# This file is part of QuTiP: Quantum Toolbox in Python.
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# Copyright (c) 2011 and later, Paul D. Nation and Robert J. Johansson.
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"""
This module contains utility functions for using QuTiP with IPython notebooks.
"""
__all__ = ['version_table', 'parfor', 'plot_animation', 'parallel_map']
from qutip.ui.progressbar import BaseProgressBar
from IPython.parallel import Client
from IPython.display import HTML, Javascript, display
import matplotlib.pyplot as plt
from matplotlib import animation
import datetime
import uuid
import sys
import os
import time
import inspect
import qutip
import numpy
import scipy
import Cython
import matplotlib
import IPython
[docs]def version_table(verbose=False):
"""
Print an HTML-formatted table with version numbers for QuTiP and its
dependencies. Use it in a IPython notebook to show which versions of
different packages that were used to run the notebook. This should make it
possible to reproduce the environment and the calculation later on.
Returns
--------
version_table: string
Return an HTML-formatted string containing version information for
QuTiP dependencies.
"""
html = "<table>"
html += "<tr><th>Software</th><th>Version</th></tr>"
packages = {"QuTiP": qutip.__version__,
"Numpy": numpy.__version__,
"SciPy": scipy.__version__,
"matplotlib": matplotlib.__version__,
"Cython": Cython.__version__,
"Python": sys.version,
"IPython": IPython.__version__,
"OS": "%s [%s]" % (os.name, sys.platform)
}
for name in packages:
html += "<tr><td>%s</td><td>%s</td></tr>" % (name, packages[name])
if verbose:
html += "<tr><th colspan='2'>Additional information</th></tr>"
qutip_install_path = os.path.dirname(inspect.getsourcefile(qutip))
html += ("<tr><td>Installation path</td><td>%s</td></tr>" %
qutip_install_path)
try:
import getpass
html += ("<tr><td>User</td><td>%s</td></tr>" %
getpass.getuser())
except:
pass
html += "<tr><td colspan='2'>%s</td></tr>" % time.strftime(
'%a %b %d %H:%M:%S %Y %Z')
html += "</table>"
return HTML(html)
class HTMLProgressBar(BaseProgressBar):
"""
A simple HTML progress bar for using in IPython notebooks. Based on
IPython ProgressBar demo notebook:
https://github.com/ipython/ipython/tree/master/examples/notebooks
Example usage:
n_vec = linspace(0, 10, 100)
pbar = HTMLProgressBar(len(n_vec))
for n in n_vec:
pbar.update(n)
compute_with_n(n)
"""
def __init__(self, iterations=0, chunk_size=1.0):
self.divid = str(uuid.uuid4())
self.textid = str(uuid.uuid4())
self.pb = HTML("""\
<div style="border: 2px solid grey; width: 600px">
<div id="%s" \
style="background-color: rgba(121,195,106,0.75); width:0%%"> </div>
</div>
<p id="%s"></p>
""" % (self.divid, self.textid))
display(self.pb)
super(HTMLProgressBar, self).start(iterations, chunk_size)
def start(self, iterations=0, chunk_size=1.0):
super(HTMLProgressBar, self).start(iterations, chunk_size)
def update(self, n):
p = (n / self.N) * 100.0
if p >= self.p_chunk:
lbl = ("Elapsed time: %s. " % self.time_elapsed() +
"Est. remaining time: %s." % self.time_remaining_est(p))
js_code = ("$('div#%s').width('%i%%');" % (self.divid, p) +
"$('p#%s').text('%s');" % (self.textid, lbl))
display(Javascript(js_code))
# display(Javascript("$('div#%s').width('%i%%')" % (self.divid,
# p)))
self.p_chunk += self.p_chunk_size
def finished(self):
self.t_done = time.time()
lbl = "Elapsed time: %s" % self.time_elapsed()
js_code = ("$('div#%s').width('%i%%');" % (self.divid, 100.0) +
"$('p#%s').text('%s');" % (self.textid, lbl))
display(Javascript(js_code))
def _visualize_parfor_data(metadata):
"""
Visualizing the task scheduling meta data collected from AsyncResults.
"""
res = numpy.array(metadata)
fig, ax = plt.subplots(figsize=(10, res.shape[1]))
yticks = []
yticklabels = []
tmin = min(res[:, 1])
for n, pid in enumerate(numpy.unique(res[:, 0])):
yticks.append(n)
yticklabels.append("%d" % pid)
for m in numpy.where(res[:, 0] == pid)[0]:
ax.add_patch(plt.Rectangle((res[m, 1] - tmin, n - 0.25),
res[m, 2] - res[m, 1], 0.5, color="green", alpha=0.5))
ax.set_ylim(-.5, n + .5)
ax.set_xlim(0, max(res[:, 2]) - tmin + 0.)
ax.set_yticks(yticks)
ax.set_yticklabels(yticklabels)
ax.set_ylabel("Engine")
ax.set_xlabel("seconds")
ax.set_title("Task schedule")
[docs]def parfor(task, task_vec, args=None, client=None, view=None,
show_scheduling=False, show_progressbar=False):
"""
Call the function ``tast`` for each value in ``task_vec`` using a cluster
of IPython engines. The function ``task`` should have the signature
``task(value, args)`` or ``task(value)`` if ``args=None``.
The ``client`` and ``view`` are the IPython.parallel client and
load-balanced view that will be used in the parfor execution. If these
are ``None``, new instances will be created.
Parameters
----------
task: a Python function
The function that is to be called for each value in ``task_vec``.
task_vec: array / list
The list or array of values for which the ``task`` function is to be
evaluated.
args: list / dictionary
The optional additional argument to the ``task`` function. For example
a dictionary with parameter values.
client: IPython.parallel.Client
The IPython.parallel Client instance that will be used in the
parfor execution.
view: a IPython.parallel.Client view
The view that is to be used in scheduling the tasks on the IPython
cluster. Preferably a load-balanced view, which is obtained from the
IPython.parallel.Client instance client by calling,
view = client.load_balanced_view().
show_scheduling: bool {False, True}, default False
Display a graph showing how the tasks (the evaluation of ``task`` for
for the value in ``task_vec1``) was scheduled on the IPython engine
cluster.
show_progressbar: bool {False, True}, default False
Display a HTML-based progress bar duing the execution of the parfor
loop.
Returns
--------
result : list
The result list contains the value of ``task(value, args)`` for each
value in ``task_vec``, that is, it should be equivalent to
``[task(v, args) for v in task_vec]``.
"""
if show_progressbar:
progress_bar = HTMLProgressBar()
else:
progress_bar = None
return parallel_map(task, task_vec, task_args=args,
client=client, view=view, progress_bar=progress_bar,
show_scheduling=show_scheduling)
[docs]def parallel_map(task, values, task_args=None, task_kwargs=None,
client=None, view=None, progress_bar=None,
show_scheduling=False, **kwargs):
"""
Call the function ``task`` for each value in ``values`` using a cluster
of IPython engines. The function ``task`` should have the signature
``task(value, *args, **kwargs)``.
The ``client`` and ``view`` are the IPython.parallel client and
load-balanced view that will be used in the parfor execution. If these
are ``None``, new instances will be created.
Parameters
----------
task: a Python function
The function that is to be called for each value in ``task_vec``.
values: array / list
The list or array of values for which the ``task`` function is to be
evaluated.
task_args: list / dictionary
The optional additional argument to the ``task`` function.
task_kwargs: list / dictionary
The optional additional keyword argument to the ``task`` function.
client: IPython.parallel.Client
The IPython.parallel Client instance that will be used in the
parfor execution.
view: a IPython.parallel.Client view
The view that is to be used in scheduling the tasks on the IPython
cluster. Preferably a load-balanced view, which is obtained from the
IPython.parallel.Client instance client by calling,
view = client.load_balanced_view().
show_scheduling: bool {False, True}, default False
Display a graph showing how the tasks (the evaluation of ``task`` for
for the value in ``task_vec1``) was scheduled on the IPython engine
cluster.
show_progressbar: bool {False, True}, default False
Display a HTML-based progress bar during the execution of the parfor
loop.
Returns
--------
result : list
The result list contains the value of
``task(value, task_args, task_kwargs)`` for each
value in ``values``.
"""
submitted = datetime.datetime.now()
if task_args is None:
task_args = tuple()
if task_kwargs is None:
task_kwargs = {}
if client is None:
client = Client()
# make sure qutip is available at engines
dview = client[:]
dview.block = True
dview.execute("from qutip import *")
if view is None:
view = client.load_balanced_view()
ar_list = [view.apply_async(task, value, *task_args, **task_kwargs)
for value in values]
if progress_bar is None:
view.wait(ar_list)
else:
if progress_bar is True:
progress_bar = HTMLProgressBar()
n = len(ar_list)
progress_bar.start(n)
while True:
n_finished = sum([ar.progress for ar in ar_list])
progress_bar.update(n_finished)
if view.wait(ar_list, timeout=0.5):
progress_bar.update(n)
break
progress_bar.finished()
if show_scheduling:
metadata = [[ar.engine_id,
(ar.started - submitted).total_seconds(),
(ar.completed - submitted).total_seconds()]
for ar in ar_list]
_visualize_parfor_data(metadata)
return [ar.get() for ar in ar_list]
def plot_animation(plot_setup_func, plot_func, result, name="movie",
verbose=False):
"""
Create an animated plot of a Result object, as returned by one of
the qutip evolution solvers.
.. note :: experimental
"""
fig, axes = plot_setup_func(result)
def update(n):
plot_func(result, n, fig=fig, axes=axes)
anim = animation.FuncAnimation(
fig, update, frames=len(result.times), blit=True)
anim.save(name + '.mp4', fps=10, writer="avconv", codec="libx264")
plt.close(fig)
if verbose:
print("Created %s.m4v" % name)
video = open(name + '.mp4', "rb").read()
video_encoded = video.encode("base64")
video_tag = '<video controls src="data:video/x-m4v;base64,{0}">'.format(
video_encoded)
return HTML(video_tag)