#!/usr/bin/env python
# coding: utf-8

# # How to add new gauge-optimizations to GST results
# This example demonstrates how to take a previously computed `Results` object and add new gauge-optimized version of to one of the estimates.  First, let's "pre-compute" a `Results` object using `do_long_sequence_gst`, which contains a single `Estimate` called "default":

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from __future__ import print_function
import pygsti, pickle
from pygsti.construction import std1Q_XYI


# In[2]:


#Generate some fake data and run GST on it.
gs_target = std1Q_XYI.gs_target.copy()
gs_datagen = std1Q_XYI.gs_target.depolarize(gate_noise=0.1, spam_noise=0.001)
listOfExperiments = pygsti.construction.make_lsgst_experiment_list(
    gs_target, std1Q_XYI.fiducials, std1Q_XYI.fiducials, std1Q_XYI.germs, [1,2,4])
ds = pygsti.construction.generate_fake_data(gs_datagen, listOfExperiments, nSamples=1000,
                                            sampleError="binomial", seed=1234)
gs_target.set_all_parameterizations("TP")
results = pygsti.do_long_sequence_gst(
    ds, gs_target, std1Q_XYI.fiducials, std1Q_XYI.fiducials, std1Q_XYI.germs, [1,2,4],
    gaugeOptParams={'itemWeights': {'gates': 1, 'spam': 1}}, verbosity=1)

with open("example_files/regaugeopt_result.pkl","wb") as f:
    pickle.dump(results, f) # pickle the results, to mimic typical workflow


# Next, let's load in the pre-computed results and use the `add_gauge_optimization` method of the `pygsti.objects.Estimate` object to add a new gauge-optimized version of the (gauge un-fixed) gate set estimate stored in `my_results.estimates['default']`.  The first argument of `add_gauge_optimization` is just a dictionary of arguments to `pygsti.gaugeopt_to_target` **except** that you don't need to specify the `GateSet` to gauge optimize or the target `GateSet` (just like the `gaugeOptParams` argument of `do_long_sequence_gst`).  The optional "`label`" argument defines the key name for the gauge-optimized `GateSet` and the corresponding parameter dictionary within the `Estimate`'s `.gatesets` and `.goparameters` dictionaries, respectively.

# In[3]:


my_results = pickle.load(open("example_files/regaugeopt_result.pkl","rb"))


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estimate = my_results.estimates['default']
estimate.add_gaugeoptimized( {'itemWeights': {'gates': 1, 'spam': 0.001}}, label="Spam 1e-3" )
gs_gaugeopt = estimate.gatesets['Spam 1e-3']

print(list(estimate.goparameters.keys())) # 'go0' is the default gauge-optimization label
print(gs_gaugeopt.frobeniusdist(estimate.gatesets['target']))


# One can also perform the gauge optimization separately and specify it using the `gateset` argument (this is useful when you want or need to compute the gauge optimization elsewhere):

# In[5]:


gs_unfixed = estimate.gatesets['final iteration estimate']
gs_gaugefixed = pygsti.gaugeopt_to_target(gs_unfixed, estimate.gatesets['target'], {'gates': 1, 'spam': 0.001})
estimate.add_gaugeoptimized( {'any': "dictionary", 
                              "doesn't really": "matter",
                              "but could be useful it you put gaugeopt params": 'here'},
                            gateset=gs_gaugefixed, label="Spam 1e-3 custom" )
print(list(estimate.goparameters.keys()))
print(estimate.gatesets['Spam 1e-3 custom'].frobeniusdist(estimate.gatesets['Spam 1e-3']))


# You can look at the gauge optimization parameters using `.goparameters`:

# In[6]:


import pprint
pp = pprint.PrettyPrinter()
pp.pprint(dict(estimate.goparameters['Spam 1e-3']))


# Finally, note that if, in the original call to `do_long_sequence_gst`, you set **`gaugeOptParams=False`** then no gauge optimizations are performed (there would be no "`go0`" elements) and you start with a blank slate to perform whatever gauge optimizations you want on your own.

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