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factory.py
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factory.py
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""" Report generation functions. """
from __future__ import division, print_function, absolute_import, unicode_literals
#***************************************************************************************************
# Copyright 2015, 2019 National Technology & Engineering Solutions of Sandia, LLC (NTESS).
# Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government retains certain rights
# in this software.
# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
# in compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0 or in the LICENSE file in the root pyGSTi directory.
#***************************************************************************************************
import pickle as _pickle
import os as _os
import time as _time
import collections as _collections
import warnings as _warnings
import zipfile as _zipfile
import numpy as _np
from ..baseobjs import VerbosityPrinter as _VerbosityPrinter
from ..objects import DataComparator as _DataComparator
from ..tools import timed_block as _timed_block
from ..tools.mpitools import distribute_indices as _distribute_indices
from .. import tools as _tools
from .. import objects as _objs
from .. import _version
from . import workspace as _ws
from . import autotitle as _autotitle
from . import merge_helpers as _merge
from . import reportables as _reportables
from .notebook import Notebook as _Notebook
from ..baseobjs.label import Label as _Lbl
#maybe import these from drivers.longsequence so they stay synced?
ROBUST_SUFFIX_LIST = [".robust", ".Robust", ".robust+", ".Robust+", ".wildcard"]
DEFAULT_BAD_FIT_THRESHOLD = 2.0
def _errgen_formula(errgen_type, typ):
assert(typ in ('html', 'latex'))
notDuringTxt = ("This is <em>not</em> the Lindblad-type generator that would produce this noise if it "
"acted continuously <em>during</em> the gate (i.e., simultaneously with a Hamiltonian that "
"generates the ideal gate). This choice is explicit; the authors of pyGSTi are concerned that "
"reporting the continuous-time-generator would encourage a false sense of understanding the "
"physics behind the noise, which is explicitly invalid if the gates were produced by anything "
"other than a simple pulse.")
if errgen_type == "logTiG": # G = T*exp(L) (pre-error)
gen = '<span class="math">G = G_0 e^{\mathbb{L}}</span>'
desc = ('<em>pre-gate</em> generator, so it answers the question '
'"If all the noise occurred <em>before</em> the ideal gate,'
' what Lindbladian would generate it?" ') + notDuringTxt
elif errgen_type == "logGTi": # G = exp(L)*T (post-error)
gen = '<span class="math">G = e^{\mathbb{L}} G_0</span>'
desc = ('<em>post-gate</em> generator, so it answers the question '
'"If all the noise occurred <em>after</em> the ideal gate,'
' what Lindbladian would generate it?" ') + notDuringTxt
elif errgen_type == "logG-logT":
gen = '<span class="math">G = e^{\mathbb{L} + \log G_0}</span>'
desc = ('<em>during-gate</em> generator, so it answers the question '
'"What Lindblad-type generate would produce this noise if it'
' acted continuously <em>during</em> the gate?" Note that '
'this does <em>not necessarily</em> give insight into physics'
' producing the noise.')
else:
gen = desc = "???"
if typ == "latex": # minor modifications for latex version
gen = gen.replace('<span class="math">', '$')
gen = gen.replace('</span>', '$')
desc = desc.replace('<em>', '\\emph{')
desc = desc.replace('</em>', '}')
return gen, desc
def _add_new_labels(running_lbls, current_lbls):
"""
Simple routine to add current-labels to a list of
running-labels without introducing duplicates and
preserving order as best we can.
"""
if running_lbls is None:
return current_lbls[:] # copy!
elif running_lbls != current_lbls:
for lbl in current_lbls:
if lbl not in running_lbls:
running_lbls.append(lbl)
return running_lbls
def _add_new_estimate_labels(running_lbls, estimates, combine_robust):
"""
Like _add_new_labels but perform robust-suffix processing.
In particular, if `combine_robust == True` then do not add
labels which have a ".robust" counterpart.
"""
current_lbls = list(estimates.keys())
def _add_lbl(lst, lbl):
if combine_robust and any([(lbl + suffix in current_lbls)
for suffix in ROBUST_SUFFIX_LIST]):
return # don't add label
lst.append(lbl) # add label
if running_lbls is None:
running_lbls = []
if running_lbls != current_lbls:
for lbl in current_lbls:
if lbl not in running_lbls:
_add_lbl(running_lbls, lbl)
return running_lbls
#def _robust_estimate_has_same_models(estimates, est_lbl):
# lbl_robust = est_lbl+ROBUST_SUFFIX
# if lbl_robust not in estimates: return False #no robust estimate
#
# for mdl_lbl in list(estimates[est_lbl].goparameters.keys()) \
# + ['final iteration estimate']:
# if mdl_lbl not in estimates[lbl_robust].models:
# return False #robust estimate is missing mdl_lbl!
#
# mdl = estimates[lbl_robust].models[mdl_lbl]
# if estimates[est_lbl].models[mdl_lbl].frobeniusdist(mdl) > 1e-8:
# return False #model mismatch!
#
# return True
def _get_viewable_crf(est, est_lbl, mdl_lbl, verbosity=0):
printer = _VerbosityPrinter.build_printer(verbosity)
if est.has_confidence_region_factory(mdl_lbl, 'final'):
crf = est.get_confidence_region_factory(mdl_lbl, 'final')
if crf.can_construct_views():
return crf
else:
printer.log(
("Note: Confidence interval factory for {estlbl}.{gslbl} "
"model exists but cannot create views. This could be "
"because you forgot to create a Hessian *projection*"
).format(estlbl=est_lbl, gslbl=mdl_lbl))
else:
printer.log(
("Note: no factory to compute confidence "
"intervals for the '{estlbl}.{gslbl}' model."
).format(estlbl=est_lbl, gslbl=mdl_lbl))
return None
def create_offline_zip(outputDir="."):
"""
Creates a zip file containing the a directory ("offline") of files
need to display "offline" reports (generated with `connected=False`).
For offline reports to display, the "offline" folder must be placed
in the same directory as the report's HTML file. This function can
be used to easily obtain a copy of the offline folder for the purpose
of sharing offline reports with other people. If you're just creating
your own offline reports using pyGSTi, the offline folder is
automatically copied into it's proper position - so you don't need
to call this function.
Parameters
----------
outputDir : str, optional
The directory in which "offline.zip" should be place.
Returns
-------
None
"""
templatePath = _os.path.join(_os.path.dirname(_os.path.abspath(__file__)),
"templates")
zipFName = _os.path.join(outputDir, "offline.zip")
zipHandle = _zipfile.ZipFile(zipFName, 'w', _zipfile.ZIP_DEFLATED)
for root, _, files in _os.walk(_os.path.join(templatePath, "offline")):
for f in files:
fullPath = _os.path.join(root, f)
zipHandle.write(fullPath, _os.path.relpath(fullPath, templatePath))
zipHandle.close()
def _set_toggles(results_dict, brevity, combine_robust):
#Determine when to get circuit weight (scaling) values and show via
# ColorBoxPlots below by checking whether any estimate has "weights"
# parameter (a dict) with > 0 entries.
toggles = {}
toggles["ShowScaling"] = False
toggles["ShowUnmodeledError"] = False
for res in results_dict.values():
for est in res.estimates.values():
weights = est.parameters.get("weights", None)
if (weights is not None and len(weights) > 0):
toggles["ShowScaling"] = True
if est.parameters.get("unmodeled_error", None):
toggles["ShowUnmodeledError"] = True
toggles['BrevityLT1'] = bool(brevity < 1)
toggles['BrevityLT2'] = bool(brevity < 2)
toggles['BrevityLT3'] = bool(brevity < 3)
toggles['BrevityLT4'] = bool(brevity < 4)
toggles['CombineRobust'] = bool(combine_robust)
return toggles
def _create_master_switchboard(ws, results_dict, confidenceLevel,
nmthreshold, printer, fmt,
combine_robust, idt_results_dict=None):
"""
Creates the "master switchboard" used by several of the reports
"""
if isinstance(results_dict, _collections.OrderedDict):
dataset_labels = list(results_dict.keys())
else:
dataset_labels = sorted(list(results_dict.keys()))
est_labels = None
gauge_opt_labels = None
Ls = None
for results in results_dict.values():
est_labels = _add_new_estimate_labels(est_labels, results.estimates,
combine_robust)
Ls = _add_new_labels(Ls, results.circuit_structs['final'].Ls)
for est in results.estimates.values():
gauge_opt_labels = _add_new_labels(gauge_opt_labels,
list(est.goparameters.keys()))
Ls = list(sorted(Ls)) # make sure Ls are sorted in increasing order
if fmt == "latex" and len(Ls) > 0:
swLs = [Ls[-1]] # "switched Ls" = just take the single largest L
else:
swLs = Ls # switch over all Ls
multidataset = bool(len(dataset_labels) > 1)
multiest = bool(len(est_labels) > 1)
multiGO = bool(len(gauge_opt_labels) > 1)
#multiL = bool(len(swLs) > 1)
switchBd = ws.Switchboard(
["Dataset", "Estimate", "Gauge-Opt", "max(L)"],
[dataset_labels, est_labels, gauge_opt_labels, list(map(str, swLs))],
["dropdown", "dropdown", "buttons", "slider"], [0, 0, 0, len(swLs) - 1],
show=[multidataset, multiest, multiGO, False] # "global" switches only + gauge-opt (OK if doesn't apply)
)
switchBd.add("ds", (0,))
switchBd.add("prepStrs", (0,))
switchBd.add("effectStrs", (0,))
switchBd.add("strs", (0,))
switchBd.add("germs", (0,))
switchBd.add("eff_ds", (0, 1))
switchBd.add("modvi_ds", (0, 1))
switchBd.add("wildcardBudget", (0, 1))
switchBd.add("scaledSubMxsDict", (0, 1))
switchBd.add("gsTarget", (0, 1))
switchBd.add("params", (0, 1))
switchBd.add("objective", (0, 1))
switchBd.add("objective_tvd_tuple", (0, 1))
switchBd.add("objective_modvi", (0, 1))
switchBd.add("mpc", (0, 1))
switchBd.add("mpc_modvi", (0, 1))
switchBd.add("clifford_compilation", (0, 1))
switchBd.add("meta_stdout", (0, 1))
switchBd.add("profiler", (0, 1))
switchBd.add("gsGIRep", (0, 1))
switchBd.add("gsGIRepEP", (0, 1))
switchBd.add("gsFinal", (0, 1, 2))
switchBd.add("gsEvalProjected", (0, 1, 2))
switchBd.add("gsTargetAndFinal", (0, 1, 2)) # general only!
switchBd.add("goparams", (0, 1, 2))
switchBd.add("gsL", (0, 1, 3))
switchBd.add("gsL_modvi", (0, 1, 3))
switchBd.add("gss", (0, 3))
switchBd.add("gssFinal", (0,))
switchBd.add("gsAllL", (0, 1))
switchBd.add("gsAllL_modvi", (0, 1))
switchBd.add("gssAllL", (0,))
switchBd.add("gsFinalGrid", (2,))
switchBd.add("idtresults", (0,))
if confidenceLevel is not None:
switchBd.add("cri", (0, 1, 2))
switchBd.add("criGIRep", (0, 1))
for d, dslbl in enumerate(dataset_labels):
results = results_dict[dslbl]
switchBd.ds[d] = results.dataset
switchBd.prepStrs[d] = results.circuit_lists['prep fiducials']
switchBd.effectStrs[d] = results.circuit_lists['effect fiducials']
switchBd.strs[d] = (results.circuit_lists['prep fiducials'],
results.circuit_lists['effect fiducials'])
switchBd.germs[d] = results.circuit_lists['germs']
switchBd.gssFinal[d] = results.circuit_structs['final']
for iL, L in enumerate(swLs): # allow different results to have different Ls
if L in results.circuit_structs['final'].Ls:
k = results.circuit_structs['final'].Ls.index(L)
switchBd.gss[d, iL] = results.circuit_structs['iteration'][k]
switchBd.gssAllL[d] = results.circuit_structs['iteration']
if idt_results_dict is not None:
switchBd.idtresults[d] = idt_results_dict.get(dslbl, None)
for i, lbl in enumerate(est_labels):
est = results.estimates.get(lbl, None)
if est is None: continue
for suffix in ROBUST_SUFFIX_LIST:
if combine_robust and lbl.endswith(suffix):
est_modvi = results.estimates.get(lbl[:-len(suffix)], est)
break
else:
est_modvi = est
def rpt_objective(opt_objective):
""" If optimized using just LGST, compute logl values """
if opt_objective == "lgst": return "logl"
else: return opt_objective
switchBd.params[d, i] = est.parameters
switchBd.objective[d, i] = rpt_objective(est.parameters['objective'])
switchBd.objective_tvd_tuple[d, i] = (rpt_objective(est.parameters['objective']), 'tvd')
switchBd.objective_modvi[d, i] = rpt_objective(est_modvi.parameters['objective'])
if est.parameters['objective'] == "logl":
switchBd.mpc[d, i] = est.parameters['minProbClip']
switchBd.mpc_modvi[d, i] = est_modvi.parameters['minProbClip']
elif est.parameters['objective'] == "chi2":
switchBd.mpc[d, i] = est.parameters['minProbClipForWeighting']
switchBd.mpc_modvi[d, i] = est_modvi.parameters['minProbClipForWeighting']
else: # "lgst" - just use defaults for logl
switchBd.mpc[d, i] = 1e-4
switchBd.mpc_modvi[d, i] = 1e-4
switchBd.clifford_compilation[d, i] = est.parameters.get("clifford compilation", 'auto')
if switchBd.clifford_compilation[d, i] == 'auto':
switchBd.clifford_compilation[d, i] = find_std_clifford_compilation(
est.models['target'], printer)
switchBd.profiler[d, i] = est_modvi.parameters.get('profiler', None)
switchBd.meta_stdout[d, i] = est_modvi.meta.get('stdout', [('LOG', 1, "No standard output recorded")])
GIRepLbl = 'final iteration estimate' # replace with a gauge-opt label if it has a CI factory
if confidenceLevel is not None:
if _get_viewable_crf(est, lbl, GIRepLbl) is None:
for l in gauge_opt_labels:
if _get_viewable_crf(est, lbl, l) is not None:
GIRepLbl = l; break
# NOTE on modvi_ds (the dataset used in model violation plots)
# if combine_robust is True, modvi_ds is the unscaled dataset.
# if combine_robust is False, modvi_ds is the effective dataset
# for the estimate (potentially just the unscaled one)
#if this estimate uses robust scaling or wildcard budget
NA = ws.NotApplicable()
if est.parameters.get("weights", None):
effds, scale_subMxs = est.get_effective_dataset(True)
switchBd.eff_ds[d, i] = effds
switchBd.scaledSubMxsDict[d, i] = {'scaling': scale_subMxs, 'scaling.colormap': "revseq"}
switchBd.modvi_ds[d, i] = results.dataset if combine_robust else effds
else:
switchBd.modvi_ds[d, i] = results.dataset
switchBd.eff_ds[d, i] = NA
switchBd.scaledSubMxsDict[d, i] = NA
if est.parameters.get("unmodeled_error", None):
switchBd.wildcardBudget[d, i] = est.parameters['unmodeled_error']
else:
switchBd.wildcardBudget[d, i] = NA
switchBd.gsTarget[d, i] = est.models['target']
switchBd.gsGIRep[d, i] = est.models[GIRepLbl]
try:
switchBd.gsGIRepEP[d, i] = _tools.project_to_target_eigenspace(est.models[GIRepLbl],
est.models['target'])
except AttributeError: # Implicit models don't support everything, like set_all_parameterizations
switchBd.gsGIRepEP[d, i] = None
switchBd.gsFinal[d, i, :] = [est.models.get(l, NA) for l in gauge_opt_labels]
switchBd.gsTargetAndFinal[d, i, :] = \
[[est.models['target'], est.models[l]] if (l in est.models) else NA
for l in gauge_opt_labels]
switchBd.goparams[d, i, :] = [est.goparameters.get(l, NA) for l in gauge_opt_labels]
for iL, L in enumerate(swLs): # allow different results to have different Ls
if L in results.circuit_structs['final'].Ls:
k = results.circuit_structs['final'].Ls.index(L)
switchBd.gsL[d, i, iL] = est.models['iteration estimates'][k]
switchBd.gsL_modvi[d, i, iL] = est_modvi.models['iteration estimates'][k]
switchBd.gsAllL[d, i] = est.models['iteration estimates']
switchBd.gsAllL_modvi[d, i] = est_modvi.models['iteration estimates']
if confidenceLevel is not None:
misfit_sigma = est.misfit_sigma(use_accurate_Np=True)
for il, l in enumerate(gauge_opt_labels):
if l in est.models:
switchBd.cri[d, i, il] = None # default
crf = _get_viewable_crf(est, lbl, l, printer - 2)
if crf is not None:
#Check whether we should use non-Markovian error bars:
# If fit is bad, check if any reduced fits were computed
# that we can use with in-model error bars. If not, use
# experimental non-markovian error bars.
region_type = "normal" if misfit_sigma <= nmthreshold \
else "non-markovian"
switchBd.cri[d, i, il] = crf.view(confidenceLevel, region_type)
else: switchBd.cri[d, i, il] = NA
# "Gauge Invariant Representation" model
# If we can't compute CIs for this, ignore SILENTLY, since any
# relevant warnings/notes should have been given above.
switchBd.criGIRep[d, i] = None # default
crf = _get_viewable_crf(est, lbl, GIRepLbl)
if crf is not None:
region_type = "normal" if misfit_sigma <= nmthreshold \
else "non-markovian"
switchBd.criGIRep[d, i] = crf.view(confidenceLevel, region_type)
results_list = [results_dict[dslbl] for dslbl in dataset_labels]
for i, gokey in enumerate(gauge_opt_labels):
if multidataset:
switchBd.gsFinalGrid[i] = [
[(res.estimates[el].models.get(gokey, None)
if el in res.estimates else None) for el in est_labels]
for res in results_list]
else:
switchBd.gsFinalGrid[i] = [
(results_list[0].estimates[el].models.get(gokey, None)
if el in results_list[0].estimates else None) for el in est_labels]
if multidataset:
switchBd.add_unswitched('gsTargetGrid', [
[(res.estimates[el].models.get('target', None)
if el in res.estimates else None) for el in est_labels]
for res in results_list])
else:
switchBd.add_unswitched('gsTargetGrid', [
(results_list[0].estimates[el].models.get('target', None)
if el in results_list[0].estimates else None) for el in est_labels])
return switchBd, dataset_labels, est_labels, gauge_opt_labels, Ls, swLs
def _construct_idtresults(idtIdleOp, idtPauliDicts, gst_results_dict, printer):
"""
Constructs a dictionary of idle tomography results, parallel
to the GST results in `gst_results_dict`, where possible.
"""
if idtPauliDicts is None:
return {}
idt_results_dict = {}
GiStr = _objs.Circuit((idtIdleOp,))
from ..extras import idletomography as _idt
autodict = bool(idtPauliDicts == "auto")
for ky, results in gst_results_dict.items():
if autodict:
for est in results.estimates.values():
if 'target' in est.models:
idt_target = est.models['target']
break
else: continue # can't find any target models
idtPauliDicts = _idt.determine_paulidicts(idt_target)
if idtPauliDicts is None:
continue # automatic creation failed -> skip
gss = results.circuit_structs['final']
if GiStr not in gss.germs: continue
try: # to get a dimension -> nQubits
estLabels = list(results.estimates.keys())
estimate0 = results.estimates[estLabels[0]]
dim = estimate0.models['target'].dim
nQubits = int(round(_np.log2(dim) // 2))
idStr = ('Gi',) if 'Gi' in estimate0.models['target'].get_primitive_op_labels() else ((),)
except:
printer.log(" ! Skipping idle tomography on %s dataset (can't get # qubits) !" % ky)
continue # skip if we can't get dimension
maxLengths = gss.Ls
# just use "L0" (first maxLength) - all should have same fidpairs
plaq = gss.get_plaquette(maxLengths[0], GiStr)
pauli_fidpairs = _idt.fidpairs_to_pauli_fidpairs(plaq.fidpairs, idtPauliDicts, nQubits)
idt_advanced = {'pauli_fidpairs': pauli_fidpairs, 'jacobian mode': "together"}
printer.log(" * Running idle tomography on %s dataset *" % ky)
idtresults = _idt.do_idle_tomography(nQubits, results.dataset, maxLengths, idtPauliDicts,
maxweight=2, # HARDCODED for now (FUTURE)
idle_string=idStr, advancedOptions=idt_advanced)
idt_results_dict[ky] = idtresults
return idt_results_dict
def _create_single_metric_switchboard(ws, results_dict, bGaugeInv,
dataset_labels, est_labels):
op_labels = None
for results in results_dict.values():
for est in results.estimates.values():
if 'target' in est.models:
op_labels = _add_new_labels(op_labels,
list(est.models['target'].operations.keys()))
if bGaugeInv:
metric_abbrevs = ["evinf", "evagi", "evnuinf", "evnuagi", "evdiamond",
"evnudiamond"]
else:
metric_abbrevs = ["inf", "agi", "trace", "diamond", "nuinf", "nuagi",
"frob"]
metric_names = [_reportables.info_of_opfn_by_name(abbrev)[0].replace('|', ' ')
for abbrev in metric_abbrevs]
if len(dataset_labels) > 1: # multidataset
metric_switchBd = ws.Switchboard(
["Metric", "Operation"], [metric_names, op_labels],
["dropdown", "dropdown"], [0, 0], show=[True, True])
metric_switchBd.add("opLabel", (1,))
metric_switchBd.add("metric", (0,))
metric_switchBd.add("cmpTableTitle", (0, 1))
metric_switchBd.opLabel[:] = op_labels
for i, gl in enumerate(op_labels):
metric_switchBd.cmpTableTitle[:, i] = ["%s %s" % (gl, nm) for nm in metric_names]
else:
metric_switchBd = ws.Switchboard(
["Metric"], [metric_names],
["dropdown"], [0], show=[True])
metric_switchBd.add("metric", (0,))
metric_switchBd.add("cmpTableTitle", (0,))
metric_switchBd.cmpTableTitle[:] = metric_names
metric_switchBd.metric[:] = metric_abbrevs
return metric_switchBd
def create_general_report(results, filename, title="auto",
confidenceLevel=None,
linlogPercentile=5, errgen_type="logGTi",
nmthreshold=50, precision=None,
comm=None, ws=None, auto_open=False,
cachefile=None, brief=False, connected=False,
link_to=None, resizable=True, autosize='initial',
verbosity=1):
""" DEPRECATED: use pygsti.report.create_standard_report(...) """
_warnings.warn(
('create_general_report(...) will be removed from pyGSTi.\n'
' This function only ever existed in beta versions and will\n'
' be removed completely soon. Please update this call with:\n'
' pygsti.report.create_standard_report(...)\n'))
def create_standard_report(results, filename, title="auto",
confidenceLevel=None, comm=None, ws=None,
auto_open=False, link_to=None, brevity=0,
advancedOptions=None, verbosity=1):
"""
Create a "standard" GST report, containing details about each estimate
in `results` individually.
Either a PDF or HTML report is generated, based on whether `filename` ends
in ".pdf" or not. In the richer HTML-mode, switches (drop-down boxes,
buttons, etc.) allow the viewer to choose which estimate is displayed. The
estimates in multiple :class:`Results` objects can be viewed by providing
a dictionary of `Results` objects as the `results` argument. Note that
when comparing many estimates it is often more convenient to view the report
generated by :func:`create_comparison_report`, which is organized for this
purpose.
In PDF-mode this interactivity is not possible and so `results` may contain
just a *single* estimate. The chief advantage of this more limited mode
is that is produces a highly-portable and self-contained PDF file.
Parameters
----------
results : Results
An object which represents the set of results from one *or more* GST
estimation runs, typically obtained from running
:func:`do_long_sequence_gst` or :func:`do_stdpractice_gst`, OR a
dictionary of such objects, representing multiple GST runs to be
compared (typically all with *different* data sets). The keys of this
dictionary are used to label different data sets that are selectable
in the report.
filename : string, optional
The output filename where the report file(s) will be saved. If
None, then no output file is produced (but returned Workspace
still caches all intermediate results).
title : string, optional
The title of the report. "auto" causes a random title to be
generated (which you may or may not like).
confidenceLevel : int, optional
If not None, then the confidence level (between 0 and 100) used in
the computation of confidence regions/intervals. If None, no
confidence regions or intervals are computed.
comm : mpi4py.MPI.Comm, optional
When not None, an MPI communicator for distributing the computation
across multiple processors.
ws : Workspace, optional
The workspace used as a scratch space for performing the calculations
and visualizations required for this report. If you're creating
multiple reports with similar tables, plots, etc., it may boost
performance to use a single Workspace for all the report generation.
auto_open : bool, optional
If True, automatically open the report in a web browser after it
has been generated.
link_to : list, optional
If not None, a list of one or more items from the set
{"tex", "pdf", "pkl"} indicating whether or not to
create and include links to Latex, PDF, and Python pickle
files, respectively. "tex" creates latex source files for
tables; "pdf" renders PDFs of tables and plots ; "pkl" creates
Python versions of plots (pickled python data) and tables (pickled
pandas DataFrams).
brevity : int, optional
Amount of detail to include in the report. Larger values mean smaller
"more briefr" reports, which reduce generation time, load time, and
disk space consumption. In particular:
- 1: Plots showing per-sequences quantities disappear at brevity=1
- 2: Reference sections disappear at brevity=2
- 3: Germ-level estimate tables disappear at brevity=3
- 4: Everything but summary figures disappears at brevity=4
advancedOptions : dict, optional
A dictionary of advanced options for which the default values are usually
are fine. Here are the possible keys of `advancedOptions`:
- connected : bool, optional
Whether output HTML should assume an active internet connection. If
True, then the resulting HTML file size will be reduced because it
will link to web resources (e.g. CDN libraries) instead of embedding
them.
- cachefile : str, optional
filename with cached workspace results
- linlogPercentile : float, optional
Specifies the colorscale transition point for any logL or chi2 color
box plots. The lower `(100 - linlogPercentile)` percentile of the
expected chi2 distribution is shown in a linear grayscale, and the
top `linlogPercentile` is shown on a logarithmic colored scale.
- errgen_type: {"logG-logT", "logTiG", "logGTi"}
The type of error generator to compute. Allowed values are:
- "logG-logT" : errgen = log(gate) - log(target_op)
- "logTiG" : errgen = log( dot(inv(target_op), gate) )
- "logGTi" : errgen = log( dot(gate, inv(target_op)) )
- nmthreshold : float, optional
The threshold, in units of standard deviations, that triggers the
usage of non-Markovian error bars. If None, then non-Markovian
error bars are never computed.
- precision : int or dict, optional
The amount of precision to display. A dictionary with keys
"polar", "sci", and "normal" can separately specify the
precision for complex angles, numbers in scientific notation, and
everything else, respectively. If an integer is given, it this
same value is taken for all precision types. If None, then
`{'normal': 6, 'polar': 3, 'sci': 0}` is used.
- resizable : bool, optional
Whether plots and tables are made with resize handles and can be
resized within the report.
- autosize : {'none', 'initial', 'continual'}
Whether tables and plots should be resized, either initially --
i.e. just upon first rendering (`"initial"`) -- or whenever
the browser window is resized (`"continual"`).
- combine_robust : bool, optional
Whether robust estimates should automatically be combined with
their non-robust counterpart when displayed in reports. (default
is True).
- confidence_interval_brevity : int, optional
Roughly specifies how many figures will have confidence intervals
(when applicable). Defaults to '1'. Smaller values mean more
tables will get confidence intervals (and reports will take longer
to generate).
- idt_basis_dicts : tuple, optional
Tuple of (prepDict,measDict) pauli-basis dictionaries, which map
between 1-qubit Pauli basis strings (e.g. `'-X'` or `'Y'`) and tuples
of gate names (e.g. `('Gx','Gx')`). If given, idle tomography will
be performed on the 'Gi' gate and included in the report.
- idt_idle_oplabel : Label, optional
The label identifying the idle gate (for use with idle tomography).
verbosity : int, optional
How much detail to send to stdout.
Returns
-------
Workspace
The workspace object used to create the report
"""
tStart = _time.time()
printer = _VerbosityPrinter.build_printer(verbosity, comm=comm)
if advancedOptions is None: advancedOptions = {}
linlogPercentile = advancedOptions.get('linlog percentile', 5)
errgen_type = advancedOptions.get('error generator type', "logGTi")
nmthreshold = advancedOptions.get('nmthreshold', DEFAULT_BAD_FIT_THRESHOLD)
precision = advancedOptions.get('precision', None)
cachefile = advancedOptions.get('cachefile', None)
connected = advancedOptions.get('connected', False)
resizable = advancedOptions.get('resizable', True)
autosize = advancedOptions.get('autosize', 'initial')
combine_robust = advancedOptions.get('combine_robust', True)
ci_brevity = advancedOptions.get('confidence_interval_brevity', 1)
idtPauliDicts = advancedOptions.get('idt_basis_dicts', 'auto')
idtIdleOp = advancedOptions.get('idt_idle_oplabel', _Lbl('Gi'))
if filename and filename.endswith(".pdf"):
fmt = "latex"
else:
fmt = "html"
printer.log('*** Creating workspace ***')
if ws is None: ws = _ws.Workspace(cachefile)
if isinstance(title, int): # to catch backward compatibility issues
raise ValueError(("'title' argument must be a string. You may be accidentally"
" specifying an int here because in older versions of pyGSTi"
" the third argument to create_general_report was the"
" confidence interval - please note the updated function signature"))
if title is None or title == "auto":
if filename is not None:
autoname = _autotitle.generate_name()
title = "GST Report for " + autoname
_warnings.warn(("You should really specify `title=` when generating reports,"
" as this makes it much easier to identify them later on. "
"Since you didn't, pyGSTi has generated a random one"
" for you: '{}'.").format(autoname))
else:
title = "N/A" # No title - but it doesn't matter since filename is None
results_dict = results if isinstance(results, dict) else {"unique": results}
toggles = _set_toggles(results_dict, brevity, combine_robust)
#DEBUG
renderMath = True
#_ws.WorkspaceOutput.default_render_options['click_to_display'] = True #don't render any plots until they're clicked
#_ws.WorkspaceOutput.default_render_options['render_math'] = renderMath #don't render any math
qtys = {} # stores strings to be inserted into report template
def addqty(b, name, fn, *args, **kwargs):
"""Adds an item to the qtys dict within a timed block"""
if b is None or brevity < b:
with _timed_block(name, formatStr='{:45}', printer=printer, verbosity=2):
qtys[name] = fn(*args, **kwargs)
qtys['title'] = title
qtys['date'] = _time.strftime("%B %d, %Y")
qtys['confidenceLevel'] = "%d" % \
confidenceLevel if confidenceLevel is not None else "NOT-SET"
qtys['linlg_pcntle'] = "%d" % round(linlogPercentile) # to nearest %
qtys['linlg_pcntle_inv'] = "%d" % (100 - int(round(linlogPercentile)))
qtys['errorgenformula'], qtys['errorgendescription'] = _errgen_formula(errgen_type, fmt)
pdfInfo = [('Author', 'pyGSTi'), ('Title', title),
('Keywords', 'GST'), ('pyGSTi Version', _version.__version__)]
qtys['pdfinfo'] = _merge.to_pdfinfo(pdfInfo)
# Perform idle tomography on datasets if desired (need to do
# this before creating main switchboard)
try:
idt_results_dict = _construct_idtresults(idtIdleOp, idtPauliDicts,
results_dict, printer)
except Exception as e:
_warnings.warn("Idle tomography failed:\n" + str(e))
idt_results_dict = {}
toggles['IdleTomography'] = bool(len(idt_results_dict) > 0)
# Generate Switchboard
printer.log("*** Generating switchboard ***")
#Create master switchboard
switchBd, dataset_labels, est_labels, gauge_opt_labels, Ls, swLs = \
_create_master_switchboard(ws, results_dict, confidenceLevel,
nmthreshold, printer, fmt,
combine_robust, idt_results_dict)
if fmt == "latex" and (len(dataset_labels) > 1 or len(est_labels) > 1
or len(gauge_opt_labels) > 1 or len(swLs) > 1):
raise ValueError("PDF reports can only show a *single* dataset,"
" estimate, and gauge optimization.")
if len(Ls) > 0 and Ls[0] == 0:
_warnings.warn(("Setting the first 'max-length' to zero, e.g. using"
" [0,1,2,4] instead of [1,2,4], is deprecated and"
" may cause 'no data to plot' errors when creating"
" this report. Please remove this leading zero."))
# Generate Tables
printer.log("*** Generating tables ***")
if confidenceLevel is not None:
#TODO: make plain text fields which update based on switchboards?
for some_cri in switchBd.cri.flat: # can have only some confidence regions
if some_cri is not None and not isinstance(some_cri, _ws.NotApplicable):
qtys['confidenceIntervalScaleFctr'] = "%.3g" % some_cri.intervalScaling
qtys['confidenceIntervalNumNonGaugeParams'] = "%d" % some_cri.nNonGaugeParams
multidataset = bool(len(dataset_labels) > 1)
#multiest = bool(len(est_labels) > 1)
#multiGO = bool(len(gauge_opt_labels) > 1)
multiL = bool(len(swLs) > 1)
##goView = [multidataset,multiest,multiGO,False]
##maxLView = [multidataset,multiest,False,multiL]
#goView = [False,False,multiGO,False]
maxLView = [False, False, False, multiL]
if fmt == "html":
qtys['topSwitchboard'] = switchBd
#qtys['goSwitchboard1'] = switchBd.view(goView,"v1")
#qtys['goSwitchboard2'] = switchBd.view(goView,"v2")
qtys['maxLSwitchboard1'] = switchBd.view(maxLView, "v6")
gsTgt = switchBd.gsTarget
ds = switchBd.ds
eff_ds = switchBd.eff_ds
modvi_ds = switchBd.modvi_ds
prepStrs = switchBd.prepStrs
effectStrs = switchBd.effectStrs
germs = switchBd.germs
strs = switchBd.strs
cliffcomp = switchBd.clifford_compilation
A = None # no brevity restriction: always display; for "Summary"- & "Help"-tab figs
#Brevity key:
# 1: Plots involving quantities for individual sequences disappear at brevity=1
# 2: Input & Meta reference tables disappear at brevity=2
# 3: Germ estimate tables disappear at brevity=3
# 4: Everything but summary figs disappear at brevity=4
addqty(2, 'targetSpamBriefTable', ws.SpamTable, gsTgt, None, display_as='boxes', includeHSVec=False)
addqty(2, 'targetGatesBoxTable', ws.GatesTable, gsTgt, display_as="boxes")
addqty(2, 'datasetOverviewTable', ws.DataSetOverviewTable, ds)
gsFinal = switchBd.gsFinal
gsGIRep = switchBd.gsGIRep
gsEP = switchBd.gsGIRepEP
cri_base = switchBd.cri if (confidenceLevel is not None) else None
criGIRep_base = switchBd.criGIRep if (confidenceLevel is not None) else None
def cri(l): return cri_base if ci_brevity <= l else None
def criGIRep(l): return criGIRep_base if ci_brevity <= l else None
# Non-summary gate estimates
# Germ
addqty(4, 'bestGatesetSpamParametersTable', ws.SpamParametersTable, switchBd.gsTargetAndFinal,
['Target', 'Estimated'], cri(1))
addqty(4, 'bestGatesetSpamBriefTable', ws.SpamTable, switchBd.gsTargetAndFinal,
['Target', 'Estimated'], 'boxes', cri(1), includeHSVec=False)
addqty(4, 'bestGatesetSpamVsTargetTable', ws.SpamVsTargetTable, gsFinal, gsTgt, cri(1))
addqty(A, 'bestGatesetGaugeOptParamsTable', ws.GaugeOptParamsTable, switchBd.goparams)
addqty(4, 'bestGatesetGatesBoxTable', ws.GatesTable, switchBd.gsTargetAndFinal,
['Target', 'Estimated'], "boxes", cri(1))
addqty(4, 'bestGatesetChoiEvalTable', ws.ChoiTable, gsFinal, None, cri(1), display=("boxplot", "barplot"))
addqty(4, 'bestGatesetDecompTable', ws.GateDecompTable, gsFinal, gsTgt, cri(0))
addqty(4, 'bestGatesetEvalTable', ws.GateEigenvalueTable, gsGIRep, gsTgt, criGIRep(1),
display=('evals', 'target', 'absdiff-evals', 'infdiff-evals', 'log-evals', 'absdiff-log-evals'))
addqty(3, 'bestGermsEvalTable', ws.GateEigenvalueTable, gsGIRep, gsEP, criGIRep(1),
display=('evals', 'target', 'absdiff-evals', 'infdiff-evals', 'log-evals', 'absdiff-log-evals'),
virtual_ops=germs)
#addqty('bestGatesetRelEvalTable', ws.GateEigenvalueTable, gsFinal, gsTgt, cri(1), display=('rel','log-rel'))
addqty(4, 'bestGatesetVsTargetTable', ws.ModelVsTargetTable, gsFinal, gsTgt, cliffcomp, cri(1))
addqty(4, 'bestGatesVsTargetTable_gv', ws.GatesVsTargetTable, gsFinal, gsTgt, cri(1),
display=('inf', 'agi', 'trace', 'diamond', 'nuinf', 'nuagi'))
addqty(3, 'bestGatesVsTargetTable_gvgerms', ws.GatesVsTargetTable, gsFinal, gsTgt, cri(0),
display=('inf', 'trace', 'nuinf'), virtual_ops=germs)
addqty(4, 'bestGatesVsTargetTable_gi', ws.GatesVsTargetTable, gsGIRep, gsTgt, criGIRep(1),
display=('evinf', 'evagi', 'evnuinf', 'evnuagi', 'evdiamond', 'evnudiamond'))
addqty(3, 'bestGatesVsTargetTable_gigerms', ws.GatesVsTargetTable, gsGIRep, gsEP, criGIRep(0),
display=('evdiamond', 'evnudiamond'), virtual_ops=germs)
summary_display = ('inf', 'trace', 'diamond', 'evinf', 'evdiamond'); wildcardBudget = None
if toggles["ShowUnmodeledError"]:
summary_display += ('unmodeled',)
wildcardBudget = switchBd.wildcardBudget
addqty(A, 'bestGatesVsTargetTable_sum', ws.GatesVsTargetTable, gsFinal, gsTgt, cri(1),
display=summary_display, wildcard=wildcardBudget)
addqty(4, 'bestGatesetErrGenBoxTable', ws.ErrgenTable, gsFinal, gsTgt, cri(1), ("errgen", "H", "S", "A"),
"boxes", errgen_type)
addqty(2, 'metadataTable', ws.MetadataTable, gsFinal, switchBd.params)
addqty(2, 'stdoutBlock', ws.StdoutText, switchBd.meta_stdout)
addqty(2, 'profilerTable', ws.ProfilerTable, switchBd.profiler)
addqty(2, 'softwareEnvTable', ws.SoftwareEnvTable)
addqty(A, 'exampleTable', ws.ExampleTable)
qtys['exampleTable'].set_render_options(click_to_display=True)
# single-metric comparison tables
gvmetric_switchBd = _create_single_metric_switchboard(ws, results_dict, False,
dataset_labels, est_labels)
gimetric_switchBd = _create_single_metric_switchboard(ws, results_dict, True,
dataset_labels, est_labels)
qtys['metricSwitchboard_gv'] = gvmetric_switchBd
qtys['metricSwitchboard_gi'] = gimetric_switchBd
if multidataset:
addqty(4, 'singleMetricTable_gv', ws.GatesSingleMetricTable, gvmetric_switchBd.metric,
switchBd.gsFinalGrid, switchBd.gsTargetGrid, est_labels, dataset_labels,
gvmetric_switchBd.cmpTableTitle, gvmetric_switchBd.opLabel, confidenceRegionInfo=None)
addqty(4, 'singleMetricTable_gi', ws.GatesSingleMetricTable, gimetric_switchBd.metric,
switchBd.gsFinalGrid, switchBd.gsTargetGrid, est_labels, dataset_labels,
gimetric_switchBd.cmpTableTitle, gimetric_switchBd.opLabel, confidenceRegionInfo=None)
else:
addqty(4, 'singleMetricTable_gv', ws.GatesSingleMetricTable, gvmetric_switchBd.metric,
switchBd.gsFinalGrid, switchBd.gsTargetGrid, est_labels, None,
gvmetric_switchBd.cmpTableTitle, confidenceRegionInfo=None)
addqty(4, 'singleMetricTable_gi', ws.GatesSingleMetricTable, gimetric_switchBd.metric,
switchBd.gsFinalGrid, switchBd.gsTargetGrid, est_labels, None,
gimetric_switchBd.cmpTableTitle, confidenceRegionInfo=None)
if len(idt_results_dict) > 0:
#Plots & tables for idle tomography tab
#idt_switchBd = _create_idle_tomography_switchboard(ws, idt_results_dict)
#qtys['idtSwitchboard'] = idt_switchBd
addqty(A, 'idtIntrinsicErrorsTable', ws.IdleTomographyIntrinsicErrorsTable, switchBd.idtresults)
addqty(3, 'idtObservedRatesTable', ws.IdleTomographyObservedRatesTable, switchBd.idtresults,
20, gsGIRep) # HARDCODED - show only top 20 rates
#Ls and Germs specific
gss = switchBd.gss
gsL = switchBd.gsL
gsL_modvi = switchBd.gsL_modvi
gssAllL = switchBd.gssAllL
addqty(2, 'fiducialListTable', ws.CircuitTable, strs, ["Prep.", "Measure"], commonTitle="Fiducials")
addqty(2, 'prepStrListTable', ws.CircuitTable, prepStrs, "Preparation Fiducials")
addqty(2, 'effectStrListTable', ws.CircuitTable, effectStrs, "Measurement Fiducials")
addqty(1, 'colorBoxPlotKeyPlot', ws.BoxKeyPlot, prepStrs, effectStrs)
addqty(2, 'germList2ColTable', ws.CircuitTable, germs, "Germ", nCols=2)
addqty(4, 'progressTable', ws.FitComparisonTable,
Ls, gssAllL, switchBd.gsAllL_modvi, modvi_ds, switchBd.objective_modvi, 'L', comm=comm)
# Generate plots
printer.log("*** Generating plots ***")
addqty(4, 'gramBarPlot', ws.GramMatrixBarPlot, ds, gsTgt, 10, strs)
addqty(4, 'progressBarPlot', ws.FitComparisonBarPlot,
Ls, gssAllL, switchBd.gsAllL_modvi, modvi_ds, switchBd.objective_modvi, 'L', comm=comm)
# just duplicate for now
addqty(A, 'progressBarPlot_sum', ws.FitComparisonBarPlot,
Ls, gssAllL, switchBd.gsAllL_modvi, modvi_ds, switchBd.objective_modvi, 'L', comm=comm)
# Don't display "Target" in model violation summary, as it's often
# huge and messes up the plot scale.
est_inds_mt = [i for i, l in enumerate(est_labels) if l != "Target"]
est_lbls_mt = [est_labels[i] for i in est_inds_mt] # "minus target"
Nd = len(dataset_labels)
Ne = len(est_inds_mt)
grid_objective = switchBd.objective_modvi[0, 0] # just take first one for now
def na_to_none(x):
return None if isinstance(x, _ws.NotApplicable) else x
if multidataset:
dsGrid = [[na_to_none(switchBd.modvi_ds[d, i]) for i in est_inds_mt]
for d in range(Nd)]
gssGrid = [[na_to_none(switchBd.gssFinal[i])] * Ne for i in range(Nd)]