From dd4a2f76547e3422d86ac62e1b1d1c53bcc679d5 Mon Sep 17 00:00:00 2001
From: Chandan Singh <chandan_singh@berkeley.edu>
Date: Sat, 12 Sep 2020 23:12:59 -0700
Subject: [PATCH] remove all unnecessary files

---
 data/boston.csv                               | 507 ------------------
 .../optimal_classification_tree/__init__.py   |   2 -
 .../{pyoptree => }/example.py                 |   0
 .../{pyoptree => }/localsearch.py             |   0
 .../{pyoptree => }/optree.py                  |   0
 .../pyoptree/__init__.py                      |   0
 .../{pyoptree => }/tree.py                    |   0
 imodels/skrules/datasets/__init__.py          |   3 -
 imodels/skrules/datasets/credit_data.py       |  45 --
 notebooks/1_model_based.ipynb                 |   6 +-
 readme.md                                     |   2 +-
 11 files changed, 4 insertions(+), 561 deletions(-)
 delete mode 100644 data/boston.csv
 rename imodels/optimal_classification_tree/{pyoptree => }/example.py (100%)
 rename imodels/optimal_classification_tree/{pyoptree => }/localsearch.py (100%)
 rename imodels/optimal_classification_tree/{pyoptree => }/optree.py (100%)
 delete mode 100644 imodels/optimal_classification_tree/pyoptree/__init__.py
 rename imodels/optimal_classification_tree/{pyoptree => }/tree.py (100%)
 delete mode 100644 imodels/skrules/datasets/__init__.py
 delete mode 100644 imodels/skrules/datasets/credit_data.py

diff --git a/data/boston.csv b/data/boston.csv
deleted file mode 100644
index 392edcaf..00000000
--- a/data/boston.csv
+++ /dev/null
@@ -1,507 +0,0 @@
-"","crim","zn","indus","chas","nox","rm","age","dis","rad","tax","ptratio","black","lstat","medv"
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-"446",10.6718,0,18.1,0,0.74,6.459,94.8,1.9879,24,666,20.2,43.06,23.98,11.8
-"447",6.28807,0,18.1,0,0.74,6.341,96.4,2.072,24,666,20.2,318.01,17.79,14.9
-"448",9.92485,0,18.1,0,0.74,6.251,96.6,2.198,24,666,20.2,388.52,16.44,12.6
-"449",9.32909,0,18.1,0,0.713,6.185,98.7,2.2616,24,666,20.2,396.9,18.13,14.1
-"450",7.52601,0,18.1,0,0.713,6.417,98.3,2.185,24,666,20.2,304.21,19.31,13
-"451",6.71772,0,18.1,0,0.713,6.749,92.6,2.3236,24,666,20.2,0.32,17.44,13.4
-"452",5.44114,0,18.1,0,0.713,6.655,98.2,2.3552,24,666,20.2,355.29,17.73,15.2
-"453",5.09017,0,18.1,0,0.713,6.297,91.8,2.3682,24,666,20.2,385.09,17.27,16.1
-"454",8.24809,0,18.1,0,0.713,7.393,99.3,2.4527,24,666,20.2,375.87,16.74,17.8
-"455",9.51363,0,18.1,0,0.713,6.728,94.1,2.4961,24,666,20.2,6.68,18.71,14.9
-"456",4.75237,0,18.1,0,0.713,6.525,86.5,2.4358,24,666,20.2,50.92,18.13,14.1
-"457",4.66883,0,18.1,0,0.713,5.976,87.9,2.5806,24,666,20.2,10.48,19.01,12.7
-"458",8.20058,0,18.1,0,0.713,5.936,80.3,2.7792,24,666,20.2,3.5,16.94,13.5
-"459",7.75223,0,18.1,0,0.713,6.301,83.7,2.7831,24,666,20.2,272.21,16.23,14.9
-"460",6.80117,0,18.1,0,0.713,6.081,84.4,2.7175,24,666,20.2,396.9,14.7,20
-"461",4.81213,0,18.1,0,0.713,6.701,90,2.5975,24,666,20.2,255.23,16.42,16.4
-"462",3.69311,0,18.1,0,0.713,6.376,88.4,2.5671,24,666,20.2,391.43,14.65,17.7
-"463",6.65492,0,18.1,0,0.713,6.317,83,2.7344,24,666,20.2,396.9,13.99,19.5
-"464",5.82115,0,18.1,0,0.713,6.513,89.9,2.8016,24,666,20.2,393.82,10.29,20.2
-"465",7.83932,0,18.1,0,0.655,6.209,65.4,2.9634,24,666,20.2,396.9,13.22,21.4
-"466",3.1636,0,18.1,0,0.655,5.759,48.2,3.0665,24,666,20.2,334.4,14.13,19.9
-"467",3.77498,0,18.1,0,0.655,5.952,84.7,2.8715,24,666,20.2,22.01,17.15,19
-"468",4.42228,0,18.1,0,0.584,6.003,94.5,2.5403,24,666,20.2,331.29,21.32,19.1
-"469",15.5757,0,18.1,0,0.58,5.926,71,2.9084,24,666,20.2,368.74,18.13,19.1
-"470",13.0751,0,18.1,0,0.58,5.713,56.7,2.8237,24,666,20.2,396.9,14.76,20.1
-"471",4.34879,0,18.1,0,0.58,6.167,84,3.0334,24,666,20.2,396.9,16.29,19.9
-"472",4.03841,0,18.1,0,0.532,6.229,90.7,3.0993,24,666,20.2,395.33,12.87,19.6
-"473",3.56868,0,18.1,0,0.58,6.437,75,2.8965,24,666,20.2,393.37,14.36,23.2
-"474",4.64689,0,18.1,0,0.614,6.98,67.6,2.5329,24,666,20.2,374.68,11.66,29.8
-"475",8.05579,0,18.1,0,0.584,5.427,95.4,2.4298,24,666,20.2,352.58,18.14,13.8
-"476",6.39312,0,18.1,0,0.584,6.162,97.4,2.206,24,666,20.2,302.76,24.1,13.3
-"477",4.87141,0,18.1,0,0.614,6.484,93.6,2.3053,24,666,20.2,396.21,18.68,16.7
-"478",15.0234,0,18.1,0,0.614,5.304,97.3,2.1007,24,666,20.2,349.48,24.91,12
-"479",10.233,0,18.1,0,0.614,6.185,96.7,2.1705,24,666,20.2,379.7,18.03,14.6
-"480",14.3337,0,18.1,0,0.614,6.229,88,1.9512,24,666,20.2,383.32,13.11,21.4
-"481",5.82401,0,18.1,0,0.532,6.242,64.7,3.4242,24,666,20.2,396.9,10.74,23
-"482",5.70818,0,18.1,0,0.532,6.75,74.9,3.3317,24,666,20.2,393.07,7.74,23.7
-"483",5.73116,0,18.1,0,0.532,7.061,77,3.4106,24,666,20.2,395.28,7.01,25
-"484",2.81838,0,18.1,0,0.532,5.762,40.3,4.0983,24,666,20.2,392.92,10.42,21.8
-"485",2.37857,0,18.1,0,0.583,5.871,41.9,3.724,24,666,20.2,370.73,13.34,20.6
-"486",3.67367,0,18.1,0,0.583,6.312,51.9,3.9917,24,666,20.2,388.62,10.58,21.2
-"487",5.69175,0,18.1,0,0.583,6.114,79.8,3.5459,24,666,20.2,392.68,14.98,19.1
-"488",4.83567,0,18.1,0,0.583,5.905,53.2,3.1523,24,666,20.2,388.22,11.45,20.6
-"489",0.15086,0,27.74,0,0.609,5.454,92.7,1.8209,4,711,20.1,395.09,18.06,15.2
-"490",0.18337,0,27.74,0,0.609,5.414,98.3,1.7554,4,711,20.1,344.05,23.97,7
-"491",0.20746,0,27.74,0,0.609,5.093,98,1.8226,4,711,20.1,318.43,29.68,8.1
-"492",0.10574,0,27.74,0,0.609,5.983,98.8,1.8681,4,711,20.1,390.11,18.07,13.6
-"493",0.11132,0,27.74,0,0.609,5.983,83.5,2.1099,4,711,20.1,396.9,13.35,20.1
-"494",0.17331,0,9.69,0,0.585,5.707,54,2.3817,6,391,19.2,396.9,12.01,21.8
-"495",0.27957,0,9.69,0,0.585,5.926,42.6,2.3817,6,391,19.2,396.9,13.59,24.5
-"496",0.17899,0,9.69,0,0.585,5.67,28.8,2.7986,6,391,19.2,393.29,17.6,23.1
-"497",0.2896,0,9.69,0,0.585,5.39,72.9,2.7986,6,391,19.2,396.9,21.14,19.7
-"498",0.26838,0,9.69,0,0.585,5.794,70.6,2.8927,6,391,19.2,396.9,14.1,18.3
-"499",0.23912,0,9.69,0,0.585,6.019,65.3,2.4091,6,391,19.2,396.9,12.92,21.2
-"500",0.17783,0,9.69,0,0.585,5.569,73.5,2.3999,6,391,19.2,395.77,15.1,17.5
-"501",0.22438,0,9.69,0,0.585,6.027,79.7,2.4982,6,391,19.2,396.9,14.33,16.8
-"502",0.06263,0,11.93,0,0.573,6.593,69.1,2.4786,1,273,21,391.99,9.67,22.4
-"503",0.04527,0,11.93,0,0.573,6.12,76.7,2.2875,1,273,21,396.9,9.08,20.6
-"504",0.06076,0,11.93,0,0.573,6.976,91,2.1675,1,273,21,396.9,5.64,23.9
-"505",0.10959,0,11.93,0,0.573,6.794,89.3,2.3889,1,273,21,393.45,6.48,22
-"506",0.04741,0,11.93,0,0.573,6.03,80.8,2.505,1,273,21,396.9,7.88,11.9
\ No newline at end of file
diff --git a/imodels/optimal_classification_tree/__init__.py b/imodels/optimal_classification_tree/__init__.py
index d3fe8352..e69de29b 100644
--- a/imodels/optimal_classification_tree/__init__.py
+++ b/imodels/optimal_classification_tree/__init__.py
@@ -1,2 +0,0 @@
-'''(in progress) [optimal classification tree](https://link.springer.com/article/10.1007/s10994-017-5633-9) (based on [this implementation](https://github.com/pan5431333/pyoptree)) - learns succinct trees using global optimization rather than greedy heuristics
-'''
\ No newline at end of file
diff --git a/imodels/optimal_classification_tree/pyoptree/example.py b/imodels/optimal_classification_tree/example.py
similarity index 100%
rename from imodels/optimal_classification_tree/pyoptree/example.py
rename to imodels/optimal_classification_tree/example.py
diff --git a/imodels/optimal_classification_tree/pyoptree/localsearch.py b/imodels/optimal_classification_tree/localsearch.py
similarity index 100%
rename from imodels/optimal_classification_tree/pyoptree/localsearch.py
rename to imodels/optimal_classification_tree/localsearch.py
diff --git a/imodels/optimal_classification_tree/pyoptree/optree.py b/imodels/optimal_classification_tree/optree.py
similarity index 100%
rename from imodels/optimal_classification_tree/pyoptree/optree.py
rename to imodels/optimal_classification_tree/optree.py
diff --git a/imodels/optimal_classification_tree/pyoptree/__init__.py b/imodels/optimal_classification_tree/pyoptree/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/imodels/optimal_classification_tree/pyoptree/tree.py b/imodels/optimal_classification_tree/tree.py
similarity index 100%
rename from imodels/optimal_classification_tree/pyoptree/tree.py
rename to imodels/optimal_classification_tree/tree.py
diff --git a/imodels/skrules/datasets/__init__.py b/imodels/skrules/datasets/__init__.py
deleted file mode 100644
index f43d66ec..00000000
--- a/imodels/skrules/datasets/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from .credit_data import load_credit_data
-
-__all__ = ['load_credit_data']
diff --git a/imodels/skrules/datasets/credit_data.py b/imodels/skrules/datasets/credit_data.py
deleted file mode 100644
index 2f2979ae..00000000
--- a/imodels/skrules/datasets/credit_data.py
+++ /dev/null
@@ -1,45 +0,0 @@
-"""default of credit card clients dataset.
-
-The original database is available from UCI Machine Learning Repository:
-
-    https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients
-
-The data contains 30000 observations on 24 variables.
-
-References
-----------
-
-Lichman, M. (2013). UCI Machine Learning Repository
-[http://archive.ics.uci.edu/ml].
-Irvine, CA: University of California, School of Information and Computer
-Science.
-
-"""
-
-import pandas as pd
-import numpy as np
-from sklearn.datasets.base import get_data_home, Bunch
-from sklearn.datasets.base import _fetch_remote, RemoteFileMetadata
-from os.path import exists, join
-
-
-def load_credit_data():
-    sk_data_dir = get_data_home()
-    archive = RemoteFileMetadata(
-        filename='default of credit card clients.xls',
-        url='https://archive.ics.uci.edu/ml/machine-learning-databases/'
-            '00350/default%20of%20credit%20card%20clients.xls',
-        checksum=('30c6be3abd8dcfd3e6096c828bad8c2f'
-                  '011238620f5369220bd60cfc82700933'))
-
-    if not exists(join(sk_data_dir, archive.filename)):
-        _fetch_remote(archive, dirname=sk_data_dir)
-
-    data = pd.read_excel(join(sk_data_dir, archive.filename),
-                         sheet_name='Data', header=1)
-
-    dataset = Bunch(
-        data=(data.drop('default payment next month', axis=1)),
-        target=np.array(data['default payment next month'])
-        )
-    return dataset
diff --git a/notebooks/1_model_based.ipynb b/notebooks/1_model_based.ipynb
index 977b3781..02f5d735 100644
--- a/notebooks/1_model_based.ipynb
+++ b/notebooks/1_model_based.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -48,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -60,7 +60,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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B7bffTmhoKKGhodf1sX37dsxmM99//z2xsbFs3ryZoUOHkpCQwNSpUxk5ciRSSrp0UcFgRXVqG59paWns2rWr1uWDFStWkJeXx/vvv8/dd99NeHh4rQ4zMTGRp59+GoCEhATbEkVJSQkpKSns3r2bvLw8wDoxeOGFF1x4lc0Hlb1QB3VFh48fP05KSgqRkZG1nldaWspLL73En/70J1dLBFSEuKXSkPGZm5vLO++8w+LFi7WUqMZmHSinWweuSsk5c+YMzz77LB999BGvvvoqOTk5PPHEE/Ts2ZPU1FQSExO5du0af/jDH/j444/Zt28fu3fvJi4ujm+//ZY1a9bQtm1be51qYLdAnD0+8/LyePfdd/n0009JSEhgzZo1lJaWEh0dTWBgIGazmSNHjnDixAmeeeYZ2zh95JFH2LhxIz4+PqxcubKmRjU2a0EtLzjIqlWrCAwMJD09nS5dumAwGDh69ChBQUFMmjSJuLg4fHx8iI6OxsvLiwMHDlwXnKioqODTTz+1LS+UlpYSFRXFli1bmDNnDiEhIYSEhLBixYrrghOhoaGYzWa8vb11s4HCPXHG2LQPnJWWlhIcHEzHjh05dOgQ48aNIywsjDZt2tCrV69q4/To0aM8/PDDvP7661y9erXahEBROyqQ5iBCCJ555hluvvlmli1bxrFjx+jduzeFhYUcPHiQdu3a4evrS35+fp1tfPPNN2RnZ2M2m0lPT6e2t7Ns3ryZ++67D6genBg6dCjTpk3j8uXLLrk+hefijLEJPwfObrrpJnx8fEhOTsbH5+d52d69exk7dizw8zgdM2YMe/fu5ccff8TLS7kTR1AzXQfx8vJCCIGvr2/V1yYKCwuxWCwMGzaMY8eOERwcTPv27QFqDU7cdtttrFy5kpiYGPr164evry9Go5GZM2eSkJBAjx492LlzJ+Xl5QwaNMgWnLBYLMTHx3P69GlGjRqlx+Ur3BhnjE2oHjgrKyujbdu2jBw50hbMLS0txdfXF7PZbBunPXv2pKKigvDwcPz9/TW9bk9FrenWgSdsswS1btZS8YTxqcZm7ajvAwqFQqEhyum6iKrdZQ3lzJkztjXdzZs3s2jRIs6fP8+GDRt46aWXOHLkiDNlKlowjRmjNbcHb926ldjYWC5cuOAChc0TtaZbD/Hx8Xh5eREZGcnGjRu5ePEiRqORiRMncuutt9K/f3/S09NZuHAhL774It27d+e5554D4K233qKoqIi+ffuSlpZGUFAQs2fPBnAouyE0NJT9+/fj4+PD5cuXWb58OUajkSFDhuhjDIVbouUYrbk9ODExEYPBQKtWrXS7fk9DzXTroW/fvuTn5yOlREpJZmYmYHWIU6ZM4c4777TtEjMYDNx1112kpaUBkJSURIcOHcjNzbVFk2+0Dlczu2HgwIEsXLiQzMxMW6ZDbRkPipaNlmMUqm8PDggIYMaMGWzfvt2FV9i8UE63HiwWC2VlZSQnJ1NSUkJpaSkAPj4+CCFsfwKYzWZ2797N7bffDlgHuMVioX///rZoclFREWCNIC9YsIAFCxYwaNAg4OfshrCwMPr168fq1at5++236dy5M506dWLVqlUqe0FxHVqOUbBmOUyYMAGwjtn4+HjCwsK0vGSPRmUv1EFDo8Nnz54lKSmpzrKOrkJFiFsmjcle0HqMqrFZO8rp1oEnpOSAGtgtFU8Yn2ps1o5aXmgkjc1OAFi0aBEJCQmUl5cTFRXFokWLKCwsBGDnzp2sW7eODRs2cOrUKdauXcv69esB2LNnD1FRUU7Rr2i+NGVsxsXFMW/ePK5evYrJZOL++++3lYdMTU1lzZo1rFy5ksLCQkwmE4sWLUJKyZIlS5g3bx6nT5921mU0W5TTrQej0YiUktjYWBITE4mKiuLkyZOANaUrKyuLtWvX8sMPP9gKmVdUWN+UfeDAAUwmEyaTieTkZFub8+bNA6zVn4KDgzEYDBw6dAiAlJQU/vd//5eDBw8yYMAAvL29KS4uJjs7m6KiIoKCgrQ1gMJtccXYDA0NJTs7G29vbxYsWEBISAh9+/YFICQkhKVLl1JeXk5gYCADBgwgNzcXIYRtLbhTp07aG8LDUE63Hnr06MGOHTswGAwUFBTQtWtXW65sVXCivLycQ4cOObzHvYra9rg/8MADmEwm25bKhQsXEhAQwMGDBzl37hxms9kW6FC0bFwxNu1rfFgsluse8va1QSIiIggLC8NisRAWFsbs2bNtBc4VdaPydOthwoQJREREYDabMZvNtG7d2jZbCAkJYdOmTZw6dYrIyEiOHj3q0B737du3c+LECcaNG3fdHvcBAwbw008/MWnSJD7//HMOHjyIj48Pc+fOBayv/2nTpo12BlC4Lc4emzVrfOzcudP2SqqatUHat2/Ptm3bOHv2LDNmzMBsNpOammobp4q6UYG0OvCEQAWoYEVLxRPGpxqbtaOWFxQKhUJD1PJCHfj5+WULITrrraM+/Pz8svXWoNAeTxifamzWjlpeaCRCiJuBo8BTUspEJ7f9AvA74HdSyp+c2baiZSCEeB24BZjizHUIIUR/4DAwVkr5tbPabUmo5YVGIITwBv4GbHW2w63kRaAIWO2CthXNHCHEw0AE8KizF36llN8ATwEfCCE6OLPtloKa6TYCIcRLwG+BMVLKMhf10Qn4GlgopfzYFX0omh9CiIHA58BoKWWKC/uJBfoD90opK1zVT3NEzXQbiBBiPDADmOYqhwsgpbwMPAT8WQjR11X9KJoPQohAYAew2JUOt5IlwC+ApS7up9mhZroNQAhxK/AlcJ+U0qxRn3OAWcBvpZRqV4SiVoR1N8Q24IqUcqZGfXYHjgD/I6X8uxZ9NgeU03UQIYQf1gDCX6WUr2rYrwDeA0qBx9w+OVOhC0KIZ4DHgP+WUhZr2O/dwF+BIVLK77Xq15NRTtdBhBBvAZ2AB7V2fEKItsBXwKtSyne07Fvh/gghhgK7sH4b0rzijBDieWA8MFJl29SPcroOIIT4H2A51qd5gU4aBgCHsAbvjumhQeF+CCFuwpq6OFdKuUsnDV7A/wHfSikX6qHBk1BOtx6EECHAP4C7pZSpOmt5CFgDDJZS5umpRaE/lamLe4BjUkpda34KIdpjdf5LpJQf6KnF3VFO9wYIIdphDRS8JKXcqrceACHEeqAXMFml6rRshBDRwF1Y08NclknTAD3/BewFhlXm8ypqQTndOqgMYH0AXJZSztZbTxVCCF/gM2CXlLLx1aoVHo0QIgJ4B+u3niy99VQhhHgSmAcYpJRX9dbjjiinWwdCiIXAdKxP7RK99dgjhLgF6wz8YSnlP/XWo9AWIURPrKmLD0opD+mtx57KycpmrHsAHlHZNtejnG4tCCGGYU0yN0gpz+osp1aEEOHAFqzBvQt661FogxCiNdaA6nYp5Tq99dSGEKINkAS8KaV8U2897oZyujWorNx0FHhSSrlbbz03QgixHOse+1EqVadlIISIB7oAD7jzLLJyF6UZGC+lPKK3HndCbQO2Qwjhg3VXzyZ3d7iVrAYsgFrbbQFUFrIZjQdskpFSZgCzsRbG6ai3HndCzXTtEEKsBoYAEVLKcr31OEJlpaejwLNSyh1661G4BiHE7VgDqL+TUv5HZzkOI4RYCwwEJqhsGytqpluJEOJeIBJrcMojHC6AlDIXeBB4UwjRT289CudjV8jmWU9yuJUsBQKBZXoLcRfUTBcQQvwS+AJr7usXeutpDEKI2cAcYKgqjNN8qMwGSAAsUson9dbTGIQQ3bCWKZ0hpdyvtx69afFOt7KQjRnYIqVcr7eexlJ5c24BKnBB8WqFPggh5mEtJRrmbqmLDUEIMRJrvCRUSnleXzX6opyuEH/BWhd0qqc7qsrCOF8CcVLKv+itR9E0hBC/BXZi/fbync5ymowQIgqYBIyQUpbqrUcvWrTTFULMwLrmNERKWai3Hmdg9w6rCCnlUb31KBqHK9/BpxeVhXF2At9JKefrLEc3WqTTFUIMBwKwfh0fKaVM01mSUxFCPAC8jPVdVmcq03cUHkBl0OwerJ/dV1LK53WW5FQqC+N8DUQDRVLKD3WWpDkt1ekeAG4H3pBSvqi3HmdTOaPYBgwC3pdSLtdZksJBhBD3Yc27zsdaxrPZVZOrvMZ3AAl08vRlvYbS4lLGKgNOw4COQG+d5bgKP6yVyH4JPKCzFkXDeADoA3QGbtZZi6voBfgCHWi+92CdtDinW8nnWF9r8qjeQlxBZcqYAWvecZLOchQN4yTWr959mmt5xMqaEQOA3YDuJSm1pkUuLygUCoVetNSZrkKhUOiCjx6d+vv7Z5WUlHTWo29H8PPzyy4uLu6itw5HcHdbgrKns/EUe3qCLUF7e+qyvCCEcOuApRACKaXQW4cjuLstQdnT2XiKPT3BlqC9PdXygkKhUGiIcroKhUKhIbqs6VaxdetWhg4dytatWxk5ciTHjx9n1KhR/P3vfycpKYk33niDxYsX884771x3bnl5Od7e3rW2u2jRIkJDQ5k6dSoAp06dIjExkVatWjFhwgTeffddsrKyePnll5k9ezYTJkwgMjLSpdeqBbXZc/r06axdu5aAgACeeuqpRtkzPj6ekpISnnzySQIDA9m5cyenT58mKCiIjh072v7ev39/Pv/8cwICApg/3/N3edZmz3nz5rFnzx4+//xzFi9e3Ch7RkdHU1xczLJlywgMDCQrK4u3336bAQMG0LdvX/7yl78QERHBr371KzZu3IiPjw8rV6508dW6ltpsedddd9muddiwYQ22pZSSqKgoiouLmT9/Pr1798ZsNnPkyBFOnDjBnDlzbO2PGjWKDRs2cO7cOWJjY7W45DrR1elGRkYybdo05s+fz7Vr1wgNDSUkJIQuXbrQqlUrOnToQJ8+fWzHFxYW8uGHH5Kdnc20adPIyMjg+PHjAIwYMYJBgwYBMG/ePJKSfk5PHTBgAHv27KG4uBgfHx9ycnJo27YtXl5etG/fnqKi5lEJsTZ7fvbZZ8ycOZO9e/fSunXrBtvTYrFgNpsZPHgwPj7W4ZKSksLKlSuZMWMGvXr1sv398ccf57//+7+Jjo7W5fqdTW32zM7OpqioiKCgoEaNz5ycHG655RYMBgOHDh1i3LhxfPDBBwQGBgLg6+tLYGAgxcXFHD16lIcffpjXX3+dq1ev0rZtW13s4Axqs6X9tTbGlkIILBYLZWVldOrUCYCwsDDatGlDr169qrUfGBjIgAEDSElJ0ePyq6Hr8kJhYSH+/v7k5ORU+/3HH3/Mfffdd93xJpOJ7OxsnnrqKXr27NmgvhYuXEhAQACZmZk8/fTTBAcH4+Pjw5tvvklxcTGFhZ5f76Yue9aFI/YsKyvjlltuYcSIERw4cACABx54AJPJhL+/f7W/A8TGxvL4448754J0pjZ7Hjx4kHPnzmE2m697WDd2fP7000/cc889pKWlMXDgQIxGIxkZGYwZM4a9e/fy448/4uXl2SuBtdnS/lpr4ogtr169SlhYGLNnz642ydq7dy9jx469rv2IiAjCwsK4du2ak6+uYeg6033jjTdYv349cXFxDBkyBD8/PwAuXrxI165drzt+xYoV5OXl8f7773P33XcTHh5OeHj4dcdt376dEydOMG7cOHbv3k3Xrl05ePAgPj4+tG/fno0bNyKEIC8vj7i4OC5dukRAQIDLr9fV1GbPkSNHsm7dOgICAq6bKTliz44dO+Lv78+OHTuYM2cOCQkJDBgwgJ9++olJkyZRVlZm+3tCQgLHjx+nZ8+eBAcHa3npLqE2ez744IMAXLt2jTZt2lQ73lF7Xrhwgffee49ly5aRkJDAxIkT2bRpE23atCEtLY1du3ZRXl5OWVkZFRUVhIeH2x5qnkpttrS/1po4YstWrVphNptJTU1l7ty5JCQkMHXqVEpLS/H19a3W/tmzZ9m2bRtnz57Vf1IgpdT8x9ptdVJTU+XWrVur/S4nJ0cajcbrjnU1lfp0sU1Df2qzpZTKno39UfZsWbaUUnt7Nos83StXrhAXF0dmZiavvfYa27dv58cff+T3v/893bt3JycnxxZMWrJkCcuXL8fLy4slS5awevVqysvLeeWVV7DWwvGcPEhwXS5kVTDywQcfZNmyZZSWlhIdHU1gYCCHDx+2BczuvvtuW+Bz27ZtGI1GfH19WbRokb3GFm3PM2fO8Oyzz/LRRx8RFxdHUVERUVFRAKSmppKYmMi1a9d45JFHbIGzF154oVZbVmr0CHu6amzGxcXx7bffsmbNGj766KNq93pxcTGvvPIK3bp1Y8yYMbz33ntkZWVhMpmuC7rZ6dTUnrouLwCsWrWKwMBA0tPT6dKlCwaDgaNHjxIUFMSkSZOIi4vDx8eH6OhovLy8OHDgwHUL6gEBATz//PO88sorlJSUkJiYiMFgoFWrVgDVgklffvklo0ePpri4mJSUFEaMGEFqaionT55k4MCBeprCKTjDnvBzMDI3N5fg4GA6duxoC/wMGzbMFjCzD3ympqby/fff079/f+sTXbi9X7ghzrBlRUUFn376KaGhoYDVrjExMbY+QkJCCAkJYcWKFdUCZ//5z3+alS3BeWMzNDQUs9mMt7f3dff6/v37KSsrw8vLix49erB06VJWrFhRa9BNL3RfnRdC8Mwzz3DzzTezbNkyjh07Ru/evSksLOTgwYO0a9cOX19f8vPzb9jOl19+Sbdu3WjXrh0BAQHMmDGD7du33/CcTp06kZ6ezrfffmuLzHs6zrJnFTfddBM+Pj4kJydXs5F9wKwq8FlWVsbAgQPp1q2b7WbxZJxhy2+++Ybs7GzMZjPp6em1HrN582buu+++aoGzK1euNCtbgvPG5tChQ5k2bRqXL1++7l4vKytj6NCh5OXlUVhYSGJiIsOHD68z6KYHunsaLy8vhBD4+vpWTfMpLCzEYrEwbNgwjh07RnBwMO3btweodUG9oKCAxYsXM2XKFCwWC7fddhvx8fFMmDCBhIQERo8ebQsmGQwG25Nv7Nix7N69m379+tGvX/N4e7kz7AnVg5FlZWW0bduWkSNHkpCQAFAtYFYV+LzppptswYqIiAhNr9sVOMOWt912GytXriQmJoZ+/fqxfft2zGYz33//PYcPH6ZHjx7s3LmT8vJyevbsaQucGQwGdu7c2WxsCc6xp8ViIT4+ntOnTzNq1Kjr7vUxY8awevVqfHx8uHTpEuvXr2f8+PGMGDGiWtBNT5rFmq6z8ZQ1M3B/W4Kyp7PxFHt6gi1B1V5wCPs1MUe5cuUKq1evZtasWZSWljJz5kxMJhMlJSUsWrQIk8nEpUuXXKDWvWmMLcEaaEtISKC8vByTycT9999PRkZGi7alPY2165kzZ2w56lu3biU2NpYLFy44U5rH0VhbxsXFMW/ePK5evcqrr77K8uXLyczMdLK6hqPr8kJ8fDxeXl5ERkayceNGLl68iNFoZOLEidx6663079+f9PR0Fi5cyIsvvkj37t157rnnAHjrrbcoKiqib9++pKWlERQUxOzZswEcCrZ17NjRltzesWNHrl696tHBCi1tCT8H2ry9vVmwYAH5+fn07du3WdjSHi3tWjPoVjNI5OloPUbtA26lpaVERUWxZcsW5syZo48BKtF1ptu3b1/y8/Nt+WtVT6HQ0FCmTJnCnXfeSZcu1jKXBoOBu+66i7Q064t7k5KS6NChA7m5ubbF+Pq+ytgH24xGI+PHj+fAgQMsW7aMuXPn1ht4c2e0tqU9FouFoKAggGZhS3u0tGvNoJujAWFPQesxah9wc6dJgK4z3aoUjuTkZEpKSigtLbWK8vFBCGH7E8BsNnPq1Cmio6P57LPPMBgMWCwWDAYDJ06cwGKxUFRURNu2bR0Ktr399tucO3eOJUuW8Je//IX09HSmT5+uuQ2chZa2hOqBtp07dzJ58mSAZmFLe7S0a82gm32QqDmgpS1rBtx8fX0xGo3MnDlT8+uuiUcE0s6ePUtSUpKtapir8ZRABbi/LaF527MKLe3qKfb0BFuC9vb0CKerNZ4yqMH9bQnKns7GU+zpCbYElb1go7ERy5pZCps3b2bRokWcP38esBbTiY2NJTo6mvz8fKZOncp7773nTOluRWPtCD9nKADk5eUxatQo2/8lJSWxatUqYmNjSUlJYc6cOezatQspJTExMbrXLHUVTbGnfTTdaDQya9YsW5bHuXPnmDx5MllZWRQUFPDoo4/akvjtz2tONMWW9lke9plIADk5OSxdupRVq1Zx5swZli9fzsqVK8nMzMRkMjFu3Din6G8sujtdo9GIlJLY2FgSExOJiori5MmTgHWnTlZWFmvXruWHH36wbemrqKgArFFLk8mEyWQiOTkZwJal0KdPH0pKSmw1UKt2U3Xr1o3OnTtTUFDQrOrpOtuOYM1QqKJqk0kVhw8fZsGCBfz73/+uVre0aitwq1atGhSMczdcYc+qsejt7c2SJUsYPnw4eXl5AAQHB9vWxdu1a8ejjz5a63meiLNtWTPLwz4TCX7e9h8UFMShQ4d4+OGHuXTpEp06dWLu3LmEhYVpbIHq6O50e/TowY4dOzAYDBQUFNC1a1eOHDkCYFtULy8v59ChQ43aEjxw4EAWLlxYLT9v+vTp9OrVi3bt2jWberqusGMVOTk5nD17li+++IKvv/4agKlTp/LnP/+Z0tLSanVLm8tWYFfY0z6afvr0aXJycujfv3+9WuzP80ScbcuaWR72mUg1mTx5crWaxPv37681MKwlum8DnjBhAhEREZjNZsxmM61bt7Y95UJCQti0aROnTp0iMjKSo0ePNnhLcHx8PJmZmURFRdlqwe7evZu8vDwuXLjA1q1bm0U9XWfbEapnKBiNRmJiYhg8eDAJCQkYDAYqKiqYOHFitbqld9xxR7PYCuxse9aMpkdGRjJp0iTOnz+P2WxmzJgx7Nu3D4vFwty5c/nwww9p1aqVLYOh6jxPxNm2rJnlsXbtWlsmUs1t/+Xl5dVqEn/11Ve88MILmtvAHhVIqwVPCVSA+9sSlD2djafY0xNsCSqQplAoFM0aXZYX/Pz8soUQnfXo2xH8/Pyy9dbgKO5uS1D2dDaeYk9PsCVob09dlhcagxDCH/ge+LWU8nw9x7YDzgH9pZQeMUC1RggxGoiRUv6XA8dOBBZLKYe5XplnIoSIA3KllCsdOPZt4LSUsvE5U80YIYQX8C3wkJTy63qO9QEygXuklGla6GsqnrS8cB9wpD6HCyClLAA+Bv7H5ao8l8eBjQ4euwfoLYSoP9TeAhFC+AEPA5sdPGUj8Jhwp4IA7sUI4ApwtL4DpZRlwLvAY64W5Sw8yek+DmxowPFqYNeBEKIDEAH8zZHjpZQ/AVvwoIGtMZOAZCnlWQePTwLKAPXNoXYeBzY0IAq3EfgfIYSvCzU5DY9wukKIXwIhwK4GnHYY8AaGukSUZzMd2C2lzGvAOZuAR4QQzaPOoHN5DMe/NVDpTDaiHmLXIYQIAiYAf3X0HCnlt8DJyvPcHo9wusAfgL9KKa85eoLdwNb5JffuReXMv6HfGpBSngJOA2NdoctTEUL0BP4L63JWQ9gKTKmMPyh+ZhqwT0rZ0J0gG/CQe93tna4Qwht4lAbMJOzYAtwvhPDsnQ/OZRDwC+CfjThXzc6uZwaQIKUsachJlQHefwIPuUSV59Kgbw127AB+K4To7mQ9TsftnS4wGsiSUv6noSdKKX8ADgEPOl2V5/I4sElKWdGIc98HRgghujhZk0dSGWX/Aw381mCHx8zOtEAIcQfQBdjf0HOllFeBD7A+BN0aT3C6jX3yVaGWGCqpTLubijXa22CklFewzigecaYuD+ZuIF9KmVzvkbWzF+gphBjoRE2ezGPAZilleSPP94jguVs7XSFEJ+AeYFsTmvl/QB+V7gTAFOBrKWVT3s7nEQNbIx6j8bNcj0x3chVCiNZYA7ybmtDMV0AJcJdTRLkIt3a6WD+ERCllfmMbqEx32or1a2BLp6nfGgC+ACTw302X47kIIdoD43Aw7e4GbMKa7tTSs0ImAqlSyjONbcBTskLc1uk2NspeBxuBGZW7V1okQohewK+BnU1px1MGtgY8DOyRUuY2pREpZTrwDR6S7uRCnHWvbwUmCSF+4YS2XILbOl2saTgBwOdNbUhKeRL4jpad7vQo8LeGpN3dgC3AfUKIQCe05ak4y0lAC3+ICSF6AEOAj5ralpTyR+DvwO+b2parcGen+ziwsZFR9tposZHiyrS7pkTZq1GZ7vQ5LTQrRAgxCOgA/MNJTX4AhAkhujmpPU/jUWC7lLLYSe259b3ulk5XCNEG65NqsxObbcnpTr8DLkkp/+3ENt16YLuYx2h82t11VKY7fYgHpDs5Gyek3dXGp0B3IcSvnNim03BLp4u1uM2XUsrvndWglLKQllsEpyHFbRxlD9BLCDHAye26NZXFbabh3AkBtNyskJFAIXDMWQ1Wppy5bVaIuzpdZ66X2bOBFjawhRAdgTE0Pcpejcp0p5ZYBGcycKyJaXe18SVQCgx3crvuTkOL2zjKJiDSHYvguJ3TFUL0Bm4HPnFB8/8CBPBbF7TtrkwH/l9T0u5uQEssguOMtLvraIlZIZVpd+NpQHEbR6ksgnMCuNfZbTcVt3O6NKK4jaO0tIHt5LS765BSfgNkYM1XbfZUFrf5DU1Mu7sBW4HJLagIzjTgUylljovad8u4g1s53SYWt3GUllQE5zdAIPCZC/toSdusHwW2NbS4jaNIKS9hzYhw23QnJ+OSbw127ACGCiFucWEfDcatnC7WLb8XpZSprupASpkFHKRlpDs1pbiNo3wADBdCdHVhH7rjoih7bbjl7MzZCCHuBG4GDriqDyllEdasJbfKCnE3p+vqJ18VzX52Vlnc5vc0sriNo7SgIjh3Y30HWoqL+/kU6CGEuN3F/ehNU4vbOEpVVojb+Dq3ESKEuAlrGcemFLdxlN00/yI4Ve+UO6dBXy0hK8Rla+P2tIQiOJXFbR6macVtHOUIUIQbFcFxG6eLNcr+iZTS4uqOWsg7v7T61gDWd36VA2Ea9acple+UG4uT0+5ugNumOzmJScB/pJTfubojdwyeu4XTdXWUvQ420kzTnSrfKXcH8H9a9NcCXo30MA1/p1yjkVJmAKdovkVwtL7XtwIT3aUIju5Ot7KAcwzgjxOK2zSAb7C+82u1EKLZzNCEEI8Ay3BR2t0NqHrn16uV27ibBUKItcBMtHUSVPY3Wwjxgsb9ugwhRFchxCvAYBr+TrlGU/m+tf3Ai0II3R9kujtdrK/n+ANQgbZbdMMr+54B9NSwX1fzG6xvh3hACHGzhv0uBS4Dc4DmVELzIeBWrA8yTaj85vcc1t1pEVr1qwG/wDrLvQzM06rTyhoMoUAkoPu2dXdwupeBm7AueDt9Z8oNOIA16n4TcFXDfl1NG6zfGv63Mu9TK1Zirdrvg3UvfXPBC2vR9lladVi5XFNVdN9fq3414AegPXABWK9Vp1LK48C6yr51D/YK5295bqAA64aI54GXXLD/2pH+nwXe1iKApwWVZQf7SykTdOi7LfCMlDJG675dhRDiMeBwZbFxrfs2ALdIKXdo3bcrqJzBLwfWVGZpaN3/U1jX5p1dN6NhOvR2ugqFQtGScIflBYVCoWg5SCnr/fHz88vCuq7lVj9+fn5Z7qyvFr3lemtoLjr9/PyyPOFzrxqj7ngvubM2RzS7m96a9qzrx6HlBSGEHsut9SKEQEop3FVfTSr16i2jXjxBZ9XmN0/QKaUUdv92m7Hqztrqwl6zu+mtac+6UMsLCoVCoSGaO928vDxMJhNjx47lypUrvPTSSyxdupTDhw8DcO7cOSZPnkxWVhYAR44c4YknngAgPj6edevWUVioTUbSokWLSEhIsOkeNWqU7f9Onz7NH//4R5588kksFgtRUVE8+eSTFBQU8NBDD/HGG29oorGmzri4OGJifk4euHjxIrGxsURHR3PmzBmWL1/OypUrKSgo4NFHHyUpKUlzjTVtWVxczJ/+9CfeeecdMjMzMZlMjBtnLdF75swZ7rvvPk00XrlyhdWrVzNr1ixKS0t59dVXWb58OZmZ1mC3/disaT/769MKi8XCkiVLePbZZ20z/iNHjvDyyy+zefNmvvnmG1544QVee+01UlNTMZlMTJ06VVON9napea/bf+72Y7PmGNCLnJwcli5dyqpVq2y/++ijj3j55ZdJTExsdLsNSmJftWoVgYGBpKen06VLFwwGA0ePHiUoKIhJkyYRFxeHj48P0dHReHl5ceDAAY4fPw7AiBEjGDRoEO3bt2fBggXk5+cTEBBAQUEBBQUFdO/eHYDg4GAmT54MQFFREcePH6d3795YLBbMZjODBw/Gx+fGsp2hE2DevHm2myohIYHRo0f/bDgfH3Jycmjbti3e3t7k5uZSUVFBQEAAHTt2pLi42Lp+c4MaMK7QOW/evGpOt1u3bnTu3JkLFy5w9OhRHn74YV5//XW8vb159NFHNfnM67Pl/v37KSsrw8vLi549ezJ37lyuXr1KRUUFn376KaGhoZroDAgI4Pnnn+eVV16hpKSE0tJSoqKi2LJlC3PmzKk2Ntu1a1fNfvbX5wjO0Juens6IESNITU3l5MmTDBw4kCFDhrBjxw5uvvlmdu/ezfLly4mNjSUkJIQuXbrQqpVju95d8bnXvNftP3f7sdmpUyfbGGgoztIN8NlnnzFz5kz27t3L1atXadu2LRkZGSxZsgSj0ciECY3b3Nagma4QgmeeeYabb76ZZcuWcezYMXr37k1hYSEHDx6kXbt2+Pr6kp+ff8N2qgYIwC9/+Uteeukl9u3bd91xX331FZcuXcJsNnPlyhVuueUWRowYwYEDNy7B6SydVeTk5HD27Fm++OILvv76awAyMzN5+umnCQ4O5ty5czz44INERESQkZHBm2++SZ8+fUhLS9NUZ11Mnz6dXr16MWbMGPbu3cuPP/6Il5djH70WtiwrK2Po0KHk5eVRWFjI/v37CQ8P55tvviE7Oxuz2Ux6+o3TZJ2l88svv6Rbt260a9fuhg/MpuIMvb/5zW9IT0/n22+/rTYRiYmJ4cqVK9cd//HHHzv8rcEVY7PmvW7/ud9zzz3VxmbVGGgoztB9/vx5TCZTne03lQbNdL28vBBC4Ovrawu2FBYWYrFYGDZsGMeOHSM4OJj27dsDEB4eXqvhEhMTefrppwE4deoUr732Gvfeey8JCQmMGTOGffv2YbFYmD9/PiNHjkRKSffu3fH392fHjh3MmTNHE53bt2/nxIkTjBs3DqPRSExMDIMHDyYhIYHbb7+djRs3IoSgY8eOxMfH07p1a+655x7WrFnDhQsX6h00rtC5Z88ezGYz33//PYcPH2bAgAHs3r2bvLw8ysrKqKioIDw8HF9fXz788ENatWrF0KFDdbXlmDFjWL16NT4+PgQEBPDVV1/xwgsvALBy5UpiYmLo16+fy21ZUFDA4sWLmTJlChaLBV9fX4xGIzNnzrxubM6dO7ea/eyvr127+t+24yy7/vTTT/Tr149+/fqRkJBAmzZtOHbsGF27dmX06NGsWrWKTp06Adalpq5dHas174rPvbZ7vepztx+b/v7+1cZAQ3CG7h49erBgwQJycnJYt24dAQEBXLt2jX/961/06dOHl19+mV/9qvFvd1fZCxriCVkB4Bk6VfZC03FnbXWhshcUCoVC0SA0dbr2AZ6GoEU2QGO01Yx2z5w5E5PJRElJCRs2bOCll17iyJEjumqE6hFk+4j8li1biIqKcpq+mjRGb83slpqZDq6gsXa1z6zYvHkzixYt4vz5886Udh2N1RoXF8e8efMaFZxqCE29j65du0ZUVBSLFi3SJEvJGfdUQ2lUCb74+Hi8vLyIjIxk48aNXLx4EaPRyMSJE7n11lvp378/6enpLFy4kBdffJHu3bvz3HPPAfDWW29RVFRE3759SUtLIygoiNmzZwPUGUl0NBtAa201o90dO3akqKgIgMuXL7N8+XKMRiNDhgzR1X72EeSaEXlHBp2Wemtmt7z55pvVMh3cRWfNzIrQ0FD2799fb2aNHlqr9JnNZry9vd1On/19lJWVRXBwMB07duTQoUMOp43peU81lEbNdPv27Ut+fr5tW1tVHmNoaChTpkzhzjvvpEuXLgAYDAbuuusuWyQ/KSmJDh06kJuba4sq1rcu42g2gB7a7KPdRqOR8ePHc+DAAdvDobaHhNYa7WlM9FVrvVXZLbVlOriLzpqZFQMHDmThwoW2Pt1JK8DQoUOZNm0aly9fdkt9VfdRz5498fHxITk52eEHmB56m0KjnK7FYqGsrIzk5GRbPiNYc1eFELY/AcxmM7t37+b2260vNzUYDFgsFvr372+LKlbNDsPDw1mwYAELFiywPVGKiopYs2YN+/fvp1evXm6lrSra/eOPP2KxWFi7di0bNmxg0KBBdOrUiVWrVtX61VhLjWCNIO/Zs4eCggJbRH78+PG2TIcTJ064jU3Bmt0yYcIEOnbsiNFoJCwsjMGDB7vVZ3/bbbexcuVKwsLC6NevH6tXr+btt9+mc+fO9erUWqvFYmH16tV88sknBAYGup2+mvdRWVkZbdu2ZeTIkQ5p1VovVL+nGowjBRqshzWc7777Tm7btq1R5zpCpa5G6XO1ttpoqE49NErZcJ1VaKmXyiIjjUFrnbIJ95IrtTZVm5Taj1F7ze6mt6Y96/pRKWMa4gmpWOAZOlXKWNNxZ211oVLGKmlsBBCqR4Dto/8AKSkpzJkzh127dl1X30ALfTWzE4xGI7NmzeLSJetbcJKSkli1ahWxsbHV9mnn5+czdepU3nvvPU10ws/R1PLyckwmE/fffz8ZGRlA9ZoBhYWFmEwmFi1ahJSSJUuWMG/ePE6fPq2pTri+ToS9Pe1rBTT2s2+KTvtof83MBPt6FvBzfZDaaiE0FGfZtrasjz179jQ6W8VV97h9DRNn119w1n1/o3HaGBrkdI1GI1JKYmNjSUxMJCoqipMnTwLWlJmsrCzWrl3LDz/8wNKlS1mxYgUVFRWANQpoMpkwmUwkJycDXBcBto/+A/j6+hIYGEhxcfF19Q200FcVVe3Tpw8lJSUsWbKE4cOHk5eXB8Dhw4dZsGAB//73v237tIOCghBC0L59+2rX4kqdYI2mAnh7e7NgwQJCQkLo27cvUL2eRWBgIAMGDCA3NxchhG39qmrXklY6a/69pj1DQkKYPn06w4cPr/ezd4XO0NBQsrOz8fb2tv29KrBTVc+ioKCgWn2QqloIN910k63/unC1bWvWt8jOzqaoqIigoCBNddV3j9vXMKmqv3Dp0iVb/YWwsPpf1O3q+/5G47QxNMjp9ujRgx07dmAwGCgoKKBr1662PNSqr3vl5eUcOnTIoT3ONSPA9tF/gIEDB2I0GsnIyODSpUvV6htooQ+qZyecPn2anJwc+vfvD8DUqVP585//bFu0r8LHx4c333yT4uLiWnMNXaHTHovFcsObKyIigrCwMCwWC2FhYcyePbvW9BdX66xJTXtW1Qqo77N3hU77aH9tmQlV9Szs64N069at1loIteFK29aW9XHw4EHOnTuH2WyuczLgCl313eP2NUxq1gZxtP6Cq+/7mtR13ztKg/J0J0yYQEREBGazGbPZTOvWrW1PjJCQEDZt2sSpU6eIjIzk6NGj9e5xrooAV+2tX7t2LefOnWPJkiUkJCQQEhLCrl27KC8vp23btuzcuZPWrVsTEVH7W6mdra/mXvzHH3+cSZMmcf78ecxmMwaDgYqKCiZOnMjIkSNt+7Tz8/N57bXXuHTpUq0zM2frhOp73Hfu3Gmb2dasGTBp0iS2bdvG2bNnmTFjBmazmdTUVObOnau5zpp1IuztCT/XCsjOzr7hZ+9snRaLhfj4eE6fPs2oUaNYvXo1mZmZREVFkZCQUK2exdy5c231Qbp06VKtFsKNcLVta9a3qCrpeO3aNdq0aaOZrvrucfsaJj/99FOj6i+4+r7fu3fvDcdpQ1GBNA3xhAAVeIZOFUhrOu6srS5UIE2hUCgUDcKh5QU/P79sIYRjWd8a4ufnl131pzvqq4mfn1+FEMLtH3SeoLPqs3f3z71Kp/2/3UWzO2urC3vN7qa3pj3rwqHlBYVCoVA4B7eezSgUCkVzQzldhUKh0BDldBUKhUJDlNNVKBQKDVFOV6FQKDREOV2FQqHQEOV0FQqFQkOU01UoFAoNUU5XoVAoNEQ5XYVCodAQ5XQVCoVCQ5TTVSgUCg1RTlehUCg0RDldhUKh0BDldBUKhUJDlNNVKBQKDVFOV6FQKDREOV2FQqHQEOV0FQqFQkOU01UoFAoNUU5XoVAoNEQ5XYVCodCQ/w+Jypku/h4iYAAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
diff --git a/readme.md b/readme.md
index a32320a1..ee2e2a20 100644
--- a/readme.md
+++ b/readme.md
@@ -40,7 +40,7 @@ preds_proba = model.predict_proba(X_test) # predicted probabilities: shape is (n
 ## Demo notebooks
 The demos are contained in 3 main [notebooks](notebooks), following this cheat-sheet:![cheat_sheet](docs/cheat_sheet.png)
 
-1. [model_based.ipynb](notebooks/1_model_based.ipynb) - how to use different interpretable models
+1. [model_based.ipynb](notebooks/1_model_based.ipynb) - how to use different interpretable models and examples with the **imodels** package
 2. [posthoc.ipynb](notebooks/2_posthoc.ipynb) - different simple analyses to interpret a trained model
 3. [uncertainty.ipynb](notebooks/3_uncertainty.ipynb) - code to get uncertainty estimates for a model