Here is example script that I have made so far.
import jupyter_client
import pprint
code = '''import math
a = math.sin(1)'''
def getIn(r, pth):
for p in pth:
r = r.get(p, {})
return r
def getMessagesUntilIdle(kern):
resp = []
while True:
msg = kern.get_iopub_msg()
estate = getIn(msg, ['content', 'execution_state'])
resp.append(msg)
if estate == 'idle':
break
return resp
with jupyter_client.run_kernel(kernel_name='python3') as kern:
kern.execute(code=code, user_expressions={'a':'a'})
# after executing given code, kernel replies with the message
# containing the expressions we have asked (i.e. 'a')
msg = kern.get_shell_msg()
# each expression we asked for is returned as a dict
resultDict = getIn(msg, ['content', 'user_expressions', 'a'])
# result as plain text
result = getIn(msg, ['content', 'user_expressions', 'a', 'data',
'text/plain'])
g.es('---------code--------')
g.es(code)
g.es('---result:', result)
g.es(pprint.pformat(resultDict))
It doesn't do too much. It calculates sin(1).
Function jupyter_client.run_kernel returns a blocking version of jupyter
client enclosed in a context. That means that the methods get_shell_msg,
and others get_..._msg will block until the message arrives. This
simplifies the example but for real use, we should use non-blocking client
and read messages on iopub channel which inform us when kernel has started
to execute our code (it becomes busy), and when it becomes idle again, we
can read the result message from the shell channel.
Instead of using every time another kernel enclosed in a python context
which will expire when the context is left, perhaps kernel client can be
created directly using jupyter_client.KernelClient(**kwargs) and instance
stored in c.user_dict. It is non-blocking client.
HTH Vitalije
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