diff --git a/data/.gitignore b/data/.gitignore
index 0f9d847..70d580a 100644
--- a/data/.gitignore
+++ b/data/.gitignore
@@ -1,4 +1,6 @@
 Brent_Oil_Prices.csv
 gdp_data.csv
 natural_gas_daily.csv
-natural_gas_monthly.csv
\ No newline at end of file
+natural_gas_monthly.csv
+
+new/
\ No newline at end of file
diff --git a/data/Brent_Oil_Prices.csv b/data/Brent_Oil_Prices.csv
deleted file mode 100644
index 7278303..0000000
--- a/data/Brent_Oil_Prices.csv
+++ /dev/null
@@ -1,9012 +0,0 @@
-Date,Price
-20-May-87,18.63
-21-May-87,18.45
-22-May-87,18.55
-25-May-87,18.6
-26-May-87,18.63
-27-May-87,18.6
-28-May-87,18.6
-29-May-87,18.58
-01-Jun-87,18.65
-02-Jun-87,18.68
-03-Jun-87,18.75
-04-Jun-87,18.78
-05-Jun-87,18.65
-08-Jun-87,18.75
-09-Jun-87,18.78
-10-Jun-87,18.78
-11-Jun-87,18.68
-12-Jun-87,18.78
-16-Jun-87,18.9
-17-Jun-87,19.03
-18-Jun-87,19.05
-19-Jun-87,19.05
-22-Jun-87,19.1
-23-Jun-87,18.9
-24-Jun-87,18.75
-25-Jun-87,18.7
-26-Jun-87,19.08
-29-Jun-87,19.15
-30-Jun-87,19.08
-01-Jul-87,18.98
-02-Jul-87,19.25
-03-Jul-87,19.33
-06-Jul-87,19.48
-07-Jul-87,19.5
-08-Jul-87,19.48
-09-Jul-87,19.68
-10-Jul-87,19.73
-13-Jul-87,19.83
-14-Jul-87,19.88
-15-Jul-87,20.28
-16-Jul-87,20.4
-17-Jul-87,20.63
-20-Jul-87,20.55
-21-Jul-87,20.35
-22-Jul-87,20.33
-23-Jul-87,20.15
-24-Jul-87,19.58
-27-Jul-87,19.3
-28-Jul-87,19.78
-29-Jul-87,19.98
-30-Jul-87,20.2
-31-Jul-87,20.03
-03-Aug-87,20.95
-04-Aug-87,20.65
-05-Aug-87,19.8
-06-Aug-87,19.75
-07-Aug-87,19.65
-10-Aug-87,19.43
-11-Aug-87,19.45
-12-Aug-87,19.5
-13-Aug-87,19.4
-14-Aug-87,19.25
-17-Aug-87,18.85
-18-Aug-87,18.75
-19-Aug-87,18.5
-20-Aug-87,18.3
-21-Aug-87,18.1
-24-Aug-87,17.48
-25-Aug-87,17.55
-26-Aug-87,18.1
-27-Aug-87,18.28
-28-Aug-87,18.2
-31-Aug-87,18.63
-01-Sep-87,18.43
-02-Sep-87,18.4
-03-Sep-87,18.18
-04-Sep-87,18.13
-07-Sep-87,17.6
-08-Sep-87,17.68
-09-Sep-87,17.9
-10-Sep-87,18.3
-11-Sep-87,18.18
-14-Sep-87,18.15
-15-Sep-87,18.53
-16-Sep-87,18.53
-17-Sep-87,18.43
-18-Sep-87,18.3
-21-Sep-87,18.28
-22-Sep-87,18.48
-23-Sep-87,18.48
-24-Sep-87,18.68
-25-Sep-87,18.6
-28-Sep-87,18.65
-29-Sep-87,18.5
-30-Sep-87,18.48
-01-Oct-87,18.5
-02-Oct-87,18.65
-05-Oct-87,18.78
-06-Oct-87,18.6
-07-Oct-87,18.58
-08-Oct-87,18.63
-09-Oct-87,18.6
-12-Oct-87,18.55
-13-Oct-87,18.55
-14-Oct-87,18.68
-15-Oct-87,18.68
-16-Oct-87,19
-19-Oct-87,19.1
-20-Oct-87,18.78
-21-Oct-87,18.93
-22-Oct-87,19.13
-23-Oct-87,18.98
-26-Oct-87,18.75
-27-Oct-87,18.8
-28-Oct-87,18.85
-29-Oct-87,18.75
-30-Oct-87,18.8
-02-Nov-87,18.63
-03-Nov-87,18.38
-04-Nov-87,17.93
-05-Nov-87,17.85
-06-Nov-87,17.95
-09-Nov-87,17.5
-10-Nov-87,17.75
-11-Nov-87,17.8
-12-Nov-87,17.85
-13-Nov-87,17.8
-16-Nov-87,17.68
-17-Nov-87,17.4
-18-Nov-87,17.18
-19-Nov-87,17.48
-20-Nov-87,17.6
-23-Nov-87,17.9
-24-Nov-87,17.83
-25-Nov-87,17.68
-26-Nov-87,17.73
-27-Nov-87,17.78
-30-Nov-87,17.7
-01-Dec-87,17.65
-02-Dec-87,17.7
-03-Dec-87,17.93
-04-Dec-87,18
-07-Dec-87,17.78
-08-Dec-87,17.58
-09-Dec-87,17.43
-10-Dec-87,17.55
-11-Dec-87,17.73
-14-Dec-87,16.8
-15-Dec-87,16.2
-16-Dec-87,15.93
-17-Dec-87,15.03
-18-Dec-87,15.6
-21-Dec-87,15.4
-22-Dec-87,16.7
-23-Dec-87,17.25
-24-Dec-87,17.1
-28-Dec-87,17
-29-Dec-87,17.38
-30-Dec-87,17.85
-31-Dec-87,17.6
-04-Jan-88,17.95
-05-Jan-88,17.08
-06-Jan-88,17.9
-08-Jan-88,16.88
-11-Jan-88,16.65
-12-Jan-88,15.95
-13-Jan-88,16.38
-14-Jan-88,16.55
-15-Jan-88,16.65
-18-Jan-88,16.83
-19-Jan-88,17.1
-20-Jan-88,16.83
-21-Jan-88,17.08
-22-Jan-88,16.7
-25-Jan-88,16.45
-27-Jan-88,16.13
-28-Jan-88,16.1
-29-Jan-88,16.28
-01-Feb-88,16.1
-02-Feb-88,16.18
-03-Feb-88,16.15
-04-Feb-88,16.18
-05-Feb-88,16.1
-08-Feb-88,16.5
-09-Feb-88,16.4
-10-Feb-88,16.13
-11-Feb-88,16.1
-12-Feb-88,15.75
-15-Feb-88,15.68
-16-Feb-88,15.63
-17-Feb-88,15.85
-18-Feb-88,15.48
-19-Feb-88,15.55
-22-Feb-88,15.38
-23-Feb-88,15.58
-24-Feb-88,15.35
-25-Feb-88,14.85
-26-Feb-88,14.65
-29-Feb-88,14.73
-01-Mar-88,14.18
-02-Mar-88,13.8
-03-Mar-88,14
-04-Mar-88,14
-07-Mar-88,13.9
-08-Mar-88,13.8
-09-Mar-88,13.98
-10-Mar-88,14.48
-11-Mar-88,14.88
-14-Mar-88,14.28
-15-Mar-88,14.3
-16-Mar-88,14.45
-17-Mar-88,14.43
-18-Mar-88,14.93
-21-Mar-88,15.38
-22-Mar-88,15.03
-23-Mar-88,15.4
-24-Mar-88,15.65
-25-Mar-88,15.45
-28-Mar-88,15.55
-29-Mar-88,15.6
-30-Mar-88,15.7
-31-Mar-88,15.65
-05-Apr-88,15.5
-06-Apr-88,15.38
-07-Apr-88,15.53
-08-Apr-88,15.55
-11-Apr-88,16.2
-12-Apr-88,16.48
-13-Apr-88,16.55
-14-Apr-88,16.65
-15-Apr-88,16.85
-18-Apr-88,17.45
-19-Apr-88,17.05
-20-Apr-88,16.78
-21-Apr-88,17
-22-Apr-88,17.15
-25-Apr-88,17.13
-26-Apr-88,17.18
-27-Apr-88,17.4
-28-Apr-88,16.88
-29-Apr-88,16.6
-02-May-88,15.95
-03-May-88,16.08
-04-May-88,16.15
-05-May-88,16.15
-06-May-88,16.45
-09-May-88,16.5
-10-May-88,16.38
-11-May-88,16.48
-12-May-88,16.4
-13-May-88,16.5
-16-May-88,16.6
-17-May-88,16.6
-18-May-88,16.4
-19-May-88,16.25
-20-May-88,16.45
-23-May-88,16.23
-24-May-88,16.3
-25-May-88,16.18
-26-May-88,16.18
-27-May-88,16.25
-30-May-88,16.23
-31-May-88,16.2
-01-Jun-88,16.33
-02-Jun-88,16.33
-03-Jun-88,16.45
-06-Jun-88,16.25
-07-Jun-88,16.23
-08-Jun-88,16.28
-09-Jun-88,16.3
-10-Jun-88,15.85
-13-Jun-88,15.53
-14-Jun-88,15.85
-15-Jun-88,15.7
-16-Jun-88,15.43
-17-Jun-88,15.48
-20-Jun-88,15
-21-Jun-88,15
-22-Jun-88,15.13
-23-Jun-88,15.18
-24-Jun-88,15.15
-27-Jun-88,14.93
-28-Jun-88,14.83
-29-Jun-88,14.55
-30-Jun-88,14.18
-01-Jul-88,13.95
-04-Jul-88,14.05
-05-Jul-88,13.98
-06-Jul-88,14.5
-07-Jul-88,15.5
-08-Jul-88,15.05
-11-Jul-88,14.63
-12-Jul-88,14
-13-Jul-88,14.1
-14-Jul-88,14.1
-15-Jul-88,14.25
-18-Jul-88,15
-19-Jul-88,14.93
-20-Jul-88,15.48
-21-Jul-88,15.63
-22-Jul-88,15.83
-25-Jul-88,15.8
-26-Jul-88,15.38
-27-Jul-88,15.45
-28-Jul-88,15.7
-29-Jul-88,15.75
-01-Aug-88,15.6
-02-Aug-88,15.35
-03-Aug-88,15
-04-Aug-88,14.5
-05-Aug-88,14.85
-08-Aug-88,15.48
-09-Aug-88,15.4
-10-Aug-88,15.1
-11-Aug-88,15.13
-12-Aug-88,14.98
-15-Aug-88,14.8
-16-Aug-88,14.85
-17-Aug-88,14.75
-18-Aug-88,14.78
-19-Aug-88,14.85
-22-Aug-88,15.03
-23-Aug-88,14.8
-24-Aug-88,14.85
-25-Aug-88,14.63
-26-Aug-88,14.5
-29-Aug-88,14.48
-30-Aug-88,14.45
-31-Aug-88,14.4
-01-Sep-88,14.15
-02-Sep-88,14
-05-Sep-88,13.68
-06-Sep-88,13.35
-07-Sep-88,13.18
-08-Sep-88,13.3
-09-Sep-88,13.25
-12-Sep-88,12.7
-13-Sep-88,13.4
-14-Sep-88,13.9
-15-Sep-88,13.5
-16-Sep-88,13.23
-19-Sep-88,12.85
-20-Sep-88,13.15
-21-Sep-88,13.05
-22-Sep-88,13.2
-23-Sep-88,13.2
-26-Sep-88,13.1
-27-Sep-88,12.58
-28-Sep-88,12.75
-29-Sep-88,12.5
-30-Sep-88,11.93
-03-Oct-88,11.6
-04-Oct-88,11.65
-05-Oct-88,11.2
-06-Oct-88,11.3
-07-Oct-88,11.35
-10-Oct-88,12.2
-11-Oct-88,12.35
-12-Oct-88,12.45
-13-Oct-88,12.55
-14-Oct-88,13.2
-17-Oct-88,13.55
-18-Oct-88,12.9
-19-Oct-88,13.35
-20-Oct-88,13.5
-21-Oct-88,13.48
-24-Oct-88,12.2
-25-Oct-88,12.08
-26-Oct-88,12.08
-27-Oct-88,12.33
-28-Oct-88,12.6
-31-Oct-88,12.6
-01-Nov-88,12.18
-02-Nov-88,12.35
-03-Nov-88,12.5
-04-Nov-88,12.75
-07-Nov-88,12.75
-08-Nov-88,12.6
-09-Nov-88,12.7
-10-Nov-88,13.03
-11-Nov-88,13.03
-14-Nov-88,13
-15-Nov-88,13
-16-Nov-88,12.7
-17-Nov-88,12.05
-18-Nov-88,12.08
-21-Nov-88,12.13
-22-Nov-88,13.35
-23-Nov-88,13.33
-24-Nov-88,12.98
-25-Nov-88,14.7
-28-Nov-88,14.73
-29-Nov-88,14.25
-30-Nov-88,14.35
-01-Dec-88,14.93
-02-Dec-88,14.8
-05-Dec-88,14.73
-06-Dec-88,14.4
-07-Dec-88,14.7
-08-Dec-88,15.05
-09-Dec-88,14.9
-12-Dec-88,15.2
-13-Dec-88,15.18
-14-Dec-88,15.35
-15-Dec-88,15.58
-16-Dec-88,15.6
-19-Dec-88,15.3
-20-Dec-88,15.28
-21-Dec-88,15.33
-22-Dec-88,15.25
-23-Dec-88,15.38
-27-Dec-88,16.25
-28-Dec-88,16.1
-29-Dec-88,15.9
-30-Dec-88,16.23
-03-Jan-89,16.4
-04-Jan-89,16.53
-05-Jan-89,16.58
-06-Jan-89,16.85
-09-Jan-89,17
-10-Jan-89,16.75
-11-Jan-89,16.9
-12-Jan-89,16.85
-13-Jan-89,17.4
-16-Jan-89,17.5
-17-Jan-89,17.78
-18-Jan-89,17.95
-19-Jan-89,18.1
-20-Jan-89,18.15
-23-Jan-89,16.98
-24-Jan-89,17.05
-25-Jan-89,17.7
-26-Jan-89,17.73
-27-Jan-89,17.18
-30-Jan-89,16.85
-31-Jan-89,16.38
-01-Feb-89,16.4
-02-Feb-89,16.95
-03-Feb-89,16.75
-06-Feb-89,16.5
-07-Feb-89,16.6
-08-Feb-89,16.75
-09-Feb-89,16.58
-10-Feb-89,16.4
-13-Feb-89,16.5
-14-Feb-89,16.83
-15-Feb-89,16.75
-16-Feb-89,17.15
-17-Feb-89,17.15
-20-Feb-89,17.38
-21-Feb-89,17.2
-22-Feb-89,16.95
-23-Feb-89,17.1
-24-Feb-89,17.05
-27-Feb-89,17.55
-28-Feb-89,17.23
-01-Mar-89,17.25
-02-Mar-89,17.45
-03-Mar-89,17.73
-06-Mar-89,17.73
-07-Mar-89,17.55
-08-Mar-89,17.55
-09-Mar-89,17.8
-10-Mar-89,17.63
-13-Mar-89,18.1
-14-Mar-89,18.63
-15-Mar-89,19.08
-16-Mar-89,18.95
-17-Mar-89,19.3
-20-Mar-89,19.18
-21-Mar-89,19.63
-22-Mar-89,19.6
-23-Mar-89,19.93
-28-Mar-89,19.73
-29-Mar-89,19.45
-30-Mar-89,20
-31-Mar-89,20.45
-03-Apr-89,19.65
-04-Apr-89,19.9
-05-Apr-89,19.75
-06-Apr-89,19.08
-07-Apr-89,19.4
-10-Apr-89,19.6
-11-Apr-89,19.95
-12-Apr-89,19.85
-13-Apr-89,19.95
-14-Apr-89,19.63
-17-Apr-89,19.88
-18-Apr-89,20.2
-19-Apr-89,21.5
-20-Apr-89,22.25
-21-Apr-89,21.6
-24-Apr-89,21.2
-25-Apr-89,21.15
-26-Apr-89,21.05
-27-Apr-89,20.65
-28-Apr-89,20.15
-02-May-89,19.15
-03-May-89,18.88
-04-May-89,19.7
-05-May-89,19.4
-08-May-89,18.8
-09-May-89,19.03
-10-May-89,18.88
-11-May-89,19.3
-12-May-89,19.2
-15-May-89,19.2
-16-May-89,19.8
-17-May-89,18.58
-18-May-89,18.6
-19-May-89,18.3
-22-May-89,17.4
-23-May-89,17.7
-24-May-89,18.15
-25-May-89,18.2
-26-May-89,17.65
-29-May-89,17.73
-30-May-89,18.03
-31-May-89,18.25
-01-Jun-89,18.08
-02-Jun-89,18.15
-05-Jun-89,18.55
-06-Jun-89,18.5
-07-Jun-89,18.15
-08-Jun-89,17.8
-09-Jun-89,17.95
-12-Jun-89,17.18
-13-Jun-89,16.8
-14-Jun-89,16.9
-15-Jun-89,16.95
-16-Jun-89,16.65
-19-Jun-89,16.85
-20-Jun-89,16.88
-21-Jun-89,17.13
-22-Jun-89,17.3
-23-Jun-89,18.03
-26-Jun-89,18.23
-27-Jun-89,18.2
-28-Jun-89,18
-29-Jun-89,18.2
-30-Jun-89,18.28
-03-Jul-89,18.4
-04-Jul-89,18.6
-05-Jul-89,18.75
-06-Jul-89,18.3
-07-Jul-89,17.85
-10-Jul-89,17.83
-11-Jul-89,17.63
-12-Jul-89,17.65
-13-Jul-89,17.6
-14-Jul-89,17.9
-17-Jul-89,18.05
-18-Jul-89,18.05
-19-Jul-89,17.85
-20-Jul-89,17.98
-21-Jul-89,17.83
-24-Jul-89,17.3
-25-Jul-89,17.05
-26-Jul-89,16.5
-27-Jul-89,16.28
-28-Jul-89,16.38
-31-Jul-89,16.3
-01-Aug-89,16.23
-02-Aug-89,16.1
-03-Aug-89,16.4
-04-Aug-89,16.15
-07-Aug-89,16.05
-08-Aug-89,16.28
-09-Aug-89,16.45
-10-Aug-89,16.93
-11-Aug-89,17
-14-Aug-89,16.85
-15-Aug-89,17
-16-Aug-89,17
-17-Aug-89,16.95
-18-Aug-89,16.83
-21-Aug-89,16.9
-22-Aug-89,17.1
-23-Aug-89,17.08
-24-Aug-89,17.05
-25-Aug-89,16.95
-28-Aug-89,16.98
-29-Aug-89,17.1
-30-Aug-89,17.13
-31-Aug-89,17.2
-01-Sep-89,17.33
-04-Sep-89,17.43
-05-Sep-89,17.45
-06-Sep-89,17.8
-07-Sep-89,17.8
-08-Sep-89,17.83
-11-Sep-89,17.98
-12-Sep-89,17.9
-13-Sep-89,18.05
-14-Sep-89,17.9
-15-Sep-89,17.88
-18-Sep-89,18.05
-19-Sep-89,17.9
-20-Sep-89,17.8
-21-Sep-89,17.85
-22-Sep-89,17.53
-25-Sep-89,17.45
-26-Sep-89,17.45
-27-Sep-89,17.55
-28-Sep-89,17.93
-29-Sep-89,18.23
-02-Oct-89,18.58
-03-Oct-89,18.73
-04-Oct-89,18.78
-05-Oct-89,18.43
-06-Oct-89,18.45
-09-Oct-89,18.25
-10-Oct-89,18.55
-11-Oct-89,18.85
-12-Oct-89,19.23
-13-Oct-89,19.6
-16-Oct-89,19.38
-17-Oct-89,19.53
-18-Oct-89,19.43
-19-Oct-89,19.33
-20-Oct-89,19.2
-23-Oct-89,18.88
-24-Oct-89,18.8
-25-Oct-89,18.88
-26-Oct-89,18.48
-27-Oct-89,18.7
-30-Oct-89,18.98
-31-Oct-89,18.93
-01-Nov-89,19.23
-02-Nov-89,19.2
-03-Nov-89,19
-06-Nov-89,18.95
-07-Nov-89,18.9
-08-Nov-89,18.9
-09-Nov-89,18.85
-10-Nov-89,19.05
-13-Nov-89,18.85
-14-Nov-89,18.7
-15-Nov-89,18.65
-16-Nov-89,18.65
-17-Nov-89,18.75
-20-Nov-89,18.8
-21-Nov-89,18.68
-22-Nov-89,18.6
-23-Nov-89,18.4
-24-Nov-89,18.48
-27-Nov-89,18.5
-28-Nov-89,18.23
-29-Nov-89,18.15
-30-Nov-89,18.48
-01-Dec-89,18.68
-04-Dec-89,19.28
-05-Dec-89,19.18
-06-Dec-89,19.28
-07-Dec-89,19.33
-08-Dec-89,19.15
-11-Dec-89,19.45
-12-Dec-89,19.53
-13-Dec-89,19.78
-14-Dec-89,19.68
-15-Dec-89,19.68
-18-Dec-89,19.98
-19-Dec-89,20.28
-20-Dec-89,20.23
-21-Dec-89,20.13
-22-Dec-89,20.5
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-12-Jan-90,22.13
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-08-Mar-90,18.73
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-13-Aug-90,26.63
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-14-Sep-90,33.35
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-01-Oct-90,38.95
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-08-Oct-90,39.2
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-15-Oct-90,38.28
-16-Oct-90,38.93
-17-Oct-90,35.33
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-22-Oct-90,27.45
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-01-Nov-90,35.65
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-14-Dec-90,28.45
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-05-Feb-98,14.83
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-01-Sep-98,12.11
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-01-Oct-98,14.46
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-08-Oct-98,13.23
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-12-Oct-98,13.18
-13-Oct-98,12.73
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-10-Nov-98,11.18
-11-Nov-98,11.45
-12-Nov-98,11.56
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-18-Nov-98,10.25
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-23-Nov-98,10.54
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-01-Dec-98,9.87
-02-Dec-98,9.71
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-04-Dec-98,9.73
-07-Dec-98,9.7
-08-Dec-98,9.68
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-11-Dec-98,9.26
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-17-Dec-98,9.92
-18-Dec-98,9.62
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-08-Jan-99,11.7
-11-Jan-99,12.07
-12-Jan-99,11.78
-13-Jan-99,10.9
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-21-Jan-99,11.14
-22-Jan-99,11.23
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-08-Mar-99,11.49
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-10-Mar-99,11.46
-11-Mar-99,11.86
-12-Mar-99,12.3
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-12-Apr-99,14.23
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-01-Jun-99,14.22
-02-Jun-99,14.28
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-04-Jun-99,15.21
-07-Jun-99,15.98
-08-Jun-99,16.25
-09-Jun-99,16.19
-10-Jun-99,15.99
-11-Jun-99,16.25
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-17-Jun-99,16
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-24-Jun-99,16.28
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-09-Jul-99,18.9
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-14-Jul-99,19.48
-15-Jul-99,18.9
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-22-Jul-99,19.01
-23-Jul-99,19.69
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-04-Aug-99,19.38
-05-Aug-99,19.64
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-05-May-00,24.93
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-09-May-00,26.69
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-05-Jun-00,28.27
-06-Jun-00,28.42
-07-Jun-00,28.32
-08-Jun-00,29.34
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-12-Jun-00,30.1
-13-Jun-00,30.4
-14-Jun-00,30.12
-15-Jun-00,29.77
-16-Jun-00,28.68
-19-Jun-00,27.69
-20-Jun-00,28.86
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-28-Jun-00,31.15
-29-Jun-00,31.05
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-03-Jul-00,32.15
-04-Jul-00,30.73
-05-Jul-00,30.35
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-10-Jul-00,30.58
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-12-Jul-00,30.27
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-14-Jul-00,30.82
-17-Jul-00,28.44
-18-Jul-00,28.46
-19-Jul-00,28.33
-20-Jul-00,27.85
-21-Jul-00,26.68
-24-Jul-00,26.84
-25-Jul-00,25.78
-26-Jul-00,25.92
-27-Jul-00,25.6
-28-Jul-00,25.96
-31-Jul-00,25.3
-01-Aug-00,25.24
-02-Aug-00,26.8
-03-Aug-00,27.04
-04-Aug-00,28.01
-07-Aug-00,27.56
-08-Aug-00,27.58
-09-Aug-00,28.13
-10-Aug-00,28.98
-11-Aug-00,29.44
-14-Aug-00,29.24
-15-Aug-00,30
-16-Aug-00,29.66
-17-Aug-00,30.71
-18-Aug-00,30.76
-21-Aug-00,31.34
-22-Aug-00,30.18
-23-Aug-00,32.38
-24-Aug-00,31.67
-25-Aug-00,33.26
-28-Aug-00,33.29
-29-Aug-00,34.03
-30-Aug-00,34.13
-31-Aug-00,35.08
-01-Sep-00,35.09
-04-Sep-00,36.02
-05-Sep-00,35.72
-06-Sep-00,36.7
-07-Sep-00,37.43
-08-Sep-00,36.27
-11-Sep-00,36.87
-12-Sep-00,33.3
-13-Sep-00,31.08
-14-Sep-00,31.35
-15-Sep-00,33.68
-18-Sep-00,34.55
-19-Sep-00,33.48
-20-Sep-00,33.67
-21-Sep-00,32.18
-22-Sep-00,31.59
-25-Sep-00,30.01
-26-Sep-00,29.94
-27-Sep-00,29.78
-28-Sep-00,28.91
-29-Sep-00,28.42
-02-Oct-00,29.65
-03-Oct-00,30.64
-04-Oct-00,30.07
-05-Oct-00,29.19
-06-Oct-00,29.62
-09-Oct-00,29.99
-10-Oct-00,30.95
-11-Oct-00,31.25
-12-Oct-00,33.45
-13-Oct-00,33.5
-16-Oct-00,32.03
-17-Oct-00,30.38
-18-Oct-00,30.98
-19-Oct-00,31.29
-20-Oct-00,30.16
-23-Oct-00,30.98
-24-Oct-00,31.81
-25-Oct-00,31.17
-26-Oct-00,31.48
-27-Oct-00,31.31
-30-Oct-00,31.09
-31-Oct-00,30.15
-01-Nov-00,31.62
-02-Nov-00,30.81
-03-Nov-00,30.81
-06-Nov-00,31
-07-Nov-00,31.59
-08-Nov-00,31.3
-09-Nov-00,31.79
-10-Nov-00,32.26
-13-Nov-00,32.5
-14-Nov-00,33.05
-15-Nov-00,33.82
-16-Nov-00,34.16
-17-Nov-00,33.09
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-22-Nov-00,33.11
-23-Nov-00,33.29
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-29-Nov-00,32.45
-30-Nov-00,32.53
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-05-Dec-00,28.88
-06-Dec-00,27.47
-07-Dec-00,27.85
-08-Dec-00,26.85
-11-Dec-00,27.28
-12-Dec-00,26.81
-13-Dec-00,26.84
-14-Dec-00,24.32
-15-Dec-00,24.44
-18-Dec-00,25.11
-19-Dec-00,24.36
-20-Dec-00,23.29
-21-Dec-00,22.4
-22-Dec-00,22.23
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-02-Jan-01,23.43
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-05-Jan-01,24.77
-08-Jan-01,24.75
-09-Jan-01,24.13
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-17-Jan-01,24.27
-18-Jan-01,24.74
-19-Jan-01,26.29
-22-Jan-01,27.69
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-05-Feb-01,29.25
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-12-Feb-01,29.32
-13-Feb-01,28
-14-Feb-01,27.89
-15-Feb-01,26.33
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-16-May-01,28.19
-17-May-01,28.57
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-11-Jun-01,29.57
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-13-Jun-01,29.13
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-22-Jun-01,26.86
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-12-Jul-01,23.62
-13-Jul-01,24.02
-16-Jul-01,23.58
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-23-Jul-01,24.44
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-25-Jul-01,24.9
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-06-Aug-01,25.39
-07-Aug-01,25.94
-08-Aug-01,25.61
-09-Aug-01,25.28
-10-Aug-01,25.78
-13-Aug-01,25.88
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-22-Aug-01,25.71
-23-Aug-01,25.66
-24-Aug-01,26.33
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-03-Sep-01,26.52
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-05-Sep-01,26.27
-06-Sep-01,26.61
-07-Sep-01,27.54
-10-Sep-01,27.59
-11-Sep-01,29.12
-12-Sep-01,28.24
-13-Sep-01,28.2
-14-Sep-01,29.22
-17-Sep-01,28.32
-18-Sep-01,27.54
-19-Sep-01,25.44
-20-Sep-01,25.57
-21-Sep-01,25.17
-24-Sep-01,20.63
-25-Sep-01,20.13
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-01-Oct-01,21.22
-02-Oct-01,21.29
-03-Oct-01,20.63
-04-Oct-01,20.94
-05-Oct-01,21.46
-08-Oct-01,20.91
-09-Oct-01,20.65
-10-Oct-01,21.02
-11-Oct-01,21.38
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-18-Oct-01,19.21
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-22-Oct-01,20.38
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-01-Nov-01,19.39
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-05-Nov-01,18.78
-06-Nov-01,18.64
-07-Nov-01,18.86
-08-Nov-01,19.54
-09-Nov-01,20.72
-12-Nov-01,19.48
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-22-Nov-01,19.65
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-06-Apr-11,123.01
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-22-Jun-11,113.59
-23-Jun-11,108.27
-24-Jun-11,104.79
-27-Jun-11,104.57
-28-Jun-11,107.57
-29-Jun-11,111.49
-30-Jun-11,111.71
-01-Jul-11,109.82
-05-Jul-11,113.21
-06-Jul-11,113.55
-07-Jul-11,117.4
-08-Jul-11,117.4
-11-Jul-11,117.35
-12-Jul-11,117.36
-13-Jul-11,118.46
-14-Jul-11,117.38
-15-Jul-11,118.06
-18-Jul-11,117.05
-19-Jul-11,118.18
-20-Jul-11,118.52
-21-Jul-11,118.25
-22-Jul-11,118.99
-25-Jul-11,118.27
-26-Jul-11,118.14
-27-Jul-11,117.99
-28-Jul-11,118.16
-29-Jul-11,115.93
-01-Aug-11,116.37
-02-Aug-11,116.02
-03-Aug-11,113.74
-04-Aug-11,110.22
-05-Aug-11,106.92
-08-Aug-11,103.06
-09-Aug-11,103.63
-10-Aug-11,103.84
-11-Aug-11,107.82
-12-Aug-11,108.17
-15-Aug-11,108.89
-16-Aug-11,109.69
-17-Aug-11,111.37
-18-Aug-11,108.36
-19-Aug-11,109.37
-22-Aug-11,108.83
-23-Aug-11,110.35
-24-Aug-11,111.91
-25-Aug-11,111.91
-26-Aug-11,112.29
-30-Aug-11,115.59
-31-Aug-11,116.48
-01-Sep-11,116.43
-02-Sep-11,115.92
-06-Sep-11,113.29
-07-Sep-11,117.5
-08-Sep-11,117.99
-09-Sep-11,115.1
-12-Sep-11,114.75
-13-Sep-11,114.08
-14-Sep-11,113.1
-15-Sep-11,116.71
-16-Sep-11,116.26
-19-Sep-11,112.89
-20-Sep-11,114.39
-21-Sep-11,114.26
-22-Sep-11,109.21
-23-Sep-11,109.17
-26-Sep-11,107.9
-27-Sep-11,109.54
-28-Sep-11,108.52
-29-Sep-11,107.08
-30-Sep-11,105.42
-03-Oct-11,103.61
-04-Oct-11,101.84
-05-Oct-11,103.77
-06-Oct-11,104.38
-07-Oct-11,106.56
-10-Oct-11,109.49
-11-Oct-11,109.22
-12-Oct-11,112.44
-13-Oct-11,112.45
-14-Oct-11,114.33
-17-Oct-11,112.92
-18-Oct-11,112.08
-19-Oct-11,111.76
-20-Oct-11,109
-21-Oct-11,111.6
-24-Oct-11,111.67
-25-Oct-11,112.11
-26-Oct-11,110.43
-27-Oct-11,112.45
-28-Oct-11,110.01
-31-Oct-11,108.43
-01-Nov-11,106.97
-02-Nov-11,110.82
-03-Nov-11,110.76
-04-Nov-11,112.22
-07-Nov-11,114.75
-08-Nov-11,115.61
-09-Nov-11,115.29
-10-Nov-11,113.32
-11-Nov-11,114.43
-14-Nov-11,112.57
-15-Nov-11,111.9
-16-Nov-11,111.91
-17-Nov-11,109.25
-18-Nov-11,107.82
-21-Nov-11,105.98
-22-Nov-11,107.77
-23-Nov-11,106.83
-25-Nov-11,106.08
-28-Nov-11,109.38
-29-Nov-11,111.25
-30-Nov-11,111.22
-01-Dec-11,108.83
-02-Dec-11,109.59
-05-Dec-11,110.18
-06-Dec-11,110.16
-07-Dec-11,110.07
-08-Dec-11,108.23
-09-Dec-11,107.91
-12-Dec-11,107.82
-13-Dec-11,109.25
-14-Dec-11,105.72
-15-Dec-11,104.52
-16-Dec-11,104
-19-Dec-11,104.55
-20-Dec-11,107.8
-21-Dec-11,108
-22-Dec-11,108.98
-23-Dec-11,109.28
-28-Dec-11,107.54
-29-Dec-11,106.89
-30-Dec-11,108.09
-03-Jan-12,111.12
-04-Jan-12,113.37
-05-Jan-12,113.59
-06-Jan-12,111.96
-09-Jan-12,111.07
-10-Jan-12,113.3
-11-Jan-12,111.66
-12-Jan-12,112.97
-13-Jan-12,109.88
-17-Jan-12,110.55
-18-Jan-12,109.81
-19-Jan-12,109.54
-20-Jan-12,108.5
-23-Jan-12,109.46
-24-Jan-12,108.38
-25-Jan-12,108.48
-26-Jan-12,109.08
-27-Jan-12,110.5
-30-Jan-12,110.24
-31-Jan-12,110.26
-01-Feb-12,111.96
-02-Feb-12,110.96
-03-Feb-12,112.56
-06-Feb-12,115.47
-07-Feb-12,116.86
-08-Feb-12,117.18
-09-Feb-12,118.4
-10-Feb-12,118.13
-13-Feb-12,118.73
-14-Feb-12,118.3
-15-Feb-12,120.25
-16-Feb-12,121
-17-Feb-12,120.69
-21-Feb-12,120.85
-22-Feb-12,123.07
-23-Feb-12,124.53
-24-Feb-12,124.89
-27-Feb-12,126.46
-28-Feb-12,124.02
-29-Feb-12,122.23
-01-Mar-12,125.76
-02-Mar-12,125.93
-05-Mar-12,126.68
-06-Mar-12,125.03
-07-Mar-12,125.37
-08-Mar-12,127.96
-09-Mar-12,128.08
-12-Mar-12,127.27
-13-Mar-12,128.14
-14-Mar-12,126.98
-15-Mar-12,123.63
-16-Mar-12,125.09
-19-Mar-12,125.76
-20-Mar-12,124.38
-21-Mar-12,123.89
-22-Mar-12,122.49
-23-Mar-12,125.21
-26-Mar-12,125.85
-27-Mar-12,125.25
-28-Mar-12,124.41
-29-Mar-12,123.23
-30-Mar-12,123.41
-02-Apr-12,124.44
-04-Apr-12,123.04
-05-Apr-12,123.58
-10-Apr-12,121.89
-11-Apr-12,120.41
-12-Apr-12,120.57
-13-Apr-12,120.62
-16-Apr-12,118.23
-17-Apr-12,117.41
-18-Apr-12,115.18
-19-Apr-12,117
-20-Apr-12,118.08
-23-Apr-12,116.66
-24-Apr-12,117.74
-25-Apr-12,117.45
-26-Apr-12,119.33
-27-Apr-12,119.3
-30-Apr-12,118.66
-01-May-12,119.57
-02-May-12,117.57
-03-May-12,115.91
-04-May-12,111.66
-08-May-12,110.48
-09-May-12,111.89
-10-May-12,112.24
-11-May-12,112.5
-14-May-12,110.79
-15-May-12,111.4
-16-May-12,109.8
-17-May-12,109.31
-18-May-12,108.03
-21-May-12,109.02
-22-May-12,109.76
-23-May-12,106.88
-24-May-12,107.2
-25-May-12,107.86
-29-May-12,107.55
-30-May-12,103.85
-31-May-12,103.86
-01-Jun-12,98.63
-04-Jun-12,97.74
-05-Jun-12,98.65
-06-Jun-12,101.14
-07-Jun-12,100.05
-08-Jun-12,97.57
-11-Jun-12,98.6
-12-Jun-12,96.59
-13-Jun-12,97.29
-14-Jun-12,96.46
-15-Jun-12,97.13
-18-Jun-12,95.21
-19-Jun-12,95.14
-20-Jun-12,93.5
-21-Jun-12,89.22
-22-Jun-12,89.22
-25-Jun-12,88.69
-26-Jun-12,90.19
-27-Jun-12,92.06
-28-Jun-12,91.02
-29-Jun-12,94.17
-02-Jul-12,95.28
-03-Jul-12,99.89
-05-Jul-12,101.54
-06-Jul-12,98.5
-09-Jul-12,99.94
-10-Jul-12,99.15
-11-Jul-12,99.23
-12-Jul-12,99.18
-13-Jul-12,101.91
-16-Jul-12,102.1
-17-Jul-12,104.21
-18-Jul-12,105.92
-19-Jul-12,107.79
-20-Jul-12,106.98
-23-Jul-12,103.91
-24-Jul-12,103.57
-25-Jul-12,102.35
-26-Jul-12,104.77
-27-Jul-12,106.3
-30-Jul-12,106.54
-31-Jul-12,105.93
-01-Aug-12,106.78
-02-Aug-12,107.55
-03-Aug-12,109.57
-06-Aug-12,110.01
-07-Aug-12,112.39
-08-Aug-12,113.42
-09-Aug-12,113.52
-10-Aug-12,113.13
-13-Aug-12,114.48
-14-Aug-12,113.9
-15-Aug-12,115.51
-16-Aug-12,116.12
-17-Aug-12,115.2
-20-Aug-12,115.5
-21-Aug-12,116.03
-22-Aug-12,115.77
-23-Aug-12,117.45
-24-Aug-12,115.76
-27-Aug-12,113.74
-28-Aug-12,112.62
-29-Aug-12,112.53
-30-Aug-12,112.28
-31-Aug-12,113.93
-04-Sep-12,114.98
-05-Sep-12,113.32
-06-Sep-12,114.5
-07-Sep-12,113.64
-10-Sep-12,113.84
-11-Sep-12,114.86
-12-Sep-12,114.86
-13-Sep-12,116
-14-Sep-12,117.48
-17-Sep-12,116.7
-18-Sep-12,113.29
-19-Sep-12,108.49
-20-Sep-12,109.41
-21-Sep-12,111.27
-24-Sep-12,109.2
-25-Sep-12,110.77
-26-Sep-12,108.99
-27-Sep-12,111.45
-28-Sep-12,111.36
-01-Oct-12,112.58
-02-Oct-12,112.49
-03-Oct-12,109.32
-04-Oct-12,110.48
-05-Oct-12,112.17
-08-Oct-12,112.6
-09-Oct-12,114.32
-10-Oct-12,116.11
-11-Oct-12,116.18
-12-Oct-12,115.17
-15-Oct-12,115
-16-Oct-12,115
-17-Oct-12,113.49
-18-Oct-12,112.58
-19-Oct-12,111.89
-22-Oct-12,109.62
-23-Oct-12,107.53
-24-Oct-12,107.66
-25-Oct-12,107.64
-26-Oct-12,108.9
-29-Oct-12,109.33
-30-Oct-12,109.4
-31-Oct-12,109.89
-01-Nov-12,108.84
-02-Nov-12,106.79
-05-Nov-12,105.59
-06-Nov-12,109.27
-07-Nov-12,108.21
-08-Nov-12,107.23
-09-Nov-12,108.61
-12-Nov-12,110.23
-13-Nov-12,108.82
-14-Nov-12,109.66
-15-Nov-12,110.07
-16-Nov-12,106.81
-19-Nov-12,110.06
-20-Nov-12,110.01
-21-Nov-12,110.81
-23-Nov-12,110.14
-26-Nov-12,110.18
-27-Nov-12,109.33
-28-Nov-12,108.26
-29-Nov-12,110.47
-30-Nov-12,110.84
-03-Dec-12,111.27
-04-Dec-12,109.99
-05-Dec-12,108.96
-06-Dec-12,108.01
-07-Dec-12,107.16
-10-Dec-12,108.25
-11-Dec-12,107.6
-12-Dec-12,110.17
-13-Dec-12,110.18
-14-Dec-12,109.28
-17-Dec-12,109.35
-18-Dec-12,109.96
-19-Dec-12,109.95
-20-Dec-12,110.57
-21-Dec-12,109.13
-24-Dec-12,108.44
-26-Dec-12,110.72
-27-Dec-12,110.04
-28-Dec-12,110.05
-31-Dec-12,110.8
-02-Jan-13,112.98
-03-Jan-13,113.03
-04-Jan-13,112.58
-07-Jan-13,112.49
-08-Jan-13,113.03
-09-Jan-13,113.07
-10-Jan-13,112.97
-11-Jan-13,110.3
-14-Jan-13,111.32
-15-Jan-13,111.72
-16-Jan-13,110.97
-17-Jan-13,111.01
-18-Jan-13,111.71
-22-Jan-13,112.72
-23-Jan-13,113.68
-24-Jan-13,114.59
-25-Jan-13,113.88
-28-Jan-13,113.92
-29-Jan-13,115.22
-30-Jan-13,115.42
-31-Jan-13,115.55
-01-Feb-13,115.55
-04-Feb-13,116.06
-05-Feb-13,117.03
-06-Feb-13,116.61
-07-Feb-13,117.17
-08-Feb-13,118.9
-11-Feb-13,118.29
-12-Feb-13,117.89
-13-Feb-13,118.43
-14-Feb-13,118.48
-15-Feb-13,117.4
-19-Feb-13,117.04
-20-Feb-13,116.23
-21-Feb-13,114.19
-22-Feb-13,113.74
-25-Feb-13,114.55
-26-Feb-13,112.96
-27-Feb-13,112.24
-28-Feb-13,112.2
-01-Mar-13,110.14
-04-Mar-13,109.9
-05-Mar-13,110.42
-06-Mar-13,110.27
-07-Mar-13,110.42
-08-Mar-13,108.91
-11-Mar-13,108.64
-12-Mar-13,108.48
-13-Mar-13,107.51
-14-Mar-13,108.1
-15-Mar-13,109.32
-18-Mar-13,108.54
-19-Mar-13,106.91
-20-Mar-13,108.27
-21-Mar-13,106.41
-22-Mar-13,106.51
-25-Mar-13,106.66
-26-Mar-13,107.1
-27-Mar-13,108.51
-28-Mar-13,108.46
-01-Apr-13,108.76
-02-Apr-13,109.66
-03-Apr-13,107.82
-04-Apr-13,105.09
-05-Apr-13,103.98
-08-Apr-13,103.16
-09-Apr-13,104.08
-10-Apr-13,104.8
-11-Apr-13,103.62
-12-Apr-13,100.58
-15-Apr-13,99.32
-16-Apr-13,97.88
-17-Apr-13,96.84
-18-Apr-13,97.48
-19-Apr-13,98.94
-22-Apr-13,99.07
-23-Apr-13,99.25
-24-Apr-13,100.71
-25-Apr-13,101.62
-26-Apr-13,102.39
-29-Apr-13,102.88
-30-Apr-13,101.53
-01-May-13,98.34
-02-May-13,100.32
-03-May-13,104.6
-06-May-13,105
-07-May-13,105.18
-08-May-13,103.79
-09-May-13,103.59
-10-May-13,101.31
-13-May-13,102.17
-14-May-13,102.7
-15-May-13,101.57
-16-May-13,104.27
-17-May-13,103.83
-20-May-13,104.55
-21-May-13,103.1
-22-May-13,102.14
-23-May-13,100.46
-24-May-13,101.24
-28-May-13,103.77
-29-May-13,102.14
-30-May-13,101.79
-31-May-13,100.43
-03-Jun-13,101.63
-04-Jun-13,102.04
-05-Jun-13,103.51
-06-Jun-13,103.37
-07-Jun-13,104.07
-10-Jun-13,103.87
-11-Jun-13,101.5
-12-Jun-13,103.11
-13-Jun-13,103.38
-14-Jun-13,105.1
-17-Jun-13,105.8
-18-Jun-13,105.21
-19-Jun-13,105.56
-20-Jun-13,102.72
-21-Jun-13,100.36
-24-Jun-13,99.8
-25-Jun-13,101.51
-26-Jun-13,100.62
-27-Jun-13,102.74
-28-Jun-13,102.49
-01-Jul-13,103.19
-02-Jul-13,103.96
-03-Jul-13,106.12
-05-Jul-13,107.46
-08-Jul-13,107.75
-09-Jul-13,107.9
-10-Jul-13,108.43
-11-Jul-13,108.18
-12-Jul-13,109.03
-15-Jul-13,109.05
-16-Jul-13,109.29
-17-Jul-13,109.67
-18-Jul-13,109.71
-19-Jul-13,109.34
-22-Jul-13,108.82
-23-Jul-13,109.27
-24-Jul-13,108.23
-25-Jul-13,108.1
-26-Jul-13,107.57
-29-Jul-13,108.1
-30-Jul-13,107.47
-31-Jul-13,107.89
-01-Aug-13,109.94
-02-Aug-13,109.63
-05-Aug-13,109.81
-06-Aug-13,108.77
-07-Aug-13,108.39
-08-Aug-13,107.32
-09-Aug-13,108.49
-12-Aug-13,109.28
-13-Aug-13,110.69
-14-Aug-13,110.26
-15-Aug-13,111.58
-16-Aug-13,111.82
-19-Aug-13,111.41
-20-Aug-13,110.74
-21-Aug-13,110.82
-22-Aug-13,110.51
-23-Aug-13,112.12
-26-Aug-13,112.23
-27-Aug-13,115.21
-28-Aug-13,116.27
-29-Aug-13,116.91
-30-Aug-13,115.97
-03-Sep-13,115.49
-04-Sep-13,115.65
-05-Sep-13,115.81
-06-Sep-13,117.15
-09-Sep-13,115.2
-10-Sep-13,112.1
-11-Sep-13,112.37
-12-Sep-13,113.11
-13-Sep-13,113.31
-16-Sep-13,110.86
-17-Sep-13,109.05
-18-Sep-13,109.09
-19-Sep-13,110.66
-20-Sep-13,110.22
-23-Sep-13,108.56
-24-Sep-13,107.68
-25-Sep-13,109.46
-26-Sep-13,108.86
-27-Sep-13,109.45
-30-Sep-13,107.85
-01-Oct-13,107.32
-02-Oct-13,109.09
-03-Oct-13,109.49
-04-Oct-13,109.42
-07-Oct-13,109.66
-08-Oct-13,110.56
-09-Oct-13,109.02
-10-Oct-13,111.63
-11-Oct-13,110.65
-14-Oct-13,110.13
-15-Oct-13,110.67
-16-Oct-13,110.79
-17-Oct-13,109.55
-18-Oct-13,109.4
-21-Oct-13,109.47
-22-Oct-13,109.57
-23-Oct-13,107.74
-24-Oct-13,106.63
-25-Oct-13,105.7
-28-Oct-13,108.29
-29-Oct-13,108.04
-30-Oct-13,108.41
-31-Oct-13,107.53
-01-Nov-13,105.78
-04-Nov-13,104.85
-05-Nov-13,105.01
-06-Nov-13,105.46
-07-Nov-13,103.08
-08-Nov-13,104.29
-11-Nov-13,105.76
-12-Nov-13,106.29
-13-Nov-13,106.9
-14-Nov-13,108.29
-15-Nov-13,108.25
-18-Nov-13,108.8
-19-Nov-13,108.29
-20-Nov-13,108.27
-21-Nov-13,109.9
-22-Nov-13,111.36
-25-Nov-13,110.83
-26-Nov-13,112.04
-27-Nov-13,111.32
-29-Nov-13,111.07
-02-Dec-13,111.49
-03-Dec-13,113.06
-04-Dec-13,113.27
-05-Dec-13,112.07
-06-Dec-13,111.5
-09-Dec-13,110.07
-10-Dec-13,108.91
-11-Dec-13,109.47
-12-Dec-13,108.99
-13-Dec-13,108.08
-16-Dec-13,110.3
-17-Dec-13,108.91
-18-Dec-13,109.56
-19-Dec-13,110.78
-20-Dec-13,112.15
-23-Dec-13,111.58
-24-Dec-13,111.57
-26-Dec-13,111.65
-27-Dec-13,112.06
-30-Dec-13,110.47
-31-Dec-13,109.95
-02-Jan-14,107.94
-03-Jan-14,106.57
-06-Jan-14,106.71
-07-Jan-14,107.01
-08-Jan-14,107.42
-09-Jan-14,107.49
-10-Jan-14,106.44
-13-Jan-14,108.02
-14-Jan-14,107.12
-15-Jan-14,108.09
-16-Jan-14,107.46
-17-Jan-14,108.45
-20-Jan-14,108.01
-21-Jan-14,109.17
-22-Jan-14,109.69
-23-Jan-14,109.69
-24-Jan-14,109.14
-27-Jan-14,108.72
-28-Jan-14,109.1
-29-Jan-14,108.83
-30-Jan-14,109.36
-31-Jan-14,108.16
-03-Feb-14,106.55
-04-Feb-14,107.04
-05-Feb-14,106.81
-06-Feb-14,108.15
-07-Feb-14,110.12
-10-Feb-14,110.18
-11-Feb-14,109.21
-12-Feb-14,108.62
-13-Feb-14,108.98
-14-Feb-14,108.63
-18-Feb-14,110.14
-19-Feb-14,110.37
-20-Feb-14,109.42
-21-Feb-14,109.03
-24-Feb-14,109.76
-25-Feb-14,109.19
-26-Feb-14,109.39
-27-Feb-14,108.54
-28-Feb-14,108.98
-03-Mar-14,111.26
-04-Mar-14,109.17
-05-Mar-14,108.15
-06-Mar-14,107.99
-07-Mar-14,109.14
-10-Mar-14,108.27
-11-Mar-14,108.35
-12-Mar-14,107.88
-13-Mar-14,107.48
-14-Mar-14,108.08
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-17-Nov-16,44.57
-18-Nov-16,44.41
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-23-Nov-16,46.54
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-25-Nov-16,46.32
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-29-Nov-16,44.68
-30-Nov-16,47.95
-01-Dec-16,52.28
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-05-Dec-16,53.3
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-07-Dec-16,51.9
-08-Dec-16,51.6
-09-Dec-16,52.19
-12-Dec-16,53.99
-13-Dec-16,53.28
-14-Dec-16,53.15
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-09-Jan-17,54.39
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-12-Jan-17,54.51
-13-Jan-17,54.37
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-18-Jan-17,53.77
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-06-Feb-17,55.02
-07-Feb-17,53.79
-08-Feb-17,54.42
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-12-Jun-17,47.18
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-01-Aug-17,50.77
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-14-Aug-17,51.29
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-06-Sep-17,54.48
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-08-Sep-17,54.55
-11-Sep-17,54.2
-12-Sep-17,55.06
-13-Sep-17,55.52
-14-Sep-17,56.76
-15-Sep-17,56.18
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-11-Oct-17,56.13
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-13-Oct-17,56.86
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-01-Nov-17,60.98
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-13-Aug-18,70.62
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-01-Oct-18,84.94
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-04-Apr-19,69.8
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-08-Apr-19,71.12
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-03-May-19,71.95
-06-May-19,71.95
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-22-May-19,71.94
-23-May-19,68.37
-24-May-19,67.98
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-04-Jun-19,63.56
-05-Jun-19,62.14
-06-Jun-19,62.77
-07-Jun-19,64.1
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-11-Jun-19,63.56
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-13-Jun-19,63.28
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-20-Jun-19,65.44
-21-Jun-19,65.99
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-27-Jun-19,66.78
-28-Jun-19,67.52
-01-Jul-19,65.1
-02-Jul-19,62.72
-03-Jul-19,63.53
-04-Jul-19,63.62
-05-Jul-19,64.23
-08-Jul-19,64.89
-09-Jul-19,64.3
-10-Jul-19,66.41
-11-Jul-19,67.64
-12-Jul-19,66.65
-15-Jul-19,66.86
-16-Jul-19,65.87
-17-Jul-19,63.67
-18-Jul-19,60.7
-19-Jul-19,61.04
-22-Jul-19,61.96
-23-Jul-19,62.28
-24-Jul-19,63.83
-25-Jul-19,63.47
-26-Jul-19,62.46
-29-Jul-19,62.29
-30-Jul-19,62.55
-31-Jul-19,64.07
-01-Aug-19,62.9
-02-Aug-19,61.12
-05-Aug-19,59.32
-06-Aug-19,58.63
-07-Aug-19,55.03
-08-Aug-19,56.29
-09-Aug-19,57.37
-12-Aug-19,57.13
-13-Aug-19,59.9
-14-Aug-19,57.86
-15-Aug-19,57.37
-16-Aug-19,59
-19-Aug-19,59.79
-20-Aug-19,59.03
-21-Aug-19,60.6
-22-Aug-19,59.81
-23-Aug-19,58.64
-26-Aug-19,58.64
-27-Aug-19,58.44
-28-Aug-19,60.42
-29-Aug-19,60.59
-30-Aug-19,61.04
-02-Sep-19,58.55
-03-Sep-19,57.93
-04-Sep-19,60.68
-05-Sep-19,62.7
-06-Sep-19,61.28
-09-Sep-19,63.99
-10-Sep-19,64.67
-11-Sep-19,63.02
-12-Sep-19,60.76
-13-Sep-19,61.25
-16-Sep-19,68.42
-17-Sep-19,65.59
-18-Sep-19,64.29
-19-Sep-19,64.25
-20-Sep-19,65.23
-23-Sep-19,64.66
-24-Sep-19,64.13
-25-Sep-19,62.41
-26-Sep-19,62.08
-27-Sep-19,62.48
-30-Sep-19,60.99
-01-Oct-19,60.06
-02-Oct-19,57.92
-03-Oct-19,58.01
-04-Oct-19,59.13
-07-Oct-19,59.46
-08-Oct-19,58.14
-09-Oct-19,59.7
-10-Oct-19,59.08
-11-Oct-19,60.59
-14-Oct-19,58.81
-15-Oct-19,59.19
-16-Oct-19,59.3
-17-Oct-19,59.35
-18-Oct-19,59.96
-21-Oct-19,58.95
-22-Oct-19,60.5
-23-Oct-19,60.52
-24-Oct-19,61.71
-25-Oct-19,62.06
-28-Oct-19,60.39
-29-Oct-19,61.05
-30-Oct-19,60.22
-31-Oct-19,59.3
-01-Nov-19,60.17
-04-Nov-19,62.52
-05-Nov-19,62.72
-06-Nov-19,62.11
-07-Nov-19,62.6
-08-Nov-19,62
-11-Nov-19,62.58
-12-Nov-19,62.19
-13-Nov-19,62.27
-14-Nov-19,62.46
-15-Nov-19,63.32
-18-Nov-19,62.82
-19-Nov-19,62.37
-20-Nov-19,63.8
-21-Nov-19,64.99
-22-Nov-19,64.83
-25-Nov-19,64.67
-26-Nov-19,64.82
-27-Nov-19,65.03
-28-Nov-19,64.68
-29-Nov-19,64.5
-02-Dec-19,63.2
-03-Dec-19,62.95
-04-Dec-19,65.25
-05-Dec-19,65.67
-06-Dec-19,66.5
-09-Dec-19,66.44
-10-Dec-19,66.57
-11-Dec-19,65.37
-12-Dec-19,66.67
-13-Dec-19,67.44
-16-Dec-19,68.04
-17-Dec-19,68.99
-18-Dec-19,69.12
-19-Dec-19,69.7
-20-Dec-19,68.66
-23-Dec-19,67.49
-24-Dec-19,69.26
-25-Dec-19,69.26
-26-Dec-19,69.26
-27-Dec-19,68.91
-30-Dec-19,68.3
-31-Dec-19,67.77
-01-Jan-20,67.77
-02-Jan-20,67.05
-03-Jan-20,69.08
-06-Jan-20,70.25
-07-Jan-20,68.74
-08-Jan-20,67.31
-09-Jan-20,66.58
-10-Jan-20,66.77
-13-Jan-20,64.14
-14-Jan-20,64.45
-15-Jan-20,63.29
-16-Jan-20,64.63
-17-Jan-20,64.05
-20-Jan-20,64.63
-21-Jan-20,63.66
-22-Jan-20,62.11
-23-Jan-20,61.26
-24-Jan-20,59.34
-27-Jan-20,58.54
-28-Jan-20,59.37
-29-Jan-20,59.46
-30-Jan-20,57.72
-31-Jan-20,57.77
-03-Feb-20,54
-04-Feb-20,53.9
-05-Feb-20,55.36
-06-Feb-20,55.18
-07-Feb-20,54.53
-10-Feb-20,53.39
-11-Feb-20,54
-12-Feb-20,55.54
-13-Feb-20,56.34
-14-Feb-20,57.37
-17-Feb-20,57.83
-18-Feb-20,57.35
-19-Feb-20,59.72
-20-Feb-20,59.72
-21-Feb-20,58.6
-24-Feb-20,56.04
-25-Feb-20,56.71
-26-Feb-20,54.96
-27-Feb-20,52.19
-28-Feb-20,51.31
-02-Mar-20,52.52
-03-Mar-20,52.24
-04-Mar-20,51.86
-05-Mar-20,51.29
-06-Mar-20,45.6
-09-Mar-20,35.33
-10-Mar-20,36.66
-11-Mar-20,34.45
-12-Mar-20,31.05
-13-Mar-20,33
-16-Mar-20,28.04
-17-Mar-20,28.04
-18-Mar-20,26.93
-19-Mar-20,23.3
-20-Mar-20,25.4
-23-Mar-20,25.06
-24-Mar-20,23.75
-25-Mar-20,25.62
-26-Mar-20,23.55
-27-Mar-20,22.39
-30-Mar-20,19.07
-31-Mar-20,19.19
-01-Apr-20,14.97
-02-Apr-20,20.24
-03-Apr-20,24.33
-06-Apr-20,22.58
-07-Apr-20,22.1
-08-Apr-20,25.22
-09-Apr-20,20.23
-14-Apr-20,21.74
-15-Apr-20,19.8
-16-Apr-20,18.69
-17-Apr-20,19.75
-20-Apr-20,17.36
-21-Apr-20,9.12
-"Apr 22, 2020",13.77
-"Apr 23, 2020",15.06
-"Apr 24, 2020",15.87
-"Apr 27, 2020",15.17
-"Apr 28, 2020",15.6
-"Apr 29, 2020",17.86
-"Apr 30, 2020",18.11
-"May 01, 2020",18.49
-"May 04, 2020",20.4
-"May 05, 2020",25.46
-"May 06, 2020",24.2
-"May 07, 2020",24.23
-"May 11, 2020",25.53
-"May 12, 2020",26.67
-"May 13, 2020",27.89
-"May 14, 2020",29.87
-"May 15, 2020",30.95
-"May 18, 2020",33.3
-"May 19, 2020",33.06
-"May 20, 2020",34.76
-"May 21, 2020",34.78
-"May 22, 2020",33.8
-"May 26, 2020",33.95
-"May 27, 2020",32.73
-"May 28, 2020",33.98
-"May 29, 2020",34.15
-"Jun 01, 2020",36.74
-"Jun 02, 2020",37.72
-"Jun 03, 2020",37.98
-"Jun 04, 2020",38.41
-"Jun 05, 2020",41
-"Jun 08, 2020",39.66
-"Jun 09, 2020",40.45
-"Jun 10, 2020",41.18
-"Jun 11, 2020",37.76
-"Jun 12, 2020",38.54
-"Jun 15, 2020",39.44
-"Jun 16, 2020",40.75
-"Jun 17, 2020",40.47
-"Jun 18, 2020",41.75
-"Jun 19, 2020",42.33
-"Jun 22, 2020",43.2
-"Jun 23, 2020",42.72
-"Jun 24, 2020",40.4
-"Jun 25, 2020",41.18
-"Jun 26, 2020",40.97
-"Jun 29, 2020",41.58
-"Jun 30, 2020",41.64
-"Jul 01, 2020",42.18
-"Jul 02, 2020",43.19
-"Jul 03, 2020",42.92
-"Jul 06, 2020",42.73
-"Jul 07, 2020",43.28
-"Jul 08, 2020",43.67
-"Jul 09, 2020",42.35
-"Jul 10, 2020",43.27
-"Jul 13, 2020",42.85
-"Jul 14, 2020",42.97
-"Jul 15, 2020",43.96
-"Jul 16, 2020",43.71
-"Jul 17, 2020",43.53
-"Jul 20, 2020",43.3
-"Jul 21, 2020",44.31
-"Jul 22, 2020",43.98
-"Jul 23, 2020",42.96
-"Jul 24, 2020",43.29
-"Jul 27, 2020",43.39
-"Jul 28, 2020",43.11
-"Jul 29, 2020",43.51
-"Jul 30, 2020",42.98
-"Jul 31, 2020",43.13
-"Aug 03, 2020",43.76
-"Aug 04, 2020",43.99
-"Aug 05, 2020",44.92
-"Aug 06, 2020",45.04
-"Aug 07, 2020",44.07
-"Aug 10, 2020",44.19
-"Aug 11, 2020",43.68
-"Aug 12, 2020",45.09
-"Aug 13, 2020",44.87
-"Aug 14, 2020",44.86
-"Aug 17, 2020",44.91
-"Aug 18, 2020",45.34
-"Aug 19, 2020",45.21
-"Aug 20, 2020",44.56
-"Aug 21, 2020",43.94
-"Aug 24, 2020",44.43
-"Aug 25, 2020",46.01
-"Aug 26, 2020",45.79
-"Aug 27, 2020",44.84
-"Aug 28, 2020",45.22
-"Sep 01, 2020",45.72
-"Sep 02, 2020",42.7
-"Sep 03, 2020",42.72
-"Sep 04, 2020",41.1
-"Sep 07, 2020",40.67
-"Sep 08, 2020",38.53
-"Sep 09, 2020",39.98
-"Sep 10, 2020",39.27
-"Sep 11, 2020",38.8
-"Sep 14, 2020",38.57
-"Sep 15, 2020",39.54
-"Sep 16, 2020",41.23
-"Sep 17, 2020",42.35
-"Sep 18, 2020",42.16
-"Sep 21, 2020",40.37
-"Sep 22, 2020",40.84
-"Sep 23, 2020",41.09
-"Sep 24, 2020",41.24
-"Sep 25, 2020",40.91
-"Sep 28, 2020",41.59
-"Sep 29, 2020",40.33
-"Sep 30, 2020",40.3
-"Oct 01, 2020",39.75
-"Oct 02, 2020",38
-"Oct 05, 2020",39.78
-"Oct 06, 2020",41.27
-"Oct 07, 2020",40.62
-"Oct 08, 2020",42
-"Oct 09, 2020",41.63
-"Oct 12, 2020",40.5
-"Oct 13, 2020",41.34
-"Oct 14, 2020",41.81
-"Oct 15, 2020",41.61
-"Oct 16, 2020",41.34
-"Oct 19, 2020",41.29
-"Oct 20, 2020",41.62
-"Oct 21, 2020",40.09
-"Oct 22, 2020",41.28
-"Oct 23, 2020",40.71
-"Oct 26, 2020",39.06
-"Oct 27, 2020",39.72
-"Oct 28, 2020",37.86
-"Oct 29, 2020",36.56
-"Oct 30, 2020",36.33
-"Nov 02, 2020",37.78
-"Nov 03, 2020",38.17
-"Nov 04, 2020",39.68
-"Nov 05, 2020",39.47
-"Nov 06, 2020",38.08
-"Nov 09, 2020",40.93
-"Nov 10, 2020",42.25
-"Nov 11, 2020",42.5
-"Nov 12, 2020",42.16
-"Nov 13, 2020",41.51
-"Nov 16, 2020",42.71
-"Nov 17, 2020",42.54
-"Nov 18, 2020",42.91
-"Nov 19, 2020",43.09
-"Nov 20, 2020",43.79
-"Nov 23, 2020",45
-"Nov 24, 2020",46.63
-"Nov 25, 2020",47.3
-"Nov 26, 2020",46.32
-"Nov 27, 2020",46.88
-"Nov 30, 2020",46.84
-"Dec 01, 2020",47.03
-"Dec 02, 2020",47.8
-"Dec 03, 2020",48.37
-"Dec 04, 2020",49.1
-"Dec 07, 2020",48.63
-"Dec 08, 2020",48.84
-"Dec 09, 2020",48.81
-"Dec 10, 2020",50.33
-"Dec 11, 2020",50.01
-"Dec 14, 2020",50.27
-"Dec 15, 2020",50.77
-"Dec 16, 2020",50.83
-"Dec 17, 2020",51.2
-"Dec 18, 2020",52.17
-"Dec 21, 2020",50.61
-"Dec 22, 2020",49.88
-"Dec 23, 2020",51.05
-"Dec 24, 2020",50.88
-"Dec 28, 2020",50.88
-"Dec 29, 2020",50.44
-"Dec 30, 2020",50.74
-"Dec 31, 2020",51.22
-"Jan 04, 2021",50.37
-"Jan 05, 2021",53.16
-"Jan 06, 2021",53.8
-"Jan 07, 2021",53.7
-"Jan 08, 2021",55.51
-"Jan 11, 2021",54.84
-"Jan 12, 2021",55.98
-"Jan 13, 2021",55.52
-"Jan 14, 2021",55.76
-"Jan 15, 2021",54.8
-"Jan 18, 2021",54.21
-"Jan 19, 2021",55.38
-"Jan 20, 2021",55.66
-"Jan 21, 2021",55.68
-"Jan 22, 2021",55.22
-"Jan 25, 2021",55.44
-"Jan 26, 2021",55.26
-"Jan 27, 2021",55.07
-"Jan 28, 2021",54.87
-"Jan 29, 2021",55.25
-"Feb 01, 2021",56.42
-"Feb 02, 2021",57.62
-"Feb 03, 2021",58.61
-"Feb 04, 2021",58.98
-"Feb 05, 2021",59.48
-"Feb 08, 2021",60.17
-"Feb 09, 2021",60.74
-"Feb 10, 2021",61.17
-"Feb 11, 2021",61.09
-"Feb 12, 2021",62.47
-"Feb 15, 2021",63.58
-"Feb 16, 2021",63.96
-"Feb 17, 2021",65.02
-"Feb 18, 2021",64.09
-"Feb 19, 2021",62.84
-"Feb 22, 2021",64.73
-"Feb 23, 2021",65.16
-"Feb 24, 2021",66.85
-"Feb 25, 2021",66.69
-"Feb 26, 2021",65.86
-"Mar 01, 2021",64.56
-"Mar 02, 2021",63.17
-"Mar 03, 2021",64.7
-"Mar 04, 2021",67.32
-"Mar 05, 2021",69.95
-"Mar 08, 2021",68
-"Mar 09, 2021",67.03
-"Mar 10, 2021",67.53
-"Mar 11, 2021",69.34
-"Mar 12, 2021",68.87
-"Mar 15, 2021",68.78
-"Mar 16, 2021",67.95
-"Mar 17, 2021",67.73
-"Mar 18, 2021",62.11
-"Mar 19, 2021",64
-"Mar 22, 2021",63.89
-"Mar 23, 2021",59.96
-"Mar 24, 2021",63.7
-"Mar 25, 2021",61.21
-"Mar 26, 2021",63.77
-"Mar 29, 2021",64.06
-"Mar 30, 2021",63.28
-"Mar 31, 2021",63.52
-"Apr 01, 2021",63.85
-"Apr 06, 2021",61.47
-"Apr 07, 2021",61.86
-"Apr 08, 2021",62.09
-"Apr 09, 2021",61.89
-"Apr 12, 2021",62.38
-"Apr 13, 2021",62.83
-"Apr 14, 2021",66.11
-"Apr 15, 2021",66.13
-"Apr 16, 2021",65.98
-"Apr 19, 2021",66.54
-"Apr 20, 2021",65.34
-"Apr 21, 2021",64.02
-"Apr 22, 2021",65.07
-"Apr 23, 2021",65.75
-"Apr 26, 2021",65.5
-"Apr 27, 2021",66.25
-"Apr 28, 2021",67.08
-"Apr 29, 2021",68.26
-"Apr 30, 2021",67.73
-"May 04, 2021",68.91
-"May 05, 2021",69.71
-"May 06, 2021",68.62
-"May 07, 2021",68.73
-"May 10, 2021",68.61
-"May 11, 2021",68.83
-"May 12, 2021",69.62
-"May 13, 2021",67.36
-"May 14, 2021",69.24
-"May 17, 2021",69.62
-"May 18, 2021",69.01
-"May 19, 2021",66.88
-"May 20, 2021",65.18
-"May 21, 2021",66.72
-"May 24, 2021",68.59
-"May 25, 2021",68.8
-"May 26, 2021",68.93
-"May 27, 2021",69.43
-"May 28, 2021",69.36
-"Jun 01, 2021",70.03
-"Jun 02, 2021",70.6
-"Jun 03, 2021",70.71
-"Jun 04, 2021",71.3
-"Jun 07, 2021",70.89
-"Jun 08, 2021",71.39
-"Jun 09, 2021",71.31
-"Jun 10, 2021",71.65
-"Jun 11, 2021",72.05
-"Jun 14, 2021",72.27
-"Jun 15, 2021",73.38
-"Jun 16, 2021",73.88
-"Jun 17, 2021",72.92
-"Jun 18, 2021",73.1
-"Jun 21, 2021",74.49
-"Jun 22, 2021",74.92
-"Jun 23, 2021",75.22
-"Jun 24, 2021",75.95
-"Jun 25, 2021",76.45
-"Jun 28, 2021",74.78
-"Jun 29, 2021",75.38
-"Jun 30, 2021",76.94
-"Jul 01, 2021",76.69
-"Jul 02, 2021",77.51
-"Jul 05, 2021",78.34
-"Jul 06, 2021",75.81
-"Jul 07, 2021",74.31
-"Jul 08, 2021",75.07
-"Jul 09, 2021",77.14
-"Jul 12, 2021",76.77
-"Jul 13, 2021",77.5
-"Jul 14, 2021",75.74
-"Jul 15, 2021",74.53
-"Jul 16, 2021",74.46
-"Jul 19, 2021",69.33
-"Jul 20, 2021",70.03
-"Jul 21, 2021",72.54
-"Jul 22, 2021",74.25
-"Jul 23, 2021",74.86
-"Jul 26, 2021",74.79
-"Jul 27, 2021",74.87
-"Jul 28, 2021",75.09
-"Jul 29, 2021",76.3
-"Jul 30, 2021",77.72
-"Aug 02, 2021",73.91
-"Aug 03, 2021",73.24
-"Aug 04, 2021",70.99
-"Aug 05, 2021",72.14
-"Aug 06, 2021",71.02
-"Aug 09, 2021",69.65
-"Aug 10, 2021",71.14
-"Aug 11, 2021",71.99
-"Aug 12, 2021",71.79
-"Aug 13, 2021",70.9
-"Aug 16, 2021",70.07
-"Aug 17, 2021",69.4
-"Aug 18, 2021",68.61
-"Aug 19, 2021",66.8
-"Aug 20, 2021",65.51
-"Aug 23, 2021",69.07
-"Aug 24, 2021",71.21
-"Aug 25, 2021",72.12
-"Aug 26, 2021",70.42
-"Aug 27, 2021",72.26
-"Aug 31, 2021",73.45
-"Sep 01, 2021",71.99
-"Sep 02, 2021",73.56
-"Sep 03, 2021",73.07
-"Sep 06, 2021",72.43
-"Sep 07, 2021",71.52
-"Sep 08, 2021",72.36
-"Sep 09, 2021",71.32
-"Sep 10, 2021",72.44
-"Sep 13, 2021",72.97
-"Sep 14, 2021",73.05
-"Sep 15, 2021",74.84
-"Sep 16, 2021",75.14
-"Sep 17, 2021",74.64
-"Sep 20, 2021",73.59
-"Sep 21, 2021",73.65
-"Sep 22, 2021",75.5
-"Sep 23, 2021",76.44
-"Sep 24, 2021",77.42
-"Sep 27, 2021",78.85
-"Sep 28, 2021",78.3
-"Sep 29, 2021",77.86
-"Sep 30, 2021",77.81
-"Oct 01, 2021",79.4
-"Oct 04, 2021",81.44
-"Oct 05, 2021",82.72
-"Oct 06, 2021",81.39
-"Oct 07, 2021",82.34
-"Oct 08, 2021",82.17
-"Oct 11, 2021",83.75
-"Oct 12, 2021",83.53
-"Oct 13, 2021",83.53
-"Oct 14, 2021",83.86
-"Oct 15, 2021",84.67
-"Oct 18, 2021",84.13
-"Oct 19, 2021",85.02
-"Oct 20, 2021",85.76
-"Oct 21, 2021",84.58
-"Oct 22, 2021",85.43
-"Oct 25, 2021",84.85
-"Oct 26, 2021",85.11
-"Oct 27, 2021",84.12
-"Oct 28, 2021",83.4
-"Oct 29, 2021",83.1
-"Nov 01, 2021",84.51
-"Nov 02, 2021",84.42
-"Nov 03, 2021",81.1
-"Nov 04, 2021",80.15
-"Nov 05, 2021",82.43
-"Nov 08, 2021",83.22
-"Nov 09, 2021",84.52
-"Nov 10, 2021",82.91
-"Nov 11, 2021",83.4
-"Nov 12, 2021",82.9
-"Nov 15, 2021",81.94
-"Nov 16, 2021",82.85
-"Nov 17, 2021",80.67
-"Nov 18, 2021",82.45
-"Nov 19, 2021",80.24
-"Nov 22, 2021",80.97
-"Nov 23, 2021",83.43
-"Nov 24, 2021",82.37
-"Nov 25, 2021",82.05
-"Nov 26, 2021",72.37
-"Nov 29, 2021",73.34
-"Nov 30, 2021",70.86
-"Dec 01, 2021",69.53
-"Dec 02, 2021",70.56
-"Dec 03, 2021",70.71
-"Dec 06, 2021",73.38
-"Dec 07, 2021",75.54
-"Dec 08, 2021",75.94
-"Dec 09, 2021",74.1
-"Dec 10, 2021",74.98
-"Dec 13, 2021",74.12
-"Dec 14, 2021",73.37
-"Dec 15, 2021",73.71
-"Dec 16, 2021",74.64
-"Dec 17, 2021",72.97
-"Dec 20, 2021",70.51
-"Dec 21, 2021",72.85
-"Dec 22, 2021",74.69
-"Dec 23, 2021",76.26
-"Dec 24, 2021",75.24
-"Dec 29, 2021",78.63
-"Dec 30, 2021",78.61
-"Dec 31, 2021",77.24
-"Jan 03, 2022",78.25
-"Jan 04, 2022",79.39
-"Jan 05, 2022",80.6
-"Jan 06, 2022",81.99
-"Jan 07, 2022",82.28
-"Jan 10, 2022",81.56
-"Jan 11, 2022",84.98
-"Jan 12, 2022",85.83
-"Jan 13, 2022",85.8
-"Jan 14, 2022",87.17
-"Jan 17, 2022",87.82
-"Jan 18, 2022",88.83
-"Jan 19, 2022",89.64
-"Jan 20, 2022",89.75
-"Jan 21, 2022",89.75
-"Jan 24, 2022",87.74
-"Jan 25, 2022",89.49
-"Jan 26, 2022",91.22
-"Jan 27, 2022",90.7
-"Jan 28, 2022",91.47
-"Jan 31, 2022",92.35
-"Feb 01, 2022",90.24
-"Feb 02, 2022",91.43
-"Feb 03, 2022",92.99
-"Feb 04, 2022",96.86
-"Feb 07, 2022",97.28
-"Feb 08, 2022",96.07
-"Feb 09, 2022",94.95
-"Feb 10, 2022",96.37
-"Feb 11, 2022",97.5
-"Feb 14, 2022",101.66
-"Feb 15, 2022",98.43
-"Feb 16, 2022",97.44
-"Feb 17, 2022",95.28
-"Feb 18, 2022",96.18
-"Feb 21, 2022",98.95
-"Feb 22, 2022",98.73
-"Feb 23, 2022",99.29
-"Feb 24, 2022",101.29
-"Feb 25, 2022",98.56
-"Feb 28, 2022",103.08
-"Mar 01, 2022",110.93
-"Mar 02, 2022",118.94
-"Mar 03, 2022",115.36
-"Mar 04, 2022",123.86
-"Mar 07, 2022",129.02
-"Mar 08, 2022",133.18
-"Mar 09, 2022",116.58
-"Mar 10, 2022",114.54
-"Mar 11, 2022",118.11
-"Mar 14, 2022",110.39
-"Mar 15, 2022",105.14
-"Mar 16, 2022",104.61
-"Mar 17, 2022",113.5
-"Mar 18, 2022",114.32
-"Mar 21, 2022",122.29
-"Mar 22, 2022",121.53
-"Mar 23, 2022",127.52
-"Mar 24, 2022",123.98
-"Mar 25, 2022",122.67
-"Mar 28, 2022",114.5
-"Mar 29, 2022",112.79
-"Mar 30, 2022",115.59
-"Mar 31, 2022",107.29
-"Apr 01, 2022",106.13
-"Apr 04, 2022",108.15
-"Apr 05, 2022",106.6
-"Apr 06, 2022",100.81
-"Apr 07, 2022",99.83
-"Apr 08, 2022",101.26
-"Apr 11, 2022",97.92
-"Apr 12, 2022",104.42
-"Apr 13, 2022",108.49
-"Apr 14, 2022",110.83
-"Apr 19, 2022",105.49
-"Apr 20, 2022",105.05
-"Apr 21, 2022",107.2
-"Apr 22, 2022",105.15
-"Apr 25, 2022",99.27
-"Apr 26, 2022",102.89
-"Apr 27, 2022",103.3
-"Apr 28, 2022",105.78
-"Apr 29, 2022",108.36
-"May 03, 2022",104.94
-"May 04, 2022",110.53
-"May 05, 2022",112.11
-"May 06, 2022",113.86
-"May 09, 2022",106.67
-"May 10, 2022",102.61
-"May 11, 2022",107.7
-"May 12, 2022",108.06
-"May 13, 2022",112.12
-"May 16, 2022",114.86
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-"Jun 09, 2022",128.47
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-"Jun 15, 2022",124.96
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-"Oct 24, 2022",91.57
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-"Oct 26, 2022",92.93
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diff --git a/data/evaluation_result.csv b/data/evaluation_result.csv
index 6783483..6b2c3b3 100644
--- a/data/evaluation_result.csv
+++ b/data/evaluation_result.csv
@@ -1,5 +1,5 @@
 ,MAE,MSE,RMSE,R²
 CatBoost,3.2055909069711648,19.908919920446998,4.461941272635376,0.9819031030495275
-DecisionTree,1.3872153377393859,4.677235053179066,2.162691622302881,0.9957484664608255
-RandomForest,1.3876994943072294,4.680556911272043,2.1634594776126597,0.9957454469437531
+DecisionTree,1.3872153377393857,4.677235053179065,2.1626916223028805,0.9957484664608255
+RandomForest,1.3842257475957955,4.659565797823521,2.158602742012416,0.9957645275377015
 GradientBoosting,3.894591633871208,34.59382614172185,5.8816516508309,0.968554752879068
diff --git a/data/exchange_rate/inflation_unemployment_data.csv b/data/exchange_rate/inflation_unemployment_data.csv
new file mode 100644
index 0000000..ffd08d9
--- /dev/null
+++ b/data/exchange_rate/inflation_unemployment_data.csv
@@ -0,0 +1,37 @@
+,Date,Inflation (annual %),Unemployment rate (%)
+0,2022,7.96757361617945,
+1,2021,3.46692585313082,6.19066252537703
+2,2020,1.9209680056685,6.70898273878473
+3,2019,2.20607305781525,5.86456974134929
+4,2018,2.45036243850175,5.97918633166657
+5,2017,2.25427651934813,5.61009077826539
+6,2016,1.60553917414213,
+7,2015,1.44385719270808,
+8,2014,2.35449052820358,5.60839624684106
+9,2013,2.65167342916577,5.65083373686793
+10,2012,3.72532666110646,5.43877797121523
+11,2011,4.82239636013325,5.91977448583024
+12,2010,3.32634463368643,5.48105498588106
+13,2009,2.86044855902672,6.33775927367331
+14,2008,8.94995335353387,
+15,2007,4.81023704342912,
+16,2006,4.26717463390264,
+17,2005,4.10725070715931,5.78923459564211
+18,2004,3.51799903099596,
+19,2003,3.02504526252236,6.43343762715344
+20,2002,2.90799857032207,
+21,2001,3.83657262052543,
+22,2000,3.43351563418767,5.51218583143303
+23,1999,3.04194667094533,6.20688088480093
+24,1998,5.09729149331441,
+25,1997,5.55412988949405,
+26,1996,6.5260956937258,5.63555813987656
+27,1995,9.07738095238091,
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+29,1993,7.14458706876256,
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+31,1991,8.99693873494331,
+32,1990,8.06346090938833,
+33,1989,6.92390503711238,
+34,1988,7.11340652931347,
+35,1987,5.71011938486679,
diff --git a/data/exchange_rate/usd_eur_exchange_rate_fred.csv b/data/exchange_rate/usd_eur_exchange_rate_fred.csv
new file mode 100644
index 0000000..c6050b7
--- /dev/null
+++ b/data/exchange_rate/usd_eur_exchange_rate_fred.csv
@@ -0,0 +1,6261 @@
+,Date,DEXUSEU
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diff --git a/data/exchange_rate/usd_eur_exchange_rates_alpha_vantage.csv b/data/exchange_rate/usd_eur_exchange_rates_alpha_vantage.csv
new file mode 100644
index 0000000..547d63a
--- /dev/null
+++ b/data/exchange_rate/usd_eur_exchange_rates_alpha_vantage.csv
@@ -0,0 +1,2609 @@
+,Date,Open,High,Low,Close
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diff --git a/figures/ARIMA_forcat_vs_actual.png b/figures/ARIMA_forcat_vs_actual.png
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diff --git a/notebooks/kaim_week_10_SARIMA_LSTM.ipynb b/notebooks/kaim_week_10_SARIMA_LSTM.ipynb
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+++ b/notebooks/kaim_week_10_SARIMA_LSTM.ipynb
@@ -0,0 +1,1274 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2e63e703af5def35",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "# BRENT CRUDE OIL PRICE FORECASTING (SARIMA and LSTM models)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "initial_id",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-08-15T22:13:29.586718300Z",
+     "start_time": "2023-08-15T22:13:29.546822Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Required modules\n",
+    "\n",
+    "# For common operations\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# For SARIMA model\n",
+    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
+    "from statsmodels.tsa.seasonal import seasonal_decompose # For time series decomposition\n",
+    "from pmdarima import auto_arima\n",
+    "\n",
+    "\n",
+    "# For LSTM model\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, LSTM, Dropout\n",
+    "from keras.models import load_model  # Allows load a previously saved model.\n",
+    "\n",
+    "# To evaluate the models\n",
+    "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error\n",
+    "\n",
+    "# To enable interactive plots\n",
+    "\n",
+    "## For Jupyter web (requires ipympl module)\n",
+    "#%matplotlib widget\n",
+    "\n",
+    "## For IDEs, like PyCharm\n",
+    "import matplotlib\n",
+    "matplotlib.use('nbagg')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d84bdc3d1edf1cb",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### Data loading"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "6e02f0772a44afc5",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-08-15T22:13:31.407841500Z",
+     "start_time": "2023-08-15T22:13:31.197403700Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\hp\\AppData\\Local\\Temp\\ipykernel_8684\\3791287256.py:5: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  data.fillna(method='ffill', inplace=True)  # Replace NaN values with the last valid observation.\n",
+      "C:\\Users\\hp\\AppData\\Local\\Temp\\ipykernel_8684\\3791287256.py:6: FutureWarning: PeriodDtype[B] is deprecated and will be removed in a future version. Use a DatetimeIndex with freq='B' instead\n",
+      "  data.index = pd.DatetimeIndex(data.index).to_period('B').to_timestamp()  # Sets the frequency for the time series.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "date\n",
+       "1987-05-20    18.63\n",
+       "1987-05-21    18.45\n",
+       "1987-05-22    18.55\n",
+       "1987-05-25    18.60\n",
+       "1987-05-26    18.63\n",
+       "              ...  \n",
+       "2023-08-01    85.34\n",
+       "2023-08-02    84.01\n",
+       "2023-08-03    86.19\n",
+       "2023-08-04    87.38\n",
+       "2023-08-07    86.47\n",
+       "Freq: B, Name: brent_crude_oil, Length: 9449, dtype: float64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "full_data = pd.read_csv('../data/brent_daily_prices.csv', parse_dates=['DATE'], date_format='%d/%m/%Y', index_col='DATE', na_values='.')  # In the original time series, NA values are represented by a period (.)\n",
+    "data = full_data['DCOILBRENTEU']\n",
+    "data.rename_axis('date', inplace=True)\n",
+    "data.rename('brent_crude_oil', inplace=True)\n",
+    "data.fillna(method='ffill', inplace=True)  # Replace NaN values with the last valid observation.\n",
+    "data.index = pd.DatetimeIndex(data.index).to_period('B').to_timestamp()  # Sets the frequency for the time series.\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9451d24e47f98efa",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### Exploratory Data Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "aed6054ea507168d",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-08-15T22:13:33.568178700Z",
+     "start_time": "2023-08-15T22:13:33.419574400Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "count    9449.000000\n",
+      "mean       49.155899\n",
+      "std        32.956536\n",
+      "min         9.100000\n",
+      "25%        19.150000\n",
+      "50%        40.800000\n",
+      "75%        72.050000\n",
+      "max       143.950000\n",
+      "Name: brent_crude_oil, dtype: float64\n",
+      "\n",
+      "\n",
+      "<class 'pandas.core.series.Series'>\n",
+      "DatetimeIndex: 9449 entries, 1987-05-20 to 2023-08-07\n",
+      "Freq: B\n",
+      "Series name: brent_crude_oil\n",
+      "Non-Null Count  Dtype  \n",
+      "--------------  -----  \n",
+      "9449 non-null   float64\n",
+      "dtypes: float64(1)\n",
+      "memory usage: 147.6 KB\n",
+      "None\n",
+      "Missing values:  0\n"
+     ]
+    },
+    {
+     "data": {
+      "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n    if (typeof WebSocket !== 'undefined') {\n        return WebSocket;\n    } else if (typeof MozWebSocket !== 'undefined') {\n        return MozWebSocket;\n    } else {\n        alert(\n            'Your browser does not have WebSocket support. ' +\n                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n                'Firefox 4 and 5 are also supported but you ' +\n                'have to enable WebSockets in about:config.'\n        );\n    }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n    this.id = figure_id;\n\n    this.ws = websocket;\n\n    this.supports_binary = this.ws.binaryType !== undefined;\n\n    if (!this.supports_binary) {\n        var warnings = document.getElementById('mpl-warnings');\n        if (warnings) {\n            warnings.style.display = 'block';\n            warnings.textContent =\n                'This browser does not support binary websocket messages. ' +\n                'Performance may be slow.';\n        }\n    }\n\n    this.imageObj = new Image();\n\n    this.context = undefined;\n    this.message = undefined;\n    this.canvas = undefined;\n    this.rubberband_canvas = undefined;\n    this.rubberband_context = undefined;\n    this.format_dropdown = undefined;\n\n    this.image_mode = 'full';\n\n    this.root = document.createElement('div');\n    this.root.setAttribute('style', 'display: inline-block');\n    this._root_extra_style(this.root);\n\n    parent_element.appendChild(this.root);\n\n    this._init_header(this);\n    this._init_canvas(this);\n    this._init_toolbar(this);\n\n    var fig = this;\n\n    this.waiting = false;\n\n    this.ws.onopen = function () {\n        fig.send_message('supports_binary', { value: fig.supports_binary });\n        fig.send_message('send_image_mode', {});\n        if (fig.ratio !== 1) {\n            fig.send_message('set_device_pixel_ratio', {\n                device_pixel_ratio: fig.ratio,\n            });\n        }\n        fig.send_message('refresh', {});\n    };\n\n    this.imageObj.onload = function () {\n        if (fig.image_mode === 'full') {\n            // Full images could contain transparency (where diff images\n            // almost always do), so we need to clear the canvas so that\n            // there is no ghosting.\n            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n        }\n        fig.context.drawImage(fig.imageObj, 0, 0);\n    };\n\n    this.imageObj.onunload = function () {\n        fig.ws.close();\n    };\n\n    this.ws.onmessage = this._make_on_message_function(this);\n\n    this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n    var titlebar = document.createElement('div');\n    titlebar.classList =\n        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n    var titletext = document.createElement('div');\n    titletext.classList = 'ui-dialog-title';\n    titletext.setAttribute(\n        'style',\n        'width: 100%; text-align: center; padding: 3px;'\n    );\n    titlebar.appendChild(titletext);\n    this.root.appendChild(titlebar);\n    this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n    var fig = this;\n\n    var canvas_div = (this.canvas_div = document.createElement('div'));\n    canvas_div.setAttribute('tabindex', '0');\n    canvas_div.setAttribute(\n        'style',\n        'border: 1px solid #ddd;' +\n            'box-sizing: content-box;' +\n            'clear: both;' +\n            'min-height: 1px;' +\n            'min-width: 1px;' +\n            'outline: 0;' +\n            'overflow: hidden;' +\n            'position: relative;' +\n            'resize: both;' +\n            'z-index: 2;'\n    );\n\n    function on_keyboard_event_closure(name) {\n        return function (event) {\n            return fig.key_event(event, name);\n        };\n    }\n\n    canvas_div.addEventListener(\n        'keydown',\n        on_keyboard_event_closure('key_press')\n    );\n    canvas_div.addEventListener(\n        'keyup',\n        on_keyboard_event_closure('key_release')\n    );\n\n    this._canvas_extra_style(canvas_div);\n    this.root.appendChild(canvas_div);\n\n    var canvas = (this.canvas = document.createElement('canvas'));\n    canvas.classList.add('mpl-canvas');\n    canvas.setAttribute(\n        'style',\n        'box-sizing: content-box;' +\n            'pointer-events: none;' +\n            'position: relative;' +\n            'z-index: 0;'\n    );\n\n    this.context = canvas.getContext('2d');\n\n    var backingStore =\n        this.context.backingStorePixelRatio ||\n        this.context.webkitBackingStorePixelRatio ||\n        this.context.mozBackingStorePixelRatio ||\n        this.context.msBackingStorePixelRatio ||\n        this.context.oBackingStorePixelRatio ||\n        this.context.backingStorePixelRatio ||\n        1;\n\n    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n        'canvas'\n    ));\n    rubberband_canvas.setAttribute(\n        'style',\n        'box-sizing: content-box;' +\n            'left: 0;' +\n            'pointer-events: none;' +\n            'position: absolute;' +\n            'top: 0;' +\n            'z-index: 1;'\n    );\n\n    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n    if (this.ResizeObserver === undefined) {\n        if (window.ResizeObserver !== undefined) {\n            this.ResizeObserver = window.ResizeObserver;\n        } else {\n            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n            this.ResizeObserver = obs.ResizeObserver;\n        }\n    }\n\n    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n        // There's no need to resize if the WebSocket is not connected:\n        // - If it is still connecting, then we will get an initial resize from\n        //   Python once it connects.\n        // - If it has disconnected, then resizing will clear the canvas and\n        //   never get anything back to refill it, so better to not resize and\n        //   keep something visible.\n        if (fig.ws.readyState != 1) {\n            return;\n        }\n        var nentries = entries.length;\n        for (var i = 0; i < nentries; i++) {\n            var entry = entries[i];\n            var width, height;\n            if (entry.contentBoxSize) {\n                if (entry.contentBoxSize instanceof Array) {\n                    // Chrome 84 implements new version of spec.\n                    width = entry.contentBoxSize[0].inlineSize;\n                    height = entry.contentBoxSize[0].blockSize;\n                } else {\n                    // Firefox implements old version of spec.\n                    width = entry.contentBoxSize.inlineSize;\n                    height = entry.contentBoxSize.blockSize;\n                }\n            } else {\n                // Chrome <84 implements even older version of spec.\n                width = entry.contentRect.width;\n                height = entry.contentRect.height;\n            }\n\n            // Keep the size of the canvas and rubber band canvas in sync with\n            // the canvas container.\n            if (entry.devicePixelContentBoxSize) {\n                // Chrome 84 implements new version of spec.\n                canvas.setAttribute(\n                    'width',\n                    entry.devicePixelContentBoxSize[0].inlineSize\n                );\n                canvas.setAttribute(\n                    'height',\n                    entry.devicePixelContentBoxSize[0].blockSize\n                );\n            } else {\n                canvas.setAttribute('width', width * fig.ratio);\n                canvas.setAttribute('height', height * fig.ratio);\n            }\n            /* This rescales the canvas back to display pixels, so that it\n             * appears correct on HiDPI screens. */\n            canvas.style.width = width + 'px';\n            canvas.style.height = height + 'px';\n\n            rubberband_canvas.setAttribute('width', width);\n            rubberband_canvas.setAttribute('height', height);\n\n            // And update the size in Python. We ignore the initial 0/0 size\n            // that occurs as the element is placed into the DOM, which should\n            // otherwise not happen due to the minimum size styling.\n            if (width != 0 && height != 0) {\n                fig.request_resize(width, height);\n            }\n        }\n    });\n    this.resizeObserverInstance.observe(canvas_div);\n\n    function on_mouse_event_closure(name) {\n        /* User Agent sniffing is bad, but WebKit is busted:\n         * https://bugs.webkit.org/show_bug.cgi?id=144526\n         * https://bugs.webkit.org/show_bug.cgi?id=181818\n         * The worst that happens here is that they get an extra browser\n         * selection when dragging, if this check fails to catch them.\n         */\n        var UA = navigator.userAgent;\n        var isWebKit = /AppleWebKit/.test(UA) && !/Chrome/.test(UA);\n        if(isWebKit) {\n            return function (event) {\n                /* This prevents the web browser from automatically changing to\n                 * the text insertion cursor when the button is pressed. We\n                 * want to control all of the cursor setting manually through\n                 * the 'cursor' event from matplotlib */\n                event.preventDefault()\n                return fig.mouse_event(event, name);\n            };\n        } else {\n            return function (event) {\n                return fig.mouse_event(event, name);\n            };\n        }\n    }\n\n    canvas_div.addEventListener(\n        'mousedown',\n        on_mouse_event_closure('button_press')\n    );\n    canvas_div.addEventListener(\n        'mouseup',\n        on_mouse_event_closure('button_release')\n    );\n    canvas_div.addEventListener(\n        'dblclick',\n        on_mouse_event_closure('dblclick')\n    );\n    // Throttle sequential mouse events to 1 every 20ms.\n    canvas_div.addEventListener(\n        'mousemove',\n        on_mouse_event_closure('motion_notify')\n    );\n\n    canvas_div.addEventListener(\n        'mouseenter',\n        on_mouse_event_closure('figure_enter')\n    );\n    canvas_div.addEventListener(\n        'mouseleave',\n        on_mouse_event_closure('figure_leave')\n    );\n\n    canvas_div.addEventListener('wheel', function (event) {\n        if (event.deltaY < 0) {\n            event.step = 1;\n        } else {\n            event.step = -1;\n        }\n        on_mouse_event_closure('scroll')(event);\n    });\n\n    canvas_div.appendChild(canvas);\n    canvas_div.appendChild(rubberband_canvas);\n\n    this.rubberband_context = rubberband_canvas.getContext('2d');\n    this.rubberband_context.strokeStyle = '#000000';\n\n    this._resize_canvas = function (width, height, forward) {\n        if (forward) {\n            canvas_div.style.width = width + 'px';\n            canvas_div.style.height = height + 'px';\n        }\n    };\n\n    // Disable right mouse context menu.\n    canvas_div.addEventListener('contextmenu', function (_e) {\n        event.preventDefault();\n        return false;\n    });\n\n    function set_focus() {\n        canvas.focus();\n        canvas_div.focus();\n    }\n\n    window.setTimeout(set_focus, 100);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n    var fig = this;\n\n    var toolbar = document.createElement('div');\n    toolbar.classList = 'mpl-toolbar';\n    this.root.appendChild(toolbar);\n\n    function on_click_closure(name) {\n        return function (_event) {\n            return fig.toolbar_button_onclick(name);\n        };\n    }\n\n    function on_mouseover_closure(tooltip) {\n        return function (event) {\n            if (!event.currentTarget.disabled) {\n                return fig.toolbar_button_onmouseover(tooltip);\n            }\n        };\n    }\n\n    fig.buttons = {};\n    var buttonGroup = document.createElement('div');\n    buttonGroup.classList = 'mpl-button-group';\n    for (var toolbar_ind in mpl.toolbar_items) {\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) {\n            /* Instead of a spacer, we start a new button group. */\n            if (buttonGroup.hasChildNodes()) {\n                toolbar.appendChild(buttonGroup);\n            }\n            buttonGroup = document.createElement('div');\n            buttonGroup.classList = 'mpl-button-group';\n            continue;\n        }\n\n        var button = (fig.buttons[name] = document.createElement('button'));\n        button.classList = 'mpl-widget';\n        button.setAttribute('role', 'button');\n        button.setAttribute('aria-disabled', 'false');\n        button.addEventListener('click', on_click_closure(method_name));\n        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n\n        var icon_img = document.createElement('img');\n        icon_img.src = '_images/' + image + '.png';\n        icon_img.srcset = '_images/' + image + '_large.png 2x';\n        icon_img.alt = tooltip;\n        button.appendChild(icon_img);\n\n        buttonGroup.appendChild(button);\n    }\n\n    if (buttonGroup.hasChildNodes()) {\n        toolbar.appendChild(buttonGroup);\n    }\n\n    var fmt_picker = document.createElement('select');\n    fmt_picker.classList = 'mpl-widget';\n    toolbar.appendChild(fmt_picker);\n    this.format_dropdown = fmt_picker;\n\n    for (var ind in mpl.extensions) {\n        var fmt = mpl.extensions[ind];\n        var option = document.createElement('option');\n        option.selected = fmt === mpl.default_extension;\n        option.innerHTML = fmt;\n        fmt_picker.appendChild(option);\n    }\n\n    var status_bar = document.createElement('span');\n    status_bar.classList = 'mpl-message';\n    toolbar.appendChild(status_bar);\n    this.message = status_bar;\n};\n\nmpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n    // which will in turn request a refresh of the image.\n    this.send_message('resize', { width: x_pixels, height: y_pixels });\n};\n\nmpl.figure.prototype.send_message = function (type, properties) {\n    properties['type'] = type;\n    properties['figure_id'] = this.id;\n    this.ws.send(JSON.stringify(properties));\n};\n\nmpl.figure.prototype.send_draw_message = function () {\n    if (!this.waiting) {\n        this.waiting = true;\n        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n    }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n    var format_dropdown = fig.format_dropdown;\n    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n    fig.ondownload(fig, format);\n};\n\nmpl.figure.prototype.handle_resize = function (fig, msg) {\n    var size = msg['size'];\n    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n        fig._resize_canvas(size[0], size[1], msg['forward']);\n        fig.send_message('refresh', {});\n    }\n};\n\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n    var x0 = msg['x0'] / fig.ratio;\n    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n    var x1 = msg['x1'] / fig.ratio;\n    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n    x0 = Math.floor(x0) + 0.5;\n    y0 = Math.floor(y0) + 0.5;\n    x1 = Math.floor(x1) + 0.5;\n    y1 = Math.floor(y1) + 0.5;\n    var min_x = Math.min(x0, x1);\n    var min_y = Math.min(y0, y1);\n    var width = Math.abs(x1 - x0);\n    var height = Math.abs(y1 - y0);\n\n    fig.rubberband_context.clearRect(\n        0,\n        0,\n        fig.canvas.width / fig.ratio,\n        fig.canvas.height / fig.ratio\n    );\n\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n    fig.canvas_div.style.cursor = msg['cursor'];\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n    fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n    fig.image_mode = msg['mode'];\n};\n\nmpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n    for (var key in msg) {\n        if (!(key in fig.buttons)) {\n            continue;\n        }\n        fig.buttons[key].disabled = !msg[key];\n        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n    }\n};\n\nmpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n    if (msg['mode'] === 'PAN') {\n        fig.buttons['Pan'].classList.add('active');\n        fig.buttons['Zoom'].classList.remove('active');\n    } else if (msg['mode'] === 'ZOOM') {\n        fig.buttons['Pan'].classList.remove('active');\n        fig.buttons['Zoom'].classList.add('active');\n    } else {\n        fig.buttons['Pan'].classList.remove('active');\n        fig.buttons['Zoom'].classList.remove('active');\n    }\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n    // Called whenever the canvas gets updated.\n    this.send_message('ack', {});\n};\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function (fig) {\n    return function socket_on_message(evt) {\n        if (evt.data instanceof Blob) {\n            var img = evt.data;\n            if (img.type !== 'image/png') {\n                /* FIXME: We get \"Resource interpreted as Image but\n                 * transferred with MIME type text/plain:\" errors on\n                 * Chrome.  But how to set the MIME type?  It doesn't seem\n                 * to be part of the websocket stream */\n                img.type = 'image/png';\n            }\n\n            /* Free the memory for the previous frames */\n            if (fig.imageObj.src) {\n                (window.URL || window.webkitURL).revokeObjectURL(\n                    fig.imageObj.src\n                );\n            }\n\n            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n                img\n            );\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        } else if (\n            typeof evt.data === 'string' &&\n            evt.data.slice(0, 21) === 'data:image/png;base64'\n        ) {\n            fig.imageObj.src = evt.data;\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n\n        var msg = JSON.parse(evt.data);\n        var msg_type = msg['type'];\n\n        // Call the  \"handle_{type}\" callback, which takes\n        // the figure and JSON message as its only arguments.\n        try {\n            var callback = fig['handle_' + msg_type];\n        } catch (e) {\n            console.log(\n                \"No handler for the '\" + msg_type + \"' message type: \",\n                msg\n            );\n            return;\n        }\n\n        if (callback) {\n            try {\n                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n                callback(fig, msg);\n            } catch (e) {\n                console.log(\n                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n                    e,\n                    e.stack,\n                    msg\n                );\n            }\n        }\n    };\n};\n\nfunction getModifiers(event) {\n    var mods = [];\n    if (event.ctrlKey) {\n        mods.push('ctrl');\n    }\n    if (event.altKey) {\n        mods.push('alt');\n    }\n    if (event.shiftKey) {\n        mods.push('shift');\n    }\n    if (event.metaKey) {\n        mods.push('meta');\n    }\n    return mods;\n}\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * https://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys(original) {\n    return Object.keys(original).reduce(function (obj, key) {\n        if (typeof original[key] !== 'object') {\n            obj[key] = original[key];\n        }\n        return obj;\n    }, {});\n}\n\nmpl.figure.prototype.mouse_event = function (event, name) {\n    if (name === 'button_press') {\n        this.canvas.focus();\n        this.canvas_div.focus();\n    }\n\n    // from https://stackoverflow.com/q/1114465\n    var boundingRect = this.canvas.getBoundingClientRect();\n    var x = (event.clientX - boundingRect.left) * this.ratio;\n    var y = (event.clientY - boundingRect.top) * this.ratio;\n\n    this.send_message(name, {\n        x: x,\n        y: y,\n        button: event.button,\n        step: event.step,\n        modifiers: getModifiers(event),\n        guiEvent: simpleKeys(event),\n    });\n\n    return false;\n};\n\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n    // Handle any extra behaviour associated with a key event\n};\n\nmpl.figure.prototype.key_event = function (event, name) {\n    // Prevent repeat events\n    if (name === 'key_press') {\n        if (event.key === this._key) {\n            return;\n        } else {\n            this._key = event.key;\n        }\n    }\n    if (name === 'key_release') {\n        this._key = null;\n    }\n\n    var value = '';\n    if (event.ctrlKey && event.key !== 'Control') {\n        value += 'ctrl+';\n    }\n    else if (event.altKey && event.key !== 'Alt') {\n        value += 'alt+';\n    }\n    else if (event.shiftKey && event.key !== 'Shift') {\n        value += 'shift+';\n    }\n\n    value += 'k' + event.key;\n\n    this._key_event_extra(event, name);\n\n    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n    return false;\n};\n\nmpl.figure.prototype.toolbar_button_onclick = function (name) {\n    if (name === 'download') {\n        this.handle_save(this, null);\n    } else {\n        this.send_message('toolbar_button', { name: name });\n    }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n    this.message.textContent = tooltip;\n};\n\n///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n// prettier-ignore\nvar _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar comm_websocket_adapter = function (comm) {\n    // Create a \"websocket\"-like object which calls the given IPython comm\n    // object with the appropriate methods. Currently this is a non binary\n    // socket, so there is still some room for performance tuning.\n    var ws = {};\n\n    ws.binaryType = comm.kernel.ws.binaryType;\n    ws.readyState = comm.kernel.ws.readyState;\n    function updateReadyState(_event) {\n        if (comm.kernel.ws) {\n            ws.readyState = comm.kernel.ws.readyState;\n        } else {\n            ws.readyState = 3; // Closed state.\n        }\n    }\n    comm.kernel.ws.addEventListener('open', updateReadyState);\n    comm.kernel.ws.addEventListener('close', updateReadyState);\n    comm.kernel.ws.addEventListener('error', updateReadyState);\n\n    ws.close = function () {\n        comm.close();\n    };\n    ws.send = function (m) {\n        //console.log('sending', m);\n        comm.send(m);\n    };\n    // Register the callback with on_msg.\n    comm.on_msg(function (msg) {\n        //console.log('receiving', msg['content']['data'], msg);\n        var data = msg['content']['data'];\n        if (data['blob'] !== undefined) {\n            data = {\n                data: new Blob(msg['buffers'], { type: data['blob'] }),\n            };\n        }\n        // Pass the mpl event to the overridden (by mpl) onmessage function.\n        ws.onmessage(data);\n    });\n    return ws;\n};\n\nmpl.mpl_figure_comm = function (comm, msg) {\n    // This is the function which gets called when the mpl process\n    // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n    var id = msg.content.data.id;\n    // Get hold of the div created by the display call when the Comm\n    // socket was opened in Python.\n    var element = document.getElementById(id);\n    var ws_proxy = comm_websocket_adapter(comm);\n\n    function ondownload(figure, _format) {\n        window.open(figure.canvas.toDataURL());\n    }\n\n    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n\n    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n    // web socket which is closed, not our websocket->open comm proxy.\n    ws_proxy.onopen();\n\n    fig.parent_element = element;\n    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n    if (!fig.cell_info) {\n        console.error('Failed to find cell for figure', id, fig);\n        return;\n    }\n    fig.cell_info[0].output_area.element.on(\n        'cleared',\n        { fig: fig },\n        fig._remove_fig_handler\n    );\n};\n\nmpl.figure.prototype.handle_close = function (fig, msg) {\n    var width = fig.canvas.width / fig.ratio;\n    fig.cell_info[0].output_area.element.off(\n        'cleared',\n        fig._remove_fig_handler\n    );\n    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n\n    // Update the output cell to use the data from the current canvas.\n    fig.push_to_output();\n    var dataURL = fig.canvas.toDataURL();\n    // Re-enable the keyboard manager in IPython - without this line, in FF,\n    // the notebook keyboard shortcuts fail.\n    IPython.keyboard_manager.enable();\n    fig.parent_element.innerHTML =\n        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n    fig.close_ws(fig, msg);\n};\n\nmpl.figure.prototype.close_ws = function (fig, msg) {\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n};\n\nmpl.figure.prototype.push_to_output = function (_remove_interactive) {\n    // Turn the data on the canvas into data in the output cell.\n    var width = this.canvas.width / this.ratio;\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] =\n        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n    // Tell IPython that the notebook contents must change.\n    IPython.notebook.set_dirty(true);\n    this.send_message('ack', {});\n    var fig = this;\n    // Wait a second, then push the new image to the DOM so\n    // that it is saved nicely (might be nice to debounce this).\n    setTimeout(function () {\n        fig.push_to_output();\n    }, 1000);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n    var fig = this;\n\n    var toolbar = document.createElement('div');\n    toolbar.classList = 'btn-toolbar';\n    this.root.appendChild(toolbar);\n\n    function on_click_closure(name) {\n        return function (_event) {\n            return fig.toolbar_button_onclick(name);\n        };\n    }\n\n    function on_mouseover_closure(tooltip) {\n        return function (event) {\n            if (!event.currentTarget.disabled) {\n                return fig.toolbar_button_onmouseover(tooltip);\n            }\n        };\n    }\n\n    fig.buttons = {};\n    var buttonGroup = document.createElement('div');\n    buttonGroup.classList = 'btn-group';\n    var button;\n    for (var toolbar_ind in mpl.toolbar_items) {\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) {\n            /* Instead of a spacer, we start a new button group. */\n            if (buttonGroup.hasChildNodes()) {\n                toolbar.appendChild(buttonGroup);\n            }\n            buttonGroup = document.createElement('div');\n            buttonGroup.classList = 'btn-group';\n            continue;\n        }\n\n        button = fig.buttons[name] = document.createElement('button');\n        button.classList = 'btn btn-default';\n        button.href = '#';\n        button.title = name;\n        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n        button.addEventListener('click', on_click_closure(method_name));\n        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n        buttonGroup.appendChild(button);\n    }\n\n    if (buttonGroup.hasChildNodes()) {\n        toolbar.appendChild(buttonGroup);\n    }\n\n    // Add the status bar.\n    var status_bar = document.createElement('span');\n    status_bar.classList = 'mpl-message pull-right';\n    toolbar.appendChild(status_bar);\n    this.message = status_bar;\n\n    // Add the close button to the window.\n    var buttongrp = document.createElement('div');\n    buttongrp.classList = 'btn-group inline pull-right';\n    button = document.createElement('button');\n    button.classList = 'btn btn-mini btn-primary';\n    button.href = '#';\n    button.title = 'Stop Interaction';\n    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n    button.addEventListener('click', function (_evt) {\n        fig.handle_close(fig, {});\n    });\n    button.addEventListener(\n        'mouseover',\n        on_mouseover_closure('Stop Interaction')\n    );\n    buttongrp.appendChild(button);\n    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n};\n\nmpl.figure.prototype._remove_fig_handler = function (event) {\n    var fig = event.data.fig;\n    if (event.target !== this) {\n        // Ignore bubbled events from children.\n        return;\n    }\n    fig.close_ws(fig, {});\n};\n\nmpl.figure.prototype._root_extra_style = function (el) {\n    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n};\n\nmpl.figure.prototype._canvas_extra_style = function (el) {\n    // this is important to make the div 'focusable\n    el.setAttribute('tabindex', 0);\n    // reach out to IPython and tell the keyboard manager to turn it's self\n    // off when our div gets focus\n\n    // location in version 3\n    if (IPython.notebook.keyboard_manager) {\n        IPython.notebook.keyboard_manager.register_events(el);\n    } else {\n        // location in version 2\n        IPython.keyboard_manager.register_events(el);\n    }\n};\n\nmpl.figure.prototype._key_event_extra = function (event, _name) {\n    // Check for shift+enter\n    if (event.shiftKey && event.which === 13) {\n        this.canvas_div.blur();\n        // select the cell after this one\n        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n        IPython.notebook.select(index + 1);\n    }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n    fig.ondownload(fig, null);\n};\n\nmpl.find_output_cell = function (html_output) {\n    // Return the cell and output element which can be found *uniquely* in the notebook.\n    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n    // IPython event is triggered only after the cells have been serialised, which for\n    // our purposes (turning an active figure into a static one), is too late.\n    var cells = IPython.notebook.get_cells();\n    var ncells = cells.length;\n    for (var i = 0; i < ncells; i++) {\n        var cell = cells[i];\n        if (cell.cell_type === 'code') {\n            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n                var data = cell.output_area.outputs[j];\n                if (data.data) {\n                    // IPython >= 3 moved mimebundle to data attribute of output\n                    data = data.data;\n                }\n                if (data['text/html'] === html_output) {\n                    return [cell, data, j];\n                }\n            }\n        }\n    }\n};\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel !== null) {\n    IPython.notebook.kernel.comm_manager.register_target(\n        'matplotlib',\n        mpl.mpl_figure_comm\n    );\n}\n",
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div id='8fdf11ca-9610-4f95-b01e-4739709eeb48'></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Basic EDA of the data\n",
+    "print(data.describe())\n",
+    "print('\\n')\n",
+    "print(data.info())\n",
+    "print('Missing values: ', data.isna().sum())\n",
+    "\n",
+    "# Plots the data\n",
+    "plt.figure(figsize=(10, 4))\n",
+    "plt.plot(data)\n",
+    "plt.title('Brent Crude Oil Price since 1987')\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Price per barrel (USD)')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "ee5b1e13d6396b90",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-08-15T22:13:40.041847Z",
+     "start_time": "2023-08-15T22:13:39.420385700Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n    if (typeof WebSocket !== 'undefined') {\n        return WebSocket;\n    } else if (typeof MozWebSocket !== 'undefined') {\n        return MozWebSocket;\n    } else {\n        alert(\n            'Your browser does not have WebSocket support. ' +\n                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n                'Firefox 4 and 5 are also supported but you ' +\n                'have to enable WebSockets in about:config.'\n        );\n    }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n    this.id = figure_id;\n\n    this.ws = websocket;\n\n    this.supports_binary = this.ws.binaryType !== undefined;\n\n    if (!this.supports_binary) {\n        var warnings = document.getElementById('mpl-warnings');\n        if (warnings) {\n            warnings.style.display = 'block';\n            warnings.textContent =\n                'This browser does not support binary websocket messages. ' +\n                'Performance may be slow.';\n        }\n    }\n\n    this.imageObj = new Image();\n\n    this.context = undefined;\n    this.message = undefined;\n    this.canvas = undefined;\n    this.rubberband_canvas = undefined;\n    this.rubberband_context = undefined;\n    this.format_dropdown = undefined;\n\n    this.image_mode = 'full';\n\n    this.root = document.createElement('div');\n    this.root.setAttribute('style', 'display: inline-block');\n    this._root_extra_style(this.root);\n\n    parent_element.appendChild(this.root);\n\n    this._init_header(this);\n    this._init_canvas(this);\n    this._init_toolbar(this);\n\n    var fig = this;\n\n    this.waiting = false;\n\n    this.ws.onopen = function () {\n        fig.send_message('supports_binary', { value: fig.supports_binary });\n        fig.send_message('send_image_mode', {});\n        if (fig.ratio !== 1) {\n            fig.send_message('set_device_pixel_ratio', {\n                device_pixel_ratio: fig.ratio,\n            });\n        }\n        fig.send_message('refresh', {});\n    };\n\n    this.imageObj.onload = function () {\n        if (fig.image_mode === 'full') {\n            // Full images could contain transparency (where diff images\n            // almost always do), so we need to clear the canvas so that\n            // there is no ghosting.\n            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n        }\n        fig.context.drawImage(fig.imageObj, 0, 0);\n    };\n\n    this.imageObj.onunload = function () {\n        fig.ws.close();\n    };\n\n    this.ws.onmessage = this._make_on_message_function(this);\n\n    this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n    var titlebar = document.createElement('div');\n    titlebar.classList =\n        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n    var titletext = document.createElement('div');\n    titletext.classList = 'ui-dialog-title';\n    titletext.setAttribute(\n        'style',\n        'width: 100%; text-align: center; padding: 3px;'\n    );\n    titlebar.appendChild(titletext);\n    this.root.appendChild(titlebar);\n    this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n    var fig = this;\n\n    var canvas_div = (this.canvas_div = document.createElement('div'));\n    canvas_div.setAttribute('tabindex', '0');\n    canvas_div.setAttribute(\n        'style',\n        'border: 1px solid #ddd;' +\n            'box-sizing: content-box;' +\n            'clear: both;' +\n            'min-height: 1px;' +\n            'min-width: 1px;' +\n            'outline: 0;' +\n            'overflow: hidden;' +\n            'position: relative;' +\n            'resize: both;' +\n            'z-index: 2;'\n    );\n\n    function on_keyboard_event_closure(name) {\n        return function (event) {\n            return fig.key_event(event, name);\n        };\n    }\n\n    canvas_div.addEventListener(\n        'keydown',\n        on_keyboard_event_closure('key_press')\n    );\n    canvas_div.addEventListener(\n        'keyup',\n        on_keyboard_event_closure('key_release')\n    );\n\n    this._canvas_extra_style(canvas_div);\n    this.root.appendChild(canvas_div);\n\n    var canvas = (this.canvas = document.createElement('canvas'));\n    canvas.classList.add('mpl-canvas');\n    canvas.setAttribute(\n        'style',\n        'box-sizing: content-box;' +\n            'pointer-events: none;' +\n            'position: relative;' +\n            'z-index: 0;'\n    );\n\n    this.context = canvas.getContext('2d');\n\n    var backingStore =\n        this.context.backingStorePixelRatio ||\n        this.context.webkitBackingStorePixelRatio ||\n        this.context.mozBackingStorePixelRatio ||\n        this.context.msBackingStorePixelRatio ||\n        this.context.oBackingStorePixelRatio ||\n        this.context.backingStorePixelRatio ||\n        1;\n\n    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n        'canvas'\n    ));\n    rubberband_canvas.setAttribute(\n        'style',\n        'box-sizing: content-box;' +\n            'left: 0;' +\n            'pointer-events: none;' +\n            'position: absolute;' +\n            'top: 0;' +\n            'z-index: 1;'\n    );\n\n    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n    if (this.ResizeObserver === undefined) {\n        if (window.ResizeObserver !== undefined) {\n            this.ResizeObserver = window.ResizeObserver;\n        } else {\n            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n            this.ResizeObserver = obs.ResizeObserver;\n        }\n    }\n\n    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n        // There's no need to resize if the WebSocket is not connected:\n        // - If it is still connecting, then we will get an initial resize from\n        //   Python once it connects.\n        // - If it has disconnected, then resizing will clear the canvas and\n        //   never get anything back to refill it, so better to not resize and\n        //   keep something visible.\n        if (fig.ws.readyState != 1) {\n            return;\n        }\n        var nentries = entries.length;\n        for (var i = 0; i < nentries; i++) {\n            var entry = entries[i];\n            var width, height;\n            if (entry.contentBoxSize) {\n                if (entry.contentBoxSize instanceof Array) {\n                    // Chrome 84 implements new version of spec.\n                    width = entry.contentBoxSize[0].inlineSize;\n                    height = entry.contentBoxSize[0].blockSize;\n                } else {\n                    // Firefox implements old version of spec.\n                    width = entry.contentBoxSize.inlineSize;\n                    height = entry.contentBoxSize.blockSize;\n                }\n            } else {\n                // Chrome <84 implements even older version of spec.\n                width = entry.contentRect.width;\n                height = entry.contentRect.height;\n            }\n\n            // Keep the size of the canvas and rubber band canvas in sync with\n            // the canvas container.\n            if (entry.devicePixelContentBoxSize) {\n                // Chrome 84 implements new version of spec.\n                canvas.setAttribute(\n                    'width',\n                    entry.devicePixelContentBoxSize[0].inlineSize\n                );\n                canvas.setAttribute(\n                    'height',\n                    entry.devicePixelContentBoxSize[0].blockSize\n                );\n            } else {\n                canvas.setAttribute('width', width * fig.ratio);\n                canvas.setAttribute('height', height * fig.ratio);\n            }\n            /* This rescales the canvas back to display pixels, so that it\n             * appears correct on HiDPI screens. */\n            canvas.style.width = width + 'px';\n            canvas.style.height = height + 'px';\n\n            rubberband_canvas.setAttribute('width', width);\n            rubberband_canvas.setAttribute('height', height);\n\n            // And update the size in Python. We ignore the initial 0/0 size\n            // that occurs as the element is placed into the DOM, which should\n            // otherwise not happen due to the minimum size styling.\n            if (width != 0 && height != 0) {\n                fig.request_resize(width, height);\n            }\n        }\n    });\n    this.resizeObserverInstance.observe(canvas_div);\n\n    function on_mouse_event_closure(name) {\n        /* User Agent sniffing is bad, but WebKit is busted:\n         * https://bugs.webkit.org/show_bug.cgi?id=144526\n         * https://bugs.webkit.org/show_bug.cgi?id=181818\n         * The worst that happens here is that they get an extra browser\n         * selection when dragging, if this check fails to catch them.\n         */\n        var UA = navigator.userAgent;\n        var isWebKit = /AppleWebKit/.test(UA) && !/Chrome/.test(UA);\n        if(isWebKit) {\n            return function (event) {\n                /* This prevents the web browser from automatically changing to\n                 * the text insertion cursor when the button is pressed. We\n                 * want to control all of the cursor setting manually through\n                 * the 'cursor' event from matplotlib */\n                event.preventDefault()\n                return fig.mouse_event(event, name);\n            };\n        } else {\n            return function (event) {\n                return fig.mouse_event(event, name);\n            };\n        }\n    }\n\n    canvas_div.addEventListener(\n        'mousedown',\n        on_mouse_event_closure('button_press')\n    );\n    canvas_div.addEventListener(\n        'mouseup',\n        on_mouse_event_closure('button_release')\n    );\n    canvas_div.addEventListener(\n        'dblclick',\n        on_mouse_event_closure('dblclick')\n    );\n    // Throttle sequential mouse events to 1 every 20ms.\n    canvas_div.addEventListener(\n        'mousemove',\n        on_mouse_event_closure('motion_notify')\n    );\n\n    canvas_div.addEventListener(\n        'mouseenter',\n        on_mouse_event_closure('figure_enter')\n    );\n    canvas_div.addEventListener(\n        'mouseleave',\n        on_mouse_event_closure('figure_leave')\n    );\n\n    canvas_div.addEventListener('wheel', function (event) {\n        if (event.deltaY < 0) {\n            event.step = 1;\n        } else {\n            event.step = -1;\n        }\n        on_mouse_event_closure('scroll')(event);\n    });\n\n    canvas_div.appendChild(canvas);\n    canvas_div.appendChild(rubberband_canvas);\n\n    this.rubberband_context = rubberband_canvas.getContext('2d');\n    this.rubberband_context.strokeStyle = '#000000';\n\n    this._resize_canvas = function (width, height, forward) {\n        if (forward) {\n            canvas_div.style.width = width + 'px';\n            canvas_div.style.height = height + 'px';\n        }\n    };\n\n    // Disable right mouse context menu.\n    canvas_div.addEventListener('contextmenu', function (_e) {\n        event.preventDefault();\n        return false;\n    });\n\n    function set_focus() {\n        canvas.focus();\n        canvas_div.focus();\n    }\n\n    window.setTimeout(set_focus, 100);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n    var fig = this;\n\n    var toolbar = document.createElement('div');\n    toolbar.classList = 'mpl-toolbar';\n    this.root.appendChild(toolbar);\n\n    function on_click_closure(name) {\n        return function (_event) {\n            return fig.toolbar_button_onclick(name);\n        };\n    }\n\n    function on_mouseover_closure(tooltip) {\n        return function (event) {\n            if (!event.currentTarget.disabled) {\n                return fig.toolbar_button_onmouseover(tooltip);\n            }\n        };\n    }\n\n    fig.buttons = {};\n    var buttonGroup = document.createElement('div');\n    buttonGroup.classList = 'mpl-button-group';\n    for (var toolbar_ind in mpl.toolbar_items) {\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) {\n            /* Instead of a spacer, we start a new button group. */\n            if (buttonGroup.hasChildNodes()) {\n                toolbar.appendChild(buttonGroup);\n            }\n            buttonGroup = document.createElement('div');\n            buttonGroup.classList = 'mpl-button-group';\n            continue;\n        }\n\n        var button = (fig.buttons[name] = document.createElement('button'));\n        button.classList = 'mpl-widget';\n        button.setAttribute('role', 'button');\n        button.setAttribute('aria-disabled', 'false');\n        button.addEventListener('click', on_click_closure(method_name));\n        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n\n        var icon_img = document.createElement('img');\n        icon_img.src = '_images/' + image + '.png';\n        icon_img.srcset = '_images/' + image + '_large.png 2x';\n        icon_img.alt = tooltip;\n        button.appendChild(icon_img);\n\n        buttonGroup.appendChild(button);\n    }\n\n    if (buttonGroup.hasChildNodes()) {\n        toolbar.appendChild(buttonGroup);\n    }\n\n    var fmt_picker = document.createElement('select');\n    fmt_picker.classList = 'mpl-widget';\n    toolbar.appendChild(fmt_picker);\n    this.format_dropdown = fmt_picker;\n\n    for (var ind in mpl.extensions) {\n        var fmt = mpl.extensions[ind];\n        var option = document.createElement('option');\n        option.selected = fmt === mpl.default_extension;\n        option.innerHTML = fmt;\n        fmt_picker.appendChild(option);\n    }\n\n    var status_bar = document.createElement('span');\n    status_bar.classList = 'mpl-message';\n    toolbar.appendChild(status_bar);\n    this.message = status_bar;\n};\n\nmpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n    // which will in turn request a refresh of the image.\n    this.send_message('resize', { width: x_pixels, height: y_pixels });\n};\n\nmpl.figure.prototype.send_message = function (type, properties) {\n    properties['type'] = type;\n    properties['figure_id'] = this.id;\n    this.ws.send(JSON.stringify(properties));\n};\n\nmpl.figure.prototype.send_draw_message = function () {\n    if (!this.waiting) {\n        this.waiting = true;\n        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n    }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n    var format_dropdown = fig.format_dropdown;\n    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n    fig.ondownload(fig, format);\n};\n\nmpl.figure.prototype.handle_resize = function (fig, msg) {\n    var size = msg['size'];\n    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n        fig._resize_canvas(size[0], size[1], msg['forward']);\n        fig.send_message('refresh', {});\n    }\n};\n\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n    var x0 = msg['x0'] / fig.ratio;\n    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n    var x1 = msg['x1'] / fig.ratio;\n    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n    x0 = Math.floor(x0) + 0.5;\n    y0 = Math.floor(y0) + 0.5;\n    x1 = Math.floor(x1) + 0.5;\n    y1 = Math.floor(y1) + 0.5;\n    var min_x = Math.min(x0, x1);\n    var min_y = Math.min(y0, y1);\n    var width = Math.abs(x1 - x0);\n    var height = Math.abs(y1 - y0);\n\n    fig.rubberband_context.clearRect(\n        0,\n        0,\n        fig.canvas.width / fig.ratio,\n        fig.canvas.height / fig.ratio\n    );\n\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n    fig.canvas_div.style.cursor = msg['cursor'];\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n    fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n    fig.image_mode = msg['mode'];\n};\n\nmpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n    for (var key in msg) {\n        if (!(key in fig.buttons)) {\n            continue;\n        }\n        fig.buttons[key].disabled = !msg[key];\n        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n    }\n};\n\nmpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n    if (msg['mode'] === 'PAN') {\n        fig.buttons['Pan'].classList.add('active');\n        fig.buttons['Zoom'].classList.remove('active');\n    } else if (msg['mode'] === 'ZOOM') {\n        fig.buttons['Pan'].classList.remove('active');\n        fig.buttons['Zoom'].classList.add('active');\n    } else {\n        fig.buttons['Pan'].classList.remove('active');\n        fig.buttons['Zoom'].classList.remove('active');\n    }\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n    // Called whenever the canvas gets updated.\n    this.send_message('ack', {});\n};\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function (fig) {\n    return function socket_on_message(evt) {\n        if (evt.data instanceof Blob) {\n            var img = evt.data;\n            if (img.type !== 'image/png') {\n                /* FIXME: We get \"Resource interpreted as Image but\n                 * transferred with MIME type text/plain:\" errors on\n                 * Chrome.  But how to set the MIME type?  It doesn't seem\n                 * to be part of the websocket stream */\n                img.type = 'image/png';\n            }\n\n            /* Free the memory for the previous frames */\n            if (fig.imageObj.src) {\n                (window.URL || window.webkitURL).revokeObjectURL(\n                    fig.imageObj.src\n                );\n            }\n\n            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n                img\n            );\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        } else if (\n            typeof evt.data === 'string' &&\n            evt.data.slice(0, 21) === 'data:image/png;base64'\n        ) {\n            fig.imageObj.src = evt.data;\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n\n        var msg = JSON.parse(evt.data);\n        var msg_type = msg['type'];\n\n        // Call the  \"handle_{type}\" callback, which takes\n        // the figure and JSON message as its only arguments.\n        try {\n            var callback = fig['handle_' + msg_type];\n        } catch (e) {\n            console.log(\n                \"No handler for the '\" + msg_type + \"' message type: \",\n                msg\n            );\n            return;\n        }\n\n        if (callback) {\n            try {\n                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n                callback(fig, msg);\n            } catch (e) {\n                console.log(\n                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n                    e,\n                    e.stack,\n                    msg\n                );\n            }\n        }\n    };\n};\n\nfunction getModifiers(event) {\n    var mods = [];\n    if (event.ctrlKey) {\n        mods.push('ctrl');\n    }\n    if (event.altKey) {\n        mods.push('alt');\n    }\n    if (event.shiftKey) {\n        mods.push('shift');\n    }\n    if (event.metaKey) {\n        mods.push('meta');\n    }\n    return mods;\n}\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * https://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys(original) {\n    return Object.keys(original).reduce(function (obj, key) {\n        if (typeof original[key] !== 'object') {\n            obj[key] = original[key];\n        }\n        return obj;\n    }, {});\n}\n\nmpl.figure.prototype.mouse_event = function (event, name) {\n    if (name === 'button_press') {\n        this.canvas.focus();\n        this.canvas_div.focus();\n    }\n\n    // from https://stackoverflow.com/q/1114465\n    var boundingRect = this.canvas.getBoundingClientRect();\n    var x = (event.clientX - boundingRect.left) * this.ratio;\n    var y = (event.clientY - boundingRect.top) * this.ratio;\n\n    this.send_message(name, {\n        x: x,\n        y: y,\n        button: event.button,\n        step: event.step,\n        modifiers: getModifiers(event),\n        guiEvent: simpleKeys(event),\n    });\n\n    return false;\n};\n\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n    // Handle any extra behaviour associated with a key event\n};\n\nmpl.figure.prototype.key_event = function (event, name) {\n    // Prevent repeat events\n    if (name === 'key_press') {\n        if (event.key === this._key) {\n            return;\n        } else {\n            this._key = event.key;\n        }\n    }\n    if (name === 'key_release') {\n        this._key = null;\n    }\n\n    var value = '';\n    if (event.ctrlKey && event.key !== 'Control') {\n        value += 'ctrl+';\n    }\n    else if (event.altKey && event.key !== 'Alt') {\n        value += 'alt+';\n    }\n    else if (event.shiftKey && event.key !== 'Shift') {\n        value += 'shift+';\n    }\n\n    value += 'k' + event.key;\n\n    this._key_event_extra(event, name);\n\n    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n    return false;\n};\n\nmpl.figure.prototype.toolbar_button_onclick = function (name) {\n    if (name === 'download') {\n        this.handle_save(this, null);\n    } else {\n        this.send_message('toolbar_button', { name: name });\n    }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n    this.message.textContent = tooltip;\n};\n\n///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n// prettier-ignore\nvar _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar comm_websocket_adapter = function (comm) {\n    // Create a \"websocket\"-like object which calls the given IPython comm\n    // object with the appropriate methods. Currently this is a non binary\n    // socket, so there is still some room for performance tuning.\n    var ws = {};\n\n    ws.binaryType = comm.kernel.ws.binaryType;\n    ws.readyState = comm.kernel.ws.readyState;\n    function updateReadyState(_event) {\n        if (comm.kernel.ws) {\n            ws.readyState = comm.kernel.ws.readyState;\n        } else {\n            ws.readyState = 3; // Closed state.\n        }\n    }\n    comm.kernel.ws.addEventListener('open', updateReadyState);\n    comm.kernel.ws.addEventListener('close', updateReadyState);\n    comm.kernel.ws.addEventListener('error', updateReadyState);\n\n    ws.close = function () {\n        comm.close();\n    };\n    ws.send = function (m) {\n        //console.log('sending', m);\n        comm.send(m);\n    };\n    // Register the callback with on_msg.\n    comm.on_msg(function (msg) {\n        //console.log('receiving', msg['content']['data'], msg);\n        var data = msg['content']['data'];\n        if (data['blob'] !== undefined) {\n            data = {\n                data: new Blob(msg['buffers'], { type: data['blob'] }),\n            };\n        }\n        // Pass the mpl event to the overridden (by mpl) onmessage function.\n        ws.onmessage(data);\n    });\n    return ws;\n};\n\nmpl.mpl_figure_comm = function (comm, msg) {\n    // This is the function which gets called when the mpl process\n    // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n    var id = msg.content.data.id;\n    // Get hold of the div created by the display call when the Comm\n    // socket was opened in Python.\n    var element = document.getElementById(id);\n    var ws_proxy = comm_websocket_adapter(comm);\n\n    function ondownload(figure, _format) {\n        window.open(figure.canvas.toDataURL());\n    }\n\n    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n\n    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n    // web socket which is closed, not our websocket->open comm proxy.\n    ws_proxy.onopen();\n\n    fig.parent_element = element;\n    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n    if (!fig.cell_info) {\n        console.error('Failed to find cell for figure', id, fig);\n        return;\n    }\n    fig.cell_info[0].output_area.element.on(\n        'cleared',\n        { fig: fig },\n        fig._remove_fig_handler\n    );\n};\n\nmpl.figure.prototype.handle_close = function (fig, msg) {\n    var width = fig.canvas.width / fig.ratio;\n    fig.cell_info[0].output_area.element.off(\n        'cleared',\n        fig._remove_fig_handler\n    );\n    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n\n    // Update the output cell to use the data from the current canvas.\n    fig.push_to_output();\n    var dataURL = fig.canvas.toDataURL();\n    // Re-enable the keyboard manager in IPython - without this line, in FF,\n    // the notebook keyboard shortcuts fail.\n    IPython.keyboard_manager.enable();\n    fig.parent_element.innerHTML =\n        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n    fig.close_ws(fig, msg);\n};\n\nmpl.figure.prototype.close_ws = function (fig, msg) {\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n};\n\nmpl.figure.prototype.push_to_output = function (_remove_interactive) {\n    // Turn the data on the canvas into data in the output cell.\n    var width = this.canvas.width / this.ratio;\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] =\n        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n    // Tell IPython that the notebook contents must change.\n    IPython.notebook.set_dirty(true);\n    this.send_message('ack', {});\n    var fig = this;\n    // Wait a second, then push the new image to the DOM so\n    // that it is saved nicely (might be nice to debounce this).\n    setTimeout(function () {\n        fig.push_to_output();\n    }, 1000);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n    var fig = this;\n\n    var toolbar = document.createElement('div');\n    toolbar.classList = 'btn-toolbar';\n    this.root.appendChild(toolbar);\n\n    function on_click_closure(name) {\n        return function (_event) {\n            return fig.toolbar_button_onclick(name);\n        };\n    }\n\n    function on_mouseover_closure(tooltip) {\n        return function (event) {\n            if (!event.currentTarget.disabled) {\n                return fig.toolbar_button_onmouseover(tooltip);\n            }\n        };\n    }\n\n    fig.buttons = {};\n    var buttonGroup = document.createElement('div');\n    buttonGroup.classList = 'btn-group';\n    var button;\n    for (var toolbar_ind in mpl.toolbar_items) {\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) {\n            /* Instead of a spacer, we start a new button group. */\n            if (buttonGroup.hasChildNodes()) {\n                toolbar.appendChild(buttonGroup);\n            }\n            buttonGroup = document.createElement('div');\n            buttonGroup.classList = 'btn-group';\n            continue;\n        }\n\n        button = fig.buttons[name] = document.createElement('button');\n        button.classList = 'btn btn-default';\n        button.href = '#';\n        button.title = name;\n        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n        button.addEventListener('click', on_click_closure(method_name));\n        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n        buttonGroup.appendChild(button);\n    }\n\n    if (buttonGroup.hasChildNodes()) {\n        toolbar.appendChild(buttonGroup);\n    }\n\n    // Add the status bar.\n    var status_bar = document.createElement('span');\n    status_bar.classList = 'mpl-message pull-right';\n    toolbar.appendChild(status_bar);\n    this.message = status_bar;\n\n    // Add the close button to the window.\n    var buttongrp = document.createElement('div');\n    buttongrp.classList = 'btn-group inline pull-right';\n    button = document.createElement('button');\n    button.classList = 'btn btn-mini btn-primary';\n    button.href = '#';\n    button.title = 'Stop Interaction';\n    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n    button.addEventListener('click', function (_evt) {\n        fig.handle_close(fig, {});\n    });\n    button.addEventListener(\n        'mouseover',\n        on_mouseover_closure('Stop Interaction')\n    );\n    buttongrp.appendChild(button);\n    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n};\n\nmpl.figure.prototype._remove_fig_handler = function (event) {\n    var fig = event.data.fig;\n    if (event.target !== this) {\n        // Ignore bubbled events from children.\n        return;\n    }\n    fig.close_ws(fig, {});\n};\n\nmpl.figure.prototype._root_extra_style = function (el) {\n    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n};\n\nmpl.figure.prototype._canvas_extra_style = function (el) {\n    // this is important to make the div 'focusable\n    el.setAttribute('tabindex', 0);\n    // reach out to IPython and tell the keyboard manager to turn it's self\n    // off when our div gets focus\n\n    // location in version 3\n    if (IPython.notebook.keyboard_manager) {\n        IPython.notebook.keyboard_manager.register_events(el);\n    } else {\n        // location in version 2\n        IPython.keyboard_manager.register_events(el);\n    }\n};\n\nmpl.figure.prototype._key_event_extra = function (event, _name) {\n    // Check for shift+enter\n    if (event.shiftKey && event.which === 13) {\n        this.canvas_div.blur();\n        // select the cell after this one\n        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n        IPython.notebook.select(index + 1);\n    }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n    fig.ondownload(fig, null);\n};\n\nmpl.find_output_cell = function (html_output) {\n    // Return the cell and output element which can be found *uniquely* in the notebook.\n    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n    // IPython event is triggered only after the cells have been serialised, which for\n    // our purposes (turning an active figure into a static one), is too late.\n    var cells = IPython.notebook.get_cells();\n    var ncells = cells.length;\n    for (var i = 0; i < ncells; i++) {\n        var cell = cells[i];\n        if (cell.cell_type === 'code') {\n            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n                var data = cell.output_area.outputs[j];\n                if (data.data) {\n                    // IPython >= 3 moved mimebundle to data attribute of output\n                    data = data.data;\n                }\n                if (data['text/html'] === html_output) {\n                    return [cell, data, j];\n                }\n            }\n        }\n    }\n};\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel !== null) {\n    IPython.notebook.kernel.comm_manager.register_target(\n        'matplotlib',\n        mpl.mpl_figure_comm\n    );\n}\n",
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div id='4bf84a3c-bce9-4fbb-a81b-d3d84ed65a7a'></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Checking the composition of the data\n",
+    "seasonal_decompose(data).plot()\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab7b6d276b159c55",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### Data splitting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "f01a6bff6eb470ae",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-08-15T22:13:42.491338600Z",
+     "start_time": "2023-08-15T22:13:42.463414500Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "train_data = data.iloc[:len(data) - 30]\n",
+    "test_data = data.iloc[len(data) - 30:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "990e6254a892a3ff",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "## SARIMA model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be55016606b7ed72",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### Getting the parameters for the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "86d629b1b51b9961",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-08-15T22:15:30.321654900Z",
+     "start_time": "2023-08-15T22:13:45.366554100Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Performing stepwise search to minimize aic\n",
+      " ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=30304.148, Time=10.92 sec\n",
+      " ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=30327.930, Time=0.39 sec\n",
+      " ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=30317.381, Time=0.77 sec\n",
+      " ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=30316.773, Time=0.88 sec\n",
+      " ARIMA(0,1,0)(0,0,0)[0]             : AIC=30326.151, Time=0.34 sec\n",
+      " ARIMA(1,1,2)(0,0,0)[0] intercept   : AIC=30314.723, Time=6.53 sec\n",
+      " ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=30314.425, Time=4.24 sec\n",
+      " ARIMA(3,1,2)(0,0,0)[0] intercept   : AIC=30303.250, Time=7.19 sec\n",
+      " ARIMA(3,1,1)(0,0,0)[0] intercept   : AIC=30310.245, Time=4.26 sec\n",
+      " ARIMA(4,1,2)(0,0,0)[0] intercept   : AIC=30320.821, Time=15.32 sec\n",
+      " ARIMA(3,1,3)(0,0,0)[0] intercept   : AIC=30305.257, Time=11.04 sec\n",
+      " ARIMA(2,1,3)(0,0,0)[0] intercept   : AIC=30303.251, Time=10.98 sec\n",
+      " ARIMA(4,1,1)(0,0,0)[0] intercept   : AIC=30308.252, Time=4.49 sec\n",
+      " ARIMA(4,1,3)(0,0,0)[0] intercept   : AIC=30307.202, Time=6.75 sec\n",
+      " ARIMA(3,1,2)(0,0,0)[0]             : AIC=30301.467, Time=4.29 sec\n",
+      " ARIMA(2,1,2)(0,0,0)[0]             : AIC=30302.361, Time=4.53 sec\n",
+      " ARIMA(3,1,1)(0,0,0)[0]             : AIC=30308.469, Time=2.98 sec\n",
+      " ARIMA(4,1,2)(0,0,0)[0]             : AIC=30319.090, Time=8.14 sec\n",
+      " ARIMA(3,1,3)(0,0,0)[0]             : AIC=30303.480, Time=5.11 sec\n",
+      " ARIMA(2,1,1)(0,0,0)[0]             : AIC=30312.647, Time=2.23 sec\n",
+      " ARIMA(2,1,3)(0,0,0)[0]             : AIC=30301.460, Time=4.60 sec\n",
+      " ARIMA(1,1,3)(0,0,0)[0]             : AIC=30307.178, Time=1.86 sec\n",
+      " ARIMA(2,1,4)(0,0,0)[0]             : AIC=30303.423, Time=5.52 sec\n",
+      " ARIMA(1,1,2)(0,0,0)[0]             : AIC=30312.947, Time=3.39 sec\n",
+      " ARIMA(1,1,4)(0,0,0)[0]             : AIC=30305.774, Time=2.28 sec\n",
+      " ARIMA(3,1,4)(0,0,0)[0]             : AIC=30305.455, Time=6.38 sec\n",
+      "\n",
+      "Best model:  ARIMA(2,1,3)(0,0,0)[0]          \n",
+      "Total fit time: 135.432 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Runs auto_arima function to get the parameters for the SARIMA model\n",
+    "opt_model = auto_arima(train_data, maxiter=100, trace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a172cb9c4777376",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### Model training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "879b5afd91fcd2d3",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-08-15T22:18:28.236518900Z",
+     "start_time": "2023-08-15T22:18:23.762630900Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n    if (typeof WebSocket !== 'undefined') {\n        return WebSocket;\n    } else if (typeof MozWebSocket !== 'undefined') {\n        return MozWebSocket;\n    } else {\n        alert(\n            'Your browser does not have WebSocket support. ' +\n                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n                'Firefox 4 and 5 are also supported but you ' +\n                'have to enable WebSockets in about:config.'\n        );\n    }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n    this.id = figure_id;\n\n    this.ws = websocket;\n\n    this.supports_binary = this.ws.binaryType !== undefined;\n\n    if (!this.supports_binary) {\n        var warnings = document.getElementById('mpl-warnings');\n        if (warnings) {\n            warnings.style.display = 'block';\n            warnings.textContent =\n                'This browser does not support binary websocket messages. ' +\n                'Performance may be slow.';\n        }\n    }\n\n    this.imageObj = new Image();\n\n    this.context = undefined;\n    this.message = undefined;\n    this.canvas = undefined;\n    this.rubberband_canvas = undefined;\n    this.rubberband_context = undefined;\n    this.format_dropdown = undefined;\n\n    this.image_mode = 'full';\n\n    this.root = document.createElement('div');\n    this.root.setAttribute('style', 'display: inline-block');\n    this._root_extra_style(this.root);\n\n    parent_element.appendChild(this.root);\n\n    this._init_header(this);\n    this._init_canvas(this);\n    this._init_toolbar(this);\n\n    var fig = this;\n\n    this.waiting = false;\n\n    this.ws.onopen = function () {\n        fig.send_message('supports_binary', { value: fig.supports_binary });\n        fig.send_message('send_image_mode', {});\n        if (fig.ratio !== 1) {\n            fig.send_message('set_device_pixel_ratio', {\n                device_pixel_ratio: fig.ratio,\n            });\n        }\n        fig.send_message('refresh', {});\n    };\n\n    this.imageObj.onload = function () {\n        if (fig.image_mode === 'full') {\n            // Full images could contain transparency (where diff images\n            // almost always do), so we need to clear the canvas so that\n            // there is no ghosting.\n            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n        }\n        fig.context.drawImage(fig.imageObj, 0, 0);\n    };\n\n    this.imageObj.onunload = function () {\n        fig.ws.close();\n    };\n\n    this.ws.onmessage = this._make_on_message_function(this);\n\n    this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n    var titlebar = document.createElement('div');\n    titlebar.classList =\n        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n    var titletext = document.createElement('div');\n    titletext.classList = 'ui-dialog-title';\n    titletext.setAttribute(\n        'style',\n        'width: 100%; text-align: center; padding: 3px;'\n    );\n    titlebar.appendChild(titletext);\n    this.root.appendChild(titlebar);\n    this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n    var fig = this;\n\n    var canvas_div = (this.canvas_div = document.createElement('div'));\n    canvas_div.setAttribute('tabindex', '0');\n    canvas_div.setAttribute(\n        'style',\n        'border: 1px solid #ddd;' +\n            'box-sizing: content-box;' +\n            'clear: both;' +\n            'min-height: 1px;' +\n            'min-width: 1px;' +\n            'outline: 0;' +\n            'overflow: hidden;' +\n            'position: relative;' +\n            'resize: both;' +\n            'z-index: 2;'\n    );\n\n    function on_keyboard_event_closure(name) {\n        return function (event) {\n            return fig.key_event(event, name);\n        };\n    }\n\n    canvas_div.addEventListener(\n        'keydown',\n        on_keyboard_event_closure('key_press')\n    );\n    canvas_div.addEventListener(\n        'keyup',\n        on_keyboard_event_closure('key_release')\n    );\n\n    this._canvas_extra_style(canvas_div);\n    this.root.appendChild(canvas_div);\n\n    var canvas = (this.canvas = document.createElement('canvas'));\n    canvas.classList.add('mpl-canvas');\n    canvas.setAttribute(\n        'style',\n        'box-sizing: content-box;' +\n            'pointer-events: none;' +\n            'position: relative;' +\n            'z-index: 0;'\n    );\n\n    this.context = canvas.getContext('2d');\n\n    var backingStore =\n        this.context.backingStorePixelRatio ||\n        this.context.webkitBackingStorePixelRatio ||\n        this.context.mozBackingStorePixelRatio ||\n        this.context.msBackingStorePixelRatio ||\n        this.context.oBackingStorePixelRatio ||\n        this.context.backingStorePixelRatio ||\n        1;\n\n    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n        'canvas'\n    ));\n    rubberband_canvas.setAttribute(\n        'style',\n        'box-sizing: content-box;' +\n            'left: 0;' +\n            'pointer-events: none;' +\n            'position: absolute;' +\n            'top: 0;' +\n            'z-index: 1;'\n    );\n\n    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n    if (this.ResizeObserver === undefined) {\n        if (window.ResizeObserver !== undefined) {\n            this.ResizeObserver = window.ResizeObserver;\n        } else {\n            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n            this.ResizeObserver = obs.ResizeObserver;\n        }\n    }\n\n    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n        // There's no need to resize if the WebSocket is not connected:\n        // - If it is still connecting, then we will get an initial resize from\n        //   Python once it connects.\n        // - If it has disconnected, then resizing will clear the canvas and\n        //   never get anything back to refill it, so better to not resize and\n        //   keep something visible.\n        if (fig.ws.readyState != 1) {\n            return;\n        }\n        var nentries = entries.length;\n        for (var i = 0; i < nentries; i++) {\n            var entry = entries[i];\n            var width, height;\n            if (entry.contentBoxSize) {\n                if (entry.contentBoxSize instanceof Array) {\n                    // Chrome 84 implements new version of spec.\n                    width = entry.contentBoxSize[0].inlineSize;\n                    height = entry.contentBoxSize[0].blockSize;\n                } else {\n                    // Firefox implements old version of spec.\n                    width = entry.contentBoxSize.inlineSize;\n                    height = entry.contentBoxSize.blockSize;\n                }\n            } else {\n                // Chrome <84 implements even older version of spec.\n                width = entry.contentRect.width;\n                height = entry.contentRect.height;\n            }\n\n            // Keep the size of the canvas and rubber band canvas in sync with\n            // the canvas container.\n            if (entry.devicePixelContentBoxSize) {\n                // Chrome 84 implements new version of spec.\n                canvas.setAttribute(\n                    'width',\n                    entry.devicePixelContentBoxSize[0].inlineSize\n                );\n                canvas.setAttribute(\n                    'height',\n                    entry.devicePixelContentBoxSize[0].blockSize\n                );\n            } else {\n                canvas.setAttribute('width', width * fig.ratio);\n                canvas.setAttribute('height', height * fig.ratio);\n            }\n            /* This rescales the canvas back to display pixels, so that it\n             * appears correct on HiDPI screens. */\n            canvas.style.width = width + 'px';\n            canvas.style.height = height + 'px';\n\n            rubberband_canvas.setAttribute('width', width);\n            rubberband_canvas.setAttribute('height', height);\n\n            // And update the size in Python. We ignore the initial 0/0 size\n            // that occurs as the element is placed into the DOM, which should\n            // otherwise not happen due to the minimum size styling.\n            if (width != 0 && height != 0) {\n                fig.request_resize(width, height);\n            }\n        }\n    });\n    this.resizeObserverInstance.observe(canvas_div);\n\n    function on_mouse_event_closure(name) {\n        /* User Agent sniffing is bad, but WebKit is busted:\n         * https://bugs.webkit.org/show_bug.cgi?id=144526\n         * https://bugs.webkit.org/show_bug.cgi?id=181818\n         * The worst that happens here is that they get an extra browser\n         * selection when dragging, if this check fails to catch them.\n         */\n        var UA = navigator.userAgent;\n        var isWebKit = /AppleWebKit/.test(UA) && !/Chrome/.test(UA);\n        if(isWebKit) {\n            return function (event) {\n                /* This prevents the web browser from automatically changing to\n                 * the text insertion cursor when the button is pressed. We\n                 * want to control all of the cursor setting manually through\n                 * the 'cursor' event from matplotlib */\n                event.preventDefault()\n                return fig.mouse_event(event, name);\n            };\n        } else {\n            return function (event) {\n                return fig.mouse_event(event, name);\n            };\n        }\n    }\n\n    canvas_div.addEventListener(\n        'mousedown',\n        on_mouse_event_closure('button_press')\n    );\n    canvas_div.addEventListener(\n        'mouseup',\n        on_mouse_event_closure('button_release')\n    );\n    canvas_div.addEventListener(\n        'dblclick',\n        on_mouse_event_closure('dblclick')\n    );\n    // Throttle sequential mouse events to 1 every 20ms.\n    canvas_div.addEventListener(\n        'mousemove',\n        on_mouse_event_closure('motion_notify')\n    );\n\n    canvas_div.addEventListener(\n        'mouseenter',\n        on_mouse_event_closure('figure_enter')\n    );\n    canvas_div.addEventListener(\n        'mouseleave',\n        on_mouse_event_closure('figure_leave')\n    );\n\n    canvas_div.addEventListener('wheel', function (event) {\n        if (event.deltaY < 0) {\n            event.step = 1;\n        } else {\n            event.step = -1;\n        }\n        on_mouse_event_closure('scroll')(event);\n    });\n\n    canvas_div.appendChild(canvas);\n    canvas_div.appendChild(rubberband_canvas);\n\n    this.rubberband_context = rubberband_canvas.getContext('2d');\n    this.rubberband_context.strokeStyle = '#000000';\n\n    this._resize_canvas = function (width, height, forward) {\n        if (forward) {\n            canvas_div.style.width = width + 'px';\n            canvas_div.style.height = height + 'px';\n        }\n    };\n\n    // Disable right mouse context menu.\n    canvas_div.addEventListener('contextmenu', function (_e) {\n        event.preventDefault();\n        return false;\n    });\n\n    function set_focus() {\n        canvas.focus();\n        canvas_div.focus();\n    }\n\n    window.setTimeout(set_focus, 100);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n    var fig = this;\n\n    var toolbar = document.createElement('div');\n    toolbar.classList = 'mpl-toolbar';\n    this.root.appendChild(toolbar);\n\n    function on_click_closure(name) {\n        return function (_event) {\n            return fig.toolbar_button_onclick(name);\n        };\n    }\n\n    function on_mouseover_closure(tooltip) {\n        return function (event) {\n            if (!event.currentTarget.disabled) {\n                return fig.toolbar_button_onmouseover(tooltip);\n            }\n        };\n    }\n\n    fig.buttons = {};\n    var buttonGroup = document.createElement('div');\n    buttonGroup.classList = 'mpl-button-group';\n    for (var toolbar_ind in mpl.toolbar_items) {\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) {\n            /* Instead of a spacer, we start a new button group. */\n            if (buttonGroup.hasChildNodes()) {\n                toolbar.appendChild(buttonGroup);\n            }\n            buttonGroup = document.createElement('div');\n            buttonGroup.classList = 'mpl-button-group';\n            continue;\n        }\n\n        var button = (fig.buttons[name] = document.createElement('button'));\n        button.classList = 'mpl-widget';\n        button.setAttribute('role', 'button');\n        button.setAttribute('aria-disabled', 'false');\n        button.addEventListener('click', on_click_closure(method_name));\n        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n\n        var icon_img = document.createElement('img');\n        icon_img.src = '_images/' + image + '.png';\n        icon_img.srcset = '_images/' + image + '_large.png 2x';\n        icon_img.alt = tooltip;\n        button.appendChild(icon_img);\n\n        buttonGroup.appendChild(button);\n    }\n\n    if (buttonGroup.hasChildNodes()) {\n        toolbar.appendChild(buttonGroup);\n    }\n\n    var fmt_picker = document.createElement('select');\n    fmt_picker.classList = 'mpl-widget';\n    toolbar.appendChild(fmt_picker);\n    this.format_dropdown = fmt_picker;\n\n    for (var ind in mpl.extensions) {\n        var fmt = mpl.extensions[ind];\n        var option = document.createElement('option');\n        option.selected = fmt === mpl.default_extension;\n        option.innerHTML = fmt;\n        fmt_picker.appendChild(option);\n    }\n\n    var status_bar = document.createElement('span');\n    status_bar.classList = 'mpl-message';\n    toolbar.appendChild(status_bar);\n    this.message = status_bar;\n};\n\nmpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n    // which will in turn request a refresh of the image.\n    this.send_message('resize', { width: x_pixels, height: y_pixels });\n};\n\nmpl.figure.prototype.send_message = function (type, properties) {\n    properties['type'] = type;\n    properties['figure_id'] = this.id;\n    this.ws.send(JSON.stringify(properties));\n};\n\nmpl.figure.prototype.send_draw_message = function () {\n    if (!this.waiting) {\n        this.waiting = true;\n        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n    }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n    var format_dropdown = fig.format_dropdown;\n    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n    fig.ondownload(fig, format);\n};\n\nmpl.figure.prototype.handle_resize = function (fig, msg) {\n    var size = msg['size'];\n    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n        fig._resize_canvas(size[0], size[1], msg['forward']);\n        fig.send_message('refresh', {});\n    }\n};\n\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n    var x0 = msg['x0'] / fig.ratio;\n    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n    var x1 = msg['x1'] / fig.ratio;\n    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n    x0 = Math.floor(x0) + 0.5;\n    y0 = Math.floor(y0) + 0.5;\n    x1 = Math.floor(x1) + 0.5;\n    y1 = Math.floor(y1) + 0.5;\n    var min_x = Math.min(x0, x1);\n    var min_y = Math.min(y0, y1);\n    var width = Math.abs(x1 - x0);\n    var height = Math.abs(y1 - y0);\n\n    fig.rubberband_context.clearRect(\n        0,\n        0,\n        fig.canvas.width / fig.ratio,\n        fig.canvas.height / fig.ratio\n    );\n\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n    fig.canvas_div.style.cursor = msg['cursor'];\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n    fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n    fig.image_mode = msg['mode'];\n};\n\nmpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n    for (var key in msg) {\n        if (!(key in fig.buttons)) {\n            continue;\n        }\n        fig.buttons[key].disabled = !msg[key];\n        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n    }\n};\n\nmpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n    if (msg['mode'] === 'PAN') {\n        fig.buttons['Pan'].classList.add('active');\n        fig.buttons['Zoom'].classList.remove('active');\n    } else if (msg['mode'] === 'ZOOM') {\n        fig.buttons['Pan'].classList.remove('active');\n        fig.buttons['Zoom'].classList.add('active');\n    } else {\n        fig.buttons['Pan'].classList.remove('active');\n        fig.buttons['Zoom'].classList.remove('active');\n    }\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n    // Called whenever the canvas gets updated.\n    this.send_message('ack', {});\n};\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function (fig) {\n    return function socket_on_message(evt) {\n        if (evt.data instanceof Blob) {\n            var img = evt.data;\n            if (img.type !== 'image/png') {\n                /* FIXME: We get \"Resource interpreted as Image but\n                 * transferred with MIME type text/plain:\" errors on\n                 * Chrome.  But how to set the MIME type?  It doesn't seem\n                 * to be part of the websocket stream */\n                img.type = 'image/png';\n            }\n\n            /* Free the memory for the previous frames */\n            if (fig.imageObj.src) {\n                (window.URL || window.webkitURL).revokeObjectURL(\n                    fig.imageObj.src\n                );\n            }\n\n            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n                img\n            );\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        } else if (\n            typeof evt.data === 'string' &&\n            evt.data.slice(0, 21) === 'data:image/png;base64'\n        ) {\n            fig.imageObj.src = evt.data;\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n\n        var msg = JSON.parse(evt.data);\n        var msg_type = msg['type'];\n\n        // Call the  \"handle_{type}\" callback, which takes\n        // the figure and JSON message as its only arguments.\n        try {\n            var callback = fig['handle_' + msg_type];\n        } catch (e) {\n            console.log(\n                \"No handler for the '\" + msg_type + \"' message type: \",\n                msg\n            );\n            return;\n        }\n\n        if (callback) {\n            try {\n                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n                callback(fig, msg);\n            } catch (e) {\n                console.log(\n                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n                    e,\n                    e.stack,\n                    msg\n                );\n            }\n        }\n    };\n};\n\nfunction getModifiers(event) {\n    var mods = [];\n    if (event.ctrlKey) {\n        mods.push('ctrl');\n    }\n    if (event.altKey) {\n        mods.push('alt');\n    }\n    if (event.shiftKey) {\n        mods.push('shift');\n    }\n    if (event.metaKey) {\n        mods.push('meta');\n    }\n    return mods;\n}\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * https://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys(original) {\n    return Object.keys(original).reduce(function (obj, key) {\n        if (typeof original[key] !== 'object') {\n            obj[key] = original[key];\n        }\n        return obj;\n    }, {});\n}\n\nmpl.figure.prototype.mouse_event = function (event, name) {\n    if (name === 'button_press') {\n        this.canvas.focus();\n        this.canvas_div.focus();\n    }\n\n    // from https://stackoverflow.com/q/1114465\n    var boundingRect = this.canvas.getBoundingClientRect();\n    var x = (event.clientX - boundingRect.left) * this.ratio;\n    var y = (event.clientY - boundingRect.top) * this.ratio;\n\n    this.send_message(name, {\n        x: x,\n        y: y,\n        button: event.button,\n        step: event.step,\n        modifiers: getModifiers(event),\n        guiEvent: simpleKeys(event),\n    });\n\n    return false;\n};\n\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n    // Handle any extra behaviour associated with a key event\n};\n\nmpl.figure.prototype.key_event = function (event, name) {\n    // Prevent repeat events\n    if (name === 'key_press') {\n        if (event.key === this._key) {\n            return;\n        } else {\n            this._key = event.key;\n        }\n    }\n    if (name === 'key_release') {\n        this._key = null;\n    }\n\n    var value = '';\n    if (event.ctrlKey && event.key !== 'Control') {\n        value += 'ctrl+';\n    }\n    else if (event.altKey && event.key !== 'Alt') {\n        value += 'alt+';\n    }\n    else if (event.shiftKey && event.key !== 'Shift') {\n        value += 'shift+';\n    }\n\n    value += 'k' + event.key;\n\n    this._key_event_extra(event, name);\n\n    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n    return false;\n};\n\nmpl.figure.prototype.toolbar_button_onclick = function (name) {\n    if (name === 'download') {\n        this.handle_save(this, null);\n    } else {\n        this.send_message('toolbar_button', { name: name });\n    }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n    this.message.textContent = tooltip;\n};\n\n///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n// prettier-ignore\nvar _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar comm_websocket_adapter = function (comm) {\n    // Create a \"websocket\"-like object which calls the given IPython comm\n    // object with the appropriate methods. Currently this is a non binary\n    // socket, so there is still some room for performance tuning.\n    var ws = {};\n\n    ws.binaryType = comm.kernel.ws.binaryType;\n    ws.readyState = comm.kernel.ws.readyState;\n    function updateReadyState(_event) {\n        if (comm.kernel.ws) {\n            ws.readyState = comm.kernel.ws.readyState;\n        } else {\n            ws.readyState = 3; // Closed state.\n        }\n    }\n    comm.kernel.ws.addEventListener('open', updateReadyState);\n    comm.kernel.ws.addEventListener('close', updateReadyState);\n    comm.kernel.ws.addEventListener('error', updateReadyState);\n\n    ws.close = function () {\n        comm.close();\n    };\n    ws.send = function (m) {\n        //console.log('sending', m);\n        comm.send(m);\n    };\n    // Register the callback with on_msg.\n    comm.on_msg(function (msg) {\n        //console.log('receiving', msg['content']['data'], msg);\n        var data = msg['content']['data'];\n        if (data['blob'] !== undefined) {\n            data = {\n                data: new Blob(msg['buffers'], { type: data['blob'] }),\n            };\n        }\n        // Pass the mpl event to the overridden (by mpl) onmessage function.\n        ws.onmessage(data);\n    });\n    return ws;\n};\n\nmpl.mpl_figure_comm = function (comm, msg) {\n    // This is the function which gets called when the mpl process\n    // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n    var id = msg.content.data.id;\n    // Get hold of the div created by the display call when the Comm\n    // socket was opened in Python.\n    var element = document.getElementById(id);\n    var ws_proxy = comm_websocket_adapter(comm);\n\n    function ondownload(figure, _format) {\n        window.open(figure.canvas.toDataURL());\n    }\n\n    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n\n    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n    // web socket which is closed, not our websocket->open comm proxy.\n    ws_proxy.onopen();\n\n    fig.parent_element = element;\n    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n    if (!fig.cell_info) {\n        console.error('Failed to find cell for figure', id, fig);\n        return;\n    }\n    fig.cell_info[0].output_area.element.on(\n        'cleared',\n        { fig: fig },\n        fig._remove_fig_handler\n    );\n};\n\nmpl.figure.prototype.handle_close = function (fig, msg) {\n    var width = fig.canvas.width / fig.ratio;\n    fig.cell_info[0].output_area.element.off(\n        'cleared',\n        fig._remove_fig_handler\n    );\n    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n\n    // Update the output cell to use the data from the current canvas.\n    fig.push_to_output();\n    var dataURL = fig.canvas.toDataURL();\n    // Re-enable the keyboard manager in IPython - without this line, in FF,\n    // the notebook keyboard shortcuts fail.\n    IPython.keyboard_manager.enable();\n    fig.parent_element.innerHTML =\n        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n    fig.close_ws(fig, msg);\n};\n\nmpl.figure.prototype.close_ws = function (fig, msg) {\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n};\n\nmpl.figure.prototype.push_to_output = function (_remove_interactive) {\n    // Turn the data on the canvas into data in the output cell.\n    var width = this.canvas.width / this.ratio;\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] =\n        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n    // Tell IPython that the notebook contents must change.\n    IPython.notebook.set_dirty(true);\n    this.send_message('ack', {});\n    var fig = this;\n    // Wait a second, then push the new image to the DOM so\n    // that it is saved nicely (might be nice to debounce this).\n    setTimeout(function () {\n        fig.push_to_output();\n    }, 1000);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n    var fig = this;\n\n    var toolbar = document.createElement('div');\n    toolbar.classList = 'btn-toolbar';\n    this.root.appendChild(toolbar);\n\n    function on_click_closure(name) {\n        return function (_event) {\n            return fig.toolbar_button_onclick(name);\n        };\n    }\n\n    function on_mouseover_closure(tooltip) {\n        return function (event) {\n            if (!event.currentTarget.disabled) {\n                return fig.toolbar_button_onmouseover(tooltip);\n            }\n        };\n    }\n\n    fig.buttons = {};\n    var buttonGroup = document.createElement('div');\n    buttonGroup.classList = 'btn-group';\n    var button;\n    for (var toolbar_ind in mpl.toolbar_items) {\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) {\n            /* Instead of a spacer, we start a new button group. */\n            if (buttonGroup.hasChildNodes()) {\n                toolbar.appendChild(buttonGroup);\n            }\n            buttonGroup = document.createElement('div');\n            buttonGroup.classList = 'btn-group';\n            continue;\n        }\n\n        button = fig.buttons[name] = document.createElement('button');\n        button.classList = 'btn btn-default';\n        button.href = '#';\n        button.title = name;\n        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n        button.addEventListener('click', on_click_closure(method_name));\n        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n        buttonGroup.appendChild(button);\n    }\n\n    if (buttonGroup.hasChildNodes()) {\n        toolbar.appendChild(buttonGroup);\n    }\n\n    // Add the status bar.\n    var status_bar = document.createElement('span');\n    status_bar.classList = 'mpl-message pull-right';\n    toolbar.appendChild(status_bar);\n    this.message = status_bar;\n\n    // Add the close button to the window.\n    var buttongrp = document.createElement('div');\n    buttongrp.classList = 'btn-group inline pull-right';\n    button = document.createElement('button');\n    button.classList = 'btn btn-mini btn-primary';\n    button.href = '#';\n    button.title = 'Stop Interaction';\n    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n    button.addEventListener('click', function (_evt) {\n        fig.handle_close(fig, {});\n    });\n    button.addEventListener(\n        'mouseover',\n        on_mouseover_closure('Stop Interaction')\n    );\n    buttongrp.appendChild(button);\n    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n};\n\nmpl.figure.prototype._remove_fig_handler = function (event) {\n    var fig = event.data.fig;\n    if (event.target !== this) {\n        // Ignore bubbled events from children.\n        return;\n    }\n    fig.close_ws(fig, {});\n};\n\nmpl.figure.prototype._root_extra_style = function (el) {\n    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n};\n\nmpl.figure.prototype._canvas_extra_style = function (el) {\n    // this is important to make the div 'focusable\n    el.setAttribute('tabindex', 0);\n    // reach out to IPython and tell the keyboard manager to turn it's self\n    // off when our div gets focus\n\n    // location in version 3\n    if (IPython.notebook.keyboard_manager) {\n        IPython.notebook.keyboard_manager.register_events(el);\n    } else {\n        // location in version 2\n        IPython.keyboard_manager.register_events(el);\n    }\n};\n\nmpl.figure.prototype._key_event_extra = function (event, _name) {\n    // Check for shift+enter\n    if (event.shiftKey && event.which === 13) {\n        this.canvas_div.blur();\n        // select the cell after this one\n        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n        IPython.notebook.select(index + 1);\n    }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n    fig.ondownload(fig, null);\n};\n\nmpl.find_output_cell = function (html_output) {\n    // Return the cell and output element which can be found *uniquely* in the notebook.\n    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n    // IPython event is triggered only after the cells have been serialised, which for\n    // our purposes (turning an active figure into a static one), is too late.\n    var cells = IPython.notebook.get_cells();\n    var ncells = cells.length;\n    for (var i = 0; i < ncells; i++) {\n        var cell = cells[i];\n        if (cell.cell_type === 'code') {\n            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n                var data = cell.output_area.outputs[j];\n                if (data.data) {\n                    // IPython >= 3 moved mimebundle to data attribute of output\n                    data = data.data;\n                }\n                if (data['text/html'] === html_output) {\n                    return [cell, data, j];\n                }\n            }\n        }\n    }\n};\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel !== null) {\n    IPython.notebook.kernel.comm_manager.register_target(\n        'matplotlib',\n        mpl.mpl_figure_comm\n    );\n}\n",
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div id='aefab4cc-241a-4d90-ac18-9d9c10bf167d'></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Trains the model according to the auto_arima output -> (2,1,3)x(0,0,0,0)\n",
+    "sarima_model_eval = SARIMAX(train_data, order=(2, 1, 3), seasonal_order=(0, 0, 0, 0))\n",
+    "estimator_eval = sarima_model_eval.fit()\n",
+    "\n",
+    "# Gets forecast for evaluation\n",
+    "preds = estimator_eval.forecast(len(test_data))\n",
+    "\n",
+    "# Plot the results\n",
+    "test_data.plot(color='blue', label='Actual')\n",
+    "preds.plot(color='green', label='Forecasts (30 days)')\n",
+    "\n",
+    "plt.title('Brent Crude Oil Price Forecast (SARIMA model evaluation)')\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Oil price (USD)')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b75439027e93728b",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### Model evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "10e8c74bfbf02659",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-08-15T22:18:35.069933Z",
+     "start_time": "2023-08-15T22:18:35.032369900Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Root Mean Square Error (RMSE): 7.543 \n",
+      "Mean Absolute Error (MAE): 6.431 \n",
+      "Mean Absolute Percentage Error (MAPE): 0.078\n"
+     ]
+    }
+   ],
+   "source": [
+    "rmse = np.sqrt(mean_squared_error(test_data.values, preds.values))\n",
+    "mae = mean_absolute_error(test_data.values, preds.values)\n",
+    "mape = mean_absolute_percentage_error(test_data.values, preds.values)\n",
+    "\n",
+    "print('Root Mean Square Error (RMSE): {} \\nMean Absolute Error (MAE): {} \\nMean Absolute Percentage Error (MAPE): {}'. format(np.round(rmse, 3), np.round(mae, 3), np.round(mape, 3)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3dad982c84a2435",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### Using the model to forecasts Brent crude oil price for the following 15 days"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "5c5ba4afab7cde1",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-08-15T22:19:38.850804300Z",
+     "start_time": "2023-08-15T22:19:35.003043700Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n    if (typeof WebSocket !== 'undefined') {\n        return WebSocket;\n    } else if (typeof MozWebSocket !== 'undefined') {\n        return MozWebSocket;\n    } else {\n        alert(\n            'Your browser does not have WebSocket support. ' +\n                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n                'Firefox 4 and 5 are also supported but you ' +\n                'have to enable WebSockets in about:config.'\n        );\n    }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n    this.id = figure_id;\n\n    this.ws = websocket;\n\n    this.supports_binary = this.ws.binaryType !== undefined;\n\n    if (!this.supports_binary) {\n        var warnings = document.getElementById('mpl-warnings');\n        if (warnings) {\n            warnings.style.display = 'block';\n            warnings.textContent =\n                'This browser does not support binary websocket messages. ' +\n                'Performance may be slow.';\n        }\n    }\n\n    this.imageObj = new Image();\n\n    this.context = undefined;\n    this.message = undefined;\n    this.canvas = undefined;\n    this.rubberband_canvas = undefined;\n    this.rubberband_context = undefined;\n    this.format_dropdown = undefined;\n\n    this.image_mode = 'full';\n\n    this.root = document.createElement('div');\n    this.root.setAttribute('style', 'display: inline-block');\n    this._root_extra_style(this.root);\n\n    parent_element.appendChild(this.root);\n\n    this._init_header(this);\n    this._init_canvas(this);\n    this._init_toolbar(this);\n\n    var fig = this;\n\n    this.waiting = false;\n\n    this.ws.onopen = function () {\n        fig.send_message('supports_binary', { value: fig.supports_binary });\n        fig.send_message('send_image_mode', {});\n        if (fig.ratio !== 1) {\n            fig.send_message('set_device_pixel_ratio', {\n                device_pixel_ratio: fig.ratio,\n            });\n        }\n        fig.send_message('refresh', {});\n    };\n\n    this.imageObj.onload = function () {\n        if (fig.image_mode === 'full') {\n            // Full images could contain transparency (where diff images\n            // almost always do), so we need to clear the canvas so that\n            // there is no ghosting.\n            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n        }\n        fig.context.drawImage(fig.imageObj, 0, 0);\n    };\n\n    this.imageObj.onunload = function () {\n        fig.ws.close();\n    };\n\n    this.ws.onmessage = this._make_on_message_function(this);\n\n    this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n    var titlebar = document.createElement('div');\n    titlebar.classList =\n        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n    var titletext = document.createElement('div');\n    titletext.classList = 'ui-dialog-title';\n    titletext.setAttribute(\n        'style',\n        'width: 100%; text-align: center; padding: 3px;'\n    );\n    titlebar.appendChild(titletext);\n    this.root.appendChild(titlebar);\n    this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n    var fig = this;\n\n    var canvas_div = (this.canvas_div = document.createElement('div'));\n    canvas_div.setAttribute('tabindex', '0');\n    canvas_div.setAttribute(\n        'style',\n        'border: 1px solid #ddd;' +\n            'box-sizing: content-box;' +\n            'clear: both;' +\n            'min-height: 1px;' +\n            'min-width: 1px;' +\n            'outline: 0;' +\n            'overflow: hidden;' +\n            'position: relative;' +\n            'resize: both;' +\n            'z-index: 2;'\n    );\n\n    function on_keyboard_event_closure(name) {\n        return function (event) {\n            return fig.key_event(event, name);\n        };\n    }\n\n    canvas_div.addEventListener(\n        'keydown',\n        on_keyboard_event_closure('key_press')\n    );\n    canvas_div.addEventListener(\n        'keyup',\n        on_keyboard_event_closure('key_release')\n    );\n\n    this._canvas_extra_style(canvas_div);\n    this.root.appendChild(canvas_div);\n\n    var canvas = (this.canvas = document.createElement('canvas'));\n    canvas.classList.add('mpl-canvas');\n    canvas.setAttribute(\n        'style',\n        'box-sizing: content-box;' +\n            'pointer-events: none;' +\n            'position: relative;' +\n            'z-index: 0;'\n    );\n\n    this.context = canvas.getContext('2d');\n\n    var backingStore =\n        this.context.backingStorePixelRatio ||\n        this.context.webkitBackingStorePixelRatio ||\n        this.context.mozBackingStorePixelRatio ||\n        this.context.msBackingStorePixelRatio ||\n        this.context.oBackingStorePixelRatio ||\n        this.context.backingStorePixelRatio ||\n        1;\n\n    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n        'canvas'\n    ));\n    rubberband_canvas.setAttribute(\n        'style',\n        'box-sizing: content-box;' +\n            'left: 0;' +\n            'pointer-events: none;' +\n            'position: absolute;' +\n            'top: 0;' +\n            'z-index: 1;'\n    );\n\n    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n    if (this.ResizeObserver === undefined) {\n        if (window.ResizeObserver !== undefined) {\n            this.ResizeObserver = window.ResizeObserver;\n        } else {\n            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n            this.ResizeObserver = obs.ResizeObserver;\n        }\n    }\n\n    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n        // There's no need to resize if the WebSocket is not connected:\n        // - If it is still connecting, then we will get an initial resize from\n        //   Python once it connects.\n        // - If it has disconnected, then resizing will clear the canvas and\n        //   never get anything back to refill it, so better to not resize and\n        //   keep something visible.\n        if (fig.ws.readyState != 1) {\n            return;\n        }\n        var nentries = entries.length;\n        for (var i = 0; i < nentries; i++) {\n            var entry = entries[i];\n            var width, height;\n            if (entry.contentBoxSize) {\n                if (entry.contentBoxSize instanceof Array) {\n                    // Chrome 84 implements new version of spec.\n                    width = entry.contentBoxSize[0].inlineSize;\n                    height = entry.contentBoxSize[0].blockSize;\n                } else {\n                    // Firefox implements old version of spec.\n                    width = entry.contentBoxSize.inlineSize;\n                    height = entry.contentBoxSize.blockSize;\n                }\n            } else {\n                // Chrome <84 implements even older version of spec.\n                width = entry.contentRect.width;\n                height = entry.contentRect.height;\n            }\n\n            // Keep the size of the canvas and rubber band canvas in sync with\n            // the canvas container.\n            if (entry.devicePixelContentBoxSize) {\n                // Chrome 84 implements new version of spec.\n                canvas.setAttribute(\n                    'width',\n                    entry.devicePixelContentBoxSize[0].inlineSize\n                );\n                canvas.setAttribute(\n                    'height',\n                    entry.devicePixelContentBoxSize[0].blockSize\n                );\n            } else {\n                canvas.setAttribute('width', width * fig.ratio);\n                canvas.setAttribute('height', height * fig.ratio);\n            }\n            /* This rescales the canvas back to display pixels, so that it\n             * appears correct on HiDPI screens. */\n            canvas.style.width = width + 'px';\n            canvas.style.height = height + 'px';\n\n            rubberband_canvas.setAttribute('width', width);\n            rubberband_canvas.setAttribute('height', height);\n\n            // And update the size in Python. We ignore the initial 0/0 size\n            // that occurs as the element is placed into the DOM, which should\n            // otherwise not happen due to the minimum size styling.\n            if (width != 0 && height != 0) {\n                fig.request_resize(width, height);\n            }\n        }\n    });\n    this.resizeObserverInstance.observe(canvas_div);\n\n    function on_mouse_event_closure(name) {\n        /* User Agent sniffing is bad, but WebKit is busted:\n         * https://bugs.webkit.org/show_bug.cgi?id=144526\n         * https://bugs.webkit.org/show_bug.cgi?id=181818\n         * The worst that happens here is that they get an extra browser\n         * selection when dragging, if this check fails to catch them.\n         */\n        var UA = navigator.userAgent;\n        var isWebKit = /AppleWebKit/.test(UA) && !/Chrome/.test(UA);\n        if(isWebKit) {\n            return function (event) {\n                /* This prevents the web browser from automatically changing to\n                 * the text insertion cursor when the button is pressed. We\n                 * want to control all of the cursor setting manually through\n                 * the 'cursor' event from matplotlib */\n                event.preventDefault()\n                return fig.mouse_event(event, name);\n            };\n        } else {\n            return function (event) {\n                return fig.mouse_event(event, name);\n            };\n        }\n    }\n\n    canvas_div.addEventListener(\n        'mousedown',\n        on_mouse_event_closure('button_press')\n    );\n    canvas_div.addEventListener(\n        'mouseup',\n        on_mouse_event_closure('button_release')\n    );\n    canvas_div.addEventListener(\n        'dblclick',\n        on_mouse_event_closure('dblclick')\n    );\n    // Throttle sequential mouse events to 1 every 20ms.\n    canvas_div.addEventListener(\n        'mousemove',\n        on_mouse_event_closure('motion_notify')\n    );\n\n    canvas_div.addEventListener(\n        'mouseenter',\n        on_mouse_event_closure('figure_enter')\n    );\n    canvas_div.addEventListener(\n        'mouseleave',\n        on_mouse_event_closure('figure_leave')\n    );\n\n    canvas_div.addEventListener('wheel', function (event) {\n        if (event.deltaY < 0) {\n            event.step = 1;\n        } else {\n            event.step = -1;\n        }\n        on_mouse_event_closure('scroll')(event);\n    });\n\n    canvas_div.appendChild(canvas);\n    canvas_div.appendChild(rubberband_canvas);\n\n    this.rubberband_context = rubberband_canvas.getContext('2d');\n    this.rubberband_context.strokeStyle = '#000000';\n\n    this._resize_canvas = function (width, height, forward) {\n        if (forward) {\n            canvas_div.style.width = width + 'px';\n            canvas_div.style.height = height + 'px';\n        }\n    };\n\n    // Disable right mouse context menu.\n    canvas_div.addEventListener('contextmenu', function (_e) {\n        event.preventDefault();\n        return false;\n    });\n\n    function set_focus() {\n        canvas.focus();\n        canvas_div.focus();\n    }\n\n    window.setTimeout(set_focus, 100);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n    var fig = this;\n\n    var toolbar = document.createElement('div');\n    toolbar.classList = 'mpl-toolbar';\n    this.root.appendChild(toolbar);\n\n    function on_click_closure(name) {\n        return function (_event) {\n            return fig.toolbar_button_onclick(name);\n        };\n    }\n\n    function on_mouseover_closure(tooltip) {\n        return function (event) {\n            if (!event.currentTarget.disabled) {\n                return fig.toolbar_button_onmouseover(tooltip);\n            }\n        };\n    }\n\n    fig.buttons = {};\n    var buttonGroup = document.createElement('div');\n    buttonGroup.classList = 'mpl-button-group';\n    for (var toolbar_ind in mpl.toolbar_items) {\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) {\n            /* Instead of a spacer, we start a new button group. */\n            if (buttonGroup.hasChildNodes()) {\n                toolbar.appendChild(buttonGroup);\n            }\n            buttonGroup = document.createElement('div');\n            buttonGroup.classList = 'mpl-button-group';\n            continue;\n        }\n\n        var button = (fig.buttons[name] = document.createElement('button'));\n        button.classList = 'mpl-widget';\n        button.setAttribute('role', 'button');\n        button.setAttribute('aria-disabled', 'false');\n        button.addEventListener('click', on_click_closure(method_name));\n        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n\n        var icon_img = document.createElement('img');\n        icon_img.src = '_images/' + image + '.png';\n        icon_img.srcset = '_images/' + image + '_large.png 2x';\n        icon_img.alt = tooltip;\n        button.appendChild(icon_img);\n\n        buttonGroup.appendChild(button);\n    }\n\n    if (buttonGroup.hasChildNodes()) {\n        toolbar.appendChild(buttonGroup);\n    }\n\n    var fmt_picker = document.createElement('select');\n    fmt_picker.classList = 'mpl-widget';\n    toolbar.appendChild(fmt_picker);\n    this.format_dropdown = fmt_picker;\n\n    for (var ind in mpl.extensions) {\n        var fmt = mpl.extensions[ind];\n        var option = document.createElement('option');\n        option.selected = fmt === mpl.default_extension;\n        option.innerHTML = fmt;\n        fmt_picker.appendChild(option);\n    }\n\n    var status_bar = document.createElement('span');\n    status_bar.classList = 'mpl-message';\n    toolbar.appendChild(status_bar);\n    this.message = status_bar;\n};\n\nmpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n    // which will in turn request a refresh of the image.\n    this.send_message('resize', { width: x_pixels, height: y_pixels });\n};\n\nmpl.figure.prototype.send_message = function (type, properties) {\n    properties['type'] = type;\n    properties['figure_id'] = this.id;\n    this.ws.send(JSON.stringify(properties));\n};\n\nmpl.figure.prototype.send_draw_message = function () {\n    if (!this.waiting) {\n        this.waiting = true;\n        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n    }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n    var format_dropdown = fig.format_dropdown;\n    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n    fig.ondownload(fig, format);\n};\n\nmpl.figure.prototype.handle_resize = function (fig, msg) {\n    var size = msg['size'];\n    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n        fig._resize_canvas(size[0], size[1], msg['forward']);\n        fig.send_message('refresh', {});\n    }\n};\n\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n    var x0 = msg['x0'] / fig.ratio;\n    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n    var x1 = msg['x1'] / fig.ratio;\n    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n    x0 = Math.floor(x0) + 0.5;\n    y0 = Math.floor(y0) + 0.5;\n    x1 = Math.floor(x1) + 0.5;\n    y1 = Math.floor(y1) + 0.5;\n    var min_x = Math.min(x0, x1);\n    var min_y = Math.min(y0, y1);\n    var width = Math.abs(x1 - x0);\n    var height = Math.abs(y1 - y0);\n\n    fig.rubberband_context.clearRect(\n        0,\n        0,\n        fig.canvas.width / fig.ratio,\n        fig.canvas.height / fig.ratio\n    );\n\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n    fig.canvas_div.style.cursor = msg['cursor'];\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n    fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n    fig.image_mode = msg['mode'];\n};\n\nmpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n    for (var key in msg) {\n        if (!(key in fig.buttons)) {\n            continue;\n        }\n        fig.buttons[key].disabled = !msg[key];\n        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n    }\n};\n\nmpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n    if (msg['mode'] === 'PAN') {\n        fig.buttons['Pan'].classList.add('active');\n        fig.buttons['Zoom'].classList.remove('active');\n    } else if (msg['mode'] === 'ZOOM') {\n        fig.buttons['Pan'].classList.remove('active');\n        fig.buttons['Zoom'].classList.add('active');\n    } else {\n        fig.buttons['Pan'].classList.remove('active');\n        fig.buttons['Zoom'].classList.remove('active');\n    }\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n    // Called whenever the canvas gets updated.\n    this.send_message('ack', {});\n};\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function (fig) {\n    return function socket_on_message(evt) {\n        if (evt.data instanceof Blob) {\n            var img = evt.data;\n            if (img.type !== 'image/png') {\n                /* FIXME: We get \"Resource interpreted as Image but\n                 * transferred with MIME type text/plain:\" errors on\n                 * Chrome.  But how to set the MIME type?  It doesn't seem\n                 * to be part of the websocket stream */\n                img.type = 'image/png';\n            }\n\n            /* Free the memory for the previous frames */\n            if (fig.imageObj.src) {\n                (window.URL || window.webkitURL).revokeObjectURL(\n                    fig.imageObj.src\n                );\n            }\n\n            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n                img\n            );\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        } else if (\n            typeof evt.data === 'string' &&\n            evt.data.slice(0, 21) === 'data:image/png;base64'\n        ) {\n            fig.imageObj.src = evt.data;\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n\n        var msg = JSON.parse(evt.data);\n        var msg_type = msg['type'];\n\n        // Call the  \"handle_{type}\" callback, which takes\n        // the figure and JSON message as its only arguments.\n        try {\n            var callback = fig['handle_' + msg_type];\n        } catch (e) {\n            console.log(\n                \"No handler for the '\" + msg_type + \"' message type: \",\n                msg\n            );\n            return;\n        }\n\n        if (callback) {\n            try {\n                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n                callback(fig, msg);\n            } catch (e) {\n                console.log(\n                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n                    e,\n                    e.stack,\n                    msg\n                );\n            }\n        }\n    };\n};\n\nfunction getModifiers(event) {\n    var mods = [];\n    if (event.ctrlKey) {\n        mods.push('ctrl');\n    }\n    if (event.altKey) {\n        mods.push('alt');\n    }\n    if (event.shiftKey) {\n        mods.push('shift');\n    }\n    if (event.metaKey) {\n        mods.push('meta');\n    }\n    return mods;\n}\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * https://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys(original) {\n    return Object.keys(original).reduce(function (obj, key) {\n        if (typeof original[key] !== 'object') {\n            obj[key] = original[key];\n        }\n        return obj;\n    }, {});\n}\n\nmpl.figure.prototype.mouse_event = function (event, name) {\n    if (name === 'button_press') {\n        this.canvas.focus();\n        this.canvas_div.focus();\n    }\n\n    // from https://stackoverflow.com/q/1114465\n    var boundingRect = this.canvas.getBoundingClientRect();\n    var x = (event.clientX - boundingRect.left) * this.ratio;\n    var y = (event.clientY - boundingRect.top) * this.ratio;\n\n    this.send_message(name, {\n        x: x,\n        y: y,\n        button: event.button,\n        step: event.step,\n        modifiers: getModifiers(event),\n        guiEvent: simpleKeys(event),\n    });\n\n    return false;\n};\n\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n    // Handle any extra behaviour associated with a key event\n};\n\nmpl.figure.prototype.key_event = function (event, name) {\n    // Prevent repeat events\n    if (name === 'key_press') {\n        if (event.key === this._key) {\n            return;\n        } else {\n            this._key = event.key;\n        }\n    }\n    if (name === 'key_release') {\n        this._key = null;\n    }\n\n    var value = '';\n    if (event.ctrlKey && event.key !== 'Control') {\n        value += 'ctrl+';\n    }\n    else if (event.altKey && event.key !== 'Alt') {\n        value += 'alt+';\n    }\n    else if (event.shiftKey && event.key !== 'Shift') {\n        value += 'shift+';\n    }\n\n    value += 'k' + event.key;\n\n    this._key_event_extra(event, name);\n\n    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n    return false;\n};\n\nmpl.figure.prototype.toolbar_button_onclick = function (name) {\n    if (name === 'download') {\n        this.handle_save(this, null);\n    } else {\n        this.send_message('toolbar_button', { name: name });\n    }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n    this.message.textContent = tooltip;\n};\n\n///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n// prettier-ignore\nvar _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar comm_websocket_adapter = function (comm) {\n    // Create a \"websocket\"-like object which calls the given IPython comm\n    // object with the appropriate methods. Currently this is a non binary\n    // socket, so there is still some room for performance tuning.\n    var ws = {};\n\n    ws.binaryType = comm.kernel.ws.binaryType;\n    ws.readyState = comm.kernel.ws.readyState;\n    function updateReadyState(_event) {\n        if (comm.kernel.ws) {\n            ws.readyState = comm.kernel.ws.readyState;\n        } else {\n            ws.readyState = 3; // Closed state.\n        }\n    }\n    comm.kernel.ws.addEventListener('open', updateReadyState);\n    comm.kernel.ws.addEventListener('close', updateReadyState);\n    comm.kernel.ws.addEventListener('error', updateReadyState);\n\n    ws.close = function () {\n        comm.close();\n    };\n    ws.send = function (m) {\n        //console.log('sending', m);\n        comm.send(m);\n    };\n    // Register the callback with on_msg.\n    comm.on_msg(function (msg) {\n        //console.log('receiving', msg['content']['data'], msg);\n        var data = msg['content']['data'];\n        if (data['blob'] !== undefined) {\n            data = {\n                data: new Blob(msg['buffers'], { type: data['blob'] }),\n            };\n        }\n        // Pass the mpl event to the overridden (by mpl) onmessage function.\n        ws.onmessage(data);\n    });\n    return ws;\n};\n\nmpl.mpl_figure_comm = function (comm, msg) {\n    // This is the function which gets called when the mpl process\n    // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n    var id = msg.content.data.id;\n    // Get hold of the div created by the display call when the Comm\n    // socket was opened in Python.\n    var element = document.getElementById(id);\n    var ws_proxy = comm_websocket_adapter(comm);\n\n    function ondownload(figure, _format) {\n        window.open(figure.canvas.toDataURL());\n    }\n\n    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n\n    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n    // web socket which is closed, not our websocket->open comm proxy.\n    ws_proxy.onopen();\n\n    fig.parent_element = element;\n    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n    if (!fig.cell_info) {\n        console.error('Failed to find cell for figure', id, fig);\n        return;\n    }\n    fig.cell_info[0].output_area.element.on(\n        'cleared',\n        { fig: fig },\n        fig._remove_fig_handler\n    );\n};\n\nmpl.figure.prototype.handle_close = function (fig, msg) {\n    var width = fig.canvas.width / fig.ratio;\n    fig.cell_info[0].output_area.element.off(\n        'cleared',\n        fig._remove_fig_handler\n    );\n    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n\n    // Update the output cell to use the data from the current canvas.\n    fig.push_to_output();\n    var dataURL = fig.canvas.toDataURL();\n    // Re-enable the keyboard manager in IPython - without this line, in FF,\n    // the notebook keyboard shortcuts fail.\n    IPython.keyboard_manager.enable();\n    fig.parent_element.innerHTML =\n        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n    fig.close_ws(fig, msg);\n};\n\nmpl.figure.prototype.close_ws = function (fig, msg) {\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n};\n\nmpl.figure.prototype.push_to_output = function (_remove_interactive) {\n    // Turn the data on the canvas into data in the output cell.\n    var width = this.canvas.width / this.ratio;\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] =\n        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n    // Tell IPython that the notebook contents must change.\n    IPython.notebook.set_dirty(true);\n    this.send_message('ack', {});\n    var fig = this;\n    // Wait a second, then push the new image to the DOM so\n    // that it is saved nicely (might be nice to debounce this).\n    setTimeout(function () {\n        fig.push_to_output();\n    }, 1000);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n    var fig = this;\n\n    var toolbar = document.createElement('div');\n    toolbar.classList = 'btn-toolbar';\n    this.root.appendChild(toolbar);\n\n    function on_click_closure(name) {\n        return function (_event) {\n            return fig.toolbar_button_onclick(name);\n        };\n    }\n\n    function on_mouseover_closure(tooltip) {\n        return function (event) {\n            if (!event.currentTarget.disabled) {\n                return fig.toolbar_button_onmouseover(tooltip);\n            }\n        };\n    }\n\n    fig.buttons = {};\n    var buttonGroup = document.createElement('div');\n    buttonGroup.classList = 'btn-group';\n    var button;\n    for (var toolbar_ind in mpl.toolbar_items) {\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) {\n            /* Instead of a spacer, we start a new button group. */\n            if (buttonGroup.hasChildNodes()) {\n                toolbar.appendChild(buttonGroup);\n            }\n            buttonGroup = document.createElement('div');\n            buttonGroup.classList = 'btn-group';\n            continue;\n        }\n\n        button = fig.buttons[name] = document.createElement('button');\n        button.classList = 'btn btn-default';\n        button.href = '#';\n        button.title = name;\n        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n        button.addEventListener('click', on_click_closure(method_name));\n        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n        buttonGroup.appendChild(button);\n    }\n\n    if (buttonGroup.hasChildNodes()) {\n        toolbar.appendChild(buttonGroup);\n    }\n\n    // Add the status bar.\n    var status_bar = document.createElement('span');\n    status_bar.classList = 'mpl-message pull-right';\n    toolbar.appendChild(status_bar);\n    this.message = status_bar;\n\n    // Add the close button to the window.\n    var buttongrp = document.createElement('div');\n    buttongrp.classList = 'btn-group inline pull-right';\n    button = document.createElement('button');\n    button.classList = 'btn btn-mini btn-primary';\n    button.href = '#';\n    button.title = 'Stop Interaction';\n    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n    button.addEventListener('click', function (_evt) {\n        fig.handle_close(fig, {});\n    });\n    button.addEventListener(\n        'mouseover',\n        on_mouseover_closure('Stop Interaction')\n    );\n    buttongrp.appendChild(button);\n    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n};\n\nmpl.figure.prototype._remove_fig_handler = function (event) {\n    var fig = event.data.fig;\n    if (event.target !== this) {\n        // Ignore bubbled events from children.\n        return;\n    }\n    fig.close_ws(fig, {});\n};\n\nmpl.figure.prototype._root_extra_style = function (el) {\n    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n};\n\nmpl.figure.prototype._canvas_extra_style = function (el) {\n    // this is important to make the div 'focusable\n    el.setAttribute('tabindex', 0);\n    // reach out to IPython and tell the keyboard manager to turn it's self\n    // off when our div gets focus\n\n    // location in version 3\n    if (IPython.notebook.keyboard_manager) {\n        IPython.notebook.keyboard_manager.register_events(el);\n    } else {\n        // location in version 2\n        IPython.keyboard_manager.register_events(el);\n    }\n};\n\nmpl.figure.prototype._key_event_extra = function (event, _name) {\n    // Check for shift+enter\n    if (event.shiftKey && event.which === 13) {\n        this.canvas_div.blur();\n        // select the cell after this one\n        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n        IPython.notebook.select(index + 1);\n    }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n    fig.ondownload(fig, null);\n};\n\nmpl.find_output_cell = function (html_output) {\n    // Return the cell and output element which can be found *uniquely* in the notebook.\n    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n    // IPython event is triggered only after the cells have been serialised, which for\n    // our purposes (turning an active figure into a static one), is too late.\n    var cells = IPython.notebook.get_cells();\n    var ncells = cells.length;\n    for (var i = 0; i < ncells; i++) {\n        var cell = cells[i];\n        if (cell.cell_type === 'code') {\n            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n                var data = cell.output_area.outputs[j];\n                if (data.data) {\n                    // IPython >= 3 moved mimebundle to data attribute of output\n                    data = data.data;\n                }\n                if (data['text/html'] === html_output) {\n                    return [cell, data, j];\n                }\n            }\n        }\n    }\n};\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel !== null) {\n    IPython.notebook.kernel.comm_manager.register_target(\n        'matplotlib',\n        mpl.mpl_figure_comm\n    );\n}\n",
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div id='9714196a-79a7-40a3-a24b-4910e1107bdc'></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Forecasts for the following 15 days: \n",
+      "\n",
+      "2023-08-08    86.307389\n",
+      "2023-08-09    86.385525\n",
+      "2023-08-10    86.422531\n",
+      "2023-08-11    86.357448\n",
+      "2023-08-14    86.391188\n",
+      "2023-08-15    86.394938\n",
+      "2023-08-16    86.376393\n",
+      "2023-08-17    86.389151\n",
+      "2023-08-18    86.387762\n",
+      "2023-08-21    86.382943\n",
+      "2023-08-22    86.387353\n",
+      "2023-08-23    86.386137\n",
+      "2023-08-24    86.385046\n",
+      "2023-08-25    86.386461\n",
+      "2023-08-28    86.385860\n",
+      "Freq: B, Name: predicted_mean, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Sets the model\n",
+    "sarima_model_forecast = SARIMAX(data, order=(2,1,3), seasonal_order=(0,0,0,0))\n",
+    "estimator_forecast = sarima_model_forecast.fit()\n",
+    "\n",
+    "# Makes predictions\n",
+    "steps_ahead = 15\n",
+    "forecasts = estimator_forecast.forecast(steps_ahead)\n",
+    "ci = estimator_forecast.conf_int()\n",
+    "\n",
+    "# Displays the results\n",
+    "short_data = data[data.index.year >= 2023]\n",
+    "short_data.plot(color='blue', label='Actual')\n",
+    "forecasts.plot(color='red', label='Forecasts')\n",
+    "\n",
+    "plt.title('Brent Crude Oil Price Forecast with SARIMA (15 days ahead)')\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Price per barrel (USD)')\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "print('Forecasts for the following {} days: \\n'.format(steps_ahead))\n",
+    "print(forecasts)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "84332a9e310c3d05",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "## LSTM model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d14c64aa9499f88f",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### Data preparation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "7340b4d7e3ac23c",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-08-15T22:19:44.031226900Z",
+     "start_time": "2023-08-15T22:19:43.990239400Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Reshapes the data to feed the model\n",
+    "full_data_lstm = data.values.reshape(-1, 1)\n",
+    "train_data_lstm = train_data.values.reshape(-1, 1)\n",
+    "test_data_lstm = test_data.values.reshape(-1, 1)\n",
+    "\n",
+    "# Defines train and test sets\n",
+    "X_train = []\n",
+    "y_train = []\n",
+    "ws = 30 # Window size: indicates the number of previous time steps. The more, may lead to higher accuracy, but increases complexity and training time.\n",
+    "\n",
+    "for i in range(ws, len(train_data_lstm)):\n",
+    "    X_train.append(train_data_lstm[i - ws: i])\n",
+    "    y_train.append(train_data_lstm[i])\n",
+    "\n",
+    "X_train, y_train = np.array(X_train), np.array(y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37bd7fddec3de406",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### Model training\n",
+    "The model hyperparameters were chosen after evaluating many different combinations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "39f22e4e1f84e9d8",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-08-13T20:12:14.181032600Z",
+     "start_time": "2023-08-13T20:06:27.505624800Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\hp\\KAIM\\KAIM-W10\\.week10\\lib\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+      "  super().__init__(**kwargs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 195ms/step - loss: 8906.3828\n",
+      "Epoch 2/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 179ms/step - loss: 290606.4375\n",
+      "Epoch 3/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 144ms/step - loss: 3227427.2500\n",
+      "Epoch 4/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 151ms/step - loss: 24450568.0000\n",
+      "Epoch 5/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 144ms/step - loss: 1138792.8750\n",
+      "Epoch 6/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 140ms/step - loss: 373213.1562\n",
+      "Epoch 7/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 165ms/step - loss: 12454.3652\n",
+      "Epoch 8/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 146ms/step - loss: 2112.4336\n",
+      "Epoch 9/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 154ms/step - loss: 856.1956\n",
+      "Epoch 10/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 147ms/step - loss: 584.5531\n",
+      "Epoch 11/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 150ms/step - loss: 548.2406\n",
+      "Epoch 12/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 165ms/step - loss: 525.4077\n",
+      "Epoch 13/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 154ms/step - loss: 524.2949\n",
+      "Epoch 14/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 152ms/step - loss: 516.2444\n",
+      "Epoch 15/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 148ms/step - loss: 517.1100\n",
+      "Epoch 16/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 164ms/step - loss: 519.7084\n",
+      "Epoch 17/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 177ms/step - loss: 517.5525\n",
+      "Epoch 18/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 170ms/step - loss: 518.1655\n",
+      "Epoch 19/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 157ms/step - loss: 517.6241\n",
+      "Epoch 20/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 140ms/step - loss: 516.8340\n",
+      "Epoch 21/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 157ms/step - loss: 514.4555\n",
+      "Epoch 22/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 141ms/step - loss: 509.4087\n",
+      "Epoch 23/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 154ms/step - loss: 517.6816\n",
+      "Epoch 24/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 144ms/step - loss: 520.5711\n",
+      "Epoch 25/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 144ms/step - loss: 508.8103\n",
+      "Epoch 26/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 141ms/step - loss: 512.9596\n",
+      "Epoch 27/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 145ms/step - loss: 506.9124\n",
+      "Epoch 28/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 148ms/step - loss: 510.2070\n",
+      "Epoch 29/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 153ms/step - loss: 507.2782\n",
+      "Epoch 30/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 149ms/step - loss: 504.7595\n",
+      "Epoch 31/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 158ms/step - loss: 498.8062\n",
+      "Epoch 32/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 144ms/step - loss: 498.6979\n",
+      "Epoch 33/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 147ms/step - loss: 503.3813\n",
+      "Epoch 34/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 148ms/step - loss: 495.0640\n",
+      "Epoch 35/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 142ms/step - loss: 491.5777\n",
+      "Epoch 36/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 152ms/step - loss: 504.2186\n",
+      "Epoch 37/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 142ms/step - loss: 488.8065\n",
+      "Epoch 38/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 144ms/step - loss: 497.5757\n",
+      "Epoch 39/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 146ms/step - loss: 495.3186\n",
+      "Epoch 40/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 139ms/step - loss: 485.9297\n",
+      "Epoch 41/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 147ms/step - loss: 473.7652\n",
+      "Epoch 42/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 152ms/step - loss: 484.3695\n",
+      "Epoch 43/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 150ms/step - loss: 483.3105\n",
+      "Epoch 44/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 159ms/step - loss: 482.6473\n",
+      "Epoch 45/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 143ms/step - loss: 487.5897\n",
+      "Epoch 46/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 186ms/step - loss: 475.6985\n",
+      "Epoch 47/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 161ms/step - loss: 472.0938\n",
+      "Epoch 48/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 162ms/step - loss: 480.6802\n",
+      "Epoch 49/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 156ms/step - loss: 471.2747\n",
+      "Epoch 50/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 162ms/step - loss: 466.4544\n",
+      "Epoch 51/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 137ms/step - loss: 466.1915\n",
+      "Epoch 52/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 147ms/step - loss: 469.7241\n",
+      "Epoch 53/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 142ms/step - loss: 464.5523\n",
+      "Epoch 54/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 148ms/step - loss: 467.1182\n",
+      "Epoch 55/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 158ms/step - loss: 458.2868\n",
+      "Epoch 56/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 163ms/step - loss: 462.8828\n",
+      "Epoch 57/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 146ms/step - loss: 460.3172\n",
+      "Epoch 58/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 193ms/step - loss: 452.8856\n",
+      "Epoch 59/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 188ms/step - loss: 453.9950\n",
+      "Epoch 60/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 160ms/step - loss: 455.5670\n",
+      "Epoch 61/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 154ms/step - loss: 448.9649\n",
+      "Epoch 62/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 156ms/step - loss: 433.8644\n",
+      "Epoch 63/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 170ms/step - loss: 432.4335\n",
+      "Epoch 64/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 158ms/step - loss: 400.9525\n",
+      "Epoch 65/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 166ms/step - loss: 373.7333\n",
+      "Epoch 66/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 154ms/step - loss: 438.7653\n",
+      "Epoch 67/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 160ms/step - loss: 457.9848\n",
+      "Epoch 68/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 177ms/step - loss: 468.8891\n",
+      "Epoch 69/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 168ms/step - loss: 450.4009\n",
+      "Epoch 70/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 198ms/step - loss: 440.6667\n",
+      "Epoch 71/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 169ms/step - loss: 451.7873\n",
+      "Epoch 72/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 165ms/step - loss: 451.0915\n",
+      "Epoch 73/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 166ms/step - loss: 447.7713\n",
+      "Epoch 74/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 160ms/step - loss: 450.3023\n",
+      "Epoch 75/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 163ms/step - loss: 446.9399\n",
+      "Epoch 76/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 179ms/step - loss: 454.5841\n",
+      "Epoch 77/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 161ms/step - loss: 441.7691\n",
+      "Epoch 78/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 156ms/step - loss: 446.0479\n",
+      "Epoch 79/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 182ms/step - loss: 442.9978\n",
+      "Epoch 80/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 163ms/step - loss: 436.0324\n",
+      "Epoch 81/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 155ms/step - loss: 432.9245\n",
+      "Epoch 82/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 157ms/step - loss: 435.6631\n",
+      "Epoch 83/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 156ms/step - loss: 434.6516\n",
+      "Epoch 84/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 169ms/step - loss: 435.6400\n",
+      "Epoch 85/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 168ms/step - loss: 435.4590\n",
+      "Epoch 86/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 171ms/step - loss: 422.0802\n",
+      "Epoch 87/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 158ms/step - loss: 432.4128\n",
+      "Epoch 88/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 175ms/step - loss: 434.4842\n",
+      "Epoch 89/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 194ms/step - loss: 435.9730\n",
+      "Epoch 90/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 177ms/step - loss: 422.5783\n",
+      "Epoch 91/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 159ms/step - loss: 426.5378\n",
+      "Epoch 92/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 155ms/step - loss: 421.5165\n",
+      "Epoch 93/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 175ms/step - loss: 422.6959\n",
+      "Epoch 94/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 170ms/step - loss: 425.7914\n",
+      "Epoch 95/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 151ms/step - loss: 421.6433\n",
+      "Epoch 96/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 158ms/step - loss: 421.1930\n",
+      "Epoch 97/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 171ms/step - loss: 413.8031\n",
+      "Epoch 98/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 161ms/step - loss: 410.4409\n",
+      "Epoch 99/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 180ms/step - loss: 410.1944\n",
+      "Epoch 100/100\n",
+      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 168ms/step - loss: 411.5324\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.history.History at 0x2934541e8f0>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = Sequential()\n",
+    "model.add(LSTM(150, activation='relu', input_shape = (X_train.shape[1], 1)))\n",
+    "model.add(Dense(1))\n",
+    "model.compile(optimizer='adam', loss='mse')  \n",
+    "model.fit(X_train, y_train, epochs=100, batch_size=600)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e87ef59ebc709f1",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### Plotting loss\n",
+    "This is useful to check if the number of epochs is adequate: \n",
+    "- A flat trend at the end of the curve is desired.\n",
+    "- If the end of the curve has a downward trend, it could indicate an opportunity of improvement, requiring a larger number of epochs.\n",
+    "- If the end of the curve has an upward trend, it could indicate overfitting, so fewer epochs are required."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "ac41f53607248a94",
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n    if (typeof WebSocket !== 'undefined') {\n        return WebSocket;\n    } else if (typeof MozWebSocket !== 'undefined') {\n        return MozWebSocket;\n    } else {\n        alert(\n            'Your browser does not have WebSocket support. ' +\n                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n                'Firefox 4 and 5 are also supported but you ' +\n                'have to enable WebSockets in about:config.'\n        );\n    }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n    this.id = figure_id;\n\n    this.ws = websocket;\n\n    this.supports_binary = this.ws.binaryType !== undefined;\n\n    if (!this.supports_binary) {\n        var warnings = document.getElementById('mpl-warnings');\n        if (warnings) {\n            warnings.style.display = 'block';\n            warnings.textContent =\n                'This browser does not support binary websocket messages. ' +\n                'Performance may be slow.';\n        }\n    }\n\n    this.imageObj = new Image();\n\n    this.context = undefined;\n    this.message = undefined;\n    this.canvas = undefined;\n    this.rubberband_canvas = undefined;\n    this.rubberband_context = undefined;\n    this.format_dropdown = undefined;\n\n    this.image_mode = 'full';\n\n    this.root = document.createElement('div');\n    this.root.setAttribute('style', 'display: inline-block');\n    this._root_extra_style(this.root);\n\n    parent_element.appendChild(this.root);\n\n    this._init_header(this);\n    this._init_canvas(this);\n    this._init_toolbar(this);\n\n    var fig = this;\n\n    this.waiting = false;\n\n    this.ws.onopen = function () {\n        fig.send_message('supports_binary', { value: fig.supports_binary });\n        fig.send_message('send_image_mode', {});\n        if (fig.ratio !== 1) {\n            fig.send_message('set_device_pixel_ratio', {\n                device_pixel_ratio: fig.ratio,\n            });\n        }\n        fig.send_message('refresh', {});\n    };\n\n    this.imageObj.onload = function () {\n        if (fig.image_mode === 'full') {\n            // Full images could contain transparency (where diff images\n            // almost always do), so we need to clear the canvas so that\n            // there is no ghosting.\n            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n        }\n        fig.context.drawImage(fig.imageObj, 0, 0);\n    };\n\n    this.imageObj.onunload = function () {\n        fig.ws.close();\n    };\n\n    this.ws.onmessage = this._make_on_message_function(this);\n\n    this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n    var titlebar = document.createElement('div');\n    titlebar.classList =\n        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n    var titletext = document.createElement('div');\n    titletext.classList = 'ui-dialog-title';\n    titletext.setAttribute(\n        'style',\n        'width: 100%; text-align: center; padding: 3px;'\n    );\n    titlebar.appendChild(titletext);\n    this.root.appendChild(titlebar);\n    this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n    var fig = this;\n\n    var canvas_div = (this.canvas_div = document.createElement('div'));\n    canvas_div.setAttribute('tabindex', '0');\n    canvas_div.setAttribute(\n        'style',\n        'border: 1px solid #ddd;' +\n            'box-sizing: content-box;' +\n            'clear: both;' +\n            'min-height: 1px;' +\n            'min-width: 1px;' +\n            'outline: 0;' +\n            'overflow: hidden;' +\n            'position: relative;' +\n            'resize: both;' +\n            'z-index: 2;'\n    );\n\n    function on_keyboard_event_closure(name) {\n        return function (event) {\n            return fig.key_event(event, name);\n        };\n    }\n\n    canvas_div.addEventListener(\n        'keydown',\n        on_keyboard_event_closure('key_press')\n    );\n    canvas_div.addEventListener(\n        'keyup',\n        on_keyboard_event_closure('key_release')\n    );\n\n    this._canvas_extra_style(canvas_div);\n    this.root.appendChild(canvas_div);\n\n    var canvas = (this.canvas = document.createElement('canvas'));\n    canvas.classList.add('mpl-canvas');\n    canvas.setAttribute(\n        'style',\n        'box-sizing: content-box;' +\n            'pointer-events: none;' +\n            'position: relative;' +\n            'z-index: 0;'\n    );\n\n    this.context = canvas.getContext('2d');\n\n    var backingStore =\n        this.context.backingStorePixelRatio ||\n        this.context.webkitBackingStorePixelRatio ||\n        this.context.mozBackingStorePixelRatio ||\n        this.context.msBackingStorePixelRatio ||\n        this.context.oBackingStorePixelRatio ||\n        this.context.backingStorePixelRatio ||\n        1;\n\n    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n        'canvas'\n    ));\n    rubberband_canvas.setAttribute(\n        'style',\n        'box-sizing: content-box;' +\n            'left: 0;' +\n            'pointer-events: none;' +\n            'position: absolute;' +\n            'top: 0;' +\n            'z-index: 1;'\n    );\n\n    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n    if (this.ResizeObserver === undefined) {\n        if (window.ResizeObserver !== undefined) {\n            this.ResizeObserver = window.ResizeObserver;\n        } else {\n            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n            this.ResizeObserver = obs.ResizeObserver;\n        }\n    }\n\n    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n        // There's no need to resize if the WebSocket is not connected:\n        // - If it is still connecting, then we will get an initial resize from\n        //   Python once it connects.\n        // - If it has disconnected, then resizing will clear the canvas and\n        //   never get anything back to refill it, so better to not resize and\n        //   keep something visible.\n        if (fig.ws.readyState != 1) {\n            return;\n        }\n        var nentries = entries.length;\n        for (var i = 0; i < nentries; i++) {\n            var entry = entries[i];\n            var width, height;\n            if (entry.contentBoxSize) {\n                if (entry.contentBoxSize instanceof Array) {\n                    // Chrome 84 implements new version of spec.\n                    width = entry.contentBoxSize[0].inlineSize;\n                    height = entry.contentBoxSize[0].blockSize;\n                } else {\n                    // Firefox implements old version of spec.\n                    width = entry.contentBoxSize.inlineSize;\n                    height = entry.contentBoxSize.blockSize;\n                }\n            } else {\n                // Chrome <84 implements even older version of spec.\n                width = entry.contentRect.width;\n                height = entry.contentRect.height;\n            }\n\n            // Keep the size of the canvas and rubber band canvas in sync with\n            // the canvas container.\n            if (entry.devicePixelContentBoxSize) {\n                // Chrome 84 implements new version of spec.\n                canvas.setAttribute(\n                    'width',\n                    entry.devicePixelContentBoxSize[0].inlineSize\n                );\n                canvas.setAttribute(\n                    'height',\n                    entry.devicePixelContentBoxSize[0].blockSize\n                );\n            } else {\n                canvas.setAttribute('width', width * fig.ratio);\n                canvas.setAttribute('height', height * fig.ratio);\n            }\n            /* This rescales the canvas back to display pixels, so that it\n             * appears correct on HiDPI screens. */\n            canvas.style.width = width + 'px';\n            canvas.style.height = height + 'px';\n\n            rubberband_canvas.setAttribute('width', width);\n            rubberband_canvas.setAttribute('height', height);\n\n            // And update the size in Python. We ignore the initial 0/0 size\n            // that occurs as the element is placed into the DOM, which should\n            // otherwise not happen due to the minimum size styling.\n            if (width != 0 && height != 0) {\n                fig.request_resize(width, height);\n            }\n        }\n    });\n    this.resizeObserverInstance.observe(canvas_div);\n\n    function on_mouse_event_closure(name) {\n        /* User Agent sniffing is bad, but WebKit is busted:\n         * https://bugs.webkit.org/show_bug.cgi?id=144526\n         * https://bugs.webkit.org/show_bug.cgi?id=181818\n         * The worst that happens here is that they get an extra browser\n         * selection when dragging, if this check fails to catch them.\n         */\n        var UA = navigator.userAgent;\n        var isWebKit = /AppleWebKit/.test(UA) && !/Chrome/.test(UA);\n        if(isWebKit) {\n            return function (event) {\n                /* This prevents the web browser from automatically changing to\n                 * the text insertion cursor when the button is pressed. We\n                 * want to control all of the cursor setting manually through\n                 * the 'cursor' event from matplotlib */\n                event.preventDefault()\n                return fig.mouse_event(event, name);\n            };\n        } else {\n            return function (event) {\n                return fig.mouse_event(event, name);\n            };\n        }\n    }\n\n    canvas_div.addEventListener(\n        'mousedown',\n        on_mouse_event_closure('button_press')\n    );\n    canvas_div.addEventListener(\n        'mouseup',\n        on_mouse_event_closure('button_release')\n    );\n    canvas_div.addEventListener(\n        'dblclick',\n        on_mouse_event_closure('dblclick')\n    );\n    // Throttle sequential mouse events to 1 every 20ms.\n    canvas_div.addEventListener(\n        'mousemove',\n        on_mouse_event_closure('motion_notify')\n    );\n\n    canvas_div.addEventListener(\n        'mouseenter',\n        on_mouse_event_closure('figure_enter')\n    );\n    canvas_div.addEventListener(\n        'mouseleave',\n        on_mouse_event_closure('figure_leave')\n    );\n\n    canvas_div.addEventListener('wheel', function (event) {\n        if (event.deltaY < 0) {\n            event.step = 1;\n        } else {\n            event.step = -1;\n        }\n        on_mouse_event_closure('scroll')(event);\n    });\n\n    canvas_div.appendChild(canvas);\n    canvas_div.appendChild(rubberband_canvas);\n\n    this.rubberband_context = rubberband_canvas.getContext('2d');\n    this.rubberband_context.strokeStyle = '#000000';\n\n    this._resize_canvas = function (width, height, forward) {\n        if (forward) {\n            canvas_div.style.width = width + 'px';\n            canvas_div.style.height = height + 'px';\n        }\n    };\n\n    // Disable right mouse context menu.\n    canvas_div.addEventListener('contextmenu', function (_e) {\n        event.preventDefault();\n        return false;\n    });\n\n    function set_focus() {\n        canvas.focus();\n        canvas_div.focus();\n    }\n\n    window.setTimeout(set_focus, 100);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n    var fig = this;\n\n    var toolbar = document.createElement('div');\n    toolbar.classList = 'mpl-toolbar';\n    this.root.appendChild(toolbar);\n\n    function on_click_closure(name) {\n        return function (_event) {\n            return fig.toolbar_button_onclick(name);\n        };\n    }\n\n    function on_mouseover_closure(tooltip) {\n        return function (event) {\n            if (!event.currentTarget.disabled) {\n                return fig.toolbar_button_onmouseover(tooltip);\n            }\n        };\n    }\n\n    fig.buttons = {};\n    var buttonGroup = document.createElement('div');\n    buttonGroup.classList = 'mpl-button-group';\n    for (var toolbar_ind in mpl.toolbar_items) {\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) {\n            /* Instead of a spacer, we start a new button group. */\n            if (buttonGroup.hasChildNodes()) {\n                toolbar.appendChild(buttonGroup);\n            }\n            buttonGroup = document.createElement('div');\n            buttonGroup.classList = 'mpl-button-group';\n            continue;\n        }\n\n        var button = (fig.buttons[name] = document.createElement('button'));\n        button.classList = 'mpl-widget';\n        button.setAttribute('role', 'button');\n        button.setAttribute('aria-disabled', 'false');\n        button.addEventListener('click', on_click_closure(method_name));\n        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n\n        var icon_img = document.createElement('img');\n        icon_img.src = '_images/' + image + '.png';\n        icon_img.srcset = '_images/' + image + '_large.png 2x';\n        icon_img.alt = tooltip;\n        button.appendChild(icon_img);\n\n        buttonGroup.appendChild(button);\n    }\n\n    if (buttonGroup.hasChildNodes()) {\n        toolbar.appendChild(buttonGroup);\n    }\n\n    var fmt_picker = document.createElement('select');\n    fmt_picker.classList = 'mpl-widget';\n    toolbar.appendChild(fmt_picker);\n    this.format_dropdown = fmt_picker;\n\n    for (var ind in mpl.extensions) {\n        var fmt = mpl.extensions[ind];\n        var option = document.createElement('option');\n        option.selected = fmt === mpl.default_extension;\n        option.innerHTML = fmt;\n        fmt_picker.appendChild(option);\n    }\n\n    var status_bar = document.createElement('span');\n    status_bar.classList = 'mpl-message';\n    toolbar.appendChild(status_bar);\n    this.message = status_bar;\n};\n\nmpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n    // which will in turn request a refresh of the image.\n    this.send_message('resize', { width: x_pixels, height: y_pixels });\n};\n\nmpl.figure.prototype.send_message = function (type, properties) {\n    properties['type'] = type;\n    properties['figure_id'] = this.id;\n    this.ws.send(JSON.stringify(properties));\n};\n\nmpl.figure.prototype.send_draw_message = function () {\n    if (!this.waiting) {\n        this.waiting = true;\n        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n    }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n    var format_dropdown = fig.format_dropdown;\n    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n    fig.ondownload(fig, format);\n};\n\nmpl.figure.prototype.handle_resize = function (fig, msg) {\n    var size = msg['size'];\n    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n        fig._resize_canvas(size[0], size[1], msg['forward']);\n        fig.send_message('refresh', {});\n    }\n};\n\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n    var x0 = msg['x0'] / fig.ratio;\n    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n    var x1 = msg['x1'] / fig.ratio;\n    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n    x0 = Math.floor(x0) + 0.5;\n    y0 = Math.floor(y0) + 0.5;\n    x1 = Math.floor(x1) + 0.5;\n    y1 = Math.floor(y1) + 0.5;\n    var min_x = Math.min(x0, x1);\n    var min_y = Math.min(y0, y1);\n    var width = Math.abs(x1 - x0);\n    var height = Math.abs(y1 - y0);\n\n    fig.rubberband_context.clearRect(\n        0,\n        0,\n        fig.canvas.width / fig.ratio,\n        fig.canvas.height / fig.ratio\n    );\n\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n    fig.canvas_div.style.cursor = msg['cursor'];\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n    fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n    fig.image_mode = msg['mode'];\n};\n\nmpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n    for (var key in msg) {\n        if (!(key in fig.buttons)) {\n            continue;\n        }\n        fig.buttons[key].disabled = !msg[key];\n        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n    }\n};\n\nmpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n    if (msg['mode'] === 'PAN') {\n        fig.buttons['Pan'].classList.add('active');\n        fig.buttons['Zoom'].classList.remove('active');\n    } else if (msg['mode'] === 'ZOOM') {\n        fig.buttons['Pan'].classList.remove('active');\n        fig.buttons['Zoom'].classList.add('active');\n    } else {\n        fig.buttons['Pan'].classList.remove('active');\n        fig.buttons['Zoom'].classList.remove('active');\n    }\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n    // Called whenever the canvas gets updated.\n    this.send_message('ack', {});\n};\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function (fig) {\n    return function socket_on_message(evt) {\n        if (evt.data instanceof Blob) {\n            var img = evt.data;\n            if (img.type !== 'image/png') {\n                /* FIXME: We get \"Resource interpreted as Image but\n                 * transferred with MIME type text/plain:\" errors on\n                 * Chrome.  But how to set the MIME type?  It doesn't seem\n                 * to be part of the websocket stream */\n                img.type = 'image/png';\n            }\n\n            /* Free the memory for the previous frames */\n            if (fig.imageObj.src) {\n                (window.URL || window.webkitURL).revokeObjectURL(\n                    fig.imageObj.src\n                );\n            }\n\n            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n                img\n            );\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        } else if (\n            typeof evt.data === 'string' &&\n            evt.data.slice(0, 21) === 'data:image/png;base64'\n        ) {\n            fig.imageObj.src = evt.data;\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n\n        var msg = JSON.parse(evt.data);\n        var msg_type = msg['type'];\n\n        // Call the  \"handle_{type}\" callback, which takes\n        // the figure and JSON message as its only arguments.\n        try {\n            var callback = fig['handle_' + msg_type];\n        } catch (e) {\n            console.log(\n                \"No handler for the '\" + msg_type + \"' message type: \",\n                msg\n            );\n            return;\n        }\n\n        if (callback) {\n            try {\n                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n                callback(fig, msg);\n            } catch (e) {\n                console.log(\n                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n                    e,\n                    e.stack,\n                    msg\n                );\n            }\n        }\n    };\n};\n\nfunction getModifiers(event) {\n    var mods = [];\n    if (event.ctrlKey) {\n        mods.push('ctrl');\n    }\n    if (event.altKey) {\n        mods.push('alt');\n    }\n    if (event.shiftKey) {\n        mods.push('shift');\n    }\n    if (event.metaKey) {\n        mods.push('meta');\n    }\n    return mods;\n}\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * https://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys(original) {\n    return Object.keys(original).reduce(function (obj, key) {\n        if (typeof original[key] !== 'object') {\n            obj[key] = original[key];\n        }\n        return obj;\n    }, {});\n}\n\nmpl.figure.prototype.mouse_event = function (event, name) {\n    if (name === 'button_press') {\n        this.canvas.focus();\n        this.canvas_div.focus();\n    }\n\n    // from https://stackoverflow.com/q/1114465\n    var boundingRect = this.canvas.getBoundingClientRect();\n    var x = (event.clientX - boundingRect.left) * this.ratio;\n    var y = (event.clientY - boundingRect.top) * this.ratio;\n\n    this.send_message(name, {\n        x: x,\n        y: y,\n        button: event.button,\n        step: event.step,\n        modifiers: getModifiers(event),\n        guiEvent: simpleKeys(event),\n    });\n\n    return false;\n};\n\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n    // Handle any extra behaviour associated with a key event\n};\n\nmpl.figure.prototype.key_event = function (event, name) {\n    // Prevent repeat events\n    if (name === 'key_press') {\n        if (event.key === this._key) {\n            return;\n        } else {\n            this._key = event.key;\n        }\n    }\n    if (name === 'key_release') {\n        this._key = null;\n    }\n\n    var value = '';\n    if (event.ctrlKey && event.key !== 'Control') {\n        value += 'ctrl+';\n    }\n    else if (event.altKey && event.key !== 'Alt') {\n        value += 'alt+';\n    }\n    else if (event.shiftKey && event.key !== 'Shift') {\n        value += 'shift+';\n    }\n\n    value += 'k' + event.key;\n\n    this._key_event_extra(event, name);\n\n    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n    return false;\n};\n\nmpl.figure.prototype.toolbar_button_onclick = function (name) {\n    if (name === 'download') {\n        this.handle_save(this, null);\n    } else {\n        this.send_message('toolbar_button', { name: name });\n    }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n    this.message.textContent = tooltip;\n};\n\n///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n// prettier-ignore\nvar _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar comm_websocket_adapter = function (comm) {\n    // Create a \"websocket\"-like object which calls the given IPython comm\n    // object with the appropriate methods. Currently this is a non binary\n    // socket, so there is still some room for performance tuning.\n    var ws = {};\n\n    ws.binaryType = comm.kernel.ws.binaryType;\n    ws.readyState = comm.kernel.ws.readyState;\n    function updateReadyState(_event) {\n        if (comm.kernel.ws) {\n            ws.readyState = comm.kernel.ws.readyState;\n        } else {\n            ws.readyState = 3; // Closed state.\n        }\n    }\n    comm.kernel.ws.addEventListener('open', updateReadyState);\n    comm.kernel.ws.addEventListener('close', updateReadyState);\n    comm.kernel.ws.addEventListener('error', updateReadyState);\n\n    ws.close = function () {\n        comm.close();\n    };\n    ws.send = function (m) {\n        //console.log('sending', m);\n        comm.send(m);\n    };\n    // Register the callback with on_msg.\n    comm.on_msg(function (msg) {\n        //console.log('receiving', msg['content']['data'], msg);\n        var data = msg['content']['data'];\n        if (data['blob'] !== 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setTimeout(function () {\n        fig.push_to_output();\n    }, 1000);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n    var fig = this;\n\n    var toolbar = document.createElement('div');\n    toolbar.classList = 'btn-toolbar';\n    this.root.appendChild(toolbar);\n\n    function on_click_closure(name) {\n        return function (_event) {\n            return fig.toolbar_button_onclick(name);\n        };\n    }\n\n    function on_mouseover_closure(tooltip) {\n        return function (event) {\n            if (!event.currentTarget.disabled) {\n                return fig.toolbar_button_onmouseover(tooltip);\n            }\n        };\n    }\n\n    fig.buttons = {};\n    var buttonGroup = document.createElement('div');\n    buttonGroup.classList = 'btn-group';\n    var button;\n    for (var toolbar_ind in mpl.toolbar_items) {\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) {\n            /* Instead of a spacer, we start a new button group. */\n            if (buttonGroup.hasChildNodes()) {\n                toolbar.appendChild(buttonGroup);\n            }\n            buttonGroup = document.createElement('div');\n            buttonGroup.classList = 'btn-group';\n            continue;\n        }\n\n        button = fig.buttons[name] = document.createElement('button');\n        button.classList = 'btn btn-default';\n        button.href = '#';\n        button.title = name;\n        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n        button.addEventListener('click', on_click_closure(method_name));\n        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n        buttonGroup.appendChild(button);\n    }\n\n    if (buttonGroup.hasChildNodes()) {\n        toolbar.appendChild(buttonGroup);\n    }\n\n    // 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titlebar.firstChild);\n};\n\nmpl.figure.prototype._remove_fig_handler = function (event) {\n    var fig = event.data.fig;\n    if (event.target !== this) {\n        // Ignore bubbled events from children.\n        return;\n    }\n    fig.close_ws(fig, {});\n};\n\nmpl.figure.prototype._root_extra_style = function (el) {\n    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n};\n\nmpl.figure.prototype._canvas_extra_style = function (el) {\n    // this is important to make the div 'focusable\n    el.setAttribute('tabindex', 0);\n    // reach out to IPython and tell the keyboard manager to turn it's self\n    // off when our div gets focus\n\n    // location in version 3\n    if (IPython.notebook.keyboard_manager) {\n        IPython.notebook.keyboard_manager.register_events(el);\n    } else {\n        // location in version 2\n        IPython.keyboard_manager.register_events(el);\n    }\n};\n\nmpl.figure.prototype._key_event_extra = function (event, _name) 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= 0; j < cell.output_area.outputs.length; j++) {\n                var data = cell.output_area.outputs[j];\n                if (data.data) {\n                    // IPython >= 3 moved mimebundle to data attribute of output\n                    data = data.data;\n                }\n                if (data['text/html'] === html_output) {\n                    return [cell, data, j];\n                }\n            }\n        }\n    }\n};\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel !== null) {\n    IPython.notebook.kernel.comm_manager.register_target(\n        'matplotlib',\n        mpl.mpl_figure_comm\n    );\n}\n",
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div id='45c87b95-e8cd-49f7-9e65-4327a61e38b2'></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(range(len(model.history.history['loss'])), model.history.history['loss'])\n",
+    "plt.xlabel('epochs')\n",
+    "plt.ylabel('loss')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b91e9243e9f55c5",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### Saving the trained model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "5ada2f02c039c75a",
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Invalid filepath extension for saving. Please add either a `.keras` extension for the native Keras format (recommended) or a `.h5` extension. Use `model.export(filepath)` if you want to export a SavedModel for use with TFLite/TFServing/etc. Received: filepath=model1.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[25], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmodel1\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\hp\\KAIM\\KAIM-W10\\.week10\\lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:122\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[0;32m    120\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[0;32m    121\u001b[0m     \u001b[38;5;66;03m# `keras.config.disable_traceback_filtering()`\u001b[39;00m\n\u001b[1;32m--> 122\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    123\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m    124\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
+      "File \u001b[1;32mc:\\Users\\hp\\KAIM\\KAIM-W10\\.week10\\lib\\site-packages\\keras\\src\\saving\\saving_api.py:114\u001b[0m, in \u001b[0;36msave_model\u001b[1;34m(model, filepath, overwrite, zipped, **kwargs)\u001b[0m\n\u001b[0;32m    110\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mstr\u001b[39m(filepath)\u001b[38;5;241m.\u001b[39mendswith((\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.h5\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.hdf5\u001b[39m\u001b[38;5;124m\"\u001b[39m)):\n\u001b[0;32m    111\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m legacy_h5_format\u001b[38;5;241m.\u001b[39msave_model_to_hdf5(\n\u001b[0;32m    112\u001b[0m         model, filepath, overwrite, include_optimizer\n\u001b[0;32m    113\u001b[0m     )\n\u001b[1;32m--> 114\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    115\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid filepath extension for saving. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    116\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPlease add either a `.keras` extension for the native Keras \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    117\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mformat (recommended) or a `.h5` extension. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    118\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUse `model.export(filepath)` if you want to export a SavedModel \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    119\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfor use with TFLite/TFServing/etc. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    120\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReceived: filepath=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfilepath\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    121\u001b[0m )\n",
+      "\u001b[1;31mValueError\u001b[0m: Invalid filepath extension for saving. Please add either a `.keras` extension for the native Keras format (recommended) or a `.h5` extension. Use `model.export(filepath)` if you want to export a SavedModel for use with TFLite/TFServing/etc. Received: filepath=model1."
+     ]
+    }
+   ],
+   "source": [
+    "model.save('model1')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df5bc2c0ce337109",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### Loading a model\n",
+    "Please, load the model \"brent_price_forecast_lstm_model\" to evaluate it or make forecasts.\n",
+    "\n",
+    "That model was trained with the following parameters:\n",
+    "- epochs = 100\n",
+    "- units = 150 (indicates the number of neurons in the LSTM layer)\n",
+    "- batch_size = 600\n",
+    "- activation = 'relu' (indicates the activation function in the LSTM layer)\n",
+    "- optimizer = 'adam' \n",
+    "- loss = 'mse'\n",
+    "- The data to feed the model was prepared with a window size of 30.\n",
+    "\n",
+    "Neural network algorithms are stochastic, which means they make use of randomness, such as initializing to random weights, and in turn the same network, with the same hyperparameters, trained on the same data can produce different results [1]. This is the reason why it is necessary to load the aforementioned model.\n",
+    "\n",
+    "\n",
+    "\n",
+    "[1] https://machinelearningmastery.com/stochastic-in-machine-learning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "7ca3fa335a5eb7b2",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-08-15T22:20:15.883184200Z",
+     "start_time": "2023-08-15T22:20:11.941536700Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "File format not supported: filepath=brent_price_forecast_lstm_model. Keras 3 only supports V3 `.keras` files and legacy H5 format files (`.h5` extension). Note that the legacy SavedModel format is not supported by `load_model()` in Keras 3. In order to reload a TensorFlow SavedModel as an inference-only layer in Keras 3, use `keras.layers.TFSMLayer(brent_price_forecast_lstm_model, call_endpoint='serving_default')` (note that your `call_endpoint` might have a different name).",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[26], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mload_model\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbrent_price_forecast_lstm_model\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\hp\\KAIM\\KAIM-W10\\.week10\\lib\\site-packages\\keras\\src\\saving\\saving_api.py:206\u001b[0m, in \u001b[0;36mload_model\u001b[1;34m(filepath, custom_objects, compile, safe_mode)\u001b[0m\n\u001b[0;32m    200\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    201\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFile not found: filepath=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfilepath\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    202\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPlease ensure the file is an accessible `.keras` \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    203\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mzip file.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    204\u001b[0m     )\n\u001b[0;32m    205\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 206\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    207\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFile format not supported: filepath=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfilepath\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    208\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mKeras 3 only supports V3 `.keras` files and \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    209\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlegacy H5 format files (`.h5` extension). \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    210\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNote that the legacy SavedModel format is not \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    211\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msupported by `load_model()` in Keras 3. In \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    212\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124morder to reload a TensorFlow SavedModel as an \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    213\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minference-only layer in Keras 3, use \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    214\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`keras.layers.TFSMLayer(\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    215\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfilepath\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, call_endpoint=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mserving_default\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m)` \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    216\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m(note that your `call_endpoint` \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    217\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmight have a different name).\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    218\u001b[0m     )\n",
+      "\u001b[1;31mValueError\u001b[0m: File format not supported: filepath=brent_price_forecast_lstm_model. Keras 3 only supports V3 `.keras` files and legacy H5 format files (`.h5` extension). Note that the legacy SavedModel format is not supported by `load_model()` in Keras 3. In order to reload a TensorFlow SavedModel as an inference-only layer in Keras 3, use `keras.layers.TFSMLayer(brent_price_forecast_lstm_model, call_endpoint='serving_default')` (note that your `call_endpoint` might have a different name)."
+     ]
+    }
+   ],
+   "source": [
+    "model = load_model('brent_price_forecast_lstm_model')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ae0c1d6f966edd2",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### Model testing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "bccc86b571eb930a",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-08-15T22:20:20.090757100Z",
+     "start_time": "2023-08-15T22:20:17.467483200Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
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' +\n                'Firefox 4 and 5 are also supported but you ' +\n                'have to enable WebSockets in about:config.'\n        );\n    }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n    this.id = figure_id;\n\n    this.ws = websocket;\n\n    this.supports_binary = this.ws.binaryType !== undefined;\n\n    if (!this.supports_binary) {\n        var warnings = document.getElementById('mpl-warnings');\n        if (warnings) {\n            warnings.style.display = 'block';\n            warnings.textContent =\n                'This browser does not support binary websocket messages. 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Matplotlib will then trigger a resize in the client,\n    // which will in turn request a refresh of the image.\n    this.send_message('resize', { width: x_pixels, height: y_pixels });\n};\n\nmpl.figure.prototype.send_message = function (type, properties) {\n    properties['type'] = type;\n    properties['figure_id'] = this.id;\n    this.ws.send(JSON.stringify(properties));\n};\n\nmpl.figure.prototype.send_draw_message = function () {\n    if (!this.waiting) {\n        this.waiting = true;\n        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n    }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n    var format_dropdown = fig.format_dropdown;\n    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n    fig.ondownload(fig, format);\n};\n\nmpl.figure.prototype.handle_resize = function (fig, msg) {\n    var size = msg['size'];\n    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n        fig._resize_canvas(size[0], size[1], msg['forward']);\n        fig.send_message('refresh', {});\n    }\n};\n\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n    var x0 = msg['x0'] / fig.ratio;\n    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n    var x1 = msg['x1'] / fig.ratio;\n    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n    x0 = Math.floor(x0) + 0.5;\n    y0 = Math.floor(y0) + 0.5;\n    x1 = Math.floor(x1) + 0.5;\n    y1 = Math.floor(y1) + 0.5;\n    var min_x = Math.min(x0, x1);\n    var min_y = Math.min(y0, y1);\n    var width = Math.abs(x1 - x0);\n    var height = Math.abs(y1 - y0);\n\n    fig.rubberband_context.clearRect(\n        0,\n        0,\n        fig.canvas.width / fig.ratio,\n        fig.canvas.height / fig.ratio\n    );\n\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n    fig.canvas_div.style.cursor = msg['cursor'];\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n    fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n    fig.image_mode = msg['mode'];\n};\n\nmpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n    for (var key in msg) {\n        if (!(key in fig.buttons)) {\n            continue;\n        }\n        fig.buttons[key].disabled = !msg[key];\n        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n    }\n};\n\nmpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n    if (msg['mode'] === 'PAN') {\n        fig.buttons['Pan'].classList.add('active');\n        fig.buttons['Zoom'].classList.remove('active');\n    } else if (msg['mode'] === 'ZOOM') {\n        fig.buttons['Pan'].classList.remove('active');\n        fig.buttons['Zoom'].classList.add('active');\n    } else {\n        fig.buttons['Pan'].classList.remove('active');\n        fig.buttons['Zoom'].classList.remove('active');\n    }\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n    // Called whenever the canvas gets updated.\n    this.send_message('ack', {});\n};\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function (fig) {\n    return function socket_on_message(evt) {\n        if (evt.data instanceof Blob) {\n            var img = evt.data;\n            if (img.type !== 'image/png') {\n                /* FIXME: We get \"Resource interpreted as Image but\n                 * transferred with MIME type text/plain:\" errors on\n                 * Chrome.  But how to set the MIME type?  It doesn't seem\n                 * to be part of the websocket stream */\n                img.type = 'image/png';\n            }\n\n            /* Free the memory for the previous frames */\n            if (fig.imageObj.src) {\n                (window.URL || window.webkitURL).revokeObjectURL(\n                    fig.imageObj.src\n                );\n            }\n\n            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n                img\n            );\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        } else if (\n            typeof evt.data === 'string' &&\n            evt.data.slice(0, 21) === 'data:image/png;base64'\n        ) {\n            fig.imageObj.src = evt.data;\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n\n        var msg = JSON.parse(evt.data);\n        var msg_type = msg['type'];\n\n        // Call the  \"handle_{type}\" callback, which takes\n        // the figure and JSON message as its only arguments.\n        try {\n            var callback = fig['handle_' + msg_type];\n        } catch (e) {\n            console.log(\n                \"No handler for the '\" + msg_type + \"' message type: \",\n                msg\n            );\n            return;\n        }\n\n        if (callback) {\n            try {\n                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n                callback(fig, msg);\n            } catch (e) {\n                console.log(\n                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n                    e,\n                    e.stack,\n                    msg\n                );\n            }\n        }\n    };\n};\n\nfunction getModifiers(event) {\n    var mods = [];\n    if (event.ctrlKey) {\n        mods.push('ctrl');\n    }\n    if (event.altKey) {\n        mods.push('alt');\n    }\n    if (event.shiftKey) {\n        mods.push('shift');\n    }\n    if (event.metaKey) {\n        mods.push('meta');\n    }\n    return mods;\n}\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * https://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys(original) {\n    return Object.keys(original).reduce(function (obj, key) {\n        if (typeof original[key] !== 'object') {\n            obj[key] = original[key];\n        }\n        return obj;\n    }, {});\n}\n\nmpl.figure.prototype.mouse_event = function (event, name) {\n    if (name === 'button_press') {\n        this.canvas.focus();\n        this.canvas_div.focus();\n    }\n\n    // from https://stackoverflow.com/q/1114465\n    var boundingRect = this.canvas.getBoundingClientRect();\n    var x = (event.clientX - boundingRect.left) * this.ratio;\n    var y = (event.clientY - boundingRect.top) * this.ratio;\n\n    this.send_message(name, {\n        x: x,\n        y: y,\n        button: event.button,\n        step: event.step,\n        modifiers: getModifiers(event),\n        guiEvent: simpleKeys(event),\n    });\n\n    return false;\n};\n\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n    // Handle any extra behaviour associated with a key event\n};\n\nmpl.figure.prototype.key_event = function (event, name) {\n    // Prevent repeat events\n    if (name === 'key_press') {\n        if (event.key === this._key) {\n            return;\n        } else {\n            this._key = event.key;\n        }\n    }\n    if (name === 'key_release') {\n        this._key = null;\n    }\n\n    var value = '';\n    if (event.ctrlKey && event.key !== 'Control') {\n        value += 'ctrl+';\n    }\n    else if (event.altKey && event.key !== 'Alt') {\n        value += 'alt+';\n    }\n    else if (event.shiftKey && event.key !== 'Shift') {\n        value += 'shift+';\n    }\n\n    value += 'k' + event.key;\n\n    this._key_event_extra(event, name);\n\n    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n    return false;\n};\n\nmpl.figure.prototype.toolbar_button_onclick = function (name) {\n    if (name === 'download') {\n        this.handle_save(this, null);\n    } else {\n        this.send_message('toolbar_button', { name: name });\n    }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n    this.message.textContent = tooltip;\n};\n\n///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n// prettier-ignore\nvar _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar comm_websocket_adapter = function (comm) {\n    // Create a \"websocket\"-like object which calls the given IPython comm\n    // object with the appropriate methods. Currently this is a non binary\n    // socket, so there is still some room for performance tuning.\n    var ws = {};\n\n    ws.binaryType = comm.kernel.ws.binaryType;\n    ws.readyState = comm.kernel.ws.readyState;\n    function updateReadyState(_event) {\n        if (comm.kernel.ws) {\n            ws.readyState = comm.kernel.ws.readyState;\n        } else {\n            ws.readyState = 3; // Closed state.\n        }\n    }\n    comm.kernel.ws.addEventListener('open', updateReadyState);\n    comm.kernel.ws.addEventListener('close', updateReadyState);\n    comm.kernel.ws.addEventListener('error', updateReadyState);\n\n    ws.close = function () {\n        comm.close();\n    };\n    ws.send = function (m) {\n        //console.log('sending', m);\n        comm.send(m);\n    };\n    // Register the callback with on_msg.\n    comm.on_msg(function (msg) {\n        //console.log('receiving', msg['content']['data'], msg);\n        var data = msg['content']['data'];\n        if (data['blob'] !== undefined) {\n            data = {\n                data: new Blob(msg['buffers'], { type: data['blob'] }),\n            };\n        }\n        // Pass the mpl event to the overridden (by mpl) onmessage function.\n        ws.onmessage(data);\n    });\n    return ws;\n};\n\nmpl.mpl_figure_comm = function (comm, msg) {\n    // This is the function which gets called when the mpl process\n    // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n    var id = msg.content.data.id;\n    // Get hold of the div created by the display call when the Comm\n    // socket was opened in Python.\n    var element = document.getElementById(id);\n    var ws_proxy = comm_websocket_adapter(comm);\n\n    function ondownload(figure, _format) {\n        window.open(figure.canvas.toDataURL());\n    }\n\n    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n\n    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n    // web socket which is closed, not our websocket->open comm proxy.\n    ws_proxy.onopen();\n\n    fig.parent_element = element;\n    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n    if (!fig.cell_info) {\n        console.error('Failed to find cell for figure', id, fig);\n        return;\n    }\n    fig.cell_info[0].output_area.element.on(\n        'cleared',\n        { fig: fig },\n        fig._remove_fig_handler\n    );\n};\n\nmpl.figure.prototype.handle_close = function (fig, msg) {\n    var width = fig.canvas.width / fig.ratio;\n    fig.cell_info[0].output_area.element.off(\n        'cleared',\n        fig._remove_fig_handler\n    );\n    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n\n    // Update the output cell to use the data from the current canvas.\n    fig.push_to_output();\n    var dataURL = fig.canvas.toDataURL();\n    // Re-enable the keyboard manager in IPython - without this line, in FF,\n    // the notebook keyboard shortcuts fail.\n    IPython.keyboard_manager.enable();\n    fig.parent_element.innerHTML =\n        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n    fig.close_ws(fig, msg);\n};\n\nmpl.figure.prototype.close_ws = function (fig, msg) {\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n};\n\nmpl.figure.prototype.push_to_output = function (_remove_interactive) {\n    // Turn the data on the canvas into data in the output cell.\n    var width = this.canvas.width / this.ratio;\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] =\n        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n    // Tell IPython that the notebook contents must change.\n    IPython.notebook.set_dirty(true);\n    this.send_message('ack', {});\n    var fig = this;\n    // Wait a second, then push the new image to the DOM so\n    // that it is saved nicely (might be nice to debounce this).\n    setTimeout(function () {\n        fig.push_to_output();\n    }, 1000);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n    var fig = this;\n\n    var toolbar = document.createElement('div');\n    toolbar.classList = 'btn-toolbar';\n    this.root.appendChild(toolbar);\n\n    function on_click_closure(name) {\n        return function (_event) {\n            return fig.toolbar_button_onclick(name);\n        };\n    }\n\n    function on_mouseover_closure(tooltip) {\n        return function (event) {\n            if (!event.currentTarget.disabled) {\n                return fig.toolbar_button_onmouseover(tooltip);\n            }\n        };\n    }\n\n    fig.buttons = {};\n    var buttonGroup = document.createElement('div');\n    buttonGroup.classList = 'btn-group';\n    var button;\n    for (var toolbar_ind in mpl.toolbar_items) {\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) {\n            /* Instead of a spacer, we start a new button group. */\n            if (buttonGroup.hasChildNodes()) {\n                toolbar.appendChild(buttonGroup);\n            }\n            buttonGroup = document.createElement('div');\n            buttonGroup.classList = 'btn-group';\n            continue;\n        }\n\n        button = fig.buttons[name] = document.createElement('button');\n        button.classList = 'btn btn-default';\n        button.href = '#';\n        button.title = name;\n        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n        button.addEventListener('click', on_click_closure(method_name));\n        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n        buttonGroup.appendChild(button);\n    }\n\n    if (buttonGroup.hasChildNodes()) {\n        toolbar.appendChild(buttonGroup);\n    }\n\n    // Add the status bar.\n    var status_bar = document.createElement('span');\n    status_bar.classList = 'mpl-message pull-right';\n    toolbar.appendChild(status_bar);\n    this.message = status_bar;\n\n    // Add the close button to the window.\n    var buttongrp = document.createElement('div');\n    buttongrp.classList = 'btn-group inline pull-right';\n    button = document.createElement('button');\n    button.classList = 'btn btn-mini btn-primary';\n    button.href = '#';\n    button.title = 'Stop Interaction';\n    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n    button.addEventListener('click', function (_evt) {\n        fig.handle_close(fig, {});\n    });\n    button.addEventListener(\n        'mouseover',\n        on_mouseover_closure('Stop Interaction')\n    );\n    buttongrp.appendChild(button);\n    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n};\n\nmpl.figure.prototype._remove_fig_handler = function (event) {\n    var fig = event.data.fig;\n    if (event.target !== this) {\n        // Ignore bubbled events from children.\n        return;\n    }\n    fig.close_ws(fig, {});\n};\n\nmpl.figure.prototype._root_extra_style = function (el) {\n    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n};\n\nmpl.figure.prototype._canvas_extra_style = function (el) {\n    // this is important to make the div 'focusable\n    el.setAttribute('tabindex', 0);\n    // reach out to IPython and tell the keyboard manager to turn it's self\n    // off when our div gets focus\n\n    // location in version 3\n    if (IPython.notebook.keyboard_manager) {\n        IPython.notebook.keyboard_manager.register_events(el);\n    } else {\n        // location in version 2\n        IPython.keyboard_manager.register_events(el);\n    }\n};\n\nmpl.figure.prototype._key_event_extra = function (event, _name) {\n    // Check for shift+enter\n    if (event.shiftKey && event.which === 13) {\n        this.canvas_div.blur();\n        // select the cell after this one\n        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n        IPython.notebook.select(index + 1);\n    }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n    fig.ondownload(fig, null);\n};\n\nmpl.find_output_cell = function (html_output) {\n    // Return the cell and output element which can be found *uniquely* in the notebook.\n    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n    // IPython event is triggered only after the cells have been serialised, which for\n    // our purposes (turning an active figure into a static one), is too late.\n    var cells = IPython.notebook.get_cells();\n    var ncells = cells.length;\n    for (var i = 0; i < ncells; i++) {\n        var cell = cells[i];\n        if (cell.cell_type === 'code') {\n            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n                var data = cell.output_area.outputs[j];\n                if (data.data) {\n                    // IPython >= 3 moved mimebundle to data attribute of output\n                    data = data.data;\n                }\n                if (data['text/html'] === html_output) {\n                    return [cell, data, j];\n                }\n            }\n        }\n    }\n};\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel !== null) {\n    IPython.notebook.kernel.comm_manager.register_target(\n        'matplotlib',\n        mpl.mpl_figure_comm\n    );\n}\n",
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div id='a44bd2da-00eb-40a7-98e3-a07fd4768e91'></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "prediction_set = []\n",
+    "batch_one = train_data_lstm[-ws:]\n",
+    "new_batch = batch_one.reshape((1, ws, 1))\n",
+    "\n",
+    "for i in range(len(test_data)):\n",
+    "    pred = model.predict(new_batch, verbose=False)[0]\n",
+    "    prediction_set.append(pred)\n",
+    "    new_batch = np.append(new_batch[:, 1:, :], [[pred]], axis=1)\n",
+    "\n",
+    "prediction_set = [i[0] for i in prediction_set] # Transforms a list of arrays into a list of single float items.\n",
+    "predictions = pd.Series(prediction_set, index=test_data.index)\n",
+    "\n",
+    "\n",
+    "# Plots the results\n",
+    "test_data.plot(color='blue', label='Actual')\n",
+    "predictions.plot(color='green', label='Prediction')\n",
+    "\n",
+    "plt.title('Brent Crude Oil Price Forecast (LSTM model evaluation)')\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Price per barrel (USD)')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cce0be78ee58579f",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### Model evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "594d1322925dd777",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-08-15T22:20:27.731893300Z",
+     "start_time": "2023-08-15T22:20:27.705066700Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Root Mean Square Error (RMSE): 3.677 \n",
+      "Mean Absolute Error (MAE): 3.224 \n",
+      "Mean Absolute Percentage Error (MAPE): 0.041\n"
+     ]
+    }
+   ],
+   "source": [
+    "rmse = np.sqrt(mean_squared_error(test_data_lstm, predictions))\n",
+    "mae = mean_absolute_error(test_data_lstm, predictions)\n",
+    "mape = mean_absolute_percentage_error(test_data_lstm, predictions)\n",
+    "print('Root Mean Square Error (RMSE): {} \\nMean Absolute Error (MAE): {} \\nMean Absolute Percentage Error (MAPE): {}'. format(np.round(rmse, 3), np.round(mae, 3), np.round(mape, 3)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a3238aac5c6086b",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### Using the model to forecast the Brent crude oil price for the following 30 days"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "19997fe72545f656",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-08-15T22:20:32.715630400Z",
+     "start_time": "2023-08-15T22:20:30.491199Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n    if (typeof WebSocket !== 'undefined') {\n        return WebSocket;\n    } else if (typeof MozWebSocket !== 'undefined') {\n        return MozWebSocket;\n    } else {\n        alert(\n            'Your browser does not have WebSocket support. ' +\n                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n                'Firefox 4 and 5 are also supported but you ' +\n                'have to enable WebSockets in about:config.'\n        );\n    }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n    this.id = figure_id;\n\n    this.ws = websocket;\n\n    this.supports_binary = this.ws.binaryType !== undefined;\n\n    if (!this.supports_binary) {\n        var warnings = document.getElementById('mpl-warnings');\n        if (warnings) {\n            warnings.style.display = 'block';\n            warnings.textContent =\n                'This browser does not support binary websocket messages. ' +\n                'Performance may be slow.';\n        }\n    }\n\n    this.imageObj = new Image();\n\n    this.context = undefined;\n    this.message = undefined;\n    this.canvas = undefined;\n    this.rubberband_canvas = undefined;\n    this.rubberband_context = undefined;\n    this.format_dropdown = undefined;\n\n    this.image_mode = 'full';\n\n    this.root = document.createElement('div');\n    this.root.setAttribute('style', 'display: inline-block');\n    this._root_extra_style(this.root);\n\n    parent_element.appendChild(this.root);\n\n    this._init_header(this);\n    this._init_canvas(this);\n    this._init_toolbar(this);\n\n    var fig = this;\n\n    this.waiting = false;\n\n    this.ws.onopen = function () {\n        fig.send_message('supports_binary', { value: fig.supports_binary });\n        fig.send_message('send_image_mode', {});\n        if (fig.ratio !== 1) {\n            fig.send_message('set_device_pixel_ratio', {\n                device_pixel_ratio: fig.ratio,\n            });\n        }\n        fig.send_message('refresh', {});\n    };\n\n    this.imageObj.onload = function () {\n        if (fig.image_mode === 'full') {\n            // Full images could contain transparency (where diff images\n            // almost always do), so we need to clear the canvas so that\n            // there is no ghosting.\n            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n        }\n        fig.context.drawImage(fig.imageObj, 0, 0);\n    };\n\n    this.imageObj.onunload = function () {\n        fig.ws.close();\n    };\n\n    this.ws.onmessage = this._make_on_message_function(this);\n\n    this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n    var titlebar = document.createElement('div');\n    titlebar.classList =\n        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n    var titletext = document.createElement('div');\n    titletext.classList = 'ui-dialog-title';\n    titletext.setAttribute(\n        'style',\n        'width: 100%; text-align: center; padding: 3px;'\n    );\n    titlebar.appendChild(titletext);\n    this.root.appendChild(titlebar);\n    this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n    var fig = this;\n\n    var canvas_div = (this.canvas_div = document.createElement('div'));\n    canvas_div.setAttribute('tabindex', '0');\n    canvas_div.setAttribute(\n        'style',\n        'border: 1px solid #ddd;' +\n            'box-sizing: content-box;' +\n            'clear: both;' +\n            'min-height: 1px;' +\n            'min-width: 1px;' +\n            'outline: 0;' +\n            'overflow: hidden;' +\n            'position: relative;' +\n            'resize: both;' +\n            'z-index: 2;'\n    );\n\n    function on_keyboard_event_closure(name) {\n        return function (event) {\n            return fig.key_event(event, name);\n        };\n    }\n\n    canvas_div.addEventListener(\n        'keydown',\n        on_keyboard_event_closure('key_press')\n    );\n    canvas_div.addEventListener(\n        'keyup',\n        on_keyboard_event_closure('key_release')\n    );\n\n    this._canvas_extra_style(canvas_div);\n    this.root.appendChild(canvas_div);\n\n    var canvas = (this.canvas = document.createElement('canvas'));\n    canvas.classList.add('mpl-canvas');\n    canvas.setAttribute(\n        'style',\n        'box-sizing: content-box;' +\n            'pointer-events: none;' +\n            'position: relative;' +\n            'z-index: 0;'\n    );\n\n    this.context = canvas.getContext('2d');\n\n    var backingStore =\n        this.context.backingStorePixelRatio ||\n        this.context.webkitBackingStorePixelRatio ||\n        this.context.mozBackingStorePixelRatio ||\n        this.context.msBackingStorePixelRatio ||\n        this.context.oBackingStorePixelRatio ||\n        this.context.backingStorePixelRatio ||\n        1;\n\n    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n        'canvas'\n    ));\n    rubberband_canvas.setAttribute(\n        'style',\n        'box-sizing: content-box;' +\n            'left: 0;' +\n            'pointer-events: none;' +\n            'position: absolute;' +\n            'top: 0;' +\n            'z-index: 1;'\n    );\n\n    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n    if (this.ResizeObserver === undefined) {\n        if (window.ResizeObserver !== undefined) {\n            this.ResizeObserver = window.ResizeObserver;\n        } else {\n            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n            this.ResizeObserver = obs.ResizeObserver;\n        }\n    }\n\n    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n        var nentries = entries.length;\n        for (var i = 0; i < nentries; i++) {\n            var entry = entries[i];\n            var width, height;\n            if (entry.contentBoxSize) {\n                if (entry.contentBoxSize instanceof Array) {\n                    // Chrome 84 implements new version of spec.\n                    width = entry.contentBoxSize[0].inlineSize;\n                    height = entry.contentBoxSize[0].blockSize;\n                } else {\n                    // Firefox implements old version of spec.\n                    width = entry.contentBoxSize.inlineSize;\n                    height = entry.contentBoxSize.blockSize;\n                }\n            } else {\n                // Chrome <84 implements even older version of spec.\n                width = entry.contentRect.width;\n                height = entry.contentRect.height;\n            }\n\n            // Keep the size of the canvas and rubber band canvas in sync with\n            // the canvas container.\n            if (entry.devicePixelContentBoxSize) {\n                // Chrome 84 implements new version of spec.\n                canvas.setAttribute(\n                    'width',\n                    entry.devicePixelContentBoxSize[0].inlineSize\n                );\n                canvas.setAttribute(\n                    'height',\n                    entry.devicePixelContentBoxSize[0].blockSize\n                );\n            } else {\n                canvas.setAttribute('width', width * fig.ratio);\n                canvas.setAttribute('height', height * fig.ratio);\n            }\n            /* This rescales the canvas back to display pixels, so that it\n             * appears correct on HiDPI screens. */\n            canvas.style.width = width + 'px';\n            canvas.style.height = height + 'px';\n\n            rubberband_canvas.setAttribute('width', width);\n            rubberband_canvas.setAttribute('height', height);\n\n            // And update the size in Python. We ignore the initial 0/0 size\n            // that occurs as the element is placed into the DOM, which should\n            // otherwise not happen due to the minimum size styling.\n            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n                fig.request_resize(width, height);\n            }\n        }\n    });\n    this.resizeObserverInstance.observe(canvas_div);\n\n    function on_mouse_event_closure(name) {\n        /* User Agent sniffing is bad, but WebKit is busted:\n         * https://bugs.webkit.org/show_bug.cgi?id=144526\n         * https://bugs.webkit.org/show_bug.cgi?id=181818\n         * The worst that happens here is that they get an extra browser\n         * selection when dragging, if this check fails to catch them.\n         */\n        var UA = navigator.userAgent;\n        var isWebKit = /AppleWebKit/.test(UA) && !/Chrome/.test(UA);\n        if(isWebKit) {\n            return function (event) {\n                /* This prevents the web browser from automatically changing to\n                 * the text insertion cursor when the button is pressed. We\n                 * want to control all of the cursor setting manually through\n                 * the 'cursor' event from matplotlib */\n                event.preventDefault()\n                return fig.mouse_event(event, name);\n            };\n        } else {\n            return function (event) {\n                return fig.mouse_event(event, name);\n            };\n        }\n    }\n\n    canvas_div.addEventListener(\n        'mousedown',\n        on_mouse_event_closure('button_press')\n    );\n    canvas_div.addEventListener(\n        'mouseup',\n        on_mouse_event_closure('button_release')\n    );\n    canvas_div.addEventListener(\n        'dblclick',\n        on_mouse_event_closure('dblclick')\n    );\n    // Throttle sequential mouse events to 1 every 20ms.\n    canvas_div.addEventListener(\n        'mousemove',\n        on_mouse_event_closure('motion_notify')\n    );\n\n    canvas_div.addEventListener(\n        'mouseenter',\n        on_mouse_event_closure('figure_enter')\n    );\n    canvas_div.addEventListener(\n        'mouseleave',\n        on_mouse_event_closure('figure_leave')\n    );\n\n    canvas_div.addEventListener('wheel', function (event) {\n        if (event.deltaY < 0) {\n            event.step = 1;\n        } else {\n            event.step = -1;\n        }\n        on_mouse_event_closure('scroll')(event);\n    });\n\n    canvas_div.appendChild(canvas);\n    canvas_div.appendChild(rubberband_canvas);\n\n    this.rubberband_context = rubberband_canvas.getContext('2d');\n    this.rubberband_context.strokeStyle = '#000000';\n\n    this._resize_canvas = function (width, height, forward) {\n        if (forward) {\n            canvas_div.style.width = width + 'px';\n            canvas_div.style.height = height + 'px';\n        }\n    };\n\n    // Disable right mouse context menu.\n    canvas_div.addEventListener('contextmenu', function (_e) {\n        event.preventDefault();\n        return false;\n    });\n\n    function set_focus() {\n        canvas.focus();\n        canvas_div.focus();\n    }\n\n    window.setTimeout(set_focus, 100);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n    var fig = this;\n\n    var toolbar = document.createElement('div');\n    toolbar.classList = 'mpl-toolbar';\n    this.root.appendChild(toolbar);\n\n    function on_click_closure(name) {\n        return function (_event) {\n            return fig.toolbar_button_onclick(name);\n        };\n    }\n\n    function on_mouseover_closure(tooltip) {\n        return function (event) {\n            if (!event.currentTarget.disabled) {\n                return fig.toolbar_button_onmouseover(tooltip);\n            }\n        };\n    }\n\n    fig.buttons = {};\n    var buttonGroup = document.createElement('div');\n    buttonGroup.classList = 'mpl-button-group';\n    for (var toolbar_ind in mpl.toolbar_items) {\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) {\n            /* Instead of a spacer, we start a new button group. */\n            if (buttonGroup.hasChildNodes()) {\n                toolbar.appendChild(buttonGroup);\n            }\n            buttonGroup = document.createElement('div');\n            buttonGroup.classList = 'mpl-button-group';\n            continue;\n        }\n\n        var button = (fig.buttons[name] = document.createElement('button'));\n        button.classList = 'mpl-widget';\n        button.setAttribute('role', 'button');\n        button.setAttribute('aria-disabled', 'false');\n        button.addEventListener('click', on_click_closure(method_name));\n        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n\n        var icon_img = document.createElement('img');\n        icon_img.src = '_images/' + image + '.png';\n        icon_img.srcset = '_images/' + image + '_large.png 2x';\n        icon_img.alt = tooltip;\n        button.appendChild(icon_img);\n\n        buttonGroup.appendChild(button);\n    }\n\n    if (buttonGroup.hasChildNodes()) {\n        toolbar.appendChild(buttonGroup);\n    }\n\n    var fmt_picker = document.createElement('select');\n    fmt_picker.classList = 'mpl-widget';\n    toolbar.appendChild(fmt_picker);\n    this.format_dropdown = fmt_picker;\n\n    for (var ind in mpl.extensions) {\n        var fmt = mpl.extensions[ind];\n        var option = document.createElement('option');\n        option.selected = fmt === mpl.default_extension;\n        option.innerHTML = fmt;\n        fmt_picker.appendChild(option);\n    }\n\n    var status_bar = document.createElement('span');\n    status_bar.classList = 'mpl-message';\n    toolbar.appendChild(status_bar);\n    this.message = status_bar;\n};\n\nmpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n    // which will in turn request a refresh of the image.\n    this.send_message('resize', { width: x_pixels, height: y_pixels });\n};\n\nmpl.figure.prototype.send_message = function (type, properties) {\n    properties['type'] = type;\n    properties['figure_id'] = this.id;\n    this.ws.send(JSON.stringify(properties));\n};\n\nmpl.figure.prototype.send_draw_message = function () {\n    if (!this.waiting) {\n        this.waiting = true;\n        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n    }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n    var format_dropdown = fig.format_dropdown;\n    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n    fig.ondownload(fig, format);\n};\n\nmpl.figure.prototype.handle_resize = function (fig, msg) {\n    var size = msg['size'];\n    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n        fig._resize_canvas(size[0], size[1], msg['forward']);\n        fig.send_message('refresh', {});\n    }\n};\n\nmpl.figure.prototype.handle_rubberband = function (fig, msg) {\n    var x0 = msg['x0'] / fig.ratio;\n    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n    var x1 = msg['x1'] / fig.ratio;\n    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n    x0 = Math.floor(x0) + 0.5;\n    y0 = Math.floor(y0) + 0.5;\n    x1 = Math.floor(x1) + 0.5;\n    y1 = Math.floor(y1) + 0.5;\n    var min_x = Math.min(x0, x1);\n    var min_y = Math.min(y0, y1);\n    var width = Math.abs(x1 - x0);\n    var height = Math.abs(y1 - y0);\n\n    fig.rubberband_context.clearRect(\n        0,\n        0,\n        fig.canvas.width / fig.ratio,\n        fig.canvas.height / fig.ratio\n    );\n\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n};\n\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n    fig.canvas_div.style.cursor = msg['cursor'];\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n    fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n    fig.image_mode = msg['mode'];\n};\n\nmpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n    for (var key in msg) {\n        if (!(key in fig.buttons)) {\n            continue;\n        }\n        fig.buttons[key].disabled = !msg[key];\n        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n    }\n};\n\nmpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n    if (msg['mode'] === 'PAN') {\n        fig.buttons['Pan'].classList.add('active');\n        fig.buttons['Zoom'].classList.remove('active');\n    } else if (msg['mode'] === 'ZOOM') {\n        fig.buttons['Pan'].classList.remove('active');\n        fig.buttons['Zoom'].classList.add('active');\n    } else {\n        fig.buttons['Pan'].classList.remove('active');\n        fig.buttons['Zoom'].classList.remove('active');\n    }\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n    // Called whenever the canvas gets updated.\n    this.send_message('ack', {});\n};\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function (fig) {\n    return function socket_on_message(evt) {\n        if (evt.data instanceof Blob) {\n            var img = evt.data;\n            if (img.type !== 'image/png') {\n                /* FIXME: We get \"Resource interpreted as Image but\n                 * transferred with MIME type text/plain:\" errors on\n                 * Chrome.  But how to set the MIME type?  It doesn't seem\n                 * to be part of the websocket stream */\n                img.type = 'image/png';\n            }\n\n            /* Free the memory for the previous frames */\n            if (fig.imageObj.src) {\n                (window.URL || window.webkitURL).revokeObjectURL(\n                    fig.imageObj.src\n                );\n            }\n\n            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n                img\n            );\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        } else if (\n            typeof evt.data === 'string' &&\n            evt.data.slice(0, 21) === 'data:image/png;base64'\n        ) {\n            fig.imageObj.src = evt.data;\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n\n        var msg = JSON.parse(evt.data);\n        var msg_type = msg['type'];\n\n        // Call the  \"handle_{type}\" callback, which takes\n        // the figure and JSON message as its only arguments.\n        try {\n            var callback = fig['handle_' + msg_type];\n        } catch (e) {\n            console.log(\n                \"No handler for the '\" + msg_type + \"' message type: \",\n                msg\n            );\n            return;\n        }\n\n        if (callback) {\n            try {\n                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n                callback(fig, msg);\n            } catch (e) {\n                console.log(\n                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n                    e,\n                    e.stack,\n                    msg\n                );\n            }\n        }\n    };\n};\n\nfunction getModifiers(event) {\n    var mods = [];\n    if (event.ctrlKey) {\n        mods.push('ctrl');\n    }\n    if (event.altKey) {\n        mods.push('alt');\n    }\n    if (event.shiftKey) {\n        mods.push('shift');\n    }\n    if (event.metaKey) {\n        mods.push('meta');\n    }\n    return mods;\n}\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * https://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys(original) {\n    return Object.keys(original).reduce(function (obj, key) {\n        if (typeof original[key] !== 'object') {\n            obj[key] = original[key];\n        }\n        return obj;\n    }, {});\n}\n\nmpl.figure.prototype.mouse_event = function (event, name) {\n    if (name === 'button_press') {\n        this.canvas.focus();\n        this.canvas_div.focus();\n    }\n\n    // from https://stackoverflow.com/q/1114465\n    var boundingRect = this.canvas.getBoundingClientRect();\n    var x = (event.clientX - boundingRect.left) * this.ratio;\n    var y = (event.clientY - boundingRect.top) * this.ratio;\n\n    this.send_message(name, {\n        x: x,\n        y: y,\n        button: event.button,\n        step: event.step,\n        modifiers: getModifiers(event),\n        guiEvent: simpleKeys(event),\n    });\n\n    return false;\n};\n\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n    // Handle any extra behaviour associated with a key event\n};\n\nmpl.figure.prototype.key_event = function (event, name) {\n    // Prevent repeat events\n    if (name === 'key_press') {\n        if (event.key === this._key) {\n            return;\n        } else {\n            this._key = event.key;\n        }\n    }\n    if (name === 'key_release') {\n        this._key = null;\n    }\n\n    var value = '';\n    if (event.ctrlKey && event.key !== 'Control') {\n        value += 'ctrl+';\n    }\n    else if (event.altKey && event.key !== 'Alt') {\n        value += 'alt+';\n    }\n    else if (event.shiftKey && event.key !== 'Shift') {\n        value += 'shift+';\n    }\n\n    value += 'k' + event.key;\n\n    this._key_event_extra(event, name);\n\n    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n    return false;\n};\n\nmpl.figure.prototype.toolbar_button_onclick = function (name) {\n    if (name === 'download') {\n        this.handle_save(this, null);\n    } else {\n        this.send_message('toolbar_button', { name: name });\n    }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n    this.message.textContent = tooltip;\n};\n\n///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n// prettier-ignore\nvar _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar comm_websocket_adapter = function (comm) {\n    // Create a \"websocket\"-like object which calls the given IPython comm\n    // object with the appropriate methods. Currently this is a non binary\n    // socket, so there is still some room for performance tuning.\n    var ws = {};\n\n    ws.binaryType = comm.kernel.ws.binaryType;\n    ws.readyState = comm.kernel.ws.readyState;\n    function updateReadyState(_event) {\n        if (comm.kernel.ws) {\n            ws.readyState = comm.kernel.ws.readyState;\n        } else {\n            ws.readyState = 3; // Closed state.\n        }\n    }\n    comm.kernel.ws.addEventListener('open', updateReadyState);\n    comm.kernel.ws.addEventListener('close', updateReadyState);\n    comm.kernel.ws.addEventListener('error', updateReadyState);\n\n    ws.close = function () {\n        comm.close();\n    };\n    ws.send = function (m) {\n        //console.log('sending', m);\n        comm.send(m);\n    };\n    // Register the callback with on_msg.\n    comm.on_msg(function (msg) {\n        //console.log('receiving', msg['content']['data'], msg);\n        var data = msg['content']['data'];\n        if (data['blob'] !== 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titlebar.firstChild);\n};\n\nmpl.figure.prototype._remove_fig_handler = function (event) {\n    var fig = event.data.fig;\n    if (event.target !== this) {\n        // Ignore bubbled events from children.\n        return;\n    }\n    fig.close_ws(fig, {});\n};\n\nmpl.figure.prototype._root_extra_style = function (el) {\n    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n};\n\nmpl.figure.prototype._canvas_extra_style = function (el) {\n    // this is important to make the div 'focusable\n    el.setAttribute('tabindex', 0);\n    // reach out to IPython and tell the keyboard manager to turn it's self\n    // off when our div gets focus\n\n    // location in version 3\n    if (IPython.notebook.keyboard_manager) {\n        IPython.notebook.keyboard_manager.register_events(el);\n    } else {\n        // location in version 2\n        IPython.keyboard_manager.register_events(el);\n    }\n};\n\nmpl.figure.prototype._key_event_extra = function (event, _name) 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= 0; j < cell.output_area.outputs.length; j++) {\n                var data = cell.output_area.outputs[j];\n                if (data.data) {\n                    // IPython >= 3 moved mimebundle to data attribute of output\n                    data = data.data;\n                }\n                if (data['text/html'] === html_output) {\n                    return [cell, data, j];\n                }\n            }\n        }\n    }\n};\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel !== null) {\n    IPython.notebook.kernel.comm_manager.register_target(\n        'matplotlib',\n        mpl.mpl_figure_comm\n    );\n}\n",
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div id='a4bf95c9-ce65-4064-8973-ba56abff537c'></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Forecasts for the following 30 days: \n",
+      "2023-08-07     86.838974\n",
+      "2023-08-08     88.119522\n",
+      "2023-08-09     88.610596\n",
+      "2023-08-10     88.780853\n",
+      "2023-08-11     90.181458\n",
+      "2023-08-14     90.257935\n",
+      "2023-08-15     90.509865\n",
+      "2023-08-16     90.808083\n",
+      "2023-08-17     90.853645\n",
+      "2023-08-18     89.893936\n",
+      "2023-08-21     90.618149\n",
+      "2023-08-22     92.039726\n",
+      "2023-08-23     93.088234\n",
+      "2023-08-24     92.772865\n",
+      "2023-08-25     93.825233\n",
+      "2023-08-28     95.939301\n",
+      "2023-08-29     95.710098\n",
+      "2023-08-30     94.572220\n",
+      "2023-08-31     95.415863\n",
+      "2023-09-01     95.783859\n",
+      "2023-09-04     96.019470\n",
+      "2023-09-05     95.710281\n",
+      "2023-09-06     95.871262\n",
+      "2023-09-07     96.260857\n",
+      "2023-09-08     96.980095\n",
+      "2023-09-11     97.948135\n",
+      "2023-09-12     98.919952\n",
+      "2023-09-13     98.961479\n",
+      "2023-09-14    100.141144\n",
+      "2023-09-15    101.966057\n",
+      "Freq: B, Name: Forecast, dtype: float32\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Makes the predictions \n",
+    "prediction_set = []\n",
+    "batch_one = full_data_lstm[-ws:]\n",
+    "new_batch = batch_one.reshape((1, ws, 1))\n",
+    "days_to_forecast = 30\n",
+    "\n",
+    "for i in range(days_to_forecast):\n",
+    "    pred = model.predict(new_batch, verbose=False)[0]\n",
+    "    prediction_set.append(pred)\n",
+    "    new_batch = np.append(new_batch[:, 1:, :], [[pred]], axis=1)\n",
+    "\n",
+    "prediction_set = [i[0] for i in prediction_set]  # Transforms a list of arrays into a list of single float items.\n",
+    "date_range = pd.date_range(test_data.index[-1], periods=days_to_forecast, freq='B')   \n",
+    "forecast = pd.Series(prediction_set, index=date_range, name='Forecast')\n",
+    "\n",
+    "\n",
+    "# Displays results\n",
+    "short_data = data.iloc[-250:] # Last n datapoints of the original time series.\n",
+    "\n",
+    "short_data.plot(color='blue', label='Actual')\n",
+    "forecast.plot(color='red')\n",
+    "\n",
+    "plt.title('Brent Crude Oil Price Forecast with LSTM (30 days ahead)')\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Price per barrel (USD)')\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "print('Forecasts for the following {} days: '.format(days_to_forecast))\n",
+    "print(forecast)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae574ff73bc1a366",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "This LSTM model was trained to forecast the Brent crude oil price for 30 days ahead from the last date in the time series. That means the model could produce extremely erroneous results if it is used with long time spans (variable _days_to_forecast_), for example, 60 days or more."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bdde3478a2381d65",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "## Conclusions\n",
+    "1. Modeling crude oil prices is a complicated task due to the high variation and volatility associated with its market. However, it's necessary to do so, as oil is one of the most important energies driving the world economy and represents a crucial factor in most industries.\n",
+    "   \n",
+    "2. In the evaluation of the SARIMA model, a Root Mean Squared Error (RMSE) of 7.543 and a Mean Absolute Error (MAE) of 6.431 were obtained. The forecast graph for the next 15 days shows a horizontal line with a value of approximately 83.5. This means that the SARIMA model estimates the Brent crude oil price for the next 15 days to be around $83.5 per barrel, on average.\n",
+    "\n",
+    "3. In the evaluation of the LSTM model, a Root Mean Squared Error (RMSE) of 3.677 and a Mean Absolute Error (MAE) of 3.224 were obtained. The forecast graph for the next 30 days demonstrates its capability to capture the trend and shape of the time series of actual data.\n",
+    "\n",
+    "4. Considering the errors obtained in the evaluations of each model, along with their ability to capture the trend and shape of the original time series, it's evident that the LSTM model performs significantly better in forecasting the Brent crude oil price."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".week10",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/task-2.ipynb b/notebooks/task-2.ipynb
new file mode 100644
index 0000000..ac46110
--- /dev/null
+++ b/notebooks/task-2.ipynb
@@ -0,0 +1,1126 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "from statsmodels.tsa.vector_ar.var_model import VAR\n",
+    "from statsmodels.tsa.arima.model import ARIMA\n",
+    "from statsmodels.tsa.regime_switching.markov_regression import MarkovRegression\n",
+    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import LSTM, Dense\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('../data/BrentOilPrices.csv')\n",
+    "df['Date'] = pd.to_datetime(df['Date'], errors='coerce')\n",
+    "df.set_index('Date', inplace=True)\n",
+    "df.dropna(inplace=True)\n",
+    "\n",
+    "# Ensure 'Price' column is float\n",
+    "df['Price'] = df['Price'].astype(float)\n",
+    "\n",
+    "# Visualize Data\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "plt.plot(df['Price'], label='Brent Oil Price')\n",
+    "plt.title('Brent Oil Prices Over Time')\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Price (USD per Barrel)')\n",
+    "plt.legend()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Vector Autoregression (VAR) Model for Multivariate Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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LPn36SI//+eefOHr0KJ599lkpOBGdPXsWEydOxPz5850uxdW4cWM8+OCDmDZtGtauXetzY6nmzZvDarVi7969NmM6efIkCgoKpE7czrRu3RolJSUug57WrVtj8eLFyM/Pd5jV/umnn1BZWYkFCxbYZF7Fslyl8847D+eddx6ef/55zJ07F2PHjsXXX3+NO+64w+2LB86kpaUhLi4Ou3fvtnts165d0Ov1yM7O9vl1lMxmMwCgpKTE5b7uvv+e8OS9GzBgAFJSUjB37lw8+eSTqoH8559/DgAYOXKkZmPUUuvWrWG1WvHvv/+iR48eDvcBgPT0dLfe69atW+Phhx/Gww8/jL1796JHjx549dVX8eWXX2o5dCKiiMY52kREEUDMXk+ZMgVbt25VXTs7Pj4ejz76KK655hqbrwkTJqBt27aYM2eOy9e57777EBcXh//+978+j3nEiBEAYNeZ+rXXXgMAu1J2Nddddx3WrFmjOse8oKBAChqvvvpqCIKAadOm2e0nZlPFIE2eXS0sLMTMmTNt9j979qxdBlYMkMRlyeLi4qQxeMtgMOCSSy7Bjz/+aDP//uTJk5g7dy4GDBgglQxrSZwu0L17d5f7uvv+e0Jc+9ud9y4uLg6PPPIIdu/ejSeffNLu8YULF2LWrFkYNmyY3zqO+2rUqFHQ6/V49tln7ZakE3/Phg0bhqSkJLzwwguqfQHEddjLyspQUVFh81jr1q2RmJhot2QeERH5hhltIqII0LJlS/Tv3x8//vgjANgE2pWVlfj+++9x8cUXIyYmRvX5V1xxBd58803k5eUhPT3d4eukpqbitttuw3vvvYedO3faZcc90b17d9xyyy346KOPUFBQgEGDBmH9+vWYPXs2Ro0ahcGDB7s8xqOPPooFCxZg5MiRuPXWW9GrVy+UlpZi+/bt+O6773Dw4EE0atQIgwcPxs0334y33noLe/fuxaWXXgqr1YqVK1di8ODBmDRpEi655BJERUXh8ssvx5133omSkhJ8/PHHSE9Px4kTJ6TXnD17Nt577z1cddVVaN26NYqLi/Hxxx8jKSlJungQGxuLTp064ZtvvkG7du3QsGFDdOnSBV26dPHoPXruueewZMkSDBgwAPfccw+MRiM+/PBDVFZW4qWXXvLsDVexefNmKctZXFyMpUuX4vvvv0f//v2lZaWccff990SvXr0AAPfffz+GDRsmNfxz5IknnsCWLVvw4osvYs2aNbj66qsRGxuLv/76C19++SU6duyI2bNnezSG+tSmTRs8+eSTmD59OgYOHIjRo0cjOjoaGzZsQFZWFmbMmIGkpCS8//77uPnmm3HOOedgzJgxSEtLw+HDh7Fw4UKcf/75eOedd7Bnzx4MGTIE1113HTp16gSj0YgffvgBJ0+edPoeEhGRFwLX8JyIiOrTu+++KwAQzj33XJvt33//vQBA+PTTTx0+d/ny5QIA4c033xQEoW55LzX79+8XDAaDtEyXI4MGDRI6d+7sdJ/q6mph2rRpQsuWLQWTySRkZ2cLkydPFioqKmz2a968uXDZZZepHqO4uFiYPHmy0KZNGyEqKkpo1KiR0L9/f+GVV14RqqqqpP3MZrPw8ssvCx06dBCioqKEtLQ0Yfjw4cKmTZukfRYsWCB069ZNiImJEVq0aCG8+OKLwmeffSYAEHJycgRBEITNmzcLN9xwg9CsWTMhOjpaSE9PF0aOHCls3LjRZlyrV68WevXqJURFRblc6svR8l7i6w0bNkxISEgQ4uLihMGDBwurV6+22Udc3svZkmNqryf/MhqNQqtWrYRHH31UKC4uttnf1/ff2fenfG/MZrNw3333CWlpaYJOp3NrqS+LxSLMnDlTOP/884WkpCQhJiZG6Ny5szBt2jShpKTEbn9fl/eaN2+e0/3EJbxOnTrl8DGlzz77TOjZs6cQHR0tNGjQQBg0aJCwZMkSu9cfNmyYkJycLMTExAitW7cWbr31Vul37/Tp08K9994rdOjQQYiPjxeSk5OFvn37Ct9++63T8RIRked0guBlhxEiIiIiIiIissM52kREREREREQaYqBNREREREREpCEG2kREREREREQaYqBNREREREREpCEG2kREREREREQaYqBNREREREREpCFjoAfgDavViuPHjyMxMRE6nS7QwyEiIiIiIqIwJwgCiouLkZWVBb3eec46JAPt48ePIzs7O9DDICIiIiIioghz5MgRNG3a1Ok+IRloJyYmAqj5BpOSkgI8GiIiIiIiIgp3RUVFyM7OluJRZ0Iy0BbLxZOSkhhoExERERERUb1xZ/oym6ERERERERERaYiBNhEREREREZGGGGgTERERERERaSgk52gTEREREZG2rFYrqqqqAj0MooAxmUwwGAyaHIuBNhERERFRhKuqqkJOTg6sVmugh0IUUCkpKcjMzHSr4ZkzDLSJiIiIiCKYIAg4ceIEDAYDsrOzoddzdilFHkEQUFZWhry8PABA48aNfToeA20iIiIioghmNptRVlaGrKwsxMXFBXo4RAETGxsLAMjLy0N6erpPZeS8XEVEREREFMEsFgsAICoqKsAjIQo88WJTdXW1T8dhoE1ERERERD7PSSUKB1r9HTDQJiIiIiIiItIQA20iIiIiIgpbs2bNQkpKSkBe+9Zbb8WoUaM0O97UqVPRo0cPzY4nCuR7FK4YaBMRERERUchxFMQuX74cOp0OBQUFAIDrr78ee/bsceuYWgecb775JmbNmqXZ8Vw5ePAgdDqd9JWamopLLrkEW7Zscfo8T94jcg8DbSIiIiIiCluxsbFIT0+v19e0WCywWq1ITk4OSKb4999/x4kTJ7B48WKUlJRg+PDh0oUHperq6oC8R+GOgTYREREREYUtZZb677//xuDBg5GYmIikpCT06tULGzduxPLly3HbbbehsLBQyghPnToVAHD27FmMGzcODRo0QFxcHIYPH469e/favcaCBQvQqVMnREdH4/Dhw3ZZd6vVipdeeglt2rRBdHQ0mjVrhueff156/PHHH0e7du0QFxeHVq1a4emnn/aq+3VqaioyMzPRu3dvvPLKKzh58iTWrVsnZby/+eYbDBo0CDExMZgzZ45qJv+nn35Cnz59EBMTg0aNGuGqq66SHqusrMQjjzyCJk2aID4+Hn379sXy5cs9Hmc44zraREREREQkEQQB5dWWgLx2rMng9+7nY8eORc+ePfH+++/DYDBg69atMJlM6N+/P9544w1MmTIFu3fvBgAkJCQAqClT37t3LxYsWICkpCQ8/vjjGDFiBP7991+YTCYAQFlZGV588UV88sknSE1NVc0QT548GR9//DFef/11DBgwACdOnMCuXbukxxMTEzFr1ixkZWVh+/btmDBhAhITE/HYY495/f2Ka0NXVVVJ25544gm8+uqr6NmzJ2JiYrB48WKb5yxcuBBXXXUVnnzySXz++eeoqqrCL7/8Ij0+adIk/Pvvv/j666+RlZWFH374AZdeeim2b9+Otm3bej3WcMJAm4iIiIiIJOXVFnSastj1jn7w77PDEBflfojy888/S8GwSFwX3JHDhw/j0UcfRYcOHQDAJjBMTk6GTqdDZmamtE0MsFetWoX+/fsDAObMmYPs7GzMnz8f1157LYCaEuz33nsP3bt3V33d4uJivPnmm3jnnXdwyy23AABat26NAQMGSPs89dRT0u0WLVrgkUcewddff+11oF1QUIDp06cjISEB5557LsrLywEADzzwAEaPHu3wec8//zzGjBmDadOmSdvE7+vw4cOYOXMmDh8+jKysLADAI488gkWLFmHmzJl44YUXvBpruGGgTUREREREIWnw4MF4//33bbatW7cON910k8PnPPTQQ7jjjjvwxRdfYOjQobj22mvRunVrh/vv3LkTRqMRffv2lbalpqaiffv22Llzp7QtKioK3bp1c3qcyspKDBkyxOE+33zzDd566y3s378fJSUlMJvNSEpKcri/I/3794der0dpaSlatWqFb775BhkZGTh48CAAoHfv3k6fv3XrVkyYMEH1se3bt8NisaBdu3Y22ysrK5GamurxWMMVA20iIiKKaEfyy/DfX3dh4gWt0D07JdDDIQq4WJMB/z47LGCv7Yn4+Hi0adPGZtvRo0edPmfq1Km48cYbsXDhQvz666945pln8PXXX9vMQfZGbGys07J3sYTbkTVr1mDs2LGYNm0ahg0bhuTkZHz99dd49dVXPR7LN998g06dOiE1NVW1GVt8fLzT5zsba0lJCQwGAzZt2gSDwfbnpawuiGQMtImIiCiiTfpqC/4+UoCF209g/wsjYND7d34oUbDT6XQelW+Honbt2qFdu3Z48MEHccMNN2DmzJm46qqrEBUVZVd63rFjR5jNZqxbt04qHT9z5gx2796NTp06uf2abdu2RWxsLJYuXYo77rjD7vHVq1ejefPmePLJJ6Vthw4d8ur7y87Odpqld6Vbt25YunQpbrvtNrvHevbsCYvFgry8PAwcONDr1wh37DpOREREES3nVIl0e13OmQCOhIj8rby8HJMmTcLy5ctx6NAhrFq1Chs2bEDHjh0B1MyLLikpwdKlS3H69GmUlZWhbdu2uPLKKzFhwgT89ddf+Pvvv3HTTTehSZMmuPLKK91+7ZiYGDz++ON47LHH8Pnnn2P//v1Yu3YtPv30UwA1gfjhw4fx9ddfY//+/Xjrrbfwww8/+OV9cOWZZ57BV199hWeeeQY7d+7E9u3b8eKLLwKouUgxduxYjBs3Dv/73/+Qk5OD9evXY8aMGVi4cGFAxhuMGGgTERFRRJNnsIsrzAEcCRH5m8FgwJkzZzBu3Di0a9cO1113HYYPHy41/erfvz/uuusuXH/99UhLS8NLL70EAJg5cyZ69eqFkSNHol+/fhAEAb/88ovUcdxdTz/9NB5++GFMmTIFHTt2xPXXX4+8vDwAwBVXXIEHH3wQkyZNQo8ePbB69Wo8/fTT2r4Bbrrwwgsxb948LFiwAD169MBFF12E9evXS4/PnDkT48aNw8MPP4z27dtj1KhR2LBhA5o1axaQ8QYjnSAIQqAH4amioiIkJyejsLDQq+YARERERKJe05fgTGnNsjevX98dV/VsGuAREdWviooK5OTkoGXLloiJiQn0cIgCytnfgydxKDPaREREFNHkzYtKKgOzdjAREYUXBtpEREQU0eRNgs0Wa+AGQkREYYOBNhEREUU0eZPx0JtQR0REwYiBNhEREUU0vZN1b4mIiLzhcaC9YsUKXH755cjKyoJOp8P8+fMd7nvXXXdBp9PhjTfesNmen5+PsWPHIikpCSkpKRg/fjxKSkrUD0JERETkRwy0iYhIax4H2qWlpejevTveffddp/v98MMPWLt2LbKysuweGzt2LHbs2IElS5bg559/xooVKzBx4kRPh0JERETkM73sbIiV40REpAWjp08YPnw4hg8f7nSfY8eO4b777sPixYtx2WWX2Ty2c+dOLFq0CBs2bEDv3r0BAG+//TZGjBiBV155RTUwJyIiIvIXZrSJiEhrms/RtlqtuPnmm/Hoo4+ic+fOdo+vWbMGKSkpUpANAEOHDoVer8e6deu0Hg4RERGRU/JAW2A3NCIi0oDHGW1XXnzxRRiNRtx///2qj+fm5iI9Pd12EEYjGjZsiNzcXNXnVFZWorKyUrpfVFSk3YCJiIgoojGhTUREWtM0o71p0ya8+eabmDVrFnQa/q81Y8YMJCcnS1/Z2dmaHZuIiIgi24FTpYEeAhFR2Fm+fDl0Oh0KCgo0Pe7Bgweh0+mwdetWTY+rNU0D7ZUrVyIvLw/NmjWD0WiE0WjEoUOH8PDDD6NFixYAgMzMTOTl5dk8z2w2Iz8/H5mZmarHnTx5MgoLC6WvI0eOaDlsIiIiIiIKIZdffjkuvfRS1cdWrlwJnU6Hbdu2SdvuvPNOGAwGzJs3z27/qVOnQqfTQafTwWAwIDs7GxMnTkR+fr7TMcifJ//6/fffffvmAshfwbEjLVq0kN63+Ph4nHPOOao/I7ns7GycOHECXbp0qZcxekvTQPvmm2/Gtm3bsHXrVukrKysLjz76KBYvXgwA6NevHwoKCrBp0ybpecuWLYPVakXfvn1VjxsdHY2kpCSbLyIiIiKtcYo2UWgYP348lixZgqNHj9o9NnPmTPTu3RvdunUDAJSVleHrr7/GY489hs8++0z1eJ07d8aJEydw+PBhzJw5E4sWLcLdd9/tchzi8+RfF1xwgVffU1VVlVfPC3XPPvssTpw4gS1btqBPnz64/vrrsXr1atV9q6qqYDAYkJmZCaNR81nQmvI40C4pKZGCaADIycnB1q1bcfjwYaSmpqJLly42XyaTCZmZmWjfvj0AoGPHjrj00ksxYcIErF+/HqtWrcKkSZMwZswYdhwnIiIiIiKXRo4cibS0NMyaNctme0lJCebNm4fx48dL2+bNm4dOnTrhiSeewIoVK1SrY41GIzIzM9GkSRMMHToU1157LZYsWeJyHOLz5F9RUVEAgO3bt+Oiiy5CbGwsUlNTMXHiRJSUlEjPvfXWWzFq1Cg8//zzyMrKkuKlI0eO4LrrrkNKSgoaNmyIK6+8EgcPHrR53c8++wydO3dGdHQ0GjdujEmTJkmPvfbaa+jatSvi4+ORnZ2Ne+65x+Z1Dx06hMsvvxwNGjRAfHw8OnfujF9++QUHDx7E4MGDAQANGjSATqfDrbfeCqCm4fWMGTPQsmVLxMbGonv37vjuu+9sxvTLL7+gXbt2iI2NxeDBg+3G7EhiYiIyMzPRrl07vPvuu4iNjcVPP/0EoCbjPX36dIwbNw5JSUmYOHGiaun4jh07MHLkSCQlJSExMREDBw7E/v37pcc/+eQTdOzYETExMejQoQPee+89t8bmC48D7Y0bN6Jnz57o2bMnAOChhx5Cz549MWXKFLePMWfOHHTo0AFDhgzBiBEjMGDAAHz00UeeDoWIiIhIUwJX0iaqKe0oLQ3Ml5tlJUajEePGjcOsWbNsVguYN28eLBYLbrjhBmnbp59+iptuugnJyckYPny4XXCudPDgQSxevFgKmL1RWlqKYcOGoUGDBtiwYQPmzZuH33//3SYgBoClS5di9+7dWLJkCX7++WdUV1dj2LBhSExMxMqVK7Fq1SokJCTg0ksvlTLe77//Pu69915MnDgR27dvx4IFC9CmTRvpmHq9Hm+99RZ27NiB2bNnY9myZXjsscekx++9915UVlZixYoV2L59O1588UUkJCQgOzsb33//PQBg9+7dOHHiBN58800ANT2zPv/8c3zwwQfYsWMHHnzwQdx00034888/AdRcHBg9ejQuv/xybN26FXfccQeeeOIJj983o9EIk8lkk91/5ZVX0L17d2zZsgVPP/203XOOHTuGCy64ANHR0Vi2bBk2bdqE22+/HWazGUBN7DllyhQ8//zz2LlzJ1544QU8/fTTmD17tsfj84gQggoLCwUAQmFhYaCHQkRERCGu+eM/S18fLN8X6OEQ1bvy8nLh33//FcrLy2s2lJQIQk3IW/9fJSVuj3vnzp0CAOGPP/6Qtg0cOFC46aabpPt79uwRTCaTcOrUKUEQBOGHH34QWrZsKVitVmmfZ555RtDr9UJ8fLwQExMjABAACK+99prT15c/T/zq06ePIAiC8NFHHwkNGjQQSmTfz8KFCwW9Xi/k5uYKgiAIt9xyi5CRkSFUVlZK+3zxxRdC+/btbcZXWVkpxMbGCosXLxYEQRCysrKEJ5980u33ad68eUJqaqp0v2vXrsLUqVNV9/3jjz8EAMLZs2elbRUVFUJcXJywevVqm33Hjx8v3HDDDYIgCMLkyZOFTp062Tz++OOP2x1LqXnz5sLrr78uCELN9/nCCy8IAISff/5ZenzUqFE2z8nJyREACFu2bJFeu2XLlkJVVZXqa7Ru3VqYO3euzbbp06cL/fr1U93f7u9BxpM4NLgL24mIiIiIiFR06NAB/fv3x2effYYLL7wQ+/btw8qVK/Hss89K+3z22WcYNmwYGjVqBAAYMWIExo8fj2XLlmHIkCHSfu3bt8eCBQtQUVGBL7/8Elu3bsV9993ncgzi80TR0dEAgJ07d6J79+6Ij4+XHjv//PNhtVqxe/duZGRkAAC6du1qkzn/+++/sW/fPiQmJtq8TkVFBfbv34+8vDwcP37cZuxKv//+O2bMmIFdu3ahqKgIZrMZFRUVKCsrQ1xcHO6//37cfffd+O233zB06FBcffXV0nx2Nfv27UNZWRkuvvhim+1VVVVSlfPOnTvt+m3169fP4THlHn/8cTz11FOoqKhAQkIC/vvf/+Kyyy6THu/du7fT52/duhUDBw6EyWSye6y0tBT79+/H+PHjMWHCBGm72WxGcnKyW+PzFgNtIiIiolosHCcCEBcHyOb01vtre2D8+PG477778O6772LmzJlo3bo1Bg0aBACwWCyYPXs2cnNzbRpnWSwWfPbZZzbBalRUlFR+LQZ606ZNw/Tp052+vvx53pAH4kDNHPNevXphzpw5dvumpaVBr3c+8/fgwYMYOXIk7r77bjz//PNo2LAh/vrrL4wfPx5VVVWIi4vDHXfcgWHDhmHhwoX47bffMGPGDLz66qsOLyyI87sXLlyIJk2a2DwmXljwxaOPPopbb70VCQkJyMjIsFsmWvkeKcXGxjp8TBz7xx9/bHchwGAweDli9zDQJiIiIiKiOjod4CK4CRbXXXcd/vOf/2Du3Ln4/PPPcffdd0uB2i+//ILi4mJs2bLFJqj6559/cNttt6GgoAApKSmqx33qqadw0UUX4e677/aqYXPHjh0xa9YslJaWSoHiqlWroNfrpaZnas455xx88803SE9Pd7jSUosWLbB06VKpcZncpk2bYLVa8eqrr0pB+bfffmu3X3Z2Nu666y7cddddmDx5Mj7++GPcd999UnbdYrFI+3bq1AnR0dE4fPiwdBFD7fuVZ/YBYO3atQ6/T7lGjRr5dLGiW7dumD17Nqqrq+2y2hkZGcjKysKBAwcwduxYr1/DG5ou70VERERERFRfEhIScP3112Py5Mk4ceKE1CUbqGmCdtlll6F79+42qyKJHb3Vssaifv36oVu3bnjhhRe8GtfYsWMRExODW265Bf/88w/++OMP3Hfffbj55pulsnFHz2vUqBGuvPJKrFy5Ejk5OVi+fDnuv/9+aSmzqVOn4tVXX8Vbb72FvXv3YvPmzXj77bcBAG3atEF1dTXefvttHDhwAF988QU++OADm9d44IEHsHjxYuTk5GDz5s34448/0LFjRwBA8+bNodPp8PPPP+PUqVMoKSlBYmIiHnnkETz44IOYPXs29u/fL72m2FDsrrvuwt69e/Hoo49i9+7dmDt3rsumc1qZNGkSioqKMGbMGGzcuBF79+7FF198gd27dwMApk2bhhkzZuCtt97Cnj17sH37dsycOROvvfaaX8fFQJuIiIioFtfRJgo948ePx9mzZzFs2DAp+3zy5EksXLgQV199td3+er0eV111FT799FOnx33wwQfxySefqC4H5kpcXBwWL16M/Px89OnTB9dccw2GDBmCd955x+XzVqxYgWbNmmH06NHo2LEjxo8fj4qKCinDfcstt+CNN97Ae++9h86dO2PkyJHYu3cvAKB79+547bXX8OKLL6JLly6YM2cOZsyYYfMaFosF9957r7Tscrt27aTlrpo0aYJp06bhiSeeQEZGhtQlffr06Xj66acxY8YM6XkLFy5Ey5YtAQDNmjXD999/j/nz56N79+744IMPvL5I4anU1FQsW7YMJSUlGDRoEHr16oWPP/5Yym7fcccd+OSTTzBz5kx07doVgwYNwqxZs6Sx+4tOEELvv5SioiIkJyejsLDQYUkFERERkTtaPLFQuv3Ype1xz4XelzAShaKKigrk5OSgZcuWiImJCfRwiALK2d+DJ3EoM9pEREREtUIv/UBERMGIgTYRERFFrEX/nLC5r1d0uyUiIvIGA20iIiKKWHd9udnmvp5xNhERaYCBNhEREVEtZrSJiEgLDLSJiIgoIlmsnJBNRET+wUCbiIiIIlKl2WK37Z0/9gVgJETBIQQXIyLSnNVq1eQ4Rk2OQkRERBRiKqrtT6YKy6sDMBKiwDKZTNDpdDh16hTS0tKg4xQKikCCIKCqqgqnTp2CXq9HVFSUT8djoE1EREQRSS2jTRSJDAYDmjZtiqNHj+LgwYOBHg7VE0EArIIAA7tA2oiLi0OzZs2g1/tW/M1Am4iIiCKSWkabKFIlJCSgbdu2qK5mVUekeOL7bdhwMB8vXt0NvVs0DPRwgoLBYIDRaNSkqoOBNhEREUWkimr7jPYdA1oGYCREwcFgMMBgMAR6GFRP5m8/BQCYte4YBnTICvBowg+boREREVFEUus6bjLy1IiIIg1Lx/2B/5sQERFRRFJrsMymy0QUaThF2z8YaBMRERHVEsBIm4gii55d5v2CgTYRERGRiHE2EUUYH5trkwN8W4mIiCgiqWWvGWcTUaTRcY62XzDQJiIiIqolcJI2EUUYVo77BwNtIiIiikhshkZExDna/sJAm4iIiKgW42wiijSMs/2DgTYRERFFpHU5Z+y2WZnSJqIIw4y2fzDQJiIioohTWFaNF37ZZbe90mwNwGiIiAKHcbZ/MNAmIiKiiFNWbVbdvuif3HoeCRFRYCkz2l+sOYi7v9yEKl549AkDbSIiIoo4jpazadkovp5HQkQUWMpPw6d/3IFf/8nFD1uOBmQ84YKBNhEREUUci2Iu9nOjugAAzFbO0SaiyOKodPxIfnn9DiTMMNAmIiKiiGNVBNRNG8QCAMwWlkpq5WxpFe7/aguO5JcFeihE5ISjZmjv/LGvnkcSXoyBHgARERFRfbMoAm2jXq+6nbzXc/oSAMCCv4/j4H8vC/BoiMgRHbuh+QUz2kRERBRxlKXjBn3NiSZLx4ko0ny1/jCqWc2jOQbaREREFHEERaBtNNQE2sxoE1Ek+nzNoUAPIeww0CYiIqKIo0ze1GW0mdUhosizO7fIblvXJskBGEn4YKBNREREEcd+jnZtRtsSmRntgrIqfPjnflzz/mr8c6zQ5+MpKwaIKPTERxsCPYSQxmZoREREFHGsnKMtsVoF9Hn+d1TXXmSYtfogXrm2u0/HXLj9hBZDIyIKWcxoExERUcRRZrRNhppToqKK6kAMJ6BOl1ZKQTYA7D9V4vMxP16ZY3O/otri8zGJqH5F4HVHTTHQJiIioojjKKNdUW2NuHWfldPSHa2p64nTxZU2919ctMvnYxKR/1Wa6y6Krc/JD+BIQh8DbSIiIoo4ykBbnKMNAPM2Ha3v4QSUANv3QosVdUurzDb3Z646qMFRicjf7vlyc6CHEDYYaBMREVHEUXYdNxrqTolSYk31PJrAUpaHapDQRnmVbam4Qa9F+E5E/rZ0V16ghxA2GGgTERFRxFHO0TbIosvkSAu0Fe+FFqXjlWbbKxnKCgIiCk7RRoaHWuE7SURERBFHGfilxNUF14kxkb0oS8P4KM2PyTibKDTIPxvbpicEcCShj4E2ERERRRxlRjvGVLde7LoIawCkvOjQwA+BNhGFBvlnY2oCPwt8wUCbiIiIIk6ZolmX3Kd/5Th8LBwps80GLSZpq74O09pEwY5LemmHgTYRERFFnLvYWVeizGj7az714QhbNo0o1PHamG8YaBMRERFFMGUGy18ZrU2HzvrnwETkF4yzfcNAm4iIiCiC/X2kwOa+v0q8o40G1zsRUb1w6++ckbZPGGgTERERRbDM5Bib+/4qHeda2kTBw50/cy7L5xsG2kREREQymUkxrncKI8reZ/4qHS+uqPbPgYnIY8o/c7WYeuOhs1ix51S9jCccMdAmIiKiiOKoZPLxSzsAAM5r1bA+hxNwyqXOtMhi9WuVCgBIjY/C4PZpAICiCsed3omofjn6HBzRNdPm/rjP1tfHcMISA20iIiKKKI4ytrGmmtOi6ghb30YZaGtRLRobVTMf+/HhHZCWGA0AqKi2+H5gItKEoz/zRgnR9TqOcOZxoL1ixQpcfvnlyMrKgk6nw/z586XHqqur8fjjj6Nr166Ij49HVlYWxo0bh+PHj9scIz8/H2PHjkVSUhJSUlIwfvx4lJSU+PzNEBEREbmizOQsemAgAEAn1lBHVpztl4z20bM1S3kZ9TppbrY1wi5gEAUzR3/mldXW+h1IGPM40C4tLUX37t3x7rvv2j1WVlaGzZs34+mnn8bmzZvxv//9D7t378YVV1xhs9/YsWOxY8cOLFmyBD///DNWrFiBiRMnev9dEBEREblJfn45ZWQndMhMAgDopTg7sgJCrTPaFdUW7DlZl0ARA20zA22ioOHoc67awkBbK0ZPnzB8+HAMHz5c9bHk5GQsWbLEZts777yDc889F4cPH0azZs2wc+dOLFq0CBs2bEDv3r0BAG+//TZGjBiBV155BVlZWV58G0RERETukWdsr+ndtO4BnZh5re8RBZYy0AaAn7cdx/ajhXj80g7Qe9gtvKi8rulZYowJG3Jq1s+euSoHD17czrfBEpEmHF1QY6dx7fh9jnZhYSF0Oh1SUlIAAGvWrEFKSooUZAPA0KFDodfrsW7dOn8Ph4iIiCKcPLDUy1pui7ciLaOtzDQLACbN3YIPVxzAr//kenw8new9HdIhHbtPFgNgMzSiUGCJrI8/v/JroF1RUYHHH38cN9xwA5KSasqycnNzkZ6ebrOf0WhEw4YNkZur/mFeWVmJoqIimy8iIiIib+w8USzdls/XFoPuSKtwVma0q811Kf3TJZVeH89k0EGv1+G+i9oAADpnJfkwSiLSEjPa/ue3QLu6uhrXXXcdBEHA+++/79OxZsyYgeTkZOkrOztbo1ESERFRpEmONUm3o4x1p0JSL7QIO88sV3QDX73/tHQ72uj5qaK5tvZenJvdslE8AGDH8SLM23jE22ESkYYcVe44WvaLPOeXQFsMsg8dOoQlS5ZI2WwAyMzMRF5ens3+ZrMZ+fn5yMzMVB4KADB58mQUFhZKX0eO8EOaiIiIvCMPHqONBul23VTkyDrRLK+yDbTl3320Se/xibeY0Tbqa95nk6Hu/X70u23eDZKINOUwox1hPSr8SfNAWwyy9+7di99//x2pqak2j/fr1w8FBQXYtGmTtG3ZsmWwWq3o27ev6jGjo6ORlJRk80VERETkDbE0Mj7KYLNdh8gsHVdmtOVdh3NOlaLXc79j1qoct48nzvkW3+coL7LiDo9tseL/ftiOH7ce0+yYRJHI0cccS8e14/EnX0lJCbZu3YqtW7cCAHJycrB161YcPnwY1dXVuOaaa7Bx40bMmTMHFosFubm5yM3NRVVVFQCgY8eOuPTSSzFhwgSsX78eq1atwqRJkzBmzBh2HCciIiK/EzOudt20pdLxyDrRLKuqaVImvh1mWTekt5btQ35pFab+9K/bx9t5oqj2uDUBfJRBu0B78Y6TmLvuMP7z9VbNjkkUiRx9zkXahUZ/8viTb+PGjejZsyd69uwJAHjooYfQs2dPTJkyBceOHcOCBQtw9OhR9OjRA40bN5a+Vq9eLR1jzpw56NChA4YMGYIRI0ZgwIAB+Oijj7T7roiIiIgcEE8k5R3H5fcj7USzsromgy2W0TdpEOvT8SbN3WJzX8uMtjz7vvbAGc2OSxRpmNH2P4/X0b7wwgudXul15ypww4YNMXfuXE9fmoiIiMhn4rmKg4R2hM3QBiyCOKe65h04dKZMdb+c06X4z9dbMLZvM1zfp5nbxzdpmNFOkTWy25CTj/NapTrZm4gcYddx//P7OtpEREREwUTMWBsUkbYuQkvHrbVvSKXFeRek6T//i21HC/H499ud7nd595qpgOc0SwHgXka7qKIaeUUV2HTorN1yY3Ixprp59Y0So10el4gccBho1+8wwpnHGW0iIiKiUCYGcjoHpeMr957GrtwidMiMjOarYvOyKrPzQLuovNqt46XGRwEA+rWuyTYblaUDCoIgoNvU36T79w9pi4cubqe6r0V2ESQtwf1Au7iiGrmFFWibkej2c4jCmSfLe607cAZ9WT3iMWa0iYiIKKJYHZSOy136xsp6Gk3glFdZ8O3GI8grrnRrf3dLSsWu5WLJuM55nI1KRYD/1tK9Dvfdl1ci3T5wusThfnJVZiu6Tv0NF7++AluPFLj1HKJw50np+PUfrfXzaMITM9pEREQUUf7YlQegbjkv0cLtJwIxnICZvvBfzF13WLpv1Ouk7LYaeUBcUW2xKeOWE7uWi4G2q/i8ykXJutz0n+u6nx8vqHDrObNW1y1NtnhHLnpkp7j9ekThymEzNK6jrRlmtImIiCiivLpkDwAgt8g2UDuSr94ELFzJg2wAMBqcp553HC+Sbi/YetzhftW1Z+piybirQFvsei532EFDtiYpdR3Rq90M0OVZbGfzv4kiiePlvfg3ohUG2kRERESwn7MdaQwefP9FFY7na1crM9qy3FkjlXnVf+zOs9t2wct/IOd0qd32G87Nlm67GzTLf67yNcKJIpmjvwR3L2CRawy0iYiIiAilVRbXO9WyWAWUVZnx1frDyFNUBpilOdr265KrzYt/7Lttqq+xat9pu20FZXUBfrWbQXO0bHkxM+tiiQDYV5qIFT7lKhUm5B0G2kREREQAIjuf7RmLIODlxbsx+X/bcc0Ha2weUzZD87YUVe1Ze2XN0NwNmrMbxkm3Hc0rd3tMgoBTbjaPIwpmyq7jK/fWXNiqrHb/ghs5x0CbiIiIiNxa71pktQpYVttU7rBibruYaTbWBtry5b08mSJdXmW22ybvkO5uGXiabL3tdB/X3n73j33o8/zv+HLtIZ+OQxRwDv58Tha512SQXGOgTURERATXy1CFu7suaOX2vmar4LDJmZhpFkvHu2QlS03M1BowXdWziepx5M3XRDtP1G0rqbQPxNXIM+q+Nnp65beaRnpPzf/Hp+MQBZraX8LWIwUeTSEh5xhoExEREUW4uCgD9M4WFlf4Ys0hu0y2qNps2wxNr9dh9u19AKgHumv2n1E9jjJjfVDRHO3PPafcGqu8aRobKhPVUPtb+GjF/vofSBhjoE1EREQE4I6BLQM9hIAp8zCLdaa0yuFjyuW9gLrO32ql48pl1kTKOaRbjpz1aIwieaDN1b2Iaij/vgDbBoMtUuPsHifPMNAmIiKiiOFo7VgAaJueWI8jCT6+rjFtsQrIK6qQMtEm2ZxvvRRou/8ayl11Xrarsw20GWkTAeoZ7SX/npRuvze2Vz2OJjwZAz0AIiIiovriLJhUztHOL61Cw/goP48oOGQlx2DB38d9OsYdszfgj9115dwmvTzQrvnXlzhXrZGZIAgu1z+3yF60VDGv+2xpFWKjDD53IycKNa7+FGNMzMf6iu8gERERRQyzk0BbrwjYFmw95u/hBI0KsxWHzqjPuXaXPMgGAKOh7v30NaNdUmnGjZ+sA2AbcLuThLfIymHfW143BzW/tAo9py9BvxlL3R4TUbhwVt0D1PVYIO/xHSQiIqKI4SyjrQy0I0lZlRl9WzbU9JjyE3XxrfUo0Jbl3G74aK10Oy6qLvssrtntjMXBa246VDPn+2xZtdtjIgoXnEXhfwy0iYiIKGI4y2gbFF23XZUkhxOzRfApg6WWHTOpZLQrqq044qBbuf0x625vP1Yo3ZaXeTv7eYocXVzx9afrKiNIFMoi6OPPbxhoExERUcRwltGWB4ZAZJ1oWgXBp0ZhlWb7zLI8cJdXCzz07Vab/ZQXONQkRte1FZLPmze7kdEud9BRXb4O99hP1rqVHZerqPZsf6JgovxzV3YZj6QLjf7CQJuIiIgihtnqODgyRvicRF+6jhdXmO22RRntm6EBwJH8cpvXdPS68mz1Be3TpNvyQLva4nzMgiDgk79ybLZVmmsC7we+2SptW7XvDN5f7nwNYatinOJxiEKRcnmvuCjbHtkMs30X2f+jEBERUUTxKKPt78EEEasATB/VxevnF5bbr6sdZTNHu+7dLJDtK88iv3h1V5vnyx+TZ67X7D8jrdHt6uKAOA9brv1Ti7B6/2m77Z+tyrHbJqe8mMCMNoUyZUZbWdGi/Ms6VlAO8gwDbSIiIooYS3fmOXxMvhwVgMiqHQfQLsP7dcQf/W6b3bZoWUZb3sBMHqBWyQJoZalqpdmKtQfOoN+MpVi8o2593zOlVVJHc1fl3mol7QDwf//bbrft4o4ZAIBtRwtw0SvL8duOXOmxFXtOofuzvymOzYw2hS5lIK28aKXsQXD/V1v8PKLww0CbiIiIIsa0n3Y4fMyoyGi7MXWYam05XGC3TT5HWx5oy1XLAuH1Ofk2j1WarRjz0VqcKKywe56x9qKIq2ZojuadH1RZyiwjKQYAMPHzTThwuhQTv9gkPTbus/V2+zOjTaFMGUjvzStRPG67/96Txf4eUthhoE1EREQRw9mcXmXXbV2YF49n1gaWAPDJuN6aH18+R9tRYyXx52Ey6GBQ7PP3kQLV5zxzeScpm1xaaT83XK7KQUZbTUV1zTFzi+wDezXMaFMo87QjA5ujeY6BNhEREUWMW/o1d/iY0W55L3+PJrDEZkgLJp2PoZ0yND++PNB2RAyETQY9TEb33vB+rVOlAH3k23+5dXx3lFdb8L/NR93enxltCmWuFhlQVoOE++ehPzDQJiIioojRKCHa4WN262j7ezABJp5HG5Vz0zWivHBx16DWAICMpLqfgThH22TQu72Od6tGCW7tV22x4u45mx0+Hq24EFBeZcFD3/5ts62syozrPlij+nwxA04Umlx17Le9r2ek7TEG2kRERBQxnM3pjbTSSPGt8Me3bdTr7N7P3s0bAABOFlXiVHElgLpmZiaD3qZLuSN/P3OJXab80jdWoLii2mab1Sqg7ZO/Oj2W8nfhf1uO2e3z9PwdWH8w32474LjRGlEocJXRVj4cWZ+O2mCgTURERGEvv7QK3286iiJFQOaMxdWZaIgTmyEpM/lKfVo08PjYahc0DLJmc2JjJTHQjjbq7ZrRqUmONQGoy44DwK7cYny1/rDNfmfL7JcbU3Jn3fDvnZSSM6NNoczZb/95rRrabYu0C5FaYKBNREREYe+N3/fg4Xl/Y+aqg24/x+pGIBbKxGBYXhL60c290CI1DpOHd5C2JUQbtXlB2dsZU9uFvC6jrYOLlbpsNIw32dxXPldt/WytMaNNoczZdUS9TmfXlZxxtuc0+uQkIiIiCl6frznk8XPcyXiGMvFCgnwu9SWdM3FJ50ysPXBG2mbQaA63PDCNNRlstpkMelis7geuyvncyqrzYwXlNvezG8biSL7tNl/tP1XieieiICU4yWnrdTooP/4YZ3uOGW0iIiKiWvKSZCcrgYUFMaOtVjou36JsauYt+XJY4mvWLe+lR6qTRnVKLRrF29xvnBxrc7+kwnbZL7HkXEuHVdbiJgoVzjLaOh2QFGObj2UzNM8x0CYiIiKq9cTwDhjVIwtA+JeOW5zM0ZbPx6yW1WV3zkpy69jTR3Wx2yZfakusFiionUsdZdTjln4tMLpnEzwhK1uXu6hDunRbGTgry9tLFOtrK9fodpeyM3nTBrEYUjsOlo5TKHPVgiI9Kcbmvrvry1MdBtpEREREMmKpdLg3Q7M4y2jLNsnX031gaDuXx901/VLcfJ79euUXtEuTbs/beBTHC8rxn6+3AgCiDHrERhnw2vU9cFnXxqrHfXRYe+m28kejLPNXNkPz9pqJGEzHRRnw7o3n4K/HL8KI2vHJM/REocZZ6Thpg3O0iYiIiGTE+b7hPEdbEATp+1MrCZXH3vJA/ML2aXb7KsXUzr9WypBlyD5blYPUhCjpvslY9xqOrm80iKvbX5lpVl4U2XPSdv601cFBrz6nKYZ3ycQdn29Uf9Fan9zSG/1bNwIAaXmxKma0KYSF+XXEoMCMNhEREUWkHtkpqtvFwDKcS8fl35r6HOy6bfIycq3mawO2Aby8uVl2w1i13ZGZXBeoK0vYlT+rAkVGe2S3LMy9o6/dMY16HUxG16fDnRrXvZ4YaK/LyccHf+53+VyicLFm/xnXO5GEgTYRERFFnGt6NcWs2/qoPiYGgGprQYcLebZe76J0XP6wVmvpRhn1NkG7UdbZXKfT4YruWTb7K+dtK8eh/FGJTdZm3dYHn97SGxMGtkT/No0wRxFs6/U6t+Zvp8iy6VGywPy/v+5CYbn7a7MTBQt3MtrxUbbVKQv+Puan0YQnBtpEREQUcSYNbmMTPMmJgZejcuNwIA+01bLU8i2Oug03iLPv5C2fh62mWcM4AMAV3bNsjvv7zpM2+8nH9PXE82y6watRlo6L31+jhGgM6ZgBY23G/Pw2jWz2izHpVeeoO6MsWy+v4lxtCj3uzNFe8tAgm/vsPO4ZBtpEREQUcYwGxyeMYoY3nOdoywNTV13H1TLeANBJpQP59b2znb7umHNrHtfrbDPlSvKfz3mtUlX3eejiusZsytJxc+2a3M5+zkDNet7ntmwo3W+bnoCXr+mGJinq5euAfaAt78pOFCqcXUcUH8tKibX5O2Og7Rk2QyMiIqKII58TrCRmtMO567jF4jzQ1tuUjqufXKu9Pa6yw3qpWgAwOPsZ6F3ngiYNboN5m47gSH65zUWR+77agtMlNXO0Xc0pjzUZYNDrkDNjBEoqzUiMqcnSf/pXjsPnRBlsy2nD+YIMhS93f2vlF5Y0bNEQEZjRJiIioojjLACLhGZoNhltlUBaJysed5QUtgoC/np8MAbJysVdnYiLj1sFweZ1777QtjQ8Iyna+YFQk2lvnZYgHU8QBFRUW/DT38elfYwuAnZxvrVOp5OCbAAYq7I8mShWMW81nC/IUPgS3Py9lV88c1TdQuoYaBMREVHEcRaA1ZWO19do6p9YWg24bobmqAGaIABNG8Thjet7uNxXerw2gBcE26C8Y2PbMvSJF7TCZV0b460bejo9nhis/7DlGHo/9zsW/ZNr87ha6fiP954v3d5+rFD1uGPPbebwNRvG287tZ0abQpGz31r5/G2baSQsHfcIS8eJiIgo4jibuxsJzdDEONud5bocnVuL7448UHd1NPFYgiDYPM9itb2qERdlxLtjz3E5tuIKMwBgde2yQw98s9XmcbULKo1T6pYJi4tSX/PbWeYuOda2CRwDbQpF7n68yf8UPG0cGOmY0SYiIqKwplYiGenN0MSMtqOAMklWRq1zED6L76snJ982c7RlEbzVy+qB9QfznT6u9nNOja8rS89Mdtz0zBHl9xvOvycUzhz/3tpMHZFfSGOc7REG2kRERBTW1DI3Jiel42KWN5zn3rrKaGc3jMWlnTNxbsuGSEtUny8txpfygNlRhlgkn6Mt/xH4671W+/7kgUOMybtT4Z/vGyDdXnvgjFfHIAokp13HWTquCQbaREREFNbUgjhnpcFiICbvzB1uxIy2WiM0oObk+oObe+HbO/s5bHAmZrTlWeOmDeKcvq540i4ItllsfzWec5Vtd6d0Xk2XJsnS7ecW7vTqGESBJP7FtWwU73Q/m9JxBtoe4RxtIiIiCmuelvbqI2B5r6raTm/FlWaX+1aa1eu6xXfHZNDjsUvbo7LaimapzgNteUZb/v76672OMjrPKTFDR5FK/JNT+wuQ/zkabDLa/h1TuGGgTURERGHN06Zm4vLO4by815u/73V737Iqi+p2+dtzz4Vt3DqWbUa77gD+eq+jnKzVDTgPtNMTo5FXXOky40cUigRnkbaM/E+Ey3t5hqXjREREFNY8zWibaoOz6jAOtH9VLIPlTHZDBw3DvMhC62Ud3eVvr7cNxfq2bOj0cVfLjTVr6DgD/9XE8zCmTzZm33auV2MjCmbiX5wnoTMrQDzDjDYRERGFNU87WkuBtoOS6Uhz+/ktkV9ShSEdM2y2exMa66TScShKx70b28OXtMd1H67x+Hlz7uiLf44VYkjHdIf7tE5LwH+v7ubw8eyGsTiSX+7xaxMFA7GKxFUfA/k1MCa0PcOMNhEREYU1T+f/iuXG63LO4PCZMn8MKaTEmAx4amQn9GudarPdm3XGxRN1QRBsysWjXcyldjw27553fptGuHNQa5cZb2c+GdfH6+cSBZpZCrTt/4aevKyjdFv+d87Scc8w0CYiIqKwZvYwpW0y1pxMni2rxgUv/+GPIQVc56wkn4/hTf8yaY42gOKKamn7Nb2aejUGT9bw1lqD+Lq1xtXWaicKZhYp0Lbd3i4jAZ2z6rrq22a0GWh7goE2ERERhbUfNh/zaH+TiwZaoS6/tAo7jhcBAK71IsBNr11Xe0gHx2XXjsjnaL/y2x4AwEMXt0OMyfn62444+1mZDP4NCqINdWOuDuOl4Cj8VJotuG3WBgD2GW1lMC2/iMSEtmfC+38SIiIiinibD5/1aH+jSillOFm2K0+63Tg5xuPn/3zfALx2XXfce5F7ncblxBP15btPSdtivQyyAecZbX9nu+VLh9386ToUllU72ZsoeKzZf0a6/feRAqf7yhsV/rXvjJM9Scnj/0lWrFiByy+/HFlZWdDpdJg/f77N44IgYMqUKWjcuDFiY2MxdOhQ7N1ru4REfn4+xo4di6SkJKSkpGD8+PEoKSnx6RshIiIiUlNY7lkAFGUM77SNRVZKb/Qie5+eFIPR5zRFtNHzAFmt8tSXalSTk4siBj+XucoD7XU5+Ziy4B+/vh6RVjypv5CXjq/Ycwor9pxyvDPZ8PjTtbS0FN27d8e7776r+vhLL72Et956Cx988AHWrVuH+Ph4DBs2DBUVFdI+Y8eOxY4dO7BkyRL8/PPPWLFiBSZOnOj9d0FERETkQHGF2aP9w7103CKbsr7Fw2y/r9TmePoy79PopDzc342blBnzv/ae9uvrEWnFk54Cyn0ZaLvP4+W9hg8fjuHDh6s+JggC3njjDTz11FO48sorAQCff/45MjIyMH/+fIwZMwY7d+7EokWLsGHDBvTu3RsA8Pbbb2PEiBF45ZVXkJWV5cO3Q0RERGSrysNlusI/0K57P/7YHfiTZl8Sz8YAlo4rFVd6dkGHKFA86d2nXOO+wmzReDThS9P/SXJycpCbm4uhQ4dK25KTk9G3b1+sWVOzxuGaNWuQkpIiBdkAMHToUOj1eqxbt071uJWVlSgqKrL5IiIiInJHtYWBtpzyxLk+ad2c21npe0V1/QYErdMS6vX1iLzlyUeActfyqrrP09//PYl/jhVqM6gwpOn/JLm5uQCAjIwMm+0ZGRnSY7m5uUhPt+1SaTQa0bBhQ2kfpRkzZiA5OVn6ys7O1nLYREREFMY87QgdpQjewm3pJvnb0adFg3p9bUFldqi8OZunlFnrX+4fKDVca5QQ7fVxvTGgTarrnYiCgLPPNFcfd7tP1iQ8txw+izs+34iRb/+l5dDCSkhcsp08eTIKCwulryNHjgR6SERERBQiKj0tHVc0QwtkBtgf5KXj4we0rNfXVlvSPL+0yuvjqS3hNf/e8zGgTSN8cFMvr4/rjaU7vb9gQFSffPlI23OypoH1ij3sSeCKpoF2ZmYmAODkyZM220+ePCk9lpmZibw82w8is9mM/Px8aR+l6OhoJCUl2XwRERERuaPKwzmFytJxS5hltFPj6zK9STGmen1ttXfS7MMa1GpLsXVrmoIv7+iLLk2SvT6uu0b3bCLdbpPO0nEKFY7/5i7qmO7wMaCm58Xmw2exJ69Y60GFHU0D7ZYtWyIzMxNLly6VthUVFWHdunXo168fAKBfv34oKCjApk2bpH2WLVsGq9WKvn37ajkcIiIiilBWqyCVR3paOq5cMirsMtqyCwf9WtdvubNayerL13bz+njKZmh+XtHLzqvXdcfk4R0AANYwuyBD4cvZr+oDQ9va3Ff7kxr93mos3HZC20GFIY+7jpeUlGDfvn3S/ZycHGzduhUNGzZEs2bN8MADD+C5555D27Zt0bJlSzz99NPIysrCqFGjAAAdO3bEpZdeigkTJuCDDz5AdXU1Jk2ahDFjxrDjOBEREfnMYhVw+dt/ITnWhLkT+qJxcgwOnC6VHh/YtpHT5xsM4Vs6brZY8dh32wAAw7tkQlffkakKXzLB/l7CyxWdTocG8VEAwuv3hMKbs9/UaKPB6XMbxJlwtqxa2wGFKY8D7Y0bN2Lw4MHS/YceeggAcMstt2DWrFl47LHHUFpaiokTJ6KgoAADBgzAokWLEBMTIz1nzpw5mDRpEoYMGQK9Xo+rr74ab731lgbfDhEREUW6nNMl+PdETcMei1VAkwaxOHC6FEM7puORYe3RIjXe6fMNuvANtFftPyPdjjbWf6uehGj7U8+4KI9PRx1qnBzjeieNib8vPlTAE9UrefXFDedm46v1jvtfKX+t46ONDLTd5PEn24UXXui0U51Op8Ozzz6LZ5991uE+DRs2xNy5cz19aSIiIiKX5FnaaouAlXtrmvac07wBOmS67vOi7GQdToH2j1uPSbddZa78IVExJ7xVmvOLHp4Y1jkDKXFRmh3PXeLvy4o9p2C2WJ0uOUYUDOShXNcmKfg9MQ+niivRsbH952M3Ra+DQFygC1V8p4iIiCis6GWBdkmlWbp9uti97tbhHGjL56vHmOr/NLCr4qRdWT3gix7Z9btUmUhevv7dpqMBGQORJ+Sfi0a9Dt/e2Q83n9ccH4+z79Tfv00jtGxUd0EsKgAX6EKVdrU6REREREFAHrqVVdWdUEa5mYmxKx0PoyZXVtlFg2hT/Z8wJ8fZZrT1Ggba7v58tSZvyHa8sCIgYyDyxOT/bZduG/Q6tGwUj+mjujjcX17NHPiuDqGDGW0iIiIKW7/tqFtyNCHavcAyrJuhyRayrvJwfXF/0LKZWaBKWuUXC5xNryQKRsoKHjUHz5RJt9ld330MtImIiCisyDPQz/+yU7od42YGN5yboZVX1wXXy3fnBXAkNbSIs6/p1RRNUmJxZY9ArV5T9/thDqPfFYoM7gTacoyz3cfScSIiIgorVgfBjrsZz3Ceo2221AXapgA17TIZdNJccU9P8tW8cm13WK1CwJb6+udYkXSbjaIo1CjXondFLaO96dBZ9GoemB4JwYyfBkRERBRWfthyTHV7t6Ypbj0/XANti1XAatnyXhd1TA/ION6+4RzptlbreAdyPe2Jg1pJtzcfLgjYOIi84enfjtqn4ZH8MpWtxECbiIiIwkJJpRkbDubjveX77R4z6nXonp3i1nGU553h0gxtxZ5T0u1WjeLx4NB2ARmHPOtrCIPOSkkxJlzevaZsfcWeUw4rKoiCkRYZ7XC5GKk1lo4TERFRWLhnzmabYFLuog7uZ2+VWVazJTxOIuVL+ky4oJXbc9a1Jg+0tew6Hkj/HCuUbldbrYjWcwkkCg3ufA40SojC6ZKa5RHVrjsy0FbHjDYRERGFvEX/5DoMsgHf5gKHS5fdSlmX8eKK6oCNQ74MV2xUeASkOadLpdsMOiiUnNcq1eU+s247V7qt9nnIJoDqGGgTERFRyLvry01OH/cl0A6Xk8jyaot0O5BLe8kD7YTo8CiubJeRIN0Ol98XCl+NEqIBAMO7ZLr12dilSTJaNooHoF7hI182kOow0CYiIqKwJ19P21PhMue2UhZoJ8WaAjaOcAy07x/SVrptCZOpBhS+2qbXXBga3rWx288R+xAcKyi3e6yav/OqGGgTERFR2KuyeJ9xCZcMpfw9uMyDE2ytRcmWFYsPk0B7ZLe6Nbyrmd2jICdOb/CkEdqv2084fMzsw+drOAuPTzciIiIiPwmXjPZLi3ZLtxvGRwVsHPKMtqcdj4NZlEGPKouVc7Qp6IkXgzyZUnO2rEp1+zs/voiL39kCxMcBMTG2X7GxNV/KbdHR9vvGxAANGwLXXqvJ9xgMGGgTERFR2Bs/oKXXzw2X5b3ktFq/2hvyQNsQDut71TLodYAlfLrUU/gSLwaZPPj7K620qG5PqCxDdGkJUFri+8BatGCgTURERBRKMpKivX5uuJSOBwuDLMg3hMnyXkBddp6/LxTsxDnVBr37s4jlzRTlHr7sQfx4c1c0jdMD5eVAZWXNv+KX/L54W+3figogLU2T7y9YMNAmIiKisCefF+ypcCkdDxbytbPDKM6WsvMWztGmICfOqTZpMHWjfbfWaHpuN5+PE47YDI2IiIjCXrTJ+/WamaHUll52cq9D+ETaxtrsIH9fKJgJgoC9eTVl3noNAu0Zo7v6fIxwxUCbiIiIQpo7zaeY0a7z9cTzAvr68nP7cMpoS6XjnKNNQexUSaV0u6TC7PbzRvXIUt1u8uGzNdzxnSEiIqKQVuFg7qBctMn7U55wyFDKL0aIa+gGirzTcSCbsmnNwDnaFAKKyuuCa3ljQlfuHdxGdbsxjBoaao2BNhEREYU0d7qC+5TRDoOu4x+vPCDd9uTk2h/kc7TDaHUvKeDgHG0KZvJluvQeXOhylLlOjjX5PKZwxUCbiIiIQpq/S7tDvRRYEAT899dd0v1YH+ara8GmGVoYzdEuLK8GAJwqVl9vmCgYfLB8v3TbkwtdjmLyaGNgP0+CGQNtIiIiCmnuzNF2tDSNW8cP8Yx2kWIepjHAcyrlJ/fhlNEuKKsJtB/+dmtgB0LkxLqcfOm2J1M3PMl+Uw0G2kRERBTS3AmEfclKh3oztAJZqWj7jMQAjqSG7RztAA7ETxJiuHouBa+xfZtJt8PpQlcwYqBNREREIc2dINqdrLfD44d4oC0ffzAEtjpdeDZDu3dwawDA0I4ZAR4JkWOt0uKl22mJ0W4/L4z+VOsNA20iIiIKae6UhfsSLId6MzR5Rt4QZCmscDp5j4+uyWRXW9gMjYLX52sOSbdbpbm/AgFLxz3HQJuIiIhCWpXZdWBj9qETdKg3Q5NfZGiRGu9kz/oXTs3QxKZQlW78PhIFyo7jRV49j3G25xhoExERUUhzJ4Po0xztEM9oy8vmn7miUwBHEt6ia5dNq6xmoE3hRy2j/dLV3QIwktDBQJuIiIhCWrUbQbRPGe0Qn6MtBtpNUmKRnhgT4NHYkjdqC3VioF1h9r7DPVGwUktoX9cnu97HEUoYaBMREVFIcyejHePD2tG+NFILBmJXdn0QnfWJU8V7ZKcEdBxaiq79HWNGm8KSItJ+cGi7wIwjhHD9ASIiIgpp7gTa1/X2PvMSTIH2wm0n8Ms/J/DS1d2k5lvO/Hu8CKPfWw0AMAZRpP3X4xfhn2OFYdWhWyodZ0abwpCydLxFo7gAjSR0MNAmIiKikCbOv06INqKk0mz3+P0XtQmbjPa9czcDqFkP+/4hbV3uP+KtldLtYGo4npUSi6yU2EAPQ1N1gTYz2hR+lB8f4bQ0n78Ez6VNIiIiIi9U1Wa026TXLVWTKMv2VvgY+ARjM7SzXsxtDralvcINu45TsBNkn2Xy9bTdEUTXG0MGA20iIiIKaWJGO8qoR3xUTbDTs3kD6XGrj2eIwdIMTX6SHGX0/BTOEESl4+EoxlTz/hZXVPv8O0fkD0UVdRU/8+7s59FzlRccednONX7iEhERUUgT52hHGfT4cdL5uLV/C7xybd2yM918bLgVLEHTLTM3SLejDd4E2lqOhpTEjPbJokqM+WhtgEdDZK+4ohpAzTSH1IRoj56rnELDynHXOEebiIiIQpoYaJsMOrRJT8TUKzoDAJY8eAG2HCnA5d0a+3T8YMlor9hzSrpdUul5wy1mtP0r2lT3/q4/mB/AkRCpE4NloxfTSFITorQeTtjjJy4RERGFNHEdbaMiZds2IxHX9c72uWlPMDVDE3kzb9zADJRfRXtRzk++KSyrxlfrD6OwrDrQQwkJ4kVDb/o1RBsNWPnYYOm+jsXjLvETgYiIiEKa2VpXOu4PwRhoyxu/OSIognE2Q/MvE2vz692krzZj8v+244FvtgR6KEEnr6gCs1bloKii7iJEVW2jPvlcbU8kx5mk2ywdd42fCERERBTSxJNHo4Yp2+FdMqXbliDsOp7gxhraypJ3d55D3uOFjPq3cu9pAMAfu0+52DPyjHp3Fab+9C8mfr5R2naisNynY+oc3CZ1DLSJiIgoZAmCgOcW7gQAGDWcg/zWDT1xRfcsAIDFEvhA22zxbMkos8WKvOJKm23JsSYHe5MWTJwDT0HkeGEFAGDtgbp+ATG1Dfu8maMNcO1sT/ETgYiIiELWWdnczOIK7eZpmgx6dGycBCA4Mto3fbrO5r4A52O6+v3VOP+/y2y2MdD2L4OioqKsyrvyXCJ/EYtc3Jl6osYmo82Y2yUG2kRERBSy5POntZ5LLU65DYY52vKsFABYXSS4/z5aaLctOY5dg/1JmSX8btPRAI2EyNa6A2cA1DVR9DYzzeDaMwy0iYiIKGSZZRGn1plnfe1ZZTAE2kredB3PSo7xw0hIpAy0g/H3hiLT9bXruoufkd727dNxlrZHGGgTERFRyKo2+y+jLQZOwVA6rqQcUmF5NfblFTt9ThSXn/IrZTO0jCRe2KDgIq5EoPcyNS3/HffmYl+k4ScuERERhawqWZMws8ZNy8STymBohqaknKPdf8ZSDH1tBXaeKHL4HG9Prsk9Op0Ol9c20AOAxBh2efc3b5t6hbujZ8vstpktVogfl95+FphkfQjE1R7IMQbaREREFLJsSsc1n6Ndc5oUjBlt5bdaWmUBAKzY43iZI+VyX6S9t2/oiUYJNXPh5Y36yD+C8W8zGOw8YV/dMmnuFikL7e31Cfnc7kqzxbuDRBAG2kRERBSy5KXjZlcdwjwUTM3QlJyVbTo6AS6tZBfs+nC6pAoAcP9XWwI8kvDHOFtdkko1xaIduVLpuBZrvjOj7RoDbSIiIgpZNqXj/spoB2Wg7fix/NIq1e0lDLSJIsIPW46pbhc/LrVYD7uSgbZLDLSJiIgoZJn9Oke75t9gDLQFJ6m8kgr1gJoZbaLI8PWGI6rbD5wqAQCcKCz3+TVapMb7fIxwxy4NREREFLKqZcH1uH7NNT12MGW0uzVNxjbZ2tjOSmbVMvtRRj1GdstS2ZsoNB0v8D1YjDSvLtkDADiS7/179+O952PbsUIM6Ziu1bDCFgNtIiIiClnVtRltvQ64vk+2pscWiyvXHDij6XG9UVld832mxkfhTGmVwznalWar6oWB1U9chEYJ0X4dI1F9yiuutLkvCIImJdHkXPfsFHTPTgn0MEICS8eJiIgoZImBdo/sFM1Psn/954Smx/OF2OAsxmQA4HiO9hu/71HNaDPIrj/v3ngOAKBvy4YBHkl4i1asCx8EhSdENjQPtC0WC55++mm0bNkSsbGxaN26NaZPn24zl0gQBEyZMgWNGzdGbGwshg4dir1792o9FCIiIgpjFquAN36vOX8wGbTPHeQVVbreqZ6IjYdio2oCbUdztK2C7bx1qn/S+uuM/PxK+f5qvepAOBreJRMAMLJb4wCPJDJo/r/Siy++iPfffx/vvPMOdu7ciRdffBEvvfQS3n77bWmfl156CW+99RY++OADrFu3DvHx8Rg2bBgqKiq0Hg4RERGFqe83HcW/J4oA+CfQdraElj9tPVJgN/9UCrSljLZtAkM0omumXUZ7VA/Oza5PxtpAm+uW+1eV4oLSv8eLAjSS4HOZg0Ba/Jzs2axBfQ4nYmn+v9Lq1atx5ZVX4rLLLkOLFi1wzTXX4JJLLsH69esB1Pxn8MYbb+Cpp57ClVdeiW7duuHzzz/H8ePHMX/+fK2HQ0RERGFq/+kS6bbJoP3czPEDWml+TFf2nizGqHdXof9/lwGoOW9694990pJdYqAtvwYgz+wZ9Hq7TN9/hrbz86hJzmBgRlsLZosVt85cj5cX71J9vFqxvNRrtY2+yHFVi5j198fnJdnTPNDu378/li5dij17an7Z//77b/z1118YPnw4ACAnJwe5ubkYOnSo9Jzk5GT07dsXa9asUT1mZWUlioqKbL6IiIgosomZQ8A/Ge2mDWIBAE1SYjU/tiMbDp6VbleaLfhr32m8vHi3tC0myn6OtjxzqpPd79g4CdumXoKWjbgMT30y6JjR1sKnf+Vg+e5TePeP/aqPz9963Ob+yr2n62NYIaHawVKH4najnm266oPmXcefeOIJFBUVoUOHDjAYDLBYLHj++ecxduxYAEBubi4AICMjw+Z5GRkZ0mNKM2bMwLRp07QeKhEREYUwg86/gbZ4eGdrVmstPtog3T58psyuHDbWVPN9ykvH5ZlTna4umxVl0CEpxuTP4ZIK8QLQzhNFKK00Iz6ai/x4Y8av6pls0VfrD9fTSEKP2CSyf+tUrN5fs2pCk5RYaTsz2vVD8/+Vvv32W8yZMwdz587F5s2bMXv2bLzyyiuYPXu218ecPHkyCgsLpa8jR9QXYSciIqLIobfJaGt/4qhD/Z+MyjunV1RboVd0Uhe7jn+59pC0zWyxzWh/vaHmPIn51MAwyH4vn5r/TwBHElk6Nk4K9BCCRlVtWf2NfZvhs1t7AwAaxkdJnxX+uDBJ9jR/lx999FE88cQTGDNmDLp27Yqbb74ZDz74IGbMmAEAyMys6XZ38uRJm+edPHlSekwpOjoaSUlJNl9EREQUWf7ccwr/+XoLCsurAdgGwvKgW2v1GbCWVpql25VmC37faXu+9PeRAgDAicK6BrLKbstL/q15zrajhX4aJTljlF30+WHLsQCOJLIkxrByQFSXudbbfE6K243MaNcLzQPtsrIy6BV1/waDAdba/wRatmyJzMxMLF26VHq8qKgI69atQ79+/bQeDhEREYWJWz5bjx+3HpeaI73+e13zo5/+Pu7oaV6rKx3X/NA2rFYBv/97EjNX5eCErNt4RbUV63LybfY9U9sUTSQIgk3peM7pUv8OllwyyM6D/Xj9J6K4M32Dy9rVsFoFbD5cAKC20kf8HIOA3SeLAXCOdn3R/NLP5Zdfjueffx7NmjVD586dsWXLFrz22mu4/fbbAdSURD3wwAN47rnn0LZtW7Rs2RJPP/00srKyMGrUKK2HQ0RERPVMEAQUllcjJS7KL8ffk1tit81R8x8tCH7Maf+55xR++vs4vtt01O6xSrPFbluVrNPyNxsO4+XFe3DHwJbStr+ZxQ44eZM+nY6RthasAiBPwqoF3pHYfK7KbMWyXXk4r1VD6fP26Nly2eMCok01b9zZ0moUlNVUA3GOdv3QPNB+++238fTTT+Oee+5BXl4esrKycOedd2LKlCnSPo899hhKS0sxceJEFBQUYMCAAVi0aBFiYmK0Hg4RERHVs/u+2oKft53A/HvPR4/sFE2OmVdUVypdXm2Rysf9yd8xUl5RBW75bL3DxyvN9hk6+drBj3+/HQDwXxdNo6h+GZjG1pzZaoVBX9coUNkAUBD8e7EtWL2zbC/eWrYPXZok4ef7BgKwbZRYabYgpraB4jFZtQyv/9QPzesGEhMT8cYbb+DQoUMoLy/H/v378dxzzyEqqu6qtk6nw7PPPovc3FxUVFTg999/R7t2XOORiIgoHPy87QQA4LO/cjQ75vI9p6TbZ0oqsS+vWLNju+Kv0vFTJZVOH6+ots9o12MDdPKSTUY7gOMIJxargA/+3I93lu0FAOw5WVfVkpVcs/xeJJaOz6uthPnnWBF+3HoMgiCgQlYJo/YZAgBRBoPqdtIWuwYQERGRZk4U1mVN4qK0O5mLj6o7ZdHpdPXSNVdsIuSv2NbV96DMaJsMuojM2oUafzbmi1Sr9p2RKjfG9m2OW2fWVYL0btEAx7aWY29eCaot1ojqqC2fSvKfr7ciIdqIBvF1yc1ezRvaZLJFsRp+NpNjkfObSERERH63QpZ5jovS7np+SWVdqfixgnKbE0x/CXR5ZUW1BS0bxUv33xrT06PnN07mlLxAEJdgA4BYEwMaLUz4fKN0WwCQV1xXDXJF9yzp9uH8svocVsApL8ZtP1aIiqq6LHab9ATV58VH8/eyPjDQJiIiIr/Q8mROOSd7X559QzR/8Ve5tquLBSv3nrbpIp7hYeD83d39vRoX+aZRgny6ZAAHEqaUBQPntmwYmIEEgRLZcoCi6tr5652zapZDVvsVbJ2mHoCTthhoExERkWaqZKXNWpYnHi+osLkvz2gBwNw7+mr2WqK6IMk/kbarLsnLduVJtwe2bYRzmjVw+9hPDO+AJimxXo+NvBdtrPu9b5+ZGMCRhC5ny3kpH4qPMqJBnAlAzdJWkc5Su6Sy0cEUhu5NkyOqvD6Q+C4TERGRJsqrLFKzIgCI07BsVpnR/mvfaZv7/ds00uy1RNIc7QBltOVGn9MEANDXzeydo5Nsqh+PDmsPAEiO9c8Sd+HO2d+cVfGgXq+TOr1bIqhbYEFZlep2c+3FTvE9UVZVcAnA+sNAm4iIiDTx1rK9OFnkvJO2t6oUHYXX5+RLt/u3TvXLa/q77Lesyr7s05EzJTUn1a9e192t/ZmxCiyxfNxZZpYce/z7bQ4fU0ta62v/WM0R1CzQ0RKH4tJnRn3NZ4COve8Dhp/CREREpIl1B87Y3NfynLfaSfb3s1v7aPdCKvx16l5epb70jprLaxs+NYx3L0MaZeQpXiCJgV8kZVi1JC5bpUbt4oVYwaHMdoczR1NPxDnaXM898PgpTERERJrQKVLA4lxBLTibzxzjp87O4nfjr6xkmQeBtjjf3d1MdaOEaK/GRNqQAm3OGdacakZbH3nvt6MLdYfP1DRQdFQ6TvWHgTYRERFp4sAp207gWq75XG3x/3JeSuIJ6tmyalSa3Q+K3VVW7f4xxcDN3bnXKbXNoSgwxCAnghKsmnHV0GzwK8ul23Mn1DRBNHiQ0d54MB83f7oOu3KLvB9kEFC7UCcIwCu/7QFg38eC6h8DbSIiIvLKsYJynCgsl+5f0C7N5vFKDwJJVxw1DnO0Tqw26oLa33ac9Plor/62G9N//le6X+7BHG1xJMqqAUfapbPbdSBFYoZVK7/9m+v08XLZ50rj5JrO+gYP5mhf88EarNx7Gpe+sRKPffe3DyMNLLUeDw1ULrApPzHGD2jppxGREgNtIiIi8lhFtQXn/3cZ+s1YBnNttvl0iW0jNE9Ko11xlNF++OJ2mr2GUpxseTJfw6UqsxVvL9uHT//KQfdpv6GsyoyK6rrvaWS3xj6+Qh29DkhmRjugDJyj7bWzZepNvtSYDDXvs7ddx7/d6HgueLBTKx03udGbYc/JYn8Mh1Qw0CYiIiKPyZsViRmmVftsm6GVa5jRdjRH21/zswGgcXKMdDu7gW9rUsu7pheWV+OX7bnSBYqbz2uOZ6/s4vT5cR6sSS5m+ShwxAp/dh33XEaS+/0FssSMtlg6Xv8zTALmwOlSu22qZfeKlHYkNYwLNAbaRERE5LGn5/8j3TZbBOzOtc+SaBloi6XjysrpaJP/TmV0Oh0yk2qCbXGpHG8pS9/jogzSxQODbB1gNW+O6eGyZPyBoW2l2wzuAo+l495ztx1DVnKM9D5Ly3tFUKT98uLddttyTpfZbVMu72Xw8bOM3Md3moiIiHxSbbXiZFGF3XZPlq9y+Rq1Z9/xUUab7dFG/2W0gbrAXvCxeFwZaCfFmKQgzGRwHmhHu1EOKu9GXs3gLuDqSscDPJAQVFrpXu8Co+x33pNmaEqumq+Fks9W5Ui3E6KNqvtEublyAfmO7zQRERH5xGIVVLPXWma0xQ7myhJqd4JQX9Qt8eXbcZSBtlUQZBltvRSYqXHnYsK+vLqO76eKK53sSfVBTBqGUxBXXx74Zqtb+4nzswHZHG0vEtqF5e7PCQ8lM2/rAwDIK7a9CBpl5Hpf9YWBNhEREfnEbBFUA4rlu09ptiyXuLxWvCJLE+PH0nGgrsu3z83QLLYXHb7ffFTKaBv1Ojir5nTnYsKRfPuSUQocsZSZ82F9c1XPJg4fk1dxHKpdO/rvIwUev8bZsiqPnxMK0hJq5rorLyRwOkP9YaBNREREPqk0W2yafcl99leO6nZ35ZwuRfunfsXJoposbXqibaMkf5eOi3yd91ypyGj/uPW4NJ/UoNc5nQPuzjz027lkT1AxcI62Jl69trvDx+SBttip/J0/9nn8GtUhWt/vamnDJrUNHEsUpfj8law/DLSJiIjIJw/P2+bwZPUTHwLtimoLBr+y3CZIzUqx7agd5e/ScWmOtm/U1gEX1/w16nVwMkXbrYsJybFcziuYGJjR1oTeyR+GvHTcF1pV3dQ3o7MPDdRdiLioQ7rNdjZLrD8MtImIiMgnfx8pcHiy6upk0Jn3VLJTiTHKZmj1FGh7cW76z7FC3DNnEw6eLlW9EFFcUZNpMhh0TruKu/M9erL8F/kfu45rZ3D7NNXtJi+aep0use9fEIqBtiAINn0ZnGmfkWhzn7+T9YeBNhEREfnM0cmqL8mTtTn5dtuUJ9d+7zpe1w7N4+de8c5f+GV7Lu74fKNqRnvh9hMAAJOL5XZcfY9RBr3LMlKqX2J3/DINO+9HqvfG9sLXE8+z2+5NNUvv53632xaKcWd+aZXUTNEV5UW8UPx+QxUDbSIiIvJYkiKzLA8kz2vVULrt7smgGrUGa3/syrO5X2+l4158G+Lw9+WV2DVDk3O2tBfgeo726skXITGGpePBRKy8OFFYwc7jTmw4mI/bZ23AwdOldo+Nr+07EBtlwHmtUu0eVy71561QLO9/dcker58bit9vqGKgTURERB5TNj8T71/bqymGdMiQtl/VM8vr11A7ITwgOyHX61wHqb7yPp9tSy2jLTIq5ppefU5Tm/vOSscfu7Q9GiVEO3ycAkNeyn/gtHslvpHo2g/WYNmuPEz6arO0rV1GTXWGcm6xUnGl/bJc3qxCcEalnDzYrdhzyunjCyad7/Axxtn1h4E2ERERecRssaKi2jZwrDbXnL2ZjHpYZGdy8zYd9fp1lCWPjZNjbO57M0fT2zH4enKq7Doup5x33rGx7ZxKZ6Xjdw9qLd1u1jAOANC0Qayj3amepMm649dXZ/xQk19at6zWoTN1y9OJf2tO2hYAAFbtOyPdnjG6KwBgQJtGTp+TkWR/UequLzer7OncyaIKpxfP/O2ybo2dPt6taYrDxzhHu/4w0CYiIiKPKJeLAerWiY4y6NG/dV2ZZ0GZfdbJXVGKQPq9sefY3K+XQLv2X1879VZUOy4dT4g2Ke4bMbxLpnTfWXm8/GLEhzf3wpU9sjDnjr4+jJS0oNPppAsoodhsqz6cM32JdFse/ImVLAZFpP357efadNfvkFl3QUr8LHC1VFdGUozTx92xK7cIfV9Yims/WO3zsdyRc7oURRWKtbBrv887B7Xy+HhTr+isybjINQbaRERE5BGxW7bcu3/sBwCYrVan2RRPKOcmd2ycZHPf32XjAKRI29cc0Gd/HXT4mLLctUezFJuLFe5+nx0bJ+HNMT3RPDXeqzGStsQLRb70KYgUZptAu+Zf5dJeF7RLw9YpF0v3Y2Xl+eLqBn+6KKkuLK8JWOfd1c+rcVZUWzD6vZoA+++jhV4dwxP78kow+JXlOPf5miZuZosV7/6xT1o2McHDeepvjumB9pmJrnckTWjTRYCIiIgihjK7IldYbh+EeytGUXKrzGCLJ83+VJfR9u04u08WO3xMLC1e+vAgnCysQIfMJJwoqPDtBSngxN/XQJYYhwqTLKgWM9pq15fkFRzypQNPFtX9vVRUWxBjUi/XP1VcMx87zcu+Bq8v2VOvneTX7D8NANJUnS/WHsLLi3dLj2cke5ahN7pY4YC0xXebiIiIPKKW0RaN6uF98zOgJoMz5qM1+PtIAc6WVdk8ptcBT4/s5NPxPeVsfWutiBnt1mkJ6F87x/SCdmm4qmcTPDqsvd9fn/xDbHLH0nHXTLXTIzYczJfma+td/O3JKz3cuehWZbZKQXKDuCivxrlk50mvnucto+zi4up9p7HugO2Sh0a9TurN4Ey/VqmIjzLggnbO57CTthhoExERkUecBdq+Lrc19LU/sfZAPq58d5XdybNOp8NN5zXz6fiequs67r/yX7VmWQa9Dq9f3wP3Dm5j91iP7BS/jYW0E+XmvOFIJe/MLmb/r/1gjbTNk0A7XdZ8zlGzL/nnSUKMd0W9JU4++/xBnrVfvCMXlWbbbPq5LRti1m19MPqcJk6PM3dCX2yZcgmXAaxnDLSJiIjII8VOSsfFE2Yt1rc+eMZ+bd36Ln3UabW+lxPOlu+i0CX+LZiZ0VYlL8E+VVxp13DQVaAt/yy4pne2dNvROtGH82s+TxJjjC77HlRUW/DFmoM4erbMZnu5k6aG/iCfLrPvVAmOnC23eTw+yohWaQl47boeqqX2Ip1Op8lnMnmG7zgRERF5xJ2Mtjx4tHrZDEq5hBhgm8XKbuj/Zax0tTltf+YkHc0ndaQeqtlJA2LpuHLNeVKnDGJdXVNrm54g3ZavUODo4+b6D9cCcP75JXptyR48/eMODH9zpc12cz1XJ4i/QwBQUmnBvjzbNdnjorl0XDBjMzQiIiLyiLOMtnjCK8/EWAQBevgWHX57p32XYGs9xC9iUOtrMzRnmNEOT+4uORWJ1JbL6zRlsc19R1nneXf1w8JtJ/Dgxe1U93W0FJ+r7u9VZqt0oXDl3pomZMqg3OLPDwIV8qx9qcqyisolECm48KdDREREHpm95hAA2CxBJRJPVOVzCx3NmfRErErWt21Ggsqe2hJPtPMVjdlcOVNS6fa+8qyVO5jQDg1RLB13yJ2LD2p/8wDQp0VDTL2iM+Kj6/KF8pjc3Y+b89ukSs9t8cRCtHvqV2w6lG93PLn67iBvkme0VTLx9dGskbzHQJuIiCgCvb98P65+fzVKVLIkrohL5Kzef8buMTG4kM+T9GQdYbXsboxJj05ZdWto/3zfAFzfOxsvXt3N7eN661hBzZzI+7/a4tHz1N4bRxKiPSsw5Ml1aDAZWTruiNmNchRPplTI/yYczdFWuqlv89r967ZN+XEHAMfzwxM9/Fv1lXwt8SqL1aaBHAU/BtpEREQR6MVFu7Dp0Flc/NqfqiWJ3hJPjuUnyZ5ktNVKIX+8d4BNaWiXJsl48ZpuyEjybA3Z+lTpQebL08B5WOcMAECWh2voUv0Sy35ZOm6vUqX/glKMSjd+Z8SPCGWgvT4nHzd9ss5+f5W09Y7jRQAc90Fo6sZSWlpan1O3nFe0UY92GYn1+vrkGwbaREREEexEYQWe/2Wn2/vL5z+O6Jpp97iYkX73xnOkbZ4E2mrZ71Dsllte5b9lgG4/vyU+vLkXFtw3wG+vQb6rm6PtOqh0NK84XM1df1i63TxVPXiNifLs717MQiuT5Xd/uQl/7Tttt7/ByQUutUcEQcDOE0U225z1q9DC+8v3S7c7ZyVh65ECv74eaSv0/uciIiIiTX2z4Yjb+8o7gWc3sD9BjjbVnFp0z06RMkzulImK1IJyk4dzmIOBfOkirRkNegzrnIlGCdGud6aAEeO4che/C5e8/idaTv4F248W1sOogoM4/QRwnLn2tNGXFGgrLlqcKVXvr+CsN4JalYmY7ZYrqsd1tX/fmVdvr0XaYKBNREQU4TzJOBdX1mVw4qONWP7IhTaPR8tOmsVyb0+Or9bVNxQ767KTOC359yQA4Nmf/3W4T87pUuw5WbNk03++9qwPQCiLr12W6pZ+zaW57EqeTqnQOSgdd8TZ54paM7SicvvstYXTAsgJ/i9ARERETn21/jDGfrIWxRXVOFtad7I5uH06WjSKt9lXPpdavO3u2rOCIKgG5aFYOt4okdlmcu2srJt9RbX/qiCCyf5TJXj3j5qS6KRYk2rAe+cFrTw+rtgXwdWc+A9v7gUAMDn5XFEL8qtVPpuqLJHxMyPvhN7/XERERFSvJv9vO1btO4OPV+bYzDft2jTZ6fPEZlDuZrQd7ZcUY3JzpMFDXAZI2SX4x3vPx7NXdg7EkChAnGVO5XOzTzsocQ4342dtkG4nRBvtLqQ1SojC5BEdvT7+/zYflW6rzX0f1rmmt4TR0RpeUM9oW1SmwFS40dStvvRtWbNcWWYQN4mMNAy0iYiIyKEj+WXS7cNnSqVmZU1SYqXtM0Z3VX2uVDquONl1FFCrlY0D6t2Bg52YXVMu3dUpKwnj+rUIwIiovt13URsAwJhzsx3uI5/LX99rNAfKwTN1nykJMUapaZzodIlvFxzEJfkA59lt5evK7c0rsblfbbHi9lkbpftifwRPVhfwlKerQbx1Q0/cc2FrfHtnPz+NiDzFQJuIiIjw49ZjqtvHfLRWun2isELK6sgbCQ3pmA4AaJOeYPNco8oc7dX7T6Pb1MX4dqN9AzZP5nIHOzFoilcE2s6yaBRe3KnoKK20LT0e99l6VJrDsxzZahWw47htw7f4KKPbU0vc1SAuSrqtbMTYv3WqdNvZlJSCsropMiWVZqzce8rm8dMlNc3cvtt0FP7irEnlnRe0wo/3nm+zLS0xGo9d2gHNHHRxp/rHQJuIiIjwn6+3qm6XZ4eAugyRPGBMT4zB31Muwa//GWizr5jRlmfqHvxmK0qrLHjsu212r1Wi4XregSY2ZJI3RYsy6O3mfjbmWthhS0yYOg+0bX/nV+w5hR82q1/0CnXv/7kfl731l802qyCgUKXJmDdialc8aJVW1zdCuVzgVT2bSLfdveiVX1LlcPrKV7JlyrTmLFs+eURHdM9O8dtrkzYYaBMREZFbLFZByj4pyy6T40z222JrTk7l3XpjTOpL+QDAPlm5ZlYQBqCerHUsBlexsjnaCTF12e1FDwzE2L7N8Oejg7UbIAUVgxsZ7V259ktGhdMFJ7mXF++229a0QRxKNVpzfkSXxgCAEtmSW8psufy9dVQ6rsxSl1WbEYhaGyEgr0paYqBNREREbrEIAo6crZlfaXAjGyQGmRWyUlhnjc3io4wOHwuUQe3SpNubDp11+3liRlu+RrB8vnaHzCQ8f1XXkOyoTu5xJ6OtVnrs7vJU4cBk0EnrX/tK/FuSZ7HNFtus8PV9su32V3pk3t8296vNgubl7e6oNkfO70G44qc7ERFRhPEkMyu3P68Ek/+3HQBQ7sZSRGKQKe/MKy+lPlVcabO/mNlqm56AYJmufdN5zaXbxRXuZ97E8Ueb6r5f5XxtCm9SRtvJ39vgDul221wtTxVOWjVKsOvwfW7Lhl4dS5yWYZV9eCiX5IqTXcxzt3S82mpFzulSr8bkizIHmf6nR3aq55GQtxhoExERRZgqi3edcotkgeaJggqX+4tBpnx9YHkp9ePf287TLqttDBUXbQyarJ78ZNyTUk4xiynPaLMPWmQR+wUq5wnLHZZ14Bb5s5N1MBncPg3JcSab6pipl3fCzFv7eHU88TDyt1ue0d4+9RKb/Z2toy1Xbbbi/37Y7tWYfOFoCkFiDC/YhQoG2kRERBGmokr9RD5fsY6v8r6cWxnt2vnY8oZPrdPqOpMv25Vns78YYEQb9UGT0ZZ3V193IN/t54lZtRhZRjtYvieqH4ba2nGLkwz1RpXpCJGyzFe7zEQAsCkdv/X8ll5XfojHkV+kE6sDkmNNSFRMW3G2vrmc8sJHYowRL13dDQDQtEGs2lM04WiNboNGpfbkfwy0iYiIIkyFg+WDlB10lUvaeErM6j794w5pm7OydXEpHqNehxv7NgMAnN8m1eH+9UGebftwxQG3nycG1fLmb96W7FNoEgMiZ6XjasJ1eS+lytpAUqvqFTH+lP+d3fXlJpvH5NwtHVdms3+893y0Tq/pbO5OrwpvtXCwTFcCM9ohgz8pIiKiCFNepX4irwwE31m2z6fX+edYod02R2W01RYr/j1R04HZoNfhvovaoHfzBujVvIFPY/CVo87ErojBlfz5wVIOT/VDbR15oGYN5tT4KLul3kSRktFOja9Z79pZab0n6jLaNfe/2XBYWslAvi62yN0g+ehZ2yUOW6UlYNvRAgA1ZeX+4qhqqFFClOp2Cj7MaBMREUUYRydwZYoA3NkJ8LktXDcsUusm7Kh776S5m/HhnzUZY6NeB5NBjwvapQW8gZi3GSuxdFz+fGfdpyn8GFQC7W83HkHv537H9J93AqgLNuXmrDuM3bnF9TPIADHqdZhwQSsAQLv0RE2OKZZ4v/PHPuScLsXj3zufV+3oQoczM2+rmT8uXkCr8mPjOvHzOFHxGRhlcLxEIgUXBtpEREQRxlGgfVaR9XHWadedAFTtPFbZiK269v7iHSelbcEUj5r03p0qidlr+XsQTN8X+Z84v18sBS8oq8Jj39U0APxsVQ4A4IyDPgjD3lhRDyMMnI/H9ZamVUwf1QWjemRh3l39fDqmfOrL4FeWu/WcTo2TPHqNwe1rusSLgXa1l40l3SF2HR/ZPctme+csz8ZMgeOXQPvYsWO46aabkJqaitjYWHTt2hUbN26UHhcEAVOmTEHjxo0RGxuLoUOHYu/evf4YChERESlUOCgdjzK4n+ExurGvWkZbWRZ7ViXQEMsyg4G3GW2xdFzeuIhztCNLWkI0gLpl7Nbn2DbT23LY/XXZQ92JQtvya3nn7LTEaLwxpif6uFEl40yXJp4HoJ/d2geTBrfBY5e29+h5UfUSaNeuwhBlm8HWc/mCkKF5oH327Fmcf/75MJlM+PXXX/Hvv//i1VdfRYMGdXOsXnrpJbz11lv44IMPsG7dOsTHx2PYsGGoqHC9VAgRERH5Rsxod2+abLNd2bQptjbj1Kg2YJBzt5GQ9Jq1J43KBmunS+wDbWVmPZBMHlx8kFMrHWeYHVnSEmv+bsSstbKaY/PhgvoeUsCcUfydn9NM+94L1/bK9vg5mckxeGRYe7vybFeijIEJtNf93xC/vR5pT/NA+8UXX0R2djZmzpyJc889Fy1btsQll1yC1q1bA6i5mvvGG2/gqaeewpVXXolu3brh888/x/HjxzF//nyth0NEREQKC/4+DgD4+6hts7L9eaWY8PlGKdPWu0XNyXDfVvaZJrVstd0+srOMyf+rKZmVr8UNAGfLHC8hFgyMXjZDE8vEdTYZbS1GRKEiunYNdbGKQ1nN4UkFSTgZ27eZX7Kyzg75xyMXOn1uelKMR68lXoCrtgh+q1QRl0WMi6q7CMA1tEOL5oH2ggUL0Lt3b1x77bVIT09Hz5498fHHH0uP5+TkIDc3F0OHDpW2JScno2/fvlizZo3WwyEiIiKFH7cel27Ls9VrDpzBkn9P4qr3VgOoCwzio+yb77jTR0gejM/fehxrD5yx26fKbEVBEAfbnmbuRRYpo123TWBOO6KIWU/x78gu+xlB6yFXyPpCPH9VV7+8hrPmZi0bxTt97sUdM3Bxpwy3X8tkrPvDVlYqaKWwvKayJymWwXWo0jzQPnDgAN5//320bdsWixcvxt133437778fs2fPBgDk5uYCADIybH+ZMzIypMeUKisrUVRUZPNFREREvlsz+SJcoWi2AwAllWbsP1XTDE2eUREluFFqOf3KLjb3x3y0VrqdkVQT4FdZrChWZLkv8eCE19/cmYuuxqo6R1uTIVGIEANts1WA1SpAB9vfJfnSUDGm8OxPXGm24P3l+7Gltky+Q6Y2HcbV+HLdQq/XYdLgNm7vHyW7grZw2wmcKan0/sUdED8XG8ZxOa9QpflftdVqxTnnnIMXXngBPXv2xMSJEzFhwgR88MEHXh9zxowZSE5Olr6ysz2fg0FEREQ1ujapmZs9YWBLmAx6dFPM1QaAC176A6drTx7jo+0z2pNHdHT5Os6644rBe7XFareM2Fs39HR57Poin2M9oE0jt59X13WcgXakilJkPZWN9cQMd1piNFY/MQStXGRdQ9FHfx7Ai4t24flfapYzUzb20tLJQt96PXnS+NAkC7Qf+vZvXPnuKp9eW42Y0e7QOAkjuzXGNb2aql70pOCleaDduHFjdOrUyWZbx44dcfhwTcv9zMxMAMDJkydt9jl58qT0mNLkyZNRWFgofR05ckTrYRMREUWE0kqzND+ye3YKAPUTzHxZN/DGybF2j2e4MafRWSmn2Git2mK1WWd4aMd0admfYCBf3kseOLmy7kBNh2lvu5ZT6JNnPUsrzXa/C+K6z31aNEDD+CjcNqBlvY6vPmw4ZNtZPdaPgfaekyWq293NVHtSvaL8WR49W+5gT+9YrQJKaudoJ0Qb8c6N5+CVa7tr+hrkf5oH2ueffz52795ts23Pnj1o3rw5AKBly5bIzMzE0qVLpceLioqwbt069Ounvn5edHQ0kpKSbL6IiIjIM5VmCzo/sxh/HykAUFfW7CoYbNYwzqvXc3Zc8YS7ymyVMjcA8O7Yc7x6LX+JlpX0VjhYf1zN3ryak375BQsu7xVZ5B3r//vrLrs5+kW1v/eG2os5Y/qEX8Vmldn2bybW5L+MrNXB39e1vZu69XxX/Ri+GH+ux2Pylnzed3SYTiuIBJr/5B588EGsXbsWL7zwAvbt24e5c+fio48+wr333gug5ur2Aw88gOeeew4LFizA9u3bMW7cOGRlZWHUqFFaD4eIiIhq/bL9hOp2Vx3Eve10mxxrcvhYanzNvMN1B/Jx9fs1zdfSEqOlTs3BIkY2ngbx7s2VlAfU8veOYXZkkVd0zNt01G7qwKH8MgB1AZ7JoMdVPZsAANplJNTPIP1M2Wndn6XjjgLtaot7f3kGvfOwaGDbNI/H5C15oB3l5coHFHia/+T69OmDH374AV999RW6dOmC6dOn44033sDYsWOlfR577DHcd999mDhxIvr06YOSkhIsWrQIMTGetdYnIiIi9z34zd829zccrCnrdNVESFnK3Sbd9yCgV/OapcP+t+WYtM0QhF2Y9Xodbu3fAkBdubsr8inn8rJ7JrQjm6IVgZTRlmdSB3dIBwCkxtuvXR+KlB253f0b8oay14PIVcdxkbOM9g/39PdqTN6SX6BgoB26/FK/MXLkSIwcOdLh4zqdDs8++yyeffZZf7w8ERERueGSzu5191bOTZ55ax+fX1ttHnawzmdunFyTCHA3UJbPOY8x6fGfIW3x5tK9eOiSdv4YHgWxzllJ2HG8CClxJrupA2IQKp8bHFP7t7ZGZSm8UKTMaBdXVjvY03dmlcz1he3T3P5ccbZfkxT7PhVNUmJxrEDbudkisVGeyaDzy5rjVD/Yuo6IiChCuTv3Wp5RmTKyE7K9nLMtp3byGKyBtphod3eOtbyEdXD7dKTEmXBt76bIUmkqR+Ht8Us7YNxn65GZFGN3oUZc8kr+ey/vZr0vrxht0v23HFZ9UCaZl+3K89trqU1x8aRKxllGO12l+aO/gmyg7gIFs9mhjT89IiKiCFBaabbbJmaq1R6Ti5ZltB3Ng/SU2jmtxUHpZ6CJ6x+Xu9kMTf4eRZv00Ol0aNogjpmpCCQGzharYNcMTbQvr65btjy7/d4f+/07uHpgVfxNfzNRvfGxFiYPd73koDPKC327pl+K3x+6ALumX6q6vz9nukiBtgcrHVDw4U+PiIgoAixUaYTWMK6mudfZMuflnPKTPU+D4SeGd1DdrpZpKqtyHvAHipi5+vWfXLf2L6+qC8hdNZqj8CYGzhargN93qmdz5b8vRllDrp+2Hffv4OrB2bIqm/vikoL+0Cw1Dgf/exk6Z9WtTrTUgwy6UdEMLcZkQJv0RIfLDYqN6/xBXPrNxIx2SONPj4iIKALsyS222yZmWF2tMys/0YyP9mzWmaOO5WrZXVcBf6B8u/GIR/ufN6NuCVOWfkY2MUtqtgpY8u9J1X2apdY165Jfl7n6HPeWpQpWpZXmgPxNu1qmyxGDrJrg9etdr1ltUgTmWi7fJ87fZ0Y7tPGnR0REFAHKnJQ9x0cbse/54Q6zTTEmA2aM7orhXTLdXpNWpLacz45pw4Kyw7gjnqyfDdguJ8Ry8cgmBn3OKkGSY2VLwMl2+3qDZxd4/KnKbMXNn67D60v2uP2cvbKS+Pok/5u7sW8zt58nD9Azk1z3UzAZbf+2HXU99wZLx8MDf3pERERhpLiiGqPfW4Uhry7HH7KyyYoq58Gi0aDHF+PPdfj4Dec2w/s39fJ4nWtxfrNcfLQRLpasDSqN2cSMvFSX0ba62LOGo3ncgfLL9hN4Z9letHvqV6zcexpvLt3r9nO9zSz7Sv66jVWamDniaTNGZam5Vv0r9p4sxk2frAPg33XHyf9C6L85IiIicuW3HSex+XAB9p8qxW2zNkjbW6W5Xks2RiWInuxgjrW7lKXjL1/TDUBozV1++dpuXj2vT4sGGo+EQo0YjKktPSW64Vz3s6716fCZMtwzZzNe+c02i70rt8it58sbu9UnecAc60Gg6mmVjUnx/bl5LcWli19fIWXH46O4QFQo40+PiIgojDiaQ+1OssVk0EGns933zkGtfRrP4PbpGN2zCbo0ScbtA1pK24N1KS81zWvn0LpbxpmZFIPcogqHjeAocohT9M+UVjncp0NmXfMu5d+pxSoE7G/lRKH68lX3zd2CJQ8Ncvl8ZxcX/En+fkU7aGSmxtNpHoXltvPPLRrO0Raty8nX/JhUfxhoExERhRFHc0Hl8wfbpCfgzTE97PbR6XSINupRUa1RagY1J6+vXW//WmrZo7TEaM1eV0viWJVLFTncv/aEXVlaSpEnMcbkch95YKj8+y2vtiDBwwaEWtl+rFB1e1mVBW8t3Ys9J4vx6nXdHU4nERt6iTyZL+0Lg+zvzp+9IL7deNTmfrAuT0iBw0CbiIgojCjngooZMfEkcHD7NHx2ax/oHJyARhsNUqA9uH2a38aplj0K1gZp4nm7u82OqmsDjECVzlLwyPBgjjBgnxU9dKYUnbOStRyS255buFN1+7GCcrxW2xRtQJtGGOOg9F3sC9EoIQpPDO+Iiztl+GegCvI52t4WA3TITPT4Oe5eiPNEgzjXF2ooePFSKxERURipVpRrikFfdW0A3jotwWGQDdiWQ47o2tgPI6yhNkfbH6WXWpBnpt05mRbXwPW0cRyFJ+VcXme6NbENqp93EOz6284T7s3DLnXSZPHG2oZep0uqcE2vpkiOrZ+gUf7Z4mk5+N9TLsGayRehQXyUx6/rj8+vL+/oq/kxqf4w0CYiIgoj7y3fZ3NfzMJaagNwgwcn/f7MyKotL+1NFqk+yDPt7pxMV5prgo9oLs1D8KwfQWpCNDY8OVS6f6bE8dxuf7r/qy1u7ReMRRvyjHalh0vzJceZvF5lwB8Z7UBVM5A2+D8AERFRGDlwqtTmvrgeqxhwe7Lkjj/nGCsz2jf2bYZXr+3ut9fzhfxtcDUPUxAE6T2PNvE0iwCTh39H8l4FA9o20no4bnF3DWwt147Wivxi4uw1h/z2OsoLg1pntLlqQejj/wBERERhbNBLf6Ci2iIFiAYPTvo9KXn1lDLL98JVXZHu4XzW+iK/4OAq0DZbBYi7sHScAM+qSERDO6YDALJSgnsN97NlVbj6/dXYeNC+O/alnTMBANNHdanXMckrUArK/FcR8Op1thcGtW6GdtN5zTU9HtU/BtpERERhrLjSjHU5+VKTNJMHGe3kWM/nKbpLfjLcIjXOb6+jBZuMtouslTg/G2DpONVwVkWy/v+GqG5Piav526sya7cCgLv2nix2e993/9iPTYfO4poP1kjbBEFAeZUFi3bkihu0HqJT8vc7Nd5/Kxm0TbfNaGu1jraIZeOhj/8DEBERhYmzDtbqPVlUgUNnygB4ll1LivXf4iTyhmxGtQnbQUR+UcDVPEz5nNCoIP++qH4oqzfk3fwdVXGYan93qi31H2jvOG7fCO2281vgnRt7uvX8yf/bjo5TFkn3V+49rdnY3CF/v5+8rKPfXifKqMeSBy+Q7mtVOi4u5xao9dNJO/wfgIiIKERYrQL25ZVAcHBCt2LvKdXtp4orsXr/GQCu52i/UjtP2qjXoUNmkg+jda6ZLIstNg8LVvITXmdzUosrqvHzthMAaoJsTzseU3hS9jpwJxyLqr0gZg5AoK3MoufMGIFnLu+MZg3dqzz5esMRm/txUfU7hULexNHfpfdtM+qy2rvc7NTuijTNJ0iXOyT3cR1tIiKiEPHCLzvxyV85GNu3GZ6/qqvd446al209UiDdVltWS+6aXk1xZY8sKaPmL1nJdZm8yur6DyY8odPpoNcBVsF5RvvuLzfjr3012TuWjZNImZl0J0st/v1V1a4WYLEKeGr+P+iRnYzr+6ivW62VKsX4xOoTVxnWSrNFtS/B/UPaajc4N8g/4+rz7/CJ/23HcB+WRLRaBYyfvQHltVUxfuxFSfWEP0IiIqIQ8clfOQCAOesOqz4eF62eOSqpMEu3K9xY7sbfQTZgWzquPLEPRmKQ4ag89MkftktBNsCO41RHuUyeO5lho6J0fN2BM/hq/WE8/v127QeImsz5TZ+sw4xfdzpsguhqFYIj+eXIK66w217fDd3kVTv1GWiXVppd7+TE5sNn8cfuuqoklo6HPma0iYiIwkRyrEl1e5ksuJYvHRQsgj2jDYhZMsFhZ2HlxQ9XlQMUOZTTNf4zpB0Meh0u75bl8Dli6bgYaJ8tq5YeEwTB5kKVFu78YhP+2ncaf+07jZev6aa6j6vAL9qox6niStXt9alJg7rAvj47//s6Q7tSUbLP0vHQx0CbiIgoBOzLc90J2FFZs7xBV6u0BM3GpJVyN7LsgWbU61AJ95fwCcLlhSlAlEvqpcSZ8Nwo+6kfcnXN0Gp+keTBalmVBfHR2p7CL92VJ92W/+qunVzXFd1VfwedDjBbbH/xo416zS8KuNK1SYp0O6oeg/xYk29BvVVRLcMeD6GPdU1EREQh4NXf9rjcx1EQuCu3Lkjv3byBZmOKJKVVNRcDStwsD3XUsI4ijzJAdRWwAval4/JscnGF5yXKBWVV+GX7CbeWCxN/d4d0SEemrJeCq4y2xSpIywiK3hzjXqdyLclL9esj0Ba7hF/mw/xswP7zmxnt0MdAm4iIKAS4k91wlRke2jGj3rNL4WbZzjzXO8E+O0WRSxmgujP3VpwnXVppxq/bT+B0SV1Jtjdd+ifN3YJ75mzGq7/tdrmvGO8pP3OUc82Vqi2ClIEXxdZzx3HAtrKnPuY5TxjYqua1PFg6UU2FYgoNM9qhj6XjREREISAtwfXc6tLKmhPwWJNBNeiOMgbniZt8XeFg5+4cd/mcWops8gy2Qa9z62KXmIn99Z9c/PpPrs1jymDWHWKjvq83HMHkEc7XlhaXFFPGeY56QIh++zcX3WRl20BgsrJdmiQDAJo2qJ8mbOL75Ou1tecW/mtzn83QQh8z2kRERCGga+3JozN7TtaUiJ/fppHq4/XRTdwb6YkxrncKsCEd0gGwyRl5Tp4Jdjd4cva3qizP9kV5lf0FueOFNZ3Dlb/rcVHO83Nr9p9BtWJsjVPq/287PtqIf58dhj8eubBeXk8nBdq+RdpHz5bb3E/QeB4+1b/g/B+XiIiIbCg70qo5W1YFAGidFq/6+O5c1w3V6tNt57dAjEmPewe3CfRQXBJPoZfuOhnQcVDokZcEuzM/29V+1WbPAjr5klvyYPCeOZvQccoifLRiv83+YsDn6UUlQbBvhtaknpf2EsVFGevtwqJYoeBrRjs1Pkq6veTBC3w7GAUFBtpEREQhwJ31r8WS0rgoI1qpBNu7gizQfubyztj2zDA0S3W9rnCgLavtyrx4BwNt8szWIwXSbXcz2s6aeCmzxq7c/9UW6XZRbSO1MyWV+GV7TUn6C7/sstn/dO0SXZ4WbxRVVOPLtYcAAG3TE7DpqaGI8bETdygRfFzgKz2pJvs/rl9ztM1I1GJIFGAMtImIiEJAhRsNkMS5lSajDhd3zLB7/LreTTUfl6/qc/kdrRw6U4onf9iOg6dLAz0UCjHuZlmd7efp793aA/l22/JLqxzuv+bAGQDqGe19zw/H3Al98feUS+we23a0EH/uOQUAaJQQjVQ3+kqEA/F98mVJP6tVwM4TRQCAdDf7QFDwC73/3YiIiCJQpaz81FFDLnEpIJNeb7Puruj6Ps38M7gIc/On6zFn3WGM/WRdoIdCIaBBXF0TMXcz2s5Kx5/4frvbr6225N+/x4vcCgrVhmA06NG/dSMkx5nQIdNx1tWbzuihSqdBMzT5hdStRwp9HBEFCwbaREREIUB+ImZ1cJZcXbvdZNBhUPt0u8fdnR9Kzh3OLwMAHCsod7EnEXBpl7r1ld39GzQ5qfSosrhfOl5aZb/m9oi3VjpdT7tRQs1cYVdztJ09vvlwgXsDDAPiu+BL6bj8Qup/hrT1cUQULBhoExERhYCi8rrloqodnGiLpeNGgx69mjfA/+7pj85ZSdLjrtbBJc8pG0mJPru1dz2PhIKVvArc3Yy2qyB30T8n3DpOSYV9oA1Adfk/kRiEu1qGTM8oAkDdz8qXjLb84knXpq5XmKDQwD8RIiKiICcIAr5af0S676gDudjx11QbUJ/TrAEyk+qW1zHyzFhzykZSALDs4UG4qIP9HHmKTPIAzN2MdmmleoAsuuvLzW4dp8TBcQ6ecTzPW2yY5mqoOvDCHaDN8l5iRjsuKnKax0UC/o9LREQU5MRSZVGl2apaPi5mReSNlMQlvwBmtP3lx63HbO63SksI0EgoGK3LqWtG5m5GOzPZ+frTPbJT3DpOsYOMttrcbaUNB+2bqLnLFIGfNb70HN9y5CwAoExlXXMKXQy0iYiIgpzYjVZOrQu5mNE2ygJt+VxJZku8F2NyfMr0n6+31t9AKOSkybpv7z/lXsfwc5o1cPq4u+0WHGW03Vku8OCZMqePqzVcFC1+IHLWgdai6zg/Q8ITA20iIqIgVyibny2qkDXPEQQBu3KLpHmXJgdn4fIycvKMqzmzRI60aOTdOvH3Dm7t8DGzm1FduYMM6S/b3Zvj7UxctNHhY5FU1aFF6TiFJwbaREREQe6DPw/YbZM3M5q38SgufWMlth4pAGBbOi5fp9pVcyMi0p63vREaxEU5fGzbUfeWgHJUIr7h4FmvxiQ3uH2az8cIB3Vdx4lsMdAmIiIKcjmn68pNk2Nr1uSVZ6qe/2Wnzf7yudizbu2DKKMe00d18fMoiUhL8gtm3hIvyPljZb9x/VrgpWu64bbzW9hs//DmXtq/WBDT68Wu465D7ROF5Xhk3t/45xjXyo4EDLSJiIiCmCAIGNUjS7ovzhUW51ieLqm0Ky1PS6ybE9q/TSPsmDYMN5/XvB5GS0RK8vWVr+rZxO3nuQq0XQV2ZosVj8z7G4Bv84cdMeh1uK53Np4c0dFm+yWdIqvjvpTRduM9fuqHf/DdpqMY+fZfNtv7tmwIALwgGmYYaBMREQWpH7ceQ5/nf8epkkoAwLh+zRFrqmloJgbaHyy3X8e5tWJ+pBaZMXLPy9d0C/QQKMjIl71//foebj9P3rm7UUIUvr+7v83jroLnPSdLpNsN4x2XofvKqPh8ibgpKh6so710V550O7+0bkUI8S1Lqa1YovDA/3mJiIiC1H++3orTJVVYte8MgJq5njG1gbZYEqo8tzMZdE67AZN/jfIgY0mRwWJVX/feFXl/hc9u7YNezRtgULu6edGulugqq6rrOJ7d0LuGbOSaeFnB6iLSXrn3lM19+f5V5prfkSh+docV/jSJiIhChMmow+6TxQCA5btrTtqUmaqEaGPkZZTqQYvUeLf2M/pjMiyFtLsvbAODXoebzmvm0fPklShi1/v3xp4jbXMVaFeabQP8aVd09uj1PdEhM9Fvxw524s/GVUL75k/X29yX//yqasseeJE0vPCnSUREFCJMer1UnvjpXzmoqLbg5cW7bfaJi3K85A557/2bznG9EyKwbJZcatkoHv8+OwzPjerq0fPkgbb4a2WQXchZuuuk0+dXmusaJv59pADj+tn3aejVvAG+v7sfbj+/JV64ynZ8A9s2cnusu3KL3d433NQt7+V4n9O103/k5IF2ZTUz2uGIP00iIqIgZFXJVinnWg96+Q+7fbJSuFa2PzRPjUefFg0CPQwKUdFGg8fPka8eIGZN5YH2pLlbnD7/TEmVzX21i0A9s1PQq3lDTLm8k9163+/c4N7FpUhX1wzNcaTd+7nf7bapZ7Q9/z2h4MVAm4iIKAgpO4kDNSfe8qV0ThbZZ0kaJUTbbSNtuNPsiEgrUWoZbUWw7Kx8/NHvttlt69861ea+QRbMy4O85qlxSI5jYy53uCodP1Vs/zkNqGe0WToeXvjTJCIiCkLVFvsGSiaDDtkNnDc1YuWy/xRV2F/8IPIXtTnaekUPgF+2n/DomO+PtV3j2mKpC/bkQd6hM2UeHff3hy5Aj+wU/DRpgEfPCwtS6bh6qH3vnM2q2y2CfUabpePhhT9NIiKiIHRQ5US3YXw0TC5OxORLxpC25MslEfmbyaZ0XH2fo2fLVbc7CvqS40y4tX8L6f5nq3Kk274EeW3SEzH/3vPRtWmy18cIVXVdx+0fW7n3FNYfzFd9nm1Gu2Y+PTPa4YU/TSIioiD0/C877bZlJEUjyuA8Zb2XwSBRWLDtyaD+d58Uq978cOaqgw6PGxtVVyIuDw5jTZwf7A1npePKTuMAkFJbki8G2hargIraDvH8GYQXBtpERERBKK+owm5blEFv1xBNyexiyR8iCg3yDLOjjLajwGy3ky7gMQ4abjVtEOv+4Eiic1E6LtezWYr0/u/Nq7kouvVIgRR0K5drpNDGQJuIiCgIZTe0n4ut1+scBtri+s0TBrb067iIqH6ozdFWqqi27+UAAG0zEmzuD+mQLt2OMal/hsi7knOusPvcWd5L9MM950ud4+//agu2HS3AzZ+ukx43uriQSqGFi20SEREFoeYN47A+x3Zu35mSKocnwC9f2w3NGsahRzaXoPKXlDgTCsocN0Qb3D6tHkdD4c6ksryXUkW1RXW73KgeWZh2RRfpvrx0/M4LWvkwQgLkpePOI+2Px/UGYNuw8op3VvltXBR4vGxCQae00ozP1xzESZWySSKiSLFQpZvwoTOlNkv+yMWajOjVvKHNOrukLVfvrHgiTaQFo77ub93goDdDhdl1oD1+QCubpbrkpeO3D1CvgImP4lxhT7nKaF9YeyGOywRGDgbaFHSm//wvpvy4A31fWOpw7UEionBXVmV/Am10Mkfb5KJJGvlO52LtNJZ9kpZSE6LQtEEsWqfFo3FSjOo+FSqfEwBQLtuekRxt81i0rHTcqLgwN3l4B8SY9Jh127neDjviiJ8LVkUEraw2cNVfg8KP33/i//3vf6HT6fDAAw9I2yoqKnDvvfciNTUVCQkJuPrqq3Hy5El/D4VCxM/b6rI41324JoAjISIKDIuDhmYmg85h6Tgz2UThxWTQ4/eHBmHxAxfYrZ8tErtVK8mD6fRE2yDd2VzsOwe1xo5pl6J7doqXo4484rupzFT3fWGpR8e5pV9zbQZEQcOvgfaGDRvw4Ycfolu3bjbbH3zwQfz000+YN28e/vzzTxw/fhyjR4/251AohJRUmqXbOadLAzgSIqLAKHcw7zLGZHC4zmqjhGjV7aQdXsqg+hZjMjitlFCbo70rtwgv/LILgHrwJo/Zo1U6kPOinWccLe9VWK7ezyEtUf2zOt1B1QKFLr8F2iUlJRg7diw+/vhjNGhQ15ilsLAQn376KV577TVcdNFF6NWrF2bOnInVq1dj7dq1/hoOERFRyCirMqtuv6J7lk2mSq5NeoLqdtKOi8pxonqnDLQPnSnFpW+slO7HqCz/JW+sxiknvpPeTjfnXifFmlS382cRfvwWaN9777247LLLMHToUJvtmzZtQnV1tc32Dh06oFmzZlizRr1MuLKyEkVFRTZfRERE4UqcXxmnaEgUYzI4bIamdkJN2jpdUhXoIRBhRNdM6bZyea/7vtpic1+t5FweaLvqO0Cuie+gco623JSRnaTbmw+dVd2Hc7jDj19+ol9//TU2b96MGTNm2D2Wm5uLqKgopKSk2GzPyMhAbm6u6vFmzJiB5ORk6Ss7O9sfwyYiIgoKp0tqGkEmxtivwulomR8KLHnwQ+RP79xwDu4f0haAfUZbOeVO2ewMAAa1S0PTBrEY1I7L0WlB56B0XO7iThnSbfkUSTlHvTkodGkeaB85cgT/+c9/MGfOHMTEaDPXYPLkySgsLJS+jhw5oslxiYiIgoEgCJi5KgcbDtasm73zRDEAoF1GIl67rjt6NkvB9qmXAGD5cjCZNLiNdLtFanwAR0KRRK/XoWWjOAD2qxMUV9gGcWoX5mKjDFj+yIWYdVsf/w0ygohvseAko+2oiaXcn3tOaTUkChL2l8p9tGnTJuTl5eGcc86RtlksFqxYsQLvvPMOFi9ejKqqKhQUFNhktU+ePInMTPWrwdHR0YiOZpOXSBEXZVBd1oaIKFydM30JzpbVNM75/aEL8MbvewEA+/JKMPqcphh9TlNp38xkNswJFoM7pCM1IQrztxzDxAtaBXo4FEESo2vm+RZXqDfcEjlqbMal6LRTVzrueJ+EaM1DLgoBmv+VDRkyBNu3b8fWrVulr969e2Ps2LHSbZPJhKVL61re7969G4cPH0a/fv20Hg6FIJZFElGkEYNsAJi64F+pdPxEYYXdvtFGg5TdpsDS6YDbzm+JHycNQEpcVKCHQxFEnFaizGArsYO4/znqOi6n7LehhnO0w4/ml1cSExPRpUsXm23x8fFITU2Vto8fP/7/27vv6Ciqtw/g300vpENISAgQeg29dzB0aSoKShE72FBeQRHsYOVnAcGGYkNQUOm9SIeQQCihhRBaQk0hPdl5/9js7MzubEnYluT7OYdzdsru3pndsPPMvfd5MG3aNAQHB8Pf3x/PP/88unTpgs6dO1u7OVQBmUomQURU2VkyT8/PS561ds+MvrZqDpnAG8PkKL6lPaQ5RioUaDHQtj2VsULasn3Mfw7MOl75OOTWyfz58zF06FCMHj0aPXv2RFhYGFauXOmIppATYjIIIqrKpDcb3xvRwsSeOhGB3rZqDpnAy2JyFG0AXSJPOo5wvaklrrwZZHPaU2zp5euu6X0Uc22wR7vyscuEgR07dsiWvby8sGDBAixYsMAeb08VDDu0iagqy5NkEW5fN8iBLSFz2KNNjqINtG/eLYBaLYhlvIr1oj2l8l5kXbqs45ZdwEaF+OD0u4PQaNZ62fqX+je0etvIsXjrhJxOoeT2rFJZCiJynGUHU9Hpgy1ISstydFMqDf1MtccuZ4qPvdxYG9uZMc4mR5He5FkRZ7waD6+jbM/YyPFAH80UnyZhfgbP8XBzwZ4ZfTGlT31xXb3q1WzVRHIQBtrk1PTvzBKRY81YmYj0rAJMX3HM0U2pNP6Mu2x0m7sFJWHIcRhok6NI516/9leieMOult40EvZo2562R1v/klWbaXzuqJaKz4sI9EaNarqqSpxPX/nwF5ycWvVqLOtG5IwSr2Sa34kssuF4mtFt7rzwcioPd6gtW1ZxljY5iH5d5ozSygVqvWiPc7RtT3uGT12Tj/QqKh2haWrudVEJO5QqMwba5NT0h1QSEVU2JSb+n9PPLi616NG2AIDO0cFWbxMpe29ECwxvXUtcduFVFDlIqJ+8I2JN4jUAhjdBc81kJad7d/jiHcX1xaVBtKlAu1A/mx1VKvyJIKdm6gKUiKgyMFVpwdtE7dWBLcJx/O0B+P1Jlsa0FzdXF7SKDBSX2aNNjqIfvC0/pDxP++bdQns0p0q7cidPcX2h2KNt/P+JxjUN529T5WGXrONE5ZWRW4Rz1++iQSgTRBBR5aS+hxuK2jmAZD/Sa2aOyiVnkVtYrHjTLl9SxYBsw9joS0t6tPs1DcUnD8ageS1/m7SNHIs92uRU9OcWAUD/z3Y6oCVERPbRolaAo5tAZSBNWMTyXuRINSTDx0N8PWVBda9GNdAyIgBP9Yx2RNMIQLFa06PtZqJHW6VS4YF2kWgazkC7MmKgTU6loFh5rsqLy+JxNUN5aA4R2UexZC5Zm6hAxzXETlJu5ojJbKytRC3g1LUsqNUCFu9KVtzH34u91c5ImsXZk1nhyYHqBPuIj2NqB4g9qADw/YT2WP18d4Ms5GR9USE+BusEQRATnZnq0abKjZ88OZU8I0Oc/km4iieXHrZza4hI6m6BLqlOZR+yvPlkOnp/sgPTlh+1yevPW38Kgz7/D/M2JBnd58uxbW3y3nRvpFmc9TM/E9nTZw+1Fh8XlQjinGCViqWi7On+mFoG6xIuZYiP3Zk1scriJ09OxVigDQAnrmYZ3UZEtpdTqPv7/O/sTVkPd2WrEDBteQIAYPXRq1Z/7RK1gG//uwAA+MZIbzZgOoEOOQf2VJEjRYX4YNp9jQBoEm9Jy0mpOK3BbrQ3NXwlyStHLtwrPnZ342dRVfEXgpxKHstQEDkt/b/PpLRsFJWoUXfGWtSbuQ6FRqZ+VETZ+bb7v2jRzvOK69e/2EO2zPm/zo892uRo2vm/RcW6QNuDN4DsSvt/tbECEm7s0a6y+MmTU8krLP2R4MULkdPJKZCPOPF0c8Hf8VfE5fhU5VqiFU12fpFNX//jjacV14f4emDLtF7iMod+OqdiydW0F3+ryMG0QXWRrEeb/3fYk/aeqLEKEvw8qq7KPcmOKhzt0PFqnm64Xczaj0TOJEevR3vKb0cQ5OMhLl/NrBwJCy/eynXI+4b6e6FE0J1DxtnOqaa/l/jYjT2H5GDa6QtFagH/nb0JALiTa9ubhSSn7dE2NoOKw/irLgba5FS0gbaPhytu5zi4MUQkc/RSpmz5TPpd2fL8zWcxsk2kPZtkE0O/3O2w9/aVJJkrKqlc894ri35NQvF/AxujbVSQo5tCJI58WXvsGtYeu+bg1lRN2kC70EZVKqjiYqBNTkWbXMnL3dVgG3t3iBwrp8D0vOXKWILPFtnVawV44WpmvmzdsNKstT6S//uMlTskx3JxUeG53g0c3QwiAJxi4gykH0FuYTF8PBhekQbHPJHTEAQBN+8WAFDO5MohekSOZa6mdGXMrWCtEX97z9/EEz8dwpWMPAT5ehhsn/9QDAD5/3NqY5l1iIhKuXJYsuNJPoLkGxyOSTq85UJO4/VVx/H7wVQAQIFCmS833rUlsrtDKbdxNv0uxnaKMjuUObfQeHk+Z6dWC3hhWTzW6A29tEbVsvyiEoz99gAAYMupbYiu7muwjzTA7tGwOk6nZaNzdMi9vzkRVWouvDZyKtrfjOa1/HHiahZGtDassU1VBwNtchraIBsAkm8a3hFkoE1kfam3cvHKigQ83bM++jerabD9wUX7AACNw/xQrDbdo+1ZgXu0J/54CLvO3DBYbyyLbFnsOC1/XaX/36SWPt4RRSVCpRwhQETWxcF+zkX7m1FSOiJpVNuKn7eEyo9/nlRhZNmwri1RVfX6qkQcSrmDJ5YeNtgmHSp+826B2R7tego9tRWFfpDdMLQagLIH2kUlauw8cwN3JfPZyzqyU6VSMcgmIou4cOi440l+JkpKfzO0ZQDdWNqrSuMvORFRFZaRZ7yM3p1c3baM3ELZqBMlFTXjaqFC0rGz1zUZ1UvKOE/6w/VJmPDDQTz7S5y4zlis7u/FQWVEdG8q8kiiykL6X7ygDbTFmub8fKoyfvpERFWYq4vxn4H1iWni49f+SjT7WuaSpTmrjFzjNxuKSgQUFFs+9/y73RcAQKxnu/roVTwjCbqltEP129dhmSgiKh+lDNcv9GVWfHuS9llr781qR4Bx2mPVxkCbnNK6F3o4uglEVYKpa4D31p4s02sVFevu63+4IQn3fbYT2flF5W2a3dxWCLRHS+bV5RQYD7QPJN/CG6sSjR7n87/HK64f2ykKs4Y0w/QBjTFvdMsytpiISMNXoQThFAbadlXDz1N87Ft640Ob04Q92lUbP31ySl7uyl/NjNxCvLriKPYn37Jzi4gqJ2MlpG7nFJqdk61P2qP99Y7zOHv9LpYfvnxP7bOH41eyDNZ9OLolfDw0Na3vmsgPMeab/fj1QCpavrUJ3/2XLNt2xURd8fo1qiHY1wNT+jRAg1C/craciKo6X09Xg3WebobryHZUKhXC/L0A6PJ6FJf+fjLQrtr46ZNTMpYI6N01p/Bn3GU8/M1+O7eIqHI6ejnTYF3qrVy0fXdzmV9Laa6zUiB/6XZumYZj29qrK47Klk+8PQBuri7wdtdcrOYplBtU8t7aU7LlbvO2Gd1391nDDOdERGXlqzB0nOzPtXR4mDavh/bGM5OhVW0MtMkpGbsbe/Z6tp1bQlT17Dxz3eT2+2PkdUGnD2gMQDkZmn5C3LiLd9Djo+1o9dYmbD6Zfm8NLYfs/CJk5poezq4diqm9cDJW1izvHuqGj+9St9zPJSLS0o68IcfSpjvRZh3XjghzN5EHhSo/fvrklKQ92lP61BcfFxRVzGRLRBWJUnIdqQBvd/HxnGHN8GA7zXxmpWRoBXq93L8dSBXXP7n0MG7dLZBtT7ycib3nbpar3eaUqAW0fGsTYt7ZhI0n0szurx3yV6wwhH732ZtoOntDudsSUs2j3M8lItJSmqNN9udWGlBrR3Fpb9CyR7tqY6BNTklarsJV0iVWUcsHEVUE2h7adYnXTO4nTaAWHuAl3hhTC5pgNjNP12N8rrRMlpZ+Xeosyfzn3MJiDPtqN8Z+dwApN3PKdQym7Dit66l/+mddJvDuDaor7q/r0da1+U5OId5bcxKPfn/gntrixl4OIrIClvdyDtrfxWK1AEEQdFnHGWhXafzrJKfkIUkeIS0/dMEGF99EVZWgF/TeLdAEvVuTjA8djwr2gUpy8yu2WZgs2UtRiRrJN3TBdViAl+z5+jfLcgp0gfaRixni4zPp1p8mMvmnw7Jl/SHko9tGYu+MvuKym96cOwB4YVm8WMKrrN4f2UJ8XD/Ut1yvQUQkpdKfn0MOob0xq1YLst8MDh2v2vjpk1NykXSZMWEjkW3k603FyC8qMQi+9XVrUF28oAA0f6vSQLugWC17Xf0e7KOXMmTL2ZIebWlPubVHryjNy/5gnSZ5WU6hpg0DmtdErUBvcbu2J6JY0hZtfezyaF8nGLum98HR2bHMCkxENvE8S3s5hEvpDY8SQZCNgmKPdtXGEIacnoupQr9EVC6CIKDfpztk6/KKSsxm2PZwVeGxznUAAINahAEA3CUXEkUlallwLZ3fnFdYgst35CWvpPWnpQG80nzve3HuhmEP+bnSnvfc0jrZ+nMdtaNpPtl02iptCPJxR1SIDwJ83M3vTERURk3D/fFKbGNHN6NK0gbUJWpB9vvF8l5VGz99cgolRmr5AvI52kRkHduSruNqZr5sXV5hiayHWcm/R6+ibnVfHH97ABaOawtAPnTxyp08/JNwRVyW9gZn5BUavN7P+y+Kj6t56QJda/b45heVYPTX+4xu1/Zo62fv1d4EOJKagbTMfGTlm85Wbso3j7VDqL+X+R2JiMop38JShGR92mvVErUgu8HMQLtq46dPTiE+9Y7Rba7s0Sayuh/3phisyysqEYPLap5u4hxlKe3fYzVPN8W5gf8kXMXyw5fF5SLJTbR3Vp8UHzcL9wegGYqtvTiU3nC7W2A64DdGqW63NAmalLZOdm6hco+2tPc9r6gEA+bvKleb3hjcFLHNw8r1XCIiS13LzDO/E9mEiySnR1FpxnGVitewVR0DbXIKDyzS9TY93q0eACCmdiAAYFDLcEc0iajSOnUtS3GucV5RiZgFPNDHHf+91gffT2iPlhEB4j5v3d/c5GvHX5LfNFt5RBN0/2/LGaw/riupJS1vpR1mJ81WXpah4wt3nMNfcZcx+5/jiH59HZYdTJVtP5Muz3z+XG9NyUDtfQJtQjZT9WhzC4txTW8EgNaJtwfIlr3cdT+tU/s0wISudS06DiKie6Gfd4PsR9ujrRZ0PdpMhEYsvkdOZ/awZgCAlc92RU5hMfy93DFrSFO8t/aUg1tGVDmsPnpVcX1+YYmY0MXPyx3hAd4ID/CWZetuVNPP5GvHp2bIX7P0wu9/W87K1kuzjWvLoMxbnySuMzGbRCbu4h18tEE+h3rGykQ83DEKuYXF+HzrWRy5KA/+W5fexPvv7E0Ul6jFWt/VTNSjvXJHuafo6OxY+Hq64ele0Vi8MxkAMLhFONrXDUZ4oBf6NA617ECIiO4RZ9o5jqvYo627UcxEaMRbLeS0XF1U8PfSJA16oke0WKuXiMouK78IE344iF8PXDQouaUlHTru56UcdJqbbxau8NpbTqbLlh/vVk8crg0AX2zVBOEnrmaJ65SGgCsxVW/7y23nsHhnMg6lyANt6SvnSuY0+ngYD7SzFOauzx3VUkxsJi1J2DIyAGM7RTHIJiK7MlM0gmxIG2gXq9W6GtocNl7lMXKhCuObx9o5uglEFZIgCHh1+VHsPHMDb6w6jtn/nBC3nX1/EGKb1QSgDbQ1AaW/JNAe0Lym+NjYhcP0AZpMt3VCfAy2PbFUXr/6kY614S0Zpv3j3hSsPXZNto9+WTBjjN00AICvd5w3WPf5w63Rvk6QuLz3nG4IvambeVl5honQqlfzFB9HBunKggX7ehjsS0RElZdYR1sQUFw6R5uJ0IjfAKow+B8WUfm89e8JbNLrVQaADnWD4O7qgts5mmzgvx1IlfRo60pQSZ9rLBjVDlncn3zbbHs83Vwxb1Qr2bopvx2RLZuqRCBlrN62Uj3wj0a3wvDWEfB01wX5z/xyxGA/Je+s0SVyaxUZgOjqvujRsLq4rnmtAKWnERHZXKif5qZfm6hAxzakCtNOu8orVOvmaPO6tcrjN4AqDA7BIVN2nL6OTSfSzO9YBf2076Li+rGdogAAh0vnMJ+4miX2aEuHjktjVmMXDi5lmBwoQEDjMNNzvZVuDChR6mkGgIe/2W+wTjtfztdE0jNL/Du1O7a+0gtekoBd2otdVMLxm0RkPyue6YInutfD1+M48s9RTlzNBAC8vioRiVc0j2/cLXBkk8gJMNCmCoNJJciYrPwiTFxyCE/9HIeMXMNazaQsyMdwiPPm0gBXGkT+MrmT+NhYXfuy/HXWKO19qV7N+BDrgxfM94wDwJ0c5c/7gMLzO9YLBgDFsmTP921gsO5/Y1obfV/919AeEwAMi2GlBCKynzohvpg1tJnJqTRkWzfv6n6LZq5MBGD5yCyqvBhok1MZFlPL6LYyVPuhKkY6vzdbkrRq8c7z+GTjaaWnEOTBtFZSWjYA4O/4K+K6rvVD0CYqEDG1A40mSbM0MH53RAsx6divT3Q2ut+D7SIter23JLW5zYkM0s0fjwj0lm1T+r9nRJsIi9vl7uqCo7NjceytWHi63VuPOREREVV8DLTJKWjr12rr2yrRJpcg0qe9ewwAVzLy8Oh3BzDhh4OYuz4JX20/h2uZyqWZqgKlucpa2vnWnz/c2mCb9G/RxUWFlc92xapnu8LFyBSOZIXs3/2b1jRYN6hFmPjY00TyMVcLpopYWmv76Z7RBrWuZw5uIls2NvS9Uc1qsuVaegG6VICPu1gpgYiIiKo2BtrkFLQXuUo9bFocgkNKMnPlc3Qf/mY/dp+7iZ1nbojrMnKV5/FWBab+brSBbp8mhmWoeuuVplKpVEaDbEC5HNjHD7QyWCed4+3pbvwnqNiCv3dpiTBj9szoi5mDm8JXr0a2r14pr9rBygH02I5RsmWWGSQiIiJL8IqBnII2c7CphGdK80mpcrqSkYcxi/dho5nkZulZ+Yh5Z5PZ18tWqIFc2anVAgqKS0wm5tIOcXZ3MfwpKOvf2xuDm8qWXVRAkK8Homv4ytZLy4bpD7EO9vXA8NaaIdyW3FizZKRCmL/ynEUfSUK0JmF+Rod7P9i+tmw5wJs91kRERGQeA21yCtrhraYSnrWIYPmcquLNv4/jwIXbePrnOJP7Tf3NstJMfydcsXiYcWUx/oeD6Dp3G1Jv54rr/p7SDR+MbCkua3u03RX+7ozNxTamWS1/2fLZ9wcDAJJvyIeUS5OI6b/vkTfvQ+vagQAsGxa+59wtk9vb1QkyOgRduv7KHeMBu6+nG16NbSQulyW7OhERVV0zBjUxvxNVagy0yaqKS9T4J+FKmefEanuv7HkRy6HozuuWkUzS+g6l3LFov98OpOLzLWfvpUkVyomrmdh97iZu5RRiwP92AdCMFomJDEBYgC47tjbQdnN1kfU0AzA5TFyJ/pBqS+ZYV/M0DOa1o1os+ftMUZgXLiWtc60vKliXGM3UvGv97V3rh5htFxEREavSEgNtsqql+y7ixWUJiJ2/q0zP015T2yvQ7vnRdtR/fR32nTfdI0aVx+Jd5x3dBLtRGgkQ6OMBlUqF6tWkgbZuuPQjnaIMnlMWHpK5116SudfS9/v0wRjZc6S929qg2LV0GLslc7R/3i+vDz6lT32se6GHuGwiDxxCJUPKa0uCbiUd6gaLj+tW9zWxJxERVUVP94o2WKcqU+FLqowYaJNV7ShNQFWWObFqyQW1Pe7+5ReViMNpH/l2P05czbT9m1KZ2OJ7YGqucmVzWWEodJCPZm5xqJ8uwJQmI/NxL9tQcX3SoFma1PDA6/0wuXs9rJ7aHaMVSmP9O7UbHmofiU8f0gThZenRlto1vQ9ejW2MOiG6oHnPuZsWPbdzdLDJ7bWDfbBlWk8cntW/TG0iIqKqYUKXugbrONOIGGiTzO8HU/HRhiSTJYFMKU+ApJa8lyXDTe/Vop3yns0/Dl2y+XtS2dQx08OopJ9C5uw1z3e3RnMqBe3Q7rAAL7xyXyNMH9BYFhBL50RL5ySXhzTLu6uLCm8ObYaWkco5FlpFBuKjB2LEGwDaPA2W9GhLRYX4QKVSybKLB5pJ6Lbm+e74v4GNMaFrXbOv3yDUT9Y7T0REpGWP61eqeBhok8zMlYlYuOM8jl0uXy+v9L+Z1Fu5RveTKpEE2iozt/+kGYyTb9wtU9u0/qc3V/duQdXLSO3sfBTm7urL0yvt1LCmn/j4+wntkfTuQLSICMDX49oCACKDTM/Dreyk3/Pn+zXElD4NZNulyc+ahssTm5VV78Y1yv1c7cXKLkl5NiXSsm7fT2gv2/b2/c0REeiNWUOa6j9NpkVEAJ7r3UBWcoyIiKislKY+mrumpcqPVxekKCOvfHWH84p0wU/C5QyLniPtPDd3R3D20Gbi49Np2WVqmzErj1wp83OOXsrAPwlXyt3zT8adu56N3w6kmt3v442nZcuj2kaIj5uE+4u9tVGlQ4kLiqtG1nFBEBSHqxWbGTovLXelX0PbUpte7okPRrbEkokdyvV8ACgo0n1OOUZugiWlZcnKusWUZirXmtC1LvbM6Mv51EREZBdK5WkZZtO9TcqjSkUaNOr3FlpKejF/Nt2yQFg6dNzcyBvp3cHFu5LRr2lNg2zHplzPzldcn3Izx6KLcrVaQMNZ68X5o97urohtHmbx+5N5M/5KlC2r1YJBBuyktCz8sOeCbF2jmn54sF0kbucUolaAbh6yv5dmbvKN7ILSILRsP32/HriI1Fu5mDGoSYW4O52ZV6SYBMzcnGdPyTDy8g6Ba1TTD40kIwvKo1AyhL35nI1ImTdEXL50Oxdnr2dj88nrsucoZS8nIiKyF1eFMpkV4JKBbMzqPdpz585Fhw4d4Ofnh9DQUIwYMQKnT8t7nvLz8zFlyhSEhISgWrVqGD16NNLT063dFKe04XgaPt102il7QqUX4ulZygGpOdKavV9uO1fm9zWXdVy6NeFSBr7eYVkm6eNXMvHcr3GyLOPS8j7Sdpty7sZdWXt3n7uJ7Hx5739WfhESLmVY9Hpk6OS1LNny3ULDXs21x66JjyODvMVg7OMHY/D9xA6ygDjIVzdP96U/EsrcnjdWHcfiXclioj9nlplbhNbvbFbcFia5+aDE1UmuCIpN1M9+dcVRPP7jYfwVd1m2XjrXnIiIyN6UfkOd41eVHMnqgfbOnTsxZcoU7N+/H5s3b0ZRURFiY2ORk6Ord/ryyy9j9erVWLFiBXbu3ImrV69i1KhR1m6K01l55DKe+SUOX247hx2nne+iPadA14t9LbPsgXZxidqg/vFHG5LMPk9dhqHj+v+P/X7Q/BBjABj65W6sS0zDi8sSxHX/TOkmPr6VU2DR6zz8zX7Z8tJ9F9HyrU04knoH+87fQucPtqLVW5swYsEevPn3cYtek+Ry9UZTvPXPCYN9vvtP15tt7jvjKxkS/U/C1XK368P15r/Ljvbp5tNGt301to3J597XvCaqV/PAfc1qWrtZZaKfBK3ujLVYui8FAHDgwm0A8l7v9nWC7NY2IiIiJUrXIhVhFBzZltUD7Q0bNmDixIlo3rw5YmJi8OOPPyI1NRVxcZq6rpmZmfj+++/x2WefoW/fvmjXrh2WLFmCvXv3Yv/+/WZeveJSqwVMW35UXC5vj7Gt3LxbIJvzeOuuZYGn1OGLdwyGpy60oMdZXaYebfn2tKx8TPn1CC7fycX6xGsGvWGX7+Qa7fUO8vVA1/ohADTDii1xW+9GgtYry4/ikW/3I03yuerX+KXyWRmvm0NfXKLGRxuSZPOJfT1MDxs290N3826BRSNMktKysS7xWrmT8NnD0n3y79zA5mFoFRmA0+8NRGSQ6Uzu/l7u2DujH755rJ0tm2iWUrbx2Qo3W7TcFIbrERER2ZNyoO2AhpBTsfnEtsxMTfbq4GBNndK4uDgUFRWhf39dPdImTZogKioK+/btQ+fOnQ1eo6CgAAUFukAoKyvLYB9nl6mXXMzNybLcLtguH+at36toCf3kVFqXbueitolyTWWZo61WCIjWJl7D2kTdUOKH2kfiowc0NXlfXJaAuIt3DJ4zvksdAEDD0GrYe/4WPliXhKd61jf53h9v1PVoeru7yhK/XbiZY7C/tJ4v3Rvtd6jJmxsMArG5o1qafX7r2oHicP5pyxPwcv9GqB3sg+WHL+H//jyGp3tGY+ZgwwzV+gH4c78eAQDZvGFncV3v5t1jnevg3REtyvQaZcl3YCvG/g+Y84/yCJH9ybdt2BoiIiLzlIaOs+QX2fSqSq1W46WXXkK3bt3QooXmgi8tLQ0eHh4IDAyU7VuzZk2kpaUpvs7cuXMREBAg/qtdu7Ytm20TZ687by8YACzZkyJbXpt4DbONXNhqXcnIw4cbklBYms1Z25tcw88TLSJ05YH6fLLD5Otoy3upVOZ7H0ss6Hlcflgzf7NELSgG2YCm/A+gmXMtvraJZFFHL2VgwXZdz/jCR9uabcfFW7lGS5xdz8rHn3GXZbWLq6Ks/CK8+fdxxKdqPidj83N7fLQd8al3DILshePaGmScVvLlI7ph0yuPXMETPx0GAPzfn8cAaBLraR27nIEF28+hqESteAPFGd0tKEbHD7bK1gX6uDuoNffm4Y5RaKyQUO2nfRwhQkREzkk/aSsAeLkxf0hVZ9NAe8qUKTh+/DiWLVt2T68zc+ZMZGZmiv8uXbpkpRbaj/583VdXHJUtO2NyNP1hqPq6zduGr3ecR6NZ65FXWIKjpbW3v3i4DZY/3UXcr1gtyIaH69MG6h4W9PKbeh39/a5m5Clu+/zh1mJA36+Jbj7qTSPD5dVqAQcvyHvNmoYp1xmOruGLC3MHi8s9P96OujPWGny+T/0ch1dXHMX8zWfEdc74HTBn0c7zqDtjLQ6llK9Xcepv8fh5/0U8ufQw7hYUy+bQ6xu5cK/BOktPmf6IitPp2UZvrNz/1R58vPE0/oy7bDASRcvS76G99P90p8G6ARU0G76/lzs2vtzT4v2DJcnuiIiInIWnu+NHiZFj2ewbMHXqVKxZswbbt29HZGSkuD4sLAyFhYXIyMiQ7Z+eno6wMOULQ09PT/j7+8v+VSTf7DqP00ZKXQmCgLoz1qLezHVGA0Nbs3R+silNZ28QHzeP8IePhxt6N64hrntnzUmjz/27dA6uJXWOi8zUAtaKfn2dYp3tl/s3wvDWunrLk7rVFR9/W9qrmVdYgn8SriAztwgZuYWIfn0d3l93SvY6Nf09sX9mP+yc3hvdGoSI65c+3lGxV/6/szdly9phzAt3nMdTSw/j441JqDdzHfacu2nwXFtZe+wa5q4/Ve6gMb+oBPNKE4Q9uGhfuV5jV2km75t3CzHjr2OyKQCW0M6xL48zen+TJ69myUYYvL/2lNFa7fpTLRwtTW/Y+KJH26JFRICDWmNf0qSGREREzsKTPdpVntUDbUEQMHXqVKxatQrbtm1DvXr1ZNvbtWsHd3d3bN2qG+Z4+vRppKamokuXLvovV6FtS0pH3Rlr8cE63dzesZ2ixMd/HEpFvZnrxOWu87bZtX1aphKzZeUXYebKY/h0k3z+talkadq6xeM61RHX7U++ZWx3fLLpjNFt+tpGBcLTwnmkTyw9LFse2SYCz/dtIFsnDYq/230BdWesRdPZG/DisgSM+WafYjK3r8e1hUqlQliAF+qE+OLXJzojZd4QpMwbIiac0p/ruuWUpnzd7ZxCDP3yP9m2TSfTxWHp4747YNGxWcOU345g8c5k7Cxn2ao/9UosZeYq9/4ao9+jvOaYPMj+9MEYs68RVIbezHGSvz3AMA/B4C/+Q8M31ovLdwuKsfLIFSj5dLPl31l7G9C8Jga2CHd0M+7Z/8a0NrvPqXcGmsz/QERE5Che7NGu8qz+DZgyZQp++eUX/Pbbb/Dz80NaWhrS0tKQl6fprQ0ICMDkyZMxbdo0bN++HXFxcZg0aRK6dOmimAitInv8x8MG6z4YqUvc9NpfiQbbTc0TtpWs0uGxUcE+2Dujr2xbq7c24feDl/DltnOyoc2fbFJOfKZNMgYArSJ1PWpJadlGh+GWRai/FxJmx6Jvk1CLn3Nfs5pImTcE88e0VpxDY0xSWrZBhul5o1piUEvzQcypdwbi3eHNxeWl+y7icMpttH13M45fMZ3Mr+Eb62z+Pdh6Sle33tiQeVNK1AJm6U2HiHlnE+4WGNa8NmbTCV1OhurVPGXbhrYKx+h2kXiscx39p4kZx8PN1IXW91TPaKPvb8zBcg6Jd6Svxzk2a7i1jGgTYXYfbw/2FhARkXNijzZZPdD++uuvkZmZid69eyM8PFz898cff4j7zJ8/H0OHDsXo0aPRs2dPhIWFYeXKldZuisNNH9BYfDylT30cntXfxN4ayw9fwt5zN3EmPRsnr9ouu/ryQ5fQ99MdOH/jLjJKA+BQP0/UCvTGrul9FJ+jrV2899xN/H5QeZ78pdu65F81/b2wSJI0LObtTYrJpbQBubk6v1reHq5GMzkefKOfwbohZgJjU7F3gLeux/TJHvXwcMco4ztLuLqo8FiXurJ1D1g4vLqoRMDzvx+xaN/yyCssweSfdDeB9Ht2k2/cNRuEGksy12LORqMJzfQ9+6vuGPVH27/YryEA4Ike8hExAPBKbGOkzBuCfTMNP2tTooJ9ZL3a0gRo5gyLqYWUeUMwtU8D8zvbWY7k5sacYc3KdDPJ2UlvTOqb2LWu/RpCRERURu4sP1nl2WTouNK/iRMnivt4eXlhwYIFuH37NnJycrBy5Uqj87Mrsud618f+mf2QMm8Ipg9oYtBrp3XqnYHi45krEzH2uwOInb8Lg7/4D3eM1G2+V//31zEk38jBmMX7xJJF2p7NkGrKw3G185TH6g1vPv3eQPRvqkkqNu2+xrJt+gmZ/kkwHIp7rDSJWpi/5T2U1Y20MdTPC8NiasnWDTYTaL8z3HgJpL+O6IZHvzGkmcXt0xrSynyQv+jRtkh8K1a2fl2i+d7W8iguUeP73fIAU78s2wOL9uGpn+Pw79GrRl/njVWGozG0Xl5+1Og2Lf2eb2megE8fjEHD0qzT2mkIUvpDwC2lUqnw/siWstwBlnprmOazl36e55ykkoB2fnY1TzdM6mZ4Y6IiG6nQq923SShq+ntial/nu+lBRESk5e5kpXzJ/vgNsCHtPF5TOtQNMjn88T+95Fh3cgrRZe5WjP56b7lLQ0mHgN+8qwvkU0pLUfmYaM/lO/JyVZ88GANPN1d8O74dTrw9AC0j5QmYVCqVrOfJx8MVfxxKFROgSXvAo8ow17JD3WCDddU8NWXh50nqKkfX8DVbG3isBb3UrS0oIaVkzjDjwfmSSR2QPHcIBrYIh5+XO3a/Jh9J8PN+w6zv91oO7IutZw3mxOsHvbdLb+58ufWsbP2q+Mvo+dF2dJm7VVaubv2LPWT7rVYI0Av1Et09/I1y775KBYxqqwuugnw98PuTncXRIY92joKX+70NxTL1/QaAiEBvjGmvKyE4b1RLhJTeJGsYWk1c/+GGJIPnOkJapibQrumvfCOvInNR+NP9bnx77J/Zz+iNSyIiImfAQJv4DXCAD0frAsH/PawZLi29sJd64fd42fIH607hWmY+4i7ewYrDlxWfY87lO8rZzReO0wzzNlXLuvuH22XL2nq3KpUKvqWBrr5XYhuJAcquMzfx2l+JeOmPBOQUFMvmB9fws/zCeXjrCDzdMxqLH2uHLdN6YWircPz5rCaZnq+nGz4Y2RLjOkVh67ReZl/LxUWF357oZHKfHyZ2sLhtUqF+Xtg3sy96NKwuW3/ynQHo01g+zzwyyEdWFu3Nv49j8o+H8E/CFSzYfg77zt9CwzfWo+6MteWeVvDFNsNs2dKaxdJe2rPX72LxTk2Stsaz1uPlP44i9XYurmXqkuftmt4HTcP9Mby1bhSB/siEpftS0GjWevxSeuPgakae0XnqgmD4/etSPwRT+jTAyXcG4F0Tow8spZ8r4Ole8rnba57vjuf7NUDLiAB8/EAr2XQBN8mP5uaT6bJh2/aUeitXvOmVUZqELsS38gWeLnrfhQVj28LFRWXy/ygiIiJnwKHjpBwZkU092K42ejXSDH/UXjC+N7IFVh+7ajBfFgAu3spBnRBfAMAGydzZ8pYDe3v1CcX10iHW617ogcFf6LJjB/t6iD2dgGYI7bO96ltUQsjPyx1DW9XC/C1nsFvSQ3/hZo74mi0jAsp08ezqosLMwU3F5a/GtpVtH1vG4cVdG1THl4+0wfO/x+ODkS0REeSNCT8cBKC5AXEvtXrDA7zxvzGt0e69LeI6Hw/lP712dYJky1uTrmNr0nWD/QZ/8R/OfzDY6Fx1JZfv5KKmvyfSswoM1qvVAmLe2YTsfHngOHd9Ep7sEW209FqAj2Zo94ejW8HD1QUr4i4jLSsfR1LvoG1UELLyizD7H8337bPNZzSf20rdsPMlkzrgTFo25q433zts7JyVVe0gHwCaLPgPtY+UjaQY2SYCQb4eCPL1wOrnu5t9reWHL9l9uHZOQTF6fqy54XX2/UG4W6AJtKt5Vb7/zl0l/ye83L+R2akYREREzoI92sRvgAO4uGiGlEsDS3dXFxx58z68IQketSYtOYT8Ik0ALs20/NX2c6g7Yy2OX8ks0/tvOWUYuH3zmDxTcbNa/ogM8haXA7zlc2XfGta8THV6g3wN59qev3EXOaU3FqoZ6Q23p2ExtXDi7QEY2ykKvRrVwBePtMHix9qZneNtiRDJMFdTdzhdXVRY+VxXi17zipGRCfr2nruJujPWovuH22VBdvcG1eHh5oKcwhIkpWUbBNlapkqz+ZV+bl7urhjYQjcff9ofCSguUaPVW5vEdbdzCmVBNgD0aRwqu7kgHTZuK1MkCc1mDW0m+5uaO8p48i0t6YiUG9kFyFO4OWZL0hsvd3ILxcR0zvA3ZG3Se287zhj+v0VEROSsGGgTvwFOxMvdVTERWfLNHPx+MBWA8vDqoV/utrgclLHkarHNDZPRrXuxB2r4eWL+mBhZtvBVz3Ut0zBvQDmpVfKNHOSXBinOUqZHOvz9/phaBsnc7sWeGX0xpn1tbHypp8n92lg4H/xOrvlEeam3cg2S1wHArCFN8fWjbcWbKdLRC/qUnq8lzXBdJ0TXM5xyKxcNJDWplXw0uhUA+Xd65iDDG03WVjvYR6x77u/ljj6NQ/FqbCMsmdjBovnfYzpE4f7ShHt/HLqEprM34DM71tWWTifp+P5WLC+dQlIZe7SlNyNfV7gJSURE5KzcOHS8ymOg7WSM9Ur9sv8icgqKseeccu/i6bRsjFq4B0//bFi7W+q30oAd0ARbALDpZeXAz9/LHYfe6I+RbSLhJ7mIjwyyPGmZlqdCQrLPt57FltJ6zs4SaNtSRKA3PnygFaJrVDO5n0qlwoW5g7FkYgcMbmk80B++YI/Z9zx7PVtx/YSudeHn5Q43I0PPXx/cRHH9lmm98P2E9mgVGYC/p3STbWsQ6qf4HCUX5g7GQx00eQkiAr1Rv4YvGtf0Q8g9DNEvL5VKhal9G6JPGWqzazPb3yq9cfWFXuI4W5EmMtQXEehtdFtFdnROLPbM6KuYAJGIiMhZsUeb+A1wMsbufp2/kYPmczbK1mkDZUDTI3kkNQMbT6Rjzj/Hjb6+tJTTEz2ikTJvCBrVNB8gJcyORcuIADzWuU6Ze7MB4IqR+eSbTmoCbS+3yh9ol4VKpUKfJqFYOK4dUuYNMbqfNPN1UYkan2w8jcMpt8V1Z42UoNL+538mXXn70Fa1DNb9PLkjGoRWQ7+mNfHv1O6KmdhXTzWc11yvui8e61xHXG5XJ0jWU+nm6oINL/XEmhe6V5ga0NLee3vKMjK8HwCGKXxmlUGAt3ulvYlARESVF5OhEQNtJ5N6S1fu6r//64NFj7ZT3O/8B4PxRI9oxW0/7bsozumWysovUtjbMq4uKqx+vjveHVG+rM/FZoa2S+tVk2k9G+nqQH+947yYRfuH3Rfw1fZzeGCRrnTWPEmSsY71gtG8lj/eH6n7DD0U7rZ2iQ5BeICXrGzXZw/FoEdD8/WnW0YGyBLRzRzUBBtf6olZQ5vi2d718Wzv+lgyyTCDu7urS4W682usFN2mE2l4e/UJFN9jGTZjjOVjiKkdKMupQERERI6ldI1FVQu/AU7GTzKXuXawjyzBlNaMQU3EbNNDjWThnfrbEYN10sRU2rrE9vJIB13wpV8vmszTBr3P9a6Pzx6KkW2btz4JdwuKsezQJXGdWi3gtwOpsv2WPt4Ra1/ogXGddL3LHz6gS+wVGeSNo7NjsXRyR6hUKjQN98fPkzvi+wntMaptpMVtnR7bGF3rh+D1wU3wdK/68HBzgaebK14b2ASvDWyiOF+/olGay51yMwdP/RyHJXtS8GfcZSSlZaHujLWYu+6U4muo1QLmrU/CsoOpitv1XbyVg3FG5sv//VzXCjMagIiIqCpwY6Bd5VW+7DkV3LCYWlh//Bo6R4eI6wK83WW1f5/pVV98/N6IFlhz7JrB62w5dR2CIIhDdM+my+fqPte7vsFzbCnAxx0X5g42WsJr6eMd7dqeiqZpuL9sCPnWV3qh36c7AQC/H0wVk+VpLTt0Ca+v0mX43jKtl2JwOLJNJPo3rQkvd1cUlwgGc+Ut6cXWF+Trgd+e7Fzm51U0E7rUwU/7LorL647r/g6XH76EGaUZ1hfvSkbvxqHoUj9E9vzVx65iUWmd8vZ1g9Eg1PTc/bWJhn/nWqwrTURE5Fgv9muIz+2Us4UqBt5qcTIebi74bkIH2bBwU2V7An2MJ4/ad16XOO38jRzZNkdcmEvfUzq//OjsWNlwaDKvvpmEatIgW7O/r9F9/bzc4e7qUiUS0lnTDL0M6TeydaXTjqRmyLaduGo45DtZ8je5+uhVs+/niERxREREZJmX72skSx5MxEC7Ari/tS7J0cSudU3u27imH5qEaZKbjf/hoFj2S5r1e8UzXazfyDLSJmJLmTcEAT4Vfyixs2OPp/V5e7iic7QuE/aSPSlG9910It1gXbFaN4+7oNj8nO6bd3Xl3OqG+KCrXg85EREROZal5XapamCgXQEUSS7Cn+ppmABNW2oIAJY91RndG1QHoElANn3FUXSduxXXs/MBAKF+niyTU0ksHNfWov26RDMgs5UfJ3XEaAvmrx+UZILXup6l6wFPvqGc/V3qq23nxMfNavnjtyc74+txbbFzem/LGktEREQ2ZS75L1UtHN9QAbzYvyH+OHwJvRrVQC2FMjefj2mNafc1Qp1gH7i4qBAhyT68Mv4KAOC1vzRDia9LhrdSxTa4pXIiPH2+JqYe0L3xcndFr8Y1ypU1v0iSmXznmRsm91WrBeRJKgm8PlgzbH2Qhd8BIiIisgPG2STBHu0KwM/LHYlvDcBXY5V7MF1cVKhX3VfMOlxko9JC5HzeHNpMfNyuTpDiPkqZ68l6Bps4v9LRJsUlaqjVAgqL1Th3PRt/J+jmZRcUq00ON7t5V3eDbN6ologMckwdbyIiIjJOYKRNEgy0K6EH2tV2dBPITiZ3r4d/p3bDwOZh+OyhGFmN7Bf7NcSCsW0xqk2EA1tY+bm5umBy93oG639/srPs81i8KxnRr69Do1nr0f+zXQb7j1y4x+h7FEmC8Ic7Rhndj4iIiIicA8eUVkLBvh4I9HFHRm6RwbZoE9mnqWJqFRmIRY+1AwDUCfHF4BbhCPB2Z11lO3ptYBN8v/uCuPxo5yh0qR8CQdAFyB9vPG3yNY5dzsSJq5loXisAAGTl+a5m5Nmg1URERERkK+zRrqSOzLrP0U0gBwny9WCQbWcebvL/St8b0RJA2bO9z1ufBEBTKqzrvG34cINm+ZtdyVZoJREREdmSwJHjJMFAu5JycVHBy13+8bq6qPDh6FYOahER6fumdCSC1t2CYgDAD3su4FpmPr7ecR4AUCvAy+5tIyIiIqLy49DxKuLC3MHIL1LD28PV0U0hqpQahFbDuevmy3Rp9WhYHbHN5YnU4lMzUKIWkJ2vm/axYPs5/LTvIgCgTVSgVdpKRERE1scObZJij3Yl1jZKk4Xa3VUFlUrFIJvIhiwZJD5SkpjuldjGAAzrnB+9nIHNJ9PFZenc7vjUjHtqIxEREdmOwLHjJMFAuxKbO6olBrcMw7oXeji6KUSV3pQ+DQAAQ1sZr209f0xrjGhdC/fH1EJMpCbp2YJxbfFqbCNxn1EL9yI9S7ne/WK9oeZERERE5Jw4dLwSqxPii4XjeGFOZA8j2kSgde1A1A6W17ie2LUuftybgidKS4D97+E2su3Bvh6Y2rchPtl0xux79GpUw3oNJiIiIiKbYaBNRGQldasbls976/7meG1gE6tM3fB04yAkIiIiZ8WB4yTFqzYiIhuzJMj+eXJHk9sf6RhV5nJhREREROQYDLSJiJxAj4aGw8I/HN1SfPzu8Ob2bA4RERER3QMOHScichId6wXj4IXbAICvx7XFoJbh6BwdgohAb7i58r4oERGRM2PScZLilRsRkZN4oW9DAICfpxsGttDU2K4T4ssgm4iIqAKYPkBTuvOxznUc3BJyBiqhAhZ8y8rKQkBAADIzM+Hv7+/o5hARWYUgCEi8kom61X3h7+Xu6OYQERFRGQiCgPM3chBd3RcuLsyrUhmVJQ7l0HEiIiehUqnQKjLQ0c0gIiKiclCpVGgQWs3RzSAnwfGIRERERERERFbEQJuIiIiIiIjIihhoExEREREREVkRA20iIiIiIiIiK2KgTURERERERGRFDLSJiIiIiIiIrIiBNhEREREREZEVMdAmIiIiIiIisiIG2kRERERERERWxECbiIiIiIiIyIoYaBMRERERERFZEQNtIiIiIiIiIitioE1ERERERERkRQy0iYiIiIiIiKyIgTYRERERERGRFTHQJiIiIiIiIrIiBtpEREREREREVuTm6AaUhyAIAICsrCwHt4SIiIiIiIiqAm38qY1HTamQgXZ2djYAoHbt2g5uCREREREREVUl2dnZCAgIMLmPSrAkHHcyarUaV69ehZ+fH1QqlaObYxNZWVmoXbs2Ll26BH9/f0c3p0rgObc/nnP74zl3DJ53++M5tz+ec/vjObc/nnPHcJbzLggCsrOzUatWLbi4mJ6FXSF7tF1cXBAZGenoZtiFv78//4jtjOfc/njO7Y/n3DF43u2P59z+eM7tj+fc/njOHcMZzru5nmwtJkMjIiIiIiIisiIG2kRERERERERWxEDbSXl6emLOnDnw9PR0dFOqDJ5z++M5tz+ec8fgebc/nnP74zm3P55z++M5d4yKeN4rZDI0IiIiIiIiImfFHm0iIiIiIiIiK2KgTURERERERGRFDLSJiIiIiIiIrIiBNhEREREREZEVMdC2oV27dmHYsGGoVasWVCoV/v77b9n29PR0TJw4EbVq1YKPjw8GDhyIs2fPyvZJS0vDY489hrCwMPj6+qJt27b466+/ZPucOXMGw4cPR/Xq1eHv74/u3btj+/bttj48p2SNc37+/HmMHDkSNWrUgL+/Px566CGkp6eL21NSUjB58mTUq1cP3t7eqF+/PubMmYPCwkJ7HKLTscc511q7di06deoEb29vBAUFYcSIETY8Muc1d+5cdOjQAX5+fggNDcWIESNw+vRp2T75+fmYMmUKQkJCUK1aNYwePdrgnKampmLIkCHw8fFBaGgopk+fjuLiYtk+O3bsQNu2beHp6YkGDRrgxx9/tPXhOSV7nnOtPXv2wM3NDa1bt7bVYTk1e57zX3/9FTExMfDx8UF4eDgef/xx3Lp1y+bH6Gysdc5feOEFtGvXDp6enorf3x07dmD48OEIDw+Hr68vWrdujV9//dWWh+bU7HXeAUAQBHzyySdo1KgRPD09ERERgffff99Wh+a0rHHOjx49ikceeQS1a9eGt7c3mjZtis8//9zgvfg7qmHPc67l6N9RBto2lJOTg5iYGCxYsMBgmyAIGDFiBJKTk/HPP/8gPj4ederUQf/+/ZGTkyPuN378eJw+fRr//vsvEhMTMWrUKDz00EOIj48X9xk6dCiKi4uxbds2xMXFISYmBkOHDkVaWppdjtOZ3Os5z8nJQWxsLFQqFbZt24Y9e/agsLAQw4YNg1qtBgAkJSVBrVZj8eLFOHHiBObPn49Fixbh9ddft+uxOgt7nHMA+Ouvv/DYY49h0qRJOHr0KPbs2YOxY8fa7Tidyc6dOzFlyhTs378fmzdvRlFREWJjY2X/d7z88stYvXo1VqxYgZ07d+Lq1asYNWqUuL2kpARDhgxBYWEh9u7di59++gk//vgjZs+eLe5z4cIFDBkyBH369EFCQgJeeuklPPHEE9i4caNdj9cZ2Ouca2VkZGD8+PHo16+fXY7PGdnrnO/Zswfjx4/H5MmTceLECaxYsQIHDx7Ek08+adfjdQbWOOdajz/+OMaMGaP4Pnv37kWrVq3w119/4dixY5g0aRLGjx+PNWvW2OzYnJm9zjsAvPjii/juu+/wySefICkpCf/++y86duxok+NyZtY453FxcQgNDcUvv/yCEydO4I033sDMmTPx1Vdfifvwd1THXudcyyl+RwWyCwDCqlWrxOXTp08LAITjx4+L60pKSoQaNWoI3377rbjO19dXWLp0qey1goODxX1u3LghABB27dolbs/KyhIACJs3b7bR0VQM5TnnGzduFFxcXITMzExxn4yMDEGlUpk8nx999JFQr1496x9EBWOrc15UVCREREQI3333nX0OpIK5fv26AEDYuXOnIAia8+fu7i6sWLFC3OfUqVMCAGHfvn2CIAjCunXrBBcXFyEtLU3c5+uvvxb8/f2FgoICQRAE4f/+7/+E5s2by95rzJgxwoABA2x9SE7PVudca8yYMcKsWbOEOXPmCDExMbY/oArAVuf8448/FqKjo2Xv9cUXXwgRERG2PiSnV55zLlWW7+/gwYOFSZMmWaXdFZ2tzvvJkycFNzc3ISkpyWZtr6ju9ZxrPffcc0KfPn3EZf6OGmerc67lDL+j7NF2kIKCAgCAl5eXuM7FxQWenp7YvXu3uK5r1674448/cPv2bajVaixbtgz5+fno3bs3ACAkJASNGzfG0qVLkZOTg+LiYixevBihoaFo166dXY/J2VlyzgsKCqBSqeDp6Snu4+XlBRcXF9nnoi8zMxPBwcE2annFZa1zfuTIEVy5cgUuLi5o06YNwsPDMWjQIBw/ftyOR+O8MjMzAUD8DsbFxaGoqAj9+/cX92nSpAmioqKwb98+AMC+ffvQsmVL1KxZU9xnwIAByMrKwokTJ8R9pK+h3Uf7GlWZrc45ACxZsgTJycmYM2eOPQ6lwrDVOe/SpQsuXbqEdevWQRAEpKen488//8TgwYPtdWhOqzzn/F7ei7+jGrY676tXr0Z0dDTWrFmDevXqoW7dunjiiSdw+/Zt6x5ABWStc67/PebvqHG2OueA8/yOMtB2EO0XZ+bMmbhz5w4KCwvx4Ycf4vLly7h27Zq43/Lly1FUVISQkBB4enri6aefxqpVq9CgQQMAgEqlwpYtWxAfHw8/Pz94eXnhs88+w4YNGxAUFOSow3NKlpzzzp07w9fXF6+99hpyc3ORk5ODV199FSUlJbLPRercuXP48ssv8fTTT9vzcCoEa53z5ORkAMBbb72FWbNmYc2aNQgKCkLv3r2r/AWCWq3GSy+9hG7duqFFixYANLkdPDw8EBgYKNu3Zs2a4pSStLQ0WfCh3a7dZmqfrKws5OXl2eJwKgRbnvOzZ89ixowZ+OWXX+Dm5mbjI6k4bHnOu3Xrhl9//RVjxoyBh4cHwsLCEBAQoDgdpiop7zkvj+XLl+PQoUOYNGnSvTS5UrDleU9OTsbFixexYsUKLF26FD/++CPi4uLwwAMPWPMQKhxrnfO9e/fijz/+wFNPPSWu4++oMluec2f6HWWg7SDu7u5YuXIlzpw5g+DgYPj4+GD79u0YNGgQXFx0H8ubb76JjIwMbNmyBYcPH8a0adPw0EMPITExEYBmDuyUKVMQGhqK//77DwcPHsSIESMwbNgwo4FhVWXJOa9RowZWrFiB1atXo1q1aggICEBGRgbatm0r+1y0rly5goEDB+LBBx+skvP5zLHWOdfO1X7jjTcwevRotGvXDkuWLIFKpcKKFSscdnzOYMqUKTh+/DiWLVvm6KZUGbY65yUlJRg7dizefvttNGrUyKqvXdHZ8nt+8uRJvPjii5g9ezbi4uKwYcMGpKSk4JlnnrH6e1Uk9vq/Zfv27Zg0aRK+/fZbNG/e3KbvVRHY8ryr1WoUFBRg6dKl6NGjB3r37o3vv/8e27dvN0hKVZVY45wfP34cw4cPx5w5cxAbG2vF1lVOtjrnzvY7ytvlDtSuXTskJCQgMzMThYWFqFGjBjp16oT27dsD0GRi/uqrr3D8+HHxxycmJgb//fcfFixYgEWLFmHbtm1Ys2YN7ty5A39/fwDAwoULsXnzZvz000+YMWOGw47PGZk75wAQGxuL8+fP4+bNm3Bzc0NgYCDCwsIQHR0te62rV6+iT58+6Nq1K7755ht7H0qFYY1zHh4eDgBo1qyZ+BxPT09ER0cjNTXVvgfkRKZOnYo1a9Zg165diIyMFNeHhYWhsLAQGRkZsjvD6enpCAsLE/c5ePCg7PW0mT2l++hntU1PT4e/vz+8vb1tcUhOz5bnPDs7G4cPH0Z8fDymTp0KQHNhLAgC3NzcsGnTJvTt29fGR+h8bP09nzt3Lrp164bp06cDAFq1agVfX1/06NED7733nvj/T1VyL+e8LHbu3Ilhw4Zh/vz5GD9+vDWaXqHZ+ryHh4fDzc1NFoA0bdoUgCY7f+PGje/9ICoYa5zzkydPol+/fnjqqacwa9Ys2Tb+jhqy5Tl3tt9R9mg7gYCAANSoUQNnz57F4cOHMXz4cABAbm4uABj0pLq6uoo9fMb2cXFxkWVsJjlj51yqevXqCAwMxLZt23D9+nXcf//94rYrV66gd+/eYs+qUm83yd3LOdeWK5HecS8qKkJKSgrq1Kljt2NwFoIgYOrUqVi1ahW2bduGevXqyba3a9cO7u7u2Lp1q7ju9OnTSE1NRZcuXQBo5qUmJibi+vXr4j6bN2+Gv7+/eEOjS5custfQ7qN9jarEHufc398fiYmJSEhIEP8988wzaNy4MRISEtCpUyf7HKyTsNf3PDc3V/F3VtuGqsQa59xSO3bswJAhQ/Dhhx/Khn1WRfY67926dUNxcTHOnz8vrjtz5gwAVLnfUmud8xMnTqBPnz6YMGGCYpk0/o7q2OOcO93vqENSsFUR2dnZQnx8vBAfHy8AED777DMhPj5euHjxoiAIgrB8+XJh+/btwvnz54W///5bqFOnjjBq1Cjx+YWFhUKDBg2EHj16CAcOHBDOnTsnfPLJJ4JKpRLWrl0rCIIm63hISIgwatQoISEhQTh9+rTw6quvCu7u7kJCQoJDjtuR7vWcC4Ig/PDDD8K+ffuEc+fOCT///LMQHBwsTJs2Tdx++fJloUGDBkK/fv2Ey5cvC9euXRP/VUX2OOeCIAgvvviiEBERIWzcuFFISkoSJk+eLISGhgq3b9+227E6i2effVYICAgQduzYIfv+5ebmivs888wzQlRUlLBt2zbh8OHDQpcuXYQuXbqI24uLi4UWLVoIsbGxQkJCgrBhwwahRo0awsyZM8V9kpOTBR8fH2H69OnCqVOnhAULFgiurq7Chg0b7Hq8zsBe51xfVc46bq9zvmTJEsHNzU1YuHChcP78eWH37t1C+/bthY4dO9r1eJ2BNc65IAjC2bNnhfj4eOHpp58WGjVqJP5GaDO9b9u2TfDx8RFmzpwpe59bt27Z9Xidhb3Oe0lJidC2bVuhZ8+ewpEjR4TDhw8LnTp1Eu677z67Hq8zsMY5T0xMFGrUqCE8+uijste4fv26uA9/R3Xsdc71OfJ3lIG2DW3fvl0AYPBvwoQJgiAIwueffy5ERkYK7u7uQlRUlDBr1iyDEi9nzpwRRo0aJYSGhgo+Pj5Cq1atDMp9HTp0SIiNjRWCg4MFPz8/oXPnzsK6devsdZhOxRrn/LXXXhNq1qwpuLu7Cw0bNhQ+/fRTQa1Wi9uXLFmi+B5V9b6VPc65IGhuPL3yyitCaGio4OfnJ/Tv319WNqwqMfb9W7JkibhPXl6e8NxzzwlBQUGCj4+PMHLkSIObQSkpKcKgQYMEb29voXr16sIrr7wiFBUVyfbZvn270Lp1a8HDw0OIjo6WvUdVYs9zLlWVA217nvMvvvhCaNasmeDt7S2Eh4cL48aNEy5fvmyPw3Qq1jrnvXr1UnydCxcuCIIgCBMmTFDc3qtXL/sdrBOx13kXBEG4cuWKMGrUKKFatWpCzZo1hYkTJ1bJGxzWOOdz5sxRfI06derI3ou/oxr2POdSjvwdVQlCFRsXRURERERERGRDnFhKREREREREZEUMtImIiIiIiIisiIE2ERERERERkRUx0CYiIiIiIiKyIgbaRERERERERFbEQJuIiIiIiIjIihhoExEREREREVkRA20iIiIiIiIiK2KgTURERERERGRFDLSJiIiIiIiIrIiBNhEREREREZEVMdAmIiIiIiIisqL/B50gLs8tkVE7AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Mock additional factors data\n",
+    "np.random.seed(42)\n",
+    "df['GDP'] = np.random.normal(loc=2.0, scale=0.5, size=len(df))  # hypothetical GDP growth rate\n",
+    "df['Inflation'] = np.random.normal(loc=1.5, scale=0.2, size=len(df))  # hypothetical inflation rate\n",
+    "df['Unemployment'] = np.random.normal(loc=5.0, scale=1.0, size=len(df))  # hypothetical unemployment rate\n",
+    "\n",
+    "# Select relevant columns for VAR\n",
+    "var_data = df[['Price', 'GDP', 'Inflation', 'Unemployment']]\n",
+    "model = VAR(var_data)\n",
+    "var_result = model.fit(maxlags=15, ic='aic')\n",
+    "\n",
+    "# Forecasting with VAR\n",
+    "forecast = var_result.forecast(var_data.values[-var_result.k_ar:], steps=10)\n",
+    "forecast_df = pd.DataFrame(forecast, index=pd.date_range(df.index[-1], periods=10, freq='M'), columns=var_data.columns)\n",
+    "\n",
+    "# Plot forecast\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "plt.plot(df['Price'], label='Historical Price')\n",
+    "plt.plot(forecast_df['Price'], label='VAR Forecasted Price', color='red')\n",
+    "plt.title('VAR Forecast for Brent Oil Prices')\n",
+    "plt.legend()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Markov-Switching ARIMA for Regime-Switching"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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cWMcff7zOOussDRgwQE2aNEn6da3t265du7jbDjvsMH366adRyxLtw33p0qVLVEOziy66SEcffbSGDBmis846K6r5VqtWraIem8zfkSX2b8EaqlHVixHW7+b27dsrHCJRmQA8Wa+99ppq166t3NxcNW/ePOGQgmbNmlWqedmKFSvUrl27vTbcW758ubZu3apGjRolvN1qiNejRw+dd955uvvuuzV+/Hj17NlTffv21cUXX5zR7vcAkEkE1wBQjSIDhr59++rEE0/UxRdfrKVLl6pmzZoKBAKSpFtuuUV9+vRJ+BzJBM/7UlHn6YqWWwFnIBCQYRh67733Et63Zs2alXr92AAqkfz8/LiAuzpcf/31mjJlim688UZ169ZNderUkWEY6t+/v71f3MZar+eeey5hkBwZFN144406++yz9eabb+r999/XXXfdpdGjR+ujjz7S0UcfXa3rmY596PP51KtXLz322GNavny5Dj/8cPu2yKoDSVX6O9rX73yy2rdvrzfffFP/+9//dPLJJye8z//+9z9JUocOHar0GomcfPLJdrfwisRur1QEAgE1atRIzz//fMLbrYoCwzD06quv6vPPP9fbb7+t999/X3/+8581duxYff7555U+ZgCAlxBcA0CG+P1+jR49Wr169dITTzyhYcOG6ZBDDpEUzPzuq8HRwQcfrO+//z5ueaJl6da6dWuZpqlWrVqpbdu21f56sQ4++GB9+OGH2r59e1TW77vvvrNvj2RleCMtW7ZMRUVF9sn/q6++qoEDB2rs2LH2ffbs2aMtW7YkXIfly5erV69e9s87duzQunXrdMYZZ1T5fVkq2xDNyko2atSoUg2xWrdurZtvvlk333yzli9frk6dOmns2LH65z//mdT6Wdt36dKlcVnzpUuXxm3/dLGGJezYsWOv90vm7ygZyTSqO+usszR69Gg9++yzCYPr8vJy/etf/9IBBxyg7t27p20d06l169b6z3/+o9LS0gqrUVq3bq0PP/xQ3bt3r1TQfvzxx+v444/X/fffr3/961/605/+pBdffFGXX355ulcfABzHmGsAyKCePXuqS5cuevTRR7Vnzx41atRIPXv21FNPPaV169bF3T9yaqA+ffpo3rx5Wrx4sb3s119/rTCDlE7nnnuu/H6/7r777rjMnmma+uWXX6r19c844wyVl5friSeeiFo+fvx4GYah008/PWr5vHnzosZNr1mzRv/+97916qmn2hlLv98f914ef/xxe0x2rKeffjpq7OjEiRNVVlYW99pVUaNGjQqD+kh9+vRR7dq19cADDyQcx2r9vuzatUt79uyJuq1169aqVatWwinW9uXYY49Vo0aNNGnSpKjHv/fee/r222915plnJv2c+1JaWqoPPvhAeXl5dvl/RZL5O0pGZfeLJJ1wwgnq3bu3pkyZonfeeSfu9jvuuEPLli3TbbfdltZMcjqdd9552rx5c9zfmRTO6F944YUqLy/XvffeG3efsrIye3v99ttvcX9fVr+IqvwOAoAXkLkGgAy79dZbdcEFF2jq1Km6+uqrNWHCBJ144ok68sgjdcUVV+iQQw7Rhg0bNG/ePP3000/68ssvJUm33Xab/vnPf+r3v/+9rr/+ensqroMOOki//vprStNB7Uvr1q113333afjw4Vq1apX69u2rWrVqaeXKlXrjjTd05ZVX6pZbbqm21z/77LPVq1cv3XHHHVq1apU6duyoDz74QP/+97914403xo0zPeKII9SnT5+oqbgk6e6777bvc9ZZZ+m5555TnTp11KFDB82bN08ffvhh1BRGkUpKSnTKKafowgsv1NKlS/Xkk0/qxBNPTDincbI6d+6sDz/8UOPGjdOBBx6oVq1aqWvXrnH3q127tiZOnKhLL71UxxxzjPr376+GDRtq9erVevfdd9W9e3c98cQTWrZsmb2uHTp0UE5Ojt544w1t2LBB/fv3T3r9cnNzNWbMGA0ePFg9evTQRRddZE/F1bJlS910000pb4P33nvPrkTYuHGj/vWvf2n58uUaNmyYPZZ8byr7d5SMzp07a+LEibrvvvvUpk0bNWrUqMLx7pL07LPP6pRTTtE555yjiy++WCeddJKKi4v1+uuva86cOerXr59uvfXWpNcjUwYMGKBnn31WQ4cO1fz583XSSSdp586d+vDDD3XttdfqnHPOUY8ePXTVVVdp9OjRWrx4sU499VTl5uZq+fLleuWVV/TYY4/p/PPP17Rp0/Tkk0/qj3/8o1q3bq3t27fr73//u2rXrp2Wag8AcCOCawDIsHPPPVetW7fWI488oiuuuEIdOnTQggULdPfdd2vq1Kn65Zdf1KhRIx199NFR80y3aNFCs2fP1l/+8hc98MADatiwoa677jrVqFFDf/nLX6K6OFeHYcOGqW3btho/frwdpLZo0UKnnnpqWgLMvfH5fHrrrbc0YsQIvfTSS5oyZYpatmyphx9+WDfffHPc/Xv06KFu3brp7rvv1urVq9WhQwdNnTpVRx11lH2fxx57TH6/X88//7z27Nmj7t2768MPP6xwzO4TTzyh559/XiNGjFBpaakuuugi/e1vf0vLRY1x48bpyiuv1J133qndu3dr4MCBCYNrSbr44ot14IEH6sEHH9TDDz+s4uJiNWvWTCeddJLdEbpFixa66KKLNGvWLD333HPKycnRYYcdppdfflnnnXdeldZx0KBBKioq0oMPPqjbb79dNWrU0B//+EeNGTOmSnOcx4r8XS8oKNBhhx2miRMn6qqrrqrU4yv7d5TsOv3444966KGHtH37dvXo0WOvwXXTpk01f/58jR07Vq+88opee+015eTk6KijjtLUqVM1YMCAar0Iliq/36/p06fbJdyvvfaa6tevb1+0sEyaNEmdO3fWU089pb/+9a/KyclRy5Ytdckll9gl7z169ND8+fP14osvasOGDapTp466dOmi559/Pq4hHQBkC8OsaucOAIAr3HjjjXrqqae0Y8eOCps0AQAAoHox5hoAPGT37t1RP//yyy967rnndOKJJxJYAwAAOIiycADwkG7duqlnz55q3769NmzYoGeeeUbbtm2rcG5fAAAAZAbBNQB4yBlnnKFXX31VTz/9tAzD0DHHHKNnnnmmwnl1AQAAkBmMuQYAAAAAIEWMuQYAAAAAIEUE1wAAAAAApMgTY64DgYB+/vln1apVy9XzQwIAAAAAsoNpmtq+fbsOPPBA+Xz7zkt7Irj++eef1aJFC6dXAwAAAACwn1mzZo2aN2++z/t5IriuVauWpOCbql27tsNrAwAAAADIdtu2bVOLFi3seHRfPBFcW6XgtWvXJrgGAAAAAGRMZYcm09AMAAAAAIAUEVwDAAAAAJAigmsAAAAAAFJEcA0AAAAAQIoIrgEAAAAASBHBNQAAAAAAKSK4BgAAAAAgRQTXAAAAAACkiOAaAAAAAIAUEVwDAAAAAJAigmsAAAAAAFJEcA0AAAAAQIoIrgEAAAAASBHBNQAAAAAAKUo6uP7kk0909tln68ADD5RhGHrzzTf3+Zg5c+bomGOOUX5+vtq0aaOpU6dWYVUBAAAAAHCnpIPrnTt3qmPHjpowYUKl7r9y5UqdeeaZ6tWrlxYvXqwbb7xRl19+ud5///2kVxYAAAAAADfKSfYBp59+uk4//fRK33/SpElq1aqVxo4dK0lq3769Pv30U40fP159+vRJ9uUBAAAAAHCdpIPrZM2bN0+9e/eOWtanTx/deOONFT6muLhYxcXF9s/btm2rrtVLm0DA1N//7wfNXrpR5QFTphl9u2HE/Ky4BYm+3edj425L4r7Rj4u+cW/rEH9bxY+Nf82Y+xrhpV0Pqa+Luxykpz/5Qcs3btfuknLtLClTecCMfZJKi9vOyT3YiYfudT/t+3Wr/uCUXteD65yK2N/5pB6b0uum8OCUX9uL79mhv4cqPu6gekUaeEJLtahXlPgOpil9PlFaOl0KlFV5/ZJTzX9kGfkjzob3UI3YB5V5gWp++mp+/jotpN6jpKJ6iW/fuVl6/w5p29oqHFuSWPek3meS26Q6t2F1rLdhqLj5CRrx6+la9ctO5eX41KpBDd15Zgflle2QZj8grf8qg8d6hxxzqXT0JU6vRVpVe3C9fv16NW7cOGpZ48aNtW3bNu3evVuFhYVxjxk9erTuvvvu6l61tFq05jeNfu87p1fD0z74ZoPuf/cbpRBLA4Cnvb5orf59XffEAfY3b0rvD8/4OgHIAjUbSb+7M/Fts++X/vdiZtcHyv9hjuYWN9ZPZkNJ0v8t36zjD6mvM357XvrPRIfXLkNa/87pNUi7ag+uq2L48OEaOnSo/fO2bdvUokULB9do3zofXE93/+FwbdperMMPrB11kSs2ix0bO8bfbu7j9sjb9h6JJv3cSbxW3Cun8D5++m2XJs5ZoYAp1SnM1TU9W+uAolwV5uUoz1+1q5H72DQVP65qD6vy6wVfs2oPzvR7DL5mZq9+eGq7prSuVX3NKr7HKr5eKg/O9P4IvmYVH5fh91geMDXts1VasWmnHnp/qR6/6Oj4O/0wJ/i1+XHSCder2rNpqf2WVOLpM3Es8fp78Pr6ZwD7YO+Wvit9829p5ScV32fZB8GvnQdJrU+pxJMmsc6Vfn+VvJ9jz1fZl63k871+uSSprrZrd41mKsr3a82vu/V/yzfrjM3vBu9z2FnSUf3Su35u07Cd02uQdtUeXDdp0kQbNmyIWrZhwwbVrl07YdZakvLz85Wfn1/dq5Z2A09o6fQqeNofOjbTys07dVzLA1S/pvf2PwCkomX9Ghoweb6+XLMl8R3WfRn82m2I1OGcjK0XAA+r2yIYXO/cnPj2QEDasT74/cm3SXWaZW7d9mdzHpB+/UEFKtGVJx+iukW5uv21r7Rx2x5pu7U/bpUO7OToaiJ51R5cd+vWTdOnT49aNnPmTHXr1q26Xxoe065JLbVrUsvp1QAAR7RuVFOStG7rbgUCpny+mMz0rl+CX2tz8gugknJDQ0xKdyW+fceG0LheI1g6jszw5UqS/DJ1UL0i7SkrlySVlAek0p3B++TVcGrtkIKkp+LasWOHFi9erMWLF0sKTrW1ePFirV69WlKwpHvAgAH2/a+++mr98MMPuu222/Tdd9/pySef1Msvv6ybbropPe8AAIAs0KhWsGKntNzUb7tK4u9QvD34NZ+LkAAqyQrQSioIrn9eFPza8DDJn5uZdYJkBEMwnxFQQZ5f+Tl+SVJxaUAq3R28T27iCl+4W9LB9YIFC3T00Ufr6KOD48GGDh2qo48+WiNGjJAkrVu3zg60JalVq1Z69913NXPmTHXs2FFjx47VP/7xD6bhAgAgQq7fpwY18yRJ67ftib7RNAmuASTPHzymqDzBBTspmLmWpHqtMrM+CLKCawXkMwzl+YM/l5aVhvdVbgUzR8DVki4L79mz514b6EydOjXhYxYtWpTsSwEAsF9pWKtAm3eUaNP24ugbSneHp2QhuAZQWVY2OlCa+HZrLHZR/cysD4Ls4NqUISkvJ/Rz2e7wfchce5Iru4UDALA/KsoLlgbuKS2PvmHP1uBXw0dwDaDyfFZwXRasgImds3nbT8GvNRpmdr32d75wcO0zDHu35JZbVUuGlFPgzLohJUmXhQMAgOpRkBv8WN5TGoi+Yc+W0B3qxJ8cA0BFfP7w94Hy+NvXLgx+PTDB9H+oPqHMtaGADEPyhY7rOWYouM4p4FjvUQTXAAC4REHOPjLXBXUzu0IAvC2ySVmi0vDdW4JfmYIrsyLLwg3JH5odIjcQGhJESbhnEVwDAOASBbmhjrFlMZnr4h3Br5SEA0iGL2IEqNW3IVJJ6NiSVzMz64OgUHDtV0CGDFkzLxJcex/BNQAALpFvl4XHZK6t7rFW518AqAxfROa6PEHm2rpwx5zKGRYu+fYZks9nlYWHgmvGW3sWwTUAAC5hZa7jxlxb5ZzMQwsgGVFjrmMy12XF4WMLVTGOMGTKMAz5Q+Or8wLWNFxkrr2K4BoAAJewx1yXxWauQyfAPib5AJAEwwgfN2KD6+Lt4e8pC88sIzpzbY+5JnPteQTXAAC4REGFZeFkrgFUkVUaHlsWbgXXuTWiM9zIqMhu4YYZugDCECDPIrgGAMAl9l0WzgkXgCT5I+a6jmQF1/lkrZ1iSMGy8FDm2hcIHfsNQjSvYs8BAOASVua6uKLMNWXhAJJlZaVjg+vS3cGvjO91gBH1nd+OyELHfh8hmlex5wAAcIn8isZcWyfFlIUDSFZFZeHWccXHccU5pnyGYZeFyyRz7XXsOQAAXCI85jqmLNzOXHMSDCBJFZWFm1aWlPHWGRfR0MyIaGhmBEL7xGCfeBXBNQAALhEecx2buaahGYAqqqgs3M5cM9zEKYYUnblW6MIqFzw8i+AaAACXsMrCi8tiMtdloblPCa4BJKvCsnAy184xon4KZ64pC/c69hwAAC6RX9FUXGV7gl9zaDwEIEkVzXNNCbLjjNCYazu4tjLX7BPPIrgGAMAlckMdYssDZvQNZcXBrzn5GV4jAJ5nj7muqKEZZeEZFzPmOr6hmZHgQfACgmsAAFzCyl6UxQXXVua6IMNrBMDzrOC5nDHXbhSZufYx5trzCK4BAHCJHH/wBIvMNYC0qagsnG7hjjNkBruFG1ZwHTr2UxbuWQTXAAC4hJW9KIlraBbKXOcy5hpAkiosCye4dk647NtnhPuX+UVDM69jzwEA4BKFuVa38IoampG5BpCkChuahX4mS+oYq41ZOHNNWbjXEVwDAOASRXnBE6pdJbHBtVUWzphrAEmym2UFS44D1rATO3PNmOuMM6Iz13FjrslcexZ7DgAAlygMBde7S8tlmhHjrstDwbU/z4G1AuBpVqBmmhr++v/U8e4P9Main2ho5gLBMdeG3S3cz5hrzyO4BgDAJayycNOUiiPHXTM2EkCVhRollpfphflrtL24TDe99KUCVvdwH+GAkyIz1/Y81+wTz2LPAQDgElZwLUl7SiNKw63gmmwGgGSFMtcbt++JWrxx687gN2SuHWPIlM8wFIqtaWiWBdhzAAC4hJW9kGKm42LKHABVFSo53rRtd9TiX7ZZwXVuptcIEWOugz8GA2ym4vI+gmsAAFzCMAz7nKs8csw1mWsAVRXKgpaVR0/xV1Ji9XIguHaSL3RR1e8z5DPIXHsdew4AABexpmQJRJ4H03gIQFWFAjXTjA6uS0tLgt9wXHGMofCM1z7DiMhcE6J5FXsOAAAXsbIYUZlrkyY3AKrKumAXPcWfWVYa/IbMtQMip+IKZ679zHPteXxKAwDgIuHMNWXhANLAylxHHlMkBcqtzDXBtXNMeyiQn8x1VmDPAQDgIlZTMxqaAUgLo4LMtTUVl5+y8IyLaGhmfevzGfLRLdzz2HMAALiIj4ZmANIpUTWMJLM8VBZO5toxhkwZimhoRlm45xFcAwDgIlbmOrosnIZmAKoqeEyJbWimcsZcO8WMGnNtfaUsPBuw5wAAcBE/Dc0ApFMoUIsrCw+QuXYDw25oJsrCswB7DgAAF7E6x5bT0AxAOthTcUWXhRuMuXaMtScMVZS55ljvVQTXAAC4SLgsPGIhDc0AVJU15ro8eBypmR8KpslcOyiioVnoe0MKT8VF5tqz2HMAALiInbmmoRmAdIjJXBflBY8jhtXLgTHXDgjuC0OmHUcbhiFDDAHyOvYcAAAuknAqLhqaAaiy6IZm4cy1dVzhol2mmVGZ6zAamnkfew4AABexy8ITNjTjJBhAkqzMdWisSVG+P+pnAjnnGDLtaiXDoCw8G7DnAABwEXue64QNzfjYBpAku1t4MHCrkRfMXAdMAjmnRGWujfBXn0FDM6/jrwkAABdJOM81Dc0AVJWRuCzczlxHFSYjk4Ldwq2GZgZTcWUB9hwAAC5inWiVMRUXgHSwuoWHgumCPOs4YkbdjswxE1zQMIyIMddcSPUsgmsAAFzEbmhmJspc09AMQLJCgVwouM7zB0//DZPmWW4QzlyLzHUWYM8BAOAicWXhgUBEQzOCawBJssZch4LpXL8VzFkX8MhcZ15oKi7DjBhzTVl4NmDPAQDgIvY811ZwXbY7fGNugQNrBMDTrOgtdJEu12+d/pO5dk74gkZ05pp94nXsOQAAXCRuKq7SiOA6p9CBNQLgadZUXDHBdbgsnMx1ptmbPnL0tUFwnQ3YcwAAuIjfzlyHFljBtT9f8vGxDSBJMfNc5+XEZK4pC888I8FUXGLMdTZgzwEA4CJW/Fwem7nOJWsNoCqip+KyG5oRyDnGVLhhpWGVhRsG3cKzAH9NAAC4SFxDs7I9wa85jLcGUAV2WXjwmGJlrikLd1Jwm/tilvi54OF57DkAAFwkrqFZoDT41Z/r0BoB8LTQMcUqC49raIaMS3RdwxfVLZzMtVcRXAMA4CJx81wHyGQASIF17LAbmsVMxcWxJfMiOoRHLmKfeB97DgAAF7EamoXnuS4LfmUMHoCqqKgsXJSFO6WiVnLhsnD2iVcRXAMA4CK+2My1WR78SpkggBSYoWNJuCzcQiCXcQmuaxiGIZ9BQzOvI7gGAMBF4jPXoeCaky0AVWGXhQePKVZwzbRPTrI6hEcvYZ94H3sOAAAXscdcW8F1eUnoBhqaAagCa3xvKLjO8cWM96UEOeOssvCobuGGwlNxUankWQTXAAC4SLgsPLTADq7znVkhAN5mRM9rbR1j7CwpZeGOiW1o5mPMtecRXAMA4CKhRr4R81wXB7/mEFwDqAorUAseU/yxnaopQc44O3MdVRZuRATbBNdexV8TAAAuEtfQzM5c5zm0RgA8zcpch44pVj8zuoU7x7SC56iGZqrgB3gJwTUAAC5iZZXKyVwDSAc7Mx08pvjszDVzKjstasy1IvYJPIu/JgAAXMRqaBaIa2hG5hpAFRjWGGsrcx0TXFOC7Jy4bDX7xOsIrgEAcJG4snAy1wBSEZu5jg2uKUHOuITdwiPvwD7xLIJrAABcJG6e6/JQcE23cABVYk3FFexE7Y/JZBPIOSeyDNwwyFdnA4JrAABcxCrZLLPHXIfKwnMoCwdQBfZUXLFjru07ZH6d9nNWQzPDiN4LlOp7H8E1AAAuYp1r2fkMMtcAUmEF06GhJoYRnALKYE5l51hFA1GZ64pah8NLCK4BAHARK6tkDblWeWnwK5lrAFVhZ6oDoa/B4wzzXDvHtGbiiprnmm7h2YC/JgAAXMQ61zJjG5qRuQZQFTENzWRYwTUlyI4JbfqohmYGpfrZgOAaAAAXsTr5xpeFk7kGUBXR3cENGcHScBqaOca0qgmiMtcxaWx4EsE1AAAuYp1TBWhoBiAdrIZmoW7hhhFsnBieiotwIOPCRQRhBmXh2YC/JgAAXMRqakNDMwBpEdMdPDzmmrJwp4S7hYeXGaIsPBsQXAMA4CLWyVbAJHMNIA3sqbiszLURGt9LWbhTEuWno3YD+8SzCK4BAHCRcEOz0DdkrgGkwg6uQz/GNjSjLNwB1pzj4SWGDMrCswB/TQAAuIgvNmNhdQvPIbgGUBXRU3FJwaDOR1m4g6JL9SUrWc0+8TqCawAAXCSuLNya55pu4QCqIrahmWIz1wRymWaF0LFTcSX+AV5CcA0AgIvYDc2ss6/SncGvuUXOrBAAb4ttaGYEp/yzwzfKwjPOamimuLJweF2V/pomTJigli1bqqCgQF27dtX8+fP3ev9HH31U7dq1U2FhoVq0aKGbbrpJe/bsqdIKAwCQzeypuKzoes/W4NfCuk6sDgCvM2LLwg35DMkwKEF2TIKpuKKazLFPPCvp4Pqll17S0KFDNXLkSH3xxRfq2LGj+vTpo40bNya8/7/+9S8NGzZMI0eO1LfffqtnnnlGL730kv7617+mvPIAAGQbX+xUXFZwXVDHkfUB4HF2Zjp4VIlvaEYgl2n7DKHZJ56VdHA9btw4XXHFFRo8eLA6dOigSZMmqaioSJMnT054/88++0zdu3fXxRdfrJYtW+rUU0/VRRddtM9sNwAA+yPrnMo0TSkQkPZsCy4guAZQJaHMdagaxhpz7SO4dly4eiA0RRrdwj0vqeC6pKRECxcuVO/evcNP4POpd+/emjdvXsLHnHDCCVq4cKEdTP/www+aPn26zjjjjApfp7i4WNu2bYv6BwDA/iBqKq6S7bJzHPm1HVojAJ5mT8VlZa5j57lmzLUbGIrMZHPBw6tykrnz5s2bVV5ersaNG0ctb9y4sb777ruEj7n44ou1efNmnXjiiTJNU2VlZbr66qv3WhY+evRo3X333cmsGgAAWcHni2hoZpWE+/Ol3ALnVgqAd9ljrivIXBPIZV6CagG6hWeHar9UNWfOHD3wwAN68skn9cUXX+j111/Xu+++q3vvvbfCxwwfPlxbt261/61Zs6a6VxMAAFcJmCbjrQGkLpSZ9oUamhmG5PcZZK5dIGoqLomy8CyQVOa6QYMG8vv92rBhQ9TyDRs2qEmTJgkfc9ddd+nSSy/V5ZdfLkk68sgjtXPnTl155ZW644475PPF/0Hn5+crPz8/mVUDACArRDU0I7gGkDIj5icjlBhlzLVzEmWuDbqFZ4GkLlXl5eWpc+fOmjVrlr0sEAho1qxZ6tatW8LH7Nq1Ky6A9vv9kkLNWgAAgM06zyVzDSAtrDHXZjhzTVm4s8wE30XtBS54eFZSmWtJGjp0qAYOHKhjjz1WXbp00aOPPqqdO3dq8ODBkqQBAwaoWbNmGj16tCTp7LPP1rhx43T00Uera9eu+v7773XXXXfp7LPPtoNsAAAQ5LM7mongGkDq7IZmYT4amjnMiPg/tIR4OiskHVz369dPmzZt0ogRI7R+/Xp16tRJM2bMsJucrV69OipTfeedd8owDN15551au3atGjZsqLPPPlv3339/+t4FAABZwgidbpG5BpAWdkOzgL2IqbjcITq4piw8GyQdXEvSkCFDNGTIkIS3zZkzJ/oFcnI0cuRIjRw5siovBQDAfsWe51oiuAaQOruhmTUVV0wgR+Y64xINjPVHXuTggodn8dcEAICLGFZDM8rCAaSVNRWXEV0WTpbUFfx+g27hWYDgGgAAF7FOc6PLwms7tj4APM7KXJvhzHVwKi7rdoJr54SDab8RsU+44OFZBNcAALiIj7JwAOlkl31Hl4Vb814TXDshfpv7fZSFZwOCawAAXCRcFh6Zua7r3AoB8LbQMcUKpsNl4dbthANOiYynfQZl4dmAvyYAAFzEzlybknZvCf6QT1k4gCqyp+IKZ659dKZ2VoLMdI6PfZINCK4BAHATK3MdKJd+WR5cVu8QB1cIgLdZU3GZ9k8+QxFTcREOOCecqfZRFp4V+GsCAMBFrPOr/PJdUtme4A91Wzi3QgC8LUHmOjj8hHmunWLaFzzC/D7y1dmA4BoAABex+sUWBHYGF/jzpZx8B9cIgKcZ0ZlryZDfMMhcu4AR2y3coCzc6/hrAgDARawkUmFgR/AbpuECkIpEY659zHPtqATVAtFl4RlcF6QVwTUAAC5inV8VlIcy1zQzA5CKmOBaCjY081EW7hjTjO8KnuNjP2QDgmsAAFzEKguvWf5bcEHhAQ6uDQDvi29oJkk+g7Jwp0WG0z66hWcF/poAAHARK4lUr2xj8Ju6Bzm3MgC8z5rn2gzNc20Yobn+7Ds4sFL7u/ht7jfoFp4NCK4BAHARI3RSVVgeGnNdVM/BtQHgeUZ0Z2prKq7w7YQDTgk3MJP8UZlreBV/TQAAuIh10ptnFge/ySlwbmUAeJ895trKXEePvyZLmnlmgm0eDK4t7BOvIrgGAMBFrHOuXLMk+A3BNYBUxHYLlyG/QXDtBrFTcYVvYJ94FcE1AAAuYuUucgOhzHUuwTWAVMQ0NIvNXJMldYAR8zW2oRm8iuAaAAAXMeLKwgudWxkA3hfKXPsiArfosnDCAacYZkVjrrng4VX8NQEA4CJWQ7Nck8w1gDQw4jPXvgS3I5Pit3nUPNfsE88iuAYAwEWs86v8wJ7gN3m1nFsZAN4XO+baMOQLNTeLvB2ZFL7QYfEZBvnqLMBfEwAALmKdXuWbVnBd5ODaAPC8uIZmMVNxEdI5KFwW7osaC88+8SqCawAAXMQ66S0wdwe/yavh3MoAyALBg4ovqlkWY66dZFYYPIf2C2XhnsVfEwAALmLEBdc1nVsZAN6XYJ5rH/Ncu0LkljfYD1mB4BoAABexTrAKrDHXuZSFA0iBEfVFhmLH9hLUZV78Ng9OkVbx7fAGgmsAAFzEOqXKVWnwmxy6hQNIgT0VV2TmmoZmbhA7r7VBWbjn8dcEAICLWJlrn1keXODPcXBtAHhffJ7aZ1AW7qREY67ZC9mB4BoAABexGpr5FQquDb9zKwPA+2Iy1zJiMqZkrh0T3bTdoFt4FuCvCQAAFzFig2sfmWsAKbAbmoV+lCFfZDUymevMq2CbG/u4He5HcA0AgIsEy8JN+a0sE8E1gFSEArWobuGhsvCKp4RCdTITLKPJXHYguAYAwEUMxUyT46MsHEAK7LLw4HHFkGQQXLtCZHm+EVuuD08iuAYAwEV8hqEcqyRcIrgGkCIrcx0Krg0jHAAw3tohFZWF0y3c6/iLAgDAReKmyaEsHEAqEmWuTStzDWdY+yIicx0VcBNcexXBNQAALmIoNnNNcA0gBXZDs8gO4aHgmsy1IxJOxWUQUmcD/qIAAHARn6FwMzOJqbgApMaILQuPrI4hnHNS/NanLNzrCK4BAHCT2OCaMdcAUhEK1MJl4YZ8odiNhmZOSZC5dmAtkH4E1wAAuIjPMMJzXBt+MhgAUhSduZYRHnPN8cVZ8d3C4XUE1wAAuIghKcee45qsNYAUxTY0M8Lfk7l2hlnBRQ26hXsfwTUAAC7i8xnyG6HMNc3MAKQqdsy1wvNckyt1VmQMTbfw7EBwDQCAiwQz11ZwnevougDIAjHdwg0jMowjiHNEojnQjJiO7vAkgmsAAFzEMCKDa8rCAaQoNrhWMMCWJJPY2hkVloXv/Xa4H8E1AAAuYhiGcq3g2k/mGkCqYrqFRzY0g6OiGprF3AJvIrgGAMBFDCncLZwx1wBSFdvQTEZEUEcQ54SEVeGGQVl4FiC4BgDARXyRmWuCawCpsua5NiIy1wbdwt0gNpimW7j3EVwDAOAihhGRuaYsHECqjMjT/XD2OnRjxlcHUnju8dgliX+CdxBcAwDgIj7DUC5TcQFIl4jg2pCpHJ+hxIXJcJJhEFJnA4JrAABchqm4AFQHn0z5fYYdABBiuw1l4V5HcA0AgItEl4WTuQaQoojMtd8woxtnEcQ5wkxUFh61K9gvXkVwDQCAi9DQDEBaRURtuT5rEQ3N3CFyKi66hWcDgmsAAFzEMCgLB5BGUZnr0CIamrmSvTeoKPAsgmsAAFzEZxgRwbXf2ZUBkAXCgVqOlbkmQ+oKlIVnH4JrAABcxFBE5pqpuACkKiJzHRtcUxbujIq2Oxc9vI/gGgAAFzEMQzkGZeEA0iQiuM4NjbWm6tgdYoNpGs15H8E1AAAuYhjB6XKCP/AxDSBFEYFajj3mOsgkiHNGgu1uRC1jv3gVn9oAALiIzzDkUyD4A8E1gFRFNjTjkOIKiYq/DRFSZwP+xAAAcJGoEyyySgBSlmAqLju84xjjpNiycJ9BWbjXEVwDAOAiwbLwQPgHAEhFgqm4fDQ0c1iisvC93w5vILgGAMBFfJFnWJSFA0hVxDHFKgs3DLpSO8m0EtQRmWuDgDor8KkNAIDL2JlrTrYApCoiuM61G5oFvyFz7ZBEm92MuOBB1ZJnEVwDAOAiPp/BmGsAaWWGqmByfFYAR+baDaL6g1MWnhUIrgEAcBFDols4gDQLBmt+I/Kn6O+QOYkqBmKbm8Gb+NQGAMBFfIbBiS+AtApnroM/2w3NqI5xVHRATVl4NiC4BgDARQwj4oSLzDWANPKHGpkRuzktroSAfZIl+NQGAMBFgvNcM9cpgPSJzVzbyx1YFyRGA/fsQHANAICLGIZB5hpAmgWPJXbm2g6ruYDnpOiAmrLwbMCnNgAALmIYEeMhHV4XANnBaqDlD/1s2McYgjgn2Mf2CsvC2S9eRXANAICL+CIz15xgAUiHUORmXbijaaI7RDY0o1t4diC4BgDARYJTcVmdfPmYBpC6cIY6uqEZ4ZxDEpV9m/u4HZ7ApzYAAC7iMwyJzDWAdDKix1zLJKx2g8gjvBEVUHPs9yqCawAA3MQIn1aRuQaQDmZMWXj4yEIQ5wTTjN/uPuoIsgKf2gAAuEiwoVlAEiWbANIltizcGnpCcO2kqHHWJt3CswHBNQAALhJsaBZC5hpAGlhVMH4amrlDguCZbuHZgU9tAABcxJDkM8hcA0inYLBmGNHBtclRxjUMg32RDQiuAQBwkajMNdkLAGlgdQsPlyHTNNFJpl1BYEYttVEW7lkE1wAAuIhhSIY15pqycADpYDc0s34MjbkmuHZIgrJw0S08G1TpU3vChAlq2bKlCgoK1LVrV82fP3+v99+yZYuuu+46NW3aVPn5+Wrbtq2mT59epRUGACCbGZHdwjnBApAGZuiU32qWGK5A5hjjFgYl+lkhJ9kHvPTSSxo6dKgmTZqkrl276tFHH1WfPn20dOlSNWrUKO7+JSUl+v3vf69GjRrp1VdfVbNmzfTjjz+qbt266Vh/AACyiiEj4iSLzDWA1NlTcVljrkMxNeGcMxJfOKUsPBskHVyPGzdOV1xxhQYPHixJmjRpkt59911NnjxZw4YNi7v/5MmT9euvv+qzzz5Tbm6uJKlly5aprTUAAFnKZ4TnO+XEF0B6RJeFwx0is9VGzC3wpqT+xkpKSrRw4UL17t07/AQ+n3r37q158+YlfMxbb72lbt266brrrlPjxo11xBFH6IEHHlB5eXmFr1NcXKxt27ZF/QMAYH9gGBGZa8ZcA0gDq3+DYYbKwsWYa2ftayoueFVSn9qbN29WeXm5GjduHLW8cePGWr9+fcLH/PDDD3r11VdVXl6u6dOn66677tLYsWN13333Vfg6o0ePVp06dex/LVq0SGY1AQDwLEORJ74AkA7WVFzBn3x0C3dUeOub8QslIm0Pq/ZL4oFAQI0aNdLTTz+tzp07q1+/frrjjjs0adKkCh8zfPhwbd261f63Zs2a6l5NAABcIdjQjKwSgPQx7bJwK3MdWk4Q55BE3cL3fju8Iakx1w0aNJDf79eGDRuilm/YsEFNmjRJ+JimTZsqNzdXfr/fXta+fXutX79eJSUlysvLi3tMfn6+8vPzk1k1AACygmEY4awSZeEA0sAqCw9PxWXd4MjqIAHDYGdkg6Q+tfPy8tS5c2fNmjXLXhYIBDRr1ix169Yt4WO6d++u77//XoFAwF62bNkyNW3aNGFgDQDA/o6GZgDSK1QWbh9VQscYEqSOiprZ2qRbeDZI+pL40KFD9fe//13Tpk3Tt99+q2uuuUY7d+60u4cPGDBAw4cPt+9/zTXX6Ndff9UNN9ygZcuW6d1339UDDzyg6667Ln3vAgCALGJNl0PmGkA6hKfisua5Zsy1kxIP+TEq+B5ekvRUXP369dOmTZs0YsQIrV+/Xp06ddKMGTPsJmerV6+Wzxc+GWjRooXef/993XTTTTrqqKPUrFkz3XDDDbr99tvT9y4AAMgi1qcoY64BpINpZ66DwvNcc4xxUmRDMx9l4Vkh6eBakoYMGaIhQ4YkvG3OnDlxy7p166bPP/+8Ki8FAMB+Jzwcksw1gHSwxlwTwLlB4r1AWXg24FMbAACXsUo3OQ0GkA7WscRvxM5zDSdFZq7pFp4dCK4BAHCZcOaaEywAqbO6hdtl4dZyjjEOSbTdudSRDQiuAQBwGT9TcQFIIzOmLDwc2hFcOyl661MWng341AYAwGWs+U7JYwBIB2vKLV/oa7gsnCDOLYzIfUFw7VkE1wAAuEx4nms+pgGkLpy5tsZcw0lmguDZ4HJqVuBTGwAAlyFzDSC9rHmurZ85xjjKGvkT1dCMvZENCK4BAHAZe55rxlwDSAOr/Nsec23PqUwO220C7BNP41MbAACX8ZFVApBG1oU669hiX8AjkHNUZLbaxzjrrEBwDQCAy4RPuDjZApA664jiszPWXLpzUqIx1zJpMpcNCK4BAHAZn0FDMwDpEzsVV3g5nBQZRpO4zg58agMA4DKUhQNIJztzHfrKVFxOo1t4tspxegXSJRAIqKSkxOnVADIuLy9PPh/XyYBsYp120dAMQDpYmWvDiJmKi3SpI8xEcTRl4VkhK4LrkpISrVy5UoFAwOlVATLO5/OpVatWysvLc3pVAKSJnbk2OckCkLq4buHWco4xDouYisuwlrBPvMzzwbVpmlq3bp38fr9atGhBBg/7lUAgoJ9//lnr1q3TQQcdJIMr0EBWMAxTMitoegMASbKOJeGycPsWB9YGXNPIXp4PrsvKyrRr1y4deOCBKioqcnp1gIxr2LChfv75Z5WVlSk3N9fp1QGQBmSuAaRTfEMzSpDdIGrrm/TayAaeT/OWl5dLEiWx2G9Zv/vW3wIA77ObDXHeCyANYqfiMqwpuaiOcUSiC6eUhWcHzwfXFsphsb/idx/IPuFu4VnzMQ3AQXZDs9CxxWdayzmHcFJkh3C6hWcHPrUBAHAZu9kQJ74A0iA8FVdM5jph22pUu4SHdnNvN8IjCK6z3NSpU1W3bl2nV8NxPXv21I033pjSc8yZM0eGYWjLli0V3id2e48aNUqdOnWyfx40aJD69u2b0noAyH6+0HQ5NDQDkA7WtH6+mOwoxxhn2Rc5JBmhoJrLHd5GcO2QQYMGyTAMGYah3NxctWrVSrfddpv27NmT1tfp16+fli1bltbnrKz7779fJ5xwgoqKiiod4Pfs2dPeLgUFBerQoYOefPLJ6l3RNNrX9n7sscc0depU++d0BP0Aso/14UxDMwDpYFXBxGasqY5xRqLtTll4diC4dtBpp52mdevW6YcfftD48eP11FNPaeTIkWl9jcLCQjVq1Citz1lZJSUluuCCC3TNNdck9bgrrrhC69at0zfffKMLL7xQ1113nV544YUKX8NN9rW969SpQyUBgH0KNzTjxBdA6sLzXAfRPMt9DDq4ZwWCawfl5+erSZMmatGihfr27avevXtr5syZ9u2BQECjR49Wq1atVFhYqI4dO+rVV1+Neo633npLhx56qAoKCtSrVy9NmzYtqnS5ojLlyZMn66CDDlLNmjV17bXXqry8XA899JCaNGmiRo0a6f777496nS1btujyyy9Xw4YNVbt2bf3ud7/Tl19+udf3d/fdd+umm27SkUcemdR2KSoqUpMmTXTIIYdo1KhROvTQQ/XWW29JCmZ6hwwZohtvvFENGjRQnz59JEkff/yxunTpovz8fDVt2lTDhg1TWVlZ1POWlZVpyJAhqlOnjho0aKC77rpLZsRYo+eee07HHnusatWqpSZNmujiiy/Wxo0b49Zv7ty5Ouqoo1RQUKDjjz9eS5YssW/bVxl+ZFn4oEGD9PHHH+uxxx6zs/UrV65UmzZt9Mgjj0Q9bvHixTIMQ99//31S2xKANzEVF4B0CliZawVCX8mSuoERuRu44JEVsi64Nk1Tu0rKHPlnptAUYsmSJfrss8+iphQbPXq0nn32WU2aNElff/21brrpJl1yySX6+OOPJUkrV67U+eefr759++rLL7/UVVddpTvuuGOfr7VixQq99957mjFjhl544QU988wzOvPMM/XTTz/p448/1pgxY3TnnXfqP//5j/2YCy64QBs3btR7772nhQsX6phjjtEpp5yiX3/9tcrvubIKCwujMtTTpk1TXl6e5s6dq0mTJmnt2rU644wzdNxxx+nLL7/UxIkT9cwzz+i+++6Lep5p06YpJydH8+fP12OPPaZx48bpH//4h317aWmp7r33Xn355Zd68803tWrVKg0aNChufW699VaNHTtW//3vf9WwYUOdffbZKi0tTfp9PfbYY+rWrZudqV+3bp0OOugg/fnPf9aUKVOi7jtlyhSdfPLJatOmTdKvA8B7wg3NACB1cZlrezmckLAsnJ2RFXKcXoF0211arg4j3nfktb+5p4+K8iq/Sd955x3VrFlTZWVlKi4uls/n0xNPPCFJKi4u1gMPPKAPP/xQ3bp1kyQdcsgh+vTTT/XUU0+pR48eeuqpp9SuXTs9/PDDkqR27dppyZIlcVnnWIFAQJMnT1atWrXUoUMH9erVS0uXLtX06dPl8/nUrl07jRkzRrNnz1bXrl316aefav78+dq4caPy8/MlSY888ojefPNNvfrqq7ryyiursrn2qby8XC+88IL+97//Rb3GoYceqoceesj++Y477lCLFi30xBNPyDAMHXbYYfr55591++23a8SIEfL5gh8lLVq00Pjx42UYhtq1a6evvvpK48eP1xVXXCFJ+vOf/2w/5yGHHKK//e1vOu6447Rjxw7VrFnTvm3kyJH6/e9/LykYsDdv3lxvvPGGLrzwwqTeX506dZSXl2dn6i2DBg3SiBEjNH/+fHXp0kWlpaX617/+FZfNBpC9rOyS1YQIAFIRDq4D9pLI5XBGdEMza5/Ay7IuuPaSXr16aeLEidq5c6fGjx+vnJwcnXfeeZKk77//Xrt27bKDOEtJSYmOPvpoSdLSpUt13HHHRd3epUuXfb5uy5YtVatWLfvnxo0by+/320Gotcwqif7yyy+1Y8cO1a9fP+p5du/erRUrViTxjivnySef1D/+8Q+VlJTI7/frpptuihq33blz56j7f/vtt+rWrVvUfM/du3fXjh079NNPP+mggw6SJB1//PFR9+nWrZvGjh2r8vJy+f1+LVy4UKNGjdKXX36p3377TYFA8ANo9erV6tChQ9TjLPXq1VO7du307bffpu39H3jggTrzzDM1efJkdenSRW+//baKi4t1wQUXpO01ALibL3SoCuz9bgBQKXZwHQrmwmdDBNdOSBRAh09R2SdelnXBdWGuX9/c08ex105GjRo17DLfyZMnq2PHjnrmmWd02WWXaceOHZKkd999V82aNYt6nJU9rqrc3Nyon62O5bHLrOByx44datq0qebMmRP3XNXRnOtPf/qT7rjjDhUWFqpp06ZRQb8U3G7ptnPnTvXp00d9+vTR888/r4YNG2r16tXq06ePI03TLr/8cl166aUaP368pkyZon79+qmoqCjj6wHAGYYdVpO5BpA6u1t46GeypO4QNfadnZEVsi64NgwjqdJst/D5fPrrX/+qoUOH6uKLL1aHDh2Un5+v1atXq0ePHgkf065dO02fPj1q2X//+9+0r9sxxxyj9evXKycnRy1btkz788eqU6dOUmOL27dvr9dee02madqZ6blz56pWrVpq3ry5fb/IMeSS9Pnnn+vQQw+V3+/Xd999p19++UUPPvigWrRoIUlasGBBwtf7/PPP7Wz4b7/9pmXLlql9+/ZJvUdLXl6eysvL45afccYZqlGjhiZOnKgZM2bok08+qdLzA/AmK6QO0NAMQBoEQkeVcEMzOClxOT6l+tmAS+IucsEFF8jv92vChAmqVauWbrnlFt10002aNm2aVqxYoS+++EKPP/64pk2bJkm66qqr9N133+n222/XsmXL9PLLL9tzKBtpnL6ld+/e6tatm/r27asPPvhAq1at0meffaY77rijwgBUCpZTL168WKtXr1Z5ebkWL16sxYsX21n5dLn22mu1Zs0aXX/99fruu+/073//WyNHjtTQoUOjst6rV6/W0KFDtXTpUr3wwgt6/PHHdcMNN0iSDjroIOXl5enxxx/XDz/8oLfeekv33ntvwte75557NGvWLC1ZskSDBg1SgwYN7A7gyWrZsqX+85//aNWqVdq8ebNdLeD3+zVo0CANHz5chx56aFQpOoDsx1RcANKp4oZmHGOcFLn1w9OjwcsIrl0kJydHQ4YM0UMPPaSdO3fq3nvv1V133aXRo0erffv2Ou200/Tuu++qVatWkqRWrVrp1Vdf1euvv66jjjpKEydOtLuFp1o6HskwDE2fPl0nn3yyBg8erLZt26p///768ccf1bhx4wofN2LECB199NEaOXKkduzYoaOPPlpHH330XgPyqmjWrJmmT5+u+fPnq2PHjrr66qt12WWX6c4774y634ABA7R792516dJF1113nW644Qa7UVrDhg01depUvfLKK+rQoYMefPDBChuIPfjgg7rhhhvUuXNnrV+/Xm+//XZUl/dk3HLLLfL7/erQoYNdim657LLLVFJSosGDB1fpuQF4F/OdAkincFl4TEMzqmMcQrfwbGWYqcwflSHbtm1TnTp1tHXrVtWuXTvqtj179mjlypVq1aqVCgoKHFpD97j//vs1adIkrVmzxulVQYr+7//+T6eccorWrFmz14sY/A0A2WfxfSepU9n/9M0J49Xh1D/v+wEAsBf/e+wCHfXbB5p98A3qNfgezX31b+q+5C4tKeqiI26b6fTq7Xf++8K9Om7pI5pfq7e63PyaJGnFt4vU+qWe2qYaqj3qZ4fXEJa9xaGJeG9wMqI8+eSTOu6441S/fn3NnTtXDz/8sIYMGeL0aiEFxcXF2rRpk0aNGqULLrhgr4E1gOzkI3MNII0CcWXhNDRzAyPBHuC4720E1x63fPly3Xffffr111910EEH6eabb9bw4cOdXi2k4IUXXtBll12mTp066dlnn3V6dQA4IDweEgBSFy4LD03FZR9kCOSckKhuOFGgDe8huPa48ePHa/z48U6vBtJo0KBBGjRokNOrAcBBvtC4SJPWKADSwA6urYG9ptU00ak1ghQdUHNRNTvwqQ0AgMtYJ1kBygMBpIFdFh4KqukW7qy9zwTBPvEygmsAAFzGMKzMNSdZAFIXnoorNM+1QV8HN4iaiotx8FmB4BoAAJexM9fUbAJIg3BZePBnw/2TBWW1RFOghUvEOe57GcE1AAAuY2cw9lo6CACVEzvPtXVoIcZ2WqJu4fAygmsAAFzGmoqLsywA6RCwg2sLZeFOSrTd6RaeHQiuAQBwGeski4ZmANIhEDvm2q5A5hjjNlzw8DaC6yw3depU1a1b1+nVcFzLli316KOPpvQcldmWo0aNUqdOneyfBw0apL59+9o/9+zZUzfeeGNK6wEg+1EWDiCd4ue5Dh1jSJY6KtERnl3ibQTXDhk0aJAMw5BhGMrNzVWrVq102223ac+ePWl9nX79+mnZsmVpfc7KWLVqlS677DK1atVKhYWFat26tUaOHKmSkpK9Pq5ly5b2dqlRo4aOOeYYvfLKKxla69TdcsstmjVrVoW3v/7667r33nvtn9MR9APIPnbmmoZmANLAOpb4YkI3AjlnJNrulIVnhxynV2B/dtppp2nKlCkqLS3VwoULNXDgQBmGoTFjxqTtNQoLC1VYWJi256us7777ToFAQE899ZTatGmjJUuW6IorrtDOnTv1yCOP7PWx99xzj6644gpt27ZNY8eOVb9+/dSsWTOdcMIJcfctKSlRXl5edb2NpNWsWVM1a9as8PZ69eplcG0AeJWPsnAAaWRVwdgBnMmYazeIDKgNxsFnBTLXDsrPz1eTJk3UokUL9e3bV71799bMmTPt2wOBgEaPHm1nfzt27KhXX3016jneeustHXrooSooKFCvXr00bdo0GYahLVu2SIovZbbKlidPnqyDDjpINWvW1LXXXqvy8nI99NBDatKkiRo1aqT7778/6nW2bNmiyy+/XA0bNlTt2rX1u9/9Tl9++WWF7826cHDqqafqkEMO0R/+8Afdcsstev311/e5XWrVqqUmTZqobdu2mjBhggoLC/X2229LCmZ67733Xg0YMEC1a9fWlVdeKUl67bXXdPjhhys/P18tW7bU2LFj4553+/btuuiii1SjRg01a9ZMEyZMiLp93LhxOvLII1WjRg21aNFC1157rXbs2BH3PG+++aa9zfv06aM1a9bEbd+KRJaF9+zZUz/++KNuuukmO1u/c+dO1a5dO24/v/nmm6pRo4a2b9++z+0HwPs4yQKQToG4snDrFo4xTtjbsZ3jvrdlX3BtmlLJTmf+pTBwZcmSJfrss8+isrCjR4/Ws88+q0mTJunrr7/WTTfdpEsuuUQff/yxJGnlypU6//zz1bdvX3355Ze66qqrdMcdd+zztVasWKH33ntPM2bM0AsvvKBnnnlGZ555pn766Sd9/PHHGjNmjO6880795z//sR9zwQUXaOPGjXrvvfe0cOFCHXPMMTrllFP066+/Vvo9bt26NenMbU5OjnJzc6PKyR955BF17NhRixYt0l133aWFCxfqwgsvVP/+/fXVV19p1KhRuuuuuzR16tSo53r44Yftxw0bNkw33HBD1MUMn8+nv/3tb/r66681bdo0ffTRR7rtttuinmPXrl26//779eyzz2ru3LnasmWL+vfvn9R7srz++utq3ry57rnnHq1bt07r1q1TjRo11L9/f02ZMiXqvlOmTNH555+vWrVqVem1AHiLdWrFSRaAdLDmVbaDaztzDSdFZ65D2Cmeln1l4aW7pAcOdOa1//qzlFej0nd/5513VLNmTZWVlam4uFg+n09PPPGEJKm4uFgPPPCAPvzwQ3Xr1k2SdMghh+jTTz/VU089pR49euipp55Su3bt9PDDD0uS2rVrpyVLlsRlnWMFAgFNnjxZtWrVUocOHdSrVy8tXbpU06dPl8/nU7t27TRmzBjNnj1bXbt21aeffqr58+dr48aNys/PlxQMcN988029+uqrdvZ4b77//ns9/vjj+ywJj1RSUqKxY8dq69at+t3vfmcv/93vfqebb77Z/vlPf/qTTjnlFN11112SpLZt2+qbb77Rww8/rEGDBtn36969u4YNG2bfZ+7cuRo/frx+//vfS1JUo7GWLVvqvvvu09VXX60nn3zSXl5aWqonnnhCXbt2lSRNmzZN7du31/z589WlS5dKvzcpWCLu9/vtTL3l8ssv1wknnKB169apadOm2rhxo6ZPn64PP/wwqecH4F3WXLQE1wDSoaKGZnDG3qbiYs94W/Zlrj2kV69eWrx4sf7zn/9o4MCBGjx4sM477zxJwWB0165d+v3vf2+P461Zs6aeffZZrVixQpK0dOlSHXfccVHPWZkAr2XLllEZ0MaNG6tDhw7y+XxRyzZu3ChJ+vLLL7Vjxw7Vr18/al1Wrlxpr8verF27VqeddpouuOACXXHFFfu8/+23366aNWuqqKhIY8aM0YMPPqgzzzzTvv3YY4+Nuv+3336r7t27Ry3r3r27li9frvLycnuZdZEi8udvv/3W/vnDDz/UKaecombNmqlWrVq69NJL9csvv2jXrl32fXJycqK2+WGHHaa6detGPU+qunTposMPP1zTpk2TJP3zn//UwQcfrJNPPjltrwHA3azTroCjawEgW4Sn4goF16HlXMBzVuJu4ewTL8u+zHVuUTCD7NRrJ6FGjRpq06aNJGny5Mnq2LGjnnnmGV122WX2WN93331XzZo1i3qclT2u8mrm5kb9bHUsj10WCARP63bs2KGmTZtqzpw5cc+1r6mpfv75Z/Xq1UsnnHCCnn766Uqt36233qpBgwapZs2aaty4sYyYqWhq1Kh8dUBlrVq1SmeddZauueYa3X///apXr54+/fRTXXbZZSopKVFRUXL7NlWXX365JkyYoGHDhmnKlCkaPHhw3HYAkL3szDXdwgGkQWzmOnY5nEe38OyQfcG1YSRVmu0WPp9Pf/3rXzV06FBdfPHF6tChg/Lz87V69Wr16NEj4WPatWun6dOnRy3773//m/Z1O+aYY7R+/Xrl5OSoZcuWlX7c2rVr1atXL3Xu3FlTpkyJyozvTYMGDeyLDpXRvn17zZ07N2rZ3Llz1bZtW/n9fnvZ559/HnWfzz//XO3bt5ckLVy4UIFAQGPHjrXX8+WXX457rbKyMi1YsMCuEFi6dKm2bNliP0+y8vLyorLrlksuuUS33Xab/va3v+mbb77RwIEDq/T8ALyJrBKAdCpnzLWr7G27c9z3NsrCXeSCCy6Q3+/XhAkTVKtWLd1yyy266aabNG3aNK1YsUJffPGFHn/8cbtc+KqrrtJ3332n22+/XcuWLdPLL79sN/FKZ5azd+/e6tatm/r27asPPvhAq1at0meffaY77rhDCxYsSPiYtWvXqmfPnjrooIP0yCOPaNOmTVq/fr3Wr1+ftvWy3HzzzZo1a5buvfdeLVu2TNOmTdMTTzyhW265Jep+c+fO1UMPPaRly5ZpwoQJeuWVV3TDDTdIktq0aaPS0lI9/vjj+uGHH/Tcc89p0qRJca+Vm5ur66+/Xv/5z3+0cOFCDRo0SMcff3zS460tLVu21CeffKK1a9dq8+bN9vIDDjhA5557rm699Vadeuqpat68eZWeH4A3+cxg5jpAxQqANLCn4jJju4XDSWSrsw/BtYvk5ORoyJAheuihh7Rz507de++9uuuuuzR69Gi1b99ep512mt599121atVKktSqVSu9+uqrev3113XUUUdp4sSJdrfwVEvHIxmGoenTp+vkk0/W4MGD1bZtW/Xv318//vijGjdunPAxM2fO1Pfff69Zs2apefPmatq0qf0v3Y455hi9/PLLevHFF3XEEUdoxIgRuueee6KamUnBIHzBggU6+uijdd9992ncuHHq06ePJKljx44aN26cxowZoyOOOELPP/+8Ro8eHfdaRUVFuv3223XxxRere/fuqlmzpl566aUqr/s999yjVatWqXXr1mrYsGHUbVZJ+p///OcqPz8ArwrNc01ZOIA0sLuFG8xz7QY0NMtehmmmMH9Uhmzbtk116tTR1q1bVbt27ajb9uzZo5UrV6pVq1YqKChwaA3d4/7779ekSZOi5l6GNz333HO66aab9PPPP0dN0RaLvwEg+2y8r70alf2sD45/Tqee9genVweAx709/lqdvfV5LT/4Ih06eJK+enW0jlzyoOYW9lT32//t9Ortdz751xidvOwB/a/WSTrq5nckSRu+/UyNXzpdP5sNdODd+24YjMzYWxyaSPaNud7PPPnkkzruuONUv359zZ07Vw8//LCGDBni9GohBbt27dK6dev04IMP6qqrrtprYA0gO5HBAJBO1swDsVNxkbl2xt7HXMPLKAv3uOXLl+ucc85Rhw4ddO+99+rmm2/WqFGjnF4tpOChhx7SYYcdpiZNmmj48OFOrw4ABzEVF4B0CJjBU/5wQ7PgcgI5Z0Vf2uCiajYgc+1x48eP1/jx451eDaTRqFGjuEAC7Od8TMUFII3shmYxmWsiOWckHnMd/x28h8w1AAAuY51aBTjJApAGgVAQHTfPNW3DHZVo61Oq720E1wAAuIxhZa45yQKQBtaFuvh5rjnGOCt8sYNpubJD1gTXHmh6DlQLfveBbMRUXADSx4wJrsM3cIxxQqIzNxpZZgfPj7nOzc2VYRjatGmTGjZsKIPyFuxHTNPUpk2bZBiGcnNznV4dAGniC100C/CZBiANyq15rkPHlnAgRyjnpOiLHVQTZAPPB9d+v1/NmzfXTz/9pFWrVjm9OkDGGYah5s2by+/3O70qANImdJLFeS+ANAhnrgOhrwRyTkrc0Ix9kQ08H1xLUs2aNXXooYeqtLTU6VUBMi43N5fAGsgy1ikWJ74A0iF2zLWFY4yzjKjvueCRDbIiuJaCGWwCDABANrCyS3QLB5AOVkgdH1zDGYn7hIf/h1dlTUMzAACyhXUCTHANIB0CZvCU3+4WTpbUFSIvdth7guja0wiuAQBwGTu4ppMvgDQoV+KGZnDK3jLXHPe9jOAaAACXCWeuASB1VnNEI+aoQojtjMRd2ikLzwYE1wAAuIyVXTL5mAaQBrENzWia6A7RZeHBfcE+8TY+tQEAcBmDDAaANDLjgmtKkJ1kJhzywxE/GxBcAwDgOoy5BpA+5RG56qivJgGds8LH+HDFEsd9LyO4BgDAZegWDiCdrAt1hhkcc21nrg2OMU4wE3wXucTkoodnEVwDAOAy4W7hDq8IgKxQ4ZhrqmNcg+sc2YHgGgAAl7GySwE+pgGkgZ25jllOCbIzEm73iLJwEtfexac2AAAuxfkVgHSwjiXWhTumfXKH6G7hQSazkHsawTUAAC7jC81FS0MzAOlQbkY3NCN8cx/DYJ9kA4JrAADcxiSrBCB97Km47Mx19HJkVsJje8Rxn4Zm3lWl4HrChAlq2bKlCgoK1LVrV82fP79Sj3vxxRdlGIb69u1blZcFAGC/QLdwAOlkhdRGzFdCOGdFVxAYCZfCW5IOrl966SUNHTpUI0eO1BdffKGOHTuqT58+2rhx414ft2rVKt1yyy066aSTqryyAADsD8LdwikwA5A6M6YsXMyp7DqE1Nkh6U/tcePG6YorrtDgwYPVoUMHTZo0SUVFRZo8eXKFjykvL9ef/vQn3X333TrkkENSWmEAALJdOHPNyRaA1FnT+sXmRqk+doYZV0MQXGrdxn7xrqSC65KSEi1cuFC9e/cOP4HPp969e2vevHkVPu6ee+5Ro0aNdNlll1XqdYqLi7Vt27aofwAA7BcizqpMWqMASIPwSGsamrmB3b09cj8Y4dtM9o9nJfWpvXnzZpWXl6tx48ZRyxs3bqz169cnfMynn36qZ555Rn//+98r/TqjR49WnTp17H8tWrRIZjUBAPCuiOC6PLCX+wFAkoyYb+jr4B4G6eqsUK2XxLdv365LL71Uf//739WgQYNKP2748OHaunWr/W/NmjXVuJYAALhIRDffgMGJL4DUldtl4daYa+sWjjGOSNwuPPQ/ZeFelpPMnRs0aCC/368NGzZELd+wYYOaNGkSd/8VK1Zo1apVOvvss+1lgUDwpCEnJ0dLly5V69at4x6Xn5+v/Pz8ZFYNAIAsEVEWzjzXANIgHKxFl4VTfuysyLJwI3Shgz3ibUllrvPy8tS5c2fNmjXLXhYIBDRr1ix169Yt7v6HHXaYvvrqKy1evNj+94c//EG9evXS4sWLKfcGACBWZOaasywAaWDPc22HbnQLdxvGwWeHpDLXkjR06FANHDhQxx57rLp06aJHH31UO3fu1ODBgyVJAwYMULNmzTR69GgVFBToiCOOiHp83bp1JSluOQAAUHRDM4OGZgBSF3uhLjwxF8G1E8xEQ35MysKzQdLBdb9+/bRp0yaNGDFC69evV6dOnTRjxgy7ydnq1avl83EyAABA1UQ0NOMMC0AaxDcuMyP+h1MSdws3KNf3sKSDa0kaMmSIhgwZkvC2OXPm7PWxU6dOrcpLAgCwf4gqC+diNYDUBczosnA7c01fB0ckum7KnsgOfGoDAOAmEWddzMQFIB3seZWt4wtVMQ7bW1k4u8fLCK4BAHCTyMw1uQwAaVBxt3A4IVHZtxExCTn7xbsIrgEAcJWIzDUlmwDSwLpkZwVwRsT4XrgE6eqsQHANAICbRGSuOdcCkA6B6Bm4xFRczkq83SO7hXPw9yqCawAA3CRqzDUnvgBSF3HJLuYLQZyTIgvAw9UElOt7GcE1AAAuVc4ZFoA0MOO6hZO5dh2O91mB4BoAADeJamjGxzSA1AViysHtEmT6OjgicRwdWRaeybVBOvGpDQCAmzAVF4A0s+e5jgnaiOGcFVUWHvpqymDHeBjBNQAAbhLKXAdMTrAApEe59U3o+MJUXM5KlJlmAq7sQHANAICrhE96OdUCkA7loVN+Ixxmw0lGgnJ8M/LYz9HfqwiuAQBwk1BmyZShAAPvAKRBuRk65Q9EZ64DjLl2hHVkj9r6EXOPc+j3LoJrAADcJHRWFeAEC0CalIaCa8MMZq7DQR3BtVtQFp4dCK4BAHCVcMdYMtcA0sEuCw8F15ElyHBAog1vzz1OmO1lBNcAALhJRFk4J1gA0qHMKv8OWGOuCa7dIKpbeEQRgcmFVc8iuAYAwE3McOaaEywA6VBWQVm4SVm4a3A5NTsQXAMA4CbWVFyMuQaQBqZpqlz+0A8xmWuOMY5IeFEj8sJqhtcH6UNwDQCAq0Rmrh1eFQCeFzAjxlwHoqfiMhNNCYUMij/ImybHfi8juAYAwE1MGpoBSB/TNBWwTvmtsnAamjkq8TzW7I1sQHANAICbUBoIII0iM9fWsBPKwl0o4oJH4uAbXkBwDQCAm9jdwqVAgBMsAKkJmKbKrFP+AA3N3MAMdW9PtPVNGSSxPYzgGgAAVwmeVQXkUzlpJQBpELDnuS6LWk5wDaQXwTUAAG4SURpYVk5wDSA1AdMMl4Uzz7V7RZWFw6sIrgEAcBN7Ki6fSssD+7gzAOydmWDMNR0dnGVVDCTaD8wU4W0E1wAAuEo4e1HOmGsAKQqYpsrN0DzXMZlrDjFuws7IBgTXAAC4id3N11ApZ74AUmQqInMdCI65pqGZsxIe2aNmiuDY71UE1wAAuIlpNTQzVEZZOIAUmYHIhmbl0bcRXDvCjLi8kfB2YmvPIrgGAMBN7Km4DMrCAaQs2NAsIogOBGSYNDRzVqItz97IBgTXAAC4SjhzTUMzAKkKloX7wwsCZaJbuDtE1Q1ElYXDqwiuAQBwk4gTLDLXAFIVNRWXJJnlsoNrk7JwJ4S3e0Xdwjn2exXBNQAAbhIRXJcyzzWAFJlmzNhqc18jfpE5kRc32BvZgOAaAABXCWeUygKUhQNIjWnG9p42RVm4C0WMgydx7V0E1wAAuInd0Ix5rgGkLpAgcy0amjnKNIL7I9Hoajq4exvBNQAAbmJPxeWjLBxAykyZMQFbRFk4Y65dhON9NiC4BgDATSIy18xzDSBViQtgwr0dkHkJG5ZFdgsnzvYsgmsAAFwlnLkuoywcQIriAjkamrnA3srCg9UG8CaCawAA3CTiRDhA+gJAiuK6hUcEbhxinJF4s7MzsgHBNQAAbhIqCydzDSAdEk3FRbdwF6IsPCsQXAMA4CrR07EECLABpCCQYCouw26cCCfsfax7omJxeAXBNQAAbmI3NAuefJWTwgCQgmBwHZO5NqzbaGjmHhzrswHBNQAAbmJnlELBNZlrACkIFoFHB9GGfdGO4No1zMiqJY77XkVwDQCAm5hWoWbwpJdx1wBSYcaWhUcEbnSldoZ9aSNBEG1SFu5pBNcAALgKmWsA6ZOoW7gVvlEW7rCozc+xPhsQXAMA4CYRHWMlgmsAqQkeQiroFs7hxREJtzvdwrMCwTUAAG4SM+a6LEA/XwBVF4iL1MJ57HLGXDsqcVl4+H94D8E1AACuEt1oiMw1gFRY8VsgpjQ8fDvHmExLvMXZD9mA4BoAADexGpoZBNcAUheXuTZjRmBziHFOgh1BWbi3EVwDAOAm9glW8COa4BpAOoRDajM6kHNulfZb9r6ooCycfeJdBNcAALiJnbkOfiktZ8w1gKoLmDGjeM1wSG3KSDAmG5nCiPfsQ3ANAICrBE90DSP4Eb27hOAaQNWFY+eIzLVhf0cJsltQFp4VCK4BAHCTUOba8AU/oneVlDm5NgA8LhAzvV9w4msz7nZk0t62uSGTwnDPIrgGAMBNzOjM9a6ScifXBoDHWW0bIvqDR+SwKUx2QuLtTkCdDQiuAQBwleAJli+Uud5J5hpASuIz14y5dlbCTR4xNp5d4l0E1wAAuEmoLNwXmoprVzGZawBVF0g05jriOwI550T1ajeDx/py+dgnHkZwDQCAm1hl4T6/JMZcA0iNGVsWbkZP9kQcl3kJy8ID4eAa3sXeAwDATewx18GTrz1ldAsHUHVxDc0iZlKmLNxFQpnrgOmjoZmHEVwDAOAqVuY6+BG9p5SycABVFw6eI8ZcRwTUxNZOMGO+KipzzT7xLoJrAADcxB5zbQXXZK4BpCBhoBY5pzKRXKbtrSw8QHjmaew9AADcxCRzDSB9wlNxRU7AFfzeNA2ypG4RKgsvk9/hFUEqCK4BAHCV4Jmun+AaQBqYdpbaWhDd0Iwx15lnbXKDsvCsQ3ANAICbhMrCc3OCH9Hrt+1xcm0AeFz8VFyKmOeabuHOMGK+KtzQjPDM09h7AAC4SShlkZ+bK0lav5XgGkDVWWOqTSPc0Ewm3cKdlHCLR2auueThWQTXAAC4SShzneMPfkRv38M81wCqLj52DuerAzJIXTsoUVl4gLJwTyO4BgDAVYJnVTn+YFOb7XtKnVwZAB4XN8+1adoX8Uz5IsrGkSkJu4XbDc18XO/wMIJrAADcJCZzvaO4jKlyAFSZdfiI7BZuLQzIoATZLSLKwuFd7D0AANwkdNKbk5MjKdiMaBul4QCqKDymOiJzbZcgG2SuHRC+oBGx8a2GZqaPC6oeRnANAICbhDLXfp+hxrXzJUnfb9zu5BoB8LD4MC5cFh6c9olALtMSloUHghdRyykL9zSCawAAXMnQgXULJUmbd5Q4vC4AvMpMlLm2x1wbNM9yi0BwnzAVl7ex9wAAcBPrTNfwqXZBcDqubbtpagagaqyyb3sqLskOrulM7RDrMJ+gLLyMfeJpBNcAALhJ6KRXhqHahaHgmjHXAKoo4VRcdnBNQzPXiGpoxj7xKoJrAABcJTJzHWxqRuYaQFUlbGgWEVzT0CzzEm7y0JhrysK9jb0HAICbWJlrGapllYUz1zWAKgo3NIuciqs89B0NzZwU1dbMDGeu2SXeRXANAICbRI65LgxmrrdTFg6giuKCZzM8z3W5fGSuHZA4c21Nj0a3cC8juAYAwE0ix1zT0AxAisy4hmbRZeGM73WJiOnR4F1V2nsTJkxQy5YtVVBQoK5du2r+/PkV3vfvf/+7TjrpJB1wwAE64IAD1Lt3773eHwCA/Vt4fGS4oRnBNYCq2duYa5Mx186KrCoIfc/0aN6WdHD90ksvaejQoRo5cqS++OILdezYUX369NHGjRsT3n/OnDm66KKLNHv2bM2bN08tWrTQqaeeqrVr16a88gAAZB0zUUMzysIBVI09FZcSZK5Nxvc6wYwebW0vtW5jHLx3JR1cjxs3TldccYUGDx6sDh06aNKkSSoqKtLkyZMT3v/555/Xtddeq06dOumwww7TP/7xDwUCAc2aNSvllQcAIOsknIqLzDWAqkk85jqyWziBnCvYmWt4WVLBdUlJiRYuXKjevXuHn8DnU+/evTVv3rxKPceuXbtUWlqqevXqVXif4uJibdu2LeofAAD7BytzzZhrAKkz95K5pjO1M+wCpahQOiJznflVQpokFVxv3rxZ5eXlaty4cdTyxo0ba/369ZV6jttvv10HHnhgVIAea/To0apTp479r0WLFsmsJgAA3hUxFZfdLby4TAEGRgKoAjPigl1ogd2Z2iRz7YiEZeER+4Fd4l0ZbUf34IMP6sUXX9Qbb7yhgoKCCu83fPhwbd261f63Zs2aDK4lAAAOsnsP+ezMtWlKO0oYdw0gefFjrmVHb4GEY3/hjHDmGt6Vk8ydGzRoIL/frw0bNkQt37Bhg5o0abLXxz7yyCN68MEH9eGHH+qoo47a633z8/OVn5+fzKoBAJAdIsZcF+T6lZfjU0lZQNt2l9rBNgBUlpWZDodskWOuKQt3VoJu4aYRrjaA5ySVuc7Ly1Pnzp2jmpFZzcm6detW4eMeeugh3XvvvZoxY4aOPfbYqq8tAABZL3raHCug3r6HzDWA5MWNuaahmePCg38iRVQTsEs8K6nMtSQNHTpUAwcO1LHHHqsuXbro0Ucf1c6dOzV48GBJ0oABA9SsWTONHj1akjRmzBiNGDFC//rXv9SyZUt7bHbNmjVVs2bNNL4VAACyQMRUXJJUuzBHm3cU09QMQJXET+sUO881kVymRTWXsxdSFp4Nkg6u+/Xrp02bNmnEiBFav369OnXqpBkzZthNzlavXi2fL5wQnzhxokpKSnT++edHPc/IkSM1atSo1NYeAIBsE1EWLkm1yFwDSIEdvhnxmety+UiSOsAKoA27gWVwqfU/+8S7kg6uJWnIkCEaMmRIwtvmzJkT9fOqVauq8hIAAOynojPX+TnBr8VlgYoeAAAVsmYaiJ6KK9gtnDHXzjANKxGZOHPNPvGujHYLBwAA+2BGj8bL8wc/qkvKyx1aIQBeFo7TEo+5Jk+aeXZnjagoOrrfBryJ4BoAADeJGXOdF8pcl5C5BlAF8VNxxY65dma99memGTyuGwkz16JbuIcRXAMA4CYxY67tzDXBNYAqsBua2bG1GTXPNSXImRe+0BF/XKcs3NsIrgEAcJXEmWvGXAOoinCgFp+5DshHt3AHBEK7woiqCqdbeDYguAYAwE3M6CDaLgsvJ7gGkLxAbNBmRgfXxNZOCHULj8xc26X6jIL3MoJrAADcxC7hDJWFM+YaQAripuKSwsG1aTC+1wEBM0G3cIUvgsTPTQ6vILgGAMCNrLJwxlwDSEF82bcpBaypuBjf64SE3cIpC88KBNcAALhJzFRc1jzXpZSFA6iCuDHXlIU7LuE813bmmrJwLyO4BgDATSgLB5BG4RLjxFNxURaeeQEzNOY6ssdG5H5il3gWwTUAAG5iBss1ZfglSbl+GpoBqDp7nmsjInOt8FRczHOdeVFzjkcsjV0C7yG4BgDATUJjIeULBtdMxQUgFfFDrsvtb8vlo3mWA/Y15ppqAu8iuAYAwE1iMtc0NAOQikBsWXggfCwJyCCMc4BdRVBht/CMrxLShOAaAAA3sTPXoW7hjLkGkIK4qbgiMtcmmWtH2GOuFT/mmr3hbQTXAAC4idXgxoguC2fMNYCqMGOneAqU2bcxFZczzFAIFlUWTuY6KxBcAwDgJjFjrvPJXANIQXxZeDhzTXDtDDPBd9FjruFVBNcAALgJY64BpFE4to4vCw/IFxF8I1MCoXmuDbqFZx2CawAA3KSCbuGUhQOoivBUW5FTcYVuk49gzhHWvkg8zzUXPLyL4BoAADepaJ5rMtcAqiBuWqe4snACuUwLNzSLxJjrbEBwDQCAm8R0C8/xB0+/ygKcbQFInhWomaFSZAVK7dsYc+0MM2HmOvyFzLV3EVwDAOAmMd3Cc0JBdoDgGkAVWMeOQOiYovIS+zaaZzkjlLhOOOaacfDeRnANAICbxIy5DlWFq5yTLQBVYB05TCMn+E1ZMLgOyBDje52ROHMdbmhWzsVUzyK4BgDATWLGXPtCHX452QJQFVbwHPCFgutyK7gOhgHE1pkXMK1u4ZEYc50NCK4BAHCTmMy1VRZOcA2gKsJjrqPLwk0ruHZipRCUMHNNNYGXEVwDAOAmVuY6lGXKzQnmNkqZigtAFVjdwMNjroMNzazSZLqFZ165Kp7nWuJiqpcRXAMA4CYBq6FZ8CO6MDd4Qry7pLyiRwBAheLGXFtl4YYVXDuwUvs7w5qKK2Ljm5SFZwOCawAA3MSMLgsvCAXXe5jnGkAVBOIy17Fl4URymWZGjra2I+lQcG0aNLD0MIJrAADcJBDd0KwgJ/i1PGBSGg4gaYHYMddlxcGfQwFegMNKxkVVfVvjriO6hTPm2rsIrgEAcJPYzHVe+KN6dyml4QCSYzc0i+0WHgq2CeMyb6+ZaxliyLV3EVwDAOAmMZnrPH/4o7q4lBQTgOTQ0Mx9AlHBdYLMNdG1ZxFcAwDgJnbmOtRN1jDk94XKNzkJBpCkijLXJvNcO8Y0IkOwRJlrdopXEVwDAOAmdrdwv73IH+osy/QsAJJlBWrhea6jx1zT0Czz9j7m2uBY72EE1wAAuEnMmGvJTmJzwgUgaeGGZqHMdclOSVKpLy+4nMNKxpmRIVjMmOuoRfAcgmsAANwkZsy1JOWEomtKBQEky8pM22XhxTskSaVGMLjmml3mRW3yBJlrjvXeRXANAICbJMpch3rflHEWDCBJZlzm2gqu8yVx0c4JAe19zDXzXHsXwTUAAG6SIHNtNzQjuAaQJKsbuGldsAsF12U+K3PNcSXjjETdwsNf2CXeRXANAICbWCdavvBHtBVck80AkCzrmpw9FVexFVwHM9el5RxXMi1qkyeY55r+Gt5FcA0AgJskyFz76BYOoIrso4YvuqFZeWjMdVl5IPMrtd/b2zzXjLn2MoJrAADcJFAW/OpLVBbuxAoB8LLwVFzWPNfBqbjK/MHMNb0cMi8QGVxbQkF2QAZN5jyM4BoAADcxKx5zXUZ0DSBJ4THXOVHLy0Nl4WWUhTsgUeY6eOwPyEd/DQ8juAYAwE0C8d3C7cw1pYIAkmR3C484pkhSuZ255qJdpgXMyODaGhQfPPaXy8ex3sMIrgEAcBMrixGZubbHXDuxQgC8LK4s3FpOQzPnJOwWHhlcO7BOSAuCawAA3MTOXIc/on0+GpoBqBo7CRpTFh6wMtdctXNEuZ29tjLX1phrMtdeRnANAICbJBpzbVAWDqBqAhWUhQf8BZJoaOYEU6ZMa9x1osw1+8SzCK4BAHCTvYy55iQYQLKshmZxmescqyyczLUT7I7h9qD4cLfwci6kehbBNQAAbrKXbuFkMwAkyzpqxI25tjLXjLnOONNUfOY6dGE1YJK59jKCawAA3MI0wydaEVkmxlwDqCq7oVlMWXhZXh1JUindwh1hB9fW5Q8ammUFgmsAANzCjDjJjSwLD52DUSoIIFnWYaMs74Co5cX59YLLyVxn3F4z1zQ08zSCawAA3KK8NPx9onmuSWcASJIVqG2r0zZq+ZYDjpTEPNdOiRpzbZqyMtjMc+1tBNcAALhF6a7w97lF9rc0NAOQqtKCBlLt5sEfmnZSWUF9SQw3cUJct3CrkaWCwTX7xLty9n0XAACQEVZw7cuV/Ln24pzQnNdkmAAky8qCGoakc5+WVn4sHXeFcr7ZLYng2il25loKN7KUVRbuwAohLQiuAQBwi5JQcJ1XFLU4PycYXJeUEVwDSI51Tc4wDKll9+A/ST7fGklUxDin4sw1Q4C8i7JwAADconRn8GtujajF+bnBj+tigmsASbIy1z4jenkOsxA4xjRjx1zHBNeMufYsgmsAANyirCT4NScvanF+TrC5WXEpwTWA5FiZaWt4icVPcO2oisZcB+QTDdy9i+AaAAC3CJQFv/pyoxYX2Jnr8thHAMBelZUHL8rl+qNT1wTXzjEVOebajJqGkbJwbyO4BgDALazSQF90SxQ7c01ZOIAk2Zlrf/RpP2Xhzqo4c21QFu5hBNcAALiFnbmODa4Zcw2gasrKrbLw2My1NQsBgVzGmZHBdXjMdUA+SQYXPDyM4BoAALewxlz7KwiuSykLB5Cc0lC78PjgOviVLKkzEmWuTSO4U4itvYvgGgAAtygLzjurnMKoxfm5wbLwPTQ0A5CkcrssvILMNd2zMs6UGTPmOnTh1A6u2SdeRXANAIBblO4Jfs0tiFpsZZysDBQAVFa4LJwx124SlbkONTQzjeCFVIJr7yK4BgDALSrIXFuNiMgwAUhWmVUWHpO59hmh4JpAzhFR81zHlIVzwcO7CK4BAHCLCjLX1hQ6ZWSuASSpwsy1n8y1U8y4hmZkrrMFwTUAAG5RFgquYzPXoZPikjJOuAAkp7Q8ceY6128dV7ho5wTTjBhzHYgZc80u8SyCawAA3KKsgjHXZK4BVJGVmc6NyVzXyAtmSXeWlGV8nfZ3Zuhf8IeA3dDMylxTqu9dBNcAALhFqTXmOjq4zmPMNYAqKg0F1/6YzHXNguCUfzuLCa6dELDCsARjrgOU6nsWwTUAAG5hZ65jG5qFuoWXk7kGkJyy0HEjN2ae6xr5weC6tNxUcVl5xtdrf2aaZsLMtUKZ62JK9T2L4BoAALeoIHNtjbkuI5sBIAmBgCnrsOGPDa7zcuzvd+whe51pduZaZniQtS8YXO8p5WKHVxFcAwDgFhVkru1u4WSuASQh8oKcNaWfxe8zVGSNuy4mmMukqMukEZlrwwquqSTwLIJrAADcooLMdX5O8IRrN9kMAEmIbIKYGzPmWgqXhtPULLNMM/GY63DmmgupXkVwDQCAW9hTcUUH17ULgyfA2yndBJCEyMx1bFm4FO4YvoOmZhlV0ZhrO3NdwoVUryK4BgDALUoTT8VVpzBXkrR1d2mm1wiAh0XOMBA7FZcktahXJEn6eu3WjK0TpIAZO+aasvBsQXANAIBb2Jnr6DHXtQuCwfWuknI6hgOoNKtPg8+QfAky14cfWEeS9OOvuzK6Xvu7QIWZ63AH93IaWHoSwTUAAG6x+7fg14LaUYtrFYS7+m4jew2gkqyy8JwEWWtJalAzT5L0y46SjK0TpPKAKTPBmGufP3ysp8eGNxFcAwDgBqYpbV8X/L5W06ibcvw+FebS1RdAcqyy8JwEzcwkqV6NUHC9szhj64Tg4d6M/KEsuP2NnHz7PrsZd+1JVQquJ0yYoJYtW6qgoEBdu3bV/Pnz93r/V155RYcddpgKCgp05JFHavr06VVaWQAAslbxNqk0VJoZE1xLUs1Q9np7MZlrAJVjZT8LQhfnYrVsUEOS9PXP2zK2TgiWhQcUuuBhBqTycHBtXUhlrmtvSjq4fumllzR06FCNHDlSX3zxhTp27Kg+ffpo48aNCe//2Wef6aKLLtJll12mRYsWqW/fvurbt6+WLFmS8soDAJA1tv0c/FpQR8oriru5VmjKnG276eoLoHJ2habYKqwguG7XuJYkacuuUjqGZ1C5aeo3M7jttXOjnblWTr499/guMteelHRwPW7cOF1xxRUaPHiwOnTooEmTJqmoqEiTJ09OeP/HHntMp512mm699Va1b99e9957r4455hg98cQTKa+86+z6VSqhIUSVlOyU3vqLNPk0ae5jUoCGPQD2M8tmBL/Wb5PwZqur7/KN2zO1RgA8zgqYrYAtVo38HPvC3fqtuzO2Xvu70vKAfjQbB3/49QdpT6hbe06BCkP76rddjIP3opx93yWspKRECxcu1PDhw+1lPp9PvXv31rx58xI+Zt68eRo6dGjUsj59+ujNN9+s8HWKi4tVXBwe+7FtmwdKVYp3SP88T/ptpVT/UMmIGNtixnb7i/k5ldtTfm7t43azkrelePuuzeFGPqvnSfOeDGdv/HmxK1k5ca9f6Qdm+PWceM0U1nW/2K4Zfj1pP9mu/L5WKFAmbfkx+P0R5yW8S6cWdfXxsk0a8e+v9eaitTKMxGMo08VM6XekEs9frc8eeo1qfpFqfw/sg30/fzW/i2pf/2p+/m/XB8+hD21cs8L7tGxQQ1+t3aozHvtURzavU+nnTuYYkczbTHabJHX3JJ+8utZ7za+79aM/FFz/39jwDQd20lHldfTTb7vV/+nPdezBBySxBt5z4XEtdOGxLZxejbRKKrjevHmzysvL1bhx46jljRs31nfffZfwMevXr094//Xr11f4OqNHj9bdd9+dzKo577eVoStPW6Sf9j4GHRUoqCsdeb70xXPSjvXBfwCw3zCkzoOkrtckvPWPRzfT1M9WaevuUn2xektG1wyAt/2h44EV3nb5Sa10w4uLVVIe0MIff8vgWu3flua2i15g+KV2Z6hfk0Z6b8l6maa0IMv3x0mHNnR6FdIuqeA6U4YPHx6V7d62bZtatHD5VY0mR0rXL5TWzA/NVxeIzl4rJsMQl3EwKndb3O3JPG/sSmfiNZN43kYdpMK6Us+/SltWBUvFS3YGMzpVVsXMTpUzQilkkjL9millvby0XTP8wP1mu/L7mvbXq9tCqtO8wptbNqihT27tpQU//qrSclPBnEr1Zq+rOTlezWsfeo1qfhPV/R6qfR9kYCcY1b2VvP301f47emCdAh0aGludyDmdmqlNo5pat2WPPXXXviSzypW9a2W3Q+WfL833q+wrV/JuHZqeIu3pE0zO5eRJdVtKDdqoR2Pp/27rpe/Wba/0/gjz1tzYbRpVXFHhVUkF1w0aNJDf79eGDRuilm/YsEFNmjRJ+JgmTZokdX9Jys/PV35+foW3u1aNBtJhZzi9Ft5Xo37wHwAgSp2iXJ3SvvG+7wgASTj8wDo6/MDKl4QjTWofJjU6LG5x8wOK1PyA+MaWcL+kGprl5eWpc+fOmjVrlr0sEAho1qxZ6tatW8LHdOvWLer+kjRz5swK7w8AAAAAgNckXRY+dOhQDRw4UMcee6y6dOmiRx99VDt37tTgwYMlSQMGDFCzZs00evRoSdINN9ygHj16aOzYsTrzzDP14osvasGCBXr66afT+04AAAAAAHBI0sF1v379tGnTJo0YMULr169Xp06dNGPGDLtp2erVq+XzhRPiJ5xwgv71r3/pzjvv1F//+lcdeuihevPNN3XEEUek710AAAAAAOAgw6zuuTbSYNu2bapTp462bt2q2rVrO706AAAAAIAsl2wcmtSYawAAAAAAEI/gGgAAAACAFBFcAwAAAACQIoJrAAAAAABSRHANAAAAAECKCK4BAAAAAEgRwTUAAAAAACkiuAYAAAAAIEUE1wAAAAAApIjgGgAAAACAFBFcAwAAAACQIoJrAAAAAABSRHANAAAAAECKcpxegcowTVOStG3bNofXBAAAAACwP7DiTyse3RdPBNfbt2+XJLVo0cLhNQEAAAAA7E+2b9+uOnXq7PN+hlnZMNxBgUBAP//8s2rVqiXDMJxenWqxbds2tWjRQmvWrFHt2rWdXp39CtveOWx7Z7H9ncO2dw7b3llsf+ew7Z3DtndOqtveNE1t375dBx54oHy+fY+o9kTm2ufzqXnz5k6vRkbUrl2bPzqHsO2dw7Z3FtvfOWx757DtncX2dw7b3jlse+eksu0rk7G20NAMAAAAAIAUEVwDAAAAAJAigmuXyM/P18iRI5Wfn+/0qux32PbOYds7i+3vHLa9c9j2zmL7O4dt7xy2vXMyve090dAMAAAAAAA3I3MNAAAAAECKCK4BAAAAAEgRwTUAAAAAACkiuAYAAAAAIEUE12n0ySef6Oyzz9aBBx4owzD05ptvRt2+YcMGDRo0SAceeKCKiop02mmnafny5VH3Wb9+vS699FI1adJENWrU0DHHHKPXXnst6j7Lli3TOeecowYNGqh27do68cQTNXv27Op+e66Wjm2/YsUK/fGPf1TDhg1Vu3ZtXXjhhdqwYYN9+6pVq3TZZZepVatWKiwsVOvWrTVy5EiVlJRk4i26Via2veXdd99V165dVVhYqAMOOEB9+/atxnfmfqNHj9Zxxx2nWrVqqVGjRurbt6+WLl0adZ89e/bouuuuU/369VWzZk2dd955cdt29erVOvPMM1VUVKRGjRrp1ltvVVlZWdR95syZo2OOOUb5+flq06aNpk6dWt1vz9Uyue0tc+fOVU5Ojjp16lRdb8szMrn9n3/+eXXs2FFFRUVq2rSp/vznP+uXX36p9vfoVuna9n/5y1/UuXNn5efnJ/ydnjNnjs455xw1bdpUNWrUUKdOnfT8889X51tzvUxte0kyTVOPPPKI2rZtq/z8fDVr1kz3339/db01T0jH9v/yyy910UUXqUWLFiosLFT79u312GOPxb0Wn7nRMrntLVX9zCW4TqOdO3eqY8eOmjBhQtxtpmmqb9+++uGHH/Tvf/9bixYt0sEHH6zevXtr586d9v0GDBigpUuX6q233tJXX32lc889VxdeeKEWLVpk3+ess85SWVmZPvroIy1cuFAdO3bUWWedpfXr12fkfbpRqtt+586dOvXUU2UYhj766CPNnTtXJSUlOvvssxUIBCRJ3333nQKBgJ566il9/fXXGj9+vCZNmqS//vWvGX2vbpOJbS9Jr732mi699FINHjxYX375pebOnauLL744Y+/TjT7++GNdd911+vzzzzVz5kyVlpbq1FNPjTqm3HTTTXr77bf1yiuv6OOPP9bPP/+sc8891769vLxcZ555pkpKSvTZZ59p2rRpmjp1qkaMGGHfZ+XKlTrzzDPVq1cvLV68WDfeeKMuv/xyvf/++xl9v26SqW1v2bJliwYMGKBTTjklI+/P7TK1/efOnasBAwbosssu09dff61XXnlF8+fP1xVXXJHR9+sm6dj2lj//+c/q169fwtf57LPPdNRRR+m1117T//73Pw0ePFgDBgzQO++8U23vze0yte0l6YYbbtA//vEPPfLII/ruu+/01ltvqUuXLtXyvrwiHdt/4cKFatSokf75z3/q66+/1h133KHhw4friSeesO/DZ268TG17S0qfuSaqhSTzjTfesH9eunSpKclcsmSJvay8vNxs2LCh+fe//91eVqNGDfPZZ5+Neq569erZ99m0aZMpyfzkk0/s27dt22ZKMmfOnFlN78ZbqrLt33//fdPn85lbt26177NlyxbTMIy9bteHHnrIbNWqVfrfhEdV17YvLS01mzVrZv7jH//IzBvxqI0bN5qSzI8//tg0zeB2zM3NNV955RX7Pt9++60pyZw3b55pmqY5ffp00+fzmevXr7fvM3HiRLN27dpmcXGxaZqmedttt5mHH3541Gv169fP7NOnT3W/Jc+orm1v6devn3nnnXeaI0eONDt27Fj9b8hjqmv7P/zww+YhhxwS9Vp/+9vfzGbNmlX3W/KMqmz7SMn8Tp9xxhnm4MGD07Le2aC6tv0333xj5uTkmN999121rXs2SHX7W6699lqzV69e9s985u5bdW17SyqfuWSuM6S4uFiSVFBQYC/z+XzKz8/Xp59+ai874YQT9NJLL+nXX39VIBDQiy++qD179qhnz56SpPr166tdu3Z69tlntXPnTpWVlempp55So0aN1Llz54y+J6+ozLYvLi6WYRhRE8wXFBTI5/NF7Z9YW7duVb169appzb0vXdv+iy++0Nq1a+Xz+XT00UeradOmOv3007VkyZIMvhv327p1qyTZv5MLFy5UaWmpevfubd/nsMMO00EHHaR58+ZJkubNm6cjjzxSjRs3tu/Tp08fbdu2TV9//bV9n8jnsO5jPQeqb9tL0pQpU/TDDz9o5MiRmXgrnlRd279bt25as2aNpk+fLtM0tWHDBr366qs644wzMvXWXK8q2z6V1+IzN6y6tv3bb7+tQw45RO+8845atWqlli1b6vLLL9evv/6a3jfgcena/rG/13zm7lt1bXsp9c9cgusMsXbw8OHD9dtvv6mkpERjxozRTz/9pHXr1tn3e/nll1VaWqr69esrPz9fV111ld544w21adNGkmQYhj788EMtWrRItWrVUkFBgcaNG6cZM2bogAMOcOrtuVpltv3xxx+vGjVq6Pbbb9euXbu0c+dO3XLLLSovL4/aP5G+//57Pf7447rqqqsy+XY8JV3b/ocffpAkjRo1SnfeeafeeecdHXDAAerZsycf9iGBQEA33nijunfvriOOOEJSsIdDXl6e6tatG3Xfxo0b28NI1q9fHxVcWLdbt+3tPtu2bdPu3bur4+14SnVu++XLl2vYsGH65z//qZycnGp+J95Undu/e/fuev7559WvXz/l5eWpSZMmqlOnTsJhMPujqm77qnj55Zf13//+V4MHD05llbNGdW77H374QT/++KNeeeUVPfvss5o6daoWLlyo888/P51vwdPStf0/++wzvfTSS7ryyivtZXzm7l11bvt0fOYSXGdIbm6uXn/9dS1btkz16tVTUVGRZs+erdNPP10+X3g33HXXXdqyZYs+/PBDLViwQEOHDtWFF16or776SlJwDOt1112nRo0a6f/+7/80f/589e3bV2effXaFQeD+rjLbvmHDhnrllVf09ttvq2bNmqpTp462bNmiY445Jmr/WNauXavTTjtNF1xwwX499m5f0rXtrbHXd9xxh8477zx17txZU6ZMkWEYeuWVVxx7f25y3XXXacmSJXrxxRedXpX9TnVt+/Lycl188cW6++671bZt27Q+dzapzt/9b775RjfccINGjBihhQsXasaMGVq1apWuvvrqtL+WF2XquDN79mwNHjxYf//733X44YdX62t5RXVu+0AgoOLiYj377LM66aST1LNnTz3zzDOaPXt2XBOp/VU6tv+SJUt0zjnnaOTIkTr11FPTuHbZrbq2fbo+c7kMnkGdO3fW4sWLtXXrVpWUlKhhw4bq2rWrjj32WEnBjslPPPGElixZYn94dOzYUf/3f/+nCRMmaNKkSfroo4/0zjvv6LffflPt2rUlSU8++aRmzpypadOmadiwYY69Pzfb17aXpFNPPVUrVqzQ5s2blZOTo7p166pJkyY65JBDop7r559/Vq9evXTCCSfo6aefzvRb8Zx0bPumTZtKkjp06GA/Jj8/X4cccohWr16d2TfkQkOGDNE777yjTz75RM2bN7eXN2nSRCUlJdqyZUvU1dwNGzaoSZMm9n3mz58f9XxWd83I+8R2m92wYYNq166twsLC6nhLnlGd23779u1asGCBFi1apCFDhkgKnvSapqmcnBx98MEH+t3vflfN79Ddqvt3f/To0erevbtuvfVWSdJRRx2lGjVq6KSTTtJ9991nH5v2R6ls+2R8/PHHOvvsszV+/HgNGDAgHavuedW97Zs2baqcnJyoAKN9+/aSgh3227Vrl/qb8LB0bP9vvvlGp5xyiq688krdeeedUbfxmVux6tz26frMJXPtgDp16qhhw4Zavny5FixYoHPOOUeStGvXLkmKy5T6/X47c1fRfXw+X1RnZSRW0baP1KBBA9WtW1cfffSRNm7cqD/84Q/2bWvXrlXPnj3tzGmirDYSS2XbW1OGRF4xLy0t1apVq3TwwQdn7D24jWmaGjJkiN544w199NFHatWqVdTtnTt3Vm5urmbNmmUvW7p0qVavXq1u3bpJCo4p/eqrr7Rx40b7PjNnzlTt2rXtixndunWLeg7rPtZz7I8yse1r166tr776SosXL7b/XX311WrXrp0WL16srl27ZubNulCmfvd37dqV8DPZWof9UTq2fWXNmTNHZ555psaMGRNVurm/ytS27969u8rKyrRixQp72bJlyySJz9w0bP+vv/5avXr10sCBAxNOb8ZnbrxMbPu0feYm1f4Me7V9+3Zz0aJF5qJFi0xJ5rhx48xFixaZP/74o2mapvnyyy+bs2fPNlesWGG++eab5sEHH2yee+659uNLSkrMNm3amCeddJL5n//8x/z+++/NRx55xDQMw3z33XdN0wx2C69fv7557rnnmosXLzaXLl1q3nLLLWZubq65ePFiR963G6S67U3TNCdPnmzOmzfP/P77783nnnvOrFevnjl06FD79p9++sls06aNecopp5g//fSTuW7dOvvf/iwT2940TfOGG24wmzVrZr7//vvmd999Z1522WVmo0aNzF9//TVj79VtrrnmGrNOnTrmnDlzon4fd+3aZd/n6quvNg866CDzo48+MhcsWGB269bN7Natm317WVmZecQRR5innnqquXjxYnPGjBlmw4YNzeHDh9v3+eGHH8yioiLz1ltvNb/99ltzwoQJpt/vN2fMmJHR9+smmdr2segWHpSp7T9lyhQzJyfHfPLJJ80VK1aYn376qXnssceaXbp0yej7dZN0bHvTNM3ly5ebixYtMq+66iqzbdu29ueI1an9o48+MouKiszhw4dHvc4vv/yS0ffrJpna9uXl5eYxxxxjnnzyyeYXX3xhLliwwOzatav5+9//PqPv123Ssf2/+uors2HDhuYll1wS9RwbN26078NnbrxMbftYVfnMJbhOo9mzZ5uS4v4NHDjQNE3TfOyxx8zmzZububm55kEHHWTeeeedcdOtLFu2zDz33HPNRo0amUVFReZRRx0VNzXXf//7X/PUU08169WrZ9aqVcs8/vjjzenTp2fqbbpSOrb97bffbjZu3NjMzc01Dz30UHPs2LFmIBCwb58yZUrC19jfr1FlYtubZvDi080332w2atTIrFWrltm7d++oKb72RxX9Pk6ZMsW+z+7du81rr73WPOCAA8yioiLzj3/8Y9wFoVWrVpmnn366WVhYaDZo0MC8+eabzdLS0qj7zJ492+zUqZOZl5dnHnLIIVGvsT/K5LaPRHAdlMnt/7e//c3s0KGDWVhYaDZt2tT805/+ZP7000+ZeJuulK5t36NHj4TPs3LlStM0TXPgwIEJb+/Ro0fm3qzLZGrbm6Zprl271jz33HPNmjVrmo0bNzYHDRq0X1/YMM30bP+RI0cmfI6DDz446rX4zI2WyW0fqSqfuUZohQEAAAAAQBUxYBQAAAAAgBQRXAMAAAAAkCKCawAAAAAAUkRwDQAAAABAigiuAQAAAABIEcE1AAAAAAApIrgGAAAAACBFBNcAAAAAAKSI4BoAAAAAgBQRXAMAAAAAkCKCawAAAAAAUkRwDQAAAABAiv4fFtFXez37eGQAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Fit a Markov Switching Model on Brent Oil Prices\n",
+    "markov_model = MarkovRegression(df['Price'], k_regimes=2, trend='n', switching_variance=True)\n",
+    "markov_result = markov_model.fit()\n",
+    "\n",
+    "# Plotting the Regime-Switching probabilities\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "plt.plot(markov_result.smoothed_marginal_probabilities[0], label='Regime 1 Probability')\n",
+    "plt.plot(markov_result.smoothed_marginal_probabilities[1], label='Regime 2 Probability')\n",
+    "plt.title('Regime Probabilities for Brent Oil Prices')\n",
+    "plt.legend()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### LSTM Model for Time Series Forecasting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/20\n",
+      "8980/8980 - 38s - 4ms/step - loss: 3.8475e-04\n",
+      "Epoch 2/20\n",
+      "8980/8980 - 36s - 4ms/step - loss: 1.4121e-04\n",
+      "Epoch 3/20\n",
+      "8980/8980 - 36s - 4ms/step - loss: 1.3870e-04\n",
+      "Epoch 4/20\n",
+      "8980/8980 - 36s - 4ms/step - loss: 1.2671e-04\n",
+      "Epoch 5/20\n",
+      "8980/8980 - 42s - 5ms/step - loss: 1.1923e-04\n",
+      "Epoch 6/20\n",
+      "8980/8980 - 36s - 4ms/step - loss: 1.1813e-04\n",
+      "Epoch 7/20\n",
+      "8980/8980 - 36s - 4ms/step - loss: 1.1638e-04\n",
+      "Epoch 8/20\n",
+      "8980/8980 - 36s - 4ms/step - loss: 1.1431e-04\n",
+      "Epoch 9/20\n",
+      "8980/8980 - 36s - 4ms/step - loss: 1.1154e-04\n",
+      "Epoch 10/20\n",
+      "8980/8980 - 39s - 4ms/step - loss: 1.0962e-04\n",
+      "Epoch 11/20\n",
+      "8980/8980 - 37s - 4ms/step - loss: 1.0917e-04\n",
+      "Epoch 12/20\n",
+      "8980/8980 - 37s - 4ms/step - loss: 1.0914e-04\n",
+      "Epoch 13/20\n",
+      "8980/8980 - 36s - 4ms/step - loss: 1.0620e-04\n",
+      "Epoch 14/20\n",
+      "8980/8980 - 36s - 4ms/step - loss: 1.0812e-04\n",
+      "Epoch 15/20\n",
+      "8980/8980 - 36s - 4ms/step - loss: 1.0387e-04\n",
+      "Epoch 16/20\n",
+      "8980/8980 - 41s - 5ms/step - loss: 1.0488e-04\n",
+      "Epoch 17/20\n",
+      "8980/8980 - 37s - 4ms/step - loss: 1.0355e-04\n",
+      "Epoch 18/20\n",
+      "8980/8980 - 36s - 4ms/step - loss: 1.0438e-04\n",
+      "Epoch 19/20\n",
+      "8980/8980 - 36s - 4ms/step - loss: 1.0232e-04\n",
+      "Epoch 20/20\n",
+      "8980/8980 - 36s - 4ms/step - loss: 1.0304e-04\n",
+      "\u001b[1m281/281\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Normalize the Price data\n",
+    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
+    "scaled_data = scaler.fit_transform(df[['Price']])\n",
+    "\n",
+    "# Prepare the dataset for LSTM\n",
+    "def create_dataset(data, look_back=1):\n",
+    "    X, Y = [], []\n",
+    "    for i in range(len(data) - look_back - 1):\n",
+    "        X.append(data[i:(i + look_back), 0])\n",
+    "        Y.append(data[i + look_back, 0])\n",
+    "    return np.array(X), np.array(Y)\n",
+    "\n",
+    "look_back = 30\n",
+    "X, y = create_dataset(scaled_data, look_back)\n",
+    "X = np.reshape(X, (X.shape[0], X.shape[1], 1))\n",
+    "\n",
+    "# Build the LSTM model\n",
+    "model = Sequential()\n",
+    "model.add(LSTM(50, input_shape=(X.shape[1], 1)))\n",
+    "model.add(Dense(1))\n",
+    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
+    "model.fit(X, y, epochs=20, batch_size=1, verbose=2)\n",
+    "\n",
+    "# Predict using LSTM\n",
+    "predictions = model.predict(X)\n",
+    "predictions = scaler.inverse_transform(predictions)\n",
+    "\n",
+    "# Plotting LSTM results\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "plt.plot(df.index[look_back + 1:], predictions, label='LSTM Predicted Price')\n",
+    "plt.plot(df['Price'], label='Actual Price')\n",
+    "plt.title('LSTM Model for Brent Oil Prices')\n",
+    "plt.legend()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exploring Economic, Technological, and Political Factors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Correlation between GDP growth rate and oil prices: -0.01865756425503499\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculate correlation\n",
+    "gdp_corr = df['Price'].corr(df['GDP'])\n",
+    "print(f\"Correlation between GDP growth rate and oil prices: {gdp_corr}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Correlation between inflation rate and oil prices: 0.007940614155305694\n"
+     ]
+    }
+   ],
+   "source": [
+    "inflation_corr = df['Price'].corr(df['Inflation'])\n",
+    "print(f\"Correlation between inflation rate and oil prices: {inflation_corr}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Correlation between environmental regulations index and oil prices: 0.009733244818864104\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Hypothetically adding data and analyzing correlation with price\n",
+    "df['Environmental_Regulations'] = np.random.normal(loc=2.0, scale=0.5, size=len(df))\n",
+    "env_reg_corr = df['Price'].corr(df['Environmental_Regulations'])\n",
+    "print(f\"Correlation between environmental regulations index and oil prices: {env_reg_corr}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Defaulting to user installation because normal site-packages is not writeable\n",
+      "Collecting wbdata\n",
+      "  Using cached wbdata-1.0.0-py3-none-any.whl.metadata (2.6 kB)\n",
+      "Collecting appdirs<2.0,>=1.4 (from wbdata)\n",
+      "  Using cached appdirs-1.4.4-py2.py3-none-any.whl.metadata (9.0 kB)\n",
+      "Collecting backoff<3.0.0,>=2.2.1 (from wbdata)\n",
+      "  Using cached backoff-2.2.1-py3-none-any.whl.metadata (14 kB)\n",
+      "Requirement already satisfied: cachetools<6.0.0,>=5.3.2 in c:\\users\\getac\\appdata\\roaming\\python\\python312\\site-packages (from wbdata) (5.5.0)\n",
+      "Collecting dateparser<2.0.0,>=1.2.0 (from wbdata)\n",
+      "  Using cached dateparser-1.2.0-py2.py3-none-any.whl.metadata (28 kB)\n",
+      "Requirement already satisfied: decorator<6.0.0,>=5.1.1 in c:\\users\\getac\\appdata\\roaming\\python\\python312\\site-packages (from wbdata) (5.1.1)\n",
+      "Requirement already satisfied: requests<3.0,>=2.0 in c:\\users\\getac\\appdata\\roaming\\python\\python312\\site-packages (from wbdata) (2.32.3)\n",
+      "Collecting shelved-cache<0.4.0,>=0.3.1 (from wbdata)\n",
+      "  Using cached shelved_cache-0.3.1-py3-none-any.whl.metadata (4.7 kB)\n",
+      "Collecting tabulate<0.9.0,>=0.8.5 (from wbdata)\n",
+      "  Using cached tabulate-0.8.10-py3-none-any.whl.metadata (25 kB)\n",
+      "Requirement already satisfied: python-dateutil in c:\\users\\getac\\appdata\\roaming\\python\\python312\\site-packages (from dateparser<2.0.0,>=1.2.0->wbdata) (2.9.0.post0)\n",
+      "Requirement already satisfied: pytz in c:\\users\\getac\\appdata\\roaming\\python\\python312\\site-packages (from dateparser<2.0.0,>=1.2.0->wbdata) (2024.2)\n",
+      "Collecting regex!=2019.02.19,!=2021.8.27 (from dateparser<2.0.0,>=1.2.0->wbdata)\n",
+      "  Using cached regex-2024.9.11-cp312-cp312-win_amd64.whl.metadata (41 kB)\n",
+      "Collecting tzlocal (from dateparser<2.0.0,>=1.2.0->wbdata)\n",
+      "  Using cached tzlocal-5.2-py3-none-any.whl.metadata (7.8 kB)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\getac\\appdata\\roaming\\python\\python312\\site-packages (from requests<3.0,>=2.0->wbdata) (3.4.0)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\getac\\appdata\\roaming\\python\\python312\\site-packages (from requests<3.0,>=2.0->wbdata) (3.10)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\getac\\appdata\\roaming\\python\\python312\\site-packages (from requests<3.0,>=2.0->wbdata) (2.2.3)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\getac\\appdata\\roaming\\python\\python312\\site-packages (from requests<3.0,>=2.0->wbdata) (2024.8.30)\n",
+      "Requirement already satisfied: six>=1.5 in c:\\users\\getac\\appdata\\roaming\\python\\python312\\site-packages (from python-dateutil->dateparser<2.0.0,>=1.2.0->wbdata) (1.16.0)\n",
+      "Requirement already satisfied: tzdata in c:\\users\\getac\\appdata\\roaming\\python\\python312\\site-packages (from tzlocal->dateparser<2.0.0,>=1.2.0->wbdata) (2024.2)\n",
+      "Using cached wbdata-1.0.0-py3-none-any.whl (18 kB)\n",
+      "Using cached appdirs-1.4.4-py2.py3-none-any.whl (9.6 kB)\n",
+      "Using cached backoff-2.2.1-py3-none-any.whl (15 kB)\n",
+      "Using cached dateparser-1.2.0-py2.py3-none-any.whl (294 kB)\n",
+      "Using cached shelved_cache-0.3.1-py3-none-any.whl (7.8 kB)\n",
+      "Using cached tabulate-0.8.10-py3-none-any.whl (29 kB)\n",
+      "Using cached regex-2024.9.11-cp312-cp312-win_amd64.whl (273 kB)\n",
+      "Using cached tzlocal-5.2-py3-none-any.whl (17 kB)\n",
+      "Installing collected packages: appdirs, tzlocal, tabulate, shelved-cache, regex, backoff, dateparser, wbdata\n",
+      "Successfully installed appdirs-1.4.4 backoff-2.2.1 dateparser-1.2.0 regex-2024.9.11 shelved-cache-0.3.1 tabulate-0.8.10 tzlocal-5.2 wbdata-1.0.0\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "[notice] A new release of pip is available: 24.2 -> 24.3.1\n",
+      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install wbdata\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import wbdata\n",
+    "import pandas\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Set up the countries\n",
+    "countries = [\"CL\", \"UY\", \"HU\", \"ET\"]\n",
+    "\n",
+    "# Set up the indicator\n",
+    "indicators = {'NY.GNP.PCAP.CD': 'GNI per Capita'}\n",
+    "\n",
+    "# Retrieve the data without 'convert_date'\n",
+    "df = wbdata.get_dataframe(indicators, country=countries)\n",
+    "\n",
+    "# Unstack the DataFrame for plotting\n",
+    "dfu = df.unstack(level=0)\n",
+    "\n",
+    "# Plot with labels and a title\n",
+    "dfu.plot()\n",
+    "plt.legend(loc='best')\n",
+    "plt.title(\"GNI Per Capita ($USD, Atlas Method)\")\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('GNI Per Capita ($USD, Atlas Method)')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import wbdata\n",
+    "import pandas\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Add more countries to the list\n",
+    "countries = [\"CL\", \"UY\", \"HU\", \"ET\", \"US\", \"CA\", \"FR\", \"DE\", \"IN\", \"CN\", \"BR\", \"ZA\", \"JP\", \"AU\", \"NG\"]\n",
+    "\n",
+    "# Define the indicator for GNI per Capita\n",
+    "indicators = {'NY.GNP.PCAP.CD': 'GNI per Capita'}\n",
+    "\n",
+    "# Retrieve the data for the countries specified\n",
+    "df = wbdata.get_dataframe(indicators, country=countries)\n",
+    "\n",
+    "# Unstack the DataFrame for plotting\n",
+    "dfu = df.unstack(level=0)\n",
+    "\n",
+    "# Plot with labels and a title\n",
+    "dfu.plot(figsize=(12, 8))  # Enlarged plot for better readability with more countries\n",
+    "plt.legend(loc='best')\n",
+    "plt.title(\"GNI Per Capita ($USD, Atlas Method)\")\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('GNI Per Capita ($USD, Atlas Method)')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Defaulting to user installation because normal site-packages is not writeable\n",
+      "Collecting wbgapi\n",
+      "  Downloading wbgapi-1.0.12-py3-none-any.whl.metadata (13 kB)\n",
+      "Requirement already satisfied: requests in c:\\users\\getac\\appdata\\roaming\\python\\python312\\site-packages (from wbgapi) (2.32.3)\n",
+      "Collecting PyYAML (from wbgapi)\n",
+      "  Downloading PyYAML-6.0.2-cp312-cp312-win_amd64.whl.metadata (2.1 kB)\n",
+      "Requirement already satisfied: tabulate in c:\\users\\getac\\appdata\\roaming\\python\\python312\\site-packages (from wbgapi) (0.8.10)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\getac\\appdata\\roaming\\python\\python312\\site-packages (from requests->wbgapi) (3.4.0)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\getac\\appdata\\roaming\\python\\python312\\site-packages (from requests->wbgapi) (3.10)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\getac\\appdata\\roaming\\python\\python312\\site-packages (from requests->wbgapi) (2.2.3)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\getac\\appdata\\roaming\\python\\python312\\site-packages (from requests->wbgapi) (2024.8.30)\n",
+      "Downloading wbgapi-1.0.12-py3-none-any.whl (36 kB)\n",
+      "Downloading PyYAML-6.0.2-cp312-cp312-win_amd64.whl (156 kB)\n",
+      "Installing collected packages: PyYAML, wbgapi\n",
+      "Successfully installed PyYAML-6.0.2 wbgapi-1.0.12\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "[notice] A new release of pip is available: 24.2 -> 24.3.1\n",
+      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install wbgapi\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Macroeconomic Data with Growth Rates:\n",
+      "      Time           GDP     GDP_G       GDPPC   GDPPC_G       HCI       CPI  \\\n",
+      "time                                                                           \n",
+      "2014  2014  1.097912e+10  3.339203  453.383414  0.659691       NaN  6.080408   \n",
+      "2015  2015  1.132302e+10  3.132298  455.638035  0.497288       NaN  7.404192   \n",
+      "2016  2016  1.177517e+10  3.993146  461.736048  1.338346       NaN  6.035759   \n",
+      "2017  2017  1.223832e+10  3.933308  467.655068  1.281905  0.374000  8.609051   \n",
+      "2018  2018  1.262925e+10  3.194357  470.423898  0.592067  0.385493  8.594230   \n",
+      "2019  2019  1.318636e+10  4.411232  478.926961  1.807532       NaN  5.610514   \n",
+      "2020  2020  1.224516e+10 -7.137672  433.838240 -9.414530  0.391991  4.201793   \n",
+      "2021  2021  1.294799e+10  5.739616  447.784676  3.214663       NaN  5.812251   \n",
+      "2022  2022  1.343952e+10  3.796199  453.858147  1.356337       NaN  8.160590   \n",
+      "2023  2023  1.397513e+10  3.985371  460.834129  1.537040       NaN  9.874327   \n",
+      "\n",
+      "           FDI  \n",
+      "time            \n",
+      "2014  2.977511  \n",
+      "2015  2.897277  \n",
+      "2016  4.564608  \n",
+      "2017  3.527972  \n",
+      "2018  4.447928  \n",
+      "2019  3.362798  \n",
+      "2020  2.746571  \n",
+      "2021  2.456494  \n",
+      "2022  3.057333  \n",
+      "2023  2.585758  \n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import wbgapi as wb\n",
+    "\n",
+    "# Define macroeconomic indicators\n",
+    "GDP = 'NY.GDP.MKTP.KD'       # GDP in constant 2015 $US\n",
+    "HCI = 'HD.HCI.OVRL'          # Human Capital Index\n",
+    "GDPPC = 'NY.GDP.PCAP.KD'     # GDP per capita in constant 2015 $US\n",
+    "CPI = 'FP.CPI.TOTL.ZG'       # Inflation rate\n",
+    "FDI = 'BX.KLT.DINV.WD.GD.ZS' # Foreign Direct Investment as a share of GDP\n",
+    "\n",
+    "# Retrieve data from the World Bank API for Madagascar (MDG) from 1987 to 2023\n",
+    "Table_1 = wb.data.DataFrame([GDP, HCI, GDPPC, CPI, FDI], 'MDG', time=range(1987, 2024), numericTimeKeys=True, labels=True, columns='series')\n",
+    "\n",
+    "# Convert to DataFrame and rename columns for clarity\n",
+    "Table_1 = pd.DataFrame(Table_1)\n",
+    "Table_1.rename(columns={\n",
+    "    'NY.GDP.MKTP.KD': 'GDP',\n",
+    "    'HD.HCI.OVRL': 'HCI',\n",
+    "    'NY.GDP.PCAP.KD': 'GDPPC',\n",
+    "    'FP.CPI.TOTL.ZG': 'CPI',\n",
+    "    'BX.KLT.DINV.WD.GD.ZS': 'FDI'\n",
+    "}, inplace=True)\n",
+    "\n",
+    "# Sort by time for correct plotting sequence\n",
+    "Table_1 = Table_1.sort_values(by=['Time'], ascending=True)\n",
+    "\n",
+    "# Calculate growth rates for GDP and GDP per capita\n",
+    "for i in ['GDP', 'GDPPC']:\n",
+    "    Table_1[i + '_G'] = Table_1[i].pct_change().mul(100)\n",
+    "\n",
+    "# Plot each macroeconomic factor over time\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "for column in ['GDP', 'HCI', 'GDPPC', 'CPI', 'FDI']:\n",
+    "    plt.plot(Table_1['Time'], Table_1[column], label=column)\n",
+    "    \n",
+    "plt.title('Macroeconomic Indicators for Madagascar')\n",
+    "plt.xlabel('Year')\n",
+    "plt.ylabel('Value')\n",
+    "plt.legend(loc='upper left')\n",
+    "plt.show()\n",
+    "\n",
+    "# Display the table with growth rates\n",
+    "print(\"Macroeconomic Data with Growth Rates:\")\n",
+    "print(Table_1[['Time', 'GDP', 'GDP_G', 'GDPPC', 'GDPPC_G', 'HCI', 'CPI', 'FDI']].tail(10))  # Display the last 10 records\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Table of Macroeconomic Factors with Growth Rates:\n",
+      "\n",
+      "      Time           GDP      GDP_G       GDPPC   GDPPC_G  HCI        CPI  \\\n",
+      "time                                                                        \n",
+      "1987  1987  5.849641e+09        NaN  537.131781       NaN  NaN  14.993135   \n",
+      "1991  1991  6.082965e+09   3.988693  496.760822 -7.516025  NaN   8.592648   \n",
+      "1995  1995  6.386838e+09   4.995463  460.507184 -7.298007  NaN  49.080210   \n",
+      "1999  1999  7.360783e+09  15.249258  467.919193  1.609532  NaN   9.929534   \n",
+      "2003  2003  7.835975e+09   6.455722  442.103230 -5.517184  NaN  -1.704005   \n",
+      "2007  2007  9.626649e+09  22.851969  483.145271  9.283362  NaN  10.287966   \n",
+      "2011  2011  1.008187e+10   4.728743  451.127505 -6.626944  NaN   9.482540   \n",
+      "2015  2015  1.132302e+10  12.310733  455.638035  0.999835  NaN   7.404192   \n",
+      "2019  2019  1.318636e+10  16.456205  478.926961  5.111278  NaN   5.610514   \n",
+      "2023  2023  1.397513e+10   5.981727  460.834129 -3.777785  NaN   9.874327   \n",
+      "\n",
+      "           FDI  \n",
+      "time            \n",
+      "1987  0.108002  \n",
+      "1991  0.420352  \n",
+      "1995  0.252994  \n",
+      "1999  1.364811  \n",
+      "2003  0.202026  \n",
+      "2007  9.260115  \n",
+      "2011  7.059792  \n",
+      "2015  2.897277  \n",
+      "2019  3.362798  \n",
+      "2023  2.585758  \n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x1000 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import wbgapi as wb\n",
+    "\n",
+    "# Define macroeconomic indicators\n",
+    "GDP = 'NY.GDP.MKTP.KD'       # GDP in constant 2015 $US\n",
+    "HCI = 'HD.HCI.OVRL'          # Human Capital Index\n",
+    "GDPPC = 'NY.GDP.PCAP.KD'     # GDP per capita in constant 2015 $US\n",
+    "CPI = 'FP.CPI.TOTL.ZG'       # Inflation rate\n",
+    "FDI = 'BX.KLT.DINV.WD.GD.ZS' # Foreign Direct Investment as a share of GDP\n",
+    "\n",
+    "# Fetch data for Madagascar (MDG) from 1960 to 2023\n",
+    "Table_1 = wb.data.DataFrame([GDP, HCI, GDPPC, CPI, FDI], 'MDG', time=range(1987, 2024,4), numericTimeKeys=True, labels=True, columns='series')\n",
+    "Table_1 = pd.DataFrame(Table_1)\n",
+    "\n",
+    "# Rename columns for readability\n",
+    "Table_1.rename(columns={\n",
+    "    'NY.GDP.MKTP.KD': 'GDP',\n",
+    "    'HD.HCI.OVRL': 'HCI',\n",
+    "    'NY.GDP.PCAP.KD': 'GDPPC',\n",
+    "    'FP.CPI.TOTL.ZG': 'CPI',\n",
+    "    'BX.KLT.DINV.WD.GD.ZS': 'FDI'\n",
+    "}, inplace=True)\n",
+    "\n",
+    "# Sort by time for consistent plotting\n",
+    "Table_1 = Table_1.sort_values(by=['Time'], ascending=True)\n",
+    "\n",
+    "# Calculate the annual growth rate for GDP and GDPPC\n",
+    "for col in ['GDP', 'GDPPC']:\n",
+    "    Table_1[col + '_G'] = Table_1[col].pct_change().mul(100)\n",
+    "\n",
+    "# Display the data table with growth rates\n",
+    "print(\"Table of Macroeconomic Factors with Growth Rates:\\n\")\n",
+    "print(Table_1[['Time', 'GDP', 'GDP_G', 'GDPPC', 'GDPPC_G', 'HCI', 'CPI', 'FDI']])\n",
+    "\n",
+    "# Plotting time series for each macroeconomic factor\n",
+    "fig, ax = plt.subplots(3, 2, figsize=(15, 10))\n",
+    "fig.suptitle('Macroeconomic Factors for Madagascar (1960-2023)', fontsize=16)\n",
+    "\n",
+    "# GDP and GDP Growth Rate\n",
+    "ax[0, 0].plot(Table_1['Time'], Table_1['GDP'], label='GDP')\n",
+    "ax[0, 0].set_title('GDP (Constant 2015 $US)')\n",
+    "ax[0, 0].set_xlabel('Year')\n",
+    "ax[0, 0].set_ylabel('GDP')\n",
+    "ax[0, 0].legend()\n",
+    "\n",
+    "ax[0, 1].plot(Table_1['Time'], Table_1['GDP_G'], label='GDP Growth Rate', color='orange')\n",
+    "ax[0, 1].set_title('GDP Growth Rate (%)')\n",
+    "ax[0, 1].set_xlabel('Year')\n",
+    "ax[0, 1].set_ylabel('Growth Rate (%)')\n",
+    "ax[0, 1].legend()\n",
+    "\n",
+    "# GDP per Capita and GDPPC Growth Rate\n",
+    "ax[1, 0].plot(Table_1['Time'], Table_1['GDPPC'], label='GDP per Capita')\n",
+    "ax[1, 0].set_title('GDP per Capita (Constant 2015 $US)')\n",
+    "ax[1, 0].set_xlabel('Year')\n",
+    "ax[1, 0].set_ylabel('GDP per Capita')\n",
+    "ax[1, 0].legend()\n",
+    "\n",
+    "ax[1, 1].plot(Table_1['Time'], Table_1['GDPPC_G'], label='GDPPC Growth Rate', color='purple')\n",
+    "ax[1, 1].set_title('GDP per Capita Growth Rate (%)')\n",
+    "ax[1, 1].set_xlabel('Year')\n",
+    "ax[1, 1].set_ylabel('Growth Rate (%)')\n",
+    "ax[1, 1].legend()\n",
+    "\n",
+    "# Human Capital Index (HCI)\n",
+    "ax[2, 0].plot(Table_1['Time'], Table_1['HCI'], label='Human Capital Index', color='green')\n",
+    "ax[2, 0].set_title('Human Capital Index')\n",
+    "ax[2, 0].set_xlabel('Year')\n",
+    "ax[2, 0].set_ylabel('HCI')\n",
+    "ax[2, 0].legend()\n",
+    "\n",
+    "# Inflation Rate (CPI)\n",
+    "ax[2, 1].plot(Table_1['Time'], Table_1['CPI'], label='Inflation Rate', color='red')\n",
+    "ax[2, 1].set_title('Inflation Rate (%)')\n",
+    "ax[2, 1].set_xlabel('Year')\n",
+    "ax[2, 1].set_ylabel('Inflation Rate (%)')\n",
+    "ax[2, 1].legend()\n",
+    "\n",
+    "plt.tight_layout(rect=[0, 0, 1, 0.96])  # Adjust layout to fit main title\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Defaulting to user installation because normal site-packages is not writeable\n",
+      "Collecting plotly\n",
+      "  Downloading plotly-5.24.1-py3-none-any.whl.metadata (7.3 kB)\n",
+      "Collecting tenacity>=6.2.0 (from plotly)\n",
+      "  Downloading tenacity-9.0.0-py3-none-any.whl.metadata (1.2 kB)\n",
+      "Requirement already satisfied: packaging in c:\\users\\getac\\appdata\\roaming\\python\\python312\\site-packages (from plotly) (24.1)\n",
+      "Downloading plotly-5.24.1-py3-none-any.whl (19.1 MB)\n",
+      "   ---------------------------------------- 0.0/19.1 MB ? eta -:--:--\n",
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+      "Downloading tenacity-9.0.0-py3-none-any.whl (28 kB)\n",
+      "Installing collected packages: tenacity, plotly\n",
+      "Successfully installed plotly-5.24.1 tenacity-9.0.0\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "[notice] A new release of pip is available: 24.2 -> 24.3.1\n",
+      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install plotly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "import pandas as pd \n",
+    "import requests \n",
+    "import json \n",
+    "import plotly.graph_objects as go"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def checkindicator(url):\n",
+    "    r= requests.get(url)\n",
+    "    r = r.json()\n",
+    "    periods = r['series']['docs'][0]['period']\n",
+    "    values = r['series']['docs'][0]['value']\n",
+    "    dataset = r['series']['docs'][0]['dataset_name']\n",
+    "    indicators = pd.DataFrame(values,index=periods)\n",
+    "    indicators.columns = [dataset]\n",
+    "    return indicators"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'_meta': {'args': {'align_periods': False, 'dataset_code': 'irt_euryld_m', 'dimensions': {}, 'facets': False, 'format': 'json', 'limit': 1000, 'metadata': True, 'observations': True, 'offset': 0, 'provider_code': 'Eurostat', 'q': '', 'series_code': 'M.EA.INS_FWD.CGB_EA.Y10'}, 'version': '22.1.17'}, 'message': \"Series 'Eurostat/irt_euryld_m/M.EA.INS_FWD.CGB_EA.Y10' not found\"}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import requests\n",
+    "\n",
+    "def checkindicator(url):\n",
+    "    r = requests.get(url)\n",
+    "    print(r.json())  # Print the entire JSON response\n",
+    "    return r.json()\n",
+    "\n",
+    "# Test the URL\n",
+    "euro_yields_10y = checkindicator('https://api.db.nomics.world/v22/series/Eurostat/irt_euryld_m/M.EA.INS_FWD.CGB_EA.Y10?observations=1')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      GDP growth (annual %)\n",
+      "date                       \n",
+      "2022               3.089607\n",
+      "2021               6.259851\n",
+      "2020              -2.932043\n",
+      "2019               2.642216\n",
+      "2018               3.286613\n",
+      "2017               3.460239\n",
+      "2016               2.820557\n",
+      "2015               3.126136\n",
+      "2014               3.123870\n",
+      "2013               2.868222\n",
+      "2012               2.709115\n",
+      "2011               3.327133\n",
+      "2010               4.529546\n",
+      "2009              -1.355782\n",
+      "2008               2.062496\n",
+      "2007               4.373277\n",
+      "2006               4.442793\n",
+      "2005               4.005866\n",
+      "2004               4.469259\n",
+      "2003               3.104276\n",
+      "2002               2.302837\n",
+      "2001               2.025403\n",
+      "2000               4.531095\n",
+      "1999               3.549717\n",
+      "1998               2.853925\n",
+      "1997               3.929714\n",
+      "1996               3.574686\n",
+      "1995               3.084615\n",
+      "1994               3.323942\n",
+      "1993               1.812976\n",
+      "1992               2.019257\n",
+      "1991               1.255826\n",
+      "1990               2.764541\n",
+      "1989               3.717184\n",
+      "1988               4.591726\n",
+      "1987               3.724627\n"
+     ]
+    }
+   ],
+   "source": [
+    "import wbdata\n",
+    "import pandas as pd\n",
+    "import datetime\n",
+    "# Define the indicator for GDP growth (annual %)\n",
+    "indicators = {'NY.GDP.MKTP.KD.ZG': 'GDP growth (annual %)'}\n",
+    "# Set the date range\n",
+    "start_date = datetime.datetime(1987, 1, 1)\n",
+    "end_date = datetime.datetime(2022, 12, 31)\n",
+    "# Retrieve GDP growth data for the world\n",
+    "gdp_growth_data = wbdata.get_dataframe(indicators, country='WLD', date=(start_date, end_date), freq='Y')\n",
+    "# Display the GDP growth data\n",
+    "print(gdp_growth_data)\n",
+    "# Save to CSV\n",
+    "gdp_growth_data.to_csv('../data/world_gdp_growth_data.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      Inflation (annual %)  Unemployment rate (%)\n",
+      "date                                             \n",
+      "2022              7.967574                    NaN\n",
+      "2021              3.466926               6.190663\n",
+      "2020              1.920968               6.708983\n",
+      "2019              2.206073               5.864570\n",
+      "2018              2.450362               5.979186\n",
+      "2017              2.254277               5.610091\n",
+      "2016              1.605539                    NaN\n",
+      "2015              1.443857                    NaN\n",
+      "2014              2.354491               5.608396\n",
+      "2013              2.651673               5.650834\n",
+      "2012              3.725327               5.438778\n",
+      "2011              4.822396               5.919774\n",
+      "2010              3.326345               5.481055\n",
+      "2009              2.860449               6.337759\n",
+      "2008              8.949953                    NaN\n",
+      "2007              4.810237                    NaN\n",
+      "2006              4.267175                    NaN\n",
+      "2005              4.107251               5.789235\n",
+      "2004              3.517999                    NaN\n",
+      "2003              3.025045               6.433438\n",
+      "2002              2.907999                    NaN\n",
+      "2001              3.836573                    NaN\n",
+      "2000              3.433516               5.512186\n",
+      "1999              3.041947               6.206881\n",
+      "1998              5.097291                    NaN\n",
+      "1997              5.554130                    NaN\n",
+      "1996              6.526096               5.635558\n",
+      "1995              9.077381                    NaN\n",
+      "1994             10.247936               4.956726\n",
+      "1993              7.144587                    NaN\n",
+      "1992              7.636109                    NaN\n",
+      "1991              8.996939                    NaN\n",
+      "1990              8.063461                    NaN\n",
+      "1989              6.923905                    NaN\n",
+      "1988              7.113407                    NaN\n",
+      "1987              5.710119                    NaN\n"
+     ]
+    }
+   ],
+   "source": [
+    "import wbdata\n",
+    "\n",
+    "# Define indicators for inflation and unemployment\n",
+    "indicators = {\n",
+    "    'FP.CPI.TOTL.ZG': 'Inflation (annual %)',\n",
+    "    'SL.UEM.TOTL.NE.ZS': 'Unemployment rate (%)'\n",
+    "}\n",
+    "\n",
+    "# Retrieve data for the world from 1987 to 2022\n",
+    "inflation_unemployment_data = wbdata.get_dataframe(indicators, country='WLD', date=(start_date, end_date), freq='Y')\n",
+    "print(inflation_unemployment_data)\n",
+    "inflation_unemployment_data.to_csv('../data/inflation_unemployment_data.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            DEXUSEU\n",
+      "DATE               \n",
+      "1999-01-04   1.1812\n",
+      "1999-01-05   1.1760\n",
+      "1999-01-06   1.1636\n",
+      "1999-01-07   1.1672\n",
+      "1999-01-08   1.1554\n",
+      "...             ...\n",
+      "2022-12-26      NaN\n",
+      "2022-12-27   1.0654\n",
+      "2022-12-28   1.0622\n",
+      "2022-12-29   1.0668\n",
+      "2022-12-30   1.0698\n",
+      "\n",
+      "[6260 rows x 1 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pandas_datareader import data as pdr\n",
+    "import datetime\n",
+    "\n",
+    "# Define start and end dates\n",
+    "start_date = datetime.datetime(1987, 1, 1)\n",
+    "end_date = datetime.datetime(2022, 12, 31)\n",
+    "\n",
+    "# Get USD to EUR exchange rate from FRED (code: DEXUSEU)\n",
+    "usd_eur_exchange = pdr.get_data_fred('DEXUSEU', start=start_date, end=end_date)\n",
+    "\n",
+    "# Display and save to CSV\n",
+    "print(usd_eur_exchange)\n",
+    "usd_eur_exchange.to_csv('../data/usd_eur_exchange_rate_fred.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "               Open     High      Low    Close\n",
+      "2024-11-05  0.91940  0.91950  0.91740  0.91770\n",
+      "2024-11-04  0.92220  0.92270  0.91680  0.91790\n",
+      "2024-10-31  0.91860  0.92179  0.91709  0.91860\n",
+      "2024-10-30  0.92088  0.92208  0.91841  0.92088\n",
+      "2024-10-29  0.92416  0.92507  0.92024  0.92416\n",
+      "...             ...      ...      ...      ...\n",
+      "2014-11-09  0.80245  0.80447  0.79944  0.80248\n",
+      "2014-11-06  0.80764  0.80881  0.80366  0.80775\n",
+      "2014-11-05  0.80143  0.80632  0.79785  0.80128\n",
+      "2014-11-04  0.79656  0.80259  0.79573  0.79665\n",
+      "2014-11-03  0.80047  0.80047  0.79566  0.80026\n",
+      "\n",
+      "[2608 rows x 4 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import requests\n",
+    "import pandas as pd\n",
+    "\n",
+    "# Alpha Vantage API key\n",
+    "api_key = 'your_alpha_vantage_api_key'\n",
+    "\n",
+    "# API URL for USD to EUR exchange rate\n",
+    "url = f'https://www.alphavantage.co/query?function=FX_DAILY&from_symbol=USD&to_symbol=EUR&apikey={api_key}&outputsize=full'\n",
+    "\n",
+    "response = requests.get(url)\n",
+    "data = response.json()\n",
+    "\n",
+    "# Convert JSON data to DataFrame\n",
+    "exchange_rates = pd.DataFrame.from_dict(data['Time Series FX (Daily)'], orient='index')\n",
+    "exchange_rates = exchange_rates.rename(columns={'1. open': 'Open', '2. high': 'High', '3. low': 'Low', '4. close': 'Close'})\n",
+    "exchange_rates.index = pd.to_datetime(exchange_rates.index)\n",
+    "\n",
+    "# Display and save to CSV\n",
+    "print(exchange_rates)\n",
+    "exchange_rates.to_csv('../data/usd_eur_exchange_rates_alpha_vantage.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/requirements.txt b/requirements.txt
index e5bdf1e..c44b01a 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -6,3 +6,10 @@ Flask
 Flask-CORS
 statsmodels
 catboost
+pmdarima
+keras
+tensorflow
+ruptures
+aesara
+pymc
+arviz
\ No newline at end of file
diff --git a/scripts/catboost_info/catboost_training.json b/scripts/catboost_info/catboost_training.json
index 63bf475..de4c182 100644
--- a/scripts/catboost_info/catboost_training.json
+++ b/scripts/catboost_info/catboost_training.json
@@ -1,104 +1,104 @@
 {
 "meta":{"test_sets":[],"test_metrics":[],"learn_metrics":[{"best_value":"Min","name":"RMSE"}],"launch_mode":"Train","parameters":"","iteration_count":100,"learn_sets":["learn"],"name":"experiment"},
 "iterations":[
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diff --git a/scripts/catboost_info/learn/events.out.tfevents b/scripts/catboost_info/learn/events.out.tfevents
index 6319328..e58d508 100644
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diff --git a/scripts/catboost_info/time_left.tsv b/scripts/catboost_info/time_left.tsv
index ddfcfc8..403bf71 100644
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+23	61	193
+24	63	189
+25	65	185
+26	68	185
+27	70	181
+28	72	176
+29	74	174
+30	77	171
+31	78	167
+32	82	167
+33	84	164
+34	87	161
+35	89	159
+36	91	156
+37	94	154
+38	96	151
+39	98	147
+40	101	145
+41	103	143
+42	105	139
+43	107	137
+44	110	134
+45	111	130
+46	114	128
+47	116	126
+48	118	122
+49	120	120
+50	122	118
+51	124	114
+52	126	112
+53	128	109
+54	129	106
+55	131	103
+56	134	101
+57	135	98
+58	137	95
+59	139	92
+60	140	90
+61	142	87
+62	143	84
+63	145	81
+64	147	79
+65	148	76
+66	150	74
+67	151	71
+68	153	69
+69	155	66
+70	156	64
+71	158	61
+72	159	59
+73	161	56
+74	163	54
+75	164	52
+76	166	49
+77	168	47
+78	169	45
+79	171	42
+80	173	40
+81	174	38
+82	176	36
+83	177	33
+84	179	31
+85	181	29
+86	182	27
+87	185	25
+88	187	23
+89	188	20
+90	190	18
+91	193	16
+92	194	14
+93	196	12
+94	198	10
+95	199	8
+96	202	6
+97	203	4
+98	205	2
+99	207	0
diff --git a/scripts/main.py b/scripts/main.py
index 1671cfa..faaf455 100644
--- a/scripts/main.py
+++ b/scripts/main.py
@@ -23,7 +23,7 @@
     plot_price_trend, plot_moving_averages, plot_prices, 
     plot_with_annotation, plot_residuals, plot_forecast, 
     plot_actual_vs_predicted, plot_brent_prices_with_events_from_json, 
-    plot_residuals_mul, plot_actual_vs_predicted_mul
+    plot_residuals_mul, plot_actual_vs_predicted_mul, plot_relation_with_exchange_rate
 )
 from feature_engineering import (
     add_time_features, split_data, generate_future_dates, 
@@ -34,10 +34,15 @@
     display_metrics, get_models, train_and_predict, 
     evaluate_models, forecast_future_mul
 )
+from arima_model import run_arima_model
 
-oil_path = '../data/Brent_Oil_Prices.csv'
-gas_path = '../data/natural_gas_daily.csv'
+oil_path = '../data/natural_gas/Brent_Oil_Prices.csv'
+gas_path = '../data/natural_gas/natural_gas_daily.csv'
 events_path = '../data/events.json'
+df =  '../data/exchange_rate/Brent_Oil_Prices.csv'
+exchange_rate_fred = '../data/exchange_rate/usd_eur_exchange_rate_fred.csv'
+exchange_rate_vintage = '../data/exchange_rate/usd_eur_exchange_rates_alpha_vantage.csv'
+
 
 def utils(oil_path, gas_path, event_path):
     data = load_data(oil_path)
@@ -123,7 +128,8 @@ def main():
     utils(oil_path, gas_path, events_path)
     xgb_model(oil_path)
     multiple_models(oil_path)
-    
+    plot_relation_with_exchange_rate(df, exchange_rate_fred, exchange_rate_vintage)
+    run_arima_model()
 
 if __name__ == "__main__":
     main()
\ No newline at end of file
diff --git a/src/arima_model.py b/src/arima_model.py
new file mode 100644
index 0000000..a5cbf2c
--- /dev/null
+++ b/src/arima_model.py
@@ -0,0 +1,155 @@
+# Importing necessary libraries
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from pmdarima import auto_arima
+from statsmodels.tsa.stattools import adfuller
+from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
+from statsmodels.tsa.arima.model import ARIMA
+from statsmodels.tsa.statespace.sarimax import SARIMAX
+from sklearn.metrics import mean_squared_error
+import warnings
+warnings.filterwarnings("ignore")
+
+
+# Checking for stationarity using ADF Test
+def adf_test(series):
+    result = adfuller(series, autolag='AIC')
+    print('ADF Statistic:', result[0])
+    print('p-value:', result[1])
+    for key, value in result[4].items():
+        print(f'Critical Value ({key}): {value}')
+        
+
+def plot_differenced_price(data):
+
+    # Differencing to achieve stationarity (if needed)
+    data_diff = data['Price'].diff().dropna()
+    plt.figure(figsize=(12,6))
+    plt.plot(data_diff, label='Differenced Price')
+    plt.title('Differenced Brent Oil Price')
+    plt.xlabel('Date')
+    plt.ylabel('Differenced Price')
+    plt.legend()
+    plt.savefig('../figures/differenced_price.png', format='png', dpi=300)
+    plt.show()
+
+    # Rechecking stationarity after differencing
+    print("ADF Test Results for Differenced Price:")
+    adf_test(data_diff)
+
+    # Plotting ACF and PACF to determine ARIMA parameters
+    fig, ax = plt.subplots(1, 2, figsize=(16, 6))
+    plot_acf(data_diff, ax=ax[0])
+    plot_pacf(data_diff, ax=ax[1])
+    plt.savefig('../figures/acf_pcaf_plots.png', format='png', dpi=300)
+    plt.show()
+def arima_model(data):
+
+    # Splitting data into training and testing sets
+    train_size = int(len(data) * 0.8)
+    train, test = data['Price'][:train_size], data['Price'][train_size:]
+
+    # Building the ARIMA model (ARIMA(p,d,q))
+    # Assuming from ACF and PACF plots p=1, d=1, q=1 as a starting point; can tune further if needed.
+    model = ARIMA(train, order=(1, 1, 1))
+    arima_model = model.fit()
+
+    # Model summary
+    print(arima_model.summary())
+
+    # Forecasting on the test set
+    forecast = arima_model.forecast(steps=len(test))
+    forecast_index = test.index
+
+    # Plotting actual vs. predicted prices
+    plt.figure(figsize=(12,6))
+    plt.plot(train, label='Training Data')
+    plt.plot(test, label='Test Data', color='blue')
+    plt.plot(forecast_index, forecast, label='Forecast', color='orange')
+    plt.title('Brent Oil Price Forecast vs Actual')
+    plt.xlabel('Date')
+    plt.ylabel('Price')
+    plt.legend()
+    plt.savefig('../figures/ARIMA_forcat_vs_actual.png', format='png', dpi=300)
+    plt.show()
+
+    # Calculating the performance metrics
+    mse = mean_squared_error(test, forecast)
+    print(f"Mean Squared Error: {mse:.2f}")
+    rmse = np.sqrt(mse)
+    print(f"Root Mean Squared Error: {rmse:.2f}")
+
+    # Forecasting future prices (e.g., next 30 days)
+    future_forecast = arima_model.forecast(steps=30)
+    future_index = pd.date_range(start=data.index[-1] + pd.Timedelta(days=1), periods=30, freq='B')
+
+    plt.figure(figsize=(12,6))
+    plt.plot(data['Price'], label='Historical Price')
+    plt.plot(future_index, future_forecast, label='Future Forecast', color='red')
+    plt.title('Brent Oil Price Future Forecast')
+    plt.xlabel('Date')
+    plt.ylabel('Price')
+    plt.legend()
+    plt.savefig('../figures/ARIMA_future_forcast.png', format='png', dpi=300)
+    plt.show()
+
+def sarima_model(data):
+
+    price_data = data['Price']
+
+    # Differencing to make the series stationary (if necessary)
+    price_data_diff = price_data.diff().dropna()
+
+    # Perform grid search using auto_arima
+    model = auto_arima(
+        price_data,                # Original series
+        start_p=0, max_p=5,        # Range of p values
+        start_d=0, max_d=2,        # Range of d values
+        start_q=0, max_q=5,        # Range of q values
+        seasonal=False,            # Set to True if using SARIMA (seasonal ARIMA)
+        trace=True,                # Show the search process
+        error_action='ignore',     # Ignore errors during the search
+        suppress_warnings=True,    # Suppress warnings
+        stepwise=True,             # Use a stepwise approach
+        information_criterion='aic' # Use AIC for model selection
+    )
+
+    # Display the best ARIMA parameters
+    print("Best ARIMA model:", model.summary())
+
+    # Fit the best ARIMA model to the data
+    best_model = SARIMAX(price_data, order=model.order, enforce_stationarity=False, enforce_invertibility=False)
+    best_model_fit = best_model.fit(disp=False)
+
+    # Forecasting
+    n_periods = 30  # Number of periods to forecast
+    forecast = best_model_fit.get_forecast(steps=n_periods)
+    forecast_ci = forecast.conf_int()
+
+    # Plot the forecast
+    plt.figure(figsize=(10, 5))
+    plt.plot(price_data, label='Observed')
+    plt.plot(forecast.predicted_mean, color='red', label='Forecast')
+    plt.fill_between(forecast_ci.index, forecast_ci.iloc[:, 0], forecast_ci.iloc[:, 1], color='pink', alpha=0.3)
+    plt.title("Brent Oil Price Forecast")
+    plt.xlabel("Date")
+    plt.ylabel("Price")
+    plt.legend()
+    plt.savefig('../figures/SARIMA__future_forcast.png', format='png', dpi=300)
+    plt.show()
+
+    print('End of SARIMA model')
+
+
+def run_arima_model():
+    # Loading the dataset
+    data = pd.read_csv('../data/natural_gas/Brent_Oil_Prices.csv', parse_dates=['Date'], index_col='Date')
+
+    print("ADF Test Results for Price:")
+    adf_test(data['Price'])
+
+    plot_differenced_price(data)
+    arima_model(data)
+    sarima_model(data)
diff --git a/src/data_loader.py b/src/data_loader.py
index d7d212d..190302d 100644
--- a/src/data_loader.py
+++ b/src/data_loader.py
@@ -16,4 +16,10 @@ def load_datasets(filepath):
 
 def rename_columns(data, column_map):
     """Rename columns in the data."""
-    return data.rename(columns=column_map)
\ No newline at end of file
+    return data.rename(columns=column_map)
+
+def load_gdp(file_path):
+    df = pd.read_csv(file_path)
+    df = df.drop(['Unnamed: 0'], axis=1)
+    df['Date'] = pd.to_datetime(df['Date'], errors='coerce')
+    return df
\ No newline at end of file
diff --git a/src/relation.py b/src/relation.py
new file mode 100644
index 0000000..8f17e85
--- /dev/null
+++ b/src/relation.py
@@ -0,0 +1,59 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.preprocessing import MinMaxScaler
+
+
+df =  '../data/new/Brent_Oil_Prices.csv'
+exchange_rate_fred = '../data/new/usd_eur_exchange_rate_fred.csv'
+exchange_rate_vintage = '../data/new/usd_eur_exchange_rates_alpha_vantage.csv'
+
+
+def load_gdp(file_path):
+    df = pd.read_csv(file_path)
+    df = df.drop(['Unnamed: 0'], axis=1)
+    df['Date'] = pd.to_datetime(df['Date'], errors='coerce')
+    return df
+    
+
+def relation_with_gdp(df, exchange_rate_fred, exchange_rate_vintage):
+    # Load the datasets
+    df = load_gdp(df)
+    exchange_rate_fred = load_gdp(exchange_rate_fred)
+    exchange_rate_vintage =  load_gdp(exchange_rate_vintage)
+
+    # Merge datasets on 'Date' column using an outer join to keep all dates
+    merged_data = df.merge(exchange_rate_fred, on='Date', how='outer') \
+                    .merge(exchange_rate_vintage, on='Date', how='outer')
+
+    # Sort by 'Date' to ensure data is in chronological order
+    merged_data = merged_data.sort_values(by='Date')
+
+    # Initialize a scaler
+    scaler = MinMaxScaler()
+
+    # Apply Min-Max Scaling to relevant columns, ignoring the 'Date' column
+    columns_to_scale = ['Price', 'DEXUSEU', 'Open', 'High', 'Low', 'Close']
+    merged_data[columns_to_scale] = scaler.fit_transform(merged_data[columns_to_scale])
+
+    # Plotting
+    plt.figure(figsize=(18, 8))
+
+    # Plot each normalized column in a single plot with different colors and labels
+    plt.plot(merged_data['Date'], merged_data['Price'], label='Brent Oil Price', color='blue')
+    plt.plot(merged_data['Date'], merged_data['DEXUSEU'], label='USD/EUR Exchange Rate (FRED)', color='purple')
+    plt.plot(merged_data['Date'], merged_data['Open'], label='USD/EUR Open (Alpha Vantage)', color='red')
+    plt.plot(merged_data['Date'], merged_data['High'], label='USD/EUR High (Alpha Vantage)', color='pink')
+    plt.plot(merged_data['Date'], merged_data['Low'], label='USD/EUR Low (Alpha Vantage)', color='brown')
+    plt.plot(merged_data['Date'], merged_data['Close'], label='USD/EUR Close (Alpha Vantage)', color='gray')
+
+    # Labels and title
+    plt.xlabel('Date')
+    plt.ylabel('Scaled Values')
+    plt.title('Brent Oil Prices and USD/EUR Exchange Rates Over Time')
+    plt.legend(loc='upper left')
+    plt.grid(True)
+
+    plt.show()
+
+
+relation_with_gdp(df, exchange_rate_fred, exchange_rate_vintage)
\ No newline at end of file
diff --git a/src/visualization.py b/src/visualization.py
index fb4c4aa..fc29633 100644
--- a/src/visualization.py
+++ b/src/visualization.py
@@ -4,6 +4,8 @@
 import matplotlib.dates as mdates
 import pandas as pd
 import json
+from sklearn.preprocessing import MinMaxScaler
+from data_loader import load_gdp
 
 sns.set(style="whitegrid")
 
@@ -270,3 +272,47 @@ def plot_actual_vs_predicted_mul(y_test, predictions):
         plt.grid(True)
         plt.savefig(f'../figures/{model_name}_actual_vs_predicted.png', format='png', dpi=300)
         plt.show()
+
+
+def plot_relation_with_exchange_rate(df, exchange_rate_fred, exchange_rate_vintage):
+    # Load the datasets
+    df = load_gdp(df)
+    exchange_rate_fred = load_gdp(exchange_rate_fred)
+    exchange_rate_vintage =  load_gdp(exchange_rate_vintage)
+
+    # Merge datasets on 'Date' column using an outer join to keep all dates
+    merged_data = df.merge(exchange_rate_fred, on='Date', how='outer') \
+                    .merge(exchange_rate_vintage, on='Date', how='outer')
+
+    # Sort by 'Date' to ensure data is in chronological order
+    merged_data = merged_data.sort_values(by='Date')
+
+    # Initialize a scaler
+    scaler = MinMaxScaler()
+
+    # Apply Min-Max Scaling to relevant columns, ignoring the 'Date' column
+    columns_to_scale = ['Price', 'DEXUSEU', 'Open', 'High', 'Low', 'Close']
+    merged_data[columns_to_scale] = scaler.fit_transform(merged_data[columns_to_scale])
+
+    print('Plotting exchange rate with price ... ')
+    # Plotting
+    plt.figure(figsize=(18, 8))
+
+    # Plot each normalized column in a single plot with different colors and labels
+    plt.plot(merged_data['Date'], merged_data['Price'], label='Brent Oil Price', color='blue')
+    plt.plot(merged_data['Date'], merged_data['DEXUSEU'], label='USD/EUR Exchange Rate (FRED)', color='purple')
+    plt.plot(merged_data['Date'], merged_data['Open'], label='USD/EUR Open (Alpha Vantage)', color='red')
+    plt.plot(merged_data['Date'], merged_data['High'], label='USD/EUR High (Alpha Vantage)', color='pink')
+    plt.plot(merged_data['Date'], merged_data['Low'], label='USD/EUR Low (Alpha Vantage)', color='brown')
+    plt.plot(merged_data['Date'], merged_data['Close'], label='USD/EUR Close (Alpha Vantage)', color='gray')
+
+    # Labels and title
+    plt.xlabel('Date')
+    plt.ylabel('Scaled Values')
+    plt.title('Brent Oil Prices and USD/EUR Exchange Rates Over Time')
+    plt.legend(loc='upper left')
+    plt.grid(True)
+    plt.savefig('../figures/price_with_exchange_rates.png', format='png', dpi=300)
+
+    plt.show()
+