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

# # Tutorial showing use of a `Workspace` object
# 
# ## Part 1: making tables and plots
# PyGSTi's `Workspace` object is first a foremost a container and factory for plots and tables.  At the most basic level, it can be used to generate nice output based on quantities (e.g. GateSets, DataSets, etc.) that you've computed or loaded within a notebook.  For this, it's useful to call `init_notebook_mode` with `autodisplay=True` (see below) so that you don't have to `.display()` everything - `display()` gets called automatically when a plot or table is created.
# 
# First, let's run GST on the standard 1Q gate set to get some results to play with.

# In[1]:


import numpy as np
import pygsti
from pygsti.construction import std1Q_XYI


# In[2]:


#The usual GST setup: we're going to run GST on the standard XYI 1-qubit gateset
gs_target = std1Q_XYI.gs_target
fiducials = std1Q_XYI.fiducials
germs = std1Q_XYI.germs
maxLengths = [1,2]
listOfExperiments = pygsti.construction.make_lsgst_experiment_list(
    gs_target.gates.keys(), fiducials, fiducials, germs, maxLengths)


# In[3]:


#Create some datasets for analysis
gs_datagen1 = gs_target.depolarize(gate_noise=0.1, spam_noise=0.02)
gs_datagen2 = gs_target.depolarize(gate_noise=0.05, spam_noise=0.01).rotate(rotate=(0.01,0.01,0.01))

ds1 = pygsti.construction.generate_fake_data(gs_datagen1, listOfExperiments, nSamples=1000,
                                            sampleError="binomial", seed=1234)
ds2 = pygsti.construction.generate_fake_data(gs_datagen2, listOfExperiments, nSamples=1000,
                                            sampleError="binomial", seed=1234)
ds3 = ds1.copy_nonstatic(); ds3.add_counts_from_dataset(ds2); ds3.done_adding_data()


# In[4]:


#Run GST on all three datasets
gs_target.set_all_parameterizations("TP")
results1 = pygsti.do_long_sequence_gst(ds1, gs_target, fiducials, fiducials, germs, maxLengths, verbosity=0)
results2 = pygsti.do_long_sequence_gst(ds2, gs_target, fiducials, fiducials, germs, maxLengths, verbosity=0)
results3 = pygsti.do_long_sequence_gst(ds3, gs_target, fiducials, fiducials, germs, maxLengths, verbosity=0)

#make some shorthand variable names for later
tgt = results1.estimates['default'].gatesets['target']

ds1 = results1.dataset
ds2 = results2.dataset
ds3 = results3.dataset

gs1 = results1.estimates['default'].gatesets['go0']
gs2 = results2.estimates['default'].gatesets['go0']
gs3 = results3.estimates['default'].gatesets['go0']

gss = results1.gatestring_structs['final']


# #### Now that we have some results, let's create a `Workspace` and make some plots and tables.
# 
# To get tables and plots to display properly, one must run `init_notebook_mode`.  The `connected` argument indicates whether you want to rely on an active internet connection.  If `True`, then resources will be loaded from the web (e.g. a CDN), and if you save a notebook as HTML the file size may be smaller.  If `False`, then all the needed resources (except MathJax) are provided by pyGSTi, and an `offline` directory is automatically created in the same directory as your notebook.  This directory contains all the necessary resources, and must "tag along" with the notebook and any saved-as-HTML versions of it in order for everything to work.  The second argument, `autodisplay`, determines whether tables and plots are automatically displayed when they are created.  If `autodisplay=False`, one must call the `display()` member function of a table or plot to display it. 

# In[5]:


from pygsti.report import workspace
w = workspace.Workspace()
w.init_notebook_mode(connected=False, autodisplay=True) 


# Plots and tables are created via member functions of a `Workspace` (`w` in our case).  Note that you can start typing "`w.`" and TAB-complete to see the different things a `Workspace` can make for you.  Furthermore, pressing SHIFT-TAB after the opening parenthesis of a function,  e.g. after typing "`w.GatesVsTargetTable(`", will bring up Jupyter's help window showing you the function signature (the arguments you need to give the function).
# 
# #### The remainder of this tutorial demonstrates some of the tables and plots you can create. 
# Note that displayed objects have a resize handle in their lower right corner.

# In[6]:


w.ColorBoxPlot(("logl",), gss, ds1, gs1, typ='scatter')
w.ColorBoxPlot(("logl",), gss, ds1, gs1, typ='boxes')
w.ColorBoxPlot(("logl",), gss, ds1, gs1, typ='histogram')


# In[7]:


w.FitComparisonBarPlot(gss.Ls, results1.gatestring_structs['iteration'],
                     results1.estimates['default'].gatesets['iteration estimates'], ds1)


# In[8]:


w.GramMatrixBarPlot(ds1,tgt)


# In[9]:


w.GatesVsTargetTable(gs1, tgt)


# In[10]:


w.SpamVsTargetTable(gs2, tgt)


# In[11]:


w.ColorBoxPlot(("chi2","logl"), gss, ds1, gs1, boxLabels=True)
  #Notice how long it takes to switch between "chi2" and "logl".  This 
  # is due to drawing all of the box labels (boxLabels=True).


# In[12]:


#This one requires knowng that each Results object holds a list of gatesets
# from each GST intation along with the corresponding gate strings that were used.
w.FitComparisonTable(gss.Ls, results1.gatestring_structs['iteration'],
                     results1.estimates['default'].gatesets['iteration estimates'], ds1)


# In[13]:


# We can reuse 'gss' for all three since the gate sequences are the same.
w.FitComparisonTable(["GS1","GS2","GS3"], [gss, gss, gss], [gs1,gs2,gs3], ds1, Xlabel="GateSet")


# In[14]:


w.ChoiTable(gs3, display=('matrix','barplot'))


# In[15]:


w.GateMatrixPlot(gs1['Gx'],scale=1.0, boxLabels=True,ylabel="hello")
w.GateMatrixPlot(pygsti.tools.error_generator(gs1['Gx'], tgt['Gx'], 'pp'), scale=1.5)


# In[16]:


from pygsti.construction import std2Q_XYCNOT
w.GateMatrixPlot(std2Q_XYCNOT.gs_target['Gxi'],scale=1.0, boxLabels=False,ylabel="hello",mxBasis="pp")


# In[17]:


mx = np.array( 
[[ 7.3380823,   8.28446943,  7.4593754,   3.91256384,  0.68631199],
 [ 3.36139818,  7.42955114,  6.78516082,  0.35863173,  5.57713093],
 [ 2.61489939,  3.40182958,  6.77389064,  9.29736475,  0.33824271],
 [ 9.64258149,  9.45928809,  6.91516602,  5.61423854,  0.56480777],
 [ 2.15195669,  9.37588783,  5.1781991,   7.20087591,  1.46096288]], 'd')
cMap = pygsti.report.colormaps.LinlogColormap(vmin=0, vmax=10, n_boxes=25, pcntle=0.55, dof_per_box=1, color='blue')
w.MatrixPlot(mx, colormap=cMap, colorbar=False)


# In[18]:


mx = np.identity(3,'d')
mx[0,1] = 2.1
mx[2,2] = 4.0
mx[2,0] = 3.0
mx[0,2] = 7.0
mx[2,1] = 10.0
mx[0,0] = np.nan
cMap = pygsti.report.colormaps.PiecewiseLinearColormap(
            [[0,(0,0.5,0)],[1,(0,1.0,0)],[2,(1.0,1.0,0)],
             [4,(1.0,0.5,0)],[10,(1.0,0,0)]])
#print(cMap.get_colorscale())
w.MatrixPlot(mx, colormap=cMap, colorbar=False, grid="white:1", boxLabels=True, prec=2,
             xlabels=('TP',"CPTP","full"),ylabels=("DS0","DS1","DS2"))


# In[19]:


w.ErrgenTable(gs3,tgt)


# In[20]:


w.PolarEigenvaluePlot([np.linalg.eigvals(gs2['Gx'])],["purple"],scale=1.5)


# In[21]:


w.GateEigenvalueTable(gs2, display=('evals','polar'))


# In[22]:


w.GateDecompTable(gs1,gs_target)
#w.old_GateDecompTable(gs1) #historical; 1Q only


# In[23]:


#Note 2Q angle decompositions
from pygsti.construction import std2Q_XXYYII
from pygsti.construction import std2Q_XYCNOT

w.GateDecompTable(std2Q_XXYYII.gs_target, std2Q_XXYYII.gs_target)

import scipy
I = np.array([[1,0],[0,1]],'complex')
X = np.array([[0,1],[1,0]],'complex')
Y = np.array([[0,1j],[-1j,0]],'complex')
XX = np.kron(X,X)
YY = np.kron(Y,Y)
IX = np.kron(I,X)
XI = np.kron(X,I)
testU = scipy.linalg.expm(-1j*np.pi/2*XX)
testS = pygsti.unitary_to_process_mx(testU)
testS = pygsti.change_basis(testS,"std","pp")

std2Q_XYCNOT.gs_target.gates['Gtest'] = testS
w.GateDecompTable(std2Q_XYCNOT.gs_target, std2Q_XYCNOT.gs_target)


# In[24]:


dsLabels = ["A","B","C"]
datasets = [ds1, ds2, ds3]
dscmps = {}
for i,ds1 in enumerate(datasets):
    for j,ds2 in enumerate(datasets[i+1:],start=i+1):
        dscmps[(i,j)] = pygsti.objects.DataComparator([datasets[i],datasets[j]])

w.DatasetComparisonSummaryPlot(dsLabels, dscmps)


# In[25]:


w.DatasetComparisonHistogramPlot(dscmps[(1,2)])


# ### Saving figures to file
# You can also save plot and figures to separate files using their `saveas` method.  The output format is determined by the file extension, and allowed extensions are:
# 
# - 'pdf': Adobe portable document format
# - 'tex': LaTeX source (uncompiled, *tables only*)
# - 'pkl': Python pickle (of a pandas `DataFrame` for tables, a dict for plots)
# - 'html': A stand-alone HTML document

# In[26]:


obj = w.GatesVsTargetTable(gs1, tgt)
#obj = w.ErrgenTable(gs3,tgt)
#obj = w.ColorBoxPlot(("logl",), gss, ds1, gs1, typ='boxes')

obj.saveas("tutorial_files/tempTest/testSave.pdf")
obj.saveas("tutorial_files/tempTest/testSave.tex")
obj.saveas("tutorial_files/tempTest/testSave.pkl")
obj.saveas("tutorial_files/tempTest/testSave.html")


# ## Exporting to HTML
# If you want, you can save this notebook as an HTML file by going to **File => Download As => HTML** in the Jupyter menu.  The resulting file will retain all of the plot interactivity, so long as its in a directory with an `offline` folder (because we set `connected=False` above).

# In[ ]: