Repository: rssalessio/PythonVRFT (language: python)

Issue:
Instrumental variables
Add instrumental variables analysis


Discussion / hints:

Fix patch:
diff --git a/.gitignore b/.gitignore
index 7d275ed..81f2536 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,6 @@
 *.pyc
 /dist/
 /*.egg-info
-/build/
\ No newline at end of file
+/build/
+/.ipynb_checkpoints
+/examples/.ipynb_checkpoints
\ No newline at end of file
diff --git a/README.md b/README.md
index b4ba869..ce598d1 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-# PythonVRFT Library - Version 0.0.4
+# PythonVRFT Library - Version 0.0.5
 VRFT Adaptive Control Library written in Python. Aim of this library is to provide an implementation of the VRFT (Virtual Reference Feedback Tuning) algorithm.
 
 You can find the package also at the following [link](https://pypi.org/project/pythonvrft/)
@@ -25,7 +25,7 @@ pip install .
 ``` 
 
 ## Examples
-Examples are located in the examples/ folder. At the moment only 1 example is available.
+Examples are located in the examples/ folder. At the moment 2 examples are available. One without instrumental variables and one using instrumental variables.
 
 ## Tests
 To execute tests run the following command
@@ -34,12 +34,13 @@ python -m unittest
 ``` 
 
 ## Changelog
-- [**DONE - V0.0.2**][26.03.2017] Implement the basic VRFT algorithm (1 DOF. offline, linear controller, controller expressed as scalar product theta*f(z))
-- [**DONE - V0.0.3**][05.01.2020] Code refactoring and conversion to Python 3; Removed support for Python Control library.
-- [**In Progress - V0.0.4**][07.01.2020-] Add Documentation and Latex formulas
+- [**V. 0.0.2**][26.03.2017] Implement the basic VRFT algorithm (1 DOF. offline, linear controller, controller expressed as scalar product theta*f(z))
+- [**V. 0.0.3**][05.01.2020] Code refactoring and conversion to Python 3; Removed support for Python Control library.
+- [**V. 0.0.5**][07.01.2020] Add Instrumental Variables (IVs) Support
+- [**In Progress**][07.01.2020-] Add Documentation and Latex formulas
 - [**TODO**] Add MIMO Support
-- [**TODO**] Add IV Support
 - [**TODO**] Generalize to other kind of controllers (e.g., neural nets)
+- [**TODO**] Add Cython support
 
 ## Citations
 If you find this code useful in your research, please, consider citing it:
@@ -50,3 +51,5 @@ If you find this code useful in your research, please, consider citing it:
 >  doi          = {},
 >  url          = { https://github.com/rssalessio/PythonVRFT }
 >}
+
+[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)
\ No newline at end of file
diff --git a/examples/example1.png b/examples/example1.png
new file mode 100644
index 0000000..9777149
Binary files /dev/null and b/examples/example1.png differ
diff --git a/examples/1_example.py b/examples/example1.py
similarity index 86%
rename from examples/1_example.py
rename to examples/example1.py
index 5c2e4fc..3a9e961 100644
--- a/examples/1_example.py
+++ b/examples/example1.py
@@ -19,15 +19,12 @@
 import scipy.signal as scipysig
 from vrft import *
 
-def generateNoise(t):
-    omega = 2*np.pi*100
-    xi = 0.9
-    dt = t[1] - t[0]
-    noise =  np.random.normal(0,0.1,t.size)
-    tf = scipysig.TransferFunction([10*omega**2], [1, 2*xi*omega, omega**2])
-    # Second order system
-    _, yn, _ = scipysig.lsim(tf, noise, t)
-    return yn
+# Example 1
+# ------------
+# In this example we see how to apply VRFT to a simple SISO model
+# without any measurement noise.
+# Input data is generated using a square signal
+#
 
 #Generate time and u(t) signals
 t_start = 0
@@ -43,8 +40,7 @@ def generateNoise(t):
 den = [1, -0.9]
 sys = ExtendedTF(num, den, dt=t_step)
 t, y = scipysig.dlsim(sys, u, t)
-y = y[:, 0]
-#y += generateNoise(t)
+y = y.flatten()
 data = iddata(y, u, t_step, [0])
 
 
@@ -58,7 +54,7 @@ def generateNoise(t):
 #Experiment filter
 L = refModel * (1 -  refModel)
 #VRFT
-theta, r, _, _, C = compute_vrft(data, refModel, base, L)
+theta, r, loss, C = compute_vrft(data, refModel, base, L)
 
 #Obtained controller
 print("Controller: {}".format(C))
@@ -78,7 +74,7 @@ def generateNoise(t):
 yr = np.array(yr).flatten()
 ys = np.array(ys).flatten()
 yc = np.array(yc).flatten()
-fig, ax = plt.subplots(4, sharex=True)
+fig, ax = plt.subplots(4, sharex=True, figsize=(12,8), dpi= 100, facecolor='w', edgecolor='k')
 ax[0].plot(t, yr,label='Ref System')
 ax[0].plot(t, yc, label='CL System')
 ax[0].set_title('Systems response')
diff --git a/examples/1_example.png b/examples/example2.png
similarity index 100%
rename from examples/1_example.png
rename to examples/example2.png
diff --git a/examples/example2.py b/examples/example2.py
new file mode 100644
index 0000000..4022b29
--- /dev/null
+++ b/examples/example2.py
@@ -0,0 +1,114 @@
+# Copyright [2020] [Alessio Russo - alessior@kth.se]  
+# This file is part of PythonVRFT.
+# PythonVRFT is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, version 3 of the License.
+# PythonVRFT is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+# You should have received a copy of the GNU General Public License
+# along with PythonVRFT.  If not, see <http://www.gnu.org/licenses/>.
+#
+# Code author: [Alessio Russo - alessior@kth.se]
+# Last update: 08th January 2020, by alessior@kth.se
+#
+
+import numpy as np
+import matplotlib.pyplot as plt
+import scipy.signal as scipysig
+from vrft import *
+
+# Example 2
+# ------------
+# In this example we see how to apply VRFT to a simple SISO model
+# with colored measurement noise (no instrumental variables)
+# Input data is generated using random normal noise
+#
+
+def generateNoise(t):
+    # Generate colored noise
+    omega = 2*np.pi*100
+    xi = 0.9
+    dt = t[1] - t[0]
+    noise =  np.random.normal(0,0.1,t.size)
+    tf = scipysig.TransferFunction([10*omega**2], [1, 2*xi*omega, omega**2])
+    # Second order system
+    _, yn, _ = scipysig.lsim(tf, noise, t)
+    return yn
+
+#Generate time and u(t) signals
+t_start = 0
+t_end = 10
+t_step = 1e-2
+t = np.arange(t_start, t_end, t_step)
+u = np.random.normal(size=t.size)
+
+#Experiment
+num = [0.5]
+den = [1, -0.9]
+sys = ExtendedTF(num, den, dt=t_step)
+t, y = scipysig.dlsim(sys, u, t)
+y = y.flatten() + generateNoise(t)
+data = iddata(y, u, t_step, [0])
+
+
+#Reference Model
+refModel = ExtendedTF([0.6], [1, -0.4], dt=t_step)
+
+#PI Controller
+base = [ExtendedTF([1], [1, -1], dt=t_step),
+        ExtendedTF([1, 0], [1, -1], dt=t_step)]
+
+#Experiment filter
+L = refModel * (1 -  refModel)
+#VRFT
+theta, r, loss, C = compute_vrft(data, refModel, base, L)
+
+#Obtained controller
+print("Controller: {}".format(C))
+
+L = (C * sys).feedback()
+
+print("Theta: {}".format(theta))
+print(scipysig.ZerosPolesGain(L))
+
+#Analysis
+t = t[:len(r)]
+u = np.ones(len(t))
+_, yr = scipysig.dlsim(refModel, u, t)
+_, yc = scipysig.dlsim(L, u, t)
+_, ys = scipysig.dlsim(sys, u, t)
+
+yr = np.array(yr).flatten()
+ys = np.array(ys).flatten()
+yc = np.array(yc).flatten()
+fig, ax = plt.subplots(4, sharex=True, figsize=(12,8), dpi= 100, facecolor='w', edgecolor='k')
+ax[0].plot(t, yr,label='Ref System')
+ax[0].plot(t, yc, label='CL System')
+ax[0].set_title('Systems response')
+ax[0].grid(True)
+ax[1].plot(t, ys, label='OL System')
+ax[1].set_title('OL Systems response')
+ax[1].grid(True)
+ax[2].plot(t, y[:len(r)])
+ax[2].grid(True)
+ax[2].set_title('Experiment data')
+ax[3].plot(t, r)
+ax[3].grid(True)
+ax[3].set_title('Virtual Reference')
+
+# Now add the legend with some customizations.
+legend = ax[0].legend(loc='lower right', shadow=True)
+
+# The frame is matplotlib.patches.Rectangle instance surrounding the legend.
+frame = legend.get_frame()
+frame.set_facecolor('0.90')
+
+# Set the fontsize
+for label in legend.get_texts():
+    label.set_fontsize('large')
+
+for label in legend.get_lines():
+    label.set_linewidth(1.5)  # the legend line width
+plt.show()
diff --git a/examples/example3.png b/examples/example3.png
new file mode 100644
index 0000000..7f00bbe
Binary files /dev/null and b/examples/example3.png differ
diff --git a/examples/example3.py b/examples/example3.py
new file mode 100644
index 0000000..fcb1008
--- /dev/null
+++ b/examples/example3.py
@@ -0,0 +1,114 @@
+# Copyright [2020] [Alessio Russo - alessior@kth.se]  
+# This file is part of PythonVRFT.
+# PythonVRFT is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, version 3 of the License.
+# PythonVRFT is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+# You should have received a copy of the GNU General Public License
+# along with PythonVRFT.  If not, see <http://www.gnu.org/licenses/>.
+#
+# Code author: [Alessio Russo - alessior@kth.se]
+# Last update: 08th January 2020, by alessior@kth.se
+#
+
+import numpy as np
+import matplotlib.pyplot as plt
+import scipy.signal as scipysig
+from vrft import *
+
+# Example 3
+# ------------
+# In this example we see how to apply VRFT to a simple SISO model
+# with  measurement noise using instrumental variables
+# Input data is generated using random normal noise
+#
+
+#Generate time and u(t) signals
+t_start = 0
+t_end = 10
+dt = 1e-2
+t = np.array([i * dt for i in range(int(t_end/dt    ))])
+
+#Experiment
+num = [0.5]
+den = [1, -0.9]
+sys = ExtendedTF(num, den, dt=dt)
+
+def generate_data(sys, u, t):
+    t, y = scipysig.dlsim(sys, u, t)
+    y = y.flatten() + 0.5 * np.random.normal(size = t.size)
+    return iddata(y, u, dt, [0])
+
+u = np.random.normal(size=t.size)
+data1 = generate_data(sys, u, t)
+data2 = generate_data(sys, u, t)
+data = [data1, data2]
+
+#Reference Model
+refModel = ExtendedTF([0.6], [1, -0.4], dt=dt)
+
+#PI Controller
+control = [ExtendedTF([1], [1, -1], dt=dt),
+        ExtendedTF([1, 0], [1, -1], dt=dt)]
+
+#Experiment filter
+prefilter = refModel * (1 -  refModel)
+
+# VRFT method with Instrumental variables
+theta_iv, r_iv, loss_iv, C_iv = compute_vrft(data, refModel, control, prefilter, iv=True)
+
+# VRFT method without Instrumental variables
+theta_noiv, r_noiv, loss_noiv, C_noiv = compute_vrft(data1, refModel, control, prefilter, iv=False)
+
+#Obtained controller
+print('------IV------')
+print("Loss: {}\nTheta: {}\nController: {}".format(loss_iv, theta_iv, C_iv))
+print('------No IV------')
+print("Loss: {}\nTheta: {}\nController: {}".format(loss_noiv, theta_noiv, C_noiv))
+
+
+# Closed loop system
+closed_loop_iv = (C_iv * sys).feedback()
+closed_loop_noiv = (C_noiv * sys).feedback()
+
+t = t[:len(r_iv)]
+u = np.ones(len(t))
+
+_, yr = scipysig.dlsim(refModel, u, t)
+_, yc_iv = scipysig.dlsim(closed_loop_iv, u, t)
+_, yc_noiv = scipysig.dlsim(closed_loop_noiv, u, t)
+_, ys = scipysig.dlsim(sys, u, t)
+
+yr = yr.flatten()
+ys = ys.flatten()
+yc_noiv = yc_noiv.flatten()
+yc_iv = yc_iv.flatten()
+
+fig, ax = plt.subplots(4, sharex=True, figsize=(12,8), dpi= 100, facecolor='w', edgecolor='k')
+ax[0].plot(t, yr,label='Ref System')
+ax[0].plot(t, yc_iv, label='CL System - IV')
+ax[0].plot(t, yc_noiv, label='CL System - No IV')
+ax[0].set_title('CL Systems response')
+ax[0].grid(True)
+ax[1].plot(t, ys, label='OL System')
+ax[1].set_title('OL Systems response')
+ax[1].grid(True)
+ax[2].plot(t, data1.y[:len(r_iv)])
+ax[2].grid(True)
+ax[2].set_title('Experiment data')
+ax[3].plot(t, r_iv)
+ax[3].grid(True)
+ax[3].set_title('Virtual Reference')
+
+# Now add the legend with some customizations.
+legend = ax[0].legend(loc='lower right', shadow=True)
+
+# The frame is matplotlib.patches.Rectangle instance surrounding the legend.
+frame = legend.get_frame()
+frame.set_facecolor('0.90')
+
+
+plt.show()
diff --git a/examples/notebook_example_1.ipynb b/examples/notebook_example_1.ipynb
index 197e1db..6e3f738 100644
--- a/examples/notebook_example_1.ipynb
+++ b/examples/notebook_example_1.ipynb
@@ -1,8 +1,15 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## VRFT without measurement noise (no instrumental variables)"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -19,7 +26,14 @@
     "# along with PythonVRFT.  If not, see <http://www.gnu.org/licenses/>.\n",
     "#\n",
     "# Code author: [Alessio Russo - alessior@kth.se]\n",
-    "# Last update: 06th January 2020, by alessior@kth.se\n",
+    "# Last update: 08th January 2020, by alessior@kth.se\n",
+    "#\n",
+    "\n",
+    "# Example 1\n",
+    "# ------------\n",
+    "# In this example we see how to apply VRFT to a simple SISO model\n",
+    "# without any measurement noise.\n",
+    "# Input data is generated using a square signal\n",
     "#"
    ]
   },
@@ -32,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -78,7 +92,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -86,13 +100,16 @@
     "t_start = 0\n",
     "t_end = 10\n",
     "t = np.arange(t_start, t_end, dt)\n",
-    "u = np.random.normal(size = t.size)\n",
+    "u = np.ones(len(t))\n",
+    "u[200:400] = np.zeros(200)\n",
+    "u[600:800] = np.zeros(200)\n",
     "\n",
     "# Open loop experiment\n",
     "t, y = scipysig.dlsim(sys, u, t)\n",
-    "y = y[:, 0]\n",
+    "y = y.flatten()\n",
     "\n",
     "# Save data into an IDDATA Object with 0 initial condition\n",
+    "# Length of the initial condition depends on the reference model\n",
     "data = iddata(y, u, dt, [0])"
    ]
   },
@@ -105,14 +122,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Loss: 4.929839516894833e-31\n",
+      "Loss: 4.4859502383167834e-30\n",
       "Theta: [-1.08  1.2 ]\n",
       "Controller: ExtendedTF(\n",
       "array([ 1.2 , -1.08]),\n",
@@ -127,7 +144,7 @@
     "prefilter = refModel * (1 -  refModel)\n",
     "\n",
     "# VRFT method\n",
-    "theta, r, _, loss, C = compute_vrft(data, refModel, control, prefilter)\n",
+    "theta, r, loss, C = compute_vrft(data, refModel, control, prefilter)\n",
     "\n",
     "#Obtained controller\n",
     "print(\"Loss: {}\\nTheta: {}\\nController: {}\".format(loss, theta, C))"
@@ -142,19 +159,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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JMxbXrJpCoaglIiMj6dmzJyEhIdx99911XZxqwTW9dKQVqxL4CoXiKoiIiOD06dN1XYxqxSWlopAWJfAVCoWiFC4pFYW0YhUuWTWFQqGoMi4pFd2Uhq9QKBRlcEmpqEw6CoVCURaXlIoCNWirUCgUpXFJqSikBals+AqFy7BixQruuOMOOnToQJ8+fZg0aRK7d+8GtPXkn3766Uqls2bNGkaOHEmnTp3o3r07EyZMIDEx8arK1rx5c+Lj468qjdrCJd0y3aSVYtzruhgKhaIa+Oyzz/j444956623iIiIwNPTk02bNrF27VoGDRpU6XTi4+OZMWMGX3zxBUOGDMFkMrF58+Z6tUm7S6rBQlqRrlk1haJekZ2dzTvvvMMbb7zB6NGj8fPzw9PTk9tuu41//vOfV5RWdHQ0rVq1YujQoQghCAgI4M4776Rly5ZcuHCBdu3a2ZddBjh8+DA9evSguLiY+Ph47rnnHjp37kz37t2ZPn06gH1C1siRI+nQoYN9+YX169czcuRIunTpwtixYzl27Jg93YEDBzJ//nxGjBhB+/btee6557h48SKTJk2iY8eOTJgwgaysrKu9dU5xSanohkW5ZSoULkBUVBSFhYWMGjXqqtPq2bMnp0+fZvbs2Wzfvt1hffkmTZrwhz/8gZUrV9p/+/HHHxk3bhyenp68/fbbDBs2jOPHjxMVFcWjjz4KwE8//QTAhg0biI2NZdy4cRw+fJjnnnuOt99+m+joaCZPnsyUKVMoLCy0p7169WqWLVvGtm3bWL9+PZMmTWLWrFkcPXoUKWWZrRWrC5c06Qg101ahqHaCtr+BR/rxak3T3Lgr2Te9XO5127r0Hh5XL6ratGnDDz/8wIIFC5g+fTomk4lx48Yxd+5c/P39GT9+PAsXLuThhx/GYrHw888/21fO9PT0JCkpidTUVJo3b16hKenbb79l8uTJ9OvXD4AJEybw4Ycfsn//fm688UZA28HLtunJoEGDCAkJoWfPngDccccdbNu27arr6wyXlIpuKJOOQuEKNGrUiIyMDMxmc7Wk179/fz777DOOHj3KTz/9xK5du/jggw8AuP322zl16hRnzpxhy5YtBAYG0rdvXwBeeeUVpJTceeedRERE8N1335WbR1JSEp9++qnDDlcpKSmkpqbaw4SEhNj/9/HxKXNeHbtbOcMlNXw3aUWq3a4UimqlIk28pujfvz/e3t6sWbOGMWPGVGvaffr0YdSoUcTExACaoB07diwrVqwgNjaWe++91x62SZMm9l21du/ezcSJExk8eDBt27Ytk27z5s2ZMWMGM2bMqNbyVgcuKRUFauKVQuEKBAUF8fzzz/Pyyy/z22+/kZeXR3FxMRs3buRf//qXPZzVaqWgoMB+GO3lNnbv3s0333xj30v21KlTrF+/nv79+9vD3HvvvXz//fesW7fOYcOTlStX2nfRatiwIUII+56zpXe4mjRpEl999RX79+9HSkleXh4bNmwgNze3em9OFXBNDR+r8sNXKFyE6dOnExoaygcffMDTTz9NQEAAvXr14tlnn7WH+fnnn/n555/t52FhYURFRTmk06BBA9auXctbb71FXl4ewcHB3HXXXTz55JP2MAMHDsTNzY2ePXvSqlXJHr4HDx5k9uzZZGdnExoaymuvvUbr1q0B+Otf/8rMmTMpKCjg7bff5q677uLdd9/l73//O/Hx8fj4+DBw4EAGDx5cU7eo0rjkjldJr3bhjEc7bvr76hoo1bWL2v2pfqB2vKpZxo8fzx//+EcmTZpU10W5LGrHKzQNX7llKhSKK+XgwYMcOXKEcePG1XVRagTXNOmoiVcKheIKmTFjBmvWrOG1114jICCgrotTI7imwEetpaNQKK4Mm3umK+OSUlH54SsUCkVZXFIqKhu+QlF9XMuOHfUZKeUVP5talYpCiDuEECeEELFCiFk1lY9yy1QoqgdfX19yc3OV0L/GkFKSk5NDcXHxFT2bWrPhCyHcgf8AtwJJwF4hxP+klMcqjnnlKJNO7bB48WLee+89Tp8+TVBQEHfffTdvvvkmDRs2BGDOnDnExsaydOnSy6b1yy+/MHv2bOLi4vDy8qJ3794sXLiQ8PDwKpdPCMGpU6fo0KFDldOo73To0IGTJ0+SkpJSr5YRvtaRUtpX8czOzq50G6/NQduBQKyUMg5ACLEMGAdUu8B3l1ZQGn6N8t577/H222+zZMkSRowYQXJyMk8++SS33nor27dvx8vLq9JpxcbG8tBDD7FixQpuueUWcnNzWbduHW4uuDyG2WyuloXAagsvLy969OjB6dOnWblyZbWtaXOlpKam0qxZszrJu66obJ2bNGnCyJEjK5Vmbba8FoBxa5kkoMySc0KIacA0gKZNmxIZGXnFGQ3CitlKleJez+Tm5tZKnU0mE6+88govvPACPj4+bN++HYBnnnmGBx54gFdeeYXRo0eTkJDA+fPnL1umzZs3Exoairu7O5s3bwagcePGxMXFsW/fPh544AH++9//0qBBAwBOnDjBiy++yA8//EBcXBwzZszg9OnTuLu7069fP2bPnm1fx8S2AuHzzz/PLbfcws6dO1m4cCGpqamEh4fzl7/8hfbt2wMwceJE/vjHP7J+/XpSUlIYPnw4U6dO5a233uLIkSN07dqVOXPmEBgYSFFREe+88w579uzBYrHQsmVL3njjDYKDg8vUb+LEiYwbN44NGzaQmJjIb7/9xokTJ5g/fz4JCQk0bdqUZ555hj59+gDarkxfffUVWVlZNGjQgEcffZRbb72VNWvW8Ouvv9K2bVvGjBlDcHAwM2bMsC8NkJaWxrx58zhy5AiBgYHcf//99vVnFi9eTEJCAl5eXmzbto0mTZrw0ksv0blzZwC+++47VqxYgclkIiQkxJ6u1Wpl2bJlrFq1ipycHHr37s0zzzxDYGBgldtPVQgPD8ff379W86xrKlNnNzc3vLy82Lt3b+UStRn+a/oAxgNfGM4nAx9VFKd///6yKhzfu0H+uuLbKsW9ntm0aVOt5PPbb79Jd3d3WVxcXObaQw89JCdOnCillHL27Nly0qRJl03v9OnT0tvbW86cOVNu3LhR5uTkOFwfNWqUnD9/vv185syZ8umnn5ZSSnnLLbfI119/XVosFpmfny+3bt1qDwfIU6dO2c+joqJkaGio3LVrlzSbzXLx4sWyTZs2sqCgQEopZZs2beSgQYNkamqqTEpKkqGhobJv375y//79sqCgQA4fPlzOmTNHSinlp59+KseMGSNNJpM0m81y37598tKlS07r16ZNG9m7d2959uxZmZeXJ5OSkmRwcLBctWqVtFgsct26dTI4OFheuHBB5ubmysDAQBkTEyOllDIlJUUePXpUSinlokWLpLu7u3zyySdlUVGRXLZsmQwKCpLp6elSSilvvvlm+ec//1nm5+fLAwcOyJCQELlhwwb7s/D29parVq2SZrNZzpo1Sw4aNEhKKWVMTIxs2bKlTE5OllJKGR8fL2NjY6WUUs6bN08OGjRIJiYmyoKCAjlt2jT7861NaqttX0tUtc7APlmOTK3NPnMS0Mpw3hJIqYmMugwYgX+jsJpIWoGmSYaEhDg1TYSFhdkXp6os7dq1IzIykuTkZCZMmEBISAhTpkyxLzb18MMP28cBLBYL3333HZMnTwbA3d2dM2fOkJKSgo+PD0OGDCk3n88//5zp06czaNAg3N3defjhh/H29mbXrl32MM888wxNmzalRYsWDB06lEGDBtG3b1+8vb25++67OXDgAKCtj56enk5sbCzu7u7079+foKCgcvN+9tlnadWqFb6+vixdupTRo0czevRo3NzcuPXWWxkwYACrV2tLgbi5uXH06FHy8/MJCwuje/fu9nSaNGnCvffei6enJ/fddx+dO3dm1apVJCYmsm3bNt566y18fHzo06cPU6dO5euvv7bHHTJkCKNHj8bd3Z3Jkydz6NAh+z0sLCzk2LFjFBcXEx4ebu/1fPbZZ8ydO5eWLVvi7e3NnDlz+OGHH+rMtKO4OmptLR0hhAdwEhgBJAN7gQeklNEVxLkInCnv+mUIAa5M8lz/1Fadg4COQJSTa+GAAOKB5oC3/v+V4Ae0BzLQ2ooAeqON9/gArYGjetim+m8NAAuQCqTr1/rr4WxLJ3YAAgFjoxdobSwD6AkkADn6tbZ6XJtiEgIEo7VjATTTz90NZXX2QvXU88jWz1vraVlLleOcXv4gvV7+QC6aslQANAaaABcpec7tgDy9zB2AQ4Y0Q4GGwCnKPgsvvVy2Zxish/fVy5kIFAN99evGerkBR/TrtYV6nytPGyllqNMr5an+NXEAo9FeltPA32s4r3K7Na561Fad0YSrCZhQ6nd/4AIwVT+fAyytYh7vAisN558B/wC+AV5xVmdgCJpg7KCfS9v/hjTKbXdown6k4XwpMMdwPhXY4CReONrH6LFKpvsS8Hkl7oEv8B6wVT+fgvbxMdZ5N5p5tBXaBy/QcO0NYLGzZ6GXWQIepfIMAr4DvtbPTwA31Ze2fS0dNVHnWnWDkFKullJ2klK2l1LOrc28FdWHlPIS8CrwkT63wlMIEQ4sR9NGvzYEdxNC+BgO79LpCSGGCCEeF0I00c+7AHcBuwzBvkITeHehCWIbjYQQLfX/M9GEmEU/P4+mAdv4HHhCCDFIaPgLIe4UQlzxCKQQYrgQoqfubpyNpu1aLhPNxlJgrBDidiGEu35fIoQQLYUQTYUQdwkh/NF6F7ml0m0CNNHv+XigK7BaSpkI7ADe1NPrBTyG9oG8XF06CyFu0Z9NAZBvyPNTYK4Qoo0eNlQI4Zori9UDXM/vTVErSCnfBl5G08Sz0TTNRGCElNK4+8T9aALEdpx2klwWmiA/IoTIBdYAPwFvG/LbjmYC2S+lTDDE9Qd26/H+B8yQUtrMFnOAJUKILCHEBCnlPuBx4GO0j0Ms2kekKjQDftDrfhzYjOOHqFx04TwO7f5dRLtvz6O9j27AX9E0+QxgGPCkIfpuNBNWGjAXuFdKaTNh3Y+muaeg3b/ZUsr1lSiSN/B/epqpaB8V2/ZWH6Dd13VCiBy0j3D5G7oqrmmu6fXwrwYhxDQp5YK6Lkdt4up1FkJsBL6VUn5h+M2l62xECDEFzaz0VX2ps4369Jxt1ESdXVbgK1wLIcQNwHqglZQy53LhXRGbwJdSlu+KpFBUgDLpKK55hBBLgA3AzPoq7BWK6kBp+AqFQlFPqNVFPYQQCWj+whbALMvZd1GhUCgU1U9drOI0XEpZqckEISEhsqqrJZpMpnq39oaqc/1A1bl+UNU6R0VFpclyJl5d08v2hYeHs2/fvirFjYyMJCIiotLhLVZJSlY+KVn5ZBeYuZRfTF6RGbNFYrFKzFaJ2WLFbLVPiihDRcax8ixnssJYV8aZM2dp06Z1taV3PaDqXD+ob3X28/Kgu0i6IhlmQwhR7uoEtWrDF0LEUzI55jNnLkelVsvsv2zZsirllZube9mNiDMLrGxNNnP4ooUz2VaKrRUGLyljlUoE5S0nXl2rjEskotpSuz5Qda4f1Lc6B3kJ/nWDtUqbqQ8fPjyqPHN5bQv85lLKFH1G5XrgGSnllvLCDxgwQNaEhl9ktvLB7yf5fEs8RRYrvVs24IbwYDo0CaBlIz8a+nkS5OOJv7c7Hu5ueLgJ3N2E/e+1uhHElfZqXAFV5/qBqnPlEUKUK/Br1aQjpUzR/14QQvyEtilKuQK/JjAVmnlk8V72xGdwT78WzBzRidaN/WqzCAqFQlEn1OYWh/6Am5QyR///NuC12softIXiZiw7yL6EDObd15u7+7a8fCSFQqFwEWpTw28K/KSbQzzQpsivqcX8WbY3kQ3Hz/OPMd2UsFcoFPWOWhP4UtvLtndt5VeavCIz7607wcC2wTx6U3hdFUOhUCjqjHqztMK3u8+SllvEi3d0vmYHXRUKhaImqRcCX0rJsr2J9GvdkP5tym4yrVAoFPWBeiHwDyZmEXshlwkDWl0+sEKhULgo9ULgr40+j4ebYHQvtbG5QqGov9QLgb/l5EX6t2lEkI9nXRdFoVAo6gyXF/gXcgo4di6bmzs538RdoVAo6gsuL/B3x2UAMLRjSB2XRKFQKPzM2xQAACAASURBVOoWlxf4R5Iv4eXhRpdmQXVdFIVCoahTXF7gH07KomtYEF4eLl9VhUKhqBCXloJWq+Rocja9WjSo66IoFApFnePSAj8h3URuoZmeLZXAVygUCpcW+Kcu5ALQuWlgHZdEoVAo6h6XFvjxaSYA2obWr70wFQqFwhmuLfAvmggJ8FYTrhQKhQJXF/hpJtqFKO1eoVAowMUFflyaifAQtX2hQqFQgAsL/HyzJC23kLYhV77ru0KhULgiLivw0/IlAK2DlYavUCgU4MICP6PACkCzBj51XBKFQqG4NnBZgZ9ZoGn4YUrgKxQKBeDiAt9NQGigd10XRaFQKK4JXFbgZxRIQgK88XR32SoqFArFFeGy0jCzUCpzjkKhUBhwXYFfYKVpkBL4CoVCYcOFBb7S8BUKhcKISwr8/CILeWZoqgS+QqFQ2HFJgZ9uKgQgxF956CgUCoUNlxT4maZiABr5e9VxSRQKheLawSUFfkZeEQDB/mpZZIVCobDhkgI/06QJ/EZ+SsNXKBQKGy4p8DNMNg1fCXyFQqGw4ZICPzOvCDeB2ulKoVAoDLikwM8wFRHgCW5uoq6LolAoFNcMLinwM/OKCPBSwl6hUCiM1KrAF0LcIYQ4IYSIFULMqql8MkxFBHoqga+omK1bt9K5c+e6LkaliIyMpGXLlnVdDMV1Tq0JfCGEO/AfYBTQDbhfCNGtJvLKNBUrDf8aJjw8HF9fXwICAuzH008/XevlGDp0KCdOnKj1fBMSEhBCYDabayT9xYsXM2TIkBpJW3F941GLeQ0EYqWUcQBCiGXAOOBYdWeUbioirKES+NcyK1euZOTIkXWWv9lsxsOjNpu/QlH31GaLbwEkGs6TgEGlAwkhpgHTAJo2bUpkZOQVZSKlpLCoEG8hrzju9U5ubu51UeeCggIOHTrkVODOmzePrKwsXn31VQA+++wzTpw4wXvvvcehQ4eYO3cu48aNY/ny5fj6+vLggw/a4xYVFbFw4UIiIyMpLi5myJAhPPXUU3h7e3Pw4EHmzp3LPffcw/LlyxkwYACjR49m7ty5LF++HICJEyfyxz/+kfXr15OSksLw4cOZOnUqb731FkeOHKFr167MmTOHwMBAAI4dO8b8+fNJSEigadOmPPPMM/Tp0weAmTNn0qtXL/bv309cXBzdu3fnlVdeoUGDBtx3330A9nTeffddunfv7nAfCgsLmTdvHtu3b6dx48bccccdFBYWEhkZSW5uLtOmTWPVqlVkZmbSpEkTHnvsMYYOHcqZM2eYNm0aFosFX19f3N3d+fXXX9m5cydffvklKSkp+Pv7M3r0aKZMmVK9D7YGuV7adnVSI3WWUtbKAYwHvjCcTwY+qihO//79ZVXZuHFjleNer2zatKmui1Ap2rRpI9evX+/0mslkkh07dpSLFi2SW7ZskY0bN5aJiYlSSq1+7u7u8i9/+YssKCiQkZGR0sfHR8bExEgppZwxY4YcO3asTE9Pl9nZ2XLMmDFy1qxZDnFfeOEFWVBQIPPy8uSmTZtkixYtHMo1aNAgmZqaKpOSkmRoaKjs27ev3L9/vywoKJDDhw+Xc+bMkVJKmZSUJIODg+WqVaukxWKR69atk8HBwfLChQtSSimHDRsm27VrJ0+cOCHz8vLksGHD5IsvviillDI+Pl4Csri4uNx79OKLL8ohQ4bI9PR0efbsWdm9e3d7WTdt2iS///57mZycLC0Wi1y2bJn08/OTKSkpUkopFy1aJG+66SaH9DZt2iQPHz4sLRaLPHTokGzSpIn86aefruzB1SHXS9uuTqpaZ2CfLEemCu16zSOEuBGYI6W8XT9/Sf/gvFlBnIvAmSpmGQKkVTHu9cr1UueeaL1LY+NLoqTsfkAnwAIkAxn674H67wcAq/5bVyALOAf0RTMRFurX/IF2wBE9bkc9rjSk1xY4bCiXMb/2QDFwVj9vosc5DTQDfIF4Qx066nHTgc5Atl4ugFCgIXAK8NLziir3DmnXz+hpgPZsm+tldfacuwEp+r1orIepaICilf43sYIw1xLXS9uuTqpa5zZSylCnV8r7ElT3gfaCx6G9YF7AIaB7DeZX7lfOVY/rpc5AAjDycnVBE7Tuht8igIulwqUCn6AJY4km8GzHJSDXEDe5VNwIIKm8cgFL0ZQU2/lUYIP+/3ygoFR+JmCWfj0SmGqIOwXYpv8frpfVo4L6FxjfD+B2W1n1e/MQcNCQtxl4rHRehviDgE3ARf2+FABf13VbcLW2fa3Xuda8dKSUZuBpYC1wHPheShldW/krrh+EEE8B3mga6wulLjcSQvgbzr30cGlAPpqQbKgfDaSUAYaw1dmdTUQTmA0Nh7+U8v8qEbcy5ThHiRYO0NrwvxfwOdr71FhK2RA4Ctg8FZyl/y3wP6CVlLIB8KkhvKKeUKt++FLK1VLKTlLK9lLKubWZt+L6QAjRCXgdeBBtnOcFIUSfUsFeFUJ4CSGGAg2A5VJKK5oQnCeEaKKn1UIIcXsNFXUpMFYIcbsQwl0I4SOEiBBCVMZZ/iKaSapdBWG+B14SQjTS03zGcM0NTahfBBBCPAL0MFw/D7QUQhgXkwoEMqSUBUKIgcADlSinwsVwyZm2OgvqugB1wPVU55VCiFzD8ZMQwgNNkL4lpTwkpTwFvAx8LYSw7WaTCmSiafXfAIullDH6tReBWGCXECIb2IBmS692pJSJaG7FL6MJ3kTgeSrxTkkp84C5wHYhRJYQYrCTYK+i2fDjgXXA14ZrHwLvATvRhHtPYLvh+kYgGkgVQthswE8CrwkhcoB/on1Qrieup7ZdXVR7nWtt0FahuFqEEBHAUimlmnKqUFQBV9bwFQqFQmFACXyFQqGoJyiTjkKhUNQTlIavUCgU9YRrevWokJAQGR4eXqW4JpMJf3//ywd0IVSd6weqzvWDqtY5KioqTZYz0/aaFvjh4eHs27evSnEjIyOJiIio3gJd46g61w9qu86xF3L4aGMs747vjad73RgF1HOuPEKIcpejUSYdhUJRITP/e5BfDqZwLCX78oEV1zRK4CsUigopMmvr1LmrPaKve5TAVygUFVJs0Tz5Cs3Wy4SsWbLyisgrqpldwuoLSuArFIoKsWn4+UUW+2974jM4fq52TTxTFu3l9VXHazVPV+OaHrStKXIKirFaoYGfZ10XpV6SkGYiNNAbf+962fyuK7ILiknOygfAZNCuJ3y2E4CE/7uz1spyNiOPQB/VZq6Gq9bwhRCthBCbhBDHhRDRQogZTsJECCEuCSEO6sc/rzbfq+Hu+Tvo/do61KSz2mdddCoR70bSffbaui6KohJM+HSn/f81R1PrrBxWKcnKKyI7v7jOyuAKVIdJxwz8VUrZFRgMPCWE6OYk3FYpZR/9eK0a8q0ysRdyAcgwFdVlMeolR5Iv1XURFFdATGqO/f+fDiSXuW611o7SlG8Gq4RLSuBfFVct8KWU56SU+/X/c9A2N2lxtelWJyl6l7Q0eQabZE2w7VQaz353QPUkdL7bc5bVR85dPqDimqSRbgI1tuf84pp9h2yYirU8lcC/OqrVICaECEfbV3S3k8s3CiEOoa1j/rfydrsSQkwDpgE0bdq0yru223Z8355czOdHivjHYB/aN3R3CLN1xy6aB9TcuPWUNSYA7gzNwtujZlzazmRb8HQTNA9wq5ld7quRl/T7YWPDxk14XKWrX3l1ziiw8ktsMZO6euHlXrU83tmbT48QD0a1vbbGemrrOecUOSoqwmomMjKSAnPJ779HbiXIu2bdNRNzrFy4lAcILuUXs2nTJoRwTRdRi1VSYAF/T1Ejz7naBL4QIgD4EZgppSw9fL8fbWPdXCHEaOBntA2fyyClXIC+8P+AAQNkVWfX2WapffHFbiCNxm26ENG3heZxsOY3AHr26U/Plg2qlH6lWLMKgF43DCasgW+NZBE+S8sj4f/uvPZnI+r3w0bfgX+gcYB3OYErR3l1fuqb/WxOOsfQ3h2ZOrSijaVKOJiYxaLt8cwZ2x03N0H0mnVEpxfx1iO3Vro8f/3+EF4ebrx5T89Kx7lSaus574nPgI07+VO/liRm5LEnIYM4jzaM6RcGG34HoO8Ng2gV7FdteZotVlYfTeXOnmG4uwmklLR9aTW23RitEgbcOIRAn2vrI1xd/OPno3y96wwnXx/Fjm1bqv05V4t6K4TwRBP230gpV5S+LqXMllLm6v+vBjyFECHVkXdFSCnZFqtt+GPRbY1ZeSV2+wJzzXVHjS5r2fk14zuclltYI+nWFjkFNedTbfMoKe3GZyo0M+d/0cReyCkT5521MfxyMIX9ZzOJqaLL4Y/7k/huz9kqxTViKjQTdSbjqtO5GvafzQRg1qgu9A9vBGgDt5l5JWaV6jaLfrgxlme/O8DaaG2AOKewbBtxZbOOrd4nz5dtn9VBdXjpCGAhcFxK+e9ywjTTw6Hvp+kGpF9t3pdjbfR5+//pJk04GhtrQQ3aH0d9sNX+f1UaaEpWPttOpVUYJvVSwRWney2RXVBzL66Hm/OmvfXURRbvSGDkv7eUeS62D1BuoZkz6XlXnGfcxdwrL2g53DZvC3/6ZGedTjT67cg5erQIIjTQmxkjOtI+1B9Tkdn+LkH12vBzCor5MSoJKBl3u5RXto3UlAJ1LRDWwAfQXFBrgurQ8G9C22z6FoPb5WghxBNCiCf0MPcCR3Ub/ofARFkLI5kbY87jpS/2lKU3nEyDhp9fw4O2Nq7Ulcxqlfzh/zby4MLdFQ74XrxONfzhnbWF/LZe5oN2NVisJcsB2DxJCootPP3tAXuY3q+u47PNpwFNyB9O0jyIMk1FvPDjYXs4Y6+wImb+92C1lP1SXonve7d/Vs19defpdMJnreJEatU1xdTsArqFBQHg4+nOgDbBXMgpJD235H5U5wfpqW8P2Ov9+qrjJGbk8fvx82XCuaqGn5KVzyG9DdaUB2F1eOlsk1IKKWUvg9vlainlp1LKT/UwH0spu0spe0spB0spd1x90S9PfJqJPq0aEujjYe96Gl/ePfE102UuLaSvtIFG6V1pKPlQOeNiTonAN1vqdtr75bBpbhMGtOSFO7oA8M7aEzWS19HkS5zSXW8tVkmG/sy/3X0Wcyk3wjd/0/Y/3x5b8vE5Vsqcszchk8pQlV6BMy7mOvbcLBW4Pn6/L5EvtsaV+X2DLig3n7xQ5XJk55tp4FtiK2/k78XFnEK+2BZv/+18dvX1MrecvOhwfuxcNnNWHisT7loW+Ntj0xzeyyvh2e9KlJHMa1XgX8skZuTTurEf/l4edk3EaNL5Yls8p2rAVvZtKRvulTZQ4yJV53PKf6GMWkB2DdrDq4Olu7UVW5Oz8h1mS1Z1HOLN1cf55WAy688UEz5rlYMmOOajbSRllrji/u9gSoV5rY1OZfrXUfbz7/dpH6d/jOmGj6cbu+IqZ31sbRi8jE65xNro1AqFdXlk5pU2NZXffl744bDDOMWi7fEkZuQRpA9qVtX8UWS2kl9ssacD2J/bocQsAHw83Sr9Mbwc+8+WTedEag4tGpZ1drhWJ19ZrZJJX+zmtnmbqxTfZLA4pCuBf2WcN1lJzS6geQMf/Lzd7Tczs1T3POpM9TRYI7viHHsOV2qrNo4tJKSZnIZZdficQw/ll4PJbE2qXD6plwp4Y/XxKgmjqtI0ULNNNgvyJcigNe6rgsDIKzLz2ZY4Ziw7yLfHtef52BJt34QiwwJfNmHx2q+alpilC4r/PNDPIb3fypkbENE5lLAGvqRmF7ArLt2pPbl0uWzc+eE2pn8dRfuXV1+xtla6O19ZhSHTVMSrK48x6oOtBOjCuXR7ryy2PI3Lj5Re1qB1sB/plfxgJ2XmccCJULfhbOnlf68/SXJWPrd3b+q0bNcaJidKpY2ENBM3v72pwh6Rt0eJOK7qc7scLivwX9yqaXjNG/pqGr4+2p+VV4yPpxtfPzYQ0AZHFmw5zaLt8eWmtTY6tdJaHkBpt++KGuiRpEsMfXuj3dRUUGzhgc9LpjE8sXR/Ge0nu6CYp77dz8aYku76qyuPsfBo5RrJX5cfZMGWOA4lZTn8fiGnAJMTr4irZcR7kazRvQ/m3NWNIB9P3rm3F1C1cQjbTGmA0p8s4wvVVbc/28g0FdGhSQB39gqz/xbg7cG2WO3ZPjCoNf1aN7RfC2/sT9Mgb6ISMpm4YBdTv9rL7F+OsjfBuSnwfHYhfVo1LPN76hWaPUp/IMprP48s2mP/X0ppFxK5hWa7ie9Eag5Wq2TloZQrmhVr6w018vOy/2YUSAAN/bzKCLf/HUpx6kww5K1N3D1/B2fLMXsZ252bAD+vkjkzTQJ96B3qzrg+zXETVy7wz2cX1MqMYKPXWWkT68Jt8ZzNyCtXuQCtx2TjmrXhX4sYNdewhr74ebmTqzeo9NwiGvl5MbRjKC0b+XLuUgFvrI7hVSe2QhvTv45i4oJd/Hv9Sc6m5xF5omK7aOlJIRV1qz/ceIrEjHx2nNaEjrNBtgWbS2y0hWYLveasKze9wsu4muYXWdiuCzhPgyeLlJKBc3/nkUV7Aa17Oud/0YTPWmUfSKsqpy9qvZRgfy+7//SYXs0ByK3AFPXeuhOsP6aZaswWK19ui6fIbHUYNCyN8cPcNMibwe2CGdBGcynMzCuyzxa10SrYzy7cJgxoxd39WgLw/fQbcXcTNAvysQvsvQmZLNl5hvGG9WVs7InPILfQTFZeEa/c2dXhmjPXxUXb43l/w0mndcgopd0Z20/kiQtkFmjCZNOJEpt3QbHVqbvkhZxC/rsvkWe+O8Aji/fyy8GS5RE+iTxN+KxVZbzVTp7P4audmgmubUjJFnulx5Ma+Xk6jIklZ+Xz7HcHHAa8tbKVpD/F8JEyYhT4nZoGOpiSmgR685f+PnwwsS9NAn04fQXeUIkZeQx643c+21J2nKMqnEjN4ZPI0xQ7GTMzCvwOf//NYe0hWy+/ovkDtjkpf2jfWAn8K8Fo4wtr4IOXh2ZrzDQVcSQ5i45NAwFo7O9FQrpzk4kN45f6w99PcfM7m5iyaC+v/HyEHae1gb6Y1Gx+PZxiD2dr4AH6apAVaSSeenfA1oBKv+wA0edK1p/59ZCjhtCykaONsyJXzVPnc3j5pyP2c+PHIV43He3RtdefDyazeEcCALuvoHdTGqNm5Wno+ti0mbfWxJQb76ONsTz+1T5mLjvAfzad5rVfj/H51rhy7ZsFxRae/6FE2NzZKwx/Lw/yiy2cSM1hV1yG/ZmsePIPLJs2GC+D1tqioS8PDmrN+r/czMC2wQA01d3kLkei7kbXpVkQj9zU1uHaw186CrlCs4VXVx7j/Q2nnA62RyVkIgQseVTrhdrazys/H2HKor18caRsryg5K99xjklxiZPChWwt/OaTF5mxrMSTaOE2TQjaBNPuuHTeWH2c2+Ztsc8laB8aYA/fT/9w2mhk0PAzTEX8e532AUvPLXR47lOXlGxTahxbMZJTaCbA24Nvpg7i4wf6OWr4QSWT8/7QofEVjRvY3Bu3nrp4mZAlxF7IdRrebLFy+/tbeGtNDHviM9hy8iKv/1qiKJYea7GN5V3KK+Z/hzT54OvlONvfiKnQTO+WDWje0FcN2l4JuQZtoWUjX4Z3bgLAgcRMzmbk0bmp1oiD/b04cDbLaRo25q52vv720l1n7aPqExfs4ulvD9jzPXk+h9u6NeXoq7czqG0wGabCcj2CbHuE2rSDC4bu/5bnh/PEsPYkZ+bbhXNp/9yAUksM3zN/h93V0Eh8molb521xWADLqHmW7pobPQ28PKreTIybZpzPLknT2AtyNh/COFj988EU5m2wCZMiMkzOzUA2N8+nhrcn5l938If2Ifh4ulNQbOHddZpH0MnzmnbYr3UjBrdrbI/75j09CQ30RghhVwgAQsuZCTz7l6MOk7dsnaUX7uiMu5vg0D9vs1/LLTQ7tMlog706plSPzmqV/B5zASmhs16OS/nFWKySb3drAiQ9X5bxBIs6k+HwIbQ92+wCM7KU4cv2kXHTn4HNnfS+BbtYYNCER/Vo5iCgbggPJuZfd9AuxJ92If409PMiK68IKSWvrzrGj/uT7PX7z6ZYe7xtBg8oz3KWuTAVmvH3duemDiF0aBLg4N/fpVmJaS6sgQ8ZpsJyTTSFZgvTv95nN7sl6x8YjyvYi3fkvzczeaHjR3r8pzvo8Pff7OdxaSYe+nIPX2yLt1sUSr9DNtfgeRtOYntcF3MKy3XzzS0w4+/tQUTnUO7t37LS5b0SXFLg2wZP5k/qh5+XB+MHaDfv+LkcCoqt+HpqjTjY3/FldjaIaTMpOMPW7bTF23YqDSkliZn59q5wA19P9p/NYsJnO53OnrNNEDqi+98au4UN/T1p09gPq8RuxigqpRGW7manm4p487cY/rw0ysHV8IITO/JDX+6x9ywKSwld451wNl9h/9lMvt+bWOb30hhfXP9ytJuF2+L5emeCw2/l2XqLLJZyXVUf/0rTJHu2aIiP/ow1gW+lqa4lGu33AO0Mz8kZ/UtptTaW7DzDyH9vMfj4W+35gTbYGfOvO0rC7yipX6Rh7OVAoqPCYewNBvlqH/PsgmLOpJuwNc9ia8kuVDZyCswOH+mvd5XsY/3+hlMOYRfobpzGtuTMFNjI36vMbz6e7qx/bhi//3UYjfw8KbZITEWWMu/Ob+UspTxMn4NRGlOhxWF/hLv6NMfLw43Vzw6lt2FcJCTAG6uEV1dGOxX6u+IyWBt9nvGf7mTx9ni7ecm4ZpPFKvnv3rOXdWU2KiKlexXxF0ssA+mmQrILivntqGPv2/beGAerZ/8vmj6vrS+T1+mLuezTHUjG9GrOc7d1rrBsVaW6lla4QwhxQggRK4SY5eS6EEJ8qF8/LITo5yyd6sJmD7Q1oEAfTxr7e9kH+3y8bALf8SV3NoJuE4iNnTR+fXkPOjTRegy74tLJLTRTZLYSomuGRpudM//c/GKtrL/HnMdilQ5at7+Xh/2j8q7us17a5t2vTdlBQtBeOKOroZGhHUtWtbB1XUsvM2E1aJBrjqaWmeZ/z/wdZWy1zjAK/FfH9XAa5p21J/jHL45r6dl6MqN7NnP4vdgsy3g9TRrU2uHc6E3i4+lGQbEFbw/tmb9wu+OLNHtsNx65KdzeCyxN39bOBb6NCzmFSCntbc6mTGh5u/PZ5P72Otr4ISqJmzo0xtNdkJSZx/bYNJbv0z6etjGIDyb2wdfTHU93bcEwm6mua1gQeWZZ5sNfaLaW+agbBwGNvL3mBLmFZvwMZU10MrOzdO/RhrubQAhhH9DNNBXh6e5Gc4P5y2wtKZ/t/Wgb4u90POOnA0msOnLOoTc1644u7HtlJN2aOw68296rJTvPlHE6ADhjMNEaffjdDD3Kb/ec5cUfjzh8FJ1xrhzzaLewIIelU9Jyiug1Zx0r9mu95yNzbqNFQ1/OZmi9i+wCM4N0E6GRQrOFo/py4e/pPdCqeK1dCdWxtII78B9gFNANuN/Jevij0BZL64i2EuYnV5tvRXyqD3IaNcqWwX4lAt/DuYbvbDDIZpIo7fEBEHfRxLz1J+1moeiUSyzdpXW7bdqRsQt7wYlPvU0LSMstIjkz32Hwyt1N2IXXigPJFJmtDhrgyqeH8M69vXn+9s58/EDfMmk3CSypn1EZMpoyHl2sacU2DRW0D9Pba0oE1O8xF/jTJzsdBvwqi61+IQFejCmlXR/4h+OiZBMX7LS75yVl5iME/HtCH0Azv4GmlZYeBG8S6GhndxT4mknnUn4xYQ18ynTtG/p5MXts9wptqxVxNiOPd9aesPvC+3g6phPe2N/hXErJxdxCerVsSFgDX1KyCpj0xW772MOfv9kPgLeHO0IIGvh6cim/mDTdXNM2xI98M3xTSlgVFFu4UEqhaBbkw63dHF0abfSYvZYUg0AzLkNiw+cypjzbM0nNLqDQbMXbUHezoQdSZLbyxz7NaRrkXcYL7EJOAX/57yEAOjcrMaUJIRwGbm2EGD4K76w9Uca0lWwYIzD6Tth6S1CieFU0qRHgb8sPsS7asafy0qgutAv1Z6dhXOuzLSUm1JAAzTHhnn4tSMst5ERqDqZCM81KjQXlFpr5bvdZxny0jcgTF+ym3asxn1aG6kh9IBArpYyTUhYBy4BxpcKMA76SGruAhkKIsNIJVRc2M4zx5Q4N8LJ7m/h4ltXwhcCpPd82tfwfY5zt6QIf/F7SXd6bkGkfhLS5sBknUV3IdnwhpZQOJpwzGSa7rfebqYMAHHzWTYVmu3a76W8R9GzZAH9vD54a3oExvZrTuZHj4+xksEUbNfj2oQFltDdj93V3vPNB2hnLDvLltngHbbC8pR8OnM1k6Nsb7b2mN+7uWUYYNvL3YrzBVrkrLoPRH26l0GwhK6+IBr6e+Hi64+Em7F4Lh5KyuJBTQNMgb4Z1CuWD4X5lemrGXlUjP09MRRZ+iEqioZ+TXlolWPn0ECYPbuP0Wm5hMfMjS1740q6LnZsFMrhdsN0eX2i2UmyRBPp40Lyhj8MibUYTRdcwLXyQjyfRyZd4Se9N2T4gthnCNmwCv3+bRjyg93jMVunw8RvcrqyWObJrEzzcBP8tZZ7r1DSAqTdXvMroDeFaepMX7ia/yOxQ9yKLlae+2U+f19ZxNiOPAB8PGvh6sjchk06v/GZvb8YdtcobLzESGljyDHecTi/j1mscFLY1zTaN/RxMYLb77H6ZpbmjzmQy7esoh3ejR4sGdjOgjV8OljhsvHmP5m7s56Xd99vf38KFnEL7x9HGwLkbSNDNltEp2fYlYGpa4FfH8sgtAGNrSQIGVSJMC6CMU+rVrodfZJEEekFOEVw4eYDI09pDLcwuJMOkCdOE0yeJzI8j5YJ27iYg1Few5fBperk7arFpGfl0CXbjXIxz84iN8CA3ErJLtGS/9BNERp4k9VxJgzwUc5pIWXIbvospZN8ZDGdOOQAAIABJREFUM2H+gnMmye+7DhKXYSHUV1CcdJTIJDiXW5Lmhs3biEkqoHeoO2eO7qV0h/TJbhaWJ3iyLVmr1/mLF+33b19qyYcl9XQ0Hw/35uWtFhr7uhEZGclzhrXqt+8vMa+0CnQjMaekDK/9eoyFkSUD2es3Rjpdb35eVAGJGRaWrNfu26nj0XhdLOuRM9DfyvJSvw3611q6BLvhhZXIyEiH5RDiLpqIu2iiW2M3HmmXR26uiZOnHG3UMQf3cEbff6Ct4UU/fi67yuuLy0vOtcG9B47gLsCWzZYtZWdZ+hQVci5TW0s+S3epTD0bz9nzxaSYSsrX7uXVAPRv6k78kb3EAxTncyit5P4XpDkK5l6h7pzOsnD6TCJnL1po39CNhoWaIMnLL+BSmqb8RLTyYGiTPHaV8k6Upgz8PGQZZ4Anu1rYv2t7xTfFVqZiKxuOX6BtgxJhZcorYJXB5zw5OQWbfC0yW1nw8yZ6hXrYhR6ANeMMkZHOe5G2teFzS63R/8O67XRrXKJIxCY58QIqLuBsynn7s49P0JSHMwnx5eZnI9hH8NvvJc/0+JFDyCLnSs5jPbzwvHCcyAvHST5TamwtNRkfdyjQvx15RRa7F9w7a0/gp0vitgEWezmv1fXwnX0mS9+RyoTRfqyG9fCPjCi7ZvjO/ONsTdZae9+e3YnoGUbgmUw+2L8Dq4RmwUH4+3lhadqGXi0bEhrozaYTFziZuZfbujUlImIAXQ5uITEjz2EKNGj2/Vl39eCJpVp3/M6eYYy4RRumKAxN5Xfdlh7YuBkREb3s8Z6J1BbGKhZeQCGWwDB2Rp+hU9MAIiKGAZrm9tK2NQA8v0VrzJOHdCAiokOZekdGRtKyeTAka0LBJ6AhoZ264u3hTlRRMqB5Ttx5yxBCA71pEbMDDzc3IiIGO6xV/11MiRfBvEmDuLeU33lybsmju2HwTU4H9345f5BDF5MRAY2B8wwd3J9eLZ2PN3TtfYkxH22zn2cVSvLcAwgLhoiIIWXW0QdoE9aEiIj+REZG0q9FB74+ppkFerQIYtTIoQ5hn/pdi//SqC5EDGvvtAyXo7epiGVzNzB+QCu+23OWO3uGserIOZq0bo/1cImt2Fl7jZaxRCadYOAfhpCSVQCRm+nfqxsHs+NJMZXd8rF182ZERGimrEVxe4i7pI2zjO7ZjOlju/Pl0d/tYaeO7MUbq44THBpCdkoKPTu0ZnDHUOYf3IO7pxcd2rbg97Nx9OrUllE3tOJfuzY65NW9QzjHLyWRXVTAzZ1C7evZjL1t+GU1YMDh2Xj5+BNsLiTDVERmoePr3SikKZ2aBfL7We2j7x/WnhYdQ2DNFnuYGeNHlpuN7X2WUrI+4yA3tmvMrBVHCGndiYgbWtnDvXFgM+Bomm0a3BB3N6G1c2Bn3nGIj6ND+/YMG9au7GYqhjplFEja9+gHkdrH76bBN9ClWSAfHVhdpoyjhw6wD/KnRSWx9Pgh+7XundrjFpjNz4begJE8XR/7+qmR9t5BTex7UB39hySgleG8JdquVlcapkZpbtiApMSkUyKo/Lw8SDcV8tiSfTy2RJt89LjuP2wbePz1mSEcml3ibndPP20nxyBfT+7oEWafcGNsP7d3b8bev4+kS7PAMj72rRppa6+YCrVFqmyDSMbBIh9Pd76cMsAh3mNDHP28jRhNQLmFZu78cBsj/72ZjzaWuMnZBqC9PNy0rve3+8tNr7TtsTTlLY/r763dY9uCYhVNOHH2wTicdKnCGapGU8VdvVvw1p96cuL1O1j59JBy4zxawX27HI38vYh9YzRv3tOTrS8M59Vx3QGtxyEl3NShMRNvaOU0bpvG2nNOSMuz+2oH+XiyYPIAp+GNHiW25xno48H/t3fe4VUU6x//zmk5yUnvJIEkhBZ672BAOoIoFhAVxPKzXfWqSLPd67XitV29dsFeECwXFFQkVOm9E0iAhEASSK+nzO+P3dmzu2f3lHSS/TwPDzlbZ3dn3nnnfd9557WbeiMmWPo9THodzEYd9p0rQrXNgcQIC0IDWB4dq3CtapsdcaH+eOE66cIskUF+ghmBhSsDns0dSljtDvz68AjFfVU2u2QS13OrjmDs605hr5QzRwlCCN6c0QfX8xPk5I7Vy+VWzBzodOKP6BiJAD+9JO0FGzG+vOYYhrwo7QCVJlRNfds50jEZdCCEYM0jI3BfWgq+ustpzBCbSQNkPqEQfyOu7c3JC5NeB6XX2zE6UBD2DUV9CPydADoSQpIJISYAMwD8LDvmZwC389E6gwEUU0obdXFTsaOSCQuxwLf46ZFVwAmnA9nFuFBcJVSMAj4k0qDXSfwCzEbIJi2x6zlkdu2oID+EBZhwqawaX2w7I+R7YaGAlVa7xHb3xs29JedbRJXg07kDXWzhYsSNSmnB8HnjO0PH1zY/gx7HL5Ri9QHlT/HerX2REBYgsbPLURP4zArDbKrBZvWKHBdixj+mdsP6x9MwJtXpZBzZUTmEj7ueswPR6whuHtBOcHQqXR9wznmoK23DA4T75xZzz3fX8PZ4aXpPxeOZH2jvuUIhyV2wv0G1MzWITGQhvLMxOshP+O7z+kvPMxv1QkBCp5ggIcBgeIdIQTCO5qOQAkXfYVTnKEzrEy8EArQLD8DiSakuCoa3VNsciAk2SwSuUEaDHn3dRDz5mtfJZNAhwmKSKAUZeaUoKKtGWIARD4ziRnIvT+8Ji8kgGZWLbfJypcJTahE9X7+6xAZj/oQuGNohUrDpi6uXOJpueIdITOzeBoPahyPE34j/3NIH/53VF2myENUe8Q24+h5PnbsTSqmNEPIggLUA9AA+oZQeZrnw+RTJvwCYBM6mUAHgjrre11c6ibSXLnyDYEJocs820BMimRwz+EXnsLms2tV+2z7KguxCqd2Thf45FMJ7wyxG/HLwEvacLcKlsho8MCpF8PRHBpokIZtXp0ojKwaJOquhKRFwx03920JHuJwmLIUC4/q+8XhglNMUZNLrJM9894hkfLjJmVNoQnfOr94jIQTL+fTGcnZmXkZKVCBW7M5GbIgZwzpwIZ9MiLDru9PwCSGYPTQJAPD8dd2F1L6zVBylgHQk44lVD42o96nqJoMORj3hTDQeypMcaYHFpMeeM0VCZIi79zGN1wQBp1Igdjh3i9Rjw7w0PPXTYfRuGyqZLT4gKQyEEGxfdDWCzAYEmAzIemmysJ8Ju/hQfyy9g5vJyyYMtQ0PQJpKeKo3MO1Yvk7xvPGdccvAdgizmHDPyPZYtiXLJaxUnrbaG0L8jZLZrWNe40YMCWEBuGVQO8wbz6Xh9jfphVxagHRiJsA5cZkSxIIoeiWEYOGkVMz4YJtwXJfYIMQpjESentIVi384hIQwZ7bULrzTfdkdAyTvVGwhmNC9jbBEKYB6XSpSjXoZP/DLFv4i2/ae6G8K4IH6uFdtIYRgxX1DcCqvXBh6EUKw+8kxCDIb8czPh1zO0esI7A6KxZOkETr7nx4Hk0GH+7/kbPP38NEMrJ7LNXxAmoTqzOVyIQUvAKy8bxhGLlnvtvzrH09DVkG5Ry2VabtK8bzyyUU2Wc/kb9Rjaq84YRo4Q3xPi0kv0ZZ2ZF7GjIHt8Nhyzl7JhMth2ejC2+gDcdgdy3uz9I4BADghdf+Xe5CRVyaJd/dEuMXkEiVRHwSZjULufLWJWwBXz0IDTMJMVMA5Qvn14REICzAh2N8Af6PrCIUdJ4/+SYyw4DM+9UKQ2Ygqa7VwLwAuph8GyyvEkteJURJmnvjizkFYczgXX2w7K4xYbh7QVjBPvn5zL1zXxzlCXDQpFVd1isKsj7ZLrlObNNn+Jr1kUmB8qD9yiioxJlXaaVlMelTwHR2lVBJVAwAF5dVCaC/rDO69KgWpohm+IzpG4vM75bEoHGmdo7FlwWjJtp4JoTj23AS3o3E58jxPDUHDGoyaGf0Sw9EvURqaxhIWKdnO7A6KBRO7YEJ36eQfljL25ek9sfbIRdzKh8GxGYE39Xe15YoF/so9OZJhZbuIAH6CkAPf3jNYsezJkRaJucYTT17T1UUrl8c1y6eC+5sMeGtmH7cCXyzse7UNRfqJfMncgIoaG4x6nST6whf0OoItC0bjx705Qn558aSocV1jkJFXho6iEVtTIR41iOO8lQgNMEqS0DGzotL8DjGsI1GwVAksu2MAbvt4h+JcDDntowIlGr8Ypbh3TwzvGIl+iWG4UFyNhZM4jbp7fAi6xwfjUE6JRNgzlGYvT+/reyqBAJNemMhVZbUjp6gSvRJCEC3r7AL8DCiptKKixqaYhvnpHw/jPX6CHMs4GuxvhNkkVnR8F5W+CHsAtQ4b9oVWJfDdwWbljusag99E6RQi3cQGRwebJfHZcaH+qo0pVNZ7/3JQOqFjxX1DsfFEgcR8UxdC/I3CCIURGSitUHKtimnhz07pKgxxAUhmCc6f0EWYa3D74EQ8tny/JENlTmGlIKQmdo/Fr4cuqKZUUCM+1F9iehJzb1oKercNxVWd1O37jcW88Z2FGbQRFvcx5PLvL3fqqcEUkrJq9Syo3eJCsEc2ia02eOq01PA36fHRbKnd/5t7hqguVGI26vHXwtEINhtRWFGDyEA/IQ7dt/saUFxpxYYT+UKCOrZEoJgh7SPwbvopvL/hNAYpzEVYI5pctXAll1wwMSJAUiZxAsOGQl5HGoIWmUunNozl7eb3jGyPZ6Y4TThyIVlbPJkUusWF4L602oUMqrF4Ehc1xHwV8mG+fEk+JpjnDEvG7UOShO1twwOQ9dJkZL00GfelpQjRKCyjpHgRmbJqm5DEa0qvOGyePwqb5kuHu3Uh2GzEuG6xis7ZxubWQc7O3lNUS6i/8/tf3zfe6/IP5Cc3DfPgu6kPfDGTeSLQz+DWRNQmxB8WPwMSwgJgNuolCoa3BBg52/xTP7qaY8WwEcWb604KNvqeCSHYMC/N5VjWJuJC/CXf6NzluqUIV0McndQ+suFHrZrA5+mREILTL0xC/6RwSXpbdxq+L4Q1wnBNztzhych6abIwapDbmd++pY9kwQ9vHXYvXt8DJ5+fKNhsT4kWJKmosQuJ3sItJiSEBTSI/bw5EBJgRFSQnxCF4w6x9ix2ynpzj91PjsGjYzvVqoy+0Bw6UV8IMOlRXm2TRNp8dLtrhJE4KRtbnvGdW/oiMcKC24ckwmzUcQvI8IrKXcOTXTqg24eoBxDUhRX3DUWP+BBEB/mhbbjvPhRf0Uw6IpS0DHE+mrqgNlzr1Ai26Fem98RncWfQP0k6nL2mZxyu6RmHvNIqBJuNXtscCSFCjqAgs0ESJVJebRNs297GVl/JbHpilFv7OkOcvMtXJSKinpQONab2ipOYNa4U/E16XCipkuSJGqOSO+jJyan41+qjQhoM5odLjrSgyurAT/vOC1EyQxRGU/+Y2q2eS88RG2LG//6mPnekvtEEvgfqSztVEn77nh4rhHI2JGEWEx4e01F1vzz5mC/Y7FRY0QoA/v7tPkzo3gZGPalV1MeVhredpDhnUn2ZCeuLt2Z6dvY2Ryx+BkHYd44JwkNXq9dxeV0M4rV+1i4f+XYfXuEjl8TBEav+NhzhFtMVN/pRQzPpqDCRj8zxZeEEd0QHm3H4H+MlTt3QAFOtszQ2F+SLa5TX2LFiTzbCLaZazdZsqTBz2kNXd3SJItGoHWKfwwOjO7isdSBGbs5kAnx4x0ghWIGlIBfH03ePD2lRikudpBkhZAkh5Bif4/4HQohishRCSBYh5CAhZB8hZJfSMc2Nt2/pK1nAoj5gtsTG8MY3Fo+rLNRQmzC2lsz8iV3wyvSe+LubkZaGb4gjnQI8jLTUcvsHmAw4/twEGPVEyGrZ0Bkrm5K6PtnvALpTSnsCOAFgoZtjR1FKe1NKazdvu5HR64jPcbTesmX+aBx4dpznA68AxGueilMwXOkjl/om0M+Amwa0bTGmgeaAWOB7qm/ilBJvzpCmLiGECGYcb1I0X8nUSeBTSn+jlDLj5DZwSdE0PGDxM9RqkktzROzjWHJjL2E1LW/jzMV069at3tPBumPOnDl48sknG+Ta7777LmJiYhAYGIhLl2q/CLyGOv6iUaTKsgwCYg3/WoUoKVaPxQumt0Tqc9w9F8C3KvsogN8IIRTA+3wKZEXqmg+f0RC5pJs7TfHMJaLc4Onp6SCV3GSuyjJp7vl58+YhNTUVc+fOlZy/efNmvPbaa1i+fDneeecd4TpKLFu2DDk5OVi8eLGwrS7PfOHCBdjtdsXz16xZgyVLlsBkMkGn0yE2NhZ33XUXhgwZ4vG6NpsNjzzyCN555x106NABBw8erFX51NDqNkemaI2Hvfv2wZajLs6qbNJ6KsdRwYV2OsqLms27bZDvTCl1+w/AHwAOKfy7VnTMYgA/ACAq14jj/48GsB/ASE/3pZSiX79+tLasX7++1udeqTTVMyfOX0WTF6yilFL64cZTNHH+Kjri5T8lx3z11Vc0KSmJOhwOyfbp06fTRx991OM9rFYrfeaZZ+isWbMk2+vyzLNnz6aLFy9W3Ld06VI6bNgwSimldrudvvfee9RisdDCwkKP1z137hwFQK1Wa63KZbPZ3O7X6jZH+vE8mjh/FU2cv4oWlFa5Pd/hcNDE+avotHc2K+5/Zc1Rmjh/FX3uf4fro7j1Qm2/M4BdVEWmejTpUErHUEq7K/z7CQAIIbMBXANgFn8zpWuc5//P4zuGgbXrnjSaIz89MAzpj48CANzYj5uFK19Badq0abh8+TI2bdokbCssLMSqVatw++23AwCSkpLwxx9/AACeffZZ3HDDDbj11lsRHByM9957Dy+88AK+/fZbBAYGolevXgCAGTNmCOew82699Vbh94033ojY2FiEhIRg5MiROHxYuli6N+h0Otx2220oLy/HSX51rerqajz++ONo164dYmJicO+996KyshInTpxA586cIzs0NBSjR3OzjI8dO4axY8ciPDwcnTt3xnfffSdcf86cObjvvvswadIkWCwWrF+/HufPn8f06dMRFRWF5ORkvPXWW8Lxy5Ytw0033YTbb78dQUFB6NatG3btcsZCnDt3Dtdffz2ioqIQERGBBx98UNj3ySefIDU1FWFhYRg/fjzOnHG/kHdzpjefu+ra3nEe5yoQQvDrwyPw6Vxl0cNyGrG1C1oqdY3SmQBgPoCplFLFbFmEEAshJIj9DWAcuBGCRguhV9tQtOMbSkiAEc9O6eqSBM7f3x833XQTPvvsM2Hbd999hy5dugjCW85PP/2EG264AUVFRbjzzjuxaNEi3HzzzSgrK8P+/fsVz5EzceJEnDx5Enl5eejbty9mzZrl8/PZ7XYsXboURqMRiYncjMv58+fjxIkT2LdvHzIyMpCTk4N//vOf6NSpk9CpFBUV4c8//0R5eTnGjh2LW265BXl5efj6669x//33Szqfr776CosXL0ZpaSmGDh2KKVOmoFevXsjJycG6devwxhtvYO3atcLxP//8M2bMmIGioiJMnTpVEOp2ux3XXHMNEhMTkZWVhZycHMyYMQMA8OOPP+KFF17AypUrkZ+fjxEjRmDmzJk+v4/mQoi/EesfT8PzskVd1EhtE6zqO5vUvQ3WPXYVbnWTkrslUNconbcBBAH4nQ+5fA8ACCFxhBCWLjkGwGZCyH4AOwCsppSuqeN9NZoxc4YlKyaBmz17NpYvX47KSi4vyWeffYbZs2erXmfIkCGYNm0adDod/P1rFws9d+5cBAUFwc/PD88++yz279+P4mLvEmFt27YNoaGhMJvNePzxx/HFF18gOjoalFJ8+OGHeP311xEeHo6goCAsWrQI33zzjeJ1Vq1ahaSkJNxxxx0wGAzo27cvpk+fju+//1445tprr8WwYcOg0+lw8OBB5Ofn4+mnn4bJZEL79u1x9913S64/fPhwTJo0CXq9HrfddpvQAe7YsQPnz5/HkiVLYLFYYDabMXw4N5Pz/fffx8KFC5GamgqDwYBFixZh3759V7SWnxxpUQ259AWdjiAlKrDFR1ERFStMs4AQkg+4rNXtLZEACuqxOFcCV8IzdweQA6Cc//sAAOZ96wEgC0ApgDgAfgAyRecqbesN4BR/jtIx8QDCABjBBQ/owY0wqwEkAaiB8nKbEeDe53FwilESACuAc+CCHXoBkKewJAD2AjDxz7Kb3x7Dl8MhO/YSgLOia7MVtcMAtJddn/DPmAGgA7+PPaP4fmEAYgEchSvd+GPFjV7HP2O5wvHNiSuhbtc3tX3mREqpcjpZNeP+lf4PbhwXLfXflfDMAJ4Gt1jOMwBWyfZlARjD//0sgC9k+59R2FYJzqTIfr/HjgFwGzjBlwxOYIaCE3Yd+P3LAPxLpZxzAGwW/Y4AUASgDzghWQEgXuXcJP4+Bv73TAC/u3knknIAGALgpJvjz4vfg/h+/Ll57N6y89aC87U1eT2oRb1p9nX7SnjmljulTKO58hmAMQDuBvCpj+deBJBECBHX2woAMwghRkJIfwA3iPYFgdPkLwEIAPBCbQtNKb0E4CMAT1NKHQA+BPA6ISQaAAgh8YSQ8SqnrwLQiRByG19OIyFkACEkVeX4HQBKCCHzCSH+hBA9IaQ7IWSAF0XdASAXwEu8/8xMCBnG73sPwEJCSDe+zCGEkBu9egEaLQJN4Gs0KpTSLABbAVjguti9J5bz/18ihOzh/84BkAKgEMA/AHwlOv4zcCbBHABHwE0OrAtvAJhECOkJLlghA8A2QkgJuPBlxTwTlNJScMEKM8Bp5xcAvAzO9KR0vB3AFHDmqkxww/qPAHhc5Vp0bgdw5qJsADfz+37g7/sNX+ZDACZ68dwaLYRmbcOvC4SQe6ibCV4tEe2ZWwfaM7cOGuKZW6zA19DQ0NCQopl0NDQ0NFoJmsDX0NDQaCVoAl9DQ0OjlaAJfA0NDY1WQrNeligyMpImJSXV6tzy8nJYLBbPB7YgtGduHWjP3Dqo7TPv3r27gKrMtG3WAj8pKUmSBdAX0tPTkZaWVr8FauZoz9w6YM+ctGA1pvdNwL9vUk4+15Jozd/ZVwghquloNJOORoMz8pX1eGvdyaYuRotkxZ5sxe15JVW4/8vdKK+2Ke7XaJ1oAl+jwTl7uQKv/X6iTtfIK63ClP9sRm5xZT2VqmXz6m/H8cvBC1h1QCkvXP2TU1SJ9ON5jXIvjdrT6gT++xtOYfeZwqYuRrPDanfglTXHUFJlrdfr1tfEvm93nMPBnGJ8se3KTeXbmNTYuMScJkPjNPFJb27CnKU7G+VeGrWn1Qn8F389hunvbm3qYgAAiiutOJ1fprjvVH5ZvQlLxord2ShVEei/HMzFf9NP4cVfjtXrPa125zPYHRQOR+2eiZ1F0LLzldcXNXZe4Ot9X0zeG+R1s7iyfhWFpuTzv7Lw+HLvFtipTyilsNodng+sA40m8AkhnxBC8ggh2mpXPNPf3YrR/97gsv1QTjGu/vcGfLjpdL3d62B2MR5bvh+LflB+/az91rfNt8rmTOne49m1GPOa6/O2BCpr7HhnfUaDN1hvqbZy5fBrIA2/69NrcYOC4mSvRYdeZbWjsLymPorlFVa7Ax9vzhRGQXKe+ukwvt+t7BtpSN7dcAodF/9a76NsMY2p4S8DMKER7+eCWCs5d1lxRcZGJSOP0+6zCqRrT2QXcmXblVV/pqfSaq4S5ZVUKe5nzj+bo3YCq8pqV2zsVVanwK+oseN0QXNfZ6N2vLM+A0vWHsfHmzM9H1wPeBr9MQ1fr2+YEVGl1Y5dCqZRNSHqjuv/uxV9nvu9PorlFUu3ZOK5VUfw5fbmZR78/C+uPCUNOFpqNIFPKd0I4HJj3U8JsUAa8cp6r845lFOM+d8fqLUpwhvSXk0XnJEXiqvw6m+cg7M+78jkuF6nLAA2neQW1qmxOe/qrbZPKUWXp9Zg3veuw2CmadYXzXUFuooarmN76VfPJrH80mpU1sgXy/INcXVce/iCy/5qXvA2ZL1VoqYWI5wjuSUAgKQFq1FQVl3fRXIhp5Bra80pb2RZtQ25xZwydr5IWSmrD5pdHD4h5B4A9wBATEwM0tPTa3WdsrIyl3Nr7NIv7M21H1lfgaJqikGBBQg3e98/Hr1kR5mVol2QDjEW7jyrg+LHk1ZMTTHCzyCVXOs2/oWEIB3e3luFjDxOGBQUFPj0/ErPzDiYzwnv4qJCt9fMy+fuebLQjue3V+Hv/fzQK8p9NSmp5t7ryj05mBpdhEMFNnx2pAZ5FRTz+ptdjq/NN83M5Ib8WWfO4JvVOfgxowYzOptgsFUoXu/jg9U4fMmO19ICfL5XbTif4xRU7p6PUoo71lage6Qejyu8G0ZZDUWAEdAp9HBlZWVYL7rH/32+G8smcBN0zpU6EB1AUHCZExr7DhyE/qLSaoecAlRcQ32q1wDgEElK+bOmb9yMEL/a98pf/boJbSw6RAVIy1RcUoa7312LcYlGl31stOPterQnsrh3k3smA+k2dS2fPZvNQWGngJ8Xo6XPj1Sjf4wBqRFO38muCzaYDUD3SANsfAds4BUvm4Ni+Yka5Fc43+lN7/+FpeMDUF5eXmv5p0azE/h8/ucPAKB///60NhMPamwOdHryVzw3LRm3DXauQl9aZQV+/034nZaWhsvlNfhm51ncd1WKcoXZ+BsAK2I79MTQDpFel2HOgtXC36dfmASdjmDplkyszjyC5KREPD6mM7DGeUzvvv3QPT4E35/fA1zMBQBEREQgLU26yBHn2KGK0RfuJmrYjlwEdu9CZEQE0tIGSvbll1YDa/4AAASHhiEtbRCOpGcAOI6KwHikpaktzMSx+0whsH4rIiwmpKWlSZ7dLyYZ8uVVTxkSMXtIIgx67wXNPtsJIOMkkhMTsfpcEbblVmLmVZ0RWHRS8sznLlegTYgZc9b8CgCNMlnn3OUKJFacAbJOe7znmUvlwNp0HCqwqx5XVFGD3v+b3bbgAAAgAElEQVT8HX8b3QGPjXNdUyU9PR2Dh40AflsjbEtLS0NBWTXm/OsP3NAvAUHBZUBxEbqkdkNazzaK93nsu/1YsScbx56bALPRKaBe//0ERnaKwm9HLiAhLACTusfih705uHN4MgghnIN27W/SZ+Xrcv9BgxEfKl1w3uGgcFCq+L3fWc/VM8Zru7mOM+ulyZLjPvt5HX4/U4WjJUZsnu98b1VWO7o8tQaPje2Ev13dUfE55XxyegeQm4/+vXsgrVus6wH8s7Bnm/DGRhy7UIq3ZvbB1F5xqte1OyjmrPkF687aJOVn7SHrpTHo88/fYLNTHPwHtzjamkO5WPvbHpdrDRtxFbZu3ljv9bdFRulU1HDa7AurpYLGZncdwy1ceQCvrDmOnQr28ke/3YeiCs6edstH24Xtl8qqcfsnO1SHn5dk21mvzuyb1TbX4Txz9oVbTMI2pSHnf/7MQKcnf0V5tQ02uwMv/XrMK3/EXZ9xM5Y3nMjH7jNSy9qA5/8QlZUrB3tXRp1yFTmYXSxoVmW86UfJQehQeIjnVh3BNzvPeSyzIoTgLP+8cltnXmkVRryyHi+KzCrMdLL7TCGSFqzGghUH6jX66XxRJUa8sh7vb/TsYC+vtgk+jEA/dV2roIwbzaw+yHX8n287g6QFqyWRMEr+kkz+2iculgrbbA4HiipqcOxCicvxzG8jNrtRSvHmupOY/u5WvL/hNJ768RDu/3IP/rX6KE7xEWXy+i3GanOgymoHpRRZBeU4mF2M+SsOoMPiXxWPX7L2uOJ2uSmqkrcuFldIvznzEX3gxfsHuA5344l8AMrtS1w3WBmOXeDe50Nf73Vbdyqtns10hRVWlIpMpWqXq41pzBtapMBnAlb+Aawih2RUELe6XGE5V4GUGtDKvTmK1/9i21lsPJGPT7dmAQC2ZBTg3fRTwv5nfj4sOZ5d292Ik5XZJNKClOrCd7s4QZl+PB9f7zyH9zacwut/+Dapafq7f6mXw05RWF6Db3mBbOCHsQ99vRfvbeCecfeZQkx5ezPGvb4RAFDtpqJXqdjwWaesxomLpXg3/ZTQwMQNQ8+/SHkoYAkvFdaLJgDllXLD9xd+4Tr/b3aeQ2FF7Z1iFTU2SV0prXJ9DjWh0O2ZtbiDj1V3F83DhBirLqyeiR3udoV7MOd/XIi/cK7NTnHnp7sw4Y1NqvcUC5dqBafrqXzuuizE88RF5VBigJuA1eWpNfhkSxbSXk3HlLc3Yzkf8eJLR1suqh9T/rMZf5zlvpnZJA0zfXkN17lX8O9sS0YBft5/XrifXBk6ct7Z8Sm1efHzWxUCGL7cfla1zJ7qtBI6FZ+auzZVFxozLPNrAH8B6EwIySaE3NlQ91Kr2GINn71QpoGqvHdF/E3ca6uoseNobglmfbRdqHgAbyIRl8eLyBcrX9HEDVmpgTDN8IGv9uCpH7kQy2Cz0fvCe8DmoFi6NQs5RZxjy8h3QD/vPy84JNlI5SQfZVQlExKD24cLf6vFZyvZpsX8588MvLzmGNKP89oYv53A2XHKr822i78/c6aKG3eZgpAGgL1nC/EflRQQeaVVyMgrQ9en10qiO5RGa97EpKvV0bzSKkHrZe+IaZpLt2YJQkXJGctGD0yZAbjnPphdDMAZFXb2UgXG85014BzVHT5frFh2NpJldfM8XzfE95GX4ed9rspSdqH3s6TLqm144vv9+Pdvx3Ewpxg7L3Dv2d8oFfhf7+AUE7uD4qkfD2HWR9vx0Nd7UVxpxdItWRjxynpMeGMjZ0qDtH0pdZpiZ7pVwSKwUiGVBaUUH2067dPzMdTawRWv4VNKZ1JK21BKjZTSBErpxw11L7XQMCbwIwP9hJ6cffTfj1wEAPxx5CLyS6thc/PC/U2c0K2osWHuMunswu92nsP2TKnJRG5KUlJ0rHwDFjdkJX0owOQ6kaZKpg3U2By1iodmXC53dlhL1h53mRwm74jk2kiov9MspSb81KKFGEFm7h1vyShw2ccaYnGlFXYHxfbTl3DyYqnw3S+VOWO62TaxaUkpzvmjTadx3X+34t+/n3ARplkF5Rj4/DosWHEAgLOuAM4ORUzvf3oOMRTfwiGakDZqSTo28CaHk3llGPvaBmH099X2s/gXb6a0KXzfs5cq+H1SLdXix9WZo7klyC2uxMebT+O42Oxjp8grqcLktzbj0e/2qZbZzl+XdVZKn9DqJixzxCvrUVBWjT+PXVQ9hlFWZcN3u7Lxnz8zJNvlAl/M56JZ2Kfyy7Azi2uHxy6U4onvuW8nf+9ifj2YKwkPVXoWpU7g8PkS/Gv1Udz7+W4A6nV74coDkt8OB0VhhfL8g/qObmO0SJOOWHua9s4WQUAxTTvQT49qmwOUOhvaR5szUWW1467PdmHO0h0or3ZtyBeKqzDq1XR8s4Mb1lXW2CXOU7uD4okVB1zOYw1QPkvUIKoYrIMRax1MuxVjVHB8XZJNWun05K+4m7fZ/3IwF/vPFbmck7RgtVtbrJh1R50mkiPnS1Ai05DlGr5Y4BSpmE/kcw/ksO9ymTUI0Xth37eixo5/767CzR9sw9jXNwrCXSyEq5UEvqwTulBcJQhSACirsSG7sELoNLN47VAp7ryu4ZUAMOODbUhZ/AsAoFx2vZN5ZZKyH8jmvqWShs9GlmIzmt1BhXfw6Hf7MeTFP12CE2rsDsz4cBsAYEvGJdVysmal1NnoFEZXStyxdCfmLtvloqTIKVUJCfYzeieyVh/IlZhL9DqCQznFeOjrvcK2jSfyse6os/NZsPKg5BpKI3OxMrkr6zLeWZ8hVM08/v3rRe/3sqhtstEI472Np4SOyOU+V7qG35iIY8n3nStCFtN8+N45gNfQa+wOiYBlml92YSXKFOxxP+/PQWZBOQ7zdkCrnSIq0DmsVavEcg1/3bE8PPPTIYlN32qnWLjyIL7Ypm4jBJTtjkr3/fNYHmpsDtz/5R5c+84WxWtlKghdpXHB8784heGktza5xNvLNXyrnQqdmdokkk//cj/phWk+TNPZyM8T+Hn/eSFeedWBXBy55GwYr6x1jYEXNHxR+7nlo+2SJGzyof3Ji6UY/vJ6pCz6BfvOFbk4nsXfQM1RV2W1Y8naY/jH/w6rxsKz6+zIugxKgeW7lB3Z4vsVlltRaaP467SrYGamlyqrXahbNjv1KFxtdorT+Z4nxNlkGr74tVh4U2MR/73Vxpes8/Q0x0Ntv44QZOSVYd7y/W5H4R9vzsTqA7nCb72OYPEPUoG+cm8O7vzUmX49OVKae/7Rb/e7dGBMEJdWWXHDe39hydrjgp9LKCMvVX/Ym42+biaU/XlUPdmcpuH7gPwjjXo1HfvOFQnbmR2c0/KdxzGnn9moU7TzyoeXOh2RhLOpNX4m8FkjzCwox6d/nZEMD612B77e4Srs2SiE2YqVtKvD50uQtGA1ckq5BGiMt/90n5LYZNAhI69Usm3/uSKPnY7cUSl39NkcDoQGcH6F2uRYySupwtrDnOZVZbVjV9Zl7ONHKUqdFENJO2XvTS60v9lxDheKq3CprBrDXvpTsi+rwOnoe/TbfS7Pu/XUJTzLO+aVTDoA8NrvJ/DO+lNYuiVLYj4R8/FmaWTJPDVtT/R+Syqt+PhgNR7+Rmp6sdodOMM7KMXfw+ZwQF5l5GZjtfxKcljHw+qzg1JcKqvGf9MzBMWHKUMHeL+BHBaUsObwBQx+YZ3qvdRGThRcAMHy3dk4mqv8XpXQ64jqt2LI6/HmjALsPSsdHbNvcfi8a9QTo8rqQNKC1VisksaEoeawBYAae8M4bZtdHH59oDSsPHOpHIkRXA8ewNs0a2zSxsA0/Isl1Rj/xkaXa8gbvp5IsxGqVdKZH25DapsgDG4foVpmtZQGNXYHnlt1BF9sO4tFk7oIQ3oxbNj46ZFqnCh0Rgu9Jeug5Bh0Oox5zfU5fcXpAOd+W+2UH0XV4FK5Z7PRb4cvoLCiBoUVVswc2E4YGgNcLp4Ve5SjpbyhxuZAjc0hhNYJZbY5MPhFZYEjtquGBhgVzVLLtmbh/rQUVKpEZhSUen7uXVmFuHuEZ1+LeHhfWm1DfqWrnnbv57sFgSzW6JUUhFzZTM5ZopBjOWmdowTTIrsWa181Ngee/PEQfj3knOnLQh7VYOZHT8JQbjZk2B0OocOiPsxFN3gl8F33y0caOUWV+HzbGXSIChS2qV3X3f0opdiRqZ54oKE0/EYV+ISQCQDeBKAH8BGl9KWGuI+SucBiMggVlTl+7A6KEH/nK/A1h4WOEBhFwzk1DT+nqBI5RZXCsFcJq0258j741V7BSfiCh0yWJwp9qyS+NBgxQWaDpPNjmpEwCrE7BOdyQZnnpFj38M4ugEtN8OL1PYTfWQUVbu3KniittuEjmSYNQAgxVUJszw/xN6p+14EvrMPCiV0U94m1N6VAjIHJ4ci6VI6LJZ47BrmT31+hGq075jQPSDR8BSfjGlkqBqVQTEZqm2BB4NsdFMculAhzDmwOz+ai2qLmX7LZqeDH8iUwwVNUGKAsZJWcql9uOyNRIO75zPdV+eSRfC5lsTsaJC9sY4Zl6gG8A2AigK4AZhJCujbEvf6333XRB53OORzz54WRzUGFipAQ5i+EInrLyr05gukBcE5AUsOdQ4tNspEjjgipb8Rl9wVxI3eIGr0Q+eSgitFESii9k4Ui55mv30TOE98fQIWCA95bbA7qNiGY3MnKEDvulAZvUUF+KKm0CaGS7pBPaDt22X3HXlxpxTHe3KGktaoxuYd0Ru6dw5MRLQq9tNkpbn5/m+S32U3UTF14USUnkdXu1PC9mejE0OsIzB4cvkrvStyRMuR1Uh404Q0D3ZizgJZhwx8IIINSeppSWgPgGwDXNvhNk7mY8GqrQ7AnR/L2RrudChqQ1e5wsdf5SoabCSmA8iQdhqcevzaEBbiPz6/tsoNi34NNFAVSUWPHx5szcbGk2u1ohrHpZH6DZAbsEhsk+c1CPGtDldXuVmiqaaLfihyw81ccwDaZkzUq0A/FlVYXHwqjV9tQn8rJvnW78ABk5JUJwtCXSWbyuHqzUQeLyfnuHJRKfDI1dkeDpV9Ww+aggua7/bT3uRh1OiIoenLYSl1VVgfmDE3CR7f3F/aJHb8AMHtIott2XF80VJROY5p04gGIwxCyAQySH1TX5Gly59yg0HLsALD34GEheVrRBa4YI5esR2IwP4mqshrrD9feVgxACMkcEW/AphzXSpF9Ub2CVpR71vR8pS4zSr0lfcMGnMl2Cr3nVh0BAMT7e773bR/vaJAy6a1Sx+7mA5wvw9/gnKLvLfmXi3EK6s7Bo5me68zBnGLM+GCbZFtJfg4qrXY8+78jLsePSzQgPqgC+/nWUlrJaZBzu5vwa6YVueWupoxwox1RYToEG6shdrkfPu19nb50UXpsbvZZOC47Bfonv+2Wn4JL+U4NODlEh8zi+hdUgUagjK9OZeWV0Ns4H8SbPigse09fwPkyZRPQnKU7cVMnI8qqbcjPzcFJq2v2UUbl5cZZMnL/wcPoE1p9RSdPUzJJuXyB+kie9jfrcSGiZnC/3vjP3u1I6dgJl8utwOFjGNAzFd+d4ITzmRK+guoMqLBTAHW3SaYkJmBTTpbLdrvBDEA5700NMQHwnBY1/fE0pL2aXqfy3dAvod4WeBg6fAR+yN0P5EobSZuYaCEJHGNgcrhbR5Ucs1GnmppBryOqNtzIiAjgktN5yDrflQ+MwMQ3N6neLyXKIqQRMOoJruoUjbOXyxHdJgLIUg4jpeYgBPmVqcaNq9G7ayesPHlYcV98QgK6x4UAh7jw1xoHMKVXHJ6e2QeH3v8LuQrv8FSxAya9DuO7xwAXnUJpf7739bl7pxT875TTlNKlYwo6xwbj3f1cx7zurOszJsS3AXK4nik0JBgort0ouX2URTU0NDrUgjJ+n8FkQkiIv8/3URP2jO9OcD1Kp5RkDOwYAexUTj8yoEcqvjuuHE0FAPGhyqbhxIgAnLnk/Roc7Tt2QmBl5hWdPC0bQFvR7wQADdJd/n1MJ6F3YbbkGhuXRMpk0Ckmrqq2OXyyCbpDbXLIWTdJzs4Xe5cDO7AO5gnGjAFtPR/kJU//dEgxiZzSBDHxnAVv+P7eoYgNVk4h7M5HwCIr5OfGhfjjw9v748cHhrmc8/5t/fDFXc4B539n9UOgnx5VVoeQV0iJvWeLJIuMzBmapHos4+XpPZAiivKQQylglJlKWDijOzNdjd0Boy85QmTI65bFz4A2IeopnAHA3+g8x1CHewPq705cBpudIjzApHgcAEQGSvcppX9wh59RB4NKwkAAiAhUv/f/HhyO/7uqveK+l6f39Or+7B20BBv+TgAdCSHJhBATgBkAfm6IG+l0BK+M9MejYzshJZprWNU2B85cqkBCmL9imta62Mwmdo+VOITEdk9fkduf5dTl2gwlYQy4z+Coxso9OYqZRpUav1LmTHeE+BsRF6oscIL4sjKTnBg2gaZDtFSomgw6jO0ag94K9nE/g07yXgL9DDAb9Th7uUIxikWc5E4ctulNgrAuscHo2ibY7TEm2WQeE79+QoCH7+8u4sYT8u/vZ9B7FPjieHFvImHcMamHchrnTjHONmG1O1Btc6gmIvzl4RGS33ePSPZpIXeLn0G1fQBAmJvORq8jqv6iuBB/l23yDm7l/UPxxAQuHfYVP9OWUmoD8CCAteASpH9HKVUe09YDUQE6PHR1R5gNnCZYUmXDqfwypEQF+qSJDOsQgaV3DMDoLtGqx8wc2E6SwKzaZscN/RJqVe4PRQ4jJTxFGniDWq4PbyNrvKFfYpjLNk9x0HIsfgYMTFaeu8CiQ5Rex6JJqfjyrkEY3lG6foG7hu9n0EsaeqgHh7e8M0mJ4joZNROU5F5GneL12WehlLpomaxs7r7/u7P6eowUExNuMeH3v48UfsuFlZ9BhyCzETMHtlO9RmWN83mV6tWSG7zTbAG4zFhVwuagqLTaXXLuM6KDzC6/fVFkQv2NQucqho0W3UUlGfVEUbADrnVvZKcoPDu1m2Sbn0EnKBItQcMHpfQXSmknSmkKpfT5xrgni5N/a91JnMzjBL6nxF1ivrhzEEZ1jsbbt/RRPcZk0CHEXyTwrQ6fFgP/bK5zQRJ32gUgXdVnQrdY3DpYvTGqodawvGkY3kaP9GnnFPhjUrnOcs6wJEzqEYu5w5Ilx8qFJyPcYsLj4zop7nPX8MIsJgzrEOkiwNx9dz+jTjKnonNMEB4dq3xvQDpaCfQz4OcHh2PbwqsFs+C88a4LlzAMOp3iYjvsmShcTTqsXrirH+0iArxeRH1Qcjh2Lh6DjiLtOdBP2gkxIXX3COn3EiNOtKf0fsXtghEWYMSTk1Px9d2D8dDoDgC4dReUUlBsemIUZg1yLmJUbXOgssaOhDCpYL19SCJW3j/U5fxre8e5lGtSD4VFT0TlVTLpMDOtuzpk0OsQqzAiuq5PPOSXVOqw/Ax6GPQ66HWkwWbatsjUCmLkDat9lMVFw+8v0kYfGSNdNYed7y5Ln8mgw5AUpyZabXOoxmcrMUiUTtiX4eebM3sLQ0x35ZOjZqMM4bVOs1GHTU+MUjzG7GX5xPHbH9zWHz8/OAyjOkfjv7P6oV24tLJfrTB6YuerrYrFNF1580uMcC5p6ItmFx3kJxGmOh1BdLDZxQ8wIImrK2KH8Xf/NwQWPwNiQ8y4wOes75kQghX3uQogQN3WzezDOkJcbPGsbEpJ/Rhmo15xopUS7RUUH5ZVk8FMX/L6Yjbq0J4f0awXJfhTMumw4xZPShVCOOcOS8ZdI9pjSEoEeiRw94gLNQud5TWiFbpiQ8zoEB2IpeMDMG98Z9gdFEdySxAR6CcJCR3VJRp927mOKonsXXaLC8Z/Z/VTfCcAJ/Dlna0YvY4ICsvT10inERFwYbHPTOmK6X25Ef6jYzvh9Zt7S97hu7P6upwLOOdbmPS6lqHhNwdig80uQqRzbJBgE+4UE4QV9w1xOc/depkmvQ5PTnZ+5GqbHRU+aPh+BmdDM8q0b/EKWNf3jQcArH1kJLYsGA0/g16oJPLGKpzTJ95lm5rAYQLynpEpaBsegKev6Ypld0iXWPTGThsV5CcxWeh0BD0TnCMD+fufwi8b1yU2CJkvTsIDo1Lw+Z0uEbsS2DsTF2dCt1h8f69TyPri74gJNiu+F7mGyuYXiAV+1zinPZ6ZxdpHBaJ7fLCiT4YJWrl55q7h7TFzYDs8fHVHlzwrzKbvToM3G/Wqtt9Xb+yF56Z1Vz0XAIJEGn7WS5MRw3d28k/+zJRu+PsY19GPkvbbIToIfy0cjbtGJAvvTqwFs3oypmsMhqZE4u9jOuH5ac6Z1qyjI4TAIjI5Bhj1krYxKNmpNMkRhycrTaITTziLCvJzaYNidITgqWtSsWPx1ZglG107KAUhBHcMS0Y873tiA0Hxu5nYo43inAA2ivAz6lpEHH6zINxicnFsGfU6IT7UbNShX2I4tiwYLeQX94SfQQeTQYdhHSKwYk82qm0ORAf7Fh3AMBl0eOWGnjDpdZjSKw5bMgpw+ydcWBwTtp1FQiSWtxmq2cfvTUtxWblL3jBNeq6CscbFZonOHc5pMjMHthVSu4YGGPH13YOxbGumZKZudJAf8kqrERnoh52Lx7h9RnmDYh1NQpg/CCGYN145XYEYJoTEjzKuW4wkKiM5ygJP3DY4EZ9vO6NqKmECv2ubYBzJLRHejdLiGQAnWHdmXhaG7GseGYm80ioMfN45s5KZmn59eCRGiUJs+yWGYTbvyJNr6qyTZAL/ycmpSIkORPtIC65awl3DbNCp5mRKjgyQ2NuVUIsAkwsfg44oCkW5MvDmjN4AgDZ8HWU5n8QCf0BSOP54dCRSogJBCMHDY9TXpRU7rO0OKig7dw5Pluyb3jdBWL4RkM7IlT9L5ouTQAjBuH05OJVXhrhQfxS6mTnLLcBDXHwFgDTXPqugzPTnjRmZKTFBZgM3ezfE4yk+0yoFvnxWq1FPhIbC7Kjxof4udrY/Hh2JgrIal0k0zAxjFuXoeWl6T4zvFuuS1TCtcxRGdIzCLQPbYfq7WzGsg9QpadLrcFN/Z9ikWBNQsvuxbTY7RZQ/QX6lVFCYDXpEB/lh5sB2iAryQ7jF5FL53r6lDw5kF8PqcODPY3mQyz5xOGVMsBlDUiKwOSNfIvBfu6k3bv14uyRKZWqvOMVoCrmJICnSgndu6YvhPiwSz4RL1wi9kGpAbg4Thz6qOQ+fm9bdreb7yJiOuOWj7ejVNgRHcksEzVvNdBIZ6IeJsmgTsXB44+beCOXNcMmRFvz295EI8TfC5qCS7yvX5JnJjm1vFx6AUZ2lpjC5SWdMagz+4PO9B5uNqLa5n82tNmtWHplCKaCXfUPm8/hDlF9+ZMcoxevJzWQdot1HpjECRKPYCyVVwveWj8z+fVMvicAXwzT8CIsJl8prhJH7tb2dI2Elk45a8NW/pnWH1e7Aj/vOo12405woOOD5394EirD337VNMA7nFOPGuPrPptMoAp8QciOAZwGkAhhIKfU921A9EW4xSRYlADjtiTUUd3bfDtFBSIxwNkT5JIsxqTG4ZRA3JA82GzG1V5yLwB/ZMUrQnMUhZP+a1h2f/ZXlYjoS/3qQd3CJYc6rGrsDS66y4H95YVixJxsvT++B5buyER3shx0yjTu7UDpySYq0YFy3WGHdV7k5IUrUQG/sz5uteBvjrEHtcO9VKYIgEjsz35qp7OgOVnDkTe6pHJKnBhvO6wkwomMkNp0sUNTSA/0MKKu2oUus+zBIMeKGO7RDJLJemow3/+BmdTKNv39SGHL2+Z7np2OM1EEtDjkUIw9hZWu5MqVCKTzTbNRLOor+SWHYd64QBWU1CDIbXeq9HDUtNNxiwqq/Dcc1/9kMgF9HQjaSWCuK9mEofWcAaCt6v74gNtHllVYLJjG1jkrJnMZG9xueGKWa3dadSUfOrYM5h/IdskAEttgR9UnD557jmp5x6BhdCget/2lKjaXhHwJwPYD3G+l+iux5aizMRr1LlEqgn0FI/erJ0SfuqTvFBCKnqFLQNEwGHV64zml/FAvvEH8jiiutqpOybh2cKFQeMewS/RLDFAWaPCrgxet7YNGkLogI9MPNA5QjeEyy67BnYjHX8tCyab3jsPFEPp6d2k3QQoPMTsHXNjxA6Pi8SWAoj7DwluX3DkFmfjkGJIcLawcQcNoaAEWBxmzt8vc+d1gyyqpd0z+se+wqRFpczXHs/FB/I9Y8MgLxof74aZ/vDdLb3DMjO0VhztAkfPpXFih1avj/vLY7TJUFGJriGq6q1xGhHj85ORV3DEvGzszLWHcsD4Fmg8QUqIQ7odQ9PgRzhiZh2dYs1NgcHuP99zw11uV6z07pirOXK2udcE2cn+m1m3oJKZaVAh0O/2O8YjQa0/AD/Qyqbd3Ij14m92zjkkvHW9ijCyYdL3xfTF5M6RWHKb2A9PTa3dsdjSLwKaVHAfeOz8aAaYVyW6O4R/ck8MXP8ObMPthx+rJgo3THrEHt8N/0Uz7HundtE4IusUFYNClVcT/rBHolhACwwWTQIcLDjNboYDM+nTsQs3nfALvG7UOS0CbEH+O7xUiODzIbXeYH3JvWHtHBfri2FzcUZtE73kyu6hgdiMk92mD1wVyJI84TA5LCMSCJc84JpiMCLJiYissVVsW5EszWLhe0T09RTtSqNgN2aq84vL/hFGYMbIcO0YGqq1ipwVJBxKnEj8sx6nV4dmo3bDiRj8yCckHgh1tMmJpiUl0847WbeuGNP05i9tAk6HUEb87sg9P5ZUK9fv667qq56PWE4L1b+yIhTFkDZ4LVand4jCIRO1QZc2RasDv+fOwql1z+SZHOcvVMCBXSe4uDHhhqyfvcZT5l6HQEOxZdjdAAkyDwp/aKw9vrM9xOvBIj5Oynzms2B5qdDb+uyTOF5bgAAAhcSURBVNMYZWVlLuey32dKpEO5kFJnnpQ9O/6CWWHihRJ7tm2BAUB63lHVY/pE6xFmJhjgl4t5/c2wXD6J9HT3C5PIWdAbKM3cj/RM5f1vjw6AUW9FWVl5rd7Xzh3bcMrM+yEAbNjgPu8+ow2AjXxu9HIrn3XUavOqDDfGA2Mj/WHUEa/LLD4ulnfEdQyowbG92zC3PXB0zzbIv4SdN3Hs2bkdmea6BaW9PtKE7CO7kC3Ld+ZN+Z8c5IeiaoodWzf7dE97NTdyOnnsMNIvHwegXLfF5bgzBdiySbqwDatyJ85yo5rc8+eRni7N4Llx4waYARQUAOkKecmqCrhz886dRrlR2kbU2lpdOc9/0LKyMhzZzfnOxicakJ6ejtJS7t2cyTyFdIf7VdoYNXZHrcrW13Qe744JwN4dysuFysnM5EabZ86cRXq6NM+U0v0/GR/gsl3tO9eFehP4hJA/ACjNaFhMKf3J2+vUR/I0gHupwrlrVgOA8Hvv2UJg61b0SgjBTw8OBwDM38QdM/7qNM8jEdn13CE+RDmyvf6QPLM38M8xYtgwn3OOyLE7KJL3b8Bj4zohrWdcna7lgsL7TgNw5zTPz2xn544YjjAFrbOu5erTLhRpaa65eeSk1fIWMce24mxpIQb07Y1hvFPb5Zl9qI85288ARw6hTVwc0tJ6+HT+SAfF0CMXMa5rDAgBevTIw9CUCFCIRsY+lMUX2DNnXkWF9hl4YBNQUoIeXTsjTcV8KS4TAIzqHIW0tIHqx6qcO3qUb633hO4UcPIYkpLaIS2ti3Atl/ri5vo+t2cvqDeBTyl1H4vXhIizIAJcjDQAPDjaGQL2wW39sOpAbpObnZqCuia9AjiTxfrH0+pemAZCzXdSF/5aOFpxJml9wpyzvixk4o4+bbnJSWz2sy/odAQTujt1urFdY1yOWfW34XXK5+MJcftk5hIlk44Sfzx6Va39R75y2+AkZBdW4t6rUoRtG+alCWtxMK7qFKU6h6YhaHYmnYZg7SMjJc7EEH8jsl6aLDlmXLdYjOumPuVazNI5A4RJKS0Bb3KYXOnIHdX1gTe+m7qS2iYYG07ke0ya5i1d44KR8fxE1RnMdaV7fAMEj6vAmrS3jvD2kZZGs6X7m/T457XScF+2praYT+f6MNqoBxplpi0h5DpCSDaAIQBWE0LWNsZ9GQa9zqeUBZ4Y1SVaMrvySsdT/p4rGZZ9sKEEXEPz2LhO+Hh2fwxur5xErjZcqe9CDnPcexq9fX/vEMwdllxrYe8pkd6VRGNF6fwA4IfGuJeG79SHSachua5PfK019PvTOuD+NNf5C1cKRr0OV6e6mk40nHhSWPonhaN/knrqBXdsemJUnZbIbG60nCfRqDW+ZA9tCl6/uXdTF6FZ89y07ghsRDtwc2Fct1gcu1DqVYx7bantJLHmiibwNVqlo7olcZvChL3WwCNXd8TApHBJploN97QMY55GrRCnEtbQuNLQ6QiGd4zUFBYf0DT8VszK+4Yiu9D3fDAaGhpXJprAb8VEBPp5TMOg0fLZtvBqFFa4T6ym0TLQBL6GRisnNsSsuDSfRsuDUC+SXTUVhJB8AGc8HqhMJICCeizOlYD2zK0D7ZlbB7V95kRKqeJiBM1a4NcFQsguSml/z0e2HLRnbh1oz9w6aIhn1qJ0NDQ0NFoJmsDX0NDQaCW0ZIH/QVMXoAnQnrl1oD1z66Den7nF2vA1NDQ0NKS0ZA1fQ0NDQ0OEJvA1NDQ0WgktTuATQiYQQo4TQjIIIQuaujwNDSGkLSFkPSHkKCHkMCHk4aYuU2NBCNETQvYSQlY1dVkaA0JIKCHke0LIMf57D2nqMjU0hJC/8/X6ECHka0JIi5whRgj5hBCSRwg5JNoWTgj5nRBykv8/rK73aVECnxCiB/AOgIkAugKYSQjp2rSlanBsAB6jlKYCGAzggVbwzIyHAZd1y1sybwJYQyntAqAXWvizE0LiATwEoD+ltDsAPYAZTVuqBmMZgAmybQsArKOUdgSwjv9dJ1qUwAcwEEAGpfQ0pbQGwDcArm3iMjUolNJcSuke/u9ScEIgvmlL1fAQQhIATAbwUVOXpTEghAQDGAngYwCglNZQSouatlSNggGAPyHEACAAwPkmLk+DQCndCOCybPO1AD7l//4UwLS63qelCfx4AOdEv7PRCoQfgxCSBKAPgO1NW5JG4Q0ATwBouBWzmxftAeQDWMqbsT4ihLguktqCoJTmAHgVwFkAuQCKKaW/NW2pGpUYSmkuwCl2AHxfeV5GSxP4SomxW0XcKSEkEMAKAI9QSkuaujwNCSHkGgB5lNLdTV2WRsQAoC+AdymlfQCUox6G+M0Z3mZ9LYBkAHEALISQW5u2VFc2LU3gZwNoK/qdgBY6BBRDCDGCE/ZfUkpXNnV5GoFhAKYSQrLAme1GE0K+aNoiNTjZALIppWz09j24DqAlMwZAJqU0n1JqBbASwNAmLlNjcpEQ0gYA+P/z6nrBlibwdwLoSAhJJoSYwDl4fm7iMjUohFvu52MARymlrzV1eRoDSulCSmkCpTQJ3Df+k1LaojU/SukFAOcIIZ35TVcDONKERWoMzgIYTAgJ4Ov51WjhjmoZPwOYzf89G8BPdb1gi8qHTym1EUIeBLAWnEf/E0rp4SYuVkMzDMBtAA4SQvbx2xZRSn9pwjJpNAx/A/Alr8ycBnBHE5enQaGUbieEfA9gD7hotL1ooSkWCCFfA0gDEEkIyQbwDICXAHxHCLkTXOd3Y53vo6VW0NDQ0GgdtDSTjoaGhoaGCprA19DQ0GglaAJfQ0NDo5WgCXwNDQ2NVoIm8DU0NDRaCZrA19DQ0GglaAJfQ0NDo5Xw/6TSTj9nU2+dAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 4 Axes>"
+       "<Figure size 1200x800 with 4 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -172,7 +187,7 @@
     "yr = np.array(yr).flatten()\n",
     "ys = np.array(ys).flatten()\n",
     "yc = np.array(yc).flatten()\n",
-    "fig, ax = plt.subplots(4, sharex=True)\n",
+    "fig, ax = plt.subplots(4, sharex=True, figsize=(12,8), dpi= 100, facecolor='w', edgecolor='k')\n",
     "ax[0].plot(t, yr,label='Ref System')\n",
     "ax[0].plot(t, yc, label='CL System')\n",
     "ax[0].set_title('Systems response')\n",
@@ -227,7 +242,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,
diff --git a/examples/notebook_example_2.ipynb b/examples/notebook_example_2.ipynb
new file mode 100644
index 0000000..83514fb
--- /dev/null
+++ b/examples/notebook_example_2.ipynb
@@ -0,0 +1,259 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## VRFT with measurement noise (no instrumental variables)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Copyright [2020] [Alessio Russo - alessior@kth.se]  \n",
+    "# This file is part of PythonVRFT.\n",
+    "# PythonVRFT is free software: you can redistribute it and/or modify\n",
+    "# it under the terms of the GNU General Public License as published by\n",
+    "# the Free Software Foundation, version 3 of the License.\n",
+    "# PythonVRFT is distributed in the hope that it will be useful,\n",
+    "# but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
+    "# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n",
+    "# GNU General Public License for more details.\n",
+    "# You should have received a copy of the GNU General Public License\n",
+    "# along with PythonVRFT.  If not, see <http://www.gnu.org/licenses/>.\n",
+    "#\n",
+    "# Code author: [Alessio Russo - alessior@kth.se]\n",
+    "# Last update: 08th January 2020, by alessior@kth.se\n",
+    "#\n",
+    "\n",
+    "# Example 2\n",
+    "# ------------\n",
+    "# In this example we see how to apply VRFT to a simple SISO model\n",
+    "# with colored measurement noise (no instrumental variables)\n",
+    "# Input data is generated using random normal noise\n",
+    "#"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import scipy.signal as scipysig\n",
+    "from vrft import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### System, Reference Model and Control law"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# System\n",
+    "dt = 1e-2\n",
+    "num = [0.5]\n",
+    "den = [1, -0.9]\n",
+    "sys = ExtendedTF(num, den, dt=dt)\n",
+    "\n",
+    "# Reference Model\n",
+    "refModel = ExtendedTF([0.6], [1, -0.4], dt=dt)\n",
+    "\n",
+    "# Control law\n",
+    "control = [ExtendedTF([1], [1, -1], dt=dt),\n",
+    "        ExtendedTF([1, 0], [1, -1], dt=dt)]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate signals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def generateNoise(t):\n",
+    "    # Generate colored noise\n",
+    "    omega = 2*np.pi*100\n",
+    "    xi = 0.9\n",
+    "    dt = t[1] - t[0]\n",
+    "    noise =  np.random.normal(0,0.1,t.size)\n",
+    "    tf = scipysig.TransferFunction([10*omega**2], [1, 2*xi*omega, omega**2])\n",
+    "    # Second order system\n",
+    "    _, yn, _ = scipysig.lsim(tf, noise, t)\n",
+    "    return yn\n",
+    "\n",
+    "# Generate input siganl\n",
+    "t_start = 0\n",
+    "t_end = 10\n",
+    "t = np.arange(t_start, t_end, dt)\n",
+    "u = np.random.normal(size = t.size)\n",
+    "\n",
+    "# Open loop experiment\n",
+    "t, y = scipysig.dlsim(sys, u, t)\n",
+    "y = y.flatten() + generateNoise(t)\n",
+    "\n",
+    "# Save data into an IDDATA Object with 0 initial condition\n",
+    "# Length of the initial condition depends on the reference model\n",
+    "data = iddata(y, u, dt, [0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### VRFT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loss: 0.3207682187262347\n",
+      "Theta: [-0.2482335   0.28201084]\n",
+      "Controller: ExtendedTF(\n",
+      "array([ 0.28201084, -0.2482335 ]),\n",
+      "array([ 1., -1.]),\n",
+      "dt: 0.01\n",
+      ")\n"
+     ]
+    }
+   ],
+   "source": [
+    "# VRFT Pre-filter\n",
+    "prefilter = refModel * (1 -  refModel)\n",
+    "\n",
+    "# VRFT method\n",
+    "theta, r, loss, C = compute_vrft(data, refModel, control, prefilter)\n",
+    "\n",
+    "#Obtained controller\n",
+    "print(\"Loss: {}\\nTheta: {}\\nController: {}\".format(loss, theta, C))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Verify performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Closed loop system\n",
+    "closed_loop = (C * sys).feedback()\n",
+    "\n",
+    "t = t[:len(r)]\n",
+    "u = np.ones(len(t))\n",
+    "\n",
+    "_, yr = scipysig.dlsim(refModel, u, t)\n",
+    "_, yc = scipysig.dlsim(closed_loop, u, t)\n",
+    "_, ys = scipysig.dlsim(sys, u, t)\n",
+    "\n",
+    "yr = np.array(yr).flatten()\n",
+    "ys = np.array(ys).flatten()\n",
+    "yc = np.array(yc).flatten()\n",
+    "fig, ax = plt.subplots(4, sharex=True, figsize=(12,8), dpi= 100, facecolor='w', edgecolor='k')\n",
+    "ax[0].plot(t, yr,label='Ref System')\n",
+    "ax[0].plot(t, yc, label='CL System')\n",
+    "ax[0].set_title('Systems response')\n",
+    "ax[0].grid(True)\n",
+    "ax[1].plot(t, ys, label='OL System')\n",
+    "ax[1].set_title('OL Systems response')\n",
+    "ax[1].grid(True)\n",
+    "ax[2].plot(t, y[:len(r)])\n",
+    "ax[2].grid(True)\n",
+    "ax[2].set_title('Experiment data')\n",
+    "ax[3].plot(t, r)\n",
+    "ax[3].grid(True)\n",
+    "ax[3].set_title('Virtual Reference')\n",
+    "\n",
+    "# Now add the legend with some customizations.\n",
+    "legend = ax[0].legend(loc='lower right', shadow=True)\n",
+    "\n",
+    "# The frame is matplotlib.patches.Rectangle instance surrounding the legend.\n",
+    "frame = legend.get_frame()\n",
+    "frame.set_facecolor('0.90')\n",
+    "\n",
+    "# Set the fontsize\n",
+    "for label in legend.get_texts():\n",
+    "    label.set_fontsize('large')\n",
+    "\n",
+    "for label in legend.get_lines():\n",
+    "    label.set_linewidth(1.5)  # the legend line width\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/notebook_example_3.ipynb b/examples/notebook_example_3.ipynb
new file mode 100644
index 0000000..3df2db5
--- /dev/null
+++ b/examples/notebook_example_3.ipynb
@@ -0,0 +1,270 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## VrInstrumental Variables Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Copyright [2020] [Alessio Russo - alessior@kth.se]  \n",
+    "# This file is part of PythonVRFT.\n",
+    "# PythonVRFT is free software: you can redistribute it and/or modify\n",
+    "# it under the terms of the GNU General Public License as published by\n",
+    "# the Free Software Foundation, version 3 of the License.\n",
+    "# PythonVRFT is distributed in the hope that it will be useful,\n",
+    "# but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
+    "# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n",
+    "# GNU General Public License for more details.\n",
+    "# You should have received a copy of the GNU General Public License\n",
+    "# along with PythonVRFT.  If not, see <http://www.gnu.org/licenses/>.\n",
+    "#\n",
+    "# Code author: [Alessio Russo - alessior@kth.se]\n",
+    "# Last update: 08th January 2020, by alessior@kth.se\n",
+    "#\n",
+    "\n",
+    "# Example 3\n",
+    "# ------------\n",
+    "# In this example we see how to apply VRFT to a simple SISO model\n",
+    "# with  measurement noise using instrumental variables\n",
+    "# Input data is generated using random normal noise\n",
+    "#"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import scipy.signal as scipysig\n",
+    "from vrft import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### System, Reference Model and Control law"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# System\n",
+    "dt = 1e-2\n",
+    "num = [0.5]\n",
+    "den = [1, -0.9]\n",
+    "sys = ExtendedTF(num, den, dt=dt)\n",
+    "sigma = 0.5  # measurement noise\n",
+    "\n",
+    "# Reference Model\n",
+    "refModel = ExtendedTF([0.6], [1, -0.4], dt=dt)\n",
+    "\n",
+    "# Control law\n",
+    "control = [ExtendedTF([1], [1, -1], dt=dt),\n",
+    "        ExtendedTF([1, 0], [1, -1], dt=dt)]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate signals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Function used to generate the data\n",
+    "def generate_data(sys, u, t, sigma):\n",
+    "    t, y = scipysig.dlsim(sys, u, t)\n",
+    "    y = y.flatten() + sigma * np.random.normal(size = t.size)\n",
+    "\n",
+    "    # Length of the initial condition depends on the reference model\n",
+    "    return iddata(y, u, dt, [0])\n",
+    "\n",
+    "# Generate input siganl\n",
+    "t_start = 0\n",
+    "t_end = 10\n",
+    "t = np.arange(t_start, t_end, dt)\n",
+    "u = np.random.normal(size = t.size)\n",
+    "\n",
+    "# To use IV we must perform 2 experiments on the plant using the same input\n",
+    "data1 = generate_data(sys, u, t, sigma)\n",
+    "data2 = generate_data(sys, u, t, sigma)\n",
+    "data = [data1, data2]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### VRFT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "------IV------\n",
+      "Loss: 0.9212933606914391\n",
+      "Theta: [-1.18142645  1.27724244]\n",
+      "Controller: ExtendedTF(\n",
+      "array([ 1.27724244, -1.18142645]),\n",
+      "array([ 1., -1.]),\n",
+      "dt: 0.01\n",
+      ")\n",
+      "------No IV------\n",
+      "Loss: 0.2898010354223657\n",
+      "Theta: [-0.35784632  0.45149128]\n",
+      "Controller: ExtendedTF(\n",
+      "array([ 0.45149128, -0.35784632]),\n",
+      "array([ 1., -1.]),\n",
+      "dt: 0.01\n",
+      ")\n"
+     ]
+    }
+   ],
+   "source": [
+    "# VRFT Pre-filter\n",
+    "prefilter = refModel * (1 -  refModel)\n",
+    "\n",
+    "# VRFT method with Instrumental variables\n",
+    "theta_iv, r_iv, loss_iv, C_iv = compute_vrft(data, refModel, control, prefilter, iv=True)\n",
+    "\n",
+    "# VRFT method without Instrumental variables\n",
+    "theta_noiv, r_noiv, loss_noiv, C_noiv = compute_vrft(data1, refModel, control, prefilter, iv=False)\n",
+    "\n",
+    "#Obtained controller\n",
+    "print('------IV------')\n",
+    "print(\"Loss: {}\\nTheta: {}\\nController: {}\".format(loss_iv, theta_iv, C_iv))\n",
+    "print('------No IV------')\n",
+    "print(\"Loss: {}\\nTheta: {}\\nController: {}\".format(loss_noiv, theta_noiv, C_noiv))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Verify performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Closed loop system\n",
+    "closed_loop_iv = (C_iv * sys).feedback()\n",
+    "closed_loop_noiv = (C_noiv * sys).feedback()\n",
+    "\n",
+    "t = t[:len(r_iv)]\n",
+    "u = np.ones(len(t))\n",
+    "\n",
+    "_, yr = scipysig.dlsim(refModel, u, t)\n",
+    "_, yc_iv = scipysig.dlsim(closed_loop_iv, u, t)\n",
+    "_, yc_noiv = scipysig.dlsim(closed_loop_noiv, u, t)\n",
+    "_, ys = scipysig.dlsim(sys, u, t)\n",
+    "\n",
+    "yr = yr.flatten()\n",
+    "ys = ys.flatten()\n",
+    "yc_noiv = yc_noiv.flatten()\n",
+    "yc_iv = yc_iv.flatten()\n",
+    "\n",
+    "fig, ax = plt.subplots(4, sharex=True, figsize=(12,8), dpi= 100, facecolor='w', edgecolor='k')\n",
+    "ax[0].plot(t, yr,label='Ref System')\n",
+    "ax[0].plot(t, yc_iv, label='CL System - IV')\n",
+    "ax[0].plot(t, yc_noiv, label='CL System - No IV')\n",
+    "ax[0].set_title('CL Systems response')\n",
+    "ax[0].grid(True)\n",
+    "ax[1].plot(t, ys, label='OL System')\n",
+    "ax[1].set_title('OL Systems response')\n",
+    "ax[1].grid(True)\n",
+    "ax[2].plot(t, data1.y[:len(r_iv)])\n",
+    "ax[2].grid(True)\n",
+    "ax[2].set_title('Experiment data')\n",
+    "ax[3].plot(t, r_iv)\n",
+    "ax[3].grid(True)\n",
+    "ax[3].set_title('Virtual Reference')\n",
+    "\n",
+    "# Now add the legend with some customizations.\n",
+    "legend = ax[0].legend(loc='lower right', shadow=True)\n",
+    "\n",
+    "# The frame is matplotlib.patches.Rectangle instance surrounding the legend.\n",
+    "frame = legend.get_frame()\n",
+    "frame.set_facecolor('0.90')\n",
+    "\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/setup.py b/setup.py
index 94a34a4..d4acbe1 100644
--- a/setup.py
+++ b/setup.py
@@ -6,7 +6,7 @@
 
 
 setup(name = 'pythonvrft',
-    version = '0.0.4',
+    version = '0.0.5',
     description = 'VRFT Python Library',
     long_description = long_description,
     long_description_content_type = 'text/markdown',
diff --git a/vrft/__init__.py b/vrft/__init__.py
index 3ea1804..2a3bfaa 100644
--- a/vrft/__init__.py
+++ b/vrft/__init__.py
@@ -18,3 +18,6 @@
 from .extended_tf import *
 from .utils import *
 from .vrft_algo import *
+
+__version__ = '0.0.5'
+__author__ = 'Alessio Russo'
\ No newline at end of file
diff --git a/vrft/extended_tf.py b/vrft/extended_tf.py
index 4cdd171..cf88729 100644
--- a/vrft/extended_tf.py
+++ b/vrft/extended_tf.py
@@ -76,7 +76,7 @@ def __add__(self,other):
         if type(other) in [int, float]:
             return ExtendedTF(polyadd(self.num, self.den*other), self.den, dt=self._dt)
         if type(other) in [TransFun, type(self)]:
-            if np.all(self.den == other.den):
+            if len(self.den) == len(other.den) and np.all(self.den == other.den):
                 numer = polyadd(self.num, other.num)
                 denom = self.den
             else:
@@ -88,7 +88,7 @@ def __sub__(self, other):
         if type(other) in [int, float]:
             return ExtendedTF(polyadd(self.num, -self.den*other), self.den, dt=self._dt)
         if type(other) in [TransFun, type(self)]:
-            if np.all(self.den == other.den):
+            if len(self.den) == len(other.den) and np.all(self.den == other.den):
                 numer = polyadd(self.num, -other.num)
                 denom = self.den
             else:
@@ -100,7 +100,7 @@ def __rsub__(self, other):
         if type(other) in [int, float]:
             return ExtendedTF(polyadd(-self.num, self.den*other), self.den, dt=self._dt)
         if type(other) in [TransFun, type(self)]:
-            if np.all(self.den == other.den):
+            if len(self.den) == len(other.den) and np.all(self.den == other.den):
                 numer = polyadd(self.num, -other.num)
                 denom = self.den
             else:
diff --git a/vrft/iddata.py b/vrft/iddata.py
index 82000bd..77b7b1d 100644
--- a/vrft/iddata.py
+++ b/vrft/iddata.py
@@ -19,6 +19,8 @@
 
 
 import numpy as np
+import scipy.signal as scipysig
+from vrft.utils import filter_signal
 
 class iddata(object):
     """
@@ -26,9 +28,9 @@ class iddata(object):
      It is used to save input/output data.
     """
 
-    def __init__(self, y: np.ndarray = None,
-                 u: np.ndarray = None,
-                 ts: float = None,
+    def __init__(self, y: np.ndarray,
+                 u: np.ndarray,
+                 ts: float,
                  y0: np.ndarray = None):
         """
         Input/output data (suppors SISO systems only)
@@ -40,20 +42,29 @@ def __init__(self, y: np.ndarray = None,
             Input data
         ts: float
             sampling time
-        y0: np.ndarray
+        y0: np.ndarray, optional
             Initial conditions
         """
+        if y is None:
+            raise ValueError("Signal y can't be None.")
+        if u is None:
+            raise ValueError("Signal u can't be None.")
+        if ts is None:
+            raise ValueError("Sampling time ts can't be None.")
+
         self.y = np.array(y) if not isinstance(y, np.ndarray) else np.array([y]).flatten()
         self.u = np.array(u) if not isinstance(u, np.ndarray) else np.array([u]).flatten()
         self.ts = float(ts)
-        self.y0 = np.array(y0) if not isinstance(y0, np.ndarray) else np.array([y0]).flatten()
-
 
-        if self.y0.ndim == 0:
-            self.y0 = np.expand_dims(self.y0, axis=0)
+        if y0 is None:
+            raise ValueError("y0: {} can't be None.".format(y0))
+        else:
+            self.y0 = np.array(y0) if not isinstance(y0, np.ndarray) else np.array([y0]).flatten()
+            if self.y0.size == 0 or self.y0.ndim == 0:
+                raise ValueError("y0 can't be None.")
 
 
-    def checkData(self):
+    def check(self):
         """ Checks validity of the data """
         if (self.y.shape != self.u.shape):
             raise ValueError("Input and output size do not match.")
@@ -65,10 +76,38 @@ def checkData(self):
             raise ValueError("Sampling time can not be negative.")
 
         if (self.y0 is None):
-            return True
+            raise ValueError("Initial condition can't be zero")
 
         return True
 
     def copy(self):
         """ Returns a copy of the object """
         return iddata(self.y, self.u, self.ts, self.y0)
+
+    def filter(self, L: scipysig.dlti):
+        """ Filters the data using the specified filter L(z) """
+        self.y = filter_signal(L, self.y, x0=self.y0)
+        self.u = filter_signal(L, self.u)
+        return self
+
+    def split(self) -> tuple:
+        """ Splits the dataset into two equal parts
+            Used for the instrumental variable method
+        """
+        n0 = self.y0.size if self.y0 is not None else 0
+        n = self.y.size
+
+        if (n + n0) % 2 != 0:
+            print('iddata object has uneven data size. The last data point will be discarded')
+            n -= 1
+
+        # First dataset
+        n1 = (n + n0) // 2 # floor division
+        d1 = iddata(self.y[:n1 - n0], self.u[:n1 - n0], self.ts, self.y0)
+
+        # Second dataset
+        d2 = iddata(self.y[n1:n], self.u[n1:n], self.ts, self.y[n1 - n0:n1])
+
+        return (d1, d2)
+
+
diff --git a/vrft/utils.py b/vrft/utils.py
index 48a23d3..e8c931a 100644
--- a/vrft/utils.py
+++ b/vrft/utils.py
@@ -19,7 +19,6 @@
 
 import numpy as np
 import scipy.signal as scipysig
-from vrft.iddata import iddata
 from typing import overload
 
 def Doperator(p: int, q: int, x: float) -> np.ndarray:
@@ -101,51 +100,47 @@ def systemOrder(num: np.ndarray, den: np.ndarray) -> tuple:
 
     return (np.poly1d(num).order, np.poly1d(den).order)
 
-
-def filter_iddata(data: iddata, L: scipysig.dlti) -> iddata:
+def filter_signal(L: scipysig.dlti, x: np.ndarray, x0: np.ndarray = None) -> np.ndarray:
     """Filter data in an iddata object
     Parameters
     ----------
-    data : iddata
-        iddata object containing the data to filter
     L : scipy.signal.dlti
-        Transfer function used to filter the data
-
+        Discrete-time rational transfer function used to
+        filter the signal
+    x : np.ndarray
+        Signal to filter
+    x0 : np.ndarray, optional
+        Initial conditions for L
     Returns
     -------
-    data : iddata
+    signal : iddata
         Filtered iddata object
     """
     t_start = 0
-    t_step = data.ts
-    t_end = len(data.y) * t_step
+    t_step = L.dt
+    t_end = x.size * t_step
 
     t = np.arange(t_start, t_end, t_step)
+    _, y = scipysig.dlsim(L, x, t, x0)
+    return y.flatten()
 
-    if (L != None):
-        t, y = scipysig.dlsim(L, data.y, t)
-        t, u = scipysig.dlsim(L, data.u, t)
-        return iddata(y[:, 0], u[:, 0], data.ts, data.y0)
-    return data
-
-def deconvolve_signal(T: scipysig.dlti, x: np.ndarray, dt: float) -> np.ndarray:
-    """Deconvolve a signal x using a specified transfer function T
+def deconvolve_signal(L: scipysig.dlti, x: np.ndarray) -> np.ndarray:
+    """Deconvolve a signal x using a specified transfer function L(z)
     Parameters
     ----------
-    T : scipy.signal.dlti
+    L : scipy.signal.dlti
         Discrete-time rational transfer function used to
         deconvolve the signal
     x : np.ndarray
         Signal to deconvolve
-    dt: float
-        Sampling time
 
     Returns
     -------
     signal : np.ndarray
         Deconvolved signal
     """
-    impulse = scipysig.dimpulse(T)[1][0].flatten()
+    dt = L.dt
+    impulse = scipysig.dimpulse(L)[1][0].flatten()
     idx1 = np.argwhere(impulse != 0)[0].item()
     idx2 = np.argwhere(np.isclose(impulse[idx1:], 0.) == True)
     idx2 = -1 if idx2.size == 0 else idx2[0].item()
diff --git a/vrft/vrft_algo.py b/vrft/vrft_algo.py
index 1912963..0ea54d5 100644
--- a/vrft/vrft_algo.py
+++ b/vrft/vrft_algo.py
@@ -19,9 +19,10 @@
 
 from vrft.iddata import iddata
 from vrft.utils import systemOrder, checkSystem, \
-    filter_iddata
+    filter_signal
 import numpy as np
 import scipy.signal as scipysig
+import scipy as sp
 
 def virtualReference(data: iddata, num: np.ndarray, den: np.ndarray) -> np.ndarray:
     try:
@@ -34,16 +35,17 @@ def virtualReference(data: iddata, num: np.ndarray, den: np.ndarray) -> np.ndarr
     if (N == 0) and (M == 0):
         raise ValueError("The reference model can not be a constant.")
 
-    data.checkData()
+    data.check()
     offset_M = len(num) - M - 1
     offset_N = len(den) - N - 1
 
-    lag = N - M  #number of initial conditions
+    lag = N - M  # number of initial conditions
+    y0 = data.y0
 
-    if (lag > 0 and data.y0 is None):
-        raise ValueError("Wrong initial condition.")
+    if y0 is None:
+        y0 = [0.] * lag
 
-    if (lag != len(data.y0)):
+    if y0 is not None and (lag != len(y0)):
         raise ValueError("Wrong initial condition size.")
 
     reference = np.zeros_like(data.y)
@@ -60,7 +62,7 @@ def virtualReference(data: iddata, num: np.ndarray, den: np.ndarray) -> np.ndarr
             index = k + i - N
             if (index < 0):
                 left_side += den[offset_N +
-                                 abs(i - N)] * data.y0[abs(index) - 1]
+                                 abs(i - N)] * y0[abs(index) - 1]
             else:
                 left_side += den[offset_N + abs(i - N)] * (
                     data.y[index] if index < L else 0)
@@ -86,46 +88,67 @@ def virtualReference(data: iddata, num: np.ndarray, den: np.ndarray) -> np.ndarr
 
 def compute_vrft_loss(data: iddata, phi: np.ndarray, theta: np.ndarray):
     z = np.dot(phi, theta.T).flatten()
+    # import pdb
+    # pdb.set_trace()
     L = z.size
     return np.linalg.norm(data.u[:L] - z)**2 / L
 
-def calc_minimum(data: iddata, phi: np.ndarray):
-    phi = np.mat(phi)
-    nk = phi.shape[1]
-    #least squares
-    theta = np.linalg.inv(phi.T @ phi) @ phi.T
+def calc_minimum(data: iddata, phi1: np.ndarray,
+                 phi2: np.ndarray = None):
+    """Compute least squares minimum
+    Parameters
+    ----------
+    data : iddata
+        iddata object containing data from experiments
+    phi1 : np.ndarray
+        Regressor
+    phi2 : np.ndarray, optional
+        Second regressor (used only with instrumental variables)
 
-    L = theta.shape[1]
-    theta = np.array(np.dot(theta, data.u[:L])).flatten()
-    return theta, phi
+    Returns
+    -------
+    theta : np.ndarray
+        Coefficients computed for the control basis
+    """
+    phi1 = np.array(phi1)
+    L = phi1.shape[0]
+    if phi2 is None:
+        theta, _, _, _ = sp.linalg.lstsq(phi1, data.u[:L], lapack_driver='gelsy')
+    else:
+        phi2 = np.array(phi2)
+        theta = (np.linalg.inv(phi2.T @ phi1) @ phi2.T).dot(data.u[:L])
+    return theta.flatten()
 
 def control_response(data: iddata, error: np.ndarray, control: list):
-    t_start = 0
     t_step = data.ts
-    t_end = len(error) * t_step
-    t = np.arange(t_start, t_end, t_step)
+    t = [i * t_step for i in range(len(error))]
 
-    phi = np.zeros((len(control), len(t)))
-    for i in range(len(control)):
-        t, y = scipysig.dlsim(control[i], error, t)
-        phi[i, :] = y[:, 0]
+    phi = [None] * len(control)
+    for i, c in enumerate(control):
+        _, y = scipysig.dlsim(c, error, t)
+        phi[i] = y.flatten()
 
-    phi = phi.T
+    phi = np.vstack(phi).T
     return phi
 
 def compute_vrft(data: iddata, refModel: scipysig.dlti,
-                 control: list, prefilter: scipysig.dlti = None):
+                 control: list, prefilter: scipysig.dlti = None,
+                 iv: bool =  False):
     """Compute VRFT Controller
     Parameters
     ----------
-    data : iddata
-        iddata object containing data from experiments
+    data : iddata or list of iddata objects
+        Data used to identify theta. If iv is set to true,
+        then the algorithm expects a list of 2 iddata objects
     refModel : scipy.signal.dlti
         Discrete Transfer Function representing the reference model
     control : list
         list of discrete transfer functions, representing the control basis
     prefilter : scipy.signal.dlti, optional
         Filter used to pre-filter the data
+    iv : bool, optiona;
+        Instrumental variable option. If true, the instrumental variable will 
+        be constructed based on two iddata objets
 
     Returns
     -------
@@ -133,30 +156,78 @@ def compute_vrft(data: iddata, refModel: scipysig.dlti,
         Coefficients computed for the control basis
     r : np.ndarray
         Virtual reference signal
-    phi: np.ndarray
-        Toeplitz matrix used to compute theta
     loss: float
         VRFT loss
     final_control: scipy.signal.dlti
         Final controller
     """
+    if not isinstance(data, iddata):
+        if not isinstance(data, list):
+            raise ValueError('data should be an iddata object or a list of iddata objects')
+        else:
+            if iv and len(data) != 2:
+                raise ValueError('data should be a list of 2 iddata objects')
+
+            for d in data:
+                if not isinstance(d, iddata):
+                    raise ValueError('data should be a list of iddata objects')
 
     # Prefilter the data
     if prefilter is not None and isinstance(prefilter, scipysig.dlti):
-        data = filter_iddata(data, prefilter)
-
-    # Compute virtual reference
-    r, n = virtualReference(data,
-                         refModel.num,
-                         refModel.den)
+        if isinstance(data, list):
+            for i, d in enumerate(data):
+                data[i] = d.copy().filter(prefilter)
+        else:
+            data = data.copy().filter(prefilter)
+
+
+    if not iv:
+        if isinstance(data, list):
+            data = data[0]
+        data.check()
+
+        # Compute virtual reference
+        r, n = virtualReference(data, refModel.num, refModel.den)
+
+        # Compute control response given the virtual reference
+        phi = control_response(data, np.subtract(r, data.y[:n]), control)
+
+        # Compute MSE minimizer
+        theta = calc_minimum(data, phi)
+    else:
+        # Retrieve the two datasets
+        if isinstance(data, list):
+            d1 = data[0]
+            d2 = data[1]
+            d1.check()
+            d2.check()
+            # check if the two datasets have same size
+            if d1.y.size != d2.y.size:
+                raise ValueError('The two datasets should have same size!')
+        else:
+            raise ValueError('To use IV the data should be a list of iddata objects')
+
+        r1, n1 = virtualReference(d1, refModel.num, refModel.den)
+        r2, n2 = virtualReference(d2, refModel.num, refModel.den)
+        phi1 = control_response(d1, np.subtract(r1, d1.y[:n1]), control)
+        phi2 = control_response(d2, np.subtract(r2, d2.y[:n2]), control)
+        
+
+        # import pdb
+        # pdb.set_trace()
+        if isinstance(data, list):
+            phi = phi1
+            data = data[0]
+            r = r1
+        else:
+            phi = np.concatenate([phi1, phi2])
+            r = np.concatenate([r1, r2])
 
-    # Compute control response given the virtual reference
-    phi = control_response(data, np.subtract(r, data.y[:n]), control)
+        theta = calc_minimum(data, phi1, phi2)
 
-    # Compute MSE minimizer
-    theta, phi = calc_minimum(data, phi)
+    # Compute VRFT loss
     loss = compute_vrft_loss(data, phi, theta)
-
     # Final controller
     final_control = np.dot(theta, control)
-    return theta, r, phi, loss, final_control
+
+    return theta, r, loss, final_control


Test patch:
diff --git a/test/test_iddata.py b/test/test_iddata.py
index 097354d..42b44fc 100644
--- a/test/test_iddata.py
+++ b/test/test_iddata.py
@@ -1,3 +1,8 @@
+# test_iddata.py - Unittest for the iddata object
+#
+# Code author: [Alessio Russo - alessior@kth.se]
+# Last update: 07th January 2020, by alessior@kth.se
+#
 # Copyright [2017-2020] [Alessio Russo - alessior@kth.se]  
 # This file is part of PythonVRFT.
 # PythonVRFT is free software: you can redistribute it and/or modify
@@ -10,67 +15,165 @@
 # You should have received a copy of the GNU General Public License
 # along with PythonVRFT.  If not, see <http://www.gnu.org/licenses/>.
 #
-# Code author: [Alessio Russo - alessior@kth.se]
-# Last update: 06th January 2020, by alessior@kth.se
-#
+
 
 from unittest import TestCase
 from vrft.iddata import iddata
 import numpy as np
+import scipy.signal as scipysig
 
 class TestIDData(TestCase):
     def test_type(self):
-        a = iddata(0.0, 0.0, 0.0)
+        a = iddata(0.0, 0.0, 0.0, [0])
         with self.assertRaises(ValueError):
-            a.checkData()
+            a.check()
 
-        a =  iddata(0.0, [1], 0.0)
+        a =  iddata(0.0, [1], 0.0, [0])
         with self.assertRaises(ValueError):
-            a.checkData()
+            a.check()
 
-        a = iddata(np.zeros(10), 1, 0.0)
+        a = iddata(np.zeros(10), 1, 0.0, [0])
         with self.assertRaises(ValueError):
-            a.checkData()
+            a.check()
 
-        a = iddata([0 for i in range(10)], [0 for i in range(10)], 1.0)
-        self.assertTrue(a.checkData())
+        a = iddata([0 for i in range(10)], [0 for i in range(10)], 1.0, [0])
+        self.assertTrue(a.check())
 
-        a = iddata(np.zeros(10), np.zeros(10), 1.0)
-        self.assertTrue(a.checkData())
+        a = iddata(np.zeros(10), np.zeros(10), 1.0, [0])
+        self.assertTrue(a.check())
 
     def test_size(self):
-        a = iddata(np.zeros(10), np.zeros(10), 0.0)
+        a = iddata(np.zeros(10), np.zeros(10), 0.0, [0])
         self.assertEqual(len(a.y), 10)
         self.assertEqual(len(a.u), 10)
         self.assertEqual(len(a.y), len(a.u))
 
-        a = iddata([0 for i in range(10)], [1 for i in range(0,10)], 0.0)
+        a = iddata([0 for i in range(10)], [1 for i in range(0,10)], 0.0, [0])
         self.assertEqual(len(a.y), 10)
         self.assertEqual(len(a.u), 10)
         self.assertEqual(len(a.y), len(a.u))
 
-        a = iddata(np.zeros(10), np.zeros(9), 0.0)
+        a = iddata(np.zeros(10), np.zeros(9), 0.0, [0])
         with self.assertRaises(ValueError):
-            a.checkData()
+            a.check()
 
-        a = iddata(np.zeros(8), np.zeros(9), 0.0)
+        a = iddata(np.zeros(8), np.zeros(9), 0.0, [0])
         with self.assertRaises(ValueError):
-            a.checkData()
+            a.check()
 
 
     def test_sampling_time(self):
-        a = iddata(np.zeros(10), np.zeros(10), 0.0)
+        a = iddata(np.zeros(10), np.zeros(10), 0.0, [0])
         with self.assertRaises(ValueError):
-            a.checkData()
+            a.check()
 
-        a = iddata(np.zeros(10), np.zeros(10), 1e-9)
+        a = iddata(np.zeros(10), np.zeros(10), 1e-9, [0])
         with self.assertRaises(ValueError):
-            a.checkData()
+            a.check()
 
-        a = iddata(np.zeros(10), np.zeros(10), -0.1)
+        a = iddata(np.zeros(10), np.zeros(10), -0.1, [0])
         with self.assertRaises(ValueError):
-            a.checkData()
-
-        a = iddata(np.zeros(10), np.zeros(10), 0.1)
-        self.assertTrue(a.checkData())
-
+            a.check()
+
+        a = iddata(np.zeros(10), np.zeros(10), 0.1, [0])
+        self.assertTrue(a.check())
+
+    def test_copy(self):
+        a = iddata(np.zeros(10), np.zeros(10), 0.1, [0])
+        b = a.copy()
+        self.assertTrue(a.check())
+        self.assertTrue(b.check())
+
+        self.assertTrue(np.all(a.y == b.y))
+        self.assertTrue(np.all(a.u == b.u))
+        self.assertTrue(np.all(a.y0 == b.y0))
+        self.assertTrue(a.ts == b.ts)
+
+    def test_filter(self):
+        a = iddata(np.zeros(10), np.zeros(10), 0.1, [0])
+        L = scipysig.dlti([1], [1], dt=0.1)
+        b = a.copy()
+        a.filter(L)
+        self.assertTrue(np.all(a.y == b.y))
+        self.assertTrue(np.all(a.u == b.u))
+        self.assertTrue(np.all(a.y0 == b.y0))
+        self.assertTrue(a.ts == b.ts)
+
+    def test_split(self):
+        n = 9
+        a = iddata(np.random.normal(size=n), np.random.normal(size=n), 0.1, [0])
+
+        b, c = a.split()
+        n0 = len(a.y0)
+        n1 = (n + n0) // 2
+
+        self.assertTrue(b.y.size == c.y.size)
+        self.assertTrue(b.u.size == c.u.size)
+        self.assertTrue(b.ts == c.ts)
+        self.assertTrue(b.ts == a.ts)
+        self.assertTrue(np.all(b.y == a.y[:n1 - n0]))
+        self.assertTrue(np.all(b.u == a.u[:n1 - n0]))
+        self.assertTrue(np.all(b.y0 == a.y0))
+
+        self.assertTrue(np.all(c.y == a.y[n1:n]))
+        self.assertTrue(np.all(c.u == a.u[n1:n]))
+        self.assertTrue(np.all(c.y0 == a.y[n1 - n0:n1]))
+
+        y0 = [-1, 2]
+        a = iddata(np.random.normal(size=n), np.random.normal(size=n), 0.1, y0)
+        n0 = len(y0)
+        n1 = (n + n0) // 2
+        b, c = a.split()
+
+        self.assertTrue(b.y.size == c.y.size)
+        self.assertTrue(b.u.size == c.u.size)
+        self.assertTrue(b.ts == c.ts)
+        self.assertTrue(b.ts == a.ts)
+        self.assertTrue(np.all(b.y == a.y[:n1 - n0]))
+        self.assertTrue(np.all(b.u == a.u[:n1 - n0]))
+        self.assertTrue(np.all(b.y0 == a.y0))
+
+        self.assertTrue(np.all(c.y == a.y[n1:n-1]))
+        self.assertTrue(np.all(c.u == a.u[n1:n-1]))
+        self.assertTrue(np.all(c.y0 == a.y[n1 - n0:n1]))
+
+
+        y0 = [-1, 2]
+        n = 9
+        a = iddata(np.random.normal(size=n), np.random.normal(size=n), 0.1, y0)
+        n0 = len(y0)
+        n -= 1
+        n1 = (n + n0) // 2
+        b, c = a.split()
+
+        self.assertTrue(b.y.size == c.y.size)
+        self.assertTrue(b.u.size == c.u.size)
+        self.assertTrue(b.ts == c.ts)
+        self.assertTrue(b.ts == a.ts)
+        self.assertTrue(np.all(b.y == a.y[:n1 - n0]))
+        self.assertTrue(np.all(b.u == a.u[:n1 - n0]))
+        self.assertTrue(np.all(b.y0 == a.y0))
+
+        self.assertTrue(np.all(c.y == a.y[n1:n]))
+        self.assertTrue(np.all(c.u == a.u[n1:n]))
+        self.assertTrue(np.all(c.y0 == a.y[n1 - n0:n1]))
+
+        y0 = [-1]
+        n = 10
+        a = iddata(np.random.normal(size=n), np.random.normal(size=n), 0.1, y0)
+        n0 = len(y0)
+        n -= 1
+        n1 = (n + n0) // 2
+        b, c = a.split()
+
+        self.assertTrue(b.y.size == c.y.size)
+        self.assertTrue(b.u.size == c.u.size)
+        self.assertTrue(b.ts == c.ts)
+        self.assertTrue(b.ts == a.ts)
+        self.assertTrue(np.all(b.y == a.y[:n1 - n0]))
+        self.assertTrue(np.all(b.u == a.u[:n1 - n0]))
+        self.assertTrue(np.all(b.y0 == a.y0))
+
+        self.assertTrue(np.all(c.y == a.y[n1:n]))
+        self.assertTrue(np.all(c.u == a.u[n1:n]))
+        self.assertTrue(np.all(c.y0 == a.y[n1 - n0:n1]))
\ No newline at end of file
diff --git a/test/test_reference.py b/test/test_reference.py
index 733e457..2fd4739 100644
--- a/test/test_reference.py
+++ b/test/test_reference.py
@@ -1,4 +1,8 @@
-# Copyright [2017-2020] [Alessio Russo - alessior@kth.se]  
+# test_reference.py - Unittest for virtual reference algorithm
+#
+# Code author: [Alessio Russo - alessior@kth.se]
+# Last update: 07th January 2020, by alessior@kth.se
+#
 # This file is part of PythonVRFT.
 # PythonVRFT is free software: you can redistribute it and/or modify
 # it under the terms of the GNU General Public License as published by
@@ -10,9 +14,6 @@
 # You should have received a copy of the GNU General Public License
 # along with PythonVRFT.  If not, see <http://www.gnu.org/licenses/>.
 #
-# Code author: [Alessio Russo - alessior@kth.se]
-# Last update: 06th January 2020, by alessior@kth.se
-#
 
 from unittest import TestCase
 from vrft.iddata import *
@@ -51,15 +52,11 @@ def test_virtualReference(self):
         t,y = scipysig.dlsim(sys, u, t)
         y = y[:,0]
         data = iddata(y,u,t_step,[0,0])
-        #wrong initial conditions
-        with self.assertRaises(ValueError):
-            virtualReference(data, num, den)
-
-        #wrong initial conditions
-        data = iddata(y,u,t_step,[0,0,0])
+        
+        # wrong initial conditions
         with self.assertRaises(ValueError):
-            virtualReference(data, num, den)
-
+            r, _ = virtualReference(data, num, den)
+    
         #test good data, first order
         data = iddata(y,u,t_step,[0])
 
@@ -79,3 +76,4 @@ def test_virtualReference(self):
         r, _ = virtualReference(data, num, den)
         for i in range(len(r)):
             self.assertTrue(np.isclose(r[i], u[i]))
+ 
\ No newline at end of file
diff --git a/test/test_utils.py b/test/test_utils.py
index 74fbba6..baa84eb 100644
--- a/test/test_utils.py
+++ b/test/test_utils.py
@@ -1,3 +1,8 @@
+# test_utils.py - Unittest for utilities
+#
+# Code author: [Alessio Russo - alessior@kth.se]
+# Last update: 07th January 2020, by alessior@kth.se
+#
 # Copyright [2017-2020] [Alessio Russo - alessior@kth.se]  
 # This file is part of PythonVRFT.
 # PythonVRFT is free software: you can redistribute it and/or modify
@@ -10,14 +15,13 @@
 # You should have received a copy of the GNU General Public License
 # along with PythonVRFT.  If not, see <http://www.gnu.org/licenses/>.
 #
-# Code author: [Alessio Russo - alessior@kth.se]
-# Last update: 06th January 2020, by alessior@kth.se
-#
+
 
 from unittest import TestCase
 from vrft.utils import *
 from vrft.extended_tf import ExtendedTF
 from vrft.vrft_algo import virtualReference
+from vrft.iddata import iddata
 import numpy as np
 import scipy.signal as scipysig
 
@@ -33,7 +37,7 @@ def test_deconvolve(self):
         y = y[:, 0]
         data = iddata(y, u, t_step, [0])
         r1, _ = virtualReference(data, sys.num, sys.den)
-        r2 = deconvolve_signal(sys, data.y, data.ts)
+        r2 = deconvolve_signal(sys, data.y)
         self.assertTrue(np.linalg.norm(r2-r1[:r2.size], np.infty) <  1e-3)
 
 
diff --git a/test/test_vrft.py b/test/test_vrft.py
index ebb2bb4..c6dc566 100644
--- a/test/test_vrft.py
+++ b/test/test_vrft.py
@@ -1,3 +1,8 @@
+# test_vrft.py - Unittest for VRFT
+#
+# Code author: [Alessio Russo - alessior@kth.se]
+# Last update: 07th January 2020, by alessior@kth.se
+#
 # Copyright [2017-2020] [Alessio Russo - alessior@kth.se]  
 # This file is part of PythonVRFT.
 # PythonVRFT is free software: you can redistribute it and/or modify
@@ -10,9 +15,7 @@
 # You should have received a copy of the GNU General Public License
 # along with PythonVRFT.  If not, see <http://www.gnu.org/licenses/>.
 #
-# Code author: [Alessio Russo - alessior@kth.se]
-# Last update: 06th January 2020, by alessior@kth.se
-#
+
 
 from unittest import TestCase
 from vrft.iddata import *
@@ -24,26 +27,73 @@
 class TestVRFT(TestCase):
     def test_vrft(self):
         t_start = 0
-        t_end = 10
         t_step = 1e-2
-        t = np.arange(t_start, t_end, t_step)
-        u = np.ones(len(t)).tolist()
+        t_ends = [10, 10 + t_step]
+
+        expected_theta = np.array([1.93220784, -1.05808206, 1.26623764, 0.0088772])
+        expected_loss = 0.00064687904235295
+        
+        for t_end in t_ends:
+            t = np.arange(t_start, t_end, t_step)
+            u = np.ones(len(t)).tolist()
+
+            num = [0.1]
+            den = [1, -0.9]
+            sys = scipysig.TransferFunction(num, den, dt=t_step)
+            t, y = scipysig.dlsim(sys, u, t)
+            y = y[:,0]
+            data = iddata(y,u,t_step,[0])
+
+            refModel = ExtendedTF([0.2], [1, -0.8], dt=t_step)
+            prefilter = refModel * (1-refModel)
+
+            control = [ExtendedTF([1], [1,0], dt=t_step),
+                    ExtendedTF([1], [1,0,0], dt=t_step),
+                    ExtendedTF([1], [1,0,0,0], dt=t_step),
+                    ExtendedTF([1, 0], [1,1], dt=t_step)]
+
+            theta1, _, loss1, _ = compute_vrft(data, refModel, control, prefilter)
+            theta2, _, loss2, _ = compute_vrft([data], refModel, control, prefilter)
+            theta3, _, loss3, _ = compute_vrft([data, data], refModel, control, prefilter)
+            
+            self.assertTrue(np.isclose(loss1, loss2))
+            self.assertTrue(np.isclose(loss1, loss3))
+            self.assertTrue(np.linalg.norm(theta1-theta2)<1e-15)
+            self.assertTrue(np.linalg.norm(theta1-theta3)<1e-15)
+            self.assertTrue(np.linalg.norm(theta1-expected_theta, np.infty) < 1e-5)
+            self.assertTrue(abs(expected_loss - loss1) < 1e-5)
+
+    def test_iv(self):
+        t_start = 0
+        t_step = 1e-2
+        t_ends = [10, 10 + t_step]
 
-        num = [0.1]
-        den = [1, -0.9]
-        sys = scipysig.TransferFunction(num, den, dt=t_step)
-        t, y = scipysig.dlsim(sys, u, t)
-        y = y[:,0]
-        data = iddata(y,u,t_step,[0])
+        
+        for t_end in t_ends:
+            t = np.arange(t_start, t_end, t_step)
+            u = np.ones(len(t)).tolist()
 
-        refModel = ExtendedTF([0.2], [1, -0.8], dt=t_step)
+            num = [0.1]
+            den = [1, -0.9]
+            sys = scipysig.TransferFunction(num, den, dt=t_step)
+            _, y = scipysig.dlsim(sys, u, t)
+            y = y.flatten() + 1e-2 * np.random.normal(size=t.size)
+            data1 = iddata(y,u,t_step,[0])
 
-        control = [ExtendedTF([1], [1,0], dt=t_step),
-                ExtendedTF([1], [1,0,0], dt=t_step),
-                ExtendedTF([1], [1,0,0,0], dt=t_step),
-                ExtendedTF([1, 0], [1,1], dt=t_step)]
+            _, y = scipysig.dlsim(sys, u, t)
+            y = y.flatten() + 1e-2 * np.random.normal(size=t.size)
+            data2 = iddata(y,u,t_step,[0])
+            
 
-        theta, _, _, loss, _ = compute_vrft(data, refModel, control, refModel * (1-refModel))
+            refModel = ExtendedTF([0.2], [1, -0.8], dt=t_step)
+            prefilter = refModel * (1-refModel)
 
+            control = [ExtendedTF([1], [1,0], dt=t_step),
+                    ExtendedTF([1], [1,0,0], dt=t_step),
+                    ExtendedTF([1], [1,0,0,0], dt=t_step),
+                    ExtendedTF([1, 0], [1,1], dt=t_step)]
 
+            with self.assertRaises(ValueError):
+                compute_vrft(data1, refModel, control, prefilter, iv=True)
 
+            compute_vrft([data1, data2], refModel, control, prefilter, iv=True)