Source code for qutip.ipynbtools

# This file is part of QuTiP: Quantum Toolbox in Python.
#
#    Copyright (c) 2011 and later, Paul D. Nation and Robert J. Johansson.
#    All rights reserved.
#
#    Redistribution and use in source and binary forms, with or without
#    modification, are permitted provided that the following conditions are
#    met:
#
#    1. Redistributions of source code must retain the above copyright notice,
#       this list of conditions and the following disclaimer.
#
#    2. Redistributions in binary form must reproduce the above copyright
#       notice, this list of conditions and the following disclaimer in the
#       documentation and/or other materials provided with the distribution.
#
#    3. Neither the name of the QuTiP: Quantum Toolbox in Python nor the names
#       of its contributors may be used to endorse or promote products derived
#       from this software without specific prior written permission.
#
#    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
#    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
#    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
#    PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
#    HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
#    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
#    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
#    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
#    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
#    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
#    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
###############################################################################
"""
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%%">&nbsp;</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)