From e64776f3e3d2a6686b31add89a3ccfeedfe8dbb9 Mon Sep 17 00:00:00 2001 From: Morten Hjorth-Jensen Date: Fri, 7 Oct 2022 08:32:32 +0200 Subject: [PATCH] update on jupyter-book --- doc/BookChapters/chapter1.dlog | 10 + doc/BookChapters/chapter1.do.txt | 8 +- doc/BookChapters/chapteroptimization.do.txt | 13 +- .../_build/.doctrees/chapter1.doctree | Bin 559498 -> 561716 bytes .../.doctrees/chapteroptimization.doctree | Bin 902683 -> 903882 bytes .../_build/.doctrees/environment.pickle | Bin 172779 -> 172852 bytes .../_build/html/_images/chapter1_17_0.png | Bin 13839 -> 13612 bytes .../_build/html/_images/chapter1_19_1.png | Bin 18513 -> 18741 bytes .../_build/html/_images/chapter1_33_0.png | Bin 25482 -> 26215 bytes .../_build/html/_images/chapter1_9_0.png | Bin 17426 -> 18349 bytes .../_images/chapteroptimization_123_1.png | Bin 23710 -> 23247 bytes .../_images/chapteroptimization_132_1.png | Bin 21777 -> 22203 bytes .../_images/chapteroptimization_148_1.png | Bin 21829 -> 22195 bytes .../_build/html/_sources/chapter1.ipynb | 596 ++++++++--------- .../html/_sources/chapteroptimization.ipynb | 468 +++++++------ doc/LectureNotes/_build/html/chapter1.html | 20 +- .../_build/html/chapteroptimization.html | 131 ++-- doc/LectureNotes/_build/html/searchindex.js | 2 +- .../_build/jupyter_execute/chapter1.ipynb | 616 +++++++++--------- .../_build/jupyter_execute/chapter1.py | 8 +- .../_build/jupyter_execute/chapter1_17_0.png | Bin 13839 -> 13612 bytes .../_build/jupyter_execute/chapter1_19_1.png | Bin 18513 -> 18741 bytes .../_build/jupyter_execute/chapter1_33_0.png | Bin 25482 -> 26215 bytes .../_build/jupyter_execute/chapter1_9_0.png | Bin 17426 -> 18349 bytes .../jupyter_execute/chapteroptimization.ipynb | 561 ++++++++-------- .../jupyter_execute/chapteroptimization.py | 14 +- .../chapteroptimization_123_1.png | Bin 23710 -> 23247 bytes .../chapteroptimization_132_1.png | Bin 21777 -> 22203 bytes .../chapteroptimization_148_1.png | Bin 21829 -> 22195 bytes doc/LectureNotes/chapter1.ipynb | 596 ++++++++--------- doc/LectureNotes/chapteroptimization.ipynb | 468 +++++++------ 31 files changed, 1764 insertions(+), 1747 deletions(-) diff --git a/doc/BookChapters/chapter1.dlog b/doc/BookChapters/chapter1.dlog index 49f0f53cc..c30a72cfc 100644 --- a/doc/BookChapters/chapter1.dlog +++ b/doc/BookChapters/chapter1.dlog @@ -77,3 +77,13 @@ found info about 5 exercises *** warning: latex envir \begin{bmatrix} does not work well in Markdown. Stick to \[ ... \], equation, equation*, align, or align* environments in math environments. output in chapter1.ipynb +Translating doconce text in chapter1.do.txt to ipynb +*** replacing \bm{...} by \boldsymbol{...} (\bm is not supported by MathJax) +found info about 5 exercises + +*** warning: latex envir \begin{bmatrix} does not work well in Markdown. Stick to \[ ... \], equation, equation*, align, or align* environments in math environments. + +*** warning: latex envir \begin{bmatrix} does not work well in Markdown. Stick to \[ ... \], equation, equation*, align, or align* environments in math environments. + +*** warning: latex envir \begin{bmatrix} does not work well in Markdown. Stick to \[ ... \], equation, equation*, align, or align* environments in math environments. +output in chapter1.ipynb diff --git a/doc/BookChapters/chapter1.do.txt b/doc/BookChapters/chapter1.do.txt index a35734a39..4ac46ba31 100644 --- a/doc/BookChapters/chapter1.do.txt +++ b/doc/BookChapters/chapter1.do.txt @@ -660,7 +660,7 @@ import os # Where to save the figures and data files PROJECT_ROOT_DIR = "Results" FIGURE_ID = "Results/FigureFiles" -DATA_ID = "datafiles/" +DATA_ID = "DataFiles/" if not os.path.exists(PROJECT_ROOT_DIR): os.mkdir(PROJECT_ROOT_DIR) @@ -1102,7 +1102,7 @@ import os # Where to save the figures and data files PROJECT_ROOT_DIR = "Results" FIGURE_ID = "Results/FigureFiles" -DATA_ID = "datafiles/" +DATA_ID = "DataFiles/" if not os.path.exists(PROJECT_ROOT_DIR): os.mkdir(PROJECT_ROOT_DIR) @@ -1607,7 +1607,7 @@ from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error # Where to save the figures and data files PROJECT_ROOT_DIR = "Results" FIGURE_ID = "Results/FigureFiles" -DATA_ID = "datafiles/" +DATA_ID = "DataFiles/" if not os.path.exists(PROJECT_ROOT_DIR): os.mkdir(PROJECT_ROOT_DIR) @@ -1771,7 +1771,7 @@ from sklearn.model_selection import train_test_split # Where to save the figures and data files PROJECT_ROOT_DIR = "Results" FIGURE_ID = "Results/FigureFiles" -DATA_ID = "datafiles/" +DATA_ID = "DataFiles/" if not os.path.exists(PROJECT_ROOT_DIR): os.mkdir(PROJECT_ROOT_DIR) diff --git a/doc/BookChapters/chapteroptimization.do.txt b/doc/BookChapters/chapteroptimization.do.txt index 692f5f28b..eabaef351 100644 --- a/doc/BookChapters/chapteroptimization.do.txt +++ b/doc/BookChapters/chapteroptimization.do.txt @@ -300,25 +300,22 @@ ensures convexity of a function $f$. We write $D_f$ to denote the domain of $f$, i.e the subset of $R^n$ where $f$ is defined. For more details and proofs we refer to: "S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press":"http://stanford.edu/boyd/cvxbook/, 2004". -!bblock First order condition -Suppose $f$ is differentiable (i.e $\nabla f(x)$ is well defined for +_First order condition_: Suppose $f$ is differentiable (i.e $\nabla f(x)$ is well defined for all $x$ in the domain of $f$). Then $f$ is convex if and only if $D_f$ -is a convex set and $$f(y) \geq f(x) + \nabla f(x)^T (y-x) $$ holds +is a convex set and $f(y) \geq f(x) + \nabla f(x)^T (y-x)$ holds for all $x,y \in D_f$. This condition means that for a convex function the first order Taylor expansion (right hand side above) at any point is a global under estimator of the function. To convince yourself you can make a drawing of $f(x) = x^2+1$ and draw the tangent line to $f(x)$ and note that it is always below the graph. -!eblock -!bblock Second order condition -Assume that $f$ is twice + +_Second order condition_: Assume that $f$ is twice differentiable, i.e the Hessian matrix exists at each point in $D_f$. Then $f$ is convex if and only if $D_f$ is a convex set and its Hessian is positive semi-definite for all $x\in D_f$. For a single-variable function this reduces to $f''(x) \geq 0$. Geometrically this means that $f$ has nonnegative curvature everywhere. -!eblock This condition is particularly useful since it gives us an procedure for determining if the function under consideration is convex, apart from using the definition. @@ -1705,6 +1702,7 @@ Autograd supports many features. However, there are some functions that is not s Assigning a value to the variable being differentiated with respect to !bc pycod +""" import autograd.numpy as np from autograd import grad def f8(x): # Assume x is an array @@ -1716,6 +1714,7 @@ f8_grad = grad(f8) x = 8.4 print("The derivative of f8 is:",f8_grad(x)) +""" !ec Here, Autograd tells us that an 'ArrayBox' does not support item assignment. The item assignment is done when the program tries to assign x[2] to the value 3. 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The intercept alpha: 
- [2.03523311]
+ [1.72261919]
 Coefficient beta : 
- [[4.99498108]]
-Mean squared error: 0.27
-Variance score: 0.87
+ [[5.39493843]]
+Mean squared error: 0.23
+Variance score: 0.91
 Mean squared log error: 0.01
-Mean absolute error: 0.41
+Mean absolute error: 0.39
_images/chapter1_19_1.png @@ -1063,7 +1063,7 @@ a linear \(x\)-dependence we s
_images/chapter1_33_0.png -
0.00499999999999999
+
0.0050000000000000044
@@ -1148,7 +1148,7 @@ After having downloaded this file to our own computer, we are now ready to read # Where to save the figures and data files PROJECT_ROOT_DIR = "Results" FIGURE_ID = "Results/FigureFiles" -DATA_ID = "datafiles/" +DATA_ID = "DataFiles/" if not os.path.exists(PROJECT_ROOT_DIR): os.mkdir(PROJECT_ROOT_DIR) @@ -1611,7 +1611,7 @@ our matrix as \(\boldsymbol{X}\in {\m # Where to save the figures and data files PROJECT_ROOT_DIR = "Results" FIGURE_ID = "Results/FigureFiles" -DATA_ID = "datafiles/" +DATA_ID = "DataFiles/" if not os.path.exists(PROJECT_ROOT_DIR): os.mkdir(PROJECT_ROOT_DIR) @@ -2048,7 +2048,7 @@ instead of our own matrix inversion implementation.

# Where to save the figures and data files PROJECT_ROOT_DIR = "Results" FIGURE_ID = "Results/FigureFiles" -DATA_ID = "datafiles/" +DATA_ID = "DataFiles/" if not os.path.exists(PROJECT_ROOT_DIR): os.mkdir(PROJECT_ROOT_DIR) @@ -2213,7 +2213,7 @@ but now splitting the data into a training set and a test set.

# Where to save the figures and data files PROJECT_ROOT_DIR = "Results" FIGURE_ID = "Results/FigureFiles" -DATA_ID = "datafiles/" +DATA_ID = "DataFiles/" if not os.path.exists(PROJECT_ROOT_DIR): os.mkdir(PROJECT_ROOT_DIR) diff --git a/doc/LectureNotes/_build/html/chapteroptimization.html b/doc/LectureNotes/_build/html/chapteroptimization.html index 15b97e436..6610c1f92 100644 --- a/doc/LectureNotes/_build/html/chapteroptimization.html +++ b/doc/LectureNotes/_build/html/chapteroptimization.html @@ -781,17 +781,15 @@ line as discussed below.

ensures convexity of a function \(f\). We write \(D_f\) to denote the domain of \(f\), i.e the subset of \(R^n\) where \(f\) is defined. For more details and proofs we refer to: [S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press](http://stanford.edu/boyd/cvxbook/, 2004).

-

First order condition.

-

Suppose \(f\) is differentiable (i.e \(\nabla f(x)\) is well defined for +

First order condition: Suppose \(f\) is differentiable (i.e \(\nabla f(x)\) is well defined for all \(x\) in the domain of \(f\)). Then \(f\) is convex if and only if \(D_f\) -is a convex set and $\(f(y) \geq f(x) + \nabla f(x)^T (y-x) \)\( holds -for all \)x,y \in D_f\(. This condition means that for a convex function +is a convex set and \(f(y) \geq f(x) + \nabla f(x)^T (y-x)\) holds +for all \(x,y \in D_f\). This condition means that for a convex function the first order Taylor expansion (right hand side above) at any point is a global under estimator of the function. To convince yourself you can -make a drawing of \)f(x) = x^2+1\( and draw the tangent line to \)f(x)$ and +make a drawing of \(f(x) = x^2+1\) and draw the tangent line to \(f(x)\) and note that it is always below the graph.

-

Second order condition.

-

Assume that \(f\) is twice +

Second order condition: Assume that \(f\) is twice differentiable, i.e the Hessian matrix exists at each point in \(D_f\). Then \(f\) is convex if and only if \(D_f\) is a convex set and its Hessian is positive semi-definite for all \(x\in D_f\). For a @@ -940,11 +938,11 @@ which equals

-
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_96694/483257001.py:18: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().
+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_9414/483257001.py:18: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().
   ax = fig.gca(projection="3d")
 

 

-
<mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x11db14850>
+
<mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x13b240850>
 

 

 _images/chapteroptimization_61_2.png
@@ -1002,7 +1000,7 @@ which equals

 

 

 

-
[<matplotlib.lines.Line2D at 0x11e09b370>]
+
[<matplotlib.lines.Line2D at 0x13b8c72e0>]
 

 

 _images/chapteroptimization_69_1.png
@@ -1259,11 +1257,11 @@ when \(||\nabla_\beta C(\beta_k) || \
 

 

 

-
[0.2831603  4.55553537]
-[[3.91511388]
- [3.13030182]]
-[[3.91511388]
- [3.13030182]]
+
[0.25881631 4.66111673]
+[[4.01840062]
+ [2.89545727]]
+[[4.01840062]
+ [2.89545727]]
 

 

 _images/chapteroptimization_123_1.png
@@ -1292,9 +1290,9 @@ when \(||\nabla_\beta C(\beta_k) || \
 

 

 

-
[[4.1509778 ]
- [2.92461411]]
-[4.13288373] [2.92817032]
+
[[3.79441434]
+ [3.07608141]]
+[3.80994952] [3.12302855]
 

 

 

@@ -1365,10 +1363,10 @@ C_{\text{ridge}}(\beta) = \frac{1}{n}||X\beta -\mathbf{y}||^2 + \lambda ||\beta|
 

 

 

-
[[4.0795449 ]
- [2.86893619]]
-[[4.04785727]
- [2.89298533]]
+
[[3.78596961]
+ [3.12387751]]
+[[3.71441535]
+ [3.17942122]]
 

 

 _images/chapteroptimization_132_1.png
@@ -1618,15 +1616,15 @@ function.

 

 

 
Own inversion
-[[4.41170104]
- [2.6431453 ]]
-Eigenvalues of Hessian Matrix:[0.31228042 4.55571665]
+[[4.42130182]
+ [2.83757843]]
+Eigenvalues of Hessian Matrix:[0.27660123 4.17938393]
 theta from own gd
-[[4.41170104]
- [2.6431453 ]]
+[[4.42130182]
+ [2.83757843]]
 theta from own sdg
-[[4.39272691]
- [2.63430285]]
+[[4.44198566]
+ [2.79512696]]
 

 

 _images/chapteroptimization_148_1.png
@@ -2221,27 +2219,52 @@ The analytical derivative of f7 at n = 2 is: 1
 
Assigning a value to the variable being differentiated with respect to

 

 

+
"""
+import autograd.numpy as np
+from autograd import grad
+def f8(x): # Assume x is an array
+    x[2] = 3
+    return x*2
+
+f8_grad = grad(f8)
+
+x = 8.4
+
+print("The derivative of f8 is:",f8_grad(x))
+"""
+

+

+

+

+
'\nimport autograd.numpy as np\nfrom autograd import grad\ndef f8(x): # Assume x is an array\n    x[2] = 3\n    return x*2\n\nf8_grad = grad(f8)\n\nx = 8.4\n\nprint("The derivative of f8 is:",f8_grad(x))\n'
+

+

+

+

+
Here, Autograd tells us that an ‘ArrayBox’ does not support item assignment. The item assignment is done when the program tries to assign x[2] to the value 3. However, Autograd has implemented the computation of the derivative such that this assignment is not possible.

+

+

 
import autograd.numpy as np
 from autograd import grad
-def f8(x): # Assume x is an array
-    x[2] = 3
-    return x*2
+def f9(a): # Assume a is an array with 2 elements
+    b = np.array([1.0,2.0])
+    return a.dot(b)
 
-f8_grad = grad(f8)
+f9_grad = grad(f9)
 
-x = 8.4
+x = np.array([1.0,0.0])
 
-print("The derivative of f8 is:",f8_grad(x))
+print("The derivative of f9 is:",f9_grad(x))
 

 

 

 

 
---------------------------------------------------------------------------
-TypeError                                 Traceback (most recent call last)
-Input In [22], in <cell line: 11>()
-      7 f8_grad = grad(f8)
-      9 x = 8.4
----> 11 print("The derivative of f8 is:",f8_grad(x))
+AttributeError                            Traceback (most recent call last)
+Input In [23], in <cell line: 11>()
+      7 f9_grad = grad(f9)
+      9 x = np.array([1.0,0.0])
+---> 11 print("The derivative of f9 is:",f9_grad(x))
 
 File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20, in unary_to_nary.<locals>.nary_operator.<locals>.nary_f(*args, **kwargs)
      18 else:
@@ -2280,30 +2303,12 @@ The analytical derivative of f7 at n = 2 is: 1
      14     subargs = subvals(args, zip(argnum, x))
 ---> 15 return fun(*subargs, **kwargs)
 
-Input In [22], in f8(x)
-      3 def f8(x): # Assume x is an array
-----> 4     x[2] = 3
-      5     return x*2
+Input In [23], in f9(a)
+      3 def f9(a): # Assume a is an array with 2 elements
+      4     b = np.array([1.0,2.0])
+----> 5     return a.dot(b)
 
-TypeError: 'ArrayBox' object does not support item assignment
-

-

-

-

-
Here, Autograd tells us that an ‘ArrayBox’ does not support item assignment. The item assignment is done when the program tries to assign x[2] to the value 3. However, Autograd has implemented the computation of the derivative such that this assignment is not possible.

-

-

-
import autograd.numpy as np
-from autograd import grad
-def f9(a): # Assume a is an array with 2 elements
-    b = np.array([1.0,2.0])
-    return a.dot(b)
-
-f9_grad = grad(f9)
-
-x = np.array([1.0,0.0])
-
-print("The derivative of f9 is:",f9_grad(x))
+AttributeError: 'ArrayBox' object has no attribute 'dot'
 

 

 

diff --git a/doc/LectureNotes/_build/html/searchindex.js b/doc/LectureNotes/_build/html/searchindex.js
index 57853623b..008c4d249 100644
--- a/doc/LectureNotes/_build/html/searchindex.js
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},titles:["3. 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Linear Regression","14. Building a Feed Forward Neural Network","15. Solving Differential Equations  with Deep Learning","16. Convolutional Neural Networks","17. Recurrent neural networks: Overarching view","4. Ridge and Lasso Regression","5. Resampling Methods","6. Logistic Regression","8. Support Vector Machines, overarching aims","9. Decision trees, overarching aims","10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","11. Basic ideas of the Principal Component Analysis (PCA)","13. Neural networks","7. Optimization, the central part of any Machine Learning algortithm","12. Clustering and Unsupervised Learning","Applied Data Analysis and Machine Learning","2. Linear Algebra, Handling of Arrays and more Python Features","Teaching schedule with links to material","1. Elements of Probability Theory and Statistical Data Analysis","Teachers and 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diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb
index f35523f45..b64f202bc 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "27b96943",
+   "id": "d66e8057",
    "metadata": {
     "editable": true
    },
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ee293e0b",
+   "id": "3f6464d0",
    "metadata": {
     "editable": true
    },
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f6481760",
+   "id": "fab3a8f9",
    "metadata": {
     "editable": true
    },
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fd2c7eef",
+   "id": "66513db3",
    "metadata": {
     "editable": true
    },
@@ -167,7 +167,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ff3dccf1",
+   "id": "0d1f19f9",
    "metadata": {
     "editable": true
    },
@@ -202,7 +202,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "89a1286b",
+   "id": "0479c66e",
    "metadata": {
     "editable": true
    },
@@ -253,7 +253,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bf0c0745",
+   "id": "609d8174",
    "metadata": {
     "editable": true
    },
@@ -286,7 +286,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aff5ae6b",
+   "id": "7e580e18",
    "metadata": {
     "editable": true
    },
@@ -298,7 +298,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9bf3106e",
+   "id": "bb8f332d",
    "metadata": {
     "editable": true
    },
@@ -335,7 +335,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "8f9fe452",
+   "id": "9e479f11",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -343,7 +343,7 @@
    "outputs": [
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "
"
       ]
@@ -383,7 +383,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a7ce677a",
+   "id": "8c7b818b",
    "metadata": {
     "editable": true
    },
@@ -400,7 +400,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a9463a86",
+   "id": "201888d1",
    "metadata": {
     "editable": true
    },
@@ -412,7 +412,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "87b5de04",
+   "id": "e6a16258",
    "metadata": {
     "editable": true
    },
@@ -433,7 +433,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e9e757d9",
+   "id": "c3aab7c4",
    "metadata": {
     "editable": true
    },
@@ -446,7 +446,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "40298a87",
+   "id": "f2957240",
    "metadata": {
     "editable": true
    },
@@ -477,7 +477,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9c2c9d9d",
+   "id": "2a15d7ec",
    "metadata": {
     "editable": true
    },
@@ -489,7 +489,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9baeab03",
+   "id": "12af21ef",
    "metadata": {
     "editable": true
    },
@@ -507,7 +507,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "bd76613f",
+   "id": "16f86276",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -515,7 +515,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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UGRmpLVu2aMyYMfXms9vtstvtodkIAADQaoW1RSgqKkppaWkqKChwG19QUKCMjIx608fGxurjjz/W7t27XUNOTo769u2r3bt3a/jw4c1VOgAAaAPC2iIkSbNnz9a0adOUnp6uESNGaOXKlSopKVFOTo4k52mtw4cPa82aNWrXrp0GDRrkNv8555yj6OjoeuMBAAAaE/YglJ2drRMnTmjx4sUqLS3VoEGDlJ+fr+TkZElSaWlpo30KAQAABCLs/QiFA/0IAQDQ+rS5foQAAADCiSAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsiyAEAAAsq0UEoeXLlyslJUXR0dFKS0tTUVGR12m3bdumSy65RPHx8erQoYP69eunhx9+uBmrBQAAbUVkuAtYv369Zs2apeXLl+uSSy7R448/rvHjx2vPnj3q2bNnveljYmJ011136cILL1RMTIy2bdumGTNmKCYmRv/+7/8ehi0AAACtlc0YY8JZwPDhw5WamqoVK1a4xvXv31+TJk1SXl6eT8uYPHmyYmJi9Mwzz/g0fUVFheLi4lReXq7Y2NiA6gYAAM0rFMfvsJ4aq6qq0s6dO5WVleU2PisrS9u3b/dpGcXFxdq+fbtGjhzpdZrKykpVVFS4DQAAAGENQmVlZXI4HEpMTHQbn5iYqCNHjjQ4b/fu3WW325Wenq4777xTt912m9dp8/LyFBcX5xp69OgRlPoBAEDr1iIulrbZbG4/G2PqjaurqKhIO3bs0GOPPaalS5dq3bp1XqfNzc1VeXm5azh48GBQ6gYAAK1bWC+WTkhIUERERL3Wn2PHjtVrJaorJSVFkvTTn/5UR48e1cKFC3X99dd7nNZut8tutwenaAAA0GaEtUUoKipKaWlpKigocBtfUFCgjIwMn5djjFFlZWWwywMAAG1c2G+fnz17tqZNm6b09HSNGDFCK1euVElJiXJyciQ5T2sdPnxYa9askSQtW7ZMPXv2VL9+/SQ5+xV68MEH9etf/zps2wAAAFqnsAeh7OxsnThxQosXL1ZpaakGDRqk/Px8JScnS5JKS0tVUlLimv7MmTPKzc3V/v37FRkZqfPPP1+///3vNWPGjHBtAgAAaKXC3o9QONCPEAAArU+b60cIAAAgnAhCAADAsghCAADAsghCAADAsghCAADAsghCAADAsghCAADAsghCAADAsghCAADAspoUhN5//32tXbtWknTy5EkdOnQoKEUBAAA0h4CfNbZw4ULt2rVLn376qaZOnarvv/9eU6ZM0bZt24JZHwAAQMgE3CL08ssva/PmzYqJiZEkdevWTadPnw5aYQAAAKEWcBCy2+2SJJvNJkk6deqU6/8AAACtQcBB6Pbbb1d2drbKysq0ZMkSZWZmas6cOcGsDQAAIKRsxhgT6Mx79+7Vm2++KWOMxowZo4EDBwaztpCpqKhQXFycysvLFRsbG+5yAACAD0Jx/A74Yun8/HxlZWWpf//+QSkEAACguQV8amzDhg3q27evpk+frvz8fFVXVwezLgAAgJALOAg9/fTT+uyzzzRlyhRt3LhR/fr10y233BLM2gAAAEIq4FNjkhQZGamMjAwdP35cX331lQoLC4NUFgAAQOgF3CK0evVqTZgwQcOGDdPHH3+sRYsWaf/+/cGsDQAAIKQCbhHau3evFi1apPT09GDWAwAA0GyadPt8a8Xt8wAAtD4t4vb5adOm6ZlnntFFF13k1pO0MUY2m00ffPBBUAoDAAAINb+D0AMPPCBJuuaaa3TDDTe4xhtjXE+iBwAAaA0CPjWWmpqqXbt2uY0bPHiwPvroo6AUFkqcGgMAoPVpEafGnnjiCa1cuVKfffaZhg0b5hp/+vRpDR06NChFAQAANAe/W4TKy8v19ddf695779Xvfvc71/hOnTqpc+fOQS8wFGgRAgCg9QnF8bvJd40dPXpUlZWVrp979uzZ5KJCjSAEAEDrE4rjd8AdKr788svq37+/zj//fI0bN04pKSmaOHFiUIoCAABoDgEHofvuu0/vv/++LrjgAu3du1fvvfeehgwZEsTSAAAAQivgIGS3213NUlVVVRo2bFiruGMMAACgRsCP2EhKStKpU6d01VVX6YorrlB8fLy6dOkSzNoAAABCKiiP2CgsLFRFRYXGjRsnu90ejLpCioulAQBofVpEP0KejBo1KhiLAQAAaFZ+B6G6zxiri2eNAQCA1sLvILRhw4ZQ1AEAANDs/L5rLDk52TUcOXJE7777rpKTkxUbG6uIiIhQ1AgAABASAV8jtHDhQu3atUuffvqppk6dqu+++05TpkzRtm3bglkfAABAyDSpZ+nNmzcrJiZGktStWzdVVFQErTAAAIBQa1KHipJcF06fOnVK7doFvDgAAIBmF3Byuf3225Wdna2ysjItWbJEmZmZmjNnTjBrAwAACKmAO1T84Ycf9M9//lNvvvmmjDEaM2aMBg4cGOz6QoIOFQEAaH1aTIeKZ86c0UUXXaTdu3erf//+QSkEAACguQV0aqxdu3YaNmyYPvnkk2DXAwAA0GwCvn3+gw8+0NChQ9WnTx917NhRxhjZbDZ6lgYAAK1GwEFo8+bNwawDAACg2fkchMaOHav/+I//0Pjx4yU5e5iWJIfDQY/SAACgVfL5GqEdO3aoV69ekqT9+/e7xj/11FOaNm1a0AsDAAAINZ+DUFVVlTp16iRJGjx4sL744gtJUkZGht58883QVAcAABBCPp8au+CCC/T++++rU6dO+vbbb3Xq1ClJUqdOnXTy5MlQ1QcAABAyPrcI3XHHHbrttts0cuRIDR48WCtXrpQkFRUVKTExMWQFAgAAhIrPLUI5OTnq0qWLPv/8c/3qV7/SlClTdN5556m0tFR33XVXKGsEAAAIiYAfsVFdXa2XXnpJVVVVmjJlSqu6c4xHbAAA0Pq0mEdsSFJkZKR+8YtfBKUIAACAcAj46fMAAACtHUEIAABYFkEIAABYFkEIAABYFkEIAABYFkEIAABYFkEIAABYFkEIAABYFkEIAABYVosIQsuXL1dKSoqio6OVlpamoqIir9Nu2rRJl19+ubp06aLY2FiNGDFCr7/+ejNWCwAA2oqwB6H169dr1qxZmj9/voqLi5WZmanx48erpKTE4/TvvPOOLr/8cuXn52vnzp0aPXq0rrrqKhUXFzdz5QAAoLUL+KGrwTJ8+HClpqZqxYoVrnH9+/fXpEmTlJeX59MyBg4cqOzsbN13330+Tc9DVwEAaH1CcfwOa4tQVVWVdu7cqaysLLfxWVlZ2r59u0/LOHPmjE6fPq3OnTt7naayslIVFRVuAwAAQFiDUFlZmRwOhxITE93GJyYm6siRIz4t46GHHtK3336r6667zus0eXl5iouLcw09evRoUt0AAKBtCPs1QpJks9ncfjbG1Bvnybp167Rw4UKtX79e55xzjtfpcnNzVV5e7hoOHjzY5JoBAEDrFxnOlSckJCgiIqJe68+xY8fqtRLVtX79ev3yl7/Uiy++qLFjxzY4rd1ul91ub3K9AACgbQlri1BUVJTS0tJUUFDgNr6goEAZGRle51u3bp2mT5+utWvXasKECaEuEwAAtFFhbRGSpNmzZ2vatGlKT0/XiBEjtHLlSpWUlCgnJ0eS87TW4cOHtWbNGknOEHTTTTfpkUce0cUXX+xqTerQoYPi4uLCth0AAKD1CXsQys7O1okTJ7R48WKVlpZq0KBBys/PV3JysiSptLTUrU+hxx9/XNXV1brzzjt15513usbffPPNWr16dXOXDwAAWrGw9yMUDvQjBABA69Pm+hECAAAIJ4IQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwLIIQAACwrBYRhJYvX66UlBRFR0crLS1NRUVFXqctLS3V1KlT1bdvX7Vr106zZs1qvkIBAECbEvYgtH79es2aNUvz589XcXGxMjMzNX78eJWUlHicvrKyUl26dNH8+fM1ePDgZq4WAAC0JTZjjAlnAcOHD1dqaqpWrFjhGte/f39NmjRJeXl5Dc47atQoDRkyREuXLvVrnRUVFYqLi1N5ebliY2MDKRsAADSzUBy/w9oiVFVVpZ07dyorK8ttfFZWlrZv3x609VRWVqqiosJtAAAACGsQKisrk8PhUGJiotv4xMREHTlyJGjrycvLU1xcnGvo0aNH0JYNAABar7BfIyRJNpvN7WdjTL1xTZGbm6vy8nLXcPDgwaAtGwAAtF6R4Vx5QkKCIiIi6rX+HDt2rF4rUVPY7XbZ7fagLQ8AALQNYW0RioqKUlpamgoKCtzGFxQUKCMjI0xVAQAAqwhri5AkzZ49W9OmTVN6erpGjBihlStXqqSkRDk5OZKcp7UOHz6sNWvWuObZvXu3JOmbb77R8ePHtXv3bkVFRWnAgAHh2AQAANBKhT0IZWdn68SJE1q8eLFKS0s1aNAg5efnKzk5WZKzA8W6fQoNHTrU9f+dO3dq7dq1Sk5O1oEDB5qzdAAA0MqFvR+hcKAfIQAAWp82148QAABAOBGEAACAZRGEAACAZRGEAACAZRGEAACAZRGEAACAZRGEAACAZRGEAACAZRGEAACAZRGEAACAZRGEAACAZRGEAACAZRGEAACAZUWGu4BWz+GQioqk0lIpKUnKzJQiIsJdFQAA8AFBqCk2bZJmzpQOHfpxXPfu0iOPSJMnh68uAADgE06NBWrTJunaa91DkCQdPuwcv2lTeOoCAAA+IwgFwuFwtgQZU/93xjiHnBypqqr5awMAAD4jCAWiqKh+S1Bdx487T5PRMgQAQItFEApEaalv0x0/zmkyAABaMIJQIJKS/Jt+1izn6TQAANCiEIQCkZnpPO1lszU+rTHSwYPO02kAAKBFIQgFIiLCeYu8P3w9nQYAAJoNQShQkydLGzZICQm+Te/v6TQAABByBKGmmDzZ2W9Qly4NTxcf7zydFioOh1RYKK1b5/yX65EAAPAJQaipoqKkxx5reJoTJ6TNm0Oz/k2bpF69pNGjpalTnf/26sWdagAA+IAgFAwTJzpbfbyx2UJz5xi9WwMA0CQEoWAoKnK2+ngTijvHGuvdWuK2fQAAGkEQCgZf7wgL5p1jjfVuzW37AAA0iiAUDL7eERbMO8fCEb4AAGhjCELB0FgHizab1KNHcO8cC0f4AgCgjSEIBUPtDhbrhqGan5cudU4XLOEIXwAAtDEEoWCp6WCxWzf38d27O8dPnhzc9YUjfAEA0MbYjPF021HbVlFRobi4OJWXlys2Nja4C3c4nBcol5Y6T0tlZoY2jGza5Lx7rPaF0z16OENQsMMXAABhFIrjN0Eo2EEoHJo7fAEAEAahOH5HBmUpCK+ICGnUqHBXAQBAq0MQCidacgAACCuCULh4urane3fnBdBc2wMAQLPgrrFw4BlhAAC0CASh5uJwSIWF0nPPSTk5PCMMAIAWgFNjzcHTaTBvaj8jzNcLoLnWCACAgBCEQq3mNJi/vRT4+owwrjUCACBgnBoLJYfDGVIC6arJl2eEca0RAABNQhAKpaIi306H1RURIWVkNDxNQyHLGOfwq19Jb77J9UYAAHhBEAolX09v1eVwSNu3NzyNLyHr5Elp7FipVy9ahwAA8IAgFEq+nN7yprEQ5U/I4lQZAAAeEYQaUnPL+7p1zn/9PcWUmem8cLnu0+F9cfRow+v1J2RxWz4AAB4RhLyFnU2bnKeURo+Wpk51/uvvKaaICOfdW5J/YSgiQrr77obXWxOyfFX7tnwAACDJ6kHolVc8h525c4N3N9bkydKGDVK3br7PU7fVxtN6N2+Wvv/e92XWCPS6pWBramsbAABBYDMmkHu7W7eKigrFxcWpXFKsvzPbbM6WmP37/eu0sG6nh8ePS7Nnu4etiAjvgaD2ejdvDqxvIknaujX8T6qn7yMAQABcx+/ycsXG+n0E94ggFOhCghEoaoejo0edp8Ma8/rr0g03SGVl/q2rdpCSwtcTtbcOJmtOHW7YQBgCAHhEEAqSoAShe++VBgwIXpBYt855eq4xnTpJp0/7t+zaIUMKX2uMw+E89ejttv9AW9sAAJYQiiDEIzYCtWTJj/8PRpDw9S4wf0OQ5Kxv6VLn/z21xtRcgxTq1pjG+j6quaC7sNAZhHh2GgAgxGgRCsYCg3Fap6a15PDhwK79qevhh6XExB+DhBT+1hhfW706d3Z2BlmD64cAAApNi5C17xprjK+3vAejn55Ab7X3pEsX6Y47pOuvd17HFBHhe2tMKG+v97XVq3YIkugQEgAQMgShuqHDZnMO99zj3y3vwQgS3m6179zZv+UcPy6df757cPD1tvlQ3l4faAeTdAgJAAgRawehZ56pHzq6d3eGkQcekA4ccN4dtnat9POf+7bMpgaJyZPd17t1q/TCC/4vp24riq+tMU15LIgntfsLKiqS/vQn5/hAwhAdQgIAgsza1wiVlys2JqbxW8kdjh/7/mlM3WtzgnG9TaDXD9W9Zb6hZYTiGiFv/QVdf70zGNUeX/e6IG/WrnXOH4i6fTlxETZChfcaEBLcPh8kfr+QhYXOXqcb066ddObMjz8HcpGvty/Qmv53JP8vpq7p88jbMkLRh09j/QW98IKUkPDjdjoc0tixjS/Xl/6bPL2Gmzd77zZg4kTnPi4sdI4fNerHa6taCk/bJHGwbYnoMLR5hDJsEmRbrFAEIRkLKi8vN5JMeXm5bzOsXWuM85Du32CzOYeNG31bz8aNxnTv7r6M7t1/nN/T730Z1q5teB09evheoy+qqxuu02ZzrrO62n2ebt38m8fX1zA+3vsyJWN+8pP6v4uPD+5r4q/qamO2bnXuu0WL6r828fH1t6v2e6Up69u6tfHXuamae32hXmfNsmfNCvy7oGYZzz5rzMMPO/9trtcmlAJ93Ruar7HvyqYI5bJ9EY7PRivi9/HbBwQhX2zdGlgQ8vcAXnNgbugLtPaH5OGHfath0SL3dYX6g+br67V1q/v2NxRYfAmUL7wQ+H7yNjS2zmC9lnWDTyCB19/gXaO5v/jDcaAJxjq97Wtf/0Bp6LugoWU0x0G49ra98YZzCMb3Q6Cv+8aN9cN/t27O8b5+VwZab6iWXZs/76XmDGGhEsRjDkEoSPx+IWtaODx9QHwdah/0vS3f3y/QxuarGTp3Ds1fv97e1L62oNW0VG3c2PB0vrTOvPiiMRERwQ9C3bsbU1kZ2i+tQFv6/D3Yelt3c3zx+7q+RYuCcwCuGyybso3V1c5ldO5cf1/fc4//3wt1vwu8vSZ1a23OYBqMA3Gg7y1fvg+C8f6v+z1WWenb97C37wN/Xpe660lIMObKK72v19Pr5el7uCW2JgX5j5DyV181BKEgCChR1nyoAw1DtU9P1RVIC0qNRYt8m7duq1CgfHlT+7M91dUNf7HVLL+hD3RjX5xNHbp0qV+PtwNgIKdDmxKw/Xmv1OVvAPf2JVtd7WxBuPde5/DGG573l6/BPdAvy9qvqa/raWgb33jDmAULPJ86bcpQc8qrZh0NnRKuPdQ9CPvbcuNp//kawmw2Z4tr3eDg7aBbWVn/c9PQ6177fRSM1/vhh+svu3atL7zgOYz4suy603l7n3pqZfN26tTf96m3SwACPV0eSIDyZZ7G3l+zZjW+vjrbWi6ZNhmEli1bZnr16mXsdrtJTU0177zzToPTFxYWmtTUVGO3201KSopZsWKFX+sLuGnN05uvoQ977aGhA5O/LSiBzBsf3/S/Dvw5fddQC1rtD7WvQc7b6+dLkAr24MuBw5e/Sv0NBv4MDQXvGv4EVm8B+J57PL/+nlrx/D3F7M91NbUPcIEES2/bGIrB1+8MT0NDB+uGDnqetq1bN/8+O3Vf17otsDXr37jR91BR87kOxWtfu55Q7ldP79NQrbPmferre9yXz1AgLTa+zOPP95u39XnY1jYZhJ5//nnTvn1788QTT5g9e/aYmTNnmpiYGPPll196nP6LL74wHTt2NDNnzjR79uwxTzzxhGnfvr3ZsGGDz+ts0jlGb82pvhz0vWlKi5A/BxdfWgka2m5/Wg+8taDV/mBWV9c/3eBt8HZg9zVIhWNo7PVuyrVnwdjXvoboWbMCb7Wq/eUWyE0H/l5XE+jp0aZsY0sZvB30QtXq6Gn9/s6zdm3z1Rfqba95n4Zye5591v+A1dhnyN+WbV/n8ef7zdP6vBxz2mQQGjZsmMnJyXEb169fPzNv3jyP08+dO9f069fPbdyMGTPMxRdf7PM6g36xlS8H/Yb404Liad6mhglfBHoBdEN3qDU1xPmz7eEYGnu9A70bMdAvvUD3aVNaMGqf1mxK8Avkuhp/Bl9bMFr64OlUX3O0cgU6vPFGy66vpW2PrzfIeBrqfoYCvbvX13n8/X6ruz4v3xehCEJh7Vm6qqpKO3fuVFZWltv4rKwsbd++3eM87733Xr3px40bpx07duiHH34IWa0N8vZojJpeqhvrO6Sh54zV/Lx0qed+LCIinH2W+KIpvUYH8ogOT71k79//4+vh6zI7d/6x35zaiop864SxRnx8wz8HW2Ovd7B78W7svVJXY488sdmcz63zpSNRbw4d+rE38MxMZ99Rgaj9XnE4nO95YwKvq0bNNpaVNX1ZLYEx7j2wN/aMwXCx2aQePZz/D7S+pj6TMRQKC0P3enfu7HyvBqru920gz5/0Zx5/v9/qri+Uj3uqI7LZ1uRBWVmZHA6HEhMT3cYnJibqyJEjHuc5cuSIx+mrq6tVVlamJA8vfmVlpSorK10/l5eXS3J2zBQ0Y8dK/+//Sdu3S0eOSOeeK2VkOA9Ivqxn7FhpzRrpP/9T+uqrH8d37Sr9/vfO33tbzq9/7Tz4ff219+V36yYNHuxbLZ742nFVbGz9daSm/vj/b7/1f5kzZrjPV2PfPt/ml6TVq6Wrr66/f/72N+k3v6n/2nXqJJ0+7fvy6/Ll9R482Ll/a+9vX519tvPf2nX78l6pKy9PmjbN8++McXaKuWKF//XVtm/fj++BX/wisOXVfl8F8+AerG1saWpec38+I83JGOn++51/KPnr7LOd33e5uYF9dkKp1nEm6GbMkM46K/D56343+/reqP359WeeyZMD+36rWZ+X40PNFphg/CFUI2htSwE4fPiwkWS2b9/uNn7JkiWmb9++Hufp3bu3uf/++93Gbdu2zUgypaWlHudZsGCB0f81pzEwMDAwMDC07mHfvn3BCSLGmLC2CCUkJCgiIqJe68+xY8fqtfrUOPfccz1OHxkZqXgvpzpyc3M1e/Zs18+nTp1ScnKySkpKFBcX18StQFNUVFSoR48eOnjwYPC6S0dA2BctB/uiZWF/tBzl5eXq2bOnOnfuHLRlhjUIRUVFKS0tTQUFBbrmmmtc4wsKCjRx4kSP84wYMUJ//etf3cZt2bJF6enpat++vcd57Ha77HZ7vfFxcXG8qVuI2NhY9kULwb5oOdgXLQv7o+Vo1y54lziH9WJpSZo9e7aefPJJPf3009q7d6/uvvtulZSUKCcnR5KzNeemm25yTZ+Tk6Mvv/xSs2fP1t69e/X000/rqaee0pw5c8K1CQAAoJUKa4uQJGVnZ+vEiRNavHixSktLNWjQIOXn5ys5OVmSVFpaqpKSEtf0KSkpys/P1913361ly5apa9euevTRR/Xzn/88XJsAAABaqbAHIUm64447dMcdd3j83erVq+uNGzlypHbt2hXw+ux2uxYsWODxdBmaF/ui5WBftBzsi5aF/dFyhGJf2IwJ5j1oAAAArUfYrxECAAAIF4IQAACwLIIQAACwLIIQAACwrDYbhJYvX66UlBRFR0crLS1NRbUfHOfB22+/rbS0NEVHR+u8887TY4891kyVtn3+7ItNmzbp8ssvV5cuXRQbG6sRI0bo9ddfb8Zq2zZ/Pxc13n33XUVGRmrIkCGhLdBC/N0XlZWVmj9/vpKTk2W323X++efr6aefbqZq2zZ/98Vzzz2nwYMHq2PHjkpKStItt9yiEydONFO1bdc777yjq666Sl27dpXNZtPLL7/c6DxBOXYH7WEdLcjzzz9v2rdvb5544gmzZ88eM3PmTBMTE2O+/PJLj9N/8cUXpmPHjmbmzJlmz5495oknnjDt27c3GzZsaObK2x5/98XMmTPNH/7wB/PBBx+Yf/zjHyY3N9e0b9/e7Nq1q5krb3v83Rc1Tp06Zc477zyTlZVlBg8e3DzFtnGB7Iurr77aDB8+3BQUFJj9+/eb999/37z77rvNWHXb5O++KCoqMu3atTOPPPKI+eKLL0xRUZEZOHCgmTRpUjNX3vbk5+eb+fPnm40bNxpJ5qWXXmpw+mAdu9tkEBo2bJjJyclxG9evXz8zb948j9PPnTvX9OvXz23cjBkzzMUXXxyyGq3C333hyYABA8yiRYuCXZrlBLovsrOzzb333msWLFhAEAoSf/fF//7v/5q4uDhz4sSJ5ijPUvzdF3/84x/Neeed5zbu0UcfNd27dw9ZjVbkSxAK1rG7zZ0aq6qq0s6dO5WVleU2PisrS9u3b/c4z3vvvVdv+nHjxmnHjh364YcfQlZrWxfIvqjrzJkzOn36dFAfsGdFge6LVatWad++fVqwYEGoS7SMQPbFK6+8ovT0dD3wwAPq1q2b+vTpozlz5uj7779vjpLbrED2RUZGhg4dOqT8/HwZY3T06FFt2LBBEyZMaI6SUUuwjt0tomfpYCorK5PD4aj39PrExMR6T62vceTIEY/TV1dXq6ysTElJSSGrty0LZF/U9dBDD+nbb7/VddddF4oSLSOQffH5559r3rx5KioqUmRkm/uqCJtA9sUXX3yhbdu2KTo6Wi+99JLKysp0xx136OTJk1wn1ASB7IuMjAw999xzys7O1r/+9S9VV1fr6quv1p///OfmKBm1BOvY3eZahGrYbDa3n40x9cY1Nr2n8fCfv/uixrp167Rw4UKtX79e55xzTqjKsxRf94XD4dDUqVO1aNEi9enTp7nKsxR/PhdnzpyRzWbTc889p2HDhumKK67Qn/70J61evZpWoSDwZ1/s2bNHv/nNb3Tfffdp586deu2117R//37Xg8LRvIJx7G5zf+YlJCQoIiKiXpo/duxYveRY49xzz/U4fWRkpOLj40NWa1sXyL6osX79ev3yl7/Uiy++qLFjx4ayTEvwd1+cPn1aO3bsUHFxse666y5JzoOxMUaRkZHasmWLxowZ0yy1tzWBfC6SkpLUrVs3xcXFucb1799fxhgdOnRIvXv3DmnNbVUg+yIvL0+XXHKJ7rnnHknShRdeqJiYGGVmZmrJkiWcQWhGwTp2t7kWoaioKKWlpamgoMBtfEFBgTIyMjzOM2LEiHrTb9myRenp6Wrfvn3Iam3rAtkXkrMlaPr06Vq7di3n3YPE330RGxurjz/+WLt373YNOTk56tu3r3bv3q3hw4c3V+ltTiCfi0suuURfffWVvvnmG9e4f/zjH2rXrp26d+8e0nrbskD2xXfffad27dwPnREREZJ+bI1A8wjasduvS6tbiZrbIZ966imzZ88eM2vWLBMTE2MOHDhgjDFm3rx5Ztq0aa7pa27Bu/vuu82ePXvMU089xe3zQeLvvli7dq2JjIw0y5YtM6Wlpa7h1KlT4dqENsPffVEXd40Fj7/74vTp06Z79+7m2muvNZ988ol5++23Te/evc1tt90Wrk1oM/zdF6tWrTKRkZFm+fLlZt++fWbbtm0mPT3dDBs2LFyb0GacPn3aFBcXm+LiYiPJ/OlPfzLFxcWurgxCdexuk0HIGGOWLVtmkpOTTVRUlElNTTVvv/2263c333yzGTlypNv0hYWFZujQoSYqKsr06tXLrFixopkrbrv82RcjR440kuoNN998c/MX3gb5+7mojSAUXP7ui71795qxY8eaDh06mO7du5vZs2eb7777rpmrbpv83RePPvqoGTBggOnQoYNJSkoyN9xwgzl06FAzV932bN26tcHv/1Adu23G0JYHAACsqc1dIwQAAOArghAAALAsghAAALAsghAAALAsghAAALAsghAAALAsghAAALAsghAAALAsghAAALAsghAAALAsghCAVm/dunWKjo7W4cOHXeNuu+02XXjhhSovLw9jZQBaOp41BqDVM8ZoyJAhyszM1F/+8hctWrRITz75pP7+97+rW7du4S4PQAsWGe4CAKCpbDabfve73+naa69V165d9cgjj6ioqIgQBKBRtAgBaDNSU1P1ySefaMuWLRo5cmS4ywHQCnCNEIA24fXXX9enn34qh8OhxMTEcJcDoJWgRQhAq7dr1y6NGjVKy5Yt0/PPP6+OHTvqxRdfDHdZAFoBrhEC0KodOHBAEyZM0Lx58zRt2jQNGDBAF110kXbu3Km0tLRwlweghaNFCECrdfLkSV1yySW69NJL9fjjj7vGT5w4UZWVlXrttdfCWB2A1oAgBAAALIuLpQEAgGURhAAAgGURhAAAgGURhAAAgGURhAAAgGURhAAAgGURhAAAgGURhAAAgGURhAAAgGURhAAAgGURhAAAgGURhAAAgGX9fyAnsMmDVjFxAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "
"
       ]
@@ -550,7 +550,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5c78adc9",
+   "id": "87da55cc",
    "metadata": {
     "editable": true
    },
@@ -572,7 +572,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "d38e9c7c",
+   "id": "221ebafe",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -583,18 +583,18 @@
      "output_type": "stream",
      "text": [
       "The intercept alpha: \n",
-      " [2.03523311]\n",
+      " [1.72261919]\n",
       "Coefficient beta : \n",
-      " [[4.99498108]]\n",
-      "Mean squared error: 0.27\n",
-      "Variance score: 0.87\n",
+      " [[5.39493843]]\n",
+      "Mean squared error: 0.23\n",
+      "Variance score: 0.91\n",
       "Mean squared log error: 0.01\n",
-      "Mean absolute error: 0.41\n"
+      "Mean absolute error: 0.39\n"
      ]
     },
     {
      "data": {
-      "image/png": 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t27fX6NGj3W4KAACxDR4sFRdLsR7IoZBUUmKeh4zzRLDZs2ePZs+erQkTJig31zOdSAAARMvJkWbNMv+7cbgJf3zPPeZ5yDhPBJvXX39d33zzjc4//3y3mwIAQHxlZdKzz0pdukQeLy42j5eVudMueKt4OBk1NTUqKirS1q1bqbEBAGRWXZ20aJG0bp3UqZM5/ERPjS3pen4z7gMAQLJycqRhw9xuBRrwxFAUAADwkVtvNeuJzjrL7ZZEIdgAAAB7vvzSDDTXXWd+XFHhbnssEGwAAAiyujqpqkqaM8d832Dj6YQUF0s/+1nksU6dpDjrz2UawQYAgKCqrJRKS6Xhw6Xx4833paWJhZEvvjB7aaqroz+3fr1UXu6pcMOsKABA6pgd5D2VlWboaPyYD6+1Y2daup1VgUMhszdn1aqE7nm6nt/02AAAUuNErwCcVVcnTZoUHWqkn45Nnhx7WGr1anuhJvx6a9aYwdYDCDYAgOSFewXWro08Xl3tuSGKrLJoUfQ9aaipMBIKSd26JX7NdesS/5o0INgAAJKTaq8A0sduyGh43rffWvfSvPGGvdfq1MneeWlGsAEAJCeVXgGkl92QET4vFIreHuKww8x7OGSIrzb9JNgAAJKTTK8AMsPuDuS9e1ufs2uXtGyZ+d8+2/STYAMASE6ivQJB4NSaMOlmJ4ysWSO1axf9tYYh5TbacclHm34y3RsAkJy6OnP2U3W1dZ1NktOAPauy0qwpajj8VlxsBggPPdgjxGqz1RBiTY20995Nv56D0/rT9fwm2AAAkheeFSVFhptE1krxAyfWhHFLwzAyfrz1OS5EAdaxAQB4j4+GKJLm99lfOTlmz4pVqInV2+ZjufFPAQCgCWVl0pgxwV15OJHZX8OGZaxZtsUqIA5YoAkj2AAAUpeT482Heqrq6qT58+2d68XZX1ah5u23pYEDM9+WDCHYAABgxarwtilNzf7K9F5aWdZL0xA1NgAANBZrqwgr8Raoy/ReWlah5vHHsyLUSPTYAAAQqali4cbiLVAXazZVeC+tv/5V2ndfZ3pysriXpiF6bAAAaChesXBDTc3+ijebyjCkn//cmZ4cq1Bz6aVZF2okemwAAIhktwh46lRp2rTYPSx2AlLjKeLhnhy7U+Vzc62nmWdhoAmjxwYAgIbsbgFx3HFNDxslM0sqkXVxQqHocwYNyupQIxFsAABe4/Z+THY3kIy3m3Wye2TF2xX9mGOs22YY0ltvJXfNACHYAAC8I9MziKw4tZt1vIAUj1WPTyhkrkPTUG5u1vfSNESwAQB4Q6wp1uG6k0yGGye2imgqINnRsMfn0ktj99Ls2pX4awcYm2ACANwX3ik8VrGtWzuFO7GwntVCfzk5sYfYGn+vyU7jzvSigAlid+8YCDYAEABVVeawUzwLFvhz64bGIeM//5HGjjU/F2tX9A0bpEsuiX4tO49tqzBVXGz2IHlkY9J0Pb+Z7g0AcJ/dGURe3I/JDqu9tHJyrMPHPfdIp59u/Tp2Q01TiwIGZdf1GAg2AAD32Z1BlOxMo3RKdsjHalf07dulk06KPtfu4Eq8RQFDIXMq+ZgxnhqWchLBBgDgvvAMoupq64dyuO4k3hTrTEt1yKdhT05TtTR2w1O8RQEbTiX345CeDcyKAgC4z6kp1pnk1Cyujz6yDjW7d5tBJJEp8EEf0rOBYAMA8AYnplhnSrwhH8n+6sGHHGL9Gjk5iYcnPw/pOYRZUQAAb/H4NGVJqc/iqq42A1tj27ZJe+1l/ncyU+DDXxNvSC/T0+YtMCsKAJAdrGYQeU0qQz5216VJpl4mPKRXXm5ex2oqudeG9BzGUBQAAIlKZshnxw7rULN6tXXvSrLhyU9DemlAjw0AwNu8ODSV6CyuZFYPTqVexmoquZM/Ny/ek/+hxwYA4F1e2BTTit1ZXKGQdah58834a9Okust4eEhv3DjzvVPBw6v35H8INgAAb/LSpphW4g35nH66dZgwDHvr8XhxCrzX74mYFQUA8Xm42z2wvLopphWr349ci0qPBx6Qfv3rxF/fahHAkhIz1GSyXsbhe8ImmDEQbACklQ82Ewwkv26KmexO3PF4IVw7fE/S9fxmKAoAYvFBt3tg+XEFXatQ88tfph5qpPTVyyTCJ/eEWVEAYIXNBN3lpxV009VL4zU+uSf02ACAlUQWR4PzUp0RlClW7TvkkOCFGsk394RgAwBWfNLtHlhenBHUuA1WD3jDkP71r8y3JxO8fk/+h2ADAFZ80u0eaF5dQTdbhp6sePWeNMCsKACw4qPNBAPPCzOCpOwONI05cE/YBBMAMonNBL3DC5tiEmoieeGexMBQFADE4oNud6RZly6xa2myNdR4HD02ANCUdG8mCO9Kdy+NV4bYAoZgAwDxeLjb3fe8+HCfMEH6y1+ijzvZQ8OK1mlDsAEAuMOLD/dM1NKEV7Ru/JrhFa0Z5kwJNTYAgMzz2nYVDzxgHWr27HE21MRb0VoyV7Suq3PumlmGHhsAgLV0DRN5bbuKTM54SmRFa4Y/k0KPDQAgWmWluY7P8OHS+PHm+9JSZ3pSvLJdxZtvWoeaH35I34wnVrROO3psAACR0l0D4oWHu1vr0rCiddp5osemurpav/jFL9S2bVu1aNFCffv21fvvv+92swAg+2SiBsTNh/uaNdahZu3azKxL45ONJP3M9WDz/fffa9CgQcrLy9M//vEPrVy5UnfddZdat27tdtMAIPtkYpjIrYd7KCTtt1/0ccOIXoQxXXyykaSfuR5sbrvtNpWUlGj27Nnq37+/SktLddxxx6lHjx5uNw0Ask8mhoky/XD/4QfrEPXWW+6sHsyK1mnlerB54YUX1K9fP51xxhlq3769DjvsMD3yyCMxz6+trVVNTU3EGwDAIZkaJsrUwz0Uklq0iD5uGNKgQc5cIxllZdLq1dKCBVJFhfl+1SpCjQNc3927oKBAkjRlyhSdccYZevfddzV58mQ99NBDOuecc6LOnzZtmqZPnx51nN29AcABTu9qHm/KeLqmlBuG1Mzib/dHHpEuvDD110fK0rW7t+vBpnnz5urXr5/++c9/1h+7/PLLtXTpUi1evDjq/NraWtXW1tZ/XFNTo5KSEoINADglPCtKst7V3G6PilsrC7MTty+kK9i4PhTVqVMn9erVK+LYQQcdpG+++cby/Pz8fLVq1SriDQDgICeGidxaWdgq1Pzyl4SaZNXVSVVV0pw55nsfrIjs+jo2gwYN0qeffhpx7LPPPlPXrl1dahEAIKVdze1MGf/Vr6SiInN1XSeGnuilcZ4X9/KywfUemyuuuEJLlizRjBkz9MUXX6iiokIPP/ywJk6c6HbTACC7hXc1HzcusQASb8q4JG3aJI0Y4cxqxlahpls3Qk0qvLaXVwJcr7GRpJdeeknXXXedPv/8c3Xr1k1TpkzRL3/5S1tfm64xOgBAkubMMbdhsCPRuh2rr23M/ceav4ULyGOF00QLyGMIbPFwqgg2AOAxVVXm3lJ2JfOgJNSkj937t2BBSht1BrZ4GAAQMPFWFm4skdWMQyHr1zWMzIcaHxbW2uKFvbxS4HrxMADABxJZbya8snB5uRlC7AaOeA9KL/XS+LSw1tZ99PlGnfTYAEBjQf1LPFmVlWbNxfDhZu3M8OHxi35jTRlvSvv21se91Esj+bewtqn72PB3vq7O1xt1UmMDAA359S/xdAk/xBs/KuwW/dbVmfs+XXVV/Gu9/rp03HHW12nMrUdXhgprHdfUfTQMqW1bc6ZaWPjjxj1uqRR7N0KNDQCkm1//Ek8XO+vRTJ7cdI9WTo7UubO9623c+NN/77eft3ppwjKx+7nT7NzHhqFGkjZvNt+3aRN53AcbdVJjAwBS/P/5h0LmQ3zMGG/9JZ5OiTzEm5odk2jNhtd6aRryY2GtnXWFGgv/zhcWmj1pGzc6u5dXGtFjAwCSP/8STzenHuLxZkmFazb+3//zZi9NQ34srE02ZBmG+W8iJyfxRRpdRLABAMmff4mnm1MP8fAsKSk6uIQ/XrNGeuyx6K9NJNBkoujbbkjzUmFtqiHLZ7/zBBsAkPz5l3i6OfkQjzVLqnVr6/BSV5dYqElm5lYy7IS0e+7xVs9GousKNeaz33mCDQBI/vxLPN2cfoiXlUmrV5sr1lZUmMe+/z76PMOQmiXweMp00bcTu59nUlP3sSk+/Z0n2ACA5M+/xDMhkYe4naGgnBwpL896L6mtWxOvpXFi5lYyGoe0BQvMKd5eCzVhse5j27bm+wD9zrOODQA0ZLWOTUmJ+T94rz60MiHeirV21/9xesZThvY1Cgyr+/i3v7nyO88mmDEQbAA4LpHtA7JR45/Pf/4jjR3b9CJ+/fubD8vGPvlEOvDA5NtidyfxigpzZg+sufA7n67nN+vYAEBjOTnB++veqQeXVc9MTk7T6/+cfrr1aznxdzVF384I0O88NTYAEHROzRiKVaTbVP2KVXipqHBuXRqKvtEIwQYAvMTptVicmjHUVJFuIubOdXZIiKJvNEKwAQCvcHotFidnDCWzLH9joRDTr5F2FA8DgFsa1r18/rk0bVryu2hbcXLGkN0i3XjSufs1Rd++QvEwAASJVRGulVQ24HRymwinim/tbpyZjAAVwCJ5DEUBQKbFqnuJJdkNOJ2cMTR4sNSmTWLXb4rP9h+CfxBsACCTUinCTTQMODljKCfHbLdTnJ5+nYkNMOELBBsAyKRUinATDQNOzxi64YafluC3Eq6f6dIls9OvM7UBJnyBYAMAmZTMEEwqYcDJGUM5OdKmTbHbKJlB6t57I481PsfJ6deZ3gATnkewAYBMSrTXxYkw4MSGjaFQ0ztDNwxKmZp+7dYGmPA0pnsDQCbV1ZnDJNXV9upsvLABZ6xAs2BB01Or0z39mg0wfY3p3gAQBOG6l/JyMzA0DDfhj6dPl3r2dH8tlliBpqLCXtvSPf3ayensCAyCDQBkWniopvE6NsXF7vfOhDU17BReqK+42AxpbrXXzxtgsphg2jAUBQBu8eLDranZTE6uiuyEeMN66VzlOBVWizO6HRJdkK7nN8EGAGCKFWqKi2NPUXc7PIRnRUnRw3qS9/aKCrfXayHRBel6fjMrCgCy3XHHWYcawzALb5tadyfZVZGd4qcNMJnFlRHU2ABANovVSxN+0PqhQLeszNxHy2vDeo3FW5wxnftoZRGCDQBkozvvlH7zm+jjP/4o5eX99HH79vZer/F5ma4f8sMGmH4IiQFAsAGATHO7aDheL02qKI615udZXD5CjQ2AYPPa5ohu7mv09tvWoWbt2tihZuNGe68dPo8tDmJzclNSxESwARBcXtsc0c2HfigkHXNM9HHDiC68bSiRXgaKY5vm9KaksESwARBMXus5cOuhv2GDdQ/BwoX2hp4S6WVIpDg2W/lpFpdPEWwABI8Xew7ceOiHQlLHjtbXGjLE3msk0stAcaw9TmxKipgINgCCx4s9B5l86O/ebd3DMnOm+b0nWndkt5eB4lj7wrO4xo0z3zP85BhmRQEIHi/2HGTqoR9vxlOyM5bsrBUTHraKt8UBxbFII3psAASPF3sOMjEjxuq1hw2LDDWp1B3F62WgOBYeQLABEDxenFabzod+KNT0lghS5uqOKI6Fywg2APwnXo2IV3sO0vHQt7vYXibrjiiOhYuosQHgL3ZrRMIhwurce+5x7yHr1L5Gia4enOm6Iz9scYBAItgA8I9wjUjjh3e4RqRxr4dXN0e0eugnss1CMlsieLHuCEiDkGE4tTmIO2pqalRUVKStW7eqVatWbjcHQLrU1ZmrBscaTgnPuFm1yv3gkii7vVCp7PEU/vnFm7Hkx58ffCldz29qbAD4gxfXpnGC3ZlKqW5c6dW6I8BhBBsA/uDFtWlSZWem0umnW4eaBQvMhfgavla8RfeYsYQsQI0NAH8IYo2InV6oWIYP/2m4SrK/6J5X644Ah1BjA8AfglgjMmeOuet4skKh2OEn3MtDTww8ihobANktiDUiqfYuNfV3qVubfQIuI9gA8I+g1YjEWyFZktq1S/71/VpQDaSAYAPAX4K0qm24F6qp4aSzzkr9On4qqE5EoruUIytQPAzAf4Kyqu348eZD2UpJiTm01qaN+T4VfiqotivZXcoReAQbAHBDrOGniorImUp1deYDO1bRdLxrFBdndrPPTEh0BWpkFYaiACCTHn7YOtRs324+qMeNM3ujwkXQdoqmm/qc3wqq48nULuXwLYINAGRKKCRddFH0ccOQWrSI/XVNFU3PnWu+BaWgOp6grkANx7g+FDVt2jRNnz494liHDh20fv16l1oEAA5btkw64ojo459+Ku2/v73XiLewnlOL7iWyGacbgrgCNRzlerCRpIMPPlivv/56/cc5XvpHBACpSHWPp4aaKpp2oqDaDwW5QVyBGo7yxFBUbm6uOnbsWP/WLpV1GwDAC77/3jrUPPNMcqEm3exuxum2eGv/hELmjLKgFUzDNk8Em88//1ydO3dWt27d9POf/1xfffWV200CgOSFQuY07cYMwwwJXuOngtwgrkANR7kebI466ij95S9/0bx58/TII49o/fr1GjhwoDZt2mR5fm1trWpqaiLeAMAT9uyx7kn49a+92UsT5reC3KCtQA1HuV5jc+KJJ9b/9yGHHKIBAwaoR48e+vOf/6wpU6ZEnT9z5syoYmMAcJ2TtTSZ5seCXHYpRwyuB5vGWrZsqUMOOUSff/655eevu+66iMBTU1OjkpKSTDUPAKJZhZoOHSS/zO70a0FuUFaghqM8F2xqa2v1ySefaHCMwq/8/Hzl5+dnuFUAYMHPvTQNhQtyY61uHNQVjBFIrtfYXHXVVVq4cKFWrVqld955R+Xl5aqpqdGECRPcbhoAxBaUUCNRkItAcT3YrF27VuPGjdMBBxygsrIyNW/eXEuWLFHXrl3dbhoARAuFrEONYfgz1IRRkIuACBmGn/8lmjU2RUVF2rp1q1q1auV2cwAEWZB6aWLx+srDCIx0Pb89V2MDICC8+IBMtk3ZEGjCKMiFzxFsADjPi0vzJ9umbAo1QAC4XmMDIGC8uDR/Mm0Kai0NEHDU2ABwTl2dVFoaexXb8LThVasyNyyVTJvopQHSLl3Pb3psADjHi0vzJ9ImemkA3yPYAHCOF5fmt3ut4cOtjxNoAF+heBiAc7y4NH+y1yLQAL5Ejw0A54SX5o9VoxIKSSUlmV2aP16brBBqAN8i2ABwjheX5m+qTY3t2UOoAXyOYAPAWV5cmj9WmxoyjMR6dQB4EjU2AJxXViaNGeO9lYetZkdt3izts0/m22KHF1dvBjyOYAM4jYeRyUtL8/txXRovrt4M+ABDUYCTKivNxeCGD5fGjzffl5a6s9oupC++sA41y5d7P9R4bfXmZNXVSVVV0pw55vu6OrdbhIBj5WHAKeGHUeN/UuEHq1v1JdnKj700kjdXb04WvU5oAisPA15WV2f+D9zqoRk+Nnkyf61mwo4d1qHmT3/yfqiRvLl6czKC1OsEXyHYAE4IysPI70IhqWXL6OOGIZ1/fubbkwwvrt6cKII+XESwAZwQhIeRn8Waqn3WWf7opWnIi6s3J4qgDxcxKwpwQhAeRn7l11qaWMIrJVdXW38P4RqbTK7enCiCPlxEjw3gBC9uJSAFf0aK1c+7VSv/hhrJm6s3J4qgDxcRbAAnePFhFOSp56GQdagxDGnr1sy3x2leXL05EV4N+sgKBBvAKV56GAV5RkrQhp5iKSuTVq+WFiyQKirM96tWeT/USN4M+sgarGMDOM3tlYeDtA5KQ/ECjds/d0SzWsempMQMNX4IaEirdD2/CTZA0FRVmcNO8SxY4J0tD+KJF2qycSE4vwQ5v7QTGZeu5zezooCgCdKMFDvDTrFWfA4Pu/mhJiVRfgpyXtozDFmBGhsgaIIyI8VOqMnGheCCXD8FOIBgAwSNl2ek2Jl+3tSMp8YBJtsWgsvGIAckiGADBI1XZ6TYmX6e6IynIA272ZFtQQ5IAsEGSJUXF8Hz0tRzKf7wSSK9NA0FZdjNrmwLckASKB4GUuHlIs6yMmnMmORmpDg5k8XO8IkVOxM2g7D9QCKyLcgBSWC6N5CsWLNxwj0Pfp2N43RYszv9PGz37sRCVfg+SJH3wu/3wUp4jaJ4Qc5vaxQhK6Xr+c1QFJCMoBZxpmPGTSLDInPnJr4NhNeG3dLJq/VTgIcQbIBkBLGIM11hze6wyPTpyYcqP28/kKhsCnJAEqixAZIRxCLORMJaIguuhetgmtrioUsX6ZFHYoeqUMgMVWPGxO6NyKaF4FKpnwICjmADJCMoRZwNi4RXrrT3NYmGtfffbzrUSNIvfynddFPs10g2VAVZNgU5IAEEGyAZQZiNY1UkbEciYS3WujRhxcVmTUhtrb3Xmz+fngkATaLGBkiG34s4Kyul009PLNQksmLxxo3WoWbxYus6GLth6fe/j19MDCCrMd0bSIVVr0dJiRlqvFrEWVcndeggbdpk/2sSmTqd6OrB4TY1NY052bYA8CymewNe5MfZOLfckliokezNuNm92zrUPPZY/LDSVA9YY36eTg8g7eixAbJJXZ3Uvr20eXP8c6dOlXr1sjfjJpleGiuJ1v0sWEABLeBT9NgASN2iRfZCjSQdd5w0bpwZHBINNRdemHiokX7qAZs61d75fppODyAjEgo2a9asSVc7AGSC3SDQpk38IuGmNq585JHE2xaWk2OGKju8Pp0eQMYlFGwOPPBA/fa3v9X27dvT1R4A6WQ3CEyalHgvTYcOyfXSWAlPp481xJXIDC0AWSWhYPPaa6/p1VdfVc+ePTV79ux0tQlAusQLDJLUtq10ww3Wn2uql2b9emfaKPl/Oj0A1yQUbAYOHKh33nlHt956q2688UYddthhqqqqSlPTADgu3uyjUEh6+GHrwOBUgbBd7IkEIAlJFQ+fc845+uyzz3TyySdr9OjROu200/TFF1843TYA6RArMJSUWAeGpnpp0j2p0o/T6QG4Kunp3jt27NCyZcs0d+5c3XvvvcrLy9PEiRM1bdo07b333k63MyamewNJarhPVKwp3ZnupQGQNdL1/E5or6g//vGPWrp0qZYuXapPPvlEOTk56tOnjyZOnKi+ffvqySefVK9evfTcc8+pX79+jjUSQBo0tYkigQaATyXUY1NSUqKjjz66/q1fv37Kz8+POGfGjBmqqKjQRx995HhjrdBjAziMUAMgA9L1/HZ85eENGzaoc+fOqsvQUucEG8AhXg40dobNAPiKJ4ai7Gjfvr3eeOMNp18WQDp5OdRYbbNQXGzO7qKIGEAjjm+pEAqFNHToUKdfFkA6HHigezOe7KislMrLo/eOqq42j1dWutMuAJ7FXlFAtgqFpE8/jT7uhUAjmcNPkyZZt4cdvgHEQLABss3Eid7upQlbtKjpXb4NQ1qzxjwPAP7H8RobABmSTEGtl2tpGrO7YSc7fANogB4bwI8qK6XSUmn4cGn8ePN9aWnsmpOKCutQs3OnN0ONZH/DTnb4BtCA49O9M43p3sg64YLaxv90w8Gl8bYIbvfSJDtVu67ODGvV1dZtDYXM2VGrVjH1G/ChdD2/PdVjM3PmTIVCIU2ePNntpgDeVFcn/epX9gpqV6ywDjVr1mQu1CTas9QQO3wDSIJngs3SpUv18MMPq0+fPm43BfCuW26RNm2K/flwQW1urmT1b8kwzF6OTHBiqjY7fANIkCeCzX//+1+dddZZeuSRR7TPPvu43RzAm+rqfurBSNT8+ZmtpXFyqjY7fANIgCeCzcSJEzV69GiNGDHC7aYA3rVokbR5c+JfZxjSscc6356mOD1VO7xh57hx5nuGnwDE4Pp076eeekrLli3T0qVLbZ1fW1ur2tra+o9ramrS1TTAWxKd1nzbbdLVV6enLfEwVRuAS1wNNmvWrNGkSZP06quvqqCgwNbXzJw5U9OnT09zywAPSmRas9uTHZmqDcAlrk73fv7553Xaaacpp0G3cl1dnUKhkJo1a6ba2tqIz0nWPTYlJSVM94b70r0Ddbzpz5JZNLxzp/tDNUzVBhCHb3b3TsRxxx2nFStWRBw777zzdOCBB+qaa66JCjWSlJ+fr/z8/Ew1EbAnEztQh6c/n3567HOeftobQSHc1vJyM8Q0DDdM1QaQRq4WD++9997q3bt3xFvLli3Vtm1b9e7d282mAfZlcgfqWKGmpESaO9dbM4WYqg3ABa4XDwO+Fm9acyhkTmseMya13olYqwdXVKRn2MspZWXm9241RJfuoTsAWclzwaaqqsrtJgD2JTKtediw5K7h9pYIqQpP1W4oE0N3ALKSJ9axAXwrndOaQyHrUGMY/gk1VjI5dAcg6xBsgFSka1qz33tpYnFyRWIAsECwAVIxeLA5hBIriIRCZmHv4MH2Xi+ovTRhTq9IDACNEGyAVDi5A3VQe2kaYkViAGlGsAFSleq05qD30jTEisQA0szVlYedkK6VC4GEJTN9ORt6aRpiRWIA/xPIlYeBQLGa1hzL8OGS1dIGQQ00YaxIDCDNGIoCMi0Uys5QE8aKxADSiGADZMott1gPPe3Zkz2hJqysTFq9WlqwwFw9ecECc/iJUAMgRQxFAZmQbbU0diQydAcANtFjA6TTvHnWoWbbtuwONQCQJvTYAOlCLw0AZBw9NoBkTkOuqpLmzDHfp7Kk/1dfWYeazz4j1ABAmtFjAzi50zS9NADgKnpskN2c2ml6xw7rUPPCC4QaAMggVh5G9gqvghtrU0a7q+DSSwMACUvX85seG2SvVHeaNgzrUHPDDYQaAHAJNTbIXqnsNE0vDQB4Ej02yF7J7jRtFWqOPZZQAwAeQI8NstfgwWYNTbydpgcP/uljKwQaAPAMemzgP06tORPeaVqKDi2Nd5om1ACALxBs4C+VleZMpuHDpfHjzfelpfanZTcWb6fp00+3DjWGQagBAA9iujf8I7zmTONf2XDwePbZ5HeHrqszZz+tW2fW1AweLOXGGKn19z8ZAPCEdD2/CTbwB6fWnLGDYScASDvWsUF2S3XNGbsINQDga8yKgj+ksuaMHQQaAAgEemzgD8muOWMHoQYAAoNgA38IrzkTK4SEQlJJyU9rztjRuzczngAgYBiKgj+E15wpLzfDSMPg0XjNGTtS7aWxmkWVatEyACBl9NjAP+KtOWNnqvfll6feS+P0WjoAAMcw3Rv+k2xviRO1NOlcS8cueosABADr2MRAsEFcTzwhnX129PG6OqlZAp2WmVxLJ5bKSmnSpMg2FBebw3TpDlQA4CDWsQGSEQpZhxrDSCzUSJlbSyeWcG9R4zZUV5vHGQoDgCwKNk5tnIjEufGz//BD66GnLVush57stDHda+k0pa7O7Kmxanv42OTJ/F4DyHrZMSuK7vvkpVrP4cbPPtFaGrttTOdaOvEk0ls0bJjz1wcAnwh+jw3d98lLdfZPpn/2331nHWo+/bTpUGO3jelYS8cuN3uLAMBHgh1s6L5PXqqhJNM/+1BIatfO+lr77+9MG8Nr6YSv1/j6UmJr6STCzd4iAPCRYAcbt4s9/cqJUJKpn/2uXdY9KC+/HH8adzJtdGItnWS42VsEAD4S7GBD931ynAgldn+m8+cn32sTCknNm1u3b9So+F+f7O9HWZm0erW0YIFUUWG+X7UqvfVabvYWAYCPBDvY0H2fHCcCod2f6e9/n9yqvVY9F7fckthie6n8fuTkmEW648aZ7zMRKNzqLQIAHwn2An3hBdWqq60feJlYUM2PqqrMQuF4FiyIPQMn3s++oURW7XVyJ26//n6w8jCAAGCBvmTQfZ8cJ+o5mvrZN2a3bsfqdUaPTn4nbr/+frjRWwQAPhHsYCPRfZ8Mpx74sX72Vpqq2wmFYm9c+dJL8V87mTby+wEAvhTsoaiG6L5PnNXCdSUlZqhJ5IFfVydNm2bW08RTUWH2RIRZBZru3aUvv7R/fTv4/QCAjGITzBjYBDPNnHrgJ1q342QtDQDAc9L1/M6OLRWykVOBJFzPkapw3U68Qt3Bgwk1AICkBb/GJhuluhVCOtip21mzRsq1yNqGQagBANhCsAkaL++N1VShbqzgkuxO3ACArESNTZCE12WJtWqwV9ZlaThM9tvfWhcCp7oTt1+4XbTs9vUBZC1qbBBfIlshOFE3k6xw3U6itTTh3qjGnw/3RvlterbbIc3t6wNAGjAUFSR+2Rvrmmtir0sTK9QEbad2t4cM3b4+AKQJwSZI/LA3Vigk3X579PF07MTtVW6HNLevDwBpRLAJEie2QkiXJ55IvJemIb/0Rtnhdkhz+/oAkEbU2ARJeEp1ebkZIhoGBjf3PnJiXRo/9EbZ5XZIc/v6AJBG9NgEjZf2Plq61DrU7NqV+Lo0Xu6NSpTbIc3t6wNAGjHdO6gyOY3X6lpWC+1JqS20Fy54bfw64bDjl1lR4Wn58VZhTte0fLevDwBK3/ObHpugCk+pHjfOfJ+uB5TVKsdWoWbr1tRXD/ZSb1QqnNo93a/XB4A0cj3YPPjgg+rTp49atWqlVq1aacCAAfrHP/7hdrNgR6wpw40ZhuRUGi8rk1avNjfLrKgw369a5Z9QE+Z2SHP7+gCQJq4PRb344ovKycnRz372M0nSn//8Z91xxx1avny5Dj744Lhfz1CUS+KtciyZw1Jr1vCXf1PcXvnX7esDyFrpen67HmystGnTRnfccYcuuOCCuOcSbFxSVWUOO8WzYIG7qxwDADwpK7ZUqKur0zPPPKPt27drwIABlufU1taqtra2/uOamppMNQ8NVVfbO8/tKcP0SABAVvFEsFmxYoUGDBignTt3aq+99tJzzz2nXr16WZ47c+ZMTZ8+PcMtRIRYU66tuDllON5eSIQeAAgcTwxF/fjjj/rmm2+0ZcsWzZ07V48++qgWLlxoGW6semxKSkoYisoUu6HG7SnDsTbMDLf/qqukOXPYABIAXJJVNTYjRoxQjx499NBDD8U9lxqbDGlqYTzJW+vK2ClstuJ2uwEgi2TVOjaGYUT0ysBlVqHmttvMMOPFKcPx9kKKhQ0gAcD3XK+xuf7663XiiSeqpKRE27Zt01NPPaWqqiq98sorbjctuOzWlhx2mPTBB9HHG/bOlJVJY8Z4q1YllYLlhhtAMpsLAHzH9WCzYcMGnX322Vq3bp2KiorUp08fvfLKKzr++OPdblowxSuoDbPqpbn88p9WrG0ovMqxVzhRsOz2bC4AQFI8WWOTCGpsEhCvoPbZZ6XnnpOeeCL6a/30axJvLyQ7WH8HANIqq2pskAZ1dWZPjdWDPnzs9NOjQ83Eif4KNZK9vZBi8dMu4QCAKASbbBGvoDZW4PnDH9LXpnRqai+k3/zGDDBsAAkAgUOwyRaJ1IwMH+6/XhorsTbMvP12b87mAgCkzPXiYWSI3YLaoNWWxCps9uJsLgBAygg22WLwYLNHoqmCWru1JUHZisBrs7kAACljKCpb5OTELgQO15vYqS2prDRnHA0fLo0fb74vLTWPAwDgMnpsskVTs4GKi81QE6+2JNZ08epq8zj1KQAAl7GOTdB99pl0wAHRx994Q1q/3v5QUrz9l9ze9BIA4Cvpen7TYxNksXppksmydqaLp2srgqDU9AAA0o4amyDautU61Ozenfw0brvTxZ3eioCaHgBAAgg2QRMKSa1bRx83jNR6OexOF3din6awcE1P456icE0P4QYA0AjBJihqa617aXbudGaxvfB08VjDW05vRWBnC4jJk83zAAD4H4JNEHTsKBUURB83DCk/35lr2Nl/ycmtCBKp6QEA4H8INn5WV2eGig0bIo9v3ZqeLRGa2n/J6anebtX0AAB8jVlRfjVwoLR4cfTxdM/ez9RWBG7U9AAAfI9g4zeGITWz6Ghbt84cksqETGxFEG8LiPC6OU7V9AAAAoGhKD856yzrUGMYmQs1mZLpmh4AQCAQbPwiFJIqKiKPffZZ+oee3JTJmh4AQCAwFOV1V18t3XFH9PEgB5qGMlXTAwAIBIKNl1mtGfPuu9KRR2a+LW7KRE0PACAQGIryolmzrEONYWRfqAEAIAH02DjJic0arQLNK69II0c600YAAAKMHhunpLpZ49NPx+6lIdQAAGALwcYJqW7WGApJP/955LEnnsieAmEAABxCsElVKps1zp8fu5fmrLMcbSYAANmAYJOqZDdrDIWkESMij91xB700AACkgOLhVCW6WePy5dLhh0d/nkADAEDK6LFJVSKbNYZC0aHmiisINQAAOIQem1TZ2ayxY0dzllRjBBoAABxFj02q4m3WaBjRw1Xl5YQaAADSgB4bJ4Q3a5w0KbKQ2Cq87NljPRPKT5xYiBAAgDSgx8YpZWXS6tXSggXWnz/iCDPo+D3UpLoQIQAAaUSwcVJtrXUtze7d0nvvZb49Tkt1IUIAANKMYOOUoUOlli0jj+2zj9lLE4RhmlQWIgQAIEOosUnV7t1SXl708R9+kAoKMt+edElkIcJhwzLWLAAAGqLHJhXjx0eHmkMPNR/yQQo1UuILEQIA4AJ6bJJhGFIzi0xYUyPtvXfm25NudXXShg32zrW7YCEAAGlAj02irrwyOtSEa2mCGGrCs6CuuKLp80IhqaTEnPoNAIBLgtNjU1cnVVWld20Vq6naGzdK7do5ex2vCM+CireYYPjncs89wSiUBgD4VnCCTe/e0rff/vRxcbG5InBZWeqvffvt0jXXRB/38urBqS6i19QsqMaKi81Q48TPGgCAFAQn2DQMNdJPa6s8+2xqD1yrXprVq6WuXZN/zXSrrIxeBTnRoBdvFlTY//2/0mWX0VMDAPCE4NbYpLq2yuzZ1qHGMLwfapxYRM/u7KYOHQg1AADPCG6wkSLXVklEKCSdf37ksRUrvD30JDm7iJ7d2U3MggIAeEiwg02Y3d6Hv/0tdi9N797OtikdEllEL57Bg83hq1h7WzELCgDgQdkRbOz0KoRC0qmnRh57+23v99I05OQiejk5Zk2OFB1umAUFAPCoYAcbO70KixbF7qUZODB9bUsHp4ePysrM4usuXSKPFxenXpQNAEAahAzDT10S0WpqalRUVKStklo1/EQ4rDT1ALYKNC+/LI0a5XArM6SuzlxMr7rauqcpFDJDyapViU/9TmXqOAAAjdQ/v7duVatWreJ/gU3Bme7duXP0Ojax1lb517/MPZ0a83fG+2n4qLzcDDENv59Uho9yctjYEgDgC8EZivroI2nBAqmiwny/apV1qAmFokPNX/7i/1ATxvARACCLBWcoKl5X1oYNUseO0cf9/e3HxvARAMDD0jUUFZwem6aMHBkdau66K7ihRvpp+GjcOPM9oQYAkAWCU2NjZedOqbAw+niQAw0AAFksuD02lZXRoeaJJwg1AAAEWPB6bHbtMvdyargI3YAB5mJ7sVbRBQAAgRCsHpt586TmzSNDzTvvSP/8J6EGAIAsEJxgs3279H/+z08f9+9vzgzq39+9NgEAgIxyPdjMnDlTRx55pPbee2+1b99ep556qj799NPEX6igQBo61Pzvqiqzp6aZ698eAADIINef/AsXLtTEiRO1ZMkSvfbaa9q9e7dOOOEEbd++PbEXyskxA41h/BRwAABAVvHcAn3/+c9/1L59ey1cuFBDhgyJe37cBX5YqA4AAM/Jmr2itm7dKklq06aN5edra2tVW1tb/3FNTU3sF6uslCZNktau/elYcbG5nxJbCwAAEDiuD0U1ZBiGpkyZomOOOUa9e/e2PGfmzJkqKiqqfyspKbF+scpKczPIhqFGMne+Li83Pw8AAALFU0NREydO1Msvv6y33npLxcXFludY9diUlJREdmXV1UmlpdGhJiwUMntuVq1iWAoAABcEfijqsssu0wsvvKA333wzZqiRpPz8fOXn5zf9YosWxQ41kllgvGaNed6wYck1GAAAeI7rwcYwDF122WV67rnnVFVVpW7duqX+og0X6HPiPAAA4AuuB5uJEyeqoqJCf/vb37T33ntr/fr1kqSioiIVWm1gaUenTs6eBwAAfMH1GptQjK0OZs+erXPPPTfu11uO0YVrbKqrrTe9pMYGAABXBbbGJi25KifHnNJdXm6GmIbXCAepe+4h1AAAEDCemu7tqLIy6dlnpS5dIo8XF5vHWccGAIDAcb3HJq3KyqQxY1h5GACALBHsYCOZIYYp3QAAZIXgDkUBAICsQ7ABAACBQbABAACBQbABAACBQbABAACBQbABAACBQbABAACBQbABAACBQbABAACBQbABAACBQbABAACBQbABAACBQbABAACBQbABAACBQbABAACBket2A+BhdXXSokXSunVSp07S4MFSTo7brQIAICaCDaxVVkqTJklr1/50rLhYmjVLKitzr10AADSBoShEq6yUyssjQ40kVVebxysr3WkXAABxEGwQqa7O7KkxjOjPhY9NnmyeBwCAxxBsEGnRouiemoYMQ1qzxjwPAACPIdgg0rp1zp4HAEAGEWwQqVMnZ88DACCDCDaINHiwOfspFLL+fCgklZSY5wEA4DG+n+5t/K+gtaamxuWWBMjMmdLZZ1t/zjCkGTOk7dsz2yYAQKCEn9uG1WSVFIQMp18xw7766iv16NHD7WYAAIAkfPnll+revbtjr+f7Hps2bdpIkr755hsVFRW53JrsVlNTo5KSEq1Zs0atWrVyuzlZj/vhHdwL7+BeeMfWrVu133771T/HneL7YNOsmVkmVFRUxC+pR7Rq1Yp74SHcD+/gXngH98I7ws9xx17P0VcDAABwEcEGAAAEhu+DTX5+vm666Sbl5+e73ZSsx73wFu6Hd3AvvIN74R3puhe+nxUFAAAQ5vseGwAAgDCCDQAACAyCDQAACAyCDQAACAxfBJsHHnhA3bp1U0FBgY444ggtWrSoyfMXLlyoI444QgUFBerevbv++Mc/ZqilwZfIvaisrNTxxx+vdu3aqVWrVhowYIDmzZuXwdYGW6L/LsLefvtt5ebmqm/fvultYJZJ9H7U1tbqhhtuUNeuXZWfn68ePXrosccey1Brgy3Re/Hkk0/q0EMPVYsWLdSpUyedd9552rRpU4ZaG1xvvvmmTj75ZHXu3FmhUEjPP/983K9x5PlteNxTTz1l5OXlGY888oixcuVKY9KkSUbLli2Nr7/+2vL8r776ymjRooUxadIkY+XKlcYjjzxi5OXlGc8++2yGWx48id6LSZMmGbfddpvx7rvvGp999plx3XXXGXl5ecayZcsy3PLgSfRehG3ZssXo3r27ccIJJxiHHnpoZhqbBZK5H6eccopx1FFHGa+99pqxatUq45133jHefvvtDLY6mBK9F4sWLTKaNWtmzJo1y/jqq6+MRYsWGQcffLBx6qmnZrjlwfP3v//duOGGG4y5c+cakoznnnuuyfOden57Ptj079/fuPjiiyOOHXjggca1115ref7VV19tHHjggRHHLrroIuPoo49OWxuzRaL3wkqvXr2M6dOnO920rJPsvRg7dqwxdepU46abbiLYOCjR+/GPf/zDKCoqMjZt2pSJ5mWVRO/FHXfcYXTv3j3i2L333msUFxenrY3ZyE6wcer57emhqB9//FHvv/++TjjhhIjjJ5xwgv75z39afs3ixYujzh85cqTee+897dq1K21tDbpk7kVje/bs0bZt2xzf8CzbJHsvZs+erS+//FI33XRTupuYVZK5Hy+88IL69eun22+/XV26dNH++++vq666Sj/88EMmmhxYydyLgQMHau3atfr73/8uwzC0YcMGPfvssxo9enQmmowGnHp+e3oTzO+++051dXXq0KFDxPEOHTpo/fr1ll+zfv16y/N3796t7777Tp06dUpbe4MsmXvR2F133aXt27frzDPPTEcTs0Yy9+Lzzz/Xtddeq0WLFik319P/7H0nmfvx1Vdf6a233lJBQYGee+45fffdd7rkkku0efNm6mxSkMy9GDhwoJ588kmNHTtWO3fu1O7du3XKKafovvvuy0ST0YBTz29P99iEhUKhiI8Nw4g6Fu98q+NIXKL3ImzOnDmaNm2ann76abVv3z5dzcsqdu9FXV2dxo8fr+nTp2v//ffPVPOyTiL/Nvbs2aNQKKQnn3xS/fv316hRo3T33Xfr8ccfp9fGAYnci5UrV+ryyy/XjTfeqPfff1+vvPKKVq1apYsvvjgTTUUjTjy/Pf2n27777qucnJyopL1x48aoVBfWsWNHy/Nzc3PVtm3btLU16JK5F2FPP/20LrjgAj3zzDMaMWJEOpuZFRK9F9u2bdN7772n5cuX69JLL5VkPlgNw1Bubq5effVVHXvssRlpexAl82+jU6dO6tKli4qKiuqPHXTQQTIMQ2vXrlXPnj3T2uagSuZezJw5U4MGDdJvfvMbSVKfPn3UsmVLDR48WL///e/p5c8gp57fnu6xad68uY444gi99tprEcdfe+01DRw40PJrBgwYEHX+q6++qn79+ikvLy9tbQ26ZO6FZPbUnHvuuaqoqGDM2iGJ3otWrVppxYoV+uCDD+rfLr74Yh1wwAH64IMPdNRRR2Wq6YGUzL+NQYMG6dtvv9V///vf+mOfffaZmjVrpuLi4rS2N8iSuRc7duxQs2aRj8KcnBxJP/UWIDMce34nVGrsgvDUvT/96U/GypUrjcmTJxstW7Y0Vq9ebRiGYVx77bXG2WefXX9+eLrYFVdcYaxcudL405/+xHRvhyR6LyoqKozc3Fzj/vvvN9atW1f/tmXLFre+hcBI9F40xqwoZyV6P7Zt22YUFxcb5eXlxscff2wsXLjQ6Nmzp3HhhRe69S0ERqL3Yvbs2UZubq7xwAMPGF9++aXx1ltvGf369TP69+/v1rcQGNu2bTOWL19uLF++3JBk3H333cby5cvrp96n6/nt+WBjGIZx//33G127djWaN29uHH744cbChQvrPzdhwgRj6NChEedXVVUZhx12mNG8eXOjtLTUePDBBzPc4uBK5F4MHTrUkBT1NmHChMw3PIAS/XfREMHGeYnej08++cQYMWKEUVhYaBQXFxtTpkwxduzYkeFWB1Oi9+Lee+81evXqZRQWFhqdOnUyzjrrLGPt2rUZbnXwLFiwoMlnQLqe3yHDoK8NAAAEg6drbAAAABJBsAEAAIFBsAEAAIFBsAEAAIFBsAEAAIFBsAEAAIFBsAEAAIFBsAEAAIFBsAEAAIFBsAEAAIFBsAHgOXPmzFFBQYGqq6vrj1144YXq06ePtm7d6mLLAHgde0UB8BzDMNS3b18NHjxYf/jDHzR9+nQ9+uijWrJkibp06eJ28wB4WK7bDQCAxkKhkG655RaVl5erc+fOmjVrlhYtWkSoARAXPTYAPOvwww/Xxx9/rFdffVVDhw51uzkAfIAaGwCeNG/ePP373/9WXV2dOnTo4HZzAPgEPTYAPGfZsmUaNmyY7r//fj311FNq0aKFnnnmGbebBcAHqLEB4CmrV6/W6NGjde211+rss89Wr169dOSRR+r999/XEUcc4XbzAHgcPTYAPGPz5s0aNGiQhgwZooceeqj++JgxY1RbW6tXXnnFxdYB8AOCDQAACAyKhwEAQGAQbAAAQGAQbAAAQGAQbAAAQGAQbAAAQGAQbAAAQGAQbAAAQGAQbAAAQGAQbAAAQGAQbAAAQGAQbAAAQGAQbAAAQGD8f18J0+uuOxrGAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "
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@@ -639,7 +639,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c76d7e9f",
+   "id": "aa05a791",
    "metadata": {
     "editable": true
    },
@@ -650,7 +650,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6a528c0f",
+   "id": "bac50889",
    "metadata": {
     "editable": true
    },
@@ -663,7 +663,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "74377872",
+   "id": "825fa9b8",
    "metadata": {
     "editable": true
    },
@@ -684,7 +684,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "27ad828e",
+   "id": "a3fa2a46",
    "metadata": {
     "editable": true
    },
@@ -696,7 +696,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b7bf4db8",
+   "id": "7627c75a",
    "metadata": {
     "editable": true
    },
@@ -706,7 +706,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7530e179",
+   "id": "52686744",
    "metadata": {
     "editable": true
    },
@@ -718,7 +718,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4672d1e1",
+   "id": "f6cb9efa",
    "metadata": {
     "editable": true
    },
@@ -730,7 +730,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "00840b14",
+   "id": "4822580d",
    "metadata": {
     "editable": true
    },
@@ -742,7 +742,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0c5e43af",
+   "id": "fb61578b",
    "metadata": {
     "editable": true
    },
@@ -753,7 +753,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a4a68023",
+   "id": "6cb0d520",
    "metadata": {
     "editable": true
    },
@@ -765,7 +765,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "adbc5f5f",
+   "id": "87835e2a",
    "metadata": {
     "editable": true
    },
@@ -787,7 +787,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "be191d7c",
+   "id": "69fc895e",
    "metadata": {
     "editable": true
    },
@@ -799,7 +799,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f4549dc2",
+   "id": "debfac94",
    "metadata": {
     "editable": true
    },
@@ -814,7 +814,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "a6bd8c76",
+   "id": "96164cb3",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -822,7 +822,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "
"
       ]
@@ -838,7 +838,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.00499999999999999\n"
+      "0.0050000000000000044\n"
      ]
     }
    ],
@@ -877,7 +877,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6c8b9965",
+   "id": "7cac7e62",
    "metadata": {
     "editable": true
    },
@@ -892,7 +892,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2852a934",
+   "id": "8b9b62fe",
    "metadata": {
     "editable": true
    },
@@ -904,7 +904,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "74fd99a0",
+   "id": "81afb7da",
    "metadata": {
     "editable": true
    },
@@ -914,7 +914,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "386c640d",
+   "id": "266b9b6d",
    "metadata": {
     "editable": true
    },
@@ -926,7 +926,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9e7c7132",
+   "id": "26bfbb29",
    "metadata": {
     "editable": true
    },
@@ -936,7 +936,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3bfd0139",
+   "id": "7e4ee0e4",
    "metadata": {
     "editable": true
    },
@@ -948,7 +948,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0ddf3bb3",
+   "id": "40627d20",
    "metadata": {
     "editable": true
    },
@@ -958,7 +958,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e9c65dac",
+   "id": "b4fbd650",
    "metadata": {
     "editable": true
    },
@@ -970,7 +970,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6addb221",
+   "id": "40eecb7a",
    "metadata": {
     "editable": true
    },
@@ -986,7 +986,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "75fd0e61",
+   "id": "97f2b230",
    "metadata": {
     "editable": true
    },
@@ -998,7 +998,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "209a6361",
+   "id": "34eed0f8",
    "metadata": {
     "editable": true
    },
@@ -1009,7 +1009,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ecf9b9da",
+   "id": "37ca2a5b",
    "metadata": {
     "editable": true
    },
@@ -1021,7 +1021,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a931cfa6",
+   "id": "36071972",
    "metadata": {
     "editable": true
    },
@@ -1035,7 +1035,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f2cbfc44",
+   "id": "32a6cbaf",
    "metadata": {
     "editable": true
    },
@@ -1047,7 +1047,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "719d770d",
+   "id": "68d9be7d",
    "metadata": {
     "editable": true
    },
@@ -1072,7 +1072,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4983af73",
+   "id": "7ec5f81c",
    "metadata": {
     "editable": true
    },
@@ -1089,7 +1089,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "b5c8db7d",
+   "id": "fb39ace0",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1108,7 +1108,7 @@
     "# Where to save the figures and data files\n",
     "PROJECT_ROOT_DIR = \"Results\"\n",
     "FIGURE_ID = \"Results/FigureFiles\"\n",
-    "DATA_ID = \"datafiles/\"\n",
+    "DATA_ID = \"DataFiles/\"\n",
     "\n",
     "if not os.path.exists(PROJECT_ROOT_DIR):\n",
     "    os.mkdir(PROJECT_ROOT_DIR)\n",
@@ -1133,7 +1133,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b54a87d1",
+   "id": "fc71e4e1",
    "metadata": {
     "editable": true
    },
@@ -1144,7 +1144,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "180faa92",
+   "id": "30e59ca4",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1166,7 +1166,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6fe942e7",
+   "id": "e58a020b",
    "metadata": {
     "editable": true
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@@ -1183,7 +1183,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "68ed7165",
+   "id": "de0f27ba",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1215,7 +1215,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2b775c80",
+   "id": "5da52984",
    "metadata": {
     "editable": true
    },
@@ -1229,7 +1229,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "2c94c74f",
+   "id": "ba67d882",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1272,7 +1272,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7be1a100",
+   "id": "b75681d2",
    "metadata": {
     "editable": true
    },
@@ -1292,7 +1292,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "b1e553c9",
+   "id": "d9760898",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1309,7 +1309,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b1e4faff",
+   "id": "5199cc2b",
    "metadata": {
     "editable": true
    },
@@ -1321,7 +1321,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "c460ff45",
+   "id": "d48c42f2",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1339,7 +1339,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8095634f",
+   "id": "91ca751e",
    "metadata": {
     "editable": true
    },
@@ -1355,7 +1355,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "61ce83d5",
+   "id": "ea82f2ad",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1368,7 +1368,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4fd2409e",
+   "id": "0fb84108",
    "metadata": {
     "editable": true
    },
@@ -1380,7 +1380,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "dbaacd6c",
+   "id": "3b24153e",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1410,7 +1410,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b4982c89",
+   "id": "d9acc526",
    "metadata": {
     "editable": true
    },
@@ -1421,7 +1421,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "931f039d",
+   "id": "942b4d36",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1462,7 +1462,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e3f80f20",
+   "id": "8fe6a29e",
    "metadata": {
     "editable": true
    },
@@ -1484,7 +1484,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "e56d424f",
+   "id": "5dc4b33c",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1524,7 +1524,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "27dbd129",
+   "id": "8dd1a395",
    "metadata": {
     "editable": true
    },
@@ -1594,7 +1594,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "936b8537",
+   "id": "9845070b",
    "metadata": {
     "editable": true
    },
@@ -1606,7 +1606,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d6fb0e7c",
+   "id": "fc1de086",
    "metadata": {
     "editable": true
    },
@@ -1625,7 +1625,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c370ec54",
+   "id": "e9b27dfb",
    "metadata": {
     "editable": true
    },
@@ -1637,7 +1637,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "df7b365e",
+   "id": "d80ac08b",
    "metadata": {
     "editable": true
    },
@@ -1649,7 +1649,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "96f2187d",
+   "id": "59de586a",
    "metadata": {
     "editable": true
    },
@@ -1667,7 +1667,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6433b033",
+   "id": "3a0a6d11",
    "metadata": {
     "editable": true
    },
@@ -1677,7 +1677,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d7d4ff3b",
+   "id": "f545dd3f",
    "metadata": {
     "editable": true
    },
@@ -1689,7 +1689,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5a52a847",
+   "id": "a4484e53",
    "metadata": {
     "editable": true
    },
@@ -1699,7 +1699,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3911b9d1",
+   "id": "7a1e5488",
    "metadata": {
     "editable": true
    },
@@ -1711,7 +1711,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "069cbce7",
+   "id": "65f8b020",
    "metadata": {
     "editable": true
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@@ -1721,7 +1721,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "98c47380",
+   "id": "0ded825d",
    "metadata": {
     "editable": true
    },
@@ -1733,7 +1733,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "69851a2e",
+   "id": "4747b482",
    "metadata": {
     "editable": true
    },
@@ -1743,7 +1743,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "27367380",
+   "id": "1a50e6b1",
    "metadata": {
     "editable": true
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@@ -1762,7 +1762,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "57e81e74",
+   "id": "2404cda8",
    "metadata": {
     "editable": true
    },
@@ -1772,7 +1772,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "306e51b5",
+   "id": "23bc5fc5",
    "metadata": {
     "editable": true
    },
@@ -1784,7 +1784,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "076e49a1",
+   "id": "ae3d3214",
    "metadata": {
     "editable": true
    },
@@ -1800,7 +1800,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6ca152bd",
+   "id": "bd07fd5c",
    "metadata": {
     "editable": true
    },
@@ -1820,7 +1820,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aa30be64",
+   "id": "cd7bdfc0",
    "metadata": {
     "editable": true
    },
@@ -1832,7 +1832,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "138531d5",
+   "id": "acb43d02",
    "metadata": {
     "editable": true
    },
@@ -1851,7 +1851,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e2760fce",
+   "id": "f1e9d7fc",
    "metadata": {
     "editable": true
    },
@@ -1861,7 +1861,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8df047bc",
+   "id": "c41dcd08",
    "metadata": {
     "editable": true
    },
@@ -1873,7 +1873,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dee93942",
+   "id": "89ebc548",
    "metadata": {
     "editable": true
    },
@@ -1885,7 +1885,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2dcb17e6",
+   "id": "8e0a376a",
    "metadata": {
     "editable": true
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@@ -1905,7 +1905,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ecf88b59",
+   "id": "b2c90704",
    "metadata": {
     "editable": true
    },
@@ -1922,7 +1922,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "f56b01eb",
+   "id": "e89031dc",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1939,7 +1939,7 @@
     "# Where to save the figures and data files\n",
     "PROJECT_ROOT_DIR = \"Results\"\n",
     "FIGURE_ID = \"Results/FigureFiles\"\n",
-    "DATA_ID = \"datafiles/\"\n",
+    "DATA_ID = \"DataFiles/\"\n",
     "\n",
     "if not os.path.exists(PROJECT_ROOT_DIR):\n",
     "    os.mkdir(PROJECT_ROOT_DIR)\n",
@@ -2002,7 +2002,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9987d583",
+   "id": "7a1e8f92",
    "metadata": {
     "editable": true
    },
@@ -2012,7 +2012,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "de65ddd1",
+   "id": "84bf3673",
    "metadata": {
     "editable": true
    },
@@ -2024,7 +2024,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a11d4702",
+   "id": "e10c4fc5",
    "metadata": {
     "editable": true
    },
@@ -2036,7 +2036,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2050adb4",
+   "id": "20866df8",
    "metadata": {
     "editable": true
    },
@@ -2048,7 +2048,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "233838f8",
+   "id": "a075809a",
    "metadata": {
     "editable": true
    },
@@ -2058,7 +2058,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a849e753",
+   "id": "ba0e251b",
    "metadata": {
     "editable": true
    },
@@ -2070,7 +2070,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "56d0fde7",
+   "id": "93a04fb7",
    "metadata": {
     "editable": true
    },
@@ -2080,7 +2080,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4b42a997",
+   "id": "56c4fcf1",
    "metadata": {
     "editable": true
    },
@@ -2092,7 +2092,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "02d1ba2c",
+   "id": "e8fc405e",
    "metadata": {
     "editable": true
    },
@@ -2105,7 +2105,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1ca3f7c3",
+   "id": "fefb3fd3",
    "metadata": {
     "editable": true
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@@ -2117,7 +2117,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2bab5254",
+   "id": "d0eeb2dc",
    "metadata": {
     "editable": true
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@@ -2129,7 +2129,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0a2dcdbd",
+   "id": "1f408b23",
    "metadata": {
     "editable": true
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@@ -2141,7 +2141,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d00d31ad",
+   "id": "dee2a248",
    "metadata": {
     "editable": true
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@@ -2152,7 +2152,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0b9759c4",
+   "id": "f1d1d292",
    "metadata": {
     "editable": true
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@@ -2164,7 +2164,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f98e5455",
+   "id": "8b2075d1",
    "metadata": {
     "editable": true
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@@ -2183,7 +2183,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "03e2f2c5",
+   "id": "0c3145a6",
    "metadata": {
     "editable": true
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@@ -2196,7 +2196,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e8920d79",
+   "id": "e72f34b6",
    "metadata": {
     "editable": true
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@@ -2206,7 +2206,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "98ad07e2",
+   "id": "f2f9ee3f",
    "metadata": {
     "editable": true
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@@ -2218,7 +2218,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d0783b2b",
+   "id": "f15f85e7",
    "metadata": {
     "editable": true
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@@ -2228,7 +2228,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a33f49bc",
+   "id": "ded06fd1",
    "metadata": {
     "editable": true
    },
@@ -2240,7 +2240,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bb935afa",
+   "id": "dc9c099b",
    "metadata": {
     "editable": true
    },
@@ -2250,7 +2250,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "19eacabb",
+   "id": "40bc212d",
    "metadata": {
     "editable": true
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@@ -2262,7 +2262,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5975a309",
+   "id": "7bc3483f",
    "metadata": {
     "editable": true
    },
@@ -2272,7 +2272,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ed1537ed",
+   "id": "22662e2a",
    "metadata": {
     "editable": true
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@@ -2284,7 +2284,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c897d003",
+   "id": "6798151b",
    "metadata": {
     "editable": true
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@@ -2294,7 +2294,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "199d48f4",
+   "id": "e63dc09e",
    "metadata": {
     "editable": true
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@@ -2306,7 +2306,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "931416f6",
+   "id": "a2f541a1",
    "metadata": {
     "editable": true
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@@ -2316,7 +2316,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fc031f0e",
+   "id": "fd8bba77",
    "metadata": {
     "editable": true
    },
@@ -2328,7 +2328,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6e04f9f2",
+   "id": "2163c93e",
    "metadata": {
     "editable": true
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@@ -2352,7 +2352,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c35c6bf5",
+   "id": "48634f87",
    "metadata": {
     "editable": true
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@@ -2364,7 +2364,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cf43264c",
+   "id": "9117c119",
    "metadata": {
     "editable": true
    },
@@ -2374,7 +2374,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f5f866f4",
+   "id": "c7cd5b11",
    "metadata": {
     "editable": true
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@@ -2386,7 +2386,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d9fa677c",
+   "id": "1159b476",
    "metadata": {
     "editable": true
    },
@@ -2396,7 +2396,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eaade1fd",
+   "id": "75098194",
    "metadata": {
     "editable": true
    },
@@ -2408,7 +2408,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "612c99ac",
+   "id": "3bd3d52b",
    "metadata": {
     "editable": true
    },
@@ -2421,7 +2421,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d5b941a8",
+   "id": "b086e3f6",
    "metadata": {
     "editable": true
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@@ -2433,7 +2433,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eba447d8",
+   "id": "6fe2d631",
    "metadata": {
     "editable": true
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@@ -2445,7 +2445,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f08187a8",
+   "id": "26bd4db6",
    "metadata": {
     "editable": true
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@@ -2457,7 +2457,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "177b3ca3",
+   "id": "141de40c",
    "metadata": {
     "editable": true
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@@ -2472,7 +2472,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "20190e91",
+   "id": "d44398a3",
    "metadata": {
     "editable": true
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@@ -2484,7 +2484,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c4ece1ae",
+   "id": "fb4c7d58",
    "metadata": {
     "editable": true
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@@ -2494,7 +2494,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bc014cc9",
+   "id": "3c1a0371",
    "metadata": {
     "editable": true
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@@ -2506,7 +2506,7 @@
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   {
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-   "id": "21b86234",
+   "id": "84a0c456",
    "metadata": {
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@@ -2516,7 +2516,7 @@
   },
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-   "id": "99699900",
+   "id": "1c5edf48",
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@@ -2528,7 +2528,7 @@
   },
   {
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-   "id": "2c98eb03",
+   "id": "332a3b12",
    "metadata": {
     "editable": true
    },
@@ -2544,7 +2544,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "8da0a506",
+   "id": "f52e02a6",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2559,7 +2559,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c1c6fbb0",
+   "id": "36ae01f6",
    "metadata": {
     "editable": true
    },
@@ -2570,7 +2570,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "104e0f22",
+   "id": "142d2f33",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2583,7 +2583,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "340d198a",
+   "id": "46296414",
    "metadata": {
     "editable": true
    },
@@ -2594,7 +2594,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "ade033f9",
+   "id": "86ed7e92",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2617,7 +2617,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9b722931",
+   "id": "942647c0",
    "metadata": {
     "editable": true
    },
@@ -2629,7 +2629,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "bf4c610b",
+   "id": "e26cee7f",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2642,7 +2642,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4d3682f3",
+   "id": "462de17c",
    "metadata": {
     "editable": true
    },
@@ -2653,7 +2653,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "28c41936",
+   "id": "e50560cf",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2665,7 +2665,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "643d4ba8",
+   "id": "ad325590",
    "metadata": {
     "editable": true
    },
@@ -2676,7 +2676,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "28f76048",
+   "id": "682dcbff",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2692,7 +2692,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1a31aa74",
+   "id": "7a3c40b6",
    "metadata": {
     "editable": true
    },
@@ -2703,7 +2703,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "cf5ba28a",
+   "id": "1cc29e48",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2717,7 +2717,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1fcd9105",
+   "id": "1ddbddcd",
    "metadata": {
     "editable": true
    },
@@ -2739,7 +2739,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7c37062a",
+   "id": "90a8edfe",
    "metadata": {
     "editable": true
    },
@@ -2751,7 +2751,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7494594e",
+   "id": "4ce63e21",
    "metadata": {
     "editable": true
    },
@@ -2763,7 +2763,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "31f0536d",
+   "id": "d4c3d110",
    "metadata": {
     "editable": true
    },
@@ -2775,7 +2775,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "80b342bd",
+   "id": "cf9ad9c2",
    "metadata": {
     "editable": true
    },
@@ -2785,7 +2785,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6ae0b01c",
+   "id": "3b1f5fc1",
    "metadata": {
     "editable": true
    },
@@ -2797,7 +2797,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4738cefc",
+   "id": "559f6a04",
    "metadata": {
     "editable": true
    },
@@ -2807,7 +2807,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b60ccc66",
+   "id": "bba9e13a",
    "metadata": {
     "editable": true
    },
@@ -2819,7 +2819,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "14251ed9",
+   "id": "c97cf725",
    "metadata": {
     "editable": true
    },
@@ -2831,7 +2831,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d7774e34",
+   "id": "6256d393",
    "metadata": {
     "editable": true
    },
@@ -2843,7 +2843,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b44744ad",
+   "id": "4274f29f",
    "metadata": {
     "editable": true
    },
@@ -2853,7 +2853,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2d48b6d2",
+   "id": "6424e32f",
    "metadata": {
     "editable": true
    },
@@ -2865,7 +2865,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "68536d4b",
+   "id": "2babfc95",
    "metadata": {
     "editable": true
    },
@@ -2875,7 +2875,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ec458e7f",
+   "id": "1a25daa2",
    "metadata": {
     "editable": true
    },
@@ -2887,7 +2887,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ad31b9e9",
+   "id": "d2a18a90",
    "metadata": {
     "editable": true
    },
@@ -2897,7 +2897,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c188e550",
+   "id": "c2730e09",
    "metadata": {
     "editable": true
    },
@@ -2909,7 +2909,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "80f77d5d",
+   "id": "dab1abb8",
    "metadata": {
     "editable": true
    },
@@ -2919,7 +2919,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a2916ab9",
+   "id": "be9b74c2",
    "metadata": {
     "editable": true
    },
@@ -2931,7 +2931,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1f74a112",
+   "id": "38e7188c",
    "metadata": {
     "editable": true
    },
@@ -2941,7 +2941,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d4dc1c29",
+   "id": "c568814d",
    "metadata": {
     "editable": true
    },
@@ -2953,7 +2953,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "338246d7",
+   "id": "37fb03f1",
    "metadata": {
     "editable": true
    },
@@ -2963,7 +2963,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ca1e4df5",
+   "id": "753cb5d9",
    "metadata": {
     "editable": true
    },
@@ -2975,7 +2975,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bea336e4",
+   "id": "a887be4f",
    "metadata": {
     "editable": true
    },
@@ -2985,7 +2985,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ce3eb821",
+   "id": "c99c1576",
    "metadata": {
     "editable": true
    },
@@ -2997,7 +2997,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a117a708",
+   "id": "ddb752ed",
    "metadata": {
     "editable": true
    },
@@ -3007,7 +3007,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8dd2c2d0",
+   "id": "d5d9bc16",
    "metadata": {
     "editable": true
    },
@@ -3019,7 +3019,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a131a505",
+   "id": "971e3cbe",
    "metadata": {
     "editable": true
    },
@@ -3029,7 +3029,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "db3ad647",
+   "id": "14a81f3f",
    "metadata": {
     "editable": true
    },
@@ -3041,7 +3041,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1e2b9af1",
+   "id": "daa744f2",
    "metadata": {
     "editable": true
    },
@@ -3052,7 +3052,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8bb14967",
+   "id": "bcdd91b0",
    "metadata": {
     "editable": true
    },
@@ -3064,7 +3064,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e111117b",
+   "id": "78646001",
    "metadata": {
     "editable": true
    },
@@ -3076,7 +3076,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dbbc1f3b",
+   "id": "8eb6b0de",
    "metadata": {
     "editable": true
    },
@@ -3088,7 +3088,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "13a154e3",
+   "id": "f5641168",
    "metadata": {
     "editable": true
    },
@@ -3100,7 +3100,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a2529176",
+   "id": "c14fcf77",
    "metadata": {
     "editable": true
    },
@@ -3112,7 +3112,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4c93ae77",
+   "id": "9b374023",
    "metadata": {
     "editable": true
    },
@@ -3122,7 +3122,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b4e80da6",
+   "id": "5f34ff5d",
    "metadata": {
     "editable": true
    },
@@ -3134,7 +3134,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5b40d231",
+   "id": "349a1654",
    "metadata": {
     "editable": true
    },
@@ -3146,7 +3146,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ea093e3a",
+   "id": "bdc8cc84",
    "metadata": {
     "editable": true
    },
@@ -3160,7 +3160,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0e3a2d2b",
+   "id": "8e31d846",
    "metadata": {
     "editable": true
    },
@@ -3186,7 +3186,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "b0874c6b",
+   "id": "ba56fb3a",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3205,7 +3205,7 @@
     "# Where to save the figures and data files\n",
     "PROJECT_ROOT_DIR = \"Results\"\n",
     "FIGURE_ID = \"Results/FigureFiles\"\n",
-    "DATA_ID = \"datafiles/\"\n",
+    "DATA_ID = \"DataFiles/\"\n",
     "\n",
     "if not os.path.exists(PROJECT_ROOT_DIR):\n",
     "    os.mkdir(PROJECT_ROOT_DIR)\n",
@@ -3268,7 +3268,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "202c5365",
+   "id": "982e0c69",
    "metadata": {
     "editable": true
    },
@@ -3279,7 +3279,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9d0addca",
+   "id": "acb30447",
    "metadata": {
     "editable": true
    },
@@ -3307,7 +3307,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "270f88ea",
+   "id": "301a7664",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3356,7 +3356,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6f52faab",
+   "id": "655f046e",
    "metadata": {
     "editable": true
    },
@@ -3367,7 +3367,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "9249b1f2",
+   "id": "413acb4d",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3392,7 +3392,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7ce8cc8a",
+   "id": "f46a080f",
    "metadata": {
     "editable": true
    },
@@ -3409,7 +3409,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "9413ed10",
+   "id": "96391e08",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3424,7 +3424,7 @@
     "# Where to save the figures and data files\n",
     "PROJECT_ROOT_DIR = \"Results\"\n",
     "FIGURE_ID = \"Results/FigureFiles\"\n",
-    "DATA_ID = \"datafiles/\"\n",
+    "DATA_ID = \"DataFiles/\"\n",
     "\n",
     "if not os.path.exists(PROJECT_ROOT_DIR):\n",
     "    os.mkdir(PROJECT_ROOT_DIR)\n",
@@ -3484,7 +3484,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5ef898c1",
+   "id": "46a1885f",
    "metadata": {
     "editable": true
    },
@@ -3528,7 +3528,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6270da59",
+   "id": "a2041293",
    "metadata": {
     "editable": true
    },
@@ -3540,7 +3540,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "a1b6612b",
+   "id": "f813a977",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3556,7 +3556,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b88aea50",
+   "id": "5fc086ec",
    "metadata": {
     "editable": true
    },
@@ -3567,7 +3567,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "8f40fe64",
+   "id": "bf72511a",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3585,7 +3585,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b86e3fa0",
+   "id": "70823078",
    "metadata": {
     "editable": true
    },
@@ -3596,7 +3596,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "1f8b1d5a",
+   "id": "85e192bf",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3610,7 +3610,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "222c84bd",
+   "id": "13fd362f",
    "metadata": {
     "editable": true
    },
@@ -3621,7 +3621,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "id": "780459dc",
+   "id": "8f379d9b",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3634,7 +3634,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "53bd2e9b",
+   "id": "c5f9dc18",
    "metadata": {
     "editable": true
    },
@@ -3645,7 +3645,7 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "id": "5bac6f50",
+   "id": "cb9f3445",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3662,7 +3662,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7c03d8ad",
+   "id": "7a16f238",
    "metadata": {
     "editable": true
    },
@@ -3673,7 +3673,7 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "id": "d32ac0d5",
+   "id": "a5386de5",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3689,7 +3689,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9bbbf5d2",
+   "id": "1d2e90ee",
    "metadata": {
     "editable": true
    },
@@ -3700,7 +3700,7 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "id": "435eabf3",
+   "id": "b494903c",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3724,7 +3724,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1d0316d8",
+   "id": "c580e7c5",
    "metadata": {
     "editable": true
    },
@@ -3735,7 +3735,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "428ca983",
+   "id": "45830e09",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3748,7 +3748,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1f48b507",
+   "id": "fe0e34ba",
    "metadata": {
     "editable": true
    },
@@ -3759,7 +3759,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "id": "82c7f88e",
+   "id": "f73e5b0f",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3779,7 +3779,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "af5e67ec",
+   "id": "a99e4f96",
    "metadata": {
     "editable": true
    },
@@ -3790,7 +3790,7 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "id": "2e5a49d5",
+   "id": "8c972941",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3833,7 +3833,7 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "id": "193f2fa9",
+   "id": "91ef994e",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3848,7 +3848,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7fd81229",
+   "id": "b2302654",
    "metadata": {
     "editable": true
    },
@@ -3925,7 +3925,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7791df68",
+   "id": "9f2998ff",
    "metadata": {
     "editable": true
    },
@@ -3937,7 +3937,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7540dd3b",
+   "id": "bc8c9873",
    "metadata": {
     "editable": true
    },
@@ -3957,7 +3957,7 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "id": "3336893a",
+   "id": "810f6b81",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3991,7 +3991,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "68146245",
+   "id": "565a1cd1",
    "metadata": {
     "editable": true
    },
@@ -4006,7 +4006,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5f73e12a",
+   "id": "0a65c309",
    "metadata": {
     "editable": true
    },
@@ -4018,7 +4018,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3a6f0106",
+   "id": "8b0d4688",
    "metadata": {
     "editable": true
    },
@@ -4028,7 +4028,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "489bf6a3",
+   "id": "d1f9ca89",
    "metadata": {
     "editable": true
    },
@@ -4052,7 +4052,7 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "id": "dc6f77ee",
+   "id": "28d4c3a0",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -4096,7 +4096,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2e94dcd3",
+   "id": "dfe532c5",
    "metadata": {
     "editable": true
    },
@@ -4106,7 +4106,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7298cf1d",
+   "id": "86f0a026",
    "metadata": {
     "editable": true
    },
@@ -4175,7 +4175,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "10db6d60",
+   "id": "fd6857da",
    "metadata": {
     "editable": true
    },
@@ -4189,7 +4189,7 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "id": "1db38720",
+   "id": "c84d4e1f",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -4202,7 +4202,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3802c10c",
+   "id": "10579805",
    "metadata": {
     "editable": true
    },
@@ -4216,7 +4216,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0e298fa7",
+   "id": "30c31580",
    "metadata": {
     "editable": true
    },
@@ -4229,7 +4229,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6e6b1642",
+   "id": "af797776",
    "metadata": {
     "editable": true
    },
@@ -4240,7 +4240,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4d691079",
+   "id": "9c6eebe7",
    "metadata": {
     "editable": true
    },
@@ -4252,7 +4252,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aec0693e",
+   "id": "06a2f8be",
    "metadata": {
     "editable": true
    },
@@ -4262,7 +4262,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "082dce1f",
+   "id": "0b5633b3",
    "metadata": {
     "editable": true
    },
@@ -4274,7 +4274,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "980a908b",
+   "id": "108886bb",
    "metadata": {
     "editable": true
    },
@@ -4290,7 +4290,7 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "id": "dfd1e291",
+   "id": "94716007",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -4341,7 +4341,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2b9113a4",
+   "id": "3aa5be35",
    "metadata": {
     "editable": true
    },
@@ -4351,7 +4351,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e1b66025",
+   "id": "1af6d7cd",
    "metadata": {
     "editable": true
    },
@@ -4397,7 +4397,7 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "id": "1f61fef1",
+   "id": "1f4f0115",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -4410,7 +4410,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2a387540",
+   "id": "8904ba25",
    "metadata": {
     "editable": true
    },
@@ -4421,7 +4421,7 @@
   {
    "cell_type": "code",
    "execution_count": 43,
-   "id": "d4ca9adf",
+   "id": "04fdf05f",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -4436,7 +4436,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "549fd34a",
+   "id": "a22f1ef9",
    "metadata": {
     "editable": true
    },
@@ -4454,7 +4454,7 @@
   {
    "cell_type": "code",
    "execution_count": 44,
-   "id": "452e7ddf",
+   "id": "ad96211c",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -4471,7 +4471,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d3b3c08c",
+   "id": "a103eff1",
    "metadata": {
     "editable": true
    },
@@ -4487,7 +4487,7 @@
   {
    "cell_type": "code",
    "execution_count": 45,
-   "id": "4aad4eb0",
+   "id": "d9b5eed5",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -4531,7 +4531,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1224d3b5",
+   "id": "9decc489",
    "metadata": {
     "editable": true
    },
@@ -4541,7 +4541,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0f4f1cc3",
+   "id": "f2ffecd1",
    "metadata": {
     "editable": true
    },
@@ -4552,7 +4552,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aca40014",
+   "id": "356683e0",
    "metadata": {
     "editable": true
    },
@@ -4563,7 +4563,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2afa7dbc",
+   "id": "01abcbae",
    "metadata": {
     "editable": true
    },
@@ -4574,7 +4574,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "50a555bd",
+   "id": "8b2a42d8",
    "metadata": {
     "editable": true
    },
@@ -4597,7 +4597,7 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "id": "8286a35b",
+   "id": "4ae34751",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -4610,7 +4610,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5167b3ba",
+   "id": "559e2cff",
    "metadata": {
     "editable": true
    },
@@ -4627,7 +4627,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cb3d13c4",
+   "id": "03b05ffa",
    "metadata": {
     "editable": true
    },
@@ -4640,7 +4640,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0001b4b7",
+   "id": "1ff2cf84",
    "metadata": {
     "editable": true
    },
@@ -4651,7 +4651,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "54d64bca",
+   "id": "fe18f837",
    "metadata": {
     "editable": true
    },
@@ -4663,7 +4663,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a0d12cd4",
+   "id": "49eff4b4",
    "metadata": {
     "editable": true
    },
@@ -4673,7 +4673,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e106067a",
+   "id": "acc25706",
    "metadata": {
     "editable": true
    },
@@ -4685,7 +4685,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bd211dd6",
+   "id": "5f945b64",
    "metadata": {
     "editable": true
    },
@@ -4702,7 +4702,7 @@
   {
    "cell_type": "code",
    "execution_count": 47,
-   "id": "4394cc31",
+   "id": "f9578f4e",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -4794,7 +4794,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "27144669",
+   "id": "88be64bc",
    "metadata": {
     "editable": true
    },
@@ -4804,7 +4804,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f8f1b0c1",
+   "id": "3a0e09e7",
    "metadata": {
     "editable": true
    },
@@ -4826,7 +4826,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3bb29f8e",
+   "id": "fd41f7f2",
    "metadata": {
     "editable": true
    },
@@ -4838,7 +4838,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1ca6a904",
+   "id": "49d2bdba",
    "metadata": {
     "editable": true
    },
@@ -4848,7 +4848,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e096bb31",
+   "id": "fd5a139b",
    "metadata": {
     "editable": true
    },
@@ -4860,7 +4860,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2c4a87f8",
+   "id": "af22b4d2",
    "metadata": {
     "editable": true
    },
@@ -4870,7 +4870,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9d6848d3",
+   "id": "b120000f",
    "metadata": {
     "editable": true
    },
@@ -4882,7 +4882,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9f070f76",
+   "id": "0e7df31e",
    "metadata": {
     "editable": true
    },
@@ -4897,7 +4897,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "14885913",
+   "id": "faa1b4d0",
    "metadata": {
     "editable": true
    },
@@ -4909,7 +4909,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f5b4f42b",
+   "id": "02f78802",
    "metadata": {
     "editable": true
    },
@@ -4919,7 +4919,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "abca9fed",
+   "id": "e85b6015",
    "metadata": {
     "editable": true
    },
@@ -4931,7 +4931,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a3931447",
+   "id": "12ae8696",
    "metadata": {
     "editable": true
    },
@@ -4941,7 +4941,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bff5fde9",
+   "id": "d90de117",
    "metadata": {
     "editable": true
    },
@@ -4953,7 +4953,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cfcbbd41",
+   "id": "15a6c139",
    "metadata": {
     "editable": true
    },
@@ -4963,7 +4963,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f21eecf5",
+   "id": "f5e7aa62",
    "metadata": {
     "editable": true
    },
@@ -4975,7 +4975,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5d1bccf8",
+   "id": "5a1f86bc",
    "metadata": {
     "editable": true
    },
@@ -4985,7 +4985,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cd0ac1aa",
+   "id": "57018aa2",
    "metadata": {
     "editable": true
    },
@@ -4997,7 +4997,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e2689f44",
+   "id": "6a9c70a8",
    "metadata": {
     "editable": true
    },
@@ -5007,7 +5007,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3578ef22",
+   "id": "75aaf0ec",
    "metadata": {
     "editable": true
    },
@@ -5019,7 +5019,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1b9ae1e2",
+   "id": "31a142ea",
    "metadata": {
     "editable": true
    },
@@ -5029,7 +5029,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3c67b152",
+   "id": "2552b361",
    "metadata": {
     "editable": true
    },
@@ -5041,7 +5041,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7e4a7fe2",
+   "id": "91b53dc5",
    "metadata": {
     "editable": true
    },
@@ -5051,7 +5051,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0c255fd7",
+   "id": "bf83b147",
    "metadata": {
     "editable": true
    },
@@ -5063,7 +5063,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f0b00e3d",
+   "id": "0c8668a6",
    "metadata": {
     "editable": true
    },
@@ -5073,7 +5073,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7c9a7b92",
+   "id": "89704701",
    "metadata": {
     "editable": true
    },
@@ -5085,7 +5085,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a8e3e7e8",
+   "id": "0054bb58",
    "metadata": {
     "editable": true
    },
@@ -5095,7 +5095,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "007406a0",
+   "id": "24f3ee5f",
    "metadata": {
     "editable": true
    },
@@ -5107,7 +5107,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "20f80df6",
+   "id": "0827a002",
    "metadata": {
     "editable": true
    },
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter1.py b/doc/LectureNotes/_build/jupyter_execute/chapter1.py
index 0ce9fd445..821b2fc8d 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter1.py
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter1.py
@@ -625,7 +625,7 @@ import os
 # Where to save the figures and data files
 PROJECT_ROOT_DIR = "Results"
 FIGURE_ID = "Results/FigureFiles"
-DATA_ID = "datafiles/"
+DATA_ID = "DataFiles/"
 
 if not os.path.exists(PROJECT_ROOT_DIR):
     os.mkdir(PROJECT_ROOT_DIR)
@@ -1089,7 +1089,7 @@ import os
 # Where to save the figures and data files
 PROJECT_ROOT_DIR = "Results"
 FIGURE_ID = "Results/FigureFiles"
-DATA_ID = "datafiles/"
+DATA_ID = "DataFiles/"
 
 if not os.path.exists(PROJECT_ROOT_DIR):
     os.mkdir(PROJECT_ROOT_DIR)
@@ -1579,7 +1579,7 @@ from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error
 # Where to save the figures and data files
 PROJECT_ROOT_DIR = "Results"
 FIGURE_ID = "Results/FigureFiles"
-DATA_ID = "datafiles/"
+DATA_ID = "DataFiles/"
 
 if not os.path.exists(PROJECT_ROOT_DIR):
     os.mkdir(PROJECT_ROOT_DIR)
@@ -1745,7 +1745,7 @@ from sklearn.model_selection import train_test_split
 # Where to save the figures and data files
 PROJECT_ROOT_DIR = "Results"
 FIGURE_ID = "Results/FigureFiles"
-DATA_ID = "datafiles/"
+DATA_ID = "DataFiles/"
 
 if not os.path.exists(PROJECT_ROOT_DIR):
     os.mkdir(PROJECT_ROOT_DIR)
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter1_17_0.png b/doc/LectureNotes/_build/jupyter_execute/chapter1_17_0.png
index 4b6e6f4df531b6e1d5aa8a58919ad1adc91ac803..b5a6ce6c4fff72c8b9f5f64307c7a05f5ed2b20d 100644
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diff --git a/doc/LectureNotes/_build/jupyter_execute/chapteroptimization.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapteroptimization.ipynb
index 2c0534d8a..076976c77 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapteroptimization.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapteroptimization.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "f84a9d0d",
+   "id": "6b6b0d7a",
    "metadata": {
     "editable": true
    },
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d40fde79",
+   "id": "eb2769bd",
    "metadata": {
     "editable": true
    },
@@ -39,7 +39,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5e16ce97",
+   "id": "7ef2e04a",
    "metadata": {
     "editable": true
    },
@@ -54,7 +54,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "48b8210e",
+   "id": "64c36bcf",
    "metadata": {
     "editable": true
    },
@@ -70,7 +70,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "160bfec6",
+   "id": "0b5a1717",
    "metadata": {
     "editable": true
    },
@@ -82,7 +82,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cf829958",
+   "id": "b51524a2",
    "metadata": {
     "editable": true
    },
@@ -93,7 +93,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a0ee64a1",
+   "id": "276aac60",
    "metadata": {
     "editable": true
    },
@@ -105,7 +105,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9d6e5d8f",
+   "id": "70a606cb",
    "metadata": {
     "editable": true
    },
@@ -119,7 +119,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4af11aa5",
+   "id": "e9908c02",
    "metadata": {
     "editable": true
    },
@@ -131,7 +131,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8801711a",
+   "id": "be51af28",
    "metadata": {
     "editable": true
    },
@@ -141,7 +141,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "34c3d5a7",
+   "id": "8f626746",
    "metadata": {
     "editable": true
    },
@@ -153,7 +153,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "452bbf0a",
+   "id": "9bbd1fdc",
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@@ -181,7 +181,7 @@
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   {
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-   "id": "e7d6fca4",
+   "id": "fa96322d",
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@@ -197,7 +197,7 @@
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   {
    "cell_type": "markdown",
-   "id": "afcf6c29",
+   "id": "ca935f27",
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@@ -208,7 +208,7 @@
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-   "id": "e0e9aca3",
+   "id": "2996d958",
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@@ -220,7 +220,7 @@
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-   "id": "5d70696d",
+   "id": "42b78480",
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@@ -230,7 +230,7 @@
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-   "id": "df759be1",
+   "id": "42e0431e",
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@@ -242,7 +242,7 @@
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-   "id": "d9f2bf15",
+   "id": "5ba17ae7",
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@@ -252,7 +252,7 @@
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-   "id": "70cf6701",
+   "id": "9136d7a1",
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@@ -264,7 +264,7 @@
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   {
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-   "id": "e2a70850",
+   "id": "a67fa215",
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@@ -287,7 +287,7 @@
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-   "id": "883881e4",
+   "id": "f4f7b38b",
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@@ -300,7 +300,7 @@
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-   "id": "8704ba9a",
+   "id": "5d0fd8da",
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@@ -310,7 +310,7 @@
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+   "id": "9c370ae4",
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@@ -328,7 +328,7 @@
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-   "id": "884ca09b",
+   "id": "c0901da4",
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@@ -338,7 +338,7 @@
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+   "id": "f8a7dc72",
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@@ -353,7 +353,7 @@
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+   "id": "648a3018",
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@@ -363,7 +363,7 @@
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-   "id": "d20911ef",
+   "id": "ac8ceb0d",
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@@ -377,7 +377,7 @@
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@@ -387,7 +387,7 @@
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@@ -401,7 +401,7 @@
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@@ -416,7 +416,7 @@
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@@ -433,7 +433,7 @@
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@@ -445,7 +445,7 @@
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+   "id": "c72f27f1",
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@@ -464,7 +464,7 @@
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@@ -476,7 +476,7 @@
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-   "id": "0b3f7fe6",
+   "id": "9a43f87d",
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@@ -517,7 +517,7 @@
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-   "id": "c1a307a1",
+   "id": "c271932e",
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@@ -555,20 +555,16 @@
     "domain of $f$, i.e the subset of $R^n$ where $f$ is defined. For more\n",
     "details and proofs we refer to: [S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press](http://stanford.edu/boyd/cvxbook/, 2004).\n",
     "\n",
-    "**First order condition.**\n",
-    "\n",
-    "Suppose $f$ is differentiable (i.e $\\nabla f(x)$ is well defined for\n",
+    "**First order condition**: Suppose $f$ is differentiable (i.e $\\nabla f(x)$ is well defined for\n",
     "all $x$ in the domain of $f$). Then $f$ is convex if and only if $D_f$\n",
-    "is a convex set and $$f(y) \\geq f(x) + \\nabla f(x)^T (y-x) $$ holds\n",
+    "is a convex set and $f(y) \\geq f(x) + \\nabla f(x)^T (y-x)$ holds\n",
     "for all $x,y \\in D_f$. This condition means that for a convex function\n",
     "the first order Taylor expansion (right hand side above) at any point\n",
     "is a global under estimator of the function. To convince yourself you can\n",
     "make a drawing of $f(x) = x^2+1$ and draw the tangent line to $f(x)$ and\n",
-    "note that it is always below the graph.\n",
+    "note that it is always below the graph.  \n",
     "\n",
-    "**Second order condition.**\n",
-    "\n",
-    "Assume that $f$ is twice\n",
+    "**Second order condition**: Assume that $f$ is twice\n",
     "differentiable, i.e the Hessian matrix exists at each point in\n",
     "$D_f$. Then $f$ is convex if and only if $D_f$ is a convex set and its\n",
     "Hessian is positive semi-definite for all $x\\in D_f$. For a\n",
@@ -598,7 +594,7 @@
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   {
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-   "id": "d648ed6e",
+   "id": "87f52dcb",
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@@ -628,7 +624,7 @@
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-   "id": "0ca978c0",
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@@ -648,7 +644,7 @@
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+   "id": "d54a453e",
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@@ -660,7 +656,7 @@
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-   "id": "d228b529",
+   "id": "b1b84a57",
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@@ -670,7 +666,7 @@
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@@ -682,7 +678,7 @@
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-   "id": "d7fcfada",
+   "id": "d2cc7ff0",
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@@ -696,7 +692,7 @@
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@@ -708,7 +704,7 @@
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+   "id": "813d3073",
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@@ -722,7 +718,7 @@
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+   "id": "3d0d9d18",
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@@ -734,7 +730,7 @@
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@@ -744,7 +740,7 @@
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-   "id": "5c2d4c87",
+   "id": "bfabfe26",
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@@ -756,7 +752,7 @@
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@@ -768,7 +764,7 @@
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@@ -780,7 +776,7 @@
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+   "id": "fa3d7c41",
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@@ -792,7 +788,7 @@
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@@ -804,7 +800,7 @@
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@@ -817,7 +813,7 @@
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@@ -829,7 +825,7 @@
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+   "id": "ac2290e7",
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@@ -839,7 +835,7 @@
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-   "id": "dcabf00c",
+   "id": "33b872cd",
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@@ -851,7 +847,7 @@
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-   "id": "405bd44d",
+   "id": "b0ef2463",
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@@ -861,7 +857,7 @@
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-   "id": "b917b358",
+   "id": "9bdb71be",
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@@ -873,7 +869,7 @@
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-   "id": "dc88b39b",
+   "id": "8b526371",
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@@ -883,7 +879,7 @@
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-   "id": "8bec0c4c",
+   "id": "f2304289",
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@@ -895,7 +891,7 @@
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-   "id": "b2b3ee64",
+   "id": "4b00e41e",
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@@ -905,7 +901,7 @@
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-   "id": "86bd52fd",
+   "id": "0e67a647",
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@@ -918,7 +914,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "4ce511b9",
+   "id": "f96e45cc",
    "metadata": {
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     "editable": true
@@ -928,14 +924,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_96694/483257001.py:18: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().\n",
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_9414/483257001.py:18: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().\n",
       "  ax = fig.gca(projection=\"3d\")\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
      "execution_count": 1,
@@ -984,7 +980,7 @@
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-   "id": "0221d5fb",
+   "id": "16ce9afc",
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@@ -995,7 +991,7 @@
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-   "id": "107c7b63",
+   "id": "de682254",
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@@ -1024,7 +1020,7 @@
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+   "id": "ebbe4b30",
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@@ -1035,7 +1031,7 @@
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-   "id": "683d97c5",
+   "id": "5f2c4a9c",
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     "editable": true
@@ -1048,7 +1044,7 @@
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+   "id": "83f88bfa",
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@@ -1059,7 +1055,7 @@
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-   "id": "a633351f",
+   "id": "2da02da5",
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@@ -1085,7 +1081,7 @@
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+   "id": "02d36ca5",
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@@ -1096,7 +1092,7 @@
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-   "id": "54f7d8ef",
+   "id": "53313492",
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@@ -1105,7 +1101,7 @@
     {
      "data": {
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+       "[]"
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@@ -1136,7 +1132,7 @@
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-   "id": "3c8bd3d6",
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@@ -1150,7 +1146,7 @@
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-   "id": "beada985",
+   "id": "6f709878",
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@@ -1162,7 +1158,7 @@
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@@ -1173,7 +1169,7 @@
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+   "id": "ddf2d65f",
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@@ -1185,7 +1181,7 @@
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-   "id": "a3832e7e",
+   "id": "3245eaab",
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@@ -1198,7 +1194,7 @@
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   {
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-   "id": "a08e7ad5",
+   "id": "69cc7635",
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@@ -1210,7 +1206,7 @@
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   {
    "cell_type": "markdown",
-   "id": "9e7fb89c",
+   "id": "e4a46e89",
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@@ -1223,7 +1219,7 @@
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-   "id": "6112eb0a",
+   "id": "69c46421",
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@@ -1235,7 +1231,7 @@
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-   "id": "87453181",
+   "id": "90770121",
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@@ -1247,7 +1243,7 @@
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-   "id": "02ee78ea",
+   "id": "3a5e36b1",
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@@ -1259,7 +1255,7 @@
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-   "id": "51806386",
+   "id": "c144cca2",
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@@ -1269,7 +1265,7 @@
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    "cell_type": "markdown",
-   "id": "a706ddd7",
+   "id": "dd5316be",
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@@ -1281,7 +1277,7 @@
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-   "id": "d74cb3eb",
+   "id": "d22dc91f",
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@@ -1291,7 +1287,7 @@
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-   "id": "d3232b9d",
+   "id": "e07ec0c1",
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@@ -1303,7 +1299,7 @@
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-   "id": "eb42d3d2",
+   "id": "63340018",
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@@ -1313,7 +1309,7 @@
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-   "id": "41db5953",
+   "id": "c41887ce",
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@@ -1325,7 +1321,7 @@
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+   "id": "b161780a",
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@@ -1343,7 +1339,7 @@
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+   "id": "1abed167",
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@@ -1355,7 +1351,7 @@
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+   "id": "bd840c05",
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@@ -1365,7 +1361,7 @@
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@@ -1377,7 +1373,7 @@
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-   "id": "72df369b",
+   "id": "a7d6e5ee",
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@@ -1389,7 +1385,7 @@
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+   "id": "8e31bd0e",
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@@ -1401,7 +1397,7 @@
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-   "id": "ca35d289",
+   "id": "b337f9b4",
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@@ -1413,7 +1409,7 @@
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+   "id": "6740500b",
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@@ -1425,7 +1421,7 @@
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-   "id": "ecb94eeb",
+   "id": "03216125",
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@@ -1440,7 +1436,7 @@
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-   "id": "121043dd",
+   "id": "9b45c7ad",
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@@ -1452,7 +1448,7 @@
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+   "id": "1c275ef3",
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@@ -1468,7 +1464,7 @@
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+   "id": "099132d8",
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@@ -1480,7 +1476,7 @@
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+   "id": "2a0c62f2",
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@@ -1490,7 +1486,7 @@
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+   "id": "d04ba7fb",
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@@ -1502,7 +1498,7 @@
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-   "id": "a043cbbf",
+   "id": "b0846f39",
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     "editable": true
    },
@@ -1512,7 +1508,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e14a453e",
+   "id": "a467360c",
    "metadata": {
     "editable": true
    },
@@ -1524,7 +1520,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "124c177b",
+   "id": "d41094c1",
    "metadata": {
     "editable": true
    },
@@ -1534,7 +1530,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8f8a774d",
+   "id": "1740a0a7",
    "metadata": {
     "editable": true
    },
@@ -1546,7 +1542,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1485ffbe",
+   "id": "df099649",
    "metadata": {
     "editable": true
    },
@@ -1556,7 +1552,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "72383dcb",
+   "id": "75ea0e90",
    "metadata": {
     "editable": true
    },
@@ -1568,7 +1564,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b94fbe5f",
+   "id": "69cad896",
    "metadata": {
     "editable": true
    },
@@ -1592,7 +1588,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "685e34ab",
+   "id": "b7c5dad0",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1606,7 +1602,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f6db7782",
+   "id": "f97fbc43",
    "metadata": {
     "editable": true
    },
@@ -1617,7 +1613,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7c9405b5",
+   "id": "7e249cc0",
    "metadata": {
     "editable": true
    },
@@ -1629,7 +1625,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "18b543bb",
+   "id": "7335ac71",
    "metadata": {
     "editable": true
    },
@@ -1639,7 +1635,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f51c5b8a",
+   "id": "fa5ee00d",
    "metadata": {
     "editable": true
    },
@@ -1651,7 +1647,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ae853b5e",
+   "id": "c5e55d7b",
    "metadata": {
     "editable": true
    },
@@ -1663,7 +1659,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2b0dba62",
+   "id": "80980c3d",
    "metadata": {
     "editable": true
    },
@@ -1679,7 +1675,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9523ebb8",
+   "id": "bd680445",
    "metadata": {
     "editable": true
    },
@@ -1689,7 +1685,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b2fce916",
+   "id": "e6d47679",
    "metadata": {
     "editable": true
    },
@@ -1701,7 +1697,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fe25134c",
+   "id": "ee872c38",
    "metadata": {
     "editable": true
    },
@@ -1713,7 +1709,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fa866d39",
+   "id": "c286e8a4",
    "metadata": {
     "editable": true
    },
@@ -1727,7 +1723,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1a26f3e1",
+   "id": "268e5249",
    "metadata": {
     "editable": true
    },
@@ -1739,7 +1735,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "13e655d3",
+   "id": "43dead94",
    "metadata": {
     "editable": true
    },
@@ -1754,7 +1750,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4077ffc7",
+   "id": "32db94fc",
    "metadata": {
     "editable": true
    },
@@ -1766,7 +1762,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ae32f491",
+   "id": "11418e4d",
    "metadata": {
     "editable": true
    },
@@ -1778,7 +1774,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2755532a",
+   "id": "1d0005da",
    "metadata": {
     "editable": true
    },
@@ -1796,7 +1792,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "0babeef2",
+   "id": "bbbaf736",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1806,16 +1802,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[0.2831603  4.55553537]\n",
-      "[[3.91511388]\n",
-      " [3.13030182]]\n",
-      "[[3.91511388]\n",
-      " [3.13030182]]\n"
+      "[0.25881631 4.66111673]\n",
+      "[[4.01840062]\n",
+      " [2.89545727]]\n",
+      "[[4.01840062]\n",
+      " [2.89545727]]\n"
      ]
     },
     {
      "data": {
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\n",
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gLCzMIOir6JVXXkFOTg66du2KadOmIS4uDrdu3cLx48eRlZWF+fPnIzw8HDk5OUhPT8dLL72kL82Qnp6OZ555BklJSRg2bBgAbXC1aNEixMfHo3Xr1ti+fTveeustk4FJTQUHB6NPnz546aWXULt2bcybNw8HDhwwO71+wYIFSE5OxoABAzBu3Dg0adIEly9fxv79+7Fjxw4sXbrUIe0lsomLE7iJJGnXrl1i/PjxIjo6Wvj6+go/Pz/RvHlzMXbsWLF69WqDfVNTU0Xt2rWNHmffvn2iX79+om7duqJBgwbigQceECdPnhQAxMsvv2yw78qVK0Xr1q2Fj4+PaNq0qXjjjTfEyy+/bHY2mRBCXL9+Xbz44osiLi5O+Pj4iICAAHHXXXeJp556ymAmEAAxefLkKu00dsyZM2eKsLAw4eHhUWWmkDGfffaZiI2NFT4+PqJFixbiiy++EKmpqQazyYTQTol/++23RZs2bYSfn5+oU6eOiI+PFxMnThSHDx8WQmhngQ0bNkxERkYKX19fERQUJHr16iVWrlxpcKybN2+Kf//73/r3DQoKEn369BEbN2402O+LL74QnTp1ErVr1xb+/v4iJiZGjB07Vmzbtk2/T69evURiYmKV8zJ2DhkZGSI+Pl54e3sb/S4ru3Dhgpg2bZqIjo4W3t7eIjAwUHTo0EG88MIL4vr16+LMmTMiJCRE9OnTx2CWlUajEYMHDxb169fXz+y7cuWKmDBhgggJCRG1atUS3bt3F+vXrxe9evUSvXr10r9WN8Or4hR4Ie6UQag8+1D3u3bhwgX9Nt3vy7x580RMTIzw9vYW8fHx4ptvvjF4rbHZZEIIsXv3bjFy5EgREhIivL29RaNGjUSfPn3E/Pnzq/28iJxNJYQQrgrEiIhIulQqFSZPnoy5c+e6uilEDsXZZERERKRoDIaIiIhI0ZhATURERjGLgpSCPUNERESkaAyGiIiISNEYDBEREZGiKSJnSKPR4MyZM6hbt67RZROIiIhIeoQQuHbtmtl1CGtKEcHQmTNnEBER4epmEBERkQ0KCgocVmUdUEgwpFtCoKCgwKJlFIiIiMj1iouLERERYXQpIHtSRDCkGxqrV68egyEiIiKZcXSKCxOoiYiISNEYDBEREZGiMRgiIiIiRWMwRERERIrGYIiIiIgUjcEQERERKRqDISIiIlI0BkNERESkaAyGiIiISNEYDBEREZGiMRgiIiIiRWMwRERERIrGYIiIiIgUjcEQERERKRqDISIiIlI0BkNERESkaAyGiIiISNFcHgytW7cOgwcPRlhYGFQqFb7//nuT+06cOBEqlQrvv/++09pHRERE7s3lwVBJSQnatGmDuXPnVrvf999/jy1btiAsLMxJLSMiIiIl8HJ1A5KTk5GcnFztPqdPn8aUKVPw66+/YuDAgU5qGRERESmBy3uGzNFoNBgzZgyeffZZJCYmuro5RERE5GZc3jNkzptvvgkvLy9MmzbN4teUlpaitLRU/7i4uNgRTSMiIiI3IOmeoe3bt2POnDlYtGgRVCqVxa9LT09HQECA/iciIsKBrSQiIiI5k3QwtH79epw/fx5NmzaFl5cXvLy8cOLECTz99NOIiooy+bqZM2eiqKhI/1NQUOC8RhMREZGsSHqYbMyYMbj33nsNtg0YMABjxozB+PHjTb7O19cXvr6+jm4eERERuQGXB0PXr19Hfn6+/vGxY8ewa9cuBAYGomnTpggKCjLY39vbG40aNUJcXJyzm0pERERuyOXB0LZt29C7d2/94+nTpwMAUlNTsWjRIhe1ioiIiJTC5cFQUlIShBAW73/8+HHHNYaIiIgUR9IJ1ERERESOxmCIiIiIFI3BEBERESkagyEiIiJSNAZDREREpGgMhoiIiEjRGAwRERGRojEYIiIiIkVjMERERESKxmCIiIiIFI3BEBERESkagyEiIiJSNAZDREREpGgMhoiIiEjRGAwRERGRojEYIiIiIkVjMERERESKxmCIiIiIFI3BEBERESkagyEiIiJSNAZDREREpGgMhoiIiEjRGAwRERGRojEYIiIiIkVjMERERESKxmCIiIiIFI3BEBERESkagyEiIiJSNAZDREREpGgMhoiIiEjRGAwRERGRojEYIiIiIkVjMERERESKxmCIiIiIFI3BEBERESkagyEiIiJSNAZDREREpGgMhoiIiEjRGAwRERGRork8GFq3bh0GDx6MsLAwqFQqfP/99/rnbt++jeeffx533XUXateujbCwMIwdOxZnzpxxXYOJiIjIrbg8GCopKUGbNm0wd+7cKs/duHEDO3bswEsvvYQdO3YgMzMThw4dwj/+8Q8XtJSIiIjckUoIIVzdCB2VSoUVK1Zg6NChJvfZunUr7rnnHpw4cQJNmza16LjFxcUICAhAUVER6tWrZ6fWEhERkSM56/7t5bAjO0hRURFUKhXq169vcp/S0lKUlpbqHxcXFzuhZURERCRHLh8ms8atW7cwY8YMjB49utoIMT09HQEBAfqfiIgIJ7aSiIjIQdRqIC8PyMjQ/qtWu7pFbkE2wdDt27cxatQoaDQazJs3r9p9Z86ciaKiIv1PQUGBk1pJRETkIJmZQFQU0Ls3MHq09t+oKO12qhFZDJPdvn0bI0eOxLFjx7BmzRqz44a+vr7w9fV1UuuIiIgcLDMTGDECqJzme/q0dvuyZcDw4a5pmxuQfM+QLhA6fPgwfvvtNwQFBbm6SURERM6jVgNPPlk1EALubEtL45BZDbi8Z+j69evIz8/XPz527Bh27dqFwMBAhIWFYcSIEdixYwd++uknqNVqnD17FgAQGBgIHx8fVzWbiIjIOdavB06dMv28EEBBgXa/pCSnNcuduDwY2rZtG3r37q1/PH36dABAamoqZs2ahZUrVwIA2rZta/C63NxcJPFLJyIid1dYaN/9qAqXB0NJSUmortSRhMogEREROV/jxvbdj6qQfM4QERGRovXoAYSHAyqV8edVKiAiQrsf2YTBEBERkZR5egJz5mj/u3JApHv8/vva/cgmDIaIiIikbvhw7fT5Jk0Mt4eHA999BwQGshBjDbg8Z4iIiIgsMHw4MGSIdtZYYaE2R+jCBeCppwxnm4WHa3uSWHfIYuwZIiIikgtPT+30+YceAi5fBh58sOq0e10hRlamthiDISIiIrlhIUa7YjBEREQkN9YUYiSzGAwRERHJDQsx2hWDISIiIrlhIUa7YjBEREQkNyzEaFcMhoiIiOSGhRjtisEQERGRHFVXiHHZMtYZsgKLLhIREcmVsUKMPXqwR8hKDIaIiIjkTFeIkWzGYTIiIiJSNAZDREREpGgMhoiIiEjRGAwRERGRojGBmoiIyB2o1ZxVZiMGQ0RERHKXmaldxb7i4q3h4drCjKw3ZBaHyYiIiOQsMxMYMaLqKvanT2u3Z2a6pl0ywmCIiIhIrtRqbY+QEFWf021LS9PuRyYxGCIiIpKr9eur9ghVJARQUKDdj0xiMERERCRXhYX23U+hGAwRERHJVePG9t1PoRgMERERyVWPHtpZYyqV8edVKiAiQrsfmcRgiIiISK48PbXT54GqAZHu8fvvs96QGQyGiIiI5Gz4cGDZMqBJE8Pt4eHa7awzZBaLLhIREcnd8OHAkCGsQG0jBkNERETuwNMTSEpydStkicEQERGRvXB9MFliMERERGQPXB9MtphATUREVFNcH0zWGAwRERHVBNcHkz0GQ0REZF9qNZCXB2RkaP919yCA64PJHnOGiIjIfpSYN8P1wWSPPUNERGQfSs2b4fpgtpNILyKDISIiqjkl581wfTDbZGYCUVFA797A6NHaf6OiXBI0uzwYWrduHQYPHoywsDCoVCp8//33Bs8LITBr1iyEhYXB398fSUlJ2Lt3r2saS0RExik5b4brg1lPYr2ILg+GSkpK0KZNG8ydO9fo8//973/x7rvvYu7cudi6dSsaNWqEfv364dq1a05uKRERmaT0vBmuD2Y5CfYiujyBOjk5GcnJyUafE0Lg/fffxwsvvIDhf/8iffnllwgNDcXixYsxceJEZzaViIhMYd4M1wezlDW9iO3bO6VJLg+GqnPs2DGcPXsW/fv312/z9fVFr169sHHjRpPBUGlpKUpLS/WPi4uLHd5WIiJF0+XNnD5t/C9+lUr7vC15M3Ja4oLrg5knwV5Elw+TVefs2bMAgNDQUIPtoaGh+ueMSU9PR0BAgP4nIiLCoe0kIlI8R+XNSCjJlkywdkaYBHsRJR0M6agqXVhCiCrbKpo5cyaKior0PwUFBY5uIhER2TtvRmJJtmSELcGqBGffSToYatSoEQBU6QU6f/58ld6iinx9fVGvXj2DHyIicoLhw4Hjx4HcXGDxYu2/x45ZHwhJMMmWKrE1WJXg7DtJB0PR0dFo1KgRcnJy9NvKysqwdu1adO3a1YUtIyIik3R5Mw89pP3Xlpuakqfqy0FNg1ULexE15Rr7tbkaLk+gvn79OvLz8/WPjx07hl27diEwMBBNmzZFWloaXn/9dcTGxiI2Nhavv/46atWqhdGjR7uw1URE5FASTLKlCqwJVk0llJuYfXfl5DWsenIjsn/W4OcjYQ5pfmUuD4a2bduG3r176x9Pnz4dAJCamopFixbhueeew82bNzFp0iRcuXIFnTp1wqpVq1C3bl1XNZmI7EVOs4TIuSSYZOtSUrtW7BWsenpC9OyF3UsPIev1QmRv2YONxa2ggW70xzmzwVVCGOvjci/FxcUICAhAUVER84eIpEKJC3qS5dRqbSKuuan6x465fwAtxWslL0+bLG1Obq7RnqGik0X47cN9yPqhHNlHYlGoaWTwfIJvPlLuOoWeQz3xjxd7Ovz+zWCIiJxPl3hZ+X8/uuRJVuwl4M7vCWD4u6Kk3xOpXitWBqtCI7BnxWFkf34GWRvr4/eiRJTDW797LZSgb+gepPQpRfLkZojsFg7AefdvBkNE5Fy6/4mayjdQ0l/8ZJ6xXpGICO1sIykHQvYY1pL6tWImWL35yVf49UhzZH9fiqzDzXFKbZj/E+dzFMmJBUgZWQc9Hk+EX32/Km/BYMiOGAwRSUgNu9dJgaSWL2OOvYa15HCtGDnXYr+GeNfzObxeMg234aPf7oeb6NNwD5J73UDyE1GI6RNp9vDOun+7PIGaiBSGs4TIFFNBj5yWuDA1rKWrvWPNsJYMrpVbRbcgrpbCv8K2ols++BPNcBs+iPE6gZSE40h5oDZ6TUqEf2BHl7W1OgyGiMi5OEuIjJFikrC1zNXeUam0tXeGDLGsZ0ui18rhnOPI+vgEinK24MXrMwAYnm8TnMZy3I/Cf3+MsNmPAzDfA2SgYlDspNEcDpMRkXNxlpDzyGV4SapJwtay97CWRK6Vm5dvIm/uHmQvv4GsfVE4Uh4JD6hxHFFoglPGqzfb2rZKQXExgADA4fdvSVegJiI3JMFS/G5JLgucutOyG/Ye1nLhtXI07yTmPrAWA0O2IjAISHm5Iz78sxeOlEfCG2VIq/M5IkwFQoBtFcJNLe/hBAyGiMj57L2gJxmS0wKn7rTshiOGtZxxrajVKPt5Ff4a9Rrmxr6PBJ/DiOndFFOX9ULWhY64BX+Ee57BY/Hr8P3MLbh0uhTvfGJh4WNLA7/qgmIn4DAZEbmOXIZx5ETq07Ery8jQ9lyZs3ixdq0zKXPksJYDrpUTv5/CgRmL0GHjhwjWnNdvL0A4puMdXAhogZSuV5HyaBMkDmkOlUeF3il7DwmaOJ6zhsmYQE1EriOnWUJyYY81o+zJ3E1coknCNtENa40YoQ18jBWKtHVYyw7XStn1MmxYsBfZ3xYh669wxJX+iWX4NyonQIfjFL5TjYLqi2p6nnr00AZ25gK/Hj0sa5yLZ49ymIyIyJ1IaTq2JXlLuptq5ZwYHZVKW2TR0puqq0lsCPjU1kJ8OnYdhodtRlDdUvR9ph3e3paEA6XRmIMnAYgqgYDq759qc7Xsnc/k4mCXwRARkTuRSk+LpXlL7phQP3w4cPy4doho8WLtv8eOOSUQun3jNtZ9uBszOuehjf9BRNzTGI991RMrCjvjOuoiRHUBqTEbkDP845onQNsz8DMXFDsYc4aIiNyJFKZj25K3JNdlNySgcNc5/PLBIWSt8kLO6QQUIUD/nAoadKqzFyn3XELK+FC0GxUHDy8P++Zq2SufycjyHswZIiIi6zkyb8VStuQtDR+uLUbIhHqz1GVqbFm4D1lfXULWjlDsvNkSQKj++WDVRQyIPIiUFKD/tHgEx91V9SD27EG0V+6frqepclDsBAyGiIjcjambSni4c3pabM1bYkK9Sef3XsAvcw4i+1cP/FrQEleEYYDTsfZeJHe4gJSxwbh7TEt4+nSrepCKPTghIdrhrTNn7JMAbS+Vg+J69YBBgxz+tgyGiIjckSt7WlyVt+RGpRrUZWps+2o/sv53EdnbG2JbSUsIdNc/30B1BQMi9iMlWYMBU+MQkphY/QGNDUMGBd1ZJsQVPYimVAyKi4ud8pbMGSIiIvtyRd6SG6xtdunwZfz6/n5kZQG/nojDRRFs8Hw7//1IaX8OKWOCcE9qS3j5WdifUd1yJ0Jog6JLl+5sl1CulrPu3wyGiIjI/owkwwJwzHpjMl3bTFOuwY7FB5D95Xlk/RGELdcTDSa6B6AI/ZrsQ0r/ctw3rQUatw2t5mgmmEtmB4DgYOCxxwAPD22PTFKSZHrUGAzZEYMhIiIXcMYMMZlV3L5y7CpWvb8P2T9rkH00DudFQ4PnW/sdRHKbQqQ83ABdJiTAu5Z3zd7Q0krROhLrTXPW/Zs5Q0RE5Bg1yVuyNP9HahW3K7+9RmD30kPI+rwQ2VsaYGNxK2jQVf98HVxDv8Z7kXxvGZKnxiK8YxyAOPs1wNrimro6UBLtTXMUBkNEROQ4tswQsyb/x5kVty0M0IpOFuG3D/ch64dyZB+JRaHGMMBJ8M1Hyl2nkDwqAN0nJsKnTueat80Ua5PUdQnVaWnaQFYCvWnOwGCIiIikw1T+j6keC2fNXKsmQBNDh2HvD/nI+vQ0sjbWx+9FiShHF/1utVCCfiG78X+ttqJLD080TGrlvJlu5tYQM8bFvWmuwJwhIiKSBlvyf2oyc83SoTgTAZru0WMen+IzzT8NnovzOYrkxAKkjKyDXk0Ow+dfz7puppupZHZzLKk+7WDOun9zbTIiIpIGa/J/dGxd28ySRWQBbcD05JMw1m+gAiCgwkuaV1AL15HScCs+HLEW+atP4EBpM7y3oxf6tTgBn9TR5tdocyRTa4iZ4+LFU52JwRAREUmDrfk/1i4YauEisiXnS7Bp/CfAqVMwtXyoBwSaogCXf/gdP5/viClLeyGmT6T2yb8DKaO9Mbpt1a0Mb08VF4/9+mugYcPq94+IcH71aRdizhAREUlDTfJ/LJ25ZiZAEVDh+iNPYKR/E+ReboNhqF8h+8c035LLVTdKbaZbxWT23buBt94yve+oUYpJngYYDBERkVSYS/Y1t16WJTPXzAQoKgjUvXkeN2/eRCn8IDy8AI0FbTcWoDlzpps11GrtqvXV+fZbID1dMQERh8mIiEgabM3/sYaFgcez7VZj349HkHFrmDYAq9yeiu0yNaTkqjXazDHXYwVUzc1ycwyGiIhIOqzN/7FAaXEpct7Yjqfar8XYsZbNphr4bl+0HBQDlbeX7QGarqfLlkDKkaTaY+VCHCYjIiLHs2ZF+ZpUrv7bid9PIfujo8ha44vV51rhBjoAADygxmt4Dk1wBh6wcChOF6AZqzNU3dIiup6uESOktTJ8SIh993MDDIaIiMixbFlR3srK1WXXy/D7J3uRlVGErL/Csa+0OYBw/fONPc4iOeYwUoZ4IbB5OjyeSAVgRYBia4BmayBFTsVgiIiIHMfaitJWOL2tENkf5iMrxxs5hYm4jnb65zygRtd6e5Dc6QpSJjRGmwdaQOXR6O9nuwANa9vW02PLrC879HTZxFRv3Pnzlr3e0v3cACtQExGRY6jVQGgocOmS8eetXFH+9o3b2PT5PmR9cwXZuxvjz1t31vvygBr/wI/oH7ILcd2D0e7Nh9CgeZD59jk7QHGW6nrjAgMtW8k+N9fly3Fw1XoiIpK3114zHQgBFtXZKdx1Dr++vx/Hft6LUxf9cAQxWI8e0MATKmjQqc5eTI9agSFn5sHn8jngPIBMAH+8aX65C1t7eqTOXG/ckiU1K2HghhgMERGR/anVd2ZhmVNh1pK6TI0tC/ch66tLyNoRiqib+zEHTyICd3o4Lnk2xN5705AwZyKC9x4GRsxyyDCcLJmreq1SAU8/Dbz3HjBypLQSu12IU+uJiMj+1q8HLhupymzElVv++N9jG/BQ5EY09CtGt8fvwmu/JyHq5n4swwg0gWFNnCDNRfRc9SKC/1wjneUupMLSqtfBwXYvYSBn7BkiIiL7s7BGzVUEoOH/DYQa3vptDVRXcF/4Hnx6+XGoSkTVdcF0PRyTJwMXLpg+uLOXu5ACa2oIPfSQaxK7JYjBEBER2T+Z2MKqypkYjgewDHV8ytDo7nDc90hDdBqfAK/NaqC3mUCnukCoIgUVD7S66rWj8qZklpwu+WGy8vJyvPjii4iOjoa/vz+aNWuGV155BRqNJYvFEBGRWZmZQFSUdobR6NHaf6Oi9Ku320LTpRvKAkONlTUEAAgAaqjwf1iIDIzGp2Xj8J+T49AtNB9efl72DWCcvdyFK5mreg0AQUGOTY52wO+To0k+GHrzzTcxf/58zJ07F/v378d///tfvPXWW/jwww9d3TQiIvnTzTyqnGeiS0C24gZ25dhVfPfURoxrvgGN/a9i1OV5EFBBU2mgSxcgeVYOlSq+p6UBTMOG0lvuwpV0Va+rq5pz6RLwww+OeX87/j45k+TrDA0aNAihoaH4/PPP9dvuv/9+1KpVC1999ZVFx2CdISKqMZl1+1tErdb+xW4q4dZMHSChEdi99BCyvyhE1uYG2FScCHWF7Is6uIYX63+EyWXvos6NCkNanp6mk5p175mfD8TEmJ/+/e672llRgPFZUQpMBrZ3fSer3rcGv0/GOOv+Lfmeoe7du2P16tU4dOgQAGD37t3YsGEDUlJSXNwyIlIMGXb7W8TSmUcVVi8vOlmE5c9uwoQW6xHufRbtRsXhX6uSsKG4DdTwQoJvPp65Ow+r396JS9d88fyVGahTXKgt4Ld4sXZKd3Wzu3TvuXGjZQuk6qbPc1bUHevXW17fyd7va+Xvk1RIPoH6+eefR1FREeLj4+Hp6Qm1Wo3XXnsNDz30kMnXlJaWorS0VP+4uLjYGU0lInfkwOUknMZUr5aFeTkFK3ci479A1sb6+L0oEeXoon+uFkrQN3QPUvqUInlyM0R2aw6gueEBKibpZmRY1mbdbCdL1vVy1XIXUuWqVeld9b52IPlgaMmSJfj666+xePFiJCYmYteuXUhLS0NYWBhSU1ONviY9PR2zZ892ckuJyO1YUsAuLU17I5bqjbe6ZRkszMsZ8147rEWS/nGcz1EkJxYgZWQd9Hg8EX71O1neHmtnO1ka6LhrNWlbWPsZy/197UDyOUMRERGYMWMGJk+erN/26quv4uuvv8aBAweMvsZYz1BERARzhojIOnl5slnDyShTvVq6YabvvgOeegri9GmojNwKNFDhFMLREvuQ1HA/knvdQPITUYjpE2l7m3R5JeZygeydz6IkrvqMHfC+zBn6240bN+DhYdhMT0/PaqfW+/r6ol69egY/RERWk3G3P9Rq4LHHTPZqCQA3J0zBp3gUQqDKjC8NVFBB4NKoKbh4yRM/n++IKUt71SwQAu7MdgKqzwViIGQ7V33GMv5uJR8MDR48GK+99hp+/vlnHD9+HCtWrMC7776LYcOGubpp5Epqtfav9owM7b9KKrdPziPjbn9zi6SqhIB/8Tl8c6onRmAZziDM8PmIcKiWL0e7jOfgH+hv37YNH86kZ0dz1Wcs0+9W8sNk165dw0svvYQVK1bg/PnzCAsLw0MPPYR///vf8PHxsegYnFrvZqrLgZDohUYyJdchHbUaCAmxaG2wL8JfQsj4Qej9RDxqH9zh3ARkdyxXIDWu+ozt9L7Oun9LPhiyBwZDbsRcDoSE//IgmdL9zgGyqGNzNO8kds/KxLC1T1n2AqnmOxGBOUNEVZmb2QMob4VqchzdUGxpKTBrFhBmOIxk127/Ggz7lhaXIueN7ZjeIQ/xvkcR07splq1taNmLAwOVVZ2ZyATJT60n0rOmoBf/0qWaMDUUO3s2EBtr3+EGG4Z9T/x+CtkfHUXWGl+sOZeIEnTQP+eF2+jitwu4ZcF7u6okAIfHSGKsCoYKCgoQERHhqLYQVU/OM3tIPqorsjhrlrY3yF7BtoUFHcuul+H3T/YiK6MIWX+FY19pcwDh+t0be5xFcsxhpAzxwr1TExCwvi3wiAXv37evfc7DGsz5IwmyKhiKj4/H9OnTMWPGDNSuXdtRbSIyTs4ze0genFlk0cx7CahQMuYJjJscil/PtsZ1tNM/7QE1utbbg+ROV5AyoTHaPNACKo9Gd15feSaPKZbuZy/uUM2b3JJVOUM5OTlYtWoVYmNjsXDhQke1ici4Hj20f0FyhWpyFGeurWTmvVQQqHPjPC6evY3rqIsQ1QWkxmzAt9M24uLRa1hf1Ab/WpWEtg/GQeVR6ZrQXSvVcfa1wpw/01gqxOWsCoa6du2KLVu24I033sC///1vtGvXDnl5eQ5qGlElMi7oJTn8n69xzhyKtfAY0xJWY9tX+1FYFoRF+d3x4JyuaBBdv/oX6a6V6v5wcPa1IuNFPB3KXRcBlhmbZpONHTsWhw4dwuDBgzFw4EAMGzYM+fn59m4bUVUyLeglKa76n68cAjAHD8Wqy9TYuOAvvNg9D4+OK7PoNcM/6osOj7SEh5eV/7vWXSuVe4giIlxzrTDnryrdsGHlIFE3bMiAyGlsrjN048YN7NixA8uXL8cHH3wAb29vTJ48GbNmzULdunXt3c4aYZ0hN8TZKLZxVZ0muSTNOqDI4vm9F/DrhweRle2BXwta4opoAECb93McUWiC0/CAAws6SuVakfs6b/am+10z1Vsm1YKeTibJoovz58/H1q1bsXXrVuzfvx+enp5o3bo1OnfujLZt2+Kbb77BoUOHsGLFCtx9990Oa7S1GAwRwXX/85VbocwaFllUl6mx7av9yP7qIrK2NcS2kpYQFTrhG6iuYEDEfiQP0OAfCYdRf/oEm99LVuRazdtRGBxaxGn3b2GF8PBwMWLECPH222+LDRs2iFu3blXZ57XXXhOJiYnWHNbhioqKBABRVFTk6qYQ2a68XIjcXCEWL9b+W15u3etzc4XQ3oaq/8nNtW+bw8NNv5dKJUREhPXn4mjLl1dtd0SEdrsRFw9dEt9M2iAeiV4vglUXqpxmO/994oVuuWLDvN3i9s3bNXovWVu+XPudq1RVfw9UKvc8Z1MWL7bsely82NUtdSln3b+trjNkzoQJE/DSSy/ZGJoRkVH2GGZyRc6GXAtlDh8ODBoEzJsHHDkCxMQAkyYBf6+HqCnXYOe3B5G18Byy/gjCH9cToEE3/cvroQj9m+xDSv9y3DetBRq3bQmgpen3GjJE+xmcPg1cuAA0bKitDq1Wu1cviS6Pydjv8vvvu08vmCVYKkRS7F6BOiQkBGvWrLH3YYmUy161WVzxP1+5Js0aCT41b76FTXdPxqf7eyL7aBzOC8MAp7XfQSS3KUTKww3QZUICvGt1sfz9PD21i6rOmCH9vKqaqhj8uTqPyZV05Q/MDRuyVIhTcKFWIimzZ56PK3I25JgX8XfwKYRAxYnpmr8fjcAyrMBw1ME19Gu8F8n3liF5aizCO5oIIi1JYJZbXhXZh8wWAXYFSSZQyxWDIZItewcTjvyfr7GbPiCrpNmiY5fh3bol/K+fNwiEdDRQocg7GDtfy0L3J1rDp45P9Qe0ZHiTs4pMk8pMOEcy9jsSEaG8YUMTuGo9Edl/mMlUnabgYGDJEtv/52uqdtEPP0i6UKbQCOxZcRj/TclDUv1dGN5sB2qZCIQAwAMCDW5fQJ+O1y0LhCypIWNrMUI51G2qCaUUIxw+HDh+XPsHzeLF2n+PHWMg5GwOTc+WCM4mI9ly1AywpUuFaNjQ8Bjh4bbN5tHNEDI2U0w3Q0hCM6auFV4T38/cLB6LXyvCPU8bNOkdpNlnho81s+hsmVVk7PO09fuzRE1nMlrLkt8pUgRn3b8ZDBFJme6mauzGYOvUdHveaKy56Tv7hvo3jVoj9v2YL975R67o22C78EapQRP9cEOkNPxDzL1/tSgPCLRP8GlNEGttwOvsQMEVgZccyzGQQzAYsiMGQyRr9qzNYu8bTU16rhwYHF0/d138+NIWMalVnojyOlmlOc28jouprfNE1uw/xI1LN6w7l4YNzbfVmt4eawJeZwcKruihcUU9LJIsSdYZIiIXsGdtFnvX/bE1p8kBy3MczjmOrI9PIHtdbeRdaoVS3KN/zgelSAr6Cyk9S5A8sSli+0VB5RFp27k8/LD5HCdryhjoFlUdMUKbRyXEnecr51Xl5TmvbpO5VeZVKu0q80OG2DfnS67lGEjWGAwRyYG9arPY+0ZjS+0iO9VNunn5JtbO24uspSXI2heFI+VRAKL0z0d6nkJK/FGkDPdD7ymJqB1iZokgS89lyBDz+1hbQ8bSgNeZgYKrCmayGCG5AIMhsi8lTIV1FU/Pmt907H2jsfamX8PehqN5J5E97xiy8moh90IibuJOgOONMvRssAfJ3YqRMjEC8SnNoPIIr3IMm88F0E55tqQInjW9PTqWBLzODBRc1UPDYoTkCg4dhJMI5gw5ibMTLcl6jkzItiSnydJ8kPfeE2LxYlH6069i1WtbxFPtc0Wcz5Equ4V7nhaPxa8VK2ZsFsWni2v++dh77Sx7z6JzxPdniitzd7iGGf2NCdR2xGDICTgVVj4ccaOx9KZvaWJxhZ+TCBfDsFwAQnihTPQK2CneTM4Vfy47KDRqjX0+E1vOxVL2ThR3VqDgzMDLGAmVYyDXcdb9mxWoqeZYQVd+HFH11pIhUksraleggQoqCGz+x+tI+HASApoG2NY+a9gy3OvMIWJnVS129XIRHHZXPC7HYUcMhhxMjutPSYUr/2fvivf+O3AWp05Dhar/6xGA8erPUg+oHTA7zixnfX9cLoJcyFn3byZQU81xKqxtXHEDrahiQraDb6y3b9zGps/3IXvxFZSeew5v40kIqOBRKSAytQyGw2Yu2YOdZsdZzR4J9ZbgKvOkAAyGqOY4FdZ6rrqBmmqLA4Kywl3n8MsHh5Cd44VVpxJQhDZ/P5OEE2iMeaopCBXnrDyoxAJqV9XicTZnBV5ELsJhMqo5Xc6QTFYmdzkp5ViZCspsyAlRl6mxZeE+ZH11Cdk7QrHjZkuD54NUl3Bf5AGkpAD9p8UjuHn9O70N584BTz1l/k2kNtTKIWIih+IwGcmHLTVVlMxVxewqs0Ovxvm9F/DrhweRle2BXwta4oq4y+D5jrX3IrnDBaSMDcbdY1rC06eb4QEqDtO98478astwiJjILTAYIvuw55IR7k4qN1AbgjJ1mRrbvtqP7K8uImtbQ2wraQmB7vqXNFBdwYCI/UgeoMGAaXEIbZVoWVvsGVA7MzGcQ8REboHBENkPEy0tI5UbqIXB1rWdh/HjUm9kZwv8cjweF0Urg+fb+e9HSvtzSH44EJ3GJ8DLr6tt7bE1oK4Y/Bw+DHz6qfOS0l1RLZnTzYnsjjlDRM4mlRwrC/Nd+uA35KKv/nE9FKF/k31I6V+O+6a1QOO2ofZtlzU3e2PJ35U5uiaOM2vxuHoGIpGTsc6QHTEYchHdTe30aeDCBaBhQ6BJE/4lC7i+mB1gNijTQIVTCEc0jiHR7whS2pxBysMN0GVCArxreTu2bZYwlfxtjKMDTGfU4rFjsjuRXDAYsiMGQy5Q3V/s7vSXbE2GLFxYzE5oBHYvPYQj6UswfPcsCMCg5o+u6vP2Nv+H6BEdENQ9QVpBrLkZeaZYO6vLmu/XkcNXUpqBSORETrt/O3SxD4ng2mROZmqdssrrGsl9jSF7LExr73WrqnH1xFWx7JmNYkKLdSLM44y+ycOwXJyE4XloAoOECAqS7qK7li4iWvln8WLL30NKCw+7ctFUIhdy1v2bCdRkX9VN165ICHkVo6v8V/+FC8CDD9a8aKIDi9kJjcDeH/KR9elpZG8KwIarrVCOLvrna6EEfUP3oH+fYGge3wBojumTkFWzZkmjIKQpts60szQp3VFFMS3pPTK2j1RmIBK5K4eGWhLBniEnsvYvdjn8JWush8DTs/peL0eu5l2Na4XXxPczN4vH4teKCM9TVZrWwvuoSGuXJ1albxM3r9ysegDdSuUSPDcD1v6eWdNuR30GlvQ0mdpn9mz3uZ6IrMCeIZIna/8ylfpfsqZ6CNRq069xVtFEaHt/DmQdRfanBchaXw/rrrTCbXTSP++Hm+jTcA+Se91A8hNRiOkTDSDa9AGlUhDSHHNT2iuytk6RIz4DS3qaANP7vPwyEBQEXL5s/HylWpSSSCYYDJF9WVsbR8rF6Cwd8jPFQYFeyfkS5H7wJ/K/2YKjJ73wp6YV1qMHNNDe6Jt5ncDAhONIvr8Wkqa0gn9gR/u32dVBbHVFGiuztvCnvT8DSyp9656vbh8dVnknsjtZBEOnT5/G888/j+zsbNy8eRMtWrTA559/jg4dOri6aVSZNX+xR0RI+y9Zcz0E5tgx0DuccxxZH59A9rraCLiUj7fxLAbhTtsueIRgy91T0OKVRxDbLwoqj0jb3kgqBSEtUV2RxkcfBWJjbZvVZe/PwJKeJnO/Z0IAly4Bs2cbLyrJKu9ENSL5YOjKlSvo1q0bevfujezsbISEhODIkSOoX7++q5tGxlT8i706KpX0/5K1tffDDkMWNy/fxNp5e5G1tARZ+6JwpDwKQBSGIRMZGA3AMNBsKC5g0NaXgZJEwKOaYTBzXFFRuSYcUfXc3p+BPXvRYmOB48dZgZrIziQfDL355puIiIjAwoUL9duioqJc1yAyz9Rf7DpOqqVTY7b0ftRgyOJo3klkzzuGrLxayL2QiJu4W/+cN8rQq/5ufHnrcahuCagqv1g3lFLTGXpyXHTX3jPy7P0Z2LMXrXFjh85AJFIqyRddTEhIwIABA3Dq1CmsXbsWTZo0waRJk/Doo4+afE1paSlKS0v1j4uLixEREcGii84m9wrU5pbNALTnUTGZ2opAr7S4FOvm7UH20mvI2tMUB8uaGTwf7nkGybH5SBnqi75TE1D30HaLls+wurCgMS4sCCkZ9voMLFl+pUkT7XNnzlTfG8WiiqQwrED9Nz8/PwDA9OnT8cADD+CPP/5AWloaFixYgLFjxxp9zaxZszB79uwq2xkMVcIFH80zt2zGd98BwcEWf4Ynfj+F7I+OIjvXF6vPJqIEdfTPeaIc3QP2IKXrVSRPCEOrYbFQeVToA8rIAEaPNt/mxYuBhx6y5iyN4++H/T4DS5ZfAVy/RAuRxLAC9d+8vb1Fly5dDLZNnTpVdO7c2eRrbt26JYqKivQ/BQUFrDNUmZSq60qdsc8qIsKiz6r0WqlY884O8czduSLR91CVsjCNPQrF/8WuE8ue2Siunrha/cFYhVjeLPk9qsHvGpE7cladIcn3DEVGRqJfv3747LPP9Ns+/vhjvPrqqzh9+rRFx+DaZJW404KPlv7lXtO/8K14/elthcj+MB9ZOd74rTAB13Dnd84DanSpuxcpnS8jZUJjtHmghWHvj7k2SGG1e7KdrRWo+X2SQjnr/i35BOpu3brh4MGDBtsOHTqEyEgbpw4rnSU1T+SyTIaxnA5ji8Baul91qklavX3jNjZ9vg/Zi68ga1dj/HkrDsCdpNkQ1QUkNzuI5IEe6DetJQJjWlt+jpXbILfkZjJkSfIzE6SJnE7yPUNbt25F165dMXv2bIwcORJ//PEHHn30UXzyySd4+OGHLToGe4YqyMtzXhKuI1XXuyXEnYDu4kVg5Ei794IV7jqHXz44hOwcL6w6lYAiBNw5NDToVGcvUu65hOTUELQfHQ8PLw+r38MkJjcbYk8KkdtiAnUFP/30E2bOnInDhw8jOjoa06dPr3Y2WWUMhipwdhKuI+iGiywpiFh5tldFlg4rqdVQr8nDkSXbsH6LL+bn34ttt1oZ7BKkuoT7Ig8gJQXoPy0ewXFBlp+PLRgAaNmj14+IJIvBkB0xGKrAHXqGLD0HS5k41/N7L2DfU5/grjVzEKS+oN9egHA8iTk4VTsOyR0uIGVsMO4e0xKePm4WjEg94HKn3DciMoo5Q+QYcqswbIy918X6+3jqMjW2fbUf2V9dRNa2hogo2Y+leAmVqz2H4xSWq0ZA9T83vtlKvcfFnXLfiMjl7JjIQLKgS8IFDBd/rPhY6km4dl4X67clFzGm2QY08ruCzv9shdlrk7C9JB7v4ykAospFovr7B2lp1a9eL1e6HpfKw5C6FdYzM13TroqsWVmeiMgMBkNKpFsuo0kTw+3h4fIYWtD1blUO5qykgQonEY4BP0zC18e646IIRj0UYUSTTfjpvo8QgVOmLxB3vdma63EBpBEE2ntleSJSNAZDSjV8uHbBx9xcbbJ0bq42kVjqgRBQfe+WhTR/r+6VhjlI9DuC5zvlYe0Hu3GxpBaWnuqC5LENLTuQu91s5dLjYu+V5YlI0ZgzpGRyrmdibjHYCjQANPCEF+70ZlzyaIhdPabig7e6ILxjYwAtDF+k1JutXHpc3CH3jYgkgz1DJF8VerduTXgCAtrApyLN3xk+z3i+h49bvIe9D7+O21mr0LDsDPrlvfh3IGSEuaE4lUpb28fdbrZyCQLdIfeNiCSDU+tJloRGYO8P+cj69DSyNwVgw9VWGIwfMQdPIgJ3eoqu1QrBzRdeRci/LK9LpWfJ4ppyGFa0htyW/GABSiK3xjpDdsRgyD1cP3sdqz/Yi6wVpcg+HIMCtWECeAvvYxiYeAyjOx5G63v84NM8sua1cZR4s5VbECj1ekhEZDMGQ3ak+GBIpjcLoRE4kHUU2Z8WIGt9Pay70gq34aN/3g830afhHiT3uoHkJ6IQ08dB69VJ+fNzVNuUGAQSkeQwGLIjRQdDUi+eV0nJ+RLkzt2L7BU3kXWgGY6XRxg838zrBFJaHkfKiFpImtIK/oH+LmqpEc4Omhz93Uo5CCQiRWAwZEeKDYYsWa5gyBCX3/AO5xxH1scnkL2uNvIutUIp/PTP+aAUSUF/IblHCVIeb4rYflFQedSsvpBDODvo5FIUysCAlBSOwZAdKTIYMreYqUoFBAYCfn7aZFkdJ/Qa3bx8E2vn7UXW0hJk749E/u0og+cjPU8hJf4oUob7ofeURNQOqe2wttiFswMTS75bKSU5k21k1qtL5AgMhuxIkcGQrYuZOugGfjTvJLLnHUNWXi3kXkjETdTSP+eNMvRosAcp3YqRMjEC8SnNpNn7Y4wrAhN3WGyXqseePyIAXKiVasrWonh2WuSytLgU6+btQfbSa8ja0xQHy5oBaKp/PtzzDJJj85Ey1Bd9pyagblh729rratZUbLZXYCKXwohkGy5CS+R0DIbcVU2K4tl4Az/x+ylkf3QU2bm+WH02ESXooH/OE+XoHrAHKV2vInlCGFr9oxlUvx8CCo8Ch24CoTLNhXBFYCKXwohkG1cE2EQKx2DIXZlbrsASZm7gZdfL8Psne5GVUYTsv5pgb2ksgHD98409ziI55jBShnjh3qkJCGjaVvtEZibQrK975EK4IjDhUhTujT1/RE7HYMhd6ZYruP9+249h5AZ+elshsj/MR1aON34rTMA1tNM/5wE1utTdi5TOl5EyoTHaPNACKo9GhgcwlQtx+rR2u9xyIVwRmOi+2xEjtMc3VhiRS1HIF3v+iJyOCdTuLDPTtmCoQtLv7VINNn2+D9mLryBrV2P8eSvOYNcQ1QUkNzuI5IEe6DetJQJjGpg+rqOSjV09/dhVFZtZGNE9yW1JFCIH4mwyO1JkMGQu8DBFpYIAkJv0CuYf7otVpxJQhIA7T0ODTnX2IuWeS0hODUH70fHw8LJwvV9HzIKSyvRjVwUmrg4EyTHktiQKkYNwNhnVjLkkTJ3gYODiRf3DsyIUk/ARVuTe+R9tkOoS7os8gORkYMCT8QiOu8u2Ntk7F0JKQ27Dh7umgKWnJ5No3dHw4drfX2OBPnv+iOyOwZC7sjCg+FAzCatwN+rgOgrRGOvRAxp44u5a+5By93mkjA3G3WNawtOnW83bZM9cCClOP2ZgQvbkqgCbSIEYDFlCjkMRFgYeyy/3xlokoYHqCvqH78fC+zZhwLQ4hLZKAJBg3zZZMsPN09Ogp8okTj8mJWCATeQUFiZ7KFhmpjb3pndvYPRo7b9RUdrtUtajB9SNwmAqIUwDFc6iEbp3UWPDvD9x/kZdfHuyK8Z+0h2hrRo6pk26WVDVUauBkSPNf76cfkxERHbCYKg6upyUyj0QupwUiQVEmnINtn+9H//pm4eu9ffhwbNzIKCCBoZLWwhoR5EaLf8Ir27si25PtIaXn5M6CYcPB5YsMd+zlpamDYxM4fRjIiKyE84mM0Umi2FeOXYVOR/sQ9aPGvxyrAXOaUIMnk/znouXVa+gftmFOxtdPf3aHrPKOP2YiMjtcTaZq9krJ8XO+UZCI7B76SFkf1GIrM0NsKk4EWp01T9fB9dwb+O9SLm3DMlTYxHecQqgfkJaOU+WDl2tXm26zSw8SEREdsJgyBR75KTYqQZO0cki/PbhPmSvLEd2fnOc0cQBuFP8MME3Hyl3nULyqAB0n5gInzqdDQ8gtSRMS4euXn31zn8b+9w4/ZiIiOyAw2Sm1HQox1QNHAuKpgmNwN4f8pH16WlkbwrAhqutUA5v/fO1UIK+oXuQ0qcUyZObIbJbuNHjSJa5IS5jqvvc5Djbj4iIzGIFajuqUc6QLTkpNuQbXT97Has/2IusFaXIPhyDAnUTg5e08D6GlFYnkfxAHfR8IhF+9f2qb7vUgwNTFXarwzwgIiJFYc6Qq9UkJ8XCfKOTby3Bsk1hyFpfD+uutMJtdNLv4oeb6N1wD1J63UDyE1GI6RMNINp8u6WyPIU5poa4qsPaQURE5AAMhqpja06KhflGz89U4Vsk6R838zqBlJbHkTKiFpKmtIJ/YEfr2iul5SksUbnC7r59hnlCprB2EBER2ZHyhslq17Z+CMnaYScL84364Vd4BAUiuUcJUh5vith+UVB5qMy+zmQbZVAKoFqOWMiViIhki8NkjrByJTBzpvVDSFbMxrp5+SbWrvNHJ++GCLh9ER5GakALALfqheL7A51Ru7Gdvlx3WJ7C3HIduoCuRw/nt42IiNyWsipQjxnjkGrSR/NO4qORazEwZCuCggSSX+6ECbfnA0DV6s8qFVQqFfwXzrNfIAS4x/IUFZfrUFXqIWPtICIichBlBUPG6HogzC3/UEFpcSly3tiO6R3yEO97FDG9m2LK0l7IutARN1EL4Z5nEBwfjK3DXq9SU0cVHu6Y3B13WZ5Cl6fVxHA2HRz1uRERkeIpK2cIQLV9MdXkopz4/RSyPzqK7FxfrD6biBLU0T/niXJ0D9iDlK5XkTwhDK2Gxd7J/XHWNHd3W55CDuUBiIjIoZgz5AoVhpDKrpfh90/2IiujCNl/NcHe0lgAd4obNvI4h5SYQ0gZ4oV7pyYgoGlb48d0VvVnd1ueQmpVs4mIyG0xGKrgQrEPfkhdj6wcb/xWmIBraKd/zgNqdKm7FymdLyN5fCO0fTAOKo9QF7bWCC5PQUREZDXZDZOlp6fjX//6F5588km8//77Fr3G3DCZAHAWjRCOU9DgTs9JiOoC7os+iJRBHug3rSUCYxrY4xQMOWI4iENMRETkBjhMZsTWrVvxySefoHXr1jYfQ6hUUFWI/3SzvSbjIwio0LnOX0jueAkp40LQfnQ8PLy617jdJjmqWrQ7DTExsCMiIgeTzWyy69ev4+GHH8ann36KBg1s66F5wftNnBKGs5TOIAwfNHwV9z8RivMHrmDTtbvw7zVJuHtsAjy8HPjx6KpFO2Cqv9vIzNQmhffuDYwerf03KoqfDRER2ZVshslSU1MRGBiI9957D0lJSWjbtq3JYbLS0lKUlpbqHxcXFyMiIgJAETxQG//0/Qr3Rh9B4qBmiHvlYXj6+zjnJHTcoVq0o5laWqS61euJiMitcJisgm+//RY7duzA1q1bLdo/PT0ds2fPrrJ9fuomDH2mPUJbjbNzC63kDtWiHUmt1g4fGovThdAGRGlp2nXNlBosEhGR3Uh+mKygoABPPvkkvv76a/j5+Vn0mpkzZ6KoqEj/U1BQAAB46IMuCG3V0JHNtYw7VIt2JGuCRSIiohqSfM/Q9u3bcf78eXTo0EG/Ta1WY926dZg7dy5KS0vhWal3wNfXF76+vs5uquXcpVq0ozBYJCIiJ5J8MNS3b1/89ddfBtvGjx+P+Ph4PP/881UCIVnggqTVY7BIREROJPlgqG7dumjVqpXBttq1ayMoKKjKdtlwt2rR9sZgkYiInEjyOUNuiwuSmsbV64mIyIlkM7W+Jpw1Nc8mLCpomrGilBERXFqEiEghnHX/ZjBE0sZgkYhIsVhniAhwr6VFiIhIkpgzRERERIrGniGyPw5tERGRjDAYIvsylvQcHq6dHcakZyIikiAOk5H96BZXrbyUxunT2u1cbZ6IiCSIwRDZh7nFVQHt4qpqtVObRUREZA6DIVdSq4G8PCAjQ/uvnAMFLq5KREQyxZwhV3G33BourkpERDLFniFXcMfcGi6uSkREMsVgyNncNbdGt7hq5bXEdFQq7VIaXFyViIgkhsGQs7lrbg0XVyUiIpliMORs7pxbM3w4sGwZ0KSJ4fbwcO12OeZCERGR22MCtbNZm1sjt2rOw4cDQ4bIq81ERKRoDIacTZdbc/q08bwhlUr7fI8e8p1xxsVViYhIRjhM5myW5tb88IP7zTgjIiKSIAZDrmAut2bIEPeccUZERCRBHCZzlepya/LyLJ9xxuEoIiKiGmEw5EqmcmvcecYZERGRxHCYTIpYzZmIiMhpGAxJEas5ExEROQ2DISliNWciIiKnYTAkVazmTERE5BRMoJYyVnMmIiJyOAZDUsdqzkRERA7FYTIiIiJSNAZDREREpGgMhoiIiEjRGAwRERGRojEYIiIiIkVjMERERESKxmCIiIiIFI3BEBERESkagyEiIiJSNAZDREREpGgMhoiIiEjRGAwRERGRojEYIiIiIkWTfDCUnp6Ojh07om7duggJCcHQoUNx8OBBVzeLiIiI3ITkg6G1a9di8uTJ2Lx5M3JyclBeXo7+/fujpKTE1U0jIiIiN6ASQghXN8IaFy5cQEhICNauXYuePXta9Jri4mIEBASgqKgI9erVc3ALiYiIyB6cdf/2ctiRHaSoqAgAEBgYaHKf0tJSlJaW6h8XFxc7vF1EREQkT5IfJqtICIHp06eje/fuaNWqlcn90tPTERAQoP+JiIhwYiuJiIhITmQ1TDZ58mT8/PPP2LBhA8LDw03uZ6xnKCIigsNkREREMsJhskqmTp2KlStXYt26ddUGQgDg6+sLX19fJ7WMiIiI5EzywZAQAlOnTsWKFSuQl5eH6OhoVzeJiIiI3Ijkg6HJkydj8eLF+OGHH1C3bl2cPXsWABAQEAB/f38Xt46IiIjkTvI5QyqVyuj2hQsXYty4cRYdg1PriYiI5Ic5Q3+TeKxGREREMierqfVERERE9sZgiIiIiBSNwRAREREpGoMhIiIiUjQGQ0RERKRoDIaIiIhI0RgMERERkaIxGCIiIiJFYzBEREREisZgiIiIiBSNwRAREREpGoMhIiIiUjQGQ0RERKRoDIaIiIhI0RgMERERkaIxGCIiIiJFYzBEREREisZgiIiIiBSNwRAREREpGoMhIiIiUjQGQ0RERKRoDIaIiIhI0RgMERERkaIxGCIiIiJFYzBEREREisZgiIiIiBSNwRAREREpGoMhIiIiUjQGQ0RERKRoDIaIiIhI0RgMERERkaIxGCIiIiJFYzBEREREisZgiIiIiBSNwRAREREpGoMhIiIiUjQGQ0RERKRoDIaIiIhI0WQTDM2bNw/R0dHw8/NDhw4dsH79elc3iYiIiNyALIKhJUuWIC0tDS+88AJ27tyJHj16IDk5GSdPnnR104iIiEjmVEII4epGmNOpUye0b98eH3/8sX5by5YtMXToUKSnp5t9fXFxMQICAlBUVIR69eo5sqlERERkJ866f0u+Z6isrAzbt29H//79Dbb3798fGzdudFGriIiIyF14uboB5ly8eBFqtRqhoaEG20NDQ3H27FmjryktLUVpaan+cVFREQBthElERETyoLtvO3oQS/LBkI5KpTJ4LISosk0nPT0ds2fPrrI9IiLCIW0jIiIix7l06RICAgIcdnzJB0PBwcHw9PSs0gt0/vz5Kr1FOjNnzsT06dP1j69evYrIyEicPHnSoR+m1BQXFyMiIgIFBQWKypXiefO8lYDnzfNWgqKiIjRt2hSBgYEOfR/JB0M+Pj7o0KEDcnJyMGzYMP32nJwcDBkyxOhrfH194evrW2V7QECAon6JdOrVq8fzVhCet7LwvJVFqeft4eHYFGfJB0MAMH36dIwZMwZ33303unTpgk8++QQnT57E448/7uqmERERkczJIhh68MEHcenSJbzyyisoLCxEq1atkJWVhcjISFc3jYiIiGROFsEQAEyaNAmTJk2y6bW+vr54+eWXjQ6duTOeN89bCXjePG8l4Hk79rxlUXSRiIiIyFEkX3SRiIiIyJEYDBEREZGiMRgiIiIiRWMwRERERIomy2Bo3rx5iI6Ohp+fHzp06ID169dXu//atWvRoUMH+Pn5oVmzZpg/f36VfZYvX46EhAT4+voiISEBK1ascFTzbWbNeWdmZqJfv35o2LAh6tWrhy5duuDXX3812GfRokVQqVRVfm7duuXoU7GKNeedl5dn9JwOHDhgsJ+7fd/jxo0zet6JiYn6feTwfa9btw6DBw9GWFgYVCoVvv/+e7OvcYfr29rzdpfr29rzdpfr29rzdpfrOz09HR07dkTdunUREhKCoUOH4uDBg2Zf54xrXHbB0JIlS5CWloYXXngBO3fuRI8ePZCcnIyTJ08a3f/YsWNISUlBjx49sHPnTvzrX//CtGnTsHz5cv0+mzZtwoMPPogxY8Zg9+7dGDNmDEaOHIktW7Y467TMsva8161bh379+iErKwvbt29H7969MXjwYOzcudNgv3r16qGwsNDgx8/PzxmnZBFrz1vn4MGDBucUGxurf84dv+85c+YYnG9BQQECAwPxwAMPGOwn9e+7pKQEbdq0wdy5cy3a312ub2vP212ub2vPW0fu17e15+0u1/fatWsxefJkbN68GTk5OSgvL0f//v1RUlJi8jVOu8aFzNxzzz3i8ccfN9gWHx8vZsyYYXT/5557TsTHxxtsmzhxoujcubP+8ciRI8V9991nsM+AAQPEqFGj7NTqmrP2vI1JSEgQs2fP1j9euHChCAgIsFcTHcLa887NzRUAxJUrV0weUwnf94oVK4RKpRLHjx/Xb5PD910RALFixYpq93GX67siS87bGDle3xVZct7ucn1XZMv37Q7XtxBCnD9/XgAQa9euNbmPs65xWfUMlZWVYfv27ejfv7/B9v79+2Pjxo1GX7Np06Yq+w8YMADbtm3D7du3q93H1DGdzZbzrkyj0eDatWtVFru7fv06IiMjER4ejkGDBlX5y9KVanLe7dq1Q+PGjdG3b1/k5uYaPKeE7/vzzz/HvffeW6VKu5S/b1u4w/VtD3K8vmtCzte3PbjL9V1UVAQA1S7C6qxrXFbB0MWLF6FWq6usVh8aGlplVXuds2fPGt2/vLwcFy9erHYfU8d0NlvOu7J33nkHJSUlGDlypH5bfHw8Fi1ahJUrVyIjIwN+fn7o1q0bDh8+bNf228qW827cuDE++eQTLF++HJmZmYiLi0Pfvn2xbt06/T7u/n0XFhYiOzsb//znPw22S/37toU7XN/2IMfr2xbucH3XlLtc30IITJ8+Hd27d0erVq1M7uesa1w2y3FUpFKpDB4LIapsM7d/5e3WHtMVbG1jRkYGZs2ahR9++AEhISH67Z07d0bnzp31j7t164b27dvjww8/xAcffGC/hteQNecdFxeHuLg4/eMuXbqgoKAAb7/9Nnr27GnTMV3F1jYuWrQI9evXx9ChQw22y+X7tpa7XN+2kvv1bQ13ur5t5S7X95QpU/Dnn39iw4YNZvd1xjUuq56h4OBgeHp6Von2zp8/XyUq1GnUqJHR/b28vBAUFFTtPqaO6Wy2nLfOkiVLMGHCBHz33Xe49957q93Xw8MDHTt2lMxfEjU574o6d+5scE7u/H0LIfDFF19gzJgx8PHxqXZfqX3ftnCH67sm5Hx924vcru+acJfre+rUqVi5ciVyc3MRHh5e7b7OusZlFQz5+PigQ4cOyMnJMdiek5ODrl27Gn1Nly5dquy/atUq3H333fD29q52H1PHdDZbzhvQ/sU4btw4LF68GAMHDjT7PkII7Nq1C40bN65xm+3B1vOubOfOnQbn5K7fN6CdrZGfn48JEyaYfR+pfd+2cIfr21Zyv77tRW7Xd03I/foWQmDKlCnIzMzEmjVrEB0dbfY1TrvGLU61lohvv/1WeHt7i88//1zs27dPpKWlidq1a+uz6mfMmCHGjBmj3//o0aOiVq1a4qmnnhL79u0Tn3/+ufD29hbLli3T7/P7778LT09P8cYbb4j9+/eLN954Q3h5eYnNmzc7/fxMsfa8Fy9eLLy8vMRHH30kCgsL9T9Xr17V7zNr1izxyy+/iCNHjoidO3eK8ePHCy8vL7Flyxann58p1p73e++9J1asWCEOHTok9uzZI2bMmCEAiOXLl+v3ccfvW+eRRx4RnTp1MnpMOXzf165dEzt37hQ7d+4UAMS7774rdu7cKU6cOCGEcN/r29rzdpfr29rzdpfr29rz1pH79f3EE0+IgIAAkZeXZ/B7e+PGDf0+rrrGZRcMCSHERx99JCIjI4WPj49o3769wbS81NRU0atXL4P98/LyRLt27YSPj4+IiooSH3/8cZVjLl26VMTFxQlvb28RHx9vcHFJhTXn3atXLwGgyk9qaqp+n7S0NNG0aVPh4+MjGjZsKPr37y82btzoxDOyjDXn/eabb4qYmBjh5+cnGjRoILp37y5+/vnnKsd0t+9bCCGuXr0q/P39xSeffGL0eHL4vnVTp0393rrr9W3tebvL9W3tebvL9W3L77k7XN/GzhmAWLhwoX4fV13jqr8bSERERKRIssoZIiIiIrI3BkNERESkaAyGiIiISNEYDBEREZGiMRgiIiIiRWMwRERERIrGYIiIiIgUjcEQERERKRqDISIiIlI0BkNERESkaAyGiEiWMjIy4Ofnh9OnT+u3/fOf/0Tr1q1RVFTkwpYRkdxwbTIikiUhBNq2bYsePXpg7ty5mD17Nj777DNs3rwZTZo0cXXziEhGvFzdACIiW6hUKrz22msYMWIEwsLCMGfOHKxfv56BEBFZjT1DRCRr7du3x969e7Fq1Sr06tXL1c0hIhlizhARydavv/6KAwcOQK1WIzQ01NXNISKZYs8QEcnSjh07kJSUhI8++gjffvstatWqhaVLl7q6WUQkQ8wZIiLZOX78OAYOHIgZM2ZgzJgxSEhIQMeOHbF9+3Z06NDB1c0jIplhzxARycrly5fRrVs39OzZEwsWLNBvHzJkCEpLS/HLL7+4sHVEJEcMhoiIiEjRmEBNREREisZgiIiIiBSNwRAREREpGoMhIiIiUjQGQ0RERKRoDIaIiIhI0RgMERERkaIxGCIiIiJFYzBEREREisZgiIiIiBSNwRAREREpGoMhIiIiUrT/B91fsuB25cNvAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "
"
       ]
@@ -1879,7 +1875,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fe2faeda",
+   "id": "00a02162",
    "metadata": {
     "editable": true
    },
@@ -1890,7 +1886,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "89600ea6",
+   "id": "5e5c5cec",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1900,9 +1896,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[4.1509778 ]\n",
-      " [2.92461411]]\n",
-      "[4.13288373] [2.92817032]\n"
+      "[[3.79441434]\n",
+      " [3.07608141]]\n",
+      "[3.80994952] [3.12302855]\n"
      ]
     }
    ],
@@ -1927,7 +1923,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c277543b",
+   "id": "215a1d5f",
    "metadata": {
     "editable": true
    },
@@ -1937,7 +1933,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f929cf55",
+   "id": "b88e153c",
    "metadata": {
     "editable": true
    },
@@ -1949,7 +1945,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dff0af0d",
+   "id": "9ab32b33",
    "metadata": {
     "editable": true
    },
@@ -1959,7 +1955,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3722fdb9",
+   "id": "6f285d39",
    "metadata": {
     "editable": true
    },
@@ -1973,7 +1969,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "074790fe",
+   "id": "98b1fab8",
    "metadata": {
     "editable": true
    },
@@ -1983,7 +1979,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3469f911",
+   "id": "1d731c50",
    "metadata": {
     "editable": true
    },
@@ -1996,7 +1992,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "61a75ef6",
+   "id": "9e58f547",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2006,15 +2002,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[4.0795449 ]\n",
-      " [2.86893619]]\n",
-      "[[4.04785727]\n",
-      " [2.89298533]]\n"
+      "[[3.78596961]\n",
+      " [3.12387751]]\n",
+      "[[3.71441535]\n",
+      " [3.17942122]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "
"
       ]
@@ -2075,7 +2071,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "967a83d3",
+   "id": "bba77347",
    "metadata": {
     "editable": true
    },
@@ -2097,7 +2093,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ba71e94a",
+   "id": "d888b6a7",
    "metadata": {
     "editable": true
    },
@@ -2136,7 +2132,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "02c72775",
+   "id": "bb7f68c3",
    "metadata": {
     "editable": true
    },
@@ -2149,7 +2145,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "11665768",
+   "id": "8f0504db",
    "metadata": {
     "editable": true
    },
@@ -2160,7 +2156,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0995d8db",
+   "id": "a8b282a3",
    "metadata": {
     "editable": true
    },
@@ -2173,7 +2169,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3cdc1697",
+   "id": "97da117c",
    "metadata": {
     "editable": true
    },
@@ -2200,7 +2196,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5af510b6",
+   "id": "4dc0d7a8",
    "metadata": {
     "editable": true
    },
@@ -2215,7 +2211,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "490f4197",
+   "id": "a92a3c90",
    "metadata": {
     "editable": true
    },
@@ -2225,7 +2221,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "219a6868",
+   "id": "80c357e0",
    "metadata": {
     "editable": true
    },
@@ -2238,7 +2234,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8d3502b5",
+   "id": "fe754d21",
    "metadata": {
     "editable": true
    },
@@ -2253,7 +2249,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "2a2c6fab",
+   "id": "ae39eaed",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2278,7 +2274,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "de5275cd",
+   "id": "4b5ab1a0",
    "metadata": {
     "editable": true
    },
@@ -2318,7 +2314,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "1f118c97",
+   "id": "1aa9624a",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2361,7 +2357,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9250c537",
+   "id": "5a1b9a95",
    "metadata": {
     "editable": true
    },
@@ -2371,7 +2367,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "54c13f2b",
+   "id": "eece48be",
    "metadata": {
     "editable": true
    },
@@ -2382,7 +2378,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "250dbe92",
+   "id": "15a1c324",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2393,20 +2389,20 @@
      "output_type": "stream",
      "text": [
       "Own inversion\n",
-      "[[4.41170104]\n",
-      " [2.6431453 ]]\n",
-      "Eigenvalues of Hessian Matrix:[0.31228042 4.55571665]\n",
+      "[[4.42130182]\n",
+      " [2.83757843]]\n",
+      "Eigenvalues of Hessian Matrix:[0.27660123 4.17938393]\n",
       "theta from own gd\n",
-      "[[4.41170104]\n",
-      " [2.6431453 ]]\n",
+      "[[4.42130182]\n",
+      " [2.83757843]]\n",
       "theta from own sdg\n",
-      "[[4.39272691]\n",
-      " [2.63430285]]\n"
+      "[[4.44198566]\n",
+      " [2.79512696]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "
"
       ]
@@ -2493,7 +2489,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e0bdcd22",
+   "id": "360cca72",
    "metadata": {
     "editable": true
    },
@@ -2506,7 +2502,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "86fcc0af",
+   "id": "1a0e415c",
    "metadata": {
     "editable": true
    },
@@ -2521,7 +2517,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3b656ad4",
+   "id": "1def9354",
    "metadata": {
     "editable": true
    },
@@ -2533,7 +2529,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ec7ef032",
+   "id": "391f8e0d",
    "metadata": {
     "editable": true
    },
@@ -2551,7 +2547,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "842b17dd",
+   "id": "ca5f2b4a",
    "metadata": {
     "editable": true
    },
@@ -2570,7 +2566,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "549a9b7e",
+   "id": "6d6e32d1",
    "metadata": {
     "editable": true
    },
@@ -2582,7 +2578,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "310fe216",
+   "id": "8cec056e",
    "metadata": {
     "editable": true
    },
@@ -2598,7 +2594,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e1b6edcb",
+   "id": "57d668cd",
    "metadata": {
     "editable": true
    },
@@ -2610,7 +2606,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "49e7b650",
+   "id": "90802b47",
    "metadata": {
     "editable": true
    },
@@ -2620,7 +2616,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "67564cfb",
+   "id": "ad5b8b19",
    "metadata": {
     "editable": true
    },
@@ -2632,7 +2628,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ebb0e17a",
+   "id": "925285b7",
    "metadata": {
     "editable": true
    },
@@ -2642,7 +2638,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6d0cfa1c",
+   "id": "74962fe4",
    "metadata": {
     "editable": true
    },
@@ -2654,7 +2650,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e8252d81",
+   "id": "4532fe7b",
    "metadata": {
     "editable": true
    },
@@ -2668,7 +2664,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ed9da45e",
+   "id": "c513fcdc",
    "metadata": {
     "editable": true
    },
@@ -2680,7 +2676,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "11c47009",
+   "id": "d9ce9d46",
    "metadata": {
     "editable": true
    },
@@ -2713,7 +2709,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7fd92874",
+   "id": "f7c21684",
    "metadata": {
     "editable": true
    },
@@ -2725,7 +2721,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5fa3a569",
+   "id": "196d654d",
    "metadata": {
     "editable": true
    },
@@ -2743,7 +2739,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1534657f",
+   "id": "252fb3d1",
    "metadata": {
     "editable": true
    },
@@ -2774,7 +2770,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "85b9db6c",
+   "id": "de9bbf4a",
    "metadata": {
     "editable": true
    },
@@ -2789,7 +2785,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0593ecb5",
+   "id": "82921918",
    "metadata": {
     "editable": true
    },
@@ -2807,7 +2803,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8d8d8609",
+   "id": "d100fcf9",
    "metadata": {
     "editable": true
    },
@@ -2819,7 +2815,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e22f9446",
+   "id": "5c6dd6a5",
    "metadata": {
     "editable": true
    },
@@ -2831,7 +2827,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1b88fbaa",
+   "id": "91a73753",
    "metadata": {
     "editable": true
    },
@@ -2849,7 +2845,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "558d9648",
+   "id": "482ace07",
    "metadata": {
     "editable": true
    },
@@ -2872,7 +2868,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3711c9bc",
+   "id": "bdf3643f",
    "metadata": {
     "editable": true
    },
@@ -2890,7 +2886,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "86d38c7e",
+   "id": "210ec4d9",
    "metadata": {
     "editable": true
    },
@@ -2902,7 +2898,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8fee2361",
+   "id": "3eff0da8",
    "metadata": {
     "editable": true
    },
@@ -2914,7 +2910,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "705e9f9b",
+   "id": "b1479583",
    "metadata": {
     "editable": true
    },
@@ -2926,7 +2922,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "281da053",
+   "id": "ae6c4d9f",
    "metadata": {
     "editable": true
    },
@@ -2938,7 +2934,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e5ed01f4",
+   "id": "213c59cd",
    "metadata": {
     "editable": true
    },
@@ -2950,7 +2946,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7ab5a8ef",
+   "id": "4a418e6b",
    "metadata": {
     "editable": true
    },
@@ -2967,7 +2963,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f47fe0de",
+   "id": "5a8ff160",
    "metadata": {
     "editable": true
    },
@@ -2986,7 +2982,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "78c5a239",
+   "id": "125d7fef",
    "metadata": {
     "editable": true
    },
@@ -2998,7 +2994,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "23d5750c",
+   "id": "bcf9e94e",
    "metadata": {
     "editable": true
    },
@@ -3016,7 +3012,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "78629315",
+   "id": "e18768d0",
    "metadata": {
     "editable": true
    },
@@ -3054,7 +3050,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "39d7472b",
+   "id": "e0f3885d",
    "metadata": {
     "editable": true
    },
@@ -3066,7 +3062,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "79a08e26",
+   "id": "67b8af63",
    "metadata": {
     "editable": true
    },
@@ -3076,7 +3072,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2fca7cf2",
+   "id": "98709557",
    "metadata": {
     "editable": true
    },
@@ -3088,7 +3084,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f55b402b",
+   "id": "3660eaa4",
    "metadata": {
     "editable": true
    },
@@ -3099,7 +3095,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "83ffc6ab",
+   "id": "045c878d",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3166,7 +3162,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "09c698dc",
+   "id": "f7f7a1fb",
    "metadata": {
     "editable": true
    },
@@ -3181,7 +3177,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "23ec9da1",
+   "id": "aafa5873",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3218,7 +3214,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c1d2dcbf",
+   "id": "6758a831",
    "metadata": {
     "editable": true
    },
@@ -3231,7 +3227,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "0ff70c42",
+   "id": "a01d4e52",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3289,7 +3285,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8ad0ae71",
+   "id": "f2871382",
    "metadata": {
     "editable": true
    },
@@ -3300,7 +3296,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "b9bf2265",
+   "id": "b0060765",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3337,7 +3333,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0fae646d",
+   "id": "743f91b4",
    "metadata": {
     "editable": true
    },
@@ -3353,7 +3349,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "44a8fa94",
+   "id": "eb6c8a53",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3391,7 +3387,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "962c21ba",
+   "id": "5d332825",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3425,7 +3421,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "83468113",
+   "id": "f5cfd0ff",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3470,7 +3466,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "9d7685e7",
+   "id": "fed0da90",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3499,7 +3495,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "666db882",
+   "id": "7cc40c34",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3546,7 +3542,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5461498d",
+   "id": "1343bf83",
    "metadata": {
     "editable": true
    },
@@ -3561,31 +3557,25 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "3543f315",
+   "id": "5d283e6c",
    "metadata": {
     "collapsed": false,
     "editable": true
    },
    "outputs": [
     {
-     "ename": "TypeError",
-     "evalue": "'ArrayBox' object does not support item assignment",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Input \u001b[0;32mIn [22]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m f8_grad \u001b[38;5;241m=\u001b[39m grad(f8)\n\u001b[1;32m      9\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m8.4\u001b[39m\n\u001b[0;32m---> 11\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe derivative of f8 is:\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[43mf8_grad\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m)\n",
-      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20\u001b[0m, in \u001b[0;36munary_to_nary..nary_operator..nary_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     19\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(args[i] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m argnum)\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43munary_operator\u001b[49m\u001b[43m(\u001b[49m\u001b[43munary_f\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_kwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:25\u001b[0m, in \u001b[0;36mgrad\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;129m@unary_to_nary\u001b[39m\n\u001b[1;32m     19\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgrad\u001b[39m(fun, x):\n\u001b[1;32m     20\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     21\u001b[0m \u001b[38;5;124;03m    Returns a function which computes the gradient of `fun` with respect to\u001b[39;00m\n\u001b[1;32m     22\u001b[0m \u001b[38;5;124;03m    positional argument number `argnum`. The returned function takes the same\u001b[39;00m\n\u001b[1;32m     23\u001b[0m \u001b[38;5;124;03m    arguments as `fun`, but returns the gradient instead. The function `fun`\u001b[39;00m\n\u001b[1;32m     24\u001b[0m \u001b[38;5;124;03m    should be scalar-valued. The gradient has the same type as the argument.\"\"\"\u001b[39;00m\n\u001b[0;32m---> 25\u001b[0m     vjp, ans \u001b[38;5;241m=\u001b[39m \u001b[43m_make_vjp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     26\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m vspace(ans)\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m     27\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGrad only applies to real scalar-output functions. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     28\u001b[0m                         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTry jacobian, elementwise_grad or holomorphic_grad.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:10\u001b[0m, in \u001b[0;36mmake_vjp\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmake_vjp\u001b[39m(fun, x):\n\u001b[1;32m      9\u001b[0m     start_node \u001b[38;5;241m=\u001b[39m VJPNode\u001b[38;5;241m.\u001b[39mnew_root()\n\u001b[0;32m---> 10\u001b[0m     end_value, end_node \u001b[38;5;241m=\u001b[39m  \u001b[43mtrace\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_node\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     11\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m end_node \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     12\u001b[0m         \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvjp\u001b[39m(g): \u001b[38;5;28;01mreturn\u001b[39;00m vspace(x)\u001b[38;5;241m.\u001b[39mzeros()\n",
-      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:10\u001b[0m, in \u001b[0;36mtrace\u001b[0;34m(start_node, fun, x)\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trace_stack\u001b[38;5;241m.\u001b[39mnew_trace() \u001b[38;5;28;01mas\u001b[39;00m t:\n\u001b[1;32m      9\u001b[0m     start_box \u001b[38;5;241m=\u001b[39m new_box(x, t, start_node)\n\u001b[0;32m---> 10\u001b[0m     end_box \u001b[38;5;241m=\u001b[39m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_box\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     11\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m isbox(end_box) \u001b[38;5;129;01mand\u001b[39;00m end_box\u001b[38;5;241m.\u001b[39m_trace \u001b[38;5;241m==\u001b[39m start_box\u001b[38;5;241m.\u001b[39m_trace:\n\u001b[1;32m     12\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m end_box\u001b[38;5;241m.\u001b[39m_value, end_box\u001b[38;5;241m.\u001b[39m_node\n",
-      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:15\u001b[0m, in \u001b[0;36munary_to_nary..nary_operator..nary_f..unary_f\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     14\u001b[0m     subargs \u001b[38;5;241m=\u001b[39m subvals(args, \u001b[38;5;28mzip\u001b[39m(argnum, x))\n\u001b[0;32m---> 15\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msubargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "Input \u001b[0;32mIn [22]\u001b[0m, in \u001b[0;36mf8\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mf8\u001b[39m(x): \u001b[38;5;66;03m# Assume x is an array\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m     x[\u001b[38;5;241m2\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m3\u001b[39m\n\u001b[1;32m      5\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m x\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m\n",
-      "\u001b[0;31mTypeError\u001b[0m: 'ArrayBox' object does not support item assignment"
-     ]
+     "data": {
+      "text/plain": [
+       "'\\nimport autograd.numpy as np\\nfrom autograd import grad\\ndef f8(x): # Assume x is an array\\n    x[2] = 3\\n    return x*2\\n\\nf8_grad = grad(f8)\\n\\nx = 8.4\\n\\nprint(\"The derivative of f8 is:\",f8_grad(x))\\n'"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
+    "\"\"\"\n",
     "import autograd.numpy as np\n",
     "from autograd import grad\n",
     "def f8(x): # Assume x is an array\n",
@@ -3596,12 +3586,13 @@
     "\n",
     "x = 8.4\n",
     "\n",
-    "print(\"The derivative of f8 is:\",f8_grad(x))"
+    "print(\"The derivative of f8 is:\",f8_grad(x))\n",
+    "\"\"\""
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "cc829644",
+   "id": "2125f374",
    "metadata": {
     "editable": true
    },
@@ -3612,12 +3603,30 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "9dd71229",
+   "id": "da6fd9e3",
    "metadata": {
     "collapsed": false,
     "editable": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'ArrayBox' object has no attribute 'dot'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [23]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m f9_grad \u001b[38;5;241m=\u001b[39m grad(f9)\n\u001b[1;32m      9\u001b[0m x \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;241m1.0\u001b[39m,\u001b[38;5;241m0.0\u001b[39m])\n\u001b[0;32m---> 11\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe derivative of f9 is:\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[43mf9_grad\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m)\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20\u001b[0m, in \u001b[0;36munary_to_nary..nary_operator..nary_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     19\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(args[i] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m argnum)\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43munary_operator\u001b[49m\u001b[43m(\u001b[49m\u001b[43munary_f\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_kwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:25\u001b[0m, in \u001b[0;36mgrad\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;129m@unary_to_nary\u001b[39m\n\u001b[1;32m     19\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgrad\u001b[39m(fun, x):\n\u001b[1;32m     20\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     21\u001b[0m \u001b[38;5;124;03m    Returns a function which computes the gradient of `fun` with respect to\u001b[39;00m\n\u001b[1;32m     22\u001b[0m \u001b[38;5;124;03m    positional argument number `argnum`. The returned function takes the same\u001b[39;00m\n\u001b[1;32m     23\u001b[0m \u001b[38;5;124;03m    arguments as `fun`, but returns the gradient instead. The function `fun`\u001b[39;00m\n\u001b[1;32m     24\u001b[0m \u001b[38;5;124;03m    should be scalar-valued. The gradient has the same type as the argument.\"\"\"\u001b[39;00m\n\u001b[0;32m---> 25\u001b[0m     vjp, ans \u001b[38;5;241m=\u001b[39m \u001b[43m_make_vjp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     26\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m vspace(ans)\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m     27\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGrad only applies to real scalar-output functions. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     28\u001b[0m                         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTry jacobian, elementwise_grad or holomorphic_grad.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:10\u001b[0m, in \u001b[0;36mmake_vjp\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmake_vjp\u001b[39m(fun, x):\n\u001b[1;32m      9\u001b[0m     start_node \u001b[38;5;241m=\u001b[39m VJPNode\u001b[38;5;241m.\u001b[39mnew_root()\n\u001b[0;32m---> 10\u001b[0m     end_value, end_node \u001b[38;5;241m=\u001b[39m  \u001b[43mtrace\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_node\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     11\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m end_node \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     12\u001b[0m         \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvjp\u001b[39m(g): \u001b[38;5;28;01mreturn\u001b[39;00m vspace(x)\u001b[38;5;241m.\u001b[39mzeros()\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:10\u001b[0m, in \u001b[0;36mtrace\u001b[0;34m(start_node, fun, x)\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trace_stack\u001b[38;5;241m.\u001b[39mnew_trace() \u001b[38;5;28;01mas\u001b[39;00m t:\n\u001b[1;32m      9\u001b[0m     start_box \u001b[38;5;241m=\u001b[39m new_box(x, t, start_node)\n\u001b[0;32m---> 10\u001b[0m     end_box \u001b[38;5;241m=\u001b[39m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_box\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     11\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m isbox(end_box) \u001b[38;5;129;01mand\u001b[39;00m end_box\u001b[38;5;241m.\u001b[39m_trace \u001b[38;5;241m==\u001b[39m start_box\u001b[38;5;241m.\u001b[39m_trace:\n\u001b[1;32m     12\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m end_box\u001b[38;5;241m.\u001b[39m_value, end_box\u001b[38;5;241m.\u001b[39m_node\n",
+      "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:15\u001b[0m, in \u001b[0;36munary_to_nary..nary_operator..nary_f..unary_f\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     14\u001b[0m     subargs \u001b[38;5;241m=\u001b[39m subvals(args, \u001b[38;5;28mzip\u001b[39m(argnum, x))\n\u001b[0;32m---> 15\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msubargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "Input \u001b[0;32mIn [23]\u001b[0m, in \u001b[0;36mf9\u001b[0;34m(a)\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mf9\u001b[39m(a): \u001b[38;5;66;03m# Assume a is an array with 2 elements\u001b[39;00m\n\u001b[1;32m      4\u001b[0m     b \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;241m1.0\u001b[39m,\u001b[38;5;241m2.0\u001b[39m])\n\u001b[0;32m----> 5\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdot\u001b[49m(b)\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'ArrayBox' object has no attribute 'dot'"
+     ]
+    }
+   ],
    "source": [
     "import autograd.numpy as np\n",
     "from autograd import grad\n",
@@ -3634,7 +3643,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "128e658a",
+   "id": "b26b3471",
    "metadata": {
     "editable": true
    },
@@ -3647,7 +3656,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "098a13f3",
+   "id": "18b1ae5f",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3672,7 +3681,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2db7526b",
+   "id": "138f3f1e",
    "metadata": {
     "editable": true
    },
@@ -3683,7 +3692,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "314170c6",
+   "id": "caebcdc6",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3698,7 +3707,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5aa6151e",
+   "id": "73211a47",
    "metadata": {
     "editable": true
    },
@@ -3713,7 +3722,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2d017a74",
+   "id": "a38103f7",
    "metadata": {
     "editable": true
    },
@@ -3728,7 +3737,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "f784b385",
+   "id": "89f0a17f",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3788,7 +3797,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9eb6dbe2",
+   "id": "c6607dea",
    "metadata": {
     "editable": true
    },
@@ -3799,7 +3808,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "408e6211",
+   "id": "d3818505",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3863,7 +3872,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "07f1dd70",
+   "id": "8bc11302",
    "metadata": {
     "editable": true
    },
@@ -3875,7 +3884,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "7eff4d61",
+   "id": "e04cac92",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3924,7 +3933,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "98ae5663",
+   "id": "fabacfbb",
    "metadata": {
     "editable": true
    },
@@ -3936,7 +3945,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "cd7caecf",
+   "id": "850fbafa",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -4020,7 +4029,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "87e8ab65",
+   "id": "6325ae4d",
    "metadata": {
     "editable": true
    },
@@ -4031,7 +4040,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "id": "3183015a",
+   "id": "cf10fd6b",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -4109,7 +4118,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9ba705cd",
+   "id": "450296fc",
    "metadata": {
     "editable": true
    },
@@ -4120,7 +4129,7 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "id": "be7a85c7",
+   "id": "e61b44d8",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -4184,7 +4193,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0e711ae2",
+   "id": "6ddb5f5e",
    "metadata": {
     "editable": true
    },
@@ -4197,7 +4206,7 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "id": "8b34e5b1",
+   "id": "3d87e7f3",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -4266,7 +4275,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7a3b6455",
+   "id": "137ad886",
    "metadata": {
     "editable": true
    },
@@ -4285,7 +4294,7 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "id": "2c30f41b",
+   "id": "07ab244c",
    "metadata": {
     "collapsed": false,
     "editable": true
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapteroptimization.py b/doc/LectureNotes/_build/jupyter_execute/chapteroptimization.py
index 0a4a034cd..6b7b8f6cf 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapteroptimization.py
+++ b/doc/LectureNotes/_build/jupyter_execute/chapteroptimization.py
@@ -269,20 +269,16 @@
 # domain of $f$, i.e the subset of $R^n$ where $f$ is defined. For more
 # details and proofs we refer to: [S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press](http://stanford.edu/boyd/cvxbook/, 2004).
 # 
-# **First order condition.**
-# 
-# Suppose $f$ is differentiable (i.e $\nabla f(x)$ is well defined for
+# **First order condition**: Suppose $f$ is differentiable (i.e $\nabla f(x)$ is well defined for
 # all $x$ in the domain of $f$). Then $f$ is convex if and only if $D_f$
-# is a convex set and $$f(y) \geq f(x) + \nabla f(x)^T (y-x) $$ holds
+# is a convex set and $f(y) \geq f(x) + \nabla f(x)^T (y-x)$ holds
 # for all $x,y \in D_f$. This condition means that for a convex function
 # the first order Taylor expansion (right hand side above) at any point
 # is a global under estimator of the function. To convince yourself you can
 # make a drawing of $f(x) = x^2+1$ and draw the tangent line to $f(x)$ and
-# note that it is always below the graph.
+# note that it is always below the graph.  
 # 
-# **Second order condition.**
-# 
-# Assume that $f$ is twice
+# **Second order condition**: Assume that $f$ is twice
 # differentiable, i.e the Hessian matrix exists at each point in
 # $D_f$. Then $f$ is convex if and only if $D_f$ is a convex set and its
 # Hessian is positive semi-definite for all $x\in D_f$. For a
@@ -1689,6 +1685,7 @@ print("The analytical derivative of f7 at n = %d is: %g"%(n,f7_grad_analytical))
 # In[22]:
 
 
+"""
 import autograd.numpy as np
 from autograd import grad
 def f8(x): # Assume x is an array
@@ -1700,6 +1697,7 @@ f8_grad = grad(f8)
 x = 8.4
 
 print("The derivative of f8 is:",f8_grad(x))
+"""
 
 
 # Here, Autograd tells us that an 'ArrayBox' does not support item assignment. The item assignment is done when the program tries to assign x[2] to the value 3. However, Autograd has implemented the computation of the derivative such that this assignment is not possible.
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapteroptimization_123_1.png b/doc/LectureNotes/_build/jupyter_execute/chapteroptimization_123_1.png
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