
else:
logging.warning('Using multiprocessing backend instead')
self._exec_sample = _exec_sample_multiprocessing(
self.wrapper, self.n_cpus)
elif backend == 'joblib':
# Check that joblib is installed
try:
import joblib
joblib_backend = True
except ImportError:
try:
from sklearn.externals import joblib
joblib_backend = True
except ImportError:
joblib_backend = False
import logging
logging.warning('joblib package missing.')
if joblib_backend:
self._exec_sample = _exec_sample_joblib(
self.wrapper, self.n_cpus, self.verbosity)
else:
logging.warning('Using multiprocessing backend instead')
self._exec_sample = _exec_sample_multiprocessing(
self.wrapper, self.n_cpus)
elif backend == 'multiprocessing':
self._exec_sample = _exec_sample_multiprocessing(
self.wrapper, self.n_cpus)
elif backend == 'pathos':
self._exec_sample = _exec_sample_pathos(self.wrapper, self.n_cpus)
elif backend == 'dask':
assert 'scheduler' in self.dask_args, 'dask_args must have "scheduler" as key'
assert 'workers' in self.dask_args, 'dask_args must have "workers" as key'
self._exec_sample, self.dask_cluster, self.dask_client = _exec_sample_dask(
self.wrapper, self.dask_args, self.verbosity)
def close_dask():
from time import sleep
self.dask_client.close()
sleep(1)
self.dask_cluster.close()
self.close_dask = close_dask
import ipyparallel as ipp
import pandas as pd
import tables as tb
import os
import argparse
parser = argparse.ArgumentParser(description='Model and year to estimate.')
parser.add_argument('model', type=str, nargs='?', default='gpin')
parser.add_argument('year', type=int, nargs='?', default=2014)
args = parser.parse_args()
print(vars(args))
rc = ipp.Client(cluster_id="{0}-{1}".format(args.model, args.year))
print(len(rc))
dv = rc[:]
dv.push(vars(args))
lv = rc.load_balanced_view()
h5 = tb.open_file('/scratch/nyu/hue/taqdf_1319.h5', mode='r')
df = h5.get_node('/data/table')
idx = list(
set(
filter(lambda x: x[1] == args.year,
zip(df.col('permno'), df.col('yyyy')))))
@ipp.interactive
def est(x):
import os
import pandas as pd
import tables as tb
def get_client(cluster_id, profile, engines, timeout, cores, quiet, **kwargs):
"""
Creates a client to view ipcluster engines for a given profile and
returns it with at least one engine spun up and ready to go. If no
engines are found after nwait amount of time then an error is raised.
If engines==MPI it waits a bit longer to find engines. If the number
of engines is set then it waits even longer to try to find that number
of engines.
"""
## save stds for later, we're gonna hide them to prevent external printing
devnull = open(os.devnull, 'w')
save_stdout = sys.stdout
save_stderr = sys.stderr
sys.stdout = devnull
sys.stderr = devnull
## get cluster_info print string
connection_string = " establishing parallel connection:"
## wrapped search for ipcluster
try:
## are we looking for a running ipcluster instance?
if profile not in [None, "default"]:
args = {'profile': profile, "timeout": timeout}
else:
clusterargs = [cluster_id, profile, timeout]
argnames = ["cluster_id", "profile", "timeout"]
args = {key:value for key, value in zip(argnames, clusterargs)}
## get connection within timeout window of wait time and hide messages
ipyclient = ipp.Client(**args)
sys.stdout = save_stdout
sys.stderr = save_stderr
## check that all engines have connected
if (engines == "MPI") or ("pta-cli-" in cluster_id):
if not quiet:
print(connection_string)
for _ in range(6000):
initid = len(ipyclient)
time.sleep(0.01)
## If MPI then wait for all engines to start so we can report
## how many cores are on each host. If Local then only wait for
## one engine to be ready and then just go.
if (engines == "MPI") or ("pta-cli-" in cluster_id):
## wait for cores to be connected
if cores:
time.sleep(0.1)
if initid == cores:
break
if initid:
time.sleep(3)
if len(ipyclient) == initid:
break
else:
if cores:
if initid == cores:
break
else:
if initid:
break
except KeyboardInterrupt as inst:
raise inst
## This is raised if ipcluster is not running ------------
except IOError as inst:
if "pta-cli-" in cluster_id:
raise PTAError(NO_IPCLUSTER_CLI)
else:
raise PTAError(NO_IPCLUSTER_API)
except (ipp.TimeoutError, ipp.NoEnginesRegistered) as inst:
raise inst
except Exception as inst:
raise inst
finally:
## ensure that no matter what we reset the stds
sys.stdout = save_stdout
sys.stderr = save_stderr
return ipyclient
def setup(self, number_of_engines, is_coalescing, depth):
self.client = ipp.Client(profile='asv', cluster_id=f'depth_{depth}')
self.view = self.client.broadcast_view(is_coalescing=is_coalescing)
self.view.targets = list(range(number_of_engines))
wait_for(lambda: len(self.client) >= number_of_engines)
def setup(self, number_of_engines, number_of_bytes):
self.client = ipp.Client(profile='asv')
self.view = get_view(self)
self.view.targets = list(range(number_of_engines))
wait_for(lambda: len(self.client) >= number_of_engines)
# import IPython.parallel as ipp # Python 2
import ipyparallel as ipp # Python 3
rc = ipp.Client(profile='default', cluster_id='')
rc.ids
dview = rc[:]
dview.block = True
dview.apply(lambda : "Hello, World")
lview = rc.load_balanced_view()
lview.block = True
import pandas
dat = pandas.read_csv('/global/scratch/paciorek/bayArea.csv', header = None, encoding = 'latin1')
dat.columns = ('Year','Month','DayofMonth','DayOfWeek','DepTime','CRSDepTime',
'ArrTime','CRSArrTime','UniqueCarrier','FlightNum','TailNum',
'ActualElapsedTime','CRSElapsedTime','AirTime','ArrDelay','DepDelay',
'Origin','Dest','Distance','TaxiIn','TaxiOut','Cancelled','CancellationCode',
'Diverted','CarrierDelay','WeatherDelay','NASDelay','SecurityDelay','LateAircraftDelay')
dview.execute('import statsmodels.api as sm')
dat2 = dat.loc[:, ('DepDelay','Year','Dest','Origin')]
dests = dat2.Dest.unique()
mydict = dict(dat2 = dat2, dests = dests)
dview.push(mydict)
def f(id):
sub = dat2.loc[dat2.Dest == dests[id],:]
sub = sm.add_constant(sub)
def run(func_parallel_loop,
func_gen_args,
func_init=None,
base_dir=None,
results_dir=None,
description=None):
'''
Runs the simulation
Parameters
----------
func_parallel_loop: function
The function that should be parallelized
func_gen_args: function
The function that will generate all the different inputs
for func_parallel_loop
func_init: function, optional
A function that will be run before the simulation starts. This might
generate some data or import some files for example
base_dir: str, optional
The location of the base directory for the simulation
results_dir: str, optional
The name of the directory where to save results
description: str, optional
A short description of the simulation for the help function
'''
import os, json
if description is None:
description = 'Generic simulation script'
if base_dir is None:
base_dir = './'
base_dir = os.path.abspath(base_dir)
if results_dir is None:
results_dir = os.path.join(base_dir, 'data/')
elif not os.path.isabs(results_dir):
results_dir = os.path.join(base_dir, results_dir)
# create results directory if it doesn't exist
if not os.path.exists(results_dir):
os.mkdir(results_dir)
parser = argparse.ArgumentParser(description=description)
parser.add_argument('-d',
'--dir',
type=str,
help='directory to store sim results')
parser.add_argument('-p',
'--profile',
type=str,
help='ipython profile of cluster')
parser.add_argument('-t',
'--test',
action='store_true',
help='test mode, runs a single loop of the simulation')
parser.add_argument('-s',
'--serial',
action='store_true',
help='run in a serial loop, ipyparallel not called')
parser.add_argument(
'--dummy',
action='store_true',
help=
'tags the directory as dummy, can be used for running small batches')
parser.add_argument('parameters',
type=str,
help='JSON file containing simulation parameters')
cli_args = parser.parse_args()
ipcluster_profile = cli_args.profile
test_flag = cli_args.test
serial_flag = cli_args.serial
dummy_flag = cli_args.dummy
data_dir_name = None
parameter_file = cli_args.parameters
# Check the state of the github repository
if dummy_flag:
tag = 'dummy'
else:
# Not a dummy run, try to get the git hash
try:
tag = get_git_hash(base_dir, length=10)
except DirtyGitRepositoryError:
if test_flag:
import warnings
warnings.warn(
'The git repo has uncommited modifications. Going ahead for test.'
)
tag = 'test'
else:
raise ValueError(
'The git repo has uncommited modifications. Aborting simulation.'
)
except InvalidGitRepositoryError:
tag = ''
# get all the parameters
with open(parameter_file, 'r') as f:
parameters = json.load(f)
# if no name is given, use the parameters file name
if 'name' not in parameters:
name = os.path.splitext(os.path.basename(parameter_file))[0]
parameters['name'] = name
else:
name = parameters['name']
# record date and time
date = time.strftime("%Y%m%d-%H%M%S")
# for convenient access to parameters:
p = collections.namedtuple('Struct',
parameters.keys())(*parameters.values())
# Save the result to a directory
if data_dir_name is None:
ttag = '_' + tag if tag != '' else tag
data_dir = os.path.join(
results_dir, data_dir_format.format(date=date, name=name,
tag=ttag))
else:
data_dir = data_dir_name
data_file_name = os.path.join(data_dir, data_file)
# create directory if it doesn't exist
if not os.path.exists(data_dir):
os.mkdir(data_dir)
# add a few practical things to the parameters
parameters['_git_sha'] = tag
parameters['_date'] = date
parameters['_base_dir'] = base_dir
parameters['_results_dir'] = data_dir
parameters['_parallel'] = not serial_flag
# Save the arguments in a json file
param_file_name = os.path.join(data_dir, param_file)
with open(param_file_name, "w") as f:
json.dump(parameters, f, indent=2)
f.close()
# run the user provided init method
if func_init is not None:
func_init(parameters)
# generate all the arguments to simulate
arguments = func_gen_args(parameters)
# Save the arguments in a json file
args_file_name = os.path.join(data_dir, args_file)
with open(args_file_name, "w") as f:
json.dump(arguments, f, indent=0)
f.close()
# There is the option to only run one loop for test
if test_flag:
print('Running one test loop only.')
arguments = arguments[:2]
# Prepare a few things for the status line
n_tasks = len(arguments)
digits = int(math.log10(n_tasks) + 1)
dformat = '{:' + str(digits) + 'd}'
status_line = (' ' + dformat + '/' + dformat +
(' tasks done. '
'Forecast end {:>20s}. '
'Ellapsed: {:>8s} Remaining: {:>8s}'))
print('/!\\ the time estimate will only be correct '
'when all tasks take about the same time to finish /!\\')
forecast = 'NA'
time_remaining = 'NA'
# Main processing loop
if serial_flag:
# add parameters to builtins so that it is accessible in the namespace
# of the calling script
import builtins
builtins.parameters = parameters
print('Running everything in a serial loop.')
# record start timestamp
then = time.time()
start_time = datetime.datetime.now()
# Serial processing
for i, ag in enumerate(arguments):
result = func_parallel_loop(ag)
# save the new result!
json_append(data_file_name, result)
# Now format some timing estimation
n_remaining = n_tasks - (i + 1)
ellapsed = int(time.time() - then)
ellapsed_fmt = '{:02}:{:02}:{:02}'.format(ellapsed // 3600,
ellapsed % 3600 // 60,
ellapsed % 60)
# estimate remaining time
if ellapsed > 0:
rate = (i + 1) / ellapsed # tasks per second
delta_finish_min = int(rate * n_remaining / 60) + 1
tdelta = datetime.timedelta(minutes=delta_finish_min)
end_date = datetime.datetime.now() + tdelta
# convert to strings
forecast = end_date.strftime('%Y-%m-%d %H:%M:%S')
s = int(tdelta.total_seconds())
time_remaining = '{:02}:{:02}:{:02}'.format(
s // 3600, s % 3600 // 60, s % 60)
formatted_status_line = status_line.format(i + 1, n_tasks,
forecast, ellapsed_fmt,
time_remaining)
print(formatted_status_line, end='\r')
# clean the output
print(' ' * len(formatted_status_line))
all_loops = int(time.time() - then)
all_loops_format = '{:02}:{:02}:{:02}'.format(all_loops // 3600,
all_loops % 3600 // 60,
all_loops % 60)
print('Total actual processing time: {} ({} s)'.format(
all_loops_format, all_loops))
else:
# Parallel processing code
import ipyparallel as ip
print('Using ipyparallel processing.')
# Start the parallel processing
c = ip.Client(profile=ipcluster_profile)
NC = len(c.ids)
print(NC, 'workers on the job')
# Clear the engines namespace
c.clear(block=True)
# Push the global config to the workers
var_space = dict(parameters=parameters, )
c[:].push(var_space, block=True)
# record start timestamp
then = time.time()
start_time = datetime.datetime.now()
# use a load balanced view
lbv = c.load_balanced_view()
# dispatch to workers
ar = lbv.map_async(func_parallel_loop, arguments)
# We use a try here so that if something happens,
# we can catch it and abort the jobs on all engines
try:
for i, result in enumerate(ar):
# save the new result!
json_append(data_file_name, result)
# Now format some timing estimation
n_remaining = n_tasks - ar.progress
ellapsed = int(time.time() - then)
ellapsed_fmt = '{:02}:{:02}:{:02}'.format(
ellapsed // 3600, ellapsed % 3600 // 60,
round(ellapsed % 60))
if ar.progress > NC and n_remaining > NC:
# estimate remaining time
rate = ellapsed / ar.progress # tasks per second
delta_finish_min = int(rate * n_remaining / 60) + 1
tdelta = datetime.timedelta(minutes=delta_finish_min)
end_date = datetime.datetime.now() + tdelta
# convert to strings
forecast = end_date.strftime('%Y-%m-%d %H:%M:%S')
s = int(tdelta.total_seconds())
time_remaining = '{:02}:{:02}:{:02}'.format(
s // 3600, s % 3600 // 60, s % 60)
formatted_status_line = status_line.format(
ar.progress, n_tasks, forecast, ellapsed_fmt,
time_remaining)
print(formatted_status_line, end='\r')
# clean the output
print(' ' * len(formatted_status_line))
print('Show all output from nodes, if any:')
ar.display_outputs()
except:
# so here, things went south. Show the traceback
# and abort all the jobs scheduled
import traceback
traceback.print_exc()
print('Aborting all remaining jobs...')
c.abort(block=True)
all_loops = int(time.time() - then)
all_loops_format = '{:02}:{:02}:{:02}'.format(all_loops // 3600,
all_loops % 3600 // 60,
all_loops % 60)
print('Total actual processing time: {} ({} s)'.format(
all_loops_format, all_loops))
print('Saved data to folder: ' + data_dir)
import ipyparallel
ipp_client = ipyparallel.Client(
url_file="/groups/turaga/home/grisaitisw/.ipython/profile_greentea/security/ipcontroller-client.json",
timeout=60 * 60 # 1 hour
)
executor = ipp_client.load_balanced_view()
executor.set_flags(retries=100000)
def main():
""" main function """
## turn off traceback for the CLI
ip.__interactive__ = 0
## Check for a new version on anaconda
_check_version()
## parse params file input (returns to stdout if --help or --version)
args = parse_command_line()
## Turn the debug output written to ipyrad_log.txt up to 11!
## Clean up the old one first, it's cleaner to do this here than
## at the end (exceptions, etc)
if os.path.exists(ip.__debugflag__):
os.remove(ip.__debugflag__)
if args.debug:
print("\n ** Enabling debug mode ** ")
ip._debug_on()
atexit.register(ip._debug_off)
## create new paramsfile if -n
if args.new:
## Create a tmp assembly, call write_params to make default params.txt
try:
tmpassembly = ip.Assembly(args.new, quiet=True, cli=True)
tmpassembly.write_params("params-{}.txt".format(args.new),
force=args.force)
except Exception as inst:
print(inst)
sys.exit(2)
print("\n New file 'params-{}.txt' created in {}\n".\
format(args.new, os.path.realpath(os.path.curdir)))
sys.exit(2)
## if params then must provide action argument with it
if args.params:
if not any([args.branch, args.results, args.steps]):
print("""
Must provide action argument along with -p argument for params file.
e.g., ipyrad -p params-test.txt -r ## shows results
e.g., ipyrad -p params-test.txt -s 12 ## runs steps 1 & 2
e.g., ipyrad -p params-test.txt -b newbranch ## branch this assembly
""")
sys.exit(2)
if not args.params:
if any([args.branch, args.results, args.steps]):
print("""
Must provide params file for branching, doing steps, or getting results.
e.g., ipyrad -p params-test.txt -r ## shows results
e.g., ipyrad -p params-test.txt -s 12 ## runs steps 1 & 2
e.g., ipyrad -p params-test.txt -b newbranch ## branch this assembly
""")
## if branching, or merging do not allow steps in same command
## print spacer
if any([args.branch, args.merge]):
args.steps = ""
print("")
## always print the header when doing steps
header = \
"\n -------------------------------------------------------------"+\
"\n ipyrad [v.{}]".format(ip.__version__)+\
"\n Interactive assembly and analysis of RAD-seq data"+\
"\n -------------------------------------------------------------"
## Log the current version. End run around the LOGGER
## so it'll always print regardless of log level.
with open(ip.__debugfile__, 'a') as logfile:
logfile.write(header)
logfile.write("\n Begin run: {}".format(time.strftime("%Y-%m-%d %H:%M")))
logfile.write("\n Using args {}".format(vars(args)))
logfile.write("\n Platform info: {}".format(os.uname()))
## if merging just do the merge and exit
if args.merge:
print(header)
merge_assemblies(args)
sys.exit(1)
## create new Assembly or load existing Assembly, quit if args.results
elif args.params:
parsedict = parse_params(args)
if args.branch:
branch_assembly(args, parsedict)
elif args.steps:
## print header
print(header)
## Only blank the log file if we're actually going to run a new
## assembly. This used to be in __init__, but had the side effect
## of occasionally blanking the log file in an undesirable fashion
## for instance if you run a long assembly and it crashes and
## then you run `-r` and it blanks the log, it's crazymaking.
if os.path.exists(ip.__debugfile__):
if os.path.getsize(ip.__debugfile__) > 50000000:
with open(ip.__debugfile__, 'w') as clear:
clear.write("file reset")
## run Assembly steps
## launch or load assembly with custom profile/pid
data = getassembly(args, parsedict)
## set CLI ipcluster terms
data._ipcluster["threads"] = args.threads
## if ipyclient is running (and matched profile) then use that one
if args.ipcluster:
ipyclient = ipp.Client(profile=args.ipcluster)
data._ipcluster["cores"] = len(ipyclient)
## if not then we need to register and launch an ipcluster instance
else:
## set CLI ipcluster terms
ipyclient = None
data._ipcluster["cores"] = args.cores if args.cores else detect_cpus()
data._ipcluster["engines"] = "Local"
if args.MPI:
data._ipcluster["engines"] = "MPI"
if not args.cores:
raise IPyradWarningExit("must provide -c argument with --MPI")
## register to have a cluster-id with "ip- name"
data = register_ipcluster(data)
## set to print headers
data._headers = 1
## run assembly steps
steps = list(args.steps)
data.run(
steps=steps,
force=args.force,
preview=args.preview,
show_cluster=1,
ipyclient=ipyclient)
if args.results:
showstats(parsedict)
def __init__(self, profile=None, cluster_id=None):
self.client = ipp.Client(profile=profile, cluster_id=cluster_id)
self.statusDict = {}
self.sleepSeconds = SLEEP_SECONDS
self.keyField = 'key'
def calculate_background(
filename,
output_dir,
*,
check_validity_channel=False,
th_factor=3.0,
above_threshold_pixel_ratio_max=0.05,
below_threshold_pixel_ratio_max=0.05,
valid_ratio_threshold=0.4,
intensity_bin_size=25,
thumbnail_size=20,
quantile=0.001,
ipcluster_nproc=1,
):
params_dict = locals()
cli = ipp.Client(profile="default")
dview = cli[:]
dview.clear()
bview = cli.load_balanced_view()
dview.execute("""
import javabridge
import bioformats as bf
import pycziutils
javabridge.start_vm(class_path=bf.JARS)
""")
os.makedirs(output_dir, exist_ok=True)
log_dir = path.join(output_dir, "calcluate_background_log")
os.makedirs(log_dir, exist_ok=True)
def savefig(fig, name):
fig.savefig(path.join(log_dir, name), bbox_inches="tight")
############## Load files ################
meta = pycziutils.get_tiled_omexml_metadata(filename)
with open(path.join(output_dir, "metadata.xml"), "w") as f:
f.write(meta)
reader = pycziutils.get_tiled_reader(filename)
sizeS, sizeT, sizeC, sizeX, sizeY, sizeZ = pycziutils.summarize_image_size(
reader)
pixel_sizes = pycziutils.parse_pixel_size(meta)
assert pixel_sizes[1] == "µm"
channels = pycziutils.parse_channels(meta)
channel_names = [c["@Fluor"] for c in channels]
print(channel_names)
params_dict.update({
"channel_names": channel_names,
})
if check_validity_channel:
check_validity_channel_index = [
j for j, c in enumerate(channels)
if check_validity_channel in c["@Fluor"]
][0]
planes_df = pycziutils.parse_planes(meta)
null_indices = planes_df.isnull().any(axis=1)
params_dict["null_indices"] = list(planes_df[null_indices].index)
planes_df = planes_df.loc[~null_indices, :]
planes_df["S_index"] = planes_df["image"]
if check_validity_channel:
############## Summarize image intensities ################
send_variable(dview, "filename", path.abspath(filename))
send_variable(dview, "read_image", read_image)
send_variable(dview, "summarize_image", summarize_image)
dview.execute("_reader = pycziutils.get_tiled_reader(filename)")
check_ipcluster_variable_defined(dview, "_reader", timeout=120)
sleep(10)
check_ipcluster_variable_defined(dview, "read_image", timeout=120)
check_ipcluster_variable_defined(dview, "summarize_image", timeout=120)
@ipp.require(summarize_image)
def _summarize_image(row):
return summarize_image(row, _reader, thumbnail_size, quantile) # pylint: disable=undefined-variable
res = bview.map_async(_summarize_image,
[row for _, row in list(planes_df.iterrows())])
res.wait_interactive()
keys = ["thumbnail", "max", "min", "mean", "median", "stdev"]
for i, k in enumerate(keys):
planes_df[k] = [r[i] for r in res.get()]
display(planes_df)
############## Calculate most frequent "standard" mean and stdev for a image ##############
mean_mode = {}
stdev_mode = {}
for iC, grp in planes_df.groupby("C_index"):
fig, ax = plt.subplots(1, 1, figsize=(10, 10))
c_name = channel_names[iC]
h, *edges, im = ax.hist2d(grp["mean"],
grp["stdev"],
bins=intensity_bin_size)
mean_mode[iC], stdev_mode[iC] = [
float((edge[x[0]] + edge[x[0] + 1]) / 2.0)
for edge, x in zip(edges, np.where(h == np.max(h)))
]
ax.plot(mean_mode[iC], stdev_mode[iC], "ro")
ax.set_xlabel("mean intensity")
ax.set_ylabel("stdev intensity")
ax.set_title(c_name)
savefig(fig, f"1_mean_and_stdev_instensities_{iC}_{c_name}.pdf")
m, s = (
mean_mode[check_validity_channel_index],
stdev_mode[check_validity_channel_index],
)
th_low = m - th_factor * s
th_high = m + th_factor * s
params_dict.update({
"mean_mode": mean_mode,
"stdev_mode": stdev_mode,
"ph_th_low": float(th_low),
"ph_th_high": float(th_high),
})
ph_planes_df = planes_df[planes_df["C_index"] ==
check_validity_channel_index].copy()
thumbail_output_name = "2_thresholded_thumbnail"
thumbail_output_path = path.join(log_dir, thumbail_output_name)
os.makedirs(thumbail_output_path, exist_ok=True)
for iS, grp in ph_planes_df.groupby("S_index"):
fig, axes = plt.subplots(1, 2, figsize=(10, 5))
img_mean = grp["thumbnail"].iloc[0]
axes[0].imshow(img_mean, vmin=th_low, vmax=th_high)
axes[1].hist(img_mean.flatten(), bins=20, range=(0, 8000))
axes[1].set_xlabel("intensity")
axes[1].set_ylabel("freq")
fig.suptitle("series " + str(iS) + " below th count: " +
str(np.sum(img_mean < m - th_factor * s)) +
" above th count: " +
str(np.sum(img_mean > m + th_factor * s)))
savefig(
fig,
path.join(thumbail_output_name,
f"2_thresholded_thumbnails_{iS}.pdf"),
)
plt.close("all")
sigma = 20 / float(pixel_sizes[0])
params_dict.update({"sigma": sigma})
send_variable(dview, "threshold_image", threshold_image)
res = bview.map_async(
lambda row: threshold_image(row, _reader, sigma, th_low, th_high), # pylint: disable=undefined-variable
[row for _, row in list(ph_planes_df.iterrows())],
)
res.wait_interactive()
print("ok")
ph_planes_df["below_th_count"] = [r[0] for r in res.get()]
ph_planes_df["above_th_count"] = [r[1] for r in res.get()]
ph_planes_df[
"below_th_ratio"] = ph_planes_df["below_th_count"] / sizeX / sizeY
ph_planes_df[
"above_th_ratio"] = ph_planes_df["above_th_count"] / sizeX / sizeY
print("ok")
ph_planes_df.drop("thumbnail",
axis=1).to_csv(path.join(log_dir,
"ph_planes_df.csv"))
############## judge if the position is valid to calculate background ##############
fig, ax = plt.subplots(1, 1, figsize=(5, 5))
ph_planes_df["is_valid"] = (
ph_planes_df["below_th_ratio"] < below_threshold_pixel_ratio_max
) & (ph_planes_df["above_th_ratio"] < above_threshold_pixel_ratio_max)
ax.scatter(
ph_planes_df["below_th_ratio"],
ph_planes_df["above_th_ratio"],
c=ph_planes_df["is_valid"],
s=1,
marker="o",
cmap=plt.get_cmap("viridis"),
alpha=0.3,
)
ax.set_xlabel("below threshold ratios")
ax.set_ylabel("above threshold ratios")
fig.tight_layout()
savefig(fig, f"4_threshold_results.pdf")
series_df = pd.DataFrame()
for Si, grp in ph_planes_df.groupby("S_index"):
X = grp["X"].iloc[0]
assert np.all(X == grp["X"])
Y = grp["Y"].iloc[0]
assert np.all(Y == grp["Y"])
series_df = series_df.append(
pd.DataFrame(
{
"thumbnail": [np.mean(grp["thumbnail"], axis=0)],
"is_valid_ratio": grp["is_valid"].sum() / len(grp),
"X": X,
"Y": Y,
},
index=[Si],
))
fig, axes = plt.subplots(1, 2, figsize=(10, 5))
im = axes[0].scatter(series_df["X"],
series_df["Y"],
c=series_df["is_valid_ratio"])
axes[0].set_title("valid_ratio")
fig.colorbar(im, ax=axes[0])
axes[1].scatter(
series_df["X"],
series_df["Y"],
c=series_df["is_valid_ratio"] > valid_ratio_threshold,
)
axes[1].set_title("thresholded")
fig.tight_layout()
savefig(fig, f"5_valid_positions.pdf")
series_df[
"is_valid"] = series_df["is_valid_ratio"] > valid_ratio_threshold
series_df.drop("thumbnail",
axis=1).to_csv(path.join(log_dir, "series_df.csv"))
valid_series = series_df[series_df["is_valid"]].index
planes_df["is_valid"] = planes_df["S_index"].isin(valid_series)
else:
planes_df["is_valid"] = True
valid_planes_df = planes_df[planes_df["is_valid"]]
print("valid_positions:", len(valid_planes_df))
planes_df.drop("thumbnail", axis=1, errors="ignore").to_csv(
path.join(output_dir, "planes_df.csv"))
############## calclulate backgrounds ##############
# t.c.z.y.x
median_images = np.empty((sizeT, sizeC, sizeZ, sizeY, sizeX))
mean_images = np.empty((sizeT, sizeC, sizeZ, sizeY, sizeX))
median_images[...] = np.nan
mean_images[...] = np.nan
print(sizeT)
# assert np.array_equal(valid_planes_df["T_index"].unique(),np.arange(sizeT))
# assert np.array_equal(valid_planes_df["C_index"].unique(),np.arange(sizeC))
# assert np.array_equal(valid_planes_df["Z_index"].unique(),np.arange(sizeZ))
for (iC, iT, iZ), grp in tqdm(
valid_planes_df.groupby(["C_index", "T_index", "Z_index"])):
imgs = []
for i, row in grp.iterrows():
imgs.append(read_image(row, reader))
imgs = np.array(imgs)
lq = np.quantile(imgs, quantile, axis=0)
hq = np.quantile(imgs, 1.0 - quantile, axis=0)
mask = np.logical_or(imgs < lq, imgs > hq)
imgs_trunc = ma.array(imgs, mask=mask)
median_images[iT, iC, iZ, ...] = np.median(imgs, axis=0)
mean_images[iT, iC, iZ, ...] = imgs_trunc.mean(axis=0)
print("saving background...")
with h5py.File(path.join(output_dir, "background_per_tile.hdf5"),
"w") as h5f:
h5f.create_dataset("median_images", data=median_images)
h5f.create_dataset("mean_images", data=mean_images)
# h5f.attrs["channels"]=channels
h5f.attrs["dimension_order"] = "tczyx"
print("saved background")
############## check correlation of backgrounds ##############
for iC, iZ in itertools.product(range(sizeC), range(sizeZ)):
c_name = channel_names[iC]
for img_key, img in zip(["median", "mean"],
[median_images, mean_images]):
fig, axes = plt.subplots(1, 6, figsize=(18, 3))
ps = []
j = sizeT // 2
ims = [img[i, iC, iZ] for i in (0, j, -1)]
ps.append(axes[0].imshow(ims[0]))
ps.append(axes[1].imshow(ims[1]))
ps.append(axes[2].imshow(ims[2]))
ps.append(axes[3].imshow(ims[1] - ims[0]))
ps.append(axes[4].imshow(ims[1] - ims[2]))
for p, ax in zip(ps, axes):
fig.colorbar(p, ax=ax)
axes[5].plot(ims[0].flatten(), ims[-1].flatten(), ".")
axes[0].set_title("at time 0")
axes[1].set_title(f"at time {j}")
axes[2].set_title(f"at time {iT-1}")
axes[3].set_title(f"diff at time {j} and 0")
axes[4].set_title(f"diff at time {j} and {iT-1}")
fig.tight_layout()
savefig(
fig,
f"6_background_correlation_C{iC}_{c_name}_Z{iZ}_{img_key}.png")
plt.close("all")
############## summarize and save backgrounds ##############
background_directory = path.join(output_dir, "averaged_background")
os.makedirs(background_directory, exist_ok=True)
for iC, iZ in itertools.product(range(sizeC), range(sizeZ)):
c_name = channel_names[iC]
for img_key, img in zip(["median", "mean"],
[median_images, mean_images]):
filename = f"{img_key}_C{iC}_{c_name}_Z{iZ}"
averaged_img = np.nanmean(img[:, iC, iZ], axis=0)
fig, ax = plt.subplots(1, 1, figsize=(5, 5))
p = ax.imshow(averaged_img)
fig.colorbar(p, ax=ax)
savefig(fig, f"7_time_averaged_background_{filename}.pdf")
plt.close("all")
io.imsave(
path.join(background_directory, filename + ".tiff"),
averaged_img,
check_contrast=False,
)
params_path = path.join(background_directory,
"calculate_background_params.yaml")
with open(params_path, "w") as f:
yaml.dump(params_dict, f)
image_props = {
"channel_names": channel_names,
"pixel_sizes": pixel_sizes,
"sizeS": sizeS,
"sizeT": sizeT,
"sizeC": sizeC,
"sizeZ": sizeZ,
"sizeY": sizeY,
"sizeX": sizeX,
}
image_props_path = path.join(output_dir, "image_props.yaml")
with open(image_props_path, "w") as f:
yaml.dump(image_props, f)
#########################################################################3
# Example: Initializing IPyParallel
#
# This example demonstrates how to access the individual
# ipython engines running within the cluster.
#
import ipyparallel
import os
import socket
#Create a client instance, used to connect the controller to the remote engines
rc = ipyparallel.Client(profile='crestone-cpu')
nengines = len(rc)
#create direct views into each engine
all_proc = rc[:] # all_proc is a list of ipython DirectView objects
#Only the controller prints this
print('\n ', nengines, " Python engines are active.\n")
# Each Python engine calls the gethostname and getpid functions
hostnames = all_proc.apply_sync(socket.gethostname)
pids = all_proc.apply_sync(os.getpid)
for i in range(nengines):
istr = '{:02d}'.format(i) # returns a 2-digit string whose value is i
pstr = str(pids[i])
hstr = str(hostnames[i])
msg = 'Engine ' + istr + ': pid = ' + pstr + '; hostname =' + hstr
print(msg)
grid = ns.grid
partition = ns.partition
Lx = ns.Lx
Ly = ns.Ly
c = ns.c
tstop = ns.tstop
if ns.save:
user_action = wave_saver
else:
user_action = None
num_cells = 1.0 * (grid[0] - 1) * (grid[1] - 1)
final_test = True
# create the Client
rc = ipp.Client(profile=ns.profile)
num_procs = len(rc.ids)
if partition is None:
partition = [1, num_procs]
assert (partition[0] * partition[1] == num_procs
), "can't map partition %s to %i engines" % (partition, num_procs)
view = rc[:]
print("Running %s system on %s processes until %f" %
(grid, partition, tstop))
# functions defining initial/boundary/source conditions
def I(x, y):
from numpy import exp
def svd4tet(data, nboots=100, method="all", nquarts=None, force=False):
"""
API wrapper for svd4tet analysis
data
ipyrad Assembly object
nboots
number of non-parametric bootstrap replicates to run
method
all, random, or equal. Default is all, which samples all possible
quartets. For very large trees (>50 tips) this may take too long,
in which case you should use random or equal. The arg nquarts
determines how many quartets will be samples. In random, nquarts
are sampled and used. In equal, a starting tree is inferred and
the random quartets are drawn so that they are spread ~equally
across splits of the tree.
nquarts
The numer of random quartets sampled in random or equal method.
Default is 10000, or all if all < 10000.
force
Overwrite existing
"""
## check that method was entered correctly
assert method in ["all", "random", "equal"], \
"method type not recognized, must be one of ['all', 'random', 'equal']"
if method != "all":
## require nquarts if method not all
assert nquarts, "if method != all, must enter a value for nquarts"
## don't allow nquarts to be greater than all
totalquarts = n_choose_k(len(data.samples), 4)
if nquarts > totalquarts:
print(" nquarts > total quartets, switching to method='all'")
method = "all"
if nquarts < 500:
print(" few possible quartets, only method='all' available")
method = "all"
## launch ipclient, assumes ipyparallel is running
ipyclient = ipp.Client(timeout=10)
## protects it from KBD
try:
run(data, nboots, method, nquarts, force, ipyclient)
except (KeyboardInterrupt, SystemExit):
## protect from KBD while saving
try:
## cancel submitted jobs
#ipyclient.abort()
## kill running jobs
#ipyclient.close()
## remove any abandoned tmp arrays
abandon = glob.glob(os.path.join(data.dirs.svd, "*_tmp_*.h5"))
for afile in abandon:
os.remove(afile)
except KeyboardInterrupt:
pass
finally:
## checkpoint the state and save
LOGGER.info("\n saving checkpoints to [Assembly].svd")
LOGGER.info(" array checkpoint: %s", data.svd.checkpoint_arr)
LOGGER.info(" boot checkpoint: %s", data.svd.checkpoint_boot)
data.save()
def wait_for_connection(self):
"""
Creates a client to view ipcluster engines for a given profile and
returns it with at least one engine spun up and ready to go. If no
engines are found after nwait amount of time then an error is raised.
If engines==MPI it waits a bit longer to find engines. If the number
of engines is set then it waits even longer to try to find that number
of engines.
"""
# save stds for later, hide here to prevent ipp enforced print()
save_stdout = sys.stdout
save_stderr = sys.stderr
sys.stdout = StringIO()
sys.stderr = StringIO()
# wrapped search for ipcluster
try:
args = {
"profile": self.tool.ipcluster["profile"],
"timeout": self.tool.ipcluster["timeout"],
"cluster_id": self.tool.ipcluster["cluster_id"],
}
ipyclient = ipp.Client(**args)
# restore std printing now that Client print statement has passed
# sys.stdout = save_stdout
# sys.stderr = save_stderr
# allow time to find the connection; count cores to break
for _ in range(6000):
# how many cores can we find right now?
ncores = len(ipyclient)
self.update_message(
"Establishing parallel connection: {} cores"
.format(ncores))
time.sleep(0.01)
# If we know ncores, then wait for all
print(self.tool.ipcluster["cores"])
if self.tool.ipcluster["cores"]:
time.sleep(0.1)
if ncores == self.tool.ipcluster["cores"]:
break
# Looking for all available cores, auto stop
else:
# If MPI and not all found break if no more in 3 secs
if self.tool.ipcluster["engines"] == "MPI":
# are any cores found yet? do long wait.
if ncores:
time.sleep(3)
if len(ipyclient) == ncores:
break
# if Local then wait 1 second between checks
else:
if ncores:
time.sleep(1.)
if len(ipyclient) == ncores:
break
except KeyboardInterrupt as inst:
raise inst
except (IOError, OSError, ipp.TimeoutError, ipp.NoEnginesRegistered):
raise IPyradError(
"\nipcluster not found, use 'auto=True' or see docs.")
finally:
# no matter what we reset the stds
sys.stdout = save_stdout
sys.stderr = save_stderr
# self.update_message(
# "Parallel connection: {}".format(len(ipyclient)))
return ipyclient
agent.salience = random.random()
new_model.step()
return new_model
# Load data
book_data = pd.read_csv("BDM_ColdWar.csv")
book_data.Position = (book_data.Position + 100) / 200
agents = []
for i, row in book_data.iterrows():
new_agent = BDMActor(row.Country, row.Capability, row.Position, 1)
new_agent.decision_model.Q = 0.5
new_agent.decision_model.T = 0.5
agents.append(new_agent)
model = NegotiationModel_(agents)
clients = ipyparallel.Client()
print(clients.ids)
dview = clients[:]
with dview.sync_imports():
import copy
import random
all_models = dview.map_sync(run_model, [model] * 10, [25] * 10)
all_model_out = [Model_Output(m) for m in all_models]
with open("ColdWar_Experiment2_1.pickle", "wb") as f:
pickle.dump(all_model_out, f)
print("Done!")
def parallel_serv_start():
import subprocess
subprocess.Popen(['ipcluster', 'start'])
import ipyparallel as ipp
rc = ipp.Client()
'''
TODO!!! Could we use doxygen or similar to convert docstrings to .org and thus avoid the
need to manually syncronise the code and the slides?
'''
'''This simply fills the non-ghosted part of the lattice with squares of consequtive
integers, stsrting from rank number, does the ghost exchange, computes gradients (2nd
order central differences) in the non-ghosted area, calculates maxima of local gradients,
and Allreduces the global maximum.
Along the way, it prints some diagnostics about the lattice and eventually of the
gradients.
'''
try:
import ipyparallel
c = ipyparallel.Client(profile="mpi")
directview = c[:]
directview.block = True
except IOError:
try:
del ipyparallel
except:
pass
import mpi4py
from mpi4py import MPI
except ImportError:
try:
del ipyparallel
except:
pass
import mpi4py
# ======== Header for ipyparallel kernels ========
import os, sys, types
import ipyparallel as ipp
# -------- Parallel kernels --------
print("Initializing cluster ...")
# variables
global kernels, cluster, nKernels
kernels = ipp.Client()
print(" Client variable \'kernels\'")
cluster = kernels[:]
print(" Cluster Direct View variable \'cluster\'")
nKernels = len(kernels.ids)
print(" Variable \'nKernels\' =", nKernels)
# change cluster current working directory
def f(cwd):
os.chdir(cwd)
print(os.getcwd)
return
cwd = os.getcwd()
cwdList = []
for i in range(nKernels):
cwdList.append(cwd)
with cluster.sync_imports():
import os
dataHDDM["stim"] = dataHDDM.apply(lambda row: 1 if row['stim'] == 'Right' else 0, axis=1)
dataHDDM["response"] = dataHDDM.apply(lambda row: 1 if row['givenResp'] == 'Right' else 0, axis=1)
def v_link_func(x, data=dataHDDM):
stim = (np.asarray(dmatrix('0 + C(s, [[1], [-1]])',
{'s': data.stim.ix[x.index]})))
return x*stim
if id < 4:
############## M1
LM = [{'model':'t ~ SAT + FC + contrast + SAT:FC + SAT:contrast + FC:contrast + SAT:FC:contrast', 'link_func': lambda x: x} ,
{'model':'v ~ contrast', 'link_func':v_link_func} ,
{'model':'a ~ FC + SAT + SAT:FC', 'link_func': lambda x: x} ]
deps = {'sz' : 'SAT'}
inc = ['sv','sz','st','z']
model_name = "Joint_t0"
else :
return np.nan()
name = 'light_reg_PMT_%s' %str(id)
m = hddm.HDDMRegressor(dataHDDM, LM , depends_on = deps,
include=inc, group_only_nodes=['sv', 'sz','st', "sz_SAT"], group_only_regressors=False, keep_regressor_trace=True)
m.find_starting_values()
m.sample(iter=10000, burn=8500, thin=1, dbname='DDM/traces/db_%s'%name, db='pickle')
m.save('DDM/Fits/%s'%name)
return m
v = ipyparallel.Client(profile="reg_PMT")[:]#sept
jobs = v.map(run_model, range(4 * 1))#4 chains for each model
wait_watching_stdout(jobs)
models = jobs.get()
iter_list = create_list()
scores = list(dview.map(parallel_method, iter_list).get())
#score_parameter_pairs = zip(scores,iter_list)
#print(iter_list)
from neuronunit import tests
#from deap import hypervolume
#test_0_run_exhaust()
os.system('ipcluster start -n 8 --profile=default & sleep 5;')
import ipyparallel as ipp
rc = ipp.Client(profile='default')
rc[:].use_cloudpickle()
dview = rc[:]
class ReducedModelTestCase(unittest.TestCase):
"""Test instantiation of the reduced model"""
"""Testing model optimization"""
def setUp(self):
#import sys
#sys.path.append('../')
#import neuronunit
from neuronunit.models.reduced import ReducedModel
#self.ReducedModel = ReducedModel
#path = ReducedModelTestCase().path
def run_cnmfe(tiff_files, param_file, output_file):
""" Run the CNMFe algorithm through CaImAn.
:param tiff_files: A list of .tiff files corresponding to a calcium imaging movie.
:param param_file: A .yaml parameter file, containing values for the following parameters:
num_processes : int
The number of processes to run in parallel. The more parallel processes, the more memory that is used.
rf : array-like
An array [half-width, half-height] that specifies the size of a patch.
stride : int
The amount of overlap in pixels between patches.
K : int
The maximum number of cells per patch.
gSiz : int
The expected diameter of a neuron in pixels.
gSig : int
The standard deviation a high pass Gaussian filter applied to the movie prior to seed pixel search, roughly
equal to the half-size of the neuron in pixels.
min_pnr : float
The minimum peak-to-noise ratio that is taken into account when searching for seed pixels.
min_corr : float
The minimum pixel correlation that is taken into account when searching for seed pixels.
min_SNR : float
Cells with an signal-to-noise (SNR) less than this are rejected.
rval_thr : float
Cells with a spatial correlation of greater than this are accepted.
decay_time : float
The expected decay time of a calcium event in seconds.
ssub_B : int
The spatial downsampling factor used on the background term.
merge_threshold : float
Cells that are spatially close with a temporal correlation of greater than merge_threshold are automatically merged.
:param output_file: The path to a .hdf5 file that will be written to contain the traces, footprints, and deconvolved
events identified by CNMFe.
"""
for tiff_file in tiff_files:
if not os.path.exists(tiff_file):
raise FileNotFoundError(tiff_file)
if not os.path.exists(param_file):
raise FileNotFoundError(param_file)
with open(param_file, 'r') as f:
params = yaml.load(f)
expected_params = [
'gSiz', 'gSig', 'K', 'min_corr', 'min_pnr', 'rf', 'stride',
'decay_time', 'min_SNR', 'rval_thr', 'merge_threshold', 'ssub_B',
'frame_rate', 'num_rows', 'num_cols', 'num_frames', 'num_processes'
]
for pname in expected_params:
if pname not in params:
raise ValueError('Missing parameter {} in file {}'.format(
pname, param_file))
gSiz = params['gSiz']
gSig = params['gSig']
K = params['K']
min_corr = params['min_corr']
min_pnr = params['min_pnr']
rf = params['rf']
stride = params['stride']
decay_time = params['decay_time']
min_SNR = params['min_SNR']
rval_thr = params['rval_thr']
merge_threshold = params['merge_threshold']
ssub_B = params['ssub_B']
frame_rate = params['frame_rate']
num_rows = params['num_rows']
num_cols = params['num_cols']
num_frames = params['num_frames']
num_processes = params['num_processes']
# write memmapped file
print('Exporting .isxd to memmap file...')
mmap_file = _export_movie_to_memmap(tiff_files,
num_frames,
num_rows,
num_cols,
overwrite=False)
print('Wrote .mmap file to: {}'.format(mmap_file))
# open memmapped file
Yr, dims, T = load_memmap(mmap_file)
Y = Yr.T.reshape((T, ) + dims, order='F')
# grab parallel IPython handle
dview = None
if num_processes > 1:
import ipyparallel as ipp
c = ipp.Client()
dview = c[:]
print('Running using parallel IPython, # clusters = {}'.format(
len(c.ids)))
num_processes = len(c.ids)
# initialize CNMFE parameter object and set user params
cnmfe_params = CNMFParams()
if gSiz is None:
raise ValueError(
'You must set gSiz to an integer, ideally roughly equal to the expected half-cell width.'
)
gSiz = _turn_into_array(gSiz)
if gSig is None:
raise ValueError(
'You must set gSig to a non-zero integer. The default value is 5.')
gSig = _turn_into_array(gSig)
cnmfe_params.set('preprocess', {'p': 1})
cnmfe_params.set(
'init', {
'K': K,
'min_corr': min_corr,
'min_pnr': min_pnr,
'gSiz': gSiz,
'gSig': gSig
})
if rf is None:
cnmfe_params.set('patch', {'rf': None, 'stride': 1})
else:
cnmfe_params.set('patch', {'rf': np.array(rf), 'stride': stride})
cnmfe_params.set('data', {'decay_time': decay_time})
cnmfe_params.set('quality', {'min_SNR': min_SNR, 'rval_thr': rval_thr})
cnmfe_params.set('merging', {'merge_thr': merge_threshold})
# set parameters that force CNMF into one-photon mode with no temporal or spatial downsampling,
# except for the background term
cnmfe_params.set(
'init', {
'center_psf': True,
'method_init': 'corr_pnr',
'normalize_init': False,
'nb': -1,
'ssub_B': ssub_B,
'tsub': 1,
'ssub': 1
})
cnmfe_params.set(
'patch', {
'only_init': True,
'low_rank_background': None,
'nb_patch': -1,
'p_tsub': 1,
'p_ssub': 1
})
cnmfe_params.set('spatial', {
'nb': -1,
'update_background_components': False
})
cnmfe_params.set('temporal', {'nb': -1, 'p': 1})
# construct and run CNMFE
print('Running CNMFe...')
cnmfe = CNMF(num_processes, dview=dview, params=cnmfe_params)
cnmfe.fit(Y)
# run auto accept/reject
print('Estimating component quality...')
idx_components, idx_components_bad, comp_SNR, r_values, pred_CNN = estimate_components_quality_auto(
Y,
cnmfe.estimates.A,
cnmfe.estimates.C,
cnmfe.estimates.b,
cnmfe.estimates.f,
cnmfe.estimates.YrA,
frame_rate,
cnmfe_params.get('data', 'decay_time'),
cnmfe_params.get('init', 'gSiz'),
cnmfe.dims,
dview=None,
min_SNR=cnmfe_params.get('quality', 'min_SNR'),
use_cnn=False)
save_cnmfe(cnmfe, output_file, good_idx=idx_components)
def optimize(
self,
method,
quantiles=(0.1, 0.3, 0.5, 0.7, 0.9),
n_runs=3,
n_bootstraps=0,
parallel_profile=None,
):
"""
Optimize model using ML, chi^2 or G^2.
:Input:
method : str
Optimization method ('ML', 'chisquare' or 'gsquare').
quantiles : tuple
A sequence of quantiles to be used for chi^2 and G^2.
Default values are the ones used by Ratcliff (.1, .3, .5, .7, .9).
n_runs : int <default=3>
Number of attempts to optimize.
n_bootstraps : int <default=0>
Number of bootstrap iterations.
parrall_profile : str <default=None>
IPython profile for parallelization.
:Output:
results <dict> - a results dictionary of the parameters values.
:Note:
The values of the nodes in single subject model is updated according to the results.
The nodes of group models are not updated
"""
results = self._run_optimization(method=method,
quantiles=quantiles,
n_runs=n_runs)
# bootstrap if requested
if n_bootstraps == 0:
return results
# init DataFrame to save results
res = pd.DataFrame(np.zeros((n_bootstraps, len(self.values))),
columns=list(self.values.keys()))
# prepare view for parallelization
if parallel_profile is not None: # create view
client = ipyparallel.Client(profile=parallel_profile)
view = client.load_balanced_view()
runs_list = [None] * n_bootstraps
else:
view = None
# define single iteration bootstrap function
def single_bootstrap(
data,
accumulator_class=self.__class__,
class_kwargs=self._kwargs,
method=method,
quantiles=quantiles,
n_runs=n_runs,
):
# resample data
new_data = data.iloc[np.random.randint(0, len(data), len(data))]
new_data = new_data.set_index(pd.Index(list(range(len(data)))))
h = accumulator_class(new_data, **class_kwargs)
# run optimization
h._run_optimization(method=method,
quantiles=quantiles,
n_runs=n_runs)
return pd.Series(h.values, dtype=np.float)
# bootstrap iterations
for i_strap in range(n_bootstraps):
if view is None:
res.iloc[i_strap] = single_bootstrap(self.data)
else:
# append to job queue
runs_list[i_strap] = view.apply_async(single_bootstrap,
self.data)
# get parallel results
if view is not None:
view.wait(runs_list)
for i_strap in range(n_bootstraps):
res.iloc[i_strap] = runs_list[i_strap].get()
# get statistics
stats = res.describe()
for q in [2.5, 97.5]:
stats = stats.append(
pd.DataFrame(res.quantile(q / 100.0),
columns=[repr(q) + "%"]).T)
self.bootstrap_stats = stats.sort_index()
return results
G['industry_imputed']
except:
G.vs['industry_imputed'] = [x == 'nan' for x in G.vs['industry']]
industry_dist = np.array(
[x['industry'] for x in G.vs if not x['industry_imputed']])
imputed_industry = np.random.choice(industry_dist,
len(G.vs(industry_imputed_eq=True)),
replace=True)
for v, s in zip(G.vs(industry_imputed_eq=True), imputed_industry):
v['industry'] = s
has_ipyparallel = True
try:
dv = ipyparallel.Client(
)[:] # This should be global (or a singleton) to avoid an error with too many files open https://github.com/ipython/ipython/issues/6039
dv.block = False
dv.use_dill()
except:
has_ipyparallel = False
print("Loading without ipyparallel support")
callbacks = [
some_terminal_suppliers_reachable,
percent_terminal_suppliers_reachable,
]
def failure_reachability_single(r,
G,
med_suppliers=False,
import bubbles
# Dynesty imports
import pickle
import dynesty
from dynesty import plotting as dyplot
from dynesty import DynamicNestedSampler
from dynesty import utils as dyfunc
from multiprocessing import Pool
import ipyparallel as ipp
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
# To use multiprocessing run following command:
# > ipcluster start -n 7 &
rc = ipp.Client()
nprocs = len(rc.ids)
print(rc.ids)
dview = rc[:]
dview.use_dill()
# =====================================================================
import argparse # argument managing
# ==============================================================================
# Managing arguments with argparse (see http://docs.python.org/howto/argparse.html)
parser = argparse.ArgumentParser()
# ---- required arguments ---- :
parser.add_argument("file_name",
type=str,
help="File name, saved in ../chains/")
# Main processing loop
if serial_flag:
print 'Running everything in a serial loop.'
# Serial processing
out = []
for ag in args:
out.append(parallel_loop(algo_names, parameters, ag))
else:
import ipyparallel as ip
print 'Using ipyparallel processing.'
# Start the parallel processing
c = ip.Client()
NC = len(c.ids)
print NC, 'workers on the job'
# replicate some parameters
algo_names_ls = [algo_names] * len(args)
params_ls = [parameters] * len(args)
# evaluate the runtime
then = time.time()
out1 = c[:].map_sync(parallel_loop, algo_names_ls[:NC], params_ls[:NC],
args[:NC])
now = time.time()
one_loop = now - then
print 'Total estimated processing time:', len(args) * one_loop / len(
c[:])
