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Backport PR #2126: ipcluster broken with any batch (PBS/LSF/SGE)...
Backport PR #2126: ipcluster broken with any batch (PBS/LSF/SGE) I have setup ipcluster_config.py to start with LSF: ``` c.IPClusterStart.controller_launcher_class = 'LSF' c.IPClusterStart.engine_launcher_class = 'LSF' ``` But the ipcluster command fails to start the engines: ``` ipcluster start --profile=lsf -n 10 ``` The problem is fixed if I add quotes to the launch command string ```cmd``` in ```launcher.py```. ``` diff --git a/IPython/parallel/apps/launcher.py b/IPython/parallel/apps/launcher.py index e752d2a..6035303 100644 --- a/IPython/parallel/apps/launcher.py +++ b/IPython/parallel/apps/launcher.py @@ -73,7 +73,7 @@ WINDOWS = os.name == 'nt' # Paths to the kernel apps #----------------------------------------------------------------------------- -cmd = "from IPython.parallel.apps.%s import launch_new_instance; launch_new_instance()" +cmd = "\"from IPython.parallel.apps.%s import launch_new_instance; launch_new_instance()\"" ipcluster_cmd_argv = [sys.executable, "-c", cmd % "ipclusterapp"] ```

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phistogram.py
40 lines | 1.2 KiB | text/x-python | PythonLexer
"""Parallel histogram function"""
import numpy
from IPython.parallel import Reference
def phistogram(view, a, bins=10, rng=None, normed=False):
"""Compute the histogram of a remote array a.
Parameters
----------
view
IPython DirectView instance
a : str
String name of the remote array
bins : int
Number of histogram bins
rng : (float, float)
Tuple of min, max of the range to histogram
normed : boolean
Should the histogram counts be normalized to 1
"""
nengines = len(view.targets)
# view.push(dict(bins=bins, rng=rng))
with view.sync_imports():
import numpy
rets = view.apply_sync(lambda a, b, rng: numpy.histogram(a,b,rng), Reference(a), bins, rng)
hists = [ r[0] for r in rets ]
lower_edges = [ r[1] for r in rets ]
# view.execute('hist, lower_edges = numpy.histogram(%s, bins, rng)' % a)
lower_edges = view.pull('lower_edges', targets=0)
hist_array = numpy.array(hists).reshape(nengines, -1)
# hist_array.shape = (nengines,-1)
total_hist = numpy.sum(hist_array, 0)
if normed:
total_hist = total_hist/numpy.sum(total_hist,dtype=float)
return total_hist, lower_edges