diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..e931de1
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,72 @@
+### Python ###
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+.DS_Store
+
+
+## Core latex/pdflatex auxiliary files:
+*.aux
+#*.bbl
+*.blg
+*.fdb_latexmk
+*.fls
+*.log
+*Notes.bib
+*.out
+*.synctex.gz
diff --git a/Code/PyROQ/pyroq.py b/Code/PyROQ/pyroq.py
index b07e1e9..46cd516 100644
--- a/Code/PyROQ/pyroq.py
+++ b/Code/PyROQ/pyroq.py
@@ -11,6 +11,8 @@
 import warnings
 import random
 import multiprocessing as mp
+from functools import partial
+from datetime import datetime 
 
 def howmany_within_range(row, minimum, maximum):
     """Returns how many numbers lie within `maximum` and `minimum` in a given `row`"""
@@ -146,11 +148,9 @@ def compute_modulus_quad(paramspoint, known_quad_bases, distance, deltaF, f_min,
 def least_match_waveform_unnormalized(parallel, nprocesses, paramspoints, known_bases, distance, deltaF, f_min, f_max, waveFlags, approximant):
     if parallel == 1:
         paramspointslist = paramspoints.tolist()
-        #pool = mp.Pool(mp.cpu_count())
-        pool = mp.Pool(processes=nprocesses)
-        modula = [pool.apply(compute_modulus, args=(paramspoint, known_bases, distance, deltaF, f_min, f_max, approximant)) for paramspoint in paramspointslist]
-        pool.close()
-    if parallel == 0:
+        with mp.Pool(processes=nprocesses) as pool:
+            modula = pool.map(partial(compute_modulus, known_bases=known_bases, distance=distance, deltaF=deltaF, f_min=f_min, f_max=f_max, approximant=approximant), paramspointslist)
+    else:
         npts = len(paramspoints)
         modula = numpy.zeros(npts)
         for i in numpy.arange(0,npts):
@@ -183,9 +183,8 @@ def least_match_waveform_unnormalized(parallel, nprocesses, paramspoints, known_
 def least_match_quadratic_waveform_unnormalized(parallel, nprocesses, paramspoints, known_quad_bases, distance, deltaF, f_min, f_max, waveFlags, approximant):
     if parallel == 1:
         paramspointslist = paramspoints.tolist()
-        pool = mp.Pool(processes=nprocesses)
-        modula = [pool.apply(compute_modulus_quad, args=(paramspoint, known_quad_bases, distance, deltaF, f_min, f_max, approximant)) for paramspoint in paramspointslist]
-        pool.close()
+        with mp.Pool(processes=nprocesses) as pool:
+            modula = pool.map(partial(compute_modulus_quad, known_quad_bases=known_quad_bases, distance=distance, deltaF=deltaF, f_min=f_min, f_max=f_max, approximant=approximant), paramspointslist)
     if parallel == 0:
         npts = len(paramspoints)
         modula = numpy.zeros(npts)
@@ -220,25 +219,41 @@ def bases_searching_results_unnormalized(parallel, nprocesses, npts, nparams, nb
     if nparams == 10: print("The parameters are Mc, q, s1(mag, theta, phi), s2(mag, theta, phi), iota, and phiRef\n")
     if nparams == 11: print("The parameters are Mc, q, s1(mag, theta, phi), s2(mag, theta, phi), iota, phiRef, and eccentricity\n")
     if nparams == 12: print("The parameters are Mc, q, s1(mag, theta, phi), s2(mag, theta, phi), iota, phiRef, lambda1, and lambda2\n") 
+        
+    t_start = datetime.now()
+    print(str(t_start), "Start linear bases searching...")
     for k in numpy.arange(0,nbases-1):
         paramspoints = generate_params_points(npts, nparams, params_low, params_high)
         basis_new, params_new, rm_new = least_match_waveform_unnormalized(parallel, nprocesses, paramspoints, known_bases, distance, deltaF, f_min, f_max, waveFlags, approximant)
-        print("Linear Iter: ", k+1, "and new basis waveform", params_new)
+        print(str(datetime.now()), "Linear Iter:", k+1, "and new basis waveform", params_new)
         known_bases= numpy.append(known_bases, numpy.array([basis_new]), axis=0)
         params = numpy.append(params, numpy.array([params_new]), axis = 0)
         residual_modula = numpy.append(residual_modula, rm_new)
+        
+    t_end = datetime.now()
+    print(str(t_start), "Finish linear bases searching...")
+    print(f"It takes {(t_end - t_start).total_seconds()} seconds.")
+    
     numpy.save('./linearbases.npy',known_bases)
     numpy.save('./linearbasiswaveformparams.npy',params)
     return known_bases, params, residual_modula
 
 def bases_searching_quadratic_results_unnormalized(parallel, nprocesses, npts, nparams, nbases_quad, known_quad_bases, basis_waveforms, params_quad, residual_modula, params_low, params_high, distance, deltaF, f_min, f_max, waveFlags, approximant):
+    
+    t_start = datetime.now()
+    print(str(t_start), "Start quadratic bases searching...")
     for k in numpy.arange(0,nbases_quad-1):
-        print("Quadratic Iter: ", k+1)
+        print(str(datetime.now()), "Quadratic Iter: ", k+1)
         paramspoints = generate_params_points(npts, nparams, params_low, params_high)
         basis_new, params_new, rm_new= least_match_quadratic_waveform_unnormalized(parallel, nprocesses, paramspoints, known_quad_bases, distance, deltaF, f_min, f_max, waveFlags, approximant)
         known_quad_bases= numpy.append(known_quad_bases, numpy.array([basis_new]), axis=0)
         params_quad = numpy.append(params_quad, numpy.array([params_new]), axis = 0)
         residual_modula = numpy.append(residual_modula, rm_new)
+        
+    t_end = datetime.now()
+    print(str(t_start), "Finish quadratic bases searching...")
+    print(f"It takes {(t_end - t_start).total_seconds()} seconds.")
+    
     numpy.save('./quadraticbases.npy',known_quad_bases)
     numpy.save('./quadraticbasiswaveformparams.npy',params_quad)
     return known_quad_bases, params_quad, residual_modula
diff --git a/Tutorial/.ipynb_checkpoints/PyROQ-tutorial-checkpoint.ipynb b/Tutorial/.ipynb_checkpoints/PyROQ-tutorial-checkpoint.ipynb
index 647a72c..fff962b 100644
--- a/Tutorial/.ipynb_checkpoints/PyROQ-tutorial-checkpoint.ipynb
+++ b/Tutorial/.ipynb_checkpoints/PyROQ-tutorial-checkpoint.ipynb
@@ -2,30 +2,16 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Collecting PyROQ==0.1.25\n",
-      "  Downloading https://files.pythonhosted.org/packages/43/62/95ca4b782154bda1e963890e0891347d7e6f200dc4e073657824ee9a70c4/PyROQ-0.1.25-py3-none-any.whl\n",
-      "Installing collected packages: PyROQ\n",
-      "  Found existing installation: PyROQ 0.1.24\n",
-      "    Uninstalling PyROQ-0.1.24:\n",
-      "      Successfully uninstalled PyROQ-0.1.24\n",
-      "Successfully installed PyROQ-0.1.25\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "!pip install --user PyROQ==0.1.25"
+    "# !pip install --user PyROQ==0.1.25"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -52,6 +38,8 @@
     "pylab.rcParams.update(plot_params)\n",
     "from mpl_toolkits.mplot3d import axes3d\n",
     "#import PyROQ.pyroq as pyroq\n",
+    "import sys\n",
+    "sys.path.insert(0, \"../Code/PyROQ/\")\n",
     "import pyroq as pyroq"
    ]
   },
@@ -64,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -102,13 +90,13 @@
     "approximant = lalsimulation.IMRPhenomPv2\n",
     "print(\"mass-min, mass-max: \", pyroq.massrange(mc_low, mc_high, q_low, q_high))\n",
     "\n",
-    "parallel = 0 # The parallel=1 will turn on multiprocesses to search for a new basis. To turn it off, set it to be 0.\n",
+    "parallel = 1 # The parallel=1 will turn on multiprocesses to search for a new basis. To turn it off, set it to be 0.\n",
     "             # Do not turn it on if the waveform generation is not slow compared to data reading and writing to files.\n",
     "             # This is more useful when each waveform takes larger than 0.01 sec to generate.\n",
     "nprocesses = 4 # Set the number of parallel processes when searching for a new basis.  nprocesses=mp.cpu_count()\n",
     "\n",
     "\n",
-    "nts = 123 # Number of random test waveforms\n",
+    "nts = 10**3 # Number of random test waveforms\n",
     "          # For diagnostics, 1000 is fine.\n",
     "          # For real ROQs calculation, set it to be 1000000.\n",
     "\n",
@@ -133,7 +121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -145,7 +133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -171,7 +159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -180,191 +168,29 @@
      "text": [
       "The parameters are Mc, q, s1(mag, theta, phi), s2(mag, theta, phi), iota, and phiRef\n",
       "\n",
-      "Linear Iter:  1 and new basis waveform [29.614911  1.271912  0.043185  1.272497  5.817337  0.169412  1.082078\n",
-      "  4.820442  3.035298  1.619856]\n",
-      "Linear Iter:  2 and new basis waveform [28.794826  1.213989  0.030177  0.036484  4.08501   0.19912   2.037582\n",
-      "  4.946962  3.080541  0.174687]\n",
-      "Linear Iter:  3 and new basis waveform [20.14874   1.331149  0.129653  1.006368  3.104629  0.198682  0.767833\n",
-      "  0.363031  2.830839  2.114186]\n",
-      "Linear Iter:  4 and new basis waveform [29.910708  1.973347  0.129228  1.580673  4.250203  0.058538  0.664838\n",
-      "  1.704622  0.082262  5.134216]\n",
-      "Linear Iter:  5 and new basis waveform [29.658094  1.722257  0.076688  3.125209  0.544965  0.049523  1.872679\n",
-      "  4.615169  0.249438  5.543772]\n",
-      "Linear Iter:  6 and new basis waveform [25.933673  1.369327  0.129837  0.17734   3.737802  0.131967  0.97688\n",
-      "  4.31931   0.156845  5.905206]\n",
-      "Linear Iter:  7 and new basis waveform [2.5371301e+01 1.0589600e+00 1.9241000e-02 2.0896220e+00 3.1745130e+00\n",
-      " 3.9381000e-02 2.0879000e+00 3.6485400e-01 2.1116100e-01 2.5852670e+00]\n",
-      "Linear Iter:  8 and new basis waveform [2.6166685e+01 1.8170070e+00 9.5021000e-02 9.9594700e-01 5.8333860e+00\n",
-      " 1.4251400e-01 3.1335340e+00 2.9145390e+00 1.4447000e-02 4.9772970e+00]\n",
-      "Linear Iter:  9 and new basis waveform [26.888898  1.422945  0.046333  1.662173  4.979861  0.036355  0.168095\n",
-      "  4.800205  0.476879  0.176604]\n",
-      "Linear Iter:  10 and new basis waveform [23.7879    1.96106   0.066201  0.02941   3.064604  0.082754  1.311234\n",
-      "  4.542395  0.221936  0.618309]\n",
-      "Linear Iter:  11 and new basis waveform [23.761441  1.47384   0.137335  1.081256  4.261831  0.079562  0.258539\n",
-      "  6.149372  0.202257  4.608246]\n",
-      "Linear Iter:  12 and new basis waveform [22.631084  1.599403  0.063175  2.002032  3.890306  0.082409  2.219852\n",
-      "  4.037299  0.07419   2.10976 ]\n",
-      "Linear Iter:  13 and new basis waveform [2.2210135e+01 1.9941440e+00 1.9266000e-02 1.6014060e+00 5.0934560e+00\n",
-      " 6.7234000e-02 3.0655850e+00 5.8534190e+00 2.7845980e+00 2.7005630e+00]\n",
-      "Linear Iter:  14 and new basis waveform [2.215633e+01 1.402031e+00 4.072200e-02 6.716800e-02 4.816329e+00\n",
-      " 1.911800e-02 2.687205e+00 4.609183e+00 7.291000e-02 2.376675e+00]\n",
-      "Linear Iter:  15 and new basis waveform [21.791822  1.297611  0.141381  2.757046  5.688823  0.050672  0.144866\n",
-      "  0.140383  2.915878  1.857038]\n",
-      "Linear Iter:  16 and new basis waveform [20.762337  1.400414  0.05173   1.603689  0.918445  0.062857  2.646583\n",
-      "  2.308411  0.089014  2.819046]\n",
-      "Linear Iter:  17 and new basis waveform [20.663774  1.541349  0.096879  2.620584  2.080292  0.159156  1.057058\n",
-      "  1.079427  2.934078  3.238433]\n",
-      "Linear Iter:  18 and new basis waveform [20.53477   1.377534  0.107647  0.57247   2.166592  0.025403  2.077651\n",
-      "  0.294269  0.143765  2.736921]\n",
-      "Linear Iter:  19 and new basis waveform [20.387409  1.658261  0.076272  0.202989  0.427194  0.057296  0.987188\n",
-      "  1.985975  2.966257  0.695228]\n",
-      "Linear Iter:  20 and new basis waveform [27.801489  1.317552  0.029103  0.946511  0.201473  0.082791  1.959707\n",
-      "  3.785979  0.470216  1.751319]\n",
-      "Linear Iter:  21 and new basis waveform [28.302911  1.120927  0.17306   1.977733  3.747616  0.128648  0.211403\n",
-      "  1.474456  2.37367   2.598043]\n",
-      "Linear Iter:  22 and new basis waveform [2.2330996e+01 1.3405830e+00 1.0075800e-01 9.8808000e-01 6.0362220e+00\n",
-      " 1.6638500e-01 1.9524300e-01 4.2476510e+00 1.0286000e-02 5.0579830e+00]\n",
-      "Linear Iter:  23 and new basis waveform [21.368399  1.824337  0.151201  1.144609  4.364405  0.171214  1.584195\n",
-      "  1.69361   2.830274  0.15041 ]\n",
-      "Linear Iter:  24 and new basis waveform [29.932828  1.286292  0.125723  2.631496  3.452425  0.127014  1.306762\n",
-      "  1.569442  2.447714  3.012377]\n",
-      "Linear Iter:  25 and new basis waveform [29.700022  1.767737  0.156239  1.507584  5.667798  0.168199  1.688496\n",
-      "  5.182115  2.876674  1.233746]\n",
-      "Linear Iter:  26 and new basis waveform [28.650595  1.918791  0.1833    2.489977  1.571073  0.072298  2.210506\n",
-      "  5.094309  1.163854  4.572117]\n",
-      "Linear Iter:  27 and new basis waveform [29.804079  1.915049  0.13567   2.004957  3.609306  0.198298  2.347696\n",
-      "  2.337589  1.95077   2.863374]\n",
-      "Linear Iter:  28 and new basis waveform [24.711804  1.310025  0.111135  0.207389  3.660623  0.177372  1.668351\n",
-      "  0.108415  0.093689  2.383488]\n",
-      "Linear Iter:  29 and new basis waveform [24.119197  1.948967  0.141682  2.945474  6.11302   0.126101  0.337582\n",
-      "  1.109053  2.787679  2.286557]\n",
-      "Linear Iter:  30 and new basis waveform [2.024197e+01 1.273233e+00 1.575540e-01 2.704800e-02 2.231000e-03\n",
-      " 9.288000e-02 4.696850e-01 6.254000e-01 3.139670e-01 4.524988e+00]\n",
-      "Linear Iter:  31 and new basis waveform [20.316326  1.073746  0.060565  1.788856  3.864748  0.177481  0.689781\n",
-      "  5.448775  2.639271  0.81441 ]\n",
-      "Linear Iter:  32 and new basis waveform [21.015457  1.787761  0.091573  3.062577  0.298003  0.072127  0.320966\n",
-      "  2.680568  2.93094   3.499225]\n",
-      "Linear Iter:  33 and new basis waveform [2.7897861e+01 1.9418270e+00 1.9171400e-01 2.5627900e+00 4.0604130e+00\n",
-      " 2.3183000e-02 2.2713980e+00 2.4766750e+00 4.0524300e-01 1.8361340e+00]\n",
-      "Linear Iter:  34 and new basis waveform [27.819236  1.018167  0.141252  0.647847  4.587993  0.035203  1.047697\n",
-      "  0.665082  2.808607  4.587717]\n",
-      "Linear Iter:  35 and new basis waveform [2.1875173e+01 1.7038700e+00 1.1362000e-02 2.6328040e+00 5.9863060e+00\n",
-      " 4.1900000e-02 3.0184360e+00 6.0708550e+00 4.5405500e-01 2.1484500e+00]\n",
-      "Linear Iter:  36 and new basis waveform [28.865314  1.802096  0.138793  2.474724  4.831972  0.155063  2.905339\n",
-      "  4.185608  2.418633  5.966491]\n",
-      "Linear Iter:  37 and new basis waveform [2.8145238e+01 1.7878930e+00 1.1952200e-01 3.9548200e-01 5.0454600e-01\n",
-      " 7.1010000e-03 2.9762370e+00 3.6539090e+00 3.6717000e-01 5.4325110e+00]\n",
-      "Linear Iter:  38 and new basis waveform [20.617458  1.230068  0.198063  0.17304   3.629282  0.081376  0.744234\n",
-      "  0.508731  3.008019  3.916883]\n",
-      "Linear Iter:  39 and new basis waveform [21.544953  1.114772  0.097155  0.342667  5.587778  0.193336  0.037187\n",
-      "  4.690346  2.259814  2.000502]\n",
-      "Linear Iter:  40 and new basis waveform [28.842905  1.916756  0.185234  2.868286  0.67479   0.063576  0.632067\n",
-      "  1.521568  0.672443  1.668159]\n",
-      "Linear Iter:  41 and new basis waveform [2.5397377e+01 1.4512290e+00 1.6053100e-01 2.5634140e+00 4.4048740e+00\n",
-      " 4.0140000e-02 1.6134710e+00 3.9218590e+00 2.8429830e+00 1.0332000e-02]\n",
-      "Linear Iter:  42 and new basis waveform [20.528895  1.287704  0.195544  0.258964  1.492667  0.037216  1.309022\n",
-      "  0.629773  0.045699  1.168751]\n",
-      "Linear Iter:  43 and new basis waveform [2.0621479e+01 1.0160130e+00 1.7110000e-02 1.8855270e+00 4.2207240e+00\n",
-      " 1.9873100e-01 6.8905800e-01 4.7115800e-01 2.8302030e+00 4.9707300e+00]\n",
-      "Linear Iter:  44 and new basis waveform [29.470985  1.363406  0.170084  0.349959  5.66966   0.194841  0.441959\n",
-      "  3.823785  2.931965  3.142751]\n",
-      "Linear Iter:  45 and new basis waveform [29.425984  1.936035  0.094217  1.905204  1.332125  0.068538  2.320147\n",
-      "  3.226821  0.703619  3.839742]\n",
-      "Linear Iter:  46 and new basis waveform [29.99608   1.146721  0.195942  2.757396  4.548558  0.141368  2.652828\n",
-      "  4.486381  2.713309  1.558506]\n",
-      "Linear Iter:  47 and new basis waveform [29.849579  1.956373  0.136239  1.954649  2.826876  0.126958  2.673773\n",
-      "  4.507313  1.853212  1.058977]\n",
-      "Linear Iter:  48 and new basis waveform [25.059997  1.653962  0.118514  2.051447  5.461904  0.047332  0.47721\n",
-      "  6.084408  0.279778  0.291678]\n",
-      "Linear Iter:  49 and new basis waveform [20.004542  1.84635   0.169199  0.280811  4.846262  0.196586  0.64275\n",
-      "  3.763326  0.591268  5.034428]\n",
-      "Linear Iter:  50 and new basis waveform [26.601217  1.689996  0.169325  0.612038  3.330823  0.195379  0.820279\n",
-      "  2.07791   0.406306  1.130659]\n",
-      "Linear Iter:  51 and new basis waveform [23.283288  1.373404  0.159538  1.412339  0.94686   0.177526  0.371503\n",
-      "  1.635211  2.690497  0.113873]\n",
-      "Linear Iter:  52 and new basis waveform [20.414261  1.047228  0.082227  1.863195  0.598306  0.139516  2.699696\n",
-      "  5.659721  0.255706  4.183571]\n",
-      "Linear Iter:  53 and new basis waveform [29.385473  1.232548  0.117339  2.004748  6.16897   0.083661  2.944266\n",
-      "  2.472091  2.726867  1.521591]\n",
-      "Linear Iter:  54 and new basis waveform [2.7106804e+01 1.9403970e+00 1.0797000e-02 5.3774900e-01 4.3312090e+00\n",
-      " 1.0415800e-01 1.4509860e+00 9.0660700e-01 3.5252000e-02 3.2915720e+00]\n",
-      "Linear Iter:  55 and new basis waveform [23.449841  1.88626   0.159362  0.026026  1.031569  0.049043  1.201658\n",
-      "  3.238452  1.804717  6.049915]\n",
-      "Linear Iter:  56 and new basis waveform [23.895972  1.129434  0.160924  0.400013  0.271521  0.141672  2.85598\n",
-      "  5.432075  0.72699   2.123649]\n",
-      "Linear Iter:  57 and new basis waveform [26.447241  1.701449  0.119315  0.53021   3.084354  0.060009  0.109993\n",
-      "  1.350889  3.025911  4.862251]\n",
-      "Linear Iter:  58 and new basis waveform [29.961618  1.61944   0.061171  0.664055  1.6921    0.035021  1.762734\n",
-      "  6.025414  2.511266  6.13675 ]\n",
-      "Linear Iter:  59 and new basis waveform [29.763013  1.867919  0.195806  2.547849  1.809466  0.094426  0.371602\n",
-      "  2.44858   2.255239  1.661905]\n",
-      "Linear Iter:  60 and new basis waveform [23.566741  1.080898  0.172064  2.212517  3.456362  0.11491   2.644624\n",
-      "  3.754526  0.165104  3.490022]\n",
-      "Linear Iter:  61 and new basis waveform [2.7144287e+01 1.1102460e+00 1.6649000e-01 1.6067790e+00 1.3483570e+00\n",
-      " 1.8102200e-01 2.3487000e-02 4.8957000e-01 3.1275910e+00 5.0136700e-01]\n",
-      "Linear Iter:  62 and new basis waveform [20.581393  1.149782  0.196952  2.841824  2.820959  0.199901  2.427662\n",
-      "  2.315828  0.073093  3.478905]\n",
-      "Linear Iter:  63 and new basis waveform [24.482132  1.003181  0.047135  3.024657  0.064354  0.077192  2.292529\n",
-      "  3.964986  0.063723  1.72104 ]\n",
-      "Linear Iter:  64 and new basis waveform [29.955788  1.819837  0.045478  2.344409  0.99733   0.133571  2.787342\n",
-      "  3.125261  0.281854  4.8429  ]\n",
-      "Linear Iter:  65 and new basis waveform [20.320629  1.541106  0.177981  0.546076  2.962742  0.140417  0.305524\n",
-      "  0.550902  0.731668  2.869013]\n",
-      "Linear Iter:  66 and new basis waveform [21.321295  1.996709  0.12148   2.701959  6.160019  0.104015  2.552771\n",
-      "  1.181904  2.931763  0.749447]\n",
-      "Linear Iter:  67 and new basis waveform [24.009618  1.741323  0.129833  1.837983  1.37641   0.161777  2.803454\n",
-      "  3.865504  2.50491   0.373744]\n",
-      "Linear Iter:  68 and new basis waveform [2.0476638e+01 1.9785550e+00 1.3158400e-01 8.9513200e-01 4.9179020e+00\n",
-      " 9.2760000e-03 4.5650500e-01 2.8339800e+00 1.8408700e-01 3.6647900e-01]\n",
-      "Linear Iter:  69 and new basis waveform [29.31246   1.197368  0.154271  3.055387  0.397884  0.149273  0.32189\n",
-      "  4.626799  0.038651  3.625417]\n",
-      "Linear Iter:  70 and new basis waveform [20.250456  1.120644  0.180746  0.071889  0.109174  0.148804  0.232857\n",
-      "  1.449483  0.131936  3.587055]\n",
-      "Linear Iter:  71 and new basis waveform [29.167318  1.976668  0.154848  2.912794  0.183426  0.181321  1.884295\n",
-      "  2.613816  0.117377  2.290952]\n",
-      "Linear Iter:  72 and new basis waveform [22.965313  1.659574  0.175184  0.508376  5.057768  0.161299  0.72649\n",
-      "  4.086714  2.830375  6.239801]\n",
-      "Linear Iter:  73 and new basis waveform [21.836824  1.998858  0.171667  0.023845  4.053513  0.070337  0.18335\n",
-      "  2.879539  0.205551  2.073151]\n",
-      "Linear Iter:  74 and new basis waveform [29.969617  1.530517  0.156111  1.942404  0.588585  0.176322  2.654416\n",
-      "  0.641167  0.341472  4.178086]\n",
-      "Linear Iter:  75 and new basis waveform [21.605871  1.186649  0.030389  1.204275  0.647993  0.067065  0.443754\n",
-      "  4.999607  0.035473  2.336399]\n",
-      "Linear Iter:  76 and new basis waveform [26.425885  1.742774  0.179428  3.013828  2.387662  0.129166  1.988722\n",
-      "  1.846013  2.27757   4.157913]\n",
-      "Linear Iter:  77 and new basis waveform [29.19146   1.674189  0.198357  3.101583  0.544042  0.148913  3.089837\n",
-      "  1.31994   3.091417  1.128466]\n",
-      "Linear Iter:  78 and new basis waveform [2.1783066e+01 1.1570260e+00 3.1320000e-03 2.9078070e+00 5.2949620e+00\n",
-      " 1.8547300e-01 7.1412600e-01 3.5693410e+00 2.4540670e+00 6.1131630e+00]\n",
-      "Linear Iter:  79 and new basis waveform [25.483844  1.861167  0.119814  2.620264  2.121999  0.123393  2.573555\n",
-      "  2.595053  2.838931  0.678256]\n",
-      "(80, 4016) [0.00000000e+00+0.j 2.62993621e-20+0.j 4.74354398e-20+0.j\n",
-      " 2.77056026e-20+0.j 2.38192913e-20+0.j 3.37741145e-20+0.j\n",
-      " 2.33770921e-20+0.j 4.30929881e-20+0.j 2.24690708e-20+0.j\n",
-      " 3.83540269e-20+0.j 2.06726898e-20+0.j 2.94115966e-20+0.j\n",
-      " 2.57125961e-20+0.j 2.80480925e-20+0.j 2.00185173e-20+0.j\n",
-      " 3.48851092e-20+0.j 1.93038289e-20+0.j 3.52089162e-20+0.j\n",
-      " 2.23080182e-20+0.j 2.53083619e-20+0.j 6.81638462e-21+0.j\n",
-      " 9.19208009e-21+0.j 3.49069029e-21+0.j 6.20975536e-21+0.j\n",
-      " 1.44368454e-21+0.j 4.77688533e-21+0.j 9.66657537e-22+0.j\n",
-      " 1.24382872e-21+0.j 1.15003799e-21+0.j 8.68841655e-22+0.j\n",
-      " 5.32406174e-21+0.j 1.02691868e-21+0.j 9.29258341e-22+0.j\n",
-      " 7.16618843e-22+0.j 4.37075269e-22+0.j 1.42081641e-22+0.j\n",
-      " 1.32208597e-22+0.j 1.32090172e-22+0.j 1.67408810e-22+0.j\n",
-      " 2.16316191e-22+0.j 7.06409463e-23+0.j 9.09627801e-23+0.j\n",
-      " 3.75334945e-23+0.j 6.28505507e-23+0.j 6.68207247e-23+0.j\n",
-      " 3.15356492e-23+0.j 4.57920272e-23+0.j 2.55161030e-23+0.j\n",
-      " 2.75960808e-23+0.j 1.37419009e-22+0.j 1.76187489e-23+0.j\n",
-      " 2.42499326e-23+0.j 1.58674025e-23+0.j 1.09175128e-23+0.j\n",
-      " 1.29066648e-23+0.j 8.90265906e-24+0.j 7.81525620e-24+0.j\n",
-      " 6.05759354e-24+0.j 6.43153081e-24+0.j 8.80506447e-24+0.j\n",
-      " 6.26801377e-24+0.j 4.19516783e-24+0.j 9.70590797e-24+0.j\n",
-      " 4.70082148e-24+0.j 4.56118798e-24+0.j 6.54353052e-24+0.j\n",
-      " 3.97222091e-24+0.j 3.48067615e-24+0.j 4.64517719e-24+0.j\n",
-      " 3.37081214e-24+0.j 7.11450695e-24+0.j 4.42313737e-24+0.j\n",
-      " 4.37156160e-24+0.j 2.95274435e-24+0.j 2.99114226e-24+0.j\n",
-      " 3.09552467e-24+0.j 2.15512355e-24+0.j 3.17386728e-24+0.j\n",
-      " 2.41738031e-24+0.j 2.18747898e-24+0.j]\n"
+      "2019-11-18 11:56:46.752123 Start linear bases searching...\n",
+      "2019-11-18 11:56:47.015183 Linear Iter: 1 and new basis waveform [29.71156   1.744159  0.147363  2.27577   4.964986  0.175048  1.095642\n",
+      "  4.452341  3.047921  5.45821 ]\n",
+      "2019-11-18 11:56:47.294294 Linear Iter: 2 and new basis waveform [29.816605  1.734986  0.071557  3.057311  4.730095  0.101606  0.469418\n",
+      "  0.85915   0.238686  6.101951]\n",
+      "2019-11-18 11:56:47.581598 Linear Iter: 3 and new basis waveform [2.6260027e+01 1.8419590e+00 1.8335500e-01 5.3645000e-02 2.1918100e-01\n",
+      " 2.1851000e-02 1.3289460e+00 1.0602100e+00 3.1301860e+00 2.7229680e+00]\n",
+      "2019-11-18 11:56:47.864843 Linear Iter: 4 and new basis waveform [25.872574  1.277741  0.070184  1.578235  3.616557  0.062608  2.615309\n",
+      "  1.022258  0.184225  2.318501]\n",
+      "2019-11-18 11:56:48.154640 Linear Iter: 5 and new basis waveform [20.065286  1.560627  0.086849  1.948623  5.165675  0.116372  0.65928\n",
+      "  0.669824  2.945945  4.972504]\n",
+      "2019-11-18 11:56:48.449108 Linear Iter: 6 and new basis waveform [2.8584572e+01 1.5486710e+00 2.4114000e-02 2.5818700e-01 1.3930610e+00\n",
+      " 1.1850300e-01 2.4508100e-01 3.6655950e+00 3.0651310e+00 3.3319140e+00]\n",
+      "2019-11-18 11:56:48.755869 Linear Iter: 7 and new basis waveform [29.247984  1.279516  0.16987   0.499811  1.719289  0.101323  0.731098\n",
+      "  4.37761   0.65088   5.373389]\n",
+      "2019-11-18 11:56:49.062899 Linear Iter: 8 and new basis waveform [23.944165  1.154245  0.195244  3.091338  4.365975  0.025967  1.404601\n",
+      "  2.555551  3.056567  2.51117 ]\n",
+      "2019-11-18 11:56:49.366037 Linear Iter: 9 and new basis waveform [24.875017  1.252381  0.110762  0.596374  4.882903  0.1657    1.981505\n",
+      "  2.855088  2.303904  1.750223]\n",
+      "2019-11-18 11:56:49.698160 Linear Iter: 10 and new basis waveform [2.323202e+01 1.224434e+00 1.279720e-01 2.151380e-01 1.490311e+00\n",
+      " 1.205900e-02 1.970279e+00 1.398870e+00 3.053968e+00 1.865143e+00]\n",
+      "2019-11-18 11:56:50.038313 Linear Iter: 11 and new basis waveform [22.362519  1.548708  0.162553  1.696655  5.274348  0.114412  3.083495\n",
+      "  2.867717  0.376455  3.573466]\n"
      ]
     }
    ],
@@ -378,18 +204,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "40 basis elements gave 0 bad points of surrogate error >  1e-08\n",
-      "Number of linear basis elements is  40 and the linear ROQ data are saved in B_linear.npy\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "known_bases = numpy.load('./linearbases.npy')\n",
     "pyroq.roqs(tolerance, freq, ndimlow, ndimhigh, ndimstepsize, known_bases, nts, nparams, params_low, params_high, distance, deltaF, f_min, f_max, waveFlags, approximant)\n"
@@ -397,38 +214,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[ 1.00000000e+00+6.82121026e-13j  9.09494702e-13+1.45519152e-11j\n",
-      "  -1.45519152e-11+7.27595761e-12j ...  2.54658516e-11-9.09494702e-12j\n",
-      "  -2.52384780e-11-2.72848411e-11j  8.18545232e-12+4.00177669e-11j]\n",
-      " [ 1.36424205e-12+2.16004992e-12j  1.00000000e+00+5.45696821e-12j\n",
-      "  -9.09494702e-12-9.09494702e-12j ... -9.09494702e-12-2.54658516e-11j\n",
-      "  -1.45519152e-11+3.45607987e-11j  1.90993887e-11-1.45519152e-11j]\n",
-      " [-1.36424205e-12-1.36424205e-12j  5.45696821e-12+3.63797881e-12j\n",
-      "   1.00000000e+00+1.27329258e-11j ...  4.72937245e-11-1.09139364e-11j\n",
-      "  -3.09228199e-11-4.00177669e-11j  7.27595761e-12+3.84261511e-11j]\n",
-      " ...\n",
-      " [ 0.00000000e+00+0.00000000e+00j  0.00000000e+00+0.00000000e+00j\n",
-      "   0.00000000e+00+0.00000000e+00j ...  0.00000000e+00+0.00000000e+00j\n",
-      "   0.00000000e+00+0.00000000e+00j  0.00000000e+00+0.00000000e+00j]\n",
-      " [ 0.00000000e+00+0.00000000e+00j  0.00000000e+00+0.00000000e+00j\n",
-      "   0.00000000e+00+0.00000000e+00j ...  0.00000000e+00+0.00000000e+00j\n",
-      "   0.00000000e+00+0.00000000e+00j  0.00000000e+00+0.00000000e+00j]\n",
-      " [ 0.00000000e+00+0.00000000e+00j  0.00000000e+00+0.00000000e+00j\n",
-      "   0.00000000e+00+0.00000000e+00j ...  0.00000000e+00+0.00000000e+00j\n",
-      "   0.00000000e+00+0.00000000e+00j  0.00000000e+00+0.00000000e+00j]]\n",
-      "emp_nodes [   0    1    2    5    7   10   13   18   25   33   43   52   62   85\n",
-      "  122  142  166  183  203  219  245  358  537  696  755  795  829  858\n",
-      "  897  936  984 1019 1066 1137 1182 1217 1298 1391 1511 1663]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "fnodes_linear = numpy.load('./fnodes_linear.npy')\n",
     "b_linear = numpy.transpose(numpy.load('./B_linear.npy'))\n",
@@ -441,22 +229,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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atNr+njTXfWZFRERERKRImBmPPvpoocNoMFdeeSU9e/bEzJg6dWrGMS3tZ97RlMyKiIiIiMg2ysrKMDPMjJKSEvr27cuZZ56Z7D7b0hx99NGUlZU1yL1effVVbrrpJu677z7Wrl3LD3/4wwa5r6RSMisiIiIiIhkdfvjhrF27lvfff58ZM2bwxhtv8P3vf7/QYTWoyspKGnrp5bvvvkssFuOkk06iV69eDdoErKysjAkTJjTY/ZozJbMiIiIiIpJRmzZt6NWrF3369OFb3/oW55xzDi+//DKbNm1KGTdlyhT22msv2rVrx9e+9jVuuOEGqqqqku/PmDGDgw8+mC5dutCtWzdGjhzJsmXL6hTL1KlTadWqFbNnz2bQoEG0a9eOgw46iNdffz055pNPPuGMM85gjz32oH379gwYMIDbb789JVktKyvj6KOPZsqUKZSWltK2bVu+//3v8/zzzzNt2rRkNXrOnDlZY5k2bRp77703bdu2pW/fvlx99dXJn7esrIzRo0cTj8eT96rNpk2bGD16NJ06dWL33Xdn0qRJdfpeilmT7WYsIiIiItISXfeXxbz9wabcAxvY3rt15trvDKr39R988AFPPPEEJSUllJSUJM9PmDCBhx9+mDvuuIP999+fJUuWcO6557J161auv/56AMrLyxk/fjwDBw5k06ZNXHvttYwcOZLFixfTpk2bvGOIx+OMGzeO3/3ud+yyyy5ceeWVjBw5kuXLl7PTTjtRXl7OvvvuyyWXXMIuu+zCSy+9xLnnnkvXrl0566yzkveZP38+nTp14qmnnqKkpIQ99tiD9evX07t3b+68804AunbtmjGGmTNn8pOf/ISJEydy6qmn8sYbb3DuuediZlx//fXceeedHHDAAVx66aWsXr0658903XXXMXHiRCZMmMDMmTO56KKLGDJkCEcccUTe30uxUjIrIiIiIiIZzZkzh44dOxKPx9myZQsAl156KR06dABg8+bNTJo0iSeffJJjjz0WgP79+zNx4kQuvPDCZDIbTSQhqLLuuuuuLFiwgMMOOyzveNydW2+9lWHDhgEwffp0dt99d2bMmMGYMWPo1asXV1xxRXJ8//79WbBgATNmzEiJIRaLMX36dDp27Jg816ZNG9q3b0+vXr1qjeHmm2/m1FNP5Ve/+hUAX//611m3bh2//OUvGT9+PF26dKFLly4AOe8F8MMf/pCzzz4bgAsvvJDf/e53/P3vf1cymwclsyIiIiIiO9D2VEd3tIMPPphp06axdetW/vjHPzJr1qxkggqwePFitmzZwqmnnpoynba6upqtW7eyYcMGunfvzqJFi7juuutYtGgRGzduTE77XbVqVZ2SWYBDDz00+XqXXXZh4MCBvP3220BQuZ00aRKPPfYYq1evZuvWrVRWVtKvX7+UewwcODAlka2LxYsXb9PQadiwYWzdupXly5fXeSum/fffP+W4T58+fPjhh8njc889N6XjcXl5OWbGbbfdljx37733cvrpp9fpc1sCJbMiIiIiIpJR+/bt2XPPPQHYZ599WLZsGeeffz4PPfQQECSPAI8//jhf//rXt7m+a9eubN68mREjRjB06FAeeuihZLVy0KBBVFRUbHeM0fWwt99+OzfddBO/+c1v+K//+i86derE5MmTmTlzZso1icpyfaWvg03EkGt9bCbp06zNLPm9Avz617/msssuSx5fccUV9OnThwsvvDB5rmfPnnX+3JZAyaw0Saf+fh4H7L4zV5+wd6FDEREREZHQhAkTGDRoEOeddx6DBw9ONmJ67733OP744zNes2TJEjZs2MANN9yQrFrOmzev3h2EX3nlFY488kgAPv30U5YuXcrYsWMBmDt3Lsceeyw//elPk+PffffdvO7bpk0bqqurc44bNGgQL7zwAueff37y3Ny5c2nfvj1f+cpX6vKj5KVHjx706NEjedypUye6du2a/EeGYqZuxtIkvbbqEx54cUWhwxARERGRiL322osTTjghuV60Y8eOXHnllVx55ZXcddddvPPOOyxevJjHHnssuXa1X79+tG3blilTprB8+XKef/55LrroonpVMc2McePGMXfuXN566y3OPPNMOnTowGmnnQbAgAEDmDNnDv/85z9ZtmwZV199Na+++mpe9+7fvz+vvfYay5cvZ+PGjVRWVmYc96tf/Yo//elP3HzzzSxbtow//vGPTJgwgUsvvbROzaxk+ymZFRERERGRvI0bN47Zs2fz/PPPAzB+/HgmT57MAw88wH777cfQoUOZPHkypaWlAHTr1o1HH32UWbNmMWjQIC677DJuu+02YrG6pyKxWIwbb7yRsWPHMnjwYNauXcvMmTOT04bHjx/PsGHDOOmkkzj00EP55JNPUqbj1ubSSy+lW7du7LfffnTv3p2XXnop47jjjz+ehx56iGnTprHPPvtw8cUXc95553HttdfW+eeR7WMNvUGwNJzBgwf7woULCx1GQZT+MljXsPLmkQWORERERKT+lixZUueGQJLZ1KlTGTNmTMr+tdIy1Pb3xMxec/fBmd5TZVZERERERESaHSWzIiIiIiIi0uwomRURERERkSavrKxMU4wlhZJZERERERERaXaUzIqIiIiIiEizo2RWREREREREmh0lsyIiIiIiItLsKJkVERERERGRZkfJrIiIiIiIiDQ7SmZFRERERKTJGz58OGPGjCl0GC3GW2+9xUEHHUS7du0oLS3NOGbChAnsueeeOzawOmhV6ABERERERKTpKSsrY/Xq1cyePbvQoQDw5JNP0qpV8aYvL774IocffjgrVqzImnzWxbhx4+jcuTNLly6lQ4cO2x9gAagyKyIiIiIiTV7Xrl3p3Llzo39ORUVFo39GXTVGTO+++y7Dhg2jtLSU7t27N9h9V65ciZk12P1qo2RWRERERERyKisr4+ijj2bKlCn07duXjh07MmbMGCorK7nnnnvo168fu+yyC+ecc05K8jVr1iyGDx9O165d6dKlC8OGDWP+/Pkp916xYgUjRoygXbt27LHHHtx9993bTCvOdnz99dfTq1cvunbtSllZGV9++WVyzOuvv85xxx1Hjx496NixI0OGDOG5555L+ezS0lKuvvpqzjvvPHbddVcOO+wwfvzjHzNixIhtvoMjjjiCsrKyrN9RaWkpV111FWPGjKFz585069aNK664gng8nhwzY8YMDj74YLp06UK3bt0YOXIky5YtS76fSAb/8Ic/cPzxx9OhQwdOO+00Dj/8cAD69++PmTF8+PCscaxdu5ZRo0ax88470759e4YPH87ChQtT7r98+XKuueYazIwJEyZkvRfA008/zV577UWHDh044ogjWL58ea3jdxQlsyIiIiIikpcFCxawcOFCZs2axYwZM3j00Uc56aSTmDdvHs8++yzTp09n+vTpPPjgg8lrvvjiC84//3xeeeUV5s2bx9e+9jWOPfZYPvroIwDcnVNOOYXPPvuMuXPn8swzzzBz5kzeeOONnPE88cQTfPzxx8yZM4cZM2bw1FNPMWnSpOT7mzZtYtSoUcyZM4fXX3+dY445hhNPPDEleQT47W9/S48ePXj55ZeZNm0a5557LrNnz2bFihXJMcuXL+eFF17g7LPPrjWmKVOmsNtuu7FgwQImT57MXXfdxR133JF8v7y8nPHjx/P6668za9YsSkpKGDly5DbV1yuuuILTTjuNt956i1tuuYWnn34agPnz57N27VqefPLJjJ/v7px88sksXbqUv/71r8yfP5+ePXvy7W9/m40bN7L77ruzdu1a+vbtyxVXXMHatWu57LLLsv48a9eu5fe//z1/+MMfmDdvHp9++ik/+clPav0OdpTinXQuIiIiIlIIz/4S1r214z+3175w3M3bdYu2bdty//3306ZNGwYOHMhRRx3Fq6++ypo1a2jbti177703I0aM4Pnnn+dnP/sZAKecckrKPe677z7+9Kc/8dxzz3H66acze/Zs3nzzTd59991ks6FHH32Uvn375oxnjz32YPLkyQDstddejBo1ir///e9cd911ANtULydOnMhf/vIXHn/8ca666qrk+SFDhmxTndxnn3148MEHmThxIgAPPPAAAwcO5LDDDqs1pv32249f//rXAAwYMIAlS5bwm9/8hksuuQSAs846K2X81KlT2XXXXVmwYEHKvceOHcsZZ5yRPF67di0A3bt3p1evXlk//x//+Afz589n8eLF7L333gA88sgjlJaW8rvf/Y5rrrmGXr16UVJSQseOHWu9FwTJ9/Tp05NTkRNJ9tatW2nXrl2t1zY2VWZFRERERCQvAwcOpE2bNsnjXr16MWDAANq2bZtybv369cnjFStWMHr0aPbcc086d+5M586d+eyzz1i1ahUAb7/9Nt26dUvpmtu1a1cGDBiQM579998/5bhPnz58+OGHyeMNGzZw3nnnsddee7HzzjvTsWNHFi9enPzshIMOOmibe48dO5aHH36Y6upqqqqqmDp1as6qLMChhx6acnzYYYexZs0aNm3aBMCiRYs45ZRT6N+/P506dWKPPfYAyCumfCxevJhdd901mchC8I8QBx98MIsXL67z/XbbbbeUNbV9+vTB3ZO/4/fff5+OHTsmH4MGDQLIeK6hqTIrIiIiIrIjbWd1tJBat26dcmxmGc9F14iecMIJdOvWjbvvvpvdd9+dNm3aMHTo0JRptfVtGBRNrDN9dllZGe+//z6TJk2if//+tG/fnlGjRm0zpTdTN9/Ro0dzxRVXMHPmTOLxOJ988glnnnlmnWN09+TrzZs3M2LECIYOHcpDDz2UrIoOGjQor5jylen7dPd6fc+ZvmMg+T3vtttuLFq0KPn+mjVrGD58eMq59D8jDUXJrIiIiIiINIqPPvqIt99+m7/97W8cc8wxAKxevTqlcrv33nuzYcMG/vOf/ySrs5988gnLli3jwAMP3K7Pnzt3LpMmTeLEE08E4Msvv+S9995jn332yXlt586dGTVqFPfffz/xeJxTTz2Vrl275rzulVdeSTl++eWX2W233ejcuTOvvfYaGzZs4IYbbmDgwIEAzJs3LyXhzSaRVFZXV9c6btCgQWzcuJG33347WZ0tLy9n/vz5nHfeeTk/p65atWqVUlVPbJ+0I/an1TRjaXLy+cssIiIiIk3fLrvsQvfu3bn//vtZtmwZL7/8Mj/60Y9o3759cszRRx/Nfvvtx5lnnsmCBQt48803GT16NK1atdruLV4GDBjAH/7wB9566y0WLVrEj370o5zJYNTYsWN59tln+Z//+R/OOeecvK5ZtGgREyZMYNmyZcyYMYM777yTiy++GIB+/frRtm1bpkyZwvLly3n++ee56KKL8vo5+/XrRywW429/+xvr16/ns88+yzjuyCOP5KCDDuK0007jpZde4t///jdnnnkmW7duTa5jbimUzEqTo1xWREREpGWIxWI8/vjjLF++nG984xuUlZXxi1/8gt69eyfHmBl//vOf6dChA4cffjgnnHACxx13HAMGDNjuBkMPP/ww8Xicgw46iJNPPpljjz2WIUOG5H39kCFD2HffffnqV7/KsGHD8rrmggsuYNWqVQwePJif//zn/OxnP0sms926dePRRx9l1qxZDBo0iMsuu4zbbruNWCx3WtazZ09uuukmbr75Znr37s1JJ52UcZyZ8dRTT7HXXnsxcuRIhgwZwrp165g1axbdunXL+2dvDkxVsKZr8ODBntgPqphUx52vXvk3AFbePLLA0YiIiIjU35IlS5LTSSV/n3/+OX379mXixIlccMEFBYujqqqKfv36cckll3DppZfmHF9aWsqYMWO4+uqrd0B0LUdtf0/M7DV3H5zpPa2ZlSYnrn9gERERESkqzzzzDK1atWLgwIGsX7+e6667DjPjBz/4QUHiicfjrF+/nnvvvZcvvviCMWPGFCQOqZ2SWWlylMyKiIiIFJfNmzfz61//mpUrV9KhQwcOPPBAXnzxRXr27FmQeN5//3369+9P7969efjhh+nSpUtB4pDaKZmVJke5rIiIiEhxGTVqFKNGjSp0GEmlpaX1akq6cuXKhg9GslIDKGlylMyKiIiIiEguSmalyYlOM1aDMhERERERyUTJrA9Mt2UAACAASURBVDQ50fQ1rlxWREREmrl4PF7oEESarO35+6FkVpqcaGVWzaBERESkOevQoQNr1qyhoqJCM85EItydiooK1qxZQ4cOHep1DzWAkiYn+r/zSmZFRESkOevbty8bN25k1apVVFVVFTockSalVatWdOnShW7dutXv+gaOR2S7ecqa2QIGIiIiIrKdYrEYPXr0oEePHoUORaTF0TRjaXLiqsyKiIiIiEgOSmalyVFlVkREREREclEyK02OKrMiIiIiIpKLkllpcjylm3EBAxERERERkSZLyaw0OdH8VS3sRUREREQkEyWz0uTEVZkVEREREZEclMxKk6M1syIiIiIikouSWWlyUtfMKpkVEREREZFtKZmVJieavyqXFRERERGRTJTMSpOjZFZERERERHJRMitNTlzTjEVEREREJIcmncya2fFmtsjMys1spZldksc1rc1skpmtNbMtZvaimR2YYdw4M1tlZlvN7A0zG5FhTJmZvRN+/lIzO72uMZrZMWb2spltDD9ruZlNNLM2df0+ioWSWRERERERyaXJJrNmNhh4GngO2B+YANxoZufmuPRW4KfAWGAI8B4w28x6Re79C+A6YDxwADAL+IuZfSMy5mTgQeAeYD/gfuARMzuujjFuAu4EhgMDgEuBc4Bb8v0uik3qPrMFC0NERERERJow8yaaLZjZDKDU3b8ZOXcr8D1375/lmk7ABuBCd78vPFcCrAHucfcJZmbAamCau18ZuXYBsNjdy8LjecBKdz8tMuZxoLu7D69vjOGYycBwdz+gtu9g8ODBvnDhwtqGtEj/Wf85R/9mLgAvXD6cfrt2KHBEIiIiIiJSCGb2mrsPzvRek63MAocRVDyjngNKzaxvlmsGA22j17l7NUHldWh4qhTYLcu9hwKEU4CHZBlzSJgg1ytGM9sLOA74Z5afoeil7jNbuDhERERERKTpasrJbG9gXdq5dZH3sl0THRe9rncdxnQDWmUZ0xboWtcYzWy1mZUDS4AXgMsz/QBmdo6ZLTSzhRs2bMg0pMXzlGRW2ayIiIiIiGyrKSeztalPhpPPNfnetz73Ohz4L2A0cAJwTcaL3O9z98HuPrh79+55htOyRBPYpjoNXkRERERECqtVoQOoxVqgV9q5nuFzejU0eg3hde+nXbcuw5hlWcZsBKqyfH458EldY3T3FeHLxWZWDTxqZpPc/cssP0vRSu1mXMBARERERESkyWrKldmXgGPSzh0LrHL31VmueY0g2UxeZ2Yx4GjgxfDUSuCDLPd+EcDdK4AFWca8Eq7DrW+MEHzvMaB1LWOKVrQYq8KsiIiIiIhk0pQrs5OBeWZ2AzAdOAi4ALg4McDMTgFuAo5y9zXuvsnM7iHYHmctsIJgbWp74F4Ad/ew4/CNZrYEWAiUEWy/c3bk8ycBT5jZfIKmTiOB7wLfqWOMlwJLCarATtCkahLwjLt/ur1fUkvwP4vXUVXtjPxGsMxYa2ZFRERERCSXJpvMuvuCcK/XG4HLCKbtXuXu90SGdSHYuzVa4bwcqAAeAHYmqNZ+290T04tx9zvCjsU3EkwLXgKc6O5vRsY8ZWZjgCsJ9q5dAZS5+7N1jLF1eH0/IA6sAu4G7qjvd9PSjJ3+GgAjvzESACc6zVjJrIiIiIiIbKvJJrMA7j4TmFnL+1OBqWnnKoFx4aO2e08iqJDWNmab+9cjxpuBm2u7h6SKa5qxiIiIiIjk0JTXzEqRSXQuTm0ApWxWRERERES2pWRWmozK6iBxTV0zW6BgRERERESkSVMyK03G1qqgSbRrn1kREREREclByaw0GVsrg2Q2rsqsiIiIiIjkoGRWmozqeGKacTSDVTYrIiIiIiLbUjIrTUZVdaIBVM05VWZFRERERCQTJbPSZFQlKrNE18wWKhoREREREWnKlMxKQVVHSq/V8TiQ3s1Y2ayIiIiIiGxLyawUVGV1PPk6UZmNuyqzIiIiIiJSOyWzUlAV0WQ2wz6z2ppHREREREQyUTIrBVVZVZPMVmeqzO7wiEREREREpDlQMisFVVldk65Wac2siIiIiIjkScmsFFRlpmnG6mYsIiIiIiI5KJmVgoqumU1OM645pcqsiIiIiIhkpGRWCqoqZZqx1syKiIiIiEh+lMxKQUUT10RlNiWBVTYrIiIiIiIZKJmVgkoksFBTmY1ux6NpxiIiIiIikomSWSmoaLJaVb1tN2PlsiIiIiIikomSWSmoSGE2smY2+r6yWRERERER2ZaSWSmo6DTjajWAEhERERGRPCmZlYJKmWacoQGUqzIrIiIiIiIZKJmVgoqnVGYTa2YjlVnlsiIiIiIikoGSWSmo6gyV2XhKN+MdHpKIiIiIiDQDSmaloKKV16pq3+aca9WsiIiIiIhkoGRWCirTPrOp3Yx3dEQiIiIiItIcKJmVgopOM06smU3pZqxFsyIiIiIikoGSWSmoaLJaGU4zjs4sVi4rIiIiIiKZKJmVgqqO17xOJLap+8wqmxURERERkW0pmZWCiqdMMw6eo+lrPI6IiIiIiMg2lMxKQUX3mY1nrMyKiIiIiIhsS8msFFS1Z0pm2eaciIiIiIhIlJJZKaiMiWvqRrMiIiIiIiLbUDIrBRWdZpxYMxtPyWWVzYqIiIiIyLaUzEpBVedYMxtXLisiIiIiIhkomZWCSklcw8zVtc+siIiIiIjkoGRWCipla56MlVllsyIiIiIisi0ls1JQ8QxVWPV/EhERERGRXJTMSkFVpzSACqcZR1JYV2VWREREREQyUDIrBZVpSnGmaq2IiIiIiEiUklkpqETTp9Yllkxmowms1syKiIiIiEgmSmaloKrDXLV1SSw5zTiawCqXFRERERGRTJTMSkEl1sS2illyerGrm7GIiIiIiOSgZFYKqjo5zTiWcZ9ZERERERGRTJTMSkEl9pZtFVkzG9eaWRERERERyUHJrBRUIldtFYsl189qzayIiIiIiOSiZFYKqjpTN+PI+3ElsyIiIiIikoGSWSmoRDLbKmXNbKQyi7JZERERERHZlpJZKSh3xyzoZlydoQGUphlLXuJx+M/s4FlEREREioKSWSmoandiZsSsZmue1DWzymYlD/Pvg0dPhSXPFDoSEREREdlBlMxKQcUdSsyIxUjpZhyz4H3lspKXTauD543LChuHiIiIiOwwSmaloOLxYJpxiUUbQDklYTarBlCSl4rNwfOWTwobh4iIiIjsMEpmpaDi4TRjs9Q1s4lkVg2gJC9frg+fNxQ2DhERERHZYZTMSkHFw8S1JFZTmY3HgwQ38b5ITl9uDJ+VzIqIiIgUiyadzJrZ8Wa2yMzKzWylmV2SxzWtzWySma01sy1m9qKZHZhh3DgzW2VmW83sDTMbkWFMmZm9E37+UjM7va4xmtlZZvZPM9tgZp+b2WuZ7lOs4u4YwRrZRCNah7BaixbNSn62fBo8f/lRYeMQERERkR2mySazZjYYeBp4DtgfmADcaGbn5rj0VuCnwFhgCPAeMNvMekXu/QvgOmA8cAAwC/iLmX0jMuZk4EHgHmA/4H7gETM7ro4xHgU8AxwfftZjwHQz+2H+30bL5Q5mQfJanWwAFayjNVSZlTxVhmtmyz8rbBwiIiIissO0ymeQmbVx94rGDibNJcACd/9leLzEzAYBVxAkmNsws07AucCF7v5MeO4sYE14foKZGXA5MNndHwkvHWdmR4SfWZY4B/y3u08Oj5ea2SHh5z+bb4zufkZamLea2beAHwD/XZcvpCUK9pkNphlXVsfDc4TVWtOaWclPIpnduqmwcYiIiIjIDpOzMmtmrYDNYZK2Ix1GUPGMeg4oNbO+Wa4ZDLSNXufu1QSV16HhqVJgtyz3HgpB8k5Q1c005hAzK9mOGAG6ABtreb9oJLbhiaU0gHJisWCasSqzkpdEN+PyzzU1XURERKRI5Exm3b0KWJ3P2AbWG1iXdm5d5L1s10THRa/rXYcx3Qiq1pnGtAW61jdGMzsDOAS4N8v755jZQjNbuGFDy29m4wTNnmIxSyau8bAya2bKSyQ396AyW9IGvBoqtxQ6IhERERHZAfJNUCcD482sbWMGUwf1SXHyuSbf+9brXmZ2EsHa25+6++sZL3K/z90Hu/vg7t275xlO8xVPrpklZZ/ZmBlB/ydls5JD1VbAoWO4LL7884KGIyIiIiI7Rl5rZoFjgW8Ca8xsMfBl9E13P76hAwPWAr3SzvUMn9OrodFrCK97P+26dRnGLMsyZiNQleXzy4FP6hqjmY0CpgJnu/v0LPEXneSa2cg04yDBtXDNrEgOiUpsp57w2ftBMtupZ+3XiIiIiEizl29ldiNBR95nCZLEj9IejeEl4Ji0c8cCq9x9dZZrXiNINpPXmVkMOBp4MTy1Evggy71fBAibXS3IMuaVcB1u3jGa2dkEieyPlcim8sSa2cg0Y090M7Zgz1mRWlWE/7bWMUxgy9UESkRERKQY5FWZdffRjR1IBpOBeWZ2AzAdOAi4ALg4McDMTgFuAo5y9zXuvsnM7iHYHmctsIKgc3F7wjWq7u5mdms4ZgmwkKCD8X7A2ZHPnwQ8YWbzCZo6jQS+C3ynjjFeTLBd0PnAC5Etgirc/ePt+4qav2CfWQv3mU00gKppCqVUVnJKVGZ32jX1WERERERatHynGQNgZr2BvQnWgy5x97U5Lqk3d18Q7vV6I3AZwbTdq9w9ui1PF2AA0Dpy7nKgAngA2JmgWvvtaKzufkfYsfhGgmnBS4AT3f3NyJinzGwMcCVBMroCKHP3ZyNj8onxIqCEYKue6PkXgOF1/V5amkQ345KYJdfMxr1mzWxca2Yll8S2PDuFfdmUzIqIiIgUhXz3mW0P3AX8mJqpyXEzmwr83N23NkZw7j4TmFnL+1MJpu9Gz1US7BE7Lse9JxFUX2sbs8396xFjaW3XFzsP18eaGdUeWTMLYNplRfJQXRk8t9s5eK5SMisiIiJSDPJdMzsJGAH8AOgO9AB+SLBetNaEUKQ2ifWxJZFteDzSAEokp+ry4Lldl+BZlVkRERGRopDvNOPvE0yxfS5y7k9mtgV4CLiwwSOTopCYUhwzkt2MUxpAqTQruVRXBM/JZHZz4WIRERERkR0m38rszsB7Gc4vJ1i3KlIvTk0345qteRIJrmmaseRWlZ7MNsqqBxERERFpYvJNZhcDP81wfkz4nki9JPaUDaYZh5VZggRXDaAkL8nKbLhmVpVZERERkaKQ7zTjCcCfzewwYC5BvjEMOBg4uXFCk2KQmFIcS28AFTaFUiorOSWS2badANOaWREREZEikVdl1t3/AgwB3ifYb/WE8PWQsJuvSL0Ee8oasZgRTzaAqlkz66rMSi6JZLZVG2jdXpVZERERkSKRszJrZq2Bs4C/uPtpjR+SFJO4O0YwrTiebABFsimUclnJqSrsZlzSNkhmq7RmVkRERKQY5KzMhvu23gG0afxwpNgkEteSmCXXxyYSXMO0ZlZyS+wzW9IGWrXXNGMRERGRIpFvA6jXgUGNGYgUp3h0zawqs1IfiX1mNc1YREREpKjk2wBqInCbmXUEFgBfRt909/UNHZgUh0Szp5jVrJmNJ9fM1pwTySqxZrYkkcxqmrGIiIhIMcg3mf1b+PwYpDSYtfC4pCGDkmLixAxKYkSmGSe6GYOrn7HkUpWezKoyKyIiIlIM8k1mv92oUUjRiienFNdMM04kuKZpxpKP6nKItQ7+wLRuDxVKZkVERESKQb7djI8C7nH39xs/JCkmyTWzMUsmrkFlNkhytTWP5FRdCa3aBq9L2kL1J4WNR0RERER2iHy7GV9IMKVYpEF5cs0sVHuiAZQTM8NAa2Ylt6pyKGkdvG7VpmbasYiIiIi0aPl2M/4XcGhjBiLFKe7hmtnINOOaNbOmFbOSW3VFUJGFYN1soruxiIiIiLRo+a6ZnQbcYmZ7kLmb8fyGDkyKg3tQ8jez8Nhr9pk1tM+s5FZdESSxEE4zrixsPCIiIiKyQ+SbzM4In2/O8J66GUu9OcGU4pJYkMwmqrMxC+e1K5eVXKorgunFEE4zVmVWREREpBjkm8x+rVGjkKIVj5OSzMY9MfU46HCsrXkkp6rytMqsklkRERGRYpBXMuvuyxs7EClO8XCecTjLmLg78XhwbBYkuyK1qq6sSWbVAEpERESkaNTaAMrMfmNmHSLHp5jZTpHjLmb2ZGMGKC2bQ7IBFATJrONhh2NVZiUP1dHKbJtg2rGIiIiItHi5uhlfBHSIHE8DekWO2wEnNXRQUjw8MqUYgjWzcQ8SXNDWPJKHaGW2pC14NcSrCxuTiIiIiDS6XMls+t6y2mtWGlSwDQ/EEmtm40GCa4SVWSWzkku8CkrCFROJRlBqAiUiIiLS4uW7z6xIo0hUZksia2bdIRYLklxXNiu5VFdCLExmE/vNqgmUiIiISIunZFYKKqjMWrIyW53cZzaxZlYkh3hVJJltHTyrCZSIiIhIi5dPN+MTzWxT+LoEON7M1ofHXRonLCkWwZRikmtm4/Gg5VOym7Eqs5JLvLommW2VqMwqmRURERFp6fJJZu9LO/5t2rGyDam3ZDfjlH1mg+TWtGZW8pFSmVUyKyIiIlIsciWzrXdIFFK04sluxsFxtXtQrbWg25gqs5JTNJlVAygRERGRolFrMuvu2t9CGlU8HnYzjk4zDiuzMfXOlnxkrMwqmRURERFp6dQASgoqWB9rkWnGHlZrg/OqzEpOKclsWJmtrixcPCIiIiKyQyiZlYLyMHFNVGar407cAYLKrHJZyUn7zIqIiIgUJSWzUlDJbXgildlEgmuoMit50DRjERERkaKkZFYKyh1iMSixmm7G7sE6WlSZlXxkbAClbsYiIiIiLZ2SWSmouDsW7WYc95QOx8plJafoPrPJNbNKZkVERERaunz2mQXAzE4HRgA9SUuC3X1EA8clRcI92IInMc24Ou7h3rOGYbjHCxqfNAPVlRArCV5rn1kRERGRopFXMmtmtwCXAHOANahgJg0kkbgmphm7h3vLWjD9WJtDSU7aZ1ZERESkKOVbmf0xMNrdH2vMYKT4JLbhiYW1/mp38JrKrBpASa3cg3/xiLUOjtUASkRERKRo5Ltmtg2woDEDkeJUs2Y2ujVPYp9ZTQGQHOJh6V4NoERERESKTr7J7KPAyY0ZiBSnROfiklhimnGwz6wBZhbuOSuSRbwqeE6umVUDKBEREZFike804w3AlWZ2CPAmkPJfiu4+qaEDk+Lg4ZTiaGXWqelmrL15pFbJZDZ9n1klsyIiIiItXb7J7DnAZuCQ8BHlgJJZqZe4e9DNOJHMuhOPB1VZA1VmpXbxyuA5mcy2AoupAZSIiIhIEcgrmXX33Rs7EClOicpszTTjYKqxWXDetWpWapNYM1vSuuZcSVs1gBIREREpAvmumU0ys9Zm1jr3SJHc4u7EYhDmspF9ZoO1tHFtMyu1SV8zC0FiW11VmHhEREREZIfJO5k1s9FmtpRguvFmM1tiZmc0XmhSDIJpxEYsFplm7I5hmJnqslK79DWzECSzienHIiIiItJi5TXN2Mx+DtwG3Ae8QNBsdhhwv5l1cfe7Gy9EadmCbXhKrKabsXuw76yFxyJZZUxm26gBlIiIiEgRyLcB1C+AX7j7PZFzT5jZ28AlgJJZqZf4Nt2Mg3OJvWeVy0qtqjMks7HWUK3KrIiIiEhLl+80492BWRnOzwL2aLhwpNjEE82ewj+J1XEPGkARrplVNiu1ybpmVsmsiIiISEuXbzK7BvhWhvPfAlY3XDhSbLbtZpxoAGWYoTWzUrtkMhvtZqxpxiIiIiLFIN9pxvcCU8zsK8C/CHKMYQTTj69rpNikCMQj2/BATQOooJuxqTIrtcu4ZraVKrMiIiIiRSDffWZvMbOtwBXAVeHpdcCv3H1KYwUnLZ87GNE1s0487pgZBirNSu0S+8ymN4BSN2MRERGRFi/fyizufidwp5ntDJi7f9J4YUmx8LAKm9hn1j3IXxPVWuWyUqtMa2ZjrTXNWERERKQI5J3MJrj7p40RiBSnuEMsVrNmNmgAVbNmVtOMpVaJCmz6PrOaZiwiIiLS4mVNZs3sb8CP3P2z8HVW7n58g0cmRSEedi5OTDOOh2tmE+eUy0qtEpXZkrQGUJVbChOPiIiIiOwwtXUz/giIh68/Do+zPRqFmR1vZovMrNzMVprZJXlc09rMJpnZWjPbYmYvmtmBGcaNM7NVZrbVzN4wsxEZxpSZ2Tvh5y81s9PrGqOZDTKzx83sXTOLm9kDdf0eWrJgSrERi9Uksx5Waw1VZiWHjA2gNM1YREREpBhkrcy6++jI6zN2TDg1zGww8DRwO/Aj4GDgHjPb7O731HLprcBo4CzgPWAcMNvMBrr7uvDeiS7MY4EF4di/mNkQd//fcMzJwIPAZcCzwEjgETP72N2frUOMOwHvA88AOZPxFunjFfDb/eGMJ2HPo1LeSqyZLUk2gKqp1poqs5JLsgGU9pkVERERKTZ57TNrZveZWccM5zuY2X0NHxYQJH4L3P2X7r7E3acCUwg6KmeLsxNwLkGX5Wfc/d8EiWp5eB4zM+ByYLK7PxLeexzwv6Qmm+OA/3b3ye6+1N1vB55M+/ycMbr7Ane/1N2nA59t1zfSXP3f/OD5zce2eSvuQbOnkqovubzVY1C1NVmtNQuSXZGsMlZm1c1YREREpBjklcwCPyWoMKZrH77XGA4Dnks79xxQamZ9s1wzGGgbvc7dq4FZwNDwVCmwW5Z7DwUwszbAkCxjDjGzRBmoPjEWoewJaVCZNXZ67R7Ob/UMe/7f4ykdjpXKSop/Pwmfvl9zXJ2hAZS6GYuIiIgUhXyTWSNzXnEIsKHhwknRm2Av26h1kfeyXRMdF72udx3GdCOYgp1pTFug63bEWCszO8fMFprZwg0bGuurbTqCyqwRq94KQKvKL5LVWsO0ZlZqbPoAnjgLpp9Scy7rmllVZkVERERaulq35jGzSoIk1oE1Fq5rDMUIkty7Gi267OqT4eRzTb73bch7pV7kfh9wH8DgwYNbRibniT5iqT9OYgpxYn0sQBxLVmtjMbRmVmp8/F7w/NF/as4l18ymTTNWMisiIiLS4uXaZ3YMQa7xEEEjpOiazwpghbu/0kixrQV6pZ3rGT6nV0Oj1xBeF5mLSM/INdExy7KM2QhUZfn8cuCT7Yix+CSqZ6T8Y0gyUY2ZEfMgKYl7LKjMhuPjSmYlYWuGJeeqzIqIiIgUrVqTWXefBmBm/wfMdfcd+V+ILwHHAL+OnDsWWOXuq7Nc8xpBsnkMcD+AmcWAowmrncBK4INwzNy0e78I4O4VZrYgHPNI2phXwnW49Y2x+CST2dTMNDGFOGY1ldnEVxuLGcFuPcpmJVT++bbntDWPiIiISNHKVZkFwN2fT7w2s25Am7T3P2jguAAmA/PM7AZgOnAQcAFwcSSWU4CbgKPcfY27bzKze4AbzWwtsIKgc3F74N4wVjezW8MxS4CFQBmwH3B25PMnAU+Y2XyCpk4jge8C36ljjG2AvcPDjkBXM9sfqHD3t7fvK2omElNB00+HeWqwPjacchxW1GJhN2NVZiUp72RW3YxFREREikFeyWy4Lc9kgr1U22cYUpLh3HZx9wXhXq83EkxxXgdclbbHbBdgANA6cu5yginQDwA7E1Rrv+3uienFuPsdYZJ5I8G04CXAie7+ZmTMU2Y2BriSYO/aFUBZYo/ZOsS4G/BG5PhA4BRgFUFn5ZYvWZlN5YkE1iyZxCaSkKCbsWlrHqlRvqnmtYddwjIls7HWwfl4HGL59rgTERERkeYmr2QWuAU4nKCC+QhwPrA7cA5B8tgo3H0mMLOW96cCU9POVRLsETsux70nEVRfaxuzzf3rEeNK0heLFptEwuHpDaCC55gZFiaxFk4PjcUMQ5VZidgaSWarK6BV25o/WyVp04wh+IeRWNsdF5+IiIiI7FD5JrMnAGe5+z/M7GHgX+7+n3At7Y+A/9doEUrzl6Uym1gzawZWXR68TiSzZpgqsxIVnWZc8WVqMps+zRiCJlCtlMyKiIiItFT5zsHrBiT2w9hEMH0XYA5wRAPHJC1NlgZQNZXZmrWyFplmbKb2TxJR8WXk9RfBc7YGUKAmUCIiIiItXL7J7PtAn/D1coJmSADfAr5o6KCkhcnaACrRzdiSa2VrklnDMO0zKzWqtta8Lk8ks5n2mU0ks2oCJSIiItKS5ZvMPgUcGb7+LTA+nGL8AMEetCLZJapnaUltynrY8L1YvGaacczQNGOpEa20Vm0Jz4UJq0X+pywxzVgdjUVERERatHy35vlV5PUTZjYMOAx4x92fbqzgpIVIJLHpa2cjDaBqktnUacZqACVJKcls+DpeFVRlLdJjLaZpxiIiIiLFIN+teb4JvOru1QDu/hLwkpmVmNk33X1eYwYpzVyyMpuazNZMMwY8LZmNWbA1j1bNSkJVec3rsGFYkMy2Th2nacYiIiIiRSHfacb/AnbNcH7n8D2R7BKVWY+nnvaafWYTiW4ssmYWVWYlqroC2nQKXicrs9Wp62UhtZuxiIiIiLRY+SazRubGsl2AzQ0XjrRIyX1mU5PZxB+omJFMeEsiyWxM7Ywlqqoc2nYMXqdUZktSx6mbsYiIiEhRqHWasZndF7504DdmtiXydgkwGHijkWKTliJrA6htK7MlHlkzGxkjQnUFtO0En6+tSVQTa2ajmvA048rqOB99UUGvLu0KHYqIiIhIs5drzezXwmcDvgJESx0VwMvArY0Ql7Qk2SqzYZ5qVvNeiUe7GZsKs1KjugLadg5eJ6cZV2afZtwEuxmf88hC/vnOBqaeNYThA3oUOhwRERGRZq3WZNbdjwAws+nA+e6+aYdEJS1LljWzntLNOEh4LRwTi1nYzVjprISqKqBzuGY2Oc24uqYSm1DXbsaffwj/mQX7nQaxfFde1N27H37OP9/ZcvvMtQAAIABJREFUAMCDL65QMisiIiKynfLdmmd0YwciLViWymxKN+Mw4bVEV2MLph8rl5Wk6vJgmjGkbc2TvmY20QAqbSuobGZeAkv/Cq3awb7fa5hYM5j77kYAjtunF88vXc/WymratS7JcZWIiIiIZJN3GcLMTjezaWb2nJn9PfpozAClBciRzBpWszVPMpk1EjuHujJagbABVDjNOKUBVLY1s3lUZuNxWP7P4PXKFxsmzixeXv4R/bt14MT9dqOiKs67H37RqJ8nIiIi0tLllcya2S3AVGA3YB2wJu0h/5+98w6Po7rb9n1m+6p3F8m9U4wrNsbB9A4BQmih1yQQ8hJI4UsB3oSQQGgJPS+dUEMPvZnghm2MMRgXucmWZau31fY53x9ntmi1klayDbY593XtNbuzZ2bP7o5W88zzK5ru6aYAVKec2ViYMYkwY0OITuM033GioUQ148hOErON6yHsU/drPt858+yG1dtb2WdQLqPLlLu8ZnvbLn09jUaj0Wg0mr2djMKMgfOBc6WUz+zKyWj2UnopAKVyZi0RG8uZFZbIRTm4Rtyn1XxniYbAmWXdt4o7RdOJ2VgBqAzCjBvXq2XpPtC0qf9zq3wP3vwVnPogDJ7S5WlfMMLmRj8/nFLBsCIvTpuhxaxGo9FoNBrNDpJpmLETWLwrJ6LZi4kXgOquNQ9x4ZE2zPibmKNm98Y01TFid6sCTzurz2zrFrUcOhP8jRDy9W9+n9wJDZWw5OG0T6+tVSHFo8tysNsMRpRkUVmrw4w1Go1Go9FodoRMxeyTwPd35UQ0ezHdObPW0hBJObMkxKxhKDmrKxpr4uLV5gS7K6U1zw5UM27dCsKA8unqcfPmfswtAtVL1f2N89IOqWrsAGBEiXKWhxR64+s0Go1Go9FoNP0j0zDjOuB6IcQMYDmd+80ipfzrzp6YZi9Cpm/Nk96ZTYQZxzfXWlYTy5G1u5SgjYnbaLhra56+VDNuqYacgZA3WD1u3wal4/o2t6YNEO6A/CHQtFHN1e7qNKS6yQ/A4HwPoMTs3DV1SCkRQofQazQajUaj0fSHTJ3Zy4AOYAZwOXBV0u3KXTM1zV5DvABUas5sTMwm5cwmO7P6JF8Tw3JZO6I2ooYjkTPbSwGoqCl5dN4GalsD6ffbugVyB0P2APW4bXvf59ZWo5bDDwFkIg83iS1NHeR7HWS51FyHFnkJRkxq24J9fz2NRqPRaDQaDZChmJVSVvRwG7KrJ6nZwzHTO7OJAlAknNl4NePOBaA033EsZ/aBeZupaY8SjYUZp3VmE2L2k8p6bnhtJb9/5av0+/XVQ3Yp5JSpx+3b+j639lq1LJ+mli1bugypbvZTXuCJP64o9AKwqUGHGms0Go1Go9H0l4z7zMYQQuQJHRen6QvxnNnUAlBqmZwza4uJWSHiocZay2pizmxVS5SQtNPabhVqSpczm1TNeEOdKrLUbX5qRyN4C8GVA46s/jmzvjq1HLCfWrZ33Ud1kz8eYgwwtCir53lpNBqNRqPRaHol0z6zNiHE74QQdUADMNxaf7MQ4tJdOUHNXkA3BaDiObMQd29tnaoZ6wJQGgtLzIZwEMZOIGiFDUcjXZ1Zw6aKOkVD8TDetMeQlKqCsadAPc4uTStEe6V9uwp1LhmbeNzpZSRbmvwMzvfG1w3O92AIqGroZ/VkjUaj0Wg0Gk3Gzux1wKXAL+lc/GkFcOHOnpRmL6OXPrMqZzYlzFiIeJhxqgwJR01q27rJgdTsnVhhxiHshLETiolZM9w1Zxas9j0JMdvcEe46JtyhRLKnUD3OGdBPMVsHWaWqB64rNxF2bNEaiOAPRxmY546vc9oNBuZ5tDOr0Wg0Go1GswNkKmYvAC6XUj4CJMeKLgfG7uxJafYyYjmzZvo+s4YgLnQ758wqNZuigbnlzVVM/9P7tAbSCBTN3kmKMxsO9VDNGKyKxxHqLDHb6AvFC47F6WhUS68lZrPLoK0fObO+WsgusfbR1d2tb1dzKM5xdlo/pNDLJi1mNRqNRqPRaPpNpmJ2KLAyzfoI4E2zXqNJEHdmO4uJdM6sPV3ObIo3+8Eq5Xytq23fRRPW7HYkObPScGDG+8xGuubMghK40RDNfnXBIxQ1aQ+mtOrxW2I2Fmbcb2e2VjmzYAniFDFrCeqSbHen9UOLvGzWYlaj0Wg0Go2m32QqZjcB+6VZfziwaudNR7NX0k0BqJhIVdWMY7myEpBWzqy1eYqhFrHa+PiCnfen2YuxnNkIdgyHM/5YObNpwowtMdvmT7j3XUKN/U1q6UlyZoOtEOqjwGyvVY4sQFZxoiCURX27mmuqM1tR6KW+PdRVZGs0Go1Go9FoMiJTMXsfcJcQ4jDr8XAhxGXAzcA9u2Rmmr2HbgtAqaWR5MyCqmhsM8CwrNnU8NDYw46QFgHfGSzxajjcCJszqc9smmrGoMKMzQitgQhFWUpEdglLTxdmDH1rzyOlEq9ZVpixtyjh+FrUWfndxdmuTuuHFqmgFu3OajQajUaj0fSPTPvM3gU8DbwOZAHvAncBd0kp/7nrpqfZK+imz2yiwqwEGSWCctjsRBE9OLMOmzps/WHtzH5nsMKM7Q43ht2JiDuzaaoZgyoKFQ3RFgjH+7u2+FOd2dQw45iY7VzAqUf8TUpQx4Swp9BalzjW69tDGAIKvF1zZkH3mtVoNBqNRqPpLxn3mZVS/hYoAQ4CDgZKpZQ37KJ5afYmYq5rSgGomJY1rHDjsHBYj02rmrHlzKbkzNosx9Yf0mL2O4MlXu0uF4bdiSGTndl0YcZOzEiIYMSkvECJxtYuYjYWZhxrzTNALftSBCoWUhwLM/YWqYs2geb4kPr2IEXZrvhxG2Nooeo1q51ZjUaj0Wg0mv6RsZgFkFL6pJQLgQXAICGELv6k6Z1unNlY+LDNKpAdFTFn1sQQJFrzpDizNuuJDi1mvztYzqzT5cHmcGIzI0SiZo/VjCNhtc1gy5lt9acWgGoGhxfsVvhvjiVm+1IEKjY2OcwYEiHMQF1bsEuIMUCe10Gu286mRt1rVqPRaDQajaY/ZCRmhRB/EUJclLTqHeBrYKsQ4sBdMjPN3kM3BaDiObOWWo2mOLNGzJlNFbMxZ3ZvCTM2o13fpKYzUSVMHS43docLu4jS3BHqIWfWTiSs3Nxuw4yDbaovbAxPoXJ5++LMxkKS486slX+blDdb3x6kONtJOoYWZVHV6M/89TQajUaj0Wg0cTJ1Zs9AiVeEEEcDk4HZwL+AP++aqWn2GrptzROrZqwc25gza4uFGcc2T93OWu7xBaDq18LLP4WbB8E9B8L2dN2vNABYrXicTg92pwsnEZp9lgjsxpk1LWd2QK4bQ3QnZrMTjw1DtdjpizMbCzPOShGzHQ3xIfXtIUpyujqzoPJmqxq0M6vRaDQajUbTHzIVswOALdb9Y4DnpZTzgDuBSbtiYpq9iF6qGcdEa0LMRjGMpDDjlN2Foya5+JBJgmGPY/VbcP9s+Ool2OcUCLTAE9/v0qNUY2E5sy63B4fTjYMIze2WmO0mZzYaUeI11+Mg1+PoWs041A6unM7rcsr6GGZcq14/lncba/NjhRlLKalrTx9mDDCkyMuWJj/R1CpnGo1Go9FoNJpeyVTMNgNWuU4OBT6y7ksgzZmkRpNEvI1KagEoy5lFiVwzyZm1JReASnFmQxGT912/4JfLj9uVs9511K2G5y+A0nHws8/glPvh3JeUoH3lpzrkOB2WM+tye3BaYral3Sqc1E01Y2ltk+2yk+dxpHdmndmd12UP6NsFhfZalS9rWD+l8ZxZdaGlPRghFDG7DTMeUuglYkq2NutQY41Go9FoNJq+kqmYfRt4QAjxIDDKegwwAdi4C+al2ZvoxpmNSbZYkdeolftow+zUmidV24UiJiWi1dr3HpY3KyW8djU43HDWs4miQ2UT4IgboPJdWP3mtznD3ZNokIg0yPK4cLlcOIjQFgsz7q7PrFUBOctlJ9edTsy2d86ZBcuZ7Us149pE8SdQTq9hj+fMNvrUHAqz0juzQwt1r1mNRqPRaDSa/pKpmP0ZsAgYBJwupbR6WnAg8MKumJhmz6Omxc+ht33Eii0tiZVmlLhs7VIAqhtnVqhqxt0VgApHk0RxrL3KnkLl+1C1AA77XaKvaYxpl0LxGHj39wk3WwNAJOQniINslx2324NLRGj1xZzZdGHGDlUcCvA4bOR5HF1b8wRbO+fMguoX66tX/Wszob02UfwJVGy8tyjuzNa3KzFb1I0zWxHrNavFrEaj0Wg0Gk2fyUjMSilbpJQ/kVKeIKV8M2n99VLKG3fd9DR7Ep9XNbOh3sdd769NrIy5ssLWQ86sWi9TC0CJ2LjOata0wkcBJTz2FKSEj/4MeRUw6dyuz9vsyp1tWAsrnv+mZ7dbEwkFCOEgy2nD4VQup8/Xrp5M68w64hcEYmI2fQGolJzZ7DJAKsc1E3x1ieJPMTyF8ZzZhnaV61vcjTM7KN+D3RBUaTGr0Wg0Go1G02cy7jMrhCgQQvxECHGHEKLIWjdNCFGx66an2ZNw2NTh1El8xhxGu0uJ2aTnEjmzamkaidY8NiOpNU/K69ijSSf+oT2oEmzl+1C9BGb/AuzpnTrGHgdl+8F/b9/zQqh3IUrM2sly2RHWZxfosELNuykAJSxn1u00yPXYaUntMxtq75ozGwv7zqQ9j5RdnVmwnNmUMONunFmbISgv8FDVoMWsRqPRaDQaTV/JtM/sBGAVcB1wJZBnPfV94I+7ZmqaPY2Y6IyL2aWPwp8Hq/s262S+k5hVy4SYVaLEThQhSOvMSilxRZMEbKh9Z76FXYeUMPcW5coecE7344SA2dcod3blK9/c/HZzIqEgIanCjGNObKjD+u7TFoByIMwwNkPgtBmqmrE/nCgmFgmqnNou1YxjYram90n5m1QocxcxWxDPmW2wxGxRVjcXL4AhRVnamdVoNBqNRqPpB5k6s7ejcmNHAIGk9W+i+s1qNPFc1niXkbeuTzwZF7MmtG6Ftm1xkSqspRQxZ1bisBlJ1YyTX0OSRVLl1/AeIgKqFsCWxTDr6u5d2RgTToaC4fDpQ9/M3PYAzLByZrPd9rh4Dfnb1JNpnVkHhhnB47AhhCDP4yAUNfGHLbc7aAnh1AJQeVagSUt175OKubfZKbnPnXJmgyrP12HrdjdDCj1s0r1mNRqNRqPRaPpMpmJ2OnCXTO2RApuBgTt3Spo9lXDU5Dr7M1zU8De1IilHNmK5aUs31sHt4+FvYxPOrLDErKFO+GNhxolqxonDLhQ1yU4Ws3tKmPG8u1UuZU+ubAzDBlMvhKr5sH3lrp/bHkA0HCCIkyyXPX5hJBKwvvt0zqzNgSHDcREZy1ltsAoyEbRClFMLQGWVgM0FLVW9TypW9Tgn5ScwljMrJY2+ULfFn2IMLcyiNRChpUMX/dJoNBqNRqPpC5mKWUH6frLlQOvOm45mTyYUMfmp/VXm+N5SK5JExpY2JWzPfmhhfF1qNWOZ1JrHYRhpc2ZDEZNskRQcsCeI2dpVsOZNmH4ZOL2ZbXPAOUq0LX1k185tF3P7O6s57b75rK/bsXBwGQ4qZzZJzEaDPRSAMhwYMoLHqX7iinPUNvVWQaZ4eHpqmLEQkFcOLVt6n1SsH20sNDmGt0hV7g600NAeorCHEGNIrmi8BxzLGo1Go9FoNLsRmYrZD4HLkx5LIYQDuB54d6fPSrNHEo4myc7Hv59wv4CAmahUHKNLNWMjMcZmS1/NOBiJdnJmZaidcNTkz29+vfv26lzwd7B7YPqlmW+TVQwTvg/Ln0mExO5hzKus5+4PKlm6qYm/vbNmh/YlI0GCOCxnVolXGe65NY/NjOB1qOeKujizVohyagEoyFzMtncXZlyolv5G6tuD8dfujqFFSszqvFmNRqPRaDSavpGpmP01cKYQ4mPACdwFrAYmoQSt5rtKNAKfP00gFObdlUkVYNd/2GlYyDL2DZLb83SuZhzLfRRI7IbAEWnHQaRTzmwwbJIlEmI2EvDxSWU9D8xdz42vfbXz3tfOorUGlj8Lk85RArUvTL1IXRD48t+7Zm67mLveW8vgfA9nTa/gva+3EwjvQHXmaJCQtJPtTDizjqjl0HfTmsdGBLfTCjPOUYIy7swGu3FmQeXNZuTMbgNnTtdQZW+RWnY00ugLUdxLmHHcmdUVjTUajUaj0Wj6RKZ9ZtcAE4G5KJfWDTwDTJZSZpBcptlr+e/f4OUr+PcTf+fD1XXdDguRKO4Uw0ypZpzszNoNwZGvTOVp5x9TnFmTnCRnNuxvY2O9Cs+sawvunPe0M1l0vwo5nfnTvm87ZAaUToAlD+/8ee1iVmxp4dONjVw4axhHjC8jGDFZvrm53/sT0ZDqM+uyxcWsF+v7Tpsz68RA4rWrYydWTThWXZiwFdLrzOq6bV65EqrJ/YzT0bYNcsq6rvcoZ1Z2NNDo6z3MONtlpzjbqdvzaDQajUaj0fSRXsWsEMIhhLgZcEopfyelPEZKeZSU8nopZQbNGDV7Ndu/BKC6yd/jsJDs6syapur7KeLOrJUzK1QBKICpxppOzmwoYpJlFdT2SRfRoC8uYkPR1Ppk3zKBViVEx58EhSP6vr0Qyp2t+Ryql+78+e1CHp63gWyXnTOmVbDfYNXJa2VNSnp92K8+owwQ0SBRw4HdZsTFqzeWO21PE8ZrXRjJscSs22Ejx2VPXPAIW8erw9N127xyQEKrqmjc3BHij6+v7CrG27dD9oCu21thxv7mOiKm7FXMAowoyaZyB/OKNRqNRqPRaL5r9CpmpZRh4GcQLy6r0SSwREG72XNeYMyZTc6ZNSzny0jJmTUw4215oHNrnmAkSo7oICTcdOAmEgrQFlCiuNG3mzmznz2mwoRn/az/+9j/DHBk7VHubG1bgNe/2MoPppST43ZQkuOiMMvJqpq2xKC178Gto+AvQ+HjW3vdpxENETUsUWg5s/Hcabu76waW4M12JA6eomxnwpmNFQ5zpHFm82PteVSo8c1vfM0/P9nApY8vIRRJCpNvq+la/AniYtbXXAtAaW6a+aUwpiybNdvb6FowXqPRaDQajUbTHZnmzP4XmLkrJ6LZU1En32Yv5+BhYvmwSUSD1jqr36ytayiyeoXOYcbZ+AnZswhIJ2bYT2tAtTRp9IWUGNi2AgIt/X1DO4dICBbcC8Nmw+Ap/d+POxf2+wGs+Df4+x+m+03yr0VVhKOS8w8aBoAQgnEDcli1zXJhg+3w4qWQPwTGHgcf/BEq3+txn4YZUi1zIC5mc4QVlptWzFqC1544doqzXdRn5MwmxGwgHOWNFdsoyXFR2xbkg1VKoCKlqmacTsy68kDYCLSosWU5PV/oARhTlkNbIMK21kCvYzUazY7xny9quOm1lVzz3Oc8+PE6Pllbv2M5/RqNRqP51kjXbicdjwF/EUIMARYDnXpISCk/3dkT0+whWE6SyzDjVYnTEUxTAEpEw9a6zgWgbCn7Mbs4s34ijhyCwRDuUIC2iHJmw1FJ+9ZV5Dx0MIw7Ac58qu/vx1cPWz8HTwEMmgRGptd7UvjyBWjbCif9vX/bJzPtYuXyfv5U/3Jvv0FCEZMnF1Zx6NgShhcnXM8xZTk8v2QzUkrEF8+AvxHOeR4G7Af/mAYf3QKjjuh2vzYzjHTExKy66BHPne4hzNjrSBxLZbluvo6FOscqITvStErKHaSWLVtYvLGR9mCEO884gOtfWsHzSzZzzL4DwN8EEX9ibKfXNsBTQLi9AYABeZk4s6oQ1Zrt7QzMSyOwvw2C7eo43jQfIkEoGasqbJdN+LZnptH0m2AkypVPf0ZqEMQ5Bw7hT6fs9+1MSqPRaDT9JlMx+y9reUua5yRg2znT0eypOI0oDrq/sh0LM3YQSayMObPSEhxGzJntLGaTQy9DEZMcOjCd2QRpQ4b9tJrh+PP+dfPJAVj1et/egBmFD/8E8/8RnxfFY+DEu2DoQX3clwnz7obSfWDU4X3bNh0DJ8LQWbDgHph2Kdh7z8H8tnht+Vbq24NcOGt4p/UjS7PxhaLUtAQYVLUQcgZB+VT15IFXwNu/gdqvoXR82v3aZQhh73uYcZYtceyUF3h49+vtmKbECHeo7dJdrHB4IKsUmjeyQiqHf+qwAk6bUs4Dc9dR1xakpG2TGps/NP0H4S1C+pSYLc3JXMyu3d7GIWNKeh2/y1n3Ibz8YyuUeqBqYfT1qzD3LyoH/Lhb07vSGs1uTihiIiVcNGs4P54zEkPA6Q8sYHvrbpamotFoNJqMyNR2Gt3DbcyumZpmd6dyexusex8AlzCx9yRmrQJQdpE0xhKxcWfWluTemglBm3wBPRgxyRZ+cOUSwIkMq5xZr9WCxaxdlfSinQIIuicagefOU5WZ9zkFLngDTnkQoiF47ET4/OnM9hNj9RtQ9zXMuhrETko1n32NKki0vI9z6Y3aVfDeDfD4yXDfLLhrItw7E545BxY9CO21Ge8qakru+bCS8QNzmT26cxuiUSWqfU1lbbtyvgdNSjy576mAgJWvdLtvJWYtB9Za5glfp8fJSCMmZhPHUXmBh1DEVO15Qh3pQ4xjFI2EhvV8tbWVwfke8r1OTj5gEKaEt77aBk0xMTsk/fbeQoxAE7luOx5n79f6CrOcFGc7Wb2trdexu5wvX4SnfgDufLjwLbjma7hqCVy7Fg75Nax9Vx0ja97+tmeq0fSZWN770CIvJTkuirJdFGU56QhFetlSo9FoNLsjmbbmWdfTbVdPUrN78vSTD8bvuwyzs+uaQkQocZEseKWp7sfCkw1bUpEomTQumthvMKycWbsnl6B0ICMBWgNhhhWpkFajYW3iRRsyODSlhLd+rZzco2+GUx+AYbNg4hlw+cfKEX35x/DF873vC5Qwfu8GzMLRBMd/P7NtMmHk4UoAfnyrEmI7gmmqAkxPnAr3HqjcaH8zFAyD8ulQMFxVqX7zOrh9PDx7rhKgvfD6F1tZX+/j6sNHdSrgBTCqVInZTVu3QcPazmI2ZwAMngzrP0q/YylxEkE4LIfTEqEJMdvV+QxbwSIee7Izq0KKNzf5Vc5suuJPMYpGQUMlK7e2su/gXADGluUwsiSL/3yxFZqtjmTditkinKEmyjIo/hRjTFkOa2q/5YrGjRvglStVnvfF78DQmbQGIyxc38C/VwV4LudHLDnmZaK5g+HpM2Hh/d/ufDWaPhK2qt477YnTH6/TTlsggtlb8QeNRqPR7HZkGmaMEGJ/4OdALGHqa+BOKeXyXTExze5Lc0eItbXtuERCZA6NbsTByG63CQt1qCWHIotUMWtPKgBlJvYtIomiOOGgj2FiG+GSUwiu3wZhP22BCJOHFLCyphV301pq7QMpjdSoarQD9+92Tr5ghO3v3MGIpQ9hzrgSIzUf1Z0HZz8HT54GL1+hijGNObrHz6byjTsZ1bCWK8LX8N4f3mPO2FL+cOIEhhb1IJwyQQg46k/w6HHw39vg8N/3OPyLLc18tLoOfziK02YwIM/NmAI4oOkdbJ8+APVrVFuZw34LUy6ErOKuO6ldBcuegGVPqhDTcSfAkTcp1zKFcNTk7vfXMrYsh6MmdA0/Lc52kudxENj8mVox6IDOA4bNVmHUoQ5wpuSxRlUFYlssZ9bKc80XPkwMDKPrz1hI2nEC3hRnFqCq0ceUcG/O7CjwPUFDsI4JBwwGVCGr4/cbyD8+rKSjdANedx548tNv7ynAE2nps5h9bslmFQZtfEvF4z+6BZD4TnqIt1e28vLnq5hXWU805SQ/27iGx/MeYvJbv0I2bUQcfXP/88t3JlKq/r9NG1URuGAbGDZ1zHgKoGAoZJftvIiJ3RQpZaKPt6DLxaXvMjFn1mFLHK95Hgdz19Qx7ndvMSDPzYA8N4Py3AzI8zAwz23dPAzMd1Oc3XtBN41Go9F8c2QkZoUQZwBPAUuBeaiitDOBpUKIc6SUz+6KyQkhjgNuBsYDNcDdUsrbe9nGAfwJOBfIt+Z8tZRyacq4XwI/BcpQwvxXUsp3UsZcAPwGGAZsAP5XSvlUyphe5yiEOBC4A5gMNAGPAr+VUu6R5RN/9H+L+LK6lSvKEofPcW0v8IpR0e02AZRw6OTeSnU/FmZss3IibZidxCxJYta+/QucIopt+EzkksUQaaE9GGFokRcvAbID1bwcOYLz7DVEmqq48+3VHDmhjIkVnUXHa8u38u7Lj3Kn+VfeMqfxly8O4859mruMw+GGs55W4cbPngvnPAcj5nR5f1JKHnntA85c+lcW2iYz/pAzGR41+deiKr5/zzyeu3wmo628yH4zbBZMPAs+uRNGHgbDDu4yJBQxufG1r3hqURVCqBO2CdE1nGX7gHG2BdhEkBrvODzH3EP+1B/2nH9bOg6O/hMc8ktYeB/M/7sKL519Dcz6ufpsLJ5auIl1dT4ePHdKWiEmhGBUaTau2hVqxcCEmG0NhGkvmMIgMwxbPu3y+cpIAAHYnDFnVonZbPwEhRtXmhP1oLSRDXiSxOyw4iycdoOva9o4JZxGNCdTPFptQw3jBs6Orz5+/0Hc/UEljdWVeLtzZQG8ReSYrZTmZJ7fPGFgLh2hKJsaOzoVz/rGaNmCXPE8i0p/yAV3ryQQNikv8HD590YwfXghw4qysBmCzY0d/LeynisW/5wrIrlctOg+Ohq34j3jofTFuHYlpgnVSzHXfUBk3Vzs25ZjhHtxt+0elZtdPhXKp6mc+Lzyb2a+veAPRaltC7C9Ncj21gDbWwPUtgVpaA/REYrQEYrSEYrgC1rLUJRw1CQalUSlJGJKotYtGZshsAmhlkk3uyFwOQzcdhtuhw23w8Blt5YOm7XeiD/ncdjwOO14nTa8Thsehw2vU4XSx9c5rXUOW7xf+O5EKKr+7SY7s788ZixThhZQ0xKgpsVPTXOApVVNbGupiTu5MW45dT/OnN7D3z7A1mXQWqMuoHjpnRFmAAAgAElEQVQLIask3rKrW/zNULdaXTR15aqLLraM/YZvjM+qmnh5WTVZLjvZyTe3nRxrOSjfo0W/RqP5xsj0l/JPwF+llNcnrxRC/Akl5Ha6mBVCTAVeAf4GnAUcCNwvhOiQUvYU23YrSsheCKwHfgm8J4QYL6XcZu3758CNwOWo6swXAq8JIaZJKb+wxnwf+D/gWuBN4HjgcSFEo5TyzUznKISoAN4F/g1cisozfhh1QeDXO/xBfQt8Wa0qwlbXNULSufpP7N3nPHYIJRw6iVnLmY2LWUdSAahkZzbWRgXIq1fXJGxDZ2DaH4wL3aJsF7Pc6zGQvG9O5gz5EZWrV3L/qoF8sPQrXv/1KRiGQEqV1/nhu6/xpPsufEX7YM56gNC7mzj9gQU8euE0DhqZ4lK6c+FHLypB+68zVRXe4bM7Dbn/7c+Zs+RqhN3BAT99jBlF6mTnzGlDOP3+BVzx5FJev2p2RvmTPXLsX2HLEnjufDj/VSjbJ/6UlJLrXljOK59v5YqDh/CzQV/hXfqgOtl3eKkefCL3ReZw77oC7K/ZuKJtIz+ZMxK3o5c5ufNgzq9h8vnwzv+Dj/4MXzwLx94Ko4+gyRfijvfWMmtUEUdOKOt2N6NKshnw1ZfIvApEtipy9Mnaeq54cilGMMAXbjC3fIYxYk6n7QJ+dSnEHhOzdhfqz0cSEk7SnTIFTXWi6k4Ssw6bwfgBOXxZ3QLOjvSVjGMUjQJguKhh/IDc+OoxZdlUFHoQzVUwct9uN5eeQhxEqMjK/HrVhEHqdb6sbslIzG5vDXDTayuZv66eH06t4H+OHNP7d9kD0eXPYpNRrquazvGTBnHW9AqmDC3o4upVFHo5aFQxVx8+mifmj+K294u4du1T1N1fQ8klL6jjZRfTtvkrts79P0o3vU5BeDtIwVo5hKXmTNbKwWySZTTJHHy4KfbaGJ4nGJUVZLSzgSFiO2W+NXiXPYX41EqVKBmvirWNOlylFuxkUR6MRKltDcaFam1rgO1tSrDWJgnX1kDXVA2X3aAoy0mWy47XZSfLaWNQvgOvJSpddgPDEqaxpc0wsFnfW1RKTFMJXVNKIlFraZpEopJgxCQQjhIIRwlGTDpCERp9JoFIlGDYjK8PhKNE+hiG67IblshNCF4lgNW6jQ0+/KFo4jmnHY/DiI+PjfU4bXgdtvi6CdtfZVSuVBfUHF6VauDwqmiL/AqVMmGxtdlPezCCIcAQgqpGlabhTHJmywu88VZiyZimpMEXUgK3JcDlTyxlc1MGaR6PnQzB5PZwAi54Pe0FyDj/uQa+/Hfi8bDZaptvg9YaVa3d5lIF92wOsLvY3BLhgoc/pT0YwWaILkI/htdp47PfHblDv0e7Df5mVd/D5lCfhRlRqUlhv7pYUT5dLZ1ZnW8Or1oae8FnoNHs5mQqZgcBj6RZ/yjwPzttNp25BlgspYwJvq+FEPsAvwLSilkhRA5wBfAzKeWr1roLgWpr/Q1CnZldB9whpXzc2vSXQohDrde8ILYOeFZKeYf1eJUQYob1+m/2YY4/BlqBi6WUJvCVEGIw8FchxP9KKTOsUrT7MJAGphqrmW6s6rR+orG+2218lphNnzOr/iHaLZcwtQBUsjOb31bJdlFCWVYxhsOD0aEqUOZ7HMxyrCUaEiw1R1Mti9iwbhV/cazjhOAiln02kH0mTue2Z9/G+fVLPO1+GXvBEDwXvMBxuQOZMa6CMx5YwKWPLeHZy2ey7+CUE/KsIjjvFXj0eHjiFDjyRph+GdgcvPTBPGbN/yljjK0YZ7+AKEpctR9enMWdZxzAj/5vEXe8t4brj0tfrTdj3Llw1jNKWD9yrBKU+54GNjt3vfM1m5Z/zAujNzB11RuwZDsUjoRjb8U44CwqXDlcC5zR2MFt76zm7vfX8uaKGh46byrDMnECcwfCDx6GSefCG9fCU6fB+JO4O3wu7cEIvzthQo/hjBMG5nDAiq8JDDwcD8qF+vmzyxiU72bmiMFsXlqCWP0p5d/rvJ2vox0P4IiJWSHUiULYF6+SnUrQtHJmjc5ict/Beby0rJpIuQ+7pwfRVTAME4Nxjm3x8GT10oLDxhRTsKyGSO4x3f6AtjkKyQWGuTLPgR1TloPDJvhqaysnTuzc8kdKSV17kAKvk0jAx9x3XmTF54uZKFs4MStK2/w27lk5jYsv/wX5Of1wdaWkccETbDTHcNWpR/LDad1HWcRwO2xceshIqif+jb8/MpAr6u5g652HUXDZq3gKB/d9DhnMce2CVwj+9x/s61/MSGmwQEzk66IL8A07jKLiAeR5HMwwDKaaJq3+MI2+MDUtfjbU+5i7vYNtrbH3dTQ2oszKqeVo72pmBD5j2MIHsC34B6bdS2jYHGxjj8Yx7pi0VZullISiJr5glIb2IHXtyj2tbw9S3x5UArXNEq2tAZo6wl324bAJSnPclOa6GFmSzUEjiyjNdVOW66Ys16WWOW5yPfbdJkw4FDHxh6J0hJVL7A9F425x/H44ij+U+nwUfzgSv1/fHqIj1IEpYViRF0MIOkJRWv1htreo/fut/UbDQewygoMIDqKMNLbyjPOPPc6zKX8f6seezZcDvs//PJs+EyrL1bvIMDApsfspyW5jf6eP85wfMr3qI1gyQbmtWcWJpStX/TaFOpSQnXaJ6qPdtFEJ1YbKzmJ2ycOwebHlxOYQ3riQppx9WDXifMZteZ7C2jW9nqBJKfmyupW2QBhTginVhQopVUE+0wo3l1KS5bInvWd1PMUOq9jRJYTA1bKe8S8cmvb1KoAbogezYMKvuPXc7xGMRGkPRGgPRmizlnPX1HHfR+u49PElZLvsSRdYEhda7CnRATZDYBfQEQrz5KIqJAYOm4HdJrAbBk6bwG49dsbWC8GJ8iPO2T8bt9Op2rEZBgibdd9m3bdR0xbmo7WN8cfCsCNsNgzDBoYDw2ZDGDaEzY5hs2ETBoZhUNbyOVO+TH+sRYUdm4zApw+mfT6O3aOigJxZqiq8w0vA8FI5+iL8heMBoS60GCKeEmAIgbAuvggBEoPtkSyEEAjUekOoL85IGu+229h3cG7/fi+iEVW9Xlg7FkbSfetx7L4zm0h7PUgTW05potOARvMtkamYXYgKkV2bsn4SsKt6zM5COaPJvAVcK4Qol1JuSbPNVMBljQNAShkVQrwLxP6LDEOJ87dStn0L5a4ihHAC0+gqmt8C7hFC2KwQ4UzmOAt4xxKyyWP+gfr8PknzPnZbVi58iwXuq/q8nV94QaZWM7bErPXR2OxJBaCSnFkjkrgS7g420Govogxwe7w4fCqfMs/jYKRYw2o5hPIBZWxrKqGcOg6wBPbI139I0xtefmtuAwfI0ccgTr4nnitamOXkiYsP5JR753HJY0t4+aezuvYHzS6Bi9+GFy+Ht6+Hj2+jxVnGCc1riNqc8MMnEaMO6/LeDx5dzOlTynl03kbOnTGUisIeHMFMKB6l5vH8BfDSZfDGtfhxcWWggZ+7TOQWA0YfBVMvVr1bU3IZKwq93HXmJE6ZNJifP/s5J98zj3vOnszBo9PkzaZj5KHw4/kw/26iH93KtdG3OXTM5Ywr7r5PLMCU3GZKRTMrvfszAXhq0Sbq20Pc/6MpTB5SwIIvRzKk5gvVizbpn7Gvo4NiwOlKynF1eCDsIyjTi9mAtJxZo7NzcPz+A3lqURVrt9QSzPUy1nKFumB3UW2vYKqxuUvY9NEVUbyfB1lDebel3LdRpMSso6nHzyQZpwGPee+iaFk7HPJaPCxRSslVTy/j9S9qOMX2CTfZH+EY4ecYAdLmQIhcQl4TZ/vHVN75Mq7LnsVTNjrj1wXY8NVChvs38En5LzISsskMzvfw45/9P156YQjHrbyO1r/Poe6s5xky5oDeN86ESJANHz6Cc9E9jI5UUUc+Hw2+jOJDLmfWqJHM7kMoa0cowsb6DtbXt7Ohzsf6+iE8Vz+eW+qOIBJoZ6axkkMjn3PY2kUMrnwD/gNfMpJPxBTm2aaymuF0hCX+cLRLKG8MmyEoyXZRluuiotDL1GEFlOUokVoaE6m5bvI9jm8vN7qfOO0GTrtBXvJFJCnVBcdAKwR9EGxVleTDfgj7lLgLW7ee7pNyEx1gdJA29AL4feFfqYyUEQn6MEMdREN+KuR2TrQt4ICmtXgW3kFlZDnn2TzkH/ITRpXlYloh2C6HwYwRRYmdrXoDFj9kvYc29R6CbRDqfDHqJgN1abw6zYQMh8qhd6qLSTcsErz6mUmebQAfAk3rP6OgNKlH8/s3QSSkQomDbTikyXORqdy2qJzf2Es537681xO0L6tbOfEf6U8hDEychHERZpKxlmvsL+AgikBiYCpRFKtXkfTYI0Ig4C/hM2kkBydhnKiLCb92PMOptk84OWqDdVFcIR+ukI+iULv6zkM+9vNV86OsD3Ft6Ui8ljQx4q+buG9IE4HELhKnR9faHGzJ2gfsLoQZRcgIhhlR96MRDBnBkFHyIg1kSR982MuHBAzEOrnrJ7eFTyeAM/45tOLl0ejR5NPOY6dXMLbAhgy3I4O++Oegbh3qGAp3QNhHXUMjZns7w1o+Zt/NH/dpDgNlFq3SiwCEUL89se9NIBFIJIJXzfFssA8nmjuUfTsWYUSDgIyfZ6mlGj+cagqkirTLwYdNZB55ETs2/bhY5xiL6jshrdeQSGteEhASPNJHsVnXp/es+fZoOectKkZP/LankTGZitmHgNuEEKNRwhZgBnAZ8CshxPTYQCnlzhK3A4FtKeu2JT2XTswOTBmXvN3kDMbEnitGfTbpxriAQqAuwzkOROUZdzemE0KIy1CfK0OG9JKX8y2QU9y74zI/OoE7I6cxxtjCXHN/ponVGNYVYWdSmLEqAGWLO7NZHiVWbMLstgBUTrQRn0fNwevNwo0Ss/leB+XmVj6WoxlZms3QgrEM3vACAPXuIRT6N7NaDqdl0qWMO+gkRElXGTIgz83DF0zjB/fN5+LHFvPc5TPJcqX8iXgK4OxnofI9ts3/F6vXraMx5wcce9HvsBV1LwJ+cdRYXl2+lX98UMlfftB9UaqMyR8CF78Lq9+gZtlb/HdVNY68AZx45JHYR3xPCe9emDO2lFd/ejCXPL6Y8x5exK+PHcels0dkdlXX7mLVmMu5+v0B/NH5GN/beDfc/SLM/ClM+pFyG1IY07EMgEXmWEaEozzw8XoOGlnE1GFKtOUMncTgtYtYubGGfYYnnMlAhwpecLiTHEcrRLid9AWWAlF1vLltnZ3ZmSOKuPusSZS9YfJxi8m9zyzjgXOndHnPUkqWRYZxqH2FOllPen6KdzsA85oLuxWzmyMFjAEGisZuRqRh/YccFFY/r/KxExDnvgLZJbz15Tbe/mIz/xr8Mgc1vEhVziSqZ1zDuEkHIyzB6zRNVrz/BIM/uZ7IA4djXvwKxuBJPb1ap/e6+q0HKMfGYaddlvl8k7DbDE4/43yWLqhg2NsX4HzqBBYcfAczjzy9X/sDINjGpnfvJWfZgwyP1rOKYXww/iYOPOES5mT1L6fY67QzYVBuPKQ7hpQqnHRD/aHUtwX5yBfEqF3JgO0fMap5Hpf5nueKyHO02ouoLD6ITcWzqSuZid2dQ1G2k5JsF8U5LoqzXXukSAXUcR7yga8OfPXWsi7x2N+UEHmBFnU/Jv7Mrs5zt8RDgr3KsYrd9xaCo7zreodbCcVYuKvNCd4ibhp7bKe/SylVyHRbIILtv3+l/NPbuM7xnHoyewzIQuWYBlpU2GjkZ2BTVdb5/CmoWgQV0yFvsHJZXbmJ/FVXDjizuOrFtRRPmMMfjiyHjvqkz8q6H2hBBlp4p76YmpIZHD9kIL5gmNaVHgq+egy+eqzTR7Fx6m8ZdsJ1ICVn3fsBps3D4nOmsOSJD3HXhpA3FiS+GxJRTDH2A752OYlmD8BBBCMaxIgGEdEgRprvpGHQoZhWn26JgYw5bwikUBcAI8Jgk7uYKfv+Agxb/BWllMwNXMnB8y/GtnEubJyb9uvNcmSRlVcGQ+coh1RYbqkw1M2wJZy+lPUyEsZVNY9R4Q4wIup7N9xqG5vDclzVLWAK/vVVK7eHf0AIGzZM7JgYmNgwsYmoWlq3aUNy+dPJ41XUl4yqcwwzihmNEo2G40sZjVih+aqw4fpQPmNzJsTD8iOmZIzbwYn5bk69dz4nPZ8cTu5AlWrppjCgxXTbai4e5WNMWY76fCWY1mcsra87cV/i8teSH6ohS8SSsmLyVd1iZTRLNrzCyWI+yPlgTavNlo/fpqIGZMxZtb7viMhno2cSYZuHYESy0SwhZHhUjQohlWOMtRQSA5UqVhLeQtRbgi0SoKB5BTYp4w5yks9v3VWPA8LNGs++ROzfQj0ITZ8ZldNLjv9uRqZiNlb06KYengN1WeabSBDoT/38TLbJdL87ui/Z3Rgp5YPAgwBTp07d7foEVIzar9cxD0ZP4FM5nk+jKqR2syzjZKmq2HYNM7bFc2Y9bnUJfniRN8WZTQozNptpcqurRdlZ2bgIA5I8l6AgUs8WOZMhhV4Gu8eokl1A/jmPsiQwiInDSnvN4Rk/MJd/nD2Zix9bzNXPLOOBc6d2LWIiBIsdUzi3MsKwoiyevXwmbk96hzDGgDw3p00p54WlW7jumLE7pziGYaNm0BEc97yLonwX//7xQdh7mUcqQ4q8vPSTWVz3wnJufmMVizc2cdPJ+zAwr4dKv0BlbRsXPLwY6RpI+U9ehbr5qk/v27+B929UzvC+p6owOyv/0Ln2DbYaA5jbWIj4tIq6tiB/PyshuEZMmIZReR/Lly/tLGb9yh1xerITE7CKN7XI9C6338qZdYnOYlYIwUkTB8G7EcZWDODnK7fz0rJqTp3cuQDQpoYOloSHcRJzoXWrOsG1cDVVAvBydQ4XdvP5rA3kcjhQGO3Dleh1HxA1nFwWuIp/NtwL9x0EB/8Paz5t5TXPc4xrWAcHXcWQw2/oWhjGMNjvyPN5zhzCQfMvwfbIKXiveE+5+L0w9+tqprW9x5bSOQwv6hpS2xemzDyM2kFv0/H46cycdwkLV7/BAeffjjunION9yOYqtrx3HwVfPc5Q2c5isS+LDriJOceewbjUi0s7CSEExdmulL/LYcBx6q6vHirfI3fNW0yufJ/JDa9BpVPlNQ6fDXlTIH8SuHazcLtICDoaOovSnu5H/On348xRF/Ji4i53ELjGJgm+nIQAtIRfPG/Q4el8fxflEAohrEJVNjjud3Dktepv9++TVa5/KoMmwdhj1f1Qu6o/cN7LPb7G4lff53u41e9BXvoLu8FwlMuXvsWv9h/Hj+eMRErJ9Q/9g3BDlSqqZRXcWr61HdvWA9n/ndW0+MOsrI8ya5SLkhwX1UNP4c6tDdiFFTZM8v8gdV+dbJmMdzdy6LASbA6P+q21u9MvS8dTNGRGxp/n0LRrB8D4t6D6s5Qc0ezEd7wDVc37cgnIDcxp9jM5EMYmhBWemwi7NYyk+wIKspxg6zo3g557VPZ0WfjWH+xPXXsQQSxEOBYaHAsHJj4vIQROu8GJ+w/C4zy+D++0D5gPqOr//kYV1u7KJWfQAfRUejL5P18PGd0azW5NpmcGfYtZ2znUAKlnVrHqMqluaPI2WNtVpWy3Lc2YNd2MqQci3bx+EFWRONM5phszIGXMnsn0ywgs/zfuYEOn1aE0h1XUFGB0FrOpYcbC6jN70UFDUqoZqxOsaNQkX7YS9ajwsLzcXAyhepAWROuwEaValnD0sAIIJFxS+4AJTO+pDUsKh44r5YaT9uH3r3zF/76+kt+fMKGT0/Lxmjp+8tRnDMrz8MTFB5KXoYC8aNZw/rWoiqcWVnH1ETv+JxWJmlz9zOcEIyYPnDsl43mkkuWyc8/Zk/m/TzZw2zurOeJvc7nikJGcd9CwLvuUUvKfFTX8v5e+xGEzePKS6QzM90L+ETD6CFWc6otn4auXVDufigPh3JeVq7P+Q1YXnM7ijU2srGlj+vDCTqF+2YPHAbB9wwrgxPj6YEdMzCY7s+r7bDY9XcKSAQLR9GI2TtjP2IpSJkXyueXNVRy778BO4cafVTXxhWm1INryKeSdkti2bhV+RwHLG2xsavClbbu0qcWkiVwKfDVdnuuWTfPpKDmA9zdN4dPDnubAlX+Ct3/D1YDPUQinPgnjT+xxF6cfNYe/1t3FxWt/jHz4JLKu/KTHKqrBSJQPX3uSOaKN3MP758qmUjp0POHrFvHpI9cwY/sztP7tHbbseykjj7oMkTso/Ub+Jsw179Kw8CkKaz5msJR8bEyjbfpVHHnk8d9+IZmsYph4prpFw1C1ANa8DWvfgfduUGOEASXjlCgqHqMqYheNVsLPU7Bz2gFFgkqcdjQoARq7Hxes9Z1FaqA5/X5szs45nyVjk/I/Szo/5y3uVLl8j8HhUW3EfrJQneC785TYbt8O985QLnSMkE8Jst52aRdEuil6FMMXVP+7YvmpQgj+fNkPuow77b75LF3fxH83tJHrcZDrsXPo2FIAjpxxAI9EryXLZSPX7SDPk7jlJi1zrJzUbxRvofqt3w0YlO9hEJn/b9/ZnD61bykZuxzDUE62Y5D63dFoviNkJGallOt29UTSMA84ms5u8DHApm7yZUG14Qla2z0EIIQwgCOw3E5gI7DVGpOctHAMVv6qlDIkhFhsjXk8ZczCpJY6mcxxHnCuEMJIyps9BpUYtKyH97/7c9ytfFBxDcf9e1yn1RHZ9cQzbP3/d3QJM04KnbL6hbptMl7pGBLObEtbG4VCYnerk47CPHW90UWYfJTguebkmZSNK4MNSf9k+iBkY5w3cxibGjr4v082sK6unSsOGYnHaePlZdU8uXATY8pyeOTCaZTkZO6wjirNZvboYp5fupmrDhu1wych989dx6cbGvnb6RMZWdL7iVhPCCG4ZPYIjt5nADe+tpK/vbuG++euY864Ug4ozyfP46C62c+7K7ezsqaVieV5/P2syQwpSnFGy6eq2zG3wBfPwcs/hjevU+Fk0qRj4vn43mjEF4pyxxkpOZWFI9SyYR2+YCQe4u23xGxWdtL1Za8Swa3SSzBidhE7HTExa5h0wQqnNJxZ/PqYcZzx4EKeXLiJS783Ij7ks6omNjhHI125iHUfwD7JYna1Ei1tMH9dQ1oxu6Wpg0Z7CQWtW7v9zDsRbIOa5bhm/hxRBYv85Rx4yXu8N28hd72+mBsvOIPJw0p73Y0Qgv858zhuuO8mbqj/BU1PXUTBxS9265bc/u4ajmx/lWB2Ga6deILqcGcx/ccPsGzR2YTeuZEDv7yD6Jd30Zw7Flf5AWQVDEDICNH2eoJbvsDduAqDKKbM50nHqWQfdBHHz57x7YvYdNgcMPx76nb0n6CjEaqXqgs51UtVuOqK51O2capWK1klyrmMh9J6lAiWphVbaKrfvpAPQm1qGbTyEQMtal1ahPqbiInQAfslCdI0IjVWrOi7QGlK0b2wVYMh5FMhp9tXqN7EA3vPD3PYDEJR9ZsSNSVbm/1UNXawscHHpoYONjX4WF+nRLLX2fPp1TOXzSAQjpLt6lrga2hRFjectE83W2o0Go0mmYxjtoQQs4GrgFHASVLKLVYf1vVSyr5lsmfGHcB8q/3PE8B06/Xj1ZOFEKcAfwYOl1JWSylbhRD3AzcLIWpQgabXAR7gAQAppRRC3GqN+RpYgqpgPBHVOifGX4EXhBCfogo2HQ+cSrJllMEcgfuAK4GHhBC3AyOB/wX+vidWMgbgR/+GDmVOO9KE7dx1zjRmPtm5cEbEFGBLcWZjYjZW/MFyZpGyc85sVDmzLa0tFJIIN7U5lUg9sMKLPaw+yrJiq4jRwP2heCxMvajfb/O3x49naJGX295ezTn/XASosKGzDxzCr48dT3Y/Qh5Pm1zOz5/9nMUbGzkwuQBJH1lX187d71dy/H4DOW3KzuuRWVHo5Z/nT+WrrS08sWAT76+q5T9fKHdRCNh3UB63nLofp00pT/vdxzFscMBZKtTpv7epdTOv5KApU5iwbBGThuQza1RKwSmHB793EMPaalhZ08o0K5c2ljObm52U55ilgr9apRdfMNJF9Pgjam4O0bXVCdGwigpweDhwRBGzRxdz39x1nDNjSPwEdMG6BvYfUoTIngOVHyTyZk0T6lbh3u+HlNW7mL+ugbPS9Jzc0uTH5yqDlnSVYtKw+VOQUZwjZjF8hY1lVerv6z9b3FR7xzFxSO850DGcdoPrLjqH++9eyc+qH2TBk39g4pl/6HJy/foXW1n433f5jfMrOPh/d0lPy0kHHkJ46nu8OvcTmhb9i5FNXzC25Q1ctBPFoJls1pjlfCFPpG7gHCYfdATn7D8Ye0/H1u6GtxBGH6luMUId6thvqFRCqX27datVIsrfaBVA8gMykTcYyyV0ZqubO1/1wHVmKwGaVaREq9cSqbH7nnzdAiRTYi25Pn8KPvgj+GrV4ykX9Lqp02Ywf10Dh/3tIzY3dnRqTeO0Gwwt9DK0yMshY0o4bFzPF58cNqPn31CNRqPRZERGZy9CiJOA51D9ZCeQ6C6ag2pDs9PFrJRysdXr9WZUr9dtwP9L6TGbB4yFTv05rgNCwD9RWfhLgSOllPF4PynlnVbF4ptRYcFfowT68qQxLwshLgGuR/Wu3QBcEOsxm+kcpZSbhRBHAbdbc2lGucS/3ZHP51tlVMLBsdu6Xt0fWJgLpIhZqcZ1EhcpYcYxZxYz2knMOgON4KvH71OuhM1yZrGr0Ld/nr0v1K5U61zWc+48uHLHapEJIThv5jBOnVzO4o2NhCImU4YW7FC+61H7lJHltPHiZ9X9FrOmKfnNiytwOwz+cNKE3jfoB/sMyuOW0/ZHSklrIEKrP0yh1euyTxx6vWrpg4DJ51Ngs/PG1bO7HW4rHsWI9i0s2dISF7PBgDqW3N4k99lyZtvw4AtGKUoxpn3W4eMSaZzZmDNjnTVLaL0AACAASURBVNT+/IjRnHbfAv61qIpLZo9gY72PdXU+fjRjKLiPUOHSdauhdJwSJ8FWxOBJzGwv4pPK+i5hzlFTUt3kJzx4ILSuzOxz2jRfudcVB/K90Zt4+tMq2oMRPlpdy6FjS7vmbfdCQZaT8372R5bc8zWT193L2X8ZyWlHHcZJB6jQs8fmb+T2d9fwbM7rSHIRGZzI9xeHzeCkw76HPHQ26+raeXt9IzXNHQQiknyPgzEDcjhzB/+udjucXnVBbeBOKPam2bk4s1X/1M2LVE/hI29SFdrTtGBK5ZAxJXy8tp5hRV6OmjCAYUVehhZlMazYS1mOe88s/KXRaDR7OJmemf4O1bv1QSHEqUnr5wG/2fnTUkgp/wP8p4fnH0X1uk1eF0b1iP1lL/v+K8p97WlMl/33dY7WmIXAQT2N2VNxpruybHTN3YxaJRYcSc6skEpxGLJzmHG80qDFyOW3wvJbCZ30DgB2l3Vl3RKzRIIqFA9UoZKdTLYrkcu0o3iddo7dbyD/WVHDDSftk74tTC88t2Qzn25o5C+n7Udpzq7NZRNCxHO1+oVhU/0WM8RZMpKhVct4tDpRITLstwIYksPFrQsgHdJNe7Cr+9pmObN2mabKakzMWkWkpgwtZOaIIh78eD0/mjGUd1eqasWHjSsF++FqbOV7SsxWL1WPB0/hoEg2L3++lbW17YwpSxx3VY0dhKImjsIKqG1WFV/TVHfuRNUCFeboyuaoCWU8On8j1z63nKaOMIePL+t5227Iz3Ix9ScPE7l7Kn+UD3DcS0Vc/9KK+PPXjqhi6tZFcPjve5/fTkAIwajSHEaV7vy/UY0mY+xOuPQDdeGzYFifNv3NceN33QmPRqPRaPpFpjEuE4B30qxvRLWp0XxHsRuCWYG7Oq+0Ofn3j2dy7VGJxiWmVacwOWcW0yr8FMuZjYUZm9FOObMxwgElauxu62Q4Lmb9iVwy147ljn4TnDp5MO3BCO+s7Hv9r+aOEH9+cxUzRhTyw92t+MTOIL+CfNpYV709vioSTCNm9zmVkKuQD80D6Ah1FbPtYcshMdOEGYetiq2ORL7vVYePorYtyNOfVvHkok1MHpKvcmHzyqFkPKyx2lJXLwVHFhSPYeZI5Q7Pr6zvtPs129WxmDPYytVrqOz5PUeCKt9yqLreNXNkERMr8nnrq21UFHo4ckL/xCwA2aXYj/sL48Mr+Xjm51x71BiuOXIMr5w/kp+23aUKFM28sv/712j2RAbs22chq9FoNJrdk0zFbDNdK/KCyjPNsMKJZm/EYTfYSkq4rM3OlKGFncJoY60F0lYzJhZmHMuZNdOKkIgVbuqIhRk70jmzu7+YnTG8iPICD88v6a6OWff844NK2gJhbjxp38z6we5p5CmBHmrYTCRWaCXYOSwYgIpprDj7M1bLIWmd2daYmI2mcWZjVUyT9jdzRBFThhZw42sr2dTQwU/mJLW0GXuMCgP2N8HGT6BiGhg2Kgq9VBR6mL+uczXvylp1LJaOsNpY1a/t+T1XfwbRIAyZCSgH84EfTeFXx4zjyYsPxGnfwby6iWfCvqdRsew2row+wc9KPmfiO2ciAi1w+iPx1kkajUaj0Wg0exqZniW9CPxRCBEr2ymFEGOAW1C5tJrvKA7DsBplJ2E1ZfckFeWJSjXGSZqcWVJyZlPCjGPExGy832jMmQ37VZ9A2CPErGEIfjClnHnr6qlu7qavYxo2N3bw+IJNnD6lgrED/n97dx4nWV0e+v/znOplpnuGmQEGhn0RAUVFYEQjaHBDxCUSt6Axwd3c/Iw7cUkMasQIMWhuEtGg16jXX7xmcZcINyYGEQMImiCLAgMCMzADA8NMz3RPd33vH+dU9+nqqu7qprurq+bzfr3Oq6pOfevUd9rjDE8/z/f5dmmp5qq8mdXatJlfbc1/Nmn3EGNUJjL3hVoDrh3DU7P4D48UT8ZGprw3kZmdyPRGBB97yeM547h1nPfCx/Lscjb0mDPze/K6/w2bb4QjnzH+1lOP3JerbrufsepEI5hb7n2Yg1YvZ3D/R+frYO+fIZi988r8sQhmId+X+PdOe1TDTsmzFgEv/hQc/0r44Sfhn16f/3l+5+t511tJkqQO1Wow+17ypk/3AQPAVeRNk+4BzluQmakj9PY0yA4WGdaB0nrQaoPMbBQBa9TWzNa6qVYnMrO70kQAUx3Oyzf7Bor/wK9fM9s7+Ig2bF9MLznxYFKCf7y29ezshf9yM1kGb3/O0TMP7lRFMHtg3M+tRYaTkSF2Z1PXBtf2cdzRKDNbi2Eblhk3yPQCR+23kotffRLnnHLE5PEHnZR3T/7e+/PXR542/tZTj9qHbbtG+fk928bP/fyebRy9/4p8bd6aw2HLLUzrjivzztuDc+9uPaOefjjrU/DWn8Hr/xXe8hM45OSF+z5JkqRF0NJ/+aeUtqeUng78BvB+4JPA81JKz0op7VrICWpp62kUPFZqwexEf7Fqcav1TOpmXLdmdrzMeGw8a7uTiRLIbOcDACwbqF8zuytfM9sB62VrDtl7gKc+ah/+4dq7qJayes387K4H+cZP7+ENTzuSdasWtulTW608gBQZB8Vmbt28nZ0jY8ToTsZ6pv6Za5nZRmXGO3anvOlYw8zs5AZQM8oq8Ojn5s/7V8G6iQ61tXWzP/jFZgAe2rmbX9y3nRMOXZMPWHssbPrv5teujuXb8hy2SP3h1hwGB580JcstSZLUiWaVxkopXZ5S+rOU0vkppe8BRMTCt8HUktXbYGseluf/Ib92ZT9nnXAQv/WkQ8bXzPY22me2fmueVB1/b7i061K2K+9w27+8QTA7vL0jSozLXr7+EO58YIirbrt/2nEpJc7/zo3sM9jHG59+5CLNrk0qvcTKAzmy90Fu3bydTdt2sTxGSD1TA8/aL0saNYDaMTzKWPQ0XjPboAHUjI47K3983FmTsv/7rVzG4w7ai+/flO9Vef2vHgTghENX5wMOeRI8cCvsmNwkatymn8HwtsULZiVJkrrInGsyI6I/Is4FbpvH+ajDjNVnFc/+Sr5GD6hkwUWveCJPOHh1aWueicAj6tfMRpYf1bHxrO0oE6XKlZE8mM36izLj3nJmdjv0d9Y60jMet47VA7186cd3TDvuX2+6j6tue4C3PvvRrFy2B2TUVh3MET33s2HLEBsf2slyRoi+5VOG9fVk9FUytjdYM/vAjhGq0du4zLhBA6gZHfUs+N1vwfOm7ub1zGP35yd3buX+7cP8+82b6evJOOmwIjN7yFPyxzuubHzdW7+fPx7x9NbnIkmSJGCGYDYieiLivIj4UUT8e0Q8rzj/W8CtwLuAv1yEeWqJKpcSA3nn1zo9WTRZMztGRGnNbGR5w5w0BsW566oTXWX7Rop1ib11+8zuLjKzHRbMLuut8Ir1h/AvN9zLpocaV+sPj47x4W/9nCPXDnL2yYcu8gzbZK8D2S+2cvv9O9j00C6WM0ylv3EjpMH+ypQ1sykltg6NUM16Wm4ANaMIOOJpDTv/vvAJB1BN8OUf38n3fr6JXztyn4n/Xxy8Pq9U+PnXG1/3tu/D/o+DlY2axUuSJGk6M2Vm/wh4J3AvcCzwtYi4ALgA+AhwaErpQws7RS1l61Yt41tvOXXaMVkW4x2Pe+u25skixrOwROTrE0uZ2c+MvoAbj30LAH2jD+dlx1mRra1fM9thZcYAr3ryYVRT4sv/eWfD9y/5j9vZcP8Q573wOHorndHc6hFbsT+rxray+eFhbrl3OytiJ73LG69mGOzvmRLMbts1yu6xlK/BblhmXGsANYtgdhqP3n8lv370Wj5+2S3ctXUnZ59c2v+30guP/Q24+TsTGeGakSG486pJDaUkSZLUup4Z3n8l8NqU0lcj4iTgauAY4NEppeEFn506wuMOWjXt+5WscTdjUnG2HMxGlr8ulRk/OHA4AP2j2ximf6IlVP2a2Q5qAFVz6D4DnHb0Wj7/w9v5xb0Ps/nhYU48bA3Peez+9GTBJy//BWcct46nH7223VNdPCv2o29sB8vZxWU/38TLK7uoNAlmV/T3sKNuzewDO/JsbKo0KTMeLbLgDZpKzdXHXvIEPvztn/Po/Vbw3OPqsqyPeylc+3m4+bvw+JdOnL/jyjxz/Khnzts8JEmS9iQzBbOHkG/DQ0rp2ojYDXzIQFazkUVMrJktdTPO0mhdZrZWZlyFosNxlWCkuE2XjW1nOEplnpWevGlUbc1sB2ZmAd7zvMfwO5/7Mf95+wMcvu8g/+uHt/OZH+RL0Q9avZzzf3MP2wt0Rb7H677xELduXsaqwV3Q3ziYHeirTNln9u5if9qs0tu8zLhn+fja7vmwbtUy/vqVJzZ+87BTYNUh8JMvTA5mb/1XqPTb/EmSJGmOZgpm+4Fy4DoCbF246agbZTGxZrbcAIo0VsQTpTWz2eQGUFUyRtJEMDsSfZMv3rNsYp/ZDszMAhyzbiVXvfdZpJSXZD+8azf/dvNmbtu8g7NPPoS9B/tmvkg3WZkHs4f0bONXu/dnRexsuh56sL+Hh3dNzr7+5M6tREBfX3/zbsa9i7i9UZbBSb8L//qnsOWXsG+xDvzWf80D2Xkqd5YkSdrTzBTMAvx+RNQWe/UAb4qISXuJpJSmtvjUnuWpf5AHow1kEeNb80xqAJWqeTDbKDNbnEvAcMrXyC6vbmd77Dv54j39eVZ2dCf0dVYDqLKIGE8UrlzWywuPP7C9E2qnIjP7xhNXMLxxNX337WgazK7o7+HebZObZ117x1aO3m8lld4mwezoztl1Mp4PJ/wO/NufwTWfgzPOhwdug803womvXtx5SJIkdZGZgtl7gNeWXm8hX0dblsgbQmlPdvqHm76Vr5nNA93J3YwblRlnk7oZp1KZ8UB1ByO9B02+eM/yiT08OzQzqzpFMHvaQYnTzjwePpqaBrMDfT1Tyoxv3LiNpz16LWztgWqjzOyueV0v25KV+8NjXgjX/294xvvgpu/k5499/uLOQ5IkqYtMG8ymlA5erImoe0VpzWzfpH1mq1OD2fFuxrU1sxm7qvltmlFlNOq2Runph6GiUKBD18yqzsA++b2w/V4Yfjg/12TN7GB/haFSA6itO0a47+Fhjl23Erb1TVNm3IbS3l97C9zwz3DNZ/Ngdr/jYM3hiz8PSZKkLrGH7PWhdiqvmZ2+m3F5n9n8XE+WsbNaGf/I7qwuo9a7HHZszp+bme0OWQUG9yuC2WJv4ekysyMT99TN9+bB79HrVubb4jQrM17szCzAwSflnYsv+wD86io4/hWLPwdJkqQuYjCrBVfJIKWiAVSUG0ClYs1suQFUpXidn1uxvI+tu9L4R0YrDTKztTLjDl4zqzor9oOHW8jM9lUYGa2yeyz/5ceGLfny/ketHcw7XTcrM25X06VnfQCWrYIDngjrX9eeOUiSJHWJVhpASY9IRIyvme2d1ABqjCyrXzMbk7oZ772in/uGSsFsfWa2ZznsejB/bma2e6zYvy4z2/h/24H+/K+woZExVi3PuOfBnWQB6/ZaBpW+ic+X7R7Kg+V2OPAEOHdDsafy/G0NJEmStCcyM6sFVynvM1vemodUt2Y2ppQZrxns576h6vgnqpX6YLaUqXXNbPdYsT9svw+GHshfD+zTcNhgX16CXls3e/eDu1i31zJ6KllRZjw69UOjbWgAVZZlBrKSJEnzwGBWC67Z1jwN18zWNYDae3AZm3ZMZGbH6oPZcrnossalqOpAK/aDHfdNrIce2LfhsFpmttbR+J4Hd3Lg6uKeqPQ2KTNuw9Y8kiRJmncGs1pwWcZ4A6hJmdlUJRpuzTOxz+zgsj4eKG0jWq3PqJUzs03WVaoDrVwH1VHYckt+Tyxf03BYfWb2nodKwWzWC2MjUz+0eyf0tjEzK0mSpHnRdM1sRHyn1YuklM6cn+moGzXrZpxvzcO03YyX9/WwbXdA0dC4Wqlr3FMObpt0vFUHqq1pvfeGvMQ4a/x7t4G+icxstZrY+OAunve4Uma2aZlxmxpASZIkad5M1wDq/kWbhbpaVm4A1bCbcX2ZcZVaN+OB/l52l27TqZnZ4nXvQB68qDus2D9/vOd62OeopsMG+ycys/fvGGFkrMqBq4t7YtoyYzOzkiRJna5pMJtSevViTkTdq5IxpZvxKBWCal0DqKllxgP9vYwxsc9sqs+o1YJZS4y7y6qD88exYdj7iKbDxjOzI2Ns2T4MwNoVRel5ozLjsdE8wHXNrCRJUsdzzawWXDQoMx6LHkjNgtmJBlDL+yf/viX11QUhtQybzZ+6y6pDJgLOvY9sOmw8Mzs8yv3b88B1n1ow26jMeHRn/tjObsaSJEmaFy3vMxsRpwJnA4cBfeX3Ukqnz/O81EUqjYJZKkUDKMYD10bdjAf7J5cOV/vqgtZymbG6RwTsdRDc/wtY9/imw2qZ2e3Do9y/I8/M7j1Y/PVUaZCZ3V10E+t1zawkSVKnaykzGxFnA98nD2SfAwwXz08G7lqw2akrTFozW3QzHoseggZrZusbQPVP+r0JY82C2crkceoCL/gLOP6VcOzzmw4ZGO9mPMaWIjO774riXsgarJndPZQ/GsxKkiR1vFYzs+8B3plS+suIeBh4K7ABuAS4bYHmpi6RTVozO0o1BWNkRTfjcplx5JnZ8prZvroy42WrJl98vLw4oS5zxNPzYxq9lYy+nowdw6Ps2j1GTxbstazI5lf68u19qtWJbsijRWbWMmNJkqSO1+qa2aOAbxbPR4DBlFIV+Djw5oWYmLpHeWueSiTGyEgEpDQRzEZxK0Y2qZtxf2/d71vqGz0Nrs0fawGx9jh7Letl2658zezeg31kWX6vje9BXC413l2smTUzK0mS1PFaDWYfAmqLEjcCjy6eLwdWNfyEVCgHswDVyPJMbSrO1gezpTLj/t7Ja2Zj+erJF9+nuBWPes4CzV5L3ZqBXrbuGOH+HSMT62VhIpitZWPBYFaSJKmLtFpmfBVwKnAD8G3gLyLiBODFwJULNDd1iUpGnoktVIvMbMbYxJrZWjCbVWB0uBTM5msiH+zdn9W77yXrH5x88bVHw//4Maw5fBH+JFqK1gz0sXUo32N231onYygFs8MT58a7GRvMSpIkdbpWg9l3ASuK5x8EVgMvAW4mXz8rNRURkO8qS0aVMSqMkTUvM07V8W7GfUUw+1eHfJyrbtzAO+rLjgH2O3aR/iRaitYM9rJhyxA7d49x6KGlrta1dbFjpWB2vJuxa2YlSZI6XUvBbErpttLzIeBNCzYjdZ1K5FnZKkFGnpmtEkSq5jHspGB2cjfjZX15mfFt1XX8d8roq1Ta8CfQUrZmoI+fDD3I0PAo+wyWM7NFwFrOzNa6GZuZlSRJ6ngt7zMrzVVWBLOJDBijGlnxvNbNOE0uM65OBLO14PXhXfkWK309rS7z1p5i9UAfmx/OA9Z9VpTWzNa2ayqvmR01MytJktQtWt1ndu+I+NuIuCMidkXESPlY6EmqsxWxLGk8Q1vLzKa8BHlKZjZR62acVSr0VTIe3pXvT2swq3prBiaahO23slFmtvRX1MiO/LFvBZIkSepsrWZmLwHWA58G7sZNPTULlWwiiIU8Q1sl8lW04w2giog3oigzrt1iQX/vRDDbWwmksnIH4wNWlcqHG3UzHtmePxrMSpIkdbxWg9lnAmemlOxcrFnLSmtmAapRoZqKNbONuhmXyoyJjP6eCtuKMuN+M7Oqc8S+Ex2u161qlJktBbPD2/Psf09pnCRJkjpSq5HBg8DWhZyIuldW3GXjmdnIisC2UTfjyuTMbAT9PaUyYxtAqc6j1k5kWRtnZksNoEZ2QP+KiUoASZIkdaxWM7MfBd4bEa9NKY0u5ITUfSYaQJXXzGZFZjamZmZTtZSZDZb1TvzOxTWzqrdmsI+nPmofVi3vZbC/9FdaLZgdqwtmLTGWJEnqCq0Gsy8GngLcGRE/ByY1fUopnTnfE1P3qG3NMzaema2Mr5kNmLrPbHUMmOhw3N8zkY11zawa+dLrnkyW1d0bDTOzD0PfIJIkSep8rQazW4BvLeRE1L3qM7MpsmLNbCo1gCqXGVcnnes3M6sZTAlkofGaWTOzkiRJXaOlYDal9OqFnoi6V0xZM1spdTOuLzPOSg2g8gBlWSkzazCrljXammd4u5lZSZKkLtFqZhaAiDgAeCz51jw3ppQ2Lsis1FVqZcbVVMrMAqRaA6jUIDObGmdmKwazalGl2LKnPjO7+pD2zEeSJEnzqqXIICKWR8RngV8BlwGXA7+KiEsiYtlCTlCdL2u4ZjYjqObJ10n7zGZFN+NSmXGRje3ryfKGUVIrxjOz5TWzZmYlSZK6RatprguA04GXA2uB/YBXAM8t3pOamtiap4U1s+V9ZovAdVlvXmbsHrOalUpPnumflJnd7ppZSZKkLtFqmfHLgHNSSpeWzv1jROwEPgf8wbzPTF2jlpmdCGZ7GGM3vY3WzNbKjGFKZrbc1VhqSc+yBlvzmJmVJEnqBq2mulYDtzU4fyuwav6mo240vjVPqpUSFw2gUsqTr/Vb89R3M+4xM6s56umH3Tvz59Ux2D1kZlaSJKlLtBod3AC8rsH51xfvSU3VlrmOZ2azSl5m3LCbcWVqN+PeqY2gpJb0rYCRofz5yI78sd9gVpIkqRu0WmZ8HvDPEXEK8APybsa/DjwZePHCTE3dIiKIgLGU5fFpVBgrtuaJKWXGtQZQaTwKrmVm7WSsWesbgN1FEDuyvThnmbEkSVI3aCk6SCl9E3gScCfwfOAFxfMnpZS+vXDTU7eoRIxnZqllZps1gKorM65lZnsqdjLWLPUNTmRkh2vB7Mr2zUeSJEnzpuV9ZlNK1wGvXMC5qItlEVTrtuYh7c7D20n7zGZTuhnb+Elz1jswEczuejB/XL66ffORJEnSvLFuU4siyyCVMrOJIEilNbMTzaFIY8BEgOtaWc1Z34qJYHbn1vxx+Zr2zUeSJEnzpmlmNiKGgMNSSpuLLXhSs7EppYGFmJy6RxbB2KTMbBCptGa2FOgCUB2dKDM2M6u56itlZncWmdllZmYlSZK6wXQpr7cA20rPpzsWREScGRHXR8RwRGyIiHe08JneiLggIjZGxM6IuCIiTmow7tyIuCMidkXEdRFxeoMx50TEzcX33xQRr5rtHCPiuIj4akT8IiKqEXHJbH8O3aC8Zna8zJhqg615asHsGLUA18ys5qxvMN+OBywzliRJ6jJNM7Mppc+Wni96ABYR64GvAx8HzibvnHxxRAyllC6e5qMXAq8GXkO+N+65wOUR8ZiU0qbi2m8DPgi8Cbi6GPvNiHhSSulnxZgXA58F3gV8l7zx1Rci4oGU0ndnMccB8mZZ3wBmDMa7VQRUU6kBFOUGUOU1s7V9fCYys7X9ZVPT2gCpid7BqWXGZmYlSZK6Qkspr4i4JSL2bnB+dUTcMv/TAvLA7+qU0ntSSjemlD4P/E/gD6eZ50rgzcB7U0rfSCn9N3mgOlycJyICeDdwUUrpC8W1zwV+xuRg81zgKymli1JKN6WUPg78U933zzjHlNLVKaV3ppS+CDz0iH4iHSzLglQqM05FmXHDfWYBxnaX1szm55b3Wm6sWeobzLfkSSkvM+5bCZWW+95JkiRpCWu1fvMoGmdx+4DD5202k50CXFp37lLg8Ig4uMln1gP95c+llMaAy4BTi1OHAwc2ufapABHRR74VUaMxT4mo1cLOaY7Tiog3RsQ1EXHN5s2b53KJJalSWjNL1kOVrNhnliZlxqPjWdqBIojt67HcWLPUN5DfX6PDeZmxJcaSJEldY9oURUQ8tfTySRGxtfS6ApwO3NXql0XEAHnZ7XSGUkpDwAHAprr3aq8PaPK9B9SNK3/uxBbG1N7bl/xn02hMP7A3sHmOc5xWSukzwGcA1q9f3zWFtVHeZ7bWAIpSA6jy1jyQr5ktgtkTD1vDy046mNecckQbZq6O1juYP47syMuMLTGWJEnqGjPV211B3sU4Ad9s8P4Q8Puz+L5zgT+ZYcxHgD+aYcxcgrxWPtPqdefzWnuEyqStebLxbsZNy4yrE2XGvZWMC192fBtmrY7XVwtmt8P2+2DFfu2djyRJkubNTMHsEeQtZW8DTibPRtaMAPemlKqz+L4LgL+aYUzRepSNwLq69/YvHuuzoTUbi8d15E2Xyp/b1GDMLU3GbAFGm3z/MFDLUM9ljnukLIJqqmVfa2XGjRpAlcqMa8GvNFfLVuWPux6ChzfBfo9p73wkSZI0b6ZdhJhSuiOltAHoTSldU7yuHRtnGciSUhpKKW2Z4agFsz8Enlt3iTOAO1JKzcp3ryUPNsc/FxEZ8GzyLDPABuCeJte+opjnCHmX40ZjrirW4c51jnukfJ/ZYmueoptxlorC41Sd6GKc1cqMJ7oZS3O2fE3+OHQ/bL8XVtb/7kmSJEmdqtW2nm8vtqT5XPlkRLwG2Lvo9DvfLgKujIiPAF8kzwy/BXh76fvPAj4KPCuldHdKaVtEXAycHxEbgdvJOxcvBz4NkFJKEXFhMeZG4BrgHOB44A2l778A+IeI+E/ypk7PB34TeOEs59gHPLZ4uQLYOyKeCIyklH7+yH5EnSPLKPaWpdiap5aZrZUZ9+bv1QLYMYNZzYOBogn7/b+ENAYrDGYlSZK6RavB7O+Rb3FT7xbg78j3WZ1XKaWri71ezyff63UT8P66PWZXAccAvaVz7yYvgb4EWE2erX1OSqlWXkxK6RNFkHk+eVnwjcCLUko/LY35WkS8Hngf+d61twPn1PaYncUcDwSuK70+CTgLuIOF6wS95GQR42tmI8u35slIDRpATe1mLM1ZLTN73435o5lZSZKkrtFqMHsQk9eg1txdvLcgUkrfBr49zfufBz5fd243eaOpc2e49gXk2dfpxky5/hzmuAEXf1IpdTNO0UM1BZElKhkzNoCS5qwWzN773/njZladKAAAHUtJREFUygOaj5UkSVJHaTVa2Awc1+D844EH5m866lYRjO8zG0WZcUaDbsaTMrMGs3qEepdD/17wqx/nr/c9qr3zkSRJ0rxpNVr4Z+CiiHh87UREPIG8vPifFmJi6i6VLEpb8xQNoEhk2TT7zJrQ1nxYc1j+OLh2IlMrSZKkjtdqMPs+4F7g+ojYVDRXuo48Y/vehZqcukdWXv9aWjNbabbP7Jhlxponaw7PHw8+ua3TkCRJ0vxqac1sSmk78LSIeC5wYnH6WuCylFJaqMmpe0QpmI2shypBUKWSRd0+s7XM7G6I5W2YqbrOEb8ON34TnvCyds9EkiRJ86jVBlAApJT+BfiXBZqLulglg6D4vcf4mtny1jxFsFsuM7absebD+tfBsS+AvWz+JEmS1E1aDmYjYhVwOnAY0Fd+L6V0/jzPS10ma5CZzZium7ENoDRPssxAVpIkqQu1FMxGxEnApeRB7CB5B+N9gSHgPvJ9VqWm8n1mawFrT7FmttqgAZRrZiVJkiTNrNVo4c+BbwB7AzuBpwBHkDeBevvCTE3dJAvG95mtbc0T0zWAqo5iN2NJkiRJzbQazD4R+POU0hhQBfpSSncAf4hZWbWgkkVdMFsrM55max4zs5IkSZKaaDVaqAIjxfP7gIOL55vJM7TStCJiPM8alYk1s1GfmZ3UzdhgVpIkSVJjrTaA+i/geOBW4MfAeyMiAW8EfrFAc1MXyQIyqgCknuUkMjKq05cZ281YkiRJUhOtBrMfJW/8BPAB4LvAZeSNoF66APNSl6lkMR7M0tNf7DNb62acpjaAspuxJEmSpGm0Gsx+H9gNkFK6DTgmIvYDtqSUqgs1OXWPLPKyYoAogtmM1KCbcSmANZiVJEmS1MSM0UJE9JBvwfPY8vmU0n0GsmrVpGA2oghmy2XGRUlxrcxYkiRJkqYxYzCbUhoF7mplrNRMFnBfWg1A7+5tJDIq0aibcSmYNTMrSZIkqYlWo4WLgD+OiP6FnIy6VyULPjp6NlesfB5DjzqTasozsRlYZixJkiRp1lpdM3sG8FTg7oi4AdhRfjOldOZ8T0zdJSLYyl58ed27eVff4Piesz1ZqutmXA5m7WYsSZIkqbFWg9ktwDcWciLqblltSWwElSyoFkUBWSTLjCVJkiTNWkvBbErp1Qs9EXW3SlbLxAZZBKmWmYXG+8yCwawkSZKkpqaNFiLi5RHRt1iTUfeKomQ4y2qZ2fx1Jap1+8yWb0nLjCVJkiQ1NlPq6/8HVtdeRMQvIuLQhZ2SulElYvyxHMxaZixJkiRpLmaKFupTY+tofZ2tNK62ZraSBRGMr5ntITXfZ9ZgVpIkSVITRgtaFFktM5sFlfKa2fpuxmE3Y0mSJEkzmymYTcVRf06alSwrBbNZMNa0m7HBrCRJkqSZzVQyHMBXI2KkeL0M+EJE7CwPSimdvhCTU/eorZnNIsjKDaDAMmNJkiRJszZTMPt3da+/tFATUXerVCaXGY+vmY1qEcwWQWy5AZTdjCVJkiQ1MW0wm1J6zWJNRN2tt7TPbCWbWDM7pczYzKwkSZKkFhgtaFFUsmKNbBZkMVFm3EMVaLLPrMGsJEmSpCaMFrQoeisT+8z2ZBNlxlkU/cRsACVJkiRpFgxmtSgqRZlxltU3gBrLB1hmLEmSJGkWjBa0KMb3mS0ea2tmK6maD6hlYSdlZsvNoCRJkiRpgsGsFkVWVzFcTbXM7Gh+YrzM2MysJEmSpJkZLWhxFJnXasrXyNbWzPZFLTPboMw48/aUJEmS1JjRghbFWDUPWnuKFG1tzWxfVhfMmpmVJEmS1AKjBS2K0Wqeke2p5LdcLTObb81Dk27GrpmVJEmS1JjBrBZFUF9m3CQzWy4tzgxmJUmSJDVmMKtF0deT32rDo3nwWsvM9tZvzQMTGVnLjCVJkiQ1YbSgRbHvij4ABvryQLW2NU9vfWYWJjKylhlLkiRJaqKn3RPQnuGVJx/KWDXxqicfBkyUGU+smS3t3TO+frZuPx9JkiRJKhjMalH0VDJec8oR469rwezynnwNbcMyY9fMSpIkSWrCMmO1xaH7rAAadDMGy4wlSZIkzchgVm3xnjMfmz+pjuaPkzKzMfWcJEmSJJUYLagt+nuLCveGwaxlxpIkSZKmZzCr9qgFr9VGW/PUGkAZzEqSJElqzGBW7VELWMd2T37daIwkSZIk1TFaUHuMZ2YblBnXZN6ekiRJkhozWlB71EqIpwtmzcxKkiRJasJoQe0xZc1slN6rdTN2zawkSZKkxgxm1R7TlRmnYu/ZrGdx5yRJkiSpYxjMqj2mC2Zr2dpK7+LOSZIkSVLHMJhVe7SSma30Le6cJEmSJHUMg1m1R21dbKN9Zs3MSpIkSZrBkg5mI+LMiLg+IoYjYkNEvKOFz/RGxAURsTEidkbEFRFxUoNx50bEHRGxKyKui4jTG4w5JyJuLr7/poh41WznGBGviYjvR8TmiHg4Iq5tdJ09zrSZ2Vowa2ZWkiRJUmNLNpiNiPXA14FLgScC5wHnR8SbZ/johcDrgDcBTwJuAy6PiHWla78N+CDwx8AJwGXANyPiCaUxLwY+C1wMHA/8LfCFiHjeLOf4LOAbwJnFd/098MWIeEXrP40uNO0+s0XW1sysJEmSpCaWcrvYdwBXp5TeU7y+MSKOA/6QPMCcIiJWAm8G/iCl9I3i3GuAu4vz50VEAO8GLkopfaH46LkR8YziO8+pnQO+klK6qHh9U0Q8pfj+77Y6x5TSb9dN88KIeDrwcuArs/mBdJXpgtms2JInM5iVJEmS1NiSzcwCp5BnPMsuBQ6PiIObfGY90F/+XEppjDzzempx6nDgwCbXPhUgIvrIs7qNxjwlYnwD1LnMEWAVsGWa97vfdMFs7bllxpIkSZKaWNTMbEQMAAMzDBtKKQ0BBwCb6t6rvT4AuKvBZw+oG1f+3IktjKm9ty/5z6bRmH5gb2DzXOYYEb8NPAV4W4P5ExFvBN4IcOihhzYa0h2mBLNRfjN/sMxYkiRJUhOLnZk9lzwInO54XwvXSXP47lY+0+p153StiPgN8rW3r0sp/aThh1L6TEppfUpp/dq1a1ucTgeqlRI33Gd2d/7Yt2Jx5yRJkiSpYyz2mtkLgL+aYcxQ8bgRWFf33v7FY302tGZj8bgOuLPuc5sajLmlyZgtwGiT7x8Gts52jhHxW8DngTeklL7YZP57junKjMeKYLZ/5eLOSZIkSVLHWNTMbEppKKW0ZYajFsz+EHhu3SXOAO5IKTUqMQa4ljzYHP9cRGTAs4ErilMbgHuaXPuKYp4jwNVNxlxVrMNteY4R8QbyQPZ3DWQL4/vMNghm+4pK9N7lizsnSZIkSR1jKTeAugg4OSI+EhHHRsTvAG8B/qw2ICLOKvZ/PQggpbSNvIvw+RHxgqKz8OeA5cCnizGJfPuet0fEbxfX/jPy7XcuKn3/BcArIuKtEXFMsX/sbwIfm+Uc3w58Cngr8O8Rsa449p7Hn1XnGc/Mjk1+DfDii2HN4bBiv0WfliRJkqTOsGS35kkpXV3s9Xo+8C7yst33p5TK2/KsAo4Byp2C3g2MAJcAq8mztc9JKdXKi0kpfaLoWHw+eVnwjcCLUko/LY35WkS8nnwN74XA7cA5KaXvlsa0Mse3AhXyILt8/t+B02b7c+ka05UZH3tmfkiSJElSE5EnKrUUrV+/Pl1zzTXtnsbCeOguuOg4OO4suOGf4bXfg0Of3O5ZSZIkSVpCIuLalNL6Ru8t5TJjdbPpMrOSJEmSNAMjCLXHdGtmJUmSJGkGRhBqjymZ2WjfXCRJkiR1HINZtYdlxpIkSZIeASMItUcteB3bPfm1JEmSJLXACELt4ZpZSZIkSY+AEYTawzJjSZIkSY+AEYTaw2BWkiRJ0iNgBKH2MJiVJEmS9AgYQag9XDMrSZIk6REwglB7uM+sJEmSpEfAYFbtYZmxJEmSpEfACELtYTArSZIk6REwglB71MqKXTMrSZIkaQ6MINQeEUCYmZUkSZI0J0YQap/IoLp74rkkSZIktcgIQu0TmZlZSZIkSXNiBKH2ySqumZUkSZI0J0YQah8zs5IkSZLmyAhC7TMpmI32zkWSJElSRzGYVfuYmZUkSZI0R0YQap9yNtZgVpIkSdIsGEGofcoBrMGsJEmSpFkwglD7GMxKkiRJmiMjCLWPwawkSZKkOTKCUPsYzEqSJEmaIyMItY/BrCRJkqQ5MoJQ+xjMSpIkSZojIwi1T1RKz6P5OEmSJEmqYzCr9hkPYMNgVpIkSdKsGMyqfWqlxZYYS5IkSZolowi1j8GsJEmSpDkyilD7GMxKkiRJmiOjCLWPwawkSZKkOTKKUPuMB7M2f5IkSZI0Owazap/xYLYy/ThJkiRJqmMwq/apBbOZwawkSZKk2TGYVfvUyosNZiVJkiTNksGs2scyY0mSJElzZDCr9qllZLOe9s5DkiRJUscxmFX7uGZWkiRJ0hwZzKp93GdWkiRJ0hwZRah9zMxKkiRJmiODWbXPeDDrmllJkiRJs2Mwq/axm7EkSZKkOTKYVfu4z6wkSZKkOTKYVfu4ZlaSJEnSHBnMqn0sM5YkSZI0Rwazah8zs5IkSZLmyGBW7WNmVpIkSdIcGcyqfdyaR5IkSdIcGcyqfWoZ2czbUJIkSdLsGEWofTLLjCVJkiTNzZIOZiPizIi4PiKGI2JDRLyjhc/0RsQFEbExInZGxBURcVKDcedGxB0RsSsirouI0xuMOScibi6+/6aIeNVs5xgRz42IH0XEluK7bo2IP42Ivtn+PLpO1ls8GsxKkiRJmp0lG8xGxHrg68ClwBOB84DzI+LNM3z0QuB1wJuAJwG3AZdHxLrStd8GfBD4Y+AE4DLgmxHxhNKYFwOfBS4Gjgf+FvhCRDxvlnPcBnwSOA04Bngn8EbgY63+LLpWpRbMumZWkiRJ0uxESqndc2goIr4MHJ5Semrp3IXAS1NKRzT5zEpgM/AHKaXPFOcqwN3AxSml8yIigLuAv0spva/02auBG1JK5xSvrwQ2pJReWRrzVWBtSum0uc6xGHMRcFpK6YTpfgbr169P11xzzXRDOtvXfh+u/xIc83w4+8vtno0kSZKkJSYirk0prW/03pLNzAKnkGc8yy4FDo+Ig5t8Zj3QX/5cSmmMPPN6anHqcODAJtc+FaAoAX5SkzFPKQLkOc0xIo4Fngd8v8mfYc9RKTKyNoCSJEmSNEuLWt8ZEQPAwAzDhlJKQ8ABwKa692qvDyDPrtY7oG5c+XMntjCm9t6+5D+bRmP6gb3JM8AtzzEi7gLWAn3AZ4B3N5g/EfFG8jJkDj300EZDukdmmbEkSZKkuVnslNi55EHgdMf7mn56wlxqo1v5TKvXncu1nkYeUL8aeAHwgYYfSukzKaX1KaX1a9eubXE6HaoWxNrNWJIkSdIsLXZK7ALgr2YYM1Q8bgTW1b23f/FYnw2t2Vg8rgPurPvcpgZjbmkyZgsw2uT7h4Gts51jSun24ukNETEGfCkiLkgp7WjyZ+l+42XGBrOSJEmSZmdRM7MppaGU0pYZjlow+0PguXWXOAO4I6XUqMQY4FryYHP8cxGRAc8GrihObQDuaXLtK4p5jgBXNxlzVbEOd65zhPznngG904zpfrUyYzOzkiRJkmZpKS9WvAi4MiI+AnwROBl4C/D22oCIOAv4KPCslNLdKaVtEXEx+fY4G4HbydemLgc+DZBSSkXH4fMj4kbgGuAc8u133lD6/guAf4iI/yRv6vR84DeBF85yju8EbiLPAifyJlUXAN9IKT34SH9IHW18ax4bQEmSJEmanSUbzKaUri72ej0feBd52e77U0oXl4atIt+7tZzhfDcwAlwCrCbP1j4npVQrLyal9ImiY/H55GXBNwIvSin9tDTmaxHxevI1vBeSB8bnpJS+O8s59hafPwyoAncAfw18Yq4/m66R7dmJaUmSJElzt2T3mdUesM/sFRfB5efBE18FL/6bds9GkiRJ0hLTqfvMqtvVMrOp2t55SJIkSeo4BrNqn4oNoCRJkiTNjcGs2md8n9lo7zwkSZIkdRyDWbVPZJMfJUmSJKlFRhFqn9paWYNZSZIkSbNkFKH2MZiVJEmSNEdGEWqf6lj+aDArSZIkaZaMItQ+qw7KHw86qb3zkCRJktRxeto9Ae3BjjkT3vQDOOD4ds9EkiRJUocxM6v2ySoGspIkSZLmxGBWkiRJktRxDGYlSZIkSR3HYFaSJEmS1HEMZiVJkiRJHcdgVpIkSZLUcQxmJUmSJEkdx2BWkiRJktRxDGYlSZIkSR3HYFaSJEmS1HEMZiVJkiRJHcdgVpIkSZLUcQxmJUmSJEkdx2BWkiRJktRxDGYlSZIkSR3HYFaSJEmS1HEMZiVJkiRJHcdgVpIkSZLUcQxmJUmSJEkdx2BWkiRJktRxDGYlSZIkSR0nUkrtnoOaiIjNwB0LdPl9gS0LdG3J+0sLyftLC8V7SwvJ+0sLpdvvrcNSSmsbvWEwu4eKiGtSSuvbPQ91J+8vLSTvLy0U7y0tJO8vLZQ9+d6yzFiSJEmS1HEMZiVJkiRJHcdgds/1mXZPQF3N+0sLyftLC8V7SwvJ+0sLZY+9t1wzK0mSJEnqOGZmJUmSJEkdx2BWkiRJktRxDGb3MBFxZkRcHxHDEbEhIt7R7jlpaYuId0fEjyJia0Q8GBFXRMQZDcY9OSKujIhdEbExIj4aEZW6MUdHxL9ExFBEbImIiyNicPH+NFrqIuKZETEWEb+sO+/9pVmLiH0j4lMRcU/x797tEfHmujHeW5q1iMgi4gMR8cuI2BkRd0bEX9bfF95fakVEPD0ivh4Rd0REiog/ajBmXu6liDggIv5PRGwrjr+PiP0W+s+4UAxm9yARsR74OnAp8ETgPOD8+n/YpTrPBD4HPAN4MnAV8K2IOKU2ICIOAS4DbgZOAn4PeBPwkdKYFcD/BUaBpwIvB84APrsofwoteRGxP/B35PdS+bz3l2atuCd+ABwFnA0cA7wS+HlpjPeW5uqdwLuBPwQeA7wBeCnwF7UB3l+ahRXkfzedC2yqf3O+7qWIyIBvAUcAzwFOB44GvhYRsQB/roWXUvLYQw7gy8CVdecuBG5v99w8OusA/gv4eOn1+cBdQFY69/vADmCweP1GYCewqjTm+UACjmj3n8mjvQf5L1cvB95D/ou2X5be8/7ymMs99UFgA9A/zRjvLY85HcDXgH+sO/dx4LrSa+8vj1kfxd9bf1R3bl7uJfLgNQHHlMYcV5w7rd1/9rkcZmb3LKeQZ2XLLgUOj4iD2zAfdaDit3orgS2l06cA30spVUvnLgUGgBNKY36UUnqoNOZ7QLV4T3u2Pyb/x/SCBu95f2kuXgJcAVxUlOTdFBEXRsRAaYz3lubqCuCUiHgCQEQcCZwJfLs0xvtL82W+7qVTyJNYN9cGpJRuIA+UT12guS8og9k9ywFMLV3YVHpPasX7gNXAF0vnWrm3poxJKe0GHsD7b48WEc8A3gy8uu4f6hrvL83Fo8jLPgeBF5KX770C+NvSGO8tzdXHgb8GfhIRu4Fbgf8g/8VcjfeX5st83UuNrlO7Vkfebz3tnoCWDDcc1owi4n+QB7MvSindNcPwVPfYyljtYSJiX+BLwGtTSo3+gW3G+0szycgrSF6XUhoFiIg+4KsR8ZaU0gNNPue9pVa8lHzd4muA68nXZF8E/Cnw/mk+5/2l+TLf91JH3m8Gs3uWjcC6unP7F4+z+Y9I7YEi4l3ka9BelFK6vO7tRvdW7fWm0phD6q7ZC+yN99+e7HHAgcA3S70nMiAiYhT4Hby/NDcbgQ21QLZwQ/F4GHm2wntLc/Vx4JMppVqV0n9FxHLgcxHx4ZTSLry/NH/m617aCDy7wfX3p0PvN8uM9yw/BJ5bd+4M4I4Wsmzag0XEh4A/Ac5sEMhCfm89p1hPW3MGMARcVxrzaxGxV2nMc8j/Hvrh/M9aHeJq4PHkHdZrx8XAr4rn38b7S3PzH8Cj6rauOKZ43FA8em9prgbJ1yKWjQFRHOD9pfkzX/fSD4EjIuLRtQER8RjyIPiKBZr7wmp3ByqPxTuAJwG7ydt4H0ue8dgJvLndc/NYugfwieI+eTH5bwFrR7lb3iHANvL278cBLwLuB/6sNGYFeYDyLeB48q1+bgf+vt1/Ro+ldTC1m7H3l8dc7qPjgWHgU+RB7DOAXwJ/VxrjveUx1/vrs8C9wFnA4eTJgtuAb5bGeH95tHo/rWDiF7r3AH9VPD9qPu8l8sD2WuDHwMnkWy5eA/wIiHb/HOb0s2v3BDwW+X/wvEX3T4t/4O8A3tHuOXks7YN8DUWj4/N1454CXAnsIi9V+ShQqRtzDHlnvaHiL+FPU7SU9/CoHdQFs8U57y+PWR/As8iz/7vIs7EXAgN1Y7y3PGZ9kGdmLywC2F3AncDfAHvXjfP+8mjlfjqtyX9r/dt830vkjZ6+CjxMHiB/Bdiv3T+DuR5R/KEkSZIkSeoYrpmVJEmSJHUcg1lJkiRJUscxmJUkSZIkdRyDWUmSJElSxzGYlSRJkiR1HINZSZIkSVLHMZiVJEmSJHUcg1lJkpaQiPh8RKQGx2+1e26SJC0lPe2egCRJmuI/gJfXnXuwflBE9KaUdi/OlCRJWlrMzEqStPSMpJQ21R27IuJLEXFpRLwtIu4AhiNiWeTeGhE3R8SuiLglIt4bEeO/tI6IfSLiqxGxIyLujYgP1q5XGnNFRFxcnkhEnBcRv6w796qI+GnxXbdHxJ9HxEDddT4dEX9SfNf9EfHZ8phi3NkR8ZPiOvdHxHciYq+IeEPxelnd+A9HxC8iIubp5yxJ6mAGs5IkdZanAqcCLwKOB0aADwNvA84FHgO8Hfh94P2lz32+GP8C4FnA0cU1ZiUiXg/8JXAh8FjgHOAM4K/rhr4CWAk8Hfht4GXAO0vXeQPwBeAfgROAZwCXkVeNfbl4fElpfKX4rktSSmm285YkdZ/w3wNJkpaOiPg8efC3q3T63pTSoyLiS8CZwMEppaFi/ApgM/DClNLlpeu8FrggpbRvRBwL3Ag8M6X0/eL9ZcAG4PqU0hnFuSuA/04pvbl0nfOA304pHVW8vgs4L6V0SWnMM4HLgVUppYeL6wyklE4sjbkEOCal9LQis3o38H9SSm9r8nP4G+CxKaXTitfPB/4ZOCSldG/rP1FJUrdyzawkSUvPj4HfLb0eLT2/oRbIFh4PLAO+HhHl31BXgGURsYY8g5qAH9XeLMqWr2EW/y0QEQcABwF/GRGfKL9VHEcB1xXnrq/7+N3kWVqAA4rje9N83cXATyPi6JTSLcAbgG8YyEqSagxmJUlaenamlH7Z5L0dda9rS4bOAm5rMH4beaDZimqDsb0Nvuv/A37Q4PO/Kj0fqXsvMXV5U9PysJTSzyLiKuD1EfEXwPOLQ5IkwGBWkqRO91/AMHBkSqlhpjMibiAPUn8NqJUZ9wMnAT8tDb0POLDu4yeWnt8DbASOTil97hHMeWNxPBf47jTjPg18DNgO3EW+plaSJMBgVpKkjpZS2hYRHwM+FhEZ8H/J/30/Hnh8Sum9KaWbIuI7wKci4k3AFuB9wGDd5S4HPhkRLyUPcl9GHgBvKb4rRcT7gYsjYhvwdfIS6McCp6eUfq/FOaeI+BDwPyPiPvK1sBl5Y6ovpZQeKIZ+BbiIvJHVh2z8JEkqs5uxJEkdLqX0J8C7gTcBPwOuAP6AvMFTze8CN5BnQr8P3A58s+5SnwU+A/wN8J/AOuq6FKeU/hdwNnkn5GuAq4EPkK+Jnc2cLwZeB/wW+frafwdOp7Q+OKW0E/gS+X+vPJJMsCSpC9nNWJKkPVTRHXnfWjfjpSgi/on8v1fOavdcJElLi2XGkiRpyYmIvcm7H78IOK29s5EkLUUGs5IkaSn6GbAKOD+ldEW7JyNJWnosM5YkSZIkdRwbQEmSJEmSOo7BrCRJkiSp4xjMSpIkSZI6jsGsJEmSJKnjGMxKkiRJkjqOwawkSZIkqeP8P1vqc/4r9uxLAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 1080x648 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "test_mc = 25\n",
     "test_q = 2\n",
@@ -473,7 +248,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -484,22 +259,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x648 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.figure(figsize=(15,9))\n",
     "plt.semilogy(surros,'o',color='black')\n",
@@ -519,95 +281,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Quadratic Iter:  1\n",
-      "Quadratic Iter:  2\n",
-      "Quadratic Iter:  3\n",
-      "Quadratic Iter:  4\n",
-      "Quadratic Iter:  5\n",
-      "Quadratic Iter:  6\n",
-      "Quadratic Iter:  7\n",
-      "Quadratic Iter:  8\n",
-      "Quadratic Iter:  9\n",
-      "Quadratic Iter:  10\n",
-      "Quadratic Iter:  11\n",
-      "Quadratic Iter:  12\n",
-      "Quadratic Iter:  13\n",
-      "Quadratic Iter:  14\n",
-      "Quadratic Iter:  15\n",
-      "Quadratic Iter:  16\n",
-      "Quadratic Iter:  17\n",
-      "Quadratic Iter:  18\n",
-      "Quadratic Iter:  19\n",
-      "Quadratic Iter:  20\n",
-      "Quadratic Iter:  21\n",
-      "Quadratic Iter:  22\n",
-      "Quadratic Iter:  23\n",
-      "Quadratic Iter:  24\n",
-      "Quadratic Iter:  25\n",
-      "Quadratic Iter:  26\n",
-      "Quadratic Iter:  27\n",
-      "Quadratic Iter:  28\n",
-      "Quadratic Iter:  29\n",
-      "Quadratic Iter:  30\n",
-      "Quadratic Iter:  31\n",
-      "Quadratic Iter:  32\n",
-      "Quadratic Iter:  33\n",
-      "Quadratic Iter:  34\n",
-      "Quadratic Iter:  35\n",
-      "Quadratic Iter:  36\n",
-      "Quadratic Iter:  37\n",
-      "Quadratic Iter:  38\n",
-      "Quadratic Iter:  39\n",
-      "Quadratic Iter:  40\n",
-      "Quadratic Iter:  41\n",
-      "Quadratic Iter:  42\n",
-      "Quadratic Iter:  43\n",
-      "Quadratic Iter:  44\n",
-      "Quadratic Iter:  45\n",
-      "Quadratic Iter:  46\n",
-      "Quadratic Iter:  47\n",
-      "Quadratic Iter:  48\n",
-      "Quadratic Iter:  49\n",
-      "Quadratic Iter:  50\n",
-      "Quadratic Iter:  51\n",
-      "Quadratic Iter:  52\n",
-      "Quadratic Iter:  53\n",
-      "Quadratic Iter:  54\n",
-      "Quadratic Iter:  55\n",
-      "Quadratic Iter:  56\n",
-      "Quadratic Iter:  57\n",
-      "Quadratic Iter:  58\n",
-      "Quadratic Iter:  59\n",
-      "Quadratic Iter:  60\n",
-      "Quadratic Iter:  61\n",
-      "Quadratic Iter:  62\n",
-      "Quadratic Iter:  63\n",
-      "Quadratic Iter:  64\n",
-      "Quadratic Iter:  65\n",
-      "Quadratic Iter:  66\n",
-      "Quadratic Iter:  67\n",
-      "Quadratic Iter:  68\n",
-      "Quadratic Iter:  69\n",
-      "Quadratic Iter:  70\n",
-      "Quadratic Iter:  71\n",
-      "Quadratic Iter:  72\n",
-      "Quadratic Iter:  73\n",
-      "Quadratic Iter:  74\n",
-      "Quadratic Iter:  75\n",
-      "Quadratic Iter:  76\n",
-      "Quadratic Iter:  77\n",
-      "Quadratic Iter:  78\n",
-      "Quadratic Iter:  79\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "hp1_quad = (numpy.absolute(hp1))**2\n",
     "known_quad_bases_start = numpy.array([hp1_quad/numpy.sqrt(numpy.vdot(hp1_quad,hp1_quad))])\n",
@@ -619,21 +295,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "20 basis elements gave 95 bad points of surrogate error >  1e-10\n",
-      "30 basis elements gave 23 bad points of surrogate error >  1e-10\n",
-      "40 basis elements gave 6 bad points of surrogate error >  1e-10\n",
-      "50 basis elements gave 0 bad points of surrogate error >  1e-10\n",
-      "Number of quadratic basis elements is  50 and the linear ROQ data save in B_quadratic.npy\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "known_quad_bases = numpy.load('./quadraticbases.npy')\n",
     "pyroq.roqs_quad(tolerance_quad, freq, ndimlow_quad, ndimhigh_quad, ndimstepsize_quad, known_quad_bases, nts, nparams, params_low, params_high, distance, deltaF, f_min, f_max, waveFlags, approximant)\n"
@@ -641,7 +305,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -654,22 +318,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x648 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "test_mc_quad =22\n",
     "test_q_quad = 1.2\n",
diff --git a/Tutorial/.ipynb_checkpoints/pyroq-checkpoint.py b/Tutorial/.ipynb_checkpoints/pyroq-checkpoint.py
deleted file mode 100644
index b07e1e9..0000000
--- a/Tutorial/.ipynb_checkpoints/pyroq-checkpoint.py
+++ /dev/null
@@ -1,539 +0,0 @@
-import numpy
-import numpy as np
-import scipy
-import matplotlib
-matplotlib.use('Agg') 
-import matplotlib.pyplot as plt
-import lal
-import lalsimulation
-from lal.lal import PC_SI as LAL_PC_SI
-import h5py
-import warnings
-import random
-import multiprocessing as mp
-
-def howmany_within_range(row, minimum, maximum):
-    """Returns how many numbers lie within `maximum` and `minimum` in a given `row`"""
-    count = 0
-    for n in row:
-        if minimum <= n <= maximum:
-            count = count + 1
-    return count
-
-# Calculating the projection of complex vector v on complex vector u
-def proj(u, v):
-    # notice: this algrithm assume denominator isn't zero
-    return u * numpy.vdot(v,u) / numpy.vdot(u,u) 
-
-# Calculating the normalized residual (= a new basis) of a vector vec from known bases
-def gram_schmidt(bases, vec):
-    for i in numpy.arange(0,len(bases)):
-        vec = vec - proj(bases[i], vec)
-    return vec/numpy.sqrt(numpy.vdot(vec,vec)) # normalized new basis
-
-# Calculating overlap of two waveforms
-def overlap_of_two_waveforms(wf1, wf2):
-    wf1norm = wf1/numpy.sqrt(numpy.vdot(wf1,wf1)) # normalize the first waveform
-    wf2norm = wf2/numpy.sqrt(numpy.vdot(wf2,wf2)) # normalize the second waveform
-    diff = wf1norm - wf2norm
-    #overlap = 1 - 0.5*(numpy.vdot(diff,diff))
-    overlap = numpy.real(numpy.vdot(wf1norm, wf2norm))
-    return overlap
-
-def spherical_to_cartesian(sph):
-    x = sph[0]*numpy.sin(sph[1])*numpy.cos(sph[2])
-    y = sph[0]*numpy.sin(sph[1])*numpy.sin(sph[2])
-    z = sph[0]*numpy.cos(sph[1])
-    car = [x,y,z]
-    return car
-
-def get_m1m2_from_mcq(mc, q):
-    m2 = mc * q ** (-0.6) * (1+q)**0.2
-    m1 = m2 * q
-    return numpy.array([m1,m2])
-
-def generate_a_waveform(m1, m2, spin1, spin2, ecc, lambda1, lambda2, iota, phiRef, distance, deltaF, f_min, f_max, waveFlags, approximant):
-    test_mass1 = m1 * lal.lal.MSUN_SI
-    test_mass2 = m2 * lal.lal.MSUN_SI
-    lalsimulation.SimInspiralWaveformParamsInsertTidalLambda1(waveFlags, lambda1)
-    lalsimulation.SimInspiralWaveformParamsInsertTidalLambda2(waveFlags, lambda2)     
-    [plus_test, cross_test]=lalsimulation.SimInspiralChooseFDWaveform(test_mass1, test_mass2, spin1[0], spin1[1], spin1[2], spin2[0], spin2[1], spin2[2], distance, iota, phiRef, 0, ecc, 0, deltaF, f_min, f_max, 0, waveFlags, approximant)
-    hp = plus_test.data.data
-    hp_test = hp[numpy.int(f_min/deltaF):numpy.int(f_max/deltaF)]
-    return hp_test
-
-def generate_a_waveform_from_mcq(mc, q, spin1, spin2, ecc, lambda1, lambda2, iota, phiRef, distance, deltaF, f_min, f_max, waveFlags, approximant):
-    m1,m2 = get_m1m2_from_mcq(mc,q)
-    test_mass1 = m1 * lal.lal.MSUN_SI
-    test_mass2 = m2 * lal.lal.MSUN_SI
-    lalsimulation.SimInspiralWaveformParamsInsertTidalLambda1(waveFlags, lambda1)
-    lalsimulation.SimInspiralWaveformParamsInsertTidalLambda2(waveFlags, lambda2) 
-    [plus_test, cross_test]=lalsimulation.SimInspiralChooseFDWaveform(test_mass1, test_mass2, spin1[0], spin1[1], spin1[2], spin2[0], spin2[1], spin2[2], distance, iota, phiRef, 0, ecc, 0, deltaF, f_min, f_max, 0, waveFlags, approximant)
-    hp = plus_test.data.data
-    hp_test = hp[numpy.int(f_min/deltaF):numpy.int(f_max/deltaF)]
-    return hp_test
-
-def generate_params_points(npts, nparams, params_low, params_high):
-    paramspoints = numpy.random.uniform(params_low, params_high, size=(npts,nparams))
-    paramspoints = paramspoints.round(decimals=6)
-    return paramspoints
-
-def compute_modulus(paramspoint, known_bases, distance, deltaF, f_min, f_max, approximant):
-    waveFlags = lal.CreateDict()
-    m1, m2 = get_m1m2_from_mcq(paramspoint[0],paramspoint[1])
-    s1x, s1y, s1z = spherical_to_cartesian(paramspoint[2:5]) 
-    s2x, s2y, s2z = spherical_to_cartesian(paramspoint[5:8]) 
-    iota = paramspoint[8]  
-    phiRef = paramspoint[9]
-    ecc = 0
-    if len(paramspoint)==11:
-        ecc = paramspoint[10]
-    if len(paramspoint)==12:
-        lambda1 = paramspoint[10]
-        lambda2 = paramspoint[11]
-        lalsimulation.SimInspiralWaveformParamsInsertTidalLambda1(waveFlags, lambda1)
-        lalsimulation.SimInspiralWaveformParamsInsertTidalLambda2(waveFlags, lambda2) 
-    f_ref = 0 
-    RA=0    
-    DEC=0   
-    psi=0   
-    phi=0   
-    m1 *= lal.lal.MSUN_SI
-    m2 *= lal.lal.MSUN_SI
-    [plus,cross]=lalsimulation.SimInspiralChooseFDWaveform(m1, m2, s1x, s1y, s1z, s2x, s2y, s2z, distance, iota, phiRef, 0, ecc, 0, deltaF, f_min, f_max, f_ref, waveFlags, approximant)
-    hp_tmp = plus.data.data[numpy.int(f_min/deltaF):numpy.int(f_max/deltaF)] # data_tmp is hplus and is a complex vector 
-    residual = hp_tmp
-    for k in numpy.arange(0,len(known_bases)):
-        residual -= proj(known_bases[k],hp_tmp)
-    modulus = numpy.sqrt(numpy.vdot(residual, residual))
-    return modulus
-
-def compute_modulus_quad(paramspoint, known_quad_bases, distance, deltaF, f_min, f_max, approximant):
-    waveFlags = lal.CreateDict()
-    m1, m2 = get_m1m2_from_mcq(paramspoint[0],paramspoint[1])
-    s1x, s1y, s1z = spherical_to_cartesian(paramspoint[2:5]) 
-    s2x, s2y, s2z = spherical_to_cartesian(paramspoint[5:8]) 
-    iota=paramspoint[8]  
-    phiRef=paramspoint[9]
-    ecc = 0
-    if len(paramspoint)==11:
-        ecc = paramspoint[10]
-    if len(paramspoint)==12:
-        lambda1 = paramspoint[10]
-        lambda2 = paramspoint[11]
-        lalsimulation.SimInspiralWaveformParamsInsertTidalLambda1(waveFlags, lambda1)
-        lalsimulation.SimInspiralWaveformParamsInsertTidalLambda2(waveFlags, lambda2) 
-    f_ref = 0 
-    RA=0    
-    DEC=0   
-    psi=0   
-    phi=0   
-    m1 *= lal.lal.MSUN_SI
-    m2 *= lal.lal.MSUN_SI
-    [plus,cross]=lalsimulation.SimInspiralChooseFDWaveform(m1, m2, s1x, s1y, s1z, s2x, s2y, s2z, distance, iota, phiRef, 0, ecc, 0, deltaF, f_min, f_max, f_ref, waveFlags, approximant)
-    hp_tmp = plus.data.data[numpy.int(f_min/deltaF):numpy.int(f_max/deltaF)] # data_tmp is hplus and is a complex vector 
-    hp_quad_tmp = (numpy.absolute(hp_tmp))**2
-    residual = hp_quad_tmp
-    for k in numpy.arange(0,len(known_quad_bases)):
-        residual -= proj(known_quad_bases[k],hp_quad_tmp)
-    modulus = numpy.sqrt(numpy.vdot(residual, residual))
-    return modulus
-
-# now generating N=npts waveforms at points that are 
-# randomly uniformly distributed in parameter space
-# and calculate their inner products with the 1st waveform
-# so as to find the best waveform as the new basis
-def least_match_waveform_unnormalized(parallel, nprocesses, paramspoints, known_bases, distance, deltaF, f_min, f_max, waveFlags, approximant):
-    if parallel == 1:
-        paramspointslist = paramspoints.tolist()
-        #pool = mp.Pool(mp.cpu_count())
-        pool = mp.Pool(processes=nprocesses)
-        modula = [pool.apply(compute_modulus, args=(paramspoint, known_bases, distance, deltaF, f_min, f_max, approximant)) for paramspoint in paramspointslist]
-        pool.close()
-    if parallel == 0:
-        npts = len(paramspoints)
-        modula = numpy.zeros(npts)
-        for i in numpy.arange(0,npts):
-            paramspoint = paramspoints[i]
-            modula[i] = compute_modulus(paramspoint, known_bases, distance, deltaF, f_min, f_max, approximant)
-    arg_newbasis = numpy.argmax(modula) 
-    paramspoint = paramspoints[arg_newbasis]
-    mass1, mass2 = get_m1m2_from_mcq(paramspoints[arg_newbasis][0],paramspoints[arg_newbasis][1])
-    mass1 *= lal.lal.MSUN_SI
-    mass2 *= lal.lal.MSUN_SI
-    sp1x, sp1y, sp1z = spherical_to_cartesian(paramspoints[arg_newbasis,2:5]) 
-    sp2x, sp2y, sp2z = spherical_to_cartesian(paramspoints[arg_newbasis,5:8]) 
-    inclination = paramspoints[arg_newbasis][8]
-    phi_ref = paramspoints[arg_newbasis][9]
-    ecc = 0
-    if len(paramspoint)==11:
-        ecc = paramspoints[arg_newbasis][10]
-    if len(paramspoint)==12:
-        lambda1 = paramspoints[arg_newbasis][10]
-        lambda2 = paramspoints[arg_newbasis][11]
-        lalsimulation.SimInspiralWaveformParamsInsertTidalLambda1(waveFlags, lambda1)
-        lalsimulation.SimInspiralWaveformParamsInsertTidalLambda2(waveFlags, lambda2) 
-    [plus_new, cross_new]=lalsimulation.SimInspiralChooseFDWaveform(mass1, mass2, sp1x, sp1y, sp1z, sp2x, sp2y, sp2z, distance, inclination, phi_ref, 0, ecc, 0, deltaF, f_min, f_max, 0, waveFlags, approximant)
-    hp_new = plus_new.data.data
-    hp_new = hp_new[numpy.int(f_min/deltaF):numpy.int(f_max/deltaF)]
-    basis_new = gram_schmidt(known_bases, hp_new)
-    return numpy.array([basis_new, paramspoints[arg_newbasis], modula[arg_newbasis]]) # elements, masses&spins, residual mod
-
-
-def least_match_quadratic_waveform_unnormalized(parallel, nprocesses, paramspoints, known_quad_bases, distance, deltaF, f_min, f_max, waveFlags, approximant):
-    if parallel == 1:
-        paramspointslist = paramspoints.tolist()
-        pool = mp.Pool(processes=nprocesses)
-        modula = [pool.apply(compute_modulus_quad, args=(paramspoint, known_quad_bases, distance, deltaF, f_min, f_max, approximant)) for paramspoint in paramspointslist]
-        pool.close()
-    if parallel == 0:
-        npts = len(paramspoints)
-        modula = numpy.zeros(npts)
-        for i in numpy.arange(0,npts):
-            paramspoint = paramspoints[i]
-            modula[i] = compute_modulus_quad(paramspoint, known_quad_bases, distance, deltaF, f_min, f_max, approximant)
-    arg_newbasis = numpy.argmax(modula)    
-    paramspoint = paramspoints[arg_newbasis]
-    mass1, mass2 = get_m1m2_from_mcq(paramspoints[arg_newbasis][0],paramspoints[arg_newbasis][1])
-    mass1 *= lal.lal.MSUN_SI
-    mass2 *= lal.lal.MSUN_SI
-    sp1x, sp1y, sp1z = spherical_to_cartesian(paramspoints[arg_newbasis,2:5]) 
-    sp2x, sp2y, sp2z = spherical_to_cartesian(paramspoints[arg_newbasis,5:8]) 
-    inclination = paramspoints[arg_newbasis][8]
-    phi_ref = paramspoints[arg_newbasis][9]
-    ecc = 0
-    if len(paramspoint)==11:
-        ecc = paramspoints[arg_newbasis][10]
-    if len(paramspoint)==12:
-        lambda1 = paramspoints[arg_newbasis][10]
-        lambda2 = paramspoints[arg_newbasis][11]
-        lalsimulation.SimInspiralWaveformParamsInsertTidalLambda1(waveFlags, lambda1)
-        lalsimulation.SimInspiralWaveformParamsInsertTidalLambda2(waveFlags, lambda2)     
-    [plus_new, cross_new]=lalsimulation.SimInspiralChooseFDWaveform(mass1, mass2, sp1x, sp1y, sp1z, sp2x, sp2y, sp2z, distance, inclination, phi_ref, 0, ecc, 0, deltaF, f_min, f_max, 0, waveFlags, approximant)
-    hp_new = plus_new.data.data
-    hp_new = hp_new[numpy.int(f_min/deltaF):numpy.int(f_max/deltaF)]
-    hp_quad_new = (numpy.absolute(hp_new))**2
-    basis_quad_new = gram_schmidt(known_quad_bases, hp_quad_new)    
-    return numpy.array([basis_quad_new, paramspoints[arg_newbasis], modula[arg_newbasis]]) # elements, masses&spins, residual mod
-
-def bases_searching_results_unnormalized(parallel, nprocesses, npts, nparams, nbases, known_bases, basis_waveforms, params, residual_modula, params_low, params_high, distance, deltaF, f_min, f_max, waveFlags, approximant):
-    if nparams == 10: print("The parameters are Mc, q, s1(mag, theta, phi), s2(mag, theta, phi), iota, and phiRef\n")
-    if nparams == 11: print("The parameters are Mc, q, s1(mag, theta, phi), s2(mag, theta, phi), iota, phiRef, and eccentricity\n")
-    if nparams == 12: print("The parameters are Mc, q, s1(mag, theta, phi), s2(mag, theta, phi), iota, phiRef, lambda1, and lambda2\n") 
-    for k in numpy.arange(0,nbases-1):
-        paramspoints = generate_params_points(npts, nparams, params_low, params_high)
-        basis_new, params_new, rm_new = least_match_waveform_unnormalized(parallel, nprocesses, paramspoints, known_bases, distance, deltaF, f_min, f_max, waveFlags, approximant)
-        print("Linear Iter: ", k+1, "and new basis waveform", params_new)
-        known_bases= numpy.append(known_bases, numpy.array([basis_new]), axis=0)
-        params = numpy.append(params, numpy.array([params_new]), axis = 0)
-        residual_modula = numpy.append(residual_modula, rm_new)
-    numpy.save('./linearbases.npy',known_bases)
-    numpy.save('./linearbasiswaveformparams.npy',params)
-    return known_bases, params, residual_modula
-
-def bases_searching_quadratic_results_unnormalized(parallel, nprocesses, npts, nparams, nbases_quad, known_quad_bases, basis_waveforms, params_quad, residual_modula, params_low, params_high, distance, deltaF, f_min, f_max, waveFlags, approximant):
-    for k in numpy.arange(0,nbases_quad-1):
-        print("Quadratic Iter: ", k+1)
-        paramspoints = generate_params_points(npts, nparams, params_low, params_high)
-        basis_new, params_new, rm_new= least_match_quadratic_waveform_unnormalized(parallel, nprocesses, paramspoints, known_quad_bases, distance, deltaF, f_min, f_max, waveFlags, approximant)
-        known_quad_bases= numpy.append(known_quad_bases, numpy.array([basis_new]), axis=0)
-        params_quad = numpy.append(params_quad, numpy.array([params_new]), axis = 0)
-        residual_modula = numpy.append(residual_modula, rm_new)
-    numpy.save('./quadraticbases.npy',known_quad_bases)
-    numpy.save('./quadraticbasiswaveformparams.npy',params_quad)
-    return known_quad_bases, params_quad, residual_modula
-
-def massrange(mc_low, mc_high, q_low, q_high):
-    mmin = get_m1m2_from_mcq(mc_low,q_high)[1]
-    mmax = get_m1m2_from_mcq(mc_high,q_high)[0]
-    return [mmin, mmax]
-
-def initial_basis(mc_low, mc_high, q_low, q_high, s1sphere_low, s1sphere_high, s2sphere_low, s2sphere_high, ecc_low, ecc_high, lambda1_low, lambda1_high, lambda2_low, lambda2_high, iota_low, iota_high, phiref_low, phiref_high, distance, deltaF, f_min, f_max, waveFlags, approximant):
-    try:
-        if approximant==lalsimulation.IMRPhenomPv2:
-            nparams = 10
-            params_low = [mc_low, q_low, s1sphere_low[0], s1sphere_low[1], s1sphere_low[2], s2sphere_low[0], s2sphere_low[1], s2sphere_low[2], iota_low, phiref_low] 
-            params_high = [mc_high, q_high, s1sphere_high[0], s1sphere_high[1], s1sphere_high[2], s2sphere_high[0], s2sphere_high[1], s2sphere_high[2], iota_high, phiref_high]
-            params_start = numpy.array([[mc_low, q_low, s1sphere_low[0], s1sphere_low[1], s1sphere_low[2], s2sphere_low[0], s2sphere_low[1], s2sphere_low[2], 0.33333*np.pi, 1.5*np.pi]])
-            hp1 = generate_a_waveform_from_mcq(mc_low, q_low, spherical_to_cartesian(s1sphere_low), spherical_to_cartesian(s2sphere_low), 0, 0, 0, iota_low, phiref_low, distance, deltaF, f_min, f_max, waveFlags, approximant)
-    except AttributeError: 
-        pass
-    try:
-        if approximant==lalsimulation.IMRPhenomPv3:
-            nparams = 10
-            params_low = [mc_low, q_low, s1sphere_low[0], s1sphere_low[1], s1sphere_low[2], s2sphere_low[0], s2sphere_low[1], s2sphere_low[2], iota_low, phiref_low] 
-            params_high = [mc_high, q_high, s1sphere_high[0], s1sphere_high[1], s1sphere_high[2], s2sphere_high[0], s2sphere_high[1], s2sphere_high[2], iota_high, phiref_high]
-            params_start = numpy.array([[mc_low, q_low, s1sphere_low[0], s1sphere_low[1], s1sphere_low[2], s2sphere_low[0], s2sphere_low[1], s2sphere_low[2], 0.33333*np.pi, 1.5*np.pi]])
-            hp1 = generate_a_waveform_from_mcq(mc_low, q_low, spherical_to_cartesian(s1sphere_low), spherical_to_cartesian(s2sphere_low), 0, 0, 0, iota_low, phiref_low, distance, deltaF, f_min, f_max, waveFlags, approximant)
-    except AttributeError: 
-        pass
-    try:
-        if approximant==lalsimulation.IMRPhenomPv3HM:
-            nparams = 10
-            params_low = [mc_low, q_low, s1sphere_low[0], s1sphere_low[1], s1sphere_low[2], s2sphere_low[0], s2sphere_low[1], s2sphere_low[2], iota_low, phiref_low] 
-            params_high = [mc_high, q_high, s1sphere_high[0], s1sphere_high[1], s1sphere_high[2], s2sphere_high[0], s2sphere_high[1], s2sphere_high[2], iota_high, phiref_high]
-            params_start = numpy.array([[mc_low, q_low, s1sphere_low[0], s1sphere_low[1], s1sphere_low[2], s2sphere_low[0], s2sphere_low[1], s2sphere_low[2], 0.33333*np.pi, 1.5*np.pi]])
-            hp1 = generate_a_waveform_from_mcq(mc_low, q_low, spherical_to_cartesian(s1sphere_low), spherical_to_cartesian(s2sphere_low), 0, 0, 0, iota_low, phiref_low, distance, deltaF, f_min, f_max, waveFlags, approximant)
-    except AttributeError: 
-        pass
-    try:
-        if approximant==lalsimulation.IMRPhenomXHM:
-            nparams = 10
-            params_low = [mc_low, q_low, s1sphere_low[0], s1sphere_low[1], s1sphere_low[2], s2sphere_low[0], s2sphere_low[1], s2sphere_low[2], iota_low, phiref_low] 
-            params_high = [mc_high, q_high, s1sphere_high[0], s1sphere_high[1], s1sphere_high[2], s2sphere_high[0], s2sphere_high[1], s2sphere_high[2], iota_high, phiref_high]
-            params_start = numpy.array([[mc_low, q_low, s1sphere_low[0], s1sphere_low[1], s1sphere_low[2], s2sphere_low[0], s2sphere_low[1], s2sphere_low[2], 0.33333*np.pi, 1.5*np.pi]])
-            hp1 = generate_a_waveform_from_mcq(mc_low, q_low, spherical_to_cartesian(s1sphere_low), spherical_to_cartesian(s2sphere_low), 0, 0, 0, iota_low, phiref_low, distance, deltaF, f_min, f_max, waveFlags, approximant)
-    except AttributeError: 
-        pass
-    try:
-        if approximant==lalsimulation.TaylorF2Ecc:
-            nparams = 11
-            params_low = [mc_low, q_low, s1sphere_low[0], s1sphere_low[1], s1sphere_low[2], s2sphere_low[0], s2sphere_low[1], s2sphere_low[2], iota_low, phiref_low, ecc_low] 
-            params_high = [mc_high, q_high, s1sphere_high[0], s1sphere_high[1], s1sphere_high[2], s2sphere_high[0], s2sphere_high[1], s2sphere_high[2], iota_high, phiref_high, ecc_high]
-            params_start = numpy.array([[mc_low, q_low, s1sphere_low[0], s1sphere_low[1], s1sphere_low[2], s2sphere_low[0], s2sphere_low[1], s2sphere_low[2], 0.33333*np.pi, 1.5*np.pi, ecc_low]])
-            hp1 = generate_a_waveform_from_mcq(mc_low, q_low, spherical_to_cartesian(s1sphere_low), spherical_to_cartesian(s2sphere_low), ecc_low, 0, 0, iota_low, phiref_low, distance, deltaF, f_min, f_max, waveFlags, approximant)
-    except AttributeError: 
-        pass
-    try:    
-        if approximant==lalsimulation.IMRPhenomPv2_NRTidal:
-            nparams = 12
-            params_low = [mc_low, q_low, s1sphere_low[0], s1sphere_low[1], s1sphere_low[2], s2sphere_low[0], s2sphere_low[1], s2sphere_low[2], iota_low, phiref_low, lambda1_low, lambda2_low]
-            params_high = [mc_high, q_high, s1sphere_high[0], s1sphere_high[1], s1sphere_high[2], s2sphere_high[0], s2sphere_high[1], s2sphere_high[2], iota_high, phiref_high, lambda1_high, lambda2_high]
-            params_start = numpy.array([[mc_low, q_low, s1sphere_low[0], s1sphere_low[1], s1sphere_low[2], s2sphere_low[0], s2sphere_low[1], s2sphere_low[2], 0.33333*np.pi, 1.5*np.pi, lambda1_low, lambda2_low]])
-
-            hp1 = generate_a_waveform_from_mcq(mc_low, q_low, spherical_to_cartesian(s1sphere_low), spherical_to_cartesian(s2sphere_low), 0, lambda1_low, lambda2_low, iota_low, phiref_low, distance, deltaF, f_min, f_max, waveFlags, approximant) 
-    except AttributeError: 
-        pass
-    try:
-        if approximant==lalsimulation.IMRPhenomNSBH:
-            nparams = 12
-            params_low = [mc_low, q_low, s1sphere_low[0], s1sphere_low[1], s1sphere_low[2], s2sphere_low[0], s2sphere_low[1], s2sphere_low[2], iota_low, phiref_low, lambda1_low, lambda2_low]
-            params_high = [mc_high, q_high, s1sphere_high[0], s1sphere_high[1], s1sphere_high[2], s2sphere_high[0], s2sphere_high[1], s2sphere_high[2], iota_high, phiref_high, lambda1_high, lambda2_high]
-            params_start = numpy.array([[mc_low, q_low, s1sphere_low[0], s1sphere_low[1], s1sphere_low[2], s2sphere_low[0], s2sphere_low[1], s2sphere_low[2], 0.33333*np.pi, 1.5*np.pi, lambda1_low, lambda2_low]])
-            hp1 = generate_a_waveform_from_mcq(mc_low, q_low, spherical_to_cartesian(s1sphere_low), spherical_to_cartesian(s2sphere_low), 0, lambda1_low, lambda2_low, iota_low, phiref_low, distance, deltaF, f_min, f_max, waveFlags, approximant) 
-    except AttributeError: 
-        pass
-    return numpy.array([nparams, params_low, params_high, params_start, hp1])
-
-def empnodes(ndim, known_bases): # Here known_bases is the full copy known_bases_copy. Its length is equal to or longer than ndim.
-    emp_nodes = numpy.arange(0,ndim)*100000000
-    emp_nodes[0] = numpy.argmax(numpy.absolute(known_bases[0]))
-    c1 = known_bases[1,emp_nodes[0]]/known_bases[0,1]
-    interp1 = numpy.multiply(c1,known_bases[0])
-    diff1 = interp1 - known_bases[1]
-    r1 = numpy.absolute(diff1)
-    emp_nodes[1] = numpy.argmax(r1)
-    for k in numpy.arange(2,ndim):
-        emp_tmp = emp_nodes[0:k]
-        Vtmp = numpy.transpose(known_bases[0:k,emp_tmp])
-        inverse_Vtmp = numpy.linalg.pinv(Vtmp)
-        e_to_interp = known_bases[k]
-        Ci = numpy.dot(inverse_Vtmp, e_to_interp[emp_tmp])
-        interpolantA = numpy.zeros(len(known_bases[k]))+numpy.zeros(len(known_bases[k]))*1j
-        for j in numpy.arange(0, k):
-            tmp = numpy.multiply(Ci[j], known_bases[j])
-            interpolantA += tmp
-        diff = interpolantA - known_bases[k]
-        r = numpy.absolute(diff)
-        emp_nodes[k] = numpy.argmax(r)
-        emp_nodes = sorted(emp_nodes)
-    u, c = numpy.unique(emp_nodes, return_counts=True)
-    dup = u[c > 1]
-    #print(len(emp_nodes), "\nDuplicates indices:", dup)
-    emp_nodes = numpy.unique(emp_nodes)
-    ndim = len(emp_nodes)
-    #print(len(emp_nodes), "\n", emp_nodes)
-    V = numpy.transpose(known_bases[0:ndim, emp_nodes])
-    inverse_V = numpy.linalg.pinv(V)
-    return numpy.array([ndim, inverse_V, emp_nodes])
-
-def surroerror(ndim, inverse_V, emp_nodes, known_bases, test_mc, test_q, test_s1, test_s2, test_ecc, test_lambda1, test_lambda2, test_iota, test_phiref, distance, deltaF, f_min, f_max, waveFlags, approximant):
-    hp_test = generate_a_waveform_from_mcq(test_mc, test_q, test_s1, test_s2, test_ecc, test_lambda1, test_lambda2, test_iota, test_phiref, distance, deltaF, f_min, f_max, waveFlags, approximant)
-    Ci = numpy.dot(inverse_V, hp_test[emp_nodes])
-    interpolantA = numpy.zeros(len(hp_test))+numpy.zeros(len(hp_test))*1j
-    #ndim = len(known_bases)
-    for j in numpy.arange(0, ndim):
-        tmp = numpy.multiply(Ci[j], known_bases[j])
-        interpolantA += tmp
-    surro = (1-overlap_of_two_waveforms(hp_test, interpolantA))*deltaF
-    return surro
-
-def surros(tolerance, ndim, inverse_V, emp_nodes, known_bases, nts, nparams, params_low, params_high, distance, deltaF, f_min, f_max, waveFlags, approximant): # Here known_bases is known_bases_copy
-    test_points = generate_params_points(nts, nparams, params_low, params_high)
-    surros = numpy.zeros(nts)
-    count = 0
-    for i in numpy.arange(0,nts):
-        test_mc =  test_points[i,0]
-        test_q = test_points[i,1]
-        test_s1 = spherical_to_cartesian(test_points[i,2:5])
-        test_s2 = spherical_to_cartesian(test_points[i,5:8])
-        test_iota = test_points[i,8]
-        test_phiref = test_points[i,9]
-        test_ecc = 0
-        test_lambda1 = 0
-        test_lambda2 = 0
-        if nparams == 11: test_ecc = test_points[i,10]
-        if nparams == 12: 
-            test_lambda1 = test_points[i,10]
-            test_lambda2 = test_points[i,11]
-        surros[i] = surroerror(ndim, inverse_V, emp_nodes, known_bases[0:ndim], test_mc, test_q, test_s1, test_s2, test_ecc, test_lambda1, test_lambda2, test_iota, test_phiref, distance, deltaF, f_min, f_max, waveFlags, approximant)
-        if (surros[i] > tolerance):
-            count = count+1
-    print(ndim, "basis elements gave", count, "bad points of surrogate error > ", tolerance)
-    if count == 0: val =0
-    else: val = 1
-    return val
-
-def roqs(tolerance, freq,  ndimlow, ndimhigh, ndimstepsize, known_bases_copy, nts, nparams, params_low, params_high, distance, deltaF, f_min, f_max, waveFlags, approximant):
-    for num in np.arange(ndimlow, ndimhigh, ndimstepsize):
-        ndim, inverse_V, emp_nodes = empnodes(num, known_bases_copy)
-        if surros(tolerance, ndim, inverse_V, emp_nodes, known_bases_copy, nts, nparams, params_low, params_high, distance, deltaF, f_min, f_max, waveFlags, approximant)==0:
-            b_linear = numpy.dot(numpy.transpose(known_bases_copy[0:ndim]),inverse_V)
-            f_linear = freq[emp_nodes]
-            numpy.save('./B_linear.npy',numpy.transpose(b_linear))
-            numpy.save('./fnodes_linear.npy',f_linear)
-            print("Number of linear basis elements is ", ndim, "and the linear ROQ data are saved in B_linear.npy")
-            break
-    return
-
-def testrep(b_linear, emp_nodes, test_mc, test_q, test_s1, test_s2, test_ecc, test_lambda1, test_lambda2, test_iota, test_phiref, distance, deltaF, f_min, f_max, waveFlags, approximant):
-    hp_test = generate_a_waveform_from_mcq(test_mc, test_q, test_s1, test_s2, test_ecc, test_lambda1, test_lambda2, test_iota, test_phiref, distance, deltaF, f_min, f_max, waveFlags, approximant)
-    hp_test_emp = hp_test[emp_nodes]
-    hp_rep = numpy.dot(b_linear,hp_test_emp)
-    diff = hp_rep - hp_test
-    rep_error = diff/numpy.sqrt(numpy.vdot(hp_test,hp_test))
-    freq = numpy.arange(f_min,f_max,deltaF)
-    plt.figure(figsize=(15,9))
-    plt.plot(freq, numpy.real(rep_error), label='Real part of h+') 
-    plt.plot(freq, numpy.imag(rep_error), label='Imaginary part of h+')
-    plt.xlabel('Frequency')
-    plt.ylabel('Fractional Representation Error')
-    plt.title('Rep Error with numpy.linalg.pinv()')
-    plt.legend(loc=0)
-    plt.show()
-    return
-
-def empnodes_quad(ndim_quad, known_quad_bases):
-    emp_nodes_quad = numpy.arange(0,ndim_quad)*100000000
-    emp_nodes_quad[0] = numpy.argmax(numpy.absolute(known_quad_bases[0]))
-    c1_quad = known_quad_bases[1,emp_nodes_quad[0]]/known_quad_bases[0,1]
-    interp1_quad = numpy.multiply(c1_quad,known_quad_bases[0])
-    diff1_quad = interp1_quad - known_quad_bases[1]
-    r1_quad = numpy.absolute(diff1_quad)
-    emp_nodes_quad[1] = numpy.argmax(r1_quad)
-    for k in numpy.arange(2,ndim_quad):
-        emp_tmp_quad = emp_nodes_quad[0:k]
-        Vtmp_quad = numpy.transpose(known_quad_bases[0:k,emp_tmp_quad])
-        inverse_Vtmp_quad = numpy.linalg.pinv(Vtmp_quad)
-        e_to_interp_quad = known_quad_bases[k]
-        Ci_quad = numpy.dot(inverse_Vtmp_quad, e_to_interp_quad[emp_tmp_quad])
-        interpolantA_quad = numpy.zeros(len(known_quad_bases[k]))+numpy.zeros(len(known_quad_bases[k]))*1j
-        for j in numpy.arange(0, k):
-            tmp_quad = numpy.multiply(Ci_quad[j], known_quad_bases[j])
-            interpolantA_quad += tmp_quad
-        diff_quad = interpolantA_quad - known_quad_bases[k]
-        r_quad = numpy.absolute(diff_quad)
-        emp_nodes_quad[k] = numpy.argmax(r_quad)
-        emp_nodes_quad = sorted(emp_nodes_quad)
-    u_quad, c_quad = numpy.unique(emp_nodes_quad, return_counts=True)
-    dup_quad = u_quad[c_quad > 1]
-    #print(len(emp_nodes_quad), "\nduplicates quad indices:", dup_quad)
-    emp_nodes_quad = numpy.unique(emp_nodes_quad)
-    ndim_quad = len(emp_nodes_quad)
-    #print(len(emp_nodes_quad), "\n", emp_nodes_quad)
-    V_quad = numpy.transpose(known_quad_bases[0:ndim_quad,emp_nodes_quad])
-    inverse_V_quad = numpy.linalg.pinv(V_quad)
-    return numpy.array([ndim_quad, inverse_V_quad, emp_nodes_quad])
-
-def surroerror_quad(ndim_quad, inverse_V_quad, emp_nodes_quad, known_quad_bases, test_mc_quad, test_q_quad, test_s1_quad, test_s2_quad, test_ecc_quad, test_lambda1_quad, test_lambda2_quad, test_iota_quad, test_phiref_quad, distance, deltaF, f_min, f_max, waveFlags, approximant):
-    hp_test_quad = (numpy.absolute(generate_a_waveform_from_mcq(test_mc_quad, test_q_quad, test_s1_quad, test_s2_quad, test_ecc_quad, test_lambda1_quad, test_lambda2_quad, test_iota_quad, test_phiref_quad, distance, deltaF, f_min, f_max, waveFlags, approximant)))**2
-    Ci_quad = numpy.dot(inverse_V_quad, hp_test_quad[emp_nodes_quad])
-    interpolantA_quad = numpy.zeros(len(hp_test_quad))+numpy.zeros(len(hp_test_quad))*1j    
-    #ndim_quad = len(known_quad_bases)
-    for j in numpy.arange(0, ndim_quad):
-        tmp_quad = numpy.multiply(Ci_quad[j], known_quad_bases[j])
-        interpolantA_quad += tmp_quad
-    surro_quad = (1-overlap_of_two_waveforms(hp_test_quad, interpolantA_quad))*deltaF
-    return surro_quad
-
-def surros_quad(tolerance_quad, ndim_quad, inverse_V_quad, emp_nodes_quad, known_quad_bases, nts, nparams, params_low, params_high, distance, deltaF, f_min, f_max, waveFlags, approximant):
-    test_points = generate_params_points(nts, nparams, params_low, params_high)
-    surros = numpy.zeros(nts)
-    count = 0
-    for i in numpy.arange(0,nts):
-        test_mc_quad =  test_points[i,0]
-        test_q_quad = test_points[i,1]
-        test_s1_quad = spherical_to_cartesian(test_points[i,2:5])
-        test_s2_quad = spherical_to_cartesian(test_points[i,5:8])
-        test_iota_quad = test_points[i,8]
-        test_phiref_quad = test_points[i,9]
-        test_ecc_quad = 0
-        test_lambda1_quad = 0
-        test_lambda2_quad = 0
-        if nparams == 11: test_ecc_quad = test_points[i,10]
-        if nparams == 12: 
-            test_lambda1_quad = test_points[i,10]
-            test_lambda2_quad = test_points[i,11]
-        surros[i] = surroerror_quad(ndim_quad, inverse_V_quad, emp_nodes_quad, known_quad_bases[0:ndim_quad], test_mc_quad, test_q_quad, test_s1_quad, test_s2_quad, test_ecc_quad, test_lambda1_quad, test_lambda2_quad, test_iota_quad, test_phiref_quad, distance, deltaF, f_min, f_max, waveFlags, approximant)
-        if (surros[i] > tolerance_quad):
-            count = count+1
-    print(ndim_quad, "basis elements gave", count, "bad points of surrogate error > ", tolerance_quad)
-    if count == 0: val =0
-    else: val = 1
-    return val
-
-def roqs_quad(tolerance_quad, freq,  ndimlow_quad, ndimhigh_quad, ndimstepsize_quad, known_quad_bases_copy, nts, nparams, params_low, params_high, distance, deltaF, f_min, f_max, waveFlags, approximant):
-    for num in np.arange(ndimlow_quad, ndimhigh_quad, ndimstepsize_quad):
-        ndim_quad, inverse_V_quad, emp_nodes_quad = empnodes_quad(num, known_quad_bases_copy)
-        if surros_quad(tolerance_quad, ndim_quad, inverse_V_quad, emp_nodes_quad, known_quad_bases_copy, nts, nparams, params_low, params_high, distance, deltaF, f_min, f_max, waveFlags, approximant)==0:
-            b_quad = numpy.dot(numpy.transpose(known_quad_bases_copy[0:ndim_quad]), inverse_V_quad)
-            f_quad = freq[emp_nodes_quad]
-            numpy.save('./B_quadratic.npy', numpy.transpose(b_quad))
-            numpy.save('./fnodes_quadratic.npy', f_quad)
-            print("Number of quadratic basis elements is ", ndim_quad, "and the linear ROQ data save in B_quadratic.npy")
-            break
-    return
-
-def testrep_quad(b_quad, emp_nodes_quad, test_mc_quad, test_q_quad, test_s1_quad, test_s2_quad, test_ecc_quad, test_lambda1_quad, test_lambda2_quad, test_iota_quad, test_phiref_quad, distance, deltaF, f_min, f_max, waveFlags, approximant):
-    hp_test_quad = (numpy.absolute(generate_a_waveform_from_mcq(test_mc_quad, test_q_quad, test_s1_quad, test_s2_quad, test_ecc_quad, test_lambda1_quad, test_lambda2_quad, test_iota_quad, test_phiref_quad, distance, deltaF, f_min, f_max, waveFlags, approximant)))**2
-    hp_test_quad_emp = hp_test_quad[emp_nodes_quad]
-    hp_rep_quad = numpy.dot(b_quad,hp_test_quad_emp)
-    diff_quad = hp_rep_quad - hp_test_quad
-    rep_error_quad = diff_quad/numpy.vdot(hp_test_quad,hp_test_quad)**0.5
-    freq = numpy.arange(f_min,f_max,deltaF)
-    plt.figure(figsize=(15,9))
-    plt.plot(freq, numpy.real(rep_error_quad))
-    plt.xlabel('Frequency')
-    plt.ylabel('Fractional Representation Error for Quadratic')
-    plt.title('Rep Error with numpy.linalg.pinv()')
-    plt.show()
-    return
-
-def surros_of_test_samples(nsamples, nparams, params_low, params_high, tolerance, b_linear, emp_nodes, distance, deltaF, f_min, f_max, waveFlags, approximant):
-    nts=nsamples
-    ndim = len(emp_nodes)
-    test_points = generate_params_points(nts, nparams, params_low, params_high)
-    surros = numpy.zeros(nts)
-    for i in numpy.arange(0,nts):
-        test_mc =  test_points[i,0]
-        test_q = test_points[i,1]
-        test_s1 = spherical_to_cartesian(test_points[i,2:5])
-        test_s2 = spherical_to_cartesian(test_points[i,5:8])
-        test_iota = test_points[i,8]
-        test_phiref = test_points[i,9]
-        test_ecc = 0
-        test_lambda1 = 0
-        test_lambda2 = 0
-        if nparams == 11: test_ecc = test_points[i,10]
-        if nparams == 12: 
-            test_lambda1 = test_points[i,10]
-            test_lambda2 = test_points[i,11]
-        hp_test = generate_a_waveform_from_mcq(test_mc, test_q, test_s1, test_s2, test_ecc, test_lambda1, test_lambda2, test_iota, test_phiref, distance, deltaF, f_min, f_max, waveFlags, approximant)
-        hp_test_emp = hp_test[emp_nodes]
-        hp_rep = numpy.dot(b_linear,hp_test_emp) 
-        surros[i] = (1-overlap_of_two_waveforms(hp_test, hp_rep))*deltaF
-    if (surros[i] > tolerance):
-        print("iter", i, surros[i], test_points[i])
-    if i%100==0:
-        print("iter", i, surros[i])
-    return surros
diff --git a/Tutorial/B_linear.npy b/Tutorial/B_linear.npy
index c86ea87..df76aa2 100644
Binary files a/Tutorial/B_linear.npy and b/Tutorial/B_linear.npy differ
diff --git a/Tutorial/B_quadratic.npy b/Tutorial/B_quadratic.npy
index d731123..27cd8ac 100644
Binary files a/Tutorial/B_quadratic.npy and b/Tutorial/B_quadratic.npy differ
diff --git a/Tutorial/PyROQ-tutorial.ipynb b/Tutorial/PyROQ-tutorial.ipynb
index 647a72c..6b3dda6 100644
--- a/Tutorial/PyROQ-tutorial.ipynb
+++ b/Tutorial/PyROQ-tutorial.ipynb
@@ -2,30 +2,16 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Collecting PyROQ==0.1.25\n",
-      "  Downloading https://files.pythonhosted.org/packages/43/62/95ca4b782154bda1e963890e0891347d7e6f200dc4e073657824ee9a70c4/PyROQ-0.1.25-py3-none-any.whl\n",
-      "Installing collected packages: PyROQ\n",
-      "  Found existing installation: PyROQ 0.1.24\n",
-      "    Uninstalling PyROQ-0.1.24:\n",
-      "      Successfully uninstalled PyROQ-0.1.24\n",
-      "Successfully installed PyROQ-0.1.25\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "!pip install --user PyROQ==0.1.25"
+    "# !pip install --user PyROQ==0.1.25"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -52,6 +38,8 @@
     "pylab.rcParams.update(plot_params)\n",
     "from mpl_toolkits.mplot3d import axes3d\n",
     "#import PyROQ.pyroq as pyroq\n",
+    "import sys\n",
+    "sys.path.insert(0, \"../Code/PyROQ/\")\n",
     "import pyroq as pyroq"
    ]
   },
@@ -64,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -102,13 +90,13 @@
     "approximant = lalsimulation.IMRPhenomPv2\n",
     "print(\"mass-min, mass-max: \", pyroq.massrange(mc_low, mc_high, q_low, q_high))\n",
     "\n",
-    "parallel = 0 # The parallel=1 will turn on multiprocesses to search for a new basis. To turn it off, set it to be 0.\n",
+    "parallel = 1 # The parallel=1 will turn on multiprocesses to search for a new basis. To turn it off, set it to be 0.\n",
     "             # Do not turn it on if the waveform generation is not slow compared to data reading and writing to files.\n",
     "             # This is more useful when each waveform takes larger than 0.01 sec to generate.\n",
     "nprocesses = 4 # Set the number of parallel processes when searching for a new basis.  nprocesses=mp.cpu_count()\n",
     "\n",
     "\n",
-    "nts = 123 # Number of random test waveforms\n",
+    "nts = 10**3 # Number of random test waveforms\n",
     "          # For diagnostics, 1000 is fine.\n",
     "          # For real ROQs calculation, set it to be 1000000.\n",
     "\n",
@@ -133,7 +121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -145,7 +133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -171,7 +159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -180,191 +168,194 @@
      "text": [
       "The parameters are Mc, q, s1(mag, theta, phi), s2(mag, theta, phi), iota, and phiRef\n",
       "\n",
-      "Linear Iter:  1 and new basis waveform [29.614911  1.271912  0.043185  1.272497  5.817337  0.169412  1.082078\n",
-      "  4.820442  3.035298  1.619856]\n",
-      "Linear Iter:  2 and new basis waveform [28.794826  1.213989  0.030177  0.036484  4.08501   0.19912   2.037582\n",
-      "  4.946962  3.080541  0.174687]\n",
-      "Linear Iter:  3 and new basis waveform [20.14874   1.331149  0.129653  1.006368  3.104629  0.198682  0.767833\n",
-      "  0.363031  2.830839  2.114186]\n",
-      "Linear Iter:  4 and new basis waveform [29.910708  1.973347  0.129228  1.580673  4.250203  0.058538  0.664838\n",
-      "  1.704622  0.082262  5.134216]\n",
-      "Linear Iter:  5 and new basis waveform [29.658094  1.722257  0.076688  3.125209  0.544965  0.049523  1.872679\n",
-      "  4.615169  0.249438  5.543772]\n",
-      "Linear Iter:  6 and new basis waveform [25.933673  1.369327  0.129837  0.17734   3.737802  0.131967  0.97688\n",
-      "  4.31931   0.156845  5.905206]\n",
-      "Linear Iter:  7 and new basis waveform [2.5371301e+01 1.0589600e+00 1.9241000e-02 2.0896220e+00 3.1745130e+00\n",
-      " 3.9381000e-02 2.0879000e+00 3.6485400e-01 2.1116100e-01 2.5852670e+00]\n",
-      "Linear Iter:  8 and new basis waveform [2.6166685e+01 1.8170070e+00 9.5021000e-02 9.9594700e-01 5.8333860e+00\n",
-      " 1.4251400e-01 3.1335340e+00 2.9145390e+00 1.4447000e-02 4.9772970e+00]\n",
-      "Linear Iter:  9 and new basis waveform [26.888898  1.422945  0.046333  1.662173  4.979861  0.036355  0.168095\n",
-      "  4.800205  0.476879  0.176604]\n",
-      "Linear Iter:  10 and new basis waveform [23.7879    1.96106   0.066201  0.02941   3.064604  0.082754  1.311234\n",
-      "  4.542395  0.221936  0.618309]\n",
-      "Linear Iter:  11 and new basis waveform [23.761441  1.47384   0.137335  1.081256  4.261831  0.079562  0.258539\n",
-      "  6.149372  0.202257  4.608246]\n",
-      "Linear Iter:  12 and new basis waveform [22.631084  1.599403  0.063175  2.002032  3.890306  0.082409  2.219852\n",
-      "  4.037299  0.07419   2.10976 ]\n",
-      "Linear Iter:  13 and new basis waveform [2.2210135e+01 1.9941440e+00 1.9266000e-02 1.6014060e+00 5.0934560e+00\n",
-      " 6.7234000e-02 3.0655850e+00 5.8534190e+00 2.7845980e+00 2.7005630e+00]\n",
-      "Linear Iter:  14 and new basis waveform [2.215633e+01 1.402031e+00 4.072200e-02 6.716800e-02 4.816329e+00\n",
-      " 1.911800e-02 2.687205e+00 4.609183e+00 7.291000e-02 2.376675e+00]\n",
-      "Linear Iter:  15 and new basis waveform [21.791822  1.297611  0.141381  2.757046  5.688823  0.050672  0.144866\n",
-      "  0.140383  2.915878  1.857038]\n",
-      "Linear Iter:  16 and new basis waveform [20.762337  1.400414  0.05173   1.603689  0.918445  0.062857  2.646583\n",
-      "  2.308411  0.089014  2.819046]\n",
-      "Linear Iter:  17 and new basis waveform [20.663774  1.541349  0.096879  2.620584  2.080292  0.159156  1.057058\n",
-      "  1.079427  2.934078  3.238433]\n",
-      "Linear Iter:  18 and new basis waveform [20.53477   1.377534  0.107647  0.57247   2.166592  0.025403  2.077651\n",
-      "  0.294269  0.143765  2.736921]\n",
-      "Linear Iter:  19 and new basis waveform [20.387409  1.658261  0.076272  0.202989  0.427194  0.057296  0.987188\n",
-      "  1.985975  2.966257  0.695228]\n",
-      "Linear Iter:  20 and new basis waveform [27.801489  1.317552  0.029103  0.946511  0.201473  0.082791  1.959707\n",
-      "  3.785979  0.470216  1.751319]\n",
-      "Linear Iter:  21 and new basis waveform [28.302911  1.120927  0.17306   1.977733  3.747616  0.128648  0.211403\n",
-      "  1.474456  2.37367   2.598043]\n",
-      "Linear Iter:  22 and new basis waveform [2.2330996e+01 1.3405830e+00 1.0075800e-01 9.8808000e-01 6.0362220e+00\n",
-      " 1.6638500e-01 1.9524300e-01 4.2476510e+00 1.0286000e-02 5.0579830e+00]\n",
-      "Linear Iter:  23 and new basis waveform [21.368399  1.824337  0.151201  1.144609  4.364405  0.171214  1.584195\n",
-      "  1.69361   2.830274  0.15041 ]\n",
-      "Linear Iter:  24 and new basis waveform [29.932828  1.286292  0.125723  2.631496  3.452425  0.127014  1.306762\n",
-      "  1.569442  2.447714  3.012377]\n",
-      "Linear Iter:  25 and new basis waveform [29.700022  1.767737  0.156239  1.507584  5.667798  0.168199  1.688496\n",
-      "  5.182115  2.876674  1.233746]\n",
-      "Linear Iter:  26 and new basis waveform [28.650595  1.918791  0.1833    2.489977  1.571073  0.072298  2.210506\n",
-      "  5.094309  1.163854  4.572117]\n",
-      "Linear Iter:  27 and new basis waveform [29.804079  1.915049  0.13567   2.004957  3.609306  0.198298  2.347696\n",
-      "  2.337589  1.95077   2.863374]\n",
-      "Linear Iter:  28 and new basis waveform [24.711804  1.310025  0.111135  0.207389  3.660623  0.177372  1.668351\n",
-      "  0.108415  0.093689  2.383488]\n",
-      "Linear Iter:  29 and new basis waveform [24.119197  1.948967  0.141682  2.945474  6.11302   0.126101  0.337582\n",
-      "  1.109053  2.787679  2.286557]\n",
-      "Linear Iter:  30 and new basis waveform [2.024197e+01 1.273233e+00 1.575540e-01 2.704800e-02 2.231000e-03\n",
-      " 9.288000e-02 4.696850e-01 6.254000e-01 3.139670e-01 4.524988e+00]\n",
-      "Linear Iter:  31 and new basis waveform [20.316326  1.073746  0.060565  1.788856  3.864748  0.177481  0.689781\n",
-      "  5.448775  2.639271  0.81441 ]\n",
-      "Linear Iter:  32 and new basis waveform [21.015457  1.787761  0.091573  3.062577  0.298003  0.072127  0.320966\n",
-      "  2.680568  2.93094   3.499225]\n",
-      "Linear Iter:  33 and new basis waveform [2.7897861e+01 1.9418270e+00 1.9171400e-01 2.5627900e+00 4.0604130e+00\n",
-      " 2.3183000e-02 2.2713980e+00 2.4766750e+00 4.0524300e-01 1.8361340e+00]\n",
-      "Linear Iter:  34 and new basis waveform [27.819236  1.018167  0.141252  0.647847  4.587993  0.035203  1.047697\n",
-      "  0.665082  2.808607  4.587717]\n",
-      "Linear Iter:  35 and new basis waveform [2.1875173e+01 1.7038700e+00 1.1362000e-02 2.6328040e+00 5.9863060e+00\n",
-      " 4.1900000e-02 3.0184360e+00 6.0708550e+00 4.5405500e-01 2.1484500e+00]\n",
-      "Linear Iter:  36 and new basis waveform [28.865314  1.802096  0.138793  2.474724  4.831972  0.155063  2.905339\n",
-      "  4.185608  2.418633  5.966491]\n",
-      "Linear Iter:  37 and new basis waveform [2.8145238e+01 1.7878930e+00 1.1952200e-01 3.9548200e-01 5.0454600e-01\n",
-      " 7.1010000e-03 2.9762370e+00 3.6539090e+00 3.6717000e-01 5.4325110e+00]\n",
-      "Linear Iter:  38 and new basis waveform [20.617458  1.230068  0.198063  0.17304   3.629282  0.081376  0.744234\n",
-      "  0.508731  3.008019  3.916883]\n",
-      "Linear Iter:  39 and new basis waveform [21.544953  1.114772  0.097155  0.342667  5.587778  0.193336  0.037187\n",
-      "  4.690346  2.259814  2.000502]\n",
-      "Linear Iter:  40 and new basis waveform [28.842905  1.916756  0.185234  2.868286  0.67479   0.063576  0.632067\n",
-      "  1.521568  0.672443  1.668159]\n",
-      "Linear Iter:  41 and new basis waveform [2.5397377e+01 1.4512290e+00 1.6053100e-01 2.5634140e+00 4.4048740e+00\n",
-      " 4.0140000e-02 1.6134710e+00 3.9218590e+00 2.8429830e+00 1.0332000e-02]\n",
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-      "  0.629773  0.045699  1.168751]\n",
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-      "  3.823785  2.931965  3.142751]\n",
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-      "  3.226821  0.703619  3.839742]\n",
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-      "  6.025414  2.511266  6.13675 ]\n",
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-      "  3.964986  0.063723  1.72104 ]\n",
-      "Linear Iter:  64 and new basis waveform [29.955788  1.819837  0.045478  2.344409  0.99733   0.133571  2.787342\n",
-      "  3.125261  0.281854  4.8429  ]\n",
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-      "  0.550902  0.731668  2.869013]\n",
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-      "  3.865504  2.50491   0.373744]\n",
-      "Linear Iter:  68 and new basis waveform [2.0476638e+01 1.9785550e+00 1.3158400e-01 8.9513200e-01 4.9179020e+00\n",
-      " 9.2760000e-03 4.5650500e-01 2.8339800e+00 1.8408700e-01 3.6647900e-01]\n",
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-      "  4.626799  0.038651  3.625417]\n",
-      "Linear Iter:  70 and new basis waveform [20.250456  1.120644  0.180746  0.071889  0.109174  0.148804  0.232857\n",
-      "  1.449483  0.131936  3.587055]\n",
-      "Linear Iter:  71 and new basis waveform [29.167318  1.976668  0.154848  2.912794  0.183426  0.181321  1.884295\n",
-      "  2.613816  0.117377  2.290952]\n",
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-      "  4.086714  2.830375  6.239801]\n",
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-      "  2.879539  0.205551  2.073151]\n",
-      "Linear Iter:  74 and new basis waveform [29.969617  1.530517  0.156111  1.942404  0.588585  0.176322  2.654416\n",
-      "  0.641167  0.341472  4.178086]\n",
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-      "  4.999607  0.035473  2.336399]\n",
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-      "  1.846013  2.27757   4.157913]\n",
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-      "  1.31994   3.091417  1.128466]\n",
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-      " 1.8547300e-01 7.1412600e-01 3.5693410e+00 2.4540670e+00 6.1131630e+00]\n",
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-      "(80, 4016) [0.00000000e+00+0.j 2.62993621e-20+0.j 4.74354398e-20+0.j\n",
-      " 2.77056026e-20+0.j 2.38192913e-20+0.j 3.37741145e-20+0.j\n",
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-      " 2.57125961e-20+0.j 2.80480925e-20+0.j 2.00185173e-20+0.j\n",
-      " 3.48851092e-20+0.j 1.93038289e-20+0.j 3.52089162e-20+0.j\n",
-      " 2.23080182e-20+0.j 2.53083619e-20+0.j 6.81638462e-21+0.j\n",
-      " 9.19208009e-21+0.j 3.49069029e-21+0.j 6.20975536e-21+0.j\n",
-      " 1.44368454e-21+0.j 4.77688533e-21+0.j 9.66657537e-22+0.j\n",
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-      " 2.42499326e-23+0.j 1.58674025e-23+0.j 1.09175128e-23+0.j\n",
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-      " 3.97222091e-24+0.j 3.48067615e-24+0.j 4.64517719e-24+0.j\n",
-      " 3.37081214e-24+0.j 7.11450695e-24+0.j 4.42313737e-24+0.j\n",
-      " 4.37156160e-24+0.j 2.95274435e-24+0.j 2.99114226e-24+0.j\n",
-      " 3.09552467e-24+0.j 2.15512355e-24+0.j 3.17386728e-24+0.j\n",
-      " 2.41738031e-24+0.j 2.18747898e-24+0.j]\n"
+      "2019-11-18 11:57:02.099851 Start linear bases searching...\n",
+      "2019-11-18 11:57:02.228166 Linear Iter: 1 and new basis waveform [20.754118  1.388194  0.158121  1.282057  4.738707  0.183059  0.190052\n",
+      "  1.296208  0.205412  0.951191]\n",
+      "2019-11-18 11:57:02.350452 Linear Iter: 2 and new basis waveform [29.324562  1.814039  0.179381  0.050433  2.179353  0.193852  1.107244\n",
+      "  0.752737  2.87711   0.290906]\n",
+      "2019-11-18 11:57:02.471002 Linear Iter: 3 and new basis waveform [2.904987e+01 1.518261e+00 1.517000e-03 2.211607e+00 8.472520e-01\n",
+      " 1.968250e-01 2.056637e+00 3.598963e+00 1.295700e-02 4.923621e+00]\n",
+      "2019-11-18 11:57:02.593712 Linear Iter: 4 and new basis waveform [2.842076e+01 1.010737e+00 1.793980e-01 1.636980e+00 2.310012e+00\n",
+      " 9.388900e-02 6.540000e-03 3.541624e+00 2.911690e+00 1.121980e-01]\n",
+      "2019-11-18 11:57:02.716227 Linear Iter: 5 and new basis waveform [28.341902  1.699134  0.15059   2.702612  3.228004  0.057229  0.211062\n",
+      "  4.353572  3.032467  3.187404]\n",
+      "2019-11-18 11:57:02.838974 Linear Iter: 6 and new basis waveform [2.5889226e+01 1.2961160e+00 1.4592000e-01 7.4780700e-01 3.3492620e+00\n",
+      " 1.5660900e-01 2.4362000e-02 5.2054400e-01 2.9545470e+00 5.2346000e+00]\n",
+      "2019-11-18 11:57:02.964773 Linear Iter: 7 and new basis waveform [2.4581397e+01 1.2244480e+00 1.4749600e-01 2.7727070e+00 4.2274780e+00\n",
+      " 6.8449000e-02 1.9446490e+00 4.4900130e+00 2.6512140e+00 1.1160000e-02]\n",
+      "2019-11-18 11:57:03.187330 Linear Iter: 8 and new basis waveform [27.27472   1.652806  0.1597    0.557398  5.800934  0.157095  0.895326\n",
+      "  4.042168  0.090589  2.922874]\n",
+      "2019-11-18 11:57:03.307721 Linear Iter: 9 and new basis waveform [26.138557  1.236367  0.139718  3.120818  3.452074  0.138761  0.211713\n",
+      "  4.987042  0.272248  0.787304]\n",
+      "2019-11-18 11:57:03.429216 Linear Iter: 10 and new basis waveform [20.644447  1.099073  0.031071  1.883376  2.36535   0.039279  2.33317\n",
+      "  0.397254  0.182669  5.572275]\n",
+      "2019-11-18 11:57:03.651507 Linear Iter: 11 and new basis waveform [2.4287331e+01 1.2065260e+00 6.9630000e-03 2.1281500e+00 1.0917070e+00\n",
+      " 1.8741100e-01 7.1096000e-01 3.9197920e+00 2.8479500e+00 5.4072460e+00]\n",
+      "2019-11-18 11:57:03.874034 Linear Iter: 12 and new basis waveform [23.756058  1.245146  0.100152  2.528585  2.835676  0.170621  1.850229\n",
+      "  3.247426  0.158964  5.854706]\n",
+      "2019-11-18 11:57:04.097844 Linear Iter: 13 and new basis waveform [21.846418  1.337104  0.0749    0.478739  0.591313  0.165308  1.994063\n",
+      "  5.627032  2.902501  5.103072]\n",
+      "2019-11-18 11:57:04.323908 Linear Iter: 14 and new basis waveform [2.1405177e+01 1.7075420e+00 1.0591000e-02 5.1146200e-01 5.2886970e+00\n",
+      " 1.9000100e-01 4.4800300e-01 7.1646600e-01 1.6932500e-01 1.3677460e+00]\n",
+      "2019-11-18 11:57:04.548428 Linear Iter: 15 and new basis waveform [2.1257199e+01 1.0309790e+00 4.8910000e-03 2.7681090e+00 4.2229880e+00\n",
+      " 1.6856800e-01 2.6910100e-01 1.9997800e+00 7.3810000e-02 2.1828880e+00]\n",
+      "2019-11-18 11:57:04.774016 Linear Iter: 16 and new basis waveform [29.934504  1.368787  0.190811  2.84603   1.168404  0.110913  1.644777\n",
+      "  4.107043  0.315476  2.111046]\n",
+      "2019-11-18 11:57:04.999101 Linear Iter: 17 and new basis waveform [22.419593  1.29896   0.034169  2.668896  3.108036  0.113769  1.718239\n",
+      "  1.315685  3.006584  3.396061]\n",
+      "2019-11-18 11:57:05.225564 Linear Iter: 18 and new basis waveform [22.698011  1.194843  0.177198  1.450723  5.604236  0.050574  1.242613\n",
+      "  0.86709   2.839747  1.066915]\n",
+      "2019-11-18 11:57:05.452342 Linear Iter: 19 and new basis waveform [2.9881064e+01 1.7926000e+00 1.7150700e-01 3.0892860e+00 4.9878530e+00\n",
+      " 8.1700000e-03 1.0996070e+00 5.3121890e+00 2.4674500e-01 1.1940060e+00]\n",
+      "2019-11-18 11:57:05.678236 Linear Iter: 20 and new basis waveform [22.485327  1.247978  0.120424  3.019046  1.916905  0.121172  2.698501\n",
+      "  4.759616  2.631722  0.903774]\n",
+      "2019-11-18 11:57:05.901910 Linear Iter: 21 and new basis waveform [2.7482805e+01 1.7926110e+00 1.7520000e-03 3.1283400e-01 5.6203510e+00\n",
+      " 1.6828500e-01 2.0755380e+00 4.8173640e+00 2.6192000e-01 2.5117260e+00]\n",
+      "2019-11-18 11:57:06.127051 Linear Iter: 22 and new basis waveform [29.951216  1.671775  0.030245  0.72876   5.56416   0.146069  2.501563\n",
+      "  1.089432  2.868882  6.217753]\n",
+      "2019-11-18 11:57:06.352246 Linear Iter: 23 and new basis waveform [21.344394  1.376124  0.193221  1.551371  5.304769  0.143422  1.18731\n",
+      "  5.529487  2.516974  0.3308  ]\n",
+      "2019-11-18 11:57:06.579561 Linear Iter: 24 and new basis waveform [2.0346729e+01 1.4890740e+00 1.8500000e-01 7.4352500e-01 5.3269630e+00\n",
+      " 2.1340000e-03 2.2763160e+00 7.9543400e-01 2.5478600e+00 3.3433300e+00]\n",
+      "2019-11-18 11:57:06.805560 Linear Iter: 25 and new basis waveform [20.242654  1.113574  0.144954  0.673126  4.012113  0.072055  1.174608\n",
+      "  0.291698  2.488784  3.244929]\n",
+      "2019-11-18 11:57:07.037072 Linear Iter: 26 and new basis waveform [2.0355548e+01 1.8603510e+00 6.0386000e-02 3.5323300e-01 4.3278200e-01\n",
+      " 2.1524000e-02 1.0609720e+00 1.3037000e-02 1.4544400e-01 5.8793430e+00]\n",
+      "2019-11-18 11:57:07.267729 Linear Iter: 27 and new basis waveform [2.4041917e+01 1.2058780e+00 9.9700000e-04 2.6857060e+00 3.5107790e+00\n",
+      " 1.5240000e-01 1.8767090e+00 6.1873930e+00 3.0793330e+00 1.6390600e-01]\n",
+      "2019-11-18 11:57:07.498114 Linear Iter: 28 and new basis waveform [2.9858778e+01 1.8430480e+00 1.3037900e-01 2.8683380e+00 2.9933410e+00\n",
+      " 1.3078000e-02 1.9421240e+00 4.0349640e+00 6.2169400e-01 3.5590050e+00]\n",
+      "2019-11-18 11:57:07.725543 Linear Iter: 29 and new basis waveform [27.142574  1.329153  0.119917  2.859324  1.628101  0.169178  0.636335\n",
+      "  5.98084   0.519838  4.207455]\n",
+      "2019-11-18 11:57:07.957662 Linear Iter: 30 and new basis waveform [26.770169  1.403517  0.183869  0.563524  5.139257  0.13319   0.113673\n",
+      "  1.783668  2.343536  5.835196]\n",
+      "2019-11-18 11:57:08.182218 Linear Iter: 31 and new basis waveform [29.563517  1.116862  0.192454  2.498283  6.259477  0.143686  0.606026\n",
+      "  2.153541  2.793603  1.904083]\n",
+      "2019-11-18 11:57:08.415056 Linear Iter: 32 and new basis waveform [24.456327  1.159492  0.161615  0.739244  4.884943  0.133684  1.158164\n",
+      "  4.591115  0.358815  4.77948 ]\n",
+      "2019-11-18 11:57:08.743159 Linear Iter: 33 and new basis waveform [21.981131  1.9314    0.190242  0.690445  3.565934  0.137924  2.474365\n",
+      "  4.258423  3.073217  1.517167]\n",
+      "2019-11-18 11:57:08.974258 Linear Iter: 34 and new basis waveform [2.0063081e+01 1.4115390e+00 9.3520000e-03 1.6823970e+00 3.6943990e+00\n",
+      " 1.6668200e-01 6.6473900e-01 5.3883160e+00 1.1459500e+00 1.7802500e-01]\n",
+      "2019-11-18 11:57:09.305708 Linear Iter: 35 and new basis waveform [20.279805  1.271917  0.197143  1.165245  3.408604  0.095878  2.973016\n",
+      "  3.042768  2.696736  1.948265]\n",
+      "2019-11-18 11:57:09.637295 Linear Iter: 36 and new basis waveform [20.48984   1.263792  0.168538  0.061925  3.162193  0.170596  0.286741\n",
+      "  4.686257  2.702666  5.735142]\n",
+      "2019-11-18 11:57:09.968427 Linear Iter: 37 and new basis waveform [26.816499  1.241648  0.118415  0.407327  4.521302  0.108028  2.210829\n",
+      "  3.623694  0.081407  0.052666]\n",
+      "2019-11-18 11:57:10.299648 Linear Iter: 38 and new basis waveform [2.5797346e+01 1.1452180e+00 1.1289700e-01 1.1066740e+00 3.7418070e+00\n",
+      " 2.6870000e-03 5.5865100e-01 5.7167860e+00 2.7973300e-01 4.8455730e+00]\n",
+      "2019-11-18 11:57:10.627895 Linear Iter: 39 and new basis waveform [2.9869046e+01 1.9594450e+00 7.3274000e-02 1.8119110e+00 4.3998830e+00\n",
+      " 1.3991400e-01 2.9611690e+00 4.3742550e+00 4.0150000e-03 1.9929670e+00]\n",
+      "2019-11-18 11:57:10.960635 Linear Iter: 40 and new basis waveform [2.9810689e+01 1.8704610e+00 8.6207000e-02 2.1745320e+00 5.3778080e+00\n",
+      " 9.1168000e-02 2.1648290e+00 9.3220000e-03 6.8232500e-01 1.2819000e-02]\n",
+      "2019-11-18 11:57:11.289670 Linear Iter: 41 and new basis waveform [2.0050373e+01 1.1442820e+00 2.7520000e-03 2.3032830e+00 2.2169990e+00\n",
+      " 1.4664700e-01 7.7655800e-01 4.1078190e+00 2.8048450e+00 1.9223610e+00]\n",
+      "2019-11-18 11:57:11.621356 Linear Iter: 42 and new basis waveform [27.410936  1.945436  0.173516  2.688312  2.558352  0.087358  2.160479\n",
+      "  6.132342  2.440595  5.926553]\n",
+      "2019-11-18 11:57:11.949313 Linear Iter: 43 and new basis waveform [29.781415  1.828133  0.184837  2.124532  2.388706  0.186457  3.038234\n",
+      "  5.653028  2.273665  3.796675]\n",
+      "2019-11-18 11:57:12.279973 Linear Iter: 44 and new basis waveform [24.299012  1.200723  0.150035  0.035216  2.920208  0.155765  0.395513\n",
+      "  3.189177  2.337703  0.477239]\n",
+      "2019-11-18 11:57:12.610753 Linear Iter: 45 and new basis waveform [21.35556   1.452545  0.105849  3.059008  0.095932  0.049925  1.304313\n",
+      "  3.621834  2.675616  2.832751]\n",
+      "2019-11-18 11:57:12.946757 Linear Iter: 46 and new basis waveform [27.94141   1.461782  0.198482  1.010664  2.932697  0.199151  1.144404\n",
+      "  5.151662  3.025229  3.768824]\n",
+      "2019-11-18 11:57:13.276963 Linear Iter: 47 and new basis waveform [23.288111  1.771043  0.118755  0.218844  4.197292  0.156936  0.783238\n",
+      "  1.937323  0.204141  2.117041]\n",
+      "2019-11-18 11:57:13.611384 Linear Iter: 48 and new basis waveform [29.29655   1.878994  0.19353   2.185298  4.721826  0.148541  2.993592\n",
+      "  4.439558  1.024265  0.547602]\n",
+      "2019-11-18 11:57:13.952242 Linear Iter: 49 and new basis waveform [29.935615  1.837532  0.101075  0.525605  4.122129  0.10884   3.121546\n",
+      "  0.896344  0.51215   3.988138]\n",
+      "2019-11-18 11:57:14.285515 Linear Iter: 50 and new basis waveform [21.322441  1.346622  0.147882  0.112944  5.220859  0.161445  0.606218\n",
+      "  2.459472  0.485346  2.160331]\n",
+      "2019-11-18 11:57:14.624065 Linear Iter: 51 and new basis waveform [28.049135  1.419239  0.044421  2.047703  6.094307  0.086997  1.222396\n",
+      "  2.222364  3.03012   4.291607]\n",
+      "2019-11-18 11:57:14.964411 Linear Iter: 52 and new basis waveform [20.160999  1.448966  0.161537  0.518225  0.671929  0.087993  0.433413\n",
+      "  6.009418  1.051708  0.445918]\n",
+      "2019-11-18 11:57:15.297875 Linear Iter: 53 and new basis waveform [25.836235  1.750988  0.115382  2.96289   2.701814  0.133048  1.608988\n",
+      "  2.050003  2.899819  0.509887]\n",
+      "2019-11-18 11:57:15.632266 Linear Iter: 54 and new basis waveform [20.341213  1.154623  0.082521  0.457314  6.170451  0.140792  0.07234\n",
+      "  4.009442  0.676546  1.084765]\n",
+      "2019-11-18 11:57:15.971229 Linear Iter: 55 and new basis waveform [29.646482  1.908298  0.192231  2.310515  4.128875  0.175452  2.196638\n",
+      "  4.972642  2.281107  0.710045]\n",
+      "2019-11-18 11:57:16.318960 Linear Iter: 56 and new basis waveform [24.38938   1.998893  0.058682  2.487255  4.863415  0.138078  1.85472\n",
+      "  1.499403  3.140168  3.464972]\n",
+      "2019-11-18 11:57:16.666680 Linear Iter: 57 and new basis waveform [29.613026  1.7857    0.08627   0.627335  3.664814  0.198852  1.433093\n",
+      "  1.346629  2.969796  2.839673]\n",
+      "2019-11-18 11:57:17.001129 Linear Iter: 58 and new basis waveform [2.2953114e+01 1.2398150e+00 1.9619400e-01 6.2887300e-01 4.4040700e+00\n",
+      " 8.5030000e-03 1.8704500e+00 3.1773400e-01 3.1220170e+00 1.3552960e+00]\n",
+      "2019-11-18 11:57:17.440696 Linear Iter: 59 and new basis waveform [22.441669  1.373016  0.055952  2.156254  2.292212  0.085923  2.879007\n",
+      "  2.473752  0.260427  0.450906]\n",
+      "2019-11-18 11:57:17.776210 Linear Iter: 60 and new basis waveform [29.363324  1.6325    0.198976  0.391382  2.677789  0.136167  0.521698\n",
+      "  5.997789  0.540945  3.220531]\n",
+      "2019-11-18 11:57:18.111152 Linear Iter: 61 and new basis waveform [28.543241  1.707795  0.095152  1.554846  1.578408  0.16722   1.036529\n",
+      "  2.803957  2.58536   5.465236]\n",
+      "2019-11-18 11:57:18.461559 Linear Iter: 62 and new basis waveform [20.000401  1.797042  0.191569  0.823284  4.392634  0.156682  0.847822\n",
+      "  5.236146  2.120046  2.626837]\n",
+      "2019-11-18 11:57:18.897439 Linear Iter: 63 and new basis waveform [28.830422  1.180775  0.030528  1.662746  1.843221  0.081849  2.549001\n",
+      "  1.851739  0.129524  0.717935]\n",
+      "2019-11-18 11:57:19.332566 Linear Iter: 64 and new basis waveform [24.356183  1.86812   0.085925  1.827589  4.875923  0.172456  2.862063\n",
+      "  3.506561  2.801182  1.799458]\n",
+      "2019-11-18 11:57:19.771903 Linear Iter: 65 and new basis waveform [21.050807  1.495304  0.165109  2.326356  1.74387   0.135713  0.368478\n",
+      "  2.271283  2.586764  3.097984]\n",
+      "2019-11-18 11:57:20.115193 Linear Iter: 66 and new basis waveform [20.081883  1.830095  0.062179  2.845132  2.338636  0.19287   2.54785\n",
+      "  3.721775  0.557087  2.117104]\n",
+      "2019-11-18 11:57:20.557587 Linear Iter: 67 and new basis waveform [22.880396  1.926114  0.056263  1.033298  0.158243  0.072189  2.860917\n",
+      "  5.978393  3.054846  5.22931 ]\n",
+      "2019-11-18 11:57:21.003501 Linear Iter: 68 and new basis waveform [26.009387  1.036805  0.19886   0.103955  4.942673  0.114914  0.252271\n",
+      "  1.631303  0.371273  2.364409]\n",
+      "2019-11-18 11:57:21.445278 Linear Iter: 69 and new basis waveform [28.516604  1.077206  0.086651  1.50905   3.412224  0.186966  2.713834\n",
+      "  0.734667  2.89096   0.397718]\n",
+      "2019-11-18 11:57:21.883527 Linear Iter: 70 and new basis waveform [26.670314  1.379756  0.182797  0.691007  3.021248  0.157883  1.273956\n",
+      "  0.395218  3.020376  1.021666]\n",
+      "2019-11-18 11:57:22.326188 Linear Iter: 71 and new basis waveform [28.887625  1.953302  0.130376  2.896529  1.493557  0.040894  2.292577\n",
+      "  3.661658  0.031196  5.75913 ]\n",
+      "2019-11-18 11:57:22.774967 Linear Iter: 72 and new basis waveform [2.0050885e+01 1.2836760e+00 1.9874300e-01 3.0781460e+00 6.1996730e+00\n",
+      " 1.8280000e-03 1.0522890e+00 4.0908920e+00 2.9552040e+00 5.9668440e+00]\n",
+      "2019-11-18 11:57:23.214897 Linear Iter: 73 and new basis waveform [25.409765  1.974068  0.194104  2.551689  3.254432  0.034104  2.607687\n",
+      "  5.54469   3.014815  0.206706]\n",
+      "2019-11-18 11:57:23.652178 Linear Iter: 74 and new basis waveform [20.948179  1.534063  0.067156  0.766769  1.505668  0.068444  1.746856\n",
+      "  0.083619  0.325721  5.603432]\n",
+      "2019-11-18 11:57:24.093657 Linear Iter: 75 and new basis waveform [2.3720288e+01 1.7235710e+00 1.3393700e-01 2.7991700e+00 2.2484650e+00\n",
+      " 3.8501000e-02 2.1645220e+00 2.4385650e+00 8.1630000e-03 3.4188570e+00]\n",
+      "2019-11-18 11:57:24.540909 Linear Iter: 76 and new basis waveform [24.650809  1.85778   0.047634  1.602423  4.326276  0.132897  0.495493\n",
+      "  5.306182  0.160894  1.968846]\n",
+      "2019-11-18 11:57:24.978804 Linear Iter: 77 and new basis waveform [29.895126  1.893467  0.170073  0.930573  0.627448  0.043727  2.907983\n",
+      "  0.055732  2.334552  1.743779]\n",
+      "2019-11-18 11:57:25.421678 Linear Iter: 78 and new basis waveform [25.377038  1.3852    0.135851  0.412976  4.384058  0.092158  1.159544\n",
+      "  4.758219  3.05317   3.886777]\n",
+      "2019-11-18 11:57:25.872979 Linear Iter: 79 and new basis waveform [2.4421298e+01 1.6723640e+00 1.4060000e-02 1.6399300e+00 3.4136100e-01\n",
+      " 3.6371000e-02 9.5710700e-01 5.5557480e+00 2.9236140e+00 2.5023400e-01]\n",
+      "2019-11-18 11:57:02.099851 Finish linear bases searching...\n",
+      "It takes 23.778407 seconds.\n",
+      "(80, 4016) [0.00000000e+00+0.j 2.82070854e-20+0.j 2.52301401e-20+0.j\n",
+      " 3.25598757e-20+0.j 2.77556416e-20+0.j 3.60087294e-20+0.j\n",
+      " 2.37335093e-20+0.j 2.56713428e-20+0.j 2.57731911e-20+0.j\n",
+      " 3.53331835e-20+0.j 2.15513626e-20+0.j 2.06862979e-20+0.j\n",
+      " 2.96941960e-20+0.j 2.05306662e-20+0.j 3.04103536e-20+0.j\n",
+      " 2.52619342e-20+0.j 2.13612223e-20+0.j 2.10297414e-20+0.j\n",
+      " 3.45749913e-20+0.j 3.38047122e-20+0.j 1.00078575e-20+0.j\n",
+      " 7.69719755e-21+0.j 6.09573615e-21+0.j 8.54171760e-21+0.j\n",
+      " 4.81304633e-21+0.j 2.16694325e-21+0.j 1.83421200e-21+0.j\n",
+      " 1.28454472e-21+0.j 1.11606486e-21+0.j 7.99267983e-22+0.j\n",
+      " 9.42423862e-22+0.j 5.90643168e-22+0.j 4.62059234e-22+0.j\n",
+      " 5.24213980e-22+0.j 3.57255343e-22+0.j 2.81083829e-22+0.j\n",
+      " 3.32193517e-22+0.j 1.61154356e-22+0.j 1.92146765e-22+0.j\n",
+      " 1.12420895e-22+0.j 1.23238434e-22+0.j 1.41013462e-22+0.j\n",
+      " 1.01484375e-22+0.j 1.58275120e-22+0.j 4.15090340e-23+0.j\n",
+      " 3.44669621e-23+0.j 2.05038567e-23+0.j 3.77686095e-23+0.j\n",
+      " 3.06910645e-23+0.j 1.97119732e-23+0.j 2.47010921e-23+0.j\n",
+      " 1.58766702e-23+0.j 1.47807985e-23+0.j 1.11865855e-23+0.j\n",
+      " 1.61467899e-23+0.j 4.28402093e-23+0.j 7.38672789e-24+0.j\n",
+      " 1.02035838e-23+0.j 6.96644213e-24+0.j 7.73443182e-24+0.j\n",
+      " 6.75638002e-24+0.j 4.69883120e-24+0.j 5.79162484e-24+0.j\n",
+      " 4.83422788e-24+0.j 4.20623040e-24+0.j 3.34650556e-24+0.j\n",
+      " 3.87766024e-24+0.j 4.23276973e-24+0.j 3.37638595e-24+0.j\n",
+      " 3.30044099e-24+0.j 2.70544796e-24+0.j 4.91482724e-24+0.j\n",
+      " 3.22260890e-24+0.j 2.54259580e-24+0.j 2.19605354e-24+0.j\n",
+      " 2.01937460e-24+0.j 1.88209163e-24+0.j 2.61176501e-24+0.j\n",
+      " 2.29814496e-24+0.j 1.61303462e-24+0.j]\n"
      ]
     }
    ],
@@ -378,15 +369,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "40 basis elements gave 0 bad points of surrogate error >  1e-08\n",
-      "Number of linear basis elements is  40 and the linear ROQ data are saved in B_linear.npy\n"
+      "39 basis elements gave 255 bad points of surrogate error >  1e-08\n",
+      "49 basis elements gave 99 bad points of surrogate error >  1e-08\n",
+      "59 basis elements gave 0 bad points of surrogate error >  1e-08\n",
+      "Number of linear basis elements is  59 and the linear ROQ data are saved in B_linear.npy\n"
      ]
     }
    ],
@@ -397,22 +390,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 1.00000000e+00+6.82121026e-13j  9.09494702e-13+1.45519152e-11j\n",
-      "  -1.45519152e-11+7.27595761e-12j ...  2.54658516e-11-9.09494702e-12j\n",
-      "  -2.52384780e-11-2.72848411e-11j  8.18545232e-12+4.00177669e-11j]\n",
-      " [ 1.36424205e-12+2.16004992e-12j  1.00000000e+00+5.45696821e-12j\n",
-      "  -9.09494702e-12-9.09494702e-12j ... -9.09494702e-12-2.54658516e-11j\n",
-      "  -1.45519152e-11+3.45607987e-11j  1.90993887e-11-1.45519152e-11j]\n",
-      " [-1.36424205e-12-1.36424205e-12j  5.45696821e-12+3.63797881e-12j\n",
-      "   1.00000000e+00+1.27329258e-11j ...  4.72937245e-11-1.09139364e-11j\n",
-      "  -3.09228199e-11-4.00177669e-11j  7.27595761e-12+3.84261511e-11j]\n",
+      "[[ 1.00000000e+00+2.27373675e-12j -7.27595761e-12+3.27418093e-11j\n",
+      "  -7.09405867e-11+5.82076609e-11j ... -8.73114914e-11+1.81898940e-10j\n",
+      "   5.82076609e-11-5.52972779e-10j  1.16415322e-10+4.94765118e-10j]\n",
+      " [-1.36424205e-11+3.63797881e-12j  1.00000000e+00-4.09272616e-11j\n",
+      "   3.63797881e-12-1.14596332e-10j ... -2.72848411e-10-1.45519152e-10j\n",
+      "   3.71073838e-10+4.07453626e-10j -9.31322575e-10-3.49245965e-10j]\n",
+      " [ 6.23610264e-02-1.47905890e-02j  5.81302370e-02+4.05792280e-01j\n",
+      "   1.88944969e-01-9.70805493e-01j ...  1.74896616e-01-9.18449812e-03j\n",
+      "  -2.76031196e-01-5.12262297e-02j  5.16839933e-01-1.59194581e-01j]\n",
       " ...\n",
       " [ 0.00000000e+00+0.00000000e+00j  0.00000000e+00+0.00000000e+00j\n",
       "   0.00000000e+00+0.00000000e+00j ...  0.00000000e+00+0.00000000e+00j\n",
@@ -423,9 +416,11 @@
       " [ 0.00000000e+00+0.00000000e+00j  0.00000000e+00+0.00000000e+00j\n",
       "   0.00000000e+00+0.00000000e+00j ...  0.00000000e+00+0.00000000e+00j\n",
       "   0.00000000e+00+0.00000000e+00j  0.00000000e+00+0.00000000e+00j]]\n",
-      "emp_nodes [   0    1    2    5    7   10   13   18   25   33   43   52   62   85\n",
-      "  122  142  166  183  203  219  245  358  537  696  755  795  829  858\n",
-      "  897  936  984 1019 1066 1137 1182 1217 1298 1391 1511 1663]\n"
+      "emp_nodes [   0    1    3    5    8   11   15   19   23   31   40   51   66   80\n",
+      "   97  110  120  134  147  156  170  180  191  201  210  221  232  237\n",
+      "  261  312  470  585  727  757  779  792  807  826  860  872  899  923\n",
+      "  951  969  998 1022 1049 1071 1093 1122 1146 1176 1223 1256 1302 1387\n",
+      " 1482 1641 1874]\n"
      ]
     }
    ],
@@ -441,12 +436,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1080x648 with 1 Axes>"
       ]
@@ -473,7 +468,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -484,12 +479,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1080x648 with 1 Axes>"
       ]
@@ -519,92 +514,95 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Quadratic Iter:  1\n",
-      "Quadratic Iter:  2\n",
-      "Quadratic Iter:  3\n",
-      "Quadratic Iter:  4\n",
-      "Quadratic Iter:  5\n",
-      "Quadratic Iter:  6\n",
-      "Quadratic Iter:  7\n",
-      "Quadratic Iter:  8\n",
-      "Quadratic Iter:  9\n",
-      "Quadratic Iter:  10\n",
-      "Quadratic Iter:  11\n",
-      "Quadratic Iter:  12\n",
-      "Quadratic Iter:  13\n",
-      "Quadratic Iter:  14\n",
-      "Quadratic Iter:  15\n",
-      "Quadratic Iter:  16\n",
-      "Quadratic Iter:  17\n",
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-      "Quadratic Iter:  19\n",
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-      "Quadratic Iter:  79\n"
+      "2019-11-18 11:57:41.969170 Start quadratic bases searching...\n",
+      "2019-11-18 11:57:41.969413 Quadratic Iter:  1\n",
+      "2019-11-18 11:57:42.096735 Quadratic Iter:  2\n",
+      "2019-11-18 11:57:42.218413 Quadratic Iter:  3\n",
+      "2019-11-18 11:57:42.339979 Quadratic Iter:  4\n",
+      "2019-11-18 11:57:42.463914 Quadratic Iter:  5\n",
+      "2019-11-18 11:57:42.586843 Quadratic Iter:  6\n",
+      "2019-11-18 11:57:42.712099 Quadratic Iter:  7\n",
+      "2019-11-18 11:57:42.834360 Quadratic Iter:  8\n",
+      "2019-11-18 11:57:42.958660 Quadratic Iter:  9\n",
+      "2019-11-18 11:57:43.079561 Quadratic Iter:  10\n",
+      "2019-11-18 11:57:43.203209 Quadratic Iter:  11\n",
+      "2019-11-18 11:57:43.325076 Quadratic Iter:  12\n",
+      "2019-11-18 11:57:43.448521 Quadratic Iter:  13\n",
+      "2019-11-18 11:57:43.569851 Quadratic Iter:  14\n",
+      "2019-11-18 11:57:43.692984 Quadratic Iter:  15\n",
+      "2019-11-18 11:57:43.817311 Quadratic Iter:  16\n",
+      "2019-11-18 11:57:43.940085 Quadratic Iter:  17\n",
+      "2019-11-18 11:57:44.064523 Quadratic Iter:  18\n",
+      "2019-11-18 11:57:44.186551 Quadratic Iter:  19\n",
+      "2019-11-18 11:57:44.310510 Quadratic Iter:  20\n",
+      "2019-11-18 11:57:44.434018 Quadratic Iter:  21\n",
+      "2019-11-18 11:57:44.559003 Quadratic Iter:  22\n",
+      "2019-11-18 11:57:44.682992 Quadratic Iter:  23\n",
+      "2019-11-18 11:57:44.806803 Quadratic Iter:  24\n",
+      "2019-11-18 11:57:44.932070 Quadratic Iter:  25\n",
+      "2019-11-18 11:57:45.054203 Quadratic Iter:  26\n",
+      "2019-11-18 11:57:45.177415 Quadratic Iter:  27\n",
+      "2019-11-18 11:57:45.301573 Quadratic Iter:  28\n",
+      "2019-11-18 11:57:45.424829 Quadratic Iter:  29\n",
+      "2019-11-18 11:57:45.547839 Quadratic Iter:  30\n",
+      "2019-11-18 11:57:45.674941 Quadratic Iter:  31\n",
+      "2019-11-18 11:57:45.802252 Quadratic Iter:  32\n",
+      "2019-11-18 11:57:45.923150 Quadratic Iter:  33\n",
+      "2019-11-18 11:57:46.045890 Quadratic Iter:  34\n",
+      "2019-11-18 11:57:46.169177 Quadratic Iter:  35\n",
+      "2019-11-18 11:57:46.293818 Quadratic Iter:  36\n",
+      "2019-11-18 11:57:46.418870 Quadratic Iter:  37\n",
+      "2019-11-18 11:57:46.544767 Quadratic Iter:  38\n",
+      "2019-11-18 11:57:46.669229 Quadratic Iter:  39\n",
+      "2019-11-18 11:57:46.793221 Quadratic Iter:  40\n",
+      "2019-11-18 11:57:46.918157 Quadratic Iter:  41\n",
+      "2019-11-18 11:57:47.042121 Quadratic Iter:  42\n",
+      "2019-11-18 11:57:47.168036 Quadratic Iter:  43\n",
+      "2019-11-18 11:57:47.292795 Quadratic Iter:  44\n",
+      "2019-11-18 11:57:47.416636 Quadratic Iter:  45\n",
+      "2019-11-18 11:57:47.543953 Quadratic Iter:  46\n",
+      "2019-11-18 11:57:47.668095 Quadratic Iter:  47\n",
+      "2019-11-18 11:57:47.793995 Quadratic Iter:  48\n",
+      "2019-11-18 11:57:47.918784 Quadratic Iter:  49\n",
+      "2019-11-18 11:57:48.043703 Quadratic Iter:  50\n",
+      "2019-11-18 11:57:48.168600 Quadratic Iter:  51\n",
+      "2019-11-18 11:57:48.294625 Quadratic Iter:  52\n",
+      "2019-11-18 11:57:48.419776 Quadratic Iter:  53\n",
+      "2019-11-18 11:57:48.546029 Quadratic Iter:  54\n",
+      "2019-11-18 11:57:48.676825 Quadratic Iter:  55\n",
+      "2019-11-18 11:57:48.806069 Quadratic Iter:  56\n",
+      "2019-11-18 11:57:48.934120 Quadratic Iter:  57\n",
+      "2019-11-18 11:57:49.059478 Quadratic Iter:  58\n",
+      "2019-11-18 11:57:49.185266 Quadratic Iter:  59\n",
+      "2019-11-18 11:57:49.309864 Quadratic Iter:  60\n",
+      "2019-11-18 11:57:49.437172 Quadratic Iter:  61\n",
+      "2019-11-18 11:57:49.566502 Quadratic Iter:  62\n",
+      "2019-11-18 11:57:49.691416 Quadratic Iter:  63\n",
+      "2019-11-18 11:57:49.816603 Quadratic Iter:  64\n",
+      "2019-11-18 11:57:49.941982 Quadratic Iter:  65\n",
+      "2019-11-18 11:57:50.070118 Quadratic Iter:  66\n",
+      "2019-11-18 11:57:50.198616 Quadratic Iter:  67\n",
+      "2019-11-18 11:57:50.324160 Quadratic Iter:  68\n",
+      "2019-11-18 11:57:50.451369 Quadratic Iter:  69\n",
+      "2019-11-18 11:57:50.576571 Quadratic Iter:  70\n",
+      "2019-11-18 11:57:50.705241 Quadratic Iter:  71\n",
+      "2019-11-18 11:57:50.933831 Quadratic Iter:  72\n",
+      "2019-11-18 11:57:51.161377 Quadratic Iter:  73\n",
+      "2019-11-18 11:57:51.392349 Quadratic Iter:  74\n",
+      "2019-11-18 11:57:51.621817 Quadratic Iter:  75\n",
+      "2019-11-18 11:57:51.850325 Quadratic Iter:  76\n",
+      "2019-11-18 11:57:52.077227 Quadratic Iter:  77\n",
+      "2019-11-18 11:57:52.309151 Quadratic Iter:  78\n",
+      "2019-11-18 11:57:52.538624 Quadratic Iter:  79\n",
+      "2019-11-18 11:57:41.969170 Finish quadratic bases searching...\n",
+      "It takes 10.798186 seconds.\n"
      ]
     }
    ],
@@ -619,16 +617,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "20 basis elements gave 95 bad points of surrogate error >  1e-10\n",
-      "30 basis elements gave 23 bad points of surrogate error >  1e-10\n",
-      "40 basis elements gave 6 bad points of surrogate error >  1e-10\n",
+      "20 basis elements gave 940 bad points of surrogate error >  1e-10\n",
+      "30 basis elements gave 115 bad points of surrogate error >  1e-10\n",
+      "40 basis elements gave 2 bad points of surrogate error >  1e-10\n",
       "50 basis elements gave 0 bad points of surrogate error >  1e-10\n",
       "Number of quadratic basis elements is  50 and the linear ROQ data save in B_quadratic.npy\n"
      ]
@@ -641,7 +639,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -654,12 +652,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1080x648 with 1 Axes>"
       ]
diff --git a/Tutorial/SurrogateErrorsRandomTestPoints.png b/Tutorial/SurrogateErrorsRandomTestPoints.png
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diff --git a/Tutorial/fnodes_linear.npy b/Tutorial/fnodes_linear.npy
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diff --git a/Tutorial/linearbases.npy b/Tutorial/linearbases.npy
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diff --git a/Tutorial/pyroq.py b/Tutorial/pyroq.py
deleted file mode 100644
index b07e1e9..0000000
--- a/Tutorial/pyroq.py
+++ /dev/null
@@ -1,539 +0,0 @@
-import numpy
-import numpy as np
-import scipy
-import matplotlib
-matplotlib.use('Agg') 
-import matplotlib.pyplot as plt
-import lal
-import lalsimulation
-from lal.lal import PC_SI as LAL_PC_SI
-import h5py
-import warnings
-import random
-import multiprocessing as mp
-
-def howmany_within_range(row, minimum, maximum):
-    """Returns how many numbers lie within `maximum` and `minimum` in a given `row`"""
-    count = 0
-    for n in row:
-        if minimum <= n <= maximum:
-            count = count + 1
-    return count
-
-# Calculating the projection of complex vector v on complex vector u
-def proj(u, v):
-    # notice: this algrithm assume denominator isn't zero
-    return u * numpy.vdot(v,u) / numpy.vdot(u,u) 
-
-# Calculating the normalized residual (= a new basis) of a vector vec from known bases
-def gram_schmidt(bases, vec):
-    for i in numpy.arange(0,len(bases)):
-        vec = vec - proj(bases[i], vec)
-    return vec/numpy.sqrt(numpy.vdot(vec,vec)) # normalized new basis
-
-# Calculating overlap of two waveforms
-def overlap_of_two_waveforms(wf1, wf2):
-    wf1norm = wf1/numpy.sqrt(numpy.vdot(wf1,wf1)) # normalize the first waveform
-    wf2norm = wf2/numpy.sqrt(numpy.vdot(wf2,wf2)) # normalize the second waveform
-    diff = wf1norm - wf2norm
-    #overlap = 1 - 0.5*(numpy.vdot(diff,diff))
-    overlap = numpy.real(numpy.vdot(wf1norm, wf2norm))
-    return overlap
-
-def spherical_to_cartesian(sph):
-    x = sph[0]*numpy.sin(sph[1])*numpy.cos(sph[2])
-    y = sph[0]*numpy.sin(sph[1])*numpy.sin(sph[2])
-    z = sph[0]*numpy.cos(sph[1])
-    car = [x,y,z]
-    return car
-
-def get_m1m2_from_mcq(mc, q):
-    m2 = mc * q ** (-0.6) * (1+q)**0.2
-    m1 = m2 * q
-    return numpy.array([m1,m2])
-
-def generate_a_waveform(m1, m2, spin1, spin2, ecc, lambda1, lambda2, iota, phiRef, distance, deltaF, f_min, f_max, waveFlags, approximant):
-    test_mass1 = m1 * lal.lal.MSUN_SI
-    test_mass2 = m2 * lal.lal.MSUN_SI
-    lalsimulation.SimInspiralWaveformParamsInsertTidalLambda1(waveFlags, lambda1)
-    lalsimulation.SimInspiralWaveformParamsInsertTidalLambda2(waveFlags, lambda2)     
-    [plus_test, cross_test]=lalsimulation.SimInspiralChooseFDWaveform(test_mass1, test_mass2, spin1[0], spin1[1], spin1[2], spin2[0], spin2[1], spin2[2], distance, iota, phiRef, 0, ecc, 0, deltaF, f_min, f_max, 0, waveFlags, approximant)
-    hp = plus_test.data.data
-    hp_test = hp[numpy.int(f_min/deltaF):numpy.int(f_max/deltaF)]
-    return hp_test
-
-def generate_a_waveform_from_mcq(mc, q, spin1, spin2, ecc, lambda1, lambda2, iota, phiRef, distance, deltaF, f_min, f_max, waveFlags, approximant):
-    m1,m2 = get_m1m2_from_mcq(mc,q)
-    test_mass1 = m1 * lal.lal.MSUN_SI
-    test_mass2 = m2 * lal.lal.MSUN_SI
-    lalsimulation.SimInspiralWaveformParamsInsertTidalLambda1(waveFlags, lambda1)
-    lalsimulation.SimInspiralWaveformParamsInsertTidalLambda2(waveFlags, lambda2) 
-    [plus_test, cross_test]=lalsimulation.SimInspiralChooseFDWaveform(test_mass1, test_mass2, spin1[0], spin1[1], spin1[2], spin2[0], spin2[1], spin2[2], distance, iota, phiRef, 0, ecc, 0, deltaF, f_min, f_max, 0, waveFlags, approximant)
-    hp = plus_test.data.data
-    hp_test = hp[numpy.int(f_min/deltaF):numpy.int(f_max/deltaF)]
-    return hp_test
-
-def generate_params_points(npts, nparams, params_low, params_high):
-    paramspoints = numpy.random.uniform(params_low, params_high, size=(npts,nparams))
-    paramspoints = paramspoints.round(decimals=6)
-    return paramspoints
-
-def compute_modulus(paramspoint, known_bases, distance, deltaF, f_min, f_max, approximant):
-    waveFlags = lal.CreateDict()
-    m1, m2 = get_m1m2_from_mcq(paramspoint[0],paramspoint[1])
-    s1x, s1y, s1z = spherical_to_cartesian(paramspoint[2:5]) 
-    s2x, s2y, s2z = spherical_to_cartesian(paramspoint[5:8]) 
-    iota = paramspoint[8]  
-    phiRef = paramspoint[9]
-    ecc = 0
-    if len(paramspoint)==11:
-        ecc = paramspoint[10]
-    if len(paramspoint)==12:
-        lambda1 = paramspoint[10]
-        lambda2 = paramspoint[11]
-        lalsimulation.SimInspiralWaveformParamsInsertTidalLambda1(waveFlags, lambda1)
-        lalsimulation.SimInspiralWaveformParamsInsertTidalLambda2(waveFlags, lambda2) 
-    f_ref = 0 
-    RA=0    
-    DEC=0   
-    psi=0   
-    phi=0   
-    m1 *= lal.lal.MSUN_SI
-    m2 *= lal.lal.MSUN_SI
-    [plus,cross]=lalsimulation.SimInspiralChooseFDWaveform(m1, m2, s1x, s1y, s1z, s2x, s2y, s2z, distance, iota, phiRef, 0, ecc, 0, deltaF, f_min, f_max, f_ref, waveFlags, approximant)
-    hp_tmp = plus.data.data[numpy.int(f_min/deltaF):numpy.int(f_max/deltaF)] # data_tmp is hplus and is a complex vector 
-    residual = hp_tmp
-    for k in numpy.arange(0,len(known_bases)):
-        residual -= proj(known_bases[k],hp_tmp)
-    modulus = numpy.sqrt(numpy.vdot(residual, residual))
-    return modulus
-
-def compute_modulus_quad(paramspoint, known_quad_bases, distance, deltaF, f_min, f_max, approximant):
-    waveFlags = lal.CreateDict()
-    m1, m2 = get_m1m2_from_mcq(paramspoint[0],paramspoint[1])
-    s1x, s1y, s1z = spherical_to_cartesian(paramspoint[2:5]) 
-    s2x, s2y, s2z = spherical_to_cartesian(paramspoint[5:8]) 
-    iota=paramspoint[8]  
-    phiRef=paramspoint[9]
-    ecc = 0
-    if len(paramspoint)==11:
-        ecc = paramspoint[10]
-    if len(paramspoint)==12:
-        lambda1 = paramspoint[10]
-        lambda2 = paramspoint[11]
-        lalsimulation.SimInspiralWaveformParamsInsertTidalLambda1(waveFlags, lambda1)
-        lalsimulation.SimInspiralWaveformParamsInsertTidalLambda2(waveFlags, lambda2) 
-    f_ref = 0 
-    RA=0    
-    DEC=0   
-    psi=0   
-    phi=0   
-    m1 *= lal.lal.MSUN_SI
-    m2 *= lal.lal.MSUN_SI
-    [plus,cross]=lalsimulation.SimInspiralChooseFDWaveform(m1, m2, s1x, s1y, s1z, s2x, s2y, s2z, distance, iota, phiRef, 0, ecc, 0, deltaF, f_min, f_max, f_ref, waveFlags, approximant)
-    hp_tmp = plus.data.data[numpy.int(f_min/deltaF):numpy.int(f_max/deltaF)] # data_tmp is hplus and is a complex vector 
-    hp_quad_tmp = (numpy.absolute(hp_tmp))**2
-    residual = hp_quad_tmp
-    for k in numpy.arange(0,len(known_quad_bases)):
-        residual -= proj(known_quad_bases[k],hp_quad_tmp)
-    modulus = numpy.sqrt(numpy.vdot(residual, residual))
-    return modulus
-
-# now generating N=npts waveforms at points that are 
-# randomly uniformly distributed in parameter space
-# and calculate their inner products with the 1st waveform
-# so as to find the best waveform as the new basis
-def least_match_waveform_unnormalized(parallel, nprocesses, paramspoints, known_bases, distance, deltaF, f_min, f_max, waveFlags, approximant):
-    if parallel == 1:
-        paramspointslist = paramspoints.tolist()
-        #pool = mp.Pool(mp.cpu_count())
-        pool = mp.Pool(processes=nprocesses)
-        modula = [pool.apply(compute_modulus, args=(paramspoint, known_bases, distance, deltaF, f_min, f_max, approximant)) for paramspoint in paramspointslist]
-        pool.close()
-    if parallel == 0:
-        npts = len(paramspoints)
-        modula = numpy.zeros(npts)
-        for i in numpy.arange(0,npts):
-            paramspoint = paramspoints[i]
-            modula[i] = compute_modulus(paramspoint, known_bases, distance, deltaF, f_min, f_max, approximant)
-    arg_newbasis = numpy.argmax(modula) 
-    paramspoint = paramspoints[arg_newbasis]
-    mass1, mass2 = get_m1m2_from_mcq(paramspoints[arg_newbasis][0],paramspoints[arg_newbasis][1])
-    mass1 *= lal.lal.MSUN_SI
-    mass2 *= lal.lal.MSUN_SI
-    sp1x, sp1y, sp1z = spherical_to_cartesian(paramspoints[arg_newbasis,2:5]) 
-    sp2x, sp2y, sp2z = spherical_to_cartesian(paramspoints[arg_newbasis,5:8]) 
-    inclination = paramspoints[arg_newbasis][8]
-    phi_ref = paramspoints[arg_newbasis][9]
-    ecc = 0
-    if len(paramspoint)==11:
-        ecc = paramspoints[arg_newbasis][10]
-    if len(paramspoint)==12:
-        lambda1 = paramspoints[arg_newbasis][10]
-        lambda2 = paramspoints[arg_newbasis][11]
-        lalsimulation.SimInspiralWaveformParamsInsertTidalLambda1(waveFlags, lambda1)
-        lalsimulation.SimInspiralWaveformParamsInsertTidalLambda2(waveFlags, lambda2) 
-    [plus_new, cross_new]=lalsimulation.SimInspiralChooseFDWaveform(mass1, mass2, sp1x, sp1y, sp1z, sp2x, sp2y, sp2z, distance, inclination, phi_ref, 0, ecc, 0, deltaF, f_min, f_max, 0, waveFlags, approximant)
-    hp_new = plus_new.data.data
-    hp_new = hp_new[numpy.int(f_min/deltaF):numpy.int(f_max/deltaF)]
-    basis_new = gram_schmidt(known_bases, hp_new)
-    return numpy.array([basis_new, paramspoints[arg_newbasis], modula[arg_newbasis]]) # elements, masses&spins, residual mod
-
-
-def least_match_quadratic_waveform_unnormalized(parallel, nprocesses, paramspoints, known_quad_bases, distance, deltaF, f_min, f_max, waveFlags, approximant):
-    if parallel == 1:
-        paramspointslist = paramspoints.tolist()
-        pool = mp.Pool(processes=nprocesses)
-        modula = [pool.apply(compute_modulus_quad, args=(paramspoint, known_quad_bases, distance, deltaF, f_min, f_max, approximant)) for paramspoint in paramspointslist]
-        pool.close()
-    if parallel == 0:
-        npts = len(paramspoints)
-        modula = numpy.zeros(npts)
-        for i in numpy.arange(0,npts):
-            paramspoint = paramspoints[i]
-            modula[i] = compute_modulus_quad(paramspoint, known_quad_bases, distance, deltaF, f_min, f_max, approximant)
-    arg_newbasis = numpy.argmax(modula)    
-    paramspoint = paramspoints[arg_newbasis]
-    mass1, mass2 = get_m1m2_from_mcq(paramspoints[arg_newbasis][0],paramspoints[arg_newbasis][1])
-    mass1 *= lal.lal.MSUN_SI
-    mass2 *= lal.lal.MSUN_SI
-    sp1x, sp1y, sp1z = spherical_to_cartesian(paramspoints[arg_newbasis,2:5]) 
-    sp2x, sp2y, sp2z = spherical_to_cartesian(paramspoints[arg_newbasis,5:8]) 
-    inclination = paramspoints[arg_newbasis][8]
-    phi_ref = paramspoints[arg_newbasis][9]
-    ecc = 0
-    if len(paramspoint)==11:
-        ecc = paramspoints[arg_newbasis][10]
-    if len(paramspoint)==12:
-        lambda1 = paramspoints[arg_newbasis][10]
-        lambda2 = paramspoints[arg_newbasis][11]
-        lalsimulation.SimInspiralWaveformParamsInsertTidalLambda1(waveFlags, lambda1)
-        lalsimulation.SimInspiralWaveformParamsInsertTidalLambda2(waveFlags, lambda2)     
-    [plus_new, cross_new]=lalsimulation.SimInspiralChooseFDWaveform(mass1, mass2, sp1x, sp1y, sp1z, sp2x, sp2y, sp2z, distance, inclination, phi_ref, 0, ecc, 0, deltaF, f_min, f_max, 0, waveFlags, approximant)
-    hp_new = plus_new.data.data
-    hp_new = hp_new[numpy.int(f_min/deltaF):numpy.int(f_max/deltaF)]
-    hp_quad_new = (numpy.absolute(hp_new))**2
-    basis_quad_new = gram_schmidt(known_quad_bases, hp_quad_new)    
-    return numpy.array([basis_quad_new, paramspoints[arg_newbasis], modula[arg_newbasis]]) # elements, masses&spins, residual mod
-
-def bases_searching_results_unnormalized(parallel, nprocesses, npts, nparams, nbases, known_bases, basis_waveforms, params, residual_modula, params_low, params_high, distance, deltaF, f_min, f_max, waveFlags, approximant):
-    if nparams == 10: print("The parameters are Mc, q, s1(mag, theta, phi), s2(mag, theta, phi), iota, and phiRef\n")
-    if nparams == 11: print("The parameters are Mc, q, s1(mag, theta, phi), s2(mag, theta, phi), iota, phiRef, and eccentricity\n")
-    if nparams == 12: print("The parameters are Mc, q, s1(mag, theta, phi), s2(mag, theta, phi), iota, phiRef, lambda1, and lambda2\n") 
-    for k in numpy.arange(0,nbases-1):
-        paramspoints = generate_params_points(npts, nparams, params_low, params_high)
-        basis_new, params_new, rm_new = least_match_waveform_unnormalized(parallel, nprocesses, paramspoints, known_bases, distance, deltaF, f_min, f_max, waveFlags, approximant)
-        print("Linear Iter: ", k+1, "and new basis waveform", params_new)
-        known_bases= numpy.append(known_bases, numpy.array([basis_new]), axis=0)
-        params = numpy.append(params, numpy.array([params_new]), axis = 0)
-        residual_modula = numpy.append(residual_modula, rm_new)
-    numpy.save('./linearbases.npy',known_bases)
-    numpy.save('./linearbasiswaveformparams.npy',params)
-    return known_bases, params, residual_modula
-
-def bases_searching_quadratic_results_unnormalized(parallel, nprocesses, npts, nparams, nbases_quad, known_quad_bases, basis_waveforms, params_quad, residual_modula, params_low, params_high, distance, deltaF, f_min, f_max, waveFlags, approximant):
-    for k in numpy.arange(0,nbases_quad-1):
-        print("Quadratic Iter: ", k+1)
-        paramspoints = generate_params_points(npts, nparams, params_low, params_high)
-        basis_new, params_new, rm_new= least_match_quadratic_waveform_unnormalized(parallel, nprocesses, paramspoints, known_quad_bases, distance, deltaF, f_min, f_max, waveFlags, approximant)
-        known_quad_bases= numpy.append(known_quad_bases, numpy.array([basis_new]), axis=0)
-        params_quad = numpy.append(params_quad, numpy.array([params_new]), axis = 0)
-        residual_modula = numpy.append(residual_modula, rm_new)
-    numpy.save('./quadraticbases.npy',known_quad_bases)
-    numpy.save('./quadraticbasiswaveformparams.npy',params_quad)
-    return known_quad_bases, params_quad, residual_modula
-
-def massrange(mc_low, mc_high, q_low, q_high):
-    mmin = get_m1m2_from_mcq(mc_low,q_high)[1]
-    mmax = get_m1m2_from_mcq(mc_high,q_high)[0]
-    return [mmin, mmax]
-
-def initial_basis(mc_low, mc_high, q_low, q_high, s1sphere_low, s1sphere_high, s2sphere_low, s2sphere_high, ecc_low, ecc_high, lambda1_low, lambda1_high, lambda2_low, lambda2_high, iota_low, iota_high, phiref_low, phiref_high, distance, deltaF, f_min, f_max, waveFlags, approximant):
-    try:
-        if approximant==lalsimulation.IMRPhenomPv2:
-            nparams = 10
-            params_low = [mc_low, q_low, s1sphere_low[0], s1sphere_low[1], s1sphere_low[2], s2sphere_low[0], s2sphere_low[1], s2sphere_low[2], iota_low, phiref_low] 
-            params_high = [mc_high, q_high, s1sphere_high[0], s1sphere_high[1], s1sphere_high[2], s2sphere_high[0], s2sphere_high[1], s2sphere_high[2], iota_high, phiref_high]
-            params_start = numpy.array([[mc_low, q_low, s1sphere_low[0], s1sphere_low[1], s1sphere_low[2], s2sphere_low[0], s2sphere_low[1], s2sphere_low[2], 0.33333*np.pi, 1.5*np.pi]])
-            hp1 = generate_a_waveform_from_mcq(mc_low, q_low, spherical_to_cartesian(s1sphere_low), spherical_to_cartesian(s2sphere_low), 0, 0, 0, iota_low, phiref_low, distance, deltaF, f_min, f_max, waveFlags, approximant)
-    except AttributeError: 
-        pass
-    try:
-        if approximant==lalsimulation.IMRPhenomPv3:
-            nparams = 10
-            params_low = [mc_low, q_low, s1sphere_low[0], s1sphere_low[1], s1sphere_low[2], s2sphere_low[0], s2sphere_low[1], s2sphere_low[2], iota_low, phiref_low] 
-            params_high = [mc_high, q_high, s1sphere_high[0], s1sphere_high[1], s1sphere_high[2], s2sphere_high[0], s2sphere_high[1], s2sphere_high[2], iota_high, phiref_high]
-            params_start = numpy.array([[mc_low, q_low, s1sphere_low[0], s1sphere_low[1], s1sphere_low[2], s2sphere_low[0], s2sphere_low[1], s2sphere_low[2], 0.33333*np.pi, 1.5*np.pi]])
-            hp1 = generate_a_waveform_from_mcq(mc_low, q_low, spherical_to_cartesian(s1sphere_low), spherical_to_cartesian(s2sphere_low), 0, 0, 0, iota_low, phiref_low, distance, deltaF, f_min, f_max, waveFlags, approximant)
-    except AttributeError: 
-        pass
-    try:
-        if approximant==lalsimulation.IMRPhenomPv3HM:
-            nparams = 10
-            params_low = [mc_low, q_low, s1sphere_low[0], s1sphere_low[1], s1sphere_low[2], s2sphere_low[0], s2sphere_low[1], s2sphere_low[2], iota_low, phiref_low] 
-            params_high = [mc_high, q_high, s1sphere_high[0], s1sphere_high[1], s1sphere_high[2], s2sphere_high[0], s2sphere_high[1], s2sphere_high[2], iota_high, phiref_high]
-            params_start = numpy.array([[mc_low, q_low, s1sphere_low[0], s1sphere_low[1], s1sphere_low[2], s2sphere_low[0], s2sphere_low[1], s2sphere_low[2], 0.33333*np.pi, 1.5*np.pi]])
-            hp1 = generate_a_waveform_from_mcq(mc_low, q_low, spherical_to_cartesian(s1sphere_low), spherical_to_cartesian(s2sphere_low), 0, 0, 0, iota_low, phiref_low, distance, deltaF, f_min, f_max, waveFlags, approximant)
-    except AttributeError: 
-        pass
-    try:
-        if approximant==lalsimulation.IMRPhenomXHM:
-            nparams = 10
-            params_low = [mc_low, q_low, s1sphere_low[0], s1sphere_low[1], s1sphere_low[2], s2sphere_low[0], s2sphere_low[1], s2sphere_low[2], iota_low, phiref_low] 
-            params_high = [mc_high, q_high, s1sphere_high[0], s1sphere_high[1], s1sphere_high[2], s2sphere_high[0], s2sphere_high[1], s2sphere_high[2], iota_high, phiref_high]
-            params_start = numpy.array([[mc_low, q_low, s1sphere_low[0], s1sphere_low[1], s1sphere_low[2], s2sphere_low[0], s2sphere_low[1], s2sphere_low[2], 0.33333*np.pi, 1.5*np.pi]])
-            hp1 = generate_a_waveform_from_mcq(mc_low, q_low, spherical_to_cartesian(s1sphere_low), spherical_to_cartesian(s2sphere_low), 0, 0, 0, iota_low, phiref_low, distance, deltaF, f_min, f_max, waveFlags, approximant)
-    except AttributeError: 
-        pass
-    try:
-        if approximant==lalsimulation.TaylorF2Ecc:
-            nparams = 11
-            params_low = [mc_low, q_low, s1sphere_low[0], s1sphere_low[1], s1sphere_low[2], s2sphere_low[0], s2sphere_low[1], s2sphere_low[2], iota_low, phiref_low, ecc_low] 
-            params_high = [mc_high, q_high, s1sphere_high[0], s1sphere_high[1], s1sphere_high[2], s2sphere_high[0], s2sphere_high[1], s2sphere_high[2], iota_high, phiref_high, ecc_high]
-            params_start = numpy.array([[mc_low, q_low, s1sphere_low[0], s1sphere_low[1], s1sphere_low[2], s2sphere_low[0], s2sphere_low[1], s2sphere_low[2], 0.33333*np.pi, 1.5*np.pi, ecc_low]])
-            hp1 = generate_a_waveform_from_mcq(mc_low, q_low, spherical_to_cartesian(s1sphere_low), spherical_to_cartesian(s2sphere_low), ecc_low, 0, 0, iota_low, phiref_low, distance, deltaF, f_min, f_max, waveFlags, approximant)
-    except AttributeError: 
-        pass
-    try:    
-        if approximant==lalsimulation.IMRPhenomPv2_NRTidal:
-            nparams = 12
-            params_low = [mc_low, q_low, s1sphere_low[0], s1sphere_low[1], s1sphere_low[2], s2sphere_low[0], s2sphere_low[1], s2sphere_low[2], iota_low, phiref_low, lambda1_low, lambda2_low]
-            params_high = [mc_high, q_high, s1sphere_high[0], s1sphere_high[1], s1sphere_high[2], s2sphere_high[0], s2sphere_high[1], s2sphere_high[2], iota_high, phiref_high, lambda1_high, lambda2_high]
-            params_start = numpy.array([[mc_low, q_low, s1sphere_low[0], s1sphere_low[1], s1sphere_low[2], s2sphere_low[0], s2sphere_low[1], s2sphere_low[2], 0.33333*np.pi, 1.5*np.pi, lambda1_low, lambda2_low]])
-
-            hp1 = generate_a_waveform_from_mcq(mc_low, q_low, spherical_to_cartesian(s1sphere_low), spherical_to_cartesian(s2sphere_low), 0, lambda1_low, lambda2_low, iota_low, phiref_low, distance, deltaF, f_min, f_max, waveFlags, approximant) 
-    except AttributeError: 
-        pass
-    try:
-        if approximant==lalsimulation.IMRPhenomNSBH:
-            nparams = 12
-            params_low = [mc_low, q_low, s1sphere_low[0], s1sphere_low[1], s1sphere_low[2], s2sphere_low[0], s2sphere_low[1], s2sphere_low[2], iota_low, phiref_low, lambda1_low, lambda2_low]
-            params_high = [mc_high, q_high, s1sphere_high[0], s1sphere_high[1], s1sphere_high[2], s2sphere_high[0], s2sphere_high[1], s2sphere_high[2], iota_high, phiref_high, lambda1_high, lambda2_high]
-            params_start = numpy.array([[mc_low, q_low, s1sphere_low[0], s1sphere_low[1], s1sphere_low[2], s2sphere_low[0], s2sphere_low[1], s2sphere_low[2], 0.33333*np.pi, 1.5*np.pi, lambda1_low, lambda2_low]])
-            hp1 = generate_a_waveform_from_mcq(mc_low, q_low, spherical_to_cartesian(s1sphere_low), spherical_to_cartesian(s2sphere_low), 0, lambda1_low, lambda2_low, iota_low, phiref_low, distance, deltaF, f_min, f_max, waveFlags, approximant) 
-    except AttributeError: 
-        pass
-    return numpy.array([nparams, params_low, params_high, params_start, hp1])
-
-def empnodes(ndim, known_bases): # Here known_bases is the full copy known_bases_copy. Its length is equal to or longer than ndim.
-    emp_nodes = numpy.arange(0,ndim)*100000000
-    emp_nodes[0] = numpy.argmax(numpy.absolute(known_bases[0]))
-    c1 = known_bases[1,emp_nodes[0]]/known_bases[0,1]
-    interp1 = numpy.multiply(c1,known_bases[0])
-    diff1 = interp1 - known_bases[1]
-    r1 = numpy.absolute(diff1)
-    emp_nodes[1] = numpy.argmax(r1)
-    for k in numpy.arange(2,ndim):
-        emp_tmp = emp_nodes[0:k]
-        Vtmp = numpy.transpose(known_bases[0:k,emp_tmp])
-        inverse_Vtmp = numpy.linalg.pinv(Vtmp)
-        e_to_interp = known_bases[k]
-        Ci = numpy.dot(inverse_Vtmp, e_to_interp[emp_tmp])
-        interpolantA = numpy.zeros(len(known_bases[k]))+numpy.zeros(len(known_bases[k]))*1j
-        for j in numpy.arange(0, k):
-            tmp = numpy.multiply(Ci[j], known_bases[j])
-            interpolantA += tmp
-        diff = interpolantA - known_bases[k]
-        r = numpy.absolute(diff)
-        emp_nodes[k] = numpy.argmax(r)
-        emp_nodes = sorted(emp_nodes)
-    u, c = numpy.unique(emp_nodes, return_counts=True)
-    dup = u[c > 1]
-    #print(len(emp_nodes), "\nDuplicates indices:", dup)
-    emp_nodes = numpy.unique(emp_nodes)
-    ndim = len(emp_nodes)
-    #print(len(emp_nodes), "\n", emp_nodes)
-    V = numpy.transpose(known_bases[0:ndim, emp_nodes])
-    inverse_V = numpy.linalg.pinv(V)
-    return numpy.array([ndim, inverse_V, emp_nodes])
-
-def surroerror(ndim, inverse_V, emp_nodes, known_bases, test_mc, test_q, test_s1, test_s2, test_ecc, test_lambda1, test_lambda2, test_iota, test_phiref, distance, deltaF, f_min, f_max, waveFlags, approximant):
-    hp_test = generate_a_waveform_from_mcq(test_mc, test_q, test_s1, test_s2, test_ecc, test_lambda1, test_lambda2, test_iota, test_phiref, distance, deltaF, f_min, f_max, waveFlags, approximant)
-    Ci = numpy.dot(inverse_V, hp_test[emp_nodes])
-    interpolantA = numpy.zeros(len(hp_test))+numpy.zeros(len(hp_test))*1j
-    #ndim = len(known_bases)
-    for j in numpy.arange(0, ndim):
-        tmp = numpy.multiply(Ci[j], known_bases[j])
-        interpolantA += tmp
-    surro = (1-overlap_of_two_waveforms(hp_test, interpolantA))*deltaF
-    return surro
-
-def surros(tolerance, ndim, inverse_V, emp_nodes, known_bases, nts, nparams, params_low, params_high, distance, deltaF, f_min, f_max, waveFlags, approximant): # Here known_bases is known_bases_copy
-    test_points = generate_params_points(nts, nparams, params_low, params_high)
-    surros = numpy.zeros(nts)
-    count = 0
-    for i in numpy.arange(0,nts):
-        test_mc =  test_points[i,0]
-        test_q = test_points[i,1]
-        test_s1 = spherical_to_cartesian(test_points[i,2:5])
-        test_s2 = spherical_to_cartesian(test_points[i,5:8])
-        test_iota = test_points[i,8]
-        test_phiref = test_points[i,9]
-        test_ecc = 0
-        test_lambda1 = 0
-        test_lambda2 = 0
-        if nparams == 11: test_ecc = test_points[i,10]
-        if nparams == 12: 
-            test_lambda1 = test_points[i,10]
-            test_lambda2 = test_points[i,11]
-        surros[i] = surroerror(ndim, inverse_V, emp_nodes, known_bases[0:ndim], test_mc, test_q, test_s1, test_s2, test_ecc, test_lambda1, test_lambda2, test_iota, test_phiref, distance, deltaF, f_min, f_max, waveFlags, approximant)
-        if (surros[i] > tolerance):
-            count = count+1
-    print(ndim, "basis elements gave", count, "bad points of surrogate error > ", tolerance)
-    if count == 0: val =0
-    else: val = 1
-    return val
-
-def roqs(tolerance, freq,  ndimlow, ndimhigh, ndimstepsize, known_bases_copy, nts, nparams, params_low, params_high, distance, deltaF, f_min, f_max, waveFlags, approximant):
-    for num in np.arange(ndimlow, ndimhigh, ndimstepsize):
-        ndim, inverse_V, emp_nodes = empnodes(num, known_bases_copy)
-        if surros(tolerance, ndim, inverse_V, emp_nodes, known_bases_copy, nts, nparams, params_low, params_high, distance, deltaF, f_min, f_max, waveFlags, approximant)==0:
-            b_linear = numpy.dot(numpy.transpose(known_bases_copy[0:ndim]),inverse_V)
-            f_linear = freq[emp_nodes]
-            numpy.save('./B_linear.npy',numpy.transpose(b_linear))
-            numpy.save('./fnodes_linear.npy',f_linear)
-            print("Number of linear basis elements is ", ndim, "and the linear ROQ data are saved in B_linear.npy")
-            break
-    return
-
-def testrep(b_linear, emp_nodes, test_mc, test_q, test_s1, test_s2, test_ecc, test_lambda1, test_lambda2, test_iota, test_phiref, distance, deltaF, f_min, f_max, waveFlags, approximant):
-    hp_test = generate_a_waveform_from_mcq(test_mc, test_q, test_s1, test_s2, test_ecc, test_lambda1, test_lambda2, test_iota, test_phiref, distance, deltaF, f_min, f_max, waveFlags, approximant)
-    hp_test_emp = hp_test[emp_nodes]
-    hp_rep = numpy.dot(b_linear,hp_test_emp)
-    diff = hp_rep - hp_test
-    rep_error = diff/numpy.sqrt(numpy.vdot(hp_test,hp_test))
-    freq = numpy.arange(f_min,f_max,deltaF)
-    plt.figure(figsize=(15,9))
-    plt.plot(freq, numpy.real(rep_error), label='Real part of h+') 
-    plt.plot(freq, numpy.imag(rep_error), label='Imaginary part of h+')
-    plt.xlabel('Frequency')
-    plt.ylabel('Fractional Representation Error')
-    plt.title('Rep Error with numpy.linalg.pinv()')
-    plt.legend(loc=0)
-    plt.show()
-    return
-
-def empnodes_quad(ndim_quad, known_quad_bases):
-    emp_nodes_quad = numpy.arange(0,ndim_quad)*100000000
-    emp_nodes_quad[0] = numpy.argmax(numpy.absolute(known_quad_bases[0]))
-    c1_quad = known_quad_bases[1,emp_nodes_quad[0]]/known_quad_bases[0,1]
-    interp1_quad = numpy.multiply(c1_quad,known_quad_bases[0])
-    diff1_quad = interp1_quad - known_quad_bases[1]
-    r1_quad = numpy.absolute(diff1_quad)
-    emp_nodes_quad[1] = numpy.argmax(r1_quad)
-    for k in numpy.arange(2,ndim_quad):
-        emp_tmp_quad = emp_nodes_quad[0:k]
-        Vtmp_quad = numpy.transpose(known_quad_bases[0:k,emp_tmp_quad])
-        inverse_Vtmp_quad = numpy.linalg.pinv(Vtmp_quad)
-        e_to_interp_quad = known_quad_bases[k]
-        Ci_quad = numpy.dot(inverse_Vtmp_quad, e_to_interp_quad[emp_tmp_quad])
-        interpolantA_quad = numpy.zeros(len(known_quad_bases[k]))+numpy.zeros(len(known_quad_bases[k]))*1j
-        for j in numpy.arange(0, k):
-            tmp_quad = numpy.multiply(Ci_quad[j], known_quad_bases[j])
-            interpolantA_quad += tmp_quad
-        diff_quad = interpolantA_quad - known_quad_bases[k]
-        r_quad = numpy.absolute(diff_quad)
-        emp_nodes_quad[k] = numpy.argmax(r_quad)
-        emp_nodes_quad = sorted(emp_nodes_quad)
-    u_quad, c_quad = numpy.unique(emp_nodes_quad, return_counts=True)
-    dup_quad = u_quad[c_quad > 1]
-    #print(len(emp_nodes_quad), "\nduplicates quad indices:", dup_quad)
-    emp_nodes_quad = numpy.unique(emp_nodes_quad)
-    ndim_quad = len(emp_nodes_quad)
-    #print(len(emp_nodes_quad), "\n", emp_nodes_quad)
-    V_quad = numpy.transpose(known_quad_bases[0:ndim_quad,emp_nodes_quad])
-    inverse_V_quad = numpy.linalg.pinv(V_quad)
-    return numpy.array([ndim_quad, inverse_V_quad, emp_nodes_quad])
-
-def surroerror_quad(ndim_quad, inverse_V_quad, emp_nodes_quad, known_quad_bases, test_mc_quad, test_q_quad, test_s1_quad, test_s2_quad, test_ecc_quad, test_lambda1_quad, test_lambda2_quad, test_iota_quad, test_phiref_quad, distance, deltaF, f_min, f_max, waveFlags, approximant):
-    hp_test_quad = (numpy.absolute(generate_a_waveform_from_mcq(test_mc_quad, test_q_quad, test_s1_quad, test_s2_quad, test_ecc_quad, test_lambda1_quad, test_lambda2_quad, test_iota_quad, test_phiref_quad, distance, deltaF, f_min, f_max, waveFlags, approximant)))**2
-    Ci_quad = numpy.dot(inverse_V_quad, hp_test_quad[emp_nodes_quad])
-    interpolantA_quad = numpy.zeros(len(hp_test_quad))+numpy.zeros(len(hp_test_quad))*1j    
-    #ndim_quad = len(known_quad_bases)
-    for j in numpy.arange(0, ndim_quad):
-        tmp_quad = numpy.multiply(Ci_quad[j], known_quad_bases[j])
-        interpolantA_quad += tmp_quad
-    surro_quad = (1-overlap_of_two_waveforms(hp_test_quad, interpolantA_quad))*deltaF
-    return surro_quad
-
-def surros_quad(tolerance_quad, ndim_quad, inverse_V_quad, emp_nodes_quad, known_quad_bases, nts, nparams, params_low, params_high, distance, deltaF, f_min, f_max, waveFlags, approximant):
-    test_points = generate_params_points(nts, nparams, params_low, params_high)
-    surros = numpy.zeros(nts)
-    count = 0
-    for i in numpy.arange(0,nts):
-        test_mc_quad =  test_points[i,0]
-        test_q_quad = test_points[i,1]
-        test_s1_quad = spherical_to_cartesian(test_points[i,2:5])
-        test_s2_quad = spherical_to_cartesian(test_points[i,5:8])
-        test_iota_quad = test_points[i,8]
-        test_phiref_quad = test_points[i,9]
-        test_ecc_quad = 0
-        test_lambda1_quad = 0
-        test_lambda2_quad = 0
-        if nparams == 11: test_ecc_quad = test_points[i,10]
-        if nparams == 12: 
-            test_lambda1_quad = test_points[i,10]
-            test_lambda2_quad = test_points[i,11]
-        surros[i] = surroerror_quad(ndim_quad, inverse_V_quad, emp_nodes_quad, known_quad_bases[0:ndim_quad], test_mc_quad, test_q_quad, test_s1_quad, test_s2_quad, test_ecc_quad, test_lambda1_quad, test_lambda2_quad, test_iota_quad, test_phiref_quad, distance, deltaF, f_min, f_max, waveFlags, approximant)
-        if (surros[i] > tolerance_quad):
-            count = count+1
-    print(ndim_quad, "basis elements gave", count, "bad points of surrogate error > ", tolerance_quad)
-    if count == 0: val =0
-    else: val = 1
-    return val
-
-def roqs_quad(tolerance_quad, freq,  ndimlow_quad, ndimhigh_quad, ndimstepsize_quad, known_quad_bases_copy, nts, nparams, params_low, params_high, distance, deltaF, f_min, f_max, waveFlags, approximant):
-    for num in np.arange(ndimlow_quad, ndimhigh_quad, ndimstepsize_quad):
-        ndim_quad, inverse_V_quad, emp_nodes_quad = empnodes_quad(num, known_quad_bases_copy)
-        if surros_quad(tolerance_quad, ndim_quad, inverse_V_quad, emp_nodes_quad, known_quad_bases_copy, nts, nparams, params_low, params_high, distance, deltaF, f_min, f_max, waveFlags, approximant)==0:
-            b_quad = numpy.dot(numpy.transpose(known_quad_bases_copy[0:ndim_quad]), inverse_V_quad)
-            f_quad = freq[emp_nodes_quad]
-            numpy.save('./B_quadratic.npy', numpy.transpose(b_quad))
-            numpy.save('./fnodes_quadratic.npy', f_quad)
-            print("Number of quadratic basis elements is ", ndim_quad, "and the linear ROQ data save in B_quadratic.npy")
-            break
-    return
-
-def testrep_quad(b_quad, emp_nodes_quad, test_mc_quad, test_q_quad, test_s1_quad, test_s2_quad, test_ecc_quad, test_lambda1_quad, test_lambda2_quad, test_iota_quad, test_phiref_quad, distance, deltaF, f_min, f_max, waveFlags, approximant):
-    hp_test_quad = (numpy.absolute(generate_a_waveform_from_mcq(test_mc_quad, test_q_quad, test_s1_quad, test_s2_quad, test_ecc_quad, test_lambda1_quad, test_lambda2_quad, test_iota_quad, test_phiref_quad, distance, deltaF, f_min, f_max, waveFlags, approximant)))**2
-    hp_test_quad_emp = hp_test_quad[emp_nodes_quad]
-    hp_rep_quad = numpy.dot(b_quad,hp_test_quad_emp)
-    diff_quad = hp_rep_quad - hp_test_quad
-    rep_error_quad = diff_quad/numpy.vdot(hp_test_quad,hp_test_quad)**0.5
-    freq = numpy.arange(f_min,f_max,deltaF)
-    plt.figure(figsize=(15,9))
-    plt.plot(freq, numpy.real(rep_error_quad))
-    plt.xlabel('Frequency')
-    plt.ylabel('Fractional Representation Error for Quadratic')
-    plt.title('Rep Error with numpy.linalg.pinv()')
-    plt.show()
-    return
-
-def surros_of_test_samples(nsamples, nparams, params_low, params_high, tolerance, b_linear, emp_nodes, distance, deltaF, f_min, f_max, waveFlags, approximant):
-    nts=nsamples
-    ndim = len(emp_nodes)
-    test_points = generate_params_points(nts, nparams, params_low, params_high)
-    surros = numpy.zeros(nts)
-    for i in numpy.arange(0,nts):
-        test_mc =  test_points[i,0]
-        test_q = test_points[i,1]
-        test_s1 = spherical_to_cartesian(test_points[i,2:5])
-        test_s2 = spherical_to_cartesian(test_points[i,5:8])
-        test_iota = test_points[i,8]
-        test_phiref = test_points[i,9]
-        test_ecc = 0
-        test_lambda1 = 0
-        test_lambda2 = 0
-        if nparams == 11: test_ecc = test_points[i,10]
-        if nparams == 12: 
-            test_lambda1 = test_points[i,10]
-            test_lambda2 = test_points[i,11]
-        hp_test = generate_a_waveform_from_mcq(test_mc, test_q, test_s1, test_s2, test_ecc, test_lambda1, test_lambda2, test_iota, test_phiref, distance, deltaF, f_min, f_max, waveFlags, approximant)
-        hp_test_emp = hp_test[emp_nodes]
-        hp_rep = numpy.dot(b_linear,hp_test_emp) 
-        surros[i] = (1-overlap_of_two_waveforms(hp_test, hp_rep))*deltaF
-    if (surros[i] > tolerance):
-        print("iter", i, surros[i], test_points[i])
-    if i%100==0:
-        print("iter", i, surros[i])
-    return surros
diff --git a/Tutorial/quadraticbases.npy b/Tutorial/quadraticbases.npy
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diff --git a/Tutorial/quadraticbasiswaveformparams.npy b/Tutorial/quadraticbasiswaveformparams.npy
index f8b10e7..8704860 100644
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