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Merge pull request #2179 from dopplershift/pylab-switch...
Merge pull request #2179 from dopplershift/pylab-switch Enable switching %pylab mode between inline and a single gui mode in a single notebook. With this merge, `%pylab` can be called interactively to toggle inline/GUI (matplotlib floating windows) mode. After initializing `%pylab inline`, now one can call `%pylab` without arguments to activate the default GUI or ask for a specific one as usual. IPython will detect if a different GUI is requested if one was already activated and will refuse to do so (to prevent multiple event loops from running concurrently, which often leads to problems).

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customresults.py
61 lines | 1.8 KiB | text/x-python | PythonLexer
MinRK
update parallel docs with some changes from scipy tutorial...
r5169 """An example for handling results in a way that AsyncMapResult doesn't provide
Specifically, out-of-order results with some special handing of metadata.
This just submits a bunch of jobs, waits on the results, and prints the stdout
and results of each as they finish.
Authors
-------
* MinRK
"""
import time
import random
from IPython import parallel
# create client & views
rc = parallel.Client()
dv = rc[:]
v = rc.load_balanced_view()
# scatter 'id', so id=0,1,2 on engines 0,1,2
dv.scatter('id', rc.ids, flatten=True)
Thomas Kluyver
Update print syntax in parallel examples.
r6455 print(dv['id'])
MinRK
update parallel docs with some changes from scipy tutorial...
r5169
def sleep_here(count, t):
"""simple function that takes args, prints a short message, sleeps for a time, and returns the same args"""
import time,sys
Thomas Kluyver
Update print syntax in parallel examples.
r6455 print("hi from engine %i" % id)
MinRK
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r5169 sys.stdout.flush()
time.sleep(t)
return count,t
amr = v.map(sleep_here, range(100), [ random.random() for i in range(100) ], chunksize=2)
pending = set(amr.msg_ids)
while pending:
try:
rc.wait(pending, 1e-3)
except parallel.TimeoutError:
# ignore timeouterrors, since they only mean that at least one isn't done
pass
# finished is the set of msg_ids that are complete
finished = pending.difference(rc.outstanding)
# update pending to exclude those that just finished
pending = pending.difference(finished)
for msg_id in finished:
# we know these are done, so don't worry about blocking
ar = rc.get_result(msg_id)
Thomas Kluyver
Update print syntax in parallel examples.
r6455 print("job id %s finished on engine %i" % (msg_id, ar.engine_id))
print("with stdout:")
print(' ' + ar.stdout.replace('\n', '\n ').rstrip())
print("and results:")
MinRK
update parallel docs with some changes from scipy tutorial...
r5169
# note that each job in a map always returns a list of length chunksize
# even if chunksize == 1
for (count,t) in ar.result:
Thomas Kluyver
Update print syntax in parallel examples.
r6455 print(" item %i: slept for %.2fs" % (count, t))
MinRK
update parallel docs with some changes from scipy tutorial...
r5169